Autoimmune Valvular Carditis Requires Endothelial Cell TNFR1 Expression
Bibliographic record
Abstract
BACKGROUND: Inflammation is a key driver of cardiovascular pathology, and many systemic autoimmune/rheumatic diseases are accompanied by increased cardiac risk. In the K/B.g7 mouse model of coexisting systemic autoantibody-mediated arthritis and valvular carditis, valve inflammation depends on macrophage production of TNF (tumor necrosis factor) and IL-6 (interleukin-6). Here, we sought to determine if other canonical inflammatory pathways participate and to determine whether TNF signaling through TNFR1 (tumor necrosis factor receptor 1) on endothelial cells is required for valvular carditis. METHODS: We first asked if type 1, 2, or 3 inflammatory cytokine systems (typified by IFNγ, IL-4, and IL-17, respectively) were critical for valvular carditis in K/B.g7 mice, using a combination of in vivo monoclonal antibody blockade and targeted genetic ablation studies. To define the key cellular targets of TNF, we conditionally deleted its main proinflammatory receptor, TNFR1, in endothelial cells. We analyzed how the absence of endothelial cell TNFR1 affected valve inflammation, lymphangiogenesis, and the expression of proinflammatory genes and molecules. RESULTS: We found that typical type 1, 2, and 3 inflammatory cytokine systems were not required for valvular carditis, apart from a known initial requirement of IL-4 for autoantibody production. Despite expression of TNFR1 on a wide variety of cell types in the cardiac valve, deleting TNFR1 specifically on endothelial cells protected K/B.g7 mice from valvular carditis. This protection was accompanied by reduced expression of VCAM-1 (vascular cell adhesion molecule), fewer valve-infiltrating macrophages, reduced pathogenic lymphangiogenesis, and diminished proinflammatory gene expression. CONCLUSIONS: TNF and IL-6 are the main cytokines driving valvular carditis in K/B.g7 mice. The interaction of TNF with TNFR1 specifically on endothelial cells promotes cardiovascular pathology in the setting of systemic autoimmune/rheumatic disease, suggesting that therapeutic targeting of the TNF:TNFR1 interaction could be beneficial in this clinical context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".