98 Characterization of Factors Affecting the Fecal Microbiome in Young Canadian Pigs
Bibliographic record
Abstract
Abstract Although established as an important contributor to pig health and performance, high diversity and variability of the microbiome has challenged identification of a beneficial community composition that could inform best management practice and gut modifier development. To address this challenge, rectal swabs were collected at intervals from near birth to one-week postweaning from 4 piglets in 10 litters from 13 conventional and 9 RWA barns across Canada. The 16S rRNA gene was sequenced in 1,997 fecal samples collected at ~4 days of age (4d), 1 day before weaning (W-1), and 7 days after weaning (W+7), piglets were also weighed at the time of sampling. Raw sequences were denoised and assigned to genera using DADA2. After quality control and filtering there was an average of 32,710 reads per sample, with 649 genera identified in the samples. Principal Coordinate Analysis (PCoA) plots were created using the vegan and phyloseq packages (cao model and MDS settings). Relationships between metadata factors and the microbiome were investigated using PERMANOVA analysis in the adonis2 package. In PCoA plots samples clustered by production stage, with an expected marked change of the bacterial community following weaning. Because production stage had a large effect on the microbiome the effects of metadata factors were investigated within sampling visit. Metadata factors investigated included farrowing pen (FP) location at the 4 d-old sampling, barn and rearing system [conventional versus raised without antibiotics (RWA)] and the lifetime health (LH) status including always healthy, received a health score 1 or greater at any one visit or did not complete the study. At ~4 d of age, PERMANOVA indicated that all these factors were significantly linked to the microbiome composition (P = 0.001), FP explained the most microbiome variation (R2 = 0.338), followed by barn (R2 = 0.217). Rearing system and LH explained much less of the variation, R2 = 0.009 and R2 = 0.005, respectively. At W-1, the microbiome was not significantly correlated to LH (P = 0.053); however, the other metadata factors were significant (P = 0.001) with similar R2 values to the previous timepoint (FP; R2 = 0.317, barn R2 = 0.210, and system R2 = 0.008). At W+7 all metadata factors were once again significant (P < 0.005). The correlation to FP had reduced to R2 = 0.245, cohort increased to R2 = 0.266, system effects doubled to R2 = 0.21, and LH remained very low at R2 = 0.006. However, interactions between LH and FP (R2 = 0.059) and system and LH (R2=0.003) were also observed. These results suggest that farrowing pen has a significant and lasting effect on the microbiome. The effect of being raised RWA or conventionally was very small in preweaning piglets, but that affect increased post weaning. The immediate environment early in life appears to have a large effect on piglet microbiomes and may present an opportunity for beneficial intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".