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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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2
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Murray D, Petrey D, Honig B. Integrating 3D structural information into systems biology. J Biol Chem 2021; 296:100562. [PMID: 33744294 PMCID: PMC8095114 DOI: 10.1016/j.jbc.2021.100562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/18/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.
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Affiliation(s)
- Diana Murray
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Donald Petrey
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Barry Honig
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Department of Medicine, Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, USA.
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3
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Broyde J, Simpson DR, Murray D, Paull EO, Chu BW, Tagore S, Jones SJ, Griffin AT, Giorgi FM, Lachmann A, Jackson P, Sweet-Cordero EA, Honig B, Califano A. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nat Biotechnol 2021; 39:215-224. [PMID: 32929263 PMCID: PMC7878435 DOI: 10.1038/s41587-020-0652-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 07/23/2020] [Indexed: 02/08/2023]
Abstract
Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.
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Affiliation(s)
- Joshua Broyde
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - David R Simpson
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, UCSF Benioff Children's Hospital, San Francisco, CA, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Evan O Paull
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Brennan W Chu
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Somnath Tagore
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sunny J Jones
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Federico M Giorgi
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Alexander Lachmann
- Mount Sinai Center for Bioinformatics; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peter Jackson
- Baxter Laboratory, Department of Microbiology & Immunology, Stanford University, Palo Alto, CA, USA
- Department of Pathology, Stanford University, Palo Alto, CA, USA
| | - E Alejandro Sweet-Cordero
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, UCSF Benioff Children's Hospital, San Francisco, CA, USA.
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
- Department of Medicine, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
- Department of Medicine, Columbia University, New York, NY, USA.
- JP Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
- Institute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Motor Neuron Center and Columbia Initiative in Stem Cells, Columbia University, New York, NY, USA.
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4
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Paull EO, Aytes A, Jones SJ, Subramaniam PS, Giorgi FM, Douglass EF, Tagore S, Chu B, Vasciaveo A, Zheng S, Verhaak R, Abate-Shen C, Alvarez MJ, Califano A. A modular master regulator landscape controls cancer transcriptional identity. Cell 2021; 184:334-351.e20. [PMID: 33434495 PMCID: PMC8103356 DOI: 10.1016/j.cell.2020.11.045] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 08/06/2020] [Accepted: 11/25/2020] [Indexed: 02/06/2023]
Abstract
Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.
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Affiliation(s)
- Evan O Paull
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alvaro Aytes
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Molecular Mechanisms and Experimental Therapeutics in Oncology (ONCOBell), Bellvitge Institute for Biomedical Research, L'Hospitalet de Llobregat, Barcelona 08908, Spain; Program Against Cancer Therapeutics Resistance (ProCURE), Catalan Institute of Oncology, Bellvitge Institute for Biomedical Research, L'Hospitalet de Llobregat, Barcelona 08908, Spain
| | - Sunny J Jones
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Eugene F Douglass
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Somnath Tagore
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Brennan Chu
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alessandro Vasciaveo
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Siyuan Zheng
- Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Roel Verhaak
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Cory Abate-Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Molecular Pharmacology and Therapeutics, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Urology, Columbia University Irving Medical Center, New York, NY 10032, USA.
| | - Mariano J Alvarez
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; DarwinHealth, Inc. New York, NY 10018, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; DarwinHealth, Inc. New York, NY 10018, USA; Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA.
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5
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Mishra V, Re DB, Le Verche V, Alvarez MJ, Vasciaveo A, Jacquier A, Doulias PT, Greco TM, Nizzardo M, Papadimitriou D, Nagata T, Rinchetti P, Perez-Torres EJ, Politi KA, Ikiz B, Clare K, Than ME, Corti S, Ischiropoulos H, Lotti F, Califano A, Przedborski S. Systematic elucidation of neuron-astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow. Nat Commun 2020; 11:5579. [PMID: 33149111 PMCID: PMC7642391 DOI: 10.1038/s41467-020-19177-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 10/02/2020] [Indexed: 12/31/2022] Open
Abstract
Cell-to-cell communications are critical determinants of pathophysiological phenotypes, but methodologies for their systematic elucidation are lacking. Herein, we propose an approach for the Systematic Elucidation and Assessment of Regulatory Cell-to-cell Interaction Networks (SEARCHIN) to identify ligand-mediated interactions between distinct cellular compartments. To test this approach, we selected a model of amyotrophic lateral sclerosis (ALS), in which astrocytes expressing mutant superoxide dismutase-1 (mutSOD1) kill wild-type motor neurons (MNs) by an unknown mechanism. Our integrative analysis that combines proteomics and regulatory network analysis infers the interaction between astrocyte-released amyloid precursor protein (APP) and death receptor-6 (DR6) on MNs as the top predicted ligand-receptor pair. The inferred deleterious role of APP and DR6 is confirmed in vitro in models of ALS. Moreover, the DR6 knockdown in MNs of transgenic mutSOD1 mice attenuates the ALS-like phenotype. Our results support the usefulness of integrative, systems biology approach to gain insights into complex neurobiological disease processes as in ALS and posit that the proposed methodology is not restricted to this biological context and could be used in a variety of other non-cell-autonomous communication mechanisms.
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Affiliation(s)
- Vartika Mishra
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Spark Therapeutics, 3737 Market Street, Philadelphia, PA, 19104, USA
| | - Diane B Re
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Department of Environmental Health Sciences, Columbia University, New York, NY, 10032, USA
| | - Virginia Le Verche
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Center for Gene Therapy, City of Hope, 1500 E. Duarte Road, Duarte, CA, 91010, USA
| | - Mariano J Alvarez
- Department of Systems Biology, Columbia University, New York, NY, 10032, USA
- DarwinHealth Inc., New York, NY, 10032, USA
| | - Alessandro Vasciaveo
- Department of Systems Biology, Columbia University, New York, NY, 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - Arnaud Jacquier
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Institut NeuroMyoGène, CNRS UMR 5310 - INSERM U1217 - Université de Lyon - Université Claude Bernard Lyon 1, Lyon, France
| | - Paschalis-Tomas Doulias
- Department of Pediatrics, Children's Hospital of Philadelphia Research Institute and the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Todd M Greco
- Department of Pediatrics, Children's Hospital of Philadelphia Research Institute and the University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Molecular Biology, Princeton University, Princeton, USA
| | - Monica Nizzardo
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, Neurology Unit, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan, 20122, Italy
| | - Dimitra Papadimitriou
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Henry Dunant Hospital, BRFAA, Athens, Greece
| | - Tetsuya Nagata
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Department of Neurology and Neurological Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Paola Rinchetti
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, Neurology Unit, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan, 20122, Italy
| | - Eduardo J Perez-Torres
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
| | - Kristin A Politi
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
| | - Burcin Ikiz
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
| | - Kevin Clare
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
- New York Medical College, Valhalla, NY, 10595, USA
| | - Manuel E Than
- Protein Crystallography Group, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Beutenbergstr. 11, 07745, Jena, Germany
| | - Stefania Corti
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, Neurology Unit, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan, 20122, Italy
| | - Harry Ischiropoulos
- Department of Pediatrics, Children's Hospital of Philadelphia Research Institute and the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Francesco Lotti
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA
| | - Andrea Califano
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA.
- Department of Systems Biology, Columbia University, New York, NY, 10032, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
- J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
| | - Serge Przedborski
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA.
- Center for Motor Neuron Biology and Diseases, Columbia University, New York, NY, 10032, USA.
- Departments of Neurology and Neuroscience, Columbia University, New York, NY, 10032, USA.
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Guzzi PH, Mercatelli D, Ceraolo C, Giorgi FM. Master Regulator Analysis of the SARS-CoV-2/Human Interactome. J Clin Med 2020; 9:E982. [PMID: 32244779 PMCID: PMC7230814 DOI: 10.3390/jcm9040982] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/20/2022] Open
Abstract
The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.
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Affiliation(s)
- Pietro H. Guzzi
- Department of Surgical and Medical Science, University of Catanzaro, 88100 Catanzaro, Italy;
| | - Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy; (D.M.); (C.C.)
| | - Carmine Ceraolo
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy; (D.M.); (C.C.)
| | - Federico M. Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy; (D.M.); (C.C.)
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7
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Muldoon JJ, Yu JS, Fassia MK, Bagheri N. Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants. Bioinformatics 2019; 35:3421-3432. [PMID: 30932143 PMCID: PMC6748731 DOI: 10.1093/bioinformatics/btz105] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 01/24/2019] [Accepted: 02/11/2019] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. RESULTS We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets. AVAILABILITY AND IMPLEMENTATION Code is available at http://github.com/bagherilab/networkinference/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joseph J Muldoon
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA
| | - Jessica S Yu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
| | - Mohammad-Kasim Fassia
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
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8
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Singh AJ, Ramsey SA, Filtz TM, Kioussi C. Differential gene regulatory networks in development and disease. Cell Mol Life Sci 2018; 75:1013-1025. [PMID: 29018868 PMCID: PMC11105524 DOI: 10.1007/s00018-017-2679-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 09/19/2017] [Accepted: 10/04/2017] [Indexed: 02/02/2023]
Abstract
Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.
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Affiliation(s)
- Arun J Singh
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA
| | - Stephen A Ramsey
- Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, OR, 97331, USA
- School of Electrical Engineering and Computer Science, College of Engineering, Oregon State University, Corvallis, OR, 97331, USA
| | - Theresa M Filtz
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA
| | - Chrissa Kioussi
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA.
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9
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He J, Zhou Z, Reed M, Califano A. Accelerated parallel algorithm for gene network reverse engineering. BMC SYSTEMS BIOLOGY 2017; 11:83. [PMID: 28950860 PMCID: PMC5615246 DOI: 10.1186/s12918-017-0458-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture. Result Here, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks. Conclusion Given that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0458-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jing He
- Department of Biomedical Informatics, Columbia University, 168th Street, New York, 10032, NY, USA.,Department of Systems Biology, 1130 St Nicholas Street, New York, 10032, NY, USA
| | - Zhou Zhou
- Department of Computer Science, New York, 10027, NY, USA
| | - Michael Reed
- Department of Computer Science, New York, 10027, NY, USA
| | - Andrea Califano
- Department of Systems Biology, 1130 St Nicholas Street, New York, 10032, NY, USA.
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10
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Kim SJ, Ka S, Ha JW, Kim J, Yoo D, Kim K, Lee HK, Lim D, Cho S, Hanotte O, Mwai OA, Dessie T, Kemp S, Oh SJ, Kim H. Cattle genome-wide analysis reveals genetic signatures in trypanotolerant N'Dama. BMC Genomics 2017; 18:371. [PMID: 28499406 PMCID: PMC5427609 DOI: 10.1186/s12864-017-3742-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 04/27/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Indigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, genetic characteristics of each breed have been poorly studied. RESULTS We performed the comparative genome-wide analysis to assess evidence for subspeciation within species at the genetic level in trypanotolerant N'Dama cattle. We analysed genetic variation patterns in N'Dama from the genomes of 101 cattle breeds including 48 samples of five indigenous African cattle breeds and 53 samples of various commercial breeds. Analysis of SNP variances between cattle breeds using wMI, XP-CLR, and XP-EHH detected genes containing N'Dama-specific genetic variants and their potential associations. Functional annotation analysis revealed that these genes are associated with ossification, neurological and immune system. Particularly, the genes involved in bone formation indicate that local adaptation of N'Dama may engage in skeletal growth as well as immune systems. CONCLUSIONS Our results imply that N'Dama might have acquired distinct genotypes associated with growth and regulation of regional diseases including trypanosomiasis. Moreover, this study offers significant insights into identifying genetic signatures for natural and artificial selection of diverse African cattle breeds.
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Affiliation(s)
- Soo-Jin Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.,C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Sojeong Ka
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jung-Woo Ha
- Clova, NAVER Corp., Seongnam, 13561, Republic of Korea
| | - Jaemin Kim
- C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - DongAhn Yoo
- C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kwondo Kim
- C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hak-Kyo Lee
- Department of Animal Biotechnology, Chonbuk National University, Jeonju, 66414, Republic of Korea
| | - Dajeong Lim
- Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA, Jeonju, 55365, Republic of Korea
| | - Seoae Cho
- C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Olivier Hanotte
- University of Nottingham, School of Life Sciences, Nottingham, NG7 2RD, UK.,International Livestock Research Institute, Addis Ababa, Ethiopia
| | - Okeyo Ally Mwai
- International Livestock Research Institute, Box 30709-00100, Nairobi, Kenya
| | - Tadelle Dessie
- International Livestock Research Institute, Addis Ababa, Ethiopia
| | - Stephen Kemp
- International Livestock Research Institute, Box 30709-00100, Nairobi, Kenya.,The Centre for Tropical Livestock Genetics and Health, The Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, Scotland, UK
| | - Sung Jong Oh
- National Institute of Animal Science, RDA, Wanju, 55365, Republic of Korea.
| | - Heebal Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea. .,C&K Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea. .,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
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11
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Giorgi FM, Lopez G, Woo JH, Bisikirska B, Califano A, Bansal M. Correction: Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information. PLoS One 2016; 11:e0163402. [PMID: 27632225 PMCID: PMC5024985 DOI: 10.1371/journal.pone.0163402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0109569.].
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12
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Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers. Sci Rep 2016; 6:23035. [PMID: 26972162 PMCID: PMC4789788 DOI: 10.1038/srep23035] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 02/23/2016] [Indexed: 12/14/2022] Open
Abstract
Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.
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13
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Lee SB, Frattini V, Bansal M, Castano AM, Sherman D, Hutchinson K, Bruce JN, Califano A, Liu G, Cardozo T, Iavarone A, Lasorella A. An ID2-dependent mechanism for VHL inactivation in cancer. Nature 2016; 529:172-7. [PMID: 26735018 PMCID: PMC5384647 DOI: 10.1038/nature16475] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 11/23/2015] [Indexed: 01/19/2023]
Abstract
Mechanisms that maintain cancer stem cells are crucial to tumour progression. The ID2 protein supports cancer hallmarks including the cancer stem cell state. HIFα transcription factors, most notably HIF2α (also known as EPAS1), are expressed in and required for maintenance of cancer stem cells (CSCs). However, the pathways that are engaged by ID2 or drive HIF2α accumulation in CSCs have remained unclear. Here we report that DYRK1A and DYRK1B kinases phosphorylate ID2 on threonine 27 (Thr27). Hypoxia downregulates this phosphorylation via inactivation of DYRK1A and DYRK1B. The activity of these kinases is stimulated in normoxia by the oxygen-sensing prolyl hydroxylase PHD1 (also known as EGLN2). ID2 binds to the VHL ubiquitin ligase complex, displaces VHL-associated Cullin 2, and impairs HIF2α ubiquitylation and degradation. Phosphorylation of Thr27 of ID2 by DYRK1 blocks ID2-VHL interaction and preserves HIF2α ubiquitylation. In glioblastoma, ID2 positively modulates HIF2α activity. Conversely, elevated expression of DYRK1 phosphorylates Thr27 of ID2, leading to HIF2α destabilization, loss of glioma stemness, inhibition of tumour growth, and a more favourable outcome for patients with glioblastoma.
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Affiliation(s)
- Sang Bae Lee
- Institute for Cancer Genetics, Columbia University Medical Center, New York
| | - Veronique Frattini
- Institute for Cancer Genetics, Columbia University Medical Center, New York
| | - Mukesh Bansal
- Department of Systems Biology, Columbia University Medical Center, New York
- Center for Computational Biology and Bioinformatics, Columbia University Medical Center, New York
| | | | - Dan Sherman
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York
| | - Keino Hutchinson
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York
| | - Jeffrey N. Bruce
- Department of Neurosurgery, Columbia University Medical Center, New York
| | - Andrea Califano
- Department of Systems Biology, Columbia University Medical Center, New York
- Center for Computational Biology and Bioinformatics, Columbia University Medical Center, New York
| | - Guangchao Liu
- Institute for Cancer Genetics, Columbia University Medical Center, New York
| | - Timothy Cardozo
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York
- Department of Neurology, Columbia University Medical Center, New York
- Department of Pathology, Columbia University Medical Center, New York
| | - Anna Lasorella
- Institute for Cancer Genetics, Columbia University Medical Center, New York
- Department of Pathology, Columbia University Medical Center, New York
- Department of Pediatrics, Columbia University Medical Center, New York
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14
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MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation. J Immunol Res 2015; 2015:140819. [PMID: 26881263 PMCID: PMC4735977 DOI: 10.1155/2015/140819] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 11/09/2015] [Indexed: 01/08/2023] Open
Abstract
Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.
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