Itch E3 ubiquitin ligase regulates large tumor suppressor 1 stability
Bibliographic record
Abstract
The large tumor suppressor 1 (LATS1) is a serine/threonine kinase and tumor suppressor found down-regulated in a broad spectrum of human cancers. LATS1 is a central player of the emerging Hippo-LATS suppressor pathway, which plays important roles in cell proliferation, apoptosis, and stem cell differentiation. Despite the ample data supporting a role for LATS1 in tumor suppression, how LATS1 is regulated at the molecular level remains largely unknown. In this study, we have identified Itch, a HECT class E3 ubiquitin ligase, as a unique binding partner of LATS1. Itch can complex with LATS1 both in vitro and in vivo through the PPxY motifs of LATS1 and the WW domains of Itch. Significantly, we found that overexpression of Itch promoted LATS1 degradation by polyubiquitination through the 26S proteasome pathway. On the other hand, knockdown of endogenous Itch by shRNAs provoked stabilization of endogenous LATS1 proteins. Finally, through several functional assays, we also revealed that change of Itch abundance alone is sufficient for altering LATS1-mediated downstream signaling, negative regulation of cell proliferation, and induction of apoptosis. Taking these data together, our study identifies E3 ubiquitin ligase Itch as a unique negative regulator of LATS1 and presents a possibility of targeting LATS1/Itch interaction as a therapeutic strategy in cancer.
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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".