Cecal bacterial communities and lung immunity in layers fed omega-3 fatty acids or yeast bioactives under different space allowance
Bibliographic record
Abstract
The poultry-rearing environment and diet can influence gut microbiota and immunity. However, the long-term effects of early-life nutritional interventions on gut heath in layers under various spacing allowances (SA) are not well understood. This study investigated how early-life dietary yeast bioactives (YB) or co-extruded full-fat flaxseed, a source of omega-3 fatty acids (N-3 FA), from placement to 16 weeks of age (woa) influenced the development of cecal microbiota and immune function in Lohmann LSL Lite pullets reared under different SA, with follow-up to 72 woa. The experiment involved 2,832 newly hatched chicks reared in an enriched cage system under high (HSA, 348 cm 2 /bird) or low (LSA, 284 cm 2 /bird) SA and fed either control diet (C), C + 3 % N-3 FA, or C + 0.05 % YB. Ceca were sampled at 4, 8, 16, 35, and 72 woa for bacterial plate counts, 16S rRNA sequencing, and short-chain fatty acid (SCFA) quantification. Lungs were analyzed for immune gene expression. At 4 and 16 woa, β-diversity ( P = 0.01 ) revealed dissimilarity between the bacterial communities of birds under HSA and LSA groups. High Bacteroides abundances, propionic and iso-butyric acids concentrations were observed in LSA at both sampling points ( P < 0.05 ). At 16 woa, the highest and lowest n-butyrate concentrations were noted in N-3 FA- and control-fed birds, respectively ( P < 0.05 ), regardless of SA. Barnesiellaceae positively correlated with n-valeric and acetic acid in N-3 FA-fed birds at 4 woa. In YB-fed pullets, Lactobacillaceae and Enterobacteriaceae showed a positive correlation with n-butyric acid. In the lungs, about 15 genes, including IL1β , IL2 , and IL8 , were differentially expressed depending on SA and diets ( P < 0.05 ). Early dietary YB or N-3 FA under different SA modulated cecal bacterial community diversity and structure, SCFA profiles, and enhanced lung immune responses in layer.
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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".