ENaC and Its Regulatory Proteins as Drug Targets for Blood Pressure Control
Bibliographic record
Abstract
Hypertension is a serious medical problem affecting millions of people worldwide. A key protein regulating blood pressure is the Epithelial Na(+) Channel (ENaC). In accord, loss of function mutations in ENaC (PHA1) cause hypotension, whereas gain of function mutations (Liddle syndrome) result in hypertension. The region mutated in Liddle syndrome, called the PY motif (L/PPxY), serves as a binding site for the ubiquitin ligase Nedd4-2, a C2-WW-Hect E3 ubiquitin ligase. Nedd4-2 binds the ENaC-PY motif via it WW domains, ubiquitylates the channel and targets it for endocytosis, a process impaired in Liddle syndrome due to poor binding of the channel to Nedd4-2. This leads to accumulation of active channels at the cell surface and increased Na(+) (and fluid) absorption in the distal nephron, resulting in elevated blood volume and blood pressure. Compounds that destabilize cell surface ENaC, or enhance Nedd4-2 activity in the kidney, could potentially serve as drug targets for hypertension. In addition, recent discoveries of regulation of activation of ENaC by proteases such as furin, prostasin and elastase, which cleave the extracellular domain of this channel leading to it activation, as well as the identification of inhibitors that block the activity of these proteases, provide further avenues for drug targeting of ENaC and the control of blood pressure.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".