Pathological Oxidation of PTPN12 Underlies ABL1 Phosphorylation in HLRCC
Bibliographic record
Abstract
Abstract Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC) is an inherited cancer syndrome associated with a highly aggressive form of type 2 papillary renal cell carcinoma (PRCC). Germ line inactivating alterations in Fumarate Hydratase ( FH ) cause HLRCC, and result in elevated levels of reactive oxygen species (ROS). Recent work indicates that FH -/- PRCC cells have increased ABL1 activation, which promotes tumor growth, but how ABL1 is activated remained unclear. Oxidation can regulate protein-tyrosine phosphatase (PTP) catalytic activity; conceivably, ROS-catalyzed inactivation of an ABL-directed PTP might account for ABL1 activation in this malignancy. Previously, our group developed “q-oxPTPome,” a method that can globally monitor the oxidation of classical PTPs. We have now refined the q-oxPTPome approach, increasing its sensitivity by >10X. Applying q-oxPTPome to FH-deficient cell models shows that multiple PTPs are either highly oxidized (including PTPN12) or overexpressed. In general, highly oxidized PTPs were those that have relatively high sensitivity to exogenous H 2 O 2 . Most PTP oxidation in FH-deficient cells is reversible, although nearly 40% of PTPN13 is oxidized irreversibly to the sulfonic acid state. Using “substrate-trapping mutants”, we mapped PTPs to their putative substrates, and found that only PTPN12 could target ABL1. Furthermore, knockdown experiments identify PTPN12 as the major ABL1 phosphatase in HLRCC. Overall, our results show that ROS-induced PTPN12 oxidation accounts for ABL1 phosphorylation in HLRCC-associated PRCC, reveal a novel mechanism for inactivating a tumor suppressor gene product, and establish a direct link between pathological PTP oxidation and neoplastic disease.
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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) | 0.001 | 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".