Identification and Genetic Characterization of MERS-Related Coronavirus Isolated from Nathusius' Pipistrelle (Pipistrellus nathusii) near Zvenigorod (Moscow Region, Russia).

Being diverse and widely distributed globally, bats are a known reservoir of a series of emerging zoonotic viruses. We studied fecal viromes of twenty-six bats captured in 2015 in the Moscow Region and found 13 of 26 (50%) samples to be coronavirus positive. Of P. nathusii (the Nathusius' pipistrelle), 3 of 6 samples were carriers of a novel MERS-related betacoronavirus. We sequenced and assembled the complete genome of this betacoronavirus and named it MOW-BatCoV strain 15-22. Whole genome phylogenetic analysis suggests that MOW-BatCoV/15-22 falls into a distinct subclade closely related to human and camel MERS-CoV. Unexpectedly, the phylogenetic analysis of the novel MOW-BatCoV/15-22 spike gene showed the closest similarity to CoVs from Erinaceus europaeus (European hedgehog). We suppose MOW-BatCoV could have arisen as a result of recombination between ancestral viruses of bats and hedgehogs. Molecular docking analysis of MOW-BatCoV/15-22 spike glycoprotein binding to DPP4 receptors of different mammals predicted the highest binding ability with DPP4 of the Myotis brandtii bat (docking score -320.15) and the E. europaeus (docking score -294.51). Hedgehogs are widely kept as pets and are commonly found in areas of human habitation. As this novel bat-CoV is likely capable of infecting hedgehogs, we suggest hedgehogs can act as intermediate hosts between bats and humans for other bat-CoVs.

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia.

Delta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to the emergence of multiple sublineages, most of which are well-mixed between countries. By contrast, here we show that nearly the entire Delta epidemic in Russia has probably descended from a single import event, or from multiple closely timed imports from a single poorly sampled geographic location. Indeed, over 90 per cent of Delta samples in Russia are characterized by the nsp2:K81N + ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect rather than a transmission advantage. The apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.

Case report: change of dominant strain during dual SARS-CoV-2 infection.

BACKGROUND: The dual infection with SARS-CoV-2 is poorly described and is currently under discussion. We present a study of two strains of SARS-CoV-2 detected in the same patient during the same disease presentation. CASE PRESENTATION: A patient in their 90 s was hospitalised with fever. Oropharyngeal swab obtained on the next day (sample 1) tested positive for SARS-CoV-2. Five days later, the patient was transferred to the ICU (intensive care unit) of the hospital specialising in the treatment of COVID-19 patients, where the patient's condition progressively worsened and continuous oxygen insufflation was required. Repeated oropharyngeal swab (sample 2), which was taken eight days after the first one, also tested positive for SARS-CoV-2. After 5 days of ICU treatment, the patient died. The cause of death was a coronavirus infection, which progressed unfavourably due to premorbid status. We have performed sequencing of full SARS-CoV-2 genomes from oropharyngeal swabs obtained eight days apart. Genomic analysis revealed the presence of two genetically distant SARS-CoV-2 strains in both swabs. Detected strains belong to different phylogenetic clades (GH and GR) and differ in seven nucleotide positions. The relative abundance of strains was 70% (GH) and 30% (GR) in the first swab, and 3% (GH) and 97% (GR) in the second swab. CONCLUSIONS: Our findings suggest that the patient was infected by two genetically distinct SARS-CoV-2 strains at the same time. One of the possible explanations is that the second infection was hospital-acquired. Change of the dominant strain ratio during disease manifestation could be explained by the advantage or higher virulence of the GR clade strain.

Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples.

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.