Differential Type-I Interferon Response in Buffy Coat Transcriptome of Individuals Infected with SARS-CoV-2 Gamma and Delta Variants.

The innate immune system is the first line of defense against pathogens such as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The type I-interferon (IFN) response activation during the initial steps of infection is essential to prevent viral replication and tissue damage. SARS-CoV and SARS-CoV-2 can inhibit this activation, and individuals with a dysregulated IFN-I response are more likely to develop severe disease. Several mutations in different variants of SARS-CoV-2 have shown the potential to interfere with the immune system. Here, we evaluated the buffy coat transcriptome of individuals infected with Gamma or Delta variants of SARS-CoV-2. The Delta transcriptome presents more genes enriched in the innate immune response and Gamma in the adaptive immune response. Interactome and enriched promoter analysis showed that Delta could activate the INF-I response more effectively than Gamma. Two mutations in the N protein and one in the nsp6 protein found exclusively in Gamma have already been described as inhibitors of the interferon response pathway. This indicates that the Gamma variant evolved to evade the IFN-I response. Accordingly, in this work, we showed one of the mechanisms that variants of SARS-CoV-2 can use to avoid or interfere with the host Immune system.

Live Cell Imaging Reveals the Relocation of dsRNA Binding Proteins Upon Viral Infection.

Viral infection triggers a range of plant responses such as the activation of the RNA interference (RNAi) pathway. The double-stranded RNA binding (DRB) proteins DRB3 and DRB4 are part of this pathway and aid in defending against DNA and RNA viruses, respectively. Using live cell imaging, we show that DRB2, DRB3, and DRB5 relocate from their uniform cytoplasmic distribution to concentrated accumulation in nascent viral replication complexes (VRC) that develop following cell invasion by viral RNA. Inactivation of the DRB3 gene in Arabidopsis by T-DNA insertion rendered these plants less able to repress RNA viral replication. We propose a model for the early stages of virus defense in which DRB2, DRB3, and DRB5 are invasion sensors that relocate to nascent VRC, where they bind to viral RNA and inhibit virus replication.

microRNAs in Macrobrachium olfersii embryos: Identification, their biogenesis components and potential targets.

In embryonic development, microRNAs (miRNAs) regulate the complex gene expression associated with the complexity of embryogenesis. Today, few studies have been conducted on the identification of miRNAs and components of miRNA biogenesis on embryonic development in crustaceans, especially in prawns. In this context, the aim of this study was to identify in silico components of miRNA biogenesis, and miRNAs and potential target genes during embryonic development in the prawn Macrobrachium olfersii through small RNAs and transcriptome analyses. Using the miRDeep2 program, we identified 17 miRNA precursors in M. olfersii, which seven (miR-9, miR-10, miR-92, miR-125, miR-305, miR-1175, and miR-2788) were reported in the miRBase database, indicating high evolutionary conservation of these sequences among animals. The other 10 miRNAs of M. olfersii were novel miRNAs and only similar to Macrobrachium niponnense miRNAs, indicating genus-specific miRNAs. In addition, eight key components of miRNA biogenesis (DROSHA, PASHA/DGCR8, XPO5, RAN, DICER, TRBP2, AGO, and PIWI) were identified in M. olfersii embryos unigenes. In the annotation of miRNA targets, 516 genes were similar to known sequences in the GenBank database. Regarding the conserved miRNAs, we verified that they were differentially expressed during embryonic development in M. olfersii. In conclusion, this is the first study that identifies conserved and novel miRNAs in the prawn M. olfersii with some miRNA target genes involved in embryonic development. Our results will allow further studies on the function of these miRNAs and miRNA biogenesis components during embryonic development in M. olfersii and other prawns of commercial interest.

Unveiling Chloroplast RNA Editing Events Using Next Generation Small RNA Sequencing Data.

Organellar RNA editing involves the modification of nucleotide sequences to maintain conserved protein functions, mainly by reverting non-neutral codon mutations. The loss of plastid editing events, resulting from mutations in RNA editing factors or through stress interference, leads to developmental, physiological and photosynthetic alterations. Recently, next generation sequencing technology has generated the massive discovery of sRNA sequences and expanded the number of sRNA data. Here, we present a method to screen chloroplast RNA editing using public sRNA libraries from Arabidopsis, soybean and rice. We mapped the sRNAs against the nuclear, mitochondrial and plastid genomes to confirm predicted cytosine to uracil (C-to-U) editing events and identify new editing sites in plastids. Among the predicted editing sites, 40.57, 34.78, and 25.31% were confirmed using sRNAs from Arabidopsis, soybean and rice, respectively. SNP analysis revealed 58.2, 43.9, and 37.5% new C-to-U changes in the respective species and identified known and new putative adenosine to inosine (A-to-I) RNA editing in tRNAs. The present method and data reveal the potential of sRNA as a reliable source to identify new and confirm known editing sites.

RFMirTarget: predicting human microRNA target genes with a random forest classifier.

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.