Metagenomics meets time series analysis: unraveling microbial community dynamics

Abstract

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research.

Small-scale variability of protistan planktonic communities relative to environmental pressures and biotic interactions at two adjacent coastal stations

“…Undoubtedly, the use of molecular data is foreseeable in the future exploration of ecological questions (e.g. Faust & Raes 2012, Faust et al 2015, Fuhrman et al 2015 and references therein). However, the terminology and metrics used in macroecology must be applied with greater caution because the methods available with which to characterize microbial communities remain at 'intermediate resolution'.…”

Engineering Microbiomes to Improve Plant and Animal Health

“…We predict that the 'generalized' host-microbiome co-propagation regimes outlined in Boxes 2 and 3 and in Figure 1 will help to stimulate such research on diverse host systems and develop efficient protocols to shape microbiomes through host-mediated artificial microbiome selection (see Outstanding Questions). Future research should optimize selection regimes by varying experimental parameters summarized in Box 3; combine optimized selection regimes with advanced methods to infer genetic and metabolic properties of the engineered microbiomes (e.g., microbiome-wide association studies [75] and with methods to quantify the microbiome changes resulting from artificial microbiome selection (e.g., metagenomic time-series analyses [76]); and build on the principal methods summarized in Box 2 to develop the full potential of host-mediated microbiome engineering.…”

The legacy of the past: effects of historical processes on microbial metacommunities

“…Other methods, such as extended local similarity analysis (eLSA) (Xia et al 2011(Xia et al , 2013 and similar network approaches (e.g. Faust et al 2015) can identify time-delayed associations between occurences of taxa and environmental variables. They might yield important insights into time-lags in species sorting processes structuring metacommunities if they can be expan ded to include a spatial dimension as well.…”