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Record W4322771745 · doi:10.1016/j.isci.2023.106313

NLRC5 affects diet-induced adiposity in female mice and co-regulates peroxisome proliferator-activated receptor PPARγ target genes

2023· article· en· W4322771745 on OpenAlexafffund
Sarah Bauer, Vanessa Aeissen, Alena M. Bubeck, Ioannis Kienes, Kornelia Ellwanger, Mona Scheurenbrand, Fjolla Rexhepi, Sheela Ramanathan, Philip Rosenstiel, W. Florian Fricke, Thomas A. Kufer

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchMinisterium für Wissenschaft, Forschung und Kunst Baden-WürttembergDeutscher Akademischer AustauschdienstUniversité de Sherbrooke
KeywordsPeroxisome proliferator-activated receptorPeroxisomeBiologyCell biologyTranscriptional regulationReceptorAdipose tissueGeneEndocrinologyTranscription factorBiochemistry

Abstract

fetched live from OpenAlex

mice presented with higher weight gain and more adipose tissue (AT) compared to wild-type (WT) animals. Mechanistically, we demonstrate that NLRC5 enhanced the expression of peroxisome proliferator-activated receptor (PPAR) γ target genes in human cells. We identify Sin3A and negative elongation factor (NELF) B as two novel NLRC5 interaction partners and show that Sin3A partly modulates the synergistic transcriptional effect of NLRC5 on PPARγ. Collectively, we show that NLRC5 contributes to weight gain in mice, which involves transcriptional enhancement of PPARγ targets by NLRC5 that is co-regulated by Sin3A.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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 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

Citations8
Published2023
Admission routes2
Has abstractyes

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