Occurrence of blaNDM-7 and association with blaKPC-2, blaCTX-M15, aac, aph, mph(A), catB3 and virulence genes in a clinical isolate of Klebsiella pneumoniae with different plasmids in Brazil.

To characterize phenotypically and genotypically an isolate of multidrug-resistant (MDR) K. pneumoniae from a patient with septicemia in a hospital in Recife-PE, Brazil, resistance and virulence genes were investigated using PCR and sequencing the amplicons, and the plasmid DNA was also sequenced. The K74-A3 isolate was resistant to all ß-lactams, including carbapenems, as well as to aminoglycosides and quinolones. By conducting a PCR analysis and sequencing, the variants blaNDM-7 associated with blaKPC-2 and the cps, wabG, fim-H, mrkD and entB virulence genes were identified. The analysis of plasmid revealed the presence of blaCTX-M15, aac(3)-IVa, aph(3')-Ia, aph(4)-Ia, aac(6')ib-cr, mph(A) and catB3, and also the plasmids IncX3, IncFIB, IncQ1, ColRNAI and ColpVC. To our knowledge, this is the first report of the blaNDM-7 gene in Recife-PE and we suggest that this variant is located in IncX3. These results alert us to the risk of spreading an isolate with a vast genetic arsenal of resistance, in addition to which several plasmids are present that favor the horizontal transfer of these genes.

Systematic in silico Evaluation of Leishmania spp. Proteomes for Drug Discovery.

Leishmaniasis is a group of neglected infectious diseases, with approximately 1. 3 million new cases each year, for which the available therapies have serious limitations. Therefore, it is extremely important to apply efficient and low-cost methods capable of selecting the best therapeutic targets to speed up the development of new therapies against those diseases. Thus, we propose the use of integrated computational methods capable of evaluating the druggability of the predicted proteomes of Leishmania braziliensis and Leishmania infantum, species responsible for the different clinical manifestations of leishmaniasis in Brazil. The protein members of those proteomes were assessed based on their structural, chemical, and functional contexts applying methods that integrate data on molecular function, biological processes, subcellular localization, drug binding sites, druggability, and gene expression. These data were compared to those extracted from already known drug targets (BindingDB targets), which made it possible to evaluate Leishmania proteomes for their biological relevance and treatability. Through this methodology, we identified more than 100 proteins of each Leishmania species with druggability characteristics, and potential interaction with available drugs. Among those, 31 and 37 proteins of L. braziliensis and L. infantum, respectively, have never been tested as drug targets, and they have shown evidence of gene expression in the evolutionary stage of pharmacological interest. Also, some of those Leishmania targets showed an alignment similarity of <50% when compared to the human proteome, making these proteins pharmacologically attractive, as they present a reduced risk of side effects. The methodology used in this study also allowed the evaluation of opportunities for the repurposing of compounds as anti-leishmaniasis drugs, inferring potential interaction between Leishmania proteins and ~1,000 compounds, of which only 15 have already been tested as a treatment for leishmaniasis. Besides, a list of potential Leishmania targets to be tested using drugs described at BindingDB, such as the potential interaction of the DEAD box RNA helicase, TRYR, and PEPCK proteins with the Staurosporine compound, was made available to the public.

Epitranscriptome machinery in Trypanosomatids: New players on the table?

Trypanosoma and Leishmania parasites cause devastating tropical diseases resulting in serious global health consequences. These organisms have complex life cycles with mammalian hosts and insect vectors. The parasites must, therefore, survive in different environments, demanding rapid physiological and metabolic changes. These responses depend upon regulation of gene expression, which primarily occurs posttranscriptionally. Altering the composition or conformation of RNA through nucleotide modifications is one posttranscriptional mechanism of regulating RNA fate and function, and modifications including N6-methyladenosine (m6A), N1-methyladenosine (m1A), N5-methylcytidine (m5C), N4-acetylcytidine (ac4C), and pseudouridine (Ψ), dynamically regulate RNA stability and translation in diverse organisms. Little is known about RNA modifications and their machinery in Trypanosomatids, but we hypothesize that they regulate parasite gene expression and are vital for survival. Here, we identified Trypanosomatid homologs for writers of m1A, m5C, ac4C, and Ψ and analyze their evolutionary relationships. We systematically review the evidence for their functions and assess their potential use as therapeutic targets. This work provides new insights into the roles of these proteins in Trypanosomatid parasite biology and treatment of the diseases they cause and illustrates that Trypanosomatids provide an excellent model system to study RNA modifications, their molecular, cellular, and biological consequences, and their regulation and interplay.

Building protein-protein interaction networks for Leishmania species through protein structural information.

BACKGROUND: Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. RESULTS: The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. CONCLUSIONS: The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.