Effects of Multiple Stressors on Pancreatic Human Islets Proteome Reveal New Insights into the Pathways Involved

Abstract

Pancreatic β-cell dysfunction is an early hallmark of type 1 diabetes mellitus. Among the potentially critical factors that cause β-cell dysfunction are cytokine attack, glucotoxicity, induction of endoplasmic reticulum (ER) or mitochondria stress. However, the exact molecular mechanism underlying β-cell’s inability to maintain glucose homeostasis under severe stresses is unknown. This study used proinflammatory cytokines, thapsigargin, and rotenone in the presence of high concentration glucose to mimicking the conditions experienced by dysfunctional β-cells in human pancreatic islets, and profiled the alterations to the islet proteome with TMT-based proteomics. The results were further verified with label-free quantitative proteomics. The differentially expressed proteins under stress conditions reveal that immune related pathways are mostly perturbed by cytokines, while the respiratory electron transport chains and protein processing in ER pathways by rotenone. Thapsigargin together with high glucose induces dramatic increases of proteins in lipid synthesis and peroxisomal protein import pathways, with energy metabolism and vesicle secretion related pathways downregulated. High concentration glucose, on the other hand, alleviated complex I inhibition induced by rotenone. Our results contribute to a more comprehensive understanding of the molecular events involved in β-cell dysfunction.

1. Introduction

Pancreatic β-cells are primarily responsible for regulating the homeostasis of blood glucose levels by secreting insulin into the circulation.[1] Dysfunction of β-cell is an important early hallmark of type 1 diabetes mellitus (T1D).[2] Among the potentially critical mechanisms that cause β-cell dysfunction or death are cytokine attack, induction of endoplasmic reticulum (ER) or mitochondria stress and glucotoxicity induced by hyperglycemia.[1, 3–6]

Proinflammatory cytokines such as interleukin-1β (IL-1β), interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) contribute to β-cell dysfunction through different mechanisms such as activating pro-apoptotic proteins and inducing ER stress.[3, 7] The effect of cytokine-induced β- cell damage has been demonstrated in different cell models and human islets. Recent studies have also demonstrated that glucose-induced β-cell production of IL-1β contributes to glucotoxicity in human pancreatic islets, suggesting concerted effects of cytokine and glucose in damaging β-cells.[8]

ER is an important organelle for insulin biosynthesis, which accounts for half of the total protein production in pancreatic β-cells.[9] Increased insulin synthesis in pancreatic β-cells leads to an accumulation of unfolded insulin in the ER, which was reported as one of the potential mechanism for T1D progression.[1] Other factors such as inhibition of protein glycosylation, disruption of Ca²⁺ homeostasis, and viral infection also cause ER stress and ultimately develop into the pathogenesis of diabetes.[1] For example, abnormal protein modifications in pancreatic islet β-cells induced chemically by ER stressor thapsigargin are immunogenic, causing recognition by autoreactive T cells in T1D.[10] As a protective mechanism, β-cells reduce the excessive protein loading by activating the unfolded protein response (UPR) signaling pathway in response to ER stress. However, under severe ER stress, cells advance to the irreversible programmed cell death.

Mitochondria are cell’s energy powerhouse and responsible for oxidative phosphorylation (OXPHOS) and β-oxidation of fatty acids, playing important roles in glucose and lipid metabolism.[11] In recent years, mitochondrial dysfunction in β-cells and immune cells has been implicated in the pathogenesis of T1D [12] and T2D.[6] The high energy demand in insulin synthesis, processing and secretion in pancreatic β-cells makes their mitochondria also major sites of reactive oxygen species (ROS) production.[13] Dysregulation of these processes has been reported to link with β-cell dysfunction and death. For example, Li et al.[14] reported that inhibition of mitochondrial complex I by rotenone resulted in ATP depletion and increased ROS. More recently, the cross-talk between ER and mitochondria is implicated in β-cell destruction in T1D.[15]

We recently reported that thapsigargin and rotenone induce distinct proteomic profile changes to INS-1 β-cell line.[16] In addition to the stresses in ER and mitochondria, β-cells in vivo are exposed to additional stresses during the initiation and progression stages of diabetes. In this regard, recent studies identified significant proteome changes in β-cells upon exposure to proinflammatory cytokines.[17, 18] Therefore, it would be valuable to see if there are differences in the response of β-cells to the cytokines and the chemical stressors of ER and mitochondria, and to perform this in primary human pancreatic islets - the organoids where the β-cells naturally reside with an environment closely mimicking the stressing conditions β-cells experienced in vivo. To this end, we employed isobaric labeling-based quantitative proteomics to uncover the proteome alterations and pathways perturbed by these classical, chemically unrelated stress conditions on human pancreatic islets. Our data provide a broad view of the synergistic effect of these stressors and identified specific pathways unique to each stress condition.

2. Materials and Methods

Human Pancreatic Islets Culture and Treatment

Pancreatic islets were purchased from Prodo Labs (https://prodolabs.com/). As the tissues were from cadaveric donors, this study was not deemed as human subject research, and the consent to conduct this research was not required. The characteristics of the tissue donors (n = 3) are shown in Supplementary Table S1. The average age of donors was 57.0 ± 10.4 years with body mass index of 28.0 ± 6.2. The initial purity of islet preparation was 85–95% while the viability was 95%, as determined by the supplier prior to shipment.

Human islets from individual donors were counted and 500 islet equivalents were aliquoted to eight tubes by the Prodo Labs. Upon receiving, the transport media was replaced by precooled CMRL media (Gibco, catalog #: 11530–037, containing 5.6 mM glucose) supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher, catalog #: 16000–044), 100 U/mL penicillin-streptomycin, 2 mM L-Glutamine and 25 mM HEPES. The islets were then cultured under the following treatment conditions for 48 h at 37°C and 5% CO₂: (i) glucose (22.3 mM), (ii) cytokine (R&D Systems, 50 U/mL IL-1β, 1000 U/mL IFN-γ, and 1000 U/mL TNF-α), (iii) cytokine + glucose (50 U/mL IL-1β, 1000 U/mL IFNγ,1000 U/mL TNFα, and 22.3 mM glucose), (iv) thapsigargin (Tocris Bioscience, 0.5 μM), (v) thapsigargin + glucose (0.5 μM thapsigargin + 22.3 mM glucose), (vi) rotenone (Tocris Bioscience, 0.1 μM), (vii) rotenone + glucose (0.1 μM rotenone + 22.3 mM glucose) and (viii) control with medium only. Conditions (ii), (iv), (vi) and (viii) all contained 5.6 mM glucose. At the end of incubation, islet samples were centrifuged at 180 g for 2 min at 6 °C. The islet pellets were washed twice with ice-cold PBS. Samples were then frozen at −80 °C before processing.

Cell Lysis and S-Trap-Based Protein Digestion

Cell lysis and protein digestion were performed as previously described.[16] Briefly, islet samples were lysed in a lysis buffer containing 5% SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 8.0. Protein concentration was determined using a reducing agent compatible BCA assay kit (Thermo Fisher Scientific, Catalog#: 23252,), then 50 μg of proteins were digested using S-trap micro columns (ProtiFi, NY). The eluted peptides from S-trap column were dried, and peptide concentration was determined using Pierce fluorometric peptide assay kit (Thermo Fisher Scientific, catalog#: 23290) after reconstituted in 0.1% FA. Eluted peptide samples were aliquoted for TMT-labeling and label-free based analysis, respectively.

TMT 10-plex Labeling and High pH-Reversed Phase StageTip Fractionation

Peptides from each sample were labeled with TMT 10plex kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. After labeling, samples were combined and fractionated by using high-pH reversed-phase stop-and-go extraction (Hp-RP StageTips) as described in detail previously.[16] The fractionated samples were then dried using Speedvac and reconstituted in 0.1% FA. The resulting peptides were desalted and concentrated using Evotips (EvoSep, Denmark).

Fractionation and Library Construction for Label-Free Quantitative Proteomics

To build the label-free quantitation (LFQ) spectrum library, equal peptide amounts (1 μg) were pooled from each digested sample (total pooled peptide amount: 24 ug). The pooled peptides were fractionated using the Hp-RP StageTips to collect six fractions. After drying, the fractionated peptide samples were reconstituted in 0.1% FA and subjected to Evotip loading.

LC-MS/MS Analysis

All peptide samples were separated by the Evosep One system (EvoSep, Odense, Denmark) on a 15 cm × 150 μm i.d. capillary column (1.9 μm C18 particles) using the pre-programmed gradient of 15 samples per day method with gradient length of 88 min. The mobile phases were comprised of 0.1% FA as solvent A and 0.1% FA in ACN as solvent B, and the peptides were eluted off column at 0.22 μL/min flow rate within 35% solvent B. The Evosep One system was coupled online to Orbitrap Exploris 240 Mass spectrometer (ThermoFisher Scientific) equipped with an Easyspray source. For the TMT-labeled peptides, the instrument was performed in DDA mode with full MS scan settings: resolution 60k, mass range m/z 350–1600, RF lens: 70%, normalized AGC target: 300%, followed by the top 20 MS/MS scans with resolution 45k, standard AGC target, isolation window of 0.7 m/z at HCD collision energy of 31 and dynamic exclusion of 25 s.

For label-free quantification acquisition method, the spectrum libraries were built using the following MS setting: 120k resolution, mass range 375–1500 m/z, 25 ms injection time, 300% of AGC target. The top 20 precursor ions in 40s exclusion duration were selected with 1.5 m/z isolation window and 7E4 minimum intensity, and fragmented with HCD collision energy 30.

Database Search

For TMT data, the MS/MS raw files were processed with Proteome Discoverer (PD, version 2.5.0.400, Thermo Fisher Scientific). Briefly, Sequest HT search engine was applied to search the raw data against a Uniprot human protein database (release 2021.07, 20307 entries) supplemented with commonly observed MS contaminants (containing 246 entries). Searches were configured with static modifications on lysine and N terminus (+229.163) Da) for the TMT 10plex reagents, carbamidomethyl on cysteines (+57.021 Da), dynamic modifications for oxidation of methionine residues (+15.995 Da), precursor mass tolerance of 20 ppm, fragment mass tolerance of 0.5 Da. Trypsin was used as digestion enzyme with maximum of two missed cleavages. The minimum and maximum peptide lengths were set as 7 and 144, respectively. For high confidence results, protein identification was filtered to 1% false discovery rate (FDR) in peptide spectra match (PSM), peptide, and protein levels. The FDR was calculated using the Percolator algorithm embedded in PD.

For label-free data, the raw files were processed using PD software (version 2.5.0.400). In brief, two-stage Sequest HT search engine was applied and matched against the same protein database and MS contaminants as used in the TMT data processing. The Minora feature detector was used as a match between runs to increase identification.[19] The search also performed intensity-based rescoring of PSMs using INFERYS to enhance confident identifications.[20] The modifications for label-free data were set as: carbamidomethyl as static modification on cysteines (+57.021 Da), and oxidation as variable modification on methionines (+15.995 Da) while precursor mass tolerance was set as 10 ppm and fragment mass tolerance of 0.02 Da. Other parameters were the same as described in the TMT data.

Statistical Analysis

The exported protein abundance values were analyzed and visualized using Perseus[21] software (version 1.6.14.0). To ensure high confidence in statistical analysis, data were further filtered to include: 1) only proteins identified without any missing values in all the 24 biological samples; 2) quantified with more than 2 unique peptides; and 3) excluded potential contaminants. The quantitative protein data were log₂ transformed and further normalized using median centering followed by batch effect correction using ComBat algorism (R software embedded in Perseus). Pearson’s correlation coefficient, histogram distribution and boxplots were performed to evaluate the reproducibility of samples. Two-tailed student’s t-test was applied for comparisons between two conditions (FDR < 0.05, by Permutation-based FDR) to determine if each treatment group was significantly different from the control group. To better interpret biological pathways perturbed by the treatments, unadjusted p values were also used in cases where few proteins with FDR < 0.05. Unsupervised hierarchical clustering analysis was performed to generate a visual heat map. GraphPad Prism (version 9.3.0, GraphPad software, San Diego, CA) were applied for further graphing of the results.

Function and Pathway Enrichment Analysis

Enrichment of functions and signaling pathways of the differentially expressed proteins (DEPs) identified from different treatment conditions was performed using Metascape[22] (http://metascape.org), a powerful tool integrating several functional databases such as Gene Ontology, KEEG, Reactome, etc., to explore the cognition of protein functions. In brief, the upregulated or downregulated proteins of each treatment group were submitted to Metascape software (version 3.5.20211101), then the enriched pathways and biological processes were identified based on statistically significant p-value < 0.01, with a minimum count of three and an enrichment factor > 1.5. The top enriched terms were displayed as heatmap and further extrapolated in table to show the number of proteins associated to the enriched biological terms in each cluster. The protein-protein interaction (PPI) enrichment analysis was done in MetaScape using STRING database. If the PPI network contains more than 3 proteins, the Molecular Complex Detection (MCODE) algorithm is applied to identify densely connected network components.[23] Then pathway and process enrichment analysis is applied to each MCODE component independently, and the best-scoring terms by p-value are retained as the functional description of the corresponding components. In the PPI figures, color represents different MCODE components. Vertices in the network are proteins, and the edges are interactions between the proteins.

3. Results

Overview of the Study Design and Proteomic Analysis

The aim of our current study was to investigate the molecular responses in human pancreatic β-cells upon exposure to a variety of commonly known stress inducers. To this end, human primary pancreatic islets were cultured and treated with a variety of chemically unrelated stressors namely high concentration glucose (Glc: induce glucotoxicity), pro-inflammatory cytokines (Cyto: including IL-1β, IFN-γ, and TNF-α), rotenone (Rot: an inhibitor to mitochondrial complex I) [24], and thapsigargin (Tha: an ER stress inducer) [25] (Figure 1A). To evaluate the synergistic effects, we also treated the human islets with chemical stressors in the presence or absence of high concentration glucose: cytokine + glucose (CytoGlc), rotenone + glucose (RotGlc), and thapsigargin + glucose (ThaGlc). Following these treatments, we applied a peptide level isobaric labeling-based quantitative proteomics strategy to accurately profile the proteomic changes in human islets in response to these stresses.

Figure 1.

(A) Experimental workflow for quantitative proteomic analysis of human islets treated with different stressors including proinflammatory cytokines, glucose, thapsigargin, rotenone, and the combinations of glucose and other stressors (biological replicates n = 3). After treatment, protein digestion was performed using S-trap micro columns, and samples from each group were labeled with TMT10-plex reagents. The high-pH reversed-phase StageTips were used to fractionate the pooled TMT-labeled peptide mixtures before LC-MS/MS analysis. Biological function readouts of pancreatic cell types under different stressing conditions demonstrated for β-cells (B), and α-cells (C). ns: no statistically significant change; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001 based on Dunnett’s multiple comparison test with the Control group.

The proteomic analysis resulted in identification of 7006 protein groups (Supplementary Table S2) at 1% FDR after strictly filtering the processed data matrix as described in the method section, representing a deep proteome coverage of human islets. Among these protein groups, 3810 were confidentially quantified without missing values in any of the 24 samples; of which 3651 (~96%) were quantifiable with ≥ 2 unique peptides and used for further data analysis. Data visualization analysis of the quantifiable proteins showed high Pearson’s correlation among the three biological replicates of each treatment conditions (Figure S1A), consistent protein abundance distribution (Figure S1B), and equal peptide loadings on column demonstrated by box plots among each treatment conditions (Figure S1C). Using the PANTHER classification system, we found that, as shown in Figure S2A, most of the quantifiable proteins were associated with cellular processes (36%), metabolic processes (21%) and biological regulation (13%); likewise, ~43% of the proteins were involved in molecular binding functions and 35% related to catalytic activities (Figure S2B).

The secreted proteins specific to each pancreatic islet cell type, i.e. insulin (INS, β-cells), glucagon (GCG, α-cells), somatostatin (SST, δ-cells) and pancreatic polypeptide (PPY, PP cells) were all detected in the proteomic data. INS levels were significantly reduced in Cyto, CytoGlc, Glc, and ThaGlc conditions compared to control, but the expressions were not significantly altered in Rot, RotGlc and Tha treatments (Figure 1B). GCG decreased significantly in Cyto, CytoGlc, Rot, RotGlc, and ThaGlc stress conditions (Figure 1C). The levels of SST and PPY were not significantly changed under all stress conditions except increased expressions were observed in Tha treatment for SST and in ThaGlc for PPY(Figure S3). To identify dysregulated proteins associated with the stressed human islets, we performed two-tailed t-test (FDR< 0.05) comparing each treatment condition with the control (Cont) group. Next, the DEPs identified in each comparison were further processed for functional enrichment and network analysis to identify the perturbed biological functions and pathways as described in the succeeding sections.

Differentially Expressed Proteins and Pathways Perturbed by Cytokines

The unsupervised hierarchical clustering analysis of the DEPs (277 upregulated and 91 downregulated proteins) identified in cytokine treatment of human islets revealed distinct clustering against the control group (Figure 2A, left and Supplementary Table S3). To further investigate the proteomic changes, we performed Gene Ontology (GO) analysis for the identified DEPs using Metascape visualization tool. The top biological processes that were significantly activated in cytokine treatment are interferon signaling (P < 6.0E-29) and cytokine response (P < 1.2E-13) processes, aligning with the responses of cells upon exposure to pro-inflammatory cytokines (Figure 2A, right). Conversely, the top biological processes that were down-regulated in cytokine treatment include hormone metabolic process (P < 2.2E-11) and response to wounding (P < 4.8E-05) among others, indicating cytokine-induced β-cell dysfunction of human islets.

Figure 2.

Cytokine induced proteomic changes in human islets. (A) Unsupervised hierarchical clustering of DEPs identified in cytokine treatment compared to control. (B) Protein-protein interaction networks of upregulated proteins. (C) Expression level of selected proteins involved in cytokine signaling in immune system. (D) Protein-protein interaction networks of downregulated proteins in the same treatment condition. (E) Unsupervised hierarchical clustering of quantitative changes of DEPs in CytoGlc vs. Cyto-treated human islets. (F) Protein-protein interaction networks of upregulated proteins in CytoGlc vs. Cyto-treated human islets. *, q < 0.05; **, q < 0.01; ***, q < 0.001; ****, q < 0.0001 based on two-sample t-test with permutation based FDR correction.

PPI network analysis of the 277 upregulated proteins identified six main interaction modules, representing 84 proteins (Figure 2B, Supplementary Table S4). One of the well-annotated modules is cytokine signaling in immune system, consisting of several MHC target molecules known to be highly relevant to T1D such as HLA-A, HLA-B, HLA-DRB5.[26] In this module, 22 of 48 proteins were significantly enriched in the PPI network with higher interaction scores (Figure 2B (i)). Of the 22 proteins, the expression level of 12 (CASP7, WARS1, CHD2, B2M, IFIT2, CXCL10, CCL5, CMPK2, GBP1, GBP4, ICAM1, ISG15) were demonstrated in Figure 2C. Most of these 12 proteins are reported as related to chemotaxis of immune cells and apoptosis. For example, caspase-7 (CASP7) is a pro-apoptotic protein involved in autoreactive T cells in T1D.[27] CCL5 and CXCL10 are two chemokines that are well known as drivers for autoimmune destruction in T1D and their circulating levels are highly elevated in T1D serum.[28, 29] Tryptophan-tRNA Synthetase 1 (WARS1) plays a key role in innate immunity, IFN-γ signaling and immune cell proliferation,[30] which was also reported to have positive association with diabetes pathophysiology and complications.[31] In line with these reports, we found that the levels of WARS1 upregulated in human islets treated with cytokines (Figure 2C). Similar upregulations were observed for IFIT2 (Figure 2C), an INF-induced antiviral protein, and Ubiquitin-like protein ISG15, which acts as an IFN-γ-inducing cytokine and is known to promote apoptosis. As a component of MHC-I complex, Beta-2-microglobulin (B2M) was also found as upregulated upon cytokine stimulation, which agrees well with the elevated antigen processing and presentation activities.

Peroxisomes are membrane-bound organelles involved in multiple pathways and play an important role in lipid and ROS metabolism and anti-inflammatory responses.[32, 33] The Peroxisome Proliferator-Activated Receptor (PPAR) family proteins modulate glucose stimulated insulin secretion in pancreatic β-cells,[34], and normal peroxisome metabolism is crucial to preserve the structure and function of β-cells in mice.[32] In the present study, 16 proteins were involved in peroxisomal protein import pathway with consistently increased expression levels in cytokine-treated group (Figure 2B(ii)); of which peroxisomal biogenesis factor 3 (PEX3) and peroxisomal membrane protein (PEX14) are essential components of the peroxisomal import machinery. An immunohistochemistry study on mouse model demonstrated that PEX14 immunoreactivity was significantly higher in the endocrine compared to exocrine pancreas.[32, 33] In line with this observation, a recent study has also reported PEX14 to express and participate in the regulation of immune signaling responses.[35] These results reflect the increased activities of peroxisome to cytokine-induced inflammation and immune signaling.

On the other hand, proteins significantly downregulated in cytokine-treated group were highlighted in three major functional modules including regulation of insulin-like growth factor (IGF) transport and uptake by IGF binding proteins (IGFBPs), neutrophil degranulation, and peptide hormone processing (Figure 2D). IGF and IGFBPs have been explored as potential emerging biomarkers for diabetes mellitus.[36] The expression levels of IGF and IGFBPs may vary in different conditions. For example, the expression levels of IGFBP1 and IGFBP2 were reported to be significantly increased in T1D.[37] Of note, most of these proteins belong to the granin family including chromogranin (CHGB or SCG1), secretogranin 2 (SCG2), and secretogranin 3 (SCG3), which recently were demonstrated as key contributors to diabetes mellitus.[38, 39] A study reported SCG3 to be secreted from dysfunctional β-cells and indicated the expression level as upregulated in T1D.[40]

Differentially Expressed Proteins and Pathways Perturbed by Cytokine and High Glucose

We also evaluated the combined effect of high glucose and cytokine exposure to human islets compared to the non-treated controls. A total of 223 DEPs were identified in this treatment condition (Figure S4, Supplementary Table S5a). Of note, 181 DEPs were commonly identified by either cytokine treatment alone or by combined treatment effects (CytoGlc), while 42 were uniquely identified in CytoGlc-treated group (Figure S4A), indicating synergistic effect of the combined treatment may cause specific proteomic changes. Among these 42 proteins, few of them (PPA1, SDC2, DNAJA1, PSME2, PSMB8, SLC29A1) were previously reported as dysregulated in IL-1β + IFN-γ treatment of human islets.[18] The combined treatment effect underscores biological processes predominantly involved in innate immune response, inflammatory response, and positive regulation of type I interferon production (Supplementary Table S5b). Six functional modules were highlighted in the network analysis of the upregulated 44 proteins in CytoGlc-treated group (Figure S4B). We observed that pathways including cytokine signaling in immune system, antigen processing and presentation of endogenous peptide antigen via MHC class I, and protein polyubiquitination were commonly enriched in both Cyto- and CytoGlc-treated groups, while interleukin-1 signaling, chemokine receptors bind chemokines, and antiviral mechanism by IFN-stimulated genes were specifically enriched in CytoGlc-treated group.

To further investigate changes induced by high glucose on top of cytokines, we also evaluated the treatment effects of CytoGlc compared to Cyto-treated groups. The analysis resulted in 223 DEPs (74 up- and 149 down-regulated proteins, p < 0.05) (Figure 2E, Supplementary Table S6). Further filtration resulted in 66 upregulated and 134 downregulated proteins that were uniquely detected in CytoGlc-treated groups compared to cytokine alone or non-treated control human islets. PPI network analysis of the 66 upregulated proteins identified two main interaction modules, representing 13 proteins (Figure 2F). Several studies suggested that alternative splicing of transcripts play a vital role in the normal functions of β-cell. In dysregulated RNA repair systems, RNA decay pathways such as nonsense-mediated decay (NMD) is required as a quality control mechanism. [41] Insulin intron-2-containing pre-mRNA expression levels were reported to significantly increase in human islets exposure to high glucose. [42]On the other hand, increased expression levels of inflammatory cytokines have been demonstrated in diabetic mellitus and stimulate NMD components. In the present study, the 7 proteins involved in NMD pathway had consistently increased expression levels in CytoGlc-treated groups (Figure 2F(i)); of which EIF4A3 is one of the core component of the splicing-dependent multiprotein exon junction complex (EJC) deposited at splice junctions on mRNAs. [43] Studies showed that EIF4A3 could be used as a potential marker for various cancers and may play a vital role in the pathogenesis of gestational diabetes mellitus (GDM),[43] although the exact molecular mechanism of EIF4A3 in the pathogenesis of GDM is not clear. In line with these reports, the increased levels of EIF4A3 may suggest potential targets for glucotoxicity.

On the other hand, proteins significantly downregulated in CytoGlc-treated group were highlighted in three major functional modules including negative regulation of MET activity, SNARE interactions in vascular transport, and DNA damage response (Figure S4C). These signaling pathways were associated in diabetes mellitus.[44–46] For example, recent data in diabetic patients indicated that an imbalance between DNA damage and repair might be associated with other complications.[46] In line with the above literature, we observed proteins significantly downregulated in DNA damage response signaling pathway (Figure S4C), suggesting the impairment of DNA repair system under high glucose concentrations.

Differentially Expressed Proteins and Pathways Perturbed by Rotenone

Rotenone treatment resulted in 1105 DEPs (566 upregulated and 539 downregulated proteins) (Figure 3A, left, and Supplementary Table S7). Functional enrichment analysis showed that mitochondrial dysfunction related processes were highlighted in the upregulated proteins (Figure S5A, Supplementary Table S8) while downregulated proteins involved in regulation of mRNA stability (Figure S5B). The upregulated proteins include key enzymes involved in the citric acid (TCA) cycle and respiratory electron transport (ETC) pathway (P < 4.6E-61) (Figure 3A, right). For example, of the 41 proteins identified in complex I of the ETC, 17 were significantly upregulated (Figure S5C). These include various NADH:ubiquinone oxidoreductase (complex I) subunits NDUFB11 (1.5-fold), NDUFB9 (1.4-fold), NDUFC2 (1.4-fold), and NDUFA13 (1.3-fold) (Figure 3B). We also identified that 13 proteins of the ATP synthase (complex V) were significantly upregulated (Figure S5D). Further, some of the key proteins involved in complex II (SDHA, SDHB, SDHD), complex III (UQCRC1, UQCRC2, UQCRFS1, UQCRQ), and complex IV (COX4I1, COX6C, COX2, COX5A), were upregulated as well. Furthermore, among the 566 upregulated proteins, we identified 224 proteins that are potentially involved in 12 interaction modules using PPI network analysis (Supplementary Table S9), of which six major interaction modules are shown in Figure 3C. Among the most annotated modules is the protein processing in ER, in which 54 proteins were identified having consistently elevated expression levels in the rotenone-treated group (Figure S6). Rotenone inhibits electron transfer from NADH to ubiquinone in complex I, leading to a blockade of oxidative phosphorylation with limited synthesis of ATP. With reduced activity of complex I, to maintain homeostasis, increased synthesis of complex proteins is needed to compensate the energy production. Increased protein synthesis in turn increases the metabolic processes of amino acids, carbohydrates, ADP and transmembrane transport in mitochondria (Figure S5A).

Figure 3.

Proteomic analysis of rotenone-exposed human islets. (A) Unsupervised hierarchical clustering of quantitative changes of DEPs in rotenone-treated human islets compared to control. (B) Protein expression level of selected proteins involved in the respiratory chain OXPHOS complexes. (C, D) Protein-protein interaction networks of upregulated (C) and downregulated (D) proteins, respectively in rotenone-treated human islets. (E) Unsupervised hierarchical clustering of quantitative changes of DEPs in RotGlc vs. Rot-treated human islets. (F) Protein-protein interaction networks of downregulated proteins in RotGlc vs. Rot-treated human islets.

On the other hand, proteins significantly downregulated in rotenone-treated group were highlighted in ten functional interaction modules (Figure 3D, Supplementary Table S10), indicating responses to oxidative stress and reduced cell proliferation. For example, the pentose phosphate pathway (PPP) is a crucial part of glucose metabolism and known to be related to diabetes.[47] In the present study, 41 proteins with reduced expression levels were enriched in PPP (Figure 3D(ii)). This includes key enzymes such as transketolase (TKT) and transaldolase 1 (TALDO1).[47] The deficiency of TKT has been reported to generate ribose-5phosphate (R5P) causing accumulation and reduced glycolysis.[48] Gluconeogenesis, the pathway by which glucose is synthesized from non-carbohydrate sources, was also found as significantly downregulated in rotenone-treated human islets (Figure 3D (iii)). This indicates that mitochondria are dysfunctional for efficient fatty acid-based glucose synthesis, as the initial steps of gluconeogenesis occur in mitochondria, such as the formation of oxaloacetate and malate. In addition, microtubule-based processes were downregulated, aligning with several studies that rotenone inhibits microtubule assembly, resulting in mitotic arrest and inhibition of cell proliferation.[49]

Differentially Expressed Proteins and Pathways Perturbed by Rotenone and High Glucose

The combined treatment effects of rotenone and glucose (RotGlc) resulted in 69 DEPs (FDR < 0.05) compared to non-treated human islets (Supplementary Table S11a). The lower number of DEPs identified in RotGlc-treated groups (RotGlc vs. Cont) compared to rotenone alone (Rot vs. Cont), suggesting a reduction of rotenone-induced toxicity through high concentration glucose. Although less changes in proteome were observed in the combined treatment, key regulatory proteins with elevated expression levels were identified in RotGlc-treated group, including TMEM259, TAX1BP1, TUBGCP6, and TENM3 (Supplementary Table S11a). For example, membralin (TMEM259) has been reported as one of the most altered proteins in gestational diabetes mellitus.[50] This protein is also annotated to have a role in the ERAD pathway that is required for clearance of misfolded proteins in the ER.

To further evaluate changes induced by high glucose on top of rotenone, we also evaluated the treatment effects of RotGlc compared to Rot-treated groups. A total of 98 DEPs (26 up- and 72 down-regulated proteins, p < 0.05) were identified in this condition (Figure 3E, Supplementary Table S11b). Further filtration and functional enrichment analysis indicated that the 19 uniquely upregulated proteins in RotGlc-treated groups were predominantly associated with spinocerebullar ataxia (Figure S5E), a proposed mechanism for hypoglycemia-induced cerebellar dysfunction in diabetic complications.[51] Our results indicate that three proteins namely SPTBN2, PUM1, and ATXN2L were involved in this pathway with consistently increased expression levels in RotGlc-treated group.

Furthermore, functional enrichment analysis on the 60 uniquely downregulated proteins in RotGlc-treated group showed that carbon metabolism, monocarboxylic acid metabolic process, amino acid metabolic process, and response to increased oxygen levels were the most enriched Gene ontology terms (Figure S5F). The PPI network analysis showed that ATP5MJ, ATP6AP1, ATP6 were the cores of the downregulated proteins in amino acid metabolic process (Figure 3F).

Differentially Expressed Proteins and Pathways Perturbed by Thapsigargin and High Glucose

Compared to the treatment with thapsigargin alone, the combination of thapsigargin and high glucose (ThaGlc) resulted in more extensive changes in the proteome, therefore this condition was chosen for further analysis. Overall, 849 (308 down and 541 upregulated) DEPs were identified in this treatment group compared to control (Figure 4A and Supplementary Table S12). Further functional enrichment analysis of the DEPs showed that upregulated proteins were associated with protein catabolic process, endomembrane system organization, ncRNA metabolic process, among others (Figure S7A), while generation of precursor metabolites and energy, cellular amide metabolic process, and ribose phosphate metabolic process were reduced under the same treatment (Figure S7B). These results reflect the increased activities in clearance of misfolded or accumulated proteins in the ER. For example, overexpression of TAOK3, a lipid droplet-associated protein, was recently found to exacerbate lipid accumulation and ER stress in human and mouse hepatocytes.[52] In agreement with this study, our data identified TAOK3 with elevated expression levels in ThaGlc-treated human islets compared to control (Figure 4B, upper panel)). Conversely, the expression level of proteins involved in the processing of other protein precursors such as RBP4 (Retinol-binding protein 4), PCSK1 (neuroendocrine convertase 1), and CPE (carboxypeptidase E) were found as significantly reduced compared to the control group (Figure 4B, lower panel), which indicates reduced protein processing as a result of ER stress.

Figure 4.

Proteomic analysis of ThaGlc-treated human islets. (A) Unsupervised hierarchical clustering of quantitative changes of DEPs in ThaGlc-treated human islets compared to control (left), and trends of altered proteins (right). (B) selected protein expressions having significantly upregulated (upper panel) and downregulated (lower panel) in ThaGlc –treated human islets. (C, D) Protein-protein interaction networks of upregulated (C) and downregulated (D) proteins in ThaGlc-treated human islets. (E) Unsupervised hierarchical clustering of quantitative changes of DEPs in ThaGlc vs. Tha-treated human islets. (F) Protein-protein interaction networks of downregulated proteins in ThaGlc vs. Tha-treated human islets.

PPI network analysis of the upregulated proteins in ThaGlc-treated group identified 133 that are involved in 13 interaction modules, of which six major modules are shown here (Figure 4C, Supplementary Table S13a). Among them, inositol phosphate metabolism was the most annotated one, with 33 proteins identified (Figure 4C(i)). Six of the 33 proteins involved in this pathway were identified with ~2-fold change (LSM2, BIRC6, LDLR, PLCD1, PIKFYVE, and HSPBP1), in which LDLR, PLCD1, PIKFYVE directly involve with lipid synthesis, transport and endocytosis.

Conversely, 144 proteins significantly downregulated in ThaGlc-treated group were highlighted in nine functional interaction modules (Figure 4D, Supplementary Table S13b). Out of these 144 proteins, 30 are involved in regulation of IGF transport and uptake by IGFBPs pathway (Figure 4D(i)), which include key proteins whose dysregulation linked to diabetic mellitus such as the secretogranin family proteins (CHGB, SCG2, and SCG3) that are critical for insulin/prohormone processing and secretion in pancreatic beta cells. In addition to these canonical pathways of INS and IGF regulation, oxidative phosphorylation and fatty acid beta oxidation, we also identified novel pathways significantly downregulated under ThaGlc such as vesicle docking in exocytosis (Figure 4D(vi)), again demonstrating impaired hormone secretion in islets under ER stress.

We also compared ThaGlc vs. Tha treatment conditions to evaluate the effect of high glucose induced on top of thapsigargen. The analysis resulted in 362 DEPs (327 up- and 35 down-regulated proteins, q < 0.05) (Figure 4E, Supplementary Table S14) Further filtration and functional enrichment analysis indicated that the 97 uniquely upregulated proteins in ThaGlc-treated groups were predominantly associated with signaling by MET, vesicle-mediated transport, and organelle localization, among others (Figure S7C). PPI network analysis identified SNARE interactions in vesicle transport signaling pathway (Figure S7D) with 13 proteins (NUP98, BRD4, KPNA6, VPS18, IRF2BP1, PPP1R9B, PPP4R3B, STX8, MED23, SNAP29, KPNA4, BET1L, GATAD2B) consistently increased expression levels in this condition.

On the other hand, proteins significantly downregulated in ThaGlc-treated group (ThaGlc vs Tha) were highlighted in five major functional modules including antigen processing and presentation, cytosolic tRNA aminoacylation, PID TCPTP pathway, COPI-mediated anterograde transport, and translation (Figure 4F). Antigen processing and presentation pathways are vital in adaptive immunity and hence diabetes [53] although little is known about the peptides that are naturally processed and presented by pancreatic beta cells. Recent study analyzed the effect of high glucose concentrations in antigen presenting cells; and suggested that an impairment of the innate and adaptive immunity observed in type 2 diabetes (T2D) patients under high glucose concentrations compared to low glucose concentrations.[54] The observations are in line with present analysis of protein expression in ThaGlc-treated human islets (Figure 4F(i)). On the other hand, alterations in the expression of mitochondrial aminoacylation-tRNA synthetases has been reported in skeletal muscle from diabetic mice and patients.[55] In line with the above literature, we found that cytosolic tRNA aminoacylation proteins were dysregulated by ThaGlc-treated group (Figure 4F(ii)).

Differentially Expressed Proteins and Pathways Perturbed by High Glucose

Unlike cytokine and the other chemical stressors of rotenone and thapsigargin, only a few DEPs were identified (DENND4A, CHGA, SCG3, INS, TIMP2) with adjusted p value (FDR) < 0.05 from human islets exposed to glucose alone. By relaxing the criteria (p < 0.05), 214 additional DEPs were identified (Supplementary Table S15). The top biological processes that were activated in high glucose treatment included positive regulation of catabolic process, protein folding, and regulation of cellular amide metabolic process, among others (Figure S8A). For example, the role of glycogen synthase kinase-3 (GSK3) has been implicated in glucose homeostasis and the development of insulin resistance.[56] Other interesting biological processes/pathways with elevated protein expression in the glucose-treated group include membrane trafficking (DENND4A, TMF1, TRIP10), intracellular steroid hormone receptor signaling pathway (RNF14, TMF1, UBR5), and activation of GTPase activity (CRK, RANGAP1, RABGAP1).

In contrast, the top biological processes that were downregulated under high concentration glucose include insulin processing and protein localization to secretory granule, among others (Figure S8B). This reflects, in agreement to previous reports,[8] significantly reduced levels of secreted insulin upon exposure of human islets to high glucose (Figure 1B & S8C). In accord with this, secretory proteins, secretogranin III (SCG3) and chromogranin A (CHGA), two secretory proteins having functions in sorting and proteolytic processing of proinsulin also decreased proportionally upon incubation with high glucose (Figure S8C). On the other hand, metalloproteinase inhibitor 2 (TIMP2), an inhibitor of metalloproteinases degrading extracellular matrix components, showed decreased expression in our study (Figure S8C), which indicates increased activity in extracellular matrix degradation and remodeling as a response to glucotoxicity in human islets.

Label-Free Quantitative Proteomics Validated Findings in TMT-Based Proteomics Study of Stressed Human Islets

Label-free quantification (LFQ) was performed to validate altered proteins in our TMT data using samples collected under the same treatment conditions. In total, we identified 6327 proteins at FDR < 1%, of which ~65% quantified without missing any value across all 24 individual samples (Supplementary Table S16). Of the identified proteins in TMT data, ~70% (5453 proteins) were detected in LFQ data (Figure S9A). In addition, we also detected a total of 468 DEPs after combining comparisons from multiple treatment conditions in LFQ data. The Pearson’s correlation between LFQ and TMT data of these commonly identified DEPs (n = 468) or commonly quantified proteins (n = 3130) were high (R ~ 0.9 for the DEPs) (Figure 5A, B and Figure S9B). Importantly, the target proteins selected from our TMT data using PPI network analysis are among the 468 proteins and their expression trends in the TMT data are similar as in the LFQ data. For example, five proteins shown in Figure 5C are involved in the respiratory electron transport pathway identified in both TMT and LFQ data, with consistently upregulated expression levels in the Rot-treated group in comparison to the controls.

Figure 5.

Validation of TMT data using Label-free proteomic quantification method. (A, B) Representative Pearson’s correlation between LFQ and TMT data of commonly identified DEPs in (A) Rot vs Control and (B) Cyto vs Control. (C) Representative proteins involved in respiratory electron transport pathway commonly identified in both TMT and LFQ data having consistently increased expression levels in Rot-treated group.

4. Discussion

Insulin secreting β-cell dysfunction is a key hallmark of the early progression of T1D. Comprehensive identification of the molecular mechanisms triggered by hyperglycemia and other cellular stressing conditions is thus crucial for a deep understanding of β-cell’s inability to survive under severe stress conditions. We previously reported that sarcoplasmic/ER Ca²⁺ ATPase pathway blocker thapsigargin and complex I of the mitochondria respiratory chain inhibitor rotenone demonstrated distinct features of β-cell dysfunction in a rat INS-1 beta cell model.[16] To translate these findings and observations to more sophisticated β-cell models, in the current study, we applied MS-based proteomics to quantitatively profile the changes of protein expression in human islets under different stressing conditions. To this end, we stimulated isolated human islets using seven stress inducer-mimicking conditions experienced by dysfunctional β-cells. These chemical stressors and the dose conditions were selected on the basis of literatures[18, 57–60] and our own observations of cultured islet being healthy.

Interestingly, only minor alterations were observed upon exposure of human islets to either glucose or thapsigargin alone, whereas their combination (ThaGlc) resulted in a dramatic increase in proteomic changes. Our results showed that proinflammatory cytokines and ThaGlc share similarities in the perturbation of islet proteome: both resulted in upregulation of proteins in class I MHC mediated antigen processing and presentation, peroxisomal protein import pathway and mitochondrial translation and downregulation of IGF and IGFBP related pathways, peptide hormone processing and exocytosis related pathways, which is in line with ER stress induced by both ThaGlc and proinflammatory cytokines and secretory functions of the pancreatic islet cells.[61] The upregulation of islet MHC class I antigen expression has been demonstrated as critical features of T1D,[62] suggesting the potential effect of these stressors in triggering cellular immune response as a molecular mechanism of β-cell dysfunction. In contrary, our recent study found increased levels of proteins involved in regulation of IGF transport and uptake by IGFBPs pathway in thapsigargin-treated INS-1 cells.[16] The inconsistency in expression levels of these proteins may result from the different treatment conditions or cell types.

Rotenone, on the other hand, has quite different effect compared to the other stressors. When used alone, it increased levels of proteins involved in oxidative phosphorylation, protein processing in the ER, TCA cycle and respiratory electron transport, but decreased those related to PPP and gluconeogenesis pathway. Most of these observations are consistent with our recent study on rotenone treated INS-1 cells model[16] except some additional pathways perturbed in the present data, which may reflect the use of higher rotenone dose (~10 times) in human islets treatment. What is unique about rotenone is when it is combined with high concentration glucose, the damaging effect was dramatically reduced, resulting in merely 69 DEPs. This is likely a result of enhanced glycolysis through PPP in the cytoplasma to counter the reduced energy production in mitochondria and concomitant increase in the levels of glutathione, a critical antioxidant species, as an antioxidant response to the increased ROS production in stressed mitochondria, resulting in preservation of the normal functions of mitochondria. High glucose counteracting rotenone induced stress has been reported by Mendivil-Perez et al.,[63] who observed that in the presence of high glucose (55 mM), cell death was dramatically (>50%) reduced in rotenone exposed Jurkat cells in comparison to low dose glucose (11 mM). Conversely, Hou et al. reported that rotenone ameliorated hyperglycemia in db/db mice by inhibiting complex I and enhancing glycolysis.[64] In line with the above literature, our observation that rotenone and high glucose together sharply reduced the number of DEPs suggests high glucose may be one way to reduce rotenone induced cell toxicity, and may have therapeutic implications in pancreatic β-cell death resulted from mitochondrial stress.

Our results also highlighted that cytokine mix (IL-1β+IFN-γ+TNF-α) specifically perturbed immune response related pathways (Figure 2B). One of the most annotated pathways was cytokine signaling in immune system, which was significantly enriched in this treatment condition with 22 upregulated proteins involved. Majority of these proteins (17 out of 22 proteins) were also previously reported with consistently increased expression levels in human islets exposed to IL-1β+IFN-γ.[18] Additional five proteins (HLA-A, HLA-DRB5, GBP1, HLA-B, and GBP4) uniquely identified in the current data with significantly elevated expression levels may be due to the synergistic effect of additional cytokine component (TNF-α) in our treatment condition and the different islet donor sources. In contrast to our observations, a recent study reported that downregulation of HLA-A and HLA-B genes in response to verapamil (a calcium channel blocker drug) treatment of human islets.[39] This may indicate the action mechanism differences in the toxicity of verapamil and cytokine induced stress. Use of global sequencing of RNAs expression in human islets study also reported several genes modified by cytokine (IFN-g+ IL-1β) exposure.[65] The observations are in line with the present analysis of protein expression in cytokine-treated human islets.

In contrast to our previous observations on INS-1 cells, rotenone treatment of human islets also induced pathways related to ER stress. This was reflected in enriched protein processing in ER pathway, with elevated expression levels of 54 proteins involved (Figure 3C(ii), Figure S6). These include heat shock protein family members, which play critical roles in facilitating folding of translocated proteins such as translocon-associated protein subunit alpha (SSR1), binding calcium to the ER membrane and thereby regulating the retention of ER resident proteins. These identified novel proteins may link to UPR signaling pathway to regulate rotenone-induced ER or mitochondrial stress.

Another pathway that draw our attention was the regulation of IGF transport and uptake by IGFBPs, which was among the most significantly downregulated pathways in Cyto-treated and ThaGlc-treated groups (Figure 2D(i) and Figure 4D(i))). These proteins were recently demonstrated as potential markers in diabetic mellitus. For example, chromogranin A (CHGA) was identified as one of the potential therapeutic markers in global T1D serum proteomics analysis in response to verapamil.[39] In the present study, despite the expression level of this protein was not significant, we identified other chromogranin families that involve in regulating insulin signaling pathway, including CHGB, SCG2, and SCG3. In contrast, we previously reported this pathway as upregulated in thapsigargin treatment of INS-1 β-cells. The expression level differences may be attributed to the synergistic effect of glucose in the combined treatment.

In line with our previous observations in INS-1 cells,[16] we also found that OXPHOS proteins dysregulated by both rotenone and the combined ThaGlc in human islets. Despite recent studies demonstrated the critical regulatory link between oxidative protein folding in ER and mitochondrial dysfunction through OXPHOS, the molecular mechanism of how these oxidative stresses interact in variety of disease progression is still elusive. Our results highlight increased expression levels of 30 proteins in rotenone treatment, while decreased expression levels of 28 proteins in ThaGlc treatment involved in OXPHOS signaling pathways (Supplementary Table S9 and Table S13b). These results reflect different mechanisms of action of both stressors, and that the dysregulated proteins may involve in regulating the tight connections between ER redox control and mitochondrial metabolism. On the other hand, L13a-mediated translational silencing of ceruloplasmin expression was among the dysregulated pathways induced by both stressors (Rot and ThaGlc). This observation was also consistent with our recent reports in rotenone-treated INS-1 cells.[16] Many of these associated proteins relating to RNA binding that contribute to structural integrity of ribosomes were reduced in these stress responses, reflecting the acute conditions leading to accumulation of misfolded proteins in ER.

In conclusion, the present findings show that human primary pancreatic islet cells respond in a stressor-specific manner when exposed to proinflammatory cytokines, ER inhibitor thapsigargin, mitochondria inhibitor rotenone and high concentration glucose. When acting alone, rotenone and cytokines have the most perturbation to the islet proteome, but thapsigargin and high glucose have minimal effect. In combination with glucose, thapsigargin dramatically increases the number of dysregulated proteins, while rotenone’s effect is ameliorated. Proinflammatory cytokines, rotenone and thapsigargin-glucose all downregulate peptide hormone processing and secretion related proteins. In addition to the immune response pathways, proinflammatory cytokines can also induce ER like stress, and both cytokines and combined effect of glucose and thapsigargin affect antigen processing and presentation. Although mitochondria stress induced by rotenone can also induce significant dysregulations in the energy metabolism and ER related protein processing, as T1D is an autoimmune disease, our results suggest aberrant antigen processing and presentation observed in proinflammatory cytokines and thapsigargin-glucose conditions maybe more relevant to T1D pathogenesis. In this respect, studying β-cells isolated from human primary pancreatic islets cultured with cytokines or thapsigargin-glucose would help further delineate how the molecular changes, particularly the proteins bearing abnormal posttranslational modifications are linked to the initiation of β-cell dysfunction, death and ultimately, the progression of T1D.

A limitation of this study is the low number of biological replicates (n = 3). The corresponding statistical power could be improved by increasing the number of biological replicates (islet donors). It is also beyond the scope of this work to study in detail every pathways identified in each treatment conditions.

Supplementary Material

SI_1

Table S1. Characteristics of human islet donors. Table S2. All proteins identified from TMT proteomics data. Table S3. DEPs identified in Cyto treatment. Table S4. PPI network analysis of upregulated proteins in Cyto treatment. Table S5a. DEPs identified in CytoGlc treatment. Table S5b. Functional enrichment results of upregulated proteins in CytoGlc treatment. Table S6. DEPs identified in CytoGlc vs Cyto (p < 0.05). Table S7. DEPs identified in Rot treatment. Table S8. Functional enrichment results of upregulated proteins in Rot treatment. Table S9. PPI network analysis of upregulated proteins in Rot treatment. Table S10. PPI network analysis of downregulated proteins in Rot treatment. Table S11a. DEPs identified in RotGlc. Table S11b. DEPs identified in RotGlc vs Rot (p < 0.05). Table S12. DEPs identified in ThaGlc treatment. Table S13a. PPI network analysis of upregulated proteins in ThaGlc treatment. Table S13b. PPI network analysis of downregulated proteins in ThaGlc treatment. Table S14. DEPs identified in ThaGlc vs Tha (p < 0.05). Table S15. DEPs identified in Glc treatment. Table S16. Protein list of all identified and quantified proteins from LFQ proteomics data (XLSX).

SI_2

Figure S1: Reproducibility of three biological replicates of each treatment conditions. Figure S2: Classification of quantifiable proteins based on their biological processes or molecular functions. Figure S3: Changes of somatostatin and pancreatic polyppetides under different stressing conditions. Figure S4: Comparison between Cyto and CytoGlc treatment conditions. Figure S5: Biological functions of dysregulated proteins induced by Rot. Figure S6: 54 proteins dysregulated in protein processing in ER pathway induced by Rot. Figure S7: Functional enrichment analysis of dysregulated proteins induced by ThaGlc. Figure S8: Functional enrichment analysis of dysregulated proteins by Glc. Figure S9: Reproducibility between LFQ and TMT data (PDF).

Significance of the Study.

Exposure of insulin producing pancreatic β-cells to stressing conditions causes β-cell dysfunction and may ultimately lead to type 1 diabetes mellitus. Whether different stressing conditions work in concert, however, is not well understood. Our results show that acting alone, proinflammatory cytokines, ER stressor thapsigargin, mitochondrial stressor rotenone, and high concentration glucose all induce stressor-specific dysregulations in the proteome of human pancreatic islets, aligning with their respective mechanism of action, with the most dramatic changes occur when exposed to cytokines or rotenone. Thapsigargin has synergistic perturbing effect to the islet proteome when combined with high concentration glucose, but the impact of rotenone is reduced in the presence of high glucose. Investigating human islet proteome under multiple stressing conditions provides valuable information for understanding the pancreatic islet and β-cell dysfunctional mechanisms in vivo. The alleviating effect of high concentration glucose to rotenone may suggest a protective mechanism for rotenone induced cell toxicity and may have therapeutic implications in pancreatic β-cell death resulted from mitochondrial stress.

Data Availability Statement

All the mass spectrometry raw files and database search results from this study were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034608.