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Record W4206946700 · doi:10.3410/f.739532974.793585242

Faculty Opinions recommendation of BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8+ T cells.

2021· dataset· en· W4206946700 on OpenAlexaff
Wilfred A. Jefferies, Hazel Cui

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2021
Typedataset
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStem cellEpigeneticsCD8BiologyCancer researchCell biologyGeneticsGeneImmune system

Abstract

fetched live from OpenAlex

During chronic infection and cancer, a self-renewing CD8+ T cell subset maintains long-term immunity and is critical to the effectiveness of immunotherapy. These stem-like CD8+ T cells diverge from other CD8+ subsets early after chronic viral infection. However, pathways guarding stem-like CD8+ T cells against terminal exhaustion remain unclear. Here, we show that the gene encoding transcriptional repressor BACH2 is transcriptionally and epigenetically active in stem-like CD8+ T cells but not terminally exhausted cells early after infection. BACH2 overexpression enforced stem-like cell fate, whereas BACH2 deficiency impaired stem-like CD8+ T cell differentiation. Single-cell transcriptomic and epigenomic approaches revealed that BACH2 established the transcriptional and epigenetic programs of stem-like CD8+ T cells. In addition, BACH2 suppressed the molecular program driving terminal exhaustion through transcriptional repression and epigenetic silencing. Thus, our study reveals a new pathway that enforces commitment to stem-like CD8+ lineage and prevents an alternative terminally exhausted cell fate. PMID: 33574619 Funding information This work was supported by: NIAID NIH HHS, United States Grant ID: R01 AI105343 Intramural NIH HHS, United States Grant ID: ZIA AI001240 NIAID NIH HHS, United States Grant ID: P01 AI108545 Intramural NIH HHS, United States Grant ID: ZIA BC011480 NIA NIH HHS, United States Grant ID: R00 AG056524 Intramural NIH HHS, United States Grant ID: ZIA AR041159 Intramural NIH HHS, United States Grant ID: ZIA AR041167 NIAID NIH HHS, United States Grant ID: R01 AI115712 NIA NIH HHS, United States Grant ID: K99 AG056524 NIAID NIH HHS, United States Grant ID: U19 AI082630 NCI NIH HHS, United States Grant ID: F99 CA234842 Intramural NIH HHS, United States Grant ID: ZIA NS003112 Intramural NIH HHS, United States Grant ID: ZIA DK075149 NCI NIH HHS, United States Grant ID: P01 CA210944 Intramural NIH HHS, United States Grant ID: ZIA AR041106 Intramural NIH HHS, United States Grant ID: ZIA NS003111 NCI NIH HHS, United States Grant ID: K00 CA234842 NIAID NIH HHS, United States Grant ID: P01 AI112521 NIAID NIH HHS, United States Grant ID: U19 AI117950 Intramural NIH HHS, United States Grant ID: ZIA AI001241 More Less keyboard_arrow_down

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score0.749

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4750.159

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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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