A systems view of disease: From single genes to complex molecular networks

“ When resistance/susceptibility is clearly defined and controlled by a single gene, scientists can use the recently developed swine genetic map (Rohrer et al. 1996) to identify the chromosomal region of the genome which possesses the gene. Genetic improvement of disease resistance in pigs requires identifying heritable phenotypes (measurements) of resistance which are well defined, accurately measured, and highly correlated with incidence of disease (marker traits) . If we focus selection on resistance to a single pathogen, we may produce animals that are only resistant to a specific disease. ”

“ A well-known example is Phenylketonuria, where the function of the gene encoding the PAH enzyme was studied with respect to the mechanism of the disease [3] . However, we are still far from fully understanding disease causation, especially regarding complex diseases such as cancer [2] . Even for mendelian diseases this is not fully achieved because phenotypic outcome cannot be predicted solely based on the genotype [3] . It has become evident, that many human diseases cannot be attributed to malfunction of single genes but arise due to complex interactions among multiple genetic variants [4] . ”

“ Moreover, influences of environmental factors, infectious agents or drugs have to be considered when studying the occurrence and evolution of a disease. In complex diseases, alterations in several genes can make subtle contributions to the susceptibility of a particular individual. ”

“ However, great challenges exist because many common diseases are caused by multiple disease genes with small to moderate effects. Even diseases that show Mendelian inheritance may involve multiple genes due to heterogeneity. Gene-gene interactions, as well as gene-environment interactions, also play an important role in the development of diseases. Classifications of diseases, which are mostly based on observed phenotypes, may not necessarily reflect their underlying mechanisms. ”

“ At the end of the day, how a disease is caused and thus how it can be treated can only be studied on the basis of the entire body of knowledge including all genes that are associated with the disease and their interactions through biological pathways. However, with the unprecedented wealth of information available, it is extremely difficult to obtain a complete picture of the genetic basis of diseases. In order to obtain such a complete picture, data integration from different sources is required. ”

“ Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. ”

“ OMIM has become a highly popular source in medical genetics [6] . It traditionally focused on mendelian diseases and later started to include complex diseases as well. In the last years, other databases have been built, among them the Pharmacogenomics Knowledge Base (PHARMGKB) specialized on the knowledge about genes that are involved in modulating drug response [7] or the Comparative Toxicogenomics Database (CTD) focused on the effect of environmental chemicals on human disease [8] . Each of the databases focuses on different aspects of phenotype-genotype relationships. ”

“ In this work we developed a comprehensive database of human gene-disease associations by integrating both, information from different databases and from literature, in order to bridge the gaps between the aforementioned knowledge pockets. The resulting database (DisGeNET database) comprises the whole spectrum of human diseases with genetic origin, including mendelian, complex and environmental diseases, and represents, to the best of our knowledge, the most complete view on human gene-disease associations that is currently publicly available. ”

“ Model organism communities benefit from centralized collections of curated research, where a scientist can search for extensively cross-referenced gene expression, phenotype, and genomic data, referred to as “model organism databases” (MODs) . Research in the field of human biology suffers because there is no equivalent resource for the human research community, and linking these diverse datasets requires searching many detached resources. ”

“ Previous work has shown that topological network analysis of gene-disease associations uncovers important properties of the nature of mendelian diseases [22] . We used four different bipartite networks called OMIM, CURATED, LHGDN and ALL (see Methods) to study human diseases at a global scale, including mendelian, complex and environmental diseases. By comparing the four networks we show that our integration effort results in a huge increase in coverage of (i) diseases, (ii) genes and (iii) their associations compared to the individual data sources (see Fig. S2) . ”

“ In summary, the network analysis of our integrated database points out that data integration is needed to obtain a comprehensive view of the genetic landscape of human diseases and that the genetic origin of mendelian, complex and environmental diseases is much more common than expected. ”

“ This contrasts with previous observations based on OMIM data. We show in several case studies how the network representation of human genetic diseases and the adjacent detection of functional modules can be used not only to shed light on the molecular basis of human diseases but also to gain a better understanding of the influence of environmental factors, including drugs, on human health. Moreover, our analysis confirms the need of integrating human gene-disease associations from various sources. ”

“ For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. ”

“ Our computational analysis has important implications for understanding disease mechanisms and how environmental factors, such as drugs, influence human health. Finally, we provide all gene-disease networks in a user-friendly way through DisGeNET [28] , a Cytoscape plugin and as SQLite database for direct computational access to aid future research of disease-related processes that will also benefit drug discovery and development. ”

“ A direct interaction between a pair of proteins either has been observed or has not been observed yet. The relationships between a candidate gene (encoding the corresponding protein) and all other genes/proteins can be thus defined. Such information can also be obtained from other data sources. For example, gene expression data can be transformed into gene co-expression networks by connecting genes with similar expression patterns. To represent known knowledge from biological pathways, a simple network can be built by connecting genes (or their products) that coexist in any pathway. ”

“ Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. ”

“ This concept of modularity is often assumed to be valid for all human diseases; however it has not been tested before for monogenic, complex and environmental diseases in a systematic manner. The DisGeNET database represents an appropriate resource to perform such an evaluation. Thus, we wanted to assess if the disease-related genes in our integrated data set were involved in the same biological processes. In other words, we wanted to study if the concept of modularity applies to the whole spectrum of human genetic diseases. ”

“ To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. ”

“ Moreover, it has been shown how cluster analysis of protein-protein interaction networks can be used to identify functional modules [23] , [27] . Hence, we use topological and functional analysis of gene-disease association networks to assess the modularity of mendelian, complex and environmental diseases. Our results indicate that for most human diseases functional modules do exist. This is also observed for groups of diseases sharing gene associations. Moreover, our results point out that most human diseases are associated with more than one biological process. ”

“ All in all, cluster analysis of the gene projection network uncovered an interesting relationship between environmental exposure to arsenic compounds and breast cancer. This relationship deserves further investigation at the epidemiological and molecular levels.
Case study 4: Identification of shared mechanisms of different diseases
Another cluster containing 79 genes is an example of a heterogeneous cluster in which genes are associated with different diseases. ”

“ In conclusion, clustering analysis of the gene projection network followed by functional enrichment analysis can be used to propose new candidate disease genes. We would also like to mention here that the CURATED set of DisGeNET represents a suitable dataset to benchmark computational methods for the prediction of candidate disease genes. ”

“ Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases. ”

“ This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.==Abstract== Author SummaryModel organisms such as fruit flies, mice, and zebrafish are useful for investigating gene function because they are easy to grow, dissect, and genetically manipulate in the laboratory. ”

“ By examining mutations in these organisms, one can identify candidate genes that cause disease in humans, and develop models to better understand human disease and gene function. A fundamental roadblock for analysis is, however, the lack of a computational method for describing and comparing phenotypes of mutant animals and of human diseases when the genetic basis is unknown. ”

“ The similar physiology and genome structure between the mouse and human facilitate ortholog comparison and functional identification between the two species. To determine if differences exist between essential and non-essential disease genes we examined several parameters in our analyses, including the physiological systems affected by each disease gene, the connectivity of each gene in protein-protein interaction networks, and the genetic mechanisms by which genes cause human disease. ”

“ These examples for identifying animal models of disease relied on knowledge of the genetic basis of the human disease, but there are many human diseases for which it is not yet known. If a researcher could compare human model organism, and even ancestral phenotypes directly, they would have a mechanism to more rapidly identify candidate genes and models of disease. ”

“ Most importantly, our results suggest that the reciprocal search will work, where we will be able to identify animal models of human disease (or disease pathways) where the human gene is not yet known. In order to implement this, we intend to annotate the remainder of OMIM using the EQ method on OMIM phenotype synopses, and supplement the database with other disease data, to provide the necessary phenotypes for comparison. Because a mutated gene in an animal model is more readily available or identifiable, our method may hasten the identification of the genetic basis of human diseases. ”

“ We expect that, as more data are accumulated using the EQ methodology, annotation to generic nodes will comprise an increasingly smaller percentage of the total annotations.
The genes identified by this similarity algorithm are good candidates for further investigation of biological function, pathway elucidation, and identification as animal models of disease. Although some models of disease may already be in existence, the importance of having a variety of animal models for the same disease should not be underestimated. ”

“ Abstract A growing literature in human social genomics has begun to analyze how everyday life circumstances influence human gene expression. Social-environmental conditions such as urbanity, low socioeconomic status, social isolation, social threat, and low or unstable social status have been found to associate with differential expression of hundreds of gene transcripts in leukocytes and diseased tissues such as metastatic cancers. ”