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

Abstract

Improving the ability of animals to convert feed resources into food for humans is needed for more sustainable livestock systems. Genetic selection for animals eating less while maintaining their performance (i.e., low residual feed intake [RFI]) appears a smart strategy but its effectiveness relies on high-throughput animal phenotyping. Here, we explored plasma nitrogen (N) isotope ratios in an attempt to identify easily superior young bulls in terms of RFI. For this, 48 Charolais young bulls fed two contrasting diets (corn vs. grass silage diets) were selected from a larger population as extreme RFI animals (24 low-RFI vs. 24 high-RFI) and their plasma analyzed for natural 15N abundance (δ15N) in the whole protein (bulk protein) and in the individual protein-bound amino acids (PbAA). For the first time, we showed that the δ 15N in plasma bulk protein differed (P = 0.007) between efficient (low-RFI) and inefficient (high-RFI) cattle regardless of diet. Furthermore, most analyzed PbAA followed the same trend as the bulk protein, with lower (P < 0.05) δ 15N values in more efficient (low-RFI) compared with less efficient (high-RFI) cattle, again regardless of diet. The only three exceptions were Phe, Met, and Lys (P > 0.05) for which the first metabolic reaction before being catabolized does not involve transamination, a pathway known naturally to enrich AAs in 15N. The contrasted isotopic signatures across RFI groups only in those PbAA undergoing transamination are interpreted as differences in transamination rates and N-use efficiency between low- and high-RFI phenotypes. Natural isotopic N signatures in bulk proteins and specific PbAA can be proposed as biomarkers of RFI in growing beef cattle fed different diets. However, the current study cannot delineate whether this effect only occurs post-absorption or to some extent also in the rumen. Our data support the conclusion that most efficient cattle in terms of RFI upregulate N conservation mechanisms compared with less efficient cattle and justify future research on this topic.

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

“…A higher level of IGF1 in the subgroup of less efficient energy-utilizing animals indicates a higher stimulus to body for making metabolic changes for growth; however, secretion of higher IGF1 in circulation might also suggest inhibitory feedback influence on the GH/pituitary axis, thus affecting feed utilization efficiency. Significantly low ( p < 0.001) SGPT (59.5 ± 0.64 U/L) was recorded in less efficient animals compared to efficient animals having a higher level of 67.72 ± 0.78 U/L, which corroborates with other reports in cattle ( 7 , 24 ), further indicating gluconeogenesis as the preferred energy pathway in efficient animals. A significant ( p < 0.05) difference in serum urea level of less efficient vs. highly efficient animals, i.e., 23.83 ± 0.35 vs. 20.94 ± 0.51 mg/dl (on day 30 of trial), indicates the effect of change in season during this particular period of July to August months covered during the trial in the present study.…”

“…Onset of rains in the month of August may influence the dietary patterns in animals along with climate change. Also, downregulation of different transaminases with corresponding lowering in serum urea levels was reported by other researchers ( 24 ) in cattle. Contrary to earlier studies ( 7 ) performed in beef cattle, serum SGOT levels were recorded to be higher in efficient buffalo heifers than in the inefficient subgroup of animals in the present study.…”

“…An insight into the relationship of various blood parameters with ADG with FCE can shed light on physiological dynamics underlying the metabolic changes, using machine learning approaches ( 20 – 22 ). The biological closeness between feed efficiency and the animal's ability to convert feed nitrogen ( N ) into animal protein, i.e., N-use efficiency or N partitioning and protein turnover across individuals ( 23 ), has been used for predicting RFI in growing cattle due to the difference in rates of amino acid transamination ( 24 ).…”

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

“…In growing cattle, most studies have failed to prove this link through N balance measurements 24 , 25 . However, results from our team using a novel isotopic approach applied to this same experimental setup (n = 48) suggested that more efficient animals in terms of RFI also show higher N use efficiency compared to less efficient RFI animals 26 . Data from the present study showed that plasma urea concentration, a confirmed biomarker of N use efficiency 27 , was decreased in more efficient animals in agreement with some reports 18 , 22 , 28 but unlike others 6 , 29 .…”

“…This study is part of a large program aiming to explore plasma biomarkers of RFI in Charolais beef cattle. Details on the feed efficiency test, experimental diets and basic data on animal performances as DM intake, average daily gain and feed efficiency ranking have been previously reported 26 . Briefly, this study used 364 pure bred Charolais young bulls (380 ± 58 kg body weight) tested in 3 different experimental farms.…”