Decoding the chicken gastrointestinal microbiome
Bibliographic record
Abstract
Metataxonomic studies have underpinned a vast understanding of microbial communities residing within livestock gastrointestinal tracts, albeit studies have often not been combined to provide a global census. Consequently, in this study we characterised the overall and common 'core' chicken microbiota associated with the gastrointestinal tract (GIT), whilst assessing the effects of GIT site, bird breed, age and geographical location on the GIT resident microbes using metataxonomic data compiled from studies completed across the world. Specifically, bacterial 16S ribosomal DNA sequences from GIT samples associated with various breeds, differing in age, GIT sites (caecum, faeces, ileum and jejunum) and geographical location were obtained from the Sequence Read Archive and analysed using the MGnify pipeline. Metataxonomic profiles produced across the 602 datasets illustrated the presence of 3 phyla, 25 families and 30 genera, of which core genera (defined by presence in over 90% of datasets) belonged to Lactobacillus, Faecalibacterium, Butyricicoccus, Eisenbergiella, Subdoligranulum, Oscillibacter, Clostridium & Blautia. PERMANOVA analysis also showed that GIT site, bird breed, age and geographical location all had a significant effect on GIT microbial diversity, regardless of dietary factors, which were not considered in this study. On a genus level, Faecalibacterium was most abundant in the caeca, Lactobacillus was most abundant in the faeces, ileum and jejunum, with the data showing that the caeca and faeces were most diverse. AIL F8 progeny, Ross 308 and Cobb 500 breeds GIT bacteria were dominated by Lactobacillus, and Eisenbergiella, Megamonas and Bacteroides were most abundant amongst Sasso-T451A and Tibetan chicken breeds. Microbial communities within each GIT site develop with age, from a Lactobacillus and Streptococcus dominated community during the earlier stages of growth, towards a Faecalibacterium, Eisenbergiella, Bacteroides, Megamonas, and Lactobacillus dominated community during the later stages of life. Geographical locations, and thus environmental effectors, also impacted upon gastrointestinal tract microbiota, with Canadian and European datasets being dominated by Lactobacillus, whilst UK and Chinese datasets were dominated by Eisenbergiella and Bacteroides respectively. This study aids in defining what 'normal' is within poultry gastrointestinal tract microbiota globally, which is imperative to enhancing the microbiome for productive and environmental improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".