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Najm M, Cornet M, Albergante L, Zinovyev A, Sermet-Gaudelus I, Stoven V, Calzone L, Martignetti L. Representation and quantification of module activity from omics data with rROMA. NPJ Syst Biol Appl 2024; 10:8. [PMID: 38242871 PMCID: PMC10799004 DOI: 10.1038/s41540-024-00331-x] [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: 05/31/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.
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Affiliation(s)
- Matthieu Najm
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Matthieu Cornet
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Luca Albergante
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Andrei Zinovyev
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Isabelle Sermet-Gaudelus
- Faculté de Médecine, Université de Paris, Paris, France
- Institut Necker Enfants Malades, INSERM U1151, Paris, France
- AP-HP. Centre - Université Paris Cité; Hôpital Necker Enfants Malades, Centre de Référence Maladie Rare - Mucoviscidose, Paris, France
| | - Véronique Stoven
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Laurence Calzone
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Loredana Martignetti
- INSERM U900, 75428, Paris, France.
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France.
- Institut Curie, PSL Research University, 75248, Paris, France.
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2
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Pasquereau-Kotula E, Nigro G, Dingli F, Loew D, Poullet P, Xu Y, Kopetz S, Davis J, Peduto L, Robbe-Masselot C, Sansonetti P, Trieu-Cuot P, Dramsi S. Global proteomic identifies multiple cancer-related signaling pathways altered by a gut pathobiont associated with colorectal cancer. Sci Rep 2023; 13:14960. [PMID: 37696912 PMCID: PMC10495336 DOI: 10.1038/s41598-023-41951-3] [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: 03/02/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023] Open
Abstract
In this work, we investigated the oncogenic role of Streptococcus gallolyticus subsp. gallolyticus (SGG), a gut bacterium associated with colorectal cancer (CRC). We showed that SGG UCN34 accelerates colon tumor development in a chemically induced CRC murine model. Full proteome and phosphoproteome analysis of murine colons chronically colonized by SGG UCN34 revealed that 164 proteins and 725 phosphorylation sites were differentially regulated. Ingenuity Pathway Analysis (IPA) indicates a pro-tumoral shift specifically induced by SGG UCN34, as ~ 90% of proteins and phosphoproteins identified were associated with digestive cancer. Comprehensive analysis of the altered phosphoproteins using ROMA software revealed up-regulation of several cancer hallmark pathways such as MAPK, mTOR and integrin/ILK/actin, affecting epithelial and stromal colonic cells. Importantly, an independent analysis of protein arrays of human colon tumors colonized with SGG showed up-regulation of PI3K/Akt/mTOR and MAPK pathways, providing clinical relevance to our findings. To test SGG's capacity to induce pre-cancerous transformation of the murine colonic epithelium, we grew ex vivo organoids which revealed unusual structures with compact morphology. Taken together, our results demonstrate the oncogenic role of SGG UCN34 in a murine model of CRC associated with activation of multiple cancer-related signaling pathways.
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Affiliation(s)
- Ewa Pasquereau-Kotula
- Biology of Gram-Positive Pathogens Unit, Institut Pasteur, Université Paris Cité, CNRS UMR6047, 75015, Paris, France.
| | - Giulia Nigro
- Stroma, Inflammation and Tissue Repair Unit, Institut Pasteur, Université Paris Cité, INSERM U1224, 75015, Paris, France
- Microenvironment and Immunity Unit, Institut Pasteur, Université Paris Cité, INSERM U1224, 75015, Paris, France
| | - Florent Dingli
- Institut Curie, PSL Research University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Damarys Loew
- Institut Curie, PSL Research University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Patrick Poullet
- Institut Curie, Bioinformatics Core Facility (CUBIC), INSERM U900, PSL Research University, Mines Paris Tech, 75005, Paris, France
| | - Yi Xu
- Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M Health Science Center, Houston, TX, USA
- Department of Microbial Pathogenesis and Immunology, School of Medicine, Bryan, TX, USA
- Department of Microbiology and Molecular Genetics, University of Texas Health Science Center, Houston, TX, USA
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer Davis
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- University of Kansas, Kansas City, KS, USA
| | - Lucie Peduto
- Stroma, Inflammation and Tissue Repair Unit, Institut Pasteur, Université Paris Cité, INSERM U1224, 75015, Paris, France
| | - Catherine Robbe-Masselot
- Université de Lille, CNRS, UMR8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, 59000, Lille, France
| | - Philippe Sansonetti
- Institut Pasteur, Unité de Pathogénie Microbienne Moléculaire, INSERM U1202, and College de France, 75005, Paris, France
| | - Patrick Trieu-Cuot
- Biology of Gram-Positive Pathogens Unit, Institut Pasteur, Université Paris Cité, CNRS UMR6047, 75015, Paris, France
| | - Shaynoor Dramsi
- Biology of Gram-Positive Pathogens Unit, Institut Pasteur, Université Paris Cité, CNRS UMR6047, 75015, Paris, France.
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3
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Landais Y, Vallot C. Multi-modal quantification of pathway activity with MAYA. Nat Commun 2023; 14:1668. [PMID: 36966153 PMCID: PMC10039856 DOI: 10.1038/s41467-023-37410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/16/2023] [Indexed: 03/27/2023] Open
Abstract
Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.
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Affiliation(s)
| | - Céline Vallot
- CNRS UMR3244, Institut Curie, PSL University, Paris, France.
- Translational Research Department, Institut Curie, PSL University, Paris, France.
- Single Cell Initiative, Institut Curie, PSL University, Paris, France.
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4
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Sobrino S, Magnani A, Semeraro M, Martignetti L, Cortal A, Denis A, Couzin C, Picard C, Bustamante J, Magrin E, Joseph L, Roudaut C, Gabrion A, Soheili T, Cordier C, Lortholary O, Lefrere F, Rieux-Laucat F, Casanova JL, Bodard S, Boddaert N, Thrasher AJ, Touzot F, Taque S, Suarez F, Marcais A, Guilloux A, Lagresle-Peyrou C, Galy A, Rausell A, Blanche S, Cavazzana M, Six E. Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy. Cell Rep Med 2023; 4:100919. [PMID: 36706754 PMCID: PMC9975109 DOI: 10.1016/j.xcrm.2023.100919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/20/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023]
Abstract
X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPβ expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.
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Affiliation(s)
- Steicy Sobrino
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Alessandra Magnani
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Michaela Semeraro
- Clinical Investigation Center CIC 1419, Necker-Enfants Malades Hospital, GH Paris Centre, Université Paris Cité, AP-HP, Paris, France
| | - Loredana Martignetti
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Akira Cortal
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Adeline Denis
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Chloé Couzin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Capucine Picard
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Lymphocyte Activation and Susceptibility to EBV Infection Laboratory, INSERM UMR 1163, Imagine Institute, Paris, France; Centre de Références des Déficits Immunitaires Héréditaires (CEREDIH), Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Jacinta Bustamante
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Elisa Magrin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Laure Joseph
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Cécile Roudaut
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Aurélie Gabrion
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Tayebeh Soheili
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Corinne Cordier
- Plateforme de Cytométrie en Flux, Structure Fédérative de Recherche Necker, INSERM US24-CNRS UAR3633, Paris, France
| | - Olivier Lortholary
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - François Lefrere
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Department of Adult Hematology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Frédéric Rieux-Laucat
- Immunogenetics of Pediatric Autoimmune Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Jean-Laurent Casanova
- Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Sylvain Bodard
- Department of Adult Radiology, Necker Enfants-Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Laboratoire d'Imagerie Biomédicale, LIB, Sorbonne Université, CNRS, INSERM, Paris, France
| | - Nathalie Boddaert
- Département de Radiologie Pédiatrique, INSERM UMR 1163 and UMR 1299, Imagine Institute, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Adrian J Thrasher
- UCL Great Ormond Street Institute of Child Health, London, UK; Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Fabien Touzot
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Sophie Taque
- CHU de Rennes, Département de Pédiatrie, Rennes, France
| | - Felipe Suarez
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France; Imagine Institute, Université Paris Cité, Paris, France
| | - Ambroise Marcais
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - Agathe Guilloux
- Mathematics and Modelization Laboratory, CNRS, Université Paris-Saclay, Université d'Evry, Evry, France
| | - Chantal Lagresle-Peyrou
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Anne Galy
- Genethon, Evry-Courcouronnes, France; Université Paris-Saclay, University Evry, Inserm, Genethon (UMR_S951), Evry-Courcouronnes, France
| | - Antonio Rausell
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Service de Médecine Génomique des Maladies Rares, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Stephane Blanche
- Department of Pediatric Immunology, Hematology, and Rheumatology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Marina Cavazzana
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France; Imagine Institute, Université Paris Cité, Paris, France.
| | - Emmanuelle Six
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
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Zaman A, Bivona TG. Quantitative Framework for Bench-to-Bedside Cancer Research. Cancers (Basel) 2022; 14:5254. [PMID: 36358671 PMCID: PMC9658824 DOI: 10.3390/cancers14215254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/29/2022] Open
Abstract
Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.
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Affiliation(s)
- Aubhishek Zaman
- Department of Medicine, University of California, San Francisco, CA 94158, USA
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Trever G. Bivona
- Department of Medicine, University of California, San Francisco, CA 94158, USA
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
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Kim KH, Park J, Cho Y, Cho SY, Lee B, Jeong H, Lee Y, Yi JW, Oh Y, Lee JJ, Wang TC, Lim KM, Nam KT. Histamine Signaling Is Essential for Tissue Macrophage Differentiation and Suppression of Bacterial Overgrowth in the Stomach. Cell Mol Gastroenterol Hepatol 2022; 15:213-236. [PMID: 36167263 PMCID: PMC9672892 DOI: 10.1016/j.jcmgh.2022.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 01/21/2023]
Abstract
BACKGROUND & AIMS Histamine in the stomach traditionally is considered to regulate acid secretion but also has been reported to participate in macrophage differentiation, which plays an important role in tissue homeostasis. Therefore, this study aimed to uncover the precise role of histamine in mediating macrophage differentiation and in maintaining stomach homeostasis. METHODS Here, we expand on this role using histidine decarboxylase knockout (Hdc-/-) mice with hypertrophic gastropathy. In-depth in vivo studies were performed in Hdc-/- mice, germ-free Hdc-/- mice, and bone-marrow-transplanted Hdc-/- mice. The stomach macrophage populations and function were characterized by flow cytometry. To identify stomach macrophages and find the new macrophage population, we performed single-cell RNA sequencing analysis on Hdc+/+ and Hdc-/- stomach tissues. RESULTS Single-cell RNA sequencing and flow cytometry of the stomach cells of Hdc-/- mice showed alterations in the ratios of 3 distinct tissue macrophage populations (F4/80+Il1bhigh, F4/80+CD93+, and F4/80-MHC class IIhighCD74high). Tissue macrophages of the stomachs of Hdc-/- mice showed impaired phagocytic activity, increasing the bacterial burden of the stomach and attenuating hypertrophic gastropathy in germ-free Hdc-/- mice. The transplantation of bone marrow cells of Hdc+/+ mice to Hdc-/- mice recovered the normal differentiation of stomach macrophages and relieved the hypertrophic gastropathy of Hdc-/- mice. CONCLUSIONS This study showed the importance of histamine signaling in tissue macrophage differentiation and maintenance of gastric homeostasis through the suppression of bacterial overgrowth in the stomach.
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Affiliation(s)
- Kwang H. Kim
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Yejin Cho
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Soo Young Cho
- Department of Molecular and Life Science, Hanyang University, Ansan, Republic of Korea
| | - Buhyun Lee
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Haengdueng Jeong
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yura Lee
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ja-Woon Yi
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Yeseul Oh
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin-Jae Lee
- Department of Life Science, Hallym University, Chuncheon, Republic of Korea
| | - Timothy C. Wang
- Division of Digestive and Liver Diseases, Department of Medicine and Irving Cancer Center, Columbia University, New York, New York
| | - Kyung-Min Lim
- College of Pharmacy, Ewha Womans University, Seoul, Republic of Korea.
| | - Ki Taek Nam
- Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps. NPJ Syst Biol Appl 2022; 8:13. [PMID: 35473910 PMCID: PMC9042890 DOI: 10.1038/s41540-022-00222-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/22/2022] [Indexed: 01/09/2023] Open
Abstract
Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.
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8
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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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9
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Malenová G, Rowson D, Boeva V. Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning. Front Genet 2021; 12:771301. [PMID: 34912376 PMCID: PMC8667553 DOI: 10.3389/fgene.2021.771301] [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: 09/06/2021] [Accepted: 10/27/2021] [Indexed: 11/22/2022] Open
Abstract
Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially solved by applying regularization techniques such as lasso, the models still suffer from unsatisfactory predictive power and low stability. Methods: Here, we investigated two methods to improve survival models. Firstly, we leveraged the biological knowledge that groups of genes act together in pathways and regularized both at the group and gene level using latent group lasso penalty term. Secondly, we designed and applied a multi-task learning penalty that allowed us leveraging the relationship between survival models for different cancers. Results: We observed modest improvements over the simple lasso model with the inclusion of latent group lasso penalty for six of the 16 cancer types tested. The addition of a multi-task penalty, which penalized coefficients in pairs of cancers from diverging too greatly, significantly improved accuracy for a single cancer, lung squamous cell carcinoma, while having minimal effect on other cancer types. Conclusion: While the use of pathway information and multi-tasking shows some promise, these methods do not provide a substantial improvement when compared with standard methods.
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Affiliation(s)
- Gabriela Malenová
- Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland
| | - Daniel Rowson
- Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland
| | - Valentina Boeva
- Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland.,Swiss Institute for Bioinformatics (SIB), Zürich, Switzerland.,Institut Cochin, Inserm U1016, CNRS UMR 8104, Université de Paris UMR-S1016, Paris, France
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10
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Monraz Gomez LC, Kondratova M, Sompairac N, Lonjou C, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Atlas of Cancer Signaling Network: A Resource of Multi-Scale Biological Maps to Study Disease Mechanisms. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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11
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Simon LM, Yan F, Zhao Z. DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data. Gigascience 2020; 9:giaa122. [PMID: 33301553 PMCID: PMC7727875 DOI: 10.1093/gigascience/giaa122] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 05/27/2020] [Accepted: 10/07/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. FINDINGS Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data. CONCLUSIONS By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms.
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Affiliation(s)
- Lukas M Simon
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, USA
| | - Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX 77030, USA
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End, Nashville, TN 37203, USA
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12
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Zhang Y, Ma Y, Huang Y, Zhang Y, Jiang Q, Zhou M, Su J. Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data. Comput Struct Biotechnol J 2020; 18:2953-2961. [PMID: 33209207 PMCID: PMC7642725 DOI: 10.1016/j.csbj.2020.10.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/29/2020] [Accepted: 10/02/2020] [Indexed: 12/16/2022] Open
Abstract
Biological pathway analysis provides new insights for cell clustering and functional annotation from single-cell RNA sequencing (scRNA-seq) data. Many pathway analysis algorithms have been developed to transform gene-level scRNA-seq data into functional gene sets representing pathways or biological processes. Here, we collected seven widely-used pathway activity transformation algorithms and 32 available datasets based on 16 scRNA-seq techniques. We proposed a comprehensive framework to evaluate their accuracy, stability and scalability. The assessment of scRNA-seq preprocessing showed that cell filtering had the less impact on scRNA-seq pathway analysis, while data normalization of sctransform and scran had a consistent well impact across all tools. We found that Pagoda2 yielded the best overall performance with the highest accuracy, scalability, and stability. Meanwhile, the tool PLAGE exhibited the highest stability, as well as moderate accuracy and scalability.
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Affiliation(s)
- Yaru Zhang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yunlong Ma
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yan Zhang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Qi Jiang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Meng Zhou
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
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13
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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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14
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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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15
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Colaprico A, Olsen C, Bailey MH, Odom GJ, Terkelsen T, Silva TC, Olsen AV, Cantini L, Zinovyev A, Barillot E, Noushmehr H, Bertoli G, Castiglioni I, Cava C, Bontempi G, Chen XS, Papaleo E. Interpreting pathways to discover cancer driver genes with Moonlight. Nat Commun 2020; 11:69. [PMID: 31900418 PMCID: PMC6941958 DOI: 10.1038/s41467-019-13803-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 11/22/2019] [Indexed: 12/28/2022] Open
Abstract
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.
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Affiliation(s)
- Antonio Colaprico
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium.
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA.
| | - Catharina Olsen
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium
- Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), VUB-ULB, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Matthew H Bailey
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University, St. Louis, MO, 63108, USA
| | - Gabriel J Odom
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA
- Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, FL, 33199, USA
| | - Thilde Terkelsen
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Tiago C Silva
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA
- Department of Genetics, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
| | - André V Olsen
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Laura Cantini
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, Paris Sciences et Lettres Research University, 75005, Paris, France
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, F-75248, Paris, France
- INSERM, U900, Paris, F-75248, France
- Mines ParisTech, Fontainebleau, F-77300, France
| | - Houtan Noushmehr
- Department of Genetics, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
- Department of Neurosurgery, Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA
| | - Gloria Bertoli
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Claudia Cava
- Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
| | - Gianluca Bontempi
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Xi Steven Chen
- Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA.
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
| | - Elena Papaleo
- Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.
- Translational Disease System Biology, Faculty of Health and Medical Science, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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16
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Čuklina J, Pedrioli PGA, Aebersold R. Review of Batch Effects Prevention, Diagnostics, and Correction Approaches. Methods Mol Biol 2020; 2051:373-387. [PMID: 31552638 DOI: 10.1007/978-1-4939-9744-2_16] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Systematic technical variation in high-throughput studies consisting of the serial measurement of large sample cohorts is known as batch effects. Batch effects reduce the sensitivity of biological signal extraction and can cause significant artifacts. The systematic bias in the data caused by batch effects is more common in studies in which logistical considerations restrict the number of samples that can be prepared or profiled in a single experiment, thus necessitating the arrangement of subsets of study samples in batches. To mitigate the negative impact of batch effects, statistical approaches for batch correction are used at the stage of experimental design and data processing. Whereas in genomics batch effects and possible remedies have been extensively discussed, they are a relatively new challenge in proteomics because methods with sufficient throughput to systematically measure through large sample cohorts have only recently become available. Here we provide general recommendations to mitigate batch effects: we discuss the design of large-scale proteomic studies, review the most commonly used tools for batch effect correction and overview their application in proteomics.
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Affiliation(s)
- Jelena Čuklina
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, Zürich, Switzerland
| | - Patrick G A Pedrioli
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- ETH Zürich, PHRT-MS, Zürich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
- Faculty of Science, University of Zürich, Zürich, Switzerland.
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17
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Do KT, Rasp DJNP, Kastenmüller G, Suhre K, Krumsiek J. MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions. Bioinformatics 2019; 35:532-534. [PMID: 30032270 PMCID: PMC6361241 DOI: 10.1093/bioinformatics/bty650] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/18/2018] [Indexed: 11/13/2022] Open
Abstract
Summary Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kieu Trinh Do
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - David J N-P Rasp
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
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18
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Katsogiannou M, Boyer JB, Valdeolivas A, Remy E, Calzone L, Audebert S, Rocchi P, Camoin L, Baudot A. Integrative proteomic and phosphoproteomic profiling of prostate cell lines. PLoS One 2019; 14:e0224148. [PMID: 31675377 PMCID: PMC6824562 DOI: 10.1371/journal.pone.0224148] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 10/06/2019] [Indexed: 12/15/2022] Open
Abstract
Background Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression. Methods We conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status. Results We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation. Conclusions Overall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation.
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Affiliation(s)
- Maria Katsogiannou
- Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
- Obstetrics and Gynecology department, Hôpital Saint Joseph, Marseille, France
| | - Jean-Baptiste Boyer
- Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Alberto Valdeolivas
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
- Aix Marseille Univ, INSERM, MMG, Marseille, France
- ProGeLife, Marseille, France
| | - Elisabeth Remy
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
| | - Laurence Calzone
- Mines Paris Tech, Institut Curie, PSL Research University, Paris, France
| | - Stéphane Audebert
- Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Palma Rocchi
- Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
- * E-mail: (PR); (LC); (AB)
| | - Luc Camoin
- Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France
- * E-mail: (PR); (LC); (AB)
| | - Anaïs Baudot
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
- Aix Marseille Univ, INSERM, MMG, Marseille, France
- * E-mail: (PR); (LC); (AB)
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19
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Guo T, Luna A, Rajapakse VN, Koh CC, Wu Z, Liu W, Sun Y, Gao H, Menden MP, Xu C, Calzone L, Martignetti L, Auwerx C, Buljan M, Banaei-Esfahani A, Ori A, Iskar M, Gillet L, Bi R, Zhang J, Zhang H, Yu C, Zhong Q, Varma S, Schmitt U, Qiu P, Zhang Q, Zhu Y, Wild PJ, Garnett MJ, Bork P, Beck M, Liu K, Saez-Rodriguez J, Elloumi F, Reinhold WC, Sander C, Pommier Y, Aebersold R. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines. iScience 2019; 21:664-680. [PMID: 31733513 PMCID: PMC6889472 DOI: 10.1016/j.isci.2019.10.059] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/15/2022] Open
Abstract
Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses. High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS Proteotypes improve drug response prediction in multi-omics regression analysis ∼3000 measured proteins allow investigation into protein complex stoichiometry CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration
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Affiliation(s)
- Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
| | - Augustin Luna
- cBio Center, Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Vinodh N Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ching Chiek Koh
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Zhicheng Wu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Wei Liu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China; Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China
| | - Yaoting Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Huanhuan Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Michael P Menden
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany; Bioscience, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Chao Xu
- Faculty of Software, Fujian Normal University, Fuzhou, China
| | - Laurence Calzone
- Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech 75005, Paris, France
| | - Loredana Martignetti
- Institut Curie, PSL Research University, INSERM, U900, Mines Paris Tech 75005, Paris, France
| | - Chiara Auwerx
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Marija Buljan
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Amir Banaei-Esfahani
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; PhD Program in Systems Biology, Life Science Zurich Graduate School, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Alessandro Ori
- Leibniz Institute on Aging, Fritz Lipmann Institute (FLI), Beutenbergstrasse 11, 07745 Jena, Germany
| | - Murat Iskar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ran Bi
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China
| | - Jiangnan Zhang
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China
| | - Huanhuan Zhang
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Chenhuan Yu
- Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang, China
| | - Qing Zhong
- Institute of Surgical Pathology, University Hospital Zurich, Zurich, Switzerland; Cancer Data Science Group, Children's Medical Research Institute, University of Sydney, Sydney, NSW, Australia
| | | | - Uwe Schmitt
- Scientific IT Services, ETH Zurich, Zurich, Switzerland
| | - Peng Qiu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr., Atlanta, GA 30332, USA
| | - Qiushi Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China
| | - Yi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, P. R. China; Guomics Laboratory of Proteomic Big Data, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang Province, China; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Peter J Wild
- Institute of Surgical Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Mathew J Garnett
- Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany; Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany; Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Kexin Liu
- Department of Clinical Pharmacology, College of Pharmacy, Dalian Medical University, Dalian, Liaoning, China
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany
| | - Fathi Elloumi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William C Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Chris Sander
- cBio Center, Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland.
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20
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Amadoz A, Hidalgo MR, Çubuk C, Carbonell-Caballero J, Dopazo J. A comparison of mechanistic signaling pathway activity analysis methods. Brief Bioinform 2019; 20:1655-1668. [PMID: 29868818 PMCID: PMC6917216 DOI: 10.1093/bib/bby040] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 03/31/2018] [Indexed: 12/11/2022] Open
Abstract
Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.
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Affiliation(s)
- Alicia Amadoz
- Department of Bioinformatics, Igenomix S.L., 46980 Valencia, Spain
| | - Marta R Hidalgo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
| | - Cankut Çubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
| | - José Carbonell-Caballero
- Chromatin and Gene expression Lab, Gene Regulation, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG), The Barcelona Institute of Science and Technology, PRBB, Barcelona 08003, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
- Chromatin and Gene expression Lab, Gene Regulation, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG), The Barcelona Institute of Science and Technology, PRBB, Barcelona 08003, Spain
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain, Functional Genomics Node (INB), FPS, Hospital Virgen del Rocío, Sevilla 41013, Spain and Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, Sevilla 41013, Spain
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21
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DeTomaso D, Jones MG, Subramaniam M, Ashuach T, Ye CJ, Yosef N. Functional interpretation of single cell similarity maps. Nat Commun 2019; 10:4376. [PMID: 31558714 PMCID: PMC6763499 DOI: 10.1038/s41467-019-12235-0] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 08/23/2019] [Indexed: 12/11/2022] Open
Abstract
We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration. The increasing accessibility of single cell RNA sequencing demands tools that enable data visualization and interpretation. Here, the authors introduce Vision, a flexible annotation tool that operates directly on the manifold of cell-cell similarity and aids interpretation of cellular heterogeneity.
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Affiliation(s)
- David DeTomaso
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
| | - Matthew G Jones
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
| | - Meena Subramaniam
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
| | - Tal Ashuach
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
| | - Chun J Ye
- Department of Epidemiology and Biostatistics, Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA. .,Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, USA. .,Chan-Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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22
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Sompairac N, Modamio J, Barillot E, Fleming RMT, Zinovyev A, Kuperstein I. Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer. BMC Bioinformatics 2019; 20:140. [PMID: 30999838 PMCID: PMC6471697 DOI: 10.1186/s12859-019-2682-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease. Results We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration (https://navicell.curie.fr/pages/maps_ReconMap%202.html). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified. Conclusions As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases. Electronic supplementary material The online version of this article (10.1186/s12859-019-2682-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicolas Sompairac
- Institut Curie, 26 rue d'Ulm, F-75005, Paris, France.,Inserm, U900, F-75005, Paris, France.,Mines Paris Tech, F-77305, Fontainebleau cedex, France.,PSL Research University, F-75005, Paris, France
| | - Jennifer Modamio
- Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, F-75005, Paris, France.,Inserm, U900, F-75005, Paris, France.,Mines Paris Tech, F-77305, Fontainebleau cedex, France.,PSL Research University, F-75005, Paris, France
| | - Ronan M T Fleming
- Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, F-75005, Paris, France.,Inserm, U900, F-75005, Paris, France.,Mines Paris Tech, F-77305, Fontainebleau cedex, France.,PSL Research University, F-75005, Paris, France
| | - Inna Kuperstein
- Institut Curie, 26 rue d'Ulm, F-75005, Paris, France. .,Inserm, U900, F-75005, Paris, France. .,Mines Paris Tech, F-77305, Fontainebleau cedex, France. .,PSL Research University, F-75005, Paris, France.
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23
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Forget A, Martignetti L, Puget S, Calzone L, Brabetz S, Picard D, Montagud A, Liva S, Sta A, Dingli F, Arras G, Rivera J, Loew D, Besnard A, Lacombe J, Pagès M, Varlet P, Dufour C, Yu H, Mercier AL, Indersie E, Chivet A, Leboucher S, Sieber L, Beccaria K, Gombert M, Meyer FD, Qin N, Bartl J, Chavez L, Okonechnikov K, Sharma T, Thatikonda V, Bourdeaut F, Pouponnot C, Ramaswamy V, Korshunov A, Borkhardt A, Reifenberger G, Poullet P, Taylor MD, Kool M, Pfister SM, Kawauchi D, Barillot E, Remke M, Ayrault O. Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling. Cancer Cell 2018; 34:379-395.e7. [PMID: 30205043 DOI: 10.1016/j.ccell.2018.08.002] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 05/12/2018] [Accepted: 08/03/2018] [Indexed: 12/20/2022]
Abstract
The current consensus recognizes four main medulloblastoma subgroups (wingless, Sonic hedgehog, group 3 and group 4). While medulloblastoma subgroups have been characterized extensively at the (epi-)genomic and transcriptomic levels, the proteome and phosphoproteome landscape remain to be comprehensively elucidated. Using quantitative (phospho)-proteomics in primary human medulloblastomas, we unravel distinct posttranscriptional regulation leading to highly divergent oncogenic signaling and kinase activity profiles in groups 3 and 4 medulloblastomas. Specifically, proteomic and phosphoproteomic analyses identify aberrant ERBB4-SRC signaling in group 4. Hence, enforced expression of an activated SRC combined with p53 inactivation induces murine tumors that resemble group 4 medulloblastoma. Therefore, our integrative proteogenomics approach unveils an oncogenic pathway and potential therapeutic vulnerability in the most common medulloblastoma subgroup.
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Affiliation(s)
- Antoine Forget
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France.
| | - Loredana Martignetti
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Stéphanie Puget
- Department of Pediatric Neurosurgery, Necker University Hospital, University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Laurence Calzone
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Sebastian Brabetz
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Daniel Picard
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Arnau Montagud
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Stéphane Liva
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Alexandre Sta
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Florent Dingli
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Guillaume Arras
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Jaime Rivera
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Damarys Loew
- Proteomics and Mass Spectrometry Laboratory, Institut Curie, PSL Research University, 75005 Paris, France
| | - Aurore Besnard
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Joëlle Lacombe
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Mélanie Pagès
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Pascale Varlet
- Department of Neuropathology, Sainte-Anne Hospital, 75014 Paris, France; University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Christelle Dufour
- Department of Pediatric and Adolescent Oncology, Gustave Roussy, Rue Edouard Vaillant, 94805 Villejuif, France
| | - Hua Yu
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Audrey L Mercier
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Emilie Indersie
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Anaïs Chivet
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Sophie Leboucher
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Institut Curie, Centre de Recherche, Plateforme d'Histologie, Orsay 91405, France
| | - Laura Sieber
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Kevin Beccaria
- Department of Pediatric Neurosurgery, Necker University Hospital, University Paris Descartes, Sorbonne Paris Cité, 75015 Paris, France
| | - Michael Gombert
- Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Frauke D Meyer
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Nan Qin
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Jasmin Bartl
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany
| | - Lukas Chavez
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Konstantin Okonechnikov
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Tanvi Sharma
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Venu Thatikonda
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Franck Bourdeaut
- Paris-Sciences-Lettres Research University, Institut Curie Research Center, SiRIC, Laboratory of Translational Research in Pediatric Oncology, Paris 75005, France; Paris-Sciences-Lettres Research University, Institut Curie Research Center, INSERM U830, Laboratory of Biology and Genetics of Cancers, Paris 75005, France
| | - Celio Pouponnot
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France
| | - Vijay Ramaswamy
- Division of Haematology/Oncology, Hospital for Sick Children and Department of Paediatrics, Hospital for Sick Children, Toronto, ON, Canada
| | - Andrey Korshunov
- Clinical Cooperation Unit Neuropathology (G380), German Cancer Research Center (DKFZ), and Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Arndt Borkhardt
- Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Guido Reifenberger
- Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick Poullet
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France
| | - Michael D Taylor
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada; Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada; Departments of Surgery, Laboratory Medicine and Pathobiology, and Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Marcel Kool
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Stefan M Pfister
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany; Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Daisuke Kawauchi
- Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany.
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, 75005 Paris, France; PSL Research University, 75005 Paris, France; Inserm, U900, 75005 Paris, France; Mines Paris Tech, 77305 cedex Fontainebleau, France.
| | - Marc Remke
- Department of Pediatric Neuro-Oncogenomics, DKFZ, Heidelberg, Germany; Department of Pediatric Oncology, Hematology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany; Institute of Neuropathology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; DKTK, Partner Site, Essen/Düsseldorf, Germany.
| | - Olivier Ayrault
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Orsay, France; Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay, France.
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24
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Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, De Meulder B, Pellet J, Balaur I, Saqi M, Nogueira MM, He F, Parton A, Lemonnier N, Gawron P, Gebel S, Hainaut P, Ollert M, Dogrusoz U, Barillot E, Zinovyev A, Schneider R, Balling R, Auffray C. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms. NPJ Syst Biol Appl 2018; 4:21. [PMID: 29872544 PMCID: PMC5984630 DOI: 10.1038/s41540-018-0059-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/26/2018] [Accepted: 05/04/2018] [Indexed: 12/18/2022] Open
Abstract
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
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Affiliation(s)
- Alexander Mazein
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Marek Ostaszewski
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Steven Watterson
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nicolas Le Novère
- 8The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT UK
| | - Diane Lefaudeux
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Bertrand De Meulder
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Johann Pellet
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Irina Balaur
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Mansoor Saqi
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Maria Manuela Nogueira
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg
| | - Andrew Parton
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nathanaël Lemonnier
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Piotr Gawron
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Pierre Hainaut
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg.,11Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, University of Southern Denmark, Odense, Denmark
| | - Ugur Dogrusoz
- 12Faculty of Engineering, Computer Engineering Department, Bilkent University, Ankara, 06800 Turkey
| | - Emmanuel Barillot
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Andrei Zinovyev
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Reinhard Schneider
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Rudi Balling
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charles Auffray
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
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25
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Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Application of Atlas of Cancer Signalling Network in preclinical studies. Brief Bioinform 2018; 20:701-716. [DOI: 10.1093/bib/bby031] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 03/28/2018] [Indexed: 12/27/2022] Open
Affiliation(s)
- L Cristobal Monraz Gomez
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Jean-Marie Ravel
- Genetic Laboratory, Nancy's Regional University Hospital, Vandœuvre-lès-Nancy and INSERM UMR 954, Lorraine University, Vandœuvre-lès-Nancy
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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26
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Cantini L, Calzone L, Martignetti L, Rydenfelt M, Blüthgen N, Barillot E, Zinovyev A. Classification of gene signatures for their information value and functional redundancy. NPJ Syst Biol Appl 2017; 4:2. [PMID: 29263798 PMCID: PMC5736638 DOI: 10.1038/s41540-017-0038-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 11/15/2017] [Accepted: 11/21/2017] [Indexed: 12/21/2022] Open
Abstract
Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defined as informative and demonstrate that they have a higher probability to appear enriched in comparative cancer studies. We show that the majority of informative signatures conserve their weights for the genes composing the signature (eigengenes) from one cancer type to another. We finally construct InfoSigMap, an interactive online map of these signatures and their cross-correlations. This map highlights the structure of compositional and functional redundancies between informative signatures, and it charts the territories of biological functions. InfoSigMap can be used to visualize the results of omics data analyses and suggests a rearrangement of existing gene sets. An informative collection of gene signatures for transcriptomic data analysis is constructed. The number of transcriptomic signatures grows fast and their collections are highly redundant that hampers omics data analyses interpretation. A computational biology team from Institut Curie led by Andrei Zinovyev selected a collection of 962 gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression. The signatures were filtered from a large compendium without requiring any manual curation by experts through a large-scale unbiased analysis of pancancer data. They have much higher chance to obtain significant enrichment scores in a comparative trancriptomic study. The authors integrated the 962 signatures into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.
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Affiliation(s)
- Laura Cantini
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, 26, rue d'Ulm, F-75248 Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, 26, rue d'Ulm, F-75248 Paris, France
| | - Loredana Martignetti
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, 26, rue d'Ulm, F-75248 Paris, France
| | - Mattias Rydenfelt
- Institute of Pathology, Charite Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.,IRI Life Sciences and Institute for Theoretical Biology, Humboldt University, Philippstr. 13, Haus 18, 10115 Berlin, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charite Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.,IRI Life Sciences and Institute for Theoretical Biology, Humboldt University, Philippstr. 13, Haus 18, 10115 Berlin, Germany
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, 26, rue d'Ulm, F-75248 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, 26, rue d'Ulm, F-75248 Paris, France
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27
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Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations. NPJ Syst Biol Appl 2017; 3:28. [PMID: 28948040 PMCID: PMC5608949 DOI: 10.1038/s41540-017-0029-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 08/18/2017] [Accepted: 08/25/2017] [Indexed: 12/27/2022] Open
Abstract
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered. Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.
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