[Molecular Mechanisms to Optimize Gene Translation Elongation Differ Significantly in Bacteria with and without Nonribosomal Peptides].

Nonribosomal peptides play an important role in the vital activity of bacteria and have an extremely broad field of biological activity. In particular, they act as antibiotics, toxins, surfactants, siderophores, and also perform a number of other specific functions. Biosynthesis of these molecules does not occur on ribosomes but by special enzymes that form gene clusters in bacterial genomes. We hypothesized that the presence of nonribosomal peptide synthesis pathways is a specific feature of bacterial metabolism, which may affect other vital processes of the cell, including translational ones. This work was the first to show the relationship between the translation regulation mechanism of protein-coding genes in bacteria, which is largely determined by the efficiency of translation elongation, and the presence of gene clusters in the genomes for the biosynthesis of nonribosomal peptides. Bioinformatic analysis of the translation elongation efficiency of protein-coding genes was performed in 11679 bacterial genomes, some of which contained gene clusters of nonribosomal peptide biosynthesis and some of which did not. The analysis showed that bacteria whose genomes contained clusters of nonribosomal peptide biosynthetic genes and those without such gene clusters differ significantly in the molecular mechanisms that ensure translation efficiency. Thus, among microorganisms whose genomes contain gene clusters of nonribosomal peptide synthetases, a significantly smaller part of them is characterized by optimized regulation of the number of local inverted repeats, while most of them have genomes optimized by the averaged energy of inverted repeats studs in mRNA and additionally by codon composition. Our results suggest that the presence of nonribosomal peptide biosynthetic pathways in bacteria may influence the structure of the overall bacterial metabolism, which is also expressed in the specific mechanisms of ribosomal protein biosynthesis.

Evolution of human genes encoding cell surface receptors involved in the regulation of appetite: an analysis based on the phylostratigraphic age and divergence indexes.

Genes encoding cell surface receptors make up a significant portion of the human genome (more than a thousand genes) and play an important role in gene networks. Cell surface receptors are transmembrane proteins that interact with molecules (ligands) located outside the cell. This interaction activates signal transduction pathways in the cell. A large number of exogenous ligands of various origins, including drugs, are known for cell surface receptors, which accounts for interest in them from biomedical researchers. Appetite (the desire of the animal organism to consume food) is one of the most primitive instincts that contribute to survival. However, when the supply of nutrients is stable, the mechanism of adaptation to adverse factors acquired in the course of evolution turned out to be excessive, and therefore obesity has become one of the most serious public health problems of the twenty-first century. Pathological human conditions characterized by appetite violations include both hyperphagia, which inevitably leads to obesity, and anorexia nervosa induced by psychosocial stimuli, as well as decreased appetite caused by neurodegeneration, inflammation or cancer. Understanding the evolutionary mechanisms of human diseases, especially those related to lifestyle changes that have occurred over the past 100-200 years, is of fundamental and applied importance. It is also very important to identify relationships between the evolutionary characteristics of genes in gene networks and the resistance of these networks to changes caused by mutations. The aim of the current study is to identify the distinctive features of human genes encoding cell surface receptors involved in appetite regulation using the phylostratigraphic age index (PAI) and divergence index (DI). The values of PAI and DI were analyzed for 64 human genes encoding cell surface receptors, the orthologs of which were involved in the regulation of appetite in model animal species. It turned out that the set of genes under consideration contains an increased number of genes with the same phylostratigraphic age (PAI = 5, the stage of vertebrate divergence), and almost all of these genes (28 out of 31) belong to the superfamily of G-protein coupled receptors. Apparently, the synchronized evolution of such a large group of genes (31 genes out of 64) is associated with the development of the brain as a separate organ in the first vertebrates. When studying the distribution of genes from the same set by DI values, a significant enrichment with genes having a low DIs was revealed: eight genes (GPR26, NPY1R, GHSR, ADIPOR1, DRD1, NPY2R, GPR171, NPBWR1) had extremely low DIs (less than 0.05). Such low DI values indicate that most likely these genes are subjected to stabilizing selection. It was also found that the group of genes with low DIs was enriched with genes that had brain-specific patterns of expression. In particular, GPR26, which had the lowest DI, is in the group of brain-specific genes. Because the endogenous ligand for the GPR26 receptor has not yet been identified, this gene seems to be an extremely interesting object for further theoretical and experimental research. We believe that the features of the genes encoding cell surface receptors we have identified using the evolutionary metrics PAI and DI can be a starting point for further evolutionary analysis of the gene network regulating appetite.

Laboratory information systems for research management in biology.

Modern investigations in biology often require the efforts of one or more groups of researchers. Often these are groups of specialists from various scientific fields who generate and share data of different formats and sizes. Without modern approaches to work automation and data versioning (where data from different collaborators are stored at different points in time), teamwork quickly devolves into unmanageable confusion. In this review, we present a number of information systems designed to solve these problems. Their application to the organization of scientific activity helps to manage the flow of actions and data, allowing all participants to work with relevant information and solving the issue of reproducibility of both experimental and computational results. The article describes methods for organizing data flows within a team, principles for organizing metadata and ontologies. The information systems Trello, Git, Redmine, SEEK, OpenBIS and Galaxy are considered. Their functionality and scope of use are described. Before using any tools, it is important to understand the purpose of implementation, to define the set of tasks they should solve, and, based on this, to formulate requirements and finally to monitor the application of recommendations in the field. The tasks of creating a framework of ontologies, metadata, data warehousing schemas and software systems are key for a team that has decided to undertake work to automate data circulation. It is not always possible to implement such systems in their entirety, but one should still strive to do so through a step-by-step introduction of principles for organizing data and tasks with the mastery of individual software tools. It is worth noting that Trello, Git, and Redmine are easier to use, customize, and support for small research groups. At the same time, SEEK, OpenBIS, and Galaxy are more specific and their use is advisable if the capabilities of simple systems are no longer sufficient.

Intratumor heterogeneity: models of malignancy emergence and evolution.

Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.

The finding and researching algorithm for potentially oscillating enzymatic systems.

Many processes in living organisms are subject to periodic oscillations at different hierarchical levels of their organization: from molecular-genetic to population and ecological. Oscillatory processes are responsible for cell cycles in both prokaryotes and eukaryotes, for circadian rhythms, for synchronous coupling of respiration with cardiac contractions, etc. Fluctuations in the numbers of organisms in natural populations can be caused by the populations' own properties, their age structure, and ecological relationships with other species. Along with experimental approaches, mathematical and computer modeling is widely used to study oscillating biological systems. This paper presents classical mathematical models that describe oscillatory behavior in biological systems. Methods for the search for oscillatory molecular-genetic systems are presented by the example of their special case - oscillatory enzymatic systems. Factors influencing the cyclic dynamics in living systems, typical not only of the molecular-genetic level, but of higher levels of organization as well, are considered. Application of different ways to describe gene networks for modeling oscillatory molecular-genetic systems is considered, where the most important factor for the emergence of cyclic behavior is the presence of feedback. Techniques for finding potentially oscillatory enzymatic systems are presented. Using the method described in the article, we present and analyze, in a step-by-step manner, first the structural models (graphs) of gene networks and then the reconstruction of the mathematical models and computational experiments with them. Structural models are ideally suited for the tasks of an automatic search for potential oscillating contours (linked subgraphs), whose structure can correspond to the mathematical model of the molecular-genetic system that demonstrates oscillatory behavior in dynamics. At the same time, it is the numerical study of mathematical models for the selected contours that makes it possible to confirm the presence of stable limit cycles in them. As an example of application of the technology, a network of 300 metabolic reactions of the bacterium Escherichia coli was analyzed using mathematical and computer modeling tools. In particular, oscillatory behavior was shown for a loop whose reactions are part of the tryptophan biosynthesis pathway.

[Theories of biological evolution from the viewpoint of the modern systemic biology].

Theories of biological evolution advanced in the last 200 years are reviewed from the viewpoint of advances of modern genetics. The theory of gene networks as a key direction of systemic biology is a link connecting different evolutionary theories.

[Evolutionary trends in the prokaryotic community and prokaryotic community-phage systems].

The Evolutionary Constructor software has been used for computer simulation of the life and evolution of communities of unicellular haploid organisms (prokaryotic cells). Opposite trends of the community evolution (simplification and complication of the genome) have been studied. It has been demonstrated that species with reduced genomes tend to replace genetically and metabolically rich species under highly favorable environmental conditions. Under unfavorable conditions, the opposite tendency is observed. It has also been shown that introduction of phages capable of killing the cells into the system may radically change the current evolutionary trend.