1
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Nunes L, Li F, Wu M, Luo T, Hammarström K, Torell E, Ljuslinder I, Mezheyeuski A, Edqvist PH, Löfgren-Burström A, Zingmark C, Edin S, Larsson C, Mathot L, Osterman E, Osterlund E, Ljungström V, Neves I, Yacoub N, Guðnadóttir U, Birgisson H, Enblad M, Ponten F, Palmqvist R, Xu X, Uhlén M, Wu K, Glimelius B, Lin C, Sjöblom T. Prognostic genome and transcriptome signatures in colorectal cancers. Nature 2024:10.1038/s41586-024-07769-3. [PMID: 39112715 DOI: 10.1038/s41586-024-07769-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/01/2024] [Indexed: 08/17/2024]
Abstract
Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.
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Affiliation(s)
- Luís Nunes
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Fuqiang Li
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen, China
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China
| | - Meizhen Wu
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen, China
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China
| | - Tian Luo
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen, China
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China
| | - Klara Hammarström
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Emma Torell
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ingrid Ljuslinder
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Artur Mezheyeuski
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Per-Henrik Edqvist
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Carl Zingmark
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Sofia Edin
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Chatarina Larsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lucy Mathot
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Erik Osterman
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Emerik Osterlund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Viktor Ljungström
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Inês Neves
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Nicole Yacoub
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Unnur Guðnadóttir
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Helgi Birgisson
- Department of Surgical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden
| | - Malin Enblad
- Department of Surgical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden
| | - Fredrik Ponten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Richard Palmqvist
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Xun Xu
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China
| | - Mathias Uhlén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Kui Wu
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China.
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen, China.
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China.
| | - Bengt Glimelius
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Cong Lin
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), BGI Research, Hangzhou, China.
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, BGI Research, Shenzhen, China.
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China.
| | - Tobias Sjöblom
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
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2
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Bleker C, Ramšak Ž, Bittner A, Podpečan V, Zagorščak M, Wurzinger B, Baebler Š, Petek M, Križnik M, van Dieren A, Gruber J, Afjehi-Sadat L, Weckwerth W, Županič A, Teige M, Vothknecht UC, Gruden K. Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses. PLANT COMMUNICATIONS 2024; 5:100920. [PMID: 38616489 PMCID: PMC11211517 DOI: 10.1016/j.xplc.2024.100920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
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Affiliation(s)
- Carissa Bleker
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
| | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Andras Bittner
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Vid Podpečan
- Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Maja Zagorščak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Bernhard Wurzinger
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Špela Baebler
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Marko Petek
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Maja Križnik
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Annelotte van Dieren
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Juliane Gruber
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Leila Afjehi-Sadat
- Mass Spectrometry Unit, Core Facility Shared Services, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Wolfram Weckwerth
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Anže Županič
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Markus Teige
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Ute C Vothknecht
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
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3
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Bäuerle F, Döbel GO, Camus L, Heilbronner S, Dräger A. Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum. FRONTIERS IN BIOINFORMATICS 2023; 3:1214074. [PMID: 37936955 PMCID: PMC10626998 DOI: 10.3389/fbinf.2023.1214074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.
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Affiliation(s)
- Famke Bäuerle
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Gwendolyn O. Döbel
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
| | - Laura Camus
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Simon Heilbronner
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
- Faculty of Biology, Microbiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
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4
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Geffen Y, Anand S, Akiyama Y, Yaron TM, Song Y, Johnson JL, Govindan A, Babur Ö, Li Y, Huntsman E, Wang LB, Birger C, Heiman DI, Zhang Q, Miller M, Maruvka YE, Haradhvala NJ, Calinawan A, Belkin S, Kerelsky A, Clauser KR, Krug K, Satpathy S, Payne SH, Mani DR, Gillette MA, Dhanasekaran SM, Thiagarajan M, Mesri M, Rodriguez H, Robles AI, Carr SA, Lazar AJ, Aguet F, Cantley LC, Ding L, Getz G. Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation. Cell 2023; 186:3945-3967.e26. [PMID: 37582358 PMCID: PMC10680287 DOI: 10.1016/j.cell.2023.07.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 01/06/2023] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.
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Affiliation(s)
- Yifat Geffen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Shankara Anand
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yo Akiyama
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Tomer M Yaron
- Weill Cornell Medical College, Meyer Cancer Center, New York, NY 10021, USA
| | - Yizhe Song
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jared L Johnson
- Weill Cornell Medical College, Meyer Cancer Center, New York, NY 10021, USA
| | - Akshay Govindan
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Özgün Babur
- Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Yize Li
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Emily Huntsman
- Weill Cornell Medical College, Meyer Cancer Center, New York, NY 10021, USA
| | - Liang-Bo Wang
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Chet Birger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David I Heiman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Qing Zhang
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Mendy Miller
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yosef E Maruvka
- Biotechnology and Food Engineering, Lokey Center for Life Science and Engineering, Technion, Israel Institute of Technology, Haifa, Israel
| | - Nicholas J Haradhvala
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anna Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Saveliy Belkin
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Alexander Kerelsky
- Weill Cornell Medical College, Meyer Cancer Center, New York, NY 10021, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA
| | | | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - François Aguet
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.
| | - Lewis C Cantley
- Weill Cornell Medical College, Meyer Cancer Center, New York, NY 10021, USA.
| | - Li Ding
- Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
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5
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Gawron P, Hoksza D, Piñero J, Peña-Chilet M, Esteban-Medina M, Fernandez-Rueda JL, Colonna V, Smula E, Heirendt L, Ancien F, Groues V, Satagopam VP, Schneider R, Dopazo J, Furlong LI, Ostaszewski M. Visualization of automatically combined disease maps and pathway diagrams for rare diseases. FRONTIERS IN BIOINFORMATICS 2023; 3:1101505. [PMID: 37502697 PMCID: PMC10369067 DOI: 10.3389/fbinf.2023.1101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 07/29/2023] Open
Abstract
Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
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Affiliation(s)
- Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Prague, Czechia
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | | | | | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council of Italy, Naples, Rome
- Department of Genetics, Genomics and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ewa Smula
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - François Ancien
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Valentin Groues
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Joaquin Dopazo
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
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6
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Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. FRONTIERS IN BIOINFORMATICS 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
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7
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Leonidou N, Renz A, Mostolizadeh R, Dräger A. New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells. PLoS Comput Biol 2023; 19:e1010903. [PMID: 36952396 PMCID: PMC10035753 DOI: 10.1371/journal.pcbi.1010903] [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: 07/27/2022] [Accepted: 01/30/2023] [Indexed: 03/25/2023] Open
Abstract
COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
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8
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Braun TP, Estabrook J, Schonrock Z, Curtiss BM, Darmusey L, Macaraeg J, Enright T, Coblentz C, Callahan R, Yashar W, Taherinasab A, Mohammed H, Coleman DJ, Druker BJ, Demir E, Lusardi TA, Maxson JE. Asxl1 deletion disrupts MYC and RNA polymerase II function in granulocyte progenitors. Leukemia 2023; 37:478-487. [PMID: 36526735 PMCID: PMC9899319 DOI: 10.1038/s41375-022-01792-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022]
Abstract
Mutations in the gene Additional Sex-Combs Like 1 (ASXL1) are recurrent in myeloid malignancies as well as the pre-malignant condition clonal hematopoiesis, where they are universally associated with poor prognosis. However, the role of ASXL1 in myeloid lineage maturation is incompletely described. To define the role of ASXL1 in myelopoiesis, we employed single cell RNA sequencing and a murine model of hematopoietic-specific Asxl1 deletion. In granulocyte progenitors, Asxl1 deletion leads to hyperactivation of MYC and a quantitative decrease in neutrophil production. This loss of granulocyte production was not accompanied by significant changes in the landscape of covalent histone modifications. However, Asxl1 deletion results in a decrease in RNAPII promoter-proximal pausing in granulocyte progenitors, indicative of a global increase in productive transcription. These results suggest that ASXL1 inhibits productive transcription in granulocyte progenitors, identifying a new role for this epigenetic regulator in myeloid development.
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Affiliation(s)
- Theodore P. Braun
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA.,Division of Hematology & Medical Oncology, Oregon
Health & Science University, Portland, Oregon, 97239, USA.,CORRESPONDENCE: Theodore P. Braun,
Knight Cancer Institute, 3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon,
97239, , Julia E. Maxson, Knight Cancer Institute,
3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon, 97239,
, Theresa A. Lusardi, Cancer Early Detection
Advanced Research Center, 3181 SW Sam Jackson Pk. Rd., KR-CEDR, Portland,
Oregon, 97239,
| | - Joseph Estabrook
- Cancer Early Detection Advanced Research Center, Oregon
Health & Science University, Portland, Oregon, 97239, USA
| | - Zachary Schonrock
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Brittany M. Curtiss
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Lucie Darmusey
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Jommel Macaraeg
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Trevor Enright
- Cancer Early Detection Advanced Research Center, Oregon
Health & Science University, Portland, Oregon, 97239, USA
| | - Cody Coblentz
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Rowan Callahan
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - William Yashar
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Akram Taherinasab
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Hisham Mohammed
- Cancer Early Detection Advanced Research Center, Oregon
Health & Science University, Portland, Oregon, 97239, USA
| | - Daniel J. Coleman
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA
| | - Brian J. Druker
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA.,Division of Hematology & Medical Oncology, Oregon
Health & Science University, Portland, Oregon, 97239, USA
| | - Emek Demir
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA.,Cancer Early Detection Advanced Research Center, Oregon
Health & Science University, Portland, Oregon, 97239, USA
| | - Theresa A. Lusardi
- Cancer Early Detection Advanced Research Center, Oregon
Health & Science University, Portland, Oregon, 97239, USA.,CORRESPONDENCE: Theodore P. Braun,
Knight Cancer Institute, 3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon,
97239, , Julia E. Maxson, Knight Cancer Institute,
3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon, 97239,
, Theresa A. Lusardi, Cancer Early Detection
Advanced Research Center, 3181 SW Sam Jackson Pk. Rd., KR-CEDR, Portland,
Oregon, 97239,
| | - Julia E. Maxson
- Knight Cancer Institute, Oregon Health & Science
University, Portland, Oregon, 97239, USA.,CORRESPONDENCE: Theodore P. Braun,
Knight Cancer Institute, 3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon,
97239, , Julia E. Maxson, Knight Cancer Institute,
3181 SW Sam Jackson Pk. Rd., KR-HEM, Portland, Oregon, 97239,
, Theresa A. Lusardi, Cancer Early Detection
Advanced Research Center, 3181 SW Sam Jackson Pk. Rd., KR-CEDR, Portland,
Oregon, 97239,
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9
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Pratt D, Pillich RT, Morris JH. Translating desktop success to the web in the cytoscape project. FRONTIERS IN BIOINFORMATICS 2023; 3:1125949. [PMID: 37035036 PMCID: PMC10076771 DOI: 10.3389/fbinf.2023.1125949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/16/2023] [Indexed: 04/11/2023] Open
Abstract
Cytoscape is an open-source bioinformatics environment for the analysis, integration, visualization, and query of biological networks. In this perspective piece, we describe our project to bring the Cytoscape desktop application to the web while explaining our strategy in ways relevant to others in the bioinformatics community. We examine opportunities and challenges in developing bioinformatics software that spans both the desktop and web, and we describe our ongoing efforts to build a Cytoscape web application, highlighting the principles that guide our development.
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Affiliation(s)
- Dexter Pratt
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, United States
- *Correspondence: Dexter Pratt,
| | - Rudolf T. Pillich
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - John H. Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California San Francisco, San Francisco, CA, United States
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10
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Franz M, Lopes CT, Fong D, Kucera M, Cheung M, Siper MC, Huck G, Dong Y, Sumer O, Bader GD. Cytoscape.js 2023 update: a graph theory library for visualization and analysis. Bioinformatics 2023; 39:6988031. [PMID: 36645249 PMCID: PMC9889963 DOI: 10.1093/bioinformatics/btad031] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 01/13/2023] [Indexed: 01/17/2023] Open
Abstract
SUMMARY Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js. This update describes new features and enhancements introduced over many new versions from 2015 to 2022. AVAILABILITY AND IMPLEMENTATION Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Max Franz
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | | | - Dylan Fong
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Mike Kucera
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Manfred Cheung
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Metin Can Siper
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Gerardo Huck
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Yue Dong
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Onur Sumer
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
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11
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Aichem M, Klein K, Czauderna T, Garkov D, Zhao J, Li J, Schreiber F. Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality. J Integr Bioinform 2022; 19:jib-2022-0034. [PMID: 36215728 PMCID: PMC9800044 DOI: 10.1515/jib-2022-0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/06/2022] [Indexed: 01/09/2023] Open
Abstract
Biomolecular networks, including genome-scale metabolic models (GSMMs), assemble the knowledge regarding the biological processes that happen inside specific organisms in a way that allows for analysis, simulation, and exploration. With the increasing availability of genome annotations and the development of powerful reconstruction tools, biomolecular networks continue to grow ever larger. While visual exploration can facilitate the understanding of such networks, the network sizes represent a major challenge for current visualisation systems. Building on promising results from the area of immersive analytics, which among others deals with the potential of immersive visualisation for data analysis, we present a concept for a hybrid user interface that combines a classical desktop environment with a virtual reality environment for the visual exploration of large biomolecular networks and corresponding data. We present system requirements and design considerations, describe a resulting concept, an envisioned technical realisation, and a systems biology usage scenario. Finally, we discuss remaining challenges.
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Affiliation(s)
- Michael Aichem
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Tobias Czauderna
- Faculty of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
| | - Dimitar Garkov
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Jinxin Zhao
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, Melbourne, Australia
| | - Jian Li
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, Melbourne, Australia
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Melbourne, Australia
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12
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SyBLaRS: A web service for laying out, rendering and mining biological maps in SBGN, SBML and more. PLoS Comput Biol 2022; 18:e1010635. [DOI: 10.1371/journal.pcbi.1010635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/28/2022] [Accepted: 10/04/2022] [Indexed: 11/15/2022] Open
Abstract
Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support.
To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of well-known graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest.
We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools.
SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.
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13
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Maier BD, Aguilera LU, Sahle S, Mutz P, Kalra P, Dächert C, Bartenschlager R, Binder M, Kummer U. Stochastic dynamics of Type-I interferon responses. PLoS Comput Biol 2022; 18:e1010623. [PMID: 36269758 PMCID: PMC9629604 DOI: 10.1371/journal.pcbi.1010623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/02/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments. We investigate the impact of intrinsic and extrinsic noise on the reliability of interferon signaling. Information must be transduced robustly despite existing biochemical variability and at the same time the system has to allow for cellular variability to tune it against changing environments. Getting insights into stochasticity in signaling networks is crucial to understand cellular dynamics and decision-making processes. To this end, we developed a detailed stochastic computational model based on single cell data. We are able to show that reliability is achieved despite high noise at the receptor level.
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Affiliation(s)
- Benjamin D. Maier
- Department of Modeling of Biological Processes, COS Heidelberg / Bioquant, Heidelberg University, Heidelberg, Germany
| | - Luis U. Aguilera
- Department of Modeling of Biological Processes, COS Heidelberg / Bioquant, Heidelberg University, Heidelberg, Germany
| | - Sven Sahle
- Department of Modeling of Biological Processes, COS Heidelberg / Bioquant, Heidelberg University, Heidelberg, Germany
| | - Pascal Mutz
- Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department for Infectious Diseases, Molecular Virology, Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Priyata Kalra
- Department of Modeling of Biological Processes, COS Heidelberg / Bioquant, Heidelberg University, Heidelberg, Germany
| | - Christopher Dächert
- Research Group “Dynamics of early viral infection and the innate antiviral response”, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department for Infectious Diseases, Molecular Virology, Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Ralf Bartenschlager
- Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department for Infectious Diseases, Molecular Virology, Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Marco Binder
- Research Group “Dynamics of early viral infection and the innate antiviral response”, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ursula Kummer
- Department of Modeling of Biological Processes, COS Heidelberg / Bioquant, Heidelberg University, Heidelberg, Germany
- * E-mail:
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14
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Schreiber F, Czauderna T. Design considerations for representing systems biology information with the Systems Biology Graphical Notation. J Integr Bioinform 2022; 19:jib-2022-0024. [PMID: 35786424 PMCID: PMC9377698 DOI: 10.1515/jib-2022-0024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/07/2022] [Indexed: 12/15/2022] Open
Abstract
Visual representations are commonly used to explore, analyse, and communicate information and knowledge in systems biology and beyond. Such visualisations not only need to be accurate but should also be aesthetically pleasing and informative. Using the example of the Systems Biology Graphical Notation (SBGN) we will investigate design considerations for graphically presenting information from systems biology, in particular regarding the use of glyphs for types of information, the style of graph layout for network representation, and the concept of bricks for visual network creation.
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Affiliation(s)
- Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Clayton, Australia.,Faculty of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
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15
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Luna A, Siper MC, Korkut A, Durupinar F, Dogrusoz U, Aslan JE, Sander C, Demir E, Babur O. Analyzing causal relationships in proteomic profiles using CausalPath. STAR Protoc 2021; 2:100955. [PMID: 34877547 PMCID: PMC8633371 DOI: 10.1016/j.xpro.2021.100955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
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Affiliation(s)
- Augustin Luna
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Corresponding author
| | - Metin Can Siper
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Durupinar
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Joseph E. Aslan
- Knight Cardiovascular Institute, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Emek Demir
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Pacific Northwest National Laboratories, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Ozgun Babur
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
- Corresponding author
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16
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Martens M, Ammar A, Riutta A, Waagmeester A, Slenter D, Hanspers K, A. Miller R, Digles D, Lopes E, Ehrhart F, Dupuis LJ, Winckers LA, Coort S, Willighagen EL, Evelo CT, Pico AR, Kutmon M. WikiPathways: connecting communities. Nucleic Acids Res 2021; 49:D613-D621. [PMID: 33211851 PMCID: PMC7779061 DOI: 10.1093/nar/gkaa1024] [Citation(s) in RCA: 459] [Impact Index Per Article: 153.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | | | - Denise N Slenter
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Ryan A. Miller
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Chemistry/Pharmacoinformatics Research Group, University of Vienna, 1090 Vienna, Austria
| | - Elisson N Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Lauren J Dupuis
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Laurent A Winckers
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Susan L Coort
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Martina Kutmon
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 EN Maastricht, the Netherlands
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