Interaction/Modulation of PKD2 by TACAN
Bibliographic record
Abstract
Background: TACAN (also named TMEM120A), recently reported as a mechanoand pain-sensing ion channel, is distributed in diverse non-neuronal tissues such as heart, intestine and kidney, which indicates its potential role besides pain sensation. Previous proteomic screenings suggested the presence of an interaction between PKD2 and TACAN. In this study, we investigated the physical and functional interaction between the two proteins. Methods: We employed mutagenesis, molecular cloning, co-immunoprecipitation, immunofluorescence, biotinylation, two-electrode voltage clamp in Xenopus oocytes to measure whole-cell currents and patch-clamp in Chinese hamster ovary (CHO) cells to measure single-channel currents. Results: We found that TACAN is co-localized and in complex with PKD2 in primary cilia of different kidney cell lines and oocytes. Using oocyte expression, we found that TACAN inhibits the channel activity of PKD2 gain-of-function mutant F604P. Using CHO cell expression, we found that TACAN inhibits both wild-type PKD2 and mutant F604P through reducing their single-channel conductance and open probability. Co-expression of TACAN significantly enhanced the sensitivity of PKD2 to stretch. Further, our data showed that while the first and last transmembrane domains (TM1 and TM6) of TACAN are involved in interaction with transmembrane domains of PKD2 only the TACAN TM6 is functionally relevant. Conclusions: Our study revealed inhibition of PKD2 channel activity through physical TACAN-PKD2 complexing and that TACAN, but not PKD2, mediates mechanosensitivity of the channel complex. Whether and how TACAN participates in the pore formation remains to be determined. Funding: Government Support - Non-U.S.
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) | 0.000 | 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) | 0.001 | 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 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".