Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease

BackgroundCrohn’s disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients’ ongoing treatments. Additionally, most analyses of CD patients’ microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.ResultsWe sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients’ response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.ConclusionsWe demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers’ microbiome in the future.The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)—CAF/08/01.

[1]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[2]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[3]  W. Engst,et al.  Dextran Sodium Sulfate-Induced Inflammation Alters the Expression of Proteins by Intestinal Escherichia coli Strains in a Gnotobiotic Mouse Model , 2011, Applied and Environmental Microbiology.

[4]  David C Wilson,et al.  Pediatric modification of the Montreal classification for inflammatory bowel disease: The Paris classification , 2011, Inflammatory bowel diseases.

[5]  David C. Wilson,et al.  Rising incidence of pediatric inflammatory bowel disease in Scotland* , 2012, Inflammatory bowel diseases.

[6]  J. Doré,et al.  Alterations of the dominant faecal bacterial groups in patients with Crohn's disease of the colon , 2003, Gut.

[7]  M. Neovius,et al.  Patients with ulcerative colitis miss more days of work than the general population, even following colectomy. , 2013, Gastroenterology.

[8]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[9]  Eoin L. Brodie,et al.  Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB , 2006, Applied and Environmental Microbiology.

[10]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[11]  W. Petritsch,et al.  The taxonomic composition of the donor intestinal microbiota is a major factor influencing the efficacy of faecal microbiota transplantation in therapy refractory ulcerative colitis , 2017, Alimentary pharmacology & therapeutics.

[12]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[13]  Tariq Ahmad,et al.  Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn's disease , 2017, Nature Genetics.

[14]  Hélène Touzet,et al.  SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data , 2012, Bioinform..

[15]  A. Day,et al.  Effect of Exclusive Enteral Nutrition on the Microbiota of Children With Newly Diagnosed Crohn's Disease , 2015, Clinical and Translational Gastroenterology.

[16]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[17]  R. Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[18]  James Butcher,et al.  Altered intestinal microbiota–host mitochondria crosstalk in new onset Crohn's disease , 2016, Nature Communications.

[19]  Richard Hansen,et al.  A Comprehensive Evaluation of Colonic Mucosal Isolates of Sutterella wadsworthensis from Inflammatory Bowel Disease , 2011, PloS one.

[20]  Javier Palarea-Albaladejo,et al.  zCompositions — R package for multivariate imputation of left-censored data under a compositional approach , 2015 .

[21]  H. Sokol,et al.  The intestinal microbiota in inflammatory bowel diseases: time to connect with the host , 2010, Current opinion in gastroenterology.

[22]  Ayhan Hilmi Çekin,et al.  A microbial signature for Crohn's disease. , 2017, The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology.

[23]  Michael Krawczak,et al.  Increased Tryptophan Metabolism Is Associated With Activity of Inflammatory Bowel Diseases. , 2017, Gastroenterology.

[24]  Elhanan Borenstein,et al.  MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome , 2014, bioRxiv.

[25]  Hisao Tamaki.  Structural robustness of the butterfly and related networks against random faults , 1993 .

[26]  Judy H. Cho,et al.  The genetics and immunopathogenesis of inflammatory bowel disease , 2008, Nature Reviews Immunology.

[27]  U. Turunen,et al.  Family and twin studies in inflammatory bowel disease. , 2006, World journal of gastroenterology.

[28]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[29]  Subrata Ghosh,et al.  Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. , 2012, Gastroenterology.

[30]  Richard Hansen,et al.  Microbiota of De-Novo Pediatric IBD: Increased Faecalibacterium Prausnitzii and Reduced Bacterial Diversity in Crohn's But Not in Ulcerative Colitis , 2012, The American Journal of Gastroenterology.

[31]  Jesse R. Zaneveld,et al.  Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences , 2013, Nature Biotechnology.

[32]  Judy H. Cho,et al.  Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations , 2015, Nature Genetics.

[33]  A. Stadnyk,et al.  The Gut Microbiome of Pediatric Crohn's Disease Patients Differs from Healthy Controls in Genes That Can Influence the Balance Between a Healthy and Dysregulated Immune Response , 2016, Inflammatory bowel diseases.

[34]  M. Ciorba,et al.  Serum Analysis of Tryptophan Catabolism Pathway: Correlation With Crohn's Disease Activity , 2012, Inflammatory bowel diseases.

[35]  J. Hyams,et al.  Fecal Short‐Chain Fatty Acids in Children with Inflammatory Bowel Disease , 1994, Journal of pediatric gastroenterology and nutrition.

[36]  Vera Pawlowsky-Glahn,et al.  It's all relative: analyzing microbiome data as compositions. , 2016, Annals of epidemiology.

[37]  Timothy L. Tickle,et al.  Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment , 2012, Genome Biology.

[38]  A. Stadnyk,et al.  Early Changes in Microbial Community Structure Are Associated with Sustained Remission After Nutritional Treatment of Pediatric Crohn's Disease , 2016, Inflammatory bowel diseases.

[39]  J. Doré,et al.  Structural robustness of the gut mucosal microbiota is associated with Crohn's disease remission after surgery , 2015, Gut.

[40]  R. DeSalle,et al.  Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing , 2017, Scientific Reports.

[41]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[42]  G. Douglas,et al.  Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research , 2017, mSystems.

[43]  J. Coffey,et al.  Desulfovibrio Bacterial Species Are Increased in Ulcerative Colitis , 2010, Diseases of the colon and rectum.

[44]  J. Barrett,et al.  Using Genetic Prediction from Known Complex Disease Loci to Guide the Design of Next-Generation Sequencing Experiments , 2013, PloS one.

[45]  Bernard Henrissat,et al.  Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome , 2012, PLoS Comput. Biol..

[46]  N. Kaakoush.  Insights into the Role of Erysipelotrichaceae in the Human Host , 2015, Front. Cell. Infect. Microbiol..

[47]  Tariq Ahmad,et al.  Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study , 2016, The Lancet.

[48]  Judy H Cho,et al.  Genetics of Inflammatory Bowel Diseases. , 2015, Gastroenterology.

[49]  K. Schmid,et al.  Transepithelial transport processes at the intestinal mucosa in inflammatory bowel disease , 1999, International Journal of Colorectal Disease.

[50]  Gonçalo R. Abecasis,et al.  The variant call format and VCFtools , 2011, Bioinform..

[51]  A. Paterson,et al.  Association of host genome with intestinal microbial composition in a large healthy cohort , 2016, Nature Genetics.

[52]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[53]  Se Jin Song,et al.  The treatment-naive microbiome in new-onset Crohn's disease. , 2014, Cell host & microbe.

[54]  O. Delaneau,et al.  A linear complexity phasing method for thousands of genomes , 2011, Nature Methods.

[55]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[56]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[57]  Olli Simell,et al.  Gut Microbiome Metagenomics Analysis Suggests a Functional Model for the Development of Autoimmunity for Type 1 Diabetes , 2011, PloS one.

[58]  Rob Knight,et al.  UCHIME improves sensitivity and speed of chimera detection , 2011, Bioinform..

[59]  J. Evans,et al.  Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. , 2010, Ecology.

[60]  C. Huttenhower,et al.  Metagenomic microbial community profiling using unique clade-specific marker genes , 2012, Nature Methods.

[61]  A. Kane,et al.  Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. , 2015, The Journal of infectious diseases.

[62]  Ashwin N. Ananthakrishnan,et al.  Epidemiology and risk factors for IBD , 2015, Nature Reviews Gastroenterology &Hepatology.

[63]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[64]  E. El-Omar,et al.  The Microaerophilic Microbiota of De-Novo Paediatric Inflammatory Bowel Disease: The BISCUIT Study , 2013, PloS one.

[65]  Jiajie Zhang,et al.  PEAR: a fast and accurate Illumina Paired-End reAd mergeR , 2013, Bioinform..

[66]  Chris McSweeney,et al.  Association between specific mucosa‐associated microbiota in Crohn's disease at the time of resection and subsequent disease recurrence: A pilot study , 2015, Journal of gastroenterology and hepatology.

[67]  A. Abdulamir,et al.  The Impact of the Level of the Intestinal Short Chain Fatty Acids in Inflammatory Bowel Disease Patients Versus Healthy Subjects , 2010, The open biochemistry journal.

[68]  J. Doré,et al.  Highlighting new phylogenetic specificities of Crohn's disease microbiota , 2011, Inflammatory bowel diseases.

[69]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[70]  C. Fiocchi,et al.  Immunopathogenesis of IBD: current state of the art , 2016, Nature Reviews Gastroenterology &Hepatology.

[71]  C. Bernstein,et al.  Association Between the Use of Antibiotics in the First Year of Life and Pediatric Inflammatory Bowel Disease , 2010, The American Journal of Gastroenterology.

[72]  David C. Wilson,et al.  Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease , 2012, Nature.