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Microbial network disturbances in relapsing refractory Crohn’s disease

Nature Medicine volume 25, pages 323–336 (2019)Cite this article

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A Publisher Correction to this article was published on 07 March 2019

This article has been updated

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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Fig. 1: Distinct microbial features associated with IBD patients.
Fig. 2: Microbial co-occurrence analysis of IBD and non-IBD subjects with gastrointestinal ecosystems.
Fig. 3: Clinical determinants correlate with intestinal microbiota composition of IBD patients.
Fig. 4: Intestinal microbiota composition of IBD patients with responsiveness to anti-TNF-α or corticosteroid therapies.
Fig. 5: Disease locations are critically important determinants that correlate with intestinal microbiota of IBD patients.
Fig. 6: Altered microbiota profiles in inactive CD patients following surgical resection of active disease segments.

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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.

Change history

  • 07 March 2019

    Owing to an error during typesetting, a number of references were deleted from the Methods reference list. This altered all of the references in the Methods section and some of the references in Extended Data Fig. 5, making them inaccurate. References 121–134 were added back into the Methods reference list, and the references in the Methods section and in Extended Data Fig. 5 were renumbered accordingly. The error has been corrected in the PDF and HTML versions of this article.

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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.

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Author notes

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

Authors and 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

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  7. Division of Experimental Pathology, Institute of Pathology, University of Bern, Bern, Switzerland

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  8. Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

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  36. Hospital of the Canton of Jura, Porrentruy And Delémont, Jura, Switzerland

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  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

Authors
  1. Bahtiyar Yilmaz
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  2. Pascal Juillerat
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  16. Andrew J. Macpherson
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Consortia

Swiss IBD Cohort Investigators

  • Karim Abdelrahman
  • , Gentiana Ademi
  • , Patrick Aepli
  • , Amman Thomas
  • , Claudia Anderegg
  • , Anca-Teodora Antonino
  • , Eva Archanioti
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  • , Diana Bakker de Jong
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  • , Janek Binek
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  • , Stephan Boehm
  • , Tujana Boldanova
  • , Jan Borovicka
  • , Christian P. Braegger
  • , Stephan Brand
  • , Lukas Brügger
  • , Simon Brunner
  • , Patrick Bühr
  • , Bernard Burnand
  • , Sabine Burk
  • , Emanuel Burri
  • , Sophie Buyse
  • , Dahlia-Thao Cao
  • , Ove Carstens
  • , Dominique H. Criblez
  • , Sophie Cunningham
  • , Fabrizia D’Angelo
  • , Philippe de Saussure
  • , Lukas Degen
  • , Joakim Delarive
  • , Christopher Doerig
  • , Barbara Dora
  • , Susan Drerup
  • , Mara Egger
  • , Ali El-Wafa
  • , Matthias Engelmann
  • , Jessica Ezri
  • , Christian Felley
  • , Markus Fliegner
  • , Nicolas Fournier
  • , Montserrat Fraga
  • , Yannick Franc
  • , Pascal Frei
  • , Remus Frei
  • , Michael Fried
  • , Florian Froehlich
  • , Raoul Ivano Furlano
  • , Luca Garzoni
  • , Martin Geyer
  • , Laurent Girard
  • , Marc Girardin
  • , Delphine Golay
  • , Ignaz Good
  • , Ulrike Graf Bigler
  • , Beat Gysi
  • , Johannes Haarer
  • , Marcel Halama
  • , Janine Haldemann
  • , Pius Heer
  • , Benjamin Heimgartner
  • , Beat Helbling
  • , Peter Hengstler
  • , Denise Herzog
  • , Cyrill Hess
  • , Roxane Hessler
  • , Klaas Heyland
  • , Thomas Hinterleitner
  • , Claudia Hirschi
  • , Petr Hruz
  • , Pascal Juillerat
  • , Carolina Khalid-de Bakker
  • , Stephan Kayser
  • , Céline Keller
  • , Christina Knellwolf (-Grieger)
  • , Christoph Knoblauch
  • , Henrik Köhler
  • , Rebekka Koller
  • , Claudia Krieger(-Grübel)
  • , Patrizia Künzler
  • , Rachel Kusche
  • , Frank Serge Lehmann
  • , Andrew J. Macpherson
  • , Michel H. Maillard
  • , Michael Manz
  • , Astrid Marot
  • , Rémy Meier
  • , Christa Meyenberger
  • , Pamela Meyer
  • , Pierre Michetti
  • , Benjamin Misselwitz
  • , Patrick Mosler
  • , Christian Mottet
  • , Christoph Müller
  • , Beat Müllhaupt
  • , Leilla Musso
  • , Michaela Neagu
  • , Cristina Nichita
  • , Jan Niess
  • , Andreas Nydegger
  • , Nicole Obialo
  • , Diana Ollo
  • , Cassandra Oropesa
  • , Ulrich Peter
  • , Daniel Peternac
  • , Laetitia Marie Petit
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  • , Daniel Pohl
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  • , Claudia Preissler
  • , Nadia Raschle
  • , Ronald Rentsch
  • , Alexandre Restellini
  • , Sophie Restellini
  • , Jean-Pierre Richterich
  • , Frederic Ris
  • , Branislav Risti
  • , Marc Alain Ritz
  • , Gerhard Rogler
  • , Nina Röhrich
  • , Jean-Benoît Rossel
  • , Vanessa Rueger
  • , Monica Rusticeanu
  • , Markus Sagmeister
  • , Gaby Saner
  • , Bernhard Sauter
  • , Mikael Sawatzki
  • , Michael Scharl
  • , Martin Schelling
  • , Susanne Schibli
  • , Hugo Schlauri
  • , Dominique Schluckebier
  • , Daniela Schmid
  • , Sybille Schmid (-Uebelhart)
  • , Jean-François Schnegg
  • , Alain Schoepfer
  • , Vivianne Seematter
  • , Frank Seibold
  • , Mariam Seirafi
  • , Gian-Marco Semadeni
  • , Arne Senning
  • , Christiane Sokollik
  • , Joachim Sommer
  • , Johannes Spalinger
  • , Holger Spangenberger
  • , Philippe Stadler
  • , Peter Staub
  • , Dominic Staudenmann
  • , Volker Stenz
  • , Michael Steuerwald
  • , Alex Straumann
  • , Bruno Strebel
  • , Andreas Stulz
  • , Michael Sulz
  • , Aurora Tatu
  • , Michela Tempia-Caliera
  • , Joël Thorens
  • , Kaspar Truninger
  • , Radu Tutuian
  • , Patrick Urfer
  • , Stephan Vavricka
  • , Francesco Viani
  • , Jürg Vögtlin
  • , Roland Von Känel
  • , Dominique Vouillamoz
  • , Rachel Vulliamy
  • , Paul Wiesel
  • , Reiner Wiest
  • , Stefanie Wöhrle
  • , Samuel Zamora
  • , Silvan Zander
  • , Tina Wylie
  • , Jonas Zeitz
  •  & Dorothee Zimmermann

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.

Corresponding author

Correspondence to Andrew J. Macpherson.

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The authors declare no competing interests.

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Extended data

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).

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.

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%). c–f, 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 (a–j).

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 (a–d).

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.

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.

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. g–j, 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).

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). g–j, 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 (e–j). (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).

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

a–d, 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). e–g, 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. k–m, 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 (a–d,k–m). Significant taxa (a–d,k–m) 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 (e–h,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 e–h,n–o. 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).

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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Yilmaz, B., Juillerat, P., Øyås, O. et al. Microbial network disturbances in relapsing refractory Crohn’s disease. Nat Med 25, 323–336 (2019). https://doi.org/10.1038/s41591-018-0308-z

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