Sodium-coupled neutral amino acid transporter SNAT2 counteracts cardiogenic pulmonary edema by driving alveolar fluid clearance
Bibliographic record
Abstract
The constant transport of ions across the alveolar epithelial barrier regulates alveolar fluid homeostasis. Dysregulation or inhibition of Na+ transport causes fluid accumulation in the distal airspaces resulting in impaired gas exchange and respiratory failure. Previous studies have primarily focused on the critical role of amiloride-sensitive epithelial sodium channel (ENaC) in alveolar fluid clearance (AFC), yet activation of ENaC failed to attenuate pulmonary edema in clinical trials. Since 40% of AFC is amiloride-insensitive, Na+ channels/transporters other than ENaC such as Na+-coupled neutral amino acid transporters (SNATs) may provide novel therapeutic targets. Here, we identified a key role for SNAT2 ( SLC38A2) in AFC and pulmonary edema resolution. In isolated perfused mouse and rat lungs, pharmacological inhibition of SNATs by HgCl2 and α-methylaminoisobutyric acid (MeAIB) impaired AFC. Quantitative RT-PCR identified SNAT2 as the highest expressed System A transporter in pulmonary epithelial cells. Pharmacological inhibition or siRNA-mediated knockdown of SNAT2 reduced transport of l-alanine across pulmonary epithelial cells. Homozygous Slc38a2−/− mice were subviable and died shortly after birth with severe cyanosis. Isolated lungs of Slc38a2+/− mice developed higher wet-to-dry weight ratios (W/D) as compared to wild type (WT) in response to hydrostatic stress. Similarly, W/D ratios were increased in Slc38a2+/− mice as compared to controls in an acid-induced lung injury model. Our results identify SNAT2 as a functional transporter for Na+ and neutral amino acids in pulmonary epithelial cells with a relevant role in AFC and the resolution of lung edema. Activation of SNAT2 may provide a new therapeutic strategy to counteract and/or reverse pulmonary edema.
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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) | 0.001 | 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) | 0.001 | 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".