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

Dataset

Description

Nile Red images saved as .tif files for the mutant and food restricted cohorts. Image# corresponds to Fish# in Supplemental Table 1, and DRYAD# in Supplemental Table 2.

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 cardiovascular disease. 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 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 re-feeding had widespread adiposity changes when compared to 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.

Data Citation

Minchin JEN, Scahill CM, Staudt N, Busch-Nentwich EM, Rawls JF (2018) Data from: Deep phenotyping in zebrafish reveals genetic and diet-induced adiposity changes that may inform disease risk. Dryad Digital Repository. https://doi.org/10.5061/dryad.vv34p8h
Date made available20 Jun 2018
PublisherDryad

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