Additional file 1: Table S1. of Multiple biomarkers predict disease severity, progression and mortality in COPD

Association Between Biomarkers and COPD Outcomes. Table S2. Statistical Models. Table S3. Demographics of Subjects at Baseline: COPDGene Cohort*. Table S4. Demographics of Subjects at Baseline: ECLIPSE Cohort*. Table S5. Analysis of COPDGene cohort. Grey shading indicates each model with lines for each biomarker in that model. Columns are beta coefficient in model (B), odds ratio, standard error (SE), correlation coefficient (R2) or pseudo R2 Cragg and Uhler’s (CU) or R2m (the marginal portion of the R2), Akaike Information Criteria (AIC), and number of subjects analyzed (N). The type of model is listed on top right of table. The best model highlighted in yellow. Table S6. Analysis of ECLIPSE cohort. Best model in ECLIPSE cohort highlighted in yellow. Grey shading indicates each model with lines for each biomarker in that model. Columns are beta coefficient in model (B), odds ratio, standard error (SE), correlation coefficient (R2) or pseudo R2 Cragg and Uhler’s (CU) or R2m (the marginal portion of the R2), Akaike Information Criteria (AIC), and number of subjects analyzed (N). The type of model is listed on top right of table. Best model in COPDGene cohort in red font. Table S7. Biomarkers Associated with FEV1/FVC. Table S8. Biomarkers Associated with (A) Total (Moderate and Severe) Exacerbations and (B) Severe Exacerbations in the Previous 12 Months. Table S9. Biomarkers Associated with (A) Prospective Total (Moderate and Severe) Exacerbations or (B) Prospective Severe Exacerbations. Table S10. Enrollment Centers. Table S11. Baseline Characteristics of Subjects with Biomarker Data Compared with Entire COPDGene Cohort. Table S12. Correlation Between Biomarkers. Table S13. Biomarkers Associated with Mortality. Analysis of COPDGene and ECLIPSE cohorts by C-statistic. Covariates were BODE, age, age2, gender, and severe exacerbations. (ZIP 485 kb

Additional file 3: of Multiple biomarkers predict disease severity, progression and mortality in COPD

Supplemental Methods. (DOCX 76 kb

Additional file 2: Figure S1. of Multiple biomarkers predict disease severity, progression and mortality in COPD

Distribution of Biomarkers. Biomarker levels were log transformed. Figure S2. Relationship Between Individual Biomarkers and FEV1. Beeswarm/box plot of biomarker levels in never smokers, smokers with normal lung function PRISm, and Gold Stage 1–4 COPD patients. Central box bars represent the median and end box bars represent the first and third quartiles. Analysis by linear regression. *p < 10−5. Figure S3. Relationship Between Individual Biomarkers and Emphysema. Analysis performed by ordinal logistic regression. Covariates were FEV1, age, smoking status, gender, race, and BMI. % Emphysema defined as % of voxels with HU < −950. *p < 0.01. (PDF 312 kb

Top 10 module membership genes.

<p>The module membership of a gene (kME) is the correlation between the gene and the eigengene of the module. For the black, salmon, red and greenyellow modules, we list the top 10 kME genes based on the sum of ranks in the COPDGene and ECLIPSE cohort. The ranks were based on kME. For COPDGene and ECLIPSE, the correlation direction between the gene and the phenotypes are represented by “+” and “-”, if the correlation is significant at <i>α</i> = 0.05.</p

Demographics.

<p>Demographics.</p

Panther results for biological processes.

<p>For each of the salmon, red, greenyellow, and black module, the top 10 enriched biological processes GO terms are presented. The reference list consists of the 6322 genes that were mapped by PANTHER and were common to all data sets and used for module construction. The # Hits denotes the number of genes in the module that are in the GO term. Fold Enrichment gives the overrepresentation. The <i>p</i>-values were calculated by the PANTHER software based on the binomial distribution. They were adjusted based on the Benjamini-Hochberg procedure on a per module basis, considering all tests for biological processes and molecular function (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185682#pone.0185682.s012" target="_blank">S2 Table</a> for molecular function GO term results).</p

Consensus module-trait relationships across COPDGene, ECLIPSE, and TESRA for cases only.

<p><i>Z</i>-scores and meta <i>p</i>-values for FEV₁% and FEV₁/FVC were based on COPDGene, ECLIPSE, and TESRA cases. The meta <i>p</i>-value for emphysema was based on COPDGene and ECLIPSE only, since the definition of emphysema in TESRA differed (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185682#sec002" target="_blank">Materials and methods</a>). As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185682#pone.0185682.g002" target="_blank">Fig 2</a>, the relationship information is given in terms of <i>Z</i>-scores rather than correlations.</p

Overrepresentation of cell-type specific genes in modules.

<p>Only overrepresentations significant at level <i>α</i> = 0.05 are displayed for readability. For a complete table of <i>p</i>-values, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185682#pone.0185682.s013" target="_blank">S3 Table</a>. The 11 groups of distinct immune cell types considered consist of eosinophils (eos), basophils/mast cells (mast_baso), dendritic cells (dendr), neutrophils (neut), b-cells, t-cells, NK-cells, t-helper cells (thelp), monocyte Lipopolysaccharides (LPS) day 0 stimulation, monocyte LPS day 1 stimulation, monocyte LPS day 7 stimulation (mono_d0, mono_d1, mono_d7, respectively).</p

Consensus module−trait relationships across COPDGene and ECLIPSE for cases and controls.

<p>Meta-analysis <i>Z</i>-scores and <i>p</i>-values were based on cases and controls from COPDGene and ECLIPSE. The sign and magnitude of the <i>Z</i>-score give information about the overall direction and magnitude of association.</p

Gene and metabolite time-course response to cigarette smoking in mouse lung and plasma

<div><p>Prolonged cigarette smoking (CS) causes chronic obstructive pulmonary disease (COPD), a prevalent serious condition that may persist or progress after smoking cessation. To provide insight into how CS triggers COPD, we investigated temporal patterns of lung transcriptome expression and systemic metabolome changes induced by chronic CS exposure and smoking cessation. Whole lung RNA-seq data was analyzed at transcript and exon levels from C57Bl/6 mice exposed to CS for 1- or 7 days, for 3-, 6-, or 9 months, or for 6 months followed by 3 months of cessation using age-matched littermate controls. We identified previously unreported dysregulation of pyrimidine metabolism and phosphatidylinositol signaling pathways and confirmed alterations in glutathione metabolism and circadian gene pathways. Almost all dysregulated pathways demonstrated reversibility upon smoking cessation, except the lysosome pathway. Chronic CS exposure was significantly linked with alterations in pathways encoding for energy, phagocytosis, and DNA repair and triggered differential expression of genes or exons previously unreported to associate with CS or COPD, including <i>Lox</i>, involved in matrix remodeling, <i>Gp2</i>, linked to goblet cells, and <i>Slc22a12</i> and <i>Agpat3</i>, involved in purine and glycerolipid metabolism, respectively. CS-induced lung metabolic pathways changes were validated using metabolomic profiles of matched plasma samples, indicating that dynamic metabolic gene regulation caused by CS is reflected in the plasma metabolome. Using advanced technologies, our study uncovered novel pathways and genes altered by chronic CS exposure, including those involved in pyrimidine metabolism, phosphatidylinositol signaling and lysosome function, highlighting their potential importance in the pathogenesis or diagnosis of CS-associated conditions.</p></div