Infectious Disease Informatics (eBook)

Vitali Sintchenko (MBBS, PhD, FRCPA, FACHI) is a clinical microbiologist and informatician with the Sydney Medical School, The University of Sydney, Australia.

Sintchenko_FM_O.pdf 1
Anchor 1 7
Sintchenko_Ch01_O.pdf 11
Chapter 1 11
Informatics for Infectious Disease Research and Control 11
1.1 Introduction 11
1.2 Handling New Data Types 12
1.2.1 Microbial Genome Assembly and Annotation 12
1.2.2 Meta-Omics: Metagenomics and Metaproteomics 15
1.2.3 Global Genome Analysis 17
1.3 Changing the Way Discoveries Are Made 18
1.3.1 Knowledge Discovery from Comparative Genomics 18
1.3.2 Automatic Recognition of Functional Regions 19
1.3.3 Enabling the Dynamic View of Infectious Diseases 20
1.3.4 Cross-Validating the Knowledge Sources 22
1.4 Enabling Knowledge Communities: eScience 23
1.4.1 Novel Infrastructures Support Knowledge Communities 23
1.4.2 Data Aggregation 24
1.5 Translating “Omics” into Clinical Practice 25
1.5.1 Rapid Identification of Pathogens 25
1.5.2 Guiding Antibiotic Prescribing Decisions 26
1.5.3 Linking Genomics to Clinical Outcomes 27
1.5.4 Tracing Pathogens with Epidemic Potential 28
1.6 Conclusions 30
References 31
Sintchenko_Ch02_O.pdf 37
Chapter 2 37
Bioinformatics of Microbial Sequences 37
2.1 Overview of Prokaryotic Microorganisms 37
2.1.1 Nature of the Bacterial Genome 37
2.1.2 Bacterial Evolution and the Universal Tree 38
2.2 Classification of Prokaryotic Microorganisms 41
2.3 Revealing Phylogenies and Population Structures 42
2.3.1 Methods for Revealing the Extent and Frequency of LGT 42
2.3.2 Methods for Depicting Population Structures and Phylogenies 44
2.3.3 Comparisons of Entire Genomes 50
2.4 Impact of Advances in Microbial Evolution on the Practice of Microbiology 50
2.4.1 Bacillus anthracis 51
2.4.2 Staphylococcus aureus 53
2.4.3 Campylobacter jejuni and 55
2.4.4 Streptococcus agalactiae 56
2.5 Concluding Remarks 58
References 58
Sintchenko_Ch03_O.pdf 63
Chapter 3 63
Mining Databases for Microbial Gene Sequences 63
3.1 Introduction 63
3.2 Retrieval of Target Sequences 66
3.2.1 Retrieval by Similarity 66
3.2.2 Retrieval by Keywords 66
3.2.3 The Brute Force Approach: By Keywords 69
3.2.4 The Brute Force Approach: By Similarity 70
3.3 Retrieval of Published Primers 71
3.3.1 A Note of Caution About PubMed Queries 71
3.3.2 Primer Extraction 73
3.4 Assessing Primers 75
3.5 Concluding Remarks 78
References 80
Sintchenko_Ch04_O.pdf 82
Chapter 4 82
Comparative Genomics of Pathogens 82
4.1 Introduction 82
4.2 Tools for Microbial Classification and Identification of Pathogens 83
4.2.1 Sequencing of Selected Genes and Genomes 85
4.2.2 DNA Hybridization-Based Approaches 87
4.2.3 Polymerase Chain Reaction (PCR)-Based Approaches 90
4.2.4 Pyrosequencing-Based Approaches 91
4.3 Metagenomics: Principles and Perspectives 92
4.4 Emerging DNA Sequencing Technologies 93
4.5 Conclusions 96
References 97
Sintchenko_Ch05_O.pdf 101
Chapter 5 101
Systems Microbiology: Gaining Insights in Transcriptional Networks 101
5.1 Systems Microbiology: Introduction 101
5.2 High–Throughput Data Sources 103
5.2.1 Expression Data 103
5.2.2 Regulator-Target Interaction Data 104
5.3 Reconstruction of Transcriptional Networks 105
5.3.1 Reconstructing from “Omics” Data 105
5.3.2 Benchmarking Algorithms 106
5.3.3 Which Method to Choose for Network Reconstruction? 107
Box 5.1 Overview of network inference methods 109
5.3.4 Module Inference: Learning About Co-Expressed Targets 113
5.3.4.1 From Clustering to Biclustering 113
5.3.4.2 Global vs. Query-Driven Biclustering 114
Integrative Biclustering: From Co-Expression Towards Co-Regulation 114
5.3.5 Inference of the Regulatory Program 115
5.3.5.1 Regulatory Program Inference from Microarray Data Only vs. Data-Integration 116
5.3.5.2 Module-Based vs. Direct Network Inference 117
5.3.5.3 Supervised vs. Unsupervised Inference of the Regulatory Program 118
5.3.6 Data Integration 119
5.3.7 Prioritization of Predictions 120
5.4 High-Throughput Data Can Assist in the Search for Novel Drug and Vaccine Targets 121
5.4.1 5.4.1 Revealing the Mechanisms of Action 121
5.4.2 The Search for Novel Targets 121
5.5 Conclusions and Perspectives 124
References 126
Sintchenko_Ch06_O.pdf 131
Chapter 6 131
Host–Pathogen Systems Biology 131
6.1 Introduction 131
6.2 Systems Biology in Drug Discovery 133
6.3 Computational Systems Biology Models, Methods and Tools 136
6.3.1 Scales and Models 136
6.3.2 Methods 136
6.3.3 Static Networks 138
6.3.4 Response Networks 138
6.3.5 Modeling Techniques 139
6.4 Intracellular Models 139
6.4.1 Genomic Foundation of Host-Pathogen Interactions 140
6.4.2 Large-Scale Host Response Models 142
6.4.3 Immune-Receptor Signaling 142
6.5 Intercellular or Cell Host-Pathogen Interaction Models 145
6.6 Large Scale Models of Host–Pathogen Physiology 147
6.7 Conclusion 150
Anchor 48 132
Box 6.1 Immune system overview 132
References 152
Sintchenko_Ch07_O.pdf 156
Chapter 7 156
Text Mining for Discovery of Host–Pathogen Interactions 156
7.1 Introduction 156
7.2 Corpus Construction 157
7.3 Biomedical Corpora 158
7.4 Named Entity Recognition 159
7.5 Syntactic Parsing 160
7.6 Relationship Extraction 160
7.7 Case Study: Pathogen–Host Relationship Extraction 161
7.7.1 Gene and Genotype Recognition 162
7.7.2 Pathogen Recognition 164
7.7.3 Disease and Syndrome Recognition 165
7.7.4 Association Mining 165
7.7.5 Potential Directions for Relationship Extraction 167
7.8 Concluding Remarks 169
References 170
Sintchenko_Ch08_O.pdf 173
Chapter 8 173
A Network Approach to Understanding Pathogen Population Structure 173
8.1 Introduction 173
8.2 Contact Networks and Disease Transmission 175
8.2.1 Sexually Transmitted Diseases and Host Contact Networks 175
8.2.2 Directly Transmitted Diseases and Host Contact Networks 176
8.3 Host Contact Networks and Pathogen Evolution 177
8.3.1 Evolution of Pathogen Traits and Host Contact Networks 177
8.3.2 Pathogen Population Structure 178
8.3.3 Community Structure in Host Networks and Pathogen Population Structure 181
8.4 Antigen Networks 184
8.4.1 Malaria Antigen Networks 184
8.4.2 Conceptual Antigen Networks and Influenza Dynamics 186
8.5 Conclusion 187
References 190
Sintchenko_Ch09_O.pdf 192
Chapter 9 192
Computational Epitope Mapping 192
9.1 Introduction 192
9.2 The Principal Molecular Varieties of Epitope 194
9.3 T-cell and B-cell Epitope Prediction In Silico 198
9.4 Conclusion 204
References 204
Sintchenko_Ch10_O.pdf 208
Chapter 10 208
Pangenomic Reverse Vaccinology 208
10.1 Introduction 208
10.2 Single Genome Analysis 210
10.2.1 The Annotation Procedure 210
10.2.2 Review of the Methods for Protein Localization Prediction 212
10.3 Pangenomic Analysis 214
10.3.1 Methods for Ortholog Identification 214
10.3.2 Allelic Variation in Candidate Antigens 216
10.4 Experimental Validation 217
10.4.1 Experimental Validation Procedure 217
10.4.2 Reverse Vaccinology Case Studies 218
10.5 Bacterial Population Genetics and Vaccine Design 219
10.5.1 Genetic Variability Between Subpopulations 220
10.5.2 Vaccine-Oriented Antigenic Typing 222
10.6 Conclusion 222
References 223
Sintchenko_Ch11_O.pdf 227
Chapter 11 227
Immunoinformatics: The Next Step in Vaccine Design 227
11.1 Introduction 227
11.2 Technological Advances 229
11.2.1 Immunoinformatics for Vaccine Design 229
11.2.2 Improved Delivery Vehicles 231
11.2.2.1 Targeting Dendritic Cells 232
11.2.2.2 Mucosal Delivery 234
11.2.2.3 Improved Adjuvants 235
11.2.2.4 Multi-functional T Cells 236
11.3 Advantages and Disadvantages of T-cell Directed Vaccines 236
11.4 Examples of T-cell Epitope-Driven Vaccines 238
11.4.1 TulyVax 238
11.4.2 HelicoVax 239
11.4.3 VennVax 240
11.5 Concluding Remarks 242
References 243
Sintchenko_Ch12_O.pdf 249
Chapter 12 249
Understanding the Shared Bacterial Genome 249
12.1 Introduction 249
12.2 Ecological Niche and Adaptive Capacity 250
12.3 The Shared Genome 253
12.3.1 Gene Capture and Transfer 254
12.3.2 Associations Between R Genes and ME 254
12.3.3 b-Lactamases Conferring Resistance to Cephalosporins 255
12.3.4 Genetic Disequilibrium Within the Mobile Gene Pool the Multi-(Antibiotic) Resistance Region
12.3.5 The Arrival and Spread of New Members of the Gene Pool 257
12.3.6 Comparative Analysis of Multiresistance Regions 258
12.3.7 Conjugative Plasmids: The Need for a New Metagenomics Strategy 260
12.4 Concluding Remarks 262
References 263
Sintchenko_Ch13_O.pdf 266
Chapter 13 266
Computational Grammars for Interrogation of Genomes 266
13.1 Introduction 266
Box 13.1 List of common mobile genetic elements (MGEs) associated with antibiotic resistance. For a more detailed introduction 267
13.2 Automatic Annotation of Bacterial DNA 267
13.3 Computational Grammars 268
13.4 Annotating Biological Structure Using Grammar Models 270
13.4.1 DNA Tokenization 271
13.4.2 Grammar Class and Parsing Algorithm 272
13.4.3 Grammar Derivation 273
13.4.4 Validation of Grammatical Models 274
13.5 Case Study: A Grammar Model for Cassette Array Modeling and Interrogation 274
13.5.1 DNA Tokenization 275
13.5.2 Cassette Array Grammar 275
13.6 Interrogation of Annotated Structures 275
13.6.1 Indexing Hierarchical Genetic Structures 276
13.6.2 A Query Language for Structure Annotations 276
13.6.3 Structure Visualization 277
13.7 Conclusion 278
References 279
Sintchenko_Ch14_O.pdf 282
Chapter 14 282
In silico Discovery of Chemotherapeutic Agents 282
14.1 Introduction 282
14.2 In Silico Identification and Selection of Chemotherapeutic Target Candidates 284
14.2.1 Target Discovery Overlapping with In Silico Drug Discovery 284
14.2.2 Filters Combined with Boolean Logic 285
14.3 Case of Malaria In Silico Target Discovery 286
14.3.1 Targets Are Somewhere in Genomic and Postgenomic Databases 286
14.3.2 Translating Working Hypotheses into Boolean Searches 287
14.3.3 In Silico Target Discovery Tools 289
14.3.4 Toward Druggable Plasmodium Genome 290
14.4 Strategies to Identify and Select Drug Candidates 292
14.4.1 In Silico and In Vitro Drug Discovery 292
14.4.2 Structure-Based Drug Discovery 292
14.4.3 Target Similarity Searching, Substructure Searching,and QSAR 300
14.5 Grid Infrastructures for In Silico Drug Discovery 300
14.6 Conclusions 302
References 302
Sintchenko_Ch15_O.pdf 308
Chapter 15 308
Informatics for Healthcare Epidemiology 308
15.1 Introduction 308
15.2 Performance Measurement and Healthcare Associated Infections 308
15.3 Electronic Health Records 309
15.4 Building Databases for Healthcare Infection Control 311
15.4.1 Standards in Healthcare Informatics 311
15.4.2 Data Auditing and Validation 313
15.5 Information Systems for Healthcare Epidemiology 315
15.5.1 Use of Hit for Measurement 315
15.5.2 Monitoring Infection Control Interventions 317
15.5.3 Decision Support 317
15.6 Reporting Tools 319
15.7 Concluding Remarks 321
References 321
Sintchenko_Ch16_O.pdf 325
Chapter 16 325
Automated, High-throughput Surveillance Systems for Public Health 325
16.1 Introduction 325
16.2 Evaluation of Surveillance Systems 327
16.3 Surveillance Goals 327
16.4 Notifiable Disease Surveillance 328
16.4.1 Deficiencies in Existing Systems 328
16.4.2 Challenges in Automated Disease Detection 329
16.5 Syndromic Surveillance 330
16.5.1 Syndromes in Place of Specific Diseases 330
16.5.2 Choice of Syndromes and ICD Code Groupings 331
16.5.3 Early Detection and Alerting 332
16.5.4 Statistical Challenges 332
16.6 Adverse Event Surveillance 334
16.6.1 Vaccine Adverse Event Surveillance 334
16.6.2 Medication Adverse Event Surveillance 334
16.7 Non-Specific Biosurveillance 335
16.7.1 Non-Health Related Data Sources 336
16.7.2 The Challenge of Opportunity Cost 336
16.8 Finding and Harnessing Data 337
16.9 High Throughput Distributed Surveillance 338
16.10 Technical Aspects of Secure and Controlled Data Sharing 339
16.10.1 The Globus Toolkit 339
16.10.2 Internet Security 340
16.11 Examples of Public Health Surveillance Systems 341
16.11.1 The National Bioterrorism Syndromic Surveillance Program 341
16.11.2 The Electronic Medical Record Support for Public Health Project 341
16.11.3 The ESP Vaccine Adverse Event Reporting System 343
16.11.4 The Distributed Research Network 343
16.12 Concluding Remarks 344
References 346
Sintchenko_Ch17_O.pdf 347
Chapter 17 347
Microbial Genotyping Systems for Infection Control 347
17.1 Introduction 347
17.2 Hospital Infection Control Surveillance 348
17.3 Targeted Genotyping to Confirm Nosocomial Outbreaks 349
17.4 Universal Genotyping in Hospital Infection Control 351
17.5 Analysis of Genotyping Results 354
17.6 Choosing Typing Method for Genotyping Systems 355
Box 17.1 Typing methods 355
17.7 Integrating Genotyping with Surveillance Systems 356
17.8 Conclusion 358
References 358
Sintchenko_Ch18_O.pdf 361
Chapter 18 361
Temporal and Spatial Clustering of Bacterial Genotypes 361
18.1 Introduction 361
18.2 Detection of Spatio-Temporal Clusters 361
18.2.1 Temporal Surveillance Methods 362
18.2.2 Spatio-Temporal Surveillance Methods 364
18.3 New Surveillance Data Types 365
18.4 Infectious Disease Surveillance Using Genotype Clustering 366
18.4.1 Outbreak Definitions 366
18.4.2 Clustering Cases of Foodborne Disease 367
18.5 Concluding Remarks 370
References 372
Sintchenko_Ch19_O.pdf 374
Chapter 19 374
Infectious Disease Ontology 374
19.1 Vocabulary Resources for Biomedicine 374
19.2 Types of Vocabulary Resources 376
19.3 Features of Ontologies Needed to Support Informatics 378
19.4 Uses of Ontologies in Informatics-Driven Research and Care 381
19.5 Vocabulary Resources Relevant to the Field of Infectious Diseases 386
19.5.1 Medical Subject Headings Controlled Vocabulary 386
19.5.2 International Classification of Diseases 387
19.5.3 The Systematized Nomenclature of Medicine – Clinical Terms 388
19.5.4 The Disease Ontology 389
19.5.5 General Conclusions Concerning Clinical Vocabularies 389
19.5.6 The Gene Ontology and OBO Foundry Ontologies 390
19.5.7 Inadequacy of Current Resources 391
19.6 The Infectious Disease Ontology Consortium 391
19.7 Conclusions 393
References 393
Sintchenko_Ch20_O.pdf 397
Chapter 20 397
Populations, Patients, Germs and Genes: Ethics Of Genomics and Informatics in Communicable Disease Control 397
20.1 Introduction 397
20.2 Infectious Diseases Ethics 398
20.3 Challenges in Infectious Diseases Genomics Research 400
20.3.1 Genetics and Disease Susceptibility 400
20.3.2 The Malaria Genomic Epidemiology Network 401
20.3.3 The Human Microbiome Project 402
20.4 Application of Pathogenomics and Informatics Research to Communicable Disease Diagnostics and Prevention 404
20.4.1 Diagnostics and Antibiotic Resistance: Ethical Implications 404
20.4.2 Strain Typing for Pathogen Tracking 408
20.5 Information Science and Technology for Patient Management and Communicable Disease Control 409
20.5.1 Health Information Systems 409
20.5.2 Practical Application 410
20.6 Ethical Implication of Improvements in Biosurveillance 412
20.6.1 Electronic Patient Records 412
20.6.2 Communicable Disease Notification and Surveillance 413
20.6.3 The Use of New Laboratory Data 414
20.6.4 Surveillance Ethics: A New Paradigm 416
References 416
Sintchenko_BM_O.pdf 419
Sintchenko_Index_O.pdf 425