Article pubs.acs.org/EF
Peptide-Based Fluorescent Biosensing for Rapid Detection of Fuel Biocontamination Oksana M. Pavlyuk† and Oscar N. Ruiz*,‡ †
Environmental Microbiology Group, University of Dayton Research Institute, University of Dayton, Dayton, Ohio 45469, United States ‡ Fuels and Energy Branch, Aerospace Systems Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States S Supporting Information *
ABSTRACT: To reduce the impact of biodeterioration in fuel systems, effective methods for early detection and monitoring of microbial growth in the fuel are required. This study presents the development of broad-range peptide biorecognition elements (BREs) that target cell surface determinants produced by hydrocarbon-degrading microorganisms during growth in fuel. BREs were used to biofunctionalize fluorescent semiconductor particles (quantum dots, QDs) to produce a sensitive quantitative fluorescence-based assay for detection of microbial growth in fuel. A biopanning method in the presence of fuel was used to select phage-displayed heptameric peptide BREs against epitopes conserved in fuel-degrading Gram-negative bacteria including Pseudomonas spp. and Acinetobacter spp. Fluorescence microscopy analysis and fluorescence signal measurements relative to colony-forming units (CFUs) demonstrated the binding and specificity of the BRE-QDs for fuel-degrading Gram-negative bacteria. Cross-reactivity with Gram-positive bacteria Arthrobacter and Lysinibacillus was not observed. The assay was shown to be specific for detection of Gram-negative bacteria. Jet fuel samples amended with different concentrations of fuel-degrading bacteria were used to determine the sensitivity and limit of detection (LOD) of the assay; an LOD of 5 × 104 colony forming units (CFUs) with detection levels as low as 5 × 103 CFUs was established for the best performing BRE−QD conjugate. The peptide BRE−QD chemistry effectively detected biocontaminated fuel samples from fuel tanks. The peptide BREs may serve to biofunctionalize various fluorescent, chemiluminescent, and colorimetric molecules as well as optical and electrical transducers to developed effective biosensors for detection of microbial contamination in fuel. The first step toward the development of a simple biosensing method capable of detecting microorganisms in fuel is to develop a functional biorecognition element (BRE) that can detect large groups of fuel-degrading microorganisms. The Gram-negative bacteria group is highly diverse and considered a predominant group of fuel biocontaminants. Thus, development of a broadspectrum BRE for detection of Gram-negative bacteria is highly desirable. BREs can be short nucleic acid-based aptamers or peptides that mimic antibody−antigen interactions, which can be easily obtained by well-established high-throughput screening methods such as systematic evolution of ligands by exponential enrichment (SELEX)15,16 and phage display.17,18 In this regard, small 7−12 amino acid (aa) peptides are ideal BREs with numerous advantages over other molecular probes, including high chemical diversity, ease of synthesis and conjugation to the surface of the signal transducer, and high stability in harsh environments like fuel.19 Peptide BREs share the high affinity and specificity of antibody−antigen binding, but unlike antibodies, short peptides do not require immunogenic antigens and post-translational modifications such as disulfide bonds, and they are not prone to batch variation because they are chemically synthesized. Unlike large multidomain proteins and antibodies, peptide molecular probes are not prone to denaturation, have
M
icrobial contamination of fuel presents a significant problem for both military and civilian applications due the deterioration of fuel quality and subsequent long-term consequences including reduced fuel stability, tank corrosion, filter plugging, injector fouling, deactivation of fuel-water coalescers, and coating degradation.1−13 Thus, effective monitoring of fuel quality and prevention of microbial growth in the fuel is of great importance as a cost-saving strategy that serves to prolong the performance and lifetime of the fuel system. However, current tests are costly, and in many cases, must be performed by highly trained scientists at off-site laboratories requiring the shipment of fuel samples, culturing microbes, and long wait times. Even when sophisticated molecular-based techniques such as quantitative real-time polymerase chain reaction (qPCR) and DNA sequencing are used, having to extract DNA from the fuel for analysis is a difficult task. Also, DNA-based tests cannot differentiate between live and dead microorganims.14 Often, commercially available field tests have low accuracy and at best provide semiquantitative results. Some test kits require growing microorganisms for days to visualize them or rely on measuring adenosine triphosphate (ATP), which is a labile molecule of variable abundance depending on the microbial growth stage. Antibody-based detection methods are frequently affected by degradation and are negatively influenced by the presence of fuel. Overall, biodeterioration is often detected only after the normal operation of the fuel system has been impacted, requiring expensive remediation. © XXXX American Chemical Society
Received: December 15, 2016 Revised: March 4, 2017 Published: March 6, 2017 A
DOI: 10.1021/acs.energyfuels.6b03350 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels longer shelf life, and potentially can be reused. Chapleau et al.20 showed that shorter single-domain antibodies, also known as nanobodies, were able to retain antigen binding activity in the presence of jet fuel. Finally, peptides can be easily conjugated to reporter fluorescent, chemiluminescent, and colorimetric molecules and signal transducing nanomaterials for simple detection of the target without altering the antigen-binding capacity and biorecognition activity of the BRE. Fluorescent semiconductor nanoparticles, more commonly known as quantum dots (QDs), have established themselves as superior alternatives to traditional chemical dyes in terms of brightness and stability against photobleaching.21−23 Moreover, their broad absorption spectra allow for utilization of a single excitation source, and their narrow symmetrical emission spectra combined with size-dependent quantum yields and large Stokes shifts make quantum dots excellent reporter fluorophores for multiplexed detection of different microorganisms.24−30 Many species of bacteria are known to contaminate fuel.6,9,31,32 From the prokaryotes, the Gram-negative bacteria, including the notoriously difficult to eradicate Pseudomonas, are predominant in biocontaminated fuel.32−34 Persistence of P. aeruginosa and other Pseudomonas species in the harshest environments is believed to be due in part to the low permeability of outer membrane proteins (porins) and the presence of efflux transporter proteins (efflux pumps) that extrude xenobiotics, thus allowing these bacteria to proliferate in antimicrobial drugs, hydrocarbons, and fuel.34,35 For instance, OprF is a major porin of P. aeruginosa responsible for outer membrane permeability and nonspecific diffusion of small polar nutrients across the bacterial membrane and has also been implicated in other important physiological functions.36 Specifically, OprF was demonstrated to serve as a connector between the outer and inner membranes, and deletion of OprF produces an unstable outer membrane and aberrant cell morphology.37 The 326 aalong OprF consists of 15 transmembrane motifs comprising the β-barrel and 8 highly conserved extracellular loops38 that have been used as target epitopes for the development of Pseudomonas aeruginosa specific antibodies.39 The secondary structure of OprF regulates its permeability by adopting an open or a closed conformation depending on the cell requirements. Additionally, OprF is thought to be a modulator of quorum sensing and enhanced bacterial virulence, and importantly, OprF expression is involved in the formation of anaerobic biofilms.40,41 It was recently demonstrated that OprF was involved in the uptake of aromatic solvents including toluene,42,43 and is regulated at the transcriptional level by hydrocarbons.34 OprF is immunogenic and highly conserved in Pseudomonads.44,45 Therefore, OprF can be used as a biomarker to detect Pseudomonas species and other fuel-degrading Gram-negative bacteria with structurally conserved OprF protein for effective monitoring of fuel quality. Similarly, the outer membrane protein Opr86, which is highly conserved in Gram-negative bacteria, was shown to be essential in outer membrane biogenesis.46 Opr86 is responsible for the assembly and insertion of β-barrel outer membrane proteins into the outer membrane via complex formation with other lipoproteins.46 Antibodies against Opr86 prevented biofilm formation by P. aeruginosa PAO1.46 Organic solvents47 and fuel34 were shown to up-regulate the expression of Opr86. Conveniently, the amino acid composition and structure of P. aeruginosa Opr8646,48 and OprF39 are known, thus allowing utilization of some of the extracellular loop motifs as target epitopes for isolation of peptide BREs.
In the present study, conserved extracellular loop epitopes of OprF and Opr86 outer membrane proteins were used as antigens to isolate binding biorecognition elements from a peptide library using phage display in the presence of fuel. The synthetic peptide counterparts of the selected heptameric peptides were subsequently used to biofunctionalize quantum dot (QD) reporter fluorophores. The resulting peptide BRE-QD conjugates were used as labeling reagents in a lateral assay for the quantitative detection of Gram-negative fuel-degrading bacteria in the presence of fuel. The assay specificity and limit of detection were determined, and its application for detection of bacteria in contaminated fuel samples from field tanks was demonstrated.
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RESULTS AND DISCUSSION Selection of Heptapeptide Biorecognition Elements (BREs) in the Presence of Fuel. The structure of Pseudomonas aeruginosa major outer membrane protein OprF was elucidated in a previous study.39 The 326 amino acids(aa)-long OprF consists of a 15 motif transmembrane domain and 8 extracellular loops.38 While multiple extracellular loops were shown to be immunogenic, the epitope of sequence GTYETGNKKVH was shown to be most reactive for the production of monoclonal antibodies.39 The knowledge of the secondary structure of OprF and the great level of conservation across different Pseudomonas species was used as the basis for the selection of the 11 aa-long GTYETGNKKVH (amino acids 55−65 of OprF, and denoted as OprF1 in this study) extracellular loop as the target epitope for selection of BREs using phage display biopanning in the presence of fuel. An OprF1 synthetic peptide biotinylated at the N-terminal was used for solution biopanning screening of a commercially available M13 bacteriophage library displaying heptameric peptides at the N-terminal of the P3 coat protein. The advantages of solution-phase biopanning include the availability of all the OprF1 amino acid residues for interaction with the potential peptide binders and lesser likelihood of isolating unspecific peptides that might bind to the capture element used to purify the phage−antigen complex (i.e., magnetic or protein G beads). OprF1−phage complexes were captured with streptavidincoated magnetic microbeads, eluted and amplified in E. coli for use in subsequent rounds of selection biopanning. After each round of selection, the DNA of multiple phage isolates carrying a single heptapeptide biorecognition element (i.e., monoclonal phages) was sequenced to determine the amino acid sequences of the OprF-binding peptides (OBPs). After four rounds of selection, the phage pool was enriched for OprF1-binding phages with three consensus sequences (Supporting Information Table S1), and a predominant PPKINIM peptide with 80% abundance based on round 4 clones. Unfortunately, this type of binding profile is characteristic of what is known as a “library collapse”,49 where the loss of library diversity is a result of enrichment for bacteriophages displaying nonspecific peptides that confer advantageous growth properties including more efficient infectivity and phage assembly/extrusion from the bacterial host cell. This phenomenon was further confirmed when the round 4 phage pool was used as an input phage for the next round of biopanning in nonphysiological conditions at pH 8.5 and in the presence of 1% v/v Jet A fuel in Bushnell−Haas (BH) minimal media. These biopanning conditions were chosen to mimic the fuel tank milieu, where the fuel-degrading bacteria would ultimately be detected.3 Interestingly, under these conditions the binding of library phage selected so far was completely abolished, and only wild-type M13 was isolated after B
DOI: 10.1021/acs.energyfuels.6b03350 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 1. Selection of OprF-binding peptides BREs via biopanning in fuel tank conditions. An M13 phage library (1 × 1011 pfu/ml) in Bushnell−Haas (BH) pH 8.5 (a) or pH 5.5 (b) supplemented with 1% v/v Jet-A was incubated with biotinylated OprF1 target antigen, and the selected phages were amplified, and the DNA of 15 randomly picked clones per round of selection was sequenced to determine the amino acid composition of OprF-binding peptides. (a) OprF-binding peptide distribution as a function of fuel additive. After selection round number 4, the binding and washing conditions were adjusted to contain BH at pH 8.5 without fuel to monitor changes in peptide binding profile. (b) OprF-binding peptide distribution as a function of pH. After selection round number 4 with fuel and pH 5.5, the binding and washing conditions were adjusted to BH pH 7 with 1% Jet-A, and the change in peptide binding profile was monitored.
abundance and thus relative affinity for OprF. Not surprisingly, using these conditions, the relative percent abundance of PPKINIM clone was determined to be only 3%, in stark contrast to the 80% abundance under physiological conditions. Finally, the phage library was screened to identify target-unrelated peptides50 by using biotinylated BSA as a target for biopanning. In addition to the PPKINIM phage, another bacteriophage displaying the IQTNPTM peptide was found to cross-react with BSA (Supporting Information Table S5). Collectively, stringent biopanning and library screening allowed us to select six unique heptapeptides for fluorescent probe synthesis and evaluation of bacterial detection (Table 1). The binding of multiple BREs (i.e., OBP 4 through OBP 12) to the OprF1 peptide was initially assessed by Western blot analysis.
round 5 (data not shown). This observation indicated that selective pressure other than multiple rounds of biopanning and amplification must be applied to isolate OprF1-specific bacteriophages lacking growth advantages. To achieve this goal, BH minimal media supplemented with 1% v/v Jet A at nonphysiological pH (pH 5.5 or pH 8.5) was used for all rounds of biopanning and removal of nonbinding phages. Using these conditions, the profile of OprF-binding peptides (OBPs) changed dramatically (Figure 1) and many additional peptide binders were identified (Supporting Information Table S2 and S3). The M13 bacteriophage was shown to survive in nonaqueous solvents and at acidic/basic pH’s that mimicked fuel tank conditions. Interestingly, if fuel was not added (Figure 1a and Table S3) or the pH was changed back to the physiological pH of 7 (Figure 1b and Table S2), the phage library converged back to the PPKINIM peptide, obliterating the selection of phages displaying the OprF1-specific peptides and lacking advantageous growth properties. Furthermore, since the library collapse is a direct consequence of amplification in E. coli, this step was omitted in order to determine a more accurate percentage of abundance that would be reflective of the binding affinity of OprF-binding peptides (Supporting Information Table S4). Fourteen monoclonal OprF1-binding phages from the third selection round were combined at equal concentrations in Bushnell−Haas pH 8.5 supplemented with 1% v/v Jet A and incubated with biotinylated OprF1, followed by alternative washes with Bushnell−Haas pH 8.5 or pH 5.5 supplemented with Jet A. Eluted unamplified phages were titered, and 30 randomly selected clones were used to determine percent
Table 1. OprF-Binding Peptides (OBPs) and Their Initial Screening for Binding to P. stutzeri Peptide ID
Peptide Sequence
pIa
% Abundanceb
Fluorescence (RFU)c
OBP4 OBP6 OBP7 OBP9 OBP11 OBP12
RRSNSQL NMTNPPP QITLRST QMLLRLP PIKTNRK PKRTPRH
12.0 5.5 9.8 9.8 11.2 12.0
33 10 10 3 10 3
4520 ± 473 2080 ± 280 2625 ± 543 3338 ± 590 5308 ± 501 6443 ± 957
a
Theoretical pI calculated using web.expasy.org. bCalculated based on 30 randomly picked clones from the unamplified phage pool. cRelative fluorescence units for OBP-QD525conjugates. C
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Energy & Fuels Monoclonal OprF-specific bacteriophages were incubated with biotinylated OprF1 (OprF1-B) peptide epitope under conditions similar to those used in biopanning. The resulting phage− OprF1-B complexes were recovered using streptavidin-coated magnetic beads, and the phage−OprF1-B complexes were resolved via SDS-PAGE, and detected by Western blot using antibodies against the M13 phage capsid and the biotin molecule attached to OprF1. The results showed that monoclonal OprFspecific phages bound specifically to OprF1-B, and both the phage and OprF1-B were detected in immunoblots (Figure 2).
Figure 3. Opr86 sequence alignment. Multiple alignment of UniProt primary amino acid sequences was performed with PRofile ALIgNEment (PRALINE). The color scheme of the alignment is based on amino acid position conservation assigned by the PRALINE scoring scheme (0 for the least conserved alignment position and 10 for the most conserved). (a) Opr86 alignment with Pseudomonas spp.; (b) Opr86 alignment with various Gram-negative bacteria. Note the key amino acids highly conserved (9−10/10) in both Pseudomonas spp. and other Gram-negative bacteria (Pro637, Ala644, Gly645, Gly646, Val650, Arg651, Gly652, Phe/Tyr653, Leu/Ile658, Gly659, Pro660, Arg/ Lys661).
Figure 2. Western blot analysis of OprF-specific biorecognition elements. Western blotting was performed by incubating the biotinylated OprF1 peptide epitope with monoclonal OprF-specific bacteriophages (lanes 1−6: OBPs 4 thru 12) and capturing the resulting complexes with magnetic streptavidin beads, followed by SDS-PAGE, transfer to a PVDF membrane, and immunoblotting with M13 and biotin-specific antibodies. Protein bands were visualized with an alkaline phosphatase-conjugated secondary antibody. Bovine serum albuminspecific bacteriophage (BBP2, lane 7) was used as a negative control to demonstrate that nonspecific binding of phages to OprF1 epitope was not occurring. Biotinylated OprF1 (OprF1-B) and wild type M13 phage were used as positive controls for the respective antibodies used.
Table 2. Opr86-Binding Peptides (OPPs) and Their Initial Screening for Binding to P. stutzeri
When phages with specificity for bovine serum albumin (BSA) were used against the OprF1-B target, a significant signal was not detected in the immunoblots (Figure 2). This result confirmed that complexing specificity was due to the OprF1-specific peptides and not caused by nonspecific binding of M13 phage capsid and nonspecific library peptides. Having established an effective biopanning protocol with OprF to select BREs against Pseudomonads, the procedure was applied to develop BREs with broader specificity to detect multiple Gram-negative genera. To achieve this, the Opr86 outer membrane protein was targeted. The Opr86 external loop fragment spanning amino acid residues 668 to 683 was shown to be a highly immunogenic epitope that could be used in the isolation of Pseudomonas specific antibodies.46 Moreover, amino acid sequence alignment analysis performed with PRofile ALIgNEment (PRALINE)51 on the Opr86 external loop region spanning amino acids 630 to 665 from different Gram-negative species revealed the region consisted of 35 highly conserved amino acid residues presenting more than 86% sequence homology among several Pseudomonas species (Figure 3a), and more than 50% sequence homology among various Gramnegative bacteria (Figure 3b). Thus, the particular region of the Opr86 extracellular loop of sequence YGSTDGLPFYENYYAGGFNSVRGFKDSTLGPRSTP was chemically synthesized with a biotin capture element and used as target epitope for BRE development in solution biopanning in Bushnell-Haas pH 8.5 supplemented with 1% Jet-A. Following the previously established procedure for OprF, several unique Opr86 peptide BREs (OPPs) able to detect bacteria were identified after three rounds of selection (Table 2). Screening of Peptide BRE−Quantum Dot Conjugates Activity. OprF1- and Opr86-binding peptides (OBPs and
Peptide ID
Peptide Sequence
pIa
% Abundanceb
Fluorescence (RFU)c
OPP1 OPP2 OPP3 OPP4 OPP5 OPP6
PRIRKSH MHNLNLL LPSTIHR LRPLMNR IITMKRR RKKSRIR
12.0 6.5 9.8 12.0 12.0 12.3
3 2 2 3 3 2
12258 ± 2060 1847 ± 52 2251 ± 63 1383 ± 74 4408 ± 225 4126 ± 98
a
Theoretical pI calculated using web.expasy.org. bCalculated based on 30 randomly picked clones from an unamplified phage pool. cRelative fluorescence units for OPP-QD525 conjugates.
OPPs, respectively) modified with a C-terminal 3-glycine plus cysteine (GGGC) linker were cross-linked to amine-functionalized quantum dots (QD525) via succinimidyl iodoacetate (SIA) following the manufacturer’s protocol. Several important factors have influenced the selection of a reporter fluorophore for our studies. For example, in addition to their tunable optical properties and exceptional brightness, quantum dots (QDs) were suitable for peptide functionalization due to their size and the number of sites available for conjugation. Quantum dots are much larger than traditional chemical fluorophores, with diameter in the range 10−20 nm, and this size proximity to biological molecules makes them more suitable for bacterial labeling. In addition, the quantum dots used in this study were coated with multiple amine functionalities (exact number varies from batch to batch), thus allowing functionalization with multiple peptide copies, in contrast to the one peptide per one chemical fluorophore as is the case of traditional fluorescent dyes. This is particularly important in order to circumvent a possible reduction in binding affinity of a peptide-dye for the target as a result of avidity loss from the pentavalently phage-displayed peptide arrangement. The emission peak fluorescence at 525 nm of 1 × 109 P. stutzeri cells labeled with peptide BRE−QD525 D
DOI: 10.1021/acs.energyfuels.6b03350 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 4. continued
E
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Figure 4. Fluorescence imaging and fluorometry of fuel-degrading bacteria labeled with OprF and Opr86 binding peptides conjugated to QD545. Bacterial pellets corresponding to 1 × 109 cells were labeled with 1.5 μM peptide−QD545 for 30 min at r.t., washed 3× with PBS, and resuspended in 0.5 mL of PBS for fluorescence measurements; 10 μL aliquots were used for imaging. (a) Fluorescence imaging of Gram-negative bacteria (Acinetobacter venetianus and Pseudomonas spp.) using peptide−QD545 conjugates. (b) Fluorescence imaging of Gram-positive bacteria labeled with peptide−QD545 conjugates. (c) Fluorometry of A. venetianus and Pseudomonas spp. labeled with peptide−QD545 conjugates. P < 0.05 (*); P < 0.01 (**); P < 0.001 (***).
intended bacterial groups, fluorescence imaging and fluorometric analysis were performed using different Gram-negative and Gram-positive fuel-degrading bacteria. 1 × 109 cells of Gramnegative bacteria, P. stutzeri, P. alkaligenes, P. aeruginosa, and Acinetobacter venetianus, and Gram-positive bacteria, Arthrobacter sp. and Lynsinibacillus sp., were labeled with OPP1−QD545, OBP11−QD545, and OBP12−QD545, visualized using fluorescence microscopy (Figure 4a−b), and the fluorescence was quantified using a fluorometer (Figure 4c). The fluorescence micrographs showed that OPP1, OBP11, and OBP12 specifically labeled the Gram-negative bacterial species, but not the Grampositive bacteria, which do not express OprF and Opr86 outer membrane proteins. The presence of a high number of bacteria in all treatments was confirmed by bright field imaging (Figure 4a− b). To confirm that QDs without biofunctionalization did not bind to the bacteria cells, all bacterial species were exposed to
conjugates was measured in a fluorometer, and their relative fluorescence units (RFU) were compared (Table 1 and 2). Peptides OBP11 and OBP12 targeting OprF, and peptide OPP1 targeting Opr86 presented fluorescence levels that greatly surpassed the other selected peptides for the respective target (Table 1 and 2). Thus, OPP1, OBP11, and OBP12 were selected for further characterization and validation. During the use of QD525, a small interfering peak at 520 nm caused by cell autofluorescence was observed. To prevent any contribution from cell autofluorescence to the overall emission from BRE-QD labeled cells, we chose to use QD545 in all further experiments and assay validations; the QD545 with fluorescence emission at 545 nm wavelength did not present an interfering peak. Characterization of Peptide−Quantum Dot Conjugates Specificity for Gram-Negative Bacteria. To characterize the specificity of the peptide−QD545 conjugates for the F
DOI: 10.1021/acs.energyfuels.6b03350 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 5. Fluorescence imaging of P. stutzeri biofilm labeled with 1.5 μM OPP1-QD545 conjugate. (a) 40× bright field, (b) 40× fluorescence, (c) 100× fluorescence.
Figure 6. Detection of Gram-negative bacteria in fuel samples amended with bacteria using peptide-QD545. (a) Fluorescence micrograph (100×) of P. stutzeri cells labeled with peptide biofunctionalized quantum dots. Notice the cell membrane was preferentially labeled. (b) Limit of detection of P. stutzeri cells extracted from 1L of Jet A fuel in 1 mL of bacterial recovery solution (BRS). (c) Limit of detection of A. venetianus cells extracted from 1L of Jet A fuel in 1 mL of BRS.
fluorescence micrograph (OPP1 panel of Figure 4a), were also labeled in addition to the planktonic cells (Figure 4a). Fueldegrading bacteria such as Pseudomonas produce biofilms, especially at the fuel−water interface, to protect against the toxic fuel environment and increase their access to the hydrocarbons in the fuel.34 It has been indicated that extracellular polymeric substance (EPS), also known as extracellular matrix (ECM), can become an important barrier that impedes the penetration of molecules such as antibiotics and antimicrobials to cells residing within biofilms. However, several reports indicate that nanoparticles, including QDs, can be modified with specific ligands and functional groups to increase penetration into EPS and biofilms, and even permeate into cells, tumors, and the blood−brain barrier.52−54 Recently, Li et al.55 showed that hydrophilic and hydrophobic QDs can be functionalized with positively charge ligands to increase penetration into the biofilm; a similar result was not observed with neutral and negatively charged ligands. Our results showed the peptide BRE-QDs penetrated the biofilm, staining cells residing deep in the biofilm (Figure 5). Biofilm penetration may have been aided by the multiple positively charged amino acids, such as lysine (K), arginine (R), and histidine (H), present in the peptide BREs, as well as the amine group connecting the SIA-QD to the peptide. The ability of the peptide BRE-QD conjugates to bind and label planktonic cells and cells in biofilms increases the applicability of the assay for detection of bacteria in fuel systems.
QD545 at a concentration equal to the concentration of peptide BRE−QD545 conjugates used in testing. The results showed a lack of bacterial fluorescence from the QD545 treatment and from the unlabeled cells used as negative control (Figure 4a−b). Careful analysis of the fluorescence micrographs indicated that only the cell wall of Gram-negative bacteria was fluorescently stained (See inset for P. alkaligenes in Figure 4a, and Figures 5 and 6a). Labeled cells appeared dark in the center with highly fluorescent outer membranes indicative of colocalization of the peptide BRE-QDs with OprF and Opr86 at the cell wall (Figure 6a). Also, the fluorescence level of each sample was measured via fluorometry (Figure 4c). Unlabeled cells and cells exposed to nonbiofunctionalized QDs only presented background levels of fluorescence below 25 RFU; samples without cells did not fluoresce. The OPP1-QD was tested and shown to recognize 18 different Gram-negative bacteria (Table 3). This indicated that OPP1 BRE may serve as a probe for broad detection of Gramnegative bacteria. Both OBP11 and OBP12 provided good detection of Pseudomonads (Figure 4c). However, OBP11 and OBP12 provided unexpected high fluorescence with P. alkaligenes and P. stutzeri, respectively (Figure 4c). The higher fluorescence observed with OBP11 and OBP12 may have been the result of increased affinity of these two peptide BREs for the OprF epitope present in P. alkaligenes and P. stutzeri. Interestingly, it was observed that biofilms produced by Pseudomonas species, especially noticeable in the P. aeruginosa G
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Tests performed to understand the behavior of the fluorescence signal emanating from different concentrations of P. stutzeri cells labeled with OBP11-QD545 and OBP12-QD545 showed the fluorescence intensity changed as a power function with respect to cell level (Figure 7). The fluorescence decay
Table 3. Fluorescence Response of Different Gram-Negative Bacteria Species Labeled with OPP1-QD525a Gram-Negative Bacteria
RFU 525 nm
Pseudomonas aeruginosa PAO1 Pseudomonas aeruginosa ATCC33988 Pseudomonas alkaligenes Pseudomonas f rederiksbergensis Pseudomonas putida Pseudomonas stutzeri Escherichia coli ER2738 Acinetobacter venetianus ATCC31012 Achromobacter sp. Alkaligenes faecalis Enhydrobacter sp. Ralstonia picketti Chryseobacterium hispalense Stenotrophomonas maltophilia Marinobacter hydrocarbonoclasticus Rhodovulum sp. Methylobacterium sp. Alkanivorax borkumensis
20,000 30,000 63,000 10,000 4,600 33,000 80,000 17,000 25,000 32,000 87,000 15,000 80,000 13,000 100,000 64,000 43,000 80,000
Gram-Positive Bacteria Arthrobacter sp. Lysinibacillus sp.
RFU 525 nm no signal no signal
a
Cells were recovered from 1 mL of 1 O.D. bacterial culture. RFU: relative fluorescence unit.
Detection of Bacteria in Fuel Using the Peptide BREQD Chemistry. To test the applicability of the peptide BRE-QD chemistry in the detection of bacteria in fuel and characterize the limit of detection (LOD) in such a system, an assay was developed to recover, label, and detect bacterial cells from 1 L jet fuel samples (see Material and Methods). The results showed that bacteria were efficiently labeled as indicated by detection of high fluorescence levels emanating from the cell wall under fluorescence microscopy (Figure 6a). The LOD was reliably determined from assays performed by multiple testers. The OPP1−QD545 conjugate detected P. stutzeri and A. venetianus at an LOD of 5 × 104 CFU/mL of bacterial recovery solution (BRS), and detection was sometimes possible down to 5 × 103 CFU/mL. OBP11−QD545 and OBP12−QD545 presented a LOD of 5 × 105 CFU/mL of BRS for P. stutzeri and A. venetianus with detection sometimes possible down to 5 × 104 CFU/mL. This indicated that the assay had the potential to be further optimized to detect much lower bacterial levels in fuel. Differences in LOD between OPP1 and the two OBPs could have been attributed to variability in Opr86 and OprF protein levels in the different bacterial strains, as well as structural and conformational differences in the external epitopes of Opr86 and OprF that may modulate the binding of BRE to the target epitope. Intrinsic variability in how the assay protocol was performed by the different testers and the use of multiple centrifugation steps in the protocol was credited for the detection fluctuations at cell levels below the reproducible LOD, and for not achieving an even lower LOD. Currently, we are further developing this assay by substituting centrifugation steps with the use of a single filter membrane to recover cells from the fuel, carryout all washes, and perform the detection step. We expect this change will prevent the loss of labeled bacteria, reduce background fluorescence, and improve the assay LOD.
Figure 7. Characterization of the fluorescence signal profile of labeled cells. 1 × 109 P. stutzeri cells were labeled with (a) 1.5 μM OBP11QD545, and (b) 1.5 μM OBP12-QD545 and then diluted from 1 × 105 to 1 × 107 to determine the fluorescence signal intensity at different cell concentrations. The exact cell number in each labeled sample was characterized by the plate colony counting method to provide the CFUs.
model for each peptide BRE-QD could be used to estimate cell levels in samples directly from the fluorescence signal readouts. Future refinement of these models should allow quantitative determination of the cell concentration in unknown samples. The ultimate goal of this study was to use the peptide BRE-QD chemistry and the developed test method to detect microorganisms in potentially contaminated fuel samples from the field. To test if the peptide BRE-QD chemistry could effectively detect bacteria in a fuel sample from the field, a contaminated jet fuel sample from a fuel tank was obtained. One milliliter of the water layer from the fuel sample (Figure 8a) was tested with OBP11, OBP12, and OPP1 BRE-QDs following the described protocol (see Material and Methods). The fluorometer readouts indicated very high levels of fluorescence with all three peptide BRE-QD assays that ranged from 22,000 RFU and 37,000 RFU (Figure 8b−c). The high fluorescence levels detected were an indication of heavy bacterial contamination in the fuel sample. Using the fluorescence decay model of OBP11 and OBP12, a cell concentration between 7 × 106 and 4 × 107 CFU/ml was estimated. To confirm that bacteria was actually present in the fuel sample, the bacterial contamination level was determined by H
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Figure 8. Detection of biocontamination in a jet fuel sample from a fuel tank. (a) The aqueous phase of a contaminated Jet A fuel sample was separated from the organic phase, and a 1 mL aliquot was centrifuged at 10,000g for 5 min. Supernatant was removed and the cell pellet was labeled with 1.5 μM of peptide-QD545-peptide (or QD alone) in PBS. The labeled cell pellet was resuspended in 0.5 mL of PBS, and the fluorescence was measured (c). The sample was serially diluted and plated to determine CFUs (b).
fuel as an additive to isolate peptide biorecognition elements specific for OprF and Opr86 outer membrane proteins of Pseudomonas and Gram-negative bacteria. Using established techniques, quantum dots were biofunctionalized with peptide BREs, and evaluated for recognition and binding to Gramnegative bacteria such as Acinetobacter venetianus and several Pseudomonas spp. In contrast, no Gram-positive bacteria were detected, demonstrating the selectivity and specificity of peptide BREs. Overall, peptide-biofunctionalized quantum dots were demonstrated to efficiently label fuel-degrading Gram-negative bacteria in a simple test method that allowed detection in real time. The method detected bacteria in contaminated fuel samples from the field, which further supported its applicability as a field test. Improvements in the reported assay methodology, including a more efficient bacterial fuel extraction step and preventing loss of cells during the wash steps, are currently underway, and these are expected to further improve the limit of detection (LOD) and the field applicability of the assay. The goal of this study was to demonstrate that it was possible to develop broad-spectrum peptide biorecognition elements for detection of large microbial groups. Currently, we are working on the selection of peptides with broad specificity for Gram-positive bacteria, and fuel-degrading yeast and filamentous fungi. Once produced, these new broad-range peptides can be combined with
quantitative real-time PCR (qPCR), a nucleic acid-based molecular method, and using the bacterial plate count method. The qPCR method, which can detect both culturable, nonculturable, and free DNA, detected 1.5 × 106 bacterial 16S gene copies/mL; this level of contamination is considered high. Colony counts, which quantify only the culturable bacteria, detected about 80 CFU/mL. While the level of culturable bacteria appeared to be lower than expected from the qPCR test, it is well-known that bacteria adapted to the fuel environment may not form colonies in culture without prior acclimatization, and many environmental bacteria are nonculturable. DNA sequencing of a 500bp region of the bacterial 16S rrn gene identified the isolated bacteria as Pseudomonas aeruginosa, which later was confirmed to degrade hydrocarbons in the laboratory. The cell concentrations predicted from the RFU values of peptide-labeled cells are in general agreement with the level detected by the well-established qPCR method. This indicated the peptide BRE-QD chemistry and the established test method are suitable for detection and estimation of Gram-negative bacteria in fuel samples.
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CONCLUSION In the present study, we have performed phage display in nonphysiological biopanning conditions with variable pH and jet I
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Conjugation of C-Terminal Modified Synthetic Peptide BREs to Quantum Dots. The Qdot 545 ITK Amino-PEG (Cat #: Q21591MP) and Qdot 525 ITK Amino-PEG (Cat #: Q21541MP) used in this study were obtained from ThermoFisher (ThermoFisher Scientific, Waltham, MA) and have been fully characterized by the vendor. Detail physical and chemical characterization data including certificate of analysis and multiple publications describing the use and application of Qdots can be found at https://www.thermofisher.com/ order/catalog/product/Q21591MP. Qdot Q21591MP presented the following specification: full with at half-maximum ≤34 nm, relative quatum yield ≥47%, and emission maximum of 545 ± 4 nm. Qdot Q21541MP presented the following specification: full width at halfmaximum ≤32 nm, relative quatum yield ≥49%, and emission maximum 525 ± 4 nm. Immediately before use, 3 mg of succinimidyl iodoacetate (SIA; ThermoFisher Scientific, Waltham, MA) was dissolved in 1 mL of dimethylformamide (DMF), and 105 μL of this reagent was added to 60 μL of 8 μM solution of quantum dots (QDs) in 50 mM borate buffer pH 8.3 (ThermoFisher Scientific, Waltham, MA). The reaction mixture was rotated gently at 25 °C for 3 h in the dark. The reaction mixture was diluted with 1 mL of H2O and then transferred to an Amicon Ultra-4 Centrifugal Filter 100 K (Merck Millipore Ltd., Carrigtwohill, Ireland). The reaction tube was washed twice with 1 mL of H2O to remove any leftover QDs, and that was applied to the Amicon Ultra-4 Centrifugal Filter to concentrate the QDs by centrifugation at 5000 rpm for 15 min at 25 °C. The flow through was discarded, and 50 μL of SIA-QD conjugate was eluted into a clean microcentrifuge tube. The filter was washed with 100 μL of H2O and combined with 50 μL of the SIA-QD conjugate. Synthetic peptide BREs modified at the C-terminal with three glycine and a cysteine were dissolved in 50 mM borate buffer pH 8.3 at 2 mg/mL, and 300 μL was added to the 150 μL of SIA-QD conjugate. The reaction mixture was rotated gently at 25 °C for 2 h in the dark, and purified by using a gravity fed dextran desalting column MWCO 5 kDa (ThermoFisher Scientific, Waltham, MA) to remove low molecular weight molecules below 5 KDa including salts and unconjugated peptides. Product was eluted with 3−5 mL of 50 mM borate buffer pH 8.3, and UV-active fractions from the dextran column elution were collected and then concentrated using an Amicon Ultra-4 centrifugal filter 100 K at 5000 rpm for 15 min to remove any high molecular weight molecules, including unconjugated SiA-Qdot aggregates that might have formed during the process. Concentrated peptide BRE-QD conjugate was eluted in a volume of 50 μL. The filter was washed with 150 μL of H2O, and that was combined with the 50 μL of peptide BRE-QD conjugate to a final volume of 200 μL with a concentration of 2.4 μM; this highly UV-active fraction was used in the study. Bacterial Labeling Method. Bacterial stocks for experimentation were prepared by harvesting overnight grown bacterial cells by centrifugation at 11000g for 15 min at 4 °C, washing once with 1X PBS pH 7.2, and resuspending in 1X PBS to a concentration of 1 × 109 cells/mL. Bacterial titers were determined by measuring optical density at 600 nm and confirmed by colony counting on LB agar plates. Cell pellets produced by centrifuging 1 mL of the 1 × 109 cells/mL stock were resuspended in 38 μL of 1X PBS, and 62 μL of 2.4 μM peptide-QD was added to a final concentration of 1.5 μM. Cells were incubated for 30 min at 25 °C. Cell pellets were washed 3 times with 0.5 mL of PBS and resuspended in 500 μL of PBS for fluorescence assays and imaging. Dilutions ranging from 1 × 109 to 1 × 104 cells were prepared using standard bacteriological techniques, and 0.5 mL samples were used for fluorescence measurements and fluorescence microscopy. Fluorometry. Emission spectra were obtained using a Cary Eclipse fluorimeter with excitation at 330 nm, scan rate of 120 nm/min, and PMT voltage of 1000 V. The spectra were corrected for background and dilution factor when appropriate. Fluorescence Microscopy. Labeled cell samples were prepared as described previously, and 10 μL of the sample was placed on a microscope slide, covered with a coverslip, and visualized on a Nikon Eclipse Ti-E inverted microscope equipped with an X-Cite LED lamp, using a fluorescence filter set (a band-pass exciter 405 nm and a longpass emission filter), and 40X (Plan Fluor, Nikon) and 100X oilimmersion objectives (DPlan 100X, Nikon). Images were captured with a Nikon DS-sCMOS camera. Scale bar = 10 μm.
the Gram-negative peptide BREs to development a multiplex detection assay. The developed peptide BREs could be applied in the fabrication of novel biosensors through the biofunctionalization of optical and electrical transducer nanomaterials.
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MATERIALS AND METHODS
Bacterial Cultures and Methods. Pseudomonas aeruginosa ATCC33988 and Acinetobacter venetianus ATCC31012 were purchased from the American Type Culture Collection (Manassas, VA). P. stutzeri, P. alcaligenes, Arthrobacter sp., and Lysinibacillus sp., plus 12 other bacteria shown in Table 3 were isolated in our laboratory from fuelcontaminated soil and water, and stored in 15% glycerol at −80 °C. The P. aeruginosa PAO1 strain refers to the human pathogenic type strain. Escherichia coli 2738 was used from the commercially available phage display kit (New England Biolabs, Ipswich, MA). Overnight cultures of fuel-degrading and environmental bacteria were grown in Luria−Bertani (LB) broth at 28 °C with shaking at 225 rpm, and E. coli grown at 37 °C. Biopanning of Phage-Displayed Peptides. Solution-phase biopanning was carried out as described by the manufacturer (New England Biolabs, Ipswich, MA) with some modifications, including changing the pH from 7.0 to 5.5 and 8.5, and adding 1% v/v of Jet A fuel. The first round of selection was carried out by diluting the Ph.D.-7 bacteriophage library 100-fold in 0.1% Tris buffer saline plus Tween 20 (0.1%TBST) at the appropriate pH for selection plus fuel. Subsequently, the phage library was incubated with 1 μg of N-terminal biotinylated target protein fragment (either OprF1: GTYETGNKKVH, OprF2: ADIKNLADFMKQYPSTSTT, Opr86: YGSTDGLPFYENYYAGGFNSVRGFKDSTLGPRSTP) for 1 h at 25 °C. Phage−protein complexes were captured with 50 μL of streptavidin magnetic microbeads, and the pelleted sample was washed 10 times with 1 mL of 1× TBS plus 0.05% Tween 20 buffer (TBST) to remove unbound and weakly bound phage particles. Bound bacteriophages were eluted from the beads by lowering the pH (0.2 M Glycine-HCl pH 2.2) while rotating gently for 10 min at 25 °C. After neutralization with 1 M Tris-HCl pH 9.1, eluted phages were amplified by infection of E. coli strain ER 2738 grown in LB medium until early log phase (OD600 0.1−0.5). The amplified phage pool was isolated by precipitation with PEG/NaCl and titered to determine the phage concentration for the next round of selection; the titer of the amplified phage (≥1010 pfu/mL) was determined by infecting E. coli ER2738 cells and subsequently selecting the infected colonies, producing a blue color in X-gal/IPTG-containing selective media. Amplified phages from round 1 were precleared with streptavidin-coated magnetic microbeads (50 μL) to further remove nonspecific binders and were then used as the input phage for round 2 of selection. Enrichment of the bacteriophage pool was achieved by performing 4 rounds of selection under the appropriate pH plus jet fuel conditions. After each round of selection, the genomic DNA from individual phage clones was sequenced by GenScript (Piscataway, NJ). Western Blot Analysis. Western blotting was performed by preparing a 200 μL solution with each of the phage clones at a concentration of 1 × 1011 pfu/mL in 1X Tris buffer saline (TBS) pH 7.5. Then, 10 μL of OprF1-biotin target peptide of concentration 1 mg/mL was added to each phage solution, followed by incubation at 25 °C for 1 h. Phage-OprF-biotin complexes were captured and pulled-down with 25 μL of streptavidin-coated beads, and the captured complexes were washed 5 times with 1X TBST. The pelleted complexes were finally resuspended in 20 μL of 2X Laemmli buffer, heated to 95 °C for 5 min, and resolved in a 14% SDS-PAGE gel. Proteins were blotted to a nitrocellulose membrane, and blocked with TBST containing 5% BSA. To detect phages, a 1:2000 dilution of rabbit anti-M13 phage antibody (primary antibody) followed by a 1:5000 dilution of alkaline phosphatase (AP)-conjugated goat antirabbit antibody was used. For detection of biotinylated OprF1, a 1:2000 dilution of AP-conjugated goat antibiotin antibody was used. For colorimetric visualization, BCIP/ NBT reagent was used. Ten μL of 9 × 1012 pfu/mL of wild type M13 phage in 10 μL of 2X Laemmli buffer, and 20 μL of 1 mg/mL OprF1biotin in 20 μL of 2X Laemmli buffer were used per well, respectively, as the M13 phage detection and OprF1-biotin detection positive controls. J
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Limit of detection in fuel study. Samples containing 1 L of Jet A fuel were amended with 1 mL of 1X phosphate buffer solution (PBS) containing either A. venetianus ATCC 31012 or P. stutzeri at the appropriate test concentration. The inoculated jet fuel samples were thoroughly mixed for 1 min, and allowed to stand for 20 min. To recover the cells, 1 mL of 1X PBS, named bacterial recovery solution (BRS), was added to the jet fuel samples, followed by shaking by hand and allowing the sample to stand for 5 min, and then, 1 mL of the aqueous phase was collected using a long serological pipet transferred to a microcentrifuge tube and cells were pelleted by centrifuged for 5 min at 11000g; cells were washed three times with 1 mL of 1X PBS. Bacterial pellets were resuspended and labeled using a final concentration of 1.5 μM peptide BRE-QD, as explained in the bacterial labeling method. The LOD was defined as the lowest concentration level that could be determined to be statistically different from QD-exposed cells from the results of multiple testers (n = 3). The actual cell level (colony-forming units, CFU) detected was determined by plating a portion of the sample after being subjected to the labeled procedure. Quantitative Real-Time PCR. Genomic DNA in fuel and water samples was determined by quantitative real-time PCR (qPCR) using a two-step amplification program with postamplification melt curve analysis in a CFX 96 Touch real-time PCR system (BioRad, Hercules, CA) as previously described.13,31,56 Briefly, DNA levels were detected and quantified using a bacterial universal 16S rrn gene primer set,57 in combination with a serially diluted (1 × 108 copies/μL to 1 × 103 copies/μL) synthetic oligonucleotide standard spanning the amplicon length. The qPCR reaction contained 1 μL of forward and reverse primer each at 200 nM final concentration, 12.5 μL of Biorad SYBR Green SuperMix, 9.5 μL of water, and 1 μL of sample DNA for a final volume of 25 μL. Statistical Analyses. All measurements were performed in triplicate, and their mean values ± SD (standard deviation) were calculated using Microsoft Excel. Statistical analyses were performed using Student’s t tests, and values were considered significantly different when P < 0.05 (*).
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ASSOCIATED CONTENT
* Supporting Information S
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.6b03350. Heptameric amino acid sequences of OprF1 and Opr86binding peptide biorecognition elements selected through multiple rounds of biopanning (PDF)
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Article
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Oksana M. Pavlyuk: 0000-0002-7131-8628 Oscar N. Ruiz: 0000-0002-0263-6024 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Research reported in this article was supported by funds from the United States Air Force Research Laboratory, Aerospace Systems Directorate to O.N.R. This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-10-2-2934. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government. K
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