Vavilovskii Zhurnal Genetiki i Selektsii最新文献

Data on the genetics and molecular biology of diabetes are accumulating rapidly. This poses the challenge of creating research tools for a rapid search for, structuring and analysis of information in this field. We have developed a web resource, GlucoGenes®, which includes a database and an Internet portal of genes and proteins associated with high glucose (hyperglycemia), low glucose (hypoglycemia), and both metabolic disorders. The data were collected using text mining of the publications indexed in PubMed and PubMed Central and analysis of gene networks associated with hyperglycemia, hypoglycemia and glucose variability performed with ANDSystems, a bioinformatics tool. GlucoGenes® is freely available at: https://glucogenes.sysbio.ru/genes/main. GlucoGenes® enables users to access and download information about genes and proteins associated with the risk of hyperglycemia and hypoglycemia, molecular regulators with hyperglycemic and antihyperglycemic activity, genes up-regulated by high glucose and/or low glucose, genes down-regulated by high glucose and/or low glucose, and molecules otherwise associated with the glucose metabolism disorders. With GlucoGenes®, an evolutionary analysis of genes associated with glucose metabolism disorders was performed. The results of the analysis revealed a significant increase (up to 40 %) in the proportion of genes with phylostratigraphic age index (PAI) values corresponding to the time of origin of multicellular organisms. Analysis of sequence conservation using the divergence index (DI) showed that most of the corresponding genes are highly conserved (DI < 0.6) or conservative (DI < 1). When analyzing single nucleotide polymorphism (SNP) in the proximal regions of promoters affecting the affinity of the TATA-binding protein, 181 SNP markers were found in the GlucoGenes® database, which can reduce (45 SNP markers) or increase (136 SNP markers) the expression of 52 genes. We believe that this resource will be a useful tool for further research in the field of molecular biology of diabetes.

Drought is a critical factor limiting the productivity of bread wheat (Triticum aestivum L.), one of the key agricultural crops. Wheat adaptation to water deficit is ensured by complex molecular genetic mechanisms, including the coordinated work of multiple genes regulated by transcription factors and signaling non-coding RNAs, particularly microRNAs (miRNAs). miRNA-mediated regulation of gene expression is considered one of the main mechanisms of plant resistance to abiotic stresses. Studying these mechanisms necessitates computational systems biology methods. This work aims to reconstruct and analyze the gene network associated with miRNA regulation of wheat adaptation to drought. Using the ANDSystem software and the specialized Smart crop knowledge base adapted for wheat genetics and breeding, we reconstructed a wheat gene network responding to water deficit, comprising 144 genes, 1,017 proteins, and 21 wheat miRNAs. Analysis revealed that miRNAs primarily regulate genes controlling the morphogenesis of shoots and roots, crucial for morphological adaptation to drought. The key network components regulated by miRNAs are the MYBa and WRKY41 family transcription factors, heat-shock protein HSP90, and the RPM1 protein. These proteins are associated with phytohormone signaling pathways and calcium-dependent protein kinases significant in plant water deficit adaptation. Several miRNAs (MIR7757, MIR9653a, MIR9671 and MIR9672b) were identified that had not been previously discussed in wheat drought adaptation. These miRNAs regulate many network nodes and are promising candidates for experimental studies to enhance wheat resistance to water deficiency. The results obtained can find application in breeding for the development of new wheat varieties with increased resistance to water deficit, which is of substantial importance for agriculture in the context of climate change.

In this part of the study, the first component of the concept of "natural genome reconstruction" is being proven. It was shown with mouse and human model organisms that CD34+ hematopoietic bone marrow progenitors take up fragments of extracellular double-stranded DNA through a natural mechanism. It is known that the process of internalization of extracellular DNA fragments involves glycocalyx structures, which include glycoproteins/protein glycans, glycosylphosphatidylinositol-anchored proteins and scavenger receptors. The bioinformatic analysis conducted indicates that the main surface marker proteins of hematopoietic stem cells belong to the indicated groups of factors and contain specific DNA binding sites, including a heparin-binding domain and clusters of positively charged amino acid residues. A direct interaction of CD34 and CD84 (SLAMF5) glycoproteins, markers of hematopoietic stem cells, with double-stranded DNA fragments was demonstrated using an electrophoretic mobility shift assay system. In cells negative for CD34, which also internalize fragments, concatemerization of the fragments delivered into the cell occurs. In this case, up to five oligonucleotide monomers containing 9 telomeric TTAGGG repeats are stitched together into one structure. Extracellular fragments delivered to hematopoietic stem cells initiate division of the original hematopoietic stem cell in such a way that one of the daughter cells becomes committed to terminal differentiation, and the second retains its low-differentiated status. After treatment of bone marrow cells with hDNAgr, the number of CD34+ cells in the colonies increases to 3 % (humans as the model organism). At the same time, treatment with hDNAgr induces proliferation of blood stem cells and their immediate descendants and stimulates colony formation (mouse, rat and humans as the model organisms). Most often, the granulocyte-macrophage lineage of hematopoiesis is activated as a result of processing extracellular double-stranded DNA. The commitment process is manifested by the appearance and repair of pangenomic single-strand breaks. The transition time in the direction of differentiation (the time it takes for pangenomic single-strand breaks to appear and to be repaired) is about 7 days. It is assumed that at the moment of initiation of pangenomic single-strand breaks, a "recombinogenic situation" ensues in the cell and molecular repair and recombination mechanisms are activated. In all experiments with individual molecules, recombinant human angiogenin was used as a comparison factor. In all other experiments, one of the experimental groups consisted of hematopoietic stem cells treated with angiogenin.

SARS-CoV-2 is a virus for which an outstanding number of genome variants were collected, sequenced and stored from sources all around the world. Raw data in FASTA format include 16.8 million genomes, each ≈29,900 nt (nucleotides), with a total size of ≈500 ∙ 109 nt, or 465 Gb. We suggest an approach to data representation and organization, with which all this can be stored losslessly in the operative memory (RAM) of a common PC. Moreover, just ≈330 Mb will be enough. Aligning all genomes versus the initial Wuhan-Hu-1 reference sequence allows each to be represented as a data structure containing lists of point mutations, deletions and insertions. Our implementation of such data representation resulted in a 1:1500 compression ratio (for comparison, compression of the same data with the popular WinRAR archiver gives only 1:62) and fast access to genomes (and their metadata) and comparisons between different genome variants. With this approach implemented as a C++ program, we performed an analysis of various properties of the set of SARS-CoV-2 genomes available in NCBI Genbank (within a period from 24.12.2019 to 24.06.2024). We calculated the distribution of the number of genomes with undetermined nucleotides, 'N's, vs the number of such nucleotides in them, the number of unique genomes and clusters of identical genomes, and the distribution of clusters by size (the number of identical genomes) and duration (the time interval between each cluster's first and last genome). Finally, the evolution of distributions of the number of changes (editing distance between each genome and reference sequence) caused by substitutions, deletions and insertions was visualized as 3D surfaces, which clearly show the process of viral evolution over 4.5 years, with a time step = 1 week. It is in good correspondence with phylogenetic trees (usually based on 3-4 thousand of genome variant representatives), but is built over millions of genomes, shows more details and is independent of the type of lineage/clade classification.

Technologies for the production of a range of compounds using microorganisms are becoming increasingly popular in industry. The creation of highly productive strains whose metabolism is aimed to the synthesis of a specific desired product is impossible without complex directed modifications of the genome using mathematical and computer modeling methods. One of the bacterial species actively used in biotechnological production is Corynebacterium glutamicum. There are already 5 whole-genome flux balance models for it, which can be used for metabolism research and optimization tasks. The paper presents fluxMicrobiotech, a software module developed at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, which implements a series of computational protocols designed for high-performance computer analysis of C. glutamicum whole-genome flux balance models. The tool is based on libraries from the opencobra community (https://opencobra.github.io) within the Python programming language (https://www.python.org), using the Pandas (https://pandas.pydata.org) and Escher (https://escher.readthedocs.io) libraries . It is configured to operate on a 'file-in/file-out' basis. The model, environmental conditions, and model constraints are specified as separate text table files, which allows one to prepare a series of files for each section, creating databases of available test scenarios for variations of the model. Or vice versa, allowing a single model to be tested under a series of different cultivation conditions. Post-processing tools for modeling data are set up, providing visualization of summary charts and metabolic maps.

In this work, we for the first time performed a comprehensive bioinformatics analysis of 568 human genes that, according to the NCBI Gene database as on September 15, 2024, were associated with pain generation, perception and anesthesia. The SCN9A gene encoding the sodium voltage-gated channel α subunit 9 and expressed in sensory neurons for transferring signals to the central nervous system about tissue damage was the only one involved in all the processes of interest at once as a hub gene. First, with our tool called OrthoWeb, we estimated the phylostratigraphic age indices (PAIs) for each of the genes, that is, identified the taxon of the most recent common ancestor of the organisms for which that gene has been sequenced. The mean PAI for all genes under study, including SCN9A as a hub gene for pain generation, perception, response and anesthesia, was '4'. On the evolutionary scale by the Kyoto Encyclopedia of Genes and Genomes (KEGG), the ancestor is the phylum Chordata, some of the most ancient of which evolved the central and the peripheral nervous system. Next, with our tool called ANDSystem, we found that phosphorylation of ion channels is a centerpiece in pain generation, perception, response and anesthesia, on which the efficiency of signal transduction from the peripheral to the central system depends. This conclusion was consistent with literature data on a key role an efficient signal transduction from the peripheral to the central system from the peripheral to the central system for adjusting the human circadian rhythm through detection of a change from the dark of night to the light of day and for identification of the direction of the source of sound by auditory brainstem nuclei, for generating the response to cold stress and for physical coordination. 21 candidate SNP marker of significant SCN9A over- and underexpression. Finally, the ratio of SCN9A upregulating to downregulating SNPs was compared to that for all known human genes estimated by the 1000 Genomes Project Consortium. It was found that SCN9A as a hub gene for pain generation, perception, pain response and anesthesia is acted on by natural selection against its downregulation, to keep the nervous system highly informed on the status of the organism and the environment.

The description of the path from a gene to a trait, as the main task of many areas in biology, is currently being equipped with new methods affecting not only experimental techniques, but also analysis of the results. The pleiotropic effect of a gene is due to its participation in numerous biological processes involved in different traits. A widespread use of genome-wide sequencing of transcripts and transcription factor (TF) binding regions has made the following tasks relevant: unveiling pleiotropic effects of TFs based on the functions of their target genes; compiling the lists of TFs that regulate biological processes of interest; and describing the ways of TF functioning (their primary and secondary targets, higher order targets, TF interactions in the process under study). We have previously developed a method for the reconstruction of TF regulatory networks and proposed an approach that allows identifying which biological processes are controlled by these networks and how this control is exerted. In this paper, we have implemented the approach as PlantReg, a program available as a web service. The paper describes how the program works. The input consists of a list of genes and a list of TFs - known or putative transcriptional regulators of these genes. As an output, the program provides a list of biological processes enriched for these genes, as well as information about by which TFs and through which genes these processes are controlled. We illustrated the use of PlantReg deciphering transcriptional regulation of processes initiated at the early salt stress response in Arabidopsis thaliana L. With PlantReg, we identified biological processes stimulated by the stress, and specific sets of TFs that activate each process. With one of these processes (response to abscisic acid) as an example, we showed that salt stress mainly affects abscisic acid signaling and identified key TFs in this regulation. Thus, PlantReg is a convenient tool for generating hypotheses about the molecular mechanisms that control plant traits.

Gene regulatory networks (GRNs) - interpretable graph models of gene expression regulation - are a pivotal tool for understanding and investigating the mechanisms utilized by cells during development and in response to various internal and external stimuli. Historically, the first approach for the GRN reconstruction was based on the analysis of published data (including those summarized in databases). Currently, the primary GRN inference approach is the analysis of omics (mainly transcriptomic) data; a number of mathematical methods have been adapted for that. Obtaining omics data for individual cells has made it possible to conduct large-scale molecular genetic studies with an extremely high resolution. In particular, it has become possible to reconstruct GRNs for individual cell types and for various cell states. However, technical and biological features of single-cell omics data require specific approaches for GRN inference. This review describes the approaches and programs that are used to reconstruct GRNs from single-cell RNA sequencing (scRNA-seq) data. We consider the advantages of using scRNA-seq data compared to bulk RNA-seq, as well as challenges in GRN inference. We pay specific attention to state-of-the-art methods for GRN reconstruction from single-cell transcriptomes recruiting other omics data, primarily transcription factor binding sites and open chromatin profiles (scATAC-seq), in order to increase inference accuracy. The review also considers the applicability of GRNs reconstructed from single-cell omics data to recover and characterize various biological processes. Future perspectives in this area are discussed.

A software information module of the experimental computer platform "EEG_Self-Construct" was developed and tested in the framework of this study. This module can be applied for identification of neurophysiological markers of self-referential processes based on the joint use of EEG and facial video recording to induce the brain's functional states associated with participants' personality traits. This module was tested on a group of non-clinical participants with varying degrees of severity of autistic personality traits (APT) according to the Broad Autism Phenotype Questionnaire. The degree of individual severity of APT is a quantitative characteristic of difficulties that a person has when communicating with other people. Each person has some individual degree of severity of such traits. Patients with autism are found to have high rates of autistic traits. However, some individuals with high rates of autistic traits are not accompanied by clinical symptoms. Our module allows inducing the brain's functional states, in which the EEG indicators of people with different levels of APT significantly differ. In addition, the module includes a set of software tools for recording and analyzing brain activity indices. We have found that relationships between brain activity and the individual level of severity of APT in non-clinical subjects can be identified in resting-state conditions following recognition of self-referential information, while recognition of socially neutral information does not induce processes associated with APT. It has been shown that people with high scores of APT have increased spectral density in the delta and theta ranges of rhythms in the frontal cortical areas of both hemispheres compared to people with lower scores of APT. This could hypothetically be interpreted as an index of reduced brain activity associated with recognition of self-referential information in people with higher scores of autistic traits. The software module we are developing can be integrated with modules that allow identifying molecular genetic markers of personality traits, including traits that determine the predisposition to mental pathologies.

A rhizosphere (a narrow area of soil around plant roots) is an ecological niche, within which beneficial microorganisms and pathogens compete with each other for organic carbon compounds and for the opportunity to colonize roots. The roots secrete rhizodeposits into the rhizosphere, which include border cells, products of root cell death and liquids secreted by living cells (root exudates). Border cells, which have their name due to their location in the soil next to the root (at the border of the root and soil), represent terminal differentiation of columella and adjacent lateral root cap cells. Border cells can detach from the root cap surface both as single cells and as cell layers. Border cells are constantly supplied to the soil throughout plant life, and the type and intensity of border cells' sloughing depend on both plant species and soil conditions. Currently, data on the factors that control the type of border cells' release and its regulation have been described in different plant species. Border cells are specialized for interaction with the environment, in particular, they are a living barrier between soil microbiota and roots. After separation of border cells from the root tip, transcription of primary metabolism genes decreases, whereas transcription of secondary metabolism genes as well as the synthesis and secretion of mucilage containing these metabolites along with extracellular DNA, proteoglycans and other substances increase. The mucilage that the border cells are embedded in serves both to attract microorganisms promoting plant growth and to protect plants from pathogens. In this review, we describe interactions of border cells with various types of microorganisms and demonstrate their importance for plant growth and disease resistance.