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Record W3097060103 · doi:10.1096/fj.202001178r

DIAPH1 regulates ciliogenesis and trafficking in primary cilia

2020· article· en· W3097060103 on OpenAlexafffund
Oliva Palander, William S. Trimble

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCiliogenesisCiliumPrimary (astronomy)Cell biologyBiologyNeurosciencePhysics

Abstract

fetched live from OpenAlex

Primary cilia are critical hubs for several signaling pathways, and defects in ciliogenesis or cilia maintenance produce a range of diseases collectively known as ciliopathies. Ciliogenesis requires vesicle trafficking along a network of microtubules and actin filaments to the basal body. The DIAPH1 (Diaphanous-related formin) family of formins promotes both actin polymerization and EB1-dependent microtubule (MT) stability. EB1 and EB3 have previously been implicated in cilia biogenesis to carry out centrosome-related functions. However, the role of DIAPH1 proteins had not been examined. Here we show that the depletion of DIAPH1 decreased ciliogenesis, cilia length, and reduced trafficking within cilia. Additionally, both actin nucleating and microtubule-stabilizing properties of DIAPH1 are important for their cilia functions. To assess their roles in ciliogenesis in isolation, we targeted DIAPH1 specifically to the basal body, which caused an increase in cilia length and increased trafficking within cilia. Intriguingly, expression of DIAPH1 mutants associated with human deafness and microcephaly impaired ciliation and caused cilia elongation and bulb formation. These results suggest that the actin and microtubule functions of DIAPH1 proteins regulate cilia maintenance in part by regulating vesicular trafficking to the base of the primary cilia.

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 score0.237
Threshold uncertainty score0.270

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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