T1DBase, a community web-based resource for type 1 diabetes research

The Idd2 Locus Confers Prominent Resistance to Autoimmune Diabetes

Type 1 diabetes is an autoimmune disease characterized by pancreatic β cell destruction. It is a complex genetic trait driven by >30 genetic loci with parallels between humans and mice. The NOD mouse spontaneously develops autoimmune diabetes and is widely used to identify insulin-dependent diabetes (Idd) genetic loci linked to diabetes susceptibility. Although many Idd loci have been extensively studied, the impact of the Idd2 locus on autoimmune diabetes susceptibility remains to be defined. To address this, we generated a NOD congenic mouse bearing B10 resistance alleles on chromosome 9 in a locus coinciding with part of the Idd2 locus and found that NOD.B10-Idd2 congenic mice are highly resistant to diabetes. Bone marrow chimera and adoptive transfer experiments showed that the B10 protective alleles provide resistance in an immune cell-intrinsic manner. Although no T cell-intrinsic differences between NOD and NOD.B10-Idd2 mice were observed, we found that the Idd2 resistance alleles limit the formation of spontaneous and induced germinal centers. Comparison of B cell and dendritic cell transcriptome profiles from NOD and NOD.B10-Idd2 mice reveal that resistance alleles at the Idd2 locus affect the expression of specific MHC molecules, a result confirmed by flow cytometry. Altogether, these data demonstrate that resistance alleles at the Idd2 locus impair germinal center formation and influence MHC expression, both of which likely contribute to reduced diabetes incidence.

A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective

Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.

Insights on the Role of Putative Muscle-Derived Factors on Pancreatic Beta Cell Function

Skeletal muscle is a main target of insulin action that plays a pivotal role in postprandial glucose disposal. Importantly, skeletal muscle insulin sensitivity relates inversely with pancreatic insulin secretion, which prompted the hypothesis of the existence of a skeletal muscle-pancreas crosstalk mediated through an endocrine factor. The observation that changes in skeletal muscle glucose metabolism are accompanied by altered insulin secretion supports this hypothesis. Meanwhile, a muscle-derived circulating factor affecting in vivo insulin secretion remains elusive. This factor may correspond to peptides/proteins (so called myokines), exosomes and their cargo, and metabolites. We hereby review the most remarkable evidence encouraging the possibility of such inter-organ communication, with special focus on muscle-derived factors that may potentially mediate such skeletal muscle-pancreas crosstalk.

On building a diabetes centric knowledge base via mining the web

BACKGROUND

Diabetes has become one of the hot topics in life science researches. To support the analytical procedures, researchers and analysts expend a mass of labor cost to collect experimental data, which is also error-prone. To reduce the cost and to ensure the data quality, there is a growing trend of extracting clinical events in form of knowledge from electronic medical records (EMRs). To do so, we first need a high-coverage knowledge base (KB) of a specific disease to support the above extraction tasks called KB-based Extraction.

METHODS

We propose an approach to build a diabetes-centric knowledge base (a.k.a. DKB) via mining the Web. In particular, we first extract knowledge from semi-structured contents of vertical portals, fuse individual knowledge from each site, and further map them to a unified KB. The target DKB is then extracted from the overall KB based on a distance-based Expectation-Maximization (EM) algorithm.

RESULTS

During the experiments, we selected eight popular vertical portals in China as data sources to construct DKB. There are 7703 instances and 96,041 edges in the final diabetes KB covering diseases, symptoms, western medicines, traditional Chinese medicines, examinations, departments, and body structures. The accuracy of DKB is 95.91%. Besides the quality assessment of extracted knowledge from vertical portals, we also carried out detailed experiments for evaluating the knowledge fusion performance as well as the convergence of the distance-based EM algorithm with positive results.

CONCLUSIONS

In this paper, we introduced an approach to constructing DKB. A knowledge extraction and fusion pipeline was first used to extract semi-structured data from vertical portals and individual KBs were further fused into a unified knowledge base. After that, we develop a distance based Expectation Maximization algorithm to extract a subset from the overall knowledge base forming the target DKB. Experiments showed that the data in DKB are rich and of high-quality.

Risk variants disrupting enhancers of TH1 and TREG cells in type 1 diabetes

Functional interpretation of noncoding genetic variants identified by genome-wide association studies is a major challenge in human genetics and gene regulation. We generated epigenomics data using primary cells from type 1 diabetes patients. Using these data, we identified and validated multiple novel risk variants for this disease. In addition, our ranked list of candidate risk SNPs represents the most comprehensive annotation based on T1D-specific T-cell data. Because many autoimmune diseases share some genetic underpinnings, our dataset may be used to understand causal noncoding mutations in related autoimmune diseases.

Genome-wide association studies (GWASs) have revealed 59 genomic loci associated with type 1 diabetes (T1D). Functional interpretation of the SNPs located in the noncoding region of these loci remains challenging. We perform epigenomic profiling of two enhancer marks, H3K4me1 and H3K27ac, using primary T_H1 and T_REG cells isolated from healthy and T1D subjects. We uncover a large number of deregulated enhancers and altered transcriptional circuitries in both cell types of T1D patients. We identify four SNPs (rs10772119, rs10772120, rs3176792, rs883868) in linkage disequilibrium (LD) with T1D-associated GWAS lead SNPs that alter enhancer activity and expression of immune genes. Among them, rs10772119 and rs883868 disrupt the binding of retinoic acid receptor α (RARA) and Yin and Yang 1 (YY1), respectively. Loss of binding by YY1 also results in the loss of long-range enhancer–promoter interaction. These findings provide insights into how noncoding variants affect the transcriptomes of two T-cell subtypes that play critical roles in T1D pathogenesis.

OCDD: an obesity and co-morbid disease database

Background

Obesity is a medical condition that is known for increased body mass index (BMI). It is also associated with chronic low level inflammation. Obesity disrupts the immune-metabolic homeostasis by changing the secretion of adipocytes. This affects the end-organs, and gives rise to several diseases including type 2 diabetes, asthma, non-alcoholic fatty liver diseases and cancers. These diseases are known as co-morbid diseases. Several studies have explored the underlying molecular mechanisms of developing obesity associated comorbid diseases. To understand the development and progression of diseases associated with obesity, we need a detailed scenario of gene interactions and the distribution of the responsible genes in human system.

Results

Obesity and Co-morbid Disease Database (OCDD) is designed for relating obesity and its co-morbid diseases using literature mining, and computational and systems biology approaches. OCDD is aimed to investigate the genes associated with comorbidity. Several existing databases have been used to extract molecular interactions and functional annotations of each gene. The degree of co-morbid associations has been measured and made available to the users. The database is available at http://www.isical.ac.in/~systemsbiology/OCDD/home.php

Conclusions

The main objective of the database is to derive the relations among the genes that are involved in both obesity and its co-morbid diseases. Functional annotation of common genes, gene interaction networks and key driver analyses have made the database a valuable and comprehensive resource for investigating the causal links between obesity and co-morbid diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-017-0153-5) contains supplementary material, which is available to authorized users.

Pancreatic Beta Cell Survival and Signaling Pathways: Effects of Type 1 Diabetes-Associated Genetic Variants

Type 1 diabetes (T1D) is a complex autoimmune disease in which pancreatic beta cells are specifically destroyed by the immune system. The disease has an important genetic component and more than 50 loci across the genome have been associated with risk of developing T1D. The molecular mechanisms by which these putative T1D candidate genes modulate disease risk, however, remain poorly characterized and little is known about their effects in pancreatic beta cells. Functional studies in in vitro models of pancreatic beta cells, based on techniques to inhibit or overexpress T1D candidate genes, allow the functional characterization of several T1D candidate genes. This requires a multistage procedure comprising two major steps, namely accurate selection of genes of potential interest and then in vitro and/or in vivo mechanistic approaches to characterize their role in pancreatic beta cell dysfunction and death in T1D. This chapter details the methods and settings used by our groups to characterize the role of T1D candidate genes on pancreatic beta cell survival and signaling pathways, with particular focus on potentially relevant pathways in the pathogenesis of T1D, i.e., inflammation and innate immune responses, apoptosis, beta cell metabolism and function.

Linking the circadian rhythm gene Arntl2 to interleukin 21 expression in type 1 diabetes

The circadian rhythm-related aryl hydrocarbon receptor nuclear translocator-like 2 (Arntl2) gene has been identified as a candidate gene for the murine type 1 diabetes locus Idd6.3. Previous studies suggested a role in expansion of CD4(+)CD25(-) T cells, and this then creates an imbalance in the ratio between T-effector and CD4(+)CD25(+) T-regulator cells. Our transcriptome analyses identify the interleukin 21 (IL21) gene (Il21) as a direct target of ARNTL2. ARNTL2 binds in an allele-specific manner to the RNA polymerase binding site of the Il21 promoter and inhibits its expression in NOD.C3H congenic mice carrying C3H alleles at Idd6.3. IL21 is known to promote T-cell expansion, and in agreement with these findings, mice with C3H alleles at Idd6.3 produce lower numbers of CD4(+)IL21(+) and CD4(+) and CD8(+) T cells compared with mice with NOD alleles at Idd6.3. Our results describe a novel and rather unexpected role for Arntl2 in the immune system that lies outside of its predicted function in circadian rhythm regulation.

Fine mapping of type 1 diabetes regions Idd9.1 and Idd9.2 reveals genetic complexity

Nonobese diabetic (NOD) mice congenic for C57BL/10 (B10)-derived genes in the Idd9 region of chromosome 4 are highly protected from type 1 diabetes (T1D). Idd9 has been divided into three protective subregions (Idd9.1, 9.2, and 9.3), each of which partially prevents disease. In this study we have fine-mapped the Idd9.1 and Idd9.2 regions, revealing further genetic complexity with at least two additional subregions contributing to protection from T1D. Using the NOD sequence from bacterial artificial chromosome clones of the Idd9.1 and Idd9.2 regions as well as whole-genome sequence data recently made available, sequence polymorphisms within the regions highlight a high degree of polymorphism between the NOD and B10 strains in the Idd9 regions. Among numerous candidate genes are several with immunological importance. The Idd9.1 region has been separated into Idd9.1 and Idd9.4, with Lck remaining a candidate gene within Idd9.1. One of the Idd9.2 regions contains the candidate genes Masp2 (encoding mannan-binding lectin serine peptidase 2) and Mtor (encoding mammalian target of rapamycin). From mRNA expression analyses, we have also identified several other differentially expressed candidate genes within the Idd9.1 and Idd9.2 regions. These findings highlight that multiple, relatively small genetic effects combine and interact to produce significant changes in immune tolerance and diabetes onset.

Genetic interactions among Idd3, Idd5.1, Idd5.2, and Idd5.3 protective loci in the nonobese diabetic mouse model of type 1 diabetes

In the NOD mouse model of type 1 diabetes, insulin-dependent diabetes (Idd) loci control the development of insulitis and diabetes. Independently, protective alleles of Idd3/Il2 or Idd5 are able to partially protect congenic NOD mice from insulitis and diabetes, and to partially tolerize islet-specific CD8(+) T cells. However, when the two regions are combined, mice are almost completely protected, strongly suggesting the existence of genetic interactions between the two loci. Idd5 contains at least three protective subregions/causative gene candidates, Idd5.1/Ctla4, Idd5.2/Slc11a1, and Idd5.3/Acadl, yet it is unknown which of them interacts with Idd3/Il2. Through the use of a series of novel congenic strains containing the Idd3/Il2 region and different combinations of Idd5 subregion(s), we defined these genetic interactions. The combination of Idd3/Il2 and Idd5.3/Acadl was able to provide nearly complete protection from type 1 diabetes, but all three Idd5 subregions were required to protect from insulitis and fully restore self-tolerance. By backcrossing a Slc11a1 knockout allele onto the NOD genetic background, we have demonstrated that Slc11a1 is responsible for the diabetes protection resulting from Idd5.2. We also used Slc11a1 knockout-SCID and Idd5.2-SCID mice to show that both loss-of-function alleles provide protection from insulitis when expressed on the SCID host alone. These results lend further support to the hypothesis that Slc11a1 is Idd5.2.

The B10 Idd9.3 locus mediates accumulation of functionally superior CD137(+) regulatory T cells in the nonobese diabetic type 1 diabetes model

CD137 is a T cell costimulatory molecule encoded by the prime candidate gene (designated Tnfrsf9) in NOD.B10 Idd9.3 congenic mice protected from type 1 diabetes (T1D). NOD T cells show decreased CD137-mediated T cell signaling compared with NOD.B10 Idd9.3 T cells, but it has been unclear how this decreased CD137 T cell signaling could mediate susceptibility to T1D. We and others have shown that a subset of regulatory T cells (Tregs) constitutively expresses CD137 (whereas effector T cells do not, and only express CD137 briefly after activation). In this study, we show that the B10 Idd9.3 region intrinsically contributes to accumulation of CD137(+) Tregs with age. NOD.B10 Idd9.3 mice showed significantly increased percentages and numbers of CD137(+) peripheral Tregs compared with NOD mice. Moreover, Tregs expressing the B10 Idd9.3 region preferentially accumulated in mixed bone marrow chimeric mice reconstituted with allotypically marked NOD and NOD.B10 Idd9.3 bone marrow. We demonstrate a possible significance of increased numbers of CD137(+) Tregs by showing functional superiority of FACS-purified CD137(+) Tregs in vitro compared with CD137(-) Tregs in T cell-suppression assays. Increased functional suppression was also associated with increased production of the alternatively spliced CD137 isoform, soluble CD137, which has been shown to suppress T cell proliferation. We show for the first time, to our knowledge, that CD137(+) Tregs are the primary cellular source of soluble CD137. NOD.B10 Idd9.3 mice showed significantly increased serum soluble CD137 compared with NOD mice with age, consistent with their increased numbers of CD137(+) Tregs with age. These studies demonstrate the importance of CD137(+) Tregs in T1D and offer a new hypothesis for how the NOD Idd9.3 region could act to increase T1D susceptibility.

GW8510 increases insulin expression in pancreatic alpha cells through activation of p53 transcriptional activity

BACKGROUND

Expression of insulin in terminally differentiated non-beta cell types in the pancreas could be important to treating type-1 diabetes. Previous findings led us to hypothesize involvement of kinase inhibition in induction of insulin expression in pancreatic alpha cells.

METHODOLOGY/PRINCIPAL FINDINGS

Alpha (αTC1.6) cells and human islets were treated with GW8510 and other small-molecule inhibitors for up to 5 days. Alpha cells were assessed for gene- and protein-expression levels, cell-cycle status, promoter occupancy status by chromatin immunoprecipitation (ChIP), and p53-dependent transcriptional activity. GW8510, a putative CDK2 inhibitor, up-regulated insulin expression in mouse alpha cells and enhanced insulin secretion in dissociated human islets. Gene-expression profiling and gene-set enrichment analysis of GW8510-treated alpha cells suggested up-regulation of the p53 pathway. Accordingly, the compound increased p53 transcriptional activity and expression levels of p53 transcriptional targets. A predicted p53 response element in the promoter region of the mouse Ins2 gene was verified by chromatin immunoprecipitation (ChIP). Further, inhibition of Jun N-terminal kinase (JNK) and p38 kinase activities suppressed insulin induction by GW8510.

CONCLUSIONS/SIGNIFICANCE

The induction of Ins2 by GW8510 occurred through p53 in a JNK- and p38-dependent manner. These results implicate p53 activity in modulation of Ins2 expression levels in pancreatic alpha cells, and point to a potential approach toward using small molecules to generate insulin in an alternative cell type.

Evidence that Cd101 is an autoimmune diabetes gene in nonobese diabetic mice

We have previously proposed that sequence variation of the CD101 gene between NOD and C57BL/6 mice accounts for the protection from type 1 diabetes (T1D) provided by the insulin-dependent diabetes susceptibility region 10 (Idd10), a <1 Mb region on mouse chromosome 3. In this study, we provide further support for the hypothesis that Cd101 is Idd10 using haplotype and expression analyses of novel Idd10 congenic strains coupled to the development of a CD101 knockout mouse. Susceptibility to T1D was correlated with genotype-dependent CD101 expression on multiple cell subsets, including Foxp3(+) regulatory CD4(+) T cells, CD11c(+) dendritic cells, and Gr1(+) myeloid cells. The correlation of CD101 expression on immune cells from four independent Idd10 haplotypes with the development of T1D supports the identity of Cd101 as Idd10. Because CD101 has been associated with regulatory T and Ag presentation cell functions, our results provide a further link between immune regulation and susceptibility to T1D.

When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases

Background

Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data.

Results

Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

Conclusions

The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases.

EuroDia: a beta-cell gene expression resource

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms.

Database URL: http://eurodia.vital-it.ch

A genomic-based approach identifies FXYD domain containing ion transport regulator 2 (FXYD2)gammaa as a pancreatic beta cell-specific biomarker

AIMS/HYPOTHESIS

Non-invasive imaging of the pancreatic beta cell mass (BCM) requires the identification of novel and specific beta cell biomarkers. We have developed a systems biology approach to the identification of promising beta cell markers.

METHODS

We followed a functional genomics strategy based on massive parallel signal sequencing (MPSS) and microarray data obtained in human islets, purified primary rat beta cells, non-beta cells and INS-1E cells to identify promising beta cell markers. Candidate biomarkers were validated and screened using established human and macaque (Macacus cynomolgus) tissue microarrays.

RESULTS

After a series of filtering steps, 12 beta cell-specific membrane proteins were identified. For four of the proteins we selected or produced antibodies targeting specifically the human proteins and their splice variants; all four candidates were confirmed as islet-specific in human pancreas. Two splice variants of FXYD domain containing ion transport regulator 2 (FXYD2), a regulating subunit of the Na(+)-K(+)-ATPase, were identified as preferentially present in human pancreatic islets. The presence of FXYD2gammaa was restricted to pancreatic islets and selectively detected in pancreatic beta cells. Analysis of human fetal pancreas samples showed the presence of FXYD2gammaa at an early stage (15 weeks). Histological examination of pancreatic sections from individuals with type 1 diabetes or sections from pancreases of streptozotocin-treated Macacus cynomolgus monkeys indicated a close correlation between loss of FXYD2gammaa and loss of insulin-positive cells.

CONCLUSIONS/INTERPRETATION

We propose human FXYD2gammaa as a novel beta cell-specific biomarker.

Nonobese diabetic congenic strain analysis of autoimmune diabetes reveals genetic complexity of the Idd18 locus and identifies Vav3 as a candidate gene

We have used the public sequencing and annotation of the mouse genome to delimit the previously resolved type 1 diabetes (T1D) insulin-dependent diabetes (Idd)18 interval to a region on chromosome 3 that includes the immunologically relevant candidate gene, Vav3. To test the candidacy of Vav3, we developed a novel congenic strain that enabled the resolution of Idd18 to a 604-kb interval, designated Idd18.1, which contains only two annotated genes: the complete sequence of Vav3 and the last exon of the gene encoding NETRIN G1, Ntng1. Targeted sequencing of Idd18.1 in the NOD mouse strain revealed that allelic variation between NOD and C57BL/6J (B6) occurs in noncoding regions with 138 single nucleotide polymorphisms concentrated in the introns between exons 20 and 27 and immediately after the 3' untranslated region. We observed differential expression of VAV3 RNA transcripts in thymocytes when comparing congenic mouse strains with B6 or NOD alleles at Idd18.1. The T1D protection associated with B6 alleles of Idd18.1/Vav3 requires the presence of B6 protective alleles at Idd3, which are correlated with increased IL-2 production and regulatory T cell function. In the absence of B6 protective alleles at Idd3, we detected a second T1D protective B6 locus, Idd18.3, which is closely linked to, but distinct from, Idd18.1. Therefore, genetic mapping, sequencing, and gene expression evidence indicate that alteration of VAV3 expression is an etiological factor in the development of autoimmune beta-cell destruction in NOD mice. This study also demonstrates that a congenic strain mapping approach can isolate closely linked susceptibility genes.

Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information

Background

The genetic contributions to human common disorders and mouse genetic models of disease are complex and often overlapping. In common human diseases, unlike classical Mendelian disorders, genetic factors generally have small effect sizes, are multifactorial, and are highly pleiotropic. Likewise, mouse genetic models of disease often have pleiotropic and overlapping phenotypes. Moreover, phenotypic descriptions in the literature in both human and mouse are often poorly characterized and difficult to compare directly.

Methods

In this report, human genetic association results from the literature are summarized with regard to replication, disease phenotype, and gene specific results; and organized in the context of a systematic disease ontology. Similarly summarized mouse genetic disease models are organized within the Mammalian Phenotype ontology. Human and mouse disease and phenotype based gene sets are identified. These disease gene sets are then compared individually and in large groups through dendrogram analysis and hierarchical clustering analysis.

Results

Human disease and mouse phenotype gene sets are shown to group into disease and phenotypically relevant groups at both a coarse and fine level based on gene sharing.

Conclusion

This analysis provides a systematic and global perspective on the genetics of common human disease as compared to itself and in the context of mouse genetic models of disease.

The Prion Disease Database: a comprehensive transcriptome resource for systems biology research in prion diseases

Prion diseases reflect conformational conversion of benign isoforms of prion protein (PrP(C)) to malignant PrP(Sc) isoforms. Networks perturbed by PrP(Sc) accumulation and their ties to pathological events are poorly understood. Time-course transcriptomic and phenotypic data in animal models are critical for understanding prion-perturbed networks in systems biology studies. Here, we present the Prion Disease Database (PDDB), the most comprehensive data resource on mouse prion diseases to date. The PDDB contains: (i) time-course mRNA measurements spanning the interval from prion inoculation through appearance of clinical signs in eight mouse strain-prion strain combinations and (ii) histoblots showing temporal PrP(Sc) accumulation patterns in brains from each mouse-prion combination. To facilitate prion research, the PDDB also provides a suite of analytical tools for reconstructing dynamic networks via integration of temporal mRNA and interaction data and for analyzing these networks to generate hypotheses.Database URL:http://prion.systemsbiology.net.

Bioinformatics for study of autoimmunity

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Use of a systems biology approach to understand pancreatic beta-cell death in Type 1 diabetes

Accumulating evidence indicates that beta-cells die by apoptosis in T1DM (Type 1 diabetes mellitus). Apoptosis is an active gene-directed process, and recent observations suggest that beta-cell apoptosis depends on the parallel and/or sequential up- and down-regulation of hundreds of genes controlled by key transcription factors such as NF-kappaB (nuclear factor kappaB) and STAT-1 (signal transducer and activator of transcription 1). Understanding the regulation of these gene networks, and how they modulate beta-cell death and the 'dialogue' between beta-cells and the immune system, will require a systems biology approach to the problem. This will hopefully allow the search for a cure for T1DM to move from a 'trial-and-error' approach to one that is really mechanistically driven.

An adenylate kinase is involved in KATP channel regulation of mouse pancreatic beta cells

AIMS/HYPOTHESIS

In a previous study, we demonstrated that a creatine kinase (CK) modulates K(ATP) channel activity in pancreatic beta cells. To explore phosphotransfer signalling pathways in more detail, we examined whether K(ATP) channel regulation in beta cells is determined by a metabolic interaction between adenylate kinase (AK) and CK.

METHODS

Single channel activity was measured with the patch-clamp technique in the inside-out (i/o) and open-cell attached (oca) configuration.

RESULTS

The ATP sensitivity of K(ATP) channels was higher in i/o patches than in permeabilised beta cells (oca). One reason for this observation could be that the local ATP:ADP ratio in the proximity of the channels is determined by factors not active in i/o patches. AMP (0.1 mmol/l) clearly increased open channel probability in the presence of ATP (0.125 mmol/l) in permeabilised cells but not in excised patches. This suggests that AK-catalysed ADP production in the vicinity of the channels is involved in K(ATP) channel regulation. The observation that the stimulatory effect of AMP on K(ATP) channels was prevented by the AK inhibitor P (1),P (5)-di(adenosine-5')pentaphosphate (Ap(5)A; 20 micromol/l) and abolished in the presence of the non-metabolisable ATP analogue adenosine 5'-(beta,gamma-imido)triphosphate tetralithium salt (AMP-PNP; 0.12 mmol/l) strengthens this idea. In beta cells from AK1 knockout mice, the effect of AMP was less pronounced, though not completely suppressed. The increase in K(ATP) channel activity induced by AMP in the presence of ATP was outweighed by phosphocreatine (1 mmol/l). We suggest that this is due to an elevation of the ATP concentration by CK.

CONCLUSIONS/INTERPRETATION

We propose that phosphotransfer events mediated by AK and CK play an important role in determining the effective concentrations of ATP and ADP in the microenvironment of pancreatic beta cell K(ATP) channels. Thus, these enzymes determine the open probability of K(ATP) channels and eventually the actual rate of insulin secretion.

Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes

Genome-wide association studies are now identifying disease-associated chromosome regions. However, even after convincing replication, the localization of the causal variant(s) requires comprehensive resequencing, extensive genotyping and statistical analyses in large sample sets leading to targeted functional studies. Here, we have localized the type 1 diabetes (T1D) association in the interleukin 2 receptor alpha (IL2RA) gene region to two independent groups of SNPs, spanning overlapping regions of 14 and 40 kb, encompassing IL2RA intron 1 and the 5' regions of IL2RA and RBM17 (odds ratio = 2.04, 95% confidence interval = 1.70-2.45; P = 1.92 x 10(-28); control frequency = 0.635). Furthermore, we have associated IL2RA T1D susceptibility genotypes with lower circulating levels of the biomarker, soluble IL-2RA (P = 6.28 x 10(-28)), suggesting that an inherited lower immune responsiveness predisposes to T1D.

Online genetic databases informing human genome epidemiology

BACKGROUND

With the advent of high throughput genotyping technology and the information available via projects such as the human genome sequencing and the HapMap project, more and more data relevant to the study of genetics and disease risk will be produced. Systematic reviews and meta-analyses of human genome epidemiology studies rely on the ability to identify relevant studies and to obtain suitable data from these studies. A first port of call for most such reviews is a search of MEDLINE. We examined whether this could be usefully supplemented by identifying databases on the World Wide Web that contain genetic epidemiological information.

METHODS

We conducted a systematic search for online databases containing genetic epidemiological information on gene prevalence or gene-disease association. In those containing information on genetic association studies, we examined what additional information could be obtained to supplement a MEDLINE literature search.

RESULTS

We identified 111 databases containing prevalence data, 67 databases specific to a single gene and only 13 that contained information on gene-disease associations. Most of the latter 13 databases were linked to MEDLINE, although five contained information that may not be available from other sources.

CONCLUSION

There is no single resource of structured data from genetic association studies covering multiple diseases, and in relation to the number of studies being conducted there is very little information specific to gene-disease association studies currently available on the World Wide Web. Until comprehensive data repositories are created and utilized regularly, new data will remain largely inaccessible to many systematic review authors and meta-analysts.

Interleukin-2 gene variation impairs regulatory T cell function and causes autoimmunity

Autoimmune diseases are thought to result from imbalances in normal immune physiology and regulation. Here, we show that autoimmune disease susceptibility and resistance alleles on mouse chromosome 3 (Idd3) correlate with differential expression of the key immunoregulatory cytokine interleukin-2 (IL-2). In order to test directly that an approximately twofold reduction in IL-2 underpins the Idd3-linked destabilization of immune homeostasis, we show that engineered haplodeficiency of Il2 gene expression not only reduces T cell IL-2 production by twofold but also mimics the autoimmune dysregulatory effects of the naturally occurring susceptibility alleles of Il2. Reduced IL-2 production achieved by either genetic mechanism correlates with reduced function of CD4(+) CD25(+) regulatory T cells, which are critical for maintaining immune homeostasis.

T1DBase: integration and presentation of complex data for type 1 diabetes research

T1DBase () [Smink et al. (2005) Nucleic Acids Res., 33, D544–D549; Burren et al. (2004) Hum. Genomics, 1, 98–109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599–1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498–2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in β cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in β cells. T1DMart is a query tool for markers and genotypes. PosterPages are ‘home pages’ about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.

EPConDB: a web resource for gene expression related to pancreatic development, beta-cell function and diabetes

EPConDB () is a public web site that supports research in diabetes, pancreatic development and beta-cell function by providing information about genes expressed in cells of the pancreas. EPConDB displays expression profiles for individual genes and information about transcripts, promoter elements and transcription factor binding sites. Gene expression results are obtained from studies examining tissue expression, pancreatic development and growth, differentiation of insulin-producing cells, islet or beta-cell injury, and genetic models of impaired beta-cell function. The expression datasets are derived using different microarray platforms, including the BCBC PancChips and Affymetrix gene expression arrays. Other datasets include semi-quantitative RT–PCR and MPSS expression studies. For selected microarray studies, lists of differentially expressed genes, derived from PaGE analysis, are displayed on the site. EPConDB provides database queries and tools to examine the relationship between a gene, its transcriptional regulation, protein function and expression in pancreatic tissues.

Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden

We mapped the genetic influences for type 1 diabetes (T1D), using 2,360 single-nucleotide polymorphism (SNP) markers in the 4.4-Mb human major histocompatibility complex (MHC) locus and the adjacent 493 kb centromeric to the MHC, initially in a survey of 363 Swedish T1D cases and controls. We confirmed prior studies showing association with T1D in the MHC, most significantly near HLA-DR/DQ. In the region centromeric to the MHC, we identified a peak of association within the inositol 1,4,5-triphosphate receptor 3 gene (ITPR3; formerly IP3R3). The most significant single SNP in this region was at the center of the ITPR3 peak of association (P=1.7 x 10(-4) for the survey study). For validation, we typed an additional 761 Swedish individuals. The P value for association computed from all 1,124 individuals was 1.30 x 10(-6) (recessive odds ratio 2.5; 95% confidence interval [CI] 1.7-3.9). The estimated population-attributable risk of 21.6% (95% CI 10.0%-31.0%) suggests that variation within ITPR3 reflects an important contribution to T1D in Sweden. Two-locus regression analysis supports an influence of ITPR3 variation on T1D that is distinct from that of any MHC class II gene.

Novel genes identified by manual annotation and microarray expression analysis in the pancreas

The mouse PancChip, a microarray developed for studying endocrine pancreatic development and diabetes, represents over 13,000 cDNAs. After computationally assigning the cDNAs on the array to known genes, manual curation of the remaining sequences identified 211 novel transcripts. In microarray experiments, we found that 196 of these transcripts were expressed in total pancreas and/or pancreatic islets. Of 50 randomly selected clones from these 196 transcripts, 92% were confirmed as expressed by qRT-PCR. We evaluated the coding potential of the novel transcripts and found that 74% of the clones had low coding potential. Since the transcripts may be partial mRNAs, we examined their translated proteins for transmembrane or signal peptide domains and found that about 40 proteins had one of these predicted domains. Interestingly, when we investigated the novel transcripts for their overlap with noncoding microRNAs, we found that 1 of the novel transcripts overlapped a known microRNA gene.

Discovery, linkage disequilibrium and association analyses of polymorphisms of the immune complement inhibitor, decay-accelerating factor gene (DAF/CD55) in type 1 diabetes

Background

Type 1 diabetes (T1D) is a common autoimmune disease resulting from T-cell mediated destruction of pancreatic beta cells. Decay accelerating factor (DAF, CD55), a glycosylphosphatidylinositol-anchored membrane protein, is a candidate for autoimmune disease susceptibility based on its role in restricting complement activation and evidence that DAF expression modulates the phenotype of mice models for autoimmune disease. In this study, we adopt a linkage disequilibrium (LD) mapping approach to test for an association between the DAF gene and T1D.

Results

Initially, we used HapMap II genotype data to examine LD across the DAF region. Additional resequencing was required, identifying 16 novel polymorphisms. Combining both datasets, a LD mapping approach was adopted to test for association with T1D. Seven tag SNPs were selected and genotyped in case-control (3,523 cases and 3,817 controls) and family (725 families) collections.

Conclusion

We obtained no evidence of association between T1D and the DAF region in two independent collections. In addition, we assessed the impact of using only HapMap II genotypes for the selection of tag SNPs and, based on this study, found that HapMap II genotypes may require additional SNP discovery for comprehensive LD mapping of some genes in common disease.

Genetic epidemiology of type 1 diabetes

The evidence that there is clinical heterogeneity of type 1 diabetes is reviewed and the implications for genetic studies are discussed. In the past year, genome-wide linkage analysis of 1435 multiplex families was reported. Additionally, confirmed evidence for association of specific markers at two loci (PTPN22, OAS1) as well as failure to replicate three others (IL12B, SUMO4, PAX4) is discussed. Some common themes are identified and suggestions for improvements are made. We look forward to the results from genome-wide association studies.

Exploring supervised and unsupervised methods to detect topics in biomedical text

Background

Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.

Results

We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics.

Conclusion

Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.

Polymorphism discovery and association analyses of the interferon genes in type 1 diabetes

Background

The aetiology of the autoimmune disease type 1 diabetes (T1D) involves many genetic and environmental factors. Evidence suggests that innate immune responses, including the action of interferons, may also play a role in the initiation and/or pathogenic process of autoimmunity. In the present report, we have adopted a linkage disequilibrium (LD) mapping approach to test for an association between T1D and three regions encompassing 13 interferon alpha (IFNA) genes, interferon omega-1 (IFNW1), interferon beta-1 (IFNB1), interferon gamma (IFNG) and the interferon consensus-sequence binding protein 1 (ICSBP1).

Results

We identified 238 variants, most, single nucleotide polymorphisms (SNPs), by sequencing IFNA, IFNB1, IFNW1 and ICSBP1, 98 of which where novel when compared to dbSNP build 124. We used polymorphisms identified in the SeattleSNP database for INFG. A set of tag SNPs was selected for each of the interferon and interferon-related genes to test for an association between T1D and this complex gene family. A total of 45 tag SNPs were selected and genotyped in a collection of 472 multiplex families.

Conclusion

We have developed informative sets of SNPs for the interferon and interferon related genes. No statistical evidence of a major association between T1D and any of the interferon and interferon related genes tested was found.

No evidence for association of the TATA-box binding protein glutamine repeat sequence or the flanking chromosome 6q27 region with type 1 diabetes

Susceptibility to the autoimmune disease type 1 diabetes has been linked to human chromosome 6q27 and, moreover, recently associated with one of the genes in the region, TATA box-binding protein (TBP). Using a much larger sample of T1D families than those studied by others, and by extensive re-sequencing of nine other genes in the proximity, in which we identified 279 polymorphisms, 83 of which were genotyped in up to 725 T1D multiplex and simplex families, we obtained no evidence for association of the TBP CAG/CAA (glutamine) microsatellite repeat sequence with disease, or for nine other genes, PDCD2, PSMB1, KIAA1838, DLL1, dJ894D12.4, FLJ25454, FLJ13162, FLJ11152, PHF10 and CCR6. This study also provides an exon-based tag single nucleotide polymorphism map for these 10 genes that can be used for analysis of other diseases.

Construction and analysis of tag single nucleotide polymorphism maps for six human-mouse orthologous candidate genes in type 1 diabetes

Background

One strategy to help identify susceptibility genes for complex, multifactorial diseases is to map disease loci in a representative animal model of the disorder. The nonobese diabetic (NOD) mouse is a model for human type 1 diabetes. Linkage and congenic strain analyses have identified several NOD mouse Idd (insulin dependent diabetes) loci, which have been mapped to small chromosome intervals, for which the orthologous regions in the human genome can be identified. Here, we have conducted re-sequencing and association analysis of six orthologous genes identified in NOD Idd loci: NRAMP1/SLC11A1 (orthologous to Nramp1/Slc11a1 in Idd5.2), FRAP1 (orthologous to Frap1 in Idd9.2), 4-1BB/CD137/TNFRSF9 (orthologous to 4-1bb/Cd137/Tnrfrsf9 in Idd9.3), CD101/IGSF2 (orthologous to Cd101/Igsf2 in Idd10), B2M (orthologous to B2m in Idd13) and VAV3 (orthologous to Vav3 in Idd18).

Results

Re-sequencing of a total of 110 kb of DNA from 32 or 96 type 1 diabetes cases yielded 220 single nucleotide polymorphisms (SNPs). Sixty-five SNPs, including 54 informative tag SNPs, and a microsatellite were selected and genotyped in up to 1,632 type 1 diabetes families and 1,709 cases and 1,829 controls.

Conclusion

None of the candidate regions showed evidence of association with type 1 diabetes (P values > 0.2), indicating that common variation in these key candidate genes does not play a major role in type 1 diabetes susceptibility in the European ancestry populations studied.