Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes
Publication Date
April 24, 2009
Journal
PLOS Computational Biology
Authors
Curt Scharfe, Henry Horng Shing Lu, Jutta K. Neuenburg, Edward A. Allen, et al
Volume
5
Issue
4
Pages
e1000374
DOI
http://doi.org/10.1371/journal.pcbi.1000374
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000374
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19390613
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2668170
Europe PMC
http://europepmc.org/abstract/MED/19390613
Web of Science
000266214200033
Scopus
66249136027
Mendeley
http://www.mendeley.com/research/mapping-gene-associations-human-mitochondria-using-clinical-disease-phenotypes
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Mendeley | Further Information

{"title"=>"Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes", "type"=>"journal", "authors"=>[{"first_name"=>"Curt", "last_name"=>"Scharfe", "scopus_author_id"=>"6602245649"}, {"first_name"=>"Henry Horng Shing", "last_name"=>"Lu", "scopus_author_id"=>"34977061000"}, {"first_name"=>"Jutta K.", "last_name"=>"Neuenburg", "scopus_author_id"=>"6508098755"}, {"first_name"=>"Edward A.", "last_name"=>"Allen", "scopus_author_id"=>"26639131800"}, {"first_name"=>"Guan Cheng", "last_name"=>"Li", "scopus_author_id"=>"56163782900"}, {"first_name"=>"Thomas", "last_name"=>"Klopstock", "scopus_author_id"=>"6603746477"}, {"first_name"=>"Tina M.", "last_name"=>"Cowan", "scopus_author_id"=>"7004979430"}, {"first_name"=>"Gregory M.", "last_name"=>"Enns", "scopus_author_id"=>"6602187309"}, {"first_name"=>"Ronald W.", "last_name"=>"Davis", "scopus_author_id"=>"54943729100"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"354658167", "doi"=>"10.1371/journal.pcbi.1000374", "scopus"=>"2-s2.0-66249136027", "pmid"=>"19390613", "isbn"=>"10.1371/journal.pcbi.1000374", "sgr"=>"66249136027", "issn"=>"1553734X"}, "id"=>"8e4fcfee-ecb3-3520-a070-18c8a2e6469c", "abstract"=>"Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes.", "link"=>"http://www.mendeley.com/research/mapping-gene-associations-human-mitochondria-using-clinical-disease-phenotypes", "reader_count"=>88, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Researcher"=>32, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Researcher"=>32, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>8, "Mathematics"=>2, "Agricultural and Biological Sciences"=>53, "Medicine and Dentistry"=>13, "Psychology"=>2, "Social Sciences"=>1, "Computer Science"=>7, "Immunology and Microbiology"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>53}, "Computer Science"=>{"Computer Science"=>7}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Mathematics"=>{"Mathematics"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "United States"=>3, "China"=>2, "Japan"=>1, "Denmark"=>1, "United Kingdom"=>3, "Slovenia"=>1, "Switzerland"=>1, "Spain"=>2, "Russia"=>1}, "group_count"=>2}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/900180"], "description"=>"<p>The 144 features are selected from a total of 502 features (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000374#pcbi.1000374.s005\" target=\"_blank\"><b>Table S2</b></a>) and are caused by defects in 174 nuclear-encoded mitochondrial genes. Every feature is associated with the number of genes shown in parentheses. The hierarchical structure of features within the phenotype ontology was established using standardized MeSH descriptors (not shown). The fourteen CC in bold serve as headers for features within them. Unassigned features are grouped under ‘Miscellaneous’.</p>", "links"=>[], "tags"=>["mitochondrial"], "article_id"=>570624, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phenotypic_features_of_human_mitochondrial_diseases_/570624", "title"=>"Phenotypic features of human mitochondrial diseases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-24 00:10:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/446749", "https://ndownloader.figshare.com/files/446822", "https://ndownloader.figshare.com/files/446878", "https://ndownloader.figshare.com/files/446925", "https://ndownloader.figshare.com/files/446942", "https://ndownloader.figshare.com/files/446967", "https://ndownloader.figshare.com/files/447007", "https://ndownloader.figshare.com/files/447046", "https://ndownloader.figshare.com/files/447070", "https://ndownloader.figshare.com/files/447089", "https://ndownloader.figshare.com/files/447107"], "description"=>"<div><p>Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (<a href=\"http://www.mitophenome.org/\">http://www.mitophenome.org/</a>) supports the study of clinical diseases and associated genes.</p></div>", "links"=>[], "tags"=>["associations", "mitochondria", "phenotypes"], "article_id"=>148062, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000374.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s010", "https://dx.doi.org/10.1371/journal.pcbi.1000374.s011"], "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mapping_Gene_Associations_in_Human_Mitochondria_using_Clinical_Disease_Phenotypes/148062", "title"=>"Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-04-24 02:14:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/899851"], "description"=>"<p>(A) The inner circle shows the distribution of 502 phenotypic features among fourteen clinical categories (CC), plus a ‘Miscellaneous’ category containing unassigned features. The numbers show the fraction in % of all features in one CC compared to all 502 features. The outer circle shows the distribution of features related to CC within the 9,407 gene-feature pairs, with the frequency in % of all features in one CC. (B) Number of genes with features in a specific CC (y-axis) in correlation to the average number of CC-specific features caused by these genes (x-axis). 154 genes caused neurological features with an average of 20.2 neurological features per gene. Phenotypically, most mitochondrial gene defects are related to neurological, metabolic and gastrointestinal diseases.</p>", "links"=>[], "tags"=>["phenotypic", "mitochondrial"], "article_id"=>570292, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_clinical_phenotypic_features_in_mitochondrial_diseases_/570292", "title"=>"Distribution of clinical phenotypic features in mitochondrial diseases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-24 00:04:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/900224"], "description"=>"<p>For each mitochondrial disorder (col.1), we identified the mitochondrial candidate genes (col.7) among all gene loci (col.6) in the genomic linkage interval (col.4). The mitochondrial genes are further sorted into: (*) known disease genes with genes (in bold) causing phenotypic features (col.3) similar to features linked to the disease interval; and (<sup>#</sup>) predicted disease genes with genes (in bold) that interact to known disease genes causing features similar to the disease interval features. For completeness, the unlabeled mitochondrial genes are not known or predicted disease genes.</p>", "links"=>[], "tags"=>["genes", "mitochondrial"], "article_id"=>570671, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.t003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prioritizing_candidate_genes_for_mitochondrial_disorders_/570671", "title"=>"Prioritizing candidate genes for mitochondrial disorders.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-24 00:11:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/900279"], "description"=>"<p>The total 4,739 genes studied are separated into nine gene groups with the number of disease genes (DG) and candidate genes (CG) in each group in parenthesis (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000374#pcbi.1000374.s008\" target=\"_blank\"><b>Table S5</b></a> for individual gene data). DG products with intracellular localization to only mitochondria (115 genes) and mitochondria-and-other-localizations (47 genes) are subsets of the 162 mitochondrial DG. The human orthologs to mouse and yeast essential genes are subsets of all 4,739 genes. The five data columns show the average number of interactions (i) of each group to all genes in the human genome (all-genes); all known human disease genes (disease-genes); all human orthologs of essential mouse genes (mouse-essential); all human orthologs of essential yeast genes; and to all nuclear-encoded human mitochondrial genes (mito-genes). The numbers in parenthesis show the median number of interactions for each group and attribute, respectively. The findings indicated distinct properties in gene molecular interactions for DG and CG, as well as for mitochondria and non-mitochondria genes.</p>", "links"=>[], "tags"=>["interactions", "mitochondria", "non-mitochondria"], "article_id"=>570717, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.t002", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Molecular_interactions_of_mitochondria_and_non_mitochondria_genes_/570717", "title"=>"Molecular interactions of mitochondria and non-mitochondria genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-24 00:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/899957"], "description"=>"<p>(A) For each gene (black dots), the average QPA (y-axis) to all other genes within a functional module was calculated. Red lines represent the median (50th percentile) of all QPA averages within a module. Boxes indicate the 25th and 75th quartiles, with minimum and maximum data points as lines that extend from each end of the box. The grand mean of all modules (blue line) is the QPA average across all gene pairs of all nine modules, which was significantly higher than for pairs outside modules (orange line). (B) Module gene relationships are predicted through functional (LR) and phenotypic (QPA) associations showing the usefulness of phenotype similarity scores in disease gene network analysis. The edge colors are: Red – gene pairs with highest correlation of QPA and LR; blue – gene pairs with lower QPA-LR correlation; orange and light blue – gene pairs with QPA only at higher (> = 0.4) and lower confidence (<0.4), respectively (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000374#pcbi.1000374.s007\" target=\"_blank\">Table S4</a> for data). Diseases caused by the six genes labeled “*” are known to respond to vitamin treatments (riboflavin, thiamine, and pyridoxine). Abbreviations: AKDH, Alpha ketoglutarate dehydrogenase; BCKDH, Branched chain alpha keto acid dehydrogenase; GCC, Glycine cleavage system; PDH, Pyruvate dehydrogenase; RCC, Respiratory chain complex; TCA, Tricarboxylic acid cycle; UC, Urea cycle.</p>", "links"=>[], "tags"=>["genes", "mitochondrial", "complexes"], "article_id"=>570401, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phenotype_similarity_of_genes_related_to_mitochondrial_protein_complexes_and_pathways_/570401", "title"=>"Phenotype similarity of genes related to mitochondrial protein complexes and pathways.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-24 00:06:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/900101"], "description"=>"<p>For each gene group, we calculated the fraction of genes (y-axis) that interact with k other genes (x-axis). The gene group fractions were calculated for interactions to all human genes (A), all disease genes (B), and all mitochondrial genes (C). The color codes of the gene groups with their respective regression line slopes and p-values (in parenthesis) are shown in the table (D), with all results including correlation coefficients listed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000374#pcbi.1000374.s010\" target=\"_blank\">Table S7</a>. The fraction analysis of all gene interactions (A) showed a higher network connectivity for non-disease genes (CG) than for disease genes (DG), as indicated by the larger absolute value of the negative DG regression slope. In contrast, the connectivity to disease genes is relatively higher for DG than for CG suggesting a tendency for DG to interact with each other (B). Human orthologs to mouse essential genes had the highest network connectivity, while the mitochondrial gene groups had the highest tendency to interact to other mitochondrial genes (C).</p>", "links"=>[], "tags"=>["interactions", "mitochondrial"], "article_id"=>570546, "categories"=>["Neuroscience", "Mathematics", "Chemistry", "Genetics", "Medicine", "Cancer"], "users"=>["Curt Scharfe", "Henry Horng-Shing Lu", "Jutta K. Neuenburg", "Edward A. Allen", "Guan-Cheng Li", "Thomas Klopstock", "Tina M. Cowan", "Gregory M. Enns", "Ronald W. Davis"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000374.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_interactions_of_human_mitochondrial_genes_/570546", "title"=>"Functional interactions of human mitochondrial genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-24 00:09:06"}

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