Metabolomic profiles indicate distinct physiological pathways affected by two loci with major divergent effect on Bos taurus growth and lipid deposition

Abstract

Identifying trait-associated genetic variation offers new prospects to reveal novel physiological pathways modulating complex traits. Taking advantage of a unique animal model, we identified the I442M mutation in the non-SMC condensin I complex, subunit G (NCAPG) gene and the Q204X mutation in the growth differentiation factor 8 (GDF8) gene as substantial modulators of pre- and/or postnatal growth in cattle. In a combined metabolomic and genotype association approach, which is the first respective study in livestock, we surveyed the specific physiological background of the effects of both loci on body-mass gain and lipid deposition. Our data provided confirming evidence from two historically and geographically distant cattle populations that the onset of puberty is the key interval of divergent growth. The locus-specific metabolic patterns obtained from monitoring 201 plasma metabolites at puberty mirror the particular NCAPG I442M and GDF8 Q204X effects and represent biosignatures of divergent physiological pathways potentially modulating effects on proportional and disproportional growth, respectively. While the NCAPG I442M mutation affected the arginine metabolism, the 204X allele in the GDF8 gene predominantly raised the carnitine level and had concordant effects on glycerophosphatidylcholines and sphingomyelins. Our study provides a conclusive link between the well-described growth-regulating functions of arginine metabolism and the previously unknown specific physiological role of the NCAPG protein in mammalian metabolism. Owing to the confirmed effect of the NCAPG/LCORL locus on human height in genome-wide association studies, the results obtained for bovine NCAPG might add valuable, comparative information on the physiological background of genetically determined divergent mammalian growth.

Uncovering Adaptation from Sequence Data: Lessons from Genome Resequencing of Four Cattle Breeds

“…Apart from the PLAG1 signature, several others include genes involved in morphology and growth: RUNX3 ), STARD3NL ), and RASSF2 are involved in bone development; NCAPG and/or LCORL match a large QTL for many growth traits in cattle, horses, and sheep Weikard et al 2010;Lindholm-Perry et al 2011;Makvandi-Nejad et al 2012;SignerHasler et al 2012;Lindholm-Perry et al 2013;Metzger et al 2013;Tetens et al 2013;Kijas 2014;Randhawa et al 2015;Sahana et al 2015;Xu et al 2015); and CTNNBL1 ) is associated with obesity traits in humans.…”

Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics

“…Studies of the metabolome using kit-based high-throughput flow injection (FIA) mass spectrometry in large population cohorts and animal studies revealed new insights into the interplay between genetics and environmental impact (Altmaier et al 2009Gieger et al 2008;Illig et al 2010;Wang-Sattler et al 2008;Weikard et al 2010;Zhai et al 2009). At present, hundreds of endogenous metabolites can be quantified in body fluids like plasma and serum in a high-throughput manner using targeted metabolomics approaches Bogumil et al 2008;Denkert et al 2008;Freedman et al 2008;Griffin and Kauppinen 2007;Koal and Deigner 2010;Sreekumar et al 2009;Urban et al 2010).…”

Targeted Metabolomics for Clinical Biomarker Discovery in Multifactorial Diseases

“…So, for the very first time, a convincing connection between genotype and metabolic phenotype could be demonstrated in a multifactorial disease. Even so, the field may still be far from a true systems biology view on T2D covering all the different omics levels but the application of GWAS (or next generation sequencing, for that matter) on quantitative metabolic traits clearly paves the way for a better understanding of epidemiology and pathophysiology at the same time, and this approach has already been confirmed in other areas of genetic research (Weikard et al, 2010). The systematic analysis of these metabolic traits is called metabolomics.…”