This study is to investigate the difference in bovine fecal microbiota between grazing and feedlot Angus cattle. Fecal samples were collected from six Angus cattle grazed on grassland and six Angus cattle fed on a feedlot. The fecal bacterial community was analyzed by high-throughput sequencing of 16S rRNA gene. Sequencing of the V3–V4 region totally produced 1,113,170 effective tages that were computationally clustered into 775 operational taxonomic units (OTUs). These 775 OTUs were taxonomically assigned to bacterial 12 phyla, 19 classes, 25 orders, 54 families, 141 genera, and 145 species. The dominant phyla were Firmicutes and Bacteroidetes. There was similar species richness between grazing and feedlot Angus beef, while higher species diversity was observed in feedlot Angus beef. The relative abundance of Firmicutes, Cyanobacteria, Elusimicrobia and Patescibacteria was significantly different between grazing and feedlot Angus beef (p < 0.05). At a genus level, five microbiotas were significantly different between the two groups and all belonged to the Firmicutes phylum. These significant differences in microbiota composition between grazing and feedlot Angus beef may have an impact on the meat quality of Angus beef.

Pigmentation genes such as MC1R, MITF, TYR, TYRP1, and MLPH play a major role in rabbit coat color. To understand the genotypic profile underlying coat color in indigenous Chinese rabbit breeds, portions of the above-mentioned genes were amplified and variations in them were analyzed by DNA sequencing. Based on the analysis of 24 Tianfu black rabbits, 24 Sichuan white rabbits, 24 Sichuan gray rabbits, and 24 Fujian yellow rabbits, two indels in MC1R, three SNPs in MITF, five SNPs (single nucleotide polymorphisms) in TYR, one SNP in TYRP1, and three SNPs in MLPH were discovered. These variations have low-to-moderate polymorphism, and there are significant differences in their distribution among the different breeds (p < 0.05). These results provide more information regarding the genetic background of these native rabbit breeds and reveal their high-quality genetic resources.

Growth, carcass characteristics and meat quality are the most important traits used in the rabbit industry. Identification of the candidate markers and genes significantly associated with these traits will be beneficial in rabbit breeding. In this study, we enrolled 465 rabbits, including 16 male Californian rabbits and 17 female Kangda5 line rabbits as the parental generation, along with their offspring (232 male and 200 female), in a genome-wide association study (GWAS) based on SLAF-seq technology. Bodyweight at 35, 42, 49, 56, 63 and 70 d was recorded for growth traits; and slaughter liveweight (84 d) and dressing out percentage were measured as carcass traits; and cooking loss and drip loss were measured as meat quality traits. A total of 5,223,720 SLAF markers were obtained by digesting the rabbit genome using RsaI + EcoRV-HF^® restriction enzymes. After quality control, a subset of 317,503 annotated single-nucleotide polymorphisms (SNPs) was retained for subsequent analysis. A total of 28, 81 and 10 SNPs for growth, carcass and meat quality traits, respectively, were identified based on genome-wide significance (p < 3.16 × 10⁻⁷). Additionally, 16, 71 and 9 candidate genes were identified within 100 kb upstream or downstream of these SNPs. Further analysis is required to determine the biological roles of these candidate genes in determining rabbit growth, carcass traits and meat quality.

Imbalances in the gut microbiota composition may lead to several reproductive disorders and diseases during pregnancy. This study investigates the fecal microbiome composition between primiparous and multiparous cows during non-pregnancy and pregnancy to analyze the host-microbial balance at different stages. The fecal samples obtained from six cows before their first pregnancy (BG), six cows during their first pregnancy (FT), six open cows with more than three lactations (DCNP), and six pregnant cows with more than three lactations (DCP) were subjected to 16S rRNA sequencing, and a differential analysis of the fecal microbiota composition was performed. The three most abundant phyla in fecal microbiota were Firmicutes (48.68%), Bacteroidetes (34.45%), and Euryarchaeota (15.42%). There are 11 genera with more than 1.0% abundance at the genus level. Both alpha diversity and beta diversity showed significant differences among the four groups (p < 0.05). Further, primiparous women were associated with a profound alteration of the fecal microbiota. The most representative taxa included Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, Christensenellaceae_R_7_group, Ruminococcaceae UCG-005, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, Methanobrevibacter, and [Eubacterium] coprostanoligenes group, which were associated with energy metabolism and inflammation. The findings indicate that host-microbial interactions promote adaptation to pregnancy and will benefit the development of probiotics or fecal transplantation for treating dysbiosis and preventing disease development during pregnancy.

Ketosis is one of the most prevalent transition metabolic disorders in dairy cows, and has been intrinsically influenced by both genetic and nutritional factors. However, altered gene expression with respective to dairy cow ketosis has not been addressed yet, especially at the genome-wide level. In this study, we recruited nine Holsteins diagnosed with clinical ketosis and ten healthy controls, for which whole blood samples were collected at both prepartum and postpartum. Four groups of blood samples were defined: from cows with ketosis at prepartum (PCK, N = 9) and postpartum (CK, N = 9), respectively, and controls at prepartum (PHC, N = 10) and postpartum (HC, N = 10). RNA-Seq approach was used for investigating gene expression, by which a total of 27,233 genes were quantified with four billion high-quality reads. Subsequently, we revealed 75 and four differentially expressed genes (DEGs) between sick and control cows at postpartum and prepartum, respectively, which indicated that sick and control cows had similar gene expression patterns at prepartum. Meanwhile, there were 95 DEGs between postpartum and prepartum for sick cows, which showed depressed changes of gene expression during this transition period in comparison with healthy cows (428 DEGs). Functional analyses revealed the associated DEGs with ketosis were mainly involved in biological stress response, ion homeostasis, AA metabolism, energy signaling, and disease related pathways. Finally, we proposed that the expression level of STX1A would be potentially used as a new biomarker because it was the only gene that was highly expressed in sick cows at both prepartum and postpartum. These results could significantly help us to understand the underlying molecular mechanisms for incidence and progression of ketosis in dairy cows.