LaBella, Abigail Leavitt and Opulente, Dana A. and Steenwyk, Jacob L. and Hittinger, Chris Todd and Rokas, Antonis and Pál, Csaba (2021) Signatures of optimal codon usage in metabolic genes inform budding yeast ecology. PLOS Biology, 19 (4). e3001185. ISSN 1545-7885
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Abstract
Reverse ecology is the inference of ecological information from patterns of genomic variation. One rich, heretofore underutilized, source of ecologically relevant genomic information is codon optimality or adaptation. Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify highly expressed, ecologically relevant genes. To test this hypothesis, we examined the relationship between optimal codon usage in the classic galactose metabolism (GAL) pathway and known ecological niches for 329 species of budding yeasts, a diverse subphylum of fungi. We find that optimal codon usage in the GAL pathway is positively correlated with quantitative growth on galactose, suggesting that GAL codon optimization reflects increased capacity to grow on galactose. Optimal codon usage in the GAL pathway is also positively correlated with human-associated ecological niches in yeasts of the CUG-Ser1 clade and with dairy-associated ecological niches in the family Saccharomycetaceae. For example, optimal codon usage of GAL genes is greater than 85% of all genes in the genome of the major human pathogen Candida albicans (CUG-Ser1 clade) and greater than 75% of genes in the genome of the dairy yeast Kluyveromyces lactis (family Saccharomycetaceae). We further find a correlation between optimization in the GALactose pathway genes and several genes associated with nutrient sensing and metabolism. This work suggests that codon optimization harbors information about the metabolic ecology of microbial eukaryotes. This information may be particularly useful for studying fungal dark matter—species that have yet to be cultured in the lab or have only been identified by genomic material.
Item Type: | Article |
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Subjects: | STM Library > Biological Science |
Depositing User: | Managing Editor |
Date Deposited: | 18 Mar 2023 07:30 |
Last Modified: | 05 Jun 2024 09:33 |
URI: | http://open.journal4submit.com/id/eprint/836 |