Abstract 15392: Inflammation and Ischemic Heart Failure: Monocyte-Mediated Cardiac Fibroblast Activation and Matrix Remodeling Through a Direct Cell-Cell Contact Mechanism
Bibliographic record
Abstract
BACKGROUND: Following myocardial infarction (MI), activated cardiac myofibroblasts facilitate extracellular matrix (ECM) remodeling to prevent mechanical complications. However, prolonged myofibroblast activity leads to dysregulation of the ECM, maladaptive remodeling, fibrosis and heart failure (HF). Chronic inflammation is believed to drive persistent myofibroblast activity, however, the mechanisms are unclear. In this study, we explored the effects of peripheral blood monocytes on human cardiac fibroblast activation in a 3D ECM microenvironment. METHODS/RESULTS: Human cardiac fibroblasts isolated from surgical human heart biopsies were seeded into 3D collagen matrices. Peripheral blood monocytes isolated from healthy human donors were co-cultured with fibroblasts. Monocytes increased fibroblast activation measured by collagen ECM contraction (17.9±11.1% increase; p<0.01) and resulted in local ECM remodeling observed by confocal microscopy. Under co-culture conditions that prevent cell-cell contact but allow interaction via paracrine factors, monocytes had minimal effects on fibroblast activation (6.4±7.0 vs.17.9±11.1% increase, respectively; p<0.01). Multiplex analysis of the co-culture media revealed an increase in the paracrine factors Transforming Growth Factor-beta 1 (TGF-β1) and Matrix Metalloproteinase 9 when monocytes and fibroblasts were cultured under cell-cell contact conditions (162.2±11.7pg/mL and 17.5±0.5ng/mL, respectively, vs. 21.8±5.7pg/mL and 4.9 ±0.4ng/mL; p<0.001). TGF-β1 blockade abolished monocyte induced cardiac fibroblast activation, as did β1-integrin. These data suggest direct cell-cell interaction between monocytes and cardiac fibroblasts through β1-integrin results in TGF-β1 release facilitating fibroblast activation and matrix remodeling. CONCLUSION: For the first time, we demonstrate that peripheral blood monocytes stimulate human cardiac fibroblast activation through a mechanism involving TGF-β1 release as a consequence of direct cell-cell interaction through β1-integrin. These data implicate inflammation as a driver of cardiac fibrosis post-MI, highlighting potential novel therapeutic targets for the treatment of ischemic HF.
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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) | 0.001 | 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) | 0.010 | 0.002 |
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".