Abstract

Inflammatory bowel diseases (IBD) can be broadly divided into Crohn’s disease (CD) and ulcerative colitis (UC) from their clinical phenotypes. Over 150 host susceptibility genes have been described, although most overlap between CD, UC and their subtypes, and they do not adequately account for the overall incidence or the highly variable severity of disease. Replicating key findings between two long-term IBD cohorts, we have defined distinct networks of taxa associations within intestinal biopsies of CD and UC patients. Disturbances in an association network containing taxa of the Lachnospiraceae and Ruminococcaceae families, typically producing short chain fatty acids, characterize frequently relapsing disease and poor responses to treatment with anti-TNF-α therapeutic antibodies. Alterations of taxa within this network also characterize risk of later disease recurrence of patients in remission after the active inflamed segment of CD has been surgically removed.

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Data availability

All sequencing datasets from the current study have been deposited in a figshare repository and are publicly available. The Cohort 1 fasta file withthe mapping file are available at https://figshare.com/s/e9f2cffd0f0328ca5811 (https://doi.org/10.6084/m9.figshare.7335068) and the Cohort 2 fasta file with the mapping file are available at https://figshare.com/s/bbdd5dfb01e29484efa1 (https://doi.org/10.6084/m9.figshare.7335071). Associated codes for the analysis using R packages and QIIME can be found in these depositories. The genome-scale metabolic model script and dataset are available at https://figshare.com/s/a34f96698ca6fcd36ac2.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We thank all patients and the members of the Swiss IBD cohort and Bern cohort for their commitment. We also thank the staff of the University Hospital of Bern, Clinic of Visceral Medicine and Surgery, and the Bern City Hospitals led by F. Seibold and R. Tutuian for obtaining samples in Cohort 2. This research was supported by Systems X (GutX) to A.J.M. and J.S., and the Swiss IBD cohort (grant no. 33CS30-148422) to G.R., A.J.M and C.M. The founding institutions had no role in the study design, analysis or interpretation of the results. We thank G. Rahnavard and C. Huttenhower (Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA) for their help in using the HAllA pipeline. We also thank J. Harrell Rieder, A. Suter, S. Brand, C. Mooser, W. Kwong Chung and J. Hugenschmidt for helping B.Y. during the process of sample preparation. We also thank G. Weingart (Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA) for his enormous help in the optimization of MaAsLin running on the MacOS platform using R.

Author information

Author notes

  1. These authors contributed equally to this work: Bahtiyar Yilmaz, Pascal Juillerat

Affiliations

  1. Maurice Müller Laboratories, Department for Biomedical Research, University of Bern, Bern, Switzerland

    • Bahtiyar Yilmaz
    • , Pascal Juillerat
    • , Markus Geuking
    • , Reiner Wiest
    •  & Andrew J. Macpherson
  2. Department of Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland

    • Bahtiyar Yilmaz
    • , Pascal Juillerat
    • , Francisco Damian Bravo
    • , Lukas Brügger
    • , Ove Carstens
    • , Ulrike Graf Bigler
    • , Benjamin Heimgartner
    • , Pascal Juillerat
    • , Andrew J. Macpherson
    • , Monica Rusticeanu
    • , Sybille Schmid (-Uebelhart)
    • , Bruno Strebel
    • , Aurora Tatu
    • , Radu Tutuian
    • , Reiner Wiest
    • , Reiner Wiest
    •  & Andrew J. Macpherson
  3. Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland

    • Ove Øyås
    • , Charlotte Ramon
    •  & Jörg Stelling
  4. Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland

    • Yannick Franc
    • , Nicolas Fournier
    • , Valerie E. H. Pittet
    • , Bernard Burnand
    • , Mara Egger
    • , Nicolas Fournier
    • , Yannick Franc
    • , Delphine Golay
    • , Astrid Marot
    • , Leilla Musso
    • , Valérie Pittet
    • , Jean-Benoît Rossel
    • , Vivianne Seematter
    • , Joachim Sommer
    •  & Rachel Vulliamy
  5. Gastroenterology La Source-Beaulieu, Lausanne, Switzerland

    • Pierre Michetti
    • , Michel H. Maillard
    • , Céline Keller
    • , Michel H. Maillard
    • , Pierre Michetti
    • , Andreas Nydegger
    •  & Alain Schoepfer
  6. Service of Gastroenterology and Hepatology, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

    • Pierre Michetti
    • , Michel H. Maillard
    • , Eva Archanioti
    • , Jessica Ezri
    • , Montserrat Fraga
    • , Michel H. Maillard
    • , Pierre Michetti
    • , Andreas Nydegger
    •  & Alain Schoepfer
  7. Division of Experimental Pathology, Institute of Pathology, University of Bern, Bern, Switzerland

    • Christoph Mueller
    •  & Christoph Müller
  8. Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    • Gerhard Rogler
    • , Luc Biedermann
    • , Mirjam Blattmann
    • , Sabine Burk
    • , Barbara Dora
    • , Michael Fried
    • , Benjamin Misselwitz
    • , Beat Müllhaupt
    • , Nicole Obialo
    • , Daniel Pohl
    • , Nadia Raschle
    • , Gerhard Rogler
    • , Michael Scharl
    • , Stephan Vavricka
    • , Roland Von Känel
    •  & Jonas Zeitz
  9. Clinique de Montchoisi, Lausanne, Switzerland

    • Karim Abdelrahman
  10. Kantonsspital St-Gallen, St-Gallen, Switzerland

    • Gentiana Ademi
    • , Jan Borovicka
    • , Stephan Brand
    • , Remus Frei
    • , Johannes Haarer
    • , Christina Knellwolf (-Grieger)
    • , Claudia Krieger(-Grübel)
    • , Patrizia Künzler
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    • , Mikael Sawatzki
    • , Martin Schelling
    • , Gian-Marco Semadeni
    • , Michael Sulz
    •  & Dorothee Zimmermann
  11. Kantonsspital Luzern, Luzern, Switzerland

    • Patrick Aepli
    • , Dominique H. Criblez
    • , Cyrill Hess
    • , Jean-Pierre Richterich
    • , Johannes Spalinger
    • , Dominic Staudenmann
    • , Andreas Stulz
    •  & Stefanie Wöhrle
  12. GI private practice, Waldkirch, St. Gallen, Switzerland

    • Amman Thomas
  13. Kantonspital Aarau, Klinik für Kinder und Jugendliche, Aarau, Switzerland

    • Claudia Anderegg
    • , Henrik Köhler
    •  & Rachel Kusche
  14. Hôpital Riviera—Site du Samaritain, Vevey, Vaud, Switzerland

    • Anca-Teodora Antonino
  15. GI private practice, Geneva, Switzerland

    • Eviano Arrigoni
    • , José M. Bengoa
    • , Sophie Cunningham
    • , Philippe de Saussure
    •  & Laurent Girard
  16. Department of Gastroenterology and Hepatology, Basel University Hospital, Basel, Switzerland

    • Diana Bakker de Jong
    • , Polat Bastürk
    • , Simon Brunner
    • , Lukas Degen
    • , Petr Hruz
    • , Carolina Khalid-de Bakker
    •  & Jan Niess
  17. Gastroenterologische Praxis, Bern, Switzerland

    • Bruno Balsiger
    • , Janine Haldemann
    • , Gaby Saner
    •  & Frank Seibold
  18. Department Gastroenterology and Hepatology, Stadtspital Triemli, Zurich, Switzerland

    • Peter Bauerfeind
  19. Department of Pediatric, Geneva University Hospital, Geneva, Switzerland

    • Andrea Becocci
    •  & Dominique Belli
  20. Gastroenterologie am Rosenberg, St-Gallen, Switzerland

    • Janek Binek
    •  & Peter Hengstler
  21. Spital Bülach, Bülach, Zurich, Switzerland

    • Stephan Boehm
  22. Department of Biomedicine, University of Basel, Basel, Switzerland

    • Tujana Boldanova
  23. University Children’s Hospital, Zurich, Switzerland

    • Christian P. Braegger
    • , Patrick Bühr
    • , Rebekka Koller
    • , Vanessa Rueger
    •  & Arne Senning
  24. Department Gastroenterology, Kantonsspital Liestal, Liestal, Switzerland

    • Emanuel Burri
  25. GI private practice, Yverdon-les-Bains, Switzerland

    • Sophie Buyse
  26. Hôpital Neuchâtelois, La Chaux-de-fonds, Neuchâtel, Switzerland

    • Dahlia-Thao Cao
  27. Department Gastroenterology and Hepatology, Geneva University Hospital, Geneva, Switzerland

    • Fabrizia D’Angelo
  28. GI private practice, Lausanne, Switzerland

    • Joakim Delarive
  29. Clinique Cecil, Lausanne, Switzerland

    • Christopher Doerig
    •  & Roxane Hessler
  30. Schulthess Clinic, Zurich, Switzerland

    • Susan Drerup
  31. GI private practice, La Chaux-de-Fonds, Switzerland

    • Ali El-Wafa
  32. Gastropraxis Luzern, Luzern, Switzerland

    • Matthias Engelmann
    •  & Claudia Hirschi
  33. Centre de Gastroentérologie Beaulieu SA, Geneva, Switzerland

    • Christian Felley
    • , Diana Ollo
    • , Cassandra Oropesa
    • , Laetitia Marie Petit
    • , Sophie Restellini
    • , Frederic Ris
    •  & Dominique Schluckebier
  34. Medical Center Sihlcity, Zurich, Switzerland

    • Markus Fliegner
  35. Gastroenterologie Bethanien, Zurich, Switzerland

    • Pascal Frei
    •  & Beat Helbling
  36. Hospital of the Canton of Jura, Porrentruy And Delémont, Jura, Switzerland

    • Florian Froehlich
  37. Universitäts-Kinderspital beider Basel (UKBB), Basel, Switzerland

    • Raoul Ivano Furlano
  38. Clinique des Grangettes, Geneva University Hospital, Genève, Switzerland

    • Luca Garzoni
  39. GI private practice, Wettingen, Aargau, Switzerland

    • Martin Geyer
  40. Groupe Médical d’Onex, Onex, Switzerland

    • Marc Girardin
    • , Alexandre Restellini
    •  & Samuel Zamora
  41. Spital Walenstadt, Walenstadt, St. Gallen, Switzerland

    • Ignaz Good
  42. GI private practice, Reinach, Switzerland

    • Beat Gysi
  43. Aerztehaus Fluntern, Zurich, Switzerland

    • Marcel Halama
    •  & Michael Manz
  44. GI private practice, Olten, Switzerland

    • Pius Heer
    • , Frank Serge Lehmann
    •  & Alex Straumann
  45. HFR Hôpital fribourgeois—Pédiatrie, Fribourg, Switzerland

    • Denise Herzog
  46. KSW Kantonsspital Winterthur Kinderklinik, Winterthur, Switzerland

    • Klaas Heyland
    •  & Ulrich Peter
  47. GI private practice, Zurich, Switzerland

    • Thomas Hinterleitner
  48. GI private practice, Luzern, Switzerland

    • Stephan Kayser
  49. Kantonsspital Nidwalden, Stans, Nidwalden, Switzerland

    • Christoph Knoblauch
    •  & Daniela Schmid
  50. AMB - Arztpraxis MagenDarm Basel, Basel, Switzerland

    • Rémy Meier
  51. Kantonsspital Graubünden, Chur, Switzerland

    • Patrick Mosler
  52. GI private practice, Sion, Switzerland

    • Christian Mottet
  53. Spital Tiefenau, Bern, Switzerland

    • Michaela Neagu
  54. Centre médical d’Epalinges, Epalinges, Switzerland

    • Cristina Nichita
    •  & Paul Wiesel
  55. Spital Waid, Zurich, Switzerland

    • Daniel Peternac
  56. Spital Lachen, Lachen, Switzerland

    • Marc Porzner
  57. Kantonsspital Olten, Olten, Switzerland

    • Claudia Preissler
  58. GI private practice, St-Gallen, Switzerland

    • Ronald Rentsch
  59. GI practice, Dietikon, Switzerland

    • Branislav Risti
  60. GI practice, Liestal, Switzerland

    • Marc Alain Ritz
    • , Michael Steuerwald
    •  & Jürg Vögtlin
  61. GI private practice, Heerbrugg, Switzerland

    • Markus Sagmeister
  62. Klinik Hirslanden Zürich, Zurich, Switzerland

    • Bernhard Sauter
  63. Kinderklinik Bern, Bern University Hospital, Bern, Switzerland

    • Susanne Schibli
    • , Christiane Sokollik
    •  & Johannes Spalinger
  64. Derby Center, Wil, Switzerland

    • Hugo Schlauri
  65. GI private practice, Montreux, Switzerland

    • Jean-François Schnegg
  66. Clinique La Colline, Geneva, Switzerland

    • Mariam Seirafi
  67. Kantonsspital Wolhusen, Wolhusen, Switzerland

    • Holger Spangenberger
  68. GI private practice, Payerne, Switzerland

    • Philippe Stadler
  69. Spital Heiden Appenzell Ausserrhoden, Heiden, Switzerland

    • Peter Staub
  70. Kantonsspital Münsterlingen, Münsterlingen, Switzerland

    • Volker Stenz
  71. Clinique des Grangettes, Chêne-Bougeries, Switzerland

    • Michela Tempia-Caliera
  72. GI private practice, Yverdon, Switzerland

    • Joël Thorens
  73. GI private practice, Langenthal, Switzerland

    • Kaspar Truninger
  74. Hirslanden Klinik Aarau, Gastro Zentrum, Aarau, Switzerland

    • Patrick Urfer
  75. Private practice, Vevey, Switzerland

    • Francesco Viani
  76. Private practice, Pully, Switzerland

    • Dominique Vouillamoz
  77. Spital Limmattal, Schlieren, Switzerland

    • Silvan Zander
  78. Infirmière de Recherche chez CHUV Lausanne University Hospital, Lausanne, Switzerland

    • Tina Wylie

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Consortia

  1. Swiss IBD Cohort Investigators

Contributions

A.J.M. conceived, designed and supervised the study. B.Y. performed all the experiments, analyzed the data and wrote the manuscript with A.J.M. P.J. organized and collected the samples of the second cohort. P.J. F.D.B., Y.F., N.F. and M.G. were involved in data curation. O.O. and C.R. carried out metabolic reaction analysis, and J.S. supervised these analyses. A.J.M., P.J. P.M., C.M., V.E.H.P, M.H.M., G.R., R.W. and Swiss IBD cohort investigators acquired patient samples and detailed structured clinical phenotypes.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Andrew J. Macpherson.

Extended data

  1. Extended Data Fig. 1 Unique microbial taxa identified as IBD signatures using unsupervised meta-analysis of published human IBD studies.

    a, The heatmap shows significant microbial changes in CD, UC or IBD (CD + UC were combined as IBD) disease groups compared to non-IBD subjects. Each disease group (CD, UC or CD + UC) was compared independently to non-IBD and each color code reports the direction of microbial changes in each respective disease status against non-IBD subjects. Euclidean clustering was performed for sample annotations (vertical) including race/ethnicity, gender, median age, patient number, sample type, sequencing method and microbial taxa (horizontal) at different taxonomic ranks. Taxa in black bold label with asterisk demonstrate those findings verified by a subset of the findings in Fig. 1. Taxa in gray bold label with gray asterisk demonstrate findings in some of the studies verified by of the findings in Fig. 1 specifically, Lachnospiraceae family, Lachnospira, Coprococcus, Clostridiales order, Faecalibacterium, Ruminococcus, Roseburia and Ruminococcaceae family for Group 1, and Actinobacteria, Proteobacteria phyla and Enterobacteriaceae family for Group 2. 18 significant additional replicated taxa in the heterogeneous Group 3 are Bacteroidetes phylum and genera from this phylum including Bacteroides, Odoribacter, Butyricimonas, Parabacteroides, Sutterella, Prevotella (of Prevotellaceae), Prevotella (of Paraprevotellaceae) and Rikenellaceae family; also Firmicutes and genera from this phylum including Phascolarctobacterium, Dialister, Eubacterium∙∙∙ and Ruminococcus∙∙∙; Blautia, Collinsella and Bifidobacterium; Sutturella from Proteobacteria and Tenericutes. Underlined taxa are matching with Cluster CDA in Fig. 2. b, The heatmap (studies are in order as in a shows clinically relevant information collected through the studies analyzed in a. Recorded clinical phenotyping information in a given study is shown in green color (Identified) and the lack of clinical data is represented in white color (non-identified).

  2. Extended Data Fig. 2 Microbial taxa comparison of CD with UC in published IBD studies.

    The heatmap shows the comparison of microbial changes between CD and UC. Taxa higher in CD (lower in UC) are in red, while taxa higher in UC (lower CD) are in blue. Euclidean clustering was performed for sample annotations (vertical) including race/ethnicity, gender, median age, patient number, sample type and sequencing method and microbial taxa (horizontal) at different taxonomic ranks. Taxa names in bold demonstrate those findings verified by a subset of the findings in Fig. 1 and taxa names in gray bold shows the findings that partially match with our findings in Fig. 1.

  3. Extended Data Fig. 3 The dominant bacterial phyla along the gastrointestinal tract of IBD patients and dysbiosis in IBD patients.

    a,b, Distribution of predominant bacterial phylotypes along the cephalocaudal axis of the gut in CD, UC and non-IBD subjects of Cohort 1 (a) and Cohort 2 (b) are depicted after stratification according to the relative abundance of Firmicutes at each sampling site. The dominant bacterial phylotypes are Bacteroidetes (51.6% IBD Cohort 1; 56% IBD Cohort 2 and 56% non-IBD), Firmicutes (34.9, 29 and 25.7%, respectively) and Proteobacteria (9.1, 17 and 14%); with a smaller proportion of Fusobacteria (0.8, 0.4 and 1.1%), Actinobacteria (0.79, 0.46 and 0.33%) and Tenericutes (0.2, 0.04 and 0.09%). cf, Microbial composition differences between IBD patients and non-IBD subjects were identified by species richness (Observed OTUs, Shannon and Simpson indices) in Cohort 1 (c) and Cohort 2 (d) and microbiome clustering based on unweighted and weighted UniFrac PCoA metrics for Cohort 1 (e) and Cohort 2 (f). Box-and-whisker plots in c and d display first and third quartiles and whiskers are from each quartile to the minimum or maximum. g,h, Beta dispersion statistics were performed by analyzing the sampling distance to centroids for Cohort 1 (g) and Cohort 2 (h) and there is no significant differences between compared groups in g and h. i,j, Only significant taxa associated with CD or UC shown as relative abundance ratio in Cohort 1 (i) or Cohort 2 (j) were identified using MaAsLin pipeline with BH-FDR correction (q value). q < 0.05 was considered significant. Significant differences were determined by either non-parametric two-sided Mann–Whitney U-test (c,d,g,h) or Adonis test for multiple comparisons (e,f) and P < 0.05 was considered significant. Box-and-whisker plots in c,d,g,h display first and third quartiles and whiskers are from each quartile to the minimum or maximum. 494 CD and 447 UC samples in Cohort 1 and 230 CD, 195 UC and 770 non-IBD samples in Cohort 2 were used for analysis (aj).

  4. Extended Data Fig. 4 Microbial taxa and functional metabolic subsystem differences in IBD patients.

    a,b, Significant taxonomic differences are depicted as relative abundance ratios between CD and non-IBD samples (a) and between UC and non-IBD samples (b) identified in MaAsLin pipeline with BH-FDR correction. q < 0.05 was considered significant. c,d, Relative abundance of the most important matching OTUs were identified using machine learning algorithm and are depicted for Cohort 1 (c) and Cohort 2 (d) using notched box whisker showing first and third quartiles with median value. Each dot represents a single sample (c,d). e,f,h,i, After mapping OTUs to metabolic reactions, calculated the metabolic distances between all pairs of patients based on raw reaction counts are shown on PCA plots based on L2 distance of total reaction counts between UC and CD and boxplots show the respective coefficients PC1 and PC2 axis in Cohort 1 (e,f) and Cohort 2 (h,i). PC1 and PC2 are the first two principal components. g,j, The principal component analysis (PCA) analysis illustrates robust data at the metabolic reaction level: (60% variance explained by PC1/2 Blue indicates UC and red indicates CD patients and are shown using notched box whisker plots showing first and third quartiles with median value. Metabolic subsystems different between in CD and UC patients were identified in Cohort 1 (g) and in Cohort 2 (j). Similar significant metabolic pathway enrichment was detected in both cohorts. Red color represents the enrichment in CD and blue color represents the enrichment in UC. Box-and-whisker plots in g,j display first and third quartiles and whiskers are from each quartile to the minimum or maximum and possible outliers. Consistent metabolic subsystems increased in CD belonged to B-vitamin and LPS biosynthesis, heparan sulfate and chondroitin sulfate degradation and fatty acid oxidation. The BH-FDR was applied to correct for multiple testing and q < 0.05 between groups was considered significant (f,i). Fisher’s exact test was performed to determine if the subsystem was overrepresented among the significantly different reactions; subsystems with P < 0.05 were considered enriched (g,j). 494 CD and 447 UC samples in Cohort 1 and 230 CD, 195 UC and 770 non-IBD samples in Cohort 2 were used for analysis (ad).

  5. Extended Data Fig. 5 Unique microbial taxa identified as IBD signatures across different species using unsupervised meta-analysis of the published IBD studies.

    a, The Euclidean clustering of IBD patients and animal models of IBD including dogs/cats diagnosed with IBD and mice with genetically and/or chemically (DSS or TNBS) induced colitis was performed using information of taxa identified significantly changing between disease groups and is plotted using the categorical information of the study models such as according to race/ethnicity, gender, median age, species, subject number, sample type, sample size, sequencing method and experimental model of IBD induction. b, The Spearman correlation heatmap shows the correlation between 123 different human and animal IBD studies based on identified 96 differentially abundant microbial taxa that are characterized in a. The correlation values ranging from 0 to 1 show positive correlation (in red) and the values ranging from −1 to 0 show negative correlation (in blue) between compared IBD studies. c, Statistical information of data of 123 independent human and animal IBD studies in total (a,b) is shown on the same matrix. Non-parametric two-tailed Spearman correlation test was performed and P < 0.05 was considered significant. Green color shows significant correlation between taxa plotted in a.

  6. Extended Data Fig. 6 Microbial stability over time in longitudinally studied IBD patients and correlation of intestinal inflammation with microbial abundance.

    a, Biopsies were collected from 22 individuals in Cohort 1 and 12 individuals in Cohort 2 over several years (1–9 years and 0.25–2 years, respectively). Each row corresponds to the time course of an individual patient. The resulting data comprised 176 biopsy samples. b, PCoA on Bray–Curtis dissimilarity distance matrix for longitudinally collected (as shown in a) 77 CD and 44 UC samples in Cohort 1 and 49 CD and 14 UC samples in Cohort 2 are plotted. Each color in represents an individual IBD patient. Ellipsoids represent a 95% confidence interval surrounding each disease group. c, The relative abundance changes for Bacteroides, Firmicutes and Proteobacteria phyla in IBD patients are plotted based on their disease severity changes over time. The x axis shows the relative abundance difference of a given phylum compared to previous sampling time point. A value higher than zero indicates that the phylum increases and a value lower than zero indicates that the phylum decreases compared to the previous sampling time point. Disease severity worsening over time is labeled as ‘decreasing’, improving over time is labeled as ‘increasing’ and stable disease severity is identified as ‘steady’ on the y axis. d,e, Fecal calprotectin that is positively correlated with Enterobacteriaceae∙ and Klebsiella for 79 CD patients (d) and negatively correlated with Ruminococcus∙∙∙ and Prevotella in 42 UC patients are shown on continuous data plot generated in MaAsLin pipeline with q < 0.05 (e). Spearman’s rank correlation coefficient for taxa in CD: 0.284 and 0.147 and for taxa in UC: −0.322 and −0.2, respectively. Adonis test was used to determine significant differences between the distance matrix of each group (b). Data shown in b was not significant when longitudinal samples were compared for individuals (P > 0.05). However, significant microbial differences were only observed between patients (P < 0.05). Taxa significantly associated with disease severity and fecal calprotectin were identified in MaAsLin pipeline (c,d,e) with BH-FDR correction and significant taxa are plotted. The q < 0.05 was considered significant.

  7. Extended Data Fig. 7 Microbial profile along the gut in IBD patients.

    a,b, Species richness of samples collected along the gut including Ileum (I), right colon (RC), transverse colon (CT), left colon (CL) and rectum (R) were calculated with Shannon index for 494 CD and 447 UC samples shown in Cohort 1 (a) and 230 CD and 195 UC in Cohort 2 (b). c,d, Beta diversity of these samples are shown for Cohort 1 (c) and Cohort 2 (d). e,f, Samples collected from same patients cluster intra-individually, as depicted CD (e) and UC (f) patients. gj, Species richness calculated with Shannon and Simpson indices for 494 CD and 447 UC samples shown in Cohort 1 and 230 CD and 195 UC in Cohort 2 with different inflammation status are shown in g for Cohort 1 and in h for Cohort 2. Beta diversity of these samples individually analyzed for CD and UC are shown for Cohort 1 (i) and Cohort 2 (j). Significant differences between groups were determined by one-way ANOVA corrected for multiple comparisons using BH-FDR and there is no significance between compared groups (q > 0.05) (a,b,g,h). Lines indicate mean values and error bars are standard deviations (a,b,g,h). Adonis test was used to determine significant differences between the dissimilarity distance matrix of each group and groups are not significantly different than each other (c,d,i,j). The edges strongly similar to each other are connected with a solid line (pure edge) and the edges partially similar to each other are connected with dashed lines (mixed edge) (e,f).

  8. Extended Data Fig. 8 Co-occurrence patterns and degree centrality scores identify the important components of IBD.

    a,b, Ecosystem-specific co-occurrence patterns are visualized using network diagrams where microbial phyla represent nodes and the presence of a positive co-occurrence relationship based on correlation is represented by an edge in Cohort 1 (a) and Cohort 2 (b). Co-occurrence relationships with less strong Spearman’s correlation coefficients (ρ value > 0.25 and P < 0.05) are depicted with network diagram for each disease. c,d, The value of eigenvector and betweenness centralities for CD, UC and non-IBD samples were calculated in Cohort 1 (c) and in Cohort 2 (d). Significant differences between groups were determined by non-parametric two-sided Mann–Whitney U-test (c) and ordinary one-way ANOVA corrected for multiple comparisons using BH-FDR correction (d) and P < 0.05 was considered significant and significant results are shown on the plot. Lines indicate mean values and error bars are standard deviations. 65 CD and 61 UC taxa from Cohort 1 and 48 CD, 44 UC and 41 non-IBD taxa from Cohort 2 were used for analysis in c and d. e,f, Important genera based on their between centrality score for Cohort 1 (e) and Cohort 2 (f) are depicted for each cohort (CD in red, UC in blue and Non-IBD in green). gj, Prominent and influential taxa were identified using in- and out-degree scores are shown for CD (g,i) and UC (h,j) for corresponding cohorts: Cohort 1 (g,h) and Cohort 2 (i,j). Taxa with ρ value > 0.25 and P < 0.05 are plotted (ej). (Cohort 1, CD phyla 9 nodes (N) and 13 edges (E); UC phyla 8 N and 12E; CD genera 64 N, 473E; UC genera, 60 N, 440E and Cohort 2: CD phyla 6 N, 7E; UC 9 N, 11E; CD genera 40 N, 273E; UC genera 38 N, 276E).

  9. Extended Data Fig. 9 Gut microbiota differences in IBD patients with different lifestyles and different responsiveness to disease.

    ad, Major taxonomic changes were observed in 494 CD samples in Cohort 1 when samples were analyzed for sport activities (a), smoking status (b), alcohol abuse (c) and family history of disease (d). eg, Species richness biopsy samples obtained from patients responsive (success) or unresponsive (failure) to anti-TNF-α therapies (e,f) and corticosteroid therapies (g,h) are shown in e,g for Cohort 1 and in f,h for Cohort 2. i,j, Microbial clustering of intestinal biopsy samples from IBD patients responding or non-responding to corticosteroids therapies for Cohort 1 (i) and Cohort 2 (j) is shown with PCoA on Bray–Curtis distance dissimilarity metrics. CD (solid line) and UC (dashed line) are used to identify the disease groups on PCoA plot. km, Unique microbial taxa identified as a signature of responding and non-responding groups in CD (182 with success and 47 with failure) (k) and in UC (l) are shown for Cohort 1 (131 with success and 36 with failure) and in UC (146 with success and 16 with failure) for Cohort 2 (m). n,o, Species richness (of biopsy samples obtained from patients with different disease activities, characterized by the frequency of exacerbations (active) and remissions (quiescent) are shown for Cohort 1 (n) and for Cohort 2 (o). Data is not significant in n and o. p,q, Microbial clustering of intestinal biopsy samples from IBD patients with different disease activities for Cohort 1 (p) and Cohort 2 (q) is shown with PCoA on Bray–Curtis distance dissimilarity metrics. CD (solid line) and UC (dashed line) are used to identify the disease groups on PCoA plot. 494 samples in CD and 447 samples in UC for Cohort 1 (p) and 226 samples in CD and 195 samples in UC for Cohort 2 (q) were analyzed. Microbial profiles were analyzed using MaAsLin pipeline with BH-FDR correction (q value) and q < 0.05 was considered significant (ad,km). Significant taxa (ad,km) are plotted using notched box whisker showing first and third quartiles and median value. (a) Sport: actively (several times per week), sometimes (once or twice a week) and rarely (less than once a week); (c) alcohol abuse; (d) family history: N (None), Y (Yes). Mann–Whitney U-test was used for statistical analysis of alpha diversity (eh,n,o). No significant differences in species richness observed between groups (P > 0.05). Box-and-whisker plots display quartiles and range with standard deviations with possible outlier shown with dots in eh,no. Adonis test assessed the significant difference differences between the dissimilarity distance matrix of each group (i,j,p,q). P < 0.05 for each compared group in each cohort (i,j,p,q).

  10. Extended Data Fig. 10 The relative abundance changes in Cluster CDA taxa with disease activity and intestinal inflammation.

    a, The relative abundance changes of taxa in CDA cluster in longitudinally studied 34 IBD patients (77 CD and 44 UC samples in Cohort 1 and 49 CD and 14 UC samples in Cohort 2) based on the clinically defined changes in disease activity over time as described in Fig. 6c. b, The correlation between fecal calprotectin of 78 CD patients and Cluster CDA taxa. Data was analyzed using MaAsLin with BH-FDR correction and data was not significant (NS; q > 0.05).

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https://doi.org/10.1038/s41591-018-0308-z