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In this paper, we revealed the genetic structure and migration history of the Powassan virus (POWV) reconstructed based on 25 complete genomes available in NCBI and ViPR databases (accessed in June 2021). The usage of this data set allowed us to perform a more precise assessment of the evolutionary rate of this virus. In addition, we proposed a simple Bayesian technique for the evaluation and visualization of ‘temporal signal dynamics’ along the phylogenetic tree. We showed that the evolutionary rate value of POWV is 3.3 × 10−5 nucleotide substitution per site per year (95% HPD, 2.0 × 10−5–4.7 × 10−5), which is lower than values reported in the previous studies. Divergence of the most recent common ancestor (MRCA) of POWV into two independent genetic lineages most likely occurred in the period between 2600 and 6030 years ago. We assume that the divergence of the virus lineages happened due to the melting of glaciers about 12,000 years ago, which led to the disappearance of the Bering Land Bridge between Eurasia and North America (the modern Alaskan territory) and spatial division of the viral areal into two parts. Genomic data provide evidence of the virus migrations between two continents. The mean migration rate detected from the Far East of Russia to North America was one event per 1750 years. The migration to the opposite direction occurred approximately once per 475 years.

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Samples from complete genomes of SARS-CoV-2 isolated during the first wave (December 2019 - July 2020) of the global COVID-19 pandemic from 21 countries (Asia, Europe, Middle East and America) around the world, were analysed using the phylogenetic method with molecular clock dating. Results showed that the first cases of COVID-19 in the human population appeared in the period between July and November 2019 in China. The spread of the virus into other countries of the world began in the autumn of 2019. In mid-February 2020, the virus appeared in all the countries we analysed. During this time, the global population of SARS-CoV-2 was characterized by low levels of the genetic polymorphism, making it difficult to accurately assess the pathways of infection. The rate of evolution of the coding region of the SARS-CoV-2 genome equal to 7.3 × 10 −4 (5.95 × 10 −4 - 8.68 × 10 −4 ) nucleotide substitutions per site per year is comparable to those of other human RNA viruses ( Measles morbillivirus, Rubella virus, Enterovirus C ). SARS-CoV-2 was separated from its known close relative , the bat coronavirus RaTG13 of the genus Betacoronavirus, approximately 15-43 years ago (the end of the 20th century).

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В работе представлена универсальная математическая модель эпидемического процесса «SRID», позволяющая с помощью бутстрэп анализа оценить доверительные интервалы для важнейших параметров эпидемического процесса с учетом погрешностей в исходных статистических данных. На основе модели получены варианты прогнозов возможного развития эпидемии COVID-19 в г. Москва по исходным данным за период с 25.03.2020 г. по 21.04.2020 г. при «естественном» течении эпидемического процесса и при принятии эффективных противоэпидемических мер. Значение параметра R_0 (базовое репродуктивное число) в варианте без учета проведения профилактических мер составило от 3,22 до 6,89, что существенно выше этого показателя для других сезонных эпидемий – гриппа и ОРВИ. При таком R_0 пик эпидемии придется на период с 10.05.2020 г. до 05.06.2020 г., а смертность от эпидемии может составить от 24620 до 57032 человек. Для сокращения продолжительности и уменьшения последствий эпидемии необходим комплекс мер, направленных на снижение значения R_0, к которым следует отнести эффективные изоляционные, карантинные и дезинфекционные мероприятия. В случае их успешной реализации пик эпидемии придется на срок от 10.05.2020 г. до 05.06.2020 г., а смертность по итогам эпидемии может составить от 407 до 2573 человек. Оценка факторов возможного развития эпидемии имеет большое значение для принятия управляющих решений медицинского и экономического характера в масштабах всей РФ. Скрипт и для языка программирования R, реализующие модель «SRID» и первичные данные по г. Москва доступны по ссылке: https://github.com/barnsys/r_srid_model.

We evaluated the temporal signal and substitution rate of tick-borne encephalitis virus (TBEV) using 276 complete open reading frame (ORF) sequences with known collection dates. According to a permutation test, the TBEV Siberian subtype (TBEV-S) data set has no temporal structure and cannot be applied for substitution rate estimation without other TBEV subtypes. The substitution rate obtained suggests that the common clade of TBEV (TBEV-common), including all TBEV subtypes and louping-ill virus (LIV), is characterized by the lowest rate (1.87 × 10−5 substitutions per site per year (s/s/y) or 1 nucleotide substitution per ORF per 4.9 years; 95% highest posterior density (HPD) interval, 1.3–2.4 × 10−5 s/s/y) among all tick-borne flaviviruses previously assessed. Within TBEV-common, the TBEV European subtype (TBEV-E) has the lowest substitution rate (1.3 × 10−5 s/s/y or 1 nucleotide substitution per ORF per 7.5 years; 95% HPD, 1.0–1.8 × 10−5 s/s/y) as compared with TBEV Far-Eastern subtype (3.0 × 10−5 s/s/y or 1 nucleotide substitution per ORF per 3.2 years; 95% HPD, 1.6–4.5 × 10−5 s/s/y). TBEV-common representing the species tick-borne encephalitis virus diverged 9623 years ago (95% HPD interval, 6373–13,208 years). The TBEV Baikalian subtype is the youngest one (489 years; 95% HPD, 291–697 years) which differs significantly by age from TBEV-E (848 years; 95% HPD, 596–1112 years), LIV (2424 years; 95% HPD, 1572–3400 years), TBEV-FE (1936 years, 95% HPD, 1344–2598 years), and the joint clade of TBEV-S (2505 years, 95% HPD, 1700–3421 years) comprising Vasilchenko, Zausaev, and Baltic lineages.

Sponges are unusual representatives of the animal kingdom; their viromes, as part of the associated community, began to be studied quite recently, and, accordingly, these studies are gaining momentum. The diversity of viruses in sponges is high, and they most likely play a significant role in the composition of the sponge holobiont, especially under stress conditions. The objects of our metagenomic study were RNA viruses of two common endemic species of Baikal sponges, Lubomirskia baikalensis and Baikalospongia bacillifera. As a result of viral RNA sequencing, we were able to identify fragments of viral genomes related to those from the RefSeq NCBI complete viral genome database. Most of the similar genomes belonged to viruses isolated from various invertebrates; some of the scaffolds were related to known plant viruses, and one of them was related to a vertebrate virus. The similarity of the putative proteins of viral scaffolds from the Baikal sponges with proteins of known viruses turned out to be low (20.7–67.3%), indicating the detection of novel viruses. The samples of diseased and visually healthy sponges were clustered separately, suggesting a shift in sponge virome composition during the course of the disease. In a comparative analysis, the viromes of the Baikal and marine sponges differed significantly, demonstrating the influence of the host species, habitat, and geographical location on virome composition in the sponge holobiont.