Viruses are evolving at an alarming rate, spreading and inconspicuously adapting to cutting-edge therapies. Therefore, the search for rapid, informative and reliable diagnostic methods is becoming urgent as ever. Conventional clinical tests (PCR, serology, etc.) are being continually optimized, yet provide very limited data. Could high throughput sequencing (HTS) become the future gold standard in molecular diagnostics of viral infections? Compared to conventional clinical tests, HTS is universal and more precise at profiling pathogens. Nevertheless, it has not yet been widely accepted as a diagnostic tool, owing primarily to its high cost and the complexity of sample preparation and data analysis. Those obstacles must be tackled to integrate HTS into daily clinical practice. For this, three objectives are to be achieved: (1) designing and assessing universal protocols for library preparation, (2) assembling purpose-specific pipelines, and (3) building computational infrastructure to suit the needs and financial abilities of modern healthcare centers. Data harvested with HTS could not only augment diagnostics and help to choose the correct therapy, but also facilitate research in epidemiology, genetics and virology. This information, in turn, could significantly aid clinicians in battling viral infections.

Major depression (MD) and posttraumatic stress disorder (PTSD) share common brain mechanisms and treatment strategies. Nowadays, the dramatically developing COVID-19 situation unavoidably results in stress, psychological trauma, and high incidence of MD and PTSD. Hence, the importance of the development of new treatments for these disorders cannot be overstated. Herbal medicine appears to be an effective and safe treatment with fewer side effects than classic pharmaca and that is affordable in low-income countries. Currently, oxidative stress and neuroinflammation attract increasing attention as important mechanisms of MD and PTSD. We investigated the effects of a standardized herbal cocktail (SHC), an extract of clove, bell pepper, basil, pomegranate, nettle, and other plants, that was designed as an antioxidant treatment in mouse models of MD and PTSD. In the MD model of “emotional” ultrasound stress (US), mice were subjected to ultrasound frequencies of 16–20 kHz, mimicking rodent sounds of anxiety/despair and “neutral” frequencies of 25–45 kHz, for three weeks and concomitantly treated with SHC. US-exposed mice showed elevated concentrations of oxidative stress markers malondialdehyde and protein carbonyl, increased gene and protein expression of pro-inflammatory cytokines interleukin (IL)-1β and IL-6 and other molecular changes in the prefrontal cortex as well as weight loss, helplessness, anxiety-like behavior, and neophobia that were ameliorated by the SHC treatment. In the PTSD model of the modified forced swim test (modFST), in which a 2-day swim is followed by an additional swim on day 5, mice were pretreated with SHC for 16 days. Increases in the floating behavior and oxidative stress markers malondialdehyde and protein carbonyl in the prefrontal cortex of modFST-mice were prevented by the administration of SHC. Chromatography mass spectrometry revealed bioactive constituents of SHC, including D-ribofuranose, beta-D-lactose, malic, glyceric, and citric acids that can modulate oxidative stress, immunity, and gut and microbiome functions and, thus, are likely to be active antistress elements underlying the beneficial effects of SHC. Significant correlations of malondialdehyde concentration in the prefrontal cortex with altered measures of behavioral despair and anxiety-like behavior suggest that the accumulation of oxidative stress markers are a common biological feature of MD and PTSD that can be equally effectively targeted therapeutically with antioxidant therapy, such as the SHC investigated here.

Kidney stone disease is an urgent medical and social problem. Genetic factors play an important role in the disease development. This study aims to establish an association between polymorphisms in genes coding for proteins involved in calcium metabolism and the development of calcium urolithiasis in Russian population. In this case-control study, we investigated 50 patients with calcium urolithiasis (experimental group) and 50 persons lacking signs of kidney stone disease (control group). For molecular genetic analysis we used a previously developed gene panel consisting of 33 polymorphisms in 15 genes involved in calcium metabolism: VDR, CASR, CALCR, OPN, MGP, PLAU, AQP1, DGKH, SLC34A1, CLDN14, TRPV6, KLOTHO, ORAI1, ALPL, and RGS14. High-throughput target sequencing was utilized to study the loci of interest. Odds ratios and 95% confidence intervals were used to estimate the association between each SNP and risk of urolithiasis development. Multifactor dimensionality reduction analysis was also carried out to analyze the gene-gene interaction. We found statistically significant (unadjusted p-value < 0.05) associations between calcium urolithiasis and the polymorphisms in the following genes: CASR rs1042636 (OR = 3.18 for allele A), CALCR rs1801197 (OR = 6.84 for allele A), and ORAI1 rs6486795 (OR = 2.25 for allele C). The maximum OR was shown for AA genotypes in loci rs1042636 (CASR) and rs1801197 (CALCR) (OR = 4.71, OR = 11.8, respectively). After adjustment by Benjamini-Hochberg FDR we found only CALCR (rs1801197) was significantly associated with the risk of calcium urolithiasis development. There was no relationship between recurrent course of the disease and family history of urolithiasis in investigated patients. Thus we found a statistically significant association of polymorphism rs1801197 (gene CALCR) with calcium urolithiasis in Russian population.

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.

Discovery and study of viruses carried by migratory birds are tasks of high importance due to the host’s ability to spread infectious diseases over significant distances. With this paper, we present and characterize the first complete genome sequence of atadenovirus from a tern bird (common tern, Sterna hirundo) preliminarily named tern atadenovirus 1 (TeAdV-1). TeAdV-1 genome is a linear double-stranded DNA molecule, 31,334 base pairs which contain 30 methionine-initiated open reading frames with gene structure typical for Atadenovirus genus, and the shortest known inverted terminal repeats (ITRs) within the Atadenovirus genus consisted of 25 bases. The nucleotide composition of the genome is characterized by a low G + C content (33.86%), which is the most AT-rich genome of known avian adenoviruses within Atadenovirus genus. The nucleotide sequence of the TeAdV-1 genome shows high divergence compared to known representatives of the Atadenovirus genus with the highest similarity to the duck atadenovirus 1 (53.7%). Phylogenetic analysis of the protein sequences of core genes confirms the taxonomic affiliation of the new representative to the genus Atadenovirus with the degree of divergence from the known representatives exceeding the interspecies distance within the genus. Thereby we proposed a novel TeAdV-1 to be considered as a separate species.

Next generation sequencing (NGS) technologies have greatly enhanced our ability to identify new viral pathogens in various types of biological samples. This approach has led to the discovery of new viruses and has revealed hidden associations of viromes with many diseases. However, unlike the 16S rRNA, which allows for bacterial detection by metabarcoding, the diversity and variability of viral genomes render the creation of universal oligonucleotides for targeting all known and novel viruses impossible. While whole­genome sequencing solves this problem, its efficiency is inadequate due to the high cost per sample and relatively low sensitivity. Furthermore, the existing approaches to designing oligonucleotides for targeted PCR enrichment are usually incomprehensive, being oriented at detecting a particular viral species or a genus based on the presumption of its presence in the sample. In this study, we developed a computational pipeline for designing genus-specific oligonucleotides that would simultaneously cover a multitude of known viruses from different taxonomic groups. This new tool was used to design an oligonucleotide panel for targeted enrichment of viral nucleic acids in different types of samples, and its applicability for the detection of multiple viral genera at once was demonstrated. Next, we created a custom protocol for NGS library preparation adapted to the new primer panel, which was tested together on a number of samples and proved highly efficient in pathogen detection and identification. Since a reliable algorithm for bioinformatic analysis is crucial for rapid classification of the sequences, in this work, we developed an NGS­based data analysis module and demonstrated its functionality both for detecting novel viruses and analyzing virome diversity. This work was supported by an RSF (Russian Science Foundation) grant (No. 17-74-20096).