Real-time, label-free detection of Gram-negative bacteria with high selectivity and sensitivity is demonstrated using a bipolar electrode-electrochemiluminescence (BPE-ECL) platform. This platform utilizes anode luminescence and cathode modification of antimicrobial peptides (AMPs) to effectively capture bacteria. Magainin I, basic AMP from Xenopus skin, boasting an a-helix structure, exhibits a preferential affinity for the surface of Gram-negative pathogens. The covalent attachment of the peptide's C-terminal carboxylic acid to the free amines of a previously thiolated linker ensures its secure immobilization onto the surface of interdigitated gold-plated cathode of BPE. The AMP-modified BPE sensor, when exposed to varying concentrations of Gram-negative bacteria, produces reproducible ECL intensities, allowing for the detection of peptide-bacteria interactions within the range of 1 to 104 CFU mL-1. Furthermore, this AMP-modified BPE sensor demonstrates a selective capacity to detect E. coli O157:H7 amidst other Gram-negative strains, even at a concentration of 1 CFU mL-1. This study underscores the high selectivity of Magainin I in bacterial detection, and the AMP-modified BPE-ECL system holds significant promise for rapid detection of Gram-negative bacteria in various applications. The AMP-modified BPE sensor to various concentrations of Gram-negative bacteria generated reproducible ECL intensity that detected peptide-bacteria interactions in the range of 1 to 104 CFU mL-1. The AMP-modified BPE sensor also selectively detected E. coli O157:H7 from other Gram-negative strains at a concentration of 1 CFU mL-1. In this paper, AMP demonstrated high selectivity in bacterial detection. The AMP-modified BPE-ECL system prepared has a great potential for application in the field of rapid detection of Gram-negative bacteria.
Research Article
Detection of pathogenic Gram-negative bacteria using an antimicrobial peptides-modified bipolar electrode-electrochemiluminescence platform
https://doi.org/10.21203/rs.3.rs-4819327/v1
This work is licensed under a CC BY 4.0 License
You are reading this latest preprint version
AMP
Bioelectronic sensors
Bacterial sensing
According to the World Health Organization (WHO), nearly 30% of foodborne deaths occur in children under the age of 5. Foodborne diseases, caused by pathogenic bacteria, have become a serious threat to global public health (Faour-Klingbeil and Todd, 2020). A wide range of intestinal diseases caused by foodborne pathogens have caused a huge economic and health burden on people's physical and mental health (Akter et al., 2021; Ling et al., 2019; Prata et al., 2021; Qiu et al., 2021). However, rapid bacterial detection is a rather challenging task due to the large number of bacterial species and the severe interference of complex substrates in the growth environment (Grant and Hung, 2013).
At present, the detection methods of bacteria include ring-mediated isothermal amplification (LAMP), polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA) and so on. LAMP can cause false positives caused by dimerization and hybridization (Lee et al., 2023). PCR is relatively expensive, takes hours, and contamination of test samples and incorrect DNA amplification can lead to false positive or false negative results (Chakvetadzea et al., 2017). ELISA, which relies on the specific interaction between antigens and antibodies, is utilized for bacterial detection despite its limitations in sensitivity (Guo et al., 2018). As a strictly two-phase system, ELISA involves a liquid analyte and solid-phase bound antibodies, necessitating a longer time for equilibration between the two phases. Therefore, the analysis time is typically measured in hours, unlike biosensors, which can produce results in minutes (Amiri et al., 2018). While these methods indeed possess immense power and versatility in detecting, monitoring, and clinically diagnosing pathogenic infections with pinpoint specificity and sensitivity, they are nonetheless hampered by a series of limitations that hinder their widespread application. It is well-known that a biosensor is an analytical device that transforms biological interactions into measurable and processable signals (Velusamy et al., 2009). It incorporates several crucial components: (1) bioreceptors capable of specifically recognize and engaging with target analytes across a range of samples, (2) transducers that transform biological responses into measurable signals, encompassing electrochemical, optical, and piezoelectric ones, and (3) detectors designed to amplify, interpret, visualize, and document these signals for analysis (Templier et al., 2015).
Among electrochemical cells, microbial fuel cells (MFCs) are a special type of biofuel cell that generates electricity by utilizing microorganisms rather than isolated enzymes, and the presence of free electrons at the cathode of the MFCs triggers a reduction reaction of O2 to produce water (Katz et al., 2003). Bacteria can be used as biocatalysts to receive electrons from the cathode electrode (Lovley, 2006). A large number of research papers have focused on bioanodes with pure or mixed bacterial cultures (He and Angenent, 2006), while cathode applications are often neglected. In MFCs, electrode materials play an important role in performance. In such systems, current generation is highly dependent on the reduction kinetics at the cathode, and in the case of air-cathode designs, the oxygen reduction reaction (ORR) is one of the main factors limiting power generation because of its high activation energy (Min and Logan, 2004; Papiya et al., 2017). Electroactive bacteria (EAB) are microorganisms that can transfer electrons to extracellular solid receptors and play key roles in biogeochemical cycles, environmental remediation of pollutants, bioenergy and biosynthesis (Hu et al., 2022; Logan, 2009; Ye et al., 2022). Microbial electrochemistry and technology (MET), which links EAB metabolism with electrochemical systems, originated in the 20th century (Qi et al., 2021). The extracellular electron transfer (EET) mechanism electronically connects the metabolism of microorganisms to the electrode (Webster et al., 2014; Yang et al., 2017). EABs has a highly efficient ability of extracellular electron transfer and can spontaneously produce detectable fluorescence signals. Escherichia coli has also been proved to be capable of extracellular electron transfer (Zhang et al., 2008). Extracellular polymer (EPS) is the main component of EABs. Many proteins in EPS have redox activity and can be used as electron donors or receptors to participate in extracellular electron transfer, thus improving the conductivity of EPS (Borole et al., 2011; Li et al., 2020). (Yang et al., 2019) reported that the electrochemical activity of biofilm was positively correlated with the redox activity of EPS. (Kang et al., 2014) confirmed that EPS extracted from Enterobacter coli can reduce positively charged silver ions to silver nanoparticles.
Electrochemiluminescence (ECL), based on the formation of luminescent excited states of electrochemically generated substances by high-energy electron transfer reactions, is considered a novel analytical technique combining electrochemistry and chemiluminescence (Meng et al., 2023). It is widely used in the field of pathogen detection, which has the characteristics of high sensitivity, good selectivity, simple operation, rapid response, low cost, non-toxicity and so on. AMP is ubiquitous in nature and is a key component of the innate immune system of organisms (Nguyen et al., 2011). Recently, AMPs have been reported as promising biorecognition elements for biosensing platforms (de Miranda et al., 2017). Because of its high stability and multiple sites to capture bacteria, more and more people use it as ligands to conjugate chemically selective groups with AMPs, which can effectively immobilize peptides on biosensors (Islam et al., 2022). Etayash et al bound the surface active peptide leucocin An of IIa bacteriocins to the surface of gold electrode (Etayash et al., 2014). The capture of Listeria monocytogenes by leucocin A can cause a change in impedance. The minimum detection limit is 103 CFU mL− 1. Magainin I is an AMP from the skin of Xenopus laevis. It has a-helix structure and can selectively bind to the surface of gram-negative pathogens. Mannoor et al coupled antimicrobial peptide magainin I to the surface of gold electrode (Mannoor et al., 2010). By capturing the impedance change caused by E. coli O157:H7, the detection sensor was installed in the microfluidic control pool, and a device capable of real-time monitoring was constructed. As an effective ligand, the specificity of AMPs is a major problem, especially the selectivity of AMPs in specific detection of pathogens (Islam et al., 2022).
Magainin I, an antimicrobial peptide derived from the skin of the African clawed toad, Xenopus laevis, immobilized on electrode arrays for BPE-ECL, provides an optimal sensor platform for the real-time detection of E. coli O157:H7 in solutions. We use AMP-modified BPE sensor, which converts the electrochemical signal into the ECL intensity signal. It can effectively detect of foodborne pathogens in food samples.
2.1. Materials and apparatus
E. coli O157:H7 (CICC 10907), Salmonella typhimurium (S. typhimurium, ATCC 14028), Pseudomonas aeruginosa (P. aeruginosa, ATCC 27853), and Lactobacillus rhamnosus (L. rhamnosus, ATCC 7469) came from Guangdong microbial Culture and Collection Center (GDMCC). The sequence of Magainin I was GIGKFLHSAGKFGKAFVGEIMKS (23 mer) and purchased from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). Cysteamine hydrochloride (Cys) and [1-ethyl-(3-dimethylaminopropyl) carbodiamide hydrochloride] (EDC) were obtained from Shanghai Aladdin Reagent Co., Ltd. (Shanghai, China). Ternary pyridine hexahydrate ruthenium chloride (Ru(bpy)3Cl2.6H2O) and tripropylamine (TPA) were acquired from Sigma-Aldrich Ltd. (St. Louis, Missouri, USA).
The following buffer solutions were used in this study: PBS (pH 7.2–7.4, 136.89 mM sodium chloride, 2.67 mM potassium chloride, 8.24 mM disodium hydrogen phosphate, 1.76 mM sodium dihydrogen phosphate); ECL detection solution (PBS with 50 mM Ru(bpy)32+ and 250 mM TPA, prepared daily). All buffers were prepared with ultra-pure system (GWA-UN1-F40, Persee, China) to a specific resistance of 18.2 MΩ cm⁻¹.
In this experiment, CHI750E electrochemical workstation (Shanghai Chenhua Instrument Co., Ltd., China) was used to all electrochemical experiments. The redox ability of bacteria was characterized by cyclic voltammetry (CV) using the traditional three-electrode system. A gold electrode was used as the working electrode, platinum wire as counter electrode and saturated calomel electrode (SCE) as reference electrode. The ECL signal of [Ru(bpy)3]2+ (λmax, em = 620 nm) was detected by fluorescence spectrophotometer (Fmur4700, Hitachi, Japan), and the scanning speed was set to 12 000 nm min− 1, adhering strictly to the parameters established in our previous work (Lu et al., 2021).
2.2. Bacteria culture
Firstly, bacteria were inoculated in 10 mL of Luria-Bertani (LB) medium, while L. rhamnosus inoculated in 10 mL of MRS medium. Subsequently, both were activated overnight in a constant temperature shaker maintained at 37 ℃ and 160 rpm. The concentration of the bacteria could reach 108 CFU mL− 1 when the activated bacterial solution was incubated at 37 ℃ and 160 rpm shaker for 2.5 h. 100 µL bacterial culture medium was added to 900 µL aseptic 0.85% saline. After diluting the solution 1 ~ 9 times, the concentration is about 1 ~ 107 CFU mL− 1.
2.3. Preparation of AMP-modified BPE sensor
The bare screen printed BPE was designed according to our previous work (Chen et al. 2023). Adding 1% HAuCl4 solution (20 µL) to the cathode and PBS (20 µL) to the anode, applying 6.5 V constant voltage to both poles for 60 s, electrodepositing gold nanoparticles on the cathode by electrochemical reduction method (Xiong et al. 2020). The prepared gold-plated BPE was washed with ultra-pure water and dried in air. The surface of gold-plated BPE was modified by Au-S covalent bond and Cys. Cys (0.01 M) was dissolved in 10×PBS and incubated at the cathode of the BPE (> 8 h) at room temperature (Etayash et al., 2014). The surface of gold-Cys-plated BPE was cleaned with ultra-pure water. AMP (20 µg/mL) was dissolved in 1×PBS, and EDC (0.2 M) was added to the prepared AMP solution. EDC was a strong and effective amide linker as an activator. 20 µL AMP solution was added to the cathode and incubated overnight at room temperature. The AMP was self-assembled on the gold-plated cathode, and cleaned with ultra-pure water.
2.4. ECL analysis
10 µL E. coli O157:H7 at serial concentrations of 1, 10, 102, 103, and 104 CFU mL− 1 were added dropwise to the AMP-modified cathode, incubated with the immobilized peptide, and after incubating the immobilised peptide for 30 min at room temperature. Then 10 µL PBS was added dropwise to the above cathode, and 10 µL ECL detection buffer was added dropwise to the anode. A constant voltage of 5.3 V was applied to both ends of the BPE, and the ECL intensity was detected by a fluorescence spectrophotometer after a response time of 20 s.
2.5. Actual sample detection
In order to verify the practicability of the designed AMP-modified BPE sensor, pure water, pure milk, coconut milk, juice were selected as real sample detection objects according to China’s National Food Safety Standard (GB 7101 − 2022 beverage). The actual sample (100 µL) was mixed with PBS (900 µL), and then 10 and 100 CFU mL− 1 of E. coli O157:H7 were added to the samples to be tested. The constant potential of 5.3 V was applied to the electrode by electrochemical workstation, and the ECL luminescent signal was obtained by fluorescence spectrophotometer.
3.1. Designing principles and characterization of the BPE-ECL sensor
The objective of our investigation was to explore the feasibility of employing Magainin I in BPE for selective and sensitive detection of harmful microorganisms and, in particular, E. coli O157:H7. Scheme 1 showed a schematic diagram of rapid screening of Gram-negative bacteria based on BPE-ECL platforms.
In order to verify that AMP selectively recognized Gram-negative bacteria on the prepared sensor, E. coli O157: H7 was selected as the experimental bacterium and incubated on AMP-modified BPE sensor. BPE were prepared as described in Fig. 1A. The luminous intensities of bare BPE, gold-plated BPE, AMP-modified BPE and adsorbed E. coli O157:H7 BPE were measured. As shown in Fig. 1B, the ECL luminous intensity of bare BPE was 1073 a.u., and gold plated BPE is 2167 a.u. indicating that AuNPs could enhance the ability of electron transfer and improve the luminous intensity of BPE. The ECL luminous intensity of AMP-modified BPE was 2201 a.u., indicating that AMP has no electron transfer ability. A clear signal started immediately (4404 a.u.) after the adsorption of E. coli O157:H7 BPE, indicating the adherence of the bacterial cells to the immobilized AMP on the sensor surface. Moreover, E. coli O157:H7 is an electroactive bacteria, which can improve the ability of electron transfer and the luminous intensity of BPE. These data show that the components of the sensor are successful.
Therefore, E. coli O157: H7 was fixed on the surface of the electrode, which can catalyze the electrochemical reduction of O2, decrease the overpotential of O2 reduction at the cathode, promote the transfer of electrons, and improve the ECL intensity. Through the collection and processing of ECL signal, the corresponding relationship between E. coli O157:H7 concentration and ECL signal can be obtained, and the subtle change of E. coli O157:H7 concentration in the sample can be identified by the value of ECL signal, so as to achieve efficient and accurate detection of the pathogenic bacteria.
To better observe the sensor preparation, the surface morphology of the bare BPE, the gold-plated cathode of BPE and the E. coli O157:H7 adsorbed electrode were tested by scanning electron microscopy (SEM, Regulus 8100, Hitachi, Japan). The electron flow was 10 mA. As shown in Fig. 2, the surface of the naked BPE was relatively smooth (Fig. 2A). There were a large number of small particles uniformly distributed on the surface of the gold-plated BPE (Fig. 2B). On the BPE adsorbed by E. coli O157:H7, a well-distributed E. coli O157:H7 can be observed (Fig. 2C), which indicated that the sensor was successfully assembled.
3.2 CV characterisation and the BPE-ECL sensor mechanism investigation
EAB can interact with anodes or cathodes and produce H2 (Kracke et al., 2015) during anodization, which has the ability to transfer electrons, and electrons can be captured by electron receptors or bacteria to generate electricity. EET was important for EAB to transfer electrons from the electron transport chain to the extracellular electron receptor during respiration (Shi et al., 2016). EAB can transfer electrons from the cell membrane to the electrode directly through membrane-bound protein structures (such as fimbriae, c-cytochrome and filaments) or through mobile electron shuttling (such as indirect electron transfer media) (Wang and Ren 2013). Cyclic voltammetry (CV) has been proved to be effective in detecting the electrical activity of bacteria (Cournet et al., 2010). In this paper, E. coli O157:H7 and L.rhamnoides were selected and their catalytic oxygen reduction ability of cathode was verified by CV method.
The PBS aerobic condition (Fig. 3, curve a) had a reduction peak on the CV diagram, which indicated that the electrochemical reduction of O2 had taken place. After the addition of E. coli O157:H7 (Fig. 3, curve b), the peak current value could be observed and there were signs of further positive potential shift. After adding the bacterial solution, the 20 min was stirred gently (Fig. 3, curve c), and an obvious transient peak appeared. This is due to the existence of electroactive bacteria, which can provide additional electron transfer pathways and increase the reactivity of the electrode surface, thus enhancing the redox peak of the CV diagram and further confirming that the reduction of O2 is catalyzed. In contrast, no catalysis was observed when 20 min was stirred by adding heat-inactivated E. coli O157:H7 (Fig. 3, curve e) and anaerobic L.rhamnoides (Fig. 3, curve f). In the presence of E. coli O157:H7, no peak current was observed after nitrogen bubbling for 20 min (Fig. 3, curve d). After conducting an experimental comparison, it was evident that the catalytic effect exhibited a strong correlation with the reduction of O2.
The BPE sensor combined with the common driving electrode can realize the redox cycle, thus amplifying the ECL intensity produced by the anode (Borchers et al. 2021). The aerobic respiratory chain of E. coli O157:H7 involves a series of electron transport complexes that work together to transfer electrons from the two initial electron donors, nicotinamide adenine dinucleotides (NADH) and succinate, to the final electron acceptor, oxygen, and ultimately to H2O. Respiratory complex I catalyzes the oxidation of NADH and the reduction of quinone (Q) by transferring electrons provided by NADH first to the flavin mononotide (FMN) cofactor, four protons one by one to the iron-sulfur cluster chain, and finally to the bound quinone (equ. 1) (Melin and Hellwig 2020). Respiratory complex II catalyzes Q to oxidize succinic acid to fumarate during aerobic respiration. The binding site of succinic acid is located near the flavin adenine dinucleotide (FAD) cofactor in hydrophilic subunit A, and electron transfer between the two sites occurs through three iron-sulfur cluster chains located in subunit B (equ. 2) (Melin and Hellwig 2020). The presence of complex III is not required in the aerobic respiratory chain of E. coli O157:H7, and the last enzyme in this chain can directly use electrons from Quinol (QH2) to reduce O2. Using QH2 as the electron donor, the electron is transferred to oxygen, and H+ is consumed at the same time, which is eventually reduced to water (equ. 3).
| NADH + Q + 5H+ → NAD + QH2 + 4H+ | (1) |
|---|---|
| Succinate + Q→ Fumarate + QH2 | (2) |
| O2 + 2QH2 + nH+ → 2H2O + 2Q + nH+ | (3) |
In the redox reaction, the number of electron transfer must be balanced between the anode and the cathode. That is, the total number of electrons lost by the material on the anode must be equal to the total number of electrons obtained by the material on the cathode. In this experiment, Ru(bpy)32+ and TPA are oxidized to lose electrons at the anode, Ru(bpy)32+is oxidized to Ru(bpy)33+ (equ. 4), and TPA is oxidized to TPA cation radical (equ. 5) - (equ. 6). O2 is reduced to electrons at the cathode to form H+ and OH− (equ. 7). Ru(bpy)33+ in the excited state releases light energy when it returns to the ground state, resulting in luminescence (equ. 8).
Anode reaction:
| Ru(bpy)32+ - e− → Ru(bpy)33+ | (4) |
|---|---|
| TPA - e− →TPA▪+ | (5) |
| TPA▪+ → TPA▪+ H+ | (6) |
| cathode reaction: | |
| O2 + 4e− + 2H2O → 4OH− | (7) |
| luminous reaction: | |
| TPA▪ + Ru(bpy)33+ →Ru(bpy)32+ *་ hv | (8) |
3.2. Optimization of detection conditions
The ECL signal is related to the driving voltage and the incubation time of bacteria. By selecting the best conditions, the best detection results can be obtained. The first is the influence of driving voltage on the luminous intensity of [Ru(bpy)3]2+. As shown in Fig. 4A, the blank group increased slowly with the increased of voltage, and its ECL signal value was also lower. In the experimental group, the ECL signal value changed obviously with the increased of voltage, while a decreasing trend was observed after 5.3 V. A more let significant signal difference was seen at 5.3 V, which was finally selected.
The incubation time of bacteria also has an important effect on the luminous intensity of Ru(bpy)32+. Too long time will limit the activity of microorganisms, and too short time will lead to incomplete adsorption of bacteria. As shown in Fig. 4B, the value of ECL signal increased rapidly at 15 min and 30 min, but decreased when 30 min ECL signal. 30 min was selected as the best incubation time.
3.3. Sensitivity of BPE-ECL Sensor
The sensitivity of microbial detection serves as a paramount parameter, pivotal in determining the practical feasibility and usability of functionalized sensors (Arora et al., 2011). To achieve this objective, the sensitivity of the AMP-modified BPE sensor in detecting microbial cells was evaluated by subjecting the sensor to different concentrations of bacteria. As shown in (Fig. 5A), the ECL signal intensity of [Ru(bpy)3]2+ (λmax, em = 620 nm) increases with the increase of E. coli O157:H7 concentration (0 ~ 104 CFU mL− 1). The logarithmic value of E. coli O157:H7 concentration is positively correlated with the ECL signal intensity, and shows a good correlation (R2 = 0.9951) (Fig. 5B). The minimum detection limit is 1 CFU mL− 1. Compared with the previously reported method for the detection of E. coli O157:H7, this method is significantly lower than the previously reported method for the detection of bacteria. (Table 1).
3.4. Selectivity of BPE-ECL Sensor
We conducted a thorough investigation into the selectivity of the AMP-modified BPE sensor in binding to diverse bacterial species, meticulously assessing the ECL intensity of each strain. Specifically, the binding affinity of the AMP-modified BPE sensor towards three Gram-negative bacteria, that's to say, E. coli O157:H7, S. typhimurium and P. aeruginosa. On the other hand, this study demonstrated that the integration of AMP with ECL intensity enabled the discrimination of various bacterial species at a concentration of 103 CFU mL− 1. As depicted in Fig. 6, the wavelength response of E. coli O157:H7 (red line), S. typhimurium (blue line), and P. aeruginosa (green line) were shown at 620 nm when they interact with the AMP- modified sensor. It is obvious that the highest ECL intensity response to E. coli O157: H7. Jason W. Soares et al. also confirmed that magainin I had preferential binding behavior against E. coli O157:H7 (Soares et al. 2008).
AMP exhibits a poly-cationic surface related to its amphiphilic conformation, which interacts with the outer membrane of microbial cells. Once the microbial surface possesses an inherent anionic nature, initial electrostatic interactions will occur with AMP. The interaction mechanism with Gram-negative substances is related to negatively charged lipopolysaccharides (LPS) (Junior et al., 2022). Negatively charged LPS found in the outer membrane of Gram-negative bacteria and is the first site for attachment of AMP, which exhibits high stability under extreme environmental conditions and binds semi-selectively to Gram-negative microbial cell surfaces (Kulagina et al., 2005). Thus, these peptides are highly specific for Gram-negative strains.
| Method | System | Detection range (CFU mL− 1) | Detection limit (CFU mL− 1) | Reference |
|---|---|---|---|---|
| LFIA | BAPFNPs coupled to Ab double readout assay | 3.06×102~2.5×104 | 306 | (Zhang et al., 2023) |
| ELISA | Binding aptamers and DNA modification of Au@AuPtNPs | 102~107 | 59 | (Zhang et al., 2023) |
| LAMP-LFA | LAMP isothermal amplification of target DNA and visual detection by LFA | 101~104 | 10 | (Kim and Oh, 2019) |
| Fluorescent | CDs-microsphere immunosensor | 2.4×104~2.4×107 | 2.4×104 | (Zhao et al., 2021) |
| PCR | Taqman probe | 102~107 | 100 | (Liang et al., 2019) |
| BPE-ECL | High selectivity of AMP | 100~104 | 1 | This artical |
3.5. Actual sample analysis
To further evaluate the feasibility of the AMP improved BPE sensor in the field of food safety detection, we compared the results of BPE-ECL assay with plate count results. Diluted E. coli O157:H7 solutions (10 CFU, 100 CFU) were added to keg drinking water, milk, coconut milk, and fruit juice, respectively. The results of three parallel experiments obtained by plate counting method were shown in Table 2 and their mean and RSD values were calculated. Under the optimized conditions, three parallel experiments were carried out with BPE-ECL to detect the bacterial solutions and the corresponding ECL signal values were obtained, and the mean and RSD values were calculated. The RSD values of the BPE-ECL method were in the range of 1.2–6.99%, and those of the plate counting method were in the range of 4.6–29.57%. When comparing the RSD values of the two methods, the BPE-ECL sensor's detection stability was better than the plate counting method, and in time the detection time of the BPE-ECL sensor taken less time than the plate counting method. Therefore, the device has good potential for rapid, sensitive, quantitative, and accurate determination of E. coli O157:H7 at very low concentrations and it has great potential for application in the detection of foodborne pathogens.
This study has successfully demonstrated the feasibility and effectiveness of utilizing a BPE-ECL platform for real-time, label-free detection of Gram-negative bacteria with remarkable selectivity and sensitivity. The integration of Magainin I, an AMP with preferential affinity for Gram-negative pathogens, onto the cathode surface of the BPE sensor via covalent attachment significantly enhanced its bacterial capture capability. The resulting AMP-modified BPE sensor consistently produced reproducible ECL signals upon exposure to varying concentrations of Gram-negative bacteria, enabling the detection of peptide-bacteria interactions within a wide range (1 to 104 CFU mL− 1). Notably, the sensor demonstrated exceptional selectivity in distinguishing E. coli O157:H7 from other Gram-negative strains, even at extremely low concentrations (1 CFU mL− 1), highlighting the high selectivity of Magainin I in bacterial detection. The AMP-modified BPE-ECL system developed in this study presented a promising approach for rapid and accurate detection of Gram-negative bacteria across various applications. This comprehensively integrated sensor array, leveraging AMP technology, can detect a diverse array of analytes of practical significance, serving as a potent and portable biosensor tool for the efficient identification of pathogenic strains in food samples, and holding great application potential in the detection of pathogenic bacteria.
Author contribution Lei Ji: Writing – review & editing. Fengyang Wang: Formal analysis, Writing – review & editing. Yan Qi: Validation, Writing – review & editing. Fanglin Qiao: Validation, Writing – review & editing. Xiaohui Xiong: Formal analysis, Conceptualization, Methodology, Funding acquisition, Project administration. Yuanjian Liu: Funding acquisition, Writing – review & editing.
Funding This work was financially supported by the Postgraduate Research and Practice Innovation Programme Projects of Jiangsu (No. SJCX24_0497).
Data availability All data provided by this article are available on request from the corresponding author.
Ethical approval This study did not involve human or animal subjects, and thus, no ethical approval was required.
Conflict of interest The authors declare no competing interests.
- Akter, R., Rahman, M.H., Bhattacharya, T., Kaushik, D., Mittal, V., Parashar, J., Kumar, K., Kabir, M.T., Tagde, P., 2021. Environ. Sci. Pollut. Res. 28(48), 68071-68089. http://doi.org/10.1007/s11356-021-16809-8
- Amiri, M., Bezaatpour, A., Jafari, H., Boukherroub, R., Szunerits, S., 2018. ACS Sens. 3, 1069-1086. http://doi.org/10.1021/acssensors.8b00239
- Arora, P., Sindhu, A., Dilbaghi, N., Chaudhury, A., 2011. Biosens. Bioelectron. 28(1), 1-12. http://doi.org/10.1016/j.bios.2011.06.002
- Borchers, J.S., Campbell, C.R., Van Scoy, S.B., Clark, M.J., Anand, R.K., 2021. ChemElectroChem. 8(18), 3482-3491. http://doi.org/10.1002/celc.202100523
- Borole, A.P., Reguera, G., Ringeisen, B., Wang, Z.W., Feng, Y.J., Kim, B.H. 2011. Energy Environ. Sci. 4(12), 4813-4834. http://doi.org/10.1039/c1ee02511b
- Chakvetadzea, C., Purcareaa, A., Pitschc, A., Chellyb, J., Diamantis, S., 2017. IDCases. 8, 94-95. http://doi.org/10.1016/j.idcr.2017.04.013
- Chen, B., Tao, Q., OuYang, S.T., Wang, M.H., Liu, Y.J., Xiong, X.H., Liu, S.Q., 2023. Biosens. Bioelectron. 221, 114940. http://doi.org/10.1016/j.bios.2022.114940
- Cournet, A., Délia, M., Bergel, A., Roques, C., Bergé, M., 2010. Electrochem. Commun. 12(4), 505-508. http://doi.org/10.1016/j.elecom.2010.01.026
- de Miranda, J.L., Oliveira, M.D.L., Oliveira, I.S., Frias, I.A.M., Franco, O.L., Andrade, C.A.S., 2017. Biochem. Eng. J. 124, 108-114. http://doi.org/10.1016/j.bej.2017.04.013
- Etayash, H., Jiang, K., Thundat, T., Kaur, K., 2014. Anal. Chem. 86(3), 1693-1700. http://doi.org/10.1021/ac4034938
- Faour-Klingbeil, D., Todd, E.C.D., 2020. Int. J. Environ. Res. Public Health 17(1), 70. http://doi.org/10.3390/ijerph17010070
- Grant, S.S., Hung, D.T., 2013. Virulence. 4(4), 1-11. http://doi.org/ 10.4161/viru.23987
- Guo, H., Yuan, Y.H., Niu, C., Qiu, Y., Wei, J.P., Yue, T.L., 2018. Food Agric. Immunol. 29(1), 976-988. http://doi.org/10.1080/09540105.2018.1491532
- He, Z., Angenent, L.T., 2006. Electroanalysis (New York, N.Y.). 18(19-20), 2009-2015. http://doi.org/10.1002/elan.200603628
- Hu, A.D., Ye, J., Ren, G.P., Qi, Y.P., Chen, Y.P., Zhou, S.G., 2022. Angew. Chem., Int. Ed. 61(35). http://doi.org/10.1002/anie.202206508
- Islam, M.A., Karim, A., Ethiraj, B., Raihan, T., Kadier, A., 2022. Biotechnol. Adv. 55, 107901. http://doi.org/10.1016/j.biotechadv.2021.107901
- Junior, A.G.D.S., Frias, I.A.M., Lima-Neto, R.G., Franco, O.L., Oliveira, M.D.L., Andrade, C.A.S., 2022. Enzyme Microb. Technol. 160, 110088. http://doi.org/10.1016/j.enzmictec.2022.110088
- Kang, F., Alvarez, P.J., Zhu, D.Q., 2014. Environ. Sci. Technol. 48(1), 316-322. http://doi.org/10.1021/es403796x
- Katz, E., Shipway, A, N., Willner, I., in Handbook of Fuel Cells–Fundamentals, Technology and Applications (Eds: Vielstich, W., Lamm, A., Gasteiger, H.,), Wiley, 2003. http://doi.org/ 10.1016/j.fuel.2003.09.012
- Kim, J.H., Oh, S.W., 2019. J. Food Sci. Technol. 56(5), 2576-2583. http://doi.org/10.1007/s13197-019-03740-7
- Kracke, F., Vassilev, I., Krà Mer, J.O., 2015. Front. Microbiol. 6, 146906. http://doi.org/10.3389/fmicb.2015.00575
- Kulagina, N.V., Lassman, M.E., Ligler, F.S., Taitt, C.R., 2005. Anal. Chem. 77(19), 6504-6508. http://doi.org/10.1021/ac050639r
- Lee, J. E., Toushik, S.H., Park, H.J., Kim, S.A., Shim, W.B., 2023. Anal. Bioanal. Chem. 415(20), 4973-4984. http://doi.org/10.1007/s00216-023-04803-7
- Li, T., Yang, X.L., Chen, Q.L., Song, H.L., He, Z., Yang, Y.L., 2020. ACS Sustainable Chem. Eng. 8(30), 11263-11275. http://doi.org/10.1021/acssuschemeng.0c03058
- Liang, C.L., Yao, Z.Y., Du, S.C., Hong, M., Wang, K., Zhang, D.M., 2019. Environ. Pollut. 252, 1078-1086. http://doi.org/https://doi.org/10.1016/j.envpol.2019.06.019
- Ling, Y.Z., Zhu, Y.F., Fan, H.H., Zha, H.L., Yang, M., Wu, L., Chen, H., Li, W., Wu, Y.P., Chen, H.B., 2019. J. Biomed. Nanotechnol. 15(6), 1290-1298. http://doi.org/10.1166/jbn.2019.2781
- Logan, B.E., 2009. Nat. Rev. Microbiol. 7(5), 375-381. http://doi.org/10.1038/nrmicro2113
- Lovley, D.R. (2006). Curr. Opin. Biotechnol. 17, 327-332. http://doi.org/10.1016/j.copbio.2006.04.006
- Lu, L.X., Yuan, W., Xiong, Q., Wang, M.H., Liu, Y.J., Cao, M., Xiong, X.H., 2021. Anal. Chim. Acta. 1141, 83-90. http://doi.org/10.1016/j.aca.2020.10.035
- Mannoor, M.S., Zhang, S., Link, A.J., McAlpine, M.C., 2010. Proc. Natl. Acad. Sci. U. S. A. 107(45), 19207-19212. http://doi.org/10.1073/pnas.1008768107
- Melin, F., Hellwig, P., 2020. Chem. Rev. 120(18), 10244-10297. http://doi.org/10.1021/acs.chemrev.0c00249
- Meng, X.D., Pang, X.J., Yang, J.Y., Zhang, X.J., Dong, H.F., 2023. Small. 2307701. http://doi.org/10.1002/smll.202307701
- Min, B., Logan, B.E., 2004. Environ. Sci. Technol. 38(21), 5809-5814. http://doi.org/10.1021/es0491026
- Nguyen, L.T., Haney, E.F., Vogel, H.J., 2011. Trends Biotechnol. 29(9), 464-472. http://doi.org/10.1016/j.tibtech.2011.05.001
- Papiya, F., Nandy, A., Mondal, S., Kundu, P.P., 2017. Electrochim. Acta. 254, 1-13. http://doi.org/10.1016/j.electacta.2017.09.108
- Prata, J.C., Da Costa, J.P., Lopes, I., Andrady, A.L., Duarte, A.C., Rocha-Santos, T., 2021. Sci. Total Environ. 777, 146094. http://doi.org/10.1016/j.scitotenv.2021.146094
- Qi, X., Wang, S.Y., Li, T., Wang, X., Jiang, Y., Zhou, Y.X., Zhou, X.H., Huang, X., Liang, P., 2021. Biosens. Bioelectron. 173, 112822. http://doi.org/10.1016/j.bios.2020.112822
- Qiu, H.H., Zhang, Y.B., Li, Z.Y., Jiang, P., Guo, S.H., He, Y., Guo, Y., 2021. Front. Cardiovasc. Med. 8, 639541. http://doi.org/10.3389/fcvm.2021.639541
- Shi, L., Dong, H.L., Reguera, G., Beyenal, H., Lu, A.H., Liu, J., Yu, H.Q., Fredrickson, J.K., 2016. Nat. Rev. Microbiol. 14(10), 651-662. http://doi.org/10.1038/nrmicro.2016.93
- Soares, J.W., Kirby, R., Morin, K.M., Mello, C.M., 2008. Protein Pept. Lett. 15(10), 1086-1093. http://doi.org/ 10.2174/092986608786071049
- Templier, V., Roux, A., Roupioz, Y., Livache, T., 2015. TrAC, Trends Anal. Chem. 79, 71-79. http://doi.org/10.1016/j.trac.2015.10.015
- Velusamy, V., Arshak, K., Korostynska, O., Oliwa, K., B, C.A., 2009. Biotechnol. Adv. 28(2), 232-254. http://doi.org/ 10.1016/j.biotechadv.2009.12.004
- Wang, H.M., Ren, Z.Y.J., 2013. Biotechnol. Adv. 31(8), 1796-1807. http://doi.org/10.1016/j.biotechadv.2013.10.001
- Webster, D.P., TerAvest, M.A., Doud, D.F.R., Chakravorty, A., Holmes, E.C., Radens, C.M., Sureka, S., Gralnick, J.A., Angenent, L.T., 2014. Biosens. Bioelectron. 62, 320-324. http://doi.org/10.1016/j.bios.2014.07.003
- Yang, G.Q., Huang, L.Y., Yu, Z., Liu, X.M., Chen, S.S., Zeng, J.X., Zhou, S.G., Zhuang, L., 2019. Water. Res. 159, 294-301. http://doi.org/10.1016/j.watres.2019.05.027
- Yang, Y., Yu, Y.Y., Wang, Y.Z., Zhang, C.L., Wang, J.X., Fang, Z., Lv, H.Y., Zhong, J.J., Yong, Y.C., 2017. Biosens. Bioelectron. 98, 338-344. http://doi.org/https://doi.org/10.1016/j.bios.2017.07.008
- Ye, J., Wang, C., Gao, C., Fu, T., Yang, C.H., Ren, G.P., Lü, J., Zhou, S.G., Xiong, Y.J., 2022. Nat. Commun. 13(1), 6612. http://doi.org/10.1038/s41467-022-34423-1
- Zhang, T., Cui, C.Z., Chen, S.L., Yang, H.X., Shen, P., 2008. Electrochem. Commun. 10(2), 293-297. http://doi.org/10.1016/j.elecom.2007.12.009
- Zhang, Y.Y., Su, J.S., Fu, T.T., Zhang, W.Z., Xiao, Y.J., Huang, Y.S., 2023. Analyst 148(18), 4279-4282. http://doi.org/10.1039/D3AN01194A
- Zhao, Y., Li, Y.Z., Zhang, P.P., Yan, Z.H., Zhou, Y.G., Du, Y.P., Qu, C.Y., Song, Y.J., Zhou, D., Qu, S.N., Yang, R.F., 2021. Biosens. Bioelectron. 179, 113057. http://doi.org/10.1016/j.bios.2021.113057
No competing interests reported.