Background: Endogenous viral elements (EVEs) are sequences of viral origin integrated into the host genome. EVEs have been characterized in various insect genomes, including mosquitoes. A large EVE content has been found in Aedes aegypti and Aedes albopictus genomes among which a recently described Chuviridae viral family is of particular interest, owing to the abundance of EVEs derived from it, the discrepancy among the chuvirus endogenized gene regions and the frequent association with retrotransposons from the BEL-Pao superfamily. In order to better understand the endogenization process of chuviruses and the association between chuvirus glycoproteins and BEL-Pao retrotransposons, we performed a comparative genomics and evolutionary analysis of chuvirus-derived EVEs found in 37 mosquito genomes. Results: We identified 428 EVEs belonging to the Chuviridae family confirming the wide discrepancy among the chuvirus genomic regions endogenized: 409 glycoproteins, 18 RNA-dependent RNA polymerases and one nucleoprotein region. Most of the glycoproteins (263 out of 409) are associated specifically with retroelements from the Pao family. Focusing only on well-assembled Pao retroelement copies, we estimated that 263 out of 379 Pao elements are associated with chuvirus-derived glycoproteins. Seventy-three potentially active Pao copies were found to contain glycoproteins into their LTR boundaries. Thirteen out of these were classified as complete and likely autonomous copies, with a full LTR structure and protein domains. We also found 116 Pao copies with no trace of glycoproteins and 37 solo glycoproteins. All potential autonomous Pao copies, contained highly similar LTRs, suggesting a recent/current activity of these elements in the mosquito genomes. Conclusion: Evolutionary analysis revealed that most of the glycoproteins found are likely derived from a single or few glycoprotein endogenization events associated with a recombination event with a Pao ancestral element. A potential functional Pao-chuvirus hybrid (named Anakin) emerged and the glycoprotein was further replicated through

No abstract

Here, we report the isolation of 31 Acinetobacter baumannii strains producing OXA-253 in a single large Brazilian city. These strains belonged to five different sequence types (STs), including 4 STs not previously associated with bla . In all strains, the bla OXA-253 gene was located in a plasmid within a genetic environment similar to what was found previously in Brazil and Italy. The reported data emphasize the successful transmission of the bla OXA-253 gene through a large area and the tendency for this resistance determinant to remain in the A. baumannii population.KEYWORDS Acinetobacter, MLST, antibiotic resistance, genome analysis, oxacillinase A cinetobacter baumannii is an opportunistic Gram-negative pathogen responsible for a large number of outbreaks of hospital-acquired infections. This bacterium has been linked with serious infections affecting mainly debilitated patients in intensive care units (1), and it can become resistant to a wide range of antibiotics, leading to serious impediments to treatment. Carbapenems, such as imipenem and meropenem, have been often used as drugs of last resort, but outbreaks of carbapenem-resistant A. baumannii have been reported in several countries, including Brazil (2).Resistance to carbapenems in A. baumannii may be due to various mechanisms, including the loss of outer membrane protein (OMP), overexpression of efflux pumps, or through alterations in the penicillin-binding protein (3). An increase in reported carbapenem resistance by A. baumannii isolates, however, has been mostly attributed to yet another mechanism, the production of carbapenemases, such as those belonging to the class D OXA type (4).Here, we report OXA-253-producing A. baumannii strains isolated from different hospitals localized within the city of Recife, state of Pernambuco, in northeastern Brazil. This variant of the OXA-143-like carbapenemase belongs to the Ambler class D of ␤-lactamases (5), which has previously been the subject of studies discussing its incidence in Brazil. So far, however, the bla OXA-143 gene has only been reported for samples from the states of São Paulo and Rio de Janeiro, over 1,800 km to the south of Recife (2, 6-8).Identification of the OXA-253-producing strains occurred after sequencing of the genomes of 45 A. baumannii strains displaying an extensively drug-resistant (XDR) phenotype and isolated from different individuals hospitalized in five public hospitals in Recife between 2010 and 2014. These strains were considered resistant to the two carbapenems tested (imipenem and meropenem) according to the Brazilian Committee on Antimicrobial Susceptibility Testing (BrCAST) guidelines, using the broth microdilution method. A detailed report of the whole-genomic data analysis, in the context of resistance and virulence characteristics, will be provided in future publications. Analysis of the sequencing data led to identification in 31 strains of the recently described class D ␤-lactamase bla OXA-253 (9). According to a multilocus sequence typing (MLST) analysis...

BackgroundSystematic 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.ResultsThe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2105-6) contains supplementary material, which is available to authorized users.