PSI-10 Establishing a foundation to harvest the potential of the microbiome in poultry production
Bibliographic record
Abstract
Abstract This study aimed to characterize poultry microbiota composition in commercial farms using 16S rRNA sequencing. Animals raised in sanitized environments have lower survival rates when facing pathogenic challenges compared to animals naturally exposed to commensal organisms. We hypothesized that intensive rearing practices inadvertently impair chicken exposure to microbes and the establishment of a balanced gut microbiota. We compared gut microbiota composition of broilers (n = 78) and layers (n = 20) from different systems, including commercial intensive farms with and without in-feed antibiotics, organic free-range farms, backyard-raised chickens and chickens in an experimental farm. Microbial community composition of conventionally raised broilers was significantly different from antibiotic-free broilers (P = 0.012), from broilers raised outdoors (P = 0.048) and in an experimental farm (P = 0.006) (Fig1). Significant community composition differences were observed between antibiotic-fed and antibiotic-free chickens (Fig2). Antibiotic-free chickens presented higher alpha-diversity, higher relative abundance of Deferribacteres, Fusobacteria, Bacteroidetes and Actinobacteria, and lower relative abundance of Firmicutes, Clostridiales and Enterobacteriales than antibiotic-fed chickens (P < 0.001) (Fig3). Microbial community composition significantly changed as birds aged. In experimental farm, microbial community composition was significant different for 7, 21 and 35 day old broilers (P < 0.001), and alpha diversity increased from 7 to 21d (P < 0.024), but not from 21 to 35d; whereas, in organic systems, increases in alpha-diversity were observed from 7d to 21d, and from 21d to 35d (P < 0.05). Broilers and layers raised together showed no differences in microbiota composition and alpha diversity (P > 0.8). It is concluded that production practices consistently impact microbial composition, and that antibiotics significantly reduces microbial diversity. We are now exploring the impact of differential colonization in a controlled setting, to determine the impact of the microbes associated with extensively raised chickens. This study will support future research and the development of methods to isolate and introduce beneficial microbes to commercial systems.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".