MicroRNAs have established their role as potent regulators of the epigenome. Interestingly, most miRNAs are located within protein-coding genes with functional consequences that have yet to be fully investigated. MiRIAD is a database with an interactive and user-friendly online interface that has been facilitating research on intragenic miRNAs. In this article, we present a major update. First, data for five additional species (chimpanzee, rat, dog, cow and frog) were added to support the exploration of evolutionary aspects of the relationship between host genes and intragenic miRNAs. Moreover, we integrated data from two different sources to generate a comprehensive alternative polyadenylation dataset. The miRIAD interface was therefore redesigned and provides a completely new gene model representation, including an interactive visualization of the 3′ untranslated region (UTR) with alternative polyadenylation sites, corresponding signals and potential miRNA binding sites. Furthermore, we expanded on functional host gene network analysis. Although the previous version solely reported protein interactions, the update features a separate network analysis view that can either be accessed through the submission of a list of genes of interest or directly from a gene’s list of protein interactions. In addition to statistical properties of the submitted gene set, the interaction network graph is presented and miRNAs with seed site over- and underrepresentation are identified. In summary, the update of miRIAD provides novel datasets and bioinformatics resources with a significant increase in functionality to facilitate intragenic miRNA research in a user-friendly and interactive way. Database URL: http://www.miriad-database.org

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients’ prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving with mutations in the spike protein, especially in the receptor-binding domain (RBD). The failure of public health measures in some countries to contain the spread of the disease has given rise to novel viral variants with increased transmissibility. However, key questions about how quickly the variants can spread remain unclear. Herein, we performed a structural investigation using molecular dynamics simulations and determined dissociation constant (K D) values using surface plasmon resonance assays of three fast-spreading SARS-CoV-2 variants, alpha, beta, and gamma, as well as genetic factors in host cells that may be related to the viral infection. Our results suggest that the SARS-CoV-2 variants facilitate their entry into the host cell by moderately increased binding affinities to the human ACE2 receptor, different torsions in hACE2 mediated by RBD variants, and an increased spike exposure time to proteolytic enzymes. We also found that other host cell aspects, such as gene and isoform expression of key genes for the infection (ACE2, FURIN, and TMPRSS2), may have few contributions to the SARS-CoV-2 variant infectivity. In summary, we concluded that a combination of viral and host cell factors allows SARS-CoV-2 variants to increase their abilities to spread faster than the wild type.