PubMed Health⌕ Search

PubMed · 14692797

Synonymous codon usage in Lactococcus lactis: mutational bias versus translational selection.

Abstract

In this study codon usage bias of all experimentally known genes of Lactococcus lactis has been analyzed. Since Lactococcus lactis is an AT rich organism, it is expected to occur A and/or T at the third position of codons and detailed analysis of overall codon usage data indicates that A and/or T ending codons are predominant in this organism. However, multivariate statistical analyses based both on codon count and on relative synonymous codon usage (RSCU) detect a large number of genes, which are supposed to be highly expressed are clustered at one end of the first major axis, while majority of the putatively lowly expressed genes are clustered at the other end of the first major axis. It was observed that in the highly expressed genes C and T ending codons are significantly higher than the lowly expressed genes and also it was observed that C ending codons are predominant in the duets of highly expressed genes, whereas the T endings codons are abundant in the quartets. Abundance of C and T ending codons in the highly expressed genes suggest that, besides, compositional biases, translational selection are also operating in shaping the codon usage variation among the genes in this organism as observed in other compositionally skewed organisms. The second major axis generated by correspondence analysis on simple codon counts differentiates the genes into two distinct groups according to their hydrophobicity values, but the same analysis computed with relative synonymous codon usage values could not discriminate the genes according to the hydropathy values. This suggests that amino acid composition exerts constraints on codon usage in this organism. On the other hand the second major axis produced by correspondence analysis on RSCU values differentiates the genes into two groups according to the synonymous codon usage for cysteine residues (rarest amino acids in this organism), which is nothing but a artifactual effect induced by the RSCU values. Other factors such as length of the genes and the positions of the genes in the leading and lagging strand of replication have practically no influence in the codon usage variation among the genes in this organism.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S K Gupta, T K Bhattacharyya, T C Ghosh. 2004. Synonymous codon usage in Lactococcus lactis: mutational bias versus translational selection.. https://doi.org/10.1080/07391102.2004.10506946

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.

Self-amplifying RNA (saRNA) is an emerging RNA therapeutic modality that can facilitate higher magnitude and more durable protein expression at substantially lower doses than nonreplicating mRNA. Unlike conventional messenger RNA (mRNA), alphavirus-derived saRNA must support a replicase-driven RNA amplification step in addition to translation, raising the possibility that transgene coding sequences impose sequence-level constraints on replication. Here, saRNA replication was found to be dependent on the codon composition of the transgene; multiple therapeutic transgenes were replication defective despite an intact Venezuelan Equine Encephalitis Virus (VEEV)-derived saRNA backbone. Replication defects were rescued by synonymous codon re-optimization of the same transgenes, indicating that nucleotide-level features of the coding sequence, rather than the encoded protein, govern replication competence. Comparative compositional analyses identified a distinct signature associated with productive replication, characterized by elevated GC (>53%) and GC3 (>63%) content, higher codon adaptation to human (>0.75), and reduced UpA (<43/kb) and UpU (<41/kb) dinucleotide density. Moreover, deliberate compositional perturbation of an otherwise replication-competent transgene shifted these features and abolished replication, supporting a causal and combinatorial role for sequence composition in defining saRNA replication outcome. These findings define an underappreciated constraint in saRNA therapeutics and motivate saRNA-specific payload design frameworks that incorporate alphavirus-associated compositional biases during transgene sequence optimization.

Codon↗

CodonMoE: DNA language models for codon-dependent mRNA prediction.

MOTIVATION: Genomic language models (gLMs) face a fundamental efficiency challenge: one must either maintain separate specialized models for each biological modality (DNA and RNA) or develop large multimodal architectures. Both approaches impose significant computational burdens-modality-specific models require redundant infrastructure despite inherent biological connections, while multi-modal architectures demand increased parameter counts and extensive cross-modality pretraining. RESULTS: To address this limitation, we introduce CodonMoE (Adaptive Mixture of Codon Reformative Experts), a lightweight adapter that transforms DNA language models into effective RNA analyzers without RNA-specific pretraining. Our theoretical analysis establishes CodonMoE as a universal approximator at the codon level, capable of mapping arbitrary functions from codon sequences to codon-dependent RNA properties given sufficient expert capacity. Across four RNA prediction tasks spanning stability, expression, and regulation, DNA models augmented with CodonMoE significantly outperform their unmodified counterparts, with the HyenaDNA+CodonMoE series achieving state-of-the-art results using 80% fewer parameters than specialized RNA models. By maintaining sub-quadratic complexity while achieving superior performance, our approach provides a principled path toward unifying genomic language modeling, leveraging more abundant DNA data and reducing computational overhead while preserving modality-specific performance advantages. AVAILABILITY AND IMPLEMENTATION: Source code for the method and to reproduce the results is available at https://github.com/Kingsford-Group/CodonMoE.

Codon↗

CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network.

MOTIVATION: The mapping from codon to amino acid is surjective due to codon degeneracy, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various protein downstream tasks. However, predictive models for residue-level tasks such as phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites prediction in general, have predominantly relied on representations in amino acid space. RESULTS: We introduce a novel approach for predicting phosphorylation sites by utilizing codon-level information through embeddings from the codon adaptation language model (CaLM), trained on protein-coding DNA sequences. Protein sequences are first reverse-translated into reliable coding sequences by mapping UniProt sequences to their corresponding NCBI reference sequences and extracting the exact coding sequences from their GenBank format using a dynamic programming-based global pairwise alignment. The resulting coding sequences are encoded using the CaLM encoder to generate codon-aware embeddings, which are subsequently integrated with amino acid-aware embeddings obtained from a protein language model, through an early fusion strategy. Next, a window-level representation of the site of interest, retaining the full sequence context, is constructed from the fused embeddings. A ConvBiGRU network extracts feature maps that capture spatiotemporal correlations between proximal residues within the window. This is followed by a prediction head based on a Kolmogorov-Arnold network (KAN) using the derivative of gaussian wavelet transform to generate the inference for the site. The overall model, dubbed CaLMPhosKAN, performs better than the existing approaches across multiple datasets. AVAILABILITY AND IMPLEMENTATION: CaLMPhosKAN is publicly available at https://github.com/KCLabMTU/CaLMPhosKAN.

Codon↗