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The value of a systems biology approach in cattle nutrition
The inception of high-throughput, genome-enabled technologies (e.g., microarray, next-generation sequencing) in combination with advances in biology-driven bioinformatics has been essential for the creation of modern systems biology and concurrent decline in a reductionist approach. Systems biology is a field of study that seeks to improve the understanding of complex biological interactions occurring within cells and tissues by information at the gene, protein, and metabolite level. These systems concepts are an ideal fit for investigating the interaction between nutrition, the microbiome, and the physiological state with tissue metabolism and function during key life stages of livestock. Within the systems context, single time point studies in NutriPhysioGenomics are reductive and are unable to identify the dynamics in biological adaptations; thus, longitudinal time-course experiments are essential. With the current ease of generating large transcriptome and metabolite data sets, robust bioinformatics tools are becoming more essential to obtain meaningful interpretation of the data. The Dynamic Impact Approach (DIA) was originally developed to help link the longitudinal physiological adaptations to lactation occurring simultaneously in liver, adipose, and mammary. Results based on our own and publically available data sets indicate that DIA is robust for physiological systems analysis of complex transcriptome data sets within a tissue or among tissues and allows simultaneous visualization for the complex inter-tissue adaptations to nutrition. This tool in addition to the gene and transcriptional factor (TF) network analyses using popular software suites, such as Ingenuity Pathway Analysis, is well-suited to interpret high-throughput data sets. An example of this approach using liver, mammary, and adipose tissue during the transition period is presented. Furthermore, we present examples of novel insights obtained from the interaction between epithelial tissue and ruminal microbiome involving nutritional management of beef and dairy cattle. Overall, we demonstrate that an integrative approach across and within tissues provides a more complete understanding of the complex dynamic physiological responses to nutrition in cattle. Longitudinal analyses of functional and TF networks within liver, skeletal muscle, epithelium, and adipose in response to nutrition may be useful for fine-tuning nutritional management of beef and dairy cattle. Specific goals also include identifying key molecular players in tissue adaptations to relevant nutritional management.
Keywords: nutrition, bioinformatics, systems