Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance.

BACKGROUND: Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry. METHODS: Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual's heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods. RESULTS: The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of - 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set. CONCLUSIONS: Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.

Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle.

Feed efficiency and energy balance are important traits underpinning profitability and environmental sustainability in animal production. They are complex traits, and our understanding of their underlying biology is currently limited. One measure of feed efficiency is residual feed intake (RFI), which is the difference between actual and predicted intake. Variation in RFI among individuals is attributable to the metabolic efficiency of energy utilization. High RFI (H_RFI) animals require more energy per unit of weight gain or milk produced compared with low RFI (L_RFI) animals. Energy balance (EB) is a closely related trait calculated very similarly to RFI. Cellular energy metabolism in mitochondria involves mitochondrial protein (MiP) encoded by both nuclear (NuMiP) and mitochondrial (MtMiP) genomes. We hypothesized that MiP genes are differentially expressed (DE) between H_RFI and L_RFI animal groups and similarly between negative and positive EB groups. Our study aimed to characterize MiP gene expression in white blood cells of H_RFI and L_RFI cows using RNA sequencing to identify genes and biological pathways associated with feed efficiency in dairy cattle. We used the top and bottom 14 cows ranked for RFI and EB out of 109 animals as H_RFI and L_RFI, and positive and negative EB groups, respectively. The gene expression counts across all nuclear and mitochondrial genes for animals in each group were used for differential gene expression analyses, weighted gene correlation network analysis, functional enrichment, and identification of hub genes. Out of 244 DE genes between RFI groups, 38 were MiP genes. The DE genes were enriched for the oxidative phosphorylation (OXPHOS) and ribosome pathways. The DE MiP genes were underexpressed in L_RFI (and negative EB) compared with the H_RFI (and positive EB) groups, suggestive of reduced mitochondrial activity in the L_RFI group. None of the MtMiP genes were among the DE MiP genes between the groups, which suggests a non-rate limiting role of MtMiP genes in feed efficiency and warrants further investigation. The role of MiP, particularly the NuMiP and OXPHOS pathways in RFI, was also supported by our gene correlation network analysis and the hub gene identification. We validated the findings in an independent data set. Overall, our study suggested that differences in feed efficiency in dairy cows may be linked to differences in cellular energy demand. This study broadens our knowledge of the biology of feed efficiency in dairy cattle.

Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle.

BACKGROUND: Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear (NuMP) and mitochondrial (MtMP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses. RESULTS: MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE NuMP genes in varying proportions of over-expression and under-expression. On the other hand, DE of MtMP genes was observed in < 50% of tissues and notably MtMP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of NuMP (up to 60%) and MtMP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of NuMP, 77% of MtMP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all MtMP genes in the direction of dominant NuMP genes expression. The NuMP and MtMP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets. CONCLUSIONS: The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism.

Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits.

BACKGROUND: Topological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants. RESULTS: We used genomic synteny to map TADs and CTCF binding motifs from humans, mice, dogs and macaques to the bovine genome. We found that our mapped TADs exhibited the same hallmark properties of those sourced from experimental data, such as housekeeping genes, transfer RNA genes, CTCF binding motifs, short interspersed elements, H3K4me3 and H3K27ac. We showed that runs of genes with the same pattern of allele-specific expression (ASE) (either favouring paternal or maternal allele) were often located in the same TAD or between the same conserved CTCF binding motifs. Analyses of variance showed that when averaged across all bovine tissues tested, TADs explained 14% of ASE variation (standard deviation, SD: 0.056), while CTCF explained 27% (SD: 0.078). Furthermore, we showed that the quantitative trait loci (QTLs) associated with gene expression variation (eQTLs) or ASE variation (aseQTLs), which were identified from mRNA transcripts from 141 lactating cows' white blood and milk cells, were highly enriched at putative bovine CTCF binding motifs. The linearly-furthermost, and most-significant aseQTL and eQTL for each genic target were located within the same TAD as the gene more often than expected (Chi-Squared test P-value < 0.001). CONCLUSIONS: Our results suggest that genomic synteny can be used to functionally annotate conserved transcriptional components, and provides a tool to reduce the search space for causative regulatory variants in the bovine genome.

Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits.

BACKGROUND: Enhancers are non-coding DNA sequences, which when they are bound by specific proteins increase the level of gene transcription. Enhancers activate unique gene expression patterns within cells of different types or under different conditions. Enhancers are key contributors to gene regulation, and causative variants that affect quantitative traits in humans and mice have been located in enhancer regions. However, in the bovine genome, enhancers as well as other regulatory elements are not yet well defined. In this paper, we sought to improve the annotation of bovine enhancer regions by using publicly available mammalian enhancer information. To test if the identified putative bovine enhancer regions are enriched with functional variants that affect milk production traits, we performed genome-wide association studies using imputed whole-genome sequence data followed by meta-analysis and enrichment analysis. RESULTS: We produced a library of candidate bovine enhancer regions by using publicly available bovine ChIP-Seq enhancer data in combination with enhancer data that were identified based on sequence homology with human and mouse enhancer databases. We found that imputed whole-genome sequence variants associated with milk production traits in 16,581 dairy cattle were enriched with enhancer regions that were marked by bovine-liver H3K4me3 and H3K27ac histone modifications from both permutation tests and gene set enrichment analysis. Enhancer regions that were identified based on sequence homology with human and mouse enhancer regions were not as strongly enriched with trait-associated sequence variants as the bovine ChIP-Seq candidate enhancer regions. The bovine ChIP-Seq enriched enhancer regions were located near genes and quantitative trait loci that are associated with pregnancy, growth, disease resistance, meat quality and quantity, and milk quality and quantity traits in dairy and beef cattle. CONCLUSIONS: Our results suggest that sequence variants within enhancer regions that are located in bovine non-coding genomic regions contribute to the variation in complex traits. The level of enrichment was higher in bovine-specific enhancer regions that were identified by detecting histone modifications H3K4me3 and H3K27ac in bovine liver tissues than in enhancer regions identified by sequence homology with human and mouse data. These results highlight the need to use bovine-specific experimental data for the identification of enhancer regions.

Targeted imputation of sequence variants and gene expression profiling identifies twelve candidate genes associated with lactation volume, composition and calving interval in dairy cattle.

Dairy cattle are an interesting model for gaining insights into the genes responsible for the large variation between and within mammalian species in the protein and fat content of their milk and their milk volume. Large numbers of phenotypes for these traits are available, as well as full genome sequence of key founders of modern dairy cattle populations. In twenty target QTL regions affecting milk production traits, we imputed full genome sequence variant genotypes into a population of 16,721 Holstein and Jersey cattle with excellent phenotypes. Association testing was used to identify variants within each target region, and gene expression data were used to identify possible gene candidates. There was statistical support for imputed sequence variants in or close to BTRC, MGST1, SLC37A1, STAT5A, STAT5B, PAEP, VDR, CSF2RB, MUC1, NCF4, and GHDC associated with milk production, and EPGN for calving interval. Of these candidates, analysis of RNA-Seq data demonstrated that PAEP, VDR, SLC37A1, GHDC, MUC1, CSF2RB, and STAT5A were highly differentially expressed in mammary gland compared to 15 other tissues. For nine of the other target regions, the most significant variants were in non-coding DNA. Genomic predictions in a third dairy breed (Australian Reds) using sequence variants in only these candidate genes were for some traits more accurate than genomic predictions from 632,003 common SNP on the Bovine HD array. The genes identified in this study are interesting candidates for improving milk production in cattle and could be investigated for novel biological mechanisms driving lactation traits in other mammals.

Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.

BACKGROUND: Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. RESULTS: Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. CONCLUSIONS: In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.

Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows.

BACKGROUND: It has been suggested that traits with low heritability, such as fertility, may have proportionately more genetic variation arising from non-additive effects than traits with higher heritability, such as milk yield. Here, we performed a large genome scan with 408,255 single nucleotide polymorphism (SNP) markers to identify chromosomal regions associated with additive, dominance and epistatic (pairwise additive × additive) variability in milk yield and a measure of fertility, calving interval, using records from a population of 7,055 Holstein cows. The results were subsequently validated in an independent set of 3,795 Jerseys. RESULTS: We identified genomic regions with validated additive effects on milk yield on Bos taurus autosomes (BTA) 5, 14 and 20, whereas SNPs with suggestive additive effects on fertility were observed on BTA 5, 9, 11, 18, 22, 27, 29 and the X chromosome. We also confirmed genome regions with suggestive dominance effects for milk yield (BTA 2, 3, 5, 26 and 27) and for fertility (BTA 1, 2, 3, 7, 23, 25 and 28). A number of significant epistatic effects for milk yield on BTA 14 were found across breeds. However on close inspection, these were likely to be associated with the mutation in the diacylglycerol O-acyltransferase 1 (DGAT1) gene, given that the associations were no longer significant when the additive effect of the DGAT1 mutation was included in the epistatic model. CONCLUSIONS: In general, we observed a low statistical power (high false discovery rates and small number of significant SNPs) for non-additive genetic effects compared with additive effects for both traits which could be an artefact of higher dependence on linkage disequilibrium between markers and causative mutations or smaller size of non-additive effects relative to additive effects. The results of our study suggest that individual non-additive effects make a small contribution to the genetic variation of milk yield and fertility. Although we found no individual mutation with large dominance effect for both traits under investigation, a contribution to genetic variance is still possible from a large number of small dominance effects, so methods that simultaneously incorporate genotypes across all loci are suggested to test the variance explained by dominance gene actions.

Identification and quantification of triacylglycerols containing n-3 long-chain polyunsaturated fatty acids in bovine milk.

The n-3 long-chain polyunsaturated fatty acids (LC-PUFA) are low-abundance components in milk fat, but have great potential in promoting human health. A comprehensive survey on triacylglycerol (TAG) molecular species in milk that contain at least one type of n-3 LC-PUFA, namely eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid, was conducted in this work using HPLC-linear trap quadrupole-Orbitrap and HPLC-triple quadrupole mass spectrometry techniques. A total of 51 TAG species that contain n-3 LC-PUFA have been identified in bovine milk and their structures assigned. The TAG species containing docosahexaenoic acid were found in much smaller number and at much lower abundance compared with the other 2 types of TAG. An HPLC-triple quadrupole mass spectrometry-based method was developed, which provides relative quantification of all these TAG species in a run of 36 min. Application of this method to the quantification of n-3 LC-PUFA-incorporated TAG in 32 individual animal milk samples allowed us to determine variation between animals, identify strong metabolic relationships between TAG species, and reveal negative effect of a grape marc supplement on the accumulation of eicosapentaenoic acid in milk.

Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle.

BACKGROUND: Genome wide association studies (GWAS) in most cattle breeds result in large genomic intervals of significant associations making it difficult to identify causal mutations. This is due to the extensive, low-level linkage disequilibrium within a cattle breed. As there is less linkage disequilibrium across breeds, multibreed GWAS may improve precision of causal variant mapping. Here we test this hypothesis in a Holstein and Jersey cattle data set with 17,925 individuals with records for production and functional traits and 632,003 SNP markers. RESULTS: By using a cross validation strategy within the Holstein and Jersey data sets, we were able to identify and confirm a large number of QTL. As expected, the precision of mapping these QTL within the breeds was limited. In the multibreed analysis, we found that many loci were not segregating in both breeds. This was partly an artefact of power of the experiments, with the number of QTL shared between the breeds generally increasing with trait heritability. False discovery rates suggest that the multibreed analysis was less powerful than between breed analyses, in terms of how much genetic variance was explained by the detected QTL. However, the multibreed analysis could more accurately pinpoint the location of the well-described mutations affecting milk production such as DGAT1. Further, the significant SNP in the multibreed analysis were significantly enriched in genes regions, to a considerably greater extent than was observed in the single breed analyses. In addition, we have refined QTL on BTA5 and BTA19 to very small intervals and identified a small number of potential candidate genes in these, as well as in a number of other regions. CONCLUSION: Where QTL are segregating across breed, multibreed GWAS can refine these to reasonably small genomic intervals. However, such QTL appear to represent only a fraction of the genetic variation. Our results suggest a significant proportion of QTL affecting milk production segregate within rather than across breeds, at least for Holstein and Jersey cattle.

Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition.

BACKGROUND: The maintenance of lactation in mammals is the result of a balance between competing signals from mammary development, prolactin signalling and involution pathways. Dairy cattle are an interesting case study to investigate the effect of polymorphisms that affect the function of genes in these pathways. In dairy cattle, lactation yields and milk composition (for example protein percentage and fat percentage) are routinely recorded, and these vary greatly between individuals. In this study, we test 8058 single nucleotide polymorphisms in or close to genes in these pathways for association with milk production traits and determine the proportion of variance explained by each pathway, using data on 16 812 dairy cattle, including Holstein-Friesian and Jersey bulls and cows. RESULTS: Single nucleotide polymorphisms close to genes in the mammary development, prolactin signalling and involution pathways were significantly associated with milk production traits. The involution pathway explained the largest proportion of genetic variation for production traits. The mammary development pathway also explained additional genetic variation for milk volume, fat percentage and protein percentage. CONCLUSIONS: Genetic variants in the involution pathway explained considerably more genetic variation in milk production traits than expected by chance. Many of the associations for single nucleotide polymorphisms in genes in this pathway have not been detected in conventional genome-wide association studies. The pathway approach used here allowed us to identify some novel candidates for further studies that will be aimed at refining the location of associated genomic regions and identifying polymorphisms contributing to variation in lactation volume and milk composition.

Milk-derived ribonuclease 5 preparations induce myogenic differentiation in vitro and muscle growth in vivo.

Ribonuclease 5, also known as angiogenin, is a stable and abundant ribonuclease in milk whey protein, which is able to regulate several cellular functions, including capillary formation, neuron survival, and epithelial cell growth. Ribonuclease 5 is important for protein synthesis directly stimulating rRNA synthesis in the nucleolus. Here, we show that biologically active RNase5 can be purified from bovine milk. Furthermore, we show that milk-derived RNase5 directly stimulates muscle cell differentiation in vitro, inducing C2C12 cell differentiation and myogenesis. When supplemented into the diet of healthy adult mice, milk-derived RNase5 preparations promoted muscle weight gain and grip strength. Collectively, these data indicate that milk-derived RNase5 preparations exhibit a novel role in skeletal muscle cell function.

Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle.

BACKGROUND: Identification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a "RNASE5 pathway" based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset. RESULTS: The constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility. CONCLUSIONS: These results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits.

Improving Genomic Selection for Heat Tolerance in Dairy Cattle: Current Opportunities and Future Directions.

Heat tolerance is the ability of an animal to maintain production and reproduction levels under hot and humid conditions and is now a trait of economic relevance in dairy systems worldwide because of an escalating warming climate. The Australian dairy population is one of the excellent study models for enhancing our understanding of the biology of heat tolerance because they are predominantly kept outdoors on pastures where they experience direct effects of weather elements (e.g., solar radiation). In this article, we focus on evidence from recent studies in Australia that leveraged large a dataset [∼40,000 animals with phenotypes and 15 million whole-genome sequence variants] to elucidate the genetic basis of thermal stress as a critical part of the strategy to breed cattle adapted to warmer environments. Genotype-by-environment interaction (i.e., G × E) due to temperature and humidity variation is increasing, meaning animals are becoming less adapted (i.e., more sensitive) to changing environments. There are opportunities to reverse this trend and accelerate adaptation to warming climate by 1) selecting robust or heat-resilient animals and 2) including resilience indicators in breeding goals. Candidate causal variants related to the nervous system and metabolic functions are relevant for heat tolerance and, therefore, key for improving this trait. This could include adding these variants in the custom SNP panels used for routine genomic evaluations or as the basis to design specific agonist or antagonist compounds for lowering core body temperature under heat stress conditions. Indeed, it was encouraging to see that adding prioritized functionally relevant variants into the 50k SNP panel (i.e., the industry panel used for genomic evaluation in Australia) increased the prediction accuracy of heat tolerance by up to 10% units. This gain in accuracy is critical because genetic improvement has a linear relationship with prediction accuracy. Overall, while this article used data mainly from Australia, this could benefit other countries that aim to develop breeding values for heat tolerance, considering that the warming climate is becoming a topical issue worldwide.

De Novo Long-Read Whole-Genome Assemblies and the Comparative Pan-Genome Analysis of Ascochyta Blight Pathogens Affecting Field Pea.

Ascochyta Blight (AB) is a major disease of many cool-season legumes globally. In field pea, three fungal pathogens have been identified to be responsible for this disease in Australia, namely Peyronellaea pinodes, Peyronellaea pinodella and Phoma koolunga. Limited genomic resources for these pathogens have been generated, which has hampered the implementation of effective management strategies and breeding for resistant cultivars. Using Oxford Nanopore long-read sequencing, we report the first high-quality, fully annotated, near-chromosome-level nuclear and mitochondrial genome assemblies for 18 isolates from the Australian AB complex. Comparative genome analysis was performed to elucidate the differences and similarities between species and isolates using phylogenetic relationships and functional diversity. Our data indicated that P. pinodella and P. koolunga are heterothallic, while P. pinodes is homothallic. More homology and orthologous gene clusters are shared between P. pinodes and P. pinodella compared to P. koolunga. The analysis of the repetitive DNA content showed differences in the transposable repeat composition in the genomes and their expression in the transcriptomes. Significant repeat expansion in P. koolunga's genome was seen, with strong repeat-induced point mutation (RIP) activity being evident. Phylogenetic analysis revealed that genetic diversity can be exploited for species marker development. This study provided the much-needed genetic resources and characterization of the AB species to further drive research in key areas such as disease epidemiology and host-pathogen interactions.

Recovery of mitogenomes from whole genome sequences to infer maternal diversity in 1883 modern taurine and indicine cattle.

Maternal diversity based on a sub-region of mitochondrial genome or variants were commonly used to understand past demographic events in livestock. Additionally, there is growing evidence of direct association of mitochondrial genetic variants with a range of phenotypes. Therefore, this study used complete bovine mitogenomes from a large sequence database to explore the full spectrum of maternal diversity. Mitogenome diversity was evaluated among 1883 animals representing 156 globally important cattle breeds. Overall, the mitogenomes were diverse: presenting 11 major haplogroups, expanding to 1309 unique haplotypes, with nucleotide diversity 0.011 and haplotype diversity 0.999. A small proportion of African taurine (3.5%) and indicine (1.3%) haplogroups were found among the European taurine breeds and composites. The haplogrouping was largely consistent with the population structure derived from alternate clustering methods (e.g. PCA and hierarchical clustering). Further, we present evidence confirming a new indicine subgroup (I1a, 64 animals) mainly consisting of breeds originating from China and characterised by two private mutations within the I1 haplogroup. The total genetic variation was attributed mainly to within-breed variance (96.9%). The accuracy of the imputation of missing genotypes was high (99.8%) except for the relatively rare heteroplasmic genotypes, suggesting the potential for trait association studies within a breed.

Dietary wheat and reduced methane yield are linked to rumen microbiome changes in dairy cows.

Fermentation of pasture grasses and grains in the rumen of dairy cows and other ruminants produces methane as a by-product, wasting energy and contributing to the atmospheric load of greenhouse gasses. Many feeding trials in farmed ruminants have tested the impact of dietary components on feed efficiency, productivity and methane yield (MeY). Such diets remodel the rumen microbiome, altering bacterial, archaeal, fungal and protozoan populations, with an altered fermentation outcome. In dairy cows, some dietary grains can reduce enteric methane production. This is especially true of wheat, in comparison to corn or barley. Using a feeding trial of cows fed rolled wheat, corn or barley grain, in combination with hay and canola, we identified wheat-associated changes in the ruminal microbiome. Ruminal methane production, pH and VFA concentration data together with 16S rRNA gene amplicon sequences were used to compare ruminal bacterial and archaeal populations across diets. Differential abundance analysis of clustered sequences (OTU) identified members of the bacterial families Lachnospiraceae, Acidaminococcaceae, Eubacteriaceae, Prevotellaceae, Selenomonadaceae, Anaerovoracaceae and Fibrobacteraceae having a strong preference for growth in wheat-fed cows. Within the methanogenic archaea, (at >99% 16S rRNA sequence identity) the growth of Methanobrevibacter millerae was favoured by the non-wheat diets, while Methanobrevibacter olleyae was unaffected. From the wheat-preferring bacteria, correlation analysis found OTU strongly linked to reduced MeY, reduced pH and raised propionic acid levels. OTU from the genera Shuttleworthia and Prevotella_7 and especially Selenomonadaceae had high anti-methane correlations. An OTU likely representing (100% sequence identity) the fumarate-reducing, hydrogen-utilising, rumen bacterium Mitsuokella jalaludinii, had an especially high negative correlation coefficient (-0.83) versus MeY and moderate correlation (-0.6) with rumen pH, strongly suggesting much of the MeY suppression is due to reduced hydrogen availablity. Other OTU, representing as yet unknown species from the Selenomonadaceae family and the genera Prevotella_7, Fibrobacter and Syntrophococcus also had high to moderate negative MeY correlations, but low correlation with pH. These latter likely represent bacterial species able to reduce MeY without causing greater ruminal acidity, making them excellent candidates, provided they can be isolated, for development as anti-methane probiotics.

Global survey of the bovine salivary proteome: integrating multidimensional prefractionation, targeted, and glycocapture strategies.

Saliva is easily obtainable from a large number of animals in a noninvasive manner and contains a wide diversity of compounds including hormones, metabolites, and proteins that may be a good source of biomarkers of health and disease. Here we have used a combination of multidimensional prefractionation, targeted, and glycocapture methodologies to profile the bovine salivary proteome. The nontargeted approach used four different separation methodologies consisting of SDS-PAGE, Off-gel fractionation, RP-HPLC, and SCX-HPLC. In the targeted approach, we've employed a hypothesis-based methodology by only selecting extracellular proteins from in silico data. Finally, the hydrazide capture methodology not only enabled us to identify formerly N-linked glycoproteins but it also provided a selective enrichment process for the identification of low abundance proteins. Together, the three different approaches identified 402 salivary proteins and 45 N-linked glycoproteins. A large number of these proteins have previously been uncharacterized in bovine saliva. To date, this is the largest global survey of the bovine salivary proteome and expands the potential of the diagnostic utility of this fluid to guide development of experiments seeking biomarkers for health traits (i.e., disease resistance) as well as feed conversion efficiency and productivity traits in dairy and beef cattle.

Conservation of the ST6Gal I gene and its expression in the mammary gland.

Milk sialoglycoconjugates can protect the gastrointestinal tract of the suckling neonate by competitively binding to invading pathogens and promoting growth of beneficial flora, and their potential role in postnatal brain development is of particular interest in human infant nutrition. Although the concentration and the distribution of sialoglycoconjugates have been extensively studied in the milk of various species, the investigation of sialyltransferase gene expression in the mammary gland, in the context of lactation, has been limited. The sialyltransferase enzyme ST6Gal I transfers sialic acid from CMP-sialic acid to type 2 (Galß1,4GlcNAc) free disaccharides or the termini of N- or O-linked oligosaccharides using an α2,6-linkage. Expression of the ST6Gal I gene is primarily regulated at the level of transcription through the use of several cell and development-specific promoters, producing transcripts with divergent 5' untranslated regions (UTR). In the mouse mammary gland, the novel 5'UTR exon (L) appears to be associated with a drastic increase in ST6Gal I gene expression during lactation. We find that rats also possess an exon (L), suggesting conservation of this regulatory mechanism in rodents. In contrast, an exon (L)-containing transcript was not detected in the lactating bovine or human mammary gland. We also observed a trend of increasing ST6Gal I gene expression in the bovine mammary gland, culminating in involution. This is in contrast to species such as mice where the greatest change in ST6Gal I gene expression occurs between pregnancy and lactation, suggesting different roles in rodents vs. other mammals for α2,6-sialylated oligosaccharides present in milk.