16S rRNA gene amplicon sequencing for microbiota analysis of rumen fluid, feces, and milk of Sarda sheep fed different contents of alfalfa hay (Medicago sativa)
Bibliographic record
Abstract
The definition of dietary models that meet the animal's nutritional needs, improve its physiological state, and preserve the microbiota equilibrium, along with the challenge of reducing environmental effects is essential for producing sustainable, high-quality milk suitable for dairy production. This study aimed to investigate whether a more sustainable diet, with a higher content of alfalfa hay but equal net energy, protein, and NDF levels, has an effect on rumen fluid, feces, and milk microbiota in Sarda sheep. Additionally, it aimed to determine if microbiota changes across different niches correlated with production and traits. Twenty-four animals were divided into 2 groups and fed different diets. Rumen fluid, feces, and milk samples were collected at the beginning (d 1, T0) and at the end of the trial (d 22, T1). Microbial communities were analyzed using Ion GeneStudio S5 for sequencing the V2, V3, V4, V6-V7, V8, and V9 regions of the 16S rRNA gene. Amplicon sequence variant clustering and taxonomic assignment were performed with QIIME2 v2023.9.2. Differential abundant taxa between the 2 experimental groups (H-alfalfa, fed the high-alfalfa content diet, and L-alfalfa, fed the low-alfalfa content diet) were assessed at the 2 time points using the ANCOM-BC plugin of QIIME2, along with the analysis of the diversity indices. Similarly, milk composition, milk coagulation properties and BW, DMI, production, and energy requirements for milk production traits were analyzed for each time point using a linear model with diet as fixed effect. Results confirmed an overall homogeneity of the samples at T0 for milk composition, milk coagulation properties, and energy requirements for milk production, except for DMI. However, no significant difference in DMI was observed at T1 between experimental groups. Regarding the microbiota composition, analysis revealed minor differences between H-alfalfa and L-alfalfa groups in taxa abundances for rumen fluid and feces, both at T0 and 1. In milk, 8 taxa resulted to be enriched at T1 in the treated group. However, all the affected taxa had a low abundance, and no significant correlation was observed with the phenotypic traits. Diets did not affect the microbial structure of rumen fluid and milk, whereas significant differences in beta diversity (Bray-Curtis and unweighted UniFrac) were observed in feces at T1. Five distinct milk taxa resulted positively correlated with 3 taxa in rumen fluid and 2 in feces, supporting the idea that host microbial niches form a network of interconnected communities, even within isolated environments. In addition, a positive correlation was observed between energy requirements for milk production and feces' Oscillospiraceae family, a taxon typical of healthy gut in ruminants. The higher content of alfalfa hay showed no highly significant effects on microbiota composition, milk composition, milk coagulation traits, and energy requirements for milk production. Similarities in chemical composition and energy content between diets likely masked qualitative differences in the ingredients and resulted in minor differences between diet groups. Nevertheless, no adverse effects of the high-alfalfa hay diet were observed, suggesting its higher value as an effective strategy, particularly advantageous from an environmental perspective.
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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 | 0.000 |
| 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 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".