ABSTRACT
Rapid detection of antimicrobial resistance in both surveillance and diagnostic settings is still a major challenge for the clinical lab, compounded by the rapid evolution of antibiotic resistance mechanisms. This study compares four methods for the broad detection of antibiotic resistance genes in Enterobacterales isolates: two multiplex PCR assays (the Streck ARM-D beta-lactamase kit and the OpGen Acuitas AMR Gene Panel u5.47 (research use only [RUO]) and one microarray assay (the Check-MDR CT103XL assay), with whole-genome sequencing as a reference standard. A total of 65 Gram-negative bacterial isolates, from 56 patients, classified by phenotypic antimicrobial susceptibility testing (AST) as showing resistance to beta-lactam antimicrobials (extended-spectrum beta-lactamase [ESBL] positive or resistance to third-generation cephalosporins or carbapenems) were included in the study. Overall concordance between the molecular assays and sequencing was high. While all three assays had similar performance, the OpGen Acuitas AMR assay had the highest overall percent concordance with sequencing results. The primary differences between the assays tested were the number and diversity of targets, ranging from 9 for Streck to 34 for OpGen. This study shows that commercially available PCR-based assays can provide accurate identification of antimicrobial resistance loci in clinically significant Gram-negative bacteria. Further studies are needed to determine the clinical diagnostic role and potential benefit of such methods.
KEYWORDS: antimicrobial resistance, AMR, PCR, sequencing, NGS, AST, antimicrobial, microarray
INTRODUCTION
Increasing antimicrobial resistance (AMR) has emerged as a global threat. According to the 2019 CDC report on antibiotic resistance, more than 2.8 million antibiotic-resistant infections occur in the United States each year, resulting in >35,000 deaths (1). Resistance is no longer limited to localized areas of endemicity due to travel of people, animals, and goods; one billion people cross international borders each year, leading to rapid global spread of highly resistant bacteria. The rapid spread of carbapenemase New Delhi metallo-β-lactamase (NDM) is a classic example of this phenomenon, originally described in 2008; within 5 years, upwards of 20 variants were found in >40 countries worldwide (2–4). The ESKAPE organisms (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the leading cause of nosocomial infections and often extend beyond multidrug resistance (MDR) into the realm of pan drug resistance, leading clinicians to resort to alternative therapies and drugs with increased toxicity in humans (5–8).
Since certain sequence types are associated with carriage of specific AMR genes, the ability to differentiate infections caused by high-risk clones is of great importance (9, 10). Many of the antibiotic-resistant pathogens are associated with a higher transmission rate; a recent study on carbapenemase-positive K. pneumoniae suggests that the degree of resistance is correlated with spread and that the carbapenemase-positive isolates had the highest rate of transmissibility (11). In addition, there is also an association between these pathogens and increased intensive care unit (ICU) stays and increased mortality (12–14). This increase in mortality rate is independent of whether the colonization is obtained in the community or in the hospital (12, 14).
Both surveillance and timely identification of antibiotic-resistant infections and colonization are necessities, but rapid detection is still a major challenge for the clinical lab, compounded by the rapid evolution of antibiotic resistance mechanisms. In addition to the rapidly evolving NDM story (2, 4), 2,770 different beta-lactamases have been identified in natural isolates, and there is a long history of classification schemes and disagreement in naming (15). The number of mutations resulting in antibiotic resistance grows on a near daily basis, and it is a challenge to develop an assay for detection that is both sensitive and flexible enough to detect new mutations. While laboratories have been relying on phenotypic tests to determine phenotypic antibiotic resistance for years, these tests generally do not determine the exact genotypic mechanism of antibiotic resistance. Additionally, current culture-based methods can take days to yield results, a time frame that has the potential to negatively impact patient care and infection control. One option for rapid detection and identification of antibiotic resistance mechanisms is multiplex real-time PCR. While this is not a new assay category, the evolution of multiplex real-time PCR assays has advanced from detection of only a few to upwards of 50 targets (16–20). Another molecular option for detecting antimicrobial resistance genes is microarray technology. Microarray technology allows one to detect a large number of genes within a single reaction and can offer broad range identification of MDR (21, 22). Microarray assays can be performed in 6 to 8 h and have demonstrated a high rate of accuracy (21). Finally, direct sequence-based assays can be used in determining antibiotic resistance and have the added benefit of an unlimited number of targets and the ability to reanalyze samples as databases change (23, 24). While sequencing assays and analytic pipelines for antibiotic resistance mechanisms are still under development, they have great future potential for the clinical lab.
This study describes the comparison of 3 methods for the detection of antibiotic resistance genes in Enterobacterales isolates, namely, two multiplex PCR assays and one microarray assay, to whole-genome sequencing. Isolates included in the study were from both diagnostic and surveillance cultures collected for routine clinical care from 2016 to 2017 at St. Jude Children’s Research Hospital.
MATERIALS AND METHODS
Study/sample population, original clinical identification, and culture.
A total of 65 Gram-negative bacterial isolates, from 56 patients at St. Jude Children’s Research Hospital in Memphis, TN, from January 2016 through June 2017, were included for analysis (see Table S1 in the supplemental material). All included isolates were classified by phenotypic AST (see below) as showing resistance to beta-lactam antimicrobials (extended-spectrum beta-lactamase [ESBL] positive or third-generation cephalosporins or carbapenems). The included isolates were limited both by the aforementioned resistance profile and by inclusion of only one organism (per genospecies) per patient during the collection period. Twenty-six samples were blood isolates, and 39 were isolates obtained from rectal swab samples collected for infection control surveillance. These included 33 Escherichia coli isolates, 21 K. pneumoniae isolates, 8 Enterobacter cloacae isolates, and 1 each of Citrobacter freundii, Enterobacter asburiae, and Klebsiella oxytoca (Table S1). Blood cultures were originally collected using Bactec liquid medium bottles (BD, Franklin Lakes, NJ) and the Bactec FX (BD) blood culture system before subculturing on plate media. Diagnostic samples were plated on the appropriate media based on source and laboratory standard operating procedure. Identification of diagnostic isolates was performed using the Vitek MS or the Vitek 2 Compact (bioMérieux, Marcy-l'Etoile, France), and susceptibility testing was performed using the Vitek 2 Compact and the AST-GN69 and AST-XN06 cards (bioMérieux). For rectal surveillance samples, swabs were used to inoculate blood agar and MacConkey agar plates (BD) and 5 ml Trypticase soy broth (TSB) (BD) containing one 10-μg meropenem disc (BD). After overnight incubation, the TSB was subcultured on MacConkey agar plates. MacConkey culture and subculture plates were examined (at 24 and 48 h) for the presence of lactose-fermenting Gram-negative bacilli. Isolated lactose-fermenting Gram-negative bacilli were identified and tested for the presence of extended-spectrum beta-lactamase (ESBL) and carbapenem resistance using the Vitek 2 Compact (bioMérieux). After identification, all isolates were cultured on blood agar plates (BD) for 16 to 24 h at 37°C and 5% CO2. McFarland suspensions of 1.5 were then made using commercially prepared TSB and utilized for downstream DNA extraction.
This study was determined to be quality improvement activity and did not meet the definition of research by the Office of Human Subjects Protection (OHSP) at SJCRH; Institutional Review Board review and informed consent were not required.
Extraction.
DNA was extracted using the above-described 1.5 McFarland bacterial isolate suspensions. Briefly, 200 μl of 1.5 McFarland suspension in TSB was added to 2 ml of NucliSENS easyMAG lysis buffer (bioMérieux) and incubated for 10 min, and then DNA was automatically extracted using the bioMérieux NucliSENS easyMAG nucleic acid extractor (bioMérieux), specific A protocol. DNA was eluted in 110 μl NucliSENS easyMAG elution buffer (bioMérieux), quantified using the Qubit 3.0 fluorometer (Life Technologies, Invitrogen) according to the manufacturer’s protocol for high-sensitivity double-stranded DNA (dsDNA), and stored at −20°C until use.
Check-Points assay.
The Check-MDR CT103XL assay (Check-Points BV, Wageningen, Netherlands) was performed per manufacturer’s instructions and was previously described elsewhere (22, 25, 26). Briefly, diluted DNA (2 μl of extracted DNA plus 8 μl water) was ligated to beta-lactamase-specific probes, amplified in a multiplex PCR, hybridized to specialized microarray tubes, and visualized and analyzed using the Check-Points tube reader and software (Check-Points BV). Isolates were identified as carrying variants to ESBL, AmpC, and carbapenemase genes, with discrimination down to the group for CTX-M genes and variant for TEM and SHV genes (see Table S2).
Streck assay.
The Streck ARM-D beta-lactamase kit (Streck, Inc., Omaha, NE) was performed according to the manufacturer’s instructions. Briefly, 1 μl of extracted DNA was added to a multiplex PCR assay using Streck primers and fluorescent reporter probes and run on the Applied Biosystems 7500 real-time PCR system (Applied Biosystems). Samples were interpreted as positive if the quantification cycle (Cq) value was ≤26 cycles. Isolates were identified as carrying variants to nine beta-lactamase family genes, with distinction between CTX-M-14 and CTX-M-15 groups (Table S2).
OpGen assay.
The OpGen Acuitas AMR Gene Panel u5.47 research use only (RUO) (OpGen, Gaithersburg, MD) was performed according to the manufacturer’s instructions with a few changes. The Acuitas AMR Gene Panel protocol recommends using 140 μl of extracted DNA eluate obtained from a Qiagen EZ-1 Advanced XL instrument. Instead, the present study used 10 μl of DNA eluate from the bioMérieux NucliSENS easyMAG nucleic acid extractor in combination with 90 μl of water and 50 μl of extracted control reagent. One hundred forty microliters of the resultant mixture was added to 140 μl of AMR Gene Panel u5.47 PCR master mix. The final PCR mixture was aliquoted into the AMR Gene Panel u5.47 PCR plate that contained the appropriate primers and probes for each target. Real-time PCR was performed using the QuantStudio 5 (Thermo Fisher Scientific), and results were analyzed with the QuantStudio design and analysis software, Acuitas AMR Gene Panel u5.47 resistance gene identification software (OpGen).
Isolate sequencing.
Individual sequencing libraries were created for each isolate using the Nextera XT DNA sample preparation kit (Illumina, San Diego, CA, USA). Isolates were pooled and multiplexed for paired-end sequencing on a single MiSeq instrument, using the MiSeq reagent kit v3 150 bp (Illumina). The resulting fastq files were imported into the SeqSphere+ software version 4.1.9 (Ridom GmbH, Muenster, Germany) for automated quality trimming and assembly. The assembled files (FASTA) produced by SeqSphere+ for each isolate were then analyzed by the Comprehensive Antibiotic Resistance database (CARD) (23, 27–29).
Statistical analysis.
Resistance results from all four assays were compared using whole-genome sequencing as the reference standard. Two assays were considered concordant when they identified the same genetic variants for a sample. The concordance rate was defined as the number of samples detected by one assay that were concordant over the total number of the samples identified by the comparator.
Data availability.
The whole-genome sequences for these isolates are available through NCBI under BioProject accession PRJNA726883 (see Table S3).
RESULTS
Results from each test were compared to sequence-based analysis as determined through CARD. Many targets were not targeted by all assays, and the isolates did not contain all tested resistance determinants. The first analysis compared the various assay gene groups to sequencing (Tables 1 and 2). For the first part of this analysis, comparison was performed only at the group level; any variant present in the sample for both comparators was considered concordant at this level (even though it may not have been at the subgroup level). The availability of specific gene variants detected by each assay was considered when determining concordance, thus explaining the differences in denominators for some groups (Table 1). Subgroups of CTX-M were also evaluated, as both the Check-Points and OpGen assays can determine subgroup variants (Table 3). Additionally, the SHV and TEM mutations were also compared for both ESBL and total concordance (Table 4). Overall concordance between the molecular assays and sequencing was high, with a few exceptions.
TABLE 1.
Group level comparison to sequencing results for beta-lactamase genes
| Gene target | Concordance (no./total no. [%]) | PPVa (%) | NPVb (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|
| OpGen | |||||
| ACT/MIR | NAc | NA | NA | NA | NA |
| CMY | 3/4 (75) | 100 | 98 | 75 | 100 |
| CTX-M | 48/48 (100) | 100 | 100 | 100 | 100 |
| KPC | 2/2 (100) | 100 | 100 | 100 | 100 |
| NDM | 1/1 (100 | 100 | 100 | 100 | 100 |
| OXA | 31/31 (100) | 100 | 100 | 100 | 100 |
| SHV | 26/26 (100) | 100 | 100 | 100 | 100 |
| TEM | 41/41 (100) | 100 | 100 | 100 | 100 |
| Streck | |||||
| ACT/MIR | NA | NA | NA | NA | NA |
| CMY | 3/3 (100) | 100 | 100 | 100 | 100 |
| CTX-M | 48/48 (100) | 100 | 100 | 100 | 100 |
| KPC | 2/2 (100) | 100 | 100 | 100 | 100 |
| NDM | 0/1 (0) | 0 | 98 | 0 | 100 |
| OXA | NA | NA | NA | NA | NA |
| SHV | NA | NA | NA | NA | NA |
| TEM | NA | NA | NA | NA | NA |
| Check-Points | |||||
| ACT/MIR | 2/3 (67) | 33 | 98 | 67 | 94 |
| CMY | 4/4 (100) | 60 | 100 | 100 | 97 |
| CTX-M | 48/49 (98) | 98 | 94 | 98 | 94 |
| KPC | 2/2 (100) | 100 | 100 | 100 | 100 |
| NDM | 1/1 (100) | 100 | 100 | 100 | 100 |
| OXA | 0/4 (0) | 0 | 94 | 0 | 100 |
| SHV | 26/26 (100) | 96 | 97 | 96 | 97 |
| TEM | 40/41 (98) | 100 | 96 | 98 | 100 |
PPV, positive predictive value.
NPV, negative predictive value.
NA, not available.
TABLE 2.
OpGen group level comparison to sequencing results for other resistance genes
| Gene target | Concordance (no./total no. [%]) | PPVa (%) | NPVb (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|
| AAC | 38/38 (100) | 100 | 100 | 100 | 100 |
| AAD | 17/17 (100) | 100 | 100 | 100 | 100 |
| ANT | 1/1 (100) | 100 | 100 | 100 | 100 |
| APH | 1/1 (100) | 100 | 100 | 100 | 100 |
| DFR | 15/26 (58) | 100 | 22 | 58 | 100 |
| Gyrase | 32/34 (94) | 100 | 94 | 94 | 100 |
| SUL | 57/57 (100) | 100 | 100 | 100 | 100 |
PPV, positive predictive value.
NPV, negative predictive value.
TABLE 3.
Subgroup level comparison for CTX-M to sequencing results
| Assay | PPVa (%) | NPVb (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
| CTX-M-1/15 | ||||
| OpGen | 100 | 100 | 100 | 100 |
| Streck | 98 | 100 | 100 | 96 |
| Check-Points | 95 | 96 | 97 | 92 |
| CTX-M-9/14 | ||||
| OpGen | 100 | 100 | 100 | 100 |
| Streck | 89 | 98 | 89 | 98 |
| Check-Points | 100 | 100 | 100 | 100 |
| CTX-M-8/25 | ||||
| Check-Points | 0 | 100 | 0 | 0 |
PPV, positive predictive value.
NPV, negative predictive value.
TABLE 4.
TEM and SHV subgroup level comparison
| Target | PPVa (%) | NPVb (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
| ESBL SHV | 11 | 98 | 50 | 87 |
| Total SHV | 96 | 97 | 96 | 97 |
| ESBL TEM | 100 | 100 | 100 | 100 |
| Total TEM | 100 | 96 | 98 | 100 |
PPV, positive predictive value.
NPV, negative predictive value.
Sequencing was used as the reference standard for all analyses. Gene groups included AAC, AAD, ACT/MIR, ANT, APH, CMY, CTX-M, DFR, mutant gyrase, KPC, NDM, OXA, SHV, SUL, and TEM (Tables 1 and 2). Comparisons between all 4 assays were possible only for CMY, CTX-M, KPC, and NDM (Table 1). Groups OXA, SHV, and TEM were compared between sequencing, OpGen, and Check-Points, but not Streck. It is also important to note here that all 3 assays detected both ESBL and non-ESBL TEMs and SHVs, but only the Check-Points assay had the ability to differentiate between them (Tables 1 and 4). Additionally, AAC, AAD, ANT APH, DFR, SUL, and mutant gyrase were compared between OpGen and sequencing only, and ACT/MIR was compared between Check-Points and sequencing only (Tables 1 and 2). CMY results showed 100% concordance for Streck and Check-Points assays, while OpGen had 75% concordance (Table 1). The CTX-M group was detected with high concordance regardless of the assay compared to sequencing—OpGen and Streck assays had 100% concordance and Check-Points assay had 98% concordance (Table 2). The mutant gyrase comparison between OpGen and sequencing showed agreement for 94% of isolates (Table 2). For KPC, there was agreement for all samples and 100% concordance across the board (Table 1). OpGen and Check-Points had 100% agreement with sequencing for NDM, while the Streck assay failed to identify NDM in 1 isolate (Table 1). OXA concordance fell on both ends of the spectrum—the OpGen assay had 100% agreement with sequencing, whereas Check-Points failed to identify OXA in any isolate, though it should be noted that the Check-Points assay will only detect OXA groups 23, 24, 48, and 58 (Table 1). The TEM agreement was also high, with OpGen identifying 100% of TEM mutations and Check-Points identifying 98% (Table 1). General SHV concordance with sequencing was 100% for both OpGen and Check-Points assays (Table 1), though Check-Points failed to accurately differentiate between the ESBL and non-ESBL SHVs (Table 4). ACT/MIR agreement between Check-Points and sequencing was 67% for the stated types of ACT detectable by the Check-Points assay, though the assay did detect an additional 4 samples with ACT types not claimed to be detectable in the Check-Points product information (typing based on sequence analysis); these were not included in the analysis (data not shown). The complete list of samples with genes missed by any assay can be found in Table 5.
TABLE 5.
Mutations detected by sequencing but not by other methods
| Sample no. | Assay with negative result | Sequencing result |
|---|---|---|
| 2 | OpGen | dfrA14 |
| 5 | OpGen | E. coli gyrA |
| 5 | OpGen | dfrA14 |
| 6 | OpGen | dfrA1, dfrA12 |
| 12 | OpGen | dfrF |
| 15 | OpGen | dfrA21 |
| 18 | OpGen | dfrA12, dfrA14 |
| 25 | OpGen | dfrA14, dfrA19 |
| 26 | Check-Points | CTX-M-136, CTX-M-60 |
| 28 | OpGen | E. coli gyrA |
| 29 | OpGen | dfrA19 |
| 32 | Check-Points | OXA-33 |
| 35 | Check-Points | ACT-9 |
| 36 | OpGen | dfrA19 |
| 39 | OpGen | dfrAa, dfrA12 |
| 44 | Check-Points | OXA-33 |
| 44 | OpGen | dfrA14 |
| 50 | OpGen | dfrA19 |
| 55 | OpGen | dfrA14 |
| 60 | OpGen | dfrA1 |
| 63 | Streck | NDM-5 |
| 68 | Check-Points | OXA-33 |
| 75 | OpGen | dfrA14 |
| 76 | OpGen | CMY-72 |
| 76 | OpGen | dfrA19 |
| 78 | OpGen | dfrA14 |
| 82 | OpGen | dfrA14 |
| 83 | OpGen | dfrA19 |
| 86 | OpGen | dfrA14 |
| 95 | Check-Points | OXA-33 |
| 95 | OpGen | dfrA14 |
| 98 | OpGen | dfrA14 |
| 100 | OpGen | dfrA12 |
| 103 | Check-Points | TEM-127, TEM-81 |
| 103 | OpGen | dfrA14 |
| 109 | OpGen | dfrA12 |
| 112 | OpGen | dfrA8 |
| 113 | OpGen | dfrA14 |
Individual CTX-M subgroups were also split out and compared to sequencing, again in an assay-specific pairwise manner (Table 3). For the CTX-M groups, there was also good overall agreement among all assays. OpGen and Streck had 89% to 100% agreement for all CTX-Ms, though they only identified and differentiated between CTX-M-1/15 and CTX-M-9/14 groups (Table 3). The Check-Points assay was expected to additionally differentiate and identify the CTX-M-8/25 group but failed to do so for the one sample containing this mutation and overall had slightly lower agreement for all the CTX-M groups (Table 3).
DISCUSSION
Three molecular assays were evaluated for comparative ability to detect molecular markers of AMR, focusing primarily on beta-lactam-resistant Enterobacterales. All 3 assays had similar performance. The primary differences between the assays tested were the number and diversity of targets included. The Streck assay had the fewest targets (9 for the beta-lactamase kit) but had robust concordance with the sequencing results for all targets except NDM. The OpGen and Check-Points assays had substantially more targets (34 for OpGen and 27 for Check-Points), and the Check-Points assay had the added value of the differentiation of variants of CTX-M to the subgroup level and ESBL versus non-ESBL determination for OXA, SHV, and TEM genes (Table 4). Many of these discrepancies may be due to differences in variant detection. The Check-Points assay was previously shown to have high sensitivity and specificity for the detection of prevalent ESBL genes (21, 22, 30); however, it does not detect resistance genes or mutations for aminoglycosides, fluoroquinolones, or sulfonamides. The OpGen assay is more comprehensive and showed accuracy for these additional antibiotic classes compared with sequencing, although sequencing detected additional gene subtypes absent from OpGen’s test menu of detectable aminoglycoside and DFR subtypes.
The present study had several limitations. The sample population was limited to samples from pediatric patients at one institution, the samples did not include primary specimens (all testing was performed from isolates), and not every possible gene assay could be evaluated due to limited representation in the sample set. Because of the latter, a comparison of assay performance could not be made with respect to many resistance gene families such as IMP, GES, MCR, PER, VEB, and VIM. Additionally, as described above, the tested genetic variants were not identified for all loci, limiting accurate concordance and performance determinations in many cases. Sequence analyses were conducted using only one database, CARD, which may have affected these results. While CARD has shown overall accuracy, major error rates have been shown to be higher using CARD than when using the ResFinder database, though CARD had fewer very major errors (31). Despite CARD being a well-curated reference database, it is dependent on a multitude of individual studies and contributors and must continue to evolve in order to maintain its utility (23). Finally, this study did not consider assay cost or time requirements for each assay, which may be important considerations for clinical implementation in various practice settings.
The pursuit of a rapid assay that can determine both genotypic and phenotypic AMR for isolates in a complex population is not complete; however, there are now several viable options for clinical labs to detect molecular AMR determinants, potentially as an adjunct to phenotypic antimicrobial susceptibility testing. We compared three of these methods to sequence-based analysis and showed a high degree of concordance. Commercially available PCR-based assays can provide accurate identification of antimicrobial resistance loci in clinically significant Gram-negative bacteria. Of these assays, the OpGen Acuitas AMR Gene Panel is pending FDA clearance for use as an in vitro diagnostic (IVD) test. Further studies are needed to determine the clinical diagnostic role and potential benefit of such methods.
ACKNOWLEDGMENTS
We thank OpGen and Streck for the assay kits and OpGen for study instrumentation.
This work was made possible in part through support from ALSAC.
Footnotes
Supplemental material is available online only.
Contributor Information
R. T. Hayden, Email: Randall.Hayden@stjude.org.
Erin McElvania, NorthShore University HealthSystem.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Tables S1 to S3. Download JCM.01033-21-s0001.xlsx, XLSX file, 0.03 MB (29.6KB, xlsx)
Data Availability Statement
The whole-genome sequences for these isolates are available through NCBI under BioProject accession PRJNA726883 (see Table S3).