TNFα affects sphingosine‐1‐phosphate signaling through modulating the expression of the cystic fibrosis transmembrane regulator (CFTR) in vascular smooth muscle cells
Bibliographic record
Abstract
Cellular effects of the ubiquitous signaling molecule sphingosine‐1‐phosphate (S1P) are tightly controlled by a rheostat between the S1P‐generating enzyme sphingosine kinase 1 (Sphk1) and its functional antagonist S1P phosphohydrolase 1 (SPP1). The intracellular localization of SPP1 requires the import of S1P prior to its degradation. We propose that the import into vascular smooth muscle cells (VSMC) critically depends on the cystic fibrosis transmembrane regulator (CFTR) and that changes in CFTR function regulate S1P signaling. In the vasculature, S1P regulates resistance artery myogenic tone and hence, peripheral resistance. Both parameters are increased in heart failure (HF), a finding that is associated with down‐regulation of vascular CFTR. The pro‐inflammatory cytokine TNFα mechanistically links HF and CFTR because (1) TNFα down‐regulates CFTR mRNA and protein expression, (2) increased TNFα expression and down‐regulation of CFTR in resistance arteries co‐localize in HF and (3) sequestration of TNFα with etanercept normalizes CFTR expression. In addition to these genomic effects, TNFα also acutely controls CFTR function through regulation of its membrane abundance. Our data suggest that TNFα‐mediated down‐regulation of CFTR function shifts the balance between S1P synthesis and degradation, leading to enhanced bioavailability and vascular/cellular effects of S1P. Funding: HSFO, CIHR
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".