ArticleApplied and environmental microbiology2024
Convergent reductive evolution in bee-associated lactic acid bacteria.
Article in Applied and environmental microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Genome-guided characterization ofMicrobiology spectrum · 2026Article
- Lactobacilli biology, applications and host interactions.Nature reviews. Microbiology · 2026Review
- Origin and Evolution of Very Large Extracellular Proteins in Fructophilic Lactic Acid Bacteria.Genome biology and evolution · 2026Article
- Article
- Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.Bioinformatics (Oxford, England) · 2025Article
- Tracing and characterization of foodborne botulism caused by the new MLST typeFrontiers in microbiology · 2025Article
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Abstract
Distantly related organisms may evolve similar traits when exposed to similar environments or engaging in certain lifestyles. Several members of the Lactobacillaceae [lactic acid bacteria (LAB)] family are frequently isolated from the floral niche, mostly from bees and flowers. In some floral LAB species (henceforth referred to as bee-associated LAB), distinctive genomic (e.g., genome reduction) and phenotypic (e.g., preference for fructose over glucose or fructophily) features were recently documented. These features are found across distantly related species, raising the hypothesis that specific genomic and phenotypic traits evolved convergently during adaptation to the floral environment. To test this hypothesis, we examined representative genomes of 369 species of bee-associated and non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts toward the bee environment in LAB. In these species, we observed significant reductions of genome size, gene repertoire, and GC content. Using machine leaning, we could distinguish bee-associated from non-bee-associated species with 94% accuracy, based on the absence of genes involved in metabolism, osmotic stress, or DNA repair. Moreover, we found that the most important genes for the machine learning classifier were seemingly lost, independently, in multiple bee-associated lineages. One of these genes, acetaldehyde-alcohol dehydrogenase (
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