CFTR High Expresser BEST4+ cells are pH-sensing neuropod cells: new implications for intestinal physiology and Cystic Fibrosis disease
Bibliographic record
Abstract
ABSTRACT Single-cell RNA sequencing (scRNA-seq) studies identified a novel subpopulation of epithelial cells along the rostrocaudal axis of human intestine specifically marked by bestrophin 4 (BEST4) that are enriched for genes regulating pH, GPCR acid-sensing receptors, satiety, cGMP signaling, HCO3 - secretion, ion transport, neuropeptides, and paracrine hormones. Interestingly, BEST4+ cells in the proximal small intestine express CFTR but have not been linked to the previously described CFTR High Expresser Cell (CHE) subpopulation in rat and human intestine. ScRNA-seq studies in rat jejunum identified CHEs and a gene expression profile consistent with human small intestinal BEST4+ and neuropod cells. Protein immunolocalization confirmed that CHEs express CFTR, BEST4, neuropod proteins, high levels of intracellular uroguanylin (UGN), guanylyl cyclase-C (GC-C), and the proton channel otopetrin 2 (OTOP2), and display long basal processes connecting to neurons. OTOP2, GC-C, and CFTR traffic robustly into the apical domain of CHEs in response to acidic luminal conditions, indicating their roles in luminal pH regulation. In the ΔF508 cystic fibrosis (CF) rat jejunum, the loss of apical CFTR did not affect BEST4 protein expression in CHEs. However, there was an increased abundance of CHE cells in the ΔF508 rat jejunum compared to wild-type animals. Furthermore, ΔF508 rat CHEs expressed higher levels of GC-C at the apical domain compared to wild-type. These data implicate CHEs in intestinal CF disease pathogenesis. NEW & NOTEWORTHY This is the first study to identify CFTR High Expresser cells in the rat small intestine as neuropod cells capable of sensing and responding to luminal pH. This study also provides the first characterization of CFTR and relevant mRNA and proteins in CHEs in CF rat models that provide insights into the significance of CHEs to CF intestinal disease.
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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 | 0.000 |
| 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".