Deep phenotyping in zebrafish reveals genetic and diet-induced adiposity changes that may inform disease risk
2018; Elsevier BV; Volume: 59; Issue: 8 Linguagem: Inglês
10.1194/jlr.d084525
ISSN1539-7262
AutoresJamesE.N. Minchin, Catherine M. Scahill, Nicole Staudt, Elisabeth M. Busch‐Nentwich, John F. Rawls,
Tópico(s)Cancer-related molecular mechanisms research
ResumoThe 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. 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. Adipose tissues (ATs) are lipid-rich organs that supply and sequester circulating lipid in response to systemic energy demands. ATs thus provide "energetic insurance" to individuals and confer selective advantages during periods of adverse physiological stresses. In modern societies, where food availability is high and energy expenditure is low, ATs accumulate large quantities of lipid, which can initiate a range of secondary metabolic abnormalities that result in increased susceptibility to diabetes, CVD, and cancer. A wide range of adiposity traits can influence disease risk. For example, general adiposity levels, as measured by BMI, are associated with increased risk for disease (1.Gadde K.M. Martin C.K. Berthoud H.R. Heymsfield S.B. Obesity: pathophysiology and management.J. Am. Coll. Cardiol. 2018; 71: 69-84Crossref PubMed Scopus (260) Google Scholar). In turn, the regional distribution of AT can also influence disease risk. For example, accumulation of visceral AT (VAT) in the abdominal cavity in close proximity to visceral organs is associated with an increased risk for insulin resistance and a sequelae of accompanying diseases such as diabetes and CVD (2.Fox C.S. Massaro J.M. Hoffmann U. Pou K.M. Maurovich-Horvat P. Liu C.Y. Vasan R.S. Murabito J.M. Meigs J.B. Cupples L.A. et al.Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.Circulation. 2007; 116: 39-48Crossref PubMed Scopus (2034) Google Scholar, 3.Karpe F. Pinnick K.E. Biology of upper-body and lower-body adipose tissue–link to whole-body phenotypes.Nat. Rev. Endocrinol. 2015; 11: 90-100Crossref PubMed Scopus (278) Google Scholar). Conversely, accumulation of subcutaneous AT (SAT) in a peripheral location at the hips and upper thighs is associated with reduced disease risk (4.McLaughlin T. Lamendola C. Liu A. Abbasi F. Preferential fat deposition in subcutaneous versus visceral depots is associated with insulin sensitivity.J. Clin. Endocrinol. Metab. 2011; 96: E1756-E1760Crossref PubMed Scopus (301) Google Scholar). Understanding how genetics and the environment influence these diverse adiposity traits will be key to treating and predicting the metabolic consequences of obesity. Zebrafish are a tropical freshwater fish that offer a tractable model system to study AT biology. Zebrafish AT is morphologically and molecularly homologous to mammalian white AT (WAT) (5.Flynn 3rd, E.J. Trent C.M. Rawls J.F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio).J. Lipid Res. 2009; 50: 1641-1652Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar, 6.Imrie D. Sadler K.C. White adipose tissue development in zebrafish is regulated by both developmental time and fish size.Dev. Dyn. 2010; 239: 3013-3023Crossref PubMed Scopus (96) Google Scholar). Zebrafish AT also appears to be functionally conserved to mammalian WAT and accumulates lipid during periods of chronic energy excess and mobilizes lipid during energy deficiency (5.Flynn 3rd, E.J. Trent C.M. Rawls J.F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio).J. Lipid Res. 2009; 50: 1641-1652Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar, 7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar, 8.Oka T. Nishimura Y. Zang L. Hirano M. Shimada Y. Wang Z. Umemoto N. Kuroyanagi J. Nishimura N. Tanaka T. Diet-induced obesity in zebrafish shares common pathophysiological pathways with mammalian obesity.BMC Physiol. 2010; 10: 21Crossref PubMed Scopus (251) Google Scholar). The molecular pathways that regulate adiposity also appear to be conserved between zebrafish and mammals, as typified by zebrafish growth hormone (gh) mutants (9.McMenamin S.K. Minchin J.E. Gordon T.N. Rawls J.F. Parichy D.M. Dwarfism and increased adiposity in the gh1 mutant zebrafish vizzini.Endocrinology. 2013; 154: 1476-1487Crossref PubMed Scopus (57) Google Scholar). Importantly, zebrafish AT can be visualized and quantified in vivo at both cell and whole-animal resolutions (10.Minchin J.E. Rawls J.F. In vivo analysis of white adipose tissue in zebrafish.Methods Cell Biol. 2011; 105: 63-86Crossref PubMed Scopus (44) Google Scholar, 11.Minchin J.E. Rawls J.F. In vivo imaging and quantification of regional adiposity in zebrafish.Methods Cell Biol. 2017; 138: 3-27Crossref PubMed Scopus (23) Google Scholar). We recently utilized these imaging properties to comprehensively identify, characterize, and quantify the full complement of zebrafish ATs at distinct developmental stages (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). This methodology generates unbiased and quantitative phenotypic information on a comprehensive range of adiposity traits. We reasoned that such multivariate data could be utilized for deep phenotyping of adiposity traits. Briefly, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities (12.Robinson P.N. Deep phenotyping for precision medicine.Hum. Mutat. 2012; 33: 777-780Crossref PubMed Scopus (244) Google Scholar) and is useful for identifying traits and complex phenotypic signatures that define health or disease (13.Hur M. Gistelinck C.A. Huber P. Lee J. Thompson M.H. Monstad-Rios A.T. Watson C.J. McMenamin S.K. Willaert A. Parichy D.M. et al.MicroCT-based phenomics in the zebrafish skeleton reveals virtues of deep phenotyping in a distributed organ system.Zebrafish. 2018; 15: 77-78Crossref PubMed Scopus (9) Google Scholar, 14.Hur M. Gistelinck C.A. Huber P. Lee J. Thompson M.H. Monstad-Rios A.T. Watson C.J. McMenamin S.K. Willaert A. Parichy D.M. et al.MicroCT-based phenomics in the zebrafish skeleton reveals virtues of deep phenotyping in a distributed organ system.eLife. 2017; 6: e26014Crossref PubMed Scopus (40) Google Scholar, 15.San-Miguel A. Kurshan P.T. Crane M.M. Zhao Y. McGrath P.T. Shen K. Lu H. Deep phenotyping unveils hidden traits and genetic relations in subtle mutants.Nat. Commun. 2016; 7: 12990Crossref PubMed Scopus (23) Google Scholar). Deep phenotyping is particularly powerful when such traits may be quantitative and subtle, as found with adiposity. In this study, we generated stage-specific adiposity profiles that comprehensively capture patterns and relationships in adiposity dynamics and adipose distribution. To classify individuals according to expected patterns of adiposity, we applied linear discriminant analysis (LDA) to the adiposity profiles. We utilized this methodology to screen eight zebrafish mutants and identified neuropilin 2b (nrp2b) as a novel gene that promotes adiposity in zebrafish. Finally, we applied our deep phenotyping strategy to identify adiposity changes that occur following diet manipulation. We identified that food restriction induced profound changes in fat distribution, even after animals had fully regained weight. Closer analysis revealed that internal ATs (IATs), including VAT, did not fully regain lipid following refeeding, resulting in altered fat distribution. Altogether, we develop methodology to quantitatively extract phenotypic information for detecting gene and diet-induced adiposity phenotypes. Zebrafish experiments conformed to the US Public Health Service Policy on the Humane Care and Use of Laboratory Animals, using protocols approved by the Institutional Animal Care and Use Committees of the University of North Carolina at Chapel Hill (UNC) and Duke University. Zebrafish husbandry for experiments conducted at either UNC or Duke were performed as previously described (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). The Ekkwill (EKW) and WIK WT data used as the LDA training set (N = 456) was previously published (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar) and is available for download at DataDryad (https://doi.org/10.5061/dryad.98470). The gh mutant data used as a positive control for LDA were previously published (9.McMenamin S.K. Minchin J.E. Gordon T.N. Rawls J.F. Parichy D.M. Dwarfism and increased adiposity in the gh1 mutant zebrafish vizzini.Endocrinology. 2013; 154: 1476-1487Crossref PubMed Scopus (57) Google Scholar), and raw images are available for download at DataDryad (https://doi.org/10.5061/dryad.vv34p8h). The eight zebrafish mutants used for LDA were obtained from the Wellcome Trust Sanger Institute's Zebrafish Mutation Project (ZMP) or from Zebrafish International Resource Center (16.Kettleborough R.N. Busch-Nentwich E.M. Harvey S.A. Dooley C.M. de Bruijn E. van Eeden F. Sealy I. White R.J. Herd C. Nijman I.J. et al.A systematic genome-wide analysis of zebrafish protein-coding gene function.Nature. 2013; 496: 494-497Crossref PubMed Scopus (442) Google Scholar). Embryos were received at Duke on a Hubrecht long-fin background. In-crosses were performed between heterozygous individuals to generate experimental clutches. Genotypes were determined by Sanger sequencing. Images used for quantification were deposited at DataDryad (https://doi.org/10.5061/dryad.vv34p8h). All alleles used in this study are included in Table 1. The food restriction and refeeding was performed in EKW WT fish at Duke, and the raw images are available for download at DataDryad (https://doi.org/10.5061/dryad.vv34p8h).TABLE 1Zebrafish mutants assessed for adiposity changes in this studyGeneGene SymbolAlleleAllele ConsequenceNo. of Fish ScreenedPercent Misclassified (Phenotypic)nrp2bnrp2bsa18942Essential splice site3435.3%nuclear receptor subfamily group a member 3nr4a3sa2842Nonsense3414.7%proprotein convertase subtilisin/kexin type 1pcsk1sa1558Essential splice site339%semaphorin 3aasema3aasa10241Nonsense3514%semaphorin 3fbsema3fbsa14466Nonsense4714.8%semaphorin 3gbsema3gbsa21283Nonsense2114.2%sarcospansspnsa2992Nonsense3616.7%transmembrane protein 160tmem160sa1347Nonsense2913.7%ghghwp22e1Premature stop codon13036% Open table in a new tab Nile Red (Sigma-Aldrich, catalog no. N1142) was dissolved in acetone at a concentration of 1.25 mg/ml and diluted to 0.5 µg/ml in system water for lipid staining (10.Minchin J.E. Rawls J.F. In vivo analysis of white adipose tissue in zebrafish.Methods Cell Biol. 2011; 105: 63-86Crossref PubMed Scopus (44) Google Scholar, 11.Minchin J.E. Rawls J.F. In vivo imaging and quantification of regional adiposity in zebrafish.Methods Cell Biol. 2017; 138: 3-27Crossref PubMed Scopus (23) Google Scholar). Zebrafish were incubated in the diluted Nile Red for 30 min as previously described (10.Minchin J.E. Rawls J.F. In vivo analysis of white adipose tissue in zebrafish.Methods Cell Biol. 2011; 105: 63-86Crossref PubMed Scopus (44) Google Scholar, 11.Minchin J.E. Rawls J.F. In vivo imaging and quantification of regional adiposity in zebrafish.Methods Cell Biol. 2017; 138: 3-27Crossref PubMed Scopus (23) Google Scholar). Following the staining, melanosomes were contracted by incubation in 10 mg/ml epinephrine (Sigma-Aldrich, catalog no. E4375) for 5 min, and zebrafish were anesthetized in 1.34 g/l MS222 (Sigma-Aldrich, catalog no. A5040) for 3 min and mounted on 3% methylcellulose (Sigma-Aldrich, catalog no. MO387). The right-hand side of each fish was imaged on a Leica MZ205FA fluorescence stereomicroscope equipped with a Leica DFC365 FX fluorescence camera and using a GFP2 bandpass filter (Leica Microsystems, catalog no. 10447407). All analyses were conducted in FIJI/ImageJ (version 1.51i) (17.Schindelin J. Arganda-Carreras I. Frise E. Kaynig V. Longair M. Pietzsch T. Preibisch S. Rueden C. Saalfeld S. Schmid B. et al.Fiji: an open-source platform for biological-image analysis.Nat. Methods. 2012; 9: 676-682Crossref PubMed Scopus (30160) Google Scholar). For AT area measurements, two copies of each image were opened; one for taking measurements and one for comparisons to maximize accuracy of adipose segmentation. Standard length (SL), height at the anterior margin of the anal fin (HAA), and body area were used to determine zebrafish size and were measured using the line and polygon tools as previously described (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar, 18.Parichy D.M. Elizondo M.R. Mills M.G. Gordon T.N. Engeszer R.E. Normal table of postembryonic zebrafish development: staging by externally visible anatomy of the living fish.Dev. Dyn. 2009; 238: 2975-3015Crossref PubMed Scopus (508) Google Scholar). AT areas were defined by manually set thresholds based on pixel intensities (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). For ATs that did not touch, the magic wand tool was used to select AT area. For touching ATs, the polygon tool was used to trace the AT outline, and where a dividing line between the ATs was not visible, a straight line was drawn between the farthest distinguishing AT points. Lateral view images were used for all measurements. ATs were classified and measured as described in ref. 7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar. A schematic illustrating the location of each AT in zebrafish is included in supplemental Fig. S1. The stages used for generating adiposity profiles incorporated both postembryonic and adiposity milestones as previously documented (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar, 18.Parichy D.M. Elizondo M.R. Mills M.G. Gordon T.N. Engeszer R.E. Normal table of postembryonic zebrafish development: staging by externally visible anatomy of the living fish.Dev. Dyn. 2009; 238: 2975-3015Crossref PubMed Scopus (508) Google Scholar). As previously described, Nile Red also labels neutral lipid within the liver, intestinal epithelium, and the blood (5.Flynn 3rd, E.J. Trent C.M. Rawls J.F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio).J. Lipid Res. 2009; 50: 1641-1652Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar, 7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar); however, lipid at these sites can be readily distinguished from AT. Adiposity measurements used in this study are included in supplemental Tables S1 and S2. Food restriction and refeeding experiments were conducted largely as described (5.Flynn 3rd, E.J. Trent C.M. Rawls J.F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio).J. Lipid Res. 2009; 50: 1641-1652Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar), with the following differences; zebrafish at 38 days postfertilization [dpf; 11.4 ± 0.5 mm SL (mean ± SD); dorsal fin ray SAT (DFRSAT) stage] were housed individually in six-well plates containing ∼3 ml of system water. No food was administered during food restriction (days 1–11). Upon refeeding (days 12–22), fish were fed Artemia franciscana and powdered food as per normal husbandry procedures at Duke University as previously described (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). Every 24 h, 0.5 µg/ml Nile Red was added to the wells, and the fluorescent lipid signal was imaged on a Leica MZ205FA fluorescence stereomicroscope equipped with a Leica DFC365 FX fluorescence camera as described above. Lipid deposits were thus evaluated daily, and food restriction was stopped after 11 days once lipid was mobilized from all AT sites in all animals. System water was replaced daily during both starvation and refeeding periods. Stage-specific adiposity profiles were generated using pairwise correlations between 63 adiposity traits (listed in Table 2) using the multivariate platform in JMP Pro 13 (SAS, NC). Adiposity profiles were constructed for 14 stages (supplemental Fig. S2 and supplemental Tables S3–S16). Heatmaps to visualize the adiposity profiles were generated in JMP and show pairwise intertrait correlations expressed as the Pearson's correlation coefficient (1 is a total positive correlation between traits, 0 is no correlation, and −1 is a total inverse correlation). LDA was performed to classify individual fish according to their adiposity profiles. LDA was performed in JMP using the discriminant analysis platform and a linear common covariance method. LDA is a general linear model that derives discriminant functions—linear combinations of variables—to maximize the probability of assigning observations to a predefined group. The discriminant functions for each group (adiposity profile) (Cprofile) followed refs. 19.Tabachnick B. Fidell L. Using Multivariate Statistics. 1996; Google Scholar and 20.Quinn G.P. Keough M.J. Experimental Design and Analysis for Biologists.Cambridge University Press. 2002; Google Scholar and are expressed as: Cprofile = i + c1(log10 a1) + c2(log10 a2) + ··· + cn(log10 an), where c is the coefficient of the classification equation (19.Tabachnick B. Fidell L. Using Multivariate Statistics. 1996; Google Scholar), i is the constant for each group (adiposity profile) as determined by multiplying the matrix of classification coefficients for that group by the matrix of means for each adiposity trait variable (a) of that group. All 63 adiposity traits were used as variables for LDA. To define the adiposity characteristics, adiposity profiles from 456 WT fish were set as a validation group (training set). Analysis of interclutch variability in LDA within the training set revealed misclassification rates of 15.2% (mean) with a SE of 2.4% (25 independent clutches). This baseline rate was used to identify phenotypic fish during additional comparisons. We defined "nonnormal" phenotypes if clutches had a percent misclassification rate >3 ± SE from the training set rate. These clutches were evaluated further as phenotypic. Principal components analysis (PCA) of adiposity profiles during food restriction (days 1–11) and refeeding (days 12–22) was conducted in JMP using the default estimation method. The mean recovery, or regain, of AT-lipid at day 22 (following food restriction and refeeding) was calculated as a percent of AT-lipid at day 1. Hierarchical clustering of AT-lipid regain at day 22 was performed in JMP using the Ward method (21.Murtagh F. Legendre P. Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion?.J. Classif. 2014; 31: 274-295Crossref Scopus (1784) Google Scholar). Student's t-tests were used for pairwise comparisons, and one-way ANOVA followed by Tukey's posthoc test was used for multiple groups. ANCOVA was used to test for differences between groups following linear regression. Statistical significance was set to α = 0.05. Graphs were plotted in R using ggplot2 (22.Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2009; Google Scholar, 23.R Core Team. R: A language and environment for statistical computing..R Foundation for Statistical Computing. 2013; Google Scholar). The stage-specific adiposity profiles can be found in supplemental Fig. 2, and the accompanying correlation matrices are in supplemental Tables 3–18.TABLE 2Morphological traits used to construct adiposity profilesTraitTrait AcronymTrait CategorySL (µm)SLBody sizeHAA (µm)HAABody sizeBody area (µm2)BABody sizePVAT (µm2)PVATAT areaAbdominal VAT (µm2)AVATAT areaRenal VAT (µm2)RVATAT areaCVAT (µm2)CVATAT groupingAnterior CVAT (µm2)aCVATAT areaPosterior CVAT (µm2)pCVATAT areaVAT (µm2)VATAT areaAbdominal SAT (µm2)ASATAT areaLateral SAT (µm2)LSATAT areaDorsal SAT (µm2)DSATAT groupingAnterior DSAT (µm2)aDSATAT areapDSAT (µm2)pDSATAT areaVentral SAT (µm2)VSATAT areaTruncal SAT (µm2)TSATAT groupingDFRSAT (µm2)DFRSATAT areaAnal fin ray cluster SAT (µm2)AFCSATAT areaAFRSAT (µm2)AFRSATAT areaCaudal fin ray SAT (µm2)CFRSATAT areaPectoral fin SAT (µm2)PECSATAT groupingLoose PECSAT (µm2)lPECSATAT areaAnterior PECSAT (µm2)aPECSATAT areaPosterior PECSAT (µm2)pPECSATAT areaAppendicular SAT (µm2)APPSATAT groupingCentral IM (µm2)cIMAT areaDorsal IM (µm2)dIMAT areaVentral IM (µm2)vIMAT areaIntermuscular (µm2)IMAT groupingDorsal POS (µm2)dPOSAT areaCentral POS (µm2)cPOSAT areaVentral POS (µm2)vPOSAT areaParaosseal (µm2)POSAT groupingEsophageal (µm2)OESAT areaNonvisceral AT (µm2)NVATAT groupingDorsal OPC (µm2)dOPCAT areaVentral OPC (µm2)vOPCAT areaOpercular (µm2)OPCAT groupingOcular (µm2)OCUAT areaBranchihyal (µm2)BHDAT areaCeratohyal (µm2)CHDAT areaUrihyal (µm2)UHDAT areaHyal (µm2)HYDAT groupingCranial SAT (µm2)CSATAT groupingTotal AT (µm2)TOTALAT groupingSAT (µm2)SATAT groupingIAT (µm2)IATAT groupingVAT:IATAT ratioNVAT:IATAT ratioIAT:TOTALAT ratioSAT:TOTALAT ratioCSAT:SATAT ratioAPPSAT:SATAT ratioTSAT:SATAT ratioVAT:SATAT ratioNVAT:SATAT ratioCSAT:IATAT ratioTSAT:IATAT ratioAPPSAT:IATAT ratioVAT:TSATAT ratioVAT:CSATAT ratioVAT:APPSATAT ratioNVAT:TSATAT ratioNVAT:CSATAT ratioNVAT:APPSATAT ratioNVAT:VATAT ratioTSAT:CSATAT ratioAPPSAT:CSATAT ratioAPPSAT:TSATAT ratio Open table in a new tab Whole-animal in vivo imaging in zebrafish enables the quantification of all ATs in a single animal and can thus be used to reveal a wide range of adiposity traits, including changes in fat levels, ectopic localization to specific organs, and changes in regional distribution (5.Flynn 3rd, E.J. Trent C.M. Rawls J.F. Ontogeny and nutritional control of adipogenesis in zebrafish (Danio rerio).J. Lipid Res. 2009; 50: 1641-1652Abstract Full Text Full Text PDF PubMed Scopus (170) Google Scholar, 10.Minchin J.E. Rawls J.F. In vivo analysis of white adipose tissue in zebrafish.Methods Cell Biol. 2011; 105: 63-86Crossref PubMed Scopus (44) Google Scholar, 11.Minchin J.E. Rawls J.F. In vivo imaging and quantification of regional adiposity in zebrafish.Methods Cell Biol. 2017; 138: 3-27Crossref PubMed Scopus (23) Google Scholar). We reasoned that collecting large amounts of quantitative adiposity data could be used to classify individuals based on adiposity traits and identify subtle, quantitative adiposity phenotypes. We previously showed that stage-matched zebrafish exhibit stereotypical adiposity patterns (Fig. 1A) (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). Therefore, we utilized existing data to generate stage-specific phenotypic profiles that capture adiposity information (hereafter called adiposity profiles). In total, 456 WT zebrafish across a range of sizes, stages, and ages were used to construct adiposity profiles (Fig. 1B) (7.Minchin J.E. Rawls J.F. A classification system for zebrafish adipose tissues.Dis. Model. Mech. 2017; 10: 797-809Crossref PubMed Scopus (46) Google Scholar). From these 456 fish, 67 traits were quantified in each fish (Table 1). The 67 traits included i) AT area measurements, ii) measures of body size (including SL, HAA, and body area), iii) composite AT groupings (e.g., total AT, SAT, and VAT), and iv) AT proportionality assessments (e.g., VAT:SAT). Correlations were computed between each trait and assessed to determine how adiposity relationships and patterns change in fish of distinct stages (Fig. 1B). As expected, considerable differences were observed in adiposity profiles at distinct stages (Fig. 1B, C). For example, pancreatic VAT (PVAT) was initially positively correlated with SL [pectoral fin bud (PB) stage], before becoming progressively more inversely correlated with SL in larger fish [stage squamation through anterior (SA)] (Fig. 1C). In conclusion, adiposity profiles capture dynamic changes in adiposity patterns at distinct developmental stages and will be useful indicators of "normal" adiposity levels and variation. To effectively use stage-specific adiposity profiles as a base for in-depth phenotypic profiling we utilized LDA. LDA is a data dimensionality reduction technique that can assign membership of a group based on accompanying covariate values. LDA has been used previously to i) classify morphological phenotypes into distinct groups (24.Bertsatos A. Papageorgopoulou C. Valakos E. Chovalopoulou M.E. Investigating the sex-related geometric variation of the human cranium.Int. J. Legal Med. 2018; Crossref PubMed Scopus (13) Google Scholar, 25.Suhail Z. Denton E.R.E. Zwiggelaar R. Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.Med. Biol. Eng. Comput. 2018; Crossref PubMed Scopus (21) Google Scholar), ii) predict disease outcomes based on current symptoms (26.Oh J. Cho D. Park J. Na S.H. Kim J. Heo J. Shin C.S. Kim J.J. Park J.Y. Lee B. Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.Physiol. Meas. 2018; 39: 035004Crossref PubMed Scopus (28) Google Scholar), and iii) predict future business success based on current financial parameters (27.Kočišová K. Mišanková M. Discriminant analysis as a tool for forecasting companys financial health.Procedia Soc. Behav. Sci. 2014; 110: 1148-1157Crossref Google Scholar). We reasoned that LDA could also be applied to adiposity profiles and used to classify individual fish according to expected adiposity traits. As a training set, we applied the adiposity profiles from the 456 WT fish described above and assessed whether LDA was able to accurately assign fish to correct developmental stages (Fig. 2A). Based on adiposity profiles, LDA assigned 87.5% of the WT fish into correct stages (Fig. 2A). We next assessed how robust the LDA classification method was across multiple independent clutches. We divided the training set data into its 25 constituent clutches and applied LDA to each clutch, resulting in an average classification rate of 84.8 ± 2.4% (mean ± SE) (Fig. 2E). Next, as a proof-of-concept, we used zebrafish gh mutants to determine whether LDA can be used to detect adiposity phenotypes. We previously showed that gh mutants have increased adiposity and retarded somatic growth relative to size-matched WT siblings (Fig. 2B, C) (9.McMenamin S.K. Minchin J.E. Gordon T.N. Rawls J.F. Parichy D.M. Dwarfism and increased adiposity in the gh1 mutant zebrafish vizzini.Endocrinology. 2013; 154: 1476-1487Cr
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