RESUMO
Codon usage bias (CUB) has been described in viruses, prokaryotes, and eukaryotes and has been linked to several cellular and environmental factors, such as the organism's growth temperature, gene expression levels, and regulation of protein synthesis and folding. Most of the studies in this area have been conducted in bacteria and higher eukaryotes, in some cases with different results. In this study, a comparative analysis of CUB in yeasts isolated from cold and template environments was performed in order to evaluate the correlation of CUB with yeast optimal temperature of growth (OTG), gene expression levels, cellular function, and structure of encoded proteins. Among the main findings, highly expressed ORFs tend to have a more similar CUB within and between yeasts, and a direct correlation between codons ending in C and expression level was generally found. A low correspondence between CUB and OTG was observed, with an inverse correlation for some codons ending in C. The clustering of yeasts based on their CUB partially aligns with their OTG, being more consistent for yeasts with lower OTG. In most yeasts, the abundance of preferred codons was generally lower at the 5' end of ORFs, higher in segments encoding beta strand, lower in segments encoding extracellular and transmembrane regions, and higher in "translation" and "energy metabolism" pathways, especially in highly expressed ORFs. Based on our findings, it is suggested that the abundance and distribution of preferred and non-preferred codons along mRNAs contribute to proper protein folding and functionality by regulating protein synthesis rates, becoming a more important factor under conditions that require faster protein synthesis in yeasts.
RESUMO
Transfer RNAs (tRNAs) genes are both coded for and arranged along some viral genomes representing the entire virosphere and seem to play different biological functions during infection, other than transferring the correct amino acid to a growing peptide chain. Baculovirus genome description and annotation has focused mostly on protein-coding genes, microRNA, and homologous regions. Here we carried out a large-scale in silico search for putative tRNA genes in baculovirus genomes. Ninety-six of 257 baculovirus genomes analyzed was found to contain at least one putative tRNA gene. We found great diversity in primary and secondary structure, in location within the genome, in intron presence and size, and in anti-codon identity. In some cases, genes of tRNA-containing genomes were found to have a bias for the codons specified by the tRNAs present in such genomes. Moreover, analysis revealed that most of the putative tRNA genes possessed conserved motifs for tRNA type 2 promoters, including the A-box and B-box motifs with few mismatches from the eukaryotic canonical motifs. From publicly available small RNA deep sequencing datasets of baculovirus-infected insect cells, we found evidence that a putative Autographa californica multiple nucleopolyhedrovirus Gln-tRNA gene was transcribed and modified with the addition of the non-templated 3'-CCA tail found at the end of all tRNAs. Further research is needed to determine the expression and functionality of these viral tRNAs.
Assuntos
Baculoviridae , RNA de Transferência , Baculoviridae/genética , RNA de Transferência/genética , RNA de Transferência/química , Eucariotos/genética , Sequência de Bases , CódonRESUMO
A mainstream procedure to analyze the wealth of genomic data available nowadays is the detection of homologous regions shared across genomes, followed by the extraction of biological information from the patterns of conservation and variation observed in such regions. Although of pivotal importance, comparative genomic procedures that rely on homology inference are obviously not applicable if no homologous regions are detectable. This fact excludes a considerable portion of "genomic dark matter" with no significant similarity - and, consequently, no inferred homology to any other known sequence - from several downstream comparative genomic methods. In this review we compile several sequence metrics that do not rely on homology inference and can be used to compare nucleotide sequences and extract biologically meaningful information from them. These metrics comprise several compositional parameters calculated from sequence data alone, such as GC content, dinucleotide odds ratio, and several codon bias metrics. They also share other interesting properties, such as pervasiveness (patterns persist on smaller scales) and phylogenetic signal. We also cite examples where these homology-independent metrics have been successfully applied to support several bioinformatics challenges, such as taxonomic classification of biological sequences without homology inference. They where also used to detect higher-order patterns of interactions in biological systems, ranging from detecting coevolutionary trends between the genomes of viruses and their hosts to characterization of gene pools of entire microbial communities. We argue that, if correctly understood and applied, homology-independent metrics can add important layers of biological information in comparative genomic studies without prior homology inference.