Abstract 177: Overexpression of Cardiac Troponin I-Interacting Kinase, a Cardiac-Specific MAPKKK, Promotes Cardiac Dysfunction
Bibliographic record
Abstract
Cardiac Troponin I-interacting kinase (TNNI3K) is a cardiac specific kinase whose biological function remains largely unknown. We have recently shown that TNNI3K expression greatly accelerates cardiac dysfunction in mouse models of cardiomyopathy, indicating an important role in modulating disease progression. To further investigate TNNI3K kinase activity in vivo, we have generated transgenic mice expressing both wild-type and kinase-dead versions of the human TNNI3K protein. Importantly, we show that the increased TNNI3K kinase activity induces mouse cardiac hypertrophy, and the kinase activity is required to accelerate disease progression in a left-ventricular pressure overload model of mouse cardiomyopathy. We demonstrate the clinical relevance of these observations by identifying two potential missense mutations near the kinase activation loop of TNNI3K in idiopathic dilated cardiomyopathy (DCM) human patients. Using an in vitro kinase assay and proteomics analysis, we show that TNNI3K is a dual-function kinase with Tyr and Ser/Thr kinase activity. Using antisera to TNNI3K, we show that TNNI3K protein is located at the sarcomere Z disc. These combined data suggest that TNNI3K mediates cell signaling to modulate cardiac response to stress. The essential role of the kinase activity makes TNNI3K a strong potential pharmaceutical target of kinase inhibitors for heart disease.
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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) | 0.005 | 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".