Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle

Development and validation of a model for early prediction of residual feed intake in beef cattle using plasma biomarkers

Identification of plasma biomarkers for feed efficiency in growing beef cattle offers a promising opportunity for developing prediction models to improve precision feeding strategies. However, these models must accurately predict feed efficiency at early stages of fattening. Our study aimed to evaluate the reliability of candidate biomarkers previously identified in late-fattening cattle when analysed during early fattening stages and to develop diet-specific prediction equations for residual feed intake (RFI). From a total of 364 Charolais bulls across seven cohorts, we selected 64 animals with extreme RFI values. The animals were fed either a corn‑ or grass-silage diets. These animals were chosen from four out of the available seven cohorts. Animals from three cohorts (24 high-RFI and 24 low-RFI, having a mean RFI difference of 1.48 kg/d) were used for biomarker confirmation and prediction model training. Animals from a fourth cohort (8 high-RFI and 8 low-RFI, having a mean RFI difference of 0.98 kg/d) were used for model external validation. Blood samples were collected at the beginning of the feed efficiency test (333 ± 20 days), and plasma underwent targeted metabolomic for 630 metabolites, natural abundance of 15N (δ15N), insulin, and IGF-1 analysis. Seven previously identified plasma biomarkers for RFI in late-fattening beef cattle still kept their capability for discriminating low and high RFI animals when analysed during early fattening stages (P < 0.05). Among these confirmed biomarkers, five were common for both grass- and corn-fed animals (creatinine, β-alanine, triglyceride TG18:0_34:2, symmetric dimethyl-arginine and phosphatidylcholine PC aa C30:2) while two were diet-specific (IGF-1 for grass silage-based diet, and isoleucine for corn silage-based diet. No new plasma biomarkers of RFI were identified at early-fattening stages (false discovery rate  > 0.05). Prediction models were developed based on seven confirmed RFI biomarkers analysed during early-fattening. Two logistic regression models incorporating creatinine and either IGF-1 (for grass silage-based diet) or PC aa C30:2 (for corn silage-based diet) effectively distinguished between high- and low-RFI animals with high sensitivity and specificity (area under the curve > 0.80). The biomarkers used in the models showed moderate to high repeatability between early and late fattening stages (0.45 < r < 0.65). The models were successfully externally validated, with more than 85% of animals from the fourth cohort correctly classified. Once validated in larger cohorts and utilising cost-effective and rapid analytical methods, these models could support precision feeding and breeding programmes, aiming to reduce the cost of raising beef cattle.

Comparative analysis of signalling pathways in tissue protein metabolism in efficient and non-efficient beef cattle: acute response to an identical single meal size

Protein turnover has been associated to residual feed intake (RFI) in beef cattle. However, this relationship may be confounded by feeding level and affected by the composition of the diet being fed. Our aim was to assess postmortem the protein metabolism signalling pathways in skeletal muscle and liver of 32 Charolais young bulls with extreme RFI phenotypes. Bulls were fed two contrasting diets during the whole fattening period but were subjected to a similar and single nutritional stimulus, induced by their respective concentrate, just prior to slaughter. The key targets were protein degradation (autophagy and ubiquitin) and synthesis signalling pathways through western-blot analysis, as well as hepatic transaminase activity. To ensure a precise assessment of all animals at the same postprandial time, they were provided with a test meal (2.5 kg of either a high-starch and high-protein concentrate or high-fibre and low-protein concentrate) 3 hours prior to slaughter, irrespective of their RFI grouping. Blood and tissues were sampled at the slaughterhouse (3 h and 3 h30 postprandially, respectively). In response to an identical single meal size, efficient RFI animals showed higher (P < 0.05) postprandial plasma β-hydroxybutyrate concentrations and insulinemia (only with the high-starch concentrate) than non-efficient animals. Moreover, efficient RFI bulls had lower muscle (P = 0.04) and liver (P = 0.08) ubiquitin protein abundance (degradation pathway) and tended to have lower alanine transaminase activity in the liver (P = 0.06) compared to non-efficient bulls, regardless of diet. A positive correlation between protein degradation potential and amino acid catabolism was identified in this study (r = 0.52, P = 0.004), which was interpreted as being biologically linked to the RFI phenotype. Efficient RFI bulls also had a faster potential for protein synthesis in the muscle, as indicated by their greater ratio of phosphorylated to total form of ribosomal protein S6 kinase (P = 0.05), regardless of diet. Results on protein synthesis pathway in muscle and plasma metabolite concentrations suggested that efficient RFI cattle may have a faster nutrient absorption and insulin responsiveness after feeding than inefficient cattle. We did not find significant differences in hepatic protein synthesis pathways between the two RFI groups (P > 0.05). Our findings suggest that, in response to an identical single meal size, efficient RFI animals exhibited lower activation of tissue protein degradation pathways and faster muscle protein synthesis activation compared to their inefficient counterparts. This pattern was observed regardless of the composition of the tested meals.

Untargeted metabolomics confirms the association between plasma branched chain amino acids and residual feed intake in beef heifers

This study explored plasma biomarkers and metabolic pathways underlying feed efficiency measured as residual feed intake (RFI) in Charolais heifers. A total of 48 RFI extreme individuals (High-RFI, n = 24; Low-RFI, n = 24) were selected from a population of 142 heifers for classical plasma metabolite and hormone quantification and plasma metabolomic profiling through untargeted LC-MS. Most efficient heifers (Low-RFI) had greater (P = 0.03) plasma concentrations of IGF-1 and tended to have (P = 0.06) a lower back fat depth compared to least efficient heifers. However, no changes were noted (P ≥ 0.10) for plasma concentrations of glucose, insulin, non-esterified fatty acids, β-hydroxybutyrate and urea. The plasma metabolomic dataset comprised 3,457 ions with none significantly differing between RFI classes after false discovery rate correction (FDR > 0.10). Among the 101 ions having a raw P < 0.05 for the RFI effect, 13 were putatively annotated by using internal databases and 6 compounds were further confirmed with standards. Metabolic pathway analysis from these 6 confirmed compounds revealed that the branched chain amino acid metabolism was significantly (FDR < 0.05) impacted by the RFI classes. Our results confirmed for the first time in beef heifers previous findings obtained in male beef cattle and pointing to changes in branched-chain amino acids metabolism along with that of body composition as biological mechanisms related to RFI. Further studies are warranted to ascertain whether there is a cause-and-effect relationship between these mechanisms and RFI.

Using the natural abundance of nitrogen isotopes to identify cattle with greater efficiency in protein-limiting diets

The difficulty in selecting cattle for higher feed and nitrogen use efficiency (NUE) is an important factor contributing to poor growth and reproductive performance in dry-tropics rangelands. Therefore, the objectives were to examine the cattle variation in retaining nitrogen in a protein-deficient diet and the natural abundance of stable isotopes in body tissues as a practical alternative for the detection of more efficient cattle. In experiment 1, feed efficiency parameters were determined in 89 Brahman steers fed a protein-limiting diet for 70 days, followed by 7 days in metabolism crates for total collection of urine and faeces and calculation of nitrogen retention and NUE. The diet-animal fractionation of nitrogen isotopes (Δ¹⁵N) was quantified in tail hair and plasma proteins using isotope-ratio MS. There was a large variation in growth performance, feed efficiency and nitrogen losses among steers. Quantifying Δ¹⁵N in tail hair (Δ¹⁵N_{tail hair}) resulted in stronger correlations with feed efficiency and nitrogen metabolism parameters than when quantified in plasma proteins. Δ¹⁵N_{tail hair} was positively correlated with nitrogen losses in urine (r = 0.31, P < 0.01) and faeces (r = 0.25, P = 0.04), leading to a negative correlation with NUE (r = -0.40, P < 0.01). The group of steers with lower Δ¹⁵N_{tail hair} had greater feed efficiency, lower nitrogen losses, and greater NUE. In experiment 2, for evaluation of isotope fraction as a predictor of reproductive performance, 630 Brahman-crossed cows were classified for reproductive performance for 2 years. From this group, 25 cows with poor reproductive performance and 25 cows with good reproductive performance were selected. Tail hair representing 7 months of growth were segmented and analysed for carbon (δ¹³C) and nitrogen (δ¹⁵N) isotope enrichment. Reproductive performance was not associated with diet selection, as there was no difference in tail hair δ¹³C between groups. However, more productive cows had lower (P < 0.05) tail hair δ¹⁵N during the dry season, indicating differences in N metabolism and possibly lower N losses. In addition, cows with better reproductive performance and, therefore, greater nutrient demands, had similar body condition scores and a tendency (P = 0.09) for higher live weight at the end of the trial. In conclusion, the findings of the present study confirm that nitrogen isotope fractionation in tail hair can be used as a predictor of nitrogen losses, NUE, and reproductive performance of Brahman cattle on low-protein diets.

Common and diet-specific metabolic pathways underlying residual feed intake in fattening Charolais yearling bulls

Residual feed intake (RFI) is one of the preferred traits for feed efficiency animal breeding. However, RFI measurement is expensive and time-consuming and animal ranking may depend on the nature of the diets. We aimed to explore RFI plasma biomarkers and to unravel the underlying metabolic pathways in yearling bulls fed either a corn-silage diet rich in starch (corn diet) or a grass-silage diet rich in fiber (grass diet). Forty-eight extreme RFI animals (Low-RFI, n = 24, versus High-RFI, n = 24, balanced per diet) were selected from a population of 364 Charolais bulls and their plasma was subjected to a targeted LC-MS metabolomic approach together with classical metabolite and hormonal plasma analyses. Greater lean body mass and nitrogen use efficiency, and lower protein turnover were identified as common mechanisms underlying RFI irrespective of the diet. On the other hand, greater adiposity and plasma concentrations of branched-chain amino acids (BCAA) together with lower insulin sensitivity in High-RFI animals were only observed with corn diet. Conversely, greater plasma concentrations of BCAA and total triglycerides, but similar insulin concentrations were noted in efficient RFI cattle with grass diet. Our data suggest that there are diet-specific mechanisms explaining RFI differences in fattening Charolais yearling bulls.

Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the ¹⁵N natural abundance in plasma proteins (plasma δ¹⁵N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ¹⁵N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ¹⁵N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ¹⁵N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ¹⁵N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ¹⁵N and feed efficiency that varied according to the efficiency index and the diet utilised.

Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach

Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.