Human Intestinal Enteroids With Inducible Neurogenin-3 Expression as a Novel Model of Gut Hormone Secretion
Bibliographic record
Abstract
BACKGROUND & AIMS: Enteroendocrine cells (EECs) are specialized epithelial cells that produce molecules vital for intestinal homeostasis, but because of their limited numbers, in-depth functional studies have remained challenging. Human intestinal enteroids (HIEs) that are derived from intestinal crypt stem cells are biologically relevant in an in vitro model of the intestinal epithelium. HIEs contain all intestinal epithelial cell types; however, similar to the intestine, HIEs spontaneously produce few EECs, which limits their study. METHODS: To increase the number of EECs in HIEs, we used lentivirus transduction to stably engineer jejunal HIEs with doxycycline-inducible expression of neurogenin-3 (NGN3), a transcription factor that drives EEC differentiation (tetNGN3-HIEs). We examined the impact of NGN3 induction on EECs by quantifying the increase in the enterochromaffin cells and other EEC subtypes. We functionally assessed secretion of serotonin and EEC hormones in response to norepinephrine and rotavirus infection. RESULTS: Treating tetNGN3-HIEs with doxycycline induced a dose-dependent increase of chromogranin A (ChgA)-positive and serotonin-positive cells, showing increased enterochromaffin cell differentiation. Despite increased ChgA-positive cells, other differentiated cell types of the epithelium remained largely unchanged by gene expression and immunostaining. RNA sequencing of doxycycline-induced tetNGN3-HIEs identified increased expression of key hormones and enzymes associated with several other EEC subtypes. Doxycycline-induced tetNGN3-HIEs secreted serotonin, monocyte chemoattractant protein-1, glucose-dependent insulinotropic peptide, peptide YY, and ghrelin in response to norepinephrine and rotavirus infection, further supporting the presence of multiple EEC types. CONCLUSIONS: We have combined HIEs and inducible-NGN3 expression to establish a flexible in vitro model system for functional studies of EECs in enteroids and advance the molecular and physiological investigation of EECs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".