Systems Biology Approaches for Host-Pathogen Interaction Analysis aids biological researchers in expanding their research scope using piled up data generated through recent technological advancements. In addition, it opens avenues for bioinformatics and computer science researchers to utilize their expertise in biologically meaningful ways. Other areas covered include network biology approaches to decipher complex multiple host-pathogen interactions in addition to valuable coverage of artificial intelligence. This book bridges these gaps through a new paradigm of understanding the consequences of interactions in biological networks. Host-pathogen interactions are generally considered as highly specific interactions leading to a variety of consequences. The utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding consequences of these interaction in the biological network.
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Table of Contents
List of contributorsForeword
Preface
Acknowledgments
Chapter 1: Host-pathogen interactions: a general introduction
Rabbani Syed, Fahad M. Aldakheel, Shatha A. Alduraywish, Ayesha Mateen, Hadeel Alnajran and Huda Hussain Al-Numan
1.1 Introduction
1.1.1 Role of pathogen
1.1.2 Host-pathogen relationship and mechanisms
1.1.3 Classification of host-pathogen interactions
1.2 Methods for prediction of host-pathogen interactions
1.2.1 Ortholog-based protein interaction detection
1.2.2 Domain-based detection of protein interaction
1.2.3 Biological reasoning-based prediction of host-pathogen interactions
1.2.4 Domain/motif interaction-based predictions
1.2.5 Machine learning-based predictions of host-pathogen interactions
1.3 Online repositories for host-pathogen interactions
1.3.1 Database of fungal virulence factors
1.3.2 E-fungi
1.3.3 Fungi DB
1.3.4 Ensembl genomes
1.3.5 EuPathDB
1.3.6 HPIDB
1.3.7 PLEXdb
1.3.8 VFDB
1.4 Conclusion
Acknowledgment
References
Chapter 2: Host-pathogen interactions: databases and approaches for data generation
Yasmin Bano and Abhinav Shrivastava
2.1 Introduction
2.2 Databases for host-pathogen interactions
2.3 Bioinformatic methods to discover HPI networking
2.3.1 Biological methods
2.3.2 Computational methods
2.4 Microscopic imaging techniques as stage of the art
2.5 RNA-Seq profiling: tool for determining the HPI network
2.5.1 Bacteria-host interactions
2.5.2 Virus-host interactions
2.5.3 Fungus-host interaction and other pathogenic interactions
2.5.4 Technical approach of RNA-seq and data analysis
2.6 Artificial intelligence-driven analysis for HPIs
2.7 Challenges and opportunities
2.7.1 Challenges
2.7.2 Opportunities
2.8 Conclusion
References
Chapter 3: Generation of host-pathogen interaction data: an overview of recent technological advancements
Fatima Noor, Usman Ali Ashfaq, Hafiz Rameez Khalid and Mohsin Khurshid
3.1 Introduction
3.2 Introduction of bioinformatics in light of NGS
3.3 A short glimpse of the “integration of omics”
3.4 Why is multiomics study preferred over single omics?
3.5 Advancements in the generation of host-pathogen interaction data
3.5.1 Biological big data and omics
3.5.2 Multiomics approaches to unravel the host-pathogen interactions
3.6 Bioinformatics resources and web-based databases for host-pathogen interactions
3.7 Challenges in the generation of host-pathogen interaction data
3.8 Discussion and future prospects
3.9 Conclusion
References
Chapter 4: Molecular omics: a promising systems biology approach to unravel host-pathogen interactions
Samman Munir, Usman Ali Ashfaq, Muhammad Qasim, Tazeem Fatima, Sehar Aslam, Muhammad Hassan Sarfraz, A.K.M. Humayun Kober and Mohsin Khurshid
4.1 Introduction
4.2 Genomics approaches
4.3 Transcriptomics
4.4 Proteomics of host-pathogen interactions
4.4.1 Secretomics of host-pathogen interactions
4.5 Metabolomics approaches
4.5.1 Lipidomics approaches
4.5.2 Multiomics integration for the analysis of host-microbe interactions
4.5.3 Integrated transcriptomicsgenomics approaches
4.5.4 Integrated epigenomics and transcriptomics approaches
4.5.5 Integrated proteomics-genomics, transcriptomics, and metabolomics
4.6 Future perspectives
References
Chapter 5: Computational methods for detection of host-pathogen interactions
Samvedna Singh, Himanshi Gupta and Shakti Sahi
5.1 Introduction
5.2 Computational techniques for prediction of host-pathogen interactions
5.2.1 Protein-protein interaction methods
5.2.2 RNA-mediated interaction-based method
5.2.3 Computational approaches using integrated pipelines
5.3 Case studies
5.3.1 Case study based on host-parasite interaction
5.3.2 Case study based on host-virus interaction
5.3.3 Case study based on host-bacteria interactions
5.3.4 Case study based on host-fungus interactions
5.4 Discussion
References
Further reading
Chapter 6: Biological interaction networks and their application for microbial pathogenesis
Nirupma Singh and Sonika Bhatnagar
6.1 Introduction: biological networks
6.1.1 What is a network?
6.1.2 Types of networks
6.1.3 Biological networks
6.1.4 Properties of biological networks
6.1.5 Host-pathogen interaction networks
6.2 Tools for construction and analysis of biological networks
6.2.1 Cytoscape
6.2.2 R studio
6.2.3 Important plugins and functions
6.3 Functional annotation and biological characterization of host and microbial proteins
6.3.1 DAVID
6.3.2 KOBAS 3.0 server
6.3.3 Biocyc
6.4 Ontology and pathway analysis to understand microbial pathogenesis
6.4.1 KEGG pathways
6.4.2 Wiki pathways
6.4.3 NCBI biosystems
6.5 Case study: host-pathogen interaction networks for CVD pathways in microbial diseases
6.6 Conclusion
References
Chapter 7: Dual transcriptomics data and detection of host-pathogen interactions
Vahap Eldem, Yusuf Ula¸s C¸inar, Selahattin Bari¸s C¸ ay, Selim Can Kuralay, O¨zgecan Kayalar, Go¨kmen Zararsiz, Yakup Bakir and Fatih Dikmen
7.1 Introduction
7.2 Unraveling host-pathogen interactions via genome-wide dual RNA-Seq
7.3 Best practices in dual RNA-Seq: from experimental design to a step-wise guide to performing bioinformatic analysis
7.4 Challenges of dual RNA-Seq experiments and data analysis
7.5 Dual RNA-Seq in the era of third-generation sequencing
7.6 Dissecting the role of noncoding RNA in host-pathogen interactions using dual transcriptomic data
7.7 Future perspectives
Acknowledgments
References
Chapter 8: Functional overrepresentation analysis and their application in microbial pathogenesis
Shilpa Kumari, Neha Verma, Anil Kumar, Sunita Dalal and Kanu Priya
8.1 Introduction
8.2 Analysis via different databases
8.2.1 GO enrichment analysis/GO functional overrepresentation analysis
8.2.2 Functional overrepresentation analysis using DOSE (Disease Ontology Semantic and Enrichment Analysis)
8.2.3 Functional overrepresentation analysis using MeSH
8.2.4 Functional overrepresentation analysis using Reactome pathway (ReactomePA)
8.3 Application of statistical databases in microbial pathogenesis
References
Chapter 9: Advancements in systems biology-based analysis of microbial pathogenesis
Neha Verma, Shilpa Kumari, Anil Kumar and Kanu Priya
9.1 Introduction of microbial pathogenesis
9.1.1 Mechanism of microbial pathogenesis
9.2 Systems biology of microbial pathogenesis
9.2.1 Host-pathogen interaction
9.2.2 Pathogen’s molecular interaction network
9.2.3 Host’s reaction to a microbial infection
9.3 Systems biology techniques to study microbial pathogenesis
9.3.1 OMICS data contributing to microbial pathogenesis (including genomics, transcriptomics, metabolomics, and proteomics)
9.3.2 Computational biology of host-pathogen interaction in microbial pathogenesis
9.3.3 High-throughput techniques
9.4 Conclusion
References
Chapter 10: Host-pathogen interactions with special reference to microbiota analysis and integration of systems biology approaches
Fahad M. Aldakheel, Dalia Mohsen and Barkha Singhal
10.1 Introduction
10.2 Methods for identifying the microbiota: a brief account
10.3 Factors to be considered before identifying microbiota
10.3.1 Geographical factors and diet
10.4 Role of next-generation sequencing technologies for microbial community analysis in understanding host-pathogen interactions
10.5 Microbial community analysis for understanding the antibiotic resistance phenomenon through 16S sequencing
10.6 Gut microbiota analysis in COVID-19 through 16S metagenomic sequencing
10.7 Challenges and advantages of using systems biology in microbiota analysis
10.8 Pathogen-host interactions in bioinformatics
10.9 Systems biology and omics data
10.10 PHI and systems biology
10.11 Conclusion
Acknowledgment
References
Chapter 11: Role of noncoding RNAs in host-pathogen interactions: a systems biology approach
Kartavya Mathur, Ananya Gupta, Varun Rawat, Vineet Sharma and Shailendra Shakya
11.1 Introduction
11.2 Exploring different forms of noncoding RNAs
11.2.1 microRNAs
11.2.2 Long noncoding RNAs
11.2.3 Piwi-like RNAs
11.2.4 Small interfering RNA
11.2.5 Small nuclear RNA
11.2.6 Small nucleolar RNA
11.2.7 Ribonucleic acid enzymes (or ribozymes)
11.2.8 Circular RNAs
11.2.9 Competing endogenous RNA
11.3 Comprehending the role of ncRNAs in pathogen-host interplay
11.3.1 Role of ncRNAs in bacterial pathogenesis
11.3.2 Function of noncoding RNAs in viral infection
11.3.3 Role of ncRNAs in fungal pathogenesis
11.3.4 Role of ncRNAs in protozoan pathogenesis
11.3.5 Role of ncRNAs in helminth pathogenesis
11.4 Why is it important to study the function of ncRNAs?
11.5 RNA systems biology
11.6 Computational resources for identifying ncRNAs in host pathogenesis
11.6.1 ncRNA expression profiling
11.6.2 Functional annotation and interpretation of ncRNA transcriptome
11.6.3 ncRNA web resources
11.6.4 Predicting ncRNA function
11.6.5 Methods for predicting and investigating microRNA targets
11.6.6 Estimating interaction events of ncRNAs
11.6.7 Predicting ncRNA structure
11.6.8 Graph-based approaches for ncRNA structure and function prediction
11.7 How to explore the significance of miRNAs in infection development?
11.7.1 Mathematical modeling for comprehending host-pathogen interaction
11.8 Why network analysis is important for studying ncRNAs?
11.8.1 Network analysis to study the regulation of host-pathogen interaction
11.8.2 Predicting ncRNA-disease association and tripartite network
11.8.3 Corelational network analysis for ncRNAs
11.8.4 Competing endogenous RNA network analysis
References
Chapter 12: Systems biology in food industry: applications in food production, engineering, and pathogen detection
Ananya Srivastava and Anuradha Mishra
12.1 Introduction
12.2 Networks used in systems biology
12.2.1 Gene regulatory networks
12.2.2 Signal transduction networks
12.2.3 Protein-protein interaction networks
12.2.4 Metabolic networks
12.3 Systems biology benefits for food production
12.3.1 Applied systems biology in nutrition and health
12.3.2 Systems biology in food production and processing
12.3.3 Biofortification and development of nutraceuticals
12.3.4 Systems biology in food safety and quality
12.4 Systems biology in foodborne pathogen detection
12.4.1 Pathogen detection techniques used in food sectors
12.4.2 Limitations
12.5 Future scope
12.6 Conclusion
References
Author index
Subject index
Authors
Mohd. Tashfeen Ashraf Assistant Professor in School of Biotechnology, Gautam Buddha University. Dr. Mohd. Tashfeen Ashraf is currently working as Assistant Professor in the School of Biotechnology at Gautam Buddha University (GBU). After securing his master's degree in Biochemistry, he earned his doctorate in Biotechnology from Aligarh Muslim University during which he characterized the folding intermediates of various proteins. His research area involves the study of folding behavior of proteins, their structure-function relationship and protein-protein interactions under normal and disease conditions. Abdul Arif Khan College of Pharmacy, King Saud University, Saudi Arabia.Abdul Arif Khan is a Microbiologist by training with an interest in the field of host-pathogen interactions, systems biology, and cancer biology. He has received several awards and Fellowships from national and international organizations, including the Royal Society for Public Health and the Federation of European Microbiological Societies. He is a recipient of several grants for his research on artificial Intelligence and host-pathogen interactions. He is credited with several papers and books related to systems biology and host-pathogen interaction analysis.
Fahad M. Aldakheel Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Saudi Arabia.Dr. Fahad Mohammed Aldakheel is an Associate Professor of Clinical Immunology at King Saud University in Riyadh, Saudi Arabia. He obtained his undergraduate degree in Clinical Laboratory Sciences from King Saud University, his master's degree in Laboratory Medicine from RMIT University, Melbourne, Australia, and his PhD from the University of Melbourne, Australia. He was appointed to the Department of Clinical Laboratory Sciences in the College of Applied Medical Sciences at King Saud University in 2017 as an assistant professor. He was promoted to an associate professor in 2022. Dr. Aldakheel has several research and publications in both local and international scientific journals. He has served as a
(Fahad Aldakheel is an Associate Professor of Clinical Immunology at the Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at King Saud University in Riyadh, Saudi Arabia. He obtained his PhD from the University of Melbourne, Australia. He is a board member of the Saudi Society for Clinical Laboratory Sciences. He has several scientific publications in national and international journals, and is also working as a member and a reviewer in several scientific organizations.
