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Genomics of metabolic adaptations in the peripartal cow

  • J. J. Loor (a1)
Abstract

The peripartal period is characterized by dramatic alterations in metabolism and function of key tissues such as liver, adipose and mammary. Metabolic regulation relies partly on transcriptional control of gene networks, a collection of DNA segments, which interact with a transcription factor or nuclear receptor, as a mechanism controlling the concentration of key enzymes in cells. These ‘global’ interactions can govern the rates at which genes in the network are transcribed into mRNA. The study of the entire genome, sub-networks or candidate genes at the mRNA level encompasses the broad field of genomics. Genomics of peripartal metabolic adaptations has traditionally been focused on candidate genes and more recently, using microarrays, on the broader transcriptome landscape. The candidate gene approach has expanded our knowledge on the functional adaptations of ureagenesis, fatty acid oxidation, gluconeogenesis, inflammation and growth hormone signaling in liver. More recent work with peripartal mammary tissue has used a gene network approach to study milk fat synthesis regulation as well as a candidate gene approach to study lipid transport, glucose uptake and inflammatory response. Network and pathway analysis of microarray data from cows fed different levels of dietary energy pre partum has revealed unique clusters encompassing functional categories including signal transduction, endoplasmic reticulum stress, peroxisome proliferator-activated receptors (PPARγ) signaling, PPARα signaling, immune or inflammatory processes and cell death in subcutaneous adipose tissue as well as liver. Of interest from a nutritional perspective is the potential to alter PPARγ signaling in adipose and PPARα signaling in liver as a means to enhance insulin sensitivity as well as fatty acid oxidation post partum. Major advances in understanding the metabolic adaptations of peripartal cows will come from using a systems biology approach to integrate data generated at the mRNA, protein, metabolite and tissue level across different nutritional management approaches and with cows of different genetic merit. This will allow the assembly of the important components needed to improve existing metabolic models of the peripartal cow and provide the tools to manipulate complex processes that could have significant long-term economic impact including lactation persistency, fertility and efficiency. An important goal of the future will be to apply additional experimental tools (e.g. gene silencing) and bioinformatics (e.g. transcription factor binding site identification) to studies focused on peripartal cows.

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E-mail: jloor@illinois.edu
References
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