Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.     

Dissection of human MiRNA regulatory influence to subpathway

The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.     

ce‐Subpathway: Identification of ceRNA‐mediated subpathways via joint power of ceRNAs and pathway topologies

Competing endogenous RNAs (ceRNAs) represent a novel mechanism of gene regulation that may mediate key subpathway regions and contribute to the altered activities of pathways. However, the classical methods used to identify pathways fail to specifically consider ceRNAs within the pathways and key regions impacted by them. We proposed a powerful strategy named ce‐Subpathway for the identification of ceRNA‐mediated functional subpathways. It provided an effective level of pathway analysis via integrating ceRNAs, differentially expressed (DE) genes and their key regions within the given pathways. We respectively analysed one pulmonary arterial hypertension (PAH) and one myocardial infarction (MI) data sets and demonstrated that ce‐Subpathway could identify many subpathways whose corresponding entire pathways were ignored by those non‐ceRNA‐mediated pathway identification methods. And these pathways have been well reported to be associated with PAH/MI‐related cardiovascular diseases. Further evidence showed reliability of ceRNA interactions and robustness/reproducibility of the ce‐Subpathway strategy by several data sets of different cancers, including breast cancer, oesophageal cancer and colon cancer. Survival analysis was finally applied to illustrate the clinical application value of the ceRNA‐mediated functional subpathways using another data sets of pancreatic cancer. Comprehensive analyses have shown the power of a joint ceRNAs/DE genes and subpathway strategy based on their topologies.     

Prediction of human disease-related gene clusters by clustering analysis

Since genes associated with similar diseases/disorders show an increased tendency for their protein products to interact with each other through protein-protein interactions (PPI), clustering analysis obviously as an efficient technique can be easily used to predict human disease-related gene clusters/subnetworks. Firstly, we used clustering algorithms, Markov cluster algorithm (MCL), Molecular complex detection (MCODE) and Clique percolation method (CPM) to decompose human PPI network into dense clusters as the candidates of disease-related clusters, and then a log likelihood model that integrates multiple biological evidences was proposed to score these dense clusters. Finally, we identified disease-related clusters using these dense clusters if they had higher scores. The efficiency was evaluated by a leave-one-out cross validation procedure. Our method achieved a success rate with 98.59% and recovered the hidden disease-related clusters in 34.04% cases when removed one known disease gene and all its gene-disease associations. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the candidates of disease-related clusters with well-supported biological significance in biological process, molecular function and cellular component of Gene Ontology (GO) and expression of human tissues. We also found that most of the disease-related clusters consisted of tissue-specific genes that were highly expressed only in one or several tissues, and a few of those were composed of housekeeping genes (maintenance genes) that were ubiquitously expressed in most of all the tissues.     

Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.     

Novel insights into seed fatty acid synthesis and modification pathways from genetic diversity and quantitative trait Loci analysis of the Brassica C genome

Natural genetic variation in fatty acid synthesis and modification pathways determine the composition of vegetable oils, which are major components of human diet and renewable products. Based on known pathways we combined diversity and genetic analysis of metabolites to infer the existence of enzymes encoded by distinct loci, and associated these with specific elongation steps or subpathways. A total of 107 lines representing different Brassica genepools revealed considerable variation for 18 seed fatty acid products. The effect of genetic variation within a single biochemical step on subsequent products was demonstrated using a correlation matrix of scatterplots, and by calculating relative step yields. Surprisingly, diploid Brassica oleracea segregating populations had a similar range of variation for individual fatty acids as across the whole genepool. This allowed identification of 22 quantitative trait loci (QTL) associated with activity in the plastid, early stages of synthesis, desaturation, and elongases. Four QTL were assigned to early stages of synthesis, seven to subpathway specific or general elongase activity, one to ketoacyl acyl-carrier protein synthetase, and two each to fatty acid desaturase and either desaturase or fatty acyl-carrier protein thioesterase. An additional 10 QTL had distinct effects but were not assigned specific functions. Where contrasting behavior in more than one subpathway was detected, we inferred QTL specificity for particular combinations of substrate and product. The assignment of enzyme function to QTL was consistent with the known position of some Brassicaeae candidate genes and collinear regions of the Arabidopsis (Arabidopsis thaliana) genome.     

Comprehensive analysis of mRNA‐level and miRNA‐level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.     

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.     

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.     

A Pathway-Based View of Human Diseases and Disease Relationships

It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.     

The Evolution of Fungal Metabolic Pathways

Fungi contain a remarkable range of metabolic pathways, sometimes encoded by gene clusters, enabling them to digest most organic matter and synthesize an array of potent small molecules. Although metabolism is fundamental to the fungal lifestyle, we still know little about how major evolutionary processes, such as gene duplication (GD) and horizontal gene transfer (HGT), have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility. We examined the synteny and evolutionary history of 247,202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes. We found that gene clustering varied greatly with respect to metabolic category and lineage; for example, clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism, whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism. The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically, GD, which was an order of magnitude more abundant than HGT, was most frequently observed in Agaricomycetes, whereas HGT was much more prevalent in Pezizomycotina. The effect of HGT in some Pezizomycotina was particularly strong; for example, we identified 111 HGT events associated with the 15 Aspergillus genomes, which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum. Finally, the impact of GD within a metabolic category was typically consistent across all fungal lineages, whereas the impact of HGT was variable. These results indicate that GD is the dominant process underlying fungal metabolic diversity, whereas HGT is episodic and acts in a category- or lineage-specific manner. Both processes have a greater impact on clustered genes, suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity.     

Prioritizing complex disease risk genes by integrating multiple data

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.     

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.     

Rational confederation of genes and diseases: NGS interpretation via GeneCards,MalaCards and VarElect

Background

A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses.

Results

VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources.

Conclusions

MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.

     

Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.