Biology subjects

Korcsmaros, T.

Publications and source records attributed to Korcsmaros, T..

At least 19 recordsLinked to original sources

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

A dysregulated stromal remodelling programme characterises prior anti-TNF failure in ulcerative colitis

Prior anti-tumour necrosis factor (TNF) failure is associated with reduced efficacy of subsequent advanced therapies in ulcerative colitis (UC), but the biological basis of this treatment-refractory state remains unclear. We integrated clinical outcomes and baseline colonic transcriptomic data from UC patients in the UNIFI phase III trial programme with regulatory and signalling network inference, connectivity mapping, and single-cell-resolution spatial transcriptomics. Colonic transcriptomic analyses identified coordinated enrichment of extracellular matrix organisation, collagen remodelling and integrin-associated programmes, increased stromal cell representation and elevated inferred MAPK/EGFR activity in UC patients with prior anti-TNF failure. Causal network inference prioritised MAPK3 as a candidate regulator of this state, while connectivity mapping identified MEK/EGFR inhibitors as candidate perturbagens. MEK inhibition suppressed stromal pathways and reduced inferred MAPK/EGFR activity ex vivo. Spatial profiling of active UC and non-IBD colonic tissues localised these programmes to UC-enriched stromal niches. Ligand-receptor inference further identified reciprocal stromal-myeloid communication within these niches. Collectively, these findings define a stromal remodelling programme associated with prior anti-TNF failure and nominate MAPK/EGFR signalling as a potentially tractable component of treatment-refractory UC.

systems biology↗

bioRxiv2026

Cytokine interaction networks, not individual cytokines, drive anti-TNF response in Crohn's disease

Crohns disease (CD) is a chronic inflammatory condition of the gastrointestinal tract for which anti-tumour necrosis factor (anti-TNF) agents remain a first-line biologic therapy. However, remission rates are modest, and the mechanistic basis of non-response is poorly characterised. A common resistance mechanism is thought to emerge when alternative inflammatory cascades compensate for TNF inhibition, but the interactions underlying this rewiring have not been systematically characterised. We applied CytokineLink, our previously developed systems immunology framework, to single-cell RNA sequencing data from CD patients sampled before and after anti-TNF therapy. We reconstructed networks of interacting cytokines across samples stratified by treatment phase, response, and inflammation status, and identified condition-specific cytokine interactions and feedback loops, statistically validated against degree-matched random networks. We clustered the generated networks based on their inflammation, response, and treatment status. The pre-treatment inflamed non-responder network contained the largest set of unique interactions, organised around a connected module driven by IL17C targeting downstream TNF, IL6, IL1B, CXCL1/2/3/8, and CCL20. IL17C was produced by a population of non-ileal enteroendocrine cells, differentially abundant at baseline in non-responders. Gene set variation analysis in an independent cohort confirmed elevated non-responder module activity in colonic tissues of non-responders. Feedback loop analysis revealed that responder networks were characterised by persistent IL10 circuits sustained by macrophage populations and acquired tissue-remodelling interactions after therapy, whereas non-responders lost IL10 feedback loops post-treatment and gained TNF-containing motifs, including circuits signalling through the upstream activator TL1A. Our findings characterise the mechanism of anti-TNF non-response as a cytokine network, in which pre-existing epithelial-driven inflammatory modules and the failure to preserve regulatory feedback sustain TNF-independent inflammation in CD. By characterising cytokine interactions at the systems level, our approach moves beyond single-cytokine models of anti-TNF resistance to provide a mechanistic framework for understanding the biological basis of treatment failure in immune mediated diseases.

systems biology↗

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↗

bioRxiv2025

Plasticity in a bacterial global regulatory switch that drives a shift in antibiotic resistance and virulence

Antibiotic resistance and expression of virulence factors can impact the outcome of infection by Pseudomonas aeruginosa. Pathogenesis is often modelled using the P. aeruginosa PAO1 reference strain but laboratory lineages vary in the sequence and activity of MexT, a global regulator impacting virulence, biofilm formation and ciprofloxacin resistance. We defined the impact of active versus inactive MexT in PAO1 and observed global transcriptomic changes affecting the expression of 900 genes. Phenotyping revealed altered metabolism, antibiotic resistance and virulence - resulting in striking variation across a single model organism. We propose that antibiotic-resistance, introduced during work with the original PAO1 strain, has caused plasticity in mexT that accounts for variation across lineages. To test this, we introduced antibiotic resistance into clinical P. aeruginosa isolates and observed downstream mutations in mexT when selective pressure was removed, supporting the proposed evolutionary pathway. Overall, we have demonstrated the transcriptomic basis of MexT as a phenotypic switch in PAO1 and implicated antibiotic resistance as a cause of downstream changes in mexT in P. aeruginosa. Furthermore, MexS/MexT-regulated efflux is implicated in the antibiotic stress response and virulence, helping identify the mechanisms for rapid phenotypic switching through mexT and confirming that PAO1 is a very different organism compared to most isolates. Improved understanding of the regulatory changes linked to antibiotic resistance is particularly relevant to P. aeruginosa where cycles of antibiotic treatment are common.

microbiology↗

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↗

bioRxiv2023

FAK family proteins regulate in vivo breast cancer metastasis via distinct mechanisms.

Breast cancer is the most commonly diagnosed malignancy and the major leading cause of tumor-related deaths in women. It is estimated that the majority of breast tumor-related deaths are a consequence of metastasis, to which no cure exists at present. The FAK family proteins Proline-rich tyrosine kinase (PYK2) and focal adhesion kinase (FAK) are highly expressed in breast cancer, but the exact cellular and signaling mechanisms by which they regulate in vivo tumor cell invasiveness and consequent metastatic dissemination are mostly unknown. Using a PYK2 and FAK knockdown xenograft model we show here, for the first time, that ablation of either PYK2 or FAK decreases primary tumor size and significantly reduces Tumor MicroEnvironment of Metastasis (TMEM) doorway activation, leading to decreased intravasation and reduced spontaneous lung metastasis. Intravital imaging analysis further demonstrates that PYK2, but not FAK, regulates a motility phenotype switch between focal adhesion-mediated fast motility and invadopodia-dependent, ECM-degradation associated slow motility within the primary tumor. Furthermore, we validate our in vivo and intravital imaging results with integrated transcriptomic and proteomic data analysis from xenograft knockdown tumors and reveal new and distinct pathways by which these two homologous kinases regulate breast tumor cell invasiveness and consequent metastatic dissemination. Our findings identify PYK2 and FAK as novel mediators of mammary tumor progression and metastasis and as candidate therapeutic targets for breast cancer metastasis.

cancer biology↗

bioRxiv2023

Invariant natural killer T-cell and CD4+ T-cell derived IL22 is a regulator of epithelial-to-mesenchymal transition and extracellular matrix remodelling in perianal fistulas

Background and aimsPerianal fistulization is a challenging phenotype of Crohns disease (CD). Unravelling the cytokine networks and cellular mediators driving tissue injury in perianal CD (pCD) will help inform much needed novel treatment strategies. MethodsThe phenotype of different T-cell subsets, including unconventional lineages, such as {gamma}{delta} T-cells, MAIT and iNKT-cells in fistula tract tissue and blood samples of patients with pCD or cryptoglandular perianal fistulas was determined using multiparameter flow cytometry. Transcriptomic profiling of fistula tract tissue was performed by RNA-sequencing. ResultsCD161+ CD4+ T-cells and CD161+ CD4- CD8- iNKT-cells significantly accumulated in fistula tissue and produced interleukin-(IL)22 and IL13. Transcripts associated with epithelial-to-mesenchymal transition (EMT), extracellular matrix (ECM) remodelling, interferon-gamma, JAK-STAT and lymphocyte signalling were enriched in pCD, as well as inhibition of pathways associated with wound closure. IL22-responsive transcripts were significantly enriched in fistulas and network analysis identified IL22-mediated regulation of EMT, ECM and other inflammatory pathways. ConclusionThis study provides novel molecular and cellular insights into fistula pathogenesis, identifying IL22 producing lymphocytes as novel immune regulators of EMT and ECM dysregulation in perianal fistulas. Targeting the regulatory networks controlling IL22 producing lymphocytes may offer novel therapeutic strategies in pCD.

immunology↗

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↗

bioRxiv2021

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

BackgroundInflammatory bowel disease (IBD) patients wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiomes composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. ResultsWe collected fifteen 16S rRNA microbiome datasets (7707 samples) from North America to benchmark combinations of gut microbiome features, data normalization methods, batch effect reduction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with four binary classification metrics following leave-one dataset-out cross validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features obtained from QIIME2 lead to better classification of samples from IBD patients than inferred functional features obtained from PICRUSt2. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Prior to training a non-linear machine learning model on taxonomic features, it is important to apply a compositional normalization method and remove batch effects with the naive zero-centering method. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. ConclusionsThese findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

microbiology↗

bioRxiv2021

Extracellular vesicles produced by the human commensal gut bacterium Bacteroides thetaiotaomicron affect host immune pathways in a cell-type specific manner that are altered in inflammatory bowel disease

The gastrointestinal (GI) tract is inhabited by a complex microbial community, which contributes to its homeostasis. Disrupted microbiome can cause GI-related diseases, including inflammatory bowel disease, therefore identifying host-microbe interactions is crucial for better understanding gut health. Bacterial extracellular vesicles (BEVs), released into the gut lumen, can cross the mucus layer and access underlying immune cells. To study cross-kingdom communication between BEVs and host, we focused on the influence of BEVs, generated by Bacteroides thetaiotaomicron (VPI-5482), on host immune cells. Using single-cell RNA sequencing data and host-microbe protein-protein interaction networks, we examined the potential effect of BEVs on dendritic cells, macrophages and monocytes with particular focus on the Toll-like receptor (TLR) pathway. We identified biological processes affected in each immune cell type, and also cell-type specific processes (e.g myeloid cell differentiation). The TLR pathway analysis highlighted that BEV targets differ among cells and even between the same cells in healthy versus disease (ulcerative colitis) conditions. Our in silico findings were validated in BEV-monocyte co-cultures demonstrating the requirement for TLR4 in BEV-elicited NF-{kappa} B activation. This study demonstrates that both cell-type and health condition influence BEV-host communication. The results and the pipeline can facilitate BEV-based therapy development for the treatment of IBD.

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↗