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Record W3089724589 · doi:10.1128/mbio.02314-20

Interleukin-1β Triggers p53-Mediated Downmodulation of CCR5 and HIV-1 Entry in Macrophages through MicroRNAs 103 and 107

2020· article· en· W3089724589 on OpenAlexafffund
Robert Lodge, Nicolas Bellini, Mélanie Laporte, Syim Salahuddin, Jean‐Pierre Routy, Petronela Ancuța, Cecilia T. Costiniuk, Mohammad‐Ali Jenabian, Éric A. Cohen

Bibliographic record

VenuemBio · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversité du Québec à MontréalUniversité de MontréalMcGill University Health CentreMontreal Clinical Research Institute
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsProinflammatory cytokineGene silencingmicroRNAImmune systemSuppressorEffectorDownregulation and upregulationBiologyMacrophageImmunologyCell biologyInflammationGeneGeneticsIn vitro

Abstract

fetched live from OpenAlex

Macrophages are heterogeneous immune cells that display varying susceptibilities to HIV-1 infection, in part due to the expression of small noncoding microRNAs involved in the posttranscriptional regulation of gene expression and silencing. Here, we identify microRNAs 103 and 107 as important p53-regulated effectors of the antiviral response triggered by the proinflammatory cytokine IL-1β in macrophages. These microRNAs, which are enriched in colon macrophages of healthy donors and alveolar macrophages of HIV-infected individuals under antiretroviral therapy, act as inhibitors of HIV-1 entry through their capacity to downregulate the CCR5 coreceptor. These results highlight the important role played by miR-103/107 in modulating CCR5 expression and HIV-1 entry in macrophages. They further underscore a distinct function of the tumor suppressor p53 in enforcing proinflammatory antiviral responses in macrophages, thus providing insight into a cellular pathway that could be targeted to limit the establishment of viral reservoirs in these cells.

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 score0.061
Threshold uncertainty score0.443

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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