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Record W3153563636 · doi:10.3389/fphar.2021.631879

SARS-CoV-2 Switches ‘on’ MAPK and NFκB Signaling via the Reduction of Nuclear DUSP1 and DUSP5 Expression

2021· article· en· W3153563636 on OpenAlexaff
Swati Goel, Fatemeh Saheb Sharif‐Askari, Narjes Saheb Sharif‐Askari, Bushra Madkhana, Ahmad Munzer Alwaa, Bassam Mahboub, Adel M. Zakeri, Elaref Ratemi, Rifat Hamoudi, Qutayba Hamid, Rabih Halwani

Bibliographic record

VenueFrontiers in Pharmacology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMAPK/ERK pathwayCell biologyProinflammatory cytokineSignal transductionNF-κBDual-specificity phosphataseKinaseImmune systemBiologyDownregulation and upregulationInflammationImmunologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Mitogen-activated protein kinases (MAPK) and NF-kappaB (NF-κB) pathway regulate many cellular processes and are essential for immune cells function. Their activity is controlled by dual-specificity phosphatases ( DUSPs ). A comprehensive analysis of publicly available gene expression data sets of human airway epithelial cells (AECs) infected with SARS-CoV-2 identified DUSP1 and DUSP5 among the lowest induced transcripts within these pathways. These proteins are known to downregulate MAPK and NF-κB pathways; and their lower expression was associated with increased activity of MAPK and NF-κB signaling and enhanced expression of proinflammatory cytokines such as TNF-α . Infection with other coronaviruses did not have a similar effect on these genes. Interestingly, treatment with chloroquine and/or non-steroidal anti-inflammatory drugs counteracted the SARS-CoV-2 induced reduction of DUSP1 and DUSP5 genes expression. Therapeutically, impeding this evasion mechanism of SARS-CoV-2 may help control the exaggerated activation of these immune regulatory pathways during a COVID-19 infection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.262
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations79
Published2021
Admission routes1
Has abstractyes

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