‘Fat’s chances’: Loci for phenotypic dispersion in plasma leptin in mouse models of diabetes mellitus


Authors: Guy M. L. Perry aff001
Authors place of work: Department of Biology, University of Prince Edward Island, Charlottetown, PEI, Canada aff001
Published in the journal: PLoS ONE 14(10)
Category: Research Article
doi: 10.1371/journal.pone.0222654

Summary

Background

Leptin, a critical mediator of feeding, metabolism and diabetes, is expressed on an incidental basis according to satiety. The genetic regulation of leptin should similarly be episodic.

Methodology

Data from three mouse cohorts hosted by the Jackson Laboratory– 402 (174F, 228M) F2 Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses, 142 Non Obese Diabetic (NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.H2g7) N2 backcross females, and 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ) reciprocal backcrosses–were used to test for loci associated with absolute residuals in plasma leptin and arcsin-transformed percent fat (‘phenotypic dispersion’; PDpLep and PDAFP). Individual data from 1,780 mice from 43 inbred strains was also used to estimate genetic variances and covariances for dispersion in each trait.

Principal findings

Several loci for PDpLep were detected, including possibly syntenic Chr 17 loci, but there was only a single position on Chr 6 for PDAFP. Coding SNP in genes linked to the consensus Chr 17 PDpLep locus occurred in immunological and cancer genes, genes linked to diabetes and energy regulation, post-transcriptional processors and vomeronasal variants. There was evidence of intersexual differences in the genetic architecture of PDpLep. PDpLep had moderate heritability (hs2=0.29) and PDAFP low heritability (hs2=0.12); dispersion in these traits was highly genetically correlated r = 0.8).

Conclusions

Greater genetic variance for dispersion in plasma leptin, a physiological trait, may reflect its more ephemeral nature compared to body fat, an accrued progressive character. Genetic effects on incidental phenotypes such as leptin might be effectively characterized with randomization-detection methodologies in addition to classical approaches, helping identify incipient or borderline cases or providing new therapeutic targets.

Keywords:

Fats – Genetic loci – leptin – Molecular genetics – Mouse models – Obesity – Phenotypes

Introduction

Leptin, produced by white adipose cells, regulates appetite, basal metabolic rate, activity, growth and energetic homeostasis, providing negative feedback in the hypothalamus against hunger induced by ghrelin, neuropeptide Y and anandamide [1]. Leptin was first identified as a hunger suppressant in spontaneous hyperphagic obese (ob) mutant mice (C57BL/KsJ) with glucose intolerance and diabetes [2]. The leptin gene (Lep) was mapped to a human leukocyte antigen (HLA) cluster (Chr 6, 29.0 MB) in the obese (ob) mouse strain [3]. Leptin is linked to T2DM through its role in satiety [1] and to T1DM by glycaemia, insulin sensitivity and triglycerides [4]. Leptin treatment is corrective in animal models of diabetes [5] and leptin polymorphisms have been linked to obesity in humans [6]. Genetic loci have also been linked to leptin production in mice (Chr 3 (143.2 MB), 10 (107.1 MB), 12 (100.6 MB) [7], 7 (~ 100 MB) [8] 14 (37–73 MB) [9], 2 (141.1 MB), 17 (40.2 MB) [10], 5 (93.3 MB), 12 (100.6 MB), 15 (55.3 MB) [11]) and such models may provide further useful information on the genetic structure of diabetes and its physiological construction.

Leptin expression is affected by thyroid or coeliac disease, growth and mass, fatigue and biochemical mediators [5,12,13]. Sex affects leptin physiology in different ways; weight gain is initiated from the deactivation of leptin receptors in the proopiomelanocortin neurons of the arcuate nucleus in females and in males by deactivation of somatotropic leptin receptors [14]. Centrally, however, satiety and hunger are periodic (regulated feeding cycle) or episodic so that the expression of satiety and hunger signals like leptin have both natural entropy [12] and random ultradian pulses [15]. This core feature of hunger suggests that much of the genetic control of leptin production should also be ephemeral or semi-random over time, with some alleles conferring different periodic or episodic expression.

This in turn resembles an emergent phenomenon in which some alleles carry individual variance components in addition to means, distributed as ({Ni{μi,σ2+b}) [16] and termed ‘phenotypic dispersion’ (PD) [17] or ‘vQTL’ [18]. Heritable dispersion loci occur in various systems [19] including medical traits. One locus for insulitis dispersion occurs on murine Chr 9 (120.8 MB) [20] linked to cholecystokinin (CCK) (121.4 MB) [21] and various chemokine receptors. Instability in diabetic phenotype may have epidemiological consequences: the frequency of random hypoglycaemic episodes prior to age five has been associated with reduced long-delay spatial memory [22], for example.

Using three curated mouse mapping datasets (F2 Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses [23], female Non Obese Diabetic (NOD)×(NOD×129S1/SvImJ.H2g7) N2 backcross [24], and male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ; T2DM model) backcrosses [11]) hosted by The Jackson Laboratories (ME) I detected several loci for randomized phenotypic dispersion in plasma leptin (PDpLep), several of which were linked to insulitis loci. A possible consensus locus was detected in two cohorts on Chr 17. There was only one locus associated with dispersion in arc-transformed percent body fat (PDAPF); morphological traits may be less subject to genetic randomization. Coding polymorphisms at candidate genes linked to the Chr 17 consensus region included those involved with the response to cancer, inflammation, growth, post-transcriptional modification, metabolism and human diabetes incidence. These findings indicate that leptin expression may be partially controlled by randomizing mutations at genes for feeding behaviour and gastrointestinal function, operating on nearly-randomized schedules and undetectable by conventional approaches.

Materials and methods

Cohorts

Brockmann et al

Data from the original works used in this analysis is hosted by the Mouse Phenome Database, Jackson Laboratories (Bar Harbor, ME; https://phenome.jax.org).

In Brockmann et al., 233 F2 DBA/2×DU6i (an inbred subline of DU6 selected for high 6-wk weight (78 generations)) intercross males and 178 females were bred from F1 parents from a DU6i sire and a DBA/2 dam (‘Brockmann1’, Cross 2 (https://phenome.jax.org/projects/Brockmann1); MPD:213) [23]. Animals were provided al libitum access to a breeding diet (Altromin International #1314; 22.5% protein, 5.0% fat, 4.5% fibre, 6.5% ash, 13.5% water, 48.0% nitrogen-free extract and trace elements and minerals and housed in 350 cm2 macrolon type II cages. Fat percentage was calculated from weight and fat mass, and arc-transformed as a proportional value for analysis [25] (‘arcsin-transformed fat percentage’; AFP). Brockmann et al. quantified plasma leptin (ng/ml) using Quantikine Murine ELISA assays (R&D Systems; Weisbaden, DE). Animal welfare in that work was approved by the Bundesministerium für Ernährung und Landwirtschaft (BMEL; https://www.bmel.de) (Approval #VI 522 a/7221.3-TV-003/97). F2 mice were genotyped at 96 microsatellites (average intermarker spacing = 24.5 MB).

Leiter et al

Data from Leiter et al. was based on an NOD/ShiLtJ cross to 129S1/Sv mice (‘Leiter2’, (https://phenome.jax.org/projects/Leiter2); MPD:240). Homozygosity for the diabetogenic NOD MHC H2g7 region is required to produce diabetes in NOD outcrosses [24], so the original investigators bred a homozygous H2g7 129S1/Sv Chr 17 ‘speed congenic’ line. NOD/ShiLtJ and 129S1/Sv mice were intercrossed; non-MHC markers were fixed by backcrossing to 129S1/Sv males with maximal non-MHC heterozygosity (142 non-MHC microsatellites) and maximal MHC heterozygosity (six generations) [24]. Male 129.H2g7 homozygotes were selected from MHC heterozygotes crosses and bred with NOD/ShiLtJs to create NOD×129.H2g7 F1s. These were used to breed 310 female NOD×(NOD×129.H2g7) backcross (BC) mice. Mice were maintained on a 14:10 light:dark photoperiod, irradiated Lab Diet 5LG4 (PMI, Brentwood, MO) and acidified water in order to prevent pathogen exposure [24]. Total weight (‘TW’; g), total lean mass (‘TLM’; g) and total fat (‘TF’; g) were measured by dual-energy X-ray absorptiometry (DXA) and percent fat was calculated from these ((‘PF’ = TF/TW); g) [24]. Genomic DNA was genotyped for 146 SNP, plus at an additional 9–24 SNP around putative diabetes QTL [24] for a total of 308 polymorphic SNP (KBioscience; Hoddeston, UK) (average intermarker distance = 8.3 MB). PLep and AFP were available from 144 mice [24].

Reifsnyder et al

In Reifsnyder et al., F1 NZO/HlLt×NON/Lt hybrids and NON/Lt mice were used to create 204 NON/Lt×(NZO/HlLt×NON/Lt) backcrosses (‘Reifsnyder1’, (https://phenome.jax.org/projects/Reifsnyder1); MPD:111) [11]. BC mice were held on a 12:12 photoperiod cycle at a controlled temperature and humidity in double plexiglass boxes to the age of 24 weeks, fed NIH-31 grain meal (4% fat) with ad libitum access to food and water. Plasma leptin (pLep; ng/ml), body weight (g) and total fat (g) were measured at 24 weeks, with pLep being measured via a commercial radioimmunoassay kit (Linco, Inc.). DNA was isolated from 5mm tail clips or frozen kidney and liver and 83 microsatellite markers (average intermarker distance = 27.0 MB) genotyped using Perkin Elmer or MJ thermocyclers and agarose gels [11]. The Jackson Laboratories Animal Health Program (http://jaxmice.jax.org/genetichealth/health_program.html) ensured the ethical treatment of all animals in the Leiter et al. and Reifsnyder et al. studies. Because of incomplete marker heterozygosity in the DU/6 line after selection, the markers D3Mit77, D5Mit10, D8Mit45 and D18Mit152 were not included in this analysis (see [11]).

Association analysis

Random genetic effects on pLep and APF were estimated at each locus in each cohort using a type III GLM (Leiter et al.), or mixed model (Brockmann et al., Reifsnyder et al.) [25] in a model of the form


where yik is trait value, μthe cohort mean, αi the effect of genotype i for each locus j, βMLHXMLH is the partial regression term for the effects of multilocus heterozygosity (MLH) on and εik the OLS residual. Studentized residuals ε^iϵj=ε^i/(σ^1−hii) were estimated from OLS residuals in SAS, where εik=∑i=1mxi−P are divided by the variance of the ith residual var(ε^i)=σ2(1−hii) to control for distributional heterogeneity [26]. MLH was included to account for putative effects of Lerner’s ‘genetic homeostasis’ [27] predicts that random phenotypic aberrancy is a function of internal genetic homogeneity, so that more inbred animals should have a greater rate or degree of phenoabberancy. In order to account for this possible effect, PDAPF and PDpLep were regressed separately on multilocus heterozygosity (MLH = heterozygous loci / total loci) in a non-locus model otherwise as above to test effects of genetic homeostasis as a partial regression covariate. Locus effects were fit to account for the effects of undetected minor QTL. Sex, F1 line and/or full sub family were included as appropriate, with full sib family fit as a random effect.

Studentized residuals for each locus were absolute-transformed (|ε^ikϵj|) to express them as positive vectors of randomization within genotype (‘phenotypic dispersion’; PD) [17]. Genotypic variance for PD was tested using Tobit quantitative limited models (QLIM) [25] fitting dispersion against MLH, full sib family, sex and pedigree where appropriate (as determined from a non-locus model) and for locus effects with a latent variable yi* related to an indicator vector xi by the quantitative vector β, so that yi*=βxi+υi where υi υi is normal error (υi ~ N(0,σ2)). The observed phenotype yi=yi* where latent yi*>τ, and the defined censoring threshold τ where yi* does not exceed τ. In this system, the lower bound τ was set to zero so that yi=yi* where yi*>0, and 0 where yi*≤0(L=∑i=1Nf(y)/(1−ϕ(α))). Joint Wald contrasts were used to determine the significance of marker effects for the Brockmann et al. F2 cohort [25] and model t-tests for BC cohorts (Leiter et al., Reifsnyder et al.). Additive/dominant genetic architecture for dispersion loci was estimated by general linear contrasts [25] in the Brockman et al. group; this was not in the backcross cohorts since only two genotypes were available. Locus effects were corrected for multiple tests via Benjamini-Hochberg (‘Benj’) correction where P-values were ranked 1…m so that the largest Pi satisfying the relation PiPk = kiα/m (k = rank of the ith test) was the α0.05 experiment-wise error rate [28]. Marker positions in base pairs (BP) were obtained from the GRCm38 Mouse Genome Assembly (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/).

Linkage mapping, pLep, APF

Linkage mapping analysis of APF and pLep were performed in the Leiter et al. cohort to compare findings of means effects to those for dispersion; both pLep and APF were already mapped in Brockmann et al. [23] and Reifsnyder et al. [11]. Simple and epistatic effects on pLep in Leiter et al. were mapped in R/QTL [29] using the Cox et al. mouse linkage map [30] at LODerror > 4.0, 1 cM intervals on a Kosambi function with a maximal error tolerance of P < 0.001. Significance thresholds were set using 5000 permutations in R/QTL.

Linked SNP

SNP at nonsynonymous coding sites, mRNA-untranslated regions (UTR) and splice sites were identified over each range of markers significantly associated with dispersion traits with a minimum ±10 MB window for single markers. Gene identities for SNP were collected from Mouse Genome Informatics (MGI) (www.informatics.jax.org) and and gene functions were interpreted from GeneCards (www.genecards.org), UniProt (www.uniprot.org) and eEnsembl (useast.ensembl.org).

Heritability

The heritability of PDAPF and PDpLep was calculated in a set of 43 Mouse Phenome Project strains (n = 1,780) hosted by the Mouse Phenome Database (MPD; http://www.jax.org/phenome) with sample sizes ranging from 4–26 by strain and sex [31] (‘Naggert1’ (https://phenome.jax.org/projects/Naggert1); MPD:143) (Table 1). Studentized residuals and demographic effects on pLep and APF were estimated in the mixed model [25]


where yij was the original phenotype, μ the experiment-wide leptin mean, αi the (random) effect of strain, γj the (fixed) effect of sex, αiγj strain-sex interaction and εijk was error. Studentized residuals were absolute-transformed to PDpLep and PDAPF as above. Dispersion for each trait |ε^iϵj| was recoded in PEST4.2.3 [32] for genetic variance/covariance component analysis in VCE5.1.2 [33]. A modified genetic animal model [34] was used to estimate broad strain-level genetic variance (hs2) and genetic covariance between PDpLepp and PDAPF in the model y = Xb + Ya + e, where yi is the n×r matrix for PDpLep (r1) and PDAPF (r2), X is a fixed n×p incidence matrix (0, 1) for p nongenetic effects, b is the n×p coefficient vector for unknown fixed nongenetic effects, Y is the i×r incidence matrix for random genetic effects (strain (i)), a is the i×r coefficient matrix for random genetic effects (distributed as N~(0, σ2)) and e is the error matrix [35]. Coefficients for strain effects were solved using Gauss-Seidel (GS) iteration and sex by Jacobi iteration. Total strain genetic variance proportions for PDpLep and PDAPF were calculated as hs2=σs2/σp2 (see [36]) and genetic correlation as ra=σg(xy)/√(σg(x)2σg(y)2).

Tab. 1. Data availability for male (m) and female (f) mice from Svenson et al. [31] (‘Naggert1’ (https://phenome.jax.org/projects/Naggert1); MPD:143) with records for serum leptin (‘Lep’, ng/ml) and percent body fat (PF).
Data availability for male (m) and female (f) mice from Svenson <i>et al</i>. [<em class="ref">31</em>] (‘Naggert1’ (<a href="https://phenome.jax.org/projects/Naggert1">https://phenome.jax.org/projects/Naggert1</a>); MPD:143) with records for serum leptin (‘Lep’, ng/ml) and percent body fat (PF).

Protein variants

Coding non-synonymous (CNS) polymorphisms were identified within multiple-cohort consensus genomic locus areas for dispersion in the same trait (PDpLep or PDAPF), fitting the non-diabetic/non-obese line 129SV/SvImJ as a control (‘reference’) strain and the diabetic/obese NOD/ShiLtJ line as the comparison (‘affected’) strain from Leiter et al. [24] using the Genomic Region search function on the Mouse Genomics Informatics (www.informatics.jax.org) [37] platform. DU6–DBA DNA and protein sequence was not available for the DU6 strain [23]; similarly, NON–NZO/HlLtJ polymorphisms were not obtainable since sequence information was not available for the latter [37]. Amino acid sequences for each source strain were extracted based on CNS codon differences for submission to the PredictProtein server meta-service [38], which produces predicted protein structure and activity on a by-amino acid basis for submitted strands. The PROFsec module [39,40] uses a neural network interface to predict squared solvent accessibility scores (predicted accessibility, ‘PACC’) as square Angstroms (Å2)) based on minimal atomic bonding distances [41]. Protein-protein, protein-DNA and protein-RNA binding sites were predicted on a by-residue basis using a machine-learning module, ISIS2, using a combination of empirical three-dimensional predictions and curated known activities [42,43]. Finally, binary predictions of polypeptide flexibility were made by residue using the META-Disorder module, which compiles sequence information from into a single two-state (binary 0/1) score along the length of a polypeptide, where flexibility was scored as Bnorm=(B−B¯Cα)/σ > +3 [44,45] (flexible state), where B¯Cα is the average residue motility based on X-ray chromatography [46]. Only isoforms from well-represented transcribed RefSeq annotations (NM polypeptide accessions/NR RNA accessions) were used to predict polypeptide structure and function; unverified, probable and model sequences (XM/XR annotations) were discounted in constructing haploid protein constructs. For single genes with multiple interstrain CNS identified in MGI, all coding sequence polymorphisms were combined into single strain polypeptide strands for residue analysis.

General gene functions for alleles with CNS polymorphisms between the source strains were obtained from MGI, the Wellcome Sanger Institute (https://www.sanger.ac.uk), the Rat Genome Database (www.rgd.mcw.edu), UniProt (www.uniprot.org), WikiGenes (https://www.wikigenes.org) and NCBI (https://www.ncbi.nih.gov).

Results

Genomic homeostasis

MLH was significantly correlated with pLep in Leiter et al. (P = 0.0007), but not with PDpLep in any cohort (P > 0.3). MLH was positively associated with APF in Brockmann et al. (P = 0.0083) and marginally in Leiter et al. (P = 0.0511). MLH was positively associated with PDAPF in Leiter et al. (β = 1.00 (SE 0.483), P = 0.0379) but not with PDAPF in either other cohort (P > 0.5).

Genetic mapping

APF was transformed as log(arcsin(APF))+1 prior to analysis. No standard or epistatic QTL or joint single-locus effects were detected for log(pLep) or log(APF) at the 5% significance threshold in Leiter et al.

Dispersion analysis

PLep was affected by sex (F = 13.7, P = 0.0003) and full sib family (F = 3.25, P = 0.0003). PDpLep was affected by sex, being significantly lower in females (β = -0.335 (SE 0.0687) t = -4.89, P < 0.0001) and also by family (F = 36.7, P = 0.0001). Subfamily effects on PDpLep were significant in males (t = 23.2, P = 0.0167) and marginally so in females (t = 17.4. P = 0.0972). In Reifsnyder et al., pLep was significantly affected by MLH (F = 11.8, P = 0.0007) and pedigree line (F = 9.48, P = 0.0024).

D1Mit236, D4Mit54, D5Mit221, D12Mit46 and D17Mit72 were associated with PDpLep (PBenj < 0.05) in the Brockman et al. cohort (Fig 1; Table 2). D4Mit54 and D17Mit72 were additive in construction (Pcont < 0.001) (Fig 2). D1Mit236 appeared overdominant (significantly higher PDpLep in DBA/2×DU6i heterozygotes) (Pcont < 0.001) and D5Mit221 underdominant (significantly lower PDpLep in DBA/2×DU6i heterozygotes) (Pcont < 0.01) in the complete population. PDpLep in DBA/2×DU6i heterozygotes and DU6i homozygotes at D12Mit46 (Fig 2).

Association mapping of phenotypic dispersion (absolute Studentized residuals from genetic model; ‘<i>PD</i>’) in plasma leptin (<i>PD</i><sub><i>pLep</i></sub>) on murine chromosomes 1–19 Jackson Laboratory in 402 (228M, 174F) F<sub>2</sub> Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses (Brockman et al.), 144 Non Obese Diabetic (NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.<i>H2</i><sup><i>g7</i></sup>) N<sub>2</sub> backcross females (Leiter et al.), 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ) reciprocal backcrosses (Reifsnyder et al.) house mice (<i>Mus musculus</i>).
Fig. 1. Association mapping of phenotypic dispersion (absolute Studentized residuals from genetic model; ‘PD’) in plasma leptin (PDpLep) on murine chromosomes 1–19 Jackson Laboratory in 402 (228M, 174F) F2 Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses (Brockman et al.), 144 Non Obese Diabetic (NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.H2g7) N2 backcross females (Leiter et al.), 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ) reciprocal backcrosses (Reifsnyder et al.) house mice (Mus musculus).
Mean phenotypic dispersion in plasma leptin (<i>PD</i><sub><i>pLep</i></sub>) from Tobit limited model analysis in a cohort of 233 F<sub>2</sub> DBA/2× DU6i house mice (<i>Mus musculus</i>) (Brockmann et al.) by marker.
Fig. 2. Mean phenotypic dispersion in plasma leptin (PDpLep) from Tobit limited model analysis in a cohort of 233 F2 DBA/2× DU6i house mice (Mus musculus) (Brockmann et al.) by marker.
The significance of differences among mean PDpLep by genotype (P < 0.05*, < 0.01**, < 0.001***) was determined using general linear contrast statements.
Tab. 2. Genomic marker peaks for dispersion in plasma leptin (PDpLep) and arcsin-transformed percent body fat (PDAPF), plasma in 402 (174F, 228M) F2 Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses (Brockman et al.), 142 female Non Obese Diabetic (T1DM model; NOD/ShiLtJ)×(NOD/ShiLtJ×129S1/SvImJ.H2g7) N2 backcross female mice (Leiter et al.), and 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ; T2DM model) reciprocal backcrosses (Reifsnyder et al.) at Benjamini-Hochberg-adjusted significance thresholds.
Genomic marker peaks for dispersion in plasma leptin (<i>PD</i><sub><i>pLep</i></sub>) and arcsin-transformed percent body fat (<i>PD</i><sub><i>APF</i></sub>), plasma in 402 (174F, 228M) F<sub>2</sub> Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses (Brockman et al.), 142 female Non Obese Diabetic (T1DM model; NOD/ShiLtJ)×(NOD/ShiLtJ×129S1/SvImJ.<i>H2</i><sup><i>g7</i></sup>) N<sub>2</sub> backcross female mice (Leiter et al.), and 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ; T2DM model) reciprocal backcrosses (Reifsnyder et al.) at Benjamini-Hochberg-adjusted significance thresholds.
The Brockman cohort was divided into male (M) and female (F) mice. Cross type, number, chromosome, genetic marker, marker chromosomal location (base pairs; bp), nominal P-value, proportion of total variance in PDpLep, genetic architecture (‘Arch’; A = additive, D = (positive) dominant, D- = negative dominant, OD = overdominant, UD = underdominant [37]), contrast test coefficients (β (SE)) and the significance of architecture interpretations as estimated from contrast tests (Parch). Contrast coefficients for Leiter et al. indicates average increase in PDpLep in NOD/ShiLtJ×129S1/Sv heterozygotes over NOD/ShiLtJ homozygotes and for Reifsnyder et al. PDpLep in NON homozygotes compared to NZO/HlLtJ heterozygotes.

PDpLep loci and their architecture were highly similar in F2 DBA/2×DU6i males to those detected in the complete cohort, with D3Mit25 (overdominant), D5Mit66 (additive) and D19Mit30 (negative dominant; DU6i homozygotes having higher PDpLep than other genotypic classes) also being associated with PDpLep (PBenj < 0.05) (Figs 1 and 2; Table 2). D17Mit72 was significantly associated with PDpLep in female F2 DBA/2×DU6i intercrosses but this locus was negative dominant in females (P < 0.001). D13Mit186 also had a negative dominant association with PDpLep in female F2s (Figs 1 and 2; Table 2). No significant marker-by-sex interaction was found using two-way mixed interactive models (PBenj > 0.1) so that differences in structure by sex were likely scalar rather than interactive [47]. Variance in plasma leptin was over twice as high in males (σm2 = 25.0) as females (σf2 = 9.8) in Brockmann et al., but variance in fat percentage by sex was roughly equivalent (σm2 = 0.448, σf2 = 0.460). Of all PDpLep loci in males and females, three were over- or underdominant (D1Mit236, D3Mit25 and D5Mit221), one was dominant (D12Mit46), two were additive (D4Mit54 and D5Mit66), two negative dominant (D13Mit186 and D19Mit30) and one which was additive in males but negative dominant in females (D17Mit49) (Fig 2; Table 2).

Positions on Chr 1, 6, 7, 11, 16 and 17 were associated with PDpLep in the Leiter et al. cohort (Fig 1; Table 2) while D15Mit42 and D17Mit247 were associated with PDpLep in Reifsnyder et al. (PBenj < 0.05). Genetic architecture could not be calculated in these cohorts since backcrosses differentiate only the homozygote and heterozygote state. Pedigree group was not associated with PDpLep (P > 0.05). Chr 17 markers for PDpLep in Leiter et al. and Reifsnyder et al. were linked (23.0 and 9.2 MB, respectively) but neither was strongly linked to the Brockmann et al. Chr 17 locus (45.5 MB and 79.4 MB in males and overall, respectively). Chr 19 loci from Brockmann et al. and Leiter et al. were more distal (26.9 MB and 55.1 MB, respectively) (Fig 1; Table 2).

Only a single SNP marker, 06-141660661-P, from the Leiter et al. cohort was associated with PDAPF at the P0.05,BH threshold with lower dispersion in NOD/Lt×129S1/SvImJ.H2g7 heterozygotes (β = -0.329 (SE 0.0882), P = 0.0002, r2 = 0.0884).

Genetic variance/covariance

Males had marginally higher PDpLep than females (μm = 0.629 SE 0.0705; μf = 0745; SE 0.0705; P < 0.0807) in the Naggert et al. collection. Strain (P < 0.0001; σG2/σp2 = 0.217), and sex-by-strain interaction (P < 0.0001; σGxs2/σp2 = 0.094) both were significant modifiers of PDpLep (Table 2). Sex did not affect PDAPF (P > 0.7), and main effects of strain were only marginally significant (P = 0.0837; σG2/σp2 = 0.058), although sex-by-strain interaction effects were significant (P = 0.0002; σGxs2/σp2 = 0.099).

Of all observations, 298 had observations for both traits, 669 for PDpLep only and 457 PDAPF only. Heritability (hg2) for PDpLep was 0.293 (SE 0.0323) σg2 0.172 (SE 0.0348)) and for PDAPF was 0.122 (SE 0.0244) (σg2 0.0502 (SE 0.0176)), and the strain-level genetic correlation (rg) was 0.796 (SE 0.0723) (σpLep,APF 0.0741 (SE 0.0240)) (L = 1480). PDpLep means were highest in the AKR/J, DBA/2J, KK/HlJ, LP/J, NZB/BlNJ, NZW/LacJ, RF/J and SPRET/EiJ lines (Fig 3).

Strain-by-sex means for phenotypic dispersion in a) plasma leptin (<i>PD</i><sub><i>pLep</i></sub>) and arcin-transformed percent body fat (<i>PD</i><sub><i>APF</i></sub>) in all (solid circles), female (empty circles) and all (solid downwarn-facing triangles) from 43 mouse strains (n = 1,780; n<sub>i</sub> = 4–26 per strain and sex) [<em class="ref">31</em>] using mixed models [<em class="ref">25</em>].
Fig. 3. Strain-by-sex means for phenotypic dispersion in a) plasma leptin (PDpLep) and arcin-transformed percent body fat (PDAPF) in all (solid circles), female (empty circles) and all (solid downwarn-facing triangles) from 43 mouse strains (n = 1,780; ni = 4–26 per strain and sex) [31] using mixed models [25].

Protein variants

A single dispersion locus consensus area was called for PDpLep on Chr 17 (1–22,989,572 BP) from the Leiter et al. cohorts. NM RefSeq CNS variants within this area were identified using MGI and indexed by accession number and location (S1 Table). CNS polymorphisms were detected at i) T cell lymphoma invasion and metastasis 2 (Tiam2), which distorts allele transmission and expression [48] and regulates neurite growth [49]; ii) NADPH oxidase 3 (Nox3), which forms reactive oxygen species (ROS) by catalyzing electron transfer from NADPH to O2 [50] and has been linked to T2DM in West Africans [51]; iii) zinc finger, DHHC domain containing 14 (Zdhhc14), which is overexpressed in gastric tumours [52] and lymphoproliferation [53]; synaptojanin 2 (Synj2), an important lipid phosphatase involved in vesicle recycling [54,55]; iv) fibronectin type III domain containing 1 (Fndc1) involved with squamous and basal cell carcinomas [56]; v) brachury 2 (T2), a t-complex gene critical to the development of the embryonic axis [57]; vi) phosphodiesterase 10A (Pde10A) which mediates intracellular signal transduction by hydrolyzing intracellular cAMP and cGMP concentrations [58], linked to bipolarity [59], the suppression of which is linked to diabetes and obesity [60] via thermoregulation [61]; vii) poly (A) binding protein, cytoplasmic 6 (Pabpc6), which polyadenylates the tail of mRNA precursors [62]; viii) insulin-like growth factor 2 receptor (Igf2r); ix) adherens junction formation factor (Afdn), an Ca2+-independent target of Ras that helps create cell-cell adhesions [63]; x) hyaluronan synthase 1 (Has1), which synthesizes hyaluronan (tissue homeostasis, provides compression resistance and lubricates tissues) in connective tissues and recruits lymphocytes in cancerous tissues [64]; and xi) an array of vomeronasal and zinc-finger genes (S1 Table).

Overall, there was little to no differentiation in predicted accessibility (PACC) or binary inferences of disorder across transcript types, except for some minor shifts in PACC between Synj2 b haplotypes near polymorphism sites (S1 Fig). However, most polypeptide sequences had from 1–5 differences in the presence or position of protein-protein binding sites (i.e. Synj2 c, e, Fndc1, Pde10a, Papbc6, Vmn2r96, Vmn2r111), usually within 50–100 residues of the CNS polymorphism (S1 Fig).

Discussion

Association analysis indicated several loci for leptin dispersion in these three cohorts, with a possible consensus locus for PDpLep in the anterior area of Chr 17 (9.1–23.0 MB) in N2 NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.H2g7 backcross mice (Leiter et al.) and in unrelated NON/Lt×(NZO/HlLt×NON/Lt) backcrosses (Reifsnyder et al.) on Chr 17, suggesting possible synteny, with an additional locus occurring in both sexes on Chr 17 (79.3 MB) in Brockman et al. There also appeared to be sex differences in the genetic structure of leptin dispersion loci on Chr 1 (45.4 MB), 4 (137.5 MB), 5 (138.4 MB), 12 (35.7 MB) and Chr 19 (26.9 MB). Other works have found mostly negative dominant architectures for dispersive loci in medical traits (diabetes, albuminuria) [65,66], but in this work genetic architecture was divided overall between additivity, dominance and heterosis. The backcross structure in the Leiter et al. and Reifsnyder et al. cohorts may have limited the detection of dominant effects. In addition to genetic differences among the source strains themselves, the marked differences in rearing environment, age and methodology among the three cohorts here could limit the ability to detect consensus loci or alter apparent genetic construction either in trait means or random dispersion effects. Summed variance proportions for loci in Brockmann et al. and Leiter et al. approximated gross estimates of heritable variance in dispersion (hg2 = 30%) from the Naggert et al. strain collections. PDpLep loci were unlinked to normal murine leptin QTL (Chr 2: 141.4 MB [10]; Chr 3: 14.2 MB; Chr 10: 106.5 MB; Chr 14: 37.2 MB [7]; Chr 7: 100 MB [8]) with the exception of the 17B3 (~ 44.3 MB) PDpLep locus [10]) but five of eight PDpLep peaks from the Leiter et al. dataset were linked to insulitis QTL from that same study (Chr 1: 121 MB; Chr 11: 42 and 114 MB; Chr 17: 24 MB; Chr 19: 50 MB) [24].

Genotypes causing instability or lability in leptin production would fit with the profile of the inherent variability (periodic or episodic pulses) in plasma leptin [15,67]) according to satiety, so that heritable dispersion could well be an integral, mathematical-physiological facet of serum leptin. Significant random or apparently variation occurs in several other diabetic traits (i.e. glycaemia [68], CAPN10 [69], suggesting that periodic or episodic physiological variation might be a common feature of diabetes in general as a normal feature of hunger and gut emptying. Either form of dysregulation–excessive variability or physiological inflexibility–could be a precursor to irregular reactions to hunger, resulting in inappropriate or incorrectly controlled feeding behaviour. Such loci might thus reflect the various positions of individuals on the longer-term onset to full diabetes, or affect repeatability among analyses (i.e. [7,70]. This may be less true for accruing morphological characters like obesity, where adipose mass probably reflects complex, long-term leptin-diet relationships, leptin resistance and feedback [71,72].

Analytical solutions incorporating dispersive effects might help physiological uncertainties in diabetes. A database search (NCBI, RGD) found no identified QTL for amylin production but the Chr 6 PDpLep peak in Leiter et al. (141.5 MB) co-located with coding and UTR SNP at amylin (islet amyloid polypeptide (Iapp)) (142.3 MB), a leptin agonist and insulin/glucagon regulator which forms pancreatic amyloid processes with cytotoxic effects on pancreatic β cells in T2DM [73]. Agonistic amylin-leptin expression might operate reactively via randomization with incidental satiety or hunger touching off cycles of randomized counter-regulation.

The general basis of dispersive gene action–whether through genes with core physiological functions or those directly related to a given dispersed trait–is unclear [66]. Coding polymorphisms in the consensus Chr 17 area included those linked to cancer (Fndc1, Tiam2, Zdhhc14 Has1) [48,52,56,64], growth and development (Igf2r, Afdn, Tiam2, T2) [49,57,63], immunology [53] or diabetes itself via the energetic electron transfer chain (Tiam2, Pde10A) [50,51] or basal thermal metabolism [61]. Tiam2 modifies allele transmission and expression [48] in addition to its other roles, which could be a central modifier of the propensity to dispersive or stable physiological function. Pabpc6 was another possible core candidate linked to the consensus Chr 17 locus via its role in poly-A post-translational processing [62]. Alternatively, dispersion in leptin could be related to coding variants at the various vomeronasal Chr 17 genes; neurological pathway alleles affecting lability in food detection could similarly be involved with randomization in leptin production upstream or downstream of sensory components of feeding activity. Notably, SNP at Fndc1 were linked to loci associated with dispersion in urine albumin [66].

Fat

Only a single locus was detected for PDAPF and heritability for dispersion in fat from Naggert et al. was low (hs2 = 0.12). Morphological characters with physical benchmarks achieved on stable, progressive trajectories like weight or total fat proportion might be less susceptible to dispersion. An assay of 38 mouse mapping cohorts (x¯ = 133.2 markers, n = 238 mice/cohort, nT = 13,571) found relatively few loci for dispersion in body weight (Perry, unpub), suggesting that overall morphology is relatively immune to heritable randomization. As an anabolic process, morphological indicators of diabetes may be a result of numerous ontogenetic corrections to achieve an integrated final value for body proportion [74] commensurate with overall genetic and environmental proclivity to obesity.

Sex

Male/female differences in the expression of dispersion loci occurred in F2 DBA/2×DU6i mice: only the Chr 17 45.4–79.4 MB locus was common to males and females, and both PDpLep loci in females were negative dominant. Architecture at that locus also varied between males (additive) and females (negative dominant). Sex-based differences in the quantitative genetic structure of disease traits is common [75], including insulin resistance in mouse models [76], leptin resistance [77] and the biochemical operation of leptin [14,78]. Heritable sex-related differences in leptin pulses might be as integral to diabetes onset as conventional means and biochemical action. Women experience greater signal amplitude in leptin expression [67], which could be partially determined by greater severity of dispersive gene action. Correspondingly, the percent variance (r2) of residuals associated with locus effects was slightly higher in female F2s from Brockmann et al. (8.2%) than for males (5.1%). Leptin dispersion might even be a component of greater leptin resistance in males [77], with uncontrolled variance in leptin being ignored by a static, unresponsive physiognomy.

Genetic homeostasis

MLH was marginally positively correlated with PDAPF in the Leiter et al. set, but was unassociated with other dispersed traits in these groups. In accordance with the predictions of genetic homeostasis [27], MLH is usually negatively associated with dispersion [66] (Perry, unpub) so that more inbred individuals tend to be more phenotypically divergent from the mean. The marginal association of MLH with PDAPF may simply be likely random chance.

Conclusion

Loci for randomized variance in plasma leptin is in line with the notion of periodic or episodic variation in satiety, and the linkage of several leptin dispersion loci to diabetes susceptibility loci (see [24]) suggests a role for physiological randomization in diabetes physiology with insulitis itself. Dispersive effects on core diabetic traits like leptin production might help explain heterogenous presentation, progression and response to treatment (see [8,79]); of the nearly 400 million sufferers of diabetes mellitus, many incipient affecteds are unaware of their condition [80]. Quantification of heritable randomization in the structure of underlying diabetic phenotype might help elucidate both genetic labilities and liabilities, helping resolve unassigned statistical variance in the underlying elements of diabetic physiology.

Supporting information

S1 Table [xls]
List of 129S1/SvImJ (129)--NOD/ShiLtJ (NOD) single nucleotide polymorphisms (SNP (dbSNP Build 142)) from Leiter et al. [] linked to by mouse chromosome (Chr), co-ordinate (base pair (BP) position), MGI gene ID, standard symbol, mutation type (coding non-synonymous (CNS), splice site (SS), noncoding transcript variant (NTV), mRNA-UTR (UTR)) and genotype in each strain.

S1 Fig [jpg]
Predicted accessibility (PACC; Å)) (solid wavy lines; Y-axis), binary disorder (solid horizontal lines) and binding sites (solid symbols = NOD/ShiLtJ, open symbols = 129S1/SvImJ.; protein-protein binding sites = triangles, protein-DNA sites = squares, protein-RNA sites = circles) for candidate genes in the Chr 17 consensus region by polypeptide from Leiter et al. [] and Reifsnyder et al. [] estimated on the PredictProtein meta-platform [] based on recognized and curated inter-strain coding polymorphisms obtained from Mouse Genomics Informatics () [].


Zdroje

1. Harris R (2014) Direct and indirect effects of leptin on adipocyte metabolism. Biochim Biophys Acta 184: 414–423.

2. Leibel RL, Bahary N, Friedman JM (1990) Genetic variation and nutrition in obesity: approaches to the molecular genetics of obesity. World Rev Nutr Diet 63: 90–101. 1973864

3. Noble JA, Erlich HA (2012) Genetics of type 1 diabetes. Cold Spring Harb Perspect Med 2: a007732. doi: 10.1101/cshperspect.a007732 22315720

4. Kraus D, Herman MA, Kahn BB (2010) Leveraging leptin for type I diabetes? Proc Natl Acad Sci U S A 107: 4793–4794. doi: 10.1073/pnas.1000736107 20212134

5. Coppari R, Bjorbaek C (2012) The potential of leptin for treating diabetes and its mechanism of action. Nat Rev Drug Discov 11: 692–708. doi: 10.1038/nrd3757 22935803

6. Heo M, Leibel RL, Boyer BB, Chung WK, Koulu M, et al. (2001) Pooling analysis of genetic data: the association of leptin receptor (LEPR) polymorphisms with variables related to human adiposity. Genetics 159: 1163–1178. 11729160

7. Almind K, Kahn CR (2004) Genetic determinants of energy expenditure and insulin resistance in diet-induced obesity in mice. Diabetes 53: 3274–3285. 15561960

8. Almind K, Kulkarni RN, Lannon SM, Kahn CR (2003) Identification of interactive loci linked to insulin and leptin in mice with genetic insulin resistance. Diabetes 52: 1535–1543. doi: 10.2337/diabetes.52.6.1535 12765967

9. Brockmann GA, Kratzsch J, Haley CS, Renne U, Schwerin M, et al. (2000) Single QTL effects, epistasis, and pleiotropy account for two-thirds of the phenotypic F(2) variance of growth and obesity in DU6i x DBA/2 mice. Genome Res 10: 1941–1957. doi: 10.1101/gr.gr1499r 11116089

10. Allan MF, Eisen EJ, Pomp D (2005) Genomic mapping of direct and correlated responses to long-term selection for rapid growth rate in mice. Genetics 170: 1863–1877. doi: 10.1534/genetics.105.041319 15944354

11. Reifsnyder PC, Churchill G, Leiter EH (2000) Maternal environment and genotype interact to establish diabesity in mice. Genome Res 10: 1568–1578. doi: 10.1101/gr.147000 11042154

12. Flier J, Maratos-Flier E (2017) Leptin's physiological role: does the emperor of energy balance have no clothes? Cell Metabolism 26: 24–26. doi: 10.1016/j.cmet.2017.05.013 28648981

13. Wherett D, Ho J, Huot C, Legault L, Nakhla M, et al. (2018) Type 1 diabetes in children and adolescents. Canadian Journal of Diabetes 42: S234–S236. doi: 10.1016/j.jcjd.2017.10.036 29650103

14. Allensworth-James ML, Odle A, Haney A, Childs G (2015) Sex differences in somatotrope dependency on leptin receptors in young mice: ablation of LEPR causes severe growth hormone deficiency and abdominal obesity in males. Endocrinology 156: 3253–3264. doi: 10.1210/EN.2015-1198 26168341

15. Bagnasco M, Kalra PS, Kalra SP (2002) Ghrelin and leptin pulse discharge in fed and fasted rats. Endocrinology 143: 726–729. doi: 10.1210/endo.143.2.8743 11796530

16. Hill W, Zhang X-S (2004) Effects on phenotypic variability of directional selection arising through genetic differences in residual variability. Genetical Research Cambridge 83: 121–132.

17. Perry G, Nehrke K, Bushinsky D, Reid R, Lewandowski K, et al. (2012) Sex modifies genetic effects on residual variance in urinary calcium excretion in rat (Rattus norvegicus). Genetics 192: 1003–1013.

18. Rönnegard L, Valdar W (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics 13: 63. doi: 10.1186/1471-2156-13-63 22827487

19. Hill W, Mulder H (2010) Genetic analysis of environmental variation. Genetical Research (Cambridge) 92: 381–395.

20. Perry G (in review) A locus for phenotypic dispersion in diabetic insulitis in backcrosses of affected and unaffected house mouse (Mus musculus).

21. Bahary N, Leibel RL, Joseph L, Friedman JM (1990) Molecular mapping of the mouse db mutation. Proc Natl Acad Sci U S A 87: 8642–8646. doi: 10.1073/pnas.87.21.8642 1978328

22. Hershey T, Perantie DC, Warren SL, Zimmerman EC, Sadler M, et al. (2005) Frequency and timing of severe hypoglycemia affects spatial memory in children with type 1 diabetes. Diabetes Care 28: 2372–2377. doi: 10.2337/diacare.28.10.2372 16186265

23. Brockmann GA, Tsaih SW, Neuschl C, Churchill GA, Li R (2009) Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both. Physiol Genomics 36: 114–126. doi: 10.1152/physiolgenomics.90277.2008 18984673

24. Leiter EH, Reifsnyder PC, Wallace R, Li R, King B, et al. (2009) NOD x 129.H2(g7) backcross delineates 129S1/SvImJ-derived genomic regions modulating type 1 diabetes development in mice. Diabetes 58: 1700–1703. doi: 10.2337/db09-0120 19336673

25. SAS (2011) Base SAS(R) 9.3 Procedures Guide. Cary, NC: SAS Institute, Inc.

26. Steel R, Torrie J (1980) Principles and Procedures of Statistics. New York: McGraw-Hill Book Co.

27. Lerner I (1977) Genetic homeostasis. London: Oliver and Boyd.

28. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125: 279–284. doi: 10.1016/s0166-4328(01)00297-2 11682119

29. Broman K, Wu H, Sen S, Churchill G (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19: 889–890. doi: 10.1093/bioinformatics/btg112 12724300

30. Cox A, Ackert-Bicknell C, Dumont D, Ding Y, Bell J, et al. (2009) A new standard genetic map for the laboratory mouse. Genetics 182: 1335–1344. doi: 10.1534/genetics.109.105486 19535546

31. Svenson KL, Von Smith R, Magnani PA, Suetin HR, Paigen B, et al. (2007) Multiple trait measurements in 43 inbred mouse strains capture the phenotypic diversity characteristic of human populations. J Appl Physiol (1985) 102: 2369–2378.

32. Groeneveld E, Kovac M, Wang T (1990) PEST, a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the 4th World Congress in Genetics Applied to Livestock 13: 488–491.

33. Kovac M, Groeneveld E, Garcia-Cortes L (2002) A package for the optimization of dispersion parameters. Montpellier, France: 7th World Congress on Genetics Applied to Livestock Production.

34. Henderson C (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31: 423–447. 1174616

35. Henderson C (1986) Estimation of variances in animal model and reduced animal model for single traits and single records. Journal of Dairy Science 69: 1394–1402.

36. Mogil JS, Wilson SG, Bon K, Lee SE, Chung K, et al. (1999) Heritability of nociception II. 'Types' of nociception revealed by genetic correlation analysis. Pain 80: 83–93. doi: 10.1016/s0304-3959(98)00196-1 10204720

37. Eppig JT, Smith CL, Blake JA, Ringwald M, Kadin JA, et al. (2017) Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research. Methods Mol Biol 1488: 47–73. doi: 10.1007/978-1-4939-6427-7_3 27933520

38. Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, et al. (2014) PredictProtein—an open resource for online prediction of protein structural and functional features. Nucleic Acids Res 42: W337–343. doi: 10.1093/nar/gku366 24799431

39. Rost B, Sander C (1994) Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19: 55–72. doi: 10.1002/prot.340190108 8066087

40. Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32: W321–326. doi: 10.1093/nar/gkh377 15215403

41. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577–2637. doi: 10.1002/bip.360221211 6667333

42. Ofran Y, Rost B (2007) ISIS: interaction sites identified from sequence. Bioinformatics 23: e13–16. doi: 10.1093/bioinformatics/btl303 17237081

43. Hönigschmid P (2012) Improvement of DNA- and RNA-protein binding prediction. Munich: Technical University of Munich.

44. Karplus P, Schultz G (1985) Prediction of chain flexibility of peptide antigens. Naturwissenshaften.

45. Carugo O, Argos P (1997) Correlation between side chain mobility and conformation in protein structures. Protein Eng 10: 777–787. doi: 10.1093/protein/10.7.777 9342144

46. Schlessinger A, Yachdav G, Rost B (2006) PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22: 891–893. doi: 10.1093/bioinformatics/btl032 16455751

47. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer Associates Inc.

48. Charron Y, Willert J, Lipkowitz B, Kusecek B, Herrmann BG, et al. (2019) Two isoforms of the RAC-specific guanine nucleotide exchange factor TIAM2 act oppositely on transmission ratio distortion by the mouse t-haplotype. PLoS Genet 15: e1007964. doi: 10.1371/journal.pgen.1007964 30817801

49. Yoshizawa M, Hoshino M, Sone M, Nabeshima Y (2002) Expression of stef, an activator of Rac1, correlates with the stages of neuronal morphological development in the mouse brain. Mech Dev 113: 65–68. doi: 10.1016/s0925-4773(01)00650-5 11900975

50. Banfi B, Malgrange B, Knisz J, Steger K, Dubois-Dauphin M, et al. (2004) NOX3, a superoxide-generating NADPH oxidase of the inner ear. J Biol Chem 279: 46065–46072. doi: 10.1074/jbc.M403046200 15326186

51. Chen G, Adeyemo AA, Zhou J, Chen Y, Doumatey A, et al. (2007) A genome-wide search for linkage to renal function phenotypes in West Africans with type 2 diabetes. Am J Kidney Dis 49: 394–400. doi: 10.1053/j.ajkd.2006.12.011 17336700

52. Oo HZ, Sentani K, Sakamoto N, Anami K, Naito Y, et al. (2014) Overexpression of ZDHHC14 promotes migration and invasion of scirrhous type gastric cancer. Oncol Rep 32: 403–410. doi: 10.3892/or.2014.3166 24807047

53. Rinaldi A, Kwee I, Poretti G, Mensah A, Pruneri G, et al. (2006) Comparative genome-wide profiling of post-transplant lymphoproliferative disorders and diffuse large B-cell lymphomas. Br J Haematol 134: 27–36. doi: 10.1111/j.1365-2141.2006.06114.x 16803564

54. Khvotchev M, Sudhof TC (1998) Developmentally regulated alternative splicing in a novel synaptojanin. J Biol Chem 273: 2306–2311. doi: 10.1074/jbc.273.4.2306 9442075

55. Nemoto Y, De Camilli P (1999) Recruitment of an alternatively spliced form of synaptojanin 2 to mitochondria by the interaction with the PDZ domain of a mitochondrial outer membrane protein. EMBO J 18: 2991–3006. doi: 10.1093/emboj/18.11.2991 10357812

56. Anderegg U, Breitschwerdt K, Kohler MJ, Sticherling M, Haustein UF, et al. (2005) MEL4B3, a novel mRNA is induced in skin tumors and regulated by TGF-beta and pro-inflammatory cytokines. Exp Dermatol 14: 709–718. doi: 10.1111/j.0906-6705.2005.00349.x 16098131

57. Wu JI, Centilli MA, Vasquez G, Young S, Scolnick J, et al. (2007) Tint maps to mouse chromosome 6 and may interact with a notochordal enhancer of Brachyury. Genetics 177: 1151–1161. doi: 10.1534/genetics.107.079715 17954925

58. Wang H, Liu Y, Hou J, Zheng M, Robinson H, et al. (2007) Structural insight into substrate specificity of phosphodiesterase 10. Proc Natl Acad Sci U S A 104: 5782–5787. doi: 10.1073/pnas.0700279104 17389385

59. MacMullen CM, Vick K, Pacifico R, Fallahi-Sichani M, Davis RL (2016) Novel, primate-specific PDE10A isoform highlights gene expression complexity in human striatum with implications on the molecular pathology of bipolar disorder. Transl Psychiatry 6: e742. doi: 10.1038/tp.2016.3 26905414

60. Nawrocki AR, Rodriguez CG, Toolan DM, Price O, Henry M, et al. (2014) Genetic deletion and pharmacological inhibition of phosphodiesterase 10A protects mice from diet-induced obesity and insulin resistance. Diabetes 63: 300–311. doi: 10.2337/db13-0247 24101672

61. Hankir MK, Kranz M, Gnad T, Weiner J, Wagner S, et al. (2016) A novel thermoregulatory role for PDE10A in mouse and human adipocytes. EMBO Mol Med 8: 796–812. doi: 10.15252/emmm.201506085 27247380

62. Gorgoni B, Gray NK (2004) The roles of cytoplasmic poly(A)-binding proteins in regulating gene expression: a developmental perspective. Brief Funct Genomic Proteomic 3: 125–141. doi: 10.1093/bfgp/3.2.125 15355595

63. Takai Y, Nakanishi H (2003) Nectin and afadin: novel organizers of intercellular junctions. J Cell Sci 116: 17–27. doi: 10.1242/jcs.00167 12456712

64. Siiskonen H, Oikari S, Pasonen-Seppanen S, Rilla K (2015) Hyaluronan synthase 1: a mysterious enzyme with unexpected functions. Front Immunol 6: 43. doi: 10.3389/fimmu.2015.00043 25699059

65. Brown D (2018) The genetics of physiological dispersion in signs of diabetes using murine models. Charlottetown, PE, Canada: University of Prince Edward Island. 120 p.

66. Perry G (2019) Genetic effects on dispersion in urinary albumin and creatinine in three house mouse (Mus musculus) cohorts. G3 (Bethesda).

67. Licinio J, Negrao AB, Mantzoros C, Kaklamani V, Wong ML, et al. (1998) Sex differences in circulating human leptin pulse amplitude: clinical implications. J Clin Endocrinol Metab 83: 4140–4147. doi: 10.1210/jcem.83.11.5291 9814504

68. Kohnert KD, Heinke P, Vogt L, Augstein P, Salzsieder E (2018) Applications of variability analysis techniques for continuous glucose monitoring derived time series in diabetic patients. Frontiers in Physiology 9: 1257. doi: 10.3389/fphys.2018.01257 30237767

69. Ridderstrale M, Nilsson E (2008) Type 2 diabetes candidate gene CAPN10: first, but not last. Curr Hypertens Rep 10: 19–24. 18367022

70. Paracchini V, Pedotti P, Taioli E (2005) Genetics of leptin and obesity: a HuGE review. Am J Epidemiol 162: 101–114. doi: 10.1093/aje/kwi174 15972940

71. Myers MG Jr., Leibel RL, Seeley RJ, Schwartz MW (2010) Obesity and leptin resistance: distinguishing cause from effect. Trends Endocrinol Metab 21: 643–651. doi: 10.1016/j.tem.2010.08.002 20846876

72. Zhang S, Zhang Q, Zhang L, Li C, Jiang H (2013) Expression of ghrelin and leptin during the development of type 2 diabetes mellitus in a rat model. Mol Med Rep 7: 223–228. doi: 10.3892/mmr.2012.1154 23129112

73. Westermark P, Andersson A, Westermark GT (2011) Islet amyloid polypeptide, islet amyloid, and diabetes mellitus. Physiol Rev 91: 795–826. doi: 10.1152/physrev.00042.2009 21742788

74. Wagner G, Schwenk P (2000) Evolutionarily stable configurations: functional integration and the evolution of phenotypic stability. Evolutionary Biology 31: 155–217.

75. Gilks WP, Abbott JK, Morrow EH (2014) Sex differences in disease genetics: evidence, evolution, and detection. Trends Genet 30: 453–463. doi: 10.1016/j.tig.2014.08.006 25239223

76. Parks BW, Sallam T, Mehrabian M, Psychogios N, Hui ST, et al. (2015) Genetic architecture of insulin resistance in the mouse. Cell Metab 21: 334–347. doi: 10.1016/j.cmet.2015.01.002 25651185

77. da Silva RP, Zampieri TT, Pedroso JA, Nagaishi VS, Ramos-Lobo AM, et al. (2014) Leptin resistance is not the primary cause of weight gain associated with reduced sex hormone levels in female mice. Endocrinology 155: 4226–4236. doi: 10.1210/en.2014-1276 25144922

78. Shi H, Strader AD, Sorrell JE, Chambers JB, Woods SC, et al. (2008) Sexually different actions of leptin in proopiomelanocortin neurons to regulate glucose homeostasis. Am J Physiol Endocrinol Metab 294: E630–639. doi: 10.1152/ajpendo.00704.2007 18171913

79. Woittiez NJ, Roep BO (2015) Impact of disease heterogeneity on treatment efficacy of immunotherapy in Type 1 diabetes: different shades of gray. Immunotherapy 7: 163–174. doi: 10.2217/imt.14.104 25713991

80. Melmed S, Polonsky K, Larsen P, Kronenberger H (2011) Williams textbook of endocrinology: Saunders.


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