Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria - PubMed (original) (raw)

. 2010 Jan;6(1):e1000711.

doi: 10.1371/journal.ppat.1000711. Epub 2010 Jan 8.

Samuel Chaffron, Rina Käppeli, Siegfried Hapfelmeier, Susanne Freedrich, Thomas C Weber, Jorum Kirundi, Mrutyunjay Suar, Kathy D McCoy, Christian von Mering, Andrew J Macpherson, Wolf-Dietrich Hardt

Affiliations

Bärbel Stecher et al. PLoS Pathog. 2010 Jan.

Abstract

The intestinal ecosystem is formed by a complex, yet highly characteristic microbial community. The parameters defining whether this community permits invasion of a new bacterial species are unclear. In particular, inhibition of enteropathogen infection by the gut microbiota ( = colonization resistance) is poorly understood. To analyze the mechanisms of microbiota-mediated protection from Salmonella enterica induced enterocolitis, we used a mouse infection model and large scale high-throughput pyrosequencing. In contrast to conventional mice (CON), mice with a gut microbiota of low complexity (LCM) were highly susceptible to S. enterica induced colonization and enterocolitis. Colonization resistance was partially restored in LCM-animals by co-housing with conventional mice for 21 days (LCM(con21)). 16S rRNA sequence analysis comparing LCM, LCM(con21) and CON gut microbiota revealed that gut microbiota complexity increased upon conventionalization and correlated with increased resistance to S. enterica infection. Comparative microbiota analysis of mice with varying degrees of colonization resistance allowed us to identify intestinal ecosystem characteristics associated with susceptibility to S. enterica infection. Moreover, this system enabled us to gain further insights into the general principles of gut ecosystem invasion by non-pathogenic, commensal bacteria. Mice harboring high commensal E. coli densities were more susceptible to S. enterica induced gut inflammation. Similarly, mice with high titers of Lactobacilli were more efficiently colonized by a commensal Lactobacillus reuteri(RR) strain after oral inoculation. Upon examination of 16S rRNA sequence data from 9 CON mice we found that closely related phylotypes generally display significantly correlated abundances (co-occurrence), more so than distantly related phylotypes. Thus, in essence, the presence of closely related species can increase the chance of invasion of newly incoming species into the gut ecosystem. We provide evidence that this principle might be of general validity for invasion of bacteria in preformed gut ecosystems. This might be of relevance for human enteropathogen infections as well as therapeutic use of probiotic commensal bacteria.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. LCM mice susceptible to S. Typhimurium induced colitis.

Groups (n = 5) of CON, streptomycin-treated mice (20 mg 24 h before infection) and LCM mice were infected with 5×107 cfu S. Typhimurium wild type by gavage and sacrificed at day 3 postinfection. S. Typhimurium levels in the mLN (A), spleen (B) and cecal content (C). (D) Cecal pathology scored in HE-stained tissue sections (see M&M). (E) HE-stained sections of cecal tissue from indicated mice. Enlarged section (white box) is shown in the lower panel. Scale bar: 100 µm.

Figure 2

Figure 2. LCM gain CR by re-association with normal CON microbiota.

Groups (n = 2,4) of LCM mice were re-associated with 1 CON donor each for 21 days in the same cage. Afterwards, non-reassociated LCM (control; n = 5), CON (control; n = 5) and re-associated LCM (n = 6) were infected with 5×107 cfu S. Typhimurium wild type by gavage for 3 days. S. Typhimurium levels in the feces at day 1 post infection (A), cecal content (B), mLN (C), spleen (D). (E) Cecal pathology scored in HE-stained tissue sections (see M&M). (F) HE-stained sections of cecal tissue from indicated mice. Enlarged section (white box) is shown in the lower panel. Scale bar: 100 µm. Arrows point at 2 mice that developed inflammation.

Figure 3

Figure 3. Collectors' curves of LCM, LCMCON21 and CON mice reveal different complexity.

Collectors' curves were created for CD = 0.05 for each mouse from the total number of filtered sequences (A) or from chimera-removed sequences (B). CON mice (green), LCM mice (red) and LCMCON21 mice (blue).

Figure 4

Figure 4. Heatmap showing OTU's distribution in different groups.

Fecal microbiota of unmanipulated LCM mice was analyzed at day 0 (n = 8). 6 of these LCM mice (LCM_1 to LCM_6; blue) were conventionalized in two groups with 2 different CON-donors (CON_1 and CON_2; green) and fecal microbiota analyzed at day 21 (LCM_x_d21; grey). OTUs (CD = 0.05) were sorted according to taxon_4 (Family level; x-axis) and average clustering was performed on Euclidean distances calculated between abundance profiles for each mouse and every time-point sampled. Red color indicates high abundance (Log2), yellow color low abundance. CON_9855_d0 and CON_9856_d0 and LCM_9865_d0 and LCM_9866_d0 are 2 additional CON or LCM mice, respectively sampled only at day 0.

Figure 5

Figure 5. Infection experiments in conventional mice reveal correlation of bacterial infectivity with the prevalence of related species.

(A) Groups normal unmanipulated CON mice (6-12 weeks; symbols indicate different sources) were infected with 5×107 cfu S. Enteritidis wild type by gavage. Fecal E. coli titres before infection were determined (x-axis; Log10 cfu/g). 1 day post infection, S. Enteritidis titres in the feces were determined (y-axis; Log10 cfu/g). Spearman and linear correlation were calculated (p = 0.0015; p<0.0001). The degree of gut inflammation was determined in the infected mice. Half-filled symbols indicate mice with inflammation score ≥4. (B) Groups normal unmanipulated CON mice (6-12 weeks; symbols indicate different sources) were infected with 5×107 Lactobacillus reuteri RR (rifampicin-resistant) by gavage. Fecal levels of Lactobacilli were determined on MRS agar and plotted against fecal Lactobacillus reuteri RR titers at day 1 (left) and 5 (right) postinfection.

Figure 6

Figure 6. In CON mice, related bacterial lineages are preferentially observed together (quantitative co-occurrence).

For all possible pairs of detected OTUs (i.e. present in at least 2/3 of the analyzed mice; CD = 0.2; each dot in the graph represents an OTU-pair), abundance correlations (y-axis, Pearson) were computed from abundance measurements in 9 distinct CON mice, and plotted against the molecular divergence between their representative 16S sequences (x-axis). The latter distances between representative sequences were computed using sequence identities as defined by the complete multiple alignment of all reads and all reference sequences. For hypothesis testing, we compared the data distribution (red) to a matched random distribution of OTU abundances generated by shuffling non-null OTU abundances between all OTUs (blue). Running medians are represented in the corresponding color. The Pearson correlation coefficient is −0.248 (p-value = 7.10e-18) for the actual data, and −0.017 (p-value = 0.563) for the randomized data. We compared the deviation of the actual data on the y-axis (Pearson correlation) from the distribution of the randomized data using the Kolmogorov-Smirnov test. Both two-sided and one-sided hypotheses (greater and less) were tested for each bin of 0.1 on the x-axis (0.0-0.1; 0.1–0.1; 0.2–0.3; 0.3–0.4; 0.4–0.5). Results are indicated in boxes in the upper part of the graph. Pv = P-value; ‘KS two-sided pv’ indicates whether there is a significant difference between the distribution of the data (red dots) and the distribution of the randomized data (blue dots); ‘KS greater pv’ indicates whether the Pearson correlation coefficient of the data (red dots) is significantly higher compared to the random background in a given bin (blue dots). ‘KS less pv’ indicates whether the Pearson correlation coefficient of the data (red dots) is significantly lower compared to the random background in (blue dots) in a given bin. The histogram on the right side of the graph represents the cumulative frequencies of the binned Pearson correlation coefficient data.

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