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Osmanoglu Ö, Gupta SK, Almasi A, Yagci S, Srivastava M, Araujo GHM, Nagy Z, Balkenhol J, Dandekar T. Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection. Front Immunol 2023; 14:1285345. [PMID: 38187394 PMCID: PMC10768010 DOI: 10.3389/fimmu.2023.1285345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear. Methods We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved. Results Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients. Discussion Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.
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Affiliation(s)
- Özge Osmanoglu
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Shishir K. Gupta
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
- Institute of Botany, Heinrich Heine University, Düsseldorf, Germany
| | - Anna Almasi
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Seray Yagci
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Mugdha Srivastava
- Core Unit Systems Medicine, University of Wuerzburg, Wuerzburg, Germany
- Algorithmic Bioinformatics, Department of Computer Science, Heinrich Heine University, Düsseldorf, Germany
| | - Gabriel H. M. Araujo
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Zoltan Nagy
- University Hospital Würzburg, Institute of Experimental Biomedicine, Würzburg, Germany
| | - Johannes Balkenhol
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- Chair of Molecular Microscopy, Rudolf Virchow Center for Integrative and Translation Bioimaging, University of Würzburg, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- European Molecular Biology Laboratory (EMBL) Heidelberg, BioComputing Unit, Heidelberg, Germany
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2
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Genna A, Alter J, Poletti M, Meirson T, Sneh T, Gendler M, Saleev N, Karagiannis GS, Wang Y, Cox D, Entenberg D, Oktay MH, Korcsmaros T, Condeelis JS, Gil-Henn H. FAK family proteins regulate in vivo breast cancer metastasis via distinct mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564212. [PMID: 37961438 PMCID: PMC10634866 DOI: 10.1101/2023.10.27.564212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Breast cancer is the most commonly diagnosed malignancy and the major leading cause of tumor-related deaths in women. It is estimated that the majority of breast tumor-related deaths are a consequence of metastasis, to which no cure exists at present. The FAK family proteins Proline-rich tyrosine kinase (PYK2) and focal adhesion kinase (FAK) are highly expressed in breast cancer, but the exact cellular and signaling mechanisms by which they regulate in vivo tumor cell invasiveness and consequent metastatic dissemination are mostly unknown. Using a PYK2 and FAK knockdown xenograft model we show here, for the first time, that ablation of either PYK2 or FAK decreases primary tumor size and significantly reduces Tumor MicroEnvironment of Metastasis (TMEM) doorway activation, leading to decreased intravasation and reduced spontaneous lung metastasis. Intravital imaging analysis further demonstrates that PYK2, but not FAK, regulates a motility phenotype switch between focal adhesion-mediated fast motility and invadopodia-dependent, ECM-degradation associated slow motility within the primary tumor. Furthermore, we validate our in vivo and intravital imaging results with integrated transcriptomic and proteomic data analysis from xenograft knockdown tumors and reveal new and distinct pathways by which these two homologous kinases regulate breast tumor cell invasiveness and consequent metastatic dissemination. Our findings identify PYK2 and FAK as novel mediators of mammary tumor progression and metastasis and as candidate therapeutic targets for breast cancer metastasis.
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Gul L, Korcsmaros T, Hall N. Flaviviruses hijack the host microbiota to facilitate their transmission. Cell 2022; 185:2395-2397. [PMID: 35803242 DOI: 10.1016/j.cell.2022.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/08/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Lejla Gul
- Earlham Institute, Norwich, UK; Imperial College London, Department of Metabolism, Reproduction and Development, London, UK
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, UK; Imperial College London, Department of Metabolism, Reproduction and Development, London, UK; Quadram Institute Bioscience, Norwich, UK
| | - Neil Hall
- Earlham Institute, Norwich, UK; Department of Biological Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia; School of Biological Sciences, University of East Anglia, Norwich, UK.
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4
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Poletti M, Treveil A, Csabai L, Gul L, Modos D, Madgwick M, Olbei M, Bohar B, Valdeolivas A, Turei D, Verstockt B, Triana S, Alexandrov T, Saez-Rodriguez J, Stanifer ML, Boulant S, Korcsmaros T. Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways. NPJ Syst Biol Appl 2022; 8:15. [PMID: 35501398 PMCID: PMC9061772 DOI: 10.1038/s41540-022-00224-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/04/2022] [Indexed: 12/14/2022] Open
Abstract
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.
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Grants
- BB/CSP17270/1 Biotechnology and Biological Sciences Research Council
- BB/P016774/1 Biotechnology and Biological Sciences Research Council
- BB/R012490/1 Biotechnology and Biological Sciences Research Council
- BBS/E/T/000PR9817 Biotechnology and Biological Sciences Research Council
- BBS/E/F/000PR10355 Biotechnology and Biological Sciences Research Council
- BB/S50743X/1 Biotechnology and Biological Sciences Research Council
- BB/M011216/1 Biotechnology and Biological Sciences Research Council
- BBS/E/F/000PR10353 Biotechnology and Biological Sciences Research Council
- BB/J004529/1 Biotechnology and Biological Sciences Research Council
- The work of T.K. was supported by the Earlham Institute (Norwich, UK) in partnership with the Quadram Institute (Norwich, UK) and strategically supported by the UKRI BBSRC UK grants (BB/J004529/1, BB/P016774/1, and BB/CSP17270/1). T.K. was also funded by a BBSRC ISP grant for Gut Microbes and Health BB/R012490/1 and its constituent projects, BBS/E/F/000PR10353 and BBS/E/F/000PR10355.
- M.P. is supported by the UKRI Biotechnological and Biosciences Research Council (BBSRC) funded Norwich Research Park Biosciences Doctoral Training Partnership (grant numbers BB/M011216/1 and BB/S50743X/1).
- A.T. is supported by the UKRI Biotechnological and Biosciences Research Council (BBSRC) funded Norwich Research Park Biosciences Doctoral Training Partnership (grant numbers BB/M011216/1 and BB/S50743X/1).
- L.G. is supported by the UKRI Biotechnological and Biosciences Research Council (BBSRC) funded Norwich Research Park Biosciences Doctoral Training Partnership (grant numbers BB/M011216/1 and BB/S50743X/1).
- The work of D.M. was supported by the Earlham Institute (Norwich, UK) in partnership with the Quadram Institute (Norwich, UK) and strategically supported by the UKRI BBSRC UK grants (BB/J004529/1, BB/P016774/1, and BB/CSP17270/1). D.M. was also funded by a BBSRC ISP grant for Gut Microbes and Health BB/R012490/1 and its constituent projects, BBS/E/F/000PR10353 and BBS/E/F/000PR10355.
- M.O. is supported by the UKRI Biotechnological and Biosciences Research Council (BBSRC) funded Norwich Research Park Biosciences Doctoral Training Partnership (grant numbers BB/M011216/1 and BB/S50743X/1).
- B.V. is supported by the Clinical Research Fund (KOOR) University Hospitals Leuven.
- S.T. acknowledges the funding from the Darwin Trust of Edinburgh and from the ERC Consolidator grant METACELL from European Union’s Horizon 2020 program. S.T. acknowledges support from the EMBL Genomics Core Facility and particularly help from Vladimir Benes.
- T.A. acknowledges the funding from the Darwin Trust of Edinburgh and from the ERC Consolidator grant METACELL from European Union’s Horizon 2020 program. T.A. acknowledges support from the EMBL Genomics Core Facility and particularly help from Vladimir Benes.
- M.L.S. was supported by the DFG (416072091) and the BMBF (01KI20239B). D.T. was supported by the Federal Ministry of Education and Research (BMBF, Computational Life Sciences grant no. 031L0181B) to J.S.R.
- S.B. was supported by research grants from the Deutsche Forschungsgemeinschaft (DFG): project numbers 415089553 (Heisenberg program), 240245660 (SFB1129), 278001972 (TRR186), and 272983813 (TRR179), the state of Baden Wuerttemberg (AZ: 33.7533.-6-21/5/1) and the Bundesministerium Bildung und Forschung (BMBF) (01KI20198A).
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Affiliation(s)
- Martina Poletti
- Earlham Institute, Norwich Research Park, Norwich, UK
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Agatha Treveil
- Earlham Institute, Norwich Research Park, Norwich, UK
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Luca Csabai
- Earlham Institute, Norwich Research Park, Norwich, UK
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Leila Gul
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Dezso Modos
- Earlham Institute, Norwich Research Park, Norwich, UK
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Matthew Madgwick
- Earlham Institute, Norwich Research Park, Norwich, UK
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Marton Olbei
- Earlham Institute, Norwich Research Park, Norwich, UK
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Balazs Bohar
- Earlham Institute, Norwich Research Park, Norwich, UK
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Alberto Valdeolivas
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Denes Turei
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Bram Verstockt
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
- Department of Chronic Diseases and Metabolism, Translational Research in GI disorders, KU Leuven, Leuven, Belgium
| | - Sergio Triana
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Theodore Alexandrov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory, Heidelberg, Germany
| | - Megan L Stanifer
- Department of Infectious Diseases, Heidelberg University Hospital Heidelberg, Heidelberg, Germany
| | - Steeve Boulant
- Department of Infectious Diseases, Heidelberg University Hospital Heidelberg, Heidelberg, Germany
| | - Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich, UK.
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
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5
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Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (N Y) 2021; 2:100141. [PMID: 34557778 PMCID: PMC8454663 DOI: 10.1016/j.xinn.2021.100141] [Citation(s) in RCA: 2549] [Impact Index Per Article: 849.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022] Open
Abstract
Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms. clusterProfiler supports exploring functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation It provides a universal interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions
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Affiliation(s)
- Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Meijun Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Pingfan Guo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zehan Dai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tingze Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lang Zhou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaocong Fu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shanshan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Guangdong Provincial Key Laboratory of Proteomics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
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6
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Perfetto L, Micarelli E, Iannuccelli M, Lo Surdo P, Giuliani G, Latini S, Pugliese GM, Massacci G, Vumbaca S, Riccio F, Fuoco C, Paoluzi S, Castagnoli L, Cesareni G, Licata L, Sacco F. A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection. Genes (Basel) 2021; 12:450. [PMID: 33809949 PMCID: PMC8004236 DOI: 10.3390/genes12030450] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 02/06/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused more than 2.3 million casualties worldwide and the lack of effective treatments is a major health concern. The development of targeted drugs is held back due to a limited understanding of the molecular mechanisms underlying the perturbation of cell physiology observed after viral infection. Recently, several approaches, aimed at identifying cellular proteins that may contribute to COVID-19 pathology, have been reported. Albeit valuable, this information offers limited mechanistic insight as these efforts have produced long lists of cellular proteins, the majority of which are not annotated to any cellular pathway. We have embarked in a project aimed at bridging this mechanistic gap by developing a new bioinformatic approach to estimate the functional distance between a subset of proteins and a list of pathways. A comprehensive literature search allowed us to annotate, in the SIGNOR 2.0 resource, causal information underlying the main molecular mechanisms through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses affect the host-cell physiology. Next, we developed a new strategy that enabled us to link SARS-CoV-2 interacting proteins to cellular phenotypes via paths of causal relationships. Remarkably, the extensive information about inhibitors of signaling proteins annotated in SIGNOR 2.0 makes it possible to formulate new potential therapeutic strategies. The proposed approach, which is generally applicable, generated a literature-based causal network that can be used as a framework to formulate informed mechanistic hypotheses on COVID-19 etiology and pathology.
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Affiliation(s)
- Livia Perfetto
- Fondazione Human Technopole, Department of Biology, Via Cristina Belgioioso, 171, 20157 Milan, Italy; (L.P.); (P.L.S.)
| | - Elisa Micarelli
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Marta Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Prisca Lo Surdo
- Fondazione Human Technopole, Department of Biology, Via Cristina Belgioioso, 171, 20157 Milan, Italy; (L.P.); (P.L.S.)
| | - Giulio Giuliani
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Sara Latini
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Giusj Monia Pugliese
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Giorgia Massacci
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Simone Vumbaca
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Federica Riccio
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Claudia Fuoco
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Serena Paoluzi
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Luisa Castagnoli
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (E.M.); (M.I.); (G.G.); (S.L.); (G.M.P.); (G.M.); (S.V.); (F.R.); (C.F.); (S.P.); (L.C.); (G.C.); (L.L.)
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7
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Türei D, Valdeolivas A, Gul L, Palacio‐Escat N, Klein M, Ivanova O, Ölbei M, Gábor A, Theis F, Módos D, Korcsmáros T, Saez‐Rodriguez J. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol Syst Biol 2021; 17:e9923. [PMID: 33749993 PMCID: PMC7983032 DOI: 10.15252/msb.20209923] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 02/11/2021] [Accepted: 02/15/2021] [Indexed: 12/12/2022] Open
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.
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Affiliation(s)
- Dénes Türei
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Alberto Valdeolivas
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | | | - Nicolàs Palacio‐Escat
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Michal Klein
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Olga Ivanova
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Márton Ölbei
- Earlham InstituteNorwichUK
- Quadram Institute BioscienceNorwichUK
| | - Attila Gábor
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Fabian Theis
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- Department of MathematicsTechnical University of MunichGarchingGermany
| | - Dezső Módos
- Earlham InstituteNorwichUK
- Quadram Institute BioscienceNorwichUK
| | | | - Julio Saez‐Rodriguez
- Faculty of Medicine and Heidelberg University HospitalInstitute of Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
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Olbei M, Hautefort I, Modos D, Treveil A, Poletti M, Gul L, Shannon-Lowe CD, Korcsmaros T. SARS-CoV-2 Causes a Different Cytokine Response Compared to Other Cytokine Storm-Causing Respiratory Viruses in Severely Ill Patients. Front Immunol 2021; 12:629193. [PMID: 33732251 PMCID: PMC7956943 DOI: 10.3389/fimmu.2021.629193] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/29/2021] [Indexed: 12/21/2022] Open
Abstract
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.
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Affiliation(s)
- Marton Olbei
- Earlham Institute, Norwich, United Kingdom
- Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, United Kingdom
| | | | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, United Kingdom
| | - Agatha Treveil
- Earlham Institute, Norwich, United Kingdom
- Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, United Kingdom
| | - Martina Poletti
- Earlham Institute, Norwich, United Kingdom
- Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, United Kingdom
| | - Lejla Gul
- Earlham Institute, Norwich, United Kingdom
| | - Claire D. Shannon-Lowe
- Institute of Immunology and Immunotherapy, The University of Birmingham, Birmingham, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Gut Microbes and Health Programme, Quadram Institute Bioscience, Norwich, United Kingdom
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