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Aug 2, 2012 - John C. Lainson , Mariana Ferrer Fuenmayor , Stephen Albert Johnston .... Wei Huang , Cornelius Domhan , Walter Mier , Thomas Lindner , ...
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Detection and Classification of Related Lipopolysaccharides via a Small Array of Immobilized Antimicrobial Peptides Joshua R. Uzarski* and Charlene M. Mello* U.S. Army Natick Solider Research, Development, and Engineering Center, Natick, Massachusetts 01760, United States Chemistry and Biochemistry Department, University of Massachusetts Dartmouth, North Dartmouth, Massachusetts 02747, United States S Supporting Information *

ABSTRACT: A small array of antimicrobial peptides comprising three cysteine-terminated natural sequences covalently immobilized to pendant surface maleimide groups are used to bind and successfully discriminate five types of lipopolysaccharide (LPS) molecules. Using surface plasmon resonance, LPSs isolated from four strains of Escherichia coli and one strain of Pseudomonas aeruginosa yield distinct binding profiles to the three immobilized peptides. Linear discriminant analysis generated 100% training set and 80% validation set classification success for the 40 samples evaluated. This work demonstrates the discriminatory binding capabilities of immobilized antimicrobial peptides toward LPS molecules and alludes to their use as probes in pathogen sensing devices potentially superior to the current state-of-the-art.

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States.1,3 Worldwide, these numbers grow significantly, especially in areas without sufficient sterilization and water treatment facilities. Clearly, there is a need for improved LPS detection and classification techniques, and the work described here demonstrates the potential to detect and discriminate LPS molecules isolated from similar bacterial strains. The clinical standard test for detection of LPS is the limulus amebocyte lysate (LAL) assay, based on gel formation between the LPS and the LAL.4,5 This method, while sensitive, suffers from environmental sensitivity (pH and temperature changes) as well as false positive responses to other carbohydrate-like molecules. Researchers have since developed newer methods that circumvent some of the drawbacks of the LAL-based method for detection of LPS. Brock et al. developed a fluorescence FRET based LPS detection platform using the CD 14 LPS binding protein sensitive in the micromolar range.6 Basu et al. used a colorometric LPS sensor using functionalized polydiacetylene liposomes to provide differential responses to specific LPS strains.7 Battaglini et al. used an electronic tongue

urrent state-of-the art pathogen detection and discrimination techniques include specific antibody-based sensing and DNA amplification procedures such as polymerase chain reaction (PCR). These (and other) methods have been the result of extensive research aimed at identifying a potential pathogenic agent before harm can occur, which is most often not possible using the time intensive colony culture and plate growing techniques. Bacterial endotoxins, specifically lipopolysaccharides (LPSs), also pose a serious threat to human health as they are are naturally shed from the cell throughout the life cycle and are toxic in and of themselves. LPS is found in Gram negative bacteria at the outer cell membrane, and is comprised of a membrane-bound fatty acid region called lipid A bound to a core saccharide region. To this is attached a long polysaccharide region consisting of repeat units of various sugars. The lipid A region of the LPS molecule is recognized by the TLR4/MD2 receptors of the immune system where it begins a cascade of immunogenic responses.1,2 If not controlled, this cascade can lead to infection, septic shock, organ failure, and eventually death. LPS-induced septic shock is a significant problem in hospitals and other point-of-care environments (such as battlefields) with over 150 000 LPS related infection cases reported each year in the United © 2012 American Chemical Society

Received: April 12, 2012 Accepted: August 2, 2012 Published: August 2, 2012 7359

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bioresistant self-assembled monolayers (SAMs) (2-{2-[2-(1mercaptoundec−11−yloxy)-ethoxy]-ethoxy}-ethanol and 2-{2[2-(2-{2-[2-(1-mercaptoundec−11−yloxy)-ethoxy]-ethoxy}ethoxy)-ethoxy]-ethoxy}-ethylamine hydrochloride) were purchased from ProChimia Surfaces (Sopot, Poland). Lipopolysaccharide samples were obtained from Sigma-Aldrich as lyophilized powders. All LPS strains (Escherichia coli K235, O111:B4, O128:B12, O55:B5, and Pseudomonis aeurginosa) were purified via phenol extraction by the manufacturer. The dry samples were reconstituted in deionized H2O (18 MΩ, Millipore) to a concentration of 1.0 mg/mL, aliquoted, flash frozen, and stored at −20 °C until needed. Other common laboratory reagents and chemicals used were obtained from standard sources. Substrates. Different substrates were used for the reflection−absorption infrared spectroscopy (RAIRS) and surface plasmon resonance (SPR) experiments. For RAIRS, substrates containing 1000 nm gold with a 10 nm titanium adhesion layer were acquired from EMF Corp. (Ithaca, NY). For SPR experiments, in lieu of the blank gold chips available in the Biacore SIA Au Kit, we used custom gold substrates purchased from LGA Thin Films (Santa Clara, CA). Borosilicate glass substrates with refractive index 1.5255 were coated with a 5 nm adhesion layer of titanium followed by 50 nm of gold. The coated substrates were polished and diced to the appropriate size prior to shipping. We found minimal difference in performance between the custom substrates and the Biacore substrates, as observed previously by others.22 Prior to use, all substrates were cleaned via submersion in fresh piranha solution (7:3 ratio of conc H2SO4 and 30% H2O2; CAUTION! Piranha solution is extremely reactive and should be handled with extreme care.) for 15−30 min, rinsed copiously with deionized H2O, and dried with a stream of ultrahigh purity (UHP) N2. The slides were immediately immersed in the desired thiol solution. Monolayer Preparation. Self-assembled monolayers of oligo(ethylene glycol)-containing alkanethiols on gold were used for their well-known resistance to nonspecific adsorption of biomolecules.23−25 Two thiols containing 3 and 6 glycol units with hydroxyl and amine-terminal groups, respectively, were used to control the subsequent surface density of antimicrobial peptides by varying the ratio of the thiols. All surfaces used in the experiments contained an 8:2 solution ratio of hydroxyl to amine thiol. To form the SAMs, clean gold substrates were immersed in 1 mL of a 0.2 mM ethanolic solution of the 8:2 ratio mixed thiol containing 5 mM of freshly dissolved pyrogallol.26 After immersion in thiol solution, the samples were placed in a 2−4 °C refrigerator overnight to minimize self-assembly temperature fluctuations. After overnight self-assembly and immediately prior to further derivitization, the substrates were removed from the thiol solution, rinsed with 200 proof ethanol, sonicated for 3 min in ethanol, rinsed again with ethanol, and dried with UHP N2. Peptides. Antimicrobial peptide samples were reconstituted in phosphate buffered saline (PBS, 0.1 M sodium phosphate, 137 mM NaCl), pH 6.5, aliquoted, flash frozen, and stored at −20 °C until used. Stock concentrations were determined by measuring the tryptophan residue absorbance at 280 nm using a Nanodrop ND-1000 spectrophotometer based on the relationship A = εbc, using the extinction coefficient ε = 5690 M−1 cm−1. Peptide samples for surface immobilization were made by diluting freshly thawed stock samples in PBS, pH 6.5. The disulfide reducing agent tris(2-carboxyethyl)phosphine

based electrochemical impedance technique to detect and discriminate LPS in spiked media containing potential interferants such as proteins, nucleic acids, and phospholipids.8 Finally, Lee et al. utilized aptamer-based impedance-based detection of LPS with a linear range 0.001−1 ng/mL.9 Each of these reports individually describes a great advance in LPS detection, either in terms of limit of detection or LPS specificity. Many of the techniques, however, require complicated chemistry requiring noncommercially available and synthetically expensive molecules. Rather than focusing on increasing LPS detection sensitivity, our work here focuses on LPS strain discrimination using nature’s natural defense molecules: antimicrobial peptides. Antimicrobial peptides (AMPs) are found throughout all kingdoms of life, serving as the first line of defense for the organism against microbial invasion. The peptides are mostly unstructured, and typically consist of 10−40 residues with a varied quantity of cationic residues distributed throughout. In concert, hydrophobic residues precisely placed in the sequence provide amphiphilic character to the peptides, providing them capability to interact with the amphiphilic anionic membranes of bacterial cells.10 The molecules are primarily classified on the basis of their active secondary structure, most commonly the αhelix and the β-sheet. This simple classification protocol, however, occludes the significant structural and mechanistic diversity observed among the thousands of native AMP sequences identified.11 Many antimicrobial peptides lack defined secondary structure prior to interaction with bacterial cell membranes. This threedimensional structural flexibility imparts robustness and stability to the peptides that is most often not observed with environmentally sensitive large protein molecules, such as antibodies. Furthermore, AMPs exhibit a broad range of binding affinities to different bacteria, which provides an advantage over the antibody−antigen “lock-and-key” binding paradigm. Due to these advantages, AMPs have been used to develop a number of whole cell bacterial biosensors.12−16 Soares and Mello demonstrated that select AMPs immobilized to polystyrene microplates preferentially bind pathogenic E. coli O157:H7 over nonpathogenic strains.17 More recently, Manoor et al. showed that the AMP magainin, immobilized on interdigitated electrodes, can be used to capture and measure the binding of defined bacterial species.18 The success of using AMPs for whole cell bacterial cell detection, along with the broad and overlapping binding affinities, led to our hypothesis that a similar system based on immobilized AMP arrays19,20 could be used to not only detect but also classify lipopolysaccharide molecules isolated from Gram negative bacteria. Others have reported the use of immobilized peptide arrays for olfaction-like sensing of various analytes including carboyhydrates,21 whole cells,14 and toxins.12 Similar to the senses of taste and smell, olfaction-like sensing relies not on specific receptors, but rather multiple sensors with overlapping affinities for various analytes that generate a pattern-based signal response. Here we report the use of a small three-peptide array consisting of native sequences to detect and classify five different lipopolysaccharide molecules consisting of four Escherichia coli strains and one Pseudomonas aeruginosa strain.

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MATERIALS AND METHODS Materials. Antimicrobial peptides were purchased from New England Peptide (Gardner, MA). All sequences (as described in the text) were 85% or higher purity. Thiols for 7360

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Table 1. Physical Properties of the Antimicrobial Peptides Used To Discriminate Lipopolysaccharide Molecules via Varying Binding Specificities name

sequence

charge

hydrophobicity

hydrophobic moment

polar %

nonpolar %

CP1c SMAPc CPAc cCP1

SWLSKTAKKLENSAKKRISEGIAIAIQGGPRC RGLRRLGRKIAHGVKKYGPTVLRIIRIAGC KWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAKC CSWLSKTAKKLENSAKKRISEGIAIAIQGGPR

5 9 6 5

0.231 0.331 0.319 0.231

0.256 0.441 0.234 0.247

59.38% 53.33% 47.22% 59.38%

40.63% 46.67% 52.78% 40.63%

with the largest score. Canonical scores are similarly determined, and two-dimensional plots are prepared to visually demonstrate the clustering of the different samples. Separate LPS binding data sets were used to determine and verify the classification functions. The first training data set was used to determine the functions, and the subsequently measured validation data set was classified using the functions of the training set. The training data set classification was also validated using the leave-one-out jackknife method, which removes the test data point from classification function generation.

hydrochloride (TCEP) was added to each peptide at a concentration of 5 mM and incubated for at least 30 min prior to use in order to occlude peptide dimerization. LPS Samples. LPS samples were prepared fresh daily via two 10-fold serial dilutions of the 1 mg/mL samples (prepared as described above) into 0.1 M PBS at pH 7.4 containing 0.01% P20 nonionic surfactant. Prior to each serial dilution as well as analysis, each LPS sample was sonicated for 20−30 min in order to disperse LPS aggregates. RAIRS Experiments. RAIRS experiments were performed on a Thermo Nicolet 6700 FTIR spectrometer equipped with a liquid nitrogen-cooled MCT-A detector. Spectra (1024 scans) were recorded and averaged with a perdeuterated methyl terminated SAM used as a reference surface. Substrates were positioned on an FT-80 horizontal stage accessory that directs the IR beam to the surface 80° from normal. See the Supporting Information for details on the surface derivitization reactions used for RAIRS analysis. SPR Experiments. SPR experiments were performed on a Biacore T100 instrument, which allows up to four ligands, one per flow channel, to be immobilized on one surface. For each experiment, one antimicrobial peptide was independently immobilized in flow channels 2−4 (for a total of three peptides), and cysteine was immobilized in flow channel 1 to serve as the reference ligand. All experiments were performed at 26 °C. Data collection frequency was 1 Hz. The instrument response is provided in response units (RU), where 1 RU = 1 pg/mm2 of surface bound mass density. Both peptide immobilization and LPS binding methods were executed using the instrument methods builder software and are described in the Supporting Information. Data Analysis. To account for peptide activity loss as a function of time, the binding response of each measurement was corrected to a time (cycle) dependent exponential function. The time dependent activity loss of immobilized ligands in SPR experiments has been observed and mathematically treated previously.27,28 In separate experiments, each LPS strain was injected for 40 consecutive cycles over each of the three immobilized peptides. Independent exponential decay curves of the form y = A × exp(−x/t) + y0, where y is the measured response, x is the cycle number (time), and A, t, and y0 are curve fitting parameters, were fit to the binding decay data. The nonlinear curve fitting routine of OriginPro 8.5 was employed for each data set. For a complete explanation of the data analysis, see the Supporting Information. The binding responses herein were corrected for individual peptide activity decay. LPS Classification. Linear discriminant analysis (LDA) was performed using SYSTAT 12 software to classify the LPS samples based upon their binding responses to the three immobilized peptides. LDA determines classification functions that maximize the between-group variance while minimizing the within-group variance of the data. Classifications scores are determined for each data point which is assigned to the group

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RESULTS AND DISCUSSION Recently, antimicrobial peptides have received significant attention for numerous applications focusing both on their antimicrobial activity as well as their cell binding proclivity. The Taitt group published a number of reports outlining the use of immobilized AMPs and related arrays for fluoresce based detection of toxins and whole bacterial cells.12−14,16,29−31 Manoor et al. established the feasibility of using immobilized AMPs in an unlabeled electrochemical detection platform, demonstrating binding of E. coli O157:H7, S. typhimurium, and L. monocytogenes.18 In addition to whole cell detection, measurement of LPS-derived endotoxins is of significant importance in the biodefense and food safety arenas. While current methods for LPS quantification do exist, very few published reports encompass methods which demonstrate LPS discrimination. Here, we show that a small array of only three immobilized AMPs can be used to discriminate five different LPS preparations from different bacterial strains, four of which are from E. coli. The peptides chosen for this work were selected from the UNMC antimicrobial peptide database for their activity against E. coli, their propensity to adopt α-helical conformation, and for the lack of inherent cysteine residues.32 Of the 493 peptides lacking cysteine residues, the sequences selected were cecropin P1 (CP1), SMAP-29, and cecropin A (CPA), and Table 1 provides their sequences and basic properties (for a detailed description of the presented peptide properties, see the Supporting Information). Each native sequence was modified with a C-terminal cysteine residue to facilitate site specific covalent immobilization to maleimide-terminated self-assembled monolayers (SAMs). Custom SAMs containing protein resistant oligo(ethylene glycol) (OEG) groups were used as the substrate over the commercially available carboxymethyl dextran (CMD) for three reasons: (1) they offer greater control over peptide surface density via the use of mixed monolayers, (2) their thickness relative to CMD (10 nm vs 100 nm for CMD) allows higher binding responses upon analyte interaction due to the exponentially decreasing evanescent electromagnetic field, and, (3) they possess better resistance to nonspecific adsorption than CMD surfaces.33 Peptide Immobilization and Characterization via RAIRS. Mixed OEG SAMs were formed by overnight 7361

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Figure 1. Schematic depicting the surface derivitization reactions used to covalently immobilize antimicrobial peptides. The mixed OEG SAM is first reacted with sulfo-GMBS at basic pH (1) to provide terminal maleimide groups, which are then reacted with the thiol groups of the cysteine residues of the peptide (2).

immersion in 0.2 mM mixed solutions of 80:20 hydroxyl and amine-terminated thiols, respectively. The antimicrobial peptides were covalently immobilized using a two-step reaction, as outlined in Figure 1. First, the terminal amine groups were reacted with (γ-maleimidobutryloxy)sulfosuccinimide ester (sulfo-GMBS) under basic conditions (pH 8.5) to create an amide linkage with a terminal maleimide group. The maleimide groups were then reacted at slightly acid conditions (pH 6.5) with the free thiol of the peptide terminal cysteine residues to form covalent and stable thioether bonds. Each step of the surface derivitization process was followed using RAIRS, as shown in Figure 2. Spectrum A in Figure 2 shows the 80:20 mixed OH/NH2 OEG-SAM. The negative peaks observed near 2300 cm−1 (for all three spectra) are the result of the reference subtraction of the C−D stretching modes of the deuterated background SAM. Modes centered near 2900 are from the C−H stretching modes. The broad appearance (with two prominent peaks at νa2917 and νs-2860) is characteristic of ethylene-glycolterminated SAMs with underlying well-ordered alkane chains.34,35 The largest peak at 1136 cm−1 is from the C−O− C stretching modes, the frequency of which is dependent on structural conformation. The absorption frequency observed here is indicative of a mostly amorphous structure of the glycol chains, which is the most advantageous structure for protein resistance.34 Spectrum B was collected from the same surface after reaction with 25 mM sulfo-GMBS for 20 min at room temperature. The appearance of a new peak at 1713 cm−1, the asymmetric stretching of the carbonyl moieties (νaCO) of the new terminal maleimide groups, confirms the immobiliza-

Figure 2. RAIR spectra for each stage of the peptide immobilization reactions: (A) 80/20 mixed OH/NH2 OEG SAM, (B) SAM after reaction with sulfo-GMBS, (C) SAM after reaction with antimicrobial peptide.

tion of the sulfo-GMBS linker.35 Prominent peaks from spectrum A do not shift, indicating that the structure and order of the underlying monolayer is not perturbed by the reaction with sulfo-GMBS. Upon introduction of 10 mM cysteine-terminated cecropin P1 peptide (spectrum C), the appearances of two large peaks at 1665 and 1549 cm−1 (the amide I (νCO) and amide II (δN−H) modes respectively) of the peptide backbone were observed.36−38 The νaCO mode of the maleimide groups shifts from 1713 cm−1 to 1705 cm−1, suggesting that a change in electron distribution of the maleimide groups has taken place. This is caused by the formation of covalent thioether bonds between the maleimide 7362

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ring proximal to the carbonyl and the thiol group of the peptide’s terminal cysteine residue providing strong evidence of the covalent attachment of the peptides to the protein resistant SAM. LPS Binding to Immobilized Peptides: SPR. After characterizing the covalent binding of the peptides to the OEG-SAMs, the three AMPs and cysteine reference ligand were immobilized to a single OEG-SAM surface using the Biacore T100 instrument. Figure 3 shows representative

Figure 4. Binding profiles of five LPS molecules isolated from four strains of E. coli and one strain of P. aeruginosa to three immobilized antimicrobial peptides. The data are the averages of four replicates of decay-corrected data. The error bars are the standard deviations of the average.

of four replicates with error bars representing the standard deviation of the average after a data correction factor was applied (see Supporting Information). The different binding profiles of each LPS strain are clearly evident. SMAP binding affinity is clearly highest followed by cecropin P1 and cecropin A. Interestingly, the relative binding affinity of the two cecropin molecules for LPS from Pseudomonis aeurginosa is opposite that of the other four E. coli strains. LPS Discrimination via Linear Discriminant Analysis. Corrected LPS binding data was subjected to linear discriminant analysis which generates classification functions based on linear combinations of the inputted variables to minimize within-group and maximize between-group variation. The LPS samples were divided into two data sets: a training set (A) and a validation set (B). Data set A was used to determine the classification functions, and Figure 5 reveals that both data sets can be discriminated into five distinct groups. The data

Figure 3. Representative sensorgrams demonstrating the binding of the LPS of E. coli O111:B4 at 10 μg/mL to immobilized cysteine (black), cecropin A (blue), cecropin P1 (red), and SMAP-29 (green). Time 1 indicates the start of the LPS injection at 10 μL/min, 2 indicates the end of LPS injection and switch to running buffer, and 3 indicates the injection of regeneration solution (50 mN NaOH with 30% acetonitrile and 0.1% tween 20). The dashed line indicates the stability point where LPS binding to the immobilized ligand is quantified.

sensorgrams of the four ligands depicting the association and dissociation of the LPS from E. coli O111:B4 as well as the results of regeneration. For the peptide sensorgrams, the raw data was corrected via reference subtraction (response of the cysteine channel) as well as subtraction of a zero injection response (buffer replacing LPS sample). Each of the three peptides had very similar immobilized surface densities (±50 RU); however, the final quantity of bound LPS are different, specifically for SMAP-29 (green trace). All three peptides show a very slow dissociation, suggesting a strong interaction with the LPS molecules. The slow dissociation required a rather strong regeneration solution be used to remove the LPS and restore the free peptide. At point 3 in Figure 3, 50 mM NaOH with 30% acetonitrile and 0.1% tween 20 was injected for 30 s at a flow rate of 50 μL/min. Successful removal of the bound LPS without loss of peptide density was determined by the return of the baseline to zero RU. Resistance to nonspecific adsorption of LPS is demonstrated by the near zero response of the cysteine channel (black trace). The amount of LPS bound to each peptide was measured at the stability point, indicated by the vertical dashed line. The stability point occurs five seconds after the end of LPS injection, which allows the instrument response to stabilize for bulk refractive index changes that occur when switching from analyte to running buffer. All LPS binding data used for classification was collected in this manner. LPS Binding Profiles to Immobilized Peptides. The binding profiles of the five LPS stains to the three immobilized peptides are shown in Figure 4. The data represent the average

Figure 5. Canonical scores plot of the LPS binding data subjected to linear discriminant analysis. Both the training (A) and validation (B) data sets are given along with 95% centroid confidence ellipses. Key: ● E. coli K235, × E. coli O111:B4, + E. coli O128:B12, ▲E. coli O55:B5, ▼P. Aeruginosa. 7363

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the lipid A structures of E. coli and P. aeruginosa. The disaccharide chain and the two phosphate moieties of the two molecules are similar; however, the quantity and length of the fatty acid alkyl chains differ. This suggests that discrimination between the two LPS molecules via AMPs is due to different hydrophobic interactions. However, this explanation is probably not sufficient to describe the observed discrimination of the four E. coli strains as their lipid A structures are most likely identical. These LPS molecules are likely identified via the different composition of their polysaccharide chains. Such differences may affect the diffusion and binding of the LPS molecule to the immobilized AMPs independent of the lipid A structure. We are currently exploring the binding of isolated lipid A fractions of the LPS molecules to help further elucidate the nature of the AMP binding selectivity. In addition to specific lipid A and polysaccharide structural differences, the effects of covalent immobilization on peptide structure may also play a significant role in LPS binding selectivity. As with all molecules immobilized on planar substrates, steric hindrance caused by neighboring ligand molecules restricts trans/rotational freedom and hinders peptide conformational changes. Even though we designed our OEG-SAM to extend the attached peptides away from the interface via the use of three additional ethylene glycol moieties, the influence of surface induced steric hindrance cannot be fully eliminated. Furthermore, the terminal cysteine anchoring of the peptides also dictates the way the peptide interacts with the LPS molecules. In solution, the peptide has complete motional freedom, which allows any part of the molecule to initially interact with the analyte. Upon immobilization, the residues that are furthest from the terminal cysteine residues are most accessible to the analyte and have the highest probability of driving the binding. As an example, consider the LPS binding data shown in Figure 7. The two peptides are both cecropin P1

were classified into groups on the basis of their Mahalanobis distance from the means of the groups; the smallest distance was assigned group membership. (See Supporting Information Table S3 for the data.) Data set A had 100% classification, as is normally the case for the training data set. Jackknife validation, where the data point being classified is removed from the rest of the data used to generate classification functions, provided 95% classification with only one misclassified case. Jackknife verification is often overly optimistic; therefore, a separate validation set is used as a more robust way to verify discrimination. Data set B was separately prepared, analyzed on the same surface as data set A, and subjected to the classification functions generated with data set A. For data set B, 80% of the data were correctly classified with four misclassifications. For three of the four misclassified samples, the percent difference between the lowest Mahalanobis distance and the distance of the correct classification group was only 3%. Discussion. The LPS binding profile and linear discriminant analysis canonical scores data in Figures 4 and 5, respectively, clearly demonstrate the capability of an array of immobilized antimicrobial peptides to discriminate lipopolysaccharide molecules from different but related bacteria strains. We have demonstrated with our system that only three peptides are needed to discriminate five different LPS molecules, four of which represent strain differentiation. This is noteworthy as most other discriminatory binding platforms utilize a much larger number of probe ligands. We hypothesize that a larger array of antimicrobial peptides will increase the discriminatory power of the system. Interaction of the immobilized AMPs with the solutionbound LPS molecules is driven by a balance of electrostatic and hydrophobic attractive forces. The α-helical conformation (initiated via membrane interaction) of the peptides provides amphiphilic character to the molecule; likewise the lipid A portion of the LPS molecules is also amphiphilic. It is probable that the interaction between the immobilized AMPs and the solution bound LPS molecules occurs at the lipid A portion of the LPS, where oppositely charged electrostatic attraction and hydrophobic interactions can mutually occur. While the lipid A structure is generally conserved among different bacterial species, small structural differences may help explain some of the observed binding specificity of the AMPs. Figure 6 shows

Figure 7. LPS binding profiles to immobilized C-terminal (CP1c, 487 pg/mm2) and N-terminal (cCP1, 495 pg/mm2) cecropin P1.

with the terminal cysteine at opposite ends (C- and Nterminus). The only difference between the two peptides is the orientation in which they were immobilized; however, the LPS binding is significantly higher for the C-terminal CP1 than the N-terminal. The immobilization direction changes the order of the peptide sequence and places different residues furthest from the attachment site. In the case of C-terminal CP1, the cationic residues are further form the attachment site than for the N-

Figure 6. Lipid A structures of Escherichia coli (left) and Pseudomonas aeruginosa (right).39,40 7364

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Notes

terminal CP1, allowing them to interact more easily with the anionic LPS molecules. We hypothesize that not only the overall sequence of immobilized AMPs but also the distance and distribution of cationic residues relative to the solid surface influence their broad binding selectivities. We surmise that these surface-influenced properties are mostly responsible for the discriminatory binding of the three peptides used in this study (see Supporting Information Table S5 for surfaceinfluenced peptide properties). Other factors such as the locations and distributions of specific residues with varying side chain chemistry may also influence binding; however, their detailed consideration is outside the scope of this work. We recognize that, as a pure detection platform, our experimental setup is inferior to standard techniques used for quantifying LPS as our limits of detection are approximately 0.1−1 μM. These methods, however, respond similarly to all LPS, precluding discriminatory capability. The utility of our system lies not in low detection limits, but in discrimination power. The ultimate application would combine the standard detection techniques along with in-line discrimination methods. This would both detect as well as identify specific types of LPS, which could prevent food and water contamination as well as sepsis related infections.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to acknowledge Dr. Morris Slutsky for his insightful discussions and generous help with data analysis. This work was supported by the U.S. Army Natick Research, Development and Engineering Center In-House Laboratory Independent Research (ILIR) program, DTRA Medical Diagnostics (8.10012_08_NRL_B) and the National Research Council CBD postdoctoral fellowship program.

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CONCLUSIONS This work demonstrates two key conclusions: (1) an array of label-free immobilized antimicrobial peptides can be used to discriminate and classify lipopolysaccharide molecules, and (2) surface plasmon resonance can be used as a feasible discriminatory detection platform if proper control data is collected to mathematically correct for inherent ligand activity loss. While the 80% successful classification rate reported here is unacceptable for a sensor, the data nonetheless demonstrates the potential for a refined sensor platform based on immobilized AMPs. Our instrumental setup restricted the peptide array size to only three ligands; nonetheless, we were able to achieve LPS discrimination and correct classification using such a small array. We expect larger arrays to have much greater discriminatory power, enabling us to identify bacterial LPSs based on their original growth conditions. Such technology is of significant interest in the fields of microbial forensics and adaptation. This report expounds our initial discoveries of the discriminatory binding power of arrays of label-free antimicrobial peptides. We are currently exploring the binding of additional peptides (both natural and designed) to other LPS strains, from which we hope to obtain greater understanding of the mechanism behind the binding selectivity, analogues to studies of peptide structure/activity. Furthermore, we aim to expand our peptide array size by using alternative optical detection platforms, such as SPR imaging, which also allow us to investigate discriminatory whole cell binding.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. E-mail: charlene.m.mello2.civ@ mail.mil. 7365

dx.doi.org/10.1021/ac300987h | Anal. Chem. 2012, 84, 7359−7366

Analytical Chemistry

Article

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