Sharpea and Kandleria are associated with rumen samples from low-methane-emitting sheep. Four strains of each genus were studied in culture, and the genomes of nine strains were analysed, to understand the physiology of these bacteria. All eight cultures grew equally well with d-glucose, d-fructose, d-galactose, cellobiose, and sucrose supplementation. d-Lactate was the major end product, with small amounts of the mixed acid fermentation products formate, acetate and ethanol. Genes encoding the enzymes necessary for this fermentation pattern were found in the genomes of four strains of Sharpea and five of Kandleria. Strains of Sharpea produced traces of hydrogen gas in pure culture, but strains of Kandleria did not. This was consistent with finding that Sharpea, but not Kandleria, genomes contained genes coding for hydrogenases. It was speculated that, in co-culture with a methanogen, Sharpea and Kandleria might change their fermentation pattern from a predominately homolactic to a predominately mixed acid fermentation, which would result in a decrease in lactate production and an increase in formation of acetate and perhaps ethanol. However, Sharpea and Kandleria did not change their fermentation products when co-cultured with Methanobrevibacter olleyae, a methanogen that can use both hydrogen and formate, and lactate remained the major end product. The results of this study therefore support a hypothesis that explains the link between lower methane yields and larger populations of Sharpea and Kandleria in the rumens of sheep.

Enteric fermentation by farmed ruminant animals is a major source of methane and constitutes the second largest anthropogenic contributor to global warming. Reducing methane emissions from ruminants is needed to ensure sustainable animal production in the future. Methane yield varies naturally in sheep and is a heritable trait that can be used to select animals that yield less methane per unit of feed eaten. We previously demonstrated elevated expression of hydrogenotrophic methanogenesis pathway genes of methanogenic archaea in the rumens of high methane yield (HMY) sheep compared to their low methane yield (LMY) counterparts. Methane production in the rumen is strongly connected to microbial hydrogen production through fermentation processes. In this study, we investigate the contribution that rumen bacteria make to methane yield phenotypes in sheep.

Facial eczema (FE) is a significant metabolic disease that affects New Zealand ruminants. Ingestion of the mycotoxin sporidesmin leads to liver and bile duct damage, which can result in photosensitisation, reduced productivity and death. Strategies used to manage the incidence and severity of the disease include breeding. In sheep, there is considerable genetic variation in the response to FE. A commercial testing program is available for ram breeders who aim to increase tolerance, determined by the concentration of the serum enzyme, gamma-glutamyltransferase 21 days after a measured sporidesmin challenge (GGT21). Genome-wide association studies were carried out to determine regions of the genome associated with GGT21. Two regions on chromosomes 15 and 24 are reported, which explain 5% and 1% of the phenotypic variance in the response to FE, respectively. The region on chromosome 15 contains the β-globin locus. Of the significant SNPs in the region, one is a missense variant within the haemoglobin subunit β (HBB) gene. Mass spectrometry of haemoglobin from animals with differing genotypes at this locus indicated that genotypes are associated with different forms of adult β-globin. Haemoglobin haplotypes have previously been associated with variation in several health-related traits in sheep and warrant further investigation regarding their role in tolerance to FE in sheep. We show a strategic approach to the identification of regions of importance for commercial breeding programs with a combination of discovery, statistical and biological validation. This study highlights the power of using increased density genotyping for the identification of influential genomic regions, combined with subsequent inclusion on lower density genotyping platforms.

To assist cattle producers transition from microsatellite (MS) to single nucleotide polymorphism (SNP) genotyping for parental verification we previously devised an effective and inexpensive method to impute MS alleles from SNP haplotypes. While the reported method was verified with only a limited data set (N = 479) from Brown Swiss, Guernsey, Holstein, and Jersey cattle, some of the MS-SNP haplotype associations were concordant across these phylogenetically diverse breeds. This implied that some haplotypes predate modern breed formation and remain in strong linkage disequilibrium. To expand the utility of MS allele imputation across breeds, MS and SNP data from more than 8000 animals representing 39 breeds (Bos taurus and B. indicus) were used to predict 9410 SNP haplotypes, incorporating an average of 73 SNPs per haplotype, for which alleles from 12 MS markers could be accurately be imputed. Approximately 25% of the MS-SNP haplotypes were present in multiple breeds (N = 2 to 36 breeds). These shared haplotypes allowed for MS imputation in breeds that were not represented in the reference population with only a small increase in Mendelian inheritance inconsistancies. Our reported reference haplotypes can be used for any cattle breed and the reported methods can be applied to any species to aid the transition from MS to SNP genetic markers. While ~91% of the animals with imputed alleles for 12 MS markers had ≤1 Mendelian inheritance conflicts with their parents' reported MS genotypes, this figure was 96% for our reference animals, indicating potential errors in the reported MS genotypes. The workflow we suggest autocorrects for genotyping errors and rare haplotypes, by MS genotyping animals whose imputed MS alleles fail parentage verification, and then incorporating those animals into the reference dataset.

The potent greenhouse gas methane (CH4) is produced in the rumens of ruminant animals from hydrogen produced during microbial degradation of ingested feed. The natural animal-to-animal variation in the amount of CH4 emitted and the heritability of this trait offer a means for reducing CH4 emissions by selecting low-CH4 emitting animals for breeding. We demonstrate that differences in rumen microbial community structure are linked to high and low CH4 emissions in sheep. Bacterial community structures in 236 rumen samples from 118 high- and low-CH4 emitting sheep formed gradual transitions between three ruminotypes. Two of these (Q and S) were linked to significantly lower CH4 yields (14.4 and 13.6 g CH4/kg dry matter intake [DMI], respectively) than the third type (H; 15.9 g CH4/kg DMI; p<0.001). Low-CH4 ruminotype Q was associated with a significantly lower ruminal acetate to propionate ratio (3.7±0.4) than S (4.4±0.7; p<0.001) and H (4.3±0.5; p<0.001), and harbored high relative abundances of the propionate-producing Quinella ovalis. Low-CH4 ruminotype S was characterized by lactate- and succinate-producing Fibrobacter spp., Kandleria vitulina, Olsenella spp., Prevotella bryantii, and Sharpea azabuensis. High-CH4 ruminotype H had higher relative abundances of species belonging to Ruminococcus, other Ruminococcaceae, Lachnospiraceae, Catabacteriaceae, Coprococcus, other Clostridiales, Prevotella, other Bacteroidales, and Alphaproteobacteria, many of which are known to form significant amounts of hydrogen. We hypothesize that lower CH4 yields are the result of bacterial communities that ferment ingested feed to relatively less hydrogen, which results in less CH4 being formed.

Molecular microbial ecology techniques are widely used to study the composition of the rumen microbiota and to increase understanding of the roles they play. Therefore, sampling and DNA extraction methods that result in adequate yields of microbial DNA that also accurately represents the microbial community are crucial. Fifteen different methods were used to extract DNA from cow and sheep rumen samples. The DNA yield and quality, and its suitability for downstream PCR amplifications varied considerably, depending on the DNA extraction method used. DNA extracts from nine extraction methods that passed these first quality criteria were evaluated further by quantitative PCR enumeration of microbial marker loci. Absolute microbial numbers, determined on the same rumen samples, differed by more than 100-fold, depending on the DNA extraction method used. The apparent compositions of the archaeal, bacterial, ciliate protozoal, and fungal communities in identical rumen samples were assessed using 454 Titanium pyrosequencing. Significant differences in microbial community composition were observed between extraction methods, for example in the relative abundances of members of the phyla Bacteroidetes and Firmicutes. Microbial communities in parallel samples collected from cows by oral stomach-tubing or through a rumen fistula, and in liquid and solid rumen digesta fractions, were compared using one of the DNA extraction methods. Community representations were generally similar, regardless of the rumen sampling technique used, but significant differences in the abundances of some microbial taxa such as the Clostridiales and the Methanobrevibacter ruminantium clade were observed. The apparent microbial community composition differed between rumen sample fractions, and Prevotellaceae were most abundant in the liquid fraction. DNA extraction methods that involved phenol-chloroform extraction and mechanical lysis steps tended to be more comparable. However, comparison of data from studies in which different sampling techniques, different rumen sample fractions or different DNA extraction methods were used should be avoided.

Black-billed gulls (Larus bulleri) are endemic to New Zealand and are suspected to be undergoing substantial population declines. They primarily breed on open gravel beds in braided rivers of the South Island-a habitat that is diminishing and becoming increasingly modified. Although management of this species is increasing, little has been published on their movements and demographics. In this study, both mitochondrial DNA (mtDNA) control region domain I and nuclear single nucleotide polymorphisms (SNPs) were examined to help understand the connectivity and population structure of black-billed gulls across the country and to help inform management decisions. Mitochondrial DNA showed no population structure, with high haplotype and low nucleotide diversity, and analyses highlighted mitochondrial introgression with the closely related red-billed gulls (Larus novaehollandiae scopulinus). Nuclear DNA analyses, however, identified two groups, with Rotorua birds in the North Island being distinct from the rest of New Zealand, and isolation-by-distance evident across the South Island populations. Gene flow primarily occurs between nearby colonies with a stepwise movement across the landscape. The importance from a genetic perspective of the more isolated North Island birds (1.6% of total population) needs to be further evaluated. From our results, we infer that the South Island black-billed gull management should focus on maintaining several populations within each region rather than focusing on single specific colonies or river catchments. Future study is needed to investigate the genetic structure of populations at the northern limit of the species' range, and identify the mechanisms behind, and extent of, the hybridisation between red-billed and black-billed gulls.

Genotyping-by-sequencing (GBS) is becoming an attractive alternative to array-based methods for genotyping individuals for a large number of single nucleotide polymorphisms (SNPs). Costs can be lowered by reducing the mean sequencing depth, but this results in genotype calls of lower quality. A common analysis strategy is to filter SNPs to just those with sufficient depth, thereby greatly reducing the number of SNPs available. We investigate methods for estimating relatedness using GBS data, including results of low depth, using theoretical calculation, simulation and application to a real data set.

Investments in genetic selection have played a major role in the New Zealand sheep industry competitiveness. Selection may erode genetic diversity, which is a crucial factor for the success of breeding programs. Better understanding of linkage disequilibrium (LD) and ancestral effective population size (Ne) through quantifying this diversity and comparison between populations allows for more informed decisions with regards to selective breeding taking population genetic diversity into account. The estimation of N e can be determined via genetic markers and requires knowledge of genetic distances between these markers. Single nucleotide polymorphisms (SNP) data from a sample of 12,597 New Zealand crossbred and purebred sheep genotyped with the Illumina Ovine SNP50 BeadChip was used to perform a genome-wide scan of LD and N e . Three methods to estimate genetic distances were investigated: 1) M1: a ratio fixed across the whole genome of one Megabase per centiMorgan; 2) M2: the ratios of genetic distance (using M3, below) over physical distance fixed for each chromosome; and, 3) M3: a genetic map of inter-SNP distances estimated using CRIMAP software (v2.503).

Identification of genomic regions that have been targets of selection for phenotypic traits is one of the most important and challenging areas of research in animal genetics. However, currently there are relatively few genomic regions identified that have been subject to positive selection. In this study, a genome-wide scan using ~50,000 Single Nucleotide Polymorphisms (SNPs) was performed in an attempt to identify genomic regions associated with fat deposition in fat-tail breeds. This trait and its modification are very important in those countries grazing these breeds.

Cattle and other ruminants have evolved the ability to derive most of their metabolic energy requirement from otherwise indigestible plant matter through a symbiotic relationship with plant fibre degrading microbes within a specialised fermentation chamber, the rumen. The genetic changes underlying the evolution of the ruminant lifestyle are poorly understood. The BPI-like locus encodes several putative innate immune proteins, expressed predominantly in the oral cavity and airways, which are structurally related to Bactericidal/Permeability Increasing protein (BPI). We have previously reported the expression of variant BPI-like proteins in cattle (Biochim Biophys Acta 2002, 1579, 92-100). Characterisation of the BPI-like locus in cattle would lead to a better understanding of the role of the BPI-like proteins in cattle physiology

Ruminants obtain nutrients from microbial fermentation of plant material, primarily in their rumen, a multilayered forestomach. How the different layers of the rumen wall respond to diet and influence microbial fermentation, and how these process are regulated, is not well understood. Gene expression correlation networks were constructed from full thickness rumen wall transcriptomes of 24 sheep fed two different amounts and qualities of a forage and measured for methane production. The network contained two major negatively correlated gene sub-networks predominantly representing the epithelial and muscle layers of the rumen wall. Within the epithelium sub-network gene clusters representing lipid/oxo-acid metabolism, general metabolism and proliferating and differentiating cells were identified. The expression of cell cycle and metabolic genes was positively correlated with dry matter intake, ruminal short chain fatty acid concentrations and methane production. A weak correlation between lipid/oxo-acid metabolism genes and methane yield was observed. Feed consumption level explained the majority of gene expression variation, particularly for the cell cycle genes. Many known stratified epithelium transcription factors had significantly enriched targets in the epithelial gene clusters. The expression patterns of the transcription factors and their targets in proliferating and differentiating skin is mirrored in the rumen, suggesting conservation of regulatory systems.

Taxonomic characterization of active gastrointestinal microbiota is essential to detect shifts in microbial communities and functions under various conditions. This study aimed to identify and quantify potentially active rumen microbiota using total RNA sequencing and to compare the outcomes of this approach with the widely used targeted RNA/DNA amplicon sequencing technique. Total RNA isolated from rumen digesta samples from five beef steers was subjected to Illumina paired-end sequencing (RNA-seq), and bacterial and archaeal amplicons of partial 16S rRNA/rDNA were subjected to 454 pyrosequencing (RNA/DNA Amplicon-seq). Taxonomic assessments of the RNA-seq, RNA Amplicon-seq, and DNA Amplicon-seq datasets were performed using a pipeline developed in house. The detected major microbial phylotypes were common among the three datasets, with seven bacterial phyla, fifteen bacterial families, and five archaeal taxa commonly identified across all datasets. There were also unique microbial taxa detected in each dataset. Elusimicrobia and Verrucomicrobia phyla; Desulfovibrionaceae, Elusimicrobiaceae, and Sphaerochaetaceae families; and Methanobrevibacter woesei were only detected in the RNA-Seq and RNA Amplicon-seq datasets, whereas Streptococcaceae was only detected in the DNA Amplicon-seq dataset. In addition, the relative abundances of four bacterial phyla, eight bacterial families and one archaeal taxon were different among the three datasets. This is the first study to compare the outcomes of rumen microbiota profiling between RNA-seq and RNA/DNA Amplicon-seq datasets. Our results illustrate the differences between these methods in characterizing microbiota both qualitatively and quantitatively for the same sample, and so caution must be exercised when comparing data.

Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock. These profiles can be difficult and expensive to obtain for thousands of samples (e.g. for accurate association of microbial profiles with traits), therefore the objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeKI or PstI, and two bioinformatic pipelines (reference-based and reference-free) were compared to bacterial 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n = 236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to RE-RRS. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of laboratory and analytical method, and the relationship with methane production. The results suggested that the best method was PstI RE-RRS analyzed with the reference-free approach, which accounted for 53.3±5.9% of reads, and had repeatabilities of 0.49±0.07 and 0.50±0.07 for the first two principal components (PC1 and PC2), phenotypic correlations with methane yield of 0.43±0.06 and 0.46±0.06 for PC1 and PC2, and explained 41±8% of the variation in methane yield. These results were significantly better than for bacterial 16S rRNA gene sequencing of the same samples (p<0.05) except for the correlation between PC2 and methane yield. A Sensitivity study suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, meaning the current work using 118 samples/lane and future proposed 384 samples/lane are well within that threshold. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.

Methane is produced in the rumen of ruminant livestock by methanogens and is a major contributor to agricultural greenhouse gases. Vaccination against ruminal methanogens could reduce methane emissions by inducing antibodies in saliva which enter the rumen and impair ability of methanogens to produce methane. Presently, it is not known if vaccination can induce sufficient amounts of antibody in the saliva to target methanogen populations in the rumen and little is known about how long antibody in the rumen remains active. In the current study, sheep were vaccinated twice at a 3-week interval with a model methanogen antigen, recombinant glycosyl transferase protein (rGT2) formulated with one of four adjuvants: saponin, Montanide ISA61, a chitosan thermogel, or a lipid nanoparticle/cationic liposome adjuvant (n = 6/formulation). A control group of sheep (n = 6) was not vaccinated. The highest antigen-specific IgA and IgG responses in both saliva and serum were observed with Montanide ISA61, which promoted levels of salivary antibodies that were five-fold higher than the second most potent adjuvant, saponin. A rGT2-specific IgG standard was used to determine the level of rGT2-specific IgG in serum and saliva. Vaccination with GT2/Montanide ISA61 produced a peak antibody concentration of 7 × 1016 molecules of antigen-specific IgG per litre of saliva, and it was estimated that in the rumen there would be more than 104 molecules of antigen-specific IgG for each methanogen cell. Both IgG and IgA in saliva were shown to be relatively stable in the rumen. Salivary antibody exposed for 1-2 hours to an in vitro simulated rumen environment retained approximately 50% of antigen-binding activity. Collectively, the results from measuring antibody levels and stablility suggest a vaccination-based mitigation strategy for livestock generated methane is in theory feasible.

Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs).

Knowledge about the genetic diversity of a population is a crucial parameter for the implementation of successful genomic selection and conservation of genetic resources. The aim of this research was to establish the scientific basis for the implementation of genomic selection in a composite Terminal sheep breeding scheme by providing consolidated linkage disequilibrium (LD) measures across SNP markers, estimating consistency of gametic phase between breed-groups, and assessing genetic diversity measures, such as effective population size (Ne), and population structure parameters, using a large number of animals (n = 14,845) genotyped with a high density SNP chip (606,006 markers). Information generated in this research will be useful for optimizing molecular breeding values predictions and managing the available genetic resources.

Ruminant livestock represent the single largest anthropogenic source of the potent greenhouse gas methane, which is generated by methanogenic archaea residing in ruminant digestive tracts. While differences between individual animals of the same breed in the amount of methane produced have been observed, the basis for this variation remains to be elucidated. To explore the mechanistic basis of this methane production, we measured methane yields from 22 sheep, which revealed that methane yields are a reproducible, quantitative trait. Deep metagenomic and metatranscriptomic sequencing demonstrated a similar abundance of methanogens and methanogenesis pathway genes in high and low methane emitters. However, transcription of methanogenesis pathway genes was substantially increased in sheep with high methane yields. These results identify a discrete set of rumen methanogens whose methanogenesis pathway transcription profiles correlate with methane yields and provide new targets for CH4 mitigation at the levels of microbiota composition and transcriptional regulation.

Methane (CH(4)) is a potent greenhouse gas (GHG), having a global warming potential 21 times that of carbon dioxide (CO(2)). Methane emissions from agriculture represent around 40% of the emissions produced by human-related activities, the single largest source being enteric fermentation, mainly in ruminant livestock. Technologies to reduce these emissions are lacking. Ruminant methane is formed by the action of methanogenic archaea typified by Methanobrevibacter ruminantium, which is present in ruminants fed a wide variety of diets worldwide. To gain more insight into the lifestyle of a rumen methanogen, and to identify genes and proteins that can be targeted to reduce methane production, we have sequenced the 2.93 Mb genome of M. ruminantium M1, the first rumen methanogen genome to be completed.

The taxonomy and associated nomenclature of many taxa of rumen bacteria are poorly defined within databases of 16S rRNA genes. This lack of resolution results in inadequate definition of microbial community structures, with large parts of the community designated as incertae sedis, unclassified, or uncultured within families, orders, or even classes. We have begun resolving these poorly-defined groups of rumen bacteria, based on our desire to name these for use in microbial community profiling. We used the previously-reported global rumen census (GRC) dataset consisting of >4.5 million partial bacterial 16S rRNA gene sequences amplified from 684 rumen samples and representing a wide range of animal hosts and diets. Representative sequences from the 8,985 largest operational units (groups of sequence sharing >97% sequence similarity, and covering 97.8% of all sequences in the GRC dataset) were used to identify 241 pre-defined clusters (mainly at genus or family level) of abundant rumen bacteria in the ARB SILVA 119 framework. A total of 99 of these clusters (containing 63.8% of all GRC sequences) had no unique or had inadequate taxonomic identifiers, and each was given a unique nomenclature. We assessed this improved framework by comparing taxonomic assignments of bacterial 16S rRNA gene sequence data in the GRC dataset with those made using the original SILVA 119 framework, and three other frameworks. The two SILVA frameworks performed best at assigning sequences to genus-level taxa. The SILVA 119 framework allowed 55.4% of the sequence data to be assigned to 751 uniquely identifiable genus-level groups. The improved framework increased this to 87.1% of all sequences being assigned to one of 871 uniquely identifiable genus-level groups. The new designations were included in the SILVA 123 release (https://www.arb-silva.de/documentation/release-123/) and will be perpetuated in future releases.