Abstract

BackgroundSystems biology enables the identification of gene networks that modulate complex traits. Comprehensive metabolomic analyses provide innovative phenotypes that are intermediate between the initiator of genetic variability, the genome, and raw phenotypes that are influenced by a large number of environmental effects. The present study combines two concepts, systems biology and metabolic analyses, in an approach without prior functional hypothesis in order to dissect genes and molecular pathways that modulate differential growth at the onset of puberty in male cattle. Furthermore, this integrative strategy was applied to specifically explore distinctive gene interactions of non-SMC condensin I complex, subunit G (NCAPG) and myostatin (GDF8), known modulators of pre- and postnatal growth that are only partially understood for their molecular pathways affecting differential body weight.ResultsOur study successfully established gene networks and interacting partners affecting growth at the onset of puberty in cattle. We demonstrated the biological relevance of the created networks by comparison to randomly created networks. Our data showed that GnRH (Gonadotropin-releasing hormone) signaling is associated with divergent growth at the onset of puberty and revealed two highly connected hubs, BTC and DGKH, within the network. Both genes are known to directly interact with the GnRH signaling pathway. Furthermore, a gene interaction network for NCAPG containing 14 densely connected genes revealed novel information concerning the functional role of NCAPG in divergent growth.ConclusionsMerging both concepts, systems biology and metabolomic analyses, successfully yielded new insights into gene networks and interacting partners affecting growth at the onset of puberty in cattle. Genetic modulation in GnRH signaling was identified as key modifier of differential cattle growth at the onset of puberty. In addition, the benefit of our innovative concept without prior functional hypothesis was demonstrated by data suggesting that NCAPG might contribute to vascular smooth muscle contraction by indirect effects on the NO pathway via modulation of arginine metabolism. Our study shows for the first time in cattle that integration of genetic, physiological and metabolomics data in a systems biology approach will enable (or contribute to) an improved understanding of metabolic and gene networks and genotype-phenotype relationships.

Highlights

  • Systems biology enables the identification of gene networks that modulate complex traits

  • Since Partial Correlation coefficient Information Theory (PCIT) creates a very complex dataset of gene-gene interactions, the present study focuses on significant connections with a |partial correlations (PC)| ≥ 0.80

  • Genotyping & genome-wide association studies (GWAS) In the present study, 152 male SEGFAM cattle were genotyped for 54,609 SNPs

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Summary

Introduction

Systems biology enables the identification of gene networks that modulate complex traits. Polymorphisms in pleiomorphic adenoma gene 1 (PLAG1) [1,2,3], non-SMC condensin I complex, subunit G (NCAPG) [4,5,6], and myostatin (GDF8, known as MSTN) [7,8] were found to exert major effects on stature, postnatal growth and muscle development, respectively Several of these loci seem to be conserved modulators of mammalian growth, because concordant growth associated polymorphisms were detected in several species [7,9,10,11,12,13,14,15,16,17,18]. Epigenetic and pleiotropic mechanisms as well as a multitude of small-effect genes make the detection of contributing polymorphisms challenging [19]

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