In silico identification of NF-kappaB-regulated genes in pancreatic beta-cells

BACKGROUND: Pancreatic beta-cells are the target of an autoimmune attack in type 1 diabetes mellitus (T1DM). This is mediated in part by cytokines, such as interleukin (IL)-1β and interferon (IFN)-γ. These cytokines modify the expression of hundreds of genes, leading to beta-cell dysfunction and death by apoptosis. Several of these cytokine-induced genes are potentially regulated by the IL-1β-activated transcription factor (TF) nuclear factor (NF)-κB, and previous studies by our group have shown that cytokine-induced NF-κB activation is pro-apoptotic in beta-cells. To identify NF-κB-regulated gene networks in beta-cells we presently used a discriminant analysis-based approach to predict NF-κB responding genes on the basis of putative regulatory elements. RESULTS: The performance of linear and quadratic discriminant analysis (LDA, QDA) in identifying NF-κB-responding genes was examined on a dataset of 240 positive and negative examples of NF-κB regulation, using stratified cross-validation with an internal leave-one-out cross-validation (LOOCV) loop for automated feature selection and noise reduction. LDA performed slightly better than QDA, achieving 61% sensitivity, 91% specificity and 87% positive predictive value, and allowing the identification of 231, 251 and 580 NF-κB putative target genes in insulin-producing INS-1E cells, primary rat beta-cells and human pancreatic islets, respectively. Predicted NF-κB targets had a significant enrichment in genes regulated by cytokines (IL-1β or IL-1β + IFN-γ) and double stranded RNA (dsRNA), as compared to genes not regulated by these NF-κB-dependent stimuli. We increased the confidence of the predictions by selecting only evolutionary stable genes, i.e. genes with homologs predicted as NF-κB targets in rat, mouse, human and chimpanzee. CONCLUSION: The present in silico analysis allowed us to identify novel regulatory targets of NF-κB using a supervised classification method based on putative binding motifs. This provides new insights into the gene networks regulating cytokine-induced beta-cell dysfunction and death

Beta Cell Imaging—From Pre-Clinical Validation to First in Man Testing

There are presently no reliable ways to quantify human pancreatic beta cell mass (BCM) in vivo, which prevents an accurate understanding of the progressive beta cell loss in diabetes or following islet transplantation. Furthermore, the lack of beta cell imaging hampers the evaluation of the impact of new drugs aiming to prevent beta cell loss or to restore BCM in diabetes. We presently discuss the potential value of BCM determination as a cornerstone for individualized therapies in diabetes, describe the presently available probes for human BCM evaluation, and discuss our approach for the discovery of novel beta cell biomarkers, based on the determination of specific splice variants present in human beta cells. This has already led to the identification of DPP6 and FXYD2γa as two promising targets for human BCM imaging, and is followed by a discussion of potential safety issues, the role for radiochemistry in the improvement of BCM imaging, and concludes with an overview of the different steps from pre-clinical validation to a first-in-man trial for novel tracers

Construction and validation of the APOCHIP, a spotted oligo-microarray for the study of beta-cell apoptosis

BACKGROUND: Type 1 diabetes mellitus (T1DM) is a autoimmune disease caused by a long-term negative balance between immune-mediated beta-cell damage and beta-cell repair/regeneration. Following immune-mediated damage the beta-cell fate depends on several genes up- or down-regulated in parallel and/or sequentially. Based on the information obtained by the analysis of several microarray experiments of beta-cells exposed to pro-apoptotic conditions (e.g. double stranded RNA (dsRNA) and cytokines), we have developed a spotted rat oligonucleotide microarray, the APOCHIP, containing 60-mer probes for 574 genes selected for the study of beta-cell apoptosis. RESULTS: The APOCHIP was validated by a combination of approaches. First we performed an internal validation of the spotted probes based on a weighted linear regression model using dilution series experiments. Second we profiled expression measurements in ten dissimilar rat RNA samples for 515 genes that were represented on both the spotted oligonucleotide collection and on the in situ-synthesized 25-mer arrays (Affymetrix GeneChips). Internal validation showed that most of the spotted probes displayed a pattern of reaction close to that predicted by the model. By using simple rules for comparison of data between platforms we found strong correlations (r(median)= 0.84) between relative gene expression measurements made with spotted probes and in situ-synthesized 25-mer probe sets. CONCLUSION: In conclusion our data suggest that there is a high reproducibility of the APOCHIP in terms of technical replication and that relative gene expression measurements obtained with the APOCHIP compare well to the Affymetrix GeneChip. The APOCHIP is available to the scientific community and is a useful tool to study the molecular mechanisms regulating beta-cell apoptosis

Biomarkers of islet beta cell stress and death in type 1 diabetes

Recent work on the pathogenesis of type 1 diabetes has led to an evolving recognition of the heterogeneity of this disease, both with regards to clinical phenotype and responses to therapies to prevent or revert diabetes. This heterogeneity not only limits efforts to accurately predict clinical disease but also is reflected in differing responses to immunomodulatory therapeutics. Thus, there is a need for robust biomarkers of beta cell health, which could provide insight into pathophysiological differences in disease course, improve disease prediction, increase the understanding of therapeutic responses to immunomodulatory interventions and identify individuals most likely to benefit from these therapies. In this review, we outline current literature, limitations and future directions for promising circulating markers of beta cell stress and death in type 1 diabetes, including markers indicating abnormal prohormone processing, circulating RNAs and circulating DNAs

Detailed transcriptome atlas of the pancreatic beta cell

BACKGROUND: Gene expression patterns provide a detailed view of cellular functions. Comparison of profiles in disease vs normal conditions provides insights into the processes underlying disease progression. However, availability and integration of public gene expression datasets remains a major challenge. The aim of the present study was to explore the transcriptome of pancreatic islets and, based on this information, to prepare a comprehensive and open access inventory of insulin-producing beta cell gene expression, the Beta Cell Gene Atlas (BCGA). METHODS: We performed Massively Parallel Signature Sequencing (MPSS) analysis of human pancreatic islet samples and microarray analyses of purified rat beta cells, alpha cells and INS-1 cells, and compared the information with available array data in the literature. RESULTS: MPSS analysis detected around 7600 mRNA transcripts, of which around a third were of low abundance. We identified 2000 and 1400 transcripts that are enriched/depleted in beta cells compared to alpha cells and INS-1 cells, respectively. Microarray analysis identified around 200 transcription factors that are differentially expressed in either beta or alpha cells. We reanalyzed publicly available gene expression data and integrated these results with the new data from this study to build the BCGA. The BCGA contains basal (untreated conditions) gene expression level estimates in beta cells as well as in different cell types in human, rat and mouse pancreas. Hierarchical clustering of expression profile estimates classify cell types based on species while beta cells were clustered together. CONCLUSION: Our gene atlas is a valuable source for detailed information on the gene expression distribution in beta cells and pancreatic islets along with insulin producing cell lines. The BCGA tool, as well as the data and code used to generate the Atlas are available at the T1Dbase website (T1DBase.org).Journal Articleinfo:eu-repo/semantics/publishe

IFN-α induces a preferential long-lasting expression of MHC class I in human pancreatic beta cells

Aims/hypothesis IFN-α, a cytokine expressed in human islets from individuals affected by type 1 diabetes, plays a key role in the pathogenesis of diabetes by upregulating inflammation, endoplasmic reticulum (ER) stress and MHC class I overexpression, three hallmarks of islet histology in early type 1 diabetes. We tested whether expression of these mediators of beta cell loss is reversible upon IFN-α withdrawal or IFN-α pathway inhibition. Methods IFN-α-induced MHC class I overexpression, ER stress and inflammation were evaluated by flow cytometry, immunofluorescence and real-time PCR in human EndoC-βH1 cells or human islets exposed to IFN-α with or without the presence of Janus kinase (JAK) inhibitors. Protein expression was evaluated by western blot. Results IFN-α-induced expression of inflammatory and ER stress markers returned to baseline after 24–48 h following cytokine removal. In contrast, MHC class I overexpression at the cell surface persisted for at least 7 days. Treatment with JAK inhibitors, when added with IFN-α, prevented MHC class I overexpression, but when added 24 h after IFN-α exposure these inhibitors failed to accelerate MHC class I return to baseline. Conclusions/interpretation IFN-α mediates a long-lasting and preferential MHC class I overexpression in human beta cells, which is not affected by the subsequent addition of JAK inhibitors. These observations suggest that IFN-α-stimulated long-lasting MHC class I expression may amplify beta cell antigen presentation during the early phase of type 1 diabetes and that IFN-α inhibitors might need to be used at very early stages of the disease to be effective

Response of young and adult birds to the same environmental variables and different spatial scales during post breeding period

Context: How do young birds achieve spatial knowledge about the environment during the initial stages of their life? They may follow adults, so gaining social information and learning; alternatively, young birds may acquire knowledge of the environment themselves by experiencing habitat and landscape features. If learning is at least partially independent of adults then young birds should respond to landscape composition at finer spatial scale than adults, who possess knowledge over a larger area. Objectives: We studied the responses of juvenile, immature and adult Caspian Gull Larus cachinnans to the same habitat and landscape variables, but at several spatial scales (ranging from 2.5 to 15\ua0km), during post-breeding period. Methods: We surveyed 61 fish ponds (foraging patches) in southern Poland and counted Caspian gulls. Results: Juvenile birds responded at finer spatial scales to the factors than did adults. Immature birds showed complicated, intermediate responses to spatial scale. The abundance of juvenile birds was mostly correlated with the landscape composition (positively with the cover of corridors and negatively with barriers). Adult abundance was positively related to foraging patch quality (fish stock), which clearly required previous spatial experience of the environment. The abundance of all age classes were moderately correlated with each other indicating that social behaviour may also contribute to the learning of the environment. Conclusions: This study shows that as birds mature, they respond differently to components of their environment at different spatial scales. This has considerable ecological consequences for their distribution across environments

Cytokines Interleukin-1β and Tumor Necrosis Factor-α Regulate Different Transcriptional and Alternative Splicing Networks in Primary β-Cells

OBJECTIVE: Cytokines contribute to pancreatic beta-cell death in type 1 diabetes. This effect is mediated by complex gene networks that remain to be characterized. We presently utilized array analysis to define the global expression pattern of genes, including spliced variants, modified by the cytokines interleukin (IL)-1beta + interferon (IFN)-gamma and tumor necrosis factor (TNF)-alpha + IFN-gamma in primary rat beta-cells. RESEARCH DESIGN AND METHODS: Fluorescence-activated cell sorter-purified rat beta-cells were exposed to IL-1beta + IFN-gamma or TNF-alpha + IFN-gamma for 6 or 24 h, and global gene expression was analyzed by microarray. Key results were confirmed by RT-PCR, and small-interfering RNAs were used to investigate the mechanistic role of novel and relevant transcription factors identified by pathway analysis. RESULTS Nearly 16,000 transcripts were detected as present in beta-cells, with temporal differences in the number of genes modulated by IL-1beta + IFNgamma or TNF-alpha + IFN-gamma. These cytokine combinations induced differential expression of inflammatory response genes, which is related to differential induction of IFN regulatory factor-7. Both treatments decreased the expression of genes involved in the maintenance of beta-cell phenotype and growth/regeneration. Cytokines induced hypoxia-inducible factor-alpha, which in this context has a proapoptotic role. Cytokines also modified the expression of >20 genes involved in RNA splicing, and exon array analysis showed cytokine-induced changes in alternative splicing of >50% of the cytokine-modified genes. CONCLUSIONS: The present study doubles the number of known genes expressed in primary beta-cells, modified or not by cytokines, and indicates the biological role for several novel cytokine-modified pathways in beta-cells. It also shows that cytokines modify alternative splicing in beta-cells, opening a new avenue of research for the field.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

The role of proteomics in assessing beta-cell dysfunction and death in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is characterized by autoimmune-induced dysfunction and destruction of the pancreatic beta cells. Unfortunately, this process is poorly understood, and the current best treatment for type 1 diabetes is administration of exogenous insulin. To better understand these mechanisms and to develop new therapies, there is an urgent need for biomarkers that can reliably predict disease stage. Areas covered: Mass spectrometry (MS)-based proteomics and complementary techniques play an important role in understanding the autoimmune response, inflammation and beta-cell death. MS is also a leading technology for the identification of biomarkers. This, and the technical difficulties and new technologies that provide opportunities to characterize small amounts of sample in great depth and to analyze large sample cohorts will be discussed in this review. Expert opinion: Understanding disease mechanisms and the discovery of disease-associated biomarkers are highly interconnected goals. Ideal biomarkers would be molecules specific to the different stages of the disease process that are released from beta cells to the bloodstream. However, such molecules are likely present in trace amounts in the blood due to the small number of pancreatic beta cells in the human body and the heterogeneity of the target organ and disease process

DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients

The first genome-scale DNA methylation study on pancreatic islets from type 2 diabetic patients identifies disease-associated DNA methylation pattern that translate into aberrant gene expression in novel factors relevant for β-cell function and survival