In silico approach to identify microsatellite candidate biomarkers to differentiate the biovar of Corynebacterium pseudotuberculosis genomes

Abstract

Corynebacterium pseudotuberculosis is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of C. pseudotuberculosis. The similarities associated with SSRs amongst the different serum variants of C. pseudotuberculosis, biovars equi and ovis, were also evaluated, and it was possible to identify SSRs located in coding regions responsible for a VF enrolled in pathogenesis known to mediate bacterial adherence (SpaH-type pili virulence factor). Phylogenetic analyses revealed that strains sharing SSR patterns, including the possible polymorphisms identified in the same position of gene-coding regions, were displayed by strains with a common ancestor, corroborating with the Genome Tree Report of the NCBI. Statistical analysis showed that the microsatellite

EasySSR: a user-friendly web application with full command-line features for large-scale batch microsatellite mining and samples comparison

“…The second dataset had a heterogeneous set of complete prokaryote genomes, eukaryotic chromosomes, and a human gene and was used for benchmark EasySSR performance while running interspecific analysis for imperfect SSRs, using both FASTA and GenBank files as input. ii) Lastly, to demonstrate EasySSR capacity to process large datasets of complete genomes, the program was executed with a dataset validated by Pinheiro et al (2022) , for batch comparison of 54 whole genomes of Corynebacterium pseudotuberculosis , running interspecific analysis for perfect SSRs, using both FASTA and GenBank files as input.…”

“…Differently from the benchmark tests, this comparison aimed to demonstrate the capacity of EasySSR to handle large datasets while being a versatile shortcut for online data analysis. For this, 54 complete genomes of C. pseudotuberculosis (CP) were selected, which have been previously studied by Pinheiro et al (2022) , who also used IMEx 2.1 as the microsatellite mining tool. The sequences were obtained at NCBI through the accession numbers stated in Table 4 , with the same version as the ones stated in the original article by Pinheiro et al (2022) , and downloaded as FASTA and GenBank annotation files.…”

“…Microsatellites, also known as Simple Sequence Repeats (SSRs) or Short Tandem Repeats (STRs), are polymorphic DNA regions with tandem repetitions of a nucleotide motif ranging 1–6 bp, also called mononucleotide, di-, tri-, tetra-, penta-, and hexanucleotide repeats ( Pinheiro et al, 2022 ). They can be categorized into perfect, imperfect, and compound and are found in both coding and non-coding regions in eukaryotes, prokaryotes, and viruses ( Mudunuri and Nagarajaram, 2007 ; Beier et al, 2017 ).…”

“…They can be categorized into perfect, imperfect, and compound and are found in both coding and non-coding regions in eukaryotes, prokaryotes, and viruses ( Mudunuri and Nagarajaram, 2007 ; Beier et al, 2017 ). The SSRs have various clinical implications and a broad range of applications in many fields, such as conservation and evolutionary studies, comparative genomics, molecular biology, biotechnology, oncology, and forensics ( Laskar et al, 2022 ; Pinheiro et al, 2022 ).…”

“…In this way, many scientists end up choosing to use command-line tools for full functionality and spend a considerable amount of time learning how to install and execute the software, in addition to performing post-processing data tabulation on other tools or manually, instead of focusing more time on data analysis; thus, there is a need for a web application that can be an easy tool for online analysis that can do the same as command-line tools, filling in the gaps of other software without sacrificing the full-fledged and accurate results already obtained ( Oliveira et al, 2008 ; Pinheiro et al, 2022 ).…”