The Almond Tree Genome 3030303012, 9783030303013

Table of contents :
Foreword
Preface to the Series
Preface
Contents
Contributors
1 Genome Analysis and Breeding
Abstract
1 History
2 Evolutionary Genomics of Almond and Peach
3 Breeding
4 Rootstock Breeding
5 Biotechnology
5.1 Genomic Resources
5.2 Studies and Markers
5.3 Marker-Assisted Breeding
6 Future Directions
References
2 Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness
Abstract
1 The Origin of Almond
2 Almond Production
3 The Two Old Friends: HCN and Almond
4 Cyanogenic Glucosides
5 Amygdalin, the Bitter Compound in Almond
TheAmygdalinDegradationPathway
6 The Sweet Kernel (Sk) Gene
7 The Sk Marker
8 Future Perspectives
Acknowledgements
Funding
References
3 The Complete Sequence of the Almond Genome
Abstract
1 Introduction
2 The First Chromosome-Scale Almond Genome—The Lauranne Genome
3 The Second Almond Genome Sequenced—The Texas Genome
4 The Third Almond Genome Sequenced—The Nonpareil Genome
5 Future Perspectives
Funding
References
4 Almond miRNA Expression and Horticultural Implications
Abstract
1 Introduction
2 MicroRNA-Mediated Gene Regulation Under Cold Stress in Almond
2.1 Gene Ontology Analysis
2.2 Identification and Verification of Cold-Responsive miRNA Target Genes Using Degradome Sequencing Method
2.3 Pdu-miR168 Under Cold Stress
2.4 Pdu-miR171 Under Cold Stress
2.5 Pdu-miR319 Under Cold Stress
2.6 Pdu-miR398 Under Cold Stress
2.7 Pdu-miR403 Under Cold Stress
2.8 Pdu-miR477a-3p Under Cold Stress
2.9 Pdu-miR7122-3p Under Cold Stress
3 MicroRNA-Mediated Gene Regulation Under Drought Stress in Almond
3.1 Pdu-miR156 Under Drought Stress
3.2 Pdu-miR167 Under Drought Stress
3.3 Pdu-miR408 Under Drought Stress
3.4 Pdu-miR2275 Under Drought Stress
4 Identification of Symbiosis-Related miRNAs Under Salt and Drought Stresses in Almond
5 MicroRNA-Mediated Gene Regulation During Fruit Development
References
5 Epigenetic Regulation in Almond
Abstract
1 Introduction
2 Epigenetic Regulation in Plants
2.1 The Dynamic Plant Genome
2.2 Plants as Epigenetic Machineries
2.3 Epigenetic Machinery in Plants
2.4 Propagation, Reproduction, Reprograming, and Extended Limited Inheritance
3 Epigenetic Mechanisms in Almond
3.1 Transposable Elements as Architects of the Almond Genome and Its Domestication
3.2 Epigenetic Regulation of Dormancy and Flowering
3.3 Dysfunctionalization of the Sf Allele Conferring Gametophytic Self-compatibility in Breeding Germplasm
3.4 The Role of Genome-Wide DNA-(De)methylation in Noninfectious Bud Failure
4 Concluding Remarks and Perspectives
Acknowledgements
References
6 Metabolomic Studies in Almond
Abstract
1 Introduction
2 Target Metabolomic in Different Prunus Spp.
2.1 Metabolomics in Salt-Stress Response in Plum
2.2 Endodormancy and Target Metabolomics in Prunus Spp.
3 Non-target Metabolomics in Almond
3.1 Almond Classification
3.2 Endodormancy in Almond Flower Buds
4 Non-target Metabolomics in Other Prunus Spp.
4.1 Antioxidant Activity in Peach Fruits
4.2 Endodormancy in Sweet Cherry Flower Buds
5 Conclusions and Future Sights
Funding
References
7 Recent Advances on Self-incompatibility in Almond: A Glance at Genomic and Transcriptomic Levels
Abstract
1 Genetic Control of GSI in Almond
1.1 The Almond S-locus
1.2 The S-RNase Gene
1.3 The Almond S-RNase Gene Structure
2 The SFB Gene
2.1 The SLF Gene
2.2 LTRs
2.3 The Molecular Basis of Self-recognition and Rejection in the Almond GSI
2.4 Characterization and Marker Assays for S-RNase Alleles in Almond
2.5 Three-Dimensional (3D) Models of the S-RNase
3 Proteomic and Transcriptomic Analyses of Pistils and Anthers from Self-incompatible and Self-compatible Almonds
4 Practical Aspects of GSI in Almond
5 Self-incompatibility in Almond Production and Breeding
6 Implications of Self-fertility for Almond Production
7 Conclusion and Future Perspectives
References
8 Transcriptional Changes Associated to Flower Bud Dormancy and Flowering in Almond: DNA Sequence Motifs, mRNA Expression, Epigenetic Modifications and Phytohormone Signaling
Abstract
1 Introduction
2 Dormancy and Flowering in Almond Trees
2.1 Paradormancy
2.2 Endodormancy Factors
2.3 Ecodormancy
3 DNA Sequence Motifs Linked to Breaking Dormancy and Flowering Date in Almond Trees
4 mRNA Expression Associated to Flowering in Almond Trees
5 Epigenetic Regulation of Flowering in Almond Trees
6 Phytohormone Signaling of Bud Dormancy and Flowering in Almond Trees
7 Concluding Remarks and Future Prospects in the New Post-genomic Context
Acknowledgements
References
9 Molecular Basis of the Abiotic Stresses in Almond
Abstract
1 Introduction
2 Rootstock Development to Overcome Abiotic Stress
3 Other Important Criteria for Almond Rootstocks
4 Water and Nutrient Uptake
5 Physiological Metabolic and Molecular Response of Abiotic Stresses
5.1 Calcareus Soils Stress
5.2 Waterlogging Stress
5.3 Drought Stress
5.4 Salinity Stress
5.5 Scion Cold Stress
6 Final Outlook
7 Conclusion
References
10 Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond
Abstract
1 Introduction
2 Brief History of Linkage and QTL Map Construction in Almond
3 Marker-Assisted Breeding in Crops
4 Marker-Assisted Selection (MAS) in Almond
5 Use of Marker Assisted Introgression (MAI) in Almond
6 Molecular Tools in Almond Breeding
7 Marker-Assisted Pyramiding
8 QTL Mapping in Almond
9 Association Mapping (AM) in Almond
10 A Well Characterised Trait in Almond Using QTL Mapping
11 New Approaches and Technologies
11.1 Genomic Selection (GS)
11.2 High-Throughput Sequencing
11.3 High-Throughput Phenotyping (HTP)
12 Conclusion
References
11 Accelerating Almond Breeding in Post-genomic Era
Abstract
1 Introduction
2 Genomic Selection in Almond Breeding
3 Aspects Influencing Genomic Selection in Almond
4 Previous Studies in Prunus and Nuts Species
5 Almond Breeding Cycle Updating
References
12 Prospects and Future Questions
Abstract
References

Citation preview

Compendium of Plant Genomes

Raquel Sánchez-Pérez Angel Fernandez i Marti Pedro Martinez-Gomez Editors

The Almond Tree Genome

Compendium of Plant Genomes

Whole-genome sequencing is at the cutting edge of life sciences in the new millennium. Since the first genome sequencing of the model plant Arabidopsis thaliana in 2000, whole genomes of about 100 plant species have been sequenced and genome sequences of several other plants are in the pipeline. Research publications on these genome initiatives are scattered on dedicated web sites and in journals with all too brief descriptions. The individual volumes elucidate the background history of the national and international genome initiatives; public and private partners involved; strategies and genomic resources and tools utilized; enumeration on the sequences and their assembly; repetitive sequences; gene annotation and genome duplication. In addition, synteny with other sequences, comparison of gene families and most importantly potential of the genome sequence information for gene pool characterization and genetic improvement of crop plants are described.

Raquel Sánchez-Pérez . Ángel Fernández i Martí Pedro Martínez-Gómez

.

Editors

The Almond Tree Genome

123

Editors Raquel Sánchez-Pérez Department of Plant Breeding CEBAS-CSIC Espinardo (Murcia), Spain

Ángel Fernández i Martí Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley, CA, USA

Pedro Martínez-Gómez Department of Plant Breeding CEBAS-CSIC Espinardo (Murcia), Spain

ISSN 2199-4781 ISSN 2199-479X (electronic) Compendium of Plant Genomes ISBN 978-3-030-30301-3 ISBN 978-3-030-30302-0 (eBook) https://doi.org/10.1007/978-3-030-30302-0 © Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to my two lovely daughters Rebeca and Carolina

Foreword

When one of the editors, Dra. Raquel Sánchez Pérez, asked me to write the prologue of The Almond Tree Genome, I thought it would be a good opportunity to put in my head the great advances that I have observed throughout my scientific career dedicated to this species. The almond tree is a species, but above all, it is a crop. This has been the key to its expansion throughout the world from those bitter seed shrubs scattered in the mountains of central Asia. Its rusticity, its enormous capacity for adaptation and the lasting viability of its seeds, has allowed the almond tree expansion and the culture in numerous areas on the five continents. Very recently, breeders have contributed to this expansion outside the traditional cultivation areas, by obtaining cultivars that flower so late that they can be grown in increasingly colder areas, with a reduced risk of frost. But breeders have also contributed to reducing variability of this species through the abusive use of few parents in breeding programs, mainly of self-compatible cultivars. The modern cultivation of the almond is very recent and could be located in the USA, where it arises the idea that almond was not a marginal rained crop, but rather a very profitable one, if the suitable cultivars were cultivated efficiently. Other countries like Australia followed this belief. Currently, even the countries where the cultivation is still mostly marginal are aware of its economic profitability if the cultivars adapted to each area are properly cultivated. This whole process of expansion and implantation of the almond tree has been a very gradual process, naturally, based on the observation and decisions of the farmers and more recently of the breeders. Science later saw in the almond tree a very interesting species for its study. Its genetic proximity to the peach tree (the model species of Prunus), its genetic richness, its ability to adapt and the growing social interest in a healthy product, has facilitated the financing of research projects and the advancement of scientific knowledge. The first studies on the almond tree were based on the agronomic behavior of the cultivars and their cultivation possibilities in each zone. Soon, the limiting factors of the crop in each region were identified, and the objectives of the breeding programs were established. Within a few years, these programs began to provide valuable information on the inheritance of traits, their genetic control, which had a positive impact on the development of new cultivars. Due to the genetic richness of this species, great scientific results and new improved cultivars were soon obtained. vii

viii

Foreword

Important processes such as flowering and fruit set were the focus of the investigations. Late flowering to avoid late frosts and floral self-compatibility to ensure fruit set were the targets of many breeding programs. The incorporation into the research groups of young researchers, attracted by the new biotechnological techniques, was the climax to the knowledge of this species. The development of molecular markers has allowed the unequivocal identification of cultivars, with much greater precision than the most experienced observer. They have also been fundamental in breeding programs, allowing the selection of the most suitable parents and marker-assisted selection, for the elimination of unwanted individuals at very early stages. Modern sequencing techniques have recently allowed the complete sequencing of the almond tree genome, opening up numerous possibilities for genomic selection. We also discovered the gene responsible for the domestication of the almond tree, which made a bitter seed, that ensured its survival under natural conditions, sweet. These tools are facilitating the correct choice of the most suitable parents for each objective and the selection of offspring based on their genetic, with a simple DNA analysis. Other disciplines of more recent application to the almond breeding, such as transcriptomics, metabolomics or epigenetics, are also contributing to the knowledge of how this crop works. Although the greatest advances achieved to date have been with qualitative characters such as floral incompatibility and the bitter taste of kernel, quantitative characters such as the time of flowering and ripening, chemical composition or resistance to diseases, are in a position to be approached through the bioinformatic study of the genotype and phenotype of the offspring. Much progress has been made, but the challenges are endless. The adaptation of varieties to climate change will be one of the future lines of research, due to the impact that these changes are having on production. We will also have to advance in the development of varieties resistant to pests and diseases, given the systematic reduction of authorized pesticides, and the general imposition of an organic agriculture, more respectful of the environment and human health. And, of course, we will be increasingly conditioned by the availability of water, which will make it necessary to obtain new cultivars more efficient in their use. Scientific knowledge is unlimited, and we do not know to what extent biotechnology will be able to contribute to the almond breeding. But today, and possibly never, these important advances may replace the experience of the field breeder and the need to observe the phenotype and evaluate in different growing conditions the future cultivars. I am sure that with The Almond Tree Genome book, advances will be observed soon. Dr. Federico Dicenta López-Higuera Professor at Department of Plant Breeding Head of the Fruit Breeding Group, CEBAS-CSIC Espinardo (Murcia), Spain

Preface to the Series

Genome sequencing has emerged as the leading discipline in the plant sciences coinciding with the start of the new century. For much of the twentieth century, plant geneticists were only successful in delineating putative chromosomal location, function, and changes in genes indirectly through the use of a number of “markers” physically linked to them. These included visible or morphological, cytological, protein, and molecular or DNA markers. Among them, the first DNA marker, the RFLPs, introduced a revolutionary change in plant genetics and breeding in the mid-1980s, mainly because of their infinite number and thus potential to cover maximum chromosomal regions, phenotypic neutrality, absence of epistasis, and codominant nature. An array of other hybridization-based markers, PCR-based markers, and markers based on both facilitated construction of genetic linkage maps, mapping of genes controlling simply inherited traits, and even gene clusters (QTLs) controlling polygenic traits in a large number of model and crop plants. During this period, a number of new mapping populations beyond F2 were utilized and a number of computer programs were developed for map construction, mapping of genes, and for mapping of polygenic clusters or QTLs. Molecular markers were also used in the studies of evolution and phylogenetic relationship, genetic diversity, DNA fingerprinting, and map-based cloning. Markers tightly linked to the genes were used in crop improvement employing the so-called marker-assisted selection. These strategies of molecular genetic mapping and molecular breeding made a spectacular impact during the last one and a half decades of the twentieth century. But still they remained “indirect” approaches for elucidation and utilization of plant genomes since much of the chromosomes remained unknown and the complete chemical depiction of them was yet to be unraveled. Physical mapping of genomes was the obvious consequence that facilitated the development of the “genomic resources” including BAC and YAC libraries to develop physical maps in some plant genomes. Subsequently, integrated genetic–physical maps were also developed in many plants. This led to the concept of structural genomics. Later on, emphasis was laid on EST and transcriptome analysis to decipher the function of the active gene sequences leading to another concept defined as functional genomics. The advent of techniques of bacteriophage gene and DNA sequencing in the 1970s was extended to facilitate sequencing of these genomic resources in the last decade of the twentieth century. ix

x

As expected, sequencing of chromosomal regions would have led to too much data to store, characterize, and utilize with the-then available computer software could handle. But the development of information technology made the life of biologists easier by leading to a swift and sweet marriage of biology and informatics, and a new subject was born—bioinformatics. Thus, the evolution of the concepts, strategies, and tools of sequencing and bioinformatics reinforced the subject of genomics—structural and functional. Today, genome sequencing has traveled much beyond biology and involves biophysics, biochemistry, and bioinformatics! Thanks to the efforts of both public and private agencies, genome sequencing strategies are evolving very fast, leading to cheaper, quicker, and automated techniques right from clone-by-clone and whole-genome shotgun approaches to a succession of second-generation sequencing methods. The development of software of different generations facilitated this genome sequencing. At the same time, newer concepts and strategies were emerging to handle sequencing of the complex genomes, particularly the polyploids. It became a reality to chemically—and so directly—define plant genomes, popularly called whole-genome sequencing or simply genome sequencing. The history of plant genome sequencing will always cite the sequencing of the genome of the model plant Arabidopsis thaliana in 2000 that was followed by sequencing the genome of the crop and model plant rice in 2002. Since then, the number of sequenced genomes of higher plants has been increasing exponentially, mainly due to the development of cheaper and quicker genomic techniques and, most importantly, the development of collaborative platforms such as national and international consortia involving partners from public and/or private agencies. As I write this preface for the first volume of the new series “Compendium of Plant Genomes,” a net search tells me that complete or nearly complete whole-genome sequencing of 45 crop plants, eight crop and model plants, eight model plants, 15 crop progenitors and relatives, and three basal plants is accomplished, the majority of which are in the public domain. This means that we nowadays know many of our model and crop plants chemically, i.e., directly, and we may depict them and utilize them precisely better than ever. Genome sequencing has covered all groups of crop plants. Hence, information on the precise depiction of plant genomes and the scope of their utilization are growing rapidly every day. However, the information is scattered in research articles and review papers in journals and dedicated Web pages of the consortia and databases. There is no compilation of plant genomes and the opportunity of using the information in sequence-assisted breeding or further genomic studies. This is the underlying rationale for starting this book series, with each volume dedicated to a particular plant. Plant genome science has emerged as an important subject in academia, and the present compendium of plant genomes will be highly useful to both students and teaching faculties. Most importantly, research scientists involved in genomics research will have access to systematic deliberations on the plant genomes of their interest. Elucidation of plant genomes is of interest not only for the geneticists and breeders, but also for practitioners of an array of plant science disciplines, such as taxonomy, evolution, cytology,

Preface to the Series

Preface to the Series

xi

physiology, pathology, entomology, nematology, crop production, biochemistry, and obviously bioinformatics. It must be mentioned that information regarding each plant genome is ever-growing. The contents of the volumes of this compendium are, therefore, focusing on the basic aspects of the genomes and their utility. They include information on the academic and/or economic importance of the plants, description of their genomes from a molecular genetic and cytogenetic point of view, and the genomic resources developed. Detailed deliberations focus on the background history of the national and international genome initiatives, public and private partners involved, strategies and genomic resources and tools utilized, enumeration on the sequences and their assembly, repetitive sequences, gene annotation, and genome duplication. In addition, synteny with other sequences, comparison of gene families, and, most importantly, the potential of the genome sequence information for gene pool characterization through genotyping by sequencing (GBS) and genetic improvement of crop plants have been described. As expected, there is a lot of variation of these topics in the volumes based on the information available on the crop, model, or reference plants. I must confess that as the series editor, it has been a daunting task for me to work on such a huge and broad knowledge base that spans so many diverse plant species. However, pioneering scientists with lifetime experience and expertise on the particular crops did excellent jobs editing the respective volumes. I myself have been a small science worker on plant genomes since the mid-1980s and that provided me the opportunity to personally know several stalwarts of plant genomics from all over the globe. Most, if not all, of the volume editors are my longtime friends and colleagues. It has been highly comfortable and enriching for me to work with them on this book series. To be honest, while working on this series I have been and will remain a student first, a science worker second, and a series editor last. And, I must express my gratitude to the volume editors and the chapter authors for providing me the opportunity to work with them on this compendium. I also wish to mention here my thanks and gratitude to Springer staff, particularly Dr. Christina Eckey and Dr. Jutta Lindenborn, for the earlier set of volumes and presently Ing. Zuzana Bernhart for all their timely help and support. I always had to set aside additional hours to edit books beside my professional and personal commitments—hours I could and should have given to my wife, Phullara, and our kids, Sourav and Devleena. I must mention that they not only allowed me the freedom to take away those hours from them but also offered their support in the editing job itself. I am really not sure whether my dedication of this compendium to them will suffice to do justice to their sacrifices for the interest of science and the science community. New Delhi, India

Chittaranjan Kole

Preface

For more than 20 years, I have been studying the almond tree. At the beginning, only from a genetic and breeding point of view. Later from a biochemistry and physiological perspective. Lately, thanks to the new technologies, the almond genome became a reality. This was a stepping stone to develop biotechnology tools to help the breeding programs. This volume on the Almond Tree Genome covers knowledge on the almond breeding, its origin and domestication of wild almonds, the bitterness trait and the marker development, the three genomes sequenced available so far, the first one from Lauranne, then Texas and, recently, Nonpareil cultivars, the expression of miRNA, epigenetic and metabolomic analyses, the recent advances on the self-incompatibility trait, the regulation of flower bud dormancy, the molecular basis of the abiotic stresses, the fruit quality traits, how to accelerate almond breeding in the post-genomic era and, finally, the prospects and future questions that should be raised to move forward with the almond crop. I hope this book will help us to overcome the challenges that we are facing right now, like the climate change and the increase of population. Any help is useful to move faster in this long race. Finally, I would specially like to thank Prof. Pedro Martínez-Gómez for giving me the opportunity to be part of the editorial team to write The Almond Tree Genome book, chance that was kindly offered by Prof. Chittaranjan Kole. Both professors have been extremely patience in the elaboration of this book. Murcia, Spain

Raquel Sánchez-Pérez

xiii

Contents

Genome Analysis and Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gina M. Sideli and Thomas M. Gradziel Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness . . . . . . . . . . . . . . . . . . . . . . . . Raquel Sánchez-Pérez

1

15

The Complete Sequence of the Almond Genome . . . . . . . . . . . . . . Raquel Sánchez-Pérez, Pedro José Martínez-García, and Ángel Fernández i Martí

25

Almond miRNA Expression and Horticultural Implications . . . . . Marzieh Karimi, Marjan Jafari, Roohollah Shahvali, Roudabeh Ravash, and Behrouz Shiran

33

Epigenetic Regulation in Almond . . . . . . . . . . . . . . . . . . . . . . . . . . . Jonathan Fresnedo Ramírez, Katherine D’Amico-Willman, and Thomas M. Gradziel

59

Metabolomic Studies in Almond. . . . . . . . . . . . . . . . . . . . . . . . . . . . Jesús Guillamón Guillamón and Raquel Sánchez-Pérez

77

Recent Advances on Self-incompatibility in Almond: A Glance at Genomic and Transcriptomic Levels. . . . . . . . . . . . . . Shashi N. Goonetilleke, Michelle G. Wirthensohn, Richard S. Dodd, and Ángel Fernández i Martí

87

Transcriptional Changes Associated to Flower Bud Dormancy and Flowering in Almond: DNA Sequence Motifs, mRNA Expression, Epigenetic Modifications and Phytohormone Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Ángela S. Prudencio, Raquel Sánchez-Pérez, Pedro José Martínez-García, Federico Dicenta, and Pedro Martínez-Gómez Molecular Basis of the Abiotic Stresses in Almond . . . . . . . . . . . . . 131 Beatriz Bielsa and Maria José Rubio-Cabetas Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Shashi N. Goonetilleke and Ángel Fernández i Martí xv

xvi

Accelerating Almond Breeding in Post-genomic Era . . . . . . . . . . . 159 Jorge Mas-Gómez, Francisco José Gómez-López, Ángela Sánchez Prudencio, Manuel Rubio Angulo, and Pedro José Martínez-García Prospects and Future Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Pedro Martínez-Gómez, Raquel Sánchez-Pérez, and Ángel Fernández i Martí

Contents

Contributors

Beatriz Bielsa Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Unidad de Hortofruticultura, Zaragoza, España Federico Dicenta Departamento de Mejora Vegetal Grupo de Mejora Genética de Frutales, CEBAS-CSIC, Espinardo, Murcia, Spain Richard S. Dodd Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA Katherine D’Amico-Willman Center of Applied Plant Sciences, The Ohio State University, Wooster, OH, USA Ángel Fernández i Martí Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA Jonathan Fresnedo Ramírez Department of Horticulture and Crop Science, Center of Applied Plant Sciences, and Sustainability Institute, The Ohio State University, Wooster, OH, USA Shashi N. Goonetilleke School of Agriculture, Food and Wine, Plant Research Centre, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia Thomas M. Gradziel Department of Plant Sciences, University of California, Davis, CA, USA Jesús Guillamón Guillamón Department CEBAS-CSIC, Espinardo (Murcia), Spain

of

Plant

Breeding,

Marjan Jafari Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran; Department of Horticulture, Shahrekord University, Shahrekord, Iran Marzieh Karimi Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Francisco José Gómez-López Department of Plant Breeding, CEBASCSIC, Espinardo (Murcia), Spain Pedro José Martínez-García Department of Plant Breeding, CEBASCSIC, Espinardo (Murcia), Spain

xvii

xviii

Contributors

Pedro Martínez-Gómez Department of Plant Breeding, CEBAS-CSIC, Espinardo (Murcia), Spain Jorge Mas-Gómez Department Espinardo (Murcia), Spain

of

Plant

Breeding,

CEBAS-CSIC,

Roudabeh Ravash Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Manuel Rubio Angulo Department of Plant Breeding, CEBAS-CSIC, Espinardo (Murcia), Spain Maria José Rubio-Cabetas Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Unidad de Hortofruticultura, Zaragoza, España; Instituto Agroalimentario de Aragón—IA2 (CITA-Universidad de Zaragoza), Zaragoza, España Ángela Sánchez Prudencio Department of Plant Breeding, CEBAS-CSIC, Espinardo (Murcia), Spain Roohollah Shahvali Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran Behrouz Shiran Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran; Institute of Biotechnology, Shahrekord University, Shahrekord, Iran Gina M. Sideli Department of Plant Sciences, University of California, Davis, CA, USA Raquel Sánchez-Pérez Department of Plant Breeding, CEBAS-CSIC, Espinardo (Murcia), Spain Michelle G. Wirthensohn School of Agriculture, Food and Wine, Plant Research Centre, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia

Genome Analysis and Breeding Gina M. Sideli and Thomas M. Gradziel

Abstract

Almond is a nut tree species of worldwide economic importance. Almond breeding programs have been crucial to develop new cultivars that meet both environmental challenges and economic demands, always taking into account the needs of industry. With the introduction of genomic technologies, detection of trait genetics, developments in automated phenotyping and micropropagation techniques, development of cultivars can be significantly accelerated. Molecular markers for single-gene traits have been and will continue to be progressed. For polygenic traits, pyramiding traits using modeling, CRISPR technology and genomic predictions holds potential. Altogether these developing technologies will only assist the foundation of traditional plant breeding.

1

History

Almonds are in the genus Prunus which includes four subgenus: Prunus, Amygdalus, Cerasus and Padus (Wen et al. 2008). The Amygdalus subgenus contains both Prunus dulcis (almond)

G. M. Sideli . T. M. Gradziel (&) Department of Plant Sciences, University of California, Davis, CA, USA e-mail: [email protected]

(Miller) D.A. Webb (syn. P. amygdalus Batsch) and Prunus persica (L.) Batsch (peach). Even though almond is classified as a stone fruit, it lacks a fleshy mesocarp and is horticulturally classified as a nut because of its edible embryo or kernel enclosed in a pellicle. Almonds are obligate outcrossers and readily hybridize with species in the Amygdalus subgenus (Atwell et al. 2010). Almond is a diploid species with eight chromosomes (Darlington 1930; Grasselly 1977; Socias i Company et al. 2017) and has close synteny with other Prunus species including peach. The genome size is around 300 Mbp (Baird et al. 1994). Almond is the first deciduous nut tree to bloom due to its low chilling requirements and response to growth as temperatures rise and therefore has a restricted growing region. The almond is adapted to a Mediterranean-type climate, mild and wet winter and a hot, dry summer, due to its areas of domestication (Kester and Gradziel 1996). It has been proposed that the cultivated almond originated from two possible natural populations of Amygdalus communis L. where selections were made for sweet kernels rather than the bitter, common in wild species because it provides defense to herbivory (Watkins 1979). The origin probably occurred in present-day Iran and Turkmenistan in the Kobet Dag mountain range, with a second possibility in the foothills of the Tian Shan mountains in Kyrgyzstan and western China. The geographic range extends

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_1

1

2

G. M. Sideli and T. M. Gradziel

Table 1 Commercial production of almonds inshell in major producing countries (FAOSTAT 2017; FAOSTATS 2017)

Country

Production (metric ton)

California, USA

a

1,029,655

Area harvested (ha)

Yield (hg/ha)a

404,686

25,443

Spain

255,503

633,562

4,033

Morocco

116,923

170,864

6,843

Iran (Islamic Republic of)

111,845

50,856

21,992

Turkey

90,000

34,050

26,432

Italy

79,599

58,472

13,613

Australia

75,373

38,000

19,835

hg/ha hectogram (100 g) per hectare

across Iran, eastern Turkey and present-day Syria overlapping with early documented cultivation sites (Denisov 1988; Atwell et al. 2010; Gradziel and Martinez-Gomez 2013). Since these populations contain sweet kernels, it has also been hypothesized that the overlap in range of domestic and wild populations is due to escapes from domesticated orchards (Ladizinsky 1999). As domestic almond is distinct from wild species, it is believed that domestication may have involved multiple interspecific hybridizations (Grasselly 1977; Browicz and Zohary 1996; Ladizinsky 1999; Zeinalabedini et al. 2010), possibly with P. fenzliana. Dispersal of almond has occurred in three stages: Asiatic, Mediterranean and Californian. The first was the domestication and spread along trade routes in central and southwestern Asia, where documented almond usage in Hebrew literature dating back to 2000 BCE. The second was the spread into Greece around 300 BCE and its subsequent spread and cultivation in Sicily, Spain and Portugal. Hard shell varieties were first imported to California, USA, in the early 1900s, and later soft-shelled were imported from France. The various high inputs with fertilizer, irrigated soils and pruned orchards with tolerant rootstocks have created favorable conditions allowing California to be the most productive almond production region worldwide. Because of severe genetic bottlenecks during domestication and subsequent breeding, Kester and Gradziel have placed an emphasis on introgression of interspecific traits into breeding lines to allow for greater genetic diversity.

California is the major producer of almonds (Table 1). Production in California, with 1,000,000 bearing acres and 330,00 non-bearing, has a value of $2,343,200. Production for shelled almonds 1135 (1000 tons) has $5,603,950 total value with an export value of $4483 million (CA Dept Food Ag 2019). ‘Nonpareil’, first selected by AT. Hatch in 1870s, is the most widely planted cultivar in California due to its high yield and good quality in kernel. However, it is selfincompatible, thus requiring pollinizers for optimal production. In Spain, ‘Guara’ has been an important variety due to its self-compatibility and good quality and yield (Socias i Company and Felipe 1991).

2

Evolutionary Genomics of Almond and Peach

Almond is closely related to peach [P. persica (L.) Batsch] despite evolving in the dry climates of central and southwestern Asia. Peach, having evolved in eastern Asia, is more adapted to warm, humid climates. (Dirlewanger et al. 2004) found a high level of collinearity between Prunus species with only one observed chromosomal rearrangement between linkage group (LG) 6 and LG8 in an almond × peach F2 population ‘Garfi’ × ‘Nemared’. A total of 92.96% of the almond ‘Texas’ assembly could be aligned to the peach ‘Lovell’ reference genome sequence with an average identity of 95.59%, which increased to 97.99% (20 SNPs/kb) when only regions that align 1:1 are considered (Alioto et al. 2019).

Physical traits

Shell hardness

GBS SSR SSR

Hakuho × UFGold R1000 × Desmayo Largueta R1000 × Desmayo Largueta

SSR KASP SSR SSR

R1000 × Desmayo Largueta Nonpareil × Lauranne Vivot × Blanquerna R1000 × Desmayo Largueta

2

2,5,8

Kw

1,4

1,2,3,5,6,7

DQ

SSR RFLP

2,8

Vivot × Blanquerna

6,8

SI

D

Ferragnes × Tuono

RFLP SSR

Padre × 54P455 R1000 × Desmayo Largueta

6

6

RFLP

SSR

R1000 × Desmayo Largueta

Ferragnes × Tuono

SSR

S

6

S

GBS

Hakuho × UFGold R1000 × Desmayo Largueta

SSR

RAPD, SSR

Tuono × Shahrood

RFLP

SSR

R1000 × Desmayo Largueta

R1000 × Desmayo Largueta

SSR

R1000a × Desmayo Largueta

Ferragnes × Tuono

RFLP, RAPD Candidate gene approach

Felisia × Bertina Texas × EarlyGold

Marker

Population

SI

4,5

6

SI

Selfcompatibility

1,4

Ripening date

Lf

Leafing date

1,4,7

2,7

CR

HR

Heat requirement

1,3,4,7

CR

1,4,7

BD

Chilling requirement

1,5,6

Lb

4

1,4,7

Lb

Bd

4

Lb

Bloom density

4

1,2,3,5,6,7

Lb

Bloom time

Linkage group

Lb

Name

Trait

Table 2 Known markers to be associated with agronomic traits in almond and peach

Sánchez-Pérez et al. (2007)

Fernández i Martí et al. (2012)

Goontelieke et al.

Sánchez-Pérez et al. (2007)

Arús et al. (1999)

Fernández i Martí et al. (2011)

Sánchez-Pérez et al. (2007)

Bliss et al. (2002)

Arús et al. (1999)

Ballester et al. (1998)

Sánchez-Pérez et al. (2007)

Sánchez-Pérez et al. (2007)

Sánchez-Pérez et al. (2012)

Bielenberg et al. (2015)

Sánchez-Pérez et al. (2012)

Sánchez-Pérez et al. (2007)

Bielenberg et al. (2015)

Rasouli et al. (2018)

Sánchez-Pérez et al. (2012)

Sánchez-Pérez et al. (2007)

Silva et al. (2005)

Ballester et al. (2001)

References

Almond (continued)

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Almond

Peach

Almond

Almond

Peach

Almond

Almond

Almond

Almond

Almond

Species

Genome Analysis and Breeding 3

Almond Duval et al. (2018) KASP Lauranne × Alnem 7 Rmia

R1000 has the late flowering mutation, Tardy Nonpareil as a parent a

Peach Duval et al. (2013) SSR Montclar × Nemared, [(Pamirskij × Rubira) × (Montclar × Nemared)]

Dirlewanger et al. (2004b)

Almond SSR [P2175 × GN22(Garfi × Nemared)] Rmia

2,7

Almond

Root-knot nematode

Sánchez-Pérez et al. (2019)

Sánchez-Pérez et al. (2010)

Almond CAPS

SSR

R1000 × Desmayo Largueta

R1000 × Desmayo Largueta

5

1,3,8

Sk

Sk/sk Bitter kernel

Almond

Ricciardi et al. (2018)

Almond CAPS R1000 × Desmayo Largueta Sweet kernel

Chemical traits

Sk

5

Fonti i Forcada et al. (2015) SSR GWAS 1,3,4,7

Species Name Trait

Table 2 (continued)

Marker

References Population

G. M. Sideli and T. M. Gradziel

Linkage group

4

Even though peach and almond differ in mating systems, with peach being self-compatible (Hedrick et al. 1917; Wellington et al. 1929; Velasco et al. 2016), while gametophytic selfincompatibility is the norm for almond, peach and almond readily hybridize with each other as well as other with wild Prunus species. Short simple repeat (SSR) analysis shows that almond has a close genetic relationship and plausible ancestry with P. fenzliana due to same SSRs across distinct morphologies, which further demonstrate that the two species can readily hybridize (Zeinalabedini et al. 2010). Interspecific hybrids exhibit moderate to high pollen viability revealing that there are not significant hybrid breakdown barriers. Hybridization between wild Prunus and domesticated almond can contribute to traits such as higher monounsaturated fats in kernel, well-sealed shell against navel orange worm (Amyelois transitella) and peach twig borer (Anarsia lineatella), later bloom period, disease resistance and tree size modification (Gradziel et al. 2001b). Velasco et al. (2016) estimated that almond and peach species diverged approximately 8 Mya during a corresponding period of climatic change and geological activity in the northeastern section of the Tibetan plateau (Fang et al. 2007; Molnar et al. 2010). In contrast, Alioto et al. (2019) estimated P. dulcis diverged from P. persica approximately 5.88 Mya, from P. mume 20.84 Mya and from P. avium 62.04 Mya. Almond was found to have up to seven times the genetic diversity of any peach species suggesting its diversity is from its mating system rather than from domestication (Velasco et al. 2016).

3

Breeding

An important breeding goal of any cultivar improvement program is maintenance of genetic diversity while concurrently selecting for improved commercially relevant traits. In almond, there is an emphasis on breeding for self-compatibility. Promising sources of self-compatibility have been from peach and wild almond relatives such as P. webbii. Use of PCR markers developed in the

Genome Analysis and Breeding

mid-1990s (Gradziel et al. 2001a; Barckley et al. 2006), or more recently KASP markers (Goonetilleke et al. 2020), for screening of selfcompatibility has become routine. There have been several self-compatible varieties released. In California, a self-compatible variety ‘Independence’ was recently released by Zaiger Genetics, as well as a partial selfcompatible ‘Sweetheart’ (Gradziel and MartinezGomez 2013). In CEBAS-CSIC, Spain, under the direction of F. Dicenta, recent self-compatible selections are currently being released ‘29–148’, ‘30–297’ and ‘D06-795’, and at Zaragoza CITA, Spain, ‘Vialfas’ was released as an extra-latebloom cultivar. The University of Adelaide, under the direction of M. Wirthensohn, has released ‘Capella’, ‘Carina’, ‘Mira’ and ‘Vela’ which are all self-compatible. Finally, a self-compatible variety ‘Matan’ has been released from the Volcani Center, ARO, Israel, by D. Holland. Variety breeding objectives vary between breeding regions, but are generally for traits with commercial relevance including: disease resistance (plum pox resistance, hull rot resistance, bacterial spot resistance), chemical quality traits (lipids, sweet kernel, tocopherol, vitamin B), physical quality traits (shell hardness, kernel size, kernel weight, shelling percentage, low rate of doubles, tight suture), phenology traits (bloom time, chilling requirement, hull split), tree architectural traits, vegetative vigor and resistance to non-infectious bud failure. In addition, non-infectious failure is a problem in the USA, and the clonal selection work started by D.E. Kester in 1980s and continued by T.M. Gradziel has been successful in providing low-bud failure source clones for the dominant ‘Nonpareil’ and ‘Carmel’ varieties currently planted in California. While growers often have similar opinions on what the ‘ideal’ cultivar should be like, processors, product developers and consumers offer a different perspective on important characteristics. For example, a candy company might want emphasis on smaller-sized almond kernels or crisp texture, while a processor is interested in flavor or few defects. Together, they aim to have ‘good-quality’ nuts.

5

4

Rootstock Breeding

In traditional dryland almond production, almond seedlings were used as rootstocks due to their deep root growth. However, almond rootstock does not perform well when planted in irrigated soil, as in the USA due to waterlogging of roots and inability to handle anoxic conditions, including disease susceptibility (Socias i Company et al. 2012) to crown rot (Phytophtora spp.), crown gall (Agrobacterium tumefaciens), oak root fungus (Armillaria spp.), as well as root knot (Meloidogyne incognita) and root lesion (Pratylenchus vulnus) nematodes. Therefore, rootstock breeding efforts have targeted resistance to disease. Alternatively, peach, plum or almond × plum rootstocks are utilized due to their shallow root system, good graft compatibility and may be better suited for irrigated soils. Peach × almond rootstocks demonstrate high vigor under irrigated as well as non-irrigated conditions. European rootstock ‘GF677’ and U.S. rootstocks ‘Hanson’ and ‘Nickels’ demonstrate such hybrid vigor. In CITA, Spain, nematode-resistant rootstocks ‘Felinem’, ‘Garnem’ and ‘Monegro’ were developed from breeding line ‘Garfi’ almond × ‘Nemared’ peach (Felipe 2009). Propagation of rootstocks can be through shoot-tip culture, hardwood cuttings or micropropagation on tissue culture. Hardwood cuttings have traditionally been important as they require less costs and specialized facilities. Micropropagation or leafy cuttings have recently become more important as it allows fast and virus-free propagations which have been successful for scions as well as rootstocks. Methods are also being developed for somatic embryogenesis of almond ‘Mission’ × peach hybrids with root-knot nematode and possible ring nematode resistance. First developed for Juglans regia L. (Tulecke and McGranahan 1985), the protocol utilizes immature embryos which have been excised from fruits at 6– 11 weeks after pollination and grown on a sequence of media that has undergone validation for producing embryos (Polito et al. 1989) in order to induce somatic embryogenesis where

6

G. M. Sideli and T. M. Gradziel

from cultured zygotic embryos, globular, heart, cotyledonary and complete somatic embryos are obtained. For initiation and multiplication, embryos are maintained at room temperature in the dark and transferred to new media every 7– 10 days. Desired embryo lines are then desiccated to initiate germination, obtain shoots and induce root growth. Resulting propagated plantlets appear to have good health, similar to most of almond and peach hybrids (Dandekar and Walawage, unpublished).

5

Biotechnology

Because almond is heterozygous with a long juvenile phase, traditional breeding is costly and the introgression of many traits can be challenging. Biotechnology techniques such as CRISPR Cas9 offer a solution to transfer traits within a genus or from non-related species. However, acceptance by the European Union or United States Department of Agriculture will dictate the use these newer biotechnologies.

5.1 Genomic Resources The major germplasm repositories are located in the USA, Spain, France, Italy, Iran and some countries of former USSR. These repositories serve as a genetic resource for breeders and geneticists to utilize for evaluation and introgression of traits for the purpose of providing natural genetic variation into breeding lines. Until recently, there was not an assembled genome for almond, and the genome assembly for peach was used as a resource. The first draft peach genome sequence represented a high-quality whole genome shotgun chromosome-scale assembly performed by the International Peach Genome Initiative of haploid ‘Lovell’. It consisted of eight pseudomolecules which represent the eight Prunus chromosomes, with 99.4% of the genome arranged on 202 scaffolds, 2730 contigs (*1.2% gap) for a total of 227.3 Mbp (Arús et al. 2012). The creation of four linkage maps, (‘T × E’) F2, (‘IF7310828’ × ‘Ferganensis’)

BC1, (‘Contender’ × ‘Ambra’) F2 and (‘Maria Dolce’ × ‘SD81’) F1 cross, with 3,576 markers was used to improve peach v1.0 mapped sequences resulting in 191 scaffolds with (99.2%) mapped sequences and (98.2%) oriented sequence, 2,525 contigs and 99.4% of genome on scaffolds > 50 Kb (Verde et al. 2017). The almond genome size is estimated to be less than 300 Mbp. The availability of genetic resources has increased relative to what has been available in other Prunus species such as peach. Whole genome sequences are stored and maintained for almond and peach, which are available at GDR worldwide community database for Rosaceae, also at Phytozome and IGA. Each database has the ability to search, download and BLAST genomic sequences. The P. dulcis ‘Texas’ genome assembly v2.0 was completed by an international consortium including Spain (CRG, IRTA, CRAG, CITA), Australian (University of Adelaide), USA (Washington State University) and France (INRA) research organizations. The final assembly of ‘Texas’ v2.0 (a.k.a. pdulcis26) is 227.6 Mb in total (91.5% of which is anchored to the eight pseudomolecules) and contains 27,969 protein-coding genes that have been annotated, and 92% of them have been assigned with a functional annotation to 6747 non-coding transcripts (Alioto et al. 2019). The genome assembly of ‘Lauranne’ v1.0 is a total size of 246 Mb, arranged on 4078 scaffolds, where 2572 were organized on eight pseudomolecules. The predicted number of genes is 27,817 where 16,747 have an annotation edit distance ≤ to 0.3. Estimates are that 95% of genes are conserved between P. persica and almond, while 34.6% of the almond genome was found to be repetitive (Sánchez-Pérez et al. 2019). The ‘Nonpareil’ genome has been developed as a phased haplotype genome in order to get the closest representation of the almond diploid genome including both the chloroplast and mitochondrial genomes (D’Amico-Willman et al. 2022). This allows for measures such as heterozygosity, allelic diversity within genes, allelic interactions and a better understanding of gene expression as it relates to epigenetic regulation.

Genome Analysis and Breeding

5.2 Studies and Markers The use of molecular markers can increase precision of selection for both progeny and parents in a breeding program. The earliest form of markers utilized was isozymes characterized by being codominant expression and reproducible. Examples of their usage have been performed by (Arulsekar et al. 1986; Haugge et al. 1987; Vezvaei et al. 1995; Gradziel and MartinezGomez 2013) and utilized to determine genetic variation in almond California cultivars (Gradziel and Martinez-Gomez 2013). Both isozymes and Random Amplified Polymorphic DNA (RAPDs) were used to create linkage maps in almond and related Prunus sp. (Arús et al. 1994) and both isozymes and Restriction Fragment Length Polymorphism (RFLPs) to create a genetic map for ‘Ferragnes’ × ‘Tuono’ (Viruel et al. 1995). More recently, microsatellite loci or simple sequence repeats (SSRs), characterized by being codominant and highly polymorphic, have been successfully used to assess genetic diversity among Spanish varieties (Fernandez i Martí et al. 2009), uniquely identify California varieties (Dangl et al. 2009), perform gene mapping of major agronomic traits (Sánchez-Pérez et al. 2007) and determine genetic basis of chilling and heat requirements for bud break (Sánchez-Pérez et al. 2011). Single nucleotide polymorphisms (SNPs) are variants discovered in genetic populations that can be used to assess genetic relationships, population structure and coupled with phenotypic data, the genetic basis for a trait. SNPs are bi-allelic, abundant in codominant markers and have the ability to evaluate a large number of individuals in a high-throughput manner. In contrast, previous marker systems such as SSRs are low throughput, possess a limited number of repeating motifs and have low linkage to genes (Mohan et al. 1997; Hong et al. 2007; Hayward et al. 2015). The use of SNP arrays, which contain thousands of SNPs, can be used for the discrimination and identification of SNP haplotypes where chromosomal regions of interest are present in individuals through identity by descent (IBD) (Chia et al. 2012; Thomson 2014).

7

Currently, there is an Illumina Infinium 9 K SNP array designed from 56 peach breeding accessions which serve as a source for molecular breeding (Verde et al. 2012). Forty almond varieties using the Texas almond genome have been re-sequenced, and 700,000 SNPs were chosen to design an Illumina array with cooperation of IRTA, Spain; INRA, France; CITA, Aragon, Spain; CEBAS-CSIC, Spain; University of Catania, Italy; and University of Adelaide, Australia (Duval et al. 2023). As with previous molecular marker methods, AFLP and RFLP, genotyping by sequence (GBS), first developed in maize and barley (Elshire et al. 2011) utilize restriction enzymes to cut DNA sequences at specific sites to create a reduced representation of the genome. GBS allows for variant discovery and genotyping during the same step (Gardner et al. 2014). However, due to highly heterozygous nature of a tree crops like almond, a danger with GBS is miscalled genotypes if there is not sufficient sequence depth (Myles 2013). Consequently, different genomic marker systems have advantages and disadvantages which should be considered accordingly for the scope of a project. In tree crops, marker-trait associations are typically carried out with bi-parental populations segregating for a specific trait with consideration to constraints of size of populations, resources and time. Genetic linkage maps provide the basis for mapping of loci which control important traits. The process to make a genetic map is to obtain a population of individuals segregating for a trait of interest and create a pseudo-test cross to obtain markers present in both parental lines. The first linkage map was constructed from 120 RFLPs and 7 isozymes from ‘Ferragnes’ × ‘Tuono’ with a total length of 400 cM across 8 linkage groups (Viruel et al. 1995). ‘Texas’ × ‘EarlyGold’ (peach) (T × E) was then constructed as a result of the European mapping project (Arús et al. 1994). Because RFLP is transferrable markers from other Prunus species, it was considered a reference map for Prunus (Joobeur et al. 1998). Later, the T × E was improved with the addition of SSRs and other sequence-based marker systems, including

8

isozymes and sequence tagged sites, for a total of 562 markers covering a distance of 519 cM with average density of markers every 0.92 cM (Dirlewanger et al. 2004). The ‘Ferragnes’ × ‘Tuono’ linkage map consisted of 72 markers, 7 isozymes and 65 RFLPs. The ‘R1000’ × ‘Desmayo Largueta’ cross was utilized to map major agronomic traits (Sánchez-Pérez et al. 2007) and the (sweet kernel) Sk locus (SánchezPérez et al. 2007; Ricciardi et al. 2018). A linkage map was constructed with 56 SSRs from the population ‘Vivot’ × ‘Blanquerna’, for mapping 14 putative QTLs for physical nut traits (Fernández i Martí et al. 2012). Among the most economically important traits in almond is self-compatibility and selffruitfulness. The latter is structural (stigma needs develop adjacent to the anther) and a complex trait. In the USA, many of the almond cultivars, including the most widely planted ‘Nonpareil’, are self-incompatible requiring the planting of another cultivar in adjacent orchard rows and the introduction of honeybee hives at bloom to facilitate proper pollination. However, the longterm sustainability for reliance on bees has a large economic footprint for the industry. Many breeding programs have placed a large emphasis on improvement of cultivars by releasing self-fertile varieties. For evaluating the selffruitfulness in self-compatible genotypes, a portion of a tree branch is covered with fine mesh bags prior to anthesis and left on for the duration of anthesis until selfed fruit set can be counted. The incompatibility of Prunus species is controlled by a single, multi-allelic gene which is gametophytic in the pollen (Dicenta and Garcia 1993a) with diploid S-alleles that are expressed in the style to recognize and self-prevent pollen tube growth (Tao et al. 1997). A PCR-based test was developed to differentiate the band size of each allele with consensus primers (Tamura et al. 2000; Channuntapipat et al. 2001, 2003, 2015; Martínez-Gómez et al. 2003; Ortega et al. 2005; López et al. 2006). Ushijima et al. (2003) sequenced the S-gene and found that one, SFB protein was expressed only in the pollen, and the other was also expressed in pistil. Three other S haplotypes from pollen were cloned and

G. M. Sideli and T. M. Gradziel

physically mapped. Identification of genetic control (Arús et al. 1998, 1999; Bliss et al. 2002; Sánchez-Pérez et al. 2007; Fernández i Martí et al. 2011; Goonetilleke et al. 2020) all mapped QTLs for S-alleles to LG 6 additionally found a ‘modifier’ gene located on LG 8 in a ‘Vivot’ × ‘Blanquerna’ cross. Phenological traits such as bloom time, chilling and subsequent heat requirements and fruit maturity are economically important traits that have been studied extensively in almond. Since almond has a low chilling requirement and is among the first tree species to bloom in the winter, the risk of rain, humidity and cold can impede both anther dehiscence and bee activity of pollen movement. In areas such as cold inland or mountainous areas, breeders have focused on late-blooming varieties to avoid frost damage. Flowering time is a polygenic trait (Kester et al. 1977; Sánchez-Pérez et al. 2007, 2012; Dicenta et al.); however, a major gene (Late bloom, Lb) has been identified in ‘Tardy Nonpareil’ a budsport mutant of ‘Nonpareil’ (Grasselly 1978; Socias i Company, Rafael et al. 1998; Silva et al. 2005). Ballester et al. (2001) analyzed ‘Felisia’ ‘Bertina’ where ‘Felisia’ had ‘Tardy Nonpareil’ in its background using bulk-segregant analysis (BSA) to identify RAPD markers linked to Lb gene which explained 79% of phenotypic variation of blooms up to 15 days later. Successful flowering occurs when endodormancy has ended and temperatures are suitable for continued bud development (Sánchez-Pérez et al. 2011). This can be explained by the heat unit accumulation, (the growing degree hours—hourly temperature minus 4.5 °C) (Sánchez-Pérez et al. 2012) and chilling requirement, (the minimum duration of cold exposure required before dormant buds bloom in response to optimal growth conditions) (Dennis 2003; Bielenberg et al. 2015). Quantitative trait loci were found to be positively associated on LG1, LG4, LG5 and LG7 (Sánchez-Pérez et al. 2011), which confirmed on LG1 in ‘Tuono’ × ‘Shahrood-12’ utilizing 140 RAPD, 87 nuclear SSRs, 5 chloroplast SSRs (Rasouli et al. 2018) and in peach cross ‘Hakuho’ × ‘UFGold’ on LG1, LG4 and LG7 (Bielenberg et al. 2015).

Genome Analysis and Breeding

Quality traits are important in the final almond product, but their importance differs depending upon consumer, industry and regional preferences. These traits can be divided into physical traits (i.e., shell hardness, kernel shape, kernel size, doubles, kernel weight) and chemical traits (i.e., amygdalin content, lipid fraction, soluble sugars, fiber, protein). Utilizing GBS technology in a ‘Nonpareil’ × ‘Lauranne’ cross, Goonetilleke et al. (2018) analyzed a population of 180 trees for shell hardness by calculating shell hardness percentage (kernel weight/in-shell weight) 100% and found genetic control on LG2, which confirmed earlier findings by (Sánchez-Pérez et al. 2007), and LG5 and LG8, which confirmed earlier findings by (Arús et al. 1998, 1999). Similarly, (Fornés Comas et al. 2019) evaluated shell hardness in 54 varieties of almond (soft and hard shell) by objectively utilizing a texturometer which measured the amount of stress applied to the almond shell and the point of shell deformation. (Sideli et al. 2023) evaluated 264 almond trees for shell hardness also using a texture analyzer and found marker-trait associations on chromosomes 2, 5, 7 and 8. Almond kernels are high in monounsaturated fatty acids, vitamin B2 and a-tocopherol (Stuetz et al. 2017), which are important human for nutrition. Unshelled almonds have a shelf-life of up to 12 months. However, shelling and processing of almonds, such as roasting, decreases shelf-life due to chemical and physical alterations during the roasting process (Franklin et al. 2017). Monounsaturated fatty acids such as oleic acids are less susceptible to oxidation of lipids during postharvest storage when compared to polyunsaturated fatty acids such as linoleic acids. The oxidation of lipids causes its degradation and generates by-products like peroxides and aldehydes, known for off-flavors. The first association mapping study performed in almond was for the evaluation of physical and chemical traits in 98 almond cultivars from Argentina, Australia, Europe, North Africa and USA for (Font i Forcada et al. 2015). Chemical traits (a-tocopherol, d-tocopherol, c-tocopherol and stearic acid) were found to have an impact on longevity of the almond nut and were found to be associated with

9

LG1, c-tocopherol. Fatty acids (oleic, linoleic and oil content) were associated with loci on LG4. Oleic, linoleic, d-tocopherol, c-tocopherol, stearic and palmitic acids and protein content were found to be on LG3. Breeding for higher oleic acid content should lengthen the storage life of almonds (Socias i Company et al. 2009). Bitterness in almond kernels is due to the presence of amygdalin, a cyanoglucoside which, in high concentrations, can be toxic. The bitter compounds are due to a recessive allele from the sweet kernel locus (Sk/sk) (Heppner 1923, 1926; Dicenta and García 1993a, b; Vargas et al. 2001; Dicenta et al. 2007; Sánchez-Pérez et al. 2010) and have been found to be associated with loci on LG5 (Dirlewanger et al. 2004; Howad et al. 2005; Sánchez-Pérez et al. 2007). The ecological sustainability of almond orchards is at risk from infestations with plantparasitic nematodes, including root knot (Meloidogyne incognita), root lesion (Pratylenchus vulnus) and ring (Mesocriconema xenoplax), which remain major threats to the grower’s economic return. Of these, M. incognita is the most widespread problem and where most of nematode research in almond/peach rootstock has been focused. Secondarily, P. vulnus is problematic in developing orchards giving rise to stunted and uneven growth. A long-term breeding focus is to develop rootstocks with resistance and tolerance to these soil-dwelling parasites which will aid mitigating damage to future yields. Resistance was first screened in Myrobalan plum (Prunus cerasifera L.) (Claverie et al. 2004) using AFLP markers in a bulksegregant analysis (Michelmore et al. 1991) of three segregating crosses and found to have genetic control on LG7. Genetic mapping and development of molecular markers for root-knot nematode resistance were performed using SSRs in a 3-way cross [P2175 x (‘Garfi’ × ‘Nemared’)], where P2175 is Myrobalan plum and ‘Garfi’ × ‘Nemared’ is an almond × peach cross, respectively. ‘Garfi’ is susceptible, and both ‘Nemared’ and P2175 are resistant (Dirlewanger et al. 2004; Duval et al. 2013) where the resistance gene Rmia was found to be controlled by two loci on LG2 and LG7.

10

5.3 Marker-Assisted Breeding

G. M. Sideli and T. M. Gradziel

Competitive allele-specific PCR (KASP) markers have been created for the identification and selection among seedlings for shell hardness (Goonetilleke et al. 2018; Sideli et al. 2023), Salleles (Goonetilleke et al. 2020) and root-knot nematode resistance in a separate population, ‘Alnem’ × ‘Lauranne’, and two KASP markers were developed (Duval et al. 2018).

In order to implement marker-assisted breeding (MAB), known markers first need to be validated. There are many examples of QTL mapping studies, but few markers have been applied in breeding programs. Single-locus genes identified are kernel sweetness or bitterness, shell hardness, delayed bloom and self-incompatibility (Socias i Company 1998). The use of markers for traits controlled by one to two loci can be predictive. However, if there are more loci under genetic control, prediction accuracy across population is more challenging. There are many examples of the development and deployment of molecular markers in breeding programs. Marker-assisted selection for S-alleles has been applied to seedlings in breeding programs to better evaluate self-compatible genotypes (Gradziel et al. 2001a; Ma and Oliveira 2001; Sánchez-Pérez et al. 2004; Ortega et al. 2005; López et al. 2006; Socias i Company 2017). More recently, cleaved amplified polymorphic sequence (CAPS) markers were developed for the sweet kernel locus Sk (Ricciardi et al. 2018).

Almond represents significant worldwide economic importance. The need to maintain genetic diversity in breeding programs remains paramount, while continuously developing improved cultivars that meet both environmental challenges, economic demands and the evolving needs of industry. With the advent of genomic technologies, discovery of trait genetics, advances in automated phenotyping and micropropagation techniques, development of cultivars can be significantly expedited. Molecular markers for single-gene traits have been and will continue to be developed. For polygenic traits, pyramiding

Fig. 1 Genomic-assisted breeding schematic. Both genomic resources and phenotypic information are the foundation for any genetic analyses such as QTL mapping, GWAS, candidate gene mapping or genomic

predictions. The ultimate goal is for the development of markers in marker-assisted selection (MAS) or the use of genomic estimated breeding values (GEBV) for genomic selection

6

Future Directions

Genome Analysis and Breeding

11

Fig. 2 Example of a complex pedigree in an almond breeding program. Pedigree includes F1 crosses, backcrosses, open pollinations and self-crosses

traits using modeling, CRISPR technology and genomic predictions holds promise. Altogether these developing technologies will only assist the foundation of traditional plant breeding (Figs. 1 and 2; Tables 1 and 2).

References Alioto T, Alexiou KG, Bardil A et al (2019) Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J 101:455– 472. https://doi.org/10.1111/tpj.14538 Arulsekar S, Parfitt DE, Kester DE (1986) Comparison of isozyme variability in peach and almond cultivars. jhered 77:272–274 Arús P, Ballester J, Jáuregui B, et al (1999) The European Prunus mapping project: update on marker development in almond. Acta Horticulturae 331–338. https:// doi.org/10.17660/ActaHortic.1998.484.57 Arús P, Messeguer R, Viruel M, et al (1994) The European Prunus mapping project. In: Schmidt H, Kellerhals M (eds) Progress in temperate fruit breeding: proceedings of the Eucarpia fruit breeding section meeting held at Wädenswil/Einsiedeln, Switzerland from August 30 to September 3, 1993. Springer, Netherlands, Dordrecht, pp 305–308 Arús P, Verde I, Sosinski B et al (2012) The peach genome. Tree Genet Genomes 8:531–547. https://doi. org/10.1007/s11295-012-0493-8 Atwell S, Huang YS, Vilhjálmsson BJ et al (2010) Genome-wide association study of 107 phenotypes in a common set of Arabidopsis thaliana inbred lines. Nature 465:627–758. https://doi.org/10.1038/nature 08800 Baird WV, Estager AS, Wells JK (1994) Estimating nuclear DNA content in peach and related diploid species using laser flow cytometry and DNA hybridization. J Amer Soc Horticul Sci Jashs 119:1312–1316

Ballester J, Bošković R, Batlle I, Arús P, Vargas F, De Vicente MC (1998) Location of the self‐incompatibility gene on the almond linkage map. Plant Breeding 117(1):69–72 Ballester J, Socias i Company, R., Arús P, de Vicente MC (2001) Genetic mapping of a major gene delaying blooming time in almond. Plant Breeding 120: 268–270. https://doi.org/10.1046/j.1439-0523.2001. 00604.x Barckley KK, Uratsu SL, Gradziel TM, Dandekar AM (2006) Multidimensional analysis of S-alleles from cross-incompatible groups of California almond cultivars. J Amer Soc Horticul Sci Jashs 131:632–636 Bielenberg DG, Rauh B, Fan S, et al (2015) Genotyping by sequencing for SNP-based linkage map construction and QTL analysis of chilling requirement and bloom date in peach [Prunus persica (L.) Batsch]. PLoS ONE 10:e0139406. https://doi.org/10.1371/journal.pone.013 9406 Bliss FA, Arulsekar S, Foolad MR et al (2002) An expanded genetic linkage map of Prunus based on an interspecific cross between almond and peach. Genome 45:520–529. https://doi.org/10.1139/g02-011 Browicz K, Zohary D (1996) The genus Amygdalus L. (Rosaceae): species relationships, distribution and evolution under domestication. Genet Resour Crop Evol 43:229–247. https://doi.org/10.1007/BF00123 275 CA Dept Food Ag (2019) California agricultural statistics review, 2017–2018 Channuntapipat C, Sedgley M, Collins G (2001) Sequences of the cDNAs and genomic DNAs encoding the S1, S7, S8, and Sf alleles from almond, Prunus dulcis. Theor Appl Genet 103:1115–1122 Channuntapipat C, Sedgley SM, Batlle I et al (2015) Sequences of the genomic DNAs encoding the S2, S9, S10, and S23alleles from almond, Prunus dulcis. J Hortic Sci Biotechnol 77:387–392. https://doi.org/ 10.1080/14620316.2002.11511510 Channuntapipat C, Wirthensohn M, Ramesh SA et al (2003) Identification of incompatibility genotypes in almond (Prunus dulcis Mill.) using specific primers

12 based on the introns of the S-alleles. Plant Breeding 122:164–168. https://doi.org/10.1046/j.1439-0523.2003. 00842.x Chia J-M, Song C, Bradbury PJ, et al (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nature Genetics 44:803 EP Claverie M, Dirlewanger E, Cosson P et al (2004) Highresolution mapping and chromosome landing at the root-knot nematode resistance locus Ma from Myrobalan plum using a large-insert BAC DNA library. Theor Appl Genet 109:1318–1327. https://doi.org/10. 1007/s00122-004-1749-y Dangl GS, Yang J, Golino DA, Gradziel T (2009) A practical method for almond cultivar identification and parental analysis using simple sequence repeat markers. Euphytica 168:41–48 Darlington CG (1930) Studies in Prunus, III. J Genet 22:65–93 Denisov VP (1988) Almond genetic resources in the USSR and their use in production and breeding. Acta Hort 224:299–306 Dennis FG (2003) Problems in standardizing methods for evaluating the chilling requirements for the breaking of dormancy in buds of woody plants. HortSci 38:347–350 Dicenta F, Garcia GE, Carbonell EA (2015) Heritability of flowering, productivity and maturity in almond. J Horticul Sci 68(1):113–120 Dicenta F, Garcia JE (1993a) Inheritance of selfcompatibility in almond. Heredity 70:313–317 Dicenta F, Garcia JE (1993b) Inheritance of the kernel flavour in almond. Heredity 70:308–312 Dicenta F, Ortega E, Martinez-Gomez P (2007) Use of recessive homozygous genotypes to assess genetic control of kernel bitterness in almond. Euphytica 153:221–225 Dirlewanger E, Graziano E, Joobeur T et al (2004) Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proc Natl Acad Sci USA 101:9891 Duval H, Hoerter M, Polidori J et al (2013) High-resolution mapping of the RMia gene for resistance to root-knot nematodes in peach. Tree Genet Genom 10:297–306. https://doi.org/10.1007/s11295-013-0683-z Duval H, Van Ghelder C, Callot C, Esmenjaud D (2018) Characterization of the RMjaresistance gene to rootknot nematodes from the “Alnem” almond rootstock. Acta Horticulturae 325–330. https://doi.org/10.17660/ ActaHortic.2018.1219.49 Duval H, Coindre E, Ramos-Onsins SE, Alexiou KG, Rubio-Cabetas MJ, Martínez-García PJ, Wirthensohn M, Dhingra A, Samarina A, Arús P (2023) Development and evaluation of an axiomTM 60K SNP array for almond (Prunus dulcis). Plants (Basel) https://doi.org/10.3390/plants12020242. 12(2):242. PMID: 36678957; PMCID: PMC9866729 Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379–e19379

G. M. Sideli and T. M. Gradziel Famula RA, Richards JH, Famula TR, Neale DB (2019) Association genetics of carbon isotope discrimination and leaf morphology in a breeding population of Juglans regia L. Tree Genetics Genomes 15:1–13. https://doi.org/10.1007/s11295-018-1307-4 Fang X, Zhang W, Meng Q et al (2007) High-resolution magnetostratigraphy of the Neogene Huaitoutala section in the eastern Qaidam Basin on the NE Tibetan Plateau, Qinghai Province, China and its implication on tectonic uplift of the NE Tibetan Plateau. Earth Planet Sci Lett 258:293–306. https://doi.org/10.1016/j. epsl.2007.03.042 FAOSTAT (2017) Food and agriculture organization of the United Nations Felipe AJ (2009) “Felinem”, “Garnem”, and “Monegro” almond x peach hybrid rootstocks. HortScience 44:196–197 Fernández i Martí A, Font i Forcada C, Socias i Company, Rafel (2012) Genetic analysis for physical nut traits in almond. Tree Genetics Genomes 9:455–465. https:// doi.org/10.1007/s11295-012-0566-8 Font i Forcada C, Oraguzie N, Reyes-Chin-Wo S, et al (2015) Identification of genetic loci associated with quality traits in almond via association mapping. PLoS ONE 10:e0127656–e0127656 Fornés Comas J, Socias i Company, Rafel, Alonso Segura JM (2019) Shell hardness in almond: cracking load and kernel percentage. Scientia Horticulturae 245:7–11. https://doi.org/10.1016/j.scienta.2018. 09.075 Franklin LM, Chapman DM, King ES et al (2017) Chemical and sensory characterization of oxidative changes in roasted almonds undergoing accelerated shelf life. J Agric Food Chem 65:2549–2563. https:// doi.org/10.1021/acs.jafc.6b05357 Fernández i Martí A, Alonso JM, Espiau MT et al (2009) Genetic diversity in Spanish and foreign almond Germplasm assessed by molecular characterization with simple sequence repeats. J Amer Soc Hort Sci 134:535–542 Fernández i Martí À, Howad W, Tao R, et al (2011) Identification of quantitative trait loci associated with self-compatibility in a Prunus species. Tree Genetics & Genomes 7:629–639. https://doi.org/10.17660/ ActaHortic.1998.484.57 Gardner KM, Brown P, Cooke TF et al (2014) Fast and cost-effective genetic mapping in apple using nextgeneration sequencing. G3: Genes Genomes, Genetics 4:1681–1687. https://doi.org/10.1534/g3.114.011023 Goonetilleke SN, Croxford AE, March TJ et al (2020) Variation among S-locus haplotypes and among stylar RNases in almond. Sci Rep 10:583 Goonetilleke SN, March TJ, Wirthensohn MG et al (2018) Genotyping by sequencing in almond: SNP discovery, linkage mapping, and marker design. G3: Genes Genomes, Genetics 8:161–172. https://doi.org/ 10.1534/g3.117.300376 Gradziel T, Martinez-Gomez P (2013) Plant breeding reviews, vol 37. pp 207–258

Genome Analysis and Breeding Gradziel TM, Martinez-Gomez P, Dandekar AM (2001a) The use of S-allele specific PCR analysis to improve breeding efficiency for self-fertility in almond. HortScience 36:440 Gradziel TM, Martinez-Gomez P, Dicenta F, Kester DE (2001b) The utilization of related prunus species for almond variety improvement. J Amer Pomological Soc 55:100–108 Grasselly C (1977) Réflexions sur les caractéristiques des espèces sauvages d’amandier et sur leur utilisation éventuelle dans des programmes d’amélioration génétique. In: Proceedings of the III Colloque du GREMPA, Bari, Italy, 3–7 October 1977, pp 70–76 Grasselly C (1978) Observations sur l’utilisation d’un mutant d’amandier a floraison tardive dans un programme d’hybridation. Ann Amelior Plant 28:685–695 Haugge R, Kester DE, Arulsekar S et al (1987) Isozyme variation among California cultivars II. Cultivar characterization and Origina. J Amer Soc Horticul Sci 112:693–698 Hayward AC, Tollenaere R, Dalton-Morgan J, Batley J (2015) Molecular marker applications in plants. In: Batley J (ed) Molecular marker application in plants. Springer, New York, NY, pp 13–27 Hedrick UP, Howe GH, Taylor OM, Tubergen CB (1917) The peaches of New York. JB Lyon Company, Albany, NY Heppner MJ (1923) The factor for bitterness in the sweet almond. Genetics 8:390 Heppner MJ (1926) Further evidence on the factor for bitterness in the sweet almond. Genetics 11:605–606 Hong CP, Piao ZY, Kang TW et al (2007) Genomic distribution of simple sequence repeats in Brassica rapa. Mol Cells 23:349–356 Howad W, Yamamoto T, Dirlewanger E et al (2005) Mapping with a few plants: using selective mapping for microsatellite saturation of the Prunus reference map. Genetics 171:1305–1309. https://doi.org/10. 1534/genetics.105.043661 Joobeur T, Viruel MA, de Vicente MC et al (1998) Construction of a saturated linkage map for Prunus using an almond×peach F2 progeny. Theor Appl Genet 97:1034–1041 Kester DE, Gradziel TM (1996) Almonds (Prunus). In: Fruit breeding. New York, USA, pp 1–97 Kester DE, Raddi P, Asay R (1977) Correlation of chilling requirements for germination, blooming and leafing within and among seedling populations of almond. J Amer Soc Horticul Sci 102(2):145–148 Ladizinsky G (1999) On the origin of almond. Gen Res Crop Evol 143–147 López M, Vargas FJ, Batlle I (2006) self-(in)compatibility almond genotypes: a review. Euphytica 150:1–16. https://doi.org/10.1007/s10681-005-9009-z Ma RC, Oliveira MM (2001) Molecular cloning of the selfincompatibility genes S1 and S3 from almond (Prunus dulcis cv. Ferragnès). Sex Plant Reprod 14:163–167 Martínez-Gómez P, Dandekar AM, Gradziel TM, et al (2003) Identification of self-incompatibility alleles in

13 almond and related prunus species using PCR. Acta Horticulturae 397–401. https://doi.org/10.17660/Acta Hortic.2003.622.41 Michelmore RW, Paran I, Kesseli RV (1991) Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci 88:9828–9832. https://doi.org/10.1073/pnas.88.21.9828 Mohan M, Nair S, Bhagwat A et al (1997) Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breeding 3:87–103 Molnar P, Boos WR, Battisti DS (2010) Orographic controls on climate and Paleoclimate of Asia: thermal and mechanical roles for the Tibetan Plateau. Annu Rev Earth Planet Sci 38:77–102. https://doi.org/10. 1146/annurev-earth-040809-152456 Myles S (2013) Improving fruit and wine: what does genomics have to offer? Trends Genet 29:190–196 Ortega E, Sutherland BG, Dicenta F et al (2005) Determination of incompatibility genotypes in almond using first and second intron consensus primers: detection of new S alleles and correction of reported S genotypes. Plant Breeding 124:188–196. https://doi. org/10.1111/j.1439-0523.2004.01058.x Polito VS, McGranahan G, Pinney K, Leslie C (1989) Origin of somatic embryos from repetitively embryogenic cultures of walnut (Juglans regia L.): implications for Agrobacterium-mediated transformation. Plant Cell Rep 8:219–221. https://doi.org/10.1007/ BF00778537 Rasouli M, Fattahi Moghaddam MR, Imani A et al (2018) Identification of DNA markers linked to blooming time in almond. JON 09:105–122 Ricciardi F, Del Cueto J, Bardaro N, et al (2018) Syntenybased development of CAPS markers linked to the sweet kernel LOCUS, controlling amygdalin accumulation in almond (Prunus dulcis (Mill.) D.A.Webb). Genes 9:385. https://doi.org/10.3390/genes9080385 Richardson EA, Seeley SD, Walker RD (1974) A model estimating the completion of rest for Red Haven and Elberta peach. HortScience 9:331–332 Sánchez-Pérez R, Dicenta F, Martinez-Gomez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Genom 8:379–389 Sánchez-Pérez R, Dicenta F, Martinez-Gomez P (2004) Identification of S-alleles in almond using multiplex PCR. Euphytica 138:263–269 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2011) Transmission of chilling and heat requirements for flowering in almond and development of QTLS. Acta Horticulturae 539–543. https://doi.org/10.17660/ ActaHortic.2011.912.81 Sánchez-Pérez R, Howad W, Dicenta F et al (2007) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breeding 126:310–318. https://doi.org/10.1111/j.1439-0523. 2007.01329.x

14 Sánchez-Pérez R, Howad W, Garcia-Mas J et al (2010) Molecular markers for kernel bitterness in almond. Tree Genet Genomes 6:237–245. https://doi.org/10. 1007/s11295-009-0244-7 Sánchez-Pérez R, Pavan S, Mazzeo R et al (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364:1095 Sideli GM, Mather D, Wirthensohn M, Dicenta F, Goonetilleke SN, Martínez-García PJ, Gradziel TM (2023) Genome-wide association analysis and validation with KASP markers for nut and shell traits in almond (Prunus dulcis [Mill.] DAWebb). Tree Genetics & Genomes 19(2):13 Silva C, Garcia-Mas J, Sánchez AM et al (2005) Looking into flowering time in almond (Prunus dulcis (Mill) D. A. Webb): the candidate gene approach. Theor Appl Genet 110:959–968. https://doi.org/10.1007/s00122004-1918-z Socias i Company R (1998) Fruit tree genetics at a turning point: the almond example. Theor Appl Genet 96: 588–601 Socias i Company R, Alonso JM, Kodad O (2009) Fruit quality in almond: physical aspects for breeding strategies. Acta Horticulturae 475–480. https://doi. org/10.17660/ActaHortic.2009.814.80 Socias i Company R, Felipe AJ (1991) Self-compatibility and autogamy in “Guara” almond. J Horticul Sci 67:313–317. https://doi.org/10.1080/00221589.1992.11 516254 Socias i Company R (2017) Pollen-Style (In)Compatibility: development of Autogamous cultivars. In: Socias i Company, Rafael, Gradziel TM (eds) Almonds, botany and production and uses. CABI international, Boston, MA, pp 188–227 Socias i Company R, Alonso JM, Kodad R, Gradziel TM (2012) Almonds. In: Badenes ML (ed) Fruit breeding. Wiley, New York, pp 697–728 Socias i Company R, Anson JM, Espiau MT (2017) Taxonomy, botany and physiology. In: Socias i Company, Rafael, Gradziel TM (eds) Almonds, botany and production and uses. CABI International, Boston, MA Socias i Company R, Felipe AJ, Gómez Aparisi J (1998) Genetics of late blooming in almond Acta Horticulturae 484:261–266 Stuetz W, Schlörmann W, Glei M (2017) B-vitamins, carotenoids and a-/c-tocopherol in raw and roasted nuts. Food Chem 221:222–227. https://doi.org/10. 1016/j.foodchem.2016.10.065 Tamura M, Ushijima K, Sassa H et al (2000) Identification of self-incompatibility genotypes of almond by allelespecific PCR analysis. Theor Appl Genet 101:344–349 Tao R, Yamane H, Sassa H, et al (1997) Identification of Stylar RNases associated with Gametophytic self-

G. M. Sideli and T. M. Gradziel incompatibility in Almond (Prunus dulcis). pcp 38:304–311. https://doi.org/10.1093/oxfordjournals.pcp. a029167 Thomson MJ (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breeding and Biotechnol 2:195–212 Tulecke W, McGranahan G (1985) Somatic embryogenesis and plant regeneration from cotyledons of walnut, Juglans regia L. Plant Sci 40:57–63. https://doi.org/ 10.1016/0168-9452(85)90163-3 Ushijima K, Sassa H, Dandekar AM et al (2003) Structural and transcriptional analysis of the selfincompatibility locus of almond: identification of a pollen-expressed F-box gene with haplotype-specific polymorphism. Plant Cell 15:771–781. https://doi.org/ 10.1105/tpc.009290 Vargas FJ, Romero MA, Batlle I Kernel taste inheritance in almond. Options Mediterraneennes 56:129–134 Velasco D, Hough J, Aradhya M, Ross-Ibarra J (2016) Evolutionary genomics of peach and almond domestication. G3: Genes, Genomes, Genetics 6:3985 Verde I, Bassil N, Scalabrin S, et al (2012) Development and evaluation of a 9K SNP array for peach by internationally coordinated SNP detection and validation in breeding Germplasm. PLoS ONE 7:e35668. https://doi.org/10.1371 Verde I, Jenkins J, Dondini L et al (2017) The Peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity. BMC Genomics 18:2196–2218. https://doi.org/10.1186/s12864-017-3606-9 Vezvaei A, Hancock TW, Giles LC et al (1995) Inheritance and linkage of isozyme loci in almond. Theor Appl Genet 91:432–438 Viruel MA, Messeguer R, de Vicente MC et al (1995) A linkage map with RFLP and isozyme markers for almond. Theor Appl Genet 91:964–971 Watkins R (1979) Evolution of crop plants. In: Simmonds W (ed) Cherry, plum, peach, apricot and almond. Prunus spp. Longman, London, pp 242–247 Wellington R, Stout AB, Einset O, Van Alstyne LM (1929) Pollination of fruit trees. The Bulletin of New York State Agricultural Experimental Station, Geneva, NY Wen J, Berggren ST, Lee C-H et al (2008) Phylogenetic inferences in Prunus (Rosaceae) using chloroplast ndhF and nuclear ribosomal ITS sequences. J Syst Evol 46:322–332. https://doi.org/10.3724/SP.J.1002. 2008.08065 Zeinalabedini M, Khayam-Nekoui M, Grigorian V et al (2010) The origin and dissemination of the cultivated almond as determined by nuclear and chloroplast SSR marker analysis. Sci Hortic 125:593–601. https://doi. org/10.1016/j.scienta.2010.05.007

Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness Raquel Sánchez-Pérez

Abstract

Almond is one of the oldest species to be domesticated. Wild almonds species accumulate the bitter and toxic compound amygdalin, a cyanogenic diglucoside. The key event enabling almond domestication was the selection of sweet kernel genotypes. Genetic studies demonstrated that the edible sweet almond kernels originated from a dominant mutation, in a locus found in the linkage group five, locus name as Sweet kernel (Sk). In a combination of techniques involving biochemistry, biotechnology, mapping genetics, molecular biology, etc., the Sk gene has been elucidated, codifying for a transcription factor of the bHLH family. A non-synonymous point mutation (Leu to Phe) in the bHLH2 prevents transcription of the two first genes of the amygdalin biosynthetic pathway, the P450 PdCYP79D16 and PdCYP71AN24. With this discovery, a molecular marker has been developed to be used in any almond breeding program, bringing to the field only sweet almond trees.

R. Sánchez-Pérez (&) Department of Plant Breeding, Centro de Edafología y Biología Aplicada del Segura-Consejo Superior de Investigaciones Científicas, Murcia, Spain e-mail: [email protected]

1

The Origin of Almond

Agriculture is probably the greatest human achievement dating back to ancient times. Agriculture begins with the domestication of the species, the breeding and cultivation. Indeed, more than 10,000 BC, the Neolithic revolution starts. In this sense, almond is one of the oldest species to be domesticated. However, the origin of almond is controversial. Archeobotany suggests that it was already cultivated 19,000 years ago (Kislev et al. 1992) and expanded through the Near East as a supplement of meat and other plant foods (Willcox et al. 2008; Rivera Núñez et al. 2012). Other theories place the origin of almond in the Fertile Crescent—the “Cradle of Civilization”—with domestication starting > 5000 years ago (Delplancke et al. 2012). Cultivation in the Mediterranean Basin is documented by the existence of almonds among the items found in the Franchthi Cave in Greece (Hansen and Renfrew 1978) in the Upper Paleolithic. But even earlier, about 10,000 BC, wild species of almond were found in the Hayonim Cave (Israel) (Rivera Núñez et al. 2012) and in historical places like the tomb of Tutankhamun (Zohary et al. 2012). Almonds have been mentioned through the human history. Here are a few examples: The history of toxicity of bitter almond and peach kernels (both are supposed to have the same ancestor (Alioto et al. 2019)) was already

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_2

15

16

realized in ancient Egypt, in the second millennium BCE, where traitorous priests in the capital cities of Memphis and Thebes were poisoned to death with pits of peaches (a practice reference in hieroglyphics as “death by peach” (Davis 1991; Blum 2010). In the Greek time, Hippocrates also mentioned almonds for different medicinal uses, as well as Dioscorides I (Rivera Núñez et al. 2012). Hippocrates, the father of Western medicine, was the first to discuss almonds. Rivera and collaborators (2012) suggested in his book that the oldest and most extensive medical system which records the use of almonds derives from ancient Greece. Later, Diocles of Carystus offers further details: “The green almonds are less unwholesome than the dry, the soaked than the unsoaked and the roasted than the raw”. Caius Plinius Secundus (Pliny the Elder) in his 37volume encyclopedia Naturalis Historia stated that the Romans were proud of knowing how to remove bitterness and toxicity (two different things, later it will be explained in this chapter) from bitter kernels (Pliny the Elder, 77AD). Almond has also been mentioned several times in the Bible, in the Genesis 28:19, 30:37, 43:11, etc., Numbers 17;18, Joshua 16:2, etc., like a tree bearing nuts which Hebrews did eat in Palestine, and probably, it was brought from Egypt. Almond is probably the most important nut mentioned in the 3rd millennium texts, partly because of its oil (Rivera Núñez et al. 2012). In Basil of Caesaria’s Hexameron, from the fourth century CE, it is stated that the Greek agriculturists in Cappadocia had discovered that piercing on the trunk of a highly bitter almond tree near the root, so as to introduce a fat plug of pine into the middle of the pith, resulted in production of delicious sweet almonds (Basil of Caesaria; Sánchez-Pérez et al. 2019). But almonds were also found and commercialized along the Silk Road (Albala 2009). Almonds belong to the exotic goods traveling eastward, from the Mediterranean Basin to China and South to India. In Chinese medicine, it is mentioned that almonds are able to suppress coughs and are useful for treating lung ailments. In fact, this was the same advice offered by Greek physician Galen of Pergamum nearly two

R. Sánchez-Pérez

millennia ago. In India, almonds were more considered as an aphrodisiac, sometimes difficult to digest, again echoing ancient Greek medical ideas (Albala 2009). In the next section, we are going to see how almond today have become a key nut in the human diet, with enormous healthy benefits.

2

Almond Production

Nowadays, almond is the most important tree nut species worldwide, with a production of 3 M tons in an area of 2 Mha, distributed not only in the Mediterranean Basin, in the Middle East, West Asia, and Himalaya mountains, but also in other areas compatible with its cultivation like California, Australia, Chile, Argentina and South Africa (Socias i Company et al. 2012). California is the first producer worldwide, with more than 80% of the production, distributed in 450,000 ha, followed by Spain with more than 90,000 tons produced in an area of 650,000 ha, which represents 5–6% of the worldwide production. This second place is shared with Australia, with a very similar production. The key event enabling almond domestication was the selection of the Sweet kernel (Sk)— dominant allele, which prevents the accumulation of the toxic and bitter cyanogenic glucoside amygdalin in the kernel. We have to remember that consumption of a few bitter kernels (ca. 20) can be lethal to humans. The reason why is explained below.

3

The Two Old Friends: HCN and Almond

Previously, we have read how bitter almonds had been mentioned along the history of humankind. Nevertheless, the first discovery of the toxic principle of HCN released occurred in 1802, upon distillation of bitter almonds by the pharmacist Böhm in Berlin (Lechtenberg and Nahrstedt 1999). Three decades later, Robiquet and Boutron-Charlad isolated the first HCNliberating substance from bitter seed almonds,

Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness

naming it as “amygdalin”. Finally, Haworth and Wylam discovered the structure of amygdalin as mandelonitrile gentiobioside via chemical synthesis in 1923 (Rauws et al. 1982). Amygdalin became the first discovered compound of an important group of plant secondary metabolites known as cyanogenic glucosides.

4

Cyanogenic Glucosides

Plants-like almonds are sessile organisms that need natural products to cope with the constantly challenges around. One of the mechanisms that plants have developed is the synthesis of bio-active compounds. In the plant kingdom, it is estimated that there are approximately 200,000 bio-active compounds, also called secondary metabolites (Harvey et al. 2015). An important class within the bio-active products is cyanogenic glucosides (CNglcs). Indeed, CNGlcs are found all across the plant kingdom, from the oldest of terrestrial plants, ferns, to gymnosperms and angiosperms (Bak et al. 2006; Picmanovà et al. 2015). To date, more than 3000 plant species in 130 families are known to be cyanogenic (Moller 2010; Gleadow and Møller 2014). The main function of these CNGlcs is chemical defense against herbivores (Hansen et al. 2023), but CNGlcs have served several other functions, including transport of carbon and nitrogen, seed germination stimulants, modulators of reactive oxygen species and inducers of flower development (Sánchez-Pérez et al. 2008, 2012; Del Cueto et al. 2017; Ionescu et al. 2017; Guillamón et al. 2020).

5

Amygdalin, the Bitter Compound in Almond

As we said, the first CNglc described was amygdalin, the bitter compound found in the bitter wild almond kernels (Frehner et al. 1990; Sánchez-Pérez et al. 2008; Wirthensohn et al. 2008). Amygdalin can be also found in other Prunus species seeds as well as in the other Rosaceae seeds, like apple seeds.

17

The Amygdalin Biosynthetic Pathway Despite mentioning bitter almonds since antiquity, only four years ago the entire biosynthetic pathway was elucidated (Thodberg et al. 2018). Amygdalin biosynthesis is initiated with the amino acid L-phenylalanine, which is twice N-hydroxylated, decarboxylated and dehydrated resulting in the formation of E-phenylacetaldoxime (Fig. 1). This first step is catalyzed by the first cytochrome P450 PdCYP79D16, belonging to the CYP79 family (Andersen et al. 2000; Forslund 2004; Yamaguchi et al. 2014; Clausen et al. 2015; Knoch et al. 2016; Thodberg et al. 2018; Hansen et. al. 2018). The second P450, involved in the amygdalin pathway in almond, belongs to the CYP71 family, called PdCYP71AN24. It catalyzes the rearrangement of the (E)-aldoxime into a Z-oxime, its dehydration and a final hydroxylation reaction to produce the cyanohydrin mandelonitrile (Kahn et al. 1997, 1999; Bak et al. 1998; Kannangara et al. 2011; Takos et al. 2011; Clausen et al. 2015; Knoch et al. 2016). Mandelonitrile is a very unstable compound, which is stabilized by the action of any of these UDP-glucosyltransferases (UGT85A19 or PdUGT94AF3), synthesizing prunasin. This cyanogenic glucoside is again glucosilated by the action of the UDP-glucosyltransferases PdUGT94AF1 or PdUGT94AF2, via a b-1,6 linkage, as usual (Franks et al. 2008; Gleadow and Møller 2014; Pičmanová et al. 2015; Thodberg et al. 2018). According to the qPCR performed in the different seed tissues, prunasin is synthesized in the tegument and amygdalin in the cotyledon [see Fig. 6 in (Thodberg et al. 2018)]. Indeed, the first two genes of the amygdalin pathway, the PdCYP79D16 and PdCYP71AN24, were differentially expressed in the bitter variety, when compared with the sweet variety and only detected in the tegument. However, the UGTs involved in the synthesis of prunasin were detected in the sweet and bitter varieties, with no significant differences, in the tegument and in the cotyledon. The UGTs involved in the synthesis in amygdalin were only detected in the cotyledon, and there were almost no differences between sweet and bitter varieties (Thodberg et al. 2018).

18

Fig. 1 Amygdalin biosynthetic (black arrows) and degradation (dashed arrows) pathway scheme in almonds. Prunasin is accumulated in the vegetative part of the tree and in the tegument/seed coat, whereas amygdalin is accumulated in the cotyledons of the mature seeds. When a tissue-containing prunasin or amygdalin is disrupted, the b-glucosidases amygdalin hydrolase (AH) and prunasin

The Amygdalin Degradation Pathway Upon mechanical disruption of plant tissue, amygdalin and prunasin are hydrolyzed by the sequential action of b-glucosidases and ahydroxynitrilases, resulting in the formation of glucose, benzaldehyde (the bitter flavor) and toxic hydrogen cyanide (Morant et al. 2008; Sánchez-Pérez et al. 2008, 2012a, 2012b; Gleadow and Møller 2014). The bitter seed taste is then due to the hydrolysis of amygdalin, upon mechanical by three consecutive reactions catalyzed by (1) amygdalin hydrolase, which liberates glucose and prunasin, (2) prunasin hydrolase, liberating mandelonitrile and glucose, and (3) mandelonitrile lyase, liberating benzaldehyde (the bitter compound) and hydrogen cyanide (the toxic compound) (Fig. 1). The ability to liberate hydrogen cyanide is termed as cyanogenesis. Amygdalin is present in the cotyledon from the very early stages of the fruit development, until the fruit is fully ripened. Prunasin, the cyanogenic monoglucoside, is mainly localized in the vegetative part of the tree (e.g., leaves, stems, roots, flower buds), and its content is independent of the CNGlcs present in the seed (Sánchez-Pérez et al. 2008). Significant amounts of prunasin are found in the tegument or seed coat of the bitter seeds (Dicenta et al. 2002; Sánchez-Pérez et al. 2008; Del Cueto et al. 2017) and tiny amounts in the cotyledon in the early stages of the development of the fruit (SánchezPérez et al. 2008).

R. Sánchez-Pérez

hydrolase (PH) come into contact with amygdalin and prunasin, respectively and mandelonitrile is liberated. This compound is very unstable and either spontaneously or enzymatically by mandelonitrile lyase 1 (MDL1) liberates benzaldehyde (the bitter flavour) and the toxic compound HCN

6

The Sweet Kernel (Sk) Gene

As it was mentioned before, the key event enabling almond domestication was the selection of the Sweet kernel (Sk)-dominant allele, which prevents the accumulation of the toxic cyanogenic glucoside amygdalin in the seed/kernel. Almost a century ago, Heppner found out that the determination of sweetness or bitterness depends on a single factor (gene), being the sweet kernel allele dominant over the bitter one (Heppner 1923). The inheritance of this trait was indeed also observed later in different almond breeding programs (Dicenta and García 1993). Later, by the use of molecular markers, the Sweet kernel locus could be localized on the linkage group 5, in an almond genetic linkage map (Joobeur et al. 1998; Bliss et al. 2002; SánchezPérez et al. 2007, 2008, 2010). In this century, crucial advances to discover the nature of the Sweet kernel gene have been done. In 2000, Dicenta et al. found out that the maternal genotype is responsible for the taste of the seed, regardless the pollinizer (Dicenta et al. 2000), which is confirmed later by (Arrázola et al. 2012; Sánchez-Pérez et al. 2012b). Dicenta et al. (2007) classified several sweet modern cultivars as heterozygous or homozygous and found out that today most of the almond varieties are heterozygous. In 2013, for the first time the draft of the almond genome was performed (Koepke et al. 2013), when two sweet and two bitter almond varieties were sequenced. In this

Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness

19

Fig. 2 Alignment of the amino acid sequences of the bHLH 1, 2 and 4 proteins in the sweet (sw) Lauranne and bitter (bit) S3067 genotype by CLC Genomic Workbench. bHLH4 amino acid sequence was identical for the two

genotypes analyzed. bHLH1 sweet had a big deletion on its sequence. bHLH2 differed in two different places, one in a non-synonymous substitution (L346F, shown with an asterisk) and an Asn insertion in the sweet genotype

study, out of the 311,497 polymorphisms found, 56,155 single nucleotide polymorphisms (SNPs) were identified between the markers BPPCT017

and BPPCT028, where the Sk locus was found. Only 228 polymorphisms were found with a codon change. Recently, synteny-based development of

20

R. Sánchez-Pérez

Cleaved Amplified Polymorphic Sequence (CAPS) markers linked to the Sk locus were developed (Ricciardi et al. 2018). The Sk gene was likely located in a genomic interval flanked by two markers corresponding in peach to a physical region of about 151 Kb. Out of the twenty genes included in this region, a cytochrome P450, a prunasin hydrolase and five bHLH transcription factors were found. Only three years ago, in 2019, the almond genome was sequenced, for the first time, at a chromosome level (Sánchez-Pérez et al. 2019). This allowed the authors to compare the physical map with the genetic linkage map, finding also in almond the five bHLH transcription factors, as shown previously (Ricciardi et al. 2018). qPCR analyses of the tegument of the sweet Lauranne (SkSk) and bitter S3067 (sksk) genotypes showed that the first two genes of the amygdalin biosynthetic pathway, the P450 PdCYP79D16 and PdCYP71AN24, were differently expressed in the sweet compared to the bitter genotypes [Fig. 3 in (Thodberg et al. 2018)]. This was confirmed by qPCR analyses at different time points during the seed development. This result pCYP79D16

*

400

* *

* *

pBitter

Fig. 3 Trans-activation analysis in Nicotiana benthaniana of putative downstream gene promoters PdCYP79D16 and PdCYP71AN24 by the bHLH 2 transcription factors localized in the Sk locus and expressed in the tegument during kernel development. The bar plots show the capacity of bHLH2 variants of sweet (Sw) and bitter (Bit) genotypes to trans-activate the sweet and bitter promoters of

pSweet1

* * *

*

* *

* *

pSweet2

Positive Control C (+)

TF200Bit bHLH2Bit

TF200Bit L346F bHLH2BitL346F

TF200Sw F346L bHLH2SwF346L

pBitter No TF

bHLH2Sw TF200Sw

TF200Bit L346F bHLH2BitL346F

TF200Sw F346L bHLH2SwF346L TF200Bit bHLH2Bit

pSweet2 No TF

C (+) Positive Control

TF200Bit bHLH2Bit

TF200Bit L346F bHLH2BitL346F

TF200Sw F346L bHLH2SwF346L

pBitter No TF

TF200Sw bHLH2Sw

TF200Bit L346F bHLH2BitL346F

No TF TF200Bit bHLH2Bit

0 TF200Sw F346L bHLH2SwF346L

0

pSweet No TF

100

TF200Sw bHLH2Sw

100

C (-) Negative Control

*

200

*

pSweet

* *

TF200Sw bHLH2Sw

200

*

* * *

300

pSweet1 No TF bHLH2Sw TF200Sw

300

*

TF200Bit bHLH2Bit

400

500

*

TF200Bit L346F bHLH2BitL346F

*

TF200Sw F346L bHLH2SwF346L

*

500

pCYP71AN24

b

C (-) Negative Control

a

was decisive to continue with the identification of the Sk gene, as, for the first time, at molecular level, there was a clear difference between sweet and bitter genotypes that could explain the high difference in the amygdalin content. qPCR analyses of the five bHLH confirmed that only bHLH1, 2 and 4 were expressed in the tegument of the sweet and bitter genotypes, without any significant difference among the genotypes (Fig. 3S, (Sánchez-Pérez et al. 2019)). In order to find which of the three expressed bHLH was controlling the expression of the P450s, the sweet and bitter versions of the bHLH1 and 2 were cloned. In bHLH1, an indel polymorphism of 161 bp was found in the sweet genotype (Fig. 2). In bHLH2, a C/T SNP and a 3-bp indel were observed, resulting in a nonsynonymous Leu346–> Phe (L346F) substitution and an Asn412 insertion in the sweet genotype. In bHLH4, no polymorphism was found between the sweet and bitter genotypes. In addition, the sweet and bitter versions of the promoters PdCYP79D16 (p79) and PdCYP71AN24 (p71) were also cloned. Functional characterization was performed by heterologous expression of the

pBitter

a PdCYP79D16 (green colors) and b PdCYP71AN24 (pink colors), respectively. The effects of mutations in bHLH2 bitter and sweet at position 346 are also shown. Control experiments: negative control (p19 silencing suppressor), no transcription factor (no TF) as negative controls and pCaMV35S: APL3:GUS as positive control [C(+)]. Significant differences were shown by an asterisk

Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness

M

1

2

3

4

5

6

bHLH4. However, a significant induction of GUS activity was shown for the bHLH2 bitter in combination with all the version of p79 and p71 when substitutions were done in amino acid 346 (Fig. 3). When Leu346 was present, the activation by the bHLH2 was as in the bitter genotype, but in the contrary, when F346 was present, no activation or basal activation was observed. This indicated that the bHLH2 could be the Sweet kernel gene.

7

Fig. 4 Agarose gel (1%) electrophoresis of the PCR with the primer set bHLH2F/bHLH2R using two sweet (1, 2), two slightly bitter (3, 4) and two bitter(5, 6) almond genotypes. M: DNA markers (1 Kb Plus DNA Ladder, Invitrogen)

bHLH 1, 2 and 4 in Nicotiana benthamiana (Fig. 3) and Escherichia coli, using a factorial transient b-glucuronidase (GUS) in N. benthamiana and b-galactosidase gene in E. coli. No significant differences were observed in GUS activity when expressing the two bHLH1 alleles or Fig. 5 DNA sequence analyses of bHLH2 in six almond genotypes, showing the region where the SNP T/C is related with the bitter taste in almond kernel. Chromatograms of two sweet (Desmayo and Lauranne), two slightly bitter (Garrigues and Tuono) and two bitter (S3067 and D00-633) genotypes are shown

21

The Sk Marker

In order to validate the SNP found, we performed a PCR amplification of the bHLH2 gene in 56 almond genotypes from the CEBAS-CSIC Almond Breeding Program. The PCR was done with the PCRBIO HiFi Polymerase (PCR Biosystems), with the following conditions: 1 min 95 °C, 35 cycles of 15 s at 95 °C, 15 s at 60 °C and 1 min at 72 °C, with 30 ng of gDNA. The combination of primers to get the full length of the bHLH2 was bHLH2F/bHLH2R (bHLH2F: ATGGAAGAGATCATAGCCTCATCTTCTT/ bHLH2R: CTAGTTGTACCACCTTTTTATAA TACCCA). The expected size of the bHLH2 was 1624 pb (Fig. 4). Fifteen lL of the 50-lL PCR

22

R. Sánchez-Pérez

was enough to send to sequence with the reverse primer (bHLH2R) at the Sequencing Platform at Molecular Biology Section (University of Murcia). Once analyzed by the software CLC Genomic Workbench, we could confirm that there was a co-segregation between the Sk/sk alleles and the C/T polymorphisms in position 1172 gDNA (Fig. 5). This indicated that the Sweet kernel gene in almond was indeed the bHLH2.

8

Future Perspectives

In this chapter, we described how successfully Sánchez-Pérez et al. in a more than 10-year work isolated the Sweet kernel gene, which encoded a specific bHLH transcription factor. Nonsynonymous point mutations in its dimerization domain render the transcription factor inactive. As a result, the expression of the first two genes involved in the amygdalin biosynthetic pathway, encoding the two cytochromes P450 PdCYP79D16 and PdCYP71AN24, is strongly repressed. Among the 56 genotypes analyzed, only one, Atocha (a heterozygous sweet almond genotype), did not show the C/T polymorphism at the position 346. Instead, we found another polymorphism at position 330, where a Leu was substituted by an Arg (Leu330-> Arg (L330R)) due to a T/G polymorphism. This opens the possibility to find more alleles for the bHLH2. Indeed, a very recent work published this April by Lotti et al. (2023) have detected two dominant sweet alleles, the Sk-1 and Sk-2. Sk-1 is the one detected in the 55 genotypes by Sánchez-Pérez et al. (2019) and the Sk-2, is the one first detected in Atocha, which has also been detected in the widespread cultivar Texas, among others. Finally, the development of KASP (Competitive Allele Specific PCR) markers have been achieved, suitable for the accurate and highthroughput selection of sweet kernel individuals in almond breeding programs. Acknowledgements The author would like to acknowledge her main mentor, the biochemist Dr. Birger Lindberg Møller, for all the knowledge transmitted to her, regarding life as a scientist, as a biochemist and the wonderful work that exists behind the cyanogenic glucosides. I would also like to acknowledge the precious effort done by one of the

most efficient and brilliant PhD students she has ever had, so far, Dra. Rosella Mazzeo. Dra. Mazzeo and the student helper at that time, D. Christian Moldovan, were a super team that helped to clone all the bHLH versions in Agrobacterium tumefaciens that were used to infiltrate into Nicotiana benthamiana leaves. Dra. Rosa López-Marqués is preciously appreciated here for her invaluable friendship and infinite patience, with her and the rest of the team, to perform all the clones and experiments in E. coli and her expertise in yeast-one-hybrid experiments that helped us to get the Science publication. Dr. Stefano Pavan and Dr. Lotti (supervisors of Dra. Mazzeo) were also key partners in the localization of the Sk gene with molecular markers performed by F. Ricciardi, also an excellent visiting PhD student at KU from Pavan and Lotti’s lab. Dr. Riccardo A. was especially important to transform NGS data into key information to perform bHLH experiments with the P450 identified. Dr. Del Cueto performed the first experiments with the P450s. To clone a simple gene in a perennial plant was a real adventure! In addition, I would like to thank the good atmosphere lived at Plant Biochemistry Lab (PLEN, KU), specifically to my office mates/colleagues/friends Nanna Bjarnholt and Kirsten Jørgensen, crucial people in many different experiments and aspects of my life in Denmark, where most of the experiments shown in this Chapter 2 were done. I would also give special thanks to Dr. Rubini Kanangara for her talks about transcription factors, and for her extremely kindness and patience on showing me and my PhD students molecular biology protocols, and Dr. Elizabeth H. J. Neilson (Lizzie) for her appreciated personal and scientific talks about prunasin and P450s in Eucalyptus and other plants. At CEBAS-CSIC, Dra. Livia Donaire’s support is enormously honored. Last but not least, Dr. Federico Dicenta is also greatly recognized in this work performed, as when I planned to do a postdoc, he guided me to decide which were the best places in studying the cyanogenic glucoside amygdalin, with the final aim to find a molecular marker for bitterness in almond. With his appreciated help and guidance, Dr. Federico showed me how studying bitterness can turn into a sweet happy ending. Funding Work produced with the support of a “2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation” to the AUSTRAL project. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. This work has also been supported by the project ALADINO-MAGIC funded by the Ministry of Science and Innovation (Spain).

References Albala K (2009) Scholarly commons almonds along the silk road: the exchange and adaptation of ideas from West to East Alioto T, Alexiou KG, Bardil A, Barteri F, Castanera R, Cruz F, Dhingra A, Duval H, Fernández i Martí Á,

Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness Frias L et al (2019) Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J tpj.14538 Andersen MD, Busk PK, Svendsen I, Møller BL (2000) Cytochromes P-450 from Cassava ( Manihot esculenta Crantz) catalyzing the first steps in the biosynthesis of the Cyanogenic glucosides Linamarin and Lotaustralin. J Biol Chem 275:1966–1975 Arrázola G, Sánchez-Pérez R, Dicenta F, Grané N (2012) Content of Amygdalin in almond kernels related to the bitterness genotype. Agron Colomb 30:260–265 Bak S, Kahn RA, Nielsen HL, Møller BL, Halkier BA (1998) Cloning of three A-type cytochromes P450, CYP71E1, CYP98, and CYP99 from Sorghum bicolor (L.) Moench by a PCR approach and identification by expression in Escherichia coli of CYP71E1 as a multifunctional cytochrome P450 in the biosynthesis of the cyanogen. Plant Mol Biol 36:393–405 Bak S, Paquette SM, Morant M, Morant AV, Saito S, Bjarnholt N, Zagrobelny M, Jørgensen K, Osmani S, Simonsen HT et al (2006) Cyanogenic glycosides: a case study for evolution and application of cytochromes P450. Phytochem Rev 5:309–329 Basil of Caesaria Hexaemeron. Hexaemeron. p Homily 5, paragraph 7 Bliss FA, Arulsekar S, Foolad MR, Becerra V, Gillen AM, Warburton ML, Dandekar AM, Kocsisne GM, Mydin KK (2002) An expanded genetic linkage map of Prunus based on an interspecific cross between almond and peach. Genome 45:520–529 Blum D (2010) The poisoner’s handbook. Penguin Press Clausen M, Kannangara RM, Olsen CE, Blomstedt CK, Gleadow RM, Jørgensen K, Bak S, Motawie MS, Møller BL (2015) The bifurcation of the cyanogenic glucoside and glucosinolate biosynthetic pathways. Plant J 84:558–573 Del Cueto J, Ionescu IA, Pičmanová M, Gericke O, Motawia MS, Olsen CE, Campoy JA, Dicenta F, Møller BL, Sánchez-Pérez R (2017) Cyanogenic glucosides and derivatives in almond and sweet cherry flower buds from dormancy to flowering. Front Plant Sci 8:800 Davis R (1991) Cyanogens—toxic substances in crop plants. Royal Society of Chemistry, Cambridge Delplancke M, Alvarez N, Espíndola A, Joly H, Benoit L, Brouck E, Arrigo N (2012) Gene flow among wild and domesticated almond species: insights from chloroplast and nuclear markers. Evol Appl 5:317–329 Dicenta F, García JE (1993) Inheritance of the kernel flavour in almond. Heredity (edinb) 70:308–312 Dicenta F, Martinez-Gomez P, Grane N, Martin ML, Leon A, Canovas JA, Berenguer V (2002) Relationship between Cyanogenic compounds in kernels, leaves, and roots of sweet and bitter kernelled almonds. J Agric Food Chem 50:2149–2152 Dicenta F, Martínez-Gómez P, Ortega E, Duval H (2000) Cultivar pollinizer does not affect almond flavor. HortScience 35(6): 1153-1154. Dicenta F, Ortega E, Martínez-Gómez P (2007) Use of recessive homozygous genotypes to assess genetic

23

control of kernel bitterness in almond. Euphytica 153: 221–225 Forslund K (2004) Biosynthesis of the Nitrile Glucosides Rhodiocyanoside A and D and the Cyanogenic Glucosides Lotaustralin and Linamarin in Lotus japonicus. Plant Physiol 135:71–84 Franks TK, Yadollahi A, Wirthensohn MG, Guerin JR, Kaiser BN, Sedgley M, Ford CM (2008) A seed coat cyanohydrin glucosyltransferase is associated with bitterness in almond (Prunus dulcis) kernels. Funct Plant Biol 35:236–246 Frehner M, Scalet M, Conn EE (1990) Pattern of the cyanide-potential in developing fruits1 implications for plants accumulating cyanogenic monoglucosides (Phaseolus lunatus) or Cyanogenic Diglucosides in Their Seeds (Linum usitatissimum, Prunus amygdalus) Gleadow RM, Møller BL (2014) Cyanogenic glycosides: synthesis, physiology, and phenotypic plasticity. Annu Rev Plant Biol 65:155–185 Guillamón JG, Prudencio ÁS, Yuste JE, Dicenta F, Sánchez-Pérez R (2020) Ascorbic acid and prunasin, two candidate biomarkers for endodormancy release in almond flower buds identified by a nontargeted metabolomic study. Hortic Res 7:203 Hansen J, Renfrew JM (1978) Palaeolithic-Neolithic seed remains at Franchthi Cave, Greece. Nature 271:349– 352 Hansen CC, Sørensen M, Veiga TA, Zibrandtsen JF, Heskes AM, Olsen CE, Boughton BA, Møller BL, Neilson EH (2018) Reconfigured cyanogenic glucoside biosynthesis in Eucalyptus cladocalyx involves a cytochrome P450 CYP706C55. Plant physiology 178(3):1081–95. https://doi.org/10.1104/pp.18.00998 Hansen CC, Sørensen M, Bellucci M, Brandt W, Olsen CE, Goodger JQ, Woodrow IE, Lindberg Møller B, Neilson EH (2023) Recruitment of distinct UDP‐glycosyltransferase families demonstrates dynamic evolution of chemical defense within Eucalyptus L'Hér. New Phytologist 237(3):999–1013. https://doi.org/10.1111/nph.v237.3 Harvey AL, Edrada-Ebel R, Quinn RJ (2015) The reemergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 14:111–129 Heppner MJ (1923) The factor for bitterness in the sweet almond. Genetics 8:390–391 Ionescu IA, López-Ortega G, Burow M, Bayo-Canha A, Junge A, Gericke O, Møller BL, Sánchez-Pérez R (2017) Transcriptome and metabolite changes during hydrogen cyanamide-induced floral bud break in sweet cherry. Front Plant Sci 8:1233 Joobeur T, Viruel MA, de Vicente MC, Jáuregui B, Ballester J, Dettori MT, Verde I, Truco MJ, Messeguer R, Batlle I et al (1998) Construction of a saturated linkage map for Prunus using an almondₓpeach F 2 progeny. TAG 97:1034–1041 Kahn R, Bak S, Svendsen I, Halkier BA, Møller BL (1997) Isolation and reconstitution of cytochrome P450ox and in vitro reconstitution of the entire biosynthetic pathway of the cyanogenic glucoside Dhurrin from Sorghum. Plant Physiol 115:1661–1670

24 Kahn RA, Fahrendorf T, Halkier BA, Møller BL (1999) Substrate specificity of the cytochrome P450 enzymes CYP79A1 and CYP71E1 involved in the biosynthesis of the cyanogenic glucoside dhurrin in Sorghum bicolor (L.) Moench. Arch Biochem Biophys 363:9–18 Kannangara R, Motawia MS, Hansen NKK, Paquette SM, Olsen CE, Møller BL, Jørgensen K (2011) Characterization and expression profile of two UDPglucosyltransferases, UGT85K4 and UGT85K5, catalyzing the last step in cyanogenic glucoside biosynthesis in cassava. Plant J 68:287–301 Kislev ME, Nadel D, Carmi I (1992) Epipalaeolithic (19,000 BP) cereal and fruit diet at Ohalo II, Sea of Galilee, Israel. Rev Palaeobot Palynol 73:161–166 Knoch E, Motawie MS, Olsen CE, Møller BL, Lyngkjær MF (2016) Biosynthesis of the leucine derived a-, band c-hydroxynitrile glucosides in barley (Hordeum vulgare L.). Plant J 88:247–256 Koepke T, Schaeffer S, Harper A, Dicenta F, Edwards M, Henry RJ, Møller BL, Meisel L, Oraguzie N, Silva H et al (2013) Comparative genomics analysis in Prunoideae to identify biologically relevant polymorphisms. Plant Biotechnol J 11:883–893 Lechtenberg M, Nahrstedt A (1999) Cyanogenic glucosides. In: I R, (ed) Naturally occurring glycosides. Wiley Inc., pp 147–191 Lotti C, Minervini AP, Delvento C, Losciale P, Greta L, Sánchez-Pérez R, Ricciardi L, Pavan S (2023) Detection and distribution of two dominant alleles associated with the sweet kernel phenotype in almond cultivated germplasm. Front Plant Sci 14. https://doi. org/10.3389/fpls.2023.1171195 Moller BL (2010) Dynamic metabolons. Science (80) 330:1328–1329 Morant AV, Jørgensen K, Jørgensen C, Paquette SM, Sánchez-Pérez R, Møller BL, Bak S (2008) bGlucosidases as detonators of plant chemical defense. Phytochemistry 69:1795–1813 Pičmanová M, Neilson EH, Motawia MS, Olsen CE, Agerbirk N, Gray CJ, Flitsch S, Meier S, Silvestro D, Jørgensen K et al (2015) A recycling pathway for cyanogenic glycosides evidenced by the comparative metabolic profiling in three cyanogenic plant species. Biochem J 469:375–389 Pliny the Elder (77AD) Naturalis Historiae. Liber XV. p 26 Rauws AG, Olling M, Timmerman A (1982) The pharmacokinetics of amygdalin. Arch Toxicol 493(49):311–319 Ricciardi F, Del Cueto J, Bardaro N, Mazzeo R, Ricciardi L, Dicenta F, Sánchez-Pérez R, Pavan S, Lotti C (2018) Synteny-based development of CAPS markers linked to the sweet kernel LOCUS, controlling Amygdalin accumulation in almond (Prunus dulcis (Mill.) D.A.Webb). Genes (Basel) 9:385 Rivera Núñez D, Matilla Séiquer G, Obón de Castro C, Alcaraz Ariza FJ (2012) Plants and humans in the Near East and the Caucasus: ancient and traditional uses of plants as food and medicine, a diachronic ethnobotanical review: (Armenia, Azerbaijan, Georgia, Iran, Iraq, Lebanon, Syria, and Turkey). Edit.um

R. Sánchez-Pérez Sánchez-Pérez R, Belmonte FS, Borch J, Dicenta F, Møller BL, Jørgensen K (2012a) Prunasin hydrolases during fruit development in sweet and bitter almonds. Plant Physiol 158:1916–1932 Sánchez-Pérez R, Arrázola G, Martín ML, Grané N, Dicenta F (2012b) Influence of the pollinizer in the amygdalin content of almonds. Scientia Horticulturae 13962–65. https://doi.org/10.1016/j.scienta.2012.02.028 Sánchez-Pérez R, Howad W, Dicenta F, Arús P, Martínez-Gómez P (2007) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breed 126(3):310―318 Sánchez-Pérez R, Howad W, Garcia-Mas J, Arús P, Martínez-Gómez P, Dicenta F (2010) Molecular markers for kernel bitterness in almond. Tree Genet Genomes 6:237–245 Sánchez-Pérez R, Jørgensen K, Olsen CE, Dicenta F, Møller BL (2008) Bitterness in almonds. Plant Physiol 146:1040–1052 Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Aiese Cigliano R, Del Cueto J, Ricciardi F, Lotti C, Ricciardi L, Dicenta F, et al (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science (80) 364:1095–1098 Socias i Company R, Alonso J, Kodad O, Gradziel T (2012) Almond. In: ML Badenes, DH Byrne (eds) Fruit breed. Springer, pp 697‒728 Takos AM, Knudsen C, Lai D, Kannangara R, Mikkelsen L, Motawia MS, Olsen CE, Sato S, Tabata S, Jørgensen K et al (2011) Genomic clustering of cyanogenic glucoside biosynthetic genes aids their identification in Lotus japonicus and suggests the repeated evolution of this chemical defence pathway. Plant J 68:273–286 Thodberg S, Del Cueto J, Mazzeo R, Pavan S, Lotti C, Dicenta F, Neilson EHJ, Møller BL, Sánchez-Pérez R (2018) Elucidation of the Amygdalin pathway reveals the metabolic basis of bitter and sweet almonds (Prunus dulcis). Plant Physiol 178:1096–1111 Willcox G, Fornite S, Herveux L (2008) Early Holocene cultivation before domestication in Northern Syria. Veg Hist Archaeobot 17:313–325 Wirthensohn MG, Chin WL, Franks TK, Baldock G, Ford CM, Sedgley M (2008) Characterising the flavour phenotypes of almond (Prunus dulcis Mill.) kernels. J Hortic Sci Biotechnol 83:462–468 Yamaguchi T, Yamamoto K, Asano Y (2014) Identification and characterization of CYP79D16 and CYP71AN24 catalyzing the first and second steps in l-phenylalanine-derived cyanogenic glycoside biosynthesis in the Japanese apricot, Prunus mume Sieb. et Zucc. Plant Mol Biol 86:215–223 Zohary D, Hopf M, Weiss E (2012) Domestication of plants in the Old World: the origin and spread of domesticated plants in Southwest Asia, Europe, and the Mediterranean Basin. Oxford University Press

The Complete Sequence of the Almond Genome Raquel Sánchez-Pérez, Pedro José Martínez-García, and Ángel Fernández i Martí

Abstract

Almond genome has been sequenced, and this has been possible thanks to next generation sequencing technologies like Illumina, PacBio, Oxford Nanopore and Pacific Biosciences, among others. The first chromosome-scale almond genome sequenced was done using the Lauranne cultivar, a sweet homozygous, self-compatible, hard-shell French cultivar, with an estimated size of 246 Mb. With this sequencing, the domestication of wild bitter almonds to sweet ones was finally elucidated. The second almond cultivar sequenced was Texas, a cultivar important in the California Almond Breeding Programs, which estimated size was 238 Mb. Recently, the genome of the most widely grown almond cultivar, Nonpareil, has been sequenced (257.2 Mb), in a parallel

R. Sánchez-Pérez (&) . P. J. Martínez-García Department of Plant Breeding, Centro de Edafología y Biología Aplicada del Segura-Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), Campus Universitario de Espinardo, nº 25, C.P. 30.100, Espinardo, Murcia, Spain e-mail: [email protected] Á. Fernández i Martí Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA

study of the methylome. For almond molecular breeders, this is a great opportunity to develop new cultivars to meet the new challenges we are facing today and be ready for the future ones.

1

Introduction

For decades, plant breeding has been helped by the use of molecular markers developed on genetic linkage maps. A recent revolution in DNA sequencing techniques has taken the discovery and application of molecular markers to high and ultrahigh-throughput levels (Grover and Sharma 2016). In 2000, a quantum leap in plant science took place when Arabidopsis thaliana genome was released (Kaul et al. 2000). However, for scientists working with trees, like almond, we had to wait until 2006, when the first genome of a perennial plant, the black cottonwood, Populus trichocarpa (Torr & Gray) genome, was sequenced (Tuskan et al. 2006). This was indeed a hope for the perennial plant scientists to advance as much as the annual plant forum people. In 2013, peach (Prunus persica (L.) Batsch) became the first Prunus species which genome was sequenced (Verde et al. 2013). The estimated genome size was 265 Mb, organized in eight pseudomolecules, covering 96.1% of the total assembly, with contig N50/L50 values of 4 Mb/26.8 Mb and 294 kb/214.2 kb, respectively.

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_3

25

26

2

R. Sánchez-Pérez et al.

The First Chromosome-Scale Almond Genome—The Lauranne Genome

Using the peach genome as a reference, sequencing reads from two sweet (Lauranne and Ramillete) and two bitter (S3067 and D05-187) almond accessions and Stella, a sweet cherry (Prunus avium L.) cultivar were used for comparative analyses of these three Prunus species (Koepke et al. 2013). Each of the individual almond genotypes was sequenced at 8–13 ₓ coverage by Illumina platform, which combined to yield a 10.8 Gb dataset or 43 ₓ coverage. With this data, it was represented between 96 and 99% of the total number of peach genes. Polymorphism analyses helped to reveal the number of target polymorphisms, for example, in the Sweet kernel locus, where more than 56,000 polymorphisms were found. From those, only 228 produced a codon change. This was indeed the first analysis of the almond genome, which helped to analyze an important trait in almond, such as the presence of bitterness in their seeds. Since the almond sequences were obtained only by Illumina platform, with a 76 bp Illumina paired-end reads, presenting more than 34% of repetitiveness, we realized that longer reads were needed to be able to do a chromosome-scale almond genome. Between Dr. Raquel Sánchez-Pérez, and Dr. Birger Lindberg Møller, at Plant Biochemistry Section (PLEN Department, Copenhagen University), in a close collaboration with Dr. Federico Dicenta (Almond Breeder at CEBAS-CSIC), we decided to sequence Lauranne, a French variety due to several qualities such as homozygous for sweetness, late flowering, hard shell, highproductivity, and self-compatibility. A bit later, Dr. Stefano Pavan, Dr. Ricciardi (Bari Univ.), and Dra. Lotti (Foggia University) decided to join this consortium, leaded by Dra. Raquel Sánchez Pérez, sending excellent PhD students like Mrs. F. Ricciardi and Mrs. Rosella Mazzeo, now both doctors in Science. I would like to remark that Dra. Mazzeo was one of the most efficient and brilliant PhD students I have ever had the privilege to work with. Dra Mazzeo, together with C. Moldovan,

became a very efficient team. Dr. Riccardo A. Cigliano did all the bioinformatic analyses and give good advice along the journey to sequence and assemble the almond genome. Dra. R. LópezMarqués joined this work at the most important stage, giving the quality touch as a plant biochemist that was needed for a succesfull end. Therefore, in June 2019, the genome (2n = 2 ₓ = 16, haploid genome size = 246 Mb) of the sweet homozygous almond cultivar Lauranne was sequenced in a combination of Illumina (paired-end reads) and PacBio (15-kb and 20-kb libraries) technologies. The long PacBio reads were used to perform genome assembly, whereas Illumina reads were used to perform gap filling and scaffolding (Sánchez-Pérez et al. 2019). The total assembly size of the Lauranne genome was 246,116,696 bp (246 Mb) with eight pseudomolecules. The pseudomolecules and the corresponding annotations were deposited at the DDBJ database with the following accession numbers: AP019297-AP019304. The longest pseudomolecule contained 43,698,516 bp (43 Mb). In total, 5012 contigs were obtained, where the N50 contig length was 82,269 bp and the L50 contig count 791. The number of scaffolds was 4078 with an N50 and N90 scaffold length of 21 Mb and 38 Kb, respectively, and L50 and L90 scaffold count of 5 and 306, respectively. The percentage of missing bases was only 0.32 [Table S1 (Sánchez-Pérez et al. 2019)]. In total, the number of genes found was 27,817 and the total coding region was 36,403,316 bp. The largest gene had 84,802 bp and the number of single-exon genes was 2314. In order to study the synteny between the Lauranne genome with the P. mume and P. persica, the P. dulcis genome was aligned against the P. persica and the P. mume reference genomes using the software Last. As a following step, DAGchainer was used to generate chains of syntenic genes using the “Relative Gene Order” option allowing a maximum distance of 120,000 bp between two matches and a minimum of 5 genes as anchor. Finally, KaKs ratios were calculated for each pair of syntenic genes using CodeML. The plot was generated using ggplot2

The Complete Sequence of the Almond Genome

27

Fig. 1 Manhattan plot showing the distribution of Ka/Ks values of the orthologous genes in Prunus dulcis/Prunus mume (left panel) and Prunus dulcis/Prunus persica (right panel). Each point represents an orthologous gene, and points are ordered following their position in the genome

and colored according to the chromosome. The y-axis shows the Ka/Ks values. High Ka/Ks values correspond to genes under positive selection whereas values close to zero correspond to genes under selective pressure

using R (Fig. 1). With this first almond reference genome, genotyping-by-sequencing (GBS) was applied to 149 almond cultivars from the Italian Council for Agricultural Research (CREA) and the CEBAS-CSIC, leading to the detection of 93,119 single-nucleotide polymorphisms (SNP). This together with four-year phenotypic observations, a genome-wide association study (GWAS) and, for the first time in a crop species, a homozygosity mapping (HM), was performed to identify genomic associations with nut, shell and seed weight (Pavan et al. 2021).

team of scientists from Spain, France, Australia, the USA, and UK. Although preliminary versions of the genome were internally available for the members of the consortium in 2015, it did not become published until September 2019 (Alioto et al. 2019). Texas is a non-self-fertile genotype that was obtained in the USA from materials imported from Western Europe. This variety became one of the most cultivated cultivars in California and was also one of the parents of the cross made with the peach cultivar “Early Gold” used for the construction of the reference linkage map of Prunus (Joobeur et al. 1998). Two different sequencing platforms were used to get a high-quality sequence assembly and genome. Most of it came through the platform Illumina, sequencing approximately 135 Gb at a coverage of 500X. Also, the quality assemblies were complemented with 10.2 Gb by using

3

The Second Almond Genome Sequenced—The Texas Genome

The consortium for sequencing the “Texas” almond genome was created and led by Dr. Arús in 2013. This project included an international

28

Oxford Nanopore. Paired-end (PE) and matepaired (MT) libraries were performed for the subsequent whole-genome shotgun sequencing for Texas DNA in an Illumina HiSeq2000 platform. Complementary, 2D genomic libraries were sequenced in a MinION instrument (ONT). A total of 27,969 protein-coding genes were annotated, sharing similar results with the other almond genome previously sequenced by Sánchez-Pérez et al. (2019). Similarly, it was observed high synteny and collinearity between the genome sequence of peach v.2.0 a1 (Verde et al. 2013). Additionally, the genome of ten traditional almond cultivars and one peach cultivar, Earlygold, was re-sequenced using PE Illumina sequencing with an average coverage nearly to 40X. Those cultivars were selected based on their geographically distribution and morphological attributes (shell hardness, bloom time, and selfincompatibility). After re-sequencing those 10 cultivars, it was found around 2 millions of SNPs (87%) and 330 k indels (13%). Almond cvs. Nonpareil had the highest number of SNPs and indels, 1,072,759 and 142,142, respectively, whereas Ripon had the lowest number of SNPs and indels, 827,397 and 94,070, respectively. Average SNP density was calculated as 6.2 SNPs per kb, whereas the average heterozygosity for the 10 cultivars was 0.44%. A SNP-based phylogenetic analysis grouped the almond cultivars into two main clades where the first clade contained the Italian cultivars such as Cristomorto, Falsa Barese, and Genco while the second group was separated into two subclades, the first containing Ai, Belle d’Aurons, Nonpareil, and Ripon and the second the Spanish almond cultivars such as Desmayo largueta, Marcona, and Vivot. As expected, the dendrogram grouped the samples based on their geographical origin, clustering the Italian cultivars, the French and US cultivars, and finally the Spanish cultivars in the same clade. The almond gnome was aligned versus the peach reference genome to assess the structural variability between the two Prunus species. Almost 93% of the Texas almond assembly

R. Sánchez-Pérez et al.

could be aligned to the reference peach genome sequence with an average identity of 95.59%. This indicates that most of such structural differences, particularly those of larger sizes, were related to TE (transposable elements) sequences. The LTR (long terminal repeat) retrotransposons and MITEs constitute the two most prevalent superfamilies of TEs in plants; thus, it seems that TEs may explain an important fraction of the interspecific variability between peach and almond, as well as the intraspecific variability of both species. A research study conducted by Donoso et al. (2016) suggests that TEs could be responsible for some of the genomic changes at the origin of the agronomic traits that distinguish peach from almond, such as mesocarp development and bitterness of the kernel. A phylogenomic analysis with 262 genes from seventeen plant species provided relevant information about the divergence time within Prunus species, by using a Bayesian relaxed molecular clock approach. It seems that P. dulcis diverged from P. persica approximately 5.88 million years ago (Mya), from P. mume 20.84 Mya, and from P. avium 62.04 Mya and all of them are coming from a common ancestor which apparently was compatible with the separation of the ancestral species by the uplift of the Central Asian Massif in two subpopulations with different environments: one (almond and its close relatives) in the arid steppes of central and western Asia and the other (peach) in the subtropical climate of southwestern China (Su et al. 2015).

4

The Third Almond Genome Sequenced—The Nonpareil Genome

With the improvement of DNA sequencing technologies, the third-generation highthroughput sequencing technologies such as single-molecule real-time sequencing (Pacific Biosciences) and Nanopore Sequencing (Oxford) have helped to refine existed genomes. In this sense, the most recent almond genome available is the Nonpareil genome (D’Amico-Willman

The Complete Sequence of the Almond Genome

29

et al. 2022). This variety represents 42% of the US almond production and is important to other countries like Australia. However, Nonpareil is susceptible to the aging-related disorder noninfectious bud failure, which can affect negatively the kernel yield and has been associated with genome-wide DNA methylation (FresnedoRamírez et al. 2017). Therefore, not only the genome was studied but also the methylomes of Nonpareil tissues like leaf, flower, endocarp, mesocarp, exocarp, and seed coat. In this work, they also provided the complete assembly of the

nuclear, plastidial, and mitochondrial genomes, which was published for the first time. The first version of the almond Nonpareil genome assembly was the result of a combination of Illumina technology (HiSeq X), PacBio, and Hi-C data. Using Hi-C, it was possible to infer the 8 major pseudomolecules expected. In total, by the use of Illumina, PacBio, and optical mapping technologies, Nonpareil genome has a 571 ₓ coverage (D’Amico-Willman et al. 2022). The final assembly N50 was 1,748,356, L50:49, and GC% 37.99. The BUSCO scores are

Fig. 2 Syntenic dotplot between the genome of two cultivars from P. dulcis. A. Lauranne versus Texas genome, B. Nonpareil versus Lauranne genome, and C. Nonpareil versus Texas genome. This program merges

syntenic blocks by using the algorithm “quota align merge,” with a syntenic depth algorithm “quota align” which calculates syntenic CDS pairs and color dots by synonymous (Ks) substitution rates

30

R. Sánchez-Pérez et al.

comparable to those from Texas (N50 = 115,182, L50: 511), superior to Lauranne (N50 = 82,269, L50:791). Surprisingly, the Nonpareil assembly showed a larger genome size than expected (257.2 Mb), especially when compared with Texas (227.76 Mb) and Lauranne (246.12 Mb). This could be artifacts of sequencing technologies, as well as repetitive elements. However, the three current almond genome assemblies share a similar proportion of repetitive elements in their genome structure: 34.5% for Lauranne, 38.2% for Texas, and 33.61% for Nonpareil. In this report, the first draft plastid and mitochondrion assemblies for almond was provided. This is important for maternal inherited traits and to determine parentages in breeding or natural populations. Clear patterns of synteny have been confirmed between the sequenced almond cultivars. The pairwise comparison between each of these almond genomes was obtained using the function SynMap from the CoGe platform (Fig. 2). Although two unclear events in chromosome 3 and chromosome 6 were observed, a more detailed analysis of the breakpoint regions must be carried in the future to confirm whether this could actually be an artifact due to a genome segment of low sequence quality or not. The genomics era is a new reality in almond, and depth genomic research studies will help the improvement of this important nut species.

5

Future Perspectives

In almost ten years, we have now three almond cultivars (Lauranne, Texas, and Nonpareil), which genome has been sequenced to a chromosome scale, with an average size of 247 Mb. Knowing the set of genes that almond genome contains is a stepping stone but this is just the beginning. Little is known regarding the functional analyses of most of the genes involved in important agronomic traits, which is the first step to develop molecular markers to help breeding programs like the KASP markers, recently developed by Lotti et al. (2023), for almond bitterness (for further details, please see Chapter

2). Consequently, goals are still enormous. Now we have the basis to continue developing tools to help the almond breeding programs to face the challenges and cultivate this healthy nut tree crop in a productive and environmentally-friendly way. Funding This work is produced with the support of a “2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation” to AUSTRAL project. The foundation takes no responsibility for the opinions, statements, and contents of this project, which are entirely the responsibility of its authors. This work has also been supported by the project “ALADINO-MAGIC” funded by Ministry of Science and Innovation (Spain).

References Alioto T, Alexiou KG, Bardil A, Barteri F, Castanera R, Cruz F, Dhingra A, Duval H, Fernández i Martí Á, Frias L, Galán B, García JL, Howad W, Gómez‐ Garrido J, Gut M, Julca I, Morata J, Puigdomènech P, Ribeca P, Rubio Cabetas MJ, Vlasova A, Wirthensohn M, Garcia‐Mas J, Gabaldón T, Casacuberta JM, Arús P (2019) Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J 101(2):455–472. https://doi.org/10. 1111/tpj.14538 D’Amico-Willman KM, Ouma WZ, Meulia T, Sideli GM, Gradziel TM, Fresnedo-Ramírez J (2022) Wholegenome sequence and methylome profiling of the almond [Prunus dulcis (Mill.) D.A. Webb] cultivar ‘Nonpareil’. G3 Genes|Genomes|Genetics. https://doi. org/10.1093/G3JOURNAL/JKAC065 Donoso JM, Picañol R, Serra O, Howad W, Alegre S, Arús P, Eduardo I (2016) Exploring almond genetic variability useful for peach improvement: mapping major genes and QTLs in two interspecific almond ₓ peach populations. Mol Breed 36:1–17 Fresnedo-Ramírez J, Chan HM, Parfitt DE, Crisosto CH, Gradziel TM (2017) Genome-wide DNA-(de) methylation is associated with Noninfectious Bud-failure exhibition in Almond [Prunus dulcis [Mill.] D.A. Webb]. https://doi.org/10.1038/srep42686 Grover A, Sharma PC (2016) Development and use of molecular markers: past and present. Crit Rev Biotechnol 36:290–302 Joobeur T, Viruel MA, de Vicente MC, Jáuregui B, Ballester J, Dettori MT, Verde I, Truco MJ, Messeguer R, Batlle I et al (1998) Construction of a saturated linkage map for Prunus using an almondₓpeach F2 progeny. TAG 97:1034–1041 Kaul S, Koo HL, Jenkins J, Rizzo M, Rooney T, Tallon LJ, Feldblyum T, Nierman W, Benito MI, Lin X et al (2000) (2000) Analysis of the genome

The Complete Sequence of the Almond Genome sequence of the flowering plant Arabidopsis thaliana. Nat 4086814(408):796–815 Koepke T, Schaeffer S, Harper A, Dicenta F, Edwards M, Henry RJ, Møller BL, Meisel L, Oraguzie N, Silva H et al (2013) Comparative genomics analysis in Prunoideae to identify biologically relevant polymorphisms. Plant Biotechnol J 11:883–893 Lotti C, Minervini AP, Delvento C, Losciale P, Gaeta L, Sánchez-Pérez R, Ricciardi L, Pavan S (2023) Detection and distribution of two dominant alleles associated with the sweet kernel phenotype in almond cultivated germplasm. Fron Plant Sci. https://doi.org/ 1410.3389/fpls.2023.1171195 Pavan S, Delvento C, Mazzeo R, Ricciardi F, Losciale P, Gaeta L, D’Agostino N, Taranto F, Sánchez-Pérez R, Ricciardi L, Lotti C (2021) Almond diversity and homozygosity define structure kinship inbreeding and linkage disequilibrium in cultivated germplasm and reveal genomic associations with nut and seed weight. Abs. Horticul. Res. 8(1). https://doi.org/10.1038/ s41438-020-00447-1

31 Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Aiese Cigliano R, Del Cueto J, Ricciardi F, Lotti C, Ricciardi L, Dicenta F et al (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science (80) 364:1095–1098 Su T, Wilf P, Huang Y, Zhang S, Zhou Z (2015) Peaches preceded humans: fossil evidence from SW China. Sci Reports 51(5):1–7 Tuskan GA, DiFazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam M, Ralph S, Rombauts S, Salamov A et al (2006) The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313:1596–1604 Verde I, Abbott AG, Scalabrin S, Jung S, Shu S, Marroni F, Zhebentyayeva T, Dettori MT, Grimwood J, Cattonaro F et al (2013) The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet 45:487–494

Almond miRNA Expression and Horticultural Implications Marzieh Karimi, Marjan Jafari, Roohollah Shahvali, Roudabeh Ravash, and Behrouz Shiran

Abstract

several miRNAomes studies including identification, expression profiling, miRNA-mRNA interactions, and miRNA functional analysis have provided novel intuitions on regulatory mechanisms. In almond, recent studies illustrated the microRNA-mediated gene regulation under abiotic stresses (cold stress and drought stress), symbiosis, and during fruit development. In this chapter, we have discussed the results of these studies and highlighted the candidate responsive miRNAs for further functional studies and appliance them for genetic manipulation and desired almond cultivars introduction.

From the past, till now various improved almond cultivars with desired traits have been introduced through breeding programs based on classical and biotechnological approaches. Despite the success of these methods, the development of almond cultivation has been encountered with a variety of constraints. In all plants, including almond, regulatory mechanisms at different levels of transcription, post-transcription, translation, and posttranslation have evolved under different conditions, and an in-depth understanding of these mechanisms may suggest new strategies for the development of desirable cultivars. Since the discovery of the first plant microRNA (miRNA) over the last two decades,

1 Marzieh Karimi and Marjan Jafari are contributed equally to this work. M. Karimi . M. Jafari . R. Shahvali . R. Ravash . B. Shiran (&) Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, P.O. Box 115, Shahrekord, Iran e-mail: [email protected]; [email protected] B. Shiran Institute of Biotechnology, Shahrekord University, P.O. Box 115, Shahrekord, Iran M. Jafari Department of Horticulture, Shahrekord University, P.O. Box 115, Shahrekord, Iran

Introduction

Over thousands of years, the best seedlings of almond have been selected and superior genotypes propagated by local farmers. The formal cross breeding programs were established in major almond production areas in the early 1900s. Since then, the adapted cultivars were obtained in individual growing regions (Gradziel 2009). Until now, variable almond breeding objectives have been determined depending on the almond cultivation areas. In general, both classical and biotechnology-based approaches are exploited to improve yield, quality, and resistance to pests and disease (Arús et al. 2009).

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_4

33

34

Desirable traits such as self-compatibility, lateblooming and blossom resistance (to escape late spring frost), hardy buds and wood, low chilling requirement (for subtropical areas), drought tolerance, and kernel quality have been considered by plant breeders in long-term attention (Janick and Moore 1996; Socias i Company et al. 2012). However, the conventional breeding approaches based on controlled crosses have dedicated a significant impression on almond breeding, but biotechnology researches have improved these breeding efforts. For instance, mapping of major desired genes via high-density linkage map construction (Ballester et al. 2001; Dirlewanger et al. 2004), screening of self-incompatible genotypes by PCR-based markers (Barckley et al. 2006), characterization of selfincompatibility gene (S-RNase) by cloning (Ushijima et al. 2001; Hafizi et al. 2013), identification of genes involved in drought tolerance by cDNA-AFLP technique (Campalans et al. 2001; Alimohammadi et al. 2013), and functional characterization of PduCBF1 and PduCBF2 genes under cold stress (Barros et al. 2012a) all are applied examples of biotechnology integration in almond breeding programs. Prerequisite for success in manipulation of important agronomic traits depends on profound knowledge on complex biological processes. Recent studies have revealed unprecedented achievement on genetic plant improvement through functional analyses of microRNAs (miRNAs). These molecules in plants are a class of 20–24 nucleotide small non-coding RNAs that posttranscriptionally regulate gene expression via either target mRNA degradation or translational inhibition. With the advent of high-throughput small-RNA sequencing (sRNA-seq), these molecules have gained a lot of attention in the last few years. miRNAs have been revealed to function in wide range of cellular, biological, and metabolic process. In addition, miRNAs are involved in defense responses against different pathogens invasion (Liu et al. 2017; Yu et al. 2017; Devi et al. 2018; Pegler et al. 2019). The regulatory role of miRNAs in adaptive responses under abiotic stress was first emerged by the

M. Karimi et al.

identification of miR395, miR398, and miR399 (Sunkar et al. 2012), and to date, numerous studies have detected various numbers of abiotic stress-responsive miRNAs by profiling their expression in plants subjected to stress (Zeng et al. 2018). For instance, in model plants and agronomically important crops, different miRNAs such as miR156, miR166, miR169, miR172, miR319, miR396, miR397, miR398, miR399, and miR408 were identified which particularly modulate target gene expression in the transcriptional regulatory network under salt, drought, and cold stress (Thiebaut et al. 2012; Sun et al. 2015; Huang et al. 2014a, b; Sanousi et al. 2016; Yang et al. 2017a, b; Ning et al. 2019; López-Galiano et al. 2019; Ebrahimi Khaksefidi et al. 2015). Unveiling miRNA characterization, expression pattern, target identification, and regulatory functions have deciphered new insights into the complex regulatory networks in different Rosaceae family members. Studies such as Wu et al. (2014), Niu et al. (2016a, b) and Hou et al. (2017) represented valuable information on fruit development-related miRNAs in pear, apricot, and blueberry, respectively. In another study, a comparative analysis was performed between the miRNAomes of perfect and imperfect flowers in Prunus mume and microRNAs associated with floral development were detected (Gao et al. 2012). In peach, the genome-wide identification and the expression profiles of responsive miRNAs under drought (Eldem et al. 2012) and cold stress (Barakat et al. 2012) were investigated by high-throughput Illumina deep sequencing. In study of Feng et al. (2017a, b), the responsive miRNAs and their targets involved in apple treeValsa mali interaction were detected and the crucial role of mdm-miR482b was indicated in this host–pathogen interplay. Besides above-mentioned studies, several miRNAomes studies have been recently carried out to identify and profile miRNA expression in almond (Prunus dulcis Mill.) to illustrate the behavioral effects of miRNA regulation on desired expressed traits. In this chapter, we have reviewed the consequences of these studies.

Almond miRNA Expression and Horticultural Implications

2

MicroRNA-Mediated Gene Regulation Under Cold Stress in Almond

Late-spring frost is one of the most important threats for productivity of early-blooming trees in subtropical and temperate regions, where frequent temperature fluctuations are visible during late winter and early spring (Badek et al. 2014; Albertos et al. 2019). In recent years along with shifting seasons, frost injury on fully open flowers has caused significant reduction on almond yield due to its low chilling requirement and early flowering (Arús et al. 2009; Hosseinpour et al. 2018). Since this species is considered as an economically important member in Rosacea family, hence several physiological, biochemical, and molecular assessments (Gong et al. 2007; Yu et al. 2007; Barros et al. 2012a, b, 2017; Mousavi et al. 2014; Sofo et al. 2014; Alisoltani et al. 2015; Karimi et al. 2016) have been carried out to determine the mechanism of this plant in response to cold stress. The findings of these studies approximately decipher new insights for understanding of regulatory mechanisms operating at distinct levels of transcription, posttranscriptional modifications, translation, and post-translational processing in almond under low temperature. The first comprehensive transcriptome analysis was performed using RNA-seq technique (Mousavi et al. 2014). In this study, the response of almondʼs reproductive tissues (anther and ovary) was assayed under frost stress (−2 °C (±0.5 °C)) and the involvement of thousands of differentially expressed (DE) genes was revealed in cellular and metabolic processes under freezing stress. Another achievement of this project was modeling the network of cold-responsive genes in pistil of almond. Six cold-induced genes including CBF/DREB1, WRKY21, ANK, HOS1, ICE1, and SIZ1 were characterized as central genes in this regulatory network which differentially expressed between cold-sensitive and coldtolerant genotypes of almond. On the other hand, this study highlighted the crucial role of Pdudof4

35

gene in cold tolerance induction (Alisoltani et al. 2015). In another study, the transcriptome of fully open flowers in frost-tolerant genotypes of almond has been profiled using RNA-seq technique and a considerable number of DE genes were identified under frost stress. One of the remarkable results of this study was revealing the involvement of highly expressed cold-shock protein (PduCSP2-like) in frost stress response. (Hosseinpour et al. 2018). Regarding the importance of miRNA-mediated reprogramming of gene expression, the first small-RNA sequencing technique (Karimi et al. 2016) was carried out to extend the perception of almond response molecular basis to cold stress. In this study, reproductive tissues (anther and ovary) of H genotype as a late-blooming and cold-tolerant genotype were used for sequencing and 174 conserved miRNAs and 59 novel miRNAs were identified. Among them, miR482, miR171, miR159, and miR396 families encompassed the highest number of members. Expression analysis of conserved miRNAs detected 37 miRNAs significantly up- or down-regulated under cold stress in one or both tissues.

2.1 Gene Ontology Analysis The GO (Gene Ontology) analysis of all conserved miRNA-predicted target genes in almond showed that 411 target genes were annotated and classified into three categories using AgriGO v2.0 program (Tian et al. 2017) including biological processes, cellular components, and molecular functions. In the cellular component category, the main subcategories were cell (GO: 0,005,623), cell part (GO: 0,044,464), and membrane (GO: 0,016,020). For as much as this assessment showed the amount of nearly 27% of annotated targets location in plasma membrane, it can be concluded the essential regulatory role of miRNAs in reception and transduction of cold stress signal. In molecular functions, the predominant terms were nucleic acid binding (GO: 0,003,676) (Karimi

36

2019). This observation is consistent with several studies such as Barakat et al. (2012), Song et al. (2017), and Yang et al. (2017a, b) which displayed that a high percentage of miRNAs predicted target involved in plasma membrane and its compounds. The GO analysis was also conducted on novel miRNA targets. In the cellular components and molecular functions categories, the annotations demonstrated similar results with targets of conserved miRNAs. But in biological processes the distinct results were observed. The comparative assay of GO analysis

M. Karimi et al.

revealed the highest percentage of predicted target genes of conserved miRNAs involved in organic cyclic compound metabolic process and the lowest amount of them involved in valyltRNA aminoacylation and nucleotide-excision repair. Novel miRNAs predicted targetpredominant genes implicated in cellular carbohydrate metabolic process and a minimum number of them involved in riboflavin metabolic with biosynthesis process and flavin-containing compound biosynthetic with metabolic process (Karimi 2019) (Fig. 1).

Fig. 1 The comparative assay of GO analysis between predicted target genes of conserved and novel miRNAs

Almond miRNA Expression and Horticultural Implications

2.2 Identification and Verification of Cold-Responsive miRNA Target Genes Using Degradome Sequencing Method Accurate identification of different miRNA target genes is essential to identify their function and requires high-throughput and reliable assay methods. Until now, various computational algorithms have been introduced to identify miRNA target genes with varying degrees of sensitivity and specificity. But despite the great variety of these tools, all of these methods require experimental confirmation. One of the common experimental methods to confirm predicted target genes is the modified 5’ RLM-RACE (5’ -Rapid Amplification of cDNA Ends) technique (Park and Shin 2014). This approach has been used successfully to identify target genes in various plant species, including in almond this technique has confirmed the cleavage of two target genes comprising F-box and DCL1 (Dicer-like 1) by Pdu-miR394b and Pdu-miR162, respectively (Karimi et al. 2016). Besides the advantages of modified 5′ RLM-RACE technique, its cost and time consumption have led to the development of more sophisticated methods such as Degradome sequencing. This technique is based on highthroughput sequencing technology that enables the sequencing of 5’ ends of RNA degradation products (microRNA cleavage sites) and ultimately provides the simultaneous confirmation of multiple target genes at a much lower time and cost and besides allows the identification of novel target genes (Lin et al. 2019). In view of the aforementioned advantages, this approach was applied in almond for the identification and confirmation of cold-responsive miRNAs target genes. Based on the results of the analysis, 16 different miRNA target genes were identified in the HC library (H genotype in. control condition). Among miRNAs, the highest number of target genes was identified for miR171d (Table 1). In the H2 library (H genotype under −2 °C), 20 miRNA target genes were identified that

37

miR167a had the highest number of target genes (Table 2). Comparison of the two libraries in terms of identified target genes showed similarity and overlap in most of the miRNAs target genes, although the number of target genes identified for each miRNA was different in each library. Despite high similarity, the target genes of miR159a (cationic amino acid transporter 1 (CAT1), miR169e-5p (trans-resveratrol di-Omethyltransferase-like (ROMT), miR395b-5p (UDP-glucuronate 4-epimerase 6 (GAE6)), miR399f (WRKY transcription factor 44 (WRKY44)), miR6285 (F-box protein At3g58530 (F-box), and miR6287 (V-type proton ATPase subunit c1 (ATP6V1C1)) were identified only in the H2 library. This result may reflect the specific expression of these target genes under cold stress. Several studies have also revealed the regulatory effect of these genes in response to cold stress. For example, in the study of Feng et al. (2012), the presence of cold-responsive cis elements has been identified in the promoter of the CAT gene family, and the induction of different members of this gene family has also been reported under cold stress. Another study has shown the important role of the CAT1 gene in the H2O2 scavenging caused by various environmental stresses, especially cold stress (Du et al. 2008). The positive effect of the F-box proteins has also been reported in numerous studies including the study of Chen et al. (2014), Gonzalez et al. (2017), and Li et al. (2018). WRKY has also been recognized as one of the important transcription factors under various abiotic stresses including cold stress (Şahin-Çevik and Moore 2013; Wang et al. 2014a, b, c). Based on RT-qPCR analysis in both coldtolerant genotype (H) and the cold-sensitive variety (Sh12) of almond under different thermal treatments of 10, 0, and −2 °C, some miRNAs displayed significant differential gene expression patterns between two studied genotypes and some others showed no significant changes in expression level. We discussed below the expression pattern of some differentially expressed miRNAs.

38

M. Karimi et al.

Table 1 Identified miRNA targets genes through degradome sequencing method in HC library (H genotype in control condition) Target gene description

miRNA

Target gene description

miRNA

Squamosa promoter-binding-like protein

ppe-miR156e

Carboxypeptidase A6

ppe-miR172a-3p

Probable 2-oxoglutarate-dependent dioxygenase

ppe-miR156e

Ethylene-responsive transcription factor

ppe-miR172a-3p

GPN-loop GTPase 3

ppe-miR160c-3p

Carboxypeptidase A6

ppe-miR172a-3p

UDP-glucuronic acid decarboxylase 5

ppe-miR164c

Floral homeotic protein APETALA 2

ppe-miR172a-3p

NAC domain-containing protein 21/22

ppe-miR164c

UDP-glycosyltransferase 89B2

ppe-miR390

NAC domain-containing protein 100

ppe-miR164c

Ubiquitin-40S ribosomal protein S27a

ppe-miR390

Homeobox-leucine zipper protein ATHB-15

ppe-miR166d

3-ketoacyl-CoA thiolase 2, peroxisomal

ppe-miR390

Protein-tyrosine-phosphatase MKP1

ppe-miR166d

Glucan endo-1,3-betaglucosidase 11

ppe-miR390

Cysteine protease RD19A

ppe-miR167a

Alpha, alpha-trehalose-phosphate synthase

ppe-miR390

Auxin response factor 8

ppe-miR167a

Transcription factor bHLH62

ptc-miR393a-3p

Enoyl-CoA hydratase 2, peroxisomal

ppe-miR167a

Protein AUXIN SIGNALING F-BOX 2

ptc-miR393a-3p

Auxin response factor 6

ppe-miR167a

Protein TRANSPORT INHIBITOR RESPONSE

ptc-miR393a-3p

Nuclear transcription factor Y subunit

ppe-miR169c

Transcription factor bHLH62

ptc- miR393a-3p

Protein TIFY 6B

ppe-miR169g

Subtilisin-like protease SBT1.8

ppe-miR394b

Nuclear pore complex protein NUP98A

mdm-miR171d

F-box only protein 6

ppe-miR394b

Aminoacylase-1

mdm-miR171d

Probable ubiquitin-conjugating enzyme

ppe-miR399n

Oxalate–CoA ligase

mdm-miR171d

Xyloglucan endotransglucosylase/hydrolase

ppe-miR2111b

Probable 2-oxoglutarate-dependent dioxygenase

mdm-miR171d

UDP-glucuronate 4-epimerase 6

ppe-miR2655o

nuclear pore complex protein NUP98A

mdm-miR171d

Alpha-glucosidase 2

ppe-miR2655o

Scarecrow-like protein 22

mdm-miR171d

LRR receptor kinase BAK1

ppe-miR2655o

Scarecrow-like protein 6

mdm-miR171d

Tubulin beta chain

ppe-miR6266c

AP2-like ethylene-responsive transcription

ppe-miR172a-3p

–

–

Almond miRNA Expression and Horticultural Implications

39

Table 2 Identified miRNA targets genes through degradome sequencing method in H2 library (H genotype under −2 °C) Target gene description

miRNA

Target gene description

miRNA

Squamosal promoter-binding-like protein

ppe-miR156e

Scarecrow-like protein 22

mdm-miR171d

Probable 2-oxoglutarate-dependent dioxygenase

ppe-miR156e

AP2-like ethylene-responsive transcription

ppe-miR172a-3p

Transcription factor GAMYB

mdm-miR159a

Carboxypeptidase A6

ppe-miR172a-3p

UDP-glucuronic acid decarboxylase 5

ppe-miR164c

Ethylene-responsive transcription factor

ppe-miR172a-3p

Probable LRR receptor-like serine/threonine-protein

ppe-miR164c

Floral homeotic protein APETALA 2

ppe-miR172a-3p

NAC domain-containing protein 21/22

ppe-miR164c

Pollen-specific leucine-rich repeat

ppe-miR390

NAC domain-containing protein 100

ppe-miR164c

Protein AUXIN SIGNALING F-BOX 2

ppe-miR393a-3p

Casein kinase 1-like protein HD16

ppe-miR164c

Protein TRANSPORT INHIBITOR RESPONSE

ppe-miR393a-3p

Squamosa promoter-binding-like protein

ppe-miR164c

Polyol transporter 5

ppe-miR394b

Protein-tyrosine-phosphatase MKP1

ppe-miR166d

F-box only protein 6

ppe-miR394b

Homeobox-leucine zipper protein REVOLUTA

ppe-miR166d

Trans-resveratrol di-Omethyltransferase-like

ppe-miR395b-5p

Homeobox-leucine zipper protein ATHB-15

ppe-miR166d

UDP-glucuronate 4-epimerase 6

ppe-miR399f

Homeobox-leucine zipper protein ATHB-14

ppe-miR166d

WRKY transcription factor 44

ppe-miR399f

60S acidic ribosomal protein P0

ppe-miR167a

Glycine-rich cell wall structural

ppe-miR399n

Cysteine protease RD19A

ppe-miR167a

Probable ubiquitin-conjugating enzyme

ppe-miR399n

Auxin response factor 6

ppe-miR167a

Putative beta-D-xylosidase

ppe-miR399n

Auxin response factor 8

ppe-miR167a

40S ribosomal protein S20-2

ppe-miR2111b

Neutral ceramidase

ppe-miR167a

Endochitinase 2

ppe-miR2655o

Cationic amino acid transporter 1

ppe-miR169e-5p

Serine/threonine-protein kinase ATM

ppe-miR2655o

Nuclear transcription factor Y subunit

ppe-miR169g

Protein AUXIN SIGNALING F-BOX 2

ppe-miR2655o

Protein TIFY 6B

ppe-miR169g

Aminoacylase-1

ppe-miR2655o

Scarecrow-like protein 6

mdm-miR171d

LRR receptor kinase BAK1

ppe-miR2655o

Oxalate-CoA ligase

mdm-miR171d

F-box protein At3g58530

ppe-miR6285

Probable 2-oxoglutarate-dependent dioxygenase

mdm-miR171d

V-type proton ATPase subunit c1

ppe-miR6287

60S ribosomal protein L18a-like protein

mdm-miR171d

–

–

40

2.3 Pdu-miR168 Under Cold Stress MiR168 is the most well-known miRNA that plays a key role in the stress response regulatory networks along with its exclusive target argonaute protein 1 (AGO1) (Khraiwesh et al. 2012; Li et al. 2012). The negative regulatory role of this miRNA in response to cold stress was demonstrated in almond except in the anther tissue of Sh12 variety. Pdu-miR168 was negatively correlated with PduAGO1 expression across two genotypes under the early hours of cold stress. The AREB/ABF regulon is considered as the most important transcriptional regulatory cassette in regulating ABA-dependent gene expression. These transcription factors are involved in ABA-dependent stress signal transduction by binding to ABRE motifs which are located within the promoter of stress-responsive genes like miR168 (Li et al. 2012). Since ABA is an important signal in many abiotic stresses including cold stress, this direct association between ABA signal and miR168 expression can reveal its regulatory role in cold-responsive downstream genes.

2.4 Pdu-miR171 Under Cold Stress miR171 is one of the ancient conserved miRNAs that play multiple roles in regulating plant growth and development (Ma et al. 2010). In addition, the role of this miRNA under various stresses has been reported in a variety of species such as Arabidopsis, barley, maize, and potato (Huang et al. 2017). In the study of Karimi et al. (2016), scarecrow-like 6 protein (SCL6) was predicted to be miR171 target. In research carried out on the DELLA proteins under cold stress and its effect on photosynthesis, to some extent the functional role of the SCL6 target gene has been identified. DELLA proteins participate in GA (gibberellic acid) signaling network. In this pathway, the GA signal is received by GID1 (Gibberellin Insensitive Dwarf 1) receptor. Bioactive GAs cause binding of GID1 to DELLAs domains and leads to degradation of DELLA via the ubiquitin–proteasome pathway.

M. Karimi et al.

Therefore, GA modulates plant growth by triggering of DELLA degradation. During CBF1mediated freezing stress response, plant growth is inhibited by DELLA accumulation (Achard et al. 2008). On the other hand, studies have shown that DELLA proteins inhibit ROS (reactive oxygen species) accumulation by increasing the expression of genes related to ROS scavenging (Ishibashi et al. 2012; Zhu et al. 2015; Zhou et al. 2017). Another consequence of cold stress is decreasing in the phytochemical efficiency of Photosystem II (PSII) and ultimately inhibiting the photosynthesis (Zhou et al. 2017). Ma et al. (2014) demonstrated SCL6 inhibits chlorophyll II biosynthesis through PROTOCHLOROPHYLLIDE OXIDOREDUCTASE (POR) gene suppression. Under cold stress, the plant's regulatory mechanism involves the reduction of GA biosynthesis enzyme activities and DELLA protein accumulation. Accumulated DELLA protein inhibits SCL6 through direct interaction. In almond under cold stress, the down-regulated SCL6 in anther tissue of genotype H and Sh12 may indicate a plant regulatory response at both transcriptional and posttranscriptional levels through DELLA-miR171mediated SCL6 suppression. This response modulates chlorophyll biosynthesis and prevents photosynthesis reduction under stress.

2.5 Pdu-miR319 Under Cold Stress MiR319 is the first miRNA known as miR-JAW in Arabidopsis. This miRNA involves in many biological processes such as regulation of ethylene biosynthesis, ABA and GA signaling cascades, leaf morphogenesis, gamete formation, transition from vegetative phase to flowering phase, and response to salinity and cold stress (Rhoades et al. 2002; Jones-Rhoades and Bartel 2004; Sunkar and Zhu 2004; Reyes et al. 2004; Schwab et al. 2006; Liu et al. 2008). Several studies have demonstrated the positive regulatory role of miR319 in response to cold stress. For instance, the significant induction of this miRNA was observed in Arabidopsis (Liu et al. 2008), sugarcane (Thiebaut et al. 2012), and rice.

Almond miRNA Expression and Horticultural Implications

The overexpression of Osa-miR319b resulted in an elevated tolerance to cold stress (Yang et al. 2013; Wang et al. 2014a, b, c). Despite these observations, in the study of Lv et al. (2010) on rice and study of Zeng et al. (2018) on winter turnip rape contrasting results have been provided and the down expression of this miRNA was reported under cold stress. In almond, positive regulatory role of miR319a was verified by high-throughput sRNA-sequencing and experimental validation. Two transcription factors including GAMYB-like and Teosinte-branched cycloidea/PCF (4) (TCP4) were predicted as Pdu-miR319a target genes. The comprehensive role of GAMYB genes in the GA signaling cascade has been identified (Millar and Gubler 2005). Since the plant regulatory mechanism under cold stress has evolved toward growth limitation through reduction of GA signaling, and preventing of GAMYB genes transcription (Thiebaut et al. 2012), hence the model of GA/ Pdu-miR319a-mediated PduGAMYB-like suppression can be presented to justify the downregulation of GAMYB-like in almond. Zeng et al. (2018) has shown that TCP4 controls cell wall formation by activating VND7 transcriptional network expression in Arabidopsis. The overexpression of TCP4 significantly increases cell wall thickness, cellulose, and lignin accumulation and promotes vascular formation, and ultimately, these changes lead to enhanced cold tolerance. Regarding cold-tolerant genotype of almond (H) exhibited significant up-regulation of PduTCP4 compared to susceptible variety, it may be concluded the function of this transcription factor plays a critical role in cold tolerance induction in tolerant genotype.

2.6 Pdu-miR398 Under Cold Stress The dynamism and variability of miR398 expression under cold stress in almond (Karimi et al. 2016) have also been observed in other plant species (Sunkar and Zhou 2004; Zhou et al. 2008; Jia et al. 2009; Yu et al. 2012; Chen et al. 2013; Wang et al. 2014a, b, c). This alteration in

41

miR398 expression depends on species, genotype, and the condition of cold stress treatment. Molecular mechanism of reduced expression of miR398 was assayed in Chrysanthemum dichrum under cold stress (Chen et al. 2013). This study has revealed the presence of cisregulatory elements in the promoter region of miR398b/c including MYB, WRKY, and bHLH which all three are controlled by the ICE1 as a key responsible gene under cold stress. In addition, this study displayed that in transgenic plants overexpressing ICE1, miR398 level was downregulated and conversely Superoxide dismutase genes (CSD1 and CSD2) as its target genes were up-regulated; eventually, these changes have led to enhance cold stress tolerance. In these plants, accumulation of ROS content in the early stages of stress was used as a signal to decrease miR398 expression and consequently increase CSD gene expression and the subsequent ROS detoxification by converting −O2 to H2O2 (Yamasaki et al. 2007). In almond, Copper Transporter 6 (CTR6) was predicted for Pdu-miR398a-3p. This protein complex is in the form of a homotrimer and located in the vacuole membrane and plays a critical role in regulating the amount of copper in the cell through the transportation of stored copper to the cytoplasm and nucleus. When copper ions have accumulated, in order to detoxification these ions have been inactivated through polyphosphates and other molecules in the vacuole. Then, CTR6 transfers them to the cytoplasm and the nucleus and caused Cuf1 transcription factor inactivation and subsequently resulting in the CTR4 and CTR5 suppression which are copper ion transporters into the cell (Bellemare et al. 2002). In the study of Chen et al. (2013), CSD genes were reported as positive regulatory genes under cold stress. Considering the role of CTR6 in copper ion transport to Cu chaperone such as CCS1 that delivers Cu to Cu/ZnSODs (CSD1‘CSD2’ CSD3) can deduce as a hypothesis that there is a high positive correlation between CTR6 and CDS genes. So that by increasing CTR gene expression further copper ions transformation is provided to Cu chaperone and eventually more ion flux to CSDs.

42

In almond, overexpression of PduCTR6 gene was observed in ovary tissue of tolerant genotype (Karimi et al. 2019).

2.7 Pdu-miR403 Under Cold Stress MiR403 is involved in developmental processes through the miR403-AGO2-miR168-AGO1 loop, but more dominant role of this miRNA has been revealed in response to biotic stress especially against plant viruses. Plants have evolved multiple defense mechanisms against different viruses. AGO1 and AGO2 have involved the first and second defense layers, respectively. The role of AGO2 in plant defense against viruses such as Potato virus X (PVX), tomato bushy stunt, and bacterial infection by miR393b contribution has also been identified (Harvey et al. 2011; Scholthof et al. 2011; Zhang et al. 2011; Zhang et al. 2012a, b). Various studies have revealed the role of miR403 in response to various abiotic stresses. For example, in the study of Ebrahimi et al. (2015), miR403 showed a different expression pattern under drought, salinity, heat, and cadmium stresses in sunflower. In study of Wang et al. (2015) and Sun et al. (2015), the overexpression of this miRNA was demonstrated in response to heat and salinity stresses in radish, respectively. The expression profiling of miR403 under cold stress was evaluated for the first time in reproductive tissues of almond, and different expression patterns were observed in reproductive between two tolerant and sensitive genotypes (Karimi et al. 2016). In Arabidopsis and several families of dicotyledonous plants, miR403 target loci have been identified within the 3’ UTR region of AGO2 transcripts (Allen et al. 2005). In almond, PduAGO2 was predicted as miR403 target gene. The study of miR403-mediated gene regulatory network in response to abiotic stresses revealed that AGO2 was regulated by SE and miR402 (Ebrahimi et al. 2015). miR402, as a stressresponsive miRNA, regulates the DML3 target gene (Demeter-like protein 3), which is involved in the epigenetic regulation of gene expression through methylation and demethylation

M. Karimi et al.

(Kim et al. 2010). In a study of Tang et al. (2018), it was found that cold stress induces DML3 expression. This gene encodes an enzyme from the glycosylase family which is involved in response to cold stress by cytosine methylation in promoter of cold-responsive genes. Thus, it could be concluded that methylation control by miR402/DML3 is a plant adaptation process against stress conditions. Given the apparent association between miR402 and the AGO2 gene in the study of Ebrahimi et al. (2015), it can be deduced as a hypothesis that AGO2 may be involved in the epigenetic response induced by cold stress.

2.8 Pdu-miR477a-3p Under Cold Stress MiR477a-3p has been identified as a highly conserved miRNA among fruit trees. Until now, various functions have been reported for this miRNA including fatty acid synthesis (Liu et al. 2019), regulation of disease-resistant terpene metabolites (Kim et al. 2017), flower development (Wang et al. 2014a, b, c), and response to cold stress (Lu et al. 2008). In almond, PdumiR477a-3p was identified as a cold-responsive miRNA (Karimi et al. 2016). Compared with the early stage of cold stress, higher expression level of Pdu-miR477a-3p in the cold-sensitive variety (Sh12) and further decreased expression level of this miRNA in H genotype was observed after two hours of −2 °C treatment. In almond, two target gene including PduAAE7 (ABC transporter C family member 8-like and acetate/butyrate CoA ligase AAE7) and PduABCC-MRP were predicted for Pdu-miR477a-3p. The results of Pearson correlation test showed a negative correlation (−0.53) between Pdu-miR477a-3p expression data and PduAAE7 target gene in both genotypes under both stress conditions. In the study of Allen et al. (2011), it was found that there is a direct relationship between glutamine accumulation (as a major source of carbon in the buds) and AAE7 expression level. Since an increased level of Pdu-miR477a-3p expression was revealed in tolerant genotype of almond, it

Almond miRNA Expression and Horticultural Implications

can be deduced that Pdu-miR477a-3p-mediated PduAAE7 regulation in tolerant genotype could prevent the reduction of Gln supply under cold stress which is indispensable for the survival of the plant. ABCC Multi-drug Resistance Proteins (ABCC-MRP) are a subset of ABC transducers involved in multiple physiological processes including cellular homeostasis, metal detoxification, and glutathione conjugate transport. These proteins are involved in cellular detoxification by transferring toxins from cytosol to vacuoles (Bhati et al. 2015). In this study, increased expression level of ABC transporter in H genotype under cold stress probably indicates that this regulator collaborates in the tolerance of this genotype through processes such as detoxification mechanisms in response to cold stress.

2.9 Pdu-miR7122-3p Under Cold Stress MiR7122 has been discovered in peach (Zhu et al. 2012), apple (Xia et al. 2012), and potato (Zhang et al. 2013). Until now, limited information is available about the function of this miRNA, but Xia et al. (2013) intriguingly revealed PpemiR7122 involved in pentatricopeptide repeat (PPR) phasiRNA production. For the first time, Pdu-miR7122-3p was characterized as cold-responsive miRNA in almond (Alisoltani et al. 2015). The expression of this miRNA was significantly increased under 0 and − 2 °C treatments in pistil of both frosttolerant and frost-sensitive late-blooming trees (H and Sh12 genotypes). Conversely, no significant overexpression was observed in frostsensitive early-blooming genotype (M3). In almond, PdubHLH041 (basic helix-loop-helix) and PduHOS1 (high expression of osmotically responsive genes) were predicted as PdumiR7122-3p’s target genes. Basic helix-loophelix proteins (bHLHs) are known as a largest family of transcription factors in eukaryotes and are involved in numerous biological processes such as light signaling (Leivar et al. 2008), hormone signaling (Lee et al. 2006), abiotic stress

43

responses (Kiribuchi et al. 2004), and fruit and flower development (Gremski et al. 2007). Various researches have reported the role of these transcription factors under salt stress (Kim et al. 2006; Waseem et al. 2019), drought, and cold stress (Chinnusamy et al. 2003; Peng et al. 2013; Waseem et al. 2019). One of the well-known bHLH transcription factor is ICE1 which encodes a Myc-like bHLH transcription factor. This protein regulates CBF3 through binding with the MYC recognition region in the CBF3’s promoter (Lee et al. 2005). In apple, another bHLH gene named MdCIbHLH1 (COLDINDUCED bHLH1) has been recognized which is involved in cold stress response (Feng et al. 2012). In reproductive tissues of cold-tolerant genotype of almond (H), the overexpression of PdubHLH041 has been observed in the early hours of stress under 0 °C whereas with increasing cold stress intensity the expression of this gene has been decreased. Unlike coldtolerant genotype, PdubHLH041 was significantly down-expressed after cold treatment at both 0 and − 2 °C in ovary tissue of coldsensitive variety (Sh12) (Karimi et al. 2016). Given the positive regulatory role of bHLH family members in different species in response to cold stress, it can be hypothesized that there is a high association between the significant down expression (FC = 43) of PdubHLH041 and cold stress sensitivity in Sh12. However, functional experiments are needed to prove this hypothesis. Alisoltani et al. (2015) demonstrated the negative correlation between the expression of Pdu-miR7122-3p and PduHOS1 by RT-qPCR across all early and late-blooming genotypes. (Pearson’s correlation coefficient = −0.74; pvalue = 0.03). In the CBF /DREB pathway, HOS1 gene is recognized as the negative regulator in cold signal transduction. This gene encodes a RING finger protein which is physically interacting with ICE1 and causing it to be ubiquitinated and eventually degraded (Lee et al. 2005). ICE1 is known as transcriptional activator of the CBF regulon, and the overexpression of this gene results in freezing tolerance induction (Chinnusamy et al. 2003). C-repeat binding factor/dehydration responsive element binding

44

M. Karimi et al.

(CBF/DREB) are known as transcription factors (TFs) which begin accumulated within a short period of plant exposure to cold stress (Chinnusamy et al. 2010; Vazquez-Hernandez et al. 2017). The CBFs regulate the expression of coldresponsive (COR) genes in ABA-independent pathway through binding to the CRT/DRE cisacting elements which are presented in the promoters of COR genes (Van Buskirk and Thomashow 2006). The study of Barrero-Gil and Salinas (2013) demonstrated that the overexpression of this gene caused reduction of coldresponsive gene expression and subsequently decreased cold stress tolerance. Generally, based on the mentioned studies, the expression pattern of Pdu-mIR7122a-3p and PduHOS1 in almond under cold stress implies that PduHOS1/PdumIR7122a-3p plays a substantial role in frost tolerance induction. However, further studies are needed to draw more accurate conclusions.

3

MicroRNA-Mediated Gene Regulation Under Drought Stress in Almond

Drought stress is considered as the most important factor limiting plant growth and its productivity. Plants have evolved various mechanisms under drought stress at different levels of molecular, cellular, and morphological. Unraveling of involved molecular genetic mechanisms and physiological processes of this complex phenomenon could facilitate efforts to develop drought-tolerant varieties (Salehi-Lisar et al. 2016; Li et al. 2017a, b). Almond tree has been identified as a drought-tolerant plant; hence, it could be well adapted to arid and semi-arid regions (Fatahi et al. 2018). The tolerance of almond to water-deficit stress is mostly related to the adaptive mechanisms in leaves and roots (Isaakidis et al. 2004). The degree of tolerance in different almond varieties has been measured based on a variety of important indicators including relative water content (RWC), leaf water potential (Ww), photosynthesis rate (PN), stomatal conductance (gs), leaf temperature (DT), electrolyte leakage (EL), carbon dioxide (CO2),

exchange rates (CER), and organic solutes content such as proline (Romero et al. 2003; Akbarpour et al. 2017; Fatahi et al. 2018). Several studies have approved the important role of miRNAs in drought stress response through regulation of cellular processes such as photosynthesis and respiration, response to stress hormones including auxin and ABA, and ROS scavenging mediated by antioxidants and osmotic adjustment (Li et al. 2008; Frazier et al. 2011; Eldem et al. 2012; Wang et al. 2013; Akdogan et al. 2016; Zhang et al. 2016; Guo et al. 2017; Arshad et al. 2017; Zare et al. 2019). Below the expression pattern of some differentially expressed miRNAs under drought stress was discussed in almond.

3.1 Pdu-miR156 Under Drought Stress One of the miRNAs that have been found to be involved in drought stress is miR156. The role of this miRNA has emerged in a wide range of processes such as floral transition (Fu et al. 2012), flowering time (Yu et al. 2015), biomass amount (Aung et al. 2015a, b), carotenoid content (Wei et al. 2010) and control of apical dominance (Fu et al. 2012), and response to various environmental stresses (Li et al. 2013; Budak et al. 2014, 2015; Cui et al. 2014; He et al. 2016; Duan et al. 2016). In more plant species, it was confirmed that miR156 targets different members of the squamosal promoter binding protein-like (SPL) gene family. MiR156/SPL module regulates drought stress tolerance in Arabidopsis through control of root development and accumulation of secondary metabolites (Yu et al. 2015; Stief et al. 2014). In alfalfa, Arshad et al. (2017) demonstrated that the regulation of SPL13 mediated by miR156 improves drought stress tolerance. Contrary to the trend of increased expression of miR156 under drought stress in different species including Arabidopsis (Liu et al. 2008), Populus (Lu et al. 2008), wheat (Kantar et al. 2011), barely (Kantar et al. 2010), and peach (Eldem et al. 2012), in almond the significant downregulation of miR156 expression

Almond miRNA Expression and Horticultural Implications

was observed under severe drought stress (−3 FC). Decreased expression of Pdu-miR156 can be justified according to previous findings. Since the overexpression of SPL transcription factors results in an early flowering phenotype, so it could be assumed that the down-regulation of Pdu-miR156 promotes the induction of SPL levels and ultimately provides a compatibility response for escaping the stress via early flower development and reproduction (Esmaeili et al. 2017; Wang et al. 2014a, b, c).

3.2 Pdu-miR167 Under Drought Stress MiR167 has been reported to decrease negative effects of drought stress through regulation of stress-related phytohormones including auxin and ABA (Nadarajah and Kumar 2019). Under drought stress, ABA induces a signaling message spread through whole plant cells and mediates regulation of related transcription factors through miRNAs, metabolism, and transmission of ABA and stomatal closure (Zhang et al. 2006; Daszkowska-Golec and Szarejko 2013; Daszkowska-Golec 2016). Another phytohormone which is known to respond to drought stress is auxin. Auxin signaling is regulated at post-transcriptional level by several miRNAs including miR160, miR167, and miR390. In plants such as rice (Liu et al. 2009) and maize (Wei et al. 2009), it has been shown that ABA mediates the downregulation of miR167 under drought stress and in contrast in Arabidopsis (Liu et al. 2008) and wheat (Pandey et al. 2013) induces its expression. The study of Wei et al. (2009) found that the downregulation of miR167d in maize induces the Phospholipase D (PLD) accumulation which is essential for stomatal movement mediated by ABA signaling. In the study of Esmaeili et al. (2017), the expression pattern of Pdu-miR167 under drought stress was assayed and results showed the significantly reduced expression of this miRNA under mild and severe drought stress in leaf tissues of almond. This downregulation is consistent with similar studies in peach (Eldem et al.

45

2012), rice (Liu et al. 2009), and maize (Wei et al. 2009), (Bakhshi et al. 2016). Auxin response factors (ARFs) is a well-characterized transcription factors which regulate the expression of many auxin response genes (Bouzroud et al. 2018) and involve in auxin signaling transduction and regulation of growth and development (Zhang et al. 2012a, b). In Arabidopsis (Gleeson et al. 2014) and rice (Huang et al. 2014a, b; Li et al. 2016a, b), it has been found that the miR167 targets ARF6 and ARF8. MiR167 with targeting ARFs has a major role in root architecture in plants which is imposed on different ambient conditions. Under drought stress, ARFs together with ABA participate in response to stress. In the study of Liu et al. (2009), it was presented that ABA-repressed production of miR167 in rice induces the accumulation of ARFs and inhibition of adventitious root formation and decreased lateral root growth. Since under drought stress, the main mechanism of the plant is to inhibit growth and development for allocating the nutrients and energy consumption for adaption mechanism (Chaves and Oliveira 2004), this process would aid the plant to retain its energy for survival and adaptationrelated processes (Nadarajah and Kumar 2019).

3.3 Pdu-miR408 Under Drought Stress MiR408 is known as a conserved miRNA which has been identified in many plant species (Axtell and Bowman 2008). Various studies have recorded that the expression level of this miRNA is significantly influenced by different abiotic stresses including drought stress. For instance, the accumulation of miR408 transcript level was reported in Hordeum vulgare (Kantar et al. 2010) and Medicago truncatula (Trindade et al. 2010) and chickpea (Hajyzadeh et al. 2015) while the decreased expression level of miR408 was displayed in Oryza sativa (Zhou et al. 2010) and Prunus persica (Eldem et al. 2012). In accordance with miR408 expression results in rice and peach, in almond, the significant downregulation of Pdu-miR408 was observed under drought

46

stress at both levels of mild (−1.7 FC) and severe (−2 FC) (Esmaeili et al. 2017). Mir408 and miR398 are both recognized as copperresponsive miRNAs (Jiu et al. 2019). Plantacyanins or copper-binding proteins are the member of phytocyanin family (Dong et al. 2005) which are involved in copper homeostasis and regulated by miR408. PduARPN (plantacyanin) was predicted as Pdu-miR408 target gene in almond, and RT-qPCR results revealed the down-regulation of PduARPN expression at both mild (−3.2 FC) and severe (−3.7 FC) stress levels (Esmaeili et al. 2017). Copper (Cu) is considered as a micronutrient which indispensable for plant growth and development. The impact of drought stress on the Cu imbalance has been displayed in several researches (Silva et al. 2011; Taran et al. 2017; Tadayyon et al. 2018). Some of the detrimental consequences of drought stress on the inappropriate distribution of this nutrient are stress-mediated sensitivity via disruption of key enzyme efficiency (Hajiboland 2012). Each plant has its own mechanism for the vital ion homeostasis. In almond, the observed reduction in the expression level of Pdu-miR408 and PduARPN under drought stress can be accounted as a specific regulatory response that in coordination with other copper-responsive including Pdu-miR398 inhibit imbalances in Cu which leads to negative secondary effects.

3.4 Pdu-miR2275 Under Drought Stress Recently, it has been found that miR2275 is involved in the production of phasiRNAproducing loci (PHAS) (Polydore et al. 2018), but little has been done to determine whether this miRNA contributes to drought stress. However, the regulatory role of this miRNA in response to cold stress was reported in the studies of Barakat et al. (2012) and Li et al. (2017). One of the studies that examined the role of this miRNA in response to drought stress is the study of Esmaeili et al. (2017) which assisted the expression changes of Pdu-miR2275 under both mild and severe levels of drought stress in

M. Karimi et al.

almond. In this research, the significant upregulation (1.4 FC) of this miRNA was observed under mild stress whereas under severe stress it was down-regulated (−3.5 FC). DNA-directed RNA polymerase III RPC4 (RPC4) was predicted as Pdu-miR2275 target gene. The examination of expression changes revealed a negative regulatory role of this gene in response to drought stress (with −2.1 FC under mild stress and −4.6 FC under severe stress). The study of Gharat and Shaw (2015) showed that this gene is targeted by miR320 in Suaeda maritime. Since the negative correlation between the expression level of Pdu-miR2275 and RPC4 was not observed under severe drought stress in almond, the existence of other regulatory elements, such as another miRNA, could be concluded in controlling the expression of this gene. The role of RPC4 gene was identified in transcription of 5S rRNAs and tRNAs (Nguyen et al. 2017). So far, no specific study has investigated the role of this gene in response to environmental stresses, especially drought stress, and the recognition of this gene's functions will be required further research.

4

Identification of SymbiosisRelated miRNAs Under Salt and Drought Stresses in Almond

The symbiosis is named after the Greek “mycos” and “rhiza” meaning “fungus-root,” and it is probably the oldest and most widespread plant symbiosis on Earth. Indeed, fossil records and phylogenetic evidence date their existence back more than 450 million years, which indicates a considerable selective advantage for both partners. Arbuscular mycorrhiza-forming fungi (AMF) are obligate biotrophs that require the host plant to complete their life cycle (Jung et al. 2012). Due to the obligate biotrophic nature, AM fungi need to consume plant photosynthetic and lipids to complete their life cycle, and reciprocally, AM fungi significantly contribute to plant growth not only by enhancing mineral nutrient uptake and water acquisition from surrounding soil, but also protecting plants against fungal

Almond miRNA Expression and Horticultural Implications

pathogens and a variety of abiotic stresses. Therefore, AM fungi are key endosymbionts of the plant symbiosis and have significant impacts on plant productivity and ecosystem function, and considered as a great interest for the sustainable agricultural development (Sun et al. 2018). In general, AM fungi have been shown to increase plant’s resistance to soil salinity and drought, while improving their productivity and nutrient status and alleviating their water stress. AM fungi increase plant’s tolerance to soil salinity and drought by cell osmoregulation, ion balance and compartmentalization, plant water balance and aquaporins, antioxidant mechanisms, and phytohormones (Estrada et al. 2013). Recent studies have displayed that microRNAs play an important role in symbiosis of plant with Arbuscular Mycorrhizal Fungi (Couzigou et al. 2017; Lelandais-bri, Hartmann and Crespi 2016). For instance, the accumulation of miR399 in the roots of Medicago trunctula and Nicotiana tabacum has been reported during AM symbiosis (Branscheid et al. 2010). In another study, 20 miRNAs with significantly changed expression in mycorrhizal roots including miR5229a/b, miR5206, miR160f*, miR5204, miR169d/l, miR169d*/e.2*/l*/m*, miR160c, miR171h, miR167, miR5244, miR5232, miR5281b–f, miR5250, miR2086, miR166b.2/ c.2/f.2, miR396b*, miR5213, miR162, miR4414a, and miR5285a–c were identified by high-throughput sequencing in M. trunctula under AM symbiosis (Devers et al. 2011). The role of mtr-miR396 and mtr-miR171h was specifically recognized in AM symbiosis by mediating the regulation of growth-regulating factor (MtrGRF) and Nodulation Signaling Pathway2 (MtrNSP2) genes, respectively (Wu et al. 2016). Despite the recent signs of progress in discovering symbiosis-related miRNAs in legumes, few studies have explored these miRNAs in non-legume plants (Wu et al. 2016); hence, the identification of potential conserved miRNAs and also novel lineage-specific AMresponsive miRNAs in AM symbiosis P. dulcis

47

has been limited across different plant lineages. In a recent study which is carried out in almond, the expression pattern of Pdu-miR156, Pdu-miR166, Pdu-miR169, Pdu-miR171, Pdu-miR172, PdumiR390, Pdu-miR393, Pdu-miR394, and PdumiR399 were examined in AM plants including GN (Garfi ₓ Nemared) and GF677 (Prunus amygdalus ₓ Prunus persica) (unpublished data). The results of morphological and physiological studies showed a positive effect of symbiosis with AM on the reduction of salinity damages in GF677 rootstock. The symbiotic plants had more suitable growth conditions than non-symbiotic plants under salinity stress and besides physiological traits such as prolin, soluble sugars, and antioxidant enzymes confirmed these observations. Since the AM symbiosis is regulated by the miR171 family (Couzigou et al. 2017). The expression analysis of Pdu-miR171a, PdumiR171b, and PduSCL (Scarecrow like protein) as their target gene was assessed in almond and remarkable differences were detected in both leaves and roots of symbiotic plants compared to non-symbiotic. For instance, in the leaves of the AM plants the downregulation of these two miRNAs was observed after 24 h of salinity stress at the highest salinity level but after 48 h the up-regulation of miR171a and miR171b was observed in all salinity levels. The overexpression of target gene was observed at high levels of salinity (80 and 160 mM NaCl) at both 24 h and 48 h treatments. In the root of AM plants, the similar results were observed but PduSCL was up-regulated at control condition and under 80 mM NaCl stress level after 24 h. After 48 h, no significant overexpression was detected under any salt stress levels (Shahvali et al. under review). Overall, it could be hypothetically concluded that with increasing the time of stress exposure, the up-regulated expression of miR171b, elevated the symbiosis induction rate and followed promoted stress tolerance through different uncharacterized mechanisms.

48

5

M. Karimi et al.

MicroRNA-Mediated Gene Regulation During Fruit Development

One of the most important parameters of plant fitness and the main component of the yield is seed size (Alonso-Blanco et al. 1999). In developmental biology, various studies have been carried out to know how plants control the size of their seeds. In almond as an angiosperm plant, seed development happens with a doublefertilization event, which produces a triploid endosperm and a diploid embryo (Chaudhury et al. 2001). The seed coat, which is obtained from female integuments, covers the endosperm and the embryo. To ensure coordinated growth and development, all three seed structures are interconnected and thereby specifying a seed final size (Figueiredo et al. 2014) (Fig. 2). The seed size is influenced by genetic factors and the environment condition. It is demonstrated that there are several factors that regulate endosperm growth and thus control the seed size (Luo et al. 2005). The genotype of zygotic tissues directly affects the seed size nor the genotype of maternal tissues (Sun et al. 2010). For instance, reciprocal crosses of two genotypes include the met1-6 mutant, a mutation of the MET1 gene, and wild type show that hypomethylated paternal and maternal genomes produce significantly smaller and larger F1 seeds, respectively (Xiao et al. 2006). The IKU pathway, phytohormones, the MAPKK pathway, transcriptional regulatory factors, the ubiquitin–proteasome pathway, and G-protein signaling are signaling pathways that

Fig. 2 Overview of seed structure in almond

recently have been demonstrated as effective controlling pathways in seed size (Li and Li 2016) (Fig. 3). The fruit of almond (Prunus dulcis Mill.), unlike other drupes, has a tough and leathery dry mesocarp, so that it has just two phases of growth as evaluated by size and total weight. Thereby, there is no final or the third stage rapid growth of the mesocarp in the almond. The almond seed reaches its final size at the end of the first stage, but in the second stage the size of the endosperm increases and then decreases as the embryo grows (Hawker and Buttrose 1980). The kernel is the almond edible part that is chiefly composed of cotyledons, and it is considered an important food product with high nutritional value. It is necessary that the kernels of the almond include a high quality to comply with the needs of the industry and also for the consumers to be attractive. In addition to the physical properties of the almond kernel, its chemical constituents are also essential due to its different applications in the industry and the high variety of its confectioneries (Kodad et al. 2009). Most commercial almond cultivars are selfincompatible and from where the edible portion of the almond is the embryo (kernel), fertilization of the egg is essential. Accordingly, it is necessary to plant in the almond orchards at least 2 cross-compatible and simultaneously blooming cultivars and pollinator insects are required for inter-cultivar transfer of pollen. On the other hand, several investigators have reported an effect of pollen sources on the features of fruits (xenia) including seed size in almond. Hence, the effect of xenia phenomenon and its related

Almond miRNA Expression and Horticultural Implications

49

Fig. 3 The major signaling pathways of seed size control. Seed size is regulated by several signaling pathways in Arabidopsis and rice, including the IKU pathway, the ubiquitin–proteasome pathway, G-protein signaling, MAPK signaling, phytohormones, and transcriptional regulatory factors. These regulators control seed size by influencing cell proliferation and cell expansion in maternal tissues or endosperm growth. Thin lines represent weak effects on cell proliferation or cell expansion, compared with thick lines. Dash–dot lines

mean that genetic relationships remain unclear. Gray lines indicate inconsistent results for effects of GL7/GW7/SLG7 on cell proliferation and cell expansion in different studies. L represents the grain-length direction, and W means the grain-width direction. The seed size regulators in Arabidopsis and rice are shown in red and blue, respectively (This figure and its legend are taken from Li and Li (2016) Signaling pathways of seed size control in plants. Current opinion in plant biology 33:23– 32)

pathways on the almond kernel size has been assayed through RNA-seq. The transcriptomes of almond fruits collected at 0, 18, 36, 62, and 74 days after pollination (DAA) from almond cultivar Sefid was pollinated with pollen from almond cultivar Mamaii (large seed size) and a clone of P. orientalis (small seed size) have been analyzed. Using RNA-seq, the studies revealed that genes involved in fruit development of almond and some gene effected from male parents (Jafari et al. 2022). In addition, several conserved miRNA families are identified in almond by using NGS technology. However, less is known about miRNAs in fruit development and quality of almond. To understand the role of miRNAs in fruit development, a study was carried out using an Illumina HiSeq 2000 platform with different sizes during fruit development. To further

elucidate the function of miRNAs during fruit development and seed size control process, genome-wide high-throughput sequencing was used to study miRNAs at 6 developmental stages of almond fruits collected at 18, 24, 30, 36, 62, and 74 days after pollination (DAA) from almond cultivar Sefid was pollinated with pollen from almond cultivar Mamaii (large seed size) and a clone of P. orientalis (small seed size), respectively. The differentially expressed miRNAs involved in fruit size were identified, and the corresponding target genes were also predicted. The high-throughput sequencing fruit of almond “Sefid” ₓ “Mamaii” (with large size fruits) and almond “Sefid” ₓ “P. orientalis” (with small size fruits) found that the expressions of these miRNAs (Pdu-miR172d, Pdu-miR482b3p, Pdu-miR482a-3p, Pdu-miR482d-3p,

50

Pdu-miR482a-5p, Pdu-miR1511-3p, PdumiR482c-3p, Pdu-miR482f, Pdu-miR482d-5p, Pdu-miR482c-5p, Pdu-miR395a-3p, PdumiR395b-3p, Pdu-miR167d, Pdu-miR482e, PdumiR7122b-5p, Pdu-miR7122a-5p, Pdu-miR535b, Pdu-miR8125, Pdu-miR396a, Pdu-miR398b, PdumiR8123-5p, Pdu-miR858, Pdu-miR6281, PdumiR6284, and Pdu-miR6285 were significantly different (Jafari et al. 2021). The potential targets of the identified miRNAs were predicted based on sequence homology. Afterward, the potential functions of the differentially expressed miRNAs and their target genes were discussed. These data open a novel point of view into the molecular mechanisms of almond fruit development. The identification and characterization of known and novel miRNAs will make it possible to understand the roles of these miRNAs in this area. For instances, miRNA396a is identified as an effective miRNA in the growth and development process. The Gene Regulation Factors are the target genes of this miRNA which encode putative transcription factors that are involved in the regulation of plant growth and development. In contrast, the target gene of PdumiRNA396a in almond is Ben1 (Brassinosteroid insensitive 1) which is the major receptor of the plant hormone brassinosteroid. It plays very important role in plant development, especially in the control of cell elongation and for the tolerance of environmental stresses. BRI1 enhances cell elongation, and promotes pollen development and control vasculature development. Studying the gene expression in large and small fruit showed that the expression of this gene in large fruits is more than the small fruits (Jiang and Lin 2013). Significant differences were observed between the large and small almond seeds growing for Pdu-miRNA6285. PdumiR6285 with targeting GH3.9 (Gretchen Hagen 3) has a major role in plant development. In accordance with miR398, different expression resulted in developing sea buckthorn seed (Ding et al. 2018), in almond, the different significant expression of Pdu-miR398b was observed in the kernel of almond with different sizes. PdumiR398b targets the ABCG40 (ATP-binding cassata G40) gene and regulates the level of

M. Karimi et al.

ABA hormone in the developing almond seed. Detailed study on the expression of ABCG40 in developing almond seed revealed that they are under post-transcriptional control by PdumiR398b, indicating the key role of miRNAmediated regulation of ABCG40 in developing seed. Finally, it could be concluded that some key miRNAs and their targets (Pdu-miR396aBEN1, Pdu-miR6285-CH3.9, Pdu-miR395aNFYB6, Pdu-miR398b-ABCG40) potentially involved in seed size of almond (Jafari et al. 2021).

References Achard P, Gong F, Cheminant S, Alioua M, Hedden P, Genschik P (2008) The cold-inducible CBF1 factor– dependent signaling pathway modulates the accumulation of the growth-repressing DELLA proteins via its effect on gibberellin metabolism. Plant Cell 20:2117– 2129 Akbarpour E, Imani A, Ferdowskhah Yeganeh S (2017) Physiological and morphological responses of almond cultivars under in vitro drought stress. J Nuts 8:61–72 Akdogan G, Tufekci ED, Uranbey S, Unver T (2016) miRNA-based drought regulation in wheat. Funct Integr Genom 16:221–233 Albertos P, Wagner K, Poppenberger B (2007) Cold stress signalling in female reproductive tissues. Plant, Cell Environ 42:846–853 Alimohammadi A, Shiran B, Martínez-Gómez P, Ebrahimie E (2013) Identification of water-deficit resistance genes in wild almond Prunus scoparia using cDNA-AFLP. Sci Hortic 159:19–28 Alisoltani A, Shiran B, Fallahi H, Ebrahimie E (2015) Gene regulatory network in almond (Prunus dulcis Mill.) in response to frost stress. Tree Genet Genomes 11:100 Allen E, Moing A, Wattis JA, Larson T, Maucourt M, Graham IA, Rolin D, Hooks MA (2011) Evidence that ACN1 (acetate non-utilizing 1) prevents carbon leakage from peroxisomes during lipid mobilization in Arabidopsis seedlings. Biochem J 437:505–513 Allen E, Xie Z, Gustafson AM, Carrington JC (2005) MicroRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121:207–221 Alonso-Blanco C, Blankestijn-de Vries H, Hanhart CJ, Koornneef M (1999) Natural allelic variation at seed size loci in relation to other life history traits of Arabidopsis thaliana. Proc Natl Acad Sci USA 96:4710–4717 Arshad M, Feyissa BA, Amyot L, Aung B, Hannoufa A (2017) MicroRNA156 improves drought stress tolerance in alfalfa (Medicago sativa) by silencing SPL13. Plant Sci 258:122–136

Almond miRNA Expression and Horticultural Implications Arús P, Gradziel T, Oliveira MM, Tao R (2009) Genomics of almond. In: Folta KM, Gardiner SE (eds) Plant genetics and genomics: crops and models. Vol. 6—genetics and genomics of Rosaceae, Springer, NewYork, pp 187–219 Aung B, Gruber MY, Amyot L, Omari K, Bertrand A, Hannoufa A (2015a) Ectopic expression of LjmiR156 delays flowering, enhances shoot branching, and improves forage quality in alfalfa. Plant Biotechnol Report 9:379–393 Aung B, Gruber MY, Amyot L, Omari K, Bertrand A, Hannoufa A (2015b) Micro RNA 156 as a promising tool for alfalfa improvement. Plant Biotechnol J 13:779–790 Axtell MJ, Bowman JL (2008) Evolution of plant microRNAs and their targets. Trends Plant Sci 13:343–349 Badek B, Napiórkowska B, Masny A, Korbin M (2014) Changes in the expression of three cold-regulated genes in ‘Elsanta’ and ‘Selvik’ Strawberry (Fragaria ₓ Ananassa) plants exposed to freezing. J Horticul Res 22:53–61 Bakhshi B, Fard EM, Nikpay N, Ebrahimi MA, Bihamta MR, Mardi M, Salekdeh GH (2016) MicroRNA signatures of drought signaling in rice root. PLoS ONE 11:e0156814 Ballester J, Socias i Company R, Arus P, De Vicente MC (2001) Genetic mapping of a major gene delaying blooming time in almond. Plant Breed 120:268– 270 Barakat A, Sriram A, Park J, Zhebentyayeva T, Main D, Abbott A (2012) Genome wide identification of chilling responsive microRNAs in Prunus persica. BMC Genomics 13:481 Barckley KK, Uratsu SL, Gradziel TM, Dandekar AM (2006) Multidimensional analysis of S-alleles from cross-incompatible groups of California almond cultivars. J Amer Soc Hort Sci 131:632–636 Barrero-Gil J, Salinas J (2013) Post-translational regulation of cold acclimation response. Plant Sci 205:48–54 Barros PM, Cherian S, Costa M, Sapeta H, Saibo NJ, Oliveira MM (2017) The identification of almond GIGANTEA gene and its expression under cold stress, variable photoperiod, and seasonal dormancy. Biol Plant 61:631–640 Barros PM, Gonçalves N, Saibo NJ, Oliveira MM (2012a) Functional characterization of two almond C-repeatbinding factors involved in cold response. Tree Physiol 32:1113–1128 Barros PM, Gonçalves N, Saibo NJ, Oliveira MM (2012b) Cold acclimation and floral development in almond bud break: insights into the regulatory pathways. J Exp Bot 63:4585–4596 Bellemare DR, Shaner L, Morano KA, Beaudoin J, Langlois R, Labbé S (2002) Ctr6, a Vacuolar Membrane Copper Transporter in Schizosaccharomyces pombe. J Biol Chem 277:46676–46686 Bhati KK, Sharma S, Aggarwal S, Kaur M, Shukla V, Kaur J, Mantri S, Pandey AK (2015) Genome-wide identification and expression characterization of

51 ABCC-MRP transporters in hexaploid wheat. Front Plant Sci 6:488 Bouzroud S, Gouiaa S, Hu N, Bernadac A, Mila I, Bendaou N, Smouni A, Bouzayen M, Zouine M (2018) Auxin Response Factors (ARFs) are potential mediators of auxin action in tomato response to biotic and abiotic stress (Solanum lycopersicum). PLoS ONE 13:e0193517 Branscheid A, Sieh D, Pant BD, May P, Devers EA, Elkrog A, Schauser L, Scheible WR, Krajinski F (2010) Expression pattern suggests a role of MiR399 in the regulation of the cellular response to local Pi increase during arbuscular mycorrhizal symbiosis. Mol Plant Microbe Interact 23:915–926 Budak H, Kantar M, Bulut R, Akpinar BA (2015) Stress responsive miRNAs and isomiRs in cereals. Plant Sci 235:1–3 Budak H, Khan Z, Kantar M (2014) History and current status of wheat miRNAs using next-generation sequencing and their roles in development and stress. Brief Funct Genom 14:189–198 Campalans A, Pages M, Messeguer R (2001) Identification of differentially expressed genes by the cDNAAFLP technique during dehydration of almond (Prunus amygdalus). Tree Physiol 21:633–643 Chaudhury AM, Koltunow A, Payne T, Luo M, Tucker MR, Dennis ES, Peacock WJ (2001) Control of early seed development. Annu Rev Cell Dev Biol 17:677–699 Chaves M, Oliveira M (2004) Mechanisms underlying plant resilience to water deficits: prospects for watersaving agriculture. J Exp Bot 55:2365–2384 Chen R, Guo W, Yin Y, Gong ZH (2014) A novel F-box protein CaF-box is involved in responses to plant hormones and abiotic stress in pepper (Capsicum annuum L.). Int J Mol Sci 15:2413–2430 Chen Y, Jiang J, Song A, Chen S, Shan H, Luo H, Gu C, Sun J, Zhu L, Fang W, Chen F (2013) Ambient temperature enhanced freezing tolerance of Chrysanthemum dichrum CdICE1 Arabidopsis via miR398. BMC Biol 11:121 Chinnusamy V, Ohta M, Kanrar S, Lee B, Hong X, Agarwal M, Zhu JK (2003) ICE1: a regulator of coldinduced transcriptome and freezing tolerance in Arabidopsis. Gene Dev 17:1043–1054 Chinnusamy V, Zhu JK, Sunkar R (2010) Gene regulation during cold stress acclimation in plants. In: Sunkar R (ed) Plant stress tolerance: methods and protocols, Humana Press, pp:39–55 Couzigou JM, Lauressergues D, André O, Gutjahr C, Guillotin B, Bécard G, Combier JP (2017) Positive gene regulation by a natural protective miRNA enables arbuscular mycorrhizal symbiosis. Cell Host Microbe 21:106–112 Cui LG, Shan JX, Shi M, Gao JP, Lin HX (2014) The miR156-SPL 9-DFR pathway coordinates the relationship between development and abiotic stress tolerance in plants. Plant J 80:1108–1117 Daszkowska-Golec A (2016) The role of abscisic acid in drought stress: how ABA helps plants to cope with

52 drought stress. In: Hossain MA, Wani SH, Bhattacharjee S, Burritt D, Tran LSP (eds) Drought stress tolerance in plants, vol 2. Springer, Cham, pp 123–151 Daszkowska-Golec A, Szarejko I (2013) Abiotic stressplant responses and applications in agriculture. IntechOpen; London, UK, 2013. The molecular basis of ABA-mediated plant response to drought Devers EA, Branscheid A, May P, Krajinski F (2011) Stars and symbiosis: MicroRNA-and MicroRNA*mediated transcript cleavage involved in arbuscular mycorrhizal symbiosis. Plant Physiol 156:1990–2010 Devi K, Dey KK, Singh S, Mishra SK, Modi MK, Sen P (2018) Identification and validation of plant miRNA from NGS data—an experimental approach. Brief Funct Genom 18:13–22 Ding J, Ruan C Guan Y, Krishna P (2018) Identification of microRNAs involved in lipid biosynthesis and seed size in developing sea buckthorn seeds using highthroughput sequencing. Scient Reports 8(1):1–15 Dirlewanger E, Cosson P, Howad W, CaPdueville G, Bosselut N, Claverie M, Voisin R, Poizat C, Lafargue B, Baron O, Laigret F, Kleinhentz M, Arus P, Esmenjaud D (2004) Microsatellite genetic linkage maps of myrobalan plum and an almond-peach hybrid – Location of root-knot nematode resistance genes. Theor Appl Genet 109:827–832 Dong J, Kim ST, Lord EM (2005) Plantacyanin plays a role in reproduction in Arabidopsis. Plant Physiol 138:778–789 Du YY, Wang PC, Chen J, Song CP (2008) Comprehensive functional analysis of the catalase gene family in Arabidopsis thaliana. J Integr Plant Biol 50:1318– 1326 Duan H, Lu X, Lian C, An Y, Xia X, Yin W (2016) Genome-wide analysis of microRNA responses to the phytohormone abscisic acid in Populus euphratica. Front Plant Sci 7:1184 Ebrahimi Khaksefidi R, Mirlohi S, Khalaji F, Fakhari Z, Shiran B, Fallahi H, Rafiei F, Budak H, Ebrahimie E (2015) Differential expression of seven conserved microRNAs in response to abiotic stress and their regulatory network in Helianthus annuus. Front Plant Sci 6:741 Eldem V, Akcay UC, Ozhuner E, Bakır Y, Uranbey S, Unver T (2012) Genome-wide identification of miRNAs responsive to drought in peach (Prunus persica) by high-throughput deep sequencing. PLoS ONE 7:e50298 Esmaeili F, Shiran B, Fallahi H, Mirakhorli N, Budak H, Martínez-Gómez P (2017) In silico search and biological validation of microRNAs related to drought response in peach and almond. Funct Integr Genomics 17:189–201 Estrada B, Aroca R, Barea JM, Ruiz-Lozano JM (2013) Native arbuscular mycorrhizal fungi isolated from a saline habitat improved maize antioxidant systems and plant tolerance to salinity. Plant Sci 201:42–51 Fathi HO, Esmailamiri MO, Imani A, Hajilou J, Nikbakht J (2018) Physiological and biochemical responses in some almond (Prunus amygdalus

M. Karimi et al. batsch.) genotypes (grafted on/gn15) submitted to drought stress. Pak J Bot 50:1281–1289 Feng H, Xu M, Liu Y, Dong R, Gao X, Huang L (2017) Dicer-like genes are required for H2O2 and KCl stress responses, pathogenicity and small RNA generation in Valsa mali. Front Microbiol 8:1166 Feng H, Xu M, Zheng X, Zhu T, Gao X, Huang L (2017b) Micrornas and their targets in apple (Malus domestica cv.“Fuji”) involved in response to infection of pathogen Valsa mali. Front Plant Sci 8:2081 Feng XM, Zhao Q, Zhao LL, Qiao Y, Xie XB, Li HF, Yao YX, You CX, Hao YJ (2012) The cold-induced basic helix-loop-helix transcription factor gene MdCIbHLH1encodes an ICE-like protein in apple. BMC Plant Biol 12:22 Figueiredo DD, Kohler C (2014) Signalling events regulating seed coat development. Biochem Soc Trans 42:358–363 Frazier TP, Sun G, Burklew CE, Zhang B (2011) Salt and drought stresses induce the aberrant expression of microRNA genes in tobacco. Mol Biotechnol 49:159– 165 Fu C, Sunkar R, Zhou C, Shen H, Zhang JY, Matts J, Wolf J, Mann DG, Stewart CN Jr, Tang Y, Wang ZY (2012) Overexpression of miR156 in switchgrass (Panicum virgatum L.) results in various morphological alterations and leads to improved biomass production. Plant Biotechnol J 10:443–452 Gao Z, Shi T, Luo X, Zhang Z, Zhuang W, Wang L (2012) High-throughput sequencing of small RNAs and analysis of differentially expressed microRNAs associated with pistil development in Japanese apricot. BMC Genom 13:371 Gharat SA, Shaw BP (2015) Novel and conserved miRNAs in the halophyte Suaeda maritima identified by deep sequencing and computational predictions using the ESTs of two mangrove plants. BMC Plant Biol 15:301 Gleeson M, Constantin M, Carroll BJ, Mitter N (2014) MicroRNAs as regulators of adventitious root development. J Plant Biochem Biotechnol 23:339–347 Gong P, Li XX, Li ZP, Li LM, Wang YQ, Yang B (2007) Effects of low-temperature stress on cell membrane systems and osmoregulatory matters of almond branch. Xinjiang Agricul Sci 2007:5 Gonzalez LE, Keller K, Chan KX, Gessel MM, Thines BC (2017) Transcriptome analysis uncovers Arabidopsis F-BOX STRESS INDUCED 1 as a regulator of jasmonic acid and abscisic acid stress gene expression. BMC Genom 18:533 Gradziel TM (2009) Almond (Prunus dulcis) Breeding. In: Mohan Jain S, Priyadarshan P.M (eds) Breeding plantation tree crops: temperate species. Springer, Verlag, New York, pp 1–31 Gremski K, Ditta G, Yanofsky MF (2007) The HECATE genes regulate female reproductive tract development in Arabidopsis thaliana. Development 134:3593–3601 Guo Y, Zhao S, Zhu C, Chang X, Yue C, Wang Z, Lin Y, Lai Z (2017) Identification of drought-responsive miRNAs and physiological characterization of tea

Almond miRNA Expression and Horticultural Implications plant (Camellia sinensis L.) under drought stress. BMC Plant Biology 17:211 Hafizi A, Shiran B, Maleki B, Imani A, Banović B (2013) Identification of new S-RNase self-incompatibility alleles and characterization of natural mutations in Iranian almond cultivars. Trees 27(3):497–510 Hajiboland R (2012) Effect of micronutrient deficiencies on plants stress responses. In: Ahmad P, Prasad MNV (eds) Abiotic stress responses in plants. Springer, New York, pp 281–330 Hajyzadeh M, Turktas M, Khawar KM, Unver T (2015) miR408 overexpression causes increased drought tolerance in chickpea. Gene 555:186–193 Harvey JJ, Lewsey MG, Patel K, Westwood J, Heimstädt S, Carr JP, Baulcombe DC (2011) An antiviral defense role of AGO2 in plants. PLoS ONE 6:e14639 Hawker J, Buttrose M (1980) Development of the almond nut (Prunus dulcis (Mill.) D. A. Webb). Anatomy and chemical composition of fruit parts from Anthesis to maturity. Annals of Botany 46(3):313–321 He X, Zheng W, Cao F, Wu F (2016) Identification and comparative analysis of the microRNA transcriptome in roots of two contrasting tobacco genotypes in response to cadmium stress. Sci Rep 6:32805 Hosseinpour B, Sepahvand S, Aliabad KK, Bakhtiarizadeh M, Imani A, Assareh R, Salami SA (2018) Transcriptome profiling of fully open flowers in a frost-tolerant almond genotype in response to freezing stress. Mol Genet Genom 293:151–163 Hou Y, Zhai L, Li X, Xue Y, Wang J, Yang P, Cao C, Li H, Cui Y, Bian S (2017) Comparative analysis of fruit ripening-related miRNAs and their targets in blueberry using small RNA and degradome sequencing. Int J Mol Sci 18:2767 Huang J, Si W, Deng Q, Li P, Yang S (2014a) Rapid evolution of avirulence genes in rice blast fungus Magnaporthe oryzae. BMC Genet 15:45 Huang R, Cang J, Yu J, Lu BW, Liu LJ, Wang JF, Guo RM, Xu C (2014b) Solexa sequencing and bioinformatics analysis of small RNA in winter wheat. Chin Bull Bot 49:8–18 Huang W, Peng S, Xian Z, Lin D, Hu G, Yang L, Ren M, Li Z (2017) Overexpression of a tomato miR171 target gene Sl GRAS 24 impacts multiple agronomical traits via regulating gibberellin and auxin homeostasis. Plant Biotechnol J 15:472–488 Isaakidis A, Sotiropoulos T, Almaliotis D, Therios I, Stylianidis D (2004) Response to severe water stress of the almond (Prunus amygdalus)’Ferragnès’ grafted on eight rootstocks. N Z J Crop Hortic Sci 32:355–362 Ishibashi Y, Tawaratsumida T, Kondo K, Kasa S, Sakamoto M, Aoki N, Zheng SH, Yuasa T, IwayaInoue M (2012) Reactive oxygen species are involved in gibberellin/abscisic acid signaling in barley aleurone cells. Plant Physiol 158:1705–1714 Jafari M, Shiran B, Rabiei G, Ravash R, Sayed Tabatabaei BE, Martínez-Gómez P (2021) Identification and verification of seed development related miRNAs in kernel almond by small RNA sequencing and qPCR. Plos one 16(12):e0260492

53 Jafari M, Shiran B, Rabiei G, Ravash R, Sayed Tabatabaei BE, Martínez-García PJ, Martínez-Gómez P (2022) Identification of genes associated with kernel size in almond [Prunus dulcis (Mill.) DA Webb] using RNA-Seq. Plant Growth Regul 97(2):357–373 Janick J, Moore JN (Eds.) (1996) Fruit breeding, tree and tropical fruits (Vol. 1). John Wiley & Sons Jia X, Wang WX, Ren L, Chen QJ, Mendu V, Willcut B, Dinkins R, Tang X, Tang G (2009) Differential and dynamic regulation of miR398 in response to ABA and salt stress in Populus tremula and Arabidopsis thaliana. Plant Mol Biol 71:51–59 Jiang WB, Lin WH (2013) Brassinosteroid functions in Arabidopsis seed development. Plant Signal Behav 8 (10):e25928 Jiu S, Leng X, Haider MS, Dong T, Guan L, Xie Z, Li X, Shangguan L, Fang J (2019) Identification of copper (Cu) stress-responsive grapevine microRNAs and their target genes by high-throughput sequencing. Royal Soc Open Sci 6:180735 Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14:787– 799 Jung SC, Martinez-Medina A, Lopez-Raez JA, Pozo MJ (2012) Mycorrhiza-induced resistance and priming of plant defenses. J Chem Ecol 38:651–664 Kantar M, Lucas SJ, Budak H (2011) miRNA expression patterns of Triticum dicoccoides in response to shock drought stress. Planta 233:471–484 Kantar M, Unver T, Budak H (2010) Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Funct Integr Genom 10:493– 507 Karimi M, , Shiran B, Rabiei M, Fallahi H (2019) Identification and functional analysis of novel and cold-responsive microRNAs and their target genes in reproductive tissue of almond (Prunus dulcis Mill.). PhD Thesis, Shahrekord University, Shahrekord, Iran Karimi M, Ghazanfari F, Fadaei A, Ahmadi L, Shiran B, Rabei M, Fallahi H (2016) The small-RNA profiles of almond (Prunus dulcis Mill.) reproductive tissues in response to cold stress. PLOS ONE 11:e0156519 Khraiwesh B, Zhu JK, Zhu J (2012) Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim Biophys Acta 1819:137–148 Kim J, Kim HY (2006) Functional analysis of a calciumbinding transcription factor involved in plant salt stress signaling. Eur Biochem Soc 580:5251–5256 Kim JY, Kwak KJ, Jung HJ, Lee HJ, Kang H (2010) MicroRNA402 affects seed germination of Arabidopsis thaliana under stress conditions via targeting DEMETER-LIKE Protein3 mRNA. Plant Cell Physiol 51:1079–1083 Kim T, Park JH, Lee SG, Kim S, Kim J, Lee J, Shin C (2017) Small RNA transcriptome of hibiscus Syriacus provides insights into the potential influence of microRNAs in flower development and terpene synthesis. Mol Cells 40:587

54 Kiribuchi K, Sugimori M, Takeda M, Otani T, Okada K, Onodera H, Ugaki M, Tanaka Y, Tomiyama-Akimoto C, Yamaguchi T, Minami E, Shibuya N, Omori T, Nishiyama N, Nojiri H, Yamane H (2004) RERJ1, a jasmonic acid-responsive gene from rice, encodes a basic helix-loop-helix protein. Biochem Biophys Res Commun 325:857–863 Kodad O, Estopanan G, Juan T, Socias i Company R (2009) Xenia effects on oil content and fatty acid and tocopherol concentrations in autogamous almond cultivars. J Agricul Food Chem 57(22):10809–10813 Lee BH, Henderson DA, Zhu JK (2005) The Arabidopsis cold-responsive transcriptome and its regulation by ICE1. Plant Cell 17:3155–3175 Lee S, Lee S, Yang KY, Kim YM, Park SY, Kim SY, Soh MS (2006) Overexpression of PRE1 and its homologous genes activates gibberellin-dependent responses in Arabidopsis thaliana. Plant Cell Physiol 47:591–600 Leivar P, Monte E, Al-Sady B, Carle C, Storer A, Alonso JM, Ecker JR, Quail PH (2008) The Arabidopsis phytochrome-interacting factor PIF7, together with PIF3 and PIF4, regulates responses to prolonged red light by modulating phyB levels. Plant Cell 20:337–352 Lelandais-Briere C, Moreau J, Hartmann C, Crespi M (2016) Noncoding RNAs, emerging regulators in root endosymbioses. Mol Plant Microbe Interact 29:170– 180 Li J, Li Y, Yin Z, Jiang J, Zhang M, Guo X, Ye Z, Zhao Y, Xiong H, Zhang Z, Shao Y (2017a) Os ASR 5 enhances drought tolerance through a stomatal closure pathway associated with ABA and H2O2 signalling in rice. Plant Biotechnol J 15:183–196 Li JS, FU FL, Ming AN, Zhou SF, She YH, Li WC (2013) Differential expression of microRNAs in response to drought stress in maize. J Integrat Agricul 12:1414–22 Li N, Li Y (2016) Signaling pathways of seed size control in plants. Curr Opin Plant Biol 33:23–32 Li S, Li K, Ju Z, Cao D, Fu D, Zhu H, Zhu B, Luo Y (2016a) Genome-wide analysis of tomato NF-Y factors and their role in fruit ripening. BMC Genom 17(1):36 Li S, Yu X, Lei N, Cheng Z, Zhao P, He Y, Wang W, Peng M (2017b) Genome-wide identification and functional prediction of cold and/or droughtresponsive lncRNAs in cassava. Sci Rep 7:45981 Li SB, Xie ZZ, Hu CG, Zhang JZ (2016b) A review of auxin response factors (ARFs) in plants. Front Plant Sci 7:47 Li WX, Oono Y, Zhu J, He XJ, Wu JM, Iida K, Lu XY, Cui X, Jin H, Zhu JK (2008) The Arabidopsis NFYA5 transcription factor is regulated transcriptionally and posttranscriptionally to promote drought resistance. Plant Cell 20:2238–2251 Li Q, Wang W, Wang W, Zhang G, Liu Y, Wang Y, Wang W (2018) Wheat F-box protein gene TaFBA1 is involved in plant tolerance to heat stress. Front Plant Sci 9:521

M. Karimi et al. Li W, Cui X, Meng Z, Huang X, Xie Q, Wu H, Jin H, Zhang D, Liang W (2012) Transcriptional regulation of Arabidopsis MIR168a and argonaute1 homeostasis in abscisic acid and abiotic stress responses. Plant Physiol 158:1279–1292 Lin SS, Chen Y, Lu MYJ (2019) Degradome Sequencing in Plants. In: de Folter S. (ed) Plant MicroRNAs, methods in molecular biology, vol 1932. Humana Press, New York, NY Liu HH, Tian X, Li YJ, Wu CA, Zheng CC (2008) Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. RNA 14:836–843 Liu Q, Zhang YC, Wang CY, Luo YC, Huang QJ, Chen SY, Zhou H, Qu LH, Chen YQ (2009) Expression analysis of phytohormone-regulated microRNAs in rice, implying their regulation roles in plant hormone signaling. FEBS Lett 583:723–728 Liu WW, Meng J, Cui J, Luan YS (2017) Characterization and function of MicroRNA*s in plants. Front Plant Sci 8:2200 Liu XX, Luo XF, Luo KX, Liu YL, Pan T, Li ZZ, Duns GJ, He FL, Qin ZD (2019) Small RNA sequencing reveals dynamic microRNA expression of important nutrient metabolism during development of Camellia oleifera fruit. Int J Biol Sci 15:416 López-Galiano MJ, García-Robles I, González-Hernández AI, Camañes G, Vicedo B, Real MD, Rausell C (2019) Expression of miR159 Is altered in tomato plants undergoing drought stress. Plants 8:201 Lu S, Sun YH, Chiang VL (2008) Stress-responsive microRNAs in populus. Plant J 55:131–151 Luo M, Dennis ES, Berger F, Peacock WJ, Chaudhury A (2005) MINISEED3 (MINI3), a WRKY family gene, and HAIKU2 (IKU2), a leucine-rich repeat (LRR) KINASE gene, are regulators of seed size in Arabidopsis. Proc Natl Acad Sci 102(48):17531– 17536 Lv DK, Bai X, Li Y, Ding XD, Ge Y, Cai H, Ji W, Wu N, Zhu YM (2010) Profiling of cold-stress-responsive miRNAs in rice by microarrays. Gene 459:39–47 Ma HS, Liang D, Shuai P, Xia XL, Yin WL (2010) Drought inducible poplar GRAS protein SCL7 confers salt and drought tolerance in Arabidopsis thaliana. J Exp Bot 61:4011–4019 Ma Z, Hu X, Cai W, Huang W, Zhou X, Luo Q, Yang H, Wang J, Huang J (2014) Arabidopsis miR171-targeted scarecrow-like proteins bind to GT cis-elements and mediate gibberellin-regulated chlorophyll biosynthesis under light conditions. PLOS Genet 10:e1004519 Millar AA, Gubler F (2005) The Arabidopsis GAMYBlike genes, MYB33 and MYB65, are microRNAregulated genes that redundantly facilitate anther development. Plant Cell 17:705–721 Mousavi S, Alisoltani A, Shiran B, Fallahi H, Ebrahimie E, Imani A, Houshmand S (2014) De novo transcriptome assembly and comparative analysis of differentially expressed genes in Prunus dulcis Mill. in response to freezing stress. PlOS ONE 9:e104541 Nadarajah K, Kumar IS (2019) Drought response in rice: the miRNA story. Int J Mol Sci 20:3766

Almond miRNA Expression and Horticultural Implications Nguyen GN, Yamagata Y, Shigematsu Y, Watanabe M, Miyazaki Y, Doi K, Tashiro K, Kuhara S, Kanamori H, Wu J, Matsumoto T (2017) Duplication and loss of function of genes encoding RNA polymerase III subunit C4 causes hybrid incompatibility in rice. G3 Genes Genomes Genetics 7:2565–75 Ning LH, Du WK, Song HN, Shao HB, Qi WC, Sheteiwy MS, Yu DY (2019) Identification of responsive miRNAs involved in combination stresses of phosphate starvation and salt stress in soybean root. Environ Exp Bot 167:103823 Niu J, Wang J, An J, Liu L, Lin Z, Wang R, Wang L, Ma C, Shi L, Lin S (2016a) Integrated mRNA and miRNA transcriptome reveal a cross-talk between developing response and hormone signaling for the seed kernels of Siberian apricot. Sci Rep 6(1):1–2 Niu J, Wang J, An J, Liu L, Lin Z, Wang R, Wang L, Ma C, Shi L, Lin S (2016b) Integrated mRNA and miRNA transcriptome reveal a cross-talk between developing response and hormone signaling for the seed kernels of Siberian apricot. Sci Rep 6:35675 Pandey B, Gupta OP, Pandey DM, Sharma I, Sharma P (2013) Identification of new stress-induced microRNA and their targets in wheat using computational approach. Plant Signal Behav 8:e23932 Park JH, Shin C (2014) MicroRNA-directed cleavage of targets: mechanism and experimental approaches. BMB Rep 47:417 Pegler JL, Oultram JM, Grof CP, Eamens AL (2019) Profiling the abiotic stress responsive microRNA landscape of Arabidopsis thaliana. Plants 8:58 Peng HH, Shan W, Kuang JF, Lu WJ, Chen JY (2013) Molecular characterization of cold-responsive basic helix-loop-helix transcription factors MabHLHs that interact with MaICE1 in banana fruit. Planta 238:937– 953 Polydore S, Lunardon A, Axtell MJ (2018) Several phased siRNA annotation methods can frequently misidentify 24 nucleotide siRNA-dominated PHAS loci. Plant Direct 2:e00101 Reyes JC, Muro-Pastor MI, Florencio FJ (2004) The GATA family of transcription factors in Arabidopsis and rice. Plant Physiol 134:1718–1732 Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP (2002) Prediction of plant microRNA targets. Cell 110:513–520 Romero P, Navarro J, Garcia F, Ordaz P (2003) Effects of regulated deficit irrigation during the preharvest period on gas exchange, leaf development and crop yield of matur almond trees. Tree Physiol 24:303–312 Şahin-Çevik M, Moore GA (2013) Identification of a drought-and cold-stress inducible WRKY gene in the cold-hardy Citrus relative Poncirus trifoliata. N Z J Crop Hortic Sci 41:57–68 Salehi-Lisar SY, Bakhshayeshan-Agdam H (2016) Drought stress in plants: causes, consequences, and tolerance. In: Hossain M, Wani S, Bhattacharjee S, Burritt D, Tran LS (eds) Drought stress tolerance in plants, vol 1. Springer, Cham

55 Sanousi RSE, Hamza NB, Abdelmula AA, Mohammed IA, Gasim SM, Sanan-Mishra N (2016) Differential expression of miRNAs in Sorghum bicolor under drought and salt stress. Am J Plant Sci 7:870–878 Scholthof HB, Alvarado VY, Vegaarreguin JC, Ciomperlik J, Odokonyero D, Brosseau C, Jaubert M, Zamora A, Moffett P (2011) Identification of an Argonaute for antiviral RNA silencing in Nicotiana Benthamiana. Plant Physiol 156:1548–1555 Schwab R, Ossowski S, Riester M, Warthmann N, Weigel D (2006) Highly specific gene silencing by artificial microRNAs in Arabidopsis. Plant Cell 18:1121–1133 Shahvali R, Shiran, B, Ravash R, Fallahi H (under review) Effects of arbuscular mycorrhizal symbiosis on morphological parameters, ion uptake, Na/K transporters genes and mir171 under salt stress in GF677 (peach ₓ almond) rootstocks Silva EC, Nogueira RJMC, Silva MA, Albuquerque M (2011) Drought stress and plant nutrition. Plant Stress 5:32–41 Socias i Company R, Alonso JM, Kodad O, Gradziel TM (2012) Almond. In: Badenes ML, Byrne D (eds) Fruit breeding. Handbook of plant breeding. Springer, Verlag, Heidelberg, pp:697–728 Sofo A, Rajabpoor S, Yaghini H, Shirani M, Archangi A, Sangi SE, Tavakoli F, Khodai M, Sorkheh K (2014) Cold-induced changes in antioxidant defenses and reactive oxygen species in eight wild almond species. Free Rad Antiox 4:70 Song G, Zhang R, Zhang S, Li Y, Gao J, Han X, Chen M, Wang J, Li W, Li G (2017) Response of microRNAs to cold treatment in the young spikes of common wheat. BMC Genom 18:212 Stief A, Altmann S, Hoffmann K, Pant BD, Scheible WR, Bäurle I (2014) Arabidopsis miR156 regulates tolerance to recurring environmental stress through SPL transcription factors. Plant Cell 26:1792–1807 Sun X, Shantharaj D, Kang X, Ni M (2010) Transcriptional and hormonal signaling control of Arabidopsis seed development. Curr Opin Plant Biol 13(5):611– 620 Sun X, Xu L, Wang Y, Yu R, Zhu X, Luo X, Gong Y, Wang R, Limera C, Zhang K, Liu L (2015) Identification of novel and salt-responsive miRNAs to explore miRNA-mediated regulatory network of salt stress response in radish (Raphanus sativus L.). BMC Genom 16:197 Sun Z, Song J, Xin X, Xie X, Zhao B (2018) Arbuscular mycorrhizal fungal 14-3-3 proteins are involved in arbuscule formation and responses to abiotic stresses during AM symbiosis. Front Microbiol 9:91 Sunkar R, Li YF, Jagadeeswaran G (2012) Functions of microRNAs in plant stress responses. Trends Plant Sci 17:196–203 Sunkar R, Zhu JK (2004) Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell 16:2001–2019

56 Tadayyon A, Nikneshan P, Pessarakli M (2018) Effects of drought stress on concentration of macro-and micronutrients in Castor (Ricinus communis L.) plant. J Plant Nutr 41:304–310 Tang X, Wang Q, Yuan H, Huang X (2018) Chillinginduced DNA Demethylation is associated with the cold tolerance of Hevea brasiliensis. BMC Plant Biol 18:70 Taran N, Storozhenko V, Svietlova N, Batsmanova L, Shvartau V, Kovalenko M (2017) Effect of zinc and copper nanoparticles on drought resistance of wheat seedlings. Nanoscale Res Lett 12:60 Thiebaut F, Rojas CA, Almeida KL, Grativol C, Domiciano GC, Lamb CR, Engler Jde A, Hemerly AS, Ferreira PC (2012) Regulation of miR319 during cold stress in sugarcane. Plant, Cell Environ 35:502–512 Tian T, Liu Y, Yan H, You Q, Yi X, Du Z, Xu W, Su Z (2017) AgriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res 45:W122–W129 Trindade I, Capitao C, Dalmay T, Fevereiro MP, Santos DM (2010) miR398 and miR408 are up-regulated in response to water deficit in Medicago truncatula. Planta 231:705–716 Ushijima K, Sassa H, Tamura M, Kusaba M, Tao R, Gradziel TM, ... , Hirano H (2001) Characterization of the S-locus region of almond (Prunus dulcis): analysis of a somaclonal mutant and a cosmid contig for an S haplotype. Genetics 158(1):379–386 Van Buskirk HA, Thomashow MF (2006) Arabidopsis transcription factors regulating cold acclimation. Physiol Plant 126:72–80 Vazquez-Hernandez M, Romero I, Escribano MI, Merodio C, Sanchez-Ballesta MT (2017) Deciphering the role of CBF/DREB transcription factors and dehydrins in maintaining the quality of table grapes cv. autumn royal treated with high CO2 levels and stored at 0 C. Front in Plant Sci 8:1591 Wang B, Duan CG, Wang X, Hou YJ, Yan J, Gao C, Kim JH, Zhang H, Zhu JK (2015) HOS1 regulates Argonaute1 by promoting transcription of the microRNA gene MIR168b in Arabidopsis. Plant J 81:861– 870 Wang B, Sun YF, Song N, Wei JP, Wang XJ, Feng H, Yin ZY, Kang ZS (2014a) MicroRNAs involving in cold, wounding and salt stresses in Triticum aestivum L. Plant Physiol Biochem 80:90–96 Wang L, Zhu W, Fang L, Sun X, Su L, Liang Z, Wang N, Londo JP, Li S, Xin H (2014b) Genome-wide identification of WRKY family genes and their response to cold stress in Vitis vinifera. BMC Plant Biol 14:103 Wang M, Wang Q, Zhang B (2013) Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum L.). Gene 530:26–32 Wang T, Pan H, Wang J, Yang W, Cheng T, Zhang Q (2014c) Identification and profiling of novel and conserved microRNAs during the flower opening process in Prunus mume via deep sequencing. Mol Genet Genom 289:169–183

M. Karimi et al. Waseem M, Rong X, Li Z (2019) Dissecting the role of a basic helix-loop-helix transcription factor, SlbHLH22, under salt and drought stresses in transgenic Solanum lycopersicum L. Front Plant Sci 10:734 Wei L, Zhang D, Xiang F, Zhang Z (2009) Differentially expressed miRNAs potentially involved in the regulation of defense mechanism to drought stress in maize seedlings. Int J Plant Sci 170:979–989 Wei S, Yu B, Gruber MY, Khachatourians GG, Hegedus DD, Hannoufa A (2010) Enhanced seed carotenoid levels and branching in transgenic Brassica napus expressing the Arabidopsis miR156b gene. J Agric Food Chem 58:9572–9578 Wu J, Wang D, Liu Y, Wang L, Qiao X, Zhang S (2014) Identification of miRNAs involved in pear fruit development and quality. BMC Genom 15:953 Wu P, Wu Y, Liu CC, Liu LW, Ma FF, Wu XY, Wu M, Hang YY, Chen JQ, Shao ZQ, Wang B (2016) Identification of arbuscular mycorrhiza (AM)-responsive microRNAs in tomato. Front Plant Sci 7:429 Xia R, Zhu H, An YQ, Beers EP, Liu Z (2012) Apple miRNAs and tasiRNAs with novel regulatory networks. Genome Biol 13:R47 Xia R, Meyers BC, Liu Z, Beers EP, Ye S, Liu Z (2013) MicroRNA superfamilies descended from miR390 and their roles in secondary small interfering RNA biogenesis in eudicots. The Plant Cell 25(5):1555– 1572 Xiao W, Brown RC, Lemmon BE, Harada JJ, Goldberg RB, Fischer RL (2006) Regulation of seed size by hypomethylation of maternal and paternal genomes. Plant Physiol 142(3):1160–1168 Yamasaki H, Abdel-Ghany SE, Cohu CM, Kobayashi Y, Shikanai T, Pilon M (2007) Regulation of copper homeostasis by micro-RNA in Arabidopsis. J Biol Chem 282:16369–16378 Yang C, Li D, Mao D, Liu XU, Ji C, Li X, Zhao X, Cheng Z, Chen C, Zhu L (2013) Overexpression of micro RNA 319 impacts leaf morphogenesis and leads to enhanced cold tolerance in rice (Oryza sativa L.). Plant, Cell Environ 36:2207–2218 Yang X, Liu F, Zhang Y, Wang L, Cheng YF (2017a) Cold-responsive miRNAs and their target genes in the wild eggplant species Solanum aculeatissimum. BMC Genomics 18:1000 Yang Y, Zhang X, Su Y, Zou J, Wang Z, Xu L, Que Y (2017b) miRNA alteration is an important mechanism in sugarcane response to low-temperature environment. BMC Genomics 18:833 Yu H, Xia NH, Ye HG (2007) Validation of Prunus hainanensis (Rosaceae). Nord J Bot 25:31–32 Yu N, Niu QW, Ng KH, Chua NH (2015) The role of miR156/SPL s modules in Arabidopsis lateral root development. Plant J 83:673–685 Yu X, Wang H, Lu Y, de Ruiter M, Cariaso M, Prins M, van Tunen A, He Y (2012) Identification of conserved and novel microRNAs that are responsive to heat stress in Brassica rapa. J Exp Bot 63:1025–1038 Yu Y, Jia T, Chen X (2017) The ‘how’and ‘where’of plant micro RNAs. New Phytol 216:1002–1017

Almond miRNA Expression and Horticultural Implications Zare S, Nazarian-Firouzabadi F, Ismaili A, Pakniyat H (2019) Identification of miRNAs and evaluation of candidate genes expression profile associated with drought stress in barley. Plant Gene 20:100205 Zeng X, Xu Y, Jiang J, Zhang F, Ma L, Wu D, Wang Y, Sun W (2018) Identification of cold stress responsive microRNAs in two winter turnip rape (Brassica rapa L.) by high throughput sequencing. BMC plant biology 18:52 Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA (2006) Conservation and divergence of plant microRNA genes. Plant J 46:243–259 Zhang F, Luo X, Zhou Y, Xie J (2016) Genome-wide identification of conserved microRNA and their response to drought stress in Dongxiang wild rice (Oryza rufipogon Griff.). Biotech Lett 38:711–721 Zhang Q, Li J, Zhang W, Yan S, Wang R, Zhao J, Li Y, Qi Z, Sun Z, Zhu Z (2012a) The putative auxin efflux carrier OsPIN3t is involved in the drought stress response and drought tolerance. Plant J 72:805–816 Zhang R, Marshall D, Bryan GJ, Hornyik C (2013) Identification and characterization of miRNA transcriptome in potato by high-throughput sequencing. PLoS ONE 8:e57233 Zhang X, Zhang X, Singh J, Li D, Qu F (2012b) Temperature-dependent survival of turnip crinkle virus-infected Arabidopsis plants relies on an RNA

57 silencing-based defense that requires DCL2, AGO2, and HEN1. J Virol 86:6847–6854 Zhang X, Zhao H, Gao S, Wang WC, Katiyaragarwal S, Huang HD, Raikhel N, Jin H (2011) Arabidopsis Argonaute 2 regulates innate immunity via miRNA393-mediated silencing of a Golgi-localized SNARE gene, MEMB12. Mol Cell 42:356–366 Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L (2010) Genome-wide identification and analysis of droughtresponsive microRNAs in Oryza sativa. J Exp Bot 61:4157–4168 Zhou X, Chen S, Wu H, Xu H (2017) effects of cold stress on the photosynthesis and antioxidant system of Rhododendron chrysanthum Pall. Preprint 1:1–10 Zhou X, Wang G, Sutoh K, Zhu JK, Zhang W (2008) Identification of cold-inducible microRNAs in plants by transcriptome analysis. Biochimica et Biophysica Acta (BBA)-Gene Regulatory Mechan 1779:780–788 Zhu H, Xia R, Zhao B, An YQ, Dardick CD, Callahan AM, Liu Z (2012) Unique expression, processing regulation, and regulatory network of peach (Prunus persica) miRNAs. BMC Plant Biol 12:149 Zhu X, Leng X, Sun X, Mu Q, Wang B, Li X, Wang C, Fang J (2015) Discovery of conservation and diversification of miR171 genes by phylogenetic analysis based on global genomes. The Plant Genome 8:1–11

Epigenetic Regulation in Almond Jonathan Fresnedo Ramírez, Katherine D’Amico-Willman, and Thomas M. Gradziel

Abstract

Rapid advances in sequencing technologies have enabled researchers to reconceptualize genomes as dynamic entities with functions beyond storage and organization of genetic information. Evidence of this dynamic nature includes the discovery of epigenetic mechanisms driving feedback loops between environmental stimuli and gene expression. These mechanisms are particularly complex in plant genomes where research has demonstrated epigenetic memory and maintenance, regulation, and even inheritance of genome modifications. These modifications do not alter the coded information stored in DNA but influence traits like phenology, plasticity, and fitness. In this chapter, we provide an overview of the framework of epigenetic regulation in plants and outline advances in our

J. Fresnedo Ramírez (&) Department of Horticulture and Crop Science, Center of Applied Plant Sciences, and Sustainability Institute, The Ohio State University, Wooster, OH, USA e-mail: [email protected] K. D’Amico-Willman Center of Applied Plant Sciences, The Ohio State University, Wooster, OH, USA T. M. Gradziel Department of Plant Sciences, University of California, Davis, CA, USA

understanding of epigenetic mechanisms influencing biological fitness and agricultural performance in almond.

1

Introduction

The incredible amount of available genomic data resulting from the implementation of highthroughput sequencing technologies has changed the paradigm of genome conceptualization. Today, researchers understand genomes as dynamic entities whose organization, structure, and modification overtime influence how genetic information is deciphered and stored. Epigenetic mechanisms represent a component of dynamic genomes as epigenetic marks [e.g., (de)methylation, chromatin folding, histone modifications, regulation, and activity of transposable elements] contribute to genome stability and integrity and thus influence performance and fitness throughout an organism’s lifetime. Epigenetic mechanisms can be understood as complementary instructions to decipher genetic information (how and when), representing an additional ‘layer’ above or wrapping the actual genetic information. The concept of epigenetic mechanisms adding additional layers of genomic information and influencing traits has a long history beginning with the ideas of Waddington (1942) from his work using animal models in the 1940s. And over the past 80 years, epigenetics has emerged as a fundamental subdiscipline in biology as a result of

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_5

59

60

seminal work in model organisms demonstrating epigenetic mechanisms as modulators of gene expression (Grafi and Ohad 2013; Meyer 2018). The increasing knowledge surrounding epigenetics and gene regulation has allowed us to rapidly expand our understanding of both organismal development and environmental plasticity (i.e., the ability to quickly respond to environmental changes). However, we have only just begun to scrape the surface in our understandings of the nature and implications of epigenetic mechanisms as well as how these mechanisms might be manipulated to address cellular, tissue, organismal, or community-level issues. Research on epigenetic mechanisms tends to have a biochemistry focus, centering on modifications in the genome, usually at the gene level (i.e., open reading frame and regulatory sequences), that impact gene expression. Thus, in the current literature there is an emphasis on biochemical modifications to the DNA. However, the study of epigenetic mechanisms needs to encompass not only the immediate effects of these chemical modifications but also the effects their persistence has on the cell and its genetic components (Jablonka 2017). The duration of the effects includes the lifespan of an organism and extends to the transmission of epigenetic mechanisms to future generations. Placing emphasis on the effects of epigenetic mechanisms on the genome that drive changes in gene expression allows us to infer how modulation in trait exhibition can affect either the performance (in an agricultural perspective the ability to produce yield) or fitness (in the biological context) of an organism. Epigenetic mechanisms can be conceptualized as mechanisms of memory, i.e., as modulators of how information (environmental cues) is stored and interpreted in the genome. These mechanisms play a role in memory in two distinct ways: (1) a dynamic mode where epigenetic marks oscillate over time according to environmental cues, organismal development, phenology, and life history, activating or silencing gene expression during the life time of an organism, and (2) a static mode in which the epigenetic mark is stable beyond the lifespan of the organism, encompassing transgenerational inheritance

J. Fresnedo Ramírez et al.

of epialleles by subsequent sexually derived progeny. This classification follows the conceptualization outlined by Jablonka and collaborators (Jablonka and Lamb 2006; Jablonka and Raz 2009; Jablonka 2017) and developed from an evolutionary perspective emphasizing the role of epigenetic mechanisms (particularly transgenerational) as sources of variation subject/ responding to selection and persistent in their effect on gene expression. In this chapter, we will provide an overview of epigenetic mechanisms as modulators of plasticity by introducing basic concepts and theoretical frameworks in plants, particularly from the model Arabidopsis thaliana. We will then shift the focus to epigenetic mechanisms putatively involved in trait plasticity in almond [Prunus dulcis (Mill.) D. A. Webb]. We will address the role of epigenetic mechanisms in regulating transposable elements across the almond genome and the influence of DNA-(de) methylation on relevant agricultural traits such as dormancy/blooming time and self-compatibility and on aging-related disorders such as noninfectious bud failure. Finally, we will provide some concluding remarks and perspectives in light of remaining questions and gaps in our understanding of the almond genome in an effort to ensure sustainability and resiliency of the almond supply chain.

2

Epigenetic Regulation in Plants

2.1 The Dynamic Plant Genome Plants exhibit incredible phenotypic plasticity resulting from an evolutionary history in which they developed mechanisms and strategies allowing them to survive diverse environmental conditions. This phenotypic plasticity, or the capacity to exhibit phenotypic modifications in response to environmental contexts (Price et al. 2003), relies on mechanisms tightly connected to genome dynamism (Fedoroff and Botstein 1992; Parfrey et al. 2008; Fontdevila 2011). Considering genomes as dynamic with the ability to regulate and maintain homeostasis (Seshasayee

Epigenetic Regulation in Almond

2014), stability, and integrity (Aguilera and Gómez-González 2008) was one of the major breakthroughs in genome analysis in the past decade. This approach to understanding the genome provides a framework for plasticity resulting, at least partially, from genome dynamism in which the genome is no longer considered a static sequence but can even be conceptualized as an organ (Lamm 2014). The concept of the dynamic genome opens the door to interpreting phenotypes as the consequence of distinct layers of genetic information maintained and interpreted within the genome. This understanding enhances our ability to explore mechanisms beyond the DNA sequence, such as the structural composition of the genome (chromatin and histones), contributing to changes in gene expression and plant phenotypic plasticity in crops. And with the continuous advances in genome sequencing tools and techniques, we are able to identify and define these mechanisms with increasingly finer resolution and clarity.

2.2 Plants as Epigenetic Machineries From the basis that epigenetic mechanisms drive feedback loops between environmental stimuli and gene expression in the cell, one can start to conceptualize plants as collections of cells that differentiate and communicate via epigenetic mechanisms (Buiatti 2011). And while this conceptualization may seem like a long leap, the view is compatible with the multifaceted life of plants. In fact, the conceptualization of plants as a collection of cells aligns with the fact that virtually any plant cell is itself a meristem, and therefore (unlike animals), every plant cell has the potential to become a self-sustaining plant (totipotency relies on this fact) under the proper conditions. This also implies that any epigenetic mark gained or lost is temporary if not incorporated into the germline or can be perpetuated through sexual, transgenerational transmission in the extended concept of limited inheritance (Merlin 2017) or through vegetative propagation. The dual ability of plants to pass information both sexually and asexually allows for clonality

61

(Klekowski 2003), a sophisticated mechanism in which perennial plants escape a mutational meltdown (Groot and Laux 2016), adding complexity to the epigenetic landscape. Viewing plants as collections of cells extends the view of (DeJong 1999; DeJong and Moing 2008) that organ growth is a function of carbohydrate partitioning, in which plants are organisms composed of semi-autonomous organs with a (epi?)genetically determined potential to compete for resources in source-sink relationships. DeJong’s principles of carbon partitioning can include the action of epigenetic mechanisms since he suggests that a given organ possesses a growth potential that can be turned on or off by feedback loops of endogenous modulation (e.g., gene expression) and environmental stimuli. However, to date, the influence of epigenetic mechanisms on fruit growth, biomass accumulation, and yield is mostly unexplored in plants, regardless of the availability of accepted theoretical frameworks. Interrogating epigenetic mechanisms under these types of theoretical frameworks could allow researchers to disentangle gene expression patterns providing plants optimal performance and fitness under diverse environments (Pikaard and Mittelsten Scheid 2014) and agricultural management practices.

2.3 Epigenetic Machinery in Plants Epigenetic mechanisms can be divided into patterns of gain or loss of DNA methylation (Zhang et al. 2018) and chromatin status (Eichten et al. 2014; Maeji and Nishimura 2018). Chromatin remodeling is the result of histone modification via methylation, acetylation, ubiquitination, or phosphorylation (Bannister and Kouzarides 2011), and chromatin differentiation results from the substitution of canonical histones with homologous proteins called histone variants (Henikoff and Smith 2015). Non-coding RNA (ncRNA) molecules function as epigenetic mechanisms tightly involved in controlling gene expression (Frías-Lasserre and Villagra 2017). Finally, the role of chemical modifications on messenger RNA (Aristizabal et al. 2019) such as

62

N6-methyladenosine (m6A) is currently being scrutinized in several plant systems (Yue et al. 2019). The role of transposable elements (TE) in shaping and influencing dynamic genomes (Fedoroff 2012), particularly in influencing genome size (Roessler et al. 2018), is also being actively explored. This includes research into TE involvement in paramutation and imprinting (Slotkin and Martienssen 2007) and their ability to drastically change gene organization in the genome as in the case of heliotron transposons (Lal et al. 2009; Thomas and Pritham 2015; Thieme and Bucher 2018). Comprehensive reviews including historical perspectives (Meyer 2018) and details on the biochemical aspects of the epigenetic mechanisms in plants can be found in Grafi and Ohad (2013), Pikkard and Mittelsten (2014) and Maeji and Nishimura (2018), as well as reviews on the implication of plant biology by Grant-Downton and Dickinson (2005, 2006). Epigenetic mechanisms have been extensively studied in select eukaryotic model organisms, and a wide range of plant species have also been considered though not in as great of depth (Niederhuth et al. 2016). Plants possess divergent epigenetic machineries with sophisticated mechanisms and unique characteristics (Pikaard and Mittelsten Scheid 2014). For example, plants exhibit a diverse methylation landscape compared to animals since methylation can occur in three distinct cytosine contexts: CpG (most often described in animals), CpHpG, and CpHpH (H = A, T, or G) (Maeji and Nishimura 2018). The divergence and diversity of epigenetic mechanisms in plants are likely the result of adaptations to survive environmental perturbations (Pikaard and Mittelsten Scheid 2014). As sessile organisms, plants exhibit a suite of characteristics contributing to phenotypic plasticity and performance that can be impacted or influenced by epigenetic mechanisms. These include a metabolically active haploid gametophyte, alternation of generations (sporophytic-to-gametophytic), non-distinctive germline throughout most of the lifespan, the presence of ubiquitous apical and lateral meristems, plasmodesmata that enable material fluxes among cells (including ncRNAs), double

J. Fresnedo Ramírez et al.

fertilization during reproduction, and resiliency to mutation and whole genome multiplications (Pikaard and Mittelsten Scheid 2014). It is important to keep these particular characteristics in mind when exploring the effects of epigenetic mechanisms on plants. The majority of studies on epigenetic mechanism in plants have focused on profiling DNA(de)methylation as the result of ncRNA activity. Analyzing histone modifications and histone variants to study chromatin status requires wellcharacterized germplasm as well as sophisticated and time-consuming techniques (for a compendium of methods and protocols see Kolvachuk 2017). Further, most studies have focused on annual plants, particularly Arabidopsis as a model organism, given their amenability for use in dissecting networks and identifying components of epigenetic mechanisms (Pikaard and Mittelsten Scheid 2014). However, relevant discoveries have been made in other plant species (Lönnig and Saedler 1997; Cubas et al. 1999; Scoville et al. 2011) including crops (Manning et al. 2006; Akimoto et al. 2007; Martin et al. 2009; Akter et al. 2019), though perennial plants have only recently been included (Daccord et al. 2017; Richards et al. 2017; Jiang et al. 2019b; Champigny et al. 2019; Wu et al. 2020; Usai et al. 2020).

2.4 Propagation, Reproduction, Reprograming, and Extended Limited Inheritance The role of epigenetic mechanisms in the development and adaptability of living organisms has a somewhat controversial history, particularly as the concepts relate to the theory of transgenerational transmission of acquired characteristics (traits) proposed by Lamarck (1809). And while current evidence provides support that epigenetic mechanisms—such as de novo methylation regulated by the RNA-dependent DNA-methylation (RdDM) pathway (Teixeira et al. 2009) or demethylation involving genes such as DEMETER (DME), REPRESSOR OF

Epigenetic Regulation in Almond

SILENCING1 (ROS1), DEMETER-LIKE2 (DML2), and DEMETER-LIKE3 (DML3) (Choi et al. 2002; Agius et al. 2006)—are involved in phenomena such as seed viability or response to infections (Pikaard and Mittelsten Scheid 2014), the idea that some epigenetic marks are passed to future generations has sparked debate beyond the possibility of their transmission. Epigenetic mechanisms in plants may be a source of phenotypic diversity even in asexual reproduction, which is common in both unmanaged and agriculturally managed contexts. Asexual reproduction occurs either by specialized organs (propagules) or techniques (e.g., grafting) as well as through more complex modes like apomixis. In the case of intentional propagation (e.g., grafting or striking), epigenetic marks in the source genome are maintained in the subsequent clonal cohorts derived through asexual multiplication since no meiotic resetting has occurred (i.e., no fusion of gametes and embryony). However, it has been seen in genets (i.e., natural communities with a given number of individual clones of single genotype) of poplar, the ramets (a single individual in the clonal population [the genet]) can have distinct DNAmethylation profiles (Guarino et al. 2015), which may influence the performance of individual ramets while influencing the overall fitness of the genet. This divergence in DNA-methylation profiles may be the result of individuals derived from specific lineages of cells within the original individual (genet) with specific DNA-methylation profiles. This strengthens the notion of defining plants as populations of cells with inherent genomic status and dynamism, Maintenance of epigenetic marks through sexual reproduction in plants represents another topic under continuous discussion. Quadrana and Colot (2016) provide a comprehensive review on transgenerational transmission of DNA methylation in plants including the tendency of inheritance. They hypothesize that parental DNA methylation is maintained in progeny produced via sexual reproduction as a result of a failure of existing epigenetic mechanisms (e.g., mechanisms such as the RdDM pathway are inactivated during embryogenesis) rather than a lack of a

63

mechanism that resets DNA-methylation status during embryogenesis (Kawakatsu et al. 2017), fitting with other recent evidence (Kenchanmane Raju et al. 2019). However, the mechanisms through which epialleles arise and persist over generations are not clear. The presence, persistence, and diversity of epialleles may be driven by their associations with transposable elements and repetitive sequences (Quadrana and Colot 2016), as suggested by patterns found in data harvested from Arabidopsis epigenetic recombinant inbreed lines (epiRIL) (Schmitz et al. 2011; Becker et al. 2011; Schmitz and Ecker 2012). Although the amount of data is vast, it does not yet provide a direct indication of actual inheritance of acquired characteristics (i.e., directly induced by the environment) and therefore does not support the idea that environments may trigger the generation of epialleles. Kawashima and Berger (2014) provide an excellent review focused on epigenetic reprogramming that can occur during sexual reproduction in plants. They provide a discussion on whether epigenetic reprograming occurs in the form of maintenance of DNA methylation and histone status during male gametogenesis, female gametogenesis, and maternal-to-zygotic transition. Kawashima and Berger (2014) argue that in animal models, after gametic fusion there is a resilencing of transposable elements and a resetting of histone modifications which contributes to genome stability and integrity. Although this silencing may be also expected in plant, they conclude that there are many unresolved issues regarding whether reprogramming occurs (and if so, how?) during plant gametogenesis. It is not clear whether methyltransferases and histone modifications occur during reactivation of the cell cycle in embryogenesis, despite evidence that de novo methylation occurs in the embryo, creating distinct DNA-methylation profiles compared to the parents (Kawashima and Berger 2014). At this stage, epigenetic reprogramming in plants (particularly from evidence in Arabidopsis) seems more ‘relaxed’ and ‘flexible’ in comparison with animal models, which may confer a source of variation to enhance plant plasticity.

64

Kawashima and Berger (2014) reject the assumption that epigenetic reprogramming does not occur in plants and on the contrary hypothesize that reprogramming may occur via distinct mechanisms and gradients and at specific stages post-fertilization. They also encourage careful consideration of epigenetic reprogramming in plant species in light of their unique reproductive biology. Sano and Kim (2013) argue that transgenerational sexual transmission of epigenetically acquired traits occurs only under three premises: (1) The acquired traits do not jeopardize fitness, (2) the trait has been transmitted through more than three generations of sexual reproduction, and (3) epigenetically targeted genes conferring the acquired trait are identifiable. Addressing premise 1, there may be situations where the epigenetic marks result in an acquired trait that is detrimental to overall fitness. Research in animal models provides evidence for epigenetic marks that negatively affect longevity and aging (Horvath and Raj 2018; Xie et al. 2018; Xavier et al. 2019), an area poorly studied in plants (Burian et al. 2016). Therefore, the idea that acquired traits are only beneficial may not be true. To address premise 2, the criteria of three generations of inheritance come from animal models in which a female’s oocytes are formed when she is a fetus, and thus, environmental exposures in the mother’s uterus will affect both the fetus and her (the fetus’) potential offspring (Mørkve Knudsen et al. 2018). Therefore, to observe true transgenerational transmission independent of this overlap, epigenetic marks need to be traced for more than three generations. As mentioned above, plants exhibit distinct reproductive features (in addition to distinct life spans) making the three-generation threshold of transgenerational inheritance of epigenetic marks inappropriate when considering plant biological systems. For example, in Arabidopsis it has been shown that epigenetic marks may persist (or arise de novo) in new epihybrids (Rigal et al. 2016) through 30 generations of sexual reproduction (Schmitz et al. 2011; Becker et al. 2011; Schmitz and Ecker 2012). Tracking such a high number of generations in plant species with long

J. Fresnedo Ramírez et al.

juvenility periods would be certainly not trivial and will require additional efforts. And to address premise 3, a focus on genes may be incorrect since, as stated above, there is evidence of extragenetic components (such as transposable elements) altering gene expression without subjecting genes to a paramutation. Epigenetic mechanisms are highly diverse in plants and can have variable impacts on gene expression. As a result, when discussing transgenerational transmission, one has to consider what is being inherited, in which form, and the actions of the epigenetic mark. These postulates are useful for guiding current discussion and will be updated as new evidence accumulates regarding epigenetic mechanisms in plants. The questions of whether and how transgenerational transmission of epigenetic marks occurs have certain implications on crop breeding (see Hung et al. 2018) for a relevant overview); however, there are additional aspects to consider in light of evolution and particularly the concept of inheritance. Usually within the concept of transgenerational biological inheritance, the focus is on the passing of genes; however, the process of sexual reproduction represents genome complementation (i.e., the union of two genomes in their minimum, ideally haploid state) as the mechanism of inception of a new organism. When considering the genome dynamism and repercussion of epigenetic mechanisms through the transgenerational inheritance of epigenetic marks, we need to carefully consider what is being inherited and how to quantify its contribution to relevant traits like genetic gain in breeding or to fitness and selection pressure for adaptation or evolution. An open discussion on the concept of inheritance in light of the contributions of epigenetic mechanisms is necessary. Authors such as Merlin (2017) argue that the concept of biological inheritance has to move from being restricted to passing genes from parents to progeny, and she proposes a limited extended inheritance concept. In the case of plants (Merlin discusses all forms of life), it may be convenient to frame the concept of inheritance by integrating transmission of not only DNA information (Miska and Ferguson-

Epigenetic Regulation in Almond

Smith 2016) but the actual material (e.g., organelles or maternal subcellular components, or cell state (see Shea et al. 2011), the genomic marks resulting from periodic environmental stimuli (e.g., epigenetic marks), and the developmental strategies or mechanisms enabling cell differentiation and survival (and therefore tissues and organs). These beyond-gene components may play a key role in the survival of an organism, the conservation and persistence of species over generations, and ultimately maintenance of the aspects of biological success that lead to speciation (or in the case of artificial selection, to domestication). In the case of crops, the extended concept of inheritance is compatible with the current concept that genetic gain is an accumulation of alleles with positive effects on the trait of interest (usually yield), regardless of whether this trait is directly linked to reproduction and/or evokes fitness. Recent evidence from Arabidopsis shows that epigenetic marks such as the hypomethylated loci quantitatively contribute as epigenetic quantitative loci (epiQTLs) to genetic variation conferring quantitative resistance to Hyaloperonospora arabidopsidis (Furci et al. 2019). These forthcoming discoveries emphasize the need to improve the framework for conceptualizing epigenetics in light of inheritance as a mechanism of transmitting information that ensures the inception of organisms with new combinations of genes, epigenetic marks, and developmental strategies. We can then apply this knowledge to answer questions related to the mechanisms and strategies that lead to evolution and speciation. Theoretical frameworks considering the role of epigenetic mechanisms in the generation of variation have been developed in the last thirty years. As an example, Shea et al. (2011) propose differentiating information channels in epigenetic inheritance. Information channels will be differentiated based on whether epigenetic information is being transmitted to assemble adaptive traits already subject to and selected for by natural selection, or for conferring phenotypic plasticity to endure environmental perturbation. Models and frameworks exist to address transgenerational transmission of epigenetic effects (Jablonka and

65

Raz 2009; Miska and Ferguson-Smith 2016), with some considering plants beyond model species (Pineda-Krch 1999; Klekowski 2003), that may be worth considering in discussions regarding aspects such as missing heritability (Bourrat et al. 2017). In the study of epigenetic mechanisms and their outcomes—in the form of marks in the genome and repercussions for gene expression that influence plasticity of traits, alteration of development, and adaptability of organisms— plant reproductive features should be considered along with a wider view of how individual performance and fitness (either ramets and genets, or conversely clones of a cultivar) are interpreted in the context of evolution, ecology, genetics, agriculture, and therefore sustainability. The wave of information on epigenetic mechanisms needs to be accompanied by broadening perspectives and redefining concepts once thought to be well-established, such as a true-type identity of a cultivar.

3

Epigenetic Mechanisms in Almond

Despite its high consumption and economic relevance as a nut crop in countries like the US, Spain, Italy, Iran, Morocco, and Australia, until recently, almond has been greatly neglected in key research fields. This neglect is particularly evident in the generation of genomic resources, especially in comparison with other Rosaceous crops such as peach, apple, strawberry, and plum. However, even with the lack of genomic resources for almond, research is ongoing to explore the influence of epigenetic mechanisms on the almond genome and their role in interspecific hybridization and the exhibition of specific traits and disorders (Arús et al. 2009). Fortunately, third-generation high-throughput sequencing technologies (van Dijk et al. 2018) and new strategies for assembling complex genomes have aided in overcoming barriers like high heterozygosity in developing genomic resources for almond. These developments allow researchers to better pursue the epigenetic mechanisms involved in speciation, evolution,

66

domestication, phenotypic plasticity, agricultural performance, and breeding in almond. As a result of this work, as of 2020, the Genome Database for Rosaceae (www.rosaceae.org) contains three publicly available, fully annotated, draft almond genome assemblies. Details on two of these sequences are provided in chapters “Origin and Domestication of Wild Bitter Almond. Recent Advancements on Almond Bitterness”, “Almond miRNA Expression and Horticultural Implications”, and this chapter of this volume as well as in their corresponding publications (SánchezPérez et al. 2019; Alioto et al. 2019). The third draft genome sequence represents ‘Nonpareil’, the most important cultivar in the US (representing * 50% of almond production worldwide), with a forthcoming description and publication. With the availability of these genomic resources comes the opportunity to mine and explore the almond genome. This exploration includes developing better understandings of the epigenetic mechanisms acting on and influencing almond traits. In this section, we will briefly address four cases in which epigenetic mechanisms are actively influencing the structure of the almond genome and affecting economically relevant traits.

3.1 Transposable Elements as Architects of the Almond Genome and Its Domestication The genomic resources generated for peach have been actively applied to almond due to their high phenotypic resemblance. Studies based on linkage maps and analyses of specific genes and traits in Prunus and using germplasm developed from interspecific crosses with almond (Arús et al. 2009) suggest that the size and general structure of genomes within Prunus are similar (this chapter). However, the recent sequencing of the ‘Lauranne’ and ‘Texas’ genomes has shed new light on patterns of divergence between Prunus genomes (Alioto et al. 2019). It is well known that Prunus genomes in diploid species

J. Fresnedo Ramírez et al.

show an extensive synteny and collinearity (Sargent et al. 2012). Through the availability of genome sequences for peach (Verde et al. 2017), cherry (Shirasawa et al. 2017), apricot (Jiang et al. 2019a), plum (Zhebentyayeva et al. 2019), wild species such as P. mume (Zhang et al. 2012) and P. yedoensis (Baek et al. 2018), and even interspecific hybrids such as Cerasus ⨯ yedoensis (Shirasawa et al. 2019), we know that the number of genes, order, and organization of the Prunus genomes is greatly conserved (Alioto et al. 2019). The similarity between genomes suggests that the ample phenotypic variability within the genus may be due to other mechanisms such as epigenetic marks that are not captured in DNA sequences. The roles of two major epigenetic mechanisms in shaping the almond genome and contributing to a divergence from peach are discussed in Alioto et al. (2019). The almond genome is enriched in genes coding for methyltransferase activity when compared to the peach genome, immediately suggesting that aspects like genetic transposition and DNA methylation may play roles in the exhibition of traits. Interestingly, 38.2% of the almond genome is composed of transposable elements, influencing the presence and distribution of insertion and deletions. These distribution patterns can in turn possibly impact almond speciation including the lack of fleshy almond fruits (Alioto et al. 2019). Thus, transposable elements represent one of the drivers of genome structure and genome dynamism in almond given that some ancestral transposon insertions show polymorphisms that can give rise to genomic variability. In addition, as discussed by Foster and Aranzana (2018) long terminal repeat-retrotransposons (accounting for more than 50% of insertions in almond) contribute to genome dynamism in almond by enabling the occurrence of somatic mutations in distinct tissues. These insertions give rise to somaclonal variants such as bud sports of relevant cultivars as has happened in cultivars such as ‘Tardy Nonpareil’ (Gradziel and MartínezGómez 2013). Another discovery by Alioto and collaborators refers to the differential DNA methylations status

Epigenetic Regulation in Almond

resulting from transposable elements in the region near the gene PdCYP71AN24, which has been identified as casual for sweet kernel exhibition, the driver trait for almond domestication (and very economically important) (Dicenta and García 1993a). Although Sánchez-Pérez and collaborators (Sánchez-Pérez et al. 2019) found that specific changes in the DNA sequence of the basic helix-loop-helix (bHLH) gene cluster are tightly involved in exhibition of sweet kernels, evidence generated by Alioto et al. (2019) demonstrates that even in the absence of DNA mutations inhibiting accumulation of amygdaline, the sweet kernel phenotype can be expressed as is the case of the ‘Texas’ cultivar (more details can be found in chapter “Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond”). The availability of genomic resources such as full genome sequences, transcriptomic data, and even methylation profiles will enable deeper investigation into key almond traits influencing the selection and domestication of nut trees such as flavor. Further, these resources will allow exploration into the impact of genome dynamism on neglected traits like tree architecture and fruit structure.

3.2 Epigenetic Regulation of Dormancy and Flowering Mechanisms related to the control of flowering and in the case of temperate perennial species such as almond, the induction, and release of dormancy are of great interest given their relevance in agricultural settings. Perennial plants with seasonal periods of (nonobvious) metabolic activity are a great model to better understand how environmental cues feed mechanisms of gene regulation in time series studies. In fact, much of the information on flowering and dormancy control available in the literature, when not produced in annual species with vernalization requirements, is concentrated on evidence generated from perennial species. Evidence from several plant species also demonstrates the effects of DNA methylation and modification/

67

remodeling of chromatin structure on gene networks and hormone signaling (Cattani et al. 2018) regulating dormancy and flowering. In terms of chilling requirements, histone modification and DNA methylation can serve as cellular memory or an epigenetic clock modulating gene activation and silencing based on environmental cues like exposure to low temperatures, such as for FLOWERING LOCUS C (FLC, in Cattani et al. 2018). Prunus species represent a model in the study of chilling requirements and bud dormancy since the expression profiles of DORMANCY-ASSOCIATED MADS-box (DAM) genes are tightly associated with dormancy stages (Li et al. 2009; Zhebentyayeva et al. 2014). These patterns resemble mechanisms of FLC described in other species, although the patterns are distinct in Prunus (Cattani et al. 2018). Differential levels of histone methylation (H3K4me3 and H3K27me3) and acetylation (H3ac) have also been observed at distinct stages of dormancy in peach (Leida et al. 2012) in the genomic region surrounding DAM6. The control of dormancy and the chilling requirement for bud break in almond has also been well-studied (Sánchez-Pérez et al. 2012). However, resolution at the genome level is limited by the lack of genomic resources specific for the species. Thus, to explore how epigenetic mechanisms like DNA methylation may be involved in the stages of dormancy, genomereference-independent approaches have been utilized based on reduced genome representation techniques such as epi-genotyping by sequencing (epiGBS, van Gurp et al. 2016). These methods have been applied in almond to compare cultivar profiles at distinct stages of dormancy (Prudencio et al. 2018) as described in chapter “Accelerating Almond Breeding in Post-genomic Era”. Although the DNA-methylation profiles showed stronger patterns linked to differences between cultivars than associated with dormancy, DNAmethylation signatures in specific genes distinguished early and late blooming cultivars as well endodormant and ecodormant buds such as LATE ELONGATED HYPOCOTYL (LHY) and MITOGEN-ACTIVATED PROTEIN KINASE (MAPK), respectively (Verde et al. 2017).

68

The recent availability of almond genome reference sequences will enhance our understanding of epigenetic mechanisms involved in dormancy, chilling requirement, and flowering and will enable a more detailed characterization of almond germplasm in breeding programs. Several hypotheses can be tested based on the evidence obtained in other plant systems. Cattani et al. (2018) provide a comprehensive review of how epigenetic mechanisms are involved in bud break and flowering in several species in addition to Prunus and emphasize the role of some genes and their epigenetic modulators (as in the case of FLOWERING TIME (FT)). The role of these genes and epigenetic mechanisms remain elusive in several perennial species, including almond.

3.3 Dysfunctionalization of the Sf Allele Conferring Gametophytic Self-compatibility in Breeding Germplasm As an outcrossing species and as for many Rosaceous species, almond exhibits a gametophytic self-incompatibility (Franklin-Tong and Franklin 2003). Information regarding the almond haplotypes has been generated (Ushijima et al. 2003) considering biological characteristics like pollen tube growth (Socias i Company et al. 2013), and the role of S-RNAses (Fernández i Martí et al. 2014) and cross-compatibility in almond is also well established (Gómez et al. 2019b). Despite the challenges that self-incompatibility brings in managing and breeding almond, cultivation of the species for the last millennia has enabled identification of self-compatible germplasm, which have been selected and bred to fix the trait in cultivated almond germplasm (Dicenta and García 1993b). Information is also currently available on the mutations that deactivate the Slocus (Socias i Company et al. 2015) as well as the interactions between pollen and the pistil (Gómez et al. 2019a). Thus, the tools exist to identify and select for self-compatible cultivars using marker-assisted selection; however, this strategy has been limited in instances where

J. Fresnedo Ramírez et al.

molecular markers suggest the haplotype corresponding to the Sf locus is present but the tree is still self-incompatible (Kodad et al. 2009). To overcome these limitations, research is now focused on the role of epigenetic mechanisms such as DNA methylation in silencing the Sf locus which reverts almond to a selfincompatible (wild) state. Fernández i Martí et al. (2014) surveyed the DNA sequence at the Sf locus in distinct almond cultivars (‘Blanquerna’, ‘Vivot’, ‘Soleta’, ‘Ponç’) and breeding germplasm (A2-199 and M-2-16). They found that differential methylation occurs in several cytosines within regulatory sequences of the Sf locus (up to * 5 kb upstream), altering expression of S-RNase in the style and suggesting inactivation of the Sf locus can be inherited by progeny (additional details provided in chapter “Molecular Basis of the Abiotic Stresses in Almond)”. This discovery is relevant given the implications for agricultural production and breeding to enhance kernel yield and produce self-compatible cultivars. It also supports the idea that we have to incorporate the effects of epigenetic mechanisms in an extended limited inheritance framework in which information from profiling DNA methylation can inform breeding programs through simple diagnostics like the methylation status at certain genes. Although the results from Fernández i Martí et al. (2014) may not satisfy the postulates of Sano and Kim (2013) for transgenerational inheritance, efforts are being pioneered to test this hypothesis in pedigrees with several generations to track the transmission of this DNAmethylation signature. In light of these efforts, discussions in the plant community surrounding epigenetic reprogramming are now increasingly relevant to almond.

3.4 The Role of Genome-Wide DNA-(De)methylation in Noninfectious Bud Failure All organisms age, decline, and subsequently die; however, our understanding of the aging process in systems other than animals is extremely

Epigenetic Regulation in Almond

limited. In the case of perennial plants, little is known about how the aging process occurs at the molecular, cellular, and organismal level and the implications of aging on plant performance, longevity, and fitness. Almond exhibits a disorder commonly called ‘crazy-top’, characterized as a decline in growth in the canopy due to the collapse and subsequent death of lateral vegetative meristems (bud) following dormancy. A symptom accompanying bud failure is cracking of bark in distinct segments of the branches, usually called ‘rough bark’ (Wilson and Schein 1956; Kester 1976). Interestingly, this disorder is not caused by an infectious agent and does not follow any epidemiological patterns for an infectious disease (Fenton et al. 1988). The disorder tends to become more severe as the tree ages (pace can be clone specific) and can be transmitted to progeny which exhibits the disorder earlier than the parent (s) and with varying degrees of severity (Kester 1976). Symptoms seem to appear earlier and are more exacerbated in places with concurrent heat stress (such as Bakersfield, in the San Joaquin Valley of California). This disorder, classified as noninfectious bud failure (NFB), has been described since the 1930s (Wilson and Schein 1956; Kester 1976) and has since been considered a latent threat. The exhibition of NBF has been reported in several almond cultivars, including ‘Nonpareil’, ‘Peerless’, and ‘Carmel’. Incidence has been reported for several cultivars in California and has also been reported in Spain (where it is called ‘fallo de yema’ pers. comm. Federico Dicenta and Pedro J. Mártinez García) and Australia (pers. comm. Michelle Wirthensohn). NBF is known to be one of the major factors for abandonment of cultivars such as ‘Jordanolo’ in the late 1950s and ‘Merced’ in the 1960s (Kester 1976) since NBF severely affects the performance of the trees. Given that NBF can be transmitted to both clonal and sexual progenies and the cumulative nature of the symptoms, it was hypothesized that a loss of juvenility, interpreted as an accumulation of defective gene–gene interactions, may lead to development of the disorder (Kester et al.

69

2003; Gradziel and Fresnedo-Ramírez 2019). Further, the gene–gene interactions may be disrupted by epigenetic-like phenomena making the genes dysfunctional (Kester et al. 2003; Gradziel and Fresnedo-Ramírez 2019). A survey of genome-wide DNA-(de)methylation patterns in five almond cultivars and four seedlings exhibiting distinct degrees of NBF severity was performed using methylationsensitive amplified fragment length polymorphisms (Fresnedo-Ramírez et al. 2017). Results of this study suggest that NBF exhibition is not independent of genome-wide methylation profiles and susceptibility to NBF exhibition depends on both the almond genotype and the age of the clone (Fresnedo-Ramírez et al. 2017). This study, though suggestive of an epigenetic mechanism such as DNA-(de)methylation mediating aging and driving NBF exhibition, lacked precision in resolving methylation patterns. The profiling technique is based on anonymous banding patterns as an almond reference genome was not available and thus did not provide information on possible genes or regions of the genome that might be associated with NBF exhibition. Although the first version of the peach genome was available for public use during this study, results from interspecific hybridizations of almond genotypes exhibiting NBF and early flowering peaches showed only 50% of the progeny exhibited symptoms. These results suggest that the genetic components associated with NBF are exclusive or divergent in the almond genome, fitting with discoveries in the recent almond reference sequence (Alioto et al. 2019). Several aspects of NBF remain unexplored (Gradziel and Fresnedo-Ramírez 2019), but one of the most important is the ability to integrate information on epigenetic marks (e.g., DNAmethylation status) into decision-making processes for breeding, nursery propagation, and orchard establishment. While it was established that using basal epicormic meristems seems to alleviate the problem, at least for propagation (Gradziel et al. 2019), unmasking the mechanisms involved in NBF exhibition remains a priority given the worldwide incidence and

70

J. Fresnedo Ramírez et al.

predisposition of preferred almond cultivars. Almond can thus serve as a model for studying not only NBF but also for addressing plant aging/loss of juvenility and transgenerational transmission, neglected issues in plant biology (Gradziel and Fresnedo-Ramírez 2019). The current availability of well-characterized experimental germplasm and a chromosomelevel genome assembly for ‘Nonpareil’ (a founder for several commercial cultivars in California) allows for a deeper exploration of epigenetic mechanisms associated with NBF exhibition. More importantly, there is a need to better understand how the environment influences NBF manifestation (Fenton et al. 1988; Pineda-Krch 1999). Advances in understanding NBF will be influential for germplasm management at breeding program and nursery levels, as well as in orchard establishment. In addition, insights regarding the ontogeny of biological, cellular, and molecular mechanisms influencing aging and longevity in perennial plants will contribute to a better understanding of the plant kingdom.

4

Concluding Remarks and Perspectives

Epigenetic mechanisms are ubiquitous and their implications may influence pivotal traits like phenotypic plasticity, agricultural performance, longevity, and fitness. Although theoretical frameworks have been developed in light of model systems, this knowledge represents the tip of the iceberg of the diversity of epigenetic mechanisms in the plant kingdom. Our ability to survey and interrogate epigenetic regulation in plant species other than Arabidopsis or model crops has been enhanced by high-throughput sequencing technologies and algorithms enabling a deeper exploration of plant genomes and their dynamism. However, given the vast array of tools at our disposal, we have to carefully consider the most appropriate approaches to ask and answer meaningful questions. This includes careful consideration of the germplasm, length of the study, environments, and biological contexts.

Given the amount of evidence being generated on epigenetic mechanisms in plants and the traits and phenomena they influence, it is imperative that we reflect on how many seemingly unalterable concepts may need updating in light of the genomic revolution. For example, epigenetic mechanisms and their transgenerational transmission call into question our current concepts of inheritance or heritability. Similarly, new discoveries on the self-regulation of genomes require us to think beyond plant genomes as a static sequence of nucleotides in a fasta file. The opportunities to perform basic, translational, and applied research on epigenetic mechanisms in almond are endless. As an example, the role of epigenetic mechanisms in the interactions between rootstock-scion in almond remains unknown. Rootstocks for almond production in California are usually the product of complex interspecific hybridization in which their luxuriance (Dobzhansky 1950) is used to overcome agrestic soil environments. Berger et al. (2018) provide a comprehensive review of the implications of epigenetic mechanisms in grafted plants emphasizing aspects of information exchange, signaling, epigenetic reprograming, and induction of paramutations. The lessons learned from interrogating these meaningful questions may necessitate reformulating the management and development of rootstocks and practices in grafting for almond breeding and production. Conceptually speaking, epigenetic mechanisms are a layer of information modulating gene expression. However, we are in an era in which accounting for the influence of marks and effects of the mechanisms will enable us to better understand the nature of traits and their plasticity, and therefore place adequate emphasis on epigenetic mechanisms in light of inheritance, (natural or artificial) selection, breeding, speciation, and evolution. Our ability to integrate conceptual frameworks, knowledge, technology, human resources, social contexts, and a willingness to solve problems will enable us to use the information gleaned from these mechanisms to answer meaningful questions and provide pertinent solutions.

Epigenetic Regulation in Almond Acknowledgements Support and funding are provided by the CFAES-SEEDS competitive grants at The Ohio State University for funding JFR and KDW in seminal work on the genomics and epigenomics for the interrogation of aging in almond. The USDA-NIFA-EWD doctoral fellowship is granted to KDW. The constant support is provided by the Almond Board of California for the development of genomic resources for their implementation in breeding and research on almond.

References Agius F, Kapoor A, Zhu J-K (2006) Role of the Arabidopsis DNA glycosylase/lyase ROS1 in active DNA demethylation. Proc Natl Acad Sci 103:11796– 11801. https://doi.org/10.1073/pnas.0603563103 Aguilera A, Gómez-González B (2008) Genome instability: a mechanistic view of its causes and consequences. Nat Rev Genet 9:204–217. https://doi.org/10. 1038/nrg2268 Akimoto K, Katakami H, Kim H-J et al (2007) Epigenetic inheritance in rice plants. Ann Bot 100:205–217. https://doi.org/10.1093/aob/mcm110 Akter A, Takahashi S, Deng W et al (2019) The histone modification H3 lysine 27 tri-methylation has conserved gene regulatory roles in the triplicated genome of Brassica rapa L. DNA Res 26:433–443. https://doi. org/10.1093/dnares/dsz021 Alioto T, Alexiou KG, Bardil A et al (2019) Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J 14538. https://doi.org/10.1111/tpj.14538 Aristizabal MJ, Anreiter I, Halldorsdottir T et al (2019) Biological embedding of experience: a primer on epigenetics. Proc Natl Acad Sci 117:201820838. https://doi.org/10.1073/pnas.1820838116 Arús P, Gradziel T, Oliveira MM, Tao R (2009) Genomics of almond. In: Folta KM, Gardiner SE (eds) Genetics and genomics of Rosaceae. Springer, New York, pp 187–219 Baek S, Choi K, Kim G-B et al (2018) Draft genome sequence of wild Prunus yedoensis reveals massive inter-specific hybridization between sympatric flowering cherries. Genome Biol. https://doi.org/10.1186/ s13059-018-1497-y Bannister AJ, Kouzarides T (2011) Regulation of chromatin by histone modifications. Cell Res 21:381–395. https://doi.org/10.1038/cr.2011.22 Becker C, Hagmann J, Müller J et al (2011) Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480:245–249. https://doi.org/10. 1038/nature10555 Berger MMJ, Gallusci P, Teyssier E (2018) Roles of epigenetic mechanisms in grafting and possible applications. In: Advances in botanical research. Elsevier, Amsterdam, pp 203–246

71 Bourrat P, Lu Q, Jablonka E (2017) Why the missing heritability might not be in the DNA. BioEssays 39:1700067. https://doi.org/10.1002/bies.201700067 Buiatti M (2011) Plants: individuals or epigenetic cell populations? In: Gissis SB, Jablonka E (eds) Transformations of Lamarckism. The MIT Press, pp 251–260 Burian A, Barbier de Reuille P, Kuhlemeier C (2016) Patterns of stem cell divisions contribute to plant longevity. Curr Biol 26:1385–1394. https://doi.org/10. 1016/j.cub.2016.03.067 Cattani AM, Sartor T, da Silveira Falavigna V et al (2018) The control of bud break and flowering time in plants. In: Advances in botanical research. Elsevier, Amsterdam, pp 277–325 Champigny MJ, Unda F, Skyba O et al (2019) Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation. Plant Biotechnol J 13299. https://doi.org/10.1111/pbi.13299 Choi Y, Gehring M, Johnson L et al (2002) DEMETER, a DNA glycosylase domain protein, is required for endosperm gene imprinting and seed viability in Arabidopsis. Cell 110:33–42. https://doi.org/10.1016/ S0092-8674(02)00807-3 Cubas P, Vincent C, Coen E (1999) An epigenetic mutation responsible for natural variation in floral symmetry. Nature 401:157–161. https://doi.org/10. 1038/43657 Daccord N, Celton J-M, Linsmith G et al (2017) Highquality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nat Genet 49:1099 DeJong TM (1999) Developmental and environmental control of dry-matter partitioning in peach. HortSci 34:1037–1040. https://doi.org/10.21273/HORTSCI. 34.6.1037 DeJong TM, Moing A (2008) Carbon assimilation, partitioning and budget modeling. In: Layne DR, Bassi D (eds) The peach: botany, production and uses. CABI Publishing, Wallingford, pp 244–263 Dicenta F, García JE (1993a) Inheritance of the kernel flavour in almond. Heredity 70:308–312. https://doi. org/10.1038/hdy.1993.44 Dicenta F, García JE (1993b) Inheritance of selfcompatibility in almond. Heredity 70:313–317. https://doi.org/10.1038/hdy.1993.45 Dobzhansky T (1950) Genetics of natural populations. XIX. Origin of heterosis through natural selection in populations of Drosophila pseudoobscura. Genetics 35:288–302 Eichten SR, Schmitz RJ, Springer NM (2014) Epigenetics: beyond chromatin modifications and complex genetic regulation. Plant Physiol 165:933–947. https:// doi.org/10.1104/pp.113.234211 Fedoroff NV (2012) Transposable elements, epigenetics, and genome evolution. Science 338:758–767. https:// doi.org/10.1126/science.338.6108.758 Fedoroff NV, Botstein D (1992) The dynamic genome: Barbara McClintock’s ideas in the century of genetics.

72 Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York Fenton CAL, Kester DE, Kuniyuki AH (1988) Models for noninfectious bud failure in almond. Phytopathology 78:139. https://doi.org/10.1094/Phyto-78-139 Fenton CAL, Kuniyuki AH, Kester DE. Search for a viroid etiology for noninfectious bud-failure in almond. HortScience 23:1050–1052 Fernández i Martí A, Gradziel TM, Socias i Company R (2014) Methylation of the Sf locus in almond is associated with S-RNase loss of function. Plant Mol Biol 86:681–689. https://doi.org/10.1007/s11103-0140258-x Fontdevila A (2011) The dynamic genome. Oxford University Press Foster TM, Aranzana MJ (2018) Attention sports fans! The far-reaching contributions of bud sport mutants to horticulture and plant biology. Horticulture Research. https://doi.org/10.1038/s41438-018-0062-x Franklin-Tong N, Franklin FCH (2003) Gametophytic self-incompatibility inhibits pollen tube growth using different mechanisms. Trends Plant Sci 8:598–605. https://doi.org/10.1016/j.tplants.2003.10.008 Fresnedo-Ramírez J, Chan HM, Parfitt DE et al (2017) Genome-wide DNA-(de)methylation is associated with noninfectious bud-failure exhibition in almond (Prunus dulcis [Mill.] D.A. Webb). Sci Rep 7:42686. https://doi.org/10.1038/srep42686 Frías-Lasserre D, Villagra CA (2017) The importance of ncRNAs as epigenetic mechanisms in phenotypic variation and organic evolution. Front Microbiol. https://doi.org/10.3389/fmicb.2017.02483 Furci L, Jain R, Stassen J et al (2019) Identification and characterisation of hypomethylated DNA loci controlling quantitative resistance in Arabidopsis. eLife 8: e40655. https://doi.org/10.7554/eLife.40655 Gómez EM, Buti M, Sargent DJ et al (2019a) Transcriptomic analysis of pollen-pistil interactions in almond (Prunus dulcis) identifies candidate genes for components of gametophytic self-incompatibility. Tree Genet Genomes. https://doi.org/10.1007/s11295-019-1360-7 Gómez EM, Dicenta F, Batlle I et al (2019b) Crossincompatibility in the cultivated almond (Prunus dulcis): updating, revision and correction. Sci Hortic 245:218–223. https://doi.org/10.1016/j.scienta.2018. 09.054 Gradziel T, Lampinen B, Preece JE (2019) Propagation from basal epicormic meristems remediates an agingrelated disorder in almond clones. Horticulturae 5:28. https://doi.org/10.3390/horticulturae5020028 Gradziel T, Fresnedo-Ramírez J (2019) Noninfectious bud-failure as a model for studying age related genetic disorders in long-lived perennial plants. J Am Pomolog Soc 73:240–253 Gradziel TM, Martínez-Gómez P (2013) Almond breeding. In: Janick J (ed) Plant breeding reviews. Wiley, Hoboken, pp 207–258 Grafi G, Ohad N (eds) (2013). Springer, Berlin

J. Fresnedo Ramírez et al. Grant-Downton RT, Dickinson HG (2005) Epigenetics and its implications for plant biology. 1. The epigenetic network in plants. Ann Bot 96:1143–1164. https://doi.org/10.1093/aob/mci273 Grant-Downton RT, Dickinson HG (2006) Epigenetics and its implications for plant biology 2. the ‘epigenetic epiphany’: epigenetics, evolution and beyond. Ann Bot 97:11–27. https://doi.org/10.1093/aob/mcj001 Groot EP, Laux T (2016) Ageing: how do long-lived plants escape mutational meltdown? Curr Biol 26:R530– R532. https://doi.org/10.1016/j.cub.2016.05.049 Guarino F, Cicatelli A, Brundu G et al (2015) Epigenetic diversity of clonal white poplar (Populus alba L.) populations: could methylation support the success of vegetative reproduction strategy? PLoS ONE 10: e0131480. https://doi.org/10.1371/journal.pone.0131480 Henikoff S, Smith MM (2015) Histone variants and epigenetics. Cold Spring Harb Perspect Biol 7: a019364. https://doi.org/10.1101/cshperspect.a019364 Horvath S, Raj K (2018) DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet 19:371–384. https://doi.org/10.1038/ s41576-018-0004-3 Hung Y-H, Liu F, Zhang X-Q et al (2018) Sexual and non-sexual reproduction. In: Advances in botanical research. Elsevier, Amsterdam, pp 117–163 Jablonka E (2017) The evolutionary implications of epigenetic inheritance. Interface Focus 7:20160135. https://doi.org/10.1098/rsfs.2016.0135 Jablonka E, Lamb MJ (2006) The changing concept of epigenetics. Ann NY Acad Sci 981:82–96. https://doi. org/10.1111/j.1749-6632.2002.tb04913.x Jablonka E, Raz G (2009) Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q Rev Biol 84:131–176. https://doi.org/10.1086/598822 Jiang F, Zhang J, Wang S et al (2019a) The apricot (Prunus armeniaca L.) genome elucidates Rosaceae evolution and beta-carotenoid synthesis. Hortic Res 6 https://doi.org/10.1038/s41438-019-0215-6 Jiang S-H, Sun Q-G, Chen M et al (2019b) Methylome and transcriptome analyses of apple fruit somatic mutations reveal the difference of red phenotype. BMC Genomics. https://doi.org/10.1186/s12864-0195499-2 Kawakatsu T, Nery JR, Castanon R, Ecker JR (2017) Dynamic DNA methylation reconfiguration during seed development and germination. Genome Biol. https://doi.org/10.1186/s13059-017-1251-x Kawashima T, Berger F (2014) Epigenetic reprogramming in plant sexual reproduction. Nat Rev Genet 15:613–624. https://doi.org/10.1038/nrg3685 Kenchanmane Raju SK, Ritter EJ, Niederhuth CE (2019) Establishment, maintenance, and biological roles of non-CG methylation in plants. Essays Biochem 63:743–755. https://doi.org/10.1042/EBC20190032 Kester DE (1976) Noninfectious bud failure in almond. In: Fulton RW (ed) Virus diseases and noninfectious

Epigenetic Regulation in Almond disorders of stone fruits in North America. U.S. Department of Agriculture, Agricultural Research Service: for sale by the superintendent of documents, U.S. Government Publishing Office, Washington, pp 278–282 Kester DE, Schackel K, Micke W et al (2003) The Noninfectious Bud-failure problem in almonds: an interaction of unique biological, adaptive and cultural conditions. HortScience 38:726 Klekowski EJ (2003) Plant clonality, mutation, diplontic selection and mutational meltdown. Biol J Lin Soc 79:61–67. https://doi.org/10.1046/j.1095-8312.2003. 00183.x Kodad O, Socias i Company R, Sánchez A, Oliveira MM (2009) The expression of self-compatibility in almond may not only be due to the presence of the Sf allele. J Am Soc Hort Sci 134:221–227. https://doi.org/10. 21273/JASHS.134.2.221 Kovalchuk I (ed) (2017). Springer, Boston Lal S, Oetjens M, Hannah LC (2009) Helitrons: enigmatic abductors and mobilizers of host genome sequences. Plant Sci 176:181–186. https://doi.org/10.1016/j. plantsci.2008.11.004 Lamarck JBPA (1809) Philosophie zoologique, ou exposition des considerations relatives a l’histoire naturelle des animaux. G. Bailliere, Paris Lamm E (2014) The genome as a developmental organ: the genome as a developmental organ. J Physiol 592:2283–2293. https://doi.org/10.1113/jphysiol.2014. 271734 Leida C, Conesa A, Llácer G et al (2012) Histone modifications and expression of DAM6 gene in peach are modulated during bud dormancy release in a cultivar-dependent manner. New Phytol 193:67–80. https://doi.org/10.1111/j.1469-8137.2011.03863.x Li Z, Reighard GL, Abbott AG, Bielenberg DG (2009) Dormancy-associated MADS genes from the EVG locus of peach [Prunus persica (L.) Batsch] have distinct seasonal and photoperiodic expression patterns. J Exp Bot 60:3521–3530. https://doi.org/10. 1093/jxb/erp195 Lönnig W-E, Saedler H (1997) Plant transposons: contributors to evolution? Gene 205:245–253. https://doi. org/10.1016/S0378-1119(97)00397-1 Maeji H, Nishimura T (2018) Epigenetic mechanisms in plants. In: Advances in botanical research. Elsevier, Amsterdam, pp 21–47 Manning K, Tör M, Poole M et al (2006) A naturally occurring epigenetic mutation in a gene encoding an SBP-box transcription factor inhibits tomato fruit ripening. Nat Genet 38:948–952. https://doi.org/10. 1038/ng1841 Martin A, Troadec C, Boualem A et al (2009) A transposon-induced epigenetic change leads to sex determination in melon. Nature 461:1135–1138. https://doi.org/10.1038/nature08498 Merlin F (2017) Limited extended inheritance. In: Huneman P, Wlash D (eds) Challenging the modern synthesis. Oxford University Press, New York

73 Meyer P (2018) Epigenetics—a historical perspective. In: Advances in botanical research. Elsevier, Amsterdam, pp 1–19 Miska EA, Ferguson-Smith AC (2016) Transgenerational inheritance: models and mechanisms of non-DNA sequence-based inheritance. Science 354:59–63. https://doi.org/10.1126/science.aaf4945 Mørkve Knudsen T, Rezwan FI, Jiang Y et al (2018) Transgenerational and intergenerational epigenetic inheritance in allergic diseases. J Allergy Clin Immunol 142:765–772. https://doi.org/10.1016/j.jaci. 2018.07.007 Niederhuth CE, Bewick AJ, Ji L et al (2016) Widespread natural variation of DNA methylation within angiosperms. Genome Biol 17:194. https://doi.org/10.1186/ s13059-016-1059-0 Parfrey LW, Lahr DJG, Katz LA (2008) The dynamic nature of eukaryotic genomes. Mol Biol Evol 25:787– 794. https://doi.org/10.1093/molbev/msn032 Pikaard CS, Mittelsten Scheid O (2014) Epigenetic regulation in plants. Cold Spring Harb Perspect Biol 6: a019315–a019315. https://doi.org/10.1101/cshperspect. a019315 Pineda-Krch F (1999) On the potential for evolutionary change in meristematic cell lineages through intraorganismal selection. J Evol Biol 12:681–688. https:// doi.org/10.1046/j.1420-9101.1999.00066.x Price TD, Qvarnström A, Irwin DE (2003) The role of phenotypic plasticity in driving genetic evolution. Proc R Soc Lond B 270:1433–1440. https://doi.org/ 10.1098/rspb.2003.2372 Prudencio Á, Werner O, Martínez-García P et al (2018) DNA methylation analysis of dormancy release in almond (Prunus dulcis) flower buds using epigenotyping by sequencing. IJMS 19:3542. https:// doi.org/10.3390/ijms19113542 Quadrana L, Colot V (2016) Plant transgenerational epigenetics. Annu Rev Genet 50:467–491. https://doi. org/10.1146/annurev-genet-120215-035254 Richards CL, Alonso C, Becker C et al (2017) Ecological plant epigenetics: evidence from model and nonmodel species, and the way forward. Ecol Lett 20:1576–1590. https://doi.org/10.1111/ele.12858 Rigal M, Becker C, Pélissier T et al (2016) Epigenome confrontation triggers immediate reprogramming of DNA methylation and transposon silencing in Arabidopsis thaliana F1 epihybrids. Proc Natl Acad Sci USA 113:E2083–E2092. https://doi.org/10.1073/ pnas.1600672113 Roessler K, Bousios A, Meca E, Gaut BS (2018) Modeling interactions between transposable elements and the plant epigenetic response: a surprising reliance on element retention. Genome Biol Evol 10:803–815. https://doi.org/10.1093/gbe/evy043 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Genomes 8:379–389. https://doi.org/10.1007/s11295011-0448-5

74 Sánchez-Pérez R, Pavan S, Mazzeo R et al (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364:1095–1098. https://doi.org/10.1126/science.aav8197 Sano H, Kim H-J (2013) Transgenerational epigenetic inheritance in plants. In: Grafi G, Ohad N (eds) Epigenetic memory and control in plants. Springer, Berlin, pp 233–253 Sargent DJ, Jung S, Main D (2012) Comparative genetics and genomics initiatives. In: Genetics, genomics and breeding of stone fruits. CRC Press, Boca Raton, pp 270–291 Schmitz RJ, Ecker JR (2012) Epigenetic and epigenomic variation in Arabidopsis thaliana. Trends Plant Sci 17:149–154. https://doi.org/10.1016/j.tplants.2012.01. 001 Schmitz RJ, Schultz MD, Lewsey MG et al (2011) Transgenerational epigenetic instability is a source of novel methylation variants. Science 334:369–373. https://doi.org/10.1126/science.1212959 Scoville AG, Barnett LL, Bodbyl-Roels S et al (2011) Differential regulation of a MYB transcription factor is correlated with transgenerational epigenetic inheritance of trichome density in Mimulus guttatus. New Phytol 191:251–263. https://doi.org/10.1111/j.14698137.2011.03656.x Seshasayee ASN (2014) Gene expression homeostasis and chromosome architecture. BioArchitecture 4:1–5. https://doi.org/10.1080/19490992.2015.1040213 Shea N, Pen I, Uller T (2011) Three epigenetic information channels and their different roles in evolution: Epigenetic mechanisms and evolution. J Evol Biol 24:1178–1187. https://doi.org/10.1111/j.1420-9101. 2011.02235.x Shirasawa K, Esumi T, Hirakawa H et al (2019) Phased genome sequence of an interspecific hybrid flowering cherry, ‘Somei-Yoshino’ (Cerasus ⨯ yedoensis). DNA Res 26:379–389. https://doi.org/10.1093/dnares/dsz016 Shirasawa K, Isuzugawa K, Ikenaga M et al (2017) The genome sequence of sweet cherry (Prunus avium) for use in genomics-assisted breeding. DNA Res 24:499– 508. https://doi.org/10.1093/dnares/dsx020 Slotkin RK, Martienssen R (2007) Transposable elements and the epigenetic regulation of the genome. Nat Rev Genet 8:272–285. https://doi.org/10.1038/nrg2072 Socias i Company R, Kodad O, Fernández i Martí À, Alonso J (2013) Pollen tube growth and selfcompatibility in almond. Plants 2:50–56. https://doi. org/10.3390/plants2010050 Socias i Company R, Kodad O, Fernández i Martí A, Alonso JM (2015) Mutations conferring selfcompatibility in Prunus species: from deletions and insertions to epigenetic alterations. Scientia Horticulturae 192:125–131. https://doi.org/10.1016/j.scienta. 2015.05.029 Teixeira FK, Heredia F, Sarazin A et al (2009) A role for RNAi in the selective correction of dna methylation defects. Science 323:1600–1604. https://doi.org/10. 1126/science.1165313

J. Fresnedo Ramírez et al. Thieme M, Bucher E (2018) Transposable elements as tool for crop improvement. In: Advances in botanical research. Elsevier, pp 165–202 Thomas J, Pritham EJ (2015) Helitrons, the eukaryotic rolling-circle transposable elements. Microbiol Spectr. https://doi.org/10.1128/microbiolspec.MDNA3-00492014 Usai G, Mascagni F, Giordani T et al (2020) Epigenetic patterns within the haplotype phased fig (Ficus carica L.) genome. Plant J. https://doi.org/10.1111/tpj.14635 Ushijima K, Sassa H, Dandekar AM et al (2003) Structural and transcriptional analysis of the selfincompatibility locus of almond: identification of a pollen-expressed f-box gene with haplotype-specific polymorphism. Plant Cell 15:771–781. https://doi.org/ 10.1105/tpc.009290 van Dijk EL, Jaszczyszyn Y, Naquin D, Thermes C (2018) The third revolution in sequencing technology. Trends Genet 34:666–681. https://doi.org/10.1016/j. tig.2018.05.008 van Gurp TP, Wagemaker NCAM, Wouters B et al (2016) epiGBS: reference-free reduced representation bisulfite sequencing. Nat Methods 13:322–324. https://doi. org/10.1038/nmeth.3763 Verde I, Jenkins J, Dondini L et al (2017) The Peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity. BMC Genomics 18 https://doi.org/ 10.1186/s12864-017-3606-9 Waddington CH (1942) The epigenotype. Int J Epidemiol 41:10–13. https://doi.org/10.1093/ije/dyr184 Wilson EE, Schein RD (1956) The nature and development of noninfectious bud failure of almonds. Hilgardia 24:519–542. https://doi.org/10.3733/hilg.v24n18 p519 Wu X, Zhou Y, Yao D et al (2020) DNA methylation of LDOX gene contributes to the floral colour variegation in peach. J Plant Physiol 246–247:153116. https://doi. org/10.1016/j.jplph.2020.153116 Xavier MJ, Roman SD, Aitken RJ, Nixon B (2019) Transgenerational inheritance: how impacts to the epigenetic and genetic information of parents affect offspring health. Hum Reprod Update 25:519–541. https://doi.org/10.1093/humupd/dmz017 Xie K, Ryan DP, Pearson BL et al (2018) Epigenetic alterations in longevity regulators, reduced life span, and exacerbated aging-related pathology in old father offspring mice. Proc Natl Acad Sci 115:E2348– E2357. https://doi.org/10.1073/pnas.1707337115 Yue H, Nie X, Yan Z, Weining S (2019) N6methyladenosine regulatory machinery in plants: composition, function and evolution. Plant Biotechnol J 17:1194–1208. https://doi.org/10.1111/pbi.13149 Zhang H, Lang Z, Zhu J-K (2018) Dynamics and function of DNA methylation in plants. Nat Rev Mol Cell Biol 19:489–506. https://doi.org/10.1038/s41580-018-0016-z Zhang Q, Chen W, Sun L et al (2012) The genome of Prunus mume. Nat Commun. https://doi.org/10.1038/ ncomms2290

Epigenetic Regulation in Almond Zhebentyayeva T, Shankar V, Scorza R et al (2019) Genetic characterization of worldwide Prunus domestica (plum) germplasm using sequence-based genotyping. Hortic Res. https://doi.org/10.1038/s41438018-0090-6 Zhebentyayeva TN, Fan S, Chandra A et al (2014) Dissection of chilling requirement and bloom date

75 QTLs in peach using a whole genome sequencing of sibling trees from an F2 mapping population. Tree Genet Genomes 10:35–51. https://doi.org/10.1007/ s11295-013-0660-6

Metabolomic Studies in Almond Jesús Guillamón Guillamón and Raquel Sánchez-Pérez

Abstract

Metabolomics is an emerging field that measures all the metabolites present in a cell, tissue, or organ. From all the next-generation sequencing platforms, metabolomics is maybe the one that advances not as fast as wanted. This is because the number of compounds to be discovered is almost infinite. Plants, the best industry to fabricate chemical compounds, represent one of the systems where the discovery and identification of new metabolites are a real challenge. The last and current advances of metabolomics in almonds are shown in this chapter.

1

Introduction

The metabolism of almond (Prunus dulcis (Mill.) D. A. Webb) and other Prunus spp. has been deeply studied over the last years (Michailidis et al. 2018; Guillamón et al. 2020; Cueto et al. 2017). These studies have unveiled the metabolites and pathways behind many physiological

J. Guillamón Guillamón . R. Sánchez-Pérez (&) Department of Plant Breeding, Centro de Edafología y Biología Aplicada del Segura-Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), Campus Universitario de Espinardo, No 25, 30100 Murcia, Spain e-mail: [email protected]

processes, such as endodormancy release, saltstress and oxidation–reduction processes (Guillamón et al. 2020; Bernal-Vicente et al. 2018; Baldermann et al. 2018). The new advances in liquid and gas chromatographies (LC and GC) coupled to mass spectrometry (MS) or nuclear magnetic resonance (NMR) have facilitated the sample analysis, obtaining a huge amount data from them (Okazaki and Saito 2012). Metabolomic studies always follow the metabolomic workflow. This workflow consists on many different stages, which conduces to the identification of the metabolic causes of a physiological process (Fig. 1). The first step is the extraction of sample metabolites using an extraction solvent, which usually consist on a mayor percentage of organic phase with a small percentage of a polar inorganic phase. In case of GC– MS analysis, it also includes a derivatization to make the metabolites more volatile. Subsequently, samples are analyzed by GC or LC coupled to MS or NMR (Michailidis et al. 2018; Stillwell et al. 1989). The most common technique used for obtaining the metabolomic profile of these samples is the LC–MS (Okazaki and Saito 2012). The recent advances in LC with the implementation of ultra-high-performance liquid chromatography (UPLC-QToF) have improved the separation of the different metabolites. This fact coupled to the advancements in MS with the development of the quadrupole time-of-flying (QToF) allows the detection and quantification of metabolites present in low concentrations, such as hormones, which

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_6

77

78

J. Guillamón Guillamón and R. Sánchez-Pérez

Fig. 1 Metabolomic workflow. Different stages in the identification of the metabolic causes of a physiological process

might play a crucial role in multiple metabolic and physiological processes (Chmielewski et al. 2018). After the analysis of the extracted metabolites, huge datasets are obtained. These datasets must be treated to detect which features display significant variations. Subsequently, the significant features must be contrasted against metabolomic libraries and pure standards to identify the metabolites of interest (Moco et al. 2007). Finally, the metabolomic workflow ends with the interpretation of the results and studying their biological sense, which connects them with the physiological process of study. Metabolomic analysis is divided in two different groups according to the target of study. Those, which focus in certain groups or pathways and metabolites, are known as target studies, while the ones, which are not focused in anything concrete, are known as non-target studies (Ribbenstedt et al. 2018). These differences in the aim of study have a great impact in the metabolite extraction. In target analysis, the metabolite extraction is focused in obtaining just the group of metabolite of interest, throwing away most of the other metabolites in previous steps of the extraction (Roberts et al. 2012). In almond, target metabolomics have supposed a great impact in the study of different metabolic processes such as

endodormancy release or the elucidation of the amygdalin pathway, responsible of bitterness (Ionescu et al. 2017; Thodberg et al. 2018). On the other hand, non-target metabolomics are not focused in any particular metabolite or pathway, extracting every single metabolite during the metabolite extraction. Therefore, non-target studies allow the discovery of new metabolites and pathways involved in different physiological processes like endodormancy release and almond classification. On the whole, both kinds of metabolomic studies are extremely useful and also complementary, since target studies are usually performed after non-target ones to accurately verify the results obtained from the previous ones (Ribbenstedt et al. 2018). In the same way, target studies are also performed for validating results obtained by genomic and transcriptomic studies (Baldermann et al. 2018).

2

Target Metabolomic in Different Prunus Spp.

Metabolomics in Prunus spp. have only been applied during the last years. Nevertheless, in this short period of time, they have unveiled some physiological processes, such as salt-stress

Metabolomic Studies in Almond

response in plum (Prunus domestica L.) and endodormancy in apricot (Prunus armeniaca L.) and sweet cherry (Prunus avium (L.) L.) (Michailidis et al. 2018; Bernal-Vicente et al. 2018; Ionescu et al. 2017; Conrad et al. 2019). These discoveries have served to confirm previous genomic and transcriptomic studies at the metabolic level (Leida et al. 2010). Therefore, metabolomics might be a key tool for solving different problems and questions about Prunus spp. that could not be answered in the past and, also, to validate previous discoveries from the metabolic point of view.

2.1 Metabolomics in Salt-Stress Response in Plum Salinity is one of the main factors that affects crop productivity. Due to the use of saline water for irrigation, more areas are suffering from this problem worldwide (Hossain and Dietz 2016). Recent studies have managed to get awareness of some mechanisms behind this problem. Reactive oxygen species (ROS) decrease under salt stress in order to dissipate safely the light energy received by the chlorophylls A and B (AcostaMotos et al. 2017). In the same way, different phytohormones such as abscisic acid (ABA), jasmonic acid (JA) and salicylic acid (SA) have demonstrated to be involved in salt-stress response, increasing when the plant is exposed to it (Bernal-Vicente et al. 2018). In this study, this group performed a target metabolomic study of some metabolites from the biosynthesis of SA using a UPLC-QToF equipment. This analysis allowed them a proper detection and quantification of the previous mentioned metabolites in presence or absence of salt stress. Moreover, they performed a gene expression analysis, where genes Non-Expressor of Pathogenesis-Related Gene 1 (NPR1) and Thioredoxin H-type 1 (TrxH) showed a significant variation under salt-stress conditions. Therefore, it was suggested a connection between SA, JA and ABA pathways and the expression of NPR1 and TrxH (BernalVicente et al. 2018).

79

2.2 Endodormancy and Target Metabolomics in Prunus Spp. Prunus spp. like almond, peach (Prunus persica (L.) Batsch), plum, apricot and sweet cherry are temperate fruit trees, which, at the end of the summer, enter into a protector state known as endodormancy (Sánchez-Pérez et al. 2012). During it, growth stops and flower buds are able to resist temperatures down to – 4 ºC (Ben Mohamed et al. 2010), making possible the survival of the tree against the adverse conditions of winter. Chilling requirements (CRs) are defined as the quantity of chilling that must be accumulated in order to release from endodormancy (Fishman et al. 1987). Throughout the last years, different biomolecular studies have been focused in the endodormancy release process, making possible the creation of highly dense linkage maps and identifying quantitative trait loci (QTL), associated to the flowering date and CR (Sánchez-Pérez et al. 2012; Castède et al. 2014). Moreover, in peach, genes from the DORMANCY-ASSOCIATED MADS-BOX (DAM) gene family have been described for their association with endodormancy release (Bielenberg et al. 2004). In spite of all these advances in the endodormancy release study, there are still many unknown spots in this area. Therefore, different target and non-target metabolomic studies have been carried out to complement or confirm other genetic or transcriptomic studies (Baldermann et al. 2018; Ionescu et al. 2017; Conrad et al. 2019). One of these studies was focused in the association of the phenylpropanoid pathway with endodormancy. During the last years, different genetic and transcriptomic studies have been targeted in this association. The accumulation of phenylpropanoids coupled with a decrease of ABA content has been described for being crucial for endodormancy release in peach (Leida et al. 2010). This fact was also studied in apricot, where a transcriptomic and target metabolomic analysis showed a clear correlation between the upregulation of genes from the phenylpropanoid

80

J. Guillamón Guillamón and R. Sánchez-Pérez

biosynthesis and endodormancy release. The increased expression of genes like 4-coumarateCoA ligase-like 7 and cinnamoyl-CoA reductase 1 was also in concordance with the high metabolite increase from the phenylpropanoid pathway, such as resveratrol and quercetin (Conrad et al. 2019). In this case, metabolomics confirmed the results from the transcriptome analysis, being vital in the validation of those results, since modifications at the transcriptomic level do not always assure changes at the proteomic or metabolic level. Other metabolites involved in endodormancy release are cyanogenic glucosides. These metabolites are by-products from the phenylalanine metabolism (Pičmanová et al. 2015). It has been described in sweet cherry flower buds that the cyanogenic glucoside prunasin increases its concentration during and after endodormancy release in presence and absence of the endodormancy release promoter cyanamide (Ionescu et al. 2017). This target metabolomic analysis supports the results obtained in a previous quantitative real-time polymerase chain reaction (qRT-PCR), where genes responsible of prunasin biosynthesis and its metabolization were upregulated during endodormancy release in presence of cyanamide (Cueto et al. 2018). This pathway has also been studied in other cyanogenic plant species like cassava, sorghum and almond, where the variation of 31 cyanogenic glucosides was studied (Thodberg et al. 2018; Pičmanová et al. 2015). Results suggested a recycling pathway for cyanogenic glucosides, in which nitrogen and carbon from CN group are recovered as NH3 and CO2 for primary metabolism (Pičmanová et al. 2015).

3

Non-target Metabolomics in Almond

During the last years, only two different nontarget metabolomic studies in almond have been performed. Nevertheless, thanks to the new techniques in metabolomics, both of them have provided a great amount of knowledge about the almond metabolism.

3.1 Almond Classification The first study was focused in the classification of almonds according to their origin and cultivar. In this study, they performed a non-target metabolomic analysis using an UHPLC-QToF. The data obtained allowed the selection of markers for country and cultivar discrimination, as well as the creation of a Partial Least Squares–Discriminant Analysis (PLS-DA) as a model for almond classification (Gil Solsona et al. 2018). Regarding the origin model, bitter, Spanish and USA almonds presented significant differences among them. However, the cultivar model did not present such differences between cultivars, except from the Spanish cultivar Desmayo Largueta. The main markers for country discrimination were sugars, while most of the cultivar discrimination markers were sugars and lipids. These results are in concordance with a previous study, which pointed sucrose and diverse saturated and monounsaturated fatty acids as the main groups that varied between almonds from different cultivars (Yada et al. 2013).

3.2 Endodormancy in Almond Flower Buds The second and most recent non-target metabolomic study in almond was focused in the metabolism behind endodormancy release. Thanks to this metabolomic approach, the variation of five different pathways in four almond cultivars was observed (Guillamón et al. 2020). The first pathway was related to glutathione metabolism, in which ascorbic acid exhibited significant variations in the four cultivars (Guillamón et al. 2020). This fact agrees with previous studies in Japanese pear (Pyrus pyrifolia (Burm. f.) Nakai) and sweet cherry, which pointed the importance of glutathione metabolism for ROS degradation during endodormancy release (Gil Solsona et al. 2018). Amino acid-based pathways were also studied for their variation through endodormancy release. As it was previously observed in sweet cherry, phenylpropanoid by-products increased during

Metabolomic Studies in Almond

and after endodormancy release (Michailidis et al. 2018; Baldermann et al. 2018). Moreover, this is also in concordance with other works in peach, where the accumulation of phenylpropanoids coupled with a decrease in ABA levels is mandatory for reaching ecodormancy (Leida et al. 2012). An increase of prunasin, a by-product from phenylalanine metabolism, was observed throughout endodormancy release in the four cultivars. These results are in concordance with previous studies in almond and sweet cherry, which found variations not only in prunasin levels, but also in the genes responsible of prunasin biosynthesis (Cueto et al. 2017; Ionescu et al. 2017). The ABA biosynthetic pathway was also studied in grapevine (Vitis vinifera L.), due to a drop in ABA levels. ABA has been widely described for playing a central role in endodormancy release, decreasing during the transition from endodormancy to ecodormancy (Zheng et al. 2018). In addition, ABA was also described for its role as a modulator of different metabolic pathways involved in endodormancy release, such as glutathione metabolism, phenylpropanoid biosynthesis and flavonoid biosynthesis (Leida et al. 2012; Ye et al. 2012; Zhao et al. 2019). Another studied pathway was D-sorbitol metabolism. D-sorbitol exhibited a huge drop during endodormancy release, while its by-product Dsorbitol-6-phosphate as well as other metabolites from the glycolysis increased at the same time. It has been detailed that in the endodormancy release of red rice seeds, the obtaining of energy through glycolysis is increased (Gianinetti et al. 2018). This fact agrees with the results observed in almond, suggesting that D-sorbitol-6phosphate works as an energy store during endodormancy release. The last pathway, where significant variations were observed, was flavonoid metabolism. Previous works in sheepgrass (Leymus chinensis (Trin.) Tzvelev) explained that during seed germinations, ABA downregulates the biosynthesis of different flavonoids (Zhao et al. 2019). These results are in concordance with the observed in almond, where flavonoids, such as petunidin-3-glucoside, increased their concentration with a concomitant decrease of ABA. In the main, prunasin and ascorbic acid were

81

proposed as two possible biomarkers for endodormancy release. These metabolites were the only ones that varied in all the cultivars at the same time, varying the rest of metabolites just in one or more cultivars. However, it is still necessary to validate them as endodormancy release biomarkers performing studies in more cultivars, species and different years.

4

Non-target Metabolomics in Other Prunus Spp.

As in almond, non-target metabolomics have been carried out in other Prunus spp. in order to determinate the role of different metabolites in some physiological processes like endodormancy release in sweet cherry. In the same way, these analyses have been also performed to elucidate the dissimilarities between groups with different properties, such as the antioxidant activity in peach fruits.

4.1 Antioxidant Activity in Peach Fruits Recently, non-target metabolomics have been applied in the biomarker discovery for the antioxidant activity of peach (Zhang et al. 2020). In this work, a double study was performed in 40 peach cultivars. Firstly, they measure the Antioxidant Potency Composite (APC) using three different methods (DPPH radical scavenging activity, the FRAP assay and ABTS radical scavenging ability), classifying the cultivars in three groups (high, medium and low). Secondly, after these results, a non-target metabolomic analysis was performed. The results obtained from the double analyses were contrasted by a mathematical model. Proanthocyanidins, quinic acid derivatives, catechins, flavonols, flavones, flavanones, phenolic glycosides, flavonoid Oglycosides, isoflavones, anthocyanidins and cyanogenic glycosides were identified for their high correlation with APC. These results are in concordance with former studies in peach, where the antioxidant capacity has been deeply

82

associated with their phenolic composition (Gil et al. 2002). Accordingly, recent studies showed that high antioxidant activities were also observed in peach with high contents of hydroxycinnamates, catechins and proanthocyanidins (Liao et al. 2019).

4.2 Endodormancy in Sweet Cherry Flower Buds In the last years, endodormancy release in sweet cherry has been studied in depth. Different pathways have been described for their role during it. For instance, metabolites involved in oxidation– reduction processes like ascorbate and dehydroascorbate, as well as reducing sugars, carotenoids and chlorophylls, exhibited meaningful changes during endodormancy release (Baldermann et al. 2018). This was in concordance with former studies, where ROS increased throughout endodormancy, decreasing after endodormancy release (Takemura et al. 2015). These metabolites, among others, were studied in sweet cherry, performing a non-target metabolomic analysis to collect as much information as possible about endodormancy release. High variations were detected in different groups of metabolites, such as phenolic compounds, carbohydrate, phospholipids and protein metabolism by-products. Accordingly, these trends have been previously described in apricot, sweet cherry, European pear (Pyrus communis L.) and almond (Guillamón et al. 2020; Conrad et al. 2019; Kaufmann and Blanke 2017; Gabay et al. 2019). After these results, two target analyses focused in the different metabolic pathways that exhibited significant changes in the first analysis were performed. The first target metabolomic analysis was focused in the identification and quantification of phenolic metabolites (Baldermann et al. 2018). The main phenolic groups that they detected and identified were caffeoylquinic acids, coumaroylquinic acids, catechins, quercetin by-products and kaempferol and peonidin derivatives. This fact is in concordance with previous studies in apple (Malus domestica Borkh.), where genes associated to the biosynthesis of flavonols, like

J. Guillamón Guillamón and R. Sánchez-Pérez

flavonoid 3-hydrolase, were repressed in response to low temperatures (Porto et al. 2015). Moreover, other studies in Prunus spp. showed that genes from the phenylpropanoid biosynthesis, like 4-COUMARATE:CoA LIGASE-LIKE 1 and 7, increased during endodormancy release (Leida et al. 2012; Prudencio et al. 2020). Even more, other metabolomic studies in sweet cherry also showed strong variations in phenylpropanoid compounds during endodormancy release (Michailidis et al. 2018; Guillamón et al. 2020). The second target metabolomic analysis aimed the identification and quantification of ascorbic and dehydroascorbic acids. Both metabolites were detected and quantified, showing a steep increase of ascorbic acid during ecodormancy. Nevertheless, it is still necessary to validate these results in other cultivars and in other Prunus spp. Previous studies in Japanese pear have explained the importance of ascorbic and dehydroascorbic acids for the degradation of ROS throughout the endodormancy release process (Takemura et al. 2015). Moreover, in grapevine leaves, the reduction of ROS has been associated with an increase in the levels of ascorbic acid and flavonols (Pérez et al. 2008, 2002). In addition, a deeper way to study the metabolomic pathways involved in endodormancy is by the use of a multi-platform analyses. In 2018, the first non-target multi-platform metabolomic analysis performed in sweet cherry (Michailidis et al. 2018) unveiled the metabolic mechanisms responsible of endodormancy release. In this study, a non-target GC–MS and LC–MS analysis was achieved (Michailidis et al. 2018), identifying primary metabolites like amino acids and sugars by GC–MS, while LC–MS analysis focused in the secondary metabolism, such as phenolic compounds, cyanogenic glucosides and high molecular weight lipids (Pičmanová et al. 2015; Redestig et al. 2010; Li et al. 2016). GC– MS results showed that the main variations in the primary metabolism occurred in the amino acids oxoproline, proline and tryptophan, as well as in phosphoric acid (Michailidis et al. 2018). This fact agrees with previous studies in grapevine, which described the role of proline as an osmotic factor against dehydration throughout winter

Metabolomic Studies in Almond

(Ben Mohamed et al. 2010). Moreover, the tryptophan increase has also been observed in almond flower buds during endodormancy release (Guillamón et al. 2020). This increase might be caused by the high level of the activity– dormancy cycle, in which metabolites like tryptophan, that varied during endodormancy, are metabolized into others involved in growth and other physiological processes (Zhang et al. 2018a, b). In contrast to GC–MS, LC–MS analysis is focused in the secondary metabolism. The main metabolites that exhibited significant variations between endodormancy and ecodormancy were phenolic metabolites, such as kaempferol, ferulic acid and caffeic acid. Kaempferol is a metabolite from the flavonol biosynthesis (Saylor and Mansell 1977). It has been described in apple, that genes responsible of flavonol biosynthesis are repressed under low temperatures. This is in concordance with these results, where kaempferol increases after endodormancy release, once chill requirements have been fulfilled (Zhao et al. 2019). On the other hand, Fig. 2 Applications of metabolomics in Prunus spp. Different physiological processes studied by metabolomics in Prunus

83

ferulic and caffeic acids are both metabolites from the phenylpropanoid biosynthesis. This pathway has been widely studied for being involved in endodormancy release (Conrad et al. 2019). Therefore, the increase observed in ferulic and caffeic acid levels is not surprising, since previous studies showed that an increase of phenylpropanoids was mandatory in order to release from endodormancy (Leida et al. 2012).

5

Conclusions and Future Sights

Molecular studies in almond have been performed during the last decades. Genomic and transcriptomic studies have answered many questions about the molecular causes of the different physiological processes that we can observe, such as flowering, bitterness, germination, growth, salt stress and so on (Fig. 2). However, owing to the low accuracy in the detection of some particular metabolites, many physiological processes remain unclear.

84

Fortunately, the recent advances in chromatography, MS and NMR coupled with the new multivariate statistical analysis have provided a new step in this field. Therefore, these advances have made possible the use of target and nontarget metabolomics in the discovery and validation of pathways and biomarkers involved in different biological processes. Nevertheless, metabolomics are still a newborn science, which must evolve to reach its greatest potential. Chromatography columns still can offer a better separation of metabolites based on their physicochemical properties, MS equipment´s can improve their resolution and precision, and NMR still needs to improve its detection limits in order to be useable in the study of metabolites with low concentration. Moreover, bioinformatics should focus in the identification of metabolites for nontarget studies, improving the current databases and browsers, making easier and faster the metabolite identification. Nevertheless, in spite of all these issues to improve, metabolomics have demonstrated their usefulness in the understanding of many physiological processes and will be playing a key role in the biomarker identification in the next coming years. Funding Work produced with the support of a “2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation”. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. This work has also been supported by the project ALADINO-MAGIC funded by Ministry of Science and Innovation (Spain). JG. Guillamón is grateful to “Fundación Tatiana Pérez de Guzmán el Bueno” for this Ph.D. fellowship.

References Acosta-Motos JR, Ortuño MF, Bernal-Vicente A, DiazVivancos P, Sanchez-Blanco MJ, Hernandez JA (2017) Plant responses to salt stress: adaptive mechanisms. Agronomy 7:1–38 Baldermann S, Homann T, Neugart S et al (2018) Selected plant metabolites involved in oxidationreduction processes during bud dormancy and ontogenetic development in sweet cherry buds (Prunus avium L.). Molecules 23:1197–1220 Ben Mohamed H, Vadel AM, Geuns JMC, Khemira H (2010) Biochemical changes in dormant grapevine

J. Guillamón Guillamón and R. Sánchez-Pérez shoot tissues in response to chilling: possible role in dormancy release. Sci Hortic (amsterdam) 124:440–447 Bernal-Vicente A, Cantabella D, Petri C, Hernández JA, Diaz-Vivancos P (2018) The salt-stress response of the transgenic plum line J8–1 and its interaction with the salicylic acid biosynthetic pathway from mandelonitrile. Int J Mol Sci 19:1–19 Bielenberg DG, Wang Y, Fan S, Reighard GL, Scorza R, Abbott AG (2004) A deletion affecting several gene candidates is present in the evergrowing peach mutant. J Hered 95:436–444 Castède S, Campoy JA, García JQ et al (2014) Genetic determinism of phenological traits highly affected by climate change in Prunus avium: flowering date dissected into chilling and heat requirements. New Phytol 202:703–715 Chmielewski FM, Huschek G, Baldermann S et al (2018) Abscisic acid related metabolites in Sweet Cherry Buds (Prunus avium L.). J Hortic 5:1–9 Conrad AO, Yu J, Staton ME et al (2019) Association of the phenylpropanoid pathway with dormancy and adaptive trait variation in apricot (Prunus armeniaca). Tree Physiol 39:1136–1148 Del Cueto J, Ionescu IA, Pičmanová M et al (2017) Cyanogenic glucosides and derivatives in almond and sweet cherry flower buds from dormancy to flowering. Front Plant Sci 8:1–16 Del Cueto J, Møller BL, Dicenta F, Sánchez-Pérez R (2018) b-Glucosidase activity in almond seeds. Plant Physiol Biochem 126:163–172 Fishman S, Erez A, Couvillon GA (1987) The temperature dependence of dormancy breaking in plants: Computer simulation of processes studied under controlled temperatures. J Theor Biol 126:309–321 Gabay G, Faigenboim A, Dahan Y et al (2019) Transcriptome analysis and metabolic profiling reveal the key role of a-linolenic acid in dormancy regulation of European pear. J Exp Bot 70:734–737 Gianinetti A, Finocchiaro F, Bagnaresi P et al (2018) Seed dormancy involves a transcriptional program that supports early plastid functionality during imbibition. Plants (basel, Switzerland) 7:35–84 Gil MI, Tomás-Barberán FA, Hess-Pierce B, Kader AA (2002) Antioxidant capacities, phenolic compounds, carotenoids, and vitamin C contents of nectarine, peach, and plum cultivars from California. J Agric Food Chem 50:4976–4982 Gil Solsona R, Boix C, Ibáñez M, Sancho JV (2018) The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics. Food Addit Contam Part A 35:395–403 Guillamón JG, Prudencio ÁS, Yuste JE, Dicenta F, Sánchez-Pérez R (2020) Ascorbic acid and prunasin, two candidate biomarkers for endodormancy release in almond flower buds identified by a nontargeted metabolomic study. Hortic Res 7:203–215 Hossain MS, Dietz KJ (2016) Tuning of redox regulatory mechanisms, reactive oxygen species and redox homeostasis under salinity stress. Front Plant Sci 2016. https://doi.org/10.3389/fpls.2016.00548

Metabolomic Studies in Almond Ionescu IA, López-Ortega G, Burow M et al (2017) Transcriptome and metabolite changes during hydrogen cyanamide-induced floral bud break in sweet cherry. Front Plant Sci 8:1–17 Kaufmann H, Blanke M (2017) Changes in carbohydrate levels and relative water content (RWC) to distinguish dormancy phases in sweet cherry. J Plant Physiol 218:1–5 Leida C, Terol J, Martí G et al (2010) Identification of genes associated with bud dormancy release in Prunus persica by suppression subtractive hybridization. Tree Physiol 30:655–666 Leida C, Conejero A, Arbona V et al (2012) Chillingdependent release of seed and bud dormancy in peach associates to common changes in gene expression. PLoS ONE 7:e35777–e35785 Li L, Zhao J, Zhao Y et al (2016) Comprehensive investigation of tobacco leaves during natural early senescence via multi-platform metabolomics analyses. Sci Rep 6:2–11 Liao X, Greenspan P, Pegg RB (2019) Characterizing the phenolic constituents and antioxidant capacity of Georgia peaches. Food Chem 271:345–353 Michailidis M, Karagiannis E, Tanou G et al (2018) Metabolic mechanisms underpinning vegetative bud dormancy release and shoot development in sweet cherry. Environ Exp Bot 155:1–11 Moco S, Vervoort J, Moco S, Bino RJ, De Vos RCH, Bino R (2007) Metabolomics technologies and metabolite identification. TrAC Trends Anal Chem 26:855–866 Okazaki Y, Saito K (2012) Recent advances of metabolomics in plant biotechnology. Plant Biotechnol Rep 6:1–15 Pérez FJ, Villegas D, Mejia N (2002) Ascorbic acid and flavonoid-peroxidase reaction as a detoxifying system of H2O2 in grapevine leaves. Phytochemistry 60:573– 580 Pérez FJ, Vergara R, Rubio S (2008) H2O2 is involved in the dormancy-breaking effect of hydrogen cyanamide in grapevine buds. Plant Growth Regul 55:149–155 Pičmanová M, Neilson EH, Motawia MS et al (2015) A recycling pathway for cyanogenic glycosides evidenced by the comparative metabolic profiling in three cyanogenic plant species. Biochem J 469:375–389 Porto DD, Bruneau M, Perini P et al (2015) Transcription profiling of the chilling requirement for bud break in apples: a putative role for FLC-like genes. J Exp Bot 66:2659–2672 Prudencio ÁS, Hoeberichts FA, Dicenta F, MartínezGómez P, And, Sánchez-Pérez R (2020) Identification of early and late flowering time candidate genes in endodormant and ecodormant almond flower buds. Tree Physiol 2020 Redestig H, Kusano M, Fukushima A, Matsuda F, Saito K, Arita M (2010) Consolidating metabolite

85 identifiers to enable contextual and multi-platform metabolomics data analysis. BMC Bioinform 11. https://doi.org/10.1186/1471-2105-11-214 Ribbenstedt A, Ziarrusta H, Benskin JP (2018) Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLoS ONE 13:1–18 Roberts LD, Souza AL, Gerszten RE, Clish CB (2012) Targeted metabolomics. Curr Protoc Mol Biol 1:1–24 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Genomes 8:379–389 Saylor MH, Mansell RL (1977) Hydroxycinnamoyl: coenzyme a transferase involved in the biosynthesis of kaempferol-3-(p-coumaroyl triglucoside) in Pisum sativum. Zeitschrift Fur Naturforsch Sect C J Biosci 32:765–768 Stillwell W, Brengle B, Hester P, Wassall SR (1989) Interaction of abscisic acid with phospholipid membranes. Biochemistry 28:2798–2804 Takemura Y, Kuroki K, Jiang M, Matsumoto K, Tamura F (2015) Identification of the expressed protein and the impact of change in ascorbate peroxidase activity related to endodormancy breaking in Pyrus pyrifolia. Plant Physiol Biochem 86:121–129 Thodberg S, Del Cueto J, Mazzeo R et al (2018) Elucidation of the amygdalin pathway reveals the metabolic basis of bitter and sweet almonds (Prunus dulcis). Plant Physiol 178:1096–1111 Yada S, Huang G, Lapsley K (2013) Natural variability in the nutrient composition of California-grown almonds. J Food Compos Anal 30:80–85 Ye N, Zhu G, Liu Y et al (2012) Ascorbic acid and reactive oxygen species are involved in the inhibition of seed germination by abscisic acid in rice seeds. J Exp Bot 63:1809–1822 Zhang J, Yan X, Su F et al (2018a) Long-term N and P additions alter the scaling of plant nitrogen to phosphorus in a Tibetan alpine meadow. Sci Total Environ 625:440–448 Zhang Z, Zhuo X, Zhao K et al (2018b) Transcriptome profiles reveal the crucial roles of hormone and sugar in the bud dormancy of Prunus mume. Sci Rep 8:1–15 Zhang X, Li X, Su M et al (2020) A comparative UPLCQ-TOF/MS-based metabolomics approach for distinguishing peach (Prunus persica (L.) Batsch) fruit cultivars with varying antioxidant activity. Food Res Int 137:109531 Zhao P, Li X, Jia J et al (2019) bHLH92 from sheepgrass acts as a negative regulator of anthocyanin/ proanthocyandin accumulation and influences seed dormancy. J Exp Bot 70:269–284 Zheng C, Acheampong AK, Shi Z et al (2018) Abscisic acid catabolism enhances dormancy release of grapevine buds. Plant Cell Environ 41:2490–2503

Recent Advances on Self-incompatibility in Almond: A Glance at Genomic and Transcriptomic Levels Shashi N. Goonetilleke, Michelle G. Wirthensohn, Richard S. Dodd, and Ángel Fernández i Martí

Abstract

Self-incompatibility (SI) systems are genetically controlled pollen–pistil interactions that allow the rejection of self-pollen (Nettancourt, Sex Plant Reprod 10:185–199, 1997). Based on the association with floral polymorphism, SI can be classified into two types: heteromorphic and homomorphic. The heteromorphic SI system, with two or three incompatibility types due to different positions of flower parts such as distyly and tristyly, whereas the homomorphic SI system, operates to inhibit selffertilization regardless of flower morphology with many different incompatibility systems (Charlesworth et al., New Phytol 168:61–69, 2005; Đorđević et al., Genetika 46:411–418, 2014). There are two types of homomorphic SI: sporophytic self-incompatibility (SSI) and gametophytic self-incompatibility (GSI) (Fig. 1). In SSI, the genotype of the diploid parental plant (sporophyte) that acts as the

S. N. Goonetilleke . M. G. Wirthensohn School of Agriculture, Food and Wine, Plant Research Centre, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia R. S. Dodd . Á. Fernández i Martí (&) Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA e-mail: [email protected]

pollen donor determines the incompatibility type (Hiscock and Tabah, Philos Trans R Soc Lond B Biol Sci 358:1037–1045, 2003), and in GSI, the genotype of the haploid pollen itself (gametophyte) determines the incompatibility type. GSI is the most common SI system in the plant kingdom and can be found in Solanaceae, Rosaceae and Plantaginaceae. Almond (Prunus dulcis) is a predominantly outcrossing species, with most cultivars which have been identified as self-incompatible. A few almond cultivars are known to be self-fertile. This phenotype has been attributed to a dominant S-RNase allele, Sf (Grasselly and Olivier, Ann Amélio Plantes 26:107–113, 1976; Socias i Company, Plant breeding reviews, vol 8. Wiley, New York, 1990) or Sfi (Sf-inactive) (Kodad and Socias i Company, Int Soc Hortic Sci (ISHS) 421–424, 2009). Due to poor transcription, plants with the Sf allele seem to lack the active Sf-RNase (Fernández i Martí et al., Sci Hortic 125:685–691, 2010), which arrest the growth of Sf pollen tubes. However, the plants with the Sfa (Sf-active) allele express an active S-RNase and confer SI (Kodad et al., J Am Soc Hortic Sci 134:221–227, 2009). In spite of their contrasting phenotypes, the Sfi- and Sfa-RNase alleles have identical nucleotide sequences and are linked with identical SFB alleles (Fernández i Martí et al., Sci Hortic 125:685–691, 2010). The Sfi and Sfa alleles are differed epigenetically (Fernández i Martí et al., Plant

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_7

87

88

S. N. Goonetilleke et al.

Mol Biol 86:681–689, 2014) by the methylation of a single nucleotide upstream of the coding sequence of the S-RNase, and this variation may determine whether the Sf-RNase allele is expressed. In 2015 Kodad et al. confirmed this experimentally and reported that the plants with SfiSfa heterozygotes were fully self-incompatible probably due to their Sfa-RNase arresting the growth of both the Sfi and Sfa pollen tubes.

1

Genetic Control of GSI in Almond

1.1 The Almond S-locus Sequencing of the highly complex and variable S-locus, which genetically controls the RNasebased GSI system in almond, from several S-haplotypes showed that S-locus F-box (SLF), stylar RNase (S-RNase) and S-haplotype-specific F-box (SFB) genes, several uncharacterized open reading frames and pairs of long-terminal-repeat retrotransposons (LTRs) are the major component of the almond S-locus and the estimated length of this region is about 72,000 bp (Ushijima et al. 2003; Goonetilleke et al. 2020). For this complex locus, the term ‘S-haplotype’ is used to describe the variant forms of the entire Slocus and the term ‘allele’ is used to describe the variant forms of a given S-locus gene (Fig. 1). Sequencing of the almond S-locus, identifying and characterizing the S-locus genes were among major interests over the past years and approximately completed sequences from several Shaplotype have been published (Goonetilleke et al. 2020; Ushijima et al. 2003), and the SLF, SRNase and/or SFB alleles have been fully or partially sequenced (Bošković et al. 2007; Ortega et al. 2006, 2005; Halász et al. 2010) (Table 1). Although high colinearity and conserved gene orientation were detected outside of the S-locus region (Entani et al. 2003; Goonetilleke et al. 2020), within the S-locus region of the different haplotypes, the varied distances between different features, and preserved gene order and orientations were detected (Goonetilleke et al. 2020; Ushijima et al. 2003) (Fig. 2). In almond,

determination of the SI specificity is mainly by the S-RNase and SFB genes (Ushijima et al. 2003, 1998), which are expressed in pistils and pollen tubes, respectively.

1.2 The S-RNase Gene Stylar RNases, which determine the pistil specificity of GSI in almond, belong to the RNase T2 family (McClure 2006) and act as cytotoxins in self-pollen tubes by blocking the pollen tube growth via degrading the cellular RNA in developing pollen tubes. Nevertheless, based on recent findings, incompatible pollen tubes of Rosaceae species exhibit several typical characteristics of programmed cell death (PCD) during the SI reaction (Liu et al. 2007; De Franceschi et al. 2011). However, in Prunus, this interaction seems to be S allele non-specific and independent of RNase activity. In general, the only process involved in the inhibition of pollen tube growth might not be the degradation of RNA, and the SRNase may have other targets than cellular RNA in the pollen tube (Chen et al. 2018).

1.3 The Almond S-RNase Gene Structure The structure of the S-RNase gene of almond consists of five conserved regions (C1, C2, C3, RC4 and C5) and six variable regions (V1, V2, V3, V4, V5 and RHV) (Gu et al. 2012; Ortega et al. 2006; Goonetilleke et al. 2020). It has two introns and three exons (Fig. 3). Between Rosaceae and Solanaceae S-RNases, high sequence similarities are detected in all conserved regions, except for C4, which is known as ‘Rosaceae Conserved Region 4’ (RC4). Of five conserved regions, C1, RC4 and C5 that are positioned near the N- or C-termini of the S-RNase contain a high number of hydrophobic amino acids and seem to have high changes in unfolding enthalpy values (ΔΔG), which may provide the instability required for the stabilization of the enzyme structure (Goonetilleke et al. 2020). Another vital feature for the RNase activity would be

Recent Advances on Self-incompatibility in Almond …

89

Fig. 1 Illustration of the genetic basis of gametophytic self-incompatibility (GSI) and sporophytic selfincompatibility (SSI). In GSI, the pollen carries one of two S-haplotypes of the pollen parent (pollen donor), in this case either S1 or S2. If the S-haplotype of the pollen matches one of the two S-haplotypes of the pistil, the pollen tube growth is retarded after growing through

approximately one-third of the style and results in rejection of pollen. In SSI, the pollen S-haplotype is determined by both S-haplotypes of the pollen parent. If the S-haplotype of the pollen donor matches one or both S-haplotypes of the pistil, the pollen tube growth is retarded and will not germinate

conserved catalytic histidine residues in the C2 and C3 regions (Ortega et al. 2006; Goonetilleke et al. 2020). Therefore, selective interaction between the S-RNase and the pollen S-determinant thought to control by the hypervariable RHV region that is located at the protein surface between C2 and C3 was considered to be crucial (Goonetilleke et al. 2020; Matton et al. 1997). However, in sweet cherry (P. avium), the pollen tube growth of the S6-RNase is not blocked by the S24-RNase, although deduced amino acid sequences in their RHV region are identical. This suggests that other than the RHV region, other factors may also contribute for selective interaction between the pollen and pistil S-proteins (Wünsch and Hormaza 2004). V1, V2 and RHV regions would be highly sensitivity to positive selection in the S-RNase appeared by the presence of large number of non-synonymous amino acid substitutions than synonymous substitutions (Ka/Ks) and would promote the origin of beneficial alleles. Based on the available S-RNase allele sequences, the size of the gene varies from 1.0 kb (S1) to 4.5 kb (S8) and their sequence identities at nucleotide level vary from 19% (between S1 and S8) to 51% (between S7 and S27). Both variation in sequences in exons and length polymorphisms

within introns (especially, intron 2) contribute to differences among the S-RNase alleles.

2

The SFB Gene

About fifteen years earlier to discover the first promising candidate for the pollen determinant of GSI, the pistil determinant has been reported from a member of Plantaginaceae, Antirrhinum hispanicum. Sequencing of a 64-kb region around the S2-RNase gene of Antirrhinum hispanicum indicated the presence of an F-box gene; AhSLF-S2 (A. hispanicum S-locus F-box of S2-haplotype) Lai et al. 2002; Zhou et al. 2003). In Rosaceae, the first report of a candidate for the pollen S-determinant (S-linked F-box genes) from Prunus species indicated that it is in the genomic region that contains the S-RNase gene and several F-box genes. However, only the Fbox gene, S-haplotype-specific F-box (SFB), which is nearest to the S-RNase gene was realized to encode the pollen S-determinant (Ushijima et al. 2003). Further, pollen-part mutations (PPMs) in SFB or a complete deletion of SFB can cause self-compatibility in Prunus. For example, self-compatibility in sweet cherry S4′ and Japanese apricot Sf was due to a PPM in

Nonpareil Mira

Sc

Sd

S7

S8

Vairo

Ferrastar, Desmayo Largueta

Marcona

Marcona

S9

S10

S11

S12

Nonpareil McKinlays

Ramillete Ramillete

Se

S6

CEBAS-CSIC, Murcia, Spain

CEBAS-CSIC, Murcia, Spain

IRTA Mas Bové, Spain CEBAS-CSIC, Murcia, Spain

USA Australia

USA Australia

CEBAS-CSIC, Murcia, Spain

USA

CEBAS-CSIC, Murcia, Spain

Aï

S4

Carmel

France Australia

Lauranne Constantí

S3

Sa

Spain

S5

Australia

Anxaneta

Source

Brown Nonpareil

Sb

S1

Almond cultivars

S2

S-genotype according to American Nomenclature

Sgenotype

Haplotype

MH064166

MH064152 MH064153

MH029536 MH029537

MH064165

MH064164

MH064163 MH064162

MH064154

S-haplotypes

MH316062

MH316073 MH316061

MH316060 MH316072

MH316059

MH316058

MH316057 MH316071

MH316056

SFL allele

AM231661

AM231660

AM231658 AM231659

MH316092

MH316089 MH316082

MH316080 MH316081

AM231657 MH316079

MH316078

AM231656

MH316094 MH316095

AF454000

MH316077

S-RNase allele

Table 1 Availability of S-haplotype sequences and S-locus genes in Prunus dulcis and GenBank accession numbers

MH316106

MH316105 MH316098

MH316104 MH316097

MH316103

MH348867

MH316112 MH348866

MH316096

SFB allele

(continued)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Goonetilleke et al. (2020)

Goonetilleke et al. (2020)

Goonetilleke et al. (2020)

Ortega et al. (2006) Goonetilleke et al. (2020)

Goonetilleke et al. (2020)

Ortega et al. (2006)

Goonetilleke et al. (2020)

Channuntapipat et al. (2003)

Goonetilleke et al. (2020)

References

90 S. N. Goonetilleke et al.

France USA USA

Reams

Dottie Won

Aldrich

S15

S16

S17 USA USA Australia CEBAS-CSIC, Murcia, Spain CEBAS-CSIC, Murcia, Spain CEBAS-CSIC, Murcia, Spain Australia Australia IRTA Mas Bové, Spain CEBAS-CSIC, Murcia, Spain Australia CEBAS-CSIC, Murcia, Spain CEBAS-CSIC, Murcia, Spain

Padre

Milow Tom Strout

CEBAS-I

Rumbeta

Atascada, Avellanera Gruesa, Del Cid, Malagueña Strout’s Papershell

Chellaston

Gabaix

La Mona Johnston’s Prolific

Avellanera Gruesa

Garrigues Keanes

S20

S21

S22

S23

S24

S25

S26

S27

S18

S19

Sh

USA Australia

Jordanolo Biggs Hardshell

Si

S14

CEBAS-CSIC, Murcia, Spain USA

CEBAS-I Kapareil

Source

Sg

Almond cultivars

S13

Haplotype

Table 1 (continued)

MH064161

MH064160

MH064159

MH064158

MH064157

MH064156

MH064155

S-haplotypes

MH316069

MH316068

MH316067

MH316066

MH316065

MH316064

MH316063

SFL allele

AM231675 MH316093

AM231674

AM231673 MH316087

AM231672

MH316086

AM231671 MH316091

AM231670

AM231669

AM231668 MH316085

AM231667

AM231666

AM231665

AM231664

AM231663 MH316084

AM231662 MH316083

S-RNase allele

MH316101

MH316110

MH348868

MH316109

MH316102

MH316108

MH316107

SFB allele

Ortega et al. (2006) Goonetilleke et al. (2020) (continued)

Ortega et al. (2006)

Ortega et al. (2006) Goonetilleke et al. (2020)

Ortega et al. (2006)

Goonetilleke et al. (2020)

Ortega et al. (2006) Goonetilleke et al. (2020)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006) Goonetilleke et al. (2020)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006) Goonetilleke et al. (2020)

Ortega et al. (2006) Goonetilleke et al. (2020)

References

Recent Advances on Self-incompatibility in Almond … 91

CEBAS-CSIC, Murcia, Spain CEBAS-CSIC, Murcia, Spain CEBAS-CSIC, Murcia, Spain Majorca Island, Balearic Islands Majorca Island, Balearic Islands NE Spain, Lérida NE Spain, Lérida NE Spain, Lérida Spain Hungary Hungary Hungary Iran Iran

Colorada

Fina del Alto

Fina del Alto

Totsol

Taiatona

Planeta de les Garrigues

Pané‐Barquets

Planeta de les Garrigues

Alzina

Nikitskyi

Szigetcsepi 55

Pozdnyi

Sher badam

Haji badam

S28

S29

S30

S31

S32

S33

S34

S35

S36

S37

S38

S39

S40

S46

S45

S44

S43

S42

S41

Source

Almond cultivars

Haplotype

Table 1 (continued) S-haplotypes

SFL allele

HQ622704

HQ622703

FJ529214

FJ529213

FJ529212

FJ876155

EF690377

EF690376

EF690375

EF690374

EF690373

AM231678

AM231677

AM231676

S-RNase allele

SFB allele

(continued)

Hafizi et al. (2013)

Hafizi et al. (2013)

Halász et al. (2010)

Halász et al. (2010)

Halász et al. (2010)

Kodad et al. (2010)

Kodad et al. (2008)

Kodad et al. (2008)

Kodad et al. (2008)

Kodad et al. (2008)

Kodad et al. (2008)

Ortega et al. (2006)

Ortega et al. (2006))

Ortega et al. (2006)

References

92 S. N. Goonetilleke et al.

Iran Italy Iran Spain Turkey Turkey Iran Iran Iran Iran Iran Iran Iran

Shahrodi-1

Cupani P

Tejari

Mollar de la Princesa Gulcan-2

Gulcan-2

Yazd-17

Yazd-17

Mashhad-40

Gr-16

Mamaei

Safari

Safari

S50

S51

S52

S53

S54

S55

S56

S57

S58

S59

S60

S61 Australia Australia

Iran

Shekofeh

S49

Mira Carina

Iran

Sefied

S48

Sf

Iran

Mamaei

S47

Sf

Source

Almond cultivars

Haplotype

Table 1 (continued)

MH029546 MH029547

S-haplotypes

MH316074 MH316070

SFL allele

MH316090 MH316088

FN599517

FN599516

FN599515

FN599514

FN599513

FN599512

FN599511

KU295457

LN624640 KU295456

FN599508

KM225272

JX067635

JX067634

JX067632

JX067631

S-RNase allele

MH316111 MH316099

SFB allele

Goonetilleke et al. (2020)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Ortega et al. (2006)

Kafkas et al. (2015)

Gomez et al. (2014) Kafkas et al. (2015)

Ortega et al. (2006)

Currò et al. (2015)

Hafizi et al. (2013)

Hafizi et al. (2013)

Hafizi et al. (2013)

Hafizi et al. (2013)

References

Recent Advances on Self-incompatibility in Almond … 93

94

S. N. Goonetilleke et al.

Fig. 2 Almond S-locus structure adapted from Goonetilleke et al. (2020). Structure of the almond S locus showing the positions of the SLF, S-RNase and SFB genes

and long terminal repeat retrotransposons (LTRs). Black lines indicate regions for which sequences were available and gray lines indicate gaps in the sequence

SFB that encodes a truncated SFB caused by a frameshift (Ushijima et al. 2004) and selfcompatible S3′ haplotype in sweet cherry is due to a complete deletion of SFB (Sonneveld et al. 2005). These findings indicated that the SFB is the pollen S gene in Prunus species and implied the importance of the SFB in the Prunus SI reaction. However, the role of the SFB is not yet fully understood, and it is generally accepted that the SFB seems to be dispensable for compatible reactions in Prunus species. It has been reported in sweet cherry (P. avium), and SFB proteins seem to protect self S-RNases from detoxification by the general inhibitor proteins, such as SFB-like and SLF-like (Matsumoto and Tao 2016b, 2019). Therefore, self S-RNases remain active and arrest the pollen tube growth (Matsumoto and Tao 2016a). The almond SFB gene structure contains an F-box motif, two hypervariable regions (HVa

and HVb) and four variable regions (V1, V2, V3 and V4) (Goonetilleke et al. 2020; Ikeda et al. 2004; Ushijima et al. 2003) (Fig. 4). In the SFB, F-box motif, HVa and HVb regions show large number of non-synonymous amino acid substitutions over synonymous substitutions (Ka/Ks), indicating high pressure of positive selection. Based on available completely sequenced SFB alleles, the length of this gene ranges from 1.1 kb (S9) to 1.5 kb (S1). Their sequence identities vary from 35% (between SFB7 and SFBf) to 86% (between SFB1 and SFB23) (Goonetilleke et al. 2020).

2.1 The SLF Gene This gene in the almond S-locus has no introns and is about 1.2 kb long. Among alleles, this gene shows the highest pairwise sequence

Recent Advances on Self-incompatibility in Almond …

95

Fig. 3 Sequence alignment of 15 almond S-RNases, showing conserved regions (C1, C2, C3, RC4 and C5), variable regions (V1–V5), a hypervariable region (RHV) and conserved active segments (CAS I and CAS II). Positions of absolutely conserved residues, conserved

histidine residues and conserved cysteine residues are indicated by arrowheads, circles and asterisks, respectively. Cysteine residues that are predicted to be linked by disulfide bridges are connected by lines

identities from 70 to 98% (Goonetilleke et al. 2020). The products of this gene suggest to act as ‘general inhibitor’ proteins (Matsumoto and Tao 2016b, 2019) in the Prunus GSI system.

The LTRs detected so far in almond S-locus are: Ty1-copia-like retrotransposons that contain TG/CA boxes in their 5ʹ and 3ʹ ends with the protein-binding sites of TyrGTA, IleAAT, MetCAT and AlaTGC (Goonetilleke et al. 2020).

2.2 LTRs The S-locus of almond contains at least one or two pairs of LTRs and mostly in relatively conserved locations (Goonetilleke et al. 2020). Interestingly, an LTR pair was detected between the SLF and S-RNase genes in the S1 haplotype, and no LTRs were detected in the SLF, S-RNase or SFB genes or between the S-RNase and SFB genes. This feature may be to encourage the recombination suppression in the S-locus region for functional sustainability of the SI system by inheriting the pistil S and pollen S genes as a single unit, as transposable elements could cause natural insertional mutations that lead to interrupt the SI in Prunus (Hauck et al. 2006; Halász et al. 2014).

2.3 The Molecular Basis of Selfrecognition and Rejection in the Almond GSI The molecular mechanisms regulating Prunus GSI including almond seem to differ from the sister tribe Maleae (Malus and Pyrus) and other families with S-RNase-based GSI. All the other families, except in Prunus where pollen Sdeterminant is a single SFB gene, have multiple paralogues of SFB genes that are known as SFBbrothers. The proposed function of the pollen S in those two is also different where Prunus tends to trigger S-RNase cytotoxicity through self-recognition and other families seem to inhibit S-RNase cytotoxicity by non-self-recognition.

96

S. N. Goonetilleke et al.

Fig. 4 Sequence alignment of 15 almond SFB proteins, showing variable regions (V1–V4) and hypervariable regions (HVa and HVb). Positions of absolutely conserved residues are indicated by arrowheads above the alignment

The initiation of the SI response of almond is by uptaking the S-RNase protein by the pollen tube from the stylar tissues in non-specifically to obtain both self and non-self S-RNases into the pollen tube (Luu et al. 2000; Goldraij et al. 2006), revealing that the self-recognition process that triggers the S-RNase cytotoxicity reaction would happen inside the pollen tube. In GSI plants, S-RNase uptake seems to occur in two ways: by endocytosis (Goldraij et al. 2006; Luu et al. 2000) or by membrane transporters (Williams et al. 2015). The general accepted model for Prunus species indicates that the SFB protein that expressed in pollen protects self S-RNases from ubiquitination by the general inhibitor (GI) that binds non-specifically to all S-RNases in the pollen tube. During an incompatible reaction, the SFB protein recognizes the enzyme complex containing the GI and the self S-RNase

and polyubiquinates the GI for degradation. This will protect and release the cytotoxic S-RNase and eventually lead to degradation of self-pollen tube. Unlike in a incompatible reaction, the SFB protein does not recognize the non-self S-RNase and GI enzyme complex in a compatible reaction and the non-self S-RNase would inhibit by the GI (Ushijima et al. 2003; Tao and Iezzoni 2010; Matsumoto and Tao 2016a) (Fig. 5). Based on the observations from protein interaction analysis and in vitro ubiquitination assays, three SLFLs in the Prunus S-locus interact both with SLFinteracting Skp1-like protein 1 (SSK1) and SRNase and direct ubiquitin molecules toward the S-RNases (Chen et al. 2018). Based on this discovery, it is possible that either these three SLFLs or either one of these can be possible candidates for the GI (Matsumoto and Tao 2016b; Chen et al. 2018). The close genetic

Recent Advances on Self-incompatibility in Almond …

97

Fig. 5 Molecular basis of self/non-self-discrimination in Prunus species. a Inhibition of non-self S-RNases with the ubiquitin proteasome system. The SLF/SFBBs form the SCF complex with the SLF/SFBB-interacting Skp1like protein (SSK1), Cul1 and Rbx1. The general inhibitor recognizes non-self S-RNases as the substrate, and the inhibition of non-self S-RNases is believed to be

unaffected by SFB, b inability to inhibit self S-RNases. Low affinity or a self-specific interaction causes the SLF/SFBBs in the complex unable to recognize self SRNases as the substrate. This makes the self S-RNases remain intact, and their cytotoxicity induces the selfincompatibility reaction

relationship of Prunus SLFLs with the SFBB genes of Malus (Aguiar et al. 2015; Akagi et al. 2016) may suggest similar mechanism of S-RNase degradation in both Prunus and Malus (Matsumoto and Tao 2016b). However, more research is required to confirm if the SLFLs are the candidates GI in Prunus.

records (Tufts and Philip 1922). Identification of S alleles prior to crossing would allow breeders to plan crosses with reliably compatible. Previously, S allele detection was mainly done by analyzing stylar ribonuclease (Bošković et al. 1997), and later, allele-specific assays were designed based on intron regions with the availability of DNA sequence information for S alleles, (Channuntapipat et al. 2003; Ortega et al. 2005; Sutherland et al. 2004) and conserved regions C1 and C2 (Goonetilleke et al. 2020). Early assays mainly relied on gel electrophoresis, and fluorescencebased KASP assays that can be used in highthroughput platforms are currently popular in discovering the almond S alleles (Table 2). Although most of the available assays are presence–absence (dominant) type that only one allele can be determined at a time, there are several

2.4 Characterization and Marker Assays for S-RNase Alleles in Almond Earlier cross-compatibility studies mainly relied on controlled hybridization followed by evaluation of fruit set. The cross-incompatibility groups were established based on fruit set evaluation, assessment of pollen tube growth and breeding

98

S. N. Goonetilleke et al.

Table 2 Some widely used gel-based assays to detect almond S alleles S allele

Gene and gene region used for assay design

Sequence (5ʹ–3ʹ)

Expected band size (bp)

References

Gel electrophoresis

Forward

Reverse

S1

S-RNase/C1 and C5 regions

CARTTYGTBCARCARTGGCC

TACCACTTCATGTAACAAGTGAG

1077

Ma and Oliveira (2002)

S2

S-RNase/intron region

GTTTTTAGAAAATTAGACTGTG

TGAATCATACAAGCAAATATAG

203

Channuntapipat et al. (2003)

S3

S-RNase/signal peptide and intron 2

TCTAAGTATGGSKATKTTGAA

AATTTTAYKGAAACRAGATG

1644

Ma and Oliveira (2002)

S5

S-RNase/intron

GGCTCTTTGTTTTTCTAGTTAC

GCAACATAAAAGCAATAAATC

S7

S-RNase/intron

ACC ATATAACATCGTGTTGC

S8

S-RNase/intron

S9

75

Channuntapipat et al. (2003)

GAGGATAATATGGTACATTC

425

Channuntapipat et al. (2003)

CAAATGGTCCTTCAGGTTTTC

CCCAAATCGCAGACTCACTCT

648

Channuntapipat et al. (2003)

S-RNase/intron

GAGGTTAGTTCTCTGGTTAGG

GTCTGTTGGATGGTTTGGG

748

Channuntapipat et al. (2003)

S23

S-RNase/intron

ATT GTCATCTGAAGACCATATAC

TGAAGCATCCAAGCAATATATAC

437

Channuntapipat et al. (2003)

Sf

S-RNase/ intron

GTGCCCTATCTAATTTGTTGAC

GACATTTTTTTAGAAAGAGTG

459

Channuntapipat et al. (2003)

Multiplex PCR assays on gel electrophoresis S-RNase

Sánchez-Pérez et al. (2004)

AS1II: TATTTTCAATTTGTGCAACAATGG AMYC5R: CAAAATACCACTTCATGTAACAAC CEBASf: AGATCTATCTATATCTTAAGTCTG

S1

1100

S2

800

S3

1200

S7

2000

S9

1800

S10

600

S11

700

S12

1600

Sf

400

co-dominant assays also available (Fig. 6). These codominant assays can be used to differentiate either among SI genotypes or between SI and SF genotypes (Goonetilleke et al. 2020).

2.5 Three-Dimensional (3D) Models of the S-RNase Three-dimensional modeling of almond SRNases indicated that S-RNases are composed

of a-helices and b-strands which are connected by loops (Fig. 7) and can be classified in family d.124.1.1 based on the SCOPe 2.06 (structural classification of proteins) database (Fox et al. 2014). There have been five 3D models available for almond from the proteins: S5-, S7-, S8-, S23and Sf-RNases (Fernández i Marti et al. 2012; Goonetilleke et al. 2020), and each of the protein structures generated has seven a-helices and between five and seven b-sheets (five for S7-, S8and S23-RNases, six for Sf-RNase and seven for

Recent Advances on Self-incompatibility in Almond …

99

Fig. 6 Results obtained with 15 primer sets designed to distinguish among S-RNase alleles of almond. For each primer set, the left panel shows results obtained for a set of almond cultivars and the right panel shows results obtained on F1 progeny from a relevant cross: Johnston (S23S25) × Lauranne (S3Sf) for WriPdSf-1 and WriPdS3, Maxima (S3S8) × Mira (S7Sf) for WriPdSf-2, Nonpareil (S7S8) × Lauranne (S3Sf) for WriPdSf-3, WriPdSf-4, WriPdS7-2 and WriPdS8, Maxima (S3S8) × Vairo (S9Sf)

for WriPdSf-5, Carmel (S5S8) × 12–350 (S1Sf) for WriPdS1, Carmel (S5S8) × Mandaline (S1Sf) for WriPdS5, Nonpareil (S7S8) × Mira (S7Sf) for WriPdS71, Nonpareil (S7S8) × Lauranne (S3Sf) for WriPdS7-2, Nonpareil (S7S8) × Vairo (S9Sf) for WriPdS9, Johnston (S23S25) × 12–350 (S1Sf) for WriPdS23 and WriPdS25-1 and Johnston (S23S25) × Vairo (S9Sf) for WriPdS25-2. The horizontal and vertical axes represent intensities of FAM and HEX fluorescences, respectively

S5-RNase) (Figs. 7a, 7b). Interestingly, in all these S-RNases, both the a-helices and b-sheets are positioned in approximately the same locations. Each of these a-helices varied from six to 15 residues, with a5 which seems the longest in all cases. Of the four b-sheets (b1, b2, b5 and b6) within the S5-, S7-, S8- and S23-RNases, three (b1, b2 and b6) form an antiparallel b-sheet which tightly connect with a-helices of the protein molecule internally. Each of these S-RNases has nearly similar molecular dimensions of 50 Å × 40 Å × 30 Å.

Of the important properties estimated for these S-RNases, solvent-accessible surface areas vary from 78% (for the S5-, S7-, S8- and S23-RNases) to 81% for the Sf-RNase, and the positively charged residues in exposed surface of the S-RNases vary from 19% (Sf-RNases) to 25% (S7- and S8-RNases). The RHV, V1, V2 and V4 regions that are electropositive have high Ka/Ks ratios with a range of 1.2–1.8. Further, all these S-RNases have higher average exposed surfaces (25%) than neutral and negatively charged regions (10%) (Fig. 7c).

100

Fig. 7 Molecular properties of almond S-RNase structural models. a Stereo representation of the superposition of 3D structures of S-RNases, whereby the template crystal structure (the MC1 RNase from seeds of bitter gourd) is in pink and the 3D models of almond Sf-RNase, S5-RNase, S7-RNase, S8-RNase and S23-RNases are blue, green, gray, yellow and tint blue, respectively. Almond structural models were superposed on the template structure with RMSD values in the range of 0.15– 0.19 Å for 179 Ca atoms. b Dispositions of secondary

S. N. Goonetilleke et al.

structure elements in the template with the Sf-, S5-, S7-, S8- and S23-RNases are indicated in pink, blue, green, gray, yellow and tint blue, respectively. c Molecular surface morphologies of the template structure and almond Sf-, S5-, S7- S8- and S23-RNase models colored by electrostatic potentials display electroneutral (white), electropositive (blue, contoured at + 5 kilotesla einstein−1) and electronegative (red, contoured at − 5 kilotesla einstein−1) regions and are presented in the same orientations as the cartoons in panel

Recent Advances on Self-incompatibility in Almond …

3

Proteomic and Transcriptomic Analyses of Pistils and Anthers from Self-incompatible and Self-compatible Almonds

Recently, several attempts were made to identify differentially expressed genes in the pistil and anthers in self-incompatible and self-compatible almond cultivars using proteomic data that were obtained from isobaric tags for relative and absolute quantification (iTRAQ) and 2D-nanoliquid chromatography-electrospray ionization tandem mass spectrometry (Gómez et al. 2015) and transcriptomic data from RNA-Seq (Gómez et al. 2019). Based on the data obtained from iTRAQ, in self-compatible and self-incompatible almond, at least 28 proteins were found to be differentially expressed. Expression of some of the genes in mature anthers and pistils seemed to be opposite, some genes that were up-regulated in the anthers were downregulated in pistil, and at least 20 proteins were differentially expressed in anthers and six of these were downregulated in the almond selection that carries Sf gene and the rest of the genes were upregulated. Most of these differentially expressed proteins were metabolic proteins (mostly related to lipid metabolism and cell wall degradation), stress-related proteins and pathogenesis-related proteins (Gómez et al. 2015). In line with these findings, Chalivendra et al. (2012) indicated that lipid plays an important role in pollen maturation in Solanum pennellii. Cell wall degradation enzymes such as glucan-endo-1,3 glucosidase that hydrolyzes the stigma wall for pollen tube entry seem to downregulate in self-compatible pistils and pollen. Pathogenesis-related proteins such as thaumatin-like proteins were upregulated in compatible pistils and known to link with the recognition of pollen signals (Sassa and Hirano 1998). Recently, transcriptome analysis from RNA sequencing of un-pollinated, incompatible and compatible pollinations of self-compatible and self-incompatible almond pistils indicated that about 1357 unigenes were differentially expressed, and based on gene ontology annotations, those genes regulate the metabolic

101

processes and molecular functions in almond pistils. Therefore, some of these identified genes could be potential candidates for the different factors involved in the gametophytic selfincompatibility system in almond (Table 3).

4

Practical Aspects of GSI in Almond

Almond fruit development has three main stages: stage one is characterized by the rapid growth of the hull, shell and integuments, stage two is the process of shell hardening and ovary (kernel) expansion and stage three is the period between post-kernel fill until harvest. The ovary expansion happens due to the releasing of plant hormones, such as auxins by the developing seeds. However, the fruit growth and expansion mainly occur at the regions where fertilized seeds are located (Devoghalaere et al. 2012; Orcheski and Brown 2012). If only a small fragment of the ovules is fertilized, small size fruits can be resulted (Westwood 1988; Goldway et al. 2008). Therefore, fertilization in a fruit is led by pollination, mainly because incompatible pollen tubes are retarred, and only compatible pollen tubes are allowed to grow through the style. Genetic determination is not the only factor that inhibits the pollen tube growth, but several external factors, such as the environment and number of pollination events (Cunningham et al. 2015), also influence the pollen tube growth. Previous studies confirm that the amount of fruit set is related to the amount of cross-pollination events, and it seems to vary depending on the pollination compatibility. For instance, self-pollination before cross-pollination (S/C) produces more seeds in incompatible almond varieties than cross-pollination before self-pollination (C/S), and both produce more seeds than a single crosspollination event. In almond, about 22–31% of flowers can be female sterile. In fact, due to low level of insect pollinator activity, open pollination in almond generally relies very low number of pollen reaching to the style, and under natural conditions, insect pollinators are unlikely to

– –

HEX

– –

HEX

HEX

HEX

HEX

– FAM

– HEX

– – – –

HEX

–

–

FAM HEX

– HEX

– – – –

GAAGGTGACCAAGTTCATGCTTGGGTTTGAATARTTACTTGGCCATAT GAAGGTCGGAGTCAACGGATTGGGTTTGAATARTTACTTGGCCATAG CAATTTGTGCAACAATGGCCACC

GAAGGTGACCAAGTTCATGCTTGGGTTTGAAWAATTACTTGGCCATAT GAAGGTCGGAGTCAACGGATTGGGTTTGAAWAATTACTTGGCCATAG CAATTTGTGCAACAATGGCCACC

GAAGGTGACCAAGTTCATGCTTGGGTTKGAATAATTACTTGGCCATAT GAAGGTCGGAGTCAACGGATTGGGTTKGAATAATTACTTGGCCATAG TTTGTGCAACAATGGCCACC

GAAGGTGACCAAGTTCATGCTGAATGGAACAAACATGGTACATGTTCG CCACATTTCGTGGGATCGCTCGAAG

GAAGGTGACCAAGTTCATGCTTGGTACGATTGAAGCGTTTTTAAGGATC GAAGGTCGGAGTCAACGGATTTGGTACGATTGAAGCGTTTTTAAGGATT GGGAAGGCGAATGGAACAAACATGG

GAAGGTGACCAAGTTCATGCTTGGATGTTGCAGGCTCCTAAAT ACGTTGGGCCAAGATATCTTCA

GAAGGTGACCAAGTTCATGCTCTTGGCCATAKGCCATGGATT GAAGGTCGGAGTCAACGGATTCTTGGCCATAKGCCATGGATG CAATTTGTGCAACAATGGCCACC

GAAGGTGACCAAGTTCATGCTAAATTTTAAATTTTGTAATATGAAAAAGTGTG CATTGGTTAATATAAACATTAAGAATTGAA

GAAGGTGACCAAGTTCATGCTGTTTTGGGAAGGCGAATGGAACAAG CGTCTTTAAGGATATTTGTAATATTGTACGACC

GAAGGTGACCAAGTTCATGCTTTGTGCGAGTACCACATGTCTTGC GAATGGAACAAACATGGTACATGTTCCG

GAAGGTGACCAAGTTCATGCTCCACTTTCCTTGCATCAAATTTCGG

WriPdSf-3

WriPdSf-4

WriPdSf-5

WriPdS1

WriPdS3

WriPdS5

WriPdS7-1

WriPdS7-2

WriPdS8

WriPdS9

WriPdS23

–

–

–

–

HEX

FAM

HEX

HEX

HEX

–

–

–

FAM

FAM

–

–

–

–

FAM

–

HEX

–

–

–

–

–

HEX

–

HEX

–

–

S8

–

–

–

S7

GAAGGTGACCAAGTTCATGCTTGGGTTTGAATAATTACTTGGCCATAT GAAGGTCGGAGTCAACGGATTGGGTTTGAATAATTACTTGGCCATAG CAATTTGTGCAACAATGGCCACC

WriPdSf-2

–

–

S5

S3

–

GAAGGTCGGAGTCAACGGATTAATGCAACTAGTCATGCATTTATTTCATG ACCAGTGTTAAGTTTAAAAGTTAGTGGAAT

WriPdSf-1

S-RNase alleles S1

Primer sequences (5ʹ–3ʹ)a

Primer set

–

FAM

–

–

HEX

–

–

–

HEX

HEX

HEX

HEX

–

S9

FAM

–

–

–

HEX

–

–

–

HEX

HEX

HEX

HEX

–

S23

–

–

–

–

Sf

–

–

–

–

HEX

–

–

–

FAM

FAM

FAM

FAM

HEX

(continued)

HEX

–

HEX

–

HEX

HEX

HEX

HEX

–

S25

Table 3 Fluorescence assays to distinguish among almond S-RNase alleles showing the fluorescence (HEX or FAM) expected for each allele (Goonetilleke et al. 2020)

102 S. N. Goonetilleke et al.

Allele-specific primers include tails (underlined) that are complementary to FRET cassettes in the KASP™ Master Mix. Nucleotides between the tails and allele-specific sequences are shown in bold text

a

– FAM – – – – – – GAAGGTGACCAAGTTCATGCTATGCTTAACCAAATGCAATACTTCGAGCGATCT CTTTAATGGGTGATACTATGTCCGAGTACTTCCATA WriPdS25-2

–

– – – HEX GAAGGTGACCAAGTTCATGCTTACGATTGAAGCGTTTTTAAGGATYTCTGTAAC GAAGGTCGGAGTCAACGGATTTACGATTGAAGCGTTTTTAAGGATYTCTGTAAT CTTAACCAAATGCAATACTTCGAGCGATC WriPdS25-1

S5 S3 S1 GCCAAGTAATTATTCAAACCCAACGAA

Primer sequences (5ʹ–3ʹ)a Primer set

Table 3 (continued)

S-RNase alleles

S7

–

S8

–

S9

S23

HEX

S25

FAM

–

Sf

Recent Advances on Self-incompatibility in Almond …

103

revisit a flower and repeated pollination events seem rare. Additionally, since insect pollinators in almond orchards typically visit multiple flowers of the same tree in almond cultivation, the pollen mixture that reaches the stigma of almond SI species mainly consists of self-pollen, and under natural conditions in almond orchards, pioneer pollen interactions may not happen frequently. Furthermore, when self-pollen fertilizes the ovule, ovule abortion is higher than in the case of cross-pollination because seeds result from self-pollination have lower amount of sink compared to those from an outcrossing event, and higher level of seed and/or fruit abortion could occur due to high competition for available limited resources.

5

Self-incompatibility in Almond Production and Breeding

The SI mechanism guarantees that fruit set in most commercial cultivars of almond highly depends on successful cross-pollination and fertilization events and crucial for both commercial almond production and breeding. To obtain successful fruit set, commercial almond orchards need to contain at least two cross-compatible cultivars and with overlapping flowering periods to permit effective cross-fertilization, that would enable seed set and fruit development. The identification and knowledge of the exact Sgenotype of different almond varieties are vital for many practical applications, such as orchard design and achieving the success of hybridization crosses in breeding programs. There are three forms of self-incompatibility: (1) when identical S-haplotypes are carried by two different parents, they are fully incompatible and cannot set any fruits, (2) when parents share only one of their S-haplotypes, they are semicompatible and can expect about 50% fruit set and (3) when parents differ in both S-haplotypes, they are fully compatible and can expect about 100% fruit set. In hand-pollination experiments, semi-compatibility does not significantly affect fruit set rate. However, it can cause significant reduction in fruit yield when environmental

104

S. N. Goonetilleke et al.

conditions are not quite suitable for pollination. It is apparent that as self-compatible varieties that carry Sf alleles have been recently released for commercial plantations, a complete lack of crosspollination is not entirely challenging in some almond orchards. Self-incompatibility is beneficial, and it can be handy when doing crosses, because emasculation of female parents does not require before pollination by the desired male parent and it also can suppress the inbreeding and maintain the genetic diversity in the almond cultivars.

6

Implications of Self-fertility for Almond Production

Transmission of self-fertility into almond cultivars allows growers to have single-block orchards where orchard management would be much more efficient and less resilient on bee pollination. Therefore, an important aim of almond breeding programs around the world is to breed for self-compatibility (Socias i Company 1990; Socias i Company et al. 2010). The very first attempt to transmit self-compatibility to the almond offspring goes back to when selfcompatibility was not recognized in almond and it was transmitted from another closely related species (Pandey 1968); subsequently, hybridization was found to be easier among the different species of the subgenus Amygdalus and with peach. The University of California obtained several self-compatible selections from peach × almond hybridization after successive backcrosses to almond, and the cultivar ‘Sweetheart’ has been released and is recommended as a pollinizer for Nonpareil. Since self-compatibility of this cultivar is obtained from peach, compatibility level was not complete (Gradziel and Martínez-Gómez 2013; Gradziel et al. 2013). Subsequently, some private breeders from California also used peach as a source of selfcompatibility and released some cultivars, i.e., ‘LeGrand’; however, this cultivar is considered to not have enough horticultural level of selfcompatibility (Weinbaum 1985). The other breeding program that attempted to transmit self-

incompatibility using a peach × almond hybrid was in India (Uppal et al. 1984). Other than peach, multiple sources of selfincompatibility alleles from P. mira, P. webbi, P. fenzliana and P. argentea were also used to introduce self-compatibility to almond through introgression (Gradziel 2009; Gradziel et al. 2013). Other mechanisms that used to obtain self-incompatibility was induced mutation through bud irradiation. Monastra et al. (1988) introduced the self-compatible cultivar, ‘Supernova’ through this technique as a mutation of ‘Fascionello’. However, using this method for obtaining self-compatibility remains a question (Marchese et al. 2008). Currently, many almond breeding programs include self-compatible cultivars and selections as a source of self-compatibility. The most active programs are in Spain, Italy, France, USA, Israel and Australia. The first self-compatible cultivars released from a breeding program were reported from Zaragoza (Spain): ‘Guara’, ‘Aylѐs’ and ‘Moncayo’ (Felipe and Socias 1987). In the USA, Californian almond cultivar ‘Independence’, a seedling of ‘All-in-one’, which was developed by Zaiger Genetics seems successful and preferred by growers (Eddy 2011). Recently, the Australian almond breeding program has released four almond cultivars (‘Carina’, ‘Capella’, ‘Mira’ and ‘Vela’) that are self-fertile (Wirthensohn 2020) (Table 4).

7

Conclusion and Future Perspectives

In the past years, many studies have provided insights into the self-incompatibility mechanism of Prunus. In recent years, major research foci have been on the identification and molecular characterization of the pollen S-determinant and of the putative general inhibitors. However, it is still unclear how many general inhibitor genes are present and which of the identified GI is actually involved in the SI reaction. Although recent research has revealed more information about the variations in the structure of the S-locus in different S-haplotypes, it is unknown how

Recent Advances on Self-incompatibility in Almond …

105

Table 4 Differentially expressed genes in the pistils of self-compatible and self-incompatible almond Gene description Pollen specific genes Pollen-specific leucine-rich repeat extensin-like protein 1 Pollen-specific leucine-rich repeat extensin-like protein 3 Pollen-specific leucine-rich repeat extensin-like protein 4 Pollen-specific protein SF3-like Pollen coat-like (PCP) Calcium ion binding Calcium-dependent phospholipid binding protein (CPBP) Ca2 + -dependent membrane-binding: annexin Transcription factor Zinc zipper CUL4 RING ubiquitin ligase Ubiquitin-protein ligase E3 Extracellular space Anther-specific protein LAT52 precursor Pollen allergen Che a 1 precursor Signal transduction Auxin-repressed protein G-protein complex WD-40 repeat protein-like (WD-40) Pollen–pistil interaction Self-incompatibility S1 family protein (S1)

Table 5 Self-incompatible almond varieties recently released from breeding programs Cultivar

Pedigree

USA Shasta Australia Capella

Nonpareil × Lauranne

Carina

Nonpareil × Lauranne

Mira

Nonpareil × Lauranne

Vela

Chellaston × 1bT47

S-locus-linked factors affect the S-locus function. The characterization of the S-locus-linked factors may provide new insights into the complex regulation of GSI in Prunus. Such knowledge can be useful, for example, in the development of selfcompatible varieties through conventional breeding or by using gene editing techniques, such as CRISPR-Cas9 (Table 5).

Self-incompatibility affects fertilization, seed set and fruit quality in almond orchards and has important implications for almond production and breeding. It is essential to explore further its underlying mechanisms and new insights into almond self-incompatibility can result in new and targeted applications that may enhance the efficacy of almond breeding and production.

106

References Aguiar B, Vieira J, Cunha AE, Fonseca NA, Iezzoni A, van Nocker S, Vieira CP (2015) Convergent evolution at the gametophytic self-incompatibility system in Malus and Prunus. PLoS ONE 10(5):e0126138. https://doi.org/10.1371/journal.pone.0126138 Akagi T, Henry IM, Morimoto T, Tao R (2016) Insights into the Prunus-specific S-RNase-Based selfincompatibility system from a genome-wide analysis of the evolutionary radiation of S locus-related F-box genes. Plant Cell Physiol 57(6):1281–1294. https:// doi.org/10.1093/pcp/pcw077 Bošković R, Tobutt K, Ortega E, Sutherland B, Godini A (2007) Self-(in)compatibility of the almonds P. dulcis and P. webbii: detection and cloning of ‘wild-type Sf ’ and new self-compatibility alleles encoding inactive SRNases. Mol Gen Genom 278(6):665–676. https://doi. org/10.1007/s00438-007-0283-4 Bošković R, Tobutt KR, Batlle I, Duval H (1997) Correlation of ribonuclease zymograms and incompatibility genotypes in almond. Euphytica 97(2):167– 176. https://doi.org/10.1023/a:1003030913308 Chalivendra SC, Lopez-Casado G, Kumar A, Kassenbrock AR, Royer S, Tovar-Mèndez A, Covey PA, Dempsey LA, Randle AM, Stack SM, Rose JKC, McClure B, Bedinger PA (2012) Developmental onset of reproductive barriers and associated proteome changes in stigma/styles of Solanum pennellii. J Exp Botany 64 (1):265–279. https://doi.org/10.1093/jxb/ers324 Channuntapipat C, Wirthensohn M, Ramesh S, Batlle I, Arus P, Sedgley M, Collins G (2003) Identification of incompatibility genotypes in almond (Prunus dulcis Mill.) using specific primers based on the introns of the S-alleles. Plant Breed 122:164–168 Charlesworth D, Vekemans X, Castric V, Glemin S (2005) Plant self-incompatibility systems: a molecular evolutionary perspective. New Phytol 168(1):61–69. https://doi.org/10.1111/j.1469-8137.2005.01443.x Chen Q, Meng D, Gu Z, Li W, Yuan H, Duan X, Yang Q, Li Y, Li T (2018) SLFL genes participate in the ubiquitination and degradation reaction of S-RNase in self-compatible peach. Front Plant Sci 9(227). https:// doi.org/10.3389/fpls.2018.00227 Cunningham S, Fournier A, Neave M, Le Feuvre D (2015) Improving spatial arrangement of honeybee colonies to avoid pollination shortfall and depressed fruit set. J Appl Ecol 53. https://doi.org/10.1111/13652664.12573 Currò S, La Malfa S, Distefano G, Long G, Sottile F, Gentile A (2015) Analysis of S-allele genetic diversity in Sicilian almond germplasm comparing different molecular methods. Plant Breed 134(6):713–718. https://doi.org/10.1111/pbr.12318 De Franceschi P, Pierantoni L, Dondini L, Grandi M, Sanzol J, Sansavini S (2011) Cloning and mapping

S. N. Goonetilleke et al. multiple S-locus F-box genes in European pear (Pyrus communis L.). Tree Genet Genomes 7(2):231–240. https://doi.org/10.1007/s11295-010-0327-5 De Nettancourt D (1997) Incompatibility in angiosperms. Sex Plant Reprod 10(4):185–199 Devoghalaere F, Doucen T, Guitton B, Keeling J, Payne W, Ling T (2012) A genomics approach to understanding the role of auxin in apple (Malus x domestica) fruit size control. BMC Plant Biol 12(1):7 Đorđević M, Cerović R, Radičević S, Nikolić D (2014) Incompatible pollen tubes in the plum style and their impact on fertilization success. Genetika 46(2):411– 418 Eddy D (2011) Bee-free almond tree. Am Fruit Grower 131(10) Entani T, Iwano M, Shiba H, Che F-S, Isogai A, Takayama S (2003) Comparative analysis of the self-incompatibility (S-) locus region of Prunus mume: identification of a pollen-expressed F-box gene with allelic diversity. Genes Cells 8(3):203–213. https:// doi.org/10.1046/j.1365-2443.2003.00626.x Felipe A, Socias R (1987) Aylés, Guarä, and Moncayö almonds. HortSci 22(5):961–962 Fernández i Martí À, Hanada T, Alonso JM, Yamane H, Tao R, Socias i Company R (2010) The almond Sf haplotype shows a double expression despite its comprehensive genetic identity. Sci Hortic 125(4):685–691. https://doi.org/10.1016/j.scienta.2010.05.024 Fernández i Marti Á, Wirthensohn M, Alonso J, Socias i Company R, Hrmova M (2012) Molecular modelling of S-RNases involved in almond self-incompatibility. Frontiers Plant Sci 3. https://doi.org/10.3389/fpls. 2012.00139 Fernández i Martí A, Gradziel T, Socias i Company R (2014) Methylation of the Sf locus in almond is associated with S-RNase loss of function. Plant Mol Biol 86(6):681–689. https://doi.org/10.1007/s11103014-0258-x Fox NK, Brenner SE, Chandonia JM (2014) SCOPe: structural classification of proteins—extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res 42:D304–D309. https://doi.org/10.1093/nar/gkt1240 Goldraij A, Kondo K, Lee CB, Hancock CN, Sivaguru M, Vazquez-Santana S, Kim S, Phillips TE, Cruz-Garcia F, McClure B (2006) Compartmentalization of SRNase and HT-B degradation in self-incompatible Nicotiana. Nature 439(7078):805–810. https://doi.org/ 10.1038/nature04491 Goldway M, Zisovich A, Raz A, Stern R (2008) Understanding the gametophytic self-incompatibility system and its impact on European pear (Pyrus communis L.) cultivation. Acta Hortic Gómez EM, Buti M, Sargent DJ, Dicenta F, Ortega E (2019) Transcriptomic analysis of pollen-pistil interactions in almond (Prunus dulcis) identifies candidate genes for components of gametophytic

Recent Advances on Self-incompatibility in Almond …

107

self-incompatibility. Tree Genet Genomes 15(4):53. https://doi.org/10.1007/s11295-019-1360-7 Gómez EM, Dicenta F, Ortega E (2014) Characterization of the region between S-RNase and SFB genes in almond selections with the Sf-haplotype but different incompatibility phenotype. ActaHort pp 91–94. https://doi.org/10.17660/ActaHortic.2014.1028.13 Gómez EM, Dicenta F, Martínez-García PJ, Ortega E (2015) iTRAQ-based quantitative proteomic analysis of pistils and anthers from self-incompatible and self-compatible almonds with the Sf haplotype. Mol Breed 35(5):1–14. https://doi.org/10.1007/s11032015-0314-5 Goonetilleke SN, Croxford AE, March TJ, Wirthensohn MG, Hrmova M, Mather DE (2020) Variation among S-locus haplotypes and among stylar RNases in almond. Sci Reports 10(1):583. https://doi.org/10. 1038/s41598-020-57498-6 Gradziel TM (2009) Almond (Prunus dulcis) breeding. In: Mohan Jain S, Priyadarshan PM (eds) Breeding plantation tree crops: temperate species. Springer New York, USA, pp 1–31. https://doi.org/10.1007/978-0387-71203-1_1 Gradziel TM, Martínez-Gómez P (2013) Almond breeding. In: Janick J (ed) Plant breeding reviews. Wiley, USA, vol 37, pp 207–258. https://doi.org/10.1002/ 9781118497869.ch4 Gradziel T, Lampinen B, Niederholzer F, Viveros M (2013) ‘Sweetheart’ Almond: a fully cross-compatible pollenizer for the early ‘Nonpareil’ bloom that exhibits very high ‘Marcona’-type kernel quality. HortSci 48(10):1320. https://doi.org/10.21273/hortsci.48.10. 1320 Grasselly C, Olivier G (1976) Mise évidence de quelques types autocompatibles parmi les cultivars d’amandier (P. amygdalus Batsch) de la population des Pouilles. Ann Amélio Plantes 26:107–113 Gu C, Wu J, Zhang S-J, Yang Y-N, Wu H-Q, Tao S-T, Zhang S-L (2012) Characterization of the S-RNase genomic DNA allele sequence in Prunus speciosa and P. pseudocerasus. Sci Hortic 144:93–101. https://doi. org/10.1016/j.scienta.2012.06.041 Hafizi A, Shiran B, Maleki B, Imani A, Banović B (2013) Identification of new S-RNase self-incompatibility alleles and characterization of natural mutations in Iranian almond cultivars.Trees 27(3):497–510. https:// doi.org/10.1007/s00468-012-0803-7 Halász J, Fodor Á, Pedryc A, Hegedüs A (2010) Sgenotyping of eastern European almond cultivars: identification and characterization of new (S36–S39) self-incompatibility ribonuclease alleles. Plant Breed 129(2):227–232. https://doi.org/10.1111/j.1439-0523. 2009.01686.x Halász J, Kodad O, Hegedűs A (2014) Identification of a recently active Prunus-specific non-autonomous Mutator element with considerable genome shaping

force. Plant J 79(2):220–231. https://doi.org/10.1111/ tpj.12551 Hauck NR, Ikeda K, Tao R, Iezzoni AF (2006) The mutated S1-haplotype in sour cherry has an altered Shaplotype-specific F-box protein gene. J Heredity 97 (5):514–520. https://doi.org/10.1093/jhered/esl029 Hiscock SJ, Tabah DA (2003) The different mechanisms of sporophytic self–incompatibility. Philos Trans R Soc Lond B Biol Sci 358(1434):1037–1045 Ikeda K, Igic B, Ushijima K, Yamane H, Hauck N, Nakano R, Sassa H, Iezzoni A, Kohn J, Tao R (2004) Primary structural features of the S haplotype-specific F-box protein, SFB, in Prunus. Sex Plant Reprod 16(5):235– 243. https://doi.org/10.1007/s00497-003-0200-x Kafkas S, Khodaeiaminjan M, Güney M, Kafkas E (2015) Identification of sex-linked SNP markers using RAD sequencing suggests ZW/ZZ sex determination in Pistacia vera L. BMC Genomics 16(1):98. https://doi. org/10.1186/s12864-015-1326-6 Kodad O, Sánchez A, Saibo N, Oliveira M (2010) Molecular characterization of five new S alleles associated with self-incompatibility in local Spanish almond cultivars. XIV GREMPA Meeting on Pistachios and Almonds. CIHEAM, Zaragoza Kodad O, Socias i Company R (2008) Variability of oil content and of major fatty acid composition in almond (Prunus amygdalus Batsch) and its relationship with kernel quality. J Agric Food Chem 56:4096–4101 Kodad O, Socias i Company R (2009) Review and update of self-incompatibility alleles in almond. Int Soc Hortic Sci (ISHS) 421–424. https://doi.org/10.17660/ ActaHortic.2009.814.70 Kodad O, Socias i Company R, Sánchez A, Oliveira MM (2009) The expression of self-compatibility in almond may not only be due to the presence of the Sf allele. J Am Soc Hortic Sci 134(2):221–227 Kodad O, Socias i Company R, Alonso JM (2015) Unilateral recognition of the Sf allele in almond. Sci Hortic 185:29–33. https://doi.org/10.1016/j.scienta. 2015.01.016 Lai Z, Ma W, Han B, Liang L, Zhang Y, Hong G, Xue Y (2002) An F-box gene linked to the selfincompatibility (S) locus of Antirrhinum is expressed specifically in pollen and tapetum. Plant Mol Biol 50 (1):29–41. https://doi.org/10.1023/A:1016050018779 Liu Z-q, Xu G-h, Zhang S-l (2007) Pyrus pyrifolia stylar S-RNase induces alterations in the actin cytoskeleton in self-pollen and tubes in vitro. Protoplasma 232 (1):61. https://doi.org/10.1007/s00709-007-0269-4 Luu D-T, Qin X, Morse D, Cappadocia M (2000) S-RNase uptake by compatible pollen tubes in gametophytic self-incompatibility. Nature 407(6804):649 Ma RC, Oliveira M (2002) Evolutionary analysis of S-RNase genes from Rosaceae species. Mol Gen Genomics 267(1):71–78. https://doi.org/10.1007/ s00438-002-0637-x

108 Marchese A, Bošković RI, Martínez-García PJ, Tobutt KR (2008) The origin of the self-compatible almond ‘Supernova.’ Plant Breed 127(1):105–107. https://doi.org/10.1111/j.1439-0523.2008.01421.x Matsumoto D, Tao R (2016a) Distinct self-recognition in the Prunus S-RNase-based gametophytic selfincompatibility system. Hortic J MI-IR06 Matsumoto D, Tao R (2016b) Recognition of a wide-range of S-RNases by S locus F-box like 2, a general-inhibitor candidate in the Prunus-specific S-RNase-based selfincompatibility system. Plant Mol Biol 91(4):459–469. https://doi.org/10.1007/s11103-016-0479-2 Matsumoto D, Tao R (2019) Recognition of S-RNases by an S locus F-box like protein and an S haplotype-specific F-box like protein in the Prunusspecific self-incompatibility system. Plant Mol Biol 100(4):367–378. https://doi.org/10.1007/s11103-01900860-8 Matton DP, Maes O, Laublin G, Xike Q, Bertrand C, Morse D, Cappadocia M (1997) Hypervariable domains of self-incompatibility RNases mediate allele-specific pollen recognition. Plant Cell 9 (10):1757–1766. https://doi.org/10.2307/3870522 McClure B (2006) New views of S-RNase-based selfincompatibility. Curr Opin Plant Biol 9(6):639–646. https://doi.org/10.1016/j.pbi.2006.09.004 Monastra F, Della Strada G, Fideghelli C, Quarta R (1988) Supernova: une nouvelle varieté d ‘amandier obtenue par mutagenese. In: 7 Colloque du GREMPA. Rapport EUR, pp 3–7 Orcheski B, Brown S (2012) A grower’s guide to self and cross-incompatibility in apple. Good Fruit Grower 20:25–28 Ortega E, Sutherland BG, Dicenta F, Boskovic R, Tobutt KR (2005) Determination of incompatibility genotypes in almond using first and second intron consensus primers: detection of new S alleles and correction of reported S genotypes. Plant Breed 124 (2):188–196. https://doi.org/10.1111/j.1439-0523. 2004.01058.x Ortega E, Bošković R, Sargent D, Tobutt K (2006) Analysis of S-RNase alleles of almond (Prunus dulcis): characterization of new sequences, resolution of synonyms and evidence of intragenic recombination. Mol Genet Genomics 276(5):413–426. https:// doi.org/10.1007/s00438-006-0146-4 Pandey KK (1968) Compatibility relationships in flowering plants: role of the S-Gene complex. Am Nat 102 (927):475–489. https://doi.org/10.1086/282560 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2004) Identification of S-alleles in almond using multiplex PCR. Euphytica 138(3):263–269. https://doi.org/10. 1023/B:EUPH.0000047097.96271.bf Sassa H, Hirano H (1998) Style-specific and developmentally regulated accumulation of a glycosylated thaumatin/PR5-like protein in Japanese pear (Pyrus serotina Rehd.). Planta 205(4):514–521. https://doi. org/10.1007/s004250050350

S. N. Goonetilleke et al. Socias i Company R (1990) Breeding self-compatible almonds. In: Janick J (ed) Plant breeding reviews, vol 8. Wiley, New York, pp 313–338. https://doi.org/10. 1002/9781118061053.ch9 Socias i Company R, Fernández i Martí À, Kodad O, Alonso JM (2010) Self-compatibility evaluation in almond: strategies, achievements, and failures. HortScience 45(8):1155–1159 Sonneveld T, Tobutt KR, Vaughan SP, Robbins TP (2005) Loss of pollen-S function in two selfcompatible selections of Prunus avium is associated with deletion/mutation of an S haplotype-specific Fbox gene. Plant Cell 17(1):37–51. https://doi.org/10. 1105/tpc.104.026963 Sutherland B, Robbins T, Tobutt K, Weber W (2004) Primers amplifying a range of Prunus S-alleles. Plant Breed 123(6):582–584. https://doi.org/10.1111/j. 1439-0523.2004.01016.x Tao R, Iezzoni AF (2010) The S-RNase-based gametophytic self-incompatibility system in Prunus exhibits distinct genetic and molecular features. Sci Hortic 124 (4):423–433. https://doi.org/10.1016/j.scienta.2010.01. 025 Tufts W, Philip G (1922) Almond pollination. Cali Agri Bul Uppal D, Dhillon D, Dhaliwal G, Chanana Y (1984) Selection of self-fruitful hybrids in intervarietal crosses of almonds. Indian J Hortic 41(1, 2):80–82 Ushijima K, Sassa H, Tao R, Yamane H, Dandekar AM, Gradziel TM, Hirano H (1998) Cloning and characterization of cDNAs encoding S-RNases from almond (Prunus dulcis): primary structural features and sequence diversity of the S-RNases in Rosaceae. Mol Gen Genet 260(2–3):261–268. https://doi.org/10. 1007/s004380050894 Ushijima K, Sassa H, Dandekar AM, Gradziel TM, Tao R, Hirano H (2003) Structural and transcriptional analysis of the self-incompatibility locus of almond: identification of a pollen-expressed F-box gene with haplotype-specific polymorphism. Plant Cell 15 (3):771–781. https://doi.org/10.1105/tpc.009290 Ushijima K, Yamane H, Watari A, Kakehi E, Ikeda K, Hauck N, Iezzoni A, Tao R (2004) The S haplotypespecific F-box protein gene, SFB, is defective in selfcompatible haplotypes of Prunus avium and P-mume. Plant J 39:573–586 Weinbaum SA (1985) Role of natural self-pollination in self-fruitfulness of almond. Sci Hortic 27(3):295–302. https://doi.org/10.1016/0304-4238(85)90034-2 Westwood MN (1988) Temperate-zone pomology. Timber Press, vol 2 Williams JS, Wu L, Li S, Sun P, Kao T-H (2015) Insight into S-RNase-based self-incompatibility in Petunia: recent findings and future directions. Front Plant Sci 6 (41). https://doi.org/10.3389/fpls.2015.00041 Wirthensohn M (2020) New cultivars from the Australian almond breeding program. HortSci 55(5):738. https:// doi.org/10.21273/hortsci14716-19

Recent Advances on Self-incompatibility in Almond …

109

Wünsch A, Hormaza JI (2004) Cloning and characterization of genomic DNA sequences of four selfincompatibility alleles in sweet cherry (Prunus avium L.). Theoret Appl Genet 108(2):299–305. https://doi. org/10.1007/s00122-003-1418-6

Zhou J, Wang F, Ma W, Zhang Y, Han B, Xue Y (2003) Structural and transcriptional analysis of S-locus F-box genes in Antirrhinum. Sex Plant Reprod 16(4):165– 177. https://doi.org/10.1007/s00497-003-0185-5

Transcriptional Changes Associated to Flower Bud Dormancy and Flowering in Almond: DNA Sequence Motifs, mRNA Expression, Epigenetic Modifications and Phytohormone Signaling Ángela S. Prudencio, Raquel Sánchez-Pérez, Pedro José Martínez-García, Federico Dicenta, and Pedro Martínez-Gómez almond including DNA sequence motifs, mRNA expression, epigenetic modifications and phytohormone signaling. Inheritance and transmission of flowering time and chilling and heat requirements in almond have been largely studied in almond being polygenic traits with high heritability although a major gene late blooming (Lb) controlling flowering time. In addition, molecular studies at DNA level have confirmed this polygenic nature identifying several genome regions (quantitative trait loci, QTLs) involved. Studies about regulation of gene expression are scarce although several transcription factors have been described as responsible. From the metabolomics point of view, the integrated analysis of the mechanisms of accumulation of cyanogenic glucosides and flowering regulation through transcription factors opens new possibilities in the analysis of this complex trait in almond. Finally, at epigenetic level, DNA methylation assays have been performed.

Abstract

Flowering time in almond [Prunus dulcis (Miller) Webb] is a complex process involving the chilling and heat requirements’ characteristic and genetic background of each cultivar. During the falling temperatures of autumn, cultivated almond, activate a winter-survival strategy called endodormancy to protect against unfavorably cold temperatures. Chill accumulation allows the progression from almond flower bud endodormancy stage to flower bud ecodormancy regulated by heat accumulation. Major breeding challenges to analyze molecular changes associated to bud dormancy and flowering will be the appropriate phenotyping together with the incorporation of genomic, transcriptomic and epigenetic tools for the development of improved breeding strategies. This chapter reviews the transcriptional changes associated to flower bud dormancy and flowering in

Á. S. Prudencio . R. Sánchez-Pérez . P. J. Martínez-García . F. Dicenta . P. Martínez-Gómez (&) Departamento de Mejora Vegetal Grupo de Mejora Genética de Frutales, CEBAS-CSIC, Espinardo, Murcia, Spain e-mail: [email protected]

1

Introduction

Almond [Prunus dulcis (Miller) Webb], a temperate tree crop cultivated around the world, is a very important crop in Spain, closely linked to the Mediterranean landscape and culture. It has

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_8

111

112

been traditionally cultivated under dry conditions, implanted in marginal lands and with poor care by farmers. However, due to the recent increase of the prices at international level, almond culture is having an amazing expansion, not only in Spain but also in all the producer countries. The efficient horticultural practices in irrigated orchards and the introduction of new cultivars better adapted to the climatic conditions of each area are allowing growers to obtain a high productivity, low costs of production and so profitable benefits. In this new context, research, experimentation and transference activities are required for the release of new almond cultivars adapted to each area, that position almond cultivation as a profitable option. Due to its great variability, almond is nowadays cultivated in a wide variety of ecological

Á. S. Prudencio et al.

niches. Almonds are cultivated in more than 50 countries, with a 95% of production located in California, Australia and the Mediterranean Basin (http://faostat.fao.org). Regarding the surface, according to the Food and Agriculture Organization of the United Nations (FAO), in 2021 there was 2,283,414 ha dedicated to almond cultivation in the world with an annual production of 3,993,998.06 tons. USA is the first productor with 2,189,040 tons 534,191 ha, followed by Spain is the first country with 744,470 ha and a production of 365,210 tons (Fig. 1). Historically, almonds were consumed as fresh and processed food, and it is nowadays considered as a functional food with both nutritional and health properties. Almonds are among the most dietetic nuts. They are a good source of

Fig. 1 Production quantities of almonds, in shell by country in 2021 evolution of production/yield quantities of almonds during the period 2000–2021 (https://www.fao.org/faostat/en/#data/)

Transcriptional Changes Associated to Flower Bud Dormancy …

essential fatty acids (between 50 and 60% of kernel weight, the oleic acid representing 65– 80%), vitamins (mainly vitamin E and folic acid), minerals and fiber (Saura-Calixto et al. 1981; Schirra 1997; Sabate and Haddad 2001). Almond consumption is associated to the prevention of coronary disease, cancer and cataracts (Fulgoni et al. 2002) with anti-inflammatory and hypocholesterolaemic properties (Poonam et al. 2011; Musa-Veloso et al. 2016). On the other hand, almonds are not highly allergenic to consumers. In addition, the uses of sweet or bitter almond ointment included the treatment of asthma and the use in soothing salves for burns. The almond kernel is consumed either in the natural state or processed, and it has many food uses. Kernels may be roasted or fried in oil. The processed kernel can be used blanched, and it is combined with chocolate in confectionery, also sliced in pastry and ground into paste for bakery products or marzipan (Gradziel 2009). Additionally, almond kernel oil can be used for cosmetics (Gradziel 2009). Morphologically, almond is a tree with a different size depending on the cultivar, soil and horticultural practices. The leaves are alternate and lanceolate, and flowers are grouped or isolated. The almond flower is hermaphrodite, white or rose depending on the genotype. After successful pollination, almond fruit starts growing during the spring and maturation takes place in summer. The almond fruit is oval-shaped with a tip, and it is classified as a drupe with a pubescent skin (exocarp), a fleshy but thin hull (mesocarp) and a hardened shell (endocarp) that contains the seed (kernel), made up of the embryo and the testa. The fruit grows during development, and the hull opens and dries at maturity. The mature endocarp hardness ranges from soft to hard depending on the genotype (Gradziel 2009). Horticulturally, almonds are classified as a ‘nut’ in which the edible seed (the kernel) is the commercial product. Environmental conditions affect almond production. The main limiting factor in most of areas in Spain (and other countries) is the late frost. During the winter, dormant buds are very resistant to low

113

temperatures, but open flowers and mainly young fruits are highly susceptible to frosts and can be seriously damaged. During flowering, bad weather conditions (wind, low temperatures, fog and rain) difficult the pollination of flowers by insects, delay anthers dehiscence and reduces successful fertilization of flowers for fruit set. The use of late-flowering and self-compatible cultivars is recommended for minimizing frost damage and ensure pollination in cold areas (Martínez-García et al. 2011; Dicenta et al. 2017). Productivity of the almond tree is mainly determined by the plant material, culture practices and environmental conditions. Selection of the plant material includes both the cultivar and the rootstock. Both determine production, fruit quality and cultivation costs. The rootstock must be compatible with the cultivar and adapted to the type of soil (permeability, minerals and pathogens’ presence) and irrigation system. Suitable culture practices are equally important, as orchard designing, pruning, fertilization, weeds control, pesticides treatments and irrigation. In Mediterranean countries, almond has been traditionally considered to be a rainfed species. However, the production is directly related to water availability. In non-irrigated orchards, low rainfall drastically reduces yield and almond quality. Moderate-to-high rainfall, together with introduction of early ripening cultivars, which ripen before the driest summer season improves production and quality. Environmental conditions also affect almond production. The objective of this chapter is an assessment of the genetic, genomic, transcriptomic and epigenetic bases of flowering date in almond.

2

Dormancy and Flowering in Almond Trees

Phenology is defined as the sequential developmental stages of the annual growth cycle and their timing. Thus, the key developmental stages of the annual growth are phenological events: growth cessation, dormancy, dormancy release and bud burst (sprouting and flowering) (Singh

Á. S. Prudencio et al.

114

et al. 2017). Phenological states from endodormancy to flowering of flower buds were described by Felipe (1977) (Fig. 2). Both flower and vegetative almond buds begin their development as vegetative buds. By midsummer, the potentially floral buds undergo a transition to the reproductive state, a process marked by the enlargement of the shoot apical meristem. The floral whorls are initiated sequentially during latesummer (Lamp et al. 2001). Almond flower buds can be presented on axial nodes or in spurs and can be differentiated from vegetative buds by shape. Vegetative buds are conical, whereas flower buds are egg-shaped (Fig. 3). Differentiation of flower buds is not possible until late-summer and may require bud dissection and microscopy (Polito et al. 2002). The formation of terminal apical buds, prior to termination of cell proliferation, is a major indicator of the dormancy beginning of trees (Hamilton et al. 2016). Dormancy has been defined as a temporary suspension of visible growth of any plant structure containing a meristem (Campoy et al. 2011a). Many types of plant dormancy have been described, regarding seed and bud dormancy (Dennis 1994). Dormancy period is a critical developmental phase of some plants of temperate climates, in which the plant must be protected from the cold injuries of the winter (Vitasse et al. 2014; Ding and Nilsson 2016). Almond harvest usually occurs between August and September.

Dormancy period is established during summer and remains during the winter. Once dormancy is overcome, flowering takes place. Depending on cultivar and region, almond flowering in Spain ranges from January to April According to Lang et al. (1987), dormancy can be divided into three sequential phases: paradormancy, endodormancy and ecodormancy. In paradormancy, the bud growth is inhibited by other buds within the tree. During endodormancy, growth inhibition is controlled by the inhibited bud meristem itself. Finally, during ecodormancy, maintenance depends on environmental cues, and the bud will be able to restart its active growth under favorable conditions. The absence of chill produces a lack of satisfaction of the chilling requirements of the almond cultivars with negative effects on productivity. The results regarding the genetic basis of endodormancy release and flowering time in almond evidenced the risk of growing extralate- and ultra-late-flowering almond cultivars in warm areas with a deficient chill unit accumulation (Fig. 4).

2.1 Paradormancy Dormancy starts with paradormancy, which consists on the inhibition of axillary buds’ growth by the apical dominance of terminal bud

Fig. 2 Almond flower buds phenological states as described by Felipe (1977). Adapted from Prudencio et al. (2018a)

Transcriptional Changes Associated to Flower Bud Dormancy …

115

Fig. 3 Almond bud formations. Vegetative (V) and flower (F) buds are indicated by arrows

Fig. 4 Chill and heat accumulations as conceptualized by Lang et al. (1987) and phenologic events related to them. Adapted from Luedeling et al. (2009)

(Hillman 1984). Paradormancy gradually becomes endodormancy which is also known as dormancy induction phase. Apical dominance is based on the acropetal circulation of nutrients, water, minerals and growth promoters as cytokinins because of the auxin synthesis by the leaves at

shoot apex. This phenomenon implies a gradient in dormancy depth that decreases basipetally (Arias and Crabbé 1975). Thus, dormancy depth is variable within the three and dormancy state can be assessed in three different levels: whole tree, shoots or single-node cuttings.

Á. S. Prudencio et al.

116

2.2 Endodormancy Factors

. Horticultural practices

Endodormancy is established in the late-summer and the deepest point is reached in autumn (beginning of November in Spain) although this characteristic depends on climatic factors, the species and even the cultivar studied. The induction and depth of endodormancy depend on environmental and endogenous factors including environmental and endogenous factors. The most important environmental factors affecting tree dormancy induction and depth are photoperiod, temperature and horticultural practices.

On the other hand, horticultural practices can also affect bud endodormancy, depending on the moment of application. If the case of pruning, if the shoot apex is removed too late, the effect on endodormancy release of axillary buds will be low. Regarding defoliation, if it is performed before endodormancy establishment, the removal of growth inhibitors synthetized in leaves that move to the buds by defoliation practice can alleviate dormancy state (Mielke and Dennis 1978; Erez 1982). Finally, regarding tree characteristics, vigor and age increase endodormancy depth. Moreover, Tromp (1976) showed that vegetative vigor, induced by high temperatures, can inhibit floral initiation. The branch position and orientation are also important (Crabbé 1984) due to hormone balance. For grafted trees, if the rootstock used is low-compatible, endodormancy release can be advanced (Erez 2000). Several methods to study the endodormancy release have been proposed (Dennis 2003; Fadón and Rodrigo 2018; Prudencio et al. 2018a). One of them is to expose plant material (single-node cuttings, branches) to forcing conditions. Such conditions of temperature (23–25 °C) and relative humidity (40–60%) allow the phenological evolution of buds. The criterion selected for defining endodormancy release is diverse (Felker and Robitaille 1985). Endodormancy release can be considered when 50% of buds shows a green tip (Tabuenca 1972; Egea et al. 2003; Prudencio et al. 2018a). During flower bud endodormancy, cell division, enlargement and differentiation are taking place (Reinoso et al. 2002; Fadón and Rodrigo 2018). Some biological processes as tetrad formation during microsporogenesis in apricot (Julian et al. 2011), xylem vessels’ differentiation (Bartolini et al. 2006; Andreini et al. 2012), carbohydrates hydrolysis and uptake by floral primordia in peach (Bonhomme et al. 2005) were related to endodormancy release.

. Photoperiod Photoperiod is necessary for endodormancy establishment in a wide range of tree species (Arora et al. 2003; Ruttink et al. 2007). However, response to photoperiod is variable within members from the Rosaceae family, and it depends on the interaction with temperature (Heide and Prestud 2005; Heide 2008; Cook et al. 2005). Summer high temperatures promote and intensify endodormancy (Jonkers 1979; Heide 2003) as well as falling temperatures of the autumn (Hatch and Walker 1969; Cook et al. 2005). . Temperature Low temperatures are needed for endodormancy overcoming. The concept of chilling requirement was introduced to define the environmental conditions controlling endodormancy release. Chilling requirement consists of the amount of chill accumulated necessary for endodormancy release, and this trait is dependent on the species and cultivar, even on the type of bud (floral or vegetative). The first researcher to observe the relationship between dormancy and low temperatures was Coville (1920). Different models for chill accumulation have been proposed from this date. Although these models can predict when chilling requirements will be satisfied, there is little information on the physiological basis of this process.

Transcriptional Changes Associated to Flower Bud Dormancy …

2.3 Ecodormancy Once endodormancy is overcome, sprouting or flowering of tree buds depends on high temperatures of spring. This period is called ecodormancy (Lang et al. 1987), and the amount of high temperatures needed for each cultivar is known defined as heat requirements. While the influence of chilling requirements on dormancy release is a well-studied phenomenon (Couvillon and Erez 1985a; Erez and Couvillon 1987), the effect of heat requirements on flowering time is less clear in Prunus species (Couvillon and Erez 1985b; Citadin et al. 2001). Heat units’ accumulation expressed as Growing Degree Hours (GDH), proposed by Richardson et al. (1975), is one of the most suitable models to estimate the heat requirements (Darbyshire et al. 2014). However, the model showed inter-annual and inter-location variability in a given cultivar (El Yaacoubi et al. 2016). During ecodormancy, starch accumulated during endodormancy starts vanishing as heat accumulation increases (Fadón et al. 2018). Following starch decrease, the ovary cells become active and processes of cell division, chromatin decondesation and vacuolization are observed (Horvath et al. 2008). The estimation of these (chilling and heat requirements) parameters under different climatic conditions using different models showed that the dynamic model presents less variation than the Richardson model, especially during warmer conditions (Prudencio et al. 2018a).

3

DNA Sequence Motifs Linked to Breaking Dormancy and Flowering Date in Almond Trees

Flowering time is a heritable quantitative trait in fruit tree species. Due to its importance for tree adaptation and success in breeding programs, many studies on the genetic control of this trait have been carried out. The transmission of flowering time has been studied by different authors in almond (Kester 1965; Grasselly 1972;

117

Vargas et al. 1984; Sánchez-Pérez et al. 2007a), and high values (0.8–1) of heritability have been calculated by regression (Kester et al. 1977; Dicenta et al. 1993). Dicenta and García (1993) showed that flowering time is controlled by nuclear DNA, and no maternal effects were observed. On the other hand, different genomic (DNA) regions involved in the control of flowering time have been identified in almond and other Prunus species including almond by quantitative trait loci (QTLs) studies. Using Random Amplified Polymorphic DNAs (RAPDs), a major gene in the variance control of flowering time was found. This and other two markers were found in linkage group (LG) 4 (Ballester et al. 2001). The localization of the Lb gene in LG4 has been confirmed using Simple Sequence Repeat markers (SSRs) in the almond population R1000 ⨯ Desmayo Largueta. Other QTLs identified in an almond (Sánchez-Pérez et al. 2007b, 2012) and almond ⨯ peach progeny (Silva et al. 2005) were located in LG1, LG2, LG3, LG5, LG6 and LG7. Moreover, some of these QTLs were linked to chilling requirements and in LG2 and LG7 QTLs linked to heat requirements were found (Sánchez-Pérez et al. 2012, 2014). Figure 5 shows the identified QTLs linked to flowering date, chill requirements and heat requirements in different Prunus species highlighting the most important genomic regions linked to breaking dormancy and flowering time in Prunus species including almond. Based on the model derived from the long-day herbaceous plant Arabidopsis, LEAFY (LFY) and Flowering Time (FT) genes, among others, were identified as determinants of flowering time. In some Prunus species, homologs to that flowering time regulators have been isolated and characterized (Srinivasan et al. 2012). In almond, LFY but no FT homolog has been reported to date (Silva et al. 2005). However, homologous genes to FT and LFY and others involved in flowering time control in the model plant Arabidopsis did not colocalize with the Lb gene. More recently, candidate genes underlying Lb were investigated in sweet cherry and key genes were identified for chilling requirements and flowering time

118

Á. S. Prudencio et al.

Fig. 5 Quantitative trait loci (QTLs) linked to chill requirements (CRs), heat requirements (HRs), flowering date (Fd) in apricot (green) and other Prunus species including peach (red), almond (blue) and cherry (violet). References are shown in brackets [1: Dirlewanger et al. 1999; 2: Quarta et al. 2000; 3: Yamamoto et al. 2001; 4: Verde et al. 2002; 5: Quilot et al. 2004; 6: Silva et al. 2005; 7: Ogundiwin et al. 2009; 8: Fan et al. 2010; 9: Eduardo et al. 2011; 10: Romeu et al. 2014; 11: Bielenberg et al. 2015; 12: Sánchez-Pérez et al. 2007b;

13: Sánchez-Pérez et al. 2012; 14: Olukolu et al. 2009; 15: Campoy et al. 2011b; 16: Salazar et al. 2013; 17: Salazar et al. 2016; 18: Wang et al. 2000; 19: Castède et al. 2014; 20: Castède et al. 2015; 21: Rasouli et al. 2013]. A tentative scale of the map is performed in cM using a framework bin map of reference in Prunus (Howad et al. 2005) indicating each bin numbered in black. Shaded in red are indicated the main regions of the Prunus genome including almond genome involved in breaking dormancy and flowering time

(Castède et al. 2015). The peach evergrowing (evg) mutant carries a deletion in EVG locus affecting up to four genes that prevents terminal buds from endodormancy (Bielenberg et al. 2004). The map-based cloning analyses of EVG locus revealed that it included six tandemly arrayed genes, called Dormancy-Associated MADS-Box (DAM) genes (Bielenberg et al. 2004, 2008). MADS-box genes are an extent gene family of transcription factors that regulate floral development. In addition, DAM genes are responsible for endodormancy maintenance and its expression decreases concomitantly with endodormancy

release (Jotatsu et al. 2011; Xu et al. 2014; Zhu et al. 2015; Rothkegel et al. 2017). Actually, CRepeat/DRE Binding Factors (CBFs) target DAM genes for transcriptional regulation in Japanese apricot (Prunus mume (Siebold) Siebold & Zucc.) (Zhao et al. 2018). Other MADSbox genes controlling flowering in Arabidopsis are those belonging to Agamous-Like (AGL) family, together with Supressor of Constans (SOC1) and Short Vegetative Phase (SVP). In different trees, genes from these families are downregulated during dormancy release (Habu et al. 2014). SVP and SOC1 expression decreased during dormancy release in poplar vegetative

Transcriptional Changes Associated to Flower Bud Dormancy …

buds (Howe et al. 2015). On the other hand, SVP accumulation is influenced by ELF3 (Yoshida et al. 2009), which relates expression pattern with photoperiod. Moreover, in Arabidopsis, SVP expression is regulated by GIGANTEA (GI), which in turn is inhibited by LATE ELONGATED HYPOCOTYLl (LHY) and CIRCADIAN CLOCK-ASSOCIATED 1 (CCA1) (Sawa and Kay 2011). According to the regulatory model proposed by Wu et al. (2017), it seems that SVP and DAM are in fact floral repressors whose expression becomes reduced during chill accumulation and dormancy release (Kumar et al. 2016). Nevertheless, a role associated to dormancy rather than to flowering transition has been proposed for SOC1 in apricot and kiwifruit (Trainin et al. 2013; Voogd et al. 2015). In almond, PdGIGANTEA expression increased during endodormancy (Barros et al. 2017) and two MADS-box genes PdMADS1 and PdMADS3 gradually increase expression levels during ecodormancy (Barros et al. 2012).

4

mRNA Expression Associated to Flowering in Almond Trees

From a molecular and transcriptomic point of view, discovering the candidate genes whose expression varies as a cause or consequence of endodormancy release and studying the biological role of those genes are priorities for breeding programs. The differential transcription regulation between early- and late-flowering cultivars and the genes related to chilling requirements must therefore be identified (Mazzitelli et al. 2007; Hedley et al. 2010; Kaufmann et al. 2010). In the last decade, high-throughput sequencing technology has allowed the transcriptomic approach to decipher which gene networks are working upon dormancy onset and release (Bai et al. 2013; Howe et al. 2015; Ionescu et al. 2017; Zhang et al. 2018). After endodormancy, cell-to-cell symplasmic connection is reestablished by callose degradation, a process triggered by cold-induced FT (van der Schoot et al. 2011; Rinne et al. 2011). At the subcellular level, cell

119

wall loosening and phytohormone synthesis signaling have been highlighted. Sugar complexes as raffinose, stachyose and galactinol are induced upon endodormancy for tree protection from chilling, drought and oxidative stress (Nishizawa et al. 2008; Ibáñez et al. 2010). Carbohydrates act not only as a source of carbon and energy upon dormancy release, but they also function as developmental signals (Anderson et al. 2005; González-Rossia et al. 2008; Rabot et al. 2012). Endodormancy release in fruit trees was proposed to be due to transcription reprogramming in response to environmental cues, in this case, cold accumulation (Halaly et al. 2008; Horvath 2009) and may be mediated by oxidative stress (Pérez et al. 2008; Sudawan et al. 2016; Beauvieux et al. 2018). In addition, differentially expressed genes between dormant and nondormant buds were identified by Suppression Subtractive Hybridization (SSH) and microarray in peach (Leida et al. 2010, 2012a). Among them, DORMANCY-ASSOCIATED MADS-BOX 5 (DAM5), ABI FIVE-BINDING PROTEIN (AFP), ABA-INDUCED WHEAT PLASMA MEMBRANE 19 (AWPM19), DEHYDRATIONRESPONSIVE ELEMENT-BINDING PROTEIN 2c (DREB2c) and a gene coding for Class III peroxidase (Prupe.1G114700) were analyzed by qRT-PCR (Leida et al. 2012b). These genes belong to LG1, excepting DREB2c (LG2). DAM transcription factors are highly expressed at dormancy onset and maintenance. DREB2C and AFP are regulators of abscisic acid sensitivity and transduction (López-Molina et al. 2003; Lee et al. 2010), whereas AWPM19 is a membrane protein that enables freezing tolerance (Koike et al. 1997), and transcription is downregulated by cold treatment (Habu et al. 2014). Differentially expressed genes (DEGs) related to metabolic switches, cell-to-cell transport, cell wall remodeling, ABA signaling and pollen development have been associated to the general endodormancy release process. In addition, some resistance genes and transcription factors displayed different behaviors between cultivars, reflecting the breeding effect of the delay of flowering time or other traits. These obtained transcriptomic results agree with the complex

120

Á. S. Prudencio et al.

Fig. 6 Gene expression analysis of PdP40 and peroxidase activity in the flower buds of ‘Desmayo Largueta’, ‘Penta’ and ‘Tardona’ almond cultivars during the 2015– 2016 and 2016–2017 seasons. Endodormancy release (transition from endodormancy to ecodormancy) is indicated by an asterisk in each case. Relative expression values are represented by means of technical

replicates ± SE. Peroxidase activity is represented by means of biological replicates ± SD, excepting those samples indicated by a cross, in which SD of technical replicates was applied. Different letters indicate statistical significant differences based on Tukey’s HSD test (P < 0.05). Adapted from Prudencio et al. (2019)

nature of the genetic determination of endodormancy release and flowering evidenced (Prudencio et al. 2021). In almond, the gene expression of Prupe.1G114700 (named PdP40) gene during endodormancy release was compared to global peroxidase activity during two seasons in three cultivars with different chilling requirements and flowering time. In general, an increase of total peroxidase activity was observed in flower buds prior to endodormancy release, as well as for PdP40 transcript expression (Fig. 6). The candidate transcript PdP40 has been described as a member of the flower-specific Class III peroxidase encoding family. Total peroxidase activity in flower buds shared a common pattern with the PdP40 expression analysis. Total peroxidase activity could therefore be another good biochemical marker for monitoring bud dormancy in almond and in other Prunus species. In addition, different authors suggest that PdP40, along with

other H2O2-scavenging enzymes, promotes endodormancy release (Prudencio et al. 2019). Gene expression regulation-related breaking dormancy and flowering can operate at different levels. Several strategies for decipheration of transcriptional networks and epigenetic marks involved in the control of dormancy and flowering time in fruit tree species have been performed. One of them was to apply the knowledge acquired from herbaceous and tree model species (Arabidopsis and poplar, respectively) to nonmodel tree species like almond. In several tree species as poplar, photoperiod plays a major role in endodormancy induction. LHY and TOC1 were the first clock genes described in chestnut (Castanea sativa (Mill.)) and poplar (Populus tremula (L).) (Ramos et al. 2005). These genes regulate the module CO/FT that control flowering, as occurs in Arabidopsis (Böhlenius et al. 2006). Additionally, induction of FT by cold toward dormancy release has been reported in

Transcriptional Changes Associated to Flower Bud Dormancy …

poplar (Rinne et al. 2011) and in sweet cherry flower buds (Gericke 2015). In almond, an increase of FT transcript abundance was observed upon anthesis (Barros et al. 2017). Another gene related to photoperiod is Early Flowering 3 (ELF3), identified as a light signal transductor for flowering in Arabidopsis. In trees, ELF3 was identified as upregulated in endodormant buds of leafy spurge (Doğramacı et al. 2010). Recently, the natural progression from endodormancy to ecodormancy of a traditional early-flowering versus two late-flowering almond cultivars from the CEBAS-CSIC Almond Breeding Program was monitored by RNA sequencing of flower buds. This monitoring allowed the identification of candidate dormancyassociated genes and cultivar-associated almond genes (2021). Such candidate genes highlighted processes as sugar synthesis and mobilization genes, as SWEET10, cell wall remodeling (especially, the role of GLUCOSYL HYDROLASES (GHs)-encoding genes) and transmembrane transport mediated by aquaporins like that encoded by NIP7 almond ortholog, whose expression was induced before endodormancy release in all cultivars studied. The obtained information may be used for the development of dormancy release molecular markers and the improvement of breeding programs efficiency, in a climate-change context.

5

Epigenetic Regulation of Flowering in Almond Trees

The dormancy release process involves sensing environmental cues as temperature, coupled to signal transduction and gene expression regulation in response to the stimuli received. Gene expression involves epigenetics, that consist on chemical modifications affecting DNA or structural proteins within the chromatin (Saze 2008; Feng and Jacobsen 2011; Pascual et al. 2014) (Fig. 7). Several works have described the role of epigenetics in the regulation of dormancy in deciduous plant species (Yaish et al. 2011; Ríos et al. 2014). Epigenetic changes are dynamic but

121

heritable, which makes them interesting regulators mediating adaptive responses to environmental changes, such as seasonal cycles and climate change (Lämke and Bäurle 2017). Santamaría et al. (2009), described a DNA methylation decrease together with histone (H4) deacetylation and the dormancy release in chestnut (Castanea sativa Mill.). In peach, de la Fuente et al. (2015) identified a genome-wide pattern of hystone trimethylation (H3K27me3) during bud dormancy release, and Lloret et al. (2017) found a relationship between gene expression, hystone modifications and sorbitol synthesis during dormancy progression and release. Rothkegel et al. (2017) showed that DNA methylation participate in the regulation of MADS-box genes in sweet cherry. In apple, genome methylation patterns have been linked to chilling acquisition during dormancy (Kumar et al. 2016). In peach, the epigenetic regulation was also reported (Leida et al. 2012a; Rios et al. 2014). Zhebentyayeva et al. (2014) described Polycomb Group (PcG) genes, which are involved in the regulation of flowering at the epigenetic level in the model species Arabidopsis. The POLYCOMB REPRESSIVE COMPLEX II (PRCII) components EMBRYONIC FLOWER 2 (EMF2) and PHOTOPERIOD-INDEPENDENT EARLY FLOWERING 1 (PIE1) colocalize with a flowering time QTL in sweet cherry (Castède et al. 2015). In the case of almond, DNA methylation has been associated with floral selfincompatibility (Fernández i Martí et al. 2014) and with bud drop (Fresnedo-Ramírez et al. 2017). In this context, an analysis of DNA methylation variants in almond flower buds has been done by bisulfite sequencing, which consists of the Next-Generation Sequencing (NGS) of digested and bisulfite-treated DNA samples. The epi-Genotyping By Sequencing (GBS) technique was designed to explore the genome and compare DNA methylation and genetic variation in hundreds of samples. Furthermore, this method let genotype samples without a prior reference genome (van Gurp et al. 2016). According to the results obtained, the DNA methylation (5mC)

Á. S. Prudencio et al.

122

Fig. 7 Representative scheme of the RNA regulation and expression including between branchets the molecular techniques used in this regulation and expression at DNA, RNA and epigenetic level

pattern was cultivar-dependent rather than dormancy state-dependent. In spite of coverage limitation of the performed sequencing, genes whose methylation state changed between the endodormant and the ecodormant state of flower buds’ DNA were identified. For instance, candidate genes related to transcription regulation processes including transcription factors, RNAmediated silencing, chromatin remodeling and LATE ELONGATED HYPOCOTYL (LHY) genes appeared as hypermethylated in flower buds from an early-flowering cultivar (Prudencio et al. 2018b). After confirmation of epigenetic and expression results, Prudencio et al. (2018b) suggested that methylation marks could be used as epigenetic markers for endodormancy release and flowering time in almond. In addition, DNA methylation studies of both traditional and almond cultivars released from breeding

programs will provide candidate epialleles linked to agronomic traits. Such polymorphisms can be screened in large populations using NGS to confirm the methylation state of loci associated to a given character of interest.

6

Phytohormone Signaling of Bud Dormancy and Flowering in Almond Trees

Hormone content and tree characteristics are the main endogenous factors affecting endodormancy establishment and depth. ABA is generally considered to be a growth inhibitor. ABA synthesis is promoted upon endodormancy induction, probably by ethylene hormone (Rodrigo et al. 2006). Then, ABA content decreases concomitantly with endodormancy progression (Tamura et al. 1992; Horvath et al.

Transcriptional Changes Associated to Flower Bud Dormancy …

2008). Moreover, exogenous ABA application prevented endodormancy release of sour cherry and peach (Mielke and Dennis 1978). The effect of ABA in endodormancy maintenance is probable mediated by other molecules, as the already mentioned CBFs and the DehydrationResponsive Element-Binding (DREB) transcription factors (Lata and Prasad 2011; Rubio et al. 2018) that regulate expression of genes responsive to cold and other abiotic stresses (Wisniewski et al. 2006). In almond, expression of genes related to ABA biosynthesis and transduction has been associated to the endodormancy state, and in contrast, the expression of AIP2 (ABI3 INTERACTING PROTEIN 2) increased in the ecodormancy state of flower buds. On the other hand, gibberellins (GAs) are growth promoter molecules that have been traditionally used to stimulate buds to growth and to measure dormancy depth. High levels of GAs are required for endodormancy release (Ramsay and Martin 1970; Tamura et al. 1992). Finally, auxin levels decreased during endodormancy while rise in ecodormancy (Bennett and Skoog 1938; Rodríguez and Sánchez-Tamés 1986; Aloni and Peterson 1997). In this regard, endodormancy has been conceptualized by two different schools: . The classical school proposed a model based on the balance of growth promoters and inhibitors (hormones) (Amen 1968; Seeley 1990) that has remained aside. . The French school focused on a ‘morphogenetic factor’ dependent on different correlative influences and beginning from paradormancy (Champagnat 1983). Regardless of the conceptualization of endodormancy, this is considered to be a quantitative state that is released progressively. For this reason, endodormancy release is established according to an arbitrary criterion to estimate chilling requirement and to compare results between species and cultivars (see Sect. 2Endodormancy). Based on the expression pattern of the GIBBERELLIN 20-OXIDASE (GA20ox) and

123

GIBBERELLIN 2-OXIDASE (GA2ox) genes, involved in GAs’ biosynthesis and degradation, respectively, a change in GA metabolism was proposed to take place upon endodormancy release (Silva et al. 2005; Barros et al. 2012). Actually, GAs have been proposed as a signal molecule for promotion of growth reactivation, together with FT (Singh et al. 2017). Interestingly, GA is involved in the regulation of cell elongation in stamen filament as well as in cellular development in anthers, in Arabidopsis (Cheng et al. 2004). In sweet cherry, GA2ox was a highlighted candidate gene related to flowering time from QTL analysis (Castède et al. 2015).

7

Concluding Remarks and Future Prospects in the New Postgenomic Context

Despite having different meanings in nature and in commercial orchards, flowering time is a key characteristic in the adaptation of a tree to its environment, ensuring that the tree shows efficient behavior in either case. Flowering time is a complex trait that is largely determined genetically and thus has high heritability although a major gene late blooming (Lb) controlling flowering time together with several genome regions (QTLs). Studies about regulation of gene expression are scarce although several transcription factors have been described as responsible. From the metabolomics point of view, the integrated analysis of the mechanisms of accumulation of cyanogenic glucosides and flowering regulation through transcription factors opens new possibilities in the analysis of this complex trait in almond. Finally, at epigenetic levels, DNA methylation cytosine methylated genes have been reported as candidate epigenetic marks associated to the regulation of bud formancy and flowering time. The recent sequencing of the almond genome (Sánchez-Pérez et al. 2019; Alioto et al. 2019) will improve genomic and transcriptomic analyses of flowering time in almond. Acknowledgements This study has been supported by the European project ‘Nut4Drought: Selection and

124 characterization of drought resistant almond cultivars from the Mediterranean basin with high nutraceutical values’ from an ERA-NET Action financed by the European Union under the Seventh Framework Program for research called ARIMNet2 (Coordination of Agricultural Research in the Mediterranean; 2014–2017; www. arimnet2.net) and Grant No. 19879/GERM/15 of the Seneca Foundation of the Region of Murcia.

References Alioto T, Alexiou K, Bardil A, Castanera R, Cruz F, Dhingra A, Duval H, i Martí AF, Frias L, Galán B, García JL, Howad W, Gómez-Garrido J, Gut M, Julca I, Morata J, Puigdomènech P, Ribeca P, Rubio Cabetas MJ, Vlasova A, Wirthensohn M, García-Mas J, Gabaldon T, Casacuberta J, Arús P (2019) Transposons played a major role in the diversification between the closely related almond (Prunus dulcis) and peach (P. persica) genomes: results from the almond genome sequence. Plant J Aloni R, Peterson CA (1997) Auxin promotes dormancy callose removal from the phloem of Magnolia hara and callose accumulation and earlywood vessel differentiation in Quercus robur. J Plant Res. https:// doi.org/10.1111/tpj.14538 Amen RD (1968) A model of seed dormancy. Bot Rev 34:1–31 Anderson JV, Gesch RW, Jia Y, Chao WS, Horvath DP (2005) Seasonal shifts in dormancy status, carbohydrate metabolism, and related gene expression in crown buds of leafy spurge. Plant Cell Environ 28:1567–1578 Andreini L, Viti R, Bartolini S, Ruiz D, Egea J, Campoy JA (2012) The relationship between xylem differentiation and dormancy evolution in apricot flower buds (Prunus armeniaca L.): the influence of environmental conditions in two Mediterranean areas. Trees 26:919–928 Arias O, Crabbé J (1975) Gradients morphogenetiques du harac d’un an des vegetaux ligneux, en repos apparent; donnees complementaires fournies par l’etude de Prunus avium L. Phys Végétale 13:69–81 Arora R, Rowland LJ, Tanino K (2003) Induction and release of bud dormancy in woody perennials: a science comes of age. HortScience 38:911–921 Bai S, Saito T, Sakamoto D, Ito A, Fujii H, Moriguchi T (2013) Transcriptome analysis of Japanese pear (Pyrus pyrifolia Nakai) flower buds transitioning through endodormancy. Plant Cell Physiol 54:1132–1151 Ballester J, Socias i Company R, Arús P, de Vicente MC (2001) Genetic mapping of a major gene delaying blooming time in almond. Plant Breed 120:268–270 Barros PM, Gonçalves N, Saibo NJM, Oliveira MM (2012) Functional characterization of two almond Crepeat-binding factors involved in cold response. Tree Physiol 32:1113–1128

Á. S. Prudencio et al. Barros PM, Cherian S, Costa M, Sapeta H, Saibo NJM, Oliveira MM (2017) The identification of almond GIGANTEA gene and its expression under cold stress, variable photoperiod, and seasonal dormancy. Biol Plant 61:631–640 Bartolini S, Viti R, Guerriero R (2006) Xylem differentiation and microsporogenesis during dormancy of apricot flower buds (Prunus armeniaca L.). Eur J Hort Sci 71:84–94 Beauvieux R, Wenden B, Dirlewanger E (2018) Bud dormancy in perennial fruit tree species: a pivotal role for oxidative cues. Front Plant Sci 9:657 Bennett J, Skoog F (1938) Preliminary experiments on the relation of growth-promoting substances to the rest period in fruit trees. Plant Physiol 13:219–239 Bielenberg DG, Wang Y, Fan S, Reighard GL, Scorza R, Abbott AG (2004) A deletion affecting several gene candidates is present in the evergrowing peach mutant. J Hered 95:436–444 Bielenberg DG, Wang Y, Li Z, Zhebentyayeva T, Fan S, Reighard GL, Scorza R, Abbott AG (2008) Sequencing and annotation of the evergrowing locus in peach Prunus persica (L.) Batsch reveals a cluster of six MADS-box transcription factors as candidate genes for regulation of terminal bud formation. Tree Genet Gen 4:495–507 Bielenberg DG, Rauh B, Fan S, Gasic K, Abbott AG, Reighard GL, Okie WR, Wells CE (2015) Genotyping by sequencing for SNP-based linkage map construction and QTL analysis of chilling requirements and Bloom date in peach [Prunus haract (L.) Batsch]. PLOS One 10:e0139406 Böhlenius H, Huang T, Charbonnel-Campaa L, Brunner AM, Jansson S, Strauss SH, Nilsson O (2006) CO/FT regulatory module controls timing of flowering and seasonal growth cessation in trees. Science 312:1040–1043 Bonhomme M, Rageau R, Lacointe A, Gendraud M (2005) Influences of cold deprivation during dormancy on carbohydrate contents on vegetative and floral primordia and nearby structures of peach buds. Sci Hort 105:223–240 Campoy JA, Ruiz D, Egea J (2011a) Dormancy in temperate fruit trees in a global warming context: a review. Sci Hort 130:357–372 Campoy JA, Ruiz D, Egea J, Rees J, Celton JM, Martínez-Gómez P (2011b) Inheritance of flowering time in apricot (Prunus armeniaca L.) and analysis of linked quantitative trait loci (QTLs) using simple sequence repeat markers. Plant Mol Biol Rep 29:404– 410 Castède S, Campoy JA, Quero García J, Le Dantec L, Lafargue M, Barreneche T, Wenden B, Dirlewanger E (2014) Genetic determinism of phenological traits highly affected by climate change in Prunus avium: flowering date dissected into chilling and heat requirements. New Phytol 202:703–715 Castède S, Campoy JA, Le Dantec L, Quero-García J, Barreneche T, Wenden B, Dirlewanger E (2015)

Transcriptional Changes Associated to Flower Bud Dormancy … Mapping of candidate genes involved in bud dormancy and flower time in sweet cherry (Prunus avium). PLoS ONE 10:e0143250 Champagnat P (1983) Bud dormancy, correlation between organs, and morphogenesis in woody plants. Soviet Plant Physiol 30:458–471 Cheng H, Qin L, Lee S, Fu X, Richards DE, Cao D, Luo D, Harberd NP, Peng J (2004) Gibberellin regulates Arabidopsis floral development via suppression of DELLA protein function. Dev 131:1055–1064 Citadin I, Raseira MC, Herter FG, da Silva JB (2001) Heat requirement for blooming and leafing in peach. HortScience 36:305–307 Cook NC, Bellen A, Cronjé PJR, Wit ID, Keulemans W, Van den Putte A, Steyn W (2005) Freezing temperature treatment induces bud dormancy in ‘Granny Smith’ apple shoots. Sci Hort 106:170–176 Couvillon G, Erez A (1985a) Influence of prolonged exposure to chilling temperatures on bud break and heat requirement for bloom of several fruit species. J Am Soc Hort Sci 110:47–50 Couvillon G, Erez A (1985b) Effect of level and duration of high temperatures on rest in the peach. J Am Soc Hort Sci 110:579–581 Coville FV (1920) The influence of cold in stimulating the growth of plants. J Agri Res 20:151–192 Crabbé JJ (1984) Correlative effects modifying the course of bud dormancy in woody plants. Zeitschrift Pflanzenphysiol 113:465–469 Darbyshire R, Webb L, Goodwin I, Barlow E (2014) Challenges in predicting climate change impacts on pome fruit phenology. Int J Biometeorol 58:1119–1133 de la Fuente L, Conesa A, Lloret A, Badenes ML, Ríos G (2015) Genome-wide changes in histone H3 lysine 27 trimethylation associated with bud dormancy release in peach. Tree Genet Gen 11:45 Dennis FG (1994) Dormancy—what we know (and don’t know). HortScience 29:1249–1255 Dennis FG (2003) Problems in standardizing methods for evaluating the chilling requirements for the breaking of dormancy in buds of woody plants. HortScience 38:347–350 Dicenta F, García J (1993) Inheritance of selfcompatibility in almond. Heredity 70:313–321 Dicenta F, García JE, Carbonell EA (1993) Heritability of flowering, productivity and maturity in almond. J Hort Sci 68:113–120 Dicenta F, Egea J, Ortega E, Martínez-Gómez P, SánchezPérez R, Rubio M, Martínez-García P, SánchezPrudencio A, Cremades T (2016) Almond Breeding: important issues and challenges for research. Options Méditerranéennes 119:23–28 Dicenta F, Sánchez-Pérez P, Batlle I, Martínez-Gómez P (2017) Late-blooming cultivar development. In: Socias i Company R, Gradizel TM (eds) Almond: Botany, production and uses. Editorial CABI. Boston (EEUU), pp 168–187 Ding J, Nilsson O (2016) Molecular regulation of phenology in trees—because the seasons they are achangin’. Curr Opinion Plant Biol 29:73–79

125

Dirlewanger E, Moing A, Rothan C, Svanella L, Pronier V, Guye A, Plomion C, Monet R (1999) Mapping QTL controlling fruit quality in peach (Prunus persica (L) Batsch). Theor Appl Genet 98:18–31 Doğramacı M, Horvath DP, Chao WS, Foley ME, Christoffers MJ, Anderson JV (2010) Low temperatures impact dormancy status, flowering competence, and transcript profiles in crown buds of leafy spurge. Plant Mol Biol 73:207–226 Eduardo I, Pacheco I, Chietera G, Bassi D, Pozzi C, Vecchietti A, Rossini L (2011) QTL analysis of fruit quality traits in two peach intraspecific populations and importance of maturity date pleiotropic effect. Tree Genet Gen 7:323–335 Egea J, Ortega E, Martínez-Gómez P, Dicenta F (2003) Chilling and heat requirements of almond cultivars for flowering. Environ Exp Bot 50:79–85 El Yaacoubi A, Malagi G, Oukabli A, Citadin I, Hafidi M, Bonhomme M, Legave JM (2016) Differentiated dynamics of bud dormancy and growth in temperate fruit trees relating to bud phenology adaptation, the case of apple and almond trees. Int J Biometeorol 60:1695–1710 Erez A (1982) The rest in the apple and natural and artificial means to break it (in German). ErwerbsObstbau 24:116–118 Erez A (2000) Bud dormancy; phenomenon, problems and solutions in the tropics and subtropics. In: Erez A (ed) Temperate fruit crops in warm climates. Springer, Dordrecht, The Netherlands, pp 17–48 Erez A, Couvillon GA (1987) Characterization of the influence of moderate temperatures on rest completion in peach. J Am Soc Hort Sci 112:677–680 Fadón E, Herrero M, Rodrigo J (2018) Dormant flower buds actively accumulate starch over winter in sweet cherry. Front Plant Sci 9:171 Fadón E, Rodrigo J (2018) Unveiling winter dormancy through empirical experiments. Environ Exp Botany 152:28–36 Fan S, Bielenberg DG, Zhebentyayeva TN, Reighard GL, Okie WR, Holland D, Abbott AG (2010) Mapping quantitative trait loci associated with chilling requirement, heat requirement and bloom date in peach (Prunus persica). New Phytol 185:917–930 Felker FC, Robitaille HA (1985) Chilling accumulation and rest of sour cherry flower buds. J Am Soc Hort Sci 110:227–232 Felipe AJ (1977) Phenological states of almond. Proceedings of the third GREMPA colloquium, Bari, Italy, pp 101–103 Feng S, Jacobsen SE (2011) Epigenetic modifications in plants: an evolutionary perspective. Curr Opin Plant Biol 14:179–186 Fernández i Martí A, Gradziel TM, Socías i Company R (2014) Methylation of the Sf locus in almond is associated with S-Rnase loss of function. Plant Mol Biol 86:681–689 Fresnedo-Ramírez J, Chan HM, Parfitt DE, Crisosto CH, Gradziel TM (2017) Genome-wide DNA-(de)

126 methylation is associated with noninfectious budfailure exhibition in almond (Prunus dulcis [Mill.] D. A. Webb). Sci Rep 7:42686 Fulgoni V, Abbey M, Davis P, Jenkins D, Lovejoy J, Most M, Sabate J, Spiller G (2002) Almonds lower blood cholesterol and LDL-cholesterol but not HDLcholesterol in human subjects: results of a metaanalysis. FASEB J 16:981–982 Gericke O (2015) Factors inducing the transition from dormancy to flowering in fruit trees: meeting the challenges of climate change. Master Thesis, University of Copenhagen, Denmark González-Rossia D, Reig C, Dovis V, Gariglio N, Agustí M (2008) Changes on carbohydrates and nitrogen content in the bark tissues induced by artificial chilling and its relationship with dormancy bud break in Prunus sp. Scientia Hort 118:275–281 Gradziel TM (2009) Almond (Prunus dulcis) breeding. Breeding plantation tree crops: temperate species. Springer, New York, pp 1–31 Grasselly C (1972) L’amandier: caractères morphologiques et physiologiques des variétés, modalité de leurs transmissions chez les haract de première haracteri. PhD Thesis, Université de Bordeaux, Bordeaux, France Habu T, Yamane H, Sasaki R, Yano K, Fujii H, Shimizu T, Yamamoto T, Tao R (2014) Custom microarray analysis for transcript profiling of dormant vegetative buds of Japanese apricot during prolonged chilling exposure. J Japan Soc Hort Sci 83:1–16 Halaly T, Pang X, Batikoff T, Crane O, Keren A, Venkateswari J, Ogrodovitch A, Sadka A, Lavee S, Or E (2008) Similar mechanisms might be triggered by alternative external stimuli that induce dormancy release in grape buds. Planta 228:79–88 Hamilton JA, El Kayal W, Hart AT, Runcie DE, ArangoVelez A, Cooke JEK (2016) The joint influence of photoperiod and temperature during growth cessation and development of dormancy in white spruce (Picea glauca). Tree Physiol 36:1432–1448 Hatch AH, Walker DR (1969) Rest intensity of dormant peach and apricot leaf buds as influenced by temperature, cold haracter and respiration. J Am Soc Hort Sci 94:304–307 Heide OM (2003) High autumn temperature delays spring bud burst in boreal trees, counterbalancing the effect of climatic warming. Tree Physiol 23:93–936 Heide OM (2008) Interaction of photoperiod and temperature in the control of growth and dormancy of Prunus species. Sci Hort 115:309–314 Heide OM, Prestud AK (2005) Low temperatures, but not photoperiod, controls growth cessation and dormancy induction and release in apple and pear. Tree Physiol 25:109–114 Hedley PE, Russell JR, Jorgensen L, Gordon S, Morris JA, Hackett CA, Cardle L, Brennan R (2010) Candidate genes associated with bud dormancy release in blackcurrant (Ribes nigrum L.). BMC Plant Biol 10:202

Á. S. Prudencio et al. Hillman JR (1984) Apical dominance. In: Wilkins MB (ed) Advanced plant physiology. Pitman, London, pp 127–148 Horvath D (2009) Common mechanisms regulate flowering and dormancy. Plant Sci 177: 523–531 Horvath DP, Chao WS, Suttle JC, Thimmapuram J, Anderson JV (2008) Transcriptome analysis identifies novel responses and potential regulatory genes involved in seasonal dormancy transitions of leafy spurge (Euphorbia esula L.). BMC Gen 9:536 Howad W, Yamamoto T, Dirlewanger E, Testolin R, Cosson P, Cipriani G, Monforte AJ, Georgi L, Abbott AG, Arús P (2005) Mapping with a few plants: using selective mapping for microsatellite saturation of the Prunus reference map. Genetics 171:1305–1310 Howe GT, Horvath DP, Dharmawardhana P, Priest HD, Mockler TC, Strauss SH (2015) Extensive transcriptome changes during natural onset and release of vegetative bud dormancy in Populus. Front Plant Sci 6:989 Ibáñez C, Kozarewa I, Johansson M, Ögren E, Rohde A, Eriksson ME (2010) Circadian clock components regulate entry and affect exit of seasonal dormancy as well as winter hardiness in Populus trees. Plant Physiol 153:1823–1833 Ionescu IA, López-Ortega G, Burow M, Bayo-Canha A, Junge A, Gericke O, Møller BL, Sánchez-Pérez R (2017) Transcriptome and metabolite changes during hydrogen cyanamide-induced floral bud break in sweet cherry. Front Plant Sci 8:1233–1233 Jonkers H (1979) Bud dormancy of apple and pear in relation to the temperature during the growth period. Sci Hort 10:149–154 Jotatsu H, Yamane H, Sasaki R, Ooka T, Hosaka Y, Tao R (2011) Expressional regulation of PpDAM5 and PpDAM6, peach (Prunus persica) dormancyassociated MADS-box genes, by low temperature and dormancy-breaking reagent treatment. J Exp Bot 62:3481–3488 Julian C, Rodrigo J, Herrero M (2011) Stamen development and winter dormancy in apricot (Prunus armeniaca). Ann Bot 108:617–625 Kaufmann K, Pajoro A, Angenent GC (2010) Regulation of transcription in plants: mechanisms controlling developmental switches. Nature Rev Genet 11:830 Kester DE (1965) Inheritance of time of bloom in certain progenies of almond. Proc Am Soc Hort Sci 87:214– 221 Kester DE, Raddi P, Asay RN (1977) Correlations of chilling requirements for germination blooming and leafing within and among seedling population of almond. J Am Soc Hort Sci 102:145–148 Koike M, Takezawa D, Arakawa K, Yoshida S (1997) Accumulation of 19-kDa plasma membrane polypeptide during induction of freezing tolerance in wheat suspension-cultured cells by abscisic acid. Plant Cell Physiol 38:707–716

Transcriptional Changes Associated to Flower Bud Dormancy … Kumar G, Arya P, Gupta K, Randhawa V, Acharya V, Singh AK (2016) Comparative phylogenetic analysis and transcriptional profiling of MADS-box gene family identified DAM and FLC-like genes in apple (Malus domestica). Sci Rep 6:20695 Lämke J, Bäurle I (2017) Epigenetic and chromatin-based mechanisms in environmental stress adaptation and stress memory in plants. Genome Biol 18:124 Lamp B, Connell J, Duncan R, Viveros M, Polito V (2001) Almond flower development: floral initiation and organogenesis. J Am Soc Hort Sci 126:689–696 Lang GA, Early JD, Martin GC, Darnell RL (1987) Endo, para- and ecodormancy: physiological terminology and classification for dormancy research. HortScience 22:371–377 Lata C, Prasad M (2011) Role of DREBs in regulation of abiotic stress responses in plants. J Exp Bot 62:4731– 4748 Lee SJ, Kang JY, Park HJ, Kim MD, Bae MS, Choi HI, Kim SY (2010) DREB2C interacts with ABF2, a bZIP protein regulating abscisic acid-responsive gene expression, and its overexpression affects abscisic acid sensitivity. Plant Physiol 153:716–727 Leida C, Terol J, Martí G, Agustí M, Llácer G, Badenes ML, Ríos G (2010) Identification of genes associated with bud dormancy release in Prunus persica by suppression subtractive hybridization. Tree Physiol 30:655–666 Leida C, Conesa A, Llácer G, Badenes ML, Ríos G (2012a) Histone modifications and expression of DAM6 gene in peach are modulated during bud dormancy release in a cultivar-dependent manner. New Phytol 193:67–80 Leida C, Romeu JF, García-Brunton J, Ríos G, Badenes ML (2012b) Gene expression analysis of chilling requirements for flower bud break in peach. Plant Breed 131:329–334 López-Molina L, Mongrand S, Kinoshita N, Chua NH (2003) AFP is a novel negative regulator of ABA signaling that promotes ABI5 protein degradation. Genes Dev 17:410–418 Lloret A, Martínez-Fuentes A, Agustí M, Badenes ML, Ríos G (2017) Chromatin-associated regulation of sorbitol synthesis in flower buds of peach. Plant Mol Biol 95:507–517 Luedeling E, Zhang M, McGranahan G, Leslie C (2009) Validation of winter chill models using historic records of walnut phenology. Agric For Meteorol 149(11):1854–1864 Martínez-García PJ, Mañas F, López P, Dicenta F, Ortega E (2011) Molecular and phenotypic characterization of the S-locus and determination of flowering time in new ‘Marcona’ and ‘Desmayo Largueta’-type almond (Prunus dulcis) selections. Euphytica 177:67– 78 Mazzitelli L, Hancock RD, Haupt S, Walker PG, Pont SDA, McNicol J, Cardle L, Morris J, Viola R, Brennan R (2007) Co-ordinated gene expression during phases of dormancy release in raspberry (Rubus idaeus L.) buds. J Exp Bot 58:1035–1045

127

Mielke EA, Dennis FG (1978) Hormonal control of flower bud dormancy in sour cherry (Prunus cerasus L.) III. Effects of leaves, defoliation and temperature on levels of abscisic acid in flower primordia. J Am Soc Hort Sci 103:446–449 Musa-Veloso K, Paulionis L, Poon T, Lee HY (2016) The effect of almond consumption on fasting blood lipid levels: a systematic review and meta-analysis of haracteri controlled trials. J Nutr Sci 5:e34 Nishizawa A, Yabuta Y, Shigeoka S (2008) Galactinol and raffinose constitute a novel function to protect plants from oxidative damage. Plant Physiol 147: 1251–1263 Olukolu B, Trainin T, Fan S, Kole C, Bielenberg D, Reighard G, Abbott A, Holland D (2009) Genetic linkage mapping for molecular dissection of chilling requirement and budbreak in apricot (Prunus armeniaca L.). Genome 52:819–828 Ogundiwin EA, Peace CP, Gradziel TM, Parfitt DE, Bliss FA, Crisosto CH (2009) A fruit quality gene map of Prunus. BMC Genomics 10:587 Pascual J, Cañal MJ, Correia B, Escandon M, Hasbún R, Meijón M, Pinto G, Valledor L (2014) Can epigenetics help forest plants to adapt to climate change? In: Álvarez-Venegas R, De la Peña C, Casas-Mollano JA (eds) Epigenetics in plants of agronomic importance: fundamentals and applications. Springer, Cham, pp 125–146 Pérez FJ, Vergara R, Rubio S (2008) H2O2 is involved in the dormancy-breaking effect of hydrogen cyanamide in grapevine buds. Plant Growth Regul 55:149–155 Polito VS, Pinney K, Heerema R, Weinbaum S (2002) Flower differentiation and spur leaf area in almond. J Hort Sci Biotechnol 77:474–478 Poonam V, Kumar G, Reddy L, Jain R, Sharma S, Prasad A, Parmar V (2011) Chemical constituents of the genus Prunus and their medicinal properties. Current Med Chem 18:3758–3824 Prudencio AS, Martínez-Gómez P, Dicenta F (2018a) Evaluation of breaking dormancy, flowering and productivity of extra-late and ultra-late flowering almond cultivars during cold and warm seasons in South-East of Spain. Sci Hort 235:39–46 Prudencio AS, Werner O, Martínez-García PJ, Dicenta F, Ros RM, Martínez-Gómez P (2018b) DNA methylation analysis of dormancy release in almond using epiGenotyping by Sequencing. Int J Mol Sci 19:3542 Prudencio AS, Díaz-Vivancos P, Dicenta F, Hernández JA, Martínez-Gómez P (2019) Monitoring the transition from endodormancy to ecodormancy in almond through the analysis and expression of a specific Class III peroxidase gene. Tree Genet Gen 15:44 Prudencio AS, Hoeberichts FA, Dicenta F, MartínezGómez P, Sánchez-Pérez R (2021) Identification of early and late flowering time candidate genes in endodormant and ecodormant almond flower buds. Tree Physiol 41(4):589–605 Quarta R, Dettori MT, Sartori A, Verde I (2000) Genetic linkage map and QTL analysis in peach. Acta Hort 521:233–241

128 Quilot B, Wu BH, Kervella J, Génard M, Foulongne M, Moreau K (2004) QTL analysis of quality traits in an advanced backcross between Prunus persica cultivars and the wild relative species P. davidiana. Theor Appl Genet 109:884–897 Rabot A, Mortreau E, Ben Baaziz K, Hamama L, Boummaza R, Sakr S, Henry C, Lothier J, Le Gourrierec J, Leduc N (2012) Insight into the role of sugars in bud burst under light in the rose. Plant Cell Physiol 53:1068–1082 Ramos A, Pérez-Solís E, Ibáñez C, Casado R, Collada C, Gómez L, Aragoncillo C, Allona I (2005) Winter disruption of the circadian clock in chestnut. Proc Natl Acad Sci 102:7037–7042 Ramsay J, Martin GC (1970) Seasonal changes in growth promoters and inhibitors in buds of apricot. J Am Soc Hort Sci 95:569–574 Rasouli M, Fatahi-Moghaddam MR, Zamani Z, Imani A, Martínez-Gómez P (2013) Microsatellite markers linked to the genes controlling flowering time and some important traits in F1 almond population resulting from controlled crosses of ‘Tuono’ (♂) ⨯ ‘Shahrood-12’ (♀). Seed Plant Improv J 29:805–822 Reinoso H, Luna V, Pharis RP, Bottini R (2002) Dormancy in peach (Prunus persica) flower buds. V. Anatomy of bud development in relation to phenological stage. Can J Bot 80:656–663 Richardson E, Seeley S, Walker D, Anderson J, Ashcroft G (1975) Pheno-climatography of spring peach bud development. HortScience 10:236–237 Rinne PLH, Welling A, Vahala J, Ripel L, Ruonala R, Kangasjärvi J, van der Schoot C (2011) Chilling of dormant buds hyperinduces FLOWERING LOCUS T and recruits GA-inducible 1,3-b-glucanases to reopen signal conduits and release dormancy in Populus. Plant Cell 23:130–146 Ríos G, Leida C, Conejero C, Badenes ML (2014) Epigenetic regulation of bud dormancy events in perennial plants. Front Plant Sci 5:247 Rodrigo MJ, Alquezar B, Zacarías L (2006) Cloning and characterization of two 9-cis-epoxycarotenoid dioxygenase genes, differentially regulated during fruit maturation and under stress conditions, from orange (Citrus sinensis L. Osbeck). J Exp Bot 57:633–643 Rodríguez A, Sánchez-Tamés R (1986) Dormancy and seasonal changes of plant growth regulators in hazel buds. Physiol Plant 66:288–292 Romeu JF, Monforte AJ, Sánchez G, Granell A, GarcíaBrunton J, Badenes ML, Ríos G (2014) Quantitative trait loci affecting reproductive phenology in peach. BMC Plant Biol 14:52 Rothkegel K, Sánchez E, Montes C, Greve M, Tapia S, Bravo S, Almeida AM (2017) DNA methylation and small interference RNAs participate in the regulation of MADS-box genes involved in dormancy in sweet cherry. Tree Physiol 37:1739–1751 Rubio S, Noriega X, Pérez FJ (2018) Abscisic acid (ABA) and low temperatures synergistically increase the expression of CBF/DREB1 transcription factors

Á. S. Prudencio et al. and cold-hardiness in grapevine dormant buds. Ann Bot 123:681–689 Ruttink T, Arend M, Morreel K, Storme V, Rombauts S, Fromm J, Bhalerao RP, Boerjan W, Rohde A (2007) A molecular timetable for apical bud formation and dormancy induction in poplar. Plant Cell 8:2370–2390 Sabate J, Haddad E (2001) Almond-rich diets simultaneously improve plasma lipoproteins and alphatocopherol levels in men and women. Ann Nutrit Metabolism 45:596 Salazar JA, Ruiz D, Egea J, Martínez-Gómez P (2013) Inheritance of fruit quality traits in apricot (Prunus armeniaca L.) and analysis of linked quantitative trait loci (QTLs) using simple sequence repeat (SSR) markers. Plant Mol Biol Rep 31:1506–1517 Salazar JA, Ruiz D, Campoy JA, Tartarini S, Dondini L, Martínez-Gómez P (2016) Inheritance of reproductive phenology traits and related QTL identification in apricot. Tree Genet Gen 12:71 Sánchez-Pérez R, Ortega E, Duval H, Martínez-Gómez P, Dicenta F (2007a) Inheritance and correlation of important agronomic traits in almond. Euphytica 155:381–391 Sánchez-Pérez R, Howad D, Dicenta F, Arús P, MartínezGómez P (2007b) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breed 126:310–318 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Gen 8:379–389 Sánchez-Pérez R, Del Cueto J, Dicenta F, MartínezGómez P (2014) Recent advancements to study flowering time in almond and other Prunus species. Fron Plant Sci 5:334 Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Cigliano RA, Del Cueto J, Ricciardi F, Lotti C, Ricciardi L, Dicenta F (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364:1095–1098 Santamaría M, Hasbún R, Valera M, Meijón M, Valledor L, Rodríguez JL, Rodríguez R (2009) Acetylated H4 histone and genomic DNA methylation patterns during bud set and bud burst in Castanea sativa. J Plant Physiol 166:1360–1369 Saura-Calixto F, Bauza M, Martinez de Toda F, Argamenteria A (1981) Amino acids, sugars, and inorganic elements in the sweet almond (Prunus amygdalus). J Agri Food Chem 29:509–511 Sawa M, Kay SA (2011) GIGANTEA directly activates Flowering Locus T in Arabidopsis thaliana. Proc Nat Acad Sci 108:11698–11703 Saze H (2008) Epigenetic memory transmission through mitosis and meiosis in plants. Sem Cell Develop Biol 19:527–536 Schirra M (1997) Postharvest technology and utilization of almonds. Hortic Rev 20:267–311 Seeley S (1990) Hormonal transduction of environmental stresses. HortScience 25:1369–1376

Transcriptional Changes Associated to Flower Bud Dormancy … Silva C, García-Mas J, Sánchez AM, Arús P, Oliveira MM (2005) Looking into flowering time in almond (Prunus dulcis (Mill.) D.A. Webb): the candidate gene approach. Theor Appl Genet 110:959–968 Singh RK, Svystun T, AlDahmash B, Jönsson AM, Bhalerao RP (2017) Photoperiod- and temperaturemediated control of phenology in trees—a molecular perspective. New Phytol 213:511–524 Srinivasan C, Dardick C, Callahan A, Scorza R (2012) Plum (Prunus domestica) trees transformed with poplar ft1 result in altered architecture, dormancy requirement, and continuous flowering. PLoS ONE 7: e40715 Sudawan B, Chang CS, Chao HF, Ku MSB, Yen YF (2016) Hydrogen cyanamide breaks grapevine bud dormancy in the summer through transient activation of gene expression and accumulation of reactive oxygen and nitrogen species. BMC Plant Biol 16:202 Tabuenca MC (1972) Chilling requirements in almond. Anales De La Estación Experimental De Aula Dei 11:325–329 Tamura F, Tanabe K, Banno K (1992) Effect of chilling treatment on intensity of bud dormancy, respiration and endogenous growth regulators in Japanese pear ‘Nijisseiki’. J Japan Soc Hortic Sci 60:763–769 Trainin T, Bar-Ya’akov I, Holland D (2013) ParSOC1, a MADS-box gene closely related to Arabidopsis AGL20/SOC1, is expressed in apricot leaves in a diurnal manner and is linked with chilling requirements for dormancy break. Tree Genet Gen 9:753–766 Tromp J (1976) Flower-bud formation and shoot growth in apple as affected by temperature. Sci Hort 5:331–338 van Gurp TP, Wagemaker NV, Wouters B, Vergeer P, Ouborg JN, Werhoeben KJV (2016) epiGBS: referencefree reduced representation bisulfite sequencing. Nat Methods 13:322–329 van der Schoot C, Paul LK, Paul SB, Rinne PLH (2011) Plant lipid bodies and cell-cell signaling: a new role for an old organelle? Plant Signal Behav 6:1732–1738 Vargas FJ, Romero MA, Rovira M, Girona J (1984) Amélioration de l’amandier par croisement de variétés. Résultats préliminaires à Tarragone (Espagne). Options Méditerr 1:101–122 Verde I, Quarta R, Cerdrola C, Dettori MT (2002) QTL analysis of agronomic traits in a BC1 peach population. Acta Hort 592:291–297 Vitasse Y, Lenz A, Körner C (2014) The interaction between freezing tolerance and phenology in temperate deciduous trees. Frontiers Plant Sci 5:541 Voogd C, Wang T, Varkonyi-Gasic E (2015) Functional and expression analyses of kiwifruit SOC1-like genes suggest that they may not have a role in the transition

129

to flowering but may affect the duration of dormancy. J Exp Bot 66:4699–4710 Wang D, Karle R, Iezzoni AF (2000) QTL analysis of flower and fruit traits in sour cherry. Theor Appl Genet 100:535–544 Wisniewski ME, Bassett CL, Renaut J, Farrell R, Tworkoski T, Artlip TS (2006) Differential regulation of two dehydrin genes from peach (Prunus persica) by photoperiod, low temperature and water deficit. Tree Physiol 26:575–584 Wu R, Tomes S, Karunairetnam S, Tustin SD, Hellens RP, Allan AC, Macknight RC, Varkonyi-Gasic E (2017) SVP-like MADS box genes control dormancy and budbreak in apple. Front Plant Sci 8:477 Xu Z, Zhang Q, Sun L, Du D, Cheng T, Pan H, Yang W, Wang J (2014) Genome-wide identification, haracterization and expression analysis of the MADS-box gene family in Prunus mume. Mol Genet Gen 289:903–920 Yamamoto T, Shimada T, Imai T, Yaegaki H, Haji T, Matsuta N, Yamaguchi M, Hayashi T (2001) Characterization of morphological traits based on a genetic linkage map in peach. Breed Sci 51:271–278 Yaish MW, Colasanti J, Rothstein SJ (2011) The role of epigenetics processes in controlling flowering time exposed to stress. J Exp Bot 62:3727–3735 Yoshida R, Fekih R, Fujiwara S, Oda A, Miyata K, Tomozoe Y, Nakagawa M, Niinuma K, Hayashi K, Ezura H (2009) Possible role of EARLY FLOWERING 3 (ELF3) in clock-dependent floral regulation by SHORT VEGETATIVE PHASE (SVP) in Arabidopsis thaliana. New Phytol 182:838–850 Zhang Z, Zhuo X, Zhao K, Zheng T, Han Y, Yuan C, Zhang Q (2018) Transcriptome profiles reveal the crucial roles of hormone and sugar in the bud dormancy of Prunus mume. Sci Rep 8:5090–5090 Zhao K, Zhou Y, Li Y, Zhuo X, Ahmad S, Han Y, Yong X, Zhang Q (2018) Crosstalk of PmCBFs and PmDAMs based on the changes of phytohormones under seasonal cold stress in the stem of Prunus mume. Int J Mol Sci 19:15 Zhebentyayeva TN, Fan S, Chandra A, Bielenberg DG, Reighard GL, Okie WR, Abbott AG (2014) Dissection of chilling requirement and bloom date QTLs in peach using a whole genome sequencing of sibling trees from an F2 mapping population. Tree Genet Gen 10:35–51 Zhu Y, Li Y, Xin D, Chen W, Shao X, Wang Y, Guo W (2015) RNA-Seq-based transcriptome analysis of dormant flower buds of Chinese cherry (Prunus pseudocerasus). Gene 555:362–376

Molecular Basis of the Abiotic Stresses in Almond Beatriz Bielsa and Maria José Rubio-Cabetas

Abstract

The advance of new technologies has increased the availability of whole genome. Next generation sequencing (NGS) technologies represent a major revolution in providing new tools for identifying the genes and/or genomic intervals controlling important traits for selection in breeding programmes. In fruit trees with long generation times and large sizes of adult plants, the impact of these techniques is considerably more important. Altogether, NGS techniques and their applications have increased the resources for plant breeding almond species, closing the former gap of genetic tools between trees and annual species. Even thought, the dissection of complex traits became possible through the availability of genome sequences along with phenotypic variation data, which allow to elucidate the causative genetic differences that give rise to observed phenotypic variation. Those techniques have allowed to decipher

B. Bielsa . M. J. Rubio-Cabetas (&) Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Unidad de Hortofruticultura, Avda. Montañana 930, 50059 Zaragoza, España e-mail: [email protected] M. J. Rubio-Cabetas Instituto Agroalimentario de Aragón—IA2 (CITA-Universidad de Zaragoza), Zaragoza, España

some complex traits in almond rootstock as a tool to develop biomarkers. However, new approaches should be done in future to obtain better results based in plant microbe interactions and improve the ability of biological inoculum and to identify root exudate to enhance the sustainability of agricultural crops in nutrient unbalance conditions that ultimately cause abiotic stresses.

1

Introduction

Almond is an important horticultural crop amongst nut fruits. Both production and consumption are dramatically increasing due to high economic return and the nutritional value of the products. Technical knowledge regarding almond production has also rapidly increased in support of the demand for greater production and quality. One of the main areas of interest and progress has been the use of suitable rootstocks to increase yield, control scion vigour, allow cultivation in poor soil and climates, overcome external stresses, and improve fruit quality. Rootstocks are one of the factors determining the ultimate performance of orchards through impacts on planting density, nutritional uptake, canopy architecture, yield, and nut quality (Day 1953; Lordan et al. 2019). In addition, rootstocks are capable of increasing usage of arable lands by alleviating external abiotic stresses (Nimbolkar et al. 2016). Rootstocks also can also influence a

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_9

131

132

scion’s ability to defend against drought stress (Bielsa et al. 2018b, 2019a). Usually, selection of scion cultivar is the grower’s top consideration for long-term productivity and profitability, whilst rootstock selection is often neglected. However, the interactions of scion and rootstock on each other can play a significant and determinative role in orchard success. Like all the fruit trees, the advantages of selected rootstocks have been recognized and utilized in the production of almond, only recently in vitro techniques allow to propagate almond cultivars and to growth in its own roots. Although several almond cultivars are grown around the world, rootstock breeding is more limited due to long generation times and large sizes of adult plants. Therefore, the molecular basis of abiotic stressed affecting the roots is more limited than the scion studies and ultimately the responsible to alleviate the abiotic stressed to the almond tree, and only some studies have achieved results. The study of genetic diversity is also essential for the breeding of fruit trees, being the increase of variability a basic step in many breeding programmes. There are numerous studies aimed at the search for new variants in cultivated or wild species of the different genera. In the genus Prunus, there are also examples of the use of wild species with the aim of creating introgression lines that contain advantageous genome regions within a commercial genetic background (Donoso et al. 2012). Studies in fruit trees have included diversity with several wild species related to almond trees (Fernández i Martí et al. 2014). The partial sequencing of chloroplastic DNA has also served to establish genetic relationships in the genus Prunus (Bielsa et al. 2014). Gene discovery and genomic regions involved in the variation of traits of interest have been more limited in abiotic stress. One of the main strategies that has been used in tree species to find an association between genotype and phenotype has been the elaboration of genetic maps with the identification of QTLs or regions of the genome linked to the characters of interest. In tree species, most of the maps made concern fruit

B. Bielsa and M. J. Rubio-Cabetas

species of economic interest such as stone fruit trees or the genus Prunus. Given the high heterozygosity of these species and the quantitative or polygenic inheritance of many of the traits of interest, it has been possible to determine the linked areas of the genome in most cases, but not with sufficient precision to be able to perform assisted breeding. However, these results represent starting points so that through the published genomes and available SNPs markers that allow saturating the target regions, obtaining markers and potential candidate genes for marker-assisted selection (MAS). One of the characters that has aroused more interest in recent years is that of the cold needs to get out of dormancy, as well as phenological characters, such as dates of flowering and fruit ripening, since they comprise the greatest limiting factor for expansion of fruit trees in warm areas and in turn represent the genetic basis on which to act under conditions of climate change. The first works were carried out in Populus (Frewen et al. 2000) and later in species of the genus Prunus. A mutant that did not go into latency (evergrowing) was found in the peach tree; the positional mapping of this mutation determined that it was due to a deletion in a tandem of MADS-box genes (Bielenberg et al. 2008). Similar QTLs, although with differences in individual contribution, have been detected in almond (Sánchez-Pérez et al. 2012). From the sequence of extreme individuals for the out-oflatency trait, polymorphisms and potential candidate genes were identified in three of the most important QTLs described linked to this trait (Zhebentayeva et al. 2014). Tolerance to abiotic stress together with resistance to pathogens is a priority in rootstock breeding. Some of Mendelian inheritance has been identified that have allowed the establishment of markers and assisted improvement for biotic stress (Esmenjaud and Dirlewanger 2007). However, less success has been achieved in abiotic stress in Prunus. Meanwhile abiotic stresses are mainly of quantitative inheritance, and some QTLs have been identified, which entails limitations for MAS (Bert et al. 2013; Gonzalo et al. 2012). Another option is selection from the genome or genomic selection (GS), a

Molecular Basis of the Abiotic Stresses in Almond

viable alternative in those species that have known genomes and have SNPs to genotype. Identification of the genes underlying these QTLs is facilitated by candidate gene expression studies. Expression studies with microarrays containing unigenes and ESTs have been carried out to identify fruit quality candidates in Prunus. So that genetic and genomic information to efficiently develop cultivars and more specially rootstock amongst the fruit species are more limited. Assisted improvement stands out in the scion and even in apple and peach trees, due to their economic importance and for the greater availability of resources (linkage maps, genome sequence, and SNP platforms). However, we will make a review of the progress last years.

2

Rootstock Development to Overcome Abiotic Stress

The first rootstocks used in almond were primarily seed source rootstocks (seedlings). Seedling rootstocks can play a crucial role in certain cultivation areas, although they do not show genetic uniformity as clonal rootstocks are present. In addition, seedling rootstocks are still used in almond because this root system is apparently more efficient in the water and nutrients uptake from the soil. The geographical origin of the seed source is believed to be important and is related to the regions where the performance of seedling rootstocks is best, as has been shown in almond (Felipe 1989, Rubio Cabetas 2016) and other nut species (Grauke 2019). Therefore, it is best to collect seed from a regional orchard where all potential male parents have a high level of local adaptation. Initially, seedling rootstocks were produced from seeds of non-selected plants, primarily from bitter almonds. Almond, as a genetically close species to the peach species of considerable economic importance, has conducted the development of several clonal rootstocks over the years (Rubio-Cabetas et al. 2017). Modern Prunus rootstock breeding programmes have released several clonal rootstocks that address currently challenges related to

133

vigour, graft incompatibility, yield, nut quality, climate change, and biotic stress amongst others. However, knowledge of scion-rootstock interactions remains limited. Considering the great advances of new technologies like next generation sequencing (NGS) has increased the capability of identifying the variability amongst the several species that conform to the genetically diverse species in interspecific hybrid used for almond nowadays. This review focusses on the recent advances related to the physiological and molecular effects of rootstocks in abiotic stress responses developed in the last years. Almond rootstock utilization has been quite different between the two main almond-growing regions: The Mediterranean basin and California, with significantly different development in each one, and being the changes much more notable in the Mediterranean basin due to the changes produced in the different almond-growing systems of that in each region. In the last decades, almond production in the Mediterranean basin has transitioned from traditional cultural practices with marginal inputs towards new orchards aiming to obtain high yields, including the new challenges of planting intensive and semi-intensive orchards. In the past, almond had been grown under endemic rainfall using seedling almonds as rootstocks for centuries. Eventually, some cultivars were selected because they produced more homogeneous plants amongst the seedling populations (Felipe 1989) or because they showed some rootknot nematode (RKN) resistance (Kochba and Spiegel-Roy 1976). In the 1970s, almond * peach hybrids like ‘GF-677’ were introduced as almond rootstocks. Although they were originally selected for peach, they showed excellent performance with almond and eventually became viable substitutes for almond seedlings. Due to their good performance under natural rainfall conditions in arid regions and in calcareous soils, these hybrids competed directly with almond seedlings as preferred rootstocks. However, the present trend in the Mediterranean region is towards the utilization of other interspecific hybrids, mainly Garnem®

134

(Felipe 2009). At the present, the interest in using the new red-leafed hybrid Garnem® for almond has increased exponentially, mainly for replanting conditions. Both hybrids, ‘GF-677’ and Garnem®, have shown good performance for years with several scion cultivars (Rubio-Cabetas et al. 2017). Moreover, under irrigation conditions, however, the production and yield from these vigorous rootstocks are significantly higher, even in the early years of the orchard. For successful almond growing, the use of peach * almond hybrid rootstocks became a necessity in the Mediterranean basin. Amongst the interspecific Prunus hybrids, some almond peach selections have been better known and more disseminated, such as Garnem® (Felipe 2009). In recent years, other hybrid clones have been studied, involving almond, peach, and several plum species parentages, especially those of interest because of their graft compatibility and satisfactory agronomical performance (Rubio-Cabetas et al. 2017). After many years of attempted crosses, there are now several clones commercially propagated, such as ‘Replantpac’, a Myrobalan * almond hybrid with compatibility with almond (Pinochet 2009, 2010). There are also several selections commercially propagated that could eventually replace ‘GF-677’, although their field behaviour with almond cultivars is still being evaluated. In the United States, the best-known and most planted interspecific Prunus hybrids are the almond * peach hybrids, being the most recent releases being ‘Nickels’ and ‘Cornerstone’. Most recently, a peach * peach hybrid, named ‘Controller™ 6’, has been also released. In Spain, the best-known new hybrids commercially propagated because of their high vigour are Garnem®, Felinem®, and Monegro®. In recent years, some progenies from a private Spanish breeding programme incorporating crosses between different Prunus species have been released as rootstocks for intensive orchard systems. Two of these are Rootpac® 20 (‘Densipac’) and Rootpac® 40 (‘Nanopac’). In California and Australia, the most used rootstock is the peach seedling

B. Bielsa and M. J. Rubio-Cabetas

‘Nemaguard’, though this may be primarily due to its availability for these rapidly expanding industries. Currently, important trials are under way to select better rootstocks for almond growing in both California and Australia. ‘Nemaguard’ had been used extensively because of its resistance to RKN in sandy soils, where the seedling peach rootstock ‘Lovell’ was susceptible. More recently, almonds on Guardian® instead of ‘Nemaguard’ are being planted on sandy, bacterial canker prone sites, and ‘Viking’ is being planted extensively on similar sites and saline soils. The American red-leafed ‘Nemared’, derived from ‘Nemaguard’, was not well adapted to the calcareous soils of most of the Mediterranean region, but it was used as a parent in crosses to produce red-leafed peach * almond hybrids with RKN resistance and tolerance to calcareous soils (Felipe 2009). The order of importance of the breeding objectives in the programmes searching for better adapted rootstocks may be ranked as follows: nematode resistance, waterlogging tolerance, vigour control, calcareous soil adaptation, drought tolerance, soil disease (fungi and bacteria) resistance, and cold hardiness (Rubio-Cabetas 2010).

3

Other Important Criteria for Almond Rootstocks

Other important traits of an almond rootstock are categorized into five groups: nursery characteristics, graft compatibility, orchard characteristics, resistance to biotic, and tolerance abiotic factors (Felipe et al. 1998). We will mention the first before going on detail the tolerance to abiotic stress. Only the nursery production system may determine if the differentiate rootstocks are to be propagated by seed or vegetative (asexually) propagated. In any case, any rootstock must fulfil all or most of the requirements from each group mentioned. Nursery Characteristics. Easy propagation is the first requirement for any rootstock. In the case of seedling rootstocks, this implies mother

Molecular Basis of the Abiotic Stresses in Almond

plants with very high fruit sets and seeds with a high germination rate. Although seedlings are always heterogeneous, some seed sources produce seedlings with a good level of homogeneity and are consequently preferred. For clonal rootstocks, cutting propagation by cuttings is usually preferred because it is less expensive than other propagation methods. However, currently, micropropagation must be considered as the best alternative method for the progressive nursery and fruit modern industries and also the only alternative for clones difficult to propagate by cuttings. In any propagation method, rooting must be easy and the resulting root system, vigorous. A good root system is always a requirement for any rootstock. Seedling rootstocks frequently have the undesirable trait of a taproot with little branching, but some seedling rootstocks produce enough lateral roots to allow good plant establishment and growth. Graft Compatibility. A commercial rootstock must be compatible with all or most cultivars of the species for which it has been selected. In almond, graft compatibility is successful with rootstocks of almond and peach (P. persica (L.) Batsch) and their hybrids. So far there have been no reported cases of incompatibility between almond and peach. With hexaploid plums (P. domestica L. and P. insititia L.), there are frequent cases of localized incompatibility, and with diploid plums (Myrobalan and Marianna), there is usually translocated incompatibility. Results with some diploid plums show that there is an increased frequency of translocated incompatibility with ‘Desmayo Largueta’. However, ‘Ardèchoise’ exhibits no translocated incompatibility and only a few cases of localized incompatibility (Felipe 1989). The diversity of soil types and the need to overcome the adverse edaphic conditions have prompted the search for rootstocks that can adapt to and grow well in a wide range of soil conditions. Since stone fruit rootstocks can often be used with different species.

135

In almond, graft incompatibility is a trait that is more genetic than genome dependent. Kester and Grasselly (1987) observed that the graft incompatibility of ‘Nonpareil’ an ‘Carmel’, two close cultivars, grafted on plum rootstocks presented different graft incompatibility between them. Several studies have sought earlier physiological and molecular predictors of graft compatibility, considering the anatomical, physiological, and molecular aspects involving in the compatible graft union formation such as the similarities/differences in scion versus rootstock vascular size and configuration (Aloni et al. 2010). In that line, Irisarri et al. (2016) identified several potential molecular biomarkers associated to the compatible graft formation. Also, several QTLs have been recently identified in apricot (Pina et al. 2021), but the specific cause and effect relationships remain still vague. The metabolic pathways involved in graft incompatibility such as auxin signalling, cell wall biosynthesis, phenylpropanoid pathway, and oxidative stress have revealed (Pina et al. 2017). Orchard Characteristics. Since almond seedlings are generally sensitive to improper transplant techniques, only new interspecific rootstocks might be a better choice for replants conditions. In dry environments, growing conditions vigorous rootstocks are preferred to induce a good growth and cropping under seasonally unfavourable growing conditions. Under irrigation, vigorous rootstocks are not so important, but they still induce rapid orchard development and increase the early productivity of the orchard. The rootstock must promote high and consistent levels of production during the whole life of the orchard. Generally, orchard life span is long with almond seedlings and almond peach hybrids. Peach rootstocks induce a shorter life span. The effects of other rootstocks, especially hybrids with plums, are not so well known. Suckering. Several rootstocks, mainly plums, may produce suckers. This trait must be selected against avoided as much as possible because

136

B. Bielsa and M. J. Rubio-Cabetas

of possible the orchard management constraints problems (weed control, labour access, irrigation) and the risk of disease infection through mechanical or chemical wounds in the suckers.

4

Water and Nutrient Uptake

There are large differences amongst rootstocks in their ability to absorb water and nutrients from the soil. High absorption efficiency is desirable in order to sustain a good productivity even under less favourable conditions. A well-developed good root system, besides improving the nutrient uptake efficiency of the rootstock, is essential to ensure anchorage of the tree. Water and nutrient uptake by the rootstock are two factors that affect directly on the nut yield of the scion cultivar. Complex scion/rootstock interactions regulate both water and nutrient assimilation, where several compounds including macromolecules, hormones, and miRNAs play an important role in the long-distance signalling regulation (Nawaz et al. 2016). It is known that the rootstock vigour improves the water and nutrient uptake. The potential transfer of water and nutrient to the scion is determined by the rate of vascular bundle development in a graft union. Therefore, the water and nutrient assimilation will be decreased whether an insufficient vascular bundle connection is present in the graft union. This causes changes in the nutrient translocation and the hormonal signalling (Martínez-Ballesta et al. 2010). Although it is believed that the Prunus scion type affects to the sensitivity to alkaline soils, this has not yet been well described. In terms to excess to boron, there are different tolerance degrees. Then, Marianna plum (P. cerasifera * P. munsoniana) and peach show a better tolerance to the excess of boron than almond, which, in turn, is more tolerant than Myrobalan plums. For this reason, almond rootstocks are recommended for locations with a lack of this compound. Generally, Marianna plum or more vigorous rootstocks are preferred when the concentration of boron is low. Furthermore, almond

and peach rootstocks are more likely to present a Zn deficiency than Marianna plums. In relation to the potassium uptake, almond scion cultivars on almond and myrobalan rootstocks show a higher susceptibility to potassium deficiency than peach that can lead to the death of the tree (Day 1953). New strategies using biofilmed biofertilizers will create a more suitable environment for biofertilizers to compete with resident organisms and to cope with the heterogeneity of biotic and abiotic factors in soil. Several examples have shown that biofilmed biofertilizers augmented P-solubilization N2 fixation siderophore production and Zn solubilization (Mitter et al. 2021). Other authors suggest particular soil amendments could act as ‘prebiotics’ to promote microbial functions (Arif et al. 2020).

5

Physiological Metabolic and Molecular Response of Abiotic Stresses

Climate change alters temperature and precipitation regimes leading to an increase of abiotic stresses which affects to the yield crop (Nimbolkar et al. 2016). In the last decades, modern almond breeding programmes consider the abiotic stress adaptation as a main goal. Therefore, several rootstocks adapted to abiotic stresses have been released. Those are generally complex hybrids and interspecific hybrids between almond and plum (Pinochet 2010). Land plants have developed a series of physiological, developmental, and biochemical mechanisms that allow them to cope with abiotic stresses (BaileySerres and Voesenek 2010; Colmer and Voesenek 2009). Nevertheless, our knowledge about the physiological and molecular mechanisms involved in abiotic stress tolerance in stone fruits remains in many cases constrained. An ideotype almond rootstock should tolerate and even thrive in most soils. Excessive soil requirements by a rootstock limit its utilization, thus restricting its commercial use. Consequently, a good adaptation to common soil problems, arid, heavy, and calcareous soils is essential. However, the physiological and

Molecular Basis of the Abiotic Stresses in Almond

molecular adaptations to those stresses are frequently different. In Spain, a new generation of almond rootstocks including three-way hybrids and complex hybrids has been developed in the last decades. The objectives are to obtain genotypes with greater resistance to abiotic stresses such as iron chlorosis, waterlogging and drought, and control of scion vigour (Xiloyannis et al. 2007; Dirlewanger et al. 2004; Pinochet 2009, 2010). Controlled interspecific crosses have been undertaken with the purpose of bringing together the desirable traits of different Prunus species. Thus, some Myrobalan genotypes were chosen as parents for their high-level and wide spectrum of RKN resistance and tolerance to waterlogging. Additionally, peach, almond, peach * almond, and peach * P. davidiana hybrids have been used as a different source of nematode resistance, tolerance to iron chlorosis, drought, replant problems, and compatibility with peach.

5.1 Calcareus Soils Stress Tolerance to calcareous high pH soils is an important trait for almond production regions with calcareous soils found most commonly in semiarid and arid zones and very common in the Mediterranean basin. High pH causes iron deficiency, which lowers leaf chlorophyll, fruit yield, fruit size, and soluble solids content according to the degree of chlorosis. The most sustainable approach to overcome iron deficiency in fruit crops is breeding for rootstocks with a higher capability to acquire Fe from the soil. The differential gene expression induced by iron deficiency in the susceptible citrus rootstock Poncirus trifoliata (L.) has been examined. The genes identified are putatively involved in cell wall modification, in determining photosynthesis rate and chlorophyll content, and reducing oxidative stress (Forner-Giner et al. 2009). In a recent study, QTL and candidate gene analyzes of rootstock mediated low-Fe tolerance in terms of fruit yield and quality traits. The most advances studies trying to understand the

137

molecular mechanisms behind citrus adaptation and tolerance to low-Fe stress, several authors have studied root transcriptional and proteomic differences between iron chlorosis tolerant and susceptible citrus rootstocks under contrasting iron fertilization conditions, the metabolic and molecular changes that take place in citrus under iron deficiency (Fu et al. 2017), and recently, Zhang et al. (2020) have provided a reduced list of 14–21 members of the basic/helix-loop-helix (bHLH) transcription factor family as putative key regulators of the iron deficiency response in C. grandis. Asins et al. (2020) identified QTLs associated to tolerance to Fe deficiency and fruit quality traits that were clustered into five genomic regions. In those regions, the authors found different putative functional candidates; amongst them, a metal-NA-transporter YSL3 (Ciclev 10019170 m), four phytochelatin synthases, an iron-chelate-transporter ATPase, as well as four basic/helix-loop helix genes coding for likely relevant transcription factors in Fe homeostasis under Fe deficiency:bHLH3 (Ciclev10019816m), bHLH137.1 (Ciclev10031873m), bHLH123 (Ciclev10008228m), and ILR3 (Ciclev1000 9354m). Tolerance to calcareous soils has been identified amongst peach, plum, but particularly almond (Jiménez et al. 2011). Presently, peach * almond hybrid rootstocks are commonly used in calcareous soils to ensure sufficient iron uptake by the plant (Rubio et al. 2017; Reighard and Loreti 2008). Selection procedures include field evaluation in calcareous soils, greenhouse evaluation at various levels of bicarbonate, and most recently via laboratory measurements of root iron reductase activity on hydroponically grown plants (Jiménez et al. 2011). There is an improved performance of trees when soil pH is maintained above pH 6.0. Deleterious effects of soil pH below 5.5 may be related to the toxicity of Al3+ or low Ca2+ availability (Cummings 1989). Unfortunately, no source of tolerance to aluminium toxicity has been identified still now, so that, this issue is managed by lime application to raise the soil pH.

138

In almond, the impact of rootstock choice on concentrations of lime, alkali, B, Zn, and K has been well studied. The previous studies in Prunus rootstocks reported that high levels of sucrose, organic acids, amino acids, and PEP carboxylase activity in the roots lead to root growth and iron uptake under Fe deficient condition (Jiménez et al. 2011). Trees on almond or almond * peach hybrids show reduced levels of chlorosis from Fe deficiency in soils of high lime content. Somewhat less tolerant is the Myrobalan rootstocks, which will often develop some chlorotic leaves at the shoot tips by late summer. The three-way, and similarly complex, hybrids tend to show more intermediate tolerance to calcareous soils. In general, almond trees on peach perform poorly on calcareous soils, whereas trees on almond rootstocks typically perform better. All Prunus rootstocks are generally sensitive to alkaline soils or water containing an excess of alkali salt. Again, trees on almond rootstocks appear to be the most tolerant, followed by Myrobalan plum, and peach, though with little difference between the latter. Some peach * almond hybrids have also demonstrated greater tolerance to alkali than peach or Myrobalan. Two significant QTLs involved in SPAD and chlorophyll concentration were identified for Felinem® in linkage groups 4 and 6. Both QTLs were detected in four of the six consecutive years of the experiment. For Myrobalan ‘P.2175’, two of the three putative QTLs identified, pspad4.1 and chl4.1, were placed in linkage group 4. These QTLs were related to the SPAD values and chlorophyll concentration, respectively, and co-localized with QTLs detected in the Felinem® map affecting the same traits. Candidate gene PFIT, related to Fe metabolism, was localized within the confidence interval of the QTL in linkage group 4. This research suggests an association of this chromosome region with tolerance to iron chlorosis in Prunus, and it provides a first approach to localize candidate genes involved in tolerance to this abiotic stress (Gonzalo et al. 2012).

B. Bielsa and M. J. Rubio-Cabetas

5.2 Waterlogging Stress Prunus trees are mainly grown in Mediterranean climate regions, which are characterized by infrequent rainfalls concentrated in few days and often leading to flooding. The identification and characterization of the adaptation mechanisms developed by waterlogging tolerant rootstocks are very important to improve the tolerance to flooding of a wider range of genotypes. Root asphyxia is an important abiotic limitation to almond production on heavy soils. The sensitiveness response to asphyxia in Prunus rootstocks varies depending on the genotype. European plum (P. domestica L.) and Myrobalan plum rootstocks are considered root asphyxia tolerant, whilst almond, peach, and their hybrids are more susceptible to waterlogging damage (Amador et al. 2012). Almond seedling rootstocks are not tolerant to waterlogging and, thus, grow poorly or die when planted in even seasonally waterlogged soils. The intensity of the waterlogging effect is more pronounced if the plant is actively growing as compared to dormant trees. Amongst the different species of Prunus, Myrobalan plum (Prunus cerasifera Ehrh.) and European plum (P. domestica L.) are considered waterlogging tolerant (Almada et al. 2013; Pistelli et al. 2012). The difference in flooding tolerance found amongst Prunus species other than peach is based on complex anatomical processes such as aerenchyma formation and biochemical adaptation involving the glycolysis and fermentative pathways to delay the eventual occurrence of the effects from lack of oxygen. Also, it is known that sensitive genotypes present their gas exchange parameters and photosynthetic activity strongly affected in comparison with tolerant genotypes (Amador et al. 2012). Pimentel and Pinto (2014) evidenced that in Prunus species, the morphoanatomical changes are important factors in tolerance to root hypoxia. Differential genes have been identified in poplar with respect to Arabidopsis (Christianson et al. 2010) in response to hypoxia. A first

Molecular Basis of the Abiotic Stresses in Almond

approach to reveal candidate genes in Prunus was done using a Chill-Peach microarray to study cold damage in peach trees (Ogundiwin et al. 2008). Therefore, different genes involved in tolerance to root asphyxia in Prunus species were revealed (Rubio-Cabetas et al. 2010). The recent discovery of oxygen sensor in plants supports the importance of adapting to low oxygen levels both in normal and under stress conditions. Particularly, the role of different ethylene-responsive proteins, including RAP2.12 (Related to Apetala 2.12), RAP2.2, and RAP2.3, in the modulation of hypoxia tolerance has been demonstrated in Arabidopsis (Gibbs et al. 2015; Licausi et al. 2011a, b). In several research, genes related to the root hypoxia stress and waterlogging have been identified clarifying the regulatory processes of the root hypoxia and waterlogging responses. Almada et al. (2013) found two haemoglobin’s genes related to root hypoxia stress response in Prunus also by comparing with Arabidopsis. Later, Arismendi et al. (2015) identified differentially expressed genes upregulated under hypoxia in tolerant, but not in sensitive, genotypes with a key role in posttranscriptional protein modifications, such as hexokinases (HXK) and fructokinases (FRK), as well as in transcription regulation, including AP2 domain-containing, ARR6 (response regulator 6), Sin3-like2, and Zinc finger (GATA type) proteins. Other strategies have also been demonstrated in tolerant and sensitive genotypes under hypoxia conditions. Rubio-Cabetas et al. (2018) confirmed that the tolerant Myrobalan ‘P.2175’ plum averts the waste of resources/ energy as root hypoxia tolerance strategy by the inhibition of the secondary metabolism gene expression. This research also proved the upregulation of genes related to protein degradation, bringing on structural adaptations that confer long-term tolerance to hypoxia in the mirobalan plum. On the other side, a set of genes involving in signal transduction were found upregulated in the sensitive almond * peach hybrid Felinem® (P. amygdalus * P. persica) Finally, Rubio-Cabetas et al. (2018) identified three genes involved in the oxygen sensing mechanism as potential biomarkers in hypoxia-

139

tolerant selection. These genes are ERF74/ RAP2.12, ACBP1/2, and HCR1.

5.3 Drought Stress The physiological performance and its genetic control of Prunus species under water scarcity conditions have been characterized in recent years, clarifying the response to drought stress. After the assessment the long-term drought response of the almond * peach hybrid Garnem®, Bielsa et al. (2016) evidenced that this hybrid rootstock consumed its water reserves during the first days of drought exposition to control the shoot growth rate. As water scarcity worsened, water consumption dropped in response to the loss of hydraulic conductivity (Bielsa et al. 2016). Later, in shorter-term drought experiments, Garnem® was capable to keep the leaf water content rates high under a low water potential, as well as preserve a high cell membrane stability, evidencing that osmotic adjustment is part of its drought-adaptiveresistance mechanism (Bielsa et al. 2019b, 2018b). Furthermore, it is known that abscisic acid (ABA) is involved in a rapid long-distance hydraulic signalling from root to shoot for inducing stomatal closure in the drought stressed Garnem® (Bielsa et al. 2019b). Considering that water use efficiency (WUE) is associated with drought tolerance, a list of almond rootstock collection with diverse in WUE was used to investigate the relationship between phenotypic and genotypic traits (Bielsa et al. 2018a). These authors examined the differences in 48 Prunus species including commercial rootstocks and wild-relative species, by evaluating leaf ash content and carbon isotope discrimination (Δ13C), which are strongly correlated with WUE. The wild-relative species to almond and peach showed the lowest Δ13C ratios, and therefore, a greater WUE than hybrid genotypes. Pilowred® (‘GN-8’), a new release belonging to the GN series (Felipe et al. 2022), showed the WUE of these hybrid rootstocks. In addition, this study led to identification of drought-responsive genes in promoter regions of effector genes involved in

140

ABA signalling in those genotypes with greater WUE. No less important has been the characterization of almond wild-relative species as a natural source of drought tolerance based on this WUE trait (Bielsa et al. 2018a). Along the same lines, Martínez-García et al. (2020) found that wild species such as P. webbii were important natural sources of drought tolerance by studying the physiological response to drought. New and advanced biotechnology techniques have accelerated the understanding of the molecular mechanisms involved in drought tolerance in almond. Recent transcriptomic research has contributed to clarify the genetic response of Prunus species under drought conditions (Bielsa et al. 2018b). In that study, several droughtresponsive genes involved in ABA signalling, antioxidant responses, stomatal regulation, osmotic adjustment, transduction of environmental signals, and leaf development including those directly related to WUE were identified. These include ERF023TF, LRR receptor-like serine/threonine-kinase ERECTA and NFYB3TF (Bielsa et al. 2018b). Alternatively, a proteomic analysis in Garnem® rootstock submitted to short-term drought stress revealed significative changes in abundance levels of different proteins identifying 15 of them, which are involved in different biological processes related to lipid metabolisms, amino acid and nitrogen metabolism, carbon metabolism, ion transport activity, transcriptional response, protein synthesis/modification, defence response, and other functional proteins with a potential drought-adaptive response (Bielsa et al. 2019b). Also, the role of aquaporins in response to water stress was also demonstrated (Opazo et al. 2020), as well as the interactions between scion and hybrid rootstocks during the drought exposure (Bielsa et al. 2019a; Opazo et al. 2020).

B. Bielsa and M. J. Rubio-Cabetas

Hence, the choice of the appropriate rootstock plays a crucial role in yield. Almond rootstock differs in salinity tolerance due to the capacity to inhibit the abortion and/or transport of Cl− and Na2+ between the roots and the scion (Massai et al. 2004). Amongst stone fruits almond can be considered the most tolerant to salinity, followed by apricot, Myrobalan plum, peach, and the interspecific hybrids are more tolerant (Day 1953). In the last years, salt tolerance knowledge has been focussed on physiological and biochemical analysis in almond rootstocks (Dejampour et al. 2012; Sorkheh et al. 2012; Zrig et al. 2011). Numerous experiments have shown that exclusion of Cl− and Na+ is a hereditary/genetic function. Thus, selection of suitable parental genotypes would restrict the translocation of Cl− or Na+ in the grafted variety or developed hybrids. Although effort to improve salinity tolerance in almond has been more limited than in other Mediterranean crops like citrus, several QTLs were initially identified in a backcrossing (BC) (Tozlu et al. 1999). Those studies are limited to crosses and have been a handicap for breeding. Even though the availability of several progenies and families in citrus together with the several genomes has limited its use (Wu et al. 2014; Xu et al. 2013). Those data have been used in a recent studied linked the genome region and QTLs (Asins et al. 2020) with an abiotic stress in citrus. In contrast to drought stress, molecular studies on salinity-tolerant rootstocks are rare in almond. More recently, Sandhu et al. (2020) confirmed that Na+ and Cl− elimination is decisive to salinity tolerance, as well as found genes induced in roots and/or leaves of tolerant-almond genotypes in response to salt stress. Amongst them, genes related to Na+ efflux as SOS1 and SOS2, genes involved in Na+ sequestration in vacuoles like AVP1, or genes implicated in antioxidant and organic solutes as SERF1 (Sandhu et al. 2020).

5.4 Salinity Stress Soil and/or water salinity can affect the growth and normal physiological processes in almond trees. Rootstock is the most important component of the tree to assess salt tolerance or sensitivity.

5.5 Scion Cold Stress Tolerance to freezing temperatures during bloom can also be an important objective in some

Molecular Basis of the Abiotic Stresses in Almond

breeding programmes in regions that are subject to crop losses from spring frost. Several approaches are possible to obtain cultivars tolerant to bloom freezes: late blooming, high bud density, and inherent bud resistance to colder temperatures. Thus, late blooming cultivars with high bud set have been developed. The first two approaches are avoidance approaches and represent traits that are moderately to highly heritable the differences in frost damages within each blooming group showed the existence of significant differences in intrinsic resistance of each genotype to frost, as already observed by Felipe (1988). Most almond cultivars show rather low chilling requirements, although high variability is observed amongst cultivars. Similarly, the almond heat requirements are quite low and very variable (Alonso et al. 2005). The extension of almond crop production is limited by low temperature in harsh environments since almond tree does not tolerate fall temperature below −5 °C and gets seriously damaged by spring frosts. The expansion of almond in land with higher altitude of Mediterranean areas (e.g. Central Spain), as well as higher European latitude, where the occurrence of spring frost is common. It overlaps with the flower induction and development periods of most almond cultivars, increasing the risk of reducing or even nullifying the yield (Alonso et al. 2017). Thus, almond fruits breeders aim to generate and select frost tolerant cultivars. Besides the late flowering, other interesting genotypes adapted to freezing temperatures during bloom could be found including those with high bud density or inherent bud resistance to colder temperatures. Frost temperatures affect to reproductive organs producing anatomical damages (Alisoltani et al. 2015). When freezing temperatures persist, branches, shoots, and leaves can suffer different symptoms and even abscission (Rodrigo 2000). Frost damage is highly dependent on the phenological stage of the bud/flower/fruit in almond, with the early developing flower being the most susceptible stage (Hosseinpour et al. 2018). In almond, the most sensitive floral organs are the base of style, the internal wall of the

141

ovary and the base of the ovule, which tissue brownish is the main frost-damage symptom. The level of frost tolerance varies amongst different cultivars and amongst different species (Bigdeli et al. 2018). Factors affecting the level of frost tolerance in the prevention of intracellular freezing in plants have been studied and reviewed previously. However, the genetics behind frost tolerance need to be understood in order to focus fruit tree breeding programmes (Alisoltani et al. 2015; Badenes et al. 2016). Metabolic changes occur during vernalization, improving cold tolerance in almond trees; as a result, winter temperatures do not hurt floral buds. During spring, temperature can drop down below zero for many hours. Thus, newly opened almond flowers can undergo frost stress. More recently, a wide variety of regulatory genes related to adaption and resistance to cold and frost including transcription factors (TFs) and target genes producing adaptive molecules have been discovered (Alisoltani et al. 2015; Hosseinpour et al. 2018; Karimi et al. 2016). The molecular perception of low temperatures probably occurs in the plasma membrane, causing an increase of cytosolic calcium (Ca2+) concentration (Knight 1999). Ca2+-sensitive proteins activate different transcription factors (TFs) that trigger different stress-induced mechanisms. The increase in Ca2+ is transient since certain cation transporters intervene to restore osmotically stable values (Knight and Knight 2012). This rapid system allows the plant to react repeatedly against a stimulus of low temperatures. C-repeatbinding factors (CBFs), along with modulators, such as inducer of CBF expression 1 (ICE1) and high expression of osmotically responsive gene1 (HOS1), play key roles during cold acclimation in plants It has been demonstrated that both CBF2 and CBF3 are responsible for activating a large number of effector genes. Adaptation and tolerance to low temperatures are responsible genes as cold-responsive (COR) genes, which are involved in osmoprotective functions (Wang et al. 2017). In recent research, Bielsa et al. (2021) reported a characterisation of the cold stress response in three commercial almond cultivars, ‘Guara’, Belona®, and Soleta®. The

142

B. Bielsa and M. J. Rubio-Cabetas

authors studied the expression of genes related to both ICE-CBF-COR and the independent CBF pathways and they identified the genes CLO and BBX20_2 as potential biomarkers of tolerance to cold that could be useful for screening in almond breeding programmes. Cold stress for rootstock breeding for almond crops in several almond-growing areas is also a concern. Spanish almond breeding programme is evaluating crosses between Russian species trying to introduce cold acclimation in the Mediterranean almond rootstocks, and some selections are already in evaluation in a private– public the Spanish breeding programme in Spain. Cold hardiness normally linked to low vigour must be balance with both rootstocks having less need in chilling hours to make the graft union to develop faster.

6

Final Outlook

Modern almond cultivation requires rootstocks that are environmentally adapted, productive, and tolerant to several stress since the expansion towards new almond-growing areas. Thus, the horticultural traits of new rootstocks should be defined as precisely as they are for modern scion cultivars. Rootstocks are ultimately the responsible of adaptation of abiotic stresses that limit the crop. The need for increased orchard efficiency and profitability elevates the need to obtain, select, and propagate preferentially clonal rootstocks. Since almonds will continue to be grown both under rainfed and irrigated conditions. The use of hybrids in both situations has given the best results when there were no other limitations or specific problems. Moreover, there remains a broad range of possibilities for recombination of positive features into future rootstocks. The necessity to increase crop production with less input in a sustainable way has been put forward the notion that plants function in harmony with their associated microbiomes, and it is now one of the leading themes in plant biology. The recent application of high-throughput omics technologies has enabled detailed

dissection of the microbial players, and molecular mechanisms involved in the complex interactions in plant-associated microbiomes. Abiotic stress tolerance as a quantitative trait and environmentally effect encoding the interaction with the soils made necessary a holistic approach in future to interact the metagenomic approaches. Some results have already shown that root exudates could improve tree nutrition (Arif et al. 2020) and inoculate involving fungi and bacteria to enhance the sustainability of almond crops under nutrient limited conditions and drought stress (has been already demonstrated in nut crops. Behrooz et al. 2019). So those concepts should be harnessed to better manage plant microbe interactions and improve the ability of tolerance to abiotic stress. Old and emerging plant microbiome concepts related to plant disease control, and address perspectives that modern and emerging microbiomics technologies can bring to functionally characterize and exploit plant-associated microbiomes for the benefit of plant health in future microbiomeassisted agriculture to cope abiotic stress (Bakker et al. 2020, ant to rethink agriculture fertilizers (Mitter et al. 2021). Those are key factors for successfully field applications of biofertilizers and suggest potential solutions based on emerging strategies for product development to cope abiotic stress in interaction with mircrobioma functions.

7

Conclusion

The quantitative inheritance of any tolerance to many abiotic stresses made difficult to identify of genes involved in all the metabolic pathways and new methods as the metagenomics could reveal more information for a more efficiently almond crops. The availability of new bio stimulant and microorganism that interact with the diverse genetics of the rootstock and ultimately responsible of a more efficiently nutrient updated because they develop more vegetative growth either in root or leaves made more alleviate any interact with abiotic stress.

Molecular Basis of the Abiotic Stresses in Almond

References Alisoltani A, Shiran B, Fallahi H, Ebrahimie E (2015) Gene regulatory network in almond (Prunus dulcis Mill.) in response to frost stress. Tree Genet Genomes 11:100-1–100-15. https://doi.org/10.1007/s11295015-0929-z Alonso JM, Socias i Company R, Kodad O (2017) Lateblooming in almond: a controversial objective. Sci Hortic 224:61–67. https://doi.org/10.1016/j.scienta. 2017.05.036 Alonso JM, Ansón JM, Espiau MT, Company RS (2005) Determination of endodormancy break in almond flower buds by a correlation model using the average temperature of different day intervals and its application to the estimation of chill and heat requirements and blooming date. J Am Soc Hortic Sci 130:308–318 Aloni B, Cohen R, Karni L, Aktas H, Edelstein M (2010) Hormonal signaling in rootstock-scion interactions. Sci Hortic 127:119–126. https://doi.org/10.1016/j. scienta.2010.09.003 Almada R, Arismendi MJ, Pimentel P, Rojas P, Hinrichsen P, Pinto M, Sagredo B (2013) Class 1 nonsymbiotic and class 3 truncated hemoglobin-like genes are differentially expressed in stone fruit rootstocks (Prunus L.) with different degrees of tolerance to root hypoxia. Tree Genet Genomes 9:1051–1063. https:// doi.org/10.1007/s11295-013-0618-8 Amador ML, Sancho S, Bielsa B, Gomez-Aparisi J, Rubio-Cabetas MJ (2012) Physiological and biochemical parameters controlling waterlogging stress tolerance in Prunus before and after drainage. Physiol Plant 144:357–368. https://doi.org/10.1111/j.13993054.2012.01568.x Arif I, Batool M, Schenk PM (2020) Plant microbiome engineering: expected benefits for improved crop growth and resilience. Trends Biotechnol 38:1385– 1396. https://doi.org/10.1016/j.tibtech.2020.04.015 Arismendi MJ, Almada R, Pimentel P, Bastias A, Salvatierra A, Rojas P, Hinrichsen P, Pinto M, Di Genova A, Travisany D, Maass A, Sagredo B (2015) Transcriptome sequencing of Prunus sp. rootstocks roots to identify candidate genes involved in the response to root hypoxia. Tree Genet Genomes 11:11. https://doi.org/10.1007/s11295-015-0838-1 Asins MJ, Raga MV, Roca D, Carbonell EA (2020) QTL and candidate gene analyses of rootstock-mediated mandarin fruit yield and quality traits under contrasting iron availabilities. Tree Genet Genomes 16:79. https://doi.org/10.1007/s11295-020-01472-w Badenes ML, Fernández i Martí A, Ríos G, RubioCabetas MJ (2016) Application of genomic technologies to the breeding trees. Front Genet 7:198. https:// doi.org/10.3389/fgene.2016.00198 Bailey-Serres J, Voesenek LACJ (2010) Life in the balance: a signalling network controlling survival of flooding. Curr Opin Plant Biol 13:489–494. https:// doi.org/10.1016/j.pbi.2010.08.002

143 Bakker PAHM, Berendsen RL, Van Pelt JA, Vismans G, Yu K, Li E, Van Bentum S, Poppeliers SWM, Sanchez Gil JJ, Zhang H, Goossens P, Stringlis IA, Song Y, de Jonge R, Pieterse CMJ (2020) The soilborne identity and microbiome-assisted agriculture: looking back to the future. Mol Plant 13:1395–1402 Behrooz A, Vahdati K, Rejali F, Lotfi M, Sarikhani S, Leslie C (2019) Arbuscular mycorrhiza and plant growth-promoting bacteria alleviate drought stress in walnut. HortScience 54(6):1087–1092 Bert PF, Bordenave L, Donnart M, Hévin C, Ollat N, Decroocq S (2013) Mapping genetic loci for tolerance to lime-induced iron deficiency chlorosis in grapevine rootstocks (Vitis sp.). Theor Appl Genet 126:451–473. https://doi.org/10.1007/s00122-012-1993-5 Bielenberg DG, Wang YE, Li Z, Zhebentyayeva T, Fan S, Reighard GL, Scorza R, Abbott AG (2008) Sequencing and annotation of the evergrowing locus in peach (Prunus persica (L.) Batsch] reveals a cluster of six MADS-box transcription factors as candidate genes for regulation of terminal bud formation. Tree Genet Genomes 4:495–507 Bielsa B, Ávila-Alonso JI, Fernández i Martí Á, Grimplet J, Rubio-Cabetas MJ (2021) Gene expression analysis in cold stress conditions reveals BBX20 and CLO as potential biomarkers for cold tolerance in almond. Hortic 7(12):527. https://doi.org/10.3390/ horticulturae7120527 Bielsa B, Jiwan D, Fernandez I Marti A, Dhingra A, RubioCabetas MJ (2014) Detection of SNP and validation of a SFP InDel (deletion) in inverted repeat region of the Prunus species chloroplast genome. Sci Hort 168:108– 112. https://doi.org/10.1016/j.scienta.2014.01.028 Bielsa B, Bassett C, Glenn DM, Rubio-Cabetas MJ (2018a) Assessing field Prunus genotypes for drought responsive potential by carbon isotope discrimination and promoter analysis. Agronomy 8(4):42. https://doi. org/10.3390/agronomy8040042 Bielsa B, Hewitt S, Reyes-Chin-Wo S, Dhingra A, RubioCabetas MJ (2018b) Identification of water use efficiency related genes in ‘Garnem’ almond-peach rootstock using time-course transcriptome analysis. PLoS ONE 13(10):e0205493. https://doi.org/10.1371/ journal.pone.0205493 Bielsa B, Leida C, Rubio-Cabetas MJ (2016) Physiological characterization of drought stress response and expression of two transcription factors and two LEA genes in three Prunus genotypes. Sci Hortic 213:260– 269. https://doi.org/10.1016/j.scienta.2016.11.006 Bielsa B, García-Brunton J, Sanz MA, Rubio-Cabetas MJ (2019a) Caracterización de la respuesta adaptativa a sequía de dos cultivares de melocotonero en condiciones de aridez. Inf Tec Econ Agrar 115:307–325. https://doi.org/10.12706/itea.2019.010 Bielsa B, Sanz M, Rubio-Cabetas MJ (2019b) Uncovering early response to drought by proteomic, physiological and biochemical changes in the almond * peach rootstock ‘Garnem.’ Funct Plant Biol 46:994–1008. https://doi.org/10.1071/FP19050

144 Bigdeli M, Imani A, Shamili M (2018) The evaluation of almond progenies of cold-susceptible and coldtolerant parents (Filippo-Ceo * Shahrood-12). Sci Hortic 234:176–183. https://doi.org/10.1016/j.scienta. 2018.02.044 Christianson JA, Llewellyn DJ, Dennis ES, Wilson IW (2010) Global gene expression responses to waterlogging in roots and leaves of cotton (Gossypium hirsutum L.). Plant Cell Physiol 51:21–37. https:// doi.org/10.1093/pcp/pcp163 Colmer TD, Voesenek LACJ (2009) Flooding tolerance: suites of plant traits in variable environments. Funct Plant Biol 36:665–681. https://doi.org/10.1071/ FP09144 Cumming GA (1989) Effect of soil pH and calcium amendments on peach yield, tree growth and longevity. Acta Hort 254:179–184 Day LH (1953) Rootstock for stone fruits: observations and experiment with plum, peach, apricot and almond roots for stone fruits. Calif Agric Exp Sta Bul 736 Dejampour J, Aliasgarzad N, Zeinalabedini M, Niya M, Hervan E (2012) Evaluation of salt tolerance in almond [Prunus dulcis (L.) Batsch] rootstocks. Afr J Biotechnol 11:11907–11912. https://doi.org/10.5897/ ajb11.299 Donoso JM et al (2012) Towards the enrichment of the peach gene pool with alleles from wild species: the collection of peach-almond introgression lines. In: 6th International Rosaceae Genomics Conference, p 40 Dirlewanger E, Kleinhentz M, Voisin R, Claverie M, Lecouls AC, Poëssel JL, Faurobert M, Arús P, Gómez-Aparisi J, Di Vito M, Xiloyannis C, Esmenjaud D (2004) Breeding for a new generation of Prunus rootstocks, an example of MAS. Acta Hort 658:581–590 Esmenjaud D, Dirlewanger E (2007) Plum. In: Kole CR (ed) Genome mapping and molecular breeding. Fruits & nuts, vol 4. Springer, Heidelberg, Alemania, pp 119–136 Felipe AJ (1988) Observaciones sobre comportamiento frente a heladas tardías en almendro. Rap EUR 11557:123–130 Felipe AJ (1989) Patrones para frutales de pepita y hueso. Ed. Técnicas Europeas SA, Barcelona, Spain, p 181 Felipe AJ (2009) ‘Felinem’, ‘Garnem’ and ‘Monegro’ almond x peach hybrid rootstocks. HortScience 44:196–197 Felipe AJ, Socias i Company R, Gómez-Aparisi J (1998) The almond rootstock ideotype. Acta Hortic 470:181– 187 Felipe AJ, Rius X, Rubio-Cabetas MJ (2022) El Cultivo del Almendro. El Almendro II. Calidad Gráfica Araconsa S.L., Zaragoza, Spain Fernandez i Marti A, Font i Forcada C, Rubio-Cabetas MJ, Kamali K, Wirthensohn M, Socias i Company R (2014) Genomic analysis of the molecular evolution and the population structure of a worldwide almond pool assessed by microsatellite markers. Genet Resour Crop Evol 62(2):205–219. https://doi.org/10.1007/ s10722-014-0146-x

B. Bielsa and M. J. Rubio-Cabetas Forner-Giner MA, Llosa MJ, Carrasco JL, Perez-Amador MA, Navarro L, Ancillo G (2009) Differential gene expression analysis provides new insights into the molecular basis of iron deficiency stress response in the citrus rootstock Poncirus trifoliata (L.). Raf J Exp Bot 61:483–490. https://doi.org/10.1093/jxb/erp328 Frewen BE, Chen THH, Howe GT, Davis J, Rohde A, Boerjan W, Bradshaw HD Jr (2000) Quantitative trait loci and candidate gene mapping of bud set and bud flush in Populus. Genetics 154:837–845 Fu LN, Zhu QQ, Sun YY, Du W, Pan ZY, Peng SA (2017) Physiological and transcriptional changes of three Citrus rootstock seedlings under iron deficiency. Front Plant Sci 8:1104. https://doi.org/10.3389/fpls. 2017.01104 Gonzalo MJ, Dirlewanger E, Moreno MA, Gogorcena Y (2012) Genetic analysis of iron chlorosis tolerance in Prunus rootstocks. Tree Genet Genomes 8:943–955 Gibbs DJ, Conde JV, Berkhan S, Prasad G, Mendiondo GM, Holdsworth MJ (2015) Group VII ethylene response factors coordinate oxygen and nitric oxide signal transduction and stress responses in plants. Plant Physiol 169:23–31. https://doi.org/10. 1104/pp.15.00338 Grauke LJ (2019) Family trees: roots and resilience. Pecan South Mag 52:12–21 Hosseinpour B, Sepahvand S, Kamali K, Bakhtiarizadeh M, Imani A, Assareh R, Alireza S (2018) Transcriptome profiling of fully open flowers in a frost-tolerant almond genotype in response to freezing stress. Mol Genet Genomics 293:151–163. https://doi.org/10.1007/s00438-017-1371-8 Irisarri P, Zhebentyayeva T, Errea P, Pina A (2016) Differential expression of phenylalanine ammonia lyase (PAL) genes implies distinct roles in development of graft incompatibility symptoms in Prunus. Sci Hortic 204:16–24. https://doi.org/10.1016/j.scienta. 2016.03.025 Jiménez S, Ollat N, Deborde C, Maucourt M, RellánÁlvarez R, Moreno MÁ, Gogorcena Y (2011) Metabolic response in roots of Prunus rootstocks submitted to iron chlorosis. J Plant Physiol 168(5):415–423 Karimi M, Ghazanfari F, Fadaei A, Ahmadi L, Shiran B, Rabei M, Fallahi HF (2016) The small-RNA profiles of almond (Prunus dulcis Mill.) reproductive tissues in response to cold stress. PLoS One 11:e0156519. https://doi.org/10.1371/journal.pone.0156519 Kochba J, Spiegel-Roy P (1976) Alnem 1, Alnem 88, Alnem 201: nematode resistant rootstock seed sources. HortScience 11:270 Kester DE, Grasselly C (1987) Almonds (Prunus). In: Rom RC, Carlson RF (eds) Rootstocks for fruit crops. John Wiley & Sons, New York, pp 265–294 Knight H (1999) Calcium signaling during abiotic stress in plants. Int Rev Cytol 195:269–324. https://doi.org/ 10.1016/S0074-7696(08)62707-2 Knight MR, Knight H (2012) Low-temperature perception leading to gene expression and cold tolerance in higher plants. New Phytol 195:737–751. https://doi. org/10.1111/j.1469-8137.2012.04239.x

Molecular Basis of the Abiotic Stresses in Almond Lordan J, Zazurca L, Maldonado M, Torguet L, Alegre S, Miarnau X (2019) Horticultural performance of ‘Marinada’ and ‘Vairo’ almond cultivars grown on a genetically diverse set of rootstocks. Sci Hortic https://doi.org/10.1016/j.scienta.2019. 256:108558. 108558 Licausi F, Kosmacz M, Weits DA, Giuntoli B, Giorgi FM, Voesenek LACJ, Perata P, van Dongen JT (2011a) Oxygen sensing in plants is mediated by an N-end rule pathway for protein destabilization. Nature 190:419–422. https://doi.org/10.1038/nature10536 Licausi F, Weits DA, Pant BD, Scheible WR, Geigenberger P, van Dongen JT (2011b) Hypoxia responsive gene expression is mediated by various subsets of transcription factors and miRNAs that are determined by the actual oxygen availability. New Phytol 190: 442–456. https://doi.org/10.1111/j.1469-8137.2010. 03451.x Martínez-Ballesta MC, Alcaraz-López C, Muries B, Mota-Cadenas C, Carvajal M (2010) Physiological aspects of rootstock–scion interactions. Sci Hortic 127 (2):112–118 Martínez-García PJ, Hartung J, de los Cobos FP, Martínez-García P, Jalili S, Sánchez-Roldán JM, Rubio M, Dicenta F, Martínez-Gómez P (2020) Temporal response to drought stress in several Prunus rootstocks and wild species. Agronomy 10:1–18. https://doi.org/10.3390/agronomy10091383 Massai R, Remorini D, Tattini M (2004) Gas exchange, water relations and osmotic adjustment in two scion/rootstock combinations of Prunus under various salinity concentrations. Plant Soil 259:153–162. https://doi.org/10.1023/B:PLSO.0000020954.71828. 13 Mitter EK, Tosi M, Obregón D, Dunfield KE, Germida JJ (2021) Rethinking crop nutrition in times of modern microbiology: innovative biofertilizer technologies. Front Sustain Food Syst 5:1–23. https://doi.org/10. 3389/fsufs.2021.606815 Nawaz MA, Imtiaz M, Kong Q, Cheng F, Ahmed W, Huang Y, Bie Z (2016) Grafting: a technique to modify ion accumulation in horticultural crops. Front Plant Sci 7:1457 Nimbolkar PK, Awachare C, Reddy YTN, Chander S, Hussain F (2016) Role of rootstocks in fruit production—a review. J Agric Eng Food Technol 3(3):183– 188 Opazo I, Toro G, Salvatierra A, Pastenes C, Pimentel P (2020) Rootstocks modulate the physiology and growth responses to water deficit and long-term recovery in grafted stone fruit trees. Agric Water Manag 228:105897. https://doi.org/10.1016/j.agwat. 2019.105897 Ogundiwin EA, Martí C, Forment J, Pons C, Granell A, Gradziel TM, Peace CP, Crisosto CH (2008) Development of ChillPeach genomic tools and identification of cold-responsive genes in peach fruit. Plant Mol Biol 68:379–397. https://doi.org/10.1007/s11103-0089378-5

145 Pimentel P, Pinto M (2014) Physiological and morphological responses of Prunus species with different degree of tolerance to long-term root hypoxia. Sci Hortic 180:14–23. https://doi.org/10.1016/j.scienta. 2014.09.055 Pinochet J (2010) ‘Replantpac’ (Rootpac R), a plumalmond hybrid rootstock for replant situations. HortScience 45:299–301 Pinochet J (2009) ‘Greenpac’ a new peach hybrid rootstock adapted to Mediterranean conditions. HortScience 44:1456–1457 Pina A, Irrisari P, Errea P, Zhebentayeva T (2021) Mapping quantitative trait loci associated with graft (In) compatibility in apricot (Prunus armeniaca L.) Front Plant Sci 19. https://doi.org/10.3389/fpls.2021. 622906 Pina A, Cookson S, Calatayud A, Trinchera A, Errea P (2017) Physiological and molecular mechanisms underlying graft compatibility. In: Colla G, PerezAlfocea F, Schwarz D (eds) Vegetable grafting: principles and practices. CABI Publishing, Wallingford, Oxfordshire, UK, pp 132–154 Pistelli L, Iacona C, Miano D, Cirilli M, Colao MC, Mensuali-Sodi A, Muleo R (2012) Novel Prunus rootstock somaclonal variants with divergent ability to tolerate waterlogging. Tree Physiol 32(3):355–368. https://doi.org/10.1093/treephys/tpr135 Rodrigo J (2000) Spring frosts in deciduous fruit trees— morphological damage and flower hardiness. Sci Hortic 85:155–173. https://doi.org/10.1016/S03044238(99)00150-8 Rubio-Cabetas MJ, Felipe AJ, Reighard GL (2017) Rootstock development. In: Socias i Company R, Gradziel TM (eds) Almonds. Botany, Production and uses. CABI, Boston, MA, pp 209–227 Rubio-Cabetas MJ, Pons C, Bielsa B, Amador ML, Marti C, Granell A (2018) Preformed and induced mechanisms underlies the differential responses of Prunus rootstock to hypoxia. J Plant Physiol 228:134– 149. https://doi.org/10.1016/j.jplph.2018.06.004 Rubio-Cabetas MJ (2016) Almond rootstocks: overview. In: Kodad O, López-Francos A, Rovira M, Socias i Company R (eds) XVI GREMPA meeting on almonds and Pistachios. Zaragoza: CIHEAM, pp 133–143 (Options Méditerranéennes: Série A. Séminaires Méditerranéens; n. 119) Rubio-Cabetas MJ (2010) Patrones para el cultivo del almendro. Revista de Fruticultura. No 10. Extraordinario 2010:44–55 Reighard GL, Loreti F (2008) Rootstock development. In: Layne D, Bassi D (eds) The Peach, Botany, production and uses. CAB International, Wallingford, U.K., pp 193–220 Sandhu D, Kaundal A, Acharya BR, Forest T, Pudussery MV, Liu X, Ferreira JFS, Suarez DL (2020) Linking diverse salinity responses of 14 almond rootstocks with physiological, biochemical, and genetic determinants. Sci Rep 10:1–13. https:// doi.org/10.1038/s41598-020-78036-4

146 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Genomes 8:379–389 Sorkheh K, Shiran B, Rouhi V, Khodambashi M, Sofo A (2012) Salt stress induction of some key antioxidant enzymes and metabolites in eight Iranian wild almond species. Acta Physiol Plant 34:203–213. https://doi. org/10.1007/s11738-011-0819-4 Tozlu I, Guy CL, Moore G (1999) QTL analysis of Na+ and Cl− accumulation related traits in an intergeneric BC1 progeny of Citrus and Poncirus under saline and nonsaline environments. Genome 42(4):692–705. https://doi.org/10.1139/g99-003 Wang DZ, Jin YN, Ding XH, Wang WJ, Zhai SS, Bai LP, Guo ZF (2017) Gene regulation and signal transduction in the ICE–CBF–COR signaling pathway during cold stress in plants. Biochem 82:1103–1117. https:// doi.org/10.1134/S0006297917100030 Wu GA, Prochnik S, Jenkins J, Salse J, Hellsten U, Murat F et al (2014) Sequencing of diverse mandarin, pummelo and orange genomes reveals complex history of admixture during citrus domestication. Nat Biotechnol 32:656–662. https://doi.org/10.1038/nbt. 2906 Xiloyannis C, Dichio B, Tuzio AC, Gomez Aparisi J, Rubio-Cabetas MJ, Kleinhentz M, Salesses G,

B. Bielsa and M. J. Rubio-Cabetas Esmenjaud D (2007) Characterization and selection of Prunus rootstocks resistant to abiotic stresses: waterlogging, water stress and iron chlorosis. Acta Hort 732:247–250 Xu Q, Chen LL, Ruan X, Chen D, Zhu A, Chen C et al (2013) The draft genome of sweet orange (Citrus sinensis). Nat Genet 45:59–66. https://doi.org/10. 1038/ng.2472 Zhang X-Y, Qiu J-Y, Hui Q-L, Xu Y-Y, He Y-Z, Peng LZ, Fu X-Z (2020) Systematic analysis of the basic/helix-loop-helix (bHLH) transcription factor family in pummelo (Citrus grandis) and identification of the key members involved in the response to iron deficiency. BMC Genomics 21(1):233. https://doi.org/ 10.1186/s12864-020-6644-7 Zhebentayeva TN, Fan S, Chandra A, Bielenberg DG, Reighard GL, Okie WR, Abbott AG (2014) Dissection of chilling requirement and bloom date QTLs in peach using a whole genome sequencing of sibling trees from an F2 mapping population. Tree Genet Genomes 10:35–51 Zrig A, Tounekti T, Vadel AM, Ben Mohamed H, Valero D, Serrano M, Chtara C, Khemira H (2011) Possible involvement of polyphenols and polyamines in salt tolerance of almond rootstocks. Plant Physiol Biochem 49:1313–1322. https://doi.org/10.1016/j. plaphy.2011.08.009

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond Shashi N. Goonetilleke and Ángel Fernández i Martí

Abstract

Within this chapter, we review the latest advances in terms of quantitative genetics for important traits such as kernel and nut quality. Almond breeding is considering kernel quality as an important breeding objective. A very limited information on the almond quality parameters that can be used in evaluation has been published and among them nut and kernel physical parameters, such as width, thickness, size, geometric diameter, and spherical index as well as chemical components, such as protein, oil content, fatty acid, and tocopherol concentration. The genetic control of these traits has recently been determined in almond. A total of 26 putative quantitative trait loci (QTL) controlling these chemical and physical traits were detected over the past years, corresponding to seven genomic regions of the eight almond linkage groups (LG). Some QTLs were clustered in the same region or

S. N. Goonetilleke School of Agriculture, Food and Wine, Plant Research Centre, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia Á. Fernández i Martí (&) Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA e-mail: [email protected]

shared the same molecular markers, indicating the correlations between those traits.

1

Introduction

In plant breeding, genetic variability is fundamental for the improvement and development of new cultivars. In the actual scenario, selections are the product of genotype (genetic component) and the environment interactions. Conventional plant breeding is about phenotypic selection of superior genotypes. Traditionally, genetic variation and characterisation are usually identified via visual selection of morphological traits. The first genetic map was constructed by Alfred H. Sturtevant in 1913, using six morphological traits (termed ‘factors’) in the fruit fly (Drosophila melanogaster) (Sturtevant 1913), then ten years later, Karl Sax uncovered the first evidence for genetic linkage between a qualitative and a quantitative trait loci (QTL) for seed colour and seed size in the common bean (Phaseolus vulgaris) (Sax 1923). In the early years of advancement in plant breeding, morphological markers were replaced with cytological and biochemical markers. With the advancement of molecular biology, these traditional markers were supplanted by genomic markers or DNA markers that are not affected by environmental factors, sample collection stages or plant developmental stages. At present, genetic markers are commonly used in both basic plant research and plant breeding to characterise plant

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_10

147

148

germplasm, for gene isolation, for marker-assisted breeding, association mapping, introgression of favourable alleles, and for variety protection. Genetic markers are inexpensive, environmentally stable, and repeatable across experiments and tissue types. A DNA marker is a small fragment of DNA that shows sequence variation between individuals within a species. Thousands of random DNA markers can be generated for any species and have been successfully used in many genome studies and in mapping of trait genes. Further, functional markers, which are designed based on the DNA polymorphs within genes that are causal for phenotypic trait variations, are used in association studies. The main disadvantage of these markers is the effect of genetic background towards the results, and currently many powerful statistical approaches have been developed to minimise the effect of unknown population structures and enhance the power and accuracy of QTL detection. Types of DNA markers: i. Restriction Fragment Length Polymorphism (RFLP): The first generation of DNA markers. RLFP markers are detected using the differences of fragment lengths in homologous DNA sequences by digesting the DNA samples of interest with specific restriction endonucleases. This marker type is specific to a single clone/restriction enzyme combination. RFLP markers are highly reproducible, with simple marker detection technique and no special knowledge or equipment is required. ii. Random Amplified Polymorphic DNA (RAPD): These markers are derived from DNA fragments obtained by polymerase chain reaction (PCR) amplification of random segments of genomic DNA using a pair of primer combination that are designed using an arbitrary nucleotide sequence. To design these markers, any specific knowledge or genome sequence information on the target organism is not required. The main disadvantage with this marker is low reproducibility and incapability of detecting allele differences in heterozygotes.

S. N. Goonetilleke and Á. Fernández i Martí

iii. Amplified Fragment Length Polymorphism (AFLP): These markers are based on the differences in restriction fragment lengths due to the presence of either single nucleotide polymorphisms (SNPs) or insertions and deletions (INDELs) in recognition sites of restriction endonucleases. These assays are performed by selective amplification of a pool of digested DNA fragments using PCR to discover unique fingerprints of an individual. Therefore, AFLP primer screening is often necessary to identify optimal primer specificities and combinations. It provides very high multiplex ratio and genotyping throughput. These are highly reproducible across laboratories. Main disadvantages with this marker type are limitation of polymorphic information content (PIC), i.e. the maximum PIC for any bi-allelic marker is 0.5 and high-quality DNA is required to ensure total digestion of the DNA fragments by the restriction enzyme, resolution of sufficient fragment mobility, and to minimise incorrect scoring of bands. iv. Cleaved Amplified Polymorphic Sequences (CAPS): CAPS assays implement combination the PCR and RFLP techniques by digesting locus-specific PCR amplicons with one or more restriction enzymes following separation of the digested products on agarose or polyacrylamide gels. These markers are versatile and can be combined with other marker types such as sequencecharacterised amplified region (SCAR), or random amplified polymorphic DNA (RAPD) analysis for improving the efficacy of finding the polymorphisms in an organism. The main disadvantage is usually, a battery of restriction enzymes must be tested to find polymorphisms. v. Expressed Sequence Tags (EST) are small pieces of DNA (200–500 nucleotide long), and their location and sequence on the chromosome are known. ESTs consist of exons only, the variations which are found at a single nucleotide position are known. It is a rapid and inexpensive technique of

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond

locating a gene. ESTs are useful in discovering new genes related to genetic diseases. They can be used for tissue specific gene expression. Main disadvantages are they have less prime specificity. It is a time consuming and labour-oriented technique. The precision is less than other techniques. It is difficult to obtain large (>6 kb) transcripts. Multiplexing is not possible for all loci. vi. Simple Sequence Repeat (SSR) or microsatellites are tandemly repeated mono-, di-, tri-, tetra-, penta-, and hexanucleotide motifs. SSR length polymorphisms are caused by differences in the number of repeats. SSR loci are individually amplified by PCR using pairs of oligonucleotide primers specific to unique DNA sequences flanking the SSR sequence. SSR markers are highly polymorphic. The genotyping throughput is high. Many SSR markers are multi-allelic and highly polymorphic. SSR markers can be multiplexed, either functionally by pooling independent PCR products or by true multiplex-PCR. Semiautomated SSR genotyping methods have been developed. Most SSRs are codominant and locus specific. Main disadvantage is the development of SSRs which is labour intensive. SSR marker development costs are very high. SSR markers are taxa specific. Start-up costs are high for automated SSR assay methods. Developing PCR multiplexes is difficult and expensive. Some markers may not multiplex. vii. Single Nucleotide Polymorphism (SNP): SNP is a variation at a single position in a DNA sequence among individuals. If a SNP occurs within a gene, then the gene is described as having more than one allele. In these cases, SNPs may lead to variations in the amino acid sequence. SNPs, however, are not just associated with genes; they can also occur in noncoding regions of DNA. They are locus specific and the genotyping throughput is very high. Main disadvantages are most of the SNPs which are bi-allelic and less informative than SSRs. Multiplexing is not possible for all loci. Some SNP assay

149

techniques are costly. Development of SNP markers is labour oriented.

2

Brief History of Linkage and QTL Map Construction in Almond

The first reference genetic linkage map for almond was constructed using isozyme and RFLP markers to progeny from a cross between the almond cultivar Texas and the peach (Prunus persica (L.) Batsch) cultivar Earlygold (Joobeur et al. 1998). Simple sequence repeat (SSR) markers were later added to this map (Aranzana et al. 2003; Dirlewanger et al. 2004). Subsequently, RAPD markers, inter-simple sequence repeat (ISSR) markers, sequence-characterised amplified region (SCAR) markers, and single nucleotide polymorphism (SNP) markers have been mapped in almond (Tavassolian et al. 2010; Joobeur et al. 2000; Wu et al. 2010, 2009; Donoso et al. 2016; Goonetilleke et al. 2018). Recently, to discover SNPs in the almond genome, next-generation sequencing (NGS) procedure using genotypingby-sequencing (GBS) was implemented (Goonetilleke et al. 2018) and SNPs were assayed using allele-specific Kompetitive Allele Specific PCR (KASP™) assays (LGC Genomics, Teddington, UK) (Goonetilleke et al. 2018).

3

Marker-Assisted Breeding in Crops

Marker-assisted breeding (MAB) uses PCR-based DNA markers to select desirable progeny based on marker trait associations and provides a good alternative to the conventional breeding approach. MAB allows indirect selection of desirable alleles without the confounding effects of environment, pleiotropic, or epistatic gene effects, enables discrimination between plants homozygous or heterozygous for a given gene, monitors the introgression of a desirable allele in backcrossing and permits identification of recombinants exhibiting the least amount of linkage drag. Marker-Assisted Selection (MAS) refers to the

S. N. Goonetilleke and Á. Fernández i Martí

150

selection of plants carrying genomic regions that encode the trait of interest using molecular markers. The efficiency of plant breeding can be improved through MAS by enabling precise transfer of genomic regions of interest (foreground selection) and hastening the recovery of the recurrent parent genome (background selection). MAS has been widely used for qualitative traits that are affected by major genes than for polygenic traits, however, there are a few success stories in employing MAS to improve quantitative traits in plants. For example, MAS is used in selecting QTLs that affect agronomics and physiological traits in many plants. For success, several critical factors need to be considered in MAS: the number of target genes to be transferred, the distance between the flanking markers and the target gene, the number of genotypes selected in each breeding generation, the nature of germplasm and the technical options available at the marker level. The development of molecular markers and genotyping technologies has evolved rapidly since its first application in 1980s with the use of isozyme markers (Tanksley 1983). Currently, a

large number of different marker types at relatively cheaper prices are available for genotyping and turnover time has also remarkably decreased. With the advent of NGS, availability of low-cost, improved, and efficient sequencing platforms and genotyping approaches to sequence whole plant genomes and to explore single nucleotide polymorphisms would provide new ways to integrate MAS into conventional plant breeding programmes. Some of the major breakthroughs of plant genotyping techniques are summarised in Table 1.

4

Marker-Assisted Selection (MAS) in Almond

A few genome sequences are available for the almond from cultivars Lauranne (Sánchez-Pérez et al. 2019), Texas (Alioto et al. 2019) and Nonpareil (D’Amico-Willman et al. 2022). Another interesting tool has been available for the genus Prunus, the ‘Prunus reference map’ that was constructed using an interspecific population of a cross between almond and peach

Table 1 Major breakthroughs of plant genotyping techniques Year

Events

1923

Sax reported a linkage map between quantitative (seed size) and qualitative trait (seed coat colour) in common bean for the first time

1961

Thodey described QTLs mapping in Drosophila melanogaster

1980

Linkage mapping in humans using RFLP (Restriction Fragment Length Polymorphism) was described for the first time by Botstein et al.

1985

Kary Mullis discovered the Polymerized Chain Reaction (PCR) which led to the designing of PCR-based markers

1989

Olson et al. reported sequence-tagged site (STS) markers

1990

Williams JGK et al. developed ‘RAPD’ (Random Amplified Polymorphic DNA)

1991

Williams MNV et al. reported ‘CAPs’ (Cleaved Amplified Polymorphic sequence)

1993

Development of Marker-assisted techniques: Paran and Michelmore developed ‘SCAR’ (SequenceCharacterised Amplified Regions) and Zabeau and Vos developed ‘AFLP’ (Amplified Fragment Length Polymorphism) technique

2001

Li and Quiros developed ‘SRAP’ (Sequence Related Amplified Polymorphism) technique

2008

Baird et al. developed RAD

2009

Collard and Mackill reported ‘SCoT’ (Start Codon Targeted Polymorphism)

2011

Elshire et al. developed GBS

2014

Singh AK et al. described ‘CAAT box-derived polymorphism marker’

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond

(Joobeur et al. 1998). Soon after, a more saturated, second generation of this map was developed adding more markers (Joobeur et al. 2000). In 2004, a comparative map which enables comparison of seven diploid Prunus species including almond was published and it was found to be highly colinear, with integration of mapping information of 28 genes segregating in Prunus species (Dirlewanger et al. 2004). This reference map has widely been used for developing markers for further saturating the regions of interested in the Prunus genome, anchoring the sequenced genomes (Verde et al. 2013) and in identifying the genomic regions that regulate different traits in Prunus (Eduardo et al. 2015; Picañol et al. 2013; Donoso et al. 2015). The traits mapped to the almond include, selfincompatibility (Ballester et al. 1998; Fernandez et al. 2011), shell hardness (Arús et al. 1998; Goonetilleke et al. 2018), and kernel taste (Sánchez-Pérez et al. 2010). There are other traits also mapped to almond, such as late blooming (Ballester et al. 2001), productivity, maturity date, double kernel (Sánchez-Pérez et al. 2007), some physical nut traits (Fernández i Martí et al. 2013), and chemical traits (Font i Forcada et al. 2012). Most of these traits were mapped using bi-parental populations where limited allelic variation is represented. However, almond breeders could use this information to implement MAS for these traits in their breeding programmes. Analysing these traits in different populations and using different approaches such as association mapping procedures, genomic selection to detect the genomic regions that control quantitative loci (QTLs) traits would provide alternative means to detect robust QTLs/genomic regions that control these traits.

5

Use of Marker Assisted Introgression (MAI) in Almond

To introduce a chromosomal region that contains a favourable allele, MAI was widely used. This is the state-of-the-art technique behind the development of introgression lines (IL) or near isogenic lines (NIL) populations. In those populations, each individual contains a unique

151

chromosome fragment of a donor cultivar in the genetic background of a recipient cultivar (Visscher et al. 1996). Usually, donors possess desirable traits such as self-compatibility, disease resistance or drought resistance, and recipients which are commercial cultivars. Interspecific crossing is widely used in almond, e.g. selfincompatibility of almond in the USA has been introduced to almond from species such as P. persica, P. mira, P. davidina and P. webbii (Hanada et al. 2009). Another trait that has been introduced to almonds is root-knot nematode resistance gene (Duval et al. 2019; Van Ghelder et al. 2010).

6

Molecular Tools in Almond Breeding

Marker technologies currently used in plant breeding amplify small amounts of DNA using PCR. PCR-based markers are more powerful as they can be easily combined with highthroughput techniques, which allow for the discrimination of many loci at a time. For almond, comparatively dense genetic maps are available, and whole genome sequencing using Illumina reads provides much more markers to develop dense genetic maps. Genetic maps have been used to identify the chromosomal regions that control the trait of interest, and in some cases, the trait could be controlled by a one major effect gene (qualitative trait) and/or by a few small effect genes (quantitative trait). Detecting the presence or absence of a molecular marker in the particular region of chromosome provides more effective, efficient, reliable and cost-effective selection method over the conventional phenotypic selection alone. In QTL mapping, if tight linkage between markers and the QTL could be detected, it can be used as a diagnostic tool to detect the trait of interest. Marker-assisted breeding provides breeders the following advantages: (i) selection can be performed more efficiently based on the genetic information independent to environmental effects. (ii) These techniques minimise the negative effects due to linkage drag and reduce

S. N. Goonetilleke and Á. Fernández i Martí

152

transfer of undesirable alleles in backcrossing and (iii) recurrent selection in multiple years may not be required and it is cost-effective. When it comes to perennial crop improvement, saving money and time is very crucial as those perennial crop plants have long juvenile periods. Theoretically, selection for major gene effect QTL or single gene QTL is beneficial as one gene/ or a QTL that explains the largest proportion of phenotypic variation. However, most of the traits are polygenic and controlled by several small effect QTLs.

due to the high cost involved in such techniques. In Prunus, gene pyramiding has been used to obtain wide-spectrum resistance to root-knot nematodes that is conferred by the Ma gene (Lecouls et al. 2004). In future, not only to combine the QTLs for biotic stresses, but MAS pyramiding could also use to combine the QTLs for abiotic stress tolerances, such as QTLs effective at tolerating the abiotic stresses different growth stages.

8 7

Marker-Assisted Pyramiding

MAS is a powerful tool that enables combining several genes into a single genotype. Although pyramiding is possible through conventional breeding, it is usually not easy to identify the plants that contain several genes for a trait of interest and testing individual plants phenotypically for all traits can be highly time consuming and complicated. The most common strategy of pyramiding is combining multiple resistance genes and/or resistance genes with different alleles, i.e. combining different pest and disease resistance genes to develop broad-spectrum and durable resistance. The non-destructive nature of DNA marker assays and possibility to test for multiple specific genes using a single DNA sample without phenotyping makes DNA marker assays efficient for the selection step in pyramiding. MAS would be the most practical method to pyramid disease or pest resistance genes that have similar phenotypic effects, differentiating phenotypes are not available, and when the matching races cannot be found, or especially where one gene masks the presence of other genes. (Lecouls et al. 2004; Sanchez et al. 2000). Thus, MAS offers promising opportunities. In practical breeding, when pyramiding is carried out, it has to be repeated after each crossing, as the pyramided resistance genes are segregating in the progeny (Werner et al. 2005). Theoretically, all tightly linked QTLs can be used as molecular markers. However, practically no more than three closely linked QTLs are used

QTL Mapping in Almond

QTL mapping is a method used to understand the general chromosomal positions of genes or genetic variants that control polygenic traits and it has been used to identify QTLs of different species including almond (Fernandez et al. 2011; Fernández i Martí et al. 2013; Font i Forcada et al. 2012; Fung et al. 2006; Salazar et al. 2014; Sánchez-Pérez et al. 2007; Goonetilleke et al. 2018). Trait variation in a population can be controlled by many QTLs and QTL mapping provides information on how many QTLs significantly contributed to the trait variation. By conducting QTL analysis, the genetic architecture of quantitative traits in the population can be explored and it would provide information on how much variation is due to the additive effects, how much is due to dominant and epistatic effects of QTL, what is the nature of genetic correlation between different traits in a genomic region, pleiotropy, or close linkage and do QTL interact with environments. However, generally, linkage mapping strategies are limited in detecting loci regulating the traits as to generate the segregating population only two extreme parents are used and only a few recombination events are studied. Previously, several studies have been carried out to identify multiple QTLs in almond using bi-parental populations: 12 QTLs controlling agronomic traits (Sánchez-Pérez et al. 2007), 20 physical nut traits (Fernández i Martí et al. 2013), and nine nut and kernel traits (Font i Forcada et al. 2012). The study reported by Sánchez-Pérez et al. (2007) had used 167 progeny of a cross between the French selection

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond

R1000 and the Spanish cultivar Desmayo Largueta to map blooming date, blooming density, productivity, leafing date, shell hardness, in-shell weight, and double kernel. Later, in 2013, (Fernández i Martí et al.) had used an almond population from a cross between Vivot (V) and Blanquerna (B) to map physical traits of nut and kernel: kernel dimension parameters such as width, thickness, length, ratios of kernel dimensions, kernel weight, geometric diameter, spherical index, and kernel size. QTLs were located on 6 linkage groups (LG) except LG4 and LG8. In summary, QTLs which control primary dimensional properties in nuts are located on LG2, LG3, LG5, LG6, and LG7, whereas the same dimensional properties in kernels were controlled by the QTLs on LG1, LG3, LG5, LG6, and LG7. The first report of identifying the QTLs controlling the kernel chemical traits was carried out using the V ) B population (Font i Forcada et al. 2012). The main traits mapped were total protein content, fatty acids, and tocopherols. In this population, QTLs were detected on 7 LGs except LG8. Two QTLs controlling total protein were detected on LG6 and LG7. Seven QTLs controlling fatty acids (oleic, palmitic, stearic, linoleic, and palmitoleic) were detected in first seven LGs. It is interesting to note that a QTL on LG2 affects the two main fatty acids, oleic, and linoleic and another in LG7 affects the all the five fatty acids that were reported. In addition, a total of five QTLs were detected for a-, b-, and ctocopherols.

9

Association Mapping (AM) in Almond

AM provides an alternative to conventional QTL mapping approaches in bi-parental crosses and to study wider genetic background with a broader genetic variation for marker and trait correlations. It is statistically more powerful than the conventional QTL mapping. AM has been applied to many crop species with unstructured and complex population histories. The main advantage of AM is that it considers all the recombination events that have taken place in the

153

evolutionary history of a sample, which provides a much higher mapping resolution than biparental mapping. Further, the number of QTL can be detected for a trait is not limited to what segregates between parents of a cross, but by the number of real QTLs that causing the trait and how much available genetic diversity in nature is represented in the mapping population available. The most attractive aspect of AM is it is simple and cost-effective compared with the laborious and often expensive process of establishing biparental mapping populations. This approach is very useful when working with organisms that cannot be crossed, cloned, or have long generation times (Nordborg and Weigel 2008). AM may not be applicable in all situations, e.g. for the populations for which large sample size are required and phenotyping is difficult, AM may not produce reliable results. In association study, the LD structure of the targeted population is analysed and as the distance between two markers gives an idea about the strength of the correlation between two. The closer the markers are, the stronger the linkage disequilibrium (LD) and LD decays over distance. Therefore, LD decay pattern (LD structure) can be used as a function to measure relations between markers and QTLs. LD structures between species seem to highly variable, and the major drawback of AM estimations is disregarding genes with small effects that affect the quantitative traits. In almond, application of AM is very limited and only two unique studies were reported (Font i Forcada et al. 2015a, b) that has been carried out to determine physical and chemical traits in 98 almond cultivars from different geographic regions of the world; i.e. USA, France, Greece, Italy, Portugal, Algeria, Argentina, Australia, Bulgaria, Tunisia, and Ukraine. The results obtained from these studies indicated that high variation occurs in physical and chemical traits, which confirms the high diversity in the almond germplasm and the representative gene pool. The physical traits analysed were nut width, nut thickness, nut length, nut width, nut thickness length ratio, and double kernel, and the chemical traits analysed were protein content, oil content,

S. N. Goonetilleke and Á. Fernández i Martí

154

percentage of five fatty acids (oleic, linoleic, stearic, palmitic, and palmitoleic), content of three tocopherol homologues (a-, b-, and c-), and content of phytosterol compounds: Campesterol, D7-Campesterol, Stigmasterol, D7-Stigmastenol, Clerosterol, b-Sitosterol, D5-Avenasterol, and D7-Avenasterol. For the phytosterol content, seven markers were revealed that several putative genomic regions affect the chemical traits in almond kernels. Interestingly, candidate gene approach conducted with the peach genome sequences revealed that those markers were located in the sterol biosynthesis pathway of peach. A total of 12 associations related to physical nut and kernel traits have been identified (Font i Forcada et al. 2015a) and some of the QTL positions detected were in accordance with the QTL mapping positions obtained by Fernández i Martí et al. (2013). For example, from both the mapping procedures (using bi-parental populations AM), the QTL positions that control nut length, kernel length, and nut thickness/length were closer to the marker CPSCT006. The results obtained here indicated that AM would be a powerful mapping tool that provides improved QTL detection approaches to accelerate breeding. The marker trait associations identified in these studies would provide efficient platforms for classical and MAS in almond breeding.

10

A Well Characterised Trait in Almond Using QTL Mapping

Of the almond nut traits, shell hardness is one of the well characterised traits. Shell hardness was first mapped in almond, using R1000 ) Desmayo Largueta bi-parental population and a QTL was detected on LG2 (Sánchez-Pérez et al. 2007). Later, Arús et al. (1998) reported another QTL on LG8 using almond ) peach F2’s: ‘Texas’ ) ‘Earlygold’ (‘T ) E’) interspecific population. In (Goonetilleke et al. 2018) mapped shell hardness to Nonpareil ) Lauranne biparental population and shell hardness QTLs for Nonpareil were detected in two regions on

LG5Shell hardness QTLs for Lauranne which were detected in four regions on three chromosomes: one on LG2, two on LG5, and one on LG8. It seemed that in all QTL regions, Nonpareil-like genotypes had paper shells with shell hardness > 55% and of the 180 progeny that were evaluated for shell hardness, less than 10% (17 progeny) had the Nonpareil-like genotype. Based on these results, six markers (WriPdK251 and WPdK50 on LG2; WriPdK129, WriPdK18, and WriPdK264 on LG5; and WriPdK282 on LG8) can be used to separate progeny with the paper-shell trait from those with harder shells (Goonetilleke et al. 2018).

11

New Approaches and Technologies

11.1 Genomic Selection (GS) Although QTL mapping and MAS have extensively been used in plant improvement programmes during the last ten decades, there are some practical limitations due to long selection cycles and limited power in capturing small gene effects and genome-wide markers have potential to overcome these problems. Genomic selection (GS), an upgraded version of MAS, utilises genome-wide markers of all loci to measure the estimated genomic breeding value (EGBV) to achieve more precise and consistent selection. It has paved the way to combine molecular breeding, quantitative genetics, association mapping between markers and phenotypic knowledge of populations to generate prediction models. One of the problems in GS models is having highdensity markers, that may end up with higher number of markers than the number of phenotypic observations. Modern developments in breeding for quantitative trait selection, highthroughput genotyping and phenotyping events, whole genome sequencing (WGS), and other advancements in data analysis have allowed plant breeders and scientists to better understand quantitative traits and accelerate the crop improvement process. Genetic selection has successfully been implemented in detection of

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond

non-shattering and free threshing prediction abilities in a perennial grain crop, intermediate wheat grass (Thinopyrumn intermedium, IWG) (Crain et al. 2020). In future, it can be expected that GS will be widely implemented in improving nut and kernel traits in almond.

11.2 High-Throughput Sequencing The current accessibility to affordable of nextgeneration sequencing provides many opportunities for breeding that were previously unavailable. Ability to conduct GS, opportunities to exchange data among laboratories, and breeding programmes worldwide and to create interactive data mining have paved the way to accelerate the breeding cycles of plants by enhancing the rate of genetic gains annually and reducing the cost. In high-throughput sequencing (HTS), Illumina, Pacific-bioscience (Pac-Bio), and Oxford Nanopore are key HTS platforms and there are many studies published using Illumina sequencing in almond (Goonetilleke et al. 2018; Zhang et al. 2017; Alioto et al. 2019). Illumina’s bridge amplification approach permits generation massive quantity of small ‘clusters’ with an identical sequence to be analysed. During the process, about ten million clusters would form on an Illumina flow cell enabling multiple primer hybridization steps to allow multiple sequencing start points in parallel. This permits the sequencing of the original template molecule from both forward and reverse ends of which is known as paired-end sequencing. Deep sampling and uniform coverage of sequence reads play an important role in Illumina’s technology by ensuring the confidence in identification of splice variants in RNA seq, and in removing duplicate copy (deduplicate) reads deriving from the same original template. Paired end reads are also important in identification of large structural variants such as inversions and deletions from whole genome sequencing that are unable to detect with short sequencing techniques such as Sanger sequencing. With the Illumina’s approach, a third read may also be used to separate out samples if each sample in the sequencing library had a unique

155

barcode read added during the adapter construction. During incorporation of the nucleotides in each sequencing cycle, all four nucleotides are present as single, separate molecules, and can lead to an overall substitution error rate of 0.11% (Minoche et al. 2011). Although the second-generation technologies have vast improvements over Sanger sequencing, their short read lengths make them less suited for genome assembly, determination of complex genetic regions, gene isoform, and methylation detection. Some of these limitations can be overcome with single-molecule real-time (SMRT) sequencing, developed by Pacific BioSciences (PacBio). PacBio sequencing is based on singlemolecule sequencing and sequence information of the target DNA molecule will be captured during the replication process. The template, known as a SMRTbell, is a closed, single-stranded circular DNA which is formed by ligating hairpin adaptors to both ends of a target double-stranded DNA (dsDNA) molecule. The major advantage of PacBio sequencing is the much longer read length and the first generation of chemistry (C1 chemistry) of PacBio generated mean read lengths around 1500 bp, while the RS II system with the current C4 chemistry produces average read lengths over 10 kb. However, when compared to Illumina sequencing, throughput of PacBio sequencing is a drawback. Typical throughput of the PacBio RS II system is 0.5–1 billion bases per SMRT cell and it is considerably less than the throughput of Illumina HiSeq platform. For example, using the new HiSeq SBS v4 reagent kits, Illumina HiSeq 2500 produces up to 8 billion paired-end 125 bp reads (1 trillion bases) per two flow cells over a 6-day run, resulting in a daily throughput of ~ 167 billion bases in High Output Run Mode. Moreover, it also gives a higher single-pass error rate (about 11– 15%) and more expensive than most other sequencing approaches.

11.3 High-Throughput Phenotyping (HTP) The interaction between genotype and environment can be detected by the phenotype. Although

S. N. Goonetilleke and Á. Fernández i Martí

156

high-throughput genotyping is growing rapidly, the limitations in procedures of obtaining plant phenotypes seem to diminish the use of breeding for crop improvement. Phenotyping procedures include specific methods and protocols to measure morphological traits, chemical traits, physiological traits, canopy traits in specific tissues, organs, whole plants, or in populations. Traditionally, phenotyping in plants is done by measuring the appearance, taste, weight using conventional tool that are labour intensive and time consuming. To overcome these limitations, scientists are trying to develop new platforms with improved methods and tools since early 2000. High-throughput phenotyping (HTP) platforms based on images that can image about hundreds and thousands of plants everyday with automation is expanding rapidly (Furbank and Tester 2011; White et al. 2012). Commonly used HTP platforms for crops are varied from ground-based (such as greenhouse, controlled environmental rooms), to aerial systems (Andrade-Sanchez et al. 2013; Crain et al. 2016). For field experiments, unmanned aerial systems (UAS) will be an effective option to ground-based phenotyping platforms, particularly for large-scale nurseries and genetic studies with a large number of replicate plots (Poland 2015). Although implementation of UAS in perennial field crops is still in its infancy, with rapid emergence of lowcost consumer-grade sensors and platforms, UAS phenotyping holds great potential to be an important component in perennial plant genomics and breeding to obtain precise, quantitative assessments of complex traits that affect by QTLs in large plant populations.

12

Conclusion

In past decades, considerable improvement has been made to develop genetic and genomic tools to accelerate the implementation of MAS in almond breeding programmes. Availability of two almond genome sequences and several resequence data from other almond cultivars would provide enormous potentials and resources required for accelerating MAS in almond.

The future of modern almond breeding would further accelerate by combining with powerful genomics, bio-informatics tools, and highthroughput phenotyping.

References Alioto T, Alexiou K, Bardil A, Barteri F, Castanera R, Cruz F, Dhingra A, Duval H, Fernández i Martí Á, Frias L, Galán B, Garcia JL, Howad W, Garrido JG, Gut M, Julca I, Morata J, Puigdomènech P, Ribeca P, Rubio Cabetas MJ, Vlasova A, Wirthensohn M, Garcia-Mas J, Gabaldón T, Casacuberta JM, Arús P (2019) Transposons played a major role in the diversification between the closely related almond (Prunus dulcis) and peach (P. persica) genomes: Results from the almond genome sequence. bioRxiv:662676. https://doi.org/10.1101/662676 Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2013) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41(1):68–79 Aranzana MJ, Pineda A, Cosson P, Dirlewanger E, Ascasibar J, Cipriani G, Ryder CD, Testolin R, Abbott A, King GJ, Iezzoni AF, Arus P (2003) A set of simple-sequence repeat (SSR) markers covering the Prunus genome. Theor Appl Genet 106(5):819– 825. https://doi.org/10.1007/s00122-002-1094-y Arús P, Ballester J, Jauregui B, Joobeur T, Truco MJ, de Vicente MC (1998) The European Prunus mapping project: Update on marker development in almond. In: Tobutt KR, Alston FH (eds) Eucarpia symposium on fruit breeding and genetics. Acta Horticulturae 484:331–336 Ballester J, Bokovi R, Batlle I, Arus P, Vargas F, Vicente M (1998) Location of the self-incompatibility gene on the almond linkage map. Plant Breed 117: 69–72 Ballester J, Company R, Arus P, de Vicente M (2001) Genetic mapping of a major gene delaying blooming time in almond. Plant Breed 120:268–270 Crain J, Bajgain P, Anderson J, Zhang X, DeHaan L, Poland J (2020) Enhancing crop domestication through genomic selection, a case study of intermediate wheatgrass. Front Plant Sci 11(319). https://doi. org/10.3389/fpls.2020.00319 Crain JL, Wei Y, Barker J III, Thompson SM, Alderman PD, Reynolds M, Zhang N, Poland J (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56(3):965–975 D’Amico-Willman KM, Ouma WZ, Meulia T, Sideli GM, Gradziel TM, Fresnedo-Ramírez J (2022) Wholegenome sequence and methylome profiling of the almond [Prunus dulcis (Mill.) D.A. Webb] cultivar ‘Nonpareil’. G3 Genes|Genomes|Genetics 12(5). https://doi.org/10.1093/g3journal/jkac065

Discovery of Quantitative Trait Loci for Nut and Quality Traits in Almond Dirlewanger E, Graziano E, Joobeur T, Garriga-Caldere F, Cosson P, Howad W, Arus P (2004) Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proc Natl Acad Sci USA 101. https://doi. org/10.1073/pnas.0307937101 Donoso JM, Eduardo I, Picañol R, Batlle I, Howad W, Aranzana MJ, Arús P (2015) High-density mapping suggests cytoplasmic male sterility with two restorer genes in almond ) peach progenies. Horticul Res 2 (1):15016. https://doi.org/10.1038/hortres.2015.16 Donoso JM, Picañol R, Serra O, Howad W, Alegre S, Arús P, Eduardo I (2016) Exploring almond genetic variability useful for peach improvement: mapping major genes and QTLs in two interspecific almond ) peach populations. Mol Breed 36(2):16. https://doi. org/10.1007/s11032-016-0441-7 Duval H, Van Ghelder C, Portier U, Confolent C, Meza P, Esmenjaud D (2019) New data completing the spectrum of the Ma, RMia, and RMja genes for resistance to root-knot nematodes (Meloidogyne spp.) in Prunus. Phytopathology® 109(4):615–622. https:// doi.org/10.1094/phyto-05-18-0173-r Eduardo I, Picañol R, Rojas E, Batlle I, Howad W, Aranzana MJ, Arús P (2015) Mapping of a major gene for the slow ripening character in peach: co-location with the maturity date gene and development of a candidate gene-based diagnostic marker for its selection. Euphytica 205(2):627–636. https://doi.org/10. 1007/s10681-015-1445-9 Fernández i Martí A, Font i Forcada C, Socias i Company R (2013) Genetic analysis for physical nut traits in almond. Tree Genet Genomes 9(2):455‒465. https://doi.org/10.1007/s11295-012-0566-8 Fernandez i, Marti A, Howad W, Tao R, Alonso J, Arus P, Socias i Company R (2011) Identification of qualitative trait loci associated with self-compatibility in aPrunusspecies. Tree Genet Genomes 7:629―639 Font i Forcada C, i Marti A, i Company R (2012) Mapping quantitative trait loci for kernel composition in almond. BMC Genet 13(1):47 Font i Forcada C, Oraguzie N, Reyes-Chin-Wo S, Espiau MT, Socias i Company R, Fernández i Martí A (2015a) Identification of genetic loci associated with quality traits in almond via association mapping. PLoS ONE 10(6):e0127656. https://doi.org/10.1371/ journal.pone.0127656 Font i Forcada C, Velasco L, Socias i Company R, Fernández i Martí Á (2015b) Association mapping for kernel phytosterol content in almond. Frontiers Plant Sci 6:530. https://doi.org/10.3389/fpls.2015.00530 Fung H, Scholz S, Matarin M, Simon-Sanchez J, Hernandez D, Britton A, Gibbs J, Langefeld C, Stiegert M, Schymick J, Okun M, Mandel R, Fernandez H, Foote K, Rodriguez R, Peckham E, De Vrieze F, Gwinn-Hardy K, Hardy J, Singleton A (2006) Genome-wide genotyping in Parkinson’s disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 5:911–916

157

Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16(12):635–644. https://doi.org/10.1016/j.tplants. 2011.09.005 Goonetilleke SN, March TJ, Wirthensohn MG, Arús P, Walker AR, Mather DE (2018) Genotyping by sequencing in almond: SNP discovery, linkage mapping, and marker design. Genes|Genomes|Genetics 8 (1):161–172. https://doi.org/10.1534/g3.117.300376 Hanada T, Fukuta K, Yamane H, Esumi T, Tao R, Gradziel TM, Dandekar AM, Fernández i Martí Á, Alonso JM, Socias i Company R (2009) Cloning and characterization of a self-compatible Sf haplotype in almond [Prunus dulcis (Mill.) D.A. Webb. syn. P. amygdalus Batsch] to resolve previous confusion in its Sf-RNase sequence. HortScience 44(3):609–613 Joobeur T, Periam N, Vicente M, King G, Arús P (2000) Development of a second generation linkage map for almond using RAPD and SSR markers. Genome 43:649–655 Joobeur T, Viruel M, de Vicente M, Jauregui B, Ballester J, Dettori M, Verde I, Truco M, Messeguer R, Batlle I (1998) Construction of a saturated linkage map for Prunus using an almond x peach F2 progeny. Theor Appl Genet 97:1034–1041 Lecouls A-C, Bergougnoux V, Rubio-Cabetas M-J, Bosselut N, Voisin R, Poessel J-L, Faurobert M, Bonnet A, Salesses G, Dirlewanger E, Esmenjaud D (2004) Marker-assisted selection for the widespectrum resistance to root-knot nematodes conferred by the Ma gene from Myrobalan plum (Prunus cerasifera) in interspecific Prunus material. Mol Breeding 13(2):113–124. https://doi.org/10.1023/B: MOLB.0000018758.56413.cf Minoche AE, Dohm JC, Himmelbauer H (2011) Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol 12(11):1–15 Nordborg M, Weigel D (2008) Next-generation genetics in plants. Nature 456(7223):720–723 Picañol R, Eduardo I, Aranzana MJ, Howad W, Batlle I, Iglesias I, Alonso JM, Arús P (2013) Combining linkage and association mapping to search for markers linked to the flat fruit character in peach. Euphytica 190(2):279– 288. https://doi.org/10.1007/s10681-012-0844-4 Poland J (2015) Breeding-assisted genomics. Curr Opin Plant Biol 24:119–124 Salazar JA, Ruiz D, Campoy JA, Sánchez-Pérez R, Crisosto CH, Martínez-García PJ, Blenda A, Jung S, Main D, Martínez-Gómez P, Rubio M (2014) Quantitative trait loci (QTL) and mendelian trait loci (MTL) analysis in Prunus: a breeding perspective and beyond. Plant Mol Biol Rep 32(1):1–18. https:// doi.org/10.1007/s11105-013-0643-7 Sánchez-Pérez R, Howad W, Dicenta F, Arús P, Martínez-Gómez P (2007) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breed 126(3):310–318. https://doi.org/ 10.1111/j.1439-0523.2007.01329.x

158 Sánchez-Pérez R, Howad W, García-Mas J, Arús P, Martínez-Gómez P, Dicenta F (2010) Molecular markers for kernel bitterness in almond. Tree Genet Genomes 6:237–245 Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Aiese Cigliano R, Del Cueto J, Ricciardi F, Lotti C, Ricciardi L, Dicenta F, López-Marqués RL, Møller BL (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364(6445):1095–1098. https://doi.org/10.1126/science.aav8197 Sanchez AC, Brar DS, Huang N, Li Z, Khush GS (2000) Sequence tagged site marker-assisted selection for three bacterial blight resistance genes in rice. Crop Sci 40(3):792–797. https://doi.org/10.2135/cropsci2000. 403792x Sax K (1923) The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8(6):552–560. https://doi.org/10. 1093/genetics/8.6.552 Sturtevant AH (1913) The linear arrangement of six sexlinked factors in Drosophila, as shown by their mode of association. J Exp Zool 14(1):43–59. https://doi. org/10.1002/jez.1400140104 Tanksley SD (1983) Molecular markers in plant breeding. Plant Mol Biol Rep 1(1):3–8. https://doi.org/10.1007/ BF02680255 Tavassolian I, Rabiei G, Gregory D, Mnejja M, Wirthensohn M, Hunt P, Gibson J, Ford C, Sedgley M, Wu SB (2010) Construction of an almond linkage map in an Australian population Nonpareil ) Lauranne. BMC Genomics 11(551):10 Van Ghelder C, Lafargue B, Dirlewanger E, Ouassa A, Voisin R, Polidori J, Kleinhentz M, Esmenjaud D (2010) Characterization of the RMja gene for resistance to root-knot nematodes in almond: spectrum, location, and interest for Prunus breeding. Tree Genet Genomes 6(3):503–511. https://doi.org/10.1007/s11295010-0268-z Verde I, Abbott AG, Scalabrin S, Jung S, Shu S, Marroni F, Zhebentyayeva T, Dettori MT, Grimwood J, Cattonaro F, Zuccolo A, Rossini L, Jenkins J, Vendramin E, Meisel LA, Decroocq V, Sosinski B, Prochnik S, Mitros T, Policriti A, Cipriani G, Dondini L, Ficklin S, Goodstein DM, Xuan P, Fabbro CD,

S. N. Goonetilleke and Á. Fernández i Martí Aramini V, Copetti D, Gonzalez S, Horner DS, Falchi R, Lucas S, Mica E, Maldonado J, Lazzari B, Bielenberg D, Pirona R, Miculan M, Barakat A, Testolin R, Stella A, Tartarini S, Tonutti P, Arus P, Orellana A, Wells C, Main D, Vizzotto G, Silva H, Salamini F, Schmutz J, Morgante M, Rokhsar DS (2013) The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet 45(5):487–494. https://doi.org/10.1038/ng.2586 http://www.nature.com/ng/journal/v45/n5/abs/ng.2586. html#supplementary-information Visscher PM, Haley CS, Thompson R (1996) Markerassisted introgression in backcross breeding programs. Genetics 144(4):1923–1932. https://doi.org/10.1093/ genetics/144.4.1923 Werner K, Friedt W, Ordon F (2005) Strategies for pyramiding resistance genes against the barley yellow mosaic virus complex (BaMMV, BaYMV, BaYMV2). Mol Breed 16(1):45–55. https://doi.org/10.1007/ s11032-005-3445-2 White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112. https://doi. org/10.1016/j.fcr.2012.04.003 Wu S-B, Franks T, Hunt P, Wirthensohn M, Gibson J, Sedgley M (2010) Discrimination of SNP genotypes associated with complex haplotypes by high resolution melting analysis in almond: implications for improved marker efficiencies. Mol Breed 25(2):351–357. https:// doi.org/10.1007/s11032-009-9324-5 Wu S-B, Tavassolian I, Rabiei G, Hunt P, Wirthensohn M, Gibson J, Ford C, Sedgley M (2009) Mapping SNPanchored genes using high-resolution melting analysis in almond. Mole Genet Genomics 282(3):273–281. https://doi.org/10.1007/s00438-009-0464-4 Zhang L, Yang X, Qi X, Guo C, Jing Z (2017) Characterizing the transcriptome and microsatellite markers for almond (Amygdalus communis L.) using the Illumina sequencing platform. Hereditas 155 (1):14. https://doi.org/10.1186/s41065-017-0049-x

Accelerating Almond Breeding in Post-genomic Era Jorge Mas-Gómez, Francisco José Gómez-López, Ángela Sánchez Prudencio, Manuel Rubio Angulo, and Pedro José Martínez-García

Abstract

Classical almond breeding is a long process due to its plurennial woody character and long juvenile period. The implementation of marker-assisted selection has had a significant impact in almond breeding programs for some simple monogenic traits. However, the last advances in genomic technologies enable now to introduce new approaches to support the improvement of quantitative traits in which only the phenotypic selection has been used up to now. Genomic selection has been explored in other Prunus and nut species being a promising approach for achieving higher rates of genetic gain per unit of time and cost. The current almond breeding cycle could evolve toward a scheme adapted to genomic selection to increase the efficiency in almond breeding programs and accelerate the genetic gain.

J. Mas-Gómez . F. J. Gómez-López . Á.S. Prudencio . M. R. Angulo . P. J. Martínez-García (&) Department of Plant Breeding, Centre of Edaphology and Applied Biology of Segura, Spanish National Research Council, (CEBAS-CSIC), Murcia, Spain e-mail: [email protected]

1

Introduction

During the last years, Next-Generation Sequencing (NGS) technologies are being implemented in almond research. As result, three reference genomes have been released (Sánchez-Pérez et al. 2019; Alioto et al. 2020; D’Amico-Willman et al. 2022) and are available for the research community. In addition, the use of the NGS techniques in almond has provided new knowledge about the genetic mechanisms controlling traits of interest. RNA-seq has been used to identify candidate genes involved in the overcoming of the endodormancy (Prudencio et al. 2021) or to provide insights into the regulatory mechanisms of the kernel size (Jafari et al. 2022). A genotyping-by-sequencing (GBS) approach was employed to perform a genome-wide association study (GWAS) and to map homozygosity, using a large collection of almonds cultivars (Pavan et al. 2021). Moreover, considering the homology among almond and peach genomes, Di Guardo et al. (2021) used the Illumina Infinium®18 K Peach SNP array to genotype a germplasm collection of almonds detecting significant marker-trait associations for volatile organic compounds. Fortunately, a new AxiomTM 60 K Almond SNP array has been designed (Duval et al. 2022; in revision) and will promote genomic studies in almond. More promising studies will be expected after the progress done in almond genomics, and the

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_11

159

160

implementation of these advances in almond breeding programs has just started. Specifically, genomic selection could have an important impact in almond breeding and has not been explored yet. Genomic selection is a set of breeding approaches that perform selection based on predictions of any trait using genotypic data across the whole genome (Lorenz et al. 2011). The general scheme consists in estimating marker effects across the whole genome of a training population (genotyped and phenotyped) and after the validation of the fine-tuned model applying it in a breeding population (genotyped but not phenotyped) (Desta and Ortiz 2014; Meuwissen et al. 2001). The genomic estimation of breeding value (GEBV) is the parameter used to select superior genotypes (Vahdati et al. 2019), which gives us an estimation of how many superior alleles are transferred to the next generation (breeding value) (Desta and Ortiz 2014). The great impact of genomic selection in breeding is the possibility to select individuals in the first steps of breeding cycle and shorten generation intervals (Biscarini et al. 2017; Lin et al. 2014; Xu et al. 2020). This implies a higher genetic gain rate per unit of time and thus higher efficiency, reducing time and costs invested in the phenotyping process and germplasm maintenance (Heffner et al. 2010). Genomic selection is generally considered suitable for quantitative traits which are controlled by many genes with small effect. A highdensity of markers is, generally, essential for genomic selection accuracy (Solberg et al. 2008; Scheben et al. 2017; Biscarini et al. 2017; Aranzana et al. 2019). In this sense, the genotyping approach chosen will determine the cost of the implementation of this strategy. The availability of reference genomes is required to implement reduced-cost genotyping approaches such as GBS techniques. The main reason is that high-throughput sequencing platforms require aligning the reads to a reference genome to reach a high imputation accuracy, making possible the use of low depths sequencing (Poland et al. 2012; Scheben et al. 2017). Moreover, new genomic tools as the new AxiomTM 60 K Almond SNP

J. Mas-Gómez et al.

can provide a great quantity of SNPs with ease of analysis for genomic selection implementation with acceptable costs (Scheben et al. 2017). Thus, the great progress done during the last years in almond genomics is helping to establish the basis for genomic selection in almond breeding.

2

Genomic Selection in Almond Breeding

As most fruit breeding, almond breeding is notably influenced by the plurennial woody character and long juvenile period (MartínezGarcía et al. 2019a). Currently, the average time to release a new almond variety is around 12 years and the phenotyping process is lengthy and time-consuming (Martínez-García et al. 2019a). In addition, the long period of the breeding cycle implies a considerable cost of maintaining many individuals for years. In long breeding cycles species, genomic selection improves the selection efficiency reducing generation intervals (Biscarini et al. 2017; Lin et al. 2014; Xu et al. 2020). Studies in diverse species with a similar idiosyncrasy to almond, such as conifer species (long generation intervals or high costs of maintaining breeding populations) have shown the superiority of genomic selection to phenotypic selection in genetic gain per unit of time and cost in crops (Wong and Bernardo 2008; Resende et al. 2012; Grattapaglia and Resende 2011; O´Connor et al. 2021; Biscarini et al. 2017; Heslot et al. 2015). The suitability of genomic selection for quantitative traits makes genomic selection a solution for the breeding of important traits in almond in which the polygenic nature of the traits makes difficult the marker-assisted selection. Flowering date is an important trait in almond mainly because determines frost vulnerability, but also effective bloom overlap when self-incompatible cultivars are combined in orchards (Battle et al. 2017). Harvest time is considered an important trait to optimize the harvesting, to commercialize earlier the almonds, and to avoid rainfall periods during the drying

Accelerating Almond Breeding in Post-genomic Era

almond process. Kernel shape, fruit size, fruit thickness and fruit weight are breeding aims in almond because of the posterior use, considering that different almond sizes have different uses (Battle et al. 2017). Kernel quality parameters such as protein and oil content are considered in breeding programs too and depending on the usage kernels with specific compositions are sought (Martínez-García et al. 2019a). Moreover, the health benefits of almond consumption dependent on the nutritional components of the kernel are appreciated by consumers (Kamil et al. 2012). Yield is probably the most desirable trait by the growers, because of the direct relation with the economic benefits. The quantitative nature of these traits has been explored by QTL studies detecting several regions controlling the trait (Sánchez-Perez et al. 2007, 2013; Ballester et al. 2001; Font i Forcada et al. 2012). For these quantitative traits is where the genomic selection could be implemented in almond breeding to accelerate the genetic gain in comparison to the phenotypic selection used until now.

3

Aspects Influencing Genomic Selection in Almond

Genomic selection accuracy has been associated with different factors related to the characteristics of the trait chosen, population types and the genome of the species studied. Generally, heritability is positively correlated with prediction accuracy in genomic selection, although some exceptions have been identified too (Xu et al. 2020; Combs and Bernardo 2013; Desta and Ortiz 2014). The heritability of some traits of interest in almond has been explored previously, obtaining high heritability values for the length and roundness of the kernel (0.93 and 0.82, respectively), kernel weight (0.71), double kernels (0.75), flowering and harvest time (0.99 and 0.83), medium heritability values for the width and globosity of the kernel (0.38 and 0.33, respectively), density of production and flowering (0.45 and 0.54, respectively) and low heritability values for the thickness of the kernel (0.09) (Dicenta et al. 1993a, b; Martínez-García

161

et al. 2014, 2019b). Following uniquely the heritability criteria, we could expect good prediction accuracies in many traits of interest, however also the heritability of some traits (e.g., kernel quality traits) remains still unexplored. Several features of the populations to study are important to be considered in genomic selection. Broadly the prediction accuracy improves when the training population size increase, although more factors should be assessed (Combs and Bernardo 2013; Desta and Ortiz 2014). The genetic architecture of the trait has relevance in the population size, when the SNP effects are small, larger training populations are required to obtain better estimations (Aranzana et al. 2019; Muranty et al. 2015). Until now, QTL studies in almond employed full-sib families from 62 to 167 seedlings (Sánchez-Perez et al. 2007; Ballester et al. 2001; Font i Forcada et al. 2012, 2013), although full-sib families in almond breeding programs can be smaller. In this sense, the use of related family sets in training populations which be representative of the breeding population (Heffner et al. 2009) may be a key point. Relatedness among training and breeding populations allows to obtain higher accuracies in predictions (Lenz et al. 2017; Ly et al. 2013; Aranzana et al. 2019; Muranty et al. 2015). Population structure analysis can be used to design effective training populations because of the increase in the genetic distance between training and breeding populations promote lower prediction accuracies (Würschum et al. 2013; Isidro et al. 2015; Werner et al. 2020; Desta and Ortiz 2014). Recently, Pérez de los Cobos et al. (2021) studied the genetic structure in modern almond breeding programs worldwide, identifying two mainstream breeding lines based only on three cultivars (“Nonpareil”, “Tuono” and “Cristomorto”). Although from the inbreeding point of view this reduced genetic background is a drawback (Pérez de los Cobos et al. 2021), the high relatedness in the pedigrees may favor higher prediction accuracies in the beginning of the genomic selection in almond breeding programs. In any case, new germplasm is being used in breeding programs to increase genetic diversity and should be included in the design of

162

J. Mas-Gómez et al.

training populations progressively in genomic selection strategies. The density of markers needed in genomic selection for better prediction accuracy is influenced by the linkage disequilibrium (LD) between markers and trait loci (Desta and Ortiz 2014). Linkage disequilibrium span shorter requires higher densities of markers to achieve higher prediction accuracies and to conserve marker-QTL associations (Liu et al. 2015; Thistlethwaite et al. 2020; Desta and Ortiz 2014). Previous studies of LD in almond would be useful to select the best quantity of markers used (Isik et al. 2015). Pavan et al. (2021) studied LD decay in a set of 149 almond cultivars, identifying a fast LD decay in comparison to other species and suggesting the self-incompatibility in almond cultivars as the reason to favor the “block-breaking”. These results may indicate that a higher density of marker will be required to obtain good prediction accuracies in genomic selection, in comparison to other species where de LD decay is slower [e.g., peach (Micheletti et al. 2015)].

4

Previous Studies in Prunus and Nuts Species

To the best of our knowledge, there has not been released any study on genomic selection research in almond. However, genomic research works in other Prunus species and nuts could be useful as a preliminary context. Fruit weight, sugar content and titratable acidity were studied for genomic prediction for the first time in peach, phenotyping 1147 individuals during 3–5 years in different locations and genotyped with IPSC 9 K SNP array V1, achieving high predictive abilities (0.60, 0.72 and 0.65) (Biscarini et al. 2017). Also, genomic prediction was implemented to study brown rot resistance in peach using 288 F1 individuals from 27 pedigree-related families and 38 cultivars/advanced selections, obtaining low to moderate values of predictive accuracy of the disease severity index (from 0.092 to 0.449) (Fu et al. 2022). Fruit quality traits were studied to implement genomic selection for the first time in

apricot using 153 individuals of an F1 population and prediction accuracy using different approaches varied from 0.31 for glucose content to 0.78 for ethylene production (Nsibi et al. 2020). The prediction accuracy of genomic selection approaches was studied in nut yield of Australian macadamia using a 295 full-sib progeny from 32 families achieving a value of 0.57 using related populations and halving the generation length from 8 to 4 years (O´Connor et al. 2021). Moreover, the genomic selection has been used in other nuts in disease resistance as blight resistance in chestnut (Westbrook et al. 2020) and sting nematode resistance, late leaf spot and rust in peanut (Chaudhari et al. 2019). The implementation of genomic selection in species related to almond and other nuts is useful as a reference to guide the future genomic selection research in almond.

5

Almond Breeding Cycle Updating

Currently, almond breeding selection approach is a phenotype-based selection supported by a marker-assisted selection of self-compatibility trait (Sánchez-Pérez et al. 2004). The design of the crosses done is complementary type, to try to assemble interesting phenotypic traits of both genitors in the descendant, or transgressive type, to obtain descendants with a superior trait in comparison to the parents (Martínez-García et al. 2019a). After the manual pollination, germination of the seeds (1 year), and a long juvenility period (3–4 years), the evaluation of the offspring is performed for 3–4 years (MartínezGarcía et al. 2019a). In addition, an evaluation in other experimental field locations to assess the behavior in different environments during several years previous to the registration and commercialization of new varieties (Martínez-García et al. 2019a). Genomic selection would introduce changes in the scheme of the current breeding cycle (Fig. 1). The zero step is the design of the training population, which for instance could be a collection of individuals related to the breeding populations present in the program with

Accelerating Almond Breeding in Post-genomic Era

163

Fig. 1 Almond breeding cycle updated using genomic selection approach

progressive entries or removals to improve the accuracies and to include possible new germplasm introduced in the breeding crosses. In any case, this training population could be used as input entirely or partially in the statistical models, varying according to the trait of interest or the breeding family, intending to achieve the highest prediction accuracies possible (Riedelsheimer et al. 2013; Hickey et al. 2014; Lorenz et al. 2015; Nsibi et al. 2020; Muranty et al. 2015). In addition, the training population has to be phenotyped to include the phenotype in the models. The design of the crosses would be done according to the aim and the GEBV obtained, and an early selection could be carried out in the nursery, after germination of the seeds and genotyping and the results of the genomic prediction performed (Heslot et al. 2015). The selection could be done for a single-trait but also for multiple traits using approaches such as index selection or look-ahead selection (Moeinizade et al. 2020; Lenz et al. 2020). This early selection will allow to reduce maintenance and phenotyping costs or to increase the number of descendants and keep the best individuals uniquely up to complete the breeding capacity of the experimental plots. Regarding the evaluation

of the descendants can be accelerated using those with the highest GEBV when just accomplish the juvenility period as progenitor of the next generation with minimum phenotyping (Heffner et al. 2010; Bernardo and Yu 2007; Wong and Bernardo 2008). Moreover, in the assessment in different locations, genomic selection could have an important role in studying training populations in multiple environments to obtain high accuracies in multi-environment predictions (in tested and no tested environments) (Guo et al. 2013; Burgueño et al. 2012; Ward et al. 2019). The new advances in almond genomics performed during the last years promote the starting of the genomic selection implementation in almond breeding programs. In addition, the convergence of the multi-omics knowledge developed in almond research will improve prediction accuracies and could solve the weaknesses (Harfouche et al. 2019). Genomic selection approaches are being implemented in many crops supplanting more traditional strategies. In almond, genomic selection could accelerate breeding cycle and rise the genetic gain per unit of time and cost in traits of interest where up to now only phenotypic selection has been employed.

164

References Alioto T, Alexiou KG, Bardil A, Barteri F, Castanera R, Cruz F et al (2020) Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence. Plant J 101:455–472 Aranzana MJ, Decroocq V, Dirlewanger E, Eduardo I, Gao ZS, Gasic K et al (2019) Prunus genetics and applications after de novo genome sequencing: achievements and prospects. Horticul Res 6:1–25 Ballester J, Socias i Company R, Arus P, De Vicente MC (2001) Genetic mapping of a major gene delaying blooming time in almond. Plant Breeding 120:268– 270. https://doi.org/10.1046/j.1439-0523.2001.00604.x Battle I, Dicenta F, Gradziel TM, Wirthensohn M, Duval H, Vargas FJ (2017) Classical genetics and breeding. In: Almonds: botany, production and uses (CABI), pp 111–148 Bernardo R, Yu J (2007) Prospects for Genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090. https://doi.org/10.2135/cropsci2006.11. 0690 Biscarini F, Nazzicari N, Bink M, Arús P, Aranzana MJ, Verde I et al (2017) Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies. BMC Genomics 18:1–15 Burgueño J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype ☓ environment interaction using pedigree and dense molecular markers. Crop Sci 52:707– 719. https://doi.org/10.2135/cropsci2011.06.0299 Chaudhari S, Khare D, Patil SC, Sundravadana S, Variath MT, Sudini HK et al. (2019) Genotype ☓ environment studies on resistance to late leaf spot and rust in genomic selection training population of peanut (Arachis hypogaea L.). Front Plant Sci 10. Available at: https://www.frontiersin.org/articles/https://doi.org/ 10.3389/fpls.2019.01338 Combs E, Bernardo R (2013) Accuracy of Genomewide selection for different traits with constant population size, heritability, and number of markers. The Plant Genome 6. https://doi.org/10.3835/plantgenome2012. 11.0030 D’Amico-Willman KM, Ouma WZ, Meulia T, Sideli GM, Gradziel TM, Fresnedo-Ramírez J (2022) Wholegenome sequence and methylome profiling of the almond [Prunus dulcis (Mill.) DA Webb] cultivar ‘Nonpareil.’ G3 12:jkac065 Desta ZA, Ortiz R (2014) Genomic selection: genomewide prediction in plant improvement. Trends Plant Sci 19:592–601 Di Guardo M, Farneti B, Khomenko I, Modica G, Mosca A, Distefano G et al. (2021) Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels. Horticul Res 8 Dicenta F, Garcia JE, Carbonell EA (1993a) Heritability of flowering, productivity and maturity in almond.

J. Mas-Gómez et al. J Horticul Sci 68:113–120. https://doi.org/10.1080/ 00221589.1993.11516334 Dicenta F, Garcia JE, Carbonell EA (1993b) Heritability of fruit characters in almond. J Horticul Sci 68:121–126. https://doi.org/10.1080/00221589.1993.11516335 Duval H, Coindre E, Ramos Onsins S, Kontantinos GA, Rubio Cabetas MJ, Martínez-García PJ, Wirthenshon M, Dhingra A, Samarina A, Arus P (2022) Development and evaluation of an AxiomTM 60K SNP array for almond (in revision) Fernández i Martí A, Font i Forcada C, Socias i Company R (2013) Genetic analysis for physical nut traits in almond. Tree Genetics and Genomes 9:455– 465. https://doi.org/10.1007/s11295-012-0566-8 Font i Forcada CF, i Martí ÀF, i Company RS (2012) Mapping quantitative trait loci for kernel composition in almond. BMC Genetics 13:47. https://doi.org/10. 1186/1471-2156-13-47 Fu W, da Silva Linge C, Lawton JM, Gasic K (2022) Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach. Fruit Res 2:1– 12. https://doi.org/10.48130/FruRes-2022-0002 Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7:241– 255. https://doi.org/10.1007/s11295-010-0328-4 Guo Z, Tucker DM, Wang D, Basten CJ, Ersoz E, Briggs WH, et al. (2013) Accuracy of acrossenvironment genome-wide prediction in maize nested association mapping populations. G3 Genes|Genomes| Genetics 3:263–272. https://doi.org/10.1534/g3.112. 005066 Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Scarascia Mugnozza G et al (2019) Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends Biotechnol 37:1217–1235. https://doi.org/10.1016/j. tibtech.2019.05.007 Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690. https://doi.org/ 10.2135/cropsci2009.11.0662 Heffner EL, Sorrells ME, Jannink J (2009) Genomic selection for crop improvement. Crop Sci 49:1–12 Heslot N, Jannink J-L, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55:1–12. https://doi.org/10.2135/ cropsci2014.03.0249 Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R, Prasanna BM et al (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54:1476–1488. https://doi.org/10.2135/ cropsci2013.03.0195 Isidro J, Jannink J-L, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128:145–158. https://doi.org/10.1007/s00122014-2418-4 Isik F, Kumar S, Martínez-García PJ, Iwata H, Yamamoto T (2015) Chapter three—acceleration of

Accelerating Almond Breeding in Post-genomic Era forest and fruit tree domestication by genomic selection. In: Plomion C, Adam-Blondon A-F (eds) Advances in botanical research, Academic Press, pp 93–124. https://doi.org/10.1016/bs.abr.2015.05.002 Jafari M, Shiran B, Rabiei G, Ravash R, Sayed Tabatabaei BE, Martínez-García PJ et al (2022) Identification of genes associated with kernel size in almond [Prunus dulcis (Mill.) DA Webb] using RNASeq. Plant Growth Regul 97:357–373 Kamil A, Chen C-YO (2012) Health benefits of almonds beyond cholesterol reduction. J Agric Food Chem 60:6694–6702. https://doi.org/10.1021/jf2044795 Lenz PRN, Beaulieu J, Mansfield SD, Clément S, Desponts M, Bousquet J (2017) Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics 18:335. https://doi.org/10.1186/s12864-017-3715-5 Lenz PRN, Nadeau S, Mottet M-J, Perron M, Isabel N, Beaulieu J et al (2020) Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evol Appl 13:76–94. https://doi.org/ 10.1111/eva.12823 Lin Z, Hayes B, Daetwyler H (2014) Genomic selection in crops, trees and forages: a review. Crop Pasture Sci 65:1177–1191 Liu H, Zhou H, Wu Y, Li X, Zhao J, Zuo T et al. (2015) The impact of genetic relationship and linkage disequilibrium on genomic selection. PLOS ONE 10:e0132379. https:// doi.org/10.1371/journal.pone.0132379 Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H et al (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123 Lorenz AJ, Smith KP (2015) Adding genetically distant individuals to training populations reduces genomic prediction accuracy in barley. Crop Sci 55:2657–2667. https://doi.org/10.2135/cropsci2014.12.0827 Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M, Gauch HG Jr et al (2013) Relatedness and genotype ☓ environment interaction affect prediction accuracies in genomic selection: a study in cassava. Crop Sci 53:1312– 1325. https://doi.org/10.2135/cropsci2012.11.0653 Martínez-García PJ, Ortega E, Cremades T, Dicenta F (2014) Heritability and phenotypic variation of double seeds in almond (Prunus dulcis). Euphytica 198:91– 99. https://doi.org/10.1007/s10681-014-1086-4 Martínez-García PJ, Prudencio AS, Sánchez-Pérez R, Cobos FPDL, Kodad O, Gouta H et al. (2019a) Almond [Prunus dulcis (Miller) DA Webb] breeding. In: Advances in plant breeding strategies: nut and beverage crops, Springer, pp 3–29 Martínez-García PJ, Rubio M, Cremades T, Dicenta F (2019b) Inheritance of shell and kernel shape in almond (Prunus dulcis). Sci Hortic 244:330–338. https://doi.org/10.1016/j.scienta.2018.09.041 Meuwissen TH, Hayes BJ, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

165 Micheletti D, Dettori MT, Micali S, Aramini V, Pacheco I, Linge CDS et al. (2015) Whole-genome analysis of diversity and SNP-major gene association in peach germplasm. PloS one 10:e0136803. https:// doi.org/10.1371/journal.pone.0136803 Moeinizade S, Kusmec A, Hu G, Wang L, Schnable PS (2020) Multi-trait genomic selection methods for crop improvement. Genetics 215:931–945. https://doi.org/ 10.1534/genetics.120.303305 Muranty H, Troggio M, Sadok IB, Rifaï MA, Auwerkerken A, Banchi E et al (2015) Accuracy and responses of genomic selection on key traits in apple breeding. Horticul Res 2:15060. https://doi.org/10. 1038/hortres.2015.60 Nsibi M, Gouble B, Bureau S, Flutre T, Sauvage C, Audergon J-M et al. (2020) Adoption and optimization of genomic selection to sustain breeding for apricot fruit quality. G3 Genes|Genomes|Genetics 10:4513– 4529. https://doi.org/10.1534/g3.120.401452 O’Connor KM, Hayes BJ, Hardner CM, Alam M, Henry RJ, Topp BL (2021) Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population. BMC Genomics 22:370. https:// doi.org/10.1186/s12864-021-07694-z Pavan S, Delvento C, Mazzeo R, Ricciardi F, Losciale P, Gaeta L et al. (2021) Almond diversity and homozygosity define structure, kinship, inbreeding, and linkage disequilibrium in cultivated germplasm, and reveal genomic associations with nut and seed weight. Horticul Res 8 Pérez de Los Cobos F, Martínez-García PJ, Romero A, Miarnau X, Eduardo I, Howad W et al (2021) Pedigree analysis of 220 almond genotypes reveals two world mainstream breeding lines based on only three different cultivars. Horticul Res 8:1–11 Poland JA, Rife TW (2012) Genotyping‐by‐sequencing for plant breeding and genetics. The Plant Genome 5 Prudencio ÁS, Hoeberichts FA, Dicenta F, MartínezGómez P, Sánchez-Pérez R (2021) Identification of early and late flowering time candidate genes in endodormant and ecodormant almond flower buds. Tree Physiol 41:589–605 Resende M Jr, Muñoz P, Acosta J, Peter G, Davis J, Grattapaglia D et al (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624 Riedelsheimer C, Melchinger AE (2013) Optimizing the allocation of resources for genomic selection in one breeding cycle. Theor Appl Genet 126:2835–2848. https://doi.org/10.1007/s00122-013-2175-9 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2004) Identification of S-alleles in almond using multiplex PCR. Euphytica 138:263–269. https://doi.org/10. 1023/B:EUPH.0000047097.96271.bf Sánchez-Pérez R, Howad W, Dicenta F, Arús P, Martínez-Gómez P (2007) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breeding 126:310–318

166 Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Cigliano RA, Del Cueto J et al (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364:1095–1098 Scheben A, Batley J, Edwards D (2017) Genotyping-bysequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J 15:149–161 Solberg T, Sonesson A, Woolliams J, Meuwissen T (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454 Thistlethwaite FR, Gamal El-Dien O, Ratcliffe B, Klápště J, Porth I, Chen C et al. (2020) Linkage disequilibrium versus pedigree: genomic selection prediction accuracy in conifer species. PLOS ONE 15:e0232201. https://doi.org/10.1371/journal.pone.0232201 Vahdati K, Arab MM, Sarikhani S, Sadat-Hosseini M, Leslie CA, Brown PJ (2019) Advances in Persian walnut (Juglans regia L.) breeding strategies. In: Advances in plant breeding strategies: nut and beverage crops, Springer, pp 401–472 Ward BP, Brown-Guedira G, Tyagi P, Kolb FL, Van Sanford DA, Sneller CH et al (2019) Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Sci 59:491–507. https://doi.org/10.2135/cropsci2018. 03.0189

J. Mas-Gómez et al. Werner CR, Gaynor RC, Gorjanc G, Hickey JM, Kox T, Abbadi A et al. (2020) How population structure impacts genomic selection accuracy in crossvalidation: implications for practical breeding. Front Plant Sci 11. Available at: https://www.frontiersin. org/articles/https://doi.org/10.3389/fpls.2020.592977 Westbrook JW, Zhang Q, Mandal MK, Jenkins EV, Barth LE, Jenkins JW et al (2020) Optimizing genomic selection for blight resistance in American chestnut backcross populations: a trade-off with American chestnut ancestry implies resistance is polygenic. Evol Appl 13:31–47. https://doi.org/10. 1111/eva.12886 Wong C, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815– 824 Würschum T, Reif JC, Kraft T, Janssen G, Zhao Y (2013) Genomic selection in sugar beet breeding populations. BMC Genet 14:85. https://doi.org/10.1186/1471-215614-85 Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C et al (2020) Enhancing genetic gain through genomic selection: from livestock to plants. Plant Commun 1:100005

Prospects and Future Questions Pedro Martínez-Gómez, Raquel Sánchez-Pérez, and Ángel Fernández i Martí

Abstract

Cultivated almond is a tree crop producing seeds of great economic interest, adapted to hot and dry climate. Domesticated in Southeast Asia, its small diploid genome and phenotypic diversity make it an ideal model to complement the genomics studies in peach species, considered as the reference Prunus species. Actually, both species represent consanguineous species which evolved under two distinct environments, being warmer and more humid in the case of peach and colder and xerophytic for almond. The genome of almond has been fully sequenced. Subsequently, several consortiums including those in Spain, Australia and USA have completed substantial resequencing projects. These results indicate a 275 Mbp genome with substantial heterozygosity as well as repetitive sequence. The advent of affordable whole genome sequencing in combination with

P. Martínez-Gómez (&) . R. Sánchez-Pérez Departamento de Mejora Vegetal Grupo de Mejora Genética de Frutales, CEBAS-CSIC, Espinardo, Murcia, Spain e-mail: [email protected] Á. Fernández i Martí (&) Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA e-mail: [email protected]

existing Prunus functional genomics data has enabled the leveraging of the significant novel diversity available in almond, providing an unmatched resource for the genetic improvement of this species. This proposed volume will expound the latest information on the current state of almond genomics and transcriptomics, with a particular focus on the latest findings, tools and strategies employed in genome sequencing and analysis in relation to the most important agronomic traits. Additionally, the knowledge of the whole genome sequence will allow the development in almond of the new methodology of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9, as a new strategy for breeding, alternative to the traditional methods. Cultivated almond [(Prunus dulcis (Miller) D. A. Webb, syn. Prunus amygdalus Batsch., Amygdalus communis L., Amygdalus dulcis Mill.)] is one of the most important species of the genus Prunus inside Rosacea family with a great economic importance. This species produces kernels of great economic interest, and it is adapted to hot and dry climate (Graziel and Martínez-Gómez 2013). Different almond genotypes have been fully sequenced at this moment including ‘Lauranne’ (SánchezPérez et al. 2019), ‘Texas’ (Alioto et al. 2020) and ‘Nonpareil’ (D’Amico et al. 2022). These results indicate a genome size of around 275 Mbp with substantial heterozygosity as well as repetitive

© Springer Nature Switzerland AG 2023 R. Sánchez-Pérez et al. (eds.), The Almond Tree Genome, Compendium of Plant Genomes, https://doi.org/10.1007/978-3-030-30302-0_12

167

168

P. Martínez-Gómez et al.

Subgenus Amygdalus - Section Euamygdalus Prunus dulcis cv. Lauranne. Genome v1.0

ALMOND & PEACH GROUP

* Prunus dulcis (Almond)

Sánchez-Pérez et al. 2019 Alioto et al. 2020

Prunus dulcis cv. Texas. Genome v2.0

D'Amico et al. 2022

Prunus dulcis cv. Nonpareil. Gen. v2.0

Prunus persica cv. Lovell. Genome v1.0

* Prunus persica (Peach)

Subgenus Prunus

Verde et al. 2013

Prunus persica cv. Lovell. Genome v2.0.a1

Verde et al. 2017

Prunus persica cv. 124. Pan. Genome v1.0

Zhang et al. 2021a

Prunus persica cv. Zhongyoutao 14. Genome v1.0

Lian et al. 2022

Prunus persica cv. Chinese Cling. Genome v1.0

Cao et al. 2021

- Section Armeniaca

Prunus

APRICOT GROUP

* Prunus armeniaca (Apricot)

Prunus armeniaca cv. Chuanzhihong and Dabaixing v1.0

Jiang et al. 2019

Prunus armeniaca cv. Marouch n14. Whole Genome v1.0

Groppi et al. 2021

Prunus armeniaca cv. Stella. Whole Genome v1.0

Groppi et al. 2021

* Prunus mume (Mei) Prunus mume cv.Tortuosa. Genome v1.0

Zheng et al. 2022

- Section Prunus PLUM GROUP

* Prunus salicina (Plum) * Prunus domestica (Prune)

Subgenus Cerasus

Prunus salicina cv. Sanyueli Genome v2.0 Prunus salicina cv. Zhongli No. 6 Genome v1.0

Liu et al. 2020 Huang et al. 2021

Prunus salicina cv. Sanyueli. Genome v1.0

Fang et al. 2022

Prunus domestica. Draft Genome v1.0.a1

Callahan et al. 2021

- Section Microcerasus CHERRY GROUP

* Prunus avium (Sweet Cherry) Prunus avium cv. Satonishiki. Genome v1.0.a1

Shirasawa et al. 2017

Prunus avium cv. Tieton. Genome v1.0.a1

Wang et al. 2020

Prunus avium cv. Tieton. Genome v2.0

Wang et al. 2020

Fig. 1 Summary of the most cultivated Prunus Genomes availables in the Genome Database for Rosaceae (https:// www.rosaceae.org/tools/jbrowse, accessed October 18, 2022)

sequence. Its small diploid genome and phenotypic diversity make it an ideal model to complement the complete genome sequence studies in Prunus species (Fig. 1). At this moment, the availability of different whole genome sequences in combination with existing functional genomic tools including the most abundant genetic variation (Single Nucleotide Polymorphisms, SNPs) and transcriptomic analysis at differential gene expression (DEG) level provide an unmatched resource for the genetic improvement of this species (Martínez-Gómez et al. 2012) to assist breeding selection methodologies. The accumulation of sequencing data over the last few years has triggered the development of huge databases at the genomic and transcriptomic level in

different Prunus species mainly in peach, considered the Prunus model species (Arús et al. 2022), but also in related Prunus such as almond. The new massive analysis methodologies produce millions of data deposited in these databases that need to be analyzed and used in the interpretation of the phenomena. This intensive data biology has been named as a new Big Data Science (Callebaut 2012) with great possibilities in the knowledge of biological process and the development of molecular (at transcriptomic and genomic level) tools. Breeding selection based on the genotype is especially efficient in fruit crops, because the savings of time and field space due to early genotype selection are potentially much higher in species with long juvenility, intergeneration periods and with an

Prospects and Future Questions

169

Fig. 2 Schematic representation of CRISPR-Cas9 system action as a gene editing tool capable of modify, cutting and paste DNA in genome determined sequences. Adapted from the original protocol described by Jinek et al (2012)

interaction with the rootstock and the environment (Arús et al. 2022). Additionally, the new methodology of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9, developed in 2012 by Jinek et al., appears as a new strategy for almond breeding outpacing the slow traditional methods (Fig. 2). For the first time, it was possible to modify the genome in the desired position, a

revolutionary genetic engineering technique that, unlike the classic genetic transformation, did allow selecting the region to be modified and not doing it randomly. A gene editing technology had been developed capable of modifying the DNA strand in specific positions, making it possible to modify a single DNA base, activate or inhibit the expression of a gene, insert, delete or invert a DNA fragment, or modulate the

170

regulation epigenetics of a gene among other applications (Wenzhi et al. 2013; Mohan et al. 2022). In fact, these researchers have received the Nobel Prize in Chemistry in 2020 for this discovery. Currently, a single plasmid is used where all the DNA fragments are inserted, which can include up to 20 different guides to act simultaneously in 30 different regions of the genome (Wang et al. 2019). However, to date, no work has been performed in almond and very few contributions have been made in Prunus species, where first results were published in peach in 2020 (Zhang et al. 2020, 2021b). More recently, this technique has been applied to plum (Fiol et al. 2022). These reference studies in other Prunus should be of great interest to develop CRISPR technology in almond for the efficient exploitation of the genome knowledge taking in account the great synteny between different Prunus species at genome (Jung et al. 2009) and transcriptome level (Martínez-Gómez et al. 2011).

References Alioto T, Alexiou K, Bardil A, Castanera R, GarcíaMas J, Gabaldon T, Casacuberta J, Arús P et al (2020) Transposons played a major role in the diversification between the closely related almond (Prunus dulcis) and peach (P. persica) genomes: results from the almond genome sequence. Plant J 101:455–472 Arús P, Aranzana MJ, Howard W, Eduardo I (2022) The peach genome and its breeding applications. Acta Hort 1352:27–32 Callahan AM, Zhebentyayeva TN, Humann JL, Saski CA, Galimba KD, Georgi LL, Scorza R, Main D, Dardick CD (2021) Defining the ‘HoneySweet’ insertion event utilizing NextGen sequencing and a de Novo genome assembly of plum (Prunus domestica). Hortic Res 2021:8 Callebaut W (2012) Scientific perspectivism: a philosopher of science´s response to the challenge of big data biology. Stud Hist Philos Biolog Biomed Sci 43:69–80 Cao K, Yang X, Li Y, Zhu G, Fang W, Chen C, Wang X, Wu J, Wang L (2021) New high-quality peach (Prunus Persica L. Batsch) genome assembly to analyze the molecular evolutionary mechanism of volatile compounds in peach fruits. Plant J 108:281–295 D’Amico KM, Ouma WZ, Meulia T, Sideli GM, Gradziel TM, Fresnedo-Ramírez J (2022) Wholegenome sequence and methylome profiling of the

P. Martínez-Gómez et al. almond [Prunus dulcis (Mill.) DA Webb] cultivar ‘nonpareil’. G3 12:jkac065 Fang Z, Lin-Wang K, Dai H, Zhou D, Jiang C, Espley RV, Deng C, Lin Y, Pan S, Ye X (2022) The genome of low-chill Chinese plum “Sanyueli” (Prunus salicina Lindl.) provides insights into the regulation of the chilling requirement of flower buds. Mol Ecol Resour 22:1919–1938 Fiol A, Jurado-Ruiz F, López-Girona E, Aranzana MJ (2022) An efficient CRISPR-Cas9 enrichment sequencing strategy for characterizing complex and highly duplicated genomic regions. A case study in the prunus Salicina LG3-MYB10 genes cluster. Plant Methods 18:1–1 Gradziel TM, Martínez-Gómez P (2013) Almond breeding. In: Jacnick J (ed) Plant breeding reviews. Editorial Wiley & Blackwel, New York, vol 37, pp 207–258 Groppi A, Liu S, Cornille A, Decroocq S, Bui QT, Tricon D, Cruaud C, Arribat S, Belser C, Marande W (2021) Population genomics of apricots unravels domestication history and adaptive events. Nat Commun 12:1–16 Huang Z, Shen F, Chen Y, Cao K, Wang L (2021) Chromosome-scale genome assembly and population genomics provide insights into the adaptation, domestication, and flavonoid metabolism of Chinese plum. Plant J 108:1174–1192 Jiang F, Zhang J, Wang S, Yang L, Luo Y, Gao S, Zhang M, Wu S, Hu S, Sun H (2019) The apricot (Prunus armeniaca L.) genome elucidates Rosaceae evolution and beta-carotenoid synthesis. Hortic Res 6 Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna A, Charpentier E (2012) A programmable dual-RNAguided DNA endonuclease in adaptive bacterial immunity. Science 2012:337 Jung S, Jiwan D, Cho I, Abbott A, Tomkins J, Main D (2009) Synteny of Prunus and other model plant species. BMC Genomics 10:76 Lian X, Zhang H, Jiang C, Gao F, Yan L, Zheng X, Cheng J, Wang W, Wang X, Ye X (2022) De Novo chromosome-level genome of a semi-dwarf cultivar of Prunus persica identifies the aquaporin PpTIP2 as responsible for temperature-sensitive semi-dwarf trait and PpB3-1 for flower type and size. Plant Biotechnol J 20:886–902 Liu C, Feng C, Peng W, Hao J, Wang J, Pan J, He Y (2020) Chromosome-level draft genome of a diploid plum (Prunus salicina). Gigascience 9:giaa130 Martínez-Gómez P, Crisosto C, Bonghi C, Rubio M (2011) New approaches to Prunus transcriptome analysis. Genetica 139:755–769 Martínez-Gómez P, Sánchez-Pérez R, Rubio M (2012) Clarifying omics concepts, challenges and opportunities for Prunus breeding in the post-genomic era. OMICS: A J Integr Biol 16:268–283 Mohan C, Satish L, Muthubharathi BC, Selvarajan D, Easterling M, Yau YY (2022) CRISPR-Cas technology: a genome-editing powerhouse for molecular plant breeding. Biotechnol Innov Environ Bioremediat 2022:803–879

Prospects and Future Questions Sánchez-Pérez R, Pavan S, Mazzeo R, Moldovan C, Cigliano RA, Del Cueto J, Ricciardi F, Lotti C, Ricciardi L, Dicenta F (2019) Mutation of a bHLH transcription factor allowed almond domestication. Science 364:1095–1098 Shirasawa K, Isuzugawa K, Ikenaga M, Saito Y, Yamamoto T, Hirakawa H, Isobe S (2017) The genome sequence of sweet cherry (Prunus avium) for use in genomics-assisted breeding. DNA Res 24:499–508 Verde I, Abbott AG, Scalabrin S, Jung S, Shu SQ, Marroni F, Zhebentyayeva T, Dettori MT, Grimwood J, Int CF, Peach Genome I (2013) The highquality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity. Domest Genome Evolut Nat Genet 45:487-U447 Verde I, Jenkins J, Dondini L, Micali S, Pagliarani G, Vendramin E, Paris R, Aramini V, Gazza L, Rossini L (2017) The Peach v2. 0 release: high-resolution linkage mapping and deep resequencing improve chromosomescale assembly and contiguity. BMC Genom 18:1–18 Wang D, Zhang C, Wang B, Li B, Wang Q, Liu D, Wang H, Zhou Y, Shi L, Lan F, Wang W (2019) Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning. Nat Commun 10:4284 Wang J, Liu W, Zhu D, Zhou X, Hong P, Zhao H, Tan Y, Chen X, Zong X, Xu L (2020) A de Novo assembly of

171 the sweet cherry (Prunus avium Cv. Tieton) genome using linked-read sequencing technology. Peer J 8: e9114 Wenzhi J, Huanbin Z, Honghao B, Michael F, Weeks BY, Donald P (2013) Demonstration of CRISPR/Cas9/ SgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res 41:e188–e188 Zhang M-M, Wang Z-Q, Xu X, Huang S, Yin W-X, Luo C (2020) MfOfd1 is crucial for stress responses and virulence in the peach brown rot fungus Monilinia fructicola. Mol Plant Pathol 21:820–833 Zhang A, Zhou H, Jiang X, Han Y, Zhang X (2021a) The draft genome of a flat peach (Prunus Persica L. Cv. ‘124 Pan’) provides insights into its good fruit flavor traits. Plants 10:538 Zhang H, Shen W, Zhang D, Shen X, Wang F, Hsiang T, Liu J, Li G (2021b) The bZIP transcription factor LtAP1 modulates oxidative stress tolerance and virulence in the peach gummosis fungus Lasiodiplodia theobromae. Front Microbiol 2021:12 Zheng T, Li P, Zhuo X, Liu W, Qiu L, Li L, Yuan C, Sun L, Zhang Z, Wang J (2022) The chromosomelevel genome provides insight into the molecular mechanism underlying the tortuous-branch phenotype of Prunus mume. New Phytol 235:141–156