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Record W2781869161 · doi:10.1016/j.stemcr.2017.12.006

Cell-Cycle-Specific Function of p53 in Fanconi Anemia Hematopoietic Stem and Progenitor Cell Proliferation

2018· article· en· W2781869161 on OpenAlexfundno aff
Xiaoli Li, Andrew F. Wilson, Wei Du, Qishen Pang

Bibliographic record

VenueStem Cell Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteCincinnati Children's Hospital Medical CenterNational Institutes of HealthUniversity of Texas MD Anderson Cancer CenterUniversité LavalCincinnati Children’s Research Foundation
KeywordsBiologyFanconi anemiaProgenitor cellHaematopoiesisCell cycleStem cellCell biologyCell growthCancer researchFunction (biology)ProgenitorCellGeneticsGeneDNA repair

Abstract

fetched live from OpenAlex

Overactive p53 has been proposed as an important pathophysiological factor for bone marrow failure syndromes, including Fanconi anemia (FA). Here, we report a p53-dependent effect on hematopoietic stem and progenitor cell (HSPC) proliferation in mice deficient for the FA gene Fanca . Deletion of p53 in Fanca −/− mice leads to replicative exhaustion of the hematopoietic stem cell (HSC) in transplant recipients. Using Fanca −/− HSCs expressing the separation-of-function mutant p53 515C transgene, which selectively impairs the p53 function in apoptosis but keeps its cell-cycle checkpoint activities intact, we show that the p53 cell-cycle function is specifically required for the regulation of Fanca −/− HSC proliferation. Our results demonstrate that p53 plays a compensatory role in preventing FA HSCs from replicative exhaustion and suggest a cautious approach to manipulating p53 signaling as a therapeutic utility in FA.

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 score0.002

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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