Abstract
It is well appreciated that oxygen- and nutrient-limiting gradients characterize microenvironments within chronic infections that foster bacterial tolerance to treatment and the immune response. However, determining how bacteria respond to these microenvironments has been limited by a lack of tools to study bacterial functions at the relevant spatial scales in situ. Here we report the application of the hybridization chain reaction (HCR) v3.0 to Pseudomonas aeruginosa aggregates as a step towards this end. As proof-of-principle, we visualize the expression of genes needed for the production of alginate (algD) and the dissimilatory nitrate reductase (narG). Using an inducible bacterial gene expression construct to calibrate the HCR signal, we were able to quantify algD and narG gene expression across microenvironmental gradients both within single aggregates and within aggregate populations using the Agar Block Biofilm Assay (ABBA). For the ABBA population, alginate gene expression was restricted to hypoxic regions within the environment (~40-200 μM O2), as measured by an oxygen microelectrode. Within individual biofilm aggregates, cells proximal to the surface expressed alginate genes to a greater extent than interior cells. Lastly, mucoid biofilms consumed more oxygen than nonmucoid biofilms. These results establish that HCR has a sensitive dynamic range and can be used to resolve subtle differences in gene expression at spatial scales relevant to microbial assemblages. Because HCR v3.0 can be performed on diverse cell types, this methodological advance has the potential to enable quantitative studies of microbial gene expression in diverse contexts, including pathogen behavior in human chronic infections.
Importance The visualization of microbial activities in natural environments is an important goal for numerous studies in microbial ecology, be the environment a sediment, soil, or infected human tissue. Here we report the application of the hybridization chain reaction (HCR) v3.0 to measure microbial gene expression in situ at single-cell resolution in aggregate biofilms. Using Pseudomonas aeruginosa with a tunable gene expression system, we show that this methodology is quantitative. Leveraging HCR v3.0 to measure gene expression within a P. aeruginosa aggregate, we find that bacteria just below the aggregate surface are the primary cells expressing genes that protect the population against antibiotics and the immune system. This observation suggests that therapies targeting bacteria growing with small amounts of oxygen may be most effective against these hard-to-treat infections. More generally, HCR v3.0 has potential for broad application into microbial activities in situ at small spatial scales.
Observation
Despite decades of research that have elucidated mechanisms of bacterial virulence, antibiotic tolerance, and antibiotic resistance, many infections remain impossible to eradicate. Phenotypic heterogeneity likely plays an important role in the failure of drugs and the immune system to clear chronic infections. Chronic Pseudomonas aeruginosa lung infections in people with cystic fibrosis (CF) are a prime example. Within individual lobes of the CF lung, genetically antibiotic susceptible and resistant P. aeruginosa co-exist (1). This likely affects treatment because resistant bacteria can protect susceptible bacteria when mixed together in vitro (2, 3). Likewise, CF lung mucus contains steep oxygen gradients, and anoxic conditions reduce antibiotic susceptibility (4–7). While we know that bacterial genetic diversity and infection site chemical heterogeneity exist, tools to measure bacterial phenotypes in situ are lacking. Here we tested the ability of the third generation of the hybridization chain reaction (HCR v3.0) to quantitatively measure gene expression in P. aeruginosa in an aggregate model system.
In situ HCR v3.0 is specific and quantitative for bacterial gene expression
HCR is a fluorescent in situ hybridization-like approach that includes a signal amplification step to help visualize low-abundant RNAs (8, 9). We previously used single HCR v2.0 probes to detect bacterial taxa in CF sputum samples (10), and HCR 2.0 was also used by Nikolakakis et al. to detect host and bacterial mRNAs in the Hawaiian bobtail squid-Vibrio fischeri symbiosis (11). We chose to test HCR v3.0 as a tool to quantify bacterial gene expression in situ because of its improved specificity over HCR v2.0. HCR v3.0 requires two paired initiator probes to anneal adjacent to one another on each RNA target before signal amplification occurs, which reduces background signal compared to HCR v2.0 non-specific binding of single initiator probes (Fig. 1A) (8, 9). Therefore, we designed and validated two types of HCR v3.0 probes which could be used to 1) differentiate species and 2) measure gene expression.
Using our previous HCR v2.0 probes as a template (10), we designed HCR v3.0 probes to detect 16S rRNA in all eubacteria and P. aeruginosa specifically. As expected, the P. aeruginosa probes detected only P. aeruginosa and not Staphylococcus aureus, while the eubacterial probe detected both organisms (Fig. 1B-C and S1). When only one initiator probe from each pair was used, no fluorescence was observed, as anticipated (Fig. S2). Thus, HCR v3.0 probes were highly specific for the intended bacteria.
To test the ability of HCR v3.0 to quantify bacterial gene expression, we designed probes to detect P. aeruginosa algD mRNA, and we cloned algD into the arabinose-inducible expression plasmid pMQ72 in a P. aeruginosa ΔalgD mutant (12, 13). mRNA-HCR analysis was highly quantitative: we observed a linear relationship between the concentration of the inducer (i.e. expression level) and HCR signal in the complemented strain, while the empty vector control strain produced no signal (Fig. 1D and S3). This demonstrated that mRNA-HCR can quantify bacterial gene expression in situ.
mRNA-HCR reveals alginate gene expression in hypoxic zones of P. aeruginosa aggregates
As a case study, we chose to measure P. aeruginosa alginate (algD) and nitrate reductase (narG) gene expression in aggregates formed by a mucoid (FRD1) and nonmucoid strain (PA14). This approach was chosen for several reasons. First, measuring algD expression in situ is of interest because alginate is overproduced by mucoid strains in CF lung infections (14, 15), and mucoid strains are associated with worsened lung function (16). Second, as a technical control, the algD gene should be more highly expressed in the mucoid than in nonmucoid strain and produce a stronger HCR signal (14). Third, previous research suggests that alginate may be expressed in hypoxic and anoxic conditions (7, 17-19), yet the precise location of alginate gene expression in aggregate biofilms has yet to be determined. Therefore, we could also quantify algD expression relative to narG, a gene induced under hypoxic and anoxic conditions (17, 20), which would help determine where algD is expressed in aggregates relative to environmental oxygen availability.
Using the Agar Block Biofilm Assay (ABBA) (20), we grew mucoid and nonmucoid aggregates suspended in an agar medium and measured narG and algD gene expression with mRNA- HCR. As expected, the mucoid strain expressed algD more highly than the nonmucoid strain (Fig. 2A-C,E). Spatially, algD expression was highest in the zones within the first 200 μm below the air-agar interface (Fig 2A-C,E). Interestingly, narG was also expressed more highly in the mucoid than nonmucoid strain (Fig 2D) and was expressed more evenly in aggregates at varying depths below the agar surface (Fig. 2A-D). Analysis of individual aggregates in the ABBA experiments showed an intriguing ring-like pattern of 16S rRNA, algD, and narG gene expression. Within individual aggregates, algD expression was detected in cells ~5-15 μm below the aggregate surface but was not detected in the innermost cells within ~10 μm of the aggregate center (Fig. 2F-G). In contrast, the innermost cells highly expressed narG, but cells within ~0-10 μm of the aggregate surface did not express narG (Fig. 2F-G). This led us to hypothesize that algD was being expressed by cells experiencing hypoxia just below the aggregate surface and not by cells in the innermost, presumably anoxic, regions of the aggregates.
To test where cells were expressing algD relative to oxygen availability, we used a microelectrode to measure oxygen concentrations from 0-600 µm below the agar surface in mucoid and nonmucoid ABBA experiments. Unexpectedly, the mucoid strain showed a modest increase in its oxygen consumption rate compared to the nonmucoid strain (Fig. 2H). However, as we predicted, the mucoid strain expressed algD highest in hypoxic regions (5-200 μM oxygen) of the agar, from 0-350 μm below the agar surface and peaking at ~75 μM oxygen (Fig. 2I-J). In regions with less than 5 μM oxygen, algD expression plummeted to <1% of the maximum value detected (Fig. 2I-J). This was surprising because in planktonic cultures we found that anoxia most strongly induced algD expression compared to oxic and hypoxic conditions (Fig. S5), similar to previous research (18). Thus, alginate gene expression patterns differ between planktonic and aggregate cells: in aggregate cells, algD expression is greatest under hypoxic rather than anoxic conditions.
Conclusion
Altogether, these experiments demonstrate the utility of HCR v3.0 for quantitatively measuring bacterial gene expression in situ at spatial scales relevant to microbial assemblages. Going forward, it will be exciting to combine mRNA-HCR with tissue clearing methods such as MiPACT (10) to determine whether the expression patterns observed in these in vitro studies similarly characterize aggregate populations of pathogens in vivo. Direct insight into how pathogen physiology develops in infected tissues, or any other context where spatial observation of microbial activities is important, promises to yield insights that will facilitate more effective control of these communities. Many applications of HCR v3.0 can be envisioned, such as using this visualization tool to analyze microbes after therapeutic interventions to identify bacterial subpopulations that either resist or succumb to treatment. Ultimately, identifying the subpopulations that survive a specific perturbation can be used to guide the development and implementation of future therapeutics.
Methods
Bacterial strains were routinely grown in Luria Bertani broth and agar. Bacterial cloning, ABBA experiments, HCR analyses, and oxygen measurements were performed as described previously (10, 12, 21–25). For experimental details see Supplemental Methods and Tables including probe sequences (Table S1), bacterial strains (Table S2), and primers (Table S3).
Acknowledgements
We would like to thank Will DePas, Ruth Lee, Niles Pierce and the Programmable Molecular Technology Center at the Caltech Beckman Institute for technical assistance and advice. Confocal microscopy was performed in the Caltech Biological Imaging Facility at the Caltech Beckman Institute, which is supported by the Arnold and Mabel Beckman Foundation. Grants to DKN from the Army Research Office (W911NF-17-1-0024) and National Institutes of Health (1R01AI127850-01A1) supported this research. PJ was supported by postdoctoral fellowships from the Cystic Fibrosis Foundation (JORTH14F0 and JORTH17F5). MAS was supported by a gift from the Doren Family Foundation.