Cancer Cell Signaling: Methods and Protocols [3 ed.] 1071607588, 9781071607589

Table of contents :
Preface
Contents
Contributors
Part I: Cancer Resistance
Chapter 1: Fighting Cancer Resistance: An Overview
1 Cancer Resistance: Main Obstacle to Overcome in Cancer Therapy
2 Challenges to Defeat Resistance Using Targeted Anticancer Therapies
3 The Role of HIFs in Cancer Resistance: Challenges to Overcome Resistance Targeting Autophagy and Metabolism
4 Cancer Stem Cells as a Hub in Cancer Resistance
5 Challenges to Beat Resistance with Immunotherapy
References
Chapter 2: Prostate Cancer Spheroids: A Three-Dimensional Model for Studying Tumor Heterogeneity
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Cell Lines
2.3 Cell Culture Equipment
3 Methods
4 Notes
References
Chapter 3: Enrichment and Transcriptional Characterization of Stem Cells Isolated from Human Glioblastoma Cell Lines
1 Introduction
2 Materials
2.1 Cell Culture (See Note 1)
2.2 Immunostaining for Flow Cytometry
2.3 Immunofluorescence
2.4 RT-qPCR
3 Methods
3.1 Glioma Stem Cells Enrichment and Culture
3.2 Determination of CD133+ and CD15+ Cells by Flow Cytometry Analysis
3.3 Glioma Stem Cells Characterization
3.4 Analysis of the Expression of Genes Associated With Stemness in GSCs
4 Notes
References
Chapter 4: Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds
1 Introduction
2 Materials
2.1 Computational Workstation
2.2 Python Environments Manager (See Note 4)
2.3 Molecular Docking Software
2.4 Bioinformatics and Cheminformatic Tools
2.5 Analysis Tools Working on Python 3.6
3 Methods
3.1 Generation of Protein Structures Database
3.2 Setting Up the Software and Conda Environments
3.3 Protein Dataset Preparation for Reverse Docking Assays (See Note 7)
3.4 Ligand Building and Preparation for Reverse Docking (See Note 8)
3.5 Protein Pocket Search for Reverse Docking
3.6 Reverse Docking
3.7 Analysis of Docking Results
4 Notes
References
Chapter 5: Mouse Model for Efficient Simultaneous Targeting of Glycolysis, Glutaminolysis, and De Novo Synthesis of Fatty Acid...
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Drug Treatments
2.3 Seahorse Assays
2.4 Mice
2.5 Glucose Tolerance Tests
2.6 Magnetic Resonance Imaging
2.7 Indirect Calorimetry
3 Methods
3.1 Cell Culture and Viability Measurement
3.2 Viability Curves
3.3 Pharmacological Interaction
3.4 Seed and Treatment of Cells for Seahorse Assays
3.5 Measurement of Oxidative Phosphorylation Parameters With the Seahorse XF Cell Mito Stress Kit
3.5.1 The Day Prior of the Assay
3.5.2 Day of Assay
3.6 Measurement of Glycolytic Parameters With the Seahorse XF Glycolysis Stress Kit
3.6.1 Day of Assay
3.7 Tumor Growth in Mice
3.8 In Vivo Evaluation of the Tumor Growth
3.9 Glucose Tolerance Tests
3.10 Magnetic Resonance Imaging
3.11 Indirect Calorimetry
4 Notes
References
Part II: Epigenetic Control of Cancer
Chapter 6: Developing a Portable Device for the Identification of miRNAs in Fluids
1 Introduction
2 Materials
2.1 Expression of has-mir-141-5p in HEK293 Cells
2.2 Extraction of microRNAs from HEK293 Cells
2.3 Preparation of Silk Gels
2.4 Preparation of Molecular Beacons and Surface Gel
2.5 Agarose Preparation for Surface Gel
2.6 Printing Microarrays on Conventional Coverslips
2.7 Production of Low-Cost Equipment for the Identification of microRNAs in Samples and Fluids
3 Methods
3.1 Expression of has-mir-141-5p in HEK293 Cells
3.2 Extraction of microRNAs from HEK293 Cells
3.3 Preparation of Silk Gels
3.4 Preparation of Molecular Beacons and Surface Gel
3.5 Agarose Preparation for Surface Gel
3.6 Printing Microarrays on Conventional Coverslips
3.7 Recording Microarray Fluorescence Increments in Real-Time
3.8 Low-Cost Equipment for the Identification of microRNAs in Samples and Fluids
3.9 Traditional Versus 3D-Enhanced TIRF Arrays
3.10 Identification of hsa-miR-141-5p, a microRNA Marker for Cancer
3.11 Microarray Analysis
4 Notes
References
Chapter 7: Methods for the Study of Long Noncoding RNA in Cancer Cell Signaling
1 Introduction
1.1 The Human Genome Contains Many Thousands of Unexplored lncRNAs
1.2 lncRNAs Regulate Gene Expression and Protein Functions Via Various Mechanisms
1.3 lncRNA Expression Is Deregulated in Human Cancer
1.4 lncRNAs Serve as Tumor Suppressor Genes or Oncogenes
1.5 lncRNAs Represent Promising Biomarker and Therapeutic Candidates for Cancer Diagnosis and Treatment
1.6 Methods in lncRNAs Research
2 Materials
2.1 lncRNA-Immunoprecipitation
2.2 lncRNA Pull-Down
2.3 lncRNA Northern Blot Analysis
2.3.1 DIG labeled RNA Probe Synthesis
2.3.2 Separating RNA by Electrophoresis
2.3.3 Transferring RNA to the Membrane
2.3.4 Probe-RNA Hybridization
2.3.5 Detection of Probe-RNA Hybrids
2.4 lncRNA In Situ Hybridization
2.5 lncRNA Knockdown
2.5.1 lncRNA Knockdown Using siRNAs
2.5.2 lncRNA Knockdown Using shRNAs
3 Methods
3.1 lncRNA-Immunoprecipitation
3.1.1 Whole Cell Lysate Preparation (See Note 6)
3.1.2 Cell Harvest and Nuclei Lysate Preparation (See Note 6)
3.1.3 RNA Immune-Precipitation and Purification
3.2 lncRNA Pull-Down
3.2.1 Biotinylated RNA Synthesis by In Vitro Transcription
3.2.2 Whole Cell Lysate Preparation (See Note 6)
3.2.3 Nuclear Lysate Preparation (See Note 6)
3.2.4 RNA Pull-Down (See Note 14)
3.3 lncRNA Northern blot Analysis (See Note 17)
3.3.1 DIG Labeled RNA Probe Synthesis by In Vitro Transcription
3.3.2 Separating RNA Samples by Electrophoresis
3.3.3 Transfer RNA from Agarose Gel to the Membrane
3.3.4 Hybridization of DIG-Labeled Probes to the Membrane (See Note 28)
3.3.5 Detection of DIG-Probe-Target RNA Hybrids
3.4 lncRNA In Situ Hybridization
3.4.1 DIG Labeled RNA Probe Synthesis by In Vitro Transcription
3.4.2 Cell Preparation and Pretreatment
3.4.3 In Situ Hybridization and Detection of Probe-Target Hybrid
3.5 Method for shRNA Knockdown
3.5.1 lncRNA Knockdown Using siRNAs
3.5.2 lncRNA Knockdown Using shRNAs
4 Notes
References
Chapter 8: RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer
1 Introduction
2 Materials
2.1 System Requirements
2.2 Data Availability
2.3 Clinical Data Availability
2.4 Data Download
2.5 Data Analysis
2.6 Quality Control
2.7 Alignment to a Reference Genome and Sorting
2.8 Assembly and Quantification
2.8.1 STAR Pipeline
2.9 Differential Expression Analysis
2.9.1 DESeq
2.10 Survival Analysis
3 Differential Expression and Survival Analysis Using RNA-Seq Data from Glioblastoma (GBM) Patients
3.1 Differential Expression Analysis
3.2 Cox-Regression and Kaplan-Meier Analysis
4 Notes
References
Part III: Metastasis Promotion
Chapter 9: Correlation of Circulating Tumor Cell Measurements with 3D Quantitative Tumor Characterization to Predict Clinical ...
1 Introduction
2 Materials
3 Methods
3.1 Protocol Overview
3.2 MR Imaging
3.3 Blood Samples (see Note 2)
3.4 Circulating Tumor Cell Analysis Using the CellSearch System
3.4.1 Processing with the CELLTRACKS AUTOPREP System
3.4.2 Analysis Using the CELLTRACKS ANALYZER II
3.5 Quality Control
3.6 Quantification of Circulating Tumor Cells
4 Notes
References
Chapter 10: Extracellular Vesicles and Their Roles in Cancer Progression
1 EVs: A Brief Introduction and History
2 EV Isolation and Classification
3 Mechanisms of EV Formation and Release
3.1 MV Biogenesis
3.2 Exosome Biogenesis
4 EVs and Cancer Progression
4.1 How EVs Mediate Cancer Cell Phenotypes
4.2 EVs and Tumor Angiogenesis
5 EVs and Metastasis
6 EVs and Cancer-Mediated Immunosuppression
7 EVs and Their Potential Uses in the Clinics
7.1 EVs and Liquid Biopsies
7.2 EVs as a Drug Delivery System
8 Concluding Remarks
References
Chapter 11: Analysis of Tumor-Derived Exosomes by Nanoscale Flow Cytometry
1 Introduction
1.1 Exosomes
2 Biological Relevance of Exosomes in Cancer
2.1 Mechanisms of Tumorigenesis and Cancer Progression via Exosomes
2.2 Tumor Exosomes as Prognostic and Diagnostic Tools in Cancer
3 Current Methods for Isolation and Characterization of Tumor Exosomes
3.1 Ultracentrifugat-ion
3.2 Density Gradients
3.3 Size Exclusion Chromatography
4 Biochemical and Physical Analysis of Exosomes
4.1 Biochemical Characterization
4.2 Physical Characterization of Exosomes
5 Flow Cytometry as an Approach for Exosome Analysis
6 Nanoscale Flow Cytometry
6.1 Considerations for the use of Nanoscale Flow Cytometry for Tumor Exosome Detection and Analysis
6.1.1 Instrument Calibration
Fluidic System Settings
Acquisition Settings
Sample Preparation
Data Analysis
References
Chapter 12: In Vitro Models for Studying Tumor Progression
1 Introduction
2 Materials
2.1 Scratch-Wound Assay to Evaluate Cancer Cell Migration in 2D Cell Cultures
2.2 Transwell Invasion Assay to Evaluate Cancer Cell Invasion in 2D Cell Cultures
2.3 Cell Migration Assay in 3D Cell Culture Conditions
2.4 Transwell Invasion Assay to Evaluate Cancer Cell Invasion in 3D Cell Culture Conditions
3 Methods
3.1 Scratch-Wound Assay to Evaluate Cancer Cell Migration in 2D Cell Cultures
3.2 Transwell Invasion Assay to Evaluate Cancer Cell Invasion in 2D Cell Cultures
3.3 Cell Migration Assay in 3D Cell Culture Conditions
3.4 Transwell Invasion Assay to Evaluate Cancer Cell Invasion in 3D Cell Culture Conditions
4 Notes
References
Chapter 13: Assessment of Cell Cycle in Primitive Chronic Myeloid Leukemia Cells by Flow Cytometry After Coculture with Endoth...
1 Introduction
2 Materials
2.1 Primary Human Cells
2.2 General
2.3 Isolating Mononuclear Cells from Human Bone Marrow Samples by Density Gradient
2.4 Enrichment of CD34+ Hematopoietic Primitive Cells
2.5 Endothelial Cells Culture
2.6 Primitive Hematopoietic Cells (PHC) in Coculture with Endothelial Cells
2.7 Cell Cycle
3 Methods
3.1 Isolating Mononuclear Cells from Human Bone Marrow Samples by Density Gradient
3.2 CD34+ PHC Enrichment
3.3 Obtaining Endothelial Cells
3.4 Contact Co-culture
3.5 Separation of Cell Populations
3.6 Cellular Identification by Sorting
3.7 Cell Cycle Assessment in CD34+ Cells
3.8 Analysis of Cell Cycle by Flow Cytometry
4 Notes
References
Part IV: New Technologies in the Study of Cancer
Chapter 14: Chimeric Antigen Receptor (CAR) T Cell Therapy for Cancer. Challenges and Opportunities: An Overview
1 Introduction
2 Chimeric Antigen Receptor (CAR) T Cells
2.1 Structure of Chimeric Antigen Receptor (CAR)
2.2 Clinical Manufacturing of CAR T Cells
3 Clinical Applications of CAR T Cells
3.1 CAR T Cell for Hematologic Malignancies
3.1.1 Leukemias
3.1.2 Lymphomas
3.2 Advances in CAR T Cell Therapies for Solid Tumors
4 Current Challenges and Perspectives
4.1 CAR T Therapy Derived-Toxicities
4.1.1 On-Target On-Tumor Toxicity
4.1.2 On-Target Off-Tumor Toxicity
4.1.3 Off-Target Toxicity
4.1.4 Genotoxicity
4.1.5 Immunogenicity
4.1.6 Neurotoxicity
4.2 Strategies to Overcome Toxicity
4.2.1 Suicide Gene Switch
4.2.2 Antibody-Mediated Suicide Switch
4.2.3 Combinatorial Target Antigen Recognition
4.2.4 Synthetic Notch Receptors
4.2.5 Inhibitory Chimeric Antigen Receptor
4.2.6 Bispecific T Cell Engager
4.2.7 On-Switch CAR
4.3 Current Challenges in CAR T Cell Therapy
4.3.1 Exhaustion of CAR T Cells
4.3.2 Resistance to CAR T Cell Therapy
Antigen Escape and Down Modulation of Target Antigen
References
Chapter 15: Cell-Internalization SELEX of RNA Aptamers as a Starting Point for Prostate Cancer Research
1 Introduction
2 Materials
2.1 Aptamer Library
2.2 Oligonucleotide for Retrotranscription (RV)
2.3 Oligonucleotides for PCR
3 Methods
3.1 Cell Culture
3.2 Library Preparation
3.3 Cell-Selex
3.4 RNA Isolation
3.5 Retrotranscription
3.6 PCR and Purification
4 Notes
References
Chapter 16: Generation of Functional Genetic Study Models in Zebrafish Using CRISPR-Cas9
1 Introduction
2 Materials
2.1 sgRNA Annealing and In Vitro Transcription
2.2 Cas9 In Vitro Transcription
2.3 CRISPR Injection
3 Methods
3.1 Single-Guide RNA In Vitro Transcription
3.2 Cas9 In Vitro Transcription
3.3 CRISPR System Injection into Zebrafish Embryos
3.4 DNA Extraction and Genotyping of Injected Embryos
4 Notes
References
Chapter 17: Developing a Model for a siRNA Delivery System by Cancer Implantation into Zebrafish Circulation
1 Introduction
2 Materials
2.1 Zebrafish Breeding Resources
2.2 Cell Culture and Stable Expression of Fluorescent Protein Components
2.3 Preparation of Zebrafish for Cancer Cell Implantation
2.4 Cancer Cell Implantation Components
2.5 Components for LPEI-Coated siRNA-PLGA Hybrid Micelles
2.6 Components and Software for Imaging
3 Methods
3.1 Fluorescent mCherry Labeling of A375, Human Malignant Melanoma Cells
3.2 Preparation of LPEI-Coated siRNA-PLGA Hybrid Micelles
3.3 Preparation for Cancer Cell Implantation
3.4 Cancer Cell Implantation
3.5 Injection of LPEI-Coated siRNA-PLGA Hybrid Micelles
3.6 Evaluation of Anticancer Effects by Image Analysis
4 Notes
References
Chapter 18: Fabrication of Adhesive Substrate for Incorporating Hydrogels to Investigate the Influence of Stiffness on Cancer ...
1 Introduction
1.1 Wound Healing, Chronic Inflammation and Cancer
1.2 The Tumor Microenvironment
1.3 Extracellular Matrix
1.4 Signalling Pathways
1.5 Cancer Cell Behaviors and Phenotypic Plasticity
1.6 Hydrogels for Mimicking Microenvironments to Study Cancer Cells
2 Materials
2.1 Preparation of Samples
2.2 Polyacrylamide (PAA) Hydrogels
2.3 Cell Culture
2.4 Immunofluorescence
2.5 Imaging and Statistics
3 Methods
3.1 Hydrogel on Loctite
3.2 Cell Culture and Immunofluorescence
4 Notes
References
Index

Citation preview

Methods in Molecular Biology 2174

Martha Robles-Flores Editor

Cancer Cell Signaling Methods and Protocols Third Edition

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Cancer Cell Signaling Methods and Protocols Third Edition

Edited by

Martha Robles-Flores Department of Biochemistry, Faculty of Medicine, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico

Editor Martha Robles-Flores Department of Biochemistry Faculty of Medicine Universidad Nacional Auto´noma de Me´xico (UNAM) Mexico City, Mexico

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-0758-9 ISBN 978-1-0716-0759-6 (eBook) https://doi.org/10.1007/978-1-0716-0759-6 © Springer Science+Business Media, LLC, part of Springer Nature 2014, 2021 1st edition: © 2003 Humana Press Inc. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Illustration Caption: Colon cancer cells derived from primary cultures obtained from colon cancer patients. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface This book intends to provide the most recent state-of-the-art advances in cancer cell signaling knowledge combined with a discussion of the current challenges and prospects in cancer therapy. As in the previous edition, the book is organized into four parts, according to key topics in cancer cell signaling. However, in this new edition, Part I was replaced with chapters concerning cancer resistance, the main obstacle to overcome in cancer therapy. However, although the other parts were conserved, the combination of rapid changes in the field of cancer cell signaling and the advent of new treatments, described in these parts, has resulted in an almost entirely new book. Retained is a chapter about the critical role played by microvesicles in intercellular communication, but it now focuses on their roles in cancer progression. In this respect, a chapter was also included describing how nanoscale flow cytometry can be adapted for the study of nanoparticles, such as exosomes. Major changes include topics such as the exploration of pathways that allow cancer cells to become resistant to therapies aimed at their elimination. Because cancer stem cells are a hub in cancer resistance, two chapters describe techniques to study cancer stem cells. Additionally, this third edition includes new material on several topics, ranging from methods to study the tumor microenvironment interplay between tumor cells and extracellular matrix to other novel cancer therapies. Among them, a chapter describes a method of cell-internalization SELEX of RNA aptamers as a technique for cancer research. Another two chapters describe CRISPR-based technologies and siRNA delivery systems, showing that zebrafish could be exploited as a model to study cell signaling pathways. Dramatic advances in immune therapy have emerged as a promising strategy in cancer therapeutics. The use of antibodies to target proteins that act as off switches for the immune system has revolutionized cancer treatment providing new approaches less evadable than chemotherapy or molecular-targeted therapies. In this book, we present a chapter reviewing the state of the art of Chimeric Antigen Receptor T cell (CAR T cell) therapy with special emphasis on the current challenges and opportunities. With each edition of this book, we marvel at the new information that cancer researchers have gathered in just a few years. However, the deeper we probe into cancer cell signaling research, and in the development of improved therapies, the more we realize how much remains to be understood. I hope that the book will provide a sufficiently up-to-date and integrated view of challenges to defeat in the cancer cell signaling field. I am very grateful to all the authors who generously have contributed to this edition not only with valuable chapters but also with their expert tips and hints to help with success in applying a technique. I am also grateful to Dr. John Walker and the Springer team for their valuable guidance, support, and kind assistance. Mexico City, Mexico

Martha Robles-Flores

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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

CANCER RESISTANCE

1 Fighting Cancer Resistance: An Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martha Robles-Flores 2 Prostate Cancer Spheroids: A Three-Dimensional Model for Studying Tumor Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mauricio Rodrı´guez-Dorantes, Carlos David Cruz-Hernandez, Sergio Alberto Corte´s-Ramı´rez, Jenie Marian Cruz-Burgos, Juan Pablo Reyes-Grajeda, Oscar Peralta-Zaragoza, and Alberto Losada-Garcia 3 Enrichment and Transcriptional Characterization of Stem Cells Isolated from Human Glioblastoma Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ a-Medina, Ana M. Herna´ndez-Vega, Ne´stor F. Dı´az, Ana G. Pin Ismael Mancilla-Herrera, and Ignacio Camacho-Arroyo 4 Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angel Jonathan Ruiz-Moreno, Alexander Do¨mling, and Marco Antonio Velasco-Vela´zquez 5 Mouse Model for Efficient Simultaneous Targeting of Glycolysis, Glutaminolysis, and De Novo Synthesis of Fatty Acids in Colon Cancer . . . . . . . ˜ as-Gonzalez Alejandro Schcolnik-Cabrera and Alfonso Duen

PART II

v ix

3

13

19

31

45

EPIGENETIC CONTROL OF CANCER

6 Developing a Portable Device for the Identification of miRNAs in Fluids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Alexander Asanov, Alicia Sampieri, and Luis Vaca 7 Methods for the Study of Long Noncoding RNA in Cancer Cell Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Yi Feng, Junjie Jiang, Zhongyi Hu, Jiao Yuan, Tianli Zhang, Yutian Pan, Mu Xu, Chunsheng Li, Youyou Zhang, Lin Zhang, and Xiaowen Hu 8 RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Sudhanshu Shukla and Seema Khadirnaikar

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Contents

PART III

METASTASIS PROMOTION

9 Correlation of Circulating Tumor Cell Measurements with 3D Quantitative Tumor Characterization to Predict Clinical Outcomes in Cancer . . Sweet Ping Ng, Clifton David Fuller, and Heath Devin Skinner 10 Extracellular Vesicles and Their Roles in Cancer Progression . . . . . . . . . . . . . . . . . Wen-Hsuan Chang, Richard A. Cerione, and Marc A. Antonyak 11 Analysis of Tumor-Derived Exosomes by Nanoscale Flow Cytometry . . . . . . . . . Cynthia Lo pez-Pacheco, Andrea Bedoya-Lo pez, Roxana Olguı´n-Alor, and Gloria Soldevila 12 In Vitro Models for Studying Tumor Progression. . . . . . . . . . . . . . . . . . . . . . . . . . . Juan Carlos Gonza´lez-Orozco, Sau´l Gaona-Domı´nguez, and Ignacio Camacho-Arroyo 13 Assessment of Cell Cycle in Primitive Chronic Myeloid Leukemia Cells by Flow Cytometry After Coculture with Endothelial Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patricia Torres-Barrera, Mireya Ramı´rez-Florencio, and Antonieta Cha´vez-Gonza´lez

PART IV 14

135 143 171

193

207

NEW TECHNOLOGIES IN THE STUDY OF CANCER

Chimeric Antigen Receptor (CAR) T Cell Therapy for Cancer. Challenges and Opportunities: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arimelek Corte´s-Herna´ndez, Evelyn Katy Alvarez-Salazar, and Gloria Soldevila Cell-Internalization SELEX of RNA Aptamers as a Starting Point for Prostate Cancer Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mauricio Rodrı´guez-Dorantes, Sergio Alberto Corte´s-Ramı´rez, Jenie Marian Cruz-Burgos, Juan Pablo Reyes-Grajeda, Alberto Losada-Garcı´a, Vanessa Gonza´lez-Covarrubias, and Carlos David Cruz-Herna´ndez Generation of Functional Genetic Study Models in Zebrafish Using CRISPR-Cas9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ ez-Martinez, Francisco Carmona-Aldana, Hober N. Nun Carlos A. Peralta-Alvarez, Gustavo Tapia-Urzua, and Fe´lix Recillas-Targa Developing a Model for a siRNA Delivery System by Cancer Implantation into Zebrafish Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasuhito Shimada and Mai Hazekawa Fabrication of Adhesive Substrate for Incorporating Hydrogels to Investigate the Influence of Stiffness on Cancer Cell Behavior . . . . . . . . . . . . . Genaro Va´zquez-Victorio, Adriana Rodrı´guez-Herna´ndez, Mariel Cano-Jorge, Ana Ximena Monroy-Romero, Marina Macı´as-Silva, and Mathieu Hautefeuille

255

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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18

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Contributors EVELYN KATY ALVAREZ-SALAZAR • Departamento de Inmunologı´a, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico MARC A. ANTONYAK • Department of Molecular Medicine, Cornell University, Ithaca, NY, USA ALEXANDER ASANOV • TIRFLabs, Cary, NC, USA ANDREA BEDOYA-LO´PEZ • Departamento de Inmunologı´a and Laboratorio Nacional de Citometrı´a de Flujo, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico IGNACIO CAMACHO-ARROYO • Unidad de Investigacion en Reproduccion Humana, Instituto Nacional de Perinatologı´a-Facultad de Quı´mica, Universidad Nacional Autonoma de Me´xico, Ciudad de Me´xico, Mexico MARIEL CANO-JORGE • Departamento de Fı´sica, Facultad de Ciencias, Universidad Nacional Autonoma de Me´xico (UNAM), Mexico City, Mexico FRANCISCO CARMONA-ALDANA • Departamento de Gene´tica Molecular, Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Mexico City, Mexico RICHARD A. CERIONE • Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA; Department of Molecular Medicine, Cornell University, Ithaca, NY, USA; Veterinary Medical Center, Cornell University, Ithaca, NY, USA WEN-HSUAN CHANG • Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA ANTONIETA CHA´VEZ-GONZA´LEZ • Laboratorio de Ce´lulas Troncales Leuce´micas, Unidad de Investigacion Me´dica en Enfermedades Oncologicas, CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Me´xico ARIMELEK CORTE´S-HERNA´NDEZ • Departamento de Inmunologı´a, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico SERGIO ALBERTO CORTE´S-RAMI´REZ • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico JENIE MARIAN CRUZ-BURGOS • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico CARLOS DAVID CRUZ-HERNA´NDEZ • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico NE´STOR F. DI´AZ • Departamento de Fisiologı´a y Desarrollo Celular, Instituto Nacional de Perinatologı´a “Isidro Espinosa de los Reyes”, Ciudad de Me´xico, Mexico ALEXANDER DO¨MLING • Department of Drug Design, Graduate School of Science and Engineering, University of Groningen (RUG), Groningen, The Netherlands ALFONSO DUEN˜AS-GONZALEZ • Unit of Biomedical Research on Cancer, Biomedical Research Institute, Universidad Nacional Autonoma de Me´xico (UNAM)/National Institute of Oncology (INCan), Mexico City, Mexico YI FENG • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

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Contributors

CLIFTON DAVID FULLER • The University of Texas MD Anderson Cancer Center, Houston, TX, USA SAU´L GAONA-DOMI´NGUEZ • Unidad de Investigacion en Reproduccion Humana, Instituto Nacional de Perinatologı´a-Facultad de Quı´mica, Universidad Nacional Autonoma de Me´xico, Mexico City, Me´xico VANESSA GONZA´LEZ-COVARRUBIAS • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico JUAN CARLOS GONZA´LEZ-OROZCO • Unidad de Investigacion en Reproduccion Humana, Instituto Nacional de Perinatologı´a-Facultad de Quı´mica, Universidad Nacional Autonoma de Me´xico, Mexico City, Me´xico MATHIEU HAUTEFEUILLE • Departamento de Fı´sica, Facultad de Ciencias, Universidad Nacional Autonoma de Me´xico (UNAM), Mexico City, Mexico MAI HAZEKAWA • Department of Immunological and Molecular Pharmacology, Faculty of Pharmaceutical Sciences, Fukuoka University, Fukuoka, Japan ANA M. HERNA´NDEZ-VEGA • Unidad de Investigacion en Reproduccion Humana, Instituto Nacional de Perinatologı´a-Facultad de Quı´mica, Universidad Nacional Autonoma de Me´xico, Ciudad de Me´xico, Mexico XIAOWEN HU • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Obstetrics and Gynecology, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA ZHONGYI HU • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA JUNJIE JIANG • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA SEEMA KHADIRNAIKAR • Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India; Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India CHUNSHENG LI • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA CYNTHIA LO´PEZ-PACHECO • Departamento de Inmunologı´a and Laboratorio Nacional de Citometrı´a de Flujo, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico ALBERTO LOSADA-GARCI´A • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico MARINA MACI´AS-SILVA • Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Mexico City, Mexico ISMAEL MANCILLA-HERRERA • Departamento de Infectologı´a e Inmunologı´a, Instituto Nacional de Perinatologı´a “Isidro Espinosa de los Reyes”, Ciudad de Me´xico, Mexico ANA XIMENA MONROY-ROMERO • Departamento de Fı´sica, Facultad de Ciencias, Universidad Nacional Autonoma de Me´xico (UNAM), Mexico City, Mexico SWEET PING NG • Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia HOBER N. NUN˜EZ-MARTINEZ • Departamento de Gene´tica Molecular, Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Mexico City, Mexico ROXANA OLGUI´N-ALOR • Departamento de Inmunologı´a and Laboratorio Nacional de Citometrı´a de Flujo, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico

Contributors

xi

YUTIAN PAN • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA CARLOS A. PERALTA-ALVAREZ • Departamento de Gene´tica Molecular, Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico OSCAR PERALTA-ZARAGOZA • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico ANA G. PIN˜A-MEDINA • Facultad de Quı´mica, Departamento de Biologı´a, Universidad Nacional Autonoma de Me´xico, Ciudad de Me´xico, Me´xico MIREYA RAMI´REZ-FLORENCIO • Laboratorio de Ce´lulas Troncales Leuce´micas, Unidad de Investigacion Me´dica en Enfermedades Oncologicas, CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Me´xico FE´LIX RECILLAS-TARGA • Departamento de Gene´tica Molecular, Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico JUAN PABLO REYES-GRAJEDA • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico MARTHA ROBLES-FLORES • Department of Biochemistry, Faculty of Medicine, Universidad Nacional Autonoma de Me´xico (UNAM), Mexico City, Me´xico MAURICIO RODRI´GUEZ-DORANTES • Oncogenomics Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico ADRIANA RODRI´GUEZ-HERNA´NDEZ • Departamento de Fı´sica, Facultad de Ciencias, Universidad Nacional Autonoma de Me´xico (UNAM), Me´xico City, Me´xico ANGEL JONATHAN RUIZ-MORENO • Departamento de Farmacologı´a y Unidad Perife´rica de Investigacion en Biomedicina Traslacional, Facultad de Medicina, Universidad Nacional Autonoma de Me´xico (UNAM), Ciudad de Me´xico, Mexico; Department of Drug Design, Graduate School of Science and Engineering, University of Groningen (RUG), Groningen, The Netherlands ALICIA SAMPIERI • Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Ciudad Universitaria, Mexico City, DF, Mexico ALEJANDRO SCHCOLNIK-CABRERA • Unit of Biomedical Research on Cancer, Biomedical Research Institute, Universidad Nacional Autonoma de Me´xico (UNAM)/National Institute of Oncology (INCan), Mexico City, Mexico YASUHITO SHIMADA • Department of Integrative Pharmacology, Mie University Graduate School of Medicine, Tsu, Mie, Japan; Mie University Zebrafish Drug Screening Center, Mie, Japan SUDHANSHU SHUKLA • Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India HEATH DEVIN SKINNER • UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA GLORIA SOLDEVILA • Departamento de Inmunologı´a and Laboratorio Nacional de Citometrı´a de Flujo, Instituto de Investigaciones Biome´dicas, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico GUSTAVO TAPIA-URZUA • Departamento de Gene´tica Molecular, Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Me´xico City, Me´xico PATRICIA TORRES-BARRERA • Laboratorio de Ce´lulas Troncales Leuce´micas, Unidad de Investigacion Me´dica en Enfermedades Oncologicas, CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico

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Contributors

LUIS VACA • Instituto de Fisiologı´a Celular, Universidad Nacional Autonoma de Me´xico, Ciudad Universitaria, Mexico City, DF, Mexico; Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA GENARO VA´ZQUEZ-VICTORIO • Departamento de Fı´sica, Facultad de Ciencias, Universidad Nacional Autonoma de Me´xico (UNAM), Mexico City, Mexico MARCO ANTONIO VELASCO-VELA´ZQUEZ • Departamento de Farmacologı´a y Unidad Perife´ rica de Investigacion en Biomedicina Traslacional, Facultad de Medicina, Universidad Nacional Autonoma de Me´xico (UNAM), Ciudad de Me´xico, Mexico MU XU • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA JIAO YUAN • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA LIN ZHANG • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Obstetrics and Gynecology, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA TIANLI ZHANG • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA YOUYOU ZHANG • Center for Reproduction and Women’s Health, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Part I Cancer Resistance

Chapter 1 Fighting Cancer Resistance: An Overview Martha Robles-Flores Abstract The inherent or developed resistance of many cancer cells to chemotherapy and irradiation is actually the main challenge to overcome in cancer treatment. It is well known that cancer cells are characterized by several hallmarks, and it seems that the ability to evolve ways to evade stressful conditions and killing therapies must be consider another typical characteristic displayed by all malignant cells. This overview aims to provide a concise description of the main mechanisms involved in the promotion of resistance to anticancer therapy and to describe the most frequent challenges faced in the war against cancer therapy resistance. Key words Cancer resistance, Therapy evasion, Intrinsic resistance, Acquired resistance, Chemotherapy resistance

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Cancer Resistance: Main Obstacle to Overcome in Cancer Therapy Mechanisms of resistance include genetic, nongenetic, and epigenetic alterations [1]. Resistance to treatment represents a complex and multifactorial phenomenon due to both intrinsic factors and extrinsic factors. Example of intrinsic factors are translocations, the acquisition of resistance-conferring genetic mutations, and reversible epigenetic mechanisms that lead to drug tolerance [1–3]. Extrinsic factors are those related with tumor microenvironment such as hypoxia, acidosis, cytokines, and other messengers present in the surroundings of a tumor cell. In addition, there is a complex interplay and communication between tumoral cells with other cells present in the microenvironment such as the immune cells which play a key role in dictating and maintaining the resistant phenotype [1–4]. In addition, the existence of inter and intratumoral phenotypic and functional heterogeneity is one of the most relevant features of cancer cells that may generate a diversity of resistance mechanisms and is responsible for treatment failure. Nongenetic events involving both chromatin remodeling and the activation of stress-related pathways are responsible for the

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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establishment of drug tolerance, a process more rapid, and massive than genetic mutation [2]. Besides, drug tolerance is a temporary condition, which can revert after the cessation of cytotoxic stimuli. Differently, in the presence of continuous drug stimulation or other cellular stresses such as hypoxia, drug tolerance stabilizes into an enduring drug resistant state. The majority of chemo/radiotherapies inhibit cancer cell growth by activating cell death pathways, such as apoptosis, necrosis, and autophagy-associated cell death [5]. But cancer cells can acquire apoptosis-resistance during treatment by upregulating multiple prosurvival factors, such as inhibitors of apoptosis proteins (IAPs), nuclear factor-κB (NF-kB), and the B cell CLL/lymphoma-2 (BCL-2) family proteins [5, 6]. Single-agent treatment, or monotherapy, has proven to be highly ineffective thus far in clinical trials. Combined therapies aimed to negatively affect cancer cell homeostasis/metabolism at multiple simultaneous targets have produced improved efficacy reducing dosage, reducing side effects, and preventing or delaying the development of acquired resistance [3, 4, 7, 8]. However, patients ultimately also develop resistance. On the other hand, although the mechanisms of action and signaling pathways affected by most treatments with single antineoplastic agents might be relatively well understood, most combinations remain poorly understood. Bioinformatics and many preclinical studies are needed to understand the mechanisms of drug interactions for combination therapy. Since most signaling pathways are overlapping, with several points of interaction with other pathways, and show compensatory mechanisms, it is certainly a difficult task to predict this complex network, especially when particular mutations of tumor cells are also taken into account. In order to improve treatment outcome, more research is required to delineate the biological mechanisms involved. Meanwhile, everything points to in order to succeed, using novel drugs and enhancing therapeutic strategies must simultaneously target multiple pathways and mechanisms.

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Challenges to Defeat Resistance Using Targeted Anticancer Therapies The identification of oncogenic driver mutations enabled the development of targeted therapies that specifically inhibited mutationinduced pathways. The so-called targeted therapies have been directed to inhibit specifically oncogenic kinases or downstream components of aberrantly activated signaling pathways [9]. However, collective data from oncogene directed therapy has shown that resistance mechanisms, which include both kinase domain

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mutations and bypass signaling via other pathways such as RTK-RAS-RAF-MAPK pathways, frequently recur with rapid tumor shrinkage often followed by regrowth of the cancer weeks or months later [9, 10]. Besides the acquisition of resistance-conferring genetic mutations, reversible epigenetic mechanisms that lead to drug tolerance have also been observed to occur in response to treatment [8, 11]. Particularly, cell plasticity has emerged as a mode of targeted therapy evasion in various cancers and represents the ability of cells to undergo phenotypic changes in response to cues from the environment without modifying their genome [9]. The investigation of the mechanisms involved in this phenotypic switch has expanded revealing the crucial role of reprogramming factors and chromatin remodeling [11]. Upon targeted therapy of a heterogeneous cancer cell population, various outcomes are possible: elimination of the bulk of tumor cells, survival of cancer cells that maintain the drug- targeted pathway active through resistance-conferring genetic mutation and emergence of drug tolerant cells through reversible reprogramming. Cross talk with the tumor microenvironment, via secretion of various cytokines by infiltrated immune cells also controls cell plasticity [12]. Because phenotypic switching is mostly regulated via epigenetic modifications, it is reasonable to propose that it can be reverted in order to resensitize cells to therapy. Unfortunately, personalized therapies targeting these genomic alterations have not yet been successful in the clinic, possibly due to the intense intratumoral heterogeneity that characterizes cancer cells [4, 9].

3 The Role of HIFs in Cancer Resistance: Challenges to Overcome Resistance Targeting Autophagy and Metabolism Hypoxia is a hallmark of the tumor microenvironment and the hypoxia-inducible factor family of transcription factors, the major regulators of cellular adaptation to oxygen deprivation, have been associated with resistance to treatment and poor prognosis [13– 15]. The high metabolic demand and insufficient vascularization of growing tumors provoke not only endoplasmic reticulum (ER) stress, but hypoxia and oxidative stress, all of them inducing stress responses and affecting autophagy to restore cellular homeostasis. Indeed, oxidative stress, hypoxia, and ER stress are closely intermingled and cancer cells develop protective mechanisms to cope with them promoting cell survival. Examples of such mechanisms to adapt to chronic stress are the metabolic reprogramming (the aerobic glycolysis or Warburg effect), the induction of

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antioxidant protein synthesis, angiogenesis induction, and autophagy addiction, all of them promoted in turn by the hypoxiainducible factors (HIFs). Indeed, it has been demonstrated that HIFs regulate multiple steps of tumorigenesis and are typically associated in cancer cells with changes in metabolic reprogramming, neovascularization, invasion, metastasis, autophagy induction, and drug resistance [13–15]. Altered cellular metabolism is recognized as a hallmark of cancer and vital for cancer cell survival and progression. Increasing experimental evidence indicates that it is exploited by cancer cells to acquire a drug resistant phenotype. Consistent with this, therapeutic strategies of targeting abnormal metabolism such as aerobic glycolysis and glutaminolysis have shown therapeutic utility for patients and have hold promise to prevent disease progression and recurrence [16, 17]. In contrast to the well-established importance of HIF-1α as a robust suppressor of apoptosis, the functional significance of HIF-2α in anticancer therapy is less known. However, growing experimental evidence has shown that HIF-2α plays a central role in cancer resistance as a cancer survival promoter [15, 18]. It is well established that both HIF-1α and HIF-2α promote autophagy as a major regulator of cellular viability under stressful conditions. But this process can be triggered in cells not only in a HIF-dependent manner but also in a HIF-independent way, stimulated by AMPK and inhibited by mTORC1, and thus induced by metabolic stress, nutrient depletion and oncogenic activation [14]. Autophagy is a highly regulated catabolic process controlling cell metabolism. It can downregulate cell metabolism leading to quiescence and survival, and as such constitutes a vital mechanism of drug resistance [5, 14, 15, 18–20]. Autophagy has also been implicated in the regulation of tumor cell survival by dormancy. In fact, autophagy is required during quiescence for recycling of amino acids and nucleotides and has been shown to be essential for the survival of disseminated dormant cancer cells [19]. Importantly, these slow dividing and/or dormant cancer cells represent an effective way to evade therapies, leading to drug resistance and cancer relapse despite an initial [19, 20]. A number of therapeutic strategies have been developed to inhibit autophagy in cancer cells and to resensitize cancer cells to drug treatments. Theoretically, autophagy inhibition should prevent tumor cell from entering quiescence and exert synergic effects with radio and chemotherapies [5, 20, 21]. However, autophagy inhibition has shown to impose another potential problem since it has been observed that antiautophagic therapeutic drugs reduce tumor-specific immune response, thereby limiting the therapeutic success [5, 22].

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Cancer Stem Cells as a Hub in Cancer Resistance Cancer stem cells (CSCs) or tumor-initiating cells are a rare population of cells with stem cell-like properties that exhibit selfrenewal, tumorigenicity, and multilineage differentiation capacity [23–25]. In addition, these cells CSC have been regarded as the cells of origin of cancer and are crucially involved in metastatic dissemination, radioresistance, chemoresistance, and disease recurrence. The identification of CSCs is classically based on the evaluation of cell surface antigens like CD34, CD44, and CD133 by flow cytometry. However, the expression of surface and cytoplasmic markers is not a static property of CSCs and may deeply vary as a consequence of adaptation ability or plasticity of cells in response to changing environmental conditions [25, 26]. For example, a transition between two phenotypic status in breast CSCs has been revealed, indicating that in response to different environmental conditions, CSCs may switch from a more proliferative epitheliallike state characterized by increased ALDH activity and a mesenchymal-like state characterized by expression of CD44+/ CD24 and a more quiescent and invasive behavior [27]. CSCs can mediate therapy resistance by several mechanisms, such as increased DNA repair, drug efflux and reduced apoptosis, all of them mediated by HIFs. Indeed, mounting evidence indicates that hypoxia represents one of the most important features of the CSC niche [28, 29]. The multidrug resistance gene (MDR1), encoding the transmembrane P-glycoprotein, which belongs to the ATP-binding cassette superfamily of transport proteins, is induced by hypoxia. CSCs escape cytoxic therapy by increased efflux of drugs mediated by MDR transporters and is a very effective way to induce chemical resistance in them [30]. In addition, CSCs display increased DNA-repair abilities and decreased apoptosis associated with the decreased expression of proapoptotic markers like caspase3 and increased expression of antiapoptotic proteins like Bcl-2 [31]. Furthermore, it has been demonstrated that HIF-2α limits DNA damage due to oxidative stress inducing the expression of ROS scavengers such as superoxide dismutase 1, 2 and catalase [18, 31–33], thereby contributing to protect CSCs from radiation therapy. CSCs metabolism shows a highly plastic profile which allows to fulfill the energy requirements, according to the most suitable environmental condition. Metabolic plasticity of CSCs and their ability to switch to different metabolic pathways in response to certain environmental stressors like hypoxia or chemotherapies is essential for CSCs survival. Therefore, targeting the metabolic flexibility in CSCs holds promise to be an effective strategy for eradicating neoplastic disease [25, 34].

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We have described so far, several characteristics by which CSCs mediate therapy resistance. But probably the most important way to mediate therapy evasion of CSCs reside in their ability to switch to a dormant/quiescent state in response to stressful environmental conditions. There is increasing evidence that a population of slowly cycling stem-like cells drives cancer cell growth and recurrence after therapy [23, 25, 26, 35]. Cell quiescence/dormancy may be defined as a reversible G0 phase growth arrest from which cells may escape to reenter the cell cycle in response to physiological cell stimuli [35]. Remarkably, the quiescent state of CSCs preserves them from antiproliferative agents and is thus an important factor of CSC-related resistance to conventional therapy. The elucidation of the mechanisms involved in promotion of cancer cell quiescence and survival is therefore of paramount importance due to their significant therapeutic implications. In summary, due to all CSCs particular characteristics, they not only need to be eradicated to provide long-term disease-free survival. According to the CSC hypothesis, the complete eradication of malignancy would only be achieved by targeting the small cell population driving the origin of cancer. Thus, identifying and eradicating CSCs represent a challenging but promising target to fully ablate cancer.

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Challenges to Beat Resistance with Immunotherapy The use and approve by FDA in 2011 of antibodies to target proteins that act as off switches for the immune system have revolutionized cancer treatment providing new approaches less evadable than chemotherapy or molecular-targeted therapies [36]. “Immune checkpoint blockade” for cancer describes the use of therapeutic antibodies that disrupt negative immune regulatory checkpoints and allow to activate preexisting antitumor immune responses [37–39]. PD-1 is a signaling receptor on activated T cells that functions as an immune checkpoint, mitigating T cell activity when it detects its counterpart, PD-L1, on a tumor cell’s surface. Another approved antibody, which binds cytotoxic T lymphocyteassociated protein 4 (CTLA-4), also expressed on the same T cells that express PD-1, serves as a checkpoint blockade releasing also the checkpoint’s break on the immune cells, allowing active T cells to attack cancer [37–39]. These antibodies have been used to treat melanomas, cancers of the lung, kidneys, bladder, head, and neck, as well as Hodgkin’s lymphoma. However, immune checkpoint inhibitors have only produced dramatic clinical improvement in some patients, suggesting the existence of intrinsic resistance mechanisms [40]. But the mechanisms of resistance for immunotherapy are literally just beginning to being investigated. Moreover, it has also been observed that after an initial positive response to

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immune checkpoint inhibitor treatment disease progression induces acquired resistance mechanisms [40, 41]. Thus, while immunotherapies provide a better chance for a long-term and durable response, there are never any guarantees for the patient that tumor will not return. Another immunotherapy is also facing resistance problems: chimeric antigen receptor (CAR) T cells. In these therapies, scientists harvest T cells from a patient’s blood, then modify the T cells, priming them for activation by tumor antigens, multiply the cells in the laboratory, and infuse them back into the patient [42]. Approved CAR T-cell therapies have led to remarkable regressions in cancers of the blood and bone marrow, the so-called liquid cancers, but clinicians are now hoping to apply CAR T therapy to treat solid tumors [43]. Solid tumors present challenges that blood and bone marrow cancers do not. For example, solid cancers microenvironments are notoriously hostile: they are hypoxic, acidic, and immunosuppressive. This makes it difficult for the T cells to infiltrate and persist in the solid cancerous mass. In addition, as it has mentioned before, the heterogeneity of tumors constitutes a problem because it means that the chosen CAR will not bind to every tumor cell. There are several important challenges to defeat in Immunotherapy [36, 40, 41, 44]: (a) Diseased cells can acquire resistance to a single immunotherapy. To avoid this, researchers and clinicians combine therapies. However, it is hard to know which therapies can be combined effectively without giving rise to toxic side effects. (b) Another challenge to take into account is that human tumors tend to be much diverse. This heterogeneity is key to cancers’ ability to develop immunotherapy resistance. To resolve this, researchers obtain primary cultures from human samples to monitor how the tumor cells respond to immune therapies. (c) Cancer cells and immune cells are not the only players in the immunotherapy game. Many small molecules and proteins are secreted to the tumor environment and act as signals that regulate the infiltrated immune cells responses. Researchers have obtained the microvesicles secreted by cells to the tumor microenvironment to analyze their content in order to search for the small molecules and cytokines in blood and fluids surrounding the tumor. In the near future, the identification of the mechanisms involved in immunotherapy resistance will not only help avoid the cases where patients stop responding to treatment, but will also help to understand the so-called primary resistance, when a cancer never responds in the first trial. Understanding complex

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interactions between the immune system and the tumor microenvironment will be essential to the rational development and optimization of immunotherapies.

Acknowledgments This work is supported by a grant from Consejo Nacional de Ciencia y Tecnologı´a (FOSSIS 2017-289600) and by Programa de Apoyo a Proyectos de Investigacio´n e Innovacio´n Tecnolo´gica (PAPIIT IV200220) to MRF. References 1. Assaraf YG, Brozovic A, Gonc¸alves AC et al (2019) The multi-factorial nature of clinical multidrug resistance in cancer. Drug Resist Updat 46:100645 2. Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B (2017) The different mechanisms of cancer drug resistance: a brief review. Adv Pharm Bull 7:339–348 3. Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT (2013) Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci 110:E170–E179 4. Delou JMA, Souza ASO, Souza LCM, Borges HL (2019) Highlights in resistance mechanism pathways for combination therapy. Cell 8:1013. https://doi.org/10.3390/ cells8091013 5. Marquez RT, Tsao BW, Faust NF, Xu L (2013) Drug resistance and molecular cancer therapy: apoptosis versus autophagy. In: Apoptosis (Chapter 8). InTech Open Science, London, pp 155–196 6. Schimmer AD (2004) Inhibitor of apoptosis proteins: translating basic knowledge into clinical practice. Cancer Res 64(20):7183–7190 7. Mokhtari RB, Homayouni TS, Baluch N, Morgatskaya E, Kumar S, Das B, Yeger H (2015) Combination therapy in combating cancer. Oncotarget 8:38022–38043 8. Morel D, Jeffery D, Aspeslagh S, Almouzni G, Postel-Vinay S (2019) Combining epigenetic drugs with other therapies for solid tumourspast lessons and future promise. Nat Rev Clin Oncol 17(2):91–107. https://doi.org/10. 1038/s41571-019-0267-4 9. Boumahdi S, de Sauvage FJ (2019) The great escape: tumour cell plasticity in resistance to targeted therapy. Nat Rev Drug Discov doi 19

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Fighting Cancer Resistance: An Overview ˜ oz N, Chinney-Herrera A, Soldevila G, Mun Bennelli R, Santoyo-Ramos P, Poggi A, Robles-Flores M (2019) Functional interaction of hypoxia inducible factor 2-alpha and autophagy mediates drug resistance in colon cancer cells. Cancer 11:755. https://doi.org/10. 3390/cancers1106 19. Li X, Zhou Y, Li Y, Yang L, Ma Y, Peng X et al (2029) Autophagy: a novel mechanism of chemoresistance in cancer. Biomed Pharmacother 119:109415 20. Wang L, Shang Z, Zhou Y, Hu X et al (2018) Autophagy mediates glucose starvation induced glioblastoma cell quiescence and chemoresistance through coordinating cell metabolism, cell cycle, and survival. Cell Death Dis 9:213 21. Galluzzi L, Bravo-San Pedro JM, Levine B, Green DR, Kroemer G (2017) Pharmacological modulation of autophagy: therapeutic potential and persisting obstacles. Nat Rev Drug Discov 16:487–511 22. Thorburn J, Frankel A, Thorburn A (2009) Regulation of HMGB1 release by autophagy. Autophagy 5(2):247–256 23. Steinbichler TB, Duda´s J, Skvortsov S, Ganswindt U, Riechelmann H, Skvortsova II (2018) Therapy resistance mediated by cancer stem cells. Semin Cancer Biol 53:156–167 24. Abdullah LN, Chow EK-H (2013) Mechanisms of chemoresistance in cancer stem cells. Clin Transl Med 2(1):3 25. De Francesco EM, Sotgia F, Lisanti MP (2018) Cancer stem cells (CSCs): metabolic strategies for their identification and eradication. Biochem J 475:1611–1634 26. De Angelis ML, Francescangeli F, La Torre F, Zeuner A (2019) Stem cell plasticity and dormancy in the development of cancer therapy resistance. Front Oncol 9:626 27. Luo M, Brooks M, Wicha MS (2015) Epithelial-mesenchymal plasticity of breast cancer stem cells: implications for metastasis and therapeutic resistance. Curr Pharm Des 21:1301–1310 28. Carnero A, Lleonart M (2016) The hypoxic microenvironment: a determinant of cancer stem cell evolution. Bioessays 38(Suppl 1): S65–S74 29. Semenza GL (2012) Hypoxia-inducible factors: mediators of cancer progression and targets for cancer therapy. Trends Pharmacol Sci 33:207–214 30. Zhou S, Schuetz JD, Bunting KD, Colapietro AM, Sampath J, Morris JJ et al (2001) The

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ABC transporter Bcrp1/ABCG2 is expressed in a wide variety of stem cells and is a molecular determinant of the side-population phenotype. Nat Med 7(9):1028–1034 31. Skvortsov S, Debbage P, Lukas P, Skvortsova I (2015) Crosstalk between DNA repair and cancer stem cell (CSC) associated intracellular pathways. Semin Cancer Biol 31:36–42 32. Scortegagna M, Ding K, Oktay Y, Gaur A, Thurmond F, Yan LJ, Marck BT, Matsumoto AM, Shelton JM, Richardson JA et al (2003) Multiple organ pathology, metabolic abnormalities and impaired homeostasis of reactive oxygen species in Epas1-/- mice. Nat Genet 35:331–340 33. Bertout JA, Majmundara AJ, Gordan JD, Lam JC, Ditsworth D, Keith B, Brown EJ, Nathanson KL, Simon MC (2009) HIF2alpha inhibition promotes p53 pathway activity, tumor cell death, and radiation responses. Proc Natl Acad Sci U S A 106:14391–14396 34. Sotgia F, Ozsvaria B, Fiorillo M, De Francesco EM, Bonuccellia G, Lisanti MP (2018) A mitochondrial based oncology platform for targeting cancer stem cells (CSCs): MITO-ONCRX. Cell Cycle 17(17):2091–2100 35. Jahanban-Esfahlan R, Seidi K, Manjili MH, Jahanban-Esfahlan A, Javaheri T, Zare P (2019) Tumor cell dormancy: threat or opportunity in the fight against cancer. Cancer 11:1207. https://doi.org/10.3390/ cancers11081207 36. Azvolinsky A (2017) How cancers evolve drug resistance. Features. The Scientist, April 2017 Issue 37. Tumeh PC, Harview CL, Yearley JH, Shintaku IP et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515:568–571 38. Koyama S, Akbay EA, Li YY, Herter-Sprie GS et al (2016) Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun 7(10501):2016. https://doi.org/10. 1038/ncomms10501 39. Boulch M, Grandjean CL, Cazaux M, Bousso P (2019) Tumor immunosurveillance and immunotherapies: a fresh look from intravital imaging. Trends Immunol pii 40(11):1022–1034. . pii: S1471-4906(19)30190-5. https://doi. org/10.1016/j.it.2019.09.002 40. Kalbasi A, Ribas A (2019) Tumour-intrinsic resistance to immune checkpoint blockade. Nature Rev Immunol 20(1):25–39. https:// doi.org/10.1038/s41577-019-0218-4

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41. Farmer JR (2019) Testing immune-related adverse events in cancer immunotherapy. Clin Lab Med 39(4):669–683 42. Li D, Li X, Zhou WL, Huang Y, Liang X, Jiang L, Yang X, Sun J, Li Z, Han WD, Wang W (2019) Genetically engineered T cells for cancer immunotherapy. Signal Transduct Target Ther 4:35. https://doi.org/10.1038/ s41392-019-0070-9. eCollection 2019

43. Schepisi G, Cursano MC, Casadei C, Menna C et al (2019) CAR-T cell therapy: a potential new strategy against prostate cancer. J Immunother Cancer 7(1):258. https://doi.org/10. 1186/s40425-019-0741-7 44. Sotillo E, Barrett DM, Black KL, Bagashev A et al (2015) Convergence of acquired mutations and alternative splicing of CD19 enables resistance to CART-19 immunotherapy. Cancer Discov 5(12):1282–1295

Chapter 2 Prostate Cancer Spheroids: A Three-Dimensional Model for Studying Tumor Heterogeneity Mauricio Rodrı´guez-Dorantes, Carlos David Cruz-Hernandez, Sergio Alberto Corte´s-Ramı´rez, Jenie Marian Cruz-Burgos, Juan Pablo Reyes-Grajeda, Oscar Peralta-Zaragoza, and Alberto Losada-Garcia Abstract Prostate cancer is one of the main causes of cancer and the sixth cause of death among men worldwide. One of the major challenges in prostate cancer research is cell heterogeneity defined as the different genomic and phenotypic characteristics in each individual cell making more difficult to assess the proper prostate cancer diagnosis and therapy. Tumor 3D spatial arrangement allow a strong interaction between the different cellular lineages and components which modulate cell proliferation, differentiation, and morphology. Prostate cancer spheroids are a cellular model which is capable to mimic the mechanical tensions of tumor tissue, providing a more representative pathophysiological model than the use of conventional 2D culture. Here, we describe a protocol to develop a 3D model of spheroids using prostate cancer cell lines (LNCaP, PC3, VCaP) which can be used to improve research considering tumoral heterogeneity role in cancer development, prognosis, and therapy. Key words Spheroids, Tumor, Prostate cancer, Tumor heterogeneity, 3D spheroid culture

1

Introduction Prostate cancer has an incidence of 29.3 and a mortality of 7.3 per 100,000 age-standardized rate (ASR) in men, occupying the second place in incidence and the sixth in deaths from neoplasms in the World [1]. Recent studies show evidence that prostate cancer tumors have a hierarchical organization, regulated by a minority of cancer stem cells [2]. Tumor clonality is crucial to the understanding of cell heterogeneity in prostate cancer. Cell heterogeneity means that the evolution of cancer is a stepped and branched process initiated by a first parental clone that progresses toward a tumor through the accumulation of genetic alterations that in turn, provide a fitness advantage for a new clone. The morphology of

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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prostate tumors is highly heterogeneous, generating a “pluriform” neoplasm with glandular, cribriform, trabecular, solid, and unicellular tumor patterns. In addition, the mode of infiltration of prostate carcinoma is particularly atypical, since the neoplastic glands and benign glands are mixed. This promotes an undistinguishable invasion, compared to other solid tumors, that show a more destructive growth with a more circumscribed invasion. Consequently, surgical pathologists are challenged with rather small tissue biopsies with even smaller areas of infiltrating tumor glands [3]. The tumor cells normally grow in a 3D environment. Their spatial arrangement comes from different lineages and their extracellular matrices allow a strong interaction between the different cellular components which together participate modulating proliferation, differentiation, and morphology. The 3D spheroids are cellular aggregates used to model types of cancer because 3D cultures better mimic the mechanical tensions of tumor tissue, providing a more representative pathophysiological condition than the use of conventional 2D culture plates. This effect is evident during in vitro chemotherapy tests, where 2D cultures are usually hypersensitive to drugs, while in 3D culture it is equivalent to the in vivo scenario [4–6]. The more sophisticated 3D systems combine tumor and stromal cells emphasizing the importance of the heterotypic stromal epithelial interactions [7]. The structure in the spheroids is composed of three cell layers. The outer layer is composed of cells with a high proliferation rate, the intermediate layer contains senescent cells generated by hypoxia and limited access to the nutrients, the last layer at the center of the spheroid is formed by necrotic cells typical of solid tumors [8]. The availability of a modifiable 3D microenvironment allowed to mimic the cellular heterogeneity of tumors observed in vivo [9]. Consequently, 3D spheroids formed by cancer cells can show distinctive phenotypes, including those of quiescent cells in contrast to proliferating cells in normal tissue. In a spheroid model, cells grow in contact with each other conveying physical communication in addition to signaling pathways and gene expression profiles, similar to those observed in solid tumors. The latter tends to simulate the development of several types of cancer such as melanoma, colorectal cancer, or hepatocellular carcinoma, among others [9]. The spheroid model is a relatively simple suspension and are produced easily and at a low cost. The most important areas of use for spheroids are cytotoxicity chemotherapies and radiotherapies, since the clinical response to chemical or physical treatments also depends on parameters such as oxygen tension, compactness and permeability [10]. They are suitable too for high performance tests like RNAseq or expression microarrays [4]. Current results strongly support the use of spheroids as an appropriate preclinical model to study in vitro drug screening, mimicking the conditions of tumors in vivo [10–13]. Here we describe a protocol to develop a 3D

Prostate Cancer Spheroids: A Three-Dimensional Model for Studying Tumor. . .

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model of spheroids using prostate cancer cell lines (LNCaP, PC3, VCaP) [14–16], which may aid in the study of biological processes in prostate cancer thus, facilitating molecular research that is more representative of in vivo cases than traditional 2D culture models.

2 2.1

Materials Cell Culture

1. Cell growth medium: Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (SFB). Prepare 50 mL aliquots. Store at 4  C. 2. Cell dissociation: StemPro Accutase Cell Dissociation Reagent (from Life technologies or from another source). 3. Spheroids growth medium: DMEM-F12 [15, 17] supplemented with 20 ng/mL EGF [18, 19], 20 ng/mL bFGF, 1 B27 [15], 5 μg/mL Insulin [20], 4 μg/mL heparin [21]. Prepare 50 mL aliquots. Store at 4  C.

2.2

Cell Lines

2.3 Cell Culture Equipment

Human prostate cell lines which can be obtained from ATCC or from another source: VCaP, LNCaP, PC-3. 1. Cell culture flasks (CLS3056). 2. Ultralow attachment multiple well plate. 3. 1000, 100, and 20 μL pipettes. 4. Neubauer hemocytometer.

3

Methods 1. Thaw the cells of choice in a water bath (37  C). Dilute cells in 5 mL of culture medium preheated at 37  C. Centrifuge for 4 min at 300  g. Suspend the cells in 1 mL of preheated culture medium. Plate cells at high density in 25 mm2 culture flasks. 2. Change the medium after 48 h. Propagate cells to 90% confluence. Wash the cells with 2 mL of PBS. Dissociate the cells with 3 mL of StemPro Accutase. Centrifuge for 4 min at 300  g. Suspend the cells in 1 mL of culture medium. Plate the cells at high density in 25 mm2 culture flasks. 3. 3.When the cells reach 90% confluence (see Note 1), wash the cells with 2 mL of PBS. Separate the cells with 3 mL of StemPro Accutase. Centrifuge for 4 min at 300  g. Suspend cells in 1 mL growth medium for spheroids.

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Fig. 1 3D Spheroids in culture; 2000 cells/mL from VCaP cell line were plated on ultralow adhesion plates and their growth was monitored weekly

4. Determine the concentration of cells using the hemocytometer (Neubauer). Clean the counting chamber and coverslip before use. The coverslip is placed over the counting. The chamber is filled with approx. 10 μL of cell suspension by capillarity. Place the chamber under the microscope using a 10 objective and focus counting grid. Count the cells of the four quadrants and average the readings. The average value is multiplied by a 10,000 factor to obtain the number of cells in 1 mL (see Note 2). 5. Use 2000 cells/mL for the formation of spheroids in 6-well, ultralow-adhesion plates. The original cell suspension is diluted according to the concentration indicated for a final volume of 3 mL spheroids medium per well. Plate the proper number of cells in each well (see Note 3). Plates are incubated under cell culture conditions 37  C and 5% CO2. The formation of spheroids is observed after 2–7 days (Fig. 1). 6. Change of medium: medium replacement is performed every 7 days. Only 50% of medium is replaced. Tilt the plate slightly without spilling the contents of the wells and extract the medium without disturbing or extracting the spheroids; the plate should remain in this position for 10 min. Subsequently take 1.5 mL from the top of each well (see Note 4). Gently add 1.5 mL of fresh growth medium and incubate as previously.

4

Notes 1. Time for cell confluence may vary according to each cell line and should be estimated by consulting the supplier’s data sheet. 2. Trypan blue (0.2%) can be used to count viable cells. 3. Swirl gently to distribute the cells throughout the well and avoid cellular aggregates. 4. Remove the medium from the highest part of the well carefully avoiding the deep immersion of a pipette tip. Remove medium slowly and slide the tip to take the desired volume.

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References 1. GLOBOCAN (2018) Prostate cancer incidence, mortality and prevalence worldwide in 2018, GLOBOCAN Project. http://globocan. iarc.fr/ 2. Patrawala L, Calhoun-Davis T, SchneiderBroussard R, Tang DG (2007) Hierarchical organization of prostate cancer cells in xenograft tumors: the CD44+α2β1+ cell population is enriched in tumor-initiating cells. Cancer Res 67(14):6796–6805. https://doi.org/10. 1158/0008-5472.CAN-07-0490 3. Tolkach Y, Kristiansen G (2018) The heterogeneity of prostate cancer: a practical approach. Pathobiology 85(1–2):108–116. https://doi. org/10.1159/000477852 4. Chambers KF, Mosaad EMO, Russell PJ, Clements JA, Doran MR (2014) 3D cultures of prostate cancer cells cultured in a novel highthroughput culture platform are more resistant to chemotherapeutics compared to cells cultured in monolayer. PLoS One 9(11): e111029. https://doi.org/10.1371/journal. pone.0111029 5. Donaldson JT, Tucker A, Keane TE, Walther PJ, Webb KS (1990) Characterization of a new model of human prostatic cancer: the multicellular tumor spheroid. Int J Cancer 46 (2):238–244 6. Lee SH, Hu W, Matulay JT, Silva MV, Owczarek TB et al (2018) Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell 173 (2):515–528.e17. https://doi.org/10.1016/ j.cell.2018.03.017 7. Weiswald LB, Bellet D, Dangles-Marie V (2015) Spherical cancer models in tumor biology. Neoplasia 17(1):1–15 8. Costa EC, Gaspar VM, Coutinho P, Correia IJ (2014) Optimization of liquid overlay technique to formulate heterogenic 3D co-cultures models. Biotechnol Bioeng 111 (8):1672–1685 9. Costa EC, Moreira AF, de Melo-Diogo D, Gaspar VM, Carvalho MP, Correia IJ (2016) 3D tumor spheroids: an overview on the tools and techniques used for their analysis. Biotechnol Adv 34(8):1427–1441 10. Zanoni M, Piccinini F, Arienti C, Zamagni A, Santi S, Polico R et al (2016) 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained. Sci Rep 6:1–11

11. Takagi A, Watanabe M, Ishii Y, Morita J, Hirokawa Y, Matsuzaki T, Shiraishi T (2007) Three-dimensional cellular spheroid formation provides human prostate tumor cells with tissue-like features. Anticancer Res 27 (1A):45–54 12. Breslin S, O’Driscoll L (2013) Threedimensional cell culture: the missing link in drug discovery. Drug Discov Today 18 (5–6):240–249 13. Lv D, Hu Z, Lu L, Lu H, Xu X (2017) Threedimensional cell culture: a powerful tool in tumor research and drug discovery. Oncol Lett 14(6):6999–7010 14. Ballangrud AM, Yang WH, Dnistrian A, Lampen NM, Sgouros G (1999) Growth and characterization of LNCaP prostate cancer cell spheroids. Clin Cancer Res 5 (10 Suppl):3171s–3176s 15. Fan X, Liu S, Su F, Pan Q, Lin T (2012) Effective enrichment of prostate cancer stem cells from spheres in a suspension culture system. Urol Oncol 30(3):314–318 16. Mittler F, Obeı¨d P, Rulina AV, Haguet V, Gidrol X, Balakirev MY (2017) High-content monitoring of drug effects in a 3D spheroid model. Front Oncol 7:293. https://doi.org/ 10.3389/fonc.2017.00293 17. Oktem G, Bilir A, Uslu R, Inan SV, Demiray SB, Atmaca H et al (2014) Expression profiling of stem cell signaling alters with spheroid formation in CD133(high)/CD44(high) prostate cancer stem cells. Oncol Lett 7(6):2103–2109 18. Gao D, Vela I, Sboner A, Iaquinta PJ, Karthaus WR, Gopalan A et al (2014) Organoid cultures derived from patients with advanced prostate cancer. Cell 159(1):176–187 19. Lukacs RU, Goldstein AS, Lawson DA, Cheng D, Witte ON (2010) Isolation, cultivation and characterization of adult murine prostate stem cells. Nat Protoc 5(4):702–713 20. Wang S, Huang S, Zhao X, Zhang Q, Wu M, Sun F et al (2014) Enrichment of prostate cancer stem cells from primary prostate cancer cultures of biopsy samples. Int J Clin Exp Pathol 7(1):184–193 21. Vazquez-Santillan K, Melendez-Zajgla J, Jimenez-Hernandez LE, Gaytan-Cervantes J, ˜ z-Galindo L, Pina˜-Sanchez P et al Muno (2016) NF-kappa B-inducing kinase regulates stem cell phenotype in breast cancer. Sci Rep 6:1–17. https://doi.org/10.1038/srep37340

Chapter 3 Enrichment and Transcriptional Characterization of Stem Cells Isolated from Human Glioblastoma Cell Lines Ana G. Pin˜a-Medina, Ana M. Herna´ndez-Vega, Ne´stor F. Dı´az, Ismael Mancilla-Herrera, and Ignacio Camacho-Arroyo Abstract Glioblastomas (GBM) are the most frequent and aggressive brain tumors due to their recurrence and resistance to current therapies. These characteristics are associated with the presence of glioma stem cells (GSCs), mainly identified by the detection of the membrane antigens CD133 and CD15. The main source of GSCs has been biopsies of tumors. However, alternatives are sought from cell lines because more homogeneous populations can be obtained with high yields. This chapter describes a method for the enrichment and characterization of GSCs from cell lines derived from human GBM by selective culture with serum-free neural stem cell medium and growth factors. The technique offers alternatives for the enrichment and characterization of GSCs, that could contribute to a better understanding of the biology of GBMs. Key words Glioblastomas, Glioma stem cells, CD133, CD15, Flow cytometry, Glioma-spheres, U87 and U251cell lines

1

Introduction Cancer Stem Cells (CSCs) or cancer-initiating cells are defined as a subpopulation of cells within a tumor that have the capacity for selfrenewal, differentiation, tumorigenic potential, and restoring the original tumor [1]. Although the subpopulation of CSCs constitutes a minority of the cells that make up a tumor, their capacity for self-renewal and resistance to therapies allows them to persist [2– 4]. CSCs were initially identified in acute myeloid lymphoma and later in different types of solid tumors such as breast, prostate and pancreatic cancer, squamous cell carcinoma of the head and neck, colon, lung, ovary, and high-grade brain tumors [5–12]. Glioblastoma (GBM) is the most frequent and aggressive brain tumor characterized by its infiltrative nature, functional heterogeneity at the cellular level, presence of CSCs, and poor prognosis due to the failure of long-term therapy [13]. Singh et al. identified

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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glioma stem cells (GSCs) as a small population with the ability to initiate tumor growth and recapitulate the initial phenotype of GBM [12]. The isolation methods of GSCs are based on protocols of neural stem cells (NSCs) isolation that was proposed by Reynolds and Weiss in 1992, which describe NSCs proliferation, and free-floating cells cluster formation, well-known as neurospheres, in serum-free NSC medium supplemented with epidermal growth factor (EGF) [14]. This medium allows undifferentiated stem cell maintenance, and basic fibroblast growth factor (bFGF) and EGF supplement enable multipotent capacity and self-renewing. GSCs grow under these conditions as floating tumorspheres with selfrenewal ability and multipotent differentiation that maintain the properties of the original tumor [15]. Singh et al. in 2003 identified GSCs by the expression of CD133 from biopsies of patients diagnosed with GBMs, and since then, this antigen has been used to isolate GSCs as well as other CSC types [11, 12, 16–18]. Additionally, in conjunction with CD133, CD15 has also been used to identify, characterize, and enrich GSCs. Still, membrane markers are insufficient. Therefore, the importance of determining the expression of other proteins and transcription factors characteristic of stem cells such as SRY-box transcription factor 2 (Sox2), Oct4, Nanog, oligodendrocyte transcription factor (Olig2), SALL2, polycomb ring finger (Bmi1), Brn2, and Nestin (NES) has been highlighted [18, 19]. Here, we describe the enrichment and characterization of GSCs from U87 and U251 cell lines derived from human GBMs, which were first characterized by the presence of CD133+ and CD15+ cells by flow cytometry. Then, GSCs were obtained by selective culture with serum-free NSC medium and growth factors and maintained until the seventh passage. In glioma-spheres of the first passage, the detection of CD133, Sox2, Ki67, and NES was observed by immunofluorescence. Additionally, we assessed the relative expression of PROM1 (Prominin 1, that coding for CD133), NES, SOX2, OLIG2, enhancer of Zeste 2 polycomb repressive complex subunit (EZH2), and BMI1 by RT-qPCR in glioma-spheres and basal culture of U87 and U251 cells, both derived from human glioblastomas. This method presents alternatives for the enrichment and characterization of GSCs from cell lines, which could contribute to a better understanding of the biology of GBM.

2

Materials

2.1 Cell Culture (See Note 1)

1. U87 and U251 cell lines (ATCC, USA) (see Note 2). 2. Dulbecco’s Modified Eagle Medium (DMEM): High glucose and red phenol supplemented with 10% fetal bovine serum (FBS), 1 mM sodium pyruvate, 1 mM antibiotic–antimycotic

Enrichment and Transcriptional Characterization of Stem Cells Isolated. . .

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(with 10,000 penicillin units, 10 mg streptomycin, and 25 μg amphotericin B per mL), 0.1 mM nonessential amino acids, and 44 mM sodium bicarbonate. Add 50 mL of FBS, 5 mL of 100 mM sodium pyruvate, 5 mL of 100 mL nonessential amino acids, and 5 mL of 100
antibiotic–antimycotic to 500 mL of DMEM with high glucose and red phenol. Store at 2–8  C (protect from light). 3. Serum-free neural stem cell medium (SFM): DMEM/Nutrient Mixture F-12 supplemented with 20 μL/mL B27 without vitamin A, 20 ng/mL recombinant human EGF (rhEGF), 20 ng/mL bFGF, 10 ng/mL human leukemia inhibitory factor (hLIF), 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) and a mix of antibiotics (10,000 units penicillin, 10 mg streptomycin and 25 μg amphotericin B per mL). Add 10 mL of 50
B-27 supplement without Vitamin A, 1 mL of 10 ng/μL rhEGF, 1 mL of bFGF, 500 μL of hLIF, and 5 mL of 1 M HEPES to 500 μL of DMEM/Nutrient Mixture F-12. Store at 2–8  C (protect from light) (see Note 3). 4. Phosphate-buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4. Weigh 8 g of NaCl, 0.2 of KCl, 1.44 g of Na2HPO4, 0.24 KH2PO4 and transfer to a 1 L graduated cylinder containing about 800 mL of ultrapure water (see Note 4). Mix for about 10 min. Adjust to pH 7.4 with 1N NaOH. Make up to 1 L with ultrapure water. Sterilize in an autoclave. Store at room temperature. 5. TrypLE™ solution (ThermoFisher) (see Note 5): 137.9 mM NaCl, 2.67 mM NaCl, 1.47 mM KH2PO4, 8.06 mM Na2HPO4·7H2O, 1.1 mM EDTA, rProtease. Without phenol red. Store at 2–8  C. 6. Trypan blue solution preparation (0.4%): Weigh 400 mg of trypan blue and transfer to a 100 mL graduated cylinder containing 80 mL of PBS solution. Bring to a slow boil and cool to room temperature. Make up to 100 mL with PBS and store at room temperature. 7. Ultra-low attachment 24-well plates. 8. Adherent 24-well plates. 9. Neubauer chamber. 10. 1.5 mL microcentrifuge tubes. 11. Microcentrifuge. 12. Inverted microscope coupled to a camera. 13. 200 μL pipette tips.

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2.2 Immunostaining for Flow Cytometry

1. Ethylenediaminetetraacetic acid (EDTA): 0.5 M EDTA. Weigh 186.1 g of disodium EDTA and transfer to a 1 L graduated cylinder containing about 800 mL of ultrapure water. Add a magnetic stir bar and mix for about 10 min. Adjust to pH 8.0 with 1N NaOH. Make up to 1 L with ultrapure water. Sterilize in an autoclave. Store at room temperature. 2. Fluorescence-activated cell sorting (FACS) buffer: 2 mM PBS– EDTA, 0.5% BSA. Add 2 mL 0.5 M EDTA and 2.5 g of bovine serum albumin (BSA) to 498 mL of 1
PBS and mix well. 3. Fluorochrome-conjugated antibodies: Anti-CD133 APC-conjugated (clone AC133) and anti-CD15 FITC-conjugated (clone MMA) antibodies. 4. Flow cytometer (FACS Aria III, BD Biosciences).

2.3 Immunofluorescence

1. Poly-L-lysine (PLL) solution: 0.1% PLL. Weigh 100 mg of PLL and transfer to a 100 mL graduated cylinder containing 80 mL of ultrapure water. Make up to 100 mL with ultrapure water and filter through a 0.22-μm filter. Store at 4  C. 2. Paraformaldehyde (PFA) solution: 4% PFA. Add 800 mL of PBS to a graduated cylinder on a stir plate and heat while stirring at 60  C. Weigh 40 g of PFA to the heated PBS solution. Slowly raise the pH by adding 1N NaOH dropwise from a pipette until the solution clears. Once the PFA is dissolved, adjust the volume of the solution to 1 L with 1
PBS. Recheck the pH and adjust it with small amounts of dilute HCl to approximately pH 6.9. Store at 2–8  C. 3. Triton buffer: 0.1% Triton X-100. Add 100 μL of Triton X-100 to a 100 mL graduated cylinder containing 80 mL of 1
PBS solution and mix. Make up to 100 mL with 1
PBS. Store at room temperature. 4. Bovine serum albumin (BSA): 5% BSA. Add 1 g of BSA to 20 mL 1
PBS and mix well. Store at 2–8  C. 5. Antibodies: (a) CD133 1 μg/mL (clone AC133). (b) CD15 20 μg/mL (clone HI98). (c) SOX2, 1 μg/mL (clone A-5). (d) Nestin 4.8 μg/mL (clone 10C2). (e) Ki67 2 μg/mL (polyclonal). (f) Alexa 488 2 μg/mL (goat anti-mouse IgG). (g) Alexa 568 2 μg/mL (goat anti-rabbit IgG). 6. DAPI (40 ,6-diamidino-2-fenilindol). 7. 4-well chamber slide. 8. Fluoro Care antifade mountant. 9. Inverted microscope coupled to a camera.

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2.4

RT-qPCR

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1. TriZol reagent. Store at 4  C. 2. Chloroform. Store at 4  C. 3. Isopropanol. Store at 4  C. 4. 75%, 80% ethanol. Store at 4  C. 5. Molecular grade water. Store at 4  C. 6. Spectrophotometer (NanoDrop 2000). 7. Tris–borate (TB) buffer: 178 mM Tris base and 178 mM boric acid. Weigh 10.8 g of Tris base and 5.5 of boric acid, and transfer to a 500 mL graduated cylinder containing about 400 mL of ultrapure water. Add a magnetic stir bar and mix for about 10 min. Store at room temperature. 8. Agarose gel: 1.5% agarose, 0.5
TB. Weigh 1.5 g of agarose and transfer to 500 mL flask containing 100 mL of 0.5
TB. Heat in microwave for 1–3 min until the agarose is completely dissolved (see Note 6). Let agarose solution cool down to about 50  C. Add nucleic acid stain and mix. Pour the agarose into a gel tray with the comb in place. Let sit at room temperature for 20–30 min, until it has completely solidified. Fill gel box with TB buffer until the gel is covered. 9. Thermocycler. 10. M-MLV Reverse Transcriptase. 11. Oligo (dT)12–18 primers. 12. LightCycler 1.5 (Roche). 13. LightCycler FastStart DNA Master SYBR Green kit. 14. Primers: PROM1, NES, SOX2, BMI1, EZH2, OLIG2, and 18S (Table 1).

Table 1 Sequences of oligonucleotides designed for the amplification of genes associated with stemness Amp. size (bp)

TARGET Forward (50 -30 )

Reverse (50 -30 )

PROM1 TACAACGCCAAACCACGACT

ACCCAGCCACCAGTATGAATC 200

NES

AGCAGAGAAGAGAGCGAGGA

GAAGCCAGGACAGCAGGAT

173

SOX2

ATGGGTTCGGTGGTCAAGTC

GCTCTGGTAGTGCTGGGACA

183

BMI1

CCTGATGTGTGTGCTTTGTG

GGTCTGGTCTTGTGAAC TTGG

150

EZH2

CCCTGACCTCTGTCTTACTTG TGGA

ACGTCAGATGGTGCCAGCAA TA

120

OLIG2

CAGAGCCCGATGACCTTTT

GCTGGACGAGGATGACTTG

200

18S

AGTGAAACTGCAATGGCTC

CTGACCGGGTTGGTTTTGAT

167

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Methods

3.1 Glioma Stem Cells Enrichment and Culture

1. Grow U87 and U251 cell lines in DMEM under at 37  C and an atmosphere of 5% CO2 (see Note 7). 2. Seed 2
104 cells in ultralow attachment 24-well plates with 500 μL complete DMEM and incubate. 3. Replace 250 μL of the medium by an equal volume of SFM containing supplemented DMEM/F12 (see Note 8). 4. Once the spheres have formed (Fig. 1), replace SFM every 48 h. 5. On day 6, transfer the cell suspension (see Note 9) to a 15 mL centrifuge tube and centrifuge for 3 min at 106
g. 6. Discard 6 mL of the supernatant. 7. Add 6 mL of fresh SFM and mix gently. 8. Seed 500 μL of the cell suspension in adherent 24-well plates (see Note 10). 9. On day 8, transfer the cell suspension to a 15 mL and centrifuge for 3 min at 106
g. 10. Discard the supernatant and gently resuspend the gliomaspheres in 1 mL of PBS. 11. Centrifuge for 3 min at 106
g and discard the supernatant. 12. Add 1 mL of prewarmed TrypLE™ 1
to the flask and incubate at 37  C for 5 min (see Note 11).

Fig. 1 Glioma-spheres obtained from glioblastoma cell lines. Glioma-spheres obtained from U87 and U251 cells. The glioma-spheres were cultured in serum-free neural stem cell medium (SFM) containing DMEM/F12 supplemented with B27 without vitamin A (20 μL/mL), rhEGF (20 ng/mL), bFGF (20 ng/mL), hLIF (10 ng/mL), HEPES (10 mM), and antibiotics. At day 8, the spheres were dissociated and seeded according to methods. The photographs were obtained with a Zeiss Axio Vert.1A microscope with 200
magnification. Each scale bar represents 100 μm

Enrichment and Transcriptional Characterization of Stem Cells Isolated. . .

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13. Discard the supernatant and gently resuspend the gliomaspheres in 1 mL of PBS. 14. Centrifuge for 3 min at 106
g and discard the supernatant. 15. Add 1 mL of SFM and mix gently to stop the reaction. Pipet gently to mechanically terminate cell dissociation. 16. Centrifuge at 106
g for 3 min. Discard the supernatant and resuspend the cell pellet with 1 mL of prewarmed complete SFM. 17. Count cells in Neubauer chamber stained with 0.4% trypan blue. 18. Seed 4.5
104 live cells in 24-well plate in 500 μL of SFM. The culture medium was replaced every 48 h. 19. Perform subsequent dissociations on day 8 with the procedure described above. 3.2 Determination of CD133+ and CD15+ Cells by Flow Cytometry Analysis

1. Grow U87 and U251 cell lines in DMEM under standard conditions. 2. Resuspend 1
106 cells in 100 mL of FACS buffer (see Note 12). 3. Incubate with 10 or 20 μL of the primary anti-CD133/APC and anti-CD15/FITC antibodies, respectively, at 4  C in the dark for 15 min. 4. Wash cells with 1 mL of FACS buffer, resuspend in 1 mL of the same buffer, and analyze in a FACS cytometer. 5. To adjust the cytometer settings, the autofluorescence and single immunostaining must be taken into account. (a) Autofluorescence control: Cells resuspended in 100 μL of FACS buffer without antibodies. (b) Single immunostaining of each antibody: Cells incubated with anti-CD133/APC or CD15/FITC antibodies treated as in the 1–4 points of the procedure. Create a compensation template with FITC and APC detectors and perform the automatic compensation. 6. Recommended gating strategies: To doublet exclusion, plot the height or width against the area for forward scatter or side scatter. Select live cells plotting the area for forward scatter against area for side scatter. Analyze the presence of CD133+ and CD15+ cells plotting area for side scatter against the area for APC or FITC detectors, respectively.

3.3 Glioma Stem Cells Characterization

1. Evaluate the expression of the CD133, CD15, Sox2, NES, and Ki67 proteins in the cell lines and the glioma-spheres. 2. Seed 5
105 U87 and U251 cells in a 4-well chamber slide with DMEM.

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Fig. 2 Identification of glioma stem cells. Detection of CD133 (green), Sox2 (green), Ki67 (red), and Nestin (green) was performed by immunofluorescence under basal conditions of U251, and U251 glioma-spheres. Hoechst (blue) was used to stain DNA. Photographs were obtained with the Olympus IX81 microscope with 200
magnification. Each scale bar represents 100 μm

3. Resuspend glioma-spheres in 1 mL of SFM, and from this suspension, place 50 μL in a 4-well chamber slide previously coated with 0.1% PLL and allow settling for 1 h. Wash cells with PBS 1
(see Note 13). 4. Fix cells with 4% PFA for 15 min at room temperature. Wash three times with 1
PBS every 5 min. 5. Permeabilize with 0.1% Triton for 15 min at room temperature. Wash three times with 1
PBS every 5 min. 6. Block cells with 1% BSA and incubate with primary antibody overnight at 4  C. Wash three times with 1
PBS every 5 min. 7. Incubate with corresponding secondary antibody for 1.5 h at room temperature. Wash three times with 1
PBS every 5 min. 8. Add DAPI at a dilution of 1: 25,000 for 7 min at room temperature. Wash three times with 1
PBS every 5 min. 9. Mount preparations with the fluorescence mounting medium and observe with an inverted microscope coupled to a camera. Take photographs with the CCD camera (Fig. 2). 3.4 Analysis of the Expression of Genes Associated With Stemness in GSCs

1. Sample collection for extraction of RNA from the basal culture: (a) Grow the cells in 6 wells plates until reaching a confluence of 80%. (b) Remove the culture medium and wash the cells three times with 2 mL of PBS. (c) Incubate the cells with 2 mL of PBS–EDTA for 5 min, and mechanically detach the cells. (d) Centrifuge the cell suspension for 3 min at 106
g to obtain a pellet.

Enrichment and Transcriptional Characterization of Stem Cells Isolated. . .

27

2. Sample collection for glioma-spheres RNA extraction: (a) Cultivate the cells, as mentioned until step 9 of Subheading 3.1. 3. Add 500 μL of guanidine isothiocyanate (TRIzol Reagent) to each pellet thoroughly previously washed. Incubate for 5 min at 4  C. 4. Pipet the lysate up and down several times to homogenize. Incubate for 5 min. 5. Add 200 μL of chloroform and incubate for 5 min at 4  C. 6. Centrifuge the sample for 15 min at 20,854
g at 4  C. 7. Transfer the aqueous phase containing the RNA to a new tube (see Note 14). 8. Add 200 μL of isopropanol to the aqueous phase and incubate at 4  C overnight. 9. Centrifuge for 10 min at 20,854
g at 4  C and discard the supernatant. 10. Wash with 500 μL 75% ethanol and centrifuge at 16,625
g for 8 min at 4  C. Wash for the second time on 80% ethanol. 11. Air-dry the RNA pellet for 5–10 min. 12. Resuspend the pellet in 20–50 μL of RNase-free water (see Note 15). 13. Quantify RNA with a NanoDrop 2000 spectrophotometer. 14. Verify the RNA integrity by a 1.5% agarose gel electrophoresis. Add loading buffer to each sample. Carefully load a molecular weight ladder into the first line of the gel. Load RNA samples into the additional wells of the gel. Run the gel at 75 V until the dye line is approximately 75–80% of the way down the gel. Turn off the power and remove the gel from the gel box. Using UV light, visualize RNA lines. 15. Synthesize cDNA from 1 μg of total RNA samples using the M-MLV Reverse Transcriptase. 16. Perform qPCR with previously synthesized cDNA.

4

Notes 1. The preparation of all supplemented media, as well as the handling of cell cultures, should be carried out in a laminarflow hood. 2. It is important that the cell line has been genotyped. 3. Once thawed, the transcription factors have a shelf life of 1 week at 4  C, only the volume of medium to be used in that time interval should be prepared.

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4. Prepare ultrapure water by successive steps of filtration and deionization to attain a resistivity of 18 MΩ cm at 25  C. 5. TrypLE™ (Gibco Thermo-Fisher Scientific, MA, USA) is an animal origin-free recombinant enzyme alternative to porcine or bovine trypsin for the dissociation of attachment-dependent cell lines from plasticware. TrypLE™ can dissociate cells cultured both in serum-free and serum-supplemented systems. 6. Not overboil the solution, as some of the buffer will evaporate and thus alter the final percentage of agarose. Heat in microwave for 30–45 s, stop and swirl, and then continue toward a boil. 7. Always incubate the cells under standardized conditions of 37  C and an atmosphere of 5% CO2. 8. To avoid discarding the glioma-spheres that are in suspension, the volume of the culture medium should be suctioned from the most superficial part possible. 9. Pool the 24-well cell suspension. 10. Some cells have likely adhered slightly to the culture box; they can be mechanically detached. 11. When half the incubation time has elapsed, shake the tube slightly. 12. Samples must be analyzed as soon as possible. 13. Aspirate slowly avoiding to detach the glioma-spheres. 14. Aspirate slowly to avoid contaminating the DNA. 15. The volume of water depends on the size of the pellet; it is recommended to resuspend in the minimum volume, quantify the sample, and depending on the concentration obtained, add more water.

Acknowledgment This work was financially supported by DGAPA-PAPIIT (IN213117), UNAM. References 1. Beck B, Blanpain C (2013) Unravelling cancer stem cell potential. Nat Rev Cancer 13 (10):727–738 2. Jackson M, Hassiotou F, Nowak A (2014) Glioblastoma stem-like cells: at the root of tumor recurrence and a therapeutic target. Carcinogenesis 36(2):177–185 3. Bao S, Wu Q, McLendon RE, Hao Y, Shi Q, Hjelmeland AB, Dewhirst MW, Bigner DD,

Rich JN (2006) Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444 (7120):756–760 4. Chen J, Li Y, Yu TS, McKay RM, Burns DK, Kernie SG, Parada LF (2012) A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488 (7412):522–526

Enrichment and Transcriptional Characterization of Stem Cells Isolated. . . 5. Eramo A, Lotti F, Sette G, Pilozzi E, Biffoni M, Di Virgilio A, Conticello C, Ruco L, Peschle C, Maria D (2008) Identification and expansion of the tumorigenic lung cancer stem cell population. Cell Death Differ 15(3):504–514 6. Bonnet D, Dick JE (1997) Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 3(7):730–737 7. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF (2003) Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci 100(7):3983–3988 8. Collins AT, Berry PA, Hyde C, Stower MJ, Maitland NJ (2005) Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res 65(23):10946–10951 9. Prince ME, Sivanandan R, Kaczorowski A, Wolf GT, Kaplan MJ, Dalerba P, Weissman IL, Clarke MF, Ailles LE (2007) Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma. Proc Natl Acad Sci 104(3):973–978 10. Ricci-Vitiani L, Lombardi DG, Pilozzi E, Biffoni M, Todaro M, Peschle C, De Maria R (2007) Identification and expansion of human colon-cancer-initiating cells. Nature 445 (7123):111–115 11. Singh SK, Clarke ID, Terasaki M, Bonn VE, Hawkins C, Squire J, Dirks PB (2003) Identification of a cancer stem cell in human brain tumors. Cancer Res 63(18):5821–5828 12. Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, Hide T, Henkelman RM, Cusimano MD, Dirks PB (2004) Identification of human brain tumour initiating cells. Nature 432

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(7015):396–401. https://doi.org/10.1038/ nature03128 13. Ostrom QT, Gittleman H, Liao P, VecchioneKoval T, Wolinsky Y, Kruchko C, BarnholtzSloan JS (2017) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology 19(Suppl_5): v1–v88 14. Reynolds B, Weiss S (1992) Generation of neurons and astrocytes from isolated cells of the adult mammalian central nervous system. Science 255(5052):1707–1710 15. Yuan X, Curtin J, Xiong Y, Liu G, Waschsmann-Hogiu S, Farkas DL, Yu JS (2004) Isolation of cancer stem cells from adult glioblastoma multiforme. Oncogene 23 (58):9392–9400 16. Monzani E, Facchetti F, Galmozzi E, Corsini E, Benetti A, Cavazzin C, Gritti A, Piccinini A, Porro D, Santinami M, Invernici G, Parati E, Alessandri G, La Porta CA (2007) Melanoma contains CD133 and ABCG2 positive cells with enhanced tumourigenic potential. Eur J Cancer 43(5):935–946 17. Richardson GD (2004) CD133, a novel marker for human prostatic epithelial stem cells. J Cell Sci 117(16):3539–3545 18. Suetsugu A, Nagaki M, Aoki H, Motohashi T, Kunisada T, Moriwaki H (2006) Characterization of CD133+ hepatocellular carcinoma cells as cancer stem/progenitor cells. Biochem Biophys Res Commun 351(4):820–824 19. Ludwig K, Kornblum HI (2017) Molecular markers in glioma. J Neuro-Oncol 134 (3):505–512

Chapter 4 Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds Angel Jonathan Ruiz-Moreno, Alexander Do¨mling, and Marco Antonio Velasco-Vela´zquez Abstract Molecular docking is a useful and powerful computational method for the identification of potential interactions between small molecules and pharmacological targets. In reverse docking, the ability of one or a few compounds to bind a large dataset of proteins is evaluated in silico. This strategy is useful for identifying molecular targets of orphan bioactive compounds, proposing new molecular mechanisms, finding alternative indications of drugs, or predicting drug toxicity. Herein, we describe a detailed reverse docking protocol for the identification of potential targets for 4-hydroxycoumarin (4-HC). Our results showed that RAC1 is a target of 4-HC, which partially explains the biological activities of 4-HC on cancer cells. The strategy reported here can be easily applied to other compounds and protein datasets. Key words Reverse docking, Orphan drug, 4-Hydroxycoumarin, Virtual screening, Hierarchical clustering

1

Introduction Molecular docking was first described by Kuntz in 1982 [1]. To date, it has become a central tool in virtual drug screening, given its ability to predict ligand–target interaction. Molecular docking comprises two major tasks. First, the sampling algorithm predicts the many conformations that the ligand can assume within the pocket of interest (referred as poses). Then, a scoring function predicts the binding affinity between ligand and receptor for each pose. The generated binding poses are then ranked based on their binding affinity scores [2]. Ideally, the top-ranked pose should correspond to the ligand-binding mode present in nature. On the other hand, scoring functions are usually employed for ranking and filtering large databases of compounds in structure-based virtual screening. The highest-ranked ligands have the best binding affinity scores and, thus, can be considered lead compounds [3].

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Fig. 1 Reported effects of 4-HC and strategy for target identification. (a) 4-HC disorganizes the actin cytoskeleton in melanoma cells and has antimetastatic activity in a murine melanoma model. However, the target mediating these effects of 4-HC is unknown. (b) Proposed screening for identification of potential targets of 4-HC by reverse docking

An additional application of molecular docking is the identification of potential molecular targets of orphan bioactive compounds; this strategy is called reverse docking. In contrast to the traditional molecular docking approach, reverse docking is used to identifying potential receptors for a given ligand among a large number of structures. Because of that, reverse docking can be used to discover targets for existing drugs, natural compounds, and novel molecules. Consequently, reverse docking allows the identification of the molecular mechanism of a substance with an unknown target, the finding of alternative indications of drugs (repurposing), or the prediction of adverse drug reactions or toxicity [4]. 4-Hydroxycoumarin (4-HC) has antimetastatic and antineoplastic activities in preclinical models of melanoma [5–7] (Fig. 1a). To identify the potential targets that mediate the reported effects of 4-HC, we performed reverse docking between 4-HC and a human protein dataset (Fig. 1b) retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (rcsbPDB— https://www.rcsb.org/-). This example allows the explanation of the process of setting up a reverse docking experiment.

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Materials

2.1 Computational Workstation

A computer with at least four available cores. For generation of data presented here, we used two Central Processor Units (CPUs) model Intel® Xeon® W3503 with two cores each (see Note 1), 12 GB of Random-access Memory (RAM), and 2 TB of Hard Drive (HD) (see Note 2) running Linux Ubuntu 16.04 Long Term Stable (LTS) for 64 bits architecture (see Note 3).

2.2 Python Environments Manager (See Note 4)

Miniconda3 for creation, management, and installation of python packages. It can be downloaded from https://docs.conda.io/en/ latest/miniconda.html.

2.3 Molecular Docking Software

GOLD from the Cambridge Structural Database suite 2019 (CSD suite 2019) of the Cambridge Crystallography Data Centre (CCDC) through the Python Application Programming Interface (Python API) on Python 2.7 (see Note 5).

2.4 Bioinformatics and Cheminformatic Tools

1. Conda packages of MDtraj 1.9.1 [8, 9] and PBDFixer 1.5 [10] for automation of protein preparation. They are available at http://mdtraj.org/1.9.3/ and https://github.com/ pandegroup/pdbfixer. 2. Command line installation of Fpocket 3.0 [11] for the identification of protein pockets in the protein dataset. Fpocket can be downloaded from http://fpocket.sourceforge.net/. 3. 3.The Graphic User Interface (GUI) of MarvinSketch and Standardizer for ligand drawing and preparation, available at ChemAxon (http://www.chemaxon.com). 4. Conda package of Pymol 2.3.1 (https://pymol.org/2/), available at https://anaconda.org/schrodinger/pymol. 5. Conda package of OpenBabel 2.4.1 [12], which can be downloaded from https://anaconda.org/openbabel/openbabel. 6. Protein-Ligand Interaction Profiler (PLIP) [13] server (https://projects.biotec.tu-dresden.de/plip-web/plip/index).

2.5 Analysis Tools Working on Python 3.6

1. Conda package of Scipy 1.3.0 [14], an open-source software for mathematics, science, and engineering available at https:// www.scipy.org/. 2. Conda package of The Macromolecular Transmission Format (MMTF) [15] downloadable at https://anaconda.org/condaforge/mmtf-python. 3. Conda package of BioPython 1.72 [16], which contains freely available tools for biological computation. Available at https:// biopython.org/.

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Methods

3.1 Generation of Protein Structures Database

The protein dataset of this example was built accordingly to the following advanced search parameters into rcsdPDB (see Note 6): – Experimental Method: X-ray. – Molecule: Protein. – Organism: Homo sapiens (only). – X-ray Resolution: 0–2.5 A˚. – Sequence identity: 90%. Our search retrieved 4757 biological assemblies of proteins that were downloaded as pdb format files to generate the protein dataset analyzed.

3.2 Setting Up the Software and Conda Environments

These procedures aim to install the tools that will be used in the protocol. At the end of these steps, all the software will be ready to start the ligand and protein preparations and the docking procedure. 1. Download and install Fpocket 3.0, MarvinSketch, and Standardizer according to the developer instructions. 2. Download and install the Miniconda3 installer for the proper platform. 3. Create two Python environments for docking and analysis, respectively by typing into the terminal: (a) $conda create -n Docking python ¼ 2.7. (b) $conda create -n Analysis python ¼ 3.6. For further info about environment activation and management of packages, see Note 4. Inside of Docking environment, install the CSD suite 2019 from CCDC, including GOLD. Follow the developer instructions for installation. Then install Mdtraj 1.9.1 and PDBFixer 1.5. by typing into the terminal: (c) $conda install -c omnia mdtraj ¼ 1.9.1 pdbfixer ¼ 1.5 Inside of the Analysis environment, install OpenBabel, Pymol, and all the tools listed on Subheading 2.5 typing: (d) $conda install -c openbabel openbabel. (e) $ conda install -c schrodinger pymol. (f) $conda install scipy. (g) $conda install -c conda-forge mmtf-python. (h) $conda install -c anaconda biopython.

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Fig. 2 Proteins and ligand preparation for docking. (a) Structure of a model protein before and after preparation. Preparation must consider the removal of cocrystallized molecules (i.e., solvents), the building of missing loops, and the proper protonation state of the residues. (b) Preparation of 4-HC. Ligand preparation must include the analysis of the prevalence of the molecule at defined pH (green line) and selection of proper protonation and tautomeric state. The 3D energetically optimized geometry of 4-HC is shown on the right 3.3 Protein Dataset Preparation for Reverse Docking Assays (See Note 7)

These steps will prepare each protein structure and store the corresponding information in new files. Once completed, the whole dataset of proteins will be ready for being employed in molecular docking. Figure 2a shows the differences in a protein from the dataset after preparation. 1. Activate and use the Analysis environment. Remove solvent molecules (water, ions, dimethyl sulfoxide, glycerol, etc.) and cocrystallized ligand(s) by using the remove_solvent() function of Mdtraj and removeHeterogens() of PDBfixer, respectively. 2. Complete the peptidic chain and replace nonstandard residues (i.e., selenomethionine (MSE) to methionine (MET)) by using the functions findNonstandardResidues() and replaceNonstandardResidues() from PDBFixer. 3. Find and add the missing residues and atoms with PDBfixer using findMissingResidues(), findMissingAtoms(), and addMissingAtoms().

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4. Add the missing hydrogens for protein structures at pH 7.4 utilizing the function and variable addMissingHydrogens(7.4) on PDBfixer. 5. Assign partial charges using assign_partial_charges() from the Protein module of the CSD suite. 6. Save the prepared protein in mol2 and pdb file formats by employing the write() function from the MoleculeWriter module of CSD suite (such files will be used later). For instance, files can be named pdbCode_prep.mol2 and pdbCode_prep.pdb, where pdbCode would be the PDB entry number of the structure in rcsbPDB. 3.4 Ligand Building and Preparation for Reverse Docking (See Note 8)

The goal of this procedure is obtaining the optimized 3D structure of the ligand(s) for docking experiments (i.e., including explicit hydrogens for pH 7.4, and the properly aromaticity perception). 1. Use MarvinSketch GUI to draw the ligand. The structure of 4-HC was generated from the SMILES code OC1 ¼ CC(¼O) C2 ¼ CC¼CC¼C2O1. Alternatively, the option “Structure>Name to Structure” can be used by typing 4-hydroxycoumarin. 2. On MarvinSketch, select the 4-HC and perform a pH-dependent protonation analysis utilizing “Calculations > Protonation > pKa” and the default settings. Figure 2b shows the optimized structure of 4-HC and the results obtained in protonation analysis. Note that the ionic form of 4-HC is prevalent at pH 7.4, whereas the percentage of the neutral species is almost zero. Because of that, we worked with the ionic form of 4-HC with SMILES code [O-]C1 ¼ CC(¼O) OC2 ¼ CC¼CC¼C12. 3. Using the SMILES code, save the ionic form of the 4-HC in a new file called 4-HC.mol2 using “File > Save As” on MarvinSketch. 4. Open Standardizer and use the 4-HC.mol2 file as input. Click “Next” and select the molecule standardization options “Add Explicit Hydrogens” and “Aromatize” (order is important). Click “Next” and save the file as 4-HC_standarized.mol2. 5. Activate the Analysis environment and use OpenBabel 2.4.1 to optimize the molecule using the MMFF94s forcefield by typing into the terminal: (a) obminimize -ff MMFF94s -sd -n 2500 -c 0.00001 -cut -rvdw 6.0 -rele 10.0 -pf 10 4-HC_standarized.mol2 > 4HC_ready.mol2, where obminimize is the module of OpenBabel for energy minimization; -ff MMFF94s is the forcefield to use for optimization; -sd is the steepest descent algorithm;-n 2500 is the number of steps; -c 0.00001 is the

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convergence criteria; -cut enables the cutoff; -rvdw 6.0 is the cutoff for the Vander Walls distance; -rele 10.0 is the electrostatics cutoff; -pf 10 is the frequency to update the nonbonded pairs; and 4-HC_standarized.mol2 > 4HC_ready.mol2 corresponds to the input and output file, respectively (see Note 9). 3.5 Protein Pocket Search for Reverse Docking

1. Utilize Fpocket 3.0 to find the pocket for each the pdbCode_prep. pdb file. Type in the terminal: $fpocket -F input_file.txt -v 500. Where input_file.txt is a text file in which each line contains the path to each pdbCode_prep.pdb file of the prepared protein dataset, and -v 500 is the number of Monte-Carlo iterations for the calculation of the pocket volume. The result of this run will be saved in a new folder for each protein-containing (among other things) the 3D coordinates of the pockets plus the protein structure in a pdb file and a txt file with several descriptors including the pocket volume (see Note 10). 2. Use Pymol inside of the Analysis environment (either using the GUI or by python programming) to extract the coordinates of pockets with a volume higher or equal to 150 A˚3 into a mol2 file for each protein. For instance, pdbCode_PocketNum.mol2. These files include all relevant pockets for one protein. Pockets smaller than 150 A˚3 are too small to allocate the 4-HC and, thus, are irrelevant for this experiment. The generated mol2 files will be used as references for the reverse docking; therefore, their number corresponds to the number of dockings to perform. For this example, the number of references is approximately 50,000.

3.6

Reverse Docking

1. Activate the Docking environment created in Subheading 3.2, step 3 to set up the reverse docking. Import the module Docker () from the CSD suite 2019 to initialize the GOLD docking engine. 2. Establish the following Docker() settings for the docking of the 4-HC into each protein using the reference coordinates of pockets: (a) receptor ¼ add_protein_file (‘pdbCode_prep.mol2’). (b) reference mol2’).

¼

MoleculeReader

(‘pdbCode_pocketNum.

(c) BindingSiteFromPoint(receptor,reference.centre_of_geometry(),6); where 6 is the number of A˚ to extend the pocket centre. (d) Fitness_funtion ¼ ‘plp’; which means using PLPchemscore function for docking scoring.

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Fig. 3 Python programming workflow for reverse docking using GOLD. The image shows a general script to establish the reverse docking settings through a python programming script in order to run the molecular docking using the Python API of GOLD

(e) autoscale ¼ 10.0; recommended value for High Throughput Screening (HTS). (f) add_ligand_file(‘4-HC_ready.mol2’,10); where 10 refers to the number of best-scored poses requested as docking result. (g) output_file ¼ ‘pdbCode_pocketNum.mol2’. For a clearer reference about the establishment of settings in a Pythonic way, see Fig. 3 (see Note 11). 3. Run the docking, which will generate a mol2 file for each pocket reference (pdbCode_pocketNum.mol2 from output_file variable). Such files contained the ten best-scored poses of 4-HC and thus were used for analysis. 3.7 Analysis of Docking Results

1. Active the Analysis environment containing the python libraries Scipy, MMTF, and BioPython. 2. Use the docking results to generate a table with the entry number of the protein and the highest score value (best docking result) for each evaluation. 3. Visualize such data in a probability distribution plot and select the potential targets among the proteins with a higher docking score. We selected as candidates 67 proteins with docking scores 55 (Fig. 4a). This cutoff value corresponds to the average docking score + 3.5 SD. 4. Generate a new table containing the entry name of the best docking results and their score. Use the MMTF python library to extract the sequence of each pdbCode using the function entity_list and add such data into a new column in the table.

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Fig. 4 Distribution of probability and Single-Linkage Hierarchical Clustering of reverse docking results. (a) The best pose scores for each ligand–pocket pair showed a normal distribution. We considered as potential targets of 4-HC the proteins with docking scores  mean + 3.5 SD (red line). (b) Potential targets were analyzed by SLHC based on their identity. We identified a protein cluster with actin cytoskeleton organization function (red square)

5. For all entries into best docking results table, perform an all vs. all sequence alignment using the function alignment_score ¼ pairwise2.align.globalxx(target, reference, score_only ¼ True) of BioPython library and calculate the identity percentage as follows: % identity ¼

aligmentscore  100 lengthðreferenceÞ

6. The result must be a matrix of size N  N; where N is the number of entries in best docking results (67 for our example), and each data inside the matrix must be the % identity for each target vs. reference sequence alignment. 7. Analyze the matrix with the identity percentage values using the Single-Linkage Hierarchical Clustering (SLHC) algorithm to identify protein clusters from best docking results by using the functions inside of the Scipy module scipy.cluster.hierarchy. Then, sort the x and y axis of the matrix according to the hierarchical map and create a heatmap of the matrix. Further analysis may include a UniProt (https://www.uniprot.org/) search to identify Gene Ontology (GO) annotations for the best docking proteins.

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Figure 4b shows the hierarchical clustering and identity heatmap for 4-HC potential targets. Clusters of proteins with high identity can be identified, suggesting that a common structure/domain could be mediating ligand–protein interaction. Our analysis identified a cluster of proteins with GO annotations that match the previously reported experimental evidence [5–7]. We focused on such group and selected the protein RAC1 (PDB code 4GZL), which participates in the control of the actin cytoskeleton organization as the best target candidate. 8. Generate a protein–ligand interaction map for the best docking pose of 4-HC in RAC1 (4GZL). For such purpose, use Pymol inside the Analysis environment to create a 4-HC-RAC1 complex using the 4GZL_1.mol2 file from dockingResults_Folder, and 4GZL_prep.mol2 file from prepProteins_Folder, corresponding to docking poses of 4-HC and RAC1, respectively. Save the #1 pose and the protein into a Complex.pdb file. Use this file to create the interaction map by the PLIP server, listed on Subheading 2.4, item 6. The binding mode of 4-HC to RAC1 (4GZL) is shown if Fig. 5. This result generates a new hypothesis about the mechanism of action of 4-HC.

Fig. 5 Interaction map of 4-HC with RAC1 (4GZL). Analysis of docking best results and SLHC indicated that RAC1 (white sticks) could be a potential target for 4-HC (green sticks) by forming several hydrogen bonds (orange dashed lines), a perpendicular π-staking (red dashed line), and hydrophobic interactions (magenta dashed line)

Reverse Docking for the Identification of Molecular Targets of Anticancer. . .

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Notes 1. Most of docking algorithms and software are optimized for running even in low-specs computers. However, recent versions of docking programs, such as GOLD [2], support running in parallel through the Python API. Because of that, reverse docking experiments can be performed in multicore machines, as supercomputers or clusters. 2. The amount of data generated in virtual HTS experiments require enough available space into Hard Drive (HD) or Solid-state Drive (SSD). In our example, the total space of the workstation employed for running the example was 2 TB, and the space utilized for protein dataset, pockets, ligand, docking results, and analysis was 11.9 GB. 3. Several of the tools employed in this method have been developed for running into Windows, Mac OS, and Linux, but we strongly advise to use a Linux based OS (e.g., Ubuntu or Debian) because it improves software stability. We worked in Linux operative system Ubuntu 16.04. 4. Many of the tools required on this protocol will be managed by Miniconda3. Moreover, most of the tasks described in methods were automatized by using Python 2.7 and Python 3.6 programming. For more info about Miniconda3 visit https:// docs.conda.io/projects/conda/en/latest/, and for Python visit https://www.python.org/. 5. Further information about GOLD can be found at https:// www.ccdc.cam.ac.uk/solutions/csd-discovery/components/ gold/. We employed GOLD due to its versatility and accuracy [17]. However, this protocol can be adapted for running using another molecular docking software. For instance, running a reverse docking using AutoDock Vina can be achieved by Bourne-again shell (BASH) scripting on Linux OS or through the Graphic User Interface (GUI) [18]. The CSD suite containing GOLD can be employed either by GUI or using the Python API. For more info about available platforms, installation, and usage, visit https://downloads.ccdc.cam.ac.uk/docu mentation/API/installation_notes.html. 6. Protein dataset definition is a key task of reverse docking because it depends on the question aimed to address. For instance, the dataset should only include kinases if the hypothesis is that the compound (or series of compounds) can bind to kinases. A different dataset should be used for evaluating the ability of a compound to bind to proteins of a specific pathogenic microorganism. Independently of the characteristics that define the dataset, structural information of the proteins can be retrieved by searching into public databases as rcsbPDB or PDBe (https://www.ebi.ac.uk/pdbe/), both members of the

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Worldwide Protein Data Bank (wwPDB -https://www.wwpdb. org/-). Additional sources of structures available for reverse docking can be found in ref. 19. 7. Alternatively, protein preparation can be achieved using GUIs as Dock Prep module of Chimera [20], which may be easier for users with less programming background. Whatever the approach selected for protein preparation, the steps are the same. 8. Ligand structures could come from various sources. This method describes the building of a ligand from zero. However, existing databases with molecules from different sources and chemical identity. For example, the ZINC15 database (https:// zinc15.docking.org/) which contains millions of molecular structures that can be used for in silico experiments. 9. Docking software cannot handle all of the file formats available to represent molecules. Thus, the user must select the proper file format for the docking software to be used. When working in GOLD, the recommended file format for proteins and ligands is mol2. 10. Identification and extraction of protein pockets allow the selection of druggable pockets. Fpocket can also search the pockets in the context of cocrystallized ligands. This option is useful if only known binding sites will be analyzed. Please note that running docking of a ligand over all the protein surface is not recommended because most docking software were not created for such purpose. 11. Different docking software packages employ different sampling algorithms and score functions to find and evaluate ligand– protein interactions. The parameters established in Fig. 3 are the criteria to reproduce this experiment using GOLD, but it does not represent a python script. It must be considered just as an example to create an automatized python script for docking running. For other GOLD score functions and setting, visit (https://www.ccdc.cam.ac.uk/support-and resources/support/search?q ¼ Scoring%20function). The use of other software than GOLD or different settings could rise to different results.

Acknowledgments Ruiz-Moreno was granted with a scholarship from CONACYT (number 584534) and received support from Programa de Apoyo a los Estudios de Posgrado (PAEP), UNAM 2018 and 2019. We thank the financial support provided by PAPIIT UNAM IN219719. Experiments and analyses presented in this chapter were performed using UNAM supercomputer “Miztli” through LANCAD-UNAMDGTIC-364 resource assignation (2018 and 2019).

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References 1. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288. https:// doi.org/10.1016/0022-2836(82)90153-X 2. Meng X-Y, Zhang H-X, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157 3. Phatak SS, Stephan CC, Cavasotto CN (2009) High-throughput and in silico screenings in drug discovery. Expert Opin Drug Discov 4:947–959. https://doi.org/10.1517/ 17460440903190961 4. Lee A, Lee K, Kim D (2016) Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 11:707–715. https://doi.org/10. 1080/17460441.2016.1190706 5. Velasco-Vela´zquez MA, Agramonte-Hevia J, Barrera D et al (2003) 4-Hydroxycoumarin disorganizes the actin cytoskeleton in B16-F10 melanoma cells but not in B82 fibroblasts, decreasing their adhesion to extracellular matrix proteins and motility. Cancer Lett 198:179–186. https://doi.org/10.1016/ S0304-3835(03)00333-1 6. Velasco-Vela´zquez MA, Salinas-Jazmı´n N, ˜ o N, Mandoki JJ (2008) Mendoza-Patin Reduced paxillin expression contributes to the antimetastatic effect of 4-hydroxycoumarin on B16-F10 melanoma cells. Cancer Cell Int 8:8. https://doi.org/10.1186/1475-2867-8-8 7. Salinas-Jazmı´n N, De La Fuente M, Jaimez R et al (2010) Antimetastatic, antineoplastic, and toxic effects of 4-hydroxycoumarin in a preclinical mouse melanoma model. Cancer Chemother Pharmacol 65:931–940. https://doi. org/10.1007/s00280-009-1100-z 8. Mcgibbon RT, Beauchamp KA, Harrigan MP et al (2015) Computational tools MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109 (8):1528–1532. https://doi.org/10.1016/j. bpj.2015.08.015 9. McGibbon RT, Beauchamp KA, Harrigan MP et al (2015) MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109:1528–1532. https://doi.org/ 10.1016/j.bpj.2015.08.015 10. Eastman P, Friedrichs MS, Chodera JD et al (2013) OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theory

Comput 9(1):461–469. https://doi.org/10. 1021/ct300857j 11. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168. https://doi.org/10.1186/14712105-10-168 12. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/ 1758-2946-3-33 13. Salentin S, Schreiber S, Haupt VJ et al (2015) PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 43: W443–W447. https://doi.org/10.1093/ nar/gkv315 14. Vitanene P, Gommers R, Oliphant TE et al (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17:261–271. https://doi.org/10.1038/ s41592-019-0686-2 15. Bradley AR, Rose AS, Pavelka A et al (2017) MMTF—an efficient file format for the transmission, visualization, and analysis of macromolecular structures. PLoS Comput Biol 13: e1005575. https://doi.org/10.1371/journal. pcbi.1005575 16. Cock PJA, Antao T, Chang JT et al (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422–1423. https://doi.org/10.1093/bioinformatics/ btp163 17. Lee M, Kim D (2012) Large-scale reverse docking profiles and their applications. BMC Bioinformatics 13:S6. https://doi.org/10. 1186/1471-2105-13-S17-S6 18. Chen F, Wang Z, Wang C et al (2017) Application of reverse docking for target prediction of marine compounds with anti-tumor activity. J Mol Graph Model 77:372–377. https://doi. org/10.1016/j.jmgm.2017.09.015 19. Xu X, Huang M, Zou X (2018) Docking-based inverse virtual screening: methods, applications, and challenges. Biophys Rep 4(1):1–16. https:// doi.org/10.1007/s41048-017-0045-8 20. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612. https://doi.org/10. 1002/jcc.20084

Chapter 5 Mouse Model for Efficient Simultaneous Targeting of Glycolysis, Glutaminolysis, and De Novo Synthesis of Fatty Acids in Colon Cancer Alejandro Schcolnik-Cabrera and Alfonso Duen˜as-Gonzalez Abstract Colon cancer is a highly anabolic entity with upregulation of glycolysis, glutaminolysis, and de novo synthesis of fatty acids, which also induces a hypercatabolic state in the patient. The blockade of either cancer anabolism or host catabolism has been previously proven to be a successful anticancer experimental treatment. However, it is still unclear whether the simultaneous blockade of both metabolic counterparts can limit malignant survival and the energetic consequences of such an approach. In this chapter, by using the CT26.WT murine colon adenocarcinoma cell line as a model of study, we provide a method to simultaneously perform a pharmacological blockade of tumor anabolism and host catabolism, as a feasible therapeutic approach to treat cancer, and to limit its energetic supply. Key words Cancer metabolism, Cancer anabolism, Drug repurposing, Glycolysis, Glutaminolysis, De novo synthesis of fatty acids

1

Introduction Altered cellular metabolism is one of the hallmarks of cancer. Metabolic reprogramming is associated with cancer development and progression [1]. Increased rates of glycolysis, glutaminolysis, and the de novo synthesis of fatty acids are common in malignancies [2–7]. The development and progression of colorectal cancer requires accumulation of genetic alterations in driver genes. Comprehensive genome analyses have revealed that driver genes, including TP53, and KRAS, among others, are frequently mutated in colon carcinoma [8–10], and these mutant gene products cause or associate with metabolic reprogramming in this tumor [11]. When P53 is mutated, glycolysis is increased while KRAS mutations favor tumor growth by regulating the metabolism of glutamine, fatty acids, and glucose as well. Besides, KRAS mutations are associated with upregulation of FASN [12, 13].

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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In this chapter, we describe how to evaluate the effects of the blockade of the malignant metabolism in a mice model of colon cancer. In particular, we show both in vitro and in vivo specific methods to assess at both cellular and metabolic levels the effects of the pharmacological blockade of HK2, GLS1, and FASN enzymes, which are critical in regulating glycolysis, glutaminolysis, and de novo synthesis of fatty acids, respectively. By targeting the referred enzymes, the concurrent use of lonidamine, 6-diazo-5-oxo-L-norleucine (DON), and orlistat are an experimental approach to ameliorate metabolic reprogramming in cancer colon cells.

2

Materials Prepare and store all reagents at room temperature, unless otherwise indicated. It is important to follow all waste disposal regulations when disposing of waste materials. For the purpose of this chapter, we are employing CT26.WT (ATCC® CRL-2638™), a cell line of mouse adenocarcinoma origin as a model of study, and the compounds orlistat, lonidamine, and 6-diazo-5-oxo-L-norleucine (DON), as inhibitors of the de novo synthesis of fatty acids, glycolysis, and glutaminolysis, respectively.

2.1

Cell Culture

1. Complete medium: RPMI-1640 supplemented with 10% fetal bovine serum (FBS) and 1% streptomycin/amphotericin. Store at 4
C. 2. 1 phosphate buffered saline (PBS) solution. 3. Trypsin solution: 1 PBS in sterile deionized water, supplemented with 0.5% trypsin and 2% 0.5 M ethylenediaminetetraacetic acid (EDTA), pH 8.0. Store at 4
C. 4. Trypan blue. 5. Cell count slides. 6. TC10™ Unity Automated Cell Counter. 7. 6-well plates and 75 mL flasks. 8. A humidified, 37
C, 5% CO2 incubator.

2.2

Drug Treatments

1. Orlistat (Sigma®): Dissolve the compound in absolute ethanol. 2. Lonidamine (Sigma®): Dissolve the compound in dimethyl sulfoxide (DMSO). 3. DON (Sigma®): Dissolve the compound in culture medium without serum. All the drug treatments must be freshly prepared for the experiments.

Mouse Model for Efficient Simultaneous Targeting of Glycolysis. . .

2.3

Seahorse Assays

47

Store all the reagents at room temperature (RT). 1. XF96 extracellular flux analyzer. 2. Seahorse XF Cell Mito Stress Test Kit, containing oligomycin, carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone (FCCP), and rotenone/antimycin A (R/Aa). 3. Seahorse XF Glycolysis Stress Test Kit, containing glucose, oligomycin and 2-deoxy-glucose (2-DG). 4. XF RPMI medium, pH 7.4. 5. XF96 cell culture microplates. 6. XF96 extracellular flux assay kit (includes calibration plates and sensor cartridges). 7. XF Calibrant. 8. Glucose, pyruvate, and glutamine. 9. Viaflo Assist robot (recommended).

(Integra®),

with

its

consumables

10. IncuCyte® ZOOM equipment (Essen BioScience). 2.4

Mice

1. 7–9 weeks old female Balb/C mice per evaluated condition (see Note 1). The sample size per group can be calculated with the formula provided by Charan and Kantharia [14]. 2. AIN-93G diet. 3. Sterilized tap water. 4. 0.9% sterile saline solution. 5. 1 mL sterile syringes. 6. Absolute ethanol. 7. A caliper. 8. An analytical balance. ∗If the objectives of the research involve the pathological analysis of tumors and/or diverse tissues, prepare a formalin solution. 9. Formalin solution: Formaldehyde 37–40% (100 mL), distillated water (900 mL), monobasic sodium phosphate (4.0 g), and dibasic sodium phosphate (anhydrous) (6.5 g). Adjust pH to 7.4.

2.5 Glucose Tolerance Tests

1. 20% glucose solution prepared in 0.9% sterile saline solution. 2. 1 mL sterile syringes. 3. Absolute ethanol. 4. A glucometer and multiple blood glucose test strips (one per measurement).

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2.6 Magnetic Resonance Imaging

1. EchoMRI-4 (Echo Medical Systems, Houston, TX, USA) quantitative magnetic resonance equipment (see Note 2). 2. A computer associated with the magnetic resonance imaging equipment. 3. Plastic cylinders. 4. Canola oil. The plastic cylinders and the canola oil are provided with the magnetic resonance equipment.

2.7 Indirect Calorimetry

1. An Oxymax-CLAMS (CLAMS®, Columbus Instruments, OH) equipment, including multiple plexiglass chambers for mice (see Note 3). 2. A compressed gas tank, containing 0.493 cmol/mol CO2, 20.02 cmol/mol O2, and nitrogen in balance, which needs to be connected to the calorimeter. 3. A computer associated with the calorimeter. 4. Drierite® desiccant-anhydrous, 98% CaSO4, 1.0 may be expected when vigorous exercise has been done, under hyperventilation states, or when there are important concentrations of circulating lactic acid. In those cases, RER would reflect ventilation rates and the levels of lactic acid in blood. Based on [21]

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24. The best way to compare the obtained data, and to find significant differences between the evaluated groups, is through the analysis of covariance (ANCOVA) method [20]. We strongly encourage to access https://calrapp.org, which is a web-based tool for analysis of indirect calorimetry experiments, based on the use of the CalR software. CalR analyses the selected metabolic parameter, such as VO2, and includes mass as a covariate. For a proper analysis of data, it is required to import .CSV raw data files, and the masses of each evaluated subject must be provided. However, both the fat and lean masses that were previously measured with the magnetic resonance equipment can also be used for comparison as a covariate. Also, indicate the hours of the light/dark cycle, and designate the caloric value of the employed diets during the assay. Finally, manually separate each subject in its corresponding group of experimentation. 25. At the end of the assay, turn off both the calorimeter and the associated computer. It is important to continuously check the humidity of Drierite®. We recommend to change the desiccant every three calorimeter experiments.

4

Notes 1. We recommend that 2–3 mice/cage must be maintained in conditions of 12 h illumination:12 h darkness, at 22  2
C, with adequate ventilation, and cleaning of the cages twice per week. 2. The magnetic resonance equipment must be maintained inside the animal facility building, under the same environmental conditions as those the evaluated mice have, and in a separated room. When the equipment is not in use, remember to turn it off and protect it from dust. 3. The indirect calorimetry equipment must be maintained inside the animal facility building, under the same environmental conditions as those the evaluated mice have, and in a separated room. If the animals and the calorimeter have to share the same room, we recommend separating the equipment behind a wall since the emitted lights and the wavelength of its sounds can stress the non-analyzed mice. When the equipment is not in use, remember to turn it off and protect it from dust. 4. Due to the small number of cells per well, we highly recommend to seed the cells with the employment of a Viaflo Assist robot, to secure very replicative experiments. Sometimes, odd measurements during the Seahorse assays can be expected on cells located at peripheral wells of the culture plates. Therefore,

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if such wells are not required for the assay, preferably seed cells only on the center of the plate. 5. In order to avoid aberrant attachment patterns related to the “edge effect,” which is induced when transferring the culture plate from room temperature to the incubator, wait 30–40 min at room temperature after seeding the cells, and then store the cells at 37
C. 6. The Seahorse assay does not require cells to die, but instead to measure changes in the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) related to metabolic modifications. Our research group has found that in CT26. WT cells, 14 h of treatment is enough to identify a strong inhibition in both the oxidative phosphorylation and glycolysis pathways, related to the use of the referred metabolic drugs. 7. Instead of storing the calibration plate in the incubator, it can be stored at room temperature. 8. We recommend to create a template with three basal measurements, and three additional quantifications after the injection of each compound supplied by the XF Cell Mito Stress Kit. Each loop should be composed of three minutes of mixing and three minutes of measurement. 9. It must be considered that the final volume in each well during the assay will be variable. 175 μL of assay medium is employed for basal measurements. However, the sequential loading of oligomycin, FCCP, and R/Aa add each 25 μL of assay medium. Therefore, the final volume after the injection of R/Aa will be 250 μL/well. This must be taken into account for the dilution of each compound, to secure that the volume of oligomycin, FCCP and R/Aa that is going to be injected generates the concentrations indicated by Zaytseva et al. 10. It must be noticed the formation of a fast-growing vesicle when the solution is being injected. Avoid the loss of volume of saline solution, by carefully removing the needle of the syringe when the inoculation has finished. 11. It is recommended to recover the whole tumor mass, as well as other tissues depending on the specific objectives of the research, for further pathological analysis, in which case a 10% formalin solution, pH 7.4, must be ready at the moment of sacrifice to store the tissues. The tissues must be submerged in 5–10 volumes of formalin. If the tumor is recovered, it can be weighted before storing it with formalin, in order to compare mass differences between control and treated groups. One section of each tissue can also be employed to extract DNA, RNA or/and protein for further molecular biology analysis, such as sequencing or microarrays, in which case-specific

Mouse Model for Efficient Simultaneous Targeting of Glycolysis. . .

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solutions such as TRIzol® must also be ready at the moment of sacrifice. 12. In order to avoid stress in the mouse and unreliable glucose measurements, make sure that the subject is holding to the cage lid when it is being punctured, and every time a drop of blood is collected. Besides, this technique will secure the absence of unnecessary movements, which could potentially hurt the mouse during the process. 13. Although the magnetic resonance and indirect calorimetry assays can be performed only in tumor-bearing mice, we strongly suggest employing mice lacking tumor as an additional comparison. This will expand the understanding of possible secondary metabolic effects of the experimental therapy employed. 14. If “System Test” is clicked instead of selecting “Pre-Scan Label,” the equipment will evaluate whether the calibration done on the past use of the equipment can still be used. However, usually, this option fails due to diverse factors such as changes in room temperature or humidity, and the equipment must be calibrated once again. 15. We recommend introducing the mouse when the cylinder is in a horizontal position. When adding the lip to the cylinder, make sure to not completely close it to avoid any harm or stress in the mouse. If the mouse is stressed, it will start to move, and the evaluation cannot be performed. In those cases, put a piece of the adhesive tape to join the lip and the cylinder, and therefore limiting the movement of the mouse. 16. In order to eliminate water vapor generated during the assays, connect a bottle filled with Drierite®, which as a desiccant. Calcium chloride can be used instead. 17. We recommend to have a register of the calibration flow, as well as of the CO2 and VO2 parameters, on the calibration at the start of each assay. 18. Consider that when the chambers are being opened to introduce the diet, the VO2 and CO2 are still being quantified. Therefore, avoid to open a cage when it is in the process of measurement. Otherwise, aberrant VO2 values will be displayed on the temporal course of quantifications.

Acknowledgments We want to thank Dr. Ariana Vargas-Castillo for her kind support with obtaining the photography of mice and of the equipment of Figs. 3, 4, 5, and 6. We are also grateful to Prof. Armando TovarPalacios and Dr. Nimbe Torres, both from the Department of

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Nutrition Physiology of the National Institute of Nutrition and Medical Sciences, in Mexico City, Mexico, for the opportunity to work with their metabolic equipment. This work was supported by the CONACyT scholarship #439704, provided to Alejandro Schcolnik-Cabrera while studying at the Plan de Estudios Combinados en Medicina (PECEM) MD/PhD program, from the National Autonomous University of Mexico. References 1. Pavlova NN, Thompson CB (2016) The emerging hallmarks of cancer metabolism. Cell Metab 23(1):27–47. https://doi.org/10. 1016/j.cmet.2015.12.006 2. Lewis NE, Abdel-Haleem AM (2013) The evolution of genome-scale models of cancer metabolism. Front Physiol 4:237. https://doi. org/10.3389/fphys.2013.00237 3. Icard P, Lincet H (2012) A global view of the biochemical pathways involved in the regulation of the metabolism of cancer cells. Biochim Biophys Acta 1826(2):423–433. https://doi. org/10.1016/j.bbcan.2012.07.001 4. Medina MA (2001) Glutamine and cancer. J Nutr 131(9 Suppl):2539S–2542S; discussion 2550S-2531S. https://doi.org/10.1093/jn/ 131.9.2539S 5. Villar VH, Nguyen TL, Delcroix V, Teres S, Bouchecareilh M, Salin B, Bodineau C, Vacher P, Priault M, Soubeyran P, Duran RV (2017) mTORC1 inhibition in cancer cells protects from glutaminolysis-mediated apoptosis during nutrient limitation. Nat Commun 8:14124. https://doi.org/10.1038/ ncomms14124 6. Menendez JA, Lupu R (2007) Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer 7(10):763–777. https://doi.org/10.1038/nrc2222 7. Beloribi-Djefaflia S, Vasseur S, Guillaumond F (2016) Lipid metabolic reprogramming in cancer cells. Oncogenesis 5:e189. https://doi. org/10.1038/oncsis.2015.49 8. Hinds PW, Finlay CA, Quartin RS, Baker SJ, Fearon ER, Vogelstein B, Levine AJ (1990) Mutant p53 DNA clones from human colon carcinomas cooperate with ras in transforming primary rat cells: a comparison of the “hot spot” mutant phenotypes. Cell Growth Differ 1(12):571–580 9. Goel S, Huang J, Klampfer L (2015) K-Ras, intestinal homeostasis and colon cancer. Curr Clin Pharmacol 10(1):73–81 10. Raskov H, Pommergaard HC, Burcharth J, Rosenberg J (2014) Colorectal

carcinogenesis—update and perspectives. World J Gastroenterol 20(48):18151–18164. https://doi.org/10.3748/wjg.v20.i48.18151 11. La Vecchia S, Sebastian C (2020) Metabolic pathways regulating colorectal cancer initiation and progression. Semin Cell Dev Biol 98:63–70. https://doi.org/10.1016/j. semcdb.2019.05.018 12. Pakiet A, Kobiela J, Stepnowski P, Sledzinski T, Mika A (2019) Changes in lipids composition and metabolism in colorectal cancer: a review. Lipids Health Dis 18(1):29. https://doi.org/ 10.1186/s12944-019-0977-8 13. Brown RE, Short SP, Williams CS (2018) Colorectal cancer and metabolism. Curr Colorectal Cancer Rep 14(6):226–241. https://doi. org/10.1007/s11888-018-0420-y 14. Charan J, Kantharia ND (2013) How to calculate sample size in animal studies? J Pharmacol Pharmacother 4(4):303–306. https://doi. org/10.4103/0976-500X.119726 15. Matthews H, Deakin J, Rajab M, Idris-UsmanM, Nirmalan NJ (2017) Investigating antimalarial drug interactions of emetine dihydrochloride hydrate using CalcuSynbased interactivity calculations. PLoS One 12 (3):e0173303. https://doi.org/10.1371/jour nal.pone.0173303 16. Zaytseva YY, Harris JW, Mitov MI, Kim JT, Butterfield DA, Lee EY, Weiss HL, Gao T, Evers BM (2015) Increased expression of fatty acid synthase provides a survival advantage to colorectal cancer cells via upregulation of cellular respiration. Oncotarget 6 (22):18891–18904. https://doi.org/10. 18632/oncotarget.3783 17. Shaw R, Miller S, Curwen J, Dymond M (2017) Design, analysis and reporting of tumor models. Lab Anim (NY) 46 (5):207–211. https://doi.org/10.1038/ laban.1257 18. Rosas-Villegas A, Sanchez-Tapia M, AvilaNava A, Ramirez V, Tovar AR, Torres N (2017) Differential effect of sucrose and fructose in combination with a high fat diet on

Mouse Model for Efficient Simultaneous Targeting of Glycolysis. . . intestinal microbiota and kidney oxidative stress. Nutrients 9(4). https://doi.org/10. 3390/nu9040393 19. Speakman JR (2013) Measuring energy metabolism in the mouse—theoretical, practical, and analytical considerations. Front Physiol 4:34. https://doi.org/10.3389/fphys.2013.00034 20. Mina AI, LeClair RA, LeClair KB, Cohen DE, Lantier L, Banks AS (2018) CalR: a web-based

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analysis tool for indirect calorimetry experiments. Cell Metab 28(4):656–666 e651. https://doi.org/10.1016/j.cmet.2018.06. 019 21. Farinatti P, Castinheiras Neto AG, Amorim PR (2016) Oxygen consumption and substrate utilization during and after resistance exercises performed with different muscle mass. Int J Exerc Sci 9(1):77–88

Part II Epigenetic Control of Cancer

Chapter 6 Developing a Portable Device for the Identification of miRNAs in Fluids Alexander Asanov, Alicia Sampieri, and Luis Vaca Abstract In the present work we describe a novel system for the identification of microRNAs (miRNAs) in fluids. The method is based on combined novel 3D microarray technology using silk as scaffold and total internal reflection fluorescence microscopy (TIRFM), which allows for the rapid identification of miRNAs using a portable device. Key words microRNAs, Cancer, Microarrays, Fluids, 3D microarray, Reflection fluorescence microscopy

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Introduction MicroRNAs or miRNAs are small noncoding RNA molecules (typically between 21 and 25 nucleotides in length) involved in the posttranscriptional modulation of hundreds of genes [1–3]. Many miRNAs have been identified as playing key roles in diverse forms of cancer by modulating cell migration, division, invasion, and other factors that contribute directly or indirectly to the establishment or progression of cancer [4–7]. Several recent studies have highlighted the role of miRNAs as markers for early stages of the disease, thus the identification of miRNAs in fluids may provide a powerful, noninvasive method for the diagnosis or prognosis of cancer [4]. There are many miRNAs that have been implicated directly or indirectly in the development of cancer in humans and in several animal models. But without any doubt, the family of mir-200 is one of the most frequently found altered in cancer [8–11]. Identifying miRNAs is a challenging task, mainly because most of the miRNAs identified to this date have very low concentrations inside cells and in fluids. The rapid development and improvement in recent years of methods such as next-generation sequencing

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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(NGS) provide a solid platform for the identification of miRNAs. However, NGS is a time-consuming, nonportable method, which is difficult to implement in clinics and hospitals [12, 13]. Immobilization of bioassay molecules onto inorganic substrates via covalent binding or irreversible physical adsorption typically results in significant conformational changes, deteriorated molecular recognition capabilities, and decreased affinity of the assay [14]. To circumvent these limitations, bioassays can be scaffolded by a construct of synthetic or natural polymer, which provides biologically friendly environment to bioassays and offer diffusional access for bioanalyze molecules [14]. Silk fibroin is a unique naturally occurring material, which allows for implementing of the 3D-enhanced TIRF arrays [15]. In classical TIRF arrays the amount of immobilized fluorescence assays is low, limited to the depth of penetration of the evanescent wave (200–400 nm), while in the case of 3D-enhanced microarrays the volume available for immobilization can be extended to several microns. Respectively, the intensity of fluorescence increases so that cost-effective low light cameras (such as the one used in this report) can be used to measure microarray responses. Silk fibers are composed of a filament core protein, silk fibroin, and a glue-protein, sericin, which attaches fibroin fibers together [16]. Fibroin consists of heavy (H) and light (L) chain polypeptides of ~390 and ~ 26 kDa, respectively, linked by a disulfide bond at the C-terminus of the two subunits, and associates with the H-L complex by hydrophobic interactions [17]. Refractive index of silk fibroin is uniquely high (n ¼ 1.54, for dry fibers). On the other hand, hydrogels with low content of silk fibroin exhibit refractive index close to that of water (n ¼ 1.33). An assortment of methods has been described in the literature that allows for producing silk fibroin scaffolds with required refractive index, sizes of pores, and regulated nanometer-scale molecular structures [15, 18–21]. In the present work we describe a novel method, which utilizes a revolutionary form of microarrays in a handheld portable device. An evolved form of this prototype may assist in the future clinics and hospitals in the identification of markers for the diagnosis and prognosis of cancer and other afflictions. The system can also be used to monitor the efficiency of different treatments, as specific markers may increase or decrease its concentration in fluids when the therapy employed is providing good results. The possible applications in molecular diagnostics for a device similar to the one presented here are numerous.

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Materials

2.1 Expression of has-mir-141-5p in HEK293 Cells

1. HEK293 cells (available from different vendors). 2. Humidity-controlled CO2 incubator. 3. DMEM medium. 4. Transfection reagent. 5. Micropipettes and tips. 6. Trypsin.

2.2 Extraction of microRNAs from HEK293 Cells

1. miRNeasy Mini extraction kit (Qiagen) or any other microRNA extraction kit. 2. PBS buffer. 3. Micropipettes and tips. 4. 20 mL glass flasks.

2.3 Preparation of Silk Gels

1. Silk cocoons from domesticated Bombyx mori silkworm. Typically these cocoons are available online from Amazon. 2. Ultrapure water of 18 MΩ cm at 25  C. 3. PBS buffer: 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH adjusted to 7.4. 4. 0.02 M Na2CO3 solution. 5. Blender. 6. 50 mL glass flasks. 7. Pierce Slide-A-Lyzer 7K or any other dialysis membrane with similar cutoff. 8. Micropipettes (10, 20, and 100 μL) and corresponding disposable pipette tips (sterile).

2.4 Preparation of Molecular Beacons and Surface Gel

1. Molecular Beacon for the identification of has-mir-141-5p: GCTACTCCAACACTGTACTGGAAGATGATATTGGAGT AGC. 2. Synthetic has-mir-141-5p: CAUCUUCCAGUACAGUGUU GGA. 3. Synthetic has-mir-141-3p: UAACACUGUCUGGUAAAGA UGG. 4. Synthetic hsa-miR-200c-5p: CGUCUUACCCAGCAGUG UUUGG. 5. Synthetic hsa-miR-141-200c mix1: CAUCUUACCCAGCAG UGUUGGA. 6. Synthetic hsa-miR-141-200c mix2: CGUCUUCCAGUACA GUGUUUGG. 7. Transfection agent.

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2.5 Agarose Preparation for Surface Gel

1. Agarose molecular biology grade. 2. Ultrapure water of 18 MΩ cm at 25  C. 3. Micropipettes and tips. 4. 20 mL glass flasks.

2.6 Printing Microarrays on Conventional Coverslips

1. BioRad solid pin Calligrapher BioOdyssey Microarrayer or any other printing microarrayer.

2.7 Production of Low-Cost Equipment for the Identification of microRNAs in Samples and Fluids

1. sCMOs camera,

2. Ultrapure water of 18 MΩ cm at 25  C. 3. Micropipettes and tips.

2. High quality 35 mm emission filter 520  14 nm bandpass. 3. 490–495 nm LED 700 mA. 4. High quality fiber optics. 5. Laptop computer with USB connector. 6. Evanescent wave generator (TIRFLabs). 7. Acquisition and analysis software (we used ImageJ free software).

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Methods

3.1 Expression of has-mir-141-5p in HEK293 Cells

1. Grow HEK293 human embryonic cells on a temperature (37  C) and humidity-controlled incubator supplied with 5% CO2 until 60–70% confluency is reached. 2. Transfect cells with 5 nmol synthetic microRNAs (has-mir141-5p, has-mir-200c-5p, hsa-miR-141-200c mix1, or hsa-miR-141-200c mix2) and maintain them for 3 h prior to extracting microRNAs for testing.

3.2 Extraction of microRNAs from HEK293 Cells

Detach from the culture dish the control HEK293 cells and cells transfected with synthetic microRNAs using the following procedure: 1. Prewarm the trypsin balanced salt solution (Ca2+ and Mg2+free solution) and growth medium to 37  C. 2. Remove and discard the culture media from the petri dish. Gently rinse the cells with balanced salt solution without Ca2+ and Mg2+ ions and remove the solution. 3. Add appropriate quantity (0.5 mL/10 cm2) of prewarmed trypsin solution to the sidewall of the petri dish. Gently swirl the contents to cover the entire cell monolayer.

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4. Incubate the petri dish at room temperate for 2–3 min (see Note 1). If less than 90% of cells are detached incubate the petri dish for another 2 min and observe the cells under microscope. 5. Once cells appear detached add two volumes of prewarmed complete growth media to inactivate trypsin. Gently disperse the medium by pipetting over the cell layer surface several times to ensure recovery of >95% of cells. 6. Transfer the cell suspension to the tube and gently centrifuge at 300–1000  g for 5–10 min. After removing the supernatant, gently resuspend the cell pellet in prewarmed complete growth medium. 7. To isolate RNA that is highly enriched for microRNAs, add 100% ethanol to bring the samples to 25% ethanol. Pass through a glass-fiber filter this lysate–ethanol mixture. Large RNAs are immobilized, and the small RNA species are collected in the filtrate. 8. The ethanol concentration of the filtrate is then increased to 55% and passed through a second glass-fiber filter where the microRNAs become immobilized. Wash this RNA few times to elute them in a low ionic strength solution. Using this approach an RNA fraction highly enriched in RNA species 200 nucleotides can be obtained. 3.3 Preparation of Silk Gels

The purpose of silk fibroin solution preparation is to separate fibroin from the gummy protein, sericin, which glues together the fibers. To obtain high-purity fibroin solution without sericin, follow the next steps: 1. Cut the cocoon into ~2 to 3 mm pieces. 2. Boil for 20 min in liberal amount of 0.02 M Na2CO3 to remove sericin. 3. Rinse, dry, and dissolve in 9 M LiBr for 3 h at 55  C to generate a 10% (w/v) fibroin solution. 4. The solution is dialyzed at 5  C in 1.5 L of distilled water (18.2 MΩ cm) using Pierce Slide-A-Lyzer 7K (molecular weight cutoff 7000 g/mol). Change the distilled water 5 times in 72 h to obtain a silk fibroin concentration of 5% (w/v). 5. To conjugate silk fibroin with bioassay molecules, molecular beacons equipped with amine-reactive groups are added to the 5% silk fibroin solution to obtain 106 M concentration of the bioassay in 4% (w/v) fibroin solution (see Note 2).

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3.4 Preparation of Molecular Beacons and Surface Gel

The molecular beacon contained the fluorophore covalently attached to the 50 end (6-FAM™) and the quencher to the 30 end (Dabcyl). The sequence of the molecular beacon to identify has-mir-141-5p is: GCTACGCTACTCCAACACTGTACTGGAAGATGATATTGGAGTAGCATATTGGAGTAGC (in blue shown the complementary sequence to has-mir-141-5p). The hairpin structure formed by the molecular beacon is illustrated in Fig. 1. In blue is highlighted the sequence that complements has-mir-141-5p (Fig. 1). 1. Design a stem 10 bases long and 20 bases for the loop, as we have found that these combination works very well for association of the molecular beacon to its target at room temperature [22]. The molecular beacon–target association is based on Watson–Crick base-pairing, thus a large portion of the loop must be complementary to the target to obtain a rapid and sustain association at room temperature [22]. We have found that when the target complements the stem from the molecular beacon, the association between target-MB is faster and more reliable at room temperature [23]. In this particular case we designed a molecular beacon that complements a portion of the stem (5 nucleotides) and the loop (17 nucleotides) (Fig. 1).

Fig. 1 Molecular beacon designed to identify hsa-miR-141-5p. Drawing illustrating the hairpin conformation of the molecular beacon for hsa-miR-141-5p. The drawing highlights the stem and loop regions of the beacon. IN blue is illustrated the sequence that complements the hsa-miR-141-5p microRNA. Attached to the 50 is the fluorophore (6-FAM™) and the quencher is attached to the 30 end (Dabcyl)

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1. Measure 50 mg of agarose and mix with agarose powder with 5 mL of saline solution. 2. Microwave for 1–3 min until the agarose is completely dissolved (do not boil the solution, as some of the buffer will evaporate and thus alter the final percentage of agarose in the gel). 3. Let agarose cool down until the solution is warm (can be hold with the hand). 4. Mix the molecular beacon with a small portion of agarose (typically for printing about 1 mL or less). This volume is sufficient to print many microarray plates. 5. Discard the solution that is not used for printing, as the agarose will solidify and cannot be boiled again without risking damaging the molecular beacon. 6. Print the microarray plate following the instruction provided in the corresponding section of this chapter (Subheading 3.6).

3.6 Printing Microarrays on Conventional Coverslips

1. Use the solution of fibroin-molecular beacon or agarosemolecular conjugates for microarray printing employing Bio-Rad solid pin Calligrapher BioOdyssey Microarrayer (can be replaced by any other microarray printer, or doing manual printing with a micropipette). 2. Freshly printed microarray spots are about 200–250 μ in diameter and 10 μ in height. After partial evaporation of water under 70% humidity in the printing chamber of Calligrapher BioOdyssey Microarrayer, height of the 3D spots decreased to approximately 8 μ. Incubate TIRF slides with printed array 10 s in 70% ethanol to obtain dense film on the top of 3D spot. Due to the high content of beta-sheet in fibroin, the ethanol-treated silk fibroin films became water-insoluble. 3. Dry ethanol-treated TIRF slides and store in the desiccator overnight to allow reaching standard dryness. With agarose printing there is no need to use ethanol, as agarose will solidify after reaching room temperature. Thus, printing using agarose requires using the agarose solution while still warm.

3.7 Recording Microarray Fluorescence Increments in Real-Time

1. Before bioanalyte measurements, engage TIRF slide with printed microarray into the fluidic cartridge and incubate with PBS pH 7.4 during 1 h or more (see Note 3). 2. Print positive and negative control spots in the microarray. Negative control can be an unrelated molecular beacon and a positive control can be a previously tested beacon that works very well with the sample studied. 3. Print the controls at four corners and the calibration spots along the perimeter.

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Fig. 2 A portable device for 3D-enhanced TIRF arrays. (a) Typical workflow of a microRNA detection experiment. Since microRNAs have very low concentrations in cells and fluids, the first step could be a polymerase chain reaction (PCR) amplification step (step 1) followed by the microarray detection step (step 2). (b) Top photograph of the microarray chamber showing the place where the microarray slide is inserted into the detection system. The photograph shows also the evanescent wave generator (TIRFLabs) and micrometer knobs to center the objective to the microarray slide. (c) Closed look at the evanescent wave generator and the connector for the fiber optic to conduct the excitation light from tis source into the evanescent wave generator. (d) Lateral view of the microarray detection system illustrating the positioning of the objective, sCMOS camera, and emission filter. Battery and LED excitation light source are also shown in the photograph. Because the equipment is battery operated the microarray reader can be used as a portable device

4. After a baseline fluorescence reading is obtained, introduce the sample using a microperfusion system (see Fig. 2) while recording fluorescence variations in real time (see Note 4). 3.8 Low-Cost Equipment for the Identification of microRNAs in Samples and Fluids

A portable device for reading microarray data most contain the following items (Fig. 2): (a) A USB camera to image the microarray. (b) An emission filter adequate to the fluorophore in the molecular beacon. (c) An excitation source to elicit fluorescence in the molecular beacon. Typically a solid-state laser or a LED. (d) The fluidics chamber where the microarray slide is contained. (e) A battery to power the camera and the excitation source. Alternatively the camera can be powered by the USB port from the laptop computer. (f) An evanescent wave generator. 1. Assemble together all the parts of the portable device as illustrated in Fig. 2. 2. The emission filter is placed between the objective and the camera to filter out undesired wavelengths. A housing for the emission filter and the objective can be produced using a lathe. In order to image the entire microarray, a low magnification objective is required (typically 10). These 10 objectives are

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fairly inexpensive and can be obtained from many microscopy vendors. For excitation light a high-quality LED of the desired wavelength can be used. New LEDs have really good power (well over 200 mW) and narrow emission spectrum. A USB-driven sCMOS camera is an excellent choice because of the high sensitivity and reduced noise. 3. Connect the camera to a laptop computer (via USB cable), which in turn powers the camera and serves as a portable image analysis workstation. Because the 3D-enhanced TIRF arrays increment notably the amount of fluorescence generated in the microarray, the use of cost-efficient sCMOS cameras is feasible. The battery is connected to the LED to power it up (Fig. 2d). The LED is coupled via fiber optic to the evanescent wave generator (Fig. 2b, c). 3.9 Traditional Versus 3D-Enhanced TIRF Arrays

1. To demonstrate the efficiency of using 3D-enhancsed TIRF arrays versus traditional arrays perform experiments to compare the signal generated by the two technologies mentioned above (Fig. 3). Printing the molecular beacon directly on the glass results in great loss of the fluorescence signal (see Note 5, Fig. 3a). Control shows the background fluorescence generated by the surface without any microRNA delivered to the microarray (background signal). Agarose shows the signal produced by the molecular beacon printed using agarose as scaffold while silk shows the signal generated by the same molecular beacon printed using the 3D-enhanced TIRF arrays consisting of silk fibroin as scaffold. 2. Print in all cases, the same amount of molecular beacon (refer to previous sections on printing the array for final concentrations used). Utilize the same excitation intensity and emission sensitivity for glass, agarose, and silk. Panel B shows the time course of fluorescence increments with the application of has-mir-141-5p synthetic miRNA. As illustrated in the figure, not only fluorescence intensity is greater with fibroin 3D-enhanced TIRF arrays, but also the increment is faster. 3. Calculate the Mean fluorescence intensity of several independent microarray experiments (n ¼ 5) as illustrated in Fig. 3c.

3.10 Identification of hsa-miR-141-5p, a microRNA Marker for Cancer

has-mir-141 is actually a double miRNA, one is produced by the 50 arm of the miRNA (thus the name has-mir-141-5p) and the other from the 30 segment (has-mir-141-3p, Fig. 4a). The selectivity is tested using synthetic has-mir-141-3p and has-mir-141-5p (Fig. 4b). Because our molecular beacon (MB) is designed considering the has-mir-141-5p sequence to be perfectly complementary, the MB cannot associate to has-mir-141-3p (Fig. 4b).

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Fig. 3 Testing the selectivity of the molecular beacon. (a) Microarray images illustrating the background fluorescence obtained from the array slide without application of the microRNA (left panels) when the molecular beacon was printed on the glass surface, on agarose or with or fibroin-based 3D-enhanced TIRF array. Right panels sow the fluorescence signal after application of the hsa-miR-141-5p microRNA. Each dot is a single printout. Notice the enhanced fluorescence obtained with the fibroin-based 3D-enhanced TIRF array. Images shown were obtained 500 s after application of the microRNA. (b) Time courses of fluorescence increments (mean  standard deviation) obtained with molecular beacons printed on the glass surface (glass), with agarose and with silk fibroin. To the right is indicated the number of independent microarray experiments performed. (c) Mean  standard deviation of the fluorescence increments obtained after 500 s of the application of the microRNA. Fluorescence is plotted relative to agarose (considered 1)

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Fig. 4 Selectivity of the molecular beacon. (a) Drawing illustrating the sequence of the hsa-miR-141-5p microRNA precursor. Notice that from this precursor 2 mature microRNAs are produced, hsa-miR-141-5p and hsa-miR-141-3p. (b) Our molecular beacon associated via Watson–Crick basepairing only to hsa-miR-141-5p and not to hsa-miR-141-3p, because our molecular beacon was designed to complement perfectly the hsa-miR-141-5p sequence. (c) Fasta analysis of the percentage identity between hsa-miR-141-5p and has-miR-200c, the two members from the microRNA 200 family that share the largest sequence identity between them (over 70% identical). (d) 3D-enhanced TIRF array with microRNAs isolated from HEK293 cells expressing the respective microRNA. MicroRNA were transfected into the cells using standard transfection procedures (see Subheading 3.1)

1. Test the system using a microRNA obtained from a cell and not only a synthetic one (as shown in the previous section). 2. Perform experiments using microRNAs isolated from culture cells or primary cultures. In this case we use HEK293 human cells expressing has-mir-141-5p. Total microRNAs is isolated from total RNA as described in Subheading 3.2. 3. As control use total microRNAs extracted from HK293 cells not expressing has-mir-141-5p. 4. Because has-mir-141-5p is over 70% identical to another member from the miRNA-200 family (has-mir-200c-5p, Fig. 4c), conduct experiments aimed to determine if the molecular beacon can distinguish between these two closed related relatives from the miR-200 family.

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5. The molecular beacon can distinguish between the two closed related members from the family, as illustrated in Fig. 4d. Use synthetic microRNAs with mutations to make has-mir-200c5p more similar to has-mir-141-5p, in an attempt to determine which nucleotide differences are responsible for the selectivity of our molecular beacon. As illustrated in Fig. 4d, the central part of the microRNA plays a critical role in the association to the molecular beacon. 6. Introduce the mutations on has-mir-200c-5p in the central region to make it similar to the sequence from has-mir-200c5p (mutation shown in red in Fig. 4d). The microRNA mutant hsa-miR-141-200c mix1, which contains the central sequence of has-mir-200c-5p and the 30 and 50 sequences from hsa-miR141-5p is no longer recognized by our molecular beacon (Fig. 4d, third array panel from the top). Conversely, introducing the nucleotides from the 30 and 50 regions from has-mir200c-5p into hsa-miR-141-5p does not alter the recognition by the molecular beacon (Fig. 4d, fourth array panel from the top). These experiments must indicate that the molecular beacon can clearly distinguish between two closed related members from the hsa-miR-200 family of microRNAs, showing greater selectivity for hsa-miR-141-5p (see Note 6). 3.11 Microarray Analysis

1. Use the raw data for analysis; do not transform the images into a compressed format, such as GIF, JPG, and compressed TIF. Doing so will result in data loss and poor resolution of the images. 2. The next step is to identify signal from noise. Because of the low emission resulting from some microarrays, the first step is to identify the signal from each microarray spot and separate it from background signal. Spot identification can be performed manually or automatically. We used ImageJ to identify each spot using a manually selected region of interest (ROI). This process is more labor intensive than automatic detection but less prone to errors. Thus, the initial step to separate signal from noise is to identify what is known as the centroid, the pixels that received the light emitted by the single molecule. 3. Store the video signal in the hard drive of the laptop computer. Laptop can be used for off-line analysis.

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Notes 1. The concentration of trypsin used must be calculated for every trypsin preparation, since trypsin potency varies significantly from stock to stock and even with time in the same stock solution. To avoid overexposing cells to trypsin (which may

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induce cell damage), we recommend monitoring the amount of cells attached to the dish during trypsin treatment. Once over 90% of the cells are detached, it is time to remove the trypsin. 2. In the case of real-time TIRF microarrays, optical properties of the scaffold also play critical role. There are two versions of TIRF microarrays that differ by refractive index of the scaffold (Fig. 5). If the refractive index of the scaffold is low (close to that of water n ¼ 1.33), total internal reflection occurs at the substrate–scaffold interface. This geometry represents classical TIRF microarrays. Respectively, the evanescent wave penetrates into the scaffold only to the distances 200–400 nm and excites only thin submicron layer of the scaffold next to the substrate. If the refractive index of the scaffold is high (close to that of the substrate n ¼ 1.47), the scaffold captures the excitation light and total internal reflection occurs at the interface between the scaffold and water. This geometry is designated for 3D-enhanced TIRF microarrays (Fig. 5). In 3D-enhanced microarrays the entire layer of the scaffold with immobilized bioassay is excited and fluoresces, while the bulk of solution, which may contain fluorophores, is not excited and does not fluoresce, eliminating in this way background noise. 3. Since we recommend using standard 1 mm thick microarray glass slides, the slides most be mounted in a chamber that allows sample application and fluidics. For this purpose the microarray slide can be mounted on a plastic chamber produced using a lathe or a 3D printer. Alternatively, a simple chamber can be produced using a coverslip to place on top of the microarray slide and a O-ring to create the internal volume by separating both glasses. THIs procedure works very well and results in chamber that can hold up to 30 μL of solution. Because of the reduced volume inside the chamber, solution exchange is very fast and sample application is instantaneous. 4. When the microRNA contained in the sample associates (via Watson-Crick interactions) to the molecular beacon, the beacon unfolds and stretches, separating the quencher from the fluorophore. This results in fluorescence increment with a time constant reflecting the speed of the association molecular beacon-microRNA. You should star image recording prior to adding the sample to the microarray chamber, in order to have baseline fluorescence prior to the association microRNAmolecular beacon. Typically 1 min or less of baseline recording is sufficient. Do not interrupt image acquisition, simply apply the sample while in continuous recording mode, in order to obtain a clean time course of fluorescence increments.

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Fig. 5 3D-enhanced TIRF arrays. Drawing illustrating the differences between traditional microarrays and 3D-enhanced TIRF arrays. As illustrated in the drawing, silk-enhanced TIRF arrays produce greater fluorescence signal due to the fact that the silk fibroin enhances fluorescence because the refractive index of the scaffold is high, the scaffold captures the excitation light and total internal reflection occurs at the interface between the scaffold and water

5. Glass provides an unfriendly environment for fluorophores, quenching most of the fluorescent generated by molecular beacons. Thus, the use of scaffolds to print microarray on glass slides greatly enhances fluorescence by providing a friendlier environment for fluorophores.

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6. It is extremely important to design experiments to determine the selectivity of the molecular beacon prior to initiate studies on the identification of microRNAs in samples. Promiscuous associations between the target and the molecular beacon result in false positive identifications. Every new molecular beacon must be carefully scrutinized for selectivity. This is particularly relevant since many microRNAs belong to families with high sequence identity among the members.

Acknowledgments This work was supported by grants from Direccio´n General de Asuntos del Personal Acade´mico (DGAPA, IN203315) to LV. LV is a Fulbright scholar. Conflict of Interest: The evanescent wave generator is property of TIRFLabs. Dr. Alexander Asanov is the CEO from TIRFLabs. References 1. Hammond SM (2015) An overview of microRNAs. Adv Drug Deliv Rev 87:3–14 2. Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9(2):102–114 3. Kim VN, Nam JW (2006) Genomics of microRNA. Trends Genet 22(3):165–173 4. Cho WCS (2010) MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol 42 (8):1273–1281 5. Velu VK, Ramesh R, Srinivasan AR (2012) Circulating microRNAs as biomarkers in health and disease. J Clin Diagn Res 6 (10):1791–1795 6. Calin GA, Croce CM (2006) MicroRNA signatures in human cancers. Nat Rev Cancer 6 (11):857–866 7. MacFarlane L-A, R. Murphy P. (2010) MicroRNA: biogenesis, function and role in cancer. Curr Genomics 11(7):537–561 8. Martello G, Rosato A, Ferrari F, Manfrin A, Cordenonsi M, Dupont S et al (2010) A microRNA targeting dicer for metastasis control. Cell 141(7):1195–1207 9. Le MTN, Hamar P, Guo C, Basar E, Perdiga˜oHenriques R, Balaj L et al (2014) miR-200–containing extracellular vesicles promote breast cancer cell metastasis. J Clin Invest 124 (12):5109–5128

10. Feng X, Wang Z, Fillmore R, Xi Y (2014) MiR-200, a new star miRNA in human cancer. Cancer Lett 344(2):166–173 11. Pecot CV, Rupaimoole R, Yang D, Akbani R, Ivan C, Lu C et al (2013) Tumour angiogenesis regulation by the miR-200 family. Nat Commun 4(1):2427 12. Park PJ (2009) ChIP–seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680 13. Niedringhaus TP, Milanova D, Kerby MB, Snyder MP, Barron AE (2011) Landscape of nextgeneration sequencing technologies. Anal Chem 83(12):4327–4341 14. Ajikumar PK, Ng JK, Tang YC, Lee JY, Stephanopoulos G, Too H-P (2007) Carboxyl-terminated dendrimer-coated bioactive interface for protein microarray: highsensitivity detection of antigen in complex biological samples. Langmuir 23 (10):5670–5677 15. Altman GH, Diaz F, Jakuba C, Calabro T, Horan RL, Chen J et al (2003) Silk-based biomaterials. Biomaterials 24(3):401–416 16. Jin H-J, Kaplan DL (2003) Mechanism of silk processing in insects and spiders. Nature 424 (6952):1057–1061 17. Lee H (2010) Biomaterials: intelligent glue. Nature 465(7296):298–299 18. Drachuk I, Suntivich R, Calabrese R, Harbaugh S, Kelley-Loughnane N, Kaplan DL et al (2015) Printed dual cell arrays for

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multiplexed sensing. ACS Biomater Sci Eng 1 (5):287–294 19. Rajkhowa R, Gil ES, Kluge J, Numata K, Wang L, Wang X et al (2010) Reinforcing silk scaffolds with silk particles. Macromol Biosci 10(6):599–611 20. Padamwar MN, Pawar AP (2004) Silk sericin and its application: a review. J Sci Ind Res (India) 63(10):323–329

21. Zhang Q, Yan S, Li M (2009) Silk fibroin based porous materials. Materials (Basel) 2 (4):2276–2295 22. Monroy-Contreras R, Vaca L (2011) Molecular beacons: powerful tools for imaging RNA in living cells. J Nucleic Acids 2011:741723 23. Asanov A, Zepeda A, Vaca L (2012) A platform for combined DNA and protein microarrays based on total internal reflection fluorescence. Sensors 12(2):1800–1815

Chapter 7 Methods for the Study of Long Noncoding RNA in Cancer Cell Signaling Yi Feng, Junjie Jiang, Zhongyi Hu, Jiao Yuan, Tianli Zhang, Yutian Pan, Mu Xu, Chunsheng Li, Youyou Zhang, Lin Zhang, and Xiaowen Hu Abstract With the advances in sequencing technology and transcriptome analysis, it is estimated that up to 75% of the human genome is transcribed into RNAs. This finding prompted intensive investigations on the biological functions of noncoding RNAs and led to very exciting discoveries of microRNAs as important players in disease pathogenesis and therapeutic applications. Research on long noncoding RNAs (lncRNAs) is in its infancy, yet a broad spectrum of biological regulations has been attributed to lncRNAs. Here, we provide a collection of detailed experimental protocols for lncRNA studies, including lncRNA immunoprecipitation, lncRNA pull-down, lncRNA northern blot analysis, lncRNA in situ hybridization, and lncRNA knockdown. We hope that the information included in this chapter can speed up research on lncRNAs biology and eventually lead to the development of clinical applications with lncRNA as novel prognostic markers and therapeutic targets. Key words Long noncoding RNA, RNA immunoprecipitation, RNA pull-down, In situ hybridization, Northern blots, Short hairpin RNA

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Introduction Cancer is a genetic disease involving multistep changes in the genome [1]. While up to 75% of the human genome is transcribed to RNA, only less than 2% of the genome encodes protein-coding transcripts, leaving most of the genome to noncoding RNA transcripts [2, 3]. The recent discovery of the noncoding RNA genes has dramatically altered our understanding on cancer genetics. In the last decade, the functional significance of small non-coding RNA, microRNA, in tumorigenesis and progression has been extensively documented; yet research on long noncoding RNA (lncRNA) is still in its infancy [4–8]. lncRNAs are operationally defined as RNA genes larger than 200 nucleotides that do not appear to have protein coding potential [4–8]. Recent studies demonstrated that lncRNAs act as key regulators of development,

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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differentiation, apoptosis, and cell proliferation, all of which have been implicated in tumor initiation and progression. In addition, the expression of lncRNAs has been found to be remarkably deregulated by epigenetic and genomic alterations in tumors. In various experimental systems, lncRNAs have reported to have tumor suppressor or oncogene activity. Therefore, it is reasoned that lncRNAs may play important roles in the development of cancer, hence represent the leading edge of cancer research. Investigations on lncRNA functions in cancer will lead to a greater understanding of molecular mechanisms of this disease, and eventually lead to the development of lncRNA-based novel applications in cancer diagnosis and therapeutic management. 1.1 The Human Genome Contains Many Thousands of Unexplored lncRNAs

lncRNAs are operationally defined as RNA transcripts larger than 200 bp that do not appear to have coding potential [4–8]. Given that up to 75% of the human genome is transcribed to RNA, while only a small portion of the transcripts encodes proteins [3], the number of lncRNA genes can be large. After the initial cloning of functional lncRNAs such as H19 [9, 10] and XIST [11] from cDNA libraries, two independent studies using high-density tiling array reported that the number of lncRNA genes is at least comparable to that of protein-coding genes [12, 13]. Recent advances in tiling array [12–15], chromatin signature [16, 17], computational analysis of cDNA libraries [18, 19], and next-generation sequencing (RNA-seq) [20–23] have revealed that thousands of lncRNA genes are abundantly expressed with exquisite cell-type and tissue specificity in human. In fact, the GENCODE consortium (version 31) within the framework of the ENCODE project recently reported 48,227 lncRNA transcripts originating from 17,904 lncRNA genes in human. These studies indicate that (1) lncRNAs are independent transcriptional units, (2) lncRNAs are spliced with fewer exons than protein-coding transcripts and utilize the canonical splice sites, (3) lncRNAs are under weaker selective constraints during evolution and many are primate specific, (4) lncRNA transcripts are subjected to typical histone modifications as proteincoding mRNAs, and (5) the expression of lncRNAs is relatively low and strikingly cell-type or tissue-specific.

1.2 lncRNAs Regulate Gene Expression and Protein Functions Via Various Mechanisms

The discovery of lncRNA has provided an important new perspective on the centrality of RNA in gene expression regulation. lncRNAs can regulate the transcriptional activity of a chromosomal region or a particular gene by recruiting epigenetic modification complexes in either cis- or trans-regulatory manner. For example, Xist, a 17-kb X-chromosome specific noncoding transcript, initiates X chromosome inactivation by targeting and tethering Polycombrepressive complexes (PRC) to X chromosome in cis [24– 26]. HOTAIR regulates the HoxD cluster genes in trans by serving as a scaffold which enables RNA-mediated assembly of PRC2 and

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LSD1 and coordinates the binding of PRC2 and LSD1 to chromatin [14, 27]. Based on the knowledge obtained from studies on a limited number of lncRNAs, lncRNAs can function as scaffolds. lncRNAs contain discrete protein-interacting domains that can bring specific protein components into the proximity of each other, resulting in the formation of unique functional complexes [27–29]. These RNA mediated complexes can also extend to RNA– DNA and RNA–RNA interactions. lncRNAs can also act as guides to recruit proteins [26, 30, 31], such as chromatin modification complexes, to chromosome [26, 31]. This may occur through RNA–DNA interactions [31] or through RNA interaction with a DNA-binding protein [26]. In addition, lncRNAs have been proposed to serve as decoys that bind to DNA-binding proteins [32], transcriptional factors [33], splicing factors [34–36], or miRNAs [37]. Some studies have also identified lncRNAs transcribed from the enhancer regions [38–40] or a neighbor locus [20, 41] of certain genes. Given that their expressions correlated with the activities of the corresponding enhancers, it was proposed that these RNAs (termed enhancer RNA/eRNA [38–40] or ncRNAactivating/ncRNA-a [20, 41]) may regulate gene transcription. 1.3 lncRNA Expression Is Deregulated in Human Cancer

The advances in high-throughput RNA quantification technologies unveiled a profound deregulation of the lncRNome in human cancer. First, lncRNA expression profiles are dramatically different between tumors and their adjacent normal tissues. A comprehensive study analyzing lncRNA expressions in 5860 tumor samples from 13 cancer types and 424 normal specimens from nine matching tissue types from the Cancer Genome Atlas project revealed that dysregulation of expression of lncRNA is common in cancer [42]. Second, given that lncRNA expression patterns are more tissue-specific that those of protein coding genes [22, 23], it has been proposed that lncRNA expression signatures may be able to accurately determine the developmental lineage and tissue origin of human cancers. Third, the association between the expressions of several lncRNAs, such as MALAT-1 [43], HOTAIR [15], PCAT-1 [21], and LET [44], and cancer metastasis have been identified by high-throughput profiling studies and validated by further independent investigations, suggesting that lncRNAs may also serve as robust biomarkers in predicting cancer prognosis and survival.

1.4 lncRNAs Serve as Tumor Suppressor Genes or Oncogenes

Though studies on lncRNAs are still in its early stage, it is clear that lncRNAs are involved in regulating proliferation [33, 36, 45], differentiation [16, 31, 46–48], migration [15, 20] and apoptosis [30, 49]. Therefore, it is reasoned that deregulation of lncRNA expression may contribute to the development and progression of cancer. In fact, some lncRNAs have been shown to function as oncogenes or tumor suppressors. For example, HOTAIR [14] can induce metastasis [15] by operating as a tether that links

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EZH2/PRC2 and LSD1, therefore coordinating their epigenetic regulatory functions [27]. ANRIL, an antisense lncRNA of the CDKN2A/CDKN2B gene, represses INK4A/INK4B expression [50] by binding to CBX7/PRC1 [28] and SUZ12/PRC2 [29]. On the other hand, deleting Xist resulted in the development of highly aggressive myeloproliferative neoplasm and myelodysplastic syndrome with 100% penetrance in female mice [51]. 1.5 lncRNAs Represent Promising Biomarker and Therapeutic Candidates for Cancer Diagnosis and Treatment

The ability to fully characterize cancer genome contributed significantly to the development of biomarkers and therapeutic applications for cancer diagnoses and treatments. PCA3, a prostate cancerassociated lncRNA [52], is the first prominent example of lncRNA as a novel biomarker. The noninvasive method to detect PCA3 transcript in urine has been developed and used clinically to detect prostate malignancy [53]. The transition from lncRNA-based diagnostics to lncRNA-based therapies is also under intensive investigations. The rapid advances in oligonucleotide/nanoparticle therapy create realistic optimism for developing lncRNA-based therapeutic tools for cancer treatment. Although the majority of cancer-related studies still focus on the protein-coding genes, given that almost 75% of the genome is transcribed to RNAs and initial studies on a handful of lncRNAs clearly demonstrated their functional significance in cancer development and high potential in clinical applications, we argue that investigations on lncRNAs is the leading edge of cancer research.

1.6 Methods in lncRNAs Research

In the following sections, we provide detailed protocols on characterizing lncRNA expression and functions. They are RNA-immunoprecipitation (RNA-IP), RNA pull-down, Northern blot analysis on lncRNA expression, In situ hybridization (ISH) of lncRNA and lncRNA knockdown, respectively. As one major mechanism for lncRNA to exert its function is to serve as a scaffold via RNA-protein interaction, it is important to investigate which lncRNAs are binding to a protein of interest. RNA-IP is developed to identify lncRNA species that bind to a protein of interest. On the other hand, if the research focus is to identify the proteins that are bound to a given lncRNA, lncRNA pull-down will help to identify the protein molecules that interact with a specific lncRNA (Fig. 1). Moreover, as a novel class of RNA transcripts, it is important to characterize the expression of lncRNAs in various systems. While the northern blot can be used to determine lncRNA abundance and identify differ splicing variants of a given lncRNA (Fig. 2); lncRNA in situ hybridization can provide information regarding the expression level of a given lncRNA, more importantly, it can reveal the cellular or tissue localization of the lncRNA of interest. Knocking down the expression of a target gene has been a gold standard assay to elucidate its endogenous function. To this aspect, we also included two lncRNA knockdown protocols in this chapter. We

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Fig. 1 Schematic diagram of lncRNA IP and lncRNA pull-down. (a) lncRNA IP, to identify lncRNA molecules interacting with a protein of interest. (b) lncRNA pull-down, to identify proteins interacting with a specific lncRNA

Fig. 2 Schematic diagram of downward transfer of northern blot analysis (adapted from Northern Max kit instruction, Invitrogen)

hope this chapter can help the readers to develop assays for their lncRNA research which will lead to a better understanding on the roles of lncRNAs in carcinogenesis and other pathological conditions.

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Materials Prepare all solutions using ultrapure RNase-free water and analytical grade reagents. Contamination of the solutions with RNase can result in RNA degradation. Use filtration or/and autoclave sterilization to ensure that all reagents and supplies used in this section are RNase-free. Use RNase ZAP to clean all equipment and work surface.

2.1 lncRNA-Immunoprecipitation

1. Sucrose. 2. 1 M Tris–HCl (pH 7.4). 3. 1 M MgCl2. 4. Triton X-100. 5. 1 M KCl. 6. 0.5 M EDTA. 7. NP-40. 8. 1 M Dithiothreitol (DTT). 9. 10
phosphate-buffered saline (PBS): to make 1
PBS, mix one part of 10
PBS with nine parts RNase-free water. Store at 4  C. 10. Protein A/G beads. 11. RNase inhibitor. 12. Protease inhibitor cocktail. 13. TRIzol RNA extraction reagent. 14. 1 mL Dounce homogenizer. 15. Nuclear Isolation Buffer: 1.28 M sucrose, 40 mM Tris–HCl (pH 7.4), 20 mM MgCl2, 4% Triton X-100. Put 40 mL RNasefree water in a beaker with a stir bar and dissolve 21.9 g sucrose in the beaker. Add 2 mL 1 M Tris–HCl (pH 7.5), 1 mL 1 M MgCl2, and 2 mL Triton X-100 and mix well. Make up to a final volume of 50 mL with RNase-free water, store at 4  C. 16. RNA Immunoprecipitation (RIP) Buffer: 150 mM KCl, 25 mM Tris (pH 7.4), 5 mM EDTA, 0.5% NP-40. Mix 7.5 mL 1 M KCl, 1.25 mL 1 M Tris–HCl (pH 7.4), 500 μL 0.5 M EDTA, and 250 μL NP-40 and make up to a final volume of 48 mL with RNase-free water. Store at 4  C. Right before use, add DTT (0.5 mM final concentration), RNase inhibitor (100 U/mL final concentration), and protease inhibitor cocktail (1
final concentration).

2.2 lncRNA Pull-Down

1. Sucrose. 2. 1 M Tris–HCl (pH 7.4). 3. 1 M MgCl2.

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4. Triton X-100. 5. 1 M KCl. 6. NP-40. 7. 1 M NaCl. 8. 1 M Dithiothreitol (DTT). 9. 10
phosphate-buffered saline (PBS): to make 1
PBS, mix one part of 10
PBS with nine parts RNase-free water. Store at 4  C. 10. DNA template (see Note 1). 11. Restriction enzyme. 12. Vanadyl-ribonucleoside complex (VRC). 13. 10
Biotin RNA labeling mix (Roche) 14. T7 RNA polymerase (10 U/μL) and 5
transcription buffer (Agilent). 15. RNase inhibitor. 16. Protease inhibitor cocktail. 17. DNase I 2000 U/mL. 18. Straptavidin agarose beads. 19. 0.5 M EDTA (pH 8.0). 20. Yeast tRNA. 21. Nuclear Isolation Buffer: 1.28 M sucrose, 40 mM Tris–HCl (pH 7.4), 20 mM MgCl2, 4% Triton X-100. Put 40 mL RNasefree water in a beaker with a stir bar and dissolve 21.9 g sucrose in the beaker. Add 2 mL 1 M Tris–HCl (pH 7.5), 1 mL 1 M MgCl2, and 2 mL Triton X-100 and mix well. Make up to a final volume of 50 mL with RNase-free water, store at 4  C. 22. RNA Immunoprecipitation (RIP) Buffer: 150 mM KCl, 25 mM Tris (pH 7.4), 5 mM EDTA, 0.5% NP-40. Mix 7.5 mL 1 M KCl, 1.25 mL 1 M Tris–HCl (pH 7.4), 500 μL 0.5 M EDTA, and 250 μL NP-40 and make up to a final volume of 48 mL with RNase-free water. Store at 4  C. Right before use, add DTT (0.5 mM final concentration), RNase inhibitor (100 U/mL final concentration) and protease inhibitor cocktail (1
final concentration). 23. NT2 Buffer: 50 mM Tris–HCl (pH 7.4), 150 mM NaCl, 1 mM MgCl2, 0.05% NP-40. Store at 4  C. For 50 mL NT2 buffer, mix 2.5 mL 1 M Tris–HCl, 5 mL 150 nM NaCl, 1 mL 1 mM MgCl2, 2.5 mL 1% NP-40, add 39 mL RNase-free water to a final volume of 50 mL, filter stock solution, store at 4  C. Right before use, add RNase inhibitor (100 U/mL final concentration), Vanadyl-ribonucleoside complex (VRC, 400 nM final concentration), DTT (1 mM final concentration), EDTA (20 mM final concentration), and protease inhibitor cocktail (1
final concentration).

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24. RNA structure buffer: 10 mM Tris (pH 7.0), 0.1 M KCl, 10 mM MgCl2. 25. 1 mL Dounce homogenizer (Fish Scientific). 26. Agarose gel electrophoresis supplies for DNA fragment purification. 27. Quick Spin Columns for radiolabeled RNA purification Sephadex G-50. 28. Gel Extraction Kit (Qiagen). 29. BCA protein assay kit. 30. 2
Laemmli loading buffer: 4% SDS, 120 mM Tris–HCl (pH 6.8), 0.02% bromophenol blue and 0.2 M DTT. Mix 4 mL 10% SDS, 1.2 mL 1 M Tris–HCl (pH 6.8), 200 μL 1% bromophenol blue, and 2 mL 1 M DTT, and add milliQ water to make the final volume to 10 mL. Make 500 μL aliquots to minimize the freeze-and-thaw cycles. 2.3 lncRNA Northern Blot Analysis

1. DNA template (see Note 1).

2.3.1 DIG labeled RNA Probe Synthesis

3. 10
DIG RNA labeling mix (Roche).

2. Restriction enzyme. 4. T7 RNA polymerase (10 U/μL) and 5
transcription buffer (Agilent). 5. Dnase I 2000 U/mL. 6. Agarose gel electrophoresis supplies for DNA fragment purification. 7. Quick Spin Columns for radiolabeled RNA purification Sephadex G-50. 8. Gel extraction kit.

2.3.2 Separating RNA by Electrophoresis

1. Nucleic acid agarose. 2. 55  C water bath. 3. 10
denaturing gel buffer (Invitrogen). 4. Heat block. 5. Gel electrophoresis apparatus. 6. 3-(N-morpholino)propanesulfonic acid (MOPS). 7. Sodium acetate. 8. 0.5 M EDTA. 9. 10
MOPS buffer: 200 mM MOPS, 50 mM sodium acetate, 20 mM EDTA, adjust pH to 7.0. To make 1
MOPS gel running buffer, mix one part of 10
MOPS buffer with nine parts of RNase-free water. 10. RNA loading buffer (Invitrogen).

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11. Ethidium bromide (only if RNA visualization is needed). 12. DIG labeled RNA marker (Roche). 2.3.3 Transferring RNA to the Membrane

1. 20
SSC (3 M NaCl in 0.3 M sodium citrate (pH 7.0)). 2. Razor blade. 3. 3 M Filter paper. 4. Positively charged nylon membrane. 5. Blunt end forceps. 6. Paper towel. 7. RNase-free flat- bottomed container as buffer reservoir. 8. Clean glass pasture pipette as roller. 9. Light weights (150–200 g) object serving as weight during transfer. 10. Supports of the reservoir (i.e., a stack of books). 11. Stratalinker® UV Crosslinker.

2.3.4 Probe–RNA Hybridization

1. 20
SSC. 2. 10% SDS. 3. DIG easy Hyb Granules (Roche). 4. 68  C shaking water bath. 5. Heat block. 6. Hybridization oven. 7. Hybridization bags. 8. Low Stringency Buffer: 2
SSC with 0.1% SDS. 9. High Stringency Buffer: 0.1
SSC with 0.1% SDS.

2.3.5 Detection of Probe–RNA Hybrids

1. Washing and Blocking buffer set (Roche). 2. Anti-DIG-alkaline phosphatase antibody (Roche). 3. NBT/BCIP Stock Solution. 4. CDP-Star, Ready-to-Use (Roche). 5. TE buffer: 10 mM Tris–HCL, 1 mM EDTA, adjust pH to approximately 8.

2.4 lncRNA In Situ Hybridization

1. 8-well chamber slide. 2. 10
Phosphate-Buffered Saline (PBS): to make 1
PBS, mix one part of 10
PBS with nine parts RNase-free water. Store at 4  C. 3. 4% paraformaldehyde (in RNase-free PBS). 4. 0.1 M triethanolamine. 5. Acetic anhydride.

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6. 0.2 M HCl in RNase-free water. 7. 20
SSC. 8. Formamide. 9. 50
Denhardt solution (Sigma). 10. Dextran sulfate. To make 50% Dextran sulfate solution, dissolve 5 g Dextran sulfate in 10 mL RNase-free water, stir at room temperature until completely dissolved. 11. Yeast tRNA. 12. Prehybridization buffer: 2
SSC, 50% formamide, 1
Denhardt solution, and 1 mg/mL yeast tRNA. For 50 mL hybridization buffer, add 1 mL 20
SSC, 5 mL formamide, 200 μL 50
Denhardt solution, and 50 mg yeast tRNA, and make up a final volume of 50 mL with RNase-free water. 13. Hybridization buffer: 2
SSC, 50% formamide, 1
Denhardt solution, 10% dextran sulfate, 1 mg/mL yeast tRNA. For 50 mL hybridization buffer, add 1 mL 20
SSC, 5 mL formamide, 200 μL 50
Denhardt solution, 10 mL 50% dextran sulfate, and 50 mg yeast tRNA, and make up a final volume of 50 mL with RNase-free water. 14. DIG Wash and Block Buffer Set (Roche). 15. Anti-DIG-alkaline phosphatase antibody. 16. NBT/BCIP stock solution (Roche). 17. TE buffer: 10 mM Tris–HCL, 1 mM EDTA, adjust pH to approximately 8. 18. Humidity oven. 2.5 lncRNA Knockdown 2.5.1 lncRNA Knockdown Using siRNAs

1. siRNA targeting lncRNA of interest: sources of siRNAs include (1) ready-to-use linecodeRNA from http://www. thermoscientificbio.com/ and (2) custom-designed siRNAs (See Note 2 for a list of siRNA design websites and see Note 3 for a list of siRNA synthesis vendors). 2. Control siRNA. 3. siRNA transfection reagents. We use Lipofectamine RNAi max from Invitrogen, but there are a handful of similar reagents that one can choose from. See Note 4 for a list of siRNA transfection reagents. 4. Opti-MEM.

2.5.2 lncRNA Knockdown Using shRNAs

1. pLKO.1, 2. shRNA (designed at http://www.broadinstitute.org/rnai/pub lic/seq/search). 3. MISSION® pLKO.1-puro Non-Mammalian shRNA Control Plasmid DNA (Sigma).

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4. pRSV-Rev (Addgene 12253). 5. pMDLg/pRRE (Addgene 12251). 6. pMD2G (Addgene 12259). 7. HEK293T cells. 8. RPMI 1640 media. 9. Fetal Bovine serum. 10. Penicillin-Streptomycin solution with 10,000 units penicillin and 10 mg streptomycin/mL. 11. FuGENE6. 12. 0.45 μm disc filter. 13. Hexadimethrine bromide (polybrene): to make 4 mg/mL stock, dissolve 40 mg powder in 10 mL MilliQ water and filter through 0.22 μm disc filter, make 500 μL aliquot and store at 20  C.

3

Methods The procedures must be performed in an RNase-free environment. Use filtered-tips and RNase-free tubes and clean all equipment and work surface with RNase ZAP before staring the experiment.

3.1 lncRNA– Immunoprecipitation

lncRNA-IP aims to identify lncRNA species that bind to a protein of interest. The protocol includes two parts: (1) preparing protein lysate from target cells and (2) immunoprecipitating the protein of interest and extract protein-bound RNAs. It is up to the readers to decide the subsequent analysis on the isolated RNAs. Before harvesting cells, precool 1
PBS, RNase-free water, nuclear isolation buffer, and RIP buffer on ice; estimate the amount of RIP buffers needed and add RNase inhibitor and protease inhibitor cocktail to the buffer accordingly (see Note 5).

3.1.1 Whole Cell Lysate Preparation (See Note 6)

If nuclear RNA-protein interaction is the focus of the research, skip this step and go directly to 3.1.2 for nuclear lysate preparation. 1. Harvest cells using regular trypsinization technique and count the cell number. 2. Wash cells in ice-cold 1
PBS once and resuspend the cell pellet (1.0
107 cells) in 1 mL ice-cold RIP buffer containing RNase and protease inhibitors. 3. Shear the cells on ice using a Dounce homogenizer with 15–20 strokes. 4. Centrifuge at 15,000
g for 15 min at 4  C and transfer the supernatant into a clean tube. This supernatant is the whole cell lysate.

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3.1.2 Cell Harvest and Nuclei Lysate Preparation (See Note 6)

1. Harvest cells using regular trypsinization technique and count the cell number. 2. Wash cells in ice-cold 1
PBS three times and resuspend 1.0
107 cells in 2 mL ice-cold PBS (see Note 7). 3. Put cell suspension in 1
PBS on ice, add 2 mL ice-cold nuclear isolation buffer and 6 mL ice-cold RNase-free water into the tube and mix well, incubate the cells on ice for 20 min with intermittent mixing (four to five times). 4. Harvest nuclei by spinning the tube at 2500
g for 15 min at 4  C. The pellet contains the purified nuclei. 5. Resuspend nuclei pellet in 1 mL freshly prepared ice-cold RIP buffer containing DTT, RNase and protease inhibitors. 6. Shear the nucleus on ice with 15–20 strokes using a Dounce homogenizer. 7. Pellet nuclear membrane and debris by centrifugation at 16,000
g for 10 min at 4  C. 8. Carefully transfer the clear supernatant (nuclear lysate) into a new tube. The supernatant is nuclear lysate.

3.1.3 RNA Immune-Precipitation and Purification

1. Wash 40 μL protein A/G beads with 500 μL ice-cold RIP buffer three times. After the wash, spin down the beads at 600
g for 30 s at 4  C, take off the RIP buffer and add 40 μL RIP buffer to resuspend the beads. 2. Add the prewashed beads and 5–10 μg IgG and into the whole cell lysate from Subheading 3.1.1 or nuclear lysate from Subheading 3.1.2. 3. Incubate the lysate with IgG and beads at 4  C with gentle rotation for 1 h. Pellet the IgG with beads by centrifugation at 16,000
g for 5 min. 4. Carefully transfer the supernatant (precleared nuclear lysate) into a new tube. At this point, the lysate can be divided into multiple portions of equal volume for different antibodies and corresponding controls. Take 50 μL lysate and set aside on ice as input control. 5. Add antibody of interest into nuclear lysate (see Note 8), incubate the lysate and antibody overnight at 4  C with gentle rotation. 6. The next day, add 40 μL prewashed protein A/G beads and incubate at 4  C for 1 h with gentle rotation. 7. Pellet the beads by spinning at 600
g for 30 s at 4  C, remove supernatant. 8. Wash the beads with 500 μL ice-cold RIP buffer three times, invert five to ten times during each wash and pellet the beads by spinning at 600
g for 30 s at 4  C.

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9. Wash the beads with 500 μL ice-cold PBS and pellet the beads by spinning at 600
g for 30 s at 4  C, and use a fine needle or tip to remove as much PBS as possible without disturbing the beads. 10. Resuspend beads in 1 mL TRIzol RNA extraction reagent and isolate coprecipitated RNA according to manufacturer’s instructions. 11. Dissolve RNA in nuclease-free water and store the RNA at 80  C for further application (see Note 9). 3.2 lncRNA Pull-Down

3.2.1 Biotinylated RNA Synthesis by In Vitro Transcription

lncRNA pull-down aims to identify proteins that bind to a lncRNA of interest. The protocol includes three parts: (1) synthesis and labeling the lncRNA of interest; (2) preparing protein lysate from target cells; (3) pull-down labeled lncRNA with its interacting proteins. The readers can decide the subsequent analyses on the lncRNA-bound proteins. Before harvesting cells, precool PBS, water, RIP buffer and NT2 buffer on ice; estimate the amount of buffers needed and add VRC, EDTA, DTT, RNase inhibitor, and protease inhibitor to the buffers accordingly (see Note 5). 1. DNA template preparation: Linearize 3–4 μg of the plasmid containing the desired template DNA (see Note 1) with a suitable restriction enzyme at the 30 end of the insert (see Note 10). 2. DNA template purification: run the digested DNA by DNA gel electrophoresis, excise the band of correct size and extract DNA from agarose using Gel Extraction Kit. 3. In vitro synthesis of biotinylated RNA using T7 RNA polymerase: add the components listed in Table 1 into an RNase-free tube on ice, mix thoroughly, centrifuge briefly and incubate at 37  C for 2 h. After incubation, add 2 μL Dnase I (RNase-free) into the reaction and incubate at 37  C for 15 min to remove DNA template. Stop the reaction by adding 0.8 μL 0.5 M EDTA (pH 8.0).

Table 1 Components for in vitro transcription of template DNA (20 μL system) Linearized plasmid DNA (1 μg) or PCR product (100–200 ng) Biotin RNA labeling mix (10
)

2 μL

5
transcription buffer

4 μL

T7 RNA polymerase (20 U/ μL)

2 μL

RNase-free sterile water

Up to 20 μL

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4. Biotinylated RNA purification of using G-50 Sephadex Columns: Before use, gently invert the column several times to resuspend the medium. Remove the top cap and then remove the bottom tip (see Note 11). Drain the buffer in the column by gravity and then centrifuge at 1100
g for 2 min to eliminate residua buffer. Place the column in an upright position (see Note 12) with a new collection tube, apply the RNA sample (up to 100 μL) to the center of the column carefully (see Note 13) and centrifuge for 4 min at 1100
g at 4  C. The elution contains the purified biotinylated RNA. Determine the RNA concentration and store at 80  C. 3.2.2 Whole Cell Lysate Preparation (See Note 6)

If nuclear RNA-protein interaction is the focus of the research, skip this step and go directly to Subheading 3.2.3 for nuclear lysate preparation. 1. Harvest cell by regular trypsinization (~107 cells) and wash cells with ice-cold 1
PBS once. 2. Resuspend cell pellet in 1 mL ice-cold RIP buffer containing RNase and protease inhibitors. 3. Shear the cell pellet on ice using a Dounce homogenizer with 1–20 strokes. 4. Centrifuge at 15,000
g for 15 min at 4  C to clear the cell lysate. The supernatant contains whole cell lysate.

3.2.3 Nuclear Lysate Preparation (See Note 6)

1. Cell harvest and nuclei isolation: harvest cells using regular trypsinization technique, wash cell pellet in ice-cold 1
PBS once and resuspend 1.0
107 cells in 2 mL ice-cold 1
PBS. Put cell suspension on ice, add 2 mL ice-cold nuclear isolation buffer and 6 mL ice-cold RNase-free water into the tube and mix well, incubate the cells on ice for 20 min with intermittent mixing. Harvest nuclei by spinning the tube at 2500
g for 15 min at 4  C. The pellet contains the purified nuclei. 2. Nuclei lysis: resuspend nuclei pellet in 1 mL freshly prepared ice-cold RIP buffer containing RNase and protease inhibitors. Shear the nuclei on ice with 15–20 strokes using a Dounce homogenizer. Pellet nuclear membrane and debris by centrifugation at 15,000
g for 15 min at 4  C. Carefully transfer the clear supernatant into a new tube. The supernatant contains nuclear lysate (see Note 7). 3. Preclear lysate: take 60 μL Straptavidin agarose beads slurry and wash the beads with precooled NT2 buffer three times. After wash, spin down the beads at 12,000
g for 1 min and resuspend the bead in 60 μL precooled NT2 buffer. Add the prewashed Straptavidin agarose beads into the whole cell lysate (Subheading 3.2.2) or nuclear lysate (Subheading 3.2.3) and

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incubate at 4  C for at least 1 h with gentle rotation. Centrifuge the lysate briefly, carefully transfer the supernatant into a new tube, and determine the protein concentration using BCA protein assay. Save 3–5% of the lysate as input. 3.2.4 RNA Pull-Down (See Note 14)

1. Dilute ~10 picomole biotinylated RNA into 40 μL of RNA structure buffer and heat the tube at 90  C for 2 min, immediately transfer the tube on ice and incubate for another 2 min. Then let the tube sit at room temperature for 20 min to allow proper RNA secondary structure formation. 2. Add the properly folded RNA from previous step into 200 μg of precleared lysate from Subheading 3.2.4., and supplement with tRNA to a final concentration of 0.1 μg/μL (see Note 15). Incubate at 4  C for 2 h with gentle rotation. 3. Add 60 μL prewashed Streptavidin Agarose Beads and incubate for 1 h at 4  C. 4. At the end of the incubation, centrifuge at 12,000
g 4  C for 1 min and take off the supernatant. Wash the beads with 1 mL ice-cold NT2 buffer at 4  C 5 times (see Note 16). 5. After the last wash, carefully remove any residual buffer without disturbing the beads. 6. Add 40 μL 2
Laemmli loading buffer and boil the beads in loading buffer for 5–10 min, centrifuge at 12,000
g for 1 min at room temperature and transfer the supernatant, which contain the lncRNA interacting proteins, into a new tube and store at 80  C for further analysis.

3.3 lncRNA Northern blot Analysis (See Note 17)

lncRNA Northern blot analysis aims to characterize lncRNA expression. The protocol includes five parts: (1) RNA probe synthesis and labeling; (2) RNA sample electrophoresis; (3) RNA transfer; (4) RNA-probe hybridization; and (5) RNA-probe hybrid detection.

3.3.1 DIG Labeled RNA Probe Synthesis by In Vitro Transcription

The DIG labeled RNA probe synthesis is very similar to the biotinylated RNA synthesis described in Subheading 3.2.1. The differences between the two procedures are: 1. Since the probe needs to be complementary to the target sequence, the probe RNA is transcribed from the 30 end of the target sequence. We clone the gene of interest in reverse orientation to make the in vitro transcription template for Northern probes. 2. Use DIG labeling mix in place for the biotin-label mix.

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3.3.2 Separating RNA Samples by Electrophoresis

1. Gel setup: (a) Wipe the gel rack, tray and combs with RNAZap, rinse with water, and let dry. (b) Weight 100 g agarose in a clean glass flask and mix with 90 mL RNase-free water. Melt the agarose completely by heating with a microwave. Put the flask with melted agarose in a 55  C water bath. (c) In a fume hood (see Note 18), add 10 mL 10
denaturing gel buffer to the gel mix that is equilibrated to 55  C. Mix the gel solution by gentle swirling to avoid generating bubbles. Slowly pour the gel mix into the gel tray, pop any bubbles or push them to the edges of the gel with a clean pipette tip. The thickness of the gel should be about 6 mm. slowly place the comb in the gel. Allow the gel to solidify before removing the comb. (d) Right before RNA electrophoresis, place the gel tray in the electrophoresis chamber with the wells near the negative lead and add 1
MOPS gel running buffer in the chamber until it is 0.5–1 cm over the top of the gel (see Note 19). 2. RNA electrophoresis (a) Mix no more than 30 μg sample RNA with 3 volumes of RNA loading buffer (see Notes 20 and 21). To destruct any secondary structure of the RNAs, incubate the RNA with loading buffer at 65  C for 15 min using a heat block. Spin briefly to collect samples to the bottom of the tube and put the tubes on ice (see Note 22). (b) Carefully draw the RNAs in the tip without trapping any bubbles at the end of pipette tip, place the pipette tip inside of the top of the well, slowly push samples into the well and exit the tip without disturbing the loaded samples. If markers are needed, load one lane with DIG-labeled RNA marker. (c) Run the gel at 5 V/cm (see Note 23). (d) (Optional) Stain the gel with ethidium bromide and visualize the RNA under UV (see Note 24).

3.3.3 Transfer RNA from Agarose Gel to the Membrane

1. Material preparation: (a) Use a razor blade to trim the gel by cutting through the wells and discard the unused gel above the wells. For marking the orientation, make a notch at a corner. (b) Cut the membrane to the size slightly larger than the gel. Make a notch at a corner to align the membrane with gel in the same orientation. Handle the membrane with care—only touching the edges with gloved hands or blunt tip forceps.

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(c) Cut eight pieces of filter paper the same size of the membrane. (d) Cut a stack of paper towels that are 3 cm in height and 1–2 cm wider than the gel. (e) Pour 20
SSC into a flat-bottomed container that has bigger dimension of the agarose gel. This serves as the buffer reservoir and can also be used to wet the paper and membrane. Put the reservoir on a support (i.e., a stack of books) so that its bottom is higher than the paper towel stack. (f) Cut three pieces of filter paper that are large enough to cover the gel and long enough reach over to the reservoir. These papers serve as the bridge to transfer buffer from the reservoir to the gel. 2. Transfer set up (a) Stack paper towel on a clean bench and put three pieces dry filter paper on top. (b) Wet two more pieces of filter paper and put on top of the dry filter paper. Gently roll out any bubbles between the filter paper layers. (c) Carefully put the membrane on top of the wet filter paper. Gently roll out any bubbles between the membrane and the filter papers. (d) Put the trimmed gel onto the center of the membrane with the bottom of the gel touching the membrane (i.e., the gel plane that faces down during electrophoresis will be in contact with the membrane), align the notches of the gel and membrane. Roll out bubbles between the membrane and the gel. (e) Place three more pieces prewet filter paper on top of the gel and roll out bubbles between filter paper layers. (f) Wet the three pieces of paper bridge and place them with one end on top of the stack and the other end in buffer reservoir. Make sure there is no bubble between any layers of paper (see Note 25). (g) Place a 150–200 g object with the size similar to the gel on top of the stack. (h) Transfer the gel for 15–20 min per mm of gel thickness. It usually takes about two hours (see Note 26). 3. RNA crosslink: disassemble the transfer stack carefully and rinse the member with 1
MOPS gel running buffer to remove residual agarose. Blot off excessive liquid and immediately subject the membrane to crosslink treatment. Cross linking the RNA to the membrane with Stratalinker® UV

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Crosslinker using the autocrosslink setting (see Note 27). Air-dry the membrane at room temperature. At this point, the membrane can be subjected to hybridization immediately or stored in a sealed bag between two pieces filter paper at 4  C for several month before hybridization. 3.3.4 Hybridization of DIG-Labeled Probes to the Membrane (See Note 28)

1. Prehybridization. (a) Reconstitute the DIG easy Hyb Granules: add 64 mL RNase-free water into one bottle of the DIG easy Hyb Granules, stir for 5 min at 37  C to complete dissolve the granules. DIG easy Hyb buffer will be used in prehybridization and hybridization. The reconstituted DIG easy Hyb buffer is stable at room temperature for up to 1 month. (b) For every 100 cm2 membrane, 10–15 mL Hyb buffer should be used for prehybridization. Measure the appropriate amount of Hyb buffer for prehybridization and place it in a clean tube and prewarm it in a 68  C water bath (see Note 29). (c) Put the membrane in a hybridization bag, add the prewarmed Hyb buffer from the previous step, seal the bag properly and incubate the membrane in Hyb buffer at 68  C for at least 30 min with gentle agitation (see Note 30). Prehybridization can be up to several hours as far as the membrane remains wet. 2. Hybridization. (a) For every 100 cm2 membrane, 3.5 mL Hyb buffer is needed for hybridization. Measure the appropriate amount of Hyb buffer for hybridization and place it in a clean tube and prewarm it in a 68  C water bath (see Note 29). (b) Determine the amount of RNA probe needed (see Note 31) and place it into a microcentrifuge tube with 50 μL RNase-free water. Denature the probe by heating the tube at 85  C for 5 min and chill on ice immediately. (c) Mix the denatured probe with prewarmed Hyb buffer by inversion. (d) Remove prehybridization buffer from the membrane and immediately replace with the prewarmed hybridization buffer containing the probe. (e) Seal the bag properly and incubate the membrane in probe-containing Hyb buffer at 68  C overnight with gentle agitation (see Note 30). (f) The next day, prewarm the High Stringency Buffer to 68  C and pour Low Stringency Buffer in an RNase-free

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container at room temperature and make sure it is enough to cover the membrane. (g) Cut open the hybridization bag, remove the Hyb buffer, and immediately submerge the membrane in the Low Stringency Buffer. (h) Wash the membrane twice in Low Stringency Buffer at room temperature for 5 min each time with shaking. (i) Wash the membrane twice in High Stringency Buffer at 68  C for 5 min each time with shaking (see Note 32). 3.3.5 Detection of DIG-Probe–Target RNA Hybrids

1. Localizing the probe–target hybrid with anti-DIG antibody. (a) Transfer the membrane from the last wash in High stringency buffer to a plastic container with 100 mL Washing buffer. Incubate for 2 min at room temperature and discard the Washing buffer. (b) Add 100 mL Blocking buffer onto the membrane and incubate for more than 30 min (up to 3 h) with shaking at room temperature. (c) Dilute anti-DIG-alkaline phosphatase antibody at the ratio of 1:5000 in Blocking buffer and incubate the membrane in 20 mL diluted antibody for 30 min at room temperature with shaking. (d) Wash membrane twice with 100 mL of Washing buffer for 15 min each time at room temperature. 2. Visualizing probe–target hybrids using chromogenic or chemiluminescent method (see Note 33). (a) Equilibrate the membrane in 20 mL Detection buffer for 3 min at room temperature. If using the chromogenic method, prepare the color substrate solution while equilibrating the membrane. (b) For chromogenic detection: l Put the membrane with the RNA side facing up in a container and incubate in 10 mL color substrate solution in the dark without shaking. l

When the desired intensity for the band is observed, discard the color substrate solution and rinse the membrane in 50 mL of TE buffer for 5 min (see Note 34).

l

Document the result by photographing the membrane (see Note 35).

(c) For chemiluminescent detection: l

Put the membrane with the RNA side facing up on a plastic sheet (i.e., cut out of a hybridization bag) and add 20 drops of CDP-Star, Ready-to-Use reagent.

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Immediately cover the membrane with another sheet to evenly distribute the reagent without creating any bubbles.

l

Incubate for 5 min at room temperature.

l

Squeeze out excess reagent and seal the bag.

l

Develop the membrane with an X-ray film in a dark room (see Note 35).

3.4 lncRNA In Situ Hybridization

lncRNA in situ hybridization aims to characterize and quantify lncRNA expression in cells. The protocol includes three parts: (1) RNA probe synthesis and labeling, (2) cell preparation and pretreatment, and (3) RNA-probe hybridization and detection.

3.4.1 DIG Labeled RNA Probe Synthesis by In Vitro Transcription

The DIG labeled RNA probe synthesis is very similar to the biotinylated RNA synthesis described in Subheading 3.2.1. The differences between the two procedures are: 1. Both sense and anti-sense probes need to be synthesized (see Note 36). The antisense probe contains the complementary sequence to the target gene and the sense probe contains the target gene sequence. The hybridization signal from the antisense probe represents target gene expression while the sense probe is used as negative control. 2. To generate antisense probes, we clone the gene of interest in reverse orientation and transcribe it using in vitro transcription as described in Subheading 3.2.1. 3. DIG labeling mix is used in place for the biotin-label mix during in vitro transcription.

3.4.2 Cell Preparation and Pretreatment

1. Culture the cells of interest on multiwell chamber glass slides. At the day of experiment, the cells should be 60–80% confluent. We use the 8-chamber glass slide to reduce the amount of hybridization buffer and probes used. The amount of reagents used in the following protocol is for 8-well chamber slides. Scale up or down according to the cell culture device used in each specific experiment. 2. On the day of the experiment, wash the cells with 400 μL 1
RNase-free-PBS per well, then fix the cells using 200 μL 4% PFA per well at room temperature for 10 min. Wash the fixated cells three times with 1
PBS at room temperature. 3. Add acetic anhydride into 0.1 M triethanolamine to make the final acetic anhydride concentration 0.25% (v/v, see Note 37). Pretreat cells with 0.1 M triethanolamine containing 0.25% acetic anhydride for 10 min at room temperature. Wash the cells with 1
PBS for 5 min afterward.

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4. Permeablize the cells with 0.2 M HCl for 10 min and wash the cells twice with 1
PBS, 5 min each time, at room temperature. 5. The slides can be either subject to hybridization immediately or stored in 1
PBS at 4  C for a couple of days before proceeding to the next step. 3.4.3 In Situ Hybridization and Detection of Probe–Target Hybrid

1. Add 200 μL prehybridization buffer (hybridization buffer without probe and dextran sulfate, see Subheading 2 for details) into each well, incubate at 60  C for 2 h (see Note 38). 2. During prehybridization, take the amount of probe needed for the experiment (see Note 39) and denature the probes by heating the probes at 85  C for 10 min and immediately cool down on ice for at least 5 min. 3. Add denatured probes into the hybridization buffer. To ensure assay specificity, always use a control probe for each target probes, see Note 36 for details. 4. Remove the prehybridization buffer from chambers and add 200 μL hybridization buffer containing denatured probes on to the slide and incubate at 60  C in humidity oven with lid on overnight. 5. The next day, wash cells by adding 500 μL 0.1
SSC with 50% formamide in each well and incubate the slides with lid at 60  C for 30 min twice (see Note 40). 6. Then wash the cells by adding 500 μL 2
SSC to each well and incubate for 5 min at room temperature twice (see Note 40). 7. Wash the cells with 500 μL washing buffer per well at room temperature for 5 min. 8. Add 200 μL blocking buffer per well and incubate for 1 h at room temperature. 9. While the cells are incubated with blocking buffer, dilute the anti-digoxygenin-alkaline phosphate antibody in blocking buffer (see Note 41). 10. Discard the blocking buffer. Add 100 μL diluted antibody per well and incubate for 1 h at room temperature. 11. Wash the slides with 500 μL wash buffer per well for 15 min at room temperature twice. 12. Incubate the slides in 500 μL detection buffer per well for 10 min at room temperature. Meanwhile, dilute 200 μL NBT/BCIP stock solution in 10 mL detection buffer. 13. Discard the detection buffer and add 500 μL diluted NBT/BCIP solution per well and incubate cells in the dark (up to 16 h).

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14. When the desired color intensity is observed (see Note 42), stop the color reaction by discarding the NBT-BCIP solution and incubating with TE buffer for 15 min. 3.5 Method for shRNA Knockdown

Like mRNAs, the endogenous lncRNA expression can be downregulated using two RNA silencing-mediated approaches. One is transfecting small-interfering RNAs (siRNAs) targeting the gene of interest into cells. The other is stably expressing gene-specific RNA hairpins (shRNAs) in target cells. The siRNA approach provides acute gene downregulation and allows for gradient knockdown of target gene expression by adding different amounts of siRNAs; whereas the shRNA-approach provides sustained downregulation of the target lncRNA, making it more suitable for experiments that take a long period to get the end-point readout. The following section will provide protocols for both methods.

3.5.1 lncRNA Knockdown Using siRNAs

The following protocol provides siRNA concentration and the amount of transfection reagents based on Lipofectamine RNAiMax transfection protocol. If you are using different transfection reagents, please follow the specific instructions from the manufacturers. 1. Make 20 μM siRNA stock solution with RNase-free water, aliquot into 20 μL per tube and store at -80  C. It is highly recommended to reduce the freeze-and-thaw cycles of siRNA stocks. 2. The day before transfection, plate cells in 24-well plate so that it will be 60–80% confluent the next day. 3. On the day of transfection, calculate the amount of control and targeting siRNAs needed for each well according to Table 2 (see Note 43). Dilute the siRNA and transfection reagents in OptiMEM in separate tubes, mix together and incubate at room temperature for at least 20 min. 4. Without removing any media from the wells, add the siRNA and transfection reagent mixture to the wells.

Table 2 siRNA transfection components and composition Reagent per well for 24-well plate

Amount of reagent

Opti-MEM for siRNA dilution

50 μL

siRNA

0.6–30 pmol

Opti-MEM for RNAiMax dilution

50 μL

RNAiMax

0.5–1.5 μL

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5. Incubate the cells with siRNA/transfection reagents for at least 6 h (see Note 44). 6. Remove the media containing siRNA/transfection reagents and feed the cells with fresh media. 7. Harvest the infected cells in TRIzol 48–72 h posttransfection. 8. Check target gene knockdown efficiency by quantitative RT-PCR of RNA from control and targeting siRNA transfected cells. 3.5.2 lncRNA Knockdown Using shRNAs

1. Generating shRNA knockdown constructs. (a) Design shRNA sequence targeting specific lncRNA at http://www.broadinstitute.org/rnai/public/seq/search (see Note 45). (b) Synthesize the DNA containing the shRNA sequence of choice and clone it into lentiviral vector pLKO.1. (c) Purify pLKO.1 constructs (containing specific shRNA or scramble controls) and other packing constructs (pRSVRev and pMDLg/pRRE) using QIAGEN Plasmid Plus Maxi Kit (see Note 46). 2. Packing virus using 293T cells. (a) Plate HEK293T cells in 6-well plate the day before transfection so it will be 80% confluent the next day. (b) Change cells transfection.

into

antibiotic-free

media

before

(c) Mix 4 μg plasmid cocktail and 12 μL FuGENE6 transfection reagent according to the FuGENE protocol. The composition of the plasmid cocktail is shown in Table 3: (d) Add the FuGENE–DNA mixture onto cells. (e) Eight hours after transfection, change into regular media with antibiotics. (f) Forty-eight hours after transfection, harvest virus by collecting the culture media and filter through 0.45 μm disc filter to get rid of cell debris. Table 3 Plasmid composition for pLKO.1 virus packaging Plasmid

μg

pRSV-Rev

0.65

pMDLg/pRRE

1.3

pMD2G

0.65

pLKO.1

1.4

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(g) The virus stock can be aliquot and store at 80  C for future use. 3. Cell Infection. (a) Plate target cells in 6-well plate the day before infection so it will be 50–60% confluent the next day. (b) Mix three parts of virus with one part of culture media (i.e., 3 mL virus with 1 mL medium) and add polybrene to final concentration of 8 μg/mL. (c) Add virus mixture directly onto cells and incubate overnight. (d) After 24 h of incubation, change media or split cells depending on the cell confluency. (e) Harvest the infected postinfection.

cells

in

TRIzol

72–96

h

(f) Check target gene knockdown efficiency by quantitative RT-PCR of RNA from scramble and shRNA infected cells.

4

Notes 1. The desired DNA template should be a plasmid containing a promoter for in vitro transcription (i.e., T7 or T3) and a target sequence whose 50 end is placed as close as possible to the 30 end of the promoter. We usually use pBluescript SK(+) and transcribe the target sequence using T7 polymerase. Minimize any unnecessary addition of non-lncRNA sequence into the plasmid to avoid impropriate RNA folding. 2. List of siRNA design websites: (a) siDESIGN center at http://www.thermoscientificbio. com/. (b) BLOCK-iT™ RNAi Designer at http://rnaidesigner.invi trogen.com/rnaiexpress/. (c) http://www.broadinstitute.org/rnai/public/seq/search. 3. List of siRNA synthesis vendors: (a) http://www.idtdna.com/. (b) http://www.invitrogen.com. (c) http://www.genscript.com/. (d) http://www.thermoscientificbio.com/. (e) http://www.sigmaaldrich.com/. 4. List of siRNA transfection reagents: (a) Lipofectamine RNAiMax (Invitrogen). (b) DharmaFECT Transfection Reagents (Thermo Scientific). (c) X-tremeGENE siRNA transfection reagent (Roche).

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5. It is utterly important that the experiment described above is conducted with extra precaution to avoid RNA degradation. All materials have to be RNase-free and the buffers need to be precooled on ice. 6. To ensure result reproducibility, the cells need to be maintained consistently. 7. The abundances of different target protein and lncRNAs may vary from cell line to cell line, therefore the amount of lysate input needs to be empirically determined for each assay. We found 1.0
107 cells is a good starting point. In cases more cells are needed, scale up the amount of buffer used to ensure high nuclear lysing efficiency. 8. The amount of antibody used for each experiment need to be empirically determined. Our suggestion is to start at around 1–2 μg antibody per million cells. 9. The amount of nuclease-free water used to dissolve the RNAs are determined by several factors, including the type of downstream analysis, the amount of lncRNA bound to the target protein and the cell type. We recommend the researchers start at 20 μL and adjust according to their specific situations. 10. Make sure the restriction enzyme digests efficiently and generate a 50 overhang. 11. The top-to-bottom sequence is necessary to avoid creating vacuum and uneven flow of buffer. 12. Maintaining the column in an upright position is very important, especially after centrifugation. Tipping the column can cause back-flow of the RNA sample and reduce the yield after purification. 13. Avoid applying the sample to the side of the column or overloading the column, since it will reduce the yield and purify of the RNA. 14. The amount of total protein used in each assay need to be empirically determined based the specific questions the researchers try to address. If the interaction between a specific lncRNA and a target protein is to be tested, the abundance of the target protein in the nuclear lysate need to be taken into account in determining the amount of total protein used in the assay. 15. The purpose of adding tRNA in the pull-down assay is to reduce nonspecific binding. Therefore, the amount of tRNA added to each reaction can be optimized according to specific conditions.

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16. We found that sometimes increasing the number of washes can greatly reduce the background, therefore it is recommended to optimize the wash condition for each specific assay. 17. Northern blot analysis is a golden-standard in RNA detection and analysis. There are many protocols developed by laboratories specialized in RNA research or companies. The protocol described here is adapted from the NorthernMax procedure from Invitrogen and DIG application manual for filter hybridization from Roche. In our hands, this protocol is time efficient and gives satisfying results without using radioactivity. 18. Always cast the gel in a fume hood as the denaturing solution contains formaldehyde. Solidified gels can be wrapped up and stored at 4  C for overnight. 19. Do not let gel soaked in running buffer for more than 1 h before loading. 20. Load no more than 30 μg total RNA in each lane. As the binding capacity of the membrane is limited, more RNA loaded does not guarantee a stronger signal. Overloading can lead to the detection of minor degradation of targeted RNAs. 21. If the total volume of sample and dye exceed the capacity of the wells, it is necessary to concentrate the RNA by precipitation and suspend the pellet in smaller volume of water before adding the loading dye. 22. Use a heat block instead of a water bath to avoid contaminating the samples with water. 23. The voltage is decided by the distance between the two electrodes (not the size of the gel). Usually, the run takes about 2 h. If the run is longer than 3 h, exchange the buffer at the two end chambers to avoid the pH gradient. 24. RNA gels that stained with Ethidium bromide are not suitable for Northern blot analysis. Therefore, if a visual examination or photograph of total RNA samples is needed as a reference for the northern blot, we suggest the researchers to either run the same set of samples on a separate gel or stain with Ethidium bromide the gel just for visualization; or de-stain the gel before continuing northern blot analysis. If a gel will be subjected to Northern analysis after UV visualization, avoid prolonged exposure of the gel to UV light. 25. It is essential to ensure that the only way for the transfer buffer to run from reservoir to the dry paper stack is through the gel. Therefore, extra care is needed to assemble the stack properly to avoid shortcut. The most common shortcut happens between the bridge and the paper beneath the gel. One can cover the edges of the gel with Parafilm to prevent this from happening.

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26. Transfer longer than 4 h may cause small RNA hydrolysis and reduce yield. 27. The autocrosslink Mode of Stratalinker® UV Crosslinker delivers a preset exposure of 1200 μJ to the membrane and takes about 40 s. Other methods of crosslinking RNA to membrane are available and can be used at this step as well. 28. Once the membrane is wet during prehybridization, it is important to avoid it getting dry during the hybridization and detection process. Dried membrane will have high background. Only if the membrane will not be stripped and reprobed, it can be dried after the last high stringency wash and stored at 4  C for future analysis. 29. For most northern blot hybridization using DIG Easy Hyb buffer, 68  C is appropriate for both prehybridization and hybridization. In cases of more heterologous RNA probes being used, the prehybridization and hybridization temperature need to be optimized. 30. Prehybridization/hybridization can be performed in containers other than bags, as far as it can be tightly sealed. Sealing the hybridization container can prevent the release of NH4, which changes the pH of the buffer, during incubation. 31. For RNA probe synthesized by in vitro transcription, it is recommended that the probe concentration should be 100 ng per mL Hyb buffer. 32. If the probe is less than 80% homologous to the target RNA, the high stringency wash should be performed at a lower temperature, which needs to be empirically determined. 33. The DIG probe–target RNA hybrids can be detected in two ways. One uses chemiluminescent method, whereas the other uses chromogenic method. The chemiluminescent method is sensitive and fast, but it requires the usage of the films and the accessibility of a darkroom. The chromogenic method requires no film or dark room and different targets can be detected simultaneously using different colored substrate. However, the chromogenic method may not be sensitive enough for low-abundant targets. 34. At this step, if there are multiple membranes, process one at a time. Depending on the abundance of target RNAs, the band may appear as quickly as a few minutes after adding the chromogenic agents. The reaction can be stopped when the band reaches a desired intensity. 35. If reprobing is needed, photograph the result while the membrane is wet and proceed to stripping and reprobing. If no reprobing is needed, dry the membrane, document the result by photograph and store the dried membrane in a clean bag at room temperature.

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36. During assay development for each specific gene target, it is critical to know whether the hybridization is specific to the gene of interest. One way to ensure hybridization specificity is to include samples hybridizing with sense probe (containing the target sequence, serving as negative controls) at the same concentration as those with the antisense probe (containing the complementary sequence). If the control probe gives comparable signal as the target probe does, the hybridization may not be specific enough to the gene of interest and optimization of the probe sequence, probe concentration, hybridization condition, and wash stringency will be needed. 37. The acetic anhydride need to be added freshly each time and discard any leftover 0.1 M triethanolamine with acetic anhydride. 38. To avoid evaporation of the buffer, we put on the lid for the chamber slide and use the humidity oven with temperature set at 60  C for the incubation. 39. The concentrations of different probes need to be empirically determined. We found that 100–400 ng probes per mL hybridization buffer are a good starting point. 40. The wash condition can be optimized by adjusting the salt concentration and temperature. 41. The recommended range for anti-DIG antibody is from 1:500 to 1:2000. The optimal concentration needs to be empirically determined. 42. The process usually takes from 5 min to 2 h. Stop the reaction once the desired signal is visible. 43. The amounts of siRNA and transfection reagents for each cell line need to be empirically determined. 44. Leaving the transfection reagents on cells for extended period of time may cause cell toxicity. 45. It is common to clone five or more shRNA and choose the two that gives the highest knockdown efficiency for future analysis. 46. We prefer to use the Qiagen Plasmid Plus Maxi Kit. However, any plasmid isolation kit that gives high quality DNA for efficient transfection can be used. A good mini-prep kit can be also used to isolate small amount plasmid during pilot experiments

Acknowledgments This work was supported, in whole or in part, the US National Institutes of Health (R01CA225929 to LZ, R01CA142776 to LZ, R01CA190415 to LZ, P50CA083638 to LZ, P50CA174523 to

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LZ), the Breast Cancer Alliance (LZ), the Marsha Rivkin Center for Ovarian Cancer Research (LZ), the Harry Fields Professorship (LZ), the Kaleidoscope of Hope Ovarian Cancer Foundation (LZ), the Ovarian Cancer Research Fund Alliance (XH), and the Foundation for Women’s Cancer (XH and Z). References 1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674 2. International Human Genome Sequencing, C (2004) Finishing the euchromatic sequence of the human genome. Nature 431:931–945 3. Djebali S, Davis CA, Merkel A et al (2012) Landscape of transcription in human cells. Nature 489:101–108 4. Rinn JL, Chang HY (2012) Genome regulation by long noncoding RNAs. Annu Rev Biochem 81:145–166 5. Lee JT (2012) Epigenetic regulation by long noncoding RNAs. Science 338:1435–1439 6. Prensner JR, Chinnaiyan AM (2011) The emergence of lncRNAs in cancer biology. Cancer Discov 1:391–407 7. Guttman M, Rinn JL (2012) Modular regulatory principles of large non-coding RNAs. Nature 482:339–346 8. Spizzo R, Almeida MI, Colombatti A et al (2012) Long non-coding RNAs and cancer: a new frontier of translational research? Oncogene 31:4577–4587 9. Pachnis V, Brannan CI, Tilghman SM (1988) The structure and expression of a novel gene activated in early mouse embryogenesis. EMBO J 7:673–681 10. Bartolomei MS, Zemel S, Tilghman SM (1991) Parental imprinting of the mouse H19 gene. Nature 351:153–155 11. Brown CJ, Ballabio A, Rupert JL et al (1991) A gene from the region of the human X inactivation centre is expressed exclusively from the inactive X chromosome. Nature 349:38–44 12. Kapranov P, Cawley SE, Drenkow J et al (2002) Large-scale transcriptional activity in chromosomes 21 and 22. Science 296:916–919 13. Rinn JL, Euskirchen G, Bertone P et al (2003) The transcriptional activity of human chromosome 22. Genes Dev 17:529–540 14. Rinn JL, Kertesz M, Wang JK et al (2007) Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129:1311–1323 15. Gupta RA, Shah N, Wang KC et al (2010) Long non-coding RNA HOTAIR reprograms

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Chapter 8 RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer Sudhanshu Shukla and Seema Khadirnaikar Abstract Sequencing analysis finds many applications in various fields of biology from comparative genomics to clinical research. Recent studies, using high-throughput sequencing method, has generated terabytes of data. It is challenging to interpret and draw a meaningful conclusion without the proper understanding of various steps involved in the analysis of such data. This chapter deals with the pipeline to be followed to process the raw RNA sequencing (RNA-Seq) reads, align, assemble, and quantify them in order to draw significant clinical conclusions from them. Key words RNA sequencing, Biomarkers, Prognosis, Differential expression, Sequencing analysis, LncRNAs

1

Introduction Innovations in sequencing technologies have led to the development of techniques which generate a massive amount of data in parallel, providing a pathway to gain insights into the genome [1]. Due to apparent advantages in transcript quantification and reproducibility, RNA-Seq is widely used for gene-level studies [2]. RNA-Seq is preferred over other techniques as it provides reliable results with small sample size, has a high signal-to-noise ratio and high sensitivity [3– 6]. Besides understanding the expression level of genes, RNA-Seq can also be used to identify novel transcripts [7, 8]. Though it finds a myriad of applications, it is most extensively used in the identification of differentially expressed genes [9, 10]. In addition to identification and understanding the functionality of protein-coding RNAs, recent studies have provided surprising insights into various functions of noncoding RNAs, especially the long noncoding RNA (LncRNA) [11, 12]. The LncRNAs are composed of more than 200 bases and do not have any coding potential [11]. Recently, many studies have shown that LncRNAs play a vital role in epigenetic regulation, gene transcription, and post-transcriptional regulation [11, 13]. Thus it is

Martha Robles-Flores (ed.), Cancer Cell Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2174, https://doi.org/10.1007/978-1-0716-0759-6_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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essential to include the LncRNAs in the biomarker identification process. Lately, RNA-Seq has become an essential component of personalized therapy [13–15]. The expression pattern of genes is an important biomarker for prognosis, diagnosis, and drug response [14, 15]. Generally, gene panels are preferred as biomarker over single gene due to the heterogeneity associated with the tumors [16]. Here in this chapter, we have described the pipeline to download raw sequencing files, analyze and perform the statistical analysis to identify the differentially regulated genes and prognostic markers.

2

Materials Analyzing the raw reads obtained after sequencing in FASTQ format involves preprocessing for quality control followed by mapping reads to an annotated reference genome. The mapped reads are then assembled and quantified to obtain gene expression level. To remove the bias, the gene expression must be normalized before using for interpretation (see Note 1). The normalized gene expression is then used for differential gene expression analysis and biomarker identification [17]. Here in this chapter, we will utilize public available data for the analysis.

2.1 System Requirements

A Quadcore Unix based OS with 1–2 TB disk space and 16–32 GB RAM is sufficient to align the raw reads to reference genome, and assemble and quantify the reads to obtain counts. Although, the requirements may vary depending on the tools and number of reads in the raw file.

2.2

The raw sequencing files and various levels of sequencing data along with clinical data can be downloaded from the following databases:

Data Availability

1. Gene Expression Omnibus (GEO)—https://www.ncbi.nlm. nih.gov/geo/. 2. ArrayExpress browse.html.

(EVA)—https://www.ebi.ac.uk/arrayexpress/

3. Sequence Read Archive (SRA)—https://www.ncbi.nlm.nih. gov/sra. 4. European Nucleotide Archive (ENA)—https://www.ebi.ac. uk/ena. 5. Encyclopedia of DNA Elements (ENCODE)—https://www. encodeproject.org/. 6. Genomic Data Commons (GDC)—https://gdc.cancer.gov/. 7. Firebrowse—http://firebrowse.org/.

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Besides transcriptome profiling data, diagnostic slide images, copy number variation, methylation beta values, miRNA expression data, isoform expression data, and somatic mutation data can also be obtained. One of the most comprehensive database for cancer research is GDC data portal. More than 10,000 samples are sequenced and deposited on the public server for the access (see Note 2). GDC divides data into four different levels: Level 1. Representing raw sequencing data. Level 2. Representing normalized data. Level 3. Representing aggregated data. Level 4. Representing regions of interest data. 2.3 Clinical Data Availability

Many publicaly available data portals mentioned above also deposit clinical data. GDC (https://gdc.cancer.gov/) portal has clinical data for more than 10,000 cancer patients which include age, gender, patient’s survival status, days to death, grade, metastasis stage, and drug treatment.

2.4

The data from the GDC data portal can be downloaded using GDC’s data transfer tool (https://gdc.cancer.gov/access-data/ gdc-data-transfer-tool), which requires a manifest file/UUIDs obtained from GDC data portal (see Note 3). R package “TCGAbiolinks” can also be used for the same [25]. Following codes can be used for downloading normal and tumor count data for patients (e.g., GBM patients) using TCGAbiolinks library in R.

Data Download

library("data.table") library("TCGAbiolinks") library("SummarizedExperiment") # Querying data query