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The viral family Coronaviridae comprises four genera, termed Alpha-, Beta-, Gamma-, and Deltacoronavirus. Recombination events have been described in many coronaviruses infecting humans and other animals. However, formal analysis of the recombination patterns, both in terms of the involved genome regions and the extent of genetic divergence between partners, are scarce. Common methods of recombination detection based on phylogenetic incongruences (e.g., a phylogenetic compatibility matrix) may fail in cases where too many events diminish the phylogenetic signal. Thus, an approach comparing genetic distances in distinct genome regions (pairwise distance deviation matrix) was set up. In alpha, beta, and delta-coronaviruses, a low incidence of recombination between closely related viruses was evident in all genome regions, but it was more extensive between the spike gene and other genome regions. In contrast, avian gammacoronaviruses recombined extensively and exist as a global cloud of genes with poorly corresponding genetic distances in different parts of the genome. Spike, but not other structural proteins, was most commonly exchanged between coronaviruses. Recombination patterns differed between coronavirus genera and corresponded to the modular structure of the spike: recombination traces were more pronounced between spike domains (N-terminal and C-terminal parts of S1 and S2) than within domains. The variability of possible recombination events and their uneven distribution over the genome suggest that compatibility of genes, rather than mechanistic or ecological limitations, shapes recombination patterns in coronaviruses.
Yulia Vakulenko; Andrei Deviatkin; Jan Drexler; Alexander Lukashev. Modular Evolution of Coronavirus Genomes. Viruses 2021, 13, 1270 .
AMA StyleYulia Vakulenko, Andrei Deviatkin, Jan Drexler, Alexander Lukashev. Modular Evolution of Coronavirus Genomes. Viruses. 2021; 13 (7):1270.
Chicago/Turabian StyleYulia Vakulenko; Andrei Deviatkin; Jan Drexler; Alexander Lukashev. 2021. "Modular Evolution of Coronavirus Genomes." Viruses 13, no. 7: 1270.
Molecular phylogenetics, particularly statistical phylogenetics, is widely used to solve the fundamental and applied problems in virology. Bayesian, or statistical, phylogenetic methods, which came into practice 10—15 years ago, markedly expanded the range of questions that can be answered based on analyzing nucleotide and amino acid sequences. An opportunity of using various evolution models allows inferring the chronology, geography and dynamics of the infection spreading. For example, analysis of globally distributed HIV group M by Bayesian methods demonstrated with a probability of 99% that the most recent common ancestor of these viruses existed in the surroundings of the city of Kinshasa (Democratic Republic of the Congo) in the early 1920s. Another study showed that H9N2 influenza virus most likely passed on to humans from wild ducks in Hong Kong in the late 1960s. In addition, using of the Bayesian analysis allows to evaluating the effect of measures taken on the development of the epidemic process. For example, it was shown retrospectively that the rate of hepatitis C virus infection cases in Egypt increased by several orders of magnitude in the mid-20th century. A sharp rise in new case rate is associated with the treatment for schistosomiasis by using non-sterile repeatedly used syringes. A set of Bayesian analysis methods has been applied in tens of thousands of researches describing various aspects of the occurrence and spread of infectious diseases in humans and animals. This was facilitated by the development and accessibility of software that implements these methods. The complexity of Bayesian phylogenetic methods imposes strict requirements on the data being analyzed. The correctness of the phylogenetic analysis data depends on various factors. For example, it is necessary to choose an evolutionary model that most adequately describes the studied objects. A mandatory step in formulating the results is the justification of the selected model. For viruses, the acquisition of genetic elements from other organisms is typical, therefore, the genomes even from closely related viruses may have non-homologous regions unsuitable for phylogenetic analysis. Another aspect is the creation of a representative dataset. Sometimes, all stages of the analysis are not indicated in publications, so that the data obtained can be interpreted ambiguously. The correct use of statistical phylogenetics methods in virology is possible only upon understanding their principles, proper methods of data preparation and evolutionary model selection criteria.
Yu. A. Vakulenko; A. N. Lukashev; A. A. Deviatkin. The use of statistical phylogenetics in virology. Russian Journal of Infection and Immunity 2021, 11, 42 -56.
AMA StyleYu. A. Vakulenko, A. N. Lukashev, A. A. Deviatkin. The use of statistical phylogenetics in virology. Russian Journal of Infection and Immunity. 2021; 11 (1):42-56.
Chicago/Turabian StyleYu. A. Vakulenko; A. N. Lukashev; A. A. Deviatkin. 2021. "The use of statistical phylogenetics in virology." Russian Journal of Infection and Immunity 11, no. 1: 42-56.
Currently, the lowest formal taxon in virus classification is species; however, unofficial lower-level units are commonly used in everyday work. Tick-borne encephalitis virus (TBEV) is a species of mammalian tick-borne flaviviruses that may cause encephalitis. Many known representatives of TBEV are grouped into subtypes, mostly according to their phylogenetic relationship. However, the emergence of novel sequences could dissolve this phylogenetic grouping; in the absence of strict quantitative criterion, it may be hard to define the borders of the first TBEV taxonomic unit below the species level. In this study, the nucleotide/amino-acid space of all known TBEV sequences was analyzed. Amino-acid sequence p-distances could not reliably distinguish TBEV subtypes. Viruses that differed by less than 10% of nucleotides in the polyprotein-coding gene belonged to the same subtype. At the same time, more divergent viruses were representatives of different subtypes. According to this distance criterion, TBEV species may be divided into seven subtypes: TBEV-Eur, TBEV-Sib, TBEV-FE, TBEV-2871 (TBEV-Ob), TBEV-Him, TBEV-178-79 (TBEV-Bkl-1), and TBEV-886-84 (TBEV-Bkl-2).
Andrei Deviatkin; Galina Karganova; Yulia Vakulenko; Alexander Lukashev. TBEV Subtyping in Terms of Genetic Distance. Viruses 2020, 12, 1240 .
AMA StyleAndrei Deviatkin, Galina Karganova, Yulia Vakulenko, Alexander Lukashev. TBEV Subtyping in Terms of Genetic Distance. Viruses. 2020; 12 (11):1240.
Chicago/Turabian StyleAndrei Deviatkin; Galina Karganova; Yulia Vakulenko; Alexander Lukashev. 2020. "TBEV Subtyping in Terms of Genetic Distance." Viruses 12, no. 11: 1240.
Tick-Borne Encephalitis Virus (TBEV) is a dangerous arbovirus widely distributed in Northern Eurasia. The area of this pathogen changes over time. At the beginning of the 2000s, the Ixodes tick populations in Karelia increased. At the same time, the area of I. persulcatus, the main vector of the Siberian TBEV subtype, also expanded. Herein, we sequenced 10 viruses isolated from ticks collected in three locations from the Karelia region in 2008–2018. PCR positive samples were passaged in suckling mice or pig embryo kidney cells (PEK). After the second passage in suckling, mice viral RNA was isolated and E-gene fragment was sequenced. Viral sequences were expected to be similar or nearly identical. Instead, there was up to a 4.8% difference in nucleotide sequence, comparable with the most diverse viruses belonging to the Baltic subgroup in Siberian TBEV subtype (Baltic TBEV-Sib). To reveal whether this was systemic or incidental, a comprehensive phylogeographical analysis was conducted. Interestingly, viruses within each geographic region demonstrated comparable diversity to the whole Baltic TBEV-Sib. Moreover, Baltic TBEV-Sib has a distribution area limited by three ecological regions. This means that active virus mixing occurs in the vast geographic area forming one common virus pool. The most plausible explanation is the involvement of flying animals in the TBEV spread.
Andrei A. Deviatkin; Ivan S. Kholodilov; Oxana A. Belova; Sergey V. Bugmyrin; Lubov A. Bespyatova; Anna Y. Ivannikova; Yulia A. Vakulenko; Alexander N. Lukashev; Galina G. Karganova. Baltic Group Tick-Borne Encephalitis Virus Phylogeography: Systemic Inconsistency Pattern between Genetic and Geographic Distances. Microorganisms 2020, 8, 1589 .
AMA StyleAndrei A. Deviatkin, Ivan S. Kholodilov, Oxana A. Belova, Sergey V. Bugmyrin, Lubov A. Bespyatova, Anna Y. Ivannikova, Yulia A. Vakulenko, Alexander N. Lukashev, Galina G. Karganova. Baltic Group Tick-Borne Encephalitis Virus Phylogeography: Systemic Inconsistency Pattern between Genetic and Geographic Distances. Microorganisms. 2020; 8 (10):1589.
Chicago/Turabian StyleAndrei A. Deviatkin; Ivan S. Kholodilov; Oxana A. Belova; Sergey V. Bugmyrin; Lubov A. Bespyatova; Anna Y. Ivannikova; Yulia A. Vakulenko; Alexander N. Lukashev; Galina G. Karganova. 2020. "Baltic Group Tick-Borne Encephalitis Virus Phylogeography: Systemic Inconsistency Pattern between Genetic and Geographic Distances." Microorganisms 8, no. 10: 1589.
Tick-borne encephalitis (TBE) is one of the most important viral zoonosis transmitted by the bite of infected ticks. In this study, all tick-borne encephalitis virus (TBEV) E gene sequences available in GenBank as of June 2019 with known date of isolation (n = 551) were analyzed. Simulation studies showed that a sample bias could significantly affect earlier studies, because small TBEV datasets (n = 50) produced non-overlapping intervals for evolutionary rate estimates. An apparent lack of a temporal signal in TBEV, in general, was found, precluding molecular clock analysis of all TBEV subtypes in one dataset. Within all subtypes and most of the smaller groups in these subtypes, there was evidence of many medium- and long-distance virus transfers. These multiple random events may play a key role in the virus spreading. For some groups, virus diversity within one territory was similar to diversity over the whole geographic range. This is best exemplified by the virus diversity observed in Switzerland or Czech Republic. These two countries yielded most of the known European subtype Eu3 subgroup sequences, and the diversity of viruses found within each of these small countries is comparable to that of the whole Eu3 subgroup, which is prevalent all over Central and Eastern Europe. Most of the deep tree nodes within all three established TBEV subtypes dated less than 300 years back. This could be explained by the recent emergence of most of the known TBEV diversity. Results of bioinformatics analysis presented here, together with multiple field findings, suggest that TBEV may be regarded as an emerging disease.
Andrei A. Deviatkin; Ivan S. Kholodilov; Yulia A. Vakulenko; Galina G. Karganova; Alexander N. Lukashev. Tick-Borne Encephalitis Virus: An Emerging Ancient Zoonosis? Viruses 2020, 12, 247 .
AMA StyleAndrei A. Deviatkin, Ivan S. Kholodilov, Yulia A. Vakulenko, Galina G. Karganova, Alexander N. Lukashev. Tick-Borne Encephalitis Virus: An Emerging Ancient Zoonosis? Viruses. 2020; 12 (2):247.
Chicago/Turabian StyleAndrei A. Deviatkin; Ivan S. Kholodilov; Yulia A. Vakulenko; Galina G. Karganova; Alexander N. Lukashev. 2020. "Tick-Borne Encephalitis Virus: An Emerging Ancient Zoonosis?" Viruses 12, no. 2: 247.
Rheumatoid arthritis (RA) is a systemic inflammatory joint disease affecting about 1% of the population worldwide. Current treatment approaches do not ensure a cure for every patient. Moreover, classical regimens are based on nontargeted systemic immune suppression and have significant side effects. Biological treatment has advanced considerably but efficacy and specificity issues remain. Gene therapy is one of the potential future directions for RA therapy, which is rapidly developing. Several gene therapy trials done so far have been of moderate success, but experimental and genetics studies have yielded novel targets. As a result, the arsenal of gene therapy tools keeps growing. Currently, both viral and nonviral delivery systems are used for RA therapy. Herein, we review recent approaches for RA gene therapy.
Andrei A. Deviatkin; Yulia A. Vakulenko; Ludmila V. Akhmadishina; Vadim V. Tarasov; Marina Beloukhova; Andrey A. Zamyatnin Jr.; Alexander N. Lukashev. Emerging Concepts and Challenges in Rheumatoid Arthritis Gene Therapy. Biomedicines 2020, 8, 9 .
AMA StyleAndrei A. Deviatkin, Yulia A. Vakulenko, Ludmila V. Akhmadishina, Vadim V. Tarasov, Marina Beloukhova, Andrey A. Zamyatnin Jr., Alexander N. Lukashev. Emerging Concepts and Challenges in Rheumatoid Arthritis Gene Therapy. Biomedicines. 2020; 8 (1):9.
Chicago/Turabian StyleAndrei A. Deviatkin; Yulia A. Vakulenko; Ludmila V. Akhmadishina; Vadim V. Tarasov; Marina Beloukhova; Andrey A. Zamyatnin Jr.; Alexander N. Lukashev. 2020. "Emerging Concepts and Challenges in Rheumatoid Arthritis Gene Therapy." Biomedicines 8, no. 1: 9.
Statistical phylogenetic methods are a powerful tool for inferring the evolutionary history of viruses through time and space. The selection of mathematical models and analysis parameters has a major impact on the outcome, and has been relatively well-described in the literature. The preparation of a sequence dataset is less formalized, but its impact can be even more profound. This article used simulated datasets of enterovirus sequences to evaluate the effect of sample bias on picornavirus phylogenetic studies. Possible approaches to the reduction of large datasets and their potential for introducing additional artefacts were demonstrated. The most consistent results were obtained using “smart sampling”, which reduced sequence subsets from large studies more than those from smaller ones in order to preserve the rare sequences in a dataset. The effect of sequences with technical or annotation errors in the Bayesian framework was also analyzed. Sequences with about 0.5% sequencing errors or incorrect isolation dates altered by just 5 years could be detected by various approaches, but the efficiency of identification depended upon sequence position in a phylogenetic tree. Even a single erroneous sequence could profoundly destabilize the whole analysis by increasing the variance of the inferred evolutionary parameters.
Yulia Vakulenko; Andrei Deviatkin; Alexander Lukashev. The Effect of Sample Bias and Experimental Artefacts on the Statistical Phylogenetic Analysis of Picornaviruses. Viruses 2019, 11, 1032 .
AMA StyleYulia Vakulenko, Andrei Deviatkin, Alexander Lukashev. The Effect of Sample Bias and Experimental Artefacts on the Statistical Phylogenetic Analysis of Picornaviruses. Viruses. 2019; 11 (11):1032.
Chicago/Turabian StyleYulia Vakulenko; Andrei Deviatkin; Alexander Lukashev. 2019. "The Effect of Sample Bias and Experimental Artefacts on the Statistical Phylogenetic Analysis of Picornaviruses." Viruses 11, no. 11: 1032.
Neurovirulent enterovirus 71 (EV-A71) caused a massive epidemic in China in 2008–2011. While subgenotype C4 was the major causative agent, a few isolates were almost identical to the prototype EV-A71 strain and belonged to genotype A. This variant was allegedly extinct since 1970, and its identification in this epidemic suggests reintroduction of the archive virus. Regression analysis of genetic distances (TempEst software) was of moderate utility due to the low resolution of classical phylogenetic methods. Bayesian phylogenetic analysis (BEAST software) suggested artificial introduction event based on highly aberrant phylogenetic tree branch rates that differed by over three standard deviations from the mean substitution rate for EV71. Manual nucleotide-level analysis was used to further explore the virus spread pattern after introduction into circulation. Upon reintroduction, the virus accumulated up to seven substitutions in VP1, most of them non-synonymous and located within the capsid’s canyon or at its rims, compatible with readaptation of a lab strain to natural circulation.
Yulia Vakulenko; Andrei Deviatkin; Alexander Lukashev. Using Statistical Phylogenetics for Investigation of Enterovirus 71 Genotype A Reintroduction into Circulation. Viruses 2019, 11, 895 .
AMA StyleYulia Vakulenko, Andrei Deviatkin, Alexander Lukashev. Using Statistical Phylogenetics for Investigation of Enterovirus 71 Genotype A Reintroduction into Circulation. Viruses. 2019; 11 (10):895.
Chicago/Turabian StyleYulia Vakulenko; Andrei Deviatkin; Alexander Lukashev. 2019. "Using Statistical Phylogenetics for Investigation of Enterovirus 71 Genotype A Reintroduction into Circulation." Viruses 11, no. 10: 895.