Preterm infant gut microbiota affects intestinal epithelial development in a humanized microbiome gnotobiotic mouse model
Bibliographic record
Abstract
Development of the infant small intestine is influenced by bacterial colonization. To promote establishment of optimal microbial communities in preterm infants, knowledge of the beneficial functions of the early gut microbiota on intestinal development is needed. The purpose of this study was to investigate the impact of early preterm infant microbiota on host gut development using a gnotobiotic mouse model. Histological assessment of intestinal development was performed. The differentiation of four epithelial cell lineages (enterocytes, goblet cells, Paneth cells, enteroendocrine cells) and tight junction (TJ) formation was examined. Using weight gain as a surrogate marker for health, we found that early microbiota from a preterm infant with normal weight gain (MPI-H) induced increased villus height and crypt depth, increased cell proliferation, increased numbers of goblet cells and Paneth cells, and enhanced TJs compared with the changes induced by early microbiota from a poor weight gain preterm infant (MPI-L). Laser capture microdissection (LCM) plus qRT-PCR further revealed, in MPI-H mice, a higher expression of stem cell marker Lgr5 and Paneth cell markers Lyz1 and Cryptdin5 in crypt populations, along with higher expression of the goblet cell and mature enterocyte marker Muc3 in villus populations. In contrast, MPI-L microbiota failed to induce the aforementioned changes and presented intestinal characteristics comparable to a germ-free host. Our data demonstrate that microbial communities have differential effects on intestinal development. Future studies to identify pioneer settlers in neonatal microbial communities necessary to induce maturation may provide new insights for preterm infant microbial ecosystem therapeutics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".