DOI: 10.5061/dryad.bcc2fqzcw
title: β-cell-specific deletion of Zfp148 improves nutrient-stimulated β-cell Ca2+ responses

description: Insulin secretion from pancreatic β-cells is essential for glucose
 homeostasis. An insufficient response to the demand for insulin results in
 diabetes. We previously showed that β-cell-specific deletion of Zfp148
 (β-Zfp148KO) improves glucose tolerance and insulin secretion in mice.
 Here, we performed Ca2+ imaging of islets from β‑Zfp148KO and control mice
 on both a chow and a Western-style diet. β-Zfp148KO islets demonstrate
 improved sensitivity and sustained Ca2+ oscillations in response to
 elevated glucose. β-Zfp148KO islets also exhibit elevated sensitivity to
 amino acid-induced Ca2+ influx under low glucose conditions, suggesting
 enhanced mitochondrial phosphoenolpyruvate (PEP)-dependent KATP channel
 closure, independent of glycolysis. RNA sequencing and proteomics of
 β-Zfp148KO islets revealed altered levels of enzymes involved in amino
 acid metabolism (SLC3A2, SLC7A8, GLS, GLS2, PSPH, PHGDH, PSAT1) and
 intermediary metabolism (GOT1, PCK2), consistent with altered PEP cycling.
 In agreement with this, β-Zfp148KO islets displayed enhanced insulin
 secretion in response to L-glutamine and activation of glutamate
 dehydrogenase. Understanding pathways controlled by ZFP148 may provide
 promising strategies for improving β-cell function that are robust to the
 metabolic challenge imposed by a Western diet.
description: These files contain the relevant raw imaging data, processed
 imaging data, analysis scripts, and relevant extended procedures used.
 This is related to: JCI Insight manuscript ID 154198. A more extensive
 materials & methods is in the Extended_materials_and_methods
 folder of this dataset.
 <strong>Mice</strong>.
 β-<em>Zfp148</em><sup>KO</sup> and control
 littermate mice were generated as previously described (1). Mice were
 maintained on either a chow diet (Purina 5008), or 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 weaning and continued until sacrifice (~20-24 weeks). Chow mice were
 sacrificed at 24-28 weeks of age to match the age range of the WD-fed
 animals, except for the mice used for the perifusion study which were
 17-19 weeks old. For each group, except those for chow-fed male amino acid
 studies, animals from at least three different litters were analyzed. We
 confirmed all genotypes prior to experiments using PCR, as previously
 described (1). Animals were sacrificed by cervical dislocation prior to
 the islet isolations. All protocols were approved by the University of
 Wisconsin-Madison Institutional Animal Care and Use Committee (Protocol
 A005821-R01). <strong>Ex vivo islet imaging
 experiments</strong>. Islets were isolated as previously described
 (2). Islets were incubated in RPMI media containing 10% FBS and 1%
 antibiotic/antimycotic, (Gibco 15240-062) for 3 days prior to imaging
 experiments. We found this optimal for signal measurement in these
 studies, though it differs from the 2 hour and overnight incubations used
 in the static and perifusion assays, respectively, for which loss of
 Zfp148 previously showed enhanced secretion ((1) and Figure 5). Islets
 from β-Zfp148<sup>KO</sup> or from control mice were imaged
 simultaneously. To differentiate animal of origin for islets in each
 experiment, islets from one mouse were loaded with DiR (1 µg/mL in media)
 for 10 minutes (3). These islets were then washed in DiR-free media, moved
 to a separate dish containing islets from its littermate, followed by
 incubation of all islets with Fura Red (2.5µM in media) at 37°C for 45min.
 We observed no effect of the DiR loading on islet
 Ca<sup>2+</sup> responses. To enhance our ability to measure
 intracellular islet Ca2+ dynamics, we used a slightly higher
 [Ca<sup>2+</sup>] (5mM) than is typically added to KRB (2.5
 mM). Each animal’s islets were imaged in two recordings, and which
 genotype had islets loaded with DiR was alternated between
 recordings. After loading, islets were placed in a
 glass-bottomed open-air perfusion chamber (RC-41LP, Warner Instruments)
 with basal imaging solution (3 mM glucose in HEPES-buffered imaging
 medium). The imaging chamber was placed on a 33°C heated microscope stage
 (TC-344C temperature controller, Warner Instruments, which also controlled
 the temperature of the perfused solution via an in-line heating element)
 over a Nikon Ti-Eclipse inverted microscope. Solution was perfused through
 the chamber at 0.25 mL/min, with flow controlled by a Fluigent MCFS-EZ
 pressure regulation system, MCFS inline sensor, and M-switch valve
 assembly (Fluigent). The scope was equipped with 20X/0.75NA SuperFluor
 objective (Nikon Instruments), a Sola SEII 365 LED light engine (Lumencor)
 set to 10% output, ET-type excitation and emission filters (Chroma
 Technology), and a FF444/521/608-Di01 dichroic beamsplitter (Semrock).
 Excitation/emission: Fura Red (430/20nm and 500/20nm excitation, 630/70nm
 emission; ratio defined as R500/430), NAD(P)H (365/20 nm excitation,
 470/24 nm emission), and DiR (748 nm excitation, 780 nm emission).
 Time-lapse images were acquired by a Hamamatsu ORCA-Flash4.0 V2 Digital
 CMOS camera at 6 sec intervals. A single region of interest was used to
 collect the average responses of each islet. Images
 were processed with NIS Elements software (Nikon) with custom scripts in
 MATLAB (MathWorks) and R (scripts available in Supplementary Data);
 statistical analyses were performed in Prism (GraphPad). In brief, duty
 cycle measurements were made for each stimulatory condition by analyzing
 the corresponding trace segment for each islet in MATLAB (4). For the
 amino acid experiments, the traces were detrended using points from the 3
 mM glucose-only conditions to model a 2-phase exponential decay curve for
 each islet. Using R, area-under-the-curve (AUC) measurements were
 determined for the segments in the detrended traces for each islet and
 amino acid condition. Example and average traces for main Figures 1 and 2
 and Supplemental Figures 2 and 3 were also created using the de-trending R
 script. Traces in Figure 5 and Supplemental figure 5 were generated in
 PRISM. All R script sets as well as the MATLAB scripts are available as
 part of this data package (File 2, the folder
 “Scripts_and_analysis_tools”), which also contains citations for the base
 packages they use. Raw data for this is stored in the FILE 1 Raw_data
 folder (see ReadMe). <strong>Ex vivo islet
 perifusion.</strong> Isolated islets were kept in RPMI-based media
 (see above) for 24 hours prior to perifusion, which was performed as
 previously described with minor modifications (5). Briefly, all solutions
 except the bead mix contained 2mM glucose and 2mM L-glutamine in Kreb’s
 ringers buffer (KRB) supplemented with 5mM HEPES and 0.5% BSA, and were
 filtered using 22 micron filters (Sigma). 100 medium-sized islets from
 each animal were transferred to a BioRep PERI-CHAMBER containing 200µL of
 bead mix (100 mg/mL Bio-Gel P-2 beads (Bio-Rad) re-hydrated in KRB) on
 BioRep fiberglass PERI-FILTER filter pads. An additional 200µL of  bead
 mix was added above the islets in the chamber, the remaining volume was
 filled with BCH-free solution, and the chambers were connected to a
 MiniPuls 3 peristaltic pump (Gilson) and BioRep PERI-NOZZLE ports using
 0.38mm inner diameter tubing (Gilson F117933). These ports were mounted to
 a FC 204 Fraction Collector (Gilson). The solutions were set to a flow
 rate of 100µL/min and 100µL samples were collected every minute following
 55 minutes of equilibration in the flowing BCH-free medium. All solutions
 and islet chambers were kept at 37°C. At the end of sample collection,
 chambers were disconnected, inverted, and flushed with 2mL of acid-ethanol
 (76.9% ethanol with 0.185N HCl) for islet insulin extraction. Fractions
 and islet extracts were frozen at -20°C until insulin
 determination. <em><strong>Secreted insulin
 assay.</strong></em> Insulin in each perifusion fraction and
 islet insulin content were determined using a custom assay as previously
 described (6). Raw data for this is stored in the FILE 1 Raw_data folder
 (see README). <em><strong>Islet RNA
 isolation and processing.</strong></em> Freshly isolated
 islets from WD-fed mice of both sexes and genotypes were collected and RNA
 from these islets was isolated using the Qiagen RNeasy-plus mini-kit
 (Qiagen). RNA concentration and purity were assessed using a Nano-Drop
 1000 spectrometer. Sequencing was done at the UW-Madison Biotechnology
 Center. RNA integrity was determined using an Agilent 2100 BioAnalyzer.
 Libraries were prepared using Illumina® TruSeq® Stranded Total RNA Sample
 Prep Gold kit (Illumina Inc., San Diego, California, USA). Complementary
 DNA (cDNA) was purified using Agencourt AMPure XP bead purification
 (Beckman-Coulter) following rRNA reduction. Libraries were quantified
 using Qubit DNA HS kit (ThermoFisher) and assayed on Agilent HS DNA Chips.
 Sequencing was done on Illumina HiSeq 2500 units. Reads were aligned back
 to the mm10 mouse genome build using the short read aligner Bowtie (7)
 followed by RSEM (8).  The resulting sequencing data were analyzed by
 EBSeq to determine significantly differently-expressed genes (FDR of 0.05)
 (9). Gene ontology enrichment of differently expressed genes in the
 RNA-seq dataset was done using Enrichr (10, 11), and results of this
 analysis are included in Supplemental File 1. Heatmap for the RNA-seq
 analysis (Figure 2) was made using R. Further information on EBSeq
 analysis can be found in FILE 3 Supplemental_tables (see
 ReadME). <strong><em>Determining candidate
 ZFP148 targets.</em></strong> These analyses used ZNF148
 ChIP-seq (GEO accessions GSE105932 and GSE136444 from the ENCODE Project
 (12) in HEK and K562 cells, respectively) and human islet transcription
 factor and HiC datasets from Miguel-Escalada et al. (13). DE orthologues
 were considered putative targets if they had ZNF148 binding peaks within 1
 kbp of their transcription start site (TSS), within their introns, or
 within 500 bp of HiC loops mapping to the DE orthologue’s TSS. Determining
 overlap of these data was done using R scripts, which are in the FILE 2
 Scripts_and_analysis_tools folder.      
 <strong><em>Isolated islet
 proteomics.</em></strong> Further details of the sample
 preparation, run, and analysis are included in the supplemental data
 (Supplemental File 3). Mass spectrometry sample preparation followed the
 SL-TMT workflow (14). All the islet samples were fitted in a TMTpro16-plex
 experiment. Islets were needle lysed using 8M Urea in 100 mM EPPS (pH 8.5)
 with protease inhibitors. 1 μL lysis buffer per 1.5 islet was used based
 on information provided (Supplemental file 3). Lysates were reduced with 5
 mM TCEP, alkylated with 10 mM iodoacetamide (dark), and quenched with 10
 mM DTT (all for 20 min). SinglePot Solid-Phase-enhanced Sample processing
 (SP3) as described previously (15) was used during protein isolation and
 digestion. The samples were mixed at a 1:1 ratio across all channels based
 on a pilot ratio check LC-MS experiment. The pooled, multiplexed samples
 were desalted using a 100 mg SepPak cartridge, of which 300 μg of peptide
 was fractionated via basic pH reversed-phase (BPRP) HPLC, collected in a
 96-well plate, and concatenated into 24 fractions prior to desalting and
 LC-MS/MS analysis (16). Mass spectrometric data were
 collected on an Orbitrap Fusion Lumos mass spectrometer coupled to a
 Proxeon NanoLC-1200 liquid chromatograph. The 100 μm capillary column was
 packed with 35 cm of Accucore 150 resin (2.6 μm, 150 Å; Thermo Fisher
 Scientific). The scan sequence began with an MS1 spectrum (Resolution 60
 000, 400-1600 Th, automatic gain control (AGC) target 400 000, maximum
 injection time 50 ms. Data were acquired using the FAIMS Pro interface
 with three compensation voltages (-40, -60, and -80 V) with each scan
 cycle set as a 1s TopSpeed method. Higher-energy collision dissociation
 (HCD) (Collision energy 37%, AGC target 125,000, maximum injection time 86
 ms, resolution 50,000) was used for MS2 fragmentation and quantification
 analysis. Mass spectra were processed using a
 Comet-based software pipeline (17). MS raw files were converted to pepXML
 for processing. Database searching included <em>Mus musculus
 </em>entries from UniProt. Reversed sequences of all proteins were
 appended to the search database for the target-decoy false discovery rate
 (FDR) analysis (18). Searches were performed using a 50-ppm precursor ion
 tolerance and a 0.02 Da for product ion tolerance to maximize sensitivity
 in conjunction with Comet searches. PSM filtering was performed using a
 linear discriminant analysis as described previously (19). TMTpro tags on
 lysine residues and peptide N termini (+304.207 Da) and
 carbamidomethylation of cysteine residues (+57.021 Da) were set as static
 modifications, and oxidation of methionine residues (+15.995 Da) was set
 as a variable modification. Peptide-spectrum matches (PSMs) were adjusted
 to a 1% FDR. Peptide intensities were quantified by summing reporter ion
 counts across all matching PSMs to give greater weight to more intense
 ions (20). We required a TMT reporter ion summed signal-to-noise of
 greater than 100. Reporter ion intensities were adjusted to correct for
 the isotopic impurities of the different TMTpro reagents according to
 manufacturer specifications. The signal-to-noise (S/N) measurements of
 peptides assigned to each protein were summed, and these values were
 normalized so the sum of the signal for all proteins in each channel was
 equivalent, thereby accounting for equal protein loading (column
 normalization). Finally, each protein abundance was scaled to a percent of
 the total, such that the summed S/N for that protein across all channels
 equaled 100, thereby generating a relative abundance (RA) measurement.
 Islet assignment metadata and the processed proteomics files are in File 3
 Supplemental_tables (see README). Citations: 1.        
 M. P. Keller<em> et al.</em>, Gene loci associated with
 insulin secretion in islets from nondiabetic mice. <em>The Journal
 of Clinical Investigation</em> <strong>129</strong>,
 4419-4432 (2019). PMC6763251 2.        
 M. E. Rabaglia<em> et al.</em>, α-Ketoisocaproate-induced
 hypersecretion of insulin by islets from diabetes-susceptible mice.
 <em>American Journal of Physiology-Endocrinology and
 Metabolism</em> <strong>289</strong>, E218-E224 (2005).
 PMID: 15741243 3.        
 M. E. Capozzi<em> et al.</em>, β Cell tone is defined by
 proglucagon peptides through cAMP signaling. <em>JCI
 Insight</em> <strong>4</strong>,  (2019). PMID:
 30720465 4.        
 S. L. Lewandowski<em> et al.</em>, Pyruvate Kinase Controls
 Signal Strength in the Insulin Secretory Pathway. <em>Cell
 metabolism</em> <strong>32</strong>, 736-750.e735
 (2020). PMC7685238 5.        
 S. Bhatnagar<em> et al.</em>, Positional Cloning of a Type 2
 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of
 Insulin Secretion. <em>PLOS Genetics</em>
 <strong>7</strong>, e1002323 (2011). PMC3188574
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 K. A. Mitok<em> et al.</em>, Islet proteomics reveals genetic
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 <em>The Journal of biological chemistry</em>
 <strong>293</strong>, 5860-5877 (2018). PMC5912463
 7.        
 B. Langmead, C. Trapnell, M. Pop, S. L. Salzberg, Ultrafast and
 memory-efficient alignment of short DNA sequences to the human genome.
 <em>Genome Biology</em> <strong>10</strong>, R25
 (2009). PMC2690996 8.        
 B. Li, C. N. Dewey, RSEM: accurate transcript quantification from RNA-Seq
 data with or without a reference genome. <em>BMC
 Bioinformatics</em> <strong>12</strong>, 323 (2011).
 PMC3163565 9.        
 N. Leng<em> et al.</em>, EBSeq: an empirical Bayes
 hierarchical model for inference in RNA-seq experiments.
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 1035-1043 (2013). PMC3624807 10.      
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 set enrichment analysis web server 2016 update. <em>Nucleic acids
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 PMC4987924 11.      
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 <em>Nature</em> <strong>489</strong>, 57-74
 (2012). PMC3439153 13.      
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 three-dimensional chromatin architecture provides insights into the
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 (2019). PMID:31253982 14.      
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 Selection-MS3. <em>J Proteome Res</em>
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description: These scripts were used in Windows-based R studio with R
 build: platform       x86_64-w64-mingw32        
  <br>arch           x86_64                    
  <br>os             mingw32                   
  <br>system         x86_64, mingw32           
  <br>status                                   
  <br>major          4                         
  <br>minor          0.3                       
  <br>year           2020                      
  <br>month          10                        
  <br>day            10                          <br>svn
 rev        79318                       <br>language      
 R                           <br>version.string R version 4.0.3
 (2020-10-10)<br>nickname       Bunny-Wunnies Freak Out 
 The MATLAB files were used in MATLAB R2018b Academic.
 Instructions for running the MATLAB scripts are included with the relevant
 subfolder.