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.

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.

Heat shock stress: Profile of differential expression in Corynebacterium pseudotuberculosis biovar Equi.

Transcriptome studies on Corynebacterium pseudotuberculosis have recently contributed to the understanding about this microorganism's survival mechanisms in various hostile conditions. The gene expression profile of the C. pseudotuberculosis strain 1002 (Ovis biovar), has revealed genes that are possible candidates responsible for its maintenance in adverse environments, such as those found in the host. In another strain of this bacterium, 258 (Equi biovar), a high temperature condition was simulated, in order to verify which genes are responsible for promoting the persistence of the bacterium in these conditions, since it tolerates temperatures higher than 40°C, despite being a mesophilic bacterium. It was possible to generate a list of genes using RNAseq technology that possibly contribute to the survival of the bacteria in this hostile environment. A total of 562 genes were considered as differentially expressed, then, after the fold-change cutoff, 113 were considered induced and 114 repressed, resulting in a total of 227 genes. Therefore, hypothetical proteins presented a fold change above 6, and genes characteristically in control for this type of stress, such as hspR, grpE, and dnaK, presented a fold change above 3. The clpB gene, a chaperone, drew attention due to presenting a fold change above 3 and located in a pathogenicity island. These genes may contribute towards efficient solutions to the effects caused by ulcerative lymphangitis in equines, thus attenuating the damage it causes to agribusiness.