Mib200345_pap 1.8

The Duodenal Microbiota Composition of Adult Celiac DiseasePatients Is Associated with the Clinical Manifestation of theDisease Pirjo Wacklin, PhD,* Katri Kaukinen, MD,† Elina Tuovinen, MSc,* Pekka Collin, MD,† Katri Lindfors, PhD,‡Jukka Partanen, PhD,* Markku Mäki, MD,‡ and Jaana Mättö, PhD* Background: Celiac disease is classically manifested in the gastrointestinal (GI) tract but extraintestinal symptoms, such as dermatitis herpetiformis(DH), are also common. Besides several well-known shared genetic risk factors and an environmental trigger, gliadin, factors determining the clinicaloutcome of the disease are not known. In this study, the role of duodenal microbiota in the celiac disease outcome was studied by analyzing mucosa-associated microbiota in celiac disease patients with a variety of intestinal and extraintestinal symptoms.
Methods: Microbiota in duodenal biopsy samples obtained from 33 patients with celiac disease with GI, DH, anemia, or mixed symptoms, as well asscreen-detected asymptomatic celiac disease and 18 control subjects were analyzed using PCR denaturing gradient gel electrophoresis and a subset ofsamples additionally by the 16S ribosomal RNA gene sequencing.
Results: The composition and diversity of mucosal microbiota was associated with the manifestation of celiac disease when analyzed using PCRdenaturing gradient gel electrophoresis and the 16S ribosomal RNA gene sequencing. The patients with celiac disease with GI symptoms or anemia hadlower microbial diversity than those with DH. Moreover, the patients with GI symptoms had different intestinal microbiota composition and structure,dominated by Proteobacteria, in comparison to those with DH or control subjects (patients with dyspepsia). The relatively similar intestinal microbiotacomposition in the control subjects and those with DH was characterized by the high abundance of Firmicutes.
Conclusions: The two common outcomes of celiac disease, classical GI and extraintestinal manifestations, had marked differences on the diversity andcomposition of intestinal microbiota. This association suggested that intestinal microbiota may have a role in the manifestation of the disease.
(Inﬂamm Bowel Dis 2013;0:1–8) Key Words: small intestine, celiac disease, duodenal microbiota Celiacdiseaseisachronicinﬂammatoryenteropathyoccurring small bowel mucosal damage, inﬂammation, and epithelial integ- in genetically predisposed individuals after dietary gluten rity are improved by commitment to a lifelong gluten-free diet.
consumption, affecting 1% to 2% of Caucasian individuals.1,2 Nowadays, the clinical picture of celiac disease is highly Currently, celiac disease with a classical gastrointestinal (GI) variable. The most typical symptoms are classical GI complaints, manifestation is diagnosed by detecting mucosal villous atrophy such as diarrhea and abdominal pain, as well as malabsorption with with crypt hyperplasia and increased inﬂammation in an intestinal weight loss or anemia. However, although the disease primarily biopsy. The serological test, especially IgA-class endomysial and affects the GI tract, a considerable number of patients diagnosed transglutaminase 2 antibodies, can support the diagnosis. Celiac with celiac disease present only with extraintestinal symptoms disease cannot be cured, but the symptoms can disappear, and including a bullous rash (dermatitis herpetiformis, DH), infertility,as well as neurologic and psychiatric problems.3 Patients withceliac disease may also be asymptomatic. Asymptomatic celiac Received for publication June 20, 2012; Accepted July 30, 2012.
disease is typically diagnosed by screening at-risk individuals such From the *Finnish Red Cross Blood Service, Helsinki, Finland; †Department of Gastroenterology and Alimentary Tract Surgery, Tampere University Hospital and as ﬁrst-degree relatives of those affected with celiac disease.
School of Medicine, University of Tampere, Tampere, Finland; and ‡Pediatric Research Celiac disease is strongly associated with histocompatibility Center, Tampere University Hospital, University of Tampere, Tampere, Finland.
complex II class HLA-DQ02 and HLA-DQ08,4 which are present Supported by the SalWe Research Program for IMO (Tekes—the Finnish Fund- in more than 95% to 99% of patients with celiac disease5 and with ing Agency for Technology and Innovation grant 648/10), the Academy of Finland,the Sigrid Juselius Foundation, and the Competitive Research Funding (EVO) of the several other genetic polymorphisms.6–8 Although they are rele- Tampere University Hospital and Helsinki and Uusimaa Hospital district.
vant as risk factors, their presence is not sufﬁcient for the devel- The authors have no conﬂicts of interest to disclose.
opment of the disease.9,10 Analysis of genetic polymorphisms Reprints: Pirjo Wacklin, PhD, Kivihaantie 7, 00310 Helsinki, Finland (e-mail: have shown that even though genetic loci speciﬁc to the two major outcomes of celiac disease, classical GI symptoms and Copyright 2013 Crohn's & Colitis Foundation of America, Inc.
DOI 10.1097/MIB.0b013e31828029a9 DH, have been identiﬁed,11 they do not explain the discordance Published online.
of celiac disease phenotypes detected in twin pairs12 or siblings.13 Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 www.ibdjournal.org 1 Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.

Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 Thus, other factors such as environmental factors or intestinalmicrobiota may play a role in the diversiﬁcation of the manifes-tation. Several risk factors, such as the introduction of gluten tothe diet at an early age,15 certain infections,16 and formula feed-ing,14 which may affect the intestinal microbiota composition,have been reported. Indeed, evidence for the dysbiosis of intesti-nal commensal microbiota in celiac disease has been presented instudies of pediatric patients with celiac disease17–22 and in a singlestudy of adult patients with celiac disease.23 The role of intestinal microbiota on different clinical manifestations of celiac disease has not been studied yet. To addressthis question, we analyzed the duodenal microbiota composition ofadult patients with celiac disease in relation to a range of intestinaland extraintestinal symptoms of the disease. Microbiota compositionin duodenal biopsy samples of 33 untreated patients with celiacdisease and 18 control subjects were analyzed by the denaturinggradient gel electrophoresis (DGGE) and a subset of samples also bythe 16S ribosomal RNA (rRNA) gene sequencing. This studyshowed that the composition and diversity of duodenal microbiotadiffered between the patients with the GI and those with extra-intestinal outcomes of celiac disease, indicating that microbiotadysbiosis may have a role in the manifestation of the disease.
MATERIALS AND METHODS Study Subjects and Sampling The study group comprised 33 adults with untreated celiac disease at the time of diagnosis (24 females and 9 males; mean age,39 years; range, 18–67 years). Eight of these patients had GIsymptoms (ie, diarrhea, abdominal pain), six had DH, and sevenhad anemia. Eight patients with celiac disease diagnosed whenscreening celiac disease family members were asymptomatic. Fur-thermore, an additional 4 patients with celiac disease had combi-nations of the above-mentioned symptoms or other complaints(Fig. 1). The small bowel biopsy and serum samples of the 33patients were taken at the time of the diagnosis of celiac disease,and thus, all patients were consuming normal Western gluten-con-taining diet on sample collection. The biopsy and serum samples of18 subjects without celiac disease with a similar age and sex dis-tribution experiencing dyspepsia served as control subjects (Fig. 1).
All the patients and control subjects had undergone an upper GI endoscopy at the Department of Gastroenterology and Alimen-tary Tract Surgery. On the endoscopy, seven forceps biopsyspecimens were taken from the distal part of the duodenum. Twoto three small bowel biopsy specimens were freshly embedded in anoptimal cutting temperature compound (Tissue-Tek, Miles; Elkhart,IN), snap frozen in liquid nitrogen, and stored at 2708C until used.
FIGURE 1. A–D, Distribution and median (indicated by square) of The rest of the biopsies were used for diagnostic purposes. The clinical parameters. The group "other" contained patients with diagnosis of celiac disease was based on the presence of small a combination of DH and GI symptoms (2), weight loss (1), and bowel mucosal, severe, partial, or subtotal villous atrophy with crypt a combination of mild GI symptoms and dementia (1). CD, celiac hyperplasia.24 To diagnose DH, a skin biopsy was taken from the disease. The P values between different CD symptom groups and uninvolved perilesional skin, and the diagnosis was based on between all CD patients and controls in Mann–Whitney U test are the demonstration of pathognomonic granular IgA deposits in the indicated in the graph: *P , 0.05, ***P , 0.001.
2 www.ibdjournal.org Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.
Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 Intestinal Microbiota Celiac Disease Manifestation dermal papillae revealed by a direct immunoﬂuorescence examina- sample. The richness was calculated as a number of detected tion.25 Even if the majority of patients with DH evince small bowel bands in the DGGE proﬁle of the sample. Principal component mucosal villous atrophy, a proportion of the patients only present analysis (PCA) based on the band intensities of all the patients with mild enteropathy. The small bowel mucosal villous height to with celiac disease and control subjects was calculated as imple- crypt depth ratio (Vh/CrD), and densities of CD3+ and gd+ intra- mented in Bionumerics, version 5.0. Statistical signiﬁcance in the epithelial lymphocytes (IEL) were analyzed from biopsy samples diversity between symptom groups was tested with t test.
and endomysial antibody (EmA) titers were measured from theserum samples, as described earlier.26 Mann–Whitney U test in Cloning of the 16S rRNA Gene StatsDirect version 2.5.6 (StatsDirect Ltd, Cheshire, United King- Cloning was performed for a subset of 20 samples including dom) was applied to calculate statistically signiﬁcant differences randomly selected samples of the control subjects (n ¼ 5), and the between the clinical parameters.
patients with celiac disease with GI symptoms (n ¼ 4), DH (n ¼ 6),anemia (n ¼ 4), and other symptoms (n ¼ 1). The samples were cloned using the pGEM-T Vector system II (Promega, Madison, WI) The total DNA was extracted from the biopsy samples kit according to the manufacturer's instructions. The 16S rRNA gene using the QIAamp Mini Kit (Qiagen, Valencia, CA) according to fragment was ampliﬁed similarly to the second PCR reaction in the manufacturer's instructions with minor modiﬁcations. Brieﬂy, DGGE analysis, except primers without GC-rich clamps were used.
the biopsy samples were lysed by incubating the sample in ATL Positive clones containing the insert were selected using Luria agar lysis buffer with proteinase K overnight at 568C, puriﬁed with with isopropyl-2-D-galactopyranoside/X-Gal/ampicillin. The clones spin columns, and then eluted with 400 mL of buffer AE. The with the correct insert size were sequenced in Euroﬁns MWG DNA concentrations were determined with NanoDrop 1000 Operon (Ebersberg, Germany).
(Thermo Scientiﬁc, Wilmington, DE). The extracted DNAs were The sequences shorter than 200 base pair or a nonbacterial stored at 2208C.
origin according to Blast30 were removed from further analysis.
Of all the anemia samples, only 43 sequences were obtained, and they were therefore excluded from the further data-analysis. The The similarity and diversity of microbiota in the biopsy rest of the sequences were assigned to bacterial taxa using the samples of the study subjects was analyzed by the nested PCR- Classiﬁer tool in Ribosomal database Project31 and aligned by DGGE. The partial 16S rRNA gene was ﬁrst ampliﬁed by PCR Mothur.32 Rarefaction curves and diversity indexes were calcu- lated in mothur using 0.03 dissimilarity threshold. Principal coor- TYMTGGCTCAG-30) and U1401R (5'-CGGTGTGTACAA- dinate analysis and Unifrac analysis were performed by Fast GACCC-30).27 In second PCR, the PCR product of the ﬁrst Unifrac software.33 A maximum likelihood tree for the Unifrac PCR reaction was ampliﬁed with primers, U968F +GC (5'- analysis was inferred by RAxML using the gamma model of rate GGGAACGCGAAGAACCTTA-3') and U1401R, as describedin the study by Mättö et al.28 A 1 mL of template DNA was usedfor both PCRs. A volume of 20 mL of the PCR product was separated in 8% polyacrylamide gel with a denaturing gradientof urea and formamide ranging from 38% to 60%.29 The DGGE gels were run at 70 V for 960 minutes using the DCode universal All the patients with celiac disease, except three patients with mutation detection system (Bio-Rad, Hercules, CA). The gels DH, had both villous atrophy and an increased titer of serum EmA were stained and documented, as described in Wacklin et al.29 (Fig. 1). Two patients with DH showed mild enteropathy and had Despite several attempts, the ampliﬁcation was not successfulfor the four samples belonging to the GI (1), anemia (2) symptom increased lymphocyte and EmA levels, whereas one patient with groups, and a control subject (1) probably because of the low DH had villous atrophy, but normal levels of lymphocytes. The amount of bacterial DNA in comparison to human DNA or small bowel mucosal structure was normal, and serum EmA was PCR inhibitors in the sample. These samples were excluded from negative for all the control subjects (Fig. 1). The medians of all the DGGE analysis.
measured clinical parameters differed between control subjects and The digitalized DGGE gel images were imported to the patients with celiac disease (Mann–Whitney U test, P .0.0001), as Bionumerics program version 5.0 (Applied Maths, Sint-Martens- expected (Fig. 1). The patients with DH had a higher median of Vh/ Latem, Belgium) for normalization and band detection, as CrD than those with celiac disease with anemia (Mann–Whitney U described in Wacklin et al.29 Matrices based on band intensities test, P ¼ 0.03) (Fig. 1). Other clinical parameters did not differ were exported from Bionumerics and used for the calculation of between the celiac disease symptom groups. Thus, the clinical pa- Shannon diversity indexes. Shannon diversity index, H', was cal- rameters indicated the presence of intestinal inﬂammation and culated using the equation H' ¼ 2(Spi ln(pi)), where pi was the mucosal damage, albeit mild, in two patients with DH in the celiac proportion of each species (ie, DGGE band intensity) in the patient group.
www.ibdjournal.org 3 Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.

Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 FIGURE 2. The microbial diversity and richness based on an intensity matrix of DGGE proﬁles in the control subjects and the patients with celiacdisease (CD) with different clinical manifestations. The P values in ANOVA are indicated in the graph. *P , 0.05, **P , 0.02, ***P , 0.001.
Microbiota Diversity and Composition (Fig 3), both of which indicate abnormal functionality of the GI track. This indicated that the composition of mucosa-associated mi- The PCR-DGGE analysis of dominant mucosa-associated crobiota in the duodenum of the patients differed depending on the microbiota showed that the patients with celiac disease presenting manifestation of celiac disease. Interestingly, the samples of patients DH had a higher microbial diversity and richness than the control reporting both GI and DH symptoms (n ¼ 2) were clustered with the subjects (analysis of variance [ANOVA], P , 0.05) and screened samples of patients with GI symptoms (data not shown), and the asymptotic patients (ANOVA, P , 0.02), especially in comparison sample of the patient with intestinal malabsorption and weight loss with the microbiota of the patients with celiac disease with anemia was clustered with the samples of the anemia symptom group. The (ANOVA, P , 0.0002) or GI symptoms (ANOVA, P , 0.0006) celiac patient group as a whole or the asymptomatic celiac patient group, which was diagnosed in the screening of risk individuals, was The patients with celiac disease with different symptoms (GI, not separable from the control subjects in the PCA of PCR-DGGE anemia, DH) were clustered separately in PCA of the DGGE proﬁles proﬁles (Fig. 3).
(Fig. 3). The patients with DH (including the two patients with mildenteropathy) clearly shared different microbiota as compared with Microbiota Diversity and Composition by the more closely clustered patients with GI symptoms and anemia 16S rRNA Sequence Analysis To extend the microbiota analysis to phylum and genus levels, the microbial composition in the control subjects and thosepatients with DH, GI, and anemia symptoms was studied using FIGURE 3. PCA based on the DGGE proﬁles of the samples of patientswith celiac disease and control subjects. The samples from the patients FIGURE 4. Rarefaction curves of the 16S rRNA gene sequences for the with celiac disease manifesting different symptoms and control control subjects, for those patients with celiac disease presenting GI subjects are indicated by different colors, and the number of samples symptoms, and for those patients with celiac disease presenting DH.
in each symptom group is in parentheses. The two samples of the patients The thin lines indicate 95% conﬁdence intervals. Operational taxo- with DH presenting mild enteropathy are indicated by arrows.
nomic units were deﬁned at a 97% similarity level.
4 www.ibdjournal.org Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.

Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 Intestinal Microbiota Celiac Disease Manifestation TABLE 1. Diversity and Richness Estimators of the 16S rRNA Gene Clone Libraries of the Control Subjects, PatientsWith Celiac Disease, and Two Disease Subgroups (Patients With GI Symptoms and Patients With DH Symptoms) No. of OTUs (Mean) Shannon Diversity Index (Mean) Patients with celiac disease Operational taxonomic units (OTUs) at 97% similarity.
aStatistical signiﬁcance at P ¼ 0.08 using t test, control subjects versus patients with celiac disease.
bStatistical signiﬁcance at P ¼ 0.04 using t test, control subjects versus patients with celiac disease with GI symptoms.
the 16S rRNA gene sequencing. Altogether, the 16S rRNA gene The samples of the patients with GI symptoms were clustered clone libraries were performed for 20 biopsy samples. We separately from the samples of the control subjects in the PCA of the obtained 725 clone sequences in total. The four samples from 16S rRNA gene clone sequences (Fig. 6). The samples of patients the patients with anemia, for which only eight to 13 clones were with DH (including the two samples with mild enteropathy) were obtained per sample, as well as 75 short sequences were excluded located between the control subjects and the patients with GI symp- from the analysis. Rarefaction curves showed a decreasing rate of toms (Fig. 6). Accordingly, the weighted and unweighted Unifrac operational taxonomic units (deﬁned by 0.03 dissimilarity analysis indicated that the microbial composition (P , 0.002) threshold) at the end of most curves (Fig. 4), demonstrating that and structure (P , 0.002) of the GI symptom group were sig- a large part of diversity was achieved.
niﬁcantly different from the control group. In addition, the Based on the sequence analysis, the microbial diversity and microbial structure differed between the DH and the GI symp- richness of samples differed depending on the symptoms of the tom groups (P , 0.002) in the weighted Unifrac analysis. The patient with celiac disease. Microbial richness based on the rarefactioncurve analysis and diversity was slightly lower in the GI symptomgroup than in the DH symptom group (Shannon diversity, ANOVA,P , 0.04) (Fig. 4; Table 1). The low diversity and richness in thepatients with GI symptoms was also detected in DGGE, thus con-ﬁrming the results. In contrast to the DGGE results, the patients withDH and control subjects shared a rather similar diversity according tothe sequence analysis. The Shannon diversity index showed a trendtoward higher diversity in the control subjects in comparison with thepatients with celiac disease (ANOVA, P , 0.08) (Table 1). Therarefaction curves for the control subjects and the all patients withceliac disease did not differ (Fig. 4).
Taxonomic assignments of the sequences showed that the mucosa-associated microbiota in the duodenum consisted ofFirmicutes, Bacteroides, and Proteobacteria and Actinobacteriaphyla. Firmicutes-related and Bacteroides-related sequences wereabundant in the control subjects and patients with DH, whereasProteobacteria-related sequences dominated (70%) in the patientswith GI symptoms (Fig. 5). The altered duodenal microbiota com-position of the patients with GI symptoms was also evident at thegenus level (Fig. 5). Several proteobacterial genera were abundantin the samples of the GI symptom group, Acinetobacter (25%) andNeisseria (12%) being the most abundant. The sequences related to FIGURE 5. Relative abundances of the sequences from the control sub- Streptococcus and Prevotella were the most abundant in the duo- jects, the patients with celiac disease presenting GI symptoms, and those denum of the patients with DH (29% and 18%) and control subjects with DH at the phyla level (A) and genera level (B). The numbers in (14% and 18%). In total, we detected 45 bacterial genera in the data parentheses show the total number of sequences identiﬁed in each set, 32 genera being detected in the control samples, and 31 genera study group at phyla and genera levels. The signiﬁcant phyla-level dif- in the celiac disease patient samples.
ferences between the study groups: *** P , 0.001, ** P , 0.01, * P , 0.05.
www.ibdjournal.org 5 Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.

Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 Our study indicated that the patients with celiac disease presenting DH symptoms shared more similar microbial composi-tion with the control subjects than those with other clinicalmanifestations of celiac disease. The patients with DH hadbiopsy-proven mucosal damage/mild enteropathy and increasedlymphocyte counts and EmA titer. As compared with the otherceliac disease symptom groups, the Vh/CrD ratio in the patientswith DH was closer to the ratio detected in the control subjects.
However, it did not differ statistically from the other celiac diseasesymptom groups, except for the anemia symptom group. The twopatients with DH showed mild enteropathy. The microbiota proﬁlesof these patients clustered tightly with those from the other patientswith DH. Thus, the Vh/CrD ratio cannot explain the highersimilarity of the microbial composition of the control subjects andpatients with DH. The ﬁnding of unexpectedly low richness anddistinct clustering of duodenal microbiota proﬁles in the patientswith anemia symptoms also suggests a possible role of microbiotain the outcome of celiac disease and warrants further studies witha larger patient cohort. Interestingly, the samples of the patientsshowing both DH and GI symptoms were clustered with the FIGURE 6. Principal coordinate analysis of the samples was associated samples of the GI symptom group, demonstrating the strong effect with the symptoms of celiac disease. The samples of the patients with of the GI symptoms on the intestinal microbiota. These ﬁndings celiac disease with GI symptoms are indicated with green triangles, indicate that a more detailed patient segmentation based on the patients with DH with red circles, and controls with blue squares. The symptoms is reasonable in further studies of microbiota in patients two samples of the patients with DH with mild enteropathy are with celiac disease.
indicated by arrows.
In contrast to the colon, the microbiota composition of the small intestine has been infrequently studied. The few studies microbial composition or structure between the control subjects and performed to proﬁle microbiota in the small intestine have revealed patients with DH did not differ. The results indicate that the com- that Firmicutes and Bacteroides are dominant phyla also in the position of the mucosa-associated microbiota in the duodenum of small intestine,35,40,41 as also supported by the present study. The the patients with celiac disease presenting GI symptoms was patients with a classical celiac disease manifestation, GI symptoms, altered, as compared with the control subjects and patients with DH.
had a higher amount of Proteobacteria than the patients withanother manifestation of the disease or the control subjects. Similarto our results of patients with celiac disease, the small intestines of patients with IBD were characterized by the parallel increase of cell Commensal microbiota interacts with the host immune wall–associated Proteobacteria and the decrease of Firmicutes in system, and the disturbance in the interaction caused by internal comparison to the patients without IBD.35 The genera (mostly or environmental factors may lead to dysbiosis and inﬂammation.
Streptococcus and Prevotella) observed in the present study were This hypothesis has been suggested in inﬂammatory bowel diseases largely matching to the genera detected by Nistal et al,23 another (IBD),34 but it may also be extended to intestinal inﬂammation in study on the microbiota composition of adult patients with celiac general. Microbiota may have a role in the development or mani- disease. In fact, the microbiota composition of the duodenum festation of celiac disease, as shown by the present study. Our resembles more the microbiota in the esophagus42 or the oral cavity43 results demonstrate that the composition, structure, and diversity than the distal parts of the intestine. In future studies, it may be of microbiota differed depending on the manifestation of celiac informative to detect the oral disease status and oral microbiota disease, especially between classical intestinal symptoms (ie, GI composition and study whether it is reﬂected on the duodenal micro- and anemia) and extraintestinal symptoms (ie, DH). The dysbiosis biota composition.
of microbiota indicated by the altered microbiota composition is Although the dysbiosis of microbiota observed in patients a common phenomenon in several intestinal inﬂammation disor- with celiac disease may be a consequence of the disease, it is ders. For example, microbial composition in Crohn's disease35,36 possible that the spectrum of a patient's intestinal microbes has and irritable bowel syndrome28,37 have been shown to differ de- a role in the actual clinical symptoms of celiac disease caused by pending on the disease phenotypes. Recently, altered intestinal mi- gliadin. The comparison of active and untreated pediatric patients crobiota populations and/or diversities have also been reported in with celiac disease has shown that certain bacterial groups (ie, patients with type 2 diabetes38 or obese subjects,39 both character- a decreased number of Biﬁdobacteria, Bacteroides, and virulent ized by a low grade of intestinal inﬂammation.
Escherichia coli) are associated with both active and treated celiac 6 www.ibdjournal.org Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.
Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 Intestinal Microbiota Celiac Disease Manifestation disease, suggesting that microbial composition changes are not from the European Genetics Cluster on Celiac Disease. Hum Immunol.
just a consequence of the disease.44 Nevertheless, no speciﬁc 5. Kaukinen K, Partanen J, Maki M, et al. HLA-DQ typing in the diagnosis bacterial agent or pathogen has been linked to the development of celiac disease. Am J Gastroenterol. 2002;97:695–699.
or manifestation of celiac disease, the situation being similar in 6. Hunt KA, Zhernakova A, Turner G, et al. Newly identiﬁed genetic risk IBD.45 Intestinal and extraintestinal manifestations of celiac dis- variants for celiac disease related to the immune response. Nat Genet.
2008;40:395–402.
ease can occur discordantly among family members13 and even 7. Dubois PC, Trynka G, Franke L, et al. Multiple common variants for among identical twins.11 Although few host genetic loci have celiac disease inﬂuencing immune gene expression. Nat Genet. 2010; been associated with the manifestation of celiac disease,11 it has 8. Einarsdottir E, Koskinen LL, de Kauwe AL, et al. Genome-wide analysis been suggested that yet unknown susceptibility genes or environ- of extended pedigrees conﬁrms IL2-IL21 linkage and shows additional mental factors or their combination play a role in the determina- regions of interest potentially inﬂuencing coeliac disease risk. Tissue Anti- tion of the outcome of celiac disease.13 Based on our results, it is tempting to speculate that microbiota composition could be one 9. Bevan S, Popat S, Braegger CP, et al. Contribution of the MHC region to the familial risk of coeliac disease. J Med Genet. 1999;36:687–690.
such factor. Recently, Rausch et al46 and we29 have shown that 10. Greco L, Romino R, Coto I, et al. The ﬁrst large population based twin host gene polymorphisms in FUT2 (coding for the histoblood study of coeliac disease. Gut. 2002;50:624–628.
group secretor status) have an effect on intestinal microbiota com- 11. Holopainen P, Mustalahti K, Uimari P, et al. Candidate gene regions and genetic heterogeneity in gluten sensitivity. Gut. 2001;48:696–701.
position. Interestingly, FUT2 association with celiac disease was 12. Hervonen K, Karell K, Holopainen P, et al. Concordance of dermatitis detected by Dickey et al47 but not by Heneghan et al.48 Thus, it is herpetiformis and celiac disease in monozygous twins. J Invest Dermatol.
possible that host genes could indirectly, by means of microbiota 13. Karell K, Korponay-Szabo I, Szalai Z, et al. Genetic dissection between composition, be involved in the manifestation of the disease.
coeliac disease and dermatitis herpetiformis in sib pairs. Ann Hum Genet.
Alternatively, bacteria, for example, those belonging to Proteo- bacteria, may contribute to the development of the disease. In this 14. Persson LA, Ivarsson A, Hernell O. Breast-feeding protects against celiac study, an increased abundance of proteobacteria was associated disease in childhood–epidemiological evidence. Adv Exp Med Biol. 2002;503:115–123.
with the commonly occurring GI manifestation of the disease but 15. Norris JM, Barriga K, Hoffenberg EJ, et al. Risk of celiac disease auto- not with DH. The combined effect of gliadin and Shigella, a mem- immunity and timing of gluten introduction in the diet of infants at ber of proteobacteria, has been shown to enhance the gut epithe- increased risk of disease. JAMA. 2005;293:2343–2351.
16. Stene LC, Honeyman MC, Hoffenberg EJ, et al. Rotavirus infection fre- lial barrier disruption in a rat model,49 and Sanz et al44 suggested quency and risk of celiac disease autoimmunity in early childhood: a lon- that intestinal bacteria could be factors enhancing immunologic gitudinal study. Am J Gastroenterol. 2006;101:2333–2340.
responses to gliadin.35 Moreover, proteobacteria with adherent, 17. Collado MC, Calabuig M, Sanz Y. Differences between the fecal micro- biota of coeliac infants and healthy controls. Curr Issues Intest Microbiol.
invasive properties have been suggested to promote inﬂammation leading to IBD in genetically or immunologically predisposed 18. Collado MC, Donat E, Ribes-Koninckx C, et al. Speciﬁc duodenal and faecal bacterial groups associated with paediatric coeliac disease. J Clin The different clinical manifestations of celiac disease showed 19. Collado MC, Donat E, Ribes-Koninckx C, et al. Imbalances in faecal and characteristic compositions of the intestinal microbiota. Hence, it is duodenal Biﬁdobacterium species composition in active and non-active possible that the spectrum of a patient's intestinal microbes has coeliac disease. BMC Microbiol. 2008;8:232.
a role in the actual clinical symptoms of celiac disease caused by 20. Nadal I, Donat E, Ribes-Koninckx C, et al. Imbalance in the composition of the duodenal microbiota of children with coeliac disease. J Med Micro- gliadin. The differences in microbiota depending on the manifesta- tion of celiac disease also demonstrate the importance of patient 21. Schippa S, Iebba V, Barbato M, et al. A distinctive ‘microbial signature' stratiﬁcation in the microbiome studies of celiac disease.
in celiac pediatric patients. BMC Microbiol. 2010;10:175.
22. Cotter PD. Small intestine and microbiota. Curr Opin Gastroenterol. 2011; 23. Nistal E, Caminero A, Herran AR, et al. Differences of small intestinal bacteria populations in adults and children with/without celiac disease: Effect The authors thank the patients with celiac disease, as well as of age, gluten diet, and disease. Inﬂamm Bowel Dis. 2011;18:649–656.
the control subjects, for providing sample donations. They also 24. United European Gastroenterology. When is a coeliac a coeliac? Report of a working group of the United European Gastroenterology Week in Am- thank Ms. Sisko Lehmonen and Ms. Karoliina Laamanen for sterdam, 2001. Eur J Gastroenterol Hepatol. 2001;13:1123–1128.
skillful technical assistance.
25. Collin P, Reunala T. Recognition and management of the cutaneous man- ifestations of celiac disease: a guide for dermatologists. Am J Clin Dermatol.
2003;4:13–20.
26. Salmi TT, Collin P, Korponay-Szabo IR, et al. Endomysial antibody- 1. Fasano A, Berti I, Gerarduzzi T, et al. Prevalence of celiac disease in at- negative coeliac disease: clinical characteristics and intestinal autoanti- risk and not-at-risk groups in the United States: a large multicenter study.
body deposits. Gut. 2006;55:1746–1753.
Arch Intern Med. 2003;163:286–292.
27. Nubel U, Engelen B, Felske A, et al. Sequence heterogeneities of genes 2. Lohi S, Mustalahti K, Kaukinen K, et al. Increasing prevalence of coeliac encoding 16S rRNAs in Paenibacillus polymyxa detected by temperature disease over time. Aliment Pharmacol Ther. 2007;26:1217–1225.
gradient gel electrophoresis. J Bacteriol. 1996;178:5636–5643.
3. Kaukinen K, Lindfors K, Collin P, et al. Coeliac disease—a diagnostic 28. Mättö J, Maunuksela L, Kajander K, et al. Composition and temporal and therapeutic challenge. Clin Chem Lab Med. 2010;48:1205–1216.
stability of gastrointestinal microbiota in irritable bowel syndrome—a lon- 4. Karell K, Louka AS, Moodie SJ, et al. HLA types in celiac disease gitudinal study in IBS and control subjects. FEMS Immunol Med Micro- patients not carrying the DQA1*05-DQB1*02 (DQ2) heterodimer: results www.ibdjournal.org 7 Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.
Inﬂamm Bowel Dis Volume 0, Number 0, Month 2013 29. Wacklin P, Makivuokko H, Alakulppi N, et al. Secretor genotype (FUT2 40. Wang M, Ahrne S, Jeppsson B, et al. Comparison of bacterial diversity gene) is strongly associated with the composition of Biﬁdobacteria in the along the human intestinal tract by direct cloning and sequencing of 16S human intestine. PLoS One. 2011;6: e20113.
rRNA genes. FEMS Microbiol Ecol. 2005;54:219–231.
30. Altschul SF, Gish W, Miller W, et al. Basic local alignment search tool.
41. Booijink CC, Zoetendal EG, Kleerebezem M, et al. Microbial communi- J Mol Biol. 1990;215:403–410.
ties in the human small intestine: coupling diversity to metagenomics.
31. Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project: Future Microbiol. 2007;2:285–295.
improved alignments and new tools for rRNA analysis. Nucleic Acids Res.
42. Yang L, Lu X, Nossa CW, et al. Inﬂammation and intestinal metaplasia of the distal esophagus are associated with alterations in the microbiome.
32. Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and com- 43. Zaura E, Keijser BJ, Huse SM, et al. Deﬁning the healthy "core micro- biome" of oral microbial communities. BMC Microbiol. 2009;9:259.
paring microbial communities. Appl Environ Microbiol. 2009;75:7537–7541.
44. Sanz Y, De Pama G, Laparra M. Unraveling the ties between celiac 33. Hamady M, Knight R. Microbial community proﬁling for human microbiome disease and intestinal microbiota. Int Rev Immunol. 2011;30:207–218.
projects: tools, techniques, and challenges. Genome Res. 2009;19:1141–1152.
45. Walker AW, Sanderson JD, Churcher C, et al. High-throughput clone 34. Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune library analysis of the mucosa-associated microbiota reveals dysbiosis responses during health and disease. Nat Rev Immunol. 2009;9:313–323.
and differences between inﬂamed and non-inﬂamed regions of the intes- 35. Frank DN, St Amand AL, Feldman RA, et al. Molecular-phylogenetic tine in inﬂammatory bowel disease. BMC Microbiol. 2011;11:7.
characterization of microbial community imbalances in human inﬂamma- 46. Rausch P, Rehman A, Kunzel S, et al. Colonic mucosa-associated micro- tory bowel diseases. Proc Natl Acad Sci U S A. 2007;104:13780–13785.
biota is inﬂuenced by an interaction of Crohn disease and FUT2 (Secretor) 36. Willing BP, Dicksved J, Halfvarson J, et al. A pyrosequencing study in genotype. Proc Natl Acad Sci U S A. 2011;108:19030–19035.
twins shows that gastrointestinal microbial proﬁles vary with inﬂammatory 47. Dickey W, Wylie JD, Collins JS, et al. Lewis phenotype, secretor status, bowel disease phenotypes. Gastroenterology. 2010;139:1844–1854.e1.
and coeliac disease. Gut. 1994;35:769–770.
37. Salonen A, de Vos WM, Palva A. Gastrointestinal microbiota in irritable bowel 48. Heneghan MA, Kearns M, Goulding J, et al. Secretor status and human leu- syndrome: present state and perspectives. Microbiology. 2010;156:3205–3215.
cocyte antigens in coeliac disease. Scand J Gastroenterol. 1996;31:973–976.
38. Larsen N, Vogensen FK, van den Berg FW, et al. Gut microbiota in 49. Cinova J, De Palma G, Stepankova R, et al. Role of intestinal bacteria in human adults with type 2 diabetes differs from non-diabetic adults. PLoS gliadin-induced changes in intestinal mucosa: study in germ-free rats.
One. 2010;5:e9085.
PLoS One. 2011;6:e16169.
39. Turnbaugh PJ, Hamady M, Yatsunenko T, et al. A core gut microbiome in 50. Mukhopadhya I, Hansen R, El-Omar EM, et al. IBD - what role do obese and lean twins. Nature. 2009;457:480–484.
Proteobacteria play? Nat Rev Gastroenterol Hepatol. 2012;9:219–230.
8 www.ibdjournal.org Copyright 2013 Crohn's & Colitis Foundation of America, Inc. Unauthorized reproduction of this article is prohibited.

Source: http://coeliac.gr/library/downloads/Docs/Documents/Wacklin%20Microbiota%20CD%202013.pdf

smar.ma

Pédiatrie 1 P1- Antibiothérapie probabiliste en milieu de réanimation pédiatrique O.EL ALLAM, Y.HARTI, Y.ALAOUI, B.HMAMOUCHI, S.NEJMI, A.CHLILEK SERVICE DE REANIMATION PEDIATRIQUE POLYVALENTE CHU IBN ROCHD DE CASABLANCA Introduction : L'antibiothérapie probabiliste correspond à une prescription d'antibiotiques réalisée avant de connaitre la nature et la sensibilité des germes responsable de l'infection. En pédiatrie l'évolution d'un processus infectieux sévère est souvent plus rapide que chez l'adulte, avec le risque d'apparition souvent précoce d'une insuffisance circulatoire. Le but de notre travail est la description et l'évaluation de l'antibiothérapie probabiliste en milieu de réanimation pédiatrique polyvalente CHU Ibn Rochd de Casablanca. Patients et méthodes : Etude rétrospective étalée sur 11 mois de janvier 2012 à novembre 2012 qui a permis le recrutement de 142 patients. Les données recueillies sont les critères épidémiologiques des patients, les antécédents médicaux, la notion de colonisation bactérienne, le type d'infection motivant l'introduction de l'antibiothérapie probabiliste, les circonstances du choix de l'antibiotique, le caractère précoce ou tardif et la durée de l'antibiothérapie probabiliste, le retentissement du changement de l'antibiothérapie sur le pronostic, l'évolution et la durée de séjour. Résultats : L'âge moyen était de 37,44 mois, le poids moyen était de 13,28kg, 7,7%des patients avaient des antécédents cardiaques, 4,2% avaient des antécédents respiratoires, 1,4% avaient un déficit immunitaire, 1,4% étaient anciens prématurés, 83,1% des patients étaient hospitalisés antérieurement avec notion de prise d'antibiotiques dans 11,3% des cas. 64,8% de nos patients avaient une infection pulmonaire, 9,2% avaient une infection urinaire, 13,4% avaient une infection neuromeningée, 15,5% une septicémie. L'antibiothérapie probabiliste prescrite était à base d'une monothérapie dans 11,5% des cas, une bithérapie dans 59,8% des cas et une trithérapie dans 28,7% des cas avec le choix du ceftriaxone dans 60,5% des cas. L'heure de début de l'antibiothérapie était le jour dans 52,8% des cas, la nuit dans 44,4% et le weekend dans 2,8% des cas. La décision était prise par un médecin junior dans 54,2% des cas et un médecin seigneur dans 45,8% des cas avec un changement de cette antibiothérapie selon la gravité dans 38% des cas et selon la bactériologie dans 21,8% des cas. La durée moyenne de l'antibiothérapie probabiliste était de 10,79 jours. L'évolution était favorable dans 66,2% des cas avec un taux de mortalité de 33,8%. Conclusion : La prescription raisonnée de l'antibiothérapie probabiliste initiale a démontré son impact sur l'amélioration du pronostic vital des patients. Le caractère nosocomial ou communautaire de l'infection, la connaissance de l'écologie bactérienne du service où l'on travaille, de la flore colonisante du patient et des données de l'examen direct des prélèvements bactériologiques jouent un rôle majeur dans cette décision.

Xyz company

FLEXIBLE BENEFIT PLAN EMPLOYEE GUIDE NEW HORIZONS REGIONAL EDUCATION CENTERS © Copyright 2013 - Flexible Benefit Administrators, Inc. TABLE OF CONTENTS FLEXIBLE BENEFIT PLAN: THE BETTER YOU PLAN, THE MORE YOU SAVE! It's more than a slogan. The Flexible Benefit Plan is a real solution to issues facing all of us. Simply stated, by taking advantage of tax laws, the Flexible Benefit Plan works with your benefits to save you money. Your insurance programs are designed to help you and your family become financially secure as well as to protect you against the high cost of medical care including catastrophic events. However, almost everyone has a number of necessary, predictable expenses that are not covered by your insurance programs. The Flexible Benefit Plan will help you pay for these predictable expenses. The Flexible Benefit Plan offers a unique way to help pay for some of your health care expenses and dependent care expenses. The key to the Flexible Benefit Plan is that your eligible expenses are paid for with Tax Free Dollars. You will not pay any federal, state or social security taxes on funds placed in the Plan. You will save between, approximately, $27.65 and $37.65 on every $100 you place in the Plan. The amount of your savings will depend on your federal tax bracket. Using the Flexible Benefit Plan can save you a significant amount of money each year, however, it is important that you understand how the Plan works and how you can make the most of the advantages the Flexible Benefit Plan offers. This handbook will help you understand the Flexible Benefit Plan. The handbook covers how the Plan works, describes the categories of the Plan, explains the rules governing the Plan, the reimbursement process and how you can elect to participate in the Flexible Benefit Plan. Prior to electing to participate in the Flexible Benefit Plan, it is important that you read and understand the Rules and Regulations section of this handbook. After you read this material, if you have any questions please feel free to contact Flexible Benefit Administrators, Inc. at (757) 340-4567 or (800) 437-FLEX.