Biology subjects

Modos, D.

Publications and source records attributed to Modos, D..

3 recordsLinked to original sources

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↗

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↗

bioRxiv2019

A systems genomics approach to uncover patient-specific pathogenic pathways in a complex disease

We describe a novel precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to identify the exact mechanisms of how SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 377 UC patients, we mapped the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. Unsupervised clustering algorithms grouped these patient-specific networks into four distinct clusters based on two large disease hubs, NFKB1 and PKCB. Pathway analysis identified the epigenetic modification as common and the T-cell specific responses as differing signalling pathways in the clusters. By integrating individual transcriptomes in active and quiescent disease setting to the patient networks, we validated the impact of non-coding SNPs. The iSNP approach identified regulatory effects of disease-associated non-coding SNPs, and identified how pathogenesis pathways are activated via different genetic modifications.

bioinformatics↗