Integrated Bacterial Identification and Antimicrobial Susceptibility

Oct 13, 2017 - Accurate and timely diagnostics are critical for managing bacterial infections. The current gold standard, culture-based diagnostics, c...
0 downloads 0 Views 2MB Size
Download PDF
Article Cite This: Anal. Chem. 2017, 89, 11529-11536

pubs.acs.org/ac

Integrated Bacterial Identification and Antimicrobial Susceptibility Testing Using PCR and High-Resolution Melt Pornpat Athamanolap,† Kuangwen Hsieh,‡ Liben Chen,‡ Samuel Yang,§ and Tza-Huei Wang*,†,‡,∥,¶ †

Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, United States Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States § Department of Emergency Medicine, Stanford University, Stanford, California 94305, United States ∥ Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States ¶ The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland 21287, United States

Downloaded via UNIV OF LOUISIANA AT LAFAYETTE on January 29, 2019 at 00:36:47 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

‡

S Supporting Information *

ABSTRACT: Accurate and timely diagnostics are critical for managing bacterial infections. The current gold standard, culture-based diagnostics, can provide clinicians with comprehensive diagnostic information including bacterial identity and antimicrobial susceptibility, but they often require several days of turnaround time, which leads to compromised clinical outcome and promotes the spread of antibiotic resistance. Nucleic acid amplification tests such as PCR have significantly accelerated the detection of specific bacteria but generally lack the capacities for broad-based bacterial identification or antimicrobial susceptibility testing. Here, we report an integrated assay based on PCR and high-resolution melt (HRM) for rapid diagnosis for bacterial infections. In our assay, we measure bacterial growth in the presence or absence of certain antibiotics with real-time quantitative PCR or digital PCR to determine antimicrobial susceptibility. In addition, we use HRM and a machine learning algorithm to identify bacterial species based on melt-curve profiles of the 16S rRNA gene in an automated fashion. As a demonstration, we correctly identified the bacterial species and their antimicrobial susceptibility profiles for multiple unknown samples in blinded tests within ∼6.5 h.

B

mature approach and offer high sensitivity and significantly shortened turnaround time for ID, exemplified by FDAapproved NAAT platforms such as Cephid’s GeneXpert for detecting a specific pathogen (e.g., MRSA)29,30 and bioMérieux’s BioFire FilmArray for detecting a panel of pathogens (e.g., respiratory panel) via nested multiplex PCR and amplicon melting analysis for specificity confirmation.31 Notably, these NAATs either use specific primers or probes for detecting a specific bacterial strain in each test or use multiple primer pairs to detect several bacterial species, which render the assay inevitably more complex to design, perform, and optimize.32 Consequently, expanding the panel of pathogens of interest for broader-scale identification would be challenging. As an alternative in enhancing the ID capability of NAATs, we and others have been developing PCR assays that can achieve broad-based bacteria detection33−42 by targeting the 16S ribosomal RNA (rRNA) gene that is present in all prokaryotic cells. The 16S rRNA gene has conserved regions that are consistent among bacteria and variable regions that are

acterial infections can result in many serious or lifethreatening complications such as sepsis and urinary tract infections (UTI). Successful disease management hinges on timely treatment with proper antibiotics, which requires rapid diagnostics that can provide both bacterial identification (ID) and antimicrobial susceptibility testing (AST) profiles.1−3 Unfortunately, the current gold standard for bacteria ID and AST remains heavily reliant on the conventional culture method. This traditional method begins with Gram staining, followed by culturing for ID; culturing-based AST is performed either in parallel with or after the ID process. This culturing workflow involves tedious processes and requires at least 48 h to produce final results.4 Consequently, although bacterial culture can provide accurate ID and comprehensive AST, the lengthy turnaround time still leads to empirical use of broadspectrum antibiotics, which has fueled the spread of antibiotics resistance.5,6 Therefore, faster diagnostics that are capable of acquiring both ID and AST remain highly desirable.7 Toward developing faster diagnostic methods, significant efforts have focused on detecting specific bacterial genes and proteins with mass spectrometry,8,9 Raman spectroscopy,10,11 immunoassays, 12 and nucleic acid amplification tests (NAATs).13−28 Among these, NAATs represent the most © 2017 American Chemical Society

Received: July 18, 2017 Accepted: October 13, 2017 Published: October 13, 2017 11529

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry specific to the bacteria’s genus and species.43−45 As such, a pair of “universal” primers can be designed to hybridize to conserved regions that flank hypervariable regions, allowing the hypervariable regions to be PCR amplified. PCR products can then be analyzed via various techniques such as sequencing, microarrays, mass spectrometry, and high-resolution melt (HRM) to identify the bacteria. Among these techniques, HRM can be performed directly after PCR without opening reaction tubes and transferring PCR products and, thus, presents a simpler, faster, more cost-effective, and less contamination-prone option. We have therefore employed HRM for identifying bacteria by differentiating their unique, species-specific melting profile.42,46,47 Moreover, we have developed a nested one-versus-one support vector machine learning algorithm (ovoSVM)48,49 to differentiate the unique melting profiles of 37 clinically relevant bacterial species in an automated fashion. Nevertheless, although the PCR-HRM method offers an effective means to broad-based bacteria identification, it does not provide AST information and thus must be integrated with an AST method. Genotypic approaches offer some capacity for determining the antimicrobial susceptibility profile of the causative bacteria by detecting the presence of resistant genes or mutations, and this genetic information can be readily detected by PCR. However, only a few genes or mutations have been found to correlate with known mechanisms of resistance.50 Moreover, because genotypic approaches rely on detecting known resistant genes and mutations, they would not be able to detect new variants of antibiotic resistant mechanisms51,52 that continue to emerge. As such, phenotypic approaches in which bacteria are grown directly with antibiotics remain the most broadly applicable way for obtaining AST profiles. Provided that our PCR-HRM assay and more broadly genetic detection assays all require extracting and amplifying DNA from lysed cells, performing growth-based phenotypic AST after these assays is impossible. This has necessitated the development of a new workflow that can seamlessly integrate these two inherently incompatible assays. Herein, we report a simple yet comprehensive bacterial diagnostic assay by effectively coupling our broad-based bacterial ID with growth-based phenotypic AST. This is achieved by simply incubating bacteria in the presence and absence of an antibiotic before performing real-time quantitative PCR (qPCR) and HRM. In doing so, bacteria of interest are still identified via HRM, which examines the profiles of melt curves; the antimicrobial susceptibility profiles are determined by qPCR, which quantifies the difference in bacterial DNA quantity from the two samples to assess the status of growth. Compared to earlier studies that coupled growth-based phenotypic AST with genetic amplification methods for bacteria detection,53,54 our current work offers true capacity for bacterial species identification from a broader panel of bacteria. As a demonstration, we use our assay to correctly identify the bacteria species and determine the antimicrobial susceptibility of multiple unknown bacterial samples in blinded tests. Finally, as an additional benefit, we can also implement an absolute quantitative digital PCR in our workflow to further enhance the diagnosis of polymicrobial infections.

(E. coli) ATCC 25922, Staphylococcus aureus (S. aureus) ATCC 29213, Enterococcus faecalis (E. faecalis) ATCC 29212, Proteus mirabilis (P. mirabilis) ATCC 12453, E. coli BAA-2471, and S. aureus BAA-44. Of these, E. coli BAA-2471 and S. aureus BAA-44 are multidrug resistant. The six bacteria strains were separately plated on tryptic soy agar plates (TSA; BD Diagnostics, Sparks, MD) at 37 °C overnight. An isolated colony from each plate was grown at 37 °C to log phase in tryptic soy broth (TSB; BD Diagnostics, Sparks, MD). The bacteria were then mixed with 20% glycerol (v/v; SigmaAldrich, St. Louis, MO), aliquoted, and frozen at −80 °C until use. After the aliquoted stocks were completely frozen, one aliquot for each strain was thawed and counted via plating in TSA, which accounts the potential effect of adding glycerol and freezing bacteria and thus provides a more accurate estimate of the stock concentrations. Gentamicin (10 mg/mL) was purchased from Quality Biological Inc. (Gaithersburg, MD), stored at 4 °C, and used without further purification. Integrated ID-AST Assay. Samples containing either gentamicin or no gentamicin with bacteria in culture broth were freshly prepared before each experiment. Gentamicin was diluted to the desired concentration with molecular grade water (Quality Biological Inc., Gaithersburg, MD). Aliquots of bacteria stock were thawed in a 37 °C water bath for ∼5 min and were subsequently diluted with Mueller-Hinton II broth (MHII; BD Diagnostics, Sparks, MD) to 5 × 106 CFU/mL. Finally, 5 μL of diluted bacteria and 5 μL of gentamicin or water were added to 40 μL of MHII broth, achieving final concentrations of 5 × 105 CFU/mL, which adheres with the CLSI standard cell concentration for AST. Subsequently, to initiate the integrated ID-AST assay, all samples were briefly cultured in an incubator at 37 °C for 2 to 4 h. Cultured bacteria samples were then immediately aliquoted into a PCR mix to perform qPCR and HRM. For optimal conditions, the 25 μL PCR mixture contained 2 μL of cultured bacteria sample, 1× Gold Buffer (Thermo Fisher Scientific, Waltham, MA), 1× Evagreen dye (Biotium, Freemont, CA), 3.5 mM MgCl2 (Thermo Fisher Scientific, Waltham, MA), 200 μM of each deoxynucleotide triphosphate (dNTP) (Thermo Fisher Scientific, Waltham, MA), 0.3 μM forward primer 5′-GYGGCGNACGGGTGAGTAA-3′ (Integrated DNA Technologies, Coralville, IA), 0.3 μM reverse primer 5′-AGCTGACGACANCCATGCA-3′ (Integrated DNA Technologies, Coralville, IA), 0.025 U/μL of Amplitaq Gold LD (Thermo Fisher Scientific, Waltham, MA), and Ultra Pure PCR water (Quality Biological Inc., Gaithersburg, MD). qPCR and HRM were performed using a CFX96 Touch Real-time PCR Detection System (BioRad, Hercules, CA) and analyzed using CFX Manager software. The qPCR cycling conditions were as follows: 10 min at 95 °C, followed by 50 cycles of 95 °C for 15 s, 65 °C for 15 s, and a 60 s extension step at 72 °C, and a final extension step at 72 °C for 7 min. HRM was performed immediately after amplification by ramping the temperature from 75 to 95 °C at 0.2 °C increments and measuring the fluorescence signals after a 2 s hold time at each temperature increment. Machine Learning Algorithm and Melt Curve Database. New experimentally generated melt curves were exported from Bio-Rad CFX software and imported into an in-house Matlab program, where the melt curves were first translated with a translation function and then compared with the melt curve database created in our previous study49 to perform bacterial identification. Specifically, the nested ovoSVM algorithm was used to classify bacterial species against

■

EXPERIMENTAL SECTION Bacterial Strains, Antibiotic, and Storage. A total of six bacteria strains were purchased from the American Type Culture Collection (ATCC; Manassas, VA): Escherichia coli 11530

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry

Figure 1. Overview of our integrated bacterial ID/AST assay. The procedure starts with growth of a bacteria sample in MHII broth with and without antibiotics (e.g., gentamicin). Both cultivated samples are then added into PCR mixtures to perform broad-based qPCR-HRM on a real-time thermocycler. After qPCR-HRM, the species of the bacteria in the sample is identified by matching the newly generated melt curve to our speciesspecific melt curve database of 37 common bacterial species via our machine learning algorithm. AST is determined by measuring the differences of Cqs between reactions with and without antibiotics (ΔCq) obtained from qPCR results. That is, bacteria that do not grow in the presence of the antibiotic would have less bacterial DNA than the no-antibiotic control, resulting in a measurable ΔCq.

be defined on the basis of different amounts of detected DNA from broth with and without gentamicin.

the stored melt curves database, which contains 4 replicates of 37 organisms. Each unknown melt curve was input into multiple SVM classifiers that distinguished between all combinations of two organisms out of 37 organisms. By using all negative derivative RFUs at each temperature point as classification features, each SVM classifier output which species the input curve was closer to. The number of times that were classified to each species were counted. Finally, an unknown curve was identified to an organism that had the highest score when comparing to all other organisms. Antimicrobial Susceptibility Testing through qPCR. Real-time amplification results were obtained via Bio-Rad CFX software. Quantification cycle (Cq) values were determined using Regression mode with Baseline Subtracted Curve Fit. ΔCq was calculated on the basis of the differences between Cqs from sample incubated with and without antibiotic [Cq_drug − Cq_no drug]. Polymicrobial Digital PCR-HRM Experiment. S. aureus and E. faecalis were mixed together at ∼106 CFU/mL and ∼2 × 104 CFU/mL, respectively, to mimic a polymicrobial infection. The experiment was performed in triplicate by first splitting each mixed sample into two reactions to incubate with and without 32 μg/mL of gentamicin (Quality Biological Inc., Gaithersburg, MD) for 2 h in MHII broth. Bacterial DNA was then extracted using a QuickExtract Bacterial DNA Extraction Kit (Epicentre, Madison, WI) according to the manufacturer’s protocol. In order to analyze individual melt curves that were derived from single bacterial species, we incorporated our previously developed technique, universal digital HRM (UdHRM).46 To achieve either zero or only one bacterial DNA in each well, the extracted DNA was diluted 60 000-fold before mixing with PCR Mastermix containing similar conditions as mentioned previously with the total final volume of 10 μL for each reaction for the whole 96-well microtiter plate. Subsequent qPCR and HRM with similar cycling conditions as mentioned above were performed using a CFX96 Touch Real-time PCR Detection System (Bio-Rad, Hercules, CA). Output melt curves generated by CFX software were entered into our in-house implemented Matlab program for bacterial identification with DovoSVM. The amount of DNA molecule of each species was determined by counting the number of positive reactions that were identified as the target species: S. aureus or E. faecalis. Antimicrobial susceptibility could then

■

RESULTS AND DISCUSSION Assay Overview. Our approach integrates qPCR and HRM with phenotypic AST to achieve bacterial ID/AST in one assay (Figure 1). Samples containing unknown bacteria are first divided into two portions, where one portion is incubated in culture media with the antibiotic condition of interest and the other is incubated in culture media without any antibiotics as the control. Due to the high sensitivity of PCR, both samples only need to be briefly cultivated (e.g., 2 to 4 h) before they are aliquoted into a qPCR mixture containing EvaGreen, which facilitates HRM after qPCR. qPCR is performed using a single pair of universal primers that amplify a ∼1000-bp hypervariable region of the 16S rRNA gene to enable broad-based detection, as demonstrated previously.49 After qPCR and HRM, bacterial species are identified by matching their distinct, species-specific melt curves to the melt curves of 37 clinically relevant bacteria in our previously established database using our ovoSVM algorithm as classifiers. Subsequently, the growth of bacteria is determined by quantifying the amount of bacterial DNA with qPCR based on the quantification cycle (Cq) value.55 Antimicrobial susceptibility is then determined by comparing the difference in Cq (ΔCq) between the samples cultivated with and without the antibiotic. Susceptible strains do not grow in the presence of the antibiotic, resulting in less DNA than the no drug control and thus a noticeably large ΔCq. In contrast, resistant strains are expected to have a similar growth profile regardless of the presence of antibiotic and therefore show minimal change in Cq between the two samples. Broad-Based Bacterial Identification Based on HRM Using Machine Learning Algorithm. In our assay, accurate bacteria ID is predicated on the exact shape of HRM profiles (i.e., melt curves), and the shape must be independent from the instrument used to generate the melt curves. As the first step toward accurate ID, we have developed a translation function (Figure 2, step 1) that allows us to match the melt curves of the same bacterial species generated from different instruments (i.e., Bio-Rad CFX platform and LightScanner). To do so, the translation function stretches and normalizes the melt curve from one instrument and fine-tunes these parameters until the 11531

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry

built melt curve database, which contains 16S melt curves (∼1000 bp) of 37 clinically relevant bacteria species49 (examples: E. coli, green; S. aureus, orange; P. mirabilis, yellow; E. faecalis, blue in Figure 2). The output of the classifiers is alikeness scores indicating how close the unknown melt curve is to the melt curve of each species. The alikeness score is subsequently ranked, and the unknown melt curve is finally identified as the species with the highest alikeness score (Figure 2, step 3). Our method is capable of identifying bacterial species even from different strains, which will be useful in clinically relevant settings where samples could be any strains. This is because the 16S primers were designed to target long amplicon covering multiple variable regions that are sufficient to differentiate bacterial species. As a result, melt curves that are generated from different strains but the same species will exhibit similar melt profiles, as demonstrated by our previous studies.42,49 As an additional verification in this work, we tested two strains of E. coli (ATCC 25922 and BAA-2471) and two strains of S. aureus (ATCC 29213 and BAA-44), via our PCR-HRM assay and our ovoSVM algorithm, which were not performed in those previous studies. The melt curves from both strains of E. coli closely overlap each other, both having a single melting temperature peak at ∼89.8 °C (Figure S-2A,B) and matching correctly with the E. coli melt curve in our database. Likewise, both strains of S. aureus also have the same melt curves with a small rise before the main melting temperature peak at ∼89 °C (Figure S-2C,D) and are also correctly identified as S. aureus by our algorithm. These results clearly support that our method can accurately identify the bacteria species despite different strains. Antimicrobial Susceptibility Testing. Bacterial strains that belong to the same species may exhibit different susceptibility to an antibiotic. This information can be effectively acquired by our integrated growth-based phenotypic test with qPCR analysis. In our method, we incubate bacteria in the presence and absence of an antibiotic and then we quantify the copy number of the 16S rRNA gene, which is highly correlated with bacterial growth (Figure S-3). As a specific example, we incubated gentamicin-susceptible and gentamicinresistant strains of E. coli with no antibiotics or with 1 μg/mL gentamicin for 2 h and then quantified bacterial growth via qPCR. For the gentamicin-susceptible E. coli, we measured a Cq of 27.86 ± 0.26 for the no-gentamicin control (Figure 3A, red) and a significantly higher Cq of 32.19 ± 0.21 for the gentamicin-treated sample (Figure 3A, black). The ΔCq of 4.33 indicates that gentamicin effectively inhibited E. coli growth. In contrast, the measured Cq from the gentamicin-resistant strain of E. coli for the no-gentamicin control was 25.10 ± 0.56 (Figure 3C, red), which is similar to the Cq of 24.94 ± 0.66 measured for the gentamicin-treated sample (Figure 3C, black). The small ΔCq of −0.16 indicates that gentamicin did not affect the growth of the resistant strain of E. coli. The difference in ΔCqs (p = 0.0012) represents an antimicrobial susceptibility profile that is indeed statistically distinct for the two strains of E. coli (Figure 3E). Likewise, our method correctly identified the gentamicin susceptibility profile of S. aureus. For the gentamicin-susceptible S. aureus strain, we measured a Cq of 29.40 ± 0.17 from the nogentamicin control (Figure 3B, red) and a Cq of 31.80 ± 0.60 for the gentamicin-treated sample (Figure 3B, black) or a noticeable ΔCq of 2.40. In contrast to the susceptible strain, MRSA, a resistant S. aureus strain, exhibited a smaller ΔCq of

Figure 2. Broad-based bacteria identification based on HRM analysis. A melt curve of an unknown bacterial species generated from a qPCRHRM machine (Bio-Rad) is first translated into the same format as previously stored HRM database (LightScanner) with our translation function. The translated unknown melt curve is then used as the input in our machine learning algorithm, one-versus-one support vector machine (ovoSVM) and compared with the melt curves of the 37 clinically relevant bacterial species in our HRM database to perform bacterial species ID. The ovoSVM outputs alikeness score between the translated unknown melt curve and melt curves from each bacterial species. After ranking the score from all species, the unknown sample is identified as the species with the highest score.

translated melt curve matches that from the other instrument. In this work, we first established the initial translation parameters through matching the melt curves of E. coli, E. faecalis, and S. aureus that were generated from a Bio-Rad instrument to the melt curves of the same three species previously generated from a LightScanner instrument and stored in our database. We subsequently validated and finalized these translation parameters, as they allowed us to translate the Bio-Rad melt curve of P. mirabilis and fully match the stored LightScanner melt curve of P. mirabilis (Figure S-1). Our work here demonstrates that, if we were to use PCR instruments from other manufacturers and generate new melt curves in the future, these new melt curves would still be matched to existing melt curves of the same species in our database with similar translation functions with new, fine-tuned parameters. For identifying a bacteria species, our identification workflow starts by obtaining its melt curve through qPCR-HRM (Figure 2). This melt curve is then preprocessed and normalized in the translation function and then transferred into the ovoSVM classifiers (Figure 2, step 2) to compare with our previously 11532

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry

Figure 3. Growth-based antimicrobial susceptibility testing (AST) through real-time quantitative PCR (qPCR). Real-time PCR curves of (A) gentamicin-susceptible E. coli, (B) gentamicin-resistant E. coli, (C) gentamicin-susceptible S. aureus, and (D) gentamicin-resistant S. aureus (MRSA) after incubating with and without gentamicin for 2 h. In each case, the ΔCq is calculated from the difference between the Cq of the sample without gentamicin (red) and the Cq of the sample with gentamicin (black). Significantly large ΔCqs of 4.3 and 2.4 were obtained from susceptible E. coli and susceptible S. aureus, while small ΔCqs of −0.2 and 0.8 were measured from gentamicin-resistant E. coli and MRSA, respectively. Statistically significant ΔCqs (p < 0.05) are observed between gentamicin-susceptible E. coli and gentamicin-resistant E. coli (E), as well as between gentamicinsusceptible S. aureus and gentamicin-resistant S. aureus (F). These results provide further support that antibiotic-susceptible and resistant strains can be effectively differentiated by our method. . (G) Reference ΔCqs for six different bacterial strains, including gentamicin-susceptible strains (green) of E. coli, S. aureus, E. faecalis, and P. mirabilis, as well as gentamicin-resistant strains (orange) of E. coli and S. aureus (MRSA), are obtained after 4 h of cultivation in culture broth with and without gentamicin. These reference ΔCqs define the ΔCq range for determining the susceptibility or resistance to gentamicin for future bacterial samples under the same cultivation conditions.

0.81, which was calculated from Cqs of 30.23 ± 0.16 and 31.04 ± 0.43 from the no-gentamicin control and the gentamicintreated sample, respectively (Figure 3D). The difference in ΔCqs between the two strains of S. aureus is statistically significant (p = 0. 0241), indicating that the gentamicin susceptibility profiles for these two strains are indeed distinct (Figure 3F). We further expanded the susceptibility profiles to gentamicin to a total of six strains of bacteria, including four gentamicinsusceptible strains (E. coli, P. mirabilis, E. faecalis, and S. aureus) and two gentamicin-resistant strains (E. coli and S. aureus). We characterized AST profiles to gentamicin of all the six strains by calculating their ΔCqs using our method with 4 h of incubation time (Figure 3G). Here, we lengthened the time of cultivation to 4 h to ensure that our assay condition is consistent and generally applicable to multiple species and that the growth of slow-growing bacteria such as E. faecalis can be detectable via our qPCR protocol. Of note, we could have shortened the cultivation to as brief as 1 h (Figure S-3) if we were targeting only fast-growing bacteria such as E. coli. For the gentamicinsusceptible E. coli, P. mirabilis, E. faecalis, and S. aureus, we measured relatively large ΔCqs of 8.73 ± 0.14, 7.28 ± 0.48, 2.41 ± 0.29, and 6.96 ± 0.51, respectively (n = 3). On the other hand, for the gentamicin-resistant E. coli and S. aureus, we measured small ΔCqs of −0.06 ± 0.27 and 0.71 ± 0.33, respectively (n = 3). These AST profiles were established in order to be used as reference ΔCq ranges in determining the susceptibility or resistance of future bacterial samples. In addition, we note that the high sensitivity of qPCR also allows us to measure bacterial growth under a wide range of antibiotic concentrations and determine minimum inhibitory concentrations (MICs). For example, we determined the MIC for E. coli against gentamicin (a commonly used intravenous

antibiotic for bacterial infections) to be 0.25 μg/mL, which is consistent with the standard MIC and the traditional broth microdilution results (Figure S-4). Blinded Tests. To validate our assay and demonstrate its usefulness in a clinically relevant scenario, we performed blinded tests where we identified bacteria species and antimicrobial susceptibility without prior knowledge of the samples. We randomly picked three out of the six strains that we tested to be our unknown samples in performing blinded tests. In this blinded test scenario, the protocol simply started with splitting each sample into two portions to briefly incubate bacterial cells in MHII broth in the presence and absence of gentamicin followed by real-time PCR amplification and HRM analysis in triplicate. In result analysis, bacterial species identification was first determined by our translation function and HRM-based identification against the stored HRM database of 37 bacteria species using ovoSVM classifiers. Three samples were identified as E. faecalis, S. aureus, and E. coli, respectively. Afterward, ΔCq of each testing sample was calculated from the qPCR result. We obtained ΔCqs of 2.93, 0.20, and 8.79 for E. faecalis, S. aureus, and E. coli, respectively. Each sample was determined to be susceptible or resistant if the ΔCq was within ±2SD of the reference ΔCq of its own species (Figure 3G). After comparing the ΔCq of each sample with our reference ΔCq ranges, the final results of the three samples were E. faecalis, susceptible; S. aureus, resistant (MRSA); E. coli, susceptible, respectively (Figure 4). We correctly identified the species and the antimicrobial susceptibility of 3 out of 3 target bacteria within 6.5 h, thus demonstrating our assay’s capacity in rapid and precise bacterial identification and antimicrobial susceptibility testing. Digital PCR for Polymicrobial Infections. The concept of briefly cultivating bacteria and then quantifying bacterial 11533

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry

DNA can be further expanded by using digital PCR (dPCR) and digital HRM (dHRM). In fact, by enumerating individual molecules of bacterial DNA, dPCR and dHRM provide additional advantages. First, whereas multiple bacteria presented polymicrobial infections may generate a composite melt curve that is challenging to resolve via HRM, they are possibly decoupled and enumerated with digital analysis. By individually assessing a single molecule of bacterial DNA, each subpopulation is detected and quantified, which also allows for distinguishing between infectious pathogen and contaminant by the bacterial load.56 Second, counting individual molecules improves detection sensitivity, especially when analyzing a sample with only a few pathogens that may not be able to reliably detect the growth with Cq values from qPCR. Due to the better sensitivity, this digital concept can also reduce the incubation time in order to detect bacterial growth. The workflow of this digital assay starts with an analogous process with the previously described method, which begins by splitting the heterogeneous sample and incubating with and without an antibiotic. We then perform cell lysis and DNA extraction for both portions in parallel. The post-DNA extracted samples are then partitioned into separate 96-well plates to perform dPCR-dHRM. In result analysis, reactions containing bacterial DNA show a strong fluorescent signal and are considered as positive reactions, in which subsequently species are identified using HRM-based classification. On the basis of the ID result, the total number of DNA molecules is counted for each species and the AST profile of each bacterial species is then determined by comparing the number of DNA molecules between the bacteria grown in broth with and without an antibiotic as illustrated in Figure S-5. To demonstrate this digital approach, we utilized a simple limiting dilution of heterogeneous samples, coupled with a “quasi-digital” assay using microtiter plates, to analyze individual bacterial DNA molecules. Here, we mixed gentamicin-susceptible S. aureus with gentamicin-susceptible E. faecalis as the sample. By using digital melt curve classification for bacterial species identification (see below), we identified on average 33 ± 7.9 positive reactions as S. aureus

Figure 4. Validation of our integrated workflow via blinded tests. Three samples with unknown bacteria species and unknown gentamicin susceptibility are all correctly diagnosed by our assay. After briefly incubating the samples with and without gentamicin followed by performing qPCR-HRM, bacterial species are identified on the basis of HRM profile using ovoSVM and the AST profile is determined on the basis of comparing ΔCq to reference values. Sample 1 is identified as E. faecalis (blue) and susceptible to gentamicin; Sample 2 is identified as S. aureus (orange) and resistant to gentamicin,; Sample 3 is identified as E. coli (green) and susceptible to gentamicin, respectively.

Figure 5. Demonstration of using digital PCR-HRM (dPCR-HRM) for bacterial ID/AST from mixed bacterial samples. Samples containing gentamicin-susceptible S. aureus and gentamicin-susceptible E. faecalis were incubated in the presence and absence of gentamicin for 2 h. Microtiter plates were used to perform dPCR-dHRM with extracted DNA from the mixed samples. By using our DovoSVM algorithm, 27 and 9 melt curves were, respectively, identified as S. aureus (orange) and E. faecalis (blue) from the no-gentamicin sample as shown in the color-coded melt curves (A) and in the 96-well plate (B). For the gentamicin-treated sample, 5 and 4 melt curves were identified as S. aureus (blue) and E. faecalis (orange), respectively, as shown in the color-coded melt curves (C) and in the 96-well plate (D). (E) Average absolute counts of each bacterial species under each condition are calculated from triplicate experiments. On the basis of the differences in absolute counts of the two bacterial species between growing with and without gentamicin, our digital bacterial ID/AST approach correctly identified that the mixed samples contained susceptible S. aureus and susceptible E. faecalis as expected. 11534

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry and 10 ± 1.5 positive reactions as E. faecalis from triplicated 96well plates of no-gentamicin control (a representative HRM result and plate layout are shown in Figure 5A,B; S. aureus, orange; E. faecalis, blue). For the gentamicin-treated sample, we identified on average 7 ± 1.5 positive reactions as S. aureus and 3 ± 0.6 positive reactions as E. faecalis from triplicated 96-well plates (a representative HRM result and plate layout are shown in Figure 5C,D; S. aureus, orange; E. faecalis, blue). On the basis of these average absolute counts of both bacterial species (Figure 5E), there were approximately 5-fold and 3-fold decreases for S. aureus and E. faecalis total counts in gentamicin-treated sample, indicating that gentamicin had effectively inhibited the growth of both bacterial species. These results demonstrate that the digital assay allows for enumerating multiple species presented in a sample, which also enables AST measurement for each individual organism by comparing the total number of molecules of each bacterial species between drug and no drug control. There are some important considerations that are essential in analyzing dPCR-dHRM. First, due to the limited dynamic range of a 96-well plate, we optimized the colony-forming unit (CFU) number of each species so that both of them could be detected at single molecule level and provided positive counts that were indicative of their AST profiles. Second, because of only a single molecule presented in each PCR reaction, the resulting melt curves exhibit higher variations in shapes. A new training database was generated on the basis of single moleculederived melt curves. Digital melt curves in this database were categorized into three groups: S. aureus, E. faecalis, and unspecific group, which was collected from water control runs and Sanger sequencing-verified contaminants (Figure S6A). The ovoSVM algorithm has also been amended for digital melt curve classification (DovoSVM) by incorporating screening criteria and identifying if the curve matches one of the three categories or there is no match as described in Figure S-6B (result shown in Figure S-6C). These additional modules help us to achieve accurate digital melt curve-based classification with a small training data set.

bacterial viability, culture broth, antibiotic type, and antibiotic dosage can be further studied and profiled for better understanding and improving assay design. Lastly, the dynamic range of the digital experiment can be increased by performing in a higher throughput format such as microfluidic digital PCR.57 This dPCR also provides superior detection sensitivity and precise quantification capability, which results in shorter incubation time compared to traditional qPCR. We envision that this work has the potential to be clinically applied in guiding antibiotic treatments, which ultimately improves global health care.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b02809. Additional experimental results and data analysis including melt curves comparison between different platforms, similar melt curves from different strains of the same bacterial species, bacterial growth measurement via qPCR, MIC determination by using our proposed method, and more details on digital bacterial ID/AST workflow (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: (+1)410-516-7086. Fax: (+1)410-516-7254. ORCID

Pornpat Athamanolap: 0000-0001-6124-4955 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work has been supported by National Institutes of Health (R01AI117032), National Science Foundation (1159771 and 1033744), and the Royal Thai Government Scholar program. We thank Siwat Jakaratanopas for help with the preparation of the table of contents/abstract graphic.

■

CONCLUSIONS In summary, we have introduced an integrated, PCR-based workflow that achieves bacterial identification and phenotypic AST in a single test. Rapid broad-based 16S PCR allows one to measure bacterial growth after brief exposure to antibiotics and that correlates with susceptibility. By integrating with HRM, any presented bacteria can be detected and species-identified with a machine learning algorithm. Our integrated workflow was validated with a blinded test in which we correctly identified the bacteria species and gentamicin susceptibility profiles of 3 unknown bacterial samples. Importantly, our assay could be completed in less than 6.5 h, representing a significant time-saver. Finally, as an added benefit, we demonstrate that ID and AST of multiple bacteria can be achieved by implementing dPCR and dHRM in our assay. While we have demonstrated and highlighted the feasibility of the proposed approach, the method should be further enhanced in several areas. First, the sample preparation process can be incorporated to retrieve viable bacterial cells from various types of sample. Second, both the HRM database and the AST references should be expanded to cover more bacterial species and different antibiotics, which would make our assay applicable to various infections. Factors that possibly affect bacterial growth including bacterial species, bacterial load,

■

REFERENCES

(1) Angus, D. C.; Linde-Zwirble, W. T.; Lidicker, J.; Clermont, G.; Carcillo, J.; Pinsky, M. R. Crit. Care Med. 2001, 29, 1303−1310. (2) Cohen, J.; Vincent, J. L.; Adhikari, N. K. J.; Machado, F. R.; Angus, D. C.; Calandra, T.; Jaton, K.; Giulieri, S.; Delaloye, J.; Opal, S.; Tracey, K.; van der Poll, T.; Pelfrene, E. Lancet Infect. Dis. 2015, 15, 581−614. (3) Davenport, M.; Mach, K. E.; Shortliffe, L. M. D.; Banaei, N.; Wang, T.-H.; Liao, J. C. Nat. Rev. Urol. 2017, 14, 296−310. (4) Dellinger, R. P.; Levy, M. M.; Rhodes, A.; Annane, D.; Gerlach, H.; Opal, S. M.; Sevransky, J. E.; Sprung, C. L.; Douglas, I. S.; Jaeschke, R.; Osborn, T. M.; Nunnally, M. E.; Townsend, S. R.; Reinhart, K.; Kleinpell, R. M.; Angus, D. C.; Deutschman, C. S.; Machado, F. R.; Rubenfeld, G. D.; Webb, S. A.; Beale, R. J.; Vincent, J.-L.; Moreno, R. Crit. Care Med. 2013, 41, 580−637. (5) U. S. Department of Health and Human Services, Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2013. https://www.cdc.gov/drugresistance/threatreport-2013/ (accessed October 19, 2017). (6) Costelloe, C.; Metcalfe, C.; Lovering, A.; Mant, D.; Hay, A. D. Bmj 2010, 340, c2096.

11535

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536

Article

Analytical Chemistry (7) The White House. National Strategy for Combating AntibioticResistant Bacteria, 2014. https://www.cdc.gov/drugresistance/pdf/ carb_national_strategy.pdf (accessed October 19, 2017). (8) Carbonnelle, E.; Mesquita, C.; Bille, E.; Day, N.; Dauphin, B.; Beretti, J. L.; Ferroni, A.; Gutmann, L.; Nassif, X. Clin. Biochem. 2011, 44, 104−109. (9) Stevenson, L. G.; Drake, S. K.; Murray, P. R. J. Clin. Microbiol. 2010, 48, 444−447. (10) Jarvis, R. M.; Goodacre, R. Anal. Chem. 2004, 76, 40−47. (11) Liu, C.-Y.; Han, Y.-Y.; Shih, P.-H.; Lian, W.-N.; Wang, H.-H.; Lin, C.-H.; Hsueh, P.-R.; Wang, J.-K.; Wang, Y.-L. Sci. Rep. 2016, 6, 23375. (12) Peruski, A. H.; Peruski, L. F. J. Clin. Diagn. Lab. Immunol. 2003, 10, 506−513. (13) Thomas, L. C.; Gidding, H. F.; Ginn, A. N.; Olma, T.; Iredell, J. J. Microbiol. Methods 2007, 68, 296−302. (14) Wellinghausen, N.; Nöckler, K.; Sigge, A.; Bartel, M.; Essig, A.; Poppert, S. J. Clin. Microbiol. 2006, 44, 1828−1830. (15) Westh, H.; Lisby, G.; Breysse, F.; Böddinghaus, B.; Chomarat, M.; Gant, V.; Goglio, A.; Raglio, A.; Schuster, H.; Stuber, F.; Wissing, H.; Hoeft, A. Clin. Microbiol. Infect. 2009, 15, 544−551. (16) Wojewoda, C. M.; Sercia, L.; Navas, M.; Tuohy, M.; Wilson, D.; Hall, G. S.; Procop, G. W.; Richter, S. S. J. Clin. Microbiol. 2013, 51, 2072−2076. (17) Peter, H.; Berggrav, K.; Thomas, P.; Pfeifer, Y.; Witte, W.; Templeton, K.; Bachmann, T. T. J. Clin. Microbiol. 2012, 50, 3990− 3998. (18) Peters, R. P. H.; Van Agtmael, M. A.; Gierveld, S.; Danner, S. A.; Groeneveld, A. B. J.; Vandenbroucke-Grauls, C. M. J. E.; Savelkoul, P. H. M. J. Clin. Microbiol. 2007, 45, 3641−3646. (19) Sango, A.; McCarter, Y. S.; Johnson, D.; Ferreira, J.; Guzman, N.; Jankowski, C. A. J. Clin. Microbiol. 2013, 51, 4008−4011. (20) Shang, S.; Chen, G.; Wu, Y.; Du, L.; Zhao, Z. Pediatr. Res. 2005, 58, 143−148. (21) Jordan, J. A.; Jones-Laughner, J.; Durso, M. B. J. Clin. Microbiol. 2009, 47, 368−372. (22) Beal, S. G.; Ciurca, J.; Smith, G.; John, J.; Lee, F.; Doern, C. D.; Gander, R. M. J. Clin. Microbiol. 2013, 51, 3988−3992. (23) Blaschke, A. J.; Heyrend, C.; Byington, C. L.; Fisher, M. A.; Barker, E.; Garrone, N. F.; Thatcher, S. A.; Pavia, A. T.; Barney, T.; Alger, G. D.; Daly, J. A.; Ririe, K. M.; Ota, I.; Poritz, M. A. Diagn. Microbiol. Infect. Dis. 2012, 74, 349−355. (24) Boedicker, J. Q.; Li, L.; Kline, T. R.; Ismagilov, R. F. Lab Chip 2008, 8, 1265−1272. (25) Brolund, A.; Tegmark Wisell, K.; Edquist, P. J.; Elfström, L.; Walder, M.; Giske, C. G. J. Microbiol. Methods 2010, 82, 229−233. (26) Buchan, B. W.; Ginocchio, C. C.; Manii, R.; Cavagnolo, R.; Pancholi, P.; Swyers, L.; Thomson, R. B.; Anderson, C.; Kaul, K.; Ledeboer, N. A. PLoS Med. 2013, 10, e1001478. (27) Forrest, G. N.; Mankes, K.; Jabra-Rizk, M. A.; Weekes, E.; Johnson, J. K.; Lincalis, D. P.; Venezia, R. A. J. Clin. Microbiol. 2006, 44, 3381−3383. (28) Grif, K.; Fille, M.; Würzner, R.; Weiss, G.; Lorenz, I.; Gruber, G.; Eschertzhuber, S.; Nachbaur, D.; Lass-Flörl, C.; Orth, D. Wien. Klin. Wochenschr. 2012, 124, 266−270. (29) Raja, S.; Ching, J.; Xi, L.; Hughes, S. J.; Chang, R.; Wong, W.; McMillan, W.; Gooding, W. E.; McCarty, K. S.; Chestney, M.; Luketich, J. D.; Godfrey, T. E. Clin. Chem. 2005, 51, 882−890. (30) Ulrich, M. P.; Christensen, D. R.; Coyne, S. R.; Craw, P. D.; Henchal, E. A.; Sakai, S. H.; Swenson, D.; Tholath, J.; Tsai, J.; Weir, A. F.; Norwood, D. A. J. Appl. Microbiol. 2006, 100, 1011−1016. (31) Poritz, M. A.; Blaschke, A. J.; Byington, C. L.; Meyers, L.; Nilsson, K.; Jones, D. E.; Thatcher, S. A.; Robbins, T.; Lingenfelter, B.; Amiott, E.; Herbener, A.; Daly, J.; Dobrowolski, S. F.; Teng, D. H. F.; Ririe, K. M. PLoS One 2011, 6, e26047. (32) Edwards, M. C.; Gibbs, R. A. Genome Res. 1994, 3, S65−S75. (33) Barghouthi, S. A. Indian J. Microbiol. 2011, 51, 430−444.

(34) Clifford, R. J.; Milillo, M.; Prestwood, J.; Quintero, R.; Zurawski, D. V.; Kwak, Y. I.; Waterman, P. E.; Lesho, E. P.; Mc Gann, P. PLoS One 2012, 7, e48558. (35) Jeng, K.; Yang, S.; Won, H.; Gaydos, C. A.; Hsieh, Y. H.; Kecojevic, A.; Carroll, K. C.; Hardick, J.; Rothman, R. E. J. Clin. Microbiol. 2012, 50, 1122−1124. (36) Masek, B. J.; Hardick, J.; Won, H.; Yang, S.; Hsieh, Y. H.; Rothman, R. E.; Gaydos, C. A. J. Mol. Diagn. 2014, 16, 261−266. (37) Perng, C. L.; Chen, H. Y.; Chiueh, T. S.; Wang, W. Y.; Huang, C. T.; Sun, J. R. J. Med. Microbiol. 2012, 61, 944−951. (38) Rothman, R.; Ramachandran, P.; Yang, S.; Hardick, A.; Won, H.; Kecojevic, A.; Quianzon, C.; Hsieh, Y. H.; Gaydos, C. Acad. Emerg. Med. 2010, 17, 741−747. (39) Sauer, P.; Gallo, J.; Kesselová, M.; Kolár, M.; Koukalová, D. Biomed. Pap. 2005, 149, 285−288. (40) Srinivasan, R.; Karaoz, U.; Volegova, M.; MacKichan, J.; KatoMaeda, M.; Miller, S.; Nadarajan, R.; Brodie, E. L.; Lynch, S. V. PLoS One 2015, 10, e0117617. (41) Won, H.; Rothman, R.; Ramachandran, P.; Hsieh, Y. H.; Kecojevic, A.; Carroll, K. C.; Aird, D.; Gaydos, C.; Yang, S. J. Clin. Microbiol. 2010, 48, 3410−3413. (42) Yang, S.; Ramachandran, P.; Rothman, R.; Hsieh, Y. H.; Hardick, A.; Won, H.; Kecojevic, A.; Jackman, J.; Gaydos, C. J. Clin. Microbiol. 2009, 47, 2252−2255. (43) Yarza, P.; Yilmaz, P.; Pruesse, E.; Glöckner, F. O.; Ludwig, W.; Schleifer, K.-H.; Whitman, W. B.; Euzéby, J.; Amann, R.; RossellóMóra, R. Nat. Rev. Microbiol. 2014, 12, 635−645. (44) Fettweis, J. M.; Serrano, M. G.; Sheth, N. U.; Mayer, C. M.; Glascock, A. L.; Brooks, J. P.; Jefferson, K. K.; Buck, G. A. BMC Genomics 2012, 13, S17. (45) Clarridge, J. E. Clin. Microbiol. Rev. 2004, 17, 840−862. (46) Fraley, S. I.; Hardick, J.; Masek, B. J.; Athamanolap, P.; Rothman, R. E.; Gaydos, C. A.; Carroll, K. C.; Wakefield, T.; Wang, T. H.; Yang, S. Nucleic Acids Res. 2013, 41, e175. (47) Andini, N.; Wang, B.; Athamanolap, P.; Hardick, J.; Masek, B. J.; Thair, S.; Hu, A.; Avornu, G.; Peterson, S.; Cogill, S. Sci. Rep. 2017, 7, 42097. (48) Athamanolap, P.; Parekh, V.; Fraley, S. I.; Agarwal, V.; Shin, D. J.; Jacobs, M. A.; Wang, T. H.; Yang, S. PLoS One 2014, 9, e109094. (49) Fraley, S. I.; Athamanolap, P.; Masek, B. J.; Hardick, J.; Carroll, K. C.; Hsieh, Y.-H.; Rothman, R. E.; Gaydos, C. A.; Wang, T.-H.; Yang, S. Sci. Rep. 2016, 6, 19218. (50) Walsh, C. Nature 2000, 406, 775−781. (51) Cirz, R. T.; Chin, J. K.; Andes, D. R.; De Crécy-Lagard, V.; Craig, W. A.; Romesberg, F. E. PLoS Biol. 2005, 3, e176. (52) Davies, J.; Davies, D. Microbiol. Mol. Biol. Rev. 2010, 74, 417− 433. (53) Waldeisen, J. R.; Wang, T.; Mitra, D.; Lee, L. P. PLoS One 2011, 6, e28528. (54) Mezger, A.; Gullberg, E.; Göransson, J.; Zorzet, A.; Herthnek, D.; Tano, E.; Nilsson, M.; Andersson, D. I. J. Clin. Microbiol. 2015, 53, 425−432. (55) Bustin, S. A.; Benes, V.; Garson, J. A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M. W.; Shipley, G. L.; Vandesompele, J.; Wittwer, C. T. Clin. Chem. 2009, 55, 611−622. (56) Park, S. Y.; Park, K. H.; Bang, K. M.; Chong, Y. P.; Kim, S. H.; Lee, S. O.; Choi, S. H.; Jeong, J. Y.; Woo, J. H.; Kim, Y. S. J. Infect. 2012, 65, 119−127. (57) Ottesen, E. A.; Hong, J. W.; Quake, S. R.; Leadbetter, J. R. Science 2006, 314, 1464−1467.

11536

DOI: 10.1021/acs.analchem.7b02809 Anal. Chem. 2017, 89, 11529−11536