Letter pubs.acs.org/acssensors
Development of Engineered Bacteriophages for Escherichia coli Detection and High-Throughput Antibiotic Resistance Determination Juhong Chen,†,‡ Samuel D. Alcaine,†,‡ Angelyca A. Jackson,† Vincent M. Rotello,*,§ and Sam R. Nugen*,†,‡ †
Department of Food Science, Cornell University, Stocking Hall, Ithaca, New York 14853, United States Department of Food Science, University of Massachusetts, 102 Holdsworth Way, Amherst, Massachusetts 01003, United States § Department of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, Massachusetts 01003, United States ‡
S Supporting Information *
ABSTRACT: T7 bacteriophages (phages) have been genetically engineered to carry the lacZ operon, enabling the overexpression of beta-galactosidase (β-gal) during phage infection and allowing for the enhanced colorimetric detection of Escherichia coli (E. coli). Following the phage infection of E. coli, the enzymatic activity of the released β-gal was monitored using a colorimetric substrate. Compared with a control T7 phage, our T7lacZ phage generated significantly higher levels of β-gal expression following phage infection, enabling a lower limit of detection for E. coli cells. Using this engineered T7lacZ phage, we were able to detect E. coli cells at 10 CFU·mL−1 within 7 h. Furthermore, we demonstrated the potential for phage-based sensing of bacteria antibiotic resistance profiling using our T7lacZ phage, and subsequent β-gal expression to detect antibiotic resistant profile of E. coli strains. KEYWORDS: engineered bacteriophages, beta-galactosidase, colorimetry, bacteria detection, antibiotic resistance profiling
B
A bacteriophage (phage) is a virus which can infect a specific bacterial host.19,20 Phage-based amplification assays for bacteria detection have been widely used for food safety, environmental monitoring, and clinical diagnosis.21−24 However, these traditional assays are complicated and the long assay times make their utilization problematic for the rapid detection of pathogenic or indicator bacteria. Fortunately, advances in molecular genetics technologies has provided researchers with new tools to improve upon current phage-based schemes, allowing for sensitive and rapid bacteria detection using engineered phages.22,23,25 For example, engineered nonlytic M13 phage with peptide-tagged capsid proteins have been utilized for biosensors.26−29 Taking advantage of the cell lysis at the latter stage of the infection cycle, a number of genes, such as green fluorescent protein, tobacco etch virus protease,
acterial infections remain a substantial burden on human health in both developing and developed countries, and are globally responsible for 10 million deaths each year.1−3 With the increasing prevalence of antibiotic resistant bacteria, these infections further exacerbate the costs of public health.4−6 Traditional bacteria detection methods, such as plate counting, can take at least 18 h to provide results after the samples are received.7,8 To ascertain whether an isolate is resistant to a given antibiotic can take even longer.9,10 In order to meet the growing demand for sensitive and rapid bacteria detection, numerous technologies have been developed such as polymerase chain reaction (PCR), surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), enzyme-linked immune sorbent assay (ELISA), electrochemistry, field effect transistor (FET), and quartz crystal microbalance (QCM).11−18 Although sensitive and accurate, these methods are expensive, require skilled operators, and are therefore unsuitable for use in low-resource settings. Thus, there remains an urgent need to develop innovative, inexpensive, sensitive, and rapid methods for the in situ detection of bacteria. © XXXX American Chemical Society
Received: January 12, 2017 Accepted: March 14, 2017 Published: March 14, 2017 A
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
Letter
ACS Sensors
Figure 1. Schematic illustration of E. coli detection using enzymatically active β-galactosidase-overexpressed via engineered bacteriophage. Initial infection of E. coli cells results in rapid propagation of phages and overexpression of β-gal. Upon phage-induced cell lysis, both phages and β-gal are released, leading to subsequent infections and catalysis of chlorophenol red-β-D-galactopyranoside to produce a colorimetric signal.
Figure 2. Genomes of engineered bacteriophage used for E. coli detection. (a) Genome of lacZ inserted construction. (b) Genome of T7Select415− 1 shows 10B capsid protein and cloning site. (c) Genome of β-gal-overexpressing T7lacZ phage created by cloning lacZ inserted construction into T7Select415−1 genome. (d) Genome of non-β-gal-overexpressing T7control phage created by cloning S·Tag into T7Select415−1 genome.
alkaline phosphatase, and firefly luciferase, have been inserted into lytic phage genomes and subsequently used as reporters to improve bacteria detection.24,25,30,31 Beta-galactosidase (β-gal), an endogenous enzyme encoded by the lacZ operon in E. coli cells, is commonly used as an indicator for the detection of E. coli cells in drinking water.32−34 Phages have been incorporated into some of these sensing protocols as lytic agents to break the host cell’s membrane and release β-gal for colorimetric, electrochemical, and bioluminescent detection of E. coli cells.33−36 The sensitivity of some of these schemes leaves significant room for improvement. In our study, we have engineered T7 phages to contain the lacZ gene driven by a T7 promoter to induce β-gal overexpression during the phage infection cycle, improving detection limit of E. coli cells via β-gal activity. Our strategy for E. coli detection is shown in Figure 1. After our engineered T7lacZ phages attach to host E. coli cells and insert their genome containing the T7 promoter-driven lacZ gene into host E. coli
cell, lacZ will be transcribed by T7 polymerase (contained on the T7 genome) and translated into β-gal by the bacterial ribosomes. At the time of phage-induced lysis, both native and overexpressed β-gal will be released into environmental media. Now free from the cell, the released enzyme can catalyze a colorimetric substrate, like chlorophenol red-β-D-galactopyranoside (CPRG), to provide a visual signal shift from yellow to red. T7 phage, which is an E. coli-specific phage and broadly infects many E. coli strains, was initially engineered to carry the lacZ gene, overexpressing intracellular β-gal during phage infection.37 The overexpression was achieved by inserting our lacZ construct into the T7Select415−1 genome using EcoRI and HindIII restriction enzyme sites (Figure 2a,b). In our construct, the lacZ gene was preceded by regulator sequences to enable T7 polymerase mediated overexpression, and a ribosome binding site for translation by the bacterial cell (Figure 2c). The T7 promoter is a very strong promoter, and is B
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
Letter
ACS Sensors
Figure 3. Comparison of one-step E. coli detection using T7lacZ and T7control engineered bacteriophage. Response curve based on absorbance intensities (574 nm) with detection time using (a) T7lacZ phage and (d) T7control phage. Contour plots of absorbance intensities (574 nm) of colorimetric response as 2D function of E. coli BL21 concentration and total detection time using (b) T7lacZ phage and (e) T7control phage. Contour plots of photograph of colorimetric response as 2D function of E. coli BL21 concentration and total detection time using (c) T7lacZ phage and (f) T7control phage.
utilized in many gene overexpression schemes.38,39 This strategy for overexpression has been found to produce over 2 × 105 copies of an enzymatic reporter per cell to enable detection.30 Finally, a stop codon was incorporated upstream of our β-gal construct to ensure the enzyme was not fused to capsid protein when inserted into the T7Select415−1 genome. As a control, T7Select415−1 control DNA, which encodes the S·Tag, was inserted into the T7 genome to create T7control phage, which would not express β-gal (Figure 2d). After propagating our modified phage, lysates were plated and PCR was used to screen the plaques that contained our mutant and the control DNA. After PCR screening, the positive T7lacZ and T7control plaques were repropagated to investigate the overexpression of β-gal during infection. The enzymatic activity of β-gal was monitored using CPRG as a colorimetric substrate. We measured the UV−vis absorption spectra of solutions containing E. coli BL21, CPRG, and our engineered phage (T7lacZ and T7control phage, respectively) following incubation for 3 h (Figure S1). We next sought to investigate the ability of our colorimetric scheme to detect E. coli cells using T7lacZ and T7control phage. E. coli BL21 was used as a model for the detection assays. Varying concentrations (101 to 107 CFU·mL−1) of E. coli BL21 in LB broth were incubated with either T7lacZ or T7control phage (105 PFU·mL−1) in the presence of CPRG. LB broth without E. coli cells was used as a control. The relationship between absorbance intensities (574 nm), E. coli concentrations and assay time using T7lacZ phage and T7control phage is shown in Figure 3a and d, respectively. The absorbance intensities correlated with the increasing E. coli concentrations. Contour plots illustrating the relationship between absorbance intensities (574 nm), E. coli BL21 concentration, and total detection time using T7lacZ phage and T7control phage are shown in Figure
3b and e. The color transition from yellow to red was scaled using absorbance intensity from 0.0 to 4.0 with an interval of 0.5. As shown, the red color indicates the E. coli detectable area incorporating both factors (E. coli cell concentration and total detection time). With increasing detection time, the limit of detection decreased, reaching a detection limit of 102 CFU· mL−1 after 7 h using T7lacZ phage, whereas the E. coli detection limit using T7control phage remained above 105 CFU·mL−1 even after incubation of 7 h. As shown in Figure 3b and e, the large difference in the red region clearly demonstrates the improvement in E. coli detection using T7lacZ phage which induced β-gal overexpression and cell lysis against the T7control phage, which only resulted in release of endemic β-gal. The corresponding photographs of the colorimetric response are shown in Figure 3c and f, which are consistent with absorbance intensity data. To further compare the differences in E. coli cell detection using T7lacZ and T7control phage, a time course study of bacteria detection can be found in Figure S2. The limits of detection using T7control were 104 CFU·mL−1 after 4 h and remained at 104 CFU·mL−1 up to 7 h of incubation. However, the detection limits using T7lacZ were 102 CFU·mL−1 after an incubation of 6 h. Although equivalent E. coli cell detection limits were reached using both phages within 3 h, T7lacZ phage showed a stronger colorimetric response than T7control phage at the same time point. After 4 h, differences became clearer, and a lower detection limit was obtained using T7lacZ phage. As noted, we were able to detect 102 CFU·mL−1 after 6 h using T7lacZ phage, which was driven by phage-overexpressed β-gal. Without overexpression of β-gal, the endemic enzyme released from E. coli cells using T7control phage resulted in a weaker colorimetric response and a 100-fold poorer detection limit of 104 CFU· mL−1 after even 7 h. These results indicate that the T7lacZ phage, which induces β-gal overexpression upon infection, has C
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
Letter
ACS Sensors
Figure 4. Two-step detection of E. coli cells at low concentrations using T7lacZ engineered bacteriophage. Response curve based on absorbance intensities (574 nm) with detection time toward E. coli concentration of (a) 103, (c) 102, (e) 101, and (g) 0 CFU·mL−1. Contour plots of absorbance intensities (574 nm) as 2D function of detection time and pre-enrichment time toward E. coli concentration of (b) 103, (d) 102, (f) 101, and (h) 0 CFU·mL−1.
the potential to improve the sensitivity of β-gal-based detection of E. coli cells. The colorimetric response from the phage-mediated β-gal enzymatic activity was driven by two factors. The first factor is the number of E. coli cells infected by our engineered phages. More E. coli cells infected by our engineered phage resulted in more β-gal enzyme expressed. The second factor was the enzyme reaction rates, which resulted in signal transduction. As expected, reaction time, pH, and temperature had a significant effect on the substrate conversion. Similar to E. coli, phage concentration can affect the time allowed for the enzyme reaction. The enzymatic reaction started after the phage infection. Furthermore, we recently reported that our engineered phages carrying lacZ gene provided higher signals compared with the control phages/no phages.16 This indicated that the detection of bacteria at the concentration higher than 107 CUF·mL−1 can be achieved in less than 2 h. Pre-enrichment steps are commonly used in bacterial detection methods. In this study, pre-enrichment steps were utilized to lower the limit of detection. We prepared E. coli cultures with various concentrations (103, 102, and 101 CFU· mL−1, LB broth was used as negative control) and allowed them to grow for varying periods of time at 37 °C. After a set pre-enrichment time, T7lacZ phage and CPRG were added to initiate the phage infection and thus reporter enzyme expression. The absorbance intensities were measured hourly for 7 h. Contour plots illustrating the relationship of absorbance intensity (574 nm), detection time, and pre-enrichment time for each initial E. coli concentrations are in Figure 4. It should be noted that the positive result area (red color) decreased with the decreasing of E. coli concentration. As expected, no red color was observed in the control experiments. The concentration of E. coli cells is critical to the colorimetric response. In our study, two-step detection (pre-enrichment step and detection step) was incorporated to detect E. coli cells at low concentrations. Pre-enrichment increased E. coli cell numbers, and the detection step was used for the phage infection and enzymatic reaction. The effect of total detection time (less than 7 h) on absorbance intensity (574 nm) was
determined for several E. coli concentrations. As shown in Figure S3, we were able to detect E. coli cells at the concentration of 103, 102, and 101 CFU·mL−1 after total detection time of 5, 6, and 7 h, respectively. At low E. coli concentrations, increasing pre-enrichment times resulted in only slight differences in the colorimetric response, suggesting that increased assay times would have more benefit during the phage infection and the enzymatic reaction. This phenomenon can be explained by the presence of limited cells in the detection solution decreased the probability of T7lacZ infection. Bacteria’s ability to become resistant to one or more antibiotics is a growing burden on human health.40 Traditional test for the determination of bacteria antibiotic resistance profiling is to look for the growth of bacteria in the presence of the antibiotic which can take days for results.41,42 As a proof-ofprinciple application, we explored the ability of our genetically modified phage to rapidly determine the antibiotic resistance profile of E. coli in 96-well plate format. E. coli BLT5403, an ampicillin resistant stain, was used as our bacterial model, and we examined phage-mediated β-gal overexpression in the presence of ampicillin, kanamycin, and ciprofloxacin. The steps of our proposed pragmatic, high-throughput, and phagebased antibiotic resistance sensing scheme are illustrated in Figure 5a. We inoculated E. coli BLT5403 cells into LB broth containing different antibiotics at varying concentrations at 37 °C for 3 h. T7lacZ phage and CPRG were then added to initiate a colorimetric response if an infection occurred. Because T7 can only replicate and express proteins in viable hosts, susceptible E. coli in the presence of antibiotics could not overexpress the enzymes. Therefore, the detection solution remained yellow. In the presence of ineffective antibiotic drugs (such as ampicillin), the resistant E. coli cells exhibited exponential growth. The degree of growth inhibition by antibiotic drugs resulted in varying amounts of β-gal expressed by the host, resulting in different levels of red intensity. By analyzing the colorimetric response of the sensing system, we were able to confirm which antibiotic our model bacteria was resistant to. As shown in Figure 5b, T7lacZ phage incubated with E. coli BLT5403 was able to determine the ampicillin D
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
Letter
ACS Sensors
membrane filtration, to increase E. coli concentrations prior to the addition of T7lacZ phage.33,34,44 In our DNA construct, a biotin acceptor tag was fused to the phage capsid protein, which in the future may enable us to conjugate the engineered phages to streptavidin-conjugated magnetic beads allowing additional schemes which combine sample purification, concentration, and detection.45 Future work will focus on applying phage-based engineering for the detection of pathogenic bacteria to improve public health in resource-limited settings.
■
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssensors.7b00021. Comparison of overexpressed β-gal activity with different treatment types; comparison of one-step E. coli detection using engineered T7control and T7lacZ bacteriophage; twostep detection of E. coli cells at low concentrations using T7lacZ engineered bacteriophage; high-throughput determination of bacteria antibiotics resistance profiling using T7control engineered bacteriophage; and experimental section. LacZ inserted construction; pUC57 plasmid; and lacZ expressing T7lacZ genome (PDF)
■
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected] (V.M.R.). *E-mail:
[email protected] (S.R.N.).
Figure 5. High-throughput determination of bacteria antibiotic resistance profile using T7lacZ engineered bacteriophage. (a) Schematic illustration of high-throughput antibiotic screening by T7 lacZ engineered phage infection of E. coli BLT5403. (b) Plot of absorbance intensities (574 nm) toward various antibiotic concentrations. (The concentration of E. coli BLT5403 was 104 CFU·mL−1.)
ORCID
Juhong Chen: 0000-0002-6484-2739 Vincent M. Rotello: 0000-0002-5184-5439 Sam R. Nugen: 0000-0003-3638-1776 Notes
resistance. In the presence of kanamycin and ciprofloxacin, the absorbance intensity at 574 nm dramatically decreased with increasing antibiotic concentration. However, we did not observe a decreasing trend for absorbance intensities (574 nm) in the presence of ampicillin at varying concentrations. As a control, T7lacZ phage was used to determine the antibiotic resistance profile of E. coli BL21. All three antibiotics completely inhibited cell growth, resulting in no significant change in color (Figure S4). Thus, these results indicated that T7lacZ phage can be used to profile the antibiotic resistance of host bacteria. Our proposed engineered phage carrying lacZ gene can shorten detection times and result in low limits of detection when determining bacterial antibiotic resistance profiling. In summary, genetic engineering tools offer powerful strategies for the rapid detection of pathogenic bacteria. In this study, we demonstrated that a scheme utilizing engineered T7lacZ phage that induces β-gal overexpression during infection, resulting in improved speed and sensitivity of E. coli detection and determination of E. coli antibiotic resistance profiling. Using this proposed strategy, we were able to detect E. coli at a concentration of 10 CFU·mL−1 within 7 h. Several methods could be used to further improve the detection limit or shorten the detection time using engineered T7lacZ phage.31 A more sensitive transduction method could be used to monitor the activity of β-gal, like an ultrasensitive enzymatic reaction incorporating redox cycling or multienzyme labels.43 There is also the potential to use a concentration step, such as
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS The author would like to acknowledge the U.S. Department of Agriculture NIFA 2013-02037, as well as the support of Nanoscale Science and Engineering Initiative of the National Science Foundation under NSF Award Number CMMI1025020. The authors would also like to acknowledge the NIH GM 077173 for the financial support.
■
REFERENCES
(1) Salwiczek, M.; Qu, Y.; Gardiner, J.; Strugnell, R. A.; Lithgow, T.; McLean, K. M.; Thissen, H. Emerging Rules for Effective Antimicrobial Coatings. Trends Biotechnol. 2014, 32 (2), 82−90. (2) Mannoor, M. S.; Tao, H.; Clayton, J. D.; Sengupta, A.; Kaplan, D. L.; Naik, R. R.; Verma, N.; Omenetto, F. G.; McAlpine, M. C. Graphene-Based Wireless Bacteria Detection on Tooth Enamel. Nat. Commun. 2012, 3, 763. (3) Chen, J.; Andler, S. M.; Goddard, J. M.; Nugen, S. R.; Rotello, V. M. Integrating Recognition Elements with Nanomaterials for Bacteria Sensing. Chem. Soc. Rev. 2017, 46, 1272. (4) Coyle, M. B. Manual of Antimicrobial Susceptibility Testing; American Society for Microbiology: Washington, DC, 2005. (5) Zhang, M.; Yang, F.; Pasupuleti, S.; Oh, J. K.; Kohli, N.; Lee, I. S.; Perez, K.; Verkhoturov, S. V.; Schweikert, E. A.; Jayaraman, A.; Cisneros-Zevallos, L.; Akbulut, M. Preventing Adhesion of Escherichia Coli O157:H7 and Salmonella Typhimurium Lt2 on Tomato Surfaces Via Ultrathin Polyethylene Glycol Film. Int. J. Food Microbiol. 2014, 185, 73−81.
E
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
Letter
ACS Sensors (6) Levy, S. B.; Marshall, B. Antibacterial Resistance Worldwide: Causes, Challenges and Responses. Nat. Med. 2004, 10, S122−S129. (7) Chen, J.; Alcaine, S. D.; Jiang, Z.; Rotello, V. M.; Nugen, S. R. Detection of Escherichia Coli in Drinking Water Using T7 Bacteriophage-Conjugated Magnetic Probe. Anal. Chem. 2015, 87 (17), 8977−8984. (8) Rompré, A.; Servais, P.; Baudart, J.; de-Roubin, M.-R.; Laurent, P. Detection and Enumeration of Coliforms in Drinking Water: Current Methods and Emerging Approaches. J. Microbiol. Methods 2002, 49 (1), 31−54. (9) Wiegand, I.; Hilpert, K.; Hancock, R. E. Agar and Broth Dilution Methods to Determine the Minimal Inhibitory Concentration (Mic) of Antimicrobial Substances. Nat. Protoc. 2008, 3 (2), 163−175. (10) Berendonk, T. U.; Manaia, C. M.; Merlin, C.; Fatta-Kassinos, D.; Cytryn, E.; Walsh, F.; Bürgmann, H.; Sørum, H.; Norström, M.; Pons, M.-N. Tackling Antibiotic Resistance: The Environmental Framework. Nat. Rev. Microbiol. 2015, 13 (5), 310−317. (11) Ray, P. C.; Khan, S. A.; Singh, A. K.; Senapati, D.; Fan, Z. Nanomaterials for Targeted Detection and Photothermal Killing of Bacteria. Chem. Soc. Rev. 2012, 41 (8), 3193−3209. (12) Wang, P.; Pang, S.; Chen, J.; McLandsborough, L.; Nugen, S. R.; Fan, M.; He, L. Label-Free Mapping of Single Bacterial Cells Using Surface-Enhanced Raman Spectroscopy. Analyst 2016, 141, 1356− 1362. (13) Belgrader, P.; Benett, W.; Hadley, D.; Richards, J. Pcr Detection of Bacteria in Seven Minutes. Science 1999, 284 (5413), 449. (14) Maalouf, R.; Fournier-Wirth, C.; Coste, J.; Chebib, H.; Saïkali, Y.; Vittori, O.; Errachid, A.; Cloarec, J.-P.; Martelet, C.; JaffrezicRenault, N. Label-Free Detection of Bacteria by Electrochemical Impedance Spectroscopy: Comparison to Surface Plasmon Resonance. Anal. Chem. 2007, 79 (13), 4879−4886. (15) Smartt, A. E.; Ripp, S. Bacteriophage Reporter Technology for Sensing and Detecting Microbial Targets. Anal. Bioanal. Chem. 2011, 400 (4), 991−1007. (16) Wang, D.; Chen, J.; Nugen, S. R. Electrochemical Detection of Escherichia Coli from Aqueous Samples Using Engineered Phages. Anal. Chem. 2017, 89, 1650. (17) Huang, Y.; Dong, X.; Liu, Y.; Li, L.-J.; Chen, P. Graphene-Based Biosensors for Detection of Bacteria and Their Metabolic Activities. J. Mater. Chem. 2011, 21 (33), 12358−12362. (18) Shen, Z.-Q.; Wang, J.-F.; Qiu, Z.-G.; Jin, M.; Wang, X.-W.; Chen, Z.-L.; Li, J.-W.; Cao, F.-H. Qcm Immunosensor Detection of Escherichia Coli O157:H7 Based on Beacon Immunomagnetic Nanoparticles and Catalytic Growth of Colloidal Gold. Biosens. Bioelectron. 2011, 26 (7), 3376−3381. (19) Hu, B.; Margolin, W.; Molineux, I. J.; Liu, J. The Bacteriophage T7 Virion Undergoes Extensive Structural Remodeling During Infection. Science 2013, 339 (6119), 576−579. (20) Lu, T. K.; Collins, J. J. Dispersing Biofilms with Engineered Enzymatic Bacteriophage. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (27), 11197−11202. (21) van der Merwe, R. G.; van Helden, P. D.; Warren, R. M.; Sampson, S. L.; Gey van Pittius, N. C. Phage-Based Detection of Bacterial Pathogens. Analyst 2014, 139 (11), 2617−2626. (22) Smartt, A. E.; Xu, T.; Jegier, P.; Carswell, J. J.; Blount, S. A.; Sayler, G. S.; Ripp, S. Pathogen Detection Using Engineered Bacteriophages. Anal. Bioanal. Chem. 2012, 402 (10), 3127−3146. (23) Zhou, X.; Cao, P.; Zhu, Y.; Lu, W. G.; Gu, N.; Mao, C. B. PhageMediated Counting by the Naked Eye of Mirna Molecules at Attomolar Concentrations in a Petri Dish. Nat. Mater. 2015, 14 (10), 1058−1065. (24) Tawil, N.; Sacher, E.; Mandeville, R.; Meunier, M. Bacteriophages: Biosensing Tools for Multi-Drug Resistant Pathogens. Analyst 2014, 139 (6), 1224−1236. (25) Edgar, R.; McKinstry, M.; Hwang, J.; Oppenheim, A. B.; Fekete, R. A.; Giulian, G.; Merril, C.; Nagashima, K.; Adhya, S. HighSensitivity Bacterial Detection Using Biotin-Tagged Phage and Quantum-Dot Nanocomplexes. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (13), 4841−4845.
(26) Wang, Y.; Ju, Z.; Cao, B.; Gao, X.; Zhu, Y.; Qiu, P.; Xu, H.; Pan, P.; Bao, H.; Wang, L.; Mao, C. Ultrasensitive Rapid Detection of Human Serum Antibody Biomarkers by Biomarker-Capturing Viral Nanofibers. ACS Nano 2015, 9 (4), 4475−4483. (27) Jin, H.; Won, N.; Ahn, B.; Kwag, J.; Heo, K.; Oh, J.-W.; Sun, Y.; Cho, S. G.; Lee, S.-W.; Kim, S. Quantum Dot-Engineered M13 Virus Layer-by-Layer Composite Films for Highly Selective and Sensitive Turn-on Tnt Sensors. Chem. Commun. 2013, 49 (54), 6045−6047. (28) Han, L.; Shao, C.; Liang, B.; Liu, A. Genetically Engineered Phage-Templated Mno2 Nanowires: Synthesis and Their Application in Electrochemical Glucose Biosensor Operated at Neutral Ph Condition. ACS Appl. Mater. Interfaces 2016, 8 (22), 13768−13776. (29) Pan, P.; Wang, Y.; Zhu, Y.; Gao, X.; Ju, Z.; Qiu, P.; Wang, L.; Mao, C. Nontoxic Virus Nanofibers Improve the Detection Sensitivity for the Anti-P53 Antibody, a Biomarker in Cancer Patients. Nano Res. 2015, 8 (11), 3562−3570. (30) Alcaine, S. D.; Tilton, L.; Serrano, M. A. C.; Wang, M.; Vachet, R. W.; Nugen, S. R. Phage-Protease-Peptide: A Novel Trifecta Enabling Multiplex Detection of Viable Bacterial Pathogens. Appl. Microbiol. Biotechnol. 2015, 99 (19), 8177−8185. (31) Alcaine, S. D.; Pacitto, D.; Sela, D. A.; Nugen, S. R. Phage & Phosphatase: A Novel Phage-Based Probe for Rapid, Multi-Platform Detection of Bacteria. Analyst 2015, 140 (22), 7629−7636. (32) Chavali, R.; Kumar Gunda, N. S.; Naicker, S.; Mitra, S. K. Detection of Escherichia Coli in Potable Water Using Personal Glucose Meters. Anal. Methods 2014, 6 (16), 6223−6227. (33) Derda, R.; Lockett, M. R.; Tang, S. K. Y.; Fuller, R. C.; Maxwell, E. J.; Breiten, B.; Cuddemi, C. A.; Ozdogan, A.; Whitesides, G. M. Filter-Based Assay for Escherichia Coli in Aqueous Samples Using Bacteriophage-Based Amplification. Anal. Chem. 2013, 85 (15), 7213− 7220. (34) Burnham, S.; Hu, J.; Anany, H.; Brovko, L.; Deiss, F.; Derda, R.; Griffiths, M. Towards Rapid on-Site Phage-Mediated Detection of Generic Escherichia Coli in Water Using Luminescent and Visual Readout. Anal. Bioanal. Chem. 2014, 406 (23), 5685−5693. (35) Neufeld, T.; Schwartz-Mittelmann, A.; Biran, D.; Ron, E. Z.; Rishpon, J. Combined Phage Typing and Amperometric Detection of Released Enzymatic Activity for the Specific Identification and Quantification of Bacteria. Anal. Chem. 2003, 75 (3), 580−585. (36) Chen, J.; Jackson, A. A.; Rotello, V. M.; Nugen, S. R. Colorimetric Detection of Escherichia Coli Based on the EnzymeInduced Metallization of Gold Nanorods. Small 2016, 12 (18), 2469− 2475. (37) Studier, F. W. Bacteriophage T7. Science 1972, 176 (4033), 367−376. (38) Dickinson, B. C.; Leconte, A. M.; Allen, B.; Esvelt, K. M.; Liu, D. R. Experimental Interrogation of the Path Dependence and Stochasticity of Protein Evolution Using Phage-Assisted Continuous Evolution. Proc. Natl. Acad. Sci. U. S. A. 2013, 110 (22), 9007−9012. (39) Carlson, J. C.; Badran, A. H.; Guggiana-Nilo, D. A.; Liu, D. R. Negative Selection and Stringency Modulation in Phage-Assisted Continuous Evolution. Nat. Chem. Biol. 2014, 10 (3), 216−222. (40) Walsh, C. Molecular Mechanisms That Confer Antibacterial Drug Resistance. Nature 2000, 406 (6797), 775−781. (41) Benveniste, R.; Davies, J. Mechanisms of Antibiotic Resistance in Bacteria. Annu. Rev. Biochem. 1973, 42 (1), 471−506. (42) Cohen, S. N.; Chang, A. C.; Hsu, L. Nonchromosomal Antibiotic Resistance in Bacteria: Genetic Transformation of Escherichia Coli by R-Factor DNA. Proc. Natl. Acad. Sci. U. S. A. 1972, 69 (8), 2110−2114. (43) Yang, H. Enzyme-Based Ultrasensitive Electrochemical Biosensors. Curr. Opin. Chem. Biol. 2012, 16 (3), 422−428. (44) Liebana, S.; Spricigo, D. A.; Pilar Cortes, M.; Barbe, J.; Llagostera, M.; Alegret, S.; Pividori, M. I. Phagomagnetic Separation and Electrochemical Magneto-Genosensing of Pathogenic Bacteria. Anal. Chem. 2013, 85 (6), 3079−3086. (45) Chen, J.; Duncan, B.; Wang, Z.; Wang, L. S.; Rotello, V. M.; Nugen, S. R. Bacteriophage-Based Nanoprobes for Rapid Bacteria Separation. Nanoscale 2015, 7 (39), 16230−16236. F
DOI: 10.1021/acssensors.7b00021 ACS Sens. XXXX, XXX, XXX−XXX
