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

Metabolism Meets Translation: Dietary and Metabolic Influences on tRNA Modifications and Codon Biased Translation DOI Creative Commons
Sherif Rashad, Aseel Marahleh

Wiley Interdisciplinary Reviews - RNA, Journal Year: 2025, Volume and Issue: 16(2)

Published: March 1, 2025

ABSTRACT Transfer RNA (tRNA) is not merely a passive carrier of amino acids, but an active regulator mRNA translation controlling codon bias and optimality. The synthesis various tRNA modifications regulated by many “writer” enzymes, which utilize substrates from metabolic pathways or dietary sources. Metabolic bioenergetic pathways, such as one‐carbon (1C) metabolism the tricarboxylic acid (TCA) cycle produce essential for synthesis, S‐Adenosyl methionine (SAM), sulfur species, α‐ketoglutarate (α‐KG). activity these can directly impact decoding via regulating levels. In this review, we discuss complex interactions between diet, metabolism, modifications, translation. We how nutrient availability, bioenergetics, intermediates modulate modification landscape to fine‐tune protein synthesis. Moreover, highlight dysregulation metabolic‐tRNA contributes disease pathogenesis, including cancer, disorders, neurodegenerative diseases. also new emerging field GlycoRNA biology drawing parallels glycobiology diseases guide future directions in area. Throughout our discussion, links specific their metabolic/dietary precursors, diseases, emphasizing importance metabolism‐centric view understanding pathologies. Future research should focus on uncovering interplay cellular contexts. Addressing gaps will into novel interventions.

Language: Английский

Citations

0
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

Citations

0