Assessing the Genotypic Differences between Strains of Corynebacterium pseudotuberculosis biovar equi through Comparative Genomics.

Seven genomes of Corynebacterium pseudotuberculosis biovar equi were sequenced on the Ion Torrent PGM platform, generating high-quality scaffolds over 2.35 Mbp. This bacterium is the causative agent of disease known as "pigeon fever" which commonly affects horses worldwide. The pangenome of biovar equi was calculated and two phylogenomic approaches were used to identify clustering patterns within Corynebacterium genus. Furthermore, other comparative analyses were performed including the prediction of genomic islands and prophages, and SNP-based phylogeny. In the phylogenomic tree, C. pseudotuberculosis was divided into two distinct clades, one formed by nitrate non-reducing species (biovar ovis) and another formed by nitrate-reducing species (biovar equi). In the latter group, the strains isolated from California were more related to each other, while the strains CIP 52.97 and 1/06-A formed the outermost clade of the biovar equi. A total of 1,355 core genes were identified, corresponding to 42.5% of the pangenome. This pangenome has one of the smallest core genomes described in the literature, suggesting a high genetic variability of biovar equi of C. pseudotuberculosis. The analysis of the similarity between the resistance islands identified a higher proximity between the strains that caused more severe infectious conditions (infection in the internal organs). Pathogenicity islands were largely conserved between strains. Several genes that modulate the pathogenicity of C. pseudotuberculosis were described including peptidases, recombination enzymes, micoside synthesis enzymes, bacteriocins with antimicrobial activity and several others. Finally, no genotypic differences were observed between the strains that caused the three different types of infection (external abscess formation, infection with abscess formation in the internal organs, and ulcerative lymphangitis). Instead, it was noted that there is a higher phenetic correlation between strains isolated at California compared to the other strains. Additionally, high variability of resistance islands suggests gene acquisition through several events of horizontal gene transfer.

The impact of quality filter for RNA-Seq.

BACKGROUND: With the emergence of large-scale sequencing platforms since 2005, there has been a great revolution regarding methods for decoding DNA sequences, which have also affected quantitative and qualitative gene expression analyses through the RNA-Sequencing technique. However, issues related to the amount of data required for the analyses have been considered because they affect the reliability of the experiments. Thus, RNA depletion during sample preparation may influence the results. Moreover, because data produced by these platforms show variations in quality, quality filters are often used to remove sequences likely to contain errors to increase the accuracy of the results. However, when reads of quality filters are removed, the expression profile in RNA-Seq experiments may be influenced. RESULT: The present study aimed to analyze the impact of different quality filter values for Corynebacterium pseudotuberculosis (sequenced by SOLiD platform), Microcystis aeruginosa and Kineococcus radiotolerans (sequenced by Illumina platform) RNA-Seq data. Although up to 47.9% of the reads produced by the SOLiD technology were removed after the QV20 quality filter is applied, and 15.85% were removed from K. radiotolerans data set using the QV30 filter, Illumina data showed the largest number of unique differentially expressed genes after applying the most stringent filter (QV30), with 69 genes. In contrast, for SOLiD, the acid stress condition with the QV20 filter yielded only 41 unique differentially expressed genes. Even for the highest quality M. aeruginosa data, the quality filter affected the expression profile. The most stringent quality filter generated a greater number of unique differentially expressed genes: 9 for high molecular weight dissolved organic matter condition and 12 for low P conditions. CONCLUSION: Even high-accuracy sequencing technologies are subject to the influence of quality filters when evaluating RNA-Seq data using the reference approach.

Corynebacterium pseudotuberculosis RNA-seq data from abiotic stresses.

Corynebacterium pseudotuberculosis causes significant loss to goat and sheep farmers because it is the causal agent of the infectious disease caseous lymphadenitis, which may lead to outcomes ranging from skin injury to animal death (Ruiz et al., 2011) [1]. This bacterium was grown under osmotic (2 M), acid (pH) and heat (50 °C) stress and under control (Normal-BHI brain heart infusion) conditions, which simulate the conditions faced by the bacteria during the infectious process. Subsequently, cDNA of each condition was sequenced by the SOLiD3 Plus platform using the RNA-Seq technique [2], [3], [4]. The data produced was processed to evaluate the differential gene expression, which is helpful to understand the adaptation mechanisms during the infection in the host. The sequencing data of all conditions are available in the European Bioinformatics Institute (EBI) repository under accession number E-MTAB-2017.

Genome Sequence of Corynebacterium pseudotuberculosis MB20 bv. equi Isolated from a Pectoral Abscess of an Oldenburg Horse in California.

The genome of Corynebacterium pseudotuberculosis MB20 bv. equi was sequenced using the Ion Personal Genome Machine (PGM) platform, and showed a size of 2,363,089 bp, with 2,365 coding sequences and a GC content of 52.1%. These results will serve as a basis for further studies on the pathogenicity of C. pseudotuberculosis bv. equi.