Advertisement

Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion

  • Kelly A. Mitok
    Footnotes
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Elyse C. Freiberger
    Footnotes
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Kathryn L. Schueler
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Mary E. Rabaglia
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Donald S. Stapleton
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Nicholas W. Kwiecien
    Affiliations
    Departments of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Paige A. Malec
    Affiliations
    Department of Chemistry, University of Michigan–Ann Arbor, Ann Arbor, Michigan 48109
    Search for articles by this author
  • Alexander S. Hebert
    Affiliations
    Genome Center of Wisconsin, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Aimee T. Broman
    Affiliations
    Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Robert T. Kennedy
    Affiliations
    Department of Chemistry, University of Michigan–Ann Arbor, Ann Arbor, Michigan 48109
    Search for articles by this author
  • Mark P. Keller
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Joshua J. Coon
    Correspondence
    To whom correspondence may be addressed:Dept. of Chemistry, University of Wisconsin-Madison, 4426 Genetics-Biotechnology Center, 425 Henry Mall, Madison, WI 53706. Tel.:608-263-1718
    Affiliations
    Departments of Chemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706

    Genome Center of Wisconsin, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Alan D. Attie
    Correspondence
    To whom correspondence may be addressed:Dept. of Biochemistry, University of Wisconsin-Madison, 543A HF Deluca Biochemistry Laboratories, 433 Babcock Dr., Madison, WI 53706. Tel.:608-262-1372; Fax:608-265-4693
    Affiliations
    From the Departments of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin 53706
    Search for articles by this author
  • Author Footnotes
    5 Please note that the JBC is not responsible for the long-term archiving and maintenance of this site or any other third party hosted site.
    1 Both authors contributed equally to this work.
    4 The abbreviations used are: T2Dtype 2 diabetesCCCollaborative CrossB6C57BL/6J129129S1/SvImJNODNOD/ShiLtJNZONZO/HILtJPWKPWK/PhJWSBWSB/EiJCASTCAST/EiJThtyrosine hydroxylasel-DOPAl-3,4-dihydroxyphenylalanineoGTToral glucose tolerance testHF/HS diethigh-fat/high-sucrose Western-style dietGLP-1glucagon-like peptide 1PApalmitateERendoplasmic reticulumWGCNAweighted gene co-expression network analysisGOGene OntologyKEGGKyoto Encyclopedia of Genes and GenomesMEmodule eigengenePC1first principal component3-MT3-methoxytyramineDOPAC3,4-dihydroxyphenylacetic acidHVAhomovanillic acidDdcDOPA decarboxylaseDbhdopamine β-hydroxylasePnmtphenylethanolamine N-methyltransferaseTCA cycletricarboxylic acid cycleOxPhosoxidative phosphorylationMaomonoamine oxidaseComtcatechol-O-methyltransferaseGSISglucose-stimulated insulin secretionDAVIDDatabase for Annotation, Visualization and Integrated DiscoveryTGtriglycerideLFQlabel-free quantificationRIAradioimmunoassayAAamino acidACNacetonitrileGcgglucagonFDRfalse discovery rateNpyneuropeptide YMRMmultiple reaction monitoring mode.
Open AccessPublished:March 01, 2018DOI:https://doi.org/10.1074/jbc.RA117.001102
      The mouse is a critical model in diabetes research, but most research in mice has been limited to a small number of mouse strains and limited genetic variation. Using the eight founder strains and both sexes of the Collaborative Cross (C57BL/6J (B6), A/J, 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), PWK/PhJ (PWK), WSB/EiJ (WSB), and CAST/EiJ (CAST)), we investigated the genetic dependence of diabetes-related metabolic phenotypes and insulin secretion. We found that strain background is associated with an extraordinary range in body weight, plasma glucose, insulin, triglycerides, and insulin secretion. Our whole-islet proteomic analysis of the eight mouse strains demonstrates that genetic background exerts a strong influence on the islet proteome that can be linked to the differences in diabetes-related metabolic phenotypes and insulin secretion. We computed protein modules consisting of highly correlated proteins that enrich for biological pathways and provide a searchable database of the islet protein expression profiles. To validate the data resource, we identified tyrosine hydroxylase (Th), a key enzyme in catecholamine synthesis, as a protein that is highly expressed in β-cells of PWK and CAST islets. We show that CAST islets synthesize elevated levels of dopamine, which suppresses insulin secretion. Prior studies, using only the B6 strain, concluded that adult mouse islets do not synthesize l-3,4-dihydroxyphenylalanine (l-DOPA), the product of Th and precursor of dopamine. Thus, the choice of the CAST strain, guided by our islet proteomic survey, was crucial for these discoveries. In summary, we provide a valuable data resource to the research community, and show that proteomic analysis identified a strain-specific pathway by which dopamine synthesized in β-cells inhibits insulin secretion.

      Introduction

      Approximately 50% of the variation in the risk of type 2 diabetes (T2D)
      The abbreviations used are: T2D
      type 2 diabetes
      CC
      Collaborative Cross
      B6
      C57BL/6J
      129
      129S1/SvImJ
      NOD
      NOD/ShiLtJ
      NZO
      NZO/HILtJ
      PWK
      PWK/PhJ
      WSB
      WSB/EiJ
      CAST
      CAST/EiJ
      Th
      tyrosine hydroxylase
      l-DOPA
      l-3,4-dihydroxyphenylalanine
      oGTT
      oral glucose tolerance test
      HF/HS diet
      high-fat/high-sucrose Western-style diet
      GLP-1
      glucagon-like peptide 1
      PA
      palmitate
      ER
      endoplasmic reticulum
      WGCNA
      weighted gene co-expression network analysis
      GO
      Gene Ontology
      KEGG
      Kyoto Encyclopedia of Genes and Genomes
      ME
      module eigengene
      PC1
      first principal component
      3-MT
      3-methoxytyramine
      DOPAC
      3,4-dihydroxyphenylacetic acid
      HVA
      homovanillic acid
      Ddc
      DOPA decarboxylase
      Dbh
      dopamine β-hydroxylase
      Pnmt
      phenylethanolamine N-methyltransferase
      TCA cycle
      tricarboxylic acid cycle
      OxPhos
      oxidative phosphorylation
      Mao
      monoamine oxidase
      Comt
      catechol-O-methyltransferase
      GSIS
      glucose-stimulated insulin secretion
      DAVID
      Database for Annotation, Visualization and Integrated Discovery
      TG
      triglyceride
      LFQ
      label-free quantification
      RIA
      radioimmunoassay
      AA
      amino acid
      ACN
      acetonitrile
      Gcg
      glucagon
      FDR
      false discovery rate
      Npy
      neuropeptide Y
      MRM
      multiple reaction monitoring mode.
      in humans is due to genetic factors (
      • Dimas A.S.
      • Lagou V.
      • Barker A.
      • Knowles J.W.
      • Mägi R.
      • Hivert M.F.
      • Benazzo A.
      • Rybin D.
      • Jackson A.U.
      • Stringham H.M.
      • Song C.
      • Fischer-Rosinsky A.
      • Boesgaard T.W.
      • Grarup N.
      • Abbasi F.A.
      • et al.
      Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.
      ). Most of the candidate genes identified in genome-wide association studies for T2D affect pancreatic islet function either directly or indirectly from other tissues (
      • Fuchsberger C.
      • Flannick J.
      • Teslovich T.M.
      • Mahajan A.
      • Agarwala V.
      • Gaulton K.J.
      • Ma C.
      • Fontanillas P.
      • Moutsianas L.
      • McCarthy D.J.
      • Rivas M.A.
      • Perry J.R.B.
      • Sim X.
      • Blackwell T.W.
      • Robertson N.R.
      • et al.
      The genetic architecture of type 2 diabetes.
      ,
      • Marullo L.
      • El-Sayed Moustafa J.S.
      • Prokopenko I.
      Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of glycaemic traits.
      ). Humans have a wide range in insulin secretory capacity, and insulin secretion shows high heritability (
      • Scott R.A.
      • Scott L.J.
      • Mägi R.
      • Marullo L.
      • Gaulton K.J.
      • Kaakinen M.
      • Pervjakova N.
      • Pers T.H.
      • Johnson A.D.
      • Eicher J.D.
      • Jackson A.U.
      • Ferreira T.
      • Lee Y.
      • Ma C.
      • Steinthorsdottir V.
      • et al.
      An expanded genome-wide association study of type 2 diabetes in europeans.
      ,
      • Wood A.R.
      • Jonsson A.
      • Jackson A.U.
      • Wang N.
      • van Leewen N.
      • Palmer N.D.
      • Kobes S.
      • Deelen J.
      • Boquete-Vilarino L.
      • Paananen J.
      • Stančáková A.
      • Boomsma D.I.
      • de Geus E.J.C.
      • Eekhoff E.M.W.
      • Fritsche A.
      • et al.
      A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants.
      ). However, model organisms are necessary for detailed mechanistic studies to understand how specific genes affect insulin secretion.
      The mouse has been indispensable in diabetes research (
      • Attie A.D.
      • Churchill G.A.
      • Nadeau J.H.
      How mice are indispensable for understanding obesity and diabetes genetics.
      ). The phenotype spectrum present in the wide array of mouse strains offers the opportunity to discover gene action in relation to diabetes traits. However, most research in mice has been limited to a small number of mouse strains covering limited genetic variation. The majority of mouse gene knockout studies has been performed in C57BL/6J (B6), with most of the remaining studies done in FVB and 129/Sv. Often, a gene deletion results in “no phenotype,” but the absence of a discernible phenotype could be due to the strain background suppressing the phenotype of the gene deletion.
      In 2002, the Collaborative Cross (CC) project was initiated to produce recombinant inbred strains from eight genetically diverse founder strains: five classical inbred mouse strains C57BL/6J (B6), A/J, 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), and three wild-derived strains PWK/PhJ (PWK), WSB/EiJ (WSB), and CAST/EiJ (CAST) (
      • Churchill G.A.
      • Airey D.C.
      • Allayee H.
      • Angel J.M.
      • Attie A.D.
      • Beatty J.
      • Beavis W.D.
      • Belknap J.K.
      • Bennett B.
      • Berrettini W.
      • Bleich A.
      • Bogue M.
      • Broman K.W.
      • Buck K.J.
      • Buckler E.
      • et al.
      The Collaborative Cross, a community resource for the genetic analysis of complex traits.
      ). Collectively, these eight strains contain as much genetic variation as the entire human population: ∼40 million SNPs and numerous insertions and deletions. Approximately 75% of the genetic variation is contributed by the three wild-derived strains. The great diversity across the strains offers an opportunity to evaluate the influence of genetic variation on metabolic phenotypes without the need to create transgenic mice.
      We assessed the variability of diabetes-related metabolic phenotypes, conducted whole-islet proteomics, and measured isolated islet insulin secretory responses from the eight CC founder strains and both sexes. Our data show a wide range of diabetes-related metabolic phenotypes among the strains and indicate that genetic background exerts a strong influence on the islet proteome, which can be causally linked to differences in insulin secretion among the strains. Furthermore, the data show that modules of highly correlated proteins are driven by specific strains and enrich for biological pathways.
      We discovered that β-cells of PWK and CAST mice uniquely have elevated levels of tyrosine hydroxylase (Th), the first step in the catecholamine synthesis pathway. We show that increased Th in CAST islets leads to enhanced dopamine production, resulting in blunted insulin secretion. Our findings demonstrate the utility of exploiting the wide genetic diversity in the CC founder mouse strains available to the research community.

      Results

      Genetic diversity drives diabetes-related phenotypic variability

      We assessed the variability of diabetes-related metabolic phenotypes of the following eight genetically diverse Collaborative Cross (CC) founder mouse strains: C57BL/6J (B6); A/J; 129S1/SvImJ (129); NOD/ShiLtJ (NOD); NZO/HILtJ (NZO); PWK/PhJ (PWK); WSB/EiJ (WSB); and CAST/EiJ (CAST), which were metabolically challenged with a Western-style diet high in fat and sucrose (HF/HS diet; 44.6% kcal from fat and 40.7% kcal from sucrose) for 16 weeks. This resulted in a large range in diet-induced weight gain and insulin resistance. All mice were obtained from The Jackson Laboratory, housed in the same vivarium, and maintained on the same diet throughout the study.
      We observed an extraordinary range in diabetes-related metabolic phenotypes among the eight mouse strains and between the sexes, reflecting their genetic diversity. Body weight (Fig. 1, A and B), fasting plasma glucose (Fig. 1, C and D), insulin (Fig. 1, E and F), and triglyceride (Fig. S1, A and B) all showed strain- and sex-dependent differences over the course of the 16-week HF/HS dietary challenge. Body weight was lowest in the three wild-derived strains (CAST, PWK, and WSB) and was highest in NZO, with NZO females becoming severely obese, reaching ∼80 g by 20 weeks of age. Food intake correlated with body weight in some but not all strains (Fig. S2, A and B). For example, the NZO male mice outweighed other males and consistently had the highest food intake. Female 129 mice consumed the least amount of food but did not have the lowest body weight. Fasting glucose levels remained within a normal range in all mice (90–180 mg/dl for HF/HS-fed mice), except for NZO males. Fasting insulin levels, a marker of insulin resistance, however, showed dramatic strain- and sex-dependent variation; there was ∼100-fold difference in fasting plasma insulin between the most insulin-resistant (NZO) and the most insulin-sensitive (CAST) strains, for the females at 20 weeks old and an ∼10-fold difference in the males. Male NZO were the only mice to become overtly diabetic (fasting glucose >300 mg/dl) and did not survive the full 16-week dietary challenge. In contrast, female NZO mice were severely obese, yet maintained euglycemia by increasing insulin.
      Figure thumbnail gr1
      Figure 1Diabetes-related metabolic phenotypes vary with genetic background. Male and female mice of the eight CC founder strains (C57BL/6J (B6); A/J; 129S1/SvImJ (129); NOD/ShiLtJ (NOD); NZO/HILtJ (NZO); PWK/PhJ (PWK); WSB/EiJ (WSB); and CAST/EiJ (CAST)) were maintained on a HF/HS diet beginning at 4 weeks of age. Body weight (A and B), fasting plasma glucose (C and D), and insulin (E and F) were measured at multiple time points during the dietary challenge. Number of islets per pancreas (G) and insulin content per islet (H) were determined for all mice at 22 weeks of age, except for NZO male mice, which were sacrificed at 14 weeks of age due to severe hyperglycemia. Body weight, plasma glucose, and insulin levels were determined after a 3–4-h fast. Data are mean ± S.E., n ≥ 3 mice/sex/strain.
      We previously evaluated the dynamic changes in plasma glucose and insulin in the eight male strains on regular chow or HF/HS diet during an oral glucose tolerance test (oGTT). We found remarkable strain-dependent variation in whole-body glucose homeostasis and circulating insulin (
      • Kreznar J.H.
      • Keller M.P.
      • Traeger L.L.
      • Rabaglia M.E.
      • Schueler K.L.
      • Stapleton D.S.
      • Zhao W.
      • Vivas E.I.
      • Yandell B.S.
      • Broman A.T.
      • Hagenbuch B.
      • Attie A.D.
      • Rey F.E.
      Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes.
      ). In particular, male CAST mice demonstrated a rapid and transient rise in plasma glucose and insulin during the oGTT. Furthermore, CAST was the only strain resistant to HF/HS diet-induced changes in all metabolic phenotypes. These results suggest that male CAST mice utilize unique physiological pathways to regulate glucose-stimulated insulin secretion and whole-body glucose homeostasis.

      Islet insulin and glucagon secretory response is determined by genetic background

      To evaluate the relationship between genetic diversity and islet function, we isolated islets from both sexes of each mouse strain that were maintained on the HF/HS diet for 16 weeks. The number of islets isolated per mouse (Fig. 1G), insulin content per islet (Fig. 1H), and glucagon content per islet (Fig. S3A) all varied greatly. In several strains (B6, A/J, WSB, CAST, NZO, and PWK), >400 islets were collected per mouse. 129 and NOD mice had fewer islets. NZO male mice yielded the fewest islets overall (∼50 pooled from four mice), a consequence of their extreme diabetes. It is likely that other factors, such as effectiveness of pancreatic digestion by collagenase, affect the number of islets isolated per mouse. However, the small number of islets isolated from the severely diabetic animals suggests that the islet number measurement is also related to the physiological state of the mouse at the time the isolation was performed. Islets from A/J and NZO females had the highest insulin content, whereas islets from WSB, PWK, and NZO males had the lowest. Islets from female PWK mice had the highest glucagon content, whereas islets from male A/J mice had the lowest. There was a sex-effect on glucagon content, with islets from male mice generally having lower glucagon content than islets from female mice. Comparison of the patterns across the strains shows that glucagon content per islet was not strongly correlated with insulin content per islet.
      To evaluate the relationship between genetic background and insulin secretion, we measured secretion in response to a variety of insulin secretagogues: glucose (3.3, 8.3, and 16.7 mm); the incretin hormone glucagon-like peptide-1 (GLP-1, 100 nm); the fatty acid palmitate (PA, 0.5 mm); amino acids (0.5 mm l-leucine, 2 mm l-glutamine, and 1.25 mm l-alanine); and a depolarizing concentration of KCl (40 mm). Glucose stimulates insulin secretion upon transport into the β-cell via Glut2, becomes phosphorylated by glucokinase, and enters the glycolytic pathway. This process induces a rise in the ATP/ADP ratio and closure of ATP-sensitive potassium channels, followed by membrane depolarization, opening of voltage-dependent calcium channels, influx of calcium ions, and fusion of insulin-containing granules with the plasma membrane, resulting in insulin secretion. Fatty acids, GLP-1 (via GLP-1 receptor), and amino acids can augment this process through “amplification pathways” (
      • Komatsu M.
      • Takei M.
      • Ishii H.
      • Sato Y.
      Glucose-stimulated insulin secretion: a newer perspective.
      ). KCl, a nonmetabolic insulin secretagogue, stimulates insulin secretion by opposing K+ efflux from the cell, resulting in membrane depolarization and calcium entry.
      The insulin secretory responses to the secretagogues varied greatly among the strains and between the sexes (Fig. 2). Insulin secretion is usually represented by one of three metrics: total insulin secreted (total secretion) (Fig. 2, 1st panel), fold-change in insulin secreted over basal (fold-change) (Fig. 2, 2nd panel), and insulin secreted as a percent of insulin content (% of content) (Fig. 2, 3rd panel). Each measure provides different information. Total insulin secreted depicts how much insulin was secreted from the islets; fold-change in insulin secreted over basal illustrates the robustness of the insulin secretory response, and insulin secreted as a percent of insulin content reports the contribution of insulin content to the amount of secreted insulin.
      Figure thumbnail gr2
      Figure 2Insulin secretory response of isolated islets is influenced by genetic background. Heat maps illustrate three metrics of the insulin secretory response of cultured islets: total amount of insulin secreted (total secretion), secretion as fold over basal (fold-change), and secretion as a percent of islet insulin content (% of content). The following conditions were used to stimulate insulin secretion from islets of the HF/HS diet-fed CC founder strains at 22 weeks of age: 3.3 mm glucose (G3.3, basal), 8.3 mm glucose (G8.3), 8.3 mm glucose + 100 nm GLP-1 (G8.3 + GLP-1), 8.3 mm glucose + 1.25 mm l-alanine, 2 mm l-glutamine, and 0.5 mm l-leucine (G8.3 + AA), 16.7 mm glucose (G16.7), 3.3 mm glucose + 40 mm KCl (G3.3 + KCl), and 16.7 mm glucose + 0.5 mm palmitic acid (G16.7 + PA). Values represent average secretory responses for ≥3 mice/sex/strain, except NZO male mice, where a pool of islets from four mice was used.
      In all strains, 16.7 mm glucose plus palmitate (G16.7 + PA) elicited the largest insulin secretory response. With some exceptions, the remaining secretagogues had decreased potency in the following rank order: 3.3 mm glucose plus KCl (G3.3 + KCl), 16.7 mm glucose (G16.7), submaximal glucose with amino acids (G8.3 + AA), submaximal glucose with GLP-1 (G8.3 + GLP-1), submaximal glucose alone (G8.3), and low glucose (G3.3). Inter-strain variability was apparent, particularly in response to more moderate secretagogues (G8.3, G8.3 + GLP-1, G8.3 + AA, G16.7, and G3.3 + KCl). At the two extreme insulin secretory conditions (G3.3 and G16.7 + PA), we observed the most consistent secretion responses across all strains, suggesting that basal release and release in response to a strong stimulus can overcome genetic influences.
      Islets from NZO mice secreted the greatest amount of total insulin in response to all secretagogues, including G3.3, the basal condition (Fig. 2, 1st panel). These results suggested that NZO islets secrete high levels of insulin under nonstimulatory conditions. When normalizing insulin secretion to insulin content, NZO islets appeared to demonstrate superb secretory capacity (Fig. 2, 3rd panel). This trend, however, is driven by the low insulin content in these mice (Fig. 1H). Indeed, NZO islets showed reduced responsiveness to several secretagogues, including G16.7 + PA, compared with the other strains, when represented as fold-change over basal (Fig. 2, 2nd panel), showing that the majority of insulin secreted from NZO islets is basal, unregulated secretion. In addition to strain, sex exerted a strong influence on insulin secretion in some (B6, CAST, 129, PWK, and NZO) but not all strains, suggesting strain–by–sex interactions. Strain–by–sex interactions became more apparent when insulin secretion was represented as fold-change over basal; fold-change in insulin secretion was much lower for females than males in several strains, including B6, 129, and CAST, and to a lesser degree in PWK and NZO. These data show that genetic background has a strong influence on insulin secretion in response to a variety of secretagogues, both metabolic and nonmetabolic.
      In addition to insulin, we measured glucagon secretion from the isolated islets in response to KCl. Islets from PWK, NZO, and NOD mice secreted the highest amount of glucagon, and islets from B6, A/J, and 129 secreted the least amount (Fig. S3B). However, when glucagon secretion was expressed as a percent of content, these strain differences were reduced, demonstrating that glucagon content strongly influences the amount of glucagon secreted (Fig. S3C).

      Whole-islet proteomics reveals strain- and sex-dependent differences

      We measured the islet proteomes of the eight HF/HS-fed CC founder strains from both sexes, using high-resolution MS coupled with nano-flow LC (
      • Baughman J.M.
      • Rose C.M.
      • Kolumam G.
      • Webster J.D.
      • Wilkerson E.M.
      • Merrill A.E.
      • Rhoads T.W.
      • Noubade R.
      • Katavolos P.
      • Lesch J.
      • Stapleton D.S.
      • Rabaglia M.E.
      • Schueler K.L.
      • Asuncion R.
      • Domeyer M.
      • et al.
      NeuCode proteomics reveals Bap1 regulation of metabolism.
      • Dittenhafer-Reed K.E.
      • Richards A.L.
      • Fan J.
      • Smallegan M.J.
      • Fotuhi Siahpirani A.
      • Kemmerer Z.A.
      • Prolla T.A.
      • Roy S.
      • Coon J.J.
      • Denu J.M.
      SIRT3 mediates multi-tissue coupling for metabolic fuel switching.
      ,
      • Floyd B.J.
      • Wilkerson E.M.
      • Veling M.T.
      • Minogue C.E.
      • Xia C.
      • Beebe E.T.
      • Wrobel R.L.
      • Cho H.
      • Kremer L.S.
      • Alston C.L.
      • Gromek K.A.
      • Dolan B.K.
      • Ulbrich A.
      • Stefely J.A.
      • Bohl S.L.
      • et al.
      Mitochondrial protein interaction mapping identifies regulators of respiratory chain function.
      ,
      • Horton J.L.
      • Martin O.J.
      • Lai L.
      • Riley N.M.
      • Richards A.L.
      • Vega R.B.
      • Leone T.C.
      • Pagliarini D.J.
      • Muoio D.M.
      • Bedi Jr, K.C.
      • Margulies K.B.
      • Coon J.J.
      • Kelly D.P.
      Mitochondrial protein hyperacetylation in the failing heart.
      ,
      • Overmyer K.A.
      • Evans C.R.
      • Qi N.R.
      • Minogue C.E.
      • Carson J.J.
      • Chermside-Scabbo C.J.
      • Koch L.G.
      • Britton S.L.
      • Pagliarini D.J.
      • Coon J.J.
      • Burant C.F.
      Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation.
      ,
      • Richards A.L.
      • Hebert A.S.
      • Ulbrich A.
      • Bailey D.J.
      • Coughlin E.E.
      • Westphall M.S.
      • Coon J.J.
      One-hour proteome analysis in yeast.
      ,
      • Riley N.M.
      • Hebert A.S.
      • Coon J.J.
      Proteomics moves into the fast lane.
      ,
      • Shishkova E.
      • Hebert A.S.
      • Coon J.J.
      Now, more than ever, proteomics needs better chromatography.
      • Stefely J.A.
      • Kwiecien N.W.
      • Freiberger E.C.
      • Richards A.L.
      • Jochem A.
      • Rush M.J.P.
      • Ulbrich A.
      • Robinson K.P.
      • Hutchins P.D.
      • Veling M.T.
      • Guo X.
      • Kemmerer Z.A.
      • Connors K.J.
      • Trujillo E.A.
      • Sokol J.
      • et al.
      Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling.
      ). This did not include male NZO mice, as these animals yielded too few islets as a result of severe diabetes. Our analysis yielded an average detection of 23,148 unique peptides (Fig. S4A), corresponding to 4,705 quantified proteins per sample (Fig. S4B). We quantified 5,255 total proteins, and 4,775 across all eight strains (Fig. S4C and Table S1), yielding >90% overlap among the samples, which permitted across-strain comparisons. Quantitative reproducibility was good with a median coefficient of variation of 16.7% across all samples (Fig. S4D).
      To identify strain-dependent patterns in the islet proteome, we computed the Z-score for all identified proteins, followed by unsupervised hierarchical clustering (Fig. 3). The Z-score indicates how many standard deviations a data point (in this case protein abundance) is from the mean (the mean abundance of that protein across all samples). All 5,255 proteins were used in the clustering, and those that were not detected in a sample are colored gray and denoted as not applicable (N/A) in Fig. 3. Clustering resulted in the samples grouping strongly by strain and sex (Fig. 3, vertical axis). CAST, NZO, PWK, and B6 grouped perfectly based on both strain and sex. All A/J mice grouped together and nearly grouped based on sex. The WSB mice clustered into two groups nearly according to sex. All but one NOD and one 129 mouse grouped by strain and sex. One male NOD and one female 129 grouped with the male WSB mice.
      Figure thumbnail gr3
      Figure 3Whole-islet proteome is strongly influenced by genetic background. Proteomics was conducted on islets of all eight CC founder strains and both sexes, except for male NZO mice, as they yielded too few islets. Z-scores were computed and used to hierarchically cluster both the proteins and mice. Proteins that were not detected in a sample are colored gray and denoted N/A. The clustering resulted in groups of proteins showing striking abundance differences among the strains. Six major clusters displayed both significant enrichment for Gene Ontology (GO) terms and marked differential expression among the mice.
      The protein clustering (horizontal axis) resulted in subsets of strain-specific up- and down-regulation of protein abundance that were significantly enriched for Gene Ontology (GO) terms (Fig. 3). The largest set contained 1,190 proteins and enriched for GO term “protein transport” (p < 10−60). These proteins were down-regulated in CAST and male WSB and up-regulated in NZO female islets. Many of these proteins are involved in vesicle fusion (Vamps and Syntaxins). The up-regulation of these proteins in the NZO female islets suggests that an increase in vesicle transport and fusion could explain the high basal, unregulated insulin secretion from these islets (Fig. 2, 1st panel). There was a smaller set of 47 proteins associated with antigen processing and presentation (p < 10−12) that was exclusively up-regulated in a subset of NOD mice, which included histocompatibility 2 class II antigen Aα (H2-Aa), Aβ1 (H2-Ab1), Eβ (H2-Eb1), and CD74 antigen (Cd74). These proteins have previously been shown to be enriched in intra-islet myeloid cells (
      • Ferris S.T.
      • Zakharov P.N.
      • Wan X.
      • Calderon B.
      • Artyomov M.N.
      • Unanue E.R.
      • Carrero J.A.
      The islet-resident macrophage is in an inflammatory state and senses microbial products in blood.
      ,
      • Carrero J.A.
      • Calderon B.
      • Towfic F.
      • Artyomov M.N.
      • Unanue E.R.
      Defining the transcriptional and cellular landscape of type 1 diabetes in the NOD mouse.
      ). It is possible that one or more of these proteins is involved in the autoimmune-mediated death of β-cells in NOD, a model for type 1 diabetes. Two clusters of proteins showed differential regulation in NZO only, endoplasmic reticulum (ER) proteins (up-regulated; p < 10−12) and mitochondrial proteins (down-regulated; p < 10−40). Some of the proteins associated with mitochondrial function that are decreased in the NZO female islets may be involved in the relatively poor insulin secretory response that we observed in NZO islets (Fig. 2, 2nd panel). Our data show that genetic background exerts a strong influence on the islet proteome that can likely be causally linked to differences in insulin secretion.

      Islet proteome co-expression modules enrich for physiological pathways

      The results presented in Fig. 3 prompted us to use a weighted gene co-expression network analysis (WGCNA) approach (
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      ,
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      ) to compute co-expression modules consisting of highly correlated protein subgroups (Table S2). Proteomics experiments can provide more information than a list of differentially expressed proteins. WGCNA can be used to analyze this higher-level information by considering relationships between measured proteins, which can be assessed by correlations between expression profiles. WGCNA starts with thousands of proteins, identifies co-expression modules, and uses correlation between an expression profile and a sample trait to identify important proteins for further validation (see “Experimental procedures” for computational details). When grouping proteins into co-expression modules, we did not utilize information about functional annotation. Among the 5,255 proteins identified from our whole-islet proteomics experiment, ∼83% were uniquely assigned to a co-expression protein module. The WGCNA approach computed 20 co-expression modules from the proteomics data, which are identified by a color name (Table S3). The modules contained varying numbers of proteins, ranging from 49 to 1,396. A cluster dendrogram shows the modules as downward branches (Fig. 4). The depth of the branches indicates the overall correlation between proteins in a module, with deeper branches having greater correlation. Table S2 lists all modules and their protein membership.
      Figure thumbnail gr4
      Figure 4Islet proteome co-expression modules enrich for physiological function. WGCNA-based unsupervised clustering of the whole-islet proteome of the CC founder strains identified co-expression modules. Modules, denoted by color, are indicated by the downward branches illustrated in the dendrogram. Enrichment analysis for Gene Ontology (GO) and the Kyoto Encyclopedia for Genes and Genomes (KEGG) was performed to determine whether modules contained proteins enriched for specific biological pathways. All modules were significantly enriched with one or more GO/KEGG terms (Z > 3). Branches are labeled with general descriptive terms; a complete list of all GO/KEGG terms for the modules are included in .
      We and others have shown that highly correlated transcripts (in this case, proteins) are often associated with common physiological pathways (
      • Carlson M.R.
      • Zhang B.
      • Fang Z.
      • Mischel P.S.
      • Horvath S.
      • Nelson S.F.
      Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks.
      • Gargalovic P.S.
      • Imura M.
      • Zhang B.
      • Gharavi N.M.
      • Clark M.J.
      • Pagnon J.
      • Yang W.P.
      • He A.
      • Truong A.
      • Patel S.
      • Nelson S.F.
      • Horvath S.
      • Berliner J.A.
      • Kirchgessner T.G.
      • Lusis A.J.
      Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids.
      ,
      • Ghazalpour A.
      • Doss S.
      • Zhang B.
      • Wang S.
      • Plaisier C.
      • Castellanos R.
      • Brozell A.
      • Schadt E.E.
      • Drake T.A.
      • Lusis A.J.
      • Horvath S.
      Integrating genetic and network analysis to characterize genes related to mouse weight.
      ,
      • Horvath S.
      • Zhang B.
      • Carlson M.
      • Lu K.V.
      • Zhu S.
      • Felciano R.M.
      • Laurance M.F.
      • Zhao W.
      • Qi S.
      • Chen Z.
      • Lee Y.
      • Scheck A.C.
      • Liau L.M.
      • Wu H.
      • Geschwind D.H.
      • et al.
      Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target.
      • Keller M.P.
      • Choi Y.
      • Wang P.
      • Davis D.B.
      • Rabaglia M.E.
      • Oler A.T.
      • Stapleton D.S.
      • Argmann C.
      • Schueler K.L.
      • Edwards S.
      • Steinberg H.A.
      • Chaibub Neto E.
      • Kleinhanz R.
      • Turner S.
      • Hellerstein M.K.
      • et al.
      A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility.
      ). GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine whether the modules contained proteins that enriched for specific biological pathways. Remarkably, all modules were significantly enriched (Z-score >3) with one or more GO and/or KEGG terms. In the cluster dendrogram, module branches are labeled with a general description of the overall GO terms or KEGG pathways enriched in each module (Fig. 4). All significantly enriched categories for the modules are included in Table S4.
      For each module, we computed a module eigengene (ME) (or first principal component (PC1)) to describe the pattern of protein abundance among the eight CC founder strains and both sexes. The ME can be considered a representative of the protein expression profiles in a module. MEs for all modules are shown in Fig. S5, A–D and illustrate the protein abundance pattern across the strain-sex combinations. The variance described by the MEs ranged from ∼31% (blue module) to ∼47% (lightcyan module) (Table S3). Proteins that were not identified within a co-expression module were put into the gray module (904 proteins), which had a variance described by the ME of ∼6%. The variance described by the MEs is the percent variance among the proteins within a module that is explained by the PC1 or the ME. Typically, they can be ∼30% or greater and much higher than the percent variance describing the ME for the gray module. This shows that proteins in the nongray modules have highly coordinated expression.
      The top-enriched module was lightcyan, which enriched for the GO term “cytosolic ribosome” (Z = 36.3). The lightcyan module contained 83 proteins, including ribosomal protein S2 (Rps2), ribosomal protein L18 (Rpl18), and many other Rpl and Rps proteins. Proteins in this module were most highly up-regulated in islets from both sexes of CAST and female WSB, and most highly down-regulated in islets from female B6 and male 129 (Fig. S5A). The abundance of these ribosomal proteins may reflect the amount of protein turnover in these islets.
      Other modules that were highly enriched for physiological pathways included tan, which enriched for the GO terms “response to interferon-γ” (Z = 15.6) and “immune response” (Z = 13.05) and the KEGG pathways “Staphylococcus aureus infection” (Z = 15.8) and “antigen processing and presentation” (Z = 10.0). This module contained histocompatibility 2 class II antigen Aα (H2-Aa), Aβ1 (H2-Ab1), Eβ (H2-Eb1), CD74 antigen (Cd74), and interferon-induced guanylate-binding protein 2 (Gbp2). This module describes the cluster of proteins highly abundant in NOD islets shown in Fig. 3.
      The midnightblue module was highly enriched for the GO term “serine-type endopeptidase activity” (Z = 15.6), and the KEGG pathway “pancreatic secretion” (Z = 14.5). This module contained pancreatic lipase, pancreatic lipase-related protein 2 (Pnliprp2), and pancreatic colipase, which reflects the unavoidable contamination of acinar tissue in isolated islet preparations. The ME for midnightblue shows that proteins in this module were up-regulated in 129 and down-regulated in A/J islets, which may reflect the amount of contaminating acinar tissue in the islet preparations from these strains (Fig. S5B).
      The turquoise module contained 1,396 proteins, which enriched for the GO term “Golgi vesicle transport” (Z = 8.6) and the KEGG pathway “SNARE interactions in vesicular transport” (Z = 7.0) and includes adaptor-related protein complex 1γ1 subunit (Ap1g1), Rab8a, Sec22b, vesicle-associated membrane protein 7 (Vamp7), syntaxin 6 (Stx6), and many other Rabs, Secs, Vamps, and Stxs. Proteins in the turquoise module were up-regulated in female NZO and down-regulated in male WSB and female CAST islets and describe the “protein transport” cluster in Fig. 3. Islets from the NZO mice had high basal insulin secretion and poor insulin secretory response when secretion was presented as fold over basal (Fig. 2), suggesting that up-regulation of proteins involved in vesicle transport and SNARE interactions results in a high rate of unregulated basal insulin secretion.
      The red module, enriched in the KEGG pathways “oxidative phosphorylation” (Z = 10.4) and “citrate cycle” (TCA cycle) (Z = 8.8), had an ME with the opposite pattern to that of turquoise. Proteins in the red module were down-regulated in female NZO and up-regulated in male WSB and female CAST islets. The red module included ATP synthase H+ transporting mitochondrial F1 (Atp5a1) and other mitochondrial ATP synthase subunits, cytochrome c oxidase subunit 5A (Cox5a), isocitrate dehydrogenase 3 (NAD+) γ (Idh3g), and pyruvate carboxylase (Pcx). This decrease in proteins in the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OxPhos) pathway in the NZO islets could also explain their poor stimulated insulin secretion.
      These results demonstrate that a network analysis yields robust protein sets (modules) that enrich for biological function, demonstrate striking strain- and sex-dependent patterns of protein abundance, and are describable by an ME that captures a large portion of the variance across the samples.

      Islet proteome co-expression modules correlate with diabetes-related phenotypes

      To determine the potential physiological significance of the islet modules, we asked whether the modules were correlated with the diabetes-related metabolic phenotypes we measured in the CC founder mice. Using MEs, we computed the correlation between the modules and several whole-body physiological traits (e.g. plasma insulin), and the insulin and glucagon secretory responses measured from the isolated islets (Fig. 5). Because islets isolated from the same mice were used for both the secretion studies and proteomics analysis, we were able to directly compare the protein and insulin secretion measurements.
      Figure thumbnail gr5
      Figure 5Islet proteome co-expression modules correlate with physiological phenotypes. Heat map illustrates the correlation between module eigengenes and diabetes-related phenotypes (body weight, fasting plasma glucose, insulin, TG, number of islets per pancreas (# islets/panc), insulin content per islet (insulin/islet), whole-pancreas insulin content (WPIC), three metrics for insulin secretion (total secretion, fold-change, % of content), glucagon content per islet (glucagon/islet), and two metrics of glucagon secretion in response to G3.3 + KCl (total secretion and % of content)) measured from the CC founder strains. Correlations computed from normalized quantile ranks; *, p ≤ 0.001.
      Several modules were significantly correlated with more than one phenotype (Fig. 5). For example, the lightgreen module, enriched for the GO terms “cell-substrate adherens junction” (Z = 8.8) and “focal adhesion” (Z = 8.8), showed the strongest positive correlation with insulin secretion in response to all secretagogue classes, as well as positive correlation with triglyceride (TG) and to a lesser extent plasma insulin. Proteins in the lightgreen module included integrin-linked kinase (Ilk), melanoma cell-adhesion molecule (Mcam), Thy-1 cell-surface antigen (Thy1), and lectin galactoside-binding soluble 1 (Lgals1) and were up-regulated in islets from NOD and NZO and down-regulated in islets from B6 (Fig. S5A). This suggests that the high total insulin secretion from the NZO and NOD islets and low insulin secretion from the B6 islets shown in Fig. 2 could be due to an increase or decrease, respectively, in proteins involved in cell-substrate junctions.
      The cyan and black modules, enriched in endoplasmic reticulum proteins, also showed a similar correlation pattern to these phenotypes. The cyan module enriched for the KEGG pathway “protein processing in endoplasmic reticulum” (Z = 3.3) and included DnaJ heat-shock protein family (Hsp40) member C1 (DnaJc1), protein-disulfide isomerase family A member 15 (Txndc5), signal sequence receptor subunit 1 (Ssr1), and SEC13 homolog nuclear pore and COPII coat complex component (Sec13). The ME for cyan showed that proteins in this module are highly up-regulated in islets from female NZO mice (Fig. S5C). The black module enriched for the GO terms “endoplasmic reticulum chaperone complex” (Z = 13.7) and “response to endoplasmic reticulum stress” (Z = 6.8) and included protein-disulfide isomerase family A member 4 (Pdia4), heat-shock protein 90 β family member 1 (Hsp90b1), calreticulin (Calr), and endoplasmic reticulum lectin 1 (Erlec1), and these proteins are also up-regulated in islets from female NZO mice and down-regulated in female B6 and PWK islets. This suggests that the high insulin secretion from the NZO islets and low insulin secretion from the B6 and PWK islets shown in Fig. 2 could be due to an increase or decrease, respectively, in proteins involved in the ER stress response. Modules enriched for the GO terms “aerobic respiration” (Z = 10.7) (red), “mRNA processing” (Z = 9.8) (blue), and “lipid metabolic process” (Z = 3.1) (gray60) showed the strongest negative correlation to insulin secretion in response to all secretagogue classes, as well as a negative correlation to body weight and plasma insulin.
      The magenta module was the most highly negatively correlated with glucagon secretion in response to KCl and enriched for the GO term “glycosphingolipid metabolic process” (Z = 8.8) and the KEGG pathway “lysosome” (Z = 6.6). Proteins in the magenta module included galactosylceramidase (Galc), GM2 ganglioside activator (Gm2a), hexosaminidase subunits a and b (Hexa and Hexb), and prosaposin (Psap). The ME for magenta showed that proteins in this module are generally up-regulated in B6, A/J, and 129 islets and down-regulated in female NZO, PWK, and CAST islets (Fig. S5B).

      Tyrosine hydroxylase is highly abundant in β-cells of PWK and CAST islets

      Previously, we have shown that CAST mice were resistant to HF/HS diet and demonstrated remarkably rapid insulin and glucose responses during an oGTT (
      • Kreznar J.H.
      • Keller M.P.
      • Traeger L.L.
      • Rabaglia M.E.
      • Schueler K.L.
      • Stapleton D.S.
      • Zhao W.
      • Vivas E.I.
      • Yandell B.S.
      • Broman A.T.
      • Hagenbuch B.
      • Attie A.D.
      • Rey F.E.
      Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes.
      ). Furthermore, a preliminary survey of the islet phosphoproteome in islets from CAST mice showed that serine 31 on Th was phosphorylated in response to glucose. Th activity is regulated by phosphorylation at specific residues (
      • Tekin I.
      • Roskoski Jr., R.
      • Carkaci-Salli N.
      • Vrana K.E.
      Complex molecular regulation of tyrosine hydroxylase.
      ). ERK1/2-mediated phosphorylation at serine 31 stabilizes the enzyme and stimulates catalytic activity (
      • Haycock J.W.
      • Ahn N.G.
      • Cobb M.H.
      • Krebs E.G.
      ERK1 and ERK2, two microtubule-associated protein 2 kinases, mediate the phosphorylation of tyrosine hydroxylase at serine-31 in situ.
      ,
      • Dunkley P.R.
      • Bobrovskaya L.
      • Graham M.E.
      • von Nagy-Felsobuki E.I.
      • Dickson P.W.
      Tyrosine hydroxylase phosphorylation: regulation and consequences.
      ). Th is the first step in the catecholamine synthesis pathway, converting l-tyrosine to l-3,4-dihydroxyphenylalanine (l-DOPA). Catecholamines are potent inhibitors of insulin secretion (
      • Rubí B.
      • Ljubicic S.
      • Pournourmohammadi S.
      • Carobbio S.
      • Armanet M.
      • Bartley C.
      • Maechler P.
      Dopamine D2-like receptors are expressed in pancreatic beta cells and mediate inhibition of insulin secretion.
      ,
      • Feldman J.M.
      • Lebovitz H.E.
      Mechanism of epinephrine and serotonin inhibition of insulin release in the golden hamster in vitro.
      • Sorenson R.L.
      • Elde R.P.
      • Seybold V.
      Effect of norepinephrine on insulin, glucagon, and somatostatin secretion in isolated perifused rat islets.
      ).
      Our survey of the islet proteome in the CC founder mice shows that Th is ∼70-fold higher in islets from CAST and PWK mice compared with the other strains (Fig. 6A). Th is present in the blue module. The ME for blue shows that blue module proteins are generally up-regulated in PWK and CAST islets and down-regulated in female NZO and male B6 islets (Fig. 6B). GO terms that describe Th in this module include “cell body” (Z = 3.4), “axon” (Z = 2.4), and “catecholamine metabolic process” (Z = 2.1) (Table S4). These GO terms for the blue module also include neural cell adhesion molecule 1 (Ncam1) and neuropeptide Y (Npy), among others. Ncam1 has been shown to be required for cell-type segregation and normal ultrastructure in pancreatic islets (
      • Esni F.
      • Täljedal I.B.
      • Perl A.K.
      • Cremer H.
      • Christofori G.
      • Semb H.
      Neural cell adhesion molecule (N-CAM) is required for cell type segregation and normal ultrastructure in pancreatic islets.
      ). Npy is a secreted neuropeptide that influences many physiological processes, including cortical excitability, stress response, and food intake (
      • Reichmann F.
      • Holzer P.
      Neuropeptide Y: A stressful review.
      ), and it also inhibits insulin secretion (
      • Schwetz T.A.
      • Ustione A.
      • Piston D.W.
      Neuropeptide Y and somatostatin inhibit insulin secretion through different mechanisms.
      ). These other proteins in the blue module should also be elevated in islets from PWK and CAST islets like Th. Indeed, Npy is 4-fold higher in PWK islets and 12-fold higher in CAST islets over B6 islets. These proteins in the blue module are associated with inhibition of insulin secretion, consistent with the negative correlation between the ME of the blue module and insulin secretion in Fig. 5.
      Figure thumbnail gr6
      Figure 6Tyrosine hydroxylase is highly expressed in β-cells of PWK and CAST mice. A, abundance of Th protein in islets from the eight CC founder strains and both sexes as determined by mass spectrometry. B, ME for the blue module illustrates that proteins in the module, which includes Th, are highly expressed in PWK and CAST islets. C, immunohistochemistry for Th (green), insulin (red), and glucagon (cyan), in pancreatic sections from 20-week-old male B6, CAST, and PWK mice maintained on the HF/HS diet. D, quantification of Th immunoreactivity in B6, PWK, and CAST islets. The number of Th-positive (Th+) β-cells (insulin-positive), α-cells (glucagon-positive), or unidentified cells (neither insulin nor glucagon-positive) per islet area is shown. Data are mean ± S.E., n = 3 mice per strain and 40 islets per mouse. Statistics were performed using unpaired, parametric, two-tailed t tests in GraphPad Prism 7. ****, p ≤ 0.0001.
      In pancreatic sections from B6, PWK, and CAST mice, we determined the proportion of β-cells (insulin-positive), α-cells (glucagon-positive), and unidentified cells (neither insulin- nor glucagon-positive) that were Th-positive using immunohistochemistry (Fig. 6, C and D). PWK and CAST islets had ∼35-fold more Th-positive β-cells per islet area, compared with B6 islets. There was no statistically significant difference in the number of Th-positive α-cells or Th-positive unidentified cells per islet area across the strains. In summary, our data show that β-cells from CAST and PWK mice have greatly elevated Th protein, suggesting that islets from these mice utilize catecholamines as an additional regulatory mechanism for insulin secretion that is absent in strains that have low Th levels.

      Increased dopamine production in CAST islets is associated with decreased insulin secretion

      Catecholamine synthesis begins with Th converting l-tyrosine to l-DOPA, which in turn becomes dopamine via DOPA decarboxylase (Ddc). Dopamine can then become norepinephrine via dopamine β-hydroxylase (Dbh) and norepinephrine can become epinephrine via phenylethanolamine N-methyltransferase (Pnmt). Excess dopamine is metabolized by two enzymes, Comt, producing 3-methoxytyramine (3-MT), and Mao, producing 3,4-dihydroxyphenylacetic acid (DOPAC). These two metabolites are further metabolized to homovanillic acid (HVA) by Mao or Comt, respectively. Interestingly, our proteomic data revealed that Ddc is highly expressed among all eight mouse strains and sexes; Comt is more abundant in CAST islets than other strains, and Mao is equally abundant among the eight mouse strains and sexes (Table S1). Dbh and Pnmt were not detected in any of the strains or sexes. Thus, we hypothesized that elevated Th activity in CAST islets would yield high levels of dopamine and its various metabolites.
      Using MS, we quantified intermediates of the dopamine biosynthetic pathway and dopamine metabolites in islets isolated from B6 and CAST mice. l-Tyrosine, the precursor to l-DOPA, was not significantly different between B6 and CAST islets (Fig. 7A). Although there was a trend for l-DOPA to be elevated in CAST islets, it was not significantly different between B6 and CAST islets (Fig. 7B). It is likely that newly synthesized l-DOPA is rapidly converted to dopamine via the high levels of Ddc in the islets of all strains. Indeed, CAST islets had an ∼5-fold higher level of dopamine compared to B6 islets (Fig. 7C). 3-MT and DOPAC were also elevated, consistent with metabolism of the elevated dopamine (Fig. 7, D and E). HVA was not detected (ND) in B6 and CAST islets maintained under normal conditions (Fig. 7F).
      Figure thumbnail gr7
      Figure 7Increased dopamine synthesis in CAST islets is associated with decreased insulin secretion. Abundance of tyrosine (A), l-DOPA (B), dopamine (C), DOPAC (D), 3-MT (E), and HVA (F) in B6 and CAST islets from 20-week-old HF/HS diet-fed mice was determined by MS. Insulin secretion from B6 and CAST islets and B6 islets were preincubated with 50 μm l-DOPA (B6 + l-DOPA) (G). Insulin secretion was stimulated with 16.7 mm glucose in the absence or presence of 1 μm dopamine. Data are mean ± S.E., n = 4, for B6 and CAST and n = 2 for B6 + l-DOPA. Statistics were performed using unpaired, parametric, two-tailed t-tests in GraphPad Prism 7. *, p ≤ 0.05; **, p ≤ 0.01. ND, not detected.
      To bypass the strain difference in Th activity, we incubated B6 islets with l-DOPA, the product of Th. l-DOPA is transported into cells via the cell-surface large amino acid transporter (Laat) (
      • Kageyama T.
      • Nakamura M.
      • Matsuo A.
      • Yamasaki Y.
      • Takakura Y.
      • Hashida M.
      • Kanai Y.
      • Naito M.
      • Tsuruo T.
      • Minato N.
      • Shimohama S.
      The 4F2hc/LAT1 complex transports l-DOPA across the blood-brain barrier.
      ,
      • Ustione A.
      • Piston D.W.
      • Harris P.E.
      Minireview: Dopaminergic regulation of insulin secretion from the pancreatic islet.
      ), which is equally abundant in all eight strains (Table S1). Pre-incubating B6 islets with l-DOPA led to a dramatic increase in islet levels of dopamine, as well as its metabolites, mimicking what we observed with CAST islets (Fig. 7, A–F). These results strongly suggest that the elevated levels of dopamine in CAST islets are due to the increased abundance and activity of Th.
      We hypothesized that the increased dopamine levels in CAST islets or that achieved in B6 islets by preincubation with l-DOPA would result in reduced glucose-stimulated insulin secretion (GSIS). We measured GSIS in islets isolated from B6 and CAST. To enhance the suppressive autocrine effect of secreted dopamine on insulin secretion, we incubated 15 islets in 125 μl of secretion media for these experiments. In response to high glucose (16.7 mm), insulin secretion from CAST islets was reduced by ∼60% compared with B6 islets (Fig. 7G). Pre-incubating B6 islets with l-DOPA (50 μm, 45 min) mimicked the response observed in CAST islets; insulin secretion from B6 islets was reduced by ∼40% in response to l-DOPA preincubation. The addition of 1 μm dopamine suppressed insulin secretion from B6 islets by ∼50%, confirming the autocrine negative feedback previously reported (
      • Rubí B.
      • Ljubicic S.
      • Pournourmohammadi S.
      • Carobbio S.
      • Armanet M.
      • Bartley C.
      • Maechler P.
      Dopamine D2-like receptors are expressed in pancreatic beta cells and mediate inhibition of insulin secretion.
      ,
      • García-Tornadú I.
      • Ornstein A.M.
      • Chamson-Reig A.
      • Wheeler M.B.
      • Hill D.J.
      • Arany E.
      • Rubinstein M.
      • Becu-Villalobos D.
      Disruption of the dopamine D2 receptor impairs insulin secretion and causes glucose intolerance.
      ,
      • Ustione A.
      • Piston D.W.
      Dopamine synthesis and D3 receptor activation in pancreatic beta-cells regulates insulin secretion and intracellular [Ca2+] oscillations.
      ). Interestingly, addition of exogenous dopamine (1 μm) did not cause an additional suppression of secretion from CAST islets, suggesting that endogenously produced dopamine was sufficient to suppress the insulin secretory response.
      In summary, β-cells of CAST islets express high levels of Th, the first step in catecholamine synthesis, resulting in elevated levels of dopamine. In response to glucose, dopamine is co-secreted with insulin, establishing a negative autocrine feedback that blunts the secretory response.

      Discussion

      In this study, we used the eight genetically diverse CC founder mouse strains fed a HF/HS diet to assess the contribution of genetic variation to diabetes-related phenotypes. We found that genetic diversity strongly influenced a host of metabolic phenotypes, including body weight, fasting plasma glucose and insulin, and insulin secretion from isolated islets in response to a range of metabolic stimuli. The eight strains displayed a wide range in insulin resistance, as judged by the level of fasting plasma insulin.
      We previously found that of the eight strains, CAST is the only strain completely resistant to HF/HS diet-induced changes in glucose homeostasis during an oGTT, consistent with the high insulin sensitivity of the CAST mice (
      • Kreznar J.H.
      • Keller M.P.
      • Traeger L.L.
      • Rabaglia M.E.
      • Schueler K.L.
      • Stapleton D.S.
      • Zhao W.
      • Vivas E.I.
      • Yandell B.S.
      • Broman A.T.
      • Hagenbuch B.
      • Attie A.D.
      • Rey F.E.
      Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes.
      ). At the other extreme, the NZO mice are the most insulin-resistant and essentially HF/HS diet-intolerant. NZO male mice become severely hyperglycemic, resulting in death by 14 weeks of age. Thus, genetic variation in the CC founder strains results in a range of phenotypes from complete resistance to lethality in response to the Western-style dietary challenge.
      We evaluated the relationship between genetic diversity and islet function. The number of islets isolated per mouse, the insulin and glucagon content per islet, and the islet insulin and glucagon secretory response varied widely among the mice. Ranking the mice based on their insulin secretory response to different classes of secretagogues illustrates how genetic background drives the insulin secretory response. When focusing on the fold-change in insulin secretion in response to the different secretagogues, islets from NOD males had the highest insulin secretion in response to G8.3 + GLP-1, whereas islets from B6 females had the lowest response. In contrast, in response to G16.7 + PA, islets from PWK males had the highest insulin secretory response, and islets from NZO males had the lowest response. In response to the nonmetabolic secretagogue KCl, islets from PWK females secreted the most insulin, whereas islets from B6 females secreted the least.
      Characterization of islet protein composition is key to unlocking the molecular details of diabetes pathophysiology. One drawback is that islet scarcity has confounded extensive analyses and has required pooling islets from multiple animals. However, recent developments in mass spectrometric technologies have improved sensitivity and permit deep profiling of single animals without the need for pooling, pre-fractionation, or heavy-isotope quantitative tagging (
      • Shishkova E.
      • Hebert A.S.
      • Coon J.J.
      Now, more than ever, proteomics needs better chromatography.
      ,
      • Hebert A.S.
      • Richards A.L.
      • Bailey D.J.
      • Ulbrich A.
      • Coughlin E.E.
      • Westphall M.S.
      • Coon J.J.
      The one hour yeast proteome.
      ). The CC founder strains have been extensively studied individually, and Chick et al. (
      • Chick J.M.
      • Munger S.C.
      • Simecek P.
      • Huttlin E.L.
      • Choi K.
      • Gatti D.M.
      • Raghupathy N.
      • Svenson K.L.
      • Churchill G.A.
      • Gygi S.P.
      Defining the consequences of genetic variation on a proteome-wide scale.
      ) completed proteome profiling of liver tissues across all eight strains, but our study represents the first in-depth analysis of the islet proteomes and insulin secretion phenotypes across the whole cohort.
      To identify islet proteins that underlie the strain- and sex-dependent differences in insulin and glucagon secretion, we conducted proteomics on islets collected from each strain and sex (excluding NZO males) and identified and quantified 5,255 total proteins. The largest murine islet proteome characterized to date contained ∼6,800 proteins (
      • Waanders L.F.
      • Chwalek K.
      • Monetti M.
      • Kumar C.
      • Lammert E.
      • Mann M.
      Quantitative proteomic analysis of single pancreatic islets.
      ). These analyses, however, required extensive peptide fractionation, resulting in 24 h of analysis per proteome; our methods quantified a proteome of ∼75% the size in ∼8% of the analysis time.
      Unsupervised clustering of the islet proteome revealed that the mice clustered based on strain and sex. This shows that genetic background has a strong influence on the islet proteome that can be linked to differences in insulin secretion. We used the WGCNA approach to compute co-expression protein modules consisting of highly correlated proteins. We found that these protein groups enriched for biological pathways and correlated with the diabetes-related phenotypic measures.
      Correlations can lead to hypotheses that can be tested for causality. For example, both the black and cyan modules enriched for protein critical for ER homeostasis and were and positively correlated with insulin secretion, plasma insulin, and body weight. Because the ER is involved in protein folding, modification, and trafficking to the Golgi, ER homeostasis is critical in β-cells (
      • Back S.H.
      • Kang S.W.
      • Han J.
      • Chung H.T.
      Endoplasmic reticulum stress in the beta-cell pathogenesis of type 2 diabetes.
      • Back S.H.
      • Kaufman R.J.
      Endoplasmic reticulum stress and type 2 diabetes.
      ,
      • Kim M.K.
      • Kim H.S.
      • Lee I.K.
      • Park K.G.
      Endoplasmic reticulum stress and insulin biosynthesis: a review.
      • Hasnain S.Z.
      • Prins J.B.
      • McGuckin M.A.
      Oxidative and endoplasmic reticulum stress in beta-cell dysfunction in diabetes.
      ). Proteins in the black module include hypoxia up-regulated 1 (Hyou1), protein-disulfide isomerase-associated 4 (Pdia4), and heat-shock protein 90 β (Grp94) member 1 (Hsp90b1), all reported to be up-regulated in islets under conditions that elicit ER stress (
      • El Ouaamari A.
      • Zhou J.Y.
      • Liew C.W.
      • Shirakawa J.
      • Dirice E.
      • Gedeon N.
      • Kahraman S.
      • De Jesus D.F.
      • Bhatt S.
      • Kim J.S.
      • Clauss T.R.
      • Camp 2nd., D.G.
      • Smith R.D.
      • Qian W.J.
      • Kulkarni R.N.
      Compensatory islet response to insulin resistance revealed by quantitative proteomics.
      ,
      • Omikorede O.
      • Qi C.
      • Gorman T.
      • Chapman P.
      • Yu A.
      • Smith D.M.
      • Herbert T.P.
      ER stress in rodent islets of Langerhans is concomitant with obesity and beta-cell compensation but not with beta-cell dysfunction and diabetes.
      • Roat R.
      • Rao V.
      • Doliba N.M.
      • Matschinsky F.M.
      • Tobias J.W.
      • Garcia E.
      • Ahima R.S.
      • Imai Y.
      Alterations of pancreatic islet structure, metabolism and gene expression in diet-induced obese C57BL/6J mice.
      ). Other potentially novel proteins in the black module may be important for ER stress-induced changes in islet function and/or health.
      The lightgreen module was enriched for proteins involved in cell–substrate junctions and positively correlated with insulin secretion. It is known that adherens junctions between β-cells is required for proper insulin secretion (
      • Cirulli V.
      Cadherins in islet beta-cells: more than meets the eye.
      • Hodson D.J.
      • Mitchell R.K.
      • Bellomo E.A.
      • Sun G.
      • Vinet L.
      • Meda P.
      • Li D.
      • Li W.H.
      • Bugliani M.
      • Marchetti P.
      • Bosco D.
      • Piemonti L.
      • Johnson P.
      • Hughes S.J.
      • Rutter G.A.
      Lipotoxicity disrupts incretin-regulated human beta cell connectivity.
      ,
      • Johansson J.K.
      • Voss U.
      • Kesavan G.
      • Kostetskii I.
      • Wierup N.
      • Radice G.L.
      • Semb H.
      N-cadherin is dispensable for pancreas development but required for beta-cell granule turnover.
      ,
      • Hauge-Evans A.C.
      • Squires P.E.
      • Persaud S.J.
      • Jones P.M.
      Pancreatic beta-cell-to-beta-cell interactions are required for integrated responses to nutrient stimuli: enhanced Ca2+ and insulin secretory responses of MIN6 pseudoislets.
      ,
      • Parnaud G.
      • Lavallard V.
      • Bedat B.
      • Matthey-Doret D.
      • Morel P.
      • Berney T.
      • Bosco D.
      Cadherin engagement improves insulin secretion of single human beta-cells.
      • Rogers G.J.
      • Hodgkin M.N.
      • Squires P.E.
      E-cadherin and cell adhesion: a role in architecture and function in the pancreatic islet.
      ). Proteins in the lightgreen module include annexin A1 (Anxa1) and paxilin (Pxn), both reported to be important in insulin secretion (
      • Rondas D.
      • Tomas A.
      • Soto-Ribeiro M.
      • Wehrle-Haller B.
      • Halban P.A.
      Novel mechanistic link between focal adhesion remodeling and glucose-stimulated insulin secretion.
      ,
      • Rackham C.L.
      • Vargas A.E.
      • Hawkes R.G.
      • Amisten S.
      • Persaud S.J.
      • Austin A.L.
      • King A.J.
      • Jones P.M.
      Annexin A1 is a key modulator of mesenchymal stromal cell-mediated improvements in islet function.
      ). There may be novel proteins in the lightgreen module important for cell–cell communication through adherens junctions and focal adhesions.
      The gray60 module enriches for the GO terms “phosphoric ester hydrolase activity” and “lipid metabolic process” and negatively correlates with insulin secretion. Proteins in the gray60 module include carnitine palmitoyltransferase 2 (Cpt2), TAM41 mitochondrial translocator assembly and maintenance homolog (Tamm41), and acyl-CoA synthetase short-chain family member 2 (Acss2). Testable hypotheses can be generated about the function of these proteins in negatively regulating insulin secretion.
      Interestingly, 5 of the 20 modules (salmon, brown, gray60, yellow, and red) were most highly enriched in distinct mitochondrial-associated pathways, enriching for mitochondrial dicarboxylic acid metabolism (salmon), transport across the mitochondrial membrane (brown), mitochondrial lipid metabolism (gray60), mitochondrial purine nucleoside, branched-chain amino acid, carbohydrate metabolism (yellow), and TCA cycle/OxPhos (red). Each of these modules consists of different proteins that have distinct expression patterns across the strains and sexes. The presence of these mitochondrially-enriched modules is consistent with the importance of mitochondrial function in islets. Mitochondrial proteins appear to be down-regulated in the islets of NZO mice, which show the greatest total insulin secretion of all the strains in Fig. 2, 1st panel. However, when represented as fold-change over basal secretion (Fig. 2, 2nd panel), islets from the NZO mice show a clear deficit in regulated insulin secretion. This shows that the NZO mice have a high nonstimulatory basal insulin secretion and a poor stimulated insulin secretion, for which mitochondrial oxidative metabolism is important (
      • Antinozzi P.A.
      • Ishihara H.
      • Newgard C.B.
      • Wollheim C.B.
      Mitochondrial metabolism sets the maximal limit of fuel-stimulated insulin secretion in a model pancreatic beta cell: a survey of four fuel secretagogues.
      ,
      • Malmgren S.
      • Nicholls D.G.
      • Taneera J.
      • Bacos K.
      • Koeck T.
      • Tamaddon A.
      • Wibom R.
      • Groop L.
      • Ling C.
      • Mulder H.
      • Sharoyko V.V.
      Tight coupling between glucose and mitochondrial metabolism in clonal beta-cells is required for robust insulin secretion.
      • Wiederkehr A.
      • Wollheim C.B.
      Mitochondrial signals drive insulin secretion in the pancreatic beta-cell.
      ).
      A caveat to performing omics studies on whole islets is that changes in islet omics may reflect differences in compositions of islet cell types. Mouse islets are composed of 60–80% β-cells producing insulin and amylin in a central core and a layer of other endocrine cells surrounding the core, which is composed of 15–20% α-cells producing glucagon, <10% δ-cells producing somatostatin, <5% PP-cells producing pancreatic polypeptide, and <1% ε-cells producing ghrelin (
      • Baetens D.
      • Malaisse-Lagae F.
      • Perrelet A.
      • Orci L.
      Endocrine pancreas: three-dimensional reconstruction shows two types of islets of Langerhans.
      • Kim A.
      • Miller K.
      • Jo J.
      • Kilimnik G.
      • Wojcik P.
      • Hara M.
      Islet architecture: a comparative study.
      ,
      • Steiner D.J.
      • Kim A.
      • Miller K.
      • Hara M.
      Pancreatic islet plasticity: interspecies comparison of islet architecture and composition.
      • Wieczorek G.
      • Pospischil A.
      • Perentes E.
      A comparative immunohistochemical study of pancreatic islets in laboratory animals (rats, dogs, minipigs, nonhuman primates).
      ). Also, axon endings can remain within islets after isolation, and fragments of acinar and ductal cells can remain attached to the islets, and their abundance could plausibly be strain-specific. Not only can these compositions be altered by genetic background, but different regions of the pancreas within the same mouse can have islets with different endocrine cell contents (
      • Baetens D.
      • Malaisse-Lagae F.
      • Perrelet A.
      • Orci L.
      Endocrine pancreas: three-dimensional reconstruction shows two types of islets of Langerhans.
      ,
      • Wieczorek G.
      • Pospischil A.
      • Perentes E.
      A comparative immunohistochemical study of pancreatic islets in laboratory animals (rats, dogs, minipigs, nonhuman primates).
      ). Furthermore, recent papers employing single-cell RNAseq and mass spectrometric studies on islet cells have found heterogeneity within islet cell types (
      • Bader E.
      • Migliorini A.
      • Gegg M.
      • Moruzzi N.
      • Gerdes J.
      • Roscioni S.S.
      • Bakhti M.
      • Brandl E.
      • Irmler M.
      • Beckers J.
      • Aichler M.
      • Feuchtinger A.
      • Leitzinger C.
      • Zischka H.
      • Wang-Sattler R.
      • et al.
      Identification of proliferative and mature beta-cells in the islets of Langerhans.
      • Baron M.
      • Veres A.
      • Wolock S.L.
      • Faust A.L.
      • Gaujoux R.
      • Vetere A.
      • Ryu J.H.
      • Wagner B.K.
      • Shen-Orr S.S.
      • Klein A.M.
      • Melton D.A.
      • Yanai I.
      A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
      ,
      • Dorrell C.
      • Schug J.
      • Canaday P.S.
      • Russ H.A.
      • Tarlow B.D.
      • Grompe M.T.
      • Horton T.
      • Hebrok M.
      • Streeter P.R.
      • Kaestner K.H.
      • Grompe M.
      Human islets contain four distinct subtypes of beta cells.
      ,
      • Johnston N.R.
      • Mitchell R.K.
      • Haythorne E.
      • Pessoa M.P.
      • Semplici F.
      • Ferrer J.
      • Piemonti L.
      • Marchetti P.
      • Bugliani M.
      • Bosco D.
      • Berishvili E.
      • Duncanson P.
      • Watkinson M.
      • Broichhagen J.
      • Trauner D.
      • et al.
      Beta cell hubs dictate pancreatic islet responses to glucose.
      ,
      • Segerstolpe Å.
      • Palasantza A.
      • Eliasson P.
      • Andersson E.M.
      • Andréasson A.C.
      • Sun X.
      • Picelli S.
      • Sabirsh A.
      • Clausen M.
      • Bjursell M.K.
      • Smith D.M.
      • Kasper M.
      • Ämmälä C.
      • Sandberg R.
      Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
      • Wang Y.J.
      • Golson M.L.
      • Schug J.
      • Traum D.
      • Liu C.
      • Vivek K.
      • Dorrell C.
      • Naji A.
      • Powers A.C.
      • Chang K.M.
      • Grompe M.
      • Kaestner K.H.
      Single-cell mass cytometry analysis of the human endocrine pancreas.
      ). There are strain- and sex-specific differences in the abundances of the major islet hormones. These differences could be plausibly due to differences in islet cell type composition and/or hormone content per cell; fluorescence-activated cell sorting (FACS) purification of the different cell types in each of the strains would need to be conducted to investigate this.
      A recent report by Cruciani-Guglielmacci et al. (
      • Cruciani-Guglielmacci C.
      • Bellini L.
      • Denom J.
      • Oshima M.
      • Fernandez N.
      • Normandie-Levi P.
      • Berney X.P.
      • Kassis N.
      • Rouch C.
      • Dairou J.
      • Gorman T.
      • Smith D.M.
      • Marley A.
      • Liechti R.
      • Kuznetsov D.
      • et al.
      Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
      ) compared the variation in body weight, glucose homeostasis, insulin secretion, and islet gene expression across six different mouse strains (C57BL/6J, DBA/2J, A/J, AKR/J, 129S2/SvPas, and BALB/cJ), all maintained on either regular chow or HF/HS diet. Three of these strains (C57BL/6J, A/J, and 129) were included in our current and previous studies (
      • Kreznar J.H.
      • Keller M.P.
      • Traeger L.L.
      • Rabaglia M.E.
      • Schueler K.L.
      • Stapleton D.S.
      • Zhao W.
      • Vivas E.I.
      • Yandell B.S.
      • Broman A.T.
      • Hagenbuch B.
      • Attie A.D.
      • Rey F.E.
      Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes.
      ). Like our study, striking strain-specific differences in diabetes-related phenotypes were observed in response to the HF/HS diet. The HF/HS diet resulted in obesity, glucose intolerance, and insulin resistance in DBA/2J and AKR/J mice, whereas these same phenotypes were separable in BALB/cJ, which only showed evidence of glucose intolerance. A major difference between the report by Cruciani-Guglielmacci et al. (
      • Cruciani-Guglielmacci C.
      • Bellini L.
      • Denom J.
      • Oshima M.
      • Fernandez N.
      • Normandie-Levi P.
      • Berney X.P.
      • Kassis N.
      • Rouch C.
      • Dairou J.
      • Gorman T.
      • Smith D.M.
      • Marley A.
      • Liechti R.
      • Kuznetsov D.
      • et al.
      Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
      ) and our studies is the inclusion of CAST mice, which were completely resistant to HF/HS dietary challenge; they showed no change in body weight, glucose homeostasis, or insulin dynamics. Thus, the wild-derived strains, which contain greater genetic diversity than the classical inbred strains, yielded a higher level of phenotypic diversity.
      In addition to surveying diabetes-related physiological phenotypes, Cruciani-Guglielmacci et al. (
      • Cruciani-Guglielmacci C.
      • Bellini L.
      • Denom J.
      • Oshima M.
      • Fernandez N.
      • Normandie-Levi P.
      • Berney X.P.
      • Kassis N.
      • Rouch C.
      • Dairou J.
      • Gorman T.
      • Smith D.M.
      • Marley A.
      • Liechti R.
      • Kuznetsov D.
      • et al.
      Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
      ) performed islet transcriptomics on mice maintained on either regular chow or HF/HS diet, enabling them to identify transcripts that were diet-responsive in each of the six strains studied. In contrast, our study surveyed whole-islet proteomics in eight mouse strains, all maintained on the HF/HS diet. Interestingly, Cruciani-Guglielmacci et al. (
      • Cruciani-Guglielmacci C.
      • Bellini L.
      • Denom J.
      • Oshima M.
      • Fernandez N.
      • Normandie-Levi P.
      • Berney X.P.
      • Kassis N.
      • Rouch C.
      • Dairou J.
      • Gorman T.
      • Smith D.M.
      • Marley A.
      • Liechti R.
      • Kuznetsov D.
      • et al.
      Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
      ) showed that the islet transcriptional profile was more closely related to genetic background than dietary conditions, length of time on a particular diet, or diet composition.
      In both studies, WGCNA-based clustering was used to compute islet gene modules (transcriptomics or proteomics), and gene set analysis was conducted on the modules to identify enriched biological pathways. Pathways that were enriched within modules from both studies included cell–substrate junction, immune response, lipid metabolism, actin cytoskeleton, ribosome (biosynthesis), tricarboxylic acid cycle, oxidative phosphorylation, carbohydrate metabolism, and antigen processing and presentation. Some pathways were enriched in only one study (e.g. DNA repair and replication, vesicular transport) and may reflect post-transcriptional regulatory mechanisms, including protein turnover. Unfortunately, Elovl2, a gene validated to play a role in the regulation of insulin secretion by Cruciani-Guglielmacci et al. (
      • Cruciani-Guglielmacci C.
      • Bellini L.
      • Denom J.
      • Oshima M.
      • Fernandez N.
      • Normandie-Levi P.
      • Berney X.P.
      • Kassis N.
      • Rouch C.
      • Dairou J.
      • Gorman T.
      • Smith D.M.
      • Marley A.
      • Liechti R.
      • Kuznetsov D.
      • et al.
      Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
      ) was not included in the 5,255 proteins that were identified in our study. Future studies would be required to directly assess the genetic dependence of Elovl2 protein abundance differences in the eight Collaborative Cross-founder strains and to what extent these differences play a role in differential insulin secretion among these strains.
      Driven by our preliminary finding that glucose promotes phosphorylation of serine 31 on Th in CAST islets, we asked whether Th was differentially abundant across the strains. Our proteomic survey showed that Th was expressed far more highly in PWK and CAST islets. It has been known for over 40 years that mouse islets can synthesize and secrete dopamine, but seemingly only after supplementing them with its precursor l-DOPA (
      • Cegrell L.
      The occurrence of biogenic monoamines in the mammalian endocrine pancreas.
      ,
      • Ericson L.E.
      • Håkanson R.
      • Lundquist I.
      Accumulation of dopamine in mouse pancreatic B-cells following injection of l-DOPA. Localization to secretory granules and inhibition of insulin secretion.
      ). Mouse β-cells contain all of the components necessary to synthesize dopamine from l-DOPA. The large aromatic amino acid transporter on the surface of the β-cell rapidly transports l-DOPA into the cell. l-DOPA is decarboxylated into dopamine by Ddc (
      • Lindström P.
      Aromatic-l-amino-acid decarboxylase activity in mouse pancreatic islets.
      ). Dopamine is packaged into insulin granules via the vesicular monoamine transporter 2 (Vmat2) (
      • Saisho Y.
      • Harris P.E.
      • Butler A.E.
      • Galasso R.
      • Gurlo T.
      • Rizza R.A.
      • Butler P.C.
      Relationship between pancreatic vesicular monoamine transporter 2 (VMAT2) and insulin expression in human pancreas.
      ,
      • Raffo A.
      • Hancock K.
      • Polito T.
      • Xie Y.
      • Andan G.
      • Witkowski P.
      • Hardy M.
      • Barba P.
      • Ferrara C.
      • Maffei A.
      • Freeby M.
      • Goland R.
      • Leibel R.L.
      • Sweet I.R.
      • Harris P.E.
      Role of vesicular monoamine transporter type 2 in rodent insulin secretion and glucose metabolism revealed by its specific antagonist tetrabenazine.
      ), resulting in co-secretion of dopamine with insulin in response to a stimulus. Dopamine acts in an autocrine fashion to inhibit insulin secretion by binding to dopamine receptors on the surface of the β-cells (
      • Rubí B.
      • Ljubicic S.
      • Pournourmohammadi S.
      • Carobbio S.
      • Armanet M.
      • Bartley C.
      • Maechler P.
      Dopamine D2-like receptors are expressed in pancreatic beta cells and mediate inhibition of insulin secretion.
      ,
      • García-Tornadú I.
      • Ornstein A.M.
      • Chamson-Reig A.
      • Wheeler M.B.
      • Hill D.J.
      • Arany E.
      • Rubinstein M.
      • Becu-Villalobos D.
      Disruption of the dopamine D2 receptor impairs insulin secretion and causes glucose intolerance.
      ,
      • Ustione A.
      • Piston D.W.
      Dopamine synthesis and D3 receptor activation in pancreatic beta-cells regulates insulin secretion and intracellular [Ca2+] oscillations.
      ). Furthermore, β-cells express Mao and Comt, which degrade excess cytoplasmic dopamine (
      • Lundquist I.
      • Panagiotidis G.
      • Stenström A.
      Effect of l-dopa administration on islet monoamine oxidase activity and glucose-induced insulin release in the mouse.
      ).
      Th, the enzyme that converts l-tyrosine to l-DOPA, is the only dopamine biosynthetic enzyme thought to be essentially absent in mouse β-cells. However, these conclusions were drawn from studies that utilized B6 mice (
      • Rubí B.
      • Ljubicic S.
      • Pournourmohammadi S.
      • Carobbio S.
      • Armanet M.
      • Bartley C.
      • Maechler P.
      Dopamine D2-like receptors are expressed in pancreatic beta cells and mediate inhibition of insulin secretion.
      ,
      • García-Tornadú I.
      • Ornstein A.M.
      • Chamson-Reig A.
      • Wheeler M.B.
      • Hill D.J.
      • Arany E.
      • Rubinstein M.
      • Becu-Villalobos D.
      Disruption of the dopamine D2 receptor impairs insulin secretion and causes glucose intolerance.
      ,
      • Ustione A.
      • Piston D.W.
      Dopamine synthesis and D3 receptor activation in pancreatic beta-cells regulates insulin secretion and intracellular [Ca2+] oscillations.
      ,
      • Cegrell L.
      The occurrence of biogenic monoamines in the mammalian endocrine pancreas.
      • Ericson L.E.
      • Håkanson R.
      • Lundquist I.
      Accumulation of dopamine in mouse pancreatic B-cells following injection of l-DOPA. Localization to secretory granules and inhibition of insulin secretion.
      ,
      • Lindström P.
      Aromatic-l-amino-acid decarboxylase activity in mouse pancreatic islets.
      ,
      • Saisho Y.
      • Harris P.E.
      • Butler A.E.
      • Galasso R.
      • Gurlo T.
      • Rizza R.A.
      • Butler P.C.
      Relationship between pancreatic vesicular monoamine transporter 2 (VMAT2) and insulin expression in human pancreas.
      ,
      • Raffo A.
      • Hancock K.
      • Polito T.
      • Xie Y.
      • Andan G.
      • Witkowski P.
      • Hardy M.
      • Barba P.
      • Ferrara C.
      • Maffei A.
      • Freeby M.
      • Goland R.
      • Leibel R.L.
      • Sweet I.R.
      • Harris P.E.
      Role of vesicular monoamine transporter type 2 in rodent insulin secretion and glucose metabolism revealed by its specific antagonist tetrabenazine.
      • Lundquist I.
      • Panagiotidis G.
      • Stenström A.
      Effect of l-dopa administration on islet monoamine oxidase activity and glucose-induced insulin release in the mouse.
      ). Therefore, the consensus has been that for mouse β-cells to synthesize and secrete dopamine, they must first import l-DOPA.
      In the central nervous system, dopamine is secreted from neurons and functions as a neurotransmitter, although it is not released into the bloodstream. Peripheral, non-neuronal production of l-DOPA results in nanomolar levels of circulating l-DOPA (
      • Goldstein D.S.
      • Eisenhofer G.
      • Kopin I.J.
      Sources and significance of plasma levels of catechols and their metabolites in humans.
      ). One source of this circulating l-DOPA is thought to be intestinal cells, which express high levels of Th (
      • Goldstein D.S.
      • Eisenhofer G.
      • Kopin I.J.
      Sources and significance of plasma levels of catechols and their metabolites in humans.
      ). Although some speculate that β-cells import l-DOPA from the circulation (
      • Ustione A.
      • Piston D.W.
      Dopamine synthesis and D3 receptor activation in pancreatic beta-cells regulates insulin secretion and intracellular [Ca2+] oscillations.
      ), evidence that circulating l-DOPA is taken up by the β-cells is elusive.
      Here, with a survey that included several strains, we show that β-cells from CAST mice express high levels of Th, leading to the synthesis of dopamine. This de novo synthesis of dopamine is associated with reduced insulin secretion, which can be mimicked in islets from B6 mice by preincubating the islets in l-DOPA, the product of Th activity. Inhibition of Th in isolated human islets greatly increases insulin secretion (
      • Simpson N.
      • Maffei A.
      • Freeby M.
      • Burroughs S.
      • Freyberg Z.
      • Javitch J.
      • Leibel R.L.
      • Harris P.E.
      Dopamine-mediated autocrine inhibitory circuit regulating human insulin secretion in vitro.
      ), suggesting that like CAST islets the human islets synthesize dopamine de novo. Therefore, strains of mice with islet cells that express Th are more appropriate to study this pathway and extend the implications to humans.
      Why would CAST β-cells synthesize a molecule that potently inhibits insulin secretion? One explanation is that they require an additional mechanism to control insulin secretion because of their high level of insulin sensitivity. We show that CAST mice are extremely insulin-sensitive, requiring the lowest plasma insulin of all the strains to maintain euglycemia and being resistant to HF/HS diet. Islets in CAST mice may employ an autocrine dopamine-mediated break on insulin secretion to ensure a brief rise in insulin, followed by a suppression of secretion (as occurs during an oGTT), to avoid hypoglycemia.
      The B6 mouse strain has become the most widely used mouse model for studying human physiology, as well as the most common strain used in gene editing. However, when a gene alteration fails to produce a phenotype, it is possible that the B6 strain was not the best choice to study the gene’s function (
      • Osterburg A.R.
      • Hexley P.
      • Supp D.M.
      • Robinson C.T.
      • Noel G.
      • Ogle C.
      • Boyce S.T.
      • Aronow B.J.
      • Babcock G.F.
      Concerns over interspecies transcriptional comparisons in mice and humans after trauma.
      ), rather than concluding that mice are not appropriate models to study human pathophysiology (
      • Seok J.
      • Warren H.S.
      • Cuenca A.G.
      • Mindrinos M.N.
      • Baker H.V.
      • Xu W.
      • Richards D.R.
      • McDonald-Smith G.P.
      • Gao H.
      • Hennessy L.
      • Finnerty C.C.
      • López C.M.
      • Honari S.
      • Moore E.E.
      • Minei J.P.
      • et al.
      Genomic responses in mouse models poorly mimic human inflammatory diseases.
      ). The “absence of a phenotype” in a single mouse strain can be the result of strain-to-strain variation.
      The phenotype variation between mouse strains motivates the search for comparable variation across the human population. In this study, we saw dramatic strain variation in Th expression in mouse islets, which determines the ability to produce dopamine de novo. Based on these results, we predict that there may be genetic variation in humans in the contribution of β-cell-derived dopamine to the regulation of insulin secretion.

      Data resource for the research community

      Our work provides a resource to identify the presence or absence of specific biological pathways and proteins in the islets of the eight genetically and phenotypically diverse CC founder mouse strains. Both collaborating labs have made available searchable databases of our islet proteomics data from the eight strains of mice (http://diabetes.wisc.edu/cc_founder.php)
      Please note that the JBC is not responsible for the long-term archiving and maintenance of this site or any other third party hosted site.
      and (http://coonlabdata.com/founder_mice).5 http://diabetes.wisc.edu/cc_founder.php5 under the “Whole-islet proteomics” link is a user-friendly web interface that allows the user to enter a gene symbol. If the query was one of the 5,255 proteins identified by our whole-islet proteomic survey, the average abundance of that protein will be displayed across the 15 experimental groups (eight strains of each sex, except NZO males). In addition, we have incorporated a protein–to–protein correlation tool that can be used to identify groups of proteins with highly correlated expression profiles. Lists of correlated proteins can be directly uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/),5 an NIH-funded bioinformatics resource that provides functional annotation to large gene lists.
      As an example of the database utility, searching for glucagon (Gcg) and then clicking the “Plot” bar graph icon under “Actions” reveals a striking strain- and sex-dependent pattern of protein abundance (Fig. S6A). A correlation analysis can then be performed by clicking the “Correlation” icon under “Actions” to determine whether other proteins show a similar pattern as Gcg. After setting the desired options and submitting, clicking “Show” for a correlated protein will produce a graph of the correlation between that protein and Gcg across the samples (Fig. S6B). Clicking “View Details,” selecting all of the proteins in the list, and selecting “Heat Map” generates a heat map of the Z-scores of the proteins across the samples, where proteins, mice, or both can be hierarchically clustered (Fig. S6C). Returning to the protein list and clicking “DAVID” automatically uploads the correlated list to the DAVID functional annotation tool website. Selecting “Functional Annotation Clustering” reveals that proteins that correlated with Gcg across the strains enrich for chaperone (p = 2.7 × 10−8) and RNA binding (p = 1.7 × 10−4). Proteins within each of these annotation clusters can be identified by clicking on the blue bar for each enrichment term.
      The http://www.coonlabdata.com/founder_mice/main.php5allows for proteins of interest to be queried to generate simple abundance column plots across strains. Individual strains can be compared with one another to determine significant changes across the dataset, and outlier analysis can be performed to identify significant changers specific to individual strains.
      Our aim is to provide a valuable tool to the scientific community to support further biological inquiry and guide future studies. Our study provides an extremely valuable tool to help determine the appropriate strain and sex in which to study a specific biological pathway or to knock out a gene.

      Experimental procedures

      Animals

      Animal care and study protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Mice were housed within the Biochemistry Department vivarium and maintained on a 12-h light/dark cycle (6 a.m. to 6 p.m.). The eight Collaborative Cross founder strains (C57BL/6J (B6); A/J; 129S1/SvImJ (129); NOD/ShiLtJ (NOD); NZO/HILtJ (NZO); PWK/PhJ (PWK); WSB/EiJ (WSB); and CAST/EiJ (CAST)) were obtained from The Jackson Laboratory (Bar Harbor, ME). Mice were bred at the University of Wisconsin-Madison Biochemistry Department, except for CAST and NZO. Mice were group-housed by strain and sex (2–5 mice/cage) except for CAST that required individual housing. Mice were housed under temperature- and humidity-controlled conditions and received ad libitum access to water and food. Beginning at 4 weeks of age, mice were maintained on HF/HS diet (TD.08811, Envigo Teklad Custom Diet, 44.6% kcal from fat, 14.7% kcal from protein, 40.7% kcal from carbohydrate). Mice were sacrificed at 22 weeks of age, except for NZO males that were sacrificed at 14 weeks, due to high mortality attributable to severe diabetes.

      Reagents

      Collagenase type XI (C7657), BSA (A4503), Ficoll type 400-DL (F9378), FBS (12306C), dopamine (H8502), l-DOPA (D9628), ascorbic acid (A5960), and all general chemicals were purchased from Sigma. Dextrose (D16) was purchased from Thermo Fisher Scientific. Hanks' balanced salt solution (14065056) and RPMI 1640 medium (11879-020) were from Thermo Fisher Scientific.

      In vivo measurements

      Body weight was measured weekly beginning at 4 weeks of age. Blood was collected by retro-orbital bleed following a 4-h fast (8 a.m. to noon) at 6, 10, and 14 weeks of age and a 3-h fast (5 a.m. to 8 a.m.) at sacrifice (22 weeks of age) and used to measure plasma glucose, insulin, and triglyceride (TG). Glucose was measured by the glucose oxidase method using a commercially available kit (TR15221, Thermo Fisher Scientific). Insulin was measured by radioimmunoassay (RIA; SRI-13K, Millipore). TG was measured using a commercially available kit (TR22421, Thermo Fisher Scientific). If plasma insulin was off the low end of the standard curve for the assay (some CAST male mice), the value of the lowest standard on the assay was reported (0.1 ng/ml). Beginning at 4 weeks of age, food intake was calculated by weighing the food remaining after 1 week, subtracting it from the amount fed, then dividing by number of mice per cage, and days to get g/mouse/day.

      Islet isolation

      For all experiments that include islet isolation, intact pancreatic islets were isolated from mice using a collagenase digestion procedure as described previously (
      • Rabaglia M.E.
      • Gray-Keller M.P.
      • Frey B.L.
      • Shortreed M.R.
      • Smith L.M.
      • Attie A.D.
      α-Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice.
      ). Islets were hand-picked and counted under a stereomicroscope to minimize contaminating acinar tissue.

      Insulin and glucagon secretion measurements

      After isolation, islets were placed in recovery media (RPMI 1640, 11.1 mm glucose, anti/anti antibiotics, 10% FBS) for 2 h at 37 °C and 5% CO2. All insulin secretion media (3.3 mm glucose (G3.3), 8.3 mm glucose (G8.3), 8.3 mm glucose plus 100 nm GLP-1 (G8.3 + GLP-1), 8.3 mm glucose plus 1.25 mm l-alanine, 2 mm l-glutamine, 0.5 mm l-leucine (G8.3 + AA), 16.7 mm glucose (G16.7), 3.3 mm glucose plus 40 mm KCl (G3.3 + KCl), and 16.7 mm glucose plus 0.5 mm palmitate (G16.7 + PA), was made in Krebs Ringer Buffer (KRB: 118.41 mm NaCl, 4.69 mm KCl, 1.18 mm MgSO4, 1.18 mm KH2PO4, 2 mm NaHCO3, 5 mm HEPES, 2.52 mm CaCl2 (pH 7.4) containing 0.5% BSA, G16.7 + PA), which contained 0.67% BSA from the PA that was conjugated to BSA. For each mouse, 50 average sized islets were transferred from the recovery media to a 35-mm Petri dish containing 3 ml of preincubation media (KRB + 0.5% BSA + 3.3 mm glucose). The rest of the islets from each mouse were washed twice with PBS, snap-frozen in liquid nitrogen, stored at −80 °C, and then used for the whole-islet proteomics (see under “Whole-islet proteomics on islets from the eight CC founder strains”). For NZO male mice, islets from four mice were pooled to have enough for the secretion measurements. There were not enough islets to conduct proteomics on the NZO males. Islets were returned to the 37 °C incubator for a 45-min preincubation period. 100 μl of each secretagogue incubation media was placed in six wells of a 96-well plate. At the end of the preincubation period, individual islets were transferred to individual wells containing the incubation media, alternating the transfer between all seven incubation conditions, to ensure similar sized islets were distributed between all seven secretion conditions. Half-way through the islet transfers, three islets were placed in 1 ml of acid EtOH for measuring insulin and glucagon content. At the end of the 45-min incubation period, the media were transferred to a 96-well polypropylene storage plate. Media were frozen at −20 °C until analyzed. Basal secretion of insulin (G3.3) was measured using the sensitive Insulin RIA (Millipore, SRI-13K). Secretion for all other conditions and insulin content was determined by an in-house–developed insulin ELISA using a pair of insulin + proinsulin antibodies (insulin + proinsulin coating antibody clone D6C4, catalog no. 10R-I136a, and insulin + proinsulin biotinylated antibody clone D3E7-BT, catalog no. 61R-I136BBT, both from Fitzgerald Industries International). Glucagon content was determined using a glucagon RIA (Millipore, GL-32K). If secreted glucagon was off the low end of the standard curve for the assay, the value of the lowest standard on the assay was reported (2 pg/islet). To generate Fig. 2, insulin secreted per islet was calculated three different ways: as total insulin secreted per islet (not normalized to anything (1st panel), as insulin secreted per islet as fold over basal secretion (normalized to basal insulin secretion at 3.3 mm glucose) (2nd panel), and as insulin secreted per islet as a percent of insulin content per islet (normalized to insulin content per islet) (3rd panel). For each mouse, the average value from the six individual average sized islets for each secretion condition (six technical replicates per condition) was calculated. Each data point (one colored square of the heat map in Fig. 2) was then calculated by averaging these values among the same strain/sex combination. Each strain/sex combination had n ≥3 mice.

      Whole-islet proteomics on islets from the eight CC founder strains

      Proteomic sample preparation

      After islets from each mouse were isolated and allowed to recover for 2 h in recovery media (see “Insulin and glucagon secretion measurements”), 50 islets from each mouse were used for the secretion measurements, and the rest of the islets were washed twice with PBS, snap-frozen in liquid nitrogen, stored at −80 °C, and then used for the whole-islet proteomics. Islets from each mouse were lysed by boiling in 6 m guanidine, and protein concentration was determined using the Pierce BCA protein assay kit (Thermo Fisher Scientific). 50 μg of protein was aliquoted from each sample, precipitated with 90% methanol, mixed, and centrifuged at 12,000 × g for 5 min. The supernatants were discarded, and the protein pellets were resuspended in 8 m urea, 10 mm tris(2-carboxyethyl)phosphine, 40 mm chloroacetamide, and 100 mm Tris (pH 8). Lysates were diluted to 1.6 m urea with 50 mm Tris (pH 8) and digested overnight at room temperature with trypsin (Promega) at a ratio of 1:50 enzyme to protein. Samples were desalted using Strata X columns (Phenomenex Strata-X Polymeric Reversed Phase, 10 mg/ml). Desalting columns were equilibrated with 1 ml of 100% acetonitrile (ACN) followed by 1 ml of 0.2% formic acid. Samples were acidified with TFA and loaded onto the equilibrated Strata X columns, which were then washed with 1 ml of 0.2% formic acid. Peptides were eluted into clean tubes with 1 ml of 80% ACN, dried, and reconstituted in 0.2% formic acid. Peptide concentration was measured prior to MS analysis using the Pierce Quantitative Colorimetric Peptide Assay (Thermo Fisher Scientific).

      LC-MS/MS analysis

      2 μg of islet peptides were loaded onto a reversed phase nano-LC column for chromatographic separation prior to MS analysis. Columns were prepared in-house from 35 cm of 75-μm inner diameter, 360-μm outer diameter fused-silica capillary tubing with polyimide coating with a laser-pulled electrospray tip. They were packed with 1.7 μm diameter, 130 Å pore size, bridged ethylene hybrid C18 particles (Waters). Columns were fitted onto an UltiMate 3000 UHPLC system (Thermo Fisher Scientific) and heated to 55 °C using a home-built column heater. Mobile phase buffer A was composed of 0.2% formic acid. Mobile phase B was composed of 70% ACN, 0.2% formic acid. Samples were separated over a 120-min gradient, including time for column re-equilibration. Flow rates were set at 325 nl/min.
      Peptide cations were converted to gas-phase ions by electrospray ionization and analyzed on an Orbitrap Fusion Lumos (Q-OT-qIT, Thermo Fisher Scientific). Precursor scans were collected from 300 to 1,350 m/z at 60,000 resolution (at 400 m/z) using a 1e6 AGC target. Precursors selected for MS/MS analysis were isolated at 0.7 Th with the quadrupole mass filter and fragmented by HCD with a collision energy of 25. The maximum injection time for MS/MS analysis was 15 ms with an AGC target of 3e4. Only precursors from charge state 2–8 were selected. Dynamic exclusion time was set to 5 s, with a mass tolerance of 25 ppm. Analyses were performed in top-speed mode with a cycle time of 2 s.

      Database searching

      The raw data were processed using MaxQuant (Version 1.5.5.1) (
      • Cox J.
      • Mann M.
      MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
      ,
      • Cox J.
      • Hein M.Y.
      • Luber C.A.
      • Paron I.
      • Nagaraj N.
      • Mann M.
      Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.
      ). Searches were performed against a target-decoy database of mouse proteins, including isoforms (Uniprot, downloaded November 6, 2014), using the Andromeda search algorithm. The precursor search tolerance was set to 4.5 ppm, and the product mass tolerance was set to 0.5 Da. Search parameters included fixed modification for carbamidomethylation of cysteine residues, variable modification for oxidation of methionine, N-terminal acetylation, and a maximum of two missed cleavages. Peptide spectral match false discovery rate (FDR), and protein FDR were both set to 1%. Proteins were quantified using MaxLFQ, a label-free, intensity-based method that obviates the need for additional chemical or metabolic labeling, with an LFQ minimum ratio count of 1. LFQ intensities were calculated using the match between runs feature, and MS/MS spectra were not required for LFQ comparisons.

      Hierarchical clustering

      MaxLFQ protein groups output was further processed using the Perseus software platform (Version 1.5.6.0) (
      • Tyanova S.
      • Temu T.
      • Sinitcyn P.
      • Carlson A.
      • Hein M.Y.
      • Geiger T.
      • Mann M.
      • Cox J.
      The Perseus computational platform for comprehensive analysis of (prote)omics data.
      ). LFQ values were log2-transformed. Z-scores were calculated for each protein across all samples ((x − μ)/σ), and the data were hierarchically clustered in an unsupervised manner using Pearson’s correlations as the distance metric. In the resulting dendrogram, clusters were defined using a distance threshold of 0.82. Uniprot accession numbers from each cluster were used for gene ontology enrichment analysis via the DAVID tool; reported p values have been corrected for multiple tests using the Benjamini-Hochberg method.

      Generating co-expression modules

      We used a previously developed method to identify protein co-expression modules (WGCNA) (
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      ,
      • Langfelder P.
      • Horvath S.
      WGCNA: an R package for weighted correlation network analysis.
      ). An extensive overview of WGCNA, including numerous tutorials, can be found at www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/.5 Because proteomics were conducted on three or more mice from each strain/sex combination, any protein that was detected in two or fewer mice/sample was considered not included in the module calculation. This resulted in the exclusion of 27 proteins out of the 5,255 identified. For those proteins that were detected in three or more samples (5,228 proteins), any samples where it was not detected we entered a zero value, followed by rank transformation of all values. An adjacency matrix was constructed for these proteins. Each entry in the matrix was the absolute Pearson’s correlation, adjusted so that the overall network is approximately scale-free. Connection strength between two proteins (xi and xj) in the network was determined according to the adjacency function, αij = |0.5 + 0.5 × cor(xi,xj)|β, using the estimated power parameter β of 12, resulting in a weighted network (
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      ,
      • Dong J.
      • Horvath S.
      Understanding network concepts in modules.
      ). This yields a “signed” co-expression network that preserves the directionality of the correlation between the protein pairs, yielding values that range from 0 to 1. We note that this allows for all correlations to be used, unlike approaches that invoke arbitrary thresholds. For a discussion of the advantage of weighted versus unweighted networks, see Ref.
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      and references therein. For the WGCNA, suggestions in the following tutorial were followed, with a power parameter of 12, Pearson correlation, and signed modules: https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-blockwise.pdf.5 The minimum number of proteins to make up a module was set at 30.

      Calculating correlations between MEs and physiological traits and GO/KEGG enrichment

      The proteins were clustered into modules by color, and the ME was calculated as the first principal component for the proteins in the module (
      • Zhang B.
      • Horvath S.
      A general framework for weighted gene co-expression network analysis.
      ). The first principal component estimate for each module was then used along with Pearson's correlation to correlate the modules with the clinical traits. Normalized ranks of the clinical trait values were used when calculating the correlation. We used a previously developed method for GO/KEGG enrichment of co-expression modules (
      • Newton M.A.
      • Quintana F.A.
      • Den Boon J.A.
      • Sengupta S.
      • Ahlquist P.
      Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis.
      ).

      Immunohistochemistry

      Immunohistochemistry was performed on 40 islets in pancreas sections from each of three B6, CAST, and PWK male mice, which were 20 weeks of age and maintained on the HF/HS diet. Mice were euthanized by CO2 asphyxiation, perfused with 4% paraformaldehyde through the heart, and the pancreas was removed, embedded in paraffin, and sectioned. Briefly, paraffin-embedded pancreas sections were de-waxed in xylenes, rehydrated in decreasing percentages of ethanol, and boiled in antigen retrieval solution (Vector Labs, H3300). After the sections were cooled and washed with PBS, sections were blocked with 10% normal donkey serum in PBS for 1 h at room temperature. Primary antibody solution in 1% normal donkey serum in PBS was incubated overnight at 4 °C. After a PBS wash, secondary antibody solution in 1% normal donkey serum in PBS was incubated 1 h at room temperature. After a PBS wash, slides were allow to dry and mounted in mounting media (Vector Labs, H-1000). Primary antibodies used were as follows: polyclonal guinea pig anti-insulin (Agilent, A056401-2); monoclonal mouse anti-glucagon antibody (Sigma, G2654); and polyclonal rabbit anti-tyrosine hydroxylase (Millipore, AB152). Secondary antibodies used were as follows: goat anti-rabbit AlexaFluor 488 (Thermo Fisher Scientific, A-11008), chicken anti-mouse AlexaFluor 647 (Thermo Fisher Scientific, A-21463), and donkey anti-guinea pig Cy3 (Jackson ImmunoResearch, 706-165-148). All primary and secondary antibodies were used at a dilution of 1:500. DAPI was added to the secondary antibody solution at a concentration of 1.3 μg/ml to view nuclei. Images were acquired on a Nikon A1R+ point scanning confocal system that uses photomultiplying tubes at room temperature with a Nikon ×40 Pan Apo oil immersion lens with a numerical aperture of 1.3. Acquisition software is NIS-Elements Ar. Post-processed using ImageJ software by adjusting the brightness and contrast. The area of each islet was measured, and the number of Th+/insulin+/glucagon (Th+ β-cells), Th+/insulin/glucagon+ (Th+ α-cells), and Th+/insulin/glucagon (Th+ unidentified cells) cells were counted. Statistics were performed using unpaired, parametric, and two-tailed t tests in GraphPad Prism 7.

      Measuring dopamine-related metabolites in B6 and CAST islets

      100 islets each from four B6 and four CAST male mice (20 weeks of age on HF/HS diet) were recovered in culture media (RPMI 1640, 1.7 mm glucose, 10% FBS) for 2 h at 37 °C and 5% CO2. Islets were then washed twice with PBS and frozen as a pellet in liquid nitrogen for storage at −80 °C.
      Metabolites were extracted from the frozen islets by the addition of 50 μl of 80% (v/v) ice-cold acetonitrile, followed by sonication. The mixture was centrifuged for 5 min at 12,100 × g. The supernatant was derivatized with benzoyl chloride as described previously (
      • Wong J.M.
      • Malec P.A.
      • Mabrouk O.S.
      • Ro J.
      • Dus M.
      • Kennedy R.T.
      Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples.
      ). Briefly, the supernatant was derivatized by sequential addition of 10 μl of 100 mm sodium carbonate, 10 μl of 2% (v/v) benzoyl chloride in acetonitrile, and 10 μl of the internal standard solution. The resulting solution was diluted with 50 μl of water. The internal standard solution consisted of metabolites derivatized with 13C6-benzoyl chloride in 20% (v/v) acetonitrile with 1% (v/v) sulfuric acid. Protein content was determined using a Pierce BCA Protein Assay kit (Thermo Fisher Scientific, Walther, MA), and metabolite concentrations were normalized to protein content. Calibration standards were prepared in water, diluted in acetonitrile to match the sample composition, and derivatized.
      Samples were analyzed using a Waters nanoAcquity UPLC coupled to an Agilent 6410B triple quadrupole mass spectrometer. An Acquity HSS T3 C18 (1 × 100 mm, 1.8 μm, 100-Å pore size) column was used, and the injection volume was 5 μl. Mobile phase A was 10 mm ammonium formate with 0.15% formic acid. Mobile phase B was acetonitrile. The flow rate was 100 μl/min, and the gradient used was as follows: initial, 0% B; 0.1 min, 17% B; 0.5 min, 17% B; 3 min, 25% B; 3.3 min, 56% B; 4.9 min, 70% B; 5 min, 100% B; 6 min, 100% B; 6.1 min, 0% B; 8 min, 0% B.
      Electrospray ionization was used in positive mode, and the capillary was at 4 kV. The nebulizer pressure was 15 p.s.i.; the drying gas was at 11 liters/min, and the gas temperature was 350 °C. Detection was performed in dynamic multiple reaction monitoring mode (MRM), and the MRM conditions are listed in Table S5. Automated peak integration was performed with Agilent MassHunter Work Station Quantitative Analysis for QQQ, version B.05.00. All peaks were inspected to ensure proper integration. Statistics were performed using unpaired, parametric, two-tailed t tests in GraphPad Prism 7.

      B6 and CAST islet insulin secretion

      Islets from four B6 and four CAST male mice (20 weeks of age on HF/HS diet) were recovered in culture media (RPMI 1640 medium, 3.3 mm glucose, 10% FBS) at 37 °C in 5% CO2 for 2 h. Islets were then preincubated in 95% O2, 5% CO2-gassed KRB (118.41 mm NaCl, 4.69 mm KCl, 1.18 mm MgSO4, 1.18 mm KH2PO4, 25 mm NaHCO2, 5 mm HEPES, 2.52 mm CaCl2) supplemented with 0.5% BSA and 3.3 mm glucose (B6 and CAST), or 3.3 mm glucose plus 50 μm l-DOPA (B6 + l-DOPA) and 100 μm ascorbic acid for 45 min at 37 °C in 5% CO2. After preincubation, 15 islets were transferred to an Eppendorf tube containing 125 μl of secretion media (KRB with 0.5% BSA and 3.3 mm glucose or 16.7 mm glucose with or without 1 μm dopamine) and placed in a 37 °C water bath for 45 min. After the secretion, the secretion media were removed from the islets, and the islets were lysed in Nonidet P-40 lysis buffer (100 mm Tris (pH 8.0), 300 mm NaCl, 10 mm NaF, 2 mm Na3VO4, 2% Nonidet P-40 alternative, cOmplete mini EDTA-free protease inhibitor mixture (Roche Applied Science)). Insulin in the media and lysates were measured using an in-house developed insulin ELISA (
      • Keller M.P.
      • Choi Y.
      • Wang P.
      • Davis D.B.
      • Rabaglia M.E.
      • Oler A.T.
      • Stapleton D.S.
      • Argmann C.
      • Schueler K.L.
      • Edwards S.
      • Steinberg H.A.
      • Chaibub Neto E.
      • Kleinhanz R.
      • Turner S.
      • Hellerstein M.K.
      • et al.
      A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility.
      ). Statistics were performed using unpaired, parametric, two-tailed t tests in GraphPad Prism 7.

      Author contributions

      A. D. A., J. J. C., and M. P. K. conceptualization; A. D. A., J. J. C., and R. T. K. resources; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., N. W. K., P. A. M., A. S. H., A. T. B., data curation; K. A. M., E. C. F., M. E. R., A. T. B., R. T. K., M. P. K., J. J. C., and A. D. A. formal analysis; R. T. K., M. P. K., J. J. C., and A. D. A. supervision; R. T. K., J. J. C., and A. D. A. funding acquisition; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., N. W. K., P. A. M., A. S. H., A. T. B., R. T. K., M. P. K., J. J. C., and A. D. A. validation; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., N. W. K., P. A. M., A. S. H., A. T. B., R. T. K., M. P. K., J. J. C., and A. D. A. investigation; K. A. M., M. P. K., and E. C. F. visualization; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., N. W. K., P. A. M., A. S. H., A. T. B., R. T. K., M. P. K., J. J. C., and A. D. A. methodology; K. A. M., M. P. K., and A. D. A. writing-original draft; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., P. A. M., N. W. K., A. S. H., A. T. B., R. T. K., M. P. K., J. J. C., and A. D. A. project administration; K. A. M., E. C. F., K. L. S., M. E. R., D. S. S., P. A. M., A. T. B., M. P. K., J. J. C., and A. D. A. writing-review and editing; N. W. K. and A. S. H. software.

      Acknowledgments

      Microscopy was performed at the University of Wisconsin-Madison Biochemistry Optical Core, which was established with support from the University of Wisconsin-Madison Department of Biochemistry Endowment.

      References

        • Dimas A.S.
        • Lagou V.
        • Barker A.
        • Knowles J.W.
        • Mägi R.
        • Hivert M.F.
        • Benazzo A.
        • Rybin D.
        • Jackson A.U.
        • Stringham H.M.
        • Song C.
        • Fischer-Rosinsky A.
        • Boesgaard T.W.
        • Grarup N.
        • Abbasi F.A.
        • et al.
        Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.
        Diabetes. 2014; 63 (24296717): 2158-2171
        • Fuchsberger C.
        • Flannick J.
        • Teslovich T.M.
        • Mahajan A.
        • Agarwala V.
        • Gaulton K.J.
        • Ma C.
        • Fontanillas P.
        • Moutsianas L.
        • McCarthy D.J.
        • Rivas M.A.
        • Perry J.R.B.
        • Sim X.
        • Blackwell T.W.
        • Robertson N.R.
        • et al.
        The genetic architecture of type 2 diabetes.
        Nature. 2016; 536 (27398621): 41-47
        • Marullo L.
        • El-Sayed Moustafa J.S.
        • Prokopenko I.
        Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of glycaemic traits.
        Curr. Diab. Rep. 2014; 14 (25344220): 551
        • Scott R.A.
        • Scott L.J.
        • Mägi R.
        • Marullo L.
        • Gaulton K.J.
        • Kaakinen M.
        • Pervjakova N.
        • Pers T.H.
        • Johnson A.D.
        • Eicher J.D.
        • Jackson A.U.
        • Ferreira T.
        • Lee Y.
        • Ma C.
        • Steinthorsdottir V.
        • et al.
        An expanded genome-wide association study of type 2 diabetes in europeans.
        Diabetes. 2017; 66 (28566273): 2888-2902
        • Wood A.R.
        • Jonsson A.
        • Jackson A.U.
        • Wang N.
        • van Leewen N.
        • Palmer N.D.
        • Kobes S.
        • Deelen J.
        • Boquete-Vilarino L.
        • Paananen J.
        • Stančáková A.
        • Boomsma D.I.
        • de Geus E.J.C.
        • Eekhoff E.M.W.
        • Fritsche A.
        • et al.
        A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants.
        Diabetes. 2017; 66 (28490609): 2296-2309
        • Attie A.D.
        • Churchill G.A.
        • Nadeau J.H.
        How mice are indispensable for understanding obesity and diabetes genetics.
        Curr. Opin. Endocrinol. Diabetes Obes. 2017; 24 (28107248): 83-91
        • Churchill G.A.
        • Airey D.C.
        • Allayee H.
        • Angel J.M.
        • Attie A.D.
        • Beatty J.
        • Beavis W.D.
        • Belknap J.K.
        • Bennett B.
        • Berrettini W.
        • Bleich A.
        • Bogue M.
        • Broman K.W.
        • Buck K.J.
        • Buckler E.
        • et al.
        The Collaborative Cross, a community resource for the genetic analysis of complex traits.
        Nat. Genet. 2004; 36 (15514660): 1133-1137
        • Kreznar J.H.
        • Keller M.P.
        • Traeger L.L.
        • Rabaglia M.E.
        • Schueler K.L.
        • Stapleton D.S.
        • Zhao W.
        • Vivas E.I.
        • Yandell B.S.
        • Broman A.T.
        • Hagenbuch B.
        • Attie A.D.
        • Rey F.E.
        Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes.
        Cell Rep. 2017; 18 (28199845): 1739-1750
        • Komatsu M.
        • Takei M.
        • Ishii H.
        • Sato Y.
        Glucose-stimulated insulin secretion: a newer perspective.
        J. Diabetes Investig. 2013; 4 (24843702): 511-516
        • Baughman J.M.
        • Rose C.M.
        • Kolumam G.
        • Webster J.D.
        • Wilkerson E.M.
        • Merrill A.E.
        • Rhoads T.W.
        • Noubade R.
        • Katavolos P.
        • Lesch J.
        • Stapleton D.S.
        • Rabaglia M.E.
        • Schueler K.L.
        • Asuncion R.
        • Domeyer M.
        • et al.
        NeuCode proteomics reveals Bap1 regulation of metabolism.
        Cell Rep. 2016; 16 (27373151): 583-595
        • Dittenhafer-Reed K.E.
        • Richards A.L.
        • Fan J.
        • Smallegan M.J.
        • Fotuhi Siahpirani A.
        • Kemmerer Z.A.
        • Prolla T.A.
        • Roy S.
        • Coon J.J.
        • Denu J.M.
        SIRT3 mediates multi-tissue coupling for metabolic fuel switching.
        Cell Metab. 2015; 21 (25863253): 637-646
        • Floyd B.J.
        • Wilkerson E.M.
        • Veling M.T.
        • Minogue C.E.
        • Xia C.
        • Beebe E.T.
        • Wrobel R.L.
        • Cho H.
        • Kremer L.S.
        • Alston C.L.
        • Gromek K.A.
        • Dolan B.K.
        • Ulbrich A.
        • Stefely J.A.
        • Bohl S.L.
        • et al.
        Mitochondrial protein interaction mapping identifies regulators of respiratory chain function.
        Mol. Cell. 2016; 63 (27499296): 621-632
        • Horton J.L.
        • Martin O.J.
        • Lai L.
        • Riley N.M.
        • Richards A.L.
        • Vega R.B.
        • Leone T.C.
        • Pagliarini D.J.
        • Muoio D.M.
        • Bedi Jr, K.C.
        • Margulies K.B.
        • Coon J.J.
        • Kelly D.P.
        Mitochondrial protein hyperacetylation in the failing heart.
        JCI Insight. 2016; 2 (26998524)e84897
        • Overmyer K.A.
        • Evans C.R.
        • Qi N.R.
        • Minogue C.E.
        • Carson J.J.
        • Chermside-Scabbo C.J.
        • Koch L.G.
        • Britton S.L.
        • Pagliarini D.J.
        • Coon J.J.
        • Burant C.F.
        Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation.
        Cell Metab. 2015; 21 (25738461): 468-478
        • Richards A.L.
        • Hebert A.S.
        • Ulbrich A.
        • Bailey D.J.
        • Coughlin E.E.
        • Westphall M.S.
        • Coon J.J.
        One-hour proteome analysis in yeast.
        Nat. Protoc. 2015; 10 (25855955): 701-714
        • Riley N.M.
        • Hebert A.S.
        • Coon J.J.
        Proteomics moves into the fast lane.
        Cell Syst. 2016; 2 (27135360): 142-143
        • Shishkova E.
        • Hebert A.S.
        • Coon J.J.
        Now, more than ever, proteomics needs better chromatography.
        Cell Syst. 2016; 3 (27788355): 321-324
        • Stefely J.A.
        • Kwiecien N.W.
        • Freiberger E.C.
        • Richards A.L.
        • Jochem A.
        • Rush M.J.P.
        • Ulbrich A.
        • Robinson K.P.
        • Hutchins P.D.
        • Veling M.T.
        • Guo X.
        • Kemmerer Z.A.
        • Connors K.J.
        • Trujillo E.A.
        • Sokol J.
        • et al.
        Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling.
        Nat. Biotechnol. 2016; 34 (27669165): 1191-1197
        • Ferris S.T.
        • Zakharov P.N.
        • Wan X.
        • Calderon B.
        • Artyomov M.N.
        • Unanue E.R.
        • Carrero J.A.
        The islet-resident macrophage is in an inflammatory state and senses microbial products in blood.
        J. Exp. Med. 2017; 214 (28630088): 2369-2385
        • Carrero J.A.
        • Calderon B.
        • Towfic F.
        • Artyomov M.N.
        • Unanue E.R.
        Defining the transcriptional and cellular landscape of type 1 diabetes in the NOD mouse.
        PLoS ONE. 2013; 8 (23555752)e59701
        • Zhang B.
        • Horvath S.
        A general framework for weighted gene co-expression network analysis.
        Stat. Appl. Genet. Mol. Biol (2005). 2005; 4 (16646834)17
        • Langfelder P.
        • Horvath S.
        WGCNA: an R package for weighted correlation network analysis.
        BMC Bioinformatics. 2008; 9 (19114008): 559
        • Carlson M.R.
        • Zhang B.
        • Fang Z.
        • Mischel P.S.
        • Horvath S.
        • Nelson S.F.
        Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks.
        BMC Genomics. 2006; 7 (16515682): 40
        • Gargalovic P.S.
        • Imura M.
        • Zhang B.
        • Gharavi N.M.
        • Clark M.J.
        • Pagnon J.
        • Yang W.P.
        • He A.
        • Truong A.
        • Patel S.
        • Nelson S.F.
        • Horvath S.
        • Berliner J.A.
        • Kirchgessner T.G.
        • Lusis A.J.
        Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids.
        Proc. Natl. Acad. Sci. U.S.A. 2006; 103 (16912112): 12741-12746
        • Ghazalpour A.
        • Doss S.
        • Zhang B.
        • Wang S.
        • Plaisier C.
        • Castellanos R.
        • Brozell A.
        • Schadt E.E.
        • Drake T.A.
        • Lusis A.J.
        • Horvath S.
        Integrating genetic and network analysis to characterize genes related to mouse weight.
        PLoS Genet. 2006; 2 (16934000): e130
        • Horvath S.
        • Zhang B.
        • Carlson M.
        • Lu K.V.
        • Zhu S.
        • Felciano R.M.
        • Laurance M.F.
        • Zhao W.
        • Qi S.
        • Chen Z.
        • Lee Y.
        • Scheck A.C.
        • Liau L.M.
        • Wu H.
        • Geschwind D.H.
        • et al.
        Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target.
        Proc. Natl. Acad. Sci. U.S.A. 2006; 103 (17090670): 17402-17407
        • Keller M.P.
        • Choi Y.
        • Wang P.
        • Davis D.B.
        • Rabaglia M.E.
        • Oler A.T.
        • Stapleton D.S.
        • Argmann C.
        • Schueler K.L.
        • Edwards S.
        • Steinberg H.A.
        • Chaibub Neto E.
        • Kleinhanz R.
        • Turner S.
        • Hellerstein M.K.
        • et al.
        A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility.
        Genome Res. 2008; 18 (18347327): 706-716
        • Tekin I.
        • Roskoski Jr., R.
        • Carkaci-Salli N.
        • Vrana K.E.
        Complex molecular regulation of tyrosine hydroxylase.
        J. Neural Transm. 2014; 121 (24866693): 1451-1481
        • Haycock J.W.
        • Ahn N.G.
        • Cobb M.H.
        • Krebs E.G.
        ERK1 and ERK2, two microtubule-associated protein 2 kinases, mediate the phosphorylation of tyrosine hydroxylase at serine-31 in situ.
        Proc. Natl. Acad. Sci. U.S.A. 1992; 89 (1347949): 2365-2369
        • Dunkley P.R.
        • Bobrovskaya L.
        • Graham M.E.
        • von Nagy-Felsobuki E.I.
        • Dickson P.W.
        Tyrosine hydroxylase phosphorylation: regulation and consequences.
        J. Neurochem. 2004; 91 (15569247): 1025-1043
        • Rubí B.
        • Ljubicic S.
        • Pournourmohammadi S.
        • Carobbio S.
        • Armanet M.
        • Bartley C.
        • Maechler P.
        Dopamine D2-like receptors are expressed in pancreatic beta cells and mediate inhibition of insulin secretion.
        J. Biol. Chem. 2005; 280 (16129680): 36824-36832
        • Feldman J.M.
        • Lebovitz H.E.
        Mechanism of epinephrine and serotonin inhibition of insulin release in the golden hamster in vitro.
        Diabetes. 1970; 19 (4393305): 480-486
        • Sorenson R.L.
        • Elde R.P.
        • Seybold V.
        Effect of norepinephrine on insulin, glucagon, and somatostatin secretion in isolated perifused rat islets.
        Diabetes. 1979; 28 (383555): 899-904
        • Esni F.
        • Täljedal I.B.
        • Perl A.K.
        • Cremer H.
        • Christofori G.
        • Semb H.
        Neural cell adhesion molecule (N-CAM) is required for cell type segregation and normal ultrastructure in pancreatic islets.
        J. Cell Biol. 1999; 144 (9922458): 325-337
        • Reichmann F.
        • Holzer P.
        Neuropeptide Y: A stressful review.
        Neuropeptides. 2016; 55 (26441327): 99-109
        • Schwetz T.A.
        • Ustione A.
        • Piston D.W.
        Neuropeptide Y and somatostatin inhibit insulin secretion through different mechanisms.
        Am. J. Physiol. Endocrinol. Metab. 2013; 304 (23211512): E211-E221
        • Kageyama T.
        • Nakamura M.
        • Matsuo A.
        • Yamasaki Y.
        • Takakura Y.
        • Hashida M.
        • Kanai Y.
        • Naito M.
        • Tsuruo T.
        • Minato N.
        • Shimohama S.
        The 4F2hc/LAT1 complex transports l-DOPA across the blood-brain barrier.
        Brain Res. 2000; 879 (11011012): 115-121
        • Ustione A.
        • Piston D.W.
        • Harris P.E.
        Minireview: Dopaminergic regulation of insulin secretion from the pancreatic islet.
        Mol. Endocrinol. 2013; 27 (23744894): 1198-1207
        • García-Tornadú I.
        • Ornstein A.M.
        • Chamson-Reig A.
        • Wheeler M.B.
        • Hill D.J.
        • Arany E.
        • Rubinstein M.
        • Becu-Villalobos D.
        Disruption of the dopamine D2 receptor impairs insulin secretion and causes glucose intolerance.
        Endocrinology. 2010; 151 (20147524): 1441-1450
        • Ustione A.
        • Piston D.W.
        Dopamine synthesis and D3 receptor activation in pancreatic beta-cells regulates insulin secretion and intracellular [Ca2+] oscillations.
        Mol. Endocrinol. 2012; 26 (22918877): 1928-1940
        • Hebert A.S.
        • Richards A.L.
        • Bailey D.J.
        • Ulbrich A.
        • Coughlin E.E.
        • Westphall M.S.
        • Coon J.J.
        The one hour yeast proteome.
        Mol. Cell. Proteomics. 2014; 13 (24143002): 339-347
        • Chick J.M.
        • Munger S.C.
        • Simecek P.
        • Huttlin E.L.
        • Choi K.
        • Gatti D.M.
        • Raghupathy N.
        • Svenson K.L.
        • Churchill G.A.
        • Gygi S.P.
        Defining the consequences of genetic variation on a proteome-wide scale.
        Nature. 2016; 534 (27309819): 500-505
        • Waanders L.F.
        • Chwalek K.
        • Monetti M.
        • Kumar C.
        • Lammert E.
        • Mann M.
        Quantitative proteomic analysis of single pancreatic islets.
        Proc. Natl. Acad. Sci. U.S.A. 2009; 106 (19846766): 18902-18907
        • Back S.H.
        • Kang S.W.
        • Han J.
        • Chung H.T.
        Endoplasmic reticulum stress in the beta-cell pathogenesis of type 2 diabetes.
        Exp. Diabetes Res. 2012; 2012 (21915177)618396
        • Back S.H.
        • Kaufman R.J.
        Endoplasmic reticulum stress and type 2 diabetes.
        Annu. Rev. Biochem. 2012; 81 (22443930): 767-793
        • Kim M.K.
        • Kim H.S.
        • Lee I.K.
        • Park K.G.
        Endoplasmic reticulum stress and insulin biosynthesis: a review.
        Exp. Diabetes Res. 2012; 2012 (22474424)509437
        • Hasnain S.Z.
        • Prins J.B.
        • McGuckin M.A.
        Oxidative and endoplasmic reticulum stress in beta-cell dysfunction in diabetes.
        J. Mol. Endocrinol. 2016; 56 (26576641): R33-R54
        • El Ouaamari A.
        • Zhou J.Y.
        • Liew C.W.
        • Shirakawa J.
        • Dirice E.
        • Gedeon N.
        • Kahraman S.
        • De Jesus D.F.
        • Bhatt S.
        • Kim J.S.
        • Clauss T.R.
        • Camp 2nd., D.G.
        • Smith R.D.
        • Qian W.J.
        • Kulkarni R.N.
        Compensatory islet response to insulin resistance revealed by quantitative proteomics.
        J. Proteome Res. 2015; 14 (26151086): 3111-3122
        • Omikorede O.
        • Qi C.
        • Gorman T.
        • Chapman P.
        • Yu A.
        • Smith D.M.
        • Herbert T.P.
        ER stress in rodent islets of Langerhans is concomitant with obesity and beta-cell compensation but not with beta-cell dysfunction and diabetes.
        Nutr. Diabetes. 2013; 3 (24145577): e93
        • Roat R.
        • Rao V.
        • Doliba N.M.
        • Matschinsky F.M.
        • Tobias J.W.
        • Garcia E.
        • Ahima R.S.
        • Imai Y.
        Alterations of pancreatic islet structure, metabolism and gene expression in diet-induced obese C57BL/6J mice.
        PLoS ONE. 2014; 9 (24505268)e86815
        • Cirulli V.
        Cadherins in islet beta-cells: more than meets the eye.
        Diabetes. 2015; 64 (25713197): 709-711
        • Hodson D.J.
        • Mitchell R.K.
        • Bellomo E.A.
        • Sun G.
        • Vinet L.
        • Meda P.
        • Li D.
        • Li W.H.
        • Bugliani M.
        • Marchetti P.
        • Bosco D.
        • Piemonti L.
        • Johnson P.
        • Hughes S.J.
        • Rutter G.A.
        Lipotoxicity disrupts incretin-regulated human beta cell connectivity.
        J. Clin. Invest. 2013; 123 (24018562): 4182-4194
        • Johansson J.K.
        • Voss U.
        • Kesavan G.
        • Kostetskii I.
        • Wierup N.
        • Radice G.L.
        • Semb H.
        N-cadherin is dispensable for pancreas development but required for beta-cell granule turnover.
        Genesis. 2010; 48 (20533404): 374-381
        • Hauge-Evans A.C.
        • Squires P.E.
        • Persaud S.J.
        • Jones P.M.
        Pancreatic beta-cell-to-beta-cell interactions are required for integrated responses to nutrient stimuli: enhanced Ca2+ and insulin secretory responses of MIN6 pseudoislets.
        Diabetes. 1999; 48 (10389845): 1402-1408
        • Parnaud G.
        • Lavallard V.
        • Bedat B.
        • Matthey-Doret D.
        • Morel P.
        • Berney T.
        • Bosco D.
        Cadherin engagement improves insulin secretion of single human beta-cells.
        Diabetes. 2015; 64 (25277393): 887-896
        • Rogers G.J.
        • Hodgkin M.N.
        • Squires P.E.
        E-cadherin and cell adhesion: a role in architecture and function in the pancreatic islet.
        Cell. Physiol. Biochem. 2007; 20 (17982281): 987-994
        • Rondas D.
        • Tomas A.
        • Soto-Ribeiro M.
        • Wehrle-Haller B.
        • Halban P.A.
        Novel mechanistic link between focal adhesion remodeling and glucose-stimulated insulin secretion.
        J. Biol. Chem. 2012; 287 (22139838): 2423-2436
        • Rackham C.L.
        • Vargas A.E.
        • Hawkes R.G.
        • Amisten S.
        • Persaud S.J.
        • Austin A.L.
        • King A.J.
        • Jones P.M.
        Annexin A1 is a key modulator of mesenchymal stromal cell-mediated improvements in islet function.
        Diabetes. 2016; 65 (26470781): 129-139
        • Antinozzi P.A.
        • Ishihara H.
        • Newgard C.B.
        • Wollheim C.B.
        Mitochondrial metabolism sets the maximal limit of fuel-stimulated insulin secretion in a model pancreatic beta cell: a survey of four fuel secretagogues.
        J. Biol. Chem. 2002; 277 (11821387): 11746-11755
        • Malmgren S.
        • Nicholls D.G.
        • Taneera J.
        • Bacos K.
        • Koeck T.
        • Tamaddon A.
        • Wibom R.
        • Groop L.
        • Ling C.
        • Mulder H.
        • Sharoyko V.V.
        Tight coupling between glucose and mitochondrial metabolism in clonal beta-cells is required for robust insulin secretion.
        J. Biol. Chem. 2009; 284 (19797055): 32395-32404
        • Wiederkehr A.
        • Wollheim C.B.
        Mitochondrial signals drive insulin secretion in the pancreatic beta-cell.
        Mol. Cell. Endocrinol. 2012; 353 (21784130): 128-137
        • Baetens D.
        • Malaisse-Lagae F.
        • Perrelet A.
        • Orci L.
        Endocrine pancreas: three-dimensional reconstruction shows two types of islets of Langerhans.
        Science. 1979; 206 (390711): 1323-1325
        • Kim A.
        • Miller K.
        • Jo J.
        • Kilimnik G.
        • Wojcik P.
        • Hara M.
        Islet architecture: a comparative study.
        Islets. 2009; 1 (20606719): 129-136
        • Steiner D.J.
        • Kim A.
        • Miller K.
        • Hara M.
        Pancreatic islet plasticity: interspecies comparison of islet architecture and composition.
        Islets. 2010; 2 (20657742): 135-145
        • Wieczorek G.
        • Pospischil A.
        • Perentes E.
        A comparative immunohistochemical study of pancreatic islets in laboratory animals (rats, dogs, minipigs, nonhuman primates).
        Exp. Toxicol. Pathol. 1998; 50 (9681646): 151-172
        • Bader E.
        • Migliorini A.
        • Gegg M.
        • Moruzzi N.
        • Gerdes J.
        • Roscioni S.S.
        • Bakhti M.
        • Brandl E.
        • Irmler M.
        • Beckers J.
        • Aichler M.
        • Feuchtinger A.
        • Leitzinger C.
        • Zischka H.
        • Wang-Sattler R.
        • et al.
        Identification of proliferative and mature beta-cells in the islets of Langerhans.
        Nature. 2016; 535 (27398620): 430-434
        • Baron M.
        • Veres A.
        • Wolock S.L.
        • Faust A.L.
        • Gaujoux R.
        • Vetere A.
        • Ryu J.H.
        • Wagner B.K.
        • Shen-Orr S.S.
        • Klein A.M.
        • Melton D.A.
        • Yanai I.
        A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.
        Cell Syst. 2016; 3 (27667365): 346-360
        • Dorrell C.
        • Schug J.
        • Canaday P.S.
        • Russ H.A.
        • Tarlow B.D.
        • Grompe M.T.
        • Horton T.
        • Hebrok M.
        • Streeter P.R.
        • Kaestner K.H.
        • Grompe M.
        Human islets contain four distinct subtypes of beta cells.
        Nat. Commun. 2016; 7 (27399229)11756
        • Johnston N.R.
        • Mitchell R.K.
        • Haythorne E.
        • Pessoa M.P.
        • Semplici F.
        • Ferrer J.
        • Piemonti L.
        • Marchetti P.
        • Bugliani M.
        • Bosco D.
        • Berishvili E.
        • Duncanson P.
        • Watkinson M.
        • Broichhagen J.
        • Trauner D.
        • et al.
        Beta cell hubs dictate pancreatic islet responses to glucose.
        Cell Metab. 2016; 24 (27452146): 389-401
        • Segerstolpe Å.
        • Palasantza A.
        • Eliasson P.
        • Andersson E.M.
        • Andréasson A.C.
        • Sun X.
        • Picelli S.
        • Sabirsh A.
        • Clausen M.
        • Bjursell M.K.
        • Smith D.M.
        • Kasper M.
        • Ämmälä C.
        • Sandberg R.
        Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes.
        Cell Metab. 2016; 24 (27667667): 593-607
        • Wang Y.J.
        • Golson M.L.
        • Schug J.
        • Traum D.
        • Liu C.
        • Vivek K.
        • Dorrell C.
        • Naji A.
        • Powers A.C.
        • Chang K.M.
        • Grompe M.
        • Kaestner K.H.
        Single-cell mass cytometry analysis of the human endocrine pancreas.
        Cell Metab. 2016; 24 (27732837): 616-626
        • Cruciani-Guglielmacci C.
        • Bellini L.
        • Denom J.
        • Oshima M.
        • Fernandez N.
        • Normandie-Levi P.
        • Berney X.P.
        • Kassis N.
        • Rouch C.
        • Dairou J.
        • Gorman T.
        • Smith D.M.
        • Marley A.
        • Liechti R.
        • Kuznetsov D.
        • et al.
        Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.
        Mol. Metab. 2017; 6 (28377873): 340-351
        • Cegrell L.
        The occurrence of biogenic monoamines in the mammalian endocrine pancreas.
        Acta Physiol. Scand. Suppl. 1968; 314 (4973243): 1-60
        • Ericson L.E.
        • Håkanson R.
        • Lundquist I.
        Accumulation of dopamine in mouse pancreatic B-cells following injection of l-DOPA. Localization to secretory granules and inhibition of insulin secretion.
        Diabetologia. 1977; 13 (404204): 117-124
        • Lindström P.
        Aromatic-l-amino-acid decarboxylase activity in mouse pancreatic islets.
        Biochim. Biophys. Acta. 1986; 884 (3533158): 276-281
        • Saisho Y.
        • Harris P.E.
        • Butler A.E.
        • Galasso R.
        • Gurlo T.
        • Rizza R.A.
        • Butler P.C.
        Relationship between pancreatic vesicular monoamine transporter 2 (VMAT2) and insulin expression in human pancreas.
        J. Mol. Histol. 2008; 39 (18791800): 543-551
        • Raffo A.
        • Hancock K.
        • Polito T.
        • Xie Y.
        • Andan G.
        • Witkowski P.
        • Hardy M.
        • Barba P.
        • Ferrara C.
        • Maffei A.
        • Freeby M.
        • Goland R.
        • Leibel R.L.
        • Sweet I.R.
        • Harris P.E.
        Role of vesicular monoamine transporter type 2 in rodent insulin secretion and glucose metabolism revealed by its specific antagonist tetrabenazine.
        J. Endocrinol. 2008; 198 (18577569): 41-49
        • Lundquist I.
        • Panagiotidis G.
        • Stenström A.
        Effect of l-dopa administration on islet monoamine oxidase activity and glucose-induced insulin release in the mouse.
        Pancreas. 1991; 6 (1946308): 522-527
        • Goldstein D.S.
        • Eisenhofer G.
        • Kopin I.J.
        Sources and significance of plasma levels of catechols and their metabolites in humans.
        J. Pharmacol. Exp. Ther. 2003; 305 (12649306): 800-811
        • Simpson N.
        • Maffei A.
        • Freeby M.
        • Burroughs S.
        • Freyberg Z.
        • Javitch J.
        • Leibel R.L.
        • Harris P.E.
        Dopamine-mediated autocrine inhibitory circuit regulating human insulin secretion in vitro.
        Mol. Endocrinol. 2012; 26 (22915827): 1757-1772
        • Osterburg A.R.
        • Hexley P.
        • Supp D.M.
        • Robinson C.T.
        • Noel G.
        • Ogle C.
        • Boyce S.T.
        • Aronow B.J.
        • Babcock G.F.
        Concerns over interspecies transcriptional comparisons in mice and humans after trauma.
        Proc. Natl. Acad. Sci. U.S.A. 2013; 110 (23847210)E3370
        • Seok J.
        • Warren H.S.
        • Cuenca A.G.
        • Mindrinos M.N.
        • Baker H.V.
        • Xu W.
        • Richards D.R.
        • McDonald-Smith G.P.
        • Gao H.
        • Hennessy L.
        • Finnerty C.C.
        • López C.M.
        • Honari S.
        • Moore E.E.
        • Minei J.P.
        • et al.
        Genomic responses in mouse models poorly mimic human inflammatory diseases.
        Proc. Natl. Acad. Sci. U.S.A. 2013; 110 (23401516): 3507-3512
        • Rabaglia M.E.
        • Gray-Keller M.P.
        • Frey B.L.
        • Shortreed M.R.
        • Smith L.M.
        • Attie A.D.
        α-Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice.
        Am. J. Physiol. Endocrinol. Metab. 2005; 289 (15741243): E218-E224
        • Cox J.
        • Mann M.
        MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
        Nat. Biotechnol. 2008; 26 (19029910): 1367-1372
        • Cox J.
        • Hein M.Y.
        • Luber C.A.
        • Paron I.
        • Nagaraj N.
        • Mann M.
        Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.
        Mol. Cell. Proteomics. 2014; 13 (24942700): 2513-2526
        • Tyanova S.
        • Temu T.
        • Sinitcyn P.
        • Carlson A.
        • Hein M.Y.
        • Geiger T.
        • Mann M.
        • Cox J.
        The Perseus computational platform for comprehensive analysis of (prote)omics data.
        Nat. Methods. 2016; 13 (27348712): 731-740
        • Dong J.
        • Horvath S.
        Understanding network concepts in modules.
        BMC Syst. Biol. 2007; 1 (17547772): 24
        • Newton M.A.
        • Quintana F.A.
        • Den Boon J.A.
        • Sengupta S.
        • Ahlquist P.
        Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis.
        Ann. Appl. Statist. 2007; 1: 85-106
        • Wong J.M.
        • Malec P.A.
        • Mabrouk O.S.
        • Ro J.
        • Dus M.
        • Kennedy R.T.
        Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples.
        J. Chromatogr. A. 2016; 1446 (27083258): 78-90