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Systems biology and the role of nutrition in coordinating adaptations to lactation
The advent of genome- and metabolome-enabled technologies (e.g., microarrays, RNA-sequencing) constituted a setback to the use of reductionism in livestock research. Those tools along with bioinformatics were essential for the advent of modern systems biology. Systems biology is a field of study widely-used in model organisms (e.g. rodents, yeast) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology 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). Within that framework, the use of a single time point to study “NutriPhysioGenomics” is reductive and insufficient to capture the dynamism of biological adaptations; therefore, implementation of time-course experiments must be undertaken. Modern bioinformatics tools compliment the ever increasing ability to generate large molecular and metabolite datasets. The Dynamic Impact Approach (DIA) was conceived to help interpret in a more biologically-relevant manner the longitudinal physiological adaptations to lactation occurring simultaneously in several tissues such as liver, adipose, and mammary. This tool along with gene and transcriptional factor (TF) network analyses using software suites such as the popular Ingenuity Pathway AnalysisÒ are ideally-suited for understanding high-throughput datasets. Results utilizing our own and publicly-available datasets demonstrate that the DIA is robust for physiological systems analysis of complex transcriptome datasets within a tissue or among tissues. Simultaneous visualization of the complex inter-tissue adaptations to physiological state and nutrition can be discerned. An example of this approach using liver, mammary, and adipose tissue during late-pregnancy and early lactation is presented. Furthermore, we present examples of new knowledge generated through the application of functional analyses and gene network tools on transcriptome and metabolome datasets encompassing nutritional management of dairy cattle, e.g. plane of dietary energy prepartum or the supplementation of amino acids or long-chain fatty acids. Overall, we demonstrate that the integrative approach across and within tissues provides a more holistic understanding of the complex dynamic physiological adaptations during lactation. The longitudinal analyses of functional and TF networks within adipose and liver in response to nutrition 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: bioinformatics, nutritional science, omics