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Cole F, Pfeiffer M, Wang D, Schröder T, Ke Y, Tinnefeld P. Controlled mechanochemical coupling of anti-junctions in DNA origami arrays. Nat Commun 2024; 15:7894. [PMID: 39256353 PMCID: PMC11387415 DOI: 10.1038/s41467-024-51721-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 08/16/2024] [Indexed: 09/12/2024] Open
Abstract
Allostery is a hallmark of cellular function and important in every biological system. Still, we are only starting to mimic it in the laboratory. Here, we introduce an approach to study aspects of allostery in artificial systems. We use a DNA origami domino array structure which-upon binding of trigger DNA strands-undergoes a stepwise allosteric conformational change. Using two FRET probes placed at specific positions in the DNA origami, we zoom in into single steps of this reaction cascade. Most of the steps are strongly coupled temporally and occur simultaneously. Introduction of activation energy barriers between different intermediate states alters this coupling and induces a time delay. We then apply these approaches to release a cargo DNA strand at a predefined step in the reaction cascade to demonstrate the applicability of this concept in tunable cascades of mechanochemical coupling with both spatial and temporal control.
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Affiliation(s)
- Fiona Cole
- Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, München, Germany
- Center for NanoScience, Ludwig-Maximilians-Universität München, Schellingstraße 4, München, Germany
| | - Martina Pfeiffer
- Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, München, Germany
- Center for NanoScience, Ludwig-Maximilians-Universität München, Schellingstraße 4, München, Germany
| | - Dongfang Wang
- Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Georgia Institute of Technology, Atlanta, GA, USA
- School of Biomedical Engineering, University of Science and Technology of China, Suzhou, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
| | - Tim Schröder
- Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, München, Germany
- Center for NanoScience, Ludwig-Maximilians-Universität München, Schellingstraße 4, München, Germany
| | - Yonggang Ke
- Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
- Georgia Institute of Technology, Atlanta, GA, USA.
| | - Philip Tinnefeld
- Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, München, Germany.
- Center for NanoScience, Ludwig-Maximilians-Universität München, Schellingstraße 4, München, Germany.
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2
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Priego Espinosa D, Espinal-Enríquez J, Aldana A, Aldana M, Martínez-Mekler G, Carneiro J, Darszon A. Reviewing mathematical models of sperm signaling networks. Mol Reprod Dev 2024; 91:e23766. [PMID: 39175359 DOI: 10.1002/mrd.23766] [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: 05/17/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Dave Garbers' work significantly contributed to our understanding of sperm's regulated motility, capacitation, and the acrosome reaction. These key sperm functions involve complex multistep signaling pathways engaging numerous finely orchestrated elements. Despite significant progress, many parameters and interactions among these elements remain elusive. Mathematical modeling emerges as a potent tool to study sperm physiology, providing a framework to integrate experimental results and capture functional dynamics considering biochemical, biophysical, and cellular elements. Depending on research objectives, different modeling strategies, broadly categorized into continuous and discrete approaches, reveal valuable insights into cell function. These models allow the exploration of hypotheses regarding molecules, conditions, and pathways, whenever they become challenging to evaluate experimentally. This review presents an overview of current theoretical and experimental efforts to understand sperm motility regulation, capacitation, and the acrosome reaction. We discuss the strengths and weaknesses of different modeling strategies and highlight key findings and unresolved questions. Notable discoveries include the importance of specific ion channels, the role of intracellular molecular heterogeneity in capacitation and the acrosome reaction, and the impact of pH changes on acrosomal exocytosis. Ultimately, this review underscores the crucial importance of mathematical frameworks in advancing our understanding of sperm physiology and guiding future experimental investigations.
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Affiliation(s)
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | - Andrés Aldana
- Network Science Institute, Northeastern University, Boston, Massachusetts, USA
| | - Maximino Aldana
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, México
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Gustavo Martínez-Mekler
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, México
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Jorge Carneiro
- Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Alberto Darszon
- Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, México
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3
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Wang H, Zhang X, Liu Y, Zhou S. A nicking enzyme-assisted allosteric strategy for self-resetting DNA switching circuits. Analyst 2023; 149:169-179. [PMID: 37999719 DOI: 10.1039/d3an01677c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
The self-regulation of biochemical reaction networks is crucial for maintaining balance, stability, and adaptability within biological systems. DNA switching circuits, serving as basic units, play essential roles in regulating pathways, facilitating signal transduction, and processing biochemical reaction networks. However, the non-reusability of DNA switching circuits hinders its application in current complex information processing. Herein, we proposed a nicking enzyme-assisted allosteric strategy for constructing self-resetting DNA switching circuits to realize complex information processing. This strategy utilizes the unique cleavage ability of the nicking enzyme to achieve the automatic restoration of states. Based on this strategy, we implemented a self-resetting DNA switch. By leveraging the reusability of the DNA switch, we constructed a DNA switching circuit with selective activation characteristics and further extended its functionality to include fan-out and fan-in processes by expanding the number of functional modules and connection modes. Furthermore, we demonstrated the complex information processing capabilities of these switching circuits by integrating recognition, translation, and decision functional modules, which could analyze and transmit multiple input signals and realize parallel logic operations. This strategy simplifies the design of switching circuits and promotes the future development of biosensing, molecular computing, and nanomachines.
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Affiliation(s)
- Haoliang Wang
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian 116622, China.
| | - Xiaokang Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Yuan Liu
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
| | - Shihua Zhou
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian 116622, China.
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4
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Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
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5
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Almowallad S, Alqahtani LS, Mobashir M. NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases. LIFE (BASEL, SWITZERLAND) 2022; 12:life12122012. [PMID: 36556376 PMCID: PMC9788026 DOI: 10.3390/life12122012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells' survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.
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Affiliation(s)
- Sanaa Almowallad
- Department of Biochemistry, Faculty of Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Leena S. Alqahtani
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah 23445, Saudi Arabia
- Correspondence: (L.S.A.); (M.M.)
| | - Mohammad Mobashir
- SciLifeLab, Department of Oncology and Pathology, Karolinska Institutet, P.O. Box 1031, S-17121 Stockholm, Sweden
- Department of Biosciences, Faculty of Natural Science, Jamia Millia Islamia, New Delhi 110025, India
- Special Infectious Agents Unit—BSL3, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21362, Saudi Arabia
- Correspondence: (L.S.A.); (M.M.)
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6
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Alkhatib H, Rubinstein AM, Vasudevan S, Flashner-Abramson E, Stefansky S, Chowdhury SR, Oguche S, Peretz-Yablonsky T, Granit A, Granot Z, Ben-Porath I, Sheva K, Feldman J, Cohen NE, Meirovitz A, Kravchenko-Balasha N. Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance. Genome Med 2022; 14:120. [PMID: 36266692 PMCID: PMC9583500 DOI: 10.1186/s13073-022-01121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.
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Affiliation(s)
- Heba Alkhatib
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Ariel M Rubinstein
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Swetha Vasudevan
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Efrat Flashner-Abramson
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Shira Stefansky
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Sangita Roy Chowdhury
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Solomon Oguche
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel
| | - Tamar Peretz-Yablonsky
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Avital Granit
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Zvi Granot
- Department of Developmental Biology and Cancer Research, Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, 91120, Jerusalem, Israel
| | - Ittai Ben-Porath
- Department of Developmental Biology and Cancer Research, Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, 91120, Jerusalem, Israel
| | - Kim Sheva
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev, Faculty of Medicine, 8410101, Beer Sheva, Israel
| | - Jon Feldman
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, 9103401, Jerusalem, Israel
| | - Noa E Cohen
- School of Software Engineering and Computer Science, Azrieli College of Engineering, 9103501, Jerusalem, Israel
| | - Amichay Meirovitz
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev, Faculty of Medicine, 8410101, Beer Sheva, Israel.
| | - Nataly Kravchenko-Balasha
- The institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, 9103401, Jerusalem, Israel.
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7
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Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups. BIOTECH 2022; 11:biotech11030035. [PMID: 35997343 PMCID: PMC9397027 DOI: 10.3390/biotech11030035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.
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8
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Cui MR, Chen Y, Zhu D, Chao J. Intelligent Programmable DNA Nanomachines for the Spatially Controllable Imaging of Intracellular MicroRNA. Anal Chem 2022; 94:10874-10884. [PMID: 35856834 DOI: 10.1021/acs.analchem.2c02299] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The high programmability of DNA molecules makes them particularly suitable for constructing artificial molecular machines to perform sophisticated functions by simulating complex living systems. However, intelligent DNA nanomachines which can perform precise tasks logically in complex environments still remain challenging. Herein, we develop a general strategy to design a pH-responsive programmable DNA (PRPD) nanomachine to perform multilayer DNA cascades, enabling precise sensing and calculation of intracellular biomolecules. The PRPD nanomachine is built on a four-stranded DNAzyme walker precursor with a DNA switch on the surface of an Au nanoparticle, which is capable of precisely responding to pH variations in living cells by sequence tuning. This multilayer DNA cascade networks have been applicated in spatially controlled imaging of intracellular microRNA, which efficiently avoided the DNA nanomachine activated by nonspecific extracellular molecules and achieved apparent signal amplification. Our strategy enables the sensing-computing-output functional integration of DNA nanomachines, facilitating the application of programmable and complex nanomachines in nanoengineering, chemistry, and biomedicine.
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Affiliation(s)
- Mei-Rong Cui
- Key Laboratory for Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing 210023, P. R. China
| | - Yan Chen
- Key Laboratory for Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing 210023, P. R. China
| | - Dan Zhu
- Key Laboratory for Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing 210023, P. R. China
| | - Jie Chao
- Key Laboratory for Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing 210023, P. R. China
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9
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Ying T, Alexander H. Quantifying information of intracellular signaling: progress with machine learning. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:10.1088/1361-6633/ac7a4a. [PMID: 35724636 PMCID: PMC9507437 DOI: 10.1088/1361-6633/ac7a4a] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.
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Affiliation(s)
- Tang Ying
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA 90095, USA
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai 519087, China
| | - Hoffmann Alexander
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA 90095, USA
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
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10
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Zhang C, Ma X, Zheng X, Ke Y, Chen K, Liu D, Lu Z, Yang J, Yan H. Programmable allosteric DNA regulations for molecular networks and nanomachines. SCIENCE ADVANCES 2022; 8:eabl4589. [PMID: 35108052 PMCID: PMC8809682 DOI: 10.1126/sciadv.abl4589] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Structure-based molecular regulations have been widely adopted to modulate protein networks in cells and recently developed to control allosteric DNA operations in vitro. However, current examples of programmable allosteric signal transmission through integrated DNA networks are stringently constrained by specific design requirements. Developing a new, more general, and programmable scheme for establishing allosteric DNA networks remains challenging. Here, we developed a general strategy for programmable allosteric DNA regulations that can be finely tuned by varying the dimensions, positions, and number of conformational signals. By programming the allosteric signals, we realized fan-out/fan-in DNA gates and multiple-layer DNA cascading networks, as well as expanding the approach to long-range allosteric signal transmission through tunable DNA origami nanomachines ~100 nm in size. This strategy will enable programmable and complex allosteric DNA networks and nanodevices for nanoengineering, chemical, and biomedical applications displaying sense-compute-actuate molecular functionalities.
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Affiliation(s)
- Cheng Zhang
- School of Computer Science, Key Lab of High Confidence Software Technologies, Peking University, Beijing 100871, China
- Corresponding author. (C.Z.); (J.Y.); (H.Y.)
| | - Xueying Ma
- School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
- Bio-evidence Sciences Academy, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
| | - Xuedong Zheng
- College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
| | - Yonggang Ke
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Chemistry, Emory University, Atlanta, GA 30322, USA
| | - Kuiting Chen
- School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
| | - Dongsheng Liu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Zuhong Lu
- The State Key Laboratory of Bioelectronics, Southeast University, Nanjing 211189, China
| | - Jing Yang
- School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
- Corresponding author. (C.Z.); (J.Y.); (H.Y.)
| | - Hao Yan
- Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- Corresponding author. (C.Z.); (J.Y.); (H.Y.)
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11
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Haga M, Okada M. Systems approaches to investigate the role of NF-κB signaling in aging. Biochem J 2022; 479:161-183. [PMID: 35098992 PMCID: PMC8883486 DOI: 10.1042/bcj20210547] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/06/2022] [Accepted: 01/10/2022] [Indexed: 12/14/2022]
Abstract
The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.
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Affiliation(s)
- Masatoshi Haga
- Laboratory for Cell Systems, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Basic Research Development Division, ROHTO Pharmaceutical Co., Ltd., Ikuno-ku, Osaka 544-8666, Japan
| | - Mariko Okada
- Laboratory for Cell Systems, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan
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12
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Kearney AL, Norris DM, Ghomlaghi M, Kin Lok Wong M, Humphrey SJ, Carroll L, Yang G, Cooke KC, Yang P, Geddes TA, Shin S, Fazakerley DJ, Nguyen LK, James DE, Burchfield JG. Akt phosphorylates insulin receptor substrate to limit PI3K-mediated PIP3 synthesis. eLife 2021; 10:e66942. [PMID: 34253290 PMCID: PMC8277355 DOI: 10.7554/elife.66942] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/30/2021] [Indexed: 01/16/2023] Open
Abstract
The phosphoinositide 3-kinase (PI3K)-Akt network is tightly controlled by feedback mechanisms that regulate signal flow and ensure signal fidelity. A rapid overshoot in insulin-stimulated recruitment of Akt to the plasma membrane has previously been reported, which is indicative of negative feedback operating on acute timescales. Here, we show that Akt itself engages this negative feedback by phosphorylating insulin receptor substrate (IRS) 1 and 2 on a number of residues. Phosphorylation results in the depletion of plasma membrane-localised IRS1/2, reducing the pool available for interaction with the insulin receptor. Together these events limit plasma membrane-associated PI3K and phosphatidylinositol (3,4,5)-trisphosphate (PIP3) synthesis. We identified two Akt-dependent phosphorylation sites in IRS2 at S306 (S303 in mouse) and S577 (S573 in mouse) that are key drivers of this negative feedback. These findings establish a novel mechanism by which the kinase Akt acutely controls PIP3 abundance, through post-translational modification of the IRS scaffold.
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Affiliation(s)
- Alison L Kearney
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Dougall M Norris
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of CambridgeCambridgeUnited Kingdom
| | - Milad Ghomlaghi
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash UniversityClaytonAustralia
- Biomedicine Discovery Institute, Monash UniversityClaytonAustralia
| | - Martin Kin Lok Wong
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Sean J Humphrey
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Luke Carroll
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Guang Yang
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Kristen C Cooke
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Pengyi Yang
- Charles Perkins Centre, School of Mathematics and Statistics, University of SydneySydneyAustralia
- Computational Systems Biology Group, Children's Medical Research Institute, University of SydneyWestmeadAustralia
| | - Thomas A Geddes
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
- Computational Systems Biology Group, Children's Medical Research Institute, University of SydneyWestmeadAustralia
| | - Sungyoung Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash UniversityClaytonAustralia
- Biomedicine Discovery Institute, Monash UniversityClaytonAustralia
| | - Daniel J Fazakerley
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of CambridgeCambridgeUnited Kingdom
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash UniversityClaytonAustralia
- Biomedicine Discovery Institute, Monash UniversityClaytonAustralia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
- School of Medical Sciences, University of SydneySydneyAustralia
| | - James G Burchfield
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
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13
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Prugger M, Einkemmer L, Beik SP, Wasdin PT, Harris LA, Lopez CF. Unsupervised logic-based mechanism inference for network-driven biological processes. PLoS Comput Biol 2021; 17:e1009035. [PMID: 34077417 PMCID: PMC8202945 DOI: 10.1371/journal.pcbi.1009035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/14/2021] [Accepted: 05/03/2021] [Indexed: 01/21/2023] Open
Abstract
Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.
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Affiliation(s)
- Martina Prugger
- Department of Biochemistry, University of Innsbruck, Innsbruck, Austria
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Lukas Einkemmer
- Department of Mathematics, University of Innsbruck, Innsbruck, Austria
| | - Samantha P. Beik
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Perry T. Wasdin
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, United States of America
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, Arkansas, United States of America
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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14
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Selvaggio G, Chaouiya C, Janody F. In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer. Int J Mol Sci 2021; 22:ijms22094897. [PMID: 34063110 PMCID: PMC8125147 DOI: 10.3390/ijms22094897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/13/2022] Open
Abstract
The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.
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Affiliation(s)
- Gianluca Selvaggio
- Fondazione the Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy;
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- CNRS, Centrale Marseille, I2M, Aix Marseille University, 13397 Marseille, France
- Correspondence: (C.C.); (F.J.)
| | - Florence Janody
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
- IPATIMUP—Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
- Correspondence: (C.C.); (F.J.)
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15
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Affiliation(s)
- Takashi Nakakuki
- Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology
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16
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Ricard N, Bailly S, Guignabert C, Simons M. The quiescent endothelium: signalling pathways regulating organ-specific endothelial normalcy. Nat Rev Cardiol 2021; 18:565-580. [PMID: 33627876 PMCID: PMC7903932 DOI: 10.1038/s41569-021-00517-4] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/18/2021] [Indexed: 02/07/2023]
Abstract
Endothelial cells are at the interface between circulating blood and tissues. This position confers on them a crucial role in controlling oxygen and nutrient exchange and cellular trafficking between blood and the perfused organs. The endothelium adopts a structure that is specific to the needs and function of each tissue and organ and is subject to tissue-specific signalling input. In adults, endothelial cells are quiescent, meaning that they are not proliferating. Quiescence was considered to be a state in which endothelial cells are not stimulated but are instead slumbering and awaiting activating signals. However, new evidence shows that quiescent endothelium is fully awake, that it constantly receives and initiates functionally important signalling inputs and that this state is actively regulated. Signalling pathways involved in the maintenance of functionally quiescent endothelia are starting to be identified and are a combination of endocrine, autocrine, paracrine and mechanical inputs. The paracrine pathways confer a microenvironment on the endothelial cells that is specific to the perfused organs and tissues. In this Review, we present the current knowledge of organ-specific signalling pathways involved in the maintenance of endothelial quiescence and the pathologies associated with their disruption. Linking organ-specific pathways and human vascular pathologies will pave the way towards the development of innovative preventive strategies and the identification of new therapeutic targets.
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Affiliation(s)
- Nicolas Ricard
- grid.47100.320000000419368710Yale Cardiovascular Research Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT USA
| | - Sabine Bailly
- grid.457348.9Université Grenoble Alpes, INSERM, CEA, BIG-Biologie du Cancer et de l’Infection, Grenoble, France
| | - Christophe Guignabert
- grid.414221.0INSERM UMR_S 999, Pulmonary Hypertension: Pathophysiology and Novel Therapies, Hôpital Marie Lannelongue, Le Plessis-Robinson, France ,grid.460789.40000 0004 4910 6535Université Paris-Saclay, Faculté de Médecine, Le Kremlin-Bicêtre, France
| | - Michael Simons
- grid.47100.320000000419368710Yale Cardiovascular Research Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Department of Cell Biology, Yale University School of Medicine, New Haven, CT USA
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17
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Abstract
MOTIVATION Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. RESULTS We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. AVAILABILITY AND IMPLEMENTATION Find the full codebase at https://github.com/gitter-lab/ssps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Merrell
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Anthony Gitter
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53726, USA
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18
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Pope RJ, Garner KL, Voliotis M, Lay AC, Betin VM, Tsaneva-Atanasova K, Welsh GI, Coward RJ, McArdle CA. An information theoretic approach to insulin sensing by human kidney podocytes. Mol Cell Endocrinol 2020; 518:110976. [PMID: 32750396 DOI: 10.1016/j.mce.2020.110976] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/29/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022]
Abstract
Podocytes are key components of the glomerular filtration barrier (GFB). They are insulin-responsive but can become insulin-resistant, causing features of the leading global cause of kidney failure, diabetic nephropathy. Insulin acts via insulin receptors to control activities fundamental to GFB integrity, but the amount of information transferred is unknown. Here we measure this in human podocytes, using information theory-derived statistics that take into account cell-cell variability. High content imaging was used to measure insulin effects on Akt, FOXO and ERK. Mutual Information (MI) and Channel Capacity (CC) were calculated as measures of information transfer. We find that insulin acts via noisy communication channels with more information flow to Akt than to ERK. Information flow estimates were increased by consideration of joint sensing (ERK and Akt) and response trajectory (live cell imaging of FOXO1-clover translocation). Nevertheless, MI values were always <1Bit as most information was lost through signaling. Constitutive PI3K activity is a predominant feature of the system that restricts the proportion of CC engaged by insulin. Negative feedback from Akt supressed this activity and thereby improved insulin sensing, whereas sensing was robust to manipulation of feedforward signaling by inhibiting PI3K, PTEN or PTP1B. The decisions made by individual podocytes dictate GFB integrity, so we suggest that understanding the information on which the decisions are based will improve understanding of diabetic kidney disease and its treatment.
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Affiliation(s)
- Robert Jp Pope
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Kathryn L Garner
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Margaritis Voliotis
- College of Engineering, Mathematics and Physical Sciences, Living Systems Institute, University of Exeter, Exeter, EX44QF, UK
| | - Abigail C Lay
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Virginie Ms Betin
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Krasimira Tsaneva-Atanasova
- College of Engineering, Mathematics and Physical Sciences, Living Systems Institute, University of Exeter, Exeter, EX44QF, UK
| | - Gavin I Welsh
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Richard Jm Coward
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK
| | - Craig A McArdle
- Bristol Renal, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK; Labs. for Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, Bristol, BS13NY, UK.
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19
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A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway. Cancers (Basel) 2020; 12:cancers12102878. [PMID: 33036375 PMCID: PMC7650612 DOI: 10.3390/cancers12102878] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/24/2020] [Accepted: 09/24/2020] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine. Abstract A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.
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20
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Rinschen MM, Saez-Rodriguez J. The tissue proteome in the multi-omic landscape of kidney disease. Nat Rev Nephrol 2020; 17:205-219. [PMID: 33028957 DOI: 10.1038/s41581-020-00348-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.
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Affiliation(s)
- Markus M Rinschen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark. .,III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany. .,Department of Chemistry, Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research, La Jolla, CA, USA.
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany
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21
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Hao Shi, Yan KK, Ding L, Qian C, Chi H, Yu J. Network Approaches for Dissecting the Immune System. iScience 2020; 23:101354. [PMID: 32717640 PMCID: PMC7390880 DOI: 10.1016/j.isci.2020.101354] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/21/2020] [Accepted: 07/08/2020] [Indexed: 02/06/2023] Open
Abstract
The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.
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Affiliation(s)
- Hao Shi
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Koon-Kiu Yan
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Liang Ding
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Chenxi Qian
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jiyang Yu
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
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22
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Burke PEP, Campos CBDL, Costa LDF, Quiles MG. A biochemical network modeling of a whole-cell. Sci Rep 2020; 10:13303. [PMID: 32764598 PMCID: PMC7411072 DOI: 10.1038/s41598-020-70145-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 07/23/2020] [Indexed: 01/18/2023] Open
Abstract
All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.
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Affiliation(s)
- Paulo E P Burke
- University of São Paulo, Bioinformatics Graduate Program, São Carlos, SP, Brazil.
| | - Claudia B de L Campos
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
| | - Luciano da F Costa
- São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil
| | - Marcos G Quiles
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
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23
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Sarkar S, García AE. Presence or Absence of Ras Dimerization Shows Distinct Kinetic Signature in Ras-Raf Interaction. Biophys J 2020; 118:1799-1810. [PMID: 32199071 DOI: 10.1016/j.bpj.2020.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 12/30/2019] [Accepted: 03/02/2020] [Indexed: 02/07/2023] Open
Abstract
Initiations of cell signaling pathways often occur through the formation of multiprotein complexes that form through protein-protein interactions. Therefore, detecting their presence is central to understanding the function of a cell signaling pathway, aberration of which often leads to fatal diseases, including cancers. However, the multiprotein complexes are often difficult to detect using microscopes due to their small sizes. Therefore, currently, their presence can be only detected through indirect means. In this article, we propose to investigate the presence or absence of protein complexes through some easily measurable kinetic parameters, such as activation rates. As a proof of concept, we investigate the Ras-Raf system, a well-characterized cell signaling system. It has been hypothesized that Ras dimerization is necessary to create activated Raf dimers. Although there are circumstantial evidences supporting the Ras dimerization hypothesis, direct proof of Ras dimerization is still inconclusive. In the absence of conclusive direct experimental proof, this hypothesis can only be examined through indirect evidences of Ras dimerization. In this article, using a multiscale simulation technique, we provide multiple criteria that distinguishes an activation mechanism involving Ras dimerization from another mechanism that does not involve Ras dimerization. The provided criteria will be useful in the investigation of not only Ras-Raf interaction but also other two-protein interactions.
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Affiliation(s)
- Sumantra Sarkar
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Angel E García
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico.
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24
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Kravchenko-Balasha N. Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches. Proteomics 2020; 20:e1900227. [PMID: 32072740 DOI: 10.1002/pmic.201900227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 01/13/2020] [Indexed: 12/17/2022]
Abstract
Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.
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Affiliation(s)
- Nataly Kravchenko-Balasha
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, 91120, Israel
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25
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Graham G, Csicsery N, Stasiowski E, Thouvenin G, Mather WH, Ferry M, Cookson S, Hasty J. Genome-scale transcriptional dynamics and environmental biosensing. Proc Natl Acad Sci U S A 2020; 117:3301-3306. [PMID: 31974311 PMCID: PMC7022183 DOI: 10.1073/pnas.1913003117] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisely react to their environment. The importance of complex feedback in controlling the real-time response to external stimuli has led to a need for the next generation of cell-based technologies that enable both the collection and analysis of high-throughput temporal data. Toward this end, we have developed a microfluidic platform capable of monitoring temporal gene expression from over 2,000 promoters. By coupling the "Dynomics" platform with deep neural network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how machine learning can be harnessed to assess patterns in transcriptional data on a genome scale and identify which genes contribute to these patterns. Furthermore, we demonstrate the utility of the Dynomics platform as a field-deployable real-time biosensor through prediction of the presence of heavy metals in urban water and mine spill samples, based on the the dynamic transcription profiles of 1,807 unique Escherichia coli promoters.
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Affiliation(s)
- Garrett Graham
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
| | - Nicholas Csicsery
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
| | - Elizabeth Stasiowski
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
| | - Gregoire Thouvenin
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
| | | | | | | | - Jeff Hasty
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093;
- Quantitative BioSciences, Inc., San Diego, CA 92121
- Molecular Biology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093
- BioCircuits Institute, University of California San Diego, La Jolla, CA 92093
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26
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Targeting MAPK Signaling in Cancer: Mechanisms of Drug Resistance and Sensitivity. Int J Mol Sci 2020; 21:ijms21031102. [PMID: 32046099 PMCID: PMC7037308 DOI: 10.3390/ijms21031102] [Citation(s) in RCA: 393] [Impact Index Per Article: 98.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 02/04/2020] [Accepted: 02/05/2020] [Indexed: 12/12/2022] Open
Abstract
Mitogen-activated protein kinase (MAPK) pathways represent ubiquitous signal transduction pathways that regulate all aspects of life and are frequently altered in disease. Here, we focus on the role of MAPK pathways in modulating drug sensitivity and resistance in cancer. We briefly discuss new findings in the extracellular signaling-regulated kinase (ERK) pathway, but mainly focus on the mechanisms how stress activated MAPK pathways, such as p38 MAPK and the Jun N-terminal kinases (JNK), impact the response of cancer cells to chemotherapies and targeted therapies. In this context, we also discuss the role of metabolic and epigenetic aberrations and new therapeutic opportunities arising from these changes.
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Bag AK, Mandloi S, Jarmalavicius S, Mondal S, Kumar K, Mandal C, Walden P, Chakrabarti S, Mandal C. Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma. PLoS Comput Biol 2019; 15:e1007090. [PMID: 31386654 PMCID: PMC6684045 DOI: 10.1371/journal.pcbi.1007090] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/13/2019] [Indexed: 12/21/2022] Open
Abstract
As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies. Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Therefore, understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics. We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach. Here we report the development, testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer. Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow. We predicted some potential novel targets before performing actual drug tests. We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme.
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Affiliation(s)
- Arup K. Bag
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Sapan Mandloi
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Saulius Jarmalavicius
- Department of Dermatology, Venerology and Allergology, Charité– Universitätsmedizin Berlin corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Susmita Mondal
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Krishna Kumar
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Chhabinath Mandal
- National Institute of Pharmaceutical Education and Research, Kolkata, India
| | - Peter Walden
- Department of Dermatology, Venerology and Allergology, Charité– Universitätsmedizin Berlin corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- * E-mail: (PW); , (SC); , (CM)
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
- * E-mail: (PW); , (SC); , (CM)
| | - Chitra Mandal
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
- * E-mail: (PW); , (SC); , (CM)
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28
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Lo Surdo P, Calderone A, Iannuccelli M, Licata L, Peluso D, Castagnoli L, Cesareni G, Perfetto L. DISNOR: a disease network open resource. Nucleic Acids Res 2019; 46:D527-D534. [PMID: 29036667 PMCID: PMC5753342 DOI: 10.1093/nar/gkx876] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 09/25/2017] [Indexed: 12/13/2022] Open
Abstract
DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred ‘patho-pathways’ at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes—either annotated in DisGeNET or user-defined—DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity.
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Affiliation(s)
- Prisca Lo Surdo
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Alberto Calderone
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Marta Iannuccelli
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Luana Licata
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Daniele Peluso
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy.,Laboratory of Bioinformatic, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
| | - Luisa Castagnoli
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Gianni Cesareni
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Livia Perfetto
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
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Lill D, Rukhlenko OS, Mc Elwee AJ, Kashdan E, Timmer J, Kholodenko BN. Mapping connections in signaling networks with ambiguous modularity. NPJ Syst Biol Appl 2019; 5:19. [PMID: 31149348 PMCID: PMC6533310 DOI: 10.1038/s41540-019-0096-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.
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Affiliation(s)
- Daniel Lill
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | | | - Eugene Kashdan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Jens Timmer
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT USA
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30
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Zhang W, Li W, Zhang J, Wang N. Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190104142228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background:
Gene Regulatory Network (GRN) inference algorithms aim to explore
casual interactions between genes and transcriptional factors. High-throughput transcriptomics
data including DNA microarray and single cell expression data contain complementary
information in network inference.
Objective:
To enhance GRN inference, data integration across various types of expression data
becomes an economic and efficient solution.
Method:
In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is
proposed to merge complementary information from microarray and single cell expression data.
This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute
importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively
evaluates the credibility levels of each information source and determines the final ranked list.
Results:
Two groups of in silico gene networks are applied to illustrate the effectiveness of the
proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene
networks suggest that the proposed E-alpha rule significantly improves performance metrics
compared with single information source.
Conclusion:
In GRN inference, the integration of hybrid expression data using E-alpha rule
provides a feasible and efficient way to enhance performance metrics than solely increasing
sample sizes.
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Affiliation(s)
- Wei Zhang
- Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, China
| | - Wenchao Li
- Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, China
| | - Jianming Zhang
- Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, China
| | - Ning Wang
- Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, China
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31
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Komatsubara AT, Goto Y, Kondo Y, Matsuda M, Aoki K. Single-cell quantification of the concentrations and dissociation constants of endogenous proteins. J Biol Chem 2019; 294:6062-6072. [PMID: 30739083 DOI: 10.1074/jbc.ra119.007685] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 01/30/2019] [Indexed: 01/23/2023] Open
Abstract
Kinetic simulation is a useful approach for elucidating complex cell-signaling systems. The numerical simulations required for kinetic modeling in live cells critically require parameters such as protein concentrations and dissociation constants (Kd ). However, only a limited number of parameters have been measured experimentally in living cells. Here we describe an approach for quantifying the concentration and Kd of endogenous proteins at the single-cell level with CRISPR/Cas9-mediated knock-in and fluorescence cross-correlation spectroscopy. First, the mEGFP gene was knocked in at the end of the mitogen-activated protein kinase 1 (MAPK1) gene, encoding extracellular signal-regulated kinase 2 (ERK2), through homology-directed repair or microhomology-mediated end joining. Next, the HaloTag gene was knocked in at the end of the ribosomal S6 kinase 2 (RSK2) gene. We then used fluorescence correlation spectroscopy to measure the protein concentrations of endogenous ERK2-mEGFP and RSK2-HaloTag fusion constructs in living cells, revealing substantial heterogeneities. Moreover, fluorescence cross-correlation spectroscopy analyses revealed temporal changes in the apparent Kd values of the binding between ERK2-mEGFP and RSK2-HaloTag in response to epidermal growth factor stimulation. Our approach presented here provides a robust and efficient method for quantifying endogenous protein concentrations and dissociation constants in living cells.
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Affiliation(s)
- Akira T Komatsubara
- From the Laboratory of Bioimaging and Cell Signaling, Graduate School of Biostudies, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; the Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yuhei Goto
- the Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; the Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan
| | - Yohei Kondo
- the Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; the Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; the Imaging Platform for Spatio-Temporal Information, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; the Department of Basic Biology, Faculty of Life Science, SOKENDAI (Graduate University for Advanced Studies), Myodaiji, Okazaki, Aichi 444-8787, Japan
| | - Michiyuki Matsuda
- From the Laboratory of Bioimaging and Cell Signaling, Graduate School of Biostudies, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; the Department of Pathology and Biology of Diseases, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Kazuhiro Aoki
- the Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; the Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi 444-8787, Japan; the Imaging Platform for Spatio-Temporal Information, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; the Department of Basic Biology, Faculty of Life Science, SOKENDAI (Graduate University for Advanced Studies), Myodaiji, Okazaki, Aichi 444-8787, Japan.
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32
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Hidalgo MR, Amadoz A, Çubuk C, Carbonell-Caballero J, Dopazo J. Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome. Biol Direct 2018; 13:16. [PMID: 30134948 PMCID: PMC6106876 DOI: 10.1186/s13062-018-0219-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 08/08/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases. RESULTS Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression. CONCLUSION We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker. REVIEWERS This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.
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Affiliation(s)
- Marta R Hidalgo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain
| | - Alicia Amadoz
- Igenomix S.A. Ronda Narciso Monturiol, 11 B, Parque Tecnológico Paterna, 46980, Paterna, Valencia, Spain
| | - Cankut Çubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain
| | | | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain. .,Functional Genomics Node (INB). FPS, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain. .,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain.
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33
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Yvinec R, Crépieux P, Reiter E, Poupon A, Clément F. Advances in computational modeling approaches of pituitary gonadotropin signaling. Expert Opin Drug Discov 2018; 13:799-813. [DOI: 10.1080/17460441.2018.1501025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Romain Yvinec
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | | | - Eric Reiter
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | - Anne Poupon
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | - Frédérique Clément
- Inria, Université Paris-Saclay, Palaiseau, France
- LMS, Ecole Polytechnique, CNRS, Université Paris-Saclay, Palaiseau, France
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34
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Gintant GA, George CH. Introduction to biological complexity as a missing link in drug discovery. Expert Opin Drug Discov 2018; 13:753-763. [PMID: 29871539 DOI: 10.1080/17460441.2018.1480608] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Despite a burgeoning knowledge of the intricacies and mechanisms responsible for human disease, technological advances in medicinal chemistry, and more efficient assays used for drug screening, it remains difficult to discover novel and effective pharmacologic therapies. Areas covered: By reference to the primary literature and concepts emerging from academic and industrial drug screening landscapes, the authors propose that this disconnect arises from the inability to scale and integrate responses from simpler model systems to outcomes from more complex and human-based biological systems. Expert opinion: Further collaborative efforts combining target-based and phenotypic-based screening along with systems-based pharmacology and informatics will be necessary to harness the technological breakthroughs of today to derive the novel drug candidates of tomorrow. New questions must be asked of enabling technologies-while recognizing inherent limitations-in a way that moves drug development forward. Attempts to integrate mechanistic and observational information acquired across multiple scales frequently expose the gap between our knowledge and our understanding as the level of complexity increases. We hope that the thoughts and actionable items highlighted will help to inform the directed evolution of the drug discovery process.
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Affiliation(s)
- Gary A Gintant
- a AbbVie, Department of Integrative Pharmacology , Integrated Science and Technology , North Chicago , IL , USA
| | - Christopher H George
- b Molecular Cardiology, Institute of Life Sciences , Swansea University Medical School , Swansea , Wales , UK
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35
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Botero D, Alvarado C, Bernal A, Danies G, Restrepo S. Network Analyses in Plant Pathogens. Front Microbiol 2018; 9:35. [PMID: 29441045 PMCID: PMC5797656 DOI: 10.3389/fmicb.2018.00035] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/09/2018] [Indexed: 11/14/2022] Open
Abstract
Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Camilo Alvarado
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Adriana Bernal
- Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Department of Design, Universidad de Los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
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Mondal M, Liao R, Guo J. Highly Multiplexed Single-Cell Protein Analysis. Chemistry 2018; 24:7083-7091. [PMID: 29194810 DOI: 10.1002/chem.201705014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Indexed: 12/17/2022]
Abstract
Single-cell proteomic analysis is crucial to advance our understanding of normal physiology and disease pathogenesis. The comprehensive protein profiling in individual cells of a heterogeneous sample can provide new insights into many important biological issues, such as the regulation of inter- and intracellular signaling pathways or the varied cellular compositions of normal and diseased tissues. With highly multiplexed molecular imaging of many different protein biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment. In this Minireview, we will describe the recent technological advances of single-cell proteomic assays, discuss their advantages and limitations, highlight their applications in biology and precision medicine, and present the current challenges and potential solutions.
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Affiliation(s)
- Manas Mondal
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona, 85287, USA
| | - Renjie Liao
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona, 85287, USA
| | - Jia Guo
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona, 85287, USA
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37
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Integrated Systems and Chemical Biology Approach for Targeted Therapies. Synth Biol (Oxf) 2018. [DOI: 10.1007/978-981-10-8693-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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38
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Follicle-Stimulating Hormone Receptor: Advances and Remaining Challenges. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2018; 338:1-58. [DOI: 10.1016/bs.ircmb.2018.02.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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39
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Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment. PLoS Comput Biol 2017; 13:e1005874. [PMID: 29267273 PMCID: PMC5739350 DOI: 10.1371/journal.pcbi.1005874] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 11/08/2017] [Indexed: 12/19/2022] Open
Abstract
Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies. One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.
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Wu Q, Finley SD. Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling. Cell Commun Signal 2017; 15:53. [PMID: 29258506 PMCID: PMC5735807 DOI: 10.1186/s12964-017-0207-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Accepted: 12/07/2017] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this function have not demonstrated clear clinical efficacy. This study explores strategies to enhance TSP1-induced apoptosis in endothelial cells. In particular, we focus on establishing a computational model to describe the signaling pathway, and using this model to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network. METHODS We constructed a molecularly-detailed mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor based on literature evidence. We employed systems biology tools to train and validate the model and further expanded the model by accounting for the heterogeneity within the cell population. The initial concentrations of signaling species or kinetic rates were altered to simulate the effects of perturbations to the signaling network. RESULTS Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species. CONCLUSIONS This new mathematical model recapitulates the intracellular dynamics of the TSP1-induced apoptosis signaling pathway. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.
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Affiliation(s)
- Qianhui Wu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.
- Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, USA.
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41
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Korcsmaros T, Schneider MV, Superti-Furga G. Next generation of network medicine: interdisciplinary signaling approaches. Integr Biol (Camb) 2017; 9:97-108. [PMID: 28106223 DOI: 10.1039/c6ib00215c] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.
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Affiliation(s)
- Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich, UK. and Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich, UK
| | | | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria and Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
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42
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Tsuchiya T, Fujii M, Matsuda N, Kunida K, Uda S, Kubota H, Konishi K, Kuroda S. System identification of signaling dependent gene expression with different time-scale data. PLoS Comput Biol 2017; 13:e1005913. [PMID: 29281625 PMCID: PMC5760096 DOI: 10.1371/journal.pcbi.1005913] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 01/09/2018] [Accepted: 12/01/2017] [Indexed: 01/11/2023] Open
Abstract
Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.
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Affiliation(s)
- Takaho Tsuchiya
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Masashi Fujii
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Naoki Matsuda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Katsuyuki Kunida
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Katsumi Konishi
- Department of Computer Science, Faculty of Informatics, Kogakuin University, Tokyo, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- CREST, Japan Science and Technology Corporation, Tokyo, Japan
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43
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Nev OA, Van Den Berg HA. Mathematical models of microbial growth and metabolism: a whole-organism perspective. Sci Prog 2017; 100:343-362. [PMID: 29113620 PMCID: PMC10365175 DOI: 10.3184/003685017x15063357842583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We review the principles underpinning the development of mathematical models of the metabolic activities of micro-organisms. Such models are important to understand and chart the substantial contributions made by micro-organisms to geochemical cycles, and also to optimise the performance of bioreactors that exploit the biochemical capabilities of these organisms. We advocate an approach based on the principle of dynamic allocation. We survey the biological background that motivates this approach, including nutrient assimilation, the regulation of gene expression, and the principles of microbial growth. In addition, we discuss the classic models of microbial growth as well as contemporary approaches. The dynamic allocation theory generalises these classic models in a natural manner and is readily amenable to the additional information provided by transcriptomics and proteomics approaches. Finally, we touch upon these organising principles in the context of the transition from the free-living unicellular mode of life to multicellularity.
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44
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Efficient synthesis of phycocyanobilin in mammalian cells for optogenetic control of cell signaling. Proc Natl Acad Sci U S A 2017; 114:11962-11967. [PMID: 29078307 DOI: 10.1073/pnas.1707190114] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Optogenetics is a powerful tool to precisely manipulate cell signaling in space and time. For example, protein activity can be regulated by several light-induced dimerization (LID) systems. Among them, the phytochrome B (PhyB)-phytochrome-interacting factor (PIF) system is the only available LID system controlled by red and far-red lights. However, the PhyB-PIF system requires phycocyanobilin (PCB) or phytochromobilin as a chromophore, which must be artificially added to mammalian cells. Here, we report an expression vector that coexpresses HO1 and PcyA with Ferredoxin and Ferredoxin-NADP+ reductase for the efficient synthesis of PCB in the mitochondria of mammalian cells. An even higher intracellular PCB concentration was achieved by the depletion of biliverdin reductase A, which degrades PCB. The PCB synthesis and PhyB-PIF systems allowed us to optogenetically regulate intracellular signaling without any external supply of chromophores. Thus, we have provided a practical method for developing a fully genetically encoded PhyB-PIF system, which paves the way for its application to a living animal.
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45
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Su Y, Shi Q, Wei W. Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis. Proteomics 2017; 17. [PMID: 28128880 DOI: 10.1002/pmic.201600267] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 01/20/2017] [Accepted: 01/23/2017] [Indexed: 11/11/2022]
Abstract
New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.
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Affiliation(s)
- Yapeng Su
- NanoSystems Biology Cancer Center, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Qihui Shi
- Key Laboratory of Systems Biomedicine (Ministry of Education), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wei
- NanoSystems Biology Cancer Center, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.,Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, CA, USA
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46
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Donati S, Sander T, Link H. Crosstalk between transcription and metabolism: how much enzyme is enough for a cell? WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017; 10. [DOI: 10.1002/wsbm.1396] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 06/20/2017] [Accepted: 07/05/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Stefano Donati
- Max Planck Institute for Terrestrial Microbiology; Marburg Germany
| | - Timur Sander
- Max Planck Institute for Terrestrial Microbiology; Marburg Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology; Marburg Germany
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47
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Skrzypczak T, Krela R, Kwiatkowski W, Wadurkar S, Smoczyńska A, Wojtaszek P. Plant Science View on Biohybrid Development. Front Bioeng Biotechnol 2017; 5:46. [PMID: 28856135 PMCID: PMC5558049 DOI: 10.3389/fbioe.2017.00046] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 07/24/2017] [Indexed: 01/07/2023] Open
Abstract
Biohybrid consists of a living organism or cell and at least one engineered component. Designing robot-plant biohybrids is a great challenge: it requires interdisciplinary reconsideration of capabilities intimate specific to the biology of plants. Envisioned advances should improve agricultural/horticultural/social practice and could open new directions in utilization of plants by humans. Proper biohybrid cooperation depends upon effective communication. During evolution, plants developed many ways to communicate with each other, with animals, and with microorganisms. The most notable examples are: the use of phytohormones, rapid long-distance signaling, gravity, and light perception. These processes can now be intentionally re-shaped to establish plant-robot communication. In this article, we focus on plants physiological and molecular processes that could be used in bio-hybrids. We show phototropism and biomechanics as promising ways of effective communication, resulting in an alteration in plant architecture, and discuss the specifics of plants anatomy, physiology and development with regards to the bio-hybrids. Moreover, we discuss ways how robots could influence plants growth and development and present aims, ideas, and realized projects of plant-robot biohybrids.
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Affiliation(s)
- Tomasz Skrzypczak
- Faculty of Biology, Department of Molecular and Cellular Biology, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Rafał Krela
- Faculty of Biology, Department of Molecular and Cellular Biology, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Wojciech Kwiatkowski
- Faculty of Biology, Department of Molecular and Cellular Biology, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Shraddha Wadurkar
- Faculty of Biology, Department of Molecular and Cellular Biology, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Aleksandra Smoczyńska
- Faculty of Biology, Department of Gene Expression, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Przemysław Wojtaszek
- Faculty of Biology, Department of Molecular and Cellular Biology, Adam Mickiewicz University in Poznań, Poznań, Poland
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48
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Mechanism-based biomarker discovery. Drug Discov Today 2017; 22:1209-1215. [DOI: 10.1016/j.drudis.2017.04.013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 04/12/2017] [Accepted: 04/20/2017] [Indexed: 11/22/2022]
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49
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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50
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A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease. G3-GENES GENOMES GENETICS 2017; 7:2271-2279. [PMID: 28500050 PMCID: PMC5499134 DOI: 10.1534/g3.117.042408] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.
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