Sustainable Agriculture Reviews 46: Mitigation of Antimicrobial Resistance Vol 1 Tools and Targets [1st ed.] 9783030530235, 9783030530242

Table of contents :
Front Matter ....Pages i-xv
Antimicrobial Resistance Paradigm and One-Health Approach (Kumar Siddharth Singh, Santosh Anand, Sunny Dholpuria, Jitendra Kumar Sharma, Yogesh Shouche)....Pages 1-32
Global Surveillance Programs on Antimicrobial Resistance (Sunil Kumar, Mayank Chaudhary, Mukesh Yadav, Vikas Kumar)....Pages 33-58
Antimicrobial Resistance, Food Systems and Climate Change (Mashkoor Mohsin, Ahtesham Ahmad Shad, Jabir Ali, Sajjad-ur-Rahman)....Pages 59-81
In Silico Approaches for Prioritizing Drug Targets in Pathogens (Mariana Santana, Stephane Fraga de Oliveira Tosta, Arun Kumar Jaiswal, Letícia de Castro Oliveira, Siomar C. Soares, Anderson Miyoshi et al.)....Pages 83-108
Molecular and Systems Biology Approaches for Analyzing Drug-Tolerant Bacterial Persister Cells (Xiangke Duan, Yang Fu, Liang Yang)....Pages 109-128
Role of Gene Editing Tool CRISPR-Cas in the Management of Antimicrobial Resistance (A. Parul Sarma, Chhavi Jain, Manu Solanki, Rajesh Ghangal, Soma Patnaik)....Pages 129-146
Control of Bacterial Biofilms for Mitigating Antimicrobial Resistance (Brij Pal Singh, Sougata Ghosh, Ashwini Chauhan)....Pages 147-176
Intrusion of Bacterial Quorum-Sensing for Antimicrobial Resistance Mitigation: A Pharmaceutical Perspective (Sandeep Kumar, Shruti Shandilya, Kumar Siddharth Singh)....Pages 177-204
Drug Discovery for Targeting Drug Resistant Bacteria (Aikaterini Valsamatzi-Panagiotou, Katya B. Popova, Robert Penchovsky)....Pages 205-228
Back Matter ....Pages 229-231

Citation preview

Sustainable Agriculture Reviews 46

Harsh Panwar Chetan Sharma Eric Lichtfouse   Editors

Sustainable Agriculture Reviews 46 Mitigation of Antimicrobial Resistance Vol 1 Tools and Targets

Sustainable Agriculture Reviews Volume 46

Series Editor Eric Lichtfouse CNRS, IRD, INRAE, Coll France, CEREGE Aix-Marseille University Aix-en-Provence, France

Other Publications by Dr. Eric Lichtfouse

Books Scientific Writing for Impact Factor Journals https://www.novapublishers.com/catalog/product_info.php?products_id=42242 Environmental Chemistry http://www.springer.com/978-3-540-22860-8 Sustainable Agriculture Volume 1: http://www.springer.com/978-90-481-2665-1 Volume 2: http://www.springer.com/978-94-007-0393-3 Book series Environmental Chemistry for a Sustainable World http://www.springer.com/series/11480 Sustainable Agriculture Reviews http://www.springer.com/series/8380 Journal Environmental Chemistry Letters http://www.springer.com/10311 Sustainable agriculture is a rapidly growing field aiming at producing food and energy in a sustainable way for humans and their children. Sustainable agriculture is a discipline that addresses current issues such as climate change, increasing food and fuel prices, poor-nation starvation, rich-nation obesity, water pollution, soil erosion, fertility loss, pest control, and biodiversity depletion. Novel, environmentally-friendly solutions are proposed based on integrated knowledge from sciences as diverse as agronomy, soil science, molecular biology, chemistry, toxicology, ecology, economy, and social sciences. Indeed, sustainable agriculture decipher mechanisms of processes that occur from the molecular level to the farming system to the global level at time scales ranging from seconds to centuries. For that, scientists use the system approach that involves studying components and interactions of a whole system to address scientific, economic and social issues. In that respect, sustainable agriculture is not a classical, narrow science. Instead of solving problems using the classical painkiller approach that treats only negative impacts, sustainable agriculture treats problem sources. Because most actual society issues are now intertwined, global, and fast-developing, sustainable agriculture will bring solutions to build a safer world. This book series gathers review articles that analyze current agricultural issues and knowledge, then propose alternative solutions. It will therefore help all scientists, decision-makers, professors, farmers and politicians who wish to build a safe agriculture, energy and food system for future generations.

More information about this series at http://www.springer.com/series/8380

Harsh Panwar • Chetan Sharma • Eric Lichtfouse Editors

Sustainable Agriculture Reviews 46 Mitigation of Antimicrobial Resistance Vol 1 Tools and Targets

Editors Harsh Panwar Department of Dairy Microbiology Guru Angad Dev Veterinary and Animal Sciences University Ludhiana, India

Chetan Sharma Alpine Biomedicals Pvt. Ltd. Haryana, India

Eric Lichtfouse Aix-Marseille University, CNRS, IRD, INRAE, Coll France, CEREGE Aix-en-Provence, France

ISSN 2210-4410     ISSN 2210-4429 (electronic) Sustainable Agriculture Reviews ISBN 978-3-030-53023-5    ISBN 978-3-030-53024-2 (eBook) https://doi.org/10.1007/978-3-030-53024-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed 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

Preface

The thoughtless person playing with penicillin treatment is morally responsible for the death of the man who succumbs to infection with the penicillin-resistant organism Sir Alexander Fleming

The World Health Organization has included antimicrobial resistance as one of the top ten threats to global health in 2019. Antimicrobial resistance is a slow but implacable evolutionary process, which has been accelerated by human activity in sectors such as human health, environment, and agriculture. Sir Alexander Fleming predicted the rise of antibiotic resistance in his 1945 Nobel Prize speech, where he emphasized on the risks associated with administration of non-lethal dosage of penicillin and its unregulated availability. Currently, various antibiotics are extensively employed in agriculture, environment, veterinary, and human medicine. Due to their wide application and unchecked usage, antibiotics and their residues have been found in almost all food products, for example, dairy, meat and vegetables, and in sewage, soils, and waters. High concentration of antibiotics is frequently associated with higher antimicrobial resistance. Therefore, guidelines and policies are needed to reduce antibiotic consumption and to prevent indiscriminate usage. Despite global efforts, antimicrobial resistance is accelerating and if present trends continue unabated, there could be as many as 10 million annual antimicrobial resistance–related deaths from a wide array of infections by 2050. The holistic multisectoral “One-Health” approach is thus needed for combating antimicrobial resistance and, in turn, save lives (Fig. 1). This book reviews the drivers, the impact, the assessment, and mitigation of antimicrobial resistance. Chapter 1 by Singh et al. explains how “One-Health” practices could efficiently mitigate emergence and spread of antimicrobial resistance. This requires effective surveillance, strict regulations, regulated use, and novel antimicrobials. Chapter 2 by Kumar et  al. presents global programs of surveillance of antimicrobial resistance in humans, animals, and the environment, with focus on monitoring aptness of therapy guidelines, public health interventions, and policies for controlling infection. Chapter 3 by Mohsin et al. discusses antimicrobial resistance in relation to climate change and food security. Chapter 4 by Santana et al. v

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Fig. 1  The ONE-HEALTH paradigm and stakeholders. “One-Health” refers to integrative strategies in design and implementation of legislation, policies, and research, which facilitates communication between multiple sectors to achieve better solutions for public health. This holistic approach aids in integration of information from different stakeholders, such as regulatory agencies, economists, consumers, and other contributors, to provide sustainable solutions against antimicrobial resistance. In Chap. 1 by Singh, K. S. et al.

describes bioinformatics strategies for prioritizing drug targets in pathogens. Comparative genomics is associated with pan-genomics, subtractive genomics, structural bioinformatics, and metabolic pathways analysis to design new antibiotics. Chapter 5 by Duan et al. summarizes approaches for analyzing native persister cells within a population. Approaches include the fluorescent label–based single-­ cell approach, high throughput screening approaches including transposon mutant library screening, knockout and overexpression library screening, and omic approaches to understand the formation of persister phenotypes. Chapter 6 by Sarma et al. presents the CRISPR-Cas system as a promising tool for addressing antimicrobial resistance. CRISPR-Cas machinery is an adaptive immune system found in bacteria and archae and is used by the organism to eliminate foreign invading genetic material. Chapter 7 by Singh et al. explains how bacterial biofilms contribute to increase in antibiotic resistance. Indeed, bacteria become more resistant to antimicrobials when they are embedded in slimy extracellular polymeric substances. The chapter presents innovative strategies to control bacterial biofilms and reviews molecular mechanisms of bacterial communication with the environment, such as quorum-sensing. Quorum-sensing communication is also discussed in Chap. 8 by Kumar et  al. with focus on the usage of quorum sensing

Preface

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inhibitors to address antimicrobial resistance. Chapter 9 by Valsamatzi-Panagiotou et  al. reviews novel approaches of antibacterial drug discovery to fight resistant bacteria. This includes the application of antisense oligonucleotides as antibacterial agents, fecal microbiota transplantation, antimicrobial peptides, and cell-penetrating peptides. We extend our sincere gratitude to all the authors who have put considerable efforts into their contributions and for their timely responses and enthusiastic cooperation during the book compilation, review, and revision process. The creation of this book would not have been possible without the assistance of several researchers around the globe who have sincerely reviewed the manuscripts deserving acknowledgment. We also extend our thanks to the Springer Nature team for their cooperation right from acceptance of proposal to the production of this book. Lastly, we hope that this book is intended to become a valuable reference for student, professors, doctors, and researchers contributing towards mitigation of antimicrobial resistance. Ludhiana, Punjab, India Ambala, Haryana, India Aix-en-Provence, France

Harsh Panwar Chetan Sharma Eric Lichtfouse

Contents

1 Antimicrobial Resistance Paradigm and One-Health Approach��������    1 Kumar Siddharth Singh, Santosh Anand, Sunny Dholpuria, Jitendra Kumar Sharma, and Yogesh Shouche 2 Global Surveillance Programs on Antimicrobial Resistance ��������������   33 Sunil Kumar, Mayank Chaudhary, Mukesh Yadav, and Vikas Kumar 3 Antimicrobial Resistance, Food Systems and Climate Change ����������   59 Mashkoor Mohsin, Ahtesham Ahmad Shad, Jabir Ali, and Sajjad-ur-Rahman 4 In Silico Approaches for Prioritizing Drug Targets in Pathogens ������   83 Mariana Santana, Stephane Fraga de Oliveira Tosta, Arun Kumar Jaiswal, Letícia de Castro Oliveira, Siomar C. Soares, Anderson Miyoshi, Luiz Carlos Junior Alcantara, Vasco Azevedo, and Sandeep Tiwari 5 Molecular and Systems Biology Approaches for Analyzing Drug-Tolerant Bacterial Persister Cells������������������������������������������������  109 Xiangke Duan, Yang Fu, and Liang Yang 6 Role of Gene Editing Tool CRISPR-Cas in the Management of Antimicrobial Resistance��������������������������������������������������������������������  129 A. Parul Sarma, Chhavi Jain, Manu Solanki, Rajesh Ghangal, and Soma Patnaik 7 Control of Bacterial Biofilms for Mitigating Antimicrobial Resistance��������������������������������������������������������������������������������������������������  147 Brij Pal Singh, Sougata Ghosh, and Ashwini Chauhan

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Contents

8 Intrusion of Bacterial Quorum-Sensing for Antimicrobial Resistance Mitigation: A Pharmaceutical Perspective����������������������������������������������������������������  177 Sandeep Kumar, Shruti Shandilya, and Kumar Siddharth Singh 9 Drug Discovery for Targeting Drug Resistant Bacteria ����������������������  205 Aikaterini Valsamatzi-Panagiotou, Katya B. Popova, and Robert Penchovsky Index������������������������������������������������������������������������������������������������������������������  229

About the Editors

Harsh  Panwar  is currently Assistant Professor in Dairy Microbiology at Guru Angad Dev Veterinary and Animal Sciences University, India. His research interests include antimicrobial resistance and its mitigation in dairy and food. Dr. Panwar has published more than 40 articles in high-impact journals. He is editorial board member for several international journals. Dr. Panwar has been awarded the Young Scientist Award 2019 by the Association of Microbiologists of India; Indo-Australian Career Boosting Gold Fellowship 2018–19 by the Department of Biotechnology, Government of India; Best Teacher 2019 and Best Researcher 2018 Award by Guru Angad Dev Veterinary and Animal Sciences University; DST Inspire Fellowship by the Department of Science and Technology, Government of India; Commonwealth Scholarship by Commonwealth Scholarship Commission, UK; and University Gold Medal by Kurukshetra University, Kurukshetra, India.

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About the Editors

Chetan Sharma  is working as Research Advisor at Alpine Biomedicals Pvt. Ltd., Haryana, India. He has completed his M.Sc. and Ph.D. in Microbiology in the Department of Microbiology at Kurukshetra University, Kurukshetra. Dr. Sharma has published several research papers of international repute and serves as a reviewer for different journals. He has also edited and published one book by Nova Science Publishers, USA, and two books by Springer Nature. His present research interest covers isolation of bioactive compounds from natural sources (plants), medical microbiology, antimicrobial resistance, and probiotics.

Eric Lichtfouse  is Biogeochemist at the University of Aix-Marseille, France, and Visiting Professor at Xi’an Jioatong University. He has invented carbon-13 dating, a method allowing to measure the relative age of organic molecules occurring in different temporal pools of complex media. Dr. Lichtfouse teaches scientific writing and communication and has published the book Scientific Writing for Impact Factors, which includes a new tool—the Micro-Article—to identify the novelty of research results. He is Founder and Chief Editor of scientific journals and series in environmental chemistry and agriculture. Dr. Lichtfouse has founded the European Association of Chemistry and the Environment. He received the Analytical Chemistry Prize from the French Chemical Society, the Grand Prize of the Universities of Nancy and Metz, and a Journal Citation Award by the Essential Indicators.

Contributors

Luiz Carlos Junior Alcantara  Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil Jabir Ali  Institute of Microbiology, University of Agriculture, Faisalabad, Pakistan Santosh Anand  Department of Dairy Microbiology, College of Dairy Technology Hansdiha, Dumka, Jharkhand, India Vasco  Azevedo  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Mayank  Chaudhary  Department of Biotechnology, Maharishi Markandeshwar (Deemed to be) University, Mullana (Ambala), Haryana, India Ashwini  Chauhan  Department Suryamaninagar, Tripura, India

of

Microbiology,

Tripura

University,

Letícia  de Castro  Oliveira  Department of Immunology, Microbiology and Parasitology, Institute of Biological Science and Natural Sciences, Federal University of Triângulo Mineiro (UFTM), Uberaba, MG, Brazil Stephane  Fraga  de Oliveira  Tosta  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Sunny Dholpuria  Department of Life Sciences, Sharda University, Greater Noida, Uttar Pradesh, India Xiangke  Duan  School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China Yang  Fu  School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China Rajesh  Ghangal  Department of Biotechnology, CGO Complex, Lodhi Road, New Delhi, India

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Contributors

Sougata Ghosh  Department of Microbiology, School of Science, RK University, Rajkot, Gujarat, India Department of Chemical Engineering, Northeastern University, Boston, MA, USA Chhavi  Jain  Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Arun  Kumar  Jaiswal  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Department of Immunology, Microbiology and Parasitology, Institute of Biological Science and Natural Sciences, Federal University of Triângulo Mineiro (UFTM), Uberaba, MG, Brazil Sandeep Kumar  ICAR-National Dairy Research Institute, Karnal, Haryana, India Sunil Kumar  Department of Biotechnology, Maharishi Markandeshwar (Deemed to be) University, Mullana (Ambala), Haryana, India Vikas Kumar  Department of Biotechnology, Maharishi Markandeshwar (Deemed to be) University, Mullana (Ambala), Haryana, India Anderson Miyoshi  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Mashkoor  Mohsin  Institute of Microbiology, University of Agriculture, Faisalabad, Pakistan Soma  Patnaik  Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Robert Penchovsky  Department of Genetics, Faculty of Biology, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria Katya  B.  Popova  Department of Genetics, Faculty of Biology, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria Sajjad-ur-Rahman  Institute of Microbiology, University of Agriculture, Faisalabad, Pakistan Mariana  Santana  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil A.  Parul  Sarma  Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Ahtesham  Ahmad  Shad  Institute of Microbiology, University of Agriculture, Faisalabad, Pakistan

Contributors

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Shruti Shandilya  University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany Jitendra Kumar Sharma  Maharshi Dayanand University, Rohtak, Haryana, India Yogesh  Shouche  National Centre for Microbial Resource  – National Centre for Cell Science, Pune, Maharashtra, India Brij Pal Singh  Department of Microbiology, School of Science, RK University, Rajkot, Gujarat, India Kumar  Siddharth  Singh  National Centre for Microbial Resource  – National Centre for Cell Science, Pune, Maharashtra, India Structure and Function of Proteins, Helmholtz Centre for Infection Research, Braunschweig, Germany Siomar  C.  Soares  Department of Immunology, Microbiology and Parasitology, Institute of Biological Science and Natural Sciences, Federal University of Triângulo Mineiro (UFTM), Uberaba, MG, Brazil Manu  Solanki  Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Sandeep  Tiwari  Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Aikaterini Valsamatzi-Panagiotou  Department of Genetics, Faculty of Biology, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria Mukesh  Yadav  Department of Biotechnology, Maharishi Markandeshwar (Deemed to be) University, Mullana (Ambala), Haryana, India Liang Yang  School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China

Chapter 1

Antimicrobial Resistance Paradigm and One-Health Approach Kumar Siddharth Singh, Santosh Anand, Sunny Dholpuria, Jitendra Kumar Sharma, and Yogesh Shouche

Abstract  The deaths caused due to drug-resistant microbes exceed 50,000 per year worldwide and antimicrobial resistance is now being considered as one of the biggest threat to human health. The emergence and spread of antimicrobial resistance warrants immediate attention from health professionals and political heads alike. The complex and multifactorial nature of antimicrobial resistance is not well understood, especially in terms of interplay of humans, animals and the environment. The lack of reliable information, slow development of new antimicrobials and high incidences of horizontal gene transfer has further worsened the situation. The issue of antimicrobial resistance demands increased awareness among public and coordinated efforts from health practitioners to regulate usage and consumption pattern of antibiotics. Policies which improve community hygiene, living conditions and vaccination coverage in population need to be strengthened, to minimize the incidences of infections. The indiscriminate mass-level usage of potent antibiotics in agriculture need to be regulated and their leakage into the environment must be checked. Effective surveillance in the environment is also warranted to understand the ground reality and develop strategies and policies to safeguard p­ ublic K. S. Singh (*) National Centre for Microbial Resource – National Centre for Cell Science, Pune, Maharashtra, India Structure and Function of Proteins, Helmholtz Centre for Infection Research, Braunschweig, Germany S. Anand Department of Dairy Microbiology, College of Dairy Technology Hansdiha, Dumka, Jharkhand, India S. Dholpuria Department of Life Sciences, Sharda University, Greater Noida, Uttar Pradesh, India J. K. Sharma Maharshi Dayanand University, Rohtak, Haryana, India Y. Shouche National Centre for Microbial Resource – National Centre for Cell Science, Pune, Maharashtra, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_1

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health. All these efforts made by government, general public and other stakeholders can be efficiently integrated and encompassed in an increasingly accepted holistic approach called as ‘One-Health’. In this chapter, we focus on different facets of the antimicrobial resistance menace and how ‘One-Health’ practices could efficiently mitigate the emergence and spread of antimicrobial resistance. Keywords  Antimicrobial resistance · Antibiotic pollution · One-health · Antibiotics and agriculture · Horizontal gene transfer · International cooperation · Environmental surveillance · Global action plan · Community hygiene · Good Microbiological practices

1.1  Introduction The usage of antibiotics and issue of antimicrobial resistance have seen many stages of development and updates. Antibiotics were discovered in 1928 and the intense research during the period 1950–1970 has given rise to many present day antibiotics. However, owing to huge financial inputs, regulatory constraints and long development durations, the pharmaceutical research into new antibiotics dampened and comparatively fewer antibiotics were developed after mid 1980s (Spellberg and Gilbert 2014). Now a days, antibiotics and/or their residues are being found in almost all the niches ranging from food (dairy, meat, vegetables, etc.) to sewage, soil and aquatic water bodies. The high level of antibiotics has been frequently associated with higher antimicrobial resistance (Llor and Bjerrum 2014), thus, main focus is being given towards reducing the consumption of antibiotics and preventing indiscriminate usage. High antibiotic usage has been proven to accelerate the rate of horizontal gene transfer and fixation of antibiotic resistance in microbial species, leading to emergence of genomic islands specific to antibiotic resistance (Gillings and Stokes 2012). One striking example is Acinetobacter baumannii which evolved (over a span of 30 years) from an antibiotic susceptible phenotype to having resistance against multiple antibiotics, by acquiring different genomic islands and 45 antibiotic resistance genes through horizontal gene transfer (Fournier et al. 2006). The sources of these antibiotics stem from human and veterinary use, production units and use of antibiotics as growth promoters in agriculture. Many hotspots of antimicrobial resistance have been identified such as hospitals, pharmaceutical waste, livestock farms and agricultural setups etc. which culminate in the overlapping ecological domains. These ecological domains witness frequent exchange of microbes between them. This leads to even rapid emergence of resistant microbes across these niches. Research into human clinical samples has shown diverse and increasing trend of antimicrobial resistance. Antimicrobial resistance has become a major public health issue and according to a prediction by the organization for

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economic cooperation and development (OECD), about 2.4 million people will die as result of antimicrobial resistance in Europe, North America and Australia only in next 30 years from now. The worldwide distribution of antibiotic resistance bacteria is aided by many factors including international travel, immigration and other activities (Church 2004). The realization of antimicrobial resistance started in 1950s and since then many studies have unequivocally reported an increasing trend (Davies and Davies 2010). In 1959, scientific group of World Health Organization (WHO) advocated research on antimicrobial resistance (The Work of WHO, 1959, Official Records of WHO no. 98) and later (1981) released guidelines for regulated usage of antibiotics (WHO/BVI/PHA/ANT/82.1). Subsequent meetings and research has generated huge amount of information about inefficacy of available antibiotics and rapid emergence and spread of resistant microbial strains among humans and animals. The previous gap in pharmaceutical research towards novel antibiotics coupled with increasing antimicrobial resistance has further worsened the situation due to less treatment availability against such conditions. However, now the issue is being recognized globally and many task forces have been established in different countries. Rapid efforts are being made to fill-in existing knowledge gaps and improve realization of interconnectedness of animals, humans and environment in context of antimicrobial resistance. Many measures have been proposed to regulate the emergence of antimicrobial resistance and its containment in different ecological domains. There is an interrelation between the different ecological niches like humans, animals, poultry, agriculture, environment and even waste. Recent outbreaks of zoonotic diseases and direct interaction with animal carriers of the disease have further underscored the importance of ‘One Health’ concept. This concept refers to a collaborative, trans-disciplinary, and multi-sectoral approach of actions applied at local, national and inter-national levels. The omnipresence of drug resistant microbes has made it necessary for a collaborative approach across different sectors, involving humans, animals, agriculture/livestock and environment. Thus using this approach an optimal outcome is achievable across all the different sectors due to the interrelatedness among humans, plants, animals and their overlapping environment (https://www.onehealthcommission.org/). Although, it is wide in definition and scope but it is particularly important in context of antimicrobial resistance. The underlying principles of One Health such as Human-Animal bond, ensuring safe food and water supply, responsible agricultural production, disease surveillance, detection of environmental agents, aligning of public policy and regulations and communication and global outreach, are critical towards global cooperation against antimicrobial resistance. Potential outcomes of this approach would be more interdisciplinary policy decisions and programs in research, education; training, disease prevention, detection and diagnosis, and rapid development of novel therapies and treatment strategies. These outcomes are also highly warranted in strategies against antimicrobial resistance, which requires highly concerted efforts across different domains (Fig. 1.1). But, as clear till now, the mitigation of the issue of antimicrobial resistance warrants careful understanding of different factors involved in the

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Fig. 1.1  ONE-HEALTH paradigm and stakeholders. ‘One-health’ refers to integrative strategies in design and implementation of legislation, policies and research, which facilitates communication between multiple sectors to achieve better solutions for public health. This holistic approach can aid in integration of information from different stakeholders such as regulatory agencies, economists, consumers and other contributors to provide sustainable solutions against antimicrobial resistance

problem. Let us have a look into these factors and the responsible stakeholders which form the complex landscape of antimicrobial resistance.

1.2  Antibiotics and Antimicrobial Resistance 1.2.1  Antibiotics in Humans, Veterinary and Agriculture The usage of antibiotics is mostly for therapeutic purposes and is frequent in humans and animals. Only few classes of antibiotics are confined to exclusive usage in either humans or animals. This is due to toxicity of those antibiotics in humans and/or different incidences of infection in humans/animals for which that antibiotic is administered usually. However, many common classes of antibiotics are administered in both animals and humans, including cattle, poultry, fish and honey bees (Church 2004; Davies and Davies 2010; Van Boeckel et  al. 2015). Limited prophylactic usage of antibiotics is usually done in humans and mostly in cases such as post-­ surgery or during treatment of certain diseases. In animals, the antibiotic administration pattern is broadly similar to humans, but in large farms and especially food

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animals because they are frequently exposed to antibiotics via water and/or feed, for preventing infection. It has been estimated that livestock consume almost 50–80% of total antibiotics used and it was estimated to be about 63,151 ± 1560 tons in 2010 (Cully 2014; Van Boeckel et al. 2015). This is in essence prophylaxis, and it contributes to huge proportion of antibiotic leakage due to surface run-off, to the environment. Even during individual infection in food animals, the whole group/flock is administered antibiotics, to prevent any possible dissemination of infection. Group level treatment of antibiotics are administered for the logic that the food animals (such as poultry, cattle etc.) are at high risk of bacterial infection owing to bad hygiene, over-crowded compartments and high exposure to infectious agents during transport and mixing of animal from different origins. Another unfortunate usage of mass level (about 26.4 million pounds every year in USA alone), low-dose antibiotics in food animals is to promote growth (Mellon et al. 2001; Oliver et al. 2010). Its use for growth promotion in animals, depends on long term usage of antibiotics at sub-therapeutic levels of dose (5–110 parts per million), and is called as ‘metaphylaxis’ (Butaye et al. 2003). Metaphylaxis could last for more than 2 weeks or entire life of animal and could bring upto 10% increase in production (World Health Organization 2003). Most alarming fact is that, most of the antibiotics (fluoroquinolones, colistin and macrolides) used on animals are reserved for treatment of humans with terminal levels of infection or more simply infections due to multiple drug resistant pathogens (WHO Advisory Group 2016). These conditions can favor the emergence of multiple drug resistant microbes, easily transferable to the humans or the environment (FAO/OIE/ WHO 2003). A ready example of such case is zoonotic diseases which are rarely transmitted through humans, such as fluoroquinolone-resistant Campylobacter spp. in chicken. Around 8–10,000 people in US alone are estimated to acquire fluoroquinolone resistant Campylobacter infections through chicken (Food and Drug Administration 2000). But many studies (for e.g. in Denmark, Sweden, etc.), have shown that raising animals in cleaner condition, appropriate vaccination, and good hygiene has similar economic effect and antibiotics might not be required (World Health Organization 2003; Tang et al. 2019). But these industry protocols prevail across 58 countries supplying food animals and stricter government regulations need to be applied for stopping this practice (World Health Organization 2003). It is well known that use of the antibiotics in food animals is largely to compensate for the prevailing poor animal management conditions (Health Canada 2014; European Union 2015). Considering many factors, the WHO has recommended and many other countries have banned or are phasing out the usage of antibiotics in farm animals (Food and Drug Administration 2013a, b; Health Canada 2014; European Union 2015). There are few striking examples which highlight the perils of using same class of antibiotics in humans and animals. Third generation Cephalosporins are critically important for human health and have wide applicability to diseases and infections in both humans and animals (WHO Advisory Group 2016). Some antibiotics of this class are mostly used for treating diseases in animals, but sometimes even in metaphylaxis. The consumption of Cephalosporins in Europe, US and other

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countries has steadily increased over the years, and is more in humans than in animals (Food and Drug Administration 2012). Resistance to these antibiotics is conferred by a specialized class of beta-lactam ring cleaving enzymes called as extended-­spectrum beta lactamases (ESBLs) and AmpC. Both of these genes are now found to be present on plasmids and are highly mobile through gene horizontal transfer (European Medicines Agency 2009). Due to its overuse, resistance to these antibiotics is often observed in human infections caused by common pathogens such as E. coli, Salmonella spp. and K. pneumoniae. Another alarming consequence is the co-transfer (due to the proximity in genetic loci) of other antibiotic resistance genes (for tetracycline, sulfonamides, aminoglycosides, etc.) with the ESBLs or AmpC. This leads to corresponding co-selection and enrichment of multi-drug resistant pathogens in affected niches, which are even more difficult to treat. This has led to drastic decrease in choice of antibiotics for therapy in these infections and many times carbapenems are left as the last option (Nordmann 2014). Many studies have found relatedness in ESBL containing E. coli strains isolated from food, animal and human sample (Jakobsen et al. 2009; Rizzo et al. 2019; Tadesse et al. 2017; Odsbu et al. 2018). Also, the mode of dissemination of cephalosporin resistance genes between pathogens of humans and animals has been found to be through plasmids (de Been et al. 2014). The mass usage of these antibiotic for metaphylaxis frequently ends up in the environment and directly through food, further exposing humans to antibiotic resistant microbes (Canadian Integrated Program for Antimicrobial Resistance 2009). Voluntary stopping the use of the cephalosporins in hatcheries has shown to bring a drastic decrease in prevalence of cephalosporin resistant microbes in the food animals, in Japan, Denmark and Canada (Bager et al. 2015; Hiki et al. 2015). The same trend has been observed regarding the usage of fluoroquinolones and fluoroquinolone resistant bacteria in food animals remain to be rare in countries like Australia, where its use in food animals was never approved (Nelson et al. 2007). Due to emergence of many multi-drug resistant pathogens and limited treatment options, Colistin is nowadays considered as one of the last resort treatments for humans infected with multi drug resistant pathogens. Among others, these pathogens include carbapenem-resistant P. aeruginosa, A. baumannii and Enterobacteriaceae which feature in the WHO priority list of pathogens for which antibiotics are urgently required (Tacconelli et al. 2018). Due to its toxicity, Colistin was not preferred earlier for human administration. But limited number of antibiotics and its high potency against multi drug resistant pathogens has made it even more important in contemporary times, as a last resort antibiotic. However, this antibiotic is also approved for usage in food animals in many countries, for metaphylaxis and is also used for growth promotion. Recent surveillance of colistin resistance in food animals such as turkey, broilers, poultry etc. in Europe showed the presence of varying percentage of colistin resistant and multi drug resistant pathogens such as Salmonella enterica. Although, this could be intrinsic resistance in the microbial population, but the presence of these resistant pathogens in food is definitely a potential hazard to humans. Another great concern was the observation of the mobilization of a colistin resistance gene (mcr-1) through horizontal gene

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transfer by plasmids (Liu et al. 2016). Usage of colistin and associated antibiotic resistance is distinct from that of cephalosporins because across many countries, the consumption of this antibiotic in food animals exceeds multiple times of its consumption in humans (European Center for Disease Prevention and Control 2015). Many countries and regulatory agencies like European Medicine Agency have recommended drastic reduction in usage of colistin in food animals to save it for usage in humans. Similar issues have been faced earlier with glycopeptide antimicrobials which were also used as growth promoter and not considered relevant drug for humans. One such example is the glycopeptide ‘avoparcin’, which was extensively used in food animals (Aarestrup et al. 1996). This led to development of resistance against glycopeptide class of antibiotics. Now, Vancomycin (also a glycopeptide antibiotic), which is an important antibiotic against multi drug resistant pathogens (especially methicillin resistant Staphylococcus aureus) is also facing resistance from many multi drug resistant pathogens from food, humans and environment (Tang et  al. 2014). Unarguably, lessons should be learnt from these previous mistakes related to rise and spread of certain resistance to classes of antibiotics and caution need to be exercised in antibiotic usage, to prevent further development of antibiotic resistance due to metaphylaxis.

1.2.2  Antimicrobial Resistance, Animals and Public Health The prevailing issue of antibiotic resistance is largely linked with irresponsible usage of antibiotics and rapid exchange of antibiotic resistance genes among microbes. The interrelatedness and rapid exchange of antibiotic resistance genes between pathogens, which can colonize and infect both animals and humans, has made the problem of antimicrobial resistance even worse (Fig. 1.2). The root cause of indiscriminate usage arises from the impetus to derive maximum economic benefits from available resources (including livestock) and irresponsible disposal of generated waste. An example is the usage of broad spectrum antibiotics as growth promoters, in livestock to increase the amount of meat and overall size. The mechanism behind this is not clear but various hypothesis has been proposed. In one of them, it has been said that the antibiotics helps in better digestion of food via suppressing the sensitive bacteria of gut that might be resulting in loss of energy due to bacterial fermentation. In another hypothesis, it has been proposed that the cytokines released during bacterial infection reduces muscle mass and thus the use of antibiotics helps in overcoming that effect (Hughes et  al. 2004). In the countries such as US, half of the antibiotics given to animals are intended for growth promotion. This practice is often applied on pigs and in major pork producing country such as China and US. The use of antibiotics in China is four times higher than that of US in order to increase the pork production (Cully 2014). This is the data which is being monitored and in comparison of that, there is very little or no information regarding antibiotics used in small farms. Most of these antibiotics, used in

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Fig. 1.2  Inter-connectedness and transferability between the different niches. Many routes of transmission exist for exchange of antimicrobial resistance determinants such as antibiotic resistance genes (ARGs) between animals, humans and the environment. Anthropogenic activities remain at the center of all major activities related to antimicrobial resistance

metaphylaxis, are poorly absorbed and upon excretion by the animal, it ends up in the animal manure. The usage of antibiotics at mass level in animals leads to emergence of antibiotic resistant pathogens in animals, many of which can infect humans. Many changes have been made to curb this practice of metaphylaxis. Use of 11 antibiotics as growth promoters had been banned in Denmark and European Union in the year 1995 and 2000 respectively. Over two decades, this has led to progressive decline of up to 50% in resistance of microbes (of human and animal origin) against these antibiotics (Hollis and Ahmed 2013). Manure is a major contributor to antimicrobial resistance because the antibiotic resistance genes get easily transferred to the genome of bacterial population that infects human (Zhu et al. 2013). This unintended exposure of sub-therapeutic levels of antibiotics in manures, and thus to soil, further increases the accumulation of antibiotic resistance genes in the environment (Pruden et al. 2012). In addition to the use of antibiotics, metals are also used on farm animals, for feed purpose, to increase their growth. The feed used in such practices generally includes metals such as Cu, Zn, As, Cr, Cd, Pb and most of these comes under the category of heavy metals, which also inhibits the bacterial growth thereby creating a co-selection pressure on bacterial population (Baker-Austin et al. 2006; Zhang et al. 2012; Seiler and Berendonk 2012). In animal manures and their adjacent agriculture fields, a positive correlation has been found between the heavy metals (Cu, Zn and Hg) and antibiotic

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resistance genes (sulA and sulIII) (Ji et al. 2012). Of course, these data only represent the culturable antibiotic resistant microbial population and huge amount of antibiotic resistance genes and antibiotic resistance bacteria must be potentially present in the unculturable portion of the microbiome of these niches (Enne et al. 2008; Zhu et al. 2013). Many speculate that these unculturable portion of the microbiome act as sink for many antibiotic resistance genes and need attention to properly mitigate antimicrobial resistance menace. Overall, these factors lead to increased antibiotics residues in the waste which ultimately end up in the environment. It has been found that manure and soil in Chinese pig farms have 28,000 times higher concentration of antibiotic resistance genes as compared to that in agricultural soils in China (Zhu et al. 2013). This could be because human waste has been dedicated to be treated in sewage treatment plants which are capable to mitigate dissemination of antibiotic resistance bacteria from sewage waste to the environment. But it is not the case that sewage treatment doesn’t cause any antibiotic resistance, although the frequency of antimicrobial resistance from the treated waste is less as compared to untreated waste (Di Cesare et al. 2016). In comparison to this, not much care is being given by animal industry towards careful and regulated disposal of animal farm waste and manures. Another aspect of indirect exposure of antibiotics to humans through animal origin lies in its overuse in street food (Campos et al. 2015). Street food is a huge phenomenon in developing and developed countries alike, but they come with questionable concerns over their preparation and raw ingredients in countries without regulation or control. Given the economic costs, street food vendors usually ignore hygiene, the raw materials are not properly stored and the prepared package is not served in clean crockery/delivery packets. To compensate for protein amount (or more money per kilogram), excess proportion of meat is used, which (given the economic constraints) could be easily sourced from farms using cheaper farm practices, including unregulated usage of antibiotics, poor rearing conditions for the animal and unhygienic transport and storage of meat items. Many a times, these vendors are inclined to buy under-treatment diseased animals which are sold at cheaper prices and have high levels of antibiotics and potentially hazardous agents (Kim et al. 2013). In South Africa, poultry litter is frequently used as high protein supplement for farm animals, which is definitely a health hazard, depending upon the levels of antibiotics to which the poultry was exposed (Soto 2013). Many antibiotic resistant bacteria have been reported from animal derived food such as 47% of resistant ones in total Salmonella isolates from meat and milk in Ethiopia (Joint Expert Advisory Committee on Antibiotic Resistance 1999) while 14% of resistant ones in total E. coli isolates from chicken, milk and egg in India (European Medicines Agency 2012). These bacteria have resistance against one or more of the commonly used antibiotics in humans such as penicillin, cephalosporin, carbapenem, tetracycline, ampicillin, sulphamethoxaxole, trimethoprim and chloramphenicol. If antibiotic usage is unregulated in livestock, it leads to emergence of many antibiotic resistant animal pathogens such as Salmonella spp., Enterococcus spp., Yersinia enterocolitica, Listeria monocytogenes, Staphylococcus spp.,

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Escherichia coli, Campylobacter jejuni, etc., which can cause infection in humans (Phillips et al. 2004). Many of these pathogens also indicate fecal contamination in water or soil, and are used as a measure of suitability for human consumption. This is important because these pathogens also have high recombination frequency and propensity towards exchange of antibiotic resistance genes through horizontal gene transfer. The phylogenetic relatedness of these bacteria further facilitates the transfer and exchange of antibiotic resistance genes among themselves. These situations potentiate the development of antibiotic resistance even more rapidly among the microbes sharing the same niche. The public health risks arising from these antibiotic resistant microbes cannot be easily estimated owing to their complex distribution pattern among food animals and humans. There is lack of fast and reliable detection techniques for manifestation of antibiotic resistance, complex pattern of exchange of antibiotic resistance genes, unreliable prediction tools to understand implications in mortality and morbidity of humans and fluctuating treatment cost associated with infections caused by such pathogens (Wegener 2012). Essentially, these antibiotic resistant zoonotic pathogens can act as vectors for antibiotic resistance in humans. Thus, antibiotic resistance in the pathogens common to both animals and humans is worrisome and certainly indicates origin of many antibiotic resistant pathogens from animal farms or related activities. Most of the veterinary use antibiotics have structural similarity with human use antibiotics and it could directly drive microbes inside humans towards resistance. Another hazard of antibiotic usage in animals is the accumulation of antibiotic residues in tissues and edible organs of animals in varying concentrations. Some reports have shown the direct presence of tetracycline and chloramphenicol residues (Cameroon, Iran, Egypt) above its maximum residue limit (European Union standards) in muscle, heart, liver and kidney of farm chicken (Tavakoli et  al. 2015; Guetiya Wadoum et al. 2016). Similar reports for presence of different antibiotics have been reported such as ciprofloxacin has been found inside eggs of terminally ill birds receiving antibiotic treatment (Billah et al. 2015), quinolones inside Chicken and Beef (Er et al. 2013) in Turkey, amoxicillin in milk and eggs in Bangladesh (Chowdhury et al. 2015), sulfonamides and quinolones in milk in China, Malaysia and India (Cheong et al. 2010; Zheng et al. 2013; Nirala et al. 2017). Exposure to antibiotics from animal derived food can create multiple complications in humans, such as neuropathy, drug hypersensitivity, aplastic anemia, mutagenesis, disturbance of normal gut flora, hepatotoxicity, reproductive disorder and of course development of antibiotic resistance in the gut colonizing bacteria (Lee et al. 2001; Nisha 2008; Beyene 2015). These manifestations in humans can be a result of exposures both acute or of long periods through the food. Ideally speaking, no antibiotics or its residues should be present in the food directly or indirectly derived from animals. However for practical reasons, it is difficult to handle and to ascertain the safe antibiotic levels in animal derived food and a maximum residue limit for antibiotics in animal derived food has been recommended by European Union and other authorities (Alimentarius 2012). The level of antibiotics excreted by animals differs dramatically depending upon the metabolization of the antibiotic, age, diet and

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Table 1.1  WHO recommendations on the use of medically important antimicrobials in food-­ producing animals 1 2 3

4a

4b

The GDG (Guideline Development Group) recommends an overall reduction in use of all classes of medically important antimicrobials in food-producing animals. The GDG recommends complete restriction of use of all classes of medically important antimicrobials in food-producing animals for growth promotion. a The GDG recommends complete restriction of use of all classes of medically important antimicrobials in food-producing animals for prevention of infectious diseases that have not yet been clinically diagnosed. b The GDG suggests that antimicrobials classified as critically important for human medicine should not be used for control of the dissemination of a clinically diagnosed infectious disease identified within a group of food-producing animals. b The GDG suggests that antimicrobials classified as critically important for human medicine should not be used for treatment of food-producing animals with a clinically diagnosed infectious disease.

Specific considerations: when a veterinary professional judges that there is a high risk of spread of a particular infectious disease, use of antimicrobials for disease prevention is justified, if such a judgement is made on the basis of recent culture and sensitivity testing results b To prevent harm to animal health and welfare, exceptions to recommendations 4a and 4b can be made when, in the judgment of veterinary professionals, bacterial culture and sensitivity results demonstrate that the selected drug is the only treatment option a

disease state of the animals. Thus to qualify these maximum residue limit levels, proper training should be provided to the farmers to facilitate careful use of antibiotics and monitoring of animal recovery. They also need to be sensitized about the withdrawal period of antibiotic to ensure its excretion and clearance from tissues within safe levels for human consumption, before its sale to the customer. This varies widely depending upon the drug type, animals and dosage form, and thus should be properly explained and monitored to the farmers by veterinary practitioners. In relation to this, the GDG (Guideline Development Group) of WHO in 2017 has recommended four guidelines, which should be followed, on the use of medically important antimicrobials in food producing animal (Table  1.1) (Aidara-Kane et al. 2018).

1.2.3  Antimicrobial Resistance and the Environment High levels of antibiotics have been frequently reported from environmental samples such as soil, manure and water bodies, especially those receiving inputs from livestock farms and/or other niches where antibiotics are used intensively. The interconnectedness and mutual interactions between these two entities have been discussed in following sections.

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1.2.4  Antimicrobial Resistance and Soil Soil is a central hub and serves as source/sink for material exchange between animals, plants, air, rocks, water, etc. and was identified as a huge reservoir for antibiotic resistance genes originating from fungi, plants, bacteria and other organisms (Figs. 1.1 and 1.2) (Monier et al. 2011). Many previously unknown antibiotic resistance genes from metagenomic studies on uncultured bacteria from soil has been reported and numerous are yet to be explored (Riesenfeld et al. 2004). Although the vegetables grown in unsupplemented soils are reported to harbor antibiotic resistance genes and thus antibiotic resistant microbes are naturally present in soils. This can be understood by the fact that most of the antibiotics are being produced by fungi or bacteria. Thus, these species also carries the genes providing resistance against the antibiotics produced by them (Allen et al. 2010). However, but, the load of antibiotic resistant microbes in manure supplemented soil is usually higher and largely dependent on the class of antibiotics being used in the animal farms from where the manure has been sourced (Marti et al. 2013). Many studies have shown that vegetables pick up the antibiotics contained in the manure/soil and many a times also during transport and distribution processes, thereby exposing humans to hazards of transmission of antimicrobial resistance (Beuchat 2002). It can also be influenced by the waste water being used for irrigation, contaminated harvesting equipments, the absorbing capacity of the soil, soil microbiome and the antibiotic resistance genes and mobile genetic elements interacting between microbes in the soil (Chee-Sanford et al. 2009; Oluyege et al. 2015). A group of researchers found high levels of tetracycline in manure and soil samples from commercial swine farms in China. They also reported the presence of about 149 unique antibiotic resistance genes and up to 43% abundance of aphA (antibiotic resistance gene against aminoglycoside antibiotic) in those samples (Zhu et  al. 2013). Another group reported the presence of blaCTX-M gene (antibiotic resistance gene against cefotaxime) as the most abundant antibiotic resistance gene providing multiple drug resistance in the isolates from the swine farm and nearby soil samples. A study shows that many paddy fields in China have been polluted (presumably through animal waste based manures or otherwise) with numerous antibiotic resistance genes showing more than 38% multiple drug resistance (Xiao et al. 2016).

1.2.5  Antimicrobial Resistance and Water Bodies Being actively involved in day-to-day activities of almost all life forms, there is heavy dependence of life on aquatic bodies and it is frequently the final endpoint for most life activities and nutrient cycles. Quite naturally, the aquatic bodies serve as natural sinks, hotspots and reservoirs for the antibiotics, antibiotic resistant bacteria, and antibiotic resistance genes. These water bodies could be marine or fresh water and constitute sea water, tap water, drinking water, ground water and waste water.

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These water bodies harbor many bacteria causing water-borne diseases such as E. coli, Salmonella spp., Shigella spp., Vibrio spp., etc., many of which could be antibiotic resistant. The situation of antimicrobial resistance in these water bodies is a result of many factors including edaphic, antibiotic levels, nutrient levels, bioaccumulation, environmental and the frequency of interactions with life forms including animals, plants and microbes (Ding and He 2010). The levels of antibiotics, antibiotic resistant bacteria and antibiotic resistance genes in the water bodies are drastically affected by manure supplemented agricultural runoff, wildlife fecal contamination, accidental discharge from industrial and/ or sewage treatment plants (Oluyege et al. 2015). There are many challenges faced by developing countries in safe disposal of their waste water and maintaining water distribution pipes. This frequently leads to mixing of waste water and drinking water, thereby exposing humans directly to fecal coliforms and cause infections, many of which might be antibiotic resistant. In developing countries, many regions do not have proper sewage disposal system and the sewage/waste water is frequently disposed-off in rivers. This increases the fecal coliforms and the load of antibiotic resistant bacteria in the ‘rivers’, which due to water scarcity is also used for drinking, and thus directly affecting the general population with infections and diseases (Normark and Normark 2002). Many genetic determinants of resistance such as ESBLs (blaCTX-M-15, bla CTX-M-15 blaOXA-58) were found in similar studies which signify the role of rivers as a possible carrier or sink for these genes. Regardless, being involved in daily life of a large population, it poses health risk to the humans and animals in vicinity of the rivers (Adelowo et al. 2018; Cacace et al. 2019). Mass gathering events (especially religious congregations, sporting events) have important epidemiological implications and have been found to increase the number of coliforms (indicator for water quality) and other microbes in the aquatic bodies including many resistant strains and antibiotic resistance genes (Khan et al. 2010a, b; Jani et al. 2018). Similar deterioration in water quality has been reported in case of recreational activities too, for example multi drug resistant bacteria belonging to Enterobacter spp., Serratia spp., Klebsiella spp., Citrobacter spp. and Pantoea spp. were found in samples taken from a resort in Malaysia (Lihan et al. 2016). Similar reports for presence of antibiotic resistant heterotrophic marine bacteria, has been observed from niches where intensive human activities exist such as fishing, high population, water sports, beaches, etc. in Iran, South Africa and Brazil (World Health Organization 2003; De Oliveira et  al. 2010; Leonard et  al. 2015). These events also require mass movement of animals for food and rituals, many of which might be sourced from countries where regulations are not stringent and good manufacturing practices are not adhered to. These sites become important hotspots for movement of infectious agents and exchange of antimicrobial resistance determinants (Memish 2001). Another big concern is contamination of groundwater with antibiotics and/or antibiotic resistant pathogens and has been known to cause outbreaks in different parts of the world (Lihan et al. 2016). Since this is the main source of drinking water for urban population and is usually considered free from harmful pathogens. But

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vicinity of animal farms, manure storage sites, landfill leachate, wastewater treatment plants, septic tanks, drug manufacturing plants, etc. can increase coliform count and also drive the groundwater microflora towards antibiotic resistance (Kümmerer 2009). Many reports have shown the presence of multi drug resistant antibiotic resistant bacteria such as Vibrio cholerae, Shigella sonnei, Salmonella enterica, etc. from taps, wells and fresh-water streams (Akoachere et al. 2013; Ma et al. 2017). Majority (75%) of the enteric bacteria isolated from a river in South Africa has been reported to show multi drug resistance (Mulamattathil et al. 2014). This study also showed a trend of lower resistance in the regions upstream of the city and higher resistance within and downstream of the city (Lin et al. 2004). In Kenya, ground water was found to be contaminated with enteric pathogens such as E. coli, P. aeruginosa, Salmonella, etc., which also showed high antibiotic resistance in the range of 87–98% out of total isolates (Wahome 2013).

1.2.6  Antimicrobial Resistance and Humans The consumption pattern of antibiotics varies between countries across the world and depends on various factors including the hygiene, living conditions, frequency of infectious diseases, antibiotic stewardship in health professionals and general awareness about antibiotic usage in the public. A primary concern for public health is the high rate of prescription of broad spectrum antibiotics, even for general low level infectious diseases. Many surveys have also shown that broad spectrum antibiotics are being prescribed even for viral diseases such as sore throat. Another aspect is the antibiotics dose regimen, which could be personalized and shortened based on individual patients and biological markers to precisely determine the presence of infection (ECDC (European Centre for Disease Prevention and Control), EFSA (European Food Safety Authority), EMA (European Medicines Agency) Joint report 2015). This leads to exhaustion of the arsenal of antibiotics which could be used in humans, in case of serious multi drug resistant infections. This calls for serious training of antibiotic stewardship among health professionals themselves, augmented by awareness drive amongst the public for evidence based usage of antibiotics during patient admissions. Creating awareness in public could include examples such as for Triclosan – a common ingredient in antimicrobial cleaning agents, which was later found out to assist in development of antibiotic resistance. Owing to mass awareness programs against it, many manufacturers have stopped or reduced its use in their products and its use has been restricted in many countries since 2014 (Laurie McGinley 2016). Humans can also act as vectors for transmission of antibiotic resistance in food animals and facilitate unintended re-circulation of antibiotic resistant strains in the food web. This includes animal farm workers, and their families who harbor high number of antibiotic resistant pathogens such as Staphylococcus aureus in their digestive tract and over their skin (Lozano et al. 2016). In many of the developing world settings, this is due to close contact between the workers and the animals

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(especially poultry) and soil/animal manure, while limited personnel protection equipments are provided to them. At these sites, intensive rearing of animals is done, biosecurity, hygiene, food security and bio-containment facilities are rarely monitored or available, and thus, the emergence of antibiotic resistant microbes is further accelerated. One study reported that both humans and poultry harbored the same Salmonella typhimurium strains shown by phage typing (phage DT56 based similarity) and also resistance against same antibiotics (Kagambèga et  al. 2013). Humans can get affected by antibiotic resistant microbes through three main mechanisms. First, humans may accidentally consume food or water contaminated with antibiotic resistant microbes, and it is not transmitted to other humans. Millions of cases of infection by Salmonella spp. and Camplylobacter spp. are reported every year in US itself and such hazards to humans are amenable to mathematical modelling for risk assessment (McEwen 2012). Such risks assessment predictions and its strong correlation with the actual disease incidences, led regulatory authorities to ban the usage of fluoroquinolones in poultry in US. Second, infected human can transmit it to other humans either directly or indirectly, and this also constitutes breaking of the ‘species barrier’ for the pathogens. Many such transmission of pathogenic infections between humans have been reported in Europe and Netherlands (Armand-Lefevre et  al. 2005; Spoor et  al. 2013). In case of vancomycin resistant enterococci, it has been proposed that the widespread usage of avoparcin (a glycopeptide) led to emergence of many resistant strains in animals and their transfer through mechanism 1 must have provided the initial seed in humans. These microbes normally colonize human gut and they further acquired resistance to multiple antibiotics over repeated exposures during their residence in the human gut (Bonten et  al. 2001). However, more information is needed in this regard to identify the source of resistant bacteria and its transfer between humans. Third, antibiotic resistance genes get transferred to human pathogens through horizontal gene transfer and these pathogens infect the humans (Lipsitch et al. 2002). Many examples exist for transfer of antibiotic resistance gene from a harmless commensal of one animal species into microbes pathogenic to humans (Dowson et al. 1993; Bowler et al. 1994). A recent examples is of vanA and vanB, which provide resistance against an advanced antibiotic – vancomycin. It has been found to easily transmit between Enterococcus spp., due to high recombination frequencies in this group of microbes and amenability of this resistance gene for horizontal gene transfer (Willems et al. 2011). This clearly suggests to another scary impendence that antibiotic resistance genes originating in harmless microbes due to different agricultural/livestock activities might be getting transferred to the human pathogens. Quite correctly, it is reasoned that the level of resistance in microbes associated with humans is dependent upon the amount of antibiotic being consumed. This dosage is further dependent upon community response towards antibiotics, bio-­ geographical conditions, population density, socio-economic conditions, drug prescription pattern of health professionals and market dynamics of incentives between the vendors and prescribers of the antibiotics (Sahoo et  al. 2012). Unarguably, antibiotic resistance in humans is adverse, but even general infection

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becomes difficult and might lead to treatment failure in immuno-compromised patients. Overall, it increases costs, hospital stays, persistence and severity of disease and thereby undue exposure to side effects (Vishnuraj et al. 2016). Antibiotic resistance severely limits the surgical and other invasive therapeutic procedures such as transplantation and anti-cancer therapies, and may need the antibiotics usually secured as the last resort treatment (Friedman et al. 2016). Many countries have initiated steps to reduce indiscriminate usage of antibiotics. To assure optimum consumption, clinical use of antibiotics in humans is operated at two levels: pre-prescription and post-prescription approach. These approaches have been designed based on regulatory restrictions, local government laws, recommended treatment regimens for the drug, clinical guidelines, pharmacy dosing programs, dosage form and route of administration, etc. The patient facing (post-prescription) is a better point for regulation of antibiotic usage, because it can be modified based upon the prevailing medical conditions in the locality, clinical status and feedback from the patient and doctor’s experience (Garnacho-Montero et al. 2015). Reduction of antibiotic prescription, orientation of prescribers for regulated usage, and feedback of antibiotic regimen has shown a marked decline in antibiotic usage in Australia (McKenzie et al. 2013). Brazil, Mexico and Thailand have passed laws to regulate sale of over-the-counter antibiotics without medical prescription and have strengthened the hospital and therapeutics committee (Santa-­ Ana-­Tellez et al. 2013; Holloway et al. 2017). India has reclassified the category of many antibiotics as Schedule H1, which demands that these antibiotics must be sold with medical prescription and the pharmacist has to retain its details in a separate register (for 3  years). This further prohibits the indiscriminate sale of over-the-­ counter antibiotics from pharmacies (Laxminarayan and Chaudhury 2016). South Africa has mandated the pharmacists to stick to the government guidelines, only registered health professionals can prescribe antibiotics and the data must be logged for checking compliance with the existing good manufacturing practices and safe medical usage of antibiotics (Gelband and Duse 2011). All of these approaches are intended for de-escalation of usage of antibiotics and are principal components of antimicrobial stewardship programmes (Masterton 2011).

1.2.7  Antimicrobial Resistance and Bacteriophages Bacteriophages are viruses which infect bacteria and kill them. They have been recently been revisited and proposed as an alternative to antibiotic therapy and for combating infections against antibiotic resistant pathogens. However, we will focus here on their potential role in antimicrobial resistance as reservoirs and/or for transfer of antibiotic resistance genes between different microbes. Many studies have found that bacteriophages are carrying many antibiotic resistance genes and might be involved in horizontal gene transfer, similar to the role played by plasmids and integrons (Brabban et al. 2005). Metagenomic and whole genomic studies on microbiome of cystic fibrosis patients has shown drastic alterations in microbial

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composition and bacteriophages to be involved in transfer of antibiotic resistance genes between the microbiome of cystic fibrosis patients (Rolain et  al. 2011). Viruses have long been known to facilitate transduction, but now many phages have been active in mobilizing genes conferring resistance to antibiotics such as imipenem, ceftazidime and aztreonam in Pseudomonas aeruginosa (Blahova et al. 2000); multiple drug resistance in Salmonella enterica (Schmieger and Schicklmaier 1999); ampicillin resistance to Escherichia coli (Colomer-Lluch et al. 2011). This attribute of bacteriophages as gene carriers has recently been reported from niches such as sewage/waste water, which contains complex nutrients, high organic matter and high concentration of antibiotics, antibiotic resistance genes and antibiotic resistance bacteria. Using quantitative PCR, bacteriophage fractions from this niche has been found to harbor many antibiotic resistance genes (qnrS, blaTEM, blaCTX-M, blaSHV, mecA, sul1) including those conferring multiple drug resistance (Colomer-Lluch et al. 2011; Marti et al. 2014; Calero-Cáceres and Muniesa 2016). The number and type of antibiotic resistance genes in the phage fractions sampled from different sections (example sludge) of sewage treatment plants varies. Similar loads of resistance determinants and phages have been found in human feces (Quirós et  al. 2014) and other environmental samples (Balcazar 2014). The presence of phages and high number of antibiotic resistant bacteria and antibiotic resistance genes in the sewage sludge makes it an active playground for transfer of antibiotic resistance genes and many studies have implicated high incidence of transfer of resistance (Colomer-Lluch et al. 2011; Calero-Cáceres et al. 2014). Although, more evidence for the impact of phages in transfer of antibiotic resistance genes is required, but the high number of antibiotic resistance genes they harbor and their active involvement in transfer of genes, makes them a potential hazard and contributing factor in spread of antimicrobial resistance.

1.2.8  Antimicrobial Resistance and Agriculture There are many ways that antibiotics can end up in the environment. These include (and are not limited to) human sewage, veterinary and livestock farming waste and surface run-off from any other sources (Gillings 2013) (Fig.  1.2). This is largely because a large portion of the administered antibiotics is not completely metabolized and excreted out of humans/animals. This is governed by various factors, including the type of antibiotic, dosage, age and species of the animal being administered. The waste material from these sources inevitably contains antibiotics and antibiotic resistance genes (Martinez 2009). The leakage of antibiotics can happen during irrigation, antibiotic contaminated dust, release from the antibiotic production pharmaceutical units, and even during transport and storage of the animal-­ derived manure. This condition may arise due to inappropriate handling conditions for aforementioned waste in the open, where it frequently mixes up with the rainwater. In addition to these, accidental spills from agricultural antibiotic storage units and direct disposal of expired antibiotics from households/small vendors also

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culminate in the environment. This is especially because there are not set guidelines and systems in place to collect expired antibiotics and most of the workers handling antibiotics in agriculture are not properly trained and are unaware of the issue. Being highly water soluble, these antibiotics can spread quickly in the environment, to surface and ground water and the soil (Du and Liu 2012). The antibiotics in the environment have different fates depending upon their chemical nature and degradability. They undergo sorption, biodegradation, photo-­ degradation and oxidation processes during their elimination from the environment (Li et al. 2014). Some artificial methods such as sedimentation, adsorption, coagulation, filtration, and advanced oxidation processes can also be applied to accelerate degradation of antibiotics, but are not so frequently used (Homem and Santos 2011). Depending on their type, different antibiotics respond variably to degradation by aerobic/anaerobic digestion and composting methods in wastewater, spent sludge and manure. Of course, these processes are highly dependent upon the temperature, moisture and composting conditions and the source animal (Bao et  al. 2009). Regardless of their source or final fate, their presence in the environment is frequently associated with high number of antibiotic resistant bacteria in that niche. About 33% of total Gram negative bacterial isolates were found to be resistant to commonly used antibiotics such as tetracycline, chloramphenicol, ampicillin, nalidixic acid and sulphamethoxazole from cattle farms in Spain (Carballo et al. 2013). There is limited data available on the use of antibiotics and antimicrobial resistance in the agricultural systems. This is largely because no such system has been in place and given its connection with animals, the logging of data and following of strict regulations, are largely ignored. Further, there is no monitoring of antimicrobial resistance and its spread from manure, sewage and waste water treatment plants, all of which are known as reservoirs of antimicrobial resistance (Michael et al. 2013).

1.3  Prevention and Regulation of Antimicrobial Resistance The menace of antimicrobial resistance is worldwide and need multi-thronged approach to achieve control and regulation over antimicrobial resistance. This includes improving awareness and understanding about antimicrobial resistance, optimize the usage of antimicrobials, effective surveillance and research, prevention of infection and more focus towards generation of novel drugs (Berendonk et al. 2015). Awareness about antimicrobial resistance is being increased through various outreach programmes (radio, TV, local engagement) by the governments worldwide, in collaboration with many non-governmental and professional organizations. Another important part is training of the health professionals (including pharmacists) to prescribe responsibly, maintain register for advanced class of antibiotics and curb the sale of over-the-counter drugs without prescription. Community level measures start with maintenance of good hygienic practices, immunization of maximum population and creating awareness about rational use of antibiotics. Healthcare systems such as hospitals and clinics need to practice good hand hygiene practices,

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rational use of antibiotics based on an standard antibiotic prescription plan (as recommended by regulatory authorities), establish infection prevention and control committee, good microbiology practices and monitoring of usage of antibiotics and antimicrobial resistance and critical checkpoints within their own premises (Uchil et al. 2014). Strengthening of healthcare systems with these regulations even in low income countries/regions with limited medical assistance would be required for effective mitigation of antimicrobial resistance. Another crucial component of preventive measures for mitigating antimicrobial resistance is surveillance, which also helps to design control strategies in clinical settings. Effective surveillance and prevention of leakage of antibiotics mitigates a large fraction of antibiotic resistance menace. It generates important and reliable data which is useful to understand the pattern of antimicrobial resistance and design strategies and national policies towards regulated drug prescription and corresponding emergence of antimicrobial resistance against particular class of antibiotics (Pereko et al. 2016). Many global partnership programmes, such as Global Action Plan, have called for such surveillance task forces and partnerships for exchange of information over the current status and the drift of antibiotic resistance over long periods of time (Duse, 2011; Leung et al. 2011). These programmes need to be both national and international, to allow for intersectoral cooperation and application of regulations. The point-of-care pathology labs and/or healthcare clinics are the point of contact for patients and can provide crucial data about the antibiotic susceptibility and the antibiotic prescription pattern. This has been practiced in many countries and is complemented by information of antibiotic resistance in that locality. Antimicrobial resistance is steadily evolving and thus requires continuous surveillance to catch the problem in time. Also, this data provides the trend of evolution of antibiotic resistance in terms of quantifiable parameters such as antibiotic resistance genes, antibiotic resistance bacteria and predictive models for antibiotic resistance. This information can also be used to identify critical breakpoints to aid in updating national policies, antibiotic prescription patterns and guidelines for standard treatment (Critchley and Karlowsky 2004). Globally, many regional surveillance systems exist such as INSAR-India, ESAC-European Union, NARMS-USA, SASCM-South Africa, etc. But, given the interconnectivity of the modern world and rapid spread of antimicrobial resistance, it is required that there is global cooperation, such as GLASS – WHO for effective mitigation of the problem and efficient surveillance is effectively established. All these precautions and regulative measures can be complimented only with innovative solutions against resistant pathogens and rapid discovery of more potent antibiotics. These are required in conjunction with novel technologies to improve drug delivery and strategies to bypass biofilm barriers, which further decrease efficacy of drugs against resistant and persistent microbes. Apart from strategies and regulations posed from the stakeholders belonging to healthcare, many steps have been suggested by economists to reduce the usage of antibiotics in agriculture. One suggestion is to impose a ban on the use of antibiotics in agriculture and recommend farmers for improving hygiene and living conditions of the animals. Another suggestion (more practical solution) is to charge a user fee on antibiotics intended for

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non-human use, thereby discouraging the mass usage of low-value antibiotics in agriculture. This move is easy to implement and would bring the production costs using antibiotics a bit higher (close to hygienic rearing) and corresponding to lower risks of infection and improvement in rearing conditions of animals (Hollis and Ahmed 2013). Although, efforts are being done to facilitate the exchange of information between these systems and integrate them in a centralized system for monitoring of antimicrobial resistance. Some examples are Pan American Health Organization (PAHO) and European Resistance Surveillance Network (EARS-Net), etc., but global networking and cooperation in this regard is required. Another problem is defining the common detection methods, safe limits and critical breakpoints across the world, for the levels of antibiotics and antibiotic resistant bacteria. An international surveillance system is warranted for this and also for integrating information about antibiotic resistance from soil, water and non-pathogenic environmental microbes, all of which could be critical as carriers of antibiotic resistance (Berendonk et al. 2015). Defining a single standard is difficult, because issue of antimicrobial resistance is complex and influenced by various factors. This calls for careful decision making and consideration of ecological factors and the niche conditions which might under- or over-represent antimicrobial resistance from the samples. An important aim for these international collaborations is to identify the factors causing the emergence, persistence and spread of antimicrobial resistance. The emergence of antibiotic resistant genes is not only due to mutations or co-selection under antibiotics, heavy metals or other antimicrobial agents. But, the role of mobile genetic elements such as plasmids, integrons and transposons, makes it even more complex and difficult to predict. The role of biofilm formation, water bodies and phyllosphere have also been implicated in persistence of resistance (Calero-Cáceres et al. 2014). It demands high-throughput techniques such as metagenomics to get holistic picture of the issue of antimicrobial resistance and understand the rate of acquisition and spread of antibiotic resistance genes. This has been structured through a classification system called as resistance readiness condition (Rescon), which takes into account the severity of antimicrobial resistance due to the antibiotic resistance genes and their propensity for rapid spread (Vorholt 2012). Understanding the transfer pathways is challenging because of multiple overlaps between the humans, animals, agriculture and the environment. At the same time, reliable data on antimicrobial resistance and driving genes and mobile genetic elements in these individual niches is also not available. Antibiotic resistance genes alone might be getting transferred between these niches and are also prone to undergo alterations in their new host. All these factors make it quite difficult to attempt any quantitative prediction for identifying the source of antibiotic resistance genes responsible for antimicrobial resistance (Martínez et al. 2015). Thus, the current strategies to assess the issue of antimicrobial resistance is trivial and is limited to culturable antibiotic susceptibility tests and few molecular estimations for presence of antibiotic resistance genes. The studies involving big data/omics approach (through metagenomics, meta-transcriptomics, meta-metabolomics, etc.) could

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have high potential impact in understanding antimicrobial resistance (Winokur et al. 2001). The usage of big data and its correlational mathematical analysis with the confounding factors is not yet fully realized and would need comprehensive efforts and action to achieve reliable predictions for emergence and spread of antimicrobial resistance (Bailar and Travers 2002; Marshall and Levy 2011). The culture based and PCR based low-throughput methods only provides limited information and are time-consuming and need prior information about the antibiotic resistance genes. Contrary to these, the multi-omics approaches utilize the samples directly from the environment/niche and provide high-throughput information into the type of antibiotic resistance genes, mobile genetic elements, expression pattern and levels of genes and gene clusters and the actual metabolites present in the niche (Spicknall et al. 2013). These chunks of data are amenable for usage in advanced statistical softwares and computer programming languages, to provide meaningful information. The meaningful information includes (1) the relative abundance and movement of antibiotic resistance genes, (2) probability for spread of antibiotic resistance genes between niches, (3) predictions for development of resistance due to mutations in known antibiotic resistance genes, (4) prevalence of mobile genetic elements like integrons, plasmids, etc. and (5) interrelation between the relative proportion of MGEs and the antibiotic resistance genes in driving the antimicrobial resistance. All these studies can be well complemented with functional metagenomics in which novel antibiotic resistance genes can be identified from different niches. However, these techniques are limited due to high costs and need for complex data analysis. Future advancements in next generation sequence analysis and more interest in environmental samples could make these technologies even cheaper for future use (Munk et al. 2017; Hendriksen et al. 2019).

1.4  Antimicrobial Resistance and One Health Approach In context of antimicrobial resistance, there are multiple benefits for adopting and working in the framework of ‘One Health’ concept. The prime concern for humans is the emergence of resistance in human pathogens and gain of resistance in human pathogens form their distant relatives, present in the environment (Forsberg et al. 2012). Even though, there is clear evidence for animal-human overlaps, but strictly confining to humans, ‘One Health’ could be beneficial in strengthening the surveillance network to improve the understanding of the changing dynamics of antimicrobial resistance in human pathogens. This includes documenting the resistant microbial phenotypes isolated from human samples during microbiological examination of patients. This reporting (every year) of the prevailing antibiotic resistance in the area would also help in identifying the pattern endemic to the region (Critchley and Karlowsky 2004). The physicians would follow evidence-based approach for requirement of antibiotics by the patient and would have clear standard guidelines to aid in prescription of antibiotics. Physicians can directly influence the patient

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behavior and attitude towards responsible and recommended usage of prescribed antibiotics. They can also educate individuals and community about the general rules of hygiene, which can bring drastic decrease in the load of infectious diseases. They can be nodal points to prevent panic and guide responsible behavior of public during disease outbreaks and safe disposal of carcasses, which have died due to zoonotic diseases. All these steps, will aid in robust design of mitigation and preventive policies against existing and prospective issues of resistant humans pathogens. The issues and identifiable terms relevant to antimicrobial resistance are used in different contexts in different domains related to public health, animal health, agricultural systems and human health. Antibiotics and their degradation products are not considered a part of antibiotic resistance, but are largely responsible for driving antibiotic resistance (Subbiah et al. 2016). Natural resistance and co-selection for resistance against heavy metals, detergents, nutrient loss/enrichment further makes the situation complex and mere reliability on few factors cannot provide correct picture (Franklin et al. 2016). Antibiotics might also be involved in acting as carbon source and elicitors for expression of different genes in microbes including motility, biofilm formation and stress response (Romero et al. 2011). The role of presence of high level of antibiotics is also sometimes not directly associated with high number of antibiotic resistant bacteria (Jechalke et al. 2015). All these factors can trivialize the strategies proposed for dealing with antibiotics in agriculture, and encourage ambiguity in behavior towards usage and response to antibiotics usage. One striking example is the remarkable difference in the regulations on usage of advanced antibiotics such as Carbapenems and Colistin in Agriculture systems, which are otherwise reserved for terminal level infection treatment in humans (Pereko et al. 2016). The estimated risks level due to resistant pathogens (such as methicillin resistant Staphylococcus aureus) can still be different for veterinarians and physicians and thus could have different priorities (and seriousness) in dealing with it. Also, there are different approaches of antibiotic prescribers when dealing with humans (focused on individual) and animals (focused on animal herds and collective protection). Further, the human-animal-environment interactions are largely ignored by general healthcare professionals and any diseases or altercations are dealt with separately in humans, animals and the environment. In many developing countries, hunting and intake of ‘bush meat’ is common to supplement food and has been linked with many humans infections including Ebola (Peterson 2003). Owing to low vaccination and lack of any veterinary management capacities, these factors pose frequent risks to humans and demands cross-disciplinary cooperation. These issue led the WHO (program on neglected tropical diseases) to adopt One Health model and foster collaborations with United Nations Food and Agricultural Organization (UN-FAO), to publish recommendations for integrative strategies and policies to deal with Africa-endemic diseases such as echinococcosis, rabies, bovine tuberculosis and brucellosis (Aidara-Kane et al. 2018). Similar medical-veterinary collaborative efforts have been initiated between UN-FAO and WHO for dealing with threat of avian influenza variants (influenza virus A) to humans (http://www. offlu.net/). The economic effects of regulating the usage of preventive antimicrobials in agriculture due to perceived threat to humans may be wrongly assessed.

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Thus, a holistic view is warranted which is possible only by working within ‘One Health’ framework. The lack of reliable information and coordination between these stakeholders further increases the knowledge divide (Fisman and Laupland 2010). It would be helpful in decreasing confusion between the relevance and limit of standards for antibiotic resistance in microbes isolated from environment and its comparability with human clinical strains (McLain et al. 2016). One Health is based on science-based risk management policies approach, works across over-lapping disciplines and administrative sectors and focusses on strengthening of skilled infrastructure and manpower in low-resource countries. One Health approach effectively bridges these gaps and provides reliable overlap between the stakeholders and ensures exchange of credible information (Gebreyes et al. 2014).

1.5  Conclusion We understand about the numerous overlaps between the animals, humans and the environment, which also forms the basis for exchange of the risks associated with each of these interacting domains. Recent outbreaks in humans originating from zoonotic reservoirs have further underscored this inter-relatedness and the importance of ‘One-Health’. Although, very wide in terms of its scope, ‘One-Health’ approach is the only solution to regulate scope with multifactorial menace of antimicrobial resistance. The confounding factors of antimicrobial resistance stem from the different biotic and abiotic spheres of earth. Although, clinicians, health professionals and regulatory agencies have initiated wonderful strides, but a lot needs to be done to generate quantifiable information for usage in reliable predictive models. We have to put more focus on the traceability of the sources and integration of information to update and accentuate the strategies and policies to mitigate the situation of antimicrobial resistance. Although applicable to both developing and developed countries, this is particularly useful for low- and middle-income countries, in which the updated safety measures and practices could be effectively implemented. Unarguably, all the spheres of life are stakeholders in the issue of antimicrobial resistance, but most of these domains have been largely ignored, due to too much focus on the human-related information. The origin and rapid exchange of antimicrobial resistance determinants between these domains have made any alienation of individual domain to be nearly impossible. The prevailing superiority and the associated romanticism with (only) human, data needs to be supplemented with information from veterinary and environmental sources. The colossal task to combat antimicrobial resistance ranges from effective surveillance, tight regulations, regulated use and development of novel antimicrobials. This requires integration of available information from each spheres of life and ‘One-Health’ approach appropriately addresses the emergence of antimicrobial resistance, regardless of the site of use of antibiotics or otherwise.

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Chapter 2

Global Surveillance Programs on Antimicrobial Resistance Sunil Kumar, Mayank Chaudhary, Mukesh Yadav, and Vikas Kumar

Abstract  Antimicrobial resistance is a huge concern for human and animal health and the environment. All these three sectors are interconnected and influenced with the involvement of a network of complex interacting factors. The lack of data on mechanisms causing the emergence and spread of antimicrobial resistance hampers global efforts to effectively manage the risks. Surveillance for antimicrobial resistance provides details of current scenario of antimicrobial resistance along with other relevant information for monitoring aptness of therapy guidelines, public health interventions, and policies for controlling infection. Several surveillance programs are functioning in the direction of gathering the global data on antimicrobial resistance. The global antimicrobial resistance surveillance system (GLASS) was launched by the World Health Organization (WHO) in 2015. The European Union surveillance program presently includes different aspects like; animal species with the highest meat production and the food derived from them, exploring antimicrobial resistance of zoonotic and indicator bacteria. The field of public health monitoring systems is beginning to exploit the power of genome and metagenome sequencing by improving our ability to accurately predict and screen for efficient and accurate surveillance of antibiotic resistance genes within environmental, agricultural, and clinical settings. Likewise, National Antimicrobial Resistance Monitoring System (NARMS) was established around two decades ago to help assess the consequences to human health arising from the use of antimicrobial drugs in food animal production in the United States. One Health approach to surveillance can increase the performance of antimicrobial resistance surveillance and, ultimately, improve health outcomes. This chapter presents an overview of globally adopted antimicrobial resistance surveillance programs focussed on humans, animals, and the environment. Keywords  Antimicrobial Resistance · Surveillance · Healthcare settings · Animal Health · One Health

S. Kumar (*) · M. Chaudhary · M. Yadav · V. Kumar Department of Biotechnology, Maharishi Markandeshwar (Deemed to be) University, Mullana (Ambala), Haryana, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_2

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2.1  Introduction Antimicrobial resistance is the acquired ability of a microbe to resist the effects of antimicrobials that once could successfully treat the microbe (Ferri et  al. 2017). Resistance is acquired horizontally by one of the following mechanisms: (i) drug efflux pumps, (ii) enzymatic modification of binding site, (iii) modification or inactivation of enzymatic drug, acquisition of a novel drug resistant target, (v) target protection by drug displacement (Barlam et al. 2016). Once the microbe becomes resistant, more difficult is to treat the infection caused by it. Microbes resistant to more than one class of antimicrobials are called multidrug resistant (Arzanlou et al. 2017). Extensively drug resistant are those which are resistant to more than two classes of antimicrobials. Healthcare associated infections and antimicrobial resistance are mounting menaces to public health and the global health care systems (MacVane 2017). Antimicrobial resistance in humans is well connected with antimicrobial resistance in other populations, particularly farm animals, and in the large environment (Arzanlou et al. 2017). Antimicrobial resistance is caused by overuse of antibiotics, which was predicted by Alexander Fleming; who said ‘The time may come when penicillin can be bought by anyone in the shops’. The lack of information substantially prohibits the coordination of approaches and evaluation of the effectiveness of interventions. The persistent tide of threats shown by healthcare associated infections and antimicrobial resistance cannot be curbed without enhancement of global surveillance systems. Specific programs should be initiated and supported financially in every region on the basis of existing network structures. A robust monitoring and evaluation platform now contributes to evidence based interventions (Tacconelli et al. 2018). An incorporated system for the surveillance of antimicrobial resistance still needs to be established. This chapter focuses on global surveillance systems to define the status, recent advancements, main limitations, and unmet needs of improved antimicrobial resistance surveillance programs worldwide (Simjee et al. 2018).

2.2  Resistance Surveillance History Around 5 decades ago, infections in the calves were reported in United Kingdom due to multidrug resistance Salmonella enterica serovar Typhimurium (Akkina et al. 1999; Threlfall et al. 1994). Such infections became prominent due to adoption of intensive farming practices such as rampant use of antimicrobial in the feed without prescriptions, which further resulted in transmission of infections to humans. This epidemic could be avoided not by use of massive antibiotics but by improvement of animal husbandry conditions and by decreasing the initiation and spread of diseases through monitoring (Braden 2006). In 1970, a monitoring program for Salmonella antimicrobial resistance surveillance in animals initiated in the United Kingdom, and surveys were conducted in

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other countries (Fedorka-Cray et al. 2002). In 1969, Martel and Coudert described the national surveillance resistance in Salmonella and E. coli animal isolates in France. In 1987, DuPont and Steele suggested national surveillance of the antimicrobials usage in food producing animals. Following the rise of multidrug resistant Salmonella Typhimurium DT104, S. enteric serovar Newport resistant to third generation cephalosporins, and fluoroquinolone resistant Campylobacter in 1996, the Center for Disease Control (CDC), Food and Drug Administration (FDA), the U.S.  Department of Agriculture (USDA), founded the National Antimicrobial Resistance Monitoring System (NARMS) (CDC 2016a, b; FDA, 2016; Zawack et  al. 2016). Similarly, in Europe, Danish Integrated Antimicrobial Resistance Monitoring Program (DANMAP) was established targeting the rise of vancomycin resistant enterococci in pigs and poultry (DANMAP 2015). The Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) was initiated in Canada in order to monitor the antimicrobial resistance. Since then, the antimicrobial resistance surveillance programs and recommendations of these programs were followed in many countries. In India, the Indian Council of Medical Research, New Delhi, developed an antimicrobial resistance surveillance network in 2014 in collaboration with tertiary care hospitals throughout India (Veeraraghavan et al. 2018).

2.3  Global and Supranational Surveillance Networks Low and middle income countries investigated the burden of global antimicrobial resistance and infectious diseases. For antimicrobial resistance surveillance, 72 supranational networks programs have been formed since 2000 in bacteria, tubercle bacilli, fungi, human immunodeficiency virus, and malaria that have included low and middle income countries. Networks are grouped as WHO/governmental (n  =  26), pharma initiated (n  =  22) or academic (n  =  24) (Ashley et  al. 2018). Funding agencies differ, with 30 networks receiving WHO or public funding, 13 foundation or trust, 25 corporate, and 4 are supported from more than one agency. The foremost global programmes for resistance surveillance in Tubercle bacilli, HIV and malaria gather data in low and middle income countries through intermittent active surveillance programs or combined approaches. The prime challenge encountered by these networks has been getting high coverage across low and middle income countries and fulfilling the recommended frequency of reporting (Amos et al. 2009). To get high quality data, representative surveillance in low and middle income countries is demanding. Antimicrobial resistance surveillance requires a set level of laboratory training and infrastructure that is not generally available in low and middle income countries (Talisuna et al. 2012). The nascent Global resistance Surveillance System aims to develop passive surveillance in all member states. Previous experience suggests harmonizing active approaches may be required in many low and middle income countries; if representative, meaningful, clinically relevant data are to be acquired (WHO 2015). Sustaining an up-to-date registry of networks would support a more coordinated approach to surveillance.

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The burden of resistant infections has been mounting from last one decade. It is estimated that the maximum numbers of lives in low and middle income countries will be lost due to these infections (O’Neill 2016). A global action plan was approved on antimicrobial resistance in 2015 by the World Health Assembly and encouraged the participating countries to toughen surveillance of antimicrobial resistance. It also requires good surveillance programs data for assessing the magnitude of the problem precisely and to direct intervention strategies. Many low and middle income countries are already contributing in surveillance start-ups for resistance in Tubercle bacilli, malaria, HIV and influenza (Ashley et al. 2018). Initiatives for the global surveillance of resistance against commonly used antibiotics have been taken in the past but the expected success has not been attained. The Global Antimicrobial Resistance Surveillance System was launched in 2015 aiming at the collection of antimicrobial resistance data of specific bacterial pathogens from different countries (WHO 2013). In West Africa, the terrible Ebola epidemic has also demanded the requirement for surveillance systems for rising diseases because most of these episodes have been seen in low and middle income countries. ‘One Health’ approach to surveillance is recommended for both antimicrobial resistance and emerging diseases using different drivers in humans, animals and the environment.

2.4  Surveillance Systems of USA The National Antimicrobial Resistance Monitoring System (NARMS) provides data to tackle the problem of rising antimicrobial resistance and also shows its positive impacts on public health (Karp et al. 2017). It was established in 1996 on the recommendations of experts organized by the Food and Drug Administration of USA. The panel recommended the establishment of a surveillance system at national level to monitor resistance among selected human disease causing enteric bacteria. National Antimicrobial Resistance Monitoring System is a combined effort of three central agencies including CDC (Centre for Diseases Control & Prevention), FDA (Food & Drug Administration) and the USDA (United States Department of Agriculture). It also includes local and state health divisions in all states of USA. National Antimicrobial Resistance Monitoring System helps in assessing the results associated with human health coming from the use of antibiotics in food animals with a vision toward alleviation. The followings are the major goals of ‘National Antimicrobial Resistance Monitoring System’: 1. To track the antimicrobial resistance in the enteric bacteria from humans, animals and retail meats. 2. To understand the spread of antimicrobial resistance, emergence, and persistence by conducting research. 3. To publish apt information on antimicrobial resistance in pathogenic and commensal organisms and provide the same to the United States stakeholders and to

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encourage interventions to reduce escalating resistance among food-borne pathogens 4. To assist FDA in making judgment related to the approval of safe and effective antimicrobials for animals by providing data. The National Antimicrobial Resistance Monitoring System deals with two main zoonotic bacterial food-borne illness in the US; namely Campylobacter and non-­ typhoidal Salmonella. Surveillance of Food animal and retail meat also take account of Escherichia coli and Enterococcus, which can serve as reservoirs of antimicrobial resistance genes and determinants of selection pressures in Gram negative and Gram positive bacteria, respectively (WHO 2013). Besides, CDC uses the human surveillance of NARMS platform for assessing resistance in Vibrio, E. coli O157, and the non-zoonotic enteric bacteria i.e. typhoidal Salmonella and Shigella. Collaborative association between epidemiologists, microbiologists, researchers from healthcare and agriculture firms have been found crucial for the effectiveness of the program. The National Antimicrobial Resistance Monitoring System has modified according to varying needs and risks by growing the horizon for surveillance, investigating new sources of strain, adding new bacteria for surveillance, modifying sampling formats, and altering antimicrobials tested over a long period of time. More meticulous information of The National Antimicrobial Resistance Monitoring System testing and sampling protocols are available in the reports of the National Antimicrobial Resistance Monitoring System surveillance (CDC 2016a, b; FDA 2016a). In present, the National Antimicrobial Resistance Monitoring System perform tests for clinical isolates of Salmonella, Shigella, E. coli O157, Vibrio, and also strains of Campylobacter isolates from the collaborating states in Food-borne Diseases Active Surveillance Network (FoodNet). It is a combined program of CDC, 10 state health departments, FDA and USDA’s Food Safety. It performs vigorous surveillance based on population for confirmed infections communicated commonly through food (Henao et al. 2015). In 1997, NARMS food animal surveillance was started with carcass testing and samples of slaughter and processing plants. In 2013, more specimens were added to NARMS like cecal contents from swine (market hogs and sows), slaughtered chickens, cattle (dairy and beef), and turkeys. Cecal samples are cultured for investigation of Salmonella, Campylobacter and E. coli. Salmonella, Enterococcus (selected sites) and E. coli (selected sites) are cultured in the Retail chicken parts, ground beef, and pork chops ground turkey.

2.5  Surveillance Systems of Europe The European Centre for Disease Prevention and Control (ECDC) and European Antimicrobial Resistance Surveillance Network perform survey for estimating resistance pattern in Europe (Table  2.1). Point prevalence survey in 2011–2012 reported methicillin resistance in 41% of invasive Staphylococcus aureus isolates, third generation cephalosporin resistance in 33% of Enterobacteriaceae isolates,

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Table 2.1  Various surveillance systems (National and Regional) of Europe National surveillance systems Austria National Reference Center for Nosocomial Infections and Antimicrobial Resistance (NRZ) Bulgaria Bulgarian Surveillance Tracking Antimicrobial Resistance (BulSTAR) Belgium The Scientific Institute of Public Health (WIV-ISSP) Croatia Croatian Institute of Public Health (CIPH) Croatia Intersectoral Coordination Mechanism for the Control of Antimicrobial Resistance (ISKRA) Czech National Institute of Public Health (NIPH) Republic Cyprus National antimicrobial resistance surveillance system Denmark Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP) France The National Observatory of the Epidemiology of Bacterial Resistance Antibiotics (ONERBA) Finland Finnish Study Group for Antimicrobial Resistance (FIRE) Germany Hospital surveillance system for nosocomial infections (KISS) Germany Monitoring antibiotic resistance in Niedersachsen (ARMIN) Germany Surveillance of antibiotic use and bacterial resistance in intensive care units (SARI) Germany Antibiotic Resistance Surveillance (ARS) Greece Greek System for the Surveillance of Antimicrobial Resistance (GSSAR) Ireland Health Protection Surveillance Centre (HPSC) Hungary National Nosocomial Surveillance System (NNSR) Italy Regional surveillance system for intensive care units (SITIER) Italy Surveillance of antibiotic resistance - National Institute of Health (AR-ISS) Italy Prospective surveillance of nosocomial infections in intensive care units (SPIN-UTI) Italy National surveillance systems for post surgical infections (SNICh) Lithuania Surveillance of antibiotic resistance - Institute of Vilnius Norway Norwegian surveillance system: healthcare associated infections module for surgical site infections; antimicrobial drug resistance module; communicable diseases (FHI) Netherlands Infectious Disease Surveillance and Information System for Antibiotic Resistance (ISIS-AR) Portugal Antibiotic Resistance Surveillance Programme in Portugal (ARSIP) Romania Sentinel surveillance system of nosocomial infections and antimicrobial resistance Spain EstudioNacional de Vigilancia de Infección Nososcomial en Servicios de MedicinaIntensiva (ENVIN-UCI) Slovakia Slovak National Antimicrobial Resistance Surveillance System (SNARS) Sweden Swedish Surveillance of Antimicrobial Resistance (Svebar) Sweden Annual resistance monitoring and quality control programme (ResNet) Switzerland CA-MRSA surveillance system (CA-MRSA) Switzerland Swiss Centre for Antibiotic Resistance (ANRESIS) (continued)

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Table 2.1 (continued) National surveillance systems Regional Italy Regional (Toscana) surveillance of antibiotic resistance (SART) Italy Regional (Emilia-Romagna) surveillance of antibiotic resistance and intravenous antibiotic usage (LAB) Spain Regional surveillance system (Asturias; SVPCIP) Spain Prevention and control of nosocomial infections and inappropriate usage of antibiotics (PIRASOA) Spain Regional surveillance system (Catalunya; VINCat) Spain Regional surveillance system (Galicia; SVIN) Switzerland Prevention and control of nosocomial infections (HPCI) UK Public Health England (PHE) UK Health Protection Scotland (HPS) UK Welsh Healthcare Associated Infections Programme (WHAIP) UK Public Health Agency (PHA) Adapted from Tacconelli et al. (2018)

vancomycin resistance in 10% of enterococcal isolates and carbapenem resistance in 81% of A. baumannii isolates. Community acquired infections show increased antimicrobial resistance with variations among countries (ECDC 2013b). Recent studies of antimicrobial resistance in food chain and animals predicts further rise in the antimicrobial resistance in relation to human beings. Lower susceptibility to tetracycline, quinolones, sulphonamides and ampicillin has been seen in Salmonella and E. coli isolates (Schwarz and Johnson 2016). In addition to this, European Medicines Agency and European Food Safety Authority has reported an association between antimicrobial intake in food producing animals and increase of resistance in bacteria isolates of human (ECDC 2015). Monitoring of antimicrobial resistance is indispensable to support appropriate antimicrobial use that will optimize clinical outcome of patients while minimizing unforeseen effects like toxicity and development of resistance (Barlam et al. 2016). ECDC and many national cohort provides European surveillance data for public (ONEBRA 2014; PHE 2016). In 1998, European Commission established a largest publicly funded resistance surveillance system in Europe, which is maintained and sponsored by ECDC since 2010 (ECDC 2016). This network improved the quality of surveillance data in Europe and provides data on antimicrobial resistance on yearly basis; but is greatly affected by heterogeneity among European countries (i.e., disparity in organization of health care program, reimbursement strategies, and blood sampling indications). In lieu of emergence of antimicrobial resistance as a substantial threat, Europe has also implemented many national surveillance systems. Because of the pressure of escalating antimicrobial resistance, several initiatives have come into action in the past few years to tackle the limitations of existing surveillance programs. The European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) project of the European Medicines Agency has collected and reported data on sales of antimicrobials used in veterinary since 2009 and has

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recently announced its policy for improved antimicrobial resistance surveillance over the next 5 years. Goals of ESVAC include spreading out of data collection to all countries in the European Union, transition to ongoing annual reporting, harmonisation and standardisation of data collection, automation of data presentation and analysis, database linkage, and integration of animal, human, and food data (EMA 2016). The Central Asian and Eastern European Surveillance of Antimicrobial Resistance (CAESAR) network is a mutual initiative of the WHO Regional Office for Europe, the Dutch National Institute for Public Health and the Environment and the European Society of Clinical Microbiology and Infectious Diseases. CAESAR is a network of national antimicrobial resistance surveillance systems and includes almost all countries of the WHO European Union that are not part of EARS-Net. The second annual CAESAR report was published in November, 2016 (WHO 2016). Twenty countries participated in CAESAR and six others have submitted the data of national surveillance to the CAESAR. In 2015, WHO launched the global antimicrobial resistance system project for improved surveillance of seven antibiotic resistant bacteria in member states. The surveillance report based on the 2016 data, were made publicly available from July, 2017. Detailed resistance surveillance in food chain and animals is necessary to understand and predict the trends in antimicrobial resistance, because the presently available information is not adequate. In report 12, the European Food Safety Authority, European Medicines Agency and ECDC emphasized foremost restrictions of existing evidence and focussed on enhanced combined surveillance after analyzing interplay between antimicrobial consumption and escalated resistance in humans and other food animals. Few European countries have started antimicrobial resistance surveillance programmes at national level but with limited goals.

2.6  Surveillance Systems of India Burden of infectious disease and antimicrobial resistance are of a great concern to global as well as national healthcare fraternity. This resistance is more significant in relation to Gram negative pathogens as compared to Gram positive organisms. No systemic or national surveillance program is present in India resulting in only few multicentre reports from individual units on antimicrobial resistance. In consideration to it, the Indian Council of Medical Research, New Delhi, developed an antimicrobial resistance surveillance network in 2014 in collaboration with tertiary care hospitals throughout India (Veeraraghavan et al. 2018; Wattal and Goel 2014). This systemic data capturing system results in generation of reliable data with greater precision. The network was developed to study the molecular mechanisms and cumulative anti biogram involved in development of antimicrobial resistance for Global Antimicrobial Resistance Surveillance System pathogens of top priority (Gram negative organisms and Gram positive). Priority pathogens include Streptococcus pneumoniae, Escherichia coli, Staphylococcus aureus, Klebsiella

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pneumoniae, Acinetobacter baumannii, Salmonella and Shigella spp. (Veeraraghavan et al. 2018; Walia et al. 2019). In response to national antimicrobial resistance crisis, Indian Council of Medical Research constituted 6 nodal and 20 regional centres in 2012 for initiating surveillance of six pathogenic groups. Priority objective of the network was evidence based treatment strategies, thus rationalising antimicrobial use. In addition to surveillance network, a nationwide antimicrobial stewardship programme was also launched (Veeraraghavan et  al. 2018). Accrediting the significance of Minimum Inhibitory Concentration (MIC) monitoring for better therapeutic outcomes, the network insists on determination of either carbapenem MIC or micro broth dilution based MIC for colistin isolates collected from different anatomical sites. It further covers molecular characterization of ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogens for formulation of suitable empirical treatment guidelines giving proper indication for newly synthesized βL/βLI (betalactam betalactamase inhibitor) drugs. Efforts for training regional labs are in process along with future goal to generate technical competency for molecular characterization of multidrug resistant pathogens (Veeraraghavan et al. 2018). At present, enough evidence is attained from the country which is not only restricted to trends and patterns but helps to monitor minimum inhibitory concentrations and resistance mechanisms from notable locations. The challenge in coming years would be expansion of this capacity and its usage for creating national treatment strategy in order to prevent misuse of antimicrobials as an effort for antimicrobial stewardship. Documentation of changing trends of antimicrobial resistance should be kept dedicatedly for future reference. Due to limited network sites in current national surveillance system, there are loopholes in retrieving hospital based literature. Improvement in surveillance networks by developing several sites at different locations will result in better data collection to highlight true picture of antimicrobial resistance in India (Walia et al. 2019; Wattal and Goel 2014).

2.7  Antimicrobial Resistance: A One Health Perspective One Health is the combined effort of several health science professionals to conquer optimal health for people, plants, wildlife, domestic animals, and environment (Fig. 2.1). The driving elements of antimicrobial resistance include antimicrobial use and misuse in animal, human, and environment and the wide spread of resistant bugs and factors conferring resistance within and between such segments and around the globe (McEwen and Collignon 2018). Research and surveillance are essential because they trace out antimicrobial resistance menace and how to deal with them (WHO 2014, 2015). There are several gaps in our understanding of the multifarious biology that characterizes the resistance associated with One Health dimensions, but many innovation programs have been utilized in recent years that sustain

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Fig. 2.1  Transmission of resistance between farm animals, the wider environment, and humans. Resistant bacteria arising in humans, animals or the environment may spread from one to another

Humans One Health Farm Animals

Environ ment

evidence based interventions to tackle rising antimicrobial resistance (Aarestrup et al. 2008; O’Neill 2016; Perry and Wright 2014). Resistance surveillance and consumption of antimicrobials in both human and non human area require investigation for estimating the patterns, extent, and magnitude of antimicrobial resistance at the regional, national, and international level (Collignon and Voss 2015; WHO 2017). These surveillance programs should be able to identify emerging trends in antimicrobial resistance of clinical significance to animals and humans (ECDC 2017). Such surveillance should notify educational efforts to reduce antimicrobial resistance, as well as use of antimicrobial policies and antimicrobial stewardship programs (Aarestrup et  al. 2008; O’Neill 2016; WHO 2015). Frequent surveillance is also required to estimate the success of interventions and other parameters that are taken to curb antimicrobial resistance (Torren-Edo et al. 2015). Resistance surveillance of One Health programs should consist sampling of target bacteria from specimens collected from different human, animal, and environmental settings including community settings, hospitals, farms, veterinary clinics, food, and the environment (Torren-Edo et al. 2015; WHO 2017). Surveillance of use of antimicrobial should also be performed in human, agricultural, and veterinary settings and should make available the estimates of antibiotic intake in humans and animals at the national point along with suitable denominators to facilitate nation wise comparisons (WHO 2017). To supply information that is valuable for assessing and guiding prescription practices and antimicrobial use behaviours, monitoring of antimicrobial use should also be performed at the level of prescribing (e.g. hospitals, veterinary clinic, community, and farm) (DANMAP 2015; Speksnijder et al. 2015; WHO 2017). Data of surveillance should be interpreted and analyzed in an organized manner across sectors, along with the reports available well on time. WHO has issued guidance on integrated and harmonized surveillance of antimicrobial resistance and use of antimicrobial to assist developing and developed countries in executing their individual surveillance programs enhancing worldwide surveillance activities, which are crucial for improved

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synchronization of international initiatives to contain use of antimicrobial agents (WHO 2017). Well designed research is required to trace out the cause of resistance development within and among the species of bacterial pathogens, enteric commensals and environmental bacteria and their ecological niches (O’Neill 2016). To curb the use of antimicrobials in animals, further research is required for suitable cost effective substitutes to antimicrobials for prevention of disease and improvement of growth and production efficiency (Aarestrup et al. 2008). Targeted research is necessary to sustain antimicrobial stewardship, like; better tools for diagnosis, different methods to develop prescribing of antimicrobial and consumption behaviours (O’Neill 2016).

2.8  Surveillance Systems in Animals 2.8.1  Livestock Antimicrobial resistance is a global threat for animal and human health. The rampant use of antibiotics in animal production is of particular concern, as this may lead to escalated resistance of animal and human pathogens (Schrijver et al. 2018; Van De Sande-Bruinsma 2008). The European Medicines Authority prepares annual reports on the sales of veterinary antimicrobial products for 29 EU/European Economic Area countries. A report of 2016 from 25 countries observed an increase in sales of more than 5% in six countries. The association between the use of antibiotic in animals and resistance development in commensal bacteria has been described in E. coli (Chantziaras et al. 2014). The transmission of resistant bacteria from food producing animals to humans is of even more concern. Another study showed many genetic resemblances between extended spectrum beta lactamase (ESBL) positive isolates of E. coli from humans and poultry (Leverstein-van et al. 2011). Besides, the fact that resistance may be transmitted horizontally among dissimilar bacteria by exchange of antimicrobial resistance genes in the form of plasmids creates an even greater threat for public health (Carattoli 2013). Due to high antibiotic usage in animals and intensive farming, transfer of antimicrobial resistance from livestock to humans is a major risk today. Therefore, global research and laboratory surveillance networks are crucial for early detection of increasing resistance patterns for continuous detection of antimicrobial resistance in bacteria that pose risks to humans and animals across the world. Various countries have set up national monitoring systems, most of them are targeting zoonotic and indicator bacteria (Directive 2003/99/EC; EU Decision 2013/652/EU). A private industry funded organization, the Centre Europeen d’Etudes pour la Sante Animale (CEESA), implements antimicrobial resistance surveillance programmes in the food producing animals of European Union (De Jong et al. 2013, 2014). However, antibiotics and bacteria of veterinary and human interest vary, and depending on the purpose, veterinary surveillance programmes cannot essentially include bacteria of

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human interest, although it might be valuable information. At present, it seems that the One Health concept, particularly involving antimicrobial resistance detection in humans and animals, is not well revealed in current human or veterinary surveillance systems (Table 2.2).

Table 2.2  Surveillance programmes in livestock of European Union or multiple countries Animals and derived meat Origin Bacteria Poultry EU (EU • Campylobacter (broilers, member jejuni and Campylobacter coli laying states) hens, • Salmonella spp. fattening • Escherichia coli turkeys), • Enterococcus Bovids, faecium and E. Pigs faecalis • ESBL or AmpC or carbapenemase producing E. coli and Salmonella spp. Pigs, EASSA • E. coli Broiler (industry) • Salmonella spp. chickens, • Enterococcus Beef • Campylobacter cattle, Slaughter Pigs, VetPath • E. coli Cattle (industry) • Staphylococcus aureus • Histophilus somni • Mannheimia haemolytica • Pasteurella multocida • P. multocida • Bordetella bronchiseptica • Haemophilus parasuis • Actinobacillus pleuropneumoniae • Streptococcus uberis • Streptococcus suis Adapted from Schrijver et al. 2018

Susceptibility Origin of samples test Readout Epidemiologic Slaughterhouse, Diffusion Farm, Retail tests, Dilution cut-off values tests

Intestinal samples Micro-­ dilution collected at slaughter

Nasopharyngeal/ nasal swabs or Lung samples; mastitis samples

Micro-­ dilution

CLSI & EUCAST/ EFSA Clinical breakpoints

CLSI breakpoints

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2.8.2  Dairy Animals Several countries have started resistance surveillance and monitoring programs focussing veterinary system. In 1997, the World Organization for Animal Health (OIE) planned standards pertaining to resistance surveillance (Diaz 2013). The OIE Terrestrial Animal Health Code chap. 6.7 describes the synchronization of surveillance and monitoring programs at national level; chap. 6.8 focuses on monitoring of the antimicrobial usage patterns in dairy animals; chap. 6.9 covers the well throughout use of antimicrobials in veterinary practice; and chap. 6.10 comprises the risk assessment for antimicrobial resistance emerging from the antimicrobial usage in animals. These standards were acknowledged in 2003 including WHO Global Strategy for the containment of antimicrobial resistance. Collaborative consultations between WHO/OIE/FAO specialists led to foundation of Codex Alimentarius Ad Hoc Inter governmental task force on antimicrobial resistance (CAC 2011). This task force suggested the “Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance” later on taken up by the Codex Alimentarius Commission in 2011 (CAC 2011). Surveillance program aims at improved recording of escalating antimicrobial resistance, enhancing the shelf life of antimicrobial drugs, and imparting guidance for the development and usage of novel antimicrobials. Organization of monitoring program needs consideration of different factors, like; selection of appropriate bacterial strains as target, sampling procedures, methods of isolation and susceptibility testing, data recording, analysis, and reporting. Different monitoring and surveillance programs and methodologies are followed between countries/agencies, which are further influenced by different agricultural practices, monitoring requirements, and availability of guidelines (Founou et al. 2016; Sharma et al. 2017) (Table 2.3).

2.9  Information Technology in Resistance Surveillance Antimicrobial resistance surveillance and usage of antibiotic is composite as resulted data from a single patient needs analysis of several microorganisms. Such microorganisms must be subjected to susceptibility testing for several antibiotics and associated with treatment regimens. When clinical laboratories capture the surveillance data; only Information Technology sector can immediately handle and club the information to perform thorough and timely analysis at local and national level. Computer systems can be deployed to investigate the upsurge of resistant genes and for detection of an outbreak inside a hospital or in a nation; to study antimicrobial resistance profiles of antibiotic resistance; to enter the data for web based reporting, which enables interpretations and timely analysis; and for providing automated reports with the aid of space time visualization. Specialists of surveillance understand the potential advantages of automation gained through hardware, software, and infrastructure, although approaches to establish an information

Italian Veterinary Antimicrobial Resistance Monitoring programme (ITAVARM) Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) Swedish Veterinary Antimicrobial Resistance Monitoring programme (SWEDRES & SVARM.) Japanese Veterinary Antimicrobial Resistance Monitoring programme (JVARM) Norwegian Surveillance System for Antimicrobial Drug Resistance (NORM-VET)

Italy

Norway

Japan

Sweden

Canada

Netherlands

Germany

Denmark

Regulatory body/ Surveillance Program National Antimicrobial Resistance Monitoring System (NARMS) Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP) German Resistance Monitoring in Veterinary Medicine (GERM-Vet) Monitoring of Antimicrobial Resistance and Antibiotic Usage in Animals in the Netherlands (MARAN)

Country United States

Table 2.3  Diverse antimicrobial resistance surveillance and monitoring programs

JSC until 2000, CLSI after 2000 MIC based automated system

http://www.maff.go.jp/nval/tyosa_kenkyu/taiseiki/ monitor/e_index.html https://www.vetinst.no

SRGA

http://www.sva.se/en/antibiotika/svarm-reports

http://www.phac-aspc.gc.ca/cipars-picra/ index-eng.php CLSI

CLSI http://www.wageningenur.nl/nl/ ExpertisesDienstverlening/Onderzoeksinstituten/ Central-Veterinary-Institute/Publicaties-CVI/ MARAN-­ Rapporten.htm http://195.45.99.82:800/pdf/itavarm.pdf CLSI

CLSI

CLSI

http://www.danmap.org

http://vetline.de/17079309/150/3130/69483

Testing protocol CLSI

Link www.cdc.gov/narms/index.html

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Adapted from Founou et al. 2016; Sharma et al. 2017

Regulatory body/ Surveillance Program Red de Vigilancia de Resistencias Antimicrobialas en Bacterias de Origen Veterinario (VIV) Finland The Finnish Veterinary Antimicrobial Resistance Monitoring and Consumption of Antimicrobial Agents report (FINRES-VET) Australia Pilot surveillance program for antimicrobial resistance in bacteria of animal origin Mexico Pilot Integrated Food Chain Surveillance System 28 European countries Monitoring and analysis of food-borne diseases in Europe (EFSA) Colombia Colombian Integrated Program for Antimicrobial Resistance Surveillance (COIPARS) Pan-European (Denmark, Belgium, The Centre Europeend’ Etudes pour la Netherlands, Ireland, The UK, France, Sante Animale (CEESA VetPath) Germany, Spain, Italy, Poland, The Czech Republic, Hungary)

Country Spain

CLSI

Testing protocol CLSI

CLSI

CLSI

https://www.ncbi.nlm.nih.gov/pubmed/25903494

http://www.ceesa.eu/

http://www.health.gov.au/internet/main/publishing. nsf/ CLSI Content/health-pubhlth-strateg-jetacar-pdfamrstrategy_ affa.htm https://www.ncbi.nlm.nih.gov/pubmed/18325258 CLSI https://www.ncbi.nlm.nih.gov/labs/ articles/22870938/ https://www.efsa.europa.eu/en/efsajournal/ pub/4380 CLSI

Link http://racve.es/publicaciones/ red-de-vigilanciaveterinaria-de-resistencias-aantimicrobianos/ https://www.evira.fi/globalassets/tietoaevirasta/ julkaisut/julkaisusarjat/elaimet/finres_ vet_2007_2009. pdf

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technology system are still not clear. Most important barriers for improving the quality are the costs associated with software, hardware, of employing staff and of maintaining systems. Insufficient infrastructure can also result in limited/sluggish internet access (Natale et al. 2017; Stelling 2016; Stelling and O’Brien 1997). Surveillance of antimicrobial resistance in Sweden was reinforced in 2012 by the initiation of Svebar – a national automated system for culture collection and acquisition of results of antibiotic susceptibility testing from Swedish clinical laboratories (Söderblom et  al. 2014; Swedres and Svarm 2013). Swedish Public Health Agency owns the system, produces immediate early alerts of critically significant antimicrobial resistance (multi drug resistance and extensive drug resistance) and owns a database of already characterized microorganisms with all antimicrobial susceptibility profiles from collaborating laboratories. Quick alert reports are automatically produced and instantly reverted to the laboratory. Such reports rely on quick warning signal (triggers). Classical examples of “triggers” are carbapenem resistant K. pneumoniae and colistin resistant E. coli in various clinical samples. In contrast, the database owned by the collaborating laboratories is utilized to investigate geographical and longitudinal trends in antimicrobial resistance. Svebar creates standardized national and local reports, generating tables of data of cumulative resistance for each antibiotic and species. To attain good quality data in an information technology system, validation of data is needed that synchronizes routine nomenclature and cooperation of all laboratories to correct the errors and inconsistency of data. Common antimicrobial susceptibility standards like; the European Committee on antimicrobial susceptibility testing, which authenticate reference genotypic and phenotypic methods for antimicrobial susceptibility that are also important when comparing data from various sources. Further validation of Swedish data is in progress through global antimicrobial surveillance and the European antimicrobial resistance Network (ECDC 2013a; WHO 2015). Although connecting with Svebar is voluntary, the system has registered most of the clinical microbiology laboratories of different countries. Public Health Agency of Sweden experts described that continuous talks between Public Health Agency of Sweden and the associated laboratories explains the huge participation. E.g., an annual conference is held at Public Health Agency of Sweden, encouragement of laboratory representatives is conducted to talk about all facets of the system, from early warnings, quality of the underlying data to the produced reports. Professional and public awareness programs about antimicrobial resistance has also encouraged political commitment and made it convenient for establishment of the system. In future, Svebar targets to add the data from sequencing of whole genome in the surveillance of antimicrobial resistance; and assimilate antibiotic intake and data of veterinary surveillance to facilitate further amend the policy. Japan Nosocomial Infections Surveillance (JANIS) is an automated and centralized programme for antimicrobial resistance surveillance to compile, analyze, and publish the data at regular intervals. Pooling of confidential summary reports from each participating hospital is conducted; resulted data is compared from all collaborating hospitals. Many countries are also approached by Japan in the region (India

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and Indonesia) by sharing the JANIS software to maintain national antimicrobial resistance surveillance (JANIS 2000; Morikane 2012; Tanihara and Suzuki 2016).

2.10  Genomics in Global Antimicrobial Resistance Surveillance Recent advancements in DNA sequencing have revolutionized microbial surveillance and diagnostic microbiology (Fig. 2.2). Prerequisite for this is availability of bioinformatics tools and easy access of online databases (Hendriksen et al. 2019a). Whole genome sequencing has brought rapid change in science of infectious disease and other specialities (Didelot et al. 2012; Fricke and Rasko 2014). Rapid gain of acceptance of these new and advanced technologies is transforming daily laboratory procedures resulting in immense amount of data on bacterial genome. Around 1,90,000 Salmonella genomes are already in the public domain with several being included on routine basis. Genomic DNA sequence represents the highest possible level of structural detail on characterising traits of an organism or population. It can also provide more reliable information on microbial identification, phylogenetic

Database of AMR Genes

Bioinformatics Tools

Raw Sequence Read

Fig. 2.2  In silico process of detection of antimicrobial resistance (AMR) determinant using an algorithm to query input DNA.  A complete database of AMR genes are prepared using bioinformatics tools. Raw data is gathered from the nucleotide sequence of each gene encoding a specific protein responsible for antimicrobial resistance. Graphical visualizations of genotypes are investigated with change in resistance patterns over time

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relationships, and act as catalogue of traits relevant for epidemiology based investigations. This has created huge impact on outbreak investigations along with diagnosis and treatment of infectious diseases. It has also impacted the practice of microbiology and epidemiology (Allard et al. 2019). Theoretically, any biological feature can be deduced from DNA sequences which might include ability to detect antimicrobial resistance as clinical application. It can further provide information regarding evolution and spread of resistant bacteria in hospital or among community. Antimicrobial resistance is a matter of global concern resulting in mortality in huge numbers (O’Neill 2016). Antimicrobial resistance has been considered as a measurement parameter to study growth inhibitory effects of a chemotherapeutic agent on cultured bacterial population. Despite some additional improvements, clinical laboratories consider diffusion and dilution methods as primary tool to guide clinical therapy and monitor antimicrobial resistance over time. Assembled data has shown accurate prediction of antimicrobial resistance using genomic sequence. Sequence based approach of antimicrobial resistance detection requires robust bioinformatics tools for visualization and analysis of microbial “resistome” comprising antimicrobial resistance genes and their precursors (Cartwright et al. 2013).

2.10.1  Whole Genome Sequencing in Surveillance Whole genome sequencing is a comprehensive method for analyzing entire genome of an organism. Whole genome sequencing is a powerful tool for genomics research, which makes it useful for sequencing any species, such as agriculturally important livestock, plants, or disease related microbes. Whole genome sequencing enriches our understanding of antimicrobial resistance spread and evolution and contributes practical information for local, national and global infection control and clinical guidance. The United States is expanding its dimensions for monitoring antimicrobial resistance through whole genome sequencing. It comprises of coordination among state public health laboratories and universities. Centre for Disease Control and Prevention (CDC) coordinate with antibiotic resistance laboratory network for rapid detection of emerging resistance threats. This comprehensive network performs whole genome sequencing in addition to other activities for numerous pathogens including all isolates of Mycobacterium tuberculosis. Whole genome sequencing is also routinely used for characterization of Neisseria gonorrhoeae and other pathogens involved in outbreaks. The National Antimicrobial Resistance Monitoring System (NARMS) focuses on bacterial transmission through food (Karp et al. 2017). NARMS began with systematic whole genome sequencing of Salmonella in 2013 and later on included Campylobacter in 2014. Resistance trends can be examined at genetic level by users using query filters provided by online tools. Graphical visualizations of genotypes with change in resistance patterns over time is provided by these tools.

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In US, the Department of Agriculture National Animal Health Laboratory Network (NAHLN) and the FDA Veterinary Laboratory Investigation and Response Network (Vet-LIRN) are initiating on gathering information on resistance and whole genome sequencing data of pathogens from different sources. The US Environmental Protection Agency conducts water surveys repeatedly for detection of resistance genes. Initial stages comprises expansion of national public health surveillance programs based on DNA sequencing information and licensing new associations from resistome analyses of the data (Hendriksen et al. 2019a).

2.10.2  Metagenomics Only a very few pathogens are targeted by current surveillance systems, primarily based on passive reporting of phenotypic results as in the DANMAP (Danish monitoring system), resulting in a narrow range of pathogen, which does not cover all relevant resistant genes. Majority of the antimicrobial resistance genes could be present in the flora of commensal bacteria of animals, healthy humans or the environment. Metagenomics techniques benefit the ability to quantify thousands of resistance conferring genes in a single sample using short read NGS (Next Generation Sequencing) data with no prior selection of target genes. Metagenomics can sequence all DNA of the sample including host DNA and food, which may lead to low sensitivity. qPCR (Quantitative Polymerase Chain Reaction) procedures and large scale capture PCR methodologies have already been developed, which provides higher sensitivity (Aarestrup et al. 1998; Lanza et al. 2018). It was recently reported that metagenomics has got more merits over conventional methods for surveillance of antimicrobial resistance in pig herds (Munk et al. 2017), which is quite beneficial for comparing antimicrobial resistance across livestock (Munk et al. 2018), as well as epidemiological data related investigations (Van Gompel et al. 2019). The usage for global surveillance of antimicrobial resistance gene dissemination by international flights (Nordahl Petersen et al. 2015) and investigating the urban sewage to determine the domestic and global resistome has also been proven (Hendriksen et al. 2019b; Pehrsson et al. 2016).

2.11  Present and Future of AMR Surveillance There is big international consensus that surveillance of the levels of antimicrobial resistance occurring in various systems emphasizes strategies to address the issue. The main reasons for resistance surveillance are to determine (i) the magnitude of the problem, (ii) whether resistance is escalating, (iii) whether a specific type of resistance is spreading (Barlam et  al. 2016) and whether previously reported unknown types of resistance are rising, (v) whether a specific type of resistance is associated with any outbreak (Simjee et al. 2018). The inference of acquiring and

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utilizing this information should be considered in the designing of a surveillance system. Surveillance is the keystone to address number of the global aspects of antimicrobial resistance. It provides a basis for assessing the resistance burden and for providing the essential evidence for developing well organized and effective control and prevention strategies. Co-development of programs for resistance surveillance in humans and animals is indispensable, but there remain several key elements that make it difficult to compare the data between human and animal antimicrobial resistance monitoring programs. Differences in antimicrobial resistance surveillance programs between countries and region also generate challenges for data comparison. In Australia, implementation of national resistance surveillance in animals has been developed by the World Health Organization Advisory Group on Integrated Surveillance of Antimicrobial Resistance, the World Organization for Animal Health, and the EFSA Working Group on Developing Harmonised Schemes for antimicrobial\ resistance monitoring in Zoonotic Agents (Shaban et  al. 2014). Nowadays, whole genome sequencing based antimicrobial resistance analysis completes in one step, what was not feasible with ordinary PCR strategies namely, identifying new alleles responsible for resistance to the same drug class. In one study of gentamicin resistant Campylobacter from retail meats and from human infections, PCR failed to identify the presence of gene for aminoglycoside resistance in many of the human isolates (Zhao et al. 2015). At present, resistance surveillance relies on straightforward in vitro antimicrobial susceptibility methods. However, the lack of synchronization across programs and the limited genetic information of antimicrobial resistance remain the foremost drawbacks of these phenotypic methods. The way forward of antimicrobial resistance surveillance is to adopt the genotypic detection, and molecular analysis methods for yielding a wealth of information. However, it is expected that these molecular techniques will outshine the phenotypic susceptibility testing in routine diagnosis. Monitoring of resistance remains a distant reality, and phenotypic testing remains necessary in the detection of new resistance mechanisms, emerging resistant bacteria, and trends of antimicrobial resistance.

2.12  Limitations of National Surveillance systems Limitations have been categorised as A. Structural problems B. Laboratory based surveillance problem C. Lack of coordination among animal and food surveillance systems There exists wide range of structural problems. The efforts at national surveillance level are heterogeneous and fragmented. Data collection systems working on antimicrobial resistance and health care related infections have different goals with little or absence of coordination and sharing of information with international

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networks. Insufficient standardization of definitions related to epidemiology, collected samples and data, included settings, testing methods (susceptibility testing), and policies related to sharing of data are obstacles in the path of informative and reliable surveillance done in a collaborative manner (Tacconelli et al. 2018). Laboratory based systems too have limitations. Foremost, reported microbiological results show no association between epidemiologically or clinically relevant data. As a result, laboratory based systems gives no information on identification of patient populations that are at risk, sources of infection, types of infection, treatment failure and real burden of disease related to health care acquired infections and antimicrobial resistance. Secondly, characterization and genetic typing is not commonly practised for relevant isolates and resistance mechanisms. This testing method can establish the cause of antimicrobial resistance that can be either due to spread of resistant strains or by transfer of determinants for resistance among different strains or species. Thirdly, biasness in sample collection might reduce external validity, create hindrance in measurement of factor based associated infections within institute or community and might prevent prediction of future trends. Differences in sampling methodology among institutions and countries and inclusion of screening isolates rather than clinical isolates undermine representation of data. Certain settings consider sample collection as best practice for more severe infections or those that do not respond to first line treatment. These cases might have inflated rate of antimicrobial resistance and usage of such data could lead to unsuitable choice of therapy, increased resistance along with costs of health care. Contrarily, occasional collection rather than routine collection of samples can lead to under reporting of antimicrobial resistance and health care associated infections. In addition to this, dependence on laboratory based surveillance can depreciate actual incidence of clinically significant health care associated infections. As samples are collected from subset of affected individuals so laboratory based surveillance of clinical samples as sole criteria is not much effective to provide strategic warning for emerging pathogens and resistance mechanisms. These must be initially colonized from urine or sputum samples.

2.13  Conclusion This era of escalating antimicrobial resistance presents urgent need for improvements in surveillance system to optimize empirical therapy, drive antimicrobial stewardship and infection control measures, and development of novel drugs and vaccines. Without such developments, it will be difficult to substantially reduce the economic and medical burdens imposed by antimicrobial resistance. New initiatives (including ESVAC, CAESAR, European Survey on Carbapenemase producing Enterobacteriaceae project, the Center for Disease Dynamics, Economics & Policy’s Resistance Map, Global Antimicrobial Resistance Surveillance System and EPI-­ Net) may improve the fragmentation, time lag, heterogeneity, and other inadequacies of existing surveillance strategies, but cannot achieve the obligatory advances

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on their own. Global coordination of political involvement and initiatives should be pursued without further delay (ECDC et al. 2015; EMA, 2016). However, the lack of coordination of various programs and the limitation of genetic information of antimicrobial resistance remain the major loopholes in Surveillance system. The future of resistance surveillance is moving toward molecular analysis, and genotypic detection methods are expected to give a wealth of information.

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Chapter 3

Antimicrobial Resistance, Food Systems and Climate Change Mashkoor Mohsin, Ahtesham Ahmad Shad, Jabir Ali, and Sajjad-ur-Rahman

Abstract  Antimicrobial resistance is one of the greatest threat to global health, food security, and development today. Every year, antimicrobial resistant infections kill 700,000 people across the globe. If dramatic actions are not taken soon, this number could skyrocket, reaching ten million deaths annually by 2050. The rapid emergence of antimicrobial-resistant infections is exacerbated by the irrantional use of antimicrobials in human and veterinary medicine and in the agriculture industry. Antimicrobial use is considered to be the single most important factor leading to resistance. Demand for meat is rising with the increase in world population, driving the expansion of large commercial livestock and poultry farming where antibiotics are frequently used in for treatment, prophylactic and growth promotion to ensure safe and sufficient food. A growing world population, temperature and the increasing food demand has put immense pressure on our food supply chains and food systems. Partly driven by public concerns over antimicrobial resistance, the food industry is slowly shifting away from meat raised with antibiotics. Recently, climatic factor like an increase in global temperature has also been attributed to be contributing towards emergence of antimicrobial resistance. Here we review, the issue of antimicrobial resistance in relation to the antibiotic use in food animals, climate change and food security. Firstly, we discussed concepts and consequences of antimicrobial resistance, food security and climate change. Later, we addressed the connections and interplay between antimicrobial resistant microorganisms and anthropogenic climate changes in relation to food safety and security. We found that mainstreaming antimicrobial resistance into broader universal health coverage, sustainable development, food system and environment agendas is key, both to scaling and to sustaining efforts to address antimicrobial resistance. A positive correlation between an increase in temperature with an increase in antimicrobial resistance has been found. These ongoing challenges suggest that the burden of antimicrobial resistance could be significantly underestimated in the face of a growing population and climate change. M. Mohsin (*) · A. A. Shad · J. Ali · Sajjad-ur-Rahman Institute of Microbiology, University of Agriculture, Faisalabad, Pakistan e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_3

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Keywords  Antimicrobial resistance · Climate change · Food system · Food safety · Food security

3.1  Introduction With the world population expected to reach 9 billion by 2050, the global demand for food is also expected to increase many folds (Guillou and Matheron 2014). The biodiversity on this planet has been persistently changing due to anthropogenic activities, more evident since the industrial revolution. To meet the growing demands for food, intensive livestock farming has been introduced where antimicrobials are used for three main purposes: therapeutic use to treat, a prophylactic use to prevent a disease outbreak, and growth promotion use (Van Boeckel et al. 2019). The introduction of antimicrobials into food animal production has contributed to improvement of animal health and productivity and ensured global food security. However, excessive use of antimicrobials in intensive livestock farming has resulted in the emergence of antimicrobial resistance (Van Boeckel et al. 2019). It is imperative to consider the impact of antibiotics and antibiotic resistance on safe food. Food safety is also linked to the sustainable development goals, in particular sustainable development goal 2 – Zero Hunger and sustainable development goals 3  – Health and well-being (WHO 2019). Animal based food products including meat, milk and eggs are often contaminated with bacteria, and thus likely to constitute the main route of transmitting resistant bacteria and resistance genes from food animals to people. Direct contact with animals or the farm environment could also involve in the spread of antimicrobial resistance. Fruits and vegetables contaminated by animal waste or contaminated water may also constitute a transmission route. Thus, antibiotic resistance is a food safety challenge (Founou et  al. 2016; Fanzo et al. 2018). Climate change, antimicrobial resistance, and global food security have become the defining issues in present times. These threats call for urgent actions to avert public health crisis. Climate change can drift several vector borne, water, and food borne illnesses, e.g., cholera and Zika virus outbreaks. In addition to these, Nipah and Hantavirus emergence has been closely related to extreme weather conditions. By the year 2030, the diseases due to climate change will result in over 250,000 deaths (Chan 2017). Antimicrobial resistance and climate change associated infectious diseases can lead to a global financial crisis (World Bank Group 2017). On the other hand, the threat to global food security worsens under the growing challenges of antimicrobial resistance and climate change (Fig.  3.1). The global demand of food has increased (FAO 2009a) over the time and due to population increase, especially in the low and middle income countries where the supply of basic food is often compromised (Popkin 2014). There is a need for coherence among

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Fig. 3.1  Antimicrobial resistance, climate change and food system: Antimicrobial resistance has a strong relationship with climatic factors such as temperature, heat, wind, rain etc. Conversely, microbial biomass has a strong tendency in changing the fate of climate components. The synergistic effect of antimicrobial resistance and climate change further make the food system compromised both qualitatively and quantitatively

government, private, and other sectors regarding addressing the antimicrobial resistance issue. This chapter aims to address the interlinking, harmony, and coherence of currently ongoing challenges on antimicrobial resistance and climate change to food security and food safety. Starting from brief review literature about the definitions and concepts regarding antimicrobial resistance, climate change, and food system to critique assessment of how the antimicrobial resistance and climate change could interplay with each other and play their role in maintaining a healthy ecosystem. Furthermore, we aim to provide policymakers a better way of making decisions in trading animal based food products at the national and international levels, addressing the global and public concerns over food borne antimicrobial resistance. Health care workers and other professionals can also play their role in the minimization of challenges associated with the threat to global food security and food safety by adapting and considering the suggested recommendations.

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3.2  Antimicrobial Resistance The first revolutionary breakthrough in the field of medicine dated back to discovery of penicillin by Sir Alexander Fleming in 1928, which marked the beginning of golden era of antibiotics. The antibiotics revolutionized the field of medicine by mitigating the burden of infectious diseases globally. Unfortunately, this era lasted for few decades as bacteria developed resistance to these antibiotics and due to lack of discovery of new antibiotics (Lewis 2020). Antimicrobial resistance is defined as the development of resistance against the antimicrobial drugs used to inhibit or kill the bacterial growth. Excessive and misuse of antimicrobials in humans as well as in animals has been shown to accelerate the selection and emergence of resistant microorganisms to multiple antimicrobials. Infections caused by multi drug resistant bacteria are increasingly common and represent a serious problem to public health all over the world (Holmes et al. 2016). These multidrug resistant bacteria are called superbugs and the World Health Organization has warned of reversing back to “post-antibiotic era”, which will result in frequent untreatable infections and small injuries or surgical procedures can be fatal (Nathan and Cars 2014; Holmes et al. 2016). The looming threat of antimicrobial resistance is no longer a new phenomenon. Sir Alexander Fleming also predicted the probability of antimicrobial resistance emergence at the time of receiving his Noble Price in 1945 (Day and Read 2016). The increased use of antibiotics means the stronger and longer exposure of antibiotics to the bacteria, which in turn, impose selective pressure on microbial communities to sustain their population (Fig. 3.2). The antibiotic consumption is one of the key factors which contribute towards development of resistance among bacteria due to selective pressure (Baym et al. 2016; Pouwels et al. 2018). The hidden problem of antibiotic selective pressure is that commensal bacterial species are unnecessarily being exposed to antibiotics which promote resistance even in non-pathogenic/commensal bacteria. Moreover, inter-species and intra-species exchange of genetic transmission further leads to serious and harder to treat infectious diseases (Naylor et al. 2018). Horizontal gene transfer mechanisms are regarded as one of the most common mechanisms of antibiotic genes transfer, in addition to the vertical gene transfer. Antibiotic selective pressure can select for chromosomal mutations conferring resistance to antibiotic, which can be transferred vertically to subsequent microbial generations. Alternatively, many genes responsible for drug resistance are found on plasmids or on transposons that can be transferred across inter-species and intra-species through horizontal gene transfer (Holmes et al. 2016; O’Neill 2016b). However, this is important to acknowledge that beside antimicrobial over use, other natural and environmental elements do exist; such as climate change is also silently driving a shift in the emergence of antibiotic resistance (Wellington et al. 2013). For instance, recent study showed increased rate of antibiotic resistance with the increasing local temperature in the USA. An increasing percentage of antibiotic resistance was found in three common pathogens Escherichia coli (4.2%), Klebsiella pneumoniae (2.2%) and Staphylococcus aureus (2.7%). Furthermore, this increase in

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Sunlight Heat Rain Wind

Carbon dioxide

Photosynthesis Respiration

Carbon dioxide

Carbon dioxide Microbial decomposition

Marine environment

Remineralization Photosynthesis

Deposition

Terrestrial environment

Carbon dioxide Interaction

Agriculture

Fig. 3.2  Marine and terrestrial environment: Carbon sequestration and liberation is the major driver of climate change; those are in strong relationship with other environmental and anthropogenic activities like agriculture, industry, deforestation etc. Antibiotics extensive use in human, animal and agricultural land cause selective pressure on human, soil and environmental microbial niche. Climatic factors such as, sunlight, heat, rain, and wind play a significant role in the balance of all mineralization and biogeochemical cycles on earth. Any outbalance of natural cycles due to human activities or by climate change has significant effects on the emergence and transmission of antimicrobial resistance, which in turn threatens the healthy food system

rate of antibiotic resistance was found to be consistent with most of the antibiotics classes including fluoroquinolones, beta-lactams, sulpha, tetracyclines, aminoglycosides, hydantoins and carbapenems. Therefore climate change should also be considered as a factor contributing to increasing antimicrobial resistance (MacFadden et  al. 2018). Another recent study identified a novel relationship between antimicrobial resistance and climate change and revealed two aspects: that the antimicrobial resistance prediction in different societies and the healthcare system significantly attributes to climatic factors. In addition, climate change could be

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responsible for increase in the antimicrobial resistance, particularly carbapenem resistance (Kaba et al. 2020). This phenomenon can be considered true for other global regions. The mechanisms underlying the rise in temperature and antibiotic resistance raised questions regarding the behaviour of transposons and mobile genetic elements bearing antibiotic resistance genes. Climate change due to global warming triggered by anthropogenic activities could be the reason for increased resistance among pathogens (MacFadden et al. 2018; Cavicchioli et al. 2019). The natural and global environmental factors have driven the emergence of several new mobile genetic elements and plasmids known to transfer novel antibiotic resistance such as a novel metallo-β-lactamase designated New Delhi metallo-β-lactamase (NDM-1) (Walsh et al. 2011), OXA-48-like carbapenemases (Poirel et al. 2012) and plasmid mediated colistin resistance mcr-1 (Liu et al. 2016). It is estimated that antimicrobial resistance could kill ten million people every year after 2050, if urgent actions are not taken (O’Neill 2016b). Moreover, antimicrobial resistance has strong potential to force 24 million people into extreme poverty (IACG 2019). It has been shown that about 70,000 people die due to drug resistant infections each year; however, the value far exceeds and reached 5.7 million deaths annually due to untreatable infections in low middle income countries (Daulaire et al. 2015; O’Neill 2016b) (Table 3.1). According to the reports of the World Health Organization, antimicrobial resistance has reached an alarming level around the globe. The Global Antimicrobial Resistance Surveillance System (GLASS) analyzed data from 22 countries and 5,00,000 isolates, reporting Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Streptococcus Table 3.1  Antimicrobial resistance related deaths and predicted consequences Antimicrobial resistance Current scenario 700,000 annual antibiotic resistant infections deaths globally 5.7 million annual antibiotic treatable deaths occur, and majority of deaths occur in low and middle income countries due to lack of access to antibiotics 23,000 deaths due to multi drug resistant tuberculosis and half a million new cases each year 2.8 million antibiotic resistance infections and 35,000 die each year in US Future 10 million deaths estimated by 2050 due to estimations antimicrobial resistance 24 million people forecasted into extreme poverty by 2030 due to antimicrobial resistance 2.4 million people could die in high income countries between 2015–2050 2 million deaths projected in India

References O’Neill (2016b) Daulaire et al. (2015)

WHO (2018)

Redfield (2019) World Bank Group (2017) Inter Agency Coordination Group IACG (2019) Organization for Economic cooperation and Development OECD (2018) Dixit et al. (2019)

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pneumoniae, and Salmonella species as the most common pathogens (Tornimbene et al. 2018). Another report from the Organization for Economic Cooperation and Development (OECD) described antimicrobial resistance as “the biggest threat to modern medicine” and estimated that infections caused by drug resistant bacteria have potential to kill 2.4 million people in Europe, North America, and Australia by 2050 (OECD 2018) (Table 3.1). In United States, estimated 2.8 million antibiotic resistant infections occur each year which results in 35,000 deaths (CDC, 2019). The deaths of ten million lives were forecasted in Sir Jim O’Neill’s review report, further highlighting the urgent need of a global surveillance system adopting specific measures to reduce or tackle the threat (O’Neill 2016a). The menace of antimicrobial resistance can be estimated by the fact that the common infections have now become hard to treat (Argudín et al. 2017). Chokshi and co-workers identified various types of contributing factors including socio economic for antimicrobial resistance between high income and low middle income countries. The key contributors in developing countries were lack of surveillance of resistance development, poor quality of available antibiotics, clinical misuse, and ease of availability of antibiotics. While in developed nations, poor hospital level regulation and excessive antibiotic use in food producing animals are the mainstream contributing factors of antibiotic resistance (Chokshi et al. 2019). It has been estimated that 73% of globally sold antimicrobials are used in food producing animals further supporting the claim that the rise in antibiotic resistant infections in animals and as well in humans is due to prophylactic and growth promotion usage of antibiotics (Van Boeckel et al. 2019). A study further highlighted the issue of antimicrobial resistance calling the multifactorial threats of antimicrobial resistance aiding in the development of complex issues of costs and implications to combat antibiotic resistance (Dadgostar 2019).

3.3  Climate Change Climate change is a phenomenon of a long term shift in global or regional climate patterns and its effects are felt both globally and locally. Effective health adaptation strategies are largely dependent upon the current health status of population. Indeed, this is often one of the most significant factors in determining the health impacts of climate change (Chan 2017; Hodson 2017). Potential catastrophic climate change has been witnessed due to rising greenhouse gases, deforestation and intensive food animal production for meat consumption (Ripple et  al. 2017). The major focus resides on the policy shifting from economic growth to conservational economy, making anthropogenic activities to work more holistically to control the climate change (Ripple et al. 2018). However, there is still a lack of much knowledge about the connections between microorganisms and anthropogenic climate change.

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3.4  Food System Food is the necessity for a living organism as it is the most common source for providing energy to human and animals. Different food components, types, and ingredients help in performing natural metabolic reactions. Thus, the food system is a system integrating several natural and environmental systems, from a simple food chain supply to the multiple interconnected networks (Hospes and Brons 2016; Béné et al. 2019). The food system has interconnection with other currently ongoing challenges that may directly or indirectly affect sustainable agriculture development and food security. Figure 3.3 gives a broader picture of the food system and its associations with environment, health, economy, politics, and society. The food system is in itself responsible for wastage, decomposition, emission of greenhouse gases, and depletion of natural resources, and biodiversity loss. Food systems have the potential to nurture human health and support environmental sustainability. Providing a growing global population with healthy diets including

Fig. 3.3  The food system has strong associations with the other dimensions like environment, society, health (Singer et al. 2016), economy and politics (Collignon et al. 2015). All these dimensions have an influential role in the development and progression of antimicrobial resistance, thus compromising the food system

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antibiotic free food from sustainable food systems is an immediate challenge (Frank et  al. 2019; Willett et  al. 2019). The road to the destination of the sustainability approach in the food system is better aimed at United Nations “Sustainable Development Goals”. Sustainable Development Goals major hallmarks are developing peace, prosperity in the world from individuals to nation level for living a better and healthful life (United Nations 2019). The food system has been compromised over the years due to the threats of antimicrobial resistant pathogens in the food chain. In particular, intensive livestock farming systems has relied on antibiotic use (Antonelli et al. 2019). Antibiotics were used substantially to treat, cure and prevent animals and human infections. However, the antibiotic use for growth has worsened the scenario as antimicrobials are used in sub therapeutic level (Kirchhelle 2018). Food safety, food security, and nutritional health are three key features of healthy food system (Table 3.2). All these three features work in coordinated fashion theoretically, but the practical influence of each component is distinct. These found to be less compatible when observed through a wider vision of policymakers and traders (Walls et al. 2019). Food safety encompasses food production, handling, preparation, and storage; also address hazards such as food borne illnesses and diseases. Nutritional health deals with the quality and quantity of food and their ingredients. While food security is an extensive and wider circle, containing both food safety and nutritional health. Food security ensures every principal, dealing with the maintenance of quality and quantity of food system in a more comprehensive way undertaking every element of the food web (Walls et  al. 2016; HLPE 2017). Food availability, food access, and food utilization are three basic pillars of food security stated by the WHO, while the stability (Table 3.2) as the fourth pillar was added in 2009 by the World Summit on Food Security (FAO 2009b).

3.4.1  The 1st Pillar The first pillar is food availability, which refers to the facility of food to be available, including from its production, distribution, and exchange. The production of food depends on several physical and environmental factors, such as land, soil, harvesting, management, selection of crops etc. Climatic factors can increase or decrease the production rate of crops (Gregory et al. 2005). The rainfall and land water are Table 3.2  Food system and four basic pillars of food security Food System Key aspects Walls et al. (2019) 1. Food security 2. Food safety 3. Healthy nutrition

Food Security Four Pillars Schmidhuber and Tubiello (2007) 1. Availability 2. Stability 3. Access 4. Utilization

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the two major sources of crop irrigation. The instability in weather and climatic condition can easily disturb the needy requirements and hence results in less production. Moreover, non reliable agricultural practices, soil erosion, and urbanization also limit food availability (Godfray et al. 2010). Food consumers are increasing day by day due to the increasing amount of population. The climate factors, temperature, and antimicrobial resistance can significantly damage the exchange or trade at the national and international levels (FAO 2017). The policymakers presently need to think about an alternate arrangement of political, financial, and exceptionally safe trade system to handle antimicrobial resistance in the food chain (George 2019).

3.4.2  The 2nd Pillar The second pillar is food access; it means food that can be allocated to a given place and under the reach of common people. This pillar has been found in strong association with the hunger and malnutrition as the food access inability in a population is due to poverty, commonly, not with the shortage or scarcity of food (FAO 2017). Food access has been categorized into two types. The first one is direct access; the human ability to produce food itself using different approaches, agriculture, farming etc. and the second one is economic access, in which one is dependent on purchasing from the first place or primary source of production. Both types of accesses are differing in their nature and depend on the demographic and regional trends of an area (Egal 2019).

3.4.3  The 3rd Pillar Food utilization is the third pillar of food security associated with food metabolism. The food insecurity risks food utilization. The entire process of food production to food consumption and the underlying steps of purification, cooking, and storage decides the food utilization pathway. The food utilization is also dependent on the cultural preference, choice of food, emotional, and psychological well being as the metabolism disturbance creates a lack of proper food utilization. Moreover, the presence of microbes, especially parasites, reduces food utilization. To cope with this, proper education and sanitation can limit the spread of diseases (FAO 2017).

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3.4.4  The 4th Pillar Food stability is the fourth pillar that talks about the sustainment of food by time. While unbalancing in food production, consumption, exchange, trade, and outbreaks cause disasters in communities. Food borne illnesses are common among them. Antimicrobial resistance and climate change have been associated with the instability of food at transitory (during certain periods), seasonal (a regular pattern of weather change) and chronic level (long term) as because of natural disasters (FAO 2017; Egal 2019).

3.5  Interplay of Microbes and Climate Change Microorganisms are present ubiquitously in the natural environment. The microbial species have a strong potential in changing the climate and ironically the changing climate also put selective pressure on the microbial communities. It is important to understand that the interplay of microbes and climate change not only affect each other but has an impact on our ecosystems (Hutchins et al. 2019). Climate change has a huge impact on the global niche of food producing microorganisms (yeast, bacteria, and molds) as well. Some microorganisms run the fermentation process, which results in the production of alcohols, organic acids, and esters (Terefe 2016). These chemical compounds play a major role in the preservation and generation of new food products against the arbitrary changes in environmental factors (precipitation, pH, temperature). However, disturbance in optimum environmental conditions significantly affects the production and preservation process. For instance, CO2 is produced in the fermentation of sugars by Saccharomyces cerevisiae, which gives the porous shape to bread and bakery products. Saccharomyces cerevisiae also contributes to flavor of beer, wine, vinegar, pickles etc. Bacterial species (Lactobacillus, Lactococcus, Bifidobacterium) produce fermented milk products (Húngaro et  al. 2014). The various types of molds such as Penicillium roqueforti and Penicillium camemberti produce cheese at an optimum temperature of 15 °C, while at 25 °C these molds produce mycotoxins which are severely damaging as well as resistant to both heat and chemicals (Húngaro et al. 2014). The role of microorganisms has been overlooked in the challenging issue of climate change. Although microorganisms are crucial in regulating climate change, they are often not the focus of climate change studies and are not considered in policy development. Due to immense microbial diversity and response to environmental change it is difficult to determine microbial role in the ecosystem. Their immense diversity and diverse responses to environmental change make determining their role in the ecosystem puzzling (Cavicchioli et  al. 2019). The microbial communities and climate change together act on the marine biome, terrestrial biome, and agriculture biome.

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The marine biome is one of the largest existing biome on Earth’s surface, comprising nearly 70%. The “Census of Marine Life” estimated that 90% of microbial mass is occupied in marine biomass. Both on the surface and deeper down in ocean, phototrophic microorganisms rely on energy. The first one absorbs sunlight while the other one’s got energy from organic and inorganic matters buried (Jørgensen and Boetius 2007). In addition to these, the half of CO2 fixation and half of Oxygen (O2) production globally are performed by the marine phytoplankton. This highlights the importance of microbial communities in absorbing and recycling of important elements in the atmosphere like Carbon (C), Nitrogen (N), and maintaining the climate. Conversely, if any significant change does happen in climate either due to human activities or by the natural climate cycle, it will affect the marine microbiome and hence the climate. On the other hand, terrestrial biomass is more than 100 folds than marine biomass. Among them, plants hold a huge proportion and perform half of the primary net production globally (Bar-On et al. 2018). Interestingly, 1.2 × 1030 bacterial and archaeal cells exist on earth (Flemming and Wuertz 2019), highlighting their vital role in sustaining the balance of life. The microbes of the terrestrial environment also regulate the carbon stored in soil and rocks. Plants also absorb CO2 during photosynthesis. Alternatively, during autotrophic respiration by plants and heterotrophic respiration, CO2 is reversed back into the atmosphere. Other climate factors directly influence the balance like warming and aridity rate, while indirect effects like change in the proportion of soil microbiota and CO2 rise, again create a jeopardized climate by enhancing the global warming (Singh et al. 2010; Ballantyne et al. 2017). Climate change effect on the land will often lead to a loss in natural habitats along with the reduction in biodiversity (Lanz et al. 2018), primarily of the microorganisms (Dai et  al. 2018). The disturbed microbial balance on land ultimately slows down the efficiency of the necessary natural processes that are originally supposed to sustain the healthy environmental system and leads to climate change.

3.6  A  ntimicrobial Resistance and Climate Change: In Relationship with Food System We are facing not only climate change, but also declining biodiversity, shortages of fertile land, wetlands and pollution. Biodiversity of macroscopic organisms is rapidly declining because of anthropogenic activities, suggesting that the biodiversity of host specific microorganisms of animal and plant species will also decrease. Contrary, there is a lack of knowledge about the connections between microorganisms and anthropogenic climate change. Although microorganisms are crucial in regulating climate change, they are rarely the focus of climate change studies (Cavicchioli et al. 2019). We are dramatically affecting our global food production system, the quality of the air and of the water and our exposure to infectious diseases caused by drug resistant pathogens. Changes to natural life support systems are already impacting our health and are projected to drive much of the global burden of infectious disease (Robinson et al.

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2018). The systems, factors, components, and drivers are all influencing each other in a complex and distinct way either directly or indirectly. The healthy food system development also requires the balanced interaction of environment, health, politics, economy and society. It is not vague to say that antimicrobial resistance, climate change, and food system are all parts of the one ecosystem and hence, possess a strong relationship with each other. For example, nearly 400,000 people die because of forborne diseases each year, because of the climate change impact on food production, processing, and consumption (Intergovernmental Panel on Climate Change 2014). Antimicrobial resistance is growing concern for policymakers, as no novel antibiotic class has come to market since 1987 (Nelson et  al. 2019). Food and Agriculture Organization estimated 820 million people are being affected by food insufficiency due to effects of climate change (FAO et al. 2019). This poses a significant threat to global food security. We approached more holistically chapter, to give view by building a strong, coherent relationship between antimicrobial resistance, climate change and the food system. This will ultimately lead to opening of new window and filling the gap among policymakers for making a better decision. The food system is an essential and integral part of all living biomass either terrestrial or marine. The plants, animals and microbes all are living entities and have taken part in the production, consumption, and emission of energy. They take and emit various forms of energies at different stages of their cycle. The interconnecting dimensions of food systems like economics, politics, environment, health, and society all play an indigenous role in the better nourishment of the food safety, food security and nutritional health (HLPE 2017). The environment is a vital force that controls the basic nutrients efficiency and integrity of a balanced healthy food system. Any change in climate or even small seasonal changes and extreme weather events leads to loss of crop productivity at the production level and outbreak of certain forborne illnesses at the consumption level. For instance, climate changes like flooding and high temperature has been known to increase prevalence of food borne diseases, Campylobacteriosis and Salmonellosis (Lake 2017). Hence, the interplay of microorganisms and climate change also vice versa, shows that there are enormous numbers of mechanisms operating simultaneously and it is just the beginning of exploring the microbial interaction with climate change. Furthermore, the shift and drift changes by climatic components, like wind, temperature, wetness, droughts, floods, etc., leads to soil erosion and environmental pollution. Because of such adversity, the natural niche of microbial communities also harmed, perturbed. This dilemma becomes worse when the interacted approach gets initiated by microbes, triggering their resistant response among animals, human and the environment. Then, the balance of food safety level drops in terms of their efficient quality of ingredients due to high microbial growth (Hammond et al. 2015), at elevated temperature in most food items. The whole food system becomes compromised in terms of quality and quantity. The collective set of harms then damages each other and raises severe problems to other chains of systems as well e.g. education, poverty. Besides these, certain types of analogies and parallel concepts contribute in antimicrobial resistance and food system. The rising one is the concept of

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“Resilience”. Resilience, however, is a borrowing term since 1973 from the socioecological system, which means the tendency or capacity of a system to revert to its original state after being disturbed. Likewise, the concept of resilience in the food system and food chain is called as Food system resilience (Tendall et al. 2015). On the other hand, certain known concepts related to antibiotic resistance already exist and “antibiotic resilience” is the just beginning of a new concept (Carvalho et al. 2019). The cooling (La Nina) and warming (El Nino) events in the Pacific Ocean create warmer and colder effects alternatively; those have a far-reaching impact on the natural balance of global climate and weather all over the world. Recently observed in China, both cycles have a remarkable effect on the production of crop yield (Li et al. 2019). The short term seasonal changes affect locally at the temporal level, while a long term silent change creates an everlasting threat globally. Unfortunately, the addition of anthropogenic activities, carbon dioxide emission and methane emission from grazing animals accounts more (Van Zanten et al. 2019) and use of antibiotics in the human and veterinary sector (Nelson et al. 2019) gives acceleration to climate change.

3.6.1  A  ntimicrobial Resistance and Climate Change: Impact on Sustainable Agriculture and Food Production Antibiotics consumption rate has estimated to increase over time ranging from 63,000 tonnes (Van Boeckel et al. 2015) to 240,000 tonnes (Grace 2015), but it varies significantly between countries due to poor surveillance. Although, it seemed to be obvious that the consumption rate in humans, agriculture and animals is increasing over time. According to estimation, antibiotics consumption in agriculture will increase from 2010–2030 by 67 percent (Van Boeckel et al. 2015). The use of antibiotics in livestock also has increased, more than 70% in the US and over 50% of antibiotics are found to be consumed in the livestock sector (O’Neill 2016b). Among the different uses of antibiotics, the use of antibiotics to promote the growth of animals is most controversial. No doubt, it is profitable and has economic value but on a large scale, it has an everlasting damaging effect on population health. Moreover, it becomes a commonly observed phenomenon, that the long term use of antibiotics makes the bacterial population to resist the antibiotics and evidence suggested that even a low use or sub-therapeutic level of usage make bacteria resistant (Allen 2014). There has been a high prevalence of antibiotic resistance genes in agricultural land with the use of antibiotics as compare to non antibiotic agricultural land (Zhu et al. 2013). This shows clearly, the threat to human health as the many countries are now revising their antibiotics usage and consumption in agriculture, livestock and at clinical sides to an optimum level, to limit and control the antimicrobial resistance issue. The 40% of the terrestrial environment has been occupied by the agriculture sector, and in the future, this proportion has been estimated to increase further, which will dramatically impact carbon, nitrogen and phosphorous fixation. The changes in these cycles can lead to loss of biodiversity (Lehman et al.

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2017), specifically of the microorganism (Dai et al. 2018). The usage of plant and animal associated microbes is now being appreciated and taking attention, as a better approach to sustaining agriculture and thus food production in the era of climate change and antimicrobial resistance. The methanogenic bacteria produce methane (a greenhouse gas) in natural and artificial anaerobic environments while methane (CH4) is also produced and associated with fossil fuels. The balance of CH4 has been regulated in the environment naturally, but the microbial communities present in the land, soil, water, etc. oxidize the CH4. Unfortunately, the CH4 amount has risen since the 2014–2017, but the actual cause of it is still unclear (Nisbet et al. 2019). This indicates increasing global warming, thus aiding climate change. On the other hand, rice has a great amount of consumption, as half of the global population consumes rice, contributing 20% emission of CH4 from rice paddies, but the estimation suggests increase in the percentage amount by the end of this century further escalating the threat of climate change (Van Groenigen et al. 2013). The meat from pigs and other non ruminant animals (poultry, fish) also produces more than 3–10 times CH4 as compared with the food producing plants (Ripple et al. 2014). However, the agriculture un-sustainability profoundly prompts to the heavy use of fertilizers, changing the biogeochemical cycles of carbon, nitrogen, and other essential elements, while at the same time burning of fossil fuels in a natural environment is also disturbing the food production (Steffen et al. 2015; Greaver et al. 2016). The entire chain of food production comes under such threat of substantial changes. The rhizobacteria present in the plant roots nodules helps in the fixation of nitrous oxide (N2O), a greenhouse gas to non-greenhouse gas, Nitrogen (N2). But the disruption in geochemical cycles through anthropogenic activities ultimately disturbs the nitrate reductase activity of soil microbiota and thus increases the amount of N2O in atmosphere aiding the increase in the effect of global warming (Itakura et al. 2013; Greaver et al. 2016). Climate change has its major impact on agriculture, as the fate of the production rate from crops and all surrounding environmental factors solely rely on climate. The weather and climate have vulnerable effects on agriculture (Howden et  al. 2007) as the required optimal growth conditions such as rainfall, precipitation, sunlight, etc. affect development and production. The high temperature results in the reduction of filling grains, leading to yield reduction by grains sterility (Hatfield et al. 2011). The substantial rise in temperature has been reported in the last few decades in Asia and the Pacific world. Moreover, Asia and the Pacific region’s agricultural production accounts for 37% of world emissions, explicitly build an association between emission rate and climate change variation patterns in these regions (Preston and Bathols 2006). While the highest figures of antimicrobial resistance deaths (4,730,000) estimated by the year 2050 also fall in the Asian. region (O’Neill 2016b). This promptly suggests a relation between the antimicrobial resistance and climate change due to increasing pattern of temperature in the Asia and Pacific regions.

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3.6.2  Infectious Diseases: Impact on Food Security The biodiversity of microorganisms has a significant relationship with the environment; the hazards of climate change affect the microbiota of terrestrial and marine biomes (Harvell et al. 2002). Disease outbreaks and spread can only be controlled by having comprehensive knowledge about the ecology of microbes with the host, respective vectors and determining the role of the environment in its progression (Altizer et al. 2013; Johnson et al. 2015). Different levels exist between the disease emergence to progression, at which environmental factors decide the fate of intensity or adversity of disease. The rise in disease burden has been associated with the increase of temperature, such as in the case of coral disease (Randall and Van Woesik 2015). The agricultural growth and development depend on the climatic conditions as well as other biotic and abiotic components, including the symbiosis with microorganisms, sequestration, and liberation of several elements up to the fixation of gases i.e. CO2. The species specific plant pathogen has a specific relationship and impact on the nourishment and propagation of crops in the natural environment. There exist a wide range of microorganisms that have potential to develop mild to deadly diseases in plants and cause severe harm to crops yield leading to the economic loses (Bebber et al. 2013). Global food security and food production thus come under the threat of the antimicrobial resistance, alongside climate change. Both antimicrobial resistance and climate change adversely affect sustainable agriculture practices. The vast plant specific species of microbes and the diseases caused by them makes the whole food web compromised in terms of their quality and quantity as well. Certain pathogens have a distinct response with the climatic conditions, like food borne, water borne and vector borne. The agriculture and human actions are directly influencing the dispersal of microbes as in the case of agro-adopted pathogens (Croll and McDonald 2017), in accounting within the space and time of climate change across the different regions and boundaries i.e. trade. These agro-adopted pathogens are more resistant, more destructive in a sense of possessing resistance to their remedy and treatment options. Table 3.3 enlists the pathogens and infectious diseases having a relative association with the environment or climate change. There is scarcity of knowledge on the transmission of antimicrobial resistance within agricultural settings and to humans via foods chain, as well as public health Table 3.3 Pathogens and diseases in relationship with climate change

Vector borne Waterborne Airborne Foodborne

West Nile Virus, Malaria, Dengue, Lyme disease Non-cholera Vibrio spp., Cryptosporidium spp., Rota virus Influenza virus, Hanta virus, Coccidioidomycosis Salmonella spp., Campylobacter spp.

Source: Adapted from Cavicchioli et al. (2019)

3  Antimicrobial Resistance, Food Systems and Climate Change Fig. 3.4 Major determinants of antimicrobial resistance selection and transmission: The usage of antimicrobials agents in human, animals, food and agriculture have direct or indirect effects on the prevalence of antibiotic resistance genes (ARGs) and the development of antimicrobial resistant bacteria which may enter the food chain

Antimicrobial Resistant Bacteria

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Antimicrobial Agents

Antibiotic Resistant Genes

risks posed by the agricultural release of antimicrobial agents, antimicrobial resistance genes, and antimicrobial resistant bacteria into the environment (Thanner et  al. 2016). Surveillance of these three components is imperative to control the infectious diseases burden and combating antimicrobial resistance (Fig. 3.4).

3.7  Future Directions, Challenges and Recommendation Therefore, in order of certain challenges running across the food system, climate change, and antimicrobial resistance, there is an urgent need for setting policies and goals. No doubts, certain regional and global organizations are already working to tackle the respective problems. Giving a sight into it will clear the blurry scenario of their aims and goals. To ensure global food security, the food system has prioritized different tasks, like achieving the sustainable development goal (goal 2) by the year 2030. The sustainable development goal 2 focuses on their commitment to ending hunger, achieving food security, improving nutrition and promoting sustainable agriculture by the year 2030 (Byerlee and Fanzo 2019). Interesting to know here, that there are some missing link in sustainable development goals that has been highlighted with possible recommendations (Veldhuizen et al. 2020), for example the differences in producers and consumers food chain, cooperation among national and global level, research communication gap and policies building differences. Antimicrobial stewardship programs and WHO/FAO work such as “Action Plan on Antimicrobial Resistance 2016–2020” plays an offensive role towards antimicrobial resistance. According to a recent report, antimicrobial resistance could lead 24 million people to extreme poverty, by the year 2030 (IACG 2019), while on the other hand sustainable development goal 2 aimed to end hunger by the year 2030. Poverty

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cannot end hunger, so to limit antimicrobial resistance and avoiding poverty along with a healthy food system at the same time; we need special adaptive measures and coordinated approaches. Moreover, the implementation of cohesive and integrated approaches to mitigate the issues relating to health, food, and environment are much needed. We proposed some more distilled and holistic challenges, listed below and possible recommendation at the end to overcome these: • The prediction and forecasting cannot be put exactly in timeframe due to certain limitations, such as modeling is not a true representative. • The collaboration is theoretical, not practically wider enough among various disciplines. • “One fits all size,” approach cannot possibly apply to all disciplines and systems. Firstly, the disturbance and problems modeling is not true enough in order of their reliability of assessment. Moreover, their impact is measured only by one dimension. Therefore, a better way of assessment by taking part in other multiple sectors is much needed. Secondly, there is a huge gap between different fields that are silently affecting and playing a significant impact on the sustainability of another system. The more comprehensive, integrated coordination is needed among all relevant fields. That can only be done by synchronization and sharing information, data, and knowledge. Lastly, it is true that one policy for all is not reliable in terms of implementation and cannot cover all aspects. However, it is possible that “all different sizes fit together to make a scrabble”. This will open a new room for making better effective decisions rather than taking individual measurements and policies of systems. The cross sight into systems will give space for establishing a better approach to fix antimicrobial resistance, climate change, and food system. Further analysis of antimicrobial resistance and climatic developments is imperative to determine whether potential climate change effects on antimicrobial resistance become greater in future.

3.8  Conclusion The food supply and system are vulnerable to the issue of antimicrobial resistance and climate change may exacerbate the antimicrobial resistance which ultimately is a threat to food quality and food safety. Both antimicrobial resistance and climate change should be closely monitored. The interplay of microbial communities with climate, especially in marine, terrestrial, and agriculture ecosystems, has a major role in sustaining the ecological balance. However, anthropogenic activities like agriculture, industry and intensive food production systems have significantly increased the emission of greenhouse gasses, while at the same time, antibiotics overuse in the human and veterinary sector has added as supplement feature. Under the dual challenge of antimicrobial resistance and climate change, the food system has become compromised either qualitatively or quantitatively, and the global food

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security threat has risen than before. Development of innovative animal production and waste management strategies to reduce antimicrobial resistance in the food production system, while maintaining productivity and profitability, animal welfare, food safety and security and environmental quality is the way forward to make a healthy food system on our planet.

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Chapter 4

In Silico Approaches for Prioritizing Drug Targets in Pathogens Mariana Santana, Stephane Fraga de Oliveira Tosta, Arun Kumar Jaiswal, Letícia de Castro Oliveira, Siomar C. Soares, Anderson Miyoshi, Luiz Carlos Junior Alcantara, Vasco Azevedo, and Sandeep Tiwari

Abstract  Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure; however, the indiscriminate use of antimicrobials is accelerating this progression. The development of resistance happens when microorganisms evolve mechanism to evade damage caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals, antimalarials, and anthelmintics, which involves genetic changes. Infections with resistant pathogens also prompt a higher health care cost (estimated budget of $20  billion annually in the United

Mariana Santana and Stephane Fraga de Oliveira Tosta contributed equally with all other contributors. M. Santana · S. F. de Oliveira Tosta · A. Miyoshi · V. Azevedo (*) · S. Tiwari (*) Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil A. K. Jaiswal Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil Department of Immunology, Microbiology and Parasitology, Institute of Biological Science and Natural Sciences, Federal University of Triângulo Mineiro (UFTM), Uberaba, MG, Brazil L. de Castro Oliveira · S. C. Soares Department of Immunology, Microbiology and Parasitology, Institute of Biological Science and Natural Sciences, Federal University of Triângulo Mineiro (UFTM), Uberaba, MG, Brazil L. C. J. Alcantara Laboratório de Flavivírus, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_4

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States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests and use of more expensive drugs. The comparative genomics associated with Pan-genomics, subtractive genomics, structural bioinformatics, and metabolic pathways analysis approaches are currently applied to reach the development of new antibiotics and fight antimicrobial resistance. Targeted drug development retains major challenges from candidate selection to in vitro and in vivo experiments and clinical trials. Yet, the advances in scientific knowledge and research and development, the advent of omics approaches for example, genomics, transcriptomics, proteomics, and bioinformatics breakthroughs conduct to a ‘big-data era’ that improved identification of putative targets via the application of in silico tools that shortened the timeline in a cost-efficient manner. In this chapter, we are focusing on different bioinformatics strategies for prioritizing drug targets in pathogens. Keywords  Antimicrobial resistance · Comparative genomics · Next generation sequencing · Pan-genomics · Subtractive genomics · Prioritizing drug targets · Metabolic pathway reconstruction

4.1  Introduction Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure to the environment; however, the indiscriminate use of antimicrobials is accelerating its progression (Holmes et al. 2016). The development of resistance happens when microorganisms evolve the mechanism to evade damage e.g. drug inactivation/alteration, efflux pumps, porin loss, biofilm formation, reduced intracellular drug accumulation, modification of drug binding sites, caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals, antimalarials, and anthelmintics, which involves genetic changes (Santajit and Indrawattana, 2016). As a result, the medicine/treatment become ineffective and the infection persists in the body, increasing the risk of spread to others, prolonged illness, disability, and death. Likewise, major medical procedures such as organ transplantation, cancer chemotherapy, diabetes management, and surgery, would be compromised. Infections with resistant pathogens also prompt a higher health care cost (estimated bugged of $20 billion annually in the United States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests and use of more expensive drugs (Marston et al. 2016). Awareness of antimicrobial traits is essential to comprehend the gain of resistance and how to overcome it. Other relevant factors that influence the prevalence of resistance are the pathogen drug and pathogen host interactions, the rate of the microorganism mutation, cross-resistance information, selection of co-resistance to unrelated drugs, and the transmission rates between human, animals, and the environment. Hence, the education of health care professionals and the general

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population is a key step to prevent antimicrobial resistance, and together with the implementation of antimicrobial stewardship programs in health care settings could improve antimicrobial prescription (Marston et al. 2016). In fact, 50% of prescribed antimicrobials are considered unnecessary and often medicaments are given without professional oversight (Centers for Disease Control and Prevention 2013). Likewise, the use of antimicrobials as animal growth promoters and in routine infection prevention in cattle further assist the dissemination of drug resistance, and the use of antimicrobials in food production is actually higher than in clinical settings (80% of total antibiotic consumption in the United States) (Van Boeckel et al. 2015). These complicate the overall resistance scenario, since most of the drugs considered ‘medically important’ for humans are also applied in animals. Moreover, this practice comprises 62% of the currently used antibiotics and the remaining percentage may also have a role (direct or indirect) (Marston et al. 2016).

4.2  Major Features on Antimicrobial Resistance The acquisition of resistance was reported even before the ‘golden era’ of antimicrobials but the selective pressure yielded by then allowed a massive proliferation and nowadays we faced an unprecedented challenge in health care with worldwide repercussions (Balouiri et al. 2016; Mayers et al. 2017). The cessation of antimicrobial selections does not reverse the issue, if the gain of resistance was already undertaken, but would decrease its expansion and prevent other pathogens to become resistant, minimizing the emergency (Holmes et al. 2016). Therefore, the development of new and alternative approaches to treat pathogen-induced illness is essential and efforts need to be carried out to avoid potential harmful outcomes and a possible scenario where extensive resistance may happen again. Mitigation should also involve public health factors such as improved access to sanitation and clean water, the extent of immunization policies, antimicrobial quality control, and microbial diagnostic optimization (Roca et  al. 2015; Balouiri et  al. 2016; Marston et al. 2016). Antimicrobial therapy is usually initiated before the identification of the causative pathogen; thus, a rapid diagnostic and stewardship programs could optimize treatments averting unnecessary antimicrobial therapies being able to differentiate colonization from infection and bacterial from viral infections. Biomarkers are being used to support diagnostics and guide health care professionals to prescribe the best treatment available (Zaas et al. 2013). Vaccination is a prevention approach that may circumvent the antimicrobial resistance issue since it would avoid the infection and consequently the use of antimicrobials. For instance, enhanced worldwide coverage of the vaccine against Streptococcus pneumoniae could prevent 11.4 million antibiotic days per year in children under 5 years (Laxminarayan et al. 2016). Furthermore, anti-virulence strategies, monoclonal antibodies, humanized immunoglobulins, and bacteriophages (phage therapy) are being used for treatment

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and prevention against infectious disease with promising results (Hauser et al. 2016; Marston et al. 2016). However, mitigations alone are not enough to overcome the threat of antimicrobial resistance being necessary an international commitment. The implementation of worldwide practices to prevent resistance spreading is urgent and requires drastic measures. The World Health Organization established a global action plan on antimicrobial resistance in 2015 to feature the ‘one health’ approach to combat antimicrobial resistance and reach a new era of sustainable development (WHO 2015). This proposal encompasses several international sectors including main stakeholders for example; food and pharmaceutical industry, finance, environment, academia, and the government, human and veterinary medicine, agriculture, and well-informed consumers, all together to achieve a common goal. The action plan was set with five major objectives: • To improve awareness and understanding of antimicrobial resistance through effective communication, education, and training; • To strengthen the knowledge and evidence base through surveillance and research; • To reduce the incidence of infection through effective sanitation, hygiene and infection prevention measures; • To optimize the use of antimicrobial medicines in human and animal health; • To develop the economic case for sustainable investment that takes account of the needs of all countries and to increase investment in new medicines, diagnostic tools, vaccines, and other interventions. Furthermore, the WHO Advisory Group on Integrated Surveillance of Antimicrobial Resistance (AGISAR), in conjunction with worldwide experts, published in 2018 an update (6th revision) of medically important antimicrobials for risk management of resistance due to non-human use (Table 4.1) (WHO 2018a). The update of the ‘WHO List of Critically Important Antimicrobials for Human Medicine’ (WHO CIA List) happens regularly to (i) gain knowledge, (ii) provide guidance on resource allocation and prioritization of risk assessment for both new and existing drug, (iii) estimate hazard consequences, and (iv) restrict the use of specific antimicrobial in countries or globally. The progression of the action plan is available in a database (https://amrcountryprogress.org/) filled by each country (i.e. self-assessment questionnaire) concerning the development of their national antimicrobial resistance action plans, the work with multiple sectors, and the implementation of key actions to address antimicrobial resistance. The international gathering and action plans are already generating outcomes that could improve the antimicrobial problem. For instance, policies were adopted in European countries to restrict the use of antimicrobial as animal promoters; however, the guidelines do not limit antimicrobial use to either human or animal to avert the crossover of resistance.

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Table 4.1  2019 WHO List of Critically Important Antimicrobials for Human Medicine’ (WHO CIA List). List and classification of antimicrobials important for human medicine. Some antimicrobials are used only in people, some in both people and animals (e.g. erythromycin, ampicillin, colistin) Antimicrobial class Critically important antimicrobials Aminoglycosides Ansamycins Carbapenems and other penems Cephalosporins (3rd, 4th and 5th generation) Glycopeptides Glycylcyclines Lipopeptides Macrolides and ketolides Monobactams Oxazolidinones Penicillins (antipseudomonal) Penicillins (aminopenicillins) Penicillins (aminopenicillin with beta-lactamase inhibitors) Phosphonic acid derivatives Polymyxins Quinolones Drugs used solely to treat tuberculosis or other mycobacterial diseases Highly important antimicrobials Amphenicols Cephalosporins (1st and 2nd generations) and cephamycins Lincosamides Penicillins (amidinopenicillins) Penicillins (narrow spectrum) Penicillins (anti-staphylococcal) Pseudomonic acids Riminofenazines Steroid antibacterials Streptogramins Sulfonamides, dihydrofolate reductase inhibitors and combinations Sulfones Tetracyclines

Example of antimicrobial(s) Gentamicin Rifampicin Meropenem Ceftriaxone, Cefepime, Ceftaroline, and Ceftobiprole Vancomycin Tigecycline Daptomycin Azithromycin, erythromycin, Telithromycin Aztreonam Linezolid Piperacillin Ampicillin Amoxicillin-clavulanic-acid Fosfomycin Colistin Ciprofloxacin Isoniazid

Chloramphenicol, Thiamphenicol Cefazolin Clindamycin Mecillinam Benzathine-benzylpenicillin, Phenoxymethylpenicillin Flucloxacillin Mupirocin Clofazimine Fusidic acid Quinupristin/Dalfopristin Sulfamethoxazole, trimethoprim Dapsone Chlortetracycline (continued)

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Table 4.1 (continued) Antimicrobial class Important antimicrobials Aminocyclitols Cyclic polypeptides Nitrofurantoins Nitroimidazoles Pleuromutilins

Example of antimicrobial(s) Spectinomycin Bacitracin Nitrofurantoin Metronidazole Retapamulin

Adapted from WHO (2018a)

4.3  Resistance in Bacteria The most well-known cases of resistance are documented through antibiotic use. The misuse of antibiotics leads to a loss of the commensal bacteria, which are responsible for the host welfare, especially in the gut, and this recklessness placed us in a ‘post-antibiotic era’. Antibiotic induced dysbiosis is a major medical concern in terms of life-threatening and health costs. Pathobionts and pathogens thrive during dysbiosis mainly because they are capable to adapt and overgrow in those new conditions which can lead to opportunistic infections (Round and Mazmanian 2009; Nicholson et al. 2012; Rigottier-Gois et al. 2014). The treatments with antibiotics increase the frequency of multi drug resistant bacteria, like Vancomycin Resistant Enterococci (e.g. Enterococcus faecalis and E. faecium), Clostridioides difficile, Mycobacterium tuberculosis, Escherichia coli, Klebsiella pneumoniae, Neisseria gonorrhoeae, and Methicillin Resistant Staphylococcus aureus (MRSA). After the antibiotic use is interrupted, the gut microbiota restoration can be rapid or take months to arise depending on the infliction (Jernberg et  al. 2007; Brandl et  al. 2008; Ubeda et  al. 2010; Becattini et  al. 2016). Multi drug resistance correspond to a high burden and a prevalent hazard to public health being associated with nosocomial infections widespread in the community, increased morbidity and mortality, high healthcare costs (i.e. annually between $21 billion and $34 billion in the United States) and antibiotic use (Singh 2013; Tanwar et al. 2014). Tuberculosis is one of the most prevalent bacteria hazards. The cases related to multi drug resistant tuberculosis were about 480,000, in 2014, yet only a quarter (123,000 cases) were detected and reported; however, only half were successfully treated. The burden of tuberculosis is much higher considering that only 3.3% of the patients were infected with a multi drug resistant strain. In 2017, WHO published an antibiotic resistant ‘priority pathogens’ list with 12 families of bacteria that pose a threat to human health, to instigate vigilance and promote research and development of new antibiotics (Table 4.2). The list includes multi drug resistant bacteria and nosocomial threats divided into 3 categories according to the urgency for new antibiotics: critical, high and medium priority.

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Table 4.2  WHO priority 2017 – pathogens list for research and development of new antibiotics. Twelve bacterial families were selected and divided into three categories according to their priority status. The list highlights the necessity of research and the development of new drugs for these bacteria.Tableacquiredfromhttps://www.who.int/news-room/detail/27-02-2017-who-publishes-list-ofbacteria-for-which-new-antibiotics-are-urgently-needed Priority Critical

Bacteria Acinetobacter baumannii Pseudomonas aeruginosa Enterobacteriaceae

High

Enterococcus faecium Staphylococcus aureus Helicobacter pylori Campylobacter spp. Salmonellae Neisseria gonorrhoeae

Medium

Streptococcus pneumoniae Haemophilus influenzae Shigella spp.

Antibiotic resistance Carbapenem resistant Carbapenem resistant Carbapenem resistant Extended spectrum beta lactamase producing Vancomycin resistant Methicillin resistant Vancomycin – intermediate and resistant Clarithromycin resistant Fluoroquinolone resistant Fluoroquinolone resistant Cephalosporin resistant Fluoroquinolone resistant Penicillin – non-susceptible Ampicillin resistant Fluoroquinolone resistant

4.4  Resistance in Fungi Fungal pathogens cause many life-threatening diseases (e.g., fungaemia, meningitis, pneumonia) and severe chronic conditions (e.g., chronic pulmonary aspergillosis, allergic bronchopulmonary aspergillosis, and chronic obstructive pulmonary disease). Fungal infections are normally associated with underlying health conditions (e.g. immunosuppression) and the favourable outcome is linked with early diagnosis and effective antifungal therapy (Brown et  al. 2012). Antifungal drug resistance occurs with all drug classes and the gain of resistance to one class or more complicates, since antifungal options are limited (e.g. azoles, echinocandins, polyenes, and flucytosine) (Odds et al. 2003; Perlin et al. 2017). The use of fungicides in agriculture further increase the drug resistance and results in reservoirs for resistant pathogens and can exclude most/all treatment options (Perlin et  al. 2017). Furthermore, the formation of biofilms is associated with resistance to most drug classes (Ramage et al. 2012). To overcome the dissemination of resistance, antimicrobial stewardship programs, which are distinctive for each institution/health care centre, and diagnostic driven approaches are essential and can reduce the overall cost without decreasing health (Andes et  al. 2009; Barnes et  al. 2009; Perlin et al. 2017). The most troubling drug resistant pathogens are azole resistant Aspergillus species and of multi drug resistant Candida glabrata and C. auris. A few symptoms caused by those and other fungi are often mistaken as bacteria driven infections (e.g.

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cryptococcal meningitis; Candida infection or colonization of the respiratory or urinary tract; febrile neutropenia in leukaemia; Aspergillus bronchitis in bronchiectasis; and allergic, chronic, and invasive fungal sinusitis and Pneumocystis pneumonia in HIV negative patients) and the accurate diagnosis is crucial to avoid antibiotics and/or antifungals misuse (Denning et al. 2017).

4.5  Resistance in Virus Viruses have an innate ability to evade antiviral drugs since its replication is prone to a high error rate (Sanjuán et al. 2010). Thus, the gain of resistance, which normally involves some type of interference in protein-protein interaction (PPI) or a mutation in the drug binding site, entails an equilibrium between the ability to evade the selective pressure without affecting the pathogen fitness (Götte 2012; Wensing et  al. 2017). Many factors contribute to the virus’s ability to overcome antiviral drugs, like its replication and mutation rate, viral fitness, and load, being specific for each drug/ microorganism (Mason et al. 2018). A rapid replication/higher viral load lead to a higher frequency of mutation whereas the viral fitness determines if the pathogen can compete and survive in the environment (under antiviral pressure) after the mutation. The viruses that denote main concerns to human health are the human immunodeficiency virus (HIV), hepatitis B (HBV) and C (HCV), and Influenza. HIV is a global problem that nowadays affects especially developing countries and currently has a variety of treatment options (e.g. over 30 antivirals, nucleoside reverse transcriptase inhibitors, protease inhibitors and non-nucleoside reverse transcriptase inhibitors) (Larder and Kemp 1989; De Clercq and Li 2016; Mason et al. 2018). The WHO recommendation is for all infected people to undertake antiretroviral treatment, which increases the probability to develop antiviral resistance and this prospect demand constant monitoring to prevent HIV spread. Influenza virus causes severe respiratory infections with annual epidemics, pandemics and endemic infections due to its rapid and constant evolution (Lee and Ison 2012). Antiviruses are associated with overall improvement and clearance of the disease; however, resistance represents a significant health issue. Antiviral resistant strains are not affected by drug therapy and retain replication and transmission fitness, being responsible for severe outbreaks (Li et al. 2015).

4.6  Resistance in Parasites Parasites cause numerous severe diseases that are normally entirely dependent on antiparasitic drugs, due to the lack of economic incentives for research and development (Elsheikha et al. 2011; Secor et al. 2015). Antiparasitic resistance is linked with biological characteristics of the pathogen that allows them to escape the drug effect, which is a direct link with the pathogen host interactions. Furthermore,

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parasite illnesses are prevalent in developing countries where the population suffers from malnutrition and have poor access to clean water and proper sanitation, factors that result in a debilitated immune system (Secor et al. 2015). Malaria is a parasitic disease caused mainly by Plasmodium falciparum and P. vivax. The parasite is transmitted by Anopheles mosquito and initially infects erythrocytes and once into the bloodstream, in severe cases can reach the brain leading to cerebral malaria. The treatment is centred on artemisinin based combination therapies with an overall efficacy rate of 95% with described multi drug resistance parasites observed in the Cambodia Thailand border (Secor et al. 2015). The spread of resistant strains to other parts of the world could pose a major public health challenge and jeopardize important recent gains in malaria control. In 2017, 219 million cases of malaria were reported with 435,000 deaths worldwide (WHO, 2018b). Approximately 70% of the reported cases were concentrated in Africa and India. A newly experimental vaccine shown partial protection against P. falciparum in children and was recommended by WHO for a pilot introduction in Ghana, Malawi, and Kenya. This new strategy may decrease the anti parasitic resistant problem and the overall burden of the disease. Bearing the obstacles generated by the use of antimicrobials, the prevention of further antimicrobial resistant community spread, and the inhibition of the propagation of pathogens are of utmost importance and require synergistic, overlapping, and complementing approaches. There is no single solution and a multi-disciplinary tactic is the best trail to guarantee and endure access to effective antimicrobial therapies. The search for alternative treatment (e.g. antimicrobial compounds, bacteriophages and phage associated enzymes, and alternative drug targets) denotes a viable option to replace antimicrobials as the main source of treatment (Bragg et al. 2018). Targeted drug development retains major challenges from candidate selection to in vitro and in vivo experiments and clinical trials. Yet, the advances in scientific knowledge (i.e. disease and pathogen) and research and development, the advent of omics approaches (e.g. genomics, transcriptomics, and proteomics), and bioinformatics breakthroughs conduct to a ‘big data era’ that improved identification of putative targets via the application of in silico tools that shortened the timeline in a cost efficient manner (Bruno et al. 2017). In this chapter, we are focusing on different bioinformatics strategies for prioritizing drug targets in pathogens.

4.7  Post Genomics Era and Prioritization of Drug Targets High throughput techniques in genome sequencing have provided an increasing number of putative drug targets to microbial, directing the efforts on how to diminish molecular targets that should be tested experimentally. Currently, comparative genomics associated with pan-genomics, subtractive genomics, structural bioinformatics, and metabolic pathways analysis approaches are applied to reach the development of new antibiotics and fight antimicrobial resistance.

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4.7.1  Pan-Genomics and Subtractive Genomics Subtractive genomics – a comparative genomics approach – is currently one of the widely used strategies throughout the last years for target prediction. Pan-genome is another comparative genomics approach, which delineates the complete genomic range of a given phylogenetic clade and encrypts all the possible lifestyles adopted by its organisms (Tettelin et al. 2005, 2008). The ultimate goal of a pan-genome is the comparison of different strains at the genomic level from the same species or even genus. Presently, different isolates of the same pathogen and their genomic data have unlocked the option of investigating numerous characteristics that are fundamental to one or more species. The pan-genomic approach is one of the options for investigating these attributes (Tettelin et al. 2005, 2008; Guimaraes et al. 2015). Tettelin et al. (2005) described the term pan-genome for the first time working with Streptococcus agalactiae, where he took eight different genomes of Streptococcus agalactiae, a pathogenic species isolated from humans. After that, other studies were conducted using pan-genomic analysis for different microorganisms, that includes Corynebacterium pseudotuberculosis (Soares et  al. 2013), Bacillus cereus (Rasko et al. 2005), Streptococcus pneumoniae (Donati et al. 2010), Escherichia coli (Rasko et al. 2008), Pantoea ananatis (De Maayer et al. 2014) and Methanobrevibacter smithii (Hansen et al. 2011). The pan-genomic studies provide vital information about the evolution of bacteria, niche adaptation, host interaction, and population structure as well as upshots in more applied issues like vaccine and drug design and the identification of virulent genes (Hansen et al. 2011). The word pan-genome refers to core genes, accessory genes, and strain specific genes. The pan-genome includes the entire range of genes accessible to the clade studied; the core genome contains genes shared by all strains, the accessory genome is composed of genes shared by a subset of the strains and strain specific genome refers to the genes only present in one strain specifically (Vernikos et al. 2015). The core and accessory genes can further be explored for the novel target’s identification against pathogenic organisms. Subtractive genomics is an approach applied to detect novel drug targets in pathogenic organisms using the whole genome. This methodology involves the subtraction of sequences between the host and the pathogen proteome/ genome (proteins/genes) with the help implementation of certain rules (Fig. 4.1). This helps in providing information for a set of proteins that are essential to the pathogen but are not present in the host (Barh et al. 2011). There are several target identification and prioritization databases, web tools and software are also available (Table  4.3). According to Barh et al. 2011, an ideal target should fulfil these properties: (a) It must be essential for survival or pathogenesis of the target organism and belongs to the core gene of the pathogen (b) The target should belong to the pathogen’s unique pathway. The pathway related targets are more advantageous and will be best if it is involved in multiple pathways. The approach of subtractive genomics for target identification has been massively applied in numerous pathogens like Treponema pallidum (Kumar Jaiswal

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Fig. 4.1  A simple overview of the Subtractive genomics based approach for target identification. The figure represents a simple overview of the Subtractive genomics based approach for target identification. Each level represents a different rule into the process until the reduced number of targets

et al. 2017), Haemophilus ducreyi (de Sarom et al. 2018), Corynebacterium diphtheriae (Jamal et  al. 2017), Corynebacterium pseudotuberculosis (Hassan et  al. 2014), Salmonella enterica subsp. (Hossain et  al. 2017), Brucella melitensis (Pradeepkiran et al. 2015). Shigella flexneri (Oany et al. 2018), Streptococcus pneumonia (Wadood et  al. 2018), Escherichia coli O157:H7 (Mondal et  al. 2015), Fusobacterium nucleatum (Kumar et al. 2016), Bacillus anthracis (Rahman et al. 2014), Salmonella typhi (Mukherjee et  al. 2019), Mycobacterium tuberculosis (Hosen et al. 2014; Waman et al. 2019), and many other pathogens. Multiple rules can be applied in the scope of subtractive genomics approach. According to the number of sequenced genomes, in silico and in vitro studies available, specific databases for each microorganism and the application order of the

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Table 4.3  The list of some available target identification and prioritization databases, web tools, and software Name AntibacTR

Standalone/Web tool Database

PDTD (Potential Drug Target Database) UniDrug-Target

Database Standalone & Web-Based tool Database Database Standalone Standalone

References Panjkovich et al. (2014) Gao et al. (2008) Chanumolu et al. (2012) Raman et al. (2008) Sosa et al. (2018) Gupta et al. (2017) Singh et al. (2006)

Web-based tool

Li et al. (2006)

Target TB Target-Pathogen TiD (Target iDentification) T-iDT (Tool for identification of drug target) TarFisDock (Target Fishing Dock)

filters selected. Recently, in vitro assays information, molecular docking and molecular dynamic simulation were associated to screen potential phototherapy molecules against already prioritized targets of multi drug resistant Acinetobacter baumannii (Skariyachan et al. 2019). Another example of multi drug resistant bacteria, Streptococcus pneumoniae is the leading cause of bacterial pneumonia. In a successful choice of filters (virulence analysis, drugability analysis, metabolic pathway enrichment, functional annotation and interactome network) resulting in just two chokepoint hub enzymes (Nayak et al. 2019).

4.7.2  Metabolic Network Analysis One useful application to prioritize drug targets in pathogens is the prediction of molecules interdependency in biochemical reactions. These biochemical interactions may involve hundreds to thousands of metabolites and enzymatic reactions, which participate in different subsets at microbial metabolism. Although different types of biomolecules (nucleotide, carbohydrate, lipid, and amino acid) constitute the complexity of microbial metabolism, protein-protein or protein-DNA interactions are currently the focus of study. Metabolites interactions prediction can provide key information about putative targets implicated in pathogenic and virulence process, adaption and response to stress. Biological networks follow a standard architecture, where genes, proteins, and compounds are represented by nodes, which are connected by edges represented through protein-compound, protein-gene, protein-protein interactions and metabolic reactions (Nikolsky et al. 2005). After sequencing or data retrieval from online databases, a whole genome or partial metabolic network can be applied (Fig. 4.2). The filtering process can consider literature, size and cellular location of the protein, quality of tertiary structure, drug ability of modelled proteins and homology with the host. Diverse online tools can be used to generate networks.

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Fig. 4.2  The workflow of metabolic network analysis. The figure represents an overview of metabolic network reconstruction and analysis for target identification

Considering gene or reaction essentiality approach, if a gene knockout or alteration in reaction leads to effects in the surrounding network, the enzymes or protein encoded by the altered node might be considered a drug target (Thiele et al. 2005; Oberhardt et al. 2009). In one of the first metabolic reconstruction pathway analysis of a pathogen (Schilling and Palsson 2000), Haemophilus influenzae gene essentiality analysis revealed 11 critical genes and six others under specific conditions. Essentiality criteria might be used alone or associated with other approaches. Moreover, essentiality can be restricted to a specific pathogen environment, where conditions of growing and access to nutrients can drive a microorganism expression profile. Klebsiella pneumoniae is an opportunistic pathogen that has been responsible for infection outbreaks in hospitals, being especially risk to adults under intensive care and newborns (Podschun and Ullmann 1998). Extended spectrum β lactamases enzymes are produced by different Klebsiella pneumoniae strains (Ramos et  al. 2018). Through an integrative approach using genomics, transcriptomics,

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structuromic and construction of the whole genome metabolic network to Klebsiella pneumoniae Kp13, twenty nine proteins were prioritized based on essentiality, drug ability level, non-host homology and metabolic network analysis (Ramos et al. 2018). Enzyme robustness analyses assess susceptible fractions of a metabolic network that can be used for drug targeting (Chavali et al. 2012). Constraining the metabolic flux through partial or complete inhibition of an enzyme catalysed reaction of a metabolic network allows the identification of the enzyme robustness (Chavali et al. 2012). The effects produced on objective flux (e.g. production of biomass, charge, mass, and energy balanced) reveal the impact of inhibition and therefore the putative relevance of the target (Chavali et al. 2012). For instance, the rise of multi resistant Mycobacterium tuberculosis (Koch and Mizrahi 2018) motivated the modelling of mycobacterial metabolism. High throughput data associated with genome scale network reconstruction with constraint based (CB) mathematical generates drug phenotype, specific growth rate, and metabolic state predictions (Rienksma et al. 2014). Although enzyme robustness analyses may provide accurate descriptions of metabolism (Schilling and Palsson 2000; Jamshidi and Palsson 2007; Fang et al. 2010; Raghunathan et al. 2010), the scarcity of kinetic parameters of each enzyme complicates genome scale modelling. More recently, Chichonska and collaborators developed a computational experimental based approach to drug target interaction mapping on kinase inhibitors (Cichonska et al. 2017). Using kernel based regression algorithm as the prediction model, they provided a model for identification of new target selectivity for drug repurposing applications (Cichonska et al. 2017). Even though this approach was not developed to drugs against microbial the same strategy can be applied. Regarding viruses, due to the reduced number of proteins, the investigation is focused on the host virus interaction and perturbation of metabolic subsets during the infection. PPI (Protein-protein-interactions) data sets such as VirHostNet (Guirimand et  al. 2015), VirusMentha (Calderone et  al. 2015), HCVpro (Kwofie et al. 2011) and HHID6 (Ptak et al. 2008) allows viral infections to be exploited as networks. Other host pathogen interaction databases and tools are shown in Table 4.4. A study investigating host virus interactions regulated in early stage HIV-1 replication cycle revealed 213 host cellular factors, 40 novel factors influencing the initiation of HIV-1 DNA synthesis and 5 proteins with diverse influence at nuclear import of viral DNA integration (Konig et al. 2008). The identification of target in early stages in initial phases at virus infection have been used as a therapeutic targets. Small molecules can be developed in order to block early stage process in the host virus interactions, such as attachment, penetration and uncoatting (Konig et al. 2008). Besides bacteria, viruses are the second main focus of host and pathogen interaction studies (Zhou et al. 2013) but metabolic network approaches have been also applied to the identification of anti-fungal drug targets (Remmele et  al. 2015; Kaltdorf et  al. 2016). Kaltdorf et  al. (2016) applied the combination of different

Integrated pathway gene relationship database (IntPath) Kegg Pathway Database

Database with information from S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens, and M. musculus Provides network and pathway maps

Database/tool Content Pathogen Host Interaction (PHI) It provides curated biological and molecular information on genes affecting pathogen-host interactions. Host-Pathogen Interaction Containing 69,441 curated entries, providing annotation, prediction, Database – (HPIDB 3.0) and display of host-pathogen interactions (HPI) Pathogen-Host Interaction It provides data for all pathogen types with experimentally verified Search Tool (PHISTO) protein interactions with the human host A manually curated database of interactions between host-­ Host-Pathogen and Coxiella pathogen-­related elements and other factors from Pseudomonas Interaction database aeruginosa and Coxiella species. (HoPaCI-db) It is a database and analysis system that aims to manually curate, Pathogen-host interaction data integration and analysis system computationally analyse pathogen-host interactions (PHIDIAS) VirHostNet Includes almost 35.000 virus-host and virus-virus manually curated protein-protein interactions VirusMentha Contains more than 5000 proteins and almost 16.000 host-virus protein interactions HCVpro It is a database of Hepatitis C Virus (HCV) protein interactions and provides molecular data, functional annotations, drug development, pathways links to other biological databases. CarveMe Genome-scale metabolic model reconstruction

Table 4.4  Host interaction and metabolic network reconstruction – databases and tools

https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC3521174/ https://www.genome.jp/kegg/ pathway.html

https://pypi.org/project/carveme/

http://www.cbrc.kaust.edu.sa/ hcvpro/

(continued)

Machado et al. (2018) Zhou et al. (2012) Kanehisa et al. (2008)

Guirimand et al. (2015) Calderone et al. (2015) Kwofie et al. (2011)

http://virhostnet.prabi.fr/ https://virusmentha.uniroma2.it/

Xiang et al. (2007)

Reference Urban et al. (2017) Ammari et al. (2016) Durmus Tekir et al. (2013) Bleves et al. (2014)

http://www.phidias.us/

http://mips.helmholtz-muenchen. de/HoPaCI/

http://www.phisto.org/

http://hpidb.igbb.msstate.edu/

Website http://www.phi-base.org/

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A suite of High-Throughput Computational Analysis Services for Metabolic Model Reconstruction It permits visualize and interact with biological pathways, merges them. Also, it identifies mapping and expression analysis. A database covering more than 170 eukaryotic pathogens (protists & fungi)

Pathosystems Resource Integration Center (PATRIC) Reactome Pathway Database

Eukaryotic pathogen genomics database resource (EuPathDB)

Content Comprehensive database for 13 families of viruses

Database/tool Virus Pathogen Resource (ViPR)

Table 4.4 (continued)

https://academic.oup.com/nar/ article/45/D1/D581/2605823

https://reactome.org/

Website https://www.viprbrc.org/brc/ home. spg?decorator=flavi_dengue https://www.patricbrc.org/ Wattam et al. (2017) Fabregat et al. (2018) Warrenfeltz et al. (2018)

Reference Pickett et al. (2012)

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bioinformatics approaches (metabolic modelling, enzyme regulation based strategy, and enzyme regulation based strategy) and 64 metabolic enzymes targets were identified (Kaltdorf et al. 2016). Protein targets can also be predicted by the host pathogen interactions caused in the process of infection. Such an approach was applied by Remmele et al., where the protein-protein interaction network of human Aspergillus fumigatus and human Candida albicans was determined based on yeast and human intraspecies networks (Remmele et al. 2015). The reconstruction of the metabolic network provided several novel host pathogen interaction candidates, for instance, the anti-fungal host protein APP and the Candida virulence factor PLB (Remmele et  al. 2015) (Table 4.5).

Table 4.5 Different in silico methods geared towards the studies of drug targets Organism Entamoeba histolytica

Disease Amoebiasis

Candida albicans

Fungal infections

Mycobacterium tuberculosis

Tuberculosis

Staphylococcus aureus

Infectious diseases caused by methicillin-­ resistant

Methods applied for analysis MOLREP; REFMAC 5.5; COOT; PyMOL; MOLPROBITY; DS suite 4.0; AutoDock Vina Tools; Gromacs 5.1.4 suite; PRODRG; MM –PBSA SWISSMODEL server; ERRAT; PROCHECK; AutoDock Tools; Accelrys DS (ver. 2.5.5); Chem- Axon Marvin Sketch 5.3.735; Avogadro v1.1.1; Accelrys DS Metabolic pathways (KEGG; Clustal Omega; Blastp; DEG); AutoDock v 4.2.6; AutoDock Tools Molecular modelling, Mesh Ewald (PME) method; GROMACS;

Outcome Reference Identified the active Malik et al. site of Entamoeba (2019) histolytica Arginase (EhArg); novel drugs from the drug library 3D model of the C. Semenyuta albicans FBA-II as et al. (2019) the target against azole resistant fungal pathogens

5 proteins as potential drug target

Uddin et al. (2019)

Nanoplexes as a promising delivery system to combat MRSA infections

Hassan et al. (2019)

(continued)

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Table 4.5 (continued) Organism Disease Shigella flexneri 2a Dysentery

LymphCorynebacterium pseudotuberculosis adenitis strains

Zika virus

Zika viral infection, dengue, chikungunya

Trypanosoma cruzi Chagas CL-Brener

Methods applied for analysis KEGG; BLASTp; STRING; ExPASy; MODELLER v9; PROCHECK; SWISS- MODEL Workspace; APBS server; PyMOL; CASTp server; BindingDB; Zinc database; PubChem; Molinspiration; SEArch; Drug-­ likeness tool; OSIRIS Property Explorer; ADME SARfari; admet- SAR; Swiss database; QikProp Pathosystems Resource Integration Center (PATRIC)

Protparam software; Aliphatic Index method; Kyte-­ Doolittle method; Kyte-Doolittle hydropathy plot; Clustal Omega software; SWISSMODEL server; RAMPAGE and PROSA (Protein Structure Analysis) software; ChemAxon; AutoDock BLASTp; MODELLER 9v16; UCSF CHIMERA; ERRAT and VERIFY 3D (SAVES server and MOLPROBITY); ClustalW; Pymol v1.8.2.1; TM-align algorithm; PubChem; Maestro; PROPKA; GlideXP;

Outcome 6 unique targets

Reference Molina et al. (2018)

New putative target, the gene nrdF2 – described as a potential target of M. tuberculosis Some compounds showed a better binding affinity with Zika envelope protein compared to dengue virus

Parise et al. (2018)

Chellasamy and Devarajan (2019)

Castilho HIV aspartic peptidase inhibitors et al. (2018) bind to the active site of the enzyme (ritonavir and lopinavir have the greater affinity); T. cruzi aspartyl peptidase can be the intracellular target (continued)

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Table 4.5 (continued) Methods applied for analysis orthoMCL software; GIPSy; BLASTp; SurfG+; SignalP and Trans- membrane Helix prediction server; InterProScan; MHOLline tool; DEG; Autodock vina; OpenBabel; Chimera; PoseView; KEGG; Treponema Syphilis orthoMCL software; pallidum MHOLline tool; DEG; Molegro Virtual Docker; KEEG GIPSy; BLASTp; SurfG+ Plasmodium Malaria MODELLER; falciparum I-TASSER; PyMOL; MolProbity; YASARA Minimization Server; PubChem; PRODRG server; Autodock Vina; MGLTools; AMBER Score Toxoplasma gondii Toxoplasmosis Gaussian 09 software; SWISS-Model web server; UCSF Chimera 1.12; ProFunc web server; AutoDock Tools v1.5.6; BIOVIA Discovery Studio Visualizer v16.1.0.15350; UCSF Chimera v1.12; MCPB.py; Gaussian 09; Particle Mesh Ewald method; SHAKE algorithm; AMBER16; AmberTools18 and self-written software

Organism Haemophilus ducreyi

Disease Chancroid

Outcome 3 drug targets harboured by pathogenicity islands

Reference de Sarom et al. (2018)

Identified six drug targets

Kumar Jaiswal et al. (2017)

Identified a compound, B02 that inhibited a drug-sensitive P. falciparum strain and multidrug-­ resistant parasite

Vydyam et al. (2019)

Compounds from the thiazolidinedione core have a preference for protein kinases of T. gondii, being promising compounds for anti-toxoplasmosis activity

Molina et al. (2018)

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4.8  Conclusion and Future Prospective Many approaches have been used for the identification of drug targets in pathogens. In the last two decades with the advent of the new sequencing platforms, drug discovery has observed a shift from the traditional approaches to rational drug target identification and target driven lead compounds discovery. One strategy that has been applied to identify new targets is the prediction of epitopes that culminates in the proposition of vaccines formed by multiple selected epitopes. The bioinformatics analyses, the discovery of potential drug targets became a rapid and less expensive way. Not only the software but also the compounds databases became important tools in the process of drug target prioritization, making them imperative of frequent updates be made. It is logical to believe that in the coming year’s specific drug discovery against antimicrobial resistance will increasingly benefit from large scale informatics, modelling, and simulations.

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Chapter 5

Molecular and Systems Biology Approaches for Analyzing Drug-Tolerant Bacterial Persister Cells Xiangke Duan, Yang Fu, and Liang Yang

Abstract  Persistence is an almost universal bacterial state, phenotypically characterized by tolerance to antibiotics. It plays a significant role in the intractability of chronic and relapsing infections. Bacterial persisters are specialized survivors which have entered a non or extremely slow growing, non replicating dormant state, while being genetically identical to non tolerant kin. Forestalling the antibiotics from inhibiting the target rather than by mutation underlies the antibiotics tolerance of persister. More insights into the mechanisms of the persister formation can accelerate the discovery of new treatment strategies. In this chapter, we summarized the approaches used for persister study. We first discuss the fluorescent label based single cell approach that could be used to analyse the native persisters within the population. We then discuss the high throughput screening approaches, including transposon mutant library screening, knockout and over expression library screening, where researchers could identify individual gene involved in bacterial persistence using these approaches. And finally, we discuss the omics approaches, including genomics, transcriptomic, proteomics and metabolomics used to understand the underlying mechanisms of persister formation. Keywords  System biology · Persistent bacterial populations · Fluorescent indicator · High throughput screening · Genomics · Transcriptomic · Proteomics · Metabolomics

X. Duan · Y. Fu · L. Yang (*) School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_5

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Abbreviations ATeam

Adenosine 5′-triphosphate indicator based on epsilon subunit for analytical measurements ATP Adenosine triphosphate BONCAT Bio-orthogonal noncanonical amino acid tagging CFP Cyan fluorescent protein CRISPR Clustered regularly interspaced short palindromic repeats CRISPRi CRISPR interference DNA Deoxyribonucleic acid EGFP Enhanced green fluorescent protein FACS Fluorescent activated cell sorting FRET Fluorescence resonance energy transfer GFP Green fluorescent protein hip high persistence LC-MS/MS Liquid chromatography – mass spectrometry/mass spectrometry LPS Lipopolysaccharides NGS Next generation sequencing PAGE Polyacrylamide gel electrophoresis pulsed-SILAC Pulsed stable isotope labelling with amino acids QS Quorum sensing QUEEN Quantitative evaluator of cellular energy RNA-seq RNA sequencing sfGFP Faster maturing superfolder green fluorescent protein TAs Toxin–antitoxin systems TCA Tricarboxylic acid cycle YFP Yellow fluorescent protein Tn-seq Transposon insertion site sequencing

5.1  Introduction Persisters are subpopulation of isogenic bacteria that can survive even upon exposure to high dose of antibiotics (Lewis 2010). Unlike the bacteria gain antibiotic resistance by mutating the target genes or acquire the resistance genes (Blair et al. 2015), the tolerance of persisters to antibiotics is due to the fact that bacterial cells enter non replicative state under drug pressure where they can resume growth after withdrawal of antibiotics from medium (Keren et  al. 2004a). The tolerance and persistence have been shown to facilitate the evolution of resistance and lead to the emergence of antibiotic resistance (Levin-Reisman et al. 2017). This phenomenon was noted more than 70 years ago (Hobby et al. 1942). Many environmental stresses such as antibiotic exposure, immune pressure and starvation can lower the growth rate of bacteria and stop cell division directly or indirectly (Eng et al. 1991). The

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phenotype and gene expression pattern are different in the persister isolated under various stresses. For example, the size of starvation induced persisters were smaller than the ciprofloxacin induced persisters, and more sensitive to heat and oxidative stress (Paranjape and Shashidhar 2019). The reduction of growth rate has been shown to be correlated with persistence both in vitro (Aldridge et al. 2012; Balaban et al. 2004; Shah et al. 2006) and in vivo (Levin and Rozen 2006). Most antibiotics are only active against growing cells by targeting the ribosome, cell wall synthesizing enzymes and deoxyribonucleic acid (DNA) gyrase or DNA topoisomerase etc. These antibiotics are not effective against slow growing or non replicating bacteria (Lewis 2010). Persisters have been characterized in both Gram positive and Gram negative bacteria. In addition, persisters have been described in the fungal pathogen Candida albicans (Van den Bergh et al. 2017). The regulatory mechanisms involved in bacterial persister formation have been identified, including SOS response (Bernier et al. 2013; Dorr et  al. 2009), toxin antitoxin systems (Page and Peti 2016), stringent response (Germain et al. 2013; Kaspy et al. 2013; Khakimova et al. 2013), quorum sensing (Leung and Levesque 2012; Moker et al. 2010) and intracellular adenosine triphosphate (ATP) level (Conlon et  al. 2016; Pu et  al. 2019; Shan et  al. 2017). Several excellent reviews on persister physiology (Helaine and Kugelberg 2014; Van den Bergh et  al. 2017), formation (Balaban et  al. 2019; Cohen et  al. 2013; Fisher et al. 2017; Harms et al. 2016; Lewis 2007; Maisonneuve and Gerdes 2014), resuscitation (Jõers et al. 2019; Matilla 2018; Wilmaerts et al. 2019) mechanisms and strategies used to combating persisters (Allison et al. 2011; Defraine et al. 2018; Keren et al. 2012; Wood 2016) have been published in recent years. In this article, we review current approaches to study persister cells and provide information on some emerging technologies which are likely be applied in persister cell research.

5.2  Classical Approaches Colony forming unit (CFU) count is one of the most basic method to quantify viable bacteria in a sample. In 1944, Joseph Bigger treated pathogenic Staphylococcus aureus with penicillin which resulted in lysis. He plated this transparent liquid and found one in a million S. aureus persister cells within the cell culture by means of CFU count (Bigger 1944). This simple and useful method provides a very straightforward result for us to analyse the proportion of persister cells within the population. However, CFU assay failed to measure the deep dormant bacteria that wouldn’t start to regrow in a short time (Pu et al. 2019).

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5.3  Fluorescent Label Based Single Cell Approaches Previous studies have focused on the persister cells in the whole population, which only provide the measurement of averages and unable to analyse the specified subpopulation. To solve this problem, some studies start to observe the persister cells at single cell level (Table  5.1), since the development of transparent microfluidic devices (Unger et  al. 2000). Balaban and coworkers tagged the high persistence (hip) mutant hipA7 and hipQ with yellow fluorescent protein and recorded the growth of individual bacterial cells within the microfluidic devices while treated with different conditions (Balaban et al. 2004). With this device, they have characterized two types of persisters that exist in the whole population. Type I persisters generated in response to hostile environment such as starvation, which activates the persister formation pathway. Type II persisters are continuously generated by a phenotype switching mechanism in the absence of external triggers (Balaban et  al. 2004). When hipA7 bacteria was inoculated in the microfluidic device and cultured in Luria-Bertani Lennox medium, most of the cells have the same growth rate as batch cultures. The persisters, however, do not divide under this condition. This study used a constitutively expressed yellow fluorescent protein (YFP) under λPR, which can distinguish the dividing cells and non dividing cells, but unable to reflect the physiological state of bacteria (Fig. 5.1a). Persister cells are expected to have a low rate of protein synthesis and the ribosomal promoter shows an extremely low activity. The Lewis group put a degradable green fluorescent protein (GFP) under the control of the ribosomal rrnBP1 promoter and found a small number of dimly fluorescent cells existed within the whole population (Fig. 5.1b). They sorted the Table 5.1  Single cell analysis methods for persister cells Methods Constitutively expressed YFP rRNA-GFPdes constructs rRNA-GFPdes -DsRed constructs Timer fluorescent protein sfGFP-Tdimer2 fusion GFP fluorescence dilution Dual fluorescence dilution QUEEN for ATP measurement FRET-based ATP biosensor FtsZ-FRET biosensor

Organisms E. coli

Reference Balaban et al. (2004)

E. coli

Shah et al. (2006)

M. tuberculosis S. typhimurium P. aeruginosa E. coli

Manina et al. (2015)

S. typhimurium E. coli

Figueira et al. (2013), Helaine et al. (2010, 2014), Saliba et al. (2016) and Stapels et al. (2018) Yaginuma et al. (2014)

Claudi et al. (2014) Xia et al. (2018) Roostalu et al. (2008)

M. smegmatis Maglica et al. (2015) E. coli

Matsumoto et al. (2018)

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Fig. 5.1  Schematic representation of the fluorescent label-based indicator for persister study. (a) Constitutively expressing YFP under the control of λPR. (b) Degradable GFP expression under the control of a ribosomal promoter, rrnBP1. (c) The insertion gfp and dsRed2 on M. tuberculosis genome. (d) The tandem fluorescent timer encoding sfGFP and Tdimer2 with a linker. (e) Structure of pDiGc and pDiGi plasmids and schematic of FD principle. (f) Schematic illustration of the genetic structure of QUEEN. (g) Schematic drawing of FRET-based ATP probe

dim and bright fluorescent cells and found that the dim cells were more tolerant to ofloxacin as compared to the bright cells (Shah et al. 2006). This single fluorescent protein based indicator enabled the identification of persister cells within the whole population and isolation by fluorescence activated cell sorting (FACS). The destabilized green fluorescent protein can effectively differentiate the individual cells with different growth rates when the expression of GFPdes is under the control of rrnBP1 promoter. However, this indicator is unable to distinguish the persister cells and dead cells if one wants to track the persister in different conditions, especially within host cells. Combine rRNA-GFPdes reporter with a constitutively expressed stable red fluorescent protein (DsRed2) allowing for identifying the non growing cells and growing cells of M. tuberculosis during the infection (Fig. 5.1d) (Manina et al. 2015). Another fluorescent indicator for slow growing cells is timerprotein. Timer is the DsRed S197T variant which changes fluorescence color from green to red overtime (Terskikh et  al. 2000). The green timer has a branched maturation pathway and emits green fluorescence rapidly in fast dividing cells, while in slow growing or non growing cells, the red fluorophore accumulated and matured owing to the lower protein synthesis rates and emits red fluorescent signal (Strack et  al. 2010). Fluorescent timer could be used to identify slow growing populations according to the red/green fluorescence ratio. With this system, the slow growing subsets as well as fast growing subsets of Salmonella population in host tissues were separated by flow cytometry based on fluorescent signal (Claudi et al. 2014). A similar type of maturation time based cell growth rate indicator is dual color fluorescent timer (Xia

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et al. 2018), which is a fusion of a slower maturing red fluorescent protein (Tdimer2) (Vrzheshch et al. 2000) and a faster maturing super folder green fluorescent protein (sfGFP) (Pedelacq et al. 2006). This fluorescent timer is expressed using a constitutive promoter, which can mark the slow growing and growth arrested P. aeruginosa cells at single cell level (Fig. 5.1c). The systems discussed above are based on the constitutive expressed fluorescent proteins. Next, we will focus on reporter systems that rely on the inducer and determine the replication dynamics based on fluorescence dilution. In this system, a fluorescent protein is expressed under the control of tightly regulated inducible promoter. After induction, the division of individual cells in inducer free condition can be monitored by measuring the concentration of GFP (Roostalu et  al. 2008). In the exponentially growing cultures, vast majority of E. coli cells were divided uniformly. However, dividing and non growing subpopulation were observed when stationary phase culture was diluted into fresh medium (Roostalu et  al. 2008). Helaine and coworkers modified this fluorescence dilution system by putting an inducible red fluorescent protein (DsRed or mCherry) and another constitutive enhanced green fluorescent protein (EGFP) on one plasmid (Fig. 5.1e). Using this approach, they demonstrated that many Salmonella cells do not replicate but appear to enter a dormant like state and therefore survive in vivo antibiotic treatment in the macrophage infection model (Figueira et al. 2013; Helaine et al. 2010, 2014; Saliba et al. 2016; Stapels et al. 2018). One common feature of persister cells is low intracellular ATP level (Conlon et al. 2016; Dorr et al. 2010; Pu et al. 2019; Shan et al. 2017). Genetically encoded fluorescent sensors for bacteria intracellular ATP measurement have been developed, including QUEEN (quantitative evaluator of cellular energy) (Yaginuma et al. 2014) and ATeam (Adenosine 5′-Triphosphate indicator based on Epsilon subunit for Analytical Measurements) (Maglica et  al. 2015). QUEEN is ratiometric ATP indicator consisting of a single GFP and a bacterial ATP binding protein (Fig. 5.1f). ATeam is a fluorescence resonance energy transfer (FRET) sensor, which is composed of a cyan fluorescent protein (CFP) and YFP that were connected by the ε subunit of Bacillus subtilis FoF1-ATP synthase (Fig. 5.1g) (Imamura et al. 2009). However, there is no study that applies ATP sensors to study persister cells at single cell level. Fluorescent reporters are very powerful tool to detect heterogeneity within bacterial populations. Combining these reporters with advanced microscope, flow cytometry and microfluidics platform can help to isolate the persister cells within the whole population.

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Table 5.2  High throughput screening approaches in persisters studies Organisms Tools Transposon mutant screening P. aeruginosa pTnMod-OGm PA14 plasposon E. coli mini-Tn10

Library scale

Number of genes

Antibiotic

Reference De Groote et al. (2009) Li and Zhang (2007) Manuel et al. (2010) Wang et al. (2015) Camacho et al. (1999) and Dhar and McKinney (2010) Parti et al. (2008)

5000

9

Ofloxacin

11,748

1

Ampicillin

P. aeruginosa PAO1 S. aureus USA500 M. tuberculosis

ISphoA/hah

5000

1

Carbenicillin

pBTn

9120

13

Levofloxacin

IS1096 based pCG113 plasmid

576

1

Isoniazid-­ treated mice

Mycobacterium fortuitum M. bovis BCG

TnphoA

125

1

Tn5370

3500

1

E. coli K12

Mini-Tn10

5000

2

Mice infection model Deficient in cord formation Kanamycin

2

Ciprofloxacin

S. aureus pTM239-­ Newman pTM244 Transposon sequencing E. coli MG1655 mini-Tn10 Uropathogenic EZ-Tn5 E. coli P. aeruginosa pIT2-Tn5 PAO1 Deletion mutant library screening E. coli K-12 Knockout E. coli K-12 Knockout Overexpression library screening E. coli K-12 Overexpression

200,000 360,000

Gentamicin Ampicillin

100,000

Ciprofloxacin

Glickman et al. (2000) Hu and Coates (2005) Wang et al. (2018) Shan et al. (2015) Molina-Quiroz et al. (2016) Cameron et al. (2018)

3985 3985

2 37

Ampicillin Ofloxacin

Ma et al. (2010) Cui et al. (2018)

4500

2

Ampicillin

Spoering et al. (2006)

5.4  High Throughput Screening Approaches 5.4.1  Transposon Mutant Library Screening Transposons are genetic elements that can jump from one genomic location to another, which can profoundly influence gene expression. Transposon mutagenesis is a powerful approach to generate randomized gene mutations among bacterial genomes. This technique has been widely used to identify individual genes involved

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in antibiotic resistance (Gallagher et al. 2011), virulence (Fu et al. 2013) and persister cells formation (Molina-Quiroz et al. 2016; Shan et al. 2015). The transposon mutant libraries of several bacterial species (Table 5.2) have been constructed and screened for persister cells formation related genes in certain condition. A total of 5000 P. aeruginosa PA14 transposon insert mutants were screened for persistence mutants under the pressure of ofloxacin treatment. This study has identified 9 persister formation related genes (De Groote et al. 2009). Screening of E. coli mutant library with ofloxacin found inactivation of phoU causes pan-­ susceptibility to various antibiotics and decreased the persistence (Li and Zhang 2007). PhoU is a negative regulator of the Pho regulon and involved in phosphate metabolism (Wanner 1990). Inactivation of homologue of PhoU in M. tuberculosis also resulted in a defect in persistence (Shi and Zhang 2010). Screening the S. aureus transposon insertion mutant library has identified 13 genes that contribute to persister cell formation, which are involved in oxidative phosphorylation, tricarboxylic acid cycle (TCA) cycle, glycolysis, cell cycle, and ABC transporters (Wang et al. 2015). By screening of 576 insertion mutants of M. tuberculosis in isoniazid treated mice persistence model, the disruption of cydC was found to decrease the persistence in isoniazid treated mice (Dhar and McKinney 2010). MT13, encoding a transcriptional regulatory factor in M. fortuitum (Parti et al. 2008), and pcaA encoding a mycolic acid synthase in M. bovis BCG (Glickman et al. 2000) were identified as persistence determining genes by insertion library screening. Transposon mutant library based high throughput screening has contributed significantly to persistence genes discovery. However, it needs considerable amount of labor and time to evaluate the phenotype of each mutant in the whole library. Instead, transposon insertion site sequencing (Tn-seq), which combines the transposon mutagenesis and next generation sequencing (NGS), provides a powerful and comprehensive approach for genome wide studies (Mazurkiewicz et  al. 2006; van Opijnen and Camilli 2013). By generating the transposon mutant library and culture it under different stress conditions, the relative abundance of mutants can be tracked via sequencing. The change in relative abundance of a certain mutation reflects that the gene is critical (decrease in relative abundance) or deleterious (increase in relative abundance) under that specific condition (Goodman et al. 2011; van Opijnen et al. 2009). Such studies led to the identification of thousands of persister genes in different microorganisms, including E. coli (Molina-Quiroz et al. 2016; Moyed and Bertrand 1983) and P. aeruginosa (Cameron et al. 2018).

5.4.2  Knockout and Overexpression Library Screening The Keio deletion mutant library consist of 3985 defined single gene deletions of all nonessential genes in E. coli K-12 (Baba et al. 2006; Yamamoto et al. 2009). Two energy production genes, sucB (encoding the E2 subunit of the 2-oxoglutarate dehydrogenase complex) and ubiF (encoding 2-octaprenyl-3-methyl-6-methoxy1,4-benzoquinol oxygenase involved in ubiquinone biosynthesis) were identified as

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persisters related genes by screening the Keio deletion mutant library (Ma et  al. 2010). The bacteriostatic antibiotics could induce the bacteria switch to persisters. By screening the Keio deletion mutant library, 37 and 9 genes were found to have deficiency in rifampin and tetracycline induced persister formation, respectively. Those genes were mapped into DNA repair, transcription, transporters, lipopolysaccharides (LPS) biosynthesis, flagella biosynthesis, metabolism, and translation pathway (Cui et al. 2018). Except the inactivation and deletion mutant library, the expression library also has been used for persisters related genes identification (Spoering et al. 2006). By screening the expression library, Lewis group found glpD (encoding an anaerobic sn-glycerol-3-phosphate dehydrogenase) and plsB (encoding an sn-glycerol-3-phosphate acyltransferase) participate in persister cell formation in E. coli (Spoering et al. 2006). Transposon based high-throughput screening or sequencing has the potential to reveal putative functions for most non-essential genes within an organism. However, this technology missed the essential genes which may play an important role in persister formation.

5.5  Omics Approaches 5.5.1  Genomics Approaches for Persister Studies Next generation sequencing (NGS) technologies nowadays can generate large amounts of whole genome sequences rapidly. The experimental evolution program has allowed investigators to obtain high persistence mutants. Combination of experimental evolution with NGS is able to identify the mutations that result in high persistence phenotype. The first study in persister genetics was accomplished by Moyed and Bertrand. In this study, they identified the first high persistence gene hipA by mutagenesis-and-selection concept (Moyed and Bertrand 1983). The pathogens in a host usually faces many stresses, such as antibiotics, starvation and immune system, and these stresses can facilitate pathogens to acquire high persistence mutations in vivo (Didelot et  al. 2016). High persister strains have been reported in clinically isolated Candida albicans (LaFleur et al. 2010), P. aeruginosa (Mojsoska et al. 2019), S. aureus (McAdam et al. 2011), S. epidermidis (Haunreiter et al. 2019) and M. tuberculosis (Muller et al. 2013). Genomics approaches have also enabled identification of the high persistence associated mutation sites of the clinical isolates. Point mutations of relA (encoding a GTP pyrophosphokinase) and rlmN (encoding a ribosomal methyltransferase) promote S. aureus persistent infection in host (Gao et al. 2010). Mojsoska and colleague screened 467 longitudinal clinical isolates of P. aeruginosa from 40 cystic fibrosis patients and classified 25.7% of them as high persister variants. Whole genome sequencing was used to identify 9 candidate ‘hip’ genes among those variants and the sigma factor encoding gene rpoN was found to be the top ‘hip’ gene (Mojsoska et al. 2019). Analysis of

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Fig. 5.2  Persisters enrichment for omics studies. (a) Cyclic exposure bacteria culture to antibiotics to enrich the high-persistence mutants for genomics analysis. (b) Isolate the triggered or native persisters for transcriptomic, proteomics and metabolomics analysis

genome sequences of within host evolved clinical S. epidermidis isolates has revealed many mutations in regulatory and metabolic genes that can result in increased antibiotic tolerance (Haunreiter et al. 2019). In the in vitro evolution experiments, investigators simulate the clinical treatments by exposing bacterial population to the high concentrations of antibiotics intermittently. Using this strategy, high persister mutants of M. tuberculosis have been enriched under streptomycin and rifampicin treatment (Torrey et al. 2016). Ten cycles of treatment with ampicillin resulted in selection of E. coli mutants with high level of antibiotic tolerance by increasing the lag time upon dilution in fresh medium (Fig. 5.2a) (Fridman et al. 2014). Once daily dosing of aminoglycoside treatments were shown to evolve extremely high levels of multi drug tolerant E. coli mutants (Van den Bergh et al. 2016). Accumulated mutations can be detected by comparing the genome sequences between ancestral and evolved mutants and it is observed that often multiple mutations are required for the evolved phenotypes.

5.5.2  Transcriptomic Approaches for Persister Studies What makes persister cells different from regular cells? Why the persister cells are more tolerant to antibiotics compared to regular cells? Investigators have asked those questions for many years. Both genomic and metagenomic approaches are very powerful tools to study the high persistence mutants. However, the genome sequence of persister cells are identical to the normal growth cells in the population,

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which means the genomic approach is unsuitable for naïve persisters study. One approach to address these questions is to analyze and compare the global expression profiles of the persister cells and regular cells. This approach has been applied exclusively to study E. coli, Acinetobacter baumannii, M. tuberculosis and Salmonella. The transcriptomic studies have previously mainly relied on microarray technology. To date, with the development of next generation sequencing technologies, RNA sequencing (RNA-seq) is now the mainstream technology to analyze the entire transcriptome of persisters. The first transcriptomic study of persisters was performed by the Lewis group. They treated the E. coli culture with high concentration of ampicillin, which caused lysis of regular cells, and collected the surviving persisters by centrifugation (Fig.  5.2b). The expression profiles of persister cells were determined using a microarray. Around 300 genes were over expressed in the persister cells, including toxin-antitoxin (TA) modules and other genes that can block important cellular functions such as translation and replication (Keren et al. 2004b). By comparing the expression profiles of ampicillin treatment induced persisters with ofloxacin isolated persisters, 22 common genes were induced, and 139 common genes were repressed in both groups. The transcriptionally activated genes in both antibiotics treatment induced persisters encoding ribosomal, stress response and regulatory proteins, most of the genes repressed are involved in energy metabolism, protein synthesis and small molecules transportation. Genes commonly changed in different antibiotic isolated persisters might be involved in the conserved persisters formation pathways (Kaldalu et al. 2004). By using RNA-seq, 943 genes were identified with twofold higher expression in the ceftazidime isolated A. baumannii persister cells compared with the regular cells (Alkasir et al. 2018). D-cycloserine is an anti Tuberculosis drug, which can lyse M. tuberculosis effectively. The transcriptome analysis of D-cycloserine treatment induced M. tuberculosis persisters characterized 282 upregulated genes and 1408 downregulated genes for at least for twofold (Keren et al. 2011). Use of bactericidal antibiotics to kill the regular cells within the population represents a very straightforward method to isolate the persister cells. However, the presence of antibiotics might induce the change of genes unrelated to persisters formation. Moreover, the global transcriptomic approaches provide an average value for gene expression over the survived population, which cannot provide information on pre-existing persisters. One approach to isolate pre-existing persisters is the combination of fluorescent activated cell sorting (FACS) with fluorescent based persister reporters. Using this approach, pre-existing persisters from the E. coli planktonic cultures were isolated for the transcriptomic analysis (Henry and Brynildsen 2016; Jain et al. 2016; Matsumoto et al. 2018; Shah et al. 2006). The analysis of gene expression profile by this approach has the potential to enlarge our understanding of the mechanisms involved in persister formation. Recently, clustered regularly interspaced short palindromic repeats (CRISPR) approaches have also been applied to study the bacterial persisters (Leung et  al. 2015). Other prospect regarding to further address the transcriptional and post transcriptional regulation includes using CRISPRi (CRISPR interference) (Lee et  al.

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2019a) to investigate the mechanisms of the persisters within the life cycle of bacterial cell dynamically.

5.5.3  Proteomics and Metabolomics Approaches for Persisters Studies Two dimensional (2-D) polyacrylamide gel electrophoresis (PAGE) is a potent tool to analyse the global protein expression of bacteria. 2D PAGE separates the whole cell protein extractions into protein spots. The differential protein spots can be cut from the gel and processed for the liquid chromatography/mass spectrometry analysis (Lilley and Friedman 2004). 2D PAGE has been used in several studies of persisters proteomes. For example, Starck et  al. used 2D PAGE to characterize the non-replicating M. tuberculosis under anaerobic condition (Starck et al. 2004). 2D PAGE has also been used to characterize the changes in proteome patterns of starvation induced M. tuberculosis persisters over time (Betts et  al. 2002). One of the highly upregulated gene Rv3290c (encoding a lysine-e aminotransferase) has been demonstrated to be involved in mycobacteria persister formation (Duan et al. 2016; Lee et  al. 2019b). Albrethsen et  al. used label free LC-MS/MS (liquid chromatography-­ mass spectrometry/mass spectrometry) and two dimensional DIGE (differential gel electrophoresis) and observed that most of the toxin–antitoxin systems (TAs) were up-regulated in the non-replicating state (Albrethsen et al. 2013). 2D PAGE still has limitations in global protein measurement owing to the low protein coverage, sensitivity, dynamic range, and precision (Gygi et al. 2000). For the shotgun proteomics analysis, complex proteins were digested with specific protease, such as trypsin, the ensuing peptide mixture then analyzed by LC-MS (Wolters et  al. 2001). Label free approaches including isotope coded affinity tag based and SWATH mass spectrometry based methods have been developed and applied to non-replicating and reactivated M. tuberculosis samples (Cho et al. 2006; Gopinath et al. 2015; Schubert et al. 2015). Protein samples from antibiotics isolated E. coli (Sulaiman et al. 2018) and Porphyromonas gingivalis (Li et al. 2018) persisters have been analyzed by LC-MS which revealed that the membrane integrity and cellular redox state are important for persisters formation. Proteomics has also been applied to study the antibiotic tolerant bacterial populations in P. aeruginosa biofilms. It has been previously reported that the antibiotic tolerant bacterial populations in P. aeruginosa biofilms are persister cells (Brooun et al. 2000; Spoering and Lewis 2001). By means of bio-orthogonal noncanonical amino acid tagging (BONCAT) proteomic analysis method, Babin et al. illustrated that flagellar motility and purine synthesis are critical for antibiotic tolerant persisters formation (Babin et al. 2017). In the other study, Chua et al. treated P. aeruginosa biofilms with colistin for 48 h, washed the cells, collected the persister fractions and investigated their proteomic profile by pulsed stable isotope labelling with amino acids (pulsed-SILAC) method. P. aeruginosa biofilms were cultivated in

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minimal medium supplemented with C12 L-lysine using flow chambers. After killing the antibiotic sensitive population, fresh medium containing colistin and C13 L-lysine was used to label the newly synthesized proteins of persister cells. The research above concluded that type IV pili and quorum sensing (QS) systems are important for antibiotic tolerance development in biofilm (Chua et al. 2016). Pulsed-­ SILAC approach also has been used to study the proteins that are important for E. coli persisters reactivation (Spanka et al. 2019). The persisters formation is associated with reduced metabolic activity (Prax and Bertram 2014). Starvation induced stringent response have been demonstrated to be involved in persisters formation (Germain et al. 2013; Kaspy et al. 2013; Khakimova et al. 2013). Many genes associated with metabolism were found to affect bacterial persistence (Amato et al. 2014). Besides, metabolomics has also been used to profile the metabolic changes of bacteria challenged with antibiotics (Campos and Zampieri 2019; Nandakumar et al. 2014; Zampieri et al. 2017). This approach has been applied to persisters study in the most recent years (Schubert et  al. 2015). Aspartic acid and glutamate have been identified to be critical for S. aureus persisters metabolism by fully 13 C-labeled glucose isotopologue profiling, a well-known metabolomics approach (Lechner et  al. 2014). Proteomics and metabolomics approaches provide global information to persisters formation mechanism from protein expression level, which helped us to develop effective strategies to eradicate persisters during the infection.

5.6  Conclusion Persisters contribute to the recalcitrance of chronic infections and can thwart the treatment efforts (LaFleur et al. 2006). More insights into persister cells will inform better control measures for chronic infections. The understanding about biology of persisters has made immense progress. Genes involved in persister cells formation or persistence were identified through transposon mutant library screening, which will provide more targets for better antibiotics or their leads. In addition, the omics data of persister cells can reveal more secrets of persisters biology. Metabolomics could be used to explore the metabolites important for persisters resuscitation. Those approaches have provided numerous information of persisters physiology for investigators. A better understanding of persisters formation and their reactivation mechanisms might facilitate the development of new strategies to eliminate them (similar to the ‘Shock and Kill’ anti-HIV approach).

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Chapter 6

Role of Gene Editing Tool CRISPR-Cas in the Management of Antimicrobial Resistance A. Parul Sarma, Chhavi Jain, Manu Solanki, Rajesh Ghangal, and Soma Patnaik

Abstract  The advent of drug discovery and advancement in developing new class of antimicrobial drugs has affected the microbial population. However, the microbial population has been very smart in developing themselves in order to overcome the onslaught of antimicrobial drugs and have evolved, rendering most of the commonly used antimicrobial drugs less effective. The world is looking at a problem where the infectious strains of microbes are emerging successful in the battle of drugs versus microbes. Researchers are working on different ways to overcome antimicrobial resistance. Gene editing using clustered regularly interspaced short palindromic repeats and its associated proteins (CRISPR-Cas) technique has emerged as one of the potential techniques to overcome antimicrobial resistance in microbes. CRISPR-Cas machinery is an adaptive immune system found in bacteria and archaea. Using this machinery, the organisms eliminate the foreign invading genetic material. Literature reports suggest the potential of CRISPR-Cas system in removing antibiotic resistant genes in various strains of bacteria. The chapter enlists some of the prominent studies carried out to mitigate antimicrobial resistance using CRISPR-Cas system. The different strategies to deliver CRISPR-Cas system in microbes has been discussed in the chapter. The chapter also outlines the challenges associated with this novel technique against the emerging antimicrobial resistance. Keywords  CRISPR · Cas · Antimicrobial resistance · Gene transfer · Guide RNA · RNA binates · CRISPR RNA · Anti-CRISPRs · Precursor CRISPR RNA · Protospacer adjacent motif A. P. Sarma · C. Jain · M. Solanki · S. Patnaik (*) Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India e-mail: [email protected] R. Ghangal Department of Biotechnology, CGO Complex, Lodhi Road, New Delhi, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_6

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Abbreviations CRISPR Clustered Regularly Interspaced Short Palindromic Repeats Cas CRISPR-associated proteins FDA Food and Drug Administration crRNA CRISPR RNA Acrs anti-CRISPRs pre-crRNA Precursor CRISPR RNA tract-RNA Trans activating RNA dsRNA Double strand RNA PAM Protospacer adjacent motif

6.1  Introduction The emergence of antimicrobial resistance in pathogenic microbes has captivated the attention of researchers (Caniça et al. 2019; Vikesland et al. 2019). Antimicrobial resistance is a condition arising due to the microbial resistance to the medications that once cured the disease or infection caused by the microbe. This results in inefficacy of standard interventions and persistence of infection (Prestinaci et al. 2015; Dixit et al. 2015). Overconsumption of medications has led to development of bacterial strains that show resistance to the existent drugs and also to the unsusceptible non-pathogenic species, which leads to the circulation of resistant genes present in the environment (Nitsch-Osuch et al. 2016; Lima et al. 2019; Pinheiro et al. 2019). According to the trend observed, the consumption of medications has a direct relation with the sudden elevation of resistance (Bell et al. 2014; Higuita-Gutiérrez et al. 2018; Auta et al. 2019). According to a WHO report (2016), around 4,90,000 people developed resistant to multiple drugs prescribed against tuberculosis alone. The indiscriminate use of antibiotics either self-prescribed or prescribed by medical practitioners has led to a rise in incidents of antimicrobial resistance. Although only 15% of throat infections are caused by bacteria, Food and Drug Administration (FDA) reports that doctors prescribe a long list of antibiotics which are ineffective in treating the sore throat (Agarwal et al. 2015). Rampant use of antibiotics in animal husbandry is one of the key contributors for antimicrobial resistance. Antibiotics have long been used as growth promoters and as a nonspecific mode of combating infections in farm animals (Economou and Gousia 2015; Founou et al. 2016). The incorrect and uncontrolled administration of antibiotics and drugs can lessen the efficiency of antibiotics in curing an infection. Researchers have been working towards eradicating the problem of antimicrobial resistance using phage therapy, re-senstizing the bacteria against antibiotics, clustered regularly interspaced short palindromic repeats (CRISPR) and others (Nam et al. 2012; Czaplewski et al. 2016; Richardson 2017; Pursey et al. 2018). Amongst these techniques, CRISPR is a promising tool to overcome antimicrobial resistance. This chapter gives an insight of how CRISPR-Cas9 system can be used in the management of antimicrobial resistance.

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6.2  The Story of CRISPR Clustered regularly interspaced short palindromic repeats serve as the immune system of many archaea and bacteria. It is an example of adaptive immune system and involves short length of nucleotides (RNA or DNA elements) obtained from either from bacteriophages or other foreign mobile genetic elements that invade the bacterial or archaeal hosts (Zhang et al. 2014b; Adli 2018). In 1987, Yoshizumi and his colleagues while carrying out a study on “iap” gene observed repeat sequences in the gene. This was attributed to the accidental cloning of CRISPR along with iap gene (Ishino et  al. 1987). These repeats contained many interspersed sequences whose function was then unknown. Later in 1993, the diversity of these sequences was observed and this property was used to design a method named spoligotyping (analysis of polymorphism and repeated units). In 2001, these interspersed sequences were named as CRISPR (Mojica and Montoliu 2016; Horvath and Barrangou 2010). It has been found that these repeats are followed by a set of homologous genes termed as CRISPR associated (Cas) genes. Motifs of helicases and nucleases were found in these Cas proteins advocating their involvement in the skeleton of CRISPR loci. The CRISPR locus constitutes of three main parts viz., Cas genes, leader sequences and spacer arrays (Horvath and Barrangou 2010; Marraffini and Sontheimer 2010). CRISPR-Cas network is a potential robust immune system that occurs in bacteria and archaea that provides immunity to the bacteria against the invaders. The network comprises not just of bacteriophages but also mobile genetic elements (Hale et al. 2009). CRISPR-Cas networks are categorized into two classes on the basis of associated effector modules. Class 1 CRISPR-Cas system forms multiple complexes due to multi-protein effector molecules, whereas in class 2 category CRISPR-Cas utilizes single protein effector molecule. CRISPR-Cas is further subdivided into types (I-VI) and subtypes based on signature genes and their characteristic arrangements. Class 1 CRISPR-Cas system represents majority (~ 90%) of the CRISPR-Cas loci and comprises of type I, III and IV. Cas3 is the signature gene present in the loci of all type I CRISPR-Cas systems. Cas10 and Csf1 are signature genes for type III and IV, respectively. The second class of CRISPR-Cas network includes type II, type V and type VI with the signature gene Cas9, Cas12 (Cpf1), and Cas13 respectively (McDonald et al. 2019). The types of CRISPR-Cas systems are further categorized into the following subtypes: type I subtypes (I-A, I-B, I-C, I-U, I-D, I-E, I-F), type II subtypes (II-A, II-B, II-C), type III subtypes (III-A, III-B, III-C, III-D), type V subtypes (V-A, V-B, V-C, V-D, V-E, V-U), and three type VI subtypes (VI-A, VI-B, VI-C) (Makarova et al. 2015; Koonin et al. 2017). Cas1 and Cas2 proteins are found in almost all CRISPR-Cas systems which play a significant role in spacer acquisition in CRISPR mechanism (Nuñez et al. 2014). The divergent class and large number of Cas proteins make the classification of CRISPR-Cas network challenging (Makarova and Koonin 2015; Makarova et al. 2015; Koonin et al. 2017). The characteristics of the subsets of CRISPR-Cas are mentioned in Table 6.1. In 2005, a research suggested that the spacers are derived from extra chromosomal DNA or foreign invaders like bacteriophage. The spacer array was the indication that CRISPR was part of adaptive immune system of bacteria (Pourcel et al.

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2005; Mojica et al. 2005; Bolotin et al. 2005). Later, first experimental evidence claiming CRISPR as adaptive immunity was published (Hsuet al. 2014). It was found that CRISPR locus comprised of DNA sequences obtained from the previous invading viruses in Streptococcus thermophilus (Hsu et al. 2014; Pennisi 2013).

6.3  Mechanism of CRISPR-Cas Machinery CRISPR mechanism of degrading foreign genome involves primarily three steps, as shown in Fig. 6.1.

6.3.1  I ntegration of Spacer Sequencer in CRISPR Array after Recognition (Spacer Acquisition) • The primary step during the encounter of bacteria with virus is to arrest viral DNA and integrate it in spacer array as a spacer. Presence of proteins like Cas1 and Cas2 in Cas network implies their involvement in integration of spacer in CRISPR locus (Aliyari and Ding 2009; Dugar et al. 2013; Hatoum-Aslan et al. 2011; Yosef et al. 2012; Swarts et al. 2012). • Two dimers of Cas1 protein linked by dimer of Cas2 protein forms a multiplex. Staging of double strand DNA (dsDNA) is done by Cas2 protein and Cas1 protein helps in addition of spacer in the array (Nuñez et al. 2014). Recent spacers are generally inserted at the starting of the CRISPR adjacent of leader sequence facilitating the progressive track of viral infections. Integration host factor (IHF) found in E. coli is a protein similar to histone which attaches with the leader sequence and is responsible for the precise integration of spacer in the CRISPR array (Nuñez et al. 2014).

6.3.2  D  evelopment of crRNA (Transcription Facilitated by RNA Polymerase) • Transcription CRISPR array: A typical CRISPR locus in a type II CRISPR-­ Cas system comprises a leader sequence, an array of repetitive sequences interspaced by spacer (segments of foreign DNA from previous invaders) as well as a set of CRISPR-associated (cas) genes. CRISPR array undergoes transcription and an unprocessed precursor CRISPR RNA (pre-crRNA) is obtained which is further subjected to post transcriptional processing (Terns and Terns 2011; Brouns et al. 2008; Hale et al. 2009). • Base pairing of tract-RNA with pre-crRNA: Preceding the cas operon is the trans-activating CRISPR RNA (tract-RNA) gene, which encodes a unique non-

Archaeglobus fulgidus, Clostridium kluyveri, Bacillus halodurans, Geobactersulfurreducens, Cyanothecesp 8802, E. coli K12, Yersinia pseudotuberculosis, Shewanella putrefaciens CN-32

Streptococcus thermophilus, L. pneumophila, N. lactamica, Micrarchaeumacidiphilum ARMAN-1

Staphylococcus epidermidis, Synechocystis sp. 6803, Methanothermobacter thermautotrophicus, Pyrococcusfuriosus

III-A, III-B, III-C, III-D

Role in nucleic acid (target) interference

crRNA biogenesis; cleavage of invader DNA

Helicase activity; nuclease activity; degradation of invader DNA I-A, I-B, I-C, I-U, I-D, I-E, I-F, I-F variant

II-A, II-B, II-C, II-C variant

III 1 Cas10

II 2 Cas9

I 1 Cas3

Thioalkalivibrio sp. K90mix, Rhodococcus jostii RHA1

Utilize crRNA; generation of crRNA directly from alien RNA IV-A, IV-B

IV 1 csf1

VI 2 Cas13

V-A, V-B, V-B variant, V-C, V-D, V-E, V-U, V-U1, V-U2, V-U3, V-U4, V-U5 Francisella cf. novicida Fx1, Alicyclobacillus acidoterrestris, Planctomycetes bacterium RBG_13_48_10, Bacterium CG09_39_24, Gordonia sp., Cyanothece sp. PCC 8801, Bacillus thuringiensis HD-771, Rothiadentocariosa M567,

Leptotrichia shahii, Ruminococcus bicirculans, Fusobacterium perfoetens, Prevotellabuccae, Bergeyellazoohelcum

VI-A, VI-B1, VI-B2, VI-C, VI-D

Combines the interference The encoded protein binds and cleaves module with adaptor RNA module

V 2 Cas12a (Cpf1)

References Nuñez et al. (2014), Makarova and Koonin (2015), Makarova et al. (2015), Abudayyeh et al. (2016), Koonin et al. (2017), Karimi et al. (2018), and McDonald et al. (2019)

Bacterial species

Subtypes

Types Class Signature gene Functions of signature genes

Table 6.1  Subsets of CRISPR-Cas system

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Fig. 6.1  Mechanism of CRISPR-Cas system inside a bacterium. Step1 Spacer aquisition: Spacer DNA of phage integrates with CRISPR locus, the CRISPR locus comprises spacer sequences of previous invaders and into pre crRNA. This pre-crRNA and trac RNA form a long RNA binate. Step 2 The RNA binate is severed by RNAase III into small units. Step3 RNA binates forms a multiplex with Cas9 protein. Step 4 Guide RNA identifies the PAM sequence present near target sequence (phage DNA). The CRISPR-Cas9 machinery severs the DNA at the target site

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Fig. 6.1 (continued)

coding RNA having homologous sequences to the repeat sequences (Jinek et al. 2012). The tract-RNA is transcribed separately and then anneals to the pre-­ crRNA and results in formation of RNA binate. • Severing of RNA binates (crRNA paired with tract-RNA) by RNAase III: In type II CRISPR system, RNase III cuts the dsRNA into small segments of mature crRNA possessing overhangs or flanks of repeats. Finally a guide RNA of 20 nucleotides is produced by cleavage with nucleases (Deltcheva et  al. 2011; Marraffini and Sontheimer 2010; Terns and Terns 2011; Chylinski et al. 2011; Karvelis et al. 2013; Carte et al. 2008).

6.3.3  Formation of Multiplex • RNA binates form a multiplex with Cas9 protein

6.3.4  R  ecognition of Foreign DNA with the Help of Protospacer Adjacent Motif • CrRNA nucleic acid complex cleavage by Cas9: The mature crRNA forms a multiplex with Cas9 protein. When a phage infects a bacterium, the multiplex of crRNA fragments and Cas9 cleaves the phage DNA. Cas9 recognizes only those sequences

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possessing Protospacer Adjacent Motif (PAM) (Brouns et  al. 2008; Waters and Storz 2009; Deveau et al. 2010; Deltcheva et al. 2011; Bolotin et al. 2005). • PAM is short length double stranded nucleotide chain usually 2–5 base pair. Different PAM sequences are found in different bacteria. These sequences help the Cas9 machinery to locate the phage DNA and aid in distinguishing between host DNA and phage DNA (Mojica et al. 2009; Strich Chertow 2019; Shah et al. 2013; Tyson and Banfield 2008; Horvath et  al. 2008; Deveau et  al. 2008; Andersson and Banfield 2008; Pride et al. 2011; Yosef et al. 2012; Díez-Villaseñor et al. 2013; Swarts et al. 2012; Goren et al. 2012; Datsenko et al. 2012). • To reach the site of action, Cas9 machinery requires the help of guide RNA. These sequences escort Cas9 to the target DNA (for genome editing). DNA cleavage takes place only when the target is followed by PAM sequence. In case of any mutation in PAM or incompatibility between spacer and foreign DNA, the DNA cleavage will not take place and host is prone to infection. The specificity of Cas proteins machinery is important in order to develop an active adaptive immune response by bacteria against bacteriophage (Tsai et al. 2015; Shah et al. 2009; Shah et al. 2013; Pourcel et al. 2005; Tyson and Banfield 2008; Horvath et al. 2008; Deveau et al. 2008; Andersson and Banfield 2008; Pride et al. 2011; Yosef et al. 2012; Díez-Villaseñor et al. 2013; Swarts et al. 2012; Goren et al. 2012; Datsenko et al. 2012; Mojica et al. 2009; Lillestøl et al. 2009; Shah et al. 2013; Anders et al. 2014; Esvelt et al. 2013; Zhang et al. 2014a).

6.4  M  anagement of Antimicrobial Resistance Using CRISPR-Cas System Antimicrobial resistance is a condition when the microbes (such as bacteria, fungi, viruses and parasite) develop resistance/ immunity against the antimicrobial agents. One of the causes of antimicrobial resistance is mutations. The sequences causing antimicrobial resistance in bacteria can be edited in order to re-sensitize the bacteria towards antibiotics. There are many nucleases which are used for gene editing like homing endonuclease (meganuclease), transcription activator like effector (TAE) and Zinc finger nucleases (ZNF). However, there are certain limitations associated with these techniques which are listed in Table 6.2. CRISPR-Cas9 machinery can be an interesting and promising gene editing tool to overcome antimicrobial resistance, as shown in Fig. 6.2. CRISPR-Cas9 is simple and unique amongst the CRISPR systems as only one protein is sufficient to cause gene silencing (Shabbir et al. 2019; Kim et al. 2015; Fernandes et al. 2019). As a result, CRISPR-Cas9 has been the tool of choice for gene editing. This potential was first reported by Doudna and Charpentier research groups (Jinek et al. 2012). A team of researchers led by Zhang were amongst the first to use CRISPR Cas9 system for gene editing in eukaryotes cells (Cong et  al. 2013). In CRISPR type II mechanism, guide DNA facilitates the Cas9 machinery to identify the target

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Table 6.2  Limitations of commonly used nucleases for gene editing Type of Nuclease Meganuclease

Limitations Low target specificity

Zinc finger nucleases Trans activator like effector

It is arduous to design Narrow range of target Uncomplicated design and highly specific to target but it in large in size Difficultly in insertion inside the cell

References Hsu et al. (2014) and Shabbir et al. (2019) Shabbir et al. (2019) Miller et al. (2011) Strong and Musunuru (2016) and Shabbir et al. (2019)

Fig. 6.2  CRISPR-Cas9 to overcome antimicrobial resistance in bacteria. The CRISPR-Cas9 system can be delivered in bacteria either using nanoparticles or bacteriophages. The redesigned guide RNA facilitates the CRISPR-Cas system to the gene responsible for antimicrobial resistance. The CRISPR nuclease complex disrupts the gene and results in re-sensitization of bacteria towards antimicrobial agent

DNA. Guide RNA can be redesigned in such a way that it recognizes the gene of interest (antimicrobial resistance causing genes) and facilitate the Cas9 nuclease complex to reach the target. In an experiment, researchers transformed the plasmid coding for CRISPR-Cas9 guide RNA into E. coli and Staphylococcus which resulted in the cessation of bacterial growth possessing antimicrobial resistance causing gene in the presence of antibiotics. The results advocate the presence of transformed plasmid facilitating degradation of antimicrobial resistance gene (Jinek et al. 2012; Citorik et al. 2014; Shabbir et al. 2019). Target (antibiotic resistant gene) oriented Cas9 assembly has better toxicity towards the bacterial cell. CRISPR-Cas9 nuclease assembly can be used to target the antimicrobial resistance genes and help in re-­ sensitization of bacteria towards the antimicrobial agent (Bikard et al. 2014; Diep et al.J 2006). In a recent study, re-sensitization of Shewanella algae to carbapenem using CRISPR-Cas9 gene edition was reported (Wu et al. 2019).

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6.5  Strategies to Deliver CRISPR-Cas9 Machinery Delivery of CRISPR-Cas system in vivo is one of the key parameters needed to be evaluated for successful application of CRISPR in mitigating antimicrobial resistance. CRISPR-Cas9 system can be delivered into the target cell using various methods. Few coherent strategies to transport CRISPR-Cas9 system are using a vector which can be delivered with the help of a virus or non-viral perspectives like transformation by electroporation or microinjection, delivery by liposomes and nanoparticles.

6.5.1  Vectors A vector can be a plasmid with genes encoding for Cas9 protein and guide RNA. Advantage of using only one vector coding for both nuclease and guide RNA is to eliminate performing transformation of vectors containing these genes multiple times. High stability of this vector makes it an efficient yet a simple approach. The challenges or hurdles using plasmid are: • Time consuming: Plasmid has to code for the Cas9 protein and guide RNA, followed by action of CRISPR (Liu et al. 2017) • Increased off the mark and undesirable effects (Fu et al. 2013; Cradick et al. 2013) • It becomes imperative to deliver the plasmid to nucleus which is a challenge A solution to above problems can be the use of mRNA which translates into Cas9 nuclease and guide RNA. This will allow for the formation of CRISPR-Cas9 assembly in a comparatively shorter time with low off target effects. Also, as the RNA is destined to cytoplasm, the need to target nucleus is mitigated. However, the low stability of mRNA is a challenge (Liu et al. 2017; Fang et al. 2014).

6.5.2  RNA Protein Complex Delivery of Cas9 nuclease and guide RNA complex using a delivery vehicle brought a new dimension in delivery systems of CRISPR-Cas9. The ribonucleoprotein complex model is a broadly used approach. The perks of using this complex are less time consuming, fast acting, performance with very high precision, decreased side/undesirable reactions. Moreover there is no compulsion of optimizing the codon. The complex can be delivered using liposomes, polymer based nanoparticles, metallic nanoparticles, etc. (Liu et al. 2017).

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6.6  Challenges Ahead The application of CRISPR network as gene editing tool has engrossed the scientific community due to its potential applications. With the advent of this tool, overcoming antimicrobial resistance using CRSIPR has opened up new vistas albeit with its share of challenges.

6.6.1  Intricate Diversity of Bacterial Population Despite the colossal future prospects of CRISPR network, currently Cas9 system for management of antimicrobial resistance is generally studied on small bacterial population. But in real time, bacteria are omnipresent and are quite diverse. This diversity of microbial community can be an obstacle to use Cas9 system for antimicrobial resistance management. For instance, even one gram of matrix containing cells in millions has more than thousand species. Variety of plasmids and genetic elements possessing different genes causing antimicrobial resistance can be found in single lineage. Unpredictability of extensive range response towards the stress by microbial community is the second challenge. Removal of certain plasmid or elimination of a particular strain may result in outgrowth of more pathogenic species. The repercussions of eliminating antimicrobial resistance causing gene by CRISPR-­ Cas9 system till date are still being assessed and needs more substantial positive outcomes to make it a preferred technique to overcome antimicrobial resistance (Pursey et al. 2018; Thomas and Nielsen 2005; Spencer et al. 2016; Marbouty et al. 2017; Theriot et al. 2014; Jorth et al. 2014).

6.6.2  CRISPR-Cas Delivery Mechanism Owing to a large size of CRISPR-Cas9 complex (160KDa), transfer of this assembly into the cell is a challenge. There are many ways used to deliver this machinery into the target cell. Bacteriophages, nanocages and nanosized Cas9 assemblies are some methods for the same. A report in 2014 suggested the use of capsids to deliver the CRISPR-Cas9 nuclease complex into the bacteria (Citorik et al. 2014). In order to fit in the capsid, special plasmids are designed. These plasmids are called phagemids. Transduction of these plasmids to facilitate the transport of Cas9 system efficiently degrades the antimicrobial resistance bacteria (Citorik et  al. 2014; Euler et  al. 2014). Since most of the antimicrobial resistance genes are located on the plasmids, their spread via horizontal gene transfer among the bacteria that populates a common niche is not an event of rare occurrence and thus these antimicrobial resistance causing genes disperse in wide range of bacterial species. The specificity

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of phage against a specific strain of host (bacteria) restricts the wide application of phagemids to deliver CRISPR-Cas machinery (Pires et al. 2016). CRISPR-Cas can also be delivered with conjugative plasmids. However, limited range of hosts, hindrances in plasmid intake and efficiency of conjugation restricts its widespread application. One of the major hindrances in using Cas9 is its inherent toxicity as evident in Corynebacterium glutamicum and Synechococcus elongatus (Naduthodi et al. 2018; Jiang et al. 2017). An alternative to this could be the usage of Cas12 in place of Cas9 which gave promising results (Hatoum-Aslan et al. 2011; Yosef et al. 2012; Swarts et al. 2012). The field of nanotechnology brought a new dimension in delivery mechanism of Cas9 system. A plethora of nano systems have been designed to help the deliver CRISPR-Cas9 assembly (Deng et al. 2019).

6.6.3  P  rogress in Resistance Against CRISPR-Cas System to Manage Antimicrobial Resistance in Bacteria Point mutation in antimicrobial resistance causing gene or the sequence which CRISPR-Cas system cleaves can hamper the antimicrobial resistance management in bacteria. Apart from point mutation, insertion and deletion of Cas gene can also affect the antimicrobial resistance in bacterial species. The obstacles to deliver CRISPR-Cas are more due to development of resistance towards it. Other than resistance caused by mutations, the resistance can also occur due to selection of anti-CRISPR genes that codes for tiny protein molecule which attaches to the key sequences of CRISPR-Cas network and deactivates the whole system (Vercoe et al. 2013). Escalated specificity and intricate diversity of sequence for anti CRISPRs (Acrs) shows that Acrs are omnipresent and can be transferred by mobile gene elements like bacteriophage and extrachromosomal determinants to prevent selection by CRISPR-Cas (Pawluk et al. 2018; Borges et al. 2017; Houte et al. 2016; Jiang et al. 2013; Pawluk et al. 2016; Harrington et al. 2017).

6.6.4  J udiciary and Legislation on Use of CRISPR-Cas System to Tackle AMR To degrade and eliminate antimicrobial resistance causing gene from bacterial population found in nature, using CRISPR-Cas network may have to go through numerous legislative and judicial challenges. Risk evaluation for usage of gene editing tools on naturally found microbes is crucial. Support of stakeholders becomes necessary to use gene editing tools like CRISPR to overcome the problem of antimicrobial resistance (Carter and Friedman 2016; Adelman et al. 2017).

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6.7  Conclusion The emergence of antimicrobial resistance has cut short the efficacy of many established therapeutics. The rampant usage of antibiotics has posed serious challenges in the treatment of various bacterial, parasitic and fungal infections. The morbid results of antimicrobial resistance may increase upto many folds in the near future if not addressed with immediate effect. In such a scenario, CRISPR Cas, an emergent technology adapted from bacteria has shown encouraging positive results to combat antimicrobial resistance. CRISPR technology is being currently investigated as an intervention against many diseases. The potential of CRISPR-Cas system to target and disrupt the antimicrobial resistance causing gene has provided new avenues that need to be explored further. The key step is to deliver CRISPR-Cas machinery into the target cells. The assembly can be directly delivered to target cell using nano sized cages and liposomes or indirectly by using vectors carrying genes which can code for guide RNA and Cas protein. The potential of CRISPR Cas system to mitigate antimicrobial resistance also has its share of shortcomings such as their unpredictable response towards intricate and diverse range of microbial communities, difficulties in delivering large assembly of 160KDa and development of anti-CRISPR Cas system resistance in bacteria. Apart from overcoming these challenges, there is a need of involvement of judicial and legislative laws that will restrict the misuse of this technology. It is important to weigh in all the parameters prior to use of this emergent technology for mankind.

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Chapter 7

Control of Bacterial Biofilms for Mitigating Antimicrobial Resistance Brij Pal Singh, Sougata Ghosh, and Ashwini Chauhan

Abstract  Antimicrobial resistance is a significant global issue across countries irrespective of the level of income and imposes a significant health and financial burden. Naturally, microorganisms become resistant when exposed to antimicrobial drugs, and overuse of antibiotics is a primary driver contributing to their increased prevalence. But when bacteria are attached to a surface and expand as a biofilm, they become more resistant to antimicrobials as they are embedded in a slimy extracellular polymeric substance as a single or multiple bacterial species. Thus, bacteria within a biofilm are protected from killing by antibiotics, biocides, and other chemical or physical challenges. Biofilms often contaminate the medical devices and food industrial equipment leading to associated infections and food spoilage, respectively. Therefore, warranting a need for novel agents and effective approach against drug resistant biofilms. Here we present innovative strategies to control and eradicate bacterial biofilms unlike the conventional antibiotic therapies. The review introduces the basics of biofilm development from a planktonic bacterium, the role of bacterial motility, structural components, and exopolysaccharides. Furthermore, signaling in biofilm, its association with antimicrobial resistance and how inhibition of biofilm signaling, using quorum sensing inhibitors molecules such as phytocompounds, signaling molecule analogues and RNA III - inhibiting peptide, can be exploited are discussed. Present review also deliberated the role of ultrasound and acidic electrolyzed water to disrupt biofilms from medical devices and food industry equipment. Besides we also reviewed the enzymatic and combination killing approaches used to remove biofilms. In the end, some emerging approaches like bacteriophage ­mediated disruption of biofilms and the role of nanomedicine to control bacterial biofilms are discussed. B. P. Singh (*) Department of Microbiology, School of Science, RK University, Rajkot, Gujarat, India S. Ghosh Department of Microbiology, School of Science, RK University, Rajkot, Gujarat, India Department of Chemical Engineering, Northeastern University, Boston, MA, USA A. Chauhan Department of Microbiology, Tripura University, Suryamaninagar, Tripura, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_7

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Keywords  Biofilms · Antimicrobial resistance · Quorum sensing · Nanomedicine · Bacteriophage · Phytocompounds · Exopolysaccharide

Abbreviations AEW Acidic Electrolyzed Water AgNPs Silver Nanoparticles AHL Acyl-Homoserine Lactone AI-2 Autoinducer-2 AIP Autoinducing Peptide CF Cystic Fibrosis CSP Competence Stimulating Peptide EPS Exopolysaccharides HSL Homo Serien Lactone MBP Myelin Basic Protein MIC Minimum Inhibitory Concentration MRSA Methicillin Resistant Staphylococcus aureus MSCRAMMs Microbial Surface Components Recognizing Adhesive Matrix Molecule OECD Organization for Economic Co-operation and Development PIA Polysaccharide Intercellular Adhesin PLL Phosphotriesterase Like Lactonase QSI Quorum Sensing Inhibitors RAP RNA III - Activating Protein RIP RNA III-Inhibiting Peptide SAAT Self Associating Autotransporter Proteins SPION Superparamagnetic Iron Oxide Nanoparticles TRAP Target of RAP UTI Urinary Tract Infections

7.1  Introduction Infectious diseases remain the major cause of morbidity and mortality worldwide. Antibiotic resistance in disease causing pathogens continuously enhances the complexity and severity of the problem. The evolution of antimicrobial resistant strains is a natural phenomenon that happens when microorganisms are exposed to antimicrobial drugs, and resistant traits can be exchanged between certain types of bacteria (Sharma et al. 2018). Besides human health and lives; there is also an economically high cost of antibiotic resistance. Already, 700,000 patients die from antimicrobial resistance worldwide each year (Ghosh et al. 2018). The latest OECD (Organization

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for Economic Co-operation and Development) report (http://www.oecd.org/health/ stemming-the-superbug-tide-9789264307599-en.htm) estimates that 2.4 million people will die from the infection of antimicrobial resistant microorganisms in Europe, North America and Australia over the next 30 years and could cost up to US$ 3.5 billion annually. This condition is already drastic in many low and middle income countries, which are expected to rise significantly (Hofer 2019). In a natural environment, bacteria exist predominantly in multicellular communities called biofilms that are attached to the surface and encased in matrix as opposed to isolated planktonic cells studied in laboratory (Singh et al. 2018). This form of lifestyle is consequential for bacterial physiology and survival as it involves significant change in genetic information and associated cellular energy. Within biofilms, cells can evade immune system and antibiotic therapy and thereby convoluting the treatment of chronic infectious diseases. Several species of pathogenic microorganisms such as Staphylococcus epidermidis, Mycobacterium tuberculosis, Mycoplasma pneumoniae, Candida albicans, Pseudomonas aeruginosa etc. are known to cause biofilm associated diseases and pose serious health concerns due to their recalcitrant nature towards antimicrobial drugs (Kumar et al. 2017). The biofilm characteristics allows microbes to survive adverse environmental situations. Even the high minimum inhibitory concentration (MIC) of many new generation antibiotics are unable to eliminate entire biofilm because of the concentration variation of antibiotics throughout a biofilm, may help microbial cells to develop resistance (Algburi et al. 2017; Roy et al. 2018). Biofilm knowledge has advanced tremendously in last decade and has provided molecular details of biofilm formation and its dispersion, using different bacterial models. Irrespective of the species, biofilm development is greatly affected by the environment and nutrition availability. Regulatory processes and signals important for biofilm development are often conserved among related bacteria. Antibiotics are frequently used to control the growth of pathogenic microorganisms in the treatment of bacterial infections and biofilm growth prevention. But, the extensive and unnecessary use of antibiotics accelerates microorganisms to develop resistance. Consequently, we are now encountering an alarming increase in multi resistant bacteria (Rasmussen and Givskov 2006). Therefore, the potential alternatives of antibiotics must be identified otherwise pre-antibiotic era may return. Several approaches have been developed to control and remove biofilms from biotic as well as abiotic surfaces. Some mechanical means are used based on avoidance of attachment of the bacteria to the surfaces and disruption the biofilm formation. Physical control methods such as, super high magnetic fields, ultrasound treatment and high pulsed electrical fields has also been used especially in medical devices. Besides, chemical approaches have also been used to control biofilm for example, sodium citrate, N-alkylpyridinium bromide and N-acetylcysteine inhibited biofilm formation by staphylococci, E. coli and Pseudomonas aeruginosa species (Satpathy et al. 2016). Besides these approaches, nowadays novel and effective strategies such as inhibition of cell-cell communication, bacteriophages mediated killing, enzymes mediated approach, nanomedicine approach and phytocompounds based inhibition has been developed to prevent and disrupt biofilm.

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This review focuses on the development of microbial biofilms, their signaling and associated drug resistance, and potential emerging strategies to control and disrupt the biofilm.

7.2  Architecture of Biofilm Biotic as well as abiotic surfaces are susceptible to biofilm formation that progresses via different stages that may be classified broadly into an adhesion stage, which is reversible, followed by an irreversible coaggregation/ maturation stage. During progression through different stages of biofilm development, the microbial cells express or repress genes required for different stages: (i) attachment to a surface, (ii) non motile growth, (iii) colony formation, and (iv) dispersion. The differentially expressed unique set of genes enables production of extracellular matrix consisting of polysaccharides, glycol peptides, lipids, proteins and nucleic acids.

7.2.1  C  ontacting the Surface: Role of Motility in Adherence to Surfaces Hydrodynamic forces and electrostatic forces act as repulsive forces that a bacterium is subjected to in a liquid environment, especially when it is approaching a surface. In order to overcome these inhibitory forces, bacteria have developed mechanisms of active motility, which increases its chances of attaching to a surface (Donlan 2002). Although biofilm development is different for two unicellular lifestyles: motile and non-motile, the generalized biofilm model constructed from the results of the prior work fits many bacterial species. Under advantageous conditions for biofilm formation, individual non motile bacteria upsurges its stickiness through increased expression of adhesins on their outer surface. This stickiness of bacteria advances both cohesive: cell-cell, as well as adhesive: cell-surface adherence upon encountering a surface (Götz 2002). For example, certain strains of staphylococcal species express surface proteins including Bap that promote binding to polystyrene surfaces as well as cell-cell interaction and are part of the extracellular matrix (Lasa and Penadés 2006). On other hand, when conditions are propitious for biofilm formation, individual motile bacteria such as E. coli and Salmonella confine to a surface and trigger a striking lifestyle switch. Bacteria lose its motility by losing its flagella and begin to produce an extracellular matrix that encapsulates and holds the cells together. It was earlier demonstrated using flagella defective or flagella and/or motility minus mutants (fliC, flhD, motA and motB) that such bacteria are comparatively defective in formation of biofilms due to lack of initial attachment of cells to the surface (Pratt and Kolter 1998). It is postulated that the motility helps in overcoming the repulsive forces that are generated between the cells and abiotic surfaces, thus, permitting favorable

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surface attachment (Geesey 2001). Microscopic observations of Pseudomonas aeruginosa biofilms indicated that motility promotes not only movement along the surface but also initial interactions with it (O’Toole and Kolter 1998). Although, motility is needed for initial surface interactions, further cellular attachments to the surface may be sterically hindered by the flagella besides its movement. Accordingly, after a bacterium attaches to a surface the motility genes are repressed. A small non coding RNA (sRNA), RsmZ was shown to be involved in switch machinery from planktonic to biofilm lifestyle in Pseudomonas aeruginosa. RsmZ is positively regulated by sensor kinases LadS and negatively regulated by sensor kinase RetS. RetS helps in maintaining Pseudomonas aeruginosa in planktonic state necessary for expression of type three secretion system, during acute infection, by activating a mRNA binding protein RsmA. LadS on other hand increase the transcription of RsmZ, which binds to RsmA and inactivates it. This RsmZ mediated inhibition of RsmA results in inhibition of Type three secretion systems by switching to biofilm formation associated with chronic infections (Ventre et al. 2006). Bacillus subtilis under biofilm forming conditions switches from flagellated, motile cells to long chains of non motile cells growing in parallel. This switch in lifestyle is governed by a global transcriptional regulator, SinR that suppresses the transcription of genes responsible for matrix production in motile cells to indirectly enhance cell separation and motility. Under biofilm forming condition, SinI, YibF and YmcA antagonisze SinR activity leading to loss of motility, and cell chain formation along with matrix production (Kearns et al. 2005; Branda et al. 2006).

7.2.2  Initial Adhesion to Surfaces: Reversible Linkages Reversible adhesion stages include loose aggregation of bacterium that may dissociate and revert back to free floating forms. Electrostatic interactions between the substratum, the bacteria and surface topography are the driving forces, during the initial stages of biofilm formation on abiotic surfaces that are facilitated by the tip adhesins on bacterial pili. Hydrodynamic forces stemming from viscosity of medium influence the probability of making initial interaction within the substratum. Furthermore, presence or absence of chemotactic molecules greatly affects the initial interactions between the substratum and microbial proteins. Bacteria with increased hydrophobicity have reduced repulsion between the substratum and the bacterium (Zobell 1943; van Loosdrecht et al. 1990). Both the nature of the surface and the environmental conditions influence the reversible attachment. The environmental conditions such as pH and the ionic force of the medium or the temperature are important players in biofilm formation (Donlan 2002). Hydrophobic surfaces such as Teflon or plastic are more prone to colonization by bacteria as compared to hydrophilic surfaces like glass and metal. Moreover, adsorption and desorption of nutrients at the surface makes a conditioning film that may influence bacterial initial attachment depending on the properties of the organic molecules (Olsen et al. 1989).

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7.2.3  Fimbriae Affect the Irreversible Adhesion to Surfaces In case of biotic surfaces, bacterial attachment to the host surface is mediated via the specific recognition of bacterial surface proteins by the extracellular proteins or carbohydrate moieties expressed on surface of the cells/ tissues. In E. coli, three categories of fimbriae strengthen the bacteria-to-surface interactions; type 1 fimbriae, curli, and conjugative pili. Type 1 fimbriae (or pili) are filamentous adhesins produced by both commensal and pathogenic E. coli isolates. Type 1 pili can adhere to wide range of eukaryotic surface receptors in mannose dependent manner. Type 1 pili tip adhesin, Fim H, binds to eukaryotic mannose oligosaccharides and is involved in pathogenesis caused by uropathogenic E. coli. Moreover, curli fimbriae, also called thin aggregative fimbriae, were initially identified in E. coli, are also produced by other Enterobacteriaceae such as Shigella, Citrobacter, and Enterobacter. Curli bind to eukaryotic proteins such as fibronectin, laminin and plasminogens, to promote adhesion to eukaryotic host. Curli production is encoded by two sets of divergently transcribed operons: csgBA (structural components of curli fimbriea) and csgDEFG (regulatory and export machinary of curli) (Ben Nasr et al. 1996). Researchers showed that F conjugative pilus could functionally substitute for Ag43, curli or type 1 fimbriea. In Pseudomonas aeruginosa, attachment and movement through the viscous cell surface is aided by type IV pili (Reisner et al. 2003). A collagen binding surface adhesion molecule, Sag, was reported in Enterococcus spp. provides focal points for adherence and aggregation on eukaryotic cells, leading to biofilm formation. Staphylococcus epidermidis and Staphylococcus aureus express several different types of microbial surface components recognizing adhesive matrix molecule (MSCRAMMs: ~12 in S. epidermidis and ~ 20 in S. aureus) that have ability to bind to host matrix proteins including fibronectin and fibrinogen (Fey and Olson 2010).

7.2.4  Surface Adhesins That Contribute to biofilm Structure Maturation stage of the biofilm leads to the three dimensional mushroom like cell growth surrounding fluid filled channels. It is characterized intercellular aggregation, which involves many different adhesive proteins or polysaccharide based exopolymers. Mostly as a consequence of bacterium-bacterium interactions, a heterogeneous physicochemical environment is created wherein bacteria shows traits distinguished from their planktonic counterparts (Romeo 2008). An autotransporter protein Antigen 43 encoded by flu locus, is found in many strains of E. coli, it and other SAAT: self associating autotransporter proteins, including AIDA-I and TibA impair bacterial motility to mediate attachment to the surface and also aids in intercommunication between the bacteria of same species (Ulett et al. 2007). Moreover, in urinary tract infections (UTI), E. coli cells that form biofilm like structures within bladder cells overexpress Ag43 (Anderson et al. 2003).

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It was demonstrated that the strains of Pseudomonas aeruginosa lacking CheR1 methyltransferase, fail to express chemotactic amino acids required for surface adherence and maturation of biofilm. Although polysaccharide intercellular adhesion, PIA, is the main molecule responsible for biofilm formation, PIA independent biofilm formation occurs wherein adhesive proteins substitute for PIA. Aggregation associated protein: AAP is most widely studied among such staphylococcal adhesins. Other adhesin proteins involved in biofilm formation are SSP-1 and SSP-2 proteins, which are identical to AAP. Staphylococcus surface proteins (SSP) contributes to cell-cell adhesion by forming protein strands on the Staphylococcus epidermidis surface. Another protein from Staphylococcus aureus isolates named biofilm associated protein, Bap, is involved in adherence to polystyrene surfaces, intercellular interactions, and biofilm formation (Cucarella et al. 2001).

7.2.5  Biofilm Matrix Polysaccharides One of the most striking features of biofilms that differentiates them from planktonic counterpart is the presence of extracellular matrix that encapsulates the biofilm bacteria and determines the architecture of mature biofilm. Extracellular matrix production is essential feature for maturation of biofilm structure. The matrix is a complex structure mostly composed of water (97%), rest it consists of exopolysaccharide polymer, lipids/phospholipids, nucleic acids, proteins, absorbed metabolites and nutrients (Ghannoum and O’Toole 2001). Composition of extracellular matrix produced by different bacteria is presented in Table 7.1. Although Extracellular matrix is a characteristic feature of biofilms, its role is not fully understood. Some of the designated roles for Extracellular matrix are (i) acts as a hydrated viscous layer protecting embedded bacteria from desiccation; (ii) provides protective layer that prevents bacteria from being recognized by the immune system; (iii) acts as a diffusion barrier thus protects from routine antimicrobials; (iv) act as a sink for toxic molecules (antimicrobials, hydroxyl radicals, and superoxide anions); and (v) contribute to development of phenotypic resistance of Table 7.1  Composition of extracellular matrix of representative bacteria Bacteria E. coli P. aeruginosa B. subtilis S. aureus

Composition of extracellular matrix Extra polymeric substance (β-1,6-GlcNAc), lipids/phospholipids, nucleic acids, proteins, absorbed metabolites and nutrients Extra polymeric substance (alginate, Pel and/or Psl), lipids, membrane vesicles, fimbriae, cupA, Typre IV pili Extra polymeric substance, extracellular proteins like TasA, fimbriae, eDNA, lipids, pili Extra polymeric substance (poly-b-1,6-N-acetylglucosamine), adhesive proteins like Biofilm associated protein (bap) and accumulation associated protein (Aap), lipids and Edna

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pathogenic bacterial biofilms leading to persistent infections. Besides, Extracellular matrix also has a structural role in biofilm formation. Extracellular matrix’s stickiness allows bacteria to interact with each other and also, with the surface. Polysaccharides (Cellulose) and the other components (eg. Curli) present in the biofilm matrix may interact and thus, may participate in three dimensional mushroom like growth of the biofilm (White et al. 2003). Polysaccharide intercellular adhesin (PIA), which is also known as PNAG (Poly-­ b-­1,6-N-acetylglucosamine (β-1,6-GlcNAc)) is a polysaccharide polymer that is an important matrix component of Staphylococcus aureus and Staphylococcus epidermidis biofilms and contributes to their virulence. In E. coli, an exopolysaccharide named β-1,6-GlcNAc, or PGA, is important for both cell-cell interaction and attachment to surfaces. Metaperiodate or a b-hexosaminidase isolated from Actinobacillus actinomycetemcomitans (DspB), depolymerizes PGA by degrading β-1, 6-GlcNAc. This results in almost complete disruption and dispersion of the biofilm. In E. coli, the synthesis (the PgaC glycosyltransferase), export and localization of the PGA polymer is encoded by pgaABCD (or ycdSRQP) operon. The pgaABCD operon is present in a variety of eubacteria. It has been proposed that β-1,6-GlcNAc adhesin stabilizes biofilms of E. coli and other bacteria such as Actinobacillus pleuropneumoniae and A. actinomycetemcomitans (Kaplan et al. 2004). P. aeruginosa produces 3 types of polysaccharides, namely alginate, pel and psl. It was earlier shown that pel and psl polysacchardies are mainly produced by environmental strains. Although alginate is widely conserved among P. aeruginosa strains, it is not important for biofilm formation in non-mucoid variants. However, alginate severely affects the biofilm structure and resistance phenotype (Franklin et al. 2011; da Silva et al. 2019). Cellulose, a glucose polymer, is produced only by a few bacterial species such as the model organism Gluconacetobacter xylinum. Using calcofluor dye it was shown that cellulose production is common in Enterobacteriaceae, including Enterica serovar enteritidis, Salmonella enterica serovar typhimurium, S. enterica subsp. and commensal and pathogenic strains of E. coli, Citrobacter spp. and Enterobacter spp. Cellulose production is clearly related to the rigid biofilm formation at the liquid-­air interface; these characteristics however, are highly strains/ serovar dependent besides on environmental conditions. Cellulose producing genes are constitutively expressed and organized as two divergently transcribed operons, bcsABZC and bcsEFG. These genes are present in most entero bacterial genomes, including Salmonella, E. coli, Shigella, Enterobacter, and Citrobacter. Moreover, cellulose synthesis is allosterically controlled by a well known secondary messenger called cyclic-di-GMP (c-di-GMP). Synthesis of two biofilm components, curli fimbriae and cellulose in Salmonella typhimurium showed a characteristic phenotype on Congo red agar plates, the red dry and rough (rdar) morphotype. Such morphotype is also reported in E. coli, wherein cellulase treatment leads to biofilm dispersion (Zogaj et al. 2003; Da Re and Ghigo 2006). Colanic acid, a negatively charged complex polymer consists of glucose, galactose, fucose, and glucuronic acid. Under specific growth and environmental settings, colanic acid forms a protective capsule around the bacterial cell (for instance,

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at 37  °C colanic acid production is hamperred in rich medium). Nineteen gene located in same cluster called wca (formerly known as cps) are involved in colanic acid synthesis. Colanic acid synthesis involves 19 genes located in the same cluster, named wca (formerly known as cps). Colanic acid synthesis is induced by RcsCDB, a 3 component system and requires a supplementary positive transcription regulator RcsA. On one hand, colanic acid impairs the initial surface attachment in bacteria on the other hand, up regulation of its synthesis in biofilms is reported, which is important for development of the mature biofilm structures (Prigent-Combaret and Lejeune 1999; Danese et  al. 2000; Stoodley et  al. 2002a; Stoodley et  al. 2002b; Hanna et al. 2003).

7.3  Signaling in Biofilm Communicating using chemical signals is an intriguing feature of microbes growing in biofilms. These chemicals cross the membranes and are sensed by not only the members of same species but also by microbes of other species (Fig. 7.1). Cells of planktonic population also produce these chemical signals but due to free floating conditions they are not concentrated enough to affect the genetic expression. On the other hand, in biofilm mode of growth, exopolysaccharide helps the cells to be in close proximity, which enables them to sense the chemical signals more efficiently so as to effect changes in cellular behavior. Once the bacterial

Fig. 7.1  Bacterial biofilm formed on a surface coordinate by chemical signals. In the pictorial depiction above, different color mushroom structures represent biofilms formed by various species of bacteria. Blue, green and red circles represent chemical signals (talk) produced by bacteria that can be perceived by different species of bacteria (listen) as indicated by big arrows OR by same species bacteria as indicated by small arrows. This process commonly called as cell-cell communication or cell-cell signaling help microbes’ carry out certain coordinated behavior such as biofilm formation

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system has sensed that there is enough number of bacteria in population that is beneficial and/or “safe” to initiate that genetic activity, they activate certain genes only. This population density dependent phenomenon is called quorum sensing. Quorum sensing was first observed as a function of cell density dependent light produced by marine bacterium Vibrio fischeri (Bassler et al. 1993; Mukherjee and Bassler 2019). Broadly, three categories of quorum sensing have been identified: a) found in Gram negative bacteria, comprising LuxI/LuxR type quorum sensing use acyl-­ homoserine lactone (AHL) as signal molecules; b) peptide based signaling system in Gram positive bacteria; and c) LuxS-encoded autoinducer-2 (AI-2), which is utilized by both Gram negative as well as Gram positive bacteria (Saxena et al. 2019; Liu et al. 2018). The AHL signaling molecules are biosynthesized by LuxI protein and are capable of diffusing across cell membrane. With an increase in cell density, AHL are recognized by transcriptional regulator protein LuxR.  This LuxR-AHL complex triggers transcription of several genes and play an important role in biofilm maturation. In P. aeruginosa, LasR-30C12-HSL complex differentially activates several genes in concentration dependent manner. P. aeruginosa LasI mutants have impaired 30C12-Homo Serien Lactone (HSL) synthesis and subsequently affected biofilm maturation. Adding LasI generated 30C12-HSL quorum sensing signal could restore the biofilm architecture (Parsek and Greenberg 2000). Unlike Gram negative bacterial species, Gram positive species utilize peptides of varying length (5 to 87 amino acids) as quorum sensing signal molecules. S. pneumoniae utilizes a 17-residue competence stimulating peptide (CSP) quorum sensing and S. mutans uses 21 a/a CSP quorum sensing to induce genetic competence during promoted biofilm growth (Okada et  al. 2005). Bacillus subtilis and Staphylococcus aureus have Ser/Thr kinases mediated regulation of biofilm growth. PrkC is a dimeric Ser/ Thr kinase like auto phosphorylating protein found in B. subtilis cell membrane that also phosphorylates external myelin basic protein (MBP). S. aureus has Ser/Thr kinase like Stk1 protein that phosphoryltes LuxS protein to eliminate production of AI-2, thereby causing biofilm formation (Cluzel et al. 2010).

7.4  Biofilm Associated Multidrug Resistance Numerous research studies have reported the high drug tolerance of bacteria in biofilms. Multi cellularity of biofilms confers protection to these cells. This tolerance is due to the intrinsic genetic drift from the free floating lifestyle to surface adhered forms (Romeo 2008; Sánchez et al. 2019). There are several hypothesis given to explain the high recalcitrance in biofilm bacteria such as development of concentration gradient of substrates create localized microenvironments, effective management of stress response within biofilms in certain bacterial cells, slowed or blocked penetration of antimicrobials in biofilms due to extracellular matrix, biofilms act as a reservoir of persisters. In planktonic state, there is excess of nutrients that bacteria can utilize but is unable to deplete substrates from the neighbouring cells due to not enough metabolic activity (Donlan 2002). On other hand, coordinated and

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community behaviour in biofilms lead to development of concentration gradient of substrates leading to localized microenvironments created with biofilm structures. Although planktonic bacteria have several genes coding for protective stress responses, they are readily overpowered by a sudden and strong stressful challenge such as antibiotic exposure. These bacterial populations are killed even before the response to stress could be triggered (Xu et al. 1998). Whereas in biofilm mode, the stress response is effectively put into action in certain bacterial cells as a trade-off to keep the population alive. Matrix of biofilm helps to overcome the environmental insults such as antibiotics. Although bacteria in planktonic cultures can also counter balance the antibiotic effects, ability of these single cells in not enough to tolerate these insults for long, In case of biofilms, the community behaviour gives the advantage of group activity to overcome the effects of antimicrobials by either slow or blocked penetration of antibiotics (Stewart 2003). Gradients of nutrient and oxygen created within biofilms assist asynchronous growth of non-dividing populations that are highly drug tolerant. On one hand phenotypically diverse population of bacteria within biofilm demonstrates quorum mediated coordination, on other hand these bacteria reprogramme genes for selective growth of resilient stress enduring cells. A fraction of cells (≈0.01%) are physiologically different compared to the parent cells but are genetically similar and are called “persisters”. Although highly drug tolerant, unlike mutant cells persisters are susceptible to minimal inhibitory concentrations of antibiotic under favorable growth conditions (Balaban et al. 2019). These cells are metabolically inactive with modified toxin antitoxin machinery and are highly tolerant to high doses of antibiotics. The hipBA toxin antitoxin system was first demonstrated to have a role in persister formation (Falla and Chopra 1998). A stringent response alarmone ppGpp, which is present in almost all the bacteria and plays a critical role in persister cell formation. In addition, SOS activation in bacteria also participate in persistence (Maisonneuve and Gerdes 2014).

7.5  Biofilm Controlling Strategies Bacteria closely associated with biofilms exhibit enhanced tolerance to antimicrobial agents compared to the free living planktonic bacteria. Thus, physical tolerance may be re-induced by effective disruption of the bacterial biofilms. To prevent biofilm development cell-cell communication can be an effective target which bacteria frequently used to reach a threshold concentration. Moreover, extracellular polymeric matrix which stabilizes the biofilms can be targeted which may lead to the weakening of the biofilm that may eventually lead to disruption. Such altered biofilm architecture may allow effective penetration and drug infiltration to the interior of the biofilm improving the efficacy of the antibiotics. Likewise, it might cause enhanced localization of polymorphonuclear leukocytes responsible for phagocytosis mediated removal of pathogenic bacteria. Such weakened biofilm release bacteria, which are more susceptible to antibiotics and immunological response.

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7.5.1  Interference with Quorum Sensing Quorum sensing is a communication behavior between single or multiple bacterial species whereby signal molecules can be produced and detected and coordinated accordingly in a manner dependent on cell density. As discussed earlier, different groups of bacteria vary with the quorum sensing system, e.g. Gram negative bacteria have acylhomoserine lactone (AHL), Gram positive bacteria contain autoinducing peptide (AIP), and autoinducer-2 (AI-2) found in both type of bacteria. Quorum sensing molecules regulate the virulence behaviors of bacterial biofilm. Biofilm resistance to antimicrobial drugs is now well established as a multicellular approach that depends essentially on the exchange of chemical signals between the cells. Quorum sensing quenchers or inhibitors can therefore provide a novel approach by interfering with bacterial cell-cell communication to prevent the formation of biofilms (Truchado et al. 2015; Algburi et al. 2017). Several approaches have been developed to interfere with bacterial quorum sensing i.e. (1) competitive binding of inhibitors to the quorum sensing receptors, (2) inhibition of quorum sensing signals, (3) enzymatic degradation of quorum sensing signals, and (4) control of quorum sensing genes (Yang and Givskovi 2015). Several phytocompounds have been explored as quorum sensing inhibitors (QSI); such as Allium sativum (garlic) crude extracts inhibit the expression of a large number of quorum sensing controlled genes. Ajoene, a sulfur containing compound found in garlic extract, has antimicrobial and antibiofilm activity against a number of bacteria, including Escherichia coli, Klebsiella pneumoniae, and Xanthomonas maltophilia (Jakobsen et al. 2012). The efficacy of plant food extracts and phytochemicals is primarily due to their resemblance to the chemical structure of quorum sensing signals (Truchado et al. 2015). Quorum sensing inhibitory activity has also identified in Oak bark (Quercus cortex) extract against Chromobacterium violaceum strain (Deryabin and Tolmacheva 2015). Moreover, eugenol inhibits the production of quorum sensing mediated violacein in Chromobacterium violaceum and virulence factors in Pseudomonas aeruginosa strains. Eugenol has also found to be effective against clinical isolates i.e. Listeria monocytogenes and Klebsiella pneumonia (Ta and Arnason 2016). Similarly, methanol extract of Albiza schimperiana root and petroleum ether extract of Justicia schimperiana seed has also found to interfere with cell to cell communication of bacterial biofilms (Bacha et al. 2016). Quercetin, a plant flavonoid, inhibited biofilm formation and quorum sensing regulated characteristics such as violacein inhibition, EPS production and alginate production efficiencies in selected food borne pathogens such as K. pneumoniae, P. aeruginosa, and Y. enterocolitica (Gopu et al. 2015). Zingerone, a potent anti-­ inflammatory compound found in Zingiber officinale (ginger), also posses potent effect on quorum sensing signal molecules in clinical isolates of P. aeruginosa (Kumar et al. 2015) (Table 7.2). Besides phytocompounds, RNAIII-inhibiting peptide (RIP) has demonstrated strong activity in preventing methicillin resistant Staphylococcus aureus graft infections in vivo. Actually, Staphylococcus aureus regulates their virulence by using the

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Table 7.2  Phytocompounds as quorum sensing inhibitors (QSI) to target bacterial biofilm Plant source Allium sativum (garlic)

Active component Ajoene

Target pathogens Escherichia coli, Klebsiella pneumoniae and Xanthomonas maltophilia Chromobacterium violaceum

References Jakobsen et al. (2012)

Quercus cortex (oak)

Bark extract

Zingerone

P. aeruginosa

Ellagic acid

Staphylococcus aureus, methicillin resistant S. aureus, Escherichia coli, and Candida albicans Staphylococcus aureus and Staphylococcus epidermidis Eikenella corrodens

Kumar et al. (2015) Bakkiyaraj et al. (2013)

Syzygium Eugenol aromaticum (clove)

Berries, Allium cepa (onions), Malusdomestica (apple) etc. Zingiber officinale (ginger) Punica granatum (pomegranate)

Quercetin

Aesculus hippocastanum

ProAC (proAnthocyanidin A2-phosphatidyl choline

Tea, Cocoa and Berries Rheum palmatum

Catechins

Cruciferous vegetables Rubiatinctorum

Emodin Allylisothiocyanate, Benzylisothiocyanate and 2-phenylethylisothiocyanate Purpurin Fruit extract

Lagerstroemia speciosa Zingiber officinale (Ginger) Cinnamon

Cinnamaldehyde

Vanilla planifolia

Vanillin

6-Gingerol

Deryabin and Tolmacheva (2015) Ta and Chromobacterium violaceum, Pseudomonas Arnason (2016) aeruginosa, Listeria monocytogenes and Klebsiella pneumoniae Gopu et al. K. pneumoniae, P. (2015) aeruginosa, and Y. enterocolitica

Escherichia coli and P. aeruginosa Chromobacterium violaceum Candida albicans Pseudomonas aeruginosa Pseudomonas aeruginosa Listeria monocytogenes, Staphylococcus epidermidis and Cronobacter sakazakii Chromobacterium violaceum and Aeromonas hydrophila

Artini et al. (2012) Matsunaga et al. (2010) Ding et al. (2011) Borges et al. (2014) Tsang et al. (2012) Singh et al. (2012) Kim et al. (2015) Ta and Arnason (2016) Kappachery et al. (2010) (continued)

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Table 7.2 (continued) Plant source Allium sativum (garlic)

Active component Allicin

Target pathogens P. aeruginosa

Berberidaceae species

Berberine

K. pneumoniae and S. Epidermidis

References Ta and Arnason (2016) Ta and Arnason (2016)

autoinducer RNAIII-activating protein (RAP), which induces the phosphorylation of the target of RAP (TRAP). Thus, if TRAP expression or phosphorylation will be blocked by RNAIII-inhibiting peptide, the bacteria do not produce toxins and do not cause disease. RNAIII-inhibiting peptide has already been tested and found to have strong activity in preventing staphylococcal infections, including drug resistant strains, such as methicillin resistant S. aureus (MRSA), glycopeptides intermediate S. aureus, and vancomycin intermediate S. aureus strains (Balaban et  al. 2007). Intestinally, a study showed that quorum sensing inhibitors increase the susceptibility of bacterial biofilms to antibiotics. In the presence of quorum sensing inhibitors, effect of tobramycin on P. aeruginosa, B. cepacia and clindamycin or vancomycin on S. aureus resulted in increased killing compared to killing by an antibiotic alone (Brackman et  al. 2011). Similarly, penicillic acid and patulin (Penicillium spp. metabolites), displayed effect on quorum sensing controlled gene expression in P. aeruginosa. This study also found that P. aeruginosa biofilms treated with patulin and tobramycin were considerably more susceptible to the antibiotic as compared to control biofilms exposed to either tobramycin or patulin alone (Rasmussen et  al. 2005; Rasamiravaka et al. 2015). Furthermore, some AHL analogues such as N-(indole-3-butanoyl)-L-HSL and N-(4-bromo-phenylacetanoyl)-L-HSL has been found to have double inhibitory activity on LasR based quorum sensing system as well as biofilm formation in P. aeruginosa. Synthetic AHLs downregulate expression of the Las I AHL synthase, resulting in reduced expression of the virulence factors pyocyanin and elastase and in an alteration of biofilm morphology (Rasamiravaka et al. 2015). Similarly, autoinducer-­2 (AI-2) analogs, isobutyl-DPD has been shown to inhibit the maturation of Escherichia coli biofilms. When isobutyl-DPD was used with gentamicin, the combination rendered almost complete clearance of preexisting E. coli biofilms (Roy et al. 2013). Vanillin, a benzoic acid derivative, has also been reported to reduce Aeromonas hydrophila biofilm by 90% on membrane filters at a concentration of 0.18 mg/ml (Ta and Arnason 2016). Meta-bromo-thiolactone has also been found to prevent virulence factor expression and biofilm formation as well as protects Caenorhabditis elegans and human A549 lung epithelial cells from quorum sensing mediated killing by Pseudomonas aeruginosa (O’Loughlin et al. 2013).

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7.5.2  Mechanical Removal by Ultrasonic Disruption Mechanical removal of biofilms is proved to be most effective; the most basic example being brushing of tooth. However, the major limitation is that its effectiveness is limited to only accessible surfaces. Ultrasonic treatment is most preferred for removal of implant associated biofilms. Various medical devices like catheters, artificial cardiac valves, pacemakers, and prosthetic joints are more prone to infections and biofilm formation. Sonication of such implants help in effective removal of bacterial cells and dispersion which may further be subjected to multiplex polymerase chain reaction (PCR) for tracing microbial pathogen derived DNA in order to identify the infectious pathogens like Propionibacterium acnes and Corynebacterium species, Finegoldia magna, and Peptostreptococcus species (Achermann et al. 2010). Biofilms of Staphylococcus aureus, Enterococcus faecalis, and P. acnes can be significantly dislodged from titanium and steel surfaces using sonication at 30 kHz with a power output of 300 W at 37 °C for 5 min. This ability of sonication mediated biofilm disruption is dependent upon equipment type, the output power, oscillation frequency, reaction volume, fluid temperature, and sonication time (Bjerkan et al. 2009). Electrophysiological cardiac devices are also susceptible to biofilms which may lead to potentially life-threatening complications. Biofilm formation by P. acnes, S. aureus, Streptococcus mitis and coagulase negative staphylococci may involve the generator pocket, the leads or both when cardiac pacemakers are used in patients with atrioventricular conduction block, sick sinus syndrome, and sinus bradycardia. Implantable cardioverter/ defibrillators (ICDs) are used for patients with heart failure after myocardial infarction and ventricular arrhythmia (Rohacek et  al. 2010). Similar process is also applicable for spinal implants. Bacterial biofilms are closely associated with chronic rhinosinusitis. Remarkable improvement in chronic rhinosinusitis symptoms is reported due to efficient biofilm disruption employing pulsed ultrasound therapy. High levels of ultrasonic treatment effectively kill bacteria due to formation of cavity in/on bacterial cell surfaces with simultaneous generation of peroxides. Further, lower levels of ultrasonic power reverts biofilm associated bacteria to planktonic state which are highly susceptible to both antibiotics as well as innate and adaptive antibacterial immunity. Thus, co-application of ultrasound and antibiotics (gentamicin) significantly kills live sessile Pseudomonas aeruginosa, which is referred to as bioacoustical effect (Fig.  7.2). Ultrasonic therapy co-administered with antibiotics may be significant against various biofilm associated infections (Young et al. 2010).

7.5.3  Enzyme Mediated Disruption Enzyme mediated biofilm disruption due to degradation of biofilm matrix is also considered as a powerful strategy to cope up with the biofilm associated diseases. Although the target of biofilm disrupting enzymes is primarily extracellular

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Fig. 7.2  Ultrasound assisted biofilm disruption. (a) Pre- and (b) post-treatment computed tomography scans of a 50 year old man with a 9 month history of left maxillary sinusitis refractory to aggressive medical treatment. He was treated with four sessions of ultrasound combined with ciprofloxacin 250 mg twice daily for 2 weeks, with complete clinical resolution of symptoms. He remained well 6 months later. L = left; R = right. (Young et al. 2010)

polymeric substance (EPS) or matrix surrounding the cells, it may have diverse mode of action which are as follows: (i) direct attack against biofilm components and degradation; (ii) induction of cellular lysis; (iii) interference with the quorum sensing; (iv) catalysis of antimicrobial substance formation (Lequette et al. 2010; Augustin et al. 2004; Simões et al. 2010; Thallinger et al. 2013). Physical integrity of the bacterial biofilms can be compromised by conversion of the extracellular polymeric substance matrix components like carbohydrates, polysaccharides, proteins (frequently exhibiting amyloid like properties), glycoproteins, lipids, phospholipids, glycolipids, and nucleic acids to their monomers which are further metabolized (Molobela et  al. 2010; Hobley et  al. 2015; Johansen et  al. 1997; Thallinger et  al. 2013). Mucoid and alginate producing strains of Pseudomonas aeruginosa infection are mostly related to illness and death in cystic fibrosis (CF) of the respiratory tract. Co-administration of alginate lyase with antibiotic like gentamicin and ceftazidime are found to be more effective biofilm disrupters. Alginate lyase, degrades the exopolysaccharide produced by mucoid strains of P. aeruginosa, and increase the efficacy of antibiotic in the respiratory tract infections (Alkawash et al. 2006). Similarly, dornase alfa (Pulmozyme; Genentech) is an inhalable DNase which can degrade extracellular DNA, and is used in cystic fibrosis (Frederiksen et al. 2006). Biofilms disruption in E. coli, S. epidermidis and Pseudomonas fluorescens can be achieved using glycoside hydrolase dispersin B that targets specifically extracellular polysaccharide poly-N-acetylglucosamine. Dispersin B (DspB) degrades poly-(β-1,6)-N-acetylglucosamine (PNAG), an extracellular polysaccharide that functions as a biofilm matrix adhesin in numerous pathogens like Staphylococcus epidermidis and S. aureus. Dispersin B can effectively detach and disrupt the bacterial biofilms in S. epidermidis, Actinobacillus pleuropneumoniae,

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E. coli, Pseudomonas fluorescens, Aggregatibacter actinomycetemcomitans, S. aureus, Bordetella bronchiseptica, B. parapertussis and B. pertussis (Kalpan 2009). Deoxyribonuclease I (DNase I) degrades extracellular DNA (eDNA), which is a major structural and functional component of the biofilm matrix that act as matrix adhesin in most bacterial species. Deoxyribonuclease treatment is able to dissolve 12–60 h old P. aeruginosa biofilms. However, streptococcal deoxyribonuclease streptodornase is more potent biofilm disruptor. Deoxyribonuclease I is also reported to detach biofilms of S. epidermidis, Enterococcus faecalis, S. pneumoniae, Campylobacter jejuni, Acinetobacter baumannii, Haemophilus influenzae, Klebsiella pneumoniae, Escherichia coli, and Streptococcus pyogenes (Kalpan 2009). Polysaccharide degrading anti biofilm enzymes (amylase, alginate lyase, cellulase and lysozyme) and certain quorum quenching enzymes (N-acyl homoserine lactonases and acylases) help in biofilm disruption. Thermostable quorum quenching lactonase from Geobacillus kaustophilus (GKL) belongs to the phosphotriesterase like lactonase (PLL) family of the amidohydrolase superfamily which is able to disrupt biofilm formation by A. baumannii most effectively (Chow et  al. 2014). Several proteolytic enzymes like Savinase, Pandion, Resinase, Spezyme and Paradigm, Pronase are reported to be effective against P. fluorescens, Pseudoalteromonas sp., P. aeruginosa biofilms (Meireles et al. 2016).

7.5.4  Acidic Electrolyzed Water More recently, acidic electrolyzed water (AEW) has been shown to be effective for eradicating biofilms associated with food borne pathogen which was experimentally confirmed employing green fluorescent protein tagged E. coli. Reduction in fluorescent signal from biofilm associated cells due to treatment confirmed the superior efficiency of acidic electrolyzed water. The underlying mechanism was confirmed to trigger extracellular polymeric substance disruption as revealed from deformation of the carbohydrate C-O-C bond and deformation of the aromatic rings in the amino acids tyrosine and phenylalanine. Further, scanning electron microscopy (SEM) images confirmed the altered biofilm architecture characterized by broken and detached non uniform bacterial biofilms. Acidic electrolyzed water also eradicates biofilms formed by both Gram negative bacteria (Vibrio parahaemolyticus) and Gram positive bacteria (Listeria monocytogenes) combined with effective inactivation of the detached cells (Fig. 7.3). However, the effectiveness is thought to be dependent upon the particular bacterial species (Han et al. 2017). Pathogenicity of S. aureus associated biofilm on the solid phase surface, particularly for indwelling medical devices and equipments in food processing industries. Electrolyzed water can effectively kill S. aureus, thereby removing the bacterial biofilm, which is otherwise difficult to remove for strong adherence to solid phase surface. Basic electrolyzed water (BEW) having more biofilm removal efficiency indicate that pH of electrolyzed water plays a significant role in biofilm control. Further, acidic

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Fig. 7.3  Representative scanning electron microscopy (SEM) images of biofilm formed by V. parahaemolyticus (a) and L. monocytogenes (b) after untreated (I), treated with sterile deionized water (II), and acidic electrolyzed water (AEW-3) for 5 min (III). Pictures were representative of three independent experiments with three replicates each. Scale bar represented 5  mm. (Han et al. 2017)

electrolyzed water shows enhanced bactericidal efficiency that can be attributed due to chlorine (Sun et al. 2012).

7.5.5  Dispersal Planktonic bacteria are small clusters of bacteria that are released from the mature biofilms due to treatment with dispersal causing agents. These planktonic cells are more antibiotic susceptible as compared to their biofilm associated counterparts. Certain conditions and agents are responsible for triggering dispersal leading to breaking up of the biofilm. Nutrient limiting conditions like depleted carbon availability induce P. aeruginosa to seed new population in order to escape from starvation. During this process of dispersal, mature biofilms releases as antibiotic susceptible planktonic bacteria. Various compounds like alginate oligomer oligoG, composed of blocks of alpha-l-guluronic acid can significantly damage bacterial biofilms by targeting exopolysaccharides. Similarly, unsaturated fatty acid cis-2-­ decenoic acid and by nitric oxide can also cause biofilm dispersal in various pathogens like P. aeruginosa (Bjarnsholt et al. 2013). Effective biofilm dispersal is also brought about by decreasing 3,5-cyclic diguanylic acid (c-di-GMP) levels using proteins that effectively bind and block 3,5-cyclic diguanylic acid. Dispersal mediator protein BdcA is highly effective against E. coli (Fig. 7.4). Additionally antimicrobial agents like sulphathiazole can significantly inhibit 3,5-cyclic diguanylic

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Fig. 7.4  Dispersal mediator protein (BdcA) increases biofilm dispersal. A. Chemical structure of 3,5-cyclic diguanylic acid (c-di-GMP); B. Relative normalized biofilm dispersal after 42 h with static biofilms formed in 96 well polystyrene plates. Biofilms were formed with Luria–Bertani (LB) medium and 30 mg ml−1 chloramphenicol at 37 °C using BW25113/pCA24N, bdcA/pCA24N and bdcA/pCA24N_bdcA. Isopropyl β-d-1-thiogalactopyranoside (0.1  mM) was added to each strain after 19 h of incubation. Biofilm formation after 23 h of isopropyl β-d-1-thiogalactopyranoside (IPTG) induction (42 h total) is compared with the biofilm formation after 12 h of isopropyl β-d-1-­ thiogalactopyranoside induction (31 h total) to obtain biofilm dispersal. Data are the average of 12 replicate wells from two independent cultures, and one standard deviation is shown; C. Representative Images images of flow cell biofilms after 42.5 h and 64.5 h of incubation with Luria-Bertani medium. Each strain has pCM18 for producing green fluorescence protein (GFP) to visualize the biofilms, and erythromycin (300  mg  ml−1) was added to retain pCM18. (Ma et al. 2011)

acid biosynthesis and prevent biofilm formation in vitro at sub-inhibitory concentrations (Ma et al. 2011; Antoniani et al. 2010) However, simultaneous antibiotic treatment is required in combination with biofilm dispersal for effective elimination of the liberated planktonic bacterial cells which would limit further spread of infection.

7.5.6  Killing and Combination Strategies Antibiotics, when used singly, are required at very high concentrations for longer duration of treatment to kill biofilm associated bacteria compared to planktonic bacteria; although the mode/mechanism of action remains the same. It is important to note that exposure of bacterial biofilm to sub inhibitory concentrations of antibiotics leads to the risk of failure to eradicate the biofilm in addition to promotion of antimicrobial resistance and enhancement of biofilm formation. Topical application like administration by inhalation are found to be effective for antibiotics like colistin, tobramycin or aztreonam which may lead to chronic suppression of bacterial biofilm, but not eradication of P. aeruginosa biofilm in patients with cystic fibrosis. In case of treating intravenous catheters associated biofilms, agents like ethanol or hydrochloric acid are used (Heijerman et  al. 2009). Similarly, gallium nitrate

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directly kills bacteria, thereby disrupting the biofilm and is now approved by the US Food and Drug Administration (FDA) for scanning in medical diagnostics during treatment of hypercalcaemia (Bjarnsholt et al. 2013). Further, combination strategies have proved to be fruitful when high doses of antibiotics are used together with drugs that targets biofilm leading to effective eradication. Candidate drugs of choice that can be used in combination strategies are meropenem, colistin and azithromycin, which are active under reduced oxygen tension and low metabolic activity, a condition which is more specific to deeper layers of biofilms. External metabolic and chemical stimuli combined with the use of antibiotics or engineered bacteriophages can also be used as novel strategy for disrupting bacterial biofilms. β-lactamase is reported to be entrapped in the biofilm matrix and can impair the penetration of β-lactams into deeper layers of thick biofilms which indicates β-lactamase stable antibiotics such as meropenem or β-lactamase inhibitors might be more effective in combination strategies (Hengzhuang et al. 2012). Additionally, use of efflux pump inhibitors like thioridazine, 1-(1-naphthylmethyl)-piperazine and Phe-Arg-naphthylamide may also help to cope up with the tolerance to antibiotics in the P. aeruginosa, E. coli and S. aureus associated biofilms (Liu et al. 2010). Another important aspect is oxidative stress mediated mutation in biofilm associated bacteria leading to development of antibiotic resistance. Thus, antioxidants such as l-proline, N-acetylcysteine, β-carotene or l-cysteine can be used in combination with antibiotics to decrease the resistance in bacteria as evident from the reports against P. aeruginosa (Boles and Singh 2008). N-acetylcysteine, a potent mucolytic agent used for treatment of cystic fibrosis, is also considered as an enhancer of antibiotic activity.

7.5.7  Bacteriophages Bacteriophages are viruses infecting bacteria, discovered independently by Frederick Twort and Felix d’Hérelle and are now considered as potent biological control agents against bacterial biofilms (Motlagh et al. 2016). Among several benefits of lytic phages, considerable host specificity, self-propagation at the site of infection, rapid clearance, great diversity, relatively easy isolation for a range of pathogens, and the opportunity to make genetic modifications are most notable (Nobrega et al. 2015). Unlike lysogeny, during lytic cycles bacteriophages replicate in the bacterial host cell interior producing large number of progeny phages which are a function of phage type and host strain. Exopolysaccharide matrix degrading enzymes like phage encoded depolymerases may lead to biofilm disruption facilitating phagocytosis by polymorphonuclear leukocytes and improved effects of antimicrobial drugs (Fig. 7.5). T4 phage can infect and replicate within E. coli biofilms killing bacterial cells and disrupting biofilm morphology. Rationally engineered T7 phage encoding a matrix degrading depolymerase is reported to have both lytic potential and enzymatic activity which was exploited owing to its enhanced efficiency of phage mediated eradication of bacterial cells as well as biofilm matrix (Lu

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Fig. 7.5  Phage therapy mechanisms to control bacterial biofilm through phage mediated disruption. As labeled in the figure, the steps of biocontrol of biofilm using bacteriophages include: 1. application of phages, 2. initiation of biofilm extracellularly polymeric substance (EPS) disruption through virion associated enzymes such as dispersin B (DspB) or EPS depolymerases, 3. biofilm disruption through disintegration of extracellular polymeric substance networks and exposing the bacteriophage infection, and 4. complete disintegration of biofilm and bacterial cell lysis. ABX stands for antibiotics (Motlagh et al. 2016)

and Collins 2007). Certain enzymes like endolysins are bacteriophage encoded peptidoglycan hydrolases that degrades the cell wall of the host bacteria which is a hallmark of the lytic multiplication cycle of the phage. Endolysins gain access to their peptidoglycan substrate from within the bacterial cell with the help of cytoplasmic membrane perforating holin proteins. Both phages encoded polysaccharide depolymerase DA7 and the purified endolysin LysK can synergistically disrupt and eradicate wide range of biofilms formed by various strains of S. aureus (Olsen et al. 2018). Phages targeting efflux pump receptor sites increase the sensitivity towards several classes of antibiotic helping in reduction of multi drug resistance. A lytic bacteriophage, OMKO1, (family Myoviridae) of P. aeruginosa is reported to utilize the outer membrane porin M (OprM) of the multi drug efflux systems MexAB and MexXY as a receptor binding site (Chan et al. 2016). An alternate strategy using cocktails of distinct phages owing to narrow host range are often used for minimizing laborious identification and evasion in pathogen. Such phage cocktail composed of an equal amount of myovirus (ϕNH-4) and a podovirus (ϕMR299–2) can eradicate biofilms of both P. aeruginosa NH57388A (mucoid) and P. aeruginosa MR299 (nonmucoid) strains while growing on human cystic fibrosis bronchial epithelial cell line (CFBE41o). The efficiency of such biofilm disruption is dependent on the

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higher penetrating power of mixed phages though alginate which is a major constituent of the biofilm matrix of P. aeruginosa. This phage mixture was also reported to be effective in killing P. aeruginosa in murine lungs infection in 6 h which substantially rationalize the promises of phage therapy for the control and treatment of multi drug resistant Pseudomonas lung infections in cystic fibrosis (CF) patients (Alemayehu et  al. 2012). However, bacteriophage therapy or therapy involving active compounds from bacteriophages may possibly induce adverse immune responses depending on the duration of treatment, the dosage (depending on the site of infection) and the composition of the bacteriophage formulation (single versus multiple strains) which should be carefully considered during clinical trials.

7.5.8  Nanomedicine Multifunctionalized nanostructured antimicrobial agents with attractive physico-­ chemical and opto-electronic properties are also reported to possess promising biofilm eradication properties. (Kale et  al. 2017). Silver nanoparticles (AgNPs) selectively attack cell membrane that consists of phospholipids and glycoprotein facilitating the entry of antibiotics to the cell surface acting as a drug carrier. Silver nanoparticles increases permeability by selectively binding to the sulphur containing proteins of the bacterial cell membrane. Further, silver (I) chelation prevents unwinding of DNA which might attribute to enhanced bactericidal activity (Ghosh et al. 2012). Silver nanoparticles of size 50 nm can effectively act against P. aeruginosa and S. epidermidis, which are causative agents of microbial keratitis (Martinez-­ Gutierrez et  al. 2013). Novel materials like quercetin functionalized silver nanoparticles can effectively inhibit the biofilm formation of multi drug resistant E. coli strain isolated from a cow with mastitis (Yu et al. 2018). Gold nanoparticles (AuNPs) conjugated with 3-(diphenylphosphino) propionic acid (Au-LPa) were also reported as potent antibiofilm agents against two Gram positive bacteria; S. aureus (ATCC 43300) and Streptococcus mutans (ATCC 25175) (Ahmed et al. 2017). Gold complexed sulfonamides are very efficient against methicillin resistant S. aureus (MRSA) and clinical isolates by reducing cell adhesion (Mizdal et  al. 2018). In our earlier reports, we have shown bimetallic nanoparticles of silver and gold synthesized using medicinal plants like Dioscorea bulbifera and Plumbago zeylanica are extremely effective in biofilm eradication against E. coli, Acinetobacter baumannii, P. aeruginosa and S. aureus (Salunke et al. 2014; Ghosh et al. 2015). In another study, naturally occurring antimicrobial cinnamaldehyde (CNMA) were conjugated to the surface of gold nanoparticles to eradicate biofilms of enterohemorrhagic E. coli O157:H7, P. aeruginosa, methicillin sensitive S. aureus organisms, and methicillin resistant S. aureus. These nanoconjugates with 0.005% (v/v) of cinnamaldehyde inhibited as well as disrupted biofilms which was indicated and confirmed by distorted cell morphology. Further cinnamaldehyde functionalized gold nanoparticles attenuated S. aureus virulence (Ramasamy et  al. 2017). Super

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paramagnetic iron oxide nanoparticles (SPION) can prevent biofilm formation against S. epidermidis, when treated at a concentration of 100 μg/mL from 12 to 48  hours indicating their promises in developing biofilm resistant orthopaedic implants (Taylor and Webster 2009; Ghosh et al. 2018). Mesoporous silica nanoparticles with superior drug loading capacity, facilitates sustained release of antimicrobial agents. Further, such types of drug carriers increase the drug concentration at the site of infection or pathogenesis, avoids frequent dosages, reduces side effects, and improves pharmacokinetics. In addition, such drug delivery systems reduce antimicrobial resistance, enhance the solubility of certain antibiotics, and broaden the therapeutic index (Ghosh 2019; Ghosh et al. 2019). Diazeniumdiolate modified silica nanoparticles (100 nm) delivers large nitric oxide (NO) payloads for effective eradication of biofilms of P. aeruginosa, E. coli, S. aureus and S. epidermidis. Nitric oxide released from the particles (61 μmol/mL) can eradicate >99% of the biofilm embedded bacteria. Smaller size of silica particles (50 nm) may lead to enhancement of biofilm disrupting activity against P. aeruginosa (Slomberg et  al. 2013). Organic nanomaterials like Ciprofloxacin loaded poly (lactic-co-glycolic acid) nanoparticles functionalized with Deoxyribonuclease I, release ciprofloxacin in a controlled fashion, target and disassemble the biofilm by degrading the extracellular DNA that stabilize the biofilm matrix. These hybrid nanostructures not only prevent biofilm formation from planktonic bacteria, but they also successfully reduce established biofilm mass, size and viable cell density. Repeated administration of Deoxyribonuclease I coated nanoparticles encapsulating ciprofloxacin can reduce biofilm formation by 95% and eradicate more than 99.8% of the established biofilm (Baelo et al. 2015).

7.6  Conclusion In the light of the above discussion it is evident that diverse strategies can be explored and are being developed to disrupt biofilms which are microbial consortium structures imparting community based drug resistance that additionally poses a global challenge towards therapeutic efficacy using conventional drugs. Biomedical surfaces can be modified by impregnation of biofilm disrupting nanoparticles to achieve efficient biofilm eradication. Similarly, more advanced strategies like laser generated shockwaves involving mechanical energy to break up biofilms should be explored. Relying solely on antibiotics and surgery to treat biofilm associated are no more effective as they incur higher cost of treatment and add up to morbidity. Moreover, current treatment algorithms are becoming increasingly less effective due to emergence of more virulent organisms and multi drug resistance. Thus, a rational integration of diverse technologies and different disciplines may help in development of advance biofilm inhibiting and disrupting technology to combat bacterial biofilms associated hazards.

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Acknowledgments  Dr. Sougata Ghosh acknowledges the Department of Science and Technology (DST), Ministry of Science and Technology, Government of India and Jawaharlal Nehru Centre for Advanced Scientific Research, India for funding under Post-doctoral Overseas Fellowship in Nano Science and Technology (Ref. JNC/AO/A.0610.1(4) 2019-2260 dated August 19, 2019).

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Chapter 8

Intrusion of Bacterial Quorum-Sensing for Antimicrobial Resistance Mitigation: A Pharmaceutical Perspective Sandeep Kumar, Shruti Shandilya, and Kumar Siddharth Singh

Abstract  Bacteria employ many molecular mechanisms to communicate and adapt the behavior according to their surrounding environment. They produce and sense small diffusible signaling molecules called autoinducers which regulate gene expression involved in virulence, biofilm formation, production of siderophores and protease. This mechanism of communication is referred to as quorum-sensing. The autoinducers are specific to strains and species i.e. Gram positive and Gram negative bacteria use the different signaling molecules to regulate their behavior. In addition, bacteria also produce quorum sensing inhibitors and enzymes to degrade the signaling molecules. In earlier studies, the quorum sensing mutants of pathogenic bacteria were found to be attenuated for virulence and pathogenicity. These naturally occurring strategies to interfere with the quorum sensing led the concept of quorum quenching to be employed in therapeutics to control the microbial diseases. Quorum sensing inhibitors and/ or quenchers are being used as a therapeutic arsenal to treat antibiotic resistant bacteria and in the development of new generation medical devices to prevent biofilm formation on them. This chapter highlights the type of mechanisms employed by bacteria for quorum sensing and the potential use of quorum sensing inhibitors as a successful strategy in the mitigation of antimicrobial resistance. Keywords  Quorum-sensing · Resistance · Antimicrobials · Auto-inducers

S. Kumar (*) ICAR-National Dairy Research Institute, Karnal, Haryana, India S. Shandilya University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany K. S. Singh National Centre for Microbial Resource – National Centre for Cell Science, Pune, Maharashtra, India Structure and Function of Proteins, Helmholtz Centre for Infection Research, Braunschweig, Germany © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_8

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8.1  Introduction Quorum sensing (QS) is a general term for a bacterial system which helps in communication between bacterial cells. This process involves the production of extracellular signaling molecules called autoinducers  (AIs). These molecules are produced by bacteria in response to changes in their population in a particular niche. After the accumulation of autoinducers in the local environment, bacteria detect and respond to AIs to activate the transcription of specific genes to regulate the physiological activities such as symbiosis, reproduction, competence, antibiotic production, antibiotic resistance, biofilm formation, motility, sporulation, bioluminescence, etc. (Miller and Bassler 2001; Rutherford and Bassler 2012). Despite the different quorum sensing signaling systems namely acylated homoserine lactones (AHLs), autoinducer-2 (AI-2), diffusible signal factors (DSFs), diketopiperazines (DKPs), 4-hydroxy-2-alkylquinolines (HAQs) and others, all known QS systems follow the three basic principles: (1) the community members produce the signaling molecules. When the population is less, the concentration of autoinducer is below the threshold required for detection. When the population is high, the concentration of autoinducer is high in the surrounding environment for their detection and responses. (2) Receptors for autoinducers are either present in the membrane or cytoplasm. (3) In concert with the activation of specific genes, detection of autoinducer itself induce the production of autoinducers (Kaplan and Greenberg 1985; Seed et al. 1995). Gram positive and Gram negative bacteria use different quorum sensing signaling systems (Fig. 8.1). Auto inducing signal peptides are produced and secreted by

Fig. 8.1  Bacterial quorum sensing (QS) signaling system. In Gram positive bacteria, quorum sensing by the two component system (a) and binding of autoinducing peptide (AIP) to transcription factor (b). In Gram negative bacteria, quorum sensing by LuxI/LuxR type system (c) and two component system (d)

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Gram positive bacteria. When the concentration of autoinducer is high, they bind to the two component membrane bound histidine kinase receptors. The binding of autoinducer activates the kinase activity of the receptor that auto phosphorylates at a conserved histidine residue and transfers this phosphate to a cognate response regulator to activate the quorum sensing genes (Figure 8.1a). In few instances, autoinducers are transported back into the cytoplasm which then interacts with the transcription factors to regulate quorum sensing genes (Figure 8.1b). Gram negative bacteria use small molecules such as acyl-homoserine lactones (acyl HSL) for communication. S- adenosylmethionine (SAM) is the substrate for the production of AHLs in the cell. When the concentration of autoinducer is high in the surrounding environment, they bind to transcription factors present in the cytoplasm and regulate the transcription of genes of quorum sensing regulon (Figure 8.1c). In a few cases, AIs are also sensed by histidine kinase receptors of a bacterial two component system that have analogous functions to Gram positive quorum sensing system as shown in Figure 8.1d (Swift et al. 2001; Wei et al. 2011). Quorum sensing plays an important role in the pathogenicity of bacteria such as the synthesis of elastase, lectin, pyocyanin, and exotoxin A in Pseudomonas aeruginosa is regulated by quorum sensing system. The quorum sensing also regulates the production and secretion of enterotoxins, hemolysins, cytotoxins, lipases, protein A, fibronectin protein, etc. in Staphylococcus aureus (Jiang et al. 2019). These virulence factors evade the immune system of the host to obtain nutrition. Many quorum sensing inhibitors have been identified as agents that inhibit the activity of virulence factors in pathogenic bacteria. The clinical trials are under investigation to explore their application in pharmaceuticals and therapeutics such as for the treatment of antibiotic resistant bacteria, as an anti biofilm forming agent in new generation medical devices, etc. The goal of this chapter is to introduce the types of quorum sensing and the potential of quorum sensing inhibitors as a therapeutic agent for the prevention of infections due to resistant pathogenic bacteria.

8.2  Quorum Sensing System in Gram Negative Bacteria 8.2.1  N-Acylhomoserine Lactone Signaling N-acylhomoserine lactones (acyl-HSL) are the most studied family of QS signaling molecules. Generally, Gram negative bacteria such as Aeromonas, Burkholderia, Brucella, Pseudomonas, and Serratia use the acyl-HSL molecules for signaling (Swift et al. 2001; Williams 2002). The basic structure of acyl-HSL is similar, consisting of a homoserine lactone ring and acyl chain that vary in length (from 4 to 18 carbons), oxidation states and saturation levels (Fig. 8.2). This variation in the acyl chain enables bacteria to recognize and differentiate between their own acyl-HSL and others (Kumari et  al. 2006). These signal molecules are synthesized by

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Acyl-HSL family

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Fig. 8.2 Representative structures of quorum sensing signaling molecules. AHL N-acyl-­ homoserine lactone, AI-2 Autoinducer-2, AIP Autoinducing peptides. Structures are drawn using Marvin Sketch software

members of the LuxI protein family using an appropriate acyl-acyl carrier protein (acyl-ACP) as an acyl donor and S-adenosylmethionine (SAM) as the amino donor. The first acyl-HSL signaling was observed in a photobacterium Vibrio fischeri, which produce light at high cell density, but not at low cell density. The acyl-HSL QS circuit is composed of an autoinducer (N-3-oxohexanoylhomoserinelactone [OHHL] in Vibrio fischeri), LuxI and LuxR proteins. Autoinducer is produced by LuxI protein that can diffuse freely in and out of cells. With the increase in cell density, the concentration of autoinducers also increases. Autoinducers are produced throughout the growth in a limited amount so high cell density is required to reach the threshold concentration of autoinducers. The receptor for autoinducers is LuxR protein. The LuxR protein is unstable in a free state and forms a stable structure when it bounds to AIs. This stable complex (LuxR-AI) acts as a transcriptional activator of specific genes to regulate bacterial physiology. This complex also induces the expression of LuxI (Fig. 8.3) (Parsek et al. 1999; Swift et al. 2001).

8.2.2  Non-acyl HSL Mediated Quorum Sensing Gram negative bacteria Ralstonia solanacearum and Xanthomonas campestris employ the non-acyl HSL mediated quorum sensing. Some Gram negative bacteria use the phenotype conversion (Phc) regulatory system and diffusible signal factors (DSF) to regulate the production of enzymes, polysaccharides, signaling molecules, motility, biofilm formation, and pathogenicity. Phc based quorum sensing system

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Fig. 8.3  The regulation of acyl-HSL based quorum sensing. When the cell density is low, the OHHL concentration does not attain a critical threshold level which leads to weak and insufficient transcription of the genes for bioluminescence (luxICDABEG) for light emission (a). whereas at high cell density, the OHHL concentration reaches a critical level to bind LuxR in order to stimulate transcription of luxICDABEG, leading to the emission of light (b)

and DSF based quorum sensing system are two best characterized examples of non-­ acyl HSL quorum sensing systems employed by plant pathogenic bacteria. (i) Phc based quorum sensing system PhcA, a LysR type regulator regulates the production of extracellular polysaccharides and enzymes involved in the pathogenicity of R. solanacearum (Chun et al. 1997). The activity of PhcA is negatively regulated by the two component system, PhcS, and PhcR. This two component system is responsive to 3-hydroxypalmitic

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acid methyl ester (3OH PAME) encoded by a phcB gene (Flavier et  al. 1997a). When the 3OH PAME concentration is low at low cell density, PhcR (response regulator) is phosphorylated by PhcS (histidine kinase) and represses the expression of PhcA. At high cell density, the 3OH PAME reaches the threshold concentration and reduces the PhcR phosphorylation ability of PhcS to increase the expression of PhcA for the production of PhcA regulated pathogenicity factors. Recently, it has been reported that chemolithoautotroph R. eutropha strains also uses the similar quorum signaling system to control the siderophore synthesis and motility (Garg et  al. 2000). In addition to Phc quorum sensing system, R. solanacearum also employs acyl HSL-mediated quorum sensing system, solI and solR homologous to luxI and luxR system (Flavier et al. 1997b). The 3OH PAME regulates the expression of solR and solI via PhcA. In addition to 3OH PAME, the RpoS (sigmaS) is also required for acyl HSL mediated quorum sensing in R. solanacearum (Flavier et al. 1998) as shown in Fig. 8.4. (ii) DSF based quorum sensing system X. campestris pv. campestris (Xcc) employs the Rpf quorum sensing network in order to manage the production of exopolysaccharides and extracellular enzymes involved in pathogenicity (Fig. 8.5). RpfF and RpfB divert the intermediates of lipid metabolism for the production of DSF (Diffusible Signal Factors). Slater et  al. reported that the DSF production is associated with the convergent transcription of

Fig. 8.4  Phc based quorum sensing. At low concentration of 3OH PAME due to low cell density, PhcS transfer the phosphate group to PhcR resulting in transcriptional regulation of PhcA. At high concentration of 3OH PAME at high cell density it decreases the kinase activity of PhcS. When the phosphate group is removed from PhcR it induces the expression of PhcA which further regulates a solI/solR quorum sensing system to modulate the expression of the gene of unknown function (aidA)

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Fig. 8.5  A model for DSF based quorum sensing system. DSF stimulates the autophosphorylation of RpfC and subsequently to the RpfG. RpfG carrying phosphate group positively regulates the transcription of pathogenicity genes and negatively regulates the rpfF and rpfB

rpfGHC, a two component regulatory system located immediately adjacent to rpfB and rpfF and is convergently transcribed (Slater et al. 2002). RpfC encodes both sensor kinase and response regulator, a hybrid two component regulator (Ishige et  al. 1994). The structural similarities are found between RpfH and membrane spanning sensor domain of RpfC but there is no histidine kinase domain in RpfH. RpfG encodes a response regulator protein carrying a typical receiver domain (Whitehead et al. 2001). In the previous study, it was reported that mutation in either rpfC or rcfG downregulate the production of pathogenicity factors and unable to produce. However, the deletion of complete rpfGHC operon or mutations within rpfC led to the overproduction of DSF. RpfC positively regulates the production of EPS and extracellular enzyme and negatively regulates the DSF production, whereas both the processes are positively regulated by RpfG (Slater et al. 2002).

8.3  Quorum Sensing System in Gram Positive Bacteria The signaling molecules for quorum sensing are different in both Gram negative bacteria and Gram positive bacteria. Gram positive bacteria do not produce acyl-­ HSLs. In Gram positive bacteria, the signaling molecules are autoinducing peptides, post translationally modified small peptides (Fig. 8.2). These signal molecules interact with the histidine kinase of a two component system mediating signal

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transduction. In certain cases, immature autoinducing peptides are secreted by the bacteria. These secreted precursor autoinducing peptides are processed by the extracellular proteases into mature autoinducing peptides. After maturation, these peptides return back to the cells to regulate the activity of transcription factors (Kievit and Iglewski 2000). In Gram positive bacteria, there are two major pathways of quorum sensing.

8.3.1  Two Component Pathway In the two component system pathway, autoinducing peptides are ribosomally synthesized and secreted via peptide ABC transporters. The secreted autoinducing peptides are modified post translationally and processed by proteases to form mature autoinducing peptides. After attaining a certain threshold concentration, they interact with the histidine kinases, specific receptors on the cell surface. This interaction activates the kinase by phosphorylating it at conserved histidine residue that subsequently activates the cognate response regulator by transferring this phosphate to conserved aspartate residue to regulate the secretion of autoinducing peptides and transcription of target genes (Bhatt 2018). (i) Competence system S. pneumoniae and B. subtilis employ a competence system (Com) for the uptake of exogenous DNA. B. subtilis also utilize this system to form homodimers of regulatory receptor to bind the inverted repeats of DNA (Bhatt 2018). The precursor autoinducing signal peptide is initially synthesized as 41 amino acid peptide known as ComC which is processed to form 17 amino acids mature autoinducing peptide called CSP (competence stimulating peptide). The ComAB ABC transporter secretes matured CSP. At high cell density, CSP reaches the critical threshold and detected via dedicated ComD histidine sensor kinase protein of the two component system. The activated histidine kinase undergoes autophosphorylation and the phosphoryl group is transferred to the response regulator, ComE (Pestova et al. 1996). Subsequently, phosphorylated ComE activates the transcription of comX gene, an alternative σ factor that mediates the transcription of structural genes involved in competence development (Lee and Morrison 1999). (ii) Accessory Gene Regulator system S. aureus pathogenicity is regulated by the peptide quorum sensing mechanism. The cell density dependent pathogenicity is regulated by RNAIII, an RNA molecule. AgrBDCA (accessory gene regulator) operon encodes several proteins, namely, AgrA, AgrB, AgrC and AgrD that regulate the RNAIII expression. The AgrD encodes the 46 amino acid precursor signal peptide that is proteolytically cleaved by AgrB resulting in thiolactone intermediate and then secreted. The final mature and active form is obtained upon subsequent cleavage (Novick et al. 1995). After attaining the critical threshold concentration, autoinducing peptide activates the sensor histidine kinase (AgrC) that further activate the intracellular response

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regulator (AgrA) via phosphorylation relay. In turn, the activated AgrA upregulates the expression of RNAIII and agr genes. In addition to that, AgrA regulated RNA III also regulates the transcription of δ-toxin and various factors involved in virulence (Mayville et al. 1999). (iii) Fsr quorum sensing system In E. faecalis, Fsr quorum sensing system is related to pathogenicity of bacteria due to its involvement in transcriptional regulation of gelatinase, an extracellular metalloprotease and SprE proteases which are involved in the regulation of protein expression and biofilm formation (Shankar et  al. 2012; Kumar et  al. 2019). Gelatinase, in turn, cleaves the Ace, a microbial surface component recognizing adhesive matrix molecule (MSCRAMM) of E. faecalis. Ace plays an essential role in host cell adherence and virulence. The disruption of GelE and Fsr system significantly increases the adherence of E. faecalis on the cell surface of the host (Sifri et al. 2002; Pinkston et al. 2011)

8.3.2  S  elf Signaling Pathway in Rap, NprR, PrgX, and PlcR Family In RNPP (Rap, NprR, PrgX, and PlcR) pathway, autoinducing peptides are ribosomally synthesized. The autoinducing peptides are modified post translationally and secreted via SecA-dependent system. The main difference between a two component system and RNPP system is that after attaining a certain threshold concentration, these autoinducing peptides are carried inside the cells by proton dependent oligopeptide transporters (POT); whereas, in two component system, autoinducing peptides (AIPs) activate the sensor kinase. The phosphate regulator, Phr-AIPs were first described in this class. The activated Phr-AIPs deactivates the Rap-phosphates and plays a key role in sporulation and competence (McQuade et al. 2001). (i) Phr-Rap system Phr pro-autoinducing peptides are ribosomally synthesized carrying the signal on N terminal for secretion. These pro-autoinducing peptides are cleaved by secreted proteases to form a mature signal peptide. The mature Phr signal peptide is returned back into the cell via oligopeptide permease (Opp), an ATP binding cassette (ABC) transporter (Gardan et al. 2009). Phr regulate the gene expression by inhibiting the regulator aspartate phosphatases (Raps). Raps are part of a complex variation of the two component regulatory system i.e. phosphorelay system and regulate the transcription of several genes such as protease secretion in Bacillus subtilis (Koetje et al. 2003). The rap and phr genes are co-transcribed. The proteins of Rap family dephosphorylate the Spo0F, P (0F, P) to control sporulation of B. subtilis. In addition, the Rap proteins also inhibit the activity of ComA, a response regulator involved in competence development and/or of DegU response regulator for the production of secreted proteases. Kin proteins belong to the histidine sensor kinase

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Fig. 8.6  Phr-Rap quorum sensing system. The phr and rap genes are often co-transcribed. Their product either dephosphorylate the Spo0F ∼ P (0F ∼ P) involved in sporulation or inhibit the transcription factors for competence

family and trigger the activation of the phosphorelay. KinB, KinC, and KinD are present on the membrane and, KinA and KinE are cytoplasmic proteins. The phosphoryl group is transferred from Spo0F, P to Spo0B phosphotransferase and subsequently to the Spo0A response regulator to regulate the expression of transcription factor required for sporulation initiation. The dephosphorylation of Spo0A, P is caused by Spo0E phosphatase (Fig. 8.6). An increase in bacterial growth induces the expression of the gene coding for RapB while, competence induces the genes encoding for RapA and RapH proteins through the activated ComP- ComA response regulator (Perego 2013). (ii) PrgX-CF10 Enterococcus has an intrinsic high capacity for uptake of plasmid encoded antibiotic resistance genes. The signaling peptides are the pheromones and induce the uptake of conjugative plasmids. In E. faecalis, PrgX act as a repressor. PrgX is a 317- amino acid protein that acts as a molecular switch of cCF10 in response to pheromone binding. CF10 is the pheromone inducible plasmid that encodes tetracycline resistance in E. faecalis (Buttaro et al. 2000). PrgX binds to cCF10 and pCF10 located on chromosome and plasmid, respectively. cCF10 together with a co-­ repressor pCF10 represses PrgX to induce conjugation (Yin et al. 2011). (iii) Plc-Pap system PlcR is the transcriptional regulator of genes coding for phospholipase C. Phospholipases are important factors involved in bacterial pathogenesis as they

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are able to cleave the phospholipids to breach the eukaryotic membrane for invasion. PapR peptide binds to PlcR which in turn, changes the conformation of PlcR to activate it. The activated PlcR binds to PlcR box found in DNA to regulate the transcription. A 48 amino acid long pro-autoinducing peptide is synthesized by papR gene and exported out of the cell. Then, this peptide is returned back into the cell through an oligopeptide transporter, OppABCDF system. The pro-peptide is processed to form an active oligopeptide (Gominet et  al. 2001; Bouillaut et  al. 2008). CodY acts as a common nutrient responsive regulator in PlcR-Pap and Spo0A-AbrB and is involved in the activation of genes in PlcR-Pap regulon (Frenzel et al. 2012).

8.4  Autoinducer-2 The autoinducer-2 (AI-2) is commonly found in both the Gram positive and Gram negative bacteria. Therefore, it is also called a “universal language” of communication between bacteria. The precursor of autoinducer-2 is 4,5,-dihydroxy-2,3-­ pentanedione (DPD) produced by LuxS, which converts S-ribosylhomocysteine to homocysteine (Vendeville et al. 2005). DPD cyclizes to activate the icaR pathway involved in the repression of N-acetyl glucosamine (UDP-GlcNAc) polymerization and attenuate the biofilm formation (Yu et al. 2012). In the previous study, it was reported that Staphylococcus aureus ΔluxS form stronger biofilm in comparison to wild type bacteria. AI-2 sensing depends on the phosphoenolpyruvate dependent sugar phosphotransferase system (Trappetti et al. 2017). AI-2 based quorum sensing plays an important role in the regulation of motility, virulence, biofilm formation and luminescence (González Barrios et al. 2006; Noor et al. 2019).

8.5  Quorum Sensing Molecules Database With time, the new mechanisms and molecules participating in quorum sensing are invented. To store the information of quorum sensing pathways and molecules, two databases were developed.

8.5.1  SigMol Database SigMol (http://bioinfo.imtech.res.in/manojk/sigmol) is an exclusive repository of prokaryotic quorum sensing signaling molecules (QSSMs) implicated in different quorum sensing systems like AHLs (N-Acyl homoserine lactones), HAQs (4-hydroxy-2-alkylquinolines), DKPs (Diketopiperazines), AI-2 (Autoinducer-2), DSFs (Diffusible Signal Factors), etc. from different bacteria and archaea. In the

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database, there are 1382 entries of 182 unique QSSMs from 215 organisms. It includes the biological information like genes, identification assays, preliminary bioassays, and applications as well as chemical details of QSSMs such as SMILES, IUPAC names, and structures. The database provides easy data retrieval and comparison of different signaling molecules due to the user friendly graphical user interface. SigMol is helpful to the scientific community associated with the field of quorum sensing and their pharmacological applications (Rajput et al. 2016).

8.5.2  Quorumpeps Database Owing to advancements in this field and numerous successful reports for quorum sensing usage, a public database such as Quorumpeps, was urgently needed. Quorumpeps (http://quorumpeps.ugent.be) is a database of experimentally proved quorum sensing signaling peptides. The browser of the database is user friendly with several search field options including sequence, trivial name, smiles, molecular formula, receptor, method, origin, literature, etc. Quorumpeps database provides the chemical information of QSSMs such as IUPAC names, structures, molecular formulas, trivial names, and smiles. In addition to that, it also provides the related information of QSSMs like the origin of microbial species, functionality related to method, result, and receptor, peptide links and chemical characteristics (3D-structure-­ derived physicochemical properties). The chemical diversity of quorum sensing signaling molecules can be used to develop the new therapeutically significant molecules. Quorumpeps database can be a valuable tool to study the quantitative structure-property relationships of quorum sensing signaling molecules. This database is updated quarterly by the authors to ensure up-to-date information to the researchers (Wynendaele et al. 2013).

8.6  R  ole of Quorum Sensing Inhibitors in Mitigation of Antimicrobial Resistance Quorum sensing is particularly involved in the regulation of the expression of virulence factors, biofilm formation, the disruption of bacterial communication, production of siderophores and protease in response to the population density of microorganisms. The autoinducing peptides in Gram positive bacteria are strains and species specific such as Staphylococcus spp., Clostridium spp., or Enterococcus spp. employ different peptides for signaling (Monnet et al. 2014). Gram negative bacteria including Acinetobacter spp., Burkholderia spp., Enterobacteria spp., Pseudomonas spp., etc. use the different class of signaling molecules as described in the above sections. In addition to acyl homoserine lactones, bacteria such as Legionella spp. and Vibrio spp. also, employ signaling molecules such as ketones

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(Tiaden and Hilbi 2012), fatty acids are used by Burkholderia spp., Xanthomonas spp., Xylella spp. (Zhou et al. 2017), quinolones are used by Pseudomonas aeruginosa (Heeb et  al. 2011) and epinephrine, nor-epinephrine, and AI-3 are used by enterohemorrhagic bacteria (Kendall and Sperandio 2007). The furanosyl borate diester, an auto inducing peptide (AI-2) is used by both Gram positive and Gram negative bacteria (Chen et al. 2002). Vibrio harveyi, a free living bioluminescent marine bacterium uses the furanosyl borate diester as a signaling molecule for bioluminescence (Skandamis and Nychas 2012). Some Gram negative bacteria integrate the different quorum sensing systems to act in a network such as P. aeruginosa uses four quorum sensing systems namely, las, rhl, iqs, and pqs. These four systems are interconnected with each other to regulate the pathogenicity of P. aeruginosa. Quorum sensing systems namely, las, iqs, and pqs invoke the rhl expression that enables the bacteria to survive in adverse conditions (Lee and Zhang 2015). Quorum quenching is the disruption of the bacterial quorum sensing system. Quorum quenching is a natural phenomenon used by bacteria to degrade quorum sensing signaling molecules and was first described in Erwinia carotovora (Rémy et  al. 2018). Inhibitors of Quorum sensing are produced by bacteria in order to inhibit the action of auto inducing peptides and quorum quenching enzymes resulting in the interference with quorum sensing mechanism. Inhibitors of Quorum sensing are listed in Table 8.1. The use of these inhibitors as therapeutic agents might be a promising strategy to reduce bacterial pathogenicity. These strategies may be used to enhance bacterial susceptibility to antibiotics and to decrease biofilm formation. The communication between bacteria can be disrupted by several mechanisms such as: (i) by the production of quorum sensing inhibitors which intervene with the autoinducer production (Tang and Zhang 2014), (ii) Utilizing quorum quenching antibodies (Park et al. 2007), and macromolecules like cyclodextrins to remove AIs (Morohoshi et  al. 2013), or (iii) by disintegrating AIs using hydrolyzing quorum quenching enzymes (Fetzner 2015a). A wide number of small molecules and antagonist peptides have been discovered to inhibit or quench the quorum sensing molecules (Tang and Zhang 2014). There are multiple methods to identify novel inhibitors and quenchers such as computer aided drug screening, high throughput screening, random screening and purification from crude extracts, etc. In addition to that many genetically modified strains that express reporter genes, are being used as quorum sensing biosensors to identify inhibitors. The inhibitors and quencher molecules can be either natural products such as ajoene from garlic, polyphenols of tea and honey, eugenol from clove and products from marine organisms or synthetic products such as azithromycin and 5-fluorouracil (5-FU) (Swatton et al. 2016; Delago et al. 2016). Besides the small molecules, some quorum quenching enzymes have been identified to target the AI-2 and AHLs molecules involved in quorum sensing. The main enzymes are lactonases, acylases, and oxidoreductases that degrade the AI-2 and AHL signaling molecules (Fetzner 2015b; Bzdrenga et al. 2017a). The quorum quenching enzymes are classified into two groups: Class I enzymes are the AHL-lactonase, AHL-­ acylase, and paraoxonase, which degrade the AHL molecules and, class II enzymes are oxidoreductases, which reduce the carbonyl to hydroxyl. The disruption of

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Table 8.1  Quorum sensing inhibitors and their targets Inhibitors Piericidin A1

Target CviR

Category Species Actinobacteria Streptomyces sp.

N-(2-Phenylethyl)isobutyramide and 3-methyl-N-(2-­ phenylethyl)butyramide Cyclo-L-proline-Ltyrosine Diketopiperazines (dkps) Yayurea A and B

LuxR, CviR and Vibrio harveyi

Bacteria

Halobacillus salinus

Chromobacterium violaceum CviR and LuxR

Bacteria

LuxN

Bacteria

Cis-9-octadecenoic acid Protoanemonin

CviR

Bacteria

LasR

Bacteria

Bacillus cereus D28 Marinobacter sp. SK-3 Staphylococcus delphini Stenotrophomonas maltophilia BJ01 Pseudomonas spp.

Bacteria

Reference Ooka et al. (2013) Teasdale et al. (2009) and Teasdale et al. (2011) Teasdale et al. (2011) Abed et al. (2013) Chu et al. (2013b) Singh et al. (2013) Bobadilla Fazzini et al. (2013) Li et al. (2011)

Agr system Cyclic dipeptides: Cyclo (L-PheL-pro) and cyclo (L-Tyr-L-pro) CepR and LuxR Brominated alkaloids compounds Cembranoids Lux R and V. harveyi

Bacteria

Lactobacillus reuteri

Bryozoan

Flustra foliacea

Peters et al. (2003)

Coral

Pseudoplexaura flagellosa and Eunicea knighti

Tumonoic acid F

V. harveyi

8-epi-malyngamide C and lyngbic acid Lyngbyoic acid

LasR LasR

Cyanobacteria Blennothrix cantharidosmum Cyanobacteria Lyngbya majuscula Cyanobacteria L. majuscula

Malyngolide

CviR and LasR

Cyanobacteria L. majuscula

Honaucins A-C

LuxR

Pitinoic acid A

LasR

Cyanobacteria Leptolyngbya crossbyana Cyanobacteria Lyngbya sp

Peptides (microcolins A and B) Crude extracts

LuxR

Cyanobacteria Lyngbya sp

Tello et al. (2011) and Tello et al. (2012) Clark et al. (2008) Kwan et al. (2010) Kwan et al. (2011) Dobretsov et al. (2010) Choi et al. (2012) Montaser et al. (2013) Dobretsov et al. (2011)

LasR

Fungi

Penicillium atramentosum

Wang et al. (2011) (continued)

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Table 8.1 (continued) Inhibitors Kojic acid

Target LuxR

Category Fungi

Species Aspergillus spp.

Patulin and Penilillic acid Farnesol (sesquiterpene) Honey and propolis

LasR and RhlR

Fungi

Penicillium spp.

PqsA

Fungi

Candida albicans

C. violaceum, LasR, and RhlR

Insect productions

Bee

Solenopsin A

Rhl circuit

Solenopisinvicta

Furanone and its derivatives

LuxR and LuxS

Insect: Fire ant Marine alga

Cyclodepsipeptides (solonomide a, b) Benzopyran

Agr system

Photobacterium

Eugenol

CviR, LasR and PQS

Plant

Cinnamaldehyde and its derivatives

LuxR and AI-2

Plant

Baccharis cassinaefolia Syzygium aromaticum Many plants

Quercetin

AI-2 and AI-3

Plant

Broccoli

Limonoids (obacunone) L-Canavanine

EHEC

Plant

Grapefruit

CviR and ExpR

Plant: Alfalfa

Medicago sativa

Catachin and naringenin

CviR and RhlR

Plant: Combretum Combretaceae albiflorum

Sesquiterpene lactones Ajoene

Pseudomonas aeruginosa LuxR family

Plant: Compositae Plant: Garlic

Marine bacteria CviR, LuxR and LasR Plant

Delisea pulchra

Centratherum punctatum Allium sativum

Reference Dobretsov et al. (2011) Rasmussen et al. (2005) Cugini et al. (2007) Truchado et al. (2009) and Bulman et al. (2011) Park et al. (2008) de Nys et al. (1993) and Rice et al. (2002) Mansson et al. (2011) Dobretsov et al. (2011) Zhou et al. (2013) Niu et al. (2006), Brackman et al. (2008) and Brackman et al. (2011a) Lee et al. (2011) Vikram et al. (2010) Keshavan et al. (2005) Vandeputte et al. (2010) and Vandeputte et al. (2011) Amaya et al. (2012) Jakobsen et al. (2012b) (continued)

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Table 8.1 (continued) Inhibitors Iberin

Target LasR and RhlR

Category Plant: Horseradish

Malabaricone C

CviR

A drimane sesquiterpene Curcumin

C. violaceum

Plant: Myristica Myristicaceae cinnamomea Plant: Tree Drimys winteri Curcuma longa

2,5-di-O-galloyl-D hamamelose p-Coumaric acid

Plant: Turmeric RNAIII Plant: Witch hazel PpuR, CviR and TraR Plants

Ellagitannins and urolithins

P. aeruginosa and Plants and Yersinia enterocolitica bacteria

Conocarpus erectus

Floridoside, betonicine and isethionic acid Exudates

TraR

Red algae

Ahnfeltiopsis flabelliformes

LuxR

Roundworm

Lumichrome

Sinorhizobiummeliloti Soil-­ freshwater alga

Caenorhabditis elegans Chlamydomonas reinhardtii

Manoalide, manoalide monoacetate, and secomanoalide Alkaloid (hymenialdisin) Emodin

LuxR and LasR

Sponge

Luffareilla variabilis

LuxR and LasR

Sponge

P. aeruginosa and S. maltophilia TraR and RhlR

TCMs

Hymeniacidon aldis Rhubarb

Flavonoid (baicalein)

CviR

Species Armoracia rusticana

TCMs

Hamamelis virginiana Various plants

Scutellaria baicalensis

Reference Jakobsen et al. (2012a) Chong et al. (2011) Paza et al. (2013) Packiavathy et al. (2014) Kiran et al. (2008) Bodini et al. (2009) Adonizio et al. (2008) and Giménez-­ Bastida et al. (2012) Liu et al. (2008) Kaplan et al. (2009) Teplitski et al. (2004) and Rajamani et al. (2008) Skindersoe et al. (2008)

Dobretsov et al. (2010) Ding et al. (2011) Zeng et al. (2008)

quorum sensing system might be a promising insight to prevent the virulent behavior of bacteria (Bzdrenga et al. 2017a). With each passing year, antibiotic resistance is emerging as a major public health concern which necessitates the need for novel therapeutic approaches to be developed. There is a multifaceted association between antibiotic resistance and quorum sensing. Quorum quenching/ inhibition of pathogenic microbes is also called anti

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pathogenic signal interference. Recently, a study shows that 4-aminoquinoline derivatives namely, 7-Cl and 7-CF3 substituted N-dodecylamino-4-aminoquinolines inhibit the quorum sensing signaling in Serratia marcescens and Pseudomonas aeruginosa. They exhibit weak bactericidal activities but are potent anti biofilm forming agents (Aleksić et  al. 2017). A new class of broad spectrum quorum quenchers, yayurea A and B were discovered that inhibit the growth of Gram negative beta- and gamma- proteobacteria. These quenchers are secreted by Staphylococcus delphini, a ‘Staphylococcus intermedius group’ of zoonotic pathogens. Their chemical formulas are N-[2-(1H-indol-3-yl)ethyl]-urea and N-(2-­ phenethyl)-urea, respectively. Their effects are opposite to acyl homoserine lactones due to their ability as quenchers (Chu et al. 2013a). The addition of AHLs to the growing phase culture of P. aeruginosa shifts the IR cell population towards the dormant phase after the treatment with carbenicillin and ciprofloxacin (Moker et al. 2010). In P. aeruginosa, the las and rhl quorum sensing systems play a key role in biofilm formation and their disruption leads to enhanced sensitivity of P. aeruginosa to antibiotics and host immune system (Shih and Huang 2002; Bjarnsholt et  al. 2005). Hamamelitannin and baicalin hydrate is the AHL targeting peptide based inhibitors of the quorum sensing system. These inhibitors disrupt the biofilm formation in Gram positive (S. aureus) and Gram negative (P. aeruginosa) bacteria and show synergistic effects in co-treatment with other antibiotics (Brackman et  al. 2011b). In the presence of quorum sensing inhibitors, the efficiency of antibiotics increases (Bulman et  al. 2017; Maura and Rahme 2017). The antibiotic resistant bacteria are the key players in hospital acquired infections. These bacteria produce biofilm and adhere to medical devices causing serious medical problems resulting in increased morbidity and mortality. Quorum quenching has been utilized to develop bacterial adhesion resistive medical devices. New generation medical devices coated with quorum quenchers such as contact lenses (Tale et al. 2016), catheters (Mandakhalikar et al. 2016), dressings (Bzdrenga et  al. 2017b), aerosols (Hraiech et  al. 2014), trauma devices, orthopedic devices (Moriarty et al. 2016) and implantable devices (Francolini et al. 2017) are being developed. 5-fluorouracil, Thiazolidinedione-8 and 3-(10-bromohexyl)-5-­ dibromomethylene-­2(5H)-furanone have been used for the coating of catheters (Mandakhalikar et  al. 2016). 5-methylene-1-(prop-2-enoyl)-4-(2-fluorophenyl)dihydropyrrol-2-one and its derivatives are being used for medical device coating. TrAIP-II, a truncated autoinducer peptide (AIP-II) with the exocyclic tail replaced by acetyl group is being used as a colonization resistant material for coating of medical devices. FS3, RNA-III inhibiting peptide (RIP) is being used in prosthetic implants (Rémy et al. 2018). The Gram positive bacteria, Staphylococcus aureus causes a variety of infections in humans. It is responsible for both, community acquired and hospital acquired infection. The treatment of Staphylococcus aureus is becoming challenging due to its potential to acquire resistance to clinically used antibiotics e.g. methicillin resistant Staphylococcus aureus. In S. aureus, pathogenesis is regulated by agr quorum sensing. Quorum sensing signal peptide binds to the histidine kinase, AgrC and subsequently activates the response regulator, AgrA that induces the RNAIII

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expressions. The enhanced expression of RNAIII positively regulates the virulence factors of bacteria. A substantial number of AgrC inhibitors have been identified as the antagonist. Benzbromarone, an AIP based receptor antagonist is being used for the treatment of infections caused by S. aureus strains (Gordon et al. 2013; Cech and Horswill 2013). Another quorum quencher, savirin was identified by screening the combinatorial library. Savirin inhibits the expression of RNAIII consequently, the production of phenol soluble modulins (PSM) is inhibited. Phenol soluble modulins (PSMs) are staphylococcal toxins. In vivo experiments on mice also show that savarin attenuate methicillin resistant Staphylococcus aureus, involved in skin and soft tissue infections. Another strategy to inhibit quorum sensing is to prevent the production of auto inducing peptides. SpsB is required for the processing of autoinducing peptide signal molecules. SpsB is a Type I signal peptidase. Inhibitors of SpsB have the ability to quench the agr quorum sensing system of Staphylococcus strains (Cech and Horswill 2013). In 2007, a study showed that some secondary fungal metabolites are also capable to inhibit the bacterial quorum signaling. Ambuic acid (KAP-21A), isolated from the fungal butanol extract was identified to inhibit the gelatinase production without affecting the growth of Enterococcus faecalis. The production of gelatinase is mediated by FsrB, homologous to AgrB quorum sensing system. In addition, ambuic acid also significantly inhibits the quormones, the quorum sensing hormones produced by S. aureus, L. innocua and other Gram positive bacteria (Nakayama et al. 2009). Two cyclic dipeptides, cyclo (L-Phe-L-Pro) and cyclo (L-Tyr-L-Pro) secreted by Lactobacillus reuteri RC-14, a human vaginal isolate were identified as staphylococcal agr quorum quencher (Li et al. 2011). Other natural compounds 1,3,6-trihydroxy-8-methyl-xanthone (norlichexanthone) and 1,3,5,6-trihydroxy-8-methyl-xanthone also act as anti virulent drugs against S. aureus (Nielsen et al. 2010). Still, the traditional approaches are being used to treat bacterial infections. The antibiotics having bacteriostatic or bactericidal activities are being used for this purpose but their unrestrictive use has led to the antibiotic resistance in bacteria. Recent studies suggest the forthcoming potential of quorum sensing inhibitors as anti pathogenic drugs to control the expression of virulence factors of antibiotic resistant bacteria.

8.7  Clinical Trials of Quorum Sensing Inhibitors Only, the previously commercialized and approved antimicrobials are being used in the clinical trials for their potential to inhibit quorum sensing system. Their antimicrobial activities and cytotoxicity were tested but their effects on quorum sensing were still untouched. In the earlier studies, the azithromycin was used for the treatment of bacterial infections. This antibiotic improved the patient’s condition but did not decrease bacterial load (Saiman et  al. 2003). In 2001, studies showed that at non-bactericidal concentrations, azithromycin disrupts the quorum sensing signaling in antibiotic resistant P. aeruginosa in ventilator associated pneumonia (Tateda et al. 2001; van Delden et al. 2012). Garlic, known as a prophylactic agent due to its

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quorum quenching properties inhibits biofilm formation, virulence factor production and antimicrobial resistance in pathogenic bacteria (Rasmussen et  al. 2005; Harjai et al. 2010). A pyrimidine analog, 5-Fluorouracil (5-FU) inhibits the quorum sensing regulated expression of genes involved in the virulence of resistant P. aeruginosa. Nowadays, 5-FU is being used in the coating of catheters (Ueda et al. 2009). In clinical trials, the results of coated catheters were shown to be significant in terms of biofilm disruption formed by antibiotic resistant bacteria (Walz et  al. 2010). ET37 molecule, N-(3-oxododecanoyl) homoserine lactone (3O-C12-HSL) conjugated with ciprofloxacin was found to be significant to reduce the biofilm formation and enhance the antibiotic susceptibility in P. aeruginosa (Bortolotti et al. 2019). In vitro studies on Sinefungin also showed reduced pneumococcal biofilm growth. Sinefungin has been shown to inhibit the synthesis of AI-2 via downregulating the expression of luxS, pfs, and speE (Yadav et  al. 2014). In a recent study, results showed that curcumin in combination with tobramycin, gentamicin, and azithromycin shows synergistic effects against P. aeruginosa. This combination reduces the quorum sensing associated virulence factors by downregulation of the quorum sensing genes (Bahari et al. 2017). Another anti quorum sensing agent Farnesol administered in combination with β-lactam antibiotics attenuated the growth rate of methicillin resistant Staphylococcus aureus. This combination inhibits the lipase activity and disrupt cytoplasmic membrane through the leakage of potassium ions (Kim et al. 2018).

8.8  C  hallenges and Prospects in the Development of Quorum Quenching Inhibitors as Therapeutic Agents The previous studies on the role of quorum sensing in the bacterial pathogenicity and resistance to antibiotics led the concept of quorum quenching or quorum sensing inhibitors to be used in pharmaceuticals for disease control. The quorum sensing mutants of pathogenic bacteria were found to be attenuated for virulence. A wide number of inhibitor molecules and quenching enzymes have been discovered as an alternative to antibiotics due to the emergence of resistance. Yet, we are still facing the obstacle in their clinical use. The major hurdles to use them in the clinic are their toxicity, delivery, stability, potency and narrow activity spectrum. In addition to that, some questions are still unanswered and open ended. At what stage of infection will quorum sensing inhibition be of value? Which pathogen should be targeted? Is there any resistance to quorum sensing inhibitors? What are the mechanisms of quorum quenching? Are only quorum sensing inhibitors are sufficient to control the disease/ infections caused by resistant bacteria or should be used in combination with other antimicrobials for synergistic effects? Future investigations are required to determine the breadth of the mechanism of action of quorum quenching inhibitor molecules and their potential in being used as an alternative for the mitigation of antibiotic resistance.

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8.9  Conclusion The study of bacterial quorum sensing systems is intended to exploit the molecular pathways and mechanisms involved in cell-cell communication. In bacteria, quorum sensing is a universal communication mechanism to regulate the traits through exchanging the extra-cellular signaling molecules called auto inducers according to surrounding conditions. The changes in the behavior depend on the type and concentration of signaling molecules. New research shows that these signaling molecules are extremely species and genus specific. Based on the information garnered from quorum sensing studies, manipulating the signaling molecules are now viewed as promising strategies in various practical applications. The continuing research directed at finding the natural quorum sensing inhibitors and their synthetic analogs for the clinical uses has developed the advanced therapies for the treatment of resistant bacteria. Presumably, these therapies will not be as prone to resistance as are the traditional antibiotics. The use of quorum sensing inhibitors in the clinical approaches could have better functions than next-generation antibiotics and would play an important role in the antimicrobial resistance mitigation.

References Abed RMM, Dobretsov S, Al-Fori M, Gunasekera SP, Sudesh K, Paul VJ (2013) Quorum-­ sensing inhibitory compounds from extremophilic microorganisms isolated from a hypersaline cyanobacterial mat. J Ind Microbiol Biotechnol 40:759–772. https://doi.org/10.1007/ s10295-013-1276-4 Adonizio A, Dawlaty J, Ausubel F, Clardy J, Mathee K (2008) Ellagitannins from Conocarpus erectus exhibit anti-quorum-sensing activity against Pseudomonas aeruginosa. Planta Med 74. https://doi.org/10.1055/s-0028-1084373 Aleksić I, Šegan S, Andrić F, Zlatović M, Moric I, Opsenica DM, Senerovic L (2017) Long-chain 4-Aminoquinolines as quorum-sensing inhibitors in Serratia marcescens and Pseudomonas aeruginosa. ACS Chem Biol 12:1425–1434. https://doi.org/10.1021/acschembio.6b01149 Amaya S, Pereira JA, Borkosky SA, Valdez JC, Bardón A, Arena ME (2012) Inhibition of quorum-­ sensing in Pseudomonas aeruginosa by sesquiterpene lactones. Phytomedicine 19:1173–1177. https://doi.org/10.1016/j.phymed.2012.07.003 Kumari A, Pasini P, Deo SK, Flomenhoft D, Shashidhar H, Daunert S (2006) Biosensing systems for the detection of bacterial quorum signaling molecules. https://doi.org/10.1021/AC061421N Bahari S, Zeighami H, Mirshahabi H, Roudashti S, Haghi F (2017) Inhibition of Pseudomonas aeruginosa quorum-sensing by subinhibitory concentrations of curcumin with gentamicin and azithromycin. Journal of global antimicrobial resistance 10:21–28. https://doi.org/10.1016/j. jgar.2017.03.006 Bhatt VS (2018) Quorum-sensing mechanisms in gram positive bacteria. In: Implication of quorum-sensing system in biofilm formation and virulence. Springer Singapore, Singapore, pp 297–311 Bjarnsholt T, Jensen PØ, Burmølle M, Hentzer M, Haagensen JAJ, Hougen HP, Calum H, Madsen KG, Moser C, Molin S, Høiby N, Givskov M (2005) Pseudomonas aeruginosa tolerance to tobramycin, hydrogen peroxide and polymorphonuclear leukocytes is quorum-sensing dependent. Microbiology 151:373–383. https://doi.org/10.1099/mic.0.27463-0

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Chapter 9

Drug Discovery for Targeting Drug Resistant Bacteria Aikaterini Valsamatzi-Panagiotou, Katya B. Popova, and Robert Penchovsky

Abstract  The alternative approaches for antibacterial drug discovery have a huge potential to develop novel antibacterial agents against multi drug resistant human pathogenic bacteria that are much needed to tackle the urgent threats of multi drug resistant human pathogenic bacteria. We may be able to develop much faster novel antibiotics against multi drug resistant human pathogenic bacteria using these alternative methods than that based on small molecule drug discovery. The alternative methods for antibacterial drug discovery can be used to target new molecules in pathogenic bacteria such as bacterial riboswitches. The combination of novel mechanisms of antibacterial drug action, with novel molecules targets, can result in the development of novel antibiotics against which bacteria have yet not developed any kind of resistance. In this book chapter, we present novel methods for antibacterial drug discovery against antimicrobial resistant bacteria based on alternative strategies for antibacterial drug discovery. These include the application of antisense oligonucleotides as antibacterial agents, fecal microbiota transplantation, and antimicrobial peptides and cell penetrating peptides with antibacterial activity. We also present the main ways of antibiotics misuse that lead to the development of antimicrobial resistance. Keywords  Drug discovery · Antimicrobial resistance: Riboswitches · Antisense oligonucleotides · Antibiotics

Aikaterini Valsamatzi-Panagiotou and Katya B.  Popova contributed equally with all other contributors. A. Valsamatzi-Panagiotou · K. B. Popova · R. Penchovsky (*) Department of Genetics, Faculty of Biology, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2_9

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9.1  Introduction The discovery of antibiotics is considered one of the greatest medical advances in the modern age. The antibiotics are a crucial line of defense against bacterial infections (Tang et  al. 2014; Munita and Arias 2016). Alexander Fleming discovered penicillin in 1928 accidentally and since then and onwards, the research for new antibiotics is a continuous process (Vellar 2002). After the introduction of sulfonamides in the 1930s, antibiotic use increased exponentially and they became along the time an integral part of health care practice worldwide (Rodriguez-Mozaz et al. 2015; Selgelid 2007). Indeed, antibiotics are regarded as “panacea” of modern medicine (Penchovsky and Traykovska 2015). Undoubtedly, they contributed to the increase of the human lifespan in comparison to the pre antibiotic era, when infectious diseases were fatal for many humans. However, every introduction of a new antibiotic is followed sooner or later by the emergence of antimicrobial resistance (McPhee et al. 2009). The emergence of antimicrobial resistance was recognized since the beginning of the antibiotic era. After the introduction of the first antibiotics, there was a belief that with their use, all infectious diseases eventually will belong to the past. Unfortunately, this is not the case due to the emergence of antimicrobial resistance against any antibiotic. Therefore, antimicrobial resistance is considered one of the biggest threats to public health in the twenty-first century worldwide. The development of antimicrobial resistance is observed mostly in countries where antibiotics use is high and is not properly controlled. Unfortunately, the rapid development of antimicrobial resistance may reverse previous medical progress and bring to light infections from the past. The antibiotics were used successfully against many bacterial infections in the past, rendering them now ineffective for the treatment of those infections nowadays due to antimicrobial resistance development (Munita and Arias 2016; Rodriguez-­ Mozaz et al. 2015; Mayers et al. 2017; Garau et al. 2014). The rise in the income of people who live in developing countries leads to easier access to antibiotics which also contributes to the increase of antibiotic use and therefore the development of antimicrobial resistance (Selgelid 2007).

9.2  Antibiotics Misuse The overuse along with the misuse of antibiotics in human and veterinary medicine, agriculture, and aquaculture are some of the reasons deemed for antimicrobial resistance development. Antibiotics are a category of drugs used in daily practice. Sometimes, due to the absence of diagnostic tools or due to the time needed for the confirmation of a pathogen, physicians start the treatment with the use of empiric therapies based on clinical symptoms and the individual characteristics of a patient. This may lead to the prescription of antibiotics not only against bacterial agents but sometimes wrongly against non bacterial agents. However, in critically ill patients

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it is crucial to start an empiric therapy because any delay at the beginning of the treatment increases the mortality rate. Nonetheless, empiric therapy should be adjusted after the confirmation of the diagnosis. Especially, this is recommended because during the treatment with empiric therapies broad spectrum agents are used (Barlam et al. 2016). If it is indicated after the confirmation of diagnosis, the broad spectrum agent should be altered with a narrower spectrum agent. This is a method called de-escalation and it aims the reduction of selective pressure on the bacterial pathogen to develop antimicrobial resistance. Indeed, the antimicrobial resistance development is closely related to selective pressure thus, if we manage to reduce selection pressure this will also have positive effects on the reduction of the emergence of antimicrobial resistance. In cases where the presence of a bacterial pathogen(s) is not proved, the therapy should be discontinued. The inability to follow the existing guidelines properly for the prescription of antimicrobials complicates the situation (Bennadi 2013). Physician’s fear of litigation creates conflicts, especially in patients in whom it is not clear if the prescription of an antimicrobial agent is needed or will improve its condition at all. This is a big dilemma and seems to contribute to the overuse of antibiotics for prophylaxis. Except for this, sometimes physicians may face pressure from patients to prescribe antibiotics that the patients have already bought and used. They may also have the demand to be treated with specific antibiotics. This usually results due to extensive advertising of pharmaceutical companies. Thus, the prescribing behavior of the physician plays a role in antimicrobial resistance development. The physicians should estimate the cost benefit ratio taking into consideration not only the individual but also the community as a whole, which is vital for the prevention of antimicrobial resistance development (Dellit et al. 2007). They should also educate their patients about the proper use of antibiotics and the devastating effects, which are caused by the overuse, and misuse of antibiotics in daily life. The main aim is to understand that antibiotics are not the treatment for any health care problem and they should be prescribed from doctors only in cases where they are the only option of treatment. Unfortunately, the lack of a patient’s education leads to the belief that a good doctor is a doctor who prescribes medications. All those mentioned above seem to contribute to the emergence of antimicrobial resistance and should not be neglected because they are significant causes for the increased rates of antibiotic prescription (Selgelid 2007; Penchovsky and Traykovska 2015; Garau et al. 2014; Septimus 2018). The under consumption of antibiotics is another important reason, which is correlated with antimicrobial resistance development. In detail, in cases where a course of therapy is not completed properly, the bacteria that would have been normally killed, survive instead and become resistant. Therefore, due to the development of antimicrobial resistance, the treatment with the drugs used the first time against the bacteria is not effective anymore. It should be highlighted that the compliance of patients is related not only to its educational interventions but also to its financial status. Indeed, some patients especially in developing countries cannot afford to complete their treatment and sometimes even pay for their transportation to health

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care facilities, which may be located far away from their homes. This is not a matter of compliance but a matter of ability. That is the reason why there is a great need for antibiotics to be more easily available and in an adequate number to cover the needs of every country around the world (de With et al. 2016). A phenomenon that is mostly observed in developing countries is the over-the-­ counter buy or improper use of antibiotics, which is attributed mainly to poor education of the population. However, improper antibiotic use and self medication are problems that also concern developed countries. Especially, in developed countries, it is observed the use of the leftovers of the drugs from previous treatment courses which may be expired or not suitable for the current health condition of a patient without taking any advice from a physician. This phenomenon is called “self medication’‘and is another factor that promotes the development of antimicrobial resistance at the community level. The worst scenario is the prescription of the leftovers of drugs from parents to their children or between family members (Selgelid 2007; WHO 2002, 2015). The hygienic conditions, proper sanitation, and good food preparation play an important role in the prevention of the spread of infections both in hospital units as well as at the community level. The health care workers are obliged to use barrier precautions to protect themselves and prevent the spread of infections between different patients and other members of the health care staff in the health care units. The use of gloves, masks, proper hand hygiene and disinfection constitute some of the main methods, which aim to protect the spread of infections in health care units. The presence of a consultant specialized in infectious diseases in health care units is vital and contributes to the improvement of infectious disease management and control (Allcock et al. 2017; Okeke et al. 2005; Dik et al. 2016). Even though there is an urgent need for the development of new antibiotics due to high rate emergence and spreading of antimicrobial resistance and multi drug resistant bacterial pathogens, the interest of pharmaceutical companies to invest in the discovery of new antibiotics during the last decades decreased dramatically. This is one of the factors, which contributed to the presence of hard to treat bacterial infections. There is a variety of reasons blamed for the decreased preference of pharmaceutical industries to invest money for the research and development of new antibiotics. The fact that antibiotics are drugs that are used for short periods against infections renders them less profitable for pharmaceutical companies in comparison to drugs that are used for the treatment of chronic diseases. For example, the duration of the treatment with drugs used for the therapy of diabetes mellitus, arterial hypertension and cancer lasts for much longer periods of time or even lifelong and those diseases plague a larger amount of population. These are some of the reasons, which render the drugs that are used for the therapy of chronic diseases more profitable for pharmaceutical companies in comparison to antimicrobial drugs. To delay the emergence of antimicrobial resistance, physicians try to hold in reserve some antibiotics and prescribe them in cases when they are truly needed and if more established antibiotics were used at first and are currently unable to treat an infection (Fishman 2006). This is in contrast with the drugs used for chronic diseases that are immediately prescribed by the physicians if needed and without

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considering the possibility of resistance development. The presence of many generic competitors in the antibiotics market along with the rapid development of antimicrobial resistance further contributes to the decrease of the interest of pharmaceutical companies in the investment for antibiotic development. The demanding approval requirement during clinical trials is another problem that should not be neglected (Stein and Castanotto 2017). The pharmaceutical companies face a great paradox. Some federal agencies call them to promote the development and research of antibiotics while at the same time others enact policies to deter that very development. All these make the investment in research and development of novel antibacterial drugs risky and costly. Especially, for the large pharmaceutical companies, this means fewer possibilities for a satisfactory reward. Therefore, all these facts mentioned above play a leading role in the decreased interest of the pharmaceutical industry to develop new antibiotics (Fair and Tor 2014).

9.3  Ways of Spreading of Antimicrobial Resistance Antimicrobial resistance can spread in multiple ways. One of the most common is through the food chain. Antibiotics were used massively as prophylaxis and control of diseases in domestic animals, which led to the development of drug resistant bacteria in the gastrointestinal tract of the animals. The resistant bacteria end up in crops either through animal defecation infected with bacteria or watering of crops with contaminated water. Thus, resistant bacteria get through the human’s gastrointestinal tract through the consumption of contaminated food (Verraes et al. 2013). Apart from that, the healthcare units are among the most favorable environments for the spread of bacterial infections. Especially, it seems that factors like inadequate infection control, lack of proper education and the absence of surveillance programs contribute to the development of antimicrobial resistance. The ways by which infections can spread are either direct from human to human or through contaminated surfaces (Penchovsky and Traykovska 2015). All these indicate that there is a great need for strategies for the prevention and containment of antimicrobial resistance globally and their absence is considered to play an important role in the emergence and spread of antimicrobial resistance (Levy and Marshall 2004). There is a variety of mechanisms, through which antimicrobial resistance emerges in bacteria. Multiple biochemical routes are used for the classification of the mechanisms of antimicrobial resistance development. It is necessary to understand the genetic basis of antimicrobial resistance development to make new methods and drugs to tackle it. Antimicrobial resistance can be either intrinsic in which a specific antimicrobial resistance is inherited, or acquired in cases where a previously sensitive bacteria became resistant (Munita and Arias 2016; Tenover 2006; Blair et al. 2015). During the past decades, the understanding of the mechanisms of antimicrobial resistance development was a central issue because of the health and economic losses worldwide (Hayes and Wolf 1990). The treatment of infections for which antimicrobial resistant bacteria are blamed is becoming more difficult. The

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failure of traditional methods of antibiotics discovery to keep pace with the evolution of antimicrobial resistance highlights the urgent need for the implementation of strategies for prevention and containment of antimicrobial resistance along with the discovery of new drugs (Smith and Romesberg 2007). For instance, in the last year, only a few new classes of antibiotics have been approved in contrast to the past where the development of new antibiotics was one step ahead from the emergence of antimicrobial resistance (Soothill et  al. 2013). This constitutes a great threat to people’s wellbeing all around the world. Thus, there is an urgent need for doing more innovative research to focus on the discovery of novel antibacterial drugs. To address the threat imposed by multi drug resistant bacteria, there is a great need for the application of faster and more adaptive pipelines so that novel antibacterial drugs can be developed within a shortened period with less money. New antibacterial drug targets are also needed for developing new antibacterial drug classes, which will not have any resistant antibacterial strains at the beginning of their application. The novel strategies should be more adaptable to the continuing emergence of antimicrobial resistance mutants and simultaneously less time consuming. This can be achieved with the help of the advancements in scientific technology during the last several decades and through the application of new molecular mechanisms for drug actions along with the use of novel drug targets. Some of the most promising agents, which can be used against bacteria are antisense oligonucleotides, phage therapies, fecal microbiota transplantation (FMT) and antimicrobial peptides and some new targets such as bacterial riboswitches (Penchovsky and Traykovska 2015; Fair and Tor 2014).

9.4  D  rug Discovery Including the Early History, Status, and Future Trends From a historical point of view, antimicrobial substances are being used since ancient times. For instance, extracts from certain plants and molds were used against various infections in ancient Egypt and Greece (Lindblad 2008). The pioneering research of Louis Pasteur led to the development of the first vaccine against such as Anthrax and Rabies. In 1928, Alexander Fleming observed by accident the antimicrobial activity of fungus Penicillium rubens against Staphylococcus. In 1942, Howard Florey and others, taking into account Alexander Fleming’s discovery were able to purify penicillin (Vellar 2002) paving the way for the emergence of the era of contemporarily antibiotics that saved the lives of countless numbers of people worldwide. For the creation of the first antibiotic, Howard Florey, Edward Abraham, and Ernst Chain were awarded the Nobel Prize in 1945. From 1945 to 1980 during the “golden era” of antibiotics, many new antibiotics were discovered. As a result, mortality from bacterial infections was significantly reduced worldwide during the twentieth century. Since 1980, the development of new antibiotics declined, in part because of the huge expense associated with it. This coupled with the emergence of

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multi drug resistant bacteria due to the misuse of the current antibiotics led to the present urgent need to develop new antibiotics. In fact, the first antibiotic was purified from microorganisms. For instance, in the laboratory of Selman Waksman dozen antibiotics were isolated from soil microorganisms, including a very potent antibiotic known as Streptomycin (Waksman 1973). For the discovery of Streptomycin, Waksman was awarded the Nobel Prize in Physiology or Medicine in 1952. This approach of antimicrobial drug discovery is based on bioactive screening on whole cell, known as classical pharmacology. Having been identified a chemical with antibacterial properties its molecular target has tried to be figured out. Most of the antibiotics were discovered applying this approach during the “golden era” (Waksman and Flynn 1973). This approach is also applicable to the present days. Another method for antibacterial drug discovery is based on the high throughput screening of chemical libraries for binding to a specific molecular target. An additional approach to antibacterial drug development is based on the rational design of molecules that specifically bind to a predefined molecular target. There are several recent trends to develop unconventional approaches to antibacterial drug discovery, which should be much more efficient than the currently applied approaches. They include the genome wide search for novel antibacterial targets and applying novel mechanisms of antibacterial drug action such as antisense oligonucleotides, phage based antibacterial and others.

9.5  A  pproaches for Antibiotic Discovery Including Unconventional and Genomic Approaches With the advancements of next generation sequencing technology, the genomes of many strains of pathogenic bacteria are now well known. We can apply various bioinformatics and experimental methods promising targets for antibacterial drug discovery (Kaloudas et al. 2018). Moreover, applying metagenomics approaches, we can investigate various horizontal transfers of genetic information among pathogenic bacteria and their interactions with other bacteria in the human body, for instance, the gut microbiome. This gives us the opportunity to apply probiotic bacteria that can inhibit the proliferation of pathogenic bacteria, also using fecal microbiota transplantation methods. Due to the advancements of structural biology and structural genomics, we can use the 3D structures of key cellular organelles to develop drug candidates using various computational methods. For instance, after solving the 3D structure of the bacterial ribosome, many researchers are trying to rationally design antibiotics that inhibit the function of the bacterial ribosome but are harmless to the human ribosome (Shasmal and Sengupta 2012). As a result of the discovery of regulation of gene expression by bacterial riboswitches (Pavlova and Penchovsky 2019), a whole class of new antibacterial drug targets are discovered that can be targeted not only

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with small molecules such as secondary metabolites but also with antisense oligonucleotides. Note that in recent years, various types of RNAs are becoming wide used targets for antibacterial drug development. In addition, the search for naturally occurring short peptides with antibacterial properties is also a promising avenue for unconventional antibacterial drug discovery (Penchovsky and Traykovska 2015). Next in this chapter, we discuss all these unconventional approaches of antibacterial drug discovery in detail.

9.6  A  ntibacterial Drug Discovery Based on Antisense Oligonucleotides Antisense oligonucleotides are short, synthetic, single stranded oligonucleotides that mimic the structure of RNA, and are capable of inhibiting RNA translation through two specific mechanisms and reducing the expression of a specific protein(s). The application of antisense oligonucleotides results in the prevention of the translation of targeted genes at the mRNA level (Penchovsky and Traykovska 2015). There are three different generations of antisense oligonucleotides, which possess various chemical modifications and can act in two different ways. The first generation of antisense oligonucleotides are phosphorothioate-oligo- deoxynucleotides. They can inhibit the target RNAs via the enzymatic function of RNase H under multiple turnover conditions. The second generation antisense oligonucleotides have a methyl modification at the 2′-OH group of the ribose (-O-CH3). The third generation of the antisense oligonucleotides includes peptic nucleic acid (PNA), locked nucleic acid (LNA) and phosphorodiamidate morpholino oligomers (PMOs). They act by blocking mRNA translation like the second generation antisense oligonucleotides. Note that chimeric antisense oligonucleotides can be designed to combine the first generation (in the middle) and the second generation (at both ends) modifications, which will be functioning via RNase cleavage (Penchovsky and Traykovska 2015). In the last decade, several antisense oligonucleotides are under clinical development (Rinaldi and Wood 2018; Geary 2009; Meng et al. 2015; Singh et al. 2007; Wright 2009; Sully and Geller 2016; Stein and Castanotto 2017) that demonstrates the general applicability of various types of antisense oligonucleotides as drug candidates. There are a great number of essential genes in bacteria with different sequence to humans that can be used as potential targets of antisense oligonucleotides for antibacterial drug development. A specific advantage that bacterial genomes possess in comparison to human genomes is that bacterial genomes are smaller and relatively simply organized. Therefore, it is easy to consider proper bacterial RNAs as a potential target for antisense oligonucleotides. Such RNA targets should be present in bacteria only and not in humans. For instance, the targeted RNA can encode an enzyme for the synthesis of an essential metabolite. To assure that bacteria are not going to survive even if an essential metabolite is present in the

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medium, the cellular influx transport mechanism of the essential metabolite should also be disturbed. This needs to be done in cases where we want to inhibit bacteria by targeting the synthesis of an essential metabolite. In these cases, two different genes should be inhibited by two different antisense oligonucleotides. Ιt should be mentioned that sometimes one antisense oligonucleotide is capable of inhibiting both genes – the one that encodes an enzyme and the other responsible for the transporter protein, for instance, the FMN riboswitch. We can also target genes, which are responsible for bacterial virulence with this technology. These genes are very suitable targets for antibacterial drug development because their inhibition does not exert selection to develop antimicrobial resistance (Penchovsky and Traykovska 2015). There are two mechanisms that antisense oligonucleotides apply to inhibit a specific RNA. As mentioned above, antisense oligonucleotides are chemically modified deoxyribonucleotide oligomers, which are designed to be complementary to the targeted RNA.  When they are transferred inside the cell with the help of a cell penetrating peptide, they apply Watson-Crick base pairing to hybridize with the complementary mRNA and an antisense oligonucleotide mRNA heteroduplex is formed. In the first case, RNase H recognizes the double stranded molecule, which is formed between the antisense oligonucleotide and mRNA and binds to it. The binding of RNase H leads to cleavage of the targeted mRNA and these results in the inhibition of protein expression via a multi turnover action (Penchovsky and Traykovska 2015). Another mechanism of antisense oligonucleotide uses peptide nucleic acids (PNAs) or locked nucleic acids (LNA). The mode of action of PNA is a single turnover, which means that if it hybridizes it cannot be used again. The binding of PNA to mRNA impedes the ribosomal subunits of binding to the mRNA. Thus, the procedure of translation is inhibited. Normally the ribosomal subunits bind to mRNA and then mRNA is translated into a functional protein. Therefore, in the presence of the antisense oligonucleotides, the binding of ribosomal subunits to mRNA is prevented which finally results in the inhibition of protein function (Chan et al. 2006; Penchovsky and Traykovska 2015; Seth et al. 2019). Antisense oligonucleotides are classified based on their chemical modifications into three generations. Those, which belong to the first generation, contain a phosphorothioate backbone, in which a sulfur atom replaces one of the non bridging oxygen in phosphodiester bonds. Antisense oligonucleotides of the first generation are capable of inducing an RNase-H mediated cleavage of target mRNAs. The PS-antisense oligonucleotides have higher bioavailability in comparison to unmodified nucleotides due to phosphorothioate modification, which is related to the higher resistance to nuclease degradation. Although, it should be mentioned that PS-antisense oligonucleotides seem to produce some non specific side effects due to their interaction with the cell surface and intracellular proteins. The second generation antisense oligonucleotides were developed to enhance nuclease resistance and increase the binding activity of the target mRNA.  They differ because of ribose assumed 2′-alkyl modifications (Penchovsky and Traykovska 2015).

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The two most studied antisense oligonucleotides which belong to the second generation are 2′-Methyl (2′-OMe) and 2′-Methoxyethyl (2′-MOE) modifications of PS-modified antisense oligonucleotides. However, due to the inability of those molecules to induce an RNase-H mediated cleavage of target mRNAs, some chimeric molecules of antisense oligonucleotides were synthesized in the aim to increase the efficacy. An example of an antisense oligonucleotide, which belongs to this category and is approved in the market, is mipomersen. Peptide nucleic acid (PNA), locked nucleic acid (LNA) and phosphorodiamidate (PMO) belong to the third generation of antisense oligonucleotides and they are the most studied antisense oligonucleotides of this category. This generation brought enhanced target affinity, nuclease resistance, bioavailability, and pharmacokinetics. The structural difference has to do with some chemical modifications of nucleotide’s furanose ring (Penchovsky and Traykovska 2015; Chan et al. 2006; Kurreck 2003; Gleave and Monia 2005). The US Food and Drug Administration (FDA) approved antisense oligonucleotides for the treatment of a virus; the cytomegalovirus (CMV) induced chorioretinitis (Traykovska et al. 2018). Their approval gave hope for the treatment of other pathogenic conditions in the future. Especially, the first antisense oligonucleotide, which was approved for the market, was fomivirsen or vitravene, which is a firsteneration anti-CMV oligonucleotide. Fomivirsen is used for local application. Its route of administration is intravitreal and it is distributed to retinal epithelium with no significant systemic distribution. Macugen or Pegaptanib is another antisense oligonucleotide that is approved by the FDA. It is used for the treatment of a leading cause of blindness in adults above the age of 50  years, the age related macular degeneration (AMD) of the retina. However, during the years, the drug’s use decreased due to the existence of more effective drugs, ranibizumab and anti-VEGF mAB bevacizumab, for the treatment of age related macular degeneration. Some antisense oligonucleotides were approved for the treatment of neurological diseases as Duchenne muscular dystrophy (DMD) and Spinal muscular atrophy (SMA). DMD and Becker muscular dystrophy (BMD) are myopathies characterized by progressive muscle degeneration (Capitanio et al. 2020). They are fatal X-linked genetic diseases with recessive inheritance. Becker muscular dystrophy is milder in comparison to Duchenne muscular dystrophy. The severity of Duchenne muscular dystrophy is due to the complete absence of dystrophin production while in Becker muscular dystrophy dystrophin is produced but it is abnormal or reduced. In Duchenne muscular dystrophy, the abnormal gene is located in Xp21 locus and it is one of the largest genes. Some of the clinical manifestations involve muscle hypotonia, delay in motor skills and weakness of proximal muscles. In the later stages, the disease presents with skeletal deformities like scoliosis, pharyngeal weakness, and contractions, which may involve the elbows, knees, and Spinal muscular atrophy is a progressive autosomal recessive disease. The pathogenesis involves a mutation in the survival motor neuron (SNM1) gene that is located in the fifth chromosome and leads to a deficiency in survival motor neuron protein. There are three types of SMA disease. Type I SMA either manifests early or affects mostly the infants, which are born with the symptoms of the disease,

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or the symptoms manifest later during their neonatal life. Type II SMA manifests later in infant life and toddlers. Some of the most common symptoms of the disease are generalized muscular weakness, difficulty in breathing, difficulty in swallowing. Some of them may be incapable to reach the developmental milestones as sitting upright. The prognosis of the disease depends on the type. Especially, in patients who suffer from type I SMA, the disease deteriorates gradually from the neonatal period and the leading cause of death is usually respiratory failure. It should be mentioned that a drug called Exondys 51, was approved by the FDA on September 19, 2016, against Duchenne muscular dystrophy, although, there were many conflicts from scientists about its utility. Another antisense oligonucleotide is indicated for infants who suffer from spinal muscular atrophy types 1, 2 and 3. It is called Speranza or Nusinersen and it was approved by the FDA on December 23, 2016 and is considered as a potentially life saving drug. ENDEAR study showed that with the application of Nusinersen there was a therapeutic efficacy even after the first seven months (Traykovska et al. 2018). Although, there is a need for a longer follow up to draw more conclusions. There is a belief that in the future antisense oligonucleotides use will have promising effects in the treatment of neurological diseases. Defitelio or Defibrotide is an oligonucleotide drug that was approved on April 1, 2016 and used in cases where after application of high dose chemotherapy or after an autologous stem cell transplantation there is severe hepatic veno-occlusive disease (sVOD) (Rinaldi and Wood 2018; Geary 2009; Stein and Castanotto 2017; Kolb and Kissel 2011, 2015; Pane et al. 2018; Yiu and Kornberg 2015; Tsoumpra et al. 2019). To find novel strategies for the therapy of atherosclerosis by lowering triglyceride levels, a second generation antisense oligonucleotide, Volanesorsen was developed. Its mechanism of action aims in the reduction of apolipoprotein C-III (Apo C-III) messenger RNA. Single nucleotide polymorphisms (SNPs) in the Apo C-II gene is related to severely elevated triglycerides. After the implementation of some studies as APPROACH and COMPASS, the drug was approved in May 2019 for the treatment of familial chylomicronemia syndrome (FCS) in adults. Some other studies are on a process to identify Volanesorsen’s utility in other conditions as hypertriglyceridemia, familial partial lipodystrophy (FPL) and partial lipodystrophy (Reiner 2018; Paik and Duggan 2019; Stein and Castanotto 2017). Nowadays, antisense oligonucleotides technologies are applied in different organisms and they seem to be very promising and popular for the treatment of bacterial infections. The idea of their use against bacteria is based on the presence of some proteins in bacteria, which are essential for their survival and cannot be found in humans. Therefore, the use of antisense oligonucleotides, which target the expression of proteins at an mRNA level, is a promising strategy for the development of novel antibiotics. More research is needed to be done in this field, due to the urgent need for the discovery of new antibacterial drugs that will be able to fight against the growing threat of antimicrobial resistant pathogenic bacteria (Rinaldi and Wood 2018; Penchovsky and Traykovska 2015). The use of antisense oligonucleotides is not limited only to the protein target identification and validation. Antisense oligonucleotides can also be used for the treatment of diseases in which

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the pathogenesis involves the dysregulation of protein expression (Chan et al. 2006). The routes of antisense oligonucleotides administration are local (or topical) and parenteral. When they are administered for systemic application, the parenteral injection is preferred either intravenously or subcutaneously. No significant changes are observed in the bioavailability between the intravenous and subcutaneous routes of administration (Geary 2009).

9.7  Antibacterial Drug Discovery Based on Phage Therapies An alternative method to control bacterial infections is the use of bacteriophages. Bacteriophages are viruses that can infect bacteria. In that way, they express their unique anti bacterial property. Their use in medicine is dated since 1919 hence, before the discovery of the first antibiotics. Twort and d’Herele discovered the first phages in 1915 and 1917. However, with the discovery of antibiotics, and particularly after the Second World War, antibiotics, especially in Western countries, replaced phage therapies (Bassetti et al. 2017). However, in some countries, including Poland, Russia, and Georgia, the use of phages remained a popular treatment strategy throughout the twentieth century and even up to present days. Bacteriophages are present almost in all the ecosystems including the human body and even in extreme environments. They are capable to fight antibacterial infections when they are applied in addition to antibiotics or as an alternative to antibiotic therapies. The overuse along with the misuse of antibiotics globally leads to the rapid development of antimicrobial resistance, which is a great threat to public health with serious socio economic impacts. The need for new alternative treatments against drug resistant strains is inevitable. There is no doubt that phage therapies consist of an unavoidable option for research and they seem to be a promising therapeutic strategy in cases where resistant bacterial strains have already developed and antibiotics are not effective anymore. Nevertheless, more researches should be done in this field. There is a great need of scientists to improve their knowledge on the subject of phages to be able to understand their biology to ensure the best conditions during their preparation and render their use against bacteria a successful strategy to fight antimicrobial resistance development (Mantravadi et al. 2019). Bacteriophages have some special characteristics. The genomic plasticity and rapid replication are two characteristics of great importance. Some point mutations, rearrangements at the level of the genome and their capacity to exchange genetic material are some of the causes of their great diversity. The use of bacteriophages as therapeutic particles seems to have many advantages. At first, they are very specific. Every phage is capable of recognizing a particular ligand on the cell wall. The ligand that is recognized turns to be specific for a certain bacterial strain. Thus, a phage affects only a particular strain and is not capable to harm other strains. In this way, phages are not able to select resistance in other strains but only to the strains that they target. Bacteria can also develop resistance against phages but in comparison to antibiotics, phages can evolve it. Thus, the development of resistance will not

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affect phage therapies because newer bacteriophages will be capable to destroy the bacterial strains, which developed resistance against the phages used before. Another advantage of phages is that if they are purified properly, they are harmless for humans and they can be used as self dose at the site of infection. They are safe for use even by immunocompromised patients because they do not cause a toxic effect on the liver and kidneys. There are two options for phage therapies. They can be either “personalized” which aims to target a causative pathogen that is causing an infection in an individual patient or a combination of several strains of phages that can be used to target more than one pathogenic bacterial species (Penchovsky and Traykovska 2015; Jonczyk-Matysiak et al. 2019; Duckworth 1976; Pelfrene et al. 2016; Sharma et al. 2017; Domingo-Calap et al. 2016; Borysowski and Gorski 2008). After the genome of a phage is injected in the bacterial cytoplasm, replication inside the host follows by the use either of lytic or lysogenic mode. Phages use the cellular machinery of the bacterium that they infect for some procedures as the synthesis of proteins and as energy generating systems. Indeed, few viruses can use both lytic and lysogenic mode (Sharma et al. 2017). When lytic phages are used, they can lyse and destroy the bacteria. Thus, progeny viruses are released. Then phages render capable to be used again and to infect other bacteria. In contrast, lysogenic phages should first integrate their genetic information into the genome of the host and replicate along with the host. By this process, the host bacteria turn to have new properties. Lytic phages are considered more suitable as therapeutic particles in comparison to lysogenic phages (Penchovsky and Traykovska 2015; Domingo-Calap et al. 2016). Unfortunately, there are cases where the failure of the treatment with the use of phages is observed. Some of the reasons which may be related to phage therapy failures are considered to be the way of phages reparation, the conditions in which they stored, the way of their application, pharmacokinetics, different immune status of patients, other drugs which are used in combination to phages and eating habits (Jonczyk-Matysiak et  al. 2019). During the last decade, one field where further research is done for the use of phages is urinary tract infections. Urinary tract infections are the second most common cause of infectious diseases following pneumonia. It is well known that urinary tract infections affect predominately the woman population due to some specific characteristics of females as the anatomy of the genitourinary system as well as, the elderly whose immune system and microbiota change during the years. The most common agent, which is blamed for urinary tract infections is Escherichia coli, a Gram negative, facultative anaerobic bacterium belonging to the family Enterobacteriaceae. A bacterium is normally found in intestines as a part of the normal gastrointestinal flora although, under some conditions, it can become pathogenic and cause infections. E. coli is included in a list that was published by the World Health Organization (WHO 2015). This list includes the priority pathogens for which antibiotics should be discovered urgently. E. coli belongs to the category of critical bacteria. The treatment options for urinary tract infections generally involve the use of antibiotics. However, the development of resistance is rapid and during the past decade, the situation became worse due to the presence of the extended spectrum beta-­lactamase

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producing E. coli (ESBL-EC) which renders the treatment of urinary tract infections with E. coli pathogen more difficult (Pena et al. 1997). The worst scenario is observed in cases where the pathogens are resistant against all the recommended antibiotics for the treatment of the infection. The presence of multi drug resistant strains along with extensively drug resistant (XDR) strains lead to the urgent need for the discovery of new drugs that will be capable to fight urinary tract infections. The use of phages as an alternative seems to be very promising and in comparison, to antibiotic use, they seem to have many advantages. The ways that phages can be used are monophage therapies in which only one phage type is used and polyphage therapies in which more phage types or a cocktail of phages are used. When polyphage therapies are used, the rise of pathogenic bacteria is avoided, the host range is increased and they are efficient against biofilm forming infections. Genetically engineered phages are a breakthrough and are an emerging field of interest for scientists’ research nowadays. With their use, they can obtain the desirable characteristics that phages should have to be used effectively against antibiotic resistant bacteria. Phage lytic enzymes or proteins, which are endolysins and virion associated lysins (VALs), are molecules that have antibacterial properties. They are produced from phages and through some mechanisms; they cause breakage of the bacterial cell wall. Another therapeutic option is the use of phages in combination with antibiotics, which act in synergism. This combination aims to fight resistant bacterial strains (Malik et  al. 2019; Goodridge 2010; Lehman and Donlan 2015; Moller-Olsen et al. 2018; Fischetti 2005; Rodriguez et al. 2011).

9.8  A  ntibacterial Drug Discovery Using Fecal Microbiota Transplantation The human gut microbiota (bacteria, archaea, viruses, and microeukaryotes) has the largest population of microorganisms in the human body (Hollister et al. 2014). It usually remains balanced and quite persistent/resilient but when the human organism is exposed to antibiotics, the balance changes (Cho and Blaser 2012). Moreover, bacterial populations from the normal microflora could disappear due to the broad spectrum effect of some antibiotics. Such changes further implicate the state of human health and new antibiotic related diseases occur (Langdon et al. 2016; Yoon and Yoon 2018). A frequent example of this is susceptibility to the development of infections, which can become chronic conditions over time. Thus, an alternative strategy i.e. the fecal microbiota transplantation was developed. Fecal microbiota transplantation (FMT) is a type of bacteriotherapy (Khoruts et al. 2010; Wilson et al. 2019). It is also known as stool transplantation because the therapeutic method consists of transferring the entire balanced stool population from a healthy donor to a patient with Clostridium difficile infection (CDI). The stool is usually fused with sterile 0.9% saline to create a liquid mixture, which is then transferred to the gastrointestinal tract of the patient (Mullish et al. 2018). Once

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the healthy microbes are transplanted, they compete with C. difficile and they suppress its growth. The transplantation is applied to patients who have already been properly treated with antibiotics but the Clostridium difficile infection has reoccurred twice (Mullish et al. 2018). However, it is possible that a candidate is considered for fecal microbiota transplantation after only one infection, if it is a severe one with high risk factors for more episodes. According to the American Gastroenterological Association and the joint British Society of Gastroenterology (BSG) and Healthcare Infection Society (HIS), caution is advised for immunosuppressed patients or patients with recent bone marrow transplant, cirrhosis, immune deficiency syndrome (AIDS) or decompensated chronic liver disease. Furthermore, fecal microbiota transplantation is not recommended for patients with anaphylactic food allergies. Usually, the fecal microbiota transplantation is performed by colonoscopy or sigmoidoscopy. However, other methods have been used successfully such as fecal enemas (given through the rectum), nasogastric (nasal), nasoduodenal or nasojejunal tube into the upper part of the gastrointestinal tract, upper gastrointestinal endoscopy or capsules with frozen fecal microbiota transplantation. Fecal microbiota transplantation is a very effective therapy that is considered successful when there is no infection for the next 8 weeks after treatment. The overall success rate varies between 65% and 80% after 1 treatment and 90% to 95% success after repeated treatments (Meyers et al. 2018; Shogbesan et al. 2018). Such favorable outcomes only prove fecal microbiota transplantation as a potent alternative to the widely used antibiotics and additionally limit the spread of antibiotic resistance.

9.9  A  ntibacterial Drug Discovery Based on Cell Penetrating Peptides with Antibacterial Activity In recent years, there has been increased interest in the research field for antimicrobial peptides (AMPs) (Pfalzgraff et al. 2018). Naturally, they are considered part of the innate immunity in various organisms but the development of synthetic ones has proven to be quite effective towards various human bacterial pathogens. Antimicrobial peptides are amphipathic and cationic. Their size is within the range of 12 and 50 amino acid residues (Fensterseifer et al. 2019). On the other hand, cell penetrating peptides (CPPs) are exogenous peptides or peptide cargo complexes with the ability to directly enter cells and deliver their cargo inside them (Kauffman et al. 2015). The majority of the cell penetrating peptides are amphipathic, have a net positive charge and do not show any cell specificity (Rodriguez et al. 2014). The size of a cell penetrating peptide usually varies between 6 and 30 residues (Nakase et al. 2012). It has been debatable whether antimicrobial peptides and cell penetrating peptides should be separated in different categories or if one of them should be a subordinate category to the other (Langel 2019). Here we briefly describe some antimicrobial peptides and cell penetrating peptides divided into 2 categories viz.

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antimicrobial peptides with cell penetrating properties (Table 9.1) and cell penetrating peptides with antibacterial properties (Table 9.2) as described by Langel (Langel 2019). The peptides mentioned in Tables 9.1 and 9.2 are briefly described in this section. Promising antibacterial therapeutics are the proline rich antimicrobial peptides (PR-AMPs). They are a diverse peptide group, sharing 4 key functions (Scocchi et al. 2011). First, as their name suggests, they have a high content of proline residues. It can reach up to 50% of all the residues. Second, they contain arginine residues, which make them cationic. Third, proline rich antimicrobial peptides have shown a broad spectrum of antimicrobial activity, especially against Gram negative bacteria. This is due to the weak damages to the bacterial membrane. Last/Four, all of their d-enantiomers significantly lose activity or they are completely inactive. Two popular examples of proline rich antimicrobial peptides are bac7 and pyrrhocoricin. Bac7 was originally derived from Bovine cathelicidin (Durzynska et al. 2015). It consists of 60 residues, of which a fragment of 35 residues starting from the N-terminus forms the so-called bac71–35 (Le et al. 2017). It was determined that bac71–35 successfully inhibits E. coli by interaction with the bacterial ribosomes and selectively inhibits the protein synthesis in vitro and in vivo (Mardirossian et al. 2014). Other pathogenic bacteria that have been successfully inhibited are Salmonella enterica and Pseudomonas aeruginosa (Runti et al. 2017). However, it should be noted that while bac7 shares the same mechanisms of action in E. coli and S. enterica, the inhibition in P. aeruginosa is based mostly on membrane damages (Table 9.1). Pyrrhocoricin is another proline rich antimicrobial peptide, composed of 20 amino acid residues. It is derived from the Pyrrhocoris apterus and it binds to the bacterial heat shock protein, DnaK in E. coli (Cociancich et al. 1994). This results in inhibition of the ATPase activity and the refolding of misfolding proteins (Taniguchi et al. 2016). However, research in 2015 suggested that DnaK is not the primary target and pyrrhocoricin preferably binds to RNA (Taniguchi et al. 2016). Thus, it inhibits protein synthesis by repressing the translation step instead of the transcription step. It should be noted that even though pyrrhocoricin inhibits E. coli, there is a surprisingly high frequency of mutation (6 × 10−7) in the bacteria. In the

Table 9.1  Antimicrobial peptides (AMPs) with cell penetrating properties Peptide Bac7 (1–35)

Sequence RIRPRPPRLPRPRPRPLPFPRPGPRPIPRPLPFP

Pyrrhocoricin VDKGSYLPRPTPPRPIYNRN Hc-CATH LL-37

KFFKRLLKSVRRAVKKFRKKPRLIGLSTLL

MIC (μM) 0.5–1 5

0.16– 20.67 LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES 5

Reference No Benincasa et al. (2009) Narayanan et al. (2014) Wei et al. (2015) Narayanan et al. (2014)

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Table 9.2  Cell penetrating peptides (CPPs) with antibacterial properties MIC (μM) Refference No 4–16, 25 Alaybeyoglu et al. (2018), Akdag and Ozkirimli (2013), Palm et al. (2006) KETWWETWWTEWSQPKKKRKV 8–32 Splith and Neundorf (2011) KKTWWKTWWTKWSQPKKKRKV 1–8 Zhu et al. (2006)

Peptide Sequence pVec LLIILRRRIRKQAHAHSK

Pep-1 Pep-­ 1-­k Tat 49–57 RKKRRQRRR a

2–4,a 8–16

Splith and Neundorf (2011)a, Lv et al. (2017)

Note: The MIC value is for Tat48–60

case of spontaneous chromosomal deletion of the sbmA gene, the bacteria become pyrrhocoricin resistant. Hc-CATH is the first antimicrobial peptide cathelicidin, which was discovered in sea snakes (Wei et al. 2015). Hc-CATH, as well as other peptides from the cathelicidins family, has a critical role against microbial infections in the vertebrates. The structure of Hc-CATH is very stable and it is composed of 30 amino acids with two α-helix regions (Wang et  al. 2018). Hc-CATH demonstrates low cytotoxicity in mammalian cells. Meanwhile, it shows high antibacterial activity in various Gram negative and Gram positive bacteria such as Aeromonas salmonicida, Bacillus subtilis, Escherichia coli, Lactococcus garvieae, Klebsiella pneumoniae, Nocardia asteroids, P. aeruginosa, Staphylococcus aureus, Shigella dysenteriae, Streptococcus iniae and Vibrio (Vibrio vulnificus, Vibrio fluvialis, Vibrio splendidus). LL-37 derives from human cathelicidin and it has an α-helical structure (Splith and Neundorf 2011; Seil et al. 2010). It consists of 37 amino acid residues and it demonstrates high antibacterial activity towards bacteria such as E. coli, L. monocytogenes, P. aeruginosa, S. aureus, and S. epidermidis. However, LL-37 also shows toxicity in mammalian cells due to disruption of the integrity of the cellular membrane and it is chemotactic for neutrophils, monocytes and T cells (Table  9.2). Therefore, LL-37 derivatives or mimicking molecules would be more suitable for the treatment of resistant bacteria in people. A popular example of antibacterial activity is pVEC (Nan et  al. 2011). It is derived from murine vascular endothelial cadherin protein and it inhibits Gram positive and Gram negative bacteria at a minimal inhibitory concentration of 4–16 μM. The N-terminus of pVEC (LLIIL) is hydrophobic and it has a significant role in the antibacterial effect according to recent research (Alaybeyoglu et  al. 2018). As stated by the research team, the N-terminus contributes to the bacterial membrane disruption and if it is removed, pVEC loses its antibacterial effect. Examples of bacteria inhibited with pVEC are E. coli and Bacillus megaterium (Palm et al. 2006). Pep-1 has a chimeric structure, consisting of the nuclear localization sequence of simian virus 40 large T antigen and of the reverse transcriptase of the human immunodeficiency virus (Splith and Neundorf 2011). Pep-1 can affect many Gram

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positive and Gram negative bacteria but even so, it has weak antibacterial activity. However, different modifications of its structure demonstrate the potential of this cell penetrating peptide. For example, Pep-1-K has replaced 3 glutamate residues (Glu-2, Glu-6, and Glu-11) with lysine (Zhu et al. 2006). As a result, the antibacterial activity is significantly increased towards Gram positive and Gram negative bacteria, in addition to clinical isolates of multi drug resistant Pseudomonas aeruginosa and methicillin resistant Staphylococcus aureus (Kauffman et al. 2015). Tat is the minimal transduction domain derived from human immunodeficiency virus-1 (HIV-1) Tat protein (Lv et al. 2017). It consists of 9 amino acid residues and it is an arginine rich peptide. It shows no toxicity in human erythrocytes. According to Splith et  al., tat demonstrates the inhibitory effect in various Gram positive and Gram negative bacteria such as S. aureus (Splith and Neundorf 2011). Indeed, the general inability of resistance development against antimicrobial peptides and cell penetrating peptides gives them a great advantage in comparison to antibiotics. Pharmaceutical companies have invested in antimicrobial peptides and cell penetrating peptides research as a potential future therapeutic option. Furthermore, a lot of antimicrobial peptides already reached the markets and there are many in preclinical along with clinical trials that are on process nowadays (Boparai and Sharma 2019).

9.10  Conclusion Eventually, bacteria may develop resistance against any antibiotic. In recent years, the rate of emergence of antimicrobial resistance is constantly increasing due to the widespread misuse of antibiotics both in human and veterinary medicine. As a result, more people are dying worldwide by infections caused by drug resistant bacterial pathogens. We need to tackle this problem by applying two main strategies. The first one is to significantly reduce the misuse of antibiotics worldwide. The second general approach we need to employ is to develop novel much more efficient approaches to antimicrobial drug development. For instance, we can apply antisense oligonucleotide technology for inhibition of any bacterial RNA that is a suitable drug target. We can use various types of cell penetrating oligopeptides attached to the antisense oligonucleotides for their delivering into the bacterial cell. Applying such drug design approaches can significantly reduce the time and the cost of antimicrobial drug development because of the main principles of design and application of antisense oligonucleotides are well understood. Even when bacteria develop resistance against an antisense oligonucleotide by mutating the target sequence, we can easily address that by manipulating the design of antisense oligonucleotide. Acknowledgments  This work was financed by a grant DN/13/14/20.12.2017 awarded by the Bulgarian National Science Fund (BNSF).

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Index

A Agriculture, v, xii, 2, 3, 8, 18–20, 22, 35–37, 51, 63, 66, 68, 69, 71–76, 86, 89, 206 Albrethsen, J., 120 Alcantara, L.C. Jr., 84–102 Ali, J., 60–77 Anand, S., 2–23 Animal health, 22, 45, 51, 52, 60, 86 Antibiotics, v, vi, 2–17, 19–22, 34, 36, 38–40, 42, 43, 45, 46, 48, 50, 60, 62–65, 67, 71, 72, 75, 76, 84, 85, 88–91, 110, 111, 114–117, 130, 136, 137, 141, 148, 149, 157, 160–162, 164–169, 178, 179, 186, 189, 192–196, 206–212, 215–219, 222 Anti-CRISPRs (Acrs), 140 Antimicrobial resistance, v–vii, xi, xii, 1–23, 34–54, 59–77, 84–86, 91, 102, 129–141, 148–169, 177–196, 206–210, 213, 216, 222 Antimicrobials, v–vii, 2–23, 34, 35, 37–43, 45–48, 52, 53, 60, 62, 65, 67, 75, 84–89, 91, 136, 137, 148, 149, 153, 156–158, 162, 164, 166, 168, 169, 194, 195, 207–211, 215, 219, 220, 222 Antisense oligonucleotides, vii, 210–216, 222 Auto-inducers, 196 Azevedo, V., 84–102 B Bacteriophages, 16, 17, 85, 91, 131, 136, 137, 139, 140, 149, 166–168, 216, 217 Barh, D., 92 Bertrand, K.P., 117 Bigger, J., 111

Biofilms, vi, 19, 20, 22, 84, 89, 120, 121, 148–169, 178–180, 185, 187–189, 193, 195, 218 C Cas, 130–141 Chaudhary, M., 34–54 Chauhan, A., 148–169 Chokshi, A., 65 Chua, S.L., 120 Climate changes, v, 59–77 Clustered regularly interspaced short palindromic repeats (CRISPR), 119, 130–141 Community hygiene, 18 Comparative genomics, vi, 91–93 CRISPR-Cas, 129–141 CRISPR RNA (crRNA), 132–135 D de Castro Oliveira, L., 84–102 de Oliveira Tosta, S.F., 84–102 Dholpuria, S., 2–23 Drug discovery, vii, 205–222 Duan, X., vi, 110–121 DuPont, A., 35 E Environmental surveillance, 3 Exopolysaccharides, 153–155, 162, 164, 166, 182

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 H. Panwar et al. (eds.), Sustainable Agriculture Reviews 46, Sustainable Agriculture Reviews 46, https://doi.org/10.1007/978-3-030-53024-2

229

Index

230 F Fleming, A. Sir, v, 34, 62 Fluorescent indicator, 113 Food safety, 14, 37, 39, 40, 60, 61, 67, 71, 76, 77 Food security, v, 15, 60, 61, 66–68, 71, 74–77 Food systems, 59–77 Fu, Y., 110–121 G Gene transfer, 15, 16, 62 Genomics, 2, 16, 49–51, 91–102, 115, 117, 118, 211–212, 216 Ghangal, R., 130–141 Ghosh, S., 148–169 Global action plan, 19, 36, 86 Global Surveillance Programs, 33–54 Guide RNA, 134–138, 141 H High throughput screening, vi, 115–117, 189, 211 Horizontal gene transfer, 2, 6–7, 10, 15, 16, 62, 139

Miyoshi, A., 84–102 Mohsin, M., v, 60–77 Moyed, H.S., 117 N Nanomedicine, 149, 168–169 Next generation sequencing (NGS), 51, 116, 117, 211 O One health, v, vi, 1–23, 36, 41–44, 86 O’Neill, J. Sir, 65 P Pan-genomics, vi, 91–94 Patnaik, S., 130–141 Penchovsky, R., 206–222 Persistent bacterial population, 109 Phytocompounds, 149, 158–160 Popova, K.B., 206–222 Precursor CRISPR RNA (pre-crRNA), 132, 134, 135 Prioritizing drug targets, vi, 83–102 Proteomics, 91, 118, 120, 121 Protospacer adjacent motif (PAM), 134–136

I International cooperation, 19 J Jain, C., 130–141 Jaiswal, A.K., 84–102

Q Quorum sensing (QS), vi, 111, 121, 156, 158–160, 162, 177–196

L Langel, U., 220 Lennox, L.-B., 112

R Remmele, C.W., 99 Resistance, v, vi, 2–23, 34–43, 45–54, 60, 62–65, 72, 74, 75, 84–86, 88–91, 102, 110, 116, 130, 136, 140, 141, 148–150, 153, 154, 156–158, 166, 167, 169, 178, 186, 192–196, 209, 213, 214, 216, 217, 219, 222 Riboswitches, 210, 211, 213 RNA binates, 134, 135

M Metabolic pathway reconstruction, 95 Metabolomics, 118, 121

S Sajjad-ur-Rahman, Ali.J., 60–77 Santana, M., v, 84–102

K Kumar, S., v, vi, 34–54, 178–196 Kumar, V., 34–54

Index Sarma, A.P., vi, 130–141 Shad, A.A., 60–77 Shandilya, S., 178–196 Sharma, J.K., 2–23 Shouche, Y., 2–23 Singh, B.P., vi, 148–169 Singh, K.S., v, vi, 2–23, 178–196 Soares, S.C., 84–102 Solanki, M., 130–141 Steele, 35 Subtractive genomics, vi, 91–94 Surveillance, v, 3, 6, 18–21, 23, 34–54, 64, 65, 72, 75, 86, 209 System biology, 110–121

231 T Tiwari, S., 84–102 Transcriptomics, 91, 95, 118, 119 V Valsamatzi-Panagiotou, A., vii, 206–222 Y Yadav, M., 34–54 Yang, L., 110–121