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Duddu AS, Majumdar SS, Sahoo S, Jhunjhunwala S, Jolly MK. Emergent dynamics of a three-node regulatory network explain phenotypic switching and heterogeneity: a case study of Th1/Th2/Th17 cell differentiation. Mol Biol Cell 2022; 33:ar46. [PMID: 35353012 PMCID: PMC9265159 DOI: 10.1091/mbc.e21-10-0521] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Naïve helper (CD4+) T-cells can differentiate into distinct functional subsets including Th1, Th2, and Th17 phenotypes. Each of these phenotypes has a 'master regulator' - T-bet (Th1), GATA3 (Th2) and RORγT (Th17) - that inhibits the other two master regulators. Such mutual repression among them at a transcriptional level can enable multistability, giving rise to six experimentally observed phenotypes - Th1, Th2, Th17, hybrid Th/Th2, hybrid Th2/Th17 and hybrid Th1/Th17. However, the dynamics of switching among these phenotypes, particularly in the case of epigenetic influence, remains unclear. Here, through mathematical modeling, we investigated the coupled transcription-epigenetic dynamics in a three-node mutually repressing network to elucidate how epigenetic changes mediated by any 'master regulator' can influence the transition rates among different cellular phenotypes. We show that the degree of plasticity exhibited by one phenotype depends on relative strength and duration of mutual epigenetic repression mediated among the master regulators in a three-node network. Further, our model predictions can offer putative mechanisms underlying relatively higher plasticity of Th17 phenotype as observed in vitro and in vivo. Together, our modeling framework characterizes phenotypic plasticity and heterogeneity as an outcome of emergent dynamics of a three-node regulatory network, such as the one mediated by T-bet/GATA3/RORγT.
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Affiliation(s)
- Atchuta Srinivas Duddu
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Sauma Suvra Majumdar
- epartment of Biotechnology, National Institute of Technology, Durgapur 713216, India
| | - Sarthak Sahoo
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Siddharth Jhunjhunwala
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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Understanding the hidden relations between pro- and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method. Sci Rep 2019; 9:3189. [PMID: 30816156 PMCID: PMC6395797 DOI: 10.1038/s41598-019-39081-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 01/18/2019] [Indexed: 02/06/2023] Open
Abstract
An understanding gene-gene interaction helps users to design the next experiments efficiently and (if applicable) to make a better decision of drugs application based on the different biological conditions of the patients. This study aimed to identify changes in the hidden relationships between pro- and anti-inflammatory cytokine genes in the bovine oviduct epithelial cells (BOECs) under various experimental conditions using a multilayer response surface method. It was noted that under physiological conditions (BOECs with sperm or sex hormones, such as ovarian sex steroids and LH), the mRNA expressions of IL10, IL1B, TNFA, TLR4, and TNFA were associated with IL1B, TNFA, TLR4, IL4, and IL10, respectively. Under pathophysiological + physiological conditions (BOECs with lipopolysaccharide + hormones, alpha-1-acid glycoprotein + hormones, zearalenone + hormones, or urea + hormones), the relationship among genes was changed. For example, the expression of IL10 and TNFA was associated with (IL1B, TNFA, or IL4) and TLR4 expression, respectively. Furthermore, under physiological conditions, the co-expression of IL10 + TNFA, TLR4 + IL4, TNFA + IL4, TNFA + IL4, or IL10 + IL1B and under pathophysiological + physiological conditions, the co-expression of IL10 + IL4, IL4 + IL10, TNFA + IL10, TNFA + TLR4, or IL10 + IL1B were associated with IL1B, TNFA, TLR4, IL10, or IL4 expression, respectively. Collectively, the relationships between pro- and anti-inflammatory cytokine genes can be changed with respect to the presence/absence of toxins, sex hormones, sperm, and co-expression of other gene pairs in BOECs, suggesting that considerable cautions are needed in interpreting the results obtained from such narrowly focused in vitro studies.
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Magnusson R, Mariotti GP, Köpsén M, Lövfors W, Gawel DR, Jörnsten R, Linde J, Nordling TEM, Nyman E, Schulze S, Nestor CE, Zhang H, Cedersund G, Benson M, Tjärnberg A, Gustafsson M. LASSIM-A network inference toolbox for genome-wide mechanistic modeling. PLoS Comput Biol 2017. [PMID: 28640810 PMCID: PMC5501685 DOI: 10.1371/journal.pcbi.1005608] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases. There are excellent methods to mathematically model time-resolved biological data on a small scale using accurate mechanistic models. Despite the rapidly increasing availability of such data, mechanistic models have not been applied on a genome-wide level due to excessive runtimes and the non-identifiability of model parameters. However, genome-wide, mechanistic models could potentially answer key clinical questions, such as finding the best drug combinations to induce an expression change from a disease to a healthy state. We present LASSIM, which is a toolbox built to infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the network inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces computation time.
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Affiliation(s)
- Rasmus Magnusson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Guido Pio Mariotti
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mattias Köpsén
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - William Lövfors
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Danuta R. Gawel
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Rebecka Jörnsten
- Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden
| | - Jörg Linde
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
- Research Group PiDOMICS, Leibniz Institute for Natural Product Research and Infection Biology -Hans Knöll Institute, Jena, Germany
| | - Torbjörn E. M. Nordling
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan
- Stockholm Bioinformatics Center, Science for Life Laboratory, Solna, Sweden
| | - Elin Nyman
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Sylvie Schulze
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
| | - Colm E. Nestor
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Huan Zhang
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Cell Biology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Mikael Benson
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Andreas Tjärnberg
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mika Gustafsson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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Cappuccio A, Tieri P, Castiglione F. Multiscale modelling in immunology: a review. Brief Bioinform 2015; 17:408-18. [PMID: 25810307 DOI: 10.1093/bib/bbv012] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 01/30/2015] [Indexed: 01/26/2023] Open
Abstract
One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
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Affiliation(s)
- Antonio Cappuccio
- Laboratory of Integrative biology of human dendritic cells and T cells, U932 Immunity and cancer, Institut Curie, 26 Rue d`Ulm, 75005 Paris, France
| | - Paolo Tieri
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
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6
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Mushthofa M, Torres G, Van de Peer Y, Marchal K, De Cock M. ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 2014; 30:3086-92. [PMID: 25028722 DOI: 10.1093/bioinformatics/btu481] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Boolean network models are suitable to simulate GRNs in the absence of detailed kinetic information. However, reducing the biological reality implies making assumptions on how genes interact (interaction rules) and how their state is updated during the simulation (update scheme). The exact choice of the assumptions largely determines the outcome of the simulations. In most cases, however, the biologically correct assumptions are unknown. An ideal simulation thus implies testing different rules and schemes to determine those that best capture an observed biological phenomenon. This is not trivial because most current methods to simulate Boolean network models of GRNs and to compute their attractors impose specific assumptions that cannot be easily altered, as they are built into the system. RESULTS To allow for a more flexible simulation framework, we developed ASP-G. We show the correctness of ASP-G in simulating Boolean network models and obtaining attractors under different assumptions by successfully recapitulating the detection of attractors of previously published studies. We also provide an example of how performing simulation of network models under different settings help determine the assumptions under which a certain conclusion holds. The main added value of ASP-G is in its modularity and declarativity, making it more flexible and less error-prone than traditional approaches. The declarative nature of ASP-G comes at the expense of being slower than the more dedicated systems but still achieves a good efficiency with respect to computational time. AVAILABILITY AND IMPLEMENTATION The source code of ASP-G is available at http://bioinformatics.intec.ugent.be/kmarchal/Supplementary_Information_Musthofa_2014/asp-g.zip.
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Affiliation(s)
- Mushthofa Mushthofa
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Gustavo Torres
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Yves Van de Peer
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Kathleen Marchal
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Martine De Cock
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
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Gustafsson M, Nestor CE, Zhang H, Barabási AL, Baranzini S, Brunak S, Chung KF, Federoff HJ, Gavin AC, Meehan RR, Picotti P, Pujana MÀ, Rajewsky N, Smith KG, Sterk PJ, Villoslada P, Benson M. Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome Med 2014; 6:82. [PMID: 25473422 PMCID: PMC4254417 DOI: 10.1186/s13073-014-0082-6] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Many common diseases, such as asthma, diabetes or obesity, involve
altered interactions between thousands of genes. High-throughput techniques (omics)
allow identification of such genes and their products, but functional understanding
is a formidable challenge. Network-based analyses of omics data have identified
modules of disease-associated genes that have been used to obtain both a systems
level and a molecular understanding of disease mechanisms. For example, in allergy a
module was used to find a novel candidate gene that was validated by functional and
clinical studies. Such analyses play important roles in systems medicine. This is an
emerging discipline that aims to gain a translational understanding of the complex
mechanisms underlying common diseases. In this review, we will explain and provide
examples of how network-based analyses of omics data, in combination with functional
and clinical studies, are aiding our understanding of disease, as well as helping to
prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve
significant problems and limitations, which will be discussed. We also highlight the
steps needed for clinical implementation.
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Affiliation(s)
- Mika Gustafsson
- Centre for Individualized Medicine, Department of Pediatrics, Faculty of Medicine, 58185 Linköping, Sweden
| | - Colm E Nestor
- Centre for Individualized Medicine, Department of Pediatrics, Faculty of Medicine, 58185 Linköping, Sweden
| | - Huan Zhang
- Centre for Individualized Medicine, Department of Pediatrics, Faculty of Medicine, 58185 Linköping, Sweden
| | - Albert-László Barabási
- Department of Physics, Biology and Computer Science, Center for Complex Network Research, Northeastern University, Boston, MA 02115 USA
| | - Sergio Baranzini
- Department of Neurology, University of California, San Francisco, CA 94143 USA
| | - Sören Brunak
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark ; Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Kian Fan Chung
- Airways Disease Section, National Heart and Lung Institute, Imperial College London, London, SW3 6LY UK
| | - Howard J Federoff
- Department of Neurology and Neuroscience, Georgetown University Medical Center, Washington, DC 20057 USA
| | | | - Richard R Meehan
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, EH4 2XU UK
| | - Paola Picotti
- Institute of Biochemistry, University of Zürich, 8093 Zürich, Switzerland
| | - Miguel Àngel Pujana
- Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, 08908 Spain
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany
| | - Kenneth Gc Smith
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XY UK ; Department of Medicine, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, CB2 0QQ UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, 1100 DE Amsterdam, The Netherlands
| | - Pablo Villoslada
- Center of Neuroimmunology and Department of Neurology, Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic of Barcelona, 08028 Barcelona, Spain
| | - Mikael Benson
- Centre for Individualized Medicine, Department of Pediatrics, Faculty of Medicine, 58185 Linköping, Sweden
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8
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Gawel DR, Rani James A, Benson M, Liljenström R, Muraro A, Nestor CE, Zhang H, Gustafsson M. The Allergic Airway Inflammation Repository--a user-friendly, curated resource of mRNA expression levels in studies of allergic airways. Allergy 2014; 69:1115-7. [PMID: 24888382 DOI: 10.1111/all.12432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2014] [Indexed: 11/28/2022]
Abstract
Public microarray databases allow analysis of expression levels of candidate genes in different contexts. However, finding relevant microarray data is complicated by the large number of available studies. We have compiled a user-friendly, open-access database of mRNA microarray experiments relevant to allergic airway inflammation, the Allergic Airway Inflammation Repository (AAIR, http://aair.cimed.ike.liu.se/). The aim is to allow allergy researchers to determine the expression profile of their genes of interest in multiple clinical data sets and several experimental systems quickly and intuitively. AAIR also provides quick links to other relevant information such as experimental protocols, related literature and raw data files.
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Affiliation(s)
- D. R. Gawel
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - A. Rani James
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - M. Benson
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - R. Liljenström
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - A. Muraro
- Department of Women and Child Health; Referral Centre for Food Allergy Diagnosis and Treatment; Veneto Region; Padua University Hospital; Padua Italy
| | - C. E. Nestor
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - H. Zhang
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
| | - M. Gustafsson
- Department of Clinical and Experimental Medicine; Centre for Individualised Medicine; Linköping University; Linköping Sweden
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Carbo A, Hontecillas R, Andrew T, Eden K, Mei Y, Hoops S, Bassaganya-Riera J. Computational modeling of heterogeneity and function of CD4+ T cells. Front Cell Dev Biol 2014; 2:31. [PMID: 25364738 PMCID: PMC4207042 DOI: 10.3389/fcell.2014.00031] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Accepted: 07/10/2014] [Indexed: 12/19/2022] Open
Abstract
The immune system is composed of many different cell types and hundreds of intersecting molecular pathways and signals. This large biological complexity requires coordination between distinct pro-inflammatory and regulatory cell subsets to respond to infection while maintaining tissue homeostasis. CD4+ T cells play a central role in orchestrating immune responses and in maintaining a balance between pro- and anti- inflammatory responses. This tight balance between regulatory and effector reactions depends on the ability of CD4+ T cells to modulate distinct pathways within large molecular networks, since dysregulated CD4+ T cell responses may result in chronic inflammatory and autoimmune diseases. The CD4+ T cell differentiation process comprises an intricate interplay between cytokines, their receptors, adaptor molecules, signaling cascades and transcription factors that help delineate cell fate and function. Computational modeling can help to describe, simulate, analyze, and predict some of the behaviors in this complicated differentiation network. This review provides a comprehensive overview of existing computational immunology methods as well as novel strategies used to model immune responses with a particular focus on CD4+ T cell differentiation.
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Affiliation(s)
- Adria Carbo
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Tricity Andrew
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Kristin Eden
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Yongguo Mei
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Stefan Hoops
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech Blacksburg, VA, USA ; Department of Biomedical Sciences and Pathobiology, Virginia-Maryland Regional College of Veterinary Medicine, Virginia Tech Blacksburg, VA, USA
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Castiglione F, Pappalardo F, Bianca C, Russo G, Motta S. Modeling biology spanning different scales: an open challenge. BIOMED RESEARCH INTERNATIONAL 2014; 2014:902545. [PMID: 25143952 PMCID: PMC4124842 DOI: 10.1155/2014/902545] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 06/25/2014] [Indexed: 02/03/2023]
Abstract
It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular.
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Affiliation(s)
- Filippo Castiglione
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | | | - Carlo Bianca
- Theoretical Physics of Condensed Matter, Sorbonne Universities, UPMC Univ Paris 6, 75252 Paris Cedex 05, France
- UMR 7600 LPTMC, CNRS, 75252 Paris Cedex 05, France
| | - Giulia Russo
- Department of Pharmaceutical Sciences, University of Catania, Catania, Italy
| | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
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Magombedze G, Eda S, Ganusov VV. Competition for antigen between Th1 and Th2 responses determines the timing of the immune response switch during Mycobaterium avium subspecies paratuberulosis infection in ruminants. PLoS Comput Biol 2014; 10:e1003414. [PMID: 24415928 PMCID: PMC3886887 DOI: 10.1371/journal.pcbi.1003414] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 11/11/2013] [Indexed: 12/15/2022] Open
Abstract
Johne's disease (JD), a persistent and slow progressing infection of ruminants such as cows and sheep, is caused by slow replicating bacilli Mycobacterium avium subspecies paratuberculosis (MAP) infecting macrophages in the gut. Infected animals initially mount a cell-mediated CD4 T cell response against MAP which is characterized by the production of interferon (Th1 response). Over time, Th1 response diminishes in most animals and antibody response to MAP antigens becomes dominant (Th2 response). The switch from Th1 to Th2 response occurs concomitantly with disease progression and shedding of the bacteria in feces. Mechanisms controlling this Th1/Th2 switch remain poorly understood. Because Th1 and Th2 responses are known to cross-inhibit each other, it is unclear why initially strong Th1 response is lost over time. Using a novel mathematical model of the immune response to MAP infection we show that the ability of extracellular bacteria to persist outside of macrophages naturally leads to switch of the cellular response to antibody production. Several additional mechanisms may also contribute to the timing of the Th1/Th2 switch including the rate of proliferation of Th1/Th2 responses at the site of infection, efficiency at which immune responses cross-inhibit each other, and the rate at which Th1 response becomes exhausted over time. Our basic model reasonably well explains four different kinetic patterns of the Th1/Th2 responses in MAP-infected sheep by variability in the initial bacterial dose and the efficiency of the MAP-specific T cell responses. Taken together, our novel mathematical model identifies factors of bacterial and host origin that drive kinetics of the immune response to MAP and provides the basis for testing the impact of vaccination or early treatment on the duration of infection. Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease, a chronic enteric disease of ruminants such as sheep and cows. Due to early culling and reduction in milk production of affected animals, MAP inflicts high economic cost to diary farms. MAP infection has a long incubation period of several years, and during the asymptomatic stage a strong cellular (T helper 1) immune response is thought to control MAP replication. Over time, Th1 response is lost and ineffective antibody response driven by Th2 cells becomes predominant. We develop the first mathematical model of helper T cell response to MAP infection to understand impact of various mechanisms on the dynamics of the switch from Th1 to Th2 response. Our results suggest that in contrast to the generally held belief, Th1/Th2 switch may be driven by the accumulation of long-lived extracellular bacteria, and therefore, may be the consequence of the disease progression of MAP-infected animals and not its cause. Our model highlights limitations of our current understanding of regulation of helper T cell responses during MAP infection and identifies areas for future experimental research.
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Affiliation(s)
- Gesham Magombedze
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennesse, United States of America
- * E-mail: ;
| | - Shigetoshi Eda
- Department of Forestry, Wildlife, and Fisheries, University of Tennessee, Knoxville, Tennesse, United States of America
| | - Vitaly V. Ganusov
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennesse, United States of America
- Department of Microbiology, University of Tennessee, Knoxville, Tennesse, United States of America
- Department of Mathematics, University of Tennessee, Knoxville, Tennesse, United States of America
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12
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Magombedze G, Reddy PBJ, Eda S, Ganusov VV. Cellular and population plasticity of helper CD4(+) T cell responses. Front Physiol 2013; 4:206. [PMID: 23966946 PMCID: PMC3744810 DOI: 10.3389/fphys.2013.00206] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Accepted: 07/21/2013] [Indexed: 12/29/2022] Open
Abstract
Vertebrates are constantly exposed to pathogens, and the adaptive immunity has most likely evolved to control and clear such infectious agents. CD4+ T cells are the major players in the adaptive immune response to pathogens. Following recognition of pathogen-derived antigens naïve CD4+ T cells differentiate into effectors which then control pathogen replication either directly by killing pathogen-infected cells or by assisting with generation of cytotoxic T lymphocytes (CTLs) or pathogen-specific antibodies. Pathogen-specific effector CD4+ T cells are highly heterogeneous in terms of cytokines they produce. Three major subtypes of effector CD4+ T cells have been identified: T-helper 1 (Th1) cells producing IFN-γ and TNF-α, Th2 cells producing IL-4 and IL-10, and Th17 cells producing IL-17. How this heterogeneity is maintained and what regulates changes in effector T cell composition during chronic infections remains poorly understood. In this review we discuss recent advances in our understanding of CD4+ T cell differentiation in response to microbial infections. We propose that a change in the phenotype of pathogen-specific effector CD4+ T cells during chronic infections, for example, from Th1 to Th2 response as observed in Mycobactrium avium ssp. paratuberculosis (MAP) infection of ruminants, can be achieved by conversion of T cells from one effector subset to another (cellular plasticity) or due to differences in kinetics (differentiation, proliferation, death) of different effector T cell subsets (population plasticity). We also shortly review mathematical models aimed at describing CD4+ T cell differentiation and outline areas for future experimental and theoretical research.
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Affiliation(s)
- Gesham Magombedze
- National Institute for Mathematical and Biological Synthesis, University of Tennessee Knoxville, TN, USA
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Blaschke C, Valencia A. The Functional Genomics Network in the evolution of biological text mining over the past decade. N Biotechnol 2012. [PMID: 23202358 DOI: 10.1016/j.nbt.2012.11.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental.
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Affiliation(s)
- Christian Blaschke
- Spanish National Cancer Research Centre, C/Melchor Fernández Almagro, 3, E-28029 Madrid, Spain.
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Aijö T, Edelman SM, Lönnberg T, Larjo A, Kallionpää H, Tuomela S, Engström E, Lahesmaa R, Lähdesmäki H. An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation. BMC Genomics 2012; 13:572. [PMID: 23110343 PMCID: PMC3526425 DOI: 10.1186/1471-2164-13-572] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Accepted: 10/02/2012] [Indexed: 01/19/2023] Open
Abstract
Background A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. Results We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. Conclusions The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.
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Affiliation(s)
- Tarmo Aijö
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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Bruhn S, Barrenäs F, Mobini R, Andersson BA, Chavali S, Egan BS, Hovig E, Sandve GK, Langston MA, Rogers G, Wang H, Benson M. Increased expression of IRF4 and ETS1 in CD4+ cells from patients with intermittent allergic rhinitis. Allergy 2012; 67:33-40. [PMID: 21919915 DOI: 10.1111/j.1398-9995.2011.02707.x] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND The transcription factor (TF) IRF4 is involved in the regulation of Th1, Th2, Th9, and Th17 cells, and animal studies have indicated an important role in allergy. However, IRF4 and its target genes have not been examined in human allergy. METHODS IRF4 and its target genes were examined in allergen-challenged CD4(+) cells from patients with IAR, using combined gene expression microarrays and chromatin immunoprecipitation chips (ChIP-chips), computational target prediction, and RNAi knockdowns. RESULTS IRF4 increased in allergen-challenged CD4(+) cells from patients with IAR, and functional studies supported its role in Th2 cell activation. IRF4 ChIP-chip showed that IRF4 regulated a large number of genes relevant to Th cell differentiation. However, neither Th1 nor Th2 cytokines were the direct targets of IRF4. To examine whether IRF4 induced Th2 cytokines via one or more downstream TFs, we combined gene expression microarrays, ChIP-chips, and computational target prediction and found a putative intermediary TF, namely ETS1 in allergen-challenged CD4(+) cells from allergic patients. ETS1 increased significantly in allergen-challenged CD4(+) cells from patients compared to controls. Gene expression microarrays before and after ETS1 RNAi knockdown showed that ETS1 induced Th2 cytokines as well as disease-related pathways. CONCLUSIONS Increased expression of IRF4 in allergen-challenged CD4(+) cells from patients with intermittent allergic rhinitis leads to activation of a complex transcriptional program, including Th2 cytokines.
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Affiliation(s)
- S Bruhn
- The Centre for Individualized Medication, Linköping University Hospital, Linköping, Sweden
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