In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression.

Background: The study of the biological basis of anxiety, depression, and intellectual disabilities in humans is one of the most actual problems of modern neurophysiology. Of particular interest is the study of complex interactions between molecular genetic factors, electrophysiological properties of the nervous system, and the behavioral characteristics of people. The neurobiological understanding of neuropsychiatric disorders requires not only the identification of genes that play a role in the molecular mechanisms of the occurrence and course of diseases, but also the understanding of complex interactions that occur between these genes. A systematic study of such interactions obviously contributes to the development of new methods of diagnosis, prevention, and treatment of disorders, as the orientation to allele variants of individual loci is not reliable enough, because the literature describes a number of genes, the same alleles of which can be associated with different, sometimes extremely different variants of phenotypic traits, depending on the genetic background, of their carriers, habitat, and other factors. Results: In our study, we have reconstructed a series of gene networks (in the form of protein–protein interactions networks, as well as networks of transcription regulation) to build a model of the influence of complex interactions of environmental factors and genetic risk factors for intellectual disability, depression, and other disorders in human behavior. Conclusion: A list of candidate genes whose expression is presumably associated with environmental factors and has potentially contentious manifestation for behavioral and neurological traits is identified for further experimental verification.

This article introduces Orthoweb (https://orthoweb.sysbio.cytogen.ru/), a software package developed for the calculation of evolutionary indices, including phylostratigraphic indices and divergence indices (K_a/K_s) for individual genes as well as for gene networks. The phylostratigraphic age index (PAI) allows the evolutionary stage of a gene’s emergence (and thus indirectly the approximate time of its origin, known as “evolutionary age”) to be assessed based on the analysis of orthologous genes across closely and distantly related taxa. Additionally, Orthoweb supports the calculation of the transcriptome age index (TAI) and the transcriptome divergence index (TDI). These indices are important for understanding the dynamics of gene expression and its impact on the development and adaptation of organisms. Orthoweb also includes optional analytical features, such as the ability to explore Gene Ontology (GO) terms associated with genes, facilitating functional enrichment analyses that link evolutionary origins of genes to biological processes. Furthermore, it offers tools for SNP enrichment analysis, enabling the users to assess the evolutionary significance of genetic variants within specific genomic regions. A key feature of Orthoweb is its ability to integrate these indices with gene network analysis. The software offers advanced visualization tools, such as gene network mapping and graphical representations of phylostratigraphic index distributions of network elements, ensur­ing intuitive interpretation of complex evolutionary relationships. To further streamline research workflows, Orthoweb includes a database of pre-calculated indices for numerous taxa, accessible via an application programming inter­face (API). This feature allows the users to retrieve pre-computed phylostratigraphic and divergence data efficiently, significantly reducing computational time and effort.

Cancer progression is a complex, multi-stage development process characterized by dynamic changes at the molecular level. Understanding these changes may provide new insights into tumorigenesis and potential therapeutic targets. This study focuses on the evolutionary transcriptomics of cancer, specifically analyzing the Transcriptome Age Index (TAI) across different pathological stages. By examining various cancers at four distinct pathological stages, we identify a significant «hourglass» pattern in TAI indices of ductal carcinoma of the breast, bladder carcinoma, and liver carcinoma, suggesting a conserved evolutionary trajectory during tumor development. The results reveal that early and late stages of these cancers exhibit higher TAI values, indicative of more novel gene expression, while intermediate stages show a dip in TAI, reflecting a more ancient evolutionary origin of expressed genes. This «hourglass» pattern underscores the evolutionary constraints and innovations at play during tumor progression. Our findings contribute to the growing body of evidence that evolutionary principles are deeply embedded in cancer biology, offering new perspectives on the dynamics of gene expression in tumors.

Autism spectrum disorder (ASD) and Alzheimer’s disease (AD) are neurodevelopmental and neurodegenerative disorders, respectively. While exome sequencing is routinely employed during the early stages of ASD diagnosis, it rarely influences therapeutic strategies. To address this gap, we have reconstructed and analyzed the gene networks linking autism spectrum disorders, Alzheimer’s disease, and mTOR signaling. In addition, we have performed a phylostratigraphic analysis that reveals similarities and differences in the evolution of both ASD and Alzheimer’s disease predisposition genes. We have shown that almost half of the genes predisposing to autism and two-fifths of the genes predisposing to Alzheimer’s disease are directly related to the mTOR signaling pathway. Analysis of Phylostratigraphic Age Index (PAI) value distributions revealed a significant enrichment of evolutionarily ancient genes in both ASD- and AD-related gene sets. When studying the distribution of ASD predisposition genes by Divergence Index (DI) values, a significant enrichment with genes having extremely low DI = 0 has been found. Such low DI values indicate that most likely these genes are under stabilizing selection. Using the ANDVisio tool, both pharmacological and natural mTOR regulators with potential for ASD treatment were selected, such as propofol, dexamethasone, celecoxib, statins, berberine, resveratrol, quercetin, myricetin, mio-inositol, and several amino acids.

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 therapy­induced 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 next­generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single­cell 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.