Evolutionary selection against short nucleotide sequences in viruses and their related hosts

Genomic characterization and molecular evolution of SARS-CoV-2 in Rio Grande do Sul State, Brazil

SARS-CoV-2 is the virus responsible for the COVID-19 and has afflicted the world since the end of 2019. Different lineages have been discovered and the Gamma lineage, which started the second wave of infections, was first described in Brazil, one of the most affected countries by pandemic. Therefore, this study analyzed SARS-CoV-2 sequenced genomes from Esteio city in Rio Grande do Sul, Southern Brazil. We also comparatively analyzed genomes of the two first years of the pandemic from Rio Grande do Sul state for understanding their genomic and evolutionary patterns. The phylogenomic analysis showed monophyletic groups for Alpha, Gamma, Delta and Omicron, as well as for other circulating lineages in the state. Molecular evolutionary analysis identified several sites under adaptive selection in membrane and nucleocapsid proteins which could be related to a prevalent stabilizing effect on membrane protein structure, as well as majoritarily destabilizing effects on C-terminal nucleocapsid domain.

RNA Viruses Linked to Eukaryotic Hosts in Thawed Permafrost

Arctic permafrost is thawing due to global warming, with unknown consequences on the microbial inhabitants or associated viruses. DNA viruses have previously been shown to be abundant and active in thawing permafrost, but little is known about RNA viruses in these systems. To address this knowledge gap, we assessed the composition of RNA viruses in thawed permafrost samples that were incubated for 97 days at 4°C to simulate thaw conditions. A diverse RNA viral community was assembled from metatranscriptome data including double-stranded RNA viruses, dominated by Reoviridae and Hypoviridae, and negative and positive single-stranded RNA viruses, with relatively high representations of Rhabdoviridae and Leviviridae, respectively. Sequences corresponding to potential plant and human pathogens were also detected. The detected RNA viruses primarily targeted dominant eukaryotic taxa in the samples (e.g., fungi, Metazoa and Viridiplantae) and the viral community structures were significantly associated with predicted host populations. These results indicate that RNA viruses are linked to eukaryotic host dynamics. Several of the RNA viral sequences contained auxiliary metabolic genes encoding proteins involved in carbon utilization (e.g., polygalacturosase), implying their potential roles in carbon cycling in thawed permafrost. IMPORTANCE Permafrost is thawing at a rapid pace in the Arctic with largely unknown consequences on ecological processes that are fundamental to Arctic ecosystems. This is the first study to determine the composition of RNA viruses in thawed permafrost. Other recent studies have characterized DNA viruses in thawing permafrost, but the majority of DNA viruses are bacteriophages that target bacterial hosts. By contrast RNA viruses primarily target eukaryotic hosts and thus represent potential pathogenic threats to humans, animals, and plants. Here, we find that RNA viruses in permafrost are novel and distinct from those in other habitats studied to date. The COVID-19 pandemic has heightened awareness of the importance of potential environmental reservoirs of emerging RNA viral pathogens. We demonstrate that some potential pathogens were detected after an experimental thawing regime. These results are important for understanding critical viral-host interactions and provide a better understanding of the ecological roles that RNA viruses play as permafrost thaws.

Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.