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      The lung extracellular matrix protein landscape in severe early-onset and moderate chronic obstructive pulmonary disease

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          Abstract

          Our study identified chronic obstructive pulmonary disease (COPD)-associated differences in the lung extracellular matrix (ECM) composition. We highlight the compartmental differences in the ECM landscape in different subtypes of COPD. The most prominent differences were observed for severe-early onset COPD. Moreover, we identified unique ECM signatures that describe airway walls and parenchyma providing insight into the intertwined nature and complexity of ECM changes in COPD that together drive ECM remodeling and may contribute to disease pathogenesis.

          Abstract

          Extracellular matrix (ECM) remodeling has been implicated in the irreversible obstruction of airways and destruction of alveolar tissue in chronic obstructive pulmonary disease (COPD). Studies investigating differences in the lung ECM in COPD have mainly focused on some collagens and elastin, leaving an array of ECM components unexplored. We investigated the differences in the ECM landscape comparing severe-early onset (SEO)-COPD and moderate COPD to control lung tissue for collagen type I α chain 1 (COL1A1), collagen type VI α chain 1 (COL6A1); collagen type VI α chain 2 (COL6A2), collagen type XIV α chain 1 (COL14A1), fibulin 2 and 5 (FBLN2 and FBLN5), latent transforming growth factor β binding protein 4 (LTBP4), lumican (LUM), versican (VCAN), decorin (DCN), and elastin (ELN) using image analysis and statistical modeling. Percentage area and/or mean intensity of expression of LUM in the parenchyma, and COL1A1, FBLN2, LTBP4, DCN, and VCAN in the airway walls, was proportionally lower in COPD compared to controls. Lowered levels of most ECM proteins were associated with decreasing forced expiratory volume in 1 s (FEV 1) measurements, indicating a relationship with disease severity. Furthermore, we identified six unique ECM signatures where LUM and COL6A1 in parenchyma and COL1A1, FBLN5, DCN, and VCAN in airway walls appear essential in reflecting the presence and severity of COPD. These signatures emphasize the need to examine groups of proteins to represent an overall difference in the ECM landscape in COPD that are more likely to be related to functional effects than individual proteins. Our study revealed differences in the lung ECM landscape between control and COPD and between SEO and moderate COPD signifying distinct pathological processes in the different subgroups.

          NEW & NOTEWORTHY Our study identified chronic obstructive pulmonary disease (COPD)-associated differences in the lung extracellular matrix (ECM) composition. We highlight the compartmental differences in the ECM landscape in different subtypes of COPD. The most prominent differences were observed for severe-early onset COPD. Moreover, we identified unique ECM signatures that describe airway walls and parenchyma providing insight into the intertwined nature and complexity of ECM changes in COPD that together drive ECM remodeling and may contribute to disease pathogenesis.

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          Most cited references44

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Is Open Access

            Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis

            Summary Background Chronic obstructive pulmonary disease (COPD) is an increasingly important cause of morbidity, disability, and mortality worldwide. We aimed to estimate global, regional, and national COPD prevalence and risk factors to guide policy and population interventions. Methods For this systematic review and modelling study, we searched MEDLINE, Embase, Global Health, and CINAHL, for population-based studies on COPD prevalence published between Jan 1, 1990, and Dec 31, 2019. We included data reported using the two main case definitions: the Global Initiative for Chronic Obstructive Lung Disease fixed ratio (GOLD; FEV1/FVC<0·7) and the lower limit of normal (LLN; FEV1/FVC
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              Lung-Function Trajectories Leading to Chronic Obstructive Pulmonary Disease.

              Chronic obstructive pulmonary disease (COPD) is thought to result from an accelerated decline in forced expiratory volume in 1 second (FEV1) over time. Yet it is possible that a normal decline in FEV1 could also lead to COPD in persons whose maximally attained FEV1 is less than population norms.

                Author and article information

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                Journal
                American Journal of Physiology-Lung Cellular and Molecular Physiology
                American Journal of Physiology-Lung Cellular and Molecular Physiology
                American Physiological Society
                1040-0605
                1522-1504
                September 01 2024
                September 01 2024
                : 327
                : 3
                : L304-L318
                Affiliations
                [1 ]University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, The Netherlands
                [2 ]University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
                [3 ]Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
                [4 ]University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
                [5 ]University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, The Netherlands
                [6 ]University of Groningen, Department of Molecular Pharmacology, Groningen, The Netherlands
                [7 ]University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences, KOLFF Institute, Groningen, The Netherlands
                Article
                10.1152/ajplung.00332.2023
                38915286
                3620b4b9-c755-4856-bb5c-4fede36a7703
                © 2024
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