Evidence map›Paper›PMID 34709707›Full record

SynthesisMolecular systems biology2021

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets.

Kuoyuan Cheng, Laura Martin-Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin

Abstract readMeta-Analysis
In one paragraph

Synthesis in Molecular systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Kuoyuan ChengCancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID https://orcid.org/0000-0001-8118-5243
Laura Martin-SanchoImmunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.ORCID https://orcid.org/0000-0003-1489-2815
Lipika R PalCancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID https://orcid.org/0000-0002-3390-110X
Yuan PuImmunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Laura RivaImmunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Xin YinImmunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.ORCID https://orcid.org/0000-0003-2357-6718
Sanju SinhaCancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.
Nishanth Ulhas NairCancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.
Sumit K ChandaImmunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Eytan RuppinCancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID https://orcid.org/0000-0002-7862-3940

Funding

Project 3 - Ex vivo immune profiling of dengue viruses and vaccinesU19AI118610 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI HARRIS, EVA · 2015 to 2021
$46.6M
SARS-CoV adaptations through a Systems Biology Lens (SYBIL)U19AI135972 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Adolfo Garcia-Sastre · 2018 to 2026
$27.2M
HHS | National Institutes of Health (NIH)NIAID NIH HHS U19 AI118610NIAID NIH HHS U19 AI135972U.S. Department of Defense (DOD) W81XWH-20-1-0270
6 · The paper itself

Abstract

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.

Indexed as

PandemicsAdenosine MonophosphateAlanineAnimalsAntiviral AgentsCaco-2 CellsChlorocebus aethiopsCOVID-19COVID-19 Drug TreatmentDatasets as TopicDrug DevelopmentDrug RepositioningHost-Pathogen InteractionsHumansMetabolic Networks and PathwaysRNA, Small InterferingAdenosine MonophosphateAlanineAntiviral AgentsremdesivirRNA, Small Interferingantiviral targetgenome-scale metabolic modelingremdesivirRNAi screenSARS-CoV-2

Identifiers

PMID34709707
PMCPMC8552660

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.