The rumen microbiota is vital for the health and growth performance of the host animal, mainly due to its role in the fermentation of ingested feed within the rumen. Attaining a better understanding of the development of the bacterial community and fermentation in the rumen can provide the theoretical basis for regulating feed utilization. This study analyzed the development of rumen bacteria in lambs from birth to 4 months of age using 16S-rRNA amplicon sequencing data and studied its relationship with ruminal fermentation. Serum levels of metabolites were monitored at 30, 60, 90, and 120 days of age, and the RandomForest approach was used to determine age-related changes in rumen bacteria. Levels of blood metabolites, ruminal fermentation, the rumen bacterial community and its functions were all affected by the age of the lambs (P < 0.05). Based on the Bray-Curtis distance within the age groups of the rumen microbiota, the similarity increased sharply after the lambs were weaned at 60 days of age (P < 0.05). The similarity between the samples collected from birth to 90 days of age and those collected at 120 days of age, increased after 20 days of age, reaching a maximum at 90 days vs. 120 days (P < 0.05). Some age-associated changes in the microbial genera were correlated with changes in the concentrations of volatile fatty acids and the levels of microbial crude protein in the rumen, including positive correlations between main volatile fatty acids and the genera of Prevotella 1, Lachnospiraceae NK3A20 group, Ruminococcus gauvreauii group, Ruminococcaceae UCG-014, and Ruminococcus 2 (P < 0.05). These results indicated that the microbial community and the function of rumen was not well-established before 20 days of age, so there is a degree of plasticity in the rumen bacterial community during the first 20 days of post-natal development in lambs, and this might provide an opportunity for interventions to improve rumen fermentation and, thus, increase their growth performance.

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.

Prolactin (PRL) has been reported to influence reproductive performance and cell apoptosis. However, its mechanism remains unclear. Hence, in the present study, ovine ovarian granulosa cells (GCs) were used as a cell model to investigate the relationship between PRL concentration and GC apoptosis, as well as its possible mechanisms. We examined the relationship between serum PRL concentration and follicle counts in sexually mature ewes. GCs were isolated from adult ewes and treated with different concentrations of PRL, while 500 ng/mL PRL was selected as the high concentration of prolactin (HPC). Then, we applied the transcriptome sequencing (RNA-Seq) combined with a gene editing approach to explore the HPC contributing to cell apoptosis and steroid hormones. The apoptosis of GCs gradually increased at PRL concentrations above 20 ng/mL, while 500 ng/mL PRL significantly decreased the secretion of steroid hormones and the expression of L-PRLR and S-PRLR. The results indicated that PRL regulates GC development and steroid hormones mainly through the target gene MAPK12. The expression of MAPK12 was increased after knocked-down L-PRLR and S-PRLR, while it decreased after overexpressed L-PRLR and S-PRLR. Cell apoptosis was inhibited and the secretion of steroid hormones increased after interfering with MAPK12, while the overexpression of MAPK12 showed the opposite trend. Overall, the number of follicles gradually decreased with increasing PRL concentration. HPCs promoted apoptosis and inhibited steroid hormone secretion in GCs by upregulating MAPK12 through reducing L-PRLR and S-PRLR.

Solid-state fermentation is known to improve plant-based feed nutritional quality; however, the association between microbes and metabolite production in fermented feed remains unclear. We inoculated corn–soybean–wheat bran (CSW) meal feed with Bacillus licheniformis Y5-39, Bacillus subtilis B-1, and lactic acid bacteria RSG-1. Then, 16S rDNA sequencing and untargeted metabolomic profiling were applied to investigate changes in the microflora and metabolites, respectively, and their integrated correlations during fermentation were assessed. The results indicated that trichloroacetic acid soluble protein levels showed a sharp increase, while glycinin and β-conglycinin levels showed a sharp decrease in the fermented feed, as confirmed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Pediococcus, Enterococcus, and Lactobacillus were predominant in the fermented feed. Overall, 699 significantly different metabolites were identified before and after fermentation. Arginine and proline, cysteine and methionine, and phenylalanine and tryptophan metabolism were the key pathways, with arginine and proline metabolism being the most important pathway in the fermentation process. By analyzing the correlation between the microbiota and metabolite production, lysyl–valine and lysyl–proline levels were found to be positively correlated with Enterococcus and Lactobacillus abundance. However, Pediococcus was positively correlated with some metabolites contributing to nutritional status and immune function. According to our data, Pediococcus, Enterococcus, and Lactobacillus mainly participate in protein degradation, amino acid metabolism, and lactic acid production in fermented feed. Our results provide new insights into the dynamic changes in metabolism that occurred during the solid-state fermentation of corn–soybean meal feed using compound strains and should facilitate the optimization of fermentation production efficiency and feed quality.

The early colonized gut microbiota during the newborn period has been reported to play important roles in the health and immunity of animals; however, whether they can affect the growth performance of suckling lambs is still unclear. In this study, a total of 84 newborn lambs were assigned into LF-1 (top 15%), LF-2 (medium 70%), and LF-3 (bottom 15%) groups according to their average body weight gain at 30 days of age. Fecal samples of lambs (LF) as well as feces (MF), vagina (VAG), colostrum (COL), teat skin (TEAT) samples of ewes, and the air sediment (AIR) in the delivery room were collected 72 h after birth, and then the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The results showed that the early colonized gut microbiota had a significant effect on the growth performance of suckling lambs with alpha and beta diversity (p < 0.05), and we observed that the contribution of early colonized bacteria on the growth performance of lambs increased with age (from BW₃₀ at 25.35% to BW₄₅ at 31.10%; from ADG₃₀ at 33.02% to ADG₄₅ at 39.79% by measuring the relative effects of factors that influence growth performance). The early colonized gut microbiota of suckling lambs with high growth performance was similar to that in VAG, MF, and AIR (p < 0.05). With the RandomForest machine learning algorithm, we detected 11, 11, 6, and 4 bacterial taxa at the genus level that were associated with BW₃₀, BW₄₅, ADG₃₀, and ADG₄₅ of suckling lambs, respectively, and the correlation analysis showed that Butyricicoccus, Ruminococcus_gnavus_group, Ruminococcaceae_Other, and Fusobacterium could significantly affect the growth performance (BW₃₀, BW₄₅, ADG₃₀, and ADG₄₅) of suckling lambs (p < 0.05). In conclusion, the early colonized gut microbiota could significantly affect the growth performance of suckling lambs, and targeting the early colonized gut microbiota might be an alternative strategy to improve the growth performance of suckling lambs.

Long non-coding RNAs (lncRNAs) have been shown to play important roles in livestock fecundity, and many lncRNAs that affect follicular development and reproductive diseases have been identified in the ovary. However, only a few of them have been functionally annotated and mechanistically validated. In this study, we identified a new lncRNA (lncGSAR) and investigated its effects on the proliferation and steroidogenesis of ovine granulosa cells (GCs). High concentrations of glucose (add 33.6 mM glucose) caused high expression of lncGSAR in GCs by regulating its stability, and lncGSAR overexpression promoted GCs proliferation, estrogen secretion, and inhibited progesterone secretion, whereas interference with lncGASR had the opposite effect. Next, we found that the RNA molecules of lncGSAR act on MiR-125b as competitive endogenous RNA (ceRNA), and SREBP-cleavage-activating protein (SCAP) was verified as a target of MiR-125b. LncGASR overexpression increased the expression of SCAP, SREBP, and steroid hormone-related proteins, which can be attenuated by MiR-125b. Our results demonstrated that lncGSAR can act as a ceRNA to activate SCAP/SREBP signaling by sponging MiR-125b to regulate steroid hormone secretion in GCs. These findings provide new insights into the mechanisms of nutrient-regulated follicle development in ewes.