Expression profiling of human genetic and protein interaction networks in type 1 diabetes

Analysis of transcriptome expression profiling data in oral leukoplakia and early and late‑stage oral squamous cell carcinoma

The present study screened, potential prognostic biomarkers for oral carcinogenesis. The GSE85195 dataset, which consisted of oral leukoplakia (OL) and early and late-stage oral squamous cell carcinoma (OSCC) samples, was used. The differentially expressed genes (DEGs) in early OSCC vs. OL, late OSCC vs. OL and late OSCC vs. early OSCC groups were screened using the limma package in R. The Short Time-series Expression Miner software package was used to cluster DEGs with similar expression patterns in the course of disease progression (from OL to early and then late-stage OSCC). Moreover, the Database for Annotation, Visualization and Integrated Discovery online analysis tool was used to perform Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Reverse transcription-quantitative PCR was performed to assess the mRNA expression levels of hub node genes in clinical samples, and receiver operating characteristic curve analysis was performed to assess the prognostic value of the hub genes. A total of 4,595, 6,042 and 2,738 DEGs were screened in the early OSCC vs. OL, late OSCC vs. OL and late OSCC vs. early OSCC groups, respectively. A total of 665 overlapping genes were identified when the screened DEGs were compared. Cluster 1 and cluster 7 were identified as the significant clusters, which contained 496 and 341 DEGs, respectively. A PPI network was constructed with 440 interaction pairs. There were five differentially expressed hub nodes identified in different stages from OL to OSCC. The results of the present study indicated that fibronectin 1, signal transducer and activator of transcription 1, collagen type II α1 chain, collagen type X α1 chain and collagen type IV α6 chain might serve as independent diagnostic factors for OL and OSCC, and as prognostic biomarkers for OL carcinogenesis.

kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

A Dual Systems Genetics Approach Identifies Common Genes, Networks, and Pathways for Type 1 and 2 Diabetes in Human Islets

Type 1 and 2 diabetes (T1/2D) are complex metabolic diseases caused by absolute or relative loss of functional β-cell mass, respectively. Both diseases are influenced by multiple genetic loci that alter disease risk. For many of the disease-associated loci, the causal candidate genes remain to be identified. Remarkably, despite the partially shared phenotype of the two diabetes forms, the associated loci for T1D and T2D are almost completely separated. We hypothesized that some of the genes located in risk loci for T1D and T2D interact in common pancreatic islet networks to mutually regulate important islet functions which are disturbed by disease-associated variants leading to β-cell dysfunction. To address this, we took a dual systems genetics approach. All genes located in 57 T1D and 243 T2D established genome-wide association studies (GWAS) loci were extracted and filtered for genes expressed in human islets using RNA sequencing data, and then integrated with; (1) human islet expression quantitative trait locus (eQTL) signals in linkage disequilibrium (LD) with T1D- and T2D-associated variants; or (2) with genes transcriptionally regulated in human islets by pro-inflammatory cytokines or palmitate as in vitro models of T1D and T2D, respectively. Our in silico systems genetics approaches created two interaction networks consisting of densely-connected T1D and T2D loci genes. The "T1D-T2D islet eQTL interaction network" identified 9 genes (GSDMB, CARD9, DNLZ, ERAP1, PPIP5K2, TMEM69, SDCCAG3, PLEKHA1, and HEMK1) in common T1D and T2D loci that harbor islet eQTLs in LD with disease-associated variants. The "cytokine and palmitate islet interaction network" identified 4 genes (ASCC2, HIBADH, RASGRP1, and SRGAP2) in common T1D and T2D loci whose expression is mutually regulated by cytokines and palmitate. Functional annotation analyses of the islet networks revealed a number of significantly enriched pathways and molecular functions including cell cycle regulation, inositol phosphate metabolism, lipid metabolism, and cell death and survival. In summary, our study has identified a number of new plausible common candidate genes and pathways for T1D and T2D.

Type 1 Diabetes Candidate Genes Linked to Pancreatic Islet Cell Inflammation and Beta-Cell Apoptosis

Type 1 diabetes (T1D) is a chronic immune-mediated disease resulting from the selective destruction of the insulin-producing pancreatic islet β-cells. Susceptibility to the disease is the result of complex interactions between environmental and genetic risk factors. Genome-wide association studies (GWAS) have identified more than 50 genetic regions that affect the risk of developing T1D. Most of these susceptibility loci, however, harbor several genes, and the causal variant(s) and gene(s) for most of the loci remain to be established. A significant part of the genes located in the T1D susceptibility loci are expressed in human islets and β cells and mounting evidence suggests that some of these genes modulate the β-cell response to the immune system and viral infection and regulate apoptotic β-cell death. Here, we discuss the current status of T1D susceptibility loci and candidate genes with focus on pancreatic islet cell inflammation and β-cell apoptosis.

Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

Molecular pathway alterations in CD4 T-cells of nonobese diabetic (NOD) mice in the preinsulitis phase of autoimmune diabetes

Type 1 diabetes (T1D) is a multigenic disease caused by T-cell mediated destruction of the insulin producing pancreatic islet ß-cells. The earliest sign of islet autoimmunity in NOD mice, islet leukocytic infiltration or insulitis, is obvious at around 5 weeks of age. The molecular alterations that occur in T cells prior to insulitis and that may contribute to T1D development are poorly understood. Since CD4 T-cells are essential to T1D development, we tested the hypothesis that multiple genes/molecular pathways are altered in these cells prior to insulitis. We performed a genome-wide transcriptome and pathway analysis of whole, untreated CD4 T-cells from 2, 3, and 4 week-old NOD mice in comparison to two control strains (NOR and C57BL/6). We identified many differentially expressed genes in the NOD mice at each time point. Many of these genes (herein referred to as NOD altered genes) lie within known diabetes susceptibility (insulin-dependent diabetes, Idd) regions, e.g. two diabetes resistant loci, Idd27 (tripartite motif-containing family genes) and Idd13 (several genes), and the CD4 T-cell diabetogenic activity locus, Idd9/11 (2 genes, KH domain containing, RNA binding, signal transduction associated 1 and protein tyrosine phosphatase 4a2). The biological processes associated with these altered genes included, apoptosis/cell proliferation and metabolic pathways (predominant at 2 weeks); inflammation and cell signaling/activation (predominant at 3 weeks); and innate and adaptive immune responses (predominant at 4 weeks). Pathway analysis identified several factors that may regulate these abnormalities: eight, common to all 3 ages (interferon regulatory factor 1, hepatic nuclear factor 4, alpha, transformation related protein 53, BCL2-like 1 (lies within Idd13), interferon gamma, interleukin 4, interleukin 15, and prostaglandin E2); and two each, common to 2 and 4 weeks (androgen receptor and interleukin 6); and to 3 and 4 weeks (interferon alpha and interferon regulatory factor 7). Others were unique to the various ages, e.g. myelocytomatosis oncogene, jun oncogene, and amyloid beta (A4) to 2 weeks; tumor necrosis factor, transforming growth factor, beta 1, NF?B, ERK, and p38MAPK to 3 weeks; and interleukin 12 and signal transducer and activator of transcription 4 to 4 weeks. Thus, our study demonstrated that expression of many genes that lie within several Idds (e.g. Idd27, Idd13 and Idd9/11) was altered in CD4 T-cells in the early induction phase of autoimmune diabetes and identified their associated molecular pathways. These data offer the opportunity to test hypotheses on the roles played by the altered genes/molecular pathways, to understand better the mechanisms of CD4 T-cell diabetogenesis, and to develop new therapeutic strategies for T1D.

Activating transcription factor 4 links metabolic stress to interleukin-6 expression in macrophages

Chronic inflammation is a molecular element of the metabolic syndrome and type 2 diabetes. Saturated fatty acids (SFAs) are considered to be an important proinflammatory factor. However, it is still incompletely understood how SFAs induce proinflammatory cytokine expression. Hereby we report that activating transcription factor (ATF) 4, a transcription factor that is induced downstream of metabolic stresses including endoplasmic reticulum (ER) stress, plays critical roles in SFA-induced interleukin-6 (Il6) expression. DNA microarray analysis using primary macrophages revealed that the ATF4 pathway is activated by SFAs. Haploinsufficiency and short hairpin RNA-based knockdown of ATF4 in macrophages markedly inhibited SFA- and metabolic stress-induced Il6 expression. Conversely, pharmacological activation of the ATF4 pathway and overexpression of ATF4 resulted in enhanced Il6 expression. Moreover, ATF4 acts in synergy with the Toll-like receptor-4 signaling pathway, which is known to be activated by SFAs. At a molecular level, we found that ATF4 exerts its proinflammatory effects through at least two different mechanisms: ATF4 is involved in SFA-induced nuclear factor-κB activation; and ATF4 directly activates the Il6 promoter. These findings provide evidence suggesting that ATF4 links metabolic stress and Il6 expression in macrophages.

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize and substantiate these networks, we performed expressional profiling in human pancreatic islets exposed to proinflammatory cytokines. Three networks were significantly enriched for cytokine-regulated genes and, thus, likely to play an important role for type 1 diabetes in pancreatic islets. Eight of the regulated genes (CD83, IFNGR1, IL17RD, TRAF3IP2, IL27RA, PLCG2, MYO1B, and CXCR7) in these networks also harbored single nucleotide polymorphisms nominally associated with type 1 diabetes. Finally, the expression and cytokine regulation of these new candidate genes were confirmed in insulin-secreting INS-1 β-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.

The molecular pathogenesis of HIV-1 associated nephropathy: recent advances

HIV-1-associated nephropathy (HIVAN) is a major complication of HIV-1 infection, frequently resulting in kidney failure. HIVAN arises due to HIV-1-induced dysregulation of podocytes, the glomerular epithelial cells that establish and maintain the kidney filtration barrier. Host genetic factors are important for the development of HIVAN. The risk of HIVAN is greatest in populations of African ancestry, and is attributable to a genetic variation at the APOL1 locus on chromosome 22. Mouse models of HIVAN enable delineation of dysregulated pathways underlying disease. Identification of HIVAN susceptibility loci in a mouse model, combined with expression quantitative trait locus mapping, has demonstrated that murine HIVAN loci transregulate podocyte gene expression. HIV-1 induces perturbations in podocyte expression response, suggesting that HIV-1 potentially interferes with compensatory pathways that normally restore cellular homeostasis in the face of genetic mutations. These findings present a framework for identification of podocyte transregulators and reconstruction of the molecular networks connecting susceptibility genes to the development of nephropathy.

Novel gene associations in type 1 diabetes

Recent genome-wide association studies have been able to identify multiple new gene loci affecting type 1 diabetes susceptibility, but the impact of these new defined loci seems to decrease in parallel with their number. The HLA gene region remains the main nominator of genetic susceptibility, although the identity of important genes and especially the mechanisms of their action are still largely unclear. Products of HLA and most other known risk genes are involved in regulation of the immune system in accordance with the autoimmune nature of the disease. The multitude of genes involved in the pathogenesis implies complex pathways where multiple steps in each may be essential in turning the balance of immune response to beta-cell destructing autoimmunity.

Prediction and pathogenesis in type 1 diabetes

A combination of genetic and immunological features is useful for prediction of autoimmune diabetes. Patterns of immune response correspond to the progression from a preclinical phase of disease to end-stage islet damage, with biomarkers indicating transition from susceptibility to active autoimmunity, and to a final loss of immune regulation. Here, we review the markers that provide evidence for immunological checkpoint failure and that also provide tools for assessment of individualized disease risk. When viewed in the context of genetic variation that influences immune response thresholds, progression from susceptibility to overt disease displays predictable modalities of clinical presentation resulting from a sequential series of failed homeostatic checkpoints for selection and activation of immunity.