Human IFT52 mutations uncover a novel role for the protein in microtubule dynamics and centrosome cohesion
Bibliographic record
Abstract
Mutations in genes encoding components of the intraflagellar transport (IFT) complexes have previously been associated with a spectrum of diseases collectively termed ciliopathies. Ciliopathies relate to defects in the formation or function of the cilium, a sensory or motile organelle present on the surface of most cell types. IFT52 is a key component of the IFT-B complex and ensures the interaction of the two subcomplexes, IFT-B1 and IFT-B2. Here, we report novel IFT52 biallelic mutations in cases with a short-rib thoracic dysplasia (SRTD) or a congenital anomaly of kidney and urinary tract (CAKUT). Combining in vitro and in vivo studies in zebrafish, we showed that SRTD-associated missense mutation impairs IFT-B complex assembly and IFT-B2 ciliary localization, resulting in decreased cilia length. In comparison, CAKUT-associated missense mutation has a mild pathogenicity, thus explaining the lack of skeletal defects in CAKUT case. In parallel, we demonstrated that the previously reported homozygous nonsense IFT52 mutation associated with Sensenbrenner syndrome [Girisha et al. (2016) A homozygous nonsense variant in IFT52 is associated with a human skeletal ciliopathy. Clin. Genet., 90, 536-539] leads to exon skipping and results in a partially functional protein. Finally, our work uncovered a novel role for IFT52 in microtubule network regulation. We showed that IFT52 interacts and partially co-localized with centrin at the distal end of centrioles where it is involved in its recruitment and/or maintenance. Alteration of this function likely contributes to centriole splitting observed in Ift52-/- cells. Altogether, our findings allow a better comprehensive genotype-phenotype correlation among IFT52-related cases and revealed a novel, extra-ciliary role for IFT52, i.e. disruption may contribute to pathophysiological mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".