Evidence map›Paper›PMID 35982655›Full record

ArticlemedRxiv : the preprint server for health sciences2022

Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

Richa Batra, William Whalen, Sergio Alvarez-Mulett, Luis G Gómez-Escobar, Katherine L Hoffman, Will Simmons, John Harrington, Kelsey Chetnik, Mustafa Buyukozkan, Elisa Benedetti and 7 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 2 institutions in 2 countries.

Richa BatraDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
William WhalenDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Sergio Alvarez-MulettDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Luis G Gómez-EscobarDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Katherine L HoffmanDepartment of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA.
Will SimmonsDepartment of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA.
John HarringtonDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Kelsey ChetnikDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Mustafa BuyukozkanDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Elisa BenedettiDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Mary E ChoiDivision of Nephrology and Hypertension, Joan and Sanford I. Weill Department of Medicine, New York, NY, USA.
Karsten SuhreBioinformatics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar.
Edward SchenckDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Augustine M K ChoiDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Frank SchmidtProteomics Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar.
Soo Jung ChoDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
Jan KrumsiekDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Cornell University · USWeill Cornell Medical College in Qatar · QA

Funding

Project 4 - Mechanistic studies on the role of the gut microbiome in models for Alzheimer's diseaseU19AG063744 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Rima F Kaddurah-Daouk · 2019 to 2026
$54.1M
Multidisciplinary Approach Training in Respiratory ResearchT32HL134629 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI Robert J Kaner, Heather Winona Stout Delgado · 2018 to 2026
$5.2M
TargetAD: A systems multi-omics approach to drug repositioning in Alzheimer's diseaseR01AG069901 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Matthias Arnold, Jan Krumsiek · 2021 to 2026
$3.7M
Necroinflammatory Cell Death in SepsisK23HL151876 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI SCHENCK, EDWARD JAMES · 2021 to 2025
$975k
NHLBI NIH HHS K23 HL151876NHLBI NIH HHS T32 HL134629NIA NIH HHS R01 AG069901NIA NIH HHS U19 AG063744
6 · The paper itself

Abstract

Background: Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. Methods and Findings: In this study, we compared COVID-19 ARDS (n=43) and bacterial sepsis-induced (non-COVID-19) ARDS (n=24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within-ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. Conclusion: We present a first comprehensive molecular characterization of differences between two ARDS etiologies - COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.

Identifiers

PMID35982655
PMCPMC9387161
OpenAlexW4281391500

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Registered trials

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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.