The primary cilium functions as a mechanical and calcium signaling nexus
Bibliographic record
Abstract
BACKGROUND: The primary cilium is an antenna-like, nonmotile structure that extends from the surface of most mammalian cell types and is critical for chemosensing and mechanosensing in a variety of tissues including cartilage, bone, and kidney. Flow-induced intracellular calcium ion (Ca(2+)) increases in kidney epithelia depend on primary cilia and primary cilium-localized Ca(2+)-permeable channels polycystin-2 (PC2) and transient receptor potential vanilloid 4 (TRPV4). While primary cilia have been implicated in osteocyte mechanotransduction, the molecular mechanism that mediates this process is not fully understood. We directed a fluorescence resonance energy transfer (FRET)-based Ca(2+) biosensor to the cilium by fusing the biosensor sequence to the sequence of the primary cilium-specific protein Arl13b. Using this tool, we investigated the role of several Ca(2+)-permeable channels that may mediate flow-induced Ca(2+) entry: PC2, TRPV4, and PIEZO1. RESULTS: Here, we report the first measurements of Ca(2+) signaling within osteocyte primary cilia using a FRET-based biosensor fused to ARL13B. We show that fluid flow induces Ca(2+) increases in osteocyte primary cilia which depend on both intracellular Ca(2+) release and extracellular Ca(2+) entry. Using siRNA-mediated knockdowns, we demonstrate that TRPV4, but not PC2 or PIEZO1, mediates flow-induced ciliary Ca(2+) increases and loading-induced Cox-2 mRNA increases, an osteogenic response. CONCLUSIONS: In this study, we show that the primary cilium forms a Ca(2+) microdomain dependent on Ca(2+) entry through TRPV4. These results demonstrate that the mechanism of mechanotransduction mediated by primary cilia varies in different tissue contexts. Additionally, we anticipate that this work is a starting point for more studies investigating the role of TRPV4 in mechanotransduction.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".