MétaCan
Menu
Back to cohort
Record W4317778069 · doi:10.1016/j.isci.2023.105975

The Staphylococcus aureus protein IsdA increases SARS CoV-2 replication by modulating JAK-STAT signaling

2023· article· en· W4317778069 on OpenAlexafffund
Mariya I. Goncheva, Richard M. Gibson, Ainslie C. Shouldice, Jimmy D. Dikeakos, David E. Heinrichs

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistryCanadian Institutes of Health ResearchBritish Society for Antimicrobial Chemotherapy
KeywordsStaphylococcus aureusSTAT proteinstatJanus kinaseViral replicationSTAT3BiologyJAK-STAT signaling pathwaySignal transductionMicrobiologyCell biologyVirologyVirusBacteriaGeneticsTyrosine kinase

Abstract

fetched live from OpenAlex

The Severe Acute Respiratory Syndrome Coronavirus 2 (CoV-2) pandemic has affected millions globally. A significant complication of CoV-2 infection is secondary bacterial co-infection, as seen in approximately 25% of severe cases. The most common organism isolated during co-infection is Staphylococcus aureus. Here, we describe the development of an in vitro co-infection model where both viral and bacterial replication kinetics may be examined. We demonstrate CoV-2 infection does not alter bacterial interactions with host epithelial cells. In contrast, S. aureus enhances CoV-2 replication by 10- to 15-fold. We identify this pro-viral activity is due to the S. aureus iron-regulated surface determinant A (IsdA) protein and demonstrate IsdA modifies host transcription. We find that IsdA alters Janus Kinase – Signal Transducer and Activator of Transcription (JAK-STAT) signaling, by affecting JAK2-STAT3 levels, ultimately leading to increased viral replication. These findings provide key insight into the molecular interactions between host cells, CoV-2 and S. aureus during co-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)0.002
metaresearch head score (Gemma)0.002
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 score0.033
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.056
GPT teacher head0.368
Teacher spread0.312 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207