A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes

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 used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes 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 data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, 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.