Southeastern Brazil has been suffering a rapid expansion of a severe sylvatic yellow fever virus (YFV) outbreak since late 2016, which has reached one of the most populated zones in Brazil and South America, heretofore a yellow fever-free zone for more than 70 years. In the current study, we describe the complete genome of 12 YFV samples from mosquitoes, humans and non-human primates from the Brazilian 2017 epidemic. All of the YFV sequences belong to the modern lineage (sub-lineage 1E) of South American genotype I, having been circulating for several months prior to the December 2016 detection. Our data confirm that viral strains associated with the most severe YF epidemic in South America in the last 70 years display unique amino acid substitutions that are mainly located in highly conserved positions in non-structural proteins. Our data also corroborate that YFV has spread southward into Rio de Janeiro state following two main sylvatic dispersion routes that converged at the border of the great metropolitan area comprising nearly 12 million unvaccinated inhabitants. Our original results can help public health authorities to guide the surveillance, prophylaxis and control measures required to face such a severe epidemiological problem. Finally, it will also inspire other workers to further investigate the epidemiological and biological significance of the amino acid polymorphisms detected in the Brazilian 2017 YFV strains.

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.

Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.

Riboswitches are RNA sensors that affect post-transcriptional processes through their ability to bind to small molecules. Thiamine pyrophosphate (TPP) riboswitch class is the most widespread riboswitch occurring in all three domains of life. Even though it controls different genes involved in the synthesis or transport of thiamine and its phosphorylated derivatives in bacteria, archaea, fungi, and plants, the TPP aptamer has a conserved structure. In this study, we aimed at understanding differences in the structural dynamics of TPP riboswitches from Escherichia coli and Arabidopsis thaliana, based on their crystallographic structures (TPPsw^ec and TPPsw^at, respectively) and dynamics in aqueous solution, both in apo and holo states. A combination of Molecular Dynamics Simulations and Network Analysis empowered to find out slight differences in the dynamical behavior of TPP riboswitches, although relevant for their dynamics in bacteria and plants species. Our results suggest that distinct interactions in the microenvironment surrounding nucleotide U36 of TPPsw^ec (and U35 in TPPsw^at) are related to different responses to TPP. The network analysis showed that minor structural differences in the aptamer enable enhanced intramolecular communication in the presence of TPP in TPPsw^ec, but not in TPPsw^at. TPP riboswitches of plants present subtler and slower regulation mechanisms than bacteria do.

Ovarian cancer is among the seven most common types of cancer in women, being the most fatal gynecological tumor, due to the difficulty of detection in early stages. Aptamers are important tools to improve tumor diagnosis through the recognition of specific molecules produced by tumors. Here, aptamers and their potential targets in ovarian cancer cells were analyzed by in silico approaches. Specific aptamers were selected by the Cell-SELEX method using Caov-3 and OvCar-3 cells. The five most frequent aptamers obtained from the last round of selection were computationally modeled. The potential targets for those aptamers in cells were proposed by analyzing proteomic data available for the Caov-3 and OvCar-3 cell lines. Overexpressed proteins for each cell were characterized as to their three-dimensional model, cell location, and electrostatic potential. As a result, four specific aptamers for ovarian tumors were selected: AptaC2, AptaC4, AptaO1, and AptaO2. Potential targets were identified for each aptamer through Molecular Docking, and the best complexes were AptaC2-FXYD3, AptaC4-ALPP, AptaO1-TSPAN15, and AptaO2-TSPAN15. In addition, AptaC2 and AptaO1 could detect different stages and subtypes of ovarian cancer tissue samples. The application of this technology makes it possible to propose new molecular biomarkers for the differential diagnosis of epithelial ovarian cancer.