Overview of data analysis on COPD cohort.

<p>Overview of data analysis on COPD cohort.</p

Correlation network illustrating functional co-clustering of analytes associated with FEV₁, FEV₁/FVC and DLCO.

<p>Analytes are plotted in a network using Cytoscape <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038629#pone.0038629-Shannon1" target="_blank">[83]</a> where nodes represent analytes and edges represent significant correlations (<i>r</i> >0.4, <i>p</i><0.05, corrected for multiple testing). Analytes are colored according to whether they were associated with FEV₁ related parameters (green), DLCO (red) or both DLCO and FEV₁ related parameters (orange) in univariate regression. Node size is proportional to the number of lung function parameters that showed significant association with a given analyte. Clusters of co-expressed analytes with similar function are highlighted by dotted regions in the graph as neutrophil function (orange), systemic inflammation (blue) and growth factor pathways (grey).</p

Univariate regression analysis of protein analytes versus lung function parameters in COPD subjects with and without metabolic syndrome.

<p>Significance (<i>p</i> values) and effect sizes (spearman correlation) are listed for biomarker associations with lung function parameters. Interaction <i>p</i> values indicate significance of differences in biomarker associations with lung function parameters, between metabolic syndrome and non- metabolic syndrome groups.</p

Association of MPO with FEV₁/FVC and Fibrinogen with DLCO in COPD patients with and without metabolic syndrome.

<p>Log2-transformed levels of MPO (A, C) and Fibrinogen (B, D) (ng/ml for MPO and mg/dl for Fibrinogen) are plotted against covariate adjusted values for FEV₁/FVC and DLCO, respectively in COPD patients with (A, B) and without (C, D) metabolic syndrome (<i>r</i> values indicate spearman correlation, covariates include age, sex, BMI, pack years and smoking status).</p

Multivariate analysis of protein analyte data for COPD subjects.

<p>Spearman correlation and adjusted R squared values were computed using test set samples, in a 5-fold nested cross-validation scheme, averaged over 10 random seeds. R squared values were adjusted for the number of predictor terms in the model.</p

Protein analyte differences between COPD and control disease severity groups.

<p>Data are expressed as median (interquartile range) in ng/ml for individual analytes, except for Fibrinogen which is in mg/dl.</p><p>All analyte data shown are from profiling on the RBM Luminex platform, except for Fibrinogen which was tested at Hospital Grosshansdorf. COPD subjects were grouped as GOLD I/II (mild/moderate) and GOLD III/IV (severe/very severe). ANOVA was used for group-wise comparisons, except for analytes noted with *, which did not follow a normal distribution and a non-parametric Kruskal Wallis test was used.</p

Persistence of Smoking-Induced Dysregulation of MiRNA Expression in the Small Airway Epithelium Despite Smoking Cessation

<div><p>Even after quitting smoking, the risk of the development of chronic obstructive pulmonary disease (COPD) and lung cancer remains significantly higher compared to healthy nonsmokers. Based on the knowledge that COPD and most lung cancers start in the small airway epithelium (SAE), we hypothesized that smoking modulates miRNA expression in the SAE linked to the pathogenesis of smoking-induced airway disease, and that some of these changes persist after smoking cessation. SAE was collected from 10^th to 12^th order bronchi using fiberoptic bronchoscopy. Affymetrix miRNA 2.0 arrays were used to assess miRNA expression in the SAE from 9 healthy nonsmokers and 10 healthy smokers, before and after they quit smoking for 3 months. Smoking status was determined by urine nicotine and cotinine measurement. There were significant differences in the expression of 34 miRNAs between healthy smokers and healthy nonsmokers (p<0.01, fold-change >1.5), with functions associated with lung development, airway epithelium differentiation, inflammation and cancer. After quitting smoking for 3 months, 12 out of the 34 miRNAs did not return to normal levels, with Wnt/β-catenin signaling pathway being the top identified enriched pathway of the target genes of the persistent dysregulated miRNAs. In the context that many of these persistent smoking-dependent miRNAs are associated with differentiation, inflammatory diseases or lung cancer, it is likely that persistent smoking-related changes in SAE miRNAs play a role in the subsequent development of these disorders.</p></div

Top function categories of miRNAs with significantly different expression in the small airway epithelium of healthy smokers <i>vs</i> healthy nonsmokers¹.

<p>¹ miRNA functional category was analyzed by “Tool for annotations of meaningful human miRNAs categories” (TAM; <a href="http://202.38.126.151/hmdd/tools/tam.html" target="_blank">http://202.38.126.151/hmdd/tools/tam.html</a>). The list of microRNAs for each category can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120824#pone.0120824.s006" target="_blank">S1 Table</a>.</p><p>² TAM includes 24 miRNA function categories.</p><p>³ Count = number of miRNAs matched to the functional category.</p><p>⁴ Percent of matched miRNA/total number of miRNA in the function category.</p><p>⁵ Significance of enrichment.</p><p>Top function categories of miRNAs with significantly different expression in the small airway epithelium of healthy smokers <i>vs</i> healthy nonsmokers<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120824#t003fn001" target="_blank">¹</a>.</p

Target genes of smoking cessation persistently altered miRNAs in the human SAE.

<p>A: Numbers of predicted target genes for each of the persistent miRNAs. The target genes prediction was based on Target Scan 6.2 database. Only genes that are expressed in human SAE were used for the analysis. Blue, target genes of persistent-down-regulated miRNAs. Red, target genes of persistent-up-regulated miRNA. The persistently dysregulated miRNAs with the highest target gene number were miR-218 and miR-133a and miR-133b targets. B: Top 10 enriched canonical pathways in the target genes of the smoking cessation persistent miRNAs. The analysis was performed using Ingenuity Pathway Analysis software. X axis,-log of p value, Fisher's exact test. The ratio of genes that were targeted by the smoking cessation persistent-miRNA in each pathway are indicated. C: Wnt/β-catenin signaling pathway associated with persistently dysregulated miRNAs despite smoking cessation. The Wnt pathway genes that are targets of the smoking cessation persistent miRNAs are highlighted by yellow. The correspondent miRNAs are marked red, with the number of red dots corresponding to the number of miRNAs targeted toward each gene. Many Wnt pathway ligands, receptors, effectors and regulators are potential targets of the smoking cessation persistent miRNAs. Abbreviations: IGF-1, insulin-like growth factor 1; ERK5, Extracellular signal-regulated kinase 5; MAPK, mitogen-activated protein kinase.</p

miRNAs Significantly Up- and Down-regulated in the Small Airway Epithelium by Cigarette Smoking¹.

<p>¹ Affymetrix miRNA 2.0 arrays.</p><p>² Fold-change: healthy smokers <i>vs</i> healthy nonsmokers.</p><p>³ p value: healthy smokers <i>vs</i> healthy nonsmokers.</p><p>miRNAs Significantly Up- and Down-regulated in the Small Airway Epithelium by Cigarette Smoking<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120824#t002fn001" target="_blank">¹</a>.</p