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Sibilio P, Conte F, Huang Y, Castaldi PJ, Hersh CP, DeMeo DL, Silverman EK, Paci P. Correlation-based network integration of lung RNA sequencing and DNA methylation data in chronic obstructive pulmonary disease. Heliyon 2024; 10:e31301. [PMID: 38807864 PMCID: PMC11130701 DOI: 10.1016/j.heliyon.2024.e31301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 05/08/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis.
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Affiliation(s)
- Pasquale Sibilio
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Yichen Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Karolinska Institutet, 17177, Stockholm, Sweden
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2
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Werder RB, Zhou X, Cho MH, Wilson AA. Breathing new life into the study of COPD with genes identified from genome-wide association studies. Eur Respir Rev 2024; 33:240019. [PMID: 38811034 PMCID: PMC11134200 DOI: 10.1183/16000617.0019-2024] [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: 02/05/2024] [Accepted: 02/23/2024] [Indexed: 05/31/2024] Open
Abstract
COPD is a major cause of morbidity and mortality globally. While the significance of environmental exposures in disease pathogenesis is well established, the functional contribution of genetic factors has only in recent years drawn attention. Notably, many genes associated with COPD risk are also linked with lung function. Because reduced lung function precedes COPD onset, this association is consistent with the possibility that derangements leading to COPD could arise during lung development. In this review, we summarise the role of leading genes (HHIP, FAM13A, DSP, AGER and TGFB2) identified by genome-wide association studies in lung development and COPD. Because many COPD genome-wide association study genes are enriched in lung epithelial cells, we focus on the role of these genes in the lung epithelium in development, homeostasis and injury.
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Affiliation(s)
- Rhiannon B Werder
- Murdoch Children's Research Institute, Melbourne, Australia
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew A Wilson
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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3
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Moll M, Silverman EK. Precision Approaches to Chronic Obstructive Pulmonary Disease Management. Annu Rev Med 2024; 75:247-262. [PMID: 37827193 DOI: 10.1146/annurev-med-060622-101239] [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: 10/14/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.
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Affiliation(s)
- Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Division of Pulmonary, Critical Care, Sleep and Allergy, Veterans Affairs Boston Healthcare System, West Roxbury, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Pan W, An S, Dai L, Xu S, Liu D, Wang L, Zhang R, Wang F, Wang Z. Identification of Potential Differentially-Methylated/Expressed Genes in Chronic Obstructive Pulmonary Disease. COPD 2023; 20:44-54. [PMID: 36655999 DOI: 10.1080/15412555.2022.2158324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. DNA methylation can regulate gene expression. Understanding the potential molecular mechanism of COPD is of great importance. The aim of this study was to find differentially methylated/expressed genes in COPD. DNA methylation and gene expression profiles in COPD were downloaded from the dataset, followed by functional analysis of differentially-methylated/expressed genes. The potential diagnostic value of these differentially-methylated/expressed genes was determined by receiver operating characteristic (ROC) analysis. Expression validation of differentially-methylated/expressed genes was performed by in vitro experiment and extra online datasets. Totally, 81 hypermethylated-low expression genes and 121 hypomethylated-high expression genes were found in COPD. Among which, 9 core hypermethylated-low expression genes (CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7 and IL7R) and 8 core hypomethylated-high expression genes (TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3 and LYZ) were identified in the protein-protein interaction (PPI) network. Moreover, these genes had a potential diagnostic utility for COPD. Some signaling pathways were identified in COPD, including T cell receptor signaling pathway, cytokine-cytokine receptor interaction, hematopoietic cell lineage, HTLV-I infection, endocytosis and Jak-STAT signaling pathway. In conclusion, differentially-methylated/expressed genes and involved signaling pathways are likely to be associated with the process of COPD.
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Affiliation(s)
- Wen Pan
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Shuyuan An
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Lina Dai
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Shuo Xu
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Dan Liu
- Clinical Laboratory, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Lizhi Wang
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Ruixue Zhang
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Fengliang Wang
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
| | - Zongling Wang
- Department of Cardiology, Qingdao Fuwai Cardiovascular Hospital, Qingdao, Shandong, China
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Zaied RE, Fadason T, O'Sullivan JM. De novo identification of complex traits associated with asthma. Front Immunol 2023; 14:1231492. [PMID: 37680636 PMCID: PMC10480836 DOI: 10.3389/fimmu.2023.1231492] [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] [Received: 05/30/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.
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Affiliation(s)
- Roan E Zaied
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Medical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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Bhardwaj A, Josse C, Van Daele D, Poulet C, Chavez M, Struman I, Van Steen K. Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses. Sci Rep 2022; 12:11027. [PMID: 35773268 PMCID: PMC9247075 DOI: 10.1038/s41598-022-14592-1] [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: 07/09/2021] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.
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Affiliation(s)
- Archana Bhardwaj
- GIGA-R Centre, BIO3 - Medical Genomics, University of Liège, Avenue de L'Hôpital, 11, 4000, Liège, Belgium.
| | - Claire Josse
- Laboratory of Human Genetics, GIGA Research, University Hospital (CHU), Liège, Belgium
- Medical Oncology Department, CHU Liège, Liège, Belgium
| | - Daniel Van Daele
- Department of Gastro-Enterology, University Hospital (CHU), Liège, Belgium
| | - Christophe Poulet
- Laboratory of Human Genetics, GIGA Research, University Hospital (CHU), Liège, Belgium
- Laboratory of Rheumatology, GIGA-R, University Hospital (CHULiege), Liège, Belgium
| | - Marcela Chavez
- Department of Medicine, Division of Hematology, University Hospital (CHU), Liège, Belgium
| | - Ingrid Struman
- GIGA-R Centre, Laboratory of Molecular Angiogenesis, University of Liège, Liège, Belgium
| | - Kristel Van Steen
- GIGA-R Centre, BIO3 - Medical Genomics, University of Liège, Avenue de L'Hôpital, 11, 4000, Liège, Belgium
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8
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Cho MH, Hobbs BD, Silverman EK. Genetics of chronic obstructive pulmonary disease: understanding the pathobiology and heterogeneity of a complex disorder. THE LANCET. RESPIRATORY MEDICINE 2022; 10:485-496. [PMID: 35427534 PMCID: PMC11197974 DOI: 10.1016/s2213-2600(21)00510-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/20/2021] [Accepted: 11/09/2021] [Indexed: 12/20/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a deadly and highly morbid disease. Susceptibility to and heterogeneity of COPD are incompletely explained by environmental factors such as cigarette smoking. Family-based and population-based studies have shown that a substantial proportion of COPD risk is related to genetic variation. Genetic association studies have identified hundreds of genetic variants that affect risk for COPD, decreased lung function, and other COPD-related traits. These genetic variants are associated with other pulmonary and non-pulmonary traits, demonstrate a genetic basis for at least part of COPD heterogeneity, have a substantial effect on COPD risk in aggregate, implicate early-life events in COPD pathogenesis, and often involve genes not previously suspected to have a role in COPD. Additional progress will require larger genetic studies with more ancestral diversity, improved profiling of rare variants, and better statistical methods. Through integration of genetic data with other omics data and comprehensive COPD phenotypes, as well as functional description of causal mechanisms for genetic risk variants, COPD genetics will continue to inform novel approaches to understanding the pathobiology of COPD and developing new strategies for management and treatment.
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Affiliation(s)
- Michael H Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Brian D Hobbs
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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Röhl A, Baek SH, Kachroo P, Morrow JD, Tantisira K, Silverman EK, Weiss ST, Sharma A, Glass K, DeMeo DL. Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease. Respir Res 2022; 23:69. [PMID: 35331221 PMCID: PMC8944072 DOI: 10.1186/s12931-022-01963-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 02/08/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation. METHODS In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks. RESULTS We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways. CONCLUSIONS The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.
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Affiliation(s)
- Annika Röhl
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Seung Han Baek
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jarrett D Morrow
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pediatric Respiratory Medicine, University of California San Diego, San Diego, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Complex Network Research, Northeastern University, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
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Lin J, Xue Y, Su W, Zhang Z, Wei Q, Huang T. Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2022; 17:475-487. [PMID: 35281477 PMCID: PMC8904782 DOI: 10.2147/copd.s349694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 02/27/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment. Methods The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset. Results A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4. Conclusion The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.
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Affiliation(s)
- Jie Lin
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Yanlong Xue
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Wenyan Su
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Zan Zhang
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Qiu Wei
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China,Correspondence: Qiu Wei; Tianxia Huang, Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, 89 Qixing Road, Nanning, Guangxi, 530022, People’s Republic of China, Tel +86 7712636163, Fax +86 7712617892, Email ;
| | - Tianxia Huang
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
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11
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Huang HH, Liang Y. Integrating molecular interactions and gene expression to identify biomarkers and network modules of chronic obstructive pulmonary disease. Technol Health Care 2022; 30:135-142. [PMID: 35124591 PMCID: PMC9028746 DOI: 10.3233/thc-228013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) causes chronic obstructive conditions, chronic bronchitis, and emphysema, and is a major cause of death worldwide. Although several efforts for identifying biomarkers and pathways have been made, specific causal COPD mechanism remains unknown. OBJECTIVE: This study combined biological interaction data with gene expression data for a better understanding of the biological process and network module for COPD. METHODS: Using a sparse network-based method, we selected 49 genes from peripheral blood mononuclear cell expression data of 136 subjects, including 42 ex-smoking controls and 94 subjects with COPD. RESULTS: These 49 genes might influence biological processes and molecular functions related to COPD. For example, our result suggests that FoxO signaling may contribute to the atrophy of COPD peripheral muscle tissues via oxidative stress. CONCLUSIONS: Our approach enhances the existing understanding of COPD disease pathogenesis and predicts new genetic markers and pathways that may influence COPD pathogenesis.
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Affiliation(s)
- Hai-Hui Huang
- Faculty of Information Technology, Macau University of Science and Technology, Macau, China
- Macau Institute of Systems Engineering and Collaborative Laboratory of Intelligent Science and Systems, Macau University of Science and Technology, Macau, China
| | - Yong Liang
- Macau Institute of Systems Engineering and Collaborative Laboratory of Intelligent Science and Systems, Macau University of Science and Technology, Macau, China
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12
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He MF, Liang Y, Huang HH. Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients. Technol Health Care 2022; 30:451-457. [PMID: 35124619 PMCID: PMC9028654 DOI: 10.3233/thc-thc228041] [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] [Indexed: 11/24/2022]
Abstract
BACKGROUND Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning. OBJECTIVE Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers. METHODS In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients. RESULTS We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment. CONCLUSIONS Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.
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Affiliation(s)
- Min-Fan He
- Macau Institute of Systems Engineering and Collaborative Laboratory of Intelligent Science and Systems, Macau University of Science and Technology, Macau, China
- School of Mathematics and Big Data, Foshan University, Foshan, China
| | - Yong Liang
- Macau Institute of Systems Engineering and Collaborative Laboratory of Intelligent Science and Systems, Macau University of Science and Technology, Macau, China
| | - Hai-Hui Huang
- Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, China
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13
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Tanaka M, Matsumoto K, Satake R, Yoshida Y, Inoue M, Hasegawa S, Suzuki T, Iwata M, Iguchi K, Nakamura M. Gentamicin-induced hearing loss: A retrospective study using the Food and Drug Administration Adverse Event Reporting System and a toxicological study using drug-gene network analysis. Heliyon 2021; 7:e07429. [PMID: 34401547 PMCID: PMC8353315 DOI: 10.1016/j.heliyon.2021.e07429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/15/2021] [Accepted: 06/24/2021] [Indexed: 11/25/2022] Open
Abstract
The objectives of the study were to evaluate the relationship between gentamicin (GEN) and hearing loss using the Food and Drug Administration Adverse Event Reporting system (FAERS) database and elucidate the potential toxicological mechanism of GEN-induced hearing loss through a drug–gene network analysis. Using the preferred terms and standardized queries from the Medical Dictionary for Regulatory Activities, we calculated the reporting odds ratios (RORs). We extracted GEN-associated genes (seed genes) and analyzed drug−gene interactions using the ClueGO plug-in in the Cytoscape software and the DIseAse MOdule Detection (DIAMOnD) algorithm. The lower limit of the 95% confidence interval (CI) of the ROR for aminoglycosides (AG) antibacterials was over 1, and the ROR was 5.5 (5.1–6.0). We retrieved 17 seed genes related to GEN from the PharmGKB and Drug Gene Interaction databases. In total, 1018 human genes interacting with GEN were investigated using ClueGO. Through Molecular Complex Detection (MCODE) analysis, we identified 17 local gene clusters. The nodes and edges of the highest-ranked local gene cluster named “Cluster 1” were 30 and 433, respectively. According to the ClueGO analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Cluster 1 genes were highly enriched in “oxidative phosphorylation.” According to the ClueGO analysis using ClinVar, Cluster 1 genes were highly enriched in “mitochondrial diseases,” “mitochondrial complex I deficiency,” “hereditary hearing loss and deafness,” and “Leigh syndrome.” We identified 60 GEN-associated genes using the DIAMOnD algorithm. Several GEN-associated genes in the DIAMOnD algorithm were highly enriched in “PI3K-Akt signaling pathway,” “Ras signaling pathway,” “focal adhesion,” “MAPK signaling pathway,” “regulation of actin cytoskeleton,” “oxidative phosphorylation,” and “ECM-receptor interaction.” Our analysis demonstrated an association between several AGs and hearing loss using the FAERS database. Drug−gene network analysis demonstrated that GEN may be associated with oxidative phosphorylation-associated genes and integrin genes, which may be associated with hearing loss.
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Affiliation(s)
- Mizuki Tanaka
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Kiyoka Matsumoto
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Riko Satake
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Yu Yoshida
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Misaki Inoue
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Shiori Hasegawa
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan.,Department of Pharmacy, Kobe City Medical Center General Hospital, 2-1-1 Minatojima Minamimachi, Chuo-ku, Kobe, 650-0047, Japan
| | - Takaaki Suzuki
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan.,Gifu Prefectural Government, 2-1-1 Yabutaminami, Gifu, 500-8570, Japan
| | - Mari Iwata
- Kifune Pharmacy, 2-23-2 Hasuike, Yanaizu-cho, Gifu, 501-6103, Japan
| | - Kazuhiro Iguchi
- Laboratory of Community Pharmacy, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Mitsuhiro Nakamura
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
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14
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Acunzo M, Nana-Sinkam P. Functional Gene Variants in Chronic Obstructive Pulmonary Disease: The Search Continues. Am J Respir Cell Mol Biol 2021; 65:8-9. [PMID: 33945772 PMCID: PMC8320122 DOI: 10.1165/rcmb.2021-0169ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Mario Acunzo
- Division of Pulmonary and Critical Care Medicine Virginia Commonwealth University Richmond, Virginia
| | - Patrick Nana-Sinkam
- Division of Pulmonary and Critical Care Medicine Virginia Commonwealth University Richmond, Virginia
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15
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Gong L, Bates S, Li J, Qiao D, Glass K, Wei W, Hsu VW, Zhou X, Silverman EK. Connecting COPD GWAS genes: FAM13A controls TGFβ2 secretion by modulating AP-3 transport. Am J Respir Cell Mol Biol 2021; 65:532-543. [PMID: 34166600 DOI: 10.1165/rcmb.2021-0016oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a common, complex disease and a major cause of morbidity and mortality. Although multiple genetic determinants of COPD have been implicated by genome-wide association studies (GWAS), the pathophysiologic significance of these associations remains largely unknown. From a COPD protein-protein interaction network module, we selected a network path between two COPD GWAS genes for validation studies: FAM13A-AP3D1-CTGF-TGFB2. We find that TGFβ2, FAM13A, and AP3D1 (but not CTGF) form a cellular protein complex. Functional characterization suggests that this complex mediates the secretion of TGFβ2 through an AP-3-dependent pathway, with FAM13A acting as a negative regulator by targeting a late stage of this transport that involves the dissociation of coat-cargo interaction. Moreover, we find that TGFβ2 is a transmembrane protein that engages the AP-3 complex for delivery to the late endosomal compartments for subsequent secretion through exosomes. These results identify a pathophysiologic context that unifies the biological network role of two COPD GWAS proteins and reveal novel mechanisms of cargo transport through an intracellular pathway.
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Affiliation(s)
- Lu Gong
- Brigham and Women's Hospital, 1861, Channing Division, Boston, Massachusetts, United States
| | - Samuel Bates
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Jian Li
- Brigham And Women's Hospital, Boston, United States
| | - Dandi Qiao
- Brigham and Women's Hospital and Harvard Medical School, Medicine, Boston, Massachusetts, United States.,Harvard School of Public Health, Biostatistics, Boston, Massachusetts, United States
| | - Kimberly Glass
- Brigham and Women\'s Hospital Channing Division of Network Medicine, 1869, Boston, Massachusetts, United States
| | - Wenyi Wei
- Beth Israel Deaconess Medical Center, 1859, Department of Pathology, Boston, Massachusetts, United States.,Harvard Medical School , Boston , Massachusetts, United States
| | - Victor W Hsu
- Brigham and Women's Hospital, 1861, Division of Rheumatology, Inflammation, and Immunity, Boston, Massachusetts, United States
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Edwin K Silverman
- Brigham and Women's Hospital, 1861, Channing Division of Network Medicine, Boston, Massachusetts, United States;
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16
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Benincasa G, DeMeo DL, Glass K, Silverman EK, Napoli C. Epigenetics and pulmonary diseases in the horizon of precision medicine: a review. Eur Respir J 2021; 57:13993003.03406-2020. [PMID: 33214212 DOI: 10.1183/13993003.03406-2020] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023]
Abstract
Epigenetic mechanisms represent potential molecular routes which could bridge the gap between genetic background and environmental risk factors contributing to the pathogenesis of pulmonary diseases. In patients with COPD, asthma and pulmonary arterial hypertension (PAH), there is emerging evidence of aberrant epigenetic marks, mainly including DNA methylation and histone modifications which directly mediate reversible modifications to the DNA without affecting the genomic sequence. Post-translational events and microRNAs can be also regulated epigenetically and potentially participate in disease pathogenesis. Thus, novel pathogenic mechanisms and putative biomarkers may be detectable in peripheral blood, sputum, nasal and buccal swabs or lung tissue. Besides, DNA methylation plays an important role during the early phases of fetal development and may be impacted by environmental exposures, ultimately influencing an individual's susceptibility to COPD, asthma and PAH later in life. With the advances in omics platforms and the application of computational biology tools, modelling the epigenetic variability in a network framework, rather than as single molecular defects, provides insights into the possible molecular pathways underlying the pathogenesis of COPD, asthma and PAH. Epigenetic modifications may have clinical applications as noninvasive biomarkers of pulmonary diseases. Moreover, combining molecular assays with network analysis of epigenomic data may aid in clarifying the multistage transition from a "pre-disease" to "disease" state, with the goal of improving primary prevention of lung diseases and its subsequent clinical management.We describe epigenetic mechanisms known to be associated with pulmonary diseases and discuss how network analysis could improve our understanding of lung diseases.
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Affiliation(s)
- Giuditta Benincasa
- Dept of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Dawn L DeMeo
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Claudio Napoli
- Dept of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy .,Clinical Dept of Internal and Specialty Medicine (DAI), University Hospital (AOU), University of Campania "Luigi Vanvitelli", Naples, Italy
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17
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Groth EE, Weber M, Bahmer T, Pedersen F, Kirsten A, Börnigen D, Rabe KF, Watz H, Ammerpohl O, Goldmann T. Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0. Respir Res 2020; 21:274. [PMID: 33076907 PMCID: PMC7574293 DOI: 10.1186/s12931-020-01544-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/11/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses. METHODS Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework. RESULTS A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma. CONCLUSIONS In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.
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Affiliation(s)
- Espen E Groth
- LungenClinic Grosshansdorf, Großhansdorf, Germany. .,Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany. .,Department of Internal Medicine I, Pneumology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. .,Department of Oncology, Hematology and BMT with Section Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Melanie Weber
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
| | - Thomas Bahmer
- LungenClinic Grosshansdorf, Großhansdorf, Germany.,Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Department of Internal Medicine I, Pneumology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Frauke Pedersen
- LungenClinic Grosshansdorf, Großhansdorf, Germany.,Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Pulmonary Research Institute at LungenClinic Grosshansdorf, Großhansdorf, Germany
| | - Anne Kirsten
- Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Pulmonary Research Institute at LungenClinic Grosshansdorf, Großhansdorf, Germany
| | - Daniela Börnigen
- Bioinformatics Core Unit, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Klaus F Rabe
- LungenClinic Grosshansdorf, Großhansdorf, Germany.,Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany
| | - Henrik Watz
- Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Pulmonary Research Institute at LungenClinic Grosshansdorf, Großhansdorf, Germany
| | - Ole Ammerpohl
- Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Institute of Human Genetics, University Medical Center Ulm, Ulm, Germany
| | - Torsten Goldmann
- Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany.,Research Center Borstel, Pathology, Borstel, Germany
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18
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Zhao K, Tu C, Liang K, Li Y, Yu Y. Long noncoding RNA MCM3AP antisense RNA 1 is downregulated in chronic obstructive pulmonary disease and regulates human bronchial smooth muscle cell proliferation. J Int Med Res 2020; 48:300060520935215. [PMID: 32940099 PMCID: PMC7503009 DOI: 10.1177/0300060520935215] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 05/26/2020] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES This study aimed to investigate the involvement of MCM3AP antisense RNA 1 (MCM3AP-AS1) in chronic obstructive pulmonary disease (COPD). METHODS The expression levels of plasma MCM3AP-AS1 in COPD patients and healthy controls were measured by quantitative PCR before treatment and at 3 months after the initiation of treatment (post-treatment) from COPD patients. The role of MCM3AP-AS1 in regulating the proliferation of human bronchial smooth muscle cells (HBSMCs) was explored by a cell proliferation assay. RESULTS We found that MCM3AP-AS1 expression was downregulated in the plasma of COPD patients compared with controls. Among controls, MCM3AP-AS1 expression was lower in smokers than never-smokers. A 3-year follow-up study showed that, among smokers, patients with low MCM3AP-AS1 expression showed a higher incidence of COPD. After treatment for COPD, MCM3AP-AS1 expression significantly increased. The cell proliferation assay showed that MCM3AP-AS1 overexpression decreased the proliferation rate of HBSMCs. MCM3AP-AS1 silencing had the opposite effect. CONCLUSIONS MCM3AP-AS1 appears to be downregulated in COPD and to predict its occurrence. MCM3AP-AS1 regulates the proliferation of HBSMCs to participate in airway remodeling.
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Affiliation(s)
| | | | - Kaiyi Liang
- Department of Radiology, Jiading Central Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, P. R. China
| | - Ying Li
- Department of Respiratory Medicine, Jiading Central Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, P. R. China
| | - Yanfang Yu
- Department of Respiratory Medicine, Jiading Central Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, P. R. China
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19
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Leopold JA, Maron BA, Loscalzo J. The application of big data to cardiovascular disease: paths to precision medicine. J Clin Invest 2020; 130:29-38. [PMID: 31895052 DOI: 10.1172/jci129203] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.
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20
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Sarkar D, Maranas CD. SNPeffect: identifying functional roles of SNPs using metabolic networks. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:512-531. [PMID: 32167625 PMCID: PMC9328443 DOI: 10.1111/tpj.14746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/20/2020] [Indexed: 05/04/2023]
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
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21
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Halu A, Liu S, Baek SH, Hobbs BD, Hunninghake GM, Cho MH, Silverman EK, Sharma A. Exploring the cross-phenotype network region of disease modules reveals concordant and discordant pathways between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. Hum Mol Genet 2020; 28:2352-2364. [PMID: 30997486 DOI: 10.1093/hmg/ddz069] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 03/12/2019] [Accepted: 03/23/2019] [Indexed: 12/16/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are two pathologically distinct chronic lung diseases that are associated with cigarette smoking. Genetic studies have identified shared loci for COPD and IPF, including several loci with opposite directions of effect. The existence of additional shared genetic loci, as well as potential shared pathobiological mechanisms between the two diseases at the molecular level, remains to be explored. Taking a network-based approach, we built disease modules for COPD and IPF using genome-wide association studies-implicated genes. The two disease modules displayed strong disease signals in an independent gene expression data set of COPD and IPF lung tissue and showed statistically significant overlap and network proximity, sharing 19 genes, including ARHGAP12 and BCHE. To uncover pathways at the intersection of COPD and IPF, we developed a metric, NetPathScore, which prioritizes the pathways of a disease by their network overlap with another disease. Applying NetPathScore to the COPD and IPF disease modules enabled the determination of concordant and discordant pathways between these diseases. Concordant pathways between COPD and IPF included extracellular matrix remodeling, Mitogen-activated protein kinase (MAPK) signaling and ALK pathways, whereas discordant pathways included advanced glycosylation end product receptor signaling and telomere maintenance and extension pathways. Overall, our findings reveal shared molecular interaction regions between COPD and IPF and shed light on the congruent and incongruent biological processes lying at the intersection of these two complex diseases.
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Affiliation(s)
- Arda Halu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shikang Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Seung Han Baek
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gary M Hunninghake
- Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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22
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Paci P, Fiscon G, Conte F, Licursi V, Morrow J, Hersh C, Cho M, Castaldi P, Glass K, Silverman EK, Farina L. Integrated transcriptomic correlation network analysis identifies COPD molecular determinants. Sci Rep 2020; 10:3361. [PMID: 32099002 PMCID: PMC7042269 DOI: 10.1038/s41598-020-60228-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/23/2020] [Indexed: 12/17/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called "switch genes" - for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.
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Affiliation(s)
- Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Valerio Licursi
- Department of Biology and Biotechnology "Charles Darwin", Sapienza University of Rome, Rome, Italy
| | - Jarrett Morrow
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Craig Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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23
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Maiorino E, Baek SH, Guo F, Zhou X, Kothari PH, Silverman EK, Barabási AL, Weiss ST, Raby BA, Sharma A. Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome. Nat Commun 2020; 11:811. [PMID: 32041952 PMCID: PMC7010776 DOI: 10.1038/s41467-020-14600-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 01/17/2020] [Indexed: 12/21/2022] Open
Abstract
The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network. We focus on asthma and COPD, two chronic respiratory diseases that have been long hypothesized to share common genetic determinants and mechanisms. We show that FC highlights potential mediator genes between the two diseases, and observe similar outcomes when applying FC to 66 additional pairs of related diseases. Further, we perform in vitro perturbation experiments on a widely replicated asthma gene, GSDMB, showing that FC identifies candidate mediators of the interactions between GSDMB and COPD-associated genes. Our results indicate that FC predicts promising gene candidates for further study of disease-disease interactions.
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Affiliation(s)
- Enrico Maiorino
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Network Science Institute, Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA, USA.
| | - Seung Han Baek
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Feng Guo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Parul H Kothari
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Albert-László Barabási
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Network Science Institute, Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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24
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Faria do Valle Í. Recent advances in network medicine: From disease mechanisms to new treatment strategies. Mult Scler 2020; 26:609-615. [DOI: 10.1177/1352458519877002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Conventional reductionist approaches have guided most of our understanding in disease diagnostic and treatment. However, most diseases are not consequence of perturbations in a single protein or metabolite, but rather of the effect that these perturbations have in their cellular context. The emerging field of network medicine offers a set of tools to explore molecular networks and to retrieve insights about mechanisms of different diseases. The study of the protein interactome, the map of physical interactions among human proteins, revealed that disease proteins tend to interact with each other, linking diseases to well-defined interactome neighborhoods. These disease-associated neighborhoods have been defined as disease modules, and they can uncover the biological significance of genes identified by genetic studies, reveal molecular mechanisms that connect different phenotypes, and help identify new pharmacological strategies for disease treatment. Therefore, network medicine offers a framework in which the complexity of different aspects of multiple sclerosis can be explored in an integrative fashion, which can ultimately provide insights about disease mechanisms and treatment.
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Affiliation(s)
- Ítalo Faria do Valle
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA, USA/ Division of Population Health and Data Science, MAVERIC, Boston Veterans Affairs Medical Center, Boston, MA, USA
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25
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Abstract
Although chronic obstructive pulmonary disease (COPD) risk is strongly influenced by cigarette smoking, genetic factors are also important determinants of COPD. In addition to Mendelian syndromes such as alpha-1 antitrypsin deficiency, many genomic regions that influence COPD susceptibility have been identified in genome-wide association studies. Similarly, multiple genomic regions associated with COPD-related phenotypes, such as quantitative emphysema measures, have been found. Identifying the functional variants and key genes within these association regions remains a major challenge. However, newly identified COPD susceptibility genes are already providing novel insights into COPD pathogenesis. Network-based approaches that leverage these genetic discoveries have the potential to assist in decoding the complex genetic architecture of COPD.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA;
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26
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Sumathipala M, Maiorino E, Weiss ST, Sharma A. Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION. Front Physiol 2019; 10:888. [PMID: 31379598 PMCID: PMC6646690 DOI: 10.3389/fphys.2019.00888] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Accepted: 06/26/2019] [Indexed: 11/13/2022] Open
Abstract
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.
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Affiliation(s)
- Marissa Sumathipala
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Harvard College, Cambridge, MA, United States
| | - Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States.,Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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27
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, United States
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