Fig 1.
Principal component analysis of taxonomy and functional classifications.
PCA plots were created using (A) taxonomic and (B) functional classifications, using the analysis method detailed previously. Control samples are coloured blue and COPD red. Triangles indicate patients who are current smokers, and black circles indicate the patient has antibiotic use in their medical history prior to giving a sample. PCA plots drawn using normalised values and Manhattan distance.
Fig 2.
‘Core microbiome’ differences between Control and COPD participants.
Abundance of the 14 bacterial species that constitute the ‘core microbiome’ in Control participants and COPD patients. Four bacterial species were found in all samples from both groups, four species were found in all of the COPD samples but not all of the Control samples, and six species were found in all of the Control samples but not all of the COPD samples. There was no bacterial species that was common to all samples in one of the two groups, but unique to that group.
Fig 3.
Significant changes in species abundance from Control to COPD.
Using MetaboAnalyst 2.0, t-tests and fold-differences were calculated from normalised percentages of reads, with only those with a P value of < 0.05 charted. Significant differences in species abundances show both higher and lower levels in COPD samples, compared to Controls. Analysis shows that the Streptococcus genus is particularly dynamic.
Fig 4.
Significant differences in functional classification abundance from Control to COPD.
Using MetaboAnalyst 2.0, t-Tests and fold-changes were calculated from normalised percentages of reads, with only those with a P value of < 0.05 charted. Functional classifications are grouped by their Level 1 classification. Only those differences at the Level 3 function are charted, with Levels 1 and 2 shown in S2 and S3 Figs respectively. Differences at Level 3 appear to centre on differences to those reads aligned to functional roles in bacterial cell division.
Table 1.
Regression analysis for COPD patients using FEV1% of predicted, pack years and age.
Fig 5.
Multivariate comparisons of metagenomic variables displaying correlation coefficients.
The pairwise correlation of multivariate parameters was performed by multiple Pearson analyses using the well-established correlograms (corrgrams) programme in R. The outputs are hierarchically clustered based on dissimilarity measures. The outputs are given in piecharts where the filled portion of the pie indicates the magnitude of the correlation and the depth of the shading indicates the magnitude of the correlation.