Patent Grant
10023918
This application is a nonprovisional of U.S. provisional patent application 61/781,564 filed Mar. 14, 2013, the specification of which is hereby incorporated by reference.
This invention relates generally to methods and compounds for the diagnosis and treatment of inflammatory bowel disease (IBD).
An intricate and essential partnership is established early in life between the host and the intestinal microbiome, assuring the maintenance of microbiota homeostasis. Disturbance of this partnership is often associated with various pathological conditions including inflammatory bowel diseases (IBD) (Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nature reviews. Genetics 13, 260-270, doi:10.1038/nrg3182 (2012)). The microbiota of IBD patients are characterized by a decreased prevalence of protective microorganisms (i.e. Clostridium IXa and IV groups) and an expansion of detrimental bacteria (i.e. Enterobacteriaceae/Escherichia coli) (Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nature reviews. Gastroenterology & hepatology 9, 599-608, doi:10.1038/nrgastro.2012.152 (2012).
Inflammatory Bowel Disease encompasses two principal conditions: ulcerative colitis (UC) and Crohn's disease (CD). Some patients have features of both subtypes and are classified as IBD-undefined (IBD-U) (Gastroenterology, 2007. 133(5): p. 1670-89). UC is defined by continuous mucosal inflammation starting in the rectum and restricted to the colon while CD inflammation can occur anywhere in the gastrointestinal tract, involves full thickness of the bowel wall and often with skip lesions (Gastroenterol Clin North Am, 2009. 38(4): p. 611-28; Gastroenterology, 2007. 133(5): p. 1670-89). Recent attempts to find new markers for IBD subtypes, such as conventional antibodies, have fared very poorly at differentiating colonic CD versus UC. As treatments and responses to medical therapies differ between CD and UC (J Pediatr Gastroenterol Nutr, 2010, S1-S13. The American journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26. Gastroenterol Clin North Am, 2009. 38(4): p. 611-28) there is an urgent need for biomarkers to differentiate between CD and UC.
The primary tool used for both diagnosis and IBD management is endoscopy (World J Gastrointest Endosc, 2012. 4(6): p. 201-11). Endoscopy enables both visualization of the mucosa and access for mucosal biopsies to diagnose disease, to define disease extent and activity, and to monitor disease progression. The diagnostic accuracy from colonoscopy ranges from 60 to 74% (J Clin Pathol, 2002. 55: p. 955-60). Accurate and early diagnosis is essential for proper disease management. The goal of IBD treatment is to bring active disease into remission and to prevent follow-up relapse (flare-ups). The choice of treatment depends on disease type (CD versus UC), disease location, severity of disease, disease complications and individual host factors (e.g. nutritional and growth status, pubertal status, child's age and size, medication allergies) (J Pediatr Gastroenterol Nutr, 2010, S1-S13. The American journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26. Gastroenterol Clin North Am, 2009. 38(4): p. 611-28). Current drug therapies consist of aminosalycylates, immune-modulators, corticosteroids, antibiotics and biological therapies (i.e. anti-TNFα monoclonal antibodies). The optimum therapeutic regimen for maintaining a disease free state still remains to be determined and the effectiveness of these drugs significantly differs between CD and UC (J Pediatr Gastroenterol Nutr, 2010, S1-S13. The American journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26. Gastroenterol Clin North Am, 2009. 38(4): p. 611-28). For example, 5-aminosalicylic acid (5-ASA) drugs are moderately effective at inducing remission and preventing relapse in mild-to-moderate-active UC, while they are not recommended in the management of active CD (The American journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26). Methotrexate is good evidence for use as maintenance therapy to prevent relapse in CD however, there is no evidence for its use in UC (The American journal of gastroenterology, 2011. 106 Suppl 1: p. S2-25; quiz S26). Greater doses of anti-TNFα therapies at more frequent intervals are being just now recognized to be required for successful treatment of severe UC as compared to standard treatment protocols in use for CD. One third of the cost associated with IBD is due to medical therapies (CCFC. 2008, report. p. 1-101) stressing the economic importance of an effective treatment and thereby an accurate diagnosis.
While the etiology of IBD is unknown, the gut microbiota is emerging as a key player in disease development and/or chronicity. Genome wide association studies in both adults and pediatric patients have identified novel IBD-associated genes but only define 25% of the genetic risk for developing IBD and excepting for very young infants (i.e. <2 years of age), no unique genes have been discovered that define pediatric IBD from adult-onset IBD. IBD is a complex polygenic disease involving multiple risk gene loci (Nature genetics, 2008. 40(8): p. 955-62. Nature genetics, 2009. 41(12): p. 1335-40. Nature genetics, 2010. 42(4): p. 332-7). These loci encode genes involved in innate and adaptive immunity, autophagy, and maintenance of epithelial barrier integrity for those genes that have known function. While these studies have shown us that multiple pathways are involved in the pathogenesis of IBD, we remain surprisingly ignorant on the root cause(s) and pathogenesis of IBD. A prevailing hypothesis is that IBD development is a consequence of functional abnormalities in the interplay between the intestinal microbiota and the host (World journal of gastroenterology: WJG, 2011. 17(5): p. 557-66). Some of the best evidence that the gut microbiota plays a key role in IBD comes from animal model studies (World journal of gastroenterology: WJG, 2011. 17(5): p. 557-66. Cell, 2007. 131(1): p. 33-45. Inflamm Bowel Dis, 2007. 13(12): p. 1457-66). Although the experimental animal models of IBD do not exactly mimic human IBD, these studies have shown that the development of the disease is dependent on the presence of resident bacteria (Cell, 2007. 131(1): p. 33-45. Inflamm Bowel Dis, 2007. 13(12): p. 1457-66). The loss of the transcriptional factor T-bet in mice, which regulates the differentiation and function of immune system cells, was shown to promote the microbiota to become colitogenic. Moreover, the induced colitis could be transmitted to other genetically intact hosts by vertical transfer of the colitogenic microbiota (Cell, 2007. 131(1): p. 33-45). Numerous studies have revealed alterations in the composition of the gut microbiota of patients with IBD (Proc Natl Acad Sci USA, 2007. 104(34): p. 13780-5. (9) Nature, 2010. 464(7285): p. 59-65. (10) Cell, 2012. 148(6): p. 1258-70; World journal of gastroenterology: WJG, 2011. 17(5): p. 557-66). However, we do not know what triggers IBD and the resulting gut microbiota dysbiosis and we have only a rudimentary understanding of the interplay between the gut microbiota and the host. Clearly, studies that longitudinally follow gut microbiota dysbiosis in humans during flare-ups and remissions could contribute important insights into the clinical significance of the gut microbiota composition.
IBD symptoms may include bloody diarrhea, abdominal pain, cramping, fatigue, various nutritional deficiencies including iron deficiency anemia, bone health problems and weight loss (Archives of disease in childhood, 2006). In children poor linear growth is also common. The onset of symptoms is slow, indolent and non-specific and so the disease may be present in certain regions of the bowel for very long periods of time prior to diagnosis. Following diagnosis, this chronic, life-long disease is characterized by episodes of flare-up and remission (quiescent, symptom-free state) (Gastroenterol Clin North Am, 2009. 38(4): p. 611-28; Archives of disease in childhood, 2006). The current therapeutic treatments aim to stop mucosal inflammation so as to maintain the quiescent period and to reduce flare-ups to reduce permanent bowel damage and alleviate the complications of disease. Corticosteroids (prednisone) remain a mainstay of treatment for IBD despite the well-known side effects of this medication (Journal of Crohn's & colitis, 2012. 6(4): p. 492-502). Alternatively, enteral nutrition (EN) is more commonly being used as a primary therapy in lieu of prednisone to induce CD remission (Current opinion in clinical nutrition and metabolic care, 2011. 14(5): p. 491-6). However, it is more difficult for most patients to adhere to these protocols that involve enteral formulas alone without eating foods for many weeks at a time. It is apparent that the microbiota composition correlates with disease and that an “abnormal” microbiota contributes to (if not triggers) mucosa alterations and immune system malfunctions (World journal of gastroenterology: WJG, 2011. 17(5): p. 557-66). It follows that interventions aimed at restoring microbiota equilibrium could promote health and/or prevent flare-up. Moreover, given that each patient is have a unique gut microbiota composition it follows that any interventions aimed at manipulating the gut microbiota should preferably be disease and patient-specific.
In view of the above there is a need for better diagnostic assays and treatments for the management of IBD.
There is provided assays and methods to diagnose and treat IBD as well as to classify gut samples into IBD, UC or CD samples. There is also provided a device for classifying gut samples into IBD, UC or CD samples.
In an embodiment there is provided an assay comprising the steps of measuring a level of proteobacteria or H2S producing bacteria or both in a gut microbioata sample from a human subject to identify the likelihood of the human subject having inflammatory bowel disease (IBD), and comparing the level of proteobacteria or H2S producing bacteria or both to a reference level of proteobacteria or H2S producing bacteria or both from gut microbiota samples of healthy human subjects, wherein a level of proteobacteria or H2S producing bacteria or both higher than the reference level is indicative of disease.
In another embodiment there is provided an assay comprising the steps of measuring a level of A. parvulum in a gut microbiota sample from a human subject to identify the likelihood of the human subject having IBD, and comparing the level of A. parvulum to a reference level of A. parvulum from gut microbiota samples of healthy human subjects, wherein a level of A. parvulum higher than the reference level is indicative of disease.
In a further embodiment there is provided an assay comprising the steps of measuring a level of butyrate producing bacteria in a gut microbiota sample from a human subject to identify the likelihood of the human subject having IBD, and comparing the level of butyrate producing bacteria to a reference level of butyrate producing bacteria from gut microbiota samples of healthy human subjects, wherein a level of butyrate producing bacteria lower than the reference level is indicative of disease.
Advantageously, the invention provides a method for distinguishing between patients with UC or CD.
In yet a further embodiment there is provided an assay for determining a severity of CD disease comprising measuring a level of one or more bacterial taxa selected from Carnobacteriaceae, Granulicatella, Mogibacterium, Proprionibacterium, Bacillaceae and Atopobium in a gut microbioata sample from the human subject wherein a level higher than a predetermined level is indicative of moderate or severe inflammation.
There is further provided an assay comprising the steps of measuring a level of sulfur dioxygenase (ETHE1), thiosulfate sulfur transferase (TST), cytochrome c oxidase subunit IV, sulfide dehydrogenase (SQR) and complexes III and IV of mithochondrial respiratory chain in a gut mucus sample from a human subject to identify the likelihood of the human subject having IBD, and wherein a lower level relative to a reference level from a healthy subject is indicative of disease.
In another aspect there is provided a method of treating IBD in a patient the method comprising: performing an assay to determine the presence of disease (IBD or UC or CD) and administering to the patient a pharmaceutically effective amount of a compound selected from aminosalycylates, immunomodulators, anti-integrins, anti-cytokines, enteral feed programs, steroids, corticosteroids, antibiotics, anti-TNFα, bismuth or a combination thereof.
These and other embodiments of the invention are further described below with reference to the Drawings and the Detailed Description.
The invention is better understood by way of the following detailed description of embodiments of the invention with reference to the appended drawings, in which:
In the present description by microbiota it is meant an ensemble of microorganisms residing in an environment and in particular by gut microbiota it is meant microorganisms found in any part of the alimentary canal from lips to the anus.
By patients having Inflammatory bowel disease (IBD) it is meant patients with ulcerative colitis (UC) or patients with Crohn's disease (CD) or IBD-undefined (IBD-U).
By level or abundance of bacteria or bacterial taxa it is meant a level or abundance obtained by a means to quantify bacteria such as culture based methods, flow cytometry, microscopy, quantitative DNA analysis and any other means that would be obvious to a person skilled in the art.
By severity of the disease it is meant a level of symptoms as described in disease activity index such Crohn's disease activity index (CDAI), Pediatric Crohn's disease activity index (PCDAI) Harvey-Bradshaw index, Ulcerative colitis activity index (UCAI), Pediatric Ulcerative colitis activity index (PUCAI), Paris classification of pediatric Crohn's disease and the like. For example severe CD corresponds to a score of 450 in the CDAI index.
By “core” it is meant the bacterial taxa that are conserved between individuals (that are present in two or more individuals).
In an aspect of the invention there is provided a method in which IBD can be detected by measuring the levels (or relative abundance) of certain bacterial taxa in samples from the gut of patients. Microbiota samples from the gut may be obtained from stools, intestinal mucosal biopsies, gut lavage or combination thereof. In an embodiment of the invention, microbiota samples are collected such as to comprise the microbiota from the mucosa-luminal interface of the gut.
In one embodiment of the invention, the collection can be performed during endoscopy by flushing a physiological solution, such as sterile saline solution or sterile water, onto the mucosa to remove the strongly adherent mucus layer overlying the intestinal mucosal epithelial cells and the microbial community embedded within the mucus layer. Aspirates are then collected directly through a colonoscope at a specific location in the gut as for example from the terminal ileum right colon and left colon and the samples are preferably immediately put on ice right in the endoscopy suite. For example the following steps can be performed: 1) a regular protocol of bowel clean out in preparation for colonoscopy is first applied to the patient, 2) then the colonoscope (“scope”) is advanced to the ascending colon or a region of the colon distal to that of interest, 3) suction out fluid and particulate matter, using either the scope's wash system or with a syringe through biopsy port, 4) flush sterile water onto mucosa until shards of mucus are dislodged, 5) aspirate mucus containing fluid into sterile trap through scope aspiration system, 6) remove the trap from scope suction and cap it and immediately place on ice, 7) advance the scope to more proximal region of interest and repeat steps 3-6, 8) transport traps with mucus to lab within 15 minutes of collection. The sample can then be analyzed at the point of care or transferred to a laboratory. The samples can also be further processed and then stored at −80° C.
Collection of the gut microbiota can also be performed on stools. Collection of bacteria from stools is known in the art. In the case of fecal microbiota collection/analysis, fresh stools may be collected and immediately processed and stored at −80° C. for DNA extraction and sequence/quantification as part of a bacterial analysis as further described below.
Samples containing gut microbiota collected as described above can be assayed for determining their microbial composition. Identification of the bacteria present in samples can be performed using DNA sequencing techniques as described in the examples below. In one embodiment, total DNA can be extracted from intestinal aspirates or stool samples. The protocol may comprise the extraction of total DNA using an extraction step with mechanical disruption. The extracted DNA can then be subjected to sequencing to identify bacteria by comparing the sequences to sequences contained in databases. In a preferred embodiment metagenomic DNA can be subjected to multiplexed massively parallel sequencing on the hypervariable V6 region of the 16S rRNA gene. It is appreciated that the sequencing of regions other than the hypervariable V6 region of the 16S rRNA gene can be used provided that such regions provide discriminating power (taxonomic resolution) for at least some bacterial taxa or operational taxonomic units (OTU's) and in particular for bacterial taxa that are preferentially associated with IBD as is further described below.
It will also be appreciated that other methods can be used to identify bacteria from the gut samples including but not limited to microscopy, metabolites identification, Gram staining, flow cytometry, immunological techniques (antibodies), culture-based methods such a colony forming unit counting and the like as would be known to a person skilled in the art.
In an aspect of the invention the relative abundance of certain bacterial taxa namely phylum, class, order, family, genus or species or combination thereof in the gut (gut microbiota profile) of patients is used to assess the presence or absence of IBD disease. It has been found that the IBD microbiota is characterized by a smaller core as compared to controls (
Assessment of the presence of CD and UC disease in a human subject can be achieved by measuring the relative abundance of taxa as exemplified in table 1. In this particular example, microbial operational taxonomic units (OTUs) that were detected in all the samples within each group and that vary significantly in abundance between CD, UC and/or controls are listed. The number of 16S rDNA reads in each sample was normalized by random subsampling to 500,000. Minimum and maximum correspond to the minimum and maximum number of reads obtained; mean corresponds to the mean of the number of reads obtained. P values were generated using a Kruskal-Wallis test with a Dunn's post hoc test. “p|Control” indicates the P values obtained by comparison to the controls; “p|UC” and “p|CD” indicate the P values obtained by comparison to the UC and CD patients respectively. Values in bold indicate significance (P<0.05). From the table it can be seen that certain taxa are more or less abundant in patients with disease than in healthy controls. Furthermore there it is also possible to distinguish between CD and UC based on the relative abundance.
In table 2 results for relative abundance of taxa are presented. Taxa that varie significantly in abundance in at least one of the three pairwise comparisons performed (controls vs. CD; controls vs. UC; and CD vs. UC) are shown. In table 2, microbial OTUs that were detected in at least 75% of the samples within each group and that vary significantly in abundance between CD, UC and/or controls are listed. The number of 16S rDNA reads in each sample was normalized by random subsampling to 500,000. Minimum and maximum correspond to the minimum and maximum number of reads obtained; mean corresponds to the mean of the number of reads obtained. P values were generated using a Kruskal-Wallis test with a Dunn's post hoc test. “p|Control” indicates the P values obtained by comparison to the controls; “p|UC” and “p|CD” indicate the P values obtained by comparison to the UC and CD patients respectively. Values in bold indicate significance (P<0.05).
0.011
0.011
0.008
0.008
0.006
0.006
0.014
0.014
0.002
0.002
0.003
0.002
0.003
0.002
0.003
0.009
0.003
0.009
0.015
0.015
0.008
0.008
0.006
0.006
0.008
0.008
0.011
0.011
0.007
0.007
0.001
0.001
0.002
0.002
0.014
0.014
0.014
0.014
0.009
0.009
0.006
0.006
0.014
0.014
0.003
0.002
0.003
0.002
0.014
0.009
0.014
0.009
0.011
0.011
0.006
0.006
0.010
0.010
0.004
0.004
0.008
0.008
0.011
0.011
Porphyromonas|
0.002
Porphyromonas|UC
0.002
0.007
Porphyromonas|CD
0.007
Lautropia|Control
0.001
Lautropia|UC
0.001
<0.0001
Lautropia|CD
<0.0001
Methylobacterium|
0.004
Methylobacterium|
0.004
Methylobacterium|
Akkermansia|
0.014
Akkermansia|UC
0.014
Akkermansia|CD
Tannerella|Control
Tannerella|UC
0.004
Tannerella|CD
0.004
Haemophilus|
0.007
0.010
Haemophilus|UC
0.007
Haemophilus|CD
0.010
Finegoldia|Control
0.014
Finegoldia|UC
0.014
0.009
Finegoldia|CD
0.009
Turicibacter|Control
0.007
0.006
Turicibacter|UC
0.007
Turicibacter|CD
0.006
Nitrincola|Control
0.008
Nitrincola|UC
0.008
Nitrincola|CD
Hydrogenophilus|
0.008
Hydrogenophilus|
0.008
0.011
Hydrogenophilus|
0.011
Listeria|Control
0.011
Listeria|UC
Listeria|CD
0.011
Actinobacillus|
0.005
Actinobacillus|UC
0.005
Actinobacillus|CD
Anaerococcus|
0.001
Anaerococcus|UC
0.001
Anaerococcus|CD
Catonella|Control
Catonella|UC
0.007
Catonella|CD
0.007
Mobiluncus|Control
0.007
Mobiluncus|UC
0.007
0.009
Mobiluncus|CD
0.009
Pantoea|Control
0.002
Pantoea|UC
Pantoea|CD
0.002
Enterobacter|
0.003
Enterobacter|UC
0.003
Enterobacter|CD
Paenibacillus|
0.013
0.007
Paenibacillus|UC
0.013
Paenibacillus|CD
0.007
Staphylococcus|
0.010
Staphylococcus|UC
Staphylococcus|CD
0.010
Vitreoscilla|Control
0.009
Vitreoscilla|UC
0.009
Vitreoscilla|CD
Alcanivorax|Control
0.012
Alcanivorax|UC
0.012
0.006
Alcanivorax|CD
0.006
Veillonella|Control
0.003
Veillonella|UC
0.003
Veillonella|CD
Tatumella|Control
0.014
Tatumella|UC
0.014
Tatumella|CD
Afipia|Control
Afipia|UC
0.012
Afipia|CD
0.012
Certain bacterial taxa exhibit higher levels (abundance) in UC or CD or IBD patients and some taxa exhibit lower levels in UC or CD or IBD patients. Therefore, an assay on a gut sample from a patient can be performed to measure an abundance (or level) of a bacterial taxa and by comparing this abundance to that of a predetermined abundance or an average abundance (as in tables 1 or 2) of the taxa derived from sample of patients with UC or CD or IBD. The result allows one to determine whether a patient has UC or CD or IBD.
The abundance or level of bacterial taxa can be determined for example by quantitative DNA analysis such as quantitative polymerase chain reaction. As described above the data can be normalized (example subsampling normalization) as would be known in the art. Therefore the results discussed in the present application can represent relative abundance. It will be appreciated that a person skilled in the art would know to interpret these values to determine the relative levels of bacteria.
A method is also provided in which a diagnosis of UC or CD or IBD is achieved by collecting a gut sample from a patient and from which bacterial taxa levels will be determined using an assay as described above. The gut sample may be from the flushing of the colon wall as described above and still further described below or from stools.
Certain taxa exhibit a statistically significant difference in their abundance between UC patients and CD patients. Therefore by comparing the relative abundance of one or more of these taxa between UC and CD patients it is possible to determine whether the patient has CD or UC disease. For example, Hydrogenophilus is more abundant in both CD and UC patients relative to healthy individuals and furthermore it is more abundant in UC patients than CD patients.
In another aspect of the invention the severity of the disease can also be assessed from the bacterial profile of the gut microbiota. Thus, the severity of CD can be established by measuring the relative abundance of certain bacterial taxa in a gut microbiota sample. In this respect, the relative abundance of one or more microbial taxa from the gut can be compared/correlated with a standard disease activity index. The resulting classification allows the use of relative abundance of bacterial taxa as an indicator of disease severity (Table 3). It will be appreciated that abundance measurements from one or more bacterial taxa can be used for that purpose.
Supplementary Table 6: Taxa that varies significantly in abundance in CD patients in at least one of the three pairwise comparisons performed (mild vs. moderate; mild vs. sever; and moderate vs. severe). In table 3 the number of 16S rDNA reads in each sample was normalized by random subsampling to 500,000. Minimum and maximum correspond to the minimum and maximum number of reads obtained; mean corresponds to the mean of the number of reads obtained. P values were generated using a Kruskal-Wallis test with a Dunn's post hoc test and a Bonferroni correction for multiple hypotheses. “p|mild” indicates the P values obtained by comparison to the CD patients with a mild inflammation; “p|moderate” and “p|severe” indicate the P values obtained by comparison to CD patients with a moderate and severe inflammation respectively. Values in bold indicate significance (P<0.05).
0.012
0.012
0.013
0.013
0.012
0.012
0.002
0.002
0.016
0.016
0.007
0.007
0.003
0.003
0.016
0.016
0.008
0.008
0.004
0.004
0.004
0.004
Atopobium|Mild
Atopobium|Severe
0.014
Atopobium|Moderate
0.014
Propionibacterium|Mild
0.016
Propionibacterium|Severe
0.016
0.007
Propionibacterium|Moderate
0.007
Trichococcus|Mild
0.002
Trichococcus|Severe
0.002
Trichococcus|Moderate
Pectobacterium|Mild
Pectobacterium|Severe
0.014
Pectobacterium|Moderate
0.014
Granulicatella|Mild
Granulicatella|Severe
0.012
Granulicatella|Moderate
0.012
Jonquetella|Mild
Jonquetella|Severe
0.014
Jonquetella|Moderate
0.014
Riemerella|Mild
0.002
Riemerella|Severe
0.000
Riemerella|Moderate
0.002
0.000
Mogibacterium|Mild
Mogibacterium|Severe
0.013
Mogibacterium|Moderate
0.013
Staphylococcus|Mild
0.002
Staphylococcus|Severe
Staphylococcus|Moderate
0.002
Sutterella|Mild
0.004
0.004
Sutterella|Severe
0.004
Sutterella|Moderate
0.004
Phascolarctobacterium|Mild
0.003
Phascolarctobacterium|
0.003
Phascolarctobacterium|
Comamonas|Mild
0.016
Comamonas|Severe
0.016
Comamonas|Moderate
Hylemonella|Mild
Hylemonella|Severe
0.014
Hylemonella|Moderate
0.014
Xenorhabdus|Mild
Xenorhabdus|Severe
0.014
Xenorhabdus|Moderate
0.014
Averyella|Mild
Averyella|Severe
0.014
Averyella|Moderate
0.014
It will be appreciated that it is possible to refine the assessment of the stage or severity of the disease by combining the measurement(s) of the abundance of bacterial taxa with the observation of a choice of symptoms underlying the classic disease indexes to arrive at the establishment of a diagnosis. For example it may be desirable or sometimes only possible to measure only a limited set of standard symptoms associated with disease indexes. This limited set of symptoms may not be sufficient to pose a diagnostic. In such cases it may be possible to combine an assay involving the measurement of bacterial taxa to provide additional information on the nature or stage of the disease.
In an aspect of the invention A. parvulum, an H2S producer, is a good marker of CD exhibiting a higher relative abundance in patient with CD than in controls. Furthermore, the relative abundance of A. parvulum compared to core bacterial taxa abundance is also a measure of the presence and severity of the disease. For example an abundance of A. parvulum relative to the core greater than 0.005% is indicative of moderate or severe stage of the disease (
In yet another aspect of the invention a decrease in the relative abundance of butyrate producers such as Firmicutes, Clostridia, Clostridiales and Lachnopiraceae Eubacterium and Faecalibacterium is indicative of the presence of disease (CD or UC).
The measurements of the abundance of bacterial taxa using DNA quatification can generally be done by methods that are known in the art. However in one aspect of the invention there is provided a method for determining the abundance of A. parvulum by absolute quantitative DNA measurement by performing PCR on the extracted metagenomic DNA. The following primers for the quantitative measurements of A. parvulum were developed: Aparv-711F 5′-GGGGAGTATTTCTTCCGTGCCG-3′ (SEQ ID NO. 1) and Aparv-881R 5′-CTTCACCTAAATGTCAA GCCCTGG-3′ (SEQ ID NO. 2). The development of these primers enables the use of an assay for measuring the abundance of A. parvulum that is highly specific, rapid and reliable. Thus in an another aspect of the invention there is also provided kits that would comprise these primers and other reagents as would be known in the art to detect A. parvulum or other taxa useful for the diagnosis, assessment or staging of UC, CD or IBD as described herein.
In further embodiment of the invention, the presence of UC and CD disease can be assessed by the presence, absence and/or relative abundance of certain host proteins. Proteins can be identified and measured by techniques known in the art such as shotgun mass-spectrometry in conjunction with protein fractionation. Other method for detecting specific proteins such as, immunology based methods (antibodies), western blots, spectrophotometry, enzyme assays, ELISA and any other method as would be known to one skilled in the art may also be used.
Table 4 provides a list of all differentially expressed proteins and their variable importance in projection scores (VIP) derived from the calculated PLS-DA. (Control v. CD with increasing inflammation severity)
Homo sapiens armadillo repeat containing 8 (ARMC8), transcript variant 2, mRNA
It has been observed that certain mitochondrial proteins are differentially expressed and their levels can be associated with the presence or absence of UC or CD disease. For example, sulfur dioxygenase (ETHE1), thiosulfate sulfur transferase (TST), cytochrome c oxidase subunit IV, sulfide dehydrogenase genes (SQR) and complexes III and IV of mithochondrial respiratory chain obtained from a gut mucus sample of a human subject can be indicative of the presence of UC or CD or IBD in the subject. However it will be appreciated that any other protein(s) listed in table 4 or 5 alone or in combination that is or are differentially expressed can also be used to assess the presence/absence/severity of UC or CD disease.
Expression of certain cytokines above normal levels can also be used to detect the presence of A. parvulum. For example the presence of A. parvulum is correlated with expression (or overexpression) of Cxcl1, II17a, II12 and II1β. Therefore there is provided an assay for identifying the likelihood of an individual of having UC or CD or IBD by measuring a relative abundance of A. parvulum by measuring the expression of Cxcl1, II17a, II12 or II1β. This correlation can also be used to provide a method of diagnostic that comprises collecting samples to measure one or more cytokines, determining the presence of A. parvulum based on the cytokine(s) measurement and establishing a diagnosis.
Table 5 List of all differentially expressed mitochondrial proteins and their variable importance in projection scores (VIP) derived from the calculated PLS-DA model.
In yet another embodiment of the invention there is provided an assay that allows the measurement of the gut microbiota composition and the meta-proteome from a same sample. More specifically the assay comprises the collection of mucus at the luminal interface of the gut during endoscopy by flushing a physiological solution, such as sterile saline, onto the mucosa to remove the strongly adherent mucus layer overlaying the intestinal mucosal epithelial cells thereby sampling the microbial community and host and bacterial proteins embedded within the mucus layer. Aspirates are then collected directly through the colonoscope and the samples are preferably immediately put on ice right in the endoscopy suite. The sample can then be analyzed at the point of care or transferred to a laboratory. Bacteria and proteins can then be identified and/or measured as described above. This method advantageously permits the establishment of a protein and bacterial profile in the same patient at a pre-determined time point.
The establishment of the presence of disease using bacterial taxa can be used to determine a course of treatment in a patient. Treatment is normally based on accepted diseases indexes. The methods and assays provided by the invention can complement or replace such disease indexes to provide more accurate diagnosis and thereby permit more efficacious treatments.
It will be appreciated that the above described assays for identifying and measuring gut proteins and bacteria can be performed as a function of time thereby allowing an assessment of the progression of the disease as well as of the efficacy of a treatment. Staging of IBD (and CD and UC) is particularly useful for choosing the appropriate treatment to be delivered. For example, treatment regimen may advantageously be adjusted taking in consideration the levels of H2S producing bacteria, which as described above, are more elevated as the severity of the disease increases. Thus regimens that are more aggressive towards mitigating the effects of the H2S producing bacteria can be timely administered to optimize the therapeutic dose. Treatment optimization using the information on the presence/stage of IBD, UC and CD provided by the measuring of bacteria and protein as described above, can be applied to known therapeutic agents such as but not limited to aminosalycylates, immunomodulators, anti-integrins, anti-cytokines, enteral feed programs, steroids, corticosteroids, antibiotics, anti-TNFα, bismuth and the like. In particular, as further described below, bismuth can be used effectively as treatment when A. parvulum is detected in a patient and or assessed to be above certain critical abundance levels.
In table 6 taxa that vary significantly in abundance in II10−/− mice in response to A. parvulum colonization and/or bismuth administration are listed. As can be seen from the table bismuth treatment may be indicated or beneficial when the relative abundance of taxa other than A. parvulum are within levels indicative of disease.
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.003
0.002
0.003
0.007
0.006
0.007
0.006
0.008
0.007
0.008
0.007
<0.0001
0.004
<0.0001
0.004
0.008
0.007
0.008
0.007
0.004
0.004
0.003
0.003
0.002
0.005
0.002
0.005
0.000
0.000
0.000
0.000
0.000
0.000
<0.0001
0.004
<0.0001
0.004
0.001
0.001
0.001
0.001
0.002
0.005
0.002
0.005
0.000
0.001
0.007
0.000
0.001
0.007
<0.0001
0.004
<0.0001
0.004
0.004
0.001
0.004
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.005
0.002
0.005
0.004
0.004
0.004
0.004
0.000
0.001
0.001
0.000
0.001
0.001
0.000
0.000
0.001
0.000
0.000
0.001
<0.0001
0.006
0.002
<0.0001
0.006
0.002
0.001
0.001
0.001
0.001
0.008
0.007
0.008
0.007
0.000
0.007
0.000
0.007
0.003
0.003
0.002
0.002
0.002
0.004
0.002
0.004
0.000
0.000
0.001
0.005
0.001
0.005
<0.0001
0.000
<0.0001
<0.0001
0.000
<0.0001
0.005
0.005
<0.0001
0.007
0.005
<0.0001
0.005
0.007
0.000
0.003
0.001
0.000
0.003
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.001
0.004
0.000
0.001
0.004
0.004
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.000
0.001
0.001
0.000
0.001
0.001
0.004
0.004
0.008
0.007
0.008
0.007
0.001
0.006
0.006
0.001
0.001
<0.0001
0.006
0.002
<0.0001
0.006
0.002
0.007
0.007
0.003
0.003
0.004
0.001
0.004
0.001
0.000
0.000
0.003
0.000
0.000
0.003
0.002
0.000
0.003
0.002
0.000
0.003
0.000
0.007
0.000
0.007
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.006
0.006
Morganella|Atopo
0.000
Morganella|AtopoBis
0.000
Morganella|Bis
0.000
Morganella|SPF
0.000
0.000
0.000
Erwinia|Atopo
0.003
Erwinia|AtopoBis
0.006
Erwinia|Bis
Erwinia|SPF
0.003
0.006
Peptostreptococcus|Atopo
0.006
Peptostreptococcus|
Peptostreptococcus|Bis
Peptostreptococcus|SPF
0.006
Dorea|Atopo
Dorea|AtopoBis
Dorea|Bis
0.008
Dorea|SPF
0.008
Ruminococcus|Atopo
0.002
Ruminococcus|AtopoBis
Ruminococcus|Bis
Ruminococcus|SPF
0.002
Kangiella|Atopo
0.003
Kangiella|AtopoBis
0.003
Kangiella|Bis
Kangiella|SPF
0.003
0.003
Enterovibrio|Atopo
<0.0001
Enterovibrio|AtopoBis
<0.0001
Enterovibrio|Bis
<0.0001
Enterovibrio|SPF
<0.0001
<0.0001
<0.0001
Coprobacillus|Atopo
0.003
0.008
0.004
Coprobacillus|AtopoBis
0.003
Coprobacillus|Bis
0.008
Coprobacillus|SPF
0.004
Actinomyces|Atopo
0.000
Actinomyces|AtopoBis
Actinomyces|Bis
0.007
Actinomyces|SPF
0.000
0.007
Marinomonas|Atopo
0.002
Marinomonas|AtopoBis
0.007
Marinomonas|Bis
0.001
Marinomonas|SPF
0.002
0.007
0.001
Vibrio|Atopo
<0.0001
Vibrio|AtopoBis
0.006
Vibrio|Bis
0.002
Vibrio|SPF
<0.0001
0.006
0.002
Dialister|Atopo
Dialister|AtopoBis
0.003
Dialister|Bis
0.003
Dialister|SPF
Halothiobacillus|Atopo
<0.0001
Halothiobacillus|AtopoBis
<0.0001
Halothiobacillus|Bis
<0.0001
Halothiobacillus|SPF
<0.0001
<0.0001
<0.0001
Trichococcus|Atopo
0.007
Trichococcus|AtopoBis
0.007
Trichococcus|Bis
Trichococcus|SPF
Nitrincola|Atopo
0.001
Nitrincola|AtopoBis
0.003
Nitrincola|Bis
0.001
Nitrincola|SPF
0.001
0.003
0.001
Serratia|Atopo
0.000
Serratia|AtopoBis
Serratia|Bis
0.000
Serratia|SPF
0.000
0.000
Ferrimonas|Atopo
<0.0001
Ferrimonas|AtopoBis
0.000
Ferrimonas|Bis
<0.0001
Ferrimonas|SPF
<0.0001
0.000
<0.0001
Butyrivibrio|Atopo
Butyrivibrio|AtopoBis
Butyrivibrio|Bis
0.006
Butyrivibrio|SPF
0.006
Oscillospira|Atopo
0.000
Oscillospira|AtopoBis
Oscillospira|Bis
Oscillospira|SPF
0.000
Epulopiscium|Atopo
Epulopiscium|AtopoBis
0.001
Epulopiscium|Bis
Epulopiscium|SPF
0.001
Escherichia|Atopo
Escherichia|AtopoBis
0.002
Escherichia|Bis
Escherichia|SPF
0.002
Alkalimonas|Atopo
0.006
Alkalimonas|AtopoBis
0.006
Alkalimonas|Bis
0.006
Alkalimonas|SPF
0.006
0.006
0.006
Listeria|Atopo
0.006
Listeria|AtopoBis
0.006
Listeria|Bis
Listeria|SPF
Streptococcus|Atopo
0.001
Streptococcus|AtopoBis
Streptococcus|Bis
0.001
Streptococcus|SPF
Actinobacillus|Atopo
0.000
Actinobacillus|AtopoBis
0.001
Actinobacillus|Bis
0.005
Actinobacillus|SPF
0.000
0.001
0.005
Roseburia|Atopo
Roseburia|AtopoBis
Roseburia|Bis
0.003
Roseburia|SPF
0.003
Bacillus|Atopo
0.001
Bacillus|AtopoBis
0.001
Bacillus|Bis
Bacillus|SPF
Parabacteroides|Atopo
Parabacteroides|AtopoBis
Parabacteroides|Bis
0.008
Parabacteroides|SPF
0.008
Sarcina|Atopo
0.005
Sarcina|AtopoBis
0.005
Sarcina|Bis
Sarcina|SPF
Enterococcus|Atopo
0.000
Enterococcus|AtopoBis
0.000
Enterococcus|Bis
Enterococcus|SPF
Carnobacterium|Atopo
0.006
Carnobacterium|AtopoBis
0.006
Carnobacterium|Bis
Carnobacterium|SPF
Coprococcus|Atopo
0.001
Coprococcus|AtopoBis
0.001
Coprococcus|Bis
Coprococcus|SPF
Enterobacter|Atopo
0.000
Enterobacter|AtopoBis
0.001
Enterobacter|Bis
0.007
Enterobacter|SPF
0.000
0.001
0.007
Neisseria|Atopo
0.003
Neisseria|AtopoBis
0.003
Neisseria|Bis
Neisseria|SPF
Photobacterium|Atopo
<0.0001
Photobacterium|AtopoBis
<0.0001
Photobacterium|Bis
<0.0001
Photobacterium|SPF
<0.0001
<0.0001
<0.0001
Brenneria|Atopo
0.000
Brenneria|AtopoBis
0.000
Brenneria|Bis
0.000
Brenneria|SPF
0.000
0.000
0.000
Oceanobacillus|Atopo
0.005
Oceanobacillus|AtopoBis
Oceanobacillus|Bis
0.005
0.006
Oceanobacillus|SPF
0.006
Lactobacillus|Atopo
0.000
<0.0001
Lactobacillus|AtopoBis
0.000
Lactobacillus|Bis
<0.0001
Lactobacillus|SPF
<0.0001
Xanthomonas|Atopo
0.007
Xanthomonas|AtopoBis
0.002
Xanthomonas|Bis
Xanthomonas|SPF
0.007
0.002
Sutterella|Atopo
0.003
Sutterella|AtopoBis
Sutterella|Bis
Sutterella|SPF
0.003
Staphylococcus|Atopo
0.000
0.001
0.003
Staphylococcus|AtopoBis
0.000
Staphylococcus|Bis
0.001
Staphylococcus|SPF
0.003
Lachnobacterium|Atopo
Lachnobacterium|AtopoBis
0.006
0.001
Lachnobacterium|Bis
0.006
Lachnobacterium|SPF
0.001
Vagococcus|Atopo
0.001
0.004
Vagococcus|AtopoBis
0.001
Vagococcus|Bis
0.004
Vagococcus|SPF
Leclercia|Atopo
<0.0001
Leclercia|AtopoBis
<0.0001
Leclercia|Bis
<0.0001
Leclercia|SPF
<0.0001
<0.0001
<0.0001
Fusobacterium|Atopo
0.006
0.005
Fusobacterium|AtopoBis
0.006
Fusobacterium|Bis
Fusobacterium|SPF
0.005
Citrobacter|Atopo
0.000
Citrobacter|AtopoBis
0.000
Citrobacter|Bis
0.000
Citrobacter|SPF
0.000
0.000
0.000
Hahella|Atopo
0.000
Hahella|AtopoBis
<0.0001
Hahella|Bis
0.000
Hahella|SPF
0.000
<0.0001
0.000
Alcanivorax|Atopo
Alcanivorax|AtopoBis
Alcanivorax|Bis
0.001
Alcanivorax|SPF
0.001
Facklamia|Atopo
0.003
Facklamia|AtopoBis
0.003
Facklamia|Bis
0.003
Facklamia|SPF
0.003
0.003
0.003
Faecalibacterium|Atopo
0.007
Faecalibacterium|AtopoBis
0.004
Faecalibacterium|Bis
0.004
0.003
Faecalibacterium|SPF
0.007
0.003
Eubacterium|Atopo
0.000
Eubacterium|AtopoBis
Eubacterium|Bis
0.001
Eubacterium|SPF
0.000
0.001
Shewanella|Atopo
<0.0001
Shewanella|AtopoBis
<0.0001
Shewanella|Bis
<0.0001
Shewanella|SPF
<0.0001
<0.0001
<0.0001
Tatumella|Atopo
<0.0001
Tatumella|AtopoBis
0.000
Tatumella|Bis
<0.0001
Tatumella|SPF
<0.0001
0.000
<0.0001
Bacteroides|Atopo
<0.0001
0.000
Bacteroides|AtopoBis
0.000
Bacteroides|Bis
<0.0001
0.000
Bacteroides|SPF
0.000
It has also been found that A. parvulum is correlated with the presence/abundance of GALT foci. Therefore there is provided an assay for identifying the likelihood of an individual of having UC or CD or IBD by measuring a relative abundance of A. parvulum by measuring the abundance of GALT foci. This correlation can also be used to provide a method of diagnostic that comprises collecting samples to measure the abundance of GAT foci, determining the presence of A. parvulum based on the cytokine(s) measurement and establishing a diagnosis.
In other aspect of the invention it has been shown that certain OTU's and/or taxa are indicative of improve therapeutic response. Table 7 exemplifies OTU's and/or taxa that exhibit a significant difference between the levels of bacteria between patients that responded to treatment. The patients in the two groups (responded to treatment/failed to respond to treatment) received a systemic corticosteroid medication (prednisone) as their acute anti-inflammatory therapy. Two patients (in the group that responded) received the mucosally active corticosteroid medication Entocort instead. Azathioprine (n=11) or methotrexate (n=4) immunomodulator medication was initiated in the patients for maintenance therapy. The clinical failure of response was determined by Physician Global Assessment and Pediatric Crohn's Disease Activity Index scoring determinations.
Clearly from the data of table 7 there is link between the level of bacteria and the efficacy of treatment. Thus when a patient exhibits bacterial levels in one or more taxa or OTU's from table 7 that are more elevated than a predetermined level or average corresponding to responders level the patient is likely not to respond to treatment. Alternatively patients exhibiting levels of bacteria lower that a predetermined level or average corresponding to non responders will profit the most from the treatment. The patient that did not respond have a different physiological or pathological status as assessed by standard diagnostic tests. For example patients with levels of Erwinia greater than about 3431 or preferably greater than about 13482 (one std dev) are likely not to respond and patient with lower levels than these likely to benefit most.
Eubacterium (OTU589746)|responded
0.011
Eubacterium (OTU589746)|Failed
0.011
Oribacteriumsinus (OTU470747)|
0.005
Oribacteriumsinus (OTU470747)|Failed
0.005
Veillonellaceae (OTU535825)|
0.024
Veillonellaceae (OTU535825)|Failed
0.024
Enterobacteriaceae (OTU323418)|
0.035
Enterobacteriaceae (OTU323418)|
0.035
Lachnospiraceae (OTU71387)|
0.015
Lachnospiraceae (OTU71387)|Failed
0.015
Atopobium (OTU529659)|responded
0.018
Atopobium (OTU529659)|Failed
0.018
Mogibacterium (OTU46159)|responded
0.043
Mogibacterium (OTU46159)|Failed
0.043
Propionibacteriumacnes (OTU368907)|
0.044
Propionibacteriumacnes (OTU368907)|
0.044
Alteromonadaceae; BD2-13
0.022
Alteromonadaceae; BD2-13
0.022
Coprococcus (OTU182512)|responded
0.045
Coprococcus (OTU182512)|Failed
0.045
Lachnospiraceae (OTU303772)|
0.043
Lachnospiraceae (OTU303772)|Failed
0.043
Clostridiales (OTU204932)|responded
0.024
Clostridiales (OTU204932)|Failed
0.024
Bacteroidales (OTU183618)|responded
0.023
Bacteroidales (OTU183618)|Failed
0.023
Ruminococcaceae (OTU195252)|
0.044
Ruminococcaceae (OTU195252)|
0.044
Sutterella (OTU295422)|responded
0.018
Sutterella (OTU295422)|Failed
0.018
Enterobacteriaceae (OTU307080)|
0.025
Enterobacteriaceae (OTU307080)|
0.025
Ruminococcus (OTU174136)|
0.033
Ruminococcus (OTU174136)|Failed
0.033
Clostridiumramosum (OTU470139)|
0.045
Clostridiumramosum (OTU470139)|
0.045
Erwinia (OTU289103)|responded
0.010
Erwinia (OTU289103)|Failed
0.010
Erwinia|responded
0.013
Erwinia|Failed
0.013
Atopobium|responded
0.045
Atopobium|Failed
0.045
Propionibacterium|
0.042
Propionibacterium|Failed
0.042
It will be appreciated that more than one taxa and/or OTU can be combined to identify patients that are more likely to respond to treatment. For example one could combine the measurement of OTU295422 and of taxa Erwinia in a patient and if the levels are below about 145 and about 3430 respectively then the patient is considered likely to respond. It should be noted that OTU's are most of the time closely related to a taxa therefore the above described approach would also be applicable using taxa associated with an OTU.
Thus in one aspect the present invention provides a method to test or assay or measure the levels of gut bacteria obtained directly from the gut or from stools and in which the actual measurement of bacterial levels is done in vitro. The measured levels can be used to assess the nature, severity or stage of IBD, CD or UC disease and determine treatment course such as the administration of certain drugs.
Furthermore there is also provided a method in which a test to measure the level(s) of bacteria as described above is requested to provide the results of an analysis to determine whether a patient has IBD, CD or UC or to determine the severity or stage of such disease by assessing bacterial levels as described above and administering a treatment if the patient exhibit the type and levels of bacteria associated with disease or the severity or stage of the disease.
Thus the present invention provides to the identification of pathological states or characteristics of patients by identifying bacteria associated with disease and of physiological states by providing levels of bacteria present in disease or at different stages or severity of disease.
The bacterial taxa and proteins described above can be referred to as diagnostic markers. These diagnostic markers can be used in a method for classifying a sample as being associated with IBD, UC or CD. The method comprises the steps of determining a presence or level of one or more of the diagnostic markers and comparing the presence or level to samples from IBD, UC or CD patients and/or normal patients. A combination of diagnostic markers may be combined together and may also further be combined with a standard diagnostic results derived from a disease activity index.
The algorithm can be a statistical algorithm which may comprise a learning statistical classifier system (or combination of such systems) such as neural network, random forest, interactive tree and the like, as would be known to a person skilled in the art. The predictive value of the classifying system maybe predetermined and may for example be at least 60%, 70%, 80%, 90% or 95%. The classification result may be provided to a clinician such as a gastroenterologist or general practitioner.
In yet a further aspect of the invention there is provided a method of classifying a gut sample to determine an association with IBD, UC or CD that comprises determining a diagnostic marker profile by detecting a presence or level of at least one gut diagnostic marker and classifying the sample as IBD, UC or CD by comparing the diagnostic marker profile to samples from IBD, UC or CD patients or normal subjects or combination thereof. The profile can be combined with a diagnostic based on a disease activity index specific for IBD, UC or CD.
The diagnostic marker can be selected from H2S producing bacteria, Proteobacteria, butyrate producing bacteria, Fusobacterium nucleatum, Veillonella parvula, Atopobium parvulum, Firmicutes, Clostridia, Clostridiales, Lachnopiraceae, Eubacterium, Roseburia, Coprococcus, Clostridium, Eubacterium rectale, Clostridium coccoides, Roseburia inulivorans, Verrucomicrobiae, Clostridiales, Verrucomicrobiales, Verrucomicrobiacae, Lachnospiraceae, Paenibacillaceae, Akkermansia, Turicibacter, Paenibacillus, Pasteurellales, Chromatialles, Hydrogenophilales, Oceanospirillales, Rhizobiales, Halomonadaceae, Pasteurellaceae, Bradyrhizobiaceae, Methylococcaceae, Hydrogenophilaceae, Porphyromonas, Lautropia, Methylobacterium, Haemophilus, Finegoldia, Nitrincola, Hydrogenophilu, Actinobacillus, Anaerococcus, Mobiluncus, Enterobacter, Vitreoscilla, Alcanivorax, Veillonella, Tatumella, Staphylococcaceae, Paenibacillaceae, Listeriaceae, Listeria, Paenibacillus, Staphylococcus, Negativicutes, Betaproteobacteria, Pasteurellales, Chromatialles, Burkholderiales, Selenomonadales, Pasteurellaceae, Haemophilus, Pantoea, Carnobacteriaceae, Granulicatella, Mogibacterium, Proprionibacterium, Bacillaceae, Atopobium, Hydrogenophilales, Rhizobiales, Bradyrhyzobiaceae, Hydrogenophylaceae, Porphyromonas, Lautropia, Tannarella, Finegoldia, Hydrogenophilus, Catonella, Mobilumcus, Alcanivorax, Afipia, sulfur dioxygenase (ETHE1), thiosulfate sulfur transferase (TST), cytochrome c oxidase subunit IV, sulfide dehydrogenase (SQR), complexes III and IV of mithochondrial respiratory chain, Cxcl1, IL17a, II12, II1β and combination thereof.
The profile may consist of level(s) of a marker or the combination of levels from different markers or the relative levels (ratios) of markers combined or not with a diagnosis based on a disease activity index. The profile may also comprise levels of markers over time or stages of the disease(s) or severity of the disease(s). The profile may also be weighted with respect to the markers or the diagnosis.
There is also provided an apparatus comprising a diagnostic marker detector capable of detecting one or more of the markers described above for example by methods described in this application, a processor configured to classify the sample as an IBD, UC or CD sample by comparing the diagnostic marker profile to samples from IBD, CD, UC or normal subjects or combination thereof and a result display unit to display to a user a classification obtained from the processor. The processor may also receive from an input a diagnostic result based on a disease activity index specific for IBD, UC or CD and combine this diagnostic result with the diagnostic marker profile to generate the classification. Thus the processor may use training data or a training cohort to identify the characterisitics of the diagnostic marker that provides a reliable classification.The data provided here (levels of bacteria and proteins for example) with their correlation to presence of disease or disease severity or progression can be used as a training cohort. However it will be appreciated that additional data could be generated to improve the training data based on the guidance of the results presented in this application.
It will be understood that the processor may use algorithms as described above.
An inception cohort of 157 patients (84 Crohn's disease (CD), 20 ulcerative colitis (UC) and 53 controls; Table 8) was recruited.
The microbiota at the intestinal mucosal interface embedded within the mucus layer and in direct contact with the site of disease was collected, and the microbial composition was characterized. The IBD microbiota was characterized by a smaller core as compared to controls (
The relative abundance of A. parvulum was validated by quantitative polymerase chain reaction (qPCR) and found to be positively correlated with disease severity (
To evaluate the colitogenic potential of A. parvulum, we utilized colitic-susceptible II10−/− mice12,13. Germ-free II10−/− mice were transferred to specific pathogen free (SPF) housing and gavaged with A. parvulum (108 CFU/mouse/week) for 6 weeks. Compared to control uninfected II10−/− mice, A. parvulum-colonized II10−/− mice showed macroscopic evidence of cecal atrophy and colon length reduction (
To gain mechanistic insights into the role of H2S-producing microbes in IBD severity, an unbiased, quantitative proteomic analysis of mucosal biopsies of IBD subjects of various disease severity (n=21) and controls (n=8) was conducted. Measurements for 3880 proteins were obtained of which 490 were identified as differentially expressed by comparing the 3 major groups, severe vs. moderate vs. control (one-way ANOVA with P<0.05). Mitochondrial proteins were identified as a major discriminant feature representing 21.7% of all differentially expressed proteins (
The findings demonstrate an alteration of the balance between bacterial-derived H2S production and host-mediated detoxification of H2S at the mucosal-luminal interface. To test the causative role of H2S-producing microbes in colitis, we assessed whether an H2S scavenger (bismuth) could alleviate Atopobium-induced colitis in II10−/− mice. Consistent with the first cohort, Atopobium-associated SPF mice developed severe colitis (
Collectively the findings shed light on the pathogenic mechanisms of early IBD onset. The emerging picture is that the pediatric IBD microbiota is characterized by a depletion in butyrate producing microbes together with an increased abundance of H2S-generating bacteria, namely A. parvulum, Fusobacterium and Veillonella, which produce H2S by protein fermentation18. Because IBD patients exhibit increased levels of fecal H2S14, sulfate-reducing bacteria (SRB) have long been proposed to be involved in the etiology of IBD, although studies have failed to demonstrate a link between SRB and IBD10. Instead, our study demonstrates a key role for microbes producing H2S through protein fermentation in CD pathogenesis. Butyrate is known to activate the expression of the genes encoding the host mitochondrial H2S detoxification components19 and our proteomic analyses indicate a diminished capacity for H2S detoxification by IBD patients. Therefore, we postulate that the depletion of butyrate-producing microbes from the gut microbiota would disable the host H2S defense systems. This “disarmed” host would be highly susceptible to further damage caused by enhanced H2S production, resulting in metabolic stress and subsequently increased mucosal inflammation. Interestingly, variants in mitochondrial DNA, which result in increased metabolic activities, protect mice from colitis20. This is in agreement with the important role of the mitochondria in modulating the mucosal barrier. More recently, excess H2S has been shown to act as an autocrine T-cell activator, potentially contributing to unwanted T-cell responses against commensal bacteria21, consistent with our observation that the gut microbiota is required for A. parvulum-induced experimental colitis. Given the essential role of butyrate in regulating regulatory T cells (Treg) homeostasis and the critical role of Treg in limiting intestinal inflammation22, H2S production may also interfere with this process by impairing butyrate oxidation and thus might lead to increased colitis severity. This result emphasises the importance of the microbial community and its interaction with the host in the pathogenesis of IBD. Altogether, our findings provide new avenues for diagnostics as well as therapies to treat IBD.
Colonic mucosal lavages and/or mucosal biopsies were collected from 157 pediatric subjects (84 Crohn's Disease, 20 Ulcerative Colitis, and 53 controls). All IBD cases were newly diagnosed with IBD and met the standard diagnostic criteria for either CD or UC. Metagenomic DNA from the intestinal lavages was extracted using the FastDNA SPIN Kit. Microbial communities were surveyed by deep sequencing the 16S rRNA V6 hypervariable region using Illumina HiSeq2500 and 454-Pyrosequencing. Reads were quality filtered and QIIME23 was used to assign reads into operational taxonomic units (OTUs) against the Greengenes reference set. Several statistical approaches (Kruskal-Wallis tests, LEFSe, PCA, PLS-DA) were used to determine differentially abundant OTUs. The correlation between A. parvulum relative abundance and CD severity was confirmed by qPCR. Proteomic analysis of mucosal biopsies was conducted using super-SILAC-based HPLC-ESI-MS/MS. The generated raw data was processed and analyzed by MaxQuant against the decoy Uniport-human database with the protein-group file imported into Persus for statistical analysis. Pathway analysis was done using the DAVID Bioinformatics Resources. The transcript levels of TST, SQRDL and COX4-1 were quantified by RT-qPCR. Gnotobiotic and specific pathogen free II10−/− mice were gavaged once weekly with A. parvulum (108 cfu) for 6 weeks. Bismuth (III) subsalicylate (7 g/kg) was added to the diet of the assigned groups one week before the gavage. Tissue samples from the colon were collected for RNA and histology as described previously24 Mouse colonoscopies were performed and histological inflammation was blindly scored as previously described25. Mice mucosal cytokines (Cxcl1, II12p40, II1β and II17a) were quantified by RT-qPCR.
Patient Cohort and Study Design:
This study involved the enrollment, detailed assessment, and biological sampling of 157 pediatric subjects (84 CD, 20 UC, and 53 controls; Table 7). All patients under 18 years of age scheduled to undergo their first diagnostic colonoscopy at the Children's Hospital of Eastern Ontario (CHEO) were potentially eligible for recruitment to this study, with the following exclusions which are known to affect the gut microbiota composition: (1) body mass index (BMI) greater than 95th percentile for age; (2) diagnosis with diabetes mellitus; (3) diagnosis with infectious gastroenteritis within the preceding 2 months; and (4) use of any antibiotics or probiotics within the last 4 weeks. All cases were newly diagnosed with IBD (inception cohort prior to the initiation of treatment) and met the standard diagnostic criteria for either Ulcerative Colitis or Crohn's Disease following thorough clinical, microbiologic, endoscopic, histologic and radiologic evaluation26; most had active inflammatory luminal disease involving the terminal ileum and/or the colon+/−perianal disease. Phenotyping of disease was based on endoscopy and clinical disease activity scores. The Simplified-Endoscopy Score-Crohn's disease was used to record macroscopic activity in each segment of the intestinal tract in Crohn's disease27, the site of involvement in CD was recorded utilizing the Paris IBD Classification28 and clinical disease activity of CD was determined using the Pediatric Crohn's Disease Activity Index (PCDAI)29. For UC, the site of disease was recorded using the Paris Classification system28, endoscopic activity was recorded using the Mayo Score Flexible Proctosigmoidoscopy Assessment in ulcerative colitis and clinical activity of UC was determined using and Pediatric Ulcerative Colitis Activity Index (PUCAI)30. The clinical activity scores are both validated for use in Pediatric IBD. All controls had a macroscopically and microscopically normal colon, and did not carry a diagnosis for any known inflammatory intestinal disorder and did not have a well-defined infectious etiology for the bowel inflammation. Data collected on all participants included: demographics (age, gender, BMI, country of birth, age of diagnosis), environmental exposures (cigarette smoke, diet, previous antibiotic exposure), and all clinical features. This study was performed in compliance with the protocol approved by the Research Ethic Board of the Children's Hospital of Eastern Ontario.
Biopsies and Mucosa-Luminal Sample Collection:
Mucosal-luminal interface samples were collected from the right colon at the time of endoscopy. Colonoscopy preparation was done the day before the procedure as per standard protocol31. During endoscopy, once the correct position is reached, loose fluid and debris was aspirated. Thereafter sterile water was flushed onto the mucosa and the collection of water, mucus and intestinal cells of the colonic mucosa was aspirated into sterile container through the colonoscope. These samples were immediately place on ice in the endoscopy suite, promptly transferred to the lab to minimize delay for processing and then storing at −80° C. Up to 2 biopsies were collected from macroscopically involved area of the right colon. Biopsies were flash frozen on dry-ice in the endoscopy suite and immediately stored at −80° C. until further processing.
Microbiota DNA Extraction and Sequencing of 16S rDNA Amplicons:
Metagenomic DNA was extracted from the mucosa-luminal samples using the Fast DNA SPIN Kit (MP Biomedicals) and the FastPrep machine (MP Biomedicals) with two mechanical lysis cycles at speed 6.0 for 40 seconds. Extracted DNA was then used for the construction of the sequencing libraries.
Two sequencing-by-synthesis platforms were used in this study: (1) pyrosequencing (Roche 454 GS-FLX) and (2) Illumina Hiseq 2500. Samples for both sequencing techniques were PCR amplified to target the same V6 hypervariable region. Samples for sequencing by Roche 454 were independently sequenced using the FLX chemistry on 12 lanes of a 16-lane sub-divided 454 FLX PicoTiter plate (70×75 mm) and using a total of 3 plates. The 454 amplicons libraries were constructed using the conserved V6 primers pair 16S-V6_907-F (5′-AAACTCAAAKGAATTGACGG-3′) (SEQ ID NO. 16) and 16S-V6_1073-R (5′-ACGAGCTGACGACARCCATG-3′)32 (SEQ ID NO. 17). The hypervariable V6 region of 16S rDNA gene was amplified using two successive PCR reactions to reduce PCR bias as previously described33. The first PCR used 16S-V6 specific primers and the 2nd PCR involved 454 fusion-tailed primers. In the first PCR, ten amplicons were generated from each extracted DNA sample. Each PCR reaction contained 2 μL DNA template, 17.5 μL molecular biology grade water, 2.5 μL 10× reaction buffer (200 mM Tris-HCl, 500 mM KCl, pH 8.4), 0.5 μL dNTPs (10 mM), 1 μl 50 mM MgCl2, 1 μL of both forward and reverse primers (10 mM each) and 0.5 μL Invitrogen's Platinum Taq polymerase (5 U/μL) in total volume of 25 μL. The PCR conditions were initiated with heated lid at 95° C. for 5 min, followed by a total of 15 cycles of 94° C. for 40 sec, 48° C. for 1 min, and 72° C. for 30 sec, and a final extension at 72° C. for 5 min, and hold at 4° C. Amplicons generated from each sample were pooled and purified to remove the excess unused primers using Qiagen's MiniElute PCR purification columns and eluted in 30 μL molecular biology grade water. The purified amplicons from the first PCR were used as templates in a second PCR with the same amplification conditions used in the first PCR with the exception of using 454 fusion-tailed primers in a 30-cycle amplification regime. An Eppendorf Mastercycler ep gradient S thermalcycler was used in all PCRs. A negative control reaction (no DNA template) was included in all experiments. PCR success was checked by agarose gel electrophoresis. The 16S-V6 amplicon of each sample was quantified by fluorometer and purified with AMPure magnetic beads. The amplicon libraries were sequenced on a 454 Genome Sequencer FLX System (Roche Diagnostics GmbH) following the amplicon sequencing protocol. Amplicons of each sample was bi-directionally sequenced in 1/16th of full sequencing run (70×75 picotiter plate).
For samples to be sequenced by Illumina Hiseq 2500, the V6 hypervariable region of the 16S rDNA gene was amplified using two successive PCR reactions as described previously34. The universal 16S rDNA-V6 primers for the first PCR step were modified from Sundquist et al32 to include the Illumina paired-end sequencing adapters, and a 4-6 nucleotide barcode sequence (Supplementary Table 10). Each PCR reaction was performed in a total volume of 50 μL using 50 ng of the extracted DNA, 1× Phusion HF PCR buffer, 0.5 μM of each primer, 0.2 mM dNTPs, and 1 U Phusion High-Fidelity DNA polymerase (Thermo Scientific). The PCR conditions included initial denaturation at 94° C. for 30 s, 10 cycles of 94° C. for 10 s, 61° C. for 10 s with a 1° C. drop each cycle and 72° C. for 15 s followed by an additional 15 cycles using an annealing temperature of 51° C. for 45 s, and a final extension at 72° C. for 2 min. The second PCR was carried out using 10 μL of the first PCR products in a final volume of 50 μL using the primers PCRFWD1/PCRRVS1 (Supplementary Table 10). The second PCR conditions were 30 s at 94° C., 15 cycles of 10 s at 94° C., 10 s at 65° C., and 15 s at 72° C. followed by a final extension step at 72° C. for 2 min. The amplicons of each sample were visualized on a 1.5% agarose gel and purified using the Montage PCR96 Cleanup Kit (Millipore). Next, the DNA concentration in each reaction was quantified using the Qubit® dsDNA BR Assay Kit (Invitrogen) following the manufacturer instructions and 100 ng of amplicons from each sample were pooled. Finally, the library consisting of the pooled amplicons was gel purified using the QIAquick Gel Extraction Kit (Qiagen), quantified and subjected to Illumina HiSeq 2500 sequencing at The Center for Applied Genomics (TCAG, Toronto) generating paired-end reads of 2×100 bases.
Microbiota Analysis:
454 Pyrosequencing Data Analysis:
A total of 346,160 reads were generated from 454 pyrosequencing of 16S rDNA-V6 region from 26 right colon samples. The generated reads were submitted to NCBI Sequence Read Archive under accession number SRP034632. The raw sequences were processed to remove low quality and short reads using Quantitative Insights Into Microbial Ecology pipeline release 1.4.0 (QIIME 1.4.0)23 according to the following parameters: (1) Minimum read length of 100 bp, (2) Exact matching to the sequencing primers, (3) No ambiguous nucleotides, and (4) The minimum average quality score of 20. This resulted in a total of 266,006 high quality reads with an average of ˜10,224 sequences per sample and a mean length of 169.58 bases including the primers. Next, sequences were clustered into operational taxonomic units (OTUs) using UCLUST based on average percentage of identity of 97%. The most abundant read from each OTU was picked as a representative sequence for that cluster, while singletons were discarded. PyNAST was used to align the representative sequences with a minimum alignment length of 100 and a minimum percentage identity of 75%, followed by identification of chimeric OTUs with the Blast Fragments Algorithm implemented in QIIME. Only 6 representative sequences were identified as chimeras and therefore were removed from the aligned representative set. Taxonomy assignments were made with BLAST by searching the representative sequences against the Greengenes database (release 4 Feb. 2011) with an e value of 1e-8 and a confidence score of ≥0.5. The resulting OTU table was used to determine the alpha and beta diversity within and between the samples using the default criteria of QIIME. Taxa significantly associated with disease status (CD, UC and control) or disease severity (mild, moderate and severe) were identified using the linear discriminant effect size (LEFSe) algorithm (http://huttenhower.org/galaxy/)11. To assign taxonomy at the species level, representative reads from OTUs of interest were retrieved from QIIME and aligned against the NCBI and RDB databases35,36.
Illumina Sequencing Data Analysis:
Paired-end sequences obtained by Illumina HiSeq 2500 (2×101 nucleotides) were merged into longer reads (with an average length per sequence of 165 nucleotides) using Fast Length Adjustment of Short reads (FLASh) software avoiding any mismatch in the overlap region that ranges from 20 to 80 nucleotides37. More than 95% of the reads was merged successfully, while the sequences that failed to merge were discarded. The merged reads were then quality filtered with a minimum quality score of 20 using the fastq_quality_filter command from the Fastx toolkit (http://hannonlab.cshl.edu/). High quality reads were sorted according to the forward and the reverse barcode sequences with barcodes trimming using the NovoBarCode software (Novocraft.com). Sequences with mismatched primers were excluded. The sorted reads were submitted to NCBI Sequence Read Archive under accession number SRP034595. Next, the reads were fed to QIIME 1.5.023 pipeline and clustered into OTUs using a closed-reference OTU picking workflow with UCLUST against the Greengenes reference set (release 4 Feb. 2011) based on average percentage of identity of 97%. The OTUs were assigned the taxonomy associated with the corresponding Greengenes reference sequence. Singletons and doubletons were removed and a table of OTU counts per sample was generated. Next, the OTU table was randomly subsampled to a total number of reads per sample of 500,000. The resulting rarefied OTU table was used to analyze the microbiota structure and diversity using the microbial ecology tools available in the QIIME package and for all other downstream analyses. For the identification of the core microbiota, OTUs detected in at least 75% of the samples within a clinical group (CD patients, UC patients or control subjects) were considered as members of the core microbiota for that particular group.
Multivariate Statistical Analysis:
Several statistical approaches were employed to identify taxa significantly associated with disease status and severity. A Kruskal-Wallis test with post hoc Dunn's test was performed to compare the relative abundance of taxa as a function of disease status (CD vs. UC vs. control) and disease severity (mild vs. moderate vs severe). A Bonferroni correction was employed to account for multiple hypotheses with a P<0.05 considered significant. The relative abundances of the taxa identified were also analyzed by principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). For PLS-DA calculation the data were log-transformed and scaled to unit variance as described in Durbin et al.38. The PLS-DA models were validated by cross-validation and permutation tests. The variable importance in projection (VIP) value was used to identify features which contribute the most to the clustering (taxa with VIP>1.0 were considered influential and with VIP>1.5 highly influential). All statistical analyses were performed using XLSTAT and/or R software package.
Atopobium parvulum qPCR Quantification:
The relative abundance of A. parvulum was determined by conducting absolute quantitative PCR on the extracted metagenomic DNA using the Applied Biosystems 7300 DNA analyzer and A. parvulum-specific 16S rRNA primers developed for the current study; Aparv-711F 5′-GGGGAGTATTTCTTCCGTGCCG-3′ (SEQ ID NO. 1) and Aparv-881R 5′-CTTCACCTAAATGTCAA GCCCTGG-3′ (SEQ ID NO. 2). Each sample was tested in duplicate in a total volume of 25 μL per reaction. 100 ng of template DNA was added to a reaction mixture containing 1 μM of each primer, and 1× QuantiFast SYBR Green PCR master mix (Qiagen). The amplification conditions were 5 min at 95° C. followed by 40 cycles of 95° C. for 10 sec and 66° C. for 1 min with data collection at the second step of each cycle. To normalize between samples, the total 16S rRNA in each sample was simultaneously quantified using the universal primers; 331F 5′-TCCTACGGGAGGCAGCAGT-3′ (SEQ ID NO. 18) and 797R 5′-GGACTACCAGGGTATCTAATCCTGTT-3′ 39 (SEQ ID NO. 19). The positive standards for A. parvulum and the total 16S rRNA quantification were prepared by conducting PCR on the DNA extracted from A. parvulum ATCC 33793 strain and one mucosal aspirate sample from a healthy subject, respectively. The amplicons were purified using PureLink™ PCR Purification Kit (Invitrogen) and quantified by Qubit® dsDNA BR Assay Kit (Invitrogen). Afterward, 106, 107, 108 and 109 copies from each gene fragment were prepared, assuming the average molecular weight of the base pair is 660, and the Ct values were determined for each concentration by qPCR following the same conditions described above. The standard curves of both A. parvulum and the total 16S rRNA gene copy numbers against Ct values were established and the relative abundance of A. parvulum in each sample was calculated as A. parvulum 16S-rRNA divided by the total 16S-rRNA copy number. To validate the specificity of Apar-711F and Aparv-881R, fresh PCR amplicons from the total DNA extracted from two different mucosal aspirates was cloned using TOPO TA cloning kit (Invitrogen) according to the manufacturer's instructions, and then, the plasmid containing the 16S rRNA gene fragment was extracted from 6 different clones by QIAprep Spin Miniprep kit (Qiagen) using its standard protocol followed by Sanger sequencing using M13F and M13R primers.
Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC):
Human hepatic HuH7 cells (HuH-7), human embryonic kidney 293 cells (HEK-293) and human colorectal cancer 116 cells (HCT-116) were individually grown at 37° C. in a 5% CO2 humidified incubator. SILAC medium was prepared as follows: DMEM lacking lysine, arginine and methionine was custom prepared by AthenaES (Baltimore, Md., USA) and supplemented with 30 mg/L methionine (Sigma Aldrich; Oakville, ON, CAN), 10% (v/v) dialyzed FBS (GIBCO-Invitrogen; Burlington, ON, CAN), 1 mM sodium pyruvate (Gibco-Invitrogen), 28 μg/mL gentamicin (Gibco-Invitrogen), and [13C6, 15N2]-L-lysine, [13C6, 15N4]-L-arginine (heavy form of amino acids; Heavy Media) from Sigma Aldrich (Oakville, ON, CAN) at final concentrations of 42 mg/L and 146 mg/L for arginine and lysine respectively. For HCT-116, the concentration of arginine was increased to 84 mg/L. Cells were grown for at least 10 doublings in SILAC media to allow for complete incorporation of the isotopically labeled amino acids into the cells.
Determination of the Rate of SILAC Amino Acids Incorporation into HuH-7, HEK-293 and HCT-116 Cells:
Cells were grown to 80% confluency in SILAC medium (5×106 cells were plated in 10-cm dish). Next, the cells were washed twice with ice-cold phosphate-buffered saline and lyzed by addition of 1 mL of 1×RIPA buffer (50 mM Tris (pH 7.6), 150 mM NaCl, 1% (v/v) NP-40, 0.5% (w/v) deoxycholate, 0.1% (w/v) SDS with protease inhibitor cocktail (Complete Mini Roche; Mississauga, ON,CAN) and phosphatase inhibitor (PhosStop Roche tablet). The lysates were then transferred to 15 mL conical tubes and the proteins were precipitated by addition of 5 mL ice-cold acetone followed by incubation at −20° C. overnight. Proteins were collected by centrifugation (3000×g, 10 min, 4° C.), washed with ice-cold acetone two times, and the protein pellets were resolubilized in 300 μL of a 50 mM NH4HCO3 solution containing 8 M urea. Protein concentrations were determined by the Bradford dye-binding method using Bio-Rad's Protein Assay Kit (Mississauga, ON, CAN). For the general in-solution digestion, 200 pg of protein lysates were reconstituted in 50 mM NH4HCO3 (200 μL) and proteins were reduced by mixing with 5 μL of 400 mM DTT at 56° C. for 15 min. The proteins were then subjected to alkylation by mixing with 20 μL of 400 mM iodoacetamide in darkness (15 min at room temperature) followed by addition of 800 μL of 50 mM NH4HCO3 to reduce the urea concentration to ˜0.8 M. Next, the proteins were digested with TPCK-trypsin solution (final ratio of 1:20 (w/w, trypsin: protein) at 37° C. for 18 h. Finally, the digested peptides were desalted using C18 Sep-Pack cartridges (Waters), dried down in a speed-vac, and reconstituted in 0.5% formic acid prior to mass spectrometric analysis (as described below) and the determination of labeling efficiency. The incorporation efficiency was calculated according to the following equation: (1−1/Ratio(H/L)); where H and L represents the intensity of heavy and light peptides detected by mass-spectrometry, respectively. Labeling was considered complete when values reached at least 95% for each cell type.
Proteomic Analysis of Biopsies Using Super-SILAC-Based Quantitative Mass Spectrometry:
Biopsies were lysed in 4% SDS (sodium dodecyl sulfate), 50 mM Tris-HCl (pH 8.0) supplemented with proteinase inhibitor cocktail (Roche) and homogenized with a Pellet pestle. The lysates were sonicated 3 times with 10 s pulses each with at least 30 s on ice between each pulse. Protein concentrations were determined using the Bio-Rad DC Protein Assay. The proteins were processed using the Filter Aided Sample Preparation Method (FASP) as previously described with some modifications40. Colon tissue lysates (45 μg of proteins) and heavy SILAC-labeled cell lysates (15 μg from each HuH-7, HEK-293 and HCT-116 cells) were mixed at a 1:1 weight ratio and transferred into the filter. The samples were centrifuged (16,000×g, 10 min), followed by two washes of 200 μL 8 M urea, 50 mM Tris-HCl pH 8.0. Samples were then reduced by incubation in 200 μL of 8 M urea, 50 mM Tris-HCl (pH 8.0) supplemented with 20 mM dithiothreitol. After centrifugation, samples were subjected to alkylation by adding 200 μL of 8 M urea, 50 mM Tris-HCl pH 8.0, containing 20 mM iodoacetamide (30 min at room temperature protected from light). Samples were washed using 200 μL 8 M urea, 50 mM Tris-HCl pH 8.0 (twice) to remove excess SDS. To further dilute urea, two washes of 200 μL 50 mM Tris-HCl pH 8.0 were performed. For the trypsin digest, samples were incubated in 200 μL of 50 mM Tris-HCl pH 8.0, containing 5 μg of Trypsin (TPCK Treated, Worthington) on a shaker (250 rpm) at 37° C. overnight. Finally, 200 μL of 50 mM Tris-HCl pH 8.0 was added to elute the peptides by centrifugation (twice). Peptides were fractionated, using an in-house constructed SCX column with five pH fractions (pH 4.0, 6.0, 8.0, 10.0, 12.0). The buffer composition was 20 mM boric acid, 20 mM phosphoric acid, and 20 mM acetic acid, with the pH adjusted by using 1 M NaOH). Finally, the fractionated samples were desalted using in-house C18 desalting cartridges and dried in a speed-vac prior to LC-MS analysis.
Mass-Spectrometry Analyses:
All resulting peptide mixtures were analyzed by high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The HPLC-ESI-MS/MS consisted of an automated Ekspert™ nanoLC 400 system (Eksigent, Dublin, Calif., USA) coupled with an LTQ Velos Pro Orbitrap Elite mass spectrometer (ThermoFisher Scientific, San Jose, Calif.) equipped with a nano-electrospray interface operated in positive ion mode. Briefly, each peptide mixture was reconstituted in 20 μL of 0.5% (v/v) formic acid and 12 μL was loaded on a 200 μm×50 mm fritted fused silica pre-column packed in-house with reverse phase Magic C18AQ resins (5 μm; 200 Å pore size; Dr. Maisch GmbH, Ammerbuch, Germany). The separation of peptides was performed on an analytical column (75 μm×10 cm) packed with reverse phase beads (3 μm; 120 Å pore size; Dr. Maisch GmbH, Ammerbuch, Germany) using a 120 min gradient of 5-30% acetonitrile (v/v) containing 0.1% formic acid (v/v) (JT Baker, Phillipsburg N.J., USA) at an eluent flow rate of 300 nL/min. The spray voltage was set to 2.2 kV and the temperature of heated capillary was 300° C. The instrument method consisted of one full MS scan from 400 to 2000 m/z followed by data-dependent MS/MS scan of the 20 most intense ions, a dynamic exclusion repeat count of 2, and a repeat duration of 90 s. The full mass was scanned in an Orbitrap analyzer with R=60,000 (defined at m/z 400), and the subsequent MS/MS analyses were performed in LTQ analyzer. To improve the mass accuracy, all the measurements in the Orbitrap mass analyzer were performed with on-the-fly internal recalibration (“Lock Mass”). The charge state rejection function was enabled with charge states “unassigned” and “single” states rejected. All data were recorded with Xcalibur software (ThermoFisher Scientific, San Jose, Calif.).
Database Search and Bioinformatic Analysis:
All raw files were processed and analyzed by MaxQuant, Version 1.2.2.5 against the decoy Uniport-human database (86,749 entries), including commonly observed contaminants. The following parameters were used: cysteine carbamidomethylation was selected as a fixed modification, with methionine oxidation, protein N-terminal acetylation and heavy proline set as variable modifications. Enzyme specificity was set to trypsin. Up to two missing cleavages of trypsin were allowed. SILAC double labeling (light: K0R0; heavy: K8R10) was set as the search parameter in order to assess the conversion efficiency. The precursor ion mass tolerances were 7 ppm and the fragment ion mass tolerance was 0.5 Da for MS/MS spectra. The false discovery rate (FDR) for peptides and proteins was set at 1% and a minimum length of six amino acids was used for peptide identification. The peptides file was imported into Persus (version 1.2.0.17) to extract the lysine- and arginine-containing peptides, respectively.
The protein-group file was imported into Persus (version 1.3.0.4) for data statistical analysis and an ANOVA—test was chosen for the protein profile with p values of less than 0.05 considered significant. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was achieved using the DAVID Bioinformatics Resources (http://david.abcc.ncifcrf.gov/). DAVID statistical analyses were performed against the whole genome. Proteomics has a tendency to oversample proteins from the cytosol and nucleus while under-sampling membrane-associated proteins. Therefore, the results from DAVID were retested against the set of proteins that were not changing in our dataset in order to eliminate any pathway/GO enrichment biases.
Total RNA Extraction and qRT-PCR Quantification of Mitochondrial Genes Expression:
RNA integrity was preserved by adding the mucosal aspirates to an equal volume of RNAlater (Ambion) before freezing at −80° C. The frozen aliquot (2 mL) was thawed on ice and the total RNA was extracted following a hot phenol protocol as described previously41. Briefly, 4 mL of each sample in RNAlater was pelleted by centrifugation at 13,000×g for 5 min at 4° C. The pellets were washed once in 50% RNAlater/PBS buffer and resuspended with lysis in 2 mL of denaturing buffer (4 M guanidium thiocyanate, 25 mM sodium citrate, 0.5% N-laurylsarcosine, 1% N-acetyl cysteine, 0.1 M 2-mercaptoethanol). The lysate was divided into 500 μL aliquots, to which 4 μL of 1M sodium acetate (pH 5.2) was added. Each aliquot was then incubated with 500 μL of buffer saturated phenol (pH 4.3) at 64° C. for 10 minutes, with intermittent mixing. One ml of chloroform was added to the solution and incubated for 15 minutes on ice, followed by centrifugation at 18,000×g for 30 min at 4° C. Afterward, RNA was precipitated from the aqueous layer by adding 1/10 volume 3M sodium acetate, 500 mM DEPC treated EDTA and 2 volumes of cold ethanol followed by overnight incubation at −80° C. The RNA was then pelleted by centrifugation at 4° C., washed with 80% cold ethanol and resuspended in 100 μL of RNAse free ddH2O. The extracted RNA was treated twice with DNase I (Epicentre) followed by PCR amplification using the 16S rRNA universal primers; Bact-8F and 1391-R; to confirm the absence of genomic DNA. The quality and the quantity of the extracted RNA was determined by NanoDrop 2000 spectrophotometer (Thermoscientific) and confirmed by BioRad's Experion StdSens RNA system according to the manufacturer's description and stored at −80° C. until use.
The quantification of the expression level of TST (Thiosulfate Sulfurtransferase), SQRDL (Sulfide Quinone Reductase Like) and COX4-1 (Cytochrome C oxidase subunit IV isoform 1) relative to GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) genes was determined using the Applied Biosystems 7300 DNA analyzer and Quantitect SYBR Green RT-PCR kit (Qiagen). The primers used were either designed by NCBI Primer-BLAST tool42 or extracted from a literature source as detailed in Supplementary Table 11. Each reaction contained 100 ng RNA template, 0.5 μM of each primer, 1× Quantitect SYBR Green RT-PCR master mix and 0.25 μL Quantitect RT-mix in a final volume of 25 μL. The one step RT-PCR conditions were 50° C. for 30 min, 95° C. for 15 min followed by 40 cycles of 15 sec at 94° C., 30 sec at 60° C. and 30 sec at 72° C. with data collection at the third step of each cycle. The amplification specificity was checked by the melting profile of the amplicon and 2% agarose gel electrophoresis. Ct values were then extracted using the Applied Biosystems 7300 sequence detection software versions 1.3.1. Ct values of TST, SQRDL, or COX4-1 were normalized to the Ct values of GAPDH generating ΔCt. Next, ΔΔCt was calculated by subtracting the average ΔCt of the control group from the ΔCt of each sample. The relative quantification were then calculated by 2−ΔΔCt as mentioned previously34.
II10−/− Mice Experiments and Tissue Processing:
Germ-free SvEv129/C57BL6 II10−/−; NF-κBEGFP mice (8-12 weeks old, n=12) were transferred to specific pathogen free (SPF) conditions and mice from one cohort (n=6) were gavaged once weekly with A. parvulum (1×108 CFUs) for 6 weeks. Atopobium parvulum ATCC 33793 was grown in fastidious anaerobic broth (FAB) (Lab M, Canada).
To investigate involvement of complex biota and H2S in the development of colitis, we performed two subsequent experiments using 129/SvEv II-10−/− mice. In the first experimental setting, gnotobiotic II10−/− mice (n=37) were randomized into 4 groups; 1: GF only (n=6), 2: GF+bismuth (III) subsalicylate (n=10); 3: A. parvulum (1×108 CFUs) (n=10) and 4: A. parvulum+bismuth (III) subsalicylate (n=11). Mice were euthanized after 6 weeks of mono-association. Bismuth (III) subsalicylate (Sigma-Aldrich, Saint Louis, Mo.) was incorporated to the chow (Teklan Global 18% Protein Rodent Diet) at a concentration of 7 g/kg (Harlan Laboratories, Madison, Wis.) and then irradiated for gnotobiotic experiments. Mice were fed with this diet starting 1 week before the colonization with A. parvulum. In the second experimental setting, gnotobiotic II10−/− mice (n=31) were transferred to SPF conditions and randomized into 4 groups; 1: SPF only (n=7), 2: SPF+bismuth (III) subsalicylate (n=8); 3: SPF plus A. parvulum (1×108 CFUs) (n=8) and 4: A. parvulum+bismuth (III) subsalicylate (n=8). Mice were euthanized after 6 weeks of weekly infection with A. parvulum. Bismuth (III) subsalicylate (Sigma-Aldrich, Saint Louis, Mo.) was incorporated to the chow (Teklan Global 18% Protein Rodent Diet) at a concentration of 7 g/kg (Harlan Laboratories, Madison, Wis.). Mice were fed with this diet starting 1 week before the colonization with A. parvulum.
All animal protocols were approved by the Institutional Animal Care and Use Committee of the University of North Carolina at Chapel Hill. Tissue samples from the colon were collected for RNA and histology as described previously24. Histological images were acquired using a DP71 camera and DP Controller 3.1.1.276 (Olympus), and intestinal inflammation was scored as previously described12. The tissue was divided into 4 quarters, a score was given to each quarter separately and then added to generate a final inflammation score on a scale of 0-16.
Mouse Endoscopy:
Colonoscopy was performed using a “Coloview System” (Karl Storz Veterinary Endoscopy) as described previously25. Mice were anesthetized using 1.5% to 2% isoflurane and ˜4 cm of the colon from the anal verge from the splenic flexure was visualized. The procedures were digitally recorded on an AIDA Compaq PC.
Real Time RT-PCR on Mouse Intestinal Samples:
Total RNA from intestinal tissues was extracted using TRIzol (Invitrogen) following the manufacturers protocol. cDNA was reverse-transcribed using M-MLV (Invitrogen) and mRNA expression levels were measured using SYBR Green PCR Master mix (Applied Biosystems) on an ABI 7900HT Fast Real-Time PCR System and normalized to β-actin. The primers used were as follows: β-actin (5′-TGGAATCCTGTGGCATCCATGAAAC-3′ (SEQ ID NO. 20) and 5′-TAAAACGCAGCTCAGTAACAGTCCG-3′ (SEQ ID NO. 21)), cxcl1 (5′-GCTGGGATTCACCTCAAGAA-3′ (SEQ ID NO. 22) and 5′-TCTCCGTTACTTGGGGACAC-3′ (SEQ ID NO. 23)), tnf (5′-ATGAGCACAGAAAGCATGATC-3′ (SEQ ID NO. 24) and 5′-TACAGGCTTGTCACTCGAATT-3′ (SEQ ID NO. 25)), il12p40 (5′-GGAAGCACGGCAGCAGCAGAATA-3′ (SEQ ID NO. 26) and 5′-AACTTGAGGGAGAAGTAGGAATGG-3′ (SEQ ID NO. 27)), il1β (5′-GCCCATCCTCTGTGACTCAT-3′ (SEQ ID NO. 28) and 5′-AGGCCACAGGTATTTTGTCG-3′ (SEQ ID NO. 29)), il-17a (5′-TCCAGAAGGCCCTCAGACTA-3′ (SEQ ID NO. 30) and 5′-ACACCCACCAGCATCTTCTC-3′ (SEQ ID NO. 31)). The PCR reactions were performed for 40 cycles according to the manufacturer's recommendation, and RNA fold changes were calculated using the ΔΔCt method.
Statistical Analyses Of II10−/− Mice Results:
Unless specifically noted, statistical analyses were performed using GraphPad Prism version 5.0a (GraphPad, La Jolla, Calif.). Comparisons of mouse studies were made with a nonparametric analysis of variance, and then a Mann-Whitney U test. All graphs depict mean±SEM. Experiments were considered statistically significant if p<0.05.
Sample Collection.
Mucosal aspirates (washings) were collected from the right colon of 40 control children, 41 crohn's disease (CD) and 20 ulcerative colitis (UC) patients aged 3-18 years old at initial diagnosis, at Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada, using a standardized protocol.28 The right colon was selected in particular because it is thought to be the most active site for butyrate synthesis.29,30 In addition, fresh stool samples were collected from a subset of patients (5 control and 10 CD) at least 24 h prior to the endoscopy procedure (Table S1 for participating patient information). Immediately following collection, samples were transported on ice to the lab where they were either processed for DNA extraction or stored at −80° C. until further use.
Extraction of Metagenomic DNA.
5 ml aliquots of the mucosal washes were spun at 20,000×g for 10 min at 4° C. Then, DNA was extracted from the sediments (or stool samples) using the FastDNA® Spin Kit (MP Biomedicals) utilizing two mechanical lysis cycles in a FastPrep® Instrument (MP Biomedicals) set at speed of 6.0 for 40 seconds. Extracted DNA was then stored at −20° C. until further use.
Characterize the Diversity of Butyrate-Producing Bacteria in Healthy and IBD Children
Relative Quantification of BCoAT Gene from Mucosal-Washes Metagenomic DNA Using qPCR.
The overall abundance of butyrate-producing bacteria was determined by quantifying the amount of BCoAT gene utilizing the primers BCoATscrF/BCoATscrR as described elsewhere.27 50 ng of metagenomic DNA from each sample was used in a 25 μl qPCR reaction containing 1× QuantiTect SYBR Green PCR Master Mix (QIAGEN) and 0.5 μM of BCoATscrF/BCoATscrR primers. The amplification conditions were as follows: 1 cycle of 95° C. for 15 min; 40 cycles of 94° C. for 15 sec, 53° C., and 72° C. each for 30 sec with data acquisition at 72° C. For the melting curve analysis, a stepwise temperature increase from 55° C. to 95° C. was performed. Quantification standards were prepared by purifying and quantifying the BCoAT gene fragment from healthy subjects using PureLink™ PCR Purification Kit (Invitrogen) and Qubit® dsDNA BR Assay Kit (Invitrogen), respectively. Then, 106, 107, and 108 gene copies were prepared assuming an average molecular weight of 660 per nucleotide. Simultaneously, the number of 16S rRNA gene copies was quantified in parallel to the BCoAT gene as described previously31, and results were expressed as copy number of BCoAT genes per 16S rRNA gene.
Preparation of BCoAT Gene and 16S rRNA Libraries from Mucosal-Washes Metagenomic DNA for Deep Sequencing.
BCoAT library construction was carried out using a two-step PCR strategy. In the 1st step, 50 ng of metagenomic DNA was used in a 50 μl PCR reaction containing 1.5 mM MgCl2, 0.5 μM of BCoATscrF/BCoATscrR primers, 0.2 mM dNTPs, and 2.5 U HotStarTaq DNA polymerase (QIAGEN). Amplification started with an initial enzyme activation step at 95° C. for 15 min. Then, amplification was carried out using 25 cycles at 94° C., 53° C., and 72° C. (each for 30 sec), and a 10 min final extension at 72° C. For the second PCR, 13 fusion primers were designed (12 forward and one reverse, SEQ ID 3-15) following Roche's Amplicon Fusion Primer Design Guidelines for GS FLX Titanium Series Lib-L Chemistry. Briefly, the forward primers contain (from 5′-3′) GS FLX Titanium Primer A, a four-base library key, 12 different Multiplex Identifiers (MIDs), and a BCoATscrF primer sequence. The reverse primer contains (from 5′-3′) GS FLX Titanium Primer B, a four-base library key, and a BCoATscrR primer sequence (Table 9). 10 μl of product from the 1st PCR was utilized in 50 μl for the second PCR reaction using a unique MID fusion primer for every 12 samples and the same concentration of PCR component as the 1st PCR. A total of 15 amplification cycles were performed utilizing the same amplification conditions as the first PCR. For each sample, a total of 5 reaction tubes were prepared. Following amplification, PCR products from the same sample were pooled together, inspected on 1.5% agarose gel, and purified using Montage PCR96 Cleanup Kit (Millipore). Finally, an equimolar amount of samples with unique MIDs (a total of 4 libraries) were pooled together and sequenced on a Roche 454 platform using a full plate of GS FLX Titanium chemistry (each library in ¼ plate) at The McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada. A 16S rRNA library was constructed. The 16S rRNA library was sequenced at The Centre for Applied Genomics (TCAG) at the Hospital for Sick Children in Toronto (Canada) using a HiSeq 2500 platform to generate paired-end reads of 2×100 bases.
Data Analysis.
For BCoAT sequencing, demultiplexed reads from each sample were filtered using RDP's Pyrosequencing Pipeline32 based on a minimum quality score of 20 and 200 nucleotides read length cutoff. Operational Taxonomic Units (OTUs) clustering at 95% sequence similarity was achieved using a de novo UCLUST algorithm integrated in Quantitative Insights Into Microbial Ecology (QIIME) software package V 1.7.0,33 after which, singleton OTUs were removed. QIIME was also used to compute alpha and beta diversity between samples using a fixed number of reads/sample of 4,600. The longest sequence from each OTU was then selected and used for taxonomy assignment as described previously.23 Sequences with <75% identity to functional gene reference database were considered unclassified OTUs. Finally, the relative abundance (RA) of assigned species was calculated and differences in butyrate producing bacteria RA were analyzed.
16S rRNA paired-end sequences were merged using Fast Length Adjustment of SHort reads (FLASH) software.34 During this step, most reads overlapped perfectly by about 10-80 nucleotides, and less than 5% of the reads failed to combine. Uncombined reads were discarded from further analysis. Subsequently, Novobarcode command from Novocraft Technologies was used to demultiplex merged reads according to the 5′ and 3′ barcode sequences and trim the barcode sequence from the corresponding read. Reads with minimum quality score of 20 were selected for further analysis using fastq_quality_filter command line from the Fastx-toolkit V 0.0.13. Taxonomy assignment to the genus level was done using QIIME V 1.5.0 aligning against Greengenes database (release 4 Feb. 2011) using UCLUST Reference-based OTU picking method at 97% sequence identity. Butyrate producers were selected from the overall micobiota using the list of bacteria that produce butyrate through BCoAT pathway found in reference23. Since each bacteria has a different copy number of the 16S rRNA gene and only one copy of the BCoAT gene, the copy number of 16S rRNA was normalized to 1 by dividing the number of reads of a given genus by its average 16S rRNA copy number obtained from rrnDB.35 The RA of identified butyrate producer genera was then calculated. Finally, correlation between BCoAT and 16S rRNA datasets was analyzed.
For phylogenetic analysis, full nucleotide sequences of BCoAT genes for the assigned butyrate producer species were obtained from the National Center for Biotechnology Information (NCBI's) Reference Sequence Database and MUSCLE aligned with the unclassified OTUs sequences. Then, a phylogenetic tree of aligned sequences was constructed using a maximum-likelihood algorithm with FastTree tool integrated into QIIME. Visual display of the rooted tree was achieved using Interactive Tree Of Life (iTOL) tool.36 Using the same strategy, another phylogenetic tree was constructed from MUSCLE aligned unclassified OTU sequences and but nucleotide database sequences (a dataset of all predicted BCoAT gene sequences (4,041 sequences) obtained from the Functional Gene Pipeline/Repository.26
Confirmation of BCoAT Sequencing Results Using qPCR.
35 control, 37 CD and 19 UC mucosal aspirate samples were used to validate the sequencing result by qPCR. Primers specific to BCoAT gene of E. rectale and F. prausnitzii were used in the qPCR. In addition, primers targeting the 16S rRNA gene of Eubacterium rectale/Clostridium coccoides group (XIVa) (20-21), F. prausnitzii 22-23, and Roseburia 24-25 were used. For stool samples, 5 control and 10 CD subjects were used to determine the relative amount of F. prausnitzii using 16S rRNA specific primers only (table 10). The complete 16S rRNA gene was amplified using the universal primer UniF/UniR (18-19) adapted from reference37. Fifty ng of metagenomic DNA was used in a 25 μl PCR reaction using QuantiTect SYBR Green PCR Master Mix (QIAGEN) as described in the previous section using 55° C. instead of 53° C. annealing temperature. The assay was done in duplicate for each sample. Delta-Ct (ΔCt) for each target was calculated by subtracting the Ct of the total 16S rRNA from the target Ct. Then, the ΔCt values were compared between groups.
Clostridium coccoides
Faecalibacterium
prausnitzii
Roseburia spp. 16S
Eubacterium rectale
Faecalibacterium
prausnitzii
Statistical Analysis.
Unless otherwise specified, result from the qPCR and sequencing was analyzed using two-tailed Mann-Whitney test comparing IBD subtypes to the control group. A P value less than 0.05 was considered significant. Correlation between BCoAT and 16S rRNA sequencing was done by calculating Spearman's rank correlation coefficient (r) of paired RA of bacterial taxa identified by the two approaches.
Butyrate Producers are Reduced in UC Patients with Colonic Inflammation.
In order to determine the relative amount of butyrate producers, the copy number of BCoAT genes to 16S rRNA was assayed. The difference in the relative number of BCoAT genes between control subjects (2.15×10−4±2.46×10−4) and IBD subgroups (CD, 2.17×10−4±1.97×10−4; UC, 1.74×10−4±2.78×10−4) was not statistically significant (P>0.2) (
Diversity of Butyrate Producers is Different in IBD Patients Compared to Healthy Subjects.
A total of 670,287 high quality reads were generated from 43 samples (13 control, 20 CD, and 10 UC) with an average of 15,570 reads per sample (range, 44,158-2,938) and an average read length of 465 nucleotides (summarized in Table S4). Clustering reads at a 0.05 distance resulted in a total of 965 OTUs from all samples with a total OTU number of 714 for controls, 804 for CD, and 744 for UC. The majority of observed OTUs were shared between the three groups (486 OTUs), and only a few were unique to each individual group (
Furthermore, multidimensional scaling analysis of UniFrac metrics, presented by principal coordinates analysis (PCoA) plot, indicates that the IBD group is different than controls. Although no separation was observed with unweighted UniFrac (data not shown), PCoA showed good separation of CD and UC samples from control with weighted UniFrac. When clustering CD samples with controls, most control and CD subjects were grouped into two distinct clusters with 52.6% of the variance accounted for by coordinate 1 (PCoA1) and an additional 12.7% of variance attributable to PCoA2 (
Eubacterium Rectale is Depleted and Faecalibacterium prausnitzii Thrives in IBD Patients.
In order to take a more in depth look at butyrate producer diversity, we looked at the assigned bacterial taxa. Overall, OTUs from all samples were assigned to 12 classified bacterial species that belong to the Firmicutes phylum in addition to 67 unclassified OTUs (
a. Comparing the identified bacterial species RA of the control group to IBD patients (both CD or UC) revealed that the control group is characterized by a higher RA of E. rectale (P<0.05) (
b. In order to test the possibility that the unclassified OTUs could represent novel butyrate producers, the complete sequences of the BCoAT gene for the assigned bacterial species were MUSCLE aligned with the unclassified OTUs sequences. The aligned sequences were then used to construct a phylogeny tree using a maximum-likelihood algorithm. Twenty-five of the 67 unclassified OTUs were clustered with known butyrate producers. Among these, the unclassified OTUs 34 and 43, which were found to be deficient in IBD, clustered with E. rectale. Interestingly, 42 of the unclassified OTUs did not cluster with any of the assigned butyrate producers. In a second step, the sequences of unclassified OTUs were MUSCLE aligned with the but nucleotide database downloaded from the Functional Gene Pipeline/Repository. The but database contains all nucleotide sequences of probable BCoAT genes identified by Hidden Markov Model searches of the NCBI bacterial protein database. Subsequently the aligned sequences were subjected to phylogenetic tree construction. This time, only 4 of the 67 unclassified OTUs were clustered with classified bacteria. The remaining OTUs clustered only with partial BCoAT coding sequences isolated from human samples that belong to unclassified uncultured bacterium. Hence, this suggests that the 63 unclassified OTUs might belong to novel butyrate producers.
Diversity of Butyrate-Producing Bacteria Revealed by 16S rRNA Sequencing:
Analyzing the diversity of butyrate producing bacteria at the genus level using 16S rRNA sequencing reveals similar results to BCoAT sequencing with minor differences. In total, 10 genera of butyrate producers were identified with the 16S rRNA approach compared to 6 genera using a functional gene approach. The majority of reads were assigned to 4 genera: Eubacterium, Faecalibacterium, Roseburia, and Coprococcus (
Relative Quantification of Key Butyrate Producers Revealed by qPCR:
BCoAT sequencing results were further validated using qPCR utilizing BCoAT and 16S rRNA specific primers. Eubacterium rectale/Clostridium coccoides group (XIVa), which is dominated by E. rectale, was reduced in both CD subtypes compared to controls. On the other hand, the UC group had similar levels of group XIVa compared to the controls (
In order to investigate if sample type (stool versus mucosal aspirate) could affect the level of detected bacteria, stool collected from 5 control and 10 CD (6 with normal and 4 with inflamed colons, table S1) patients were subjected to qPCR using 16S rRNA specific primers to F. prausnitzii. Contrary to the mucosal aspirate finding, F. prausnitzii showed similar levels to the controls in both CD subtypes (
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CA2014/050245 | 3/14/2014 | WO | 00 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2014/138999 | 9/18/2014 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 5225329 | Marks | Jul 1993 | A |
| 20070269813 | Dewhirst et al. | Nov 2007 | A1 |
| Number | Date | Country |
|---|---|---|
| WO 2007056680 | May 2007 | WO |
| WO 2013133298 | Sep 2013 | WO |
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| Number | Date | Country | |
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| 20160032363 A1 | Feb 2016 | US |
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| 61781564 | Mar 2013 | US |
