Research ArticleMICROBIOLOGY

Niche partitioning of a pathogenic microbiome driven by chemical gradients

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Science Advances  26 Sep 2018:
Vol. 4, no. 9, eaau1908
DOI: 10.1126/sciadv.aau1908
  • Fig. 1 WinCF microbial physiology and diversity changes in response to pH and oxygen gradients.

    Boxplots of the amount of gas produced (A), change in green color saturation (B), and Shannon-Weiner diversity index (C) in the WinCF capillary columns through the pH gradient in both CF and non-CF sputum samples. Letters above the boxplot denote significant differences between the 8 and 8.5 pH samples. (D) Boxplot of the Shannon-Weiner index at each millimeter depth through the WinCF capillary columns with and without bicarbonate treatment. Sputum samples are shown as a reference when applicable. ***P < 0.001, **P < 0.01, *P < 0.05 from Mann-Whitney U test.

  • Fig. 2 Effect of pH and oxygen gradients on microbial community in WinCF system.

    RF linear models (A and C) and bacterial genus distributions (B and D) in the WinCF pH and oxygen experiments. An RF linear model was run on the (A) pH and (C) depth of each 16S rDNA microbiome profile from the WinCF experiments. The model predicts the pH or depth of a sample on the basis of the microbiome data, providing a measure of the strength of the overall microbial changes through the gradients. The percent variance of the data in the model is also shown. The distributions of bacterial genera of interest are visualized for pH (B) and oxygen (D) using a visual depiction of the WinCF system experiments generated with the ‘ili software (45). The color scale for each WinCF tube represents the relative abundance of that particular operational taxonomic unit (OTU) and is shown for each panel, as they are not all at the same scale. In the oxygen experiment, each layer represents 1 mm into the WinCF media, and in the pH experiment, each column represents the WinCF columns grown in the pH gradient with 0.5 units from 5 to 8.5. The normalized abundance of bacterial genera of interest are shown as a heat map, with red being the most abundant and blue being the least abundant. The P value from the LMM for each genus in each gradient is shown.

  • Fig. 3 Molecular networks of P. aeruginosa virulence factors and their abundance through depth in the WinCF columns.

    The data are mapped onto a model of the WinCF oxygen experiment using the ‘ili software (45). The log mean intensity of each P. aeruginosa virulence metabolite through the 1- to 10-mm-depth sections of the WinCF media is shown as a heat map with the oxygen penetration shown for reference. The Pearson’s r value of the correlation with depth is shown as well as the P value from the LMM. Plots of the log area under the curve compared to depth in the WinCF media are shown colored by each patient for each metabolite of interest. The molecular networks show the metabolites of interest and their related molecules. Each node represents a unique MS/MS spectrum from the molecular networking algorithm, and edges between the nodes represent a cosine similarity between them above 0.7. Colors of the nodes are scaled to their abundance in the oxic layer of the WinCF columns (1 mm) in spectral counts, and the node size is scaled to the overall abundance of the metabolite. Structures and mass in daltons of each virulence metabolite are shown. HQNO, 2-heptyl-4-hydroxyquinolone-N-oxide.

  • Fig. 4 The 3D models of the WinCF oxygen experiments with treatment visualized using the ‘ili software (45).

    Each column represents the WinCF medium that is sectioned in 1-mm depths. The sections are colored according to the normalized abundance of bacterial genera of interest. The untreated samples (NT), bicarbonate (BC)–treated samples, and tobramycin (TB)–treated samples are shown, as well as the LMM P values with and without the depth as a mixed effect. NS, not significant.

  • Fig. 5 Profiles of Aspergillus mitochondrion reads, tobramycin abundance, and Aspergillus specialized metabolite abundance through the WinCF oxygen gradients in the 19 patients tested in this study.

    (A) Relative abundance of 16S rDNA gene reads in each patient mapping to the Aspergillus mitochondrion in tobramycin-treated WinCF tubes through the 10-mm depth. (B) Summed ion intensity of tobramycin in each patient through the WinCF 10-mm depth treated with tobramycin. (C) Spectral counts of A. fumigatus specialized metabolites in same samples.

  • Fig. 6 Results of mathematical model simulations on the CF microbiome response to pH.

    (A) Contours of P. aeruginosa θp and the fermentative anaerobe θf growth in the WinCF capillary column environment when started at low or high pH. The aerobic portion of the column is at the top of the figure, and the anaerobic portions are at the bottom. Depth, width, and intensity of growth parameters are on unitless scales. (B) Contours of θp and θf given by the simulation with the effect of inhibition chemical I turned off (set β1 = 0 in the model); therefore, in this case, P. aeruginosa has no effect on the growth of fermenters. (C) Contours of θp and θf with the effect pH on the growth of P. aeruginosa turned off (set β2 = 0 in the model). In this case, there is no difference between the low and high initial pH as expected. Contours of (D) θp and (E) θf resulting from the simulated treatment of bicarbonate (BC) and tobramycin (TB) from the top portion of the media are compared to the untreated case (NT). These results demonstrate the predictability of the community response to inhaled CF treatments.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/9/eaau1908/DC1

    Fig. S1. Schematic of the experimental design with the WinCF system modified for the pH experiments and the oxygen experiments.

    Fig. S2. Actual measurement versus predicted value from RF machine learning algorithm on the microbiome and metabolome data through pH, gas production, and depth variables.

    Fig. S3. Niche partitioning of CF lung microbiota in the pH and oxygen experiments.

    Fig. S4. O2 microenvironment (% air saturation) through the WinCF vertical depth gradient after incubation with sputum from two patients compared to a noninoculated control.

    Fig. S5. Microbiome profiles of individual patients in the pH and oxygen experiments.

    Fig. S6. Molecular network of rhamnolipids and quinolones detected in the LC-MS/MS data.

    Fig. S7. GLMM results for different bacterial genera on a per patient basis.

    Fig. S8. Mean abundance of tobramycin by ion count in the WinCF columns after incubation.

    Fig. S9. Tobramycin and N-propionyl tobramycin identification from polar LC-MS/MS data.

    Fig. S10. A. fumigatus metabolites in tobramycin-treated WinCF columns.

    Fig. S11. Mean abundance of pooled anaerobes in the WinCF columns after the different treatments.

    Fig. S12. WinCF model equations.

    Fig. S13. Principle co-ordinate analysis (PCoA) plots of metabolome and microbiome data from all samples.

    Table S1. Patient samples and information collected in this study.

    Table S2. ANOVA of qualitative and quantitative variables measured during the WinCF pH gradient experiments.

    Table S3. Metabolites that most changed with the WinCF gas production gradient according to an RF variable importance plot from the untreated samples.

    Table S4. Mean abundance through the depth gradient (1 to 10 mm) of P. aeruginosa virulence factor metabolites detected in the WinCF depth experiments and the corresponding Pearson’s correlation (r).

    Table S5. Deblurred OTUs and their sequences that most changed with the WinCF depth gradient according to an RF variable importance plot from the untreated samples.

    Table S6. Results of the assessment of bias in WinCF system.

    Table S7. Confusion matrix and out-of-bag error from an RF classification of the pH experiment metabolomics data based on patient source.

    Supplementary Methods

    References (4651)

  • Supplementary Materials

    The PDF file includes:

    • Fig. S1. Schematic of the experimental design with the WinCF system modified for the pH experiments and the oxygen experiments.
    • Fig. S2. Actual measurement versus predicted value from RF machine learning algorithm on the microbiome and metabolome data through pH, gas production, and depth variables.
    • Fig. S3. Niche partitioning of CF lung microbiota in the pH and oxygen experiments.
    • Fig. S4. O2 microenvironment (% air saturation) through the WinCF vertical depth gradient after incubation with sputum from two patients compared to a noninoculated control.
    • Fig. S5. Microbiome profiles of individual patients in the pH and oxygen experiments.
    • Fig. S6. Molecular network of rhamnolipids and quinolones detected in the LC-MS/MS data.
    • Fig. S7. GLMM results for different bacterial genera on a per patient basis.
    • Fig. S8. Mean abundance of tobramycin by ion count in the WinCF columns after incubation.
    • Fig. S9. Tobramycin and N-propionyl tobramycin identification from polar LC-MS/MS data.
    • Fig. S10. A. fumigatus metabolites in tobramycin-treated WinCF columns.
    • Fig. S11. Mean abundance of pooled anaerobes in the WinCF columns after the different treatments.
    • Fig. S12. WinCF model equations.
    • Fig. S13. Principle co-ordinate analysis (PCoA) plots of metabolome and microbiome data from all samples.
    • Legends for Tables S1 to S7
    • Supplementary Methods
    • References (4651)

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). Patient samples and information collected in this study.
    • Table S2 (Microsoft Excel format). ANOVA of qualitative and quantitative variables measured during the WinCF pH gradient experiments.
    • Table S3 (Microsoft Excel format). Metabolites that most changed with the WinCF gas production gradient according to an RF variable importance plot from the untreated samples.
    • Table S4 (Microsoft Excel format). Mean abundance through the depth gradient (1 to 10 mm) of P. aeruginosa virulence factor metabolites detected in the WinCF depth experiments and the corresponding Pearson’s correlation (r).
    • Table S5 (Microsoft Excel format). Deblurred OTUs and their sequences that most changed with the WinCF depth gradient according to an RF variable importance plot from the untreated samples.
    • Table S6 (Microsoft Excel format). Results of the assessment of bias in WinCF system.
    • Table S7 (Microsoft Excel format). Confusion matrix and out-of-bag error from an RF classification of the pH experiment metabolomics data based on patient source.

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