代谢组学和基因组学的结合提供了影响猪分子表型的遗传因素目录,这些遗传因素连接了相关的代谢途径

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Samuele Bovo, Anisa Ribani, Flaminia Fanelli, Giuliano Galimberti, Pier Luigi Martelli, Paolo Trevisi, Francesca Bertolini, Matteo Bolner, Rita Casadio, Stefania Dall’Olio, Maurizio Gallo, Diana Luise, Gianluca Mazzoni, Giuseppina Schiavo, Valeria Taurisano, Paolo Zambonelli, Paolo Bosi, Uberto Pagotto, Luca Fontanesi
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引用次数: 0

摘要

代谢组学为研究复杂性状的基本生物学机制开辟了新的途径,从代谢物的表征开始。生物流体中的代谢物及其水平代表了简单的分子表型(代谢型),它们是酶活性的直接产物,与所有代谢途径有关,包括营养物质的分解代谢和合成代谢。在这项研究中,我们展示了将代谢组学和基因组学结合在猪身上的效用,以揭示影响哺乳动物代谢的大量遗传因素。我们对来自大白猪和杜洛克猪两个品种的1300多头猪的血浆代谢组进行了针对性的表征。通过估计188种代谢物水平的遗传能力,利用这些猪的代谢组学特征来鉴定受遗传影响的代谢物。然后,结合单个代谢物及其比例的品种特异性全基因组关联研究和跨品种荟萃分析,我们共鉴定出97个代谢物数量性状位点(mQTL),与126种代谢物相关。利用这些结果,我们构建了影响代谢组学特征的遗传因素的人猪比较目录。全基因组重测序数据确定了这些mQTL的几个假定的致病突变。此外,基于犬尿氨酸水平的主要mQTL,我们设计了一项营养遗传学研究,饲养不同水平色氨酸的候选基因kynurenine 3-monooxygenase (KMO)携带不同基因型的仔猪,并证明了该遗传因素对犬尿氨酸途径的影响。此外,我们利用大白猪和杜洛克猪的代谢组学特征,使用高斯图形模型重建代谢途径,其中包括已识别的mQTL的扰动。这项研究首次提供了影响猪血液代谢组分子表型的遗传因素目录,并与重要的代谢途径相关联,为将这种牲畜物种的遗传和营养结合起来开辟了新的途径。所获得的结果与基础生物学和应用生物学以及评价猪作为生物医学模型有关。受遗传影响的代谢物可以在猪的营养遗传学方法中进一步利用。所描述的分子表型可以用于解剖复杂的性状和设计新的饲养,育种和选择方案的猪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Merging metabolomics and genomics provides a catalog of genetic factors that influence molecular phenotypes in pigs linking relevant metabolic pathways
Metabolomics opens novel avenues to study the basic biological mechanisms underlying complex traits, starting from characterization of metabolites. Metabolites and their levels in a biofluid represent simple molecular phenotypes (metabotypes) that are direct products of enzyme activities and relate to all metabolic pathways, including catabolism and anabolism of nutrients. In this study, we demonstrated the utility of merging metabolomics and genomics in pigs to uncover a large list of genetic factors that influence mammalian metabolism. We obtained targeted characterization of the plasma metabolome of more than 1300 pigs from two populations of Large White and Duroc pig breeds. The metabolomic profiles of these pigs were used to identify genetically influenced metabolites by estimating the heritability of the level of 188 metabolites. Then, combining breed-specific genome-wide association studies of single metabolites and their ratios and across breed meta-analyses, we identified a total of 97 metabolite quantitative trait loci (mQTL), associated with 126 metabolites. Using these results, we constructed a human-pig comparative catalog of genetic factors influencing the metabolomic profile. Whole genome resequencing data identified several putative causative mutations for these mQTL. Additionally, based on a major mQTL for kynurenine level, we designed a nutrigenetic study feeding piglets that carried different genotypes at the candidate gene kynurenine 3-monooxygenase (KMO) varying levels of tryptophan and demonstrated the effect of this genetic factor on the kynurenine pathway. Furthermore, we used metabolomic profiles of Large White and Duroc pigs to reconstruct metabolic pathways using Gaussian Graphical Models, which included perturbation of the identified mQTL. This study has provided the first catalog of genetic factors affecting molecular phenotypes that describe the pig blood metabolome, with links to important metabolic pathways, opening novel avenues to merge genetics and nutrition in this livestock species. The obtained results are relevant for basic and applied biology and to evaluate the pig as a biomedical model. Genetically influenced metabolites can be further exploited in nutrigenetic approaches in pigs. The described molecular phenotypes can be useful to dissect complex traits and design novel feeding, breeding and selection programs in pigs.
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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