Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

Although the rumen microbiome of domesticated ruminants has been evaluated, few studies have explored the rumen microbiome of wild ruminants, and no studies have identified the rumen microbiome in the impala (Aepyceros melampus melampus). In the present study, next-generation sequencing and real-time polymerase chain reaction were used to investigate the diversity and density of the bacteria and methanogenic archaea residing in the rumen of five adult male impalas, culled during the winter dry season in Pongola, South Africa. A total of 15,323 bacterial 16S rRNA gene sequences (from five impala), representing 3,892 different phylotypes, were assigned to 1,902 operational taxonomic units (OTUs). A total of 20,124 methanogen 16S rRNA gene sequence reads (from four impala), of which 5,028 were unique, were assigned to 344 OTUs. From the total sequence reads, Bacteroidetes, Proteobacteria, and Firmicutes were the most abundant bacterial phyla. While the majority of the bacterial genera found were unclassified, Prevotella and Cupriavidus were the most abundant classified genera. For methanogens, the genera Methanobrevibacter and Methanosphaera represented 94.3 % and 4.0 % of the classified sequences, respectively. Most notable was the identification of Methanobrevibacter thaueri-like 16S rRNA gene sequence reads in all four impala samples, representing greater than 30 % of each individual's total sequences. Both data sets are accessible through NCBI's Sequence Read Archive (SRA), under study accession number SRP [048619]. The densities of bacteria (1.26×10 10 -3.82×1010 cells/ml whole rumen contents) and methanogens (4.48×10 8 -7.2×109 cells/ml of whole rumen contents) from five individual impala were similar to those typically observed in domesticated ruminants.

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

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Epigenetic mechanisms may provide a novel prospective of bobcat (Lynx rufus) adaptation to habitat loss/fragmentation. Previous research has focused on bobcat behavior and genetics, but epigenetics has not been studied in bobcat. The aim of this study was to determine the quantity of global DNA methylation in the liver of 30 bobcats. DNA was extracted from liver samples obtained from the Vermont Fish and Wildlife Department. The percent of global DNA methylation was quantified and calculated using the MethylFlashTM Methylated DNA 5-mC Quantification Kit from Epigentek (Farmingdale, NY, USA). Age, sex, and carcass weight data were collected at sampling and analyzed with percent of global DNA methylation. Global DNA methylation was found to range from 0.46% to 2.76%. Age ranged from <1 to 12 years old and weight ranged from 3.18 to 13.61 kg. Further analysis of differential methylation may provide insight into novel means of bobcat conservation within different regions of Vermont. These results reinforce the need for genome-wide epigenetic studies in conservation biology.

To elucidate the extent to which DNA methylation varies across multiple tissues in the brain and between animals, we have quantified global DNA methylation in tissues comprising the limbic system for six Red Angus x Simmental steers. Global DNA methylation was measured in nine regions of the bovine brain: amygdala, the bed nucleus of the stria terminalis, cingulate gyrus, dorsal raphe, hippocampus, hypothalamus, nucleus accumbens, periaqueductal gray and prefrontal cortex. DNA methylation varies among animals for each tissue type and varies among tissue types for each animal. The highest amounts of DNA methylation were found in the amygdala, cingulate gyrus and dorsal raphe, while the bed nucleus of the stria terminalis, nucleus accumbens and periaqueductal gray had the lowest amounts of DNA methylation. A heatmap sorted by k-means clustering was generated to graphically display percent DNA methylation in relation to tissue type and animal number. This is the first study to report measures of DNA methylation in the limbic system of the bovine brain and can be used to inform the cattle genomics community of expected variation in cattle brain methylation.

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