DOI: 10.5061/dryad.j0zpc86jc
title: Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function

description: Insufficient insulin secretion to meet metabolic demand results in
 diabetes. The intracellular flux of Ca2+ into β-cells triggers insulin
 release. Since genetics strongly influences variation in islet secretory
 responses, we surveyed islet Ca2+ dynamics in eight genetically diverse
 mouse strains. We found high strain variation in response to four
 conditions: 1) 8 mM glucose; 2) 8 mM glucose plus amino acids; 3) 8 mM
 glucose, amino acids, plus 10nM GIP; and 4) 2 mM glucose. These stimuli
 interrogate β-cell function, α-cell to β-cell signaling, and incretin
 responses. We then correlated components of the Ca2+ waveforms to islet
 protein abundances in the same strains used for the Ca2+ measurements. To
 focus on proteins relevant to human islet function, we identified human
 orthologues of correlated mouse proteins that are proximal to
 glycemic-associated SNPs in human GWAS. Several orthologues have
 previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1,
 and GCK), supporting our mouse-to-human integration as a discovery
 platform. By integrating these data, we nominated novel regulators of
 islet Ca2+ oscillations and insulin secretion with potential relevance for
 human islet function. We also provide a resource for identifying
 appropriate mouse strains in which to study these regulators.
description: <strong>Chemicals:</strong> All general chemicals, amino acids, BSA, DMSO, glucose, gastric inhibitory polypeptide (GIP,G2269), cOmplete Mini EDTA-free Protease Inhibitor Cocktail Tablets (11836170001), and heat-inactivated FBS (12306C) were purchased from Sigma Aldrich. RPMI 1640 base medium (11-875-093), antibiotic–antimycotic solutions (15240112), NP-40 Alternative (492016), Fura Red Ca<sup>2+</sup> imaging dye (F3020), DiR (D12731), and agarose (BP1356-500) were purchased from ThermoFisher. Glass-bottomed culture dishes were ordered from Mattek (P35G-0-14-C). Fura Red stocks were prepared at 5 mM concentrations in DMSO, aliquoted into light-shielded tubes, and stored at -20°C until day of use (5 μM final concentration). DiR was prepared in DMSO at 2 mg/ mL, aliquoted to light-shielded tubes, and stored at 4°C until use. All imaging solutions were prepared in a bicarbonate/HEPES-buffered imaging medium (formula in Table 1). Amino acids were prepared as 100× stock in the biocarbonate/HEPES-buffered imaging medium, aliquoted into 1.5 mL tubes, and frozen at –20°C until day of use. Aliquots of GIP stock were prepared at 100 μM in water and kept at -20°C until day of use. <strong>Animals</strong> Animal care and experimental protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Most strains (B6, AJ, 129, NOD, PWK, and WSB) were bred in-house, although two strains (CAST and NZO) were purchased from Jackson Laboratory (Bar Harbor, ME). All mice were fed a high-fat, high-sucrose Western-style diet (WD, consisting of 44.6% kcal fat, 34% carbohydrate, and 17.3% protein) from Envigo Teklad (TD.08811) beginning at 4 weeks and continuing until sacrifice (aged ~19–20 weeks for all strains except the NZO males). The NZO males were sacrificed at 12 weeks of age owing to complications from severe diabetes. For each strain, 3–7 males and females from at least 2 litters were analyzed. Animals were sacrificed by cervical dislocation prior to islet isolation. <strong>In vivo measurements</strong> Fasting blood glucose and insulin levels were measured in mice at 19 weeks of age, except for the NZO males which were measured at 12 weeks of age. Glucose was analyzed by the glucose oxidase method using a commercially available kit (TR15221, Thermo Fisher Scientific), and insulin was measured by radioimmunoassay (RIA; SRI-13K, Millipore). <strong>Islet imaging</strong> Islets were isolated as previously described (72) and incubated in recovery medium (RPMI 1640, 11.1 mM glucose, 1% antibiotic/antimycotic, 10% FBS) overnight at 37°C and 5% CO<sub>2</sub>. Islets were then incubated with Fura Red (5 μM in recovery medium) at 37°C for 45 minutes. Imaging dishes were created from glass-bottomed 10 cm<sup>2</sup> dishes that had been filled with agarose. A channel with a central well was cut into the agarose with expanded ports on either side of the well for inflow and outflow lines. Prior to loading, the chambers were perfused with the initial imaging solution (8 mM glucose in imaging medium). Islets were then loaded into these dishes. The imaging chamber was placed on a 37°C-heated microscope stage (Tokai Hit TIZ) of a Nikon A1R-Si+ confocal microscope. All solution reservoirs were kept in a 37°C water bath. Solutions were perfused through the chamber at 0.25 mL/min, with constant flow controlled by a Fluigent MCFS-EZ and M-switch valve assembly (Fluigent). The scope was integrated with a Nikon Eclipse-Ti Inverted scope and equipped with a Nikon CFI Apochromat Lambda D 10x/0.45 objective (Nikon Instruments), fluorescence spectral detector, and multiple laser lines (Nikon LU-NV laser unit; 405, 440, 488, 514, 561, 640nm). Bound dye was excited with the 405nm laser and the spectral detector’s variable filter was set to 620–690nm. The free dye was excited with the 488nm laser and the variable filter collected from 640–690nm. Images were collected at 1 frame/sec at 6-second intervals. Each islet was considered a region of interest for further analysis. ROI intensity was collected by NIS Elements and exported for further analysis. All 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. <strong>Islet perifusion </strong> Isolated islets were kept in RPMI-based medium (see above) overnight prior to perifusion, which was performed as previously described, with minor modifications. Islets were equilibrated in 2 mM glucose for 55 minutes, after which 100 μL fractions were collected every minute with the perifusion solutions set at a flow rate of 100 μL/min. All solutions and islet chambers were kept at 37°C. After the final fraction was collected, islet chambers were disconnected, inverted, and flushed with 2 mL of NP-40 Alternative lysis buffer containing protease inhibitors for islet insulin extraction. <strong>Secreted insulin assay</strong> Insulin in each perifusion fraction and islet insulin content were determined using a custom assay, as previously described. <strong>Imaging data analysis</strong> Trace segments for each solution condition were analyzed using Matlab and R. Traces were detrended using custom R scripts and Graphpad PRISM. Custom Matlab scripts (https://github.com/hrfoster/Merrins-Lab-Matlab-Scripts, also stored on Dryad https://doi.org/10.5281/zenodo.6540721) determined oscillation peak amplitude, pulse duration, active duration (the time when calcium is above 50% peak amplitude), silent duration (the difference between period and active duration). , plateau fraction (the fraction of overall time per pulse spent in the active duration), pulse period and other parameters. Spectral density deconvolution for the trace segments to determine principal frequencies was done using R. Animal averages for the different parameters defined by Matlab and R were computed and graphed using custom R scripts. Figures were created using CorelDraw and Biorender.com. All R scripts and the citations for the relevant packages used to generate them are available via Dryad. <strong>Correlation and Z-score calculations. </strong>Correlation analysis was performed using the imaging data measurements and our published islet protein abundance data, <em>ex vivo </em>static insulin secretion measurements, and <em>in vivo</em> measurements made in a separate cohort of mice on the WD from the same strains and sexes used in these studies.  For each imaging parameter or previously published measurement, the Z-score was calculated using the formula <em>z = (x-μ) / σ</em> where <em>z </em>is the Z-score, <em>x </em>is the animal average for that trait given the strain and sex, <em>μ </em>is the average of all animals’ values for that trait, and <em>σ </em>is the standard deviation for all animals’ values for that trait. Z-scores were computed in R and excel for the imaging parameters and the previously published islet proteomic, <em>ex vivo </em>secretion, and <em>in vivo </em>measurements. Correlation coefficients between the Z-score values of the imaging parameters and Z-scores of the previously published protein abundance, islet secretion, and <em>in vivo </em>traits were computed in Excel using the CORREL function. The equation used for this function is: Where <em>X</em> and <em>Y</em> are the Z-scores for the correlated traits/parameters, <em>ẋ</em> is the population average for trait X and ẏ is the population average for trait Y. Traits were considered highly correlated if the absolute value for their Z-score correlation coefficients was ≥ 0.5. <strong>Gene enrichment and human GWAS analysis. </strong>Proteins highly correlated or anticorrelated to imaging parameters were further analyzed using pathway enrichment and presence of human GWAS SNPs. Briefly, for a given parameter, pathway analysis for the highly correlated or anti-correlated proteins to that parameter was done using Enrichr. For GWAS analysis, human orthologues for genes encoding the previously measured islet proteins were identified using BioMart. For highly correlated proteins, the protein was deemed of human interest if its orthologue had SNPs for glycemia-related traits (see table 2) either along the gene body, within +/- 100 kbp of the gene start or end, or if any region in the gene body was connected to regions with SNPs by chromatin looping. SNPs were queried using Lunaris tool of the Common Metabolic Diseases Knowledge Portal (cmdkp.org). Chromatin loop anchor points for the relevant gene orthologues were identified using previously published human islet promoter-capture HiC data and the alignment between these anchor loops and orthologues of interest was done using R scripts. For those proteins having ortholgoues with SNPs via this analysis, we conducted further literature searches using Pubmed, Google Scholar, ChEMBL, canSAR, Uniprot, Tabula Muris, and the Human Protein Atlas, and other resources to determine tissue expression and identify any prior roles in islet biology. Figures for the relevant protein examples were created using Prism, CorelDraw, and the WashU Epigenome Browser. <strong>Web resource</strong> A web resource was created to explore the islet calcium and proteomic data and their relationships (https://rstudio.it.wisc.edu/FounderCalciumStudy). This resource sits on an RStudio/Connect server (see https://posit.co/). It enables the user to select traits from the calcium and protein datasets to plot by strain, sex, and calcium parameters. Distinct mice were assayed for <strong>calcium</strong> and <strong>protein</strong>. Individual strains can be selected on the main menu using the checkboxes, or all strains (default) can be viewed. The different datasets available in the main menu are: 1)     <strong>calcium</strong>: calcium parameters & spectral density data, with stimulatory secretion conditions 2)     <strong>protein</strong>: islet proteomic measurements 3)     <strong>basal</strong>: average calcium in 2mM glucose The <strong>calcium</strong> data have three stimulatory conditions (8G, 8G/QLA, and 8G/QLA/GIP) that are displayed together for each calcium parameter. The proteomic data (<strong>protein</strong>) are displayed for each identified peptide. In rare cases of multiple peptides per gene, both gene symbol and peptide identifier (PP number) are included (e.g. Pkm_PP_1521 for the M1 isoform of the protein PKM). Desired proteins can be selected simultaneously with desired calcium parameters for correlation analysis and paired display by both datasets. The <strong>basal</strong> elements retained from the calcium data include the Average Calcium measurement for 2mM glucose. Proteomic data were log10-transformed. All traits were transformed into normal scores, keeping the sample mean and variance the same. Scatter plots display data across sex and calcium conditions. When plotting calcium against protein or basal traits, means by strain and sex are used, as the two experiments used different mice. Correlation of selected traits with all other traits in the resource use Pearson correlation on pairwise-complete data. The user can order traits by their significance or by their correlation to other selected traits. Statistical modeling terms include strain, sex, and the strain:sex interation, plus additional terms for comparing calcium condition with respect to strain and sex. Users can view volcano plots displaying deviation of term effects, measured as the standard deviation (SD = square root of mean square error) divided by the raw SD for that trait, against their significance (p-value after adjusting for all other model terms, presented on -log10 scale). In addition to the terms, a composite “signal” captures the combined effect of terms strain:condition + strain:condition:sex using a general F test computation. All data handling and web app construction for the resource was performed using R scripts in publicly available GitHub repositories, with specifics for the calcium study at https://github.com/byandell/FounderCalciumStudy and the general purpose analysis and web deployment package at https://github.com/byandell/foundr.  <strong>Statistics. </strong>For the islet perifusion insulin measurements, statistics were determined in GraphPad Prism. Fractional secretion area-under-the-curve (AUC) was determined using Prism and differences in AUCs analyzed using post-tests following 2-way ANOVA for the indicated trace segments. Islet total insulins between strains were compared using a two-tailed Student’s t-test with Welch’s correction. <strong>Study approval</strong>. All protocols were approved by the University of Wisconsin-Madison IACUC (Protocol A005821-R01).
description: Microsoft Excel/ OpenOffice R
description: README-------------------------------------------------------------------------------------------------------- TITLE: Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function CITATION: Emfinger, Christopher and Clark, Lauren et al. (2022), Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function, Dryad, Dataset AUTHORS: Christopher H. Emfinger1#, Lauren E. Clark1#, Brian Yandell2, Kathryn L. Schueler1, Shane P. Simonett1, Donnie S. Stapleton1, Kelly A. Mitok1, Matthew J. Merrins3,4, Mark P. Keller1, Alan D. Attie\*1,2 1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA 2Department of Statistics, University of Wisconsin-Madison, Madison, WI, 3706, USA 3Department of Medicine, Division of Endocrinology, University of Wisconsin Madison, Madison, WI, 53705, USA 4William S. Middleton Memorial Veterans Hospital, Madison, WI 53705 USA #These authors contributed equally to this work \*corresponding author For inquiries, please address correspondence to: Alan D. Attie 543A HF DeLuca Biochemistry Laboratories, 433 Babcock Drive, Madison, WI 53706 (608) 262-1372 adattie@wisc.edu KEYWORDS: Islet calcium, Insulin secretion, Genetic variation, Beta cell, Calcium imaging DATA COLLECTED: Date Range: 4/2021-04/2022 Location: University of Wisconsin-Madison, Madison, WI, 53706 United States RELATED TO: BIORXIV preprint BIORXIV/2022/517741 FUNDING: This work was supported by NIH R01DK101573 to A. Attie, NIH R01DK113103, NIH R01DK127637, and VA I01B005113 to M. Merrins, and ADA 7-21-PDF-157 to C. Emfinger. LICENSE: CC0 ### FILES: Please note that there is a copy of this file in each of the component Files #### File 1 (Stored on Dryad): raw\_data subfolder: NIS\_output\_files. General summary: This folder contains the output files for the microscopy experiments of mouse islets showing the signal intensity across the different fluorescence channels for each islet. see also the excel file raw\_data\_index.xlsx These are excel files generated by NIS Elements software. There are tabs in each file that are as follows: Fura\_bound: the fluorescence channel containing the emission average for each ROI corresponding to the Ca2+-bound state of the Fura Red dye. Fura\_free: the fluorescence channel containing the emission average for each ROI for the Ca2+-free state of the Fura Red dye. NIS\_fura\_ratio: A tab which is generated by the NIS Elements software. It calculates the ratio between Fura\_bound and Fura\_free emissions measurements. However, some as-yet undiscovered setting in the data skewed the values, so they were not used. We think it was calculating based on peak intensity rather than average but cannot say for certain. Excel\_fura\_ratio: a tab which calculates the ratio in Excel for the average emission measures for Fura\_bound/Fura\_free. There are a couple of exceptions to this general rule. For a few of the experiments there was a point at which acquisition was interrupted or had other corrections. These consequently have additional tabs. These files are: Exp1\_NZO\_M1and2\_M3and4\_9.30.21.xlsx had a pressure issue which was fixed after briefly stopping the acquisition for 1 minute. The acquisitions for each individual movie are designated M1 for movie 1 and M2 for movie 2. The files tabs are: Fura\_bound\_M1 Fura\_free\_M1 NIS\_fura\_ratio\_M1 Fura\_bound\_M2 Fura\_free\_M2 NIS\_fura\_ratio\_M2 Excel\_fura\_ratio\_combined The Excel\_fura\_ratio\_combined tab is the calculated ratios for both movies in sequence Exp1\_NZOF1and2\_11.10.21\_002.xlsx had a computer artefact which increased the background until 155min into the recording. Its tabs include the standard ones (see above) and also: Excel\_fura\_ratio\_bkgrd\_subtr Fura\_free\_bkgrd\_subtr Fura\_bound\_bkgrd\_subtr The Fura\_free\_bkgrd\_subtr and Fura\_bound\_bkgrd\_subtr recalculated the Fura\_free signal and Fura\_bound signals for the period of high background using a ROI which contained no islets. The Excel\_fura\_ratio\_bkgrd\_subtr is the ratio of the Fura\_bound\_bkgrd\_subtr to Fura\_free\_bkgrd\_subtr calculated in excel. Exp1\_4.13.21\_HFD\_F\_AJ.xlsx also had a pressure issue which required interrupting acquisition and restarting it. Its tabs are: Fura\_bound\_M2 Fura\_free\_M2 NADH\_M2 NIS\_fura\_ratio\_M2 Excel\_fura\_ratio\_M2 NIS\_fura\_ratio\_M1 Fura\_free\_M1 Fura\_bound\_M1 NADH\_M1 Excel\_fura\_ratio\_M1 Excel\_fura\_ratio\_combined In this initial experiment, we also attempted to measure NADH in both movies but found that the results had very low signal to noise as a result of the confocal laser not being at optimal excitation wavelength and consequently did not measure it in the other experiments. As with Exp1\_NZO\_M1and2\_M3and4\_9.30.21.xlsx, M1 designates movie 1 for each measurement, M2 designates movie 2 for each measurement, and the Excel\_fura\_ratio\_combined contains the ratio of Fura\_bound/Fura\_free for both movies in sequence. The file names are for each NIS output file are: Date created and experiment performed on Bytes name Contains 6/9/2021 3,894,542 Ex3\_PWK\_DIRPM5\_DiRNF6\_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/27/2021 2,125,327 Exp1\_4.27.21\_AJM\_DiRPAJ4DiRNAJ5.xlsx Raw ROI data for the Fura Red calcium imaging 5/18/2021 3,324,541 Exp1\_5.18.21\_DiRP\_B6F1\_DiRN\_B6F2\_HFD.xlsx Raw ROI data for the Fura Red calcium imaging 5/21/2021 2,879,081 Exp1\_5.21.21\_129M\_dirP1DirN2.xlsx Raw ROI data for the Fura Red calcium imaging 6/17/2021 2,735,621 Exp1\_129M4f\_DiRP\_129M5\_DirN\_6.17.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/23/2021 1,680,454 Exp1\_AJF\_HFD\_4.23.21\_DiRPAJF4\_DIRNAJF5.xlsx Raw ROI data for the Fura Red calcium imaging 11/4/2021 2,081,173 Exp1\_CASTF2DiRN\_CASTF1\_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/5/2021 1,481,380 Exp1\_CASTM1\_CASTM2\_11.5.21\_001.nd2.xlsx Raw ROI data for the Fura Red calcium imaging 4/30/2021 1,524,352 Exp1\_DiRP\_AJF\_DiRN\_AJM\_4.30.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/29/2021 2,401,998 Exp1\_DiRPB6M1\_DiRNB6M2\_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/16/2021 3,129,011 Exp1\_DiRPWSBF1\_DirNWSBF2\_7.16.21.xlsx Raw ROI data for the Fura Red calcium imaging 1/11/2022 1,446,358 Exp1\_NODF1\_NODF2\_1.11.22.xlsx Raw ROI data for the Fura Red calcium imaging 1/12/2022 1,616,527 Exp1\_NODF3\_NODF4\_1.12.22.xlsx Raw ROI data for the Fura Red calcium imaging 6/11/2021 2,150,207 Exp1\_NODM\_DiRP3\_DirN4\_6.11.21.xlsx Raw ROI data for the Fura Red calcium imaging 9/30/2021 934,620 Exp1\_NZO\_M1and2\_M3and4\_9.30.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/10/2021 3,579,003 Exp1\_NZOF1and2\_11.10.21\_002.xlsx Raw ROI data for the Fura Red calcium imaging 5/27/2021 2,043,676 Exp1\_PWK\_DiRF3\_DiRNF4\_5.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/9/2021 2,837,803 Exp1\_PWK\_DIRPF6\_DiRNF7\_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 5/25/2021 2,866,882 Exp1\_PWK\_M\_HFD\_5.25.21.xlsx Raw ROI data for the Fura Red calcium imaging 5/26/2021 2,822,841 Exp1\_PWKF\_HFD\_5.26.21\_DiRPF1DiRNF2.xlsx Raw ROI data for the Fura Red calcium imaging 6/8/2021 2,453,524 Exp1\_PWKM4DiRP\_129F5DiRN\_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging 10/27/2021 2,600,884 Exp1\_WSBF7DirP\_WSBF8\_10.27.21\_001.xlsx Raw ROI data for the Fura Red calcium imaging 7/9/2021 3,050,655 Exp1\_WSBM1DiRP\_WSBM2DiRN\_7.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 10/6/2021 1,816,192 Exp1\_WSBM4\_WSBF6\_10.6.21.xlsx Raw ROI data for the Fura Red calcium imaging 1/6/2022 2,272,289 Exp1\_WSBM5WSBM6\_1.6.22.xlsx Raw ROI data for the Fura Red calcium imaging 5/18/2021 2,304,600 Exp2\_5.18.21\_DiRP\_B6F2\_DiRN\_B6F3\_HFD.xlsx Raw ROI data for the Fura Red calcium imaging 7/7/2021 2,518,124 Exp2\_7.7.21\_PWKM6DirN\_PWKM7DiRP.xlsx Raw ROI data for the Fura Red calcium imaging 6/8/2021 2,140,847 Exp2\_129F4DiRP\_129F6DiRN\_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/17/2021 2,525,163 Exp2\_129M5\_DiRP\_129M6\_DirN\_6.17.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/23/2021 1,928,007 Exp2\_AJF\_HFD\_4.23.21\_DiRPAJF3\_DIRNAJF4.xlsx Raw ROI data for the Fura Red calcium imaging 7/27/2021 2,752,587 Exp2\_B6F4\_B6F5\_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/4/2021 1,998,895 Exp2\_CASTF3DiRN\_CASTF2\_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/5/2021 1,568,927 Exp2\_CASTM2\_CASTM3\_11.5.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/30/2021 2,431,428 Exp2\_DiRP\_AJM\_DiRN\_AJF\_4.30.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/29/2021 2,626,783 Exp2\_DiRPB6M2\_DiRNB6M3\_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/15/2021 2,247,339 Exp2\_DiRPNODM6\_DiRNNODM5\_6.15.21.xlsx Raw ROI data for the Fura Red calcium imaging 1/11/2022 1,402,831 Exp2\_NODF2\_NODF1\_1.11.22.xlsx Raw ROI data for the Fura Red calcium imaging 1/12/2022 1,855,180 Exp2\_NODF4\_NODF3\_1.12.22.xlsx Raw ROI data for the Fura Red calcium imaging 6/11/2021 2,041,554 Exp2\_NODM\_DiRP4\_DirN3\_6.11.21.xlsx Raw ROI data for the Fura Red calcium imaging 9/30/2021 1,167,550 Exp2\_NZO\_M3andM4\_M1andM2.xlsx Raw ROI data for the Fura Red calcium imaging 11/10/2021 1,594,199 Exp2\_NZOF2\_NZOF3\_11.10.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/11/2021 1,761,426 Exp2\_NZOF5and6\_11.11.21\_002.xlsx Raw ROI data for the Fura Red calcium imaging 5/27/2021 1,870,262 Exp2\_PWK\_DiRF4\_DiRNF3\_5.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/9/2021 2,589,142 Exp2\_PWK\_DIRPF5\_DiRNM5\_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 5/25/2021 3,333,623 Exp2\_PWK\_M\_HFD\_5.25.21.xlsx Raw ROI data for the Fura Red calcium imaging 5/26/2021 4,594,490 Exp2\_PWKF\_HFD\_5.26.21\_DiRPF2DiRNF1.xlsx Raw ROI data for the Fura Red calcium imaging 7/16/2021 2,207,546 Exp2\_WSBF1andF2\_DiR\_unclear\_7.16.21.xlsx Raw ROI data for the Fura Red calcium imaging 10/6/2021 2,394,950 Exp2\_WSBF6\_WSBM4\_10.6.21.xlsx Raw ROI data for the Fura Red calcium imaging 10/27/2021 2,444,709 Exp2\_WSBF7DirN\_WSBF8\_10.27.21\_003.xlsx Raw ROI data for the Fura Red calcium imaging 7/9/2021 2,344,808 Exp2\_WSBM1DiRN\_WSBM2DiRP\_7.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 1/6/2022 2,202,683 Exp2\_WSBM5WSBM6\_1.6.22.xlsx Raw ROI data for the Fura Red calcium imaging 5/18/2021 1,796,134 Exp3\_5.18.21\_DiRP\_B6F3\_DiRN\_B6F1\_HFD.xlsx Raw ROI data for the Fura Red calcium imaging 6/8/2021 2,448,024 Exp3\_129F6DiRP\_PWKM4DiRN\_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/27/2021 2,347,394 Exp3\_B6F6\_B6F7\_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/5/2021 2,382,385 Exp3\_CASTM3\_CASTM3\_11.5.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/30/2021 2,119,811 Exp3\_DiRP\_AJF\_DiRN\_AJM\_4.30.21\_runs\_1and2.xlsx Raw ROI data for the Fura Red calcium imaging 7/29/2021 3,012,333 Exp3\_DiRPB6M3\_DiRNB6M1\_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/27/2021 2,410,933 Exp4\_B6F6\_B6F7\_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/9/2021 2,962,993 Exp4\_PWK\_DIRPF7\_DiRNF5\_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/16/2021 358,288 AJ\_M\_Exp1\_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging 4/19/2021 1,317,356 AJ\_M\_Exp1b\_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/13/2022 2,435,773 AJ\_M\_Exp2\_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/13/2022 2,291,674 Exp1\_4.13.21\_HFD\_F\_AJ.xlsx Raw ROI data for the Fura Red calcium imaging 5/11/2021 2,990,568 Exp1\_5.11.21\_129Fs\_1and2\_absentDIR.xlsx Raw ROI data for the Fura Red calcium imaging 7/7/2021 2,617,055 Exp1\_7.7.21\_PWKM6DirP\_PWKM7DiRN.xlsx Raw ROI data for the Fura Red calcium imaging 7/27/2021 2,674,935 Exp1\_B6F5\_B6F4\_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging 6/15/2021 2,574,970 Exp1\_DiRPNODM5\_DiRNNODM6\_6.15.21.xlsx Raw ROI data for the Fura Red calcium imaging 7/20/2021 2,752,718 Exp1\_DiRPWSBF3\_DiRNWSBF4\_7.20.21.xlsx Raw ROI data for the Fura Red calcium imaging 11/11/2021 1,816,842 Exp1\_NZOF4and6\_11.11.21\_002.xlsx Raw ROI data for the Fura Red calcium imaging 4/13/2021 1,777,944 Exp2\_4.13.21\_HFD\_F\_AJ.xlsx Raw ROI data for the Fura Red calcium imaging 4/27/2021 2,256,642 Exp2\_4.27.21\_AJM\_DiRPAJ5DiRNAJ4.xlsx Raw ROI data for the Fura Red calcium imaging 5/21/2021 3,080,396 Exp2\_5.21.21\_129M\_dirP3DirN2.xlsx Raw ROI data for the Fura Red calcium imaging 5/14/2021 3,015,949 Exp2\_129\_F\_5-11-21.xlsx Raw ROI data for the Fura Red calcium imaging 7/20/2021 2,227,050 Exp2\_DiRNWSBF3\_DiRPWSBF4\_7.20.21.xlsx Raw ROI data for the Fura Red calcium imaging 2/18/2022 1,665,058 Exp3\_CASTF1DiRN\_CASTF3\_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging For each of the tabs in the files, there are standard column conventions. Each tab should have the following: Name (the file name for the experiment in NIS) which should have the same value for every point from that experiment. Time [s]: The timepoint in seconds that the data were collected. ND.T : One of the standard NIS outputs but not used in our calculations. It appears to have the frame number for each point. ND.Z : One of the standard NIS outputs the function of which is uncertain and which is not used in our calculations. ND.M: our recalculation of the time in minutes. The remaining columns designate the values for the individual ROIs. They are typically: For Fura\_bound and Fura\_free, the ROI number (e.g. #1) and the wavelength of emission (e.g. 405 for the Fura\_bound channel). So an example would be #1 (405) for a Fura\_bound ROI or #1 (488) for a Fura\_free ROI. The NIS\_fura\_ratio ROI designations contain the ROI number (e.g. #1) and the ratios of the wavelengths for each measurement in parenthesis (e.g. Ratio 405/488). So the corresponding ROI NIS\_fura\_ratio measure for the previous examples would be #1 Ratio (405/488). ##### subfolder: Perifusion\_data. Summary: This contains the raw ELISA data (xlsx format) relevant to Figure 3. Files: Date created size (bytes) ID 1/10/2022 91,330 Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx 1/12/2022 84,422 Founder\_ins\_elisa\_results\_12.7.21done\_1.12.22.xlsx 1/14/2022 83,702 Founder\_ins\_elisa\_results\_12.10.21done\_1.13.22.xlsx 1/27/2022 17,870 Founder\_Perifusion\_Insulin\_ELISA\_1.27.22.xlsx 1/26/2022 95,437 ELISA\_1.26.22\_onFounder\_1.14.22\_run.xlsx 2/2/2022 137,880 Founder Perifusion Insulin ELISA 1.26.22.xlsx 2/2/2022 13,750 Founder\_Perifusion\_Insulin\_ELISA\_Rerun\_2.1.22.xlsx 2/12/2022 113,709 ELISA\_2.11.22ofperif\_2.20.22.xlsx 2/12/2022 18,125 elisa\_perif\_cast\_2.11.21.xlsx 2/2/2022 122,689 ELISA\_1.27.22\_onFounder\_1.20.22\_run\_someglitches.xlsx The files have the following architecture: ELISA\_1.26.22\_onFounder\_1.14.22\_run.xlsx Each 96-well plate has its own sheet designated Plate\_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader Below this, OD OD OD OD ng/mL ng/mL ng/mL ng/mL OD ng/mL WSB9 NOD9 NOD10 129\_10 WSB9 NOD9 NOD10 129\_10 arranged interpolated * column A indicates the fraction collected * columns B-E represent the ODs for individual animals which are designated as (OD in row 11 followed by animal ID on row 12 (for example OD WSB9) * columns F-I, the interpolated ng/mL insulin are indicated for the respective animals. The OD arranged is a concatenation of the raw OD values for determining their ng/mL using PRISM. The ng/mL interpolated is what PRISM returns by fitting these using its standard curve values from the plate (B9:H9) The processing tab is a concatenation of all of the fraction measurements from the respective plates. * Following the columns for interpolated ng/mL (column I), the ng/mL is converted to ng/islet/fraction (columns J:M) then percent of total insulin secreted/fraction (% secreted, columns N:Q) and finally fold change of secretion vs basal secretion (FC, columns R:U). Above each column in row 1 for fold change (columns R:U) is the average percent insulin secreted during the basal period for the corresponding animal, which is used to calculate the fold change. Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx Each 96 well-plate has its own sheet designated Plate\_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader * Below the raw data are columns identifying the fraction number (column A) on the plate and the corresponding animal values for the raw OD (columns B-E, with animal IDs in row 12) and the interpolated ng/mL values from PRISM (columns F-I, animal IDs in row 12) The summary sheet shows the concatenation of all fraction raw OD & interpolated ng/mL from all plates in columns B-I, with fraction numbers indicated in column A. * Columns J-M are ng/mL \* volume of each fraction. * Columns N-Q divide the values in J-M by the number of islets in used for each animal. * Columns R-U adjust for some fractions being diluted to be on the standard curve. * In row 1 for columns V-Y are the corresponding total insulin values/islet. In these columns below the animal IDs (row 4), each of the values in R-U are divided by the total insulin value for that animal in row 1 and multiplied by 100 to give % of total insulin secreted/fraction. For row 1 in Z-AC, the values are the average % secretion for the basal period. Below the animal IDs (row 4) the values in columns V-Y are divided by the average basal values for each corresponding animal to give the fold change. ELISA\_2.11.22ofperif\_2.20.22 Each 96-well plate has its own sheet designated Plate\_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader. Below those, the structure resembles the layout of ELISA\_1.26.22\_onFounder\_1.14.22\_run.xlsx. The summary tab concatenates the values for OD and interpolated ng/mL similar to the summary tab of Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx, with a few differences. * Columns J-M include the number of islets in the calculation instead of just the volume of the fractions to give the ng/fraction/islet for each animal ID in row 4. * For columns N-Q, the total insulin values for each animal per islet are listed in row 3, and all values below the IDs are the corresponding values from rows J-M divided by the total insulin value for that animal and multiplied by 100 to give % secreted. * Columns R-U give the fold change over basal secretion period. The average % secreted for the basal secretion period for each animal is indicated in row 3, and the values for the fractions below the animal ids (row 4) are the values in N-Q divided by the average basal secretion in row 3 for each animal. elisa\_perif\_cast\_2.11.21.xlsx contains raw data for a perifusion in 3 CAST male mice. Each 96 well plate has its own sheet designated Plate\_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader The calculations for converting the raw OD data to secretion data are not in this file. ELISA\_1.27.22\_onFounder\_1.20.22\_run\_someglitches.xlsx For this set, the glitches refers to some of the values being under-diluted and slightly off the standard curve. This file shares a layout with ELISA\_1.26.22\_onFounder\_1.14.22\_run.xlsx for the sheets for each plate. It shares the structure of Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx for the summary tab. Founder Perifusion Insulin ELISA 1.26.22.xlsx shares the structure of ELISA\_1.26.22\_onFounder\_1.14.22\_run.xlsx for its plate sheets. The summary tab is structured as the summary tab is for Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx. There is a duplicate of the summary tab on Sheet1 Founder\_Perifusion\_Insulin\_ELISA\_Rerun\_2.1.22.xlsx is a single plate with raw data in columns A1-N9. The values below row 9 are organized into fraction (column A), OD (column B), interpolated ng/mL (column C) and the adjusted values for dilution (column D) for each of the animals Identified in column E. The reason for this was to test buffer issues with a previous run that limited the color development in the last part of the assay. Founder\_Perifusion\_Insulin\_ELISA\_1.27.22.xlsx is just the raw data corresponding to the ELISA\_1.27.22\_onFounder\_1.20.22\_run\_someglitches.xlsx Founder\_ins\_elisa\_results\_12.10.21done\_1.13.22.xlsx has the same format as Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx. Founder\_ins\_elisa\_results\_12.7.21done\_1.12.22.xlsx has the same format as Founder\_ins\_elisa\_results\_12.7.21done\_1.7.22.xlsx. It is a redo of those same samples. #### File 2 (Stored on Zenodo): R\_scripts These were produced using the following R builds platform x86\_64-w64-mingw32 arch x86\_64 OS mingw32 system x86\_64, mingw32statusmajor 4 minor 0.2 year 2020 month 06 day 22 svn rev 78730 language R version.string R version 4.0.2 (2020-06-22) nickname Taking Off Again Date created/last updated Size (bytes) File name 11/23/2022 17,251 for\_graphing\_matlab\_imaging\_data\_summaries\_v5\_migrated.R 11/23/2022 3,690 histogram\_data.R 11/23/2022 1,947 founder\_islet\_heatmaps.R 10/22/2022 17,033 for\_finding\_baselines3\_founderpaper.R 3/5/2022 16,668 SD\_summarizer\_V2.R 10/22/2022 54,251 decay\_correction\_8.R 5/5/2022 9,571 HiC\_protein\_CE2.R 3/18/2022 1,037 for\_Biomart\_queries\_v1.R These files are for, in the following order above: Graphing the imaging summaries from the matlab analysis scripts (seen in figure 4 and 7) For graphing histograms (such as that seen in Figure 5) For making heatmaps of the correlation coefficients For finding baseline and peak values for the pulses For creating summaries of the spectral density data generated by the decay\_correction\_8 script For processing the NIS raw excel files. This function has several features including generating the detrended curves (figure 1, supplemental figures 1 and 2), performing spectral density derivation, and others. For determining the HiC loops given the data from (1) For identifying orthologues of the relevant proteins using Biomart Note- the HiC queries were done in conjunction with scripts previously published: https://doi.org/10.5281/zenodo.6540721 #### File 3 (Stored on Zenodo): Supplemental\_data ##### subfolder: PRISM\_files. Summary: This contains the prism file for the perifusion ELISA data relevant to Figure 3. The file is Perifusions.pzfx ##### Subfolder: initial\_processing This folder contains the matlab and spectral density processed data from the scripts as well as the summary by-group averages and Z-scores. Note- that if working with group data that includes the NZO males, one must use a different population average than when that group is not included. The insulin secretion data, the calcium data, and the clinical data include a few NZO males but the proteomic sets do not. Thus, there are sets for correlating traits including the male NZO and those without, as indicated by file name. These are Excel files. The file Files: date last modified size (bytes) File name 11/15/2022 124,176 Raw\_data\_matlab\_noNZOm.xlsx 11/14/2022 126,144 Raw\_data\_matlab\_with\_NZOM.xlsx 11/23/2022 66,207 Spectral\_density\_data\_withNZOM.xlsx 11/23/2022 98,333 Spectral\_density\_data\_noNZOm.xlsx 11/15/2022 80,925 clinical\_traits\_all\_mitok\_etal.xlsx 11/14/2022 79,374 clinical\_exvivo\_traits\_noNZOm\_mitoketal.xlsx 11/15/2022 10,346,328 Islet\_proteomic\_data\_noNZOM.xlsx ##### Subfolder: correlations This folder contains the correlation data calculated from the Z-scores. For the Proteins\_to\_calcium.xlsx file, the lists of the most correlated/anticorrelated proteins to the specific calcium parameters are also included on separate tabs. Files: date last modified size (bytes) name 11/23/2022 13,477,715 Proteins\_to\_calcium.xlsx 11/17/2022 6,831,406 Proteins\_to\_clinical\_exvivo.xlsx 11/17/2022 1,603,415 calcium\_to\_clintraits.xlsx 11/18/2022 2,818,894 for\_heatmaps\_prot\_calc.xlsx 11/17/2022 2,590,213 prot\_to\_clin\_forheatmap.xlsx 11/19/2022 165 \~$Proteins\_to\_calcium.xlsx Proteins\_to\_calcium shows the correlation coefficients between the calcium parameters and islet proteins' z-scores Proteins\_to\_clinical\_exvivo shows the correlation coefficients between the proteins and clinical traits/ex vivo secretion traits z-scores Calcium\_clintraits shows correlation coefficients between the calcium parameters' z-scores and the z-scores for the clinical and ex vivo islet secretion parameters. Note- the raw proteomic data and raw clinical/ex vivo secretion data were previously published (2). ``` Subfolder: Supplemental_data This folder contains the supplemental figures as well as both Table 3, which lists the proteins absolutely correlated to the 5 parameters of interest that also have SNPs for glycemic traits in the Type-2 Diabetes Knowledge portal (https://t2d.hugeamp.org/), and Supplemental Table 1, which provides the Enrichr links for the proteins correlated to the specified parameters. Files: ``` Date last modified size (bytes) Name 11/23/2022 50,362 Table\_3\_Correlatedproteins\_with\_glycemic\_SNPs.xlsx 11/23/2022 1,926,285 Emfinger\_Clark\_Attie\_Supplemental.pdf 3/27/2023 692,377 Supplemental\_table\_1 #### Raw Movies Refer to the table below for the folder breakdown. Owing to size, these had to be uploaded in parts (tar files): * raw\_movies\_part1 contains the folders 4.31.21 through 5.27.21 * raw\_movies\_part2 contains the folders 6.8.21 through 7.9.21 * raw\_movies\_part3 contains the folders 7.16.21 through 10.27.21 * raw\_movies\_part4 contains the folders 11.4.21 through 1.25.22 * the information in the table below can be found in the raw\_movie\_index.xlsx file Within the tar files, these folders contain the raw calcium images in .nd2 file format. This is the output of NIS Elements but can be read by ImageJ ### METHODS #### Chemicals All general chemicals, amino acids, BSA, DMSO, glucose, gastric inhibitory polypeptide (GIP,G2269), cOmplete Mini EDTA-free Protease Inhibitor Cocktail Tablets (11836170001), and heat-inactivated FBS (12306C) were purchased from Sigma Aldrich. RPMI 1640 base medium (11-875-093), antibioticantimycotic solutions (15240112), NP-40 Alternative (492016), Fura Red Ca2+ imaging dye (F3020), DiR (D12731), and agarose (BP1356-500) were purchased from ThermoFisher. Glass-bottomed culture dishes were ordered from Mattek (P35G-0-14-C). Fura Red stocks were prepared at 5 mM concentrations in DMSO, aliquoted into light-shielded tubes, and stored at 20C until day of use (5 M final concentration). DiR was prepared in DMSO at 2 mg/ mL, aliquoted to light-shielded tubes, and stored at 4C until use. All imaging solutions were prepared in a bicarbonate/HEPES-buffered imaging medium (formula in Table 1). Amino acids were prepared as 100 stock in the biocarbonate/HEPES-buffered imaging medium, aliquoted into 1.5 mL tubes, and frozen at 20C until day of use. Aliquots of GIP stock were prepared at 100 M in water and kept at -20C until day of use. #### Animals Animal care and experimental protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Most strains (B6, AJ, 129, NOD, PWK, and WSB) were bred in-house, although two strains (CAST and NZO) were purchased from Jackson Laboratory (Bar Harbor, ME). All mice were fed a high-fat, high-sucrose Western-style diet (WD, consisting of 44.6% kcal fat, 34% carbohydrate, and 17.3% protein) from Envigo Teklad (TD.08811) beginning at 4 weeks and continuing until sacrifice (aged \~19-20 weeks for all strains except the NZO males). The NZO males were sacrificed at 12 weeks of age owing to complications from severe diabetes. For each strain, 3-7 males and females from at least 2 litters were analyzed. Animals were sacrificed by cervical dislocation prior to islet isolation. #### In vivo measurements Fasting blood glucose and insulin levels were measured in mice at 19 weeks of age, except for the NZO males which were measured at 12 weeks of age. Glucose was analyzed by the glucose oxidase method using a commercially available kit (TR15221, Thermo Fisher Scientific), and insulin was measured by radioimmunoassay (RIA; SRI-13K, Millipore). #### Islet imaging Islets were isolated as previously described (3) and incubated in recovery medium (RPMI 1640, 11.1 mM glucose, 1% antibiotic/antimycotic, 10% FBS) overnight at 37C and 5% CO2. Islets were then incubated with Fura Red (5 M in recovery medium) at 37C for 45 minutes. Imaging dishes were created from glass-bottomed 10 cm2 dishes that had been filled with agarose. A channel with a central well was cut into the agarose with expanded ports on either side of the well for inflow and outflow lines. Prior to loading the chambers were perfused with the initial imaging solution (8 mM glucose in imaging medium). Islets were then loaded into these dishes. The imaging chamber was placed on a 37Cheated microscope stage (Tokai Hit TIZ) of a Nikon A1R-Si+ confocal microscope. All solution reservoirs were kept in a 37C water bath. Solutions were perfused through the chamber at 0.25 mL/min, with constant flow controlled by a Fluigent MCFS-EZ and M-switch valve assembly (Fluigent). The scope was integrated with a Nikon Eclipse-Ti Inverted scope and equipped with a Nikon CFI Apochromat Lambda D 10x/0.45 objective (Nikon Instruments), fluorescence spectral detector, and multiple laser lines (Nikon LU-NV laser unit; 405, 440, 488, 514, 561, 640nm). Bound dye was excited with the 405nm laser and the spectral detectors variable filter was set to 620-690nm. The free dye was excited with the 488nm laser and the variable filter collected from 640-690nm. Images were collected at 1 frame/sec at 6-second intervals. Each islet was considered a region of interest for further analysis. ROI intensity was collected by NIS Elements and exported for further analysis. All 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. #### Islet perifusion Isolated islets were kept in RPMI-based medium (see above) overnight prior to perifusion, which was performed as previously described, with minor modifications (4, 5). Islets were equilibrated in 2 mM glucose for 55 minutes, after which 100 L fractions were collected every minute with the perifusion solutions set at a flow rate of 100 L/min. All solutions and islet chambers were kept at 37C. After the final fraction was collected, islet chambers were disconnected, inverted, and flushed with 2 mL of NP-40 Alternative lysis buffer containing protease inhibitors for islet insulin extraction. #### Secreted insulin assay Insulin in each perifusion fraction and islet insulin content were determined using a custom assay, as previously described (2). #### Imaging data analysis Trace segments for each solution condition were analyzed using Matlab and R. Traces were detrended using custom R scripts and Graphpad PRISM. Custom Matlab scripts (https://github.com/hrfoster/Merrins-Lab-Matlab-Scripts, also stored on Dryad https://doi.org/10.5281/zenodo.6540721) determined oscillation peak amplitude, pulse duration, active duration (the time when Ca2+ is above 50% peak amplitude), silent duration (the difference between period and active duration). , plateau fraction (the fraction of overall time per pulse spent in the active duration), pulse period and other parameters. Spectral density deconvolution for the trace segments to determine principal frequencies was done using R. Animal averages for the different parameters defined by Matlab and R were computed and graphed using custom R scripts. Figures were created using CorelDraw and Biorender.com. #### Correlation and Z-score calculations Correlation analysis was performed using the imaging data measurements and our published islet protein abundance data, ex vivo static insulin secretion measurements, and in vivo measurements made in a separate cohort of mice on the WD from the same strains and sexes used in these studies (2). For each imaging parameter or previously published measurement, the Z-score was calculated using the formula z = (x-) / where z is the Z-score, x is the animal average for that trait given the strain and sex, is the average of all animals values for that trait, and is the standard deviation for all animals values for that trait. Z-scores were computed in R and excel for the imaging parameters and the previously published (2) islet proteomic, ex vivo secretion, and in vivo measurements. Correlation coefficients between the Z-score values of the imaging parameters and Z-scores of the previously published protein abundance, islet secretion, and in vivo traits were computed in Excel using the CORREL function. The equation used for this function is: Correl(X,Y)=((x-)(y-))/((x-)^2\*(y-)^2 ) Where X and Y are the Z-scores for the correlated traits/parameters, is the population average for trait X and is the population average for trait Y. Traits were considered highly correlated if absolute value for their Z-score correlation coefficients was 0.5. #### Gene enrichment and human GWAS analysis Proteins highly correlated or anticorrelated to imaging parameters were further analyzed using pathway enrichment and presence of human GWAS SNPs. Briefly, for a given parameter, pathway analysis for the highly correlated or anti-correlated proteins to that parameter was done using Enrichr (6, 7). For GWAS analysis, human orthologues for genes encoding the previously measured islet proteins were identified using BioMart (8). For highly correlated proteins, the protein was deemed of human interest if its orthologue had SNPs for glycemia-related traits (see table 2) either along the gene body, within +/- 100 kbp of the gene start or end, or if any region in the gene body was connected to regions with SNPs by chromatin looping. SNPs were queried using Lunaris tool of the Common Metabolic Diseases Knowledge Portal (cmdkp.org). Chromatin loop anchor points for the relevant gene orthologues were identified using previously published human islet promoter-capture HiC data (1) and the alignment between these anchor loops and orthologues of interest was done using R scripts. For those proteins having ortholgoues with SNPs via this analysis, we conducted further literature searches using Pubmed, Google Scholar, ChEMBL (9-11), canSAR (12), Uniprot (13), Tabula Muris (14), and the Human Protein Atlas (15, 16), and other resources (17-19) to determine tissue expression and identify any prior roles in islet biology. Figures for the relevant protein examples were created using Prism, CorelDraw, and the WashU Epigenome Browser (20). #### Web resource A web resource was created to explore the islet calcium and proteomic data and their relationships (https://rstudio.it.wisc.edu/FounderCalciumStudy). This resource sits on an RStudio/Connect server (see https://posit.co/). It enables the user to select traits from the calcium and protein datasets to plot by strain, sex, and calcium parameters. Distinct mice were assayed for calcium and protein. Individual strains can be selected on the main menu using the checkboxes, or all strains (default) can be viewed. The different datasets available in the main menu are: ``` calcium: calcium parameters & spectral density data, with stimulatory secretion conditions protein: islet proteomic measurements basal: average calcium in 2mM glucose ``` The calcium data have three stimulatory conditions (8G, 8G/QLA, and 8G/QLA/GIP) that are displayed together for each calcium parameter. The proteomic data (protein) are displayed for each identified peptide. In rare cases of multiple peptides per gene, both gene symbol and peptide identifier (PP number) are included (e.g. Pkm\_PP\_1521 for the M1 isoform of the protein PKM). Desired proteins can be selected simultaneously with desired calcium parameters for correlation analysis and paired display by both datasets. The basal elements retained from the calcium data include the Average Calcium measurement for 2mM glucose. Proteomic data were log10-transformed. All traits were transformed into normal scores, keeping the sample mean and variance the same. Scatter plots display data across sex and calcium conditions. When plotting calcium against protein or basal traits, means by strain and sex are used, as the two experiments used different mice. Correlation of selected traits with all other traits in the resource use Pearson correlation on pairwise-complete data. The user can order traits by their significance or by their correlation to other selected traits. Statistical modeling terms include strain, sex, and the strain:sex interation, plus additional terms for comparing calcium condition with respect to strain and sex. Users can view volcano plots displaying deviation of term effects, measured as the standard deviation (SD = square root of mean square error) divided by the raw SD for that trait, against their significance (p-value after adjusting for all other model terms, presented on -log10 scale). In addition to the terms, a composite signal captures the combined effect of terms strain:condition + strain:condition:sex using a general F test computation. All data handling and web app construction for the resource was performed using R scripts in publicly available GitHub repositories, with specifics for the calcium study at https://github.com/byandell/FounderCalciumStudy and the general purpose analysis and web deployment package at https://github.com/byandell/foundr. #### Statistics: For the islet perifusion insulin measurements, statistics were determined in GraphPad Prism. Fractional secretion area-under-the-curve (AUC) was determined using Prism and differences in AUCs analyzed using post-tests following 2-way ANOVA for the indicated trace segments. Islet total insulins between strains were compared using a two-tailed Students t-test with Welchs correction. #### Study approval: All protocols were approved by the University of Wisconsin-Madison IACUC (Protocol A005821-R01) ### ### CITATIONS: 1. Miguel-Escalada I, Bonas-Guarch S, Cebola I, Ponsa-Cobas J, Mendieta-Esteban J, Atla G, et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nature genetics. 2019. 2. Mitok KA, Freiberger EC, Schueler KL, Rabaglia ME, Stapleton DS, Kwiecien NW, et al. Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion. The Journal of biological chemistry. 2018;293(16):5860-77. 3. Rabaglia ME, Gray-Keller MP, Frey BL, Shortreed MR, Smith LM, and Attie AD. -Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice. American Journal of Physiology-Endocrinology and Metabolism. 2005;289(2):E218-E24. 4. Emfinger CH, de Klerk E, Schueler KL, Rabaglia ME, Stapleton DS, Simonett SP, et al. Cellspecific deletion of Zfp148 improves nutrient-stimulated cell Ca2+ responses. JCI Insight. 2022;7(10). 5. Bhatnagar S, Oler AT, Rabaglia ME, Stapleton DS, Schueler KL, Truchan NA, et al. Positional cloning of a type 2 diabetes quantitative trait locus; tomosyn-2, a negative regulator of insulin secretion. PLoS Genet. 2011;7(10):e1002323. 6. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. 7. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research. 2016;44(W1):W90-7. 8. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, et al. BioMart--biological queries made easy. BMC Genomics. 2009;10:22. 9. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic acids research. 2017;45(D1):D945-D54. 10. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, et al. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic acids research. 2015;43(W1):W612-W20. 11. Jupp S, Malone J, Bolleman J, Brandizi M, Davies M, Garcia L, et al. The EBI RDF platform: linked open data for the life sciences. Bioinformatics. 2014;30(9):1338-9. 12. Coker EA, Mitsopoulos C, Tym JE, Komianou A, Kannas C, Di Micco P, et al. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic acids research. 2019;47(D1):D917-D22. 13. The UniProt C. UniProt: the universal protein knowledgebase in 2021. Nucleic acids research. 2021;49(D1):D480-D9. 14. Schaum N, Karkanias J, Neff NF, May AP, Quake SR, Wyss-Coray T, et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018;562(7727):367-72. 15. Uhln M, Fagerberg L, Hallstrm BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science (New York, NY). 2015;347(6220):1260419. 16. Thul PJ, kesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, et al. A subcellular map of the human proteome. Science (New York, NY). 2017;356(6340). 17. Varshney A, Scott LJ, Welch RP, Erdos MR, Chines PS, Narisu N, et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(9):2301-6. 18. Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome research. 2017;27(2):208-22. 19. Uhln M, Karlsson MJ, Hober A, Svensson AS, Scheffel J, Kotol D, et al. The human secretome. Science signaling. 2019;12(609). 20. Li D, Hsu S, Purushotham D, Sears RL, and Wang T. WashU Epigenome Browser update 2019. Nucleic acids research. 2019;47(W1):W158-W65.