Biology subjects

Ivanova, O.

Publications and source records attributed to Ivanova, O..

5 recordsLinked to original sources

bioRxiv2025

Neuroprotective action of agonists and modulators of A 1 adenosine receptors upon hyperexcitation: mechanism of the antiepileptic activity and role of neuron-glial interaction

Hyperexcitation of neuronal networks is believed to be the main reason for the excitotoxic death of neurons in different central nervous system pathologies, including epilepsy, ischemic stroke, and traumatic brain injury. Gi-coupled receptors can be considered as promising targets for the development of new neuroprotectors. Here, we studied the anticonvulsant activity of the agonists and positive allosteric modulators (PAM) of A1 adenosine receptors (A1Rs). Our experiments demonstrate that A1R agonists, CCPA and N6-cyclohexyladenosine (N6-CHA), suppress hyperexcitation in three different in vitro models, including acute glutamate excitotoxicity, NH4Cl- and bicuculline-induced epileptiform activity. We have found that the inhibitory action of the agonists is mediated by the activation of not only the neuronal A1Rs but also the astrocytic receptors. In astrocytes, A1R agonists enhance GABA release, possibly via induction of calcium transients. Using inhibitory analysis, we have demonstrated that G{beta}{gamma}-mediated activation of phospholipase C and subsequent Ca2+ mobilization from internal stores are essential for generating calcium transients in astrocytes following N6-CHA application. We have shown first that Ca2+-dependent activation of protein kinase C, which is involved in the mechanism of GABA release by astrocytes, is a pivotal step in the realization of the antiepileptic action of A1R agonists. Moreover, using the model of epileptiform activity induced by GABAAR blockade, we have shown that PAMs, PD81723 and VCP171, also suppress hyperexcitation. Furthermore, using the picrotoxin-induced epilepsy model in mice, we demonstrated that A1R agonists exhibit significant anticonvulsant effects and improve animal survival. The PAMs PD81723 and VCP171, when administered one hour before seizure induction, did not significantly affect seizure severity or survival rates. However, chronic administration of VCP171 produced a pronounced anticonvulsant effect and significantly increased survival.. Importantly, PAMs provided therapeutic benefits without significantly affecting overall activity levels in mice. Thus, our study demonstrates that both agonists and PAMs of A1R can be considered as potential therapeutic agents with antiepileptic and neuroprotective activity.

neuroscience↗

bioRxiv2025

OmniPath: integrated knowledgebase for multi-omics analysis

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, miRNA, and enzyme-PTM (post-translational modification) interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model (LLM) agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell and spatial multi-omics data, especially for mechanistic and causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/675512v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17c2b6borg.highwire.dtl.DTLVardef@1069835org.highwire.dtl.DTLVardef@1f2ce76org.highwire.dtl.DTLVardef@1d0b34f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

NetworkCommons: bridging data, knowledge and methods to build and evaluate context-specific biological networks

SummaryWe present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. Availability and ImplementationNetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons. Contactsaezlab@ebi.ac.uk, martin.garrido@embl.de Supplementary DataContribution guidelines, additional figures, and descriptions for data, knowledge, methods, evaluation strategies and their implementation are available in the Supplementary Data and in the NetworkCommons documentation at https://networkcommons.readthedocs.io/.

bioinformatics↗

bioRxiv2020

Integrated intra- and intercellular signaling knowledge for multicelullar omics analysis

Molecular knowledge of biological processes is a cornerstone in the analysis of omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across different resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources in a single database. It covers the interactions and roles of proteins in inter- and intracellular signal transduction, as well as transcriptional and post-transcriptional regulation. We also provide a comprehensive collection of protein complexes and rich annotations on the properties of proteins, including function, localization, and role in diseases. The resource is available for human, and via homology translation for mouse and rat. The data is accessible via OmniPaths web service, a Cytoscape plugin, and packages in R/Bioconductor and Python, providing convenient access options for both computational and experimental scientists. Our resource provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications to study SARS-CoV-2 infection and ulcerative colitis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/221242v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@15de883org.highwire.dtl.DTLVardef@4bfb6aorg.highwire.dtl.DTLVardef@67113aorg.highwire.dtl.DTLVardef@9c328a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2018

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several putative molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in bakers yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.\n\nAuthor SummaryThe relationship between genotype and phenotype is one of the major challenges in biology. While many previous studies have identified genes involved in complex genetic diseases, there is still a gap between genotype and phenotype. One of the difficulties in filling this gap has been attributed to genetic interactions. Large-scale studies have revealed that genetic interactions are widespread in model organisms such as bakers yeast. Several molecular mechanisms have been proposed for different genetic interaction types. However, differences in occurrence and underlying molecular mechanism of genetic interactions have not yet been compared between gene classes of different function. Here, we compared genetic interaction patterns identified using gene expression profiling for two classes of genes: gene specific transcription factors and signaling related genes. We modelled all possible molecular networks to unravel putative molecular differences underlying different genetic interaction patterns. Our study proposes a new mechanistic explanation for a certain genetic interaction pattern that is more strongly associated with transcription factors compared to signaling related genes. Overall, our findings and the computational methodologies implemented here can be valuable for understanding the molecular mechanisms underlying genetic interactions.

genetics↗