Dynamics of the Microbiota in Response to Host Infection

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions

Sequence and culture-based signatures for three <i>Lactobacillus</i> species in ileum and cecum.

<p>Horizontal axis indicates days post-inoculation with <i>Citrobacter rodentium</i>. For sequence-based signatures, vertical axis indicates the number of normalized sequencing counts for the Operational Taxonomic Unit (OTU). For culture-based signatures, vertical axis indicates log₁₀ Colony Forming Units (CFUs) per gram of input tissue. Dashed lines indicate the inferred median signature shape for each trajectory. Shaded regions indicate the 95% credible interval. <b>(A, G)</b> Sequence and <b>(D, J)</b> culture-based signatures for <i>Lactobaillus johnsonii</i>. <b>(B, H)</b> Sequence and <b>(E, K)</b> culture-based signatures for <i>Lactobacillus murinus</i>. <b>(C, I)</b> Sequence and <b>(F, L)</b> culture-based signatures for <i>Lactobacillus reuteri</i>.</p

Summary of sequenced samples and Good's coverage estimates.

<p>Numbers of reads are listed in thousands, and are after all preprocessing quality control and filtering steps. The average number of reads across all samples was ≈2,700. Good's coverage is a nonparametric estimate of the proportion of classes (i.e., OTUs) that are observed in a sample out of the total number of classes inferred to be present in the population. Labels in the top table row: T  =  time-point, ctrl  =  control, uninfected mice; inf  =  infected mice.</p

Consensus Signature Groups systematically characterize patterns of time-dependent microbiota changes in response to infection.

<p>Consensus Signature Groups (CSGs) represent sets of taxa that share similar dynamics within a tissue, providing a means to identify common behaviors among taxa regardless of their phylogenetic relationships. Representative signatures of individual Operational Taxonomic Units (OTUs) from CSGs are shown. Horizontal axis indicates days post-inoculation with the pathogen; vertical axis shows normalized sequencing counts for the OTU. Dashed or dotted lines indicate median signature shapes for OTUs. Shaded regions indicate 95% credible intervals for signatures; regions of overlap indicate time-periods during which changes were not detected. Phases of infection are E  =  early, A  =  acute, R  =  recovery, C  =  convalescence. (<b>A</b>) The pathogen, <i>Citrobacter rodentium</i> (OTU#6) in colon. (<b>B</b>) <i>Mucispirillum</i> (OTU#1) in colon, rapidly decreases and does not return to baseline until the convalescent phase. (<b>C</b>) <i>Parabacteroides</i> (OTU#8) in colon, decreases during early infection, but returns to baseline by the recovery phase. (<b>D</b>) <i>Parabacteroides</i> (OTU#8) in cecum had no detectable change between cohorts. (<b>E–F</b>) Two <i>Lactobacilli</i> in ileum, showing different dynamics: OTU#3 increases during acute infection, while OTU#13 decreases. (<b>G–H</b>) <i>Clostridium</i> (OTU#24) in ileum and cecum, has a delayed increase that persists into the convalescent phase. (<b>I–J</b>) Representative OTUs in colon and ileum showing no detectable changes between cohorts.</p

Filtering steps in bioinformatics pipeline and remaining sequencing reads.

<p>Filtering steps in bioinformatics pipeline and remaining sequencing reads.</p

Correlation of Sequence and Culture-based Signatures.

<p>Correlation of Sequence and Culture-based Signatures.</p

Systems-level measures of microbiota diversity dynamics and tissue recovery times in response to infection.

<p>(<b>A, C, E</b>) Shannon entropy calculated from inferred Operational Taxonomic Unit (OTU) signatures measures dynamic changes in the diversity of the microbial ecosystems in each tissue. Red line  =  entropy of infected tissue ecosystem; blue line  =  entropy for controls. Vertical bars denote 95% credible intervals. (<b>B, D, F</b>) The Microbiota Recovery Time (MRT) in each tissue measures the latest time-point post-challenge with the pathogen for which microbial communities from infected mice and controls exhibit >95% similarity overall. Red bars  =  weighted measure of detected changes in taxa increasing with infection; blue bars  =  corresponding measure for decreasing taxa. The weighted measure takes into account relative taxa abundances in both infected and uninfected cohorts. The recovery time was 62 days for ileum, 14 days for cecum, and 62 days for colon.</p

Species identified by quantitative culture.

<p>Agar media used: MAC (MacConkey agar) for selection of enteric and bile-resistant non-fermenter species; MAC+TET (MacConkey agar with 10 µg/mL of Tetracycline) to select for <i>Proteus vulgaris</i>. CAN (Colistin Naladixic Acid agar with 5% sheep's blood on a Columbia agar base) for suppression of enteric species; BKV (Brucella-Kanamycin-Vancomycin agar) for selection of aminoglycoside+vancomycin-resistant Lactobacilli;</p>1<p>Aerobic incubation conditions were in 5% CO₂ humidified atmosphere at 37°C; plates read at 24 and 48 hours of incubation.</p>2<p>Anaerobic conditions were in a Coy anaerobic chamber at 37°C. Plates were read in the chamber at 72 hours of incubation.</p

Sequence and culture-based signatures for <i>Citrobacter rodentium</i> in all tissues.

<p>Horizontal axis indicates days post-inoculation with <i>C. rodentium</i>. Dashed lines indicate the inferred median signature shape for each trajectory. Shaded regions indicate the 95% credible interval. <b>(A, B, C)</b> Signatures derived from sequencing data for the predominant <i>C. rodentium</i> Operational Taxonomic Unit (OTU) in ileum, cecum, and colon. Vertical axis indicates the number of normalized sequencing counts. <b>(D, E, F)</b> Signatures derived from culture-based data for <i>C. rodentium</i> in ileum, cecum and colon. Vertical axis indicates log₁₀ Colony Forming Units (CFUs) per gram of input tissue. Of note, although <i>C. rodentium</i> was not cultured from uninfected mice, the estimated upper bound of the 95% credible interval for the culture counts in uninfected and infected mice trends to 1X10² CFU/g, which was the threshold of detection when using MacConkey agar for selective culture (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095534#pone-0095534-t003" target="_blank">Table 3</a>).</p

Experimental and computational framework.

<p><b>(A)</b> Experimental model, with cohorts of infected mice (red) or uninfected age-matched controls (blue), from which ileum, cecum and distal colon samples were collected. Samples were subjected to high-throughput 16S rDNA sequencing as well as quantitative culture for the pathogen and predominant commensals. <b>(B)</b> Computational pipeline begins with preprocessing and clustering of 16S rDNA sequences into Operational Taxonomic Units (OTUs). Normalized OTU or culture counts serve as input to MC-TIMME, which simultaneously infers the number, shapes, and assignments of taxa to prototype signatures. <b>(C)</b> MC-TIMME outputs summarize dynamic changes in microbial communities across intestinal sites at multiple levels of detail. Systems measures detect large-scale changes in microbial community structure and dynamics. Consensus Signature Groups (CSGs) compress OTUs into sets with members exhibiting similar behaviors over time. Time-maps compactly visualize microbiota dynamics in tissues, organizing CSGs by their times of maximal change to reveal cascades of coordinate alterations. Signature Match Percentiles (SMPs) identify taxa for which sequence and culture-derived signatures have strong correspondences.</p