Proteomics and phosphoproteomics of failing human left ventricle identifies dilated cardiomyopathy-associated phosphorylation of CTNNA3
Bibliographic record
Abstract
The prognosis and treatment outcomes of heart failure (HF) patients rely heavily on disease etiology, yet the majority of underlying signaling mechanisms are complex and not fully elucidated. Phosphorylation is a major point of protein regulation with rapid and profound effects on the function and activity of protein networks. Currently, there is a lack of comprehensive proteomic and phosphoproteomic studies examining cardiac tissue from HF patients with either dilated dilated cardiomyopathy (DCM) or ischemic cardiomyopathy (ICM). Here, we used a combined proteomic and phosphoproteomic approach to identify and quantify more than 5,000 total proteins with greater than 13,000 corresponding phosphorylation sites across explanted left ventricle (LV) tissue samples, including HF patients with DCM vs. nonfailing controls (NFC), and left ventricular infarct vs. noninfarct, and periinfarct vs. noninfarct regions of HF patients with ICM. Each pair-wise comparison revealed unique global proteomic and phosphoproteomic profiles with both shared and etiology-specific perturbations. With this approach, we identified a DCM-associated hyperphosphorylation cluster in the cardiomyocyte intercalated disc (ICD) protein, αT-catenin (CTNNA3). We demonstrate using both ex vivo isolated cardiomyocytes and in vivo using an AAV9-mediated overexpression mouse model, that CTNNA3 phosphorylation at these residues plays a key role in maintaining protein localization at the cardiomyocyte ICD to regulate conductance and cell-cell adhesion. Collectively, this integrative proteomic/phosphoproteomic approach identifies region- and etiology-associated signaling pathways in human HF and describes a role for CTNNA3 phosphorylation in the pathophysiology of DCM.
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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".