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

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.

Quantitative analyses of adiposity dynamics in zebrafish

“…From 456 zebrafish, 67 traits were quantified in each fish, including AT area measurements, measures of body size (SL and body area), composite AT groupings (total AT, VAT and SAT) and AT proportionality assessments (VAT:SAT ratios). Correlations were computed between each trait and assessed to determine how adiposity relationships and patterns change in fish of distinct stages [53]. As expected, considerable differences in adiposity profiles at distinct stages were observed.…”

“…We found that FLD+ area was an accurate predictor for TAG mass for each AT (R 2 = 0.78, 0.87 and 0.82; all P < 0.0001), which supported the method of area measurements of individual ATs as an accurate estimator of lipid content. These methods are relatively fast to undertake and large sample sizes were achievable [51,53]. Therefore, FLDs allow the large-scale collection of quantitative adiposity data.…”

“…There have been multiple efforts to map the phenome of various model organisms, and a zebrafish phenome project was proposed in 2010 [60]. We recently published an article which applied deep phenotyping techniques to assess adiposity changes in zebrafish [53]. Our goal was to use the quantitative methods of zebrafish AT identification described previously [40,51] and couple this to deep phenotyping to analyse complex responses of adipose tissues to genetic and environmental perturbations [53].…”

“…We recently published an article which applied deep phenotyping techniques to assess adiposity changes in zebrafish [53]. Our goal was to use the quantitative methods of zebrafish AT identification described previously [40,51] and couple this to deep phenotyping to analyse complex responses of adipose tissues to genetic and environmental perturbations [53]. After quantification of all ATs in the zebrafish, we reasoned that collecting large amounts of quantitative adiposity data could be used to classify individuals based on their adiposity profiles and could then be used to identify subtle, quantitative adiposity phenotypes.…”

“…After quantification of all ATs in the zebrafish, we reasoned that collecting large amounts of quantitative adiposity data could be used to classify individuals based on their adiposity profiles and could then be used to identify subtle, quantitative adiposity phenotypes. Existing data was utilized to generate post-embryonic stage-specific phenotypic profiles that capture adiposity information (adiposity profiles) [53]. From 456 zebrafish, 67 traits were quantified in each fish, including AT area measurements, measures of body size (SL and body area), composite AT groupings (total AT, VAT and SAT) and AT proportionality assessments (VAT:SAT ratios).…”