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. 2020 Jul 22;5(4):e00426-20.
doi: 10.1128/mSphere.00426-20.

Spatiotemporal Distribution of Pseudomonas aeruginosa Alkyl Quinolones under Metabolic and Competitive Stress

Tianyuan Cao  1 , Jonathan V Sweedler  2   3 , Paul W Bohn  1   4   5 , Joshua D Shrout  6   7   8   9
Affiliations

Spatiotemporal Distribution of Pseudomonas aeruginosa Alkyl Quinolones under Metabolic and Competitive Stress

Tianyuan Cao et al. mSphere. .

Abstract

Pseudomonas aeruginosa is an opportunistic human pathogen important to diseases such as cystic fibrosis. P. aeruginosa has multiple quorum-sensing (QS) systems, one of which utilizes the signaling molecule 2-heptyl-3-hydroxy-4-quinolone (Pseudomonas quinolone signal [PQS]). Here, we use hyperspectral Raman imaging to elucidate the spatiotemporal PQS distributions that determine how P. aeruginosa regulates surface colonization and its response to both metabolic stress and competition from other bacterial strains. These chemical imaging experiments illustrate the strong link between environmental challenges, such as metabolic stress caused by nutritional limitations or the presence of another bacterial species, and PQS signaling. Metabolic stress elicits a complex response in which limited nutrients induce the bacteria to produce PQS earlier, but the bacteria may also pause PQS production entirely if the nutrient concentration is too low. Separately, coculturing P. aeruginosa in the proximity of another bacterial species, or its culture supernatant, results in earlier production of PQS. However, these differences in PQS appearance are not observed for all alkyl quinolones (AQs) measured; the spatiotemporal response of 2-heptyl-4-hydroxyquinoline N-oxide (HQNO) is highly uniform for most conditions. These insights on the spatiotemporal distributions of quinolones provide additional perspective on the behavior of P. aeruginosa in response to different environmental cues.IMPORTANCE Alkyl quinolones (AQs), including Pseudomonas quinolone signal (PQS), made by the opportunistic pathogen Pseudomonas aeruginosa have been associated with both population density and stress. The regulation of AQ production is known to be complex, and the stimuli that modulate AQ responses are not fully clear. Here, we have used hyperspectral Raman chemical imaging to examine the temporal and spatial profiles of AQs exhibited by P. aeruginosa under several potentially stressful conditions. We found that metabolic stress, effected by carbon limitation, or competition stress, effected by proximity to other species, resulted in accelerated PQS production. This competition effect did not require cell-to-cell interaction, as evidenced by the fact that the addition of supernatants from either Escherichia coli or Staphylococcus aureus led to early appearance of PQS. Lastly, the fact that these modulations were observed for PQS but not for all AQs suggests a high level of complexity in AQ regulation that remains to be discerned.

Keywords: HQNO; PQS; Raman spectroscopy; Staphylococcus aureus; chemical imaging; polymicrobial; principal-component analysis; quorum sensing.

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Figures

FIG 1
FIG 1
Combined CRM and PCA show that P. aeruginosa exhibits signatures of PQS by 48 h when cocultured with E. coli. Shown are images of the resultant P. aeruginosa and E. coli growth on 0.7% agar at 24 h (top) and 48 h (bottom). Raman measurements were taken in the region of the P. aeruginosa advancing edge at 24 h and at the intersection of the two strains at 48 h (shown as boxed areas on plates). One representative Raman image and one representative score image are shown for integrations over the 1,330-to-1,380-cm−1 (top row) and 1,630-to-1,680-cm−1 (bottom row) spectral windows at both 24 h and 48 h. Spectra were acquired and inspected over at least five locations within the region to validate overall consistency. Loading plots and score images of principal components were generated from principal-component analysis of the CRM microspectra acquired over the same region. HQNO and PQS features are labeled in red and blue, respectively. In the 48-h sample, PCA revealed two principal components with distinct features, which represent PQS and HQNO, respectively. Bars, 10 μm.
FIG 2
FIG 2
P. aeruginosa exhibits PQS signatures only in close proximity to E. coli, while HQNO signatures extend beyond the region of P. aeruginosa growth. (a) Images of P. aeruginosa–E. coli coculture plates at 24 h and 48 h (bars, 10 mm). Black squares indicate imaged areas 1 to 5 (along the dashed red lines). At least three regions of interest within each area were picked for scanning. (b) Raman images (integrated over 1,330 to 1,380 cm−1), Z-score spatial maps, and loading plots for the most significant principal component for 24 h as a function of position. Confocal Raman images were integrated over 1,330 to 1,380 cm−1 for areas 1 to 4 and over 2,800 to 3,000 cm−1 for area 5. Principal-component analysis was performed for all areas to generate score images and loading plots. All loading plots showed regions of Raman shifts from 600 to 1,800 cm−1. (c) CRM and PCA results of a 48-h coculture from area 1 to area 5 (all integrated over 1,330 to 1,380 cm−1), showing the locations of features from HQNO (red lettering) and PQS (blue lettering). PQS was detected within areas 2 and 3 at 48 h. Score image values range from low (dark blue) to high (red) in each plot, although the total range differs from plot to plot. All samples were grown on FAB-glucose (12 mM) medium with 0.7% agar (bars, 10 μm).
FIG 3
FIG 3
Effects of metabolic stress on PQS production with and without interspecies competition. Imaged areas are boxed on the plates, and at least three regions of interest within each area were picked for scanning. The matrices of CRM and PCA results were acquired as a function of glucose concentration (3 mM and 6 mM) and time (24 h and 48 h) for both P. aeruginosa with P. aeruginosa and P. aeruginosa with E. coli. Each panel shows (from left to right) an optical image of the plate, the Raman image, the Z-score image, and the loading plot. For 6 mM glucose samples, Raman images were integrated over the spectral window of 1,330 to 1,380 cm−1, except for the 48-h coculture of P. aeruginosa with E. coli, which also includes intensities integrated from 1,630 to 1,680 cm−1. For 3 mM glucose samples, Raman images were integrated over the range of 2,800 to 3,000 cm−1. For the 48-h sample in 6 mM glucose only, the first two principal-component Z-score images and loading plots are shown. Score image values range from low (dark blue) to high (red) in each plot, although the total range differs from plot to plot. All samples were grown on FAB-glucose (at 6 mM [top] or 3 mM [bottom]). Bars, 10 μm.
FIG 4
FIG 4
Optical images, Raman images (1,330 to 1,380 cm−1), and PCA of the advancing edge (boxed areas on plates) of P. aeruginosa exposed to 1 μl of a P. aeruginosa, E. coli, or S. aureus supernatant. Supernatants were spotted directly on the edge at 18 h postinoculation, and the plate was then returned to the incubator for another 6 h before being removed for testing. All samples were grown on FAB-glucose (12 mM) medium with 0.5% agar. Bars, 10 μm.
FIG 5
FIG 5
Schematic illustration of the two-factor interaction model. Shown are plots of PQS production as a function of growth time without (top) and with (bottom) a bacterial competitor (e.g., E. coli) at 6 mM or 12 mM glucose. The metabolically stressed condition (3 mM glucose) never achieves PQS production. Dashed horizontal lines indicate an effective level of PQS production.
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