Muscle stem cell function is impaired in absence of Talpid3 - a gene required for primary cilia formation
Bibliographic record
Abstract
Abstract Skeletal muscle stem cells (MuSC) are crucial for tissue homeostasis and repair after injury. Following activation, they proliferate to generate differentiating myoblasts. A proportion of cells selfrenew, re-enter the MuSC niche under the basal lamina outside the myofiber and become quiescent. Quiescent MuSC have a primary cilium, which is disassembled upon cell cycle entry. Ex vivo experiments suggest cilia are important for MuSC self-renewal, however, their role in muscle regeneration in vivo remains poorly understood. Talpid3 (TA 3 ) is essential for primary cilia formation and Hedgehog (Hh) signalling. Here we use tamoxifen-inducible conditional deletion of TA 3 in MuSC (iSC-KO) and show that regeneration is impaired in response to cytotoxic injury. Repeat injury exacerbates the regeneration phenotype in TA 3iSC-KO mice, indicating depletion of MuSCs. Single cell transcriptomics of MuSC progeny isolated from myofibers identifies components of several signalling pathways, which are deregulated in absence of TA 3 , including Hh and Wnt. Pharmacological activation of Wnt restores muscle regeneration, while purmorphamine, an activator of the Smoothened (Smo) co-receptor in the Hh pathway, has no effect. Together, our data suggest that TA 3 and primary cilia are important for MuSC self-renewal, and that pharmacological treatment can efficiently restore muscle regeneration.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".