Detection of bacterial metabolites for the discrimination of bacteria utilizing gold nanoparticle chemiresistor sensors

Sensors and Actuators B 220 (2015) 895–902

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Detection of bacterial metabolites for the discrimination of bacteria utilizing gold nanoparticle chemiresistor sensors Melissa S. Webster ∗ , James S. Cooper, Edith Chow, Lee J. Hubble, Andrea Sosa-Pintos, Lech Wieczorek, Burkhard Raguse ∗ CSIRO Manufacturing Flagship, 36 Bradfield Road, West Lindfield, 2070 Sydney, NSW, Australia

a r t i c l e

i n f o

Article history: Received 7 February 2015 Received in revised form 21 May 2015 Accepted 10 June 2015 Available online 19 June 2015 Keywords: Gold nanoparticle chemiresistors Bacteria Metabolites Liquid sensors Healthcare diagnostics

a b s t r a c t The current methods for detecting and diagnosing bacterial infections have limitations that put lives at risk and threaten to burden healthcare systems with antibiotic resistant strains. Within the field of diagnostics, efforts continue to focus on developing new tools that are fast, easy to use and accessible to resource poor settings. Chemiresistor sensors are amongst new technologies being investigated to meet present needs for diagnosing diseases. Potential advantages of the technology include its amenability to point of care diagnostics, inexpensive components and rapid response times. Here, we present work on utilizing gold nanoparticle chemiresistors for the rapid detection and discrimination of bacteria in liquid samples. The detection principle is based on the distinct metabolic differences associated with species specific bacterial growth. For our proof of concept phase, the supernatant of defined bacterial liquid broth cultures were used. Principal component analysis on data from an array of gold nanoparticle chemiresistors was able to discriminate the culture supernatants of four bacterial species (Escherichia coli, Bacillus subtilis, Staphylococcus epidermidis and Enterobacter aerogenes). With basic unoptimized sensors, the detection limit for E. coli was indicated to be below 3.7 × 106 CFU/mL and detection was achieved within 6 h from low inoculation levels (102 CFU/mL). Results indicated for the first time that gold nanoparticle chemiresistors can successfully detect and discriminate bacteria indirectly from liquid samples. The outcome of this investigation is positive for the continued development of gold nanoparticle chemiresistors for a much needed point of care diagnostic tool for rapidly detecting bacteria. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The correct and timely diagnosis of a bacterial infection is of vital importance in global healthcare [1]. An accurate diagnosis of a bacterial infection allows the most appropriate treatment to be identified whilst a fast diagnosis can ensure that treatment is administered as soon as possible. Unfortunately, current methods for detecting bacteria from clinical samples, such as culturing, polymerase chain reaction (PCR) and immunological methods, have significant limitations in terms of time, cost and complexity [2,3]. As yet, no single method has met the key ideal requirements of being rapid, specific, inexpensive and simple to use. To address this challenge, sensor technologies are being widely investigated as a

∗ Corresponding authors. Tel.: +61 2 9413 7549. E-mail addresses: Melissa s [email protected] (M.S. Webster), [email protected] (J.S. Cooper), [email protected] (E. Chow), [email protected] (L.J. Hubble), [email protected] (A. Sosa-Pintos), [email protected] (L. Wieczorek), [email protected] (B. Raguse). http://dx.doi.org/10.1016/j.snb.2015.06.024 0925-4005/© 2015 Elsevier B.V. All rights reserved.

means to rapidly detect bacteria [4–9] including label free detection [10]. Chemiresistors are chemical sensors consisting of a thin film of material that changes electrical resistance in response to the presence of a chemical. In the case of gold nanoparticle (AuNP ) chemiresistors, the sensor typically consists of an interdigitated microelectrode onto which the gold nanoparticle film is deposited. The AuNP are functionalized with thiol compounds through the gold sulphur bond and as a result the AuNP film is modified with the properties of that particular thiol. Depending on those properties, molecules may partition into the AuNP film and thus alter the resistance of the film (illustrated in Fig. 1(a)). Thiol functionalized AuNP chemiresistors are suited to detecting low molecular weight (MW) chemicals and have the ability to detect small chemical changes in their surrounding environment [11–14]. In terms of disease diagnostics, the field of metabonomics demonstrates that a global change in the products of metabolism (metabolites) can indicate the presence of disease [15]. Since metabolites typically consist of low MW compounds, the chemiresistor has the potential to indirectly detect the presence of a

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Fig. 1. (a) Schematic illustrating gold nanoparticle chemiresistor detection of bacteria based on bacterial growth. Bacteria take in nutrients (blue shapes) and release metabolites (red shapes) as they grow. The small molecular weight chemicals (both nutrients and metabolites) partition into the thiol functionalized gold nanoparticle (AuNP ) film. In steady state, gold nanoparticles sit a distance ‘L’ apart. As chemicals partition into and out of the gold nanoparticle film the distance between gold nanoparticles changes by ‘L’ which cause the resistance of the AuNP film to change. The interaction with target molecules is semi-specific based on the chemical properties of the target and nanoparticle ligand. (b) Image of the chemiresistor sensor array comprising 16 gold nanoparticle films deposited onto interdigitated circular gold electrodes.

bacterial infection. For example, it has been well documented that bacteria produce low MW volatile organic compounds (VOCs) during growth [16,17]. Various methods such as gas chromatography mass spectroscopy (GC–MS) [18], selected ion flow tube mass spectroscopy (SIFT-MS) [19] and nuclear magnetic resonance (NMR) [20] have been used to identify bacteria specific VOCs. Studies have found that different bacteria produce different VOCs whilst consuming the same nutrients [18–20] yielding the potential for discrimination. Based on the different metabolomic volatile profiles produced by bacteria, various chemiresistor technologies have been able to detect and discriminate bacterial species [4–6,8]. However, the focus of chemiresistor development for metabonomic based healthcare diagnostics has predominately been in the gas phase. Yet there are several advantages to directly measuring liquid phase samples: a wider range of diseases could potentially be detected and monitored by probing biological fluids other than breath (such as blood and urine); enhanced detection may be possible by capturing additional information from changes in non-volatile components; the need to carefully control humidity would be eliminated; and relatively complex sample collection techniques could be avoided [21]. In previous work [12] we demonstrated that AuNP chemiresistors can operate in liquids (including highly conductive solutions) and we have investigated applications such as environmental monitoring [22,23] and food spoilage [24]. In addition to operating in the liquid phase, the AuNP chemiresistor technology offers other advantages for disease diagnostics such as rapid response times, amenability to point of care devices and simplicity in construction from inexpensive components. Chemiresistors are a different approach to detecting bacteria compared to some other

conductometric tests that rely on bacteria being captured between electrodes [10]. In this work, we present our initial development of AuNP chemiresistors for healthcare diagnostics that probe liquid samples. AuNP chemiresistors were used to detect the change in liquid culture media associated with the growth of Escherichia coli. These metabolic changes were monitored over time in order to indicate approximate detection limits and times. An array of AuNP chemiresistors was used to discriminate between four different bacterial species in liquid cultures based on their metabolic profiles. 2. Materials and methods 2.1. Materials Gold(III) chloride trihydrate (HAuCl4 ·3H2 O), tetraoctylammonium bromide (TOAB), 4-(dimethylamino)pyridine (DMAP), toluene, sodium borohydride, sulphuric acid, sodium carbonate, N-methyl-2-pyrrolidone (NMP), bovine serum albumin (BSA), acetonitrile, 2-ethylhexanethiol, 1-16-hexadecanedithiol, 3-mercaptohexylhexanoate and 1-heptanethiol were purchased from Sigma-Aldrich, Australia; (3-mercaptopropyl)triethoxysilane (MPTES) was from Fluka, Australia; 1-hexanethiol and 1-10decanedithiol were purchased from Alfa Aesar, UK; Tryptone Soya Broth (TSB), Tryptone Soya Agar (TSA) and phosphate buffer solution (PBS) were manufactured by Oxoid (purchased from Thermo Fisher Scientific, Australia) and were prepared following the manufacturer’s instructions. Where applicable, solutions were prepared using Milli-Q deionized water (>18.0 M cm, Millipore, Australia). Bacterial strains were obtained from the Food Research Ryde

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Bacteriology (FRR B) Culture Collection maintained by the Food and Nutrition Flagship of the Commonwealth Scientific and Industrial Research Organisation (CSIRO). The following bacteria were used: E. coli FRR B 2697, Bacillus subtilis FRR B 2788, Staphylococcus epidermidis FRR B 2505 and Enterobacter aerogenes FRR B 2845. 2.2. Nanoparticle synthesis Gold nanoparticles were synthesized following the procedure described by Brust et al. [25] and were then stabilized with DMAP [26] (DMAP-AuNP ). The diameter of the resulting gold nanoparticles was typically in the range of 4–6 nm as determined using dynamic light scattering (High Performance Particle Sizer, Malvern Instruments, Worcestershire, UK) [27]. 2.3. Electrode fabrication Gold interdigitated circular microelectrodes were fabricated with an outer diameter of 900 ␮m, finger width of 5 ␮m and finger gap of 5 ␮m. The electrodes were deposited onto glass microscope slides (Borofloat® 33, Schott, Australia) following a standard photolithography procedure, as described in previous work [12]. An array of 16 microelectrodes was patterned per glass slide. After electrode deposition, the surface of the slides were treated with MPTES as previously described [12]. 2.4. Nanoparticle film deposition and functionalization A 1% w/v aqueous solution of DMAP-AuNP was prepared with 4% NMP [27]. The solution was drop cast (1 ␮L) onto the surface of each circular electrode. Glass slides were then placed into a vacuum desiccator until the nanoparticle films were dry. To functionalize the gold nanoparticles with a particular thiol, the films were incubated with thiol solutions (10 mM, acetonitrile) for 1 h. The thiols utilized in this work were: 1-hexanethiol (HT), 1-10-decanedithiol (1-10-DDT), 2-ethylhexanethiol (2-EHT), 1-16-hexadecanedithiol (1-16-HDD), 3-mercaptohexylhexanoate (3-MHH) and 1-heptanethiol (1-HEPTT). In the discriminatory work when multi-functionalized arrays were utilized, 3 or 4 sensors were allocated to each thiol. The nanoparticle films were individually exposed to the thiol solutions in custom made well plates using an automated liquid handling workstation (Microlab® STARlet, Hamilton Robotics, Reno, USA). The workstation operated from a pre-programmed method which deposited thiol solutions into designated wells and then, after the incubation period, rinsed the films with acetonitrile and then water [28]. The glass slides were then incubated at room temperature in 1% w/v BSA solution for 1 h, rinsed and stored in water until use. A typical image of the AuNP layer was reported by Chow et al. [27] and an image of the sensor array is provided in Fig. 1(b).

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required culturing time a 35 mL sample of the liquid culture was collected and centrifuged at 13 685 × g to separate the culture liquid from the bacteria. The supernatant was aspirated and the test sample (without bacteria) was stored at 4 ◦ C if being used within the next 24 h, otherwise samples were stored at −80 ◦ C. Cultures were enumerated by performing standard viable cell counts on dilutions of the culture (plated onto solid agar, TSA). Note that in all experiments, the supernatant from bacterial cultures was used i.e. it was assumed there was no or very low levels of bacteria present in the test samples. 2.6. Experimental setup To test the sensors with the bacterial culture supernatants, a test rig was used which consisted of: a fluidics system (GlobalFIA Inc., Fox Island, WA, USA) which pumped the liquid samples to the sensor array via 1/16th inch stainless steel tubing; a polycarbonate flow cell which housed the sensor array; a potentiostat that biased the sensors at +100 mV DC; and readout electronics comprising of a 24-b data acquisition device (USB-2416, Measurement Computing Corporation, Norton, USA) in combination with TracerDAQ® software recording at a rate of 1 Hz. In addition, the flow tubing passed through a heat sink immediately prior to the sensor array to eliminate any small temperature variations between the sample and baseline solution. The valve of the fluidics system was programmed to switch between the baseline solution (sterile media, not inoculated) and the sample of interest (bacterial culture supernatant). Samples were exposed to the sensors for 180 s with 240 s flush periods. Each sample was exposed three times. Flow rate was set to 2 mL/min. 2.7. Data analysis For each sample, individual sensor data were first baseline corrected to a 30 s period immediately prior to sample exposure in order to minimize the effects of any slight drift in the system. The change in sensor response with respect to the baseline signal was then calculated over time as a percentage change (R/R0 ). An Excel spreadsheet was developed to automatically extract features from the data including the average maximum and minimum R/R0 and the slope over the exposure period. For the maximum and minimum R/R0 , the largest in magnitude was taken to be the sensor’s response to that sample. Principal component analysis (PCA) was performed on the data using the statistical software JMP (JMP 10.0.0, SAS Institute Inc., USA).

2.5. Sample preparation In all experiments, bacteria were cultured aerobically at 37 ◦ C and 180 rpm in TSB. For the hourly growth monitoring experiment, a large initial volume of TSB (800 mL) was inoculated with overnight liquid culture so as to yield a starting concentration of approximately 102 CFU/mL (colony forming units per milliliter). Aliquots (35 mL) were removed at hourly intervals between 6 and 9 h for testing with the sensors. For the discrimination work, 100 mL TSB was inoculated with 10 ␮L of overnight culture and all four bacterial strains were cultured at the same time under the same conditions (24 h at 37 ◦ C, shaking 180 rpm) such that they were in the stationary growth phase at the time of use. Cultures were confirmed to be uncontaminated by plating dilutions of the culture and inspecting the colonies formed. In all experiments, after the

Fig. 2. An example of an hexanethiol functionalized gold nanoparticle chemiresistor signal trace over time for both the Escherichia coli culture supernatant and blank sample. The sensor is exposed to: sterile culture media (0–50 s); analyte (50–230 s); sterile culture media (230–450 s). ‘Blank’ samples were the culture media without bacterial inoculation.

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Fig. 3. (a) Corresponding sample points on an optical density (OD) growth curve of Escherichia coli in Tryptone Soya Broth. Arrows indicate the time points on the growth curve at which samples were tested with the sensors. Also labeled are the distinct growth phases of the bacteria: lag phase, log phase and stationary phase. (b) Signal trace of a hexanethiol functionalized gold nanoparticle chemiresistor response to E. coli culture supernatant over time at 0, 6, 7, 8 and 9 h after inoculation. (c) Average response from hexanethiol functionalized gold nanoparticle chemiresistors (n = 4) to E. coli culture supernatant over time compared to a blank control (culture not inoculated).

2.8. Optical density growth curve The growth curve of the E. coli strain in TSB was obtained using an UV/vis absorbance microplate reader (SPECTROstarNano , BMG LABTECH, Germany). Growth conditions were as close as possible to those in the sample preparation for sensor testing (Section 2.5). TSB (7 mL) was inoculated to a starting concentration of approximately 102 CFU/mL (diluted from an overnight culture). The well plate was incubated at 37 ◦ C in the plate reader and shaken in orbital mode at 100 rpm. Optical density (OD) was measured every 15 min at 600 nm over 28 h.

3. Results 3.1. Detection of bacterial growth To first test that AuNP chemiresistors could operate in culture medium and were sensitive to the chemical changes caused by bacterial growth, the supernatant of overnight cultures of E. coli were tested with hexanethiol functionalized AuNP chemiresistors (HT-AuNP chemiresistors). As a control, the sensors were also tested with ‘blank’ samples i.e. the culture medium without bacterial inoculation. The average response to the E. coli culture supernatant was 2.34% ± 0.37% (n = 3) with respect to the baseline solution (sterile medium). This is significantly different to the blank response which was 0.18% (paired t-test, ˛ = 0.05, p = 0.0090). Fig. 2 shows an example of a sensor’s signal trace for both the E. coli culture supernatant and a blank (baseline corrected and converted into percentage change). The graph clearly demonstrates the increased signal due

to E. coli growth in the culture media. At the time of sample collection, the average E. coli concentration was 4.71 × 109 CFU/mL (range: 4.03 × 109 –5.45 × 109 CFU/mL) with no bacteria detected in the control samples. 3.2. Monitoring growth over time Bacterial growth was also monitored over time using HT-AuNP chemiresistors. The E. coli culture supernatant was sampled at 0 h (at the time of inoculation) and then at 6, 7, 8 and 9 h during culturing. Fig. 3(a) shows the collection points with respect to the growth curve of the E. coli culture (same inoculation and growth conditions as the sensor test samples). It illustrates the three distinct growth phases: the lag phase when bacteria adjust to their environment and there is little growth; the log phase when bacteria replicate exponentially; and the stationary phase when cell growth matches cell death and there is no significant further increase in viable cell numbers. Fig. 3(a) shows that samples were collected during the log phase of E. coli growth. Table 1 Average concentrations (CFU/mL) of the Escherichia coli culture (and blank control) at corresponding times of sample collection for sensor testing. Time (h)

Average bacteria concentration (CFU/mL) E. coli

0 6 7 8 9

Blank

3.7 × 10 3.7 × 106 3.8 × 108 9.8 × 108 2.9 × 109 2

0 0 0 0 0

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Fig. 4. Typical response profiles to the culture supernatant of four different bacteria after 24 h culture: (a) Escherichia coli, (b) Bacillus subtilis, (c) Staphylococcus epidermidis and (d) Enterobacter aerogenes. Profiles consist of results from six different types of gold nanoparticle chemiresistors functionalized with 1-hexanethiol (HT), 2-ethylhexanethiol (2-EHT), 1-10-decanedithiol (1–10-DDT), 3-mercaptohexylhexanoate (3-MHH), 1-heptanethiol (1-HEPTT) and 1-16-hexadecanedithiol (1-16-HDT). Response is represented as a change in resistance, R/R0 (%). Note the different scale for E. coli (a).

Fig. 3(b) shows typical signal traces from a HT-AuNP chemiresistor at the collection time points. Interestingly, the change at 6 h is negative relative to both the baseline signal and the response of the culture at 0 h. Beyond 6 h, the response becomes increasingly positive, consistent with what was observed in the 24 h response (Fig. 2). The average response from the sensors (n = 4) is displayed in Fig. 3(c) along with responses from a blank culture. The data indicates that at as early as 6 h, the sensor detected significant changes in the culture supernatant (paired t-test, ˛ = 0.05, p = 0.0002). Sensor responses to the blank samples remained constant over the testing time frame, as expected, and indicated no contamination. Table 1 reports the average bacterial concentration at the sample time points. At 6 h, when HT-AuNP chemiresistors have indirectly detected the presence of E. coli, the average concentration is 3.7 × 106 CFU/mL.

3.3. Discrimination of bacterial species The potential for AuNP chemiresistors to discriminate between different bacterial species was investigated by testing an array of multi-functionalized AuNP chemiresistors with culture supernatant from four different bacterial species. The various responses of differently functionalized chemiresistor sensors to the four different bacterial species are exemplified in Fig. 4. In most cases, each type of chemiresistor gave notably different responses to the four different bacterial species yielding four distinct ‘chemical fingerprint’ response profiles. Differences were apparent in several features of the response such as the magnitude and also the gradient.

Consequently, the data were encouraging for the discrimination of the bacterial species. Discrimination is demonstrated by performing a PCA on the data, the outcome of which is presented in Fig. 5. The clustering of the data points in the PCA shows that the array of sensors was able to discriminate the four different bacterial species.

4. Discussion We have demonstrated that AuNP chemiresistors can indirectly detect bacteria in liquid culture supernatants as a result of changes that have occurred to the culture medium during bacterial growth. For the specific case of E. coli growth, HT-AuNP chemiresistors exhibited an initial decrease in resistance at the first sample point (6 h) followed by a continuous increase in resistance at 7, 8, 9 and 24 h of growth (Figs. 2 and 3). It has previously been shown that the resistance of AuNP films increase proportionally to the concentration of analytes that partition into the nanoparticle film [29]. Based on this, the data suggests that as E. coli grow, the sensors detect an initial decrease in analyte(s) concentration followed by an increase. As the results from the blank control did not indicate such a change, it can be assumed that the analytes have derived from E. coli growth. This sort of transient trend has been reported by other types of sensors utilized for monitoring bacterial growth [9,30]. A possible explanation for this trend can be gleaned from the general principle of bacterial metabolism. It is commonly known that bacteria will take up glucose and other nutrients (such as amino acids, peptides and vitamins) for cell growth and division.

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Fig. 5. PCA of sensor array data from four different bacterial species. Bacteria and average concentrations as follows: Escherichia coli (4.8 × 109 CFU/mL), Bacillus subtilis (1.4 × 109 CFU/mL), Staphylococcus epidermidis (3.9 × 109 CFU/mL), Enterobacter aerogenes (8.0 × 109 CFU/mL). A blank sample (not inoculated with bacteria) is also included. An array of 23 chemiresistors was utilized with 6 different thiol functionalizations of the gold nanoparticle chemiresistor.

In doing so, metabolic waste products are generated and expelled by the bacteria. Therefore, at the 6 h point it could be that the sensors, in the early stages of exponential growth (known as the log phase and shown as the steep slope of Fig. 3(a)), have detected a depletion of medium ingredients due to their uptake into the bacteria. As bacteria continue to grow in the log phase, metabolic by-products expelled by the bacteria accumulate in their environment. The increasing response of the sensor after 6 h indicates that these metabolites are now beginning to dominate the response over the less sensitive changes in nutrient level. As these metabolites increase with growth, there is a corresponding increase in sensor response. One component of bacterial growth which is likely to be relevant to the AuNP chemiresistor’s mode of detection is that of VOC production. The production of VOCs by bacteria is well documented [16,17]. In the specific case of E. coli growth in TSB, literature reports the production of the following volatiles: ethanol [31,32]; propanol [31]; 3-methyl-1-butanol [18,31,33]; long chain alcohols such as 1-octanol, 1-decanol and 1-dodecanol [18,31,33,34]; and indole [18,31–33,35]. It has been established that AuNP chemiresistors are well suited to the detection of VOCs [11,12]. Previous work has demonstrated that HT-AuNP chemiresistors respond to the presence of alcohols, such as ethanol and propanol, and that the response increases with increasing alcohol chain length [23]. Thus, it is probable that some of the sensor response can be attributed to the production of VOCs which remain present in the liquid medium. However, whilst there is a focus in the literature on VOC, it is likely that there will be equally significant changes in the non-volatile components which the sensor is also responding to. Fig. 2 shows a small positive response to the blank sample from the HT-AuNP chemiresistors. This was not expected but could be attributed to side effects from the autoclaving process. Importantly though, the difference between media with and without bacteria is still significant. As a result, the sensor has potential value as a proof of absence tool i.e. excluding bacterial infection in a diagnosis. If bacterial infection can be rapidly ruled out through the absence of specific biomarkers this would help prevent precautionary use of antibiotics and the development of antibiotic resistant strains.

Proof of absence is also a useful tool in drug susceptibility testing when determining the most effective antibiotic treatment [36]. Based on this set of data, it can be said that the HT-AuNP chemiresistors have indirectly detected E. coli at 6 h (from a low inoculation of 102 CFU/mL) at a level of 3.7 × 106 CFU/mL. It should be noted that the data presented here is from HT-AuNP chemiresistors only and has not been optimized. With further screening of different thiols and testing in the 0–6 h range, it could be possible to find more sensitive sensors with lower detection limits and times, 3-MHH for example is more sensitive than HT as evident from Fig. 4(a). To determine whether AuNP chemiresistors can discriminate different bacteria from liquid broth cultures, an array of different chemiresistors was tested with the supernatant of overnight (stationary phase) cultures of four different bacteria. All bacteria were grown in the same type of media (TSB) as media composition is known to influence the metabolites produced [18]. The results (Fig. 5) demonstrated clear discrimination of the bacteria after 24 h. A similar result was reported by Chu et al. [9] who used a colourimetric sensor to discriminate different strains of bacteria integrated into a blood culture bottle. Like this application, the AuNP chemiresistor sensor array described here has the potential to be used for real time monitoring within culture bottles over a 24 h period. A possible advantage of the chemiresistor array is that it can operate in the liquid phase and could thus have improved sensitivity by probing for both volatile and non-volatile markers. The reason for successful discrimination can be attributed to the fact that different bacteria can have different metabolisms and thus produce different metabolic by-products [16,17]. Consequently, the AuNP chemiresistors yielded different sensor response profiles for each of the four bacteria (Fig. 4). For example, in Fig. 4 the HT-AuNP chemiresistor’s largest response was to the E. coli supernatant and then to S. epidermidis, B. subtilis and E. aerogenes in decreasing order. As mentioned previously, E. coli is known to produce metabolites that include long chain alcohols and indole. The literature indicates that the production of these metabolites occurs less for other bacterial species [18,31–33]. Indeed, indole is known to be missing from the metabolic profile of E. aerogenes and as a result, a test for indole is often used to discriminate between E. coli and E. aerogenes in clinical laboratories. It has also been reported that VOC and polar metabolites can decrease during growth which could also be influencing the chemiresistor response [20,37–40]. In order to understand the reasons for the different responses in more detail, the chemical composition of the culture media could, in principle, be analyzed with methods such as GC–MS and LC–MS (liquid chromatography mass spectroscopy). However, the aim of the present study is to work toward developing simple sensor systems that are able to detect and discriminate between a large variety of microorganisms without the need to identify specific biomarkers or the use of complex analytical tools. Indeed, other work using AuNP chemiresistors for gas phase diagnostics have found them to be more effective in the discrimination of different cancers compared to using GC–MS [41] indicating that the sensors are picking up critical additional information. Compared to laboratory diagnostics for bacterial infections, cultures are typically left overnight (in the region of 18–20 h minimum) before being interpreted to indicate the presence of bacteria. Whilst simple kits are available that can give bacterial identification, this is only after samples have been cultured. The additional testing requires further time, labor and costs. Based on the proof of principle work reported here, our sensors have the potential to directly detect and discriminate bacterial infections from a culture bottle within 24 h, faster than current techniques. For example, for urinary tract infections, cultures are left overnight and readings indicating ≥104 –105 CFU/mL in the urine sample are taken to define infection [42]. In this study, much lower initial concentrations were used (102 CFU/mL) when detection was achieved at 6 h.

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Another potential application of the sensors could be to detect bacterial metabolites directly from a clinical sample without the need for culturing. In this work we have shown that the sensor array is sensitive to different metabolites produced by different bacteria and can respond to these analytes in a complex matrix. If sensitivity can be reached to detect low levels of these analytes in a clinical sample, then the simple nature of these sensors lend themselves to affordable point-of-care test devices. 5. Conclusion We have demonstrated for the first time that AuNP chemiresistors are capable of discriminating bacteria in a complex liquid (the supernatant of liquid culture media). Indirect detection is based on the metabonomic changes occurring to the culture media as bacteria grow. This early investigation indicates that detection times are within 6 h at low inoculations (102 CFU/mL) and detection limits are below 3.7 × 106 CFU/mL. Optimization of the sensor properties is expected to significantly improve these detection parameters. Further, by exploiting the distinct variations in bacterial metabolomics, an array of AuNP chemiresistors was able to discriminate four different bacterial species based on their unique sensor response patterns. To the best of our knowledge this is the first documentation of using AuNP chemiresistors to discriminate bacteria based on metabolic changes in the liquid phase. The results suggest that detection may be possible in less than 24 h which would be a significant reduction in time compared to the 2 to 5 days it can take to identify infections using conventional culture methods. AuNP chemiresistors represent an approach to achieving rapid bacterial detection, identification and growth monitoring with inexpensive technology that is amenable to the development of point of care diagnostics or bioreactor monitoring. In future studies, this chemiresistor could be applied to detect infection directly from liquid clinical samples by detecting the metabolomic biomarkers produced by a host in response to infection. Acknowledgements The authors would like to acknowledge the valuable contributions of Roger Chai for fabricating electrodes, Anne Mai-Prochnow for supplying the bacteria, Jan Myers for synthesizing the gold nanoparticles and Mark Roberts for the construction of the electronics system. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.snb.2015.06.024 References [1] D.C. Hay Burgess, J. Wasserman, C.A. Dahl, Global health diagnostics—foreword, Nature S1 (2006) 1–2. [2] O. Lazcka, F.J. Del Campo, F.X. Munoz, Pathogen detection: a perspective of traditional methods and biosensors, Biosens. Bioelectron. 22 (2007) 1205–1217. [3] C. Kaittanis, S. Santra, J.M. Perez, Emerging nanotechnology-based strategies for the identification of microbial pathogenesis, Adv. Drug Delivery Rev. 62 (2010) 408–423. [4] S. Aathithan, J.C. Plant, A.N. Chaudry, G.L. French, Diagnosis of bacteriuria by detection of volatile organic compounds in urine using an automated headspace analyzer with multiple conducting polymer sensors, J. Clin. Microbiol. 39 (2001) 2590–2593. [5] R. Dutta, E.L. Hines, J.W. Gardner, P. Boilot, Bacteria classification using cyranose 320 electronic nose, Biomed. Eng. Online 1 (2002) 4. [6] T.D. Gibson, O. Prosser, J.N. Hulbert, R.W. Marshall, P. Corcoran, P. Lowery, et al., Detection and simultaneous identification of microorganisms from headspace samples using an electronic nose, Sens. Actuators, B: Chem. 44 (1997) 413–422. [7] D. Ivnitski, I. Abdel-Hamid, P. Atanasov, E. Wilkins, Biosensors for detection of pathogenic bacteria, Biosens. Bioelectron. 14 (1999) 599–624.

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Biographies Melissa S. Webster was a Post-Doctoral research fellow at CSIRO after receiving her EngD in Medical Devices from The University of Strathclyde, Scotland. She is interested in developing new diagnostic technologies for healthcare applications.

James S. Cooper completed a PhD at the University of Notre Dame in USA and is currently a Senior Research Scientist with CSIRO. His research interest is engineering nanostructures with unique electrochemical properties. Edith Chow received her PhD in chemistry from the University of New South Wales in 2006 and is currently a Senior Research Scientist at CSIRO Manufacturing Flagship. Her main research interest is in the development of nanomaterials for biosensing and chemical sensing applications. Lee J. Hubble was awarded his PhD in chemistry from the University of Western Australia in 2009 and is currently a Research Scientist with CSIRO. He is actively researching gold nanoparticle/organic linker networks for the application of chemiresistor sensors to solution-based point-of-analysis testing. Andrea Sosa-Pintos is an Experimental Scientist at CSIRO Manufacturing Flagship in Australia. Currently her research is in the development of chemical and biochemical sensors for biomedical and environmental applications. Lech Wieczorek is a Senior Principal Research Scientist at CSIRO in Australia and leads the Integrated Nanoscience Research Group. Currently his research is in chemical and biochemical sensors for biomedical and environmental applications. Burkhard Raguse is a Senior Principal Research Scientist at CSIRO in Australia. His current research interests include the development of new sensor transduction mechanisms using functional nanomaterials and the development of biomimetic systems.