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1. Codon usage bias and dinucleotide preference in 29 Drosophila species

   https://doi.org/10.1093/g3journal/jkab191  

   Kokate, Prajakta P; Techtmann, Stephen M; Werner, Thomas (August 2021, G3 Genes|Genomes|Genetics)

   null (Ed.)

   Abstract Codon usage bias, where certain codons are used more frequently than their synonymous counterparts, is an interesting phenomenon influenced by three evolutionary forces: mutation, selection, and genetic drift. To better understand how these evolutionary forces affect codon usage bias, an extensive study to detect how codon usage patterns change across species is required. This study investigated 668 single-copy orthologous genes independently in 29 Drosophila species to determine how the codon usage patterns change with phylogenetic distance. We found a strong correlation between phylogenetic distance and codon usage bias and observed striking differences in codon preferences between the two subgenera Drosophila and Sophophora. As compared to the subgenus Sophophora, species of the subgenus Drosophila showed reduced codon usage bias and a reduced preference specifically for codons ending with C, except for codons with G in the second position. We found that codon usage patterns in all species were influenced by the nucleotides in the codon’s 2nd and 3rd positions rather than the biochemical properties of the amino acids encoded. We detected a concordance between preferred codons and preferred dinucleotides (at positions 2 and 3 of codons). Furthermore, we observed an association between speciation, codon preferences, and dinucleotide preferences. Our study provides the foundation to understand how selection acts on dinucleotides to influence codon usage bias. 

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2. Genomic factors shaping codon usage across the Saccharomycotina subphylum

   https://doi.org/10.1093/g3journal/jkae207  

   Zavala, Bryan; Dineen, Lauren; Fisher, Kaitlin_J; Opulente, Dana_A; Harrison, Marie-Claire; Wolters, John_F; Shen, Xing-Xing; Zhou, Xiaofan; Groenewald, Marizeth; Hittinger, Chris_Todd; et al (August 2024, G3: Genes, Genomes, Genetics)

   Abstract Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. It has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. We characterized codon usage across 1,154 strains from 1,051 species from the fungal subphylum Saccharomycotina to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns. We found a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is distinct between the 12 orders to such a degree that yeasts can be classified with an accuracy >90% using a machine learning algorithm. We also characterized the degree to which codon usage bias is impacted by translational selection. We found it was influenced by a combination of features, including the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that avoidance of the CGN codons is associated with a decline in arginine tRNA function. Consistent with previous findings, codon usage bias within the Saccharomycotina is shaped by genomic features and GC bias. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons. 

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3. Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage

   https://doi.org/10.1371/journal.pgen.1010256  

   Cope, Alexander L.; Shah, Premal (June 2022, PLOS Genetics)

   Fay, Justin C. (Ed.)

   Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies. 

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4. Lost in translation: conserved amino acid usage despite extreme codon bias in foraminifera

   https://doi.org/10.1128/mbio.03916-24  

   Cote-L'Heureux, Auden E; Sterner, Elinor G; Maurer-Alcalá, Xyrus X; Katz, Laura A (April 2025, mBio)

   Johnson, Patricia J (Ed.)

   ABSTRACT Analyses of codon usage in eukaryotes suggest that amino acid usage responds to GC pressure so AT-biased substitutions drive higher usage of amino acids with AT-ending codons. Here, we combine single-cell transcriptomics and phylogenomics to explore codon usage patterns in foraminifera, a diverse and ancient clade of predominantly uncultivable microeukaryotes. We curate data from 1,044 gene families in 49 individuals representing 28 genera, generating perhaps the largest existing dataset of data from a predominantly uncultivable clade of protists, to analyze compositional bias and codon usage. We find extreme variation in composition, with a median GC content at fourfold degenerate silent sites below 3% in some species and above 75% in others. The most AT-biased species are distributed among diverse non-monophyletic lineages. Surprisingly, despite the extreme variation in compositional bias, amino acid usage is highly conserved across all foraminifera. By analyzing nucleotide, codon, and amino acid composition within this diverse clade of amoeboid eukaryotes, we expand our knowledge of patterns of genome evolution across the eukaryotic tree of life.IMPORTANCEPatterns of molecular evolution in protein-coding genes reflect trade-offs between substitution biases and selection on both codon and amino acid usage. Most analyses of these factors in microbial eukaryotes focus on model species such asAcanthamoeba, Plasmodium,and yeast, where substitution bias is a primary contributor to patterns of amino acid usage. Foraminifera, an ancient clade of single-celled eukaryotes, present a conundrum, as we find highly conserved amino acid usage underlain by divergent nucleotide composition, including extreme AT-bias at silent sites among multiple non-sister lineages. We speculate that these paradoxical patterns are enabled by the dynamic genome structure of foraminifera, whose life cycles can include genome endoreplication and chromatin extrusion. 

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5. Alaska thermokarst monitoring and permafrost soils database 2023

   https://doi.org/10.18739/A2PR7MW2P  

   Jorgenson, Mark; Kanevskiy, Mikhail (January 2024, NSF Arctic Data Center)

   This database contains data on site, soil stratigraphy, soil physical and chemical properties, Carbon-14 (C14) and stable isotope, and vegetation composition and structure acquired from permafrost soil surveys and thermokarst monitoring sites. The data are from projects that we have conducted, as well as data compiled from numerous other project and reports, that have emphasized the study of the intermediate layer of upper permafrost and the dynamic responses of permafrost to environmental conditions. This 2023 update includes data from our recent National Science Foundation (NSF)-funded project on the upper permafrost. The Access Database has 11 main data tables (tbl_) for site (environmental), soil stratigraphy, soil physical data, soil chemical data, water Oxygen-18 (O18), soil radiocarbon dates, vegetation cover, vegetation structure, study areas, personnel, and project data sources. The Site data includes information of location, observers, geomorphology, topography, hydrology, soil summary characteristics, pH and electrical conductivity (EC), soil classification, and vegetation cover by species. Soil stratigraphy has information on soil texture and ground ice. Soil physical and chemical data includes lab data on bulk density, moisture, carbon, and nitrogen. The database has 40 reference tables (REF_) that have codes and descriptions for variables used in site, soil stratigraphy, and vegetation cover tables. Query tables (qry_) are used to link data tables and reference tables to display data with names instead of codes. In addition to the permafrost soils information, the Site data includes topographic survey control information for repeat monitoring of thermokarst study areas. The data and metadata are provided in three formats. The Access relational database has all the data and reference tables, as well as the metadata associated with each table. Two Excel workbooks are provided that separately contain all the data tables and reference tables. Finally, 52 csv files are provided that contain the information on each individual data and reference table, as well as a metadata file that serially lists information on all the fields for all the tables. 

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