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      Host lifestyle affects human microbiota on daily timescales

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          Abstract

          Background

          Disturbance to human microbiota may underlie several pathologies. Yet, we lack a comprehensive understanding of how lifestyle affects the dynamics of human-associated microbial communities.

          Results

          Here, we link over 10,000 longitudinal measurements of human wellness and action to the daily gut and salivary microbiota dynamics of two individuals over the course of one year. These time series show overall microbial communities to be stable for months. However, rare events in each subjects’ life rapidly and broadly impacted microbiota dynamics. Travel from the developed to the developing world in one subject led to a nearly two-fold increase in the Bacteroidetes to Firmicutes ratio, which reversed upon return. Enteric infection in the other subject resulted in the permanent decline of most gut bacterial taxa, which were replaced by genetically similar species. Still, even during periods of overall community stability, the dynamics of select microbial taxa could be associated with specific host behaviors. Most prominently, changes in host fiber intake positively correlated with next-day abundance changes among 15% of gut microbiota members.

          Conclusions

          Our findings suggest that although human-associated microbial communities are generally stable, they can be quickly and profoundly altered by common human actions and experiences.

          Electronic supplementary material

          The online version of this article (doi:10.1186/gb-2014-15-7-r89) contains supplementary material, which is available to authorized users.

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          Spurious regressions in econometrics

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            Short-Term Antibiotic Treatment Has Differing Long-Term Impacts on the Human Throat and Gut Microbiome

            Antibiotic administration is the standard treatment for the bacterium Helicobacter pylori, the main causative agent of peptic ulcer disease and gastric cancer. However, the long-term consequences of this treatment on the human indigenous microbiota are relatively unexplored. Here we studied short- and long-term effects of clarithromycin and metronidazole treatment, a commonly used therapy regimen against H. pylori, on the indigenous microbiota in the throat and in the lower intestine. The bacterial compositions in samples collected over a four-year period were monitored by analyzing the 16S rRNA gene using 454-based pyrosequencing and terminal-restriction fragment length polymorphism (T-RFLP). While the microbial communities of untreated control subjects were relatively stable over time, dramatic shifts were observed one week after antibiotic treatment with reduced bacterial diversity in all treated subjects in both locations. While the microbiota of the different subjects responded uniquely to the antibiotic treatment some general trends could be observed; such as a dramatic decline in Actinobacteria in both throat and feces immediately after treatment. Although the diversity of the microbiota subsequently recovered to resemble the pre treatment states, the microbiota remained perturbed in some cases for up to four years post treatment. In addition, four years after treatment high levels of the macrolide resistance gene erm(B) were found, indicating that antibiotic resistance, once selected for, can persist for longer periods of time than previously recognized. This highlights the importance of a restrictive antibiotic usage in order to prevent subsequent treatment failure and potential spread of antibiotic resistance.
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              Salmonella enterica Serovar Typhimurium Exploits Inflammation to Compete with the Intestinal Microbiota

              Introduction The evolution of pathogenic microorganisms has been shaped to a great extent by their interaction with cognate host species. Colonization is the first step of any infection. For enteropathogenic bacteria, this poses a formidable task as the target host organ is already colonized by a dense microbial community, the microflora, or “microbiota”. Intestinal colonization by microbiota begins immediately after birth and lasts for life. In a healthy intestine, the microbiota is quite stable, and its gross composition at higher taxonomic levels is similar between individuals, and even between humans and mice [1]. The intestinal ecosystem is shaped by symbiotic interactions between the host and the microbiota. Microbiota composition is influenced by nutrient availability, local pH, and possibly also by the host's immune system [2]. Conversely, the microbiota optimizes nutrient utilization [3,4], and boosts maturation of intestinal tissues and the intestinal immune system [5–7]. In addition, the microbiota provides an efficient barrier against infections (“colonization resistance”), which must be overcome by enteropathogenic bacteria. It is poorly understood how enteropathogens can achieve that task. Here, we used Salmonella enterica subspecies 1 serovar Typhimurium (S. Tm) and a mouse colitis model to study strategies by which enteropathogenic bacteria break colonization resistance. S. Tm infects a broad range of animal species and is a frequent cause of intestinal infections in the human population. The normal murine microbiota provides colonization resistance and prevents intestinal colonization upon oral S. Tm infection. Oral treatment with the antibiotic streptomycin (20 mg of streptomycin intragastric [i.g.]) transiently reduces the microbiota by >80% and disrupts colonization resistance for a period of 24 h [8,9]. The residual microbiota re-grows within 2–3 d, and colonization resistance is re-established ([9]; unpublished data). These studies have provided the basis for a “streptomycin mouse model” for Salmonella enterocolitis [10]: 1 d after streptomycin treatment, oral infection with S. Tm leads to efficient colonization of the murine intestine, especially the cecum and the colon (approximately 109 colony-forming units [CFU]/gram; Figures 1A and S1) [8,9,11]. Wild-type S. Tm (S. Tmwt) triggers pronounced intestinal inflammation (colitis) and colonizes the intestinal lumen at high densities over extended periods of time [8,10–12]. This “streptomycin mouse model” can be used to study bacterial virulence factors required for colonization and triggering of intestinal inflammation. For example, S. Tm strains lacking the two virulence-associated type III secretion systems (e.g., S. Tm ΔinvG sseD::aphT [S. Tmavir] [13]) cannot trigger colitis. In addition, these mutants were found to colonize the murine intestine only transiently [11,13]. The reason for this colonization defect has remained elusive. Figure 1 Microbiota Outcompete S. Tmavir but not S. Tmwt (A) Streptomycin-treated mouse model. The antibiotic transiently reduces the microbiota (grey circles) in the lumen of the large intestine, reduces colonization resistance, and allows colonization and induction of colitis by S. Tmwt. (B) Streptomycin-treated C57BL/6 mice (n = 7 per group) were infected with S. Tmavir (blue) or S. Tmwt (red; 5 × 107 CFU i.g.). At indicated time points mice were sacrificed, S. Tm loads were determined in cecal content, mLN, and spleen, and cecal pathology was scored. Detection limits (dotted lines): cecal content, 10 CFU/g; mLN, 10 CFU/organ; spleen, 20 CFU/organ. *, p ≤ 0.05; statistically significant difference between S. Tmavir and S. Tmwt. Boxes indicate 25th and 75th percentiles, black bars indicate medians, and whiskers indicate data ranges. (C–H) Representative confocal fluorescence microscopy images of cecum tissue sections from the mice shown in (B). Nuclei and bacterial DNA are stained by Sytox green (green), the epithelial brush border actin by Alexa-647-phalloidin (blue), and extracellular S. Tm in the intestinal lumen by anti–S. Tm LPS antiserum (red). Normal microbiota in unmanipulated mice (C), microbiota 1 d after streptomycin (sm) treatment (D), streptomycin-treated mice infected for 1 or 4 d with S. Tmavir or S. Tmwt (E–H). The S. Tm colonization levels are indicated (CFU/g); L, cecum lumen. To explore this, we analyzed microbiota compostition in S. Tmwt– and S. Tmavir–infected mice and the role of inflammation for Salmonella colonization and competition against the intrinsic microbiota. We found that inflammation shifts the balance between the protective microbiota and the pathogen S. Tm in favour of the pathogen. This principle might apply to various other pathogens and therefore constitute a novel paradigm in infectious biology. Results S. Tmavir but Not S. Tmwt Is Outcompeted by Commensal Microbiota First, we confirmed the differential colonization efficiency of S. Tmwt and S. Tmavir in the streptomycin mouse model. Unlike S. Tmwt, intestinal S. Tmavir colonization levels decreased significantly by day 4 post-infection (p.i.) in a highly reproducible fashion (Figure 1B). This coincided with re-growth of the microbiota as revealed by immunofluorescence microscopy (Figure 1C–1H). By anaerobic culture, DNA isolation, and 16S rRNA gene sequencing, high densities of characteristic members of the intestinal microbiota (Clostridium spp., Bacteroides spp., and Lactobacillus spp. [14]) were found in S. Tmavir–infected, but not in S. Tmwt–infected, animals at day 4 p.i. (Table 1). Both the S. Tm/microbiota ratio and the composition of the microbiota itself differed between mice infected with S. Tmavir and S. Tmwt. These data demonstrated that residual microbiota surviving the streptomycin treatment can re-grow, outcompete S. Tmavir, and thereby re-establish colonization resistance. In contrast, S. Tmwt can suppress re-growth of the residual microbiota. Therefore, the streptomycin mouse model allows study of the principal mechanisms by which enteropathogens manipulate the intestinal ecosystem. Table 1 Bacterial Genera Recovered by Anaerobic Culture from S. Tm Infected Mice S. Tmwt Alters Composition of the Microbiota in the Streptomycin Mouse Model To better characterize the effect of S. Tm on microbiota composition, we employed 16S rRNA gene sequencing (see Materials and Methods). This method allows a quantitative comparison of microbial communities, including bacterial species that cannot be cultivated in vitro. The analysis comprised five different groups of mice and addressed the effect of the streptomycin pretreatment per se as well as the effect of S. Tmavir and S. Tmwt infection on microbiota composition (Figure 2). Figure 2 16S rRNA Gene Sequence Analysis of Microbiota Manipulation by S. Tmwt and S. Tmavir in the Streptomycin Mouse Model Cecal contents were recovered from unmanipulated mice, mice at days 1 or 5 after streptomycin treatment (20 mg i.g.), and streptomycin-treated mice 4 d after infection with S. Tmavir and S. Tmwt (5 × 107 CFU i.g.; all n = 5). Total DNA was extracted, and bacterial 16S rRNA genes were PCR-amplified using universal bacterial primers, cloned, and sequenced (approximately 100 sequences per animal; five animals per group; see Materials and Methods). (A) Pie diagrams showing the microbiota composition at the phylum level. Numbers below the diagrams indicate bacteria/gram cecal content as defined by Sytox green staining. *The lower bacterial density in S. Tmwt–infected mice is attributable to a high proportion of cellular debris in the intestinal lumen (see Figure 1G). #In these groups no Salmonella 16S rRNA genes were identified. ‡Proteobacterial sequences belonged to Salmonella (E. coli) in the following percentages: 91 (1), 15 (70), 87 (11), 55 (38), and 100 (0). See also Table S1. (B) Visual depiction of the microbiota composition of individual mice. The animals were grouped based on the similarity of their microbiota composition at the phylum level (using the Canberra distance as metric). The resulting groupings are depicted as a dendrogram, and observed phylum counts for each mouse are shown as a heat map (0%–100% of all identified 16S rRNA gene sequences). Labels indicate unique mouse identifier numbers. The experimental groups are indicated. p.sm., post–streptomycin treatment. In line with published data, a large fraction of the murine microbiota in unmanipulated mice belonged to either the Firmicutes (including Clostridium spp. and Lactobacillus spp.; 39% ± 10%) or the Bacteroidales (53% ± 13%; Figure 2) [1,15–17]. Streptomycin treatment reduced the global density of the microbiota by approximately 90% (Figure 2; see also Figure 1C and 1D) and changed its relative composition (Figure 2A and 2B; Table 2). The composition of the remaining microbiota varied substantially between individual members of this group (Figure 2B). Most likely, this is attributable to the unstable situation created by the antibiotic and may arise from slight animal-to-animal variations in the timing or speed of the gut passage of the antibiotic and/or from species-specific differences in antibiotic susceptibility and rate of re-growth. Table 2 Phylum-Level Comparison of Microbiota in Streptomycin-Treated S. Tm–Infected Mice from Experiment Described in Figure 2 Five days after the antibiotic treatment, the microbiota had re-grown to normal density and microbiota composition, at least at the phylum level (Figure 2A and 2B; Table 2; p = 0.35078). Infection with S. Tmavir did not interfere detectably with re-growth of the normal microbiota in the streptomycin-pretreated mouse model (Figure 2B; Table 2). In contrast, S. Tmwt significantly altered the cecal microbiota composition (Figure 2A and 2B; Table 2; p 90% of all sequences, and Salmonella spp. generally represented the most prominent (up to 100%) proteobacterial species in the S. Tmwt–infected animals. These observations were confirmed by fluorescence in situ hybridization (FISH) of fixed cecal content (Figure S2). This demonstrates that S. Tmwt interferes with microbiota re-growth and represents the predominant species at day 4 p.i. It should be noted that other proteobacterial species (e.g., Escherichia coli) were also present in significant numbers in the cecum of most S. Tmwt–infected animals (Figure 2A). These proteobacterial strains are low abundance members of the normal gut microbiota of our mouse colony ( 10 different commensal species, including commensal E. coli strains from our mouse colony, grown in vitro (all negative). DNA was stained with Sytox green (0.1 μg/ml; Sigma-Aldrich, http://www.sigmaaldrich.com/) and F-Actin with Alexa-647-phalloidin (Molecular Probes, http://probes.invitrogen.com/). Sections were mounted with Vectashield hard set (Vector Laboratories, http://www.vectorlabs.com/) and sealed with nail polish. Images were recorded using a PerkinElmer (http://www.perkinelmer.com/) Ultraview confocal imaging system and a Zeiss (http://www.zeiss.com/) Axiovert 200 microscope. For quantification of total bacterial numbers, cecal contents were weighed, fixed in 4% paraformaldehyde, and stained with Sytox green (0.1 μg/ml). Bacteria were counted in a Neubauer's counting chamber using an upright fluorescence microscope (Zeiss). Broad-range bacterial 16S rRNA gene sequence analysis. Total DNA was extracted from cecal contents using a QIAmp DNA stool mini kit (Qiagen, http://www1.qiagen.com/) and a Tissuelyzer device (Qiagen). 16S rRNA genes were amplified by PCR using primers Bact-7F (5′-AGA GTT TGA TYM TGG CTC AG-3′) and Bact-1510R (5′-ACG GYT ACC TTG TTA CGA CTT-3′) and the following cycling conditions: 95 °C, 5 min; 22 cycles of 95 °C, 30 s; 58 °C, 30 s; 72 °C, 2 min; followed by 72 °C, 8 min; 4 °C, ∞. Reaction conditions (100 μl) were as follows: 50 mM KCl, 10 mM Tris-HCl (pH 8.3), 1.5 mM Mg2+, 0.2 mM dNTPs, 40 pmol of each primer, and 5 U of Taq DNA polymerase (Eppendorf, http://www.eppendorf.com/). Fragments were purified by gel electrophoresis, excised, recovered using the gene clean kit (Qbiogene; http://www.qbiogene.com/) and dried. The PCR products were suspended in 10 μl of sterile distilled water and between 2 and 5 μl was ligated into pGEM-T Easy Vectors (Promega, http://www.promega.com/). The ligated vectors were transformed into high-efficiency competent JM109 E. coli cells (Promega), plated on LB-carbenicillin agar, and subjected to blue-white screening of colonies. White colonies were picked into 96-well boxes containing 500 μl of Circlegrow medium (Qbiogene, http://www.qbiogene.com/) per well and grown overnight at 37 °C, and the plasmid DNA was then prepped using a modified semi-automated alkaline lysis method. Sequencing was carried out using Applied Biosystems (http://www.appliedbiosystems.com/) BigDye terminators (version 3.1) and run on Applied Biosystems 3730 sequencers. The 16S rRNA gene inserts were sequenced using two primers targeted towards the vector end sequences, M13r (5′-CAGGAAACAGCTATGACC-3′) and T7f (5′-TAATACGACTCACTATAGGG-3′), and one towards an internal region of the gene, 926r (5′-CCGTCAATTC[A/C]TTT[A/G]AGT-3′), in order to bridge any gaps between the sequences generated from the two end primers. Contigs were built from each three-primer set of sequences using the GAP4 software package [51] and converted to “sense” orientation using OrientationChecker software [52]. These files were then aligned using MUSCLE [53], and the alignments were manually inspected and corrected using the sequence editor function in the ARB package [54]. The files were then tested for the presence of chimeric sequences using Mallard [52] and Bellerophon [55], and putative chimeras were checked using Pintail [56] and BLAST [57]. Positively identified chimeras were removed, and the remaining sequences were examined with the Classifier function at the Ribosomal Database Project II Web site [48] in order to give a broad classification at the phylum level. To obtain more detailed taxonomic information the sequences were divided into phylotypes by generating distance matrices in ARB (with Olsen correction), which were then entered into the DOTUR program [58] set to the furthest neighbour and 99% similarity settings. The resulting phylotypes were then assigned similarities to nearest neighbours using BLAST. Statistical analysis of bacterial colonization and intestinal pathology. Statistical analyses of viable CFU and pathological scores were performed using the exact Mann-Whitney U Test and the SPSS version 14.0 software, as described before [8]. Values of p < 0.05 were considered statistically significant. Box-plots were created using GraphPad Prism 4 version 4.03 (GraphPad Software, http://www.graphpad.com/). Statistical analysis of microbiota composition. Differences in the phylogenetic compositions of samples were assessed by first assigning the detected 16S rRNA gene sequences to their respective phyla, and then computing the normalized Euclidean distance between the phyla counts. The observed differences were judged for their statistical significance by performing Monte Carlo randomizations: 16S rRNA gene sequences were shuffled between two samples, such that overall sample sizes and total counts for each phylum were maintained. Euclidean distances were then re-computed, and the fraction of distances larger than or equal to the observed distances determined the p-values. Bonferroni correction for multiple testing means that p-values below 0.005 indicate statistical significance in Figures 2 and 6 and Table 2. Supporting Information Figure S1 Colitis Score Developed for the Streptomycin-Pretreated Mouse Model for Salmonella Colitis [8] Mice were pretreated with a single dose of streptomycin (20 mg i.g.) and 24 h later infected with 5 × 107 CFU of S. Tmavir (A) or S. Tmwt i.g. (B). Mice were sacrificed 1 d p.i. Left panels of (A) and (B): macroscopic appearance of the cecum from S. Tmavir– and S. Tmwt–infected mice, respectively. Note the reduction in size and purulent cecal content in case of S. Tmwt–induced colitis. Middle panels: HE-stained cross-section of ceca shown in left panel (scale bar: 1 mm). Note the submucosal edema (se), which is a characteristic of S. Tmwt–induced colitis. L, cecal lumen. Right panels: at higher magnification, large numbers of goblet cells (gc) are observed in the cecal mucosa of healthy mice. Colitis leads to reduced numbers of goblet cells due to pronounced epithelial regeneration. Note infiltrating polymorphonuclear leukocytes and desquamated epithelium in the S. Tmwt–infected cecum (scale bar: 0.05 mm). Detailed parameters for colitis score are listed in table at bottom of figure. (272 KB PDF) Click here for additional data file. Figure S2 FISH Analysis of Microbiota Manipulation by S. Tmwt and S. Tmavir in the Streptomycin Mouse Model Cecal contents were fixed in PBS (4% paraformaldehyde [pH 7.4]; 4 °C; 12 h), washed in PBS, applied onto polylysine-coated slides, and air-dried. Bacteria were permeabilized (70.000 U/ml of lysozyme; 5 mM EDTA; 100 mM Tris/HCl [pH 7.5]; 37 °C; 10 min), dehydrated with ethanol, and hybridized with HPLC-purified, 5′-labelled 16S rRNA probes (5% formamide, 90 mM NaCl, 20 mM Tris/HCl [pH 7.5]; 46 °C; 2 h): Eub338-cy5 (5′-GCT GCC TCC CGT AGG AGT-3′; detection of all eubacteria [59]), LGC-cy3 or LGC-fluorescein (5′-TCA CGC GGC GTT GCT C-3′; detection of gram-positive bacteria with low G+C content; Firmicutes [60]), and Bac303-cy3 or Bac303-fluorescein (5′-CCA ATG TGG GGG ACC TT-3′; detection of the Bacteroidales group of the Bacteroidetes [61]). Slides were washed at 48 °C (636 mM NaCl, 5 mM EDTA, 0.01% SDS, 20 mM Tris/HCl [pH 7.5]) as described [59]. S. Tm was detected by immunostaining (see above), and FISH detection was performed using the Eub338-cy5 probe. The relative abundance of Firmicutes, Bacteroidales, and S. Tm was determined by co-staining and imaging at 630× magnification using a PerkinElmer Ultraview confocal imaging system and a Zeiss Axiovert 200 microscope. For each condition, 500–1,750 bacteria were evaluated. FISH analysis of cecal microbiota from the mice shown in Figure 2. Cecal contents from unmanipulated mice, from mice at days 1 or 5 after streptomycin treatment (20 mg, i.g.), and from streptomycin-treated mice 4 d after infection with S. Tmavir and S. Tmwt (5 × 107 CFU i.g.; all n = 5) were recovered, fixed on cover slips, and hybridized with Eub338 (all bacteria). Firmicutes and Bacteroidales were recognized by hybridization with LGC and BAC303 probes, respectively, and S. Tm by an anti–S. Tm LPS antiserum (see Materials and Methods). Firmicutes (green), Eub338+ Bac303− LGC+; Bacteroidales (yellow), Eub338+ Bac303+ LGC−; Salmonella (red with white stripes), Eub338+ LPS+; “unknown” (grey), Eub338+ LGC− Bac303− LPS−. Abundance of respective groups is expressed as percentage of total Eub338+ bacteria. The results of the FISH analysis confirmed the results obtained via 16S rRNA gene sequencing (Figure 2). Slight differences in the percent composition of the microbiota with respect to Firmicutes, Bacteroidales, and Salmonella spp. obtained via both methods are attributable to species-specific differences in lysis efficiency and 16S rRNA gene copy number. (124 KB PDF) Click here for additional data file. Figure S3 Cecal Histopathology in Acute and Chronic Mouse Colitis Models Shown in Figures 4 and 5 Frozen sections of cecal tissues (5 μm) were stained with HE (scale bar: 200 μm). Acute Salmonella colitis was observed in C57BL/6 mice infected with S. Tmwt (A) but not with S. Tmavir (B) 4 d p.i. (compare with Figure 3A). Chronic Salmonella colitis was observed in 129Sv/Ev mice infected with S. Tmwt (C) but not with S. Tmavir (D) 47 d p.i. (compare with Figure 3B). Genetic predisposition (lack of anti-inflammatory cytokine IL10) leads to sporadic occurrence of colitis in C57/BL6IL10−/− mice (E). However, some C57/BL6IL10−/− mice are not affected (F) (compare with Figure 3C). A large number of C3H/HeJBirIL10−/− mice were affected by cecal inflammation (G), but one was not (H) (compare with Figure 3C). L, cecal lumen; se, submucosal edema. (735 KB PDF) Click here for additional data file. Figure S4 Colitis Scores for C57/BL6IL10−/− and C3H/HeJBirIL10−/− Mice (A) Frozen sections of cecal tissues (5 μm) were stained with HE (scale bar: 200 μm). Histopathology was scored with respect to submucosal edema (black), polymorphonuclear leukocyte infiltration (grey), loss of goblet cells (dark grey), and epithelial destruction (light grey). The scoring scheme is shown in Figure S1. Scores are plotted as stacked vertical bars. One animal was sacrificed at the end of day 1 p.i. for humane reasons (marked with †). (B) Confocal fluorescence microscopy image of cecal lumen reveals normal high microbiota densities. Upper left: C3H/HeJBirIL10−/− animal marked with ‡ in (A). The remaining images show animals described in Figure 6B. Upper right: VILLIN-HA control, S. Tmavir infected. Lower left: VILLIN-HA+CL4-CD8 (inflammation), non-infected. Lower-right: VILLIN-HA+CL4-CD8 (inflammation), S. Tmavir infected. Bacterial DNA is stained by Sytox green (green) and extracellular S. Tm by anti-S. Tm LPS antiserum (red). Scale bar: 20 or 50 μm as specified. (1.8 MB PDF) Click here for additional data file. Figure S5 S. Tmavir Efficiently Colonizes Germ-Free Mice Germ-free C57BL/6 mice (n = 8) were infected with S. Tmavir (5 × 107 CFU i.g.) and sacrificed at day 2 or 4 p.i. (open blue boxes). For comparison, previous data [62] from five mice infected for 1 d with S. Tmwt are included (open red boxes). S. Tm colonization was analyzed in the cecum content (day 2 p.i.), and cecum pathology was scored (see Material and Methods). Detection limits (dotted line): cecum, 10 CFU/g; mLN, 10 CFU/organ; spleen, 20 CFU/organ. At day 4 p.i., S. Tmavir colonization levels in germ-free mice in the absence of re-growing microbiota were significantly higher when compared to streptomycin-treated SPF mice (p = 0.002; compare with Figure 3A, left panel). (105 KB PDF) Click here for additional data file. Figure S6 16S rRNA Gene Sequence Analysis of Microbiota in VILLIN-HACL4-CD8 Model Visual depiction of the microbiota composition of individual mice. The animals were grouped based on the similarity of their microbiota composition at the phylum level (using the Canberra distance as metric). The resulting groupings are depicted as a dendrogram, and observed phylum counts for each mouse are shown as a heat map (0%–100% of all identified 16S rRNA gene sequences). Labels give unique mouse identifier numbers. The experimental groups are indicated. (64 KB PDF) Click here for additional data file. Table S1 Broad-Range Bacterial 16S rRNA Gene Sequence Analysis of the Microbiota Composition from the Experiment Shown in Figures 2 and 6 (277 KB XLS) Click here for additional data file. Table S2 Phylum-Level Comparison of Microbiota of VILLIN-HACL4-CD8 Model from the Experiment Described in Figure 6 (35 KB DOC) Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the 16S RNA gene sequences shown in Figure 2 are EF604903–EF605247, and for those shown in Figure 6C are EF604904–EF605247 and EU006095–EU006496.
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                Author and article information

                Contributors
                l.d@duke.edu
                amaterna@clcbio.com
                yonatanf@mit.edu
                baptista@mit.edu
                matthew.blackburn@epfl.ch
                aperrott@mit.edu
                serdman@mit.edu
                ejalm@mit.edu
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                25 July 2014
                25 July 2014
                2014
                : 15
                : 7
                Affiliations
                [ ]Society of Fellows, Harvard University, Cambridge, MA 02138 USA
                [ ]FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138 USA
                [ ]QIAGEN Aarhus A/S, Silkeborgvej 2, 8000 Aarhus C, Denmark
                [ ]Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
                [ ]Department of Civil & Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]The Broad Institute of MIT and Harvard, Cambridge, MA 02139 USA
                [ ]Molecular Genetics & Microbiology and Center for Genomic & Computational Biology, Duke University, Durham, NC 27708 USA
                Article
                3286
                10.1186/gb-2014-15-7-r89
                4405912
                25146375
                2a122bea-822b-4c09-85c3-082c63993d61
                © David et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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