Tumor Necrosis Factor-α–Mediated Downregulation of the Cystic Fibrosis Transmembrane Conductance Regulator Drives Pathological Sphingosine-1-Phosphate Signaling in a Mouse Model of Heart Failure
Bibliographic record
Abstract
BACKGROUND: Sphingosine-1-phosphate (S1P) signaling is a central regulator of resistance artery tone. Therefore, S1P levels need to be tightly controlled through the delicate interplay of its generating enzyme sphingosine kinase 1 and its functional antagonist S1P phosphohydrolase-1. The intracellular localization of S1P phosphohydrolase-1 necessitates the import of extracellular S1P into the intracellular compartment before its degradation. The present investigation proposes that the cystic fibrosis transmembrane conductance regulator transports extracellular S1P and hence modulates microvascular S1P signaling in health and disease. METHODS AND RESULTS: In cultured murine vascular smooth muscle cells in vitro and isolated murine mesenteric and posterior cerebral resistance arteries ex vivo, the cystic fibrosis transmembrane conductance regulator (1) is critical for S1P uptake; (2) modulates S1P-dependent responses; and (3) is downregulated in vitro and in vivo by tumor necrosis factor-α, with significant functional consequences for S1P signaling and vascular tone. In heart failure, tumor necrosis factor-α downregulates the cystic fibrosis transmembrane conductance regulator across several organs, including the heart, lung, and brain, suggesting that it is a fundamental mechanism with implications for systemic S1P effects. CONCLUSIONS: We identify the cystic fibrosis transmembrane conductance regulator as a critical regulatory site for S1P signaling; its tumor necrosis factor-α-dependent downregulation in heart failure underlies an enhancement in microvascular tone. This molecular mechanism potentially represents a novel and highly strategic therapeutic target for cardiovascular conditions involving inflammation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".