Building a genome-based understanding of bacterial pH preferences.

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model that could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

Terrestrial connectivity, upstream aquatic history and seasonality shape bacterial community assembly within a large boreal aquatic network.

During transit from soils to the ocean, microbial communities are modified and re-assembled, generating complex patterns of ecological succession. The potential effect of upstream assembly on downstream microbial community composition is seldom considered within aquatic networks. Here, we reconstructed the microbial succession along a land-freshwater-estuary continuum within La Romaine river watershed in Northeastern Canada. We captured hydrological seasonality and differentiated the total and reactive community by sequencing both 16 S rRNA genes and transcripts. By examining how DNA- and RNA-based assemblages diverge and converge along the continuum, we inferred temporal shifts in the relative importance of assembly processes, with mass effects dominant in spring, and species selection becoming stronger in summer. The location of strongest selection within the network differed between seasons, suggesting that selection hotspots shift depending on hydrological conditions. The unreactive fraction (no/minor RNA contribution) was composed of taxa with diverse potential origins along the whole aquatic network, while the majority of the reactive pool (major RNA contribution) could be traced to soil/soilwater-derived taxa, which were distributed along the entire rank-abundance curve. Overall, our findings highlight the importance of considering upstream history, hydrological seasonality and the reactive microbial fraction to fully understand microbial community assembly on a network scale.