In silico prediction of mtDNA Gene Expression Based on Codon Usage Bias in Ants (Formicidae Latreille, 1802) that Inhabit Limestone Quarry Ecosystems
Author(s) -
Semir Dorić,
Dinko Osmanković,
Lada LukićBilela
Publication year - 2018
Publication title -
genetics and applications
Language(s) - English
Resource type - Journals
eISSN - 2566-431X
pISSN - 2566-2937
DOI - 10.31383/ga.vol2iss1pp32-37
Subject(s) - codon usage bias , biology , gene , gene expression , in silico , genetics , mitochondrial dna , transfer rna , genome , coding region , computational biology , transcriptome , evolutionary biology , rna
Codon usage is considered as a modulator of gene expression, due to high correlation between codon usage, tRNA abundance and the level of gene expression. Adaptability is primarily manifested at gene level therefore mtDNA gene expression analysis may indicate trends toward the development of adaptive traits for specific environmental conditions. Moreover, modified gene expression patterns may result from such adaptations. Due to their sensitivity to environmental disturbances, great functional importance and accessibility ants (Family: Formicidae Latreille, 1802) are excellent model organisms for molecular and bioinformatics genome analysis. This in silico simulation is based on the comparison of codon usage bias and the level of gene expression of currently available mitochondrial protein-coding genes of ant species that were sampled at quarry Ribnica (Kakanj, Bosnia and Herzegovina). MILC and MELP algorithms were used forcodon usage bias analysis and mitochondrial gene expression prediction, respectively. The analysis included four mtDNA protein-coding genes from eight selected species of ants totaling in 32 protein sequences. The results of codon usage analysis indicated no statistically significant differences in codon usage bias, as well as relative frequencies of the gene expression level. The next step should be directed to molecular ecology studies, even using whole genome measures of gene expression (RNA-seq; transcriptomics) to capture molecular response to environmental challenges.
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