Gut Online First, published on January 7, 2015 as 10.1136/gutjnl-2014-308341 Gut microbiota
ORIGINAL ARTICLE
Geographical patterns of the standing and active human gut microbiome in health and IBD Ateequr Rehman,1 Philipp Rausch,2,3 Jun Wang,2,3 Jurgita Skieceviciene,1,4 Gediminas Kiudelis,5 Ketan Bhagalia,6 Deepak Amarapurkar,6 Limas Kupcinskas,4,5 Stefan Schreiber,1,7 Philip Rosenstiel,1 John F Baines,2,3 Stephan Ott7 ▸ Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/ gutjnl-2014-308341). For numbered affiliations see end of article. Correspondence to Professor Philip Rosenstiel; Schittenhelmstr. 12, Institut für Klinische Molekular Biologie Kiel D-24105, Germany; [email protected] Professor John F Baines; Arnold-Heller-Str. 3, Haus 17, Institut für Experimentelle Medizin, Kiel D- 24105, Germany; [email protected] Dr. Stephan Ott; Arnold-Heller-Str. 3, Haus 6, Klinik für Innere Medizin I Kiel D-24105, Germany; [email protected] AR and PR contributed equally. Received 25 September 2014 Revised 19 November 2014 Accepted 30 November 2014
ABSTRACT Objective A global increase of IBD has been reported, especially in countries that previously had low incidence rates. Also, the knowledge of the human gut microbiome is steadily increasing, however, limited information regarding its variation on a global scale is available. In the light of the microbial involvement in IBDs, we aimed to (1) identify shared and distinct IBDassociated mucosal microbiota patterns from different geographical regions including Europe (Germany, Lithuania) and South Asia (India) and (2) determine whether profiling based on 16S rRNA transcripts provides additional resolution, both of which may hold important clinical relevance. Design In this study, we analyse a set of 89 mucosal biopsies sampled from individuals of German, Lithuanian and Indian origins, using bacterial community profiling of a roughly equal number of healthy controls, patients with Crohn’s disease and UC from each location, and analyse 16S rDNA and rRNA as proxies for standing and active microbial community structure, respectively. Results We find pronounced population-specific as well as general disease patterns in the major phyla and patterns of diversity, which differ between the standing and active communities. The geographical origin of samples dominates the patterns of β diversity with locally restricted disease clusters and more pronounced effects in the active microbial communities. However, two genera belonging to the Clostridium leptum subgroup, Faecalibacteria and Papillibacter, display consistent patterns with respect to disease status and may thus serve as reliable ‘microbiomarkers’. Conclusions These analyses reveal important interactions of patients’ geographical origin and disease in the interpretation of disease-associated changes in microbial communities and highlight the added value of analysing communities on both the 16S rRNA gene (DNA) and transcript (RNA) level.
INTRODUCTION
To cite: Rehman A, Rausch P, Wang J, et al. Gut Published Online First: [please include Day Month Year] doi:10.1136/gutjnl2014-308341
While a steady increase of IBD has been observed over the past decades in North America and Europe, a dramatic rise in incidence rates is observed in countries that have recently adopted a Western industrialised lifestyle, for example, East and South Asia, or states of the former Soviet Union.1 2 Suspected environmental factors include increased levels of hygiene (decrease in antigen contacts), changes in nutritional habits and the
Significance of this study What is already known on this subject?
▸ IBD impacts microbial community structure in the gut. ▸ Microbial communities differ between human populations, potentially driven by variation in genetic polymorphism, lifestyle and environmental conditions. ▸ Diversity within and between bacterial communities change over a lifetime.
What are the new findings?
▸ Pathological community patterns observed in IBD are influenced by local, population-specific factors, but also show shared elements between the different cohorts. ▸ The active bacterial component (rRNA) shows lower diversity and intercohort variation than the standing diversity (rDNA). ▸ The bacterial communities investigated on the RNA level reveal stronger disease-associated patterns.
How might it impact on clinical practice in the foreseeable future?
▸ Variation of mucosal microbial communities among human populations constitutes an important factor when considering the microbiome as a target for treatment of IBD. ▸ The identification of disease-associated microbial patterns shared between distinct geographical regions might be specifically useful to tailor a set of ‘microbiomarkers’ for a molecular assessment of IBD.
industrialisation of food production and preservation.3–6 It can be assumed that all these environmental cues specifically act on the acquisition, composition and stability of the intestinal microbiome. Only recently have international studies begun to explore microbial communities in human populations at different body sites on broader geographical scales.7 8 A common concept in microbial biogeography proposes a world-wide, passive dispersal of bacteria, followed by environmental filtering of bacterial assemblies (ie, ‘everything is everywhere, but the environment selects’).9 Many
Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
1
Copyright Article author (or their employer) 2015. Produced by BMJ Publishing Group Ltd (& BSG) under licence.
Gut microbiota studies addressed this hypothesis in an environmental context,10–12 but how those globally distinct environmental microbial assemblages are translated into the stable adult intestinal microbiome is still poorly understood. However, it seems obvious that physical distance and variables that correlate with this (eg, temperature), the genetic makeup of the host, diet and other sociocultural habits will play decisive roles. Whatever the exact reasons for geographical differences in human-associated microbial communities, they are likely important factors to be considered for understanding disease aetiologies among human populations. Qin et al13 were among the first to identify a common faecal microbial gene catalogue and to discern communities according to an underlying condition of IBD not restricted to a single human population. More recent in-depth analyses of faecal communities included divergent populations,8 but only recently focused on disease states.14 It can be hypothesised that the microbial communities tightly associated with the intestinal mucosa might be under stronger host control15 and less subject to transient perturbations compared with the luminal microbiota. Although they might have a greater impact on homeostasis, mucosal communities are comparatively understudied. In this study, we investigate the impact of the two major forms of IBD, Crohn’s disease (CD) and UC, on the bacterial communities associated with the colonic mucosa in a geographical context. Colonic biopsies were obtained from patients with IBD and controls originating from Germany, Lithuania and India. While most previous studies focused on the 16S rDNA level, we employ bacterial community profiling on the levels of both 16S rDNA and rRNA. This comprehensive approach distinguishes between standing and active microbial communities, which together enables us to explore variation among geographically distinct microbiomes and also to relate these differences to the patterns observed in IBD.
MATERIAL AND METHODS Human samples Colonic biopsies were taken from the sigmoid region of healthy subjects and patients in clinical remission. The diagnoses of UC and CD were based on standard clinical, endoscopical, radiological and histological criteria. All samples and phenotype information were pseudonymised before the procedure. All individuals agreed to participate by giving informed consent at least 24 h before sampling. Details on age, sex, disease status and medication are provided in table 1. Due to mean differences in age between population cohorts, a normalisation within populations was performed to account for this in interpopulation comparisons by subtracting the minimum age within each population.
Nucleic acids extraction and 16S rRNA pyrosequencing DNA and RNA were extracted using the Qiagen Allprep DNA/ RNA as previously described (see online supplementary material16). RNA was reverse transcribed to cDNA using random hexamers (Qiagen, Hilden, Germany). Nucleic acid extraction and reverse transcription of Indian samples were performed on-site in India. Reverse transcribed cDNA and genomic DNA were freeze dried and transported on dry ice to Germany for further processing. Frozen biopsies sampled in Lithuania were transported on dry ice to be processed in Kiel, Germany. The 16S rRNA gene (RNA and DNA) was amplified with the 27F-338R primer pair and sequenced as described before.17 Sequences were processed using Mothur V.1.15.0,18 and filtered using stringent quality criteria (see online supplementary methods). 2
Statistical analysis α Diversity and β diversity indices ( Jaccard and Bray–Curtis (square root transformed)) were calculated in R.19–21 FASTUniFrac was used to calculate the unweighted and normalised weighted UniFrac metrics.22 Statistical analysis of community distances was performed with non-parametric distance-based analysis of variance (ANOVA) using ‘adonis’, Mantel correlation, Procrustes analysis and fitting of centroids were implemented in the ‘vegan’ package for R and tested with 105 permutations to assess significance.23 24 Redundancy Analysis (RDA) was carried out on Hellinger-transformed Operational Taxonomic Unit (OTU) tables and tested using a permutative ANOVA approach.25 Comparisons of means (ie, phyla abundances, α diversity) followed a linear model framework using standard model selection procedures (minimising AIC values without a significant loss of fit) requiring normally distributed residuals. Indicator species analysis was implemented via the R package ‘indicspecies’ with 105 permutations.26 The activity of genera and species was estimated through rRNA/rDNA ratio, while divisions of and by zero were set to zero. Differentially active bacteria were detected by Kruskal–Wallis tests. p Values of the genera/OTU associations (rDNA, rRNA, activity) were adjusted using the Benjamini and Hochberg procedure.
RESULTS Phylum abundances are influenced by disease status and sampling population To investigate the influence of IBD on the mucosa-associated microbiota in a broader geographical context, sigmoidal biopsies were obtained from ∼10 each of healthy controls, patients with CD and UC, residing in Germany, Lithuania and India, totalling 89 samples (cohort details in table 1). Pyrosequencing of the V1– V2 region of the 16S rRNA gene was performed on the level of both 16S rDNA and rRNA (reverse transcribed to cDNA, see the Materials and methods section). Normalisation (∼1000 sequences per individual) yielded 88000 rDNA and 86974 rRNA sequences. A single control rDNA sample from Germany and single control and CD rRNA samples from Lithuania (ie, in total three samples) were not included in further analysis due to low sequencing coverage. Species-level OTUs (97% identity OTUs) were clustered using the combined rDNA and rRNA-level datasets, and split accordingly. This resulted in a community coverage of 83.45±5.08% and 89.22±5.54% of species for rDNA and rRNA, respectively (Good’s coverage, see online supplementary figure S1). We first analysed phylum abundances in a global manner (ie, across all three populations), whereby complex differences between standing (rDNA) and active (rRNA) communities were observed for most phyla (see online supplementary figure S2 and S3). Overall, Bacteroidetes and Proteobacteria show inverse effects among the active and standing microbial communities and are negatively associated with each other (rDNA: r=−0.527, p=1.33×10−7, rRNA: r=−0.254, p=0.0176). Bacteroidetes show a significant increase with age in the rDNA samples (figure 1A), whereas the rRNA samples further reveal influences of disease status on the abundance of active Bacteroidetes, mainly by a higher abundance in UC samples across populations (figure 1B, see online supplementary table S1). Proteobacteria abundance decreases with age in the rDNA-based samples (figure 1E), inversely with Bacteroidetes. The abundance of active Proteobacteria in contrast does not decrease with age, but displays a decrease in patients with UC compared with patients with CD and healthy samples, which is Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
Gut microbiota Table 1 Patient information for each population Metadata DNA Control Crohn’s Disease UC Male/female # NA gender Mean age (original) Mean age (normalised) Age range (original) Age range (normalised) # NA age Medication (yes/no) Antibiotics Probiotics 5-ASA TNF-block Azathioprin MTX Corticoids RNA Control Crohn’s Disease UC Male/female # NA gender Mean age (original) Mean age (normalised) Age range (original) Age range (normalised) # NA age Medication (yes/no) Antibiotics Probiotics 5-ASA TNF-block Azathioprin MTX Corticoids
Germany
Lithuania
India
9 10 10 14/15 0 35.966±12.974 SD 19.966±12.974 SD 16–63 0–47 0 10/19 2 4 2 3 4 1 5
10 9 10 10/18 1 45.893±18.089 SD 26.893±18.089 SD 19–81 0–62 1 13/16 2 0 11 0 0 0 1
11 9 10 21/9 0 37.172±13.782 SD 20.172±13.782 SD 17–67 0–50 1 25/5 16 7 17 1 4 0 9
10 10 10 14/16 0 35.667±12.853 SD 19.667±12.853 SD 16–63 0–47 0 10/20 2 5 2 3 4 1 5
9 8 10 10/17 0 46.370±18.253 SD 27.370±18.253 SD 19–81 0–62 0 13/14 2 0 11 0 0 0 1
11 9 10 21/9 0 37.172±13.782 SD 20.172±13.782 SD 17–67 0–50 1 25/5 16 7 17 1 4 0 9
ASA, 5-aminosalicylic acid; MTX, methotrexate; NA, not available; TNF, tumour necrosis factor.
also influenced by the subject’s gender (figure 1F, see online supplementary table S1). The Firmicutes abundances based on rDNA mainly display differences between healthy controls and patients with UC across populations, especially among German and Lithuanian samples (figure 1C), which is confirmed in separate analyses for each population (see below, see online supplementary table S2). The Firmicutes abundances based on rRNA show significant differences between European (Germany, Lithuania) and Indian samples, as well as between pathologies within and among the sampling cohorts (figure 1D, see online supplementary table S1). Second, we analysed each single population separately. This reveals pronounced differences especially in Firmicutes between disease groups within each population, based on both rRNA and rDNA, although the relative phylum-level patterns between investigated groups are not consistent among populations (see online supplementary table S2). In particular, Firmicutes Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
abundance is the lowest in healthy German samples compared with diseased individuals, while Lithuanian and Indian patients with CD show the lowest Firmicutes abundances. Bacteroidetes, on the other hand, show common patterns of age and pathology in Lithuanian and Indian patients but not in Germans. Bacteroidetes show also a population-independent increase in abundance in the standing and active bacteria among healthy and UC subjects. Proteobacteria also display an increased abundance in CD among Lithuanians and Indians, while no apparent effects were present in German samples (see online supplementary table S2). In summary, we revealed interesting age-related patterns for both Bacteroidetes and Proteobacteria, while population-specific disease-related patterns are present among the Firmicutes. Furthermore, basing analyses on 16S rRNA in general provided greater resolution in detecting disease and population-specific effects. 3
Gut microbiota
Figure 1 Comparative analysis of mucosa-attached bacterial communities at the phylum level. Plots of phyla abundances based on 16S rDNA (A, C, E) and rRNA (B, D, F) visualise the effects of the best statistical model (Firmicutes, Bacteroidetes, Proteobacteria, error bars represent SD). CD, Crohn’s Disease; CON, control; GER, Germany; IND, India; LIT, Lithuania.
4
Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
Gut microbiota Patterns of bacterial diversity within and between individuals is influenced by age, population-specific effects and disease α Diversity We focused our analysis on a panel of diversity measures which provide information about the approximate species number,27 entropy and evenness of the community,19 as well as its phylogenetic diversity.20 Interestingly, we find significantly higher diversity in rDNA-based samples and a moderate correlation between the species diversities of the standing and active communities (figure 2A–C). First, we analysed the panel of α diversity indices globally among all samples. Investigating species richness (using Chao1 index), we observe increases of species number with age in the standing microbial community (rDNA), while species richness in the active communities (rRNA) increases with age and shows significantly lower diversity among patients with CD (figure 2D, E, table 2). By applying Shannon entropy,19 which represents the distribution of species in a sample, we also find an increase in diversity with age in the rDNA-based and rRNA-based communities (see online supplementary figure S4A, table 2). Phylogenetic diversity of the standing community is also correlated with a subject’s age, but increases only in patients with CD (see online
supplementary figure S4C, table 2). The rRNA-based samples display differences between CD and healthy controls, between CD and UC, as well as between European and Indian samples (see online supplementary figures S4B,D, S5 and table S3; also see table 2), with the highest level of species and phylogenetic diversity among healthy individuals. The increase of community diversity with age can be a sign of community succession, that is, a change in community structure over time, or a lack of colonisation resistance. To further investigate potential confounding effects of disease on those succession patterns, we analysed each disease state and population cohort separately. Interestingly, the strongest signal of succession is present in the active and standing bacterial communities of patients with CD, while in healthy individuals and patients with UC, diversity does not consistently increase with age (see online supplementary figures S5, S6 and supplementary table S3). Thus, in summary, the rRNA-based samples display reduced diversity compared with rDNA-based samples, and at the same time provide more resolution to detect influences of sampling region and disease compared with rDNA. The age-related patterns of increasing species diversity appear to be largely limited to CD and may point towards a reduced colonisation resistance of the disturbed microbial communities in IBD. Further,
Figure 2 Analysis of mucosa-attached bacterial communities identifies a common increase of bacterial diversity with age regardless of diagnosis and geographical origin. Correlation of α diversity metrics based on rDNA and rRNA (Chao1 species richness: r=0.493, p=1.97×10−6 (A); Shannon H ( Jost): r=0.609, p=9.223×10−11 (B); phylogenetic diversity: r=0.355, p=0.001 (C)). Species richness according to the best statistical model in rDNA (D) and rRNA (E) derived communities (table 2; for details on Shannon H and phylogenetic diversity, see figure S4). CD, Crohn’s Disease; CON, control; GER, Germany; IND, India; LIT, Lithuania. Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
5
Gut microbiota Table 2 Statistical analyses of α diversity based on species distribution (Shannon H), richness (Chao1) and phylogenetic diversity in DNA-based and RNA-based samples α Diversity metric
Model factors
DF
F
DNA Shannon H Chao1 species richness Phylogenetic diversity
Normalised age Normalised age Disease
1 1 2
10.532 5.656 2.258
0.002 0.020 0.111
Normalised age Disease : Normalised age
1 2
0.203 2.935
0.654 0.059
Disease
2
5.006
0.009
Population
2
14.726
ORIGINAL ARTICLE
Geographical patterns of the standing and active human gut microbiome in health and IBD Ateequr Rehman,1 Philipp Rausch,2,3 Jun Wang,2,3 Jurgita Skieceviciene,1,4 Gediminas Kiudelis,5 Ketan Bhagalia,6 Deepak Amarapurkar,6 Limas Kupcinskas,4,5 Stefan Schreiber,1,7 Philip Rosenstiel,1 John F Baines,2,3 Stephan Ott7 ▸ Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/ gutjnl-2014-308341). For numbered affiliations see end of article. Correspondence to Professor Philip Rosenstiel; Schittenhelmstr. 12, Institut für Klinische Molekular Biologie Kiel D-24105, Germany; [email protected] Professor John F Baines; Arnold-Heller-Str. 3, Haus 17, Institut für Experimentelle Medizin, Kiel D- 24105, Germany; [email protected] Dr. Stephan Ott; Arnold-Heller-Str. 3, Haus 6, Klinik für Innere Medizin I Kiel D-24105, Germany; [email protected] AR and PR contributed equally. Received 25 September 2014 Revised 19 November 2014 Accepted 30 November 2014
ABSTRACT Objective A global increase of IBD has been reported, especially in countries that previously had low incidence rates. Also, the knowledge of the human gut microbiome is steadily increasing, however, limited information regarding its variation on a global scale is available. In the light of the microbial involvement in IBDs, we aimed to (1) identify shared and distinct IBDassociated mucosal microbiota patterns from different geographical regions including Europe (Germany, Lithuania) and South Asia (India) and (2) determine whether profiling based on 16S rRNA transcripts provides additional resolution, both of which may hold important clinical relevance. Design In this study, we analyse a set of 89 mucosal biopsies sampled from individuals of German, Lithuanian and Indian origins, using bacterial community profiling of a roughly equal number of healthy controls, patients with Crohn’s disease and UC from each location, and analyse 16S rDNA and rRNA as proxies for standing and active microbial community structure, respectively. Results We find pronounced population-specific as well as general disease patterns in the major phyla and patterns of diversity, which differ between the standing and active communities. The geographical origin of samples dominates the patterns of β diversity with locally restricted disease clusters and more pronounced effects in the active microbial communities. However, two genera belonging to the Clostridium leptum subgroup, Faecalibacteria and Papillibacter, display consistent patterns with respect to disease status and may thus serve as reliable ‘microbiomarkers’. Conclusions These analyses reveal important interactions of patients’ geographical origin and disease in the interpretation of disease-associated changes in microbial communities and highlight the added value of analysing communities on both the 16S rRNA gene (DNA) and transcript (RNA) level.
INTRODUCTION
To cite: Rehman A, Rausch P, Wang J, et al. Gut Published Online First: [please include Day Month Year] doi:10.1136/gutjnl2014-308341
While a steady increase of IBD has been observed over the past decades in North America and Europe, a dramatic rise in incidence rates is observed in countries that have recently adopted a Western industrialised lifestyle, for example, East and South Asia, or states of the former Soviet Union.1 2 Suspected environmental factors include increased levels of hygiene (decrease in antigen contacts), changes in nutritional habits and the
Significance of this study What is already known on this subject?
▸ IBD impacts microbial community structure in the gut. ▸ Microbial communities differ between human populations, potentially driven by variation in genetic polymorphism, lifestyle and environmental conditions. ▸ Diversity within and between bacterial communities change over a lifetime.
What are the new findings?
▸ Pathological community patterns observed in IBD are influenced by local, population-specific factors, but also show shared elements between the different cohorts. ▸ The active bacterial component (rRNA) shows lower diversity and intercohort variation than the standing diversity (rDNA). ▸ The bacterial communities investigated on the RNA level reveal stronger disease-associated patterns.
How might it impact on clinical practice in the foreseeable future?
▸ Variation of mucosal microbial communities among human populations constitutes an important factor when considering the microbiome as a target for treatment of IBD. ▸ The identification of disease-associated microbial patterns shared between distinct geographical regions might be specifically useful to tailor a set of ‘microbiomarkers’ for a molecular assessment of IBD.
industrialisation of food production and preservation.3–6 It can be assumed that all these environmental cues specifically act on the acquisition, composition and stability of the intestinal microbiome. Only recently have international studies begun to explore microbial communities in human populations at different body sites on broader geographical scales.7 8 A common concept in microbial biogeography proposes a world-wide, passive dispersal of bacteria, followed by environmental filtering of bacterial assemblies (ie, ‘everything is everywhere, but the environment selects’).9 Many
Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
1
Copyright Article author (or their employer) 2015. Produced by BMJ Publishing Group Ltd (& BSG) under licence.
Gut microbiota studies addressed this hypothesis in an environmental context,10–12 but how those globally distinct environmental microbial assemblages are translated into the stable adult intestinal microbiome is still poorly understood. However, it seems obvious that physical distance and variables that correlate with this (eg, temperature), the genetic makeup of the host, diet and other sociocultural habits will play decisive roles. Whatever the exact reasons for geographical differences in human-associated microbial communities, they are likely important factors to be considered for understanding disease aetiologies among human populations. Qin et al13 were among the first to identify a common faecal microbial gene catalogue and to discern communities according to an underlying condition of IBD not restricted to a single human population. More recent in-depth analyses of faecal communities included divergent populations,8 but only recently focused on disease states.14 It can be hypothesised that the microbial communities tightly associated with the intestinal mucosa might be under stronger host control15 and less subject to transient perturbations compared with the luminal microbiota. Although they might have a greater impact on homeostasis, mucosal communities are comparatively understudied. In this study, we investigate the impact of the two major forms of IBD, Crohn’s disease (CD) and UC, on the bacterial communities associated with the colonic mucosa in a geographical context. Colonic biopsies were obtained from patients with IBD and controls originating from Germany, Lithuania and India. While most previous studies focused on the 16S rDNA level, we employ bacterial community profiling on the levels of both 16S rDNA and rRNA. This comprehensive approach distinguishes between standing and active microbial communities, which together enables us to explore variation among geographically distinct microbiomes and also to relate these differences to the patterns observed in IBD.
MATERIAL AND METHODS Human samples Colonic biopsies were taken from the sigmoid region of healthy subjects and patients in clinical remission. The diagnoses of UC and CD were based on standard clinical, endoscopical, radiological and histological criteria. All samples and phenotype information were pseudonymised before the procedure. All individuals agreed to participate by giving informed consent at least 24 h before sampling. Details on age, sex, disease status and medication are provided in table 1. Due to mean differences in age between population cohorts, a normalisation within populations was performed to account for this in interpopulation comparisons by subtracting the minimum age within each population.
Nucleic acids extraction and 16S rRNA pyrosequencing DNA and RNA were extracted using the Qiagen Allprep DNA/ RNA as previously described (see online supplementary material16). RNA was reverse transcribed to cDNA using random hexamers (Qiagen, Hilden, Germany). Nucleic acid extraction and reverse transcription of Indian samples were performed on-site in India. Reverse transcribed cDNA and genomic DNA were freeze dried and transported on dry ice to Germany for further processing. Frozen biopsies sampled in Lithuania were transported on dry ice to be processed in Kiel, Germany. The 16S rRNA gene (RNA and DNA) was amplified with the 27F-338R primer pair and sequenced as described before.17 Sequences were processed using Mothur V.1.15.0,18 and filtered using stringent quality criteria (see online supplementary methods). 2
Statistical analysis α Diversity and β diversity indices ( Jaccard and Bray–Curtis (square root transformed)) were calculated in R.19–21 FASTUniFrac was used to calculate the unweighted and normalised weighted UniFrac metrics.22 Statistical analysis of community distances was performed with non-parametric distance-based analysis of variance (ANOVA) using ‘adonis’, Mantel correlation, Procrustes analysis and fitting of centroids were implemented in the ‘vegan’ package for R and tested with 105 permutations to assess significance.23 24 Redundancy Analysis (RDA) was carried out on Hellinger-transformed Operational Taxonomic Unit (OTU) tables and tested using a permutative ANOVA approach.25 Comparisons of means (ie, phyla abundances, α diversity) followed a linear model framework using standard model selection procedures (minimising AIC values without a significant loss of fit) requiring normally distributed residuals. Indicator species analysis was implemented via the R package ‘indicspecies’ with 105 permutations.26 The activity of genera and species was estimated through rRNA/rDNA ratio, while divisions of and by zero were set to zero. Differentially active bacteria were detected by Kruskal–Wallis tests. p Values of the genera/OTU associations (rDNA, rRNA, activity) were adjusted using the Benjamini and Hochberg procedure.
RESULTS Phylum abundances are influenced by disease status and sampling population To investigate the influence of IBD on the mucosa-associated microbiota in a broader geographical context, sigmoidal biopsies were obtained from ∼10 each of healthy controls, patients with CD and UC, residing in Germany, Lithuania and India, totalling 89 samples (cohort details in table 1). Pyrosequencing of the V1– V2 region of the 16S rRNA gene was performed on the level of both 16S rDNA and rRNA (reverse transcribed to cDNA, see the Materials and methods section). Normalisation (∼1000 sequences per individual) yielded 88000 rDNA and 86974 rRNA sequences. A single control rDNA sample from Germany and single control and CD rRNA samples from Lithuania (ie, in total three samples) were not included in further analysis due to low sequencing coverage. Species-level OTUs (97% identity OTUs) were clustered using the combined rDNA and rRNA-level datasets, and split accordingly. This resulted in a community coverage of 83.45±5.08% and 89.22±5.54% of species for rDNA and rRNA, respectively (Good’s coverage, see online supplementary figure S1). We first analysed phylum abundances in a global manner (ie, across all three populations), whereby complex differences between standing (rDNA) and active (rRNA) communities were observed for most phyla (see online supplementary figure S2 and S3). Overall, Bacteroidetes and Proteobacteria show inverse effects among the active and standing microbial communities and are negatively associated with each other (rDNA: r=−0.527, p=1.33×10−7, rRNA: r=−0.254, p=0.0176). Bacteroidetes show a significant increase with age in the rDNA samples (figure 1A), whereas the rRNA samples further reveal influences of disease status on the abundance of active Bacteroidetes, mainly by a higher abundance in UC samples across populations (figure 1B, see online supplementary table S1). Proteobacteria abundance decreases with age in the rDNA-based samples (figure 1E), inversely with Bacteroidetes. The abundance of active Proteobacteria in contrast does not decrease with age, but displays a decrease in patients with UC compared with patients with CD and healthy samples, which is Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
Gut microbiota Table 1 Patient information for each population Metadata DNA Control Crohn’s Disease UC Male/female # NA gender Mean age (original) Mean age (normalised) Age range (original) Age range (normalised) # NA age Medication (yes/no) Antibiotics Probiotics 5-ASA TNF-block Azathioprin MTX Corticoids RNA Control Crohn’s Disease UC Male/female # NA gender Mean age (original) Mean age (normalised) Age range (original) Age range (normalised) # NA age Medication (yes/no) Antibiotics Probiotics 5-ASA TNF-block Azathioprin MTX Corticoids
Germany
Lithuania
India
9 10 10 14/15 0 35.966±12.974 SD 19.966±12.974 SD 16–63 0–47 0 10/19 2 4 2 3 4 1 5
10 9 10 10/18 1 45.893±18.089 SD 26.893±18.089 SD 19–81 0–62 1 13/16 2 0 11 0 0 0 1
11 9 10 21/9 0 37.172±13.782 SD 20.172±13.782 SD 17–67 0–50 1 25/5 16 7 17 1 4 0 9
10 10 10 14/16 0 35.667±12.853 SD 19.667±12.853 SD 16–63 0–47 0 10/20 2 5 2 3 4 1 5
9 8 10 10/17 0 46.370±18.253 SD 27.370±18.253 SD 19–81 0–62 0 13/14 2 0 11 0 0 0 1
11 9 10 21/9 0 37.172±13.782 SD 20.172±13.782 SD 17–67 0–50 1 25/5 16 7 17 1 4 0 9
ASA, 5-aminosalicylic acid; MTX, methotrexate; NA, not available; TNF, tumour necrosis factor.
also influenced by the subject’s gender (figure 1F, see online supplementary table S1). The Firmicutes abundances based on rDNA mainly display differences between healthy controls and patients with UC across populations, especially among German and Lithuanian samples (figure 1C), which is confirmed in separate analyses for each population (see below, see online supplementary table S2). The Firmicutes abundances based on rRNA show significant differences between European (Germany, Lithuania) and Indian samples, as well as between pathologies within and among the sampling cohorts (figure 1D, see online supplementary table S1). Second, we analysed each single population separately. This reveals pronounced differences especially in Firmicutes between disease groups within each population, based on both rRNA and rDNA, although the relative phylum-level patterns between investigated groups are not consistent among populations (see online supplementary table S2). In particular, Firmicutes Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
abundance is the lowest in healthy German samples compared with diseased individuals, while Lithuanian and Indian patients with CD show the lowest Firmicutes abundances. Bacteroidetes, on the other hand, show common patterns of age and pathology in Lithuanian and Indian patients but not in Germans. Bacteroidetes show also a population-independent increase in abundance in the standing and active bacteria among healthy and UC subjects. Proteobacteria also display an increased abundance in CD among Lithuanians and Indians, while no apparent effects were present in German samples (see online supplementary table S2). In summary, we revealed interesting age-related patterns for both Bacteroidetes and Proteobacteria, while population-specific disease-related patterns are present among the Firmicutes. Furthermore, basing analyses on 16S rRNA in general provided greater resolution in detecting disease and population-specific effects. 3
Gut microbiota
Figure 1 Comparative analysis of mucosa-attached bacterial communities at the phylum level. Plots of phyla abundances based on 16S rDNA (A, C, E) and rRNA (B, D, F) visualise the effects of the best statistical model (Firmicutes, Bacteroidetes, Proteobacteria, error bars represent SD). CD, Crohn’s Disease; CON, control; GER, Germany; IND, India; LIT, Lithuania.
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Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
Gut microbiota Patterns of bacterial diversity within and between individuals is influenced by age, population-specific effects and disease α Diversity We focused our analysis on a panel of diversity measures which provide information about the approximate species number,27 entropy and evenness of the community,19 as well as its phylogenetic diversity.20 Interestingly, we find significantly higher diversity in rDNA-based samples and a moderate correlation between the species diversities of the standing and active communities (figure 2A–C). First, we analysed the panel of α diversity indices globally among all samples. Investigating species richness (using Chao1 index), we observe increases of species number with age in the standing microbial community (rDNA), while species richness in the active communities (rRNA) increases with age and shows significantly lower diversity among patients with CD (figure 2D, E, table 2). By applying Shannon entropy,19 which represents the distribution of species in a sample, we also find an increase in diversity with age in the rDNA-based and rRNA-based communities (see online supplementary figure S4A, table 2). Phylogenetic diversity of the standing community is also correlated with a subject’s age, but increases only in patients with CD (see online
supplementary figure S4C, table 2). The rRNA-based samples display differences between CD and healthy controls, between CD and UC, as well as between European and Indian samples (see online supplementary figures S4B,D, S5 and table S3; also see table 2), with the highest level of species and phylogenetic diversity among healthy individuals. The increase of community diversity with age can be a sign of community succession, that is, a change in community structure over time, or a lack of colonisation resistance. To further investigate potential confounding effects of disease on those succession patterns, we analysed each disease state and population cohort separately. Interestingly, the strongest signal of succession is present in the active and standing bacterial communities of patients with CD, while in healthy individuals and patients with UC, diversity does not consistently increase with age (see online supplementary figures S5, S6 and supplementary table S3). Thus, in summary, the rRNA-based samples display reduced diversity compared with rDNA-based samples, and at the same time provide more resolution to detect influences of sampling region and disease compared with rDNA. The age-related patterns of increasing species diversity appear to be largely limited to CD and may point towards a reduced colonisation resistance of the disturbed microbial communities in IBD. Further,
Figure 2 Analysis of mucosa-attached bacterial communities identifies a common increase of bacterial diversity with age regardless of diagnosis and geographical origin. Correlation of α diversity metrics based on rDNA and rRNA (Chao1 species richness: r=0.493, p=1.97×10−6 (A); Shannon H ( Jost): r=0.609, p=9.223×10−11 (B); phylogenetic diversity: r=0.355, p=0.001 (C)). Species richness according to the best statistical model in rDNA (D) and rRNA (E) derived communities (table 2; for details on Shannon H and phylogenetic diversity, see figure S4). CD, Crohn’s Disease; CON, control; GER, Germany; IND, India; LIT, Lithuania. Rehman A, et al. Gut 2015;0:1–11. doi:10.1136/gutjnl-2014-308341
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Gut microbiota Table 2 Statistical analyses of α diversity based on species distribution (Shannon H), richness (Chao1) and phylogenetic diversity in DNA-based and RNA-based samples α Diversity metric
Model factors
DF
F
DNA Shannon H Chao1 species richness Phylogenetic diversity
Normalised age Normalised age Disease
1 1 2
10.532 5.656 2.258
0.002 0.020 0.111
Normalised age Disease : Normalised age
1 2
0.203 2.935
0.654 0.059
Disease
2
5.006
0.009
Population
2
14.726
