Identification of PTPσ as an autophagic phosphatase
Bibliographic record
Abstract
Macroautophagy is a dynamic process whereby portions of the cytosol are encapsulated in double-membrane vesicles and delivered to the lysosome for degradation. Phosphatidylinositol-3-phosphate (PtdIns3P) is concentrated on autophagic vesicles and recruits effector proteins that are crucial for this process. The production of PtdIns3P by the class III phosphatidylinositol 3-kinase Vps34, has been well established; however, protein phosphatases that antagonize this early step in autophagy remain to be identified. To identify such enzymes, we screened human phosphatase genes by RNA interference and found that loss of PTPσ, a dual-domain protein tyrosine phosphatase (PTP), increases levels of cellular PtdIns3P. The abundant PtdIns3P-positive vesicles conferred by loss of PTPσ strikingly phenocopied those observed in cells starved of amino acids. Accordingly, we discovered that loss of PTPσ hyperactivates both constitutive and induced autophagy. Finally, we found that PTPσ localizes to PtdIns3P-positive membranes in cells, and this vesicular localization is enhanced during autophagy. We therefore describe a novel role for PTPσ and provide insight into the regulation of autophagy. Mechanistic knowledge of this process is crucial for understanding and targeting therapies for several human diseases, including cancer and Alzheimer's disease, in which abnormal autophagy might be pathological.
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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 | 0.001 | 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 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".