A revised model of energy transactions and body composition in sheep.

A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 - km) x MEI), where km is partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of km derived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and w pools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa. When evaluated with independent data, the model performed better than the current Australian feeding standards (Freer et al., 2007) for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes.

Based on prior work by Oltjen et al. (2006), a revised dynamic, mechanistic model was developed to improve the prediction of the composition of protein and fat in the body of growing ruminants. The revised model calculates heat production (HP) internally as a function of fasting HP, heat associated with feeding, and HP from changes in fat and protein within the body. Heat associated with product formation is calculated from changes in body protein and fat, with separate efficiencies for each, while heat associated with feeding is a constant proportion of metabolizable energy intake and applies at all levels of feeding above and below maintenance. When evaluated against novel data, the revised model performed similarly to current Australian feeding standards (Freer et al., 2007) Unlike the Freer model, the revised model captures variation in HP arising from feed as well as gain of protein and fat. The revised model explicitly represents protein in the body as two pools with markedly different rates of energy expenditure, improving representation of the underlying biology compared to current feeding systems. This provides a more flexible way to predict energy requirements and body composition in growing animals while achieving similar performance to current feeding systems.

Genomic predictions for enteric methane production are improved by metabolome and microbiome data in sheep (Ovis aries).

Methane production from rumen methanogenesis contributes approximately 71% of greenhouse gas emissions from the agricultural sector. This study has performed genomic predictions for methane production from 99 sheep across 3 yr using a residual methane phenotype that is log methane yield corrected for live weight, rumen volume, and feed intake. Using genomic relationships, the prediction accuracies (as determined by the correlation between predicted and observed residual methane production) ranged from 0.058 to 0.220 depending on the time point being predicted. The best linear unbiased prediction algorithm was then applied to relationships between animals that were built on the rumen metabolome and microbiome. Prediction accuracies for the metabolome-based relationships for the two available time points were 0.254 and 0.132; the prediction accuracy for the first microbiome time point was 0.142. The second microbiome time point could not successfully predict residual methane production. When the metabolomic relationships were added to the genomic relationships, the accuracy of predictions increased to 0.274 (from 0.201 when only the genomic relationship was used) and 0.158 (from 0.081 when only the genomic relationship was used) for the two time points, respectively. When the microbiome relationships from the first time point were added to the genomic relationships, the maximum prediction accuracy increased to 0.247 (from 0.216 when only the genomic relationship was used), which was achieved by giving the genomic relationships 10 times more weighting than the microbiome relationships. These accuracies were higher than the genomic, metabolomic, and microbiome relationship matrixes achieved alone when identical sets of animals were used.

Epithelial, metabolic and innate immunity transcriptomic signatures differentiating the rumen from other sheep and mammalian gastrointestinal tract tissues.

Background. Ruminants are successful herbivorous mammals, in part due to their specialized forestomachs, the rumen complex, which facilitates the conversion of feed to soluble nutrients by micro-organisms. Is the rumen complex a modified stomach expressing new epithelial (cornification) and metabolic programs, or a specialised stratified epithelium that has acquired new metabolic activities, potentially similar to those of the colon? How has the presence of the rumen affected other sections of the gastrointestinal tract (GIT) of ruminants compared to non-ruminants? Methods. Transcriptome data from 11 tissues covering the sheep GIT, two stratified epithelial and two control tissues, was analysed using principal components to cluster tissues based on gene expression profile similarity. Expression profiles of genes along the sheep GIT were used to generate a network to identify genes enriched for expression in different compartments of the GIT. The data from sheep was compared to similar data sets from two non-ruminants, pigs (closely related) and humans (more distantly related). Results. The rumen transcriptome clustered with the skin and tonsil, but not the GIT transcriptomes, driven by genes from the epidermal differentiation complex, and genes encoding stratified epithelium keratins and innate immunity proteins. By analysing all of the gene expression profiles across tissues together 16 major clusters were identified. The strongest of these, and consistent with the high turnover rate of the GIT, showed a marked enrichment of cell cycle process genes (P = 1.4 E-46), across the whole GIT, relative to liver and muscle, with highest expression in the caecum followed by colon and rumen. The expression patterns of several membrane transporters (chloride, zinc, nucleosides, amino acids, fatty acids, cholesterol and bile acids) along the GIT was very similar in sheep, pig and humans. In contrast, short chain fatty acid uptake and metabolism appeared to be different between the species and different between the rumen and colon in sheep. The importance of nitrogen and iodine recycling in sheep was highlighted by the highly preferential expression of SLC14A1-urea (rumen), RHBG-ammonia (intestines) and SLC5A5-iodine (abomasum). The gene encoding a poorly characterized member of the maltase-glucoamylase family (MGAM2), predicted to play a role in the degradation of starch or glycogen, was highly expressed in the small and large intestines. Discussion. The rumen appears to be a specialised stratified cornified epithelium, probably derived from the oesophagus, which has gained some liver-like and other specialized metabolic functions, but probably not by expression of pre-existing colon metabolic programs. Changes in gene transcription downstream of the rumen also appear have occurred as a consequence of the evolution of the rumen and its effect on nutrient composition flowing down the GIT.