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

Overview of data analysis on COPD cohort.

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

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

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

Potential miR-223 targets are repressed in a miR-223 −/− system.

<p>A) miR-223 expression across the profiled cell types (bars) is plotted against the relative expression profile (lines) of 82 genes identified as potential miR-223 targets (TargetScan, significant negative correlation). Red line represent mean expression profile of target genes, dotted line represents mean expression across cell types. B) 82 genes were identified in our study as being significant miR-223 targets. We used the data from a previously published miR-223 −/− system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029979#pone.0029979-Baek1" target="_blank">[25]</a> to see if those targets would correspondingly be de-repressed when miR-223 is knocked-out. 62 of these 82 genes had matching mouse homologs (in red). The change in expression of these genes was compared against all TargetScan predicted miRNA target genes, which included predicted targets not negatively correlated with miRNA expression in our dataset (234 genes, in blue). Fold-change for all probe sets is also plotted in this figure as a null distribution (black).</p

Significant overlap observed between Roche and HUG datasets.

<p>A) Excluding mDCs and pDCs, 749 genes were identified as cell-type specific in the Roche dataset, compared to 672 in HUG dataset. 416 genes were common to both (<i>p</i><2.2e-16). The Jaccard coefficient (i.e. the intersection to union ratio), which measures sample set similarity, is 0.41. B) Excluding mDCs and pDCs, 35 miRNAs were identified as cell-type specific in the Roche dataset, compared to 54 in HUG dataset. 24 miRNAs were common to both (<i>p</i><2.2e-16) with a Jaccard coefficient of 0.37. C) 6 miRNAs were significantly negatively correlated with their TargetScan predicted target genes in the Roche dataset, compared to 21 in the HUG dataset. 4 miRNAs were common to both datasets with a Jaccard coefficient of 0.17.</p

Cell type specific expression of miRNAs.

<p>miRNAs were grouped based on specificity to one, two or three cell types. <b>A</b>) miR-143 and miR-31 were specific to neutrophils and T cells respectively, while <b>B</b>) miR-362 and miR-125 were specific to monocytes, pDCs and T cells, neutrophils. <b>C</b>) miR-223 was specific to myeloid lineage cells (neutrophils, eosinophils and monocytes), whereas miR-155 was specific to lymphoid lineage cells (pDCs, T cells, B cells and NK cells).</p

6 cell-type specific miRNAs are negatively correlated with significant numbers of target mRNAs as predicted by TargetScan.

<p>6 cell-type specific miRNAs are negatively correlated with significant numbers of target mRNAs as predicted by TargetScan.</p