A Novel Phosphatidic Acid-Protein-tyrosine Phosphatase D2 Axis Is Essential for ERBB2 Signaling in Mammary Epithelial Cells
Bibliographic record
Abstract
Background: The role of protein-tyrosine phosphatases (PTP) in ERBB2 signaling is undefined. Results: Phosphatidic acid (PA) activated PTPD2; inhibition of PA production or PTPD2 expression attenuated ERBB2-mediated morphological changes in mammary epithelial cells. Conclusion: The PLD2-PTPD2 axis is required for ERBB2 signaling. Significance: PA-regulated PTPD2 activity is a novel, positive element of ERBB2 signaling, which may offer a new therapeutic strategy in breast cancer. We used a loss-of-function screen to investigate the role of classical protein-tyrosine phosphatases (PTPs) in three-dimensional mammary epithelial cell morphogenesis and ERBB2 signaling. The study revealed a novel role for PTPD2 as a positive regulator of ERBB2 signaling. Suppression of PTPD2 attenuated the ERBB2-induced multiacinar phenotype in three-dimensional cultures specifically by inhibiting ERBB2-mediated loss of polarity and lumen filling. In contrast, overexpression of PTPD2 enhanced the ERBB2 phenotype. We also found that a lipid second messenger, phosphatidic acid, bound PTPD2 in vitro and enhanced its catalytic activity. Small molecule inhibitors of phospholipase D (PLD), an enzyme that produces phosphatidic acid in cells, also attenuated the ERBB2 phenotype. Exogenously added phosphatidic acid rescued the PLD-inhibition phenotype, but only when PTPD2 was present. These findings illustrate a novel pathway involving PTPD2 and the lipid second messenger phosphatidic acid that promotes ERBB2 function.
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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".