Dataset Information

PhenoHM: human-mouse comparative phenome-genome server.

ABSTRACT: PhenoHM is a human-mouse comparative phenome-genome server that facilitates cross-species identification of genes associated with orthologous phenotypes (http://phenome.cchmc.org; full open access, login not required). Combining and extrapolating the knowledge about the roles of individual gene functions in the determination of phenotype across multiple organisms improves our understanding of gene function in normal and perturbed states and offers the opportunity to complement biologically the rapidly expanding strategies in comparative genomics. The Mammalian Phenotype Ontology (MPO), a structured vocabulary of phenotype terms that leverages observations encompassing the consequences of mouse gene knockout studies, is a principal component of mouse phenotype knowledge source. On the other hand, the Unified Medical Language System (UMLS) is a composite collection of various human-centered biomedical terminologies. In the present study, we mapped terms reciprocally from the MPO to human disease concepts such as clinical findings from the UMLS and clinical phenotypes from the Online Mendelian Inheritance in Man knowledgebase. By cross-mapping mouse-human phenotype terms, extracting implicated genes and extrapolating phenotype-gene associations between species PhenoHM provides a resource that enables rapid identification of genes that trigger similar outcomes in human and mouse and facilitates identification of potentially novel disease causal genes. The PhenoHM server can be accessed freely at http://phenome.cchmc.org.

SUBMITTER: Sardana D

PROVIDER: S-EPMC2896149 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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PhenoHM: human-mouse comparative phenome-genome server.

Sardana Divya D Vasa Suresh S Vepachedu Nishanth N Chen Jing J Gudivada Ranga Chandra RC Aronow Bruce J BJ Jegga Anil G AG

Nucleic acids research 20100527 Web Server issue

PhenoHM is a human-mouse comparative phenome-genome server that facilitates cross-species identification of genes associated with orthologous phenotypes (http://phenome.cchmc.org; full open access, login not required). Combining and extrapolating the knowledge about the roles of individual gene functions in the determination of phenotype across multiple organisms improves our understanding of gene function in normal and perturbed states and offers the opportunity to complement biologically the r ...[more]

PMID: 20507906

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Project description:BACKGROUND:Because species-specific gene expression is driven by species-specific regulation, understanding the relationship between sequence and function of the regulatory regions in different species will help elucidate how differences among species arise. Despite active experimental and computational research, relationships among sequence, conservation, and function are still poorly understood. RESULTS:We compared transcription factor occupied segments (TFos) for 116 human and 35 mouse TFs in 546 human and 125 mouse cell types and tissues from the Human and the Mouse ENCODE projects. We based the map between human and mouse TFos on a one-to-one nucleotide cross-species mapper, bnMapper, that utilizes whole genome alignments (WGA). Our analysis shows that TFos are under evolutionary constraint, but a substantial portion (25.1% of mouse and 25.85% of human on average) of the TFos does not have a homologous sequence on the other species; this portion varies among cell types and TFs. Furthermore, 47.67% and 57.01% of the homologous TFos sequence shows binding activity on the other species for human and mouse respectively. However, 79.87% and 69.22% is repurposed such that it binds the same TF in different cells or different TFs in the same cells. Remarkably, within the set of repurposed TFos, the corresponding genome regions in the other species are preferred locations of novel TFos. These events suggest exaptation of some functional regulatory sequences into new function. Despite TFos repurposing, we did not find substantial changes in their predicted target genes, suggesting that CRMs buffer evolutionary events allowing little or no change in the TFos - target gene associations. Thus, the small portion of TFos with strictly conserved occupancy underestimates the degree of conservation of regulatory interactions. CONCLUSION:We mapped regulatory sequences from an extensive number of TFs and cell types between human and mouse using WGA. A comparative analysis of this correspondence unveiled the extent of the shared regulatory sequence across TFs and cell types under study. Importantly, a large part of the shared regulatory sequence is repurposed on the other species. This sequence, fueled by turnover events, provides a strong case for exaptation in regulatory elements.

Project description:BACKGROUNDUnderstanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases.METHODSWe used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.RESULTSBy linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings.CONCLUSIONSThis study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.

Dataset Information

PhenoHM: human-mouse comparative phenome-genome server.

Publications

PhenoHM: human-mouse comparative phenome-genome server.

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