Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature

Antonio Cappuccio; Daniel G Chawla; Xi Chen; Aliza B Rubenstein; Wan Sze Cheng; Weiguang Mao; Thomas W Burke; Ephraim L Tsalik; Elizabeth Petzold; Ricardo Henao; Micah T McClain; Christopher W Woods; Maria Chikina; Olga G Troyanskaya; Stuart C Sealfon; Steven H Kleinstein; Elena Zaslavsky

doi:10.1016/j.cels.2022.11.008

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature

Cell Syst. 2022 Dec 21;13(12):989-1001.e8. doi: 10.1016/j.cels.2022.11.008.

Authors

Antonio Cappuccio¹, Daniel G Chawla², Xi Chen³, Aliza B Rubenstein¹, Wan Sze Cheng¹, Weiguang Mao⁴, Thomas W Burke⁵, Ephraim L Tsalik⁵, Elizabeth Petzold⁵, Ricardo Henao⁵, Micah T McClain⁵, Christopher W Woods⁵, Maria Chikina⁴, Olga G Troyanskaya⁶, Stuart C Sealfon¹, Steven H Kleinstein⁷, Elena Zaslavsky⁸

Affiliations

¹ Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
² Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
³ Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
⁴ Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁵ Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
⁶ Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA.
⁷ Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA. Electronic address: steven.kleinstein@yale.edu.
⁸ Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: elena.zaslavsky@mssm.edu.

PMID: 36549275
DOI: 10.1016/j.cels.2022.11.008

Abstract

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Keywords: COVID-19; cross-reactivity; epigenomics; host response signature; interpretability; optimization; plasmablasts; robustness; transcriptomics; viral infection diagnosis.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

COVID-19*
Humans
SARS-CoV-2
Virus Diseases*