Ranking Transitive Chemical-Disease Inferences Using Local Network Topology in the Comparative Toxicogenomics Database

Exposure to chemicals in the environment is believed to play a critical role in the etiology of many human diseases. To enhance understanding about environmental effects on human health, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) provides unique curated data that enable development of novel hypotheses about the relationships between chemicals and diseases. CTD biocurators read the literature and curate direct relationships between chemicals-genes, genes-diseases, and chemicals-diseases. These direct relationships are then computationally integrated to create additional inferred relationships; for example, a direct chemical-gene statement can be combined with a direct gene-disease statement to generate a chemical-disease inference (inferred via the shared gene). In CTD, the number of inferences has increased exponentially as the number of direct chemical, gene and disease interactions has grown. To help users navigate and prioritize these inferences for hypothesis development, we implemented a statistic to score and rank them based on the topology of the local network consisting of the chemical, disease and each of the genes used to make an inference. In this network, chemicals, diseases and genes are nodes connected by edges representing the curated interactions. Like other biological networks, node connectivity is an important consideration when evaluating the CTD network, as the connectivity of nodes follows the power-law distribution. Topological methods reduce the influence of highly connected nodes that are present in biological networks. We evaluated published methods that used local network topology to determine the reliability of protein–protein interactions derived from high-throughput assays. We developed a new metric that combines and weights two of these methods and uniquely takes into account the number of common neighbors and the connectivity of each entity involved. We present several CTD inferences as case studies to demonstrate the value of this metric and the biological relevance of the inferences.     

BacterialLectinDb: An integrated bacterial lectin database

Studies of various diversified bacterial lectins/ lectin data may serve as a tool with enormous promise to help biotechnologists/ geneticists in their innovative technology to explore a deeper understanding in proteomics/ genomics research for finding the molecular basis of infectious diseases and also to new approaches for their prevention and in development of new bacterial vaccines. Hence we developed a bacterial lectin database named 'BacterialLectinDb'. An organized database schema for BacterialLectinDb was designed to collate all the available information about all bacterial lectins as a central repository. The database was designed using HTML, XML. AVAILABILITY: The database is available for free at http://www.research-bioinformatics.in.     

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network‐based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time‐points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue‐generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases‐specific, stage‐specific, severity‐specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.     

A survey of web resources and tools for the study of TCM network pharmacology

Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM. Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis. Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

A survey on biomarker identification based on molecular networks

Background: Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way. Results: In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available. Conclusions: The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of biomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.     

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

A system architecture for genomic data analysis

MOTIVATION: Most of diseases are caused by a set of gene defects, which occur in a complex association. The association scheme of expressed genes can be modelled by genetic networks. Genetic networks are efficiently facilities to understand the dynamic of pathogenic processes by modelling molecular reality of cell conditions. In this sense a genetic network consists of first, a set of genes of specified cells, tissues or species and second, causal relations between these genes determining the functional condition of the biological system, i. e. under disease. A relation between two genes will exist if they both are directly or indirectly associated with disease [8]. Our goal is to characterize diseases (especially autoimmune diseases like chronic pancreatitis CP, multiple sclerosis MS, rheumatoid arthritis RA) by genetic networks generated by a computer system. We want to introduce this practice as a bioinformatic approach for finding targets.     

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background  

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.     

结缔组织病伴发疾病分析

目的：对结缔组织病与其常见伴发疾病进行研究分析,探讨肿瘤等疾病与与结缔组织病的相关性。方法：收集确诊有结缔组织病患者共333例,对其临床资料并进行回顾性统计分析。结果：本文中333例结缔组织病中发生肺间质病变79例（23.7%）,发生肿瘤8例（2.4%）,出现肾损害有124例（37.2%）。结论：间质性肺病、肿瘤、肾损害在结缔组织病患者中占一定的比例,其机制可能与结缔组织病本身的病理生理改变、外源性因素等共同作用所致。     

Convergence and divergence of genetic and modular networks between diabetes and breast cancer

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi‐dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM‐related genes, 386 type 2 diabetes (T2DM) genes and 873 BC‐related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non‐overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long‐term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.     

Antagomirbase- a putative antagomir database

The accurate prediction of a comprehensive set of messenger putative antagomirs against microRNAs (miRNAs) remains an open problem. In particular, a set of putative antagomirs against human miRNA is predicted in this current version of database. We have developed Antagomir database, based on putative antagomirs-miRNA heterodimers. In this work, the human miRNA dataset was used as template to design putative antagomirs, using GC content and secondary structures as parameters. The algorithm used predicted the free energy of unbound antagomirs. Although in its infancy the development of antagomirs, that can target cell specific genes or families of genes, may pave the way forward for the generation of a new class of therapeutics, to treat complex inflammatory diseases. Future versions need to incorporate further sequences from other mammalian homologues for designing of antagomirs for aid in research. AVAILABILITY: The database is available for free at http://bioinfopresidencycollegekolkata.edu.in/antagomirs.html.     

GeneComps and ChemComps: a new CTD metric to identify genes and chemicals with shared toxicogenomic profiles

The Comparative Toxicogenomics Database is a public resource that promotes understanding about the effects of environmental chemicals on human health. Currently, CTD describes over 184,000 molecular interactions for more than 5,100 chemicals and 16,300 genes/proteins. We have leveraged this dataset of chemical-gene relationships to compute similarity indices following the statistical method of the Jaccard index. These scores are used to produce lists of comparable genes (“GeneComps”) or chemicals (“ChemComps”) based on shared toxicogenomic profiles. GeneComps and ChemComps are now provided for every curated gene and chemical in CTD. ChemComps are particularly significant because they provide a way to group chemicals based upon their biological effects, instead of their physical or structural properties. These metrics provide a novel way to view and classify genes and chemicals and will help advance testable hypotheses about environmental chemical-genedisease networks.

Availability

CTD is freely available at http://ctd.mdibl.org/     

Compromise in competition between free energy and binding effect of intrinsically disordered protein p53 C-terminal domain

The C-terminal domain (CTD) of tumour suppressor p53 is an intrinsically disordered protein which has been shown to be able to bind multiple partner proteins and exercise diverse physiological functions in the cell. In this study, we performed molecular dynamics simulations on the isolated p53 CTD, as well as three regulatory binding complexes to investigate the conformational ensemble of isolated p53 CTD and its dynamic structures when different binding partner present. The results demonstrate that the isolated p53 CTD resembles a molten globule rather than extended structure. It mainly adopts random coil conformations with some tendency to form helical structures, which is consistent with experimental observations. For isolated p53 CTD, the dynamics is exclusively dominated by the intrinsic free energy and the p53 CTD could not folded spontaneously to each binding competent state which is located in high free energy region. However, when the binding partners present, the dynamics of p53 CTD are dominated by two mechanisms, the p53 CTD tending to adopt the structure with minimum free energy as isolate existed and the binding energy from partner protein tending to minimum. Each of them has an extreme tendency and corresponds to a possible characteristic state, the random coil state and each binding competent state. The compromise in competition between these two mechanisms results in alternate realisation of different characteristic states, while the relative strength of each mechanism determines the sampling frequency of each characteristic state.     

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases

Background

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. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.

Principal Findings

We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental 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. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.

Conclusions

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.

Availability

The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.     

CytReD: A database collecting human cytokinome information

The cytokines/related receptors system represents a complex regulatory network that is involved in those chronic inflammatory processes which lead to many diseases as cancers. We developed a Cytokine Receptor Database (CytReD) to collect information on cytokine receptors related to their biological activity, gene data, protein structures and diseases in which these and their ligands are implicated. This large set of information may be used by researchers as well as by physicians or clinicians to identify which cytokines, reported in the literature, are important in a given disease and, therefore, useful for purposes of diagnosis or prognostic. AVAILABILITY: The database is available for free at http://www.cro-m.eu/CytReD/