Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing dataset from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA dataset, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included ATF3, ARF4, CREB3, and COG6. Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress. 2 Page 3 of 781 Diabetes INTRODUCTION Diabetes mellitus impacts over 425 million individuals worldwide, and this number is expected to approach 629 million by the year 2045 (1). Type 1 diabetes (T1D) accounts for 5- 10% of all diabetes cases and results from autoimmune destruction of pancreatic β cells, whereas type 2 diabetes (T2D) accounts for 90-95% of cases and results from a combination of peripheral insulin resistance and β-cell dysfunction (2). While the origins of T1D and T2D have been viewed classically as distinct, inadequate insulin secretion plays a central role in the pathophysiology of both forms of diabetes. Accumulating data suggest that common β-cell stress pathways underlie the diminished insulin secretory capacity and reduced β-cell survival observed during T1D and T2D progression. The primary function of the β cell is to rapidly sense elevations in blood glucose and respond with carefully titrated levels of insulin secretion (3). To meet the heavy biosynthetic burden of insulin production, β cells possess a highly developed secretory pathway in which insulin is sequentially folded, processed, and packaged into vesicles for exocytosis. Insulin production begins within the lumen of the endoplasmic reticulum (ER) with conversion of preproinsulin to proinsulin following cleavage of the preproinsulin signal peptide by signal peptidase (3). Proinsulin exits the ER and enters the Golgi apparatus (GA), where it undergoes packaging into early secretory vesicles, and these immature vesicles undergo a series of maturation steps (3). Cleavage of proinsulin by prohormone convertases to produce mature insulin begins within the trans-GA sub-compartment, and this process is completed within the mature secretory vesicles (3). The mild acidity of the GA aids the maturation of the prohormone convertases needed for insulin maturation (3), and this process continues and accelerates in the more acidic secretory vesicles to form mature insulin granules. The vesicles are then stored near 3 Diabetes Page 4 of 781 the plasma membrane as a readily releasable pool that is rapidly mobilized during glucose- stimulated insulin secretion (3). As insulin accounts for nearly half of the total protein produced in β cells (4), disruptions in the fidelity of proinsulin processing and alterations in secretory organelle function can significantly affect β-cell function and health (5; 6). In this regard, ER stress has been well documented as playing a role in the pathogenesis of both T1D and T2D (7-10), and the tripartite ER stress response has been studied extensively, resulting in multiple well-validated protein and transcriptional markers of ER stress. The concept of GA stress has been proposed primarily in the context of neurologic disorders (11; 12) but has also been suggested to occur in other secretory cells, such as monocytes (13) and Brunner’s gland cells (14). Several candidate GA stress markers were recently identified in other cell types and linked with GA morphologic changes observed by electron microscopy (15). In contrast with ER stress, the pathways underlying GA stress are incompletely described and may be much more diverse in nature (14). Despite the importance of the GA in insulin processing and maturation, whether GA stress plays a role in β cells or if it exists in either T1D or T2D remain unclear. To this end, we identified publicly available human islet datasets from T1D and T2D models, including a combination of datasets from donors with diabetes and datasets where diabetes pathophysiology was modeled ex vivo, and we analyzed these datasets for changes in the expression of GA-associated genes. In parallel, we treated human islets with the known GA stress inducer brefeldin A (BFA) and used a computational approach to identify overlap between the T1D, T2D, and BFA datasets. From this analysis, we identified a set of genes and pathways associated with β-cell GA stress in diabetes. 4 Page 5 of 781 Diabetes RESEARCH DESIGN AND METHODS Selection and Classification of Datasets Studies of interest were identified by searching specific terms in the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds). Search terms included: “islet & cytokine,” “islet & palmitate,” “islet & type 2 diabetes,” and “islet & type 1 diabetes.” Some datasets were found in more than one search query, but these were weighted equally with datasets appearing in only one query. Datasets were excluded if the primary tissue was not human islets or if there was no non-diabetic control or control condition available for comparison. The experimental procedures of all the publicly available microarray and RNA- sequencing human pancreatic islets samples used in this paper have been fully described elsewhere (16-24). T1D datasets included cadaveric human islets treated with or without pro-inflammatory cytokines analogous to autoimmune mediated β-cell insult in T1D (datasets 1 (18) and 3 [data under review; islets treated with or without 50 U/mL IL-1β and 1000 U/mL IFNγ for 24 h] and from organ donors with T1D [dataset 2]) (25). T2D datasets included cadaveric human islets from donors diagnosed with T2D (datasets 4 & 5) (17; 24) and cadaveric human islets treated with palmitate as a model of T2D (dataset 6) (16). The BFA dataset (dataset 7) included cadaveric human islets treated with BFA or DMSO as a control. Further information on the dataset characteristics, including GEO accession numbers, number of samples, profiling technology, and number of probe sets or genes, is shown in Table 1. Human islet donor characteristics are shown in Table 2. 5 Diabetes Page 6 of 781 Generation of BFA-treated Human Islet Datasets For the generation of the BFA dataset, human cadaveric donor islets were obtained from the Integrated Islet Distribution Program (26). Upon receipt, islets were allowed to recover overnight in phenol red–free DMEM containing 10% FBS, 10 mM HEPES, 2 mM L-glutamate, and 100 U/0.1 mg/L penicillin/streptomycin. Islets were handpicked (250 per condition) and then treated for 24 h with DMSO or 0.1 µg/mL BFA. Islets were collected, washed in PBS, and lysed in TRIzol (Thermo Fisher Scientific, Waltham, MA). Total RNA was isolated using a miRNeasy kit (Qiagen, Hilden, Germany) for RNA-sequencing and RNeasy Micro kit for qRT- PCR. Raw data were deposited in the GEO database (GSE152615). RNA-Seq Library Preparation and Sequencing of BFA-treated Islets The concentration and quality of total RNA samples were first assessed using an Agilent 2100 Bioanalyzer. A RIN (RNA Integrity Number) ≥5 was required to pass quality control (RIN 8.063 ± 0.4031). Then, 100 ng of RNA per sample were used to prepare single-indexed strand- specific cDNA libraries using a KAPA mRNA Hyperprep kit (Roche Sequencing & Life Science, Indianapolis, IN). The resulting libraries were assessed for quantity and size distribution using Qubit and an Agilent 2100 Bioanalyzer. Pooled libraries (300 pM) were sequenced with a 2×100 bp paired-end configuration on a NovaSeq6000 (Illumina, San Diego, CA). A Phred quality score (Q score) was used to measure the quality of sequencing. More than 90% of the sequencing reads reached Q30 (99.9% base call accuracy). Data Preprocessing and Normalization 6 Page 7 of 781 Diabetes For microarray datasets, transcriptomic data were background adjusted, summarized, and normalized to probe sets using Robust Multi-Array Average methods from the Bioconductor package Affy in R (27) for datasets 4 and 5. Batch correction of dataset 5 was performed using the ComBat function from the R/Bioconductor package sva (28).