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Microbial dysbiosis in pediatric patients with Crohn’s disease 1

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Nadeem O. Kaakoush1, Andrew S. Day2,3,4, Karina D. Huinao1, Steven T. Leach2, Daniel A. 4

Lemberg3, Scot E. Dowd5, and Hazel Mitchell1* 5

6 1School of Biotechnology and Biomolecular Sciences, The University of New South Wales, 7

Sydney, NSW 2052, Australia 8 2School of Women's and Children's Health, The University of New South Wales, Sydney, 9

Australia 10 3Department of Gastroenterology, Sydney Children's Hospital, Sydney, Australia 11

4Department of Paediatrics, University of Otago, Christchurch, New Zealand 12 5Department of Biology, Texas Tech University, Lubbock, Texas, USA 13

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Corresponding author: Professor Hazel Mitchell 15

School of Biotechnology and Biomolecular Sciences 16

The University of New South Wales 17

Sydney, NSW 2052 18

Australia 19

Telephone number: +61293852040 20

Fax number: +61293851483 21

Email address: H.Mitchell@unsw.edu.au 22

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Running title: Microbial composition changes and Crohn’s disease 25

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Keywords: Crohn’s disease, dysbiosis, high-throughput sequencing, 27

Firmicutes, Bacteroides, Proteobacteria 28

Copyright © 2012, American Society for Microbiology. All Rights Reserved.J. Clin. Microbiol. doi:10.1128/JCM.01396-12 JCM Accepts, published online ahead of print on 25 July 2012

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Abstract 29

Microbial dysbiosis has been suggested to be involved in the pathogenesis of Crohn’s 30

disease, however, many studies of gut microbial communities have been confounded by 31

environmental and patient-related factors. In this study, the microbial flora of fecal samples 32

from nineteen children newly-diagnosed with CD and twenty-one age-matched controls were 33

analyzed using high throughput sequencing to determine differences in the microbial 34

composition between CD patients and controls. Analysis of the microbial composition of 35

specific bacterial groups revealed that Firmicutes were significantly lower in CD patients 36

than in controls, and that this was largely due to changes in the class Clostridia. Bacteroidetes 37

and Proteobacteria percentages were higher and significantly higher in CD patients than in 38

controls, respectively. Both the detection frequencies of Bacteroidetes and Firmicutes 39

correlated (positively and negatively, respectively) with the calculated Pediatric Crohn’s 40

Disease Activity Index scores of patients. Upon further analysis, differences in the microbial 41

compositions of patients with mild disease and moderate to severe disease were identified. 42

Our findings indicate that a combination of different bacterial species or a dynamic interplay 43

between individual species is important for disease, and is consistent with the dysbiosis 44

hypothesis of CD. 45

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Introduction 46

Crohn’s disease (CD) is a chronic relapsing idiopathic disease, and together with ulcerative 47

colitis, they are the two most common forms of inflammatory bowel diseases (IBD) (41). CD 48

is a multifactorial disease with unknown etiology. However, it is currently hypothesized that 49

intestinal microorganisms, in association with a disruption of the gastrointestinal epithelium, 50

stimulate and subsequently drive a dysregulated immune response in predisposed individuals 51

(47). 52

53

In recent literature, dysbiosis, a breakdown in the balance between commensal and 54

pathogenic intestinal bacteria, has been suggested to be involved in the pathogenesis of CD. 55

A consistent finding across studies investigating dysbiosis in CD is that, in patients the 56

abundance of members of the Firmicutes is decreased, whereas members of the 57

Proteobacteria (Escherichia coli in particular) are increased than in controls (41). These 58

findings are supported by observations in mouse models of CD where prolific colonization by 59

commensal bacteria, such as Enterobacteriaceae, drives the depletion of other groups such as 60

Firmicutes (39). 61

62

The results observed for CD-related changes in groups such as the Bacteroidetes are however 63

more inconsistent. For example, while Frank et al (21) and Ott et al (45) reported 64

Bacteroidetes to be significantly depleted in CD patients as compared with controls, Rehman 65

et al (46) and Swidsinski et al (53) showed Bacteroidetes to be more prevalent in CD patients 66

as compared with controls. The latter findings are supported by studies examining the 67

intestinal microbiota in the TRUC (T-bet, RAG, ulcerative colitis) mouse model of 68

spontaneous colitis, where bacteria belonging to the order Bacteroidales (phylum 69

Bacteroidetes) were reported to be present in high numbers (23). Furthermore, Rehman et al 70

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have reported the transcriptional activity of Bacteroidetes to be significantly increased in 71

patients with CD as compared with controls (46). 72

73

While the current literature on dysbiosis and CD provides significant insights into microbial 74

changes associated with CD, the data has not been consistent. Possible factors for these 75

inconsistencies include differences in the techniques employed to survey the intestinal 76

microbiota, study design, the stage of disease and its location, and the control populations 77

used. To avoid potential confounding factors associated with previous studies in adults, 78

including previous treatment with antibiotics or anti-inflammatory therapies, differences in 79

stage of disease, smoking and alcohol intake, we conducted the current study in children 80

newly diagnosed with CD who had not undergone prior antibiotic or anti-inflammatory 81

therapy for CD and age- matched controls. 82

83

Materials and Methods 84

Patients 85

Twenty-two symptomatic children undergoing diagnostic colonoscopy and upper endoscopy 86

at the Sydney Children’s Hospital Randwick (Sydney, Australia) were included in the study. 87

Based upon standard endoscopic, histologic, and radiologic investigations (24), 19/22 88

children (12 male, mean age: 11.6 ± 2.5 years) were newly diagnosed with ileo-colonic CD 89

with upper-gut involvement. The Pediatric Crohn’s Disease Activity Index (PCDAI) ranged 90

from 7.5 – 70 (Table 1). Of the remaining three children, one was diagnosed with reflux 91

esophagitis (female, 12.2 years), one duodenal ulcer disease (male, 13.1 years), and the 92

remaining child was diagnosed with a functional bowel disorder (male, 11.7 years). 93

94

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Along with the three children who underwent colonoscopy and who were not diagnosed with 95

IBD, 18 healthy children were also included in the study as controls. These 21 control 96

children with no histological features of IBD comprised 13 males and had a mean age of 9.5 97

± 4.2 years (P=0.07). 98

99

No child involved in this study had undergone prior antibiotic or anti-inflammatory therapy 100

in the previous four weeks. Informed consent was obtained from all children (or their 101

parent/guardian for younger children) to be included in the study. This study was approved 102

by the Research Ethics Committees of the University of New South Wales and the South East 103

Sydney Area Health Service-Eastern Section, Sydney (Ethics No.: 03/163, 03/165 and 104

06/164). Fecal samples were collected from each child prior to colonoscopic examination. 105

106

DNA extraction and microbial community sequencing 107

DNA extraction was performed using the ISOLATE Fecal DNA Kit (Bioline) according to 108

the manufacturer’s instructions. The concentration and quality of DNA was measured using a 109

Nanodrop ND-1000 Spectophotometer (Nanodrop Technologies; Wilmington, USA). 110

111

The microbial community was assessed by high-throughput sequencing of the 16S rRNA 112

gene. Tag-encoded FLX amplicon pyrosequencing (bTEFAP) was performed as described 113

previously using the primers Gray28F (5’TTTGATCNTGGCTCAG) and Gray519r (5’ 114

GTNTTACNGCGGCKGCTG) (2, 3, 20, 26) with the primers numbered in relation to E. coli 115

16S rRNA (variable regions 1-3). The sequence of the primers is not complementary to 116

human DNA, thus preventing co-amplification of human sequences. Moreover, the primers 117

span the variable region of the rRNA gene so that discrimination between closely related taxa 118

can be performed. At the same time, the positioning of the primers allows for amplification of 119

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a large proportion of known 16S rRNA sequences. Generation of the sequencing library 120

utilized a one-step PCR with a total of 30 cycles, a mixture of Hot Start and HotStar high 121

fidelity taq polymerases, and amplicons originating and sequencing extending from the 28F 122

with an average read length of 400 bp. Tag-encoded FLX amplicon pyrosequencing analyses 123

utilized a Roche 454 FLX instrument with Titanium reagents. This bTEFAP process was 124

performed at the Molecular Research laboratory (MR DNA; Shallowater, TX) based upon 125

established and validated protocols (http://www.mrdnalab.com/). 126

127

Data analysis 128

The sequence data derived from the high-throughput sequencing process was analyzed 129

employing a pipeline developed at Molecular Research LP (www.mrdnalab.com). Sequences 130

are first depleted of barcodes and primers, then short sequences <200 bp, sequences with 131

ambiguous base calls, and sequences with homopolymer runs exceeding 6 bp are all 132

removed. Sequences were then de-noised and chimeras were removed (Black Box Chimera 133

Check software B2C2, n = 2129). Operational taxonomic units (OTU) were defined after 134

removal of singleton sequences (sequences appearing only once in the whole dataset) with 135

clustering set at 3% divergence (97% similarity) (9, 14-17, 19, 51). OTU were then 136

taxonomically classified using BLASTn against a curated GreenGenes database (12) and 137

compiled into each taxonomic level. Taxonomy was defined based on the following 138

percentages: >97%, species; between 97% and 95%, unclassified species; between 95% and 139

90%, unclassified genus; between 90% and 85%, unclassified family; between 85% and 80%, 140

unclassified order; between 80% and 77%, unclassified phylum; <77%, unclassified. 141

Statistical analyses based on relative abundances of bacterial groups were performed using 142

Primer-E (http://www.primer-e.com/). Permutational multivariate analysis of variance 143

(PERMANOVA) (1), which overcomes multiple hypothesis testing, was performed to detect 144

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overall group differences between CD patients and controls. The hypothesis of the test was 145

“Are there differences in Bray-Curtis similarities between bacterial communities of CD 146

patients and controls?” In order to explore for key variables, differences in relative 147

abundances of bacterial taxa between CD patients and controls were tested using standard t-148

tests. Upon stratification of CD patients into mild and moderate/severe groups, a one-way 149

ANOVA with a post hoc Tukey’s correction was used to correct for multiple testing. 150

Although examining the multivariate significance of the data provides some protection 151

against increased chance of type I error, it is still likely that comparisons may achieve 152

statistical significance by chance. 153

154

Results and Discussion 155

Evidence to support the role of microorganisms in the pathogenesis of CD has been 156

demonstrated in both humans and animals (41). However, the specific microorganism or 157

group of microorganisms responsible for the initiation of CD are yet to be determined. Thus, 158

in an attempt to gain a better understanding of microbial involvement in CD, we examined 159

the microbial flora in fecal samples of children newly diagnosed with CD and age-matched 160

controls, as this population is relatively free of confounding factors. 161

162

The mean number of sequences per sample obtained for the 40 subjects in this study 163

following data analysis was 2609 ± 138 sequences. To check for sampling bias arising from 164

grouping the subjects into CD patients and controls, the number of sequences per sample 165

within each group was analyzed. The mean number of sequences per sample for CD patients 166

and controls were 2716 ± 220 and 2513 ± 174 sequences, respectively, and this difference 167

was not statistically significant (P=0.47), indicating that no sampling bias due to the grouping 168

existed. 169

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Differences in microbial compositions of the control subjects 171

Principal component analysis was performed on the detection frequencies obtained for the 172

control subjects in order to identify any outliers that could influence downstream analyses. 173

Two subjects were shown to be outliers with respect to the other controls (Figure 1). 174

Interestingly, one of these outliers was the control subject diagnosed with a functional bowel 175

disorder. Upon more specific analysis of the detection frequencies in this child, it was found 176

that they had a high Actinobacteria count (17.1%), which was due to a putative 177

Mycobacterium peregrinum infection (14.8%; 0% in all other subjects). Although M. 178

peregrinum has yet to be clearly associated with human disease, this bacterium has been 179

related to surgical site infections and catheter-related infections (44). Interestingly, infection 180

with M. peregrinum was reported to be the cause of an outbreak of mycobacteriosis in an 181

aviary containing Gouldian finches (Erythrura gouldiae) where affected birds developed 182

granulomatous lesions of the liver and intestine (54). Another study reported that exposure of 183

zebrafish to M. peregrinum led to clinical signs of mycobacteriosis (27). The second outlier 184

(Figure 1, Figure S1) was a healthy control that was found to have an unusually high level of 185

Bacteroidetes (63.8%) that was shown to result from high levels of Prevotella copri (55.2%). 186

Although P. copri has been previously isolated from human feces (28), no studies have 187

associated this bacterium with any specific disease outcome. It is unknown why this healthy 188

control had such high levels of P. copri within their fecal microbial composition. Given these 189

anomalies these two outliers were excluded from further multivariate and univariate 190

comparisons with CD patients. Thus, for the final fecal microbiota comparisons 19 control 191

children (12 males) and 19 children with CD (12 males) were included. 192

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Variations in microbial compositions between patients with Crohn’s disease and controls 194

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Analysis of the microbial composition of CD patients and controls revealed that as a whole 195

error values within the detection frequencies of CD patients were relatively higher than that 196

within the controls (percentage standard errors on the three major phyla Firmicutes, 197

Bacteroidetes and Proteobacteria were 11.6%, 26.7% and 38.2% for CD patients and 3.0%, 198

21.8% and 31.2% for controls, respectively) indicating that higher variations existed among 199

individual CD patients than among controls. Despite this, PERMANOVA analysis showed 200

that differences between CD patients and the remaining 19 controls were significant 201

(P=0.015), and several taxa were found to be significantly different in abundance in CD 202

patients as compared with controls using univariate analyses. 203

204

Firmicutes 205

Analysis of the microbial composition for specific bacterial groups revealed that Firmicutes 206

were significantly lower in CD patients as compared with controls (P=0.0035) (Table 2, 207

Figure 2). This shift in Firmicutes was found to be largely due to changes in the detection of 208

class Clostridia (and order Clostridiales) that was significantly lower in CD patients than in 209

controls (P=0.0009) (Table 2). The decrease in relative abundance of members of the 210

Firmicutes in the children with CD is consistent with recent findings on microbial 211

composition changes in CD patients (41). For example, Rehman et al showed Firmicutes to 212

be significantly lower in patients with CD (52.7%) as compared with healthy controls 213

(78.6%; P<0.01) (46). 214

215

Three families (Clostridiaceae, Lachnospiraceae and Ruminococcaceae) were found to have 216

varying relative abundances when the two subject groups were compared. Clostridiaceae 217

specifically members of the Clostridium genus, which comprises several important 218

pathogens, were numerically higher in CD patients than controls (Table 2), although this was 219

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not significant (P=0.29). In contrast, Lachnospiraceae were higher in controls than in CD 220

patients (Table 2), however, again this did not reach significance (P=0.11). Closer analysis of 221

this family showed that two genera Coprococcus and Roseburia (P=4.4 x 10-5 and P=0.031, 222

respectively) were driving the results (Table 2). Species of interest (those with significantly 223

different relative abundances between CD patients and controls) were Roseburia faecis, 224

Coprococcus eutactus and Coprococcus Clostridium sp. SS2/1 (Table 2). 225

226

Ruminococcaceae were found to be significantly higher in controls than in CD patients 227

(P=0.0033). These results were partly due to significant differences in Oscillospira (P=0.033) 228

and Subdoligranulum (P=0.0029). Unlike previous observations in adult patients (41, 48), 229

where significantly lower abundances of Faecalibacterium prausnitzii have been reported in 230

CD patients as compared with controls, no significant difference in Faecalibacterium 231

prausnitzii was observed between the CD children as compared with controls (P=0.20). 232

Although, the frequency in CD children was lower than that in controls (Table 2). 233

Interestingly, F. prausnitzii is believed to be an anti-inflammatory commensal bacterium, 234

which when decreased in abundance increases the risk of CD recurrence (49). Our contrasting 235

findings may reflect differences in the immunomodulatory roles of the gut microflora in 236

adults and children, and/or the fact that our patients were newly diagnosed with the disease. 237

238

One CD patient (CD9) who had a relatively high Firmicutes count (92.2%) as compared with 239

other CD patients (average: 57.9 ± 6.7%) was found to have unusually high counts of 240

Streptococcus spp. including S. parasanguinis (30.9%), S. thermophilus (30.3%) and S. oralis 241

(8.3%). Streptococcus spp. have been previously associated with CD, with Streptococcal 242

DNA being identified in CD patients but not in healthy controls (25). 243

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Bacteroidetes 245

In the current study, numerically higher percentages of Bacteroidetes were observed in CD 246

patients as compared with that in controls (P=0.086) (Table 2, Figure 2). The drop in 247

Firmicutes detection levels correlated with the increase in Bacteroidetes (CD: r17 = -0.71, 248

P=0.0006; controls: r17 = -0.82, P<0.0001). Similar correlations were observed when 249

comparing the detection frequencies of Bacteroidia, Bacteroidales and Bacteroidaceae in CD 250

patients and controls. The only genus within Bacteroidaceae showing consistent results in its 251

detection frequency between CD patients and controls that was close to significance was 252

Bacteroides (P=0.065) (Table 2). While the difference between the CD patients and controls 253

improved statistically from the phylum to genus level (P=0.086 to 0.065), it still did not reach 254

significance (discussed further below). As reported previously by Swidsinski et al (52), the 255

Bacteroides within each patient were not represented by a single species but by multiple 256

species. The change in the level of bacteria from the phylum Bacteroidetes was of particular 257

interest given the study by Bloom et al (5) who showed common commensal Bacteroides 258

species such as Bacteroides vulgatus and Bacteroides thetataiotaomicron to colonize IBD-259

susceptible and -nonsusceptible mice equivalently, but to induce disease exclusively in 260

susceptible animals. 261

262

Fusobacteria 263

In the current study, Fusobacteria were detected in 8/19 CD patients (patient CD2 having a 264

high detection frequency of 29.2%) and 1/19 controls, with the one positive control patient 265

being the control diagnosed with duodenal ulcer disease (Fusobacterium canifelinum). None 266

of the healthy controls had Fusobacteria. The species detected in our CD patients were 267

Fusobacterium equinum (n=2), Fusobacterium canifelinum (n=3), Fusobacterium nucleatum 268

(n=1), Fusobacterium periodonticum (n=1) and the misclassified (10) Clostridium rectum 269

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(n=1). Fusobacterium nucleatum, which was detected in patient CD14, has been recently 270

associated with colorectal carcinoma (34). Moreover, Strauss et al (50) isolated 271

Fusobacterium spp. from a significantly higher number of IBD (CD, n=17; UC, n=4; IBD-272

unclassified, n=1) patients (15/22, 68.2%) compared with non-IBD controls (9/34, 26.5%). 273

Interestingly, these authors found that Fusobacterium strains originating from inflamed 274

biopsy tissue of IBD patients were significantly more invasive than strains isolated from 275

healthy tissue from either IBD patients or control patients (50). 276

277

Actinobacteria 278

The frequency of members of the phylum Actinobacteria was similar in both the CD and 279

control subjects (P=0.88) (Table 2, Figure 2). However, in one CD patient (CD6) a very high 280

Actinobacteria count (22.2%) was noted, which was shown to be due to the presence of two 281

species Collinsella aerofaciens (8.2%) and Bifidobacterium longum (9.9%). Interestingly, 282

Swidsinski et al (52) have previously reported a preferential increase in the frequency of C. 283

aerofaciens in IBD patients. Moreover, the frequency of members of the order 284

Actinomycetales was higher in CD patients than controls (Table 2), and these bacteria were 285

detected in 13/19 CD patients in comparison to 5/19 controls. However, these differences 286

were not significant (P=0.19). 287

288

Proteobacteria 289

In the current study Proteobacteria percentages were significantly higher in CD patients than 290

in controls (P=0.040) (Table 2, Figure 2). Further analysis revealed that the detection rates of 291

Proteobacteria were mostly driven by γ-Proteobacteria detection (P=0.056) (Table 2). 292

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The increased detection of γ-Proteobacteria in CD patients resulted from the detection of 294

Enterobacteriales (P=0.058), specifically Escherichia (P=0.062) and Enterobacter (P=0.061) 295

(Table 2). The specific species detected within the Escherichia genus based on our BLAST 296

sequence analyses of the pyrosequencing reads were Escherichia coli, Escherichia fergusonii 297

and Escherichia albertii. Interestingly, previous studies have reported adherent and invasive 298

E. coli (AIEC) to be associated with ileal CD, with one study reporting the isolation of AIEC 299

in 22% of ileal biopsy samples from CD patients as compared with 6.2% of controls (11). 300

Moreover, a further study has reported the isolation of AIEC in 29% of CD patients as 301

compared with 9% of controls (42). While E. coli was detected in our CD patients, whether 302

this is an AIEC strain cannot be ascertained. Within the Enterobacter genus, only one species 303

Enterobacter hormaechei was detected in our CD patients. While the detection rate in CD 304

patients was higher than in controls (Table 2), this did not quite reach significance (P=0.059). 305

306

One CD patient (CD11) who was found to have very high Proteobacteria counts (77.4%), was 307

found to possibly have an unclassified Citrobacter infection (46.2%). This is of some interest 308

given that in mice Citrobacter rodentium infection has been shown to elicit a highly polarized 309

Th1 response including up-regulation of interleukin-12, interferon-γ and tumor necrosis 310

factor alpha, and a pathology very similar to that found in mouse models of inflammatory 311

bowel disease (29). More recently, Th17 cells have been implicated in C. rodentium 312

infections (37), a finding which is of interest considering the Th17 response has also been 313

implicated in the development of Crohn’s disease (6). 314

315

The Klebsiella species K. granulomatis and K. variicola were detected in 4 and 5 CD 316

patients, respectively and 0 controls, although in one control Klebsiella pneumoniae was 317

detected. In 2009 Gutierrez et al reported Klebsiella DNA to be present in the blood of one 318

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CD patient but not in any healthy controls (25). Moreover, the presence of K. pneumoniae has 319

been correlated with colitis in a genetically deficient mouse model (23), although this is not 320

supported by our findings. 321

322

In 2008, Wagner et al used a genus specific bacterial 16S PCR as well as nested PCR to 323

investigate the prevalence and diversity of Pseudomonas species in ileal biopsies of 32 324

children with CD at initial endoscopic examination, as well as in control children with non-325

inflammatory bowel disease (non-IBD) (55). This showed that 58% of CD patients 326

demonstrated positive results for Pseudomonas spp., which was significantly higher than for 327

non-IBD controls (33%). In the current study, members of the Pseudomonadaceae were 328

detected in 5 CD patients (26.3%) and in no controls. Further analysis revealed that 329

Pseudomonas balearica was the species detected only in the CD patients. 330

331

While a higher abundance of α-Proteobacteria was detected in CD patients as compared with 332

controls (Table 2), this difference was not significant (P=0.13). Comparison of the order 333

Rhizobiales (detected in 10/19 CD patients vs. 0/19 controls) showed a significantly higher 334

abundance in CD patients than in controls (P=0.0033) (Table 2). Bradyrhizobiaceae (detected 335

in 6/19 CD patients vs. 0/19 controls) within the Rhizobiales also showed a significantly 336

higher abundance in CD patients than controls (P=0.045) (Table 2). The species identified 337

were an unclassified Balneimonas (n=3) and Afipia broomae (n=3). Other α-Proteobacteria 338

species identified were Methylobacterium adhaesivum (n=2) and Methylobacterium 339

hispanicum (n=2) (within Methylobacteriaceae), Aurantimonas coralicida (n=2) (within 340

Aurantimonadaceae), Rhodoplanes cryptolactis (n=1) and Gemmiger formicilis (n=1) (within 341

Hyphomicrobiaceae), and Rhizobium spp. IRBG 74 (n=1) (within Rhizobiaceae). While it 342

appears that A. broomae may be an opportunistic pathogen in elderly people (7), the higher 343

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abundance of α-Proteobacteria in CD patients is more likely to have resulted from changes in 344

the gut environment of patients (eg. higher methane production and higher reactive nitrogen 345

species production) as these bacteria tend to be environmental rather than host-related. 346

347

ß-Proteobacteria were also higher in abundance in CD patients than controls, the predominant 348

order detected being Burkholderiales (Table 2). Three species of interest were detected within 349

this order, Cupriavidus gilardii (4 CD patients vs. 0 controls), Massilia timonae (5 CD 350

patients vs. 0 controls) and Paucimonas lemoignei (4 CD patients vs. 0 controls). C. gilardii 351

is not known for its pathogenicity, however, it has been reported to infect humans, in one 352

case causing fatal sepsis following the dissemination of an infection with intestinal focus 353

(33). M. timonae has also been reported to infect humans, being isolated from blood, 354

cerebrospinal fluid, and bone samples (36). Interestingly, Neisseriales were detected in 6 CD 355

patients and in 1 control, the species detected being Eikenella corrodens (3 CD patients vs. 0 356

controls) and an unclassified Vogesella (3 CD patients vs. 1 control). 357

358

δ-Proteobacteria, which have been associated with IBD (38), were detected in four CD 359

patients (CD3, CD5, CD6 and CD12) and no controls. The bacteria were identified as an 360

unclassified Entotheonella (belonging to Entotheonellales) (CD3 and CD5), Bilophila 361

wadsworthia (belonging to Desulfovibrionales) (CD6 and CD12) and an unclassified 362

Haliangium (belonging to Myxococcales) (CD6). Both Entotheonella spp. and Haliangium 363

spp. appear to be environmental bacteria with no association with human disease (8, 22). In 364

contrast, B. wadsworthia has been associated with human infections including appendicitis 365

and cholecystitis (4). 366

367

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Bacteria from the class ε-Proteobacteria were detected in two patients (CD1 and CD8) and no 368

controls. The bacterium was identified as Campylobacter concisus, a species recently been 369

associated with IBD (32, 41). Interestingly, C. concisus strains isolated from chronic 370

intestinal diseases such as CD have been shown to be more invasive than strains isolated 371

from acute intestinal disease and healthy subjects (31, 40). 372

373

Possible bacterial species associated with initiating and driving CD in the 19 CD patients in 374

this study are detailed in Table 3. These bacteria were chosen based on their previous 375

association with CD in other studies, their pathogenic potential in humans or their relative 376

abundance in CD patients versus controls in this study. 377

378

Effect of age and gender on microbial composition 379

While age has been reported to affect the intestinal microbial composition in infants or old 380

people (18) in the current study no correlation between microbial composition and age was 381

observed, and this may relate to the fact that the children were not infants and had a fairly 382

narrow age range. While Lay et al (35) and Dicksved et al (13) have both reported no 383

significant correlation between microbial composition and age, Mueller et al (43) reported 384

both Enterobacteria and Bacteroides-Prevotella detection levels to be influenced by the 385

subject’s age. In addition to age, a number of previous studies have also investigated the 386

effect of gender on the fecal microbial composition of subjects. In the current study the 387

detection frequencies of Firmicutes, Bacteroidetes and Proteobacteria were compared in 388

females and males in both the CD patients and controls. This showed that although 389

Bacteroidetes appeared to be higher in females (both CD patients and controls) and 390

Proteobacteria appeared to be higher in males (both CD patients and controls), no statistical 391

difference was observed between females and males within either subject subgroup (Table 4). 392

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393

Associations between the inflammatory index of Crohn’s disease patients 394

As one of the key hallmarks of CD is chronic inflammation, the microbial composition of 395

patients was assessed with respect to their calculated PCDAI at diagnosis. Correlation 396

analysis revealed that there existed a strong positive correlation between the detection 397

frequencies of Bacteroidetes and the calculated PCDAI scores of each patient (r17 = 0.544, 398

P=0.016) (Figure 3). In contrast, a negative correlation was identified between the detection 399

frequencies of Firmicutes and PCDAI (r17 = -0.425, P=0.070) (Figure 3), however, this did 400

not reach significance. 401

402

Due to the observed correlation between microbial composition and PCDAI, patients were 403

stratified into two groups consisting of mild disease (PCDAI < 30) and moderate to severe 404

disease (PCDAI ≥ 30) (30). This categorization of CD patients based on disease activity 405

revealed that Bacteroidetes were present at significantly higher levels in moderate to severe 406

disease (31.8 ± 9.5%) than controls (11.1 ± 2.4%, P=0.021), whereas patients with mild 407

disease (12.1 ± 5.7%) had similar levels to controls (P=0.99) (Figure 4). The finding that 408

Bacteroidetes were similar in patients with mild disease and controls, would suggest that their 409

abundance is either as a result of inflammation or is driving inflammation. Interestingly, 410

Firmicutes were present at numerically lower levels in mild (63.0 ± 10.3%, P=0.15) and 411

significantly lower levels in moderate to severe disease (53.3 ± 9.0%, P=0.0097) than in 412

controls (80.0 ± 2.4%) (Figure 4), suggesting that inflammation may have a negative effect 413

on Firmicutes or that changes in the Bacteroidetes and Proteobacteria (below) may affect 414

their abundance. Of particular interest was the finding that Proteobacteria were present at 415

higher levels in mild (18.1 ± 8.7%) compared with moderate to severe disease (4.6 ± 1.5%, 416

P=0.070) and controls (1.6 ± 0.5%, P=0.0085) (Figure 4). Given the higher levels of 417

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Proteobacteria in patients with mild disease as compared with moderate/severe disease, this 418

may suggest that this group could play a role in the initiation of the disease. 419

420

Conclusions 421

In conclusion, significant differences in the microbial composition of patients with CD and 422

controls were detected, highlighting specific bacterial groups that may be associated with 423

disease outcome. Furthermore, the observed correlation between specific bacterial groups and 424

patient PCDAI emphasizes the involvement of these groups in initiating or driving 425

inflammation, and possibly the disease. 426

427

We propose the hypothesis (Figure 5) whereby an invasive pathogen (possibly a member of 428

the Proteobacteria) capable of surviving intracellularly (as evidenced by the association 429

between autophagy and CD) establishes an infection in patients. Defective pathogen sensing 430

and reduced ability for pathogen clearance by the host (47) results in a chronic intracellular 431

infection. As a result of a dysregulated immune response, chronic inflammation occurs, 432

which is driven by the dysbiotic intestinal microflora, and in particular Bacteroides species. 433

Following treatment, disease remission occurs, however, re-infection leads to relapse of the 434

disease. 435

436

Acknowledgements 437

This work was made possible by the support of the National Health and Medical Research 438

Council, Australia and The University of New South Wales Goldstar award. NOK is 439

supported by an Early Career fellowship from the National Health and Medical Research 440

Council, Australia. 441

No conflicts of interest exist. 442

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443

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634

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Figure legends 635

Figure 1 636

Principal component analysis of the bacterial community compositions of the control 637

subjects. 638

639

Figure 2 640

Bacterial diversity observed in Crohn’s disease patients and age-matched controls. 641

Differences in detection frequencies of bacterial phyla between CD patients (gray) and 642

controls (white). Firmicutes (CD patients: 57.9 ± 6.7%, controls: 80.0 ± 2.4%, P=0.0035); 643

Proteobacteria (CD patients: 11.0 ± 4.2%, controls: 1.6 ± 0.5%, P=0.040); Bacteroidetes (CD 644

patients: 22.5 ± 6.0%, controls: 11.1 ± 2.4%, P=0.086); Actinobacteria (CD patients: 3.2 ± 645

1.2%, controls: 3.4 ± 0.8%, P=0.88). Error bars are the standard error of the mean. 646

647

Figure 3 648

Correlations between detection frequencies of Firmicutes and Bacteroidetes and patient 649

PCDAI. Firmicutes, black (r17 = -0.425, P=0.070); Bacteroidetes, grey (r17 = 0.544, 650

P=0.016). 651

652

Figure 4 653

Microbial composition of patients with mild CD, moderate/severe CD and in controls. 654

A, Bacteroidetes; B, Firmicutes; C, Proteobacteria. 655

656

Figure 5 657

Proposed mechanism of initiation and recurrence of Crohn’s disease. 658

659

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Table captions 660

Table 1 661

Characteristics of children with Crohn’s disease included in the study, the location of 662

their disease and their PCDAI at diagnosis. 663

Patient Age (years) Gender Disease location PCDAICD1 9 F L3 + L4 (ileocolon) 7.5 CD2 12 F L3 + L4 (ileocolon) 57.5 CD3 14 M L3 + L4 (ileocolon) 45 CD4 13 M L3 + L4 (ileocolon) 22.5 CD5 14 M L3 + L4 (ileocolon) 52.5 CD6 15 F L3 + L4 (ileocolon) 52 CD7 14 F L3 + L4 (ileocolon) 25 CD8 9 M L3 + L4 (ileocolon) 35 CD9 10 M L3 + L4 (ileocolon) 20 CD10 13 M L3 + L4 (ileocolon) 20CD11 14 M L3 + L4 (ileocolon) 27.5 CD12 15 M L3 + L4 (ileocolon) 37.5 CD13 9 M L3 + L4 (ileocolon) 27.5 CD14 8.6 M L3 + L4 (ileocolon) 25 CD15 11.4 F L3 + L4 (ileocolon) 37.5 CD16 13 F L3 + L4 (ileocolon) 70 CD17 8.7 M L3 + L4 (ileocolon) 10 CD18 7.2 M L3 + L4 (ileocolon) 42.5 CD19 11 F L3 + L4 (ileocolon) 52.5

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Table 2 665

Differences in detection frequencies of bacterial groups between CD patients and controls. 666

Phylum Class Order/Family Genus Species CD (%) Controls (%) P-value Firmicutes 57.9 ± 6.7 80.0 ± 2.4 0.0035* Clostridia 49.6 ± 7.3 77.4 ± 2.4 0.0009* Clostridiaceae 10.3 ± 4.1 5.7 ± 1.4 0.29 Clostridium 14.4 ± 3.9 8.8 ± 1.1 0.21 Lachnospiraceae 18.0 ± 5.7 30.8 ± 4.6 0.11 Coprococcus 0.93 ± 0.26 5.1 ± 0.8 4.4 x 10-5* Roseburia 4.4 ± 1.8 10.4 ± 2.0 0.031* Roseburia faecis 1.5 ± 0.7 4.5 ± 0.9 0.016* Coprococcus eutactus 0.025 ± 0.017 0.95 ± 0.29 0.0045* Coprococcus Clostridium sp. SS2/1 0.33 ± 0.13 2.4 ± 0.7 0.012* Ruminococcaceae 15.0 ± 3.0 27.4 ± 2.2 0.0033* Oscillospira 0.26 ± 0.16 1.8 ± 0.6 0.033* Subdoligranulum 0.68 ± 0.45 3.5 ± 0.7 0.0029* Faecalibacterium prausnitzii 9.9 ± 2.4 14.1 ± 2.2 0.20 Bacteroidetes 22.5 ± 6.0 11.1 ± 2.4 0.086 Bacteroides 19.6 ± 5.4 8.4 ± 2.3 0.065 Actinobacteria 3.2 ± 1.2 3.4 ± 0.8 0.88 Actinomycetales 0.24 ± 0.12 0.069 ± 0.031 0.19 Proteobacteria 11.0 ± 4.2 1.6 ± 0.5 0.040* γ-Proteobacteria 9.6 ± 4.1 1.1 ± 0.5 0.056 Enterobacteriales 9.2 ± 4.3 0.71 ± 0.36 0.058 Escherichia 3.2 ± 1.4 0.29 ± 0.16 0.062 Enterobacter 2.3 ± 1.0 0.13 ± 0.05 0.061 Enterobacter hormaechei 2.3 ± 1.0 0.11 ± 0.05 0.059 α-Proteobacteria 0.34 ± 0.13 0.10 ± 0.08 0.13 Rhizobiales 0.10 ± 0.03 0 0.0033* Bradyrhizobiaceae 0.036 ± 0.016 0 0.045* ß-Proteobacteria 1.0 ± 0.3 0.43 ± 0.19 0.12 Burkholderiales 0.91 ± 0.28 0.42 ± 0.19 0.11

*P<0.05 667

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Table 3 668

Possible bacterial species associated with initiating and driving Crohn’s disease in the 669

CD patients in this study. These bacteria were chosen based on their previous association 670

with CD in other studies, their pathogenic potential in humans or their relative abundance in 671

CD patients versus controls in this study. 672

Patient Possible agents associated with disease CD1 Campylobacter concisus; Pseudomonas balearica; Cupriavidus gilardii CD2 Fusobacterium equinum (27.4%); Klebsiella spp. CD3 Cupriavidus gilardii; Pseudomonas balearica; Massilia timonae;

Fusobacterium equinumCD4 Ruminococcus torques (21.1%) CD5 Klebsiella variicola; Escherichia fergusonii; Bacteroides spp. (73.7%);

Fusobacterium canifelinum CD6 Massilia timonae; Escherichia fergusonii; Bilophila wadsworthia; Collinsella

aerofaciens (8.2%); Fusobacterium canifelinum CD7 Cupriavidus gilardii; Pseudomonas balearica; Massilia timonae; Escherichia

fergusonii; Clostridium difficile (6.5%) CD8 Campylobacter concisus; Clostridium difficile (24.7%); Cupriavidus gilardii;

Pseudomonas balearica; Massilia timonae; Klebsiella spp.; Escherichia fergusonii

CD9 Streptococcus spp. (74.0%) CD10 Klebsiella spp.; Escherichia fergusonii; Bacteroides spp. (50.2%);

Fusobacterium canifelinumCD11 Citrobacter unclassified (46.2%); Escherichia fergusonii; Klebsiella spp.;

Pseudomonas balearica; Massilia timonae CD12 Bilophila wadsworthia; Fusobacterium canifelinum CD13 Clostridium difficile (2.2%) CD14 Escherichia coli; Shigella sonnei; Fusobacterium nucleatum; Fusobacterium

periodonticum; Peptostreptococcus anaerobius (13.9%) CD15 Unusual α-Proteobacteria detected CD16 Escherichia coli; Shigella sonnei; Bacteroides spp. (56.3%) CD17 Escherichia coli; Shigella sonnei CD18 Bacteroides spp. (30.0%) CD19 Clostridium rectum (Fusobacteria); Bacteroides spp. (57.1%);

Parabacteroides spp. (19.1%) 673

674

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Table 4 676

Effect of gender on the detection frequencies of three bacterial phyla. 677

Subjects Gender Bacterial detection frequencies (%) Firmicutes P-value Bacteroidetes P-value Proteobacteria P-value CD patients Female 53.9 0.66 26.6 0.61 6.8 0.47 Male 60.2 20.1 13.5 Controls Female 80.2 0.95 13.5 0.45 0.47 0.085 Male 79.9 9.6 2.3

678

679

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1.2

1.0

0 6

0.8

nent

2

0.4

0.6

Com

pon

0.2

0.0-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

-0.2Component 1

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90 P = 0.0035

70

80

50

60

ncy

(%)

40

ectio

n fr

eque

n

P = 0.086

20

30

Det

e

P = 0.040

0

10 P = 0.88

Bacteroidetes Firmicutes Proteobacteria ActinobacteriaPhylum

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100

80

90

60

70

uenc

y (%

) Firmicutes

40

50

tect

ion

freq

u

20

30Det

i

0

10Bacteroidetes

0 10 20 30 40 50 60 70 80

PCDAI

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30354045

ncy

(%)

A

51015202530

etec

tion

freq

uen

05

Controls Mild CD Moderate and severe CD

D

8090

%)B

304050607080

n fr

eque

ncy

(%

0102030

Controls Mild CD Moderate and severe CD

Det

ectio

n

C

20

25

30

quen

cy (%

)

0

5

10

15

Det

ectio

n fr

eq

0Controls Mild CD Moderate and severe CD

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1 I fe tio by i a i e atho e1. Infection by invasive pathogen

3. Reduced ability for pathogen clearance (Autophagy defect)

11. Relapse

2. Defective pathogen sensing

clearance (Autophagy defect)

5. Dysregulated immune response4. Chronic intracellular infection

6. Dysbiosis

7. Chronic inflammation10. Remission 7. Chronic inflammation

8. Crohn’s disease

9. Treatment

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