RNA Sequencing of Sperm from Healthy Cattle and Horses Reveals the Presence of a Large Bacterial Population.

RNA molecules within ejaculated sperm can be characterized through whole-transcriptome sequencing, enabling the identification of pivotal transcripts that may influence reproductive success. However, the profiling of sperm transcriptomes through next-generation sequencing has several limitations impairing the identification of functional transcripts. In this study, we explored the nature of the RNA sequences present in the sperm transcriptome of two livestock species, cattle and horses, using RNA sequencing (RNA-seq) technology. Through processing of transcriptomic data derived from bovine and equine sperm cell preparations, low mapping rates to the reference genomes were observed, mainly attributed to the presence of ribosomal RNA and bacteria in sperm samples, which led to a reduced sequencing depth of RNAs of interest. To explore the presence of bacteria, we aligned the unmapped reads to a complete database of bacterial genomes and identified bacteria-associated transcripts which were characterized. This analysis examines the limitations associated with sperm transcriptome profiling by reporting the nature of the RNA sequences among which bacterial RNA was found. These findings can aid researchers in understanding spermatozoal RNA-seq data and pave the way for the identification of molecular markers of sperm performance.

Characterization of the Hepatic Transcriptome for Divergent Immune-Responding Sheep Following Natural Exposure to Gastrointestinal Nematodes.

Infections with gastrointestinal nematodes (GINs) reduce the economic efficiency of sheep operations and compromise animal welfare. Understanding the host's response to GIN infection can help producers identify animals that are naturally resistant to infection. The objective of this study was to characterize the hepatic transcriptome of sheep that had been naturally exposed to GIN parasites. The hepatic transcriptome was studied using RNA-Sequencing technology in animals characterized as high (n = 5) or medium (n = 6) based on their innate immune acute-phase (AP) response phenotype compared with uninfected controls (n = 4), and with biased antibody-mediated (AbMR, n = 5) or cell-mediated (CMR, n = 5) adaptive immune responsiveness compared to uninfected controls (n = 3). Following the assessment of sheep selected for innate responses, 0, 136, and 167 genes were differentially expressed (DE) between high- and medium-responding animals, high-responding and uninfected control animals, and medium-responding and uninfected control animals, respectively (false discovery rate (FDR) < 0.05, and fold change |FC| > 2). When adaptive immune responses were assessed, 0, 53, and 57 genes were DE between antibody- and cell-biased animals, antibody-biased and uninfected control animals, and cell-biased and uninfected control animals, respectively (FDR < 0.05, |FC| > 2). Functional analyses identified enriched gene ontology (GO) terms and metabolic pathways related to the innate immune response and energy metabolism. Six functional candidate genes were identified for further functional and validation studies to better understand the underlying biological mechanisms of host responses to GINs. These, in turn, can potentially help improve decision making and management practices to increase the overall host immune response to GIN infection.

Novel lncRNA regulatory elements in milk somatic cells of Holstein dairy cows associated with mastitis.

Despite regulatory elements such as long non - coding RNAs representing most of the transcriptome, the functional understanding of long non - coding RNAs in relation to major health conditions including bovine mastitis is limited. This study examined the milk somatic cell transcriptome from udder quarters of 6 Holstein dairy cows to identify differentially expressed long non - coding RNAs using RNA - Sequencing. Ninety - four differentially expressed long non - coding RNAs are identified, 5 of which are previously annotated for gene name and length, 11 are annotated for gene name and 78 are novel, having no gene name or length previously annotated. Significant inflammatory response and regulation of immune response pathways (false discovery rate < 0.05) are associated with the differentially expressed long non - coding RNAs. QTL annotation analysis revealed 31 QTL previously annotated in the genomic regions of the 94 differentially expressed long non - coding RNAs, and the majority are associated with milk traits. This research provides a better understanding of long non - coding RNAs regulatory elements in milk somatic cells, which may enhance current breeding strategies for more adaptable or high mastitis resistant cattle.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies.

Ruminant supply chains contribute 5.7 gigatons of CO2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH4), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH4 production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH4 yield in ruminants indicates that genomic selection for reduced CH4 emissions is possible. Although the microbiology of CH4 production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of "-omics" technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH4 production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH4 emissions. Through a systems biology approach, various "-omics" technologies can be combined to unravel genomic regions and genetic markers associated with CH4 production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH4 emissions, and the potential for "-omics" technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH4 production traits in ruminants and aid in reducing agriculture's overall environmental footprint.