Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

Apolipoprotein M gene polymorphisms in childhood-onset type 1 diabetes in southern Brazil

Type 1 diabetes mellitus (T1DM), associated with autoimmune destruction of pancreatic β cells, is observed in children and adolescents.

OBJECTIVE

We investigated the potential association of the apolipoprotein M (APOM) polymorphisms rs707921, rs805264, rs805296, rs805297, and rs9404941 in childhood-onset T1DM (n = 144) and compared them to those in healthy (mostly Euro-Brazilian) children (n = 168).

METHODS

This project was approved by the Ethics Committee of the Federal University of Parana (CAAE 24676613.6.0000.0102). Genotyping was performed using PCR-restriction fragment length polymorphisms (rs805296 and rs9404941) and TaqMan probes (rs707921, rs805264, and rs805297).

RESULTS

All polymorphisms were in Hardy-Weinberg equilibrium. In the codominant model, no significant differences (P > 0.05) were observed in genotype and allele frequencies between healthy controls and children with T1DM. The minor allele frequencies (95% CI) for healthy subjects were rs707921 (A, 10.7%; 7-14%), rs805264 (A, 6.5%; 4-9%), rs805296 (C, 3.6%; 2-6%), rs805297 (A, 22.6%; 22-31%), and rs9404941 (C, 2.7%; 1-4%). The frequencies of the rs805297 A allele and rs805296 C allele were similar to those of other Caucasian populations; both the rs707921 and rs805264 A alleles were similar to American and Latin American populations, whereas that of the rs9404941 C allele was lower than that observed in the Caucasian and Asian populations.

CONCLUSIONS

Haplotype analysis suggests that rs805297-C, rs9404941-T, rs805296-T, rs805264-G, and rs707921-C conferred risk (OR: 4.25; 95% CI: 1.81-10.1) to childhood-onset T1DM in the Euro-Brazilian population.

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).

The Role of Heat Shock Proteins in Type 1 Diabetes

Type 1 diabetes (T1D) is a T-cell mediated autoimmune disease characterized by recognition of pancreatic β-cell proteins as self-antigens, called autoantigens (AAgs), followed by loss of pancreatic β-cells. (Pre-)proinsulin ([P]PI), glutamic acid decarboxylase (GAD), tyrosine phosphatase IA-2, and the zinc transporter ZnT8 are key molecules in T1D pathogenesis and are recognized by autoantibodies detected in routine clinical laboratory assays. However, generation of new autoantigens (neoantigens) from β-cells has also been reported, against which the autoreactive T cells show activity. Heat shock proteins (HSPs) were originally described as “cellular stress responders” for their role as chaperones that regulate the conformation and function of a large number of cellular proteins to protect the body from stress. HSPs participate in key cellular functions under both physiological and stressful conditions, including suppression of protein aggregation, assisting folding and stability of nascent and damaged proteins, translocation of proteins into cellular compartments and targeting irreversibly damaged proteins for degradation. Low HSP expression impacts many pathological conditions associated with diabetes and could play a role in diabetic complications. HSPs have beneficial effects in preventing insulin resistance and hyperglycemia in type 2 diabetes (T2D). HSPs are, however, additionally involved in antigen presentation, presenting immunogenic peptides to class I and class II major histocompatibility molecules; thus, an opportunity exists for HSPs to be employed as modulators of immunologic responses in T1D and other autoimmune disorders. In this review, we discuss the multifaceted roles of HSPs in the pathogenesis of T1D and in autoantigen-specific immune protection against T1D development.

Apolipoprotein M-A Marker or an Active Player in Type II Diabetes?

Apolipoprotein M (apoM) is a member of the lipocalin superfamily and an important carrier of the small bioactive lipid sphingosine-1-phosphate (S1P). The apoM/S1P complex is attached to all lipoproteins, but exhibits a significant preference for high-density lipoproteins. Although apoM, S1P, and the apoM/S1P complex have been discovered more than a decade earlier, the overall function of the apoM/S1P complex remains controversial. Evidence suggests that the complex plays a role in inflammation and cholesterol metabolism and is important for maintaining a healthy endothelial barrier, regulating the turnover of triglycerides from lipoproteins, and reducing cholesterol accumulation in vessel walls. Recent studies have also addressed the role of apoM and S1P in the development of diabetes and obesity. However, limited evidence is available, and the data published so far deviates. This review discusses the specific elements indicative of the protective or harmful effects of apoM, S1P, and the apoM/S1P complex on type 2 diabetes development. Since drugs targeting the S1P system and its receptors are available and could be potentially used for treating diabetes, this research topic is a pertinent one.

The influence of ICAM1 rs5498 on diabetes mellitus risk: evidence from a meta-analysis

OBJECTIVES

Both type 1 diabetes (T1D) and type 2 diabetes (T2D) are classified as forms of diabetes mellitus (DM) and commonly considered inflammatory process. Intercellular adhesion molecule-1 (ICAM-1) is involved in the development and progression of diabetes mellitus. However, the genetic association between ICAM-1 rs5498, and T1D and T2D risk was inconclusive.

MATERIALS AND METHODS

A meta-analysis by searching the PubMed, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure (CNKI) databases was performed out. The pooled odds ratio (OR) and 95% confidence interval (CI) were used to describe the strength of association of T1D and T2D risk.

RESULTS

A total of 14 studies encompassing 3233 cases and 2884 controls were included in the present meta-analysis. Significant associations were found between the allele and recessive models of ICAM1 rs5498 and DM in Asian population (allele: OR 1.13; 95% CI 1.03-1.23, p = 0.008; recessive: OR 1.25; 95% CI 1.06-1.48, p = 0.008), but not in Caucasian population (p > 0.05). In addition, the allele model of rs5498 was found to be significantly associated with the increased risk of T2D (OR 1.10; 95% CI 1.01-1.21, p = 0.03), but not T1D (p > 0.05).

CONCLUSIONS

The ICAM1 rs5498 might be a susceptible factor for T2D, but not T1D. And the allele and recessive models of ICAM1 rs5498 might be a risk factor in Asian population.

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.

RNA-seq is widely used for transcriptome analysis. Here, the authors analyse a wide spectrum of RNA-seq workflows and present a comprehensive analysis protocol named RNACocktail as well as a computational pipeline leveraging the widely used tools for accurate RNA-seq analysis.

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

HLA-DRB1*03 as a risk factor for microalbuminuria in same duration of type 1 diabetes: a case control study

BACKGROUND

Increased urinary albumin excretion rate is the earliest clinical manifestation of diabetic nephropathy. The development of microalbuminuria in patients with type 1 diabetes mellitus (T1D) usually begins 5 to 15 years after the onset of diabetes. The rate of progression of diabetic nephropathy varies considerably among patients and not always can be explained solely by glycaemic control. The evidence suggests that genetic susceptibility may play a role in the development of diabetes microvascular complications, besides the presence of such risk factors as hyperglycaemia, hypertension, dyslipidaemia and smoking. The aim of the study was to evaluate a link between known genetic risk factors for type 1 diabetes mellitus (HLA-DR3/DR4) and microalbuminuria among patients with the same durations of diabetes.

METHODS

Ninety-nine patients with T1D at the age 18-35 years were recruited for the study. The urine albumin excretion rate was normal when <30 mg/24 h; microalbuminuria 30-300 mg/24 h. Genotypes were investigated in 39 patients with normal albumin excretion rate and duration of diabetes 13.46 ± 3.72 years and in 60 patients with microalbuminuria and duration of diabetes 15.28 ± 4.08 years (p = 0.11). Genetic typing of DR3 and DR4 antigens successfully was performed for 99 subjects. Statistical analysis was performed using SPSS v. 20.0.

RESULTS

Genotyping of 99 patients with T1D was performed: no DR3 and DR4 risk alleles were found in 22 (22.22 %) cases, DR3 alleles were present in 47 (47.48 %) cases, DR4 alleles in 25 (25.25 %) cases, and DR3/DR4 alleles in 5 (5.05 %) cases. The highest 24 h albumin excretion rate was found in patients with DRB1 gene expressed DR3 risk alleles group, the lowest - in patients with DRB1 gene with no expression of both DR3 and DR4 antigen. We confirmed the 1.87 (p = 0.021) increased relative risk for microalbuminuria in patients with DR3/DR3 alleles and same duration of diabetes. The distribution of DR3 and DR4 risk alleles was not associated with cardiovascular autonomic neuropathy both in patients with normal albumin excretion rate and microalbuminuria (1.6 vs 2.1; p = 0.21).

CONCLUSIONS

The 1.87 (p = 0.021) increased relative risk for microalbuminuria was found in patients with DR3/DR3 alleles and the same duration of diabetes.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association.

Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.

Protein interaction-based genome-wide analysis of incident coronary heart disease

BACKGROUND

Network-based approaches may leverage genome-wide association (GWA) analysis by testing for the aggregate association across several pathway members. We aimed to examine if networks of genes that represent experimentally determined protein-protein interactions (PPIs) are enriched in genes associated with risk of coronary heart disease (CHD).

METHODS AND RESULTS

Genome-wide association analyses of approximately ≈700,000 single-nucleotide polymorphisms in 899 incident CHD cases and 1823 age- and sex-matched controls within the Nurses' Health and the Health Professionals Follow-up Studies were used to assign genewise P values. A large database of PPIs was used to assemble 8351 unbiased protein complexes and corresponding gene sets. Superimposed genewise P values were used to rank gene sets based on their enrichment in genes associated with CHD. After correcting for the number of complexes tested, 1 gene set was overrepresented in CHD-associated genes (P=0.002). Centered on the β1-adrenergic receptor gene (ADRB1), this complex included 18 protein interaction partners that have not been identified as candidate loci for CHD. Of the 19 genes in the top complex, 5 are involved in abnormal cardiovascular system physiological features based on knockout mice (4-fold enrichment; Fisher exact test, P=0.006). Ingenuity pathway analysis revealed that canonical pathways, especially related to blood pressure regulation, were significantly enriched in the genes from the top complex.

CONCLUSIONS

The integration of a GWA study with PPI data successfully identifies a set of candidate susceptibility genes for incident CHD that would have been missed in single-marker GWA analysis.

From SNPs to genes: disease association at the gene level

Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics. We demonstrate that the gene-wide test statistics can be controlled for the number of SNPs within each gene and show that all three methods perform considerably better than expected by chance at identifying genes with confirmed associations. By applying each method to GWAS data for Crohn's Disease and Type 1 Diabetes we identified new potential disease genes.

The type 1 diabetes - HLA susceptibility interactome--identification of HLA genotype-specific disease genes for type 1 diabetes

Background

The individual contribution of genes in the HLA region to the risk of developing type 1 diabetes (T1D) is confounded by the high linkage disequilibrium (LD) in this region. Using a novel approach we have combined genetic association data with information on functional protein-protein interactions to elucidate risk independent of LD and to place the genetic association into a functional context.

Methodology/Principal Findings

Genetic association data from 2300 single nucleotide polymorphisms (SNPs) in the HLA region was analysed in 2200 T1D family trios divided into six risk groups based on HLA-DRB1 genotypes. The best SNP signal in each gene was mapped to proteins in a human protein interaction network and their significance of clustering in functional network modules was evaluated. The significant network modules identified through this approach differed between the six HLA risk groups, which could be divided into two groups based on carrying the DRB1*0301 or the DRB1*0401 allele. Proteins identified in networks specific for DRB1*0301 carriers were involved in stress response and inflammation whereas in DRB1*0401 carriers the proteins were involved in antigen processing and presentation.

Conclusions/Significance

In this study we were able to hypothesise functional differences between individuals with T1D carrying specific DRB1 alleles. The results point at candidate proteins involved in distinct cellular processes that could not only help the understanding of the pathogenesis of T1D, but also the distinction between individuals at different genetic risk for developing T1D.