Cellular Signal Transduction in Toxicology and Pharmacology

JONATHAN W. BOYD, PHD, is an Associate Professor in the Department of Orthopaedics at West Virginia University School of Medicine. RICHARD R. NEUBIG, MD, PHD, is Chair and Professor in the Department of Pharmacology and Toxicology at Michigan State University.

List of Contributors xv

About the Editors xvii

Preface xix

1 Introduction to Cellular Signal Transduction: The Connection Between a Biological System and Its Surroundings 1
Jonathan W. Boyd, Richard R. Neubig, Alice Han, and Maren Prediger

1.1 Starting Big, but Ending Small 3

1.1.1 Key Features of Signal Transduction 3

1.2 Responding to Our Environment: Sensory Perception Begins and Ends with Signal Transduction 4

1.2.1 Taste (Gustation) 4

1.2.2 Smell (Olfaction) 5

1.2.3 Sight (Vision) 6

1.2.4 Sound (Audition) 6

1.2.5 Touch (Somatosensation) 8

1.3 Primary Transport Systems Involved in Signal Transduction 8

1.3.1 Ion Channels, Transporters, and Ion Pumps 9

1.3.2 Receptors 10

1.3.3 Endocytosis 10

1.3.4 Exosomes 11

1.4 Key Organelles Involved in Signal Transduction 12

1.4.1 Mitochondria 12

1.4.2 Endoplasmic Reticulum 14

1.4.3 Nucleus 15

References 16

2 Mechanisms of Cellular Signal Transduction 21
Richard R. Neubig, Jonathan W. Boyd, Julia A. Mouch, and Nicole Prince

2.1 Posttranslational Modifications and Their Roles in Signal Transduction 22

2.1.1 Phosphorylation 22

2.1.2 Acylation 24

2.1.3 Alkylation 25

2.1.4 Glycosylation 26

2.1.5 Other PTMs 27

2.2 Receptors 27

2.3 Receptor Signaling Mechanisms 29

2.3.1 Basic Principles of Signal Transduction Mechanisms 29

2.3.1.1 Selectivity and Recognition 31

2.3.1.2 Flexible Modularity 31

2.3.1.3 Molecular Switches 34

2.3.1.4 GPCRs and Second Messengers 36

2.3.1.5 Amplification 39

2.3.1.6 Turn‐Off Mechanisms 40

2.3.1.7 Localization 40

2.3.1.8 Biased Signaling/Functional Selectivity 41

2.4 Receptor Tyrosine Kinases 42

2.5 Steroid Receptors 43

2.6 Reactive Oxygen Species (ROS) 43

2.7 Summary 44

References 44

3 From Cellular Mechanisms to Physiological Responses: Functional Signal Integration Across Multiple Biological Levels 49
Robert H. Newman

3.1 Introduction 49

3.2 Cellular Information Flow: Mechanisms of Cellular Signal Integration and Regulation 50

3.2.1 The InsR‐aPKC‐NF‐κB Signaling Axis 51

3.2.2 Modes of Regulation in InsR‐PKC‐NF‐κB Signaling Axis 54

3.2.3 Transcriptional Regulation 54

3.2.4 Regulating the Regulators: Phosphatase‐Mediated Regulation of Signaling Molecules 59

3.3 Crosstalk and Functional Signal Integration in Response to Insulin in Hepatocytes 60

3.4 Systemic Signal Integration 65

3.4.1 Pancreatic β‐Cells 65

3.4.2 Skeletal Muscles 66

3.4.3 Adipose Tissue 67

3.5 Dysregulation of Insulin Signaling in the Etiology of Type 2

Diabetes 67

References 69

4 Signal Transduction in Disease: Relating Cell Signaling to Morbidity and Mortality 73
Patricia E. Ganey and Sean A. Misek

4.1 Introduction 73

4.2 Fibrosis as an Example of Complex Signaling 75

4.2.1 Development of Liver Fibrosis 75

4.2.2 Animal Models of Hepatic Fibrosis 76

4.2.3 Activation of Hepatic Stellate Cells 77

4.2.4 Epithelial‐to‐Mesenchymal Transition (EMT) 78

4.2.5 Other Cellular Interactions in Fibrosis 78

4.2.6 Intracellular Signaling Pathways Critical to Liver Fibrosis 80

4.2.6.1 TGF‐β1 80

4.2.6.2 Kinase Pathways Involved in Fibrotic Responses 82

4.2.6.3 HIF‐1α 83

4.2.6.4 miRNA 84

4.2.6.5 Toll‐Like Receptors (TLRs) 84

4.3 Cancer Drug Resistance: Complex Cellular and Population Changes 85

4.3.1 Genomic Resistance Mechanisms 85

4.3.2 Non‐genomic Mechanisms 88

4.3.3 Non‐cancer Drug Resistance Paradigms 88

4.3.4 Tumor Heterogeneity as a Driver of Drug Resistance 89

4.3.5 Mutational Drivers of Drug Resistance 90

4.3.6 Drug‐Induced Rewiring of Signaling Networks as a Mechanism of Drug Resistance 91

4.3.7 Parallel Pathways and Combination Treatments 93

4.3.8 Epigenetic Mechanisms of Drug Resistance 95

4.3.9 Summary of Cancer Drug Resistance 97

4.4 Summary 98

References 98

5 Experimental Design in Signal Transduction 113
Weimin Gao, Meghan Cromie, Qian Wang, Zhongwei Liu, Song Tang, and Julie Vrana Miller

5.1 Overview of Basic Experimental Design 113

5.1.1 Independent Sample t Test 114

5.1.2 Completely Randomized Analysis of Variance (ANOVA) 114

5.1.3 t Test for Dependent Sample Design 115

5.1.4 Randomized Block Design 115

5.1.5 Completely Randomized Factorial Design 116

5.1.6 Summary 116

5.2 Aseptic Technique 116

5.2.1 Sterile Work Environment and Laminar‐Flow Hood 117

5.2.2 Good Personal Hygiene Practices 117

5.2.3 Sterile Reagents and Materials 118

5.2.4 Sterile Handling 118

5.3 Biological Sample Collection, Processing, and Pretreatment Technology 119

5.3.1 Sample Collection 119

5.3.1.1 Sample Collection In Vivo 119

5.3.1.2 Cell Culture In Vitro 120

5.3.2 Sample Processing 121

5.3.2.1 DNA Isolation 121

5.3.2.2 RNA Extraction 121

5.3.2.3 Protein Extraction 122

5.4 Sample Storage 122

5.5 Common In Vitro Studies in Toxicology/Pharmacology 123

5.5.1 Cytotoxicity Studies 123

5.5.2 Viability Assays 123

5.5.2.1 Trypan Blue 123

5.5.2.2 Erythrosin 124

5.5.2.3 Crystal Violet Staining 124

5.5.2.4 Neutral Red Staining 125

5.5.3 Survival Assays 125

5.5.3.1 Clonogenic or Colony Formation Assay 125

5.5.3.2 Cell Cycle Analysis: Flow Cytometry 126

5.5.4 DNA Damage Assays 126

5.5.4.1 Comet Assay 127

5.5.4.2 Sister Chromatid Exchange Assay 127

5.5.5 Southern Blot and DNA Sequencing 127

5.5.5.1 Southern Blot 127

5.5.5.2 DNA Sequencing 128

5.5.5.3 Transfection and Gene Silencing 128

5.5.6 RNA Quantification and Identification 128

5.5.6.1 Northern Blot 128

5.5.6.2 Promoter Deletion Analysis 129

5.5.6.3 RNase Protection Assay 129

5.5.7 Gene Expression 129

5.5.7.1 Quantitative Real‐Time Polymerase Chain Reaction (qRT‐PCR) 130

5.5.7.2 Microarray 130

5.5.8 Protein‐Related Assays 131

5.5.8.1 Bradford Assay 131

5.5.8.2 Enzyme-Linked Immunosorbent Assay (ELISA) 131

5.5.8.3 Western Blot and 2D Gel Electrophoresis 131

5.5.8.4 Immunolocalization 132

5.5.8.5 Immunoprecipitation Assays 132

5.5.8.6 Chromatin Immunoprecipitation (ChIP) 132

5.5.9 Epigenetics 133

5.5.9.1 Bisulfite Pyrosequencing 133

5.5.9.2 ChIP‐on‐Chip 133

5.5.9.3 Multiplex miRNA Profiling 134

5.6 Common In Vivo Studies in Toxicology 134

5.6.1 Toxicological Endpoints 134

5.6.1.1 Maximum Tolerated Dose (MTD) 134

5.6.1.2 Acute, Subchronic, and Chronic Toxicity 135

5.6.1.3 Reproductive and Developmental Toxicity 135

5.6.1.4 Genotoxicity and Carcinogenicity Studies 136

5.6.2 Routes of Exposure 136

5.6.2.1 Oral, Dermal, and Inhalation 136

5.6.2.2 Exposure via Injection 137

5.6.3 Animal Models 137

5.6.3.1 Rodent Studies 137

5.6.3.2 Other Studies 138

5.7 Basic Advantages and Disadvantages Associated with Sample Types 138

5.8 Human Epidemiology Studies 138

5.8.1 Nonexperimental Studies 139

5.8.2 Experimental Studies 139

5.8.3 Molecular Epidemiology 140

5.9 Examples of Tox‐ and Pharm‐Based Experiments Relevant to Signal Transduction Endpoints 140

5.9.1 Cytotoxicity 141

5.9.1.1 Nicotine‐Derived Nitrosamine Ketone (NNK) 141

5.9.1.2 Doxorubicin (DOX) 142

5.9.1.3 Curcumin 142

5.9.1.4 Combination Effects of Cisplatin and/or Leptomycin B (LMB) 143

5.9.2 DNA Damage 143

5.9.3 Cell Cycle and Apoptosis 145

5.9.4 ROS Induction in A549 Cells After LMB and Epigallocatechin Gallate (EGCG) Treatment 146

5.9.5 Signaling Pathways 146

5.9.5.1 Metabolizing Alterations After Chemical Exposure 146

5.9.5.2 p53 Signaling Pathways 148

5.9.6 Protein Kinase B (Akt/PKB)/Mechanistic Target of Rapamycin (mTOR) Pathway Analysis Using Multiblot 150

5.9.7 Discovery of Unrecognized Pathways/Molecules Using Proteomics 150

5.10 Coupling Experimental Results Within the Larger Literature Framework to Generate Information 152

5.10.1 Cell Proliferation–EGFR Pathway 152

5.10.2 Cell Cycle 154

5.10.3 Signal‐Mediated Cell Death 156

5.10.4 Reactive Oxygen Species (ROS) 161

References 162

6 Techniques for Measuring Cellular Signal Transduction 171
Julie Vrana Miller, Weimin Gao, Meghan Cromie, and Zhongwei Liu

6.1 Introduction 171

6.2 High‐Throughput Versus High‐Content Data 172

6.2.1 Ergodic and Nonergodic Systems 173

6.3 Methods to Measure Signal Transduction Data 173

6.3.1 Microscopy 173

6.3.1.1 Widefield Epifluorescence Microscopy 173

6.3.1.2 Confocal Microscopy 174

6.3.1.3 Immunohistochemistry 175

6.3.1.4 FRET 178

6.3.2 Enzyme‐Linked Immunosorbent Assay (ELISA) 179

6.3.2.1 Competitive ELISA 179

6.3.2.2 Sandwich ELISA 180

6.3.2.3 Direct Cellular ELISA 180

6.3.2.4 Multiplex Suspension Array Assays 181

6.3.2.5 Electrochemiluminescence (ECL) Array 182

6.3.3 Gel Electrophoresis 183

6.3.4 Western Blot 183

6.3.5 Protein Nuclear Magnetic Resonance (NMR) 186

6.4 Techniques to Generate Large Datasets for Signal Transduction Network Analysis 187

6.4.1 ’‐omics Using Mass Spectrometry 187

6.4.1.1 Separation Techniques 188

6.4.1.2 Phosphoprotein Enrichment for Phosphoproteomics: IMAC, MOAC, and SMOAC 189

6.4.1.3 Quantitation with Chemical Tags: iTRAQ and TMT 190

6.4.2 RNA Sequencing (RNA‐Seq) 190

6.5 Bioenergetics 191

6.5.1 Oxygen Consumption 191

6.5.2 Reactive Oxygen Species (ROS) Fluorescent Probes 192

6.5.3 ATP Assays 193

6.5.4 Nicotinamide Adenine Dinucleotide (NADH) Assay 193

6.5.5 Mitochondrial Membrane Potential 194

6.6 Relating Signaling to Cellular Outcome Using Relevant Assays 194

6.6.1 MTT/MTS/WST Assays 194

6.6.2 LDH Assay 195

6.6.3 Resazurin Assay (Alamar Blue) 196

6.6.4 Cell Death: Plasma Membrane Degradation Assay 196

6.7 Summary 196

References 197

7 Computational Methods for Signal Transduction: A Network Approach 201
Giovanni Scardoni, Gabriele Tosadori, John Morris, Sakshi Pratap, Carlo Laudanna, and Alice Han

7.1 Introduction 201

7.2 Network Construction 203

7.2.1 Introduction to Network Construction 203

7.2.2 Network Construction from a Probe 203

7.2.3 Mapping Methodology 204

7.2.4 Small Networks 208

7.2.5 Large Networks 208

7.3 Facing the Network Analysis 209

7.3.1 Centralities Definition and Description 211

7.3.2 Global Parameters 211

7.3.2.1 Diameter (ΔG) 211

7.3.2.2 Average Distance 212

7.3.3 Local Parameters 213

7.3.3.1 Degree 213

7.3.3.2 Eccentricity 214

7.3.3.3 Closeness 215

7.3.3.4 Radiality 215

7.3.3.5 Centroid Value 217

7.3.3.6 Stress 219

7.3.3.7 S.‐P. Betweenness 219

7.3.3.8 Eigenvector 220

7.3.3.9 Bridging Centrality 221

7.3.3.10 Edge Betweenness 221

7.3.3.11 Normalization and Relative Centralities 222

7.3.4 Clusters 222

7.4 Employing Centrality Analysis to Evaluate Stressed Biological Systems 224

7.5 Interference Notion: How to Perform Virtual Knockout Experiments on Biological Networks 226

7.5.1 Integrating Experimental Dataset into a Topological Analysis 227

7.5.2 Integrating Expression or Activation Levels as Nodes Attributes 228

7.5.3 Edge Attributes as Distance in a Computation 228

7.6 Network Analysis Software 229

7.6.1 Cytoscape and Its Apps 229

7.6.1.1 structureViz/RINalyzer 231

7.6.1.2 CentiScaPe 231

7.6.1.3 PesCa 231

7.6.1.4 Interference 231

7.6.1.5 clusterMaker2 232

7.6.1.6 chemViz 233

7.6.2 Other Tools 233

7.6.2.1 Gephi 233

7.6.2.2 D3.js 234

7.6.2.3 VisANT 234

7.7 Conclusions 236

References 236

8 A Toxicological Application of Signal Transduction: Early Cellular Changes Can Be Indicative of Toxicity 239
Julie Vrana Miller, Nicole Prince, Julia A. Mouch, and Jonathan W. Boyd

8.1 Introduction 239

8.2 Classification of Toxic Agent and Exposure Effects: A Toxicological Perspective 240

8.2.1 Dose–Response for Chemical Exposure Toxicity Testing and Risk Assessment 240

8.2.2 Chemical Mixtures 241

8.2.3 Mode of Action Versus Mechanism of Action 242

8.3 Early Cellular Changes Post‐exposure 244

8.3.1 Intracellular Signaling Perturbations Associated with Exposure 245

8.3.2 Bioenergetic Changes Post‐exposure 248

8.3.3 Time Scale of Exposure Effects 249

8.4 Experimentally Testing Early Cellular Changes that May Contribute to Exposure Sensing and Response 250

8.4.1 Paradigm Shift Toward In Vitro Cell Culture 250

8.4.2 Real‐Time In Vitro Assays to Measure Early Cellular Changes 251

8.4.2.1 Using NADH and Oxygen Consumption to Predict ATP Generation 252

8.4.3 Prediction of Posttranslational Phosphorylation Response for Mixtures 253

8.4.3.1 Using Bliss Independence (Response Addition) to Predict Relative Phosphorylation During Critical Signaling Events 253

References 256

Appendix A 262

9 Future Research in Signaling 267
Jonathan W. Boyd, Nicole Prince, and Marc Birringer

9.1 Translational Research and a Spatiotemporal Understanding of Signal Transduction 267

9.2 Integrating Second Messengers into Signal Transduction 270

9.3 Understanding Crosstalk in Signal Transduction 272

9.4 Posttranslational Modifications (PTMs) and Target Identification in Signal Transduction 274

9.5 Epigenetic Endpoints in Signal Transduction 276

9.6 The Integration of Nutrition and Signal Transduction 278

9.6.1 Cellular AMPK Signaling 281

9.6.2 Cellular TOR Signaling 282

9.6.3 Gut Microbiota 282

9.6.4 The Integration of Endocrine Gut Signaling 283

References 284

Index 291