α-Catenin force-sensitive binding and sequestration of LZTS2 leads to cytokinesis failure
Bibliographic record
Abstract
Epithelial cells can become polyploid upon tissue injury, but mechanosensitive cues that trigger this state are poorly understood. Using an Madin Darby Canine Kidney (MDCK) cell knock-out/reconstitution system, we show that α-catenin mutants that alter force-sensitive binding to F-actin or middle (M)-domain promote cytokinesis failure and binucleation, particularly near epithelial wound-fronts. We identified Leucine Zipper Tumor Suppressor 2 (LZTS2), a factor previously implicated in abscission, as a conformation sensitive proximity partner of α-catenin. We show that LZTS2 enriches not only at midbody/intercellular bridges but also at apical adhering junctions. α-Catenin mutants with persistent M-domain opening show elevated junctional enrichment of LZTS2 compared with wild-type cells. LZTS2 knock-down leads to elevated rates of binucleation. These data implicate LZTS2 as a mechanosensitive effector of α-catenin that is critical for cytokinetic fidelity. This model rationalizes how persistent mechanoactivation of α-catenin may drive tension-induced polyploidization of epithelia after injury and suggests an underlying mechanism for how pathogenic α-catenin M-domain mutations drive macular dystrophy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".