Phenotypic and molecular characterization and complete genome sequence of a Corynebacterium diphtheriae strain isolated from cutaneous infection in an immunized individual.

Diphtheria is an infectious disease potentially fatal that constitutes a threat to global health security, with possible local and systemic manifestations that result mainly from the production of diphtheria toxin (DT). In the present work, we report a case of infection by Corynebacterium diphtheriae in a cutaneous lesion of a fully immunized individual and provided an analysis of the complete genome of the isolate. The clinical isolate was first identified by MALDI-TOF Mass Spectrometry. The commercial strip system and mPCR performed phenotypic and genotypic characterization, respectively. The antimicrobial susceptibility profile was determined by the disk diffusion method. Additionally, genomic DNA was sequenced and analyzed for species confirmation and sequence type (ST) determination. Detection of resistance and virulence genes was performed by comparisons against ResFinder and VFDB databases. The isolate was identified as a nontoxigenic C. diphtheriae biovar Gravis strain. Its genome presented a size of 2.46 Mbp and a G + C content of 53.5%. Ribosomal Multilocus Sequence Typing (rMLST) allowed the confirmation of species as C. diphtheriae with 100% identity. DDH in silico corroborated this identification. Moreover, MLST analyses revealed that the isolate belongs to ST-536. No resistance genes were predicted or mutations detected in antimicrobial-related genes. On the other hand, virulence genes, mostly involved in iron uptake and adherence, were found. Presently, we provided sufficient clinical data regarding the C. diphtheriae cutaneous infection in addition to the phenotypic and genomic data of the isolate. Our results indicate a possible circulation of ST-536 in Brazil, causing cutaneous infection. Considering that cases of C. diphtheriae infections, as well as diphtheria outbreaks, have still been reported in several regions of the world, studies focusing on taxonomic analyzes and predictions of resistance genes may help to improve the diagnosis and to monitor the propagation of resistant clones. In addition, they can contribute to understanding the association between variation in genetic factors and resistance to antimicrobials.

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.

The Spike of SARS-CoV-2: Uniqueness and Applications.

The Spike (S) protein of the SARS-CoV-2 virus is critical for its ability to attach and fuse into the host cells, leading to infection, and transmission. In this review, we have initially performed a meta-analysis of keywords associated with the S protein to frame the outline of important research findings and directions related to it. Based on this outline, we have reviewed the structure, uniqueness, and origin of the S protein of SARS-CoV-2. Furthermore, the interactions of the Spike protein with host and its implications in COVID-19 pathogenesis, as well as drug and vaccine development, are discussed. We have also summarized the recent advances in detection methods using S protein-based RT-PCR, ELISA, point-of-care lateral flow immunoassay, and graphene-based field-effect transistor (FET) biosensors. Finally, we have also discussed the emerging Spike mutants and the efficacy of the Spike-based vaccines against those strains. Overall, we have covered most of the recent advances on the SARS-CoV-2 Spike protein and its possible implications in countering this virus.

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.

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.