15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)

Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users

Computer-guided design of optimal microbial consortia for immune system modulation

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics

Additional file 6: Figure S4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Forecasts of microbial concentration trajectories for the gnotobiotic mice probiotic stability experiments. The forecasts were obtained using a hold-one-subject-out procedure. Briefly, MDSINE was run on all data from all but one of the mice (the held-out subject) and model parameters were inferred. Using the inferred model parameters (including for the perturbation) and the measured concentrations of the microbiota at an initial time point for the held-out mouse, the trajectories of the microbiota for the held-out mouse were then forecast for all the remaining time points; the procedure was repeated for each mouse in turn. Solid lines denote predicted trajectories and symbols denote actual data. (PDF 7552 kb

Additional file 7: Figure S5. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Predicted stability and steady state concentrations (log10 ng strain DNA/μg total fecal DNA) for all combinations of the 13 Clostridia strains in mice fed either high-fiber (standard) or low-fiber diets in the probiotic stability experiment. Columns and rows were ordered using hierarchical clustering using Euclidean distance with Ward linkage. No significant differences were found in the predicted stable biodiversity profiles between the high-fiber and low-fiber dietary regimes (number of strains across all predicted stable states was not significantly different; Wilcoxon rank sum test p value = 0.096). (PDF 4845 kb

Additional file 1: Tables S1–4. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Supplemental tables. (DOCX 21 kb

Additional file 5: Figure S3. of MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Experimental design for probiotic stability studies in gnotobiotic mice. Seven adult germ-free mice were gavaged with 13 Clostridia strains from the VE202 probiotic cocktail [24]. Five mice were maintained on a standard high-fiber diet for 5 weeks, after which mice were switched to a low-fiber diet for 2 weeks and then switched back to the high-fiber diet for another 2 weeks; an additional two mice were inoculated with the same strains but were not subjected to the low-fiber dietary perturbation. Fecal pellets were collected at days 1–21 (daily), 23, 25, 27, 29, 31, 33, 35–60 (daily), 62, 63, and 65 for the five mice receiving the low-fiber dietary perturbation and at days 1–21 (daily), 23, 25, 27, and 29 for the two mice not receiving the perturbation. (PDF 42 kb

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses (Software Source Code)

<p>Predicting dynamics of host-microbial ecosystems is crucial for rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then demonstrate MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with <em>Clostridium difficile</em> and an immune-modulatory probiotic. On these datasets, we demonstrate new capabilities including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity (keystoneness) in response to perturbations.</p

Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota

Manipulation of the gut microbiota holds great promise for the treatment of inflammatory and allergic diseases1, 2. Although numerous probiotic microorganisms have been identified3, there remains a compelling need to discover organisms that elicit more robust therapeutic responses, are compatible with the host, and can affect a specific arm of the host immune system in a well-controlled, physiological manner. Here we use a rational approach to isolate CD4+FOXP3+ regulatory T (Treg)-cell-inducing bacterial strains from the human indigenous microbiota. Starting with a healthy human faecal sample, a sequence of selection steps was applied to obtain mice colonized with human microbiota enriched in Treg-cell-inducing species. From these mice, we isolated and selected 17 strains of bacteria on the basis of their high potency in enhancing Treg cell abundance and inducing important anti-inflammatory molecules—including interleukin-10 (IL-) and inducible T-cell co-stimulator (ICOS)—in Treg cells upon inoculation into germ-free mice. Genome sequencing revealed that the 17 strains fall within clusters IV, XIVa and XVIII of Clostridia, which lack prominent toxins and virulence factors. The 17 strains act as a community to provide bacterial antigens and a TGF-β-rich environment to help expansion and differentiation of Treg cells. Oral administration of the combination of 17 strains to adult mice attenuated disease in models of colitis and allergic diarrhoea. Use of the isolated strains may allow for tailored therapeutic manipulation of human immune disorders