Evidence map›Paper›PMID 34907210›Full record

ArticleScientific reports2021

Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2.

Chen Su, Simon Rousseau, Amin Emad

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Chen SuDepartment of Electrical and Computer Engineering, McGill University, 755, McConnell Engineering Building, 3480 University Street, Montreal, QC, H3A 0E9, Canada.
Simon RousseauThe Meakins-Christie Laboratories at the Research Institute of McGill University Heath Centre (RI-MUHC), McGill University, E M3.2244, 1001 Décarie, Montreal, QC, H4A 3J1, Canada. simon.rousseau@mcgill.ca.
Amin EmadDepartment of Electrical and Computer Engineering, McGill University, 755, McConnell Engineering Building, 3480 University Street, Montreal, QC, H3A 0E9, Canada. amin.emad@mcgill.ca.
McGill University · CA

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2019-04460
6 · The paper itself

Abstract

Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

Indexed as

A549 CellsCell LineCOVID-19Epithelial CellsGene Expression ProfilingGene Expression RegulationHumansModels, BiologicalSARS-CoV-2Sequence Analysis, RNASystems Biology

Identifiers

PMID34907210
PMCPMC8671548
OpenAlexW4200437116

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.