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Updated: Nov 2, 2025
Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors
Published on: September 13, 2018
AI-guided discovery of the invariant host response to viral pandemics
Debashis Sahoo1, Gajanan D Katkar2, Soni Khandelwal3
1Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0730, Leichtag Building 132, La Jolla, CA 92093-0831, USA; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, USA; Moores Cancer Center, University of California San Diego, USA.
Insights
This study defines the host immune response in COVID-19 using AI, identifying a conserved 166-gene signature (ViP) and a 20-gene subset (severe-ViP) that predict disease severity and potential therapeutic targets.
Area of Science:
- Immunology
- Computational Biology
- Virology
Background:
- Coronavirus Disease 2019 (COVID-19) presents significant challenges to healthcare systems.
- The "cytokine storm" is implicated in fatal COVID-19 outcomes.
- Understanding the host immune response is crucial for managing viral pandemics.
Purpose of the Study:
- To define the host immune response in COVID-19 using an AI-based approach.
- To identify a gene signature associated with viral pandemics and disease severity.
- To explore therapeutic strategies for COVID-19.
Main Methods:
- Analysis of over 45,000 transcriptomic datasets from viral pandemics.
- Extraction of a 166-gene signature using ACE2 as a seed gene.
- Application of AI to analyze the gene signature's utility in disease management and therapeutics.
Main Results:
- A 166-gene signature (ViP) was conserved across viral pandemics, with a 20-gene subset (severe-ViP) classifying disease severity.
- Identified an IL15 cytokine storm in lung epithelial and myeloid cells; cell senescence and apoptosis correlated with severity.
- Therapeutic goals were met in hamster models using neutralizing antibodies or EIDD-2801; IL15/IL15RA levels predicted severity in patients.
Conclusions:
- The ViP signatures offer a framework for quantifying immune responses in viral pandemics.
- These signatures serve as an unbiased tool for assessing disease severity and vetting drug candidates.
- This AI-driven approach facilitates rapid assessment and therapeutic development for viral diseases.
Background:
Coronavirus Disease 2019 (Covid-19) continues to challenge the limits of our knowledge and our healthcare system. Here we sought to define the host immune response, a.k.a, the "cytokine storm" that has been implicated in fatal COVID-19 using an AI-based approach.
Method:
Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a 'seed' gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. An AI-based approach was used to explore the utility of the signature in navigating the uncharted territory of Covid-19, setting therapeutic goals, and finding therapeutic solutions.
Findings:
The 166-gene signature was surprisingly conserved across all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determine severity/fatality. Precise therapeutic goals could be formulated; these goals were met in high-dose SARS-CoV-2-challenged hamsters using either neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine prognosticated disease severity.
Interpretation:
The ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.
Funding:
This work was supported by the National Institutes for Health (NIH) [grants CA151673 and GM138385 (to DS) and AI141630 (to P.G), DK107585-05S1 (SD) and AI155696 (to P.G, D.S and S.D), U19-AI142742 (to S.
C, Cchi:
Cooperative Centers for Human Immunology)]; Research Grants Program Office (RGPO) from the University of California Office of the President (UCOP) (R00RG2628 & R00RG2642 to P.G, D.S and S.D); the UC San Diego Sanford Stem Cell Clinical Center (to P.G, D.S and S.D); LJI Institutional Funds (to S.C); the VA San Diego Healthcare System Institutional funds (to L.C.A). GDK was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists.
One Sentence Summary:
The host immune response in COVID-19.
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