Proteomic profiling of lung immune cells reveals dysregulation of phagocytotic pathways in female-dominated molecular COPD phenotype

Yang, Mingxing; Kohler, Maxie; Heyder, Tina; Forsslund, Helena; Garberg, Hilde; Karimi, Reza; Grünewald, Johan; Berven, Frode S.; Nyrén, Sven; Sköld, C. Magnus; Wheelock, Åsa M.

doi:10.1186/s12931-017-0699-2

Cited by 24 publications

(27 citation statements)

References 46 publications

(50 reference statements)

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“…Some of our highlighted genes are involved in smoke. Additionally, dysregulation of the lysosomal pathway has also been previously 375 described in COPD patients [59]. 376 We observed some down-regulated genes in the adherens junction pathway for modules for each cohort [17].…”

supporting

confidence: 60%

“…Our meta-analysis also highlighted disease genes that 48 interact with smoking status, and these genes can be used to further characterize the different microarray platforms: Affymetrix GeneChip Human Genome U133 Plus 2.0, 55 Affymetrix Human Gene 1.1 ST Array and Agilent Whole Human Genome Microarray 56 4x44K. Our current meta-analysis pipeline (similar to Brooks et al [18]), included 5 57 main steps (Fig 1): (1) data curation; (2) pre-processing of raw expression data; (3) 58 analysis of variance (ANOVA) on our linear model which compared gene expression 59 changes due to disease state, smoking status, sex and age group; (4) post-hoc analysis 60 using Tukey Honest Significance Difference test (TukeyHSD) for biological significance; 61 and (5) Gene ontology (GO) and pathway enrichment analysis of the differentially 62 expressed and biologically significant genes. 63 Microarray Data Curation from Gene Expression Omnibus and 64 Array Express 65 To gather the datasets for our meta-analysis, we searched the National Center for 66 Biotechnology Information (NCBI)'s data repository, Gene Expression Omnibus 67 (GEO) [19], and the European Bioinformatics Institute (EMBL-EBI)'s data repository, 68 Array Express (AE) [20] for microarray expression data.…”

Section: Introductionmentioning

confidence: 99%

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Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Rogers

Verlinde

Mias

2019

Preprint

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Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3 rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls. We used pre-existing, publicly available microarray expression datasets to conduct a meta-analysis. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, smoking status and study as effects that also included binary interactions. We found 1,513 statistically significant (Benjamini-Hochberg-adjusted p-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from a Tukey test post-hoc analysis (Benjamini-Hochberg-adjusted p-value <0.05 and 10% two-tailed quantiles of mean differences between COPD and control), to identify 304 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the 304 genes as features, which enabled prediction of disease status with 84% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures. 2 capacity. In COPD there is inflammation of the bronchial tubes (chronic bronchitis) [1] 3

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supporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Rogers

Verlinde

Mias

2019

Preprint

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“…This study used omics data blocks from the Karolinska COSMIC cohort (ClinicalTrials.gov ID: NCT02627872), a three-group cross-sectional study [17][18][19][20][21][22][23][24][25] with age-matched (45-65 years) and sex-matched groups of healthy never-smokers ("Healthy"), smokers with normal lung function ("Smokers") and COPD patients ("COPD"; Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I-II/A-B; FEV1 51-97%; FEV1/FVC <70) (table E1). Peripheral blood, bronchoalveolar lavage (BAL) and bronchial epithelial cells (BEC) were collected as previously described [17,19].…”

Section: Clinical Cohortmentioning

confidence: 99%

Integration of multi-omics datasets enables molecular classification of COPD

Wheelock

Sköld

et al. 2018

Eur Respir J

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Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.

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“…To investigate the impact of long‐term habitual smoking on the immune cell proteome of BAL, Yang et al applied the iTRAQ (4‐plexed labelling) procedure to BAL cells from never‐smokers (n = 17) and smokers with normal lung function (n = 25). The pathway enrichment analyses performed by the authors revealed that the significantly (p < 0.05) altered proteins (five hundred) were associated with fifteen molecular pathways.…”

Section: Cell Culturementioning

confidence: 99%

“…This shotgun proteomic method was subsequently applied (on BAL cells from smokers with normal lung function and early stage COPD patients) by the same research group to demonstrate gender differences in respiratory symptoms, lung function and molecular markers of inflammation in COPD . While in females one hundred sixty‐four proteins were significantly altered in smokers versus COPD, only twenty four proteins were altered in the corresponding males.…”

Section: Cell Culturementioning

confidence: 99%

Searching for biomarkers of chronic obstructive pulmonary disease using proteomics: The current state

et al. 2018

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Detection of proteins which may be potential biomarkers of disorders represents a big step forward in understanding the molecular mechanisms that underlie pathological processes. In this context proteomics plays the important role of opening a path for the identification of molecular signatures that can potentially assist in early diagnosis of several clinical disturbances. Aim of this report is to provide an overview of the wide variety of proteomic strategies that have been applied to the investigation of chronic obstructive pulmonary disease (COPD), a severe disorder that causes an irreversible damage to the lungs and for which there is no cure yet. The results in this area published over the past decade show that proteomics indeed has the ability of monitoring alterations in expression profiles of proteins from fluids/tissues of patients affected by COPD and healthy controls. However, these data also suggest that proteomics, while being an attractive tool for the identification of novel pathological mediators of COPD, remains a technique mainly generated and developed in research laboratories. Great efforts dedicated to the validation of these biological signatures will result in the proof of their clinical utility.

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Proteomic profiling of lung immune cells reveals dysregulation of phagocytotic pathways in female-dominated molecular COPD phenotype

Cited by 24 publications

References 46 publications

Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Integration of multi-omics datasets enables molecular classification of COPD

Searching for biomarkers of chronic obstructive pulmonary disease using proteomics: The current state

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