Conventional actin appears to be ubiquitous in eukaryotes. The occurrence of actin in diverse animal groups is interesting and researchers are curious about their nature of existence. The genes that code for actin are defined as a gene family (Pont et al., 1983). This means that the genetic information of each individual contains instructions that generate actin variants (called isoforms) that possess slightly different functions. This, in turn, means that eukaryotic organisms express different genes that give rise to: α-actin, which is found in contractile structures; β-actin, found at the expanding edge of cells that use the projection of their cellular structures as their means of mobility; and γ-actin, which is found in the filaments of stress fibres (Scott et al., 2012). The evolutionary origin of actin can be traced which have equivalent proteins (Gunning et al., 2015). In addition to the similarities that exist between an organism’s isoforms there is also an evolutionary conservation in the structure and function even between organisms contained in different eukaryotic domains.
Materials and Methods
Sequence Retrieval and Taxon Sampling
Actin gene sequences (772256) of diverse animal species available in GenBank database were retrieved using entrez key word search and PERL script. The sequences were filter searched and around 90 sequences were selected referring to specific phyla. These gene sequences were considered for phylogenetic analysis and their corresponding RNA secondary structure were also predicted.
Multiple Sequence Alignment and Phylogenetic Analysis Based on Actin Gene
Multiple sequence alignment (MSA) was performed using CLUSTALW program with default settings. Phylogenetic trees were generated by two different methods (MP and ML) by MEGA 7 (Kumar et al., 2016). All characters were equally weighted and unordered. Alignment gaps were treated as missing data.
RNA Secondary Structure Prediction
The actin gene sequences were used to generate consensus RNA secondary structures using LocARNA version 1.5.2 (Smith et al., 2010). It performed multiple alignments and generated consensus secondary structures of each category using realistic energy model for RNAs.