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.

Lung cancer is one of the most frequent types of cancer worldwide. Most patients are diagnosed at advanced stage and thus have poor prognosis. Smoking is a risk factor for lung cancer, however most smokers do not develop lung cancer while 20% of women with lung adenocarcinoma are non-smokers. Therefore, it is possible that these two groups present differences besides the smoking status, including differences in their gene expression signature. The altered expression patterns of non-coding RNAs in complex diseases make them potential biomarkers for diagnosis and treatment. We analyzed data from differentially and constitutively expressed PIWI-interacting RNAs and small nucleolar RNAs from publicly available small RNA high-throughput sequencing data in search of an expression pattern of non-coding RNA that could differentiate these two groups. Here, we report two sets of differentially expressed small non-coding RNAs identified in normal and tumoral tissues of women with lung adenocarcinoma, that discriminate between smokers and non-smokers. Our findings may offer new insights on metabolic alterations caused by tobacco and may be used for early diagnosis of lung cancer.

The main cause of cervical cancer in the world is high risks human papillomavirus infection (mainly represented by HPV-16 and HPV-18), that are associated to the development of malign transformation of the epithelium. HPV prevalence exhibits a wide geographical variability and HPV-16 variants have been related to an increased risk of developing cervical intraepithelial lesion. The aim of this study was to describe DNA-HPV prevalence and HPV-16 variants among a women population from Northern Brazil.

Extracellular vesicles (EVs) are double-membrane particles associated with intercellular communication. Since the discovery of EV production in the fungus Cryptococcus neoformans, the importance of EV release in its physiology and pathogenicity has been investigated. To date, few studies have investigated the proteomic content of EVs from multiple fungal species. Our main objective was to use an orthology approach to compare proteins identified by EV shotgun proteomics in 8 pathogenic and 1 nonpathogenic species. Using protein information from the UniProt and FungiDB databases, we integrated data for 11,433 hits in fungal EVs with an orthology perspective, resulting in 3,834 different orthologous groups. OG6_100083 (Hsp70 Pfam domain) was the unique orthologous group that was identified for all fungal species. Proteins with this protein domain are associated with the stress response, survival and morphological changes in different fungal species. Although no pathogenic orthologous group was found, we identified 5 orthologous groups exclusive to S. cerevisiae. Using the criteria of at least 7 pathogenic fungi to define a cluster, we detected the 4 unique pathogenic orthologous groups. Taken together, our data suggest that Hsp70-related proteins might play a key role in fungal EVs, regardless of the pathogenic status. Using an orthology approach, we identified at least 4 protein domains that could be novel therapeutic targets against pathogenic fungi. Our results were compiled in the herein described ExVe database, which is publicly available at http://exve.icc.fiocruz.br.

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) are the most common histological types of cervical cancer (CC). The worse prognosis of ADC cases highlights the need for better molecular characterization regarding differences between these CC types. RNA-Seq analysis of seven SCC and three ADC human papillomavirus 16-positive samples and the comparison with public data from non-tumoral human papillomavirus-negative cervical tissue samples revealed pathways exclusive to each histological type, such as the epithelial maintenance in SCC and the maturity-onset diabetes of the young (MODY) pathway in ADC. The transcriptional regulatory network analysis of cervical SCC samples unveiled a set of six transcription factor (TF) genes with the potential to positively regulate long non-coding RNA genes DSG1-AS1, CALML3-AS1, IGFL2-AS1, and TINCR. Additional analysis revealed a set of MODY TFs regulated in the sequence predicted to be repressed by miR-96-5p or miR-28-3p in ADC. These microRNAs were previously described to target LINC02381, which was predicted to be positively regulated by two MODY TFs upregulated in cervical ADC. Therefore, we hypothesize LINC02381 might act by decreasing the levels of miR-96-5p and miR-28-3p, promoting the MODY activation in cervical ADC. The novel TF networks here described should be explored for the development of more efficient diagnostic tools.