Non-invasive metabolomics biomarkers of production efficiency and beef carcass quality traits

Artegoitia, V. M.; Newman, John W.; Foote, Andrew P; Shackelford, S. D.; King, D. A.; Wheeler, T. L.; Lewis, Ronald M.; Freetly, H. C.

doi:10.1038/s41598-021-04049-2

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…Until 2023, there were 153 publications on metabolomics approaches in research with bovine species available [ 22 ]. Recently published studies predominantly aimed to identify biomarkers for desirable economic traits, such as feed efficiency [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ], growth potential [ 31 ] and dairy production [ 32 , 33 , 34 ]. Metabolomics studies on the effect of supplementing RPMET to dairy/beef diets are limited.…”

Section: Discussionmentioning

confidence: 99%

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

Inhuber

Windisch

Kleigrewe³

et al. 2023

Metabolites

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This study aimed to determine the metabolic response of growing German Simmental bulls fed rations low in crude protein (CP) supplemented with rumen-protected methionine (RPMET). In total, 69 bulls (on average 238 ± 11 days of age at start and 367 ± 25 kg of bodyweight) were assigned to three dietary treatments (n = 23/group): Positive control (CON; 13.7% CP; 2.11 g methionine/kg DM), negative control deficient in CP (RED; 9.04% CP; 1.56 g methionine/kg DM) and crude protein-deficient ration supplemented with RPMET (RED+RPMET; 9.04% CP; 2.54 g methionine/kg DM). At slaughter, samples of liver, muscle and blood serum were taken and underwent subsequent metabolomics profiling using a UHPLC-QTOF-MS system. A total of 6540 features could be detected. Twenty metabolites in the liver, five metabolites in muscle and thirty metabolites in blood serum were affected (p < 0.05) due to dietary treatments. In total, six metabolites could be reliably annotated and were thus subjected to subsequent univariate analysis. Reduction in dietary CP had minimal effect on metabolite abundance in target tissues of both RED and RED+RPMET bulls as compared to CON bulls. The addition of RPMET altered the hepatic anti-oxidant status in RED+RPMET bulls compared to both RED and CON bulls. Results exemplify nutrient partitioning in growing German Simmental bulls: bulls set maintenance as the prevailing metabolic priority (homeostasis) and nutrient trafficking as the second priority, which was directed toward special metabolic functions, such as anti-oxidant pathways.

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Section: Discussionmentioning

confidence: 99%

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

Inhuber

Windisch

Kleigrewe³

et al. 2023

Metabolites

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“…One advantage of profiling metabolites is exploring the impact of metabolism on systemic health by monitoring the production and further metabolism of compounds present in the diet, digesta, and plasma (Seidel et al, 2014). It can also be used to evaluate feed Martin et al, 2021;Artegoitia et al, 2022). Studies have also shown that the heritability of water-soluble compounds such as free amino acids, nucleotides, and sugars in beef was less than 0.30 and varied with animal age (Sakuma et al, 2016).…”

Section: Metabolomicsmentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

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“…They are the last response of biological systems to genetic and environmental effects before the expression of the phenotype, and are therefore considered as an intermediate phenotype 13 . Hence, the analysis of the metabolomic pro le can be used to study the biological mechanisms controlling the development of complex traits, as it has been shown by several authors [14][15][16][17] . A metabolomic analysis on these divergent lines will allow to take a closer look into the biological mechanisms responsible for the fat deposition, giving relevant insights that can be extrapolated to the study of obesity.…”

Section: Introductionmentioning

confidence: 99%

Divergent selection for IMF in rabbits: A story told by plasma metabolites

Zubiri-Gaitán

Blasco

Hernández

2023

Preprint

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This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity confirmed its relevant role in the lipid deposition.

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Non-invasive metabolomics biomarkers of production efficiency and beef carcass quality traits

Cited by 8 publications

References 50 publications

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

Applications of Omics Technology for Livestock Selection and Improvement

Divergent selection for IMF in rabbits: A story told by plasma metabolites

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