A disease-drug-phenotype matrix inferred by walking on a functional domain network

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder

Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.

Identification of key genes in colorectal cancer using random walk with restart

As the most common type of cancer and the second leading cause of cancer-associated mortality, colorectal cancer (CRC) has received increasing attention. The aim of the present study was to investigate the mechanisms of CRC by analyzing the microarray dataset, GSE32323. The GSE32323 dataset was downloaded from the Gene Expression Omnibus, and included 17 pairs of matched cancer and normal colorectal tissue samples. The differentially expressed genes (DEGs) were screened using the Linear Models for Microarray Data package and a search of CRC genes, also denoted as seed genes, was performed using the Online Mendelian Inheritance in Man database. Subsequently, the protein‑protein interaction (PPI) network was downloaded from the Search Tool for the Retrieval of Interacting Genes database and the sub‑network (CRC.PPI) of the DEGs and seed genes were obtained. In addition, the top 50 nodes with highest affinity scores in the CRC.PPI were identified using random walk with restart analysis. The potential functions of the DEGs included in the top 50 nodes were analyzed using the Database for Annotation, Visualization and Integrated Discovery online tool. Using the Drug Gene Interaction database, drug‑gene interaction analysis was performed to identify antineoplastic drug interacts with genes. A total of 1,640 DEGs between the CRC and normal samples were screened. The obtained seed genes included cyclin D1 (CCND1) and aurora kinase A (AURKA). The enriched functions for the 31 DEGs in the PPI network of the top 50 nodes were predominantly associated with cell cycle. The DEGs may function in CRC by interacting with other genes in the PPI network of the top 50 nodes, for example, DEP domain‑containing MTOR‑interacting protein (DEPTOR)‑CCND1, AURKA‑breast carcinoma amplified sequence‑1 (BCAS1), CCND1‑BCAS1, CCND1‑neural precursor cell expressed developmentally downregulated 9 (NEDD9) and CCND1‑mitogen‑activated protein kinase kinase 2 (MAP2K2). Only three DEGs (CCND1, AURKA and DEPTOR) had interactions with their corresponding antineoplastic drugs. Taken together, DEPTOR, AURKA, CCND1, BCAS1, NEDD9 and MAP2K2 may act in CRC.

A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary.

Function-selective domain architecture plasticity potentials in eukaryotic genome evolution

To help evaluate how protein function impacts on genome evolution, we introduce a new concept of ‘architecture plasticity potential’ – the capacity to form distinct domain architectures – both for an individual domain, or more generally for a set of domains grouped by shared function. We devise a scoring metric to measure the plasticity potential for these domain sets, and evaluate how function has changed over time for different species. Applying this metric to a phylogenetic tree of eukaryotic genomes, we find that the involvement of each function is not random but highly selective. For certain lineages there is strong bias for evolution to involve domains related to certain functions. In general eukaryotic genomes, particularly animals, expand complex functional activities such as signalling and regulation, but at the cost of reducing metabolic processes. We also observe differential evolution of transcriptional regulation and a unique evolutionary role of channel regulators; crucially this is only observable in terms of the architecture plasticity potential. Our findings provide a new layer of information to understand the significance of function in eukaryotic genome evolution. A web search tool, available at http://supfam.org/Pevo, offers a wide spectrum of options for exploring functional importance in eukaryotic genome evolution.

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A new concept to measure domain architecture plasticity potential in a genome.

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We reveal the function-selective role in eukaryotic genome evolution.

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Eukaryotic genomes expand signalling and regulations but reduce metabolism.

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We observe differential evolution between trans- and cis-acting regulations.

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We observe a unique role of channel regulators in separating eukaryotic kingdoms.

K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores

Background

Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.

Results

The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains.

Conclusion

Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.

Electronic supplementary material

The online version of this article (doi:10.1186/s13336-015-0016-6) contains supplementary material, which is available to authorized users.

dcGOR: an R package for analysing ontologies and protein domain annotations

I introduce an open-source R package 'dcGOR' to provide the bioinformatics community with the ease to analyse ontologies and protein domain annotations, particularly those in the dcGO database. The dcGO is a comprehensive resource for protein domain annotations using a panel of ontologies including Gene Ontology. Although increasing in popularity, this database needs statistical and graphical support to meet its full potential. Moreover, there are no bioinformatics tools specifically designed for domain ontology analysis. As an add-on package built in the R software environment, dcGOR offers a basic infrastructure with great flexibility and functionality. It implements new data structure to represent domains, ontologies, annotations, and all analytical outputs as well. For each ontology, it provides various mining facilities, including: (i) domain-based enrichment analysis and visualisation; (ii) construction of a domain (semantic similarity) network according to ontology annotations; and (iii) significance analysis for estimating a contact (statistical significance) network. To reduce runtime, most analyses support high-performance parallel computing. Taking as inputs a list of protein domains of interest, the package is able to easily carry out in-depth analyses in terms of functional, phenotypic and diseased relevance, and network-level understanding. More importantly, dcGOR is designed to allow users to import and analyse their own ontologies and annotations on domains (taken from SCOP, Pfam and InterPro) and RNAs (from Rfam) as well. The package is freely available at CRAN for easy installation, and also at GitHub for version control. The dedicated website with reproducible demos can be found at http://supfam.org/dcGOR.

Comparative analysis of a novel disease phenotype network based on clinical manifestations

Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN.

The 'dnet' approach promotes emerging research on cancer patient survival

We present the 'dnet' package and apply it to the 'TCGA' mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer 'survivalness', with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The 'dnet' R package is available at http://cran.r-project.org/package=dnet.