Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota

<div><p>The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated <i>Clostridium difficile</i> infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and <i>C. difficile</i> inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against <i>C. difficile</i>. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.</p></div

Colonization mechanism.

<p>(A) Mechanism of <i>C. difficile</i> colonization in mice. (B) Schematics of step-by-step dynamics leading to <i>C. difficile</i> establishment following clindamycin treatment.</p

Growth and interaction rates and susceptibilities to clindamycin application from cecal mouse data.

<p>All growth rates are found to be positive (A). Interaction parameters in row <i>i</i> and column <i>j</i> represent the effect of genus <i>j</i> on <i>i</i> where red stands for activation and blue for repression (B). Blue bars in the susceptibility panel refer to an inhibiting effect of clindamycin, while red ones refer to activation (C). The optimal regularization parameters obtained in a 3-fold cross-validation are , , .</p

Conceptual figure highlighting the difference between our approach and the currently available methods for microbiota analysis.

<p>Used input data are the temporal records of microbial total abundances (colored bars on left) and the temporal signal of external perturbations (e.g. presence/absence or concentration). (A) Example and list of current computational approaches used to analyze community data for microbiota studies. (B) Our approach uses ecological modeling to infer a network of microbial interactions, susceptibilities to external perturbations and growth rates. The inferred parameters are used in an ecological community model which can then be used to predict ecosystem dynamics and to identify steady states.</p

Comparison between observation and predicted microbial composition in the cecum.

<p>(A) refers to replicate 2 of population #1 (<i>C. difficile</i> inoculation at day 0), (B) to clindamycin administration at day 1 (replicate 2 of population #2) and (C) to clindamycin and <i>C. difficile</i> administration at day 1 and 2 respectively (replicate 2 of population #3). The composition bar is linearly scaled. Note, the total abundance of the intestinal microbiota does not decrease with antibiotic treatment. This may indicate the specific function of the bacteria that are present after the perturbation. (D) Rank correlation of measured with predicted data points. Colors indicate elapsed time. 75% confidence ellipses are drawn for the first (blue) and last (red) predicted time points.</p

Steady state microbial composition for the cases described in Figure 3A–C.

<p>(A) predicted composition of the second replicates of the three different populations. These states are asymptotically stable as depicted in (B) where the corresponding largest eigenvalues of the Jacobian matrix evaluated at each steady state is compared (red dot) against the histogram of largest eigenvalues of all attainable and biologically meaningful steady states.</p

Early <i>Clostridium difficile</i> Infection during Allogeneic Hematopoietic Stem Cell Transplantation

<div><p><i>Clostridium difficile</i> infection (CDI) is frequently diagnosed in recipients of allogeneic hematopoietic stem cell transplantation (allo-HSCT). We characterized early-transplant CDI and its associations, and analyzed serially-collected feces to determine intestinal carriage of toxigenic <i>C. difficile</i>. Fecal specimens were collected longitudinally from 94 patients during allo-HSCT hospitalization, from the start of pre-transplant conditioning until up to 35 days after stem cell infusion. Presence of <i>C. difficile</i> 16S rRNA and <i>tcdB</i> genes was determined. Clinical variables and specimen data were analyzed for association with development of CDI. Historical data from an additional 1144 allo-HSCT patients was also used. Fecal specimens from 37 patients (39%) were found to harbor <i>C. difficile</i>. Early-transplant CDI was diagnosed in 16 of 94 (17%) patients undergoing allo-HSCT; cases were generally mild and resembled non-CDI diarrhea associated with transplant conditioning. CDI was associated with preceding colonization with tcdB-positive <i>C. difficile</i> and conditioning regimen intensity. We found no associations between early-transplant CDI and graft-versus-host disease or CDI later in transplant. CDI occurs with high frequency during the early phase of allo-HSCT, where recipients are pre-colonized with toxigenic C. difficile. During this time, CDI incidence peaks during pre-transplant conditioning, and is correlated to intensity of the treatment. In this unique setting, high rates of CDI may be explained by prior colonization and chemotherapy; however, cases were generally mild and resembled non-infectious diarrhea due to conditioning, raising concerns of misdiagnosis. Further study of this unique population with more discriminating CDI diagnostic tests are warranted.</p></div

Kaplan-Meier plot of CDI during allo-HSCT.

<p>Patients receiving greater intensity conditioning regimens were more likely to develop CDI. <i>A</i>, Biospecimen group (N = 94). <i>B</i>, Observational group (N = 1144).</p

Distinct but Spatially Overlapping Intestinal Niches for Vancomycin-Resistant <i>Enterococcus faecium</i> and Carbapenem-Resistant <i>Klebsiella pneumoniae</i>

<div><p>Antibiotic resistance among enterococci and γ-proteobacteria is an increasing problem in healthcare settings. Dense colonization of the gut by antibiotic-resistant bacteria facilitates their spread between patients and also leads to bloodstream and other systemic infections. Antibiotic-mediated destruction of the intestinal microbiota and consequent loss of colonization resistance are critical factors leading to persistence and spread of antibiotic-resistant bacteria. The mechanisms underlying microbiota-mediated colonization resistance remain incompletely defined and are likely distinct for different antibiotic-resistant bacterial species. It is unclear whether enterococci or γ-proteobacteria, upon expanding to high density in the gut, confer colonization resistance against competing bacterial species. Herein, we demonstrate that dense intestinal colonization with vancomycin-resistant <i>Enterococcus faecium</i> (VRE) does not reduce <i>in vivo</i> growth of carbapenem-resistant <i>Klebsiella pneumoniae</i>. Reciprocally, <i>K</i>. <i>pneumoniae</i> does not impair intestinal colonization by VRE. In contrast, transplantation of a diverse fecal microbiota eliminates both VRE and <i>K</i>. <i>pneumoniae</i> from the gut. Fluorescence <i>in situ</i> hybridization demonstrates that VRE and <i>K</i>. <i>pneumoniae</i> localize to the same regions in the colon but differ with respect to stimulation and invasion of the colonic mucus layer. While VRE and <i>K</i>. <i>pneumoniae</i> occupy the same three-dimensional space within the gut lumen, their independent growth and persistence in the gut suggests that they reside in distinct niches that satisfy their specific <i>in vivo</i> metabolic needs.</p></div

<i>K</i>. <i>pneumoniae</i> and VRE achieve similar densities in the large intestine of co-colonized mice.

<p>Ampicillin-treated mice were inoculated with <i>K</i>. <i>pneumoniae</i> by oral gavage or left uninfected. Three days later, half of the <i>K</i>. <i>pneumoniae</i>-infected mice and the uninfected group were challenged with VRE. Microbiota composition of mice colonized with VRE alone (V), <i>K</i>. <i>pneumoniae</i> alone (K) or both (VK) was determined by sequencing of the V4-V5 region of the 16S rRNA genes. (A) Fecal microbiota composition at different time points post VRE challenge. (B) Ileal and cecal microbiota composition at day 21 of colonization. (A,B) Each stacked bar represents the average of five individually-housed mice per time point.</p