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Record W1977489133 · doi:10.1111/imm.12057

Epigenetic aspects of lymphocyte antigen receptor gene rearrangement or ‘when stochasticity completes randomness’

2012· review· en· W1977489133 on OpenAlexfundno aff
Sébastien Jaeger, Bastien Fernandez, Pierre Ferrier

Bibliographic record

VenueImmunology · 2012
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersFondation Princesse Grace de MonacoInstitut National de la Santé et de la Recherche MédicaleInstitute of Cancer ResearchInstitut National Du CancerCentre National de la Recherche ScientifiqueAssociation pour la Recherche sur le Cancer
KeywordsEpigeneticsBiologyGeneRandomnessAntigenLymphocyteGeneticsImmunologyMathematics

Abstract

fetched live from OpenAlex

To perform their specific functional role, B and T lymphocytes, cells of the adaptive immune system of jawed vertebrates, need to express one (and, preferably, only one) form of antigen receptor, i.e. the immunoglobulin or T-cell receptor (TCR), respectively. This end goal depends initially on a series of DNA cis-rearrangement events between randomly chosen units from separate clusters of V, D (at some immunoglobulin and TCR loci) and J gene segments, a biomolecular process collectively referred to as V(D)J recombination. V(D)J recombination takes place in immature T and B cells and relies on the so-called RAG nuclease, a site-specific DNA cleavage apparatus that corresponds to the lymphoid-specific moiety of the VDJ recombinase. At the genome level, this recombinase's mission presents substantial biochemical challenges. These relate to the huge distance between (some of) the gene segments that it eventually rearranges and the need to achieve cell-lineage-restricted and developmentally ordered routines with at times, mono-allelic versus bi-allelic discrimination. The entire process must be completed without any recombination errors, instigators of chromosome instability, translocation and, potentially, tumorigenesis. As expected, such a precisely choreographed and yet potentially risky process demands sophisticated controls; epigenetics demonstrates what is possible when calling upon its many facets. In this vignette, we will recall the evidence that almost from the start appeared to link the two topics, V(D)J recombination and epigenetics, before reviewing the latest advances in our knowledge of this joint venture.

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)0.004
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.009
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.067
GPT teacher head0.298
Teacher spread0.231 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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