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Islet stress, degradation and autoimmunity
review-article
Publication date (Electronic):
19 September 2018
Journal:
Diabetes, Obesity & Metabolism
Publisher:
Blackwell Publishing Ltd
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Abstract
β‐cell destruction in type 1 diabetes (T1D) results from the effect of inflammation and autoimmunity. In response to inflammatory signals, islet cells engage adaptive mechanisms to restore and maintain cellular homeostasis. Among these mechanisms, the unfolded protein response (UPR) leads to a reduction of the general protein translation rate, increased production of endoplasmic reticulum chaperones and the initiation of degradation by activation of the ER associated degradation pathway (ERAD) in which newly synthetized proteins are ubiquitinylated and processed through the proteasome. This adaptive phase is also believed to play a critical role in the development of autoimmunity by the generation of neoantigens. While we have previously investigated the effect of stress on transcription, translation and post‐translational events as possible source for neoantigens, the participation of the degradation machinery, yet crucial in the generation of antigenic peptides, remains to be investigated in the context of T1D pathology. In this review, we will describe the relation between the unfolded protein response and the Ubiquitin Proteasome System (UPS) and address the role of the cellular degradation machinery in the generation of antigens. Learning from tumour immunology, we propose how these processes may unmask β‐cells by triggering the generation of aberrant peptides recognized by the immune cells.
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The Human Pancreatic Islet Transcriptome: Expression of Candidate Genes for Type 1 Diabetes and the Impact of Pro-Inflammatory Cytokines
Introduction Type 1 diabetes (T1D) is an autoimmune disease with a strong genetic component [1]. We have previously proposed that insulitis, the pancreatic islet inflammation present in T1D, results from a “dialog” between immune cells homing into the islets and the target beta cells. Beta cells contribute to this dialog by local release of cytokines and chemokines and by delivering immunogenic signals during the cell death process; this, together with signals generated by invading immune cells, contributes to trigger and amplify (or dampen) insulitis [2]. The amplification or resolution of insulitis, and its progression or not to disease, probably depends on an interplay between environmental triggers, such as dietary components or viral infections, and the patient's genetic background [2], [3], [4] acting at least in part at the pancreatic beta cell level [5], [6], [7]. It is thus important to identify the molecular mechanisms by which immune signals and genetic and/or environmental factors affect beta cell survival and the production of inflammatory mediators such as chemokines and cytokines. Evaluation of the full transcriptome of beta cells exposed to pro-inflammatory cytokines such as interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) provides a snapshot of the responses of these cells under conditions that may prevail in early T1D [2]. Until recently, the only way to analyze large numbers of transcripts was via oligonucleotide array technology. By using this technology we have described expression of nearly 8,000 genes in rat and human islet cells, of which around 20% were modified by cytokines [8], [9], [10]. Arrays, however, can only identify known transcripts due to the need for complementary recognition of probes by the target mRNA. In recent years RNA-sequencing (RNA-seq) has emerged as a new and promising tool for transcriptomic studies. RNA-seq works in an unbiased way, without the need for a priori knowledge of the targets, and shows both high reproducibility and low frequency of false positives [11], [12]. Moreover, RNA-seq is able to identify between 25 and 75% more genes than cDNA microarrays, and allows identification of both whole genes and splice variants [12], [13], [14]. Transcripts of >90% of eukaryotic genes can undergo alternative splicing (AS), i.e. be spliced in more than one way [15]. AS is a basic mechanism for the generation of multiple structurally and functionally distinct mRNAs and protein isoforms from a single gene [15], [16], [17]. It varies in a tissue-specific manner, contributing to tissue specificity [18], [19], [20], and can be modulated by cellular signals such as those provided by pro-inflammatory cytokines [9]. The use of RNA-seq, coupled to dedicated bioinformatic tools, enables the identification of novel splice variants by transcripts with skipped exons, retained introns, alternative start sites, etc [16]. Against this background, we describe here the first RNA-seq analysis of human pancreatic islets. This was done by reverse transcribing and sequencing RNA from human islets obtained from five organ donors, exposed or not to the pro-inflammatory cytokines IL-1β and IFN-γ. The data showed very good internal consistency, and allowed us: To describe the complete human islet cell transcriptome, including splice variants, which provides a novel and valuable resource for future genetic and functional studies; To show that >60% of the candidate genes for T1D, previously believed to be mostly expressed in the immune system [21], are expressed in human islets, and that expression of many of these genes is modified by cytokines; To characterize the impact of an inflammatory challenge, i.e. exposure to pro-inflammatory cytokines, on the human islet transcriptome; To validate some of the key findings obtained by RNA-seq by other methods, e.g. real time RT-PCR, ELISA or histology, in independent samples of human islets and clonal or primary rat beta cells. For some of the novel genes, the use of specific siRNAs allowed clarification of their function in beta cells. Methods Ethics statement Human islet collection and handling were approved by the local Ethical Committee in Pisa, Italy. Wistar rats were used according to the rules of the Belgian Regulations for Animal Care with approval of the Ethical Committee for Animal Experiments of the ULB. Human islet isolation and culture and rat beta cell culture Human islet preparations were obtained in collaboration with Pisa University [5], [22], [23], [24]. The donors, aged 68±3 (n = 15), were heart-beating organ donors with no medical history of diabetes or metabolic disorders. Donor information is summarized in Table 1. Preparations 1–5 were used for RNA-seq and preparations 6–15 for independent confirmation of key findings. Isolated islets were used for research when the pancreas was not suitable for clinical transplantation. The human islets were isolated using collagenase digestion and density gradient purification [25]. The islets were cultured in M199 culture medium containing 5.5 mM glucose and shipped within 1–5 days following isolation. Upon arrival, the human islet cells were cultured in Ham's F-10 medium containing 6.1 mM glucose, 10% fetal bovine serum (FBS), 2 mM GlutaMAX, 50 µM 3-isobutyl-1-methylxanthine, 1% BSA, 50 U/ml penicillin and 50 µg/ml streptomycin. The islets were exposed or not to cytokines in the same medium without FBS for 2 days [5], [26]. The following cytokine concentrations were used, based on previous dose-response experiments from our group [5], [27], [28]: recombinant human IL-1β (specific activity 1.8×107 U/mg; a kind gift from C.W. Reinolds, National Cancer Institute, Bethesda, MD, USA) at 50 U/ml; recombinant human IFN-γ (specific activity 2×107 U/mg; R&D Systems, Abingdon, UK) at 1000 U/ml. The evaluation of islet cell purity, i.e. the percentage of beta cells present in the preparations, was done by immunocytochemistry with an anti-insulin antibody (1/1000; Sigma, Bornem, Belgium) and donkey anti-mouse IgG rhodamine (1/200; Lucron Bioproducts, De Pinte, Belgium). Only preparations with more than 40% beta cells were used for the RNA-seq analyses; on average they contained 58% beta cells (Table 1), which is similar to the reported percentage of 54% in isolated human islets [29] and 55% in the human pancreas [30]. 10.1371/journal.pgen.1002552.t001 Table 1 Characteristics of the organ donors and human islet preparations used for RNA-seq and independent confirmation. Gender Age (years) BMI (kg/m2) Cause of death Purity (%) Islets for RNA-seq ID1 F 77 24 trauma 45 ID2 F 46 23 CVD 60 ID3 F 79 28 trauma 61 ID4 M 36 26 CVD 62 ID5 M 77 25 CVD 62 Islets for RT-PCR ID6 M 59 25 trauma 70 ID7 F 84 26 CH 73 ID8 M 83 24 CH 52 ID9 F 70 25 CH 63 ID10 M 68 37 CH 57 ID11 M 69 24 CVD 57 ID12 M 70 21 CVD 69 ID13 M 75 28 CVD 59 ID14 M 58 25 CH 59 ID15 F 72 24 CH 62 ID: Donor identification number; F: Female; M: Male; BMI: Body mass index; CVD: Cardiovascular disease; CH: Cerebral hemorrhage. Purity indicates the percentage of beta cells in the human islet preparations as determined by staining for insulin. For confirmation and mechanistic studies of selected genes, we used the rat insulin-producing INS-1E cell line, kindly provided by C. Wollheim, University of Geneva, Geneva, Switzerland [31]. The cells were maintained in RPMI 1640 medium supplemented with 5% heat-inactivated FBS, 10 mM HEPES, 1 mM Na-pyruvate and 50 µM 2-mercaptoethanol [26]. Cells were exposed to 10 U/ml human IL-1β and 100 U/ml murine IFN-γ (R&D Systems). These cytokine concentrations were selected based on previous dose-response studies [28], [32]; lower cytokine concentrations and shorter time points were used for rodent experiments because rat beta cells are more sensitive than human islets to cytokine damage [33], [34]. Additional confirmation was done in autofluorescence-activated cell sorting (FACS)-purified primary rat beta cells. Pancreatic islets were isolated from adult male Wistar rats (Charles River Laboratories, Brussels, Belgium) and primary beta cells FACS-purified (FACSAria; BD Bioscience, San Jose, CA, USA) and cultured as described [35]. Primary beta cells were transfected with the synthetic double-stranded (ds) RNA polyinosinic-polycytidylic acid (PIC, InvivoGen) as described [6], [7]. RNA sequencing Five human islet preparations were used for sequencing. Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Venlo, The Netherlands) which favors purification of all RNA molecules longer than 200 nucleotides and sample preparation done as described by the manufacturer (Illumina, Eindhoven, The Netherlands). Briefly, mRNA was purified from two µg total RNA using oligo (dT) beads, before it was fragmented and randomly primed for reverse transcription followed by second-strand synthesis to create ds cDNA fragments. The generated cDNA had undergone paired-end repair to convert overhangs into blunt ends. After 3′-monoadenylation and adaptor ligation, cDNAs were purified on a 2% agarose gel and 200 basepair (bp) products were excised from the gel. Following gel digestion, purified cDNA was amplified by PCR using primers specific for the ligated adaptors. The generated libraries were submitted to quality control with the Agilent bioanalyzer 2100 (Agilent Technologies, Wokingham, UK) before sequencing. The RNA integrity number (RIN) values for all samples were 7.5 and above. 1 µL cDNA was loaded on an Agilent DNA chip (DNA-1000) to verify cDNA quality and quantity. Only libraries reaching satisfactory conditions were used for sequencing, on one sequencing lane of an Illumina Genome Analyzer II system (GAII, Illumina). The raw data generated during the sequencing procedure on the GAII will be deposited in Gene Expression Omnibus (GEO) under submission number GSE35296. RNA–seq data analysis Sequencing reads were mapped to the human genome (version GRCh37/hg19) using the program gem-mapper from the GEM suite (http://gemlibrary.sourceforge.net). The GEM mapper reports exhaustively all mappings and split-mappings up to a user-defined amount of mismatches (default 2 mismatches), disregarding presumptive base-calling errors as identified by low associated quality values. Mapped reads were used to quantify transcripts from the RefSeq reference database [36], using the Flux Capacitor approach that deconvolves reads mapping to exonic regions shared by multiple transcripts by optimizing a system of linear equations and thus obtains a number of reads specifically assigned to each alternative spliceform (http://flux.sammeth.net, see [37] for a short description). All genes and transcripts have been assigned a relative coverage rate as measured in RPKM units (“reads per kilobase per million mapped reads”) [38]. Lists of differentially expressed genes and transcripts were generated from the Flux Capacitor output using scripts in Perl or R (see legends to figures and tables). To define genes up- or downregulated by cytokines, the log2 of the proportion between the sum of the RPKM for all gene transcripts under cytokine condition and the same sum in control condition was taken as measure of change in gene expression. The p-value was obtained by performing a Fisher exact test (number of reads mapped to the gene and number of reads mapped to all other genes in the cytokine condition versus the control condition) and corrected by the Benjamini-Hochberg method (taking for each gene the 5 samples as independent tests). A difference in gene expression was considered significant if the corrected p-value was 90% [5], [46]. After overnight transfection the cells were cultured for 48 h before being retrieved for evaluation of RNA and protein expression. Western blot and chemokine and cytokine ELISA For Western blotting, equal amounts of proteins were loaded in 12% SDS-PAGE. Immunoblot analysis was performed using goat anti-Nova1 (0.03 µg/ml; Abcam, Cambridge, UK) and mouse anti-α-tubulin (1∶5000; Sigma) antibodies. The proteins were detected using horseradish peroxidase-conjugated secondary antibody (1∶5000; Santa Cruz Biotechnology) and chemiluminescence Supersignal (Pierce). Densitometric analysis was performed using analysis software Aida1D (Fujifilm, London, UK) and data were normalized for α-tubulin. Release of the human chemokines CXCL1 (Gro-α), CXCL9 (Mig), CXCL10 (IP-10), CXCL11 (Itac), CCL2 (MCP-1), CCL3 (Mip-1-α), CCL5 (Rantes) and the cytokines IL-6 and IL-8 was measured in culture medium of control and cytokine-exposed human islets using a Custom Multi-Analyte ELISArray kit (SABiosciences, Frederick, MD, USA). Samples were processed following the manufacturer's instructions. This is a semi-quantitative assay that does not include a standard curve. Absorbance at 450 nm was measured, corrected by readings at 570 nm, normalized to the geometric mean of β-actin and GAPDH expression and expressed as arbitrary units. Immunofluorescence Human pancreatic tissue obtained from biopsies or organ donors were fixed in formaldehyde and embedded in paraffin. Sections were stained for double immunofluorescence with rabbit anti-Nova1 (1∶500; Merck-Millipore, Overijse, Belgium) and guinea pig anti-insulin (I2018, 1∶2000; Sigma) or mouse anti-glucagon antibodies using FITC and Cy3 as fluorochromes, respectively. The samples were analyzed by inverted fluorescence microscopy and images captured with Axiocam (Zeiss). Assessment of apoptosis The percentage of apoptotic cells was determined by two observers (one being blind to sample identity), after staining with the DNA-binding dyes propidium iodide and Hoechst 33342 (Sigma-Aldrich) as previously described [47]. At least 500 cells were counted per condition, with an agreement between findings obtained by the two observers of >90%. Statistical analysis Data for the confirmation experiments are presented as means ± SEM. Comparisons were performed by paired two-tailed Student's t-test or Mann Whitney test as indicated in the figure legends. A p-value≤0.05 was considered statistically significant. The statistical analysis of the RNA-seq data is described above. Results Sequencing of human islets and analysis of transcripts RNA-seq data were obtained from 5 human islet preparations (Table 1) cultured under control condition or following a 48-h exposure to the cytokines IL-1β+IFN-γ. Each of these preparations was sequenced on a single lane of an Illumina GAII sequencer, with 10–51 million reads for control and 35–62 million reads for cytokine-treated islets. This provides sufficient sequencing depth to quantify gene expression and detect rare transcripts as previously shown [16]. The 51 nucleotide paired-end reads were mapped to the human genome (version hg19) using GEM software. Taking this approach, we were able to map on average 83% of the raw reads. GEM can report multiple mappings for a single read and we observed on average a redundancy (mappings to reads ratio) of 1.5 (Table S2). Reads that align with exons or with overlapping exon junctions can be used to evaluate the levels of splicing. We used the Flux Capacitor software, which in brief takes as input a list of reads mapped to the genome and a list of transcript annotations, and subsequently produces a list of reads that are uniquely assigned to one of the transcripts. As reference transcript annotation, we employed the 34,102 annotated human mRNA and ncRNA sequences from RefSeq [48]. In a first step, the program interprets the mate information of mappings and filters off mappings that do not pair properly within the boundaries of annotated transcripts. For about half of the originally sequenced reads a mate in correct orientation and within exon boundaries of the annotated RefSeq transcripts could be identified, with only spurious redundancy ( 1 in all samples were considered for analysis. For each pair of samples a Pearson correlation coefficient (PCC) was computed from the power-law normalized expression levels (i.e. the RPKM values). (A) Boxplot for each islet sample (called ID1 to ID5) with the PCC values between the individual islet sample and 4 other islet preparations. (B) Heatmap with clustering dendrograms inferred by employing (1 – PCC) as distance function and complete linkage as clustering function, showing a tight cluster of islet preparations. For internal methodological validation, we selected 4 genes for confirmation by qRT-PCR in the same samples used for RNA-seq. The gene expression data using these two methods were essentially superposable (Figure S1). The validation steps described above, including comparison between islet samples and against 5 other tissues, and the validation using qRT-PCR in the same samples, indicate that the RNA-seq of human islets provided reliable and reproducible data, as has been described for other tissues [11], [16], [38], [53], enabling us to proceed with the analyses described below. Expression of candidate genes for type 1 diabetes in pancreatic islets Based on the datasets above, we examined whether candidate genes for T1D, previously identified by genome-wide association studies (GWAS) [54], [55], are expressed in human islets. We considered genes as “expressed” with a median RPKM >1. Out of 41 candidate genes, 25 (i.e. 61%) were clearly expressed in human islets (Figure 2A and Table S3). We followed this up by functional studies in insulin-producing INS-1E cells and purified rat beta cells, to confirm gene expression and query the relevance of these genes at the beta cell level. We have previously shown that 2 of these genes, namely IFIH1/MDA5 and PTPN2, are expressed in pancreatic beta cells and regulate respectively local inflammation [6] and apoptosis [5], [56]. Pro-inflammatory cytokines and dsRNA, a by-product of viral infections, modulate expression of these 2 genes, indicating crosstalk between T1D candidate genes and environmental factors and local inflammation [5], [6], [56]. Indeed, knockdown of IFIH1/MDA5 in rat beta cells reduced the chemokine and cytokine expression induced by a 48-h exposure to PIC, a synthetic dsRNA (Figure S2). We now confirm in clonal INS-1E cells expression of an additional candidate gene, namely SH2B3 (Figure 2B), and its induction by the cytokines IL-1β+IFN-γ in a time-course study. 10.1371/journal.pgen.1002552.g002 Figure 2 Two thirds of candidate genes for T1D are expressed in pancreatic beta cells. (A) T1D candidate genes ranked by the odds ratio for their risk allele (http://t1dbase.org). Based on our present data, 25 candidate genes out of 41 (61%) were expressed in human beta cells (marked with *). (B) INS-1E cells were left untreated or treated with IL-1β+IFN-γ for the indicated times. The expression of the T1D candidate gene SH2B3 was assayed by qRT-PCR and normalized to the housekeeping gene GAPDH. The results are means ± SEM of 3–6 independent experiments. *p 2-fold the known expressed genes in human islets. There was a high correlation between the islet samples (0.90–0.96), clearly higher than the correlation observed between islets and five other tissues (0.53–0.88) used for external comparison. This, and the fact that around 20 genes identified as expressed and/or modified by cytokines in the present analysis were confirmed at the RNA and/or protein expression level by other methods, supports the reliability of the present observations. This is in line with previous studies in other tissues indicating that RNA-seq is a reliable and reproducible method to evaluate RNA expression [11], [16], [38], [53]. The human islets used in this analysis contained 58% beta cells on average (Table 1), and the transcriptome includes RNAs from non-beta endocrine cells, mostly alpha and delta cells [51], and ductal cells. The comparison against INS-1E cells suggests, nonetheless, that at least half of the presently identified cytokine-modified genes are expressed in beta cells. Use of GWAS has revealed more than 40 loci containing putative genetic contributors to the pathogenesis of T1D [54], [55]; this number was further increased by a recent genome-wide meta-analysis of six diabetes cohorts [68]. While in T2D most candidate genes impact more on islet function than on insulin resistance and are hence considered to regulate beta cell function and development [69], [70], it is usually assumed that in T1D most if not all candidate genes modulate the immune system (reviewed in [21]). In this conventional view beta cells are regarded as “passive victims” of a process that starts and is regulated elsewhere. By using the presently generated datasets, we observed that 61% of the candidate genes for T1D are consistently expressed in human pancreatic islets. Furthermore, the present and previous observations [5], [6], [56] indicate that expression of many of these genes change following exposure to pro-inflammatory cytokines or dsRNA (a by-product of virus infection), agents that may contribute to triggering of T1D [2]. For at least two of these genes, namely IFIH1/MDA5 [6] (present data) and PTPN2 [5], [6], [56], there is experimental evidence pointing to their respective roles in production of chemokines/cytokines and beta cell apoptosis. These observations are in line with the present analysis of gene expression in cytokine-treated human islets. Of note, only one time point (48 h cytokine exposure) was examined here, providing a snapshot of dynamic regulation of gene expression. It is conceivable that relevant cytokine-modulated genes at other time points were missed in the present analysis. Cytokines modified expression of 3,000 genes, mostly related to inflammation, innate immune response and apoptosis. Key chemokines and cytokines were among the most up-regulated genes in human islets, a finding confirmed at the protein level for CCL2, CCL5, CCL3, CXCL9, CXCL10, CXCL11, IL-6 and IL-8. This is in good agreement with findings in diabetes-prone NOD mice, where increased expression of CCL2, CXCL10 and other chemokines/cytokines are observed in the pre-diabetic period [42], [71], [72]. CCL2 and CXCL10 attract macrophages, and may contribute to the recruitment of immune cells during the early stages of insulitis, as suggested by the observation that transgenic expression of CCL2 in beta cells causes insulitis and diabetes [72]. Some of these observations have been recently confirmed in histological material from T1D patients. Thus, it was observed that pancreatic beta cells from islets affected by insulitis express CXCL10, while the infiltrating T cells express CXCR3, the receptor of CXCL10 [73], [74]. Islet cells themselves are probably an important source of chemokine production during inflammation, as suggested by the present findings. That chemokines are indeed produced by beta cells is supported by the observations that FACS-purified rat beta cells (>90% pure) or clonal rat beta cells (INS-1E cells) exposed to IL-1β+IFN-γ, or to dsRNA, show increased expression of mRNAs encoding CCL2, CXCL10, CCL20, CX3CL1 and IL-15, among others [9], [44], [61], [75]. This is confirmed by histology of pancreatic samples, showing expression of chemokines by beta cells [73], [74], [76]. The findings described above support the concept of a “dialogue” between beta cells and the invading macrophages and T cells in the course of insulitis, rather than a “monolog” where all action takes place at the level of the immune system and beta cells are no more than passive victims. Thus, activated mononuclear cells produce cytokines such as IFN-γ, IL-1β and TNF-α, triggering the release of chemokines and stimulatory cytokines by the beta cells. This, together with beta cell death and the putative presentation of neoantigens secondary to modified AS and up-regulation of the machinery for antigen presentation, will attract more mononuclear cells that also release multiple cytokines and chemokines, in a process modulated by candidate genes that are expressed and act at both the immune system and beta cell levels, as shown for MDA5 and PTPN2, among others. One of the most deleterious consequences of islet inflammation is the progressive loss of pancreatic beta cells via apoptosis [2]. We presently observed modulation of the expression of several apoptosis-related genes in human islets exposed to cytokines. One of them, the anti-apoptotic Bcl-2 family member BCL2A1 [77], [78], was confirmed by qRT-PCR in both independent human islet preparations and in clonal rat insulin-producing INS-1E cells. Knock down of BCL2A1 by a specific siRNA augmented both basal and cytokine-induced apoptosis, confirming the relevant function of this protein in protecting beta cells against apoptosis (present data). Cytokine-induced expression of BCL2A1 in human islets has been previously observed by array analysis [60], [79], but the function of this gene in beta cells remained to be clarified. Of interest, BCL2A1 inhibits apoptosis induced by, among others, the BH3 only protein Bim [80], [81]. Bim was recently shown to be a crucial pro-apoptotic signal following inhibition of the candidate gene PTPN2 [56], a gene also detected in the present RNAseq. We presently report another level of molecular regulation of beta cell function, namely AS. Interestingly, AS is modified by cytokine exposure as suggested by the present findings in human islets and previous observations from our group based on exon array analysis in rat beta cells [9]. Regulation of splicing in other tissues involves the cooperation between SR, hnRNPs proteins and several other tissue-specific regulators of splicing such as neuron-specific Nova or the neural/muscle-enriched Fox proteins [82], [83]. The well-characterized Nova proteins regulate numerous splicing events in the central nervous system [64], [84], and the present findings show that Nova1 is expressed in beta cells and affects splicing of at least one target gene, namely Gabrg2. Of interest, several of the known Nova target genes in brain are also expressed in beta cells, including neuroligin and neurexin family members, inhibitory synapse-associated neuroligin and neurexin binding partners [64], [85]. These findings are in line with previous observations that beta cells share expression of a large number of genes and proteins with the central nervous system [86], [87], [88]. This opens a new field of research, and new experiments are now required to determine how AS is regulated in beta cells, and how cytokines modify this process. In conclusion, the present study identifies most of the transcripts present in human islets of Langerhans, providing a valuable dataset for future genetic and functional studies in pancreatic beta cells. It also shows that pro-inflammatory cytokines modify AS and the expression of nearly 20% of the genes expressed in human islet cells. Importantly, the present observations indicate that >60% of the known candidate genes for T1D are expressed in human islets. This, taken together with the cytokine-induced expression of a large number of chemokines and cytokines in human islets, reinforces the concept of a dialog between pancreatic islets and the immune system, which might be crucial for triggering insulitis and eventual progression to diabetes. The present study identifies a large number of the words used by pancreatic islets in this dialog, and points to candidate genes for T1D as one of the writers of the beta cell speeches. Supporting Information Dataset S1 RPKM data and lists of cytokine-modified and human islet-specific genes. Includes “table_RPKM.xlsx” (detected transcripts with their expression levels), “ctrl_cyt_expr.up.xlsx” (list of upregulated genes), “ctrl_cyt_expr.down.xlsx” (list of downregulated genes), “ctrl_cyt_AS_up.xlsx” (list of upregulated splicing isoforms), “ctrl_cyt_AS_down.xlsx” (list of downregulated splicing isoforms) and “Legends.docx” (explanation of the tables). (ZIP) Click here for additional data file. Figure S1 Validation of RNA-seq gene expression data by qRT-PCR in cytokine-treated human islets. Human islets from 5 organ donors were cultured for 48 h in the presence (CYT) or absence (CTL) of the cytokines IL-1β+IFN-γ. RNA-seq gene expression results (black bars) were compared to gene expression assessed by qRT-PCR (gray bars) in the 5 human islet preparations used for RNA-seq. Data were normalized to the geometric mean of β-actin and GAPDH expression and expressed as fold induction of control. *p<0.05, **p<0.01 for CYT versus CTL. (TIF) Click here for additional data file. Figure S2 MDA5 regulates cytokine and chemokine production in primary rat beta cells exposed to intracellular dsRNA. FACS-purified rat beta cells were transfected with control siRNA (siC, black bars) or siRNA targeting MDA5 (siMDA5, grey bars). After 48 h, cells were left untreated or transfected with PIC for 48 h. MDA5, IFN-β, CCL5 and CXCL10 mRNA expression was assayed by qRT-PCR and corrected for the housekeeping gene GAPDH. Results are mean ± SEM of six independent experiments. *p<0.05, **p<0.01, ***p<0.001 versus control; p<0.05 for the comparison siC versus siMDA5 as indicated. (TIF) Click here for additional data file. Figure S3 DAVID analysis of cytokine-modified genes. (A, B, C, D) 1,416 genes were significantly up-regulated by the cytokines IL-1β+IFN-γ in at least 4 out of 5 islet samples, and significantly downregulated in none. These genes mapped to 1,395 unique entries in the DAVID database, which were submitted to gene set enrichment analysis based on Benjamini-Hochberg corrected Fisher tests against some of the compound databases available in DAVID. Results are shown for (A) 979 genes mapping to 68 entries of Gene Ontology “Biological Process” (GO_BP), (B) 1,023 genes mapping to 104 entries of Gene Ontology “Molecular Function” (GO_MF), (C) 522 genes mapping to 36 entries of KEGG Pathway, (D) 1244 genes mapping to 120 entries of InterPro. (E, F, G, H) 1,652 genes were significantly downregulated by cytokines in at least 4 out of 5 islet samples, and significantly up-regulated in none. They were mapped to 1,620 unique entries in the DAVID database: (E) 1,151 genes mapping to 188 entries of Gene Ontology “Biological Process”, (B) 1,111 genes mapping to 57 entries of Gene Ontology “Molecular Function”, (C) 462 genes mapping to 25 entries of KEGG Pathway, (D) 1421 genes mapping to 94 entries of InterPro. The length of the grey bars indicates the significance of the association between the set of genes and the entry name, expressed as minus the logarithm of the probability that a set of genes taken at random from the human genome would be associated with the same entry. Only the 30 top entries are displayed. The red vertical line indicates a probability threshold of 0.05 (corresponding to a −log(BH p-value) of 1.3). (TIF) Click here for additional data file. Figure S4 Protein–protein interaction analysis of cytokine up-regulated genes. 1,416 genes were significantly up-regulated by the cytokines IL-1β+IFN-γ in at least 4 out of 5 islet samples, and significantly downregulated in none. These genes were mapped to 1,403 unique entries in the BioProfiling database, and 55 of these entries were assembled into a unique network using as connecting nodes protein-protein interactions documented in the IntAct database. A representative figure is shown. A gene set enrichment analysis was performed and genes were color-coded to indicate association with the indicated Gene Ontology terms. (TIF) Click here for additional data file. Figure S5 RT-PCR validation of the modulation of alternative splicing by cytokines in human islets. (A) Schematic representation of DNAJA3 splice products amplified by RT-PCR, resulting in PCR products of 267 bp for variant 1 and 150 bp for variant 2. (B) Relative abundance of variants 1 and 2 was evaluated in three human islet preparations under control condition (Hi) or following exposure to the cytokines IL-1β+IFN-γ (Hi+Cyt). (TIF) Click here for additional data file. Table S1 Sequence of the primers used in this study. STD: primers used for conventional PCR, qRT: primers used for real time qRT-PCR. The RefSeq ID of the sequence used to design the primers is provided. (DOC) Click here for additional data file. Table S2 Mapping and quantification statistics for the RNA-seq data. Sequencing reads for 5 human islet samples cultured under control conditions were mapped to the human genome using GEM. Only a fraction of the total number of reads could be mapped. The number of mappings is greater than the number of mapped reads since some reads were mapped to more than one alternative location. The mappings were subsequently “paired” onto the RefSeq annotated transcripts using Flux Capacitor. Only a fraction of the reads could be paired. The number of paired mappings ( = number of transcript counts) is greater than the number of paired reads since sometimes it is not possible to choose between alternative transcripts. The last column gives the number of genetic loci for which at least one read is paired to one transcript. (DOC) Click here for additional data file. Table S3 Expression of T1D candidate genes in human islets under control and pro-inflammatory conditions. For these T1D candidate genes, RNA-seq gene expression is provided in 5 human islet preparations (see Table 1) cultured under control condition or following exposure to the pro-inflammatory cytokines IL-1β+IFN-γ, mimicking inflammation. The sum of the RPKM for all the transcripts of the same gene is taken as measure of gene expression and the median of the 5 values is provided. Genes that were not detected or had an RPKM<1 for either condition are not mentioned. (DOC) Click here for additional data file. Table S4 Expression of genes involved in radical scavenging in human islets and other tissues. For a number of genes known to be involved in radical scavenging a comparison is made between the 5 studied human islet preparations (see Table 1) cultured under control conditions and 5 selected tissues from the Illumina Human Body Map (adipose tissue, colon, kidney, liver and skeletal muscle). The sum of the RPKM for all the transcripts from the same gene is taken as measure of gene expression. The third column contains the median of the expression values for the 5 human islet samples. The log2 of the proportion between the level of gene expression for an islet preparation and the level of gene expression for a background tissue is taken as the measure of difference in gene expression. The last 5 columns contain the median of the significant differences in gene expression between the 5 islet preparations and a background tissue. If there is a significant difference (a description of the statistical analysis is provided in Materials and Methods) in one direction for at least 4 out of 5 islet samples and in the other direction for none the value is considered significant and shown in bold font, otherwise it is considered non-significant. (DOC) Click here for additional data file. Table S5 Classification of selected IL-1β+IFN-γ-modulated genes in human islet cells into functional groups. For a selected number of genes a comparison is shown between the 5 studied islet samples (see Table 1) cultured under control conditions and in the presence of cytokines (IL-1β+IFN-γ). The log2 of the proportion between the sum of the RPKM for all the transcripts from the same gene under cytokine treatment and the same sum obtained under control conditions was taken as measure of change in gene expression. A difference in gene expression was considered significant if the corrected p value<0.05. The table contains the median of the significant changes in gene expression. With a few exceptions genes were only taken up in the list when they were significantly changed in expression in one direction for at least 4 islet samples and changed in the other direction for none. JUNB and SH2B3 were significantly downregulated and BMF was significantly up-regulated in 1 islet preparation but they were added to this list for the sake of completeness. (DOC) Click here for additional data file.
- Record: found
- Abstract: found
- Article: not found
A selective inhibitor of the immunoproteasome subunit LMP7 blocks cytokine production and attenuates progression of experimental arthritis.
Tony Muchamuel, Michael Basler, Monette A Aujay … (2009)
The immunoproteasome, a distinct class of proteasome found predominantly in monocytes and lymphocytes, is known to shape the antigenic repertoire presented on class I major histocompatibility complexes (MHC-I). However, a specific role for the immunoproteasome in regulating other facets of immune responses has not been established. We describe here the characterization of PR-957, a selective inhibitor of low-molecular mass polypeptide-7 (LMP7, encoded by Psmb8), the chymotrypsin-like subunit of the immunoproteasome. PR-957 blocked presentation of LMP7-specific, MHC-I-restricted antigens in vitro and in vivo. Selective inhibition of LMP7 by PR-957 blocked production of interleukin-23 (IL-23) by activated monocytes and interferon-gamma and IL-2 by T cells. In mouse models of rheumatoid arthritis, PR-957 treatment reversed signs of disease and resulted in reductions in cellular infiltration, cytokine production and autoantibody levels. These studies reveal a unique role for LMP7 in controlling pathogenic immune responses and provide a therapeutic rationale for targeting LMP7 in autoimmune disorders.
- Record: found
- Abstract: found
- Article: not found
Regulation of proteasome activity in health and disease.
Marion Schmidt, Daniel Finley (2013)
The ubiquitin-proteasome system (UPS) is the primary selective degradation system in the nuclei and cytoplasm of eukaryotic cells, required for the turnover of myriad soluble proteins. The hundreds of factors that comprise the UPS include an enzymatic cascade that tags proteins for degradation via the covalent attachment of a poly-ubiquitin chain, and a large multimeric enzyme that degrades ubiquitinated proteins, the proteasome. Protein degradation by the UPS regulates many pathways and is a crucial component of the cellular proteostasis network. Dysfunction of the ubiquitination machinery or the proteolytic activity of the proteasome is associated with numerous human diseases. In this review we discuss the contributions of the proteasome to human pathology, describe mechanisms that regulate the proteolytic capacity of the proteasome, and discuss strategies to modulate proteasome function as a therapeutic approach to ameliorate diseases associated with altered UPS function. This article is part of a Special Issue entitled: Ubiquitin-Proteasome System. Guest Editors: Thomas Sommer and Dieter H. Wolf. © 2013 Elsevier B.V. All rights reserved.
Author and article information
Contributors
Bart O. Roep:
ORCID: http://orcid.org/0000-0002-3110-7391
broep@coh.org
Journal
Journal ID (nlm-ta): Diabetes Obes Metab
Journal ID (iso-abbrev): Diabetes Obes Metab
Journal ID (doi): 10.1111/(ISSN)1463-1326
Journal ID (publisher-id): DOM
Title:
Diabetes, Obesity & Metabolism
Publisher:
Blackwell Publishing Ltd
(Oxford, UK
)
ISSN
(Print):
1462-8902
ISSN
(Electronic):
1463-1326
Publication date
(Electronic):
19
September
2018
Publication date
(Print):
September
2018
Volume: 20
Issue: Suppl Suppl 2
(
doiID:
10.1111/dom.2018.20.issue-S2
)
Pages: 88-94
Affiliations
[
1
]
Department of Cell and Chemical Biology
Leiden University Medical Center
Leiden
The Netherlands
Author notes
[*]
[*
]
Correspondence
Bart O. Roep, Department of Diabetes Immunology, Diabetes & Metabolism Research Institute at the Beckman Research Institute, City of Hope, Duarte, CA.
Email: broep@ 123456coh.org
Bart O. Roep, Department of Diabetes Immunology, Diabetes & Metabolism Research Institute at the Beckman Research Institute, City of Hope, Duarte, CA.
Email: broep@ 123456coh.org
Author information
Arnaud Zaldumbide
http://orcid.org/0000-0001-6680-4456
Bart O. Roep
http://orcid.org/0000-0002-3110-7391
Article
Publisher ID: DOM13387DOI: 10.1111/dom.13387
PMC ID: 6174957
PubMed ID: 30230178
SO-VID: e5fdad81-917d-4a16-bebd-c6184a77018b
Copyright © © 2018 The Authors.
Diabetes, Obesity and Metabolism published by John Wiley ' Sons Ltd.
License:
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
History
Date
received
: 25
April
2018
Date
revision received
: 18
May
2018
Date
accepted
: 30
May
2018
Page count
Figures: 2,
Tables: 0,
Pages: 7,
Words: 5963
Funding
Funded by: DON foundation stichting DON (Diabetes Onderzoek Nederland)
Funded by: Diabetes Fonds
Funded by: JDRF
Categories
Subject:
Review Article
Subject:
Review Articles
Custom metadata
source-schema-version-number 2.0
component-id dom13387
header-id dom13387-hdr-0001
cover-date September 2018
details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.0 mode:remove_FC converted:08.10.2018
ScienceOpen disciplines: Endocrinology & Diabetes