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Choice of synonymous codons depends on nucleotide/dinucleotide composition of the genome (termed mutational pressure) and relative abundance of tRNAs in a cell (translational pressure). Mutational pressure is commonly simplified to genomic GC content; however mononucleotide and dinucleotide frequencies in different genomes or mRNAs may vary significantly, especially in RNA viruses. A series of in silico shuffling algorithms were developed to account for these features and analyze the relative impact of mutational pressure components on codon usage bias in RNA viruses. Total GC content was a poor descriptor of viral genome composition and causes of codon usage bias. Genomic nucleotide content was the single most important factor of synonymous codon usage. Moreover, the choice between compatible amino acids (e.g., leucine and isoleucine) was strongly affected by genomic nucleotide composition. Dinucleotide composition at codon positions 2-3 had additional effect on codon usage. Together with mononucleotide composition bias, it could explain almost the entire codon usage bias in RNA viruses. On the other hand, strong dinucleotide content bias at codon position 3-1 found in some viruses had very little effect on codon usage. A hypothetical innate immunity sensor for CpG in RNA could partially explain the codon usage bias, but due to dependence of virus translation upon biased host translation machinery, experimental studies are required to further explore the source of dinucleotide bias in RNA viruses.

Crimean Congo hemorrhagic fever virus (CCHFV) is one of the most severe viral zoonozes. It is prevalent throughout Africa, Asia and southern Europe. Limited availability of sequence data has hindered phylogeographic studies. The complete genomic sequence of all three segments of 14 Crimean Congo hemorrhagic fever virus strains isolated from 1958-2000 in Russia, Central Asia and Africa was identified. Each genomic segment was independently subjected to continuous Bayesian phylogeographic analysis. The origin of each genomic segment was traced to Africa about 1,000-5,000 years ago. The virus was first introduced to South and Central Asia in the Middle Ages, and then spread to China, India and Russia. Reverse transfers of genomic segments from Asia to Africa were also observed. The European CCHFV genotype V was introduced to Europe via the Astrakhan region in South Russia 280-400 years ago and subsequently gradually spread westward in Russia, to Turkey and the Balkans less than 150 years ago. Only a few recombination events could be suggested in S and L genomic segments, while segment reassortment was very common. The median height of a non-reassortant phylogenetic tree node was 68-156 years. There were reassortment events within the European CCHFV lineage, but not with viruses from other locations. Therefore, CCHFV in Europe is a recently emerged zoonosis that represents a spillover from the global gene pool.

Metaviromic studies of potential emerging infection reservoirs led to discovery of many novel viruses. Since metaviromes contain viruses from target host, its food or other sources, fast and robust approaches are needed to predict hosts of unknown viruses based on their genome data. Four machine learning algorithms (random forest, two gradient boosting machines, support vector machine) were used here to predict the hosts of RNA viruses that infect mammals, insects and plants. The prediction efficiency was largely dependent on the dataset composition. In the more challenging task of predicting hosts of unknown virus genera, median weighted F1-score of 0.79 was achieved using support vector machine and 4-mer frequencies, a notable improvement over baseline methods (median weighted F1-scores 0.68 for the homology-based tBLASTx and 0.72 for ML trained on mono-, di- and trinucleotide frequencies). More complicated features and feature combinations provided worse results. When predicting hosts of short virus sequence fragments quality decreased but using same-length fragments instead of full genomes for training consistently produced an improvement of prediction quality. Therefore, short k -mers carry sufficient information to predict hosts of novel RNA virus genera. This algorithm can be useful in rapid analysis of metaviromic data to highlight potential biological threats.

The evolution in Leishmania is governed by the opposite forces of clonality and sexual reproduction, with vicariance being an important factor. As such, Leishmania spp. populations may be monospecific or mixed. Leishmania turanica in Central Asia is a good model to compare these two types. In most areas, populations of L . turanica are mixed with L . gerbilli and L . major . Notably, co-infection with L . turanica in great gerbils helps L . major to withstand a break in the transmission cycle. Conversely, the populations of L . turanica in Mongolia are monospecific and geographically isolated. In this work, we compare genomes of several well-characterized strains of L . turanica originated from monospecific and mixed populations in Central Asia in order to shed light on genetic factors, which may drive evolution of these parasites in different settings. Our results illustrate that evolutionary differences between mixed and monospecific populations of L . turanica are not dramatic. On the level of large-scale genomic rearrangements, we confirmed that different genomic loci and different types of rearrangements may differentiate strains originated from mixed and monospecific populations, with genome translocations being the most prominent example. Our data suggests that L . turanica has a significantly higher level of chromosomal copy number variation between the strains compared to its sister species L . major with only one supernumerary chromosome. This suggests that L . turanica (in contrast to L . major ) is in the active phase of evolutionary adaptation.

Noroviruses infect a wide range of mammals and are the major cause of gastroenteritis in humans. Recombination at the junction of ORF1 encoding nonstructural proteins and ORF2 encoding major capsid protein VP1 is a well-known feature of noroviruses. Using all available complete norovirus sequences, we systematically analyzed patterns of natural recombination in the genus Norovirus both throughout the genome and across the genogroups. Recombination events between nonstructural (ORF1) and structural genomic regions (ORF2 and ORF3) were found in all analyzed genogroups of noroviruses, although recombination was most prominent between members of GII, the most common genogroup that infects humans. The half-life times of recombinant forms (clades without evidence of recombination) of human GI and GII noroviruses were 10.4 and 8.4–11.3 years, respectively. There was evidence of many recent recombination events, and most noroviruses that differed by more than 18% of nucleotide sequence were recombinant relative to each other. However, there were no distinct recombination events between viruses that differed by over 42% in ORF2/3, consistent with the absence of systematic recombination between different genogroups. The few inter-genogroup recombination events most likely occurred between ancient viruses before they diverged into contemporary genogroups. The recombination events within ORF1 or between ORF2/3 were generally rare. Thus, noroviruses routinely exchange full structural and nonstructural blocks of the genome, providing a modular evolution.

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).

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.

In this work we reviewed historical and recent data on Leishmania spp. infection combining data collected in Turkmenistan, Uzbekistan, Kazakhstan, Kyrgyzstan, Iran, China and Mongolia. We specifically focused on a complex of co-existing species (Leishmania major, Leishmania turanica and Leishmania gerbilli) sharing the same animal reservoirs and vectors. In addition, we analysed the presence of dsRNA viruses in these species and discussed future research directions to identify species-specific traits, which may determine susceptibility of different Leishmania spp. to viral infection.

The large virus family Paramyxoviridae includes some of the most significant human and livestock viruses, such as measles-, distemper-, mumps-, parainfluenza-, Newcastle disease-, respiratory syncytial virus and metapneumoviruses. Here we identify an estimated 66 new paramyxoviruses in a worldwide sample of 119 bat and rodent species (9,278 individuals). Major discoveries include evidence of an origin of Hendra- and Nipah virus in Africa, identification of a bat virus conspecific with the human mumps virus, detection of close relatives of respiratory syncytial virus, mouse pneumonia- and canine distemper virus in bats, as well as direct evidence of Sendai virus in rodents. Phylogenetic reconstruction of host associations suggests a predominance of host switches from bats to other mammals and birds. Hypothesis tests in a maximum likelihood framework permit the phylogenetic placement of bats as tentative hosts at ancestral nodes to both the major Paramyxoviridae subfamilies ( Paramyxovirinae and Pneumovirinae ). Future attempts to predict the emergence of novel paramyxoviruses in humans and livestock will have to rely fundamentally on these data. The large virus family, Paramyxoviridae , includes several human and livestock viruses. This study, testing 119 bat and rodent species distributed globally, identifies novel putative paramyxovirus species, providing data with potential uses in predictions of the emergence of novel paramyxoviruses in humans and livestock.

The exponential growth of viral metagenomic data has created an urgent need for accurate and scalable tools for virus discovery, yet the extreme diversity, rapid evolution, and limited reference databases for viruses pose unique computational challenges that traditional sequence comparison methods struggle to address. This systematic review, conducted in accordance with PRISMA 2020, examines current trends and methodological advances in virus discovery tools from 1990 to 2025. As virus discovery is a broad and multi-dimensional topic, this review focuses on the first-line tools used to analyze the results of high-throughput sequencing. The review was conducted using the PubMed database with a snowballing approach, with over 54 key studies selected for the analysis. These studies encompass the following approaches: alignment-based methods, rapid similarity estimation techniques, profile hidden Markov model methods, combination pipelines, k-mer-based approaches, and machine learning-based methods. The transition from alignment-based to machine learning methods has dramatically improved the detection of divergent viruses, yet challenges remain in interpreting model decisions and handling incomplete viral genomes. This review summarizes current knowledge and potential future directions for the development of virus detection capabilities.