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β-cell-specific deletion of Zfp148 improves nutrient-stimulated β-cell Ca2+ responses

Citation

Emfinger, Christopher et al. (2022), β-cell-specific deletion of Zfp148 improves nutrient-stimulated β-cell Ca2+ responses, Dryad, Dataset, https://doi.org/10.5061/dryad.bcc2fqzcw

Abstract

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.

Methods

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.

Mice. β-Zfp148KO 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).

Ex vivo islet imaging experiments. 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-Zfp148KO 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 Ca2+ responses. To enhance our ability to measure intracellular islet Ca2+ dynamics, we used a slightly higher [Ca2+] (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).

Ex vivo islet perifusion. 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.

Secreted insulin assay. 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).

Islet RNA isolation and processing. 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).

Determining candidate ZFP148 targets. 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.      

Isolated islet proteomics. 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 Mus musculus 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 et al., Gene loci associated with insulin secretion in islets from nondiabetic mice. The Journal of Clinical Investigation 129, 4419-4432 (2019). PMC6763251

2.         M. E. Rabaglia et al., α-Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice. American Journal of Physiology-Endocrinology and Metabolism 289, E218-E224 (2005). PMID: 15741243

3.         M. E. Capozzi et al., β Cell tone is defined by proglucagon peptides through cAMP signaling. JCI Insight 4,  (2019). PMID: 30720465

4.         S. L. Lewandowski et al., Pyruvate Kinase Controls Signal Strength in the Insulin Secretory Pathway. Cell metabolism 32, 736-750.e735 (2020). PMC7685238

5.         S. Bhatnagar et al., Positional Cloning of a Type 2 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of Insulin Secretion. PLOS Genetics 7, e1002323 (2011). PMC3188574

6.         K. A. Mitok et al., Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion. The Journal of biological chemistry 293, 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. Genome Biology 10, R25 (2009). PMC2690996

8.         B. Li, C. N. Dewey, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). PMC3163565

9.         N. Leng et al., EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035-1043 (2013). PMC3624807

10.       M. V. Kuleshov et al., Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research 44, W90-97 (2016). PMC4987924

11.       E. Y. Chen et al., Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013). PMC3637064

12.       ENCODE, An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74 (2012). PMC3439153

13.       I. Miguel-Escalada et al., Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nature genetics,  (2019). PMID:31253982

14.       J. Navarrete-Perea, Q. Yu, S. P. Gygi, J. A. Paulo, Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3. J Proteome Res 17, 2226-2236 (2018). PMC5994137

15.       C. S. Hughes et al., Ultrasensitive proteome analysis using paramagnetic bead technology. Mol Syst Biol 10, 757 (2014). PMC4299378

16.       J. A. Paulo et al., Quantitative mass spectrometry-based multiplexing compares the abundance of 5000 S. cerevisiae proteins across 10 carbon sources. J Proteomics 148, 85-93 (2016). PMC5035620

17.       J. K. Eng et al., A deeper look into Comet--implementation and features. J Am Soc Mass Spectrom 26, 1865-1874 (2015). PMC4607604

18.       J. E. Elias, S. P. Gygi, Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol Biol 604, 55-71 (2010). PMC2922680

19.       E. L. Huttlin et al., A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174-1189 (2010). PMC3035969

20.       G. C. McAlister et al., Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal Chem 84, 7469-7478 (2012). PMC3715028

Usage Notes

These scripts were used in Windows-based R studio with R build:

platform       x86_64-w64-mingw32          
arch           x86_64                      
os             mingw32                     
system         x86_64, mingw32             
status                                     
major          4                           
minor          0.3                         
year           2020                        
month          10                          
day            10                          
svn rev        79318                       
language       R                           
version.string R version 4.0.3 (2020-10-10)
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.

Funding

American Diabetes Association, Award: 7-21-PDF-157

National Institute of Diabetes and Digestive and Kidney Diseases, Award: R01DK101573

National Institute of Diabetes and Digestive and Kidney Diseases, Award: R01DK127637

National Institute of Diabetes and Digestive and Kidney Diseases, Award: T32DK007665

National Institute of Diabetes and Digestive and Kidney Diseases, Award: F31DK126403

National Institute of Diabetes and Digestive and Kidney Diseases, Award: R01DK113103

National Institute of Diabetes and Digestive and Kidney Diseases, Award: R01GM067945

U.S. Department of Veterans Affairs, Award: I01B005113

National Institute of Diabetes and Digestive and Kidney Diseases, Award: R01GM132129

Ministerio de Ciencia e Innovación, Award: PID2019-106640RB-I00