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The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates

Received 10 May 2011; Revised 12 December 2011; Accepted 13 December 2011Advance online publication 19 January 2012Top of pageAbstractThe human gastrointestinal tract (GI tract) harbors a complex community of microbes. The microbiota composition varies between different locations in the GI tract, but most studies focus on the fecal microbiota, and that inhabiting the colonic mucosa. Consequently, little is known about the microbiota at other parts of the GI tract, which is especially true for the small intestine because of its limited accessibility. Here we deduce an ecological model of the microbiota composition and function in the small intestine, using complementing culture independent approaches. Phylogenetic microarray analyses demonstrated that microbiota compositions that are typically found in effluent samples from ileostomists (subjects without a colon) can also be encountered in the small intestine of healthy individuals. Phylogenetic mapping of small intestinal metagenome of three different ileostomy effluent samples from a single individual indicated that Streptococcus sp., Escherichia coli, Clostridium sp. and high G organisms are most abundant in the small intestine. The compositions of these populations fluctuated in time and correlated to the short chain fatty acids profiles that were determined in parallel. Comparative functional analysis with fecal metagenomes identified functions that are overrepresented in the small intestine, including simple carbohydrate transport phosphotransferase systems (PTS), central metabolism and biotin production. Moreover, metatranscriptome analysis supported high level in situ expression of PTS and carbohydrate metabolic genes, especially those belonging to Streptococcus sp. The GI tract is colonized with approximately 1000 microbial species, commonly called the microbiota, and may harbor more than nine million unique genes (Zoetendal et al., 2008; Yang et al., 2009). Random metagenomic analysis of fecal microbiota suggested that it complements human physiology with a range of essential functions that were canonically encountered in different adults, and that a potential link between microbiota derived gene pool and health and disease exists (Gill et al., 2006; Kurokawa et al., 2007; Turnbaugh et al., 2009; Qin et al., 2010). Integrated approaches that combine multiple approaches permit moving from such list approaches to a deeper understanding of the ecosystem functioning (Raes et al., 2007; Raes and Bork, 2008; Verberkmoes et al., 2009).A major drawback of the use of fecal samples to determine the intestinal microbial composition is the fact that fecal microbiota represents only the end of the colon, leaving other parts of the GI tract, particularly the small intestine, unexplored. The small intestine is a harsh environment for microbial life because of the short transit time and excretion of digestive enzymes and bile (Johnson, 2006), thereby requiring different survival strategies of microbes compared with those residing in the colon. The few existing studies on the small intestine have generally employed invasive sampling procedures or material from sudden death victims (Wang et al., 2003, 2005; Hayashi et al., 2005; Ahmed et al., 2007; Willing et al., 2010), empeding the study of the dynamics of microbiota over time or as a function of diet. Ileostomists provide a powerful alternative to this problem (Gorbach et al., 1967). Ileostomists underwent surgery to remove the complete colon because of a clinical condition, such as colon cancer or inflammatory bowel diseases, and have the terminal part of the ileum connected to a stoma allowing non invasive, repeated sampling of the ileostomy effluent. In general, ileostomists that recover within months following the operation, can be considered healthy and enjoy an active life. A recent study suggested that oxygen might penetrate the small intestine via the stoma, resulting in increased facultative anaerobe populations (Hartman et al., 2009), but other studies have clearly established the significant abundance of typical strict anaerobes in effluent samples (Booijink et al., 2010).Here we compare the microbiota composition in samples obtained from different small intestinal positions (jejunum, ileum and terminal ileum) in healthy subjects to those obtained from ileostomists. In addition, metagenomic and metatranscriptomic analyses, complemented by fermentation end product profiling of multiple ileostomy effluent samples provided insight in the microbiota metabolic potential, its dynamics and the activity of the microbiota in the small intestine. From these data, we deduce a model that exemplifies that fast uptake and conversion of carbohydrates contributes to maintaining the microbiota in the human small intestine.Top of pageMaterials and methodsEthics statementThe study was approved by the University Hospital Maastricht Ethical Committee, and conducted in full accordance with the principles of the of Helsinki (52nd WMA General Assembly, Edinburgh, Scotland, October 2000). All volunteers were informed about the study orally and in writing, and signed a written informed consent before participation.Sample collectionFor phylogenetic profiling ileostomy effluent, small intestinal content and feces was freshly collected from five healthy ileostomists (three male, two female; 60.2 years; (Booijink et al., 2010); individuals A and four healthy subjects (four male; 24 years; individuals F and two healthy subjects (one male, one female; 56.6 years; individuals J All ileostomists had an intact small intestine with the exception of the terminal few centimeters of the ileum, which were removed during surgical removal of the colon. For metagenome and metatranscriptome analyses, ileostomy effluent samples were collected from a healthy, 74 year old male subject who has been carrying an ileostoma for 20 years. Apart from absence of the colon, the subject had no known abnormalities of the digestive system and had not been subjected to any nutrition intervention trial, specific diet or antibiotic treatment for at least 1 year before sampling. To generate maximum coverage of the population dynamics of the small intestine microbiota (4), samples were taken at day 1 (morning and afternoon; 1M and 1A, respectively), day 7 (afternoon; 7A), and compared with a morning sample that was obtained 1 year earlier (A; Supplementary Table S1). Ileostomy effluent samples were freshly collected in an unused ileostomy appliance and frozen immediately in dry ice for DNA isolation or quenched in methanol (4 (2 hydroxyethyl) 1 piperazineethanesulfonic acid) buffer ( homogenized and stored on dry ice as described previously for RNA isolation (Pieterse et al., 2006). Small intestinal fluid samples were obtained after a 10 h fasting period from the healthy volunteers without a history of GI complaints, using an intraluminal naso ileal catheter that was placed nasogastrically with the catheter tip positioned in the ileum. These samples included a jejunal sample and an ileum sample from subject G, a single ileum sample from subjects van cleef flower earrings replica F and H, and two terminal ileum samples from subject I. Catheter positioning was performed as described previously (Troost et al., 2008), under short interval fluoroscopic control. No sedatives were given to the volunteers, and the bowel was not prepared before sampling. The sampling location was determined by assessing the distance from the mouth to the catheter tip. In this way, sample locations can be specified as jejunum, ileum and terminal ileum, but not beyond this classification.Phylogenetic profilingDNA was extracted from 1.0 of ileostomy effluent samples and 0.25 from ileal content as described before (Zoetendal et al., 2006b), using the Stool DNA Isolation Kit (Qiagen, Leiden, The Netherlands) and subsequently quantified by a spectrophotometer (Nanodrop ND 1000 spectrophotometer, NanoDrop Technologies, Wilmington, DE, USA). In short, the SSU rRNA gene was amplified from 10 ng DNA with the T7prom Bact 27 for and Uni 1492 rev primers followed by in vitro transcription and labeling with Cy3 and Cy5, respectively. The Cy3 target mixes were fragmented and hybridized on the arrays at 62.5 for 16 in a rotation oven (Agilent Technologies, Amstelveen, The Netherlands). After washing and drying, the slides were scanned. Hierarchical clustering of probe profiles was carried out using the Euclidian distance and Ward's minimum variance method. These small intestinal microbiota composition profiles generated for the samples obtained from healthy subjects were compared with the ileostoma effluent profiles that were already present in the database (Booijink et al., 2010). Multivariate statistical software Canoco 4.5 for windows (Leps and Smilauer, 2003;Biometrix, Plant Research International, Wageningen, The Netherlands) was used to perform the principle component analysis on log transformed HITChip probe signal intensity profiles. (1998) with modifications. After removal of the aqueous layer, 0.7 volume of isopropanol was added.Fosmid library constructionDNA isolated from each of the four samples was cloned into the pCC1Fos vector using the protocol and kit provided by Epicentre Biotechnologies (Madison, WI, USA). In short, DNA was not additionally fragmented, as the isolation process fragmented the DNA sufficiently to be cloned directly. Two size ranges were used for cloning. DNA of 40 and 30 was isolated from (0.35 low melting point agarose, extracted using agarase and purified by ethanol precipitation. The DNA fragments were ligated to the clone ready pCC1Fos vector, and packaged and transformed according to the standard protocol provided by the manufacturer. Plasmids were isolated from pools of all transformants per library according to standard procedures performed at the GATC Biotech, and used for phylogenetic comparison with their original samples.cDNA library constructionTotal RNA was isolated from a morning sample according to the method described previously (Zoetendal et al., 2006a). The cDNA library was constructed at the GATC Biotech. Briefly, 10 total RNA was treated with Terminator 5 Exonuclease (Epicentre Biotechnologies) to digest tRNAs and rRNAs. An additional rRNA removal step was performed using the MicrobeExpress Kit from Ambion (Austin, TX, USA). The enriched mRNA was reverse transcribed and cloned using the SMART cDNA Library Construction Kit (Clontech Takara Bio Europe, Saint Germain en Laye, France). The resulting double stranded cDNA was cloned into a bBSII SK vector with a modified multiple cloning site containing two Sfi I sites at positions 716 and 740, which allowed the directional cloning of the cDNAs. This resulted in clones of which 3609 of the 10 were mRNA derived.Sanger sequencingFor Sanger end read sequencing, clones were picked and vector DNA was isolated using the alkaline extraction method (Birnboim, 1983). The replica van cleef small earrings vector DNA was sequenced using BigDye Chemistry (Applied Biosystems, Foster City, CA, USA) and reactions were subsequently analyzed on the ABI 3730xl.Before obtaining all the end reads, the quality of the sublibraries were determined by initially sequencing the end reads of 192 randomly selected clones (stored in 96 well plate). These were blasted against genomes and plasmids from a selection of 50 non redundant microbes (46 Bacteria, 4 Archaea, see Supplementary Table S2). This indicated that samples A, 1A and 1M harbored a diverse community consisting of a variety of phyla. Sample 7A was excluded for further sequencing as 75 of the sequences originated from E. coli and related bacteria, which suggests it was contaminated with DNA from the cloning host. This was comfirmed by comparative phylogenetic profiling of the original sample and the pooled fosmid DNA (Supplementary Figure S1), which demonstrated the congruency between libraries and original samples, except for sample 7A.GS FLX sequencingFosmids from the different libraries were pooled in an equimolar mixture. DNA was prepared for sequencing using the Amplicon A kit (Hoffmann La Roche AG, Basel, Switzerland) and sequenced on the GS FLX (Hoffmann La Roche AG) using the manufacturer's protocols. The sequencing data were trimmed to remove the pCC1Fos vector and assembled using the GS De Novo Assembler (Hoffmann La Roche AG).Computational and statistical analysesSequence analysis was performed using the SMASH pipeline (Arumugam et al., 2010). The remaining reads were then assembled using Forge assembler (using for metagenome assembly; (Diguistini et al., 2009)). Genes were predicted using GeneMark v2.6c with a heuristic model, based on the GC content (Besemer and Borodovsky, 2005). Functional annotation was performed as in Kunin et al. (2008) and Gianoulis et al. (2009). In short, genes were mapped to EggNOG orthologous groups (Jensen et al., 2008), and Kyoto Encyclopedia of Genes and Genomes (KEGG) modules van cleef pearl earrings replica and pathways (Kanehisa et al., 2008). Protein sequences were searched against the EggNOG and KEGG databases using BLASTP (Altschul et al., 1997). The functional entity frequency for each sample was calculated by summing the total number of instances of that entity in a particular sample, and then normalized by total number of assignments for all entities in that sample to compensate for sample coverage differences. For all analyses, entities for which the summed count over all samples constituted less than or equal to 0.01 of the total count were removed to avoid artifacts. Proteins from selected pathways were mapped to nodes of the tree of life (Ciccarelli et al., 2006) using an in house perl script based upon the last common ancestor approach (Huson et al., 2007). Input data were BLASTp results of the proteins against the STRING 7 database (Jensen et al., 2008). Only hits above 60 bits and whose scores lied within 10 of the best score were considered. Samples were compared with fecal metagenomes from Gill et al. (2006) and Kurokawa et al. (2007). For statistical comparison, the two most similar ileal samples were pooled and functional group counts were compared with the pooled non infant fecal samples using Fisher's exact test with Benjamini FDR correction for multiple testing. Finally, highlighted case studies were manually scrutinized to exclude any artifacts.pH and metabolite measurementsApproximately 3 of the directly frozen ileostomy effluent was defrosted on ice and subsequently centrifuged at 9000 for 5 at 4 Afterwards, the supernatant was collected for measuring the pH and the concentration of short chain fatty acids, lactate and alcohols by high performance liquid chromatography as described previously (Starrenburg and Hugenholtz, 1991). HITChip analysis of small intestine samples from four healthy subjects revealed a wide diversity at phylum level, depending on the subject and sampling location within the subject (Figure 1a). Bacteroidetes, Clostridium cluster XIVa and Proteobacteria were among the dominant groups in the ileum of subjects G and H, and terminal ileum of subject I. Notably, the jejunal sample (subject G) and one ileum sample (subject F) were dominated by Bacilli (Streptococcus sp.), Clostridium clusters IX (Veillonella sp.) and XIVa (several genera), and several Gamma Proteobacteria, thereby displaying higher similarity with microbiota composition profiles obtained from ileostoma effluent samples (Booijink et al., 2010), which was supported by cluster analysis (Figure 1a). Principle component analysis analysis suggested that ileostomy effluent cluster closely to the jejunal sample, whereas the terminal ileum samples were more similar to feces as explained by 48 of the data along the first principle component (Figure 1b). The ileum samples were positioned in between ileostomy effluent and feces and display large subject specific differences. These results indicated that the microbiota composition that is encountered in ileostomy effluent can also be detected in the small intestine of healthy subjects. However, the limited number of samples from healthy subjects and the impact of subject specificity on the overall variation between samples hamper a more detailed comparison between ileostomy effluent and small intestinal lumen samples.Figure 1.(a) Relative contribution of different microbial groups that are present in samples derived from the small intestine and feces of four and two healthy individuals, respectively, and those from five healthy ileostomists. The tree represents the Euclidian clustering of the HITChip probe profiles. A encode the subjects; eff, jen, ile, ter, fec encode ileostomy effluent, jenunum, ileum, terminal ileum, and feces, respectively. Microbial groups that are 2.5 of the total community in at least one of the samples are represented in the legend. (b) Principle component analysis of the microbiotas based on the HITChip probe signal profiles. Sample identities are identical as in Figure 1a. The two first components and their respective percentage of variation they explain are indicated.Full figure and legend (131K)Microbiome of small intestine is less complex than that of the colonTo obtain insight into the genetic potential and population dynamics within the small intestinal microbiota, a metagenomic library was constructed from the ileostoma effluent from a healthy individual who had the stoma for more than 20 years and did not require any stoma related medication. As the ileostomy effluent microbiota composition fluctuates over time (Booijink et al., 2010), the metagenome library was constructed on four different morning and afternoon effluent samples.After initial quality control (see Supplementary Materials), three libraries representing the morning (1M) and afternoon (1A) of the same day and a morning sample taken 1 year earlier (A) were subjected to end sequencing (Sanger) and GS FLX random sequencing (Supplementary Table S1), generating a total of 178 of sequence information. 146 of sequence information could be assembled into contigs that collectively encompass 63 with the largest contig being 78 and leaving only 13 of the sequence reads unassembled. This assembled proportion of sequences is considerably higher than previously observed with fecal metagenomes (Gill et al., 2006; Kurokawa et al., 2007), indicating a lower species diversity in the small intestine as compared with the colon, which is in line with the previous observations based on 16S rRNA genes (Hartman et al., 2009; Booijink et al., 2010). More than 170 genes could be assigned in the assembled contigs, of which 16 was complete.Microbiome of the small intestine consists of various microbial phylaPhylogenetic positioning of sequences suggested that they originated from a wide variety of phylotypes, with Clostridium sp., Streptococcus sp. and coliforms as dominant phylogenetic groups (Supplementary Figure S1), which is consistent with the 16S rRNA based analyses. In addition, an unexpectedly large fraction of high G Gram positives was observed, which may be due to the commonly encountered underestimation of these microbial groups by regular 16S rRNA gene amplification (Hayashi et al., 2004). Moreover, a fraction of sequences mapped at low phylogetic resolution, indicating the presence of several uncultured species from which close cultured relatives have not been characterized by full genome sequencing.

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