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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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2
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Zerrouk N, Alcraft R, Hall BA, Augé F, Niarakis A. Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis. NPJ Syst Biol Appl 2024; 10:10. [PMID: 38272919 PMCID: PMC10811231 DOI: 10.1038/s41540-024-00337-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.
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Affiliation(s)
- Naouel Zerrouk
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Rachel Alcraft
- Advanced Research Computing Centre, University College London, London, UK
| | - Benjamin A Hall
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Franck Augé
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Anna Niarakis
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France.
- Lifeware Group, Inria Saclay, Palaiseau, France.
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3
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Pastrello C, Kotlyar M, Abovsky M, Lu R, Jurisica I. PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful. Nucleic Acids Res 2024; 52:D663-D671. [PMID: 37994706 PMCID: PMC10767947 DOI: 10.1093/nar/gkad1027] [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: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/24/2023] Open
Abstract
Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.
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Affiliation(s)
- Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Max Kotlyar
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Mark Abovsky
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Richard Lu
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto, Toronto, ON M5G 1L7, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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4
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Chuang YH, Lin CY, Lee JC, Lee CH, Liu CL, Huang SH, Lee JY, Lai WS, Yang JM. Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer. Int J Mol Sci 2023; 24:13068. [PMID: 37685875 PMCID: PMC10487792 DOI: 10.3390/ijms241713068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/14/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.
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Affiliation(s)
- Yi-Hsuan Chuang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chun-Yu Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Jih-Chin Lee
- Department of Otolaryngology—Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chia-Hwa Lee
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
- Ph.D. Program in Medicine Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Chia-Lin Liu
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Sing-Han Huang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Jung-Yu Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Wen-Sen Lai
- Department of Otolaryngology—Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung 404, Taiwan
| | - Jinn-Moon Yang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-Sen University, Kaohsiung 804, Taiwan
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Aghakhani S, Silva-Saffar SE, Soliman S, Niarakis A. Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts. Comput Struct Biotechnol J 2023; 21:4196-4206. [PMID: 37705596 PMCID: PMC10495551 DOI: 10.1016/j.csbj.2023.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) are amongst the key players of the tumor microenvironment (TME) and are involved in cancer initiation, progression, and resistance to therapy. They exhibit aggressive phenotypes affecting extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining a cell- and disease-specific asynchronous Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, in light of our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Sacha E Silva-Saffar
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Anna Niarakis
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
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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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Rougny A, Balaur I, Luna A, Mazein A. StonPy: a tool to parse and query collections of SBGN maps in a graph database. Bioinformatics 2023; 39:7075543. [PMID: 36897014 PMCID: PMC10017094 DOI: 10.1093/bioinformatics/btad100] [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: 08/23/2022] [Revised: 01/14/2023] [Accepted: 03/09/2023] [Indexed: 03/11/2023] Open
Abstract
SUMMARY The systems biology graphical notation (SBGN) has become the de facto standard for the graphical representation of molecular maps. Having rapid and easy access to the content of large collections of maps is necessary to perform semantic or graph-based analysis of these resources. To this end, we propose StonPy, a new tool to store and query SBGN maps in a Neo4j graph database. StonPy notably includes a data model that takes into account all three SBGN languages and a completion module to automatically build valid SBGN maps from query results. StonPy is built as a library that can be integrated into other software and offers a command-line interface that allows users to easily perform all operations. AVAILABILITY AND IMPLEMENTATION StonPy is implemented in Python 3 under a GPLv3 license. Its code and complete documentation are freely available from https://github.com/adrienrougny/stonpy. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
| | - Augustin Luna
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
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8
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Wang X, Chan YS, Wong K, Yoshitake R, Sadava D, Synold TW, Frankel P, Twardowski PW, Lau C, Chen S. Mechanism-Driven and Clinically Focused Development of Botanical Foods as Multitarget Anticancer Medicine: Collective Perspectives and Insights from Preclinical Studies, IND Applications and Early-Phase Clinical Trials. Cancers (Basel) 2023; 15:701. [PMID: 36765659 PMCID: PMC9913787 DOI: 10.3390/cancers15030701] [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: 11/14/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Cancer progression and mortality remain challenging because of current obstacles and limitations in cancer treatment. Continuous efforts are being made to explore complementary and alternative approaches to alleviate the suffering of cancer patients. Epidemiological and nutritional studies have indicated that consuming botanical foods is linked to a lower risk of cancer incidence and/or improved cancer prognosis after diagnosis. From these observations, a variety of preclinical and clinical studies have been carried out to evaluate the potential of botanical food products as anticancer medicines. Unfortunately, many investigations have been poorly designed, and encouraging preclinical results have not been translated into clinical success. Botanical products contain a wide variety of chemicals, making them more difficult to study than traditional drugs. In this review, with the consideration of the regulatory framework of the USFDA, we share our collective experiences and lessons learned from 20 years of defining anticancer foods, focusing on the critical aspects of preclinical studies that are required for an IND application, as well as the checkpoints needed for early-phase clinical trials. We recommend a developmental pipeline that is based on mechanisms and clinical considerations.
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Affiliation(s)
- Xiaoqiang Wang
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Yin S. Chan
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Kelly Wong
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Ryohei Yoshitake
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - David Sadava
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Timothy W. Synold
- Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Paul Frankel
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Przemyslaw W. Twardowski
- Department of Urologic Oncology, Saint John’s Cancer Institute, 2200 Santa Monica Blvd, Santa Monica, CA 90404, USA
| | - Clayton Lau
- Department of Surgery, City of Hope Comprehensive Cancer Center, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Shiuan Chen
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
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9
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Makki Z, Malcolm J, Miguel JC. COVID-19 Adaptations with Virtual Microscopy. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1397:173-197. [DOI: 10.1007/978-3-031-17135-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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10
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The global integrative network: integration of signaling and metabolic pathways. ABIOTECH 2022; 3:281-291. [PMID: 36533264 PMCID: PMC9755797 DOI: 10.1007/s42994-022-00078-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/08/2022] [Indexed: 10/14/2022]
Abstract
The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel "meta-pathway" structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species-human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction. Supplementary Information The online version contains supplementary material available at 10.1007/s42994-022-00078-1.
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11
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Calzone L, Noël V, Barillot E, Kroemer G, Stoll G. Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells. Comput Struct Biotechnol J 2022; 20:5661-5671. [PMID: 36284705 PMCID: PMC9582792 DOI: 10.1016/j.csbj.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.
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Affiliation(s)
- Laurence Calzone
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France,Corresponding authors.
| | - Vincent Noël
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Europén Georges Pompidou, AP-HP, Paris, France
| | - Gautier Stoll
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Corresponding authors.
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12
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Vignet P, Coquet J, Auber S, Boudet M, Siegel A, Théret N. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Comput Biol 2022; 18:e1010175. [PMID: 35696426 PMCID: PMC9232147 DOI: 10.1371/journal.pcbi.1010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/24/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states. The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene). Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.
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Affiliation(s)
- Pierre Vignet
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jean Coquet
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Sébastien Auber
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Matéo Boudet
- IGEPP, Agrocampus Ouest, INRAE, Université de Rennes 1, Le Rheu, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- * E-mail: (AS); (NT)
| | - Nathalie Théret
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- * E-mail: (AS); (NT)
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13
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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14
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Montagud A, Béal J, Tobalina L, Traynard P, Subramanian V, Szalai B, Alföldi R, Puskás L, Valencia A, Barillot E, Saez-Rodriguez J, Calzone L. Patient-specific Boolean models of signalling networks guide personalised treatments. eLife 2022; 11:72626. [PMID: 35164900 PMCID: PMC9018074 DOI: 10.7554/elife.72626] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/27/2022] [Indexed: 11/22/2022] Open
Abstract
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
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Affiliation(s)
| | - Jonas Béal
- Institut Curie, PSL Research University, Paris, France
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany
| | | | - Vigneshwari Subramanian
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany
| | - Bence Szalai
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | | | | | | | | | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany
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15
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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16
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Kasikci Y, Gronemeyer H. Complexity against current cancer research - are we on the wrong track? Int J Cancer 2021; 150:1569-1578. [PMID: 34921726 DOI: 10.1002/ijc.33912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/09/2022]
Abstract
Cancer genetics has led to major discoveries, including proto-oncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the inter-individual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yasenya Kasikci
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Department of Functional Genomics and Cancer, Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France.,Université de Strasbourg, Illkirch, France
| | - Hinrich Gronemeyer
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Department of Functional Genomics and Cancer, Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U1258, Illkirch, France.,Université de Strasbourg, Illkirch, France
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17
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Glavaški M, Velicki L. Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation. BioData Min 2021; 14:45. [PMID: 34600580 PMCID: PMC8487578 DOI: 10.1186/s13040-021-00279-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/14/2021] [Indexed: 11/25/2022] Open
Abstract
Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-021-00279-2.
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Affiliation(s)
- Mila Glavaški
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
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18
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Hyung D, Baek MJ, Lee J, Cho J, Kim HS, Park C, Cho SY. Protein-gene Expression Nexus: Comprehensive characterization of human cancer cell lines with proteogenomic analysis. Comput Struct Biotechnol J 2021; 19:4759-4769. [PMID: 34504668 PMCID: PMC8405889 DOI: 10.1016/j.csbj.2021.08.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 12/30/2022] Open
Abstract
Researchers have gained new therapeutic insights using multi-omics platform approaches to study DNA, RNA, and proteins of comprehensively characterized human cancer cell lines. To improve our understanding of the molecular features associated with oncogenic modulation in cancer, we proposed a proteogenomic database for human cancer cell lines, called Protein-gene Expression Nexus (PEN). We have expanded the characterization of cancer cell lines to include genetic, mRNA, and protein data of 145 cancer cell lines from various public studies. PEN contains proteomic and phosphoproteomic data on 4,129,728 peptides, 13,862 proteins, 7,138 phosphorylation site-associated genomic variations, 117 studies, and 12 cancer. We analyzed functional characterizations along with the integrated datasets, such as cis/trans association for copy number alteration (CNA), single amino acid variation for coding genes, post-translation modification site variation for Single Amino Acid Variation, and novel peptide expression for noncoding regions and fusion genes. PEN provides a user-friendly interface for searching, browsing, and downloading data and also supports the visualization of genome-wide association between CNA and expression, novel peptide landscape, mRNA-protein abundance, and functional annotation. Together, this dataset and PEN data portal provide a resource to accelerate cancer research using model cancer cell lines. PEN is freely accessible at http://combio.snu.ac.kr/pen.
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Affiliation(s)
- Daejin Hyung
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Min-Jeong Baek
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Jongkeun Lee
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Juyeon Cho
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Hyoun Sook Kim
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Charny Park
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Soo Young Cho
- National Cancer Center, 323 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea.,Department of Molecular and Life Science, Hanyang University, Ansan 15588, Republic of Korea
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19
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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20
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Schultz B, Zaliani A, Ebeling C, Reinshagen J, Bojkova D, Lage-Rupprecht V, Karki R, Lukassen S, Gadiya Y, Ravindra NG, Das S, Baksi S, Domingo-Fernández D, Lentzen M, Strivens M, Raschka T, Cinatl J, DeLong LN, Gribbon P, Geisslinger G, Ciesek S, van Dijk D, Gardner S, Kodamullil AT, Fröhlich H, Peitsch M, Jacobs M, Hoeng J, Eils R, Claussen C, Hofmann-Apitius M. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Sci Rep 2021; 11:11049. [PMID: 34040048 PMCID: PMC8155020 DOI: 10.1038/s41598-021-90296-2] [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: 10/01/2020] [Accepted: 05/04/2021] [Indexed: 02/08/2023] Open
Abstract
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
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Affiliation(s)
- Bruce Schultz
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Andrea Zaliani
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Christian Ebeling
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jeanette Reinshagen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Denisa Bojkova
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Vanessa Lage-Rupprecht
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Sören Lukassen
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Yojana Gadiya
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Neal G Ravindra
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Sayoni Das
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Shounak Baksi
- Causality BioModels Pvt Ltd., Kinfra Hi-Tech Park, Kerala Technology Innovation Zone- KTIZ, Kalamassery, Cochin, 683503, India
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Lentzen
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Mark Strivens
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Tamara Raschka
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jindrich Cinatl
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Phil Gribbon
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Gerd Geisslinger
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
- Pharmazentrum Frankfurt/ZAFES, Institut Für Klinische Pharmakologie, Klinikum Der Goethe-Universität Frankfurt, 60590, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
| | - Sandra Ciesek
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
- DZIF, German Centre for Infection Research, External Partner Site, 60596, Frankfurt am Main, Germany
| | - David van Dijk
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Steve Gardner
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Peitsch
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Marc Jacobs
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Julia Hoeng
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Claussen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany.
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21
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Rougny A, Paulevé L, Teboul M, Delaunay F. A detailed map of coupled circadian clock and cell cycle with qualitative dynamics validation. BMC Bioinformatics 2021; 22:240. [PMID: 33975535 PMCID: PMC8114686 DOI: 10.1186/s12859-021-04158-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/21/2021] [Indexed: 12/16/2022] Open
Abstract
Background The temporal coordination of biological processes by the circadian clock is an important mechanism, and its disruption has negative health outcomes, including cancer. Experimental and theoretical evidence suggests that the oscillators driving the circadian clock and the cell cycle are coupled through phase locking. Results We present a detailed and documented map of known mechanisms related to the regulation of the circadian clock, and its coupling with an existing cell cycle map which includes main interactions of the mammalian cell cycle. The coherence of the merged map has been validated with a qualitative dynamics analysis. We verified that the coupled circadian clock and cell cycle maps reproduce the observed sequence of phase markers. Moreover, we predicted mutations that contribute to regulating checkpoints of the two oscillators. Conclusions Our approach underlined the potential key role of the core clock protein NR1D1 in regulating cell cycle progression. We predicted that its activity influences negatively the progression of the cell cycle from phase G2 to M. This is consistent with the earlier experimental finding that pharmacological activation of NR1D1 inhibits tumour cell proliferation and shows that our approach can identify biologically relevant species in the context of large and complex networks. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04158-9.
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Affiliation(s)
- Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan.,Computational Bio Big Data Open Innovation Laboratory (CBBD-OIL), AIST, Aomi, Tokyo, Japan
| | - Loïc Paulevé
- Bordeaux INP, CNRS, LaBRI, UMR5800, Univ. Bordeaux, 33400, Talence, France
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22
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Mazein A, Rougny A, Karr JR, Saez-Rodriguez J, Ostaszewski M, Schneider R. Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling. Brief Bioinform 2021; 22:6217719. [PMID: 33834185 PMCID: PMC8425390 DOI: 10.1093/bib/bbab103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/05/2022] Open
Abstract
Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS–RAF–MEK–ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.,European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan.,Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Julio Saez-Rodriguez
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, 52074 Aachen, Germany.,Institute for Computational Biomedicine, Heidelberg University Hospital and Heidelberg University, Faculty of Medicine, Bioquant Heidelberg, Heidelberg 69120, Germany
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
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23
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Baines C, Lerebours A, Thomas F, Fort J, Kreitsberg R, Gentes S, Meitern R, Saks L, Ujvari B, Giraudeau M, Sepp T. Linking pollution and cancer in aquatic environments: A review. ENVIRONMENT INTERNATIONAL 2021; 149:106391. [PMID: 33515955 DOI: 10.1016/j.envint.2021.106391] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/23/2020] [Accepted: 01/09/2021] [Indexed: 06/12/2023]
Abstract
Due to the interconnectedness of aquatic ecosystems through the highly effective marine and atmospheric transport routes, all aquatic ecosystems are potentially vulnerable to pollution. Whilst links between pollution and increased mortality of wild animals have now been firmly established, the next steps should be to focus on specific physiological pathways and pathologies that link pollution to wildlife health deterioration. One of the pollution-induced pathologies that should be at the centre of attention in ecological and evolutionary research is cancer, as anthropogenic contamination has resulted in a rapid increase of oncogenic substances in natural habitats. Whilst wildlife cancer research is an emerging research topic, systematic reviews of the many case studies published over the recent decades are scarce. This research direction would (1) provide a better understanding of the physiological mechanisms connecting anthropogenic pollution to oncogenic processes in non-model organisms (reducing the current bias towards human and lab-animal studies in cancer research), and (2) allow us to better predict the vulnerability of different wild populations to oncogenic contamination. This article combines the information available within the scientific literature about cancer occurrences in aquatic and semi-aquatic species. For the first aim, we use available knowledge from aquatic species to suggest physiological mechanisms that link pollution and cancer, including main metabolic detoxification pathways, oxidative damage effects, infections, and changes to the microbiome. For the second aim, we determine which types of aquatic animals are more vulnerable to pollution-induced cancer, which types of pollution are mainly associated with cancer in aquatic ecosystems, and which types of cancer pollution causes. We also discuss the role of migration in exposing aquatic and semi-aquatic animals to different oncogenic pollutants. Finally, we suggest novel research avenues, including experimental approaches, analysis of the effects of pollutant cocktails and long-term chronic exposure to lower levels of pollutants, and the use of already published databases of gene expression levels in animals from differently polluted habitats.
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Affiliation(s)
- Ciara Baines
- Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia.
| | - Adelaide Lerebours
- LIttoral, ENvironnement et Sociétés (LIENSs), UMR7266, CNRS Université de La Rochelle, 2 rue Olympe de Gouges, 17042 La Rochelle Cedex, France
| | - Frederic Thomas
- CREEC/CREES, 911 Avenue Agropolis, BP 6450134394 Montpellier Cedex 5, France; MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 6450134394 Montpellier Cedex 5, France
| | - Jerome Fort
- LIttoral, ENvironnement et Sociétés (LIENSs), UMR7266, CNRS Université de La Rochelle, 2 rue Olympe de Gouges, 17042 La Rochelle Cedex, France
| | - Randel Kreitsberg
- Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia
| | - Sophie Gentes
- LIttoral, ENvironnement et Sociétés (LIENSs), UMR7266, CNRS Université de La Rochelle, 2 rue Olympe de Gouges, 17042 La Rochelle Cedex, France
| | - Richard Meitern
- Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia
| | - Lauri Saks
- Estonian Marine Institute, Universty of Tartu, Mäealuse 14, 12618 Tallinn, Harju County, Estonia
| | - Beata Ujvari
- School of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Waurn Ponds, VIC, Australia
| | - Mathieu Giraudeau
- LIttoral, ENvironnement et Sociétés (LIENSs), UMR7266, CNRS Université de La Rochelle, 2 rue Olympe de Gouges, 17042 La Rochelle Cedex, France; CREEC/CREES, 911 Avenue Agropolis, BP 6450134394 Montpellier Cedex 5, France; MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 6450134394 Montpellier Cedex 5, France
| | - Tuul Sepp
- Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia
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24
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Rougny A, Touré V, Albanese J, Waltemath D, Shirshov D, Sorokin A, Bader GD, Blinov ML, Mazein A. SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks. Brief Bioinform 2021; 22:6184415. [PMID: 33758926 PMCID: PMC8425392 DOI: 10.1093/bib/bbab049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/13/2021] [Indexed: 11/14/2022] Open
Abstract
A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.
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Affiliation(s)
- Adrien Rougny
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
| | - Vasundra Touré
- Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, Realfagbygget, 7491 Trondheim, Norway
| | - John Albanese
- R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Dagmar Waltemath
- Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, D-17475 Greifswald, Germany
| | - Denis Shirshov
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
| | - Anatoly Sorokin
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia
- Moscow Institute of Physics and Technology, 9 Institutsky per., Dolgoprudny, Moscow Region, 141700, Russia
- University of Liverpool, Liverpool L7 3EA, UK
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, M5S 3E1, Toronto, Canada
| | - Michael L Blinov
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
| | - Alexander Mazein
- Corresponding authors: Adrien Rougny, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan and Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan; E-mail: ; Michael L. Blinov, R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT 06030, USA; E-mail: ; Alexander Mazein, European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Pushchino, Moscow Region, 142290, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg; E-mail:
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Moya-García AA, Pino-Ángeles A, Sánchez-Jiménez F, Urdiales JL, Medina MÁ. Histamine, Metabolic Remodelling and Angiogenesis: A Systems Level Approach. Biomolecules 2021; 11:415. [PMID: 33799732 PMCID: PMC8000605 DOI: 10.3390/biom11030415] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/11/2022] Open
Abstract
Histamine is a highly pleiotropic biogenic amine involved in key physiological processes including neurotransmission, immune response, nutrition, and cell growth and differentiation. Its effects, sometimes contradictory, are mediated by at least four different G-protein coupled receptors, which expression and signalling pathways are tissue-specific. Histamine metabolism conforms a very complex network that connect many metabolic processes important for homeostasis, including nitrogen and energy metabolism. This review brings together and analyses the current information on the relationships of the "histamine system" with other important metabolic modules in human physiology, aiming to bridge current information gaps. In this regard, the molecular characterization of the role of histamine in the modulation of angiogenesis-mediated processes, such as cancer, makes a promising research field for future biomedical advances.
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Affiliation(s)
- Aurelio A. Moya-García
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
| | - Almudena Pino-Ángeles
- Unidad de Lípidos y Arteriosclerosis, Servicio de Medicina Interna, Hospital Universitario Reina Sofia, Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Universidad de Córdoba, 14004 Córdoba, Spain;
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, 14004 Córdoba, Spain
| | - Francisca Sánchez-Jiménez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
| | - José Luis Urdiales
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
| | - Miguel Ángel Medina
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
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26
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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: 112] [Impact Index Per Article: 37.3] [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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27
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Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics 2021; 36:4473-4482. [PMID: 32403123 PMCID: PMC7575051 DOI: 10.1093/bioinformatics/btaa484] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/17/2020] [Accepted: 05/06/2020] [Indexed: 12/16/2022] Open
Abstract
Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sara Sadat Aghamiri
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Vidisha Singh
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Aurélien Naldi
- Département de Biologie, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ècole Normale Supérieure, CNRS, INSERM, Université PSL, Paris 75005, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Anna Niarakis
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
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28
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Carcereny E, Fernández-Nistal A, López A, Montoto C, Naves A, Segú-Vergés C, Coma M, Jorba G, Oliva B, Mas JM. Head to head evaluation of second generation ALK inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using an in silico systems biology-based approach. Oncotarget 2021; 12:316-332. [PMID: 33659043 PMCID: PMC7899557 DOI: 10.18632/oncotarget.27875] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 12/23/2020] [Indexed: 12/21/2022] Open
Abstract
Around 3-7% of patients with non-small cell lung cancer (NSCLC), which represent 85% of diagnosed lung cancers, have a rearrangement in the ALK gene that produces an abnormal activity of the ALK protein cell signaling pathway. The developed ALK tyrosine kinase inhibitors (TKIs), such as crizotinib, ceritinib, alectinib, brigatinib and lorlatinb present good performance treating ALK+ NSCLC, although all patients invariably develop resistance due to ALK secondary mutations or bypass mechanisms. In the present study, we compare the potential differences between brigatinib and alectinib's mechanisms of action as first-line treatment for ALK+ NSCLC in a systems biology-based in silico setting. Therapeutic performance mapping system (TPMS) technology was used to characterize the mechanisms of action of brigatinib and alectinib and the impact of potential resistances and drug interferences with concomitant treatments. The analyses indicate that brigatinib and alectinib affect cell growth, apoptosis and immune evasion through ALK inhibition. However, brigatinib seems to achieve a more diverse downstream effect due to a broader cancer-related kinase target spectrum. Brigatinib also shows a robust effect over invasiveness and central nervous system metastasis-related mechanisms, whereas alectinib seems to have a greater impact on the immune evasion mechanism. Based on this in silico head to head study, we conclude that brigatinib shows a predicted efficacy similar to alectinib and could be a good candidate in a first-line setting against ALK+ NSCLC. Future investigation involving clinical studies will be needed to confirm these findings. These in silico systems biology-based models could be applied for exploring other unanswered questions.
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Affiliation(s)
- Enric Carcereny
- Catalan Institute of Oncology B-ARGO Group, Hospital Germans Trias i Pujol, Badalona, Spain
| | | | | | | | | | | | | | - Guillem Jorba
- Anaxomics Biotech, Barcelona, Spain
- Structural Bioinformatics (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | - Baldomero Oliva
- Structural Bioinformatics (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
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29
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Nishi A, Ohbuchi K, Kaifuchi N, Shimobori C, Kushida H, Yamamoto M, Kita Y, Tokuoka SM, Yachie A, Matsuoka Y, Kitano H. LimeMap: a comprehensive map of lipid mediator metabolic pathways. NPJ Syst Biol Appl 2021; 7:6. [PMID: 33504811 PMCID: PMC7840682 DOI: 10.1038/s41540-020-00163-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 12/11/2020] [Indexed: 01/30/2023] Open
Abstract
Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.
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Affiliation(s)
- Akinori Nishi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Katsuya Ohbuchi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Noriko Kaifuchi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Chika Shimobori
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Hirotaka Kushida
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Masahiro Yamamoto
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Yoshihiro Kita
- grid.26999.3d0000 0001 2151 536XLife Sciences Core Facility, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan ,grid.26999.3d0000 0001 2151 536XDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Suzumi M. Tokuoka
- grid.26999.3d0000 0001 2151 536XDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ayako Yachie
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
| | - Yukiko Matsuoka
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
| | - Hiroaki Kitano
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
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30
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Lonjou C, Eon-Marchais S, Truong T, Dondon MG, Karimi M, Jiao Y, Damiola F, Barjhoux L, Le Gal D, Beauvallet J, Mebirouk N, Cavaciuti E, Chiesa J, Floquet A, Audebert-Bellanger S, Giraud S, Frebourg T, Limacher JM, Gladieff L, Mortemousque I, Dreyfus H, Lejeune-Dumoulin S, Lasset C, Venat-Bouvet L, Bignon YJ, Pujol P, Maugard CM, Luporsi E, Bonadona V, Noguès C, Berthet P, Delnatte C, Gesta P, Lortholary A, Faivre L, Buecher B, Caron O, Gauthier-Villars M, Coupier I, Mazoyer S, Monraz LC, Kondratova M, Kuperstein I, Guénel P, Barillot E, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Gene- and pathway-level analyses of iCOGS variants highlight novel signaling pathways underlying familial breast cancer susceptibility. Int J Cancer 2021; 148:1895-1909. [PMID: 33368296 PMCID: PMC9290690 DOI: 10.1002/ijc.33457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 12/17/2022]
Abstract
Single‐nucleotide polymorphisms (SNPs) in over 180 loci have been associated with breast cancer (BC) through genome‐wide association studies involving mostly unselected population‐based case‐control series. Some of them modify BC risk of women carrying a BRCA1 or BRCA2 (BRCA1/2) mutation and may also explain BC risk variability in BC‐prone families with no BRCA1/2 mutation. Here, we assessed the contribution of SNPs of the iCOGS array in GENESIS consisting of BC cases with no BRCA1/2 mutation and a sister with BC, and population controls. Genotyping data were available for 1281 index cases, 731 sisters with BC, 457 unaffected sisters and 1272 controls. In addition to the standard SNP‐level analysis using index cases and controls, we performed pedigree‐based association tests to capture transmission information in the sibships. We also performed gene‐ and pathway‐level analyses to maximize the power to detect associations with lower‐frequency SNPs or those with modest effect sizes. While SNP‐level analyses identified 18 loci, gene‐level analyses identified 112 genes. Furthermore, 31 Kyoto Encyclopedia of Genes and Genomes and 7 Atlas of Cancer Signaling Network pathways were highlighted (false discovery rate of 5%). Using results from the “index case‐control” analysis, we built pathway‐derived polygenic risk scores (PRS) and assessed their performance in the population‐based CECILE study and in a data set composed of GENESIS‐affected sisters and CECILE controls. Although these PRS had poor predictive value in the general population, they performed better than a PRS built using our SNP‐level findings, and we found that the joint effect of family history and PRS needs to be considered in risk prediction models.
What's new?
Genetic studies have identified more than 180 single‐nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, but these studies are reaching their limits. Here, the authors evaluated SNPs in the iCOGS genotyping array using a multilevel approach, including single variant, gene, and pathway analyses. They measured the contribution of the SNPs to breast cancer in patients who have a sister with breast cancer but do not carry a BRCA1/2 mutation. They showed that a pathway‐derived polygenic risk score performed poorly in the general population, and that the best predictive model must include family history.
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Affiliation(s)
- Christine Lonjou
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Séverine Eon-Marchais
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Thérèse Truong
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Marie-Gabrielle Dondon
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Mojgan Karimi
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Yue Jiao
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | - Laure Barjhoux
- Department of BioPathology, Centre Léon Bérard, Lyon, France
| | - Dorothée Le Gal
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Juana Beauvallet
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Noura Mebirouk
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Eve Cavaciuti
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | | | | | - Sophie Giraud
- Service de Génétique, Hospices Civils de Lyon, Groupement Hospitalier Est, Bron, France
| | - Thierry Frebourg
- Département de Génétique, Hôpital Universitaire de Rouen, Rouen, France
| | | | - Laurence Gladieff
- Service d'Oncologie Médicale, Institut Claudius Regaud-IUCT-Oncopole, Toulouse, France
| | | | - Hélène Dreyfus
- Clinique Sainte Catherine, Avignon, France.,Département de Génétique, CHU de Grenoble, Hôpital Couple-Enfant, Grenoble, France
| | | | - Christine Lasset
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | | | - Yves-Jean Bignon
- Département d'Oncogénétique, Université Clermont Auvergne, UMR INSERM, U1240, Centre Jean Perrin, Clermont Ferrand, France
| | - Pascal Pujol
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Christine M Maugard
- Département d'Oncobiologie, LBBM, Hôpitaux Universitaires de Strasbourg, Génétique Oncologique Moléculaire, UF1422, Strasbourg, France.,Hôpitaux Universitaires de Strasbourg, UF6948 Génétique Oncologique Clinique, Évaluation Familiale et Suivi, Strasbourg, France
| | - Elisabeth Luporsi
- ICL Alexis Vautrin, Unité d'Oncogénétique, Vandœuvre-lès-Nancy, France
| | - Valérie Bonadona
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | - Catherine Noguès
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France.,Aix Marseille University, INSERM, IRD, SESSTIM, Marseille, France
| | - Pascaline Berthet
- Département de Biopathologie, Centre François Baclesse, Oncogénétique, Caen, France
| | - Capucine Delnatte
- Institut de Cancérologie de l'Ouest, Unité d'Oncogénétique, Saint Herblain, France
| | - Paul Gesta
- CH Georges Renon, Service d'Oncogénétique Régional Poitou-Charentes, Niort, France
| | - Alain Lortholary
- Centre Catherine de Sienne, Service d'Oncologie Médicale, Nantes, France
| | - Laurence Faivre
- Institut GIMI, CHU de Dijon, Hôpital d'Enfants, Dijon, France.,Oncogénétique, Centre de Lutte contre le Cancer Georges François Leclerc, Dijon, France
| | | | - Olivier Caron
- Département de Médecine Oncologique, Gustave Roussy, Villejuif, France
| | | | - Isabelle Coupier
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Sylvie Mazoyer
- Equipe GENDEV, Centre de Recherche en Neurosciences de Lyon, Inserm U1028, CNRS UMR5292, Université Lyon 1, Université St Etienne, Lyon, France
| | - Luis-Cristobal Monraz
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Maria Kondratova
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Inna Kuperstein
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Pascal Guénel
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Emmanuel Barillot
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Service de Génétique, Paris, France.,Inserm, U830, Université Paris-Descartes, Paris, France
| | - Nadine Andrieu
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Fabienne Lesueur
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
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31
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Touré V, Dräger A, Luna A, Dogrusoz U, Rougny A. The Systems Biology Graphical Notation: Current Status and Applications in Systems Medicine. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11515-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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32
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Agapito G, Pastrello C, Jurisica I. Comprehensive pathway enrichment analysis workflows: COVID-19 case study. Brief Bioinform 2020. [PMCID: PMC7799312 DOI: 10.1093/bib/bbaa377] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.
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Affiliation(s)
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Igor Jurisica
- Departments of Medical Biophysics and Computer Science, University of Toronto, Canada
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33
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Aghakhani S, Zerrouk N, Niarakis A. Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches. Cancers (Basel) 2020; 13:E35. [PMID: 33374292 PMCID: PMC7795338 DOI: 10.3390/cancers13010035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/12/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
| | - Naouel Zerrouk
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
| | - Anna Niarakis
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
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34
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Tsirvouli E, Touré V, Niederdorfer B, Vázquez M, Flobak Å, Kuiper M. A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines. Front Mol Biosci 2020; 7:502573. [PMID: 33195403 PMCID: PMC7581946 DOI: 10.3389/fmolb.2020.502573] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 09/22/2020] [Indexed: 11/23/2022] Open
Abstract
Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.
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Affiliation(s)
- Eirini Tsirvouli
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Barbara Niederdorfer
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miguel Vázquez
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav’s University Hospital, Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
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35
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Hernandez C, Thomas-Chollier M, Naldi A, Thieffry D. Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors. Front Physiol 2020; 11:558606. [PMID: 33101049 PMCID: PMC7554341 DOI: 10.3389/fphys.2020.558606] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/19/2020] [Indexed: 12/31/2022] Open
Abstract
At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.
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Affiliation(s)
- Céline Hernandez
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Morgane Thomas-Chollier
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France.,Institut Universitaire de France, Paris, France
| | - Aurélien Naldi
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Denis Thieffry
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
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36
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Xu P, Wu Q, Lu D, Yu J, Rao Y, Kou Z, Fang G, Liu W, Han H. A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path. BMC Bioinformatics 2020; 21:396. [PMID: 32894041 PMCID: PMC7487489 DOI: 10.1186/s12859-020-03732-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 09/01/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. RESULTS In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. CONCLUSION In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.
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Affiliation(s)
- Peng Xu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.,School of computer science of information technology, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China
| | - Qian Wu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Deyang Lu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Jian Yu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Yongsheng Rao
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Zheng Kou
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Gang Fang
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Wenbin Liu
- Institute of computational science and technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
| | - Henry Han
- Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA.
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37
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Hoksza D, Gawron P, Ostaszewski M, Smula E, Schneider R. MINERVA API and plugins: opening molecular network analysis and visualization to the community. Bioinformatics 2020; 35:4496-4498. [PMID: 31074494 PMCID: PMC6821317 DOI: 10.1093/bioinformatics/btz286] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 03/29/2019] [Accepted: 04/16/2019] [Indexed: 11/14/2022] Open
Abstract
Summary The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform’s visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. Availability and implementation Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.
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Affiliation(s)
- David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux L-4367, Luxembourg.,Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 118 00, Czech Republic
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux L-4367, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux L-4367, Luxembourg
| | - Ewa Smula
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux L-4367, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux L-4367, Luxembourg
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38
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An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer. Cancers (Basel) 2020; 12:cancers12051324. [PMID: 32455963 PMCID: PMC7281299 DOI: 10.3390/cancers12051324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 05/19/2020] [Indexed: 12/28/2022] Open
Abstract
Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55–5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.
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39
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Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N, Preciat G, Daníelsdóttir AD, Krecke M, Merten D, Haraldsdóttir HS, Heinken A, Heirendt L, Magnúsdóttir S, Ravcheev DA, Sahoo S, Gawron P, Friscioni L, Garcia B, Prendergast M, Puente A, Rodrigues M, Roy A, Rouquaya M, Wiltgen L, Žagare A, John E, Krueger M, Kuperstein I, Zinovyev A, Schneider R, Fleming RMT, Thiele I. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res 2020; 47:D614-D624. [PMID: 30371894 PMCID: PMC6323901 DOI: 10.1093/nar/gky992] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/09/2018] [Indexed: 12/31/2022] Open
Abstract
A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.
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Affiliation(s)
- Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Yohan Jarosz
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Elisabeth Guerard
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Anna Dröfn Daníelsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Max Krecke
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Diane Merten
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Stefanía Magnúsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Dmitry A Ravcheev
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Lucia Friscioni
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Beatriz Garcia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mabel Prendergast
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Alberto Puente
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mariana Rodrigues
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Akansha Roy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mouss Rouquaya
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Luca Wiltgen
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Elisabeth John
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Maren Krueger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg.,Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden 2333, The Netherlands
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
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40
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Ravel JM, Monraz Gomez LC, Sompairac N, Calzone L, Zhivotovsky B, Kroemer G, Barillot E, Zinovyev A, Kuperstein I. Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases. Cancers (Basel) 2020; 12:E990. [PMID: 32316560 PMCID: PMC7226067 DOI: 10.3390/cancers12040990] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/10/2020] [Indexed: 12/25/2022] Open
Abstract
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.
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Affiliation(s)
- Jean-Marie Ravel
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Laboratoire de génétique médicale, CHRU-Nancy, F-54000 Nancy, France
- Inserm, NGERE, Université de Lorraine, F-54000 Nancy, France
| | - L. Cristobal Monraz Gomez
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75006 Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Boris Zhivotovsky
- Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Division of Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Box 210, 17177 Stockholm, Sweden
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, 75006 Paris, France;
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, 94805 Villejuif, France
- Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
- Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou 215163, China
- Karolinska Institute, Department of Women’s and Children’s Health, Karolinska University Hospital, 171 77 Stockholm, Sweden
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
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41
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Montagud A, Traynard P, Martignetti L, Bonnet E, Barillot E, Zinovyev A, Calzone L. Conceptual and computational framework for logical modelling of biological networks deregulated in diseases. Brief Bioinform 2020; 20:1238-1249. [PMID: 29237040 DOI: 10.1093/bib/bbx163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/24/2017] [Indexed: 01/02/2023] Open
Abstract
Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.
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42
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Humayun F, Domingo-Fernández D, Paul George AA, Hopp MT, Syllwasschy BF, Detzel MS, Hoyt CT, Hofmann-Apitius M, Imhof D. A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders. Front Bioeng Biotechnol 2020; 8:74. [PMID: 32211383 PMCID: PMC7069124 DOI: 10.3389/fbioe.2020.00074] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/28/2020] [Indexed: 01/07/2023] Open
Abstract
Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.
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Affiliation(s)
- Farah Humayun
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Ajay Abisheck Paul George
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Marie-Thérèse Hopp
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Benjamin F. Syllwasschy
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Milena S. Detzel
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Diana Imhof
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
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43
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Raimúndez E, Keller S, Zwingenberger G, Ebert K, Hug S, Theis FJ, Maier D, Luber B, Hasenauer J. Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines. PLoS Comput Biol 2020; 16:e1007147. [PMID: 32119655 PMCID: PMC7067490 DOI: 10.1371/journal.pcbi.1007147] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 03/12/2020] [Accepted: 01/27/2020] [Indexed: 12/31/2022] Open
Abstract
Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers. Unraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes responder and non-responder gastric cancer cell lines and can predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might potentially provide the basis for understanding the recent failure of several clinical studies.
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Affiliation(s)
- Elba Raimúndez
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Simone Keller
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Gwen Zwingenberger
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Karolin Ebert
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Sabine Hug
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Fabian J. Theis
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | | | - Birgit Luber
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
- * E-mail:
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44
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Romers J, Thieme S, Münzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl 2020; 6:2. [PMID: 31934349 PMCID: PMC6954118 DOI: 10.1038/s41540-019-0120-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/20/2019] [Indexed: 11/09/2022] Open
Abstract
The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.
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Affiliation(s)
- Jesper Romers
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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45
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Iannuccelli M, Micarelli E, Surdo PL, Palma A, Perfetto L, Rozzo I, Castagnoli L, Licata L, Cesareni G. CancerGeneNet: linking driver genes to cancer hallmarks. Nucleic Acids Res 2020; 48:D416-D421. [PMID: 31598703 PMCID: PMC6943052 DOI: 10.1093/nar/gkz871] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/12/2019] [Accepted: 09/30/2019] [Indexed: 12/28/2022] Open
Abstract
CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the 'hallmarks of cancer'. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and 'MiniPathways' illustrating regulatory circuits that are frequently altered in different cancers.
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Affiliation(s)
- Marta Iannuccelli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Elisa Micarelli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Prisca Lo Surdo
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ilaria Rozzo
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Luisa Castagnoli
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Luana Licata
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome, Tor Vergata, 00133 Rome, Italy
- IRCSS Fondazione Santa Lucia, 00142 Rome, Italy
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Rahmati S, Abovsky M, Pastrello C, Kotlyar M, Lu R, Cumbaa CA, Rahman P, Chandran V, Jurisica I. pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species. Nucleic Acids Res 2020; 48:D479-D488. [PMID: 31733064 PMCID: PMC7145646 DOI: 10.1093/nar/gkz989] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/11/2019] [Accepted: 11/13/2019] [Indexed: 12/14/2022] Open
Abstract
PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.
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Affiliation(s)
- Sara Rahmati
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON M5T 2S8, Canada
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
| | - Mark Abovsky
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Richard Lu
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Christian A Cumbaa
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Proton Rahman
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
| | - Vinod Chandran
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON M5T 2S8, Canada
- Department of Medicine, Memorial University of Newfoundland, Saint John's, NL A1B 3V6, Canada
- Department of Medicine, Division of Rheumatology, University of Toronto, Toronto, ON M5G 2C4, Canada
- Department of Laboratory Medicine and Pathobiology (LMP), Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Department of Medical Biophysics, University of Toronto, ON M 5G 1L7, Canada
- Department of Computer Science, University of Toronto, ON M5S 1A4, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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Balaur I, Roy L, Mazein A, Karaca SG, Dogrusoz U, Barillot E, Zinovyev A. cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats. Bioinformatics 2020; 36:2620-2622. [DOI: 10.1093/bioinformatics/btz969] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 11/20/2019] [Accepted: 01/01/2020] [Indexed: 12/27/2022] Open
Abstract
Abstract
Motivation
CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files.
Results
We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML.
Availability and implementation
The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively).
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Irina Balaur
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
| | - Ludovic Roy
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 69007 Lyon, France
- Institute of Cell Biophysics, Russian Academy of Sciences, 3 Institutskaya Street, Moscow Region, Pushchino 142290, Russia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - S Gökberk Karaca
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Emmanuel Barillot
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
| | - Andrei Zinovyev
- Institut National de la Santé et de la Recherche Médicale (INSERM), U900, F-75005 Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
- Institut Curie, PSL Research University, F-75005 Paris, France
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Singh V, Kalliolias GD, Ostaszewski M, Veyssiere M, Pilalis E, Gawron P, Mazein A, Bonnet E, Petit-Teixeira E, Niarakis A. RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis. Database (Oxford) 2020; 2020:baaa017. [PMID: 32311035 PMCID: PMC7170216 DOI: 10.1093/database/baaa017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 01/21/2020] [Accepted: 02/13/2020] [Indexed: 02/07/2023]
Abstract
Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.
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Affiliation(s)
- Vidisha Singh
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - George D Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
- Weill Cornell Medical Center, Weill Department of Medicine, 525 East 68th Street, New York, NY 10065, USA
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Maëva Veyssiere
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Eleftherios Pilalis
- eNIOS Applications P.C., R&D department, Alexandrou Pantou 25, 17671, Kallithea-Athens, Greece
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, 2 rue Gaston Crémieux, CP5706 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Elisabeth Petit-Teixeira
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Anna Niarakis
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
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49
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Ivanova O, Richards LB, Vijverberg SJ, Neerincx AH, Sinha A, Sterk PJ, Maitland‐van der Zee AH. What did we learn from multiple omics studies in asthma? Allergy 2019; 74:2129-2145. [PMID: 31004501 DOI: 10.1111/all.13833] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/25/2019] [Accepted: 04/12/2019] [Indexed: 12/13/2022]
Abstract
More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.
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Affiliation(s)
- Olga Ivanova
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Levi B. Richards
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Susanne J. Vijverberg
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Anne H. Neerincx
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Anirban Sinha
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Peter J. Sterk
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
| | - Anke H. Maitland‐van der Zee
- Department of Respiratory Medicine, Amsterdam University Medical Centres (AUMC) University of Amsterdam Amsterdam the Netherlands
- Department of Paediatric Pulmonology Amsterdam UMC/ Emma Children's Hospital Amsterdam the Netherlands
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50
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Mousa H, Elgamal M, Marei RG, Souchelnytskyi N, Lin KW, Souchelnytskyi S. Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms. Cancer Genomics Proteomics 2019; 16:505-518. [PMID: 31659104 PMCID: PMC6885374 DOI: 10.21873/cgp.20153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 08/19/2019] [Accepted: 08/21/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND/AIM Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation. MATERIALS AND METHODS Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells. RESULTS We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use. CONCLUSION Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.
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Affiliation(s)
- Hanaa Mousa
- College of Medicine, Qatar University, Doha, Qatar
| | | | | | | | - Kah-Wai Lin
- College of Medicine, Qatar University, Doha, Qatar
- Neurocentrum, Karolinska University Hospital, Solna, Sweden
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