Table 1.
Human subjects and associated clinical parameters1.
Fig 1.
Untargeted metabolite profiling identifies differentially-expressed serum metabolites among COPD smokers, healthy smokers, and non-smokers (P<0.05).
(A) A total of 1,181 aligned molecular features detected as positive and negative ions were quantified in all 115 serum samples by untargeted molecular feature extraction. (B) Heat map from hierarchical clustering analysis, showing 314 differentially-expressed metabolites among COPD smokers, healthy smokers and non-smokers. The relative level of expression of each metabolite is represented by a color coded heat map, where red represents an expression higher than the mean and blue represents an expression below the mean values. (C) Venn Diagram depicts shared metabolite changes among COPD smokers, healthy smokers and non-smokers (p < 0.05). Of these, 62 metabolites were significantly different (P<0.05) between healthy smokers and COPD smokers. And 17 of these 62 metabolites were differentially-expressed in COPD smokers compared to both healthy smokers and non-smokers, but showed no difference between healthy smokers and non-smokers.
Table 2.
Differential metabolites between COPD smoker and healthy smoker compared to non-smokers.
Fig 2.
MS/MS fragmentation of a doubly-charged ion ([M+2H] = 697.97 Da) reveals the sequence of peptide as pyroEGVNDNEEGFFSA.
Fragments derived from ion fragmentation at 30 EV match the amino acid sequence of fibrinogen peptide B with truncated C-terminal arginine and N-terminal amino acid modified as pyroglutamate.
Fig 3.
Significant positive correlations of 4 unknown peptides (doubly-charged ions) with des-arginine-fibrinogen peptide B in 115 human serum samples (Pearson correlation coefficient R > 0.67, P < 9.3E-18).
Table 3.
Differential metabolites between COPD smokers compared to healthy smokers1.
Table 4.
Correlation of serum metabolites with clinical lung parameters (Pearson correlation).
Fig 4.
Characterization of a PLS-DA prediction model for identification COPD smokers, based on 23 serum metabolites.
(A) Three-dimensional score plot of PLS-DA classification model showing the partial separation of COPD smokers from healthy smokers. (B) Diagnostic statistics for the presented PLS-DA model, as area under the receiver operator characteristics curve (AUC). The AUC was 0.87 with a 95% confidence interval of 0.761–0.957 for the constructed model. (C) The location of original between-sum squares and the within-sum squares (B/W-ratio), relative to the distribution histogram of 2000 permuted B/W ratios. The B/W ratio of the original classification assignment (COPD smokers vs. healthy smoker.) was further to the right of the distribution, with a permutation P value less than 5.0 x e-4, affirming the statistical significance and validity of the PLS-DA model.