Building a genome-based understanding of bacterial pH preferences

Abstract

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.

How to Provide Nitrite Robustly for Anaerobic Ammonium Oxidation in Mainstream Nitrogen Removal

“…Similar efforts are currently underway, such as predicting microbial metabolism by an automated science platform named BacterAI and estimating bacterial pH preferences by a machine learning approach. 215,216 These emerging technologies present opportunities for fully characterizing and optimizing culturing conditions for AOMs and NOB.…”

“…In other words, the relative abundance and activity of nitrite-supplying bacteria and NOB are predictable under different substrate concentrations or encountering different stress factors. Similar efforts are currently underway, such as predicting microbial metabolism by an automated science platform named BacterAI and estimating bacterial pH preferences by a machine learning approach. , These emerging technologies present opportunities for fully characterizing and optimizing culturing conditions for AOMs and NOB. The combination of PN and PD can be a robust and reliable pathway for achieving mainstream nitrite supply, and the contribution percentage of PN to the nitrite generation determines the economic feasibility of this combined system. Hence, it is still urgent to conduct targeted strategies for inhibiting NOB focused on their physiological and biochemical characteristics.…”

“…In other words, the relative abundance and activity of nitrite-supplying bacteria and NOB are predictable under different substrate concentrations or encountering different stress factors. Similar efforts are currently underway, such as predicting microbial metabolism by an automated science platform named BacterAI and estimating bacterial pH preferences by a machine learning approach. , These emerging technologies present opportunities for fully characterizing and optimizing culturing conditions for AOMs and NOB.…”

Functional regimes define the response of the soil microbiome to environmental change

“…Identifying which of these processes are the key drivers of the community-level metabolic response remains a challenge. Surveys of communities in the wild [16][17][18] reveal patterns relating community composition, environmental variation, and metabolic processes [19][20][21][22][23][24][25][26][27][28]. However, the origins of these patterns are difficult to dissect [14], and establishing causal relationships from observational data alone is challenging given many confounding factors.…”

“…Surveys of communities in the wild [16–18] reveal patterns relating community composition, environmental variation, and metabolic processes [19–28]. However, the origins of these patterns are difficult to dissect [14], and establishing causal relationships from observational data alone is challenging given many confounding factors.…”