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Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection

Nature Microbiology volume 9, pages 3284–3303 (2024)Cite this article

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Abstract

Pseudomonas aeruginosa frequently causes antibiotic-recalcitrant pneumonia, but the mechanisms driving its adaptation during human infections remain unclear. To reveal the selective pressures and adaptation strategies at the mucosal surface, here we investigated P. aeruginosa growth and antibiotic tolerance in tissue-engineered airways by transposon insertion sequencing (Tn-seq). Metabolic modelling based on Tn-seq data revealed the nutritional requirements for P. aeruginosa growth, highlighting reliance on glucose and lactate and varying requirements for amino acid biosynthesis. Tn-seq also revealed selection against biofilm formation during mucosal growth in the absence of antibiotics. Live imaging in engineered organoids showed that biofilm-dwelling cells remained sessile while colonizing the mucosal surface, limiting nutrient foraging and reduced growth. Conversely, biofilm formation increased antibiotic tolerance at the mucosal surface. Moreover, mutants with exacerbated biofilm phenotypes protected less tolerant but more cytotoxic strains, contributing to phenotypic heterogeneity. P. aeruginosa must therefore navigate conflicting physical and biological selective pressures to establish chronic infections.

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Fig. 1: Tn-seq identifies how P. aeruginosa adapts to the mucosal surface.
Fig. 2: P. aeruginosa adopts metabolic independence at the mucosal surface.
Fig. 3: Biofilm formation imposes a fitness burden during mucosal colonization.
Fig. 4: CdGMP causes fitness defects during mucosal colonization by stimulating polysaccharide overproduction.
Fig. 5: Colonization versus tolerance trade-offs for biofilms during antimicrobial treatment.
Fig. 6: Biofilms cross-protect acute-like sensitive populations from antibiotics.

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Data availability

Spreadsheets containing the source data used to generate each plot (main and extended data figures), the code used during metabolic modelling, the microscopy data displayed in all the figures and all files related to Tn-seq (that is, WIG and annotation files used during analyses with TRANSIT) are openly available via Zenodo at https://doi.org/10.5281/zenodo.13629466 (ref. 129). The Tn-seq sequencing data have been submitted to the NCBI Sequence Read Archive under accession number PRJNA1156351. The custom image analysis software used for quantification of fitness ratios and cluster sizes distribution is available via GitHub under the following repository: https://github.com/PersatLab/CompetitionAssay. Additional files, such as the raw files of the dozens of videos recorded during AirGel experiments as well as all strains and plasmids used in this study, are available from the corresponding author upon request.

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Acknowledgements

We thank Z. Al-Mayyah for laboratory technical assistance, the Lausanne Genomic Technologies Facility for sequencing, A. Martins Bravo for initial feedback on Tn-seq analysis and members of the Persat lab for constructive feedback throughout the development of the project. We also thank D. K. Newman, S. Saunders, E. Perry, M. Bergkessel, C. Nadell and N. Wespe (National Centres of Competence in Research (NCCR) AntiResist Research Data Officer) for their comments on the manuscript. Funding supporting this work was from Swiss National Science Foundation: 310030_189084 (A.P.), NCCR AntiResist (A.P.), European Molecular Biology Organization Postdoctoral Fellowship ALTF 12-2022 (L.A.M.), Swiss National Science Foundation: 200021_188623 (V.H.) and NCCR Microbiomes (V.H.).

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Author notes

  1. Tamara Rossy

    Present address: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Authors and Affiliations

  1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Lucas A. Meirelles, Auriane Debache, Eric Schmidt, Tamara Rossy, Tania Distler & Alexandre Persat

  2. Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Lucas A. Meirelles, Auriane Debache, Eric Schmidt, Tamara Rossy, Tania Distler & Alexandre Persat

  3. Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Evangelia Vayena & Vassily Hatzimanikatis

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Contributions

Conceptualization: L.A.M. and A.P. Data curation: L.A.M., E.V. and E.S. Formal analysis: L.A.M., E.V. and E.S. Funding acquisition: V.H. and A.P. Investigation: L.A.M. and E.V. Methodology: L.A.M., E.V., A.D., E.S., T.R. and T.D. Project administration: L.A.M. and A.P. Supervision: V.H. and A.P. Visualization: L.A.M., E.V. and E.S. Writing—original draft: L.A.M. and A.P. Writing—review and editing: L.A.M., E.V., V.H. and A.P.

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Correspondence to Alexandre Persat.

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Extended data

Extended Data Fig. 1 Detailed procedure for the Tn-seq during mucosal colonization.

A. Representative confocal image of (z-slice) showing intact epithe lium after 11 h of infection with the Tn-library. Experiment repeated twice with similar results. B. Exhaustive illustration of experiments and control conditions for the Tn-seq during mucosal colonization. Briefly, an aliquot of Tn-library was grown overnight in LB (starting OD = 0.25) and used as inoculum for infection or a new LB control culture. Infections or growth in the LB control proceeded for ~11 h, followed by sample collection and sequencing. For all details, including ODs used, inoculum sizes, and number of generations measured, see the Methods section. All five samples were sequenced (Tn-library, population used for inoculum, healthy HBE, CF HBE, and LB control). Blue arrows represent the comparisons (#1-3) made using the TRANSIT software to assess the conditional essentiality of genes. In comparison #1, the inoculum was used as the control; in comparison #2 and #3, the “LB control” condition was used as the control. Red arrows represent the two “quality control – QC” comparisons made using the TRANSIT software to check for bias in the inoculum used for infection. Abbreviations: CF, cystic fibrosis; HBE cells, human bronchial epithelial cells; LB, Luria-Bertani broth. The data underlying this figure can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 2 Functional annotation of Tn-seq data using Database for Annotation, Visualization and Integrated Discovery (DAVID).

We used the list of significant genes found in comparison #1 of our Tn-seq analysis (see Supplementary Table 1) as input for DAVID and used the web tool to calculate the fold enrichment of Gene Ontology (GOTERM_BP_DIRECT) terms. Disruption of processes found on top leads to decreased fitness (magenta), while disruptions of processes found on the bottom lead to increased fitness (green). The results of the same analysis but using KEGG Pathways can be found in Fig. 1B. For full parameters calculated in the analyses (for example, gene counts, % ID, p-values) displayed here and in Fig. 1B, see Supplementary Table 2. The data underlying this figure can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 3 Schematic of the metabolic modeling approach used in our study.

A computational approach based on genome-scale metabolic modeling and constraint-based optimization was used to assist in the interpretation of Tn-seq data. To this end, a genome-scale metabolic model was used to simulate metabolism and perform in silico single gene deletion experiments on 66 different media compositions of varying complexity (that is, single carbon media, double carbon source media, and rich media). Media selection was guided by the assumed mucus composition and Tn-seq data. Dimensionality reduction techniques and clustering were then used to compare the in silico data with the Tn-seq data to infer carbon utilization on the mucosal surface. Abbreviations: LB, Luria-Bertani broth; CF, cystic fibrosis; SCFM, synthetic cystic fibrosis sputum medium.

Extended Data Fig. 4 Relative cdGMP levels of strains used in the study.

Relative values of cdGMP were measured using a previously published fluorescence-based reporter (GFP under the control of the cdrA promoter by Rybtke et. al.92). See Methods section for details. Each data point represents an independent biological replicate (n = 3); horizontal black lines mark their mean. Statistics: 1-way ANOVA with Tukey HSD multiple comparison test, with asterisks showing significant differences relative to WT (* p < 0.05, *** p < 0.001). Due to the large difference in magnitude of the cdGMP levels for strains with the ∆wspF deletion, the comparisons were made in two parts (part 1: WT vs ∆algR or ∆bifA; part 2: WT vs. ∆wspF or ∆wspF∆pel∆psl). The data underlying this figure and information on statistical analyses can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 5 Competition between WT and an alginate overproducing strain (mucoid).

A. Pictures of the mucoid PAO1 strain, a mucA merodiploid. B. CFU-based validation of the fitness changes mediated by alginate overproduction. Competitions of WT and mucoid strain at the mucosa (left) and in LB (right). Each data point represents the CFUs ratio (mucoid divided by WT) of a single replicate (n = 4 for each condition). These ratios were calculated for the inoculum (0 h) and at the end of competition (8 h), for both conditions (mucosa vs LB). The horizontal black lines mark their mean. C. Fitness ratio of ∆wspF competed against WT, displayed for comparison. These ratios were calculated the same way using the CFU values shown in Fig. 3b. All experiments were performed with CF HBE cells. Statistics: panels B and C, Welch unpaired t-test (two-sided) (** p < 0.01, * p < 0.05, n.s. p > 0.05). Abbreviations: CFU, colony-forming units; LB, Luria-Bertani broth; WT, wild-type. The data underlying this figure and information on statistical analyses can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 6 Biofilm fitness quantification during mucosal colonization.

A. Fitness ratio based on the quantification of the total area measured from surface coverage by ∆bifA-mNeonGreen relative to WT-Scarlet during competition assays at the mucosal surface (HBE cultures). Each data point represents one imaged field of view (n = 15 for WT vs. WT; n = 12 for WT vs. ∆bifA) distributed within three biological replicates. The data for the WT-mNeonGreen vs. WT-mScarlet competition is the same from Fig. 3E but is shown here for comparison. Horizontal black lines mark the mean fitness ratio for each condition. B. Representative images of WT and ∆bifA competition assays in HBE cultures (maximum intensity projection; same field of view). For additional representative images, see Supplementary Fig. 1C. C. Cumulative distributions for cluster size formed by WT and ∆bifA. Distributions include three biological replicates. Vertical lines represent the median cluster-size for each strain. D. Percentage of small clusters (clusters smaller than 20 µm2) that comes from WT or ∆bifA populations. Each data point represents an independent biological replicate (n = 3); horizontal black lines mark their mean. Statistics: panel A and D, Welch unpaired t-test (two-sided) (*** p < 0.001, ** p < 0.01). All experiments were performed with CF HBE cells. Abbreviations: HBE cells, human bronchial epithelial cells; WT, wild-type. The data underlying this figure and information on statistical analyses can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 7 Biofilms mechanically damage epithelia while constraining pathogenicity.

A. Experimental design for imaging epithelial damage upon WT and ∆wspF infections in AirGels. AirGels were infected with WT or ∆wspF expressing mNeonGreen, and infections were monitored for 11 hours for the observation of lysis and cell viability (propidium iodide staining). B. Representative maximum intensity projection images showing differences in growth and subsequent epithelial cell lysis by WT (top) and ∆wspF (bottom). Note that after 10 h of infection, WT had completely taken over the surface and had lysed the epithelial cells. By contrast, ∆wspF formed large expanding biofilms that opened up nodules that stretched out within the tissue (white circles). See Supplementary Movies 2 and 3 for full time-lapse visualization of these images. C. Representative maximum intensity projection images showing differences in growth and the killing of epithelial cells by WT (top) and ∆wspF (bottom). Note the uniform cytotoxic effect to the WT due to its faster spreading, while ∆wspF biofilms showed only cytotoxic activity towards epithelial cells in the immediate vicinity of the nodules (white circles). See Supplementary Movie 4 for a full time-lapse visualization of these images. All experiments were performed with CF HBE cells. In B-C, for each strain, infections of two distinct AirGels were visualized, with similar patterns. Abbreviations: PI, propidium iodide; WT, wild-type. The data underlying this figure can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 8 Tn-seq analysis identifies genetic determinants of antibiotic adaptation at the mucosal surface.

A. Full experimental design of the Tn-seq during antibiotic tolerance at the mucosal surface. The library was grown on CF HBE cells, treated with CIP or TOB (or no treatment for the control), and then an outgrowth step (7 to 12 hours) on LB was performed for all samples. The three outgrowth samples were sequenced. Blue lines represent the comparisons made using the TRANSIT software to assess the conditional essentiality of genes (comparisons #4-5), and the “no antibiotic” condition was used as the control (see Methods for full experimental details) B. Fitness effects of transposon insertions in representative genes and their categories discovered in our antibiotic tolerance Tn-seq. Genes are separated by CIP and TOB-specific hits. These do not represent all the genes that made the significance cutoff. See Supplementary Table 6 for the complete dataset. All experiments were performed with CF HBE cells. Abbreviations: CIP, ciprofloxacin; CF, cystic fibrosis; HBE cells, human bronchial epithelial cells; TOB, tobramycin. The data underlying this figure can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 9 Matrix production mediates tolerance to CIP in high cdGMP strains.

A. Validation of the growth defects caused by cdGMP-mediated matrix overproduction at the mucosal surface using CFUs. Each data point represents an independent biological replicate (that is, one transwell, n = 3); the horizontal black lines mark their mean. B. Tolerance to CIP at the mucosal surface measured by CFUs for different strains used in our study. For each strain, tolerance is calculated based on total CFUs recovered before and after CIP treatment. Each data point represents an independent biological replicate (that is, one transwell, n = 3); the horizontal black lines mark their mean. Statistics: panel A, 1-way ANOVA with Tukey HSD multiple comparison test, with asterisks showing significant differences relative to WT (** p < 0.01, *** p < 0.001, n.s. p > 0.05); panel B, 1-way ANOVA with Tukey HSD multiple comparison test, with asterisks displaying significant differences for highlighted comparisons (* p < 0.05, n.s. p > 0.05). In panel B, statistical analyses were run in two parts (part 1: WT vs ∆bifA or ∆wspF∆pel∆psl; part 2: ∆wspF vs ∆wspF∆pel∆psl or WT). All experiments were performed with CF HBE cells. Abbreviations: CFUs, colony-forming units; CIP, ciprofloxacin. The data underlying this figure and information on statistical analyses can be found in the source data available with this manuscript (see Data Availability session).

Extended Data Fig. 10 Model of the colonization vs tolerance trade-offs for biofilms and its consequences for phenotypic diversification during chronic infections.

Left. During colonization, planktonic behavior is beneficial as it allows better spread on the mucosa, eventually leading to the killing of epithelial cells. However, upon antibiotic treatment selection, the biofilm lifestyle, which minimizes tissue damage, is selected. Right. Long-term infection leads to genetic diversification, where strains displaying different genotypes co-exist. Such genotypes display phenotypic lifestyles (acute or chronic behavior). During antibiotic treatment selection, biofilm-forming strains may protect genotypes associated with acute behavior. We hypothesize that upon removal of the antibiotic selection (either by treatment break and/or mutation leading to resistance), acute behavior is again beneficial, potentially leading to lung exacerbations.

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Supplementary Fig. 1, Full legends for Tables 1–8 and Full legends for Videos 1–7.

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Supplementary Tables

Supplementary Tables 1–8.

Supplementary Video 1

Growth of WT (orange) and ∆wspF (green) during AirGel co-infections. Representative case for data used in the quantification shown in Fig. 3f.

Supplementary Video 2

Growth of WT (top) and ∆wspF (bottom), both in green, during separate AirGel infections. Note the different growth patterns of each strain, with WT quickly spreading over the mucosal surface while ∆wspF expands as compact biofilms. The full time lapse for images shown in Extended Data Fig. 7b.

Supplementary Video 3

Visualization of tissue lysis by WT (top) and ∆wspF (bottom). Note quick tissue lysis caused by WT (that is, the tissue completely disintegrates), while ∆wspF formed large expanding biofilms that opened up nodules that stretched out the tissue. The full time lapse for images shown in Extended Data Fig. 7b.

Supplementary Video 4

Epithelium cell death measured with propidium iodide over the course of AirGel infections by WT (top) or ∆wspF (bottom). Note the faster and uniform cytotoxic effect caused by WT in comparison to ∆wspF (that is, complete killing of HBE cells in the displayed area by 7 h of infection). The full time lapse for images shown in Extended Data Fig. 7c.

Supplementary Video 5

Representative videos displaying the tolerance levels WT (top) and ∆bifA (bottom) upon ciprofloxacin treatment in AirGels. Both strains are shown in green. The full time lapse for images shown in Fig. 5e. Note the heterogeneous survival of ∆bifA, where rapidly expanding regions (top left corner) collapses faster than compact slower-growing biofilms (bottom right corner).

Supplementary Video 6

Representative 3D rendering videos showing differences in tolerance to ciprofloxacin of mixed infections forming large (left) or small (right) biofilms. The two distinct regions shown were collected in the same AirGel, located millimeters apart. WT is shown in orange, ∆wspF is shown in green, and the epithelium is shown in magenta. The full time-lapse rendering of the imaging data shown in Fig. 6c.

Supplementary Video 7

Representative maximum intensity projection videos showing differences in tolerance to ciprofloxacin of mixed infections forming large (left) or small (right) biofilms. The two distinct regions shown were collected in the same AirGel, located millimeters apart. WT is shown in orange, and ∆wspF is shown in green. Note the increased survival of large biofilms. The full time lapse for images shown in Fig. 6c.

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Meirelles, L.A., Vayena, E., Debache, A. et al. Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection. Nat Microbiol 9, 3284–3303 (2024). https://doi.org/10.1038/s41564-024-01842-3

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