Bacterial Dysbiosis Associated With Crohn's Disease: Advances in Gut Metagenomic Analysis

Josef Wagner^*

Murdoch Childrens Research Institute, Melbourne, Australia

*Corresponding Author:

Dr. Josef Wagner
Murdoch Childrens Research Institute
The Royal Children’s Hospital
Flemington Road Parkville Victoria 3052 Australia
Tel: 03 83416450
Fax: 03 83416449
E-mail: Josef.wagner@mcri.edu.au

Received date: March 26, 2012; Accepted date: March 28, 2012; Published date: March 30, 2012

Citation: Wagner J (2012) Bacterial Dysbiosis Associated With Crohn’s Disease: Advances in Gut Metagenomic Analysis. J Gastrointest Dig Syst 2:e105. doi: 10.4172/2161-069X.1000e105

Copyright: © 2011 Wagner J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Crohn’s disease (CD) is a growing health concern worldwide, accounting for significant human morbidity and mortality. Current understanding of CD aetiology suggests that an infectious microbe(s) initiates a persistent and immune-mediated tissue injury in a genetically susceptible host. CD is characterised by a relapsing and remitting disease course and involves segmental inflammation of the gastrointestinal tract, commonly affecting the ileo-caecal region. There is strong evidence that microbial populations within the gut are linked to CD for example, the presence of intestinal bacteria is essential for the development of colitis in several experimental animal models [1-3]. An elegant human study has shown that diversion of the faecal stream from the inflamed gut induced healing in CD [4], whereas re-infusion of intestinal content into surgically excluded ileum triggered disease recurrence [5].

There is ongoing debate about the precise mechanism by which a microbe(s) might trigger CD. Two proposals include damage arising from changes in the global gut bacterial distribution, or from infection with one or more candidate microbes. Studies over many years have described a possible link between CD and infection with a single microbial candidate, for example Mycobacterium avium subspecies paratuberculosis (MAP) [6-8]. The search for a bacterial dysbiotic role i.e. imbalance in the microbiota in CD, has been investigated during the last ten years. The analysis and study of gut microbial dysbiosis has emerged by advances in metagenomic technologies including bacterial specific microarray, terminal fragment length polymorphism (T-RFLP), denaturing gradient gel electrophoresis (DGGE), fluorescence in situ hybridisation (FISH), and sequencing. High throughput sequencing or next generation sequencing (NGS) has revolutionised bacterial identification based on large scale sequencing of the bacterial 16s ribosomal gene.

Studies have revealed that changes in the bacterial population of CD patients occur when compared with controls. It is suggested that a consequence of an imbalance in the microbiota leads to breakdown in the host-microbial association and that these alterations in proinflammatory and anti-inflammatory bacteria (dysbiosis) lead to disease.

However, a clear defined associated shift in the balance between beneficial and harmful bacteria associated with CD is still missing. This is in part due to a lack of consistency of (1) method used (2) specimen collection across studies, (3) disease status and (4) treatment regimes. This editorial aims to discuss some of the inconsistency with current studies that limit clear identification of a bacterial dysbiotic role in CD.

Methodological Inconsistency

Molecular analysis of the microbial composition of fecal and mucosal samples using bacterial 16S ribosomal DNA and RNA has increased the previously culture-based estimates of 200–300 intestinal species, to as high as 1,800 genera and between 15,000 and 36,000 individual species [9-11]. The approximately 1.5 kb 16S gene is present in every bacterium and has been used as the gold standard for bacterial characterisation. Various techniques including, T-RFLP, DGGE, FISH, microarray, and Sanger sequencing, have all been used for the profiling of bacterial communities in the human gut. The availability of NGS technology enables us to sequence tens of thousands of partial bacterial 16s genes per individual, with an enormous cost advantage compared to conventional cloning and Sanger sequencing. Differences in identity of bacterial communities have been reported between methods. For example, studies have shown an increase of Actinobacteria in stool samples [12] from CD patients using NGS, whereas a decrease of the same phyla was reported in stool samples using non NGS approaches, including DGGE, and microarray [13,14]. However, these differences might be also patient related. Despite the availability of NGS, almost all CD metagenomic studies reported in 2011 were performed using non NGS technology, including, T-RFLP, DGGE, microarray and Sanger sequencing [14-16]. The most plausible explanation for this is that the analysis of 100s of thousands bacterial 16s sequences generated by NGS technology requires complex bioinformatics analysis and that NGS is still cost intensive if large number of samples being sequenced. Large number of samples can be analysed by DGGE, T-RFLP and microarray with relatively low cost whereby microarray data analysis also requires complex bioinformatics analysis. The great advantage of NGS technology is, that it does not rely on known sequences information and hypothetically, all bacterial taxa can be characterised from a given sample, if the sequencing depth is appropriate depending on the source of sample.

Specimen Collection Inconsistency

Gut wall tissue samples for the analysis of mucosa associated microbes (MAM), and stool samples, have been used for gut metagenomic analysis. The regular involvement of the whole gut in CD makes it necessary to investigate different regions of the gut affected by CD. The bacterial concentration and species composition differ between the small bowel and large bowel with different species fulfilling different functions at different gut regions. Willing et al studied gut samples from 46 CD twins and identified significant differences in the main bacterial phyla including Firmicutes, Fusobacteria and Proteobacteria between colonic CD and ileal CD compared with healthy control twins [12]. In stool studies, a gut location specific dysbiosis cannot be determined and therefore, comparisons with gut mucosa metagenomic studies are difficult to obtain. Similarly, it is not surprising that different control individuals/samples across studies add additional inconsistencies for the comparisons of metagenomic studies. Control samples are either obtained from naive healthy individuals in the case of stool studies, or in the case of MAM studies, control samples were obtained either from unaffected gut of patients with symptoms of IBD or from individuals from a family with a history of cancer. The later control cohort is often obtained from an older population, where the desired age match with CD patients is difficult to fulfil.

Disease Status Inconsistency

CD is a lifelong disease with 25% of cases having their onset during childhood [17]. The majority of studies investigating a CD associated dysbiosis were conducted on adult patients with long term disease, with differing disease status. CD behaviour status is classified from non-stricturing, non-penetrating disease (B1) to stricturing (B2) and penetrating (B3) disease often requiring surgery to remove the affected gut region [18]. Though the majority of studies did not identify significant differences between inflamed and non-inflamed CD tissue from within the same individual [16,19], changes in bacterial composition were reported between biopsy samples and surgically removed tissue. These changes included an increase of Proteobacteria and Streptococci in CD ileal and colon tissue, compared to CD ileal and colonic biopsies. By contrast, the adverse effect was observed for Clostridium leptum and Enterococcus groups which were less common in surgical tissue compared with biopsies [20]. Metagenomic studies have investigated newly diagnosed CD patients [21] and patients with well established CD with up to 41 years duration [12]. In newly diagnosed patients, no significant differences of Bacteroides, Proteobacteria and Fusobacteria communities were observed between CD patients and controls using DGGE analysis of MAM [21]. In a similar study using the same technology and MAM analysis, proteobacteria were significantly increased in CD with an average of 8.5 years disease duration compared with controls [22]. Thus, CD status creates a challenge in comparing and interpreting bacterial metagenomic studies.

Medical Treatment Inconsistency

Medical treatment regimes, particularly antibiotics can alter the gut microbial composition for a prolonged period of time. It has been shown that Ciprofloxacin treatment (a common fluoroquinolone antibotic) influences the abundance of about a third of the bacterial taxa in the gut, decreasing the taxonomic richness and diversity [23]. These authors also published evidence that the taxonomic composition of the gut microbiota closely resembled its pre-treatment state by 4 weeks after the end of treatment, but several taxa failed to recover within 6 months. In the settings of CD, a diverse range of drugs including immunosuppressants, steroids, antibacterial, non steroidal antiinflammatory drugs, glucocorticoids, and TNFα therapy (Infliximab) are used to treat patients. The profound effect of these drugs on the gut microbiota is largely unknown and different studies have used different exclusion criteria. For example Martinez-Medina’s group excluded patients receiving antibiotic treatment within 2 months before endoscopy [22], Joosens’s group excluded patients taking antibiotics within 4 weeks before the stool sampling [13], while others used a 6 months window [24]. Comparison of studies using different antibiotic free windows is difficult to establish due to additional confounding factors such as sample used (stool versus gut tissue) and method (sequencing, DGGE, T-RFLP, and microarray).

In summary, the nature of CD with its diverse disease locations and disease behaviours makes it difficult to establish a defined CD associated/ causing dysbiosis. Furthermore, complex treatment strategies applied for CD and the use of different methods add additional levels of complexity when comparing CD metagenomic data. It seems logical that a globally consistent approach should be established for the analysis of microbial dysbiosis in CD. This should include using a single technology such as NGS, and a clear separation of results obtained from onset CD and established CD, together with separate analysis of different matched gut locations. Current pyrosequencing technology only allows sequencing of a partial region (approximately 300-400bp) of the bacterial 16s gene. This limits the comparison between NGS studies due to differences in the population structure of bacterial taxa using different variable 16s gene regions across studies [25]. The emergence of new sequencing technologies will enable the future sequencing of longer fragments or single molecules. This could lead to a widely adapted high throughput sequencing of the approximately 1.5 kb full length bacterial 16s gene across studies. As a result globally comparative metagenomic datasets should become available to allow for the interpretation of gut dysbiosis patterns implicated in Crohn’s disease.

References

1. Sellon RK, Tonkonogy S, Schultz M, Dieleman LA, Grenther W, et al. (1998) Resident enteric bacteria are necessary for development of spontaneous colitis and immune system activation in interleukin-10-deficient mice. Infect Immun 66: 5224-5231.
2. Strober W, Fuss IJ, Blumberg RS (2002) The immunology of mucosal models of inflammation. Annu Rev Immunol 20: 495-549.
3. Wirtz S, Neurath MF (2007) Mouse models of inflammatory bowel disease. Adv Drug Deliv Rev 59: 1073-1083.
4. Rutgeerts P, Goboes K, Peeters M, Hiele M, Penninckx F, et al. (1991) Effect of faecal stream diversion on recurrence of Crohn's disease in the neoterminal ileum. Lancet 338: 771-774.
5. D'Haens GR, Geboes K, Peeters M, Baert F, Penninckx F, et al. (1998) Early lesions of recurrent Crohn's disease caused by infusion of intestinal contents in excluded ileum. Gastroenterology 114: 262-267.
6. Feller M, Huwiler K, Stephan R, Altpeter E, Shang A, et al. (2007) Mycobacterium avium subspecies paratuberculosis and Crohn's disease: a systematic review and meta-analysis. Lancet Infect Dis 7: 607-613.
7. Kirkwood CD, Wagner J, Boniface K, Vaughan J, Michalski WP, et al. (2009) Mycobacterium avium subspecies paratuberculosis in children with early-onset Crohn's disease. Inflamm Bowel Dis 15: 1643-1655.
8. Wagner J, Sim W, Bishop RF, Catto-Smith AG, Cameron DJ, et al. (2011) Mycobacterium avium subspecies paratuberculosis in children with early-onset Crohn's disease: a longitudinal follow-up study. Inflamm Bowel Dis 17: 1825-1826.
9.  Sartor RB (2008) Microbial influences in inflammatory bowel diseases. Gastroenterology 134: 577-594.
10. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, et al. (2005) Diversity of the human intestinal microbial flora. Science 308: 1635-1638.
11. Mahida YR, Rolfe VE (2004) Host-bacterial interactions in inflammatory bowel disease. Clin Sci (Lond) 107: 331-341.
12. Willing BP, Dicksved J, Halfvarson J, Andersson AF, Lucio M, et al. (2010) A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139: 1844-1854 .
13. Joossens M, Huys G, Cnockaert M, De Preter V, Verbeke K, et al. (2011) Dysbiosis of the faecal microbiota in patients with Crohn's disease and their unaffected relatives. Gut 60: 631-637.
14. Mondot S, Kang S, Furet JP, Aguirre de Carcer D, McSweeney C, et al. (2011) Highlighting new phylogenetic specificities of Crohn's disease microbiota. Inflamm Bowel Dis 17: 185-192.
15. Andoh A, Imaeda H, Aomatsu T, Inatomi O, Bamba S, et al. (2011) Comparison of the fecal microbiota profiles between ulcerative colitis and Crohn's disease using terminal restriction fragment length polymorphism analysis. J Gastroenterol 46: 479-486.
16. Walker AW, Sanderson JD, Churcher C, Parkes GC, Hudspith BN, et al. (2011) High-throughput clone library analysis of the mucosa-associated microbiota reveals dysbiosis and differences between inflamed and non-inflamed regions of the intestine in inflammatory bowel disease. BMC Microbiol 11: 7.
17. Rufo PA, Bousvaros A (2007) Challenges and progress in pediatric inflammatory bowel disease. Curr Opin Gastroenterol 23: 406-412.
18. Silverberg MS, Satsangi J, Ahmad T, Arnott ID, Bernstein CN, et al. (2005) Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: Report of a Working Party of the 2005 Montreal World Congress of Gastroenterology. Can J Gastroenterol 19: 5-36.
19. Gophna U, Sommerfeld K, Gophna S, Doolittle WF, Veldhuyzen van Zanten SJ (2006) Differences between tissue-associated intestinal microfloras of patients with Crohn's disease and ulcerative colitis. J Clin Microbiol 44: 4136-4141.
20. Prindiville T, Cantrell M, Wilson KH (2004) Ribosomal DNA sequence analysis of mucosa-associated bacteria in Crohn's disease. Inflamm Bowel Dis 10: 824-833.
21. Bibiloni R, Mangold M, Madsen KL, Fedorak RN, Tannock GW (2006) The bacteriology of biopsies differs between newly diagnosed, untreated, Crohn's disease and ulcerative colitis patients. J Med Microbiol 55: 1141-1149.
22. Martinez-Medina M, Aldeguer X, Gonzalez-Huix F, Acero D, Garcia-Gil LJ (2006) Abnormal microbiota composition in the ileocolonic mucosa of Crohn's disease patients as revealed by polymerase chain reaction-denaturing gradient gel electrophoresis. Inflamm Bowel Dis 12: 1136-1145.
23. Dethlefsen L, Huse S, Sogin ML, Relman DA (2008) The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol 6: e280.
24. Swidsinski A, Ladhoff A, Pernthaler A, Swidsinski S, Loening-Baucke V, et al. (2002) Mucosal flora in inflammatory bowel disease. Gastroenterology 122: 44-54.
25. Chakravorty S, Helb D, Burday M, Connell N, Alland D (2007) A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods 69: 330-339.