MITRE: inferring features from microbiota time-series data linked to host status.

Longitudinal studies are crucial for discovering causal relationships between the microbiome and human disease. We present MITRE, the Microbiome Interpretable Temporal Rule Engine, a supervised machine learning method for microbiome time-series analysis that infers human-interpretable rules linking changes in abundance of clades of microbes over time windows to binary descriptions of host status, such as the presence/absence of disease. We validate MITRE's performance on semi-synthetic data and five real datasets. MITRE performs on par or outperforms conventional difficult-to-interpret machine learning approaches, providing a powerful new tool enabling the discovery of biologically interpretable relationships between microbiome and human host ( https://github.com/gerberlab/mitre/ ).

Contact-mediated cell-assisted cell proliferation in a model eukaryotic single-cell organism: an explanation for the lag phase in shaken cell culture.

In cell culture, when cells are inoculated into fresh media, there can be a period of slow (or lag phase) growth followed by a transition to exponential growth. This period of slow growth is usually attributed to the cells' adaptation to a new environment. However, we argue that, based on observations of shaken suspension culture of Dictyostelium discoideum, a model single-cell eukaryote, this transition is due to a density effect. Attempts to demonstrate the existence of implicit cell signaling via long-range diffusible messengers (i.e., soluble growth factors) through cell-medium separation and microfluidic flow perturbation experiments produced negative results. This, in turn, led to the development of a signaling model based on direct cell-to-cell contacts. Employing a scaling argument for the collision rate due to fluid shear, we reasonably estimate the crossover density for the transition into the exponential phase and fit the observed growth kinetics.

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.