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Record W3175747428 · doi:10.1016/j.isci.2021.102739

Myocardin-related transcription factor and serum response factor regulate cilium turnover by both transcriptional and local mechanisms

2021· article· en· W3175747428 on OpenAlexafffund
Pam Speight, Matthew Rozycki, Shruthi Venugopal, Katalin Szászi, Michael Kofler, András Kapùs

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchKidney Foundation of Canada
KeywordsSerum response factorCiliogenesisCell biologyCiliumTranscription factorBiologyMyocardinGenetics

Abstract

fetched live from OpenAlex

Turnover of the primary cilium (PC) is critical for proliferation and tissue homeostasis. Each key component of the PC resorption machinery, the HEF1/Aurora kinase A (AurA)/HDAC6 pathway harbors cis-elements potentially targeted by the transcriptional co-activator myocardin-related transcription factor (MRTF) and/or its partner serum response factor (SRF). Thus we investigated if MRTF and/or SRF regulate PC turnover. Here we show that (1) both MRTF and SRF are indispensable for serum-induced PC resorption, and (2) they act via both transcriptional and local mechanisms. Intriguingly, MRTF and SRF are present in the basal body and/or the PC, and serum facilitates ciliary MRTF recruitment. MRTF promotes the stability and ciliary accumulation of AurA and facilitates SRF phosphorylation. Ciliary SRF interacts with AurA and HDAC6. MRTF also inhibits ciliogenesis. It interacts with and is required for the correct localization of the ciliogenesis modulator CEP290. Thus, MRTF and SRF are critical regulators of PC assembly and/or disassembly, acting both as transcription factors and as PC constituents.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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
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

Citations5
Published2021
Admission routes2
Has abstractyes

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