Evidence map›Paper›PMID 35844595›Full record

ArticleFrontiers in immunology2022

A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2.

Piyush Agrawal, Narmada Sambaturu, Gulden Olgun, Sridhar Hannenhalli

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

13 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Piyush AgrawalCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Narmada SambaturuIISc Mathematics Initiative, Indian Institute of Science, Bangalore, India.
Gulden OlgunCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Sridhar HannenhalliCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
National Cancer Institute · USIndian Institute of Science Bangalore · INNational Institutes of Health · US

Funding

Machine learning, network-based models for gene expression, activity, functionZIABC011981 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI HANNENHALLI, SRIDHAR · 2020 to 2023
$1.2M
6 · The paper itself

Abstract

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

Indexed as

COVID-19COVID-19 Drug TreatmentSARS-CoV-2Cell LineHumansLeukocytes, MononuclearTranscriptomecell linesDEGs (Differentially Expressed Genes)network analysisPBMCs (Peripheral Blood Mononuclear Cells)SARS-C0V-2transcriptome

Identifiers

PMID35844595
PMCPMC9284228
OpenAlexW4283772517

What OpenQuestion 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 OpenQuestion graph.