Human Milk Oligosaccharides Protect against Necrotizing Enterocolitis by Activating Intestinal Cell Differentiation
Bibliographic record
Abstract
SCOPE: Necrotizing enterocolitis (NEC) is a devastating gastrointestinal emergency and currently the leading cause of mortality in preterm infants. Recent studies show that human milk oligosaccharides (HMOs) reduce the frequency and incidence of NEC; however, the molecular mechanisms for their protection are largely unexplored. METHODS AND RESULTS: To address this gap, a genome-wide profiling of the intestinal epithelial transcriptome in response to HMOs using RNA-sequencing is performed. It is found that HMOs alter the host transcriptome in 225 unique target genes pertaining to cell proliferation and differentiation, including upregulation of stem cell differentiation marker HMGCS2. To validate these results, differentiation in Caco-2Bbe1 (Caco-2) intestinal cells is verified by Alcian Blue staining and transepithelial electrical resistance (TER) recordings. Furthermore, an in vivo model of NEC is also employed whereby neonatal pups are gavage fed HMOs. Interestingly, HMOs-fed pups show enhanced cell MUC2 differentiation and HMGCS2 expression. CONCLUSIONS: These findings demonstrate HMOs protect against NEC in part by altering the differentiation of the crypt-villus axis. In addition, this study suggests that pooled HMOs directly induce a series of biological processes, which provide mechanistic insights to how HMOs protect the host intestine.
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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".