ABSTRACT
Next-generation sequencing technologies have revolutionized the field of virology by enabling the reading of complete viral genomes, extensive metagenomic studies, and the identification of novel viral pathogens. Although metagenomic sequencing has the advantage of not requiring specific probes or primers, it faces significant challenges in analyzing data and identifying novel viruses. Traditional bioinformatics tools for sequence identification mainly depend on homology-based strategies, which may not allow the detection of a virus significantly different from known variants due to the extensive genetic diversity and rapid evolution of viruses. In this work, we performed metagenomic analysis of bat feces from different Russian cities and identified a wide range of viral pathogens. We then selected sequences with minimal homology to a known picornavirus and used "Switching Mechanism at the 5' end of RNA Template" technology to obtain a longer genome fragment, allowing for more reliable identification. This study emphasizes the importance of integrating advanced computational methods with experimental strategies for identifying unknown viruses to better understand the viral universe.
ABSTRACT
In 2021, a patient died from Marburg virus (MARV) disease in Guinea and it was the first confirmed case in West Africa. The origin of the outbreak has not been identified. It was revealed that the patient didn't travel anywhere before the illness. Prior to outbreak, MARV had been found in bats in the neighboring Sierra Leone, but never in Guinea. Therefore, the origin of infection is unclear: was it an autochthonous case with spillover from a local population of bats or an imported case with spillover from fruit bats foraging/migrating from Sierra Leone? In this paper, we studied Rousettus aegyptiacus in Guinea as the possible source of MARV infection caused the patient death in 2021 in Guinea. We caught bats in 32 sites of Guéckédou prefecture, including seven caves and 25 locations of the flight path. A total of 501 fruit bats (Pteropodidae) were captured, including 66 R. aegyptiacus. The PCR screening showed three positive MARV R. aegyptiacus, roosting in two caves discovered in Guéckédou prefecture. After Sanger sequencing and phylogenetic analyses it was shown that found MARV belongs to the Angola-like lineage but it is not identical to the isolate obtained during the outbreak of 2021.
Subject(s)
Chiroptera , Marburg Virus Disease , Marburgvirus , Animals , Humans , Guinea/epidemiology , Marburgvirus/genetics , Phylogeny , Egypt , Marburg Virus Disease/epidemiology , Disease OutbreaksABSTRACT
According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.
