Biology subjects

Olbei, M. L.

Publications and source records attributed to Olbei, M. L..

5 recordsLinked to original sources

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↗

bioRxiv2025

SalmonAct Deciphers Transcription Factor Regulatory Activity in Salmonella Transcriptomics

Foodborne Salmonella enterica infection remains a major public health threat due to its prevalence in food, ease of transmission, and increasing antibiotic resistance. Consequently, understanding the molecular mechanisms underlying Salmonella pathogenesis is crucial for guiding novel diagnostic, preventative, and therapeutic approaches. High-throughput transcriptomic technologies are now often employed in Salmonella research to quantify how gene expression changes in response to various conditions or mutations. But due to the high dimensionality of this data, resulting from the comparison of the expression of thousands of genes from multiple strains or culture conditions with complex interactions, interpretation remains a demanding task. To address this issue, we present SalmonAct, a comprehensive signed and directed prior knowledge resource for inferring transcription factor activities in Salmonella. This new resource expands the toolkit available for Salmonella functional analysis. Built as an extension of the SalmoNet2 database, SalmonAct can be used to infer the activity of 191 transcription factors in 5991 regulatory interactions based on publicly available interaction and regulatory knockout data. SalmonAct enhances the interpretation of highly-dimensional transcriptomic data by identifying both highly influential and minimally active transcription factors that drive the observed expression state. SalmonAct aids in bridging the gap between model and non-model organisms functional analysis, and together with the SalmoNet2 resource, can be used for further downstream data analyses. ImportanceWhile modern high-throughput transcriptomics technologies provide effective ways of studying Salmonella enterica, the complexity of these datasets makes interpretation challenging. SalmonAct provides a new computational resource to connect gene expression changes with the activity of transcription factors, key regulators of bacterial adaptation and virulence. SalmonAct can help researchers progress from the analysis of gene expression profiles towards a clearer understanding of the regulatory programs driving the behaviour of the pathogen. By integrating prior knowledge of transcription factor - target gene relationships, SalmonAct contributes to uncovering novel mechanisms of bacterial pathogenesis, and supports the development of new approaches to combat Salmonella infections.

systems biology↗

bioRxiv2024

Decoding Cytokine Networks in Ulcerative Colitis to Identify Pathogenic Mechanisms and Therapeutic Targets

Ulcerative colitis (UC) is a chronic inflammatory disorder of the gastrointestinal tract characterised by dysregulated cytokine signalling. Despite the advent of advanced therapies targeting cytokine signalling, treatment outcomes for UC patients remain suboptimal. Hence, there is a pressing need to better understand the complexity of cytokine regulation in UC by comprehensively mapping the interconnected cytokine signalling networks that are perturbed in UC patients. To address this, we undertook systems immunology modelling of single-cell transcriptomics data from colonic biopsies of treatment-naive and treatment-exposed UC patients to build complex cytokine signalling networks underpinned by putative cytokine-cytokine interactions. The generated cytokine networks effectively captured known physiologically relevant cytokine-cytokine interactions which we recapitulated in vitro in UC patient-derived colonic epithelial organoids. These networks revealed new aspects of UC pathogenesis, including a cytokine subnetwork that is unique to treatment-naive UC patients, the identification of highly rewired cytokines across UC disease states (IL22, TL1A, IL23A, and OSM), JAK paralogue-specific cytokine-cytokine interactions, and the positioning of TL1A as an important upstream regulator of TNF and IL23A as well as an attractive therapeutic target. Overall, these findings open up several avenues for guiding future cytokine-targeting therapeutic approaches in UC, and the presented methodology can be readily applied to gain similar insights into other immune-mediated inflammatory diseases (IMIDs). One Sentence SummaryA systems immunology map of cytokine interaction networks in ulcerative colitis reveals novel insights into disease pathogenesis, with potential to guide future cytokine-targeting therapeutic strategies.

systems biology↗

bioRxiv2021

Multi-layered networks of SalmoNet2 enable strain comparisons of the Salmonella genus on a molecular level

Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens has the potential to identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here we present SalmoNet2, a major update to SalmoNet, the first multi-layered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory and enzyme enzyme interactions. The new version extends the number of Salmonella genomes from 11 to 20, including strains such as S. Typhimurium D23580, an epidemic multidrug-resistant strain leading to invasive non-typhoidal Salmonella Disease (iNTS), and a strain from Salmonella bongori, another species in the Salmonella genus. The database now uses strain specific metabolic models instead of a generalised model to highlight differences between strains. This has increased the coverage of high-quality protein-protein interactions, and enhances interoperability with other computational resources by adopting standardised formats. The resource website has been updated with tutorials to help researchers analyse their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. ImportanceMulti-layered network databases collate information from multiple sources, and are powerful both as a knowledge base and platform for analysis. Here we present SalmoNet2, an integrated network resource of 20 Salmonella strains, containing protein-protein, transcriptional regulatory, and metabolic interactions. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyse their data using SalmoNet2.

systems biology↗

bioRxiv2021

CytokineLink: a cytokine communication map to analyse immune responses in inflammatory and infectious diseases

Intercellular communication mediated by cytokines is critical to the development of immune responses, particularly in the context of infectious and inflammatory diseases. By releasing these small molecular weight peptides, the source cells can influence numerous intracellular processes in the target cells, including the secretion of other cytokines downstream. However, there are no readily available bioinformatic resources that can model cytokine - cytokine interactions. In this effort, we built a communication map between major tissues and blood cells that reveals how cytokine-mediated intercellular networks form during homeostatic conditions. We collated the most prevalent cytokines from literature, and assigned the proteins and their corresponding receptors to source tissue and blood cell types based on enriched consensus RNA-Seq data from the Human Protein Atlas database. To assign more confidence to the interactions, we integrated literature information on cell - cytokine interactions from two systems immunology databases, immuneXpresso and ImmunoGlobe. From the collated information, we defined two metanetworks: a cell-cell communication network connected by cytokines; and a cytokine-cytokine interaction network depicting the potential ways in which cytokines can affect the activity of each other. Using expression data from disease states, we then applied this resource to reveal perturbations in cytokine-mediated intercellular signalling in inflammatory and infectious diseases (ulcerative colitis and COVID-19, respectively). For ulcerative colitis, with CytokineLink we demonstrated a significant rewiring of cytokine-mediated intercellular communication between non-inflamed and inflamed colonic tissues. For COVID-19, we were able to identify inactive cell types and cytokine interactions that may be important following SARS-CoV-2 infection when comparing the cytokine response with other viruses capable of initiating a cytokine storm. Such findings have potential to inform the development of novel, cytokine-targeted therapeutic strategies. CytokineLink is freely available for the scientific community through the NDEx platform and the project github repository (https://github.com/korcsmarosgroup/CytokineLink).

bioinformatics↗