Mapping genotype-phenotype associations of nsSNPs in coiled-coil oligomerization domains of the human proteome.

We assessed the impact of disease mutations (DMs) versus polymorphisms (PYs) in coiled-coil (CC) domains in UniProt by modeling the structural and functional impact of variants in silico with the CC prediction program Multicoil. The structural impact of variants was evaluated with respect to three main metrics: the oligomerization score-to determine whether the variant is stabilizing or destabilizing-the oligomerization state, and the register-specific score. The functional impact was queried indirectly in several ways. First, we examined marginally stable CCs that were either stabilized or destabilized by the variant. Second, we looked for variants that altered the register of the wild-type CC near wild-type irregularities of likely functional importance, such as skips and stammers. Third, we searched for variants that altered the oligomerization state of the CC. DMs tended to be more destabilizing than PYs; but interestingly, PYs were more frequently associated with predicted changes in the oligomerization state. The functional impact was also queried by testing the association of CC variants with multiple phenotypes, that is, pleiotropy. Mutations in CC regions of proteins cause 155 different phenotypes and are more frequently associated with pleiotropy than proteins in general. Importantly, the CC region itself often encodes the pleiotropy.

Gentrepid V2.0: a web server for candidate disease gene prediction.

BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.

Cell cycle sensing of oxidative stress in Saccharomyces cerevisiae by oxidation of a specific cysteine residue in the transcription factor Swi6p.

Yeast cells begin to bud and enter the S phase when growth conditions are favorable during the G(1) phase. When subjected to some oxidative stresses, cells delay entry at G(1), allowing repair of cellular damage. Hence, oxidative stress sensing is coordinated with the regulation of cell cycle. We identified a novel function of the cell cycle regulator of Saccharomyces cerevisiae, Swi6p, as a redox sensor through its cysteine residue at position 404. When alanine was substituted at this position, the resultant mutant, C404A, was sensitive to several reactive oxygen species and oxidants including linoleic acid hydroperoxide, the superoxide anion, and diamide. This mutant lost the ability to arrest in G(1) phase upon treatment with lipid hydroperoxide. The Cys-404 residue of Swi6p in wild-type cells was oxidized to a sulfenic acid when cells were subjected to linoleic acid hydroperoxide. Mutation of Cys-404 to Ala abolished the down-regulation of expression of the G(1) cyclin genes CLN1, CLN2, PCL1, and PCL2 that occurred when cells of the wild type were exposed to the lipid hydroperoxide. In conclusion, oxidative stress signaling for cell cycle regulation occurs through oxidation of the G(1)/S-specific transcription factor Swi6p and consequently leads to suppression of the expression of G(1) cyclins and a delay in cells entering the cell cycle.

Analysis of genome-wide association study data using the protein knowledge base.

BACKGROUND: Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control. RESULTS: Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data. CONCLUSIONS: Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.

Identifying foldable regions in protein sequence from the hydrophobic signal.

Structural genomics initiatives aim to elucidate representative 3D structures for the majority of protein families over the next decade, but many obstacles must be overcome. The correct design of constructs is extremely important since many proteins will be too large or contain unstructured regions and will not be amenable to crystallization. It is therefore essential to identify regions in protein sequences that are likely to be suitable for structural study. Scooby-Domain is a fast and simple method to identify globular domains in protein sequences. Domains are compact units of protein structure and their correct delineation will aid structural elucidation through a divide-and-conquer approach. Scooby-Domain predictions are based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method employs an A*-search to identify sequence regions that form a globular structure and those that are unstructured. On a test set of 173 proteins with consensus CATH and SCOP domain definitions, Scooby-Domain has a sensitivity of 50% and an accuracy of 29%, which is better than current state-of-the-art methods. The method does not rely on homology searches and, therefore, can identify previously unknown domains.

Diversity in the intrinsic apoptosis pathway of nematodes.

Early studies of the free-living nematode C. elegans informed us how BCL-2-regulated apoptosis in humans is regulated. However, subsequent studies showed C. elegans apoptosis has several unique features compared with human apoptosis. To date, there has been no detailed analysis of apoptosis regulators in nematodes other than C. elegans. Here, we discovered BCL-2 orthologues in 89 free-living and parasitic nematode taxa representing four evolutionary clades (I, III, IV and V). Unlike in C. elegans, 15 species possess multiple (two to five) BCL-2-like proteins, and some do not have any recognisable BCL-2 sequences. Functional studies provided no evidence that BAX/BAK proteins have evolved in nematodes, and structural studies of a BCL-2 protein from the basal clade I revealed it lacks a functionally important feature of the C. elegans orthologue. Clade I CED-4/APAF-1 proteins also possess WD40-repeat sequences associated with apoptosome assembly, not present in C. elegans, or other nematode taxa studied.

Strategies to enable large-scale proteomics for reproducible research.

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.

BACKGROUND: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. RESULTS: Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. CONCLUSION: The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Cross-strand disulfides in the hydrogen bonding site of antiparallel ß-sheet (aCSDhs): Forbidden disulfides that are highly strained, easily broken.

Some disulfide bonds perform important structural roles in proteins, but another group has functional roles via redox reactions. Forbidden disulfides are stressed disulfides found in recognizable protein contexts, which currently constitute more than 10% of all disulfides in the PDB. They likely have functional redox roles and constitute a major subset of all redox-active disulfides. The torsional strain of forbidden disulfides is typically higher than for structural disulfides, but not so high as to render them immediately susceptible to reduction under physionormal conditions. Previously we characterized the most abundant forbidden disulfide in the Protein Data Bank, the aCSDn: a canonical motif in which disulfide-bonded cysteine residues are positioned directly opposite each other on adjacent anti-parallel ß-strands such that the backbone hydrogen-bonded moieties are directed away from each other. Here we perform a similar analysis for the aCSDh, a less common motif in which the opposed cysteine residues are backbone hydrogen bonded. Oxidation of two Cys in this context places significant strain on the protein system, with the ß-chains tilting toward each other to allow disulfide formation. Only left-handed aCSDh conformations are compatible with the inherent right-handed twist of ß-sheets. aCSDhs tend to be more highly strained than aCSDns, particularly when both hydrogen bonds are formed. We discuss characterized roles of aCSDh motifs in proteins of the dataset, which include catalytic disulfides in ribonucleotide reductase and ahpC peroxidase as well as a redox-active disulfide in P1 lysozyme, involved in a major conformation change. The dataset also includes many binding proteins.

Regulation through MicroRNAs in Response to Low-Energy N+ Ion Irradiation in Oryza sativa.

MicroRNAs (miRNAs) are a non-coding regulatory RNAs that play significant roles in plant growth and development, especially in the stress response. Low-energy ion radiation, a type of environmental stress, can cause multiple biological effects. To understand the roles of miRNAs in response to low-energy N+ ion radiation in Oryza sativa, high-throughput sequencing of small RNAs was carried out to detect the expression of miRNAs in the shoots of the rice after 2 × 1017 N+/cm2 irradiation. The differentially expressed 28 known miRNAs were identified, 17 of these identified miRNAs were validated by real-time quantitative fluorescent PCR (q-PCR), including 9 up-regulated miRNAs (miR1320-3p, miR1320-5p, miR156b-3p, miR156c-5p, miR156c-3p/g-3p, miR1561-5p, miR398b and miR6250) and 8 down-regulated miRNAs (miR156a/e/i, miR156k, miR160f-5p, miR166j-5p, miR1846e and miR399d). In addition, 45 novel radiationresponsive miRNAs were predicted, and 8 of them were verified by q-PCR. The target genes of radiation-responsive miRNAs were predicted and gene function enrichment analysis was performed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of 9 targets of 4 known miRNA families (miR156, miR399, miR1320 and miR398) and 2 targets of 2 novel miRNAs were quantified by q-PCR, and a strong negative regulation relation between miRNAs and their targets were observed. Those targets including SQUAMOSA promoterbinding-like protein (SPL) genes, copper/zinc superoxide dismutase (Cu/Zn-SOD), copper chaperone for SOD (CCS1) and electron transporter/ heat-shock protein binding protein (HSP), which are involved in growth and defense against various stresses, especially associated with reactive oxygen species (ROS) scavenging. This work provides important information for understanding the ROS generation and elimination mechanisms closely related to miRNAs in rice seedlings after low-energy N+ radiation exposure.

Analysis of protein sequence and interaction data for candidate disease gene prediction.

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Interleukin 33 Signaling Restrains Sporadic Colon Cancer in an Interferon-γ-Dependent Manner.

Interleukin 33 (IL33) is an inflammatory cytokine released during necrotic cell death. The epithelium and stroma of the intestine express large amounts of IL33 and its receptor St2. IL33 is therefore continuously released during homeostatic turnover of the intestinal mucosa. Although IL33 can prevent colon cancer associated with inflammatory colitis, the contribution of IL33 signaling to sporadic colon cancer remains unknown. Here, we utilized a mouse model of sporadic colon cancer to investigate the contribution of IL33 signaling to tumorigenesis in the absence of preexisting inflammation. We demonstrated that genetic ablation of St2 enhanced colon tumor development. Conversely, administration of recombinant IL33 reduced growth of colon cancer cell allografts. In reciprocal bone marrow chimeras, the concurrent loss of IL33 signaling within radioresistant nonhematopoietic, and the radiosensitive hematopoietic, compartments was associated with increased tumor burden. We detected St2 expression within the radioresistant mesenchymal cell compartment of the colon whose stimulation with IL33 induced expression of bona fide NF-κB target genes. Mechanistically, we discovered that St2 deficiency within the nonhematopoietic compartment coincided with increased abundance of regulatory T cells and suppression of an IFNγ gene expression signature, whereas IL33 administration triggered IFNγ expression by tumor allograft-infiltrating T cells. The decrease of this IFNγ gene expression signature was associated with more aggressive disease in human colon cancer patients, suggesting that lack of IL33 signaling impaired the generation of a potent IFNγ-mediated antitumor immune response. Collectively, our data reveal that IL33 functions as a tumor suppressor in sporadic colon cancer. Cancer Immunol Res; 6(4); 409-21. ©2018 AACR.

High torsional energy disulfides: relationship between cross-strand disulfides and right-handed staples.

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent beta-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained beta-sheet, are required.

Evolution of distinct EGF domains with specific functions.

EGF domains are extracellular protein modules cross-linked by three intradomain disulfides. Past studies suggest the existence of two types of EGF domain with three-disulfides, human EGF-like (hEGF) domains and complement C1r-like (cEGF) domains, but to date no functional information has been related to the two different types, and they are not differentiated in sequence or structure databases. We have developed new sequence patterns based on the different C-termini to search specifically for the two types of EGF domains in sequence databases. The exhibited sensitivity and specificity of the new pattern-based method represents a significant advancement over the currently available sequence detection techniques. We re-annotated EGF sequences in the latest release of Swiss-Prot looking for functional relationships that might correlate with EGF type. We show that important post-translational modifications of three-disulfide EGFs, including unusual forms of glycosylation and post-translational proteolytic processing, are dependent on EGF subtype. For example, EGF domains that are shed from the cell surface and mediate intercellular signaling are all hEGFs, as are all human EGF receptor family ligands. Additional experimental data suggest that functional specialization has accompanied subtype divergence. Based on our structural analysis of EGF domains with three-disulfide bonds and comparison to laminin and integrin-like EGF domains with an additional inter-domain disulfide, we propose that these hEGF and cEGF domains may have arisen from a four-disulfide ancestor by selective loss of different cysteine residues.

Comparison of sequence and structure-based datasets for nonredundant structural data mining.

Structural data mining studies attempt to deduce general principles of protein structure from solved structures deposited in the protein data bank (PDB). The entire database is unsuitable for such studies because it is not representative of the ensemble of protein folds. Given that novel folds continue to be unearthed, some folds are currently unrepresented in the PDB while other folds are overrepresented. Overrepresentation can easily be avoided by filtering the dataset. PDB_SELECT is a well-used representative subset of the PDB that has been deduced by sequence comparison. Specifically, structures with sequences that exhibit a pairwise sequence identity above a threshold value are weeded from the dataset. Although length criteria for pairwise alignments have a structural basis, this automated method of pruning is essentially sequence-based and runs into problems in the twilight zone, possibly resulting in some folds being overrepresented. The value-added structure databases SCOP and CATH are also a potential source of a nonredundant dataset. Here we compare the sequence-derived dataset PDB_SELECT with the structural databases SCOP (Structural Classification Of Proteins) and CATH (Class-Architecture-Topology-Homology). We show that some folds remain overrepresented in the PDB_SELECT dataset while other folds are not represented at all. However, SCOP and CATH also have their own problems such as the labor-intensiveness of the update process and the problem of determining whether all folds are equally or sufficiently distant. We discuss areas where further work is required.

Novel therapeutics for coronary artery disease from genome-wide association study data.

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. METHODS: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. RESULTS: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05). CONCLUSIONS: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Potential role of glutathione in evolution of thiol-based redox signaling sites in proteins.

Cysteine is susceptible to a variety of modifications by reactive oxygen and nitrogen oxide species, including glutathionylation; and when two cysteines are involved, disulfide formation. Glutathione-cysteine adducts may be removed from proteins by glutaredoxin, whereas disulfides may be reduced by thioredoxin. Glutaredoxin is homologous to the disulfide-reducing thioredoxin and shares similar binding modes of the protein substrate. The evolution of these systems is not well characterized. When a single Cys is present in a protein, conjugation of the redox buffer glutathione may induce conformational changes, resulting in a simple redox switch that effects a signaling cascade. If a second cysteine is introduced into the sequence, the potential for disulfide formation exists. In favorable protein contexts, a bistable redox switch may be formed. Because of glutaredoxin's similarities to thioredoxin, the mutated protein may be immediately exapted into the thioredoxin-dependent redox cycle upon addition of the second cysteine. Here we searched for examples of protein substrates where the number of redox-active cysteine residues has changed throughout evolution. We focused on cross-strand disulfides (CSDs), the most common type of forbidden disulfide. We searched for proteins where the CSD is present, absent and also found as a single cysteine in protein orthologs. Three different proteins were selected for detailed study-CD4, ERO1, and AKT. We created phylogenetic trees, examining when the CSD residues were mutated during protein evolution. We posit that the primordial cysteine is likely to be the cysteine of the CSD which undergoes nucleophilic attack by thioredoxin. Thus, a redox-active disulfide may be introduced into a protein structure by stepwise mutation of two residues in the native sequence to Cys. By extension, evolutionary acquisition of structural disulfides in proteins can potentially occur via transition through a redox-active disulfide state.

NKX2-5 mutations causative for congenital heart disease retain functionality and are directed to hundreds of targets.

We take a functional genomics approach to congenital heart disease mechanism. We used DamID to establish a robust set of target genes for NKX2-5 wild type and disease associated NKX2-5 mutations to model loss-of-function in gene regulatory networks. NKX2-5 mutants, including those with a crippled homeodomain, bound hundreds of targets including NKX2-5 wild type targets and a unique set of "off-targets", and retained partial functionality. NKXΔHD, which lacks the homeodomain completely, could heterodimerize with NKX2-5 wild type and its cofactors, including E26 transformation-specific (ETS) family members, through a tyrosine-rich homophilic interaction domain (YRD). Off-targets of NKX2-5 mutants, but not those of an NKX2-5 YRD mutant, showed overrepresentation of ETS binding sites and were occupied by ETS proteins, as determined by DamID. Analysis of kernel transcription factor and ETS targets show that ETS proteins are highly embedded within the cardiac gene regulatory network. Our study reveals binding and activities of NKX2-5 mutations on WT target and off-targets, guided by interactions with their normal cardiac and general cofactors, and suggest a novel type of gain-of-function in congenital heart disease.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Identification of novel therapeutics for complex diseases from genome-wide association data.

BACKGROUND: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. METHODS: We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. RESULTS: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. CONCLUSIONS: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.