Tamás Korcsmáros

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Abstract Background Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster ; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org . Conclusions With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.

Article22 March 2021Open Access Transparent process Integrated intra- and intercellular signaling knowledge for multicellular omics analysis Dénes Türei Dénes Türei orcid.org/0000-0002-7249-9379 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Alberto Valdeolivas Alberto Valdeolivas orcid.org/0000-0001-5482-9023 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Lejla Gul Lejla Gul Earlham Institute, Norwich, UK Search for more papers by this author Nicolàs Palacio-Escat Nicolàs Palacio-Escat orcid.org/0000-0002-7022-1437 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Michal Klein Michal Klein orcid.org/0000-0002-2433-6380 Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany Search for more papers by this author Olga Ivanova Olga Ivanova orcid.org/0000-0002-9111-4593 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Márton Ölbei Márton Ölbei orcid.org/0000-0002-4903-6237 Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Attila Gábor Attila Gábor orcid.org/0000-0002-0776-1182 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Fabian Theis Fabian Theis orcid.org/0000-0002-2419-1943 Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany Department of Mathematics, Technical University of Munich, Garching, Germany Search for more papers by this author Dezső Módos Dezső Módos orcid.org/0000-0001-9412-6867 Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Tamás Korcsmáros Tamás Korcsmáros orcid.org/0000-0003-1717-996X Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Julio Saez-Rodriguez Corresponding Author Julio Saez-Rodriguez [email protected] orcid.org/0000-0002-8552-8976 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany Search for more papers by this author Dénes Türei Dénes Türei orcid.org/0000-0002-7249-9379 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Alberto Valdeolivas Alberto Valdeolivas orcid.org/0000-0001-5482-9023 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Lejla Gul Lejla Gul Earlham Institute, Norwich, UK Search for more papers by this author Nicolàs Palacio-Escat Nicolàs Palacio-Escat orcid.org/0000-0002-7022-1437 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Michal Klein Michal Klein orcid.org/0000-0002-2433-6380 Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany Search for more papers by this author Olga Ivanova Olga Ivanova orcid.org/0000-0002-9111-4593 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Márton Ölbei Márton Ölbei orcid.org/0000-0002-4903-6237 Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Attila Gábor Attila Gábor orcid.org/0000-0002-0776-1182 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Search for more papers by this author Fabian Theis Fabian Theis orcid.org/0000-0002-2419-1943 Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany Department of Mathematics, Technical University of Munich, Garching, Germany Search for more papers by this author Dezső Módos Dezső Módos orcid.org/0000-0001-9412-6867 Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Tamás Korcsmáros Tamás Korcsmáros orcid.org/0000-0003-1717-996X Earlham Institute, Norwich, UK Quadram Institute Bioscience, Norwich, UK Search for more papers by this author Julio Saez-Rodriguez Corresponding Author Julio Saez-Rodriguez [email protected] orcid.org/0000-0002-8552-8976 Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany Search for more papers by this author Author Information Dénes Türei1, Alberto Valdeolivas1, Lejla Gul2, Nicolàs Palacio-Escat1,3,4, Michal Klein5, Olga Ivanova1, Márton Ölbei2,6, Attila Gábor1, Fabian Theis5,7, Dezső Módos2,6, Tamás Korcsmáros2,6 and Julio Saez-Rodriguez *,1,3 1Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany 2Earlham Institute, Norwich, UK 3Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany 4Faculty of Biosciences, Heidelberg University, Heidelberg, Germany 5Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany 6Quadram Institute Bioscience, Norwich, UK 7Department of Mathematics, Technical University of Munich, Garching, Germany *Corresponding author. Tel: +49 6221 5451334; E-mail: [email protected] Molecular Systems Biology (2021)17:e9923https://doi.org/10.15252/msb.20209923 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath’s web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis. SYNOPSIS Over 100 resources are integrated into OmniPath, a comprehensive knowledge base of intra- and inter-cellular signaling. Workflows are provided and illustrated in case studies analyzing omics data in SARS-CoV-2 infection and ulcerative colitis. OmniPath includes 4,000,000 annotations for over 20,000 proteins. A new framework defining transmitter and receiver roles generalizes the concepts of ligand and receptor. Integrated analysis of intra- and intercellular signaling can be performed to study how cells affect each other in healthy and diseased conditions. Software tools and workflows in R and Python facilitate the analysis of bulk and single-cell omics data using tools such as CellPhoneDB, NicheNet and CARNIVAL. Introduction Cells process information by physical interactions of molecules. These interactions are organized into an ensemble of signaling pathways that are often represented as a network. This network determines the response of the cell under different physiological and disease conditions. In multicellular organisms, the behavior of each cell is regulated by higher levels of organization: the tissue and, ultimately, the organism. In tissues, multiple cells communicate to coordinate their behavior to maintain homeostasis. For example, cells may produce and sense extracellular matrix (ECM), and release enzymes acting on the ECM as well as ligands. These ligands are detected by receptors in the same or different cells, that in turn trigger intracellular pathways that control other processes, including the production of ligands and the physical binding to other cells. The totality of these processes mediates the intercellular communication in tissues. Thus, to understand physiological and pathological processes at the tissue level, we need to consider both the signaling pathways within each cell type as well as the communication between them. Since the end of the nineties, databases have been collecting information about signaling pathways (Xenarios et al, 2000). These databases provide a unified source of information in formats that users can browse, retrieve, and process. Signaling databases have become essential tools in systems biology and to analyze omics data. A few resources provide ligand–receptor interactions (Kirouac et al, 2010; Fazekas et al, 2013; Ramilowski et al, 2015; Armstrong et al, 2019; Efremova et al, 2020). However, their coverage is limited, they do not include some key players of intercellular communication such as matrix proteins or extracellular enzymes, and they are not integrated with intracellular processes. This is increasingly important as new techniques allow us to measure data from single cells, enabling the analysis of inter- and intracellular signaling. For example, the recent CellPhoneDB (Efremova et al, 2020) and ICELLNET (Noël et al, 2021) tools provide computational methods to prioritize the most likely intercellular connections from single-cell transcriptomics data, and NicheNet (Browaeys et al, 2019) expands this to intracellular gene regulation. A comprehensive resource of inter- and intracellular signaling knowledge would enhance and expedite these analyses. To effectively study multicellular communication, a resource should (i) classify proteins by their roles in intercellular communication, (ii) connect them by interactions from the widest possible range of resources, and (iii) integrate all this information in a transparent and customizable way, where the users can select the resources to evaluate their quality and features, and adapt them to their context and analyses. Prompted by the lack of comprehensive efforts addressing principle (i), we built a database on top of OmniPath (Türei et al, 2016), a resource which has already shown the benefits of principles (ii) and (iii). The first version of OmniPath focused on literature curated intracellular signaling pathways. It has been used in many computational projects and omics studies. For example, to model cell senescence from phosphoarray data (An et al, 2020), or as part of a computational pipeline to predict the effect of microbial proteins on human genes (Andrighetti et al, 2020), and a community effort to integrate knowledge about the COVID-19 disease mechanism (Ostaszewski et al, 2020). The new OmniPath extends its scope to intercellular communication and its integration with intracellular signaling, providing prior knowledge for modeling and analysis methods. It combines 103 resources to build an integrated database of molecular interactions, enzyme-PTM (post-translational modification) relationships, protein complexes and annotations about intercellular communication, and other functional attributes of proteins. We demonstrate with two case studies that we provide a versatile resource for the analysis of single-cell and bulk omics data. Leveraging the intercellular communication knowledge in OmniPath, we present two examples where autocrine and paracrine signaling are key parts of pathomechanism. First, we studied the potential influence of ligands secreted in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the inflammatory response through autocrine signaling. We identified signaling mechanisms that may lead to the dysregulated inflammatory and immune response shown in severe cases. Second, we examined the rewiring of cellular communication in ulcerative colitis (UC) based on single-cell data from the colon. By analyzing downstream signaling from the intercellular interactions, we found pathways associated with the regulatory T cells targeted by myofibroblasts in UC. Results We used four major types of resources: (i) molecular interactions, (ii) enzyme-PTM relationships, (iii) protein complexes, and (iv) molecule annotations about function, localization, and other attributes (Fig 1A). The pypath Python package combined the resources from those four types to build four corresponding integrated databases. Using the annotations, pypath compiled a fifth database about the roles in intercellular communication (intercell; Fig 1B). The ensemble of these five databases is what we call OmniPath, combining data from 103 resources (Fig 1A and Dataset EV1). Figure 1. The composition and workflow of OmniPath Database contents with the respective number of resources in parentheses. Workflow and design: OmniPath is based on four major types of resources, and the pypath Python package combines the resources to build five databases. The databases are available by the database builder software pypath, the web resource at https://omnipathdb.org/, the R package OmnipathR, the Python client omnipath, the Cytoscape plug-in and can be exported to formats such as Biological Expression Language (BEL). Download figure Download PowerPoint A focus on intercellular signaling To create a database of intercellular communication, we defined the roles that proteins play in this process. Ligands and receptors are main players of intercellular communication. Many other kinds of molecules have a great impact on the behavior of the cells, such as matrix proteins and transporters (Fig 2A). We defined eight major (Fig 2) and 17 minor generic functional categories of intercellular signaling roles (Datasets EV6 and EV10). We also defined ten locational categories (e.g., plasma membrane peripheral), using in addition structural resources and prediction methods to annotate the transmembrane, secreted and peripheral membrane proteins. Furthermore, we provide 994 specific categories (e.g., neurotrophin receptors). Each generic category can be accessed by resource (e.g., ligands from HGNC) or as the combination of all contributing resources (Fig EV4). To provide highly curated annotations, we checked every entry in each category manually against UniProt datasheets to exclude wrong annotations. Overall we defined 1,170 categories and provided 54,330 functional annotations about intercellular communication roles of 5,781 proteins. Figure 2. The composition and representation of the intercellular signaling network We assigned intercellular communication roles to proteins based on evidence from multiple resources. In all panels: —transmitter; —receiver. Schematic illustration of the intercellular communication roles and their possible connections. Cells are physically connected by proteins forming tight junctions (1), gap junctions (2), and other adhesion proteins (3); they release vesicles which can be taken up by other cells (4); some receptors form complexes (5) to detect secreted ligands (6); transporters might also be affected by factors released by other cells (8); enzymes released into the extracellular space act on ligands and the extracellular matrix (7, 9); cells release the components of the extracellular matrix and bind to the matrix by adhesion proteins (10). The main intercellular communication roles (x axis) and the major contributing resources (y axis). Size of the dots represents the number of proteins annotated to have a certain role in a given resource. The darker areas represent the overlaps (proteins annotated in more than one resource for the same role) while the lighter color denotes those unique to that resource. The intercellular communication network. The circle segments represent the eight main intercellular communication roles. The edges are proportional to the number of interactions in the OmniPath PPI network connecting proteins of one role to the other. Number of unique, directed transmitter–receiver (e.g., ligand–receptor) connections by resources. Bars on the right show the coverage of each resource on a textbook dataset of 131 well-known ligand–receptor interactions. Download figure Download PowerPoint Click here to expand this figure. Figure EV4. Example of the intercell query in the OmniPath web service Each category has a parent category and a database of origin. The scope of a category is either “generic” (e.g., ligand) or “specific” (e.g., interleukin). The aspect is either “locational” or “functional”. Further attributes show whether the protein is a signal transmitter or a receiver, and whether it is secreted, or a transmembrane or peripheral protein of the plasma membrane. Download figure Download PowerPoint We collected the proteins for each intercellular communication functional category using data from 27 resources (Fig 2B, Dataset EV6). Combining them with molecular interaction networks from 48 resources (Dataset EV2), we created a corpus of putative intercellular communication pathways (Fig 2C). To have a high coverage on the intercellular molecular interactions, we also included ten resources focusing on ligand–receptor interactions (Figs 3 and EV1). Figure 3. Quantitative description of the network, complex, and enzyme–PTM databases A–C. Networks by interaction types and the network datasets within the PPI network. (A) Number of nodes and interactions. The light dots represent the shared nodes and edges (in more than one resource), while the dark ones show their total numbers. (B) Causality: number of connections by direction and effect sign. (C) Coverage of the networks on various groups of proteins. Dots show the percentage of proteins covered by network resources for the following groups: cancer driver genes from COSMIC and IntOGen, kinases from kinase.com, phosphatases from Phosphatome.net, receptors from the Human Plasma Membrane Receptome (HPMR) and transcription factors from the TF census. Gray bars show the number of proteins in the networks. The information for individual resources is in Figs EV1 and EV2, Appendix Fig S1. D–G. On each panel, the bottom rows represent the combined complex and enzyme–PTM databases contained in OmniPath (D, E). Number of complexes (D) and enzyme–PTM (E) interactions by resource. (F) Enzyme–PTM relationships by PTM type. (G) Enzyme–PTM interactions by their target. Light, medium, and dark dots represent the number of enzyme–PTM relationships targeting the enzyme itself, another protein within the same molecular complex or an independent protein, respectively. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Quantitative description of the PPI network by resource Number of nodes and interactions. The light dots represent the shared nodes and edges (in more than one resource), while the dark ones show their total numbers. Causality: number of connections by direction and effect sign. Coverage of the networks on various groups of proteins. Dots show the percentage of proteins covered by network resources for the following groups: cancer driver genes from COSMIC and IntOGen, kinases from kinase.com, phosphatases from Phosphatome.net, receptors from the Human Plasma Membrane Receptome (HPMR) and transcription factors from the TF census. Gray bars show the number of proteins in the networks. Download figure Download PowerPoint Many of the proteins in intercellular communication work as parts of complexes. We therefore built a comprehensive database of protein complexes and inferred their intercellular communication roles: a complex belongs to a category if and only if all members of the complex belong to it. We obtained 14,348 unique, directed transmitter–receiver (e.g., ligand–receptor) connections, around seven times more than the largest of the resources providing such kind of data. We also mapped a textbook table (Cameron & Kelvin, 2013) of 131 cytokine–receptor interactions to the ligand–receptor resources. As the textbook contains well-known interactions, many of the resources cover more than 90% of them (Fig 2D). This large coverage is achieved by not only integrating ten ligand–receptor resources, but also complementing these with data from annotation and interaction resources. An essential feature of this novel resource is that it combines knowledge about intercellular and intracellular signaling (Table 1). Thus, using OmniPath one can, for example, easily analyze the intracellular pathways triggered by a given ligand or check the transcription factors (TFs) and microRNAs (miRNAs) regulating the expression of such ligands. Table 1. Qualitative comparison of ligand–receptor and integrative databases. Resource Inter-actions Directed inter-actions Signs (positive/negative) Transcriptional regulation Intracellular pathways Intercellular communication roles Protein complexes Integrative resource Literature curated Baccin2019 (e) yes yes (a) no no no yes (f) yes yes yes (g) CellCellInteractions yes yes (a) no no no yes (l) no yes no CellPhoneDB yes yes (a) no no no yes (d) yes yes yes ConsensusPathDB yes no no yes yes no no yes yes (g) EMBRACE (e) yes yes (a) no no no yes no yes (k) yes (g) HPMR yes yes (a) no no no yes no no yes ICELLNET yes yes (a) no no no yes yes no yes iTALK (h) yes yes (a) no no no yes no yes yes (g) Kirouac2010 yes yes (a) no no no yes no no yes LRdb yes yes (a) no no no yes no yes yes (g) PathwayCommons yes yes (m) no yes yes no yes yes yes (g) Ramilowski2015 yes yes (a) no no no yes no yes yes (g) SignaLink yes yes yes yes (i) yes yes no yes (j) yes (g) OmniPath yes yes (b) yes yes yes yes (c) yes yes yes (g) OmniPath combines resources to build a network with directions and effect signs, including intra- and intercellular signaling, transcriptional regulation, and annotates proteins as ligands or receptors. Here, we show which of these features are covered by other databases: those specialized in ligand–receptor interactions and two large integrative network databases (ConsensusPathDB and Pathway Commons). (a) Implicit: if we assume always the ligand affects the receptor; (b) As in some of the constituent resources the directions are implicit, certain directions in the combined network are implicit; (c) Provides not only ligand and receptor annotation but further categories, for example adhesion, transporter, ECM, etc; (d) Apart from secreted (mostly ligand) and receptor provides a few further categories: integrin, collagen, transmembrane, peripheral, etc; (e) Data are for mouse, homology translation is necessary to derive human data; (f) For ligands, provides certain classification, e.g., cytokine, ECM, secreted, etc; (g) Only in part is literature curated; (h) Ligand–receptor interactions are classified as growth factor, cytokine, checkpoint, or other; (i) Contains transcriptional regulation but that part is not integrated by OmniPath; (j) OmniPath only integrates its original literature curation, not the secondary resources; (k) Only builds on Ramilowski et al; (l) Besides ligand and receptor only ECM; (m) Directionality information might be extracted from BioPAX. OmniPath: an ensemble of five databases The abovementioned intercellular database exists in OmniPath together with four further databases (Fig 1B), supporting an integrated analysis of inter- and intracellular signaling. The network of molecular interactions The network database part covers four major domains of molecular signaling: (i) protein–protein interactions (PPI), (ii) transcriptional regulation of protein-coding genes, (iii) miRNA–mRNA interactions, and (iv) transcriptional regulation of miRNA genes (TF-miRNA). We further differentiated the PPI data into four subsets based on the interaction mechanisms and the types of supporting evidence: (i) literature curated activity flow (directed and signed; corresponds to the original release of OmniPath; Türei et al, 2016), (ii) activity flow with no literature references, (iii) enzyme–PTM, and (iv) ligand–receptor interactions (Fig 3A–C). Interaction data are extensively used for a variety of purposes: for building mechanistic models, deriving pathway and TF activities from transcriptomics data and graph-based analysis methods. In total, the resource contained 103,396 PPI interactions between 12,469 proteins from 38 original resources (Dataset EV2). The large number of unique interactions added by each resource underscores the importance of their integration (Figs EV1 and EV2, Appendix Fig S1). The interactions with effect signs, essential for mechanistic modeling, are provided by the activity flow resources (Appendix 1; Fig 3B). The combined PPI network covered 53% of the human proteome (SwissProt), with an enrichment of kinases and cancer driver genes (Fig 3C). The transcriptional regulation data in OmniPath were obtained from DoRothEA (Garcia-Alonso et al, 2019), a comprehensive resource of TF regulons integrating data from 18 sources. In addition, six literature curated resources were directly integrated into OmniPath (Dataset EV8). The miRNA–mRNA and TF–miRNA interactions were integrated from five and two literature curated resources, with 6,213 and 1,803 interactions, respectively. Combining multiple resources not only increases the coverage, but also improves quality. It makes it possible to select higher confidence records based on the number of resources and references. Cross-checking the interaction directions and effect signs between resources reveal contradictory information which is either a sign of mistakes or reflects on limitations of our data representation (Appendix 1; Appendix Figs S4). Overall, we included 61 network resources in OmniPath (Dataset EV2). Furthermore, pypath provides access to additional resources, including the Human Reference Interactome (Luck et al, 2020), ConsensusPathDB (Kamburov et al, 2013), Reactome (Jassal et al, 2020), ACSN (Kuperstein et al, 2015), and WikiPathways (Slenter et al, 2018). Click here to expand this figure. Figure EV2. Quantitative description of the transcriptional network by resource A–C. Panels and notations are the same as on Fig EV1. Download figure Download PowerPoint Enzyme-PTM relationships In enzyme–PTM relationships, enzymes (e.g., kinases) alter specific residues of their substrates, producing so-called post-translational modifications (PTM). Enzyme–PTM relationships are essential for deriving networks from phosphoproteomics data or estimating kinase activities. We combined 11 resources of enzyme–PTM relationships mostly covering phosphorylation (94% of all) and dephosphorylations (3%) (Fig 3F). Overall, we included 39,201 enzyme–PTM relationships, 1,821 enzymes targeting 16,467 PTM sites (Fig 3E–G). Besides phosphorylation and dephosphorylation, only proteolytic cleavage and acetylation account for more than one hundred interactions. Most of the databases curated only phosphorylation, and DEPOD (Damle & Köhn, 2019) exclusively dephosphorylation. Only SIGNOR (Licata et al, 2020) and HPRD (Keshava Prasad et al, 2009) contained a large number of other modifications (Fig 3F). 60% of the interactions were described by only one resource, and 92% of them by only one literature reference (Fig 3E). Self-modifications, e.g., autophosphorylation and modifications between members of the same complex comprised 4 and 18% of the interactions, respectively (Fig 3G). Protein complexes Many proteins operate in complexes, for example, receptors often detect ligands in complexes. To facilitate analyses taking into consideration complexes, we added to OmniPath a comprehensive collection of 22,005 protein complexes described by 12 resources from 4,077 articles (Fig 3D). A complex is defined by its combination of unique mem

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Hyper-induction of pro-inflammatory cytokines, also known as a cytokine storm or cytokine release syndrome (CRS), is one of the key aspects of the currently ongoing SARS-CoV-2 pandemic. This process occurs when a large number of innate and adaptive immune cells activate and start producing pro-inflammatory cytokines, establishing an exacerbated feedback loop of inflammation. It is one of the factors contributing to the mortality observed with coronavirus 2019 (COVID-19) for a subgroup of patients. CRS is not unique to the SARS-CoV-2 infection; it was prevalent in most of the major human coronavirus and influenza A subtype outbreaks of the past two decades (H5N1, SARS-CoV, MERS-CoV, and H7N9). With a comprehensive literature search, we collected changing the cytokine levels from patients upon infection with the viral pathogens mentioned above. We analyzed published patient data to highlight the conserved and unique cytokine responses caused by these viruses. Our curation indicates that the cytokine response induced by SARS-CoV-2 is different compared to other CRS-causing respiratory viruses, as SARS-CoV-2 does not always induce specific cytokines like other coronaviruses or influenza do, such as IL-2, IL-10, IL-4, or IL-5. Comparing the collated cytokine responses caused by the analyzed viruses highlights a SARS-CoV-2-specific dysregulation of the type-I interferon (IFN) response and its downstream cytokine signatures. The map of responses gathered in this study could help specialists identify interventions that alleviate CRS in different diseases and evaluate whether they could be used in the COVID-19 cases.

Inflammatory bowel disease (IBD) is a chronic immune-mediated inflammatory disorder of the gastrointestinal tract that arises due to complex interactions between host genetic risk factors, environmental factors, and a dysbiotic gut microbiota. Although metagenomic approaches have attempted to characterise the dysbiosis occurring in IBD, the precise mechanistic pathways interlinking the gut microbiota and the intestinal mucosa are still yet to be unravelled. To deconvolute these complex interactions, a more reductionist approach involving microbial metabolites has been suggested. Bile acids have emerged as a key class of microbiota-associated metabolites that are perturbed in IBD patients. In recent years, metabolomics studies have revealed a consistent defect in bile acid metabolism with an increase in primary bile acids and a reduction in secondary bile acids in IBD patients. This review explores the evolving evidence that specific bile acid metabolites interact with intestinal epithelial and immune cells to contribute to the inflammatory milieu seen in IBD. Furthermore, we summarise evidence linking bile acids with intracellular pathways that are known to be relevant in IBD including autophagy, apoptosis, and the inflammasome pathway. Finally, we discuss how novel experimental and bioinformatics approaches could further advance our understanding of the role of bile acids and inform novel therapeutic strategies in IBD.

The function of interleukin-22 (IL-22) in intestinal barrier homeostasis remains controversial. Here, we map the transcriptional landscape regulated by IL-22 in human colonic epithelial organoids and evaluate the biological, functional and clinical significance of the IL-22 mediated pathways in ulcerative colitis (UC). We show that IL-22 regulated pro-inflammatory pathways are involved in microbial recognition, cancer and immune cell chemotaxis; most prominently those involving CXCR2+ neutrophils. IL-22-mediated transcriptional regulation of CXC-family neutrophil-active chemokine expression is highly conserved across species, is dependent on STAT3 signaling, and is functionally and pathologically important in the recruitment of CXCR2+ neutrophils into colonic tissue. In UC patients, the magnitude of enrichment of the IL-22 regulated transcripts in colonic biopsies correlates with colonic neutrophil infiltration and is enriched in non-responders to ustekinumab therapy. Our data provide further insights into the biology of IL-22 in human disease and highlight its function in the regulation of pathogenic immune pathways, including neutrophil chemotaxis. The transcriptional networks regulated by IL-22 are functionally and clinically important in UC, impacting patient trajectories and responsiveness to biological intervention.

Patients with inflammatory bowel disease (IBD) treated with anti-TNF therapy exhibit attenuated humoral immune responses to vaccination against SARS-CoV-2. The gut microbiota and its functional metabolic output, which are perturbed in IBD, play an important role in shaping host immune responses. We explored whether the gut microbiota and metabolome could explain variation in anti-SARS-CoV-2 vaccination responses in immunosuppressed IBD patients.Faecal and serum samples were prospectively collected from infliximab-treated patients with IBD in the CLARITY-IBD study undergoing vaccination against SARS-CoV-2. Antibody responses were measured following two doses of either ChAdOx1 nCoV-19 or BNT162b2 vaccine. Patients were classified as having responses above or below the geometric mean of the wider CLARITY-IBD cohort. 16S rRNA gene amplicon sequencing, nuclear magnetic resonance (NMR) spectroscopy and bile acid profiling with ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) were performed on faecal samples. Univariate, multivariable and correlation analyses were performed to determine gut microbial and metabolomic predictors of response to vaccination.Forty-three infliximab-treated patients with IBD were recruited (30 Crohn's disease, 12 ulcerative colitis, 1 IBD-unclassified; 26 with concomitant thiopurine therapy). Eight patients had evidence of prior SARS-CoV-2 infection. Seventeen patients (39.5%) had a serological response below the geometric mean. Gut microbiota diversity was lower in below average responders (p = 0.037). Bilophila abundance was associated with better serological response, while Streptococcus was associated with poorer response. The faecal metabolome was distinct between above and below average responders (OPLS-DA R2X 0.25, R2Y 0.26, Q2 0.15; CV-ANOVA p = 0.038). Trimethylamine, isobutyrate and omega-muricholic acid were associated with better response, while succinate, phenylalanine, taurolithocholate and taurodeoxycholate were associated with poorer response.Our data suggest that there is an association between the gut microbiota and variable serological response to vaccination against SARS-CoV-2 in immunocompromised patients. Microbial metabolites including trimethylamine may be important in mitigating anti-TNF-induced attenuation of the immune response.JLA is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-502), funded by Imperial College London and The Joyce and Norman Freed Charitable Trust. BHM is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases at Imperial College London receives financial and infrastructure support from the NIHR Imperial Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. Metabolomics studies were performed at the MRC-NIHR National Phenome Centre at Imperial College London; this work was supported by the Medical Research Council (MRC), the National Institute of Health Research (NIHR) (grant number MC_PC_12025) and infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC). The NIHR Exeter Clinical Research Facility is a partnership between the University of Exeter Medical School College of Medicine and Health, and Royal Devon and Exeter NHS Foundation Trust. This project is supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.

The network paradigm is increasingly used to describe the topology and dynamics of complex systems. Here, we review the results of the topological analysis of protein structures as molecular networks describing their small-world character, and the role of hubs and central network elements in governing enzyme activity, allosteric regulation, protein motor function, signal transduction and protein stability. We summarize available data how central network elements are enriched in active centers and ligand binding sites directing the dynamics of the entire protein. We assess the feasibility of conformational and energy networks to simplify the vast complexity of rugged energy landscapes and to predict protein folding and dynamics. Finally, we suggest that modular analysis, novel centrality measures, hierarchical representation of networks and the analysis of network dynamics will soon lead to an expansion of this field.

Despite improved rational drug design and a remarkable progress in genomic, proteomic and high-throughput screening methods, the number of novel, single-target drugs has fallen far behind expectations during the past decade. Multi-target drugs multiply the number of pharmacologically relevant target molecules by introducing a set of indirect, network-dependent effects. Parallel with this, the low-affinity binding of multi-target drugs eases the constraints of druggability and significantly increases the size of the druggable proteome. These effects tremendously expand the number of potential drug targets and introduce novel classes of multi-target drugs with smaller side effects and toxicity. Here, the authors review the recent progress in this field, compare possible network attack strategies and propose several methods to find target-sets for multi-target drugs.

Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

Endocytosis participates in downregulating incoming signals, but 'signaling endosomes' may also serve as physical platforms for crosstalk between signaling pathways. Here, we briefly review the role of endosomes in signaling crosstalk and suggest that endosome-associated scaffold proteins mediate this crosstalk. In addition, using a proteome-wide in silico approach - in which we analyze endosome-binding properties and the capacity of candidates to recruit signaling proteins from more than one distinct pathway - we extend the list of putative crosstalk-mediating endosomal scaffolds. Because endosomal crosstalk may be an important systems-level regulator of pathway communication, scaffold proteins that mediate this crosstalk could be potential targets for pharmacological intervention and synthetic engineering.

NRF2 is a well‐known, master transcription factor (TF) of oxidative and xenobiotic stress responses. Recent studies uncovered an even wider regulatory role of NRF2 influencing carcinogenesis, inflammation and neurodegeneration. Prompted by these advances here we present a systems‐level resource for NRF2 interactome and regulome that includes 289 protein–protein, 7469 TF–DNA and 85 miRNA interactions. As systems‐level examples of NRF2‐related signaling we identified regulatory loops of NRF2 interacting proteins (e.g., JNK1 and CBP) and a fine‐tuned regulatory system, where 35 TFs regulated by NRF2 influence 63 miRNAs that down‐regulate NRF2. The presented network and the uncovered regulatory loops may facilitate the development of efficient, NRF2‐based therapeutic agents.

For a long time necrosis was thought to be an uncontrolled process but evidences recently have revealed that necrosis can also occur in a regulated manner. Necroptosis, a type of programmed necrosis is defined as a death receptor-initiated process under caspase-compromised conditions. The process requires the kinase activity of receptor-interacting protein kinase 1 and 3 (RIPK1 and RIPK3) and mixed lineage kinase domain-like protein (MLKL), as a substrate of RIPK3. The further downstream events remain elusive. We applied known inhibitors to characterize the contributing enzymes in necroptosis and their effect on cell viability and different cellular functions were detected mainly by flow cytometry. Here we report that staurosporine, the classical inducer of intrinsic apoptotic pathway can induce necroptosis under caspase-compromised conditions in U937 cell line. This process could be hampered at least partially by the RIPK1 inhibitor necrotstin-1 and by the heat shock protein 90 kDa inhibitor geldanamycin. Moreover both the staurosporine-triggered and the classical death ligand-induced necroptotic pathway can be effectively arrested by a lysosomal enzyme inhibitor CA-074-OMe and the recently discovered MLKL inhibitor necrosulfonamide. We also confirmed that the enzymatic role of poly(ADP-ribose)polymerase (PARP) is dispensable in necroptosis but it contributes to membrane disruption in secondary necrosis. In conclusion, we identified a novel way of necroptosis induction that can facilitate our understanding of the molecular mechanisms of necroptosis. Our results shed light on alternative application of staurosporine, as a possible anticancer therapeutic agent. Furthermore, we showed that the CA-074-OMe has a target in the signaling pathway leading to necroptosis. Finally, we could differentiate necroptotic and secondary necrotic processes based on participation of PARP enzyme.

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species – or communities – is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

The gut microbiota's function in regulating health has seen it linked to disease progression in several cancers. However, there is limited research detailing its influence in breast cancer (BrCa). This study found that antibiotic-induced perturbation of the gut microbiota significantly increases tumor progression in multiple BrCa mouse models. Metagenomics highlights the common loss of several bacterial species following antibiotic administration. One such bacteria, Faecalibaculum rodentium, rescued this increased tumor growth. Single-cell transcriptomics identified an increased number of cells with a stromal signature in tumors, and subsequent histology revealed an increased abundance of mast cells in the tumor stromal regions. We show that administration of a mast cell stabilizer, cromolyn, rescues increased tumor growth in antibiotic treated animals but has no influence on tumors from control cohorts. These findings highlight that BrCa-microbiota interactions are different from other cancers studied to date and suggest new research avenues for therapy development.

Abstract Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor–target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor–target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor–target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/

Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention points, where autophagy can be effectively modulated in cancer therapy.

Why are YAP1 and c-Myc often overexpressed (or activated) in KRAS-driven cancers and drug resistance? Here, we propose that there are two independent pathways in tumor proliferation: one includes MAPK/ERK and PI3K/A kt/mTOR; and the other consists of pathways leading to the expression (or activation) of YAP1 and c-Myc. KRAS contributes through the first. MYC is regulated by e.g. β-catenin, Notch and Hedgehog. We propose that YAP1 and ERK accomplish similar roles in cell cycle control, as do β-catenin and PI3K. This point is compelling, since the question of how YAP1 rescues K-Ras or B-Raf ablation has recently captured much attention, as well as the mechanism of resistance to PI3K inhibitors. The similarity in cell cycle actions of β-catenin and PI3K can also clarify the increased aggressiveness of lung cancer when both K-Ras and β-catenin operate. Thus, we propose that the two pathways can substitute one another - or together amplify each other - in promoting proliferation. This new understanding of the independence and correspondence of the two pathways in cancer - MAPK/ERK and PI3K/Akt/mTOR; and YAP1 and c-Myc - provide a coherent and significant picture of signaling-driven oncogenic proliferation and may help in judicious, pathway-based drug discovery.

Although the primary role of autophagy-dependent cellular self-eating is cytoprotective upon various stress events (such as starvation, oxidative stress, and high temperatures), sustained autophagy might lead to cell death. A transcription factor called NRF2 (nuclear factor erythroid-related factor 2) seems to be essential in maintaining cellular homeostasis in the presence of either reactive oxygen or nitrogen species generated by internal metabolism or external exposure. Accumulating experimental evidence reveals that oxidative stress also influences the balance of the 5' AMP-activated protein kinase (AMPK)/rapamycin (mammalian kinase target of rapamycin or mTOR) signaling pathway, thereby inducing autophagy. Based on computational modeling here we propose that the regulatory triangle of AMPK, NRF2 and mTOR guaranties a precise oxidative stress response mechanism comprising of autophagy. We suggest that under conditions of oxidative stress, AMPK is crucial for autophagy induction via mTOR down-regulation, while NRF2 fine-tunes the process of autophagy according to the level of oxidative stress. We claim that the cellular oxidative stress response mechanism achieves an incoherently amplified negative feedback loop involving NRF2, mTOR and AMPK. The mTOR-NRF2 double negative feedback generates bistability, supporting the proper separation of two alternative steady states, called autophagy-dependent survival (at low stress) and cell death (at high stress). In addition, an AMPK-mTOR-NRF2 negative feedback loop suggests an oscillatory characteristic of autophagy upon prolonged intermediate levels of oxidative stress, resulting in new rounds of autophagy stimulation until the stress events cannot be dissolved. Our results indicate that AMPK-, NRF2- and mTOR-controlled autophagy induction provides a dynamic adaptation to altering environmental conditions, assuming their new frontier in biomedicine.

The gut microbiota appears to play a central role in health, and alterations in the gut microbiota are observed in both forms of inflammatory bowel disease [IBD], namely Crohn's disease and ulcerative colitis. Yet, the mechanisms behind host-microbiota interactions in IBD, especially at the intestinal epithelial cell level, are not yet fully understood. Dissecting the role of host-microbiota interactions in disease onset and progression is pivotal, and requires representative models mimicking the gastrointestinal ecosystem, including the intestinal epithelium, the gut microbiota, and immune cells. New advancements in organoid microfluidics technology are facilitating the study of IBD-related microbial-epithelial cross-talk, and the discovery of novel microbial therapies. Here, we review different organoid-based ex vivo models that are currently available, and benchmark their suitability and limitations for specific research questions. Organoid applications, such as patient-derived organoid biobanks for microbial screening and 'omics technologies, are discussed, highlighting their potential to gain better mechanistic insights into disease mechanisms and eventually allow personalised medicine.

The complexity of the cells can be described and understood by a number of networks such as protein–protein interaction, cytoskeletal, organelle, signalling, gene transcription and metabolic networks. All these networks are highly dynamic producing continuous rearrangements in their links, hubs, network‐skeleton and modules. Here we describe the adaptation of cellular networks after various forms of stress causing perturbations, congestions and network damage. Chronic stress decreases link‐density, decouples or even quarantines modules, and induces an increased competition between network hubs and bridges. Extremely long or strong stress may induce a topological phase transition in the respective cellular networks, which switches the cell to a completely different mode of cellular function. We summarize our initial knowledge on network restoration after stress including the role of molecular chaperones in this process. Finally, we discuss the implications of stress‐induced network rearrangements in diseases and ageing, and propose therapeutic approaches both to increase the robustness and help the repair of cellular networks.

Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult.We present SignaLink, a database containing eight major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster and humans. Based on 170 review and approximately 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the eight pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery.We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease, and underscore its importance in network-based drug target selection.http://SignaLink.org.

In the past few years, network-based tools have become increasingly important in the identification of novel molecular targets for drug development. Systems-based approaches to predict signal transduction-related drug targets have developed into an especially promising field. Here, we summarize our studies, which indicate that modular bridges and overlaps of protein-protein interaction and signaling networks may be of key importance in future drug design. Intermodular nodes are very efficient in mediating the transmission of perturbations between signaling modules and are important in network cooperation. The analysis of stress-induced rearrangements of the yeast interactome by the ModuLand modularization algorithm indicated that components of modular overlap are key players in cellular adaptation to stress. Signaling crosstalk was much more pronounced in humans than in Caenorhabditis elegans or Drosophila melanogaster in the SignaLink (http://www.SignaLink.org) database, a uniformly curated database of eight major signaling pathways. We also showed that signaling proteins that participate in multiple pathways included multiple established drug targets and drug target candidates. Lastly, we caution that the pervasive overlap of cellular network modules implies that wider use of multitarget drugs to partially inhibit multiple individual proteins will be necessary to modify specific cellular functions, because targeting single proteins for complete disruption usually affects multiple cellular functions with little specificity for a particular process. Tools for analyzing network topology and especially network dynamics have great potential to identify alternative sets of targets for developing multitarget drugs.

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert it. Despite previous key findings of bacteria-autophagy interplay, asystems-level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors SQSTM1/p62, CALCOCO2/NDP52 and MAP1LC3/LC3. Using structure-based interaction prediction, we identified bacterial proteins capable to modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus-specific proteins, and not those present in multiple genera. The complementarity between the predicted SQSTM1/p62 and CALCOCO2/NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. This complementarity potentially leaves the host more susceptible to chronic infections upon the mutation of autophagy receptors. Proteins derived from enterotoxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed apathogen-specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins that could modulate autophagy, and in turn being targeted by autophagy as ahost defense mechanism. We confirmed experimentally an interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognizes these pathogenic effectors. Abbreviations: ATG5: autophagy related 5; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; GST: glutathione S-transferase; LIR: MAP1LC3/LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3 alpha; OMV: outer membrane vesicles; SQSTM1/p62: sequestosome 1; SCV: Salmonella containing vesicle; TECPR1: tectonin beta-propeller repeat containing 1; YhjJ: hypothetical zinc-protease.

NF-E2-related factor 2 (NRF2) transcription factor has a fundamental role in cell homeostasis maintenance as one of the master regulators of oxidative and electrophilic stress responses. Previous studies have shown that a regulatory connection exists between NRF2 and autophagy during reactive oxygen species-generated oxidative stress. The aim of the present study was to investigate how autophagy is turned off during prolonged oxidative stress, to avoid overeating and destruction of essential cellular components. AMPK is a key cellular energy sensor highly conserved in eukaryotic organisms, and it has an essential role in autophagy activation at various stress events. Here the role of human AMPK and its Caenorhabditis elegans counterpart AAK-2 was explored upon oxidative stress. We investigated the regulatory connection between NRF2 and AMPK during oxidative stress induced by tert-butyl hydroperoxide (TBHP) in HEK293T cells and C. elegans. Putative conserved NRF2/protein skinhead-1 binding sites were found in AMPK/aak-2 genes by in silico analysis and were later confirmed experimentally by using EMSA. After addition of TBHP, NRF2 and AMPK showed a quick activation; AMPK was later down-regulated, however, while NRF2 level remained high. Autophagosome formation and Unc-51-like autophagy activating kinase 1 phosphorylation were initially stimulated, but they returned to basal values after 4 h of TBHP treatment. The silencing of NRF2 resulted in a constant activation of AMPK leading to hyperactivation of autophagy during oxidative stress. We observed the same effects in C. elegans demonstrating the conservation of this self-defense mechanism to save cells from hyperactivated autophagy upon prolonged oxidative stress. We conclude that NRF2 negatively regulates autophagy through delayed down-regulation of the expression of AMPK upon prolonged oxidative stress. This regulatory connection between NRF2 and AMPK may have an important role in understanding how autophagy is regulated in chronic human morbidities characterized by oxidative stress, such as neurodegenerative diseases, certain cancer types, and in metabolic diseases.-Kosztelnik, M., Kurucz, A., Papp, D., Jones, E., Sigmond, T., Barna, J., Traka, M. H., Lorincz, T., Szarka, A., Banhegyi, G., Vellai, T., Korcsmaros, T., Kapuy, O. Suppression of AMPK/aak-2 by NRF2/SKN-1 down-regulates autophagy during prolonged oxidative stress.

The epithelial lining of the small intestine consists of multiple cell types, including Paneth cells and goblet cells, that work in cohort to maintain gut health. 3D in vitro cultures of human primary epithelial cells, called organoids, have become a key model to study the functions of Paneth cells and goblet cells in normal and diseased conditions. Advances in these models include the ability to skew differentiation to particular lineages, providing a useful tool to study cell type specific function/dysfunction in the context of the epithelium. Here, we use comprehensive profiling of mRNA, microRNA and long non-coding RNA expression to confirm that Paneth cell and goblet cell enrichment of murine small intestinal organoids (enteroids) establishes a physiologically accurate model. We employ network analysis to infer the regulatory landscape altered by skewing differentiation, and using knowledge of cell type specific markers, we predict key regulators of cell type specific functions: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them. Links identified between these regulators and cellular phenotypes of inflammatory bowel disease (IBD) suggest that global regulatory rewiring during or after differentiation of Paneth cells and goblet cells could contribute to IBD aetiology. Future application of cell type enriched enteroids combined with the presented computational workflow can be used to disentangle multifactorial mechanisms of these cell types and propose regulators whose pharmacological targeting could be advantageous in treating IBD patients with Crohn's disease or ulcerative colitis.

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Increasing evidence indicates the importance of the tumor microenvironment, in particular cancer‐associated fibroblasts, in cancer development and progression. In our study, we developed a novel, visually based method to identify new immunohistochemical signatures of these fibroblasts. The method employed a protein list based on 759 protein products of genes identified by RNA profiling from our previous study, comparing fibroblasts with differential growth‐modulating effect on human cancers cells, and their first neighbors in the human protein interactome. These 2,654 proteins were analyzed in the Human Protein Atlas online database by comparing their immunohistochemical expression patterns in normal versus tumor‐associated fibroblasts. Twelve new proteins differentially expressed in cancer‐associated fibroblasts were identified (DLG1, BHLHE40, ROCK2, RAB31, AZI2, PKM2, ARHGAP31, ARHGAP26, ITCH, EGLN1, RNF19A and PLOD2), four of them can be connected to the Rho kinase signaling pathway. They were further analyzed in several additional tumor stromata and revealed that the majority showed congruence among the different tumors. Many of them were also positive in normal myofibroblast‐like cells. The new signatures can be useful in immunohistochemical analysis of different tumor stromata and may also give us an insight into the pathways activated in them in their true in vivo context. The method itself could be used for other similar analysis to identify proteins expressed in other cell types in tumors and their surrounding microenvironment.

NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2.

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual stress-history of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: what does not kill me makes me stronger.

The phenomenon of super-additivity of biological response to compounds applied jointly, termed synergy, has the potential to provide many therapeutic benefits. Therefore, high throughput screening of compound combinations has recently received a great deal of attention. Large compound libraries and the feasibility of all-pairs screening can easily generate large, information-rich datasets. Previously, these datasets have been visualized using either a heat-map or a network approach-however these visualizations only partially represent the information encoded in the dataset.A new visualization technique for pairwise combination screening data, termed "Synergy Maps", is presented. In a Synergy Map, information about the synergistic interactions of compounds is integrated with information about their properties (chemical structure, physicochemical properties, bioactivity profiles) to produce a single visualization. As a result the relationships between compound and combination properties may be investigated simultaneously, and thus may afford insight into the synergy observed in the screen. An interactive web app implementation, available at http://richlewis42.github.io/synergy-maps, has been developed for public use, which may find use in navigating and filtering larger scale combination datasets. This tool is applied to a recent all-pairs dataset of anti-malarials, tested against Plasmodium falciparum, and a preliminary analysis is given as an example, illustrating the disproportionate synergism of histone deacetylase inhibitors previously described in literature, as well as suggesting new hypotheses for future investigation.Synergy Maps improve the state of the art in compound combination visualization, by simultaneously representing individual compound properties and their interactions. The web-based tool allows straightforward exploration of combination data, and easier identification of correlations between compound properties and interactions.

The human fungal microbiota known as mycobiota is increasingly recognized as a critical factor in human gut health and disease. Non-pathogenic commensal yeasts such as Saccharomyces cerevisiae promote homeostasis in the gut, whereas dysbiosis of the gut mycobiota is associated with inflammation. Glycan-binding receptors (lectins) are key host factors in host-mycobiota interaction in the gut. They are expressed on immune cells such as dendritic cells (DCs) and recognize fungal polysaccharides. This interaction is imperative to mount appropriate immune responses for immune homeostasis in the gut as well as clearance of fungal pathogens. Recent studies demonstrate that microtubule-associated protein light-chain 3 (LC3)-associated phagocytosis (LAP) is involved in lectin-fungi interactions. Yet, the biological impact of LAP on the lectin function remains largely elusive. In this report, we demonstrate that in mouse LAP is linked to dendritic cell-associated lectin 2 (Dectin-2), a C-type lectin specific to fungal α-mannan polysaccharide. We found that mouse Dectin-2 recognizes commensal yeast S. cerevisiae and Kazachstania unispora. Mouse bone marrow-derived DCs (BMDCs) produced inflammatory cytokines TNFα and IL-1β in response to the yeasts in a Dectin-2 and spleen tyrosine kinase (Syk)-dependent manner. We found that S. cerevisiae and K. unispora induced LAP in mouse BMDCs upon internalization. Furthermore, LC3 was activated by stimulation of BMDCs with the yeasts in a Dectin-2 and Syk-dependent manner. To address the biological impact of LAP on Dectin-2 yeast interaction, we established a knock-in mouse strain (Atg16L1E230, thereafter called E230), which BMDCs exhibit autophagy-active and LAP-negative phenotypes. When stimulated with yeasts, E230 BMDCs produced significantly less amounts of TNFα and IL-1β. Taken together, we revealed a novel link between Dectin-2 and LAP that enables host immune cells to respond to mycobiota.

Toxoplasma gondii is a ubiquitous parasite and a prevalent food-borne parasitic pathogen. Infection of the host occurs principally through oral consumption of contaminated food and water with the gastrointestinal tract being the primary route for entry into the host. To promote infection, T. gondii has evolved highly specialized strategies for rapid traversal of the single cell thick intestinal epithelial barrier. Parasite transmigration via the paracellular pathway between adjacent cells enables parasite dissemination to secondary sites of infection where chronic infection of muscle and brain tissue is established. It has recently been proposed that parasite interactions with the integral tight junction (TJ) protein occludin influences parasite transmigration of the intestinal epithelium. We review here the emerging mechanisms of T. gondii transmigration of the small intestinal epithelium alongside the developing role played in modulating the wider TJ-associated proteome to rewire host cell regulatory systems for the benefit of the parasite.

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

Abstract Motivation Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. Results Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term ‘causal interaction’, and related child terms, which are children of the PSI-MI ‘molecular interaction’ term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. Availability and implementation PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). Supplementary information Supplementary data are available at Bioinformatics online.

Macroautophagy, the degradation of cytoplasmic content by lysosomal fusion, is an evolutionary conserved process promoting homeostasis and intracellular defence. Macroautophagy is initiated primarily by a complex containing ULK1 or ULK2 (two paralogs of the yeast Atg1 protein). To understand the differences between ULK1 and ULK2, we compared the human ULK1 and ULK2 proteins and their regulation. Despite the similarity in their enzymatic domain, we found that ULK1 and ULK2 have major differences in their autophagy-related interactors and their post-translational and transcriptional regulators. We identified 18 ULK1-specific and 7 ULK2-specific protein motifs serving as different interaction interfaces. We found that interactors of ULK1 and ULK2 all have different tissue-specific expressions partially contributing to diverse and ULK-specific interaction networks in various tissues. We identified three ULK1-specific and one ULK2-specific transcription factor binding sites, and eight sites shared by the regulatory region of both genes. Importantly, we found that both their post-translational and transcriptional regulators are involved in distinct biological processes-suggesting separate functions for ULK1 and ULK2. Unravelling differences between ULK1 and ULK2 could lead to a better understanding of how ULK-type specific dysregulation affects autophagy and other cellular processes that have been implicated in diseases such as inflammatory bowel disease and cancer.

Abstract Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

Patients with inflammatory bowel disease (IBD) 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 microbiome's 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. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These 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.

One of the main inducers of autophagy-dependent self-cannibalism, called ULK1, is tightly regulated by the two sensor molecules of nutrient conditions and energy status, known as mTOR and AMPK kinases, respectively. Recently, we developed a freely available mathematical model to explore the oscillatory characteristic of the AMPK-mTOR-ULK1 regulatory triangle. Here, we introduce a systems biology analysis to explain in detail the dynamical features of the essential negative and double-negative feedback loops and also the periodic repeat of autophagy induction upon cellular stress. We propose an additional regulatory molecule in the autophagy control network that delays some of AMPK’s effect on the system, making the model output more consistent with experimental results. Furthermore, a network analysis on AutophagyNet was carried out to identify which proteins could be the proposed regulatory components in the system. These regulatory proteins should satisfy the following rules: (1) they are induced by AMPK; (2) they promote ULK1; (3) they down-regulate mTOR upon cellular stress. We have found 16 such regulatory components that have been experimentally proven to satisfy at least two of the given rules. Identifying such critical regulators of autophagy induction could support anti-cancer- and ageing-related therapeutic efforts.

Pancreatic ductal adenocarcinoma (PDAC) is driven by metabolic changes in pancreatic cells caused by oncogenic mutations and dysregulation of p53. PDAC cell lines and PDAC-derived xenografts grow as a result of altered metabolic pathways, changes in stroma, and autophagy. Selective targeting and inhibition of one of these may open avenues for the development of new therapeutic strategies. In this study, we performed a genome-wide siRNA screen in a PDAC cell line using endogenous autophagy as a readout and identified several regulators of autophagy that were required for autophagy-dependent PDAC cell survival. Validation of two promising candidates, MPP7 (MAGUK p55 subfamily member 7, a scaffolding protein involved in cell-cell contacts) and MDH1 (cytosolic Malate dehydrogenase 1), revealed their role in early stages of autophagy during autophagosome formation. MPP7 was involved in the activation of YAP1 (a transcriptional coactivator in the Hippo pathway), which in turn promoted autophagy, whereas MDH1 was required for maintenance of the levels of the essential autophagy initiator serine-threonine kinase ULK1, and increased in the activity upon induction of autophagy. Our results provide a possible explanation for how autophagy is regulated by MPP7 and MDH1, which adds to our understanding of autophagy regulation in PDAC. SIGNIFICANCE: This study identifies and characterizes MPP7 and MDH1 as novel regulators of autophagy, which is thought to be responsible for pancreatic cancer cell survival.

The underlying health-driving mechanisms of Bifidobacterium during early life are not well understood, particularly how this microbiota member may modulate the intestinal barrier via programming of intestinal epithelial cells (IECs). We investigated the impact of Bifidobacterium breve UCC2003 on the transcriptome of neonatal murine IECs. Small IECs from two-week-old neonatal mice administered B. breve UCC2003 or PBS (control) were subjected to global RNA sequencing, and differentially expressed genes, pathways, and affected cell types were determined. We observed extensive regulation of the IEC transcriptome with ∼4,000 genes significantly up-regulated, including key genes linked with epithelial barrier function. Enrichment of cell differentiation pathways was observed, along with an overrepresentation of stem cell marker genes, indicating an increase in the regenerative potential of the epithelial layer. In conclusion, B. breve UCC2003 plays a central role in driving intestinal epithelium homeostatic development during early life and suggests future avenues for next-stage clinical studies.

Background: Bifidobacterium represents an important early life microbiota member. Specific bifidobacterial components, exopolysaccharides (EPS), positively modulate host responses, with purified EPS also suggested to impact microbe–microbe interactions by acting as a nutrient substrate. Thus, we determined the longitudinal effects of bifidobacterial EPS on microbial communities and metabolite profiles using an infant model colon system. Methods: Differential gene expression and growth characteristics were determined for each strain; Bifidobacterium breve UCC2003 and corresponding isogenic EPS-deletion mutant (B. breve UCC2003del). Model colon vessels were inoculated with B. breve and microbiome dynamics monitored using 16S rRNA sequencing and metabolomics (NMR). Results: Transcriptomics of EPS mutant vs. B. breve UCC2003 highlighted discrete differential gene expression (e.g., eps biosynthetic cluster), though overall growth dynamics between strains were unaffected. The EPS-positive vessel had significant shifts in microbiome and metabolite profiles until study end (405 h); with increases of Tyzzerella and Faecalibacterium, and short-chain fatty acids, with further correlations between taxa and metabolites which were not observed within the EPS-negative vessel. Conclusions: These data indicate that B. breve UCC2003 EPS is potentially metabolized by infant microbiota members, leading to differential microbial metabolism and altered metabolite by-products. Overall, these findings may allow development of EPS-specific strategies to promote infant health.

Abstract Background Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. Results To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait—the visual system—for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Conclusions Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Homeostatic functions of a living tissue, such as the gastrointestinal tract, rely on highly sophisticated and finely tuned cell-to-cell interactions. These crosstalks evolve and continuously are refined as the tissue develops and give rise to specialized cells performing general and tissue-specific functions. To study these systems, stem cell–based in vitro models, often called <i>organoids</i>, and non–stem cell–based primary cell aggregates (called spheroids) appeared just over a decade ago. These models still are evolving and gaining complexity, making them the state-of-the-art models for studying cellular crosstalk in the gastrointestinal tract, and to investigate digestive pathologies, such as inflammatory bowel disease, colorectal cancer, and liver diseases. However, the use of organoid- or spheroid-based models to recapitulate in vitro the highly complex structure of in vivo tissue remains challenging, and mainly restricted to expert developmental cell biologists. Here, we condense the founding knowledge and key literature information that scientists adopting the organoid technology for the first time need to consider when using these models for novel biological questions. We also include information that current organoid/spheroid users could use to add to increase the complexity to their existing models. We highlight the current and prospective evolution of these models through bridging stem cell biology with biomaterial and scaffold engineering research areas. Linking these complementary fields will increase the in vitro mimicry of in vivo tissue, and potentially lead to more successful translational biomedical applications. Deepening our understanding of the nature and dynamic fine-tuning of intercellular crosstalks will enable identifying novel signaling targets for new or repurposed therapeutics used in many multifactorial diseases.

The gastrointestinal tract (GIT) harbors a complex community of microbes termed the microbiota that plays a role in maintaining the host’s health and wellbeing. How this comes about and the nature of microbe-host cell interactions in the GIT is still unclear.

<h2>Summary</h2> Interactions between the epithelium and the immune system are critical in the pathogenesis of inflammatory bowel disease (IBD). In this study, we mapped the transcriptional landscape of human colonic epithelial organoids in response to different cytokines responsible for mediating canonical mucosal immune responses. By profiling the transcriptome of human colonic organoids treated with the canonical cytokines interferon gamma, interleukin-13, -17A, and tumor necrosis factor alpha with next-generation sequencing, we unveil shared and distinct regulation patterns of epithelial function by different cytokines. An integrative analysis of cytokine responses in diseased tissue from patients with IBD (n = 1,009) reveals a molecular classification of mucosal inflammation defined by gradients of cytokine-responsive transcriptional signatures. Our systems biology approach detected signaling bottlenecks in cytokine-responsive networks and highlighted their translational potential as theragnostic targets in intestinal inflammation.

Changes in assisted protein folding are largely unexplored in diabetes. In the present studies, we have identified a reductive shift in the redox status of rat liver microsomes after 4 weeks of streptozotocin-induced diabetes. This change was reflected by a significant increase in the total- and protein-sulfhydryl content, as well as in the free sulfhydryl groups of the major protein disulfide isomerases (PDIs), the 58 kDa PDI and the 57 kDa ERp57 but not other chaperones. A parallel decrease of the protein-disulfide oxidoreductase activity was detected in the microsomal fraction of diabetic livers. The oxidant of PDI, Ero1-Lα showed a more oxidized status in diabetic rats. Our results reveal major changes in the redox status of the endoplasmic reticulum and its redox chaperones in diabetic rats, which may contribute to the defective protein secretion of the diabetic liver.

Molecular chaperones play a prominent role in signaling and transcriptional regulatory networks of the cell. Recent advances uncovered that chaperones act as genetic buffers stabilizing the phenotype of various cells and organisms and may serve as potential regulators of evolvability. Chaperones have weak links, connect hubs, are in the overlaps of network modules and may uncouple these modules during stress, which gives an additional protection for the cell at the network-level. Moreover, after stress chaperones are essential to re-build inter-modular contacts by their low affinity sampling of the potential interaction partners in different modules. This opens the way to the chaperone-regulated modular evolution of cellular networks, and helps us to design novel therapeutic and anti-aging strategies.

There is a widening recognition that cancer cells are products of complex developmental processes. Carcinogenesis and metastasis formation are increasingly described as systems-level, network phenomena. Here we propose that malignant transformation is a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of carcinogenesis as a model of cellular learning. We describe the hallmarks of increased system plasticity of early, tumor initiating cells, such as increased noise, entropy, conformational and phenotypic plasticity, physical deformability, cell heterogeneity and network rearrangements. Finally, we argue that the large structural changes of molecular networks during cancer development necessitate a rather different targeting strategy in early and late phase of carcinogenesis. Plastic networks of early phase cancer development need a central hit, while rigid networks of late stage primary tumors or established metastases should be attacked by the network influence strategy, such as by edgetic, multi-target, or allo-network drugs. Cancer stem cells need special diagnosis and targeting, since their dormant and rapidly proliferating forms may have more rigid, or more plastic networks, respectively. The extremely high ability of cancer stem cells to change the rigidity/plasticity of their networks may be their key hallmark. The application of early stage-optimized anti-cancer drugs to late-stage patients may be a reason of many failures in anti-cancer therapies. Our hypotheses presented here underlie the need for patient-specific multi-target therapies applying the correct ratio of central hits and network influences - in an optimized sequence.

Even targeted chemotherapies against solid cancers show a moderate success increasing the need to novel targeting strategies. To address this problem, we designed a systems-level approach investigating the neighbourhood of mutated or differentially expressed cancer-related proteins in four major solid cancers (colon, breast, liver and lung). Using signalling and protein-protein interaction network resources integrated with mutational and expression datasets, we analysed the properties of the direct and indirect interactors (first and second neighbours) of cancer-related proteins, not found previously related to the given cancer type. We found that first neighbours have at least as high degree, betweenness centrality and clustering coefficient as cancer-related proteins themselves, indicating a previously unknown central network position. We identified a complementary strategy for mutated and differentially expressed proteins, where the affect of differentially expressed proteins having smaller network centrality is compensated with high centrality first neighbours. These first neighbours can be considered as key, so far hidden, components in cancer rewiring, with similar importance as mutated proteins. These observations strikingly suggest targeting first neighbours as a novel strategy for disrupting cancer-specific networks. Remarkably, our survey revealed 223 marketed drugs already targeting first neighbour proteins but applied mostly outside oncology, providing a potential list for drug repurposing against solid cancers. For the very central first neighbours, whose direct targeting would cause several side effects, we suggest a cancer-mimicking strategy by targeting their interactors (second neighbours of cancer-related proteins, having a central protein affecting position, similarly to the cancer-related proteins). Hence, we propose to include first neighbours to network medicine based approaches for (but not limited to) anticancer therapies.

Abstract Background Breast cancer is the second most prevalent cancer worldwide with around 1.7 million new cases diagnosed every year. Whilst prognosis is generally favourable in early stages, this worsens significantly in advanced disease. Therefore, it is pertinent to focus on mitigating factors that may slow growth or progression. Recently, the gut microbiome has been implicated in a wide-range of roles in tumour biology. Through modulation of immunity, the gut microbiota can improve the efficacy of several immunotherapies. However, despite the prevalence of breast cancer, there is still a lack of microbiota studies in this field, including exploring the influence of external microbiome-modulating factors such as antibiotics. We describe herein how disruption of the gut microbiota via antibiotics may be detrimental to patient outcomes through acceleration of tumour growth. Results Supplementing animals with a cocktail of antibiotics leads to gut microbiota alterations and is accompanied by significant acceleration of tumour growth. Surprisingly, and distinct from previous microbiome-tumour studies, the mechanism driving these effects do not appear to be due to gross immunological changes. Analysis of intratumoural immune cell populations and cytokine production are not affected by antibiotic administration. Through global tumour transcriptomics, we have uncovered dysregulated gene expression networks relating to protein and lipid metabolism that are correlated with accelerated tumour growth. Fecal metabolomics revealed a reduction of the microbial-derived short-chain fatty acid butyrate that may contribute to accelerated tumour growth. Finally, through use of a routinely administered antibiotic in breast cancer patients, Cephalexin, we have shown that tumour growth is also significantly affected. Metataxanomic sequencing and analysis highlighted significant antibiotic-associated reductions in the butyrate producing genera Odoribacter and Anaeotruncus , and increased abundance of Bacteroides . Conclusions Our data indicate that perturbation of the microbiota by antibiotics may have negative impacts on breast cancer patient outcomes. This is of importance as antibiotics are regularly prescribed to breast cancer patients undergoing mastectomy or breast reconstruction. We have also shown that the metabolic impact of disruption to the microbiome should be considered alongside the potent immunological effects. We believe our work lays the foundation for improving the use of antibiotics in patients, and with further investigation could potentially inform clinical practice.

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein–protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.

Paneth cells are key epithelial cells that provide an antimicrobial barrier and maintain integrity of the small-intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and are often associated with digestive pathologies such as Crohn's disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process that recycles cellular components. The direct effect of autophagy impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells, making these cells impaired in autophagy. Using three-dimensional intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy-targeted proteins and functional information to identify the mechanistic link between autophagy impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%) that are predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms that degrade key proteins in Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration have effects at the protein level, not on gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred downregulation of exocytosis. Our observations could explain how protein-level alterations affect Paneth cell homeostatic functions upon autophagy impairment.This article has an associated First Person interview with the joint first authors of the paper.

Background Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species. Principal Findings Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates. Conclusions Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.

Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a ‘contextual signaling hub’, i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often ‘train’ cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design ‘gentler’, differentiation and maintenance therapies.

The compound eye of the fruit fly Drosophila melanogaster is one of the most intensively studied and best understood model organs in the field of developmental genetics. Herein we demonstrate that autophagy, an evolutionarily conserved selfdegradation process of eukaryotic cells, is essential for eye development in this organism. Autophagic structures accumulate in a specific pattern in the developing eye disc, predominantly in the morphogenetic furrow (MF) and differentiation zone. Silencing of several autophagy genes (Atg) in the eye primordium severely affects the morphology of the adult eye through triggering ectopic cell death. In Atg mutant genetic backgrounds however genetic compensatory mechanisms largely rescue autophagic activity in, and thereby normal morphogenesis of, this organ. We also show that in the eye disc the expression of a key autophagy gene, Atg8a, is controlled in a complex manner by the anterior Hox paralog Lab (Labial), a master regulator of early development. Atg8a transcription is repressed in front of, while activated along, the MF by Lab. The amount of autophagic structures then remains elevated behind the moving MF. These results indicate that eye development in Drosophila depends on the cell death-suppressing and differentiating effects of the autophagic process. This novel, developmentally regulated function of autophagy in the morphogenesis of the compound eye may shed light on a more fundamental role for cellular self-digestion in differentiation and organ formation than previously thought.αTub84B, α-Tubulin at 84B; Act5C, Actin5C; AO, acridine orange; Atg, autophagy-related; Ato, Atonal; CASP3, caspase 3; Dcr-2; Dicer-2; Dfd, Deformed; DZ, differentiation zone; eGFP, enhanced green fluorescent protein; EM, electron microscopy; exd, extradenticle; ey, eyeless; FLP, flippase recombinase; FRT, FLP recognition target; Gal4, gene encoding the yeast transcription activator protein GAL4; GFP, green fluorescent protein; GMR, Glass multimer reporter; Hox, homeobox; hth, homothorax; lab, labial; L3F, L3 feeding larval stage; L3W, L3 wandering larval stage; lf, loss-of-function; MAP1LC3, microtubule-associated protein 1 light chain 3; MF, morphogenetic furrow; PE, phosphatidylethanolamine; PBS, phosphate-buffered saline; PI3K/PtdIns3K, class III phosphatidylinositol 3-kinase; PZ, proliferation zone; Ref(2)P, refractory to sigma P, RFP, red fluorescent protein; RNAi, RNA interference; RpL32, Ribosomal protein L32; RT-PCR, reverse transcription-coupled polymerase chain reaction; S.D., standard deviation; SQSTM1, Sequestosome-1, Tor, Target of rapamycin; TUNEL, terminal deoxynucleotidyl transferase mediated dUTP nick end labeling assay; UAS, upstream activation sequence; qPCR, quantitative real-time polymerase chain reaction; w, white.

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 context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

Although autophagy is a type of programmed cell death, it is also essential for cell survival upon tolerable level of various stress events. For the cell to respond adequately to an external and/or internal stimulus induced by cellular stress, autophagy must be controlled in a highly regulated manner. By using systems biology techniques, here we explore the dynamical features of autophagy induction. We propose that the switch-like characteristic of autophagy induction is achieved by a control network, containing essential feedback loops of four components, so-called autophagy inducer, autophagy controller, mTORC1 and autophagy executor, respectively. We show how an autophagy inducer is capable to turn on autophagy in a cellular stress-specific way. The autophagy controller acts as a molecular switch and not only promotes autophagy but also blocks the permanent hyperactivation of the process via downregulating the autophagy inducer. In this theoretical analysis, we explore in detail the properties of all four proposed controlling elements and their connections. Here we also prove that the kinetic features of this control network can be considered accurate in various stress processes (such as starvation, endoplasmic reticulum stress and oxidative stress), even if the exact components may be different. The robust response of the resulting control network is essential during cellular stress.

Characterization of host genetic factors contributing to COVID-19 severity promises advances on drug discovery to fight the disease. Most genetic analyses to date have identified genome-wide significant associations involving loss-of-function variants for immune response pathways. Despite accumulating evidence supporting a role for T cells in COVID-19 severity, no definitive genetic markers have been found to support an involvement of T cell responses. We analyzed 205 whole exomes from both a well-characterized cohort of hospitalized severe COVID-19 patients and controls. Significantly enriched high impact alleles were found for 25 variants within the T cell receptor beta (TRB) locus on chromosome 7. Although most of these alleles were found in heterozygosis, at least three or more in TRBV6-5, TRBV7-3, TRBV7-6, TRBV7-7, and TRBV10-1 suggested a possible TRB loss of function via compound heterozygosis. This loss-of-function in TRB genes supports suboptimal or dysfunctional T cell responses as a major contributor to severe COVID-19 pathogenesis.

Cruciferous-rich diets, particularly broccoli, have been associated with reduced risk of developing cancers of various sites, cardiovascular disease and type-2 diabetes. Sulforaphane (SF), a sulfur-containing broccoli-derived metabolite, has been identified as the major bioactive compound mediating these health benefits. Sulforaphane is a potent dietary activator of the transcription factor Nuclear factor erythroid-like 2 (NRF2), the master regulator of antioxidant cell capacity responsible for inducing cytoprotective genes, but its role in glucose homeostasis remains unclear. In this study, we set to test the hypothesis that SF regulates glucose metabolism and ameliorates glucose overload and its resulting oxidative stress by inducing NRF2 in human hepatoma HepG2 cells. HepG2 cells were exposed to varying glucose concentrations: basal (5.5 mM) and high glucose (25 mM), in the presence of physiological concentrations of SF (10 μM). SF upregulated the expression of glutathione (GSH) biosynthetic genes and significantly increased levels of reduced GSH. Labelled glucose and glutamine experiments to measure metabolic fluxes identified that SF increased intracellular utilisation of glycine and glutamate by redirecting the latter away from the TCA cycle and increased the import of cysteine from the media, likely to support glutathione synthesis. Furthermore, SF altered pathways generating NADPH, the necessary cofactor for oxidoreductase reactions, namely pentose phosphate pathway and 1C-metabolism, leading to the redirection of glucose away from glycolysis and towards PPP and of methionine towards methylation substrates. Finally, transcriptomic and targeted metabolomics LC-MS analysis of NRF2-KD HepG2 cells generated using CRISPR-Cas9 genome editing revealed that the above metabolic effects are mediated through NRF2. These results suggest that the antioxidant properties of cruciferous diets are intricately connected to their metabolic benefits.

Abstract Background Precision medicine in ulcerative colitis (UC) remain elusive. The heterogeneity in patients’ mucosal transcriptomic profiles suggests that stratification based on molecular mechanisms might be possible, which may link to treatment response. We interrogated a dataset of ustekinumab-treated patients treated to determine transcript modules (TMs) in active UC, investigate pathogenic pathways and consider which cells might be driving ustekinumab resistance. Methods Pre-treatment mucosal transcriptomes and outcome data were available for 358 ustekinumab-treated patients enrolled in the UNIFI programme. Modules of co-expressed genes were defined with weighted gene correlation network analysis (WGCNA) and their functional roles and regulatory interactions inferred with Fisher tests. Multivariate regression was used to test the ability of clinical and transcriptomic data to predict treatment resistance. Further insights into the cellular and molecular features of ustekinumab resistance were gauged from the analysis of single cell data (Single Cell Portal accession SCP259). Analyses were performed in R 4.2.3 (Vienna, Austria). Results WGCNA generated 23 transcript modules (TMs) whose correlation with mucosal healing ranged from -0.26 to 0.25. The three TMs most negatively correlated with response comprised genes enriched in neutrophil degranulation, extracellular matrix activity and endoplasmic reticulum function. The modules correlating highest with response were enriched in genes involved in mitochondrial function. The constituents of the ustekinumab resistance-related modules are regulated by a hierarchy of transcription factors downstream of NFKB1, AHR, JUN and RELA. They are also densely connected by regulatory protein-protein interactions (Figure 1). A multivariate model including TM1 enrichment score and clinical variables predicts resistance with area under the curve 0.76 (95% confidence interval: 0.69–0.82). Notably, TM1 included TREM1 and OSM, which were previously associated with anti-TNF resistance. Surmising that cell populations co-expressing these genes might be key to ustekinumab resistance too, we identified TREM1+ OSM+ inflammatory monocytes (IMs). These showed higher expression of genes such as IL1B, IL8 and CXCL2 compared to other IMs, and increased activity in pathogenic processes including interleukin (IL)10, IL4 and IL13 signalling and G-protein-coupled receptor signalling. Conclusion Gene co-expression analysis identified transcripts and molecular mechanisms associated with response and predictive of resistance to ustekinumab. TREM1+ OSM+ IMs may be key to driving resistance, suggesting an important role for innate immunity. These findings are important for developing precision medicine approaches in UC.

The intertwined interactions various immune cells have with epithelial cells in our body require sophisticated experimental approaches to be studied. Due to the limitations of immortalised cell lines and animal models, there is an increasing demand for human in vitro model systems to investigate the microenvironment of immune cells in normal and in pathological conditions. Organoids, which are self-renewing, 3D cellular structures that are derived from stem cells, have started to provide gap-filling tissue modelling solutions. In this review, we first demonstrate with some of the available examples how organoid-based immune cell co-culture experiments can advance disease modelling of cancer, inflammatory bowel disease and tissue regeneration. Then, we argue that to achieve both complexity and scale, organ-on-chip models combined with cutting-edge microfluidics-based technologies can provide more precise manipulation and readouts. Finally, we discuss how genome editing techniques and the use of patient-derived organoids and immune cells can improve disease modelling and facilitate precision medicine. To achieve maximum impact and efficiency, these efforts should be supported by novel infrastructures such as organoid biobanks, organoid facilities, as well as drug screening and host-microbe interaction testing platforms. All these together or in combination can allow researchers to shed more detailed, and often patient-specific, light on the crosstalk between immune cells and epithelial cells in health and disease.

The endoplasmic reticulum (ER), similary to other subcompartments of the eukaryotic cell possesses a relatively oxidizing environment. The special milieu of ER lumen is important for many ER-specific processes (redox protein folding, glycoprotein synthesis, quality control of secreted proteins, antigen presentation, etc.). Despite of the vital importance of redox regulation in the ER, we have a surprisingly fragmented knowledge about the mechanisms responsible for the ER redox balance. Moreover, new observations on disulfide bridge synthesis and on glutathione functions urge us to revise our recent theories based on many indirect and in vitro results. We have also very little information about the effects of different pathological conditions on the thiol metabolism and redox folding in the ER. Examining the role of molecular chaperones in the cellular pathology of diabetes mellitus we found that the ER redox environment shifted to a more reducing state, which was followed by changes of the thiol metabolism and structural-functional changes of the protein machinery involved in the redox folding process in diabetes. The possible consequences of these unexpected changes are also discussed.

Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.

Autophagy, the lysosome-mediated self-degradation process, is implicated in survival during starvation in yeast, Dictyostelium and animals. In these eukaryotic taxa (collectively called Unikonts), autophagy is induced primarily through the Atg1/ULK1 complex in response to nutrient depletion. Autophagy has also been well-studied in non-unikont parasites, such as Trypanosoma and Plasmodium, and found important in their life-cycle transitions. However, how autophagy is induced in non-unikonts remains largely unrevealed. Using a bioinformatics approach, we examined the presence of Atg1 and of its complex in the genomes of 40 non-unikonts. We found that these genomes do not encode typical Atg1 proteins: BLAST and HMMER queries matched only with the kinase domain of Atg1, while other segments responsible for regulation and protein-binding were missing. Non-unikonts also lacked other components of the Atg1-inducing complex. Orthologs of an alternative autophagy inducer, Atg6 were found only in the half of the species, indicating that the other half may possess other inducing mechanisms. As key autophagy genes have differential expression patterns during life-cycle, we raise the possibility that autophagy in these protists is induced mainly at the post-transcriptional level. Understanding Atg1-independent autophagy induction mechanisms in these parasites may lead to novel pharmacological interventions, not affecting human Atg1-dependent autophagy.

Abstract 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 OmniPath ’s 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.

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

Inflammatory diseases such as inflammatory bowel disease (IBD) require recurrent invasive tests, including blood tests, radiology, and endoscopic evaluation both to diagnose and assess disease activity, and to determine optimal therapeutic strategies. Simple ‘bedside’ biomarkers could be used in all phases of patient management to avoid unnecessary investigation and guide further management. The focal adhesion complex (FAC) has been implicated in the pathogenesis of multiple inflammatory diseases, including IBD, rheumatoid arthritis, and multiple sclerosis. Utilizing omics technologies has proven to be an efficient approach to identify biomarkers from within the FAC in the field of cancer medicine. Predictive biomarkers are paving the way for the success of precision medicine for cancer patients, but inflammatory diseases have lagged behind in this respect. This review explores the current status of biomarker prediction for inflammatory diseases from within the FAC using omics technologies and highlights the benefits of future potential biomarker identification approaches.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.

There is growing evidence that emerging malignancies in solid tissues might be kept under control by physical intercellular contacts with normal fibroblasts.Here we characterize transcriptional landscapes of fibroblasts that confronted cancer cells. We studied four pairs of in vitro and ex vivo fibroblast lines which, within each pair, differed in their capacity to inhibit cancer cells. The natural process was modeled in vitro by confronting the fibroblasts with PC-3 cancer cells. Fibroblast transcriptomes were recorded by Affymetrix microarrays and then investigated using network analysis.The network enrichment analysis allowed us to separate confrontation- and inhibition-specific components of the fibroblast transcriptional response. Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA. Inhibition-specific differences were more subtle and characterized by involvement of Rho signaling at the pathway level and by potential individual regulators such as IL6, MAPK8, MAP2K4, PRKCA, JUN, STAT3, and STAT5A.We investigated the interaction between cancer cells and fibroblasts in order to shed light on the potential mechanisms and explain the differential inhibitory capacity of the latter, which enabled both a holistic view on the process and details at the gene/protein level. The combination of our methods pointed to proteins, such as members of the Rho pathway, pro-inflammatory signature and the YAP1/TAZ cascade, that warrant further investigation via tools of experimental perturbation. We also demonstrated functional congruence between the in vitro and ex vivo models. The microarray data are made available via the Gene Expression Omnibus as GSE57199.

Molecular chaperones are not only fascinating molecular machines that help the folding, refolding, activation or assembly of other proteins, but also have a number of functions. These functions can be understood only by considering the emergent properties of cellular networks--and that of chaperones as special network constituents. As a notable example for the network-related roles of chaperones they may act as genetic buffers stabilizing the phenotype of various cells and organisms, and may serve as potential regulators of evolvability. Why are chaperones special in the context of cellular networks? Chaperones: (1) have weak links, i.e. low affinity, transient interactions with most of their partners; (2) connect hubs, i.e. act as 'masterminds' of the cell being close to several centre proteins with a lot of neighbours; and (3) are in the overlaps of network modules, which confers upon them a special regulatory role. Importantly, chaperones may uncouple or even quarantine modules of protein-protein interaction networks, signalling networks, genetic regulatory networks and membrane organelle networks during stress, which gives an additional chaperone-mediated protection for the cell at the network-level. Moreover, chaperones are essential to rebuild inter-modular contacts after stress by their low affinity, 'quasi-random' sampling of the potential interaction partners in different cellular modules. This opens the way to the chaperone-regulated modular evolution of cellular networks, and helps us to design novel therapeutic and anti-ageing strategies.

Ustekinumab (UST), an anti-IL12/23p40 monoclonal antibody, has been approved for Crohn's disease (CD). The aim of this study was to identify baseline predictors of response using several omics layers, which ultimately may result in a multi-omics panel allowing individualised UST therapy. Inflamed colonic (n = 25) and ileal (n = 22) biopsies were retrieved prior to first UST administration in patients with active CD, in addition to sorted circulating CD14+ monocytes and CD4+ T cells (n = 39). RNA was extracted from both lysed biopsies and sorted cells, and RNA sequencing performed. Proteomic analysis was performed on baseline serum samples (n = 86) using OLINK Proseek inflammation. Genotyping data were generated using Immunochip (n = 38). The genetic risk burden was determined for every patient using the SNPs which overlap with genes encoding functional proteins or RNAs. The six above-described layers of omics data were integrated and analysed using Multi-Omics Factor Analysis (MOFA). The strongest omic layers in terms of variance contribution to the latent factors explaining endoscopic response (≥50% in SES-CD by w24) were identified. Dimensionality reduction and feature extraction from the strongest -omic layers were performed followed by predictive modelling on the top-ranked features. Cross-validation using distinct test and training sets was performed for the ensemble and individual classifiers, as an internal validation to avoid over-fitting. MOFA identified 19 latent factors (LF, minimum explained variance 2%), with 3 LFs correlating with endoscopic response at w24 (r = −0.24, r = 0.27, r = −0.25; p = 0.03, p = 0.01, p = 0.02). The genomic and CD14 transcriptomic layers contributed significantly to the prediction of endoscopic response. Predictive modelling based on the results of the most dominant omic layers revealed a 10-feature panel predicting endoscopic response at w24 with an accuracy of 98%. In contrast, classification performance based on 10 randomly selected features resulted in a drastic drop in accuracy (66%). Only 2 of the 10 features exhibited significant correlation with baseline faecal calprotectin, and 1 with CRP, suggesting that this panel is not a simple surrogate of baseline inflammation. From the genetic risk burden, we identified a 15-gene panel which could classify (accuracy 96.6%) the patients based on endoscopic response. Through multi-omic data integration, we discovered pathways contributing to UST response, and identified a 10-feature transcriptomic and 15-feature genomic panel predicting endoscopic response to UST standard dosage. Further validation in larger and independent cohorts is warranted, as well as its UST specificity.

The cytokine release syndrome or cytokine storm, which is the hyper-induction of inflammatory responses has a central role in the mortality rate of COVID-19 and some other viral infections. Interleukin-6 (IL-6) is a key player in the development of cytokine storms. Shedding of interleukin-6 receptor (IL-6Rα) results in the accumulation of soluble interleukin-6 receptors (sIL-6R). Only relatively few cells express membrane-bound IL-6Rα. However, sIL-6R can act on potentially all cells and organs through the ubiquitously expressed gp130, the coreceptor of IL-6Rα. Through this, so-called trans-signaling, IL-6-sIL-6R is a powerful factor in the development of cytokine storms and multiorgan involvement. Some bacteria (e.g., Serratia marcescens, Staphylococcus aureus, Pseudomonas aeruginosa, Listeria monocytogenes), commonly considered to cause co-infections during viral pneumonia, can directly induce the shedding of membrane receptors, including IL-6Rα, or enhance endogenous shedding mechanisms causing the increase of sIL-6R level. Here we hypothesise that bacteria promoting shedding and increase the sIL-6R level can be an important contributing factor for the development of cytokine storms. Therefore, inhibition of IL-6Rα shedding by drastically reducing the number of relevant bacteria may be a critical element in reducing the chance of a cytokine storm. Validation of this hypothesis can support the consideration of the prophylactic use of antibiotics more widely and at an earlier stage of infection to decrease the mortality rate of COVID-19. Video abstract.

Macroautophagy/autophagy is a highly-conserved catabolic procss 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 eight 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, expansion, 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 user’s 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 (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Abstract Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis.

Patients with inflammatory bowel disease (IBD) are known to have perturbations in microRNA (miRNA) levels as well as altered miRNA regulation. Although experimental methods have provided initial insights into the functional consequences that may arise due to these changes, researchers are increasingly utilising novel bioinformatics approaches to further dissect the role of miRNAs in IBD. The recent exponential increase in transcriptomics datasets provides an excellent opportunity to further explore the role of miRNAs in IBD pathogenesis. To effectively understand miRNA-target gene interactions from gene expression data, multiple database resources are required, which have become available in recent years. In this technical note, we provide a step-by-step protocol for utilising these state-of-the-art resources, as well as systems biology approaches to understand the role of miRNAs in complex disease pathogenesis. We demonstrate through a case study example how to combine the resulting miRNA-target gene networks with transcriptomics data to find potential disease-specific miRNA regulators and miRNA-target genes in Crohn's disease. This approach could help to identify miRNAs that may have important disease-modifying effects in IBD and other complex disorders, and facilitate the discovery of novel therapeutic targets.

Understanding living systems requires an in-depth knowledge of the signaling networks that drive cellular homeostasis, regulate intercellular communication, and contribute to cell fates during development. Several resources exist to provide high-throughput data sets or manually curated interaction information from human or invertebrate model organisms. We previously developed SignaLink, a uniformly curated, multi-layered signaling resource containing information for human and for the model organisms nematode Caenorhabditis elegans and fruit fly Drosophila melanogaster. Until now, the use of the SignaLink database for zebrafish pathway analysis was limited. To overcome this limitation, we created SignaFish (http://signafish.org), a fish-specific signaling resource, built using the concept of SignaLink. SignaFish contains more than 200 curation-based signaling interactions, 132 further interactions listed in other resources, and it also lists potential miRNA-based regulatory connections for seven major signaling pathways. From the SignaFish website, users can reach other web resources, such as ZFIN. SignaFish provides signaling or signaling-related interactions that can be examined for each gene or downloaded for each signaling pathway. We believe that the SignaFish resource will serve as a novel navigating point for experimental design and evaluation for the zebrafish community and for researchers focusing on nonmodel fish species, such as cyclids.

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 the 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 the literature information on cell–cytokine interactions from two systems of 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 cell types and cytokine interactions following SARS-CoV-2 infection, highlighting important cytokine interactions that might contribute to severe illness in a subgroup of patients. Such findings have the 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.

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial-immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial-immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in an urgent need for identifying potential therapeutic drugs. In the first half of 2020 tropic antimalarial drugs, such as chloroquine (CQ) or hydroxochloroquine (HCQ) were the focus of tremendous public attention. In the initial periods of the pandemic, many scientific results pointed out that CQ/HCQ could be very effective for patients with severe COVID. While CQ and HCQ have successfully been used against several diseases (such as malaria, autoimmune disease and rheumatic illnesses); long term use of these agents are associated with serious adverse effects (i.e. inducing acute kidney injury, among many others) due to their role in blocking autophagy-dependent self-degradation. Recent experimental and clinical trial data also confirmed that there is no sufficient evidence about the efficient usage of CQ/HCQ against COVID-19. By using systems biology techniques, here we show that the cellular effect of CQ/HCQ on autophagy during endoplasmic reticulum (ER) stress or following SARS-CoV-2 infection results in upregulation of ER stress. By presenting a simple mathematical model, we claim that although CQ/HCQ might be able to ameliorate virus infection, the permanent inhibition of autophagy by CQ/HCQ has serious negative effects on the cell. Since CQ/HCQ promotes apoptotic cell death, here we confirm that addition of CQ/HCQ cannot be really effective even in severe cases. Only a transient treatment seemed to be able to avoid apoptotic cell death, but this type of therapy could not limit virus replication in the infected host. The presented theoretical analysis clearly points out the utility and applicability of systems biology modelling to test the cellular effect of a drug targeting key major processes, such as autophagy and apoptosis. Applying these approaches could decrease the cost of pre-clinical studies and facilitate the selection of promising clinical trials in a timely fashion.

Flaviviruses, such as Dengue and Zika viruses, infect millions of people worldwide using mosquitos as vectors. In this issue of Cell, Zhang et al. reveal how these viruses manipulate the skin microbiome of infected hosts in a way that increases vector recruitment and viral spread. They propose vitamin A as a way to counteract the virus and decrease transmission. Flaviviruses, such as Dengue and Zika viruses, infect millions of people worldwide using mosquitos as vectors. In this issue of Cell, Zhang et al. reveal how these viruses manipulate the skin microbiome of infected hosts in a way that increases vector recruitment and viral spread. They propose vitamin A as a way to counteract the virus and decrease transmission. Flaviviruses are a group of vector-borne pathogens that cause several important human diseases including Zika and dengue. Because climate change is increasing the distribution of their mosquito hosts, many of these viruses pose an increasing global health threat, and consequently there is an urgent need to control their spread. Both dengue and Zika viruses use Aedes mosquitoes to spread between hosts, including humans. These insects transmit viruses by feeding on an infected vertebrate host, followed by an uninfected host, allowing the virus to enter the host's bloodstream and causing infection. There is a strong evolutionary pressure on the virus to maximize vector attraction (Pierson and Diamond, 2020Pierson T.C. Diamond M.S. The continued threat of emerging flaviviruses.Nat. Microbiol. 2020; 5: 796-812https://doi.org/10.1038/s41564-020-0714-0Crossref PubMed Scopus (330) Google Scholar). One way in which this can occur is by directly causing the host to secrete volatiles that attract vectors. Such a mechanism was found previously for Plasmodium falciparum, the malaria-causing, mosquito-borne eukaryotic pathogen that stimulates aldehyde production in red blood cells of infected individuals (Robinson et al., 2018Robinson A. Busula A.O. Voets M.A. Beshir K.B. Caulfield J.C. Powers S.J. Verhulst N.O. Winskill P. Muwanguzi J. Birkett M.A. et al.Plasmodium-associated changes in human odor attract mosquitoes.Proc. Natl. Acad. Sci. U.S.A. 2018; 115: E4209-E4218https://doi.org/10.1073/pnas.1721610115Crossref PubMed Scopus (92) Google Scholar). Traditionally, vector-borne disease transmission dynamics can be considered as a tripartite interaction between the virus, its vector, and its host (Pierson and Diamond, 2020Pierson T.C. Diamond M.S. The continued threat of emerging flaviviruses.Nat. Microbiol. 2020; 5: 796-812https://doi.org/10.1038/s41564-020-0714-0Crossref PubMed Scopus (330) Google Scholar). In this issue of Cell, Zhang et al. revealed a fourth component in the system, the skin microbiome, and have provided a detailed examination of the mechanism by which Dengue and Zika viruses increase their ability to transmit by orienting mosquitoes to feed on infected hosts. In their paper, the authors demonstrate that the virus can alter the skin microbiome composition to favor vector attraction (Zhang et al., 2022Zhang H. Zhu Y. Liu Z. Peng Y. Peng W. Tong L. Wang J. Liu Q. Wang P. Cheng G. A volatile from the skin microbiota of flavivirus-infected hosts promotes mosquito attractiveness.Cell. 2022; 185: 2510-2522Abstract Full Text Full Text PDF Scopus (18) Google Scholar; Figure 1). Zhang et al. found that, in the initial phase of the flavivirus infection, Aedes mosquitoes did not show an increased preference for infected hosts. However, this tendency changed between days 4 and 6, when dengue- and Zika-virus-infected mice attracted significantly more mosquitoes. By measuring volatile compounds secreted by the skin of infected animals, they identified multiple molecules, including acetophenone, that may attract the mosquitoes. Acetophenone is produced predominantly by the commensal skin and intestinal microbiota (Human Microbiome Project Consortium, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214https://doi.org/10.1038/nature11234Crossref PubMed Scopus (7419) Google Scholar). By using specific antibiotic treatments and experiments with germ-free mice, the authors ruled out the involvement of the gut microbiome. In the skin microbiome, however, an increased abundance of Bacillus spp.—potent acetophenone-producing microbes—was observed during infection, and this correlated with transcriptomic changes in the skin epidermis. In particular, the authors noted a downregulation of Retnla, a gene that encodes the resistin-like molecule-α (RELMα) antimicrobial protein that normally targets B. spp. RELMα is exclusively produced in epidermal keratinocytes and sebocytes, making it a specific and adequate target that allows the virus to alter only the skin microbiome without causing major dysbiosis elsewhere, which could compromise host health. Given that flaviviruses are small, single-stranded RNA viruses, consisting of only an 11kb-long positive-RNA genome encoding 10 functional proteins (Pierson and Diamond, 2020Pierson T.C. Diamond M.S. The continued threat of emerging flaviviruses.Nat. Microbiol. 2020; 5: 796-812https://doi.org/10.1038/s41564-020-0714-0Crossref PubMed Scopus (330) Google Scholar), this is a remarkable and elegant mechanism for maximizing their own transmission. Flaviviruses infect up to 400 million people every year all over the world (Pierson and Diamond, 2020Pierson T.C. Diamond M.S. The continued threat of emerging flaviviruses.Nat. Microbiol. 2020; 5: 796-812https://doi.org/10.1038/s41564-020-0714-0Crossref PubMed Scopus (330) Google Scholar). Although vaccines exist for some of the flaviviruses (e.g. dengue and yellow fever viruses), without major drug therapies the most useful defense is controlling transmission. Although the role of microbes in vector-borne disease transmission has been studied before, the focus has been the interaction between the vector and its own microbiota (Dutra et al., 2016Dutra H.L.C. Rocha M.N. Dias F.B.S. Mansur S.B. Caragata E.P. Moreira L.A. Wolbachia Blocks Currently Circulating Zika Virus Isolates in Brazilian Aedes aegypti Mosquitoes.Cell Host Microbe. 2016; 19: 771-774https://doi.org/10.1016/j.chom.2016.04.021Abstract Full Text Full Text PDF PubMed Scopus (352) Google Scholar; Moreira et al., 2009Moreira L.A. Iturbe-Ormaetxe I. Jeffery J.A. Lu G. Pyke A.T. Hedges L.M. Rocha B.C. Hall-Mendelin S. Day A. Riegler M. et al.A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium.Cell. 2009; 139: 1268-1278https://doi.org/10.1016/j.cell.2009.11.042Abstract Full Text Full Text PDF PubMed Scopus (1131) Google Scholar). The work of Zhang et al. not only revealed a specific mechanism involving the host skin microbiota to attract vectors, but they also identified a promising countermeasure. Vitamin A has previously been found to be effective at inducing RELMα expression (Harris et al., 2019Harris T.A. Gattu S. Propheter D.C. Kuang Z. Bel S. Ruhn K.A. Chara A.L. Edwards M. Zhang C. Jo J.-H. et al.Resistin-like Molecule α Provides Vitamin-A-Dependent Antimicrobial Protection in the Skin.Cell Host Microbe. 2019; 25: 777-788.e8https://doi.org/10.1016/j.chom.2019.04.004Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar). Therefore, in an additional experiment, the authors tested the indirect effect of vitamin A on acetophenone production. As predicted, the authors found that vitamin A supplementation can decrease virus transmission by reducing the acetophenone-producing Bacillus species through increased RELMα secretion in skin cells. This result indicates that dietary supplementation of a vitamin A derivative to flavivirus-infected hosts could reduce viral transmission. The involvement of commensal (skin) microbes in infection mechanisms opens new opportunities for intervention in disease transmission. New strategies, such as modification of the microbiome composition in order to make the host less attractive for vectors, is an attractive strategy for further investigation. Although this work largely focused on mouse models, it is important to understand how these findings may translate to human systems where the skin microbiota will be quite different. We can facilitate this with homology-based translation to extend and fine-tune mouse data to humans (Türei et al., 2021Türei D. Valdeolivas A. Gul L. Palacio-Escat N. Klein M. Ivanova O. Ölbei M. Gábor A. Theis F. Módos D. et al.Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.Mol. Syst. Biol. 2021; 17: e9923https://doi.org/10.15252/msb.20209923Crossref PubMed Scopus (64) Google Scholar). Another task is to identify the exact pathways through which flaviviruses can cause host transcriptomic changes (that is, the downregulation of Retnla). Integrating existing literature as a priori information can increase the confidence of such predictive models. For example, Toll-like receptor (TLR) and retinoic acid receptor (RAR) pathways can regulate antimicrobial peptide production—including RETN, the human homolog of murine Retnla—and may respond to vitamin A derivatives. Therefore, extending the current experiments with in silico pathway modeling of human skin cells, with tools like ViralLink (Treveil et al., 2021Treveil A. Bohar B. Sudhakar P. Gul L. Csabai L. Olbei M. Poletti M. Madgwick M. Andrighetti T. Hautefort I. et al.ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways.PLoS Comput. Biol. 2021; 17: e1008685https://doi.org/10.1371/journal.pcbi.1008685Crossref PubMed Google Scholar), will improve our understanding of how flaviviruses can manipulate the regulation of host antimicrobial peptides. In addition, we must also investigate if and how other vector-borne diseases may interact with the skin microbiome. The demonstrated role of the skin microbiome increases the complexity of how flaviviruses (and potentially other viruses) interact with their hosts to increase their own transmission. From a behavioral ecology perspective, it remains an open question how the increased attraction of mosquitoes to infected hosts influences the transmission of viruses to healthy individuals. Do infected mosquitoes show the same preference for infected vertebrate hosts? We know that infected vectors have different biting behavior (Wei Xiang et al., 2022Wei Xiang B.W. Saron W.A.A. Stewart J.C. Hain A. Walvekar V. Missé D. Thomas F. Kini R.M. Roche B. Claridge-Chang A. et al.Dengue virus infection modifies mosquito blood-feeding behavior to increase transmission to the host.Proc. Natl. Acad. Sci. U.S.A. 2022; 119https://doi.org/10.1073/pnas.2117589119Crossref PubMed Scopus (9) Google Scholar). Nonetheless, this more detailed inter-kingdom connection also provides a mechanism that could be used to counteract the virus. The promising findings of this study should bring researchers closer to an understanding of how four-component systems, including a virus, bacterial communities, invertebrate vectors, and vertebrate hosts, influence disease spread. Such fundamental knowledge has the potential to be translated to prevent disease and save lives. L.G. and T.K. receive research funding from Unilever. A volatile from the skin microbiota of flavivirus-infected hosts promotes mosquito attractivenessZhang et al.CellJuly 07, 2022In BriefFlaviviruses such as dengue and Zika modulate murine host skin bacterial communities to increase acetophenone-producing bacteria. Acetophenone is a mosquito attractant, and its increased production by flavivirus-infected humans and mice make them more attractive to mosquitoes, facilitating viral transmission by mosquito vectors. Full-Text PDF Open Archive

Summary statement Using an integrative approach encompassing intestinal organoid culture, proteomics, transcriptomics and protein-protein interaction networks, we list Paneth cell functions dependent on autophagy. Abstract Paneth cells are key epithelial cells providing an antimicrobial barrier and maintaining integrity of the small intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and often associated with digestive pathologies such as Crohn’s disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process which recycles cellular components. The direct effect of autophagy-impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells making these cells impaired in autophagy. Using 3D intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type (WT) and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy targeted proteins and functional information to identify the mechanistic link between autophagy-impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%), predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms degrading proteins key to Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration affect protein level and not gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred down-regulation of exocytosis. Our observations could explain how protein level alterations affect Paneth cell homeostatic functions upon autophagy impairment.

Abstract Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is still known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically-diverse species. Using a novel approach to reconstruct GRNs in vertebrate species, we aimed to study GRN evolution in representative species of the most striking example of an adaptive radiation, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids is attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. To investigate these mechanisms across species at a genome-wide scale, our novel network-based approach identifies ancestral and extant gene co-expression modules along a phylogeny, and by integrating associated regulators, predicts candidate regulatory regions implicated in traits under selection in cichlids. As a case study, we present data from a well-studied adaptive trait - the visual system - for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate the plausibility of their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species, and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Our approach revealed numerous novel potential candidate regulatory regions across cichlid genomes with no prior association, as well as those with previously reported associations to known adaptive evolutionary traits, thus providing proof of concept.

Abstract Immune checkpoint inhibitors (CPIs) have revolutionised cancer treatment, with previously untreatable disease now amenable to potential cure. Combination regimens of anti-CTLA-4 and anti-PD-1 show enhanced efficacy but are prone to off-target immune-mediated tissue injury, particularly at the barrier surfaces. CPI-induced colitis is a common and serious complication. To probe the impact of immune checkpoints on intestinal homeostasis, mice were challenged with combination anti-CTLA-4/anti-PD-1 immunotherapy and manipulation of the intestinal microbiota. Colonic immune responses were profiled using bulk and single-cell RNA-sequencing and flow cytometry. CPI-colitis was dependent on the composition of the intestinal microbiota and was characterized by remodelling of mucosal lymphocytes with induction of polyfunctional lymphocyte responses characterized by increased expression of interferon-γ (IFNγ), other pro-inflammatory cytokines/chemokines ( Il22 , Il17a Ccl3, Ccl4 and Ccl9 ), cytotoxicity molecules ( Gzmb , Gzma , Prf1 , Nkg7 ) and the chemokine receptor Cxcr6 . In comparison with mucosal lymphocytes in the steady state, polyfunctional lymphocytes from both CD4 + and CD8 + lineages upregulated costimulatory molecules and checkpoint molecules in CPI-colitis, indicating that these cells are tightly regulated. CPI-colitis was attenuated following depletion of effector lymphocytes or following blockade of the IL23/IFNγ axis. This study provides new mechanistic insights into CPI-colitis, identifying polyfunctional, cytotoxic lymphocytes as key mediators of disease. Therapeutic targeting of their effector response or regulatory networks, including the IL23/IFNγ axis likely holds the key to preventing and reversing CPI-colitis.

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 could 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 SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. 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 analyze their data using SalmoNet2.

Macroautophagy is a ubiquitous homeostasis and health-promoting recycling process of eukaryotic cells, targeting misfolded proteins, damaged organelles and intracellular infectious agents. Some intracellular pathogens such as Salmonella enterica serovar Typhimurium hijack this process during pathogenesis. Here we investigate potential protein-protein interactions between host transcription factors and secreted effector proteins of Salmonella and their effect on host gene transcription. A systems-level analysis identified Salmonella effector proteins that had the potential to affect core autophagy gene regulation. The effect of a SPI-1 effector protein, SopE, that was predicted to interact with regulatory proteins of the autophagy process, was investigated to validate our approach. We then confirmed experimentally that SopE can directly bind to SP1, a host transcription factor, which modulates the expression of the autophagy gene MAP1LC3B. We also revealed that SopE might have a double role in the modulation of autophagy: Following initial increase of MAP1LC3B transcription triggered by Salmonella infection, subsequent decrease in MAP1LC3B transcription at 6h post-infection was SopE-dependent. SopE also played a role in modulation of the autophagy flux machinery, in particular MAP1LC3B and p62 autophagy proteins, depending on the level of autophagy already taking place. Upon typical infection of epithelial cells, the autophagic flux is increased. However, when autophagy was chemically induced prior to infection, SopE dampened the autophagic flux. The same was also observed when most of the intracellular Salmonella cells were not associated with the SCV (strain lacking sifA) regardless of the autophagy induction status before infection. We demonstrated how regulatory network analysis can be used to better characterise the impact of pathogenic effector proteins, in this case, Salmonella. This study complements previous work in which we had demonstrated that specific pathogen effectors can affect the autophagy process through direct interaction with autophagy proteins. Here we show that effector proteins can also influence the upstream regulation of the process. Such interdisciplinary studies can increase our understanding of the infection process and point out targets important in intestinal epithelial cell defense.

Abstract 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.