1
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Li CL, Liu SF. Exploring Molecular Mechanisms and Biomarkers in COPD: An Overview of Current Advancements and Perspectives. Int J Mol Sci 2024; 25:7347. [PMID: 39000454 PMCID: PMC11242201 DOI: 10.3390/ijms25137347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/30/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.
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Affiliation(s)
- Chin-Ling Li
- Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan;
| | - Shih-Feng Liu
- Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan;
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
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2
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RPJ, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol 2024; 20:e1011814. [PMID: 38527092 PMCID: PMC10994553 DOI: 10.1371/journal.pcbi.1011814] [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: 01/10/2024] [Revised: 04/04/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, Denver, Colorado, United States of America
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Rachel PJ Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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3
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Vaziri Y. The genomic landscape of chronic obstructive pulmonary disease: Insights from nutrigenomics. Clin Nutr ESPEN 2024; 59:29-36. [PMID: 38220389 DOI: 10.1016/j.clnesp.2023.11.017] [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: 09/21/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024]
Abstract
Chronic obstructivе pulmonary disеasе (COPD), a rеspiratory disеasе, is influenced by a combination of gеnеtic and еnvironmеntal factors. Thе fiеld of nutrigеnomics, which studiеs thе intеrplay bеtwееn diеt and gеnеs, provides valuable insights into thе gеnomic landscapе of COPD and its implications for production and managеmеnt. This rеviеw providеs a comprеhеnsivе ovеrviеw of thе gеnеtic aspеcts of COPD and thе rolе of nutrigеnomics in advancing our undеrstanding of thе undеrlying mеchanisms. Through studies of gеnomе-widе associations, researchers have identified gеnеtic factors that contribute to suscеptibility to COPD. Thеsе gеnеs arе associatеd with oxidativе strеss, inflammation, and antioxidant dеfеnsе mеchanisms. Nutrigеnomics rеsеarch is currеntly invеstigating how diеtary componеnts interact with gеnеtic variations to modulatе thе dеvеlopmеnt of COPD. Antioxidants, omеga-3 fatty acids and vitamin D havе dеmonstratеd potеntial bеnеfits in rеducing inflammation, improving lung function, and minimizing еxacеrbations in patients with COPD. Therefore, there are sеvеral challеngеs that must be added to the nutrigеnomic rеsеarch. The challenges include thе nееd for largеr clinical trials, adding hеtеrogеnеity and validating biomarkеrs. In the tеrms of futurе dirеctions, prеcision nutrition, gеnе-basеd thеrapiеs, biomarkеr dеvеlopmеnt, intеgration of multi-omics data, systеms biology analysis, longitudinal studiеs, and public hеalth implications arе important arеas to еxplorе. Pеrsonalizеd nutritional intеrvеntions based on an individual's gеnеtic profilе hold grеat promisе for optimizing COPD managеmеnt. In conclusion, nutrigеnomics provides valuable insights into the gеnomic landscapе of COPD and its intеraction with the disease. This knowlеdgе can guidе thе dеvеlopmеnt of pеrsonalizеd diеtary stratеgiеs and gеnе-basеd thеrapiеs for thе prеvеntion and managеmеnt of COPD. Howеvеr, morе rеsеarch is nееdеd to validatе thеsе findings, dеvеlop еffеctivе intеrvеntions, and implеmеnt thеm еffеctivеly in clinical practicе to improvе thе quality of lifе for pеoplе with COPD.
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Affiliation(s)
- Yashar Vaziri
- Department of Nutrition and Dietetics, Sarab Branch, Islamic Azad University, Sarab, Iran.
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4
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Liu W, Pratte KA, Castaldi PJ, Hersh C, Bowler RP, Banaei-Kashani F, Kechris KJ. A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576667. [PMID: 38328226 PMCID: PMC10849540 DOI: 10.1101/2024.01.22.576667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.
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Affiliation(s)
- Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Peter J. Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Craig Hersh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Russell P. Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, USA
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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5
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Maiorino E, De Marzio M, Xu Z, Yun JH, Chase RP, Hersh CP, Weiss ST, Silverman EK, Castaldi PJ, Glass K. Joint clinical and molecular subtyping of COPD with variational autoencoders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.19.23294298. [PMID: 38260473 PMCID: PMC10802661 DOI: 10.1101/2023.08.19.23294298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the disease's complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.
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Affiliation(s)
- Enrico Maiorino
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Margherita De Marzio
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Zhonghui Xu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Jeong H. Yun
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Robert P. Chase
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School
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6
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RP, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574780. [PMID: 38260498 PMCID: PMC10802464 DOI: 10.1101/2024.01.09.574780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Rachel Pj Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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7
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Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis. TOXICS 2023; 11:1014. [PMID: 38133415 PMCID: PMC10748071 DOI: 10.3390/toxics11121014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/18/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.
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Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
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8
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Angelini ED, Yang J, Balte PP, Hoffman EA, Manichaikul AW, Sun Y, Shen W, Austin JHM, Allen NB, Bleecker ER, Bowler R, Cho MH, Cooper CS, Couper D, Dransfield MT, Garcia CK, Han MK, Hansel NN, Hughes E, Jacobs DR, Kasela S, Kaufman JD, Kim JS, Lappalainen T, Lima J, Malinsky D, Martinez FJ, Oelsner EC, Ortega VE, Paine R, Post W, Pottinger TD, Prince MR, Rich SS, Silverman EK, Smith BM, Swift AJ, Watson KE, Woodruff PG, Laine AF, Barr RG. Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans. Thorax 2023; 78:1067-1079. [PMID: 37268414 PMCID: PMC10592007 DOI: 10.1136/thorax-2022-219158] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/03/2023] [Indexed: 06/04/2023]
Abstract
BACKGROUND Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. METHODS New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. RESULTS The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10-8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. CONCLUSION Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.
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Affiliation(s)
- Elsa D Angelini
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
- LTCI, Institut Polytechnique de Paris, Telecom Paris, Palaiseau, France
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College, London, UK
| | - Jie Yang
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Pallavi P Balte
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Eric A Hoffman
- Departments of Radiology, Medicine and Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Yifei Sun
- Department of Biostatistics, Columbia University Irving Medical Center, New York, New York, USA
| | - Wei Shen
- Department of Pediatrics, Institute of Human Nutrition, Columbia University Irving Medical Center, New York, New York, USA
- Columbia Magnetic Resonance Research Center (CMRRC), Columbia University Irving Medical Center, New York, New York, USA
| | - John H M Austin
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Norrina B Allen
- Institute for Public Health and Medicine (IPHAM) - Center for Epidemiology and Population Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Eugene R Bleecker
- Department of Medicine, University of Arizona Health Sciences, Tucson, Arizona, USA
| | - Russell Bowler
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Christine Kim Garcia
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - MeiLan K Han
- Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Nadia N Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Emlyn Hughes
- Department of Physics, Columbia University, New York, New York, USA
| | - David R Jacobs
- Division of Epidemiology and Community Public Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Silva Kasela
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA
- New York Genome Center, New York, New York, USA
| | - Joel Daniel Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, Washington, USA
| | - John Shinn Kim
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Tuuli Lappalainen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA
| | - Joao Lima
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Daniel Malinsky
- Department of Biostatistics, Columbia University Irving Medical Center, New York, New York, USA
| | - Fernando J Martinez
- Department of Medicine, Cornell University Joan and Sanford I Weill Medical College, New York, New York, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Victor E Ortega
- Department of Pulmonary Medicine, Mayo Clinic, Phoenix, Arizona, USA
| | - Robert Paine
- Department of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Wendy Post
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tess D Pottinger
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Martin R Prince
- Department of Radiology, Cornell University Joan and Sanford I Weill Medical College, New York, New York, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Benjamin M Smith
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Medicine, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Andrew J Swift
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Karol E Watson
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Prescott G Woodruff
- Department of Medicine, University of California, San Francisco, California, USA
| | - Andrew F Laine
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
- Columbia Magnetic Resonance Research Center (CMRRC), Columbia University Irving Medical Center, New York, New York, USA
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York, USA
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9
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Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.30.23296386. [PMID: 37873294 PMCID: PMC10592990 DOI: 10.1101/2023.09.30.23296386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.
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Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
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10
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Sánchez-Valle J, Valencia A. Molecular bases of comorbidities: present and future perspectives. Trends Genet 2023; 39:773-786. [PMID: 37482451 DOI: 10.1016/j.tig.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023]
Abstract
Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.
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Affiliation(s)
- Jon Sánchez-Valle
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain.
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain; ICREA, Barcelona, 08010, Spain.
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11
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Ma C, Liao K, Wang J, Li T, Liu L. L-Arginine, as an essential amino acid, is a potential substitute for treating COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. Cell Biosci 2023; 13:152. [PMID: 37596640 PMCID: PMC10436497 DOI: 10.1186/s13578-023-00994-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 02/20/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUNDS Chronic obstructive pulmonary disease (COPD) is a frequent and common disease in clinical respiratory medicine and its mechanism is unclear. The purpose of this study was to find the new biomarkers of COPD and elucidate its role in the pathogenesis of COPD. Analysis of metabolites in plasma of COPD patients were performed by ultra-high performance liquid chromatography (UPLC) and quadrupole time-of-flight mass spectrometry (TOF-MS). The differential metabolites were analyzed and identified by multivariate analysis between COPD patients and healthy people. The role and mechanisms of the differential biomarkers in COPD were verified with COPD rats, arginosuccinate synthetase 1 (ASS-l) KO mice and bronchial epithelial cells (BECs). Meanwhile, whether the differential biomarkers can be the potential treatment targets for COPD was also investigated. 85 differentials metabolites were identified between COPD patients and healthy people by metabonomic. RESULTS L-Arginine (LA) was the most obvious differential metabolite among the 85 metabolites. Compare with healthy people, the level of LA was markedly decreased in serum of COPD patients. It was found that LA had protective effects on COPD with in vivo and in vitro experiments. Silencing Ass-1, which regulates LA metabolism, and α-methy-DL-aspartic (NHLA), an Ass-1 inhibitor, canceled the protective effect of LA on COPD. The mechanism of LA in COPD was related to the inhibition of ROS/NLRP3/NF-κB signaling pathway. It was also found that exogenous LA significantly improved COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. L-Arginine (LA) as a key metabolic marker is identified in COPD patients and has a protective effect on COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. CONCLUSION LA may be a novel target for the treatment of COPD and also a potential substitute for treating COPD.
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Affiliation(s)
- Chunhua Ma
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Tranfusion Research, Department of Army Medical Center, Army Medical University, Chongqing, 400042, People's Republic of China
- The Affiliated Nanjing Hospital of Nanjing University of Chinese Medicine, Nanjing, 210001, China
| | - Kexi Liao
- Institute of Hepatobiliary Surgery, First Affiliated Hospital, Army Medical University, Shapingba District, Gaotanyan Road 30, Chongqing, 400038, China
| | - Jing Wang
- School of Biology and Food Engineering, Institute of Pharmaceutical Biotechnology, Suzhou University, Anhui, China
| | - Tao Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Tranfusion Research, Department of Army Medical Center, Army Medical University, Chongqing, 400042, People's Republic of China.
| | - Liangming Liu
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Tranfusion Research, Department of Army Medical Center, Army Medical University, Chongqing, 400042, People's Republic of China.
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12
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Zhuang Y, Xing F, Ghosh D, Hobbs BD, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris K. Deep learning on graphs for multi-omics classification of COPD. PLoS One 2023; 18:e0284563. [PMID: 37083575 PMCID: PMC10121008 DOI: 10.1371/journal.pone.0284563] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications.
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Affiliation(s)
- Yonghua Zhuang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Biostatistics Shared Resource, University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States of America
| | | | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
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Hobbs BD, Morrow JD, Wang XW, Liu YY, DeMeo DL, Hersh CP, Celli BR, Bueno R, Criner GJ, Silverman EK, Cho MH. Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue. BMC Pulm Med 2023; 23:115. [PMID: 37041558 PMCID: PMC10091624 DOI: 10.1186/s12890-023-02389-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 03/15/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known. METHODS We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC). RESULTS SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells. CONCLUSIONS Unsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.
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Affiliation(s)
- Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA.
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Jarrett D Morrow
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bartolome R Celli
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Raphael Bueno
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Gerard J Criner
- Division of Pulmonary and Critical Care Medicine, Temple University School of Medicine, Philadelphia, PA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Rm 460, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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14
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Gea J, Enríquez-Rodríguez CJ, Pascual-Guardia S. Metabolomics in COPD. Arch Bronconeumol 2023; 59:311-321. [PMID: 36717301 DOI: 10.1016/j.arbres.2022.12.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/28/2022] [Accepted: 12/07/2022] [Indexed: 01/20/2023]
Abstract
The clinical presentation of chronic obstructive pulmonary disease (COPD) is highly heterogeneous. Attempts have been made to define subpopulations of patients who share clinical characteristics (phenotypes and treatable traits) and/or biological characteristics (endotypes), in order to offer more personalized care. Assigning a patient to any of these groups requires the identification of both clinical and biological markers. Ideally, biological markers should be easily obtained from blood or urine, but these may lack specificity. Biomarkers can be identified initially using conventional or more sophisticated techniques. However, the more sophisticated techniques should be simplified in the future if they are to have clinical utility. The -omics approach offers a methodology that can assist in the investigation and identification of useful markers in both targeted and blind searches. Specifically, metabolomics is the science that studies biological processes involving metabolites, which can be intermediate or final products. The metabolites associated with COPD and their specific phenotypic and endotypic features have been studied using various techniques. Several compounds of particular interest have emerged, namely, several types of lipids and derivatives (mainly phospholipids, but also ceramides, fatty acids and eicosanoids), amino acids, coagulation factors, and nucleic acid components, likely to be involved in their function, protein catabolism, energy production, oxidative stress, immune-inflammatory response and coagulation disorders. However, clear metabolomic profiles of the disease and its various manifestations that may already be applicable in clinical practice still need to be defined.
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Affiliation(s)
- Joaquim Gea
- Servicio de Neumología, Hospital del Mar - IMIM, Barcelona, Spain; Dpt. MELIS, Universitat Pompeu Fabra, Barcelona, Spain; CIBERES, ISCIII, Barcelona, Spain.
| | - César J Enríquez-Rodríguez
- Servicio de Neumología, Hospital del Mar - IMIM, Barcelona, Spain; Dpt. MELIS, Universitat Pompeu Fabra, Barcelona, Spain
| | - Sergi Pascual-Guardia
- Servicio de Neumología, Hospital del Mar - IMIM, Barcelona, Spain; Dpt. MELIS, Universitat Pompeu Fabra, Barcelona, Spain; CIBERES, ISCIII, Barcelona, Spain
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15
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Gao J, Liu H, Wang X, Wang L, Gu J, Wang Y, Yang Z, Liu Y, Yang J, Cai Z, Shu Y, Min L. Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease. Front Med (Lausanne) 2023; 9:1030644. [PMID: 36714109 PMCID: PMC9877466 DOI: 10.3389/fmed.2022.1030644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/22/2022] [Indexed: 01/14/2023] Open
Abstract
We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.
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Affiliation(s)
- Junyin Gao
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongjun Liu
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Xiaolin Wang
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Liping Wang
- Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Jianjun Gu
- Department of Cardiology, Institute of Translational Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yuxiu Wang
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Zhiguang Yang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yunpeng Liu
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Jingjing Yang
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Zhibin Cai
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yusheng Shu
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China,Yusheng Shu ✉
| | - Lingfeng Min
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China,*Correspondence: Lingfeng Min ✉
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16
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Godbole S, Bowler RP. Metabolome Features of COPD: A Scoping Review. Metabolites 2022; 12:621. [PMID: 35888745 PMCID: PMC9324381 DOI: 10.3390/metabo12070621] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex heterogeneous disease state with multiple phenotypic presentations that include chronic bronchitis and emphysema. Although COPD is a lung disease, it has systemic manifestations that are associated with a dysregulated metabolome in extrapulmonary compartments (e.g., blood and urine). In this scoping review of the COPD metabolomics literature, we identified 37 publications with a primary metabolomics investigation of COPD phenotypes in human subjects through Google Scholar, PubMed, and Web of Science databases. These studies consistently identified a dysregulation of the TCA cycle, carnitines, sphingolipids, and branched-chain amino acids. Many of the COPD metabolome pathways are confounded by age and sex. The effects of COPD in young versus old and male versus female need further focused investigations. There are also few studies of the metabolome's association with COPD progression, and it is unclear whether the markers of disease and disease severity are also important predictors of disease progression.
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Affiliation(s)
- Suneeta Godbole
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Russell P. Bowler
- Division of Medicine, National Jewish Health, Denver, CO 80206, USA;
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17
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Joean O, Welte T. Vaccination and modern management of chronic obstructive pulmonary disease - a narrative review. Expert Rev Respir Med 2022; 16:605-614. [PMID: 35713962 DOI: 10.1080/17476348.2022.2092099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Chronic obstructive pulmonary disease (COPD) carries a tremendous societal and individual burden, posing significant challenges for public health systems worldwide due to its high morbidity and mortality. Due to aging and multimorbidity but also in the wake of important progress in deciphering the heterogeneous disease endotypes, an individualized approach to the prevention and management of COPD is necessary. AREAS COVERED This article tackles relevant immunization strategies that are available or still under development with a focus on the latest evidence but also controversies around different regional immunization approaches. Further, we present the crossover between chronic lung inflammation and lung microbiome disturbance as well as its role in delineating COPD endotypes. Moreover, the article attempts to underline endotype-specific treatment approaches. Lastly, we highlight non-pharmacologic prevention and management programs in view of the challenges and opportunities of the COVID-19 era. EXPERT OPINION Despite the remaining challenges, personalized medicine has the potential to offer tailored approaches to prevention and therapy and promises to improve the care of patients living with COPD.
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Affiliation(s)
- Oana Joean
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany
| | - Tobias Welte
- Department of Respiratory Medicine, Hannover Medical School, Hannover, Germany.,Biomedical Research in Endstage and Obstructive Lung Disease, Member of the German Center for Lung Research, Hannover, Germany
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18
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Reiss JD, Peterson LS, Nesamoney SN, Chang AL, Pasca AM, Marić I, Shaw GM, Gaudilliere B, Wong RJ, Sylvester KG, Bonifacio SL, Aghaeepour N, Gibbs RS, Stevenson DK. Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations. Exp Neurol 2022; 351:113988. [DOI: 10.1016/j.expneurol.2022.113988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 01/06/2022] [Accepted: 01/13/2022] [Indexed: 11/26/2022]
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