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

机译:斑马鱼的深表型揭示了遗传和饮食诱导的肥胖改变可能会告知疾病风险

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摘要

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 neuropilin 2b 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.
机译: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CVD的风险。斑马鱼提供了一个易于处理的模型系统,可通过该模型系统获得有关区域肥胖的无偏见和定量表型信息,而深表型可以探索与疾病相关的复杂肥胖性状。为了促进斑马鱼肥胖性状的深表型研究,我们使用了67个肥胖性状之间的成对相关性来生成描述生长过程中变化的肥胖型态和关系的阶段特异性肥胖型态。线性判别分析根据肥胖状况将单个鱼分类,准确度为87.5%。八个以前未鉴定的斑马鱼突变体的深表型鉴定为神经纤毛蛋白2b是改变脂肪分布的新基因。当我们进行深表型分析以识别饮食控制过程中的肥胖变化时,与连续喂食,等尺寸的对照动物相比,进行食物限制和重新喂养的斑马鱼的肥胖变化普遍。特别地,在食物限制之后,内部脂肪组织(例如内脏脂肪)表现出降低的补充脂质的能力。在一起,这些结果在斑马鱼中建立了一种新的深度表型分析技术,作为一种无偏见和定量的方法,以帮助揭示基因型,饮食和肥胖之间的新关系。

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