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Record W3006264253 · doi:10.1002/jcp.29642

Altering chromatin methylation patterns and the transcriptional network involved in regulation of hematopoietic stem cell fate

2020· review· en· W3006264253 on OpenAlexaff
Mohammad Shokouhian, Marziye Bagheri, Behzad Poopak, Rouzbeh Chegeni, Nader Davari, Najmaldin Saki

Bibliographic record

VenueJournal of Cellular Physiology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMichener InstituteUniversity Health Network
Fundersnot available
KeywordsBiologyEpigeneticsDNA methylationEpigenetic regulation of neurogenesisEpigenetics of physical exerciseChromatinCell biologyTranscription factorEnhancerHistoneRegulation of gene expressionHistone methylationGeneticsChromatin remodelingGene expressionGene

Abstract

fetched live from OpenAlex

Hematopoietic stem cells (HSCs) are quiescent cells with self-renewal capacity and potential multilineage development. Various molecular regulatory mechanisms such as epigenetic modifications and transcription factor (TF) networks play crucial roles in establishing a balance between self-renewal and differentiation of HSCs. Histone/DNA methylations are important epigenetic modifications involved in transcriptional regulation of specific lineage HSCs via controlling chromatin structure and accessibility of DNA. Also, TFs contribute to either facilitation or inhibition of gene expression through binding to enhancer or promoter regions of DNA. As a result, epigenetic factors and TFs regulate the activation or repression of HSCs genes, playing a central role in normal hematopoiesis. Given the importance of histone/DNA methylation and TFs in gene expression regulation, their aberrations, including changes in HSCs-related methylation of histone/DNA and TFs (e.g., CCAAT-enhancer-binding protein α, phosphatase and tensin homolog deleted on the chromosome 10, Runt-related transcription factor 1, signal transducers and activators of transcription, and RAS family proteins) could disrupt HSCs fate. Herewith, we summarize how dysregulations in the expression of genes related to self-renewal, proliferation, and differentiation of HSCs caused by changes in epigenetic modifications and transcriptional networks lead to clonal expansion and leukemic transformation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.666
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.022
GPT teacher head0.259
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations6
Published2020
Admission routes1
Has abstractyes

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