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Record W4378713713 · doi:10.1093/nargab/lqad052

Refining the genomic determinants underlying escape from X-chromosome inactivation

2022· article· en· W4378713713 on OpenAlexafffund
Samantha B. Peeters, Tiffany Leung, Oriol Fornés, Rachelle A. Farkas, Wyeth W. Wasserman, Carolyn J. Brown

Bibliographic record

VenueNAR Genomics and Bioinformatics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsX-inactivationBiologyGeneticsGeneTransgeneX chromosomeTranscription (linguistics)Transcription factorPhenotypeChromosomeContext (archaeology)

Abstract

fetched live from OpenAlex

Abstract X-chromosome inactivation (XCI) epigenetically silences one X chromosome in every cell in female mammals. Although the majority of X-linked genes are silenced, in humans 20% or more are able to escape inactivation and continue to be expressed. Such escape genes are important contributors to sex differences in gene expression, and may impact the phenotypes of X aneuploidies; yet the mechanisms regulating escape from XCI are not understood. We have performed an enrichment analysis of transcription factor binding on the X chromosome, providing new evidence for enriched factors at the transcription start sites of escape genes. The top escape-enriched transcription factors were detected at the RPS4X promoter, a well-described human escape gene previously demonstrated to escape from XCI in a transgenic mouse model. Using a cell line model system that allows for targeted integration and inactivation of transgenes on the mouse X chromosome, we further assessed combinations of RPS4X promoter and genic elements for their ability to drive escape from XCI. We identified a small transgenic construct of only 6 kb capable of robust escape from XCI, establishing that gene-proximal elements are sufficient to permit escape, and highlighting the additive effect of multiple elements that work together in a context-specific fashion.

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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Same venueNAR Genomics and BioinformaticsSame topicGenetic and Clinical Aspects of Sex Determination and Chromosomal AbnormalitiesFrench-language works237,207