Identification of a Novel Link between the Protein Kinase NDR1 and TGFβ Signaling in Epithelial Cells
Bibliographic record
Abstract
Transforming growth factor-beta (TGFβ) is a secreted polypeptide that plays essential roles in cellular development and homeostasis. Although mechanisms of TGFβ-induced responses have been characterized, our understanding of TGFβ signaling remains incomplete. Here, we uncover a novel function for the protein kinase NDR1 (nuclear Dbf2-related 1) in TGFβ responses. Using an immunopurification approach, we find that NDR1 associates with SnoN, a key component of TGFβ signaling. Knockdown of NDR1 by RNA interference promotes the ability of TGFβ to induce transcription and cell cycle arrest in NMuMG mammary epithelial cells. Conversely, expression of NDR1 represses TGFβ-induced transcription and inhibits the ability of TGFβ to induce cell cycle arrest in NMuMG cells. Mechanistically, we find that NDR1 acts in a kinase-dependent manner to suppress the ability of TGFβ to induce the phosphorylation and consequent nuclear accumulation of Smad2, which is critical for TGFβ-induced transcription and responses. Strikingly, we also find that TGFβ reciprocally regulates NDR1, whereby TGFβ triggers the degradation of NDR1 protein. Collectively, our findings define a novel and intimate link between the protein kinase NDR1 and TGFβ signaling. NDR1 suppresses TGFβ-induced transcription and cell cycle arrest, and counteracting NDR1's negative regulation, TGFβ signaling induces the downregulation of NDR1 protein. These findings advance our understanding of TGFβ signaling, with important implications in development and tumorigenesis.
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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) | 0.001 | 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".