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在斑马鱼中进行深度表型分析揭示了遗传和饮食诱导的肥胖变化,这些变化可能提示疾病风险。

Deep phenotyping in zebrafish reveals genetic and diet-induced adiposity changes that may inform disease risk.

机构信息

Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, EH16 4TJ

Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27710.

出版信息

J Lipid Res. 2018 Aug;59(8):1536-1545. doi: 10.1194/jlr.D084525. Epub 2018 May 23.

Abstract

The regional distribution of adipose tissues is implicated in a wide range of diseases. For example, proportional increases in visceral adipose tissue increase the risk for insulin resistance, diabetes, and CVD. Zebrafish offer a tractable model system by which to obtain unbiased and quantitative phenotypic information on regional adiposity, and deep phenotyping can explore complex disease-related adiposity traits. To facilitate deep phenotyping of zebrafish adiposity traits, we used pairwise correlations between 67 adiposity traits to generate stage-specific adiposity profiles that describe changing adiposity patterns and relationships during growth. Linear discriminant analysis classified individual fish according to an adiposity profile with 87.5% accuracy. Deep phenotyping of eight previously uncharacterized zebrafish mutants identified as a novel gene that alters adipose distribution. When we applied deep phenotyping to identify changes in adiposity during diet manipulations, zebrafish that underwent food restriction and refeeding had widespread adiposity changes when compared with continuously fed, equivalently sized control animals. In particular, internal adipose tissues (e.g., visceral adipose) exhibited a reduced capacity to replenish lipid following food restriction. Together, these results in zebrafish establish a new deep phenotyping technique as an unbiased and quantitative method to help uncover new relationships between genotype, diet, and adiposity.

摘要

脂肪组织的区域分布与多种疾病有关。例如,内脏脂肪组织的比例增加会增加胰岛素抵抗、糖尿病和心血管疾病的风险。斑马鱼提供了一个可行的模型系统,可以获得关于局部肥胖的无偏和定量表型信息,而深度表型分析可以探索与肥胖相关的复杂疾病特征。为了便于对斑马鱼肥胖特征进行深度表型分析,我们使用 67 个体脂特征之间的成对相关性,生成特定阶段的体脂特征,以描述生长过程中体脂模式和关系的变化。线性判别分析根据体脂特征以 87.5%的准确率对个体鱼进行分类。对 8 种以前未被描述的斑马鱼突变体进行深度表型分析,鉴定出了一个改变脂肪分布的新基因。当我们应用深度表型分析来识别饮食干预期间的体脂变化时,与持续喂养、同等大小的对照动物相比,进行食物限制和再喂养的斑马鱼的体脂发生了广泛的变化。特别是,内部脂肪组织(如内脏脂肪)在食物限制后补充脂质的能力降低。总之,这些在斑马鱼中的研究结果确立了一种新的深度表型分析技术,作为一种无偏和定量的方法,有助于揭示基因型、饮食和肥胖之间的新关系。

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