Jennifer M. Thomson

Invited review: Recommendations for reporting intervention studies on reproductive performance in dairy cattle: Improving design, analysis, and interpretation of research on reproduction

Abundant evidence from the medical, veterinary, and animal science literature demonstrates that there is substantial room for improvement of the clarity, completeness, and accuracy of reporting of intervention studies. More rigorous reporting guidelines are needed to improve the quality of data available for use in comparisons of outcomes (or meta-analyses) of multiple studies. Because of the diversity of factors that affect reproduction and the complexity of interactions between these, a systematic approach is required to design, conduct, and analyze basic and applied studies of dairy cattle reproduction. Greater consistency, clarity, completeness, and correctness of design and reporting will improve the value of each report and allow for greater depth of evaluation in meta-analyses. Each of these benefits will improve understanding and application of current knowledge and better identify questions that require additional modeling or primary research. The proposed guidelines and checklist will aid in the design, conduct, analysis, and reporting of intervention studies. We propose an adaptation of the REFLECT (Reporting Guidelines for Randomized Controlled Trials for Livestock and Food Safety) statement to provide guidelines and a checklist specific to reporting intervention studies in dairy cattle reproduction. Furthermore, we provide recommendations that will assist investigators to produce studies with greater internal and external validity that can more often be included in systematic reviews and global meta-analyses. Such studies will also assist the development of models to describe the physiology of reproduction.

Characterization of the vaginal microbiota of ewes and cows reveals a unique microbiota with low levels of lactobacilli and near-neutral pH

Although a number of common reproductive disorders in livestock involve bacterial infection, very little is known about their normal vaginal microbiota. Therefore, we sought to determine the species composition of sheep and cattle vaginal microbiota. Twenty Rambouillet ewes and twenty crossbred cows varying in age and reproductive status were sampled by ectocervicovaginal lavage. We amplified and sequenced the V3–V4 region of the 16S ribosomal RNA (rRNA) contents yielding a total of 907,667 high-quality reads. Good’s Coverage estimates indicated that we obtained data on 98 ± 0.01% of the total microbial genera present in each sample. Cow and ewe vaginal microbiota displayed few differences. Cow microbiota exhibited greater (P ≤ 0.05) α-diversity compared to the ewe microbiota. Both livestock species differed (P ≤ 0.05) from all previously reported vaginal communities. While bacteria were numerically dominant, Archaea were detected in 95% of cow and ewe samples, mainly of the order Desulfurococcales. Both ewes and cows were predominately colonized by the bacterial phyla Bacteroidetes, Fusobacteria, and Proteobacteria. The most abundant genera were Aggregatibacter spp., and Streptobacillus spp. Lactobacillus spp. were detected in 80% of ewe and 90% of cow samples, but only at very low abundances. Bacteria previously described from culture-based studies as common to the cow and ewe vaginal tract, except for Escherichia, were variably present, and only in low abundance. Ewe and cow pH differed (P ≤ 0.05), with means (±SD) of 6.7 ± 0.38 and 7.3 ± 0.63, respectively. In conclusion, 16S rRNA sequencing of cow and ewe vaginal ectocervicovaginal lavages showed that cow and ewe vaginal microbiota differ from culture-led results, revealing a microbiota distinct from previously described vaginal ecosystems.

Feed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiota

Several studies have revealed differences in rumen-located microbes between greatly efficient and inefficient animals; however, how the microbiota vary in the hind gastrointestinal tract (GIT) has only been sparsely explored and how they vary in the small intestine remains to be determined. We therefore sampled the microbiota of the duodenum, jejunum, ileum, colon, and colorectally-obtained feces, in addition to the rumen of 12 lambs that, in a residual feed intake trial, were found to be at either extreme of feed efficiency phenotypes. The 16S rRNA gene (V3-V4 region) profiles of all samples were analyzed and revealed unique microbiota in all GIT locations except the jejunum and ileum (ANOSIM > 0.2, < 0.001). Measures of β-diversity revealed greater dissimilarity between more anatomically distant GIT locations (e.g., Rumen-Duodenum, ANOSIM = 0.365, < 0.001; Rumen-Colon, ANOSIM = 1, < 0.001) with the nearest distal region typically more similar than the nearest proximal location. The relative abundances of 13 operational taxonomic units (OTUs) from the duodenum, jejunum, colon, and feces, as well as the rumen, differed between efficient and inefficient animals (Bonferroni corrected, < 0.05), while another 2 OTUs trended toward significance. These OTUs were classified as taxa with known roles in fibrolysis (Fibrobacteres, Ruminococcaceae, and Saccharofermentans) and others that are commonly associated with health (Bifidobacteriaceae, and Christensenellaceae) and dysbiosis (Proteobacteria). Our findings show biospatial delineations of microbiota throughout the GIT and suggest that feed efficiency extends beyond the rumen, transcending these regions, and involves increases in both rumen- and colon-located fibrolytic taxa, increases in bifidobacterial species in the small intestine, and reductions in small intestine and distal GIT-located Proteobacteria.

Genetic Markers Are Associated with the Ruminal Microbiome and Metabolome in Grain and Sugar Challenged Dairy Heifers

Dairy heifers were subjected to a non-life-threatening challenge designed to induce ruminal acidosis by feeding grain and sugar. Large among animal variation in clinical signs of acidosis, rumen metabolite concentrations, and the rumen microbiome occurred. This exploratory study investigates sources of the variation by examining associations between the genome, metabolome, and microbiome, albeit with a limited population. The broader objective is to provide a rationale for a larger field study to identify markers for susceptibility to ruminal acidosis. Initially, heifers (n = 40) allocated to five feed additive groups were fed 20-days pre-challenge with a total mixed ration and additives. Fructose (0.1% of bodyweight/day) was added for the last 10 days pre-challenge. On day 21 heifers were challenged with 1.0% of bodyweight dry matter wheat + 0.2% of bodyweight fructose + additives. Rumen samples were collected via stomach tube weekly (day 0, 7, and 14) and at five times over 3.6 h after challenge and analyzed for pH and volatile fatty acid, ammonia, D-, and L-lactate concentrations. Relative abundance of bacteria and archaea were determined using Illumina MiSeq. Genotyping was undertaken using a 150K Illumina SNPchip. Genome-wide association was performed for metabolite and microbiome measures (n = 33). Few genome associations occurred with rumen pH, concentration of acetate, propionate, total volatile fatty acids, or ammonia, or the relative abundance of the Firmicutes, Bacteroidetes, and Spirochaetes phyla. Metabolites and microbial phyla that had markers associated and quantitative trait loci (QTL) were: acetate to propionate ratio (A:P), D-, L-, and total lactate, butyrate, acidosis eigenvalue, Actinobacteria, Chloroflexi, Euryarchaeota, Fibrobacteres, Planctomycetes, Proteobacteria, and Tenericutes. A putative genomic region overlapped for Actinobacteria, Euryarchaeota, and Fibrobacteres and covered the region that codes for matrix extracellular phosphoglycoprotein (MEPE). Other overlapping regions were: (1) Chloroflexi, Tenericutes, and A:P, (2) L- and total lactate and Actinobacteria, and (3) Actinobacteria, Euryarchaeota, Fibrobacteres, and A:P. Genome-wide associations with the metabolome and microbiome occurred despite the small population, suggesting that markers for ruminal acidosis susceptibility exist. The findings may explain some of the variation in metabolomic and microbial data and provide a rationale for a larger study with a population that has variation in acidosis.

The identification of candidate genes and SNP markers for classical bovine spongiform encephalopathy susceptibility.

Classical bovine spongiform encephalopathy is a transmissible prion disease that is fatal to cattle and is a human health risk due to its association with a strain of Creutzfeldt-Jakob disease (vCJD). Mutations to the coding region of the prion gene (PRNP) have been associated with susceptibility to transmissible spongiform encephalopathies in mammals including bovines and humans. Additional loci such as the retinoic acid receptor beta (RARB) and stathmin like 2 (STMN2) have also been associated with disease risk. The objective of this study was to refine previously identified regions associated with BSE susceptibility and to identify positional candidate genes and genetic variation that may be involved with the progression of classical BSE. The samples included 739 samples of either BSE infected animals (522 animals) or non-infected controls (207 animals). These were tested using a custom SNP array designed to narrow previously identified regions of importance in bovine genome. Thirty one single nucleotide polymorphisms were identified at p < 0.05 and a minor allele frequency greater than 5%. The chromosomal regions identified and the positional and functional candidate genes and regulatory elements identified within these regions warrant further research.

Identification of genetic markers and QTL for carcass quality traits within the American Simmental Association Carcass Merit Program1

© The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. Transl. Anim. Sci. 2018.2:S39–S43 doi: 10.1093/tas/txy032

Evaluating sample size to estimate genetic management metrics in the genomics era

Inbreeding and relationship metrics among and within populations are useful measures for genetic management of wild populations, but accuracy and precision of estimates can be influenced by the number of individual genotypes analysed. Biologists are confronted with varied advice regarding the sample size necessary for reliable estimates when using genomic tools. We developed a simulation framework to identify the optimal sample size for three widely used metrics to enable quantification of expected variance and relative bias of estimates and a comparison of results among populations. We applied this approach to analyse empirical genomic data for 30 individuals from each of four different free‐ranging Rocky Mountain bighorn sheep (Ovis canadensis canadensis) populations in Montana and Wyoming, USA, through cross‐species application of an Ovine array and analysis of approximately 14,000 single nucleotide polymorphisms (SNPs) after filtering. We examined intra‐ and interpopulation relationships using kinship and identity by state metrics, as well as FST between populations. By evaluating our simulation results, we concluded that a sample size of 25 was adequate for assessing these metrics using the Ovine array to genotype Rocky Mountain bighorn sheep herds. However, we conclude that a universal sample size rule may not be able to sufficiently address the complexities that impact genomic kinship and inbreeding estimates. Thus, we recommend that a pilot study and sample size simulation using R code we developed that includes empirical genotypes from a subset of populations of interest would be an effective approach to ensure rigour in estimating genomic kinship and population differentiation.