Non-Invasive Mapping of the Gastrointestinal Microbiota Identifies Children with Inflammatory Bowel Disease
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{"title"=>"Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease", "type"=>"journal", "authors"=>[{"first_name"=>"Eliseo", "last_name"=>"Papa", "scopus_author_id"=>"25655220800"}, {"first_name"=>"Michael", "last_name"=>"Docktor", "scopus_author_id"=>"52563301500"}, {"first_name"=>"Christopher", "last_name"=>"Smillie", "scopus_author_id"=>"16647101300"}, {"first_name"=>"Sarah", "last_name"=>"Weber", "scopus_author_id"=>"7401926685"}, {"first_name"=>"Sarah P.", "last_name"=>"Preheim", "scopus_author_id"=>"14623354200"}, {"first_name"=>"Dirk", "last_name"=>"Gevers", "scopus_author_id"=>"6603379365"}, {"first_name"=>"Georgia", "last_name"=>"Giannoukos", "scopus_author_id"=>"35725353300"}, {"first_name"=>"Dawn", "last_name"=>"Ciulla", "scopus_author_id"=>"25724108100"}, {"first_name"=>"Diana", "last_name"=>"Tabbaa", "scopus_author_id"=>"26027329500"}, {"first_name"=>"Jay", "last_name"=>"Ingram", "scopus_author_id"=>"7102464293"}, {"first_name"=>"David B.", "last_name"=>"Schauer", "scopus_author_id"=>"7005864970"}, {"first_name"=>"Doyle V.", "last_name"=>"Ward", "scopus_author_id"=>"55464681600"}, {"first_name"=>"Joshua R.", "last_name"=>"Korzenik", "scopus_author_id"=>"6603822375"}, {"first_name"=>"Ramnik J.", "last_name"=>"Xavier", "scopus_author_id"=>"7006636647"}, {"first_name"=>"Athos", "last_name"=>"Bousvaros", "scopus_author_id"=>"36660676300"}, {"first_name"=>"Eric J.", "last_name"=>"Alm", "scopus_author_id"=>"6603687290"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84863105536", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pmid"=>"22768065", "issn"=>"19326203", "sgr"=>"84863105536", "doi"=>"10.1371/journal.pone.0039242", "pui"=>"365133346"}, "id"=>"c233e8ea-8667-30da-a3ae-7069c9e5c3a3", "abstract"=>"Pediatric inflammatory bowel disease (IBD) is challenging to diagnose because of the non-specificity of symptoms; an unequivocal diagnosis can only be made using colonoscopy, which clinicians are reluctant to recommend for children. Diagnosis of pediatric IBD is therefore frequently delayed, leading to inappropriate treatment plans and poor outcomes. We investigated the use of 16S rRNA sequencing of fecal samples and new analytical methods to assess differences in the microbiota of children with IBD and other gastrointestinal disorders.", "link"=>"http://www.mendeley.com/research/noninvasive-mapping-gastrointestinal-microbiota-identifies-children-inflammatory-bowel-disease", "reader_count"=>191, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>49, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>8, "Other"=>13, "Student > Master"=>22, "Student > Bachelor"=>14, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>49, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>8, "Other"=>13, "Student > Master"=>22, "Student > Bachelor"=>14, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>92, "Business, Management and Accounting"=>1, "Chemistry"=>3, "Computer Science"=>7, "Engineering"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>11, "Nursing and Health Professions"=>1, "Mathematics"=>2, "Medicine and Dentistry"=>48, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Psychology"=>2, "Social Sciences"=>1, "Immunology and Microbiology"=>10}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>48}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>92}, "Computer Science"=>{"Computer Science"=>7}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>11}}, "reader_count_by_country"=>{"Canada"=>2, "Netherlands"=>3, "Belgium"=>1, "United States"=>12, "Norway"=>1, "Denmark"=>1, "United Kingdom"=>4, "Italy"=>2, "Mexico"=>1, "Malaysia"=>1, "Germany"=>3}, "group_count"=>12}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/615383"], "description"=>"<p>(A) SLiME applied to Frank et al. biopsy data set. The black line indicates performance obtained when features were generated by taxonomical binning of the original sequence data (AUC  = 0.73); dashed line shows performance when features were selected based on their importance in the pediatric case-control data set and then applied to the Frank et al. study (AUC  = 0.71). (B) ROC curve for SLiME classification of active IBD patients vs controls in the pediatric case-control data set. Two different threshold selections are highlighted: circle, for which SLiME has 80.3% sensitivity and 69.7% specificity; triangle, for which SLiME has 45.8% sensitivity and 92.4% specificity.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology", "ecology", "pediatrics and child health", "Gastroenterology and hepatology"], "article_id"=>285856, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_disease_classification_/285856", "title"=>"Accuracy of disease classification.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:37:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/616112"], "description"=>"<p>demographics of paediatric (training) cohort.</p>", "links"=>[], "tags"=>["paediatric"], "article_id"=>286592, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_demographics_of_paediatric_training_cohort_/286592", "title"=>"demographics of paediatric (training) cohort.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-06-29 01:49:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/616014"], "description"=>"<p>(A) Above, ROC curve for the classification of CD vs UC in samples where diagnosis of IBD is already established. Below, ROC curve for the classification of each disease class against all other classes. (B) Strength of association for the best features (q-value <0.05) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039242#pone.0039242-Liang1\" target=\"_blank\">[31]</a> which allow discrimination between CD and UC.</p>", "links"=>[], "tags"=>["cd"], "article_id"=>286493, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g006", "stats"=>{"downloads"=>5, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discrimination_of_CD_and_UC_/286493", "title"=>"Discrimination of CD and UC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:48:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/615924"], "description"=>"<p>Activity levels are considered simultaneously, employing the Kruskal-Wallis test. Grey bar indicate the q-value and thus the strength of the association between the features and the disease state. Color bars indicate the average percentage of reads for each disease activity level.</p>", "links"=>[], "tags"=>["discriminate"], "article_id"=>286402, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g005", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Best_features_to_discriminate_by_activity_levels_/286402", "title"=>"Best features to discriminate by activity levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:46:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/615481"], "description"=>"<p>Center panel is a compositional heatmap of the selected taxa for each of the samples in the pediatric case-control study. Left panel indicates the significance of the association of each taxa with disease state, as measured by the q-value. Right panel shows a measure of effect size (Cohen’s delta), highlighting in red those taxa which are significantly more prevalent in IBD samples. Bottom panels show relevant metadata for each sample, including disease activity as measured by PUCAI <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039242#pone.0039242-Tomas1\" target=\"_blank\">[32]</a> and PCDAI indices <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039242#pone.0039242-Knights1\" target=\"_blank\">[33]</a>.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology", "ecology", "pediatrics and child health", "Gastroenterology and hepatology"], "article_id"=>285949, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Taxa_significantly_associated_with_IBD_/285949", "title"=>"Taxa significantly associated with IBD.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:39:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/319923", "https://ndownloader.figshare.com/files/319986", "https://ndownloader.figshare.com/files/320048", "https://ndownloader.figshare.com/files/320116", "https://ndownloader.figshare.com/files/320179", "https://ndownloader.figshare.com/files/320245", "https://ndownloader.figshare.com/files/320295", "https://ndownloader.figshare.com/files/320682", "https://ndownloader.figshare.com/files/320711", "https://ndownloader.figshare.com/files/320735", "https://ndownloader.figshare.com/files/320772", "https://ndownloader.figshare.com/files/320811", "https://ndownloader.figshare.com/files/320855", "https://ndownloader.figshare.com/files/320881", "https://ndownloader.figshare.com/files/320912", "https://ndownloader.figshare.com/files/320941", "https://ndownloader.figshare.com/files/320959", "https://ndownloader.figshare.com/files/320982", "https://ndownloader.figshare.com/files/320996", "https://ndownloader.figshare.com/files/321008", "https://ndownloader.figshare.com/files/321020", "https://ndownloader.figshare.com/files/321028", "https://ndownloader.figshare.com/files/321039"], "description"=>"<div><h3>Background</h3><p>Pediatric inflammatory bowel disease (IBD) is challenging to diagnose because of the non-specificity of symptoms; an unequivocal diagnosis can only be made using colonoscopy, which clinicians are reluctant to recommend for children. Diagnosis of pediatric IBD is therefore frequently delayed, leading to inappropriate treatment plans and poor outcomes. We investigated the use of 16S rRNA sequencing of fecal samples and new analytical methods to assess differences in the microbiota of children with IBD and other gastrointestinal disorders.</p> <h3>Methodology/Principal Findings</h3><p>We applied synthetic learning in microbial ecology (SLiME) analysis to 16S sequencing data obtained from i) published surveys of microbiota diversity in IBD and ii) fecal samples from 91 children and young adults who were treated in the gastroenterology program of Children’s Hospital (Boston, USA). The developed method accurately distinguished control samples from those of patients with IBD; the area under the receiver-operating-characteristic curve (AUC) value was 0.83 (corresponding to 80.3% sensitivity and 69.7% specificity at a set threshold). The accuracy was maintained among data sets collected by different sampling and sequencing methods. The method identified taxa associated with disease states and distinguished patients with Crohn’s disease from those with ulcerative colitis with reasonable accuracy. The findings were validated using samples from an additional group of 68 patients; the validation test identified patients with IBD with an AUC value of 0.84 (e.g. 92% sensitivity, 58.5% specificity).</p> <h3>Conclusions/Significance</h3><p>Microbiome-based diagnostics can distinguish pediatric patients with IBD from patients with similar symptoms. Although this test can not replace endoscopy and histological examination as diagnostic tools, classification based on microbial diversity is an effective complementary technique for IBD detection in pediatric patients.</p> </div>", "links"=>[], "tags"=>["non-invasive", "gastrointestinal", "microbiota", "identifies", "children", "inflammatory", "bowel"], "article_id"=>123132, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0039242.s001", "https://dx.doi.org/10.1371/journal.pone.0039242.s002", "https://dx.doi.org/10.1371/journal.pone.0039242.s003", "https://dx.doi.org/10.1371/journal.pone.0039242.s004", "https://dx.doi.org/10.1371/journal.pone.0039242.s005", "https://dx.doi.org/10.1371/journal.pone.0039242.s006", "https://dx.doi.org/10.1371/journal.pone.0039242.s007", "https://dx.doi.org/10.1371/journal.pone.0039242.s008", "https://dx.doi.org/10.1371/journal.pone.0039242.s009", "https://dx.doi.org/10.1371/journal.pone.0039242.s010", "https://dx.doi.org/10.1371/journal.pone.0039242.s011", "https://dx.doi.org/10.1371/journal.pone.0039242.s012", "https://dx.doi.org/10.1371/journal.pone.0039242.s013", "https://dx.doi.org/10.1371/journal.pone.0039242.s014", "https://dx.doi.org/10.1371/journal.pone.0039242.s015", "https://dx.doi.org/10.1371/journal.pone.0039242.s016", "https://dx.doi.org/10.1371/journal.pone.0039242.s017", "https://dx.doi.org/10.1371/journal.pone.0039242.s018", "https://dx.doi.org/10.1371/journal.pone.0039242.s019", "https://dx.doi.org/10.1371/journal.pone.0039242.s020", "https://dx.doi.org/10.1371/journal.pone.0039242.s021", "https://dx.doi.org/10.1371/journal.pone.0039242.s022", "https://dx.doi.org/10.1371/journal.pone.0039242.s023"], "stats"=>{"downloads"=>61, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Non_Invasive_Mapping_of_the_Gastrointestinal_Microbiota_Identifies_Children_with_Inflammatory_Bowel_Disease/123132", "title"=>"Non-Invasive Mapping of the Gastrointestinal Microbiota Identifies Children with Inflammatory Bowel Disease", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-06-29 00:52:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/615790"], "description"=>"<p>Overall microbial diversity as measured by the Shannon Diversity Index. Activity was assessed on the basis of patient symptoms using PCDAI and PUCAI clinical indices.</p>", "links"=>[], "tags"=>["patients"], "article_id"=>286265, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g004", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stratification_of_patients_by_activity_levels_/286265", "title"=>"Stratification of patients by activity levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:44:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/615692"], "description"=>"<p>Mean difference in normalized abundance between IBD samples and control samples is plotted for each taxa. Positive values (x-axis) mean the taxa is more prevalent in IBD samples, while negative values are associated with taxa more abundant in control samples. Stool-based and tissue-based data set are differentially colored (dark blue and light blue respectively).</p>", "links"=>[], "tags"=>["pediatric", "frank", "et"], "article_id"=>286166, "categories"=>["Microbiology", "Chemistry", "Medicine", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Eliseo Papa", "Michael Docktor", "Christopher Smillie", "Sarah Weber", "Sarah P. Preheim", "Dirk Gevers", "Georgia Giannoukos", "Dawn Ciulla", "Diana Tabbaa", "Jay Ingram", "David B. Schauer", "Doyle V. Ward", "Joshua R. Korzenik", "Ramnik J. Xavier", "Athos Bousvaros", "Eric J. Alm"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039242.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Taxa_in_the_pediatric_data_set_stool_based_and_the_Frank_et_al_data_set_tissue_based_agree_in_their_relative_abundance_/286166", "title"=>"Taxa in the pediatric data set (stool-based) and the Frank et al. data set (tissue-based) agree in their relative abundance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-06-29 01:42:46"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"14", "full-text"=>"18", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"42", "full-text"=>"20", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"11", "supp-data"=>"28", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}

Relative Metric

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