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Record W4402363619 · doi:10.1002/bies.202400173

Beyond RNAi: How the Dicer protein modulates the antiviral innate immune response in mammalian cells

2024· article· en· W4402363619 on OpenAlexfundno aff
Léa Gaucherand, Morgane Baldaccini, Sébastien Pfeffer

Bibliographic record

VenueBioEssays · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsnot available
FundersSchool of Medicine, New York UniversityNational Institutes of HealthCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheYork UniversityUniversité de StrasbourgFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche Médicale
KeywordsDicerRNA interferenceBiologyInnate immune systemCell biologymicroRNAImmune systemRibonuclease IIIRNA silencingInterferonRNAGeneticsGene

Abstract

fetched live from OpenAlex

While Dicer plays an important antiviral role through the RNAi pathway in plants and invertebrates, its contribution to antiviral immunity in vertebrates and more specifically mammals is more controversial. The apparent limited RNAi activity in mammalian cells has been attributed to the reduced long dsRNA processive activity of mammalian Dicer, as well as a functional incompatibility between the RNAi and IFN pathways. Why Dicer has lost this antiviral activity in the profit of the IFN pathway is still unclear. We propose that the primary direct antiviral activity of Dicer has been functionally replaced by other sensors in the IFN pathway, leading to its specialization toward microRNA maturation. As a result, Dicer can regulate the innate immune response and prevent basal activation of the IFN pathway in mammals. Here, we discuss this hypothesis, highlighting how the adaptation of the helicase domain of mammalian Dicer may be key to this process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.234
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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