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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)989-1001.e8
JournalCell Systems
Volume13
Issue number12
DOIs
StatePublished - Dec 21 2022

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Keywords

  • COVID-19
  • cross-reactivity
  • epigenomics
  • host response signature
  • interpretability
  • optimization
  • plasmablasts
  • robustness
  • transcriptomics
  • viral infection diagnosis

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