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Record W1877391240 · doi:10.4049/jimmunol.1400871

Immunoproteasomes Shape the Transcriptome and Regulate the Function of Dendritic Cells

2014· article· en· W1877391240 on OpenAlexafffund
Danielle A. de Verteuil, Alexandre Rouette, Marie‐Pierre Hardy, Stéphanie Lavallée, Assya Trofimov, Étienne Gaucher, Claude Perreault

Bibliographic record

VenueThe Journal of Immunology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Institutes of Health Research
KeywordsBiologyCell biologyPriming (agriculture)TranscriptomeTranscription factorPhenotypeTranscription (linguistics)ProteasomeFunction (biology)Transcriptional regulationImmune systemGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

By regulating protein degradation, constitutive proteasomes (CPs) control practically all cellular functions. In addition to CPs, vertebrates express immunoproteasomes (IPs). The major nonredundant role ascribed to IPs is their enhanced ability to generate antigenic peptides. We report that CPs and IPs differentially regulate the expression of >8000 transcripts in maturing mouse dendritic cells (DCs) via regulation of signaling pathways such as IFN regulatory factors, STATs, and NF-κB. IPs regulate the transcription of many mRNAs and maturation of a few of them. Moreover, even when engineered to present optimal amounts of antigenic peptide, IP-deficient DCs are inefficient for in vivo T cell priming. Our study shows that the role of IPs in DCs is not limited to Ag processing and reveals a major nonredundant role for IPs in transcription regulation. The dramatic effect of IPs on the transcriptional landscape could explain the various immune and nonimmune phenotypes observed in vertebrates with IP deficiency or mutations.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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