Biology subjects

Bohar, B.

Publications and source records attributed to Bohar, B..

13 recordsLinked to original sources

bioRxiv2026

The Human Gut Cell Atlas v1.0: A Unified, Community-BuiltReference of Cellular Diversity Across the Gut

Single-cell RNA-sequencing (scRNA-seq) studies have transformed our understanding of cellular diversity in the human intestine, but differences in anatomical sampling, experimental processing and cell-type nomenclature limit comparison across datasets. Here, we present the Human Gut Cell Atlas (HGCA) V1, a community-curated reference of 944,390 healthy and disease-adjacent intestinal cells from 502 samples, 265 donors and 24 landmark studies, linked to detailed anatomical, clinical, and technical metadata. As this metadata is of critical importance to human biology, we developed MetaManager, an open source data management framework for standardized metadata collection and tracking. A five-level consensus taxonomy resolves 94 homeostatic cell identities and enables transferrable, high-resolution annotation of new gut datasets. By assembling cell identity, sampling metadata, and independent spatial validation, intestinal segment and radial tissue depth were revealed to systematically shape cellular composition and transcriptional state, enabling inference of the presence of sampling-dependent tissue niches, including lymphoid follicles. Reference mapping accurately and rapidly recovered cohort-level Crohn's disease and treatment signatures without de novo annotation, while mapping human intestinal organoids quantified cell-type-specific identity, regional specification, maturation, and transcriptional fidelity to native tissue. The HGCA V1 provides a standardized cellular, anatomical coordinate, and metadata system for reproducible, high-quality analysis of intestinal biology across healthy tissue, disease-associated remodelling, and experimental models, providing a continuously evolving community reference for cumulative discovery in human intestinal biology.

systems biology↗

bioRxiv2026

A microbiota-responsive polyfunctional cytotoxic CD4⁺ T-cell state promotes mucosal inflammation in ulcerative colitis

Ulcerative colitis (UC) is characterised by chronic colonic inflammation with marked heterogeneity in disease severity and therapeutic outcomes. Here, we define a spatially organised, polyfunctional cytotoxic CD4 T-cell state associated with mucosal inflammation and adverse therapeutic outcomes in UC. Integrating ex vivo T-cell receptor stimulation with multi-cohort bulk and single-cell transcriptomics and multiparameter flow cytometry, we show that GZMB CD4 T cells are preferentially enriched in inflamed UC mucosa, but not peripheral blood, and co-express cytotoxic molecules, Th1- and Th17-associated cytokines and chemokines, and immunoregulatory receptors. Single-cell analyses implicate inflammatory cytokine and antigen-presentation signals in the acquisition or maintenance of this state. High-resolution spatial profiling localised this programme predominantly to Th17 cells, which were preferentially enriched within multicellular inflammatory and tertiary lymphoid structure-associated niches. Across independent patient cohorts, a transcriptional signature derived from this state increased with endoscopic disease severity and was associated with reduced response to anti-TNF and anti-IL-12/23p40 therapies. Adoptive transfer of Gzma/Gzmb-deficient rather than wild-type CD4 T cells into Rag2-deficient recipient mice markedly attenuated experimental colitis and abrogated the polyfunctional cytokine phenotype, demonstrating that granzyme-dependent effector activity is a key mechanism driving CD4+ T-cell-mediated intestinal inflammation. Finally, human host-microbiome analysis linked this programme to intestinal dysbiosis, while transfer of dysbiotic microbiota promoted the emergence of a corresponding state in vivo. Collectively, these findings define a microbiota-responsive, spatially organised polyfunctional cytotoxic CD4 T-cell programme that contributes to intestinal inflammation and is associated with disease severity and treatment resistance in UC.

immunology↗

bioRxiv2026

Epithelial regulatory networks link non-coding genetic variation to clinical heterogeneity in ulcerative colitis

Ulcerative colitis (UC) is a genetically heterogeneous disease causing chronic intestinal inflammation. Genome-wide association studies have linked numerous non-coding single nucleotide polymorphisms (SNPs) to UC, yet how these variants relate to intestinal epithelial function and clinical outcomes remains largely unknown. To address this, we aimed to reconstruct patient-specific epithelial signalling and regulatory networks perturbed by non-coding SNPs and to dissect patient heterogeneity in a large UC phase III trial. We analysed genotype data from 452 participants in the etrolizumab phase III HICKORY cohort using the integrated SNP network platform (iSNP) to map the downstream effects of non-coding SNPs and identify transcription factors (TFs) they perturb in each patient. TFs were filtered to retain those with differential regulatory activity in UC epithelial cells, patients were stratified by these TF profiles, and clusters were correlated with etrolizumab outcomes. We identified 14 SNP-propagated TFs with differential epithelial regulatory activity. Unsupervised clustering stratified patients into subgroups differing in disease severity and treatment response: two clusters had the highest severity and lowest remission and response rates, whereas two others had the best outcomes. iSNP-based clustering improved endoscopic healing prediction (p = 0.03), with trends for clinical response and histological endoscopic mucosal improvement. This systems genomics framework links patient-specific non-coding SNPs to epithelial signalling and regulatory networks in UC and captures clinically meaningful heterogeneity associated with treatment outcomes. It supports genetics-informed precision medicine and improved clinical trial design in inflammatory bowel disease and beyond.

systems biology↗

bioRxiv2026

OmniPath Metabo: chemical structures, interactions and mechanisms to study the metabolome

Mechanistic and functional analysis of omics data largely relies on the incorporation of prior knowledge; however, connecting metabolomics data and knowledge is a major methodological challenge. This is largely driven by the diverse prior knowledge being fragmented across many databases requiring the merging of different database records across chemical structures, identifiers, and varying levels of structural specificity. Hence, this limits mechanistic interpretation and functional characterisation of the metabolome. Here, we present OmniPath Metabo, a comprehensive, harmonized, metabolome-centric database covering metabolites, lipids, food-derived compounds, and small molecule drugs, along with their associated receptors, transporters, enzymes, reactions, allosteric regulators, and disease associations. OmniPath Metabo harmonizes attributes using controlled vocabularies and ontologies, structures and built-in cheminformatics to map identifiers and track ambiguity. OmniPath Metabo is built directly from 40+ original resources and is freely accessible via an interactive web app and API at metabo.omnipathdb.org. OmniPath Metabo enables dynamic, context-specific construction of subnetworks to serve dedicated purposes, such as cell-cell communication or integrated multi-omics metabolite-driven regulation, connecting reactions, allosteric regulation, metabolite-receptor and metabolite-transporter interactions. Combining it with the over 170 other resources in OmniPath, it can be used for integrated networks of signaling, gene regulation, and metabolism. We showcase the application of OmniPath Metabo by analysing publicly available metabolomics data of lung cancer cell lines and metabolic footprints to mutational patterns. In summary, OmniPath Metabo transforms fragmented resources into a harmonised prior knowledge framework for a mechanistic and functional analysis of the metabolome.

bioinformatics↗

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

Pathogen genomic surveillance as a scalable framework for precision phage therapy

Phage therapy is gaining increasing interest in the fight against critically resistant nosocomial pathogens. However, the narrow host range of bacteriophages hampers the development of broadly effective phage therapeutics and demands precision approaches. Here we combine large-scale phylogeographical analysis with high-throughput phage typing to guide the development of precision phage cocktails targeting carbapenem-resistant Acinetobacter baumannii, a top-priority pathogen. Our analysis reveals that a few strain types dominate infections in each world region, with their geographical distribution remaining stable within six years. As we demonstrate in Eastern Europe, this spatio-temporal distribution enables preemptive preparation of region-specific phage collections that target most local infections. Finally, we showcase the efficacy of a four-phage cocktail against the most prevalent strain type in both in vitro and in vivo animal infection models. Ultimately, genomic surveillance identifies patients benefiting from the same phages across geographical scales, thus providing a scalable framework for precision phage therapy. HighlightsO_LIA few carbapenem-resistant Acinetobacter baumannii types dominate infections worldwide C_LIO_LIPhylogeography reveals stable strain composition of individual countries over a six-year period C_LIO_LIThis spatio-temporal distribution allows preemptive preparation of region-specific phage collections C_LIO_LIA four-phage cocktail is efficacious against the most prevalent strain type in Europe. C_LI

microbiology↗

bioRxiv2024

mulea - an R package for enrichment analysis using multiple ontologies and empirical FDR correction

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs an innovative empirical false discovery rate (eFDR) correction method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, muleas architecture allows for easy integration of user-defined ontologies, expanding its applicability across diverse research areas. Availability and ImplementationSoftware for the tools demonstrated in this article is available as an R package on GitHub: https://github.com/ELTEbioinformatics/mulea.

bioinformatics↗

bioRxiv2023

AutophagyNet: High-resolution data source for the analysis of autophagy and its regulation

Autophagy is a highly-conserved catabolic process eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last seven years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, elongation, termination, etc); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the users needs. The resource is available in various file formats (e.g., CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific using (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.

bioinformatics↗

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↗

bioRxiv2021

Reprogramming of the intestinal epithelial-immune cell interactome during SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents an unprecedented worldwide health problem. Although the primary site of infection is the lung, growing evidence points towards a crucial role of the intestinal epithelium. Yet, the exact effects of viral infection and the role of intestinal epithelial-immune cell interactions in mediating the inflammatory response are not known. In this work, we apply network biology approaches to single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids to investigate how altered intracellular pathways upon infection in intestinal enterocytes leads to modified epithelial-immune crosstalk. We point out specific epithelial-immune interactions which could help SARS-CoV-2 evade the immune response. By integrating our data with existing experimental data, we provide a set of epithelial ligands likely to drive the inflammatory response upon infection. Our integrated analysis of intra- and inter-cellular molecular networks contribute to finding potential drug targets, and suggest using existing anti-inflammatory therapies in the gut as promising drug repurposing strategies against COVID-19.

bioinformatics↗

bioRxiv2020

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a cell-type specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis. The workflow is available on GitHub (https://github.com/korcsmarosgroup/ViralLink) in an easily accessible Python wrapper script, or as customisable modular R and Python scripts.Author summary Collaborative and multidisciplinary science provides increased value for experimental datasets and speeds the process of discovery. Such ways of working are especially important at present due to the urgency of the SARS-CoV-2 pandemic. Here, we present a systems biology workflow which models the effect of viral proteins on the infected host cell, to aid collaborative and multidisciplinary research. Through integration of gene expression datasets with context-specific and context-agnostic molecular interaction datasets, the workflow can be easily applied to different datasets as they are made available. Application to diverse SARS-CoV-2 datasets will increase our understanding of the mechanistic details of the infection at a cell type specific level, aid drug target discovery and help explain the variety of clinical manifestations of the infection.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗