InSysBio (Russia)

The field of human and medical genomics is undergoing two major transformations (Figure 1). First, data density and dimensionality is increasing. Second, a combination of new and old analytical techniques-notably empowered by artificial intelligence (AI)-are enabling extraction of mechanistic insight and knowledge from these data. In this editorial we highlight a fraction of the many key papers recently published in this field, with a particular emphasis on those that were invited to our research topic with speculative insights based on literature review or research that reflected state-of-the-art methods, detailed recent developments, or highlighted pivotal accomplishments. A focus of this research topic was to showcase progress and pinpoint hurdles that need to be surmounted to push the boundaries of what is medically and technically feasible in genetics.Two years ago, at the launch of this research topic, we envisioned potential advances in the following categories: (1) novel genomic technologies and their clinical application; (2) genetic determinants of complex diseases; (3) ethical, legal, and social implications of genomic research; (4) personalized medicine and genomic tailoring; (5) advances in genomic sequencing techniques; and (6) integration of genomic data with AI and machine learning approaches. These fit into a broader arc of progress in our field. Human genomics fundamentally starts with the genome and ends with personalized medicine. Variant annotation remains the foundation of human genomics and must be built on careful observation of patients 1 reported on the scale of case reports 2 all the way up to population compilations. 3,4 AlphaMissense provides an example of advancing sophistication in variant annotation (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1487608/full). The insight is that variant annotation cannot end without consideration of complex genomic context. To advance mechanistic insights the field will increasingly incorporate epistasis, 5 synergies, population and ancestry context, 6 and whole genome interactions into this functional foundation. Viewing the genome as a web of interactions, and not as a linear construct is a necessary start. Pangenome representations (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1679660/full) illustrate an important step in this direction. Furthermore, The Peruvian Genome Project (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1614021/full) highlights the importance of worldwide pan-population data generation coupled to FAIR data governance (https://www.go-fair.org/fair-principles/). Once a foundation of data is generated, it must be analyzed. The insight is that this analysis must be increasingly mechanistic and holistic, focusing on Self-critique of the field must address major questions:Are current omics data collections big enough? Dense data drives twenty-first century research. 10 Even though modern data sets may comprise data on thousands of variables (e.g., a combination of plasma proteomics and metabolomics), these may not be enough to truly illuminate the dynamic state of the entire human systems and its environment. A combination of increasing technological efficiency, brute force of scale, and careful hypothesis driven selection of assayed biological targets will be necessary to drive complete insights (e.g., shifting from left to right in Figure 1). Are we examining enough dimensions? Many analyses focus on genomics coupled to blood proteomics, metabolomics, & epigenetics. The microbiome is gaining increasing attention. For better systems understanding, we will need to look at multiple tissues and cells (and their compartments) simultaneously, and to measure the entire system dynamically over time. Larger, less biased, recruitment must occur in clinical trials and studies. Continuous monitoring devices and solutions that enable remote trials with massive population participation will drive the future.As analyses get increasingly complex, it becomes more important to carefully design controls, and to red-team (systematically testing for flaws or biases) the computational workflows. Control analyses limit the ways our complex workflows can fool us into conclusions not supported by the data. The safeguards provided by controls become critically important given the widespread availability of AI-and ML-tools that automate data handling without necessarily exercising judgment or understanding. We must use systems thinking and rational epistemological approaches such as Hill's criteria to arrive at medically useful translatable knowledge. 7Are we creating papers and disseminating data that will be part of large meta-analyses? As research becomes increasingly collaborative and cross-discipline, we must write manuscripts that will be accessible and understood by lay people, students, experts, and AI agents. Datasets must be publicly available, easing any barriers to high-throughput automated access, following the FAIR

In the 1970s, it was discovered that isotherms of antibiotic binding to DNA indicate interactions between adsorbed molecules that propagate over several turns along the DNA. These interactions were described using long-range distance-dependent potentials between ligands. The interactions between distant DNA ligands were immediately compared with the previously discovered allostery in proteins, the mutual influence of ligands bound to a protein. Both cooperative and anticooperative interactions between ligands have been observed. It was hypothesized that a ligand, upon binding to DNA, induces a perturbed state of several nearby nucleotide pairs, and this perturbation propagates along the DNA. The mechanism of this phenomenon was elucidated later. X-ray diffraction studies of DNA-protein complexes revealed that bound proteins stretch the grooves, thereby creating more or less favorable binding sites for subsequent proteins in accordance with the helical symmetry of the DNA. Models have been developed further to describe the role of this mechanism in regulating gene expression. We expect that signaling pathways in the cell will be described at the level of systems biology through allosteric interactions of entire cascades of proteins, including DNA-mediated, coupled with conformational changes in DNA. Expanding databases and algorithms developing nowadays for predicting allosteric effects in protein pairs will facilitate the construction of interaction networks in the cell and can take epigenetics and proteomics to a qualitatively new level, providing a deeper understanding of links in protein cascades and spatial changes in complexes of proteins and DNA.

For over eight decades, antibiotics have been the cornerstone of treating bacterial infections. However, the rapid rise of antibiotic-resistant pathogens has created an urgent need for alternative therapeutic strategies. Advances in nanotechnology offer a promising solution through the development of bio-derived nanoparticles. This broad class includes extracellular vesicles such as exosomes and bacterial outer membrane vesicles (OMVs), as well as bioengineered cell membrane-coated nanoparticles (CMNPs) that combine synthetic cores with natural membranes from diverse source cells. These particles possess unique physicochemical and biological properties, such as intrinsic bioactivity, biocompatibility, and structural versatility, that can be harnessed for antimicrobial therapy. This review synthesizes recent progress in the design, characterization, and application of biological nanoparticles for combating bacterial infections. We place particular emphasis on their mechanisms of action, therapeutic potential, and key research directions that could accelerate their translation into clinical use.

Bacteriophages play a significant role in the natural regulation of bacterial populations. To date, a substantial body of information has been obtained regarding the mechanisms that enhance bacterial survival upon encounter with bacteriophages. However, most available data concern the interactions between phages and actively growing bacterial cells modeled by exponential growth phase cultures. The interactions between bacteriophages and host cells in the stationary phase of growth remain poorly understood. In this work, we investigated the interactions of phages T7, capable of propagating in early stationary-phase Escherichia coli cultures. We demonstrate that visible expansion of T7 plaques during prolonged (several days) incubation occurs due to efficient phage infection of the mature microcolonies and killing the cells. In contrast to many other phages (e.g., phage T4), the bacteriophage T7 is able to form plaques even if it is applied onto a mature bacterial lawn that already reached the maximal density. The phage readily penetrates into the microcolonies of the lawn to infect the cells through the whole colony volume without getting sequestered at the surface.

Whooping cough is a serious illness affecting young children. There are two main types of vaccines for whooping cough: cellular and acellular. We focused on studying the immune response to Bordetella pertussis based on the type of vaccination to uncover factors that affect vaccine effectiveness. It is worth noting that the immune response to Bordetella pertussis depends on many factors. In this study, we examine the role of the systemic T cell response. It was found that memory T cells react to components of the pertussis bacterium, and the strength of this response is independent of the vaccination type. In most donors, the T cell response to the inactivated bacterium was more robust than to gd15PtxS1 (genetically detoxified S1 subunit of pertussis toxin), a key ingredient in acellular vaccines. This indicates the existence of dominant epitopes beyond PT that elicit an immune response. Additionally, a bioinformatics analysis was conducted on the epitopes of various B. pertussis proteins, predicting their dominance based on HLA alleles and assessing the potential for cross-reactive T cell responses to other Bordetella species and human microbiota. These findings could help in the development of new vaccines against B. pertussis or in modifying existing vaccines to improve their efficacy and minimize side effects. These results may help in the development of new vaccines against B. pertussis or in the modification of vaccines to their efficacy and minimize side effects.

Members of the insulin receptor family (InsR, IGF1R, and IRR) play a key role in metabolic regulation, yet the structural mechanisms governing their activation remain incompletely understood. While extracellular and kinase domain structures are well characterized, the transmembrane domains (TMDs), responsible for transmitting conformational changes, lack high-resolution structural data, particularly in their physiologically relevant dimeric states. This limitation impedes the development of targeted therapies for diseases like diabetes and cancer, where aberrant receptor signaling is a key driver. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for studying membrane proteins in near-native environments, offering insights into their dynamics and interactions. However, production of isotopically labeled TMDs for NMR studies remains challenging due to low expression yields and solubility issues. Here, we present an optimized cell-free continuous-exchange expression system for high-yield production of InsR, IGF1R, and IRR TMDs, coupled with efficient purification proved by heteronuclear NMR spectroscopy in a membrane mimicking environment.

Introduction. The COVID-19 pandemic has revealed a whole complex of problems related to mathematical modeling of the epidemic process and assessing the effect of preventive and anti-epidemic measures in modern complex societies. Along with this, the accumulation of significant factual data has spurred the active development of agent-based models, in which each agent (a hypothetical person) has a unique set of characteristics and interaction methods determined based on real sociological and demographic data. Aim and objectives. Development and demonstration of the capabilities of the epidemiological agent-based model POEM platform (POpulation Epidemiological Model). Materials and methods. The POEM platform is developed based on the source code of one of the most widely used agent-based models worldwide, Covasim, taking into account the demographic and organizational-administrative conditions specific to the Russian Federation. Results. Computational experiments have shown that due to individual variability in the dynamics of infection development and the specifics of disease registration, even mass events, while leading to an actual increase in the number of infected individuals, do not have a significant impact on the shape of the curve of registered disease incidence. It has been shown that intercity traffic flows at a level of 0.1% of the population per day have a minimal effect on the dynamics of the epidemic's development, while the effect of a 1% population outflow per day sharply reduces the effect of strict anti-epidemic measures implemented in only one particular city. Using the example of the Voronezh region, the transition from the Delta variant to Omicron in early 2022 was modeled, and a high degree of correlation was shown between the model dynamics and the actual ratio of virus variants observed. Conclusion. The model is fully implemented within the Russian system on the server of the Research Institute for System Biology and Medicine of Rospotrebnadzor and can be used to conduct digital epidemiological experiments to predict the effectiveness of proposed anti-epidemic measures.

Abstract Summary Nowadays, search for methylation sites in bacteria is usually performed by direct detection of nucleotide motifs over-represented in modified contexts, using classical motif enrichment approaches oriented only on context sequences themselves. Herein, we present a new algorithm Snappy, which is actually rethinking of the original Snapper algorithm but does not use any enrichment heuristics and does not require control sample sequencing. Opposite to previous methods, Snappy uses raw basecalling data simultaneously with the motif enrichment process, thus significantly enhancing the enrichment sensitivity and accuracy compared with other enrichment algorithms. The versatility of the method was shown on both our and external data, representing different bacterial taxa with complex and simple methylome. Availability and implementation Source code and documentation is hosted on GitHub (https://github.com/DNKonanov/ont-snappy) and Zenodo (zenodo.org/records/16731817). For accessibility, Snappy is installable from PyPi using ‘pip install ont-snappy’ command.

Neutral fats in living organisms are stored in lipid droplets, intracellular organelles enveloped by a phospholipid monolayer. The fusion of these lipid droplets is vital for numerous physiological functions and is regulated by specific proteins and lipids. Dysregulation of this process, leading to excessive droplet growth, is associated with various pathological conditions. Notably, changes in the lipid composition of the boundary monolayers can significantly influence the fusion rate, mirroring fusion dynamics of membranous compartments surrounded by lipid bilayers. In this study, we conducted a theoretical and computational analysis of monolayer fusion, extending the established bilayer fusion model to this context. We characterize the energy trajectory associated with monolayer fusion, tracing the process from the initial unperturbed state to the formation of physical contact between monolayers, and subsequently to the expansion of this structure, which we refer to as the monolayer stalk, analogous to bilayer fusion. Unlike bilayer fusion, monolayer fusion features a single energy barrier, determining the process efficiency. Once this barrier is overcome, further droplet merging occurs spontaneously, highlighting the dynamic nature of lipid droplet interactions. We analyze how lipid composition influences this energy barrier and explore the effects of factors such as Gaussian curvature and hydration-induced repulsion on the energy landscape. Our calculations reveal that Gaussian curvature energy significantly contributes to barrier height. An increase in the proportion of lipids exhibiting large negative spontaneous curvature, which enhances fusion likelihood, can substantially decrease this barrier. Our findings are consistent with existing experimental data and allow us to quantify the barrier height as a function of lipid composition. Specifically, we demonstrate that incorporating 50 mol % of dioleoylphosphatidylethanolamine (DOPE) into pure dioleoylphosphatidylcholine (DOPC) monolayers reduces the energy barrier height by approximately 16 kBT - half of this reduction attributed to changes in spontaneous curvature, with the other half due to modification in hydration repulsion parameters. These findings provide quantitative insights into lipid droplet fusion mechanisms, advancing our understanding of lipid metabolism and its physiological regulation.

Abstract Systematic whole-proteome screening and comprehensive profiling of antigen-specific CD4+ T cells are crucial for advancing our understanding of CD4+ T cell immunity, yet such efforts remain technically challenging. Here, we present a high-throughput platform that employs large-scale class II single-chain trimer libraries to detect antigen-specific CD4+ T cells, while simultaneously profiling their antigen specificity, TCRα/β sequences, MHC restriction, whole transcriptomes, and patient/timepoint origins at single-cell resolution. We benchmarked SCTs against conventional pMHCs and validated the SCT library-based approach in direct ex vivo identification of antigen-specific CD4+ T cells in healthy donors. We then applied the platform to screen the entire SARS-CoV-2 receptor-binding domain in a longitudinal patient cohort, identifying 2,188 antigen-specific CD4+ T cells and revealing key features that define antigen immunogenicity. Extending to cancer, we performed whole-proteome screening of HPV-16 E6/E7 for TCR repertoire profiling in a precancerous cohort, uncovering functional heterogeneity of HPV-specific TCRs. By integrating high-throughput antigen screening with high-dimensional, multi-modal cellular characterization, our approach offers an unprecedented window into CD4+ T cell immunity across diverse disease contexts and empowers the development of new therapies.