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2.13 Integrative Genomics for  Pandemic Preparedness

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INtegrative   GENomics  of Host- PathogEn  (INGEN-HOPE)  Pathogens’ genome architecture: clinical symptoms, severity and outcome  Host response: genetic and transcriptional  Role of co-infections in delineating clinical outcome  Role of non-coding RNA in modulating host response  Sequencer to samples:  MicroLabs  with Scale-Speed-Sensitivity-Sample cost (4S) Rajesh   Pandey, PhD Principal Scientist, CSIR-IGIB, Delhi

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Genomics  sequencing enabled  science of Genome/s

Pandemic preparedness  integration of the understanding

Discover Develop  Detect Diagnose Drug development

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First 2 samples of SARS-CoV-2 which arrived in IGIB on 27th March 2020 for sequencing

and thereafter 2000 more samples sequenced using Nanopore ( MicroLab )

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MegaLab   (high-throughput sequencing) MicroLab   (quick turnaround sequencing)

Combination important for balance between high-throughput and high priority sequencing.  Combines  Samples to the Lab  and  Lab to the Samples  approach.  Combines scale-speed-sensitivity of genomic surveillance.

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The WHY  Quick detection of new pathogen –  lab for the people  Fast turnaround of pathogen sequencing –  in minimal settings  Periodic screening of susceptible population(s) –  pooled sequencing

The HOW  Balance between pathogen detection & genomic surveillance  Balance between genome sequencing & amplicon-based detection  Bridge the  Sample to sequence  gap  Development of genetic assays  Trained manpower – experimental and computational  Cost effectiveness of sequencing reagents -  indigenisation  Integration and Integrative approach b/w stakeholders

Are WE ready?  Third generation sequencing platform  Sequencing cycle independent  30-50  Gb  data/Flow cell/72 hour run  Length: bps to  Kilobase  to  Megabase

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Pathogen Tiling primers Sequencing  Pores RAMPART  PCR  Amplicons Nanopore  Sequencing  Real time Analysis  Genome  Assembly MinIT MinION

Nasopharyngeal /  Oropharyngeal  / Swab Pathogen nucleic acid isolation

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INtegrative   GENomics   of SARS-CoV-2 (INGEN-CoV2)

COVID-19 Screening  Positive   Negative SARS-CoV-2 Genomes Host  transcriptome Host Genome Co-infections  Clinical manifestation  Co-morbidity Clinical Information

Hierarchical understanding  SARS-CoV-2 genomes  Host Response  Co-infections  Non-coding RNA  AI/ML

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Phylogenetic  tree (MEGA) of SARS-CoV-2.  A new India-specific  clade  (A4)

What’s unique?  1 st  genomic view of SARS-CoV-2 in India  Highlighted lineage ancestry

https://pubmed.ncbi.nlm.nih.gov/32995557/

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Sympt On Symptom Onset
Sympt R Symptom Resolution
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What’s the message  Re-infection of SARS-CoV-2  Highlighted lineage ancestry

https://pubmed.ncbi.nlm.nih.gov/33768104/

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Alpha, Delta and Kappa are VOCs  Delta variant has driven India’s 2 nd  surge  Faster detection and surveillance is critical

https://www.medrxiv.org/content/10.1101/2021.06.02.21258076v1

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https://www.medrxiv.org/content/10.1101/2021.06.28.21259546v1

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Innovation and integration for future pandemic preparedness

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Proactive genome surveillance  Sequencer to sample  Shorter sample to sequence time  Information dissemination

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Co-infections: hierarchical   understanding

Integration is the key  Co-infections modulate disease severity and outcome  Hierarchical layered understanding important  Important for diagnosis and prognosis  Implications for vaccines and therapeutics

https://www.frontiersin.org/articles/10.3389/fmicb.2021.664386/full

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Integration : key to decode the interactome

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https://lh4.googleusercontent.com/osMOyv-6F31kwSXdMOxywuokvfxNimwQJ8u0ydnfgSAFeusjGTjj4QAFExunq8DLgcTB9kS_q9MPGnBk85Aq8XmbZmpHNzQyistk772XEaqAko2TZBKJa9U7tl_8tk23VHXIJCqk

Genomics   in management of infectious diseases

Discover – Develop – Detect – Diagnose - Drug development

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Dr Rajesh Pandey, Principle Scientist, CSIR-IGIB, Delhi

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