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

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.

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.

Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review

Diabetes mellitus (DM) is a kind of chronic and metabolic disease, which can cause a number of diseases and severe complications. Network pharmacology approach is introduced to study DM, which can combine the drugs, target proteins and disease and form drug-target-disease networks. Network pharmacology has been widely used in the studies of the bioactive compounds and action mechanisms of natural products for the treatment of DM due to the multi-components, multi-targets, and lower side effects. This review provides a balanced and comprehensive summary on network pharmacology from current studies, highlighting different bioactive constituents, related databases and applications in the investigations on the treatment of DM especially type 2. The mechanisms related to type 2 DM, including α-amylase and α-glucosidase inhibitory, targeting β cell dysfunction, AMPK signal pathway and PI3K/Akt signal pathway are summarized and critiqued. It suggests that the network pharmacology approach cannot only provide a new research paradigm for natural products, but also improve the current antidiabetic drug discovery strategies. Furthermore, we put forward the perspectives on the reasonable applications of network pharmacology for the therapy of DM and related drug discovery.

TarNet: An Evidence-Based Database for Natural Medicine Research

Background

Complex diseases seriously threaten human health. Drug discovery approaches based on “single genes, single drugs, and single targets” are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.

Results

In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein–protein interactions (PPIs). TarNet includes in-depth information on both plant–compound–protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein–protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound–protein relationships.

Conclusions

TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

Ptpn22 and Cd2 Variations Are Associated with Altered Protein Expression and Susceptibility to Type 1 Diabetes in Nonobese Diabetic Mice

By congenic strain mapping using autoimmune NOD.C57BL/6J congenic mice, we demonstrated previously that the type 1 diabetes (T1D) protection associated with the insulin-dependent diabetes (Idd)10 locus on chromosome 3, originally identified by linkage analysis, was in fact due to three closely linked Idd loci: Idd10, Idd18.1, and Idd18.3. In this study, we define two additional Idd loci—Idd18.2 and Idd18.4—within the boundaries of this cluster of disease-associated genes. Idd18.2 is 1.31 Mb and contains 18 genes, including Ptpn22, which encodes a phosphatase that negatively regulates T and B cell signaling. The human ortholog of Ptpn22, PTPN22, is associated with numerous autoimmune diseases, including T1D. We, therefore, assessed Ptpn22 as a candidate for Idd18.2; resequencing of the NOD Ptpn22 allele revealed 183 single nucleotide polymorphisms with the C57BL/6J (B6) allele—6 exonic and 177 intronic. Functional studies showed higher expression of full-length Ptpn22 RNA and protein, and decreased TCR signaling in congenic strains with B6-derived Idd18.2 susceptibility alleles. The 953-kb Idd18.4 locus contains eight genes, including the candidate Cd2. The CD2 pathway is associated with the human autoimmune disease, multiple sclerosis, and mice with NOD-derived susceptibility alleles at Idd18.4 have lower CD2 expression on B cells. Furthermore, we observed that susceptibility alleles at Idd18.2 can mask the protection provided by Idd10/Cd101 or Idd18.1/Vav3 and Idd18.3. In summary, we describe two new T1D loci, Idd18.2 and Idd18.4, candidate genes within each region, and demonstrate the complex nature of genetic interactions underlying the development of T1D in the NOD mouse model.

Type 1 Diabetes Prone NOD Mice Have Diminished Cxcr1 mRNA Expression in Polymorphonuclear Neutrophils and CD4+ T Lymphocytes

In humans, CXCR1 and CXCR2 are two homologous proteins that bind ELR+ chemokines. Both receptors play fundamental roles in neutrophil functions such as migration and reactive oxygen species production. Mouse Cxcr1 and Cxcr2 genes are located in an insulin-dependent diabetes genetic susceptibility locus. The non obese diabetic (NOD) mouse is a spontaneous well-described animal model for insulin-dependent type 1 diabetes. In this disease, insulin deficiency results from the destruction of insulin-producing beta cells by autoreactive T lymphocytes. This slow-progressing disease is dependent on both environmental and genetic factors. Here, we report descriptive data about the Cxcr1 gene in NOD mice. We demonstrate decreased expression of mRNA for Cxcr1 in neutrophils and CD4+ lymphocytes isolated from NOD mice compared to other strains, related to reduced NOD Cxcr1 gene promoter activity. Looking for Cxcr1 protein, we next analyze the membrane proteome of murine neutrophils by mass spectrometry. Although Cxcr2 protein is clearly found in murine neutrophils, we did not find evidence of Cxcr1 peptides using this method. Nevertheless, in view of recently-published experimental data obtained in NOD mice, we argue for possible Cxcr1 involvement in type 1 diabetes pathogenesis.

IthaGenes: an interactive database for haemoglobin variations and epidemiology

Inherited haemoglobinopathies are the most common monogenic diseases, with millions of carriers and patients worldwide. At present, we know several hundred disease-causing mutations on the globin gene clusters, in addition to numerous clinically important trans-acting disease modifiers encoded elsewhere and a multitude of polymorphisms with relevance for advanced diagnostic approaches. Moreover, new disease-linked variations are discovered every year that are not included in traditional and often functionally limited locus-specific databases. This paper presents IthaGenes, a new interactive database of haemoglobin variations, which stores information about genes and variations affecting haemoglobin disorders. In addition, IthaGenes organises phenotype, relevant publications and external links, while embedding the NCBI Sequence Viewer for graphical representation of each variation. Finally, IthaGenes is integrated with the companion tool IthaMaps for the display of corresponding epidemiological data on distribution maps. IthaGenes is incorporated in the ITHANET community portal and is free and publicly available at http://www.ithanet.eu/db/ithagenes.

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.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

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.

T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes

Researchers are finding it more and more difficult to follow the changing status of disease candidate genes due to the exponential increase in gene mapping studies. The Text-mined Hypertension, Obesity and Diabetes candidate gene database (T-HOD) is developed to help trace existing research on three kinds of cardiovascular diseases: hypertension, obesity and diabetes, with the last disease categorized into Type 1 and Type 2, by regularly and semiautomatically extracting HOD-related genes from newly published literature. Currently, there are 837, 835 and 821 candidate genes recorded in T-HOD for hypertension, obesity and diabetes, respectively. T-HOD employed the state-of-art text-mining technologies, including a gene/disease identification system and a disease–gene relation extraction system, which can be used to affirm the association of genes with three diseases and provide more evidence for further studies. The primary inputs of T-HOD are the three kinds of diseases, and the output is a list of disease-related genes that can be ranked based on their number of appearance, protein–protein interactions and single-nucleotide polymorphisms. Unlike manually constructed disease gene databases, the content of T-HOD is regularly updated by our text-mining system and verified by domain experts. The interface of T-HOD facilitates easy browsing for users and allows T-HOD curators to verify data efficiently. We believe that T-HOD can help life scientists in search for more disease candidate genes in a less time- and effort-consuming manner.

Database URL:http://bws.iis.sinica.edu.tw/THOD

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.

Islet amyloid polypeptide triggers limited complement activation and binds complement inhibitor C4b-binding protein, which enhances fibril formation

Islet amyloid polypeptide (IAPP) is synthesized in pancreatic β-cells and co-secreted with insulin. Aggregation and formation of IAPP-amyloid play a critical role in β-cell death in type 2 diabetic patients. Because Aβ-fibrils in Alzheimer disease activate the complement system, we have here investigated specific interactions between IAPP and complement factors. IAPP fibrils triggered limited activation of complement in vitro, involving both the classical and the alternative pathways. Direct binding assays confirmed that IAPP fibrils interact with globular head domains of complement initiator C1q. Furthermore, IAPP also bound complement inhibitors factor H and C4b-binding protein (C4BP). Recombinant C4BP mutants were used to show that complement control protein (CCP) domains 8 and 2 of the α-chain were responsible for the strong, hydrophobic binding of C4BP to IAPP. Immunostaining of pancreatic sections from type 2 diabetic patients revealed the presence of complement factors in the islets and varying degree of co-localization between IAPP fibrils and C1q, C3d, as well as C4BP and factor H but not membrane attack complex. Furthermore, C4BP enhanced formation of IAPP fibrils in vitro. We conclude that C4BP binds to IAPP thereby limiting complement activation and may be enhancing formation of IAPP fibrils from cytotoxic oligomers.

Visualizing the drug target landscape

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets. Assembling a holistic picture of the current landscape of drug discovery activity remains a challenge, however, because of the lack of integration between biological, chemical and clinical resources. Although tools designed to tackle the interpretation of individual data types are abundant, systems that bring together multiple elements to directly enable decision making within drug discovery programmes are rare. In this article, we review the path that led to the development of a knowledge system to tackle this problem within our organization and highlight the influences of existing technologies on its development. Central to our approach is the use of visualization to better convey the overall meaning of an integrated set of data including disease association, druggability, competitor intelligence, genomics and text mining. Organizing such data along lines of therapeutic precedence creates clearly distinct 'zones' of pharmaceutical opportunity, ranging from small-molecule repurposing to biotherapeutic prospects and gene family exploitation. Mapping content in this way also provides a visual alerting mechanism that evaluates new evidence in the context of old, reducing information overload by filtering redundant information. In addition, we argue the need for more tools in this space and highlight the role that data standards, new technologies and increased collaboration might have in achieving this aim.

Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology

In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

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.

Paracrine signalling loops in adult human and mouse pancreatic islets: netrins modulate beta cell apoptosis signalling via dependence receptors

AIMS/HYPOTHESIS

Adult pancreatic islets contain multiple cell types that produce and secrete well characterised hormones, including insulin, glucagon and somatostatin. Although it is increasingly apparent that islets release and respond to more secreted factors than previously thought, systematic analyses are lacking. We therefore sought to identify potential autocrine and/or paracrine islet growth factor loops, and to characterise the function of the netrin family of islet-secreted factors and their receptors, which have been previously unreported in adult islets.

METHODS

Gene expression databases, islet-specific tag sequencing libraries and microarray datasets of FACS purified beta cells were used to compile a list of secreted factors and receptors present in mouse or human islets. Netrins and their receptors were further assessed using RT-PCR, Western blot analysis and immunofluorescence staining. The roles of netrin-1 and netrin-4 in beta cell function, apoptosis and proliferation were also examined.

RESULTS

We identified 233 secreted factors and 234 secreted factor receptors in islets. The presence of netrins and their receptors was further confirmed. Downregulation of caspase-3 activation was observed when MIN6 cells were exposed to exogenous netrin-1 and netrin-4 under hyperglycaemic conditions. Reduction in caspase-3 cleavage was linked to the decrease in dependence receptors, neogenin and unc-5 homologue A, as well as the activation of Akt and extracellular signal-regulated protein kinase (ERK) signalling.

CONCLUSIONS/INTERPRETATION

Our results highlight the large number of potential islet growth factors and point to a context-dependent pro-survival role for netrins in adult beta cells. Since diabetes results from a deficiency in functional beta cell mass, these studies are important steps towards developing novel therapies to improve beta cell survival.

Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of "missing heritability" has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of "missing heritability" for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp.

Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest

We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.

Type 2 diabetes genetic association database manually curated for the study design and odds ratio

Background

The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.

Description

The list of publications used in this study was collected from the HuGE Navigator, which is an online database of published genome epidemiology literature. Because type 2 diabetes genetic association database (T2DGADB) aims to provide specialized information on the genetic risk factors involved in the development of type 2 diabetes, 701 of the 1,771 publications in the type 2 Diabetes case-control study for the development of the disease were extracted.

Conclusions

In the database, the association results were grouped as either positive or negative. The gene and SNP names were replaced with gene symbols and rsSNP numbers, the association p-values were determined manually, and the results are displayed by graphs and tables. In addition, the study design in publications, such as the population type and size are described. This database can be used for research purposes, such as an association and functional study of type 2 diabetes related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.

Glucose-induced ERM protein activation and translocation regulates insulin secretion

A key step in regulating insulin secretion is insulin granule trafficking to the plasma membrane. Using live-cell time-lapse confocal microscopy, we observed a dynamic association of insulin granules with filamentous actin and PIP2-enriched structures. We found that the scaffolding protein family ERM, comprising ezrin, radixin, and moesin, are expressed in β-cells and target both F-actin and PIP2. Furthermore, ERM proteins are activated via phosphorylation in a glucose- and calcium-dependent manner. This activation leads to a translocation of the ERM proteins to sites on the cell periphery enriched in insulin granules, the exocyst complex docking protein Exo70, and lipid rafts. ERM scaffolding proteins also participate in insulin granule trafficking and docking to the plasma membrane. Overexpression of a truncated dominant-negative ezrin construct that lacks the ERM F-actin binding domain leads to a reduction in insulin granules near the plasma membrane and impaired secretion. Conversely, overexpression of a constitutively active ezrin results in more granules near the cell periphery and an enhancement of insulin secretion. Diabetic mouse islets contain less active ERM, suggestive of a novel mechanism whereby impairment of insulin granule trafficking to the membrane through a complex containing F-actin, PIP2, Exo70, and ERM proteins contributes to defective insulin secretion.

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

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.

Banting Lecture 2009: An unfinished journey: molecular pathogenesis to prevention of type 1A diabetes

The Banting Medal for Scientific Achievement Award is the American Diabetes Association's highest scientific award and honors an individual who has made significant, long-term contributions to the understanding of diabetes, its treatment, and/or prevention. The award is named after Nobel Prize winner Sir Frederick Banting, who codiscovered insulin treatment for diabetes.

Dr. Eisenbarth received the American Diabetes Association's Banting Medal for Scientific Achievement at the Association's 69th Scientific Sessions, June 5–9, 2009, in New Orleans, Louisiana. He presented the Banting Lecture, An Unfinished Journey—Type 1 Diabetes—Molecular Pathogenesis to Prevention , on Sunday, June 7, 2009.

Unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders

Chronic kidney disease is common with up to 5% of the adult population reported to have an estimated glomerular filtration rate of < 60 ml/min/1.73 m(2). A large number of pathogenic mutations have been identified that are responsible for 'single gene' renal disorders, such as autosomal dominant polycystic kidney disease and X-linked Alport syndrome. These single gene disorders account for < 15% of the burden of end-stage renal disease that requires dialysis or kidney transplantation. It has proved more difficult to identify the genetic susceptibility underlying common, complex, multifactorial kidney conditions, such as diabetic nephropathy and hypertensive nephrosclerosis. This review describes success to date and explores strategies currently employed in defining the genetic basis for a number of renal disorders. The complementary use of linkage studies, candidate gene and genome-wide association analyses are described and a collation of renal genetic resources highlighted.

Current status and the future for the genetics of type I diabetes

The Type I Diabetes Genetics Consortium (T1DGC) is an international collaboration whose primary goal is to identify genes whose variants modify an individual's risk of type I diabetes (T1D). An integral part of the T1DGC's mission is the establishment of clinical and data resources that can be used by, and that are fully accessible to, the T1D research community (http://www.t1dgc.org). The T1DGC has organized the collection and analyses of study samples and conducted several major research projects focused on T1D gene discovery: a genome-wide linkage scan, an intensive evaluation of the human major histocompatibility complex, a detailed examination of published candidate genes, and a genome-wide association scan. These studies have provided important information to the scientific community regarding the function of specific genes or chromosomal regions on T1D risk. The results are continually being updated and displayed (http://www.t1dbase.org). The T1DGC welcomes all investigators interested in using these data for scientific endeavors on T1D. The T1DGC resources provide a framework for future research projects, including examination of structural variation, re-sequencing of candidate regions in a search for T1D-associated genes and causal variants, correlation of T1D risk genotypes with biomarkers obtained from T1DGC serum and plasma samples, and in-depth bioinformatics analyses.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes.

Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

Meta-analysis of gene expression in human pancreatic islets after in vitro expansion

Pancreatic islet transplantation as a potential cure for type 1 diabetes (T1D) cannot be scaled up due to a scarcity of human pancreas donors. In vitro expansion of beta-cells from mature human pancreatic islets provides an alternative source of insulin-producing cells. The exact nature of the expanded cells produced by diverse expansion protocols and their potential for differentiation into functional beta-cells remain elusive. We performed a large-scale meta-analysis of gene expression in human pancreatic islet cells, which were processed using three different previously described protocols for expansion and for which redifferentiation was attempted. All three expansion protocols induced dramatic changes in the expression profiles of pancreatic islets; many of these changes are shared among the three protocols. Attempts at redifferentiation of expanded cells induce a limited number of gene expression changes. Nevertheless, these fail to restore a pancreatic islet-like gene expression pattern. Comparison with a collection of public microarray datasets confirmed that expanded cells are highly comparable to mesenchymal stem cells. Genes induced in expanded cells are also enriched for targets of transcription factors important for pluripotency induction. The present data increase our understanding of the active pathways in expanded and redifferentiated islets. Knowledge of the mesenchymal stem cell potential may help development of drug therapeutics to restore beta-cell mass in T1D patients.

Genomic convergence of genome-wide investigations for complex traits

Genome-wide investigations for identifying the genes for complex traits are considered to be agnostic in terms of prior assumptions for the responsible DNA alterations. The agreement of genome-wide association studies (GWAS) and genome-wide linkage scans (GWLS) has not been explored to date. In this study, a genomic convergence approach of GWAS and GWLS was implemented for the first time in order to identify genomic loci supported by both methods. A database with 376 GWLS and 102 GWAS for 19 complex traits was created. Data regarding the location and statistical significance for each genetic marker were extracted from articles or web-based databases. Convergence was quantified as the proportion of significant GWAS markers located within linked regions. Convergence was variable (0-73.3%) and was found to be significantly higher than expected by chance only for two of the 19 phenotypes. Seventy five loci of interest were identified, which being supported by independent lines of evidence, could merit prioritization in future investigations. Although convergence is supportive of genuine effects, lack of agreement between GWLS and GWAS is also indicative that these studies are designed to answer different questions and are not equally well suited for deciphering the genetics of complex traits.

PTPN2, a candidate gene for type 1 diabetes, modulates interferon-gamma-induced pancreatic beta-cell apoptosis

OBJECTIVE

The pathogenesis of type 1 diabetes has a strong genetic component. Genome-wide association scans recently identified novel susceptibility genes including the phosphatases PTPN22 and PTPN2. We hypothesized that PTPN2 plays a direct role in beta-cell demise and assessed PTPN2 expression in human islets and rat primary and clonal beta-cells, besides evaluating its role in cytokine-induced signaling and beta-cell apoptosis.

RESEARCH DESIGN AND METHODS

PTPN2 mRNA and protein expression was evaluated by real-time PCR and Western blot. Small interfering (si)RNAs were used to inhibit the expression of PTPN2 and downstream STAT1 in beta-cells, allowing the assessment of cell death after cytokine treatment.

RESULTS

PTPN2 mRNA and protein are expressed in human islets and rat beta-cells and upregulated by cytokines. Transfection with PTPN2 siRNAs inhibited basal- and cytokine-induced PTPN2 expression in rat beta-cells and dispersed human islets cells. Decreased PTPN2 expression exacerbated interleukin (IL)-1beta + interferon (IFN)-gamma-induced beta-cell apoptosis and turned IFN-gamma alone into a proapoptotic signal. Inhibition of PTPN2 amplified IFN-gamma-induced STAT1 phosphorylation, whereas double knockdown of both PTPN2 and STAT1 protected beta-cells against cytokine-induced apoptosis, suggesting that STAT1 hyperactivation is responsible for the aggravation of cytokine-induced beta-cell death in PTPN2-deficient cells.

CONCLUSIONS

We identified a functional role for the type 1 diabetes candidate gene PTPN2 in modulating IFN-gamma signal transduction at the beta-cell level. PTPN2 regulates cytokine-induced apoptosis and may thereby contribute to the pathogenesis of type 1 diabetes.

POINeT: protein interactome with sub-network analysis and hub prioritization

BACKGROUND

Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

RESULTS

We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

CONCLUSION

The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex including HLA-B and HLA-A

AIM

Until recently, human leucocyte antigen (HLA) class II-independent associations with type 1 diabetes (T1D) in the Major Histocompatibility Complex (MHC) region were not adequately characterized owing to insufficient map coverage, inadequate statistical approaches and strong linkage disequilibrium spanning the entire MHC. Here we test for HLA class II-independent associations in the MHC using fine mapping data generated by the Type 1 Diabetes Genetics Consortium (T1DGC).

METHODS

We have applied recursive partitioning to the modelling of the class II loci and used stepwise conditional logistic regression to test approximately 1534 loci between 29 and 34 Mb on chromosome 6p21, typed in 2240 affected sibpair (ASP) families.

RESULTS

Preliminary analyses confirm that HLA-B (at 31.4 Mb), HLA-A (at 30.0 Mb) are associated with T1D independently of the class II genes HLA-DRB1 and HLA-DQB1 (P = 6.0 x 10(-17) and 8.8 x 10(-13), respectively). In addition, a second class II region of association containing the single-nucleotide polymorphism (SNP), rs439121, and the class II locus HLA-DPB1, was identified as a T1D susceptibility effect which is independent of HLA-DRB1, HLA-DQB1 and HLA-B (P = 9.2 x 10(-8)). A younger age-at-diagnosis of T1D was found for HLA-B*39 (P = 7.6 x 10(-6)), and HLA-B*38 was protective for T1D.

CONCLUSIONS

These analyses in the T1DGC families replicate our results obtained previously in approximately 2000 cases and controls and 850 families. Taking both studies together, there is evidence for four T1D-associated regions at 30.0 Mb (HLA-A), 31.4 Mb (HLA-B), 32.5 Mb (rs9268831/HLA-DRA) and 33.2 Mb (rs439121/HLA-DPB1) that are independent of HLA-DRB1/HLA-DQB1. Neither study found evidence of independent associations at HLA-C, HLA-DQA1 loci nor in the UBD/MAS1L or ITPR3 gene regions. These studies show that to find true class II-independent effects, large, well-powered sample collections are required and be genotyped with a dense map of markers. In addition, a robust statistical methodology that fully models the class II effects is necessary. Recursive partitioning is a useful tool for modelling these multiallelic systems.

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).

Predicting Type 1 Diabetes Candidate Genes using Human Protein-Protein Interaction Networks

Background

Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases.

Results

In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates.

Conclusion

Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.

Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology

Although high-throughput technologies used in biology have resulted in the accumulation of vast amounts of data in the literature, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Databases have assumed a crucial role in assimilating and storing information that could enable future discoveries. To this end, our group has developed two resources - Human Protein Reference Database (HPRD) and Human Proteinpedia - that provide integrated information pertaining to human proteins. These databases contain information on a number of features of proteins that have been discovered using various experimental methods. Human Proteinpedia was developed as a portal for community participation to annotate and share proteomic data using HPRD as the scaffold. It allows proteomic investigators to even share unpublished data and provides an effective medium for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, classical genetics, and chemical genetics in understanding the relationships among genome, gene functions, and living systems.

Human Protein Reference Database--2009 update

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

GeneSpeed Beta Cell: an online genomics data repository and analysis resource tailored for the islet cell biologist

OBJECTIVE

We here describe the development of a freely available online database resource, GeneSpeed Beta Cell, which has been created for the pancreatic islet and pancreatic developmental biology investigator community.

RESEARCH DESIGN AND METHODS

We have developed GeneSpeed Beta Cell as a separate component of the GeneSpeed database, providing a genomics-type data repository of pancreas and islet-relevant datasets interlinked with the domain-oriented GeneSpeed database.

RESULTS

GeneSpeed Beta Cell allows the query of multiple published and unpublished select genomics datasets in a simultaneous fashion (multiexperiment viewing) and is capable of defining intersection results from precomputed analysis of such datasets (multidimensional querying). Combined with the protein-domain categorization/assembly toolbox provided by the GeneSpeed database, the user is able to define spatial expression constraints of select gene lists in a relatively rigid fashion within the pancreatic expression space. We provide several demonstration case studies of relevance to islet cell biology and development of the pancreas that provide novel insight into islet biology.

CONCLUSIONS

The combination of an exhaustive domain-based compilation of the transcriptome with gene array data of interest to the islet biologist affords novel methods for multidimensional querying between individual datasets in a rapid fashion, presently not available elsewhere.

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.

HGVbaseG2P: a central genetic association database

The Human Genome Variation database of Genotype to Phenotype information (HGVbaseG2P) is a new central database for summary-level findings produced by human genetic association studies, both large and small. Such a database is needed so that researchers have an easy way to access all the available association study data relevant to their genes, genome regions or diseases of interest. Such a depository will allow true positive signals to be more readily distinguished from false positives (type I error) that fail to consistently replicate. In this paper we describe how HGVbaseG2P has been constructed, and how its data are gathered and organized. We present a range of user-friendly but powerful website tools for searching, browsing and visualizing G2P study findings. HGVbaseG2P is available at http://www.hgvbaseg2p.org.

MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs

The identification of cis-regulatory modules (CRMs) can greatly advance our understanding of eukaryotic regulatory mechanism. Current methods to predict CRMs from known motifs either depend on multiple alignments or can only deal with a small number of known motifs provided by users. These methods are problematic when binding sites are not well aligned in multiple alignments or when the number of input known motifs is large. We thus developed a new CRM identification method MOPAT (motif pair tree), which identifies CRMs through the identification of motif modules, groups of motifs co-ccurring in multiple CRMs. It can identify ‘orthologous’ CRMs without multiple alignments. It can also find CRMs given a large number of known motifs. We have applied this method to mouse developmental genes, and have evaluated the predicted CRMs and motif modules by microarray expression data and known interacting motif pairs. We show that the expression profiles of the genes containing CRMs of the same motif module correlate significantly better than those of a random set of genes do. We also show that the known interacting motif pairs are significantly included in our predictions. Compared with several current methods, our method shows better performance in identifying meaningful CRMs.

T2D-Db: an integrated platform to study the molecular basis of Type 2 diabetes

Background

Type 2 Diabetes Mellitus (T2DM) is a non insulin dependent, complex trait disease that develops due to genetic predisposition and environmental factors. The advanced stage in type 2 diabetes mellitus leads to several micro and macro vascular complications like nephropathy, neuropathy, retinopathy, heart related problems etc. Studies performed on the genetics, biochemistry and molecular biology of this disease to understand the pathophysiology of type 2 diabetes mellitus has led to the generation of a surfeit of data on candidate genes and related aspects. The research is highly progressive towards defining the exact etiology of this disease.

Results

T2D-Db (Type 2 diabetes Database) is a comprehensive web resource, which provides integrated and curated information on almost all known molecular components involved in the pathogenesis of type 2 diabetes mellitus in the three widely studied mammals namely human, mouse and rat. Information on candidate genes, SNPs (Single Nucleotide Polymorphism) in candidate genes or candidate regions, genome wide association studies (GWA), tissue specific gene expression patterns, EST (Expressed Sequence Tag) data, expression information from microarray data, pathways, protein-protein interactions and disease associated risk factors or complications have been structured in this on line resource.

Conclusion

Information available in T2D-Db provides an integrated platform for the better molecular level understanding of type 2 diabetes mellitus and its pathogenesis. Importantly, the resource facilitates graphical presentation of the gene/genome wide map of SNP markers and protein-protein interaction networks, besides providing the heat map diagram of the selected gene(s) in an organism across microarray expression experiments from either single or multiple studies. These features aid to the data interpretation in an integrative way. T2D-Db is to our knowledge the first publicly available resource that can cater to the needs of researchers working on different aspects of type 2 diabetes mellitus.

PADB: published association database

Background

Although molecular pathway information and the International HapMap Project data can help biomedical researchers to investigate the aetiology of complex diseases more effectively, such information is missing or insufficient in current genetic association databases. In addition, only a few of the environmental risk factors are included as gene-environment interactions, and the risk measures of associations are not indexed in any association databases.

Description

We have developed a published association database (PADB; ) that includes both the genetic associations and the environmental risk factors available in PubMed database. Each genetic risk factor is linked to a molecular pathway database and the HapMap database through human gene symbols identified in the abstracts. And the risk measures such as odds ratios or hazard ratios are extracted automatically from the abstracts when available. Thus, users can review the association data sorted by the risk measures, and genetic associations can be grouped by human genes or molecular pathways. The search results can also be saved to tab-delimited text files for further sorting or analysis. Currently, PADB indexes more than 1,500,000 PubMed abstracts that include 3442 human genes, 461 molecular pathways and about 190,000 risk measures ranging from 0.00001 to 4878.9.

Conclusion

PADB is a unique online database of published associations that will serve as a novel and powerful resource for reviewing and interpreting huge association data of complex human diseases.

DNA microarrays: a powerful genomic tool for biomedical and clinical research

Among the many benefits of the Human Genome Project are new and powerful tools such as the genome-wide hybridization devices referred to as microarrays. Initially designed to measure gene transcriptional levels, microarray technologies are now used for comparing other genome features among individuals and their tissues and cells. Results provide valuable information on disease subcategories, disease prognosis, and treatment outcome. Likewise, they reveal differences in genetic makeup, regulatory mechanisms, and subtle variations and move us closer to the era of personalized medicine. To understand this powerful tool, its versatility, and how dramatically it is changing the molecular approach to biomedical and clinical research, this review describes the technology, its applications, a didactic step-by-step review of a typical microarray protocol, and a real experiment. Finally, it calls the attention of the medical community to the importance of integrating multidisciplinary teams to take advantage of this technology and its expanding applications that, in a slide, reveals our genetic inheritance and destiny.