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.

Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus.

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

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.

Taxonomic classification of strain PO100/5 shows a broader geographic distribution and genetic markers of the recently described Corynebacterium silvaticum.

The bacterial strain PO100/5 was isolated from a skin abscess taken from a pig (Sus scrofa domesticus) in the Alentejo region of southern Portugal. It was identified as Corynebacterium pseudotuberculosis using biochemical tests, multiplex PCR and Pulsed Field Gel Electrophoresis. After genome sequencing and rpoB phylogeny, the strain was classified as C. ulcerans. To better understand the taxonomy of this strain and improve identification methods, we compared strain PO100/5 to other publicly available genomes from C. diphtheriae group. Taxonomic analysis reclassified it and three others strains as the recently described C. silvaticum, which have been isolated from wild boar and roe deer in Germany and Austria. The results showed that PO100/5 is the first sequenced genome of a C. silvaticum strain from livestock and a different geographical region, has the unique sequence type ST709, and could be could produce the diphtheriae toxin, along with strain 05-13. Genomic analysis of PO100/5 showed four prophages, and eight conserved genomic islands in comparison to C. ulcerans. Pangenome analysis of 38 C. silvaticum and 76 C. ulcerans genomes suggested that C. silvaticum is a genetically homogeneous species, with 73.6% of its genes conserved and a pangenome near to be closed (α > 0.952). There are 172 genes that are unique to C. silvaticum in comparison to C. ulcerans. Most of these conserved genes are related to nutrient uptake and metabolism, prophages or immunity against them, and could be genetic markers for species identification. Strains PO100/5 (livestock) and KL0182T (wild boar) were predicted to be potential human pathogens. This information may be useful for identification and surveillance of this pathogen.

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.