Syntaxin 4 (STX4), a plasma membrane–localized SNARE protein, regulates human islet β-cell insulin secretion and preservation of β-cell mass. We found that human type 1 diabetes (T1D) and NOD mouse islets show reduced β-cell STX4 expression, consistent with decreased STX4 expression, as a potential driver of T1D phenotypes. To test this hypothesis, we generated inducible β-cell–specific STX4-expressing NOD mice (NOD-iβSTX4). Of NOD-iβSTX4 mice, 73% had sustained normoglycemia vs. <20% of control NOD (NOD-Ctrl) mice by 25 weeks of age. At 12 weeks of age, before diabetes conversion, NOD-iβSTX4 mice demonstrated superior whole-body glucose tolerance and β-cell glucose responsiveness than NOD-Ctrl mice. Higher β-cell mass and reduced β-cell apoptosis were also detected in NOD-iβSTX4 pancreata compared with pancreata of NOD-Ctrl mice. Single-cell RNA sequencing revealed that islets from NOD-iβSTX4 had markedly reduced interferon-γ signaling and tumor necrosis factor-α signaling via nuclear factor-κB in islet β-cells, including reduced expression of the chemokine CCL5; CD4+ regulatory T cells were also enriched in NOD-iβSTX4 islets. These results provide a deeper mechanistic understanding of STX4 function in β-cell protection and warrant further investigation of STX4 enrichment as a strategy to reverse or prevent T1D in humans or protect β-cell grafts.

Type 1 diabetes (T1D) results from autoimmune dysfunction and destruction of the insulin-producing β-cells of the endocrine pancreas. The exact causes of T1D are not yet known, but genetic and environmental factors are thought to be important (1). Loss of functional β-cell mass can progress for months or years before any symptoms appear, and the cumulative exposure to hyperglycemia can damage major organs, including the heart, blood vessels, nerves, eyes, and kidneys. The role of β-cells in their own demise as well as immune escape has become increasingly evident (2). To reduce the risk of T1D-associated complications and death, it is imperative to protect the ability of pancreatic β-cells to maintain normoglycemia while also protecting β-cells from autoimmune destruction.

Among molecules involved in β-cell vitality, resilience, and function, an abundance of soluble NSF attachment protein receptor (SNARE) proteins in β-cells is a rate-limiting feature for insulin release (3,4). In particular, syntaxin 4 (STX4) is required for glucose-stimulated insulin secretion in vivo and to resist diabetogenic β-cell loss (5). Enrichment of STX4 expression in pancreatic β-cells promotes β-cell function by increasing the capacity for insulin granule release during each phase of glucose-stimulated insulin secretion and preserves β-cell mass by reducing nuclear factor-κB (NF-κB) signaling in response to proinflammatory cytokine exposure (5,6). STX4-induced suppression of NF-κB signaling is based on the ability of STX4 to suppress cytokine-induced inhibitor of κB (IκB)β degradation, preventing translocation of IκBβ:NF-κB-p50 to the nucleus (7), which prevents expression of CXCL9, CXCL10, and CXCL11, three chemokine ligands known to drive β-cell death in autoimmune diabetes in mice (5,7) and insulitis in human T1D (8).

The activation of immune cells by distressed β-cells is an early event in T1D etiology, and identifying new therapeutic targets in both islets and the immune system to preserve functional β-cell mass is a major goal of current diabetes research. However, immune intervention therapies tested to date have failed to induce durable preservation of β-cell function (9). Teplizumab, a monoclonal antibody immunotherapy, transiently enhanced β-cell function and partially delayed the onset of clinical T1D compared with a randomized control group (10). This therapy dampened the immune response and resulted in a relative sparing of regulatory T cells (Tregs) compared with effector T cells (Teffs) (1012). Deficiency of islet cell–localized Tregs is hypothesized to be a primary cause of autoimmune diabetes, and Treg-based therapy has recently received attention for intervention of T1D (1315). But perhaps this immunotherapy is not enough for halting the progression of T1D. We may also need β-cell therapy to preserve β-cell function.

The NOD mouse is widely used as an in vivo surrogate model for human T1D and T-cell inflammation in the pancreatic islets that is particularly useful for its timed disease process before diabetes manifestation (16). In the prediabetic phase, pancreatic islets become infiltrated by macrophages and dendritic cells followed by cytotoxic T cells. This insulitis starts as early as 4 weeks of age in NOD females (17,18), resulting in diabetes onset by 17–18 weeks of age in ∼50% of females (19,20). By using new inducible β-cell–specific STX4-overexpressing NOD mice (NOD-iβSTX4), we tested the hypothesis that STX4 enrichment of β-cells in vivo can prevent autoimmune diabetes onset.

Animals

Animals were maintained under protocols approved by the City of Hope institutional animal care and use committee and according to the National Research Council Guidelines for the Care and Use of Laboratory Animals. Female NOD (Research Resource Identifier [RRID]: IMSR_JAX: 001976) and MHC-matched diabetes-resistant control strain for NOD mice (NOR) (RRID: IMSR_JAX: 002050) were obtained from The Jackson Laboratory (JAX) (Bar Harbor, ME). Conversion of controls in our NOD colony matched that of the vendor. Our C57BL/6J.TRE-STX4 mice (5) were mated with NOD mice followed by “speed congenic” backcrossing using mapped polymorphic microsatellites for genotypic selection (JAX). Incorporation reached >99% of the NOD genome at the fifth generation with no insulin-dependent diabetes mellitus disturbances detected by genome scan analyses. Then, commercially available NOD.RIP-rtTA mice [RRID: IMSR_JAX: 005734 NOD/Lt-Tg(Ins2-rtTA)1Ach/AchJ] were bred with our custom NOD.TRE-STX4 mice to generate NOD-iβSTX4 mice. To induce the STX4 transgene, the mice were treated with doxycycline (Dox) in the drinking water (1 mg/mL) at the time of weaning (4 weeks old). Uninduced double-transgenic mice were used as controls (NOD-Ctrl).

Human Pancreas Sections and Immunofluorescence

Formalin-fixed paraffin-embedded human pancreas donor sections were obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD) program (https://www.jdrfnpod.org) (Supplementary Table 1). Pancreatic sections were immunostained with guinea pig anti-insulin (cat. no. A0564; Agilent Dako, Santa Clara, CA) and rabbit anti-STX4 (cat. no. AB5330; Chemicon International, Temecula, CA) antibodies. We used Alexa Fluor 488–conjugated goat anti-guinea pig (cat. no. A-11073; Thermo Fisher Scientific, Waltham, MA) and Alexa Fluor 568–conjugated goat anti-rabbit (cat. no. A-11011; Thermo Fisher Scientific) secondary antibodies and visualized the sections using an LSM 700 confocal microscope (Carl Zeiss, Oberkochen, Germany). All human T1D samples were prepared and processed at the same time. Islet immunofluorescence was assessed by imaging 8–12 islets (grouping of 2 or 3 insulin-positive islets per subject) per group. Analysis was performed to quantify the fluorescence intensity of STX4 normalized to insulin using hybrid cell counting software (Keyence, Itasca, IL).

Human Islet Culture and Cytokine Treatment

Human islets were obtained from the City of Hope Islet Core or the Integrated Islet Distribution Program (donor information, Supplementary Table 2), allowed to recover, and then handpicked as previously described (5). Human islets were treated with a cytokine cocktail (100 ng/mL interferon-γ [IFN-γ], 10 ng/mL tumor necrosis factor-α [TNF-α], and 5 ng/mL interleukin-1β [IL-1β]; ProSpec, East Brunswick, NJ) for 72 h. The cytokine-containing medium was replaced every 24 h.

Immunoblotting of Mouse Pancreas

Pancreatic islets were isolated from mice at 13 weeks of age and lysed in 1% Nonidet P-40 lysis buffer (1% Nonidet P-40, 25 mmol/L HEPES [pH 7.4], 10% glycerol, 50 μmol/L sodium fluoride, 10 mmol/L sodium pyrophosphate, 1 mmol/L sodium vanadate, 137 mmol/L sodium chloride, 1 mmol/L phenylmethylsulfonyl fluoride, 1 μg/mL pepstatin, and 10 μg/mL aprotinin), and then cleared detergent lysates were used for immunoblotting by a method described previously (5). STX4 antibody was generated as previously described (5); STX1A (cat. no. SAB1404425; Sigma-Aldrich, St. Louis, MO), Munc18-1 (cat. no. 116 011; Synaptic Systems GmbH, Göttingen, Germany), and tubulin (cat. no. T5168; Sigma-Aldrich) antibodies were used as previously described (6).

Intraperitoneal Glucose Tolerance Test, Plasma Insulin Measurement, and Islet Perifusion

Female mice were fasted for 6 h (0800–1400 h) before the intraperitoneal glucose tolerance test (IPGTT) at a dose of 1 g glucose/kg body weight. The mean glucose concentration in tail vein blood (mg/dL) was recorded from four to six mice per group over five time points, including before the injection of glucose (time 0). For plasma insulin response to an acute glucose challenge, female mice were fasted for 6 h before an acute challenge of 2 g glucose/kg body weight injected intraperitoneally. Blood was collected from the tail vein before and 10 min after glucose injection, and the plasma was analyzed using a sensitive rat insulin radioimmunoassay kit (cat. no. SRI-13K; Millipore). For islet perifusion, islets were isolated as previously described (21) from 13-week-old female NOD-Ctrl and NOD-iβSTX4 mice. Forty islets were handpicked onto each column for perifusion analysis at a flow rate of 0.3 mL/min, and insulin secreted into eluted fractions was quantitated by radioimmunoassay.

Morphometric Assessment of Islet β-Cell Mass, TUNEL Assay, and Insulitis

Mouse islet morphometry and TUNEL staining were conducted as described previously (5). For histological scoring of insulitis, slides containing paraformaldehyde-fixed paraffin-embedded whole pancreas were stained with hematoxylin-eosin. The score given for each mouse was based on evaluation of all islets identified by morphology using a standard scale (19,20) (0 = no infiltrate, 1 = peri-infiltrate only, 2 = infiltrating cells extending peri-islet <50%, 3 = invasive insulitis >50%) as previously described (22).

Single-Cell RNA Sequencing

Islets were isolated from female NOD-Ctrl or NOD-iβSTX4 transgenic mice and dispersed into single cells with TrypLE (Invitrogen, Carlsbad, CA). Cell numbers and viability were measured using a TC20 Automated Cell Counter (Bio-Rad); only samples showing at least 70% viability were used. Cells were then loaded onto a chromium controller (10x Genomics, Pleasanton, CA) to generate single-cell Gel Bead-In Emulsions (GEMs) captured in droplets at a targeted cell recovery of 3,000 cells. GEM reverse transcriptions were performed in a Veriti 96-Well Thermal Cycler (Thermo Fisher Scientific). The Chromium Single Cell 3' Reagent Kit V2 (10x Genomics) was used to process samples into single-cell RNA sequencing (scRNA-seq) libraries according to the manufacturer’s protocol. The libraries were sequenced with the paired-end setting of 28 cycles of read 1, 101 cycles of read 2, and 8 cycles of the index read on the Illumina NovaSeq 6000 platform at the Translational Genomics Research Institute (TGen) (Phoenix, AZ). Raw sequencing data sets were aligned back to the mouse genome (mm10 v.1.2.0), using the cellranger count (v.3.1.0) tool (10x Genomics) to produce expression data at a single-cell resolution. The R package Seurat (v.3.2.3) (23) was used for gene and cell filtration, normalization, variable gene finding, and clustering analysis as well as for principal component analysis (PCA) and uniform manifold approximation and projection dimension reduction. Data were then merged using standard integration and log-normalized for subsequent analysis. PCA was performed for dimension reduction, and the first 21 principal components were used for clustering analysis. Clusters were visualized with t-distributed stochastic neighbor embedding using Seurat. Differentially expressed genes in each cluster were discovered with the function FindAllMarkers using logfc.threshold = 10100 and min.pct = 0.01. Gene ontology was performed with the full list of differentially expressed genes from each cluster using the gene set enrichment analysis (GSEA) function implemented in the clusterProfiler package (v.3.14.3) (24) and plotted with ggplot2 (v.3.3.2).

INS-1 832/13 Cell Culture, Transduction, Transfection, and Cytokine Treatments

The INS-1 832/13 β-cell line was cultured as previously described (5), adenovirally transduced (multiplicity of infection = 100) for 2 h, washed with PBS, and incubated for 48 h. For knockdown of STX4, siRNAs (siCtrl [cat. no. 1027281] or siSTX4 [cat. no. SI0316089]) were purchased from QIAGEN (Valencia, CA) and were transfected (50 nmol/L) with RNAiMAX (Invitrogen). For the last 16 h of incubation, the cells were exposed to medium containing proinflammatory cytokines, as previously described (5), and RNA was isolated for quantitative real-time PCR.

Quantitative Real-Time PCR

Samples were lysed for total RNA extraction (RNeasy; QIAGEN, Germantown, MD), and mRNA levels (Supplementary Table 3) were quantified using real-time PCR as previously described (5).

Flow Cytometry Staining and Analysis

Pancreatic lymphocytes, pancreatic lymph nodes (PLNs), and spleen were prepared as single-cell suspensions. For FACS analysis, the cell suspension was first incubated with anti-mouse CD16/CD32 (clone no. 2.4G2, cat. no. BE0307; Bio X Cell, Lebanon, NH) to diminish nonspecific binding and stained with LIVE/DEAD (cat. no. L34966; Thermo Fisher Scientific) for 5 min on ice. The cells were stained with FITC-conjugated T-cell receptor-β (TCRβ) (clone no. H57-597, cat. no. 11-5961-82; Thermo Fisher Scientific), CD4 (clone no. H129.19, cat. no. 740684; BD Biosciences), CD44 [clone no. IM7(RUO), cat. no. 740215; BD Biosciences], CD62L (clone no. MEL-14, cat. no. 47-0621-82; Thermo Fisher Scientific), and Foxp3 (clone no. NRRF-30, cat. no. 12-4771-80; Invitrogen). For intracellular Foxp3 detection, cells were fixed and washed with the Foxp3 Fixation/Permeabilization Kit (cat. no. 72-5775-40; Thermo Fisher Scientific). All antibody incubations were performed at 4°C for 30 min (isotype controls were included). Cells were immediately analyzed with flow cytometry (Fortessa; BD Biosciences). The data were analyzed with FlowJo software (FlowJo, LLC) using settings to remove doublets as well as dying and dead cells.

Statistical Analysis

All data were evaluated for statistical significance by an unpaired two-tailed Student t test for the comparison of two groups (NOD-Ctrl vs. NOD-iβSTX4) using GraphPad Prism 8.4.3. Data are expressed as the mean ± SEM and are considered statistically significant when P < 0.05.

Data and Resource Availability

The data sets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. No applicable resources were generated during this study.

STX4 Expression Is Reduced in New-Onset T1D Human Insulin-Positive Islets and Normoglycemic NOD Mouse Islets

To investigate changes in STX4 levels in the pancreatic islets of humans with T1D, we obtained paraffin-embedded slides from cadaveric donors with new-onset pediatric T1D (≤3 years with T1D; n = 3) and age-matched control subjects without diabetes (n = 4) (Supplementary Table 1) from nPOD. We observed decreased STX4 immunofluorescence intensity specifically in the insulin-positive (INS+) cells from new-onset T1D islets compared with abundant levels of STX4 intensity in the INS+ cells of the control subjects without diabetes (Fig. 1A); fewer INS+ cells were observed in islets of the T1D donors, as expected. T1D is associated with elevated levels of circulating proinflammatory cytokines, which damage β-cells (25). Thus, we used proinflammatory cytokine treatment as a model for the diabetogenic environment to ask whether T1D-like stimuli can reduce STX4 expression. Treatment of human cadaveric nondiabetic islets (Supplementary Table 2) ex vivo with a cytokine cocktail (IL-1β, TNF-α, and IFN-γ) reduced STX4 mRNA levels by ∼50% compared with islets exposed to vehicle only (Ctrl) (Fig. 1B).

Figure 1

STX4 levels are reduced in human new-onset T1D INS+ cells, cytokine-stressed human islets, and normoglycemic NOD mouse islets. A: Representative immunostaining for STX4 and INS in a pancreatic islet from a 17-year-old male with new-onset T1D compared with a 17-year-old male without diabetes (Non-T1D). The bar graph shows quantification of INS+ β-cell area/islet and STX4 intensity in INS+ β-cells (n = 3–4 human age/sex/ethnicity-matched donor sets comprising two to three islets per donor). Scale bars = 20 μm. B: Quantitative real-time PCR for STX4 mRNA in human islets exposed to proinflammatory cytokines for 72 h, relative to tubulin mRNA. Data represent the mean ± SEM of four independent sets of donor islets. C and D: Immunoblotting (IB) and quantification of STX4 protein levels, normalized to tubulin content, in islets from 13-week-old female normoglycemic NOD and control age-matched NOR mice. Data represent the mean ± SEM of islets isolated from eight mice, four per strain. *P < 0.05. Cyt, cytokines; Tub, tubulin.

Figure 1

STX4 levels are reduced in human new-onset T1D INS+ cells, cytokine-stressed human islets, and normoglycemic NOD mouse islets. A: Representative immunostaining for STX4 and INS in a pancreatic islet from a 17-year-old male with new-onset T1D compared with a 17-year-old male without diabetes (Non-T1D). The bar graph shows quantification of INS+ β-cell area/islet and STX4 intensity in INS+ β-cells (n = 3–4 human age/sex/ethnicity-matched donor sets comprising two to three islets per donor). Scale bars = 20 μm. B: Quantitative real-time PCR for STX4 mRNA in human islets exposed to proinflammatory cytokines for 72 h, relative to tubulin mRNA. Data represent the mean ± SEM of four independent sets of donor islets. C and D: Immunoblotting (IB) and quantification of STX4 protein levels, normalized to tubulin content, in islets from 13-week-old female normoglycemic NOD and control age-matched NOR mice. Data represent the mean ± SEM of islets isolated from eight mice, four per strain. *P < 0.05. Cyt, cytokines; Tub, tubulin.

To investigate whether islet STX4 protein levels are decreased before T1D onset, we compared normoglycemic NOD mice to control MHC-matched congenic NOR mice, which do not develop diabetes despite sharing 88% of their genome with NOD mice (26). Pancreatic islets from 13-week-old female NOD mice contained ∼40% less STX4 protein than age/sex-matched NOR islets (Fig. 1C and D). The average nonfasted blood glucose levels of NOD and NOR mice were <250 mg/dL at 13 weeks, confirming that the NOD mice were nondiabetic/normoglycemic. Overall, these data are consistent with a model wherein early loss of STX4 contributes to the loss of functional β-cell mass in T1D.

NOD-iβSTX4 Mice Are Protected From Glucose Intolerance and Hyperglycemia

Our Dox-inducible NOD-iβSTX4 mice have delayed diabetes onset (Fig. 2A). The cumulative incidence of diabetes in the NOD-iβSTX4 mice was markedly decreased compared with multiple controls: Dox-treated single-transgenic NOD-RIP-rtTA/+ [NOD-Ctrl-sTg (+)Dox] mice and non-Dox–treated double-transgenic NOD-RIP-rtTA/+;TRE-STX4/+ [NOD-Ctrl-dTg, (−)Dox]. The conversion rate of our NOD mouse colony is similar to the founding colony at JAX (Supplementary Fig. 1A). Low-dose Dox (1 mg/mL) in drinking water increased STX4 expression by threefold in islets (Fig. 2B and Supplementary Fig. 1B) but not in hypothalamus or cerebellum (Supplementary Fig. 1C), avoiding issues reported to be linked to administration of high-dose Dox and/or Dox delivery in food to NOD mice (27). NOD-iβSTX4 mice at 12 weeks of age showed improved glucose tolerance (Fig. 2C) and a rise in plasma insulin within 10 min of glucose injection during the IPGTT (Fig. 2D). No glucose-induced rise in plasma insulin was detectable in age-matched NOD-Ctrl mice, as reported by others (28).

Figure 2

Induced STX4 expression in NOD-iβSTX4 mice deters the onset of hyperglycemia. A: Nonfasted blood glucose levels from Dox-inducible NOD-iβSTX4 mice (n = 11), Dox-treated single-transgenic NOD-RIP-rtTA/+ [NOD-Ctrl-sTg (+)Dox] mice (n = 9), and non-Dox–treated double-transgenic NOD-RIP-rtTA/+;TRE-STX4/+ [NOD-Ctrl-dTg, (−)Dox] mice (n = 5). B: Dox-induced STX4 protein (35 kDa) levels in islets from Dox-treated NOD-iβSTX4 mice compared with two different types of littermate NOD-Ctrl: NOD-Ctrl-dTg (−)Dox and NOD-Ctrl-sTg (+)Dox. Bands represent the protein content in islets from three mice each. Vertical dashed line indicates splicing of lanes from within the same gel exposure. C: IPGTT in 12-week-old female Dox-induced NOD-iβSTX4 vs. NOD-Ctrl (dTg, no Dox) mice. Data represent the mean ± SEM of four to six mice per group. D: Plasma insulin content was measured in 13-week-old mice, shown as stimulation index (10 min after glucose administration/fasted 6 h (glucose/basal [Glu/Bas]), in the assays in panel C. n = 4–6. E: Islets were isolated from 13 week-old-female NOD-iβSTX4 or NOD-Ctrl and handpicked into groups of 40 and layered onto Cytodex bead columns for parallel perifusion analysis. Islets were preincubated for 30 min in low glucose (2.8 mmol/L) followed by basal sample collection (1–10 min) at low glucose to establish a baseline. Glucose was then elevated to 20 mmol/L for 35 min. Eluted fractions were collected at 1-min intervals at a flow rate of 0.3 mL/min, and secreted insulin was measured by radioimmunoassay. F: Quantitation of the area under the curve (AUC) for first-phase (11–18 min) and second-phase (19–45 min) insulin secretion from islets isolated from NOD-iβSTX4 or NOD-Ctrl, normalized to baseline, as described previously (21). G: Islets were solubilized in Laemmli sample buffer and resolved by 10% SDS-PAGE for immunoblotting. *P < 0.05. IB, immunoblot; Tub, tubulin.

Figure 2

Induced STX4 expression in NOD-iβSTX4 mice deters the onset of hyperglycemia. A: Nonfasted blood glucose levels from Dox-inducible NOD-iβSTX4 mice (n = 11), Dox-treated single-transgenic NOD-RIP-rtTA/+ [NOD-Ctrl-sTg (+)Dox] mice (n = 9), and non-Dox–treated double-transgenic NOD-RIP-rtTA/+;TRE-STX4/+ [NOD-Ctrl-dTg, (−)Dox] mice (n = 5). B: Dox-induced STX4 protein (35 kDa) levels in islets from Dox-treated NOD-iβSTX4 mice compared with two different types of littermate NOD-Ctrl: NOD-Ctrl-dTg (−)Dox and NOD-Ctrl-sTg (+)Dox. Bands represent the protein content in islets from three mice each. Vertical dashed line indicates splicing of lanes from within the same gel exposure. C: IPGTT in 12-week-old female Dox-induced NOD-iβSTX4 vs. NOD-Ctrl (dTg, no Dox) mice. Data represent the mean ± SEM of four to six mice per group. D: Plasma insulin content was measured in 13-week-old mice, shown as stimulation index (10 min after glucose administration/fasted 6 h (glucose/basal [Glu/Bas]), in the assays in panel C. n = 4–6. E: Islets were isolated from 13 week-old-female NOD-iβSTX4 or NOD-Ctrl and handpicked into groups of 40 and layered onto Cytodex bead columns for parallel perifusion analysis. Islets were preincubated for 30 min in low glucose (2.8 mmol/L) followed by basal sample collection (1–10 min) at low glucose to establish a baseline. Glucose was then elevated to 20 mmol/L for 35 min. Eluted fractions were collected at 1-min intervals at a flow rate of 0.3 mL/min, and secreted insulin was measured by radioimmunoassay. F: Quantitation of the area under the curve (AUC) for first-phase (11–18 min) and second-phase (19–45 min) insulin secretion from islets isolated from NOD-iβSTX4 or NOD-Ctrl, normalized to baseline, as described previously (21). G: Islets were solubilized in Laemmli sample buffer and resolved by 10% SDS-PAGE for immunoblotting. *P < 0.05. IB, immunoblot; Tub, tubulin.

These data suggest that enhanced STX4 expression in pancreatic β-cells of NOD mice preserves β-cell function. To investigate this, isolated islets from NOD-Ctrl and NOD-iβSTX4 mice at 13 weeks old were perifused in parallel chambers. The NOD-iβSTX4 islets showed improved biphasic insulin secretion compared with NOD-Ctrl islets (Fig. 2E), as quantified by area under the curve analysis (Fig. 2F). The NOD-iβSTX4 islets retrieved from perifusion chambers were verified for STX4 overexpression by immunoblotting along with analyses of other SNARE and SNARE regulatory proteins (Fig. 2G). The improved diabetes-free status and glucose tolerance were not related to changes in body weight or tissue weights between two groups (Supplementary Table 4) and add in vivo support of our proposition that STX4 protects β-cells from T1D.

NOD-iβSTX4 Mice Preserve β-Cell Mass by Reducing β-Cell Apoptosis

Next, we evaluated the islets of the NOD-iβSTX4 mice. Higher β-cell mass and reduced insulitis were detected in female NOD-iβSTX4 pancreata compared with those of age- and sex-matched NOD-Ctrl mice (13-week-old females) (Fig. 3A–C). In addition, islets from NOD-iβSTX4 mice contained ∼80% fewer TUNEL+:INS+ cells than islets from NOD-Ctrl mice, indicating reduced apoptosis (Fig. 3D and E). To determine whether NOD-iβSTX4 mice released more insulin before insulitis or the insulitis in these mice was slower, young (7 weeks old) mice were examined. NOD-Ctrl mice displayed a glucose-induced increase in plasma insulin level and had low levels of severe insulitis; NOD-iβSTX4 showed similar plasma insulin and insulitis levels (Supplementary Fig. 2A and B). These data suggest that STX4 enrichment protects islet β-cells against inflammation-induced β-cell damage, including apoptosis.

Figure 3

Preserved β-cell mass and reduced β-cell apoptosis in NOD-iβSTX4 mice. A: β-Cell mass. B: Insulitis scores (defined in Research Design and Methods). C: Representative images of islet scoring. Scale bars = 50 μm. D and E: TUNEL immunofluorescent quantification and staining of TUNEL+ β-cells (percentage of total β-cells) from three to four sets of littermate NOD-iβSTX4 (green) or NOD-Ctrl (yellow) mice (13 weeks old). Scale bars = 100 μm. Arrows denote representative TUNEL+ cells. *P < 0.05.

Figure 3

Preserved β-cell mass and reduced β-cell apoptosis in NOD-iβSTX4 mice. A: β-Cell mass. B: Insulitis scores (defined in Research Design and Methods). C: Representative images of islet scoring. Scale bars = 50 μm. D and E: TUNEL immunofluorescent quantification and staining of TUNEL+ β-cells (percentage of total β-cells) from three to four sets of littermate NOD-iβSTX4 (green) or NOD-Ctrl (yellow) mice (13 weeks old). Scale bars = 100 μm. Arrows denote representative TUNEL+ cells. *P < 0.05.

Transcriptomic Analysis of Isolated Pancreatic Islet β-Cells From NOD-iβSTX4 Mice

To determine the molecular mechanisms underpinning the protective effect of STX4 in NOD-iβSTX4 pancreatic β-cells, we used scRNA-seq. Individual β-cells dispersed from isolated pancreatic islets from normoglycemic 13-week-old female NOD-Ctrl or NOD-iβSTX4 littermate mice were subjected to scRNA-seq using a 10x Genomics platform. The top nine principal cell markers from PCA were used for clustering (Fig. 4A). Each endocrine cell cluster was individually evaluated, with ectopic expression of STX4 found only in pancreatic β-cells (defined as cluster 0 [INS+ cells] depicted in the violin plot in Fig. 4B). No other endocrine cells, such as pancreatic α-cells, defined as cluster 2 (glucagon-positive cells) (Fig. 4C) or pancreatic δ-cells, defined as cluster 6 (somatostatin-positive cells) (Fig. 4D), showed ectopic STX4. A t-distributed stochastic neighbor embedding plot was used to visualize the data in a two-dimensional subspace, which led to identification of 19 major clusters (0–18) (Supplementary Fig. 3A), and enhanced expression of the STX4 gene was detected only in pancreatic β-cells (expression cut off 2.2) (Supplementary Fig. 3B). No differences were detected in anti-apoptotic genes, such as Bcl-2, Bcl-xL, Mcl-1, Bcl-w, A-1, Bfl-M, NR13, and Brag-1, in INS+ cells from NOD-iβSTX4 versus NOD-Ctrl mice.

Figure 4

Identification of islet cell types in pancreatic islets from NOD-iβSTX4 mice by scRNA-seq analysis. A: Two single-cell preparations from pancreatic islets isolated from three NOD-Ctrl mice or four NOD-iβSTX4 mice (13 weeks old) were used for scRNA-seq. Violin plots of the expression for nine islet cell type–specific markers based on a single-cell transcriptomic map of the mouse pancreas (45) are shown for the 19 clusters that were identified. B: Violin plots are shown for STX4 expression in β-cells from NOD-Ctrl vs. NOD-iβSTX4 mice. C: Violin plots are shown for STX4 expression in α-cells from NOD-Ctrl vs. NOD-iβSTX4 mice. D: Violin plots are shown for STX4 expression in δ-cells from NOD-Ctrl vs. NOD-iβSTX4 mice.

Figure 4

Identification of islet cell types in pancreatic islets from NOD-iβSTX4 mice by scRNA-seq analysis. A: Two single-cell preparations from pancreatic islets isolated from three NOD-Ctrl mice or four NOD-iβSTX4 mice (13 weeks old) were used for scRNA-seq. Violin plots of the expression for nine islet cell type–specific markers based on a single-cell transcriptomic map of the mouse pancreas (45) are shown for the 19 clusters that were identified. B: Violin plots are shown for STX4 expression in β-cells from NOD-Ctrl vs. NOD-iβSTX4 mice. C: Violin plots are shown for STX4 expression in α-cells from NOD-Ctrl vs. NOD-iβSTX4 mice. D: Violin plots are shown for STX4 expression in δ-cells from NOD-Ctrl vs. NOD-iβSTX4 mice.

We next used Hallmark GSEA to analyze for effects of STX4 in NOD β-cells upon a variety of signaling pathways. In NOD-iβSTX4 β-cells, the inflammatory response, IFN-γ response, and TNF-α signaling via NF-κB gene sets were significantly decreased compared with NOD-Ctrl β-cells (Fig. 5A). However, these signaling pathways remained unchanged in NOD-iβ STX4 α-cells (Supplementary Fig. 4A) or δ-cells (Supplementary Fig. 5A) compared with NOD-Ctrl cells, suggesting specificity for β-cells. The signaling pathways were individually analyzed to reveal the identity and level of change for each gene by volcano plot (Fig. 5B–D). However, these signaling pathways in α-cells (Supplementary Fig. 4B–D) or δ-cells (Supplementary Fig. 5B–D) were similarly unchanged, unlike changes observed in the β-cells. Ingenuity Pathway Analysis (IPA) (Ingenuity Systems, Redwood City, CA) was also performed to identify any biological networks and signaling cascades (Fig. 5E), and reduced CCL5 expression was identified as a main factor in the STX4-mediated inhibition of NF-κB signaling.

Figure 5

Transcriptomic analysis of isolated pancreatic islets from NOD-Ctrl or NOD-iβSTX4 mice. A: Bubble plot of Hallmark GSEA from comparisons of expression profiles between NOD-iβSTX4 and NOD-Ctrl in β-cells (from 13-week-old mice as described in Fig. 4). The graph shows the top 10 enhanced or decreased signaling pathways. Color represents q value (adjusted P value). The size of each bubble reflects the number of differentially expressed genes. B: Volcano plot for genes in the inflammatory response gene set in β-cells. Red dots indicate expression changes of greater than ±1.2-fold with P < 0.05. Blue dots indicate expression changes less than ±1.2-fold with P < 0.05. Gray dots indicate expression changes less than ±1.2-fold with P ≥ 0.05. C: Volcano plot for genes in the IFN-γ response gene set in β-cells. D: Volcano plot for genes in the TNF-α signaling via gene NF-κB set in β-cells. E: The same gene set from Hallmark GSEA was evaluated using the IPA network to generate the signaling network. Data were analyzed using IPA (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). F: Evaluation of CCL5 and STX4 mRNA levels after 16 h of cytokine (Cyt) (TNF-α, IL-1β, and IFN-γ) exposure in INS-1 832/13 β-cells transduced to express STX4 with an adenoviral vector containing the rat insulin promoter (Ad-RIP-STX4) or the vector control (Ad-RIP-Ctrl) (i and ii). The adenoviral control was set to 1.0-fold for normalization of each set. Evaluation of CCL5 and STX4 mRNA (iii and iv) as in i and ii for INS-1 832/13 cells transfected with STX4 siRNA compared with a nontargeting control (siRNA-Ctrl). Data represent the mean ± SEM of three independent sets of cell passages. *P < 0.05. FC, fold change; FDR, false discovery rate; NES, normalized enrichment score.

Figure 5

Transcriptomic analysis of isolated pancreatic islets from NOD-Ctrl or NOD-iβSTX4 mice. A: Bubble plot of Hallmark GSEA from comparisons of expression profiles between NOD-iβSTX4 and NOD-Ctrl in β-cells (from 13-week-old mice as described in Fig. 4). The graph shows the top 10 enhanced or decreased signaling pathways. Color represents q value (adjusted P value). The size of each bubble reflects the number of differentially expressed genes. B: Volcano plot for genes in the inflammatory response gene set in β-cells. Red dots indicate expression changes of greater than ±1.2-fold with P < 0.05. Blue dots indicate expression changes less than ±1.2-fold with P < 0.05. Gray dots indicate expression changes less than ±1.2-fold with P ≥ 0.05. C: Volcano plot for genes in the IFN-γ response gene set in β-cells. D: Volcano plot for genes in the TNF-α signaling via gene NF-κB set in β-cells. E: The same gene set from Hallmark GSEA was evaluated using the IPA network to generate the signaling network. Data were analyzed using IPA (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). F: Evaluation of CCL5 and STX4 mRNA levels after 16 h of cytokine (Cyt) (TNF-α, IL-1β, and IFN-γ) exposure in INS-1 832/13 β-cells transduced to express STX4 with an adenoviral vector containing the rat insulin promoter (Ad-RIP-STX4) or the vector control (Ad-RIP-Ctrl) (i and ii). The adenoviral control was set to 1.0-fold for normalization of each set. Evaluation of CCL5 and STX4 mRNA (iii and iv) as in i and ii for INS-1 832/13 cells transfected with STX4 siRNA compared with a nontargeting control (siRNA-Ctrl). Data represent the mean ± SEM of three independent sets of cell passages. *P < 0.05. FC, fold change; FDR, false discovery rate; NES, normalized enrichment score.

CCL5, also known as RANTES, part of the TNF-α signaling pathway via NF-κB, was significantly downregulated in NOD-iβSTX4 β-cells compared with controls (Fig. 5D). Because CCL5 encodes a chemokine that recruits leukocytes into the inflammatory site (29), this served as an important validation of our scRNA-seq and bioinformatics. It was also consistent with our hypothesis that the effects of STX4 enrichment are mediated by NF-κB, similar to our prior finding that CXCL10 is regulated by STX4 using human islets in bulk RNA-seq (5). To validate the scRNA-seq findings, we transduced INS-1 832/13 β-cells with adenoviral control or adenoviral STX4 expression and exposed them to proinflammatory cytokines for 16 h for quantitative real-time PCR evaluation of CCL5. The levels of cytokine-induced CCL5 were reduced in STX4-enriched cells compared with control cells; STX4 knockdown had the reverse effect, increasing CCL5 levels (Fig. 5F). Reduction of CXCL10 levels by STX4 enrichment served as a positive control (Supplementary Fig. 6). Thus, our results reveal an inverse association between STX4 expression and CCL5 expression, supporting our hypothesis that STX4 regulates inflammatory responses in β-cells.

Transcriptomic Analysis of Islet Cell–Localized T Cells From NOD-iβSTX4 Mice

Given the decreased inflammatory response in β-cells from NOD-iβSTX4 mice, we next analyzed the T-cell population. T cells are identified by the CD3+ marker and are distributed in clusters 1, 4, 9, 13, 16, and 17. CD4+ T cells are in clusters 1, 4, 13, and 17, and CD4+ Foxp3+ (Treg) cells are in cluster 1, whereas CD8+ T cells are in cluster 9 (Fig. 6A). The gene set from NOD-iβSTX4 was normalized to NOD-Ctrl and then analyzed using the Hallmark GSEA with the CD4+ T-cell population (Fig. 6B). The analysis revealed that IL-2/STAT5 signaling is increased, correlating with the induction of Tregs (30). FACS studies showed that the ratio of Tregs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3+)/Teffs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3) was increased in pancreatic lymphocytes of NOD-iβSTX4 mice (Fig. 6C), but the Treg/Teff ratio in PLNs (Fig. 6D) or spleen (Fig. 6E) collected from the same mice remained unchanged. These data suggest that β-cell STX4 enrichment increases Treg abundance but decreases the Teff population in peripheral islets and intra-islet T cells, likely contributing to suppressed immune responses.

Figure 6

Transcriptome analysis of intra-islet T cells from NOD-Ctrl or NOD-iβSTX4 mice. A: Violin plots of the expression for each T-cell–type marker. Clusters 1, 4, 9, 13, 16, and 17 are defined as T cells. B: Bubble plot of Hallmark GSEA from comparisons of expression profiles from islet cell–localized T cells between NOD-iβSTX4 and NOD-Ctrl mice. The Teffs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3) or Tregs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3+) detected by FACS using the markers CD4, TCRβ, CD44, CD62L, and Foxp3 and shown in scatter plots and Treg/Teff ratios in pancreatic lymphocytes (C), PLNs (D), and spleens (E) from 13-week-old female NOD-iβSTX4 (n = 4) relative to the mean value derived from NOD-Ctrl (n = 6) mice. *P < 0.05. FDR, false discovery rate; FSC, forward scatter; NES, normalized enrichment score.

Figure 6

Transcriptome analysis of intra-islet T cells from NOD-Ctrl or NOD-iβSTX4 mice. A: Violin plots of the expression for each T-cell–type marker. Clusters 1, 4, 9, 13, 16, and 17 are defined as T cells. B: Bubble plot of Hallmark GSEA from comparisons of expression profiles from islet cell–localized T cells between NOD-iβSTX4 and NOD-Ctrl mice. The Teffs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3) or Tregs (CD4+, TCRβ+, CD44hi, CD62Llow, and Foxp3+) detected by FACS using the markers CD4, TCRβ, CD44, CD62L, and Foxp3 and shown in scatter plots and Treg/Teff ratios in pancreatic lymphocytes (C), PLNs (D), and spleens (E) from 13-week-old female NOD-iβSTX4 (n = 4) relative to the mean value derived from NOD-Ctrl (n = 6) mice. *P < 0.05. FDR, false discovery rate; FSC, forward scatter; NES, normalized enrichment score.

We describe STX4 as a potential therapeutic target in T1D that protects the functional β-cell mass, preserves glucose tolerance, and prevents conversion to autoimmune diabetes in NOD mice. STX4 levels proved to be reduced in INS+ β-cells of humans with T1D, in human β-cells exposed to a proinflammatory cytokine mixture, and in normoglycemic NOD mice, suggesting that STX4 depletion may drive islet inflammation in both mice and humans, whereas increased STX4 protects β-cells from their demise. Indeed, STX4 heterozygous mice harbor increased susceptibility to multiple low-dose–induced loss of β-cell mass and development of severe glucose intolerance (5). To determine how STX4 suppressed T1D in NOD mice, scRNA-seq was used for transcriptomics analysis of pancreatic islet β-cells and islet cell–localized T cells. Reduced CCL5 expression was observed in NOD-iβSTX4 β-cells, consistent with the reduced appearance of insulitis, β-cell apoptosis, and whole-body glucose intolerance compared with NOD-Ctrl mice. FACS studies showed that the Treg subset of CD4+ T cells was increased selectively in pancreata from diabetes-resistant NOD-iβSTX4 mice. These findings culminate in a further substantiated model for how β-cell–specific STX4 enrichment favors resilience and protection of functional β-cell mass by influencing islet cell–localized immune cells.

CCL5, along with the previously reported chemokine ligands CXCL9, CXCL10, and CXCL11 (5), was discovered as a factor attenuated by STX4 expression in β-cells. This finding further expands the mechanistic actions and breadth of STX4 impact and validates the scRNA-seq methodology. Indeed, CCL5 expression is increased in human T1D and diabetic NOD mice, similar to CXCL9 and CXCL10 expression (8,29,31,32) and is driven by NF-κB–mediated transcriptional activation (33).

An advantage to using the NOD-iβSTX4 mouse model for the present investigations was the ability to assess changes in islet-infiltrating T cells in the same mice used for islet scRNA-seq coupled to phenotypic progress, or lack thereof, toward autoimmune diabetes; NOD mouse T cells are well studied in the processes converting to diabetes (34). The scRNA-seq analyses revealed that an IL-2/STAT5 signaling cascade was increased in islet cell–localized T cells of NOD-iβSTX4 mice, which may contribute to the increased expression of Foxp3 (30,35). This is consistent with a model wherein STX4 suppresses T-cell activation and migration into pancreatic islets through downregulation of chemokine ligand genes (CCL5, CXCL9, CXCL10). For example, IL-2 can reverse autoimmune diabetes in NOD mice by locally affecting pancreatic Tregs (36) and promoting Treg survival (37). Most recently, IL-2 injection with hydrogels to prolong IL-2 half-life enhanced the Treg population and conclusively reduced the incidence of onset diabetes in NOD mice (38). In addition, inducing Tregs, in combination with anti-CD3, can reverse new-onset T1D autoimmune disease (39). Further studies will be required to determine how STX4 enrichment in the pancreatic β-cells enhances IL-2/STAT5 signaling in adjacent T cells that infiltrated the islets. Although STX4 in β-cells is tethered to the intracellular leaflet of the plasma membrane, an extracellular form of STX4, presented at the plasma membrane in a “flipped topology,” has been reported in other nonislet cell types (40,41), wherein STX4 would interface with adjacent immune cells. It is possible that such a mechanism could confer communication between β-cells and T cells in the islet niche.

NUPR1 (also known as P8 or Com1) was the most robustly increased gene aside from STX4 in NOD-iβSTX4 β-cells (Supplementary Fig. 7). NUPR1 is a transcription factor expressed in pancreatic β-cells that inhibits nuclear NF-κB translocation (42), similar to the action of STX4. Pancreatic β-cell−specific NUPR1-overexpressing mice show an attenuated response to the hyperglycemia-inducing multiple low-dose STZ paradigm (43), similar to β-cell−specific STX4-expressing mice (5). Intriguingly, NUPR1 has been reported to increase the expression of SNAP25 (44), a t-SNARE coupled to STX4 in the SNARE core complex. As such, by providing more SNARE complexes at the plasma membrane, NUPR1 could promote the function of STX4 in exocytosis, facilitating β-cell insulin release. Future experiments will be required to determine whether NUPR1 is critical for the protective function of STX4 expression.

In conclusion, enhanced STX4 expression prevents autoimmune diabetes through a novel mechanism that includes transcriptional activation of chemokines such as CCL5 and preservation of the islet cell–localized Treg population, creating a microenvironment that protects β-cells. Reinforcing β-cell resilience in addition to immunotherapy may offer the “perfect storm” to protect β-cells and prevent or intervene in T1D. Further studies will be required to determine whether STX4 can reverse established diabetes given these novel mechanistic findings.

This article contains supplementary material online at https://doi.org/10.2337/figshare.16613851.

Acknowledgments. We are grateful for the generous provision of human pancreatic samples from nPOD. We also thank Dr. Xiwei Wu, Dr. Jinhui Wang, Dr. Hanjun Qin, Min-Hsuan Chen, the City of Hope Integrative Genomics Core (for assistance with scRNA-seq analyses), and Dr. Arianne Aslamy, Cedars-Sinai Medical Center, and Dr. Jinhee Hwang, City of Hope, for expert assistance. Dr. Nancy Linford, Linford Biomedical Communications, LLC, provided editing services.

Funding. This research was performed with the support of nPOD (RRID: SCR_014641), a collaborative T1D research project sponsored by JDRF (nPOD: 5-SRA-2018-557-Q-R), and The Leona M. and Harry B. Helmsley Charitable Trust (grant 2018PG-T1D053). Organ Procurement Organizations partnering with nPOD to provide research resources are listed at https://www.jdrfnpod.org/for-partners/npod-partners. This study was supported by grants from the Wanek Family Project for Type 1 Diabetes (to E.O. and D.C.T.) and the National Institutes of Health (DK-067912, DK-112917, and DK-102233 to D.C.T.). Human islets were supplied by the Southern California Islet Cell Resource Center (City of Hope) and the Integrated Islet Distribution Program. Research reported in this publication also includes work performed in the City of Hope Islet Core supported by the National Cancer Institute, National Institutes of Health, under award number P30-CA-33572.

The content and views expressed are the responsibility of the authors and do not necessarily reflect the official view of nPOD.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. E.O., E.M.M., M.A., P.A.G., S.B., and S.T. performed experiments/analyses, contributed to the discussion, and reviewed/edited the manuscript. E.O. and D.C.T. conceived of the study, contributed to the discussion, and wrote/reviewed/edited the manuscript. D.F.Z. and B.O.R. contributed to the discussion and reviewed/edited the manuscript. All authors read and approved the final version of the manuscript. D.C.T. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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;
3
:
346
360.e4
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