Selection on synonymous codons in mammalian rhodopsins: a possible role in optimizing translational processes

Next-generation development and application of codon model in evolution

To date, numerous nucleotide, amino acid, and codon substitution models have been developed to estimate the evolutionary history of any sequence/organism in a more comprehensive way. Out of these three, the codon substitution model is the most powerful. These models have been utilized extensively to detect selective pressure on a protein, codon usage bias, ancestral reconstruction and phylogenetic reconstruction. However, due to more computational demanding, in comparison to nucleotide and amino acid substitution models, only a few studies have employed the codon substitution model to understand the heterogeneity of the evolutionary process in a genome-scale analysis. Hence, there is always a question of how to develop more robust but less computationally demanding codon substitution models to get more accurate results. In this review article, the authors attempted to understand the basis of the development of different types of codon-substitution models and how this information can be utilized to develop more robust but less computationally demanding codon substitution models. The codon substitution model enables to detect selection regime under which any gene or gene region is evolving, codon usage bias in any organism or tissue-specific region and phylogenetic relationship between different lineages more accurately than nucleotide and amino acid substitution models. Thus, in the near future, these codon models can be utilized in the field of conservation, breeding and medicine.

Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril

Most molecular evolutionary studies of natural selection maintain the decades-old assumption that synonymous substitution rate variation (SRV) across sites within genes occurs at levels that are either nonexistent or negligible. However, numerous studies challenge this assumption from a biological perspective and show that SRV is comparable in magnitude to that of nonsynonymous substitution rate variation. We evaluated the impact of this assumption on methods for inferring selection at the molecular level by incorporating SRV into an existing method (BUSTED) for detecting signatures of episodic diversifying selection in genes. Using simulated data we found that failing to account for even moderate levels of SRV in selection testing is likely to produce intolerably high false positive rates. To evaluate the effect of the SRV assumption on actual inferences we compared results of tests with and without the assumption in an empirical analysis of over 13,000 Euteleostomi (bony vertebrate) gene alignments from the Selectome database. This exercise reveals that close to 50% of positive results (i.e., evidence for selection) in empirical analyses disappear when SRV is modeled as part of the statistical analysis and are thus candidates for being false positives. The results from this work add to a growing literature establishing that tests of selection are much more sensitive to certain model assumptions than previously believed.

Insights into the nucleotide composition and codon usage pattern of human tumor suppressor genes

Tumor suppressor genes encode different proteins that inhibit the uncontrolled proliferation of cell growth and tumor development. To acquire clues for predicting gene expression level, it is essential to understand the codon usage bias (CUB) of genes to characterize genome which possesses its own compositional characteristics and unique coding sequences. We used bioinformatic tools to analyze the codon usage patterns of 637 human tumor suppressor genes as no work was reported earlier. The mean effective number of codons of these genes was 48, indicating low CUB. Our results exhibited a significant positive correlation among different nucleotide compositions and the codons ending with C base was most frequently used along with the most over-represented codon CTG and GTG codifying leucine and valine amino acid, respectively, in human tumor suppressor genes. The neutrality plot showed a significant positive correlation (Pearson, r = 0. 646; P < .01) suggesting that mutation on GC bias might affect the CUB. However, the linear regression coefficient of GC₁₂ on GC₃ in human tumor suppressor genes suggested that natural selection played a major role while mutation pressure played a minor role in the codon usage patterns of tumor suppressor genes in human. Our study would throw light into the factors that affect CUB and the codon usage patterns in the human tumor suppressor genes.

Codon Usage Differences among Genes Expressed in Different Tissues of Drosophila melanogaster

Codon usage patterns are affected by both mutational biases and translational selection. The frequency at which each codon is used in the genome is directly linked to the cellular concentrations of their corresponding tRNAs. Transfer RNA abundances—as well as the abundances of other potentially relevant factors, such as RNA-binding proteins—may vary across different tissues, making it possible that genes expressed in different tissues are subject to different translational selection regimes, and thus differ in their patterns of codon usage. These differences, however, are poorly understood, having been studied only in Arabidopsis, rice and human, with controversial results in human. Drosophila melanogaster is a suitable model organism to study tissue-specific codon adaptation given its large effective population size. Here, we compare 2,046 genes, each expressed specifically in one tissue of D. melanogaster. We show that genes expressed in different tissues exhibit significant differences in their patterns of codon usage, and that these differences are only partially due to differences in GC content, expression levels, or protein lengths. Remarkably, these differences are stronger when analyses are restricted to highly expressed genes. Our results strongly suggest that genes expressed in different tissues are subject to different regimes of translational selection.

Dissimilar substitution rates between two strands of DNA influence codon usage pattern in some human genes

We illustrated the descriptive aspects of codon usage of some important human genes and their expression potential in E. coli. By comparing the results of various codon usage parameters, effects that are due to selection and mutational pressures have been deciphered. The variation in GC3s explains a significant proportion of the variation in codon usage patterns. The codons CGC, CGG, CTG and GCG showed strong positive correlation with GC3, which suggested that codon usage had been influenced by GC bias. We also found that ACC (Thr, RSCU-1.77), GCC (Ala, RSCU-1.67), CCC (Pro, RSCU-1.54), TCC (Ser, RSCU-1.47) were frequently used which signified that C was common at 2nd and 3rd codon positions. Correspondence analysis revealed that F1 axis had significant correlation with various GC contents suggesting that compositional properties under mutation pressure might affect codon usage bias. Nc-GC3 plot analysis suggested that both mutation pressure and natural selection might affect the codon usage bias which is also supported by neutrality plot analysis. The dinucleotide CT, TG and AG were significantly over-represented and CG, TA, AT, TT, and GT were underrepresented due to high rate of spontaneous mutation resulting from cytosine deamination.

Rescue of mutant rhodopsin traffic by metformin-induced AMPK activation accelerates photoreceptor degeneration

Protein misfolding caused by inherited mutations leads to loss of protein function and potentially toxic 'gain of function', such as the dominant P23H rhodopsin mutation that causes retinitis pigmentosa (RP). Here, we tested whether the AMPK activator metformin could affect the P23H rhodopsin synthesis and folding. In cell models, metformin treatment improved P23H rhodopsin folding and traffic. In animal models of P23H RP, metformin treatment successfully enhanced P23H traffic to the rod outer segment, but this led to reduced photoreceptor function and increased photoreceptor cell death. The metformin-rescued P23H rhodopsin was still intrinsically unstable and led to increased structural instability of the rod outer segments. These data suggest that improving the traffic of misfolding rhodopsin mutants is unlikely to be a practical therapy, because of their intrinsic instability and long half-life in the outer segment, but also highlights the potential of altering translation through AMPK to improve protein function in other protein misfolding diseases.

Identification of possible siRNA molecules for TDP43 mutants causing amyotrophic lateral sclerosis: In silico design and molecular dynamics study

The DNA binding protein, TDP43 is a major protein involved in amyotrophic lateral sclerosis and other neurological disorders such as frontotemporal dementia, Alzheimer disease, etc. In the present study, we have designed possible siRNAs for the glycine rich region of tardbp mutants causing ALS disorder based on a systematic theoretical approach including (i) identification of respective codons for all mutants (reported at the protein level) based on both minimum free energy and probabilistic approaches, (ii) rational design of siRNA, (iii) secondary structure analysis for the target accessibility of siRNA, (iii) determination of the ability of siRNA to interact with mRNA and the formation/stability of duplex via molecular dynamics study for a period of 15ns and (iv) characterization of mRNA-siRNA duplex stability based on thermo-physical analysis. The stable GC-rich siRNA expressed strong binding affinity towards mRNA and forms stable duplex in A-form. The linear dependence between the thermo-physical parameters such as Tm, GC content and binding free energy revealed the ability of the identified siRNAs to interact with mRNA in comparable to that of the experimentally reported siRNAs. Hence, this present study proposes few siRNAs as the possible gene silencing agents in RNAi therapy based on the in silico approach.

Comparative sequence analyses of rhodopsin and RPE65 reveal patterns of selective constraint across hereditary retinal disease mutations

Retinitis pigmentosa (RP) comprises several heritable diseases that involve photoreceptor, and ultimately retinal, degeneration. Currently, mutations in over 50 genes have known links to RP. Despite advances in clinical characterization, molecular characterization of RP remains challenging due to the heterogeneous nature of causal genes, mutations, and clinical phenotypes. In this study, we compiled large datasets of two important visual genes associated with RP: rhodopsin, which initiates the phototransduction cascade, and the retinoid isomerase RPE65, which regenerates the visual cycle. We used a comparative evolutionary approach to investigate the relationship between interspecific sequence variation and pathogenic mutations that lead to degenerative retinal disease. Using codon-based likelihood methods, we estimated evolutionary rates (dN/dS) across both genes in a phylogenetic context to investigate differences between pathogenic and nonpathogenic amino acid sites. In both genes, disease-associated sites showed significantly lower evolutionary rates compared to nondisease sites, and were more likely to occur in functionally critical areas of the proteins. The nature of the dataset (e.g., vertebrate or mammalian sequences), as well as selection of pathogenic sites, affected the differences observed between pathogenic and nonpathogenic sites. Our results illustrate that these methods can serve as an intermediate step in understanding protein structure and function in a clinical context, particularly in predicting the relative pathogenicity (i.e., functional impact) of point mutations and their downstream phenotypic effects. Extensions of this approach may also contribute to current methods for predicting the deleterious effects of candidate mutations and to the identification of protein regions under strong constraint where we expect pathogenic mutations to occur.

Decoding mechanisms by which silent codon changes influence protein biogenesis and function

SCOPE

Synonymous codon usage has been a focus of investigation since the discovery of the genetic code and its redundancy. The occurrences of synonymous codons vary between species and within genes of the same genome, known as codon usage bias. Today, bioinformatics and experimental data allow us to compose a global view of the mechanisms by which the redundancy of the genetic code contributes to the complexity of biological systems from affecting survival in prokaryotes, to fine tuning the structure and function of proteins in higher eukaryotes. Studies analyzing the consequences of synonymous codon changes in different organisms have revealed that they impact nucleic acid stability, protein levels, structure and function without altering amino acid sequence. As such, synonymous mutations inevitably contribute to the pathogenesis of complex human diseases. Yet, fundamental questions remain unresolved regarding the impact of silent mutations in human disorders. In the present review we describe developments in this area concentrating on mechanisms by which synonymous mutations may affect protein function and human health.

PURPOSE

This synopsis illustrates the significance of synonymous mutations in disease pathogenesis. We review the different steps of gene expression affected by silent mutations, and assess the benefits and possible harmful effects of codon optimization applied in the development of therapeutic biologics.

PHYSIOLOGICAL AND MEDICAL RELEVANCE

Understanding mechanisms by which synonymous mutations contribute to complex diseases such as cancer, neurodegeneration and genetic disorders, including the limitations of codon-optimized biologics, provides insight concerning interpretation of silent variants and future molecular therapies.