Engineering Cyborg Pathogens through Intracellular Hydrogelation

Abstract

Synthetic biology primarily focuses on two kinds of cell chassis: living cells and nonliving systems. Living cells are autoreplicating systems that have active metabolism. Nonliving systems, including artificial cells and nanoparticles, are nonreplicating systems typically lacking active metabolism. In recent work, Cyborg bacteria that are nonreplicating-but-metabolically active have been engineered through intracellular hydrogelation. Intracellular hydrogelation is conducted by infusing gel monomers and photoactivators into cells, followed by the activation of polymerization of the gel monomers inside the cells. However, the previous work investigated only Escherichia coli cells. Extending the Cyborg-Cell method to pathogenic bacteria could enable the exploitation of their pathogenic properties in biomedical applications. Here, we focus on different strains of Pseudomonas aeruginosa, Staphylococcus aureus, and Klebsiella pneumoniae. To synthesize the Cyborg pathogens, we first reveal the impact of different hydrogel concentrations on the metabolism, replication, and intracellular gelation of Cyborg pathogens. Next, we demonstrate that the Cyborg pathogens are taken up by macrophages in a similar magnitude as wild-type pathogens through confocal microscopy and real-time PCR. Finally, we show that the macrophage that takes up the Cyborg pathogen exhibits a similar phenotypic response to the wild-type pathogen. Our work generalizes the intracellular hydrogelation approach from lab strains of E. coli to bacterial pathogens. The new Cyborg pathogens could be applied in biomedical applications ranging from drug delivery to immunotherapy.

Keywords: Pseudomonas aeruginosa; Staphylococcus aureus; cyborg pathogen; intracellular hydrogelation; macrophages; synthetic biology.

Figure 1.

Engineering Cyborg bacterial pathogens using intracellular hydrogelation. (A) Schematic workflow for intracellular hydrogelation. (B) Flow cytometry and fluorescence microscopy analysis depicting the detection of wild-type and Cyborg bacteria pathogens, including S. aureus, P. aeruginosa, and K. pneumoniae. The histogram illustrates the fluorescence signal obtained from two distinct markers: PEGDA–fluorescein Diacrylate, serving as an indicator of intracellular hydrogelation, and reductive chromogenic dye, a marker used to assess metabolic activity. The gray quadrant shows a population of hydrogelated cells that are metabolically active (n = 3 independent experiments). Fluorescence microscopy images of hydrogelated bacteria. The hydrogel was labeled with fluorescein (Methods Section M1) (Scale bar = 10 μm, n = 3 independent experiments). The data provide insights into the successful intracellular hydrogelation of the bacterial cells, thus creating Cyborg pathogens. (C) Representation of the percentage of hydrogelation and hydrogelated metabolically active cells in three distinct bacterial strains: S. aureus, P. aeruginosa, and K. pneumoniae while varying the hydrogelation percentages at 5, 10, 20, and 30%. Each bar within the graph represents the proportion of cells exhibiting intracellular hydrogelation (shown in blue) and the subset of these hydrogelated cells exhibiting metabolic activity (shown in magenta). The data offer insights into how different hydrogelation percentages impact the creation of Cyborg pathogens, allowing for an assessment of the most effective hydrogelation concentration for each strain. t tests were applied to confirm the statistical significance of the observed difference (n = 3 independent experiments). The error bar represents the standard deviation of the mean. (D) Representation of the comparative analysis of the rate of hydrogelation and metabolically active tracker across six distinct bacterial strains. The chart displays the hydrogelated and metabolically active populations at four different hydrogelation percentages: 5, 10, 20, and 30%. The grouped bar chart provides insights into the impact of varying hydrogelation percentages on the parameters within the bacterial population. This visualization aids in identifying the most favorable conditions, with 10% hydrogelation exhibiting the highest values among the examined percentages (n = 3 independent experiments). Error bar = standard deviation.

Figure 1.

Engineering Cyborg bacterial pathogens using intracellular hydrogelation. (A) Schematic workflow for intracellular hydrogelation. (B) Flow cytometry and fluorescence microscopy analysis depicting the detection of wild-type and Cyborg bacteria pathogens, including S. aureus, P. aeruginosa, and K. pneumoniae. The histogram illustrates the fluorescence signal obtained from two distinct markers: PEGDA–fluorescein Diacrylate, serving as an indicator of intracellular hydrogelation, and reductive chromogenic dye, a marker used to assess metabolic activity. The gray quadrant shows a population of hydrogelated cells that are metabolically active (n = 3 independent experiments). Fluorescence microscopy images of hydrogelated bacteria. The hydrogel was labeled with fluorescein (Methods Section M1) (Scale bar = 10 μm, n = 3 independent experiments). The data provide insights into the successful intracellular hydrogelation of the bacterial cells, thus creating Cyborg pathogens. (C) Representation of the percentage of hydrogelation and hydrogelated metabolically active cells in three distinct bacterial strains: S. aureus, P. aeruginosa, and K. pneumoniae while varying the hydrogelation percentages at 5, 10, 20, and 30%. Each bar within the graph represents the proportion of cells exhibiting intracellular hydrogelation (shown in blue) and the subset of these hydrogelated cells exhibiting metabolic activity (shown in magenta). The data offer insights into how different hydrogelation percentages impact the creation of Cyborg pathogens, allowing for an assessment of the most effective hydrogelation concentration for each strain. t tests were applied to confirm the statistical significance of the observed difference (n = 3 independent experiments). The error bar represents the standard deviation of the mean. (D) Representation of the comparative analysis of the rate of hydrogelation and metabolically active tracker across six distinct bacterial strains. The chart displays the hydrogelated and metabolically active populations at four different hydrogelation percentages: 5, 10, 20, and 30%. The grouped bar chart provides insights into the impact of varying hydrogelation percentages on the parameters within the bacterial population. This visualization aids in identifying the most favorable conditions, with 10% hydrogelation exhibiting the highest values among the examined percentages (n = 3 independent experiments). Error bar = standard deviation.

Figure 2.

Improving Cyborg pathogen purity via flow cytometry sorting. (A) Schematic workflow for intracellular hydrogelation and purification of the Cyborg pathogen by flow cytometry sorting. (B) CFUs of Cyborg S. aureus, P. aeruginosa, and K. pneumoniae. Flow cytometry-sorted Cyborg S. aureus, P. aeruginosa, and K. pneumoniae cells exhibit nondetectable CFU (below the detection limit) after sorting (black bars). This observation shows the successful elimination of nonhydrogelated and dividing cells within the bacterial Cyborg population (n = 3 independent experiments). Statistical significance was determined using a two-tailed t test with a significance threshold set at p < 0.05.

Figure 3.

Visualization of bacterial uptake and intracellular localization in macrophage. (A) Schematic demonstration of the invasion assay. Macrophages (RAW 264.7) were infected with wild-type bacterial strains, including S. aureus MN8, P. aeruginosa PA14, and K. pneumoniae BIDMC 2A at an MOI of 50. Postinfection, gentamycin treatment was applied to eliminate extracellular bacteria. After gentamycin treatment, the infected cells underwent three rounds of thorough washing to remove the remaining extracellular bacteria. Subsequently, cell lysis was achieved using 0.5 mL of 0.1% Triton-X in PBS, followed by enumeration of the CFU. This assay quantifies surviving intracellular bacteria, corroborating their ability to persist within macrophages after uptake. (B) CFUs of wild-type S. aureus MN8, P. aeruginosa PA14, and K. pneumoniae BIDMC 2A were determined. The gentamicin protection assay was conducted on wild-type S. aureus, K. pneumoniae, and P. aeruginosa in the RAW 264.7 macrophage cell lines. This assay indicated the successful invasion of wild-type pathogens inside the macrophage cells. The experiment was performed with three biological replicates (n = 3). (C–E) Representative confocal images of bacterial uptake by macrophages. Wild-type S. aureus, P. aeruginosa, and K. pneumoniae were stained with the SYTO-24 dye. Cyborg bacteria were labeled by PEGDA-fluorescein diacrylate. Macrophages were stained with Phalloidin (shown in red) to visualize their actin structure. White arrows indicate the bacteria. Orange arrows highlight the colocalization of S. aureus (C), P. aeruginosa (D), and K. pneumoniae (E) with macrophage actin. Confocal imaging was performed on two biological replicates (n = 2), with three technical replicates each. Five images were acquired for each replicate (scale bar = 20 μm).

Figure 3.

Visualization of bacterial uptake and intracellular localization in macrophage. (A) Schematic demonstration of the invasion assay. Macrophages (RAW 264.7) were infected with wild-type bacterial strains, including S. aureus MN8, P. aeruginosa PA14, and K. pneumoniae BIDMC 2A at an MOI of 50. Postinfection, gentamycin treatment was applied to eliminate extracellular bacteria. After gentamycin treatment, the infected cells underwent three rounds of thorough washing to remove the remaining extracellular bacteria. Subsequently, cell lysis was achieved using 0.5 mL of 0.1% Triton-X in PBS, followed by enumeration of the CFU. This assay quantifies surviving intracellular bacteria, corroborating their ability to persist within macrophages after uptake. (B) CFUs of wild-type S. aureus MN8, P. aeruginosa PA14, and K. pneumoniae BIDMC 2A were determined. The gentamicin protection assay was conducted on wild-type S. aureus, K. pneumoniae, and P. aeruginosa in the RAW 264.7 macrophage cell lines. This assay indicated the successful invasion of wild-type pathogens inside the macrophage cells. The experiment was performed with three biological replicates (n = 3). (C–E) Representative confocal images of bacterial uptake by macrophages. Wild-type S. aureus, P. aeruginosa, and K. pneumoniae were stained with the SYTO-24 dye. Cyborg bacteria were labeled by PEGDA-fluorescein diacrylate. Macrophages were stained with Phalloidin (shown in red) to visualize their actin structure. White arrows indicate the bacteria. Orange arrows highlight the colocalization of S. aureus (C), P. aeruginosa (D), and K. pneumoniae (E) with macrophage actin. Confocal imaging was performed on two biological replicates (n = 2), with three technical replicates each. Five images were acquired for each replicate (scale bar = 20 μm).

Figure 4.

Quantitative analysis of DNA amplification and bacterial uptake. (A–C) Melt-curve peaks are shown for DNA extracted from S. aureus, P. aeruginosa, and K. pneumoniae. (D–F) Linear standard curves of Ct (qPCR cycle threshold) against the log DNA concentration. R² values exceed 0.99 for S. aureus, P. aeruginosa, and K. pneumoniae. (G) Uptake of wild-type and Cyborg bacteria by macrophages was measured by the qPCR of extracted bacterial DNA, followed by conversion to CFUs using the standard curves.

Figure 5.

Inflammatory response of macrophages against Cyborg and wild-type pathogens. Macrophages (RAW 264.7) were subjected to infection with wild-type and Cyborg bacterial strains, encompassing S. aureus, K. pneumoniae, and P. aeruginosa, at an MOI of 50 for 4 h. Postinfection, supernatants were collected, and the levels of proinflammatory cytokines, including TNF-α, IL-17, and IFN-γ, were quantified using the ELISA, following the manufacturer’s instructions. (A–C) Standard curves for TNF-α, IL-17, and IFN-γ were developed as per manufacturer’s instructions. The standard curve was developed in biological replicates (n = 2). (D–F) TNF-α, IL-17, and IFN-γ from macrophages infected with S. aureus, P. aeruginosa, and K. pneumoniae. Assessed via the ELISA as per the manufacturer’s instruction. The assay was conducted using two biological replicates and two technical replicates. Error bar = standard deviation. * indicates P < 0.01 for TNF-α, IL-17, and IFN-γ. One sample two-tailed t test was applied.

Figure 6.

Intracellular level of total ROS in macrophages in response to Cyborg and wild-type pathogens. Total reactive oxygen species (ROS) levels were quantified in macrophages following infection with wild-type and Cyborg bacterial strains (S. aureus, P. aeruginosa, and K. pneumoniae) at an MOI of 50 for 4 h, utilizing the oxidant-sensitive probe H2DCFDA. (A) Histogram of the H2DCFDA (green) fluorescence channel, illustrating the marker gate used to identify cells positive for the H2DCFDA probe compared to stained uninfected cells. (B) Mean fluorescence intensity of the H2DCFDA probe in macrophages infected with wild-type and Cyborg pathogens, including S. aureus, P. aeruginosa, and K. pneumoniae. Unstained uninfected and stained uninfected cells are used as controls. n = 2. Error bars = SEM. Statistical significance was determined using one-way analysis of variance (ANOVA). **** indicates p-value <0.0001.