154 Physiological imbalances and targets in dairy cattle using metabonomics.

Abstract The life cycle of the dairy cow encompasses a number of transitions during which physiological mechanisms across multiple tissues must adapt in order to sustain a new metabolic state. Nutrition of the animal is an integral component in the cycle of life, not only to ensure that maintenance nutrient needs are met but also to provide the nutrients needed to support growth, lactation, and pregnancy among the most noteworthy processes. Although phenotypic responses to traditional management practices at distinct stages of the life cycle are often predictable, at the tissue level there is still a lack of understanding of the underlying complexity. “Omics” technologies, especially transcriptomics, have significantly expanded our knowledge of the molecular regulation of key metabolic processes in both health and disease. In this context, “metabonomics”, which aims to capture the dynamic metabolic responses of living systems to biological stimuli, including nutrition, has emerged as a powerful tool. The primary aim of metabonomics is to understand systemic change through time in complex biological systems. Within this framework, metabolomics, a core component of metabonomics, enables the characterization and quantification of small molecules produced through metabolic activity. In the process, individual metabolites can be identified, structures can be assigned to identified spectra, and applying bioinformatic approaches such as multivariate statistics and pattern recognition (e.g., partial least square analysis), metabolite patterns and potential biomarkers can be identified. We have employed these strategies in studies focused on key organs in dairy cows during the transition into lactation, with particular attention on metabolite and lipid species. Notably, we have also incorporated Weighted Gene Co-expression Network Analysis (WGCNA) to construct metabolite co-expression networks, offering a systems-level view of metabolic regulation. This method facilitates the identification of functional modules composed of highly correlated metabolites, as well as hub compounds that may play central roles in metabolic adaptation. Examples of the use of these integrative approaches in the context of nutritional management will be presented.