Evidence map›Paper›PMID 36549275›Full record

ArticleCell systems2022

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 and 7 more

Open access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 5 institutions in 1 country.

Antonio CappuccioDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Daniel G ChawlaProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Xi ChenCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
Aliza B RubensteinDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Wan Sze ChengDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Weiguang MaoDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Thomas W BurkeCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Ephraim L TsalikCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Elizabeth PetzoldCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Ricardo HenaoCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Micah T McClainCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Christopher W WoodsCenter for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA.
Maria ChikinaDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Olga G TroyanskayaCenter 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.
Stuart C SealfonDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Steven H KleinsteinProgram 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.
Elena ZaslavskyDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: elena.zaslavsky@mssm.edu.
Duke University · USIcahn School of Medicine at Mount Sinai · USPrinceton University · USUniversity of Pittsburgh · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Virus DiseasesHumansSARS-CoV-2COVID-19cross-reactivityepigenomicshost response signatureinterpretabilityoptimizationplasmablastsrobustnesstranscriptomicsviral infection diagnosis

Identifiers

PMID36549275
OpenAlexW4312105425

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.