Alteration in Fecal Microbiota Associated with Grain-induced Subacute Ruminal Acidosis Challenge in Dairy Cows
Notice bibliographique
Résumé
Introduction: High prevalence of subacute rumen acidosis (SARA) in dairy herds has been reported with large impact on production and welfare. The field diagnosis of SARA is currently unclear and primarily based on point measurements of rumen pH, which are inaccurate. Consequently, SARA cases in the field are often not detected. Thus, other and better markers of SARA are needed. The purpose of this research was to study the feces microbiome during SARA and assess the possibilities of using feces microbial markers as indicators of SARA. Methods: Six lactating, rumen cannulated, Danish Holstein cows were used in a blocked design study including two blocks. In the first block, two cows received control diet and two cows received SARA-challenge diet. In the second block, former control cows received SARA diet while two new cows received control diet. Cows received a total mixed ration (TMR; 24% concentrate) for four weeks before the trial. SARA was induced by gradual substitution of 40% of TMR with grain pellets (50:50 wheat:barley) over 3 days. Full SARA diet was fed for four days. Rumen pH was measured continuously by indwelling probes (eCow). Feces samples were taken at 9 am and 9 pm on last day of the control period, and second and last day of full SARA-feeding. DNA was extracted and the V4 region of bacterial 16S rRNA gene was amplified and subjected to Illumina sequencing. Bioinformatics were performed using QIIME and resultant operational taxonomic units (OTUs) were aligned to Greengenes database at 97% similarity threshold. The differences between phylogenetic structures of microbial communities were calculated based on weighted and unweighted UniFrac distances and tested using permutational multivariate analysis of variance (PERMANOVA). Partial least square discriminant analysis (PLS-DA) was applied to identify the taxa that were correlated to each treatment group. Goodness of fit and predictive value was evaluated by R2Y and Q2 estimates. Taxa with a Variable Importance for the Projection (VIP) below 0.5 and relative abundance below 0.3 were excluded from the dataset. Results: In total, 641,335 high quality sequences were obtained resulting in identification of 81 classified bacterial genera belonging to 15 different bacterial phyla. The fecal bacterial communities of SARA and control samples were significantly distinct with P-values of 0.0262 and 0.0001 for weighted and unweighted UniFrac distances, respectively. The proportion of several taxa was significantly higher in SARA samples compared to control during full SARA feeding. These included Lachnospiraceae, Bifidobacteriaceae, YRC22, Roseburia, Treponema, 5-7N15, Parabacteroides , Anaerovibrio, Blautia, Coprococcus, Veillonellaceae, Ruminococcus, Bifidobacterium, and Sutterella. Goodness of fit and predictive value was estimated to R2: 95.3 and Q2: 74.6, respectively. On phyla level, the proportion of Firmicutes, Actinobacteria and Spirochaetes was significantly higher in SARA samples compared to control. Goodness of fit and predictive value was estimated to R2: 87.0 and Q2: 73.2, respectively. Conclusion: Results confirm that intensive grain feeding changes the feces microbiome. The identification of specific taxa characteristic of SARA could provide new knowledge of the pathogenesis and might be useful as future biological markers of the disease. bovine, subacute ruminal acidosis, fecal microbiome, biological marker
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 ».