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Record W4412810094 · doi:10.1038/s41597-025-05669-3

Genome-wide analysis of host-encoded microRNAs modulating SARS-CoV-2 infection

2025· article· en· W4412810094 on OpenAlexfundno aff
Christina L. Rootes, Karla J. Cowley, Aaron M. Brice, Henry Beetham, Rasan Mohamed Sathiqu, Kaylene J. Simpson, Cameron R. Stewart

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersPeter MacCallum Cancer CentreAustralian GovernmentCommonwealth Scientific and Industrial Research OrganisationCanada Excellence Research Chairs, Government of CanadaAustralian Cancer Research FoundationUniversity of Melbourne
KeywordsmicroRNAGenomeBiologyHost (biology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Computational biologyCoronavirus disease 2019 (COVID-19)VirologyGeneticsGeneMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Viruses exploit cellular machinery to complete their replication cycle. Furthering our understanding of this process provides insight into the mechanism of virus replication and potential targets for antiviral therapeutics. Genome-wide CRISPR screens have identified cellular pathways important in the SARS-COV-2 infection process, including vesicular traffic, lipid homeostasis and PI3K signalling. Functional genomics-driven analysis of host-encoded microRNAs (miRNAs) impacting SARS-CoV-2 infection would provide further unbiased and discovery-driven insight into the host-pathogen interface. Here we present findings from genome-wide complementary miRNA mimic and inhibitor screens performed in a bio-safety level (BSL)-4 laboratory using a combination of high-throughput robotics, high-content imaging and novel data analysis pipelines. This dataset has identified both miRNA promoters and inhibitors of SARS-CoV-2 replication which may be used by researchers to further explore therapeutic targets against SARS-CoV-2 and the host factors influencing COVID pathogenesis.

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)0.001
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 score0.201
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.086
GPT teacher head0.390
Teacher spread0.304 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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