Comparative genomic analysis of the Dietzia genus: an insight into genomic diversity, and adaptation.

Dietzia strains are widely distributed in the environment, presenting an opportunistic role, and some species have undetermined taxonomic characteristics. Here, we propose the existence of errors in the classification of species in this genus using comparative genomics. We performed ANI, dDDH, pangenome and genomic plasticity analyses better to elucidate the phylogenomic relationships between Dietzia strains. For this, we used 55 genomes of Dietzia downloaded from public databases that were combined with a newly sequenced. Sequence analysis of a phylogenetic tree based on genome similarity comparisons and dDDH, ANI analyses supported grouping different Dietzia species into four distinct groups. The pangenome analysis corroborated the classification of these groups, supporting the idea that some species of Dietzia could be reassigned in a possible classification into three distinct species, each containing less variability than that found within the global pangenome of all strains. Additionally, analysis of genomic plasticity based on groups containing Dietzia strains found differences in the presence and absence of symbiotic Islands and pathogenic islands related to their isolation site. We propose that the comparison of pangenome subsets together with phylogenomic approaches can be used as an alternative for the classification and differentiation of new species of the genus Dietzia.

In Silico Designed Multi-Epitope Immunogen "Tpme-VAC/LGCM-2022" May Induce Both Cellular and Humoral Immunity against Treponema pallidum Infection.

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum, has seen a resurgence over the past years. T. pallidum is capable of early dissemination and immune evasion, and the disease continues to be a global healthcare burden. The purpose of this study was to design a multi-epitope immunogen through an immunoinformatics-based approach. Multi-epitope immunogens constitute carefully selected epitopes belonging to conserved and essential bacterial proteins. Several physico-chemical characteristics, such as antigenicity, allergenicity, and stability, were determined. Further, molecular docking and dynamics simulations were performed, ensuring binding affinity and stability between the immunogen and TLR-2. An in silico cloning was performed using the pET-28a(+) vector and codon adaptation for E. coli. Finally, an in silico immune simulation was performed. The in silico predictions obtained in this work indicate that this construct would be capable of inducing the requisite immune response to elicit protection against T. pallidum. Through this methodology we have designed a promising potential vaccine candidate for syphilis, namely Tpme-VAC/LGCM-2022. However, it is necessary to validate these findings in in vitro and in vivo assays.

Exploring the MALDI Biotyper for the Identification of Corynebacterium pseudotuberculosis biovar Ovis and Equi.

Biochemical, serological, and molecular methods have been developed for the laboratory diagnosis of diseases caused by C. pseudotuberculosis (CP), but the identification of the pathogen and biovars differentiation may be time-consuming, expensive, and confusing compared with other bacteria. This study aimed to evaluate MALDI Biotyper and Overall Genome Relatedness Index (OGRI) analysis to optimize the identification and differentiation of biovars of C. pseudotuberculosis. Out of 230 strains isolated from several hosts and countries, 202 (87.8%) were precisely classified using MALDI Biotyper and the BioNumerics platform. The classification accuracies for the Ovis and Equi biovars were 80 (88.75%) and 82 (92.68%), respectively. When analyzing a sampling of these strains by Average Nucleotide Identity based on BLAST and TETRA analyses using genomic sequence data, it was possible to differentiate 100% of the strains in Equi and Ovis. Our data show that MALDI Biotyper and OGRI analysis help identify C. pseudotuberculosis at the species and biovar levels.

Pan-Resistome Insights into the Multidrug Resistance of Acinetobacter baumannii.

Acinetobacter baumannii is an important Gram-negative opportunistic pathogen that is responsible for many nosocomial infections. This etiologic agent has acquired, over the years, multiple mechanisms of resistance to a wide range of antimicrobials and the ability to survive in different environments. In this context, our study aims to elucidate the resistome from the A. baumannii strains based on phylogenetic, phylogenomic, and comparative genomics analyses. In silico analysis of the complete genomes of A. baumannii strains was carried out to identify genes involved in the resistance mechanisms and the phylogenetic relationships and grouping of the strains based on the sequence type. The presence of genomic islands containing most of the resistance gene repertoire indicated high genomic plasticity, which probably enabled the acquisition of resistance genes and the formation of a robust resistome. A. baumannii displayed an open pan-genome and revealed a still constant genetic permutation among their strains. Furthermore, the resistance genes suggest a specific profile within the species throughout its evolutionary history. Moreover, the current study performed screening and characterization of the main genes present in the resistome, which can be used in applied research to develop new therapeutic methods to control this important bacterial pathogen.

From In-Person to the Online World: Insights Into Organizing Events in Bioinformatics.

Bioinformatics is a fast-evolving research field, requiring effective educational initiatives to bring computational knowledge to Life Sciences. Since 2017, an organizing committee composed of graduate students and postdoctoral researchers from the Universidade Federal de Minas Gerais (Brazil) promotes a week-long event named Summer Course in Bioinformatics (CVBioinfo). This event aims to diffuse bioinformatic principles, news, and methods mainly focused on audiences of undergraduate students. Furthermore, as the advent of the COVID-19 global pandemic has precluded in-person events, we offered the event in online mode, using free video transmission platforms. Herein, we present and discuss the insights obtained from promoting the Online Workshop in Bioinformatics (WOB) organized in November 2020, comparing it to our experience in previous in-person editions of the same event.

Complete genome analysis of Glutamicibacter creatinolyticus from mare abscess and comparative genomics provide insight of diversity and adaptation for Glutamicibacter.

Bacteria of the genusGlutamicibacterare considered ubiquitous because they can be found in soil, water and air. They have already been isolated from different habitats, including different types of soil, clinical samples, cheese and plants. Glutamicibacter creatinolyticus is a Gram-positive bacterium important to various biotechnological processes, however, as a pathogen it is associated to urinary tract infections and bacteremia. Recently,Glutamicibacter creatinolyticusLGCM 259 was isolated from a mare, which displayed several diffuse subcutaneous nodules with heavy vascularization. In this study, sequencing, genomic analysis ofG. creatinolyticusLGCM 259 and comparative analyseswere performedamong 4representatives of different members of genusfromdifferent habitats, available in the NCBI database. The LGCM 259 strain's genome carries important factors of bacterial virulence that are essential in cell viability, virulence, and pathogenicity. Genomic islands were predicted for 4 members of genusGlutamicibacter,showing ahigh number of GEIs,which may reflect a high interspecific diversity and a possible adaptive mechanism responsible for the survival of each species in its specific niche. Furthermore,G. creatinolyticusLGCM 259 sharessyntenicregions, albeit with a considerable loss of genes, in relation to the other species. In addition,G. creatinolyticusLGCM 259 presentsresistancegenes to 6 differentclasses ofantibiotics and heavy metals, such as: copper, arsenic, chromium and cobalt-zinc-cadmium.Comparative genomicsanalysescouldcontribute to the identification of mobile genetic elements particular to the speciesG. creatinolyticuscompared to other members of genus. The presence of specific regions inG. creatinolyticuscould be indicative of their rolesin host adaptation, virulence, and the characterization ofastrain that affects animals.

Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression.

Methods based around statistics and linear algebra have been increasingly used in attempts to address emerging questions in microarray literature. Microarray technology is a long-used tool in the global analysis of gene expression, allowing for the simultaneous investigation of hundreds or thousands of genes in a sample. It is characterized by a low sample size and a large feature number created a non-square matrix, and by the incomplete rank, that can generate countless more solution in classifiers. To avoid the problem of the 'curse of dimensionality' many authors have performed feature selection or reduced the size of data matrix. In this work, we introduce a new logistic regression-based model to classify breast cancer tumor samples based on microarray expression data, including all features of gene expression and without reducing the microarray data matrix. If the user still deems it necessary to perform feature reduction, it can be done after the application of the methodology, still maintaining a good classification. This methodology allowed the correct classification of breast cancer sample data sets from Gene Expression Omnibus (GEO) data series GSE65194, GSE20711, and GSE25055, which contain the microarray data of said breast cancer samples. Classification had a minimum performance of 80% (sensitivity and specificity), and explored all possible data combinations, including breast cancer subtypes. This methodology highlighted genes not yet studied in breast cancer, some of which have been observed in Gene Regulatory Networks (GRNs). In this work we examine the patterns and features of a GRN composed of transcription factors (TFs) in MCF-7 breast cancer cell lines, providing valuable information regarding breast cancer. In particular, some genes whose αi ∗ associated parameter values revealed extreme positive and negative values, and, as such, can be identified as breast cancer prediction genes. We indicate that the PKN2, MKL1, MED23, CUL5 and GLI genes demonstrate a tumor suppressor profile, and that the MTR, ITGA2B, TELO2, MRPL9, MTTL1, WIPI1, KLHL20, PI4KB, FOLR1 and SHC1 genes demonstrate an oncogenic profile. We propose that these may serve as potential breast cancer prediction genes, and should be prioritized for further clinical studies on breast cancer. This new model allows for the assignment of values to the αi ∗ parameters associated with gene expression. It was noted that some αi ∗ parameters are associated with genes previously described as breast cancer biomarkers, as well as other genes not yet studied in relation to this disease.