Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Bucci V

PROVIDER: S-EPMC4893271 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Bucci Vanni V Tzen Belinda B Li Ning N Simmons Matt M Tanoue Takeshi T Bogart Elijah E Deng Luxue L Yeliseyev Vladimir V Delaney Mary L ML Liu Qing Q Olle Bernat B Stein Richard R RR Honda Kenya K Bry Lynn L Gerber Georg K GK

Genome biology 20160603 1

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-modulat ...[more]

PMID: 27259475

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Project description:To gain improved temporal, spatial and phylogenetic resolution of marine microbial communities, in this study we expanded the original prototype genome proxy array (an oligonucleotide microarray targeting marine microbial genome fragments and genomes), evaluated it against metagenomic sequencing, and applied it to time series samples from the Monterey Bay long term ecological research site. The expanded array targeted 268 microbial genotypes (vs. 14 in the original prototype) across much of the known diversity of cultured and uncultured marine microbes. The target abundances measured by the genome proxy array were highly correlated to pyrosequence-based abundances (linear regression R2 = 0.85-0.91, p<0.0001). Fifty-seven samples from ~4-years in Monterey Bay were examined with the array, spanning the photic zone (0m), the base of the surface mixed layer (30m), and the subphotic zone (200m). A significant portion of the expanded genome proxy array’s targets showed signal (95 out of 268 targets present in ≥ 1 sample). The multi-year community survey showed the consistent presence of a core group of common and abundant targeted taxa at each depth in Monterey Bay, higher variability among shallow than deep samples, and episodic occurrences of more transient marine genotypes. The abundance of the most dominant genotypes peaked after strong episodic upwelling events. The genome-proxy array’s ability to track populations of closely-related genotypes indicated population shifts within several abundant target taxa, with specific populations in some cases clustering by depth or oceanographic season. Although 51 cultivated organisms were targeted (representing 19% of the array) the majority of targets detected and of total target signal (85% and ~92%, respectively) were from uncultivated lineages, often those derived from Monterey Bay. The array provided cost-effective (~$15 per array, for construction and use) insights into the natural history of uncultivated lineages in the wild. To gain improved temporal, spatial and phylogenetic resolution of marine microbial communities, in this study we expanded the original prototype genome proxy array (an oligonucleotide microarray targeting marine microbial genome fragments and genomes), evaluated it against metagenomic sequencing, and applied it to time series samples from the Monterey Bay long term ecological research site. The expanded array targeted 268 microbial genotypes (vs. 14 in the original prototype) across much of the known diversity of cultured and uncultured marine microbes. The target abundances measured by the genome proxy array were highly correlated to pyrosequence-based abundances (linear regression R2 = 0.85-0.91, p<0.0001). Fifty-seven samples from ~4-years in Monterey Bay were examined with the array, spanning the photic zone (0m), the base of the surface mixed layer (30m), and the subphotic zone (200m). A significant portion of the expanded genome proxy array’s targets showed signal (95 out of 268 targets present in ≥ 1 sample). The multi-year community survey showed the consistent presence of a core group of common and abundant targeted taxa at each depth in Monterey Bay, higher variability among shallow than deep samples, and episodic occurrences of more transient marine genotypes. The abundance of the most dominant genotypes peaked after strong episodic upwelling events. The genome-proxy array’s ability to track populations of closely-related genotypes indicated population shifts within several abundant target taxa, with specific populations in some cases clustering by depth or oceanographic season. Although 51 cultivated organisms were targeted (representing 19% of the array) the majority of targets detected and of total target signal (85% and ~92%, respectively) were from uncultivated lineages, often those derived from Monterey Bay. The array provided cost-effective (~$15 per array, for construction and use) insights into the natural history of uncultivated lineages in the wild.

Dataset Information

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

Publications

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

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