Effect of protein modification in synbiotic infant formula on probiotic metabolic activity and bacterial composition in an infant gut-model
Bibliographic record
Abstract
Aim: Microbial colonization of the neonatal gut is pivotal in priming the infant’s immune system. Human milk (HM) is the best nutrition for infants and supports the development of the microbiota due to prebiotic compounds and probiotic microorganisms. When exclusive breastfeeding is not possible, infant formula (IF) with probiotics is a strategy to support the infant’s microbiome development. However, knowledge about the effects of the infant gut microbiota and different compounds in IF on individual probiotic strains is limited, as strain-level detection in a complex ecosystem is challenging. The aim of the present study was to show the effects of IF with different protein forms on the metabolic activity of two probiotic strains isolated from HM in a complex ecosystem. Methods: By using an ex-vivo infant gut model containing infant donor-microbiota, the effects of IF with either intact or extensively hydrolyzed protein on the metabolic activity of the donor microbiota, as well as two probiotic strains [Limosilactobacillus fermentum (L. fermentum ) CECT 5716 (Lf) and Bifidobacterium breve (B. breve ) DSM 32583 (Bb)], were analyzed. A new bioinformatic pipeline combined with a specific infant microbiome database was used to explore shotgun metagenome datasets (1200 Megabases) for taxonomic identification and strain-level tracking. Results: Both protein forms (i.e., intact or extensively hydrolyzed protein) in IF supported infant gut microbial metabolic activity equally, as evidenced by similar levels of short-chain fatty acids (SCFAs). Interestingly, gut microbial metabolic activity was found to be differently activated in a strain-dependent manner. Taxonomic profiling of the microbiome at the strain level enabled monitoring of the prevalence and abundance of both probiotic strains, even in a complex ecosystem. Conclusion: Food matrix and host microbiota interactions should be considered when evaluating strain-specific probiotic effects in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".