Meta-analysis reveals the predictable dynamic development of the gut microbiota in commercial pigs
Bibliographic record
Abstract
ABSTRACT Understanding the mechanisms of microbiome assembly during host development is crucial for successful modulation of the gut microbiome to improve host health and growth. However, results from previous microbial intervention studies to improve swine growth have largely been inconsistent due to the constantly changing nature of the gut microbiota and limited longitudinal sampling. Detailed characterization of the swine gut microbiota through meta-analysis allows us to understand the dynamics of microbial community succession, as well as the transient and natural variations between time points and animals. A total of 3,313 fecal microbial communities from over 349 pigs covering 60 time points (from birth to market age) from 14 publications were included in this meta-analysis. Despite differences in animal breeds and management, generalizable patterns of community assembly were identified. Alpha diversity continuously increased during early stages of animal growth and more slowly in the later stages. Beta regression analysis revealed that more microbial taxa were enriched, while fewer were excluded by the gut microbiota. The microbial community structure also changed significantly between days at early ages and became more similar as pigs aged, as revealed by dissimilarity and distance metrics. Dirichlet multinomial mixtures analysis supported gradient microbial clustering in longitudinal pig fecal samples, and we found that the early samples spread to more clusters than those from older pigs. Random forest regression identified 30 operational taxonomic units as potential microbial biomarkers for modeling swine gut microbiota development. External validation showed this model could be generalized to future microbiome studies conducted in suckling and weaning pigs and adds to the toolkit to quantify community succession. IMPORTANCE The swine gut microbiome undergoes an age-dependent assembly pattern with a developmental phase at early ages and a stabilization phase at later ages. Shorter time intervals and a wider range of data sources provided a clearer understanding of the gut microbiota colonization and succession and their associations with pig growth and development. The rapidly changing microbiota of suckling and weaning pigs implies potential time targets for growth and health regulation through gut microbiota manipulation. Since swine gut microbiota development is predictable, swine microbiota age can be calculated and compared between animal treatment groups rather than relying only on static time-matched comparisons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".