Original rumen digesta, rumen liquid and solid fractions have been frequently used to assess the rumen bacterial community. However, bacterial profiles in rumen original digesta, liquid and solid fractions vary from each other and need to be better established. To compare bacterial profiles in each fraction, samples of rumen digesta from six cows fed either a high fiber diet (HFD) or a high energy diet (HED) were collected via rumen fistulas. Rumen digesta was then squeezed through four layers of cheesecloth to separate liquid and solid fractions. The bacterial profiles of rumen original digesta, liquid and solid fractions were analyzed with High-throughput sequencing technique. Rumen bacterial diversity was mainly affected by diet and individual cow (P > 0.05) rather than rumen fraction. Bias distributed bacteria were observed in solid and liquid fractions of rumen content using Venn diagram and LEfSe analysis. Fifteen out of 16 detected biomarkers (using LEfSe analysis) were found in liquid fraction, and these 15 biomarkers contributed the most to the bacterial differences among rumen content fractions. Similar results were found when using samples of original rumen digesta, rumen liquid or solid fractions to assess diversity of rumen bacteria; however, more attention should be draw onto bias distributed bacteria in different ruminal fractions, especially when liquid fraction has been used as a representative sample for rumen bacterial study.

In this study, we aimed to establish the correlation between ruminal fermentation parameters and the bacterial community by comparing those of the goat, sheep, and cow, thus illustrating the main bacteria causing the difference in rumen fermentation among goats, sheep, and cows and providing a new idea for improving the feed digestibility of ruminants. Rumen fluid from goats (Taihang White cashmere goat, n = 6), sheep (Hu sheep, n = 6), and cows (Holstein cow, n = 6) was collected using oral intubation and immediately brought back to the laboratory for a fermentation test with the same total mixed ration (TMR) feed in vitro. The rumen bacterial composition was measured by high-throughput sequencing of 16S rRNA genes in the MiSeq platform, the gas production (GP) was recorded after 2, 4, 6, 8, 10, 12, 24, 36, and 48 h of fermentation, and the feed nutrient digestibility and the rumen fluid parameters were determined after 48 h of fermentation. The results showed that the 48 h GP of the sheep group was higher than that of the cow group (p < 0.05), and the theoretical maximum GP was higher than that of the goat and cow groups (p < 0.05). The organic matter digestibility (OMD), dry matter digestibility (DMD), crude protein digestibility (CPD), and gross energy digestibility (GED) of the sheep group were higher than those of the goat and cow groups (p < 0.05). The ammonia nitrogen (NH₃-N), microbial protein (MCP), and total volatile fatty acids (TVFA) concentrations of the sheep group were higher than those of the other groups (p < 0.05), and the pH of the sheep group was lower than those of the other groups (p < 0.05). The 16S rRNA gene sequencing revealed that bacterial composition also differed in the rumens of the sheep, goat, and cow groups (ANOSIM, p < 0.05). We then used a random forest machine learning algorithm to establish models to predict the fermentation parameters by rumen bacterial composition, and the results showed that rumen bacterial composition could explain most of the ruminal fermentation parameter variation (66.56%, 56.13%, 65.75%, 80.85%, 61.30%, 4.59%, 1.41%, −3.13%, 34.76%, −25.62%, 2.73%, 60.74%, 76.23%, 47.48%, −13.2%, 80.16%, 4.15%, 69.03%, 32.29%, and 89.96% for 48 h GP, a (GP of quickly degraded part), b (GP of slowly degraded part), c (GP rate), a + b (theoretical maximum GP), DMD, OMD, GED, CPD, NDFD, ANDF, pH, NH₃-N, MCP, acetic acid, propionic acid, butyric acid, valeric acid, TVFA, and A:P (acetic acid–propionic acid ratio), respectively). A correlation analysis revealed that Lactobacillus, Prevotellaceae_UCG-003, Selenomonas, Peptostreptococcus, and Olsenella significantly correlated with most in vitro fermentation parameters (p < 0.05). A comprehensive analysis showed that rumen fermentation parameters and bacterial composition differed in goats, sheep, and cows. The ruminal fermentation parameters of GP, a, b, c, a + b, pH, NH₃-N, propionic acid, valeric acid, and A:P could be accurately predicted by rumen bacteria (explanation > 55% of variation), and the Lactobacillus, Prevotellaceae_UCG-003, Olsenella, Selenomonas, and Peptostreptococcus were the main bacteria that affected the in vitro fermentation parameters of goats, sheep, and cows.

Colonization of gastrointestinal microbiota in mammals during early life is vital to host health. The objective of this study was to investigate whether lambs with high and low ADG have a different rumen and rectum microbial community. Thus, we investigated potential relationships between rumen and rectum microbiota and average daily gain (ADG) in weaned lambs. Sixteen lambs with similar body weights (7.63 ± 1.18 kg) were selected at 30 days of age. At 60 days of age, lambs were weaned, and ADG was calculated from 60 to 90 days. Then, two groups were generated: higher ADG (HG, 134.17 ± 13.48 g/day) and lower ADG (LG, 47.50 ± 19.51 g/day). Microbiota was evaluated at 30, 60, and 90 days of age. The final live weight and ADG at 90 days of age was higher (p < 0.05) in the HG group compared to the LG group. The maturity of bacterial and fungal communities was increased (p < 0.05) in the HG group for the 30 days vs. 90 days comparison and 60 days vs. 90 days comparison. Linear discriminant analysis effect size (LEfSe) analysis revealed a total of 18 bacterial biomarkers that are ADG-specific in the rumen and 35 bacterial biomarkers in the rectum. Meanwhile, 15 fungal biomarkers were found in the rumen and 8 biomarkers were found in the rectum. Our findings indicated that ADG is related to the rumen and rectum microbiota in lambs.

Considering the frequently large price fluctuations for soybean meal, an alternative is the increased use of locally produced high-protein ingredients. The objective of this study was to evaluate the effects of the total replacement of soybean meal with different sources of protein on the growth performance, nutrient digestibility, serum parameters, rumen fermentation parameters, and bacterial communities in growing lambs. Sixty sheep with similar body weights (38.46 ± 0.71 kg) were distributed to one of five treatments: soybean meal (SBM); cottonseed meal (COM); peanut meal (PEM); rapeseed meal (RAM); and distillers’ dried grains with solubles (DDGS). The experiment lasted 62 days with a 10-day adaptation period and a 52-day growing period. The results indicated that the body weight and average daily gain were not affected by different protein sources (p > 0.05), but the dry matter intake of the SBM group was lower than that of the other groups (p < 0.05); otherwise, the feed efficiency was higher (p < 0.05). The digestion of dry matter was higher in the SBM, COM, and RAM groups than in the DDGS and PEM groups (p < 0.05). Meanwhile, compared to the other groups, the SBM group had the highest digestion of gross energy and crude protein (p < 0.05). In addition, the concentration of glutathione peroxidase was highest in the SBM group (p < 0.05). Regarding the rumen fermentation, the SBM group had the highest concentration of NH₃-N (p < 0.05). The rumen bacterial community was not affected by treatments (p > 0.05). In conclusion, the total replacement of soybean meal with cottonseed, peanut, rapeseed, or DDGS reduced digestibility but did not impact the body weight or average daily gain of growing lambs and had no effect on the immune function and rumen bacterial community; thus, they can be used to substitute the soybean meal.

The microbiota in raw milk plays an important role in the health of dairy cows and the safety of dairy products, which might be influenced by that in teat skin. However, the microbiota composition in raw milk and teat skin, as well as the bacterial interaction between the two adjacent spatial locations, remains elusive. Here, we investigated the composition, diversity, and co-occurrence network of the bacterial communities in raw milk and on teat skin, as well as the shift of bacterial communities during the teat bath using 469 samples from 156 individual cows. We observed that raw milk and teat skin harbored significantly different bacterial communities according to an assessment of the genera numbers (p < 0.05) and PCoA analysis (ANOSIM p < 0.05). The microbiota in raw milk was dominated by Proteobacteria (58.5% in relative abundance) at the phylum level and by Pseudomonas (51.2%) at the genus level, while that in teat skin was dominated by Firmicutes (46.9%) at the phylum level and by Pseudomonas (11.0%) at the genus level. We observed a massive difference between the bacterial subnetworks in raw milk and teat, and the bacterial abundance in these two adjacent spatial locations was positively correlated (p < 0.05). Using Bayesian algorithms, we identified that 92.1% of bacteria in raw milk were transferred from teat skin, while 63.6% of bacteria on teat skin were transferred from raw milk. Moreover, microbiota composition in teat skin could be affected by the teat bath with iodine disinfectant, which tended to be more similar to that in raw milk after the teat bath (p < 0.05), while the abundance of the dominant genus Pseudomonas significantly increased (p < 0.05). These findings expand our knowledge on the microbiota composition in teat skin and raw milk, as well as the interaction between these two adjacent spatial locations.