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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