Role Of Retroelements In The Development Of COVID-19 Neurological Consequences

Retroelements play a key role in brain functioning in humans and other animals, since they represent dynamic regulatory elements controlling the expression of specific neuron types. The activity of retroelements in the brain is impaired under the influence of SARS-CoV-2, penetrating the blood-brain barrier. We propose a new concept, according to which the neurological complications of COVID-19 and their long-term effects are caused by modified expression of retroelements in neurons due to viral effect. This effect is implemented in several ways: a direct effect of the virus on the promoter regions of retroelement-encoding genes, virus interaction with miRNAs causing silencing of transposons, and an effect of the viral RNA on the products of retroelement transcription. Aging-related physiological activation of retroelements in the elderly is responsible for more severe course of COVID-19. The associations of multiple sclerosis, Parkinson’s disease, Guillain-Barré syndrome, acute disseminated encephalomyelitis with coronavirus lesions also indicate the role of retroelements in such complications, because retroelements are involved in the mechanisms of the development of these diseases. According to meta-analyses, COVID-19-caused neurological complications ranged 36.4-73%. The neuropsychiatric consequences of COVID-19 are observed in patients over a long period after recovery, and their prevalence may exceed those during the acute phase of the disease. Even 12 months after recovery, unmotivated fatigue, headache, mental disorders, and neurocognitive impairment were observed in 82%, 60%, 26.2-45%, and 16.2-46.8% of patients, correspondingly. These manifestations are explained by the role of retroelements in the integration of SARS-CoV-2 into the human genome using their reverse transcriptase and endonuclease, which results in a long-term viral persistence. The research on the role of specific retroelements in these changes can become the basis for developing targeted therapy for neurological consequences of COVID-19 using miRNAs, since epigenetic changes in the functioning of the genome in neurons, affected by transposons, are reversible

ЭПИГЕНЕТИКА КАНЦЕРОГЕНЕЗА

Currently, the key mechanisms of carcinogenesis are epigenetic events. Epigenetic factors include DNA methylation, histone modifications, microRNA expression and higher chromatin organization. Non-coding RNAs include microRNAs, small interfering RNAs or siRNAs, piRNAs, long noncoding RNAs or lncRNAs. According to recent data, most of these RNAs are directly formed from mobile genetic elements or have a transposon origin. Non-coding RNAs specifically affect the methylation of the genome and the modification of histones in ontogenesis. This is facilitated by evolutionarily programmed features of activation of transposons, since non-coding RNAs are formed from transposons. Thus, the material basis of epigenetic heredity are the transposons. Stress and aging increase the likelihood of developing cancer. This can be explained by an increase in the number of abnormal activation of mobile genetic elements that are sensitive to stress and hormones. Abnormal activation of transposons in cells leads to genomic instability-most such cells undergo apoptosis. However, in some cases, progressive genomic instability leads to damage to oncospressor genes and oncogenes activation - as a result of apoptosis does not occur, and cells acquire the ability of uncontrolled proliferation with the accumulation of a variety of mutations due to the progressive genomic instability caused by the mobilization of transposons. In each type of malignant tumors, specific cascade mechanisms of activation of mobile genetic elements with the participation of non-coding RNA are triggered. The study of epigenetic mechanisms of development of each type of cancer will enable to develop effective methods for early molecular genetic diagnosis of cancer, as well as targeted therapy at different stages of carcinogenesis.В настоящее время ключевыми механизмами канцерогенеза признаны эпигенетические события, к которым относятся специфические изменения метилирования ДНК, модификации гистонов, экспрессия микроРНК и высшая хроматиновая организация. Согласно последним данным, некодирующие РНК (микроРНК, малые интерферирующие РНК или siРНК, piРНК, длинные некодирующие РНК или lncРНК) в большинстве своем либо непосредственно образуются из мобильных генетических элементов, либо имеют транспозонное происхождение. Некодирующие РНК специфически влияют на метилирование генома и модификации гистонов в онтогенезе, чему способствуют эволюционно запрограммированные особенности активации транспозонов, из последовательностей которых происходят данные РНК. Таким образом, материальной основой эпигенетической наследственности служат транспозоны. Под действием стресса и при старении увеличивается вероятность развития онкопатологии, что объясняется повышенной вероятностью аномальной активации мобильных генетических элементов, чувствительных к стрессовым воздействиям и изменению уровня гормонов. Аномальная активация транспозонов в клетках ведет к геномной нестабильности – большинство подобных клеток подвергаются апоптозу. Однако в некоторых случаях прогрессирующая геномная нестабильность ведет к повреждению генов онкосупрессоров и активации онкогенов - в результате апоптоза не происходит, а клетки обретают способность неконтролирующей пролиферации с накоплением множества мутаций вследствие прогрессирующей геномной нестабильности, вызванной мобилизацией транспозонов. В каждом типе злокачественных опухолей запускаются свои каскадные механизмы активации мобильных генетических элементов с участием некодирующих РНК. Исследование эпигенетических механизмов развития каждого типа рака даст возможность разработать эффективные методы ранней молекулярно-генетической диагностики онкопатологии, а также таргетной терапии на разных стадиях развития патологического процесса

Between Lake Baikal and the Baltic Sea: Genomic History of the Gateway to Europe

Background: The history of human populations occupying the plains and mountain ridges separating Europe from Asia has been eventful, as these natural obstacles were crossed westward by multiple waves of Turkic and Uralic speaking migrants as well as eastward by Europeans. Unfortunately, the material records of history of this region are not dense enough to reconstruct details of population history. These considerations stimulate growing interest to obtain a genetic picture of the demographic history of migrations and admixture in Northern Eurasia. Results: We genotyped and analyzed 1076 individuals from 30 populations with geographical coverage spanning from Baltic Sea to Baikal Lake. Our dense sampling allowed us to describe in detail the population structure, provide insight into genomic history of numerous European and Asian populations, and significantly increase quantity of genetic data available for modern populations in region of North Eurasia. Our study doubles the amount of genome-wide profiles available for this region. We detected unusually high amount of shared identical-by-descent (IBD) genomic segments between several Siberian populations, such as Khanty and Ket, providing evidence of genetic relatedness across vast geographic distances and between speakers of different language families. Additionally, we observed excessive IBD sharing between Khanty and Bashkir, a group of Turkic speakers from Southern Urals region. While adding some weight to the “Finno-Ugric” origin of Bashkir, our studies highlighted that the Bashkir genepool lacks the main “core”, being a multi-layered amalgamation of Turkic, Ugric, Finnish and Indo-European contributions, which points at intricacy of genetic interface between Turkic and Uralic populations. Comparison of the genetic structure of Siberian ethnicities and the geography of the region they inhabit point at existence of the “Great Siberian Vortex” directing genetic exchanges in populations across the Siberian part of Asia. Slavic speakers of Eastern Europe are, in general, very similar in their genetic composition. Ukrainians, Belarusians and Russians have almost identical proportions of Caucasus and Northern European components and have virtually no Asian influence. We capitalized on wide geographic span of our sampling to address intriguing question about the place of origin of Russian Starovers, an enigmatic Eastern Orthodox Old Believers religious group relocated to Siberia in seventeenth century. A comparative reAdmix analysis, complemented by IBD sharing, placed their roots in the region of the Northern European Plain, occupied by North Russians and Finno-Ugric Komi and Karelian people. Russians from Novosibirsk and Russian Starover exhibit ancestral proportions close to that of European Eastern Slavs, however, they also include between five to 10 % of Central Siberian ancestry, not present at this level in their European counterparts. Conclusions: Our project has patched the hole in the genetic map of Eurasia: we demonstrated complexity of genetic structure of Northern Eurasians, existence of East-West and North-South genetic gradients, and assessed different inputs of ancient populations into modern populations

Decreased Rate of Evolution in Y Chromosome STR Loci of Increased Size of the Repeat Unit

Polymorphic Y chromosome short tandem repeats (STRs) have been widely used in population genetic and evolutionary studies. Compared to di-, tri-, and tetranucleotide repeats, STRs with longer repeat units occur more rarely and are far less commonly used.In order to study the evolutionary dynamics of STRs according to repeat unit size, we analysed variation at 24 Y chromosome repeat loci: 1 tri-, 14 tetra-, 7 penta-, and 2 hexanucleotide loci. According to our results, penta- and hexanucleotide repeats have approximately two times lower repeat variance and diversity than tri- and tetranucleotide repeats, indicating that their mutation rate is about half of that of tri- and tetranucleotide repeats. Thus, STR markers with longer repeat units are more robust in distinguishing Y chromosome haplogroups and, in some cases, phylogenetic splits within established haplogroups.Our findings suggest that Y chromosome STRs of increased repeat unit size have a lower rate of evolution, which has significant relevance in population genetic and evolutionary studies

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer.

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk

A European spectrum of pharmacogenomic biomarkers: Implications for clinical pharmacogenomics

Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant interpopulation pharmacogenomic biomarker allele frequency differences, particularly in 7 clinically actionable pharmacogenomic biomarkers in 7 European populations, affecting drug efficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on the differences observed in the prevalence of high-risk genotypes in these populations, as far as common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMT pharmacogenes are concerned. Also, our data demonstrate notable differences in predicted genotype-based warfarin dosing among these populations. Our findings can be exploited not only to develop guidelines for medical prioritization, but most importantly to facilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomic testing. This may subsequently contribute towards significant cost-savings in the overall healthcare expenditure in the participating countries, where pharmacogenomics implementation proves to be cost-effective

Genetic modifiers of CHEK2*1100delC-associated breast cancer risk

Purpose: CHEK2*1100delC is a founder variant in European populations that confers a two-to threefold increased risk of breast cancer (BC). Epidemiologic and family studies have suggested that the risk associated with CHEK2*1100delC is modified by other genetic factors in a multiplicative fashion. We have investigated this empirically using data from the Breast Cancer Association Consortium (BCAC). Methods: Using genotype data from 39,139 (624 1100delC carriers) BC patients and 40,063 (224) healthy controls from 32 BCAC studies, we analyzed the combined risk effects of CHEK2*1100delC and 77 common variants in terms of a polygenic risk score (PRS) and pairwise interaction. Results: The PRS conferred odds ratios (OR) of 1.59 (95% CI: 1.212.09) per standard deviation for BC for CHEK2*1100delC carriers and 1.58 (1.55-1.62) for noncarriers. No evidence of deviation from the multiplicative model was found. The OR for the highest quintile of the PRS was 2.03 (0.86-4.78) for CHEK2*1100delC carriers, placing them in the high risk category according to UK NICE guidelines. The OR for the lowest quintile was 0.52 (0.16-1.74), indicating a lifetime risk close to the population average. Conclusion: Our results confirm the multiplicative nature of risk effects conferred by CHEK2*1100delC and the common susceptibility variants. Furthermore, the PRS could identify carriers at a high lifetime risk for clinical actions.Peer reviewe

Origin and spread of human mitochondrial DNA haplogroup U7

Human mitochondrial DNA haplogroup U is among the initial maternal founders in Southwest Asia and Europe and one that best indicates matrilineal genetic continuity between late Pleistocene hunter-gatherer groups and present-day populations of Europe. While most haplogroup U subclades are older than 30 thousand years, the comparatively recent coalescence time of the extant variation of haplogroup U7 (~16–19 thousand years ago) suggests that its current distribution is the consequence of more recent dispersal events, despite its wide geographical range across Europe, the Near East and South Asia. Here we report 267 new U7 mitogenomes that – analysed alongside 100 published ones – enable us to discern at least two distinct temporal phases of dispersal, both of which most likely emanated from the Near East. The earlier one began prior to the Holocene (~11.5 thousand years ago) towards South Asia, while the later dispersal took place more recently towards Mediterranean Europe during the Neolithic (~8 thousand years ago). These findings imply that the carriers of haplogroup U7 spread to South Asia and Europe before the suggested Bronze Age expansion of Indo-European languages from the Pontic-Caspian Steppe region