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A Review: Molecular Mechanism of Regulation of ABCA1 Expression

2022· review· en· W4225116141 on OpenAlexaff
Dongdong Wang, Andy Wai Kan Yeung, Atanas G. Atanasov

Bibliographic record

VenueCurrent Protein and Peptide Science · 2022
Typereview
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsABCA1BiologyCell biologyRegulation of gene expressionTranscriptional regulationPost-transcriptional regulationMessenger RNABiochemistryGene expressionGeneTransporter

Abstract

fetched live from OpenAlex

ATP-binding cassette subfamily A member 1 (ABCA1) protein plays an essential role in a variety of events, such as cholesterol and phospholipid efflux, nascent high-density lipoprotein (HDL) biosynthesis, phospholipid translocation. Thus, there has been much research activity aimed at understanding the molecular mechanisms of regulating ABCA1 expression. In this review, we first discuss ABCA1 structure, tissue distribution, cellular localization, and trafficking, as well as its function. Furthermore, current understanding of the molecular mechanisms involved in the regulation of ABCA1 expression is summarized. ABCA1 transcriptional regulation is mediated by a very complicated system, including nuclear receptor systems, factors binding to other sites in the ABCA1 promoter, cytokines, hormones, growth factors, lipid metabolites, enzymes, and other messengers/factors/pathways. In addition, ABCA1 posttranscriptional regulation is mediated by microRNA, long noncoding RNA, RNA-binding proteins, proteases, fatty acids, PDZ proteins, signaling proteins, and other factors. Compared to the transcriptional regulation of ABCA1, which is well established, the post-transcriptional regulation of ABCA1 expression is poorly understood.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.064
GPT teacher head0.367
Teacher spread0.303 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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