Interferons are key cytokines acting on pancreatic islets in type 1 diabetes.

AIMS/HYPOTHESIS: The proinflammatory cytokines IFN-α, IFN-γ, IL-1ß and TNF-α may contribute to innate and adaptive immune responses during insulitis in type 1 diabetes and therefore represent attractive therapeutic targets to protect beta cells. However, the specific role of each of these cytokines individually on pancreatic beta cells remains unknown. METHODS: We used deep RNA-seq analysis, followed by extensive confirmation experiments based on reverse transcription-quantitative PCR (RT-qPCR), western blot, histology and use of siRNAs, to characterise the response of human pancreatic beta cells to each cytokine individually and compared the signatures obtained with those present in islets of individuals affected by type 1 diabetes. RESULTS: IFN-α and IFN-γ had a greater impact on the beta cell transcriptome when compared with IL-1ß and TNF-α. The IFN-induced gene signatures have a strong correlation with those observed in beta cells from individuals with type 1 diabetes, and the level of expression of specific IFN-stimulated genes is positively correlated with proteins present in islets of these individuals, regulating beta cell responses to 'danger signals' such as viral infections. Zinc finger NFX1-type containing 1 (ZNFX1), a double-stranded RNA sensor, was identified as highly induced by IFNs and shown to play a key role in the antiviral response in beta cells. CONCLUSIONS/INTERPRETATION: These data suggest that IFN-α and IFN-γ are key cytokines at the islet level in human type 1 diabetes, contributing to the triggering and amplification of autoimmunity.

RedRibbon: A new rank-rank hypergeometric overlap for gene and transcript expression signatures.

High-throughput omics technologies have generated a wealth of large protein, gene, and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anticorrelation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm-based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. The package compatibility mode is a drop-in replacement to previous packages. RedRibbon holds the promise to accurately extricate detailed information from large comparative analyses.

Why does the immune system destroy pancreatic ß-cells but not α-cells in type 1 diabetes?

A perplexing feature of type 1 diabetes (T1D) is that the immune system destroys pancreatic ß-cells but not neighbouring α-cells, even though both ß-cells and α-cells are dysfunctional. Dysfunction, however, progresses to death only for ß-cells. Recent findings indicate important differences between these two cell types. First, expression of BCL2L1, a key antiapoptotic gene, is higher in α-cells than in ß-cells. Second, endoplasmic reticulum (ER) stress-related genes are differentially expressed, with higher expression levels of pro-apoptotic CHOP in ß-cells than in α-cells and higher expression levels of HSPA5 (which encodes the protective chaperone BiP) in α-cells than in ß-cells. Third, expression of viral recognition and innate immune response genes is higher in α-cells than in ß-cells, contributing to the enhanced resistance of α-cells to coxsackievirus infection. Fourth, expression of the immune-inhibitory HLA-E molecule is higher in α-cells than in ß-cells. Of note, α-cells are less immunogenic than ß-cells, and the CD8+ T cells invading the islets in T1D are reactive to pre-proinsulin but not to glucagon. We suggest that this finding is a result of the enhanced capacity of the α-cell to endure viral infections and ER stress, which enables them to better survive early stressors that can cause cell death and consequently amplify antigen presentation to the immune system. Moreover, the processing of the pre-proglucagon precursor in enteroendocrine cells might favour immune tolerance towards this potential self-antigen compared to pre-proinsulin.

Adipose tissue plasticity in pheochromocytoma patients suggests a role of the splicing machinery in human adipose browning.

Adipose tissue from pheochromocytoma patients acquires brown fat features, making it a valuable model for studying the mechanisms that control thermogenic adipose plasticity in humans. Transcriptomic analyses revealed a massive downregulation of splicing machinery components and splicing regulatory factors in browned adipose tissue from patients, with upregulation of a few genes encoding RNA-binding proteins potentially involved in splicing regulation. These changes were also observed in cell culture models of human brown adipocyte differentiation, confirming a potential involvement of splicing in the cell-autonomous control of adipose browning. The coordinated changes in splicing are associated with a profound modification in the expression levels of splicing-driven transcript isoforms for genes involved in the specialized metabolism of brown adipocytes and those encoding master transcriptional regulators of adipose browning. Splicing control appears to be a relevant component of the coordinated gene expression changes that allow human adipose tissue to acquire a brown phenotype.

Mining the transcriptome of target tissues of autoimmune and degenerative pancreatic ß-cell and brain diseases to discover therapies.

Target tissues of autoimmune and degenerative diseases show signals of inflammation. We used publicly available RNA-seq data to study whether pancreatic ß-cells in type 1 and type 2 diabetes and neuronal tissue in multiple sclerosis and Alzheimer's disease share inflammatory gene signatures. We observed concordantly upregulated genes in pairwise diseases, many of them related to signaling by interleukins and interferons. We next mined these signatures to identify therapies that could be re-purposed/shared among the diseases and identified the bromodomain inhibitors as potential perturbagens to revert the transcriptional signatures. We experimentally confirmed in human ß-cells that bromodomain inhibitors I-BET151 and GSK046 prevent the deleterious effects of the pro-inflammatory cytokines interleukin-1ß and interferon-γ and at least some of the effects of the metabolic stressor palmitate. These results demonstrate that key inflammation-induced molecular mechanisms are shared between ß-cells and brain in autoimmune and degenerative diseases and that these signatures can be mined for drug discovery.

ADAR1-dependent editing regulates human ß cell transcriptome diversity during inflammation.

Introduction: Enterovirus infection has long been suspected as a possible trigger for type 1 diabetes. Upon infection, viral double-stranded RNA (dsRNA) is recognized by membrane and cytosolic sensors that orchestrate type I interferon signaling and the recruitment of innate immune cells to the pancreatic islets. In this context, adenosine deaminase acting on RNA 1 (ADAR1) editing plays an important role in dampening the immune response by inducing adenosine mispairing, destabilizing the RNA duplexes and thus preventing excessive immune activation. Methods: Using high-throughput RNA sequencing data from human islets and EndoC-ßH1 cells exposed to IFNα or IFNγ/IL1ß, we evaluated the role of ADAR1 in human pancreatic ß cells and determined the impact of the type 1 diabetes pathophysiological environment on ADAR1-dependent RNA editing. Results: We show that both IFNα and IFNγ/IL1ß stimulation promote ADAR1 expression and increase the A-to-I RNA editing of Alu-Containing mRNAs in EndoC-ßH1 cells as well as in primary human islets. Discussion: We demonstrate that ADAR1 overexpression inhibits type I interferon response signaling, while ADAR1 silencing potentiates IFNα effects. In addition, ADAR1 overexpression triggers the generation of alternatively spliced mRNAs, highlighting a novel role for ADAR1 as a regulator of the ß cell transcriptome under inflammatory conditions.

Transcription and splicing regulation by NLRC5 shape the interferon response in human pancreatic ß cells.

IFNα is a key regulator of the dialogue between pancreatic ß cells and the immune system in early type 1 diabetes (T1D). IFNα up-regulates HLA class I expression in human ß cells, fostering autoantigen presentation to the immune system. We observed by bulk and single-cell RNA sequencing that exposure of human induced pluripotent-derived islet-like cells to IFNα induces expression of HLA class I and of other genes involved in antigen presentation, including the transcriptional activator NLRC5. We next evaluated the global role of NLRC5 in human insulin-producing EndoC-ßH1 and human islet cells by RNA sequencing and targeted gene/protein determination. NLRC5 regulates expression of HLA class I, antigen presentation-related genes, and chemokines. NLRC5 also mediates the effects of IFNα on alternative splicing, a generator of ß cell neoantigens, suggesting that it is a central player of the effects of IFNα on ß cells that contribute to trigger and amplify autoimmunity in T1D.

From Pancreatic ß-Cell Gene Networks to Novel Therapies for Type 1 Diabetes.

Completion of the Human Genome Project enabled a novel systems- and network-level understanding of biology, but this remains to be applied for understanding the pathogenesis of type 1 diabetes (T1D). We propose that defining the key gene regulatory networks that drive ß-cell dysfunction and death in T1D might enable the design of therapies that target the core disease mechanism, namely, the progressive loss of pancreatic ß-cells. Indeed, many successful drugs do not directly target individual disease genes but, rather, modulate the consequences of defective steps, targeting proteins located one or two steps downstream. If we transpose this to the T1D situation, it makes sense to target the pathways that modulate the ß-cell responses to the immune assault-in relation to signals that may stimulate the immune response (e.g., HLA class I and chemokine overexpression and/or neoantigen expression) or inhibit the invading immune cells (e.g., PDL1 and HLA-E expression)-instead of targeting only the immune system, as it is usually proposed. Here we discuss the importance of a focus on ß-cells in T1D, lessons learned from other autoimmune diseases, the "alternative splicing connection," data mining, and drug repurposing to protect ß-cells in T1D and then some of the initial candidates under testing for ß-cell protection.

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis.

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression.We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-ßH1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples.We identified a total of 264 genes stably expressed in EndoC-ßH1 cells and human islets following cytokines - or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.

Molecular Footprints of the Immune Assault on Pancreatic Beta Cells in Type 1 Diabetes.

Type 1 diabetes (T1D) is a chronic disease caused by the selective destruction of the insulin-producing pancreatic beta cells by infiltrating immune cells. We presently evaluated the transcriptomic signature observed in beta cells in early T1D and compared it with the signatures observed following in vitro exposure of human islets to inflammatory or metabolic stresses, with the aim of identifying "footprints" of the immune assault in the target beta cells. We detected similarities between the beta cell signatures induced by cytokines present at different moments of the disease, i.e., interferon-α (early disease) and interleukin-1ß plus interferon-γ (later stages) and the beta cells from T1D patients, identifying biological process and signaling pathways activated during early and late stages of the disease. Among the first responses triggered on beta cells was an enrichment in antiviral responses, pattern recognition receptors activation, protein modification and MHC class I antigen presentation. During putative later stages of insulitis the processes were dominated by T-cell recruitment and activation and attempts of beta cells to defend themselves through the activation of anti-inflammatory pathways (i.e., IL10, IL4/13) and immune check-point proteins (i.e., PDL1 and HLA-E). Finally, we mined the beta cell signature in islets from T1D patients using the Connectivity Map, a large database of chemical compounds/drugs, and identified interesting candidates to potentially revert the effects of insulitis on beta cells.