Massimo Bionaz1, Juan J Loor2
1Oregon State University, United States of America; 2University of Illinois at Urbana-Champaign
Genome sequencing efforts and the impressive development of technologies able to measure the cellular transcriptome with high accuracy have greatly enhanced the depth of research in livestock biosciences. The study of cattle physiology and nutrigenomics, in particular, has benefited from completion of the genome sequencing, ongoing annotation efforts, application of high-throughput technologies, and the development of more sophisticated bioinformatics approaches. Despite all those improvements, many challenges remain both ontological and methodological. Improper application of quantitative RT-PCR and inadequate use of statistics and bioinformatics for the analysis of large transcriptome datasets is still quite often observed in published research in cattle. The latter, in particular, limit the meaningful biological interpretation of omics data partly because of a persistent reductive approach to science. The reductionist approach has been challenged by the resurgence of the system biology approach. Such approach aims to study any system, from cells to entire organisms as they are: a complex system driven by dynamic interactive networks. Integrative systems biology is required to fully capture such dynamism. This approach is well-suited to study the complexity of cattle physiological adaptations to lactation, response to health challenges, and nutrition. The dynamic transcriptional adaptation across multiple tissue/organs, i.e., systems physiology, remains a major challenge. The Dynamic Impact Approach (DIA) appears suitable for the application of integrative system physiology. The DIA analysis of large transcriptome datasets allowed biological visualization of the dynamic adaptations of liver, mammary gland, and adipose tissue in dairy cows during the transition from pregnancy to lactation as well as the dynamic adaptation to nutrition. Interactive networks between gene products are fundamental for life; thus, their study is crucial to understand the biological adaptations of any biological system. Transcriptional network analysis has demonstrated great utility for understanding dynamic adaptations to physiological state or nutrition in dairy cows. Besides the transcriptome, including also non-coding short RNA, we are witnessing an ever-growing development of metabolomics and epigenomics technologies and, possibly, the development of bioinformatics tools able to integrate information from all those technologies. The future of integrative systems biology in dairy cattle biosciences holds great promises and appears very exciting!
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