Ecological networking of cystic fibrosis lung infections - PubMed (original) (raw)
Ecological networking of cystic fibrosis lung infections
Robert A Quinn et al. NPJ Biofilms Microbiomes. 2016.
Abstract
In the context of a polymicrobial infection, treating a specific pathogen poses challenges because of unknown consequences on other members of the community. The presence of ecological interactions between microbes can change their physiology and response to treatment. For example, in the cystic fibrosis lung polymicrobial infection, antimicrobial susceptibility testing on clinical isolates is often not predictive of antibiotic efficacy. Novel approaches are needed to identify the interrelationships within the microbial community to better predict treatment outcomes. Here we used an ecological networking approach on the cystic fibrosis lung microbiome characterized using 16S rRNA gene sequencing and metagenomics. This analysis showed that the community is separated into three interaction groups: Gram-positive anaerobes, Pseudomonas aeruginosa, and Staphylococcus aureus. The P. aeruginosa and S. aureus groups both anti-correlate with the anaerobic group, indicating a functional antagonism. When patients are clinically stable, these major groupings were also stable, however, during exacerbation, these communities fragment. Co-occurrence networking of functional modules annotated from metagenomics data supports that the underlying taxonomic structure is driven by differences in the core metabolism of the groups. Topological analysis of the functional network identified the non-mevalonate pathway of isoprenoid biosynthesis as a keystone for the microbial community, which can be targeted with the antibiotic fosmidomycin. This study uses ecological theory to identify novel treatment approaches against a polymicrobial disease with more predictable outcomes.
Figures
Fig. 1
Co-occurrence network inferred from the SparCC algorithm applied to 16S rRNA gene data of 126 CF sputum samples. Co-occurrence and anti-occurrence of taxa is denoted with grey lines and red lines, respectively. Nodes are sized by their degree closeness centrality (cc) and colored by increasing clustering coefficient (increasing from olive to green to blue). The major taxa clustering strongly support the CAM hypothesis, stating that the CF community splits into two differently pathogenic subgroups: Attack Community members such as Streptococcus, Veillonella and Porphyromonas, and the Climax Community dominated by Pseudomonas and Staphylococcus
Fig. 2
Functional co-occurrence network inferred from the SparCC algorithm applied to KEGG Module abundances from CF sputum metagenomes. Nodes are sized based on their degree of closeness centrality and colored by their number of interactions, where increasing ‘k’ is redder. Edges are sized and colored by the strength of the co-occurrence. Results of the read mapping are highlighted for particular keystone pathways in the network according to the lettering (A–J). The taxonomic distribution of reads to the KEGG module genes is shown as bar graphs of the relative abundance of assigned reads. The relative abundance of taxa from the MG-RAST output of all reads in the pooled metagenomes is shown as a reference
Fig. 3
Individual co-occurrence networks of taxa for the BETR categories of disease state. Co-occurrence and anti-occurrence of taxa is denoted with grey lines and red lines, respectively. a Baseline network with significant clustering between anaerobes strongly counteracted by Pseudomonas. b Exacerbation network characterized by re-organization of significant co-occurrence patterns. Pseudomonas engages in new specific positive interactions during exacerbation and loses the strong negative effect against the anaerobes seen during stability. c Treatment network characterized by small numbers of involved taxa. d Recovery network topology breaks into few, sporadic taxon clusters
Fig. 4
Boxplot distributions of Climax and Attack communities according to assignment of OTU membership from the 16S rRNA gene network topology (see methods). Statistical significance is tested within clinical state using the Student’s _t_-test (Bonferroni corrected _p_-value = p < 0.0125, *** = p < 0.001) and across clinical states with the Tukey’s test of a one-way ANOVA. Letters designate statistical significances where a shared letter indicates two distributions that were significantly different (a = 0.014, b = 0.04, c = 0.0012, d = 5.7 × 10−5, e = 0.0041, f = 4.5 × 10−6)
Fig. 5
Read mapping of the non-mevalonate pathway reads in CF metagenomes in the context of the chemical species and steps of the pathway. The action of the antibiotic fosmidomycin is shown as a reference
Fig. 6
The Climax and Attack Model (CAM) as inferred from co-occurrence network results from this study. Different communities containing fundamentally different physiology affect the surrounding environment differently, principally by changing the pH. This results in community stability during Climax community growth and instability during growth of the Attack community at exacerbation
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