PEX14 functions as a molecular link between optineurin and pexophagy in human cells
Bibliographic record
Abstract
Abstract Pexophagy, the selective degradation of peroxisomes, is essential for removing excess or dysfunctional peroxisomes, and its dysregulation is linked to various diseases. Previous research has shown that optineurin (OPTN), an autophagy receptor involved in mitophagy, aggrephagy, and xenophagy, can induce pexophagy in HEK-293 cells. However, the underlying mechanism remains unclear. In this study, we used proximity labeling to identify PEX14, a peroxisomal membrane protein, as a neighboring partner of OPTN. Biochemical analyses revealed that PEX14 and OPTN interact through their respective coiled-coil and ubiquitin-binding domains. Further analyses demonstrated that the C-terminal half of overexpressed OPTN triggers pexophagy, likely by forming oligomers with endogenous OPTN. The co-localization of PEX14-OPTN complexes with LC3, combined with the suppression of OPTN-mediated peroxisome degradation by bafilomycin A1, supports a model in which PEX14 acts as a docking site for OPTN on the peroxisomal membrane, enabling the recruitment of the autophagic machinery for OPTN-mediated pexophagy. Summary This study uncovers and defines the peroxisomal membrane protein PEX14 as a key player in optineurin-driven pexophagy, advancing our mechanistic understanding of this cellular process. These findings open new avenues for developing therapeutic strategies targeting diseases associated with defective pexophagy.
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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) | 0.000 | 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) | 0.003 | 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".