Heritable diseases are caused by germ-line mutations that, despite tissuewide presence, often lead to tissue-specific pathology. Here, we make a systematic analysis of the link between tissue-specific gene expression and pathological manifestations in many human diseases and cancers. Diseases were systematically mapped to tissues they affect from disease-relevant literature in PubMed to create a disease-tissue covariation matrix of high-confidence associations of >1,000 diseases to 73 tissues. By retrieving >2,000 known disease genes, and generating 1,500 disease-associated protein complexes, we analyzed the differential expression of a gene or complex involved in a particular disease in the tissues affected by the disease, compared with nonaffected tissues. When this analysis is scaled to all diseases in our dataset, there is a significant tendency for disease genes and complexes to be overexpressed in the normal tissues where defects cause pathology. In contrast, cancer genes and complexes were not overexpressed in the tissues from which the tumors emanate. We specifically identified a complex involved in XY sex reversal that is testis-specific and down-regulated in ovaries. We also identified complexes in Parkinson disease, cardiomyopathies, and muscular dystrophy syndromes that are similarly tissue specific. Our method represents a conceptual scaffold for organism-spanning analyses and reveals an extensive list of tissue-specific draft molecular pathways, both known and unexpected, that might be disrupted in disease.proteomics ͉ systems biology ͉ computational biology
Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine.
A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge, but they are expensive to generate manually and may therefore have incomplete coverage. We have developed a method that ranks 12,460 genes in the human genome on the basis of their potential relevance to a specific query drug and its putative indications. Our method uses known gene-drug interactions, networks of gene-gene interactions, and available measures of drug-drug similarity. It ranks genes by building a local network of known interactions and assessing the similarity of the query drug (by both structure and indication) with drugs that interact with gene products in the local network. In a comprehensive benchmark, our method achieves an overall area under the curve of 0.82. To showcase our method, we found novel gene candidates for warfarin, gefitinib, carboplatin, and gemcitabine, and we provide the molecular hypotheses for these predictions.Pharmacogenomics is the large-scale study of how genes and their variations impact drug response. A critical step in pharmacogenomics is identifying genes that are important for drug response. A given drug may have pharmacogenes-genes important for its pharmacology-that are involved in its absorption, distribution, metabolism, and excretion (pharmacokinetics) or that are involved in its mechanism of action (pharmacodynamics). However, it may take many years of study to identify these pharmacokinetic or pharmacodynamic genes completely. This delay in understanding impedes our ability to identify, evaluate, and use genetics to optimize drug selection and dose.1Recently, high-throughput genomic technologies have been successful in identifying new pharmacogenetic (PGx) interactions.2 RNA expression arrays can measure differential gene expression related to drug response;3 genes showing significant changes in expression are more likely to be relevant to the drug response. Similarly, genome-wide association studies (GWASs) analyze data related to patients with different drug-response phenotypes and seek polymorphisms (most often single-nucleotide polymorphisms, SNPs) that correlate with drug response;4 genes with variants that correlate with drug response are more likely to be pharmacogenetically relevant.
In the present work, the Henderson-Hasselbalch (HH) equation has been employed for the development of a tool for the prediction of pH-dependent aqueous solubility of drugs and drug candidates. A new prediction method for the intrinsic solubility was developed, based on artificial neural networks that have been trained on a druglike PHYSPROP subset of 4548 compounds. For the prediction of acid/base dissociation coefficients, the commercial tool Marvin has been used, following validation on a data set of 467 molecules from the PHYSPROP database. The best performing network for intrinsic solubility predictions has a cross-validated root mean square error (RMSE) of 0.70 log S-units, while the Marvin pKa plug-in has an RMSE of 0.71 pH-units. A data set of 27 drugs with experimentally determined pH-solubility curves was assembled from the literature for the validation of the combined pH-dependent model, giving a mean RMSE of 0.79 log S-units. Finally, the combined model has been applied on profiling the solubility space at low pH of five large vendor libraries.
Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.
Computers in chemistry V 0380 Prediction of pH-Dependent Aqueous Solubility of Druglike Molecules. -(HANSEN, N. T.; KOUSKOUMVEKAKI, I.; JOERGENSEN, F. S.; BRUNAK, S.; JONSDOTTIR*, S. O.; J. Chem. Inf. Model. (J. Chem. Inf. Comput. Sci.) 46 (2006) 6, 2601-2609; BioCent.-DTU, Tech. Univ. Den., DK-2800 Lyngby, Den.; Eng.) -Lindner 09-209
Aim-To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes.Methods-We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation.Results-A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in β-cell development and diabetic complications.Conclusions-The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.
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