GC Content in Nuclear-Encoded Genes and Effective Number of Codons (ENC) Are Positively Correlated in AT-Rich Species and Negatively Correlated in GC-Rich Species DOI Open Access
Douglas M. Ruden

Genes, Journal Year: 2025, Volume and Issue: 16(4), P. 432 - 432

Published: April 5, 2025

Background/Objectives: Codon usage bias affects gene expression and translation efficiency across species. The effective number of codons (ENC) GC content influence codon preference, often displaying unimodal or bimodal distributions. This study investigates the correlation between ENC rankings species how their relationship Methods: I analyzed nuclear-encoded genes from 17 representing six kingdoms: one bacteria (Escherichia coli), three fungi (Saccharomyces cerevisiae, Neurospora crassa, Schizosaccharomyces pombe), archaea (Methanococcus aeolicus), protists (Rickettsia hoogstraalii, Dictyostelium discoideum, Plasmodium falciparum),), plants (Musa acuminata, Oryza sativa, Arabidopsis thaliana), animals (Anopheles gambiae, Apis mellifera, Polistes canadensis, Mus musculus, Homo sapiens, Takifugu rubripes). Genes in all were ranked by ENC, correlations assessed. examined adding subtracting these influenced overall distribution a new method that call Two-Rank Order Normalization TRON. equation, TRON = SUM(ABS((GC rank1:GC rankN) − (ENC rank1:ENC rankN))/(N2/3), where (GC is rank-order series rank, sorted rank. denominator TRON, N2/3, normalization factor because it expected value sum absolute rank–ENC rank for if are not correlated. Results: positively correlated (i.e., increases as increases) AT-rich such honeybees (R2 0.60, slope 0.78) wasps 0.52, 0.72) negatively decreases GC-rich humans 0.38, −0.61) rice 0.59, −0.77). Second, distributions change to Third, rank+ENC Fourth, slopes versus ENC) with 0.98) (see Graphic Abstract). Conclusions: differs among species, shaping opposite ways depending on whether species’ GC-rich. Understanding patterns might provide insights into efficiency, epigenetics mediated CpG DNA methylation, epitranscriptomics RNA modifications, secondary structures, evolutionary pressures, potential applications genetic engineering biotechnology.

Language: Английский

GC Content in Nuclear-Encoded Genes and Effective Number of Codons (ENC) Are Positively Correlated in AT-Rich Species and Negatively Correlated in GC-Rich Species DOI Open Access
Douglas M. Ruden

Genes, Journal Year: 2025, Volume and Issue: 16(4), P. 432 - 432

Published: April 5, 2025

Background/Objectives: Codon usage bias affects gene expression and translation efficiency across species. The effective number of codons (ENC) GC content influence codon preference, often displaying unimodal or bimodal distributions. This study investigates the correlation between ENC rankings species how their relationship Methods: I analyzed nuclear-encoded genes from 17 representing six kingdoms: one bacteria (Escherichia coli), three fungi (Saccharomyces cerevisiae, Neurospora crassa, Schizosaccharomyces pombe), archaea (Methanococcus aeolicus), protists (Rickettsia hoogstraalii, Dictyostelium discoideum, Plasmodium falciparum),), plants (Musa acuminata, Oryza sativa, Arabidopsis thaliana), animals (Anopheles gambiae, Apis mellifera, Polistes canadensis, Mus musculus, Homo sapiens, Takifugu rubripes). Genes in all were ranked by ENC, correlations assessed. examined adding subtracting these influenced overall distribution a new method that call Two-Rank Order Normalization TRON. equation, TRON = SUM(ABS((GC rank1:GC rankN) − (ENC rank1:ENC rankN))/(N2/3), where (GC is rank-order series rank, sorted rank. denominator TRON, N2/3, normalization factor because it expected value sum absolute rank–ENC rank for if are not correlated. Results: positively correlated (i.e., increases as increases) AT-rich such honeybees (R2 0.60, slope 0.78) wasps 0.52, 0.72) negatively decreases GC-rich humans 0.38, −0.61) rice 0.59, −0.77). Second, distributions change to Third, rank+ENC Fourth, slopes versus ENC) with 0.98) (see Graphic Abstract). Conclusions: differs among species, shaping opposite ways depending on whether species’ GC-rich. Understanding patterns might provide insights into efficiency, epigenetics mediated CpG DNA methylation, epitranscriptomics RNA modifications, secondary structures, evolutionary pressures, potential applications genetic engineering biotechnology.

Language: Английский

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