A review on the mechanistic details of OXA enzymes of ESKAPE pathogens.

The production of ß-lactamases is a prevalent mechanism that poses serious pressure on the control of bacterial resistance. Furthermore, the unavoidable and alarming increase in the transmission of bacteria producing extended-spectrum ß-lactamases complicates treatment alternatives with existing drugs and/or approaches. Class D ß-lactamases, designated as OXA enzymes, are characterized by their activity specifically towards oxacillins. They are widely distributed among the ESKAPE bugs that are associated with antibiotic resistance and life-threatening hospital infections. The inadequacy of current ß-lactamase inhibitors for conventional treatments of 'OXA' mediated infections confirms the necessity of new approaches. Here, the focus is on the mechanistic details of OXA-10, OXA-23, and OXA-48, commonly found in highly virulent and antibiotic-resistant pathogens Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter spp. to describe their similarities and differences. Furthermore, this review contains a specific emphasis on structural and computational perspectives, which will be valuable to guide efforts in the design/discovery of a common single-molecule drug against ESKAPE pathogens.

SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.

Background/aim: The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. Materials and methods: In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk's lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. Results: It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. Conclusion: By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor (https://doi.org/10.18129/B9.bioc.SVMDO) and GitHub (https://github.com/robogeno/SVMDO).

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks.

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.