<i>Limosilactobacillus reuteri</i> promotes the expression and secretion of enteroendocrine‐ and enterocyte‐derived hormones
Bibliographic record
Abstract
Intestinal microbes can beneficially impact host physiology, prompting investigations into the therapeutic usage of such microbes in a range of diseases. For example, human intestinal microbe Limosilactobacillus reuteri strains ATCC PTA 6475 and DSM 17938 are being considered for use for intestinal ailments, including colic, infection, and inflammation, as well as for non-intestinal ailments, including osteoporosis, wound healing, and autism spectrum disorder. While many of their beneficial properties are attributed to suppressing inflammatory responses, we postulated that L. reuteri may also regulate intestinal hormones to affect physiology within and outside of the gut. To determine if L. reuteri secreted factors impact the secretion of enteric hormones, we treated an engineered jejunal organoid line, NGN3-HIO, which can be induced to be enriched in enteroendocrine cells, with L. reuteri 6475 or 17938 conditioned medium and performed transcriptomics. Our data suggest that these L. reuteri strains affect the transcription of many gut hormones, including vasopressin and luteinizing hormone subunit beta, which have not been previously recognized as produced in the gut epithelium. Moreover, we find that these hormones appear to be produced in enterocytes, in contrast to canonical gut hormones produced in enteroendocrine cells. Finally, we show that L. reuteri conditioned media promote the secretion of enteric hormones, including serotonin, GIP, PYY, vasopressin, and luteinizing hormone subunit beta, and identify by metabolomics metabolites potentially mediating these effects on hormones. These results support L. reuteri affecting host physiology through intestinal hormone secretion, thereby expanding our understanding of the mechanistic actions of this microbe.
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 | 0.000 |
| 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) | 0.001 | 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".