Evidence map›Paper›PMID 39213398›Full record

ArticleG3 (Bethesda, Md.)2024

Genomic factors shaping codon usage across the Saccharomycotina subphylum.

Bryan Zavala, Lauren Dineen, Kaitlin J Fisher, Dana A Opulente, Marie-Claire Harrison, John F Wolters, Xing-Xing Shen, Xiaofan Zhou, Marizeth Groenewald, Chris Todd Hittinger and 2 more

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Genes near tRNAs are enriched in translational machinery.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Bryan ZavalaDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, NC 28081, USA.
Lauren DineenDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, NC 28081, USA.
Kaitlin J FisherDepartment of Biological Sciences, SUNY Oswego, Oswego, NY 13126, USA.
Dana A OpulenteDepartment of Biology, Villianova University, Villanova, PA 19085, USA.
Marie-Claire HarrisonDepartment of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA.
John F WoltersLaboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA.
Xing-Xing ShenInstitute of Insect Sciences and Centre for Evolutionary and Organismal Biology, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0001-5765-1419
Xiaofan ZhouGuangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0002-2879-6317
Marizeth GroenewaldWesterdijk Fungal Biodiversity Institute, 3584 CT Utrecht, The Netherlands.
Chris Todd HittingerLaboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA.ORCID 0000-0001-5088-7461
Antonis RokasDepartment of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA.ORCID 0000-0002-7248-6551
Abigail Leavitt LaBellaDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, NC 28081, USA.ORCID 0000-0003-0068-6703

Funding

Deciphering the phenotypic and genomic traits that underlie the evolution of pathogenicity differences among Aspergillus fumigatus and its close relativesR01AI153356 · NIAID · VANDERBILT UNIVERSITY · PI Antonis Rokas · 2022 to 2026
$3.5M
Burroughs Wellcome FundDOE BER Office of Science DE-SC0018409DOE Great Lakes Bioenergy Research CenterFundamental Research Funds for the Central Universities 226-2023-00021H.I. Romnes Faculty Fellowshipkey research project of Zhejiang Lab 2021PE0AC04NIAID NIH HHS R01 AI153356NIHNSF DEB-2110403NSF for Distinguished Young Scholars of Zhejiang Province LR23C140001Office of the Vice Chancellor for Research and Graduate EducationUSDA National Institute of Food and Agriculture 1020204Wisconsin Alumni Research Foundation
6 · The paper itself

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.

Indexed as

Base CompositionCodon UsageGenome, FungalAscomycotaCodonEvolution, MolecularGenomicsProtein BiosynthesisRNA, TransferCodonRNA, Transfercodoncodon usage biasmachine learningSaccharomycotinatRNA

Identifiers

PMID39213398
PMCPMC11540330

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.