Vavilovskii Zhurnal Genetiki i Selektsii最新文献

Precocious puberty (PP, OMIM 176400, 615346) is an autosomal dominant disorder caused by the premature reactivation of the hypothalamic-pituitary-gonadal axis. Genetic, epigenetic, and environmental factors play a decisive role in determining the timing of puberty. In recent years, genetic variants in the KISS1, KISS1R, MKRN3, and DLK1 genes have been identified as genetic causes of PP. The MKRN3 and DLK1 genes are imprinted, and therefore epigenetic modifications, such as DNA methylation, which alter the expression of these genes, can also contribute to the development of PP. The aim of this study is to determine the methylation index of the imprinting centers of the DLK1 and MKRN3 genes in girls with a clinical presentation of PP. The methylation index of the imprinting centers of the DLK1 and MKRN3 genes was analyzed in a group of 45 girls (age 7.2 ± 1.9 years) with a clinical presentation of PP and a normal karyotype using targeted massive parallel sequencing after sodium bisulfite treatment of DNA. The control group consisted of girls without PP (n = 15, age 7.9 ± 1.6 years). No significant age differences were observed between the groups (p > 0.8). Analysis of the methylation index of the imprinting centers of the DLK1 and MKRN3 genes revealed no significant differences between patients with PP and the control group. However, in the group of patients with isolated adrenarche, an increased methylation index of the imprinting center of the MKRN3 gene was observed (72 ± 7.84 vs 56.92 ± 9.44 %, p = 0.005). In the group of patients with central PP, 3.8 % of patients showed a decreased methylation index of the imprinting center of the DLK1 gene, and 11.5 % of probands had a decreased methylation index of the imprinting center of the MKRN3 gene. Thus, this study demonstrates that not only genetic variants but also alterations in the methylation index of the imprinting centers of the DLK1 and MKRN3 genes can contribute to the development of PP.

The achaete-scute complex (AS-C) is a locus approximately 90 kbp in length, containing multiple enhancers. The local expression of the achaete and scute genes in proneural clusters of Drosophila melanogaster imaginal discs results in the formation of a well-defined pattern of macrochaetae in adult flies. A wide variety of easily analyzed phenotypes, along with the direct connection between individual regulatory elements and the development of specific setae make this locus a classic model in developmental genetics. One classic AS-C allele is sc8, which arose as a result of the In(1) sc8 inversion. One breakpoint of this inversion lies between the ac and sc genes, while the second is in the pericentromeric heterochromatin of chromosome X, within satellite block 1.688. The heterochromatic position of the breakpoint raised the question of whether position effect variegation contributes to the disruption of normal locus function in the In(1)sc8 flies. However, conflicting results were obtained. Previously, we found that a secondary inversion, In(1)19EHet, arose spontaneously in one of the stocks of the In(1)sc8 BDSC line, transferring most of the heterochromatin from the ac gene to the 19E region of the X chromosome. Here, we demonstrate that the In(1)19EHet inversion leads to complete rescue of the number of posterior supraalar (PSA) and partial rescue of the number of dorsocentral (DC) macrochaetes observed in the original In(1)sc8 line. The same rescue of the macrochaetes pattern was observed when the In(1)sc8 inversion was introduced into a strain with the Su(var)3-906 position effect modifier. Combining the inversion with the Rif11 mutation, a conserved factor determining late replication and underreplication, does not restore the normal pattern of bristles. Our data indicate that the phenotype of flies carrying the In(1) sc8 inversion, associated with a disturbance in bristle development, is determined by the effect of heterochromatin on the distal part of the locus. This model can be used to test the influence of various factors on the position effect variegation caused by heterochromatin. Another phenotypic manifestation of In(1)sc8, a decreased proportion of males in the offspring, was independent of the proximity of the distal part of AS-C to heterochromatin and was not affected by the Rif11 mutation.

A rapid growth of the available body of genomic data has made it possible to obtain extensive results in genomic prediction and identification of associations of SNPs with phenotypic traits. In many cases, to identify new relationships between phenotypes and genotypes, it is preferable to use machine learning, deep learning and artificial intelligence, especially explainable artificial intelligence, capable of recognizing complex patterns. 80 sources were manually selected; while there were no restrictions on the release date, the main attention was paid to the originality of the proposed approach for use in genomic prediction. The article considers models for genomic prediction, convolutional neural networks, explainable artificial intelligence and large language models. Attention is paid to Data Augmentation, Transfer Learning, Dimensionality Reduction methods and hybrid methods. Research in the field of model-specific and model-independent methods for interpretation of model solutions is represented by three main categories: sensing, perturbation, and surrogate model. The considered examples reflect the main modern trends in this area of research. The growing role of large language models, including those based on transformers, for genetic code processing, as well as the development of data augmentation methods, are noted. Among hybrid approaches, the prospect of combining machine learning models and models of plant development based on biophysical and biochemical processes is emphasized. Since the methods of machine learning and artificial intelligence are the focus of attention of both specialists in various applied fields and fundamental scientists, and also cause public resonance, the number of works devoted to these topics is growing explosively.

One of the main etiological factors in the development of cervical cancer is infection with human papillomavirus (HPV). At the same time, the risk of developing a malignant process increases with an increase in viral load. The aim of this study was to investigate the transcription level of DNA repair and cell cycle control genes in the cervical epithelial cells of women with a clinically significant HPV viral load. The material for the study was DNA and RNA samples isolated from cervical epithelial cells in women. A total of 107 samples were analyzed. 55 women were HPV-positive (with a clinically significant viral load - more than 103 HPV genomes per 100 thousand human cells); the control group consisted of 52 HPV-negative women. All women were over 30 years old. The transcription level of the APEX1, ERCC2, CHEK2, TP53, TP73, CDKN2A, SIRT1 genes was determined using RT-PCR. It was shown that the detection frequency of the APEX1 and ERCC2 gene transcripts was increased in the group of women with a clinically significant viral load. The transcription level of all the studied genes did not differ between the control group and the group with clinically significant HPV concentrations. However, the transcription level of the TP53 and TP73 genes decreased with increasing viral load. In the control, a correlation between the transcription levels of genes involved in the functioning of the p53 protein was revealed. An increase in viral load during HPV infection is associated with a change in the coexpression of DNA repair and cell cycle control genes.

Bolgar was one of the most significant mediaeval cities in Eastern Europe. Before the Mongol conquest, it served as a major administrative centre of Volga Bulgaria, and after 1236, it temporarily functioned as the capital of the Golden Horde. Historical, archaeological, and paleoanthropological evidence indicates a mixed population of this city during the 13th-15th centuries; however, the contributions of exact ethnic groups into its genetic structure remain unclear. To date, there are no genetic data for this medieval group. For the first time, using massive parallel sequencing methods, we determined whole-genome sequences for three individuals from Bolgar who were buried in the early 14th century close to the so-called "Greek Chamber". The average coverage of the studied genomes ranged from x0.5 to x1.5. We identified the genetic sex of the people (two men and one woman), and performed a population genetic analysis. The authenticity of the DNA studied and the low level of contamination were confirmed, and the mitochondrial DNA haplogroups of all three individuals as well as the Y-chromosome haplogroups of two male individuals were determined. We used more than 2.7 thousand DNA samples from representatives of ancient and modern populations that had been previously published to perform a comparative population-genetic analysis. Whole-genome data analysis employing uniparental markers (mitochondrial DNA and Y chromosome) and autosomal markers revealed genetic heterogeneity in this population. Based on PCA and f4- statistics analysis, a genetic connection was identified between one of the individuals (female) and modern Finno-Ugric peoples of the Volga-Ural region. Genomic analysis of the other two individuals suggests their Armenian origin and indicates migrant influx from the Caucasus or Anatolia. The results align well with archaeological and paleoanthropological findings and significantly enhance them by reconstructing the contributions of the indigenous population to the formation of the mediaeval Bolgar population structure.

Flax (Linum usitatissimum) is an important agricultural crop grown for fiber and oil production, playing a key role in various industries such as production of paints, linoleum, food, clothes and composite materials. Fusarium wilt caused by the fungus Fusarium oxysporum f. sp. lini is a reason of significant economic damage in flax cultivation. The spores of the fungus can persist in the soil for a long time, so obtaining resistant varieties is important. Here we used data on the resistance of 297 flax accessions from the collection of the Federal Center for Bast Crops in Torzhok (Russian Federation) to infection by a highly virulent isolate of the fungus MI39 in 2019-2021. Genotype resistance to infection was assessed by calculating the DSI index, a normalized proportion of genotypes with the same disease symptoms. The IIIVmrMLM program in Single_env mode was used to search for regions of the flax genome associated with resistance. The IIIVmrMLM model was designed to address methodological shortcomings in identifying all types of interactions between alleles, genes and environment, and to unbiasedly estimate their genetic effects. Being a multilocus MLM model, it estimates the effects of all genes as well as the effects of all interactions simultaneously. A total of 111 QTNs were found, of which 34 fell within the body of a known gene or were located in flanking regions within 1,000 bp. The genes into which the detected variants fell were associated with resistance to abiotic and biotic stresses, root, shoot and flower growth and development. Ten of the QTNs found mapped to regions of previously identified QTLs controlling the synthesis of palmitic, oleic, and other fatty acids. QTN Chr1_1706865/Chr1_1706872 and QTN Chr8_22542741 mark regions identified previously in an association search by the GAPIT program. The allelic effect was confirmed for all the QTNs found: a Mann-Whitney test was performed, which confirmed significant differences between the DSI index value in carriers of the reference and alternative allele. An increase in the number of alleles with negative effects in the genotype leads to a statistically significant decrease in the DSI value for all three years of testing. The groups of varieties with a large number of alleles reducing the DSI index had the best resistance. A total of 5 varieties were selected from the collection for which the number of alleles reducing the DSI index value did not exceed the number of alleles with the opposite effect for all three years. These varieties can be used further in breeding programs.

Peach (Prunus persica (L.) Batsch) is one of the main agricultural stone fruit crops of the family Rosaceae. Modern breeding is aimed at improving the quality of the fruit, extending the period of its production, increasing its resistance to unfavorable environmental conditions and reducing the total cost of production of cultivated varieties. However, peach breeding is an extremely long process: it takes 10-15 years from hybridization of the parental forms to obtaining fruit-bearing trees. Research into peach varieties as donors of desirable traits began in the 1980s. The first version of the peach genome was presented in 2013, and its appearance contributed to the identification and localization of loci, followed by the identification of candidate genes that control the desired trait. The development of NGS has accelerated the development of methods based on the use of diagnostic DNA markers. Approaches that allow accelerating classical breeding processes include marker-oriented selection (MOS) and genomic selection. In order to develop DNA markers associated with the traits under investigation, it is necessary to carry out preliminary mapping of loci controlling economically desirable traits and to develop linkage maps. SNP-chip approaches and genotyping by sequencing (GBS) methods are being developed. In recent years, genome-wide association analysis (GWAS) has been actively used to identify genomic loci associated with economically important traits, which requires screening of large samples of varieties for hundreds and thousands of SNPs. Study on the pangenome has shown the need to analyze a larger number of samples, since there is still not enough data to identify polymorphic regions of the genome. The aim of this review was to systematize and summarize the major advances in peach genomic research over the last 40 years: linkage and physical map construction, development of different molecular markers, full genome sequencing for peach, and existing methods for genome-wide association studies with high-density SNP markers. This review provides a theoretical basis for future GWAS analysis in order to identify high-performance markers of economically valuable traits for peach and to develop genomic selection of this crop.

Genetic diversity among biological entities, including populations, species, and communities, serves as a fundamental source of information for understanding their structure and functioning. However, many ecological and evolutionary problems arise from limited and complex datasets, complicating traditional analytical approaches. In this context, our study applies a deep learning-based approach to address a crucial question in evolutionary biology: the balance between sexual and asexual reproduction. Sexual reproduction often disrupts advantageous gene combinations favored by selection, whereas asexual reproduction allows faster proliferation without the need for males, effectively maintaining beneficial genotypes. This research focuses on exploring the coexistence patterns of sexual and asexual reproduction within a single species. We developed a convolutional neural network model specifically designed to analyze the dynamics of populations exhibiting mixed reproductive strategies within changing environments. The model developed here allows one to estimate the ratio of population members who originate from sexual reproduction to the clonal organisms produced by parthenogenetic females. This model assumes the reproductive ratio remains constant over time in populations with dual reproductive strategies and stable population sizes. The approach proposed is suitable for neutral multiallelic marker traits such as microsatellite repeats. Our results demonstrate that the model estimates the ratio of reproductive modes with an accuracy as high as 0.99, effectively handling the complexities posed by small sample sizes. When the training dataset's dimensionality aligns with the actual data, the model converges to the minimum error much faster, highlighting the significance of dataset design in predictive performance. This work contributes to the understanding of reproductive strategy dynamics in evolutionary biology, showcasing the potential of deep learning to enhance genetic data analysis. Our findings pave the way for future research examining the nuances of genetic diversity and reproductive modes in fluctuating ecological contexts, emphasizing the importance of advanced computational methods in evolutionary studies.

Pea (Pisum sativum L.) is an important crop culture and a model object for studying the molecular genetic bases of nitrogen-fixing symbiosis and arbuscular mycorrhiza (AM). Pea genotypes with high and low responsivity to inoculation with nodule bacteria (rhizobia) and AM fungi have been described: the 'responsive' genotypes demonstrate an increase in seed weight under inoculation, while 'non-responsive' ones do not show such a reaction. In order to get insight into the molecular genetic mechanisms underlying the symbiotic responsivity, a transcriptomic analysis of whole root systems of pea plants of the 'responsive' genotype k-8274 (cv. Vendevil, France) and 'non-responsive' genotype k-3358 (unnamed cultivar, Saratov region, Russia) grown in soil without inoculation (control) and inoculated either with rhizobia (single inoculation) or with rhizobia together with AM fungi (double inoculation) was performed. It was shown that the 'responsive' genotype, indeed, demonstrated a pronounced transcriptomic response to single and double inoculation, in contrast to the 'non-responsive' genotype. In k-8274, single inoculation led to specific up-regulation of genes related to catabolism of polyamines, lipid metabolism, and jasmonic acid and salicylic acid signaling. Under double inoculation, the specifically up-regulated genes in k-8274 were related to arbuscular mycorrhiza infection, and the down-regulated genes were related to nodulation. This fact matches the phenotype of the plants: the number of nodules was lower in k-8274 under double inoculation as compared to the control. Thus, strict control over the nodule number may be one of the mechanisms underlying the symbiotic responsivity of pea. Finally, a comparison of expression profiles in k-8274 and k-3358 roots under double inoculation also allowed us to identify the transcriptomic signatures characteristic of the symbiotically responsive genotype. Further work will be focused on validation of these transcriptomic markers of the symbiotic responsivity trait in pea.

For many years, the gold standard in the study of malignant tumors has been the in vitro culture of tumor cells, in vivo xenografts or genetically modified animal models. Meanwhile, three-dimensional cell models (3D cultures) have been added to the arsenal of modern biomedical research. 3D cultures reproduce tissue-specific features of tissue topology. This makes them relevant tissue models in terms of cell differentiation, metabolism and the development of drug resistance. Such models are already being used by many research groups for both basic and translational research, and may substantially reduce the number of animal studies, for example in the field of oncological research. In the current literature, 3D cultures are classified according to the technique of their formation (with or without a scaffold), cultivation conditions (static or dynamic), as well as their cellular organization and function. In terms of cellular organization, 3D cultures are divided into "spheroid models", "organoids", "organs-on-a-chip" and "microtissues". Each of these models has its own unique features, which should be taken into account when using a particular model in an experiment. The simplest 3D cultures are spheroid models which are floating spherical cell aggregates. An organoid is a more complex 3D model, in which a self-organizing 3D structure is formed from stem cells (SCs) capable of self-renewal and differentiation within the model. Organ-on-a-chip models are chips of microfluidic systems that simulate dynamic physical and biological processes found in organs and tissues in vitro. By combining different cell types into a single structure, spheroids and organoids can act as a basis for the formation of a microtissue - a hybrid 3D model imitating a specific tissue phenotype and containing tissue-specific extracellular matrix (ECM) components. This review presents a brief history of 3D cell culture. It describes the main characteristics and perspectives of the use of "spheroid models", "organoids", "organ-on-a-chip" models and "microtissues" in immune oncology research of solid tumors.