Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 145 "muscle to meat conversion" process, which is crucial for determining meat tenderness (D’Alessandro and Zolla, 2013). By integrating proteomic data with other omics approaches, researchers have established milestones in understanding the events leading to meat quality, enabling the development of strategies to produce high-quality meat (Picard et al., 2010; D’Alessandro and Zolla, 2013). 5 Metabolomics: Profiling Biochemical Pathways 5.1 Introduction to metabolomics in livestock Metabolomics is a powerful omics technology that involves the comprehensive analysis of small molecule metabolites within biological samples such as cells, tissues, and biofluids. This approach provides a detailed snapshot of the metabolic state of an organism, reflecting the end-products of complex genetic, epigenetic, and environmental interactions (Goldansaz et al., 2017). In livestock research, metabolomics has been increasingly utilized to enhance phenotypic characterization, offering insights into animal health, disease diagnosis, and economically important traits such as feed efficiency and milk production (Fontanesi, 2016; Goldansaz et al., 2017). The integration of metabolomics with other omics data, such as genomics, has the potential to refine trait descriptions and improve the prediction of breeding values, thereby advancing livestock breeding strategies (Fontanesi, 2016). 5.2 Techniques for metabolic profiling Metabolomics employs advanced analytical techniques, primarily mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, to identify and quantify metabolites. High-resolution mass spectrometry, coupled with gas or liquid chromatography, is particularly effective for the accurate measurement of a wide range of metabolites. Techniques such as reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) are used to separate lipophilic and hydrophilic metabolites, respectively, allowing for comprehensive coverage of the metabolome. Recent advancements in integrative multi-omics approaches have enabled the simultaneous analysis of proteins and metabolites from a single sample, providing a more holistic view of the biological system (Blum et al., 2018). 5.3 Impact of metabolomics on breeding decisions The application of metabolomics in livestock breeding can significantly impact breeding decisions by providing detailed phenotypic data that complements genomic information. Metabolomics can identify biomarkers associated with desirable traits, such as growth rate, milk production, and fat deposition, which can be used to select animals with superior genetic potential (Fontanesi, 2016). Additionally, metabolomics can help elucidate the biochemical pathways underlying these traits, offering insights into the metabolic mechanisms that drive phenotypic variation (Fontanesi, 2016; Goldansaz et al., 2017). By integrating metabolomic data with genomic and other omics data, researchers can develop more accurate models for predicting breeding values and optimizing selection programs. 5.4 Case study: metabolomics in nutritional efficiency A notable application of metabolomics in livestock research is the study of nutritional efficiency. Metabolomic profiling can reveal how different diets affect the metabolic pathways in livestock, identifying key metabolites and pathways associated with improved feed efficiency (Goldansaz et al., 2017). For example, a study on cattle metabolomics identified specific metabolites linked to better feed conversion ratios, which could be used as biomarkers for selecting animals with higher nutritional efficiency (Goldansaz et al., 2017). This approach not only enhances the understanding of the metabolic basis of feed efficiency but also provides practical tools for improving livestock production through targeted breeding strategies. Integrative analysis of metabolomics data with other omics layers, such as genomics and transcriptomics, further enhances the ability to identify genetic markers associated with nutritional efficiency, paving the way for more efficient and sustainable livestock production systems (Figure 2) (Jendoubi, 2021).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==