α-catenin phosphorylation is actomyosin-sensitive and required for epithelial barrier functions through Afadin
Bibliographic record
Abstract
Zonula adherens junctions (zAJ) are spatially proximal to tight junctions (TJ), in a superstructure known as the apical junctional complex (AJC). A key component of the AJC is a circumferential ring of filamentous (F)-actin, but how actomyosin contractility drives AJC structure and epithelial barrier function is incompletely understood. Here, we show that a central mechanosensitive component of zAJ, α-catenin (α-cat), undergoes force-dependent phosphorylation in an unstructured linker region. This modification in turn primes the α-cat mechanosensitive Middle-region for effector-binding. We credential Afadin, a multi-domain TJ/AJ scaffold protein, as mechano-chemical binding partner of α-cat, identifying residues in α-cat required for this interaction. α-cat phosphorylation and Afadin-binding are required for their co-enrichment at zAJ and epithelial barrier function. A mouse model that prevents α-cat phosphorylation is particularly detrimental to post-natal brain development. These data support a stepwise model where α-cat integrates mechanical and chemical signals to progressively promote zAJ enrichment, effector recruitment and epithelial barrier function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".