98 Characterization of Factors Affecting the Fecal Microbiome in Young Canadian Pigs
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».