Evidence map›Paper›PMID 36067633›Full record

ArticleComputers in biology and medicine2022

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19.

Enrique J deAndrés-Galiana, Juan Luis Fernández-Martínez, Óscar Álvarez-Machancoses, Guillermina Bea, Carlos M Galmarini, Andrzej Kloczkowski

Open access · hybridAbstract read
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 5 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

6 authors at 3 institutions in 2 countries.

Enrique J deAndrés-GalianaGroup of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain; Department of Computer Science, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain. Electronic address: andresenrique@uniovi.es.
Juan Luis Fernández-MartínezGroup of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain; DeepBioInsights, Spain. Electronic address: jlfm@uniovi.es.
Óscar Álvarez-MachancosesGroup of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain. Electronic address: oscar.alvarez@deepbioinsights.com.
Guillermina BeaGroup of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain; DeepBioInsights, Spain. Electronic address: guillermina.bea@icloud.com.
Carlos M GalmariniTopazium Artificial Intelligence, Paseo de la Castellana 40, 28046, Madrid, Spain. Electronic address: cmgalmarini@topazium.com.
Andrzej KloczkowskiBattelle Center for Mathematical Medicine, Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA. Electronic address: andrzej.kloczkowski@nationwidechildrens.org.
Universidad de Oviedo · ESAssociació de la Paràlisi Cerebral · ESNationwide Children's Hospital · US

Funding

Protein Sequence MatchingR01GM127701 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2018 to 2021
$1.4M
NIGMS NIH HHS R01 GM127701
6 · The paper itself

Abstract

backgroundTo understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.

methodsWe have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.

resultsThe transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.

conclusionsTranscriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Indexed as

COVID-19COVID-19 Drug TreatmentSARS-CoV-2Antiviral AgentsDrug RepositioningHumansInterferonsTranscriptomeAntiviral AgentsInterferonsCoronavirusDrug repositioningMachine learningSARS-CoV-2Side effectsSmall scale genetic signature

Identifiers

PMID36067633
PMCPMC9423878
OpenAlexW4293718696

What OpenQuestion holds

Textmetadata
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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.