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. Author manuscript; available in PMC: 2023 Nov 30.
Published in final edited form as: ACS Appl Mater Interfaces. 2022 May 31;14(23):27515–27522. doi: 10.1021/acsami.2c07012

Polymer-Based Multichannel Sensor for Rapid Cell-Based Screening of Antibiotic Mechanisms and Resistance Development

Sohini Basu Roy 1, Ahmed Nabawy 1,§, Aritra Nath Chattopadhyay 1,§, Yingying Geng 1, Jessa Marie Makabenta 1, Akash Gupta 1, Vincent M Rotello 1,*
PMCID: PMC10587897  NIHMSID: NIHMS1937890  PMID: 35638721

Abstract

Antibiotic resistance presents a critical threat to public health, necessitating the rapid development of novel antibiotics and appropriate choice of therapeutics to combat refractory bacterial infections. Here, we report a high-throughput polymer-based sensor platform that rapidly (30 min) profiles mechanisms of antibiotic activity. The sensor array features three fluorophore-conjugated polymers that can detect subtle antibiotic-induced phenotypic changes on bacterial surfaces, generating distinct mechanism-based fluorescence patterns. Notably, discrimination of different generations of antibiotic resistance was achieved with high efficiency. This sensor platform combines trainability, simplicity, and rapid screening into a readily translatable platform.

Keywords: Multidrug-resistant bacteria, Array-based sensing, Antibiotics, Generation of resistance, High-throughput screening

Graphical Abstract

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Introduction

The rapid emergence of antibiotic-resistant bacteria has led to the challenge of incurable infections.1,2 Multidrug-resistant (MDR) bacteria present a global health crisis, increasing morbidity and mortality in infected individuals and adversely affecting other immunocompromised groups such as cancer patients and patients undergoing organ transplantation.3,4 Annually, an estimate of ~700,000 individuals die due to antibiotic-resistant infections and research suggests that this death toll might reach 10 million by 2050 if left unresolved.5 While drug resistance in bacteria occurs naturally, its development and spread have been significantly accelerated due to the indiscriminate use of antibiotics.6,7 Recent reports of MDR bacterial strains that are resistant to last-resort antibiotics, including colistin8 and carbapenem,9 have led to the rise of potentially untreatable infections. The threat is further compounded by the significant decline in the development of new antimicrobials, with no new drug class approved over the last 30 years.1 This rapidly worsening crisis has contributed to the urgent need to discover novel antibiotic therapeutics, with the concomitant requirement for high throughput screening methods to assess the resistance profile of these therapeutics.

Rapid identification of the mechanism of potential lead antibiotics is a critical step in the discovery and optimization of new therapeutics.10 Screening methods that simultaneously profile antibiotic mechanisms and monitor the acquisition of resistance would facilitate targeting the appropriate antibiotic to a particular bacteria, especially in the emerging era of precision medicine.11 Recently, cell-based profiling of drug mechanisms using molecular/phenotypic signatures has emerged as a promising tool in drug discovery.12, 13, 14 However, these conventional screening methods that primarily use -omics signatures (genomics, proteomics, etc.),15 require multi-stage processing of samples and expensive instrumentation.16 Also, these techniques are generally non-generalizable and challenging to adapt to high-throughput platforms.

Hypothesis-free signature-based sensing is generally employed in an array-based sensing platform,17 has emerged as an alternative to conventional screening assays for identifying complex bioanalytes.18,19, 20,21 Hypothesis-free cell surface sensing strategies utilize and distinguish selective interactions between the sensor and analytes to generate unique fingerprints for each analyte, hence hypothesis-free.22,23,24,25 This approach has been successfully used to differentiate a variety of different phenotypic changes in cell surfaces including planktonic bacteria and biofilms.26

Multichannel polymer-based sensor arrays have been developed capable of identifying and differentiating between distinct bacterial biofilms.27,28 Taking advantage of the sensitivity and information-rich output of this sensor, we hypothesized that this sensor platform could detect subtle phenotypic differences in bacterial surfaces in a hypothesis-free manner, providing a rapid species-based bacteria screening. We report the use of a fully polymeric sensing platform capable of discriminating antibiotic mechanisms and tracking the development of antibiotic resistance. This sensor array employs three cationic benzyl functionalized polymers29 with three different fluorescent transducers generating four distinct ratiometric channels.30 These sensors can detect subtle changes in bacteria surface, owing to differences in structure and composition of the bacterial cell envelope induced by antibiotic treatment, 31,32 generating unique response signatures that can be used as a training set for tracking antibiotic activity. Once trained, the information-rich output of these four-channel sensors enables the determination of the mechanism of antibiotics from a single measurement, far quicker than conventional screening methods. Furthermore, our sensing strategy can distinguish between bacterial strains sensitive and resistant to a specific antibiotic and hence can provide useful information for the prescription of an effective treatment.33 The high throughput screening of antibiotic mechanisms coupled with the accurate profiling of the susceptibility of different bacteria strains to antibiotics highlight the potential of our sensor platform for antibiotic research and development.

Results and Discussion

The sensor used here was modified from our previous arrays.27 The modified sensor was engineered to maximize responses upon interaction with the bacterial cell surfaces. The sensor was composed of two key elements: a cationic benzyl functionalized recognition element and three solvatochromic dyes as transducers (Figure 1a). The polymeric sensors displayed changes in their fluorescent intensities upon the addition of planktonic bacteria due to changes in the local environment including pH, polarity, electrostatics and hydrophobicity, and the supramolecular interaction of the dyes.34 The three different solvatochromic fluorophores selected were: pyrene (Py), nitrobenzoxadiazole (NBD), and REDD (Figure S1a–c). NBD and REDD are environmentally sensitive dyes that are highly sensitive to the local environment of the solvent. Py, on the other hand, shows fluorescent properties from both its monomer state and an excimer state. The former is highly sensitive to changes in solvent polarity while the latter depends on the actual physical separation between multiple pyrenes (Table S2). Overall, the careful selection of the dyes in the polymer sensor resulted in an information-rich, multi-channel output from a single well experiment. The cationic recognition element, the positively charged benzyl group, was selected to ensure selective binding to the negatively charged bacterial cell envelope through multiple supramolecular interactions including electrostatics, hydrophobic and aromatic interactions, resulting in the generation of binding patterns (Figure 1b).35 The generated fluorescence patterns differed significantly depending on the signatures of the drug-treated cell surface and were then used for data analysis to identify mechanisms of bacterial cell death induced by drugs.

Figure 1. Polymer-based sensor arrays used for discriminating bacteria.

Figure 1.

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a) Molecular structures of three different cationic fluorescent polymers (PONI-C3-benzyl-C3-pyrene, PONI-C3-benzyl-C3-NBD, and PONI-C3-benzyl-C3-REDD). b) Selective interactions of the multi-channel sensor elements with the pathogenic bacterium resulting in fluorescent changes in all four channels, generating multi-dimensional output with a distinct fingerprint. Linear discriminant analysis was performed on the data to classify each bacteria.

Initial studies focused on the ability of the dye-conjugated polymers to discriminate between different bacteria species. The discrimination study of the different bacteria species was performed with three different bacteria concentrations measured through optical density (OD600=0.25, 0.05, and 0.005). The sensor was incubated with each of the bacteria species in 5 mM phosphate buffer solution and the fluorescence output was measured within 30 mins. This protocol was maintained throughout all the experiments performed, and is intended to minimize interactions of the sensor platform with interferents. The sensing results with a bacteria concentration of OD600=0.25 showed a high accuracy of 92% correct classification in the cross-validation studies (Figure S4) and hence was chosen as the optimal concentration for the rest of the experiments. Next, we tested the sensor against four bacteria species for the initial discrimination studies: Staphylococcus aureus (CD-35), Escherichia coli (CD-2), Bacillus subtilis (FD6b), Pseudomonas aeruginosa (ATCC 19660), of which two species are pathogenic clinical isolates (S. aureus CD-35 and E. coli CD-2). The sensor generated a distinct fluorescence pattern for each of the bacteria species (Figure 2A). Linear discriminant analysis (LDA) was used to quantify the discrimination from the fluorescence signature (Figure 2B). The LDA graph showed four distinct clusters for each of the bacteria species with different Gram classifications. Accurate phenotyping was obtained within 30 minutes, which is highly desirable for high-throughput applications. Evidence suggests a possible surface binding event by the polymer to the anionic bacterial surface as exhibited by a positive shift in the Zeta potential data.36,37

Figure 2. Discrimination between different bacteria species.

Figure 2.

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(a) Fluorescence intensity bar graph against four types of non-resistant bacterial species with two clinically isolated strains. I30 is the sensor response of bacterial species in interaction with the sensor after incubation for 30 min. I0 is the sensor-only response in absence of the bacteria. Each value is the average of eight independent measurements. (b) Canonical score plot for the first two factors of fluorescence patterns was obtained with the polymer sensor against four bacterial species. The scores were generated through LDA with 95% confidence ellipses (n = 8).

Next, we investigated the ability of the polymeric sensor platform to categorize and identify antibiotic mechanisms using a set of different antibiotics with established mechanisms. These antibacterial agents are clinically and experimentally used to treat bacterial infections and offer different modes of action against bacteria (Table. 1).38 We tested the hypothesis that these common antibiotics will generate subtle yet significant cell surface alterations that could be rapidly discerned using our sensor.39 We selected E. coli (CD-2) and S. aureus (CD-35) as representatives of Gram-negative and Gram-positive strains, respectively, for profiling antibiotic mechanisms.

Table 1.

Description of the antibiotic screened in this study using S. aureus (CD-35) and E. coli (CD-2). The antibiotics marked with an asterisk were used in the training set and their mechanism of action was known.

# Antibiotics Name Mode of Action Antibiotic Class Test Strain
1) Ciprofloxacin Interfere with bacterial DNA replication and transcription.40 Quinolones S. aureus (CD-35)
2) Levofloxacin*
3) Moxifloxacin*
4) Ceftazidime* Inhibit bacterial cell wall biosynthesis by binding with penicillin-binding proteins.41 Cephalosporins
5) Cefotaxime
6) Chloramphenicol* Inhibits synthesis of proteins preventing growth.42 Chloramphenicol
7) Vancomycin Disrupt cross-linking of bacterial peptidoglycan cell wall biosynthesis.43 Glycopeptides
1) Ciprofloxacin Interfere with bacteria DNA replication and transcription. Quinolones E.coli (CD-2)
2) Levofloxacin
3) Moxifloxacin
4) Ceftazidime Inhibit bacterial cell wall biosynthesis by binding with penicillin-binding proteins. Cephalosporins
5) Cefotaxime
6) Oxacillin Inhibit bacterial cell wall biosynthesis.44 β-lactams
7) Amoxicillin
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The antibiotic screening studies for S. aureus CD-35 followed a simple protocol as demonstrated in Figure 3A. The sensing study was carried out in a 96 well black microplate and fluorescence signals were recorded after incubating with the sensor for 30 minutes. Initially, we used 4 different antibiotics that act through different molecular mechanisms to generate a reference set based (Table 1). The polymeric sensor generated characteristic fluorescence fingerprints after interaction with the drug-treated bacteria. The multidimensional sensor data were quantitatively interpreted using LDA analysis which classified the patterned fluorescence response (4 antibiotic-resistant bacteria X 8 replicates X 4 channels) into three different and distinct clusters (Figure 3C and Table S8) corresponding to different antibiotic mechanisms. The LDA plot shows a 78% correct classification between the three groups indicating that the sensor could discriminate between different mechanistic pathways. Notably, antibiotics with similar molecular mechanisms (moxifloxacin and levofloxacin) showed overlapping clusters that were distinguishable from the other mechanistic categories. The distinctly separate clustering of the different mechanistic classes of antibiotics demonstrates the sensitivity of the sensor based on the subtle differences on the bacterial surface.

Figure 3. Workflow for the multichannel polymer-based antibiotic screening.

Figure 3.

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a) Signature-based sensing of the antibiotic-treated bacterial species S. aureus CD-35. Bacteria were cultured and treated with the antibiotic for 24 h, followed by washing and treatment with the polymer sensor for 30 mins. b) The bar plot shows differential fluorescence responses for the four antibiotics treated bacterial surfaces. I0 is the fluorescence intensity of the sensor only and I30 is the fluorescence intensity after the addition of the sensor to the bacteria. c) Clustering of the antibiotics after the raw fluorescence data were analyzed through LDA.

One of the key aspects of drug screening is to identify the mechanism of potential lead compounds. The mechanism identified through the screening may turn out to be either known or novel. We performed blinded experiments with new antibiotics ceftazidime and ciprofloxacin that exhibit mechanism similar to cefotaxime and levofloxacin, respectively, in the training set for S. aureus (CD-35) (Table 1). The training data set was developed using known compounds, separating the antibiotics based on their mechanism into distinct clusters. Once trained, the model was tested using new sets of data generated from antibiotics with ‘unknowns’. Profiling the mechanism of new drugs can be achieved by calculating the probability of a drug corresponding to the closest reference group utilizing F-distribution for the minimum Mahalanobis distance obtained from LDA.45 A properly trained model, therefore, can predict and profile novel antibiotics with pre-established mechanisms of action as well as potential lead compounds with completely new mechanisms of action. Implementing this analysis, followed by a cut-off p-value of ≥ 0.01 we correctly predicted the molecular mechanism of these two antibiotics, demonstrating the capability of the sensor to screen ‘real’ unknowns (Table 2, S9–S11). We then proceeded to check if the sensor can identify other antibiotics involving different mechanisms outside the training set. We selected vancomycin from the glycopeptide-based antibiotic class for the novel mechanism study (that is outside the reference set) and tested using our sensor. Probabilistic analysis demonstrated that vancomycin, with a p-value < 0.01, did not belong to any of the reference sets and could be classified as a ‘novel’ mechanism (Table 2 and Table S9–S11).46 As above, LDA analysis using the reference and novel antibiotic set revealed distinct clusters, with antibiotics with the same mode of action overlapping (Fig 4a). These results indicate the ability of the sensor to classify novel classes of antibiotics with high efficiency.

Table 2.

Probabilistic predictions of antibiotic mechanisms as known or novel through the fluorescence output.

Blind Experiment Antibiotic Mechanism of action P-value Correct prediction
Similar mechanism as the reference set Ciprofloxacin Interfere with bacteria DNA replication and transcription. 0.821 Yes
Ceftazidime Inhibit bacterial cell wall biosynthesis by binding with penicillin-binding proteins. 0.061 Yes
Mechanism outside the reference set Vancomycin Disrupt cross-linking of bacterial peptidoglycan cell wall biosynthesis. <0.0001 Novel
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Figure 4. Screening of antibiotic mechanisms using fluorescence fingerprints.

Figure 4.

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Heat map of the fluorescence response patterns of a) S. aureus (CD-35), and b) E. coli (CD-2) when each treated with a different set of antibiotics, where I0 and I are the fluorescence intensity before and after the addition of the sensor to the cells, respectively. c-d) canonical score plots for c) S. aureus (CD-35), and d) E. coli (CD-2), were derived from LDA of the fluorescence responses from the antibiotic sets and were plotted with 95% confidence ellipses around the centroid of each group (based on the standard error of the mean).

The generality of the four-color sensor platform was evaluated against a second bacteria species, E. coli (CD-2), with entirely different phenotypic configurations. Our main objective was to observe whether our sensor can also classify the different antibiotic-induced phenotypic changes on the Gram-negative bacterial surface of E. coli. Antibiotics from three different mechanistic classes were used: quinolones, cephalosporins, and β-lactams. We also included two completely novel antibiotics to validate the robustness and reliability of our sensor with LDA analysis (Table 1). LDA analysis showed six distinct mechanism-based clusters with 98% classification based on characteristic fluorescence patterns (Figure 4b). The antibiotics with similar molecular mechanisms clustered together while antibiotics with different mechanisms show a clear distinction between each other. Taken together, the polymeric sensor platform provides a robust and reliable tool for profiling the mechanisms of novel therapeutics.

The escalating rate of antibiotic resistance in bacterial pathogens continues to burden both global health and the economy. 47 Xu et al. have reported an aggregation-based sensor for identifying multiple MDR bacteria species.48

We hypothesized that the development of resistance in bacteria would lead to subtle phenotypic changes in the bacterial surfaces, enabling rapid cell surface-based screening using the three-color sensor.49,50 The capability of the sensor to identify and classify different generations of antibiotic resistance was established using S. aureus (CD-35) and E. coli (CD-2). Antibiotic-resistant bacteria were generated by subjecting the initially non-resistant (NR) bacteria to sub-inhibitory concentrations (50% of the minimum inhibitory concentration (MIC) of the antibiotic (Figure 5).51,52 The resulting bacterial populations for each antibiotic were defined as the first generation (R1), harvested, and their MICs determined. Subsequent generations were provided by further passaging the previous generation in presence of the corresponding sub-MIC dosing of the antibiotic (Table S5). Three different antibiotics appropriate to the species and strain were chosen to induce resistance development for each of the two bacteria: ceftazidime, levofloxacin, and vancomycin for S. aureus (CD-35) and cefotaxime, moxifloxacin and oxacillin for E. coli (CD-2).

Figure 5.

Figure 5.

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Schematic illustration of serial passage experiments for the generation of antibiotic-resistant bacterial mutants.

Next, the different resistant generations of resistant bacteria were incubated with the polymer sensor for 30 min before reading the fluorescence signals, and the resulting fluorescent patterns for the different generations of resistant bacteria were further analyzed through linear discriminant analysis (LDA) (3 bacterial species X 8 replicates X 4 channels of the sensor). As shown in Figures 6a and 6b, the sensor effectively discriminated between the different generations of resistant bacteria. We further validated the reliability of the sensor by performing unknown sample identification which showed 93% correct classification (Table S14–S18). These results demonstrate that the fluorescent signatures of the sensor-bacterial surface interactions enable efficient differentiation and identification of the non-resistant and the different resistant generations of the bacteria.

Figure 6. Schematic illustration of the classification of the different generations of antibiotic resistance of pathogenic bacteria.

Figure 6.

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Discrimination of the zero, first and the third generation of resistance for a) S. aureus (CD-35) when treated with three different antibiotics ceftazidime, levofloxacin, or vancomycin. b) E. coli (CD-2) when treated with antibiotics cefotaxime, ciprofloxacin, or oxacillin.

Conclusion

In summary, we have designed a polymer with responsive dyes that demonstrates differential binding patterns through supramolecular interactions with the bacterial cell surfaces and fabricated a rapid and robust polymer-based multichannel sensor array for the high-throughput screening of antibiotics. Our research demonstrates the ability of the four-channel sensor to detect subtle phenotypic changes in bacteria surfaces induced by different antibiotics in a single well of a microplate, allowing high-throughput profiling of their specific mechanisms of action. Furthermore, the sensor is effective in discriminating different generations of antibiotic resistance, an important attribute in the prescription of personalized antibiotic therapy.53 Taken together, the development of a modular, simple, and highly efficient sensor, particularly with the rapid spread of MDR bacteria, highlights its potential to facilitate rapid and effective antibiotic discovery and development, together with rapid screening of antimicrobial susceptibility.

Supplementary Material

Supporting Information

ACKNOWLEDGEMENT

The authors thank Dr. Caren. M. Rotello and Piyas Chakrabarty for providing useful suggestions in the statistical calculation. S.B.R was partially supported by a fellowship from the University of Massachusetts, Amherst. Clinical samples obtained from the Cooley Dickinson Hospital Microbiology Laboratory (Northampton, MA) were kindly provided by Dr. Margaret Riley.

Funding Sources

This research was funded by the NIH (DK121351, AI134770).

Footnotes

Supporting Information

Electronic supporting information (ESI) is available free of charge at DOI: xxxx.

Synthesis of fluorescent polymers, 1H NMR of polymers, preparation of the pathogenic bacteria, generation of resistant bacteria species, and sensing studies.

CONFLICTS OF INTEREST

The authors declare no competing financial interest.

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Supporting Information
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