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Li D, Pan Z, Hu G, Anderson G, He S. Active Module Identification From Multilayer Weighted Gene Co-Expression Networks: A Continuous Optimization Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2239-2248. [PMID: 32011261 DOI: 10.1109/tcbb.2020.2970400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Searching for active modules, i.e., regions showing striking changes in molecular activity in biological networks is important to reveal regulatory and signaling mechanisms of biological systems. Most existing active modules identification methods are based on protein-protein interaction networks or metabolic networks, which require comprehensive and accurate prior knowledge. On the other hand, weighted gene co-expression networks (WGCNs) are purely constructed from gene expression profiles. However, existing WGCN analysis methods are designed for identifying functional modules but not capable of identifying active modules. There is an urgent need to develop an active module identification algorithm for WGCNs to discover regulatory and signaling mechanism associating with a given cellular response. To address this urgent need, we propose a novel algorithm called active modules on the multi-layer weighted (co-expression gene) network, based on a continuous optimization approach (AMOUNTAIN). The algorithm is capable of identifying active modules not only from single-layer WGCNs but also from multilayer WGCNs such as cross-species and dynamic WGCNs. We first validate AMOUNTAIN on a synthetic benchmark dataset. We then apply AMOUNTAIN to WGCNs constructed from Th17 differentiation gene expression datasets of human and mouse, which include a single layer, a cross-species two-layer and a multilayer dynamic WGCNs. The identified active modules from WGCNs are enriched by known protein-protein interactions, and more importantly, they reveal some interesting and important regulatory and signaling mechanisms of Th17 cell differentiation.
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Nudelman I, Kudrin D, Nudelman G, Deshpande R, Hartmann BM, Kleinstein SH, Myers CL, Sealfon SC, Zaslavsky E. Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses. Front Immunol 2021; 12:691758. [PMID: 34335598 PMCID: PMC8317020 DOI: 10.3389/fimmu.2021.691758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.
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Affiliation(s)
- Irina Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Division of General Internal Medicine, New York University Langone Medical Centre, New York, NY, United States
| | - Daniil Kudrin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Boris M Hartmann
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States.,Program in Biomedical Informatics and Computational Biology, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Found In Translation: a machine learning model for mouse-to-human inference. Nat Methods 2018; 15:1067-1073. [DOI: 10.1038/s41592-018-0214-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 10/11/2018] [Indexed: 12/24/2022]
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Robertsen HL, Weber T, Kim HU, Lee SY. Toward Systems Metabolic Engineering of Streptomycetes for Secondary Metabolites Production. Biotechnol J 2017; 13. [DOI: 10.1002/biot.201700465] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 10/20/2017] [Indexed: 12/20/2022]
Affiliation(s)
- Helene Lunde Robertsen
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 Kongens Lyngby Denmark
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 Kongens Lyngby Denmark
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Korea Advanced Institute of Science and Technology (KAIST); Yuseong-gu Daejeon 306-701 Republic of Korea
| | - Sang Yup Lee
- The Novo Nordisk Foundation Center for Biosustainability; Technical University of Denmark; 2800 Kongens Lyngby Denmark
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Korea Advanced Institute of Science and Technology (KAIST); Yuseong-gu Daejeon 306-701 Republic of Korea
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Muetze T, Goenawan IH, Wiencko HL, Bernal-Llinares M, Bryan K, Lynn DJ. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks. F1000Res 2016; 5:1745. [PMID: 27853512 PMCID: PMC5105880 DOI: 10.12688/f1000research.9118.2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2016] [Indexed: 01/21/2023] Open
Abstract
Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such
contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store (
http://apps.cytoscape.org/apps/chat).
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Affiliation(s)
- Tanja Muetze
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Ivan H Goenawan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Heather L Wiencko
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Meath, Ireland
| | - Manuel Bernal-Llinares
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Kenneth Bryan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - David J Lynn
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia; School of Medicine, Flinders University, Bedford Park, Australia
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Muetze T, Goenawan IH, Wiencko HL, Bernal-Llinares M, Bryan K, Lynn DJ. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks. F1000Res 2016; 5:1745. [PMID: 27853512 PMCID: PMC5105880 DOI: 10.12688/f1000research.9118.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2016] [Indexed: 07/30/2023] Open
Abstract
UNLABELLED Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. AVAILABILITY CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).
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Affiliation(s)
- Tanja Muetze
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Ivan H. Goenawan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Heather L. Wiencko
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Meath, Ireland
| | - Manuel Bernal-Llinares
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Kenneth Bryan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - David J. Lynn
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
- School of Medicine, Flinders University, Bedford Park, Australia
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He H, Cao S, Niu T, Zhou Y, Zhang L, Zeng Y, Zhu W, Wang YP, Deng HW. Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women. PLoS One 2016; 11:e0147475. [PMID: 26808152 PMCID: PMC4726665 DOI: 10.1371/journal.pone.0147475] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 01/05/2016] [Indexed: 01/22/2023] Open
Abstract
Background Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women. Methods Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I2 values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test. Results Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment analysis of the 58 module genes revealed that these genes were enriched in several important KEGG pathways including Osteoclast differentiation, B cell receptor signaling pathway, MAPK signaling pathway, Chemokine signaling pathway and Insulin signaling pathway. The importance of module genes was replicated by demonstrating that most module genes were genetically associated with BMD in the GWAS data sets. Meta-analyses were performed at the individual gene level by combining p-values and effect sizes. Five candidate genes (ESR1, MAP3K3, PYGM, RAC1 and SYK) were identified based on gene expression meta-analysis, and their associations with BMD were also replicated by two BMD meta-analysis studies. Conclusions In summary, our network-based meta-analysis not only identified important differentially expressed genes but also discovered biologically meaningful functional modules for BMD determination. Our study may provide novel therapeutic targets for osteoporosis in women.
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Affiliation(s)
- Hao He
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Shaolong Cao
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America
| | - Tianhua Niu
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Yu Zhou
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Lan Zhang
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Yong Zeng
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Wei Zhu
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Yu-ping Wang
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America
| | - Hong-wen Deng
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
- * E-mail:
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El-Kebir M, Soueidan H, Hume T, Beisser D, Dittrich M, Müller T, Blin G, Heringa J, Nikolski M, Wessels LFA, Klau GW. xHeinz: an algorithm for mining cross-species network modules under a flexible conservation model. Bioinformatics 2015; 31:3147-55. [PMID: 26023104 DOI: 10.1093/bioinformatics/btv316] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 05/18/2015] [Indexed: 01/18/2023] Open
Abstract
MOTIVATION Integrative network analysis methods provide robust interpretations of differential high-throughput molecular profile measurements. They are often used in a biomedical context-to generate novel hypotheses about the underlying cellular processes or to derive biomarkers for classification and subtyping. The underlying molecular profiles are frequently measured and validated on animal or cellular models. Therefore the results are not immediately transferable to human. In particular, this is also the case in a study of the recently discovered interleukin-17 producing helper T cells (Th17), which are fundamental for anti-microbial immunity but also known to contribute to autoimmune diseases. RESULTS We propose a mathematical model for finding active subnetwork modules that are conserved between two species. These are sets of genes, one for each species, which (i) induce a connected subnetwork in a species-specific interaction network, (ii) show overall differential behavior and (iii) contain a large number of orthologous genes. We propose a flexible notion of conservation, which turns out to be crucial for the quality of the resulting modules in terms of biological interpretability. We propose an algorithm that finds provably optimal or near-optimal conserved active modules in our model. We apply our algorithm to understand the mechanisms underlying Th17 T cell differentiation in both mouse and human. As a main biological result, we find that the key regulation of Th17 differentiation is conserved between human and mouse. AVAILABILITY AND IMPLEMENTATION xHeinz, an implementation of our algorithm, as well as all input data and results, are available at http://software.cwi.nl/xheinz and as a Galaxy service at http://services.cbib.u-bordeaux2.fr/galaxy in CBiB Tools. CONTACT gunnar.klau@cwi.nl SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mohammed El-Kebir
- Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands, Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Hayssam Soueidan
- Computational Cancer Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Thomas Hume
- Univ. Bordeaux, CBiB, 33000 Bordeaux, France, Univ. Bordeaux, CNRS/LaBRI, 33405 Talence, France
| | - Daniela Beisser
- Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen
| | - Marcus Dittrich
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany, Institute of Human Genetics, University of Würzburg, Würzburg, Germany and
| | - Tobias Müller
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | | | - Jaap Heringa
- Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands
| | - Macha Nikolski
- Univ. Bordeaux, CBiB, 33000 Bordeaux, France, Univ. Bordeaux, CNRS/LaBRI, 33405 Talence, France
| | - Lodewyk F A Wessels
- Computational Cancer Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Gunnar W Klau
- Life Sciences, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Centre for Integrative Bioinformatics VU, VU University Amsterdam, The Netherlands, Erable Team, INRIA, Lyon, France
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