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The ongoing pandemic of SARS-CoV-2 presents novel challenges and opportunities for the use of phylogenetics to understand and control its spread. Here, we analyze the emergence of SARS-CoV-2 in Russia in March and April 2020. Combining phylogeographic analysis with travel history data, we estimate that the sampled viral diversity has originated from at least 67 closely timed introductions into Russia, mostly in late February to early March. All but one of these introductions were not from China, suggesting that border closure with China has helped delay establishment of SARS-CoV-2 in Russia. These introductions resulted in at least 9 distinct Russian lineages corresponding to domestic transmission. A notable transmission cluster corresponded to a nosocomial outbreak at the Vreden hospital in Saint Petersburg; phylodynamic analysis of this cluster reveals multiple (2-3) introductions each giving rise to a large number of cases, with a high initial effective reproduction number of 3.0 [1.9, 4.3]. The COVID-19 epidemic began later in Russia than many European countries, possibly due to restrictions on travel from China. Here, the authors analyze whole genome sequences sampled early in the epidemic in Russia, and find that most strains were not linked to China.

Human parainfluenza viruses 3 (hPIV3) are important pathogens, responsible for acute respiratory tract diseases, especially in young children. Information on hPIV3 circulation and their diversity pattern in Russia is limited. The aim of this study was to perform a molecular and genetic characterization of hPIV3 circulating in Saint Petersburg, Russia. From October 2017 to September 2023, 14,704 swabs were screened using real-time reverse transcription-PCR. A phylogenetic analysis of the complete hemagglutinin–neuraminidase (HN) gene was performed. Out of 1334 positive hPIV cases, hPIV3 was the most common subtype. Phylogenetic analysis of the studied and previously published HN sequences revealed four distinct genetic clusters, A, B, C, and D, with Cluster D being first delineated in this study. In addition, two newly subdivided genetic lineages, C5a and C5b, were documented. Phylogenetic analysis revealed that the analyzed Russian strains grouped into Cluster C and D; further subclusters C5a, C5b, C3b, C3e, and C3a. While three strains were classified within cluster D, the majority of isolates fell within subcluster C3a, followed by C5b. Taken together, these findings demonstrate the co-circulation of hPIV3 strains during the study period. This is the first study that describes the genetic and molecular aspects of hPIV3 circulating in Russia. Moreover, our results provide an up-to-date hPIV3 phylogenetic analysis.

Human adenoviruses (HAdVs) are globally distributed pathogens capable of causing a wide range of clinical manifestations, particularly acute respiratory infections. However, their genomic diversity remains insufficiently characterized, with substantial geographic gaps in available sequence data, including for Russia, where only a few complete genomes have been deposited prior to this work. In this study, we analyzed more than 1200 PCR-positive respiratory specimens collected from hospitalized patients within routine surveillance projects and the Global Influenza Hospital Surveillance Network (GIHSN) across plenty of Russian regions during 2023–2024. Virus isolation followed by next-generation sequencing yielded 128 complete HAdV genomes representing species B, C, and D. The dataset included 27 B3, 9 B7, 44 B55, 12 C1, 16 C2, 4 C5, 7 C89, 5 C108, and one D109 genome, as well as three unassigned recombinant viruses with p89h5f5, p5h6f6 and p5h57f6 genomic structures (p, penton base; h, hexon; f, fiber). Phylogenetic analyses of whole genomes and capsid genes revealed extensive variability in immunogenic regions, particularly in species C, and identified clusters within B3 viruses. Notably, HAdV-D109 was identified in Russia, marking only the second reported detection of this genotype worldwide. Together, these findings substantially expand the currently available genomic landscape of HAdVs, highlighting the circulation of diverse and recombinant strains in Russia.

The comparison of the development of the SARS-CoV-2 epidemic in several neighboring regions can help researchers to assess the risks and develop more effective strategies and approaches in the field of preventive medicine. We analyzed the infection and mortality statistics for the 2020–2022 period in ten individual regions of the Siberian Federal District of Russia. We also sequenced complete genomes, which allowed us to analyze the genetic diversity of SARS-CoV-2 circulated in each of the ten regions and to build a phylogenetic dendrogram for the virus variants. The ParSeq v.1.0 software was developed to automate and speed up the processing and analysis of viral genomes. At the beginning of the pandemic, in the first two waves, the B.1.1 variant (20B) dominated in all regions of the Siberian Federal District. The third and fourth waves were caused by the Delta variant. Mortality during this period was at a maximum; the incidence was quite high, but the number of deposited genomes with GISAID during this period was extremely low. The maximum incidence was at the beginning of 2022, which corresponds to the arrival of the Omicron variant in the region. The BA.5.2 variant became the dominant one. In addition, by using NextClade, we identified three recombinants in the most densely populated areas.

Background This study aimed to establish a Severity Scale for influenza and other acute respiratory infections (ARI), requiring hospitalization, for surveillance and research purposes (the SevScale). Such a scale could aid the interpretation of data gathered from disparate settings. This could facilitate pooled analyses linking viral genetic sequencing data to clinical severity, bringing insights to inform influenza surveillance and the vaccine strain selection process. Methods We used a subset of data from the Global Influenza Hospital Surveillance Network database, including data from different geographical areas and income levels. To quantify the underlying concept of severity, an item response model was developed using 16 indicators of severity related to the hospital stay. Each patient in the dataset was assigned a Severity Score and a Severity Category (low, medium, or high severity). Finally, we compared the model scores across different subgroups. Results Data from 9 countries were included, covering between 4 and 11 seasons from 2012 to 2022, with a total of 96,190 ARI hospitalizations. Not for all severity indicators data were available for all included seasons. Subgroups with a high percentage of patients in the high Severity Category included influenza A(H1N1)pdm09, age ≥ 50, lower‐middle income countries, and admission since the start of the COVID‐19 pandemic. Conclusions The initial model successfully highlighted severity disparities across patient subgroups. Repeating this exercise with new, more complete data would allow recalibration and validation of the current model. The SevScale proved to be a promising method to define severity for influenza vaccine strain selection, surveillance, and research.

The COVID-19 pandemic has uncovered the high genetic variability of the SARS-CoV-2 virus and its ability to evade the immune responses that were induced by earlier viral variants. Only a few monoclonal antibodies that have been reported to date are capable of neutralizing a broad spectrum of SARS-CoV-2 variants. Here, we report the isolation of a new broadly neutralizing human monoclonal antibody, iC1. The antibody was identified through sorting the SARS-CoV-1 RBD-stained individual B cells that were isolated from the blood of a vaccinated donor following a breakthrough infection. In vitro , iC1 potently neutralizes pseudoviruses expressing a wide range of SARS-CoV-2 Spike variants, including those of the XBB sublineage. In an hACE2-transgenic mouse model, iC1 provided effective protection against the Wuhan strain of the virus as well as the BA.5 and XBB.1.5 variants. Therefore, iC1 can be considered as a potential component of the broadly neutralizing antibody cocktails resisting the SARS-CoV-2 mutation escape.

Prediction models which will explicitly include the immunity levels of the population are required to plan effective measures for the containment of seasonal epidemics of influenza. The aim of the current work is to develop an approach to herd immunity dynamics modeling, with the long–term goal of employing it as a part of multicomponent model of influenza incidence dynamics. Based on serological studies performed for 52 Russian cities and 2 virus strains (A(H1N1)pdm09, A(H3N2)) in 11 years period, we propose statistical models which allow to analyze and predict the strain–specific immunity dynamics.

Nirsevimab is an extended half-life monoclonal antibody to the respiratory syncytial virus (RSV) fusion protein that has been developed to protect infants for an entire RSV season. Previous studies have shown that the nirsevimab binding site is highly conserved. However, investigations of the geotemporal evolution of potential escape variants in recent (ie, 2015–2021) RSV seasons have been minimal. Here, we examine prospective RSV surveillance data to assess the geotemporal prevalence of RSV A and B, and functionally characterise the effect of the nirsevimab binding-site substitutions identified between 2015 and 2021. We assessed the geotemporal prevalence of RSV A and B and nirsevimab binding-site conservation between 2015 and 2021 from three prospective RSV molecular surveillance studies (the US-based OUTSMART-RSV, the global INFORM-RSV, and a pilot study in South Africa). Nirsevimab binding-site substitutions were assessed in an RSV microneutralisation susceptibility assay. We contextualised our findings by assessing fusion-protein sequence diversity from 1956 to 2021 relative to other respiratory-virus envelope glycoproteins using RSV fusion protein sequences published in NCBI GenBank. We identified 5675 RSV A and RSV B fusion protein sequences (2875 RSV A and 2800 RSV B) from the three surveillance studies (2015–2021). Nearly all (25 [100%] of 25 positions of RSV A fusion proteins and 22 [88%] of 25 positions of RSV B fusion proteins) amino acids within the nirsevimab binding site remained highly conserved between 2015 and 2021. A highly prevalent (ie, >40·0% of all sequences) nirsevimab binding-site Ile206Met:Gln209Arg RSV B polymorphism arose between 2016 and 2021. Nirsevimab neutralised a diverse set of recombinant RSV viruses, including new variants containing binding-site substitutions. RSV B variants with reduced susceptibility to nirsevimab neutralisation were detected at low frequencies (ie, prevalence <1·0%) between 2015 and 2021. We used 3626 RSV fusion-protein sequences published in NCBI GenBank between 1956 and 2021 (2024 RSV and 1602 RSV B) to show that the RSV fusion protein had lower genetic diversity than influenza haemagglutinin and SARS-CoV-2 spike proteins. The nirsevimab binding site was highly conserved between 1956 and 2021. Nirsevimab escape variants were rare and have not increased over time. AstraZeneca and Sanofi.

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties. While escape from humoral immunity certainly contributes to intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin’s lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented the binding of known immunogenic SARS-CoV-2 HLA class I antigens. By experimentally assessing the effect of a subset of the escape mutations, we show that they resulted in a loss of as much as ~1% of effector CD8 T cell response. Our results indicate that CD8 T cell escape represents a major underappreciated contributor to SARS-CoV-2 evolution in humans. Here, the authors report accelerated intrahost evolution of SARS-CoV-2 in an immunocompromised patient with non-Hodgkin’s lymphoma with 318 days long COVID-19, and show that changes in the viral genome resulted in escape from T cellular immune response.

Human parainfluenza viruses (hPIVs) are major contributors to respiratory tract infections in young children worldwide. Despite their global significance, genomic surveillance of hPIV1 and hPIV2 had not previously been conducted in Russia. This study aimed to develop a robust amplicon-based sequencing protocol for these viruses. The designed primer sets were tested on clinical samples containing hPIV RNA to evaluate their performance and efficiency. Sequencing results demonstrated high-quality genome data and efficient amplification across various Ct values. As a result, 41 hPIV1 and 13 hPIV2 near-complete genome sequences were successfully obtained from clinical specimens collected in Saint Petersburg (Russia). Phylogenetic analysis of the HN gene sequences showed that Russian hPIV1 strains clustered into clades II and III, while hPIV2 strains were distributed between clusters G1a and G3. The whole-genome-based trees confirmed the same distribution of the strains. These findings highlight the potential of our primer panels and contribute to a better understanding of the molecular characteristics and phylogenetic diversity of circulating hPIV strains. Notably, this study presents the first evolutionary analysis of hPIVs in Russia.