NutriPhysioGenomics of Dairy Cattle: A Step towards Integrative Systems Physiology

Abstract Text:

The advent and application of genome- and metabolome-enabled technologies (e.g., microarrays, next-generation sequencing) constituted a setback to the widespread use of the reductionist approach in livestock research. Those tools along with bioinformatics analyses of the resulting data are the foundation of modern systems physiology. Systems physiology is a field of study widely-used in model organisms (e.g. rodents, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems physiology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of mammalian organisms including the transition from pregnancy to lactation (i.e. the peripartal period). In modern dairy cattle in particular, the nature of the physiologic and metabolic adaptations during this period is multifaceted and involves multiple tissues and cell types. Within that framework, the use of a single time point to study NutriPhysioGenomics is reductive and insufficient to capture the dynamism of the underlying biological adaptations; therefore, implementation of time-course experiments must be undertaken. We have developed and validated a bioinformatics approach for ‘omics’ data termed Dynamic Impact Approach (DIA) to help interpret longitudinal physiological adaptations to lactation occurring in liver, adipose, and mammary tissue. This tool along with gene and metabolite network analyses is ideally-suited for understanding high-throughput datasets arising from transcriptome, proteome, and metabolome studies. Results demonstrate that the DIA is a suitable tool for physiological systems analysis of complex genome- and metabolome-wide datasets. Furthermore, the systems approach allowed simultaneous visualization of the complex inter-tissue adaptations to physiological state and nutrition. The knowledge generated from this integrative approach provides a more holistic understanding of the complex dynamic physiological adaptations of tissues, and in the future may prove useful for fine-tuning nutritional management of dairy cattle. An important goal during this process is to uncover key molecular players involved in the tissue’s adaptations to physiological state or nutrition.

Keywords: metabolomics, transcriptomics, bioinformatics, physiology