Biology subjects

Tsirvouli, E.

Publications and source records attributed to Tsirvouli, E..

7 recordsLinked to original sources

bioRxiv2024

NeKo: a tool for automatic network construction from prior knowledge

Biological networks provide a structured framework for analyzing the dynamic interplay and interactions between molecular entities, facilitating deeper insights into cellular functions and biological processes. Network construction often requires extensive manual curation based on scientific literature and public databases, a time-consuming and laborious task. To address this challenge, we introduce NeKo, a Python package to automate the construction of biological networks by integrating and prioritizing molecular interactions from various databases. NeKo allows users to provide their molecules of interest (e.g., genes, proteins or phosphosites), select interaction resources and apply flexible strategies to build networks based on prior knowledge. Users can filter interactions by various criteria, such as direct or indirect links and signed or unsigned interactions, to tailor the network to their needs and downstream analysis. We demonstrate some of NeKos capabilities in two use cases: first we construct a network based on transcriptomics from medulloblastoma; in the second, we model drug synergies. NeKo streamlines the network-building process, making it more accessible and efficient for researchers. The tool is available at https://sysbio-curie.github.io/Neko/.

bioinformatics↗

bioRxiv2024

Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations

In cancer treatment, the persistent challenge of unresponsiveness of certain patients to drugs or the development of resistance post-treatment remains a significant concern. Drug combinations that synergistically reduce tumor growth emerge as a promising avenue to address this issue. Here, we aimed to characterize the mechanism of action of two synergistic drug combinations that target PI3K together with MEK1 or with TAK1 and used time course measurements of phosphoproteomics and transcriptomics in response to single inhibitors and their combinations. Our analysis untangled those responses driven by single drugs and responses that were unique to the combinations. We observed a high overlap between single-drug responses and their combinations, suggesting that single-drug mechanisms dominate the mechanism of action of the combinations of the kinase inhibitors. Despite a high overlap, both drug combinations exhibited a synergistic modulation of several cell fate regulators found at the convergence points of the targeted pathways, including the key regulator of intrinsic apoptosis BCL2L11. Interestingly, the responses in both combinations were largely limited to the targeted pathways, namely PI3K/AKT and MAPKs, with very limited change of any other additional cell fate decision pathways. In addition, we observed a strong downregulation of nucleotide metabolism and tRNA biosynthesis uniquely in the combinations, which could be attributed to the reduced activity of mTOR and ATF4. Our approach provides insights into the molecular mechanisms affected by the PI3Ki-TAK1i and PI3Ki-MEKi combinations and can serve as a flexible framework for dissecting drug combination responses based on multi-omics measurements.

cancer biology↗

bioRxiv2023

A dynamic Boolean model of molecular and cellular interactions represents psoriasis development and predicts drug candidates.

Psoriasis is a chronic skin disease affecting 2-3% of the global population. Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation and perturbed keratinocyte life cycle. Although the latest generation of drugs have greatly improved psoriasis management, the disease remains incurable, and the substantial variability in treatment response calls for novel approaches to comprehend the intricate mechanisms underlying disease development and to discover potential drug targets. In this study, we present a multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network metrics, we identify potential pairwise interventions as alternative treatment options. Specifically, The model predictions suggest that targeting neutrophil activation in conjunction with either PGE2 production or STAT3 signaling shows promise comparable to IL-17 inhibition, which is currently the most used treatment option for moderate and severe cases of psoriasis. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling cascades in psoriasis, and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification. Author summaryIn our study, we aimed to uncover the complex mechanisms underlying psoriasis and identify potential treatment options. By utilizing a computational model, we simulated the dynamic interactions between different cell types involved in psoriasis, such as immune cells and keratinocytes. Our model predicts that targeting neutrophil activation, combined with either PGE2 production or STAT3 signaling, may yield comparable effectiveness to the current standard treatment for moderate or severe psoriasis, namely IL-17 inhibition. Our study underscores the importance of computational modeling in unraveling the complexities of disease systems and provides a foundation for identifying new candidate treatment options in psoriasis that should be tested in the lab.

systems biology↗

bioRxiv2023

Patient-specific logical models replicate phenotype responses to psoriatic and anti-psoriatic stimuli.

Psoriasis is a dermatologic disease that affects 2% of the world population. Psoriasis is characterized by chronic inflammation and aberrant behavior of keratinocytes, which display increased levels of proliferation, and decreased differentiation and apoptosis. Stimulation of keratinocytes by psoriatic cytokines leads to the increased production of immunostimulatory ligands that further attract immune cells and amplify inflammatory responses. Psoriasis can have severe, moderate, or mild outcomes and while these severity levels demand custom medical treatment schemes, assigning an effective treatment to patients with moderate or severe disease is a demanding task. The varied responses of patients to treatments highlight a large disease complexity, demanding that new ways to analyze and integrate patients molecular profiles are developed to design patient-specific therapies. We have used gene expression values from psoriasis biopsies to separate patients into two clusters, each with distinct expression profiles, but nevertheless not correlating with any of the available clinical data, such as disease severity. When using these gene expression levels in logical model simulations these data became highly descriptive of patient-specific phenotype characteristics. Starting from a psoriatic keratinocyte model that we published recently, we added additional pathways highlighted by a differential gene expression analysis between the subgroups. This included components from the Interleukin-1 family, IFN-alpha/beta and IL-6 signaling pathways. Model personalization was performed by using patient gene expression levels in model configurations, exploiting the PROFILE pipeline. Personalized simulations revealed that the two patient clusters represent more innate immunity-driven, highly inflammatory phenotypes and adaptive immunity-driven, chronic phenotypes, respectively. The model was also able to finely capture differences between responses in patients with a known disease severity. A treatment response analysis among the patient cohort predicted differential responses to the inhibition of psoriatic stimuli, with IL-17, TNF and PGE2 inhibition reducing proliferation and inflammatory phenotypes. Alternative treatment with PGE2 or TNF inhibition instead of IL-17 was suggested for patients with high NF-{kappa}B activity and prosurvival factors, such as CREB1. With this project, we aim to highlight the value of combining omics data with logical modeling for the detection of emergent phenotypes and for gaining disease knowledge on the individual patient level.

systems biology↗

bioRxiv2023

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1,183 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/534849v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@ad6521org.highwire.dtl.DTLVardef@1ca8f37org.highwire.dtl.DTLVardef@1808ccdorg.highwire.dtl.DTLVardef@9c2613_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2021

Logical and experimental modeling of cytokine and eicosanoid signaling in psoriatic keratinocytes

Psoriasis is characterized by chronic inflammation, perpetuated by a Th17-dependent signaling loop between the immune system and keratinocytes that could involve phospholipase A2 (PLA2)-dependent eicosanoid release. A prior knowledge network supported by experimental observations was used to encode the regulatory network of psoriatic keratinocytes in a computational model for studying the mode of action of a cytosolic (c) PLA2 inhibitor. A combination of evidence derived from the computational model and experimental data suggests that Th17 cytokines stimulate pro-inflammatory cytokine expression in psoriatic keratinocytes via activation of cPLA2-PGE2-EP4 signaling, which could be suppressed using the anti-psoriatic calcipotriol. cPLA2 inhibition and calcipotriol showed overlapping and distinct modes of action. Model analyses revealed the immunomodulatory role of Th1 cytokines, the modulation of the physiological states of keratinocytes by Th17 cytokines, and how Th1 and Th17 cells together promote the development of psoriasis. Model simulations additionally suggest novel drug targets, including EP4 and PRKACA, for treatment that may restore a normal phenotype. Our work illustrates how the study of complex diseases can benefit from an integrated systems approach.

systems biology↗