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Codon usage bias is the preferential or non-random use of synonymous codons, a ubiquitous phenomenon observed in bacteria, plants and animals. Different species have consistent and characteristic codon biases. Codon bias varies not only with species, family or group within kingdom, but also between the genes within an organism. Codon usage bias has evolved through mutation, natural selection, and genetic drift in various organisms. Genome composition, GC content, expression level and length of genes, position and context of codons in the genes, recombination rates, mRNA folding, and tRNA abundance and interactions are some factors influencing codon bias. The factors shaping codon bias may also be involved in evolution of the universal genetic code. Codon-usage bias is critical factor determining gene expression and cellular function by influencing diverse processes such as RNA processing, protein translation and protein folding. Codon usage bias reflects the origin, mutation patterns and evolution of the species or genes. Investigations of codon bias patterns in genomes can reveal phylogenetic relationships between organisms, horizontal gene transfers, molecular evolution of genes and identify selective forces that drive their evolution. Most important application of codon bias analysis is in the design of transgenes, to increase gene expression levels through codon optimization, for development of transgenic crops. The review gives an overview of deviations of genetic code, factors influencing codon usage or bias, codon usage bias of nuclear and organellar genes, computational methods to determine codon usage and the significance as well as applications of codon usage analysis in biological research, with emphasis on plants.

Background The genus Caragana , known for its adaptability and high forage value, is commonly planted to rehabilitate barren land and prevent desertification. Several Caragana species are also used for medicinal purposes. Analysis of synonymous codon usage bias and their primary influencing factors in chloroplast genomes aims to provide insights into molecular research and germplasm innovation for Caragana plants. Results The GC content of the five Caragana species ranged from 36.00% to 37.10%, showing a preference for codons ending in A/U, although the codon bias was weak. The screening identified nine to twelve optimal codons, but their frequency of use was low. Correlation analysis, neutrality plots, ENC plots and PR2 plots of the parameters identified two potential groups among the five species: Caragana arborescens and Caragana jubata , and Caragana turkestanica , Caragana opulens and Caragana tibetica . These groups showed a high level of intragroup similarity in the parameter analyses. In the RSCU cluster tree analysis, Caragana turkestanica and Caragana arborescens grouped together, while Caragana tibetica , Caragana jubata and Caragana opulens formed a separate clade in the CDS sequence and complete sequence phylogenetic tree analysis. Conclusions The codon usage bias in the chloroplast genomes of the five Caragana species showed high similarity, suggesting that natural selection has a greater influence on codon bias than mutation. Furthermore, the identified optimal codons provide valuable insights for germplasm improvement of Caragana plants.

Abstract We present the Codon Statistics Database, an online database that contains codon usage statistics for all the species with reference or representative genomes in RefSeq (over 15,000). The user can search for any species and access two sets of tables. One set lists, for each codon, the frequency, the Relative Synonymous Codon Usage, and whether the codon is preferred. Another set of tables lists, for each gene, its GC content, Effective Number of Codons, Codon Adaptation Index, and frequency of optimal codons. Equivalent tables can be accessed for (1) all nuclear genes, (2) nuclear genes encoding ribosomal proteins, (3) mitochondrial genes, and (4) chloroplast genes (if available in the relevant assembly). The user can also search for any taxonomic group (e.g., “primates”) and obtain a table comparing all the species in the group. The database is free to access without registration at http://codonstatsdb.unr.edu.

Background The degeneracy of the genetic code is increasingly recognized for roles in regulating translation rate, protein folding, and cell response. However, the functional genomics of codon usage patterns remains poorly defined. We previously showed that prokaryotic and eukaryotic cells respond to individual stresses by uniquely reprogramming the tRNA pool and the dozens of tRNA modifications comprising the tRNA epitranscriptome to cause selective translation of mRNAs from codon-biased stress response genes. Here, we tested the hypothesis that functional gene families have distinct values of codon bias in the Saccharomyces cerevisiae genome by modeling isoacceptor codon distributions using a new approach—analysis of synonymous codon signatures (ASCS). Results Application of ASCS to the S. cerevisiae genome revealed linear relationships between patterns of codon bias and gene function using canonical correlation analysis. By mapping codon-biased open reading frames (ORFs) onto a functional network of gene ontology (GO) categories, we identified 91 gene families distinguished by unique codon usage signatures. The codon usage patterns were found to strongly predict functional clusters of genes, such as translational machinery, transcription, and metabolic processes. Conclusions The ASCS-derived model of codon usage patterns in S. cerevisiae reveals functional codon bias signatures and captures more biologically meaningful information when compared to other codon analytical approaches.

SignificanceNonsense mutations account for approximately 11% of all described gene lesions causing human inherited diseases. This premature termination codon (PTC) leads to the premature arrest of translation that generates a truncated peptide and the degradation of the corresponding mRNA through the nonsense-mediated mRNA decay (NMD) pathway. The possibility of restoring the protein expression by promoting PTC readthrough using drugs appears to be an important therapeutic strategy. Unfortunately, this strategy is limited by the small number of molecules known to promote PTC readthrough without affecting normal translation termination. In this work, we identify a new molecule, TLN468, that promotes a high level of PTC readthrough without a detectable effect on normal stop codons. Premature termination codons (PTCs) account for 10 to 20% of genetic diseases in humans. The gene inactivation resulting from PTCs can be counteracted by the use of drugs stimulating PTC readthrough, thereby restoring production of the full-length protein. However, a greater chemical variety of readthrough inducers is required to broaden the medical applications of this therapeutic strategy. In this study, we developed a reporter cell line and performed high-throughput screening (HTS) to identify potential readthrough inducers. After three successive assays, we isolated 2-guanidino-quinazoline (TLN468). We assessed the clinical potential of this drug as a potent readthrough inducer on the 40 PTCs most frequently responsible for Duchenne muscular dystrophy (DMD). We found that TLN468 was more efficient than gentamicin, and acted on a broader range of sequences, without inducing the readthrough of normal stop codons (TC).

Abstract Bifunctional stop codons that have both translation and termination functions in the same species are important for understanding the evolution and function of genetic codes in living organisms. Considering the high frequency of bifunctional codons but limited number of available genomes in ciliates, we de novo sequenced seven representative ciliate genomes to explore the evolutionary history of stop codons. We further propose a stop codon reassignment quantification method (stopCR) that can identify bifunctional codons and measure their frequencies in various eukaryotic organisms. Using our newly developed method, we found two previously undescribed genetic codes, illustrating the prevalence of bifunctional stop codons in ciliates. Overall, evolutionary genomic analyses suggest that gain or loss of reassigned stop codons in ciliates is shaped by their living environment, the eukaryotic release factor 1, and suppressor tRNAs. This study provides novel clues about the functional diversity and evolutionary history of stop codons in eukaryotic organisms.

Gene therapy is a potentially curative medicine for many currently untreatable diseases, and recombinant adeno-associated virus (rAAV) is the most successful gene delivery vehicle for in vivo applications 1 – 3 . However, rAAV-based gene therapy suffers from several limitations, such as constrained DNA cargo size and toxicities caused by non-physiological expression of a transgene 4 – 6 . Here we show that rAAV delivery of a suppressor tRNA (rAAV.sup-tRNA) safely and efficiently rescued a genetic disease in a mouse model carrying a nonsense mutation, and effects lasted for more than 6 months after a single treatment. Mechanistically, this was achieved through a synergistic effect of premature stop codon readthrough and inhibition of nonsense-mediated mRNA decay. rAAV.sup-tRNA had a limited effect on global readthrough at normal stop codons and did not perturb endogenous tRNA homeostasis, as determined by ribosome profiling and tRNA sequencing, respectively. By optimizing the AAV capsid and the route of administration, therapeutic efficacy in various target tissues was achieved, including liver, heart, skeletal muscle and brain. This study demonstrates the feasibility of developing a toolbox of AAV-delivered nonsense suppressor tRNAs operating on premature termination codons (AAV-NoSTOP) to rescue pathogenic nonsense mutations and restore gene function under endogenous regulation. As nonsense mutations account for 11% of pathogenic mutations, AAV-NoSTOP can benefit a large number of patients. AAV-NoSTOP obviates the need to deliver a full-length protein-coding gene that may exceed the rAAV packaging limit, elicit adverse immune responses or cause transgene-related toxicities. It therefore represents a valuable addition to gene therapeutics. The feasibility of adeno-associated-virus-delivered nonsense suppressor tRNAs operating on premature termination codons (AAV-NoSTOP) is explored to restore gene function, using a mouse model of mucopolysaccharidosis type I for proof of concept.

Rutaceae family comprises economically important plants due to their extensive applications in spices, food, oil, medicine, etc. The Rutaceae plants is able to better utilization through biotechnology. Modern biotechnological approaches primarily rely on the heterologous expression of functional proteins in different vectors. However, several proteins are difficult to express outside their native environment. The expression potential of functional genes in heterologous systems can be maximized by replacing the rare synonymous codons in the vector with preferred optimal codons of functional genes. Codon usage bias plays a critical role in biogenetic engineering-based research and development. In the current study, 727 coding sequences (CDSs) obtained from the chloroplast genomes of ten Rutaceae plant family members were analyzed for codon usage bias. The nucleotide composition analysis of codons showed that these codons were rich in A/T(U) bases and preferred A/T(U) endings. Analyses of neutrality plots, effective number of codons (ENC) plots, and correlations between ENC and codon adaptation index (CAI) were conducted, which revealed that natural selection is a major driving force for the Rutaceae plant family’s codon usage bias, followed by base mutation. In the ENC vs. CAI plot, codon usage bias in the Rutaceae family had a negligible relationship with gene expression level. For each sample, we screened 12 codons as preferred and high-frequency codons simultaneously, of which GCU encoding Ala, UUA encoding Leu, and AGA encoding Arg were the most preferred codons. Taken together, our study unraveled the synonymous codon usage pattern in the Rutaceae family, providing valuable information for the genetic engineering of Rutaceae plant species in the future.

Most amino acids can be encoded by several synonymous codons, which are used at unequal frequencies. The significance of unequal codon usage remains unclear. One hypothesis is that frequent codons are translated relatively rapidly. However, there is little direct, in vivo, evidence regarding codon-specific translation rates. In this study, we generate high-coverage data using ribosome profiling in yeast, analyze using a novel algorithm, and deduce events at the A- and P-sites of the ribosome. Different codons are decoded at different rates in the A-site. In general, frequent codons are decoded more quickly than rare codons, and AT-rich codons are decoded more quickly than GC-rich codons. At the P-site, proline is slow in forming peptide bonds. We also apply our algorithm to short footprints from a different conformation of the ribosome and find strong amino acid-specific (not codon-specific) effects that may reflect interactions with the exit tunnel of the ribosome. Genes contain the instructions for making proteins from molecules called amino acids. These instructions are encoded in the order of the four building blocks that make up DNA, which are symbolized by the letters A, T, C, and G. The DNA of a gene is first copied to make a molecule of RNA, and then the letters in the RNA are read in groups of three (called ‘codons’) by a cellular machine called a ribosome. ‘Sense codons’ each specify one amino acid, and the ribosome decodes hundreds or thousands of these codons into a chain of amino acids to form a protein. ‘Stop codons’ do not encode amino acids but instead instruct the ribosome to stop building a protein when the chain is completed. Most proteins are built from 20 different kinds of amino acid, but there are 61 sense codons. As such, up to six codons can code for the same amino acid. The multiple codons for a single amino acid, however, are not used equally in gene sequences—some are used much more often than others. Now, Gardin, Yeasmin et al. have instantly halted the on-going processes of decoding genes and building proteins in yeast cells. Codons being translated into amino acids are trapped inside the ribosome; and codons that take the longest to decode are trapped most often. By using a computer algorithm, Gardin, Yeasmin et al. were able to measure just how often each kind of sense codon was trapped inside the ribosome and use this as a measure of how quickly each codon is decoded. The more often a given codon is used in a gene sequence, the less likely it was found to be trapped inside the ribosome—which suggests that these codons are decoded quicker than other codons and pass through the ribosome more quickly. Put another way, it appears that genes tend to use the codons that can be read the fastest. Certain properties of a codon also affected its decoding speed. Codons with more As and Ts, for example, are decoded faster than codons with more Cs and Gs. Furthermore, whenever a chemically unusual amino acid called proline has to be added to a new protein chain, it slowed down the speed at which the protein was built. The method described by Gardin, Yeasmin et al. for peering into a decoding ribosome may now help future studies that aim to answer other questions about how proteins are built.

Trypanosomatids primarily regulate protein expression at the posttranscriptional level, with codon bias playing a crucial role in controlling protein production across all life forms. This study investigated how codon usage, tRNA abundance, and codon pairing modes influence protein production in T. cruzi . Through tRNA sequencing and the integration of epigenomic and translatome data, we discovered that infective and noninfective forms of T. cruzi exhibit similar codon usage and tRNA pool preferences, despite having different proteomes. We developed pipelines applicable to any organism to measure codon adaptation to tRNA pools and pairing modes. Our analysis revealed that highly expressed genes are better aligned with more abundant tRNAs and favor Watson-Crick or inosine pairing. These findings suggest an additional layer of gene regulation based on tRNA availability and pairing modes, which impacts protein expression in the different life forms of T. cruzi .