Evidence map›Paper›PMID 35123426›Full record

ArticleBMC nephrology2022

Possible kidney-lung cross-talk in COVID-19: in silico modeling of SARS-CoV-2 infection.

Dmitry N Grigoryev, Hamid Rabb

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

2 authors at 2 institutions in 1 country.

Dmitry N GrigoryevCenter for Translational Data Science, University of Chicago Division of Biological Sciences, 5454 South Shore Drive, Suite 2A/B, Chicago, IL, 60615, USA. gri@uchicago.edu.ORCID 0000-0002-1849-1763
Hamid RabbJohns Hopkins Medicine, Baltimore, MD, USA.
Johns Hopkins University · USUniversity of Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPublicly available genomics datasets have grown drastically during the past decades. Although most of these datasets were initially generated to answer a pre-defined scientific question, their repurposing can be useful when new challenges such as COVID-19 arise. While the establishment and use of experimental models of COVID-19 are in progress, the potential hypotheses for mechanisms of onset and progression of COVID-19 can be generated by using in silico analysis of known molecular changes during COVID-19 and targets for SARS-CoV-2 invasion.

methodsSelecting condition: COVID-19 infection leads to pneumonia and mechanical ventilation (PMV) and associated with acute kidney injury (AKI). There is increasing data demonstrating mechanistic links between AKI and lung injury caused by mechanical ventilation. Selecting targets: SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) for cell entry. We hypothesized that expression of ACE2 and TMPRSS2 would be affected in models of AKI and PMV. We therefore evaluated expression of ACE2 and TMPRSS2 as well as other novel molecular players of AKI and AKI-lung cross-talk in the publicly available microarray datasets GSE6730 and GSE60088, which represent gene expression of lungs and kidneys in mouse models of AKI and PMV, respectively.

resultsExpression of COVID-19 related genes ACE2 and TMPRSS2 was downregulated in lungs after 6 h of distant AKI effects. The expression of ACE2 decreased further after 36 h, while expression of TMPRSS2 recovered. In kidneys, both genes were downregulated by AKI, but not by distant lung injury. We also identified 53 kidney genes upregulated by PMV; and 254 lung genes upregulated by AKI, 9 genes of which were common to both organs. 3 of 9 genes were previously linked to kidney-lung cross-talk: Lcn2 (Fold Change (FC)

conclusionsUsing our in silico approach, we identified changes in COVID-19 related genes ACE2 and TMPRSS2 in traditional mouse models of AKI and kidney-lung cross-talk. We also found changes in new candidate genes, which could be involved in the combined kidney-lung injury during COVID-19.

Indexed as

Computer SimulationAnimalsCOVID-19Disease Models, AnimalKidney DiseasesLung DiseasesMaleMiceMice, Inbred C57BLSARS-CoV-2ACE2Acute kidney injuryCOVID receptorsGene expressionGenomicsKidney-lung cross-talkMicroarrayTMPRSS2

Identifiers

PMID35123426
PMCPMC8817768
OpenAlexW4210701363

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.