Present address: School of Molecular and Microbial Biosciences G08, University of Sydney, Australia.
Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections
Article first published online: 8 AUG 2012
© 2012 Society for Applied Microbiology and Blackwell Publishing Ltd
Special Issue: Environmental Ecology of Pathogens and Resistances
Volume 15, Issue 2, pages 398–408, February 2013
How to Cite
Behrends, V., Ryall, B., Zlosnik, J. E. A., Speert, D. P., Bundy, J. G. and Williams, H. D. (2013), Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections. Environmental Microbiology, 15: 398–408. doi: 10.1111/j.1462-2920.2012.02840.x
- Issue published online: 28 JAN 2013
- Article first published online: 8 AUG 2012
- Accepted manuscript online: 17 JUL 2012 12:26AM EST
- Received 14 March, 2012; revised 16 June, 2012; accepted 25 June, 2012.
Fig. S1. In the presented sample set, mucoidy is more common in isolates from later stages of infection, but does not become the only morphotye of end-stage isolates.
Fig. S2. Growth parameters with isolates grouped relative to progression of infection (on a per patient basis). Box-and-whisker plot representations of variability of (A) optical density and (B) doubling times observed for the clinical isolates. The whiskers encompass 1.5× interquartile range. The red crosses indicate individual points that fall outside this range.
Fig. S3. OD600 after 24 h growth and doubling time are not related for the strains used in this study.
Fig. S4. Relationship between length of infection and metabolite concentration for individual patients (Pearson correlation). Colour scale indicates statistical significance of correlation.
Fig. S5. Relationship between length of infection and metabolite concentration for individual patients (Spearman's rank correlation, only patients with ≥ 10 isolates included). Colour scale indicates statistical significance of correlation.
Fig. S6. Evolutionary pressures are present in the lung that lead to a convergence of compound utilization/excretion over time. Boxplot representation of acetate detected in media supernatants grouped according to infection stage. The boxes represent the interquartile range and the whiskers represent 1.5× interquartile range.
Fig. S7. Variability of compound utilization/excretion in rich media. Sunburst plots summarizing the utilization/excretion of all detected metabolites. The plots encode data on both patient and length of infection: each group of coloured rays represents the isolates of a single patient, ordered clockwise according to length of infection; the length of each ray corresponds to the metabolite concentration after bacterial growth (NB data scaled such that median = 1 for all metabolites). The blue dotted line indicates the original concentration of the metabolite in the growth medium, and the black and red dotted lines indicate median and mean respectively of the metabolite across all strains.
Table S1. Details of Pseudomonas aeruginosa strains isolated from 18 cystic fibrosis patients used in the current study.
Table S2. Additional statistical analysis of metabolic uptake behaviour. The table summarizes statistics from linear modelling against patient (categorical variable) + length of infection (numerical variable) + the interaction term patient × length of infection. The table lists R2adj for the whole model, and the P-value for each term of the model. The asterisk indicates metabolites with a non-significant interaction term that had a significantly improved model against length of infection with the interaction term excluded.
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