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/     

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.     

Gene profiling of the erythro- and megakaryoblastic leukaemias induced by the Graffi murine retrovirus

Background

Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.

Methods

We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.

Results

We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.

Conclusions

We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.     

Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.     

DiseaseComps: a metric that discovers similar diseases based upon common toxicogenomic profiles at CTD

The Comparative Toxicogenomics Database (CTD) is a free resource that describes chemical-gene-disease networks to help understand the effects of environmental exposures on human health. The database contains more than 13,500 chemical-disease and 14,200 gene-disease interactions. In CTD, chemicals and genes are associated with a disease via two types of relationships: as a biomarker or molecular mechanism for the disease (M-type) or as a real or putative therapy for the disease (T-type). We leveraged these curated datasets to compute similarity indices that can be used to produce lists of comparable diseases ("DiseaseComps") based upon shared toxicogenomic profiles. This new metric now classifies diseases with common molecular characteristics, instead of the traditional approach of using histology or tissue of origin to define the disorder. In the dawning era of "personalized medicine", this feature provides a new way to view and describe diseases and will help develop testable hypotheses about chemical-gene-disease networks. AVAILABILITY: The database is available for free at http://ctd.mdibl.org/     

Connecting Mutations of the RNA Polymerase II C-Terminal Domain to Complex Phenotypic Changes Using Combined Gene Expression and Network Analyses

The C-terminal domain (CTD) of the largest subunit in DNA-dependent RNA polymerase II (RNAP II) is essential for mRNA synthesis and processing, through coordination of an astounding array of protein-protein interactions. Not surprisingly, CTD mutations can have complex, pleiotropic impacts on phenotype. For example, insertions of five alanine residues between CTD diheptads in yeast, which alter the CTD''s overall tandem structure and physically separate core functional units, dramatically reduce growth rate and result in abnormally large cells that accumulate increased DNA content over time. Patterns by which specific CTD-protein interactions are disrupted by changes in CTD structure, as well as how downstream metabolic pathways are impacted, are difficult to target for direct experimental analyses. In an effort to connect an altered CTD to complex but quantifiable phenotypic changes, we applied network analyses of genes that are differentially expressed in our five alanine CTD mutant, combined with established genetic interactions from the Saccharomyces cerevisiae Genome Database (SGD). We were able to identify candidate genetic pathways, and several key genes, that could explain how this change in CTD structure leads to the specific phenotypic changes observed. These hypothetical networks identify links between CTD-associated proteins and mitotic function, control of cell cycle checkpoint mechanisms, and expression of cell wall and membrane components. Such results can help to direct future genetic and biochemical investigations that tie together the complex impacts of the CTD on global cellular metabolism.     

Apoptosis pathways and osteoporosis: An approach to genomic analysis

Background

Osteoporosis is a disease of the bone system that causes a decrease in skeletal density and degrades skeletal tissue. Decreased bone quality, so that bones are easily broken, damaged and fractured, is an important public health problem. Previous studies have shown that the maintenance of adult bone mass is not only due to changes in bone marrow and bone cells. By regulating apoptosis, they change the lifespan of each individual. This study influences understanding of the function of apoptosis in the pathogenesis of osteoporosis and the importance of controlling the mechanisms of osteoporosis.

Methods

On the National Institute of Biotechnology Information website, Gene Expression Omnibus (GEO) microarray data and GSE551495 GEO profiles were collected. The gene set enrichment analysis tool was used to confirm the enrichment of genetic sets in relation to the gene set. The collection of C2 gene sets is compiled from the KEGG ( https://www.gsea-msigdb.org/gsea/msigdb/human/search.jsp and https://www.kegg.jp/kegg/ ) online database and REACTOME ( https://www.gsea-msigdb.org/gsea/msigdb/human/search.jsp and https://reactome.org/ ) pathway analysis. The Search Tool for the Retrieval of Interaction Genes (STRING) website was used to construct and select proteins and genes. The comparative toxicological genomic database (CTD) tools can be used to predict the relationship between apoptosis, osteoporosis-related genes and interactions between central genes and osteoporosis.

Results

These results generally expand our understanding of the path of apoptosis in osteoporosis. We have discovered genes CASP9, CASP8, CASP3, BAX and TP53 associated with osteoporosis. In activation of KEGG apoptosis and REACTOME, caspase activation through the extrinsic apoptotic signaling pathway is characterized by the identification of a subcollection of C2. Other STRINGs show the formation of protein networks and central gene selection, and CTD can accurately predict the relationship between these apoptosis pathways and central genes.

Conclusions

Our research has highlighted the importance of the osteoporosis pathway associated with osteoporosis apoptosis with several analytical approaches. These results have broadened our understanding of the pathways of osteoporosis apoptosis. It is particularly possible to predict the sensitivity and vulnerability to osteoporosis.     

Comparative radiation hybrid map of canine chromosome 1 incorporating SNP and indel polymorphisms

We report a comparative map of canine chromosome 1 (CFA1) incorporating single nucleotide polymorphisms (SNPs) and insertion/deletion (indel) polymorphisms, developed by using cross-species primers, radiation hybrid analysis, and pool-and-sequence identification of genetic variations. Fifty-five genes were chosen with relatively even spacing (approximately 3 Mb between the human homologues) and were mapped to CFA1, with 49 of these being new assignments. Evolutionary chromosomal breakpoints between CFA1 and the corresponding human chromosomes (HSA6, HSA9, HSA18, and HSA19) were located within 1 to 5 Mb based upon the human genome sequence. The process of identifying the evolutionary chromosomal breakpoints between CFA1 and the relevant human chromosomes led to an improvement in the comparative maps of CFA7, CFA12, and CFA29 through the mapping of 21 additional genes. A manual pool-and-sequence method was used to identify 79 SNPs, 9 small indels, 7 simple tandem repeats, and 2 polymorphic SINE insertions within the genes mapped. The cross-species primers can also be used in the manner described here to improve the comparative maps for other mammalian species.     

Crystal structure of the ternary complex of a NaV C-terminal domain, a fibroblast growth factor homologous factor, and calmodulin

Voltage-gated Na? (Na(V)) channels initiate neuronal action potentials. Na(V) channels are composed of a transmembrane domain responsible for voltage-dependent Na? conduction and a cytosolic C-terminal domain (CTD) that regulates channel function through interactions with many auxiliary proteins, including fibroblast growth factor homologous factors (FHFs) and calmodulin (CaM). Most ion channel structural studies have focused on mechanisms of permeation and voltage-dependent gating but less is known about how intracellular domains modulate channel function. Here we report the crystal structure of the ternary complex of a human Na(V) CTD, an FHF, and Ca2?-free CaM at 2.2 ?. Combined with functional experiments based on structural insights, we present a platform for understanding the roles of these auxiliary proteins in Na(V) channel regulation and the molecular basis of mutations that lead to neuronal and cardiac diseases. Furthermore, we identify a critical interaction that contributes to the specificity of individual Na(V) CTD isoforms for distinctive FHFs.     

Animals as sentinels: using comparative medicine to move beyond the laboratory

The comparative medicine approach, as applied to the study of laboratory animals for the betterment of human health, has resulted in important medical and scientific progress. Much of what is known about the human health risks of many toxic and infectious hazards present in the environment derives from experimental studies in animals and observational (epidemiological) studies of exposed human populations. Yet there is a third source of "in vivo" knowledge about host-environment interactions that may be underused and -explored: the study of diseases in naturally occurring animal populations that may signal potential human health threats. Just as canaries warned coal miners of the risk of toxic gases, other nonhuman animals, due to their greater susceptibility, environmental exposure, or shorter life span, may serve as "sentinels" for human environmental health hazards. Traditionally, communication between human and animal health professionals about cross-species sentinel events has been limited, but progress in comparative genomics, animal epidemiology, and bioinformatics can now provide an enhanced forum for such communication. The "One Health" concept involves moving toward a comparative clinical approach that considers "shared risks" between humans and animals and promotes greater cooperation and collaboration between human and animal health professionals to identify and reduce such risks. In doing so, it also creates new opportunities for the field of comparative medicine that can supplement traditional laboratory animal research.     

Segmentally variable genes: a new perspective on adaptation

Genomic sequence variation is the hallmark of life and is key to understanding diversity and adaptation among the numerous microorganisms on earth. Analysis of the sequenced microbial genomes suggests that genes are evolving at many different rates. We have attempted to derive a new classification of genes into three broad categories: lineage-specific genes that evolve rapidly and appear unique to individual species or strains; highly conserved genes that frequently perform housekeeping functions; and partially variable genes that contain highly variable regions, at least 70 amino acids long, interspersed among well-conserved regions. The latter we term segmentally variable genes (SVGs), and we suggest that they are especially interesting targets for biochemical studies. Among these genes are ones necessary to deal with the environment, including genes involved in host-pathogen interactions, defense mechanisms, and intracellular responses to internal and environmental changes. For the most part, the detailed function of these variable regions remains unknown. We propose that they are likely to perform important binding functions responsible for protein-protein, protein-nucleic acid, or protein-small molecule interactions. Discerning their function and identifying their binding partners may offer biologists new insights into the basic mechanisms of adaptation, context-dependent evolution, and the interaction between microbes and their environment.     

HPA-axis activity in alcoholism: examples for a gene-environment interaction.

Genetic and environmental influences are both known to be causal factors in the development and maintenance of substance abuse disorders. This review aims to focus on the contributions of genetic and environmental research to the understanding of alcoholism and how gene-environment interactions result in a variety of addiction phenotypes. Gene-environment interactions have been reviewed by focusing on one of the most relevant environmental risk factors for alcoholism, stress. This is examined in more detail by reviewing the functioning of the hypothalamic-pituitary-adrenal (HPA) axis and its genetic and molecular components in this disorder. Recent evidence from animal and human studies have shown that the effects of stress on alcohol drinking are mediated by core HPA axis genes and are associated with genetic variations in those genes. The findings of the studies discussed here suggest that the collaborations of neuroscience, psychobiology and molecular genetics provide a promising framework to elucidate the exact mechanisms of gene-environment interactions as seen to convene upon the HPA axis and effect phenotypes of addiction.