Omics Approaches and Technologies in COVID-19

By Debmalya Barh

The COVID-19 pandemic has affected the entire world in an unprecedented way since 2019. However, novel and innovative applications of various omics, computational, and smart technologies have helped manage the pandemic of the 21st century in a very effective manner. Omics approaches and technologies in COVID-19 presents up-to-date knowledge on omics, genetic engineering, mathematical and computational approaches, and advanced technologies in the diagnosis, prevention, monitoring, and management of COVID-19.

This book contains 26 chapters written by academic and industry experts from more than 15 countries. Split into three sections (Omics; Artificial Intelligence and Bioinformatics; and Smart and Emerging Technologies), it brings an overview of novel technologies under omics such as, genomic, metagenomic, pangenomic, metabolomics and proteomics in COVID-19. In addition, it discusses hostpathogen interactions and interactomics, management options, application of genetic engineering, mathematical modeling andsimulations, systems biology, and bioinformatics approaches in COVID-19 drug discovery and vaccine development.

This is a valuable resource for students, biotechnologists, bioinformaticians, virologists, clinicians, and pharmaceutical, biomedical, and healthcare industry people who want to understand the promising omics and other technologies used in combating COVID-19 from various aspects.

• Provides novel technologies for rapid diagnostics, drug discovery, vaccine development, monitoring, prediction of future waves, etc.
• Describes various omics applications including genomics, metagenomics, epigenomics, nutrigenomics, transcriptomics,miRNAomics, proteomics, metabolomics, phenomics, multiomics, etc., in COVID-19
• Presents applications of genetic engineering, CRISPR, artificial intelligence, mathematical and in silico modeling, systems biology,and other computational approaches in COVID-19
• Discusses emerging, digital, and smart technologies for the monitoring and management of COVID-19

• Language: English
• Publisher: Academic Press
• Release date: Dec 1, 2022
• ISBN: 9780323986212

Book preview

9780323986212_FC

Omics Approaches and Technologies in COVID-19

First Edition

Debmalya Barh

Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal, India

Vasco Azevedo

Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

Table of Contents

Cover

Title page

Copyright

Dedication

Contributors

About the editors

Preface

Section A: Omics

Chapter 1: Omics approaches in COVID-19: An overview

Abstract

1: Introduction

2: Genomics, meta-genomics, and pan-genomics approaches

3: Genotype-phenotype correlations in COVID-19

4: Proteomics in COVID-19

5: Host-pathogen protein—Protein interactions and interactomics

6: Currently available COVID-19 management options

7: Transcriptomic approaches in COVID-19: From infection to vaccines

8: miRNAomics in COVID-19

9: Epigenetic implementations in COVID-19

10: Nutrigenomics and nutrition aspects in COVID-19

11: COVID-19 and phenomics

12: Metabolomics in COVID-19

13: Applications of genetic engineering in COVID-19

14: CRISPR-based assays for rapid detection of SARS-CoV2

15: Approaches to understand the emergence and dynamics of COVID-19 and future pandemics

16: Artificial intelligence (AI) in COVID-19

17: Applications of mathematical modeling and simulation in COVID-19

18: In silico disease modeling for COVID-19

19: System biology applications in COVID-19

20: Computational approaches in COVID-19 drug discovery

21: Computational approaches in COVID-19 vaccine development

22: Applications of multiomics data in COVID-19

23: Publicly available resources in COVID-19 research and their applications

24: Emerging technologies for COVID-19 diagnosis, prevention, and management

25: Applications of digital and smart technologies to control SARS-CoV2 transmission, rapid diagnosis, and monitoring

26: Technologies for prediction of a patient’s health condition and outcomes from COVID-19

27: Conclusion and overall implications

References

Chapter 2: Genomics, metagenomics, and pan-genomics approaches in COVID-19

Abstract

1: Introduction

2: Metagenomic analysis

3: Covid-19 pangenome dynamics

4: Application and advancement of pan genome-derived data in therapeutics

5: Implementations of SARS-CoV-2 genomics, metagenomics, and pan-genomics approaches

6: Conclusion and future perspectives

References

Chapter 3: Genotype and phenotype correlations in COVID-19

Abstract

1: Introduction

2: Structure and lifecycle of coronavirus

3: COVID-19 susceptibility genes identified by GWASs and implications of these genes in developing COVID-19 disease subphenotypes

4: The cellular pathway of these SNPs/genes leading to COVID-19 subphenotypes

5: Genetic variations associated with susceptibility and severity to COVID-19

6: Epigenetic mechanisms of SARS-CoV-2 infection and associated comorbidities

7: Genetic variations and impact on diagnosis and treatment

8: Implementations of genotype-phenotype correlations in COVID-19

9: Other implementations

References

Chapter 4: Proteomic understanding of SARS-CoV-2 infection and COVID-19: Biological, diagnostic, and therapeutic perspectives

Abstract

1: Introduction

2: Proteome of the SARS-CoV-2 virus

3: Host-pathogen protein-protein interactions in COVID-19

4: Proteomics in COVID-19 patients

5: Posttranslational modifications in COVID-19

6: Proteomics tools and applications for COVID-19

7: Implementations of SARS-CoV-2 and COVID-19 proteomics

8: Critical analyses of the present achievements and future perspectives

References

Chapter 5: Metabolites and metabolomics in COVID-19

Abstract

1: Introduction

2: Metabolites and metabolomics in viral infection

3: Metabolomics in SARS-CoV-2

4: Implementation of metabolites/metabolomics in COVID-19

5: Conclusions and future perspectives

References

Chapter 6: Host-pathogen protein-protein interactions and interactomics in COVID-19

Abstract

1: Introduction

2: Comparative interactomics: How the SARS-CoV-2 PPI differs from SARS-CoV-1 and other viruses

3: PPI-based pathway interactions in COVID-19

4: Interactome datasets and tools available to the community on COVID-19

5: Implementations of SARS-CoV-2 and human PPI/translational interactomics

6: The search for druggable targets through ACE2 protein-protein interaction networks (PPINs)

7: Bioinformatics-based HPI and their implementations

8: Comparative coronavirus interactomics and host targets

9: Drug repurposing

10: Understanding the mechanism of multiorgan injuries and their sequels

11: Critical analyses of the present achievements

12: Conclusion and future perspectives

References

Chapter 7: Currently available COVID-19 management options

Abstract

1: Introduction

2: General treatment strategies

3: Specific treatments

4: Antiviral therapies

5: Anti-SARS-CoV-2 neutralizing antibody products

6: Immunomodulatory agents

7: Ventilation management and oxygenation in COVID-19

8: Management of critical cases

9: Supplementation

10: Vitamin D

11: Vitamin C

12: Zinc

13: Magnesium

14: Vitamin B12

15: Alpha-lipoic acid

16: Management of postinfection complications

17: Conclusions and future perspectives

References

Chapter 8: Transcriptomic approaches in COVID-19: From infection to vaccines

Abstract

1: Introduction

2: The structural basis of SARS-CoV-2 transcriptome

3: Transcriptional host responses to SARS-CoV-2 infection

4: Implementations of transcriptomics in COVID-19

5: Single-cell transcriptomics in COVID-19

6: Conclusions and perspectives

References

Chapter 9: miRNAomics in COVID-19

Abstract

1: Introduction

2: miRNA expression profile in COVID-19

3: Interactions of SARS-CoV-2 miRNA/small RNAs and host miRNA

4: SARS-CoV-2 encoded miRNAs/small RNAs in SARS-CoV-2 infection and COVID-19 pathology

5: Host miRNA/small noncoding RNAs and COVID-19 pathology/severity/host response

6: miRNA perspective of comorbid conditions and long-haul COVID

7: Implementations of miRNAs in

8: List of miRNA therapeutics in clinical trials

9: Conclusion and future perspectives

References

Chapter 10: Epigenetic features, methods, and implementations associated with COVID-19

Abstract

1: Introduction

2: Epigenetic landscape alteration and epigenetic mechanisms in respiratory viral infections

3: Cutting-edge epigenetics and epigenomics technology applied in COVID-19

4: Epigenetic landscape and mechanism in SARS-CoV-2 entry and infection

5: Interactions between human epigenetic factors and SARS-CoV-2 proteins

6: Epigenetic biomarkers for COVID-19 risk and severity

7: Epitranscriptome profiling, technology, outcomes, and implementations in COVID-19

8: Epitherapy and epidrug repurposing in COVID-19 clinical trials

9: Implementations of SARS-CoV-2 epigenetics/epigenomics

10: Conclusion and future perspectives

References

Chapter 11: Nutrigenetics and nutrition aspects in COVID-19

Abstract

1: Introduction of nutrigenetics

2: Gene-diet interaction and precision nutrition in COVID-19

3: Various diet components and their cellular and molecular effects on COVID-19

4: Recent updates of nutrigenomic studies in COVID-19

5: Link of COVID-19 to low mortality of Asians compared to Americans and Europeans

6: Conclusions and future perspectives

References

Chapter 12: COVID-19 phenomics

Abstract

1: Introduction to phenomics in the context of viral disease

2: Phenomic approaches to COVID-19

3: Applications of phenomics to COVID-19

4: Concluding remarks

References

Chapter 13: Applications of genetic engineering in COVID-19

Abstract

Conflict of interest

1: Introduction

2: SARS-CoV-2 protein production during the lifecycle emphasizes important posttranslation modifications

3: Application of subunit spike protein production

4: Production of recombinant virus and virus-like particles

5: Genetically engineered models

6: Synthetic biology of SARS-CoV-2

7: Concluding remarks

References

Chapter 14: CRISPR-based assays for rapid detection of SARS-CoV-2

Abstract

1: Introduction

2: SHERLOCK—CRISPR-Cas13a enzyme-based COVID-19 detection assay

3: DETECTR—CRISPR-Cas12a enzyme-based detection assay

4: AIOD-CRISPR—All-in-one dual CRISPR-Cas12a assay

5: CRISPRENHANCE—Enhanced analysis of nucleic acids with crRNA extensions assay

6: CASdetec—CRISPR-Cas12b-mediated DNA detection assay

7: FELUDA—CRISPR-Cas9 enzyme-based detection assay

8: Conclusion

References

Section B: Artificial intelligence and bioinformatics

Chapter 15: Emergence and dynamics of COVID-19 and future pandemics

Abstract

1: Differentiating the disease and infection

2: The weight of preconceived ideas

3: The example of COVID-19

4: The medical approach

5: The environmental approach

6: The laboratory leak narratives

7: The Circulation model and the evolution of viruses in the human population

8: The genetic accident

9: The societal accident

10: An intermediate summary

11: What can be performed to prevent the occurrence of further pandemics?

12: Conclusion: The solution is in the societal management

References

Chapter 16: Artificial intelligence in COVID-19

Abstract

1: Introduction

2: Background

3: AI implementations for COVID-19

4: Conclusion

References

Chapter 17: Applications of mathematical modeling and simulation in COVID-19

Abstract

1: The basic principles and applications of mathematical modeling and simulation in pandemics

2: Data sets and various mathematical models applied to COVID-19

3: Implementations of modeling and simulation

4: Limitations and potential challenges of modeling and simulation in COVID-19

5: Conclusions and future perspectives

References

Chapter 18: In silico disease modeling for COVID-19

Abstract

1: Introduction: COVID-19 models

2: In silico modeling of infectious disease: Types of models and biological implications

3: In silico modeling of SARS-COV-2 dynamics within the host

4: Implementations of in silico modeling of COVID-19

5: Conclusions

References

Chapter 19: Systems biology in COVID-19

Abstract

1: Introduction

2: Strategies, tools, DBs, and other publicly available resources for COVID-19 systems biology and phenomics

3: Implementations of systems biology in COVID-19 in basic and translational research

4: Systems pharmacology approaches in identifying the targetome, therapeutics, and prophylactic agents for COVID-19

5: Other implementations: Big data, phenomics, and radiomics

6: Critical analyses of the present achievements

7: Conclusion and future perspectives

References

Chapter 20: Computational approaches for drug discovery against COVID-19

Abstract

1: Introduction

2: Computational approaches to identify drug targets in SARS-CoV-2 and humans

3: In silico approaches to identify candidate drugs

4: Potential anti-COVID drugs based on computational approaches

5: Identification of prophylaxis and COVID-19 management agents (such as NO etc.) using in silico approaches

6: In silico disease (COVID-19) modeling for testing the efficacy of candidate therapeutics

7: Critical analyses of the present achievements

8: Conclusion and future perspectives

References

Chapter 21: Computational approaches in COVID-19 vaccine development

Abstract

1: Why is the vaccine the best way to fight COVID-19?

2: Steps in the development of conventional vaccines and their difficulties and disadvantages

3: How the computational approaches speed up the vaccine design and development process

4: The concept of computational immunology and the available resources

5: Computational immune proteomics approach in COVID vaccine design and outcomes

6: Reverse vaccinology-based approach in COVID vaccine design and outcomes

7: Identified epitope and multiepitope-based peptide vaccines against COVID-19

8: Computer-aided mRNA vaccine design against COVID-19

9: Computer-aided DNA vaccine design against COVID-19

10: Artificial intelligence and systems biology approaches in COVID-19 vaccine development

11: Computational approaches for rapid design, development, and testing the efficacy of COVID-19 vaccines

12: Computational approaches applied in various vaccine development platforms for COVID-19

13: The currently available vaccines and the computational approaches behind these vaccines

14: Computational approaches to validate the efficacy of the developed vaccines against COVID-19

15: Tools and applications used in tracking the COVID-19 vaccination regime

16: Publicly available resources for COVID-19 vaccine discovery

17: Critical analyses of the present achievements

18: Conclusion and future perspectives

References

Chapter 22: Applications of multiomics data in COVID-19

Abstract

1: Omics and multiomics approaches: Data, data integration, and analysis

2: Multiomics approaches to decode SARS-CoV-2 infection biology

3: Multiomics approaches to understand how nCoV hijacks the host cell machinery

4: Multiomics approaches to understand the comorbid conditions and COVID-19 interactions

5: Multiomics approaches for personalized and targeted therapy and care to COVID-19 patients

6: Early diagnosis and multiomics-based screening of biomarkers for the COVID-susceptible population

7: Developing a preventive strategy and multiomics approach toward the formulation of prophylaxis agents/strategies

8: Developing therapeutics and management for mild and severe COVID-19 cases

9: Multiomics approaches toward multiple organ injury and response because of COVID-19

10: Multiomics-based prediction of long-term health consequences and their treatment options in recovered patients

11: Conclusion

References

Chapter 23: Publicly available resources in COVID-19 research and their applications

Abstract

1: Introduction

2: Literature resources

3: Epidemiology resources

4: SARS-CoV-2-specific genomic databases and tools for analysis

5: SARS-CoV-2-specific proteomic databases and tools for analysis

6: SARS-CoV-2-specific epigenetic databases and tools for analysis

7: SARS-CoV-2-specific transcriptomic databases and tools for analysis

8: In vivo and clinical trial databases for COVID-19 drugs

9: Bioinformatics tools and databases for SARS-CoV-2 drug designing and vaccine developments

10: Toxicogenomic databases and tools for SARS-CoV-2 research

11: Resources and tools for clinicians

12: Mobile apps for tracking the pandemic

13: Conclusion and future perspectives

References

Section C: Smart and emerging technologies

Chapter 24: Emerging technologies for COVID-19, diagnosis, prevention, and management

Abstract

1: Introduction

2: Emerging technologies for diagnosing SARS-CoV-2

3: Emerging technologies for studying the COVID-19 epidemiology

4: Technologies for prevention and control the SARS-CoV-2 transmission

5: Emerging technologies for therapeutic and vaccine development

6: Conclusion and future perspectives

References

Chapter 25: Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring

Abstract

1: Introduction

2: Digital and smart technologies used in the COVID pandemic

3: Implementation of digital and smart technologies in COVID-19

4: Technologies for Integration of various sectors (clinical, environmental, public and private, hospitals, health care centers to the individual public, etc.)

5: Emerging smart and digital tech for research purposes

6: Comparison of smart and digital techs implemented for pandemic management between first and third world economic countries

7: Advantages, disadvantages, and risks of the currently used smart and digital technologies in the COVID pandemic

8: Ethical perspective and steps to ensure data safety

9: Conclusion and future perspectives

References

Chapter 26: Application of big data analytics in the COVID-19 pandemic: Selected problems

Abstract

Acknowledgments

1: Introduction

2: Challenges in COVID-19 and big data

3: Recommendations

4: Conclusion

References

Index

Copyright

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Dedication

We dedicate the book to our mothers, mothers of our children, and mother of the universe, who are our source of powers and inspirations.

Contributors

Numbers in parenthesis indicate the pages on which the authors’ contributions begin.

Megha Bhat Agni     (87), Nitte (Deemed to be University), KS Hegde Medical Academy, Department of Physiology, Mangalore, Karnataka, India

Fares Al-Ejeh

(61), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia

College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar

Amjad Ali     (23,339), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Mariam Al-Muftah     (61), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Taís Aparecida Alvarenga     (405), Federal University of Lavras (UFLA), Department of Automatics, Lavras, Brazil

Lobna Al-Zaidan

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

K.R. Anu     (41), Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India

Farha Anwer     (23), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Maryum Arif     (177), Department of Clinical Nutrition, Rawalpindi Institute of Cardiology, Rawalpindi, Pakistan

Hayeqa Shahwar Awan     (339), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Anindya Bandyopadhyay     (239), Synthetic Biology R&D, Reliance Industries Ltd, Navi Mumbai, India

Katarina Baralić     (367), Department of Toxicology Akademik Danilo Soldatović, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Bruno Henrique Groenner Barbosa     (405), Federal University of Lavras (UFLA), Department of Automatics, Lavras, Brazil

Debmalya Barh

(87), Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal, India

Christopher Barnes     (161), Active Motif, Incorporated, Carlsbad, CA, United States

Amina Basheer     (23), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Takwa Bedhiafi

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Tulika Bhardwaj     (351), School of Computational & Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi, India

Dragica Bozic     (367), Department of Toxicology Akademik Danilo Soldatović, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Elenice Valentim Carmona     (405), School of Nursing, State University of Campinas (Unicamp), Campinas, São Paulo, Brazil

Subhash Chandra     (321), Department of Botany, Kumaun University, S S J Campus, Almora, Uttarakhand, India

Nathaniel Chapin     (191), Active Motif, Incorporated, Carlsbad, CA, United States

Almas Chaudhry     (339), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Sarantis Chlamydas

(161), Active Motif, Incorporated, Carlsbad, CA, United States

Olink Proteomics, Uppsala, Sweden

Harry Coppock     (255), GLAM—Group on Language, Audio, & Music, Imperial College London, London, United Kingdom

Emmanuel Cornillot

(219), Institut de Recherche en Cancérologie de Montpellier (IRCM), Université de Montpellier and Institut du Cancer de Montpellier (ICM), Montpellier

Wespran, Paris, France

Simone Gonçalves da Fonseca     (125), Departamento de Biociências e Tecnologia, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil

Damodara Gowda     (87), Nitte (Deemed to be University), KS Hegde Medical Academy, Department of Physiology, Mangalore, Karnataka, India

Subham Das     (41), Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India

Santanu Dasgupta     (239), Synthetic Biology R&D, Reliance Industries Ltd, Navi Mumbai, India

Said Dermime

(61), College of Health and Life Sciences, Hamad Bin Khalifa University

Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Daniela Fernanda dos Santos Alves     (405), School of Nursing, State University of Campinas (Unicamp), Campinas, São Paulo, Brazil

Danijela Đukić-Ćosić     (367), Department of Toxicology Akademik Danilo Soldatović, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Erika Christiane Marocco Duran     (405), School of Nursing, State University of Campinas (Unicamp), Campinas, São Paulo, Brazil

Queenie Fernandes

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Juan Luis Fernandez-Martinez     (427), Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo, Spain

Ana Cláudia Barbosa Honório Ferreira     (405), Centro Universitário de Lavras (UNILAVRAS), Lavras, Brazil

Danton Diego Ferreira     (405), Federal University of Lavras (UFLA), Department of Automatics, Lavras, Brazil

Roger Frutos     (245), CIRAD, Intertryp, UMR17, Montpellier, France

Michael Garbati     (161), Active Motif, Incorporated, Carlsbad, CA, United States

Luiz Gustavo Gardinassi     (125), Departamento de Biociências e Tecnologia, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil

Konstantinos I. Gourgoulianis     (301), Department of Neurology, Athens Naval Hospital, Athens, Greece

Mubashir Hassan

(427), Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore, Pakistan

Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States

Pramukh Subrahmanya Hegde     (87), Nitte (Deemed to be University), KS Hegde Medical Academy, Department of Physiology, Mangalore, Karnataka, India

Varghese Inchakalody

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Alex Joseph     (41), Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India

Tushar Joshi

(321), Department of Biotechnology, Kumaun University, Bhimtal Campus, Bhimtal

Department of Botany, Kumaun University, S S J Campus, Almora, Uttarakhand, India

Nagesh Kancharla     (239), Synthetic Biology R&D, Reliance Industries Ltd, Navi Mumbai, India

Bineypreet Kaur     (145), Biotechnology Branch, University Institute of Engineering and Technology (UIET), Panjab University, Chandigarh, India

Jaspreet Kaur     (145), Biotechnology Branch, University Institute of Engineering and Technology (UIET), Panjab University, Chandigarh, India

Adithi Kellarai     (87), Nitte (Deemed to be University), KS Hegde Medical Academy, Department of General Medicine, Mangalore, Karnataka, India

Andrzej Kloczkowski

(427), Institute for Genomic Medicine, Nationwide Children’s Hospital

Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, United States

Gustavo Barbosa Libotte     (275), Department of Computational Modeling, Polytechnic Institute, Rio de Janeiro State University, Nova Friburgo, Brazil

Davi Vinícius de Lima     (125), Departamento de Biociências e Tecnologia, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil

Fran Sérgio Lobato     (275), Chemical Engineering Faculty, Federal University of Uberlândia, Uberlândia, Brazil

Abhilash Ludhiadch     (111), Complex Disease Genomics and Precision Medicine Laboratory, Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Saul O. Lugo Reyes     (101), Immune Deficiencies Lab, National Institute of Pediatrics, Mexico City, Mexico

Kenneth Lundstrom     (87), PanTherapeutics, Lutry, Switzerland

Amit K. Maiti     (3), Department of Genetics and Genomics, Mydnavar, Southfield, MI, United States

Shalini Mathpal

(321), Department of Biotechnology, Kumaun University, Bhimtal Campus, Bhimtal

Department of Botany, Kumaun University, S S J Campus, Almora, Uttarakhand, India

Maysaloun Merhi

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Sarra Mestiri

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Mostafa M. Mohamed

(255), Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg

Artificial Intelligence Research & Development Team, SyncPilot GmbH, Augsburg, Germany

Maria Helena Baena de Moraes Lopes     (405), School of Nursing, State University of Campinas (Unicamp), Campinas, São Paulo, Brazil

Dina Moustafa

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Anjana Munshi     (111), Complex Disease Genomics and Precision Medicine Laboratory, Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Anam Naz     (427), Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore, Pakistan

Mina A. Nessiem

(255), Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg

Artificial Intelligence Research & Development Team, SyncPilot GmbH, Augsburg, Germany

Mehmet Özdemir     (389), Division of Medical Virology, Department of Medical Microbiology, Meram Medical Faculty, Necmettin Erbakan University, Konya, Turkey

Gustavo Mendes Platt     (275), Graduate Program in Agroindustrial Systems and Processes, School of Chemistry and Food, Federal University of Rio Grande, Santo Antônio da Patrulha, Brazil

Sari Eka Pratiwi     (219), Department of Biology and Pathobiology, Faculty of Medicine, Universitas Tanjungpura, Pontianak, Indonesia

Abdur Rahman     (177), Atta ur Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Afsheen Raza

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Demóstenes Zegarra Rodríguez     (405), Federal University of Lavras (UFLA), Department of Computer Science, Lavras, Brazil

Helioswilton Sales-Campos     (125), Departamento de Biociências e Tecnologia, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Brazil

Luis Otávio Santos     (405), Federal University of Lavras (UFLA), Department of Automatics, Lavras, Brazil

Shreya Sarkar     (161), New Brunswick Heart Centre (NBHC), Saint John Regional Hospital, Saint John, NB, Canada

Björn W. Schuller

(255), Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany

GLAM—Group on Language, Audio, & Music, Imperial College London, London, United Kingdom

Rwik Sen     (161,191), Active Motif, Incorporated, Carlsbad, CA, United States

Fatima Shahid

(23,339), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Faculty of Science and Technology (FST), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

Priyanka Sharma     (321), Department of Botany, Kumaun University, D S B Campus, Nainital, Uttarakhand, India

Abubakar Siddique     (177), Atta ur Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Ammara Siddique     (427), Atta ur Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan

Pallavi Somvanshi

(351), School of Computational & Integrative Sciences (SCIS)

Special Centre of Systems Medicine (SCSM), Jawaharlal Nehru University, New Delhi, India

Camila Oliveira Silva Souza     (125), Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil

Syeda Duaa Tahir     (177), Atta ur Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Nassiba Taib

(61), Department of Medical Oncology, National Center for Cancer Care and Research

Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Sushma Tamta     (321), Department of Botany, Kumaun University, D S B Campus, Nainital, Uttarakhand, India

Shahab Uddin     (61), Translational Research Institute, Hamad Medical Corporation, Doha, Qatar

Ayşe Rüveyda Uğur     (389), Division of Medical Virology, Department of Medical Microbiology, Konya City Hospital, Konya, Turkey

George D. Vavougios

(301), Department of Neurology, Faculty of Medicine, University of Cyprus, Lefkosia, Cyprus

Department of Neurology, Athens Naval Hospital, Athens, Greece

Maaz Waseem     (23), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Umesh Prasad Yadav     (111), Laboratory for Molecular Medicine, Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Eugenia Ch. Yiannakopoulou     (291), Department of Biomedical Sciences, Faculty of Health Sciences, University of West Attica, Athens, Greece

Ysrafil Ysrafil     (219), Department of Pharmacotherapy, Faculty of Medicine, Universitas Palangka Raya, Palangka Raya, Indonesia

Tahreem Zaheer

(23), Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Department of Biological Physics, Eötvös Loránd University, Budapest, Hungary

Sotirios G. Zarogiannis     (301), Department of Respiratory Medicine, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece

Katarina Živančević

(367), Department of Toxicology Akademik Danilo Soldatović, Faculty of Pharmacy

Institute of Physiology and Biochemistry Ivan Djaja, Centre for Laser Microscopy, Faculty of Biology, University of Belgrade, Belgrade, Serbia

About the editors

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Debmalya Barh holds an MSc in applied genetics, PhD in biotechnology, PhD in bioinformatics, postdoc in bioinformatics, and postgraduate in management. He is blended with both academic and industrial research for decades and is an expert in integrative omics-based biomarker and targeted drug discovery, genetic diagnosis, infectious diseases, and precision health. He is currently Visiting Full Professor (Titular, Grade-E) at the Department of Genetics, Ecology, and Evolution, Institute of Biological Sciences (ICB), Federal University of Minas Gerais (UFMG), Brazil. He is also an honorary scientist at the Institute of Integrative Omics and Applied Biotechnology (IIOAB), India. Dr. Barh has published more than 200 research and review articles, 45+ book chapters, and has edited 25+ books published by Taylor & Francis, Elsevier, and Springer. He is an editorial board member of various journals, including Frontiers, PeerJ, etc., and frequently reviews articles for Nature publications, Elsevier, MDPI, AACR, and BMC journals, to name a few.

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Vasco Azevedo is Full Professor at the Department of Genetics, Ecology, and Evolution, ICB, UFMG, Brazil. He is a CNPq researcher 1A, a member of the Brazilian Academy of Sciences, a Commander of the Order of Scientific Merit of the MCTI, of the Genetics advisory committee and the public policy working group on Biotechnology and Genetic resources of COBRG/CNPq, the coordinator of the Associated International Laboratory Bactinfl from INRAE and UFMG, and the vice president of the Brazilian Association of Bioinformatics and Computational Biology (AB3C). He holds a degree in veterinary medicine from the School of Veterinary Medicine of the Federal University of Bahia (1986), a master’s degree (1989), a doctorate (1993) in genetics of microorganisms from the Institut National Agronomique Paris Grignon, and a doctorate in bioinformatics from UFMG (2017). He did his postdoctoral research at the Department of Microbiology, School of Medicine, University of Pennsylvania, United States (1994). He was Professor at the Institute of Biomedical Sciences of the University of São Paulo (2004). Prof. Azevedo is an expert in genetics and bioinformatics, and his work is mainly focused on the following: genomics, transcriptomics, proteomics, metabolomics, vaccine development, diagnostics, and the development of next-generation probiotics. Prof. Vasco Azevedo has 600+ scientific publications, 14+ books, 60+ chapters, and 12 patents.

Preface

The COVID-19 pandemic has affected the entire world in an unprecedented way since 2019. However, novel and innovative applications of various omics, computational, smart technologies have been able to manage the pandemic of the 21st century in a very effective manner. Omics Approaches and Technologies in COVID-19 presents an up-to-date knowledge on various omics approaches, genetic engineering, mathematical and computational approaches, and advanced technologies in diagnosis, prevention, monitoring, and management of COVID-19.

This book contains 26 chapters written by academic and industry experts from more than 15 countries and is divided into three sections. In Section A (Omics), 14 chapters have been included. It starts with overview of omics approaches in COVID-19 followed by genomics, meta-genomics, and pan-genomics approaches in COVID-19; genotype and phenotype correlations in COVID-19; proteomics in COVID-19; metabolomics in COVID-19; host-pathogen interactions and interactomics in COVID-19; COVID-19 management options; transcriptomic in COVID-19; miRNAomics in COVID-19; epigenetics in COVID-19; nutrigenomic aspects in COVID-19; COVID-19 phenomics; and genetic engineering and CRISPR in COVID-19. Section B (Artificial Intelligence and Bioinformatics) consists of nine chapters that start with the emergence and dynamics of COVID-19 and future pandemics; artificial intelligence in COVID-19; mathematical modeling and simulation in COVID-19; in silico disease modeling for COVID-19; systems biology in COVID-19; bioinformatics approaches in COVID-19 drug discovery and vaccine development; applications of multiomics data in COVID-19; and publicly available resources in COVID-19 research. Three chapters (emerging, digital, and smart technologies for COVID-19 diagnosis, monitoring, prevention, and management; and applications of big data analytics in COVID-19) have been included in Section C (Smart and Emerging Technologies).

This is a valuable resource for students, biotechnologists, bioinformaticians, virologists, clinicians, and pharmaceutical, biomedical, and healthcare industry people who want to understand the promising omics and other technologies used in combatting COVID-19 from various aspects.

Editors

Debmalya Barh

Vasco Azevedo

Section A

Omics

Chapter 1: Omics approaches in COVID-19: An overview

Amit K. Maiti    Department of Genetics and Genomics, Mydnavar, Southfield, MI, United States

Abstract

COVID-19 caused by the virus SARS-CoV2 exhibits its devastating consequences worldwide in terms of health and economic loss. SARS-CoV2 is a novel coronavirus whose mechanism of infection and development of symptoms in patients leading to mortalities are poorly understood. Although primarily a respiratory virus, people infected with SARS-CoV2 show a series of symptoms involving multiple human organs including cardiac abnormalities, neuronal dysfunction, etc. Developing effective drugs to control the pandemic is overwhelmingly important and urgent. Ongoing rigorous scientific research based on molecular biology, genetics, genomics, proteomics, and informatics are in progress to develop suitable therapy. Using the omics-based approach, the correlation of vast amount of COVID-19 patient-related experimental data with patient-specific medical information is necessary prerequisite to manage COVID-19 patients and develop successful therapy especially in rural and urban areas where access to proper healthcare is limited. Conclusive data of these coordinated approaches would immensely help to manage a large number of critically ill patients to improve the treatment outcome and reduce mortalities.

Keywords

COVID-19; Omics; Origin; Genomics; Proteomics; Informatics

1: Introduction

Recently, novel coronavirus (virus, SARS-CoV2; disease, COVID-19) created pandemic with over 6 million mortalities in all over the world until May 2022. It is started in Wuhan, China but now showing its devastating consequences in Europe, USA, India, Brazil, and other parts of the world. Although mortality rates differ in various countries, such as in Wuhan (little more than 2%), in Italy, it is more than 5% of infected people. The principal mode of transmission of SARS-CoV2 is primarily air-droplet-borne with a strong possibilities of airborne [1]. Eventually, it enters to lung alveolar cells through upper respiratory tract. SARS-CoV2 has a protein coat with a spike protein that attaches with host cell and a genome consist of positive ∼  30 kb (29,903 bp) RNA strand [2]. The spike protein attaches with the ACE2 receptor of the human cells and uses TMPRSS2 enzyme to enter into cells. After entering into cells, it uses host machinery to replicate its RNA genome to make thousands of copies [3] and uses a host protein synthesis system to synthesize its coat proteins to pack its RNA genetic material to become a new full-fledged virus. The new virus comes out by exocytosis to infect other cells. Damaged lung cells are unable to carry out their function of supplying oxygen to the blood [4].

SARS-CoV2 related pathogenicity includes mild, moderate, and critical health problems with hospitalizations. COVID-19 phenotype includes severe respiratory distress, immune compromise, heart failure including stroke, blood clot, myocardial infarction, and neuronal dysfunction [5]. A subsequent fraction of SARS-CoV2 affected persons is asymptomatic, and children show more resistant to virus-related illness than adult. COVID-19-related co-morbidities are more prevalent to old age people than younger ones, and various statistical data indicate that male patients are more prone to COVID-19-related critical illness than female. These diversities in virus-related infection and mortalities suggest that apart from viral strains, the host genetic factors may play an important role in COVID-19 infection and mortalities. In addition, the patients that are recovered from COVID-19-related hospitalizations develop a series of symptoms such as fatigue, brain fog, etc., that are persisted for long time (3 months to a year). These conditions are referred as long covid syndrome or post COVID-19 syndrome.

SARS-CoV2 virus is believed to be originated from a closely related bat Coronavirus RaTG13 lineage [6]. SARS-CoV2 uses its entry-point key residues in S1 (spike) protein to attach with human ACE2 receptor [7]. SARS-CoV2 evolution comprises any of these possibilities: it entered human from bat with its poorly developed entry-point residues long before its known appearance with a slower mutation rate; or recently with efficiently developed entry-point residues having more infective power with a higher mutation rate; or through an intermediate host [8,9]. RaTG13 has 96.3% identity with SARS-CoV2 genome implying that it substituted ∼  1106 nucleotides to evolute as present-day virus. Temporal analysis of SARS-CoV2 genome shows that its nucleotide substitution rate is as low as 27 nt/year with an evolutionary rate of 9 × 10−  4/site/year, which is a little less than other retrovirus (10−  4 to 10−  6/site/year). Estimation of TMRCA (Time to the Most Recent Common Ancestor) of SARS-CoV2 from bat RaTG13 lineage appears to be in between 9 and 14 years [10,11]. Genetic codon analysis indicates that SARS-CoV2 evolution from RaTG13 lineage strictly follows neutral evolution with strong purifying selection, whereas its propagation in human disobeys neutral evolution as nonsynonymous mutations surpasses synonymous mutations with the increase of ω (dn/ds) signifying its proceedings toward divergent selection predictably for its infection power to evade multiple organs.

Within a year and half of its appearance, it infected more than 600 million people (until August 2022) worldwide with a death of over 6 million people in official count. This pandemic is a major health hazards in world population with enormous economic and human life loss and should be controlled from multi-faced approach. SARS-CoV2 is a novel virus and its many properties are unknown to the scientists. It is necessary to understand its mechanism of invasion, symptom development, emergence of new strains by mutation, and survival in the host body to control the virus and develop remedy to reduce mortalities (Fig. 1).

Fig. 1

Fig. 1 Omics-based approaches to correlate scientific data to control the COVID-19 pandemic.

2: Genomics, meta-genomics, and pan-genomics approaches

SARS-CoV2 virus-related mortalities vary population specifically and that is higher in African-Americans and Hispanics than other ethnic groups. It is expected that SARS-CoV2 infection and mortalities have susceptible genetic factors that may affect differently in various population [8,9]. In some cases, second infection to the same individual could also be feasible [12]. Furthermore, COVID-19 patients show a spectrum of phenotypes that vary from individuals to individuals. However, the mechanism of these disparities of SARS-CoV2 virus-mediated pathogenicity can be explored from the genomic sequences of COVID-19 patients by identifying genes/proteins that might play a role in developing symptoms and mortalities.

2.1: Genomic approaches to covid-19

Several genomic consortiums/databases are established to store and explore COVID-19 patient samples and genomic data in repository. These genomic resources are available to serve the world communities for genomic analysis and developing genomic therapy of COVID-19. The SARS-CoV2 genomic sequences are largely stored and freely distributed by GISAID (Global Sharing Avian Influenza Data; www.gisaid.org). Other databases are (www.covid19dataportal.org; ENA browser, European Nucleotide Archive) of European institute and NCBI, NIH repository (https://www.ncbi.nlm.nih.gov/nuccore/). Other COVID-19 resources are available in European genome phenome archive (https://ega-archive.org/access/data-access) and Chinese Genomic Institute (https://db.cngb.org/cnsa/).

Several Biobanks (UK Biobank, Estonian Biobank, Columbia University COVID-19 Biobank, Biobanque Quebeck COVID-19 (Cabada), Netherland twin registrar, Regeneron, Ancestry, 23 and ME, and NIH (COVID-19 sequencing initiative) are sequencing COVID-19 patients’ genome in large scale. These data are freely available to the researchers all over the world with data access permissions.

2.2: Metagenomic approaches

Given wide spectrum of COVID-19 phenotypes, metagenomic approaches are necessary to manage the COVID-19 patients to control the pandemic. All genomic information of SARS-CoV2 virus, such as viral DNA sequences, and COVID-19 patients’ resources, such as whole genome sequence (WGS), whole exome sequence (WES), tissue-specific RNA sequence (transcriptome), single-cell RNA sequence (ScRNA data), microbial sequences from the gut of COVID-19 patients, are available in repositories and are being analyzed to understand the mechanism of infection and mortalities in the population.

2.3: Pan-genomics

Biobanks have preapproved IRB and patients’ consent to use the data. Thus all available genetic data of a COVID-19 patient, such as WGS, WES, transcriptomic, or microbiomes can be analyzed together with medical records of the patients with subphenotypic symptoms and their treatment outcome. These information help to understand to develop personalized therapy for future infected patients.

3: Genotype-phenotype correlations in COVID-19

3.1: COVID-19 susceptibility genes identified by Genome-Wide Association Studies

Identifying genetic risk groups for SARS-CoV2 infection and disease severity are overwhelmingly urgent. Several genome-wide consortiums are established to identify the factors affecting the large phenotypic spectrum of COVID-19 patients. An initial GWAS study [13] by European consortium identified O blood group gene and subsequently other groups identified TMPRSS2, UME, and HLA-DRB gene as susceptible factor for COVID-19 infection [14,15].

Using whole-genome sequencing, COVID-19 HGI (COVID-19 Human Genome Initiative) consortium identified several susceptibility SNPs/genes (Table 1) [16]. And results from other studies (UK COVID-19 consortium, regeneration) also identified several SNPs/genes that are associated with COVID-19 [17]. Other groups (Ancestry, UK biobank, 23 & Me) also reported several genes/SNPs that are associated with COVID-19 subphenotypes [18,19]. A systemic review indicated that three genes (ACE2, TMPRSS2, and IFITM) are mostly refereed to be associated with COVID-19 phenotypes [20]. GWAS also identified variants in MX1 gene associated with COVID-19 as that has special implications [20]. The spike protein variant D614G that is present in all recently emerged virulent SARS-CoV2 strains (B.1.1.7; B.1.6.1.7; P1; P2, B.617, and BA series) shows more virulency with an SNP (rs35074065, Del C) at the intergenic region between TMPRSS2 and MX1 gene [21,22]. The future analysis will include causal SNP identification in these genes for functional significance and build genetic networks to identify the signaling pathway involved to develop COVID-19 subphenotypes.

Table 1

Cases (n)/controls(n) = 6179/1483780 (ref: www.covid19hg.org) [16].

3.2: Protein coding SNPs associated with COVID-19

For protein coding SNPs, the effect of amino acid changes on protein structure and subsequent interacting ability of the specified domain could be established using PYMOL (Releases schrodinger/pymol-open-source GitHub) and cn3D at NCBI site (www.ncbi.nlm.nih.gov). In vivo confirmation will be the next step for these SNPs for their association with COVID-19.

3.3: Regulatory SNPs associated with COVID-19

For regulatory SNPs, their position in respect to the gene can be analyzed accordingly. The functional characterization of these SNPs can be obtained in gTEX for tissue specific gene expression. Subsequently, eQTL analysis for linking other SNPs in the genome and Hi-C for interacting genomic region [23] can be deduced. Regulatory SNPs could also be tested for enhancer and other regulatory function in vivo and in vitro.

3.4: Implications of these genes in developing COVID-19 disease subphenotypes

Most importantly, COVID-19 patients show a wide spectrum of phenotypes, such as respiratory distress, immune activation, cardiovascular complications including stroke and myocardial infarction, blood clotting, etc. [5,24–28]. COVID-19 HGI divided patients with various subphenotypes, and the association results are analyzed accordingly that provided insights into the development of subphenotypes. The initial subdivision includes (1) acute respiratory distress syndrome, (2) clonal hematopoiesis [17], (3) lung fibrosis, and (4) vascular complications. Successful complications of these projects would identify the causative genes/SNPs that contribute to develop some of COVID-19 subphenotypes [16,29].

3.5: The path to develop disease prediction, detection, and therapeutic target

Identification of COVID-19 susceptibility SNPs would be immensely helpful for prediction and to develop therapeutic target. Susceptible SNPs could be used to develop SNPs arrays in CHIPs and could be used to test patients in the initial phase of infection to predict the subphenotypes or COVID-19-related complications. These genetic information in a CHIP in a hospital setting should be immensely helpful to reduce COVID-19-related mortalities. Rigorous experimentation including the animal model for these mutations could be developed to confirm their involvement to develop symptoms of COVID-19, and small molecules could be tested to rescue from these mutations to develop therapy.

4: Proteomics in COVID-19

4.1: Identification of protein level changes in various tissues

Numerous proteomic studies are reported to identify proteins involved in developing COVID-19 subphenotypes [30]. Using autopsy of COVID-19 patients, Zhao et al. [31] identified CTSL as the most expressing protein than ACE2 in lungs. Apart from ACE2, CTSL is one of the major receptors facilitating SARS-CoV2 infection [31].

Using time-resolved proteomic map, Demichev et al. [32] detected dysregulation of protein translation, glucose metabolism, and fatty acid metabolism in multiple organs. They also identified age-specific response, increased inflammation, and lipoprotein dysregulation in older patients. Their study demonstrates that accurate prognosis of COVID-19 outcome from proteomic signatures could be recorded weeks earlier developing severe illness [32].

4.2: Identification therapeutic targets

Proteomic studies are also useful to identify therapeutics. Using quantitative plasma proteomic studies, Flora et al. [33] showed that marked changes in plasma proteins occur in COVID-19 patients [33]. These are related to complement activation, blood coagulation, antimicrobial humoral response, acute inflammatory response, and endopeptidase inhibitor activity. Higher levels of IREB2, GELS, POLR3D, PON,1 and ULBP6 upon admission to hospital were found in patients with mild symptoms, while higher levels of Gal-10 were found in critical and severe patients. They suggested that these proteins could be used as a therapeutic target for covid-19. Similarly, mass spectrometric studies identified several nucleocapsid and tryptic proteins of SARS-CoV2 for diagnosis and therapy [34].

5: Host-pathogen protein—Protein interactions and interactomics

Identifying proteins are only a part of study unless interactions of these proteins are identified that contribute cellular pathway leading to pathogenic complications.

5.1: Interaction of SARS-CoV2 proteins with host proteins

SARS-CoV2 genome codes for 29 proteins that interact with the host proteins to control its own replication and packaging. Using the mass spectrometric approach, Gordon et al. [35] first prepared an interactome map of host cellular proteins after infecting SARS-CoV2 into various human cells in vitro. Crunfli et al. [36] prepared a detail interactome map of brain astrocytes proteins of COVID-19 patients and explained neural inviability due to illness [36]. Using imaging and interactive studies, Andrews et al. [37] identified DPP4-mediated injury in human cortical astrocytes [37].

5.2: Implication of protein interactions in developing therapy

Interacting proteins of SARS-CoV2 with human proteins are also important to trace the downstream pathway that contribute to disease symptoms and severities. Nevertheless, these pathways will help to identify suitable targets to target downstream proteins to reduce disease severities [38].

6: Currently available COVID-19 management options

WHO (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/patient-management)-recommended COVID-19 management systems include a series of advice from testing to discharge of patients. These recommendations meet the needs of the frontline clinicians and fulfill the treatment of all patients including mild, moderate, and severely affected patients. These include (1) rapid testing with RT-QPCR that would confirm the presence of SARS-CoV2 virus in the body. (2) Chest imaging with radiography, computer tomography, and ultrasound; these tests will confirm the different levels of disease severity in asymptomatic to severely affected patients. (3) Prehospital Emergency Medical Services, health care provider should use specific practice guidelines that would include their safety and patient safety to reduce the transmission. (4) Treatment options, health care professionals should use recommended and FDA-approved drugs to treat COVID-19 patients. They must avoid such drugs that are discontinued use recommendations by WHO or other authentic organizations, such as FDA. (5) Home care for patients with suspected infections, this includes distancing from patients but keep contact and monitor through digital communication. (6) Operational considerations for case management of COVID-19 in health facility and community, although health systems are challenged, it should provide all lifesaving equipment without compromising public health objectives and safety of health workers. (7) Maintaining a safe and adequate blood supply during the pandemic, it provides the interim guidance to the management of adequate blood supply quickly during the pandemic. (8) Global COVID-19 Clinical Data Platform for clinical characterization and management, WHO developed standard case report that should be followed and all prognostic, diagnostic, and clinical data should be disseminated as fast as possible anonymously to the global platform. (9) New data platform for anonymized COVID-19 clinical data, data of new clinical treatment outcome should be anonymously stored and digitally transmitted for large-scale clinical trials.

NIH developed a guideline (https://www.covid19treatmentguidelines.nih.gov/management/clinical-management/nonhospitalized-adults--therapeutic-management/) for treating nonhospitalized COVID-19 patients. Preferred therapies should be ritonovir-boosted nirmatrelvir (paxolovid). However, paxolovid has drug-drug interactions, thus should be taken with physicians’ recommendations. The panel also recommends the use of dexamethasone or other corticosteroids if the patient still needs oxygen supplement after hospital discharge but avoid taking it if patient does not need oxygen supplement. If a patient requires hospitalization after starting treatment, the full treatment course can be completed at the health care provider’s discretion.

In May 2022, NIH added additional recommendations over previous recommendations for Antithrombotic Therapy in Patients With COVID-19 and critical care for children (https://www.covid19treatmentguidelines.nih.gov/about-the-guidelines/whats-new/). For COVID-19 patients with antithrombotic, patients should be treated with a 35-day course of rivaroxaban. Critical care of children include (1) introduction to critical care management of children with COVID-19, (2) extracorporeal membrane oxygenation for children, (3) hemodynamic considerations for children, and (4) oxygenation and ventilation for children.

7: Transcriptomic approaches in COVID-19: From infection to vaccines

Transcriptomics including RNA sequencing provide valuable information about the SARS-CoV2 infection to develop COVID-19 symptoms. Several tissue-specific RNA sequencings identified numerous genes that modulate various signaling pathways in COVID-19 patients.

7.1: Identification of target using RNA sequencing from patients’ blood, nasopharyngeal swab, and other tissues

An initial RNA sequencing study with COVID-19 patients’ tissue revealed that SARS-CoV2 infects specific cells where ACE2 expression is maximum. These are goblet cells in nasal passage, pneumocytes in lung, and endocytes in gut, which implied that apart from lung cell damages, SARS-CoV2 also produces lesions in gut and nervous systems [39]. However, ACE2 expresses moderately in most tissues of the human body, thus SARS-CoV2 can infect various tissues. RNA sequencing study also first identified the involvement of interferon in SARS-CoV2 infection and generation of cytokine storm. Impact of comorbidities has been evaluated using transcriptomics [40]. Notably, ICU patients with COVID-19 have modulated regulations of HLA-E, HLA-C, NRP1, and NRP2 that had been projected to predict the mortality of COVID-19 patients [41]. A comprehensive review of COVID-19 gene expression atlas identified 32 genes that are differentially expressed in lung tissue in COVID-19 patients [42]. Another comprehensive transcriptomic analysis identified several myeloid subset genes in COVID-19 patients that help to design therapy [43].

7.2: Single cell sequencing to identify genetic changes in COVID-19 patients

Using nasal swabs and blood cells of COVID-19 patients, several scRNA sequencing studies are performed to understand the heterogeneity of host cell function due to viral attack. Using single-cell RNA sequencing of blood cells, severity and critical illness-specific transcriptomic signatures were identified [44].

Differential immune response has been observed in various cells in COVID-19 patients. As for example, the frequencies of peripheral T cells and NK cells were significantly decreased in severe COVID-19 patients, especially for innate-like T and various CD8  + T cell subsets, compared to healthy controls. In contrast, the proportions of various activated CD4  + T cell subsets, including Th1, Th2, and Th17-like cells were increased and more clonally expanded in severe COVID-19 patients. Patients’ peripheral T cells showed no sign of exhaustion or augmented cell death, whereas T cells in BALFs (broncho-alveolar lavage fluid) produced higher levels of IFNG, TNF, CCL4, and CCL5 [45]. ScRNA sequencing revealed that SARS-CoV2 replicates primarily in the pneumocytes in African green monkey [46]. A single-cell immune response signature identified each immune cell-specific genes in COVID-19 patients [47]. A single-cell atlas of gene expression, SCOVID, has been developed [48].

8: miRNAomics in COVID-19

MicroRNA or LincRNA that modulate genes involved in COVID-19 are extremely important as it would be easier to manipulate them with external DNA (antioligo) or RNA (anti-RNA) counterpart. In COVID-19 patients with thrombosis, several exosomal miRNA, such as miR424, is upregulated, whereas miR145, miR103a, and miR885 are downregulated [49]. Zhang et al. [50] demonstrated that each step of SARS-CoV2 infection and attack to human cells are guided by specific miRNA [50]. Sabetian et al. [51] proposed specific miRNA and LincRNA interaction in causing male infertility in COVID-19 patients [51]. Design of anti-miRNA oligo and introduction of them into specified cells or body with LNP particles could reduce COVID-19 diseases severity.

9: Epigenetic implementations in COVID-19

9.1: Whole genome methylation profiling of COVID-19 patients

Using global DNA methylation profile, Franzen et al. [52] showed that epigenetic clocks are not accelerated in COVID-19 patients. They suggested that COVID-19 patients do not show telomere attrition in old age patients and do not favor severity, thus biological age does not support at higher risk for COVID-19 [52]. Blood DNA methylation signature predicts that epigenetic clock could be a predictor of disease and mortality risk in severe COVID‐19 [53]. Whole-genome DNA methylation revealed that ACE2 gene methylation and posttranslational histone modifications may drive differences in host tissue, biological age, and sex-biased patterns of viral infection [54].

9.2: Other epigenetic approaches

Identifying SARS-CoV2 proteins binding site in human genomic DNA and modulating gene expression may be an important aspect of SARS-CoV2 function. CHIP sequencing and other epigenomic studies could be an essential area of research to understand SARS-CoV2 pathogenesis [55,56].

10: Nutrigenomics and nutrition aspects in COVID-19

10.1: Role of vitamins and other nutritional factors

Nutrients are the powerful modulator of the immune system, thus play a critical role in COVID-19 subphenotypes. Although many nutrients are initially assumed to be effective to prevent SARS-CoV2 infection, none of them has been proved by large scientific clinical trial [57]. They assessed the results of 13 nutrients (vitamin A, C, D, E, iron; selenium; zinc; antioxidants, poly-unsaturated fatty acids, protein energy metabolites) on COVID-19 patients and found no conclusive evidence that support nutritional therapy could help COVID-19 patients. However, vitamin C and D have anti-inflammatory effect that may reduce some of immunological complications, such as cytokine storm that occurs in COVID-19 patients. Butler-Laporte et al. [58] reported the outcome of a Mendelian randomized study of COVID-19 patients with Vitamin D and found no supporting evidence of effectiveness of vitamin D for COVID-19 patients [58,59]. Lepere et al. [60] showed that zinc with azithromycin supplement prevent hospitalization of COVID-19 patients [60]. However, systematic studies with one or multiple nutrients together are yet to be reported as effective in large clinical trial.

10.2: Involvement of gut as an important part of COVID-19 complications

Gut intestinal enterocytes express ACE2 abundantly [39], thus serve as excellent attachment sites for SARS-CoV2 infection. Consequently, gut microbiome should have a critical role in COVID-19 phenotypes. Livanos et al. [61] demonstrated that reduced expression of IFNG, CXCL8, CXCL2, and IL1B genes in the gut of COVID-19-hospitalized patients. They also observed that reduced levels of key inflammatory proteins including IL-6, CXCL8, IL-17A, and CCL28 in circulation are associated with reduced morbidities but was not associated with nasopharyngeal viral load.

10.3: Microbial sequencing to identify gut microbes in COVID-19 patients

Several microbiome consortiums are initiated to sequence the gut microbiota in COVID-19 patients to predict initial diagnosis and disease severity [62]. From a large cohort of COVID-19 patients, Ward et al. [63] showed that the intestinal and oral microbiome make-up predicts with 92% and 84% accuracy, respectively, in severity of COVID-19 respiratory symptoms that lead to death [63]. They also found that Enterococcus faecalis, a known pathobiont, as the top predictor of COVID-19 disease severity and easily culturable in the lab that could be developed as a tool for assessing COVID-19 pathogenicity.

11: COVID-19 and phenomics

Phenomics is the systematic study of the continuum of gene-environment interactions and the measurement of the emergent physical and chemical properties that result from those interactions to define individual and population phenotypes. COVID-19-related phenotypes are complex and need correct diagnosis, which overlaps with other related phenotypes. The diagnosis mainly includes laboratory testing and suspected or probable infection. An international guideline could be followed as described in https://covid19-phenomics.org/.

In molecular phenomics, how these chemical and biochemical signatures (metabolites, proteins, transcripts, etc.) of cells and biofluids change in the onset, development, and recovery from disease are captured. The metabolic and immunometabolic pattern in COVID-19 patients are strong components to characterize stages of disease severity [64]. However, over the time, scientists developed various cellular and a pattern of serotype responses that characterizes the primary and acute state of the diseases. Based on fluorescence microscopy imaging of infected cells for immunomodulatory variations, MIT scientists developed phenomics-based assay to characterize cytokine storms in COVID-19 and screened a series of drugs using this strategy as a high-throughput assay. They identified several potential drugs including JAK/STAT inhibitors [65].

12: Metabolomics in COVID-19

12.1: Metabolomic approaches to COVID-19 detection and prediction

Dierckx et al. [66] used metabolic fingerprinting to predict the severity of the diseases [66]. They observed that severity-associated biomarkers were equally broad that included increased inflammatory markers (glycoprotein acetylation), amino acid concentrations (increased leucine and phenylalanine), increased lipoprotein particle concentrations (except those of very low density lipoprotein, VLDL), decreased cholesterol levels (except in large HDL and VLDL), increased triglyceride levels (only in IDL), fatty acid levels (decreased poly-unsaturated fatty acid, increased mono-unsaturated fatty acid), and decreased choline concentration. Their results point to systemic metabolic biomarkers for COVID-19 severity that makes strong targets for further fundamental research into its pathology.

Bergamaschi et al. [67] predicted severity of the disease using immuno-metabolites in early COVID-19 patients. They showed that immunopathology may be inevitable in some individuals, or preventative intervention may be needed before symptom onsets [67]. Viral load does not correlate with the development of this pathological response but does with its subsequent severity. Immune recovery is complex, with profound persistent of cellular abnormalities correlating with a change in the nature of the inflammatory response, where signature characteristics of increased oxidative phosphorylation and reactive‑oxygen species (ROS) are associated. Using high-resolution untargeted LC-MS analysis, Roberts et al. [19] identified metabolites that could predict the severity of the diseases [68].

13: Applications of genetic engineering in COVID-19

Genetic engineering is the modification of nucleic acids (base) and that could be used to modify SARS-CoV2 virus and vaccine development. Although the approach to modify the virus for favored treatment of COVID-19 disease has not been addressed yet, genetic engineering is widely used for vaccine development. The adenovirus vector-based DNA vaccines (Oxford/Astra-Zeneca, Johnsson & Johnsson, Gamelya institute) needed expert genetic engineering for vector construction and insertion of viral DNA [69–72]. mRNA vaccines [70,73] are continuously being modified based on novel virulent strains that changed their RNA base.

14: CRISPR-based assays for rapid detection of SARS-CoV2

CRISPR/CAS9 or CRISPR/CAS12a is a powerful technique for base modification. This is successfully used to rapidly detect the presence of SARS-CoV2 virus in patients’ samples [74]. In diagnostic test FELUDA, the authors targeted the N501Y residue of SARS-CoV2 to develop a cheaper and rapid assay system that could be useful where only small amount of patient’s samples are available. They also developed a method to detect SARS-CoV2 in a paper strip [75]. CRISPR diagnostics could replace sophisticated RT-QPCR where those machine are not available.

Using CRISPR/CAS12a or Cas13b, a series of diagnostic tests, such as SHERLOCK (SARS-CoV2 S gene and Orf1ab gene) [76], DETECTR (SARS-CoV2 N gene and E gene) [77], AIOD-CRISPR (SARS-CoV2 N gene) [78], and ENHANCE (SARS-CoV2 N gene) [79] have been developed.

15: Approaches to understand the emergence and dynamics of COVID-19 and future pandemics

Sequence comparison and prediction of evolutionary trajectories leading SARS-CoV2 evolution and virulency has been explored using SARS-CoV2 sequence in different geographical regions. Replicating the creation of mutation profile in vitro could predict the efficacy of vaccine and its design or resistance as earlier done for other RNA virus [80].

SARS-CoV2 is believed to be originated from bat CoV virus RATG13. Extensive sequence and phylogenetic analysis of SARS-CoV2 sequences, RATG13, and other CoV virus of bat and pangolin led to this conclusion. But this conclusion is still ambiguous and more analysis are needed to trace the origin of this virus [8–11].

Recent insurgence of super infective strains carrying mutations in the spike and other proteins in SARS-CoV2 are identified. Notable new strains of SARS-CoV2 are B.1.1.7 (alpha), P.1 (gamma) and P.2 (zeta), (B.1.351) (beta), B.1.526 (iota), B1.525 (eta), B.1.6.1.7 (delta), BA.1, BA.2, BA.3, BA.4, and BA.5 (omicron), etc., that are poising threat to the ongoing pandemic. B.1.1.7 has nine mutations in spike protein including N501Y at the RBD domain and has higher infectivity with increasing mortalities [81]. When in vitro monoclonal antibody was tested for B.1.1.7 strain, reduced neutralizing efficacy is observed with the escape of antibody effect [82,83]. The South African virulent strain (B.1.351) showed ninefold less effective for neutralizing the virus efficiently for RNA vaccine (Pfizer) and threatens the efficacy of the vaccine [84]. The delta variant B.1.6.1.7 is believed to be expanded the pandemic by reducing the efficiency of currently used vaccines [85,86]. Similar phenomena are observed for omicron variants that reduce the efficacy of vaccines [87].

Thus constant genomic sequence analysis of SARS-CoV2 genome and studying their epidemiology would be immensely needed to control the pandemic. Temporal analysis of SARS-CoV2 sequences revealed that its nucleotide substitution rate is 2.22 nt/month with an evolutionary rate of 9 × 10−  4/site/year. Genetic codon analysis indicates that SARS-CoV2 evolution strictly follows neutral evolution with strong purifying selection, whereas its propagation in human disobeys neutral evolution and proceeding toward divergent selection predictably for its infection power to evade multiple organs [10,11]. Invasion of SARS-CoV2 into various human organs is predictably increasing the nonsynonymous mutation over synonymous mutations that are persisted over the deleterious mutation and increases its selection fitness. This property of SARS-CoV2 enables it to generate new mutations that help it to survive aggressively to new strains frequently to become virulent and escape antibody vaccine response, thus extending the pandemic in future directions.

16: Artificial intelligence (AI) in COVID-19

AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. Based on AI, Kowalewski and Ray [88] identified 65 molecules as therapeutic targets for COVID-19. They used the panel of NIH approved thousands of drugs and tested them for COVID-19 subphenotypes to identify target drugs [88]. Delafiori et al. [89] fed the computer with metabolomics profile of COVID-19 patients and developed algorithm to predict the automated diagnosis of COVID-19 patients [89]. A comprehensive knowledge-based host-SARS-CoV2 disease map has been developed to assess various information about disease state and treatment outcomes [90].

17: Applications of mathematical modeling and simulation in COVID-19

Mathematical modeling helps to respond to the COVID-19 pandemic by informing decisions about pandemic