Early-Life Poultry-Derived Lactobacilli Drive Microbial Succession and Gut Immune Modulation in Broiler Chickens
Bibliographic record
Abstract
Probiotic supplementation supports poultry gut health by modulating microbiome and promoting immune development, yet limited information is known about the effects of early, particularly embryonic, supplementation. In this study, we investigated the effects of administering a lactobacilli cocktail in ovo (embryonic day 18), post-hatch, or both on gut immunity and the succession of the cecal microbiota in broilers over five weeks. 16S rRNA gene-based sequencing of cecal contents revealed a steady increase in Shannon diversity during the first three weeks (PERMANOVA, p < 0.005), with community structure stabilizing by week 3 across all groups. In ovo lactobacilli administration improved early hatch rates and modulated microbial composition during early succession, including reductions in Klebsiella and Enterococcus, and enrichment of Lactobacillus, during the first two weeks (MaAsLin2, q < 0.25). These microbiome shifts were accompanied by a reduced expression of pro-inflammatory cytokines (IFN-γ, IL-1β, and IL-8) in cecal tonsils. These findings highlight the transient yet critical role of early-life probiotic interventions in shaping gut microbial colonization and immune response in broiler chickens. More importantly, a single in ovo lactobacilli dose yielded effects comparable to weekly oral or combined administration.
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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 | 0.001 |
| 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".