Role of S-Palmitoylation by ZDHHC13 in Mitochondrial function and Metabolism in Liver
Bibliographic record
Abstract
Palmitoyltransferase (PAT) catalyses protein S-palmitoylation which adds 16-carbon palmitate to specific cysteines and contributes to various biological functions. We previously reported that in mice, deficiency of Zdhhc13, a member of the PAT family, causes severe phenotypes including amyloidosis, alopecia, and osteoporosis. Here, we show that Zdhhc13 deficiency results in abnormal liver function, lipid abnormalities, and hypermetabolism. To elucidate the molecular mechanisms underlying these disease phenotypes, we applied a site-specific quantitative approach integrating an alkylating resin-assisted capture and mass spectrometry-based label-free strategy for studying the liver S-palmitoylome. We identified 2,190 S-palmitoylated peptides corresponding to 883 S-palmitoylated proteins. After normalization using the membrane proteome with TMT10-plex labelling, 400 (31%) of S-palmitoylation sites on 254 proteins were down-regulated in Zdhhc13-deficient mice, representing potential ZDHHC13 substrates. Among these, lipid metabolism and mitochondrial dysfunction proteins were overrepresented. MCAT and CTNND1 were confirmed to be specific ZDHHC13 substrates. Furthermore, we found impaired mitochondrial function in hepatocytes of Zdhhc13-deficient mice and Zdhhc13-knockdown Hep1-6 cells. These results indicate that ZDHHC13 is an important regulator of mitochondrial activity. Collectively, our study allows for a systematic view of S-palmitoylation for identification of ZDHHC13 substrates and demonstrates the role of ZDHHC13 in mitochondrial function and metabolism in liver.
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".