Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data

A meta-analysis of changes in bacterial and archaeal communities with time

Ecologists have long studied the temporal dynamics of plant and animal communities with much less attention paid to the temporal dynamics exhibited by microbial communities. As a result, we do not know if overarching temporal trends exist for microbial communities or if changes in microbial communities are generally predictable with time. Using microbial time series assessed via high-throughput sequencing, we conducted a meta-analysis of temporal dynamics in microbial communities, including 76 sites representing air, aquatic, soil, brewery wastewater treatment, human- and plant-associated microbial biomes. We found that temporal variability in both within- and between-community diversity was consistent among microbial communities from similar environments. Community structure changed systematically with time in less than half of the cases, and the highest rates of change were observed within ranges of 1 day to 1 month for all communities examined. Microbial communities exhibited species–time relationships (STRs), which describe the accumulation of new taxa to a community, similar to those observed previously for plant and animal communities, suggesting that STRs are remarkably consistent across a broad range of taxa. These results highlight that a continued integration of microbial ecology into the broader field of ecology will provide new insight into the temporal patterns of microbial and ‘macro'-bial communities alike