Dissolvable alginate hydrogel-based biofilm microreactors for antibiotic susceptibility assays

Abstract

Biofilms are found in many infections in the forms of surface-adhering aggregates on medical devices, small clumps in tissues, or even in synovial fluid. Although antibiotic resistance genes are studied and monitored in the clinic, the structural and phenotypic changes that take place in biofilms can also lead to significant changes in how bacteria respond to antibiotics. Therefore, it is important to better understand the relationship between biofilm phenotypes and resistance and develop approaches that are compatible with clinical testing. Current methods for studying antimicrobial susceptibility are mostly planktonic or planar biofilm reactors. In this work, we develop a new type of biofilm reactor-three-dimensional (3D) microreactors-to recreate biofilms in a microenvironment that better mimics those in vivo where bacteria tend to form surface-independent biofilms in living tissues. The microreactors are formed on microplates, treated with antibiotics of 1000 times of the corresponding minimal inhibitory concentrations (1000 × MIC), and monitored spectroscopically with a microplate reader in a high-throughput manner. The hydrogels are dissolvable on demand without the need for manual scraping, thus enabling measurements of phenotypic changes. Bacteria inside the biofilm microreactors are found to survive exposure to 1000 × MIC of antibiotics, and subsequent comparison with plating results reveals no antibiotic resistance-associated phenotypes. The presented microreactor offers an attractive platform to study the tolerance and antibiotic resistance of surface-independent biofilms such as those found in tissues.

Keywords: Antibiotic resistance; Antibiotic susceptibility assays; Biofilm microreactors; Biofilm phenotype; Hydrogel.

Fig. 1

Schematic of the dissolvable alginate hydrogel-based biofilm microreactors for antibiotic susceptibility assays. (A) Formation of microreactors: (1) a synthesized bacteria-encapsulated alginate gel was cultured for 24 h to form microreactors; (2) schematic of microreactors within the alginate gel; and (3) chemical structure of cross-linked alginate chains where Ca²⁺ chelates the G-blocks of alginate chains. (B) The microreactors in alginate gels were exposed to antibiotic assay, followed by downstream analyses: visualized using confocal laser scanning and fluorescence microscopies (left); monitored using microplate reader for fluorescence/luminescence signal (middle); or reversely dissolved to obtain the bacteria in planktonic form for agar plating to assess antibiotic-induced mutation and viability (right).

Fig. 2

Log10(CFU/mL) of microbial growth in alginate hydrogels and in tubes, indicating they were within the same order of magnitudes.

Fig. 3

Distribution of biofilm microreactors in alginate gel. (A) Confocal image of alginate-encapsulated RP437/pRSH103 microreactors grown in vitro for 24 h. (B) Rebuilt 3D image of the microreactors. (C) Live/dead staining of the corresponding gels. Scale bars as indicated.

Fig. 4

Microscopic images of biofilm matrix and cell aggregate of P. aeruginosa strain PA14. (A) Brightfield image of the microreactors grown in vitro for 24 h. (B) SYPRO® Ruby staining of biofilm matrix in red. (C) FM® 1–43 staining of cells within the microreactors in orange. (D) Co-registered image of two stains showing microbes embedded in biofilm matrix. Scale bar as indicated. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Procedure for the antibiotic assays of biofilm microreactors. Alginate hydrogels were formed in a 96-well microplate followed by 24-h culturing at 37^oC to form microreactors. Antibiotic sensitivity was assessed by treating each microreactor in gel with antibiotic dose of 1000 × MIC for three days followed by reading fluorescence/bioluminescence signals daily with a microplate reader.

Fig. 6

Biofilm tolerance, cell viability, and antibiotic resistance mutation-induced phenotype of E. coli. (A) Fluorescence signals of E. coli RP437/pRSH103 treated with bleach, and 1000 × MIC of antibiotics from Day 0 to Day 3. (B) Log10(CFU/mL) counts on nutrient plates of the retrieved cells from dissolved biofilm microreactors with 1000 × MIC antibiotic treatments for 0, 1 and 2 days. (C) CFU counts on MIC antibiotic plates of the corresponding retrieved cells. Day 0 indicates 24-h microreactors in CMHB before antibiotic treatments. (*) indicates significant differences (p < 0.05) between groups or time points, (#) indicates significant differences (p < 0.05) within the group or time points. All columns and error bars represent mean and ± standard deviation.

Fig. 7

Biofilm tolerance, cell viability, and antibiotic resistance mutations of P. aeruginosa. (A) Fluorescence signals of PAO1/pTdK-GFP treated with bleach and 1000 × MIC ciprofloxacin from Day 0 to Day 3. (B) Log10(CFU/mL) counts on nutrient plates of the retrieved cells from dissolved microreactors with 1000 × MIC ciprofloxacin treatments for 0, 1, and 2 days. (C) CFU counts on MIC ciprofloxacin plates of the corresponding retrieved cells. Day 0 indicates 24-h microreactors in CMHB before antibiotic treatments. (*) indicates significant differences (p < 0.05) between groups or time points, (#) indicates significant differences (p < 0.05) within the group or time points. All columns and error bars represent mean and ± standard deviation.

Fig. 8

Biofilm tolerance, cell viability, and antibiotic resistance mutations of S. aureus. (A) Fluorescence signals of AH2547/pCM29 treated with bleach and 1000 × MIC effective antibiotics from Day 0 to Day 3. (B) Log10(CFU/mL) counts on nutrient plats of the retrieved cells from dissolved microreactors with 1000 × MIC antibiotic treatments for 0, 1, and 2 days. (C) CFU counts on MIC antibiotic plates of the corresponding retrieved cells. (Day 0) indicates 24-h microreactors in CMHB before antibiotic treatments. (*) indicates significant differences (p < 0.05) between groups or time points, (#) indicates significant differences (p < 0.05) within the group or time points. All columns and error bars represent mean and ± standard deviation.