ArticleScience advances2023
Building a genome-based understanding of bacterial pH preferences.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Functional-Taxonomic Scaling Resolves Conflicting Average Genome Size Estimates Across Environmental Gradients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genome-based predictions of metabolic preferences and substrate phenotypes in psychrotrophic bacteria from permafrost environments.Microbiology spectrum · 2026Article
- Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.BMC genomics · 2026Article
- Machine learning-based linking of bacterial genomes to optimal growth pH: a foundation for rational microbial engineering.Journal of animal science and biotechnology · 2026Article
- Bacterial Siderophore Production in Metal-Rich Environments: Underexplored Sources of Siderophores and Insights into Bioremediation.Journal of natural products · 2026Review
- Constructing a "periodic table" of bacteria to map diversity in trait space.The ISME journal · 2026Article
- Does genome size drive the pH-related shifts in bacterial biodiversity within forest soils?Frontiers in microbiology · 2026Article
- Fit for purpose: evaluation and evolution of agricultural bioinoculants across production landscapes.ISME communications · 2026Review
- Ecological constraints and evolutionary trade-offs shape nitrogen fixation across habitats.ISME communications · 2026Article
- Root microbiome dynamics favor slow-growth strategies duringISME communications · 2026Article
- Microbial micronutrient sharing, gut redox balance and keystone taxa as a basis for a new perspective to solutions targeting health from the gut.Gut microbes · 2025Review
- Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.BMC microbiology · 2025Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies.Integrative and comparative biology · 2025Review
- Functional proteomic analysis of Streptomyces sp. F-3 reveals its potential to effectively degrade waste-yeast.Applied microbiology and biotechnology · 2025Article
- Trait-Based Life History Strategies Shape Bacterial Niche Breadth.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recovery of 679 metagenome-assembled genomes from different soil depths along a precipitation gradient.Scientific data · 2025Article
- Soil properties in agricultural systems affect microbial genomic traits.FEMS microbes · 2025Article
- An ecological-evolutionary perspective on the genomic diversity and habitat preferences of theMicrobial genomics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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