Figures
Abstract
Accurate identification of slowly growing nontuberculous mycobacteria (SG-NTM) of clinical significance remains problematic. This study evaluated a novel method of SG-NTM identification by amplification of the mycobacterial 16S-23S rRNA internal transcribed spacer (ITS) region followed by resolution of amplified fragments by sequencer-based capillary gel electrophoresis (SCGE). Fourteen American Type Culture Collection (ATCC) strains and 103 clinical/environmental isolates (total n = 24 species) of SG-NTM were included. Identification was compared with that achieved by high performance liquid chromatography (HPLC), in-house PCR and 16S/ITS sequencing. Isolates of all species yielded a SCGE profile comprising a single fragment length (or peak) except for M. scrofulaceum (two peaks). SCGE peaks of ATCC strains were distinct except for peak overlap between Mycobacterium kansasii and M. marinum. Of clinical/environmental strains, unique peaks were seen for 7/17 (41%) species (M. haemophilum, M. kubicae, M. lentiflavum, M. terrae, M. kansasii, M. asiaticum and M. triplex); 3/17 (18%) species were identified by HPLC. There were five SCGE fragment length types (I–V) each of M. avium, M. intracellulare and M. gordonae. Overlap of fragment lengths was seen between M. marinum and M. ulcerans; for M. gordonae SCGE type III and M. paragordonae; M. avium SCGE types III and IV, and M. intracellulare SCGE type I; M. chimaera, M. parascrofulaceum and M. intracellulare SCGE types III and IV; M. branderi and M. avium type V; and M. vulneris and M. intracellulare type V. The ITS-SCGE method was able to provide the first line rapid and reproducible species identification/screening of SG-NTM and was more discriminatory than HPLC.
Citation: Subedi S, Kong F, Jelfs P, Gray TJ, Xiao M, Sintchenko V, et al. (2016) 16S-23S Internal Transcribed Spacer Region PCR and Sequencer-Based Capillary Gel Electrophoresis has Potential as an Alternative to High Performance Liquid Chromatography for Identification of Slowly Growing Nontuberculous Mycobacteria. PLoS ONE 11(10): e0164138. https://doi.org/10.1371/journal.pone.0164138
Editor: Luis Angel Maldonado Manjarrez, Universidad Autonoma Metropolitana, MEXICO
Received: May 6, 2016; Accepted: September 20, 2016; Published: October 17, 2016
Copyright: © 2016 Subedi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Slowly growing nontuberculous mycobacteria (SG-NTM) are environmental bacteria that can cause lung, lymph node, bone, skin and soft tissue, as well as disseminated, infections [1–4]. Infections are acquired following environmental exposure, and outbreaks of public health significance have been reported particularly in relation to contaminated water supplies [1, 5]. SG-NTM also colonize the airways of patients with chronic lung disease and are increasingly encountered in respiratory tract specimens [1, 6, 7]. The spectrum of recognized SG-NTM species (over 160) is ever widening (http://www.bacterio.net/mycobacterium.html) with evolving taxonomy, re-definition of species complex and identification of individual species within complexes [4, 8, 9]; an increasing number may cause human disease [1]. The most common pathogenic species or species groups include Mycobacterium avium complex (MAC; comprising M. avium, M. intracellulare and other species), M. simiae complex, M. terrae complex, M. haemophilum, M. kansasii and M. marinum [10]. Newer pathogenic species include M. asiaticum, M. branderi, M. lentiflavum and M. triplex [4]. Because of differences in clinical relevance and species-specific antimicrobial susceptibity and treatment regimens, accurate and rapid species identificaion of SG-NTM is of great importance [7, 9, 11].
Traditionally, a combination of phenotypic, morphological and mycolic acid-based methods have been used to identify/screen SG-NTM. In our laboratory (Fig 1a), high performance liquid chromatography (HPLC) has been performed in parallel with the TB Ag MPT64 rapid test (SD Bioline, Standard Diagnostics, Suwon, Korea) for identification/screening of SG-NTM and exclusion of Mycobacterium tuberculosis [10]. However, this approach requires 1–2 days for identification and lacks discriminatory power to distinguish closely-related SG-NTM species [10]. Matrix-assisted laser desorption ionization—time of flight mass spectrometry although used routinely for bacterial identification currently is also unable to reliably distinguish closely related species [12–14]. Molecular methods such as probe-based assays, restriction enzyme analysis (REA) and gene sequencing are alternative approaches for identification of SG-NTM [10, 15–17]. In particular, sequencing of the 16S rRNA gene and the 16S-23S internal transcribed spacer (ITS) region has been often taken as the benchmark for species resolution [15, 16]. Multiplex PCR based assays targeting the 16S-23S rRNA to differentiate common NTM species have also been developed [18]. Sequencing of the heat shock protein 65 (hsp65) and beta subunit of RNA polymerase (rpoB) genes also yield good species discrimination [19, 20] yet no single target gene seems to be sufficiently discriminatory to accurately speciate all NTM.
a. Algorithm used for identification of slowly growing nontuberculous mycobacteria in our laboratory. b. Proposed new algorithm incorporating SCGE for identification of slowly growing nontuberculous mycobacteria. Species-specific PCR; “TB/MAC” PCR, targeting M. tuberculosis [IS6110] and MAC [ITS]; “KGSA 1” PCR, targeting M. gordonae [ITS] and M. kansasii [16S]; “KGSA2” PCR, targeting M. marinum/M.ulcerans [ITS], M. asiaticum [ITS] and M. szulgai [ITS].
Sequencer-based capillary gel electrophoresis (SCGE) is an alternative approach for fast, accurate organism identification/screening and has produced high resolution for species identification of Clostridium difficile [21, 22], Vibrio [23], Nocardia [24], rapidly growing mycobacteria (RGM) [25] and M. tuberculosis [26]. Unlike traditional gel electrophoresis, SCGE uses a 5’-end fluorescein-labelled primers and a DNA analyser, with resultant rapid and accurate resolution of amplicons. We recently applied SCGE to identify RGM and therein, had included several American Type Culture Collection (ATCC; Manassas, VA) SG-NTM strains (Table 1) to broadly illustrate the distinctiveness in profiles between these two groups of mycobacteria; however, no clinical SG-NTM isolates were studied [25]. Here we applied an ITS-SCGE assay to identify 103 clinical/environmental SG-NTM isolates and compared the results with those of a combination of HPLC, in-house multiplex PCR and 16S rRNA gene/ITS sequencing. The identification provided by DNA sequencing is definitive. We hypothesised that SCGE will improve the initial identification of SG-NTM and provide an alternative to HPLC. Fig 1b shows the proposed algorithm for identification of SG-NTM where SCGE is used as an alternative to HPLC.
Materials and Methods
Isolates
A total of 117 SG-NTM isolates representing 24 species/species complex referred to the New South Wales Mycobacterium Reference laboratory (Westmead Hospital, Sydney, Australia) from 1/1/2015-31/10/2016 were studied. These comprised of 101 clinical isolates, one environmental M. gordonae, and a “quality assurance” M. kubicae, isolates (Table 2). Fourteen ATCC strains (14 species) (Table 1) were also analysed and 3/14 ATCC strains previously analysed were repeated for comparison [25]. A clinical strain of M. bovis was included for comparison as a representative of M. tuberculosis complex.
The clinical SG-NTM isolates (from 92 patients) were from sputum (n = 65 isolates), broncho-alveolar lavage fluid (n = 26), tissue (n = 8), and one isolate each from pleural fluid, and blood culture. Nine patients had two isolates cultured during the same illness episode, and one had three; these repeat isolates were analyzed to assess reproducibility of the SCGE procedure. Thirty-six isolates were M, intracellulare, 33 were M. avium, and 12, M. gordonae. Smaller numbers isolates of M. marinum M. chimaera, M. kansasii, M. paragordonae, M. parascrofulaceum, M. asiaticum, M. branderi, M. haemophilum, M. lentiflavum, M. terrae, M. triplex, M. ulcerans and M. vulneris were also examined (Table 2). Isolates had been received fresh in Mycobacterial growth Indicator Tube (MGIT), Middlebrook 7H10 or Lowenstein Jenson media. DNA extraction was performed on fresh cultures and extracts were stored in -20°C prior to analysis by SCGE.
Identification of mycobacteria
SG-NTM were subjected to HPLC in conjunction with testing for TB Ag MPT64 (SD Bioline) [9, 27] (Fig 1a). For the purposes of the present study, all isolates were subjected to one or more in-house real—time multiplex PCR assays using primers and species-specific probes as appropriate, was then performed. Three different assays were used: 1) “TB/MAC PCR” with detects M. tuberculosis (IS6110 target) or MAC (ITS-directed); 2) “KGSA-1 PCR” with targets for M. kansasii (16S), M. gordonae (ITS); and 3) “KGSA-2 PCR” with targets for M. asiaticum (ITS), M. szulgai (ITS), and M. marinum/M. ulcerans (ITS) [28]. For identification of M. ulcerans, a species-specific-PCR targeting the M. ulcerans insertion sequence IS2404 was used to confirm identification [29]. A subset of isolates (n = 35) further underwent 16S rRNA gene and/or ITS sequencing as required (see below); all 35 could not be identified to species level by SCGE, HPLC or in-house PCR. Hence DNA sequencing was considered the reference method for identification. 16S rRNA gene and ITS sequences were compared with archived sequences of type strains in NCBI GenBank using the NCBI BLASTn (http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome). All ATCC strains underwent 16S rRNA/ITS sequencing to verify their identity to exclude potential misclassification during storage.
DNA extraction
DNA extraction was performed using the commercial InstaGene Matrix method (Bio-Rad Laboratories Inc, California, USA). Two to four colonies were retrieved from Middlebrook 7H11 agar and placed into 1 mL of sterile water, vortexed and then sonicated for 20 minutes. The resultant suspensions were centrifuged at 11,000 rpm for 1 minute. The supernatant was discarded and the residual pellet was resuspended in 200 mL of InstaGene Matrix (Bio-Rad), mixed by vortex and incubated at 56°C for 20 minutes. Samples were then boiled for 8 minutes and centrifuged (13,200 rpm) for 2 minutes prior to use. Samples were used within 24 hours or stored at -20°C. The resultant DNA extract was diluted to 1:100 using molecular-biology grade water (Eppendorf, North Ryde, Australia) prior to undergoing PCR.
Amplification of the 16S-23S rRNA internal transcribed spacer region (ITS)
The Mycobacteria ITS region(s) were amplified prior to SCGE. The PCR Master mix comprised of 12.5 μL of hotstart master mix x2 (Qiagen, Doncaster, Vic.), 0.1 μL of 6-FAM labelled forward primer ITS1F (5’GTGCGGCTGGATCACCTCCT3’), 0.1 μL of ITS 1 reverse primer ITS1R (5’ AGCCTCCYACGTCCTTC[A/T]TCGGCT 3’), 5 μL of extracted DNA and molecular-biology grade water (Eppendorf) to make up a total volume of 25 μL. PCR conditions were 95°C for 15 minutes, 35 cycles of 95°C for 30 seconds, 62°C for 30 seconds, 72°C for 1 minute, followed by a further extension at 72°C for 10 minutes. Sequence analysis was performed by SCGE of the amplified ITS region. ITS sequencing was performed using the primer pair ITS2F and ITS2R: ITS2F forward primer 5’ GAAGTCGTAACAAGGTAGCCG 3’, ITS2R reverse primer 5’ GACAGCTCCCCGAGGC(A/T)TATCGCA 3’ (Sigma-Aldrich, Castle Hill, NSW). PCR conditions and mastermix (other than the primers) components were the same for PCR amplification prior to SCGE. A single band on agarose gel electrophoresis confirmed the presence of DNA amplicons.
Sequencer-based capillary gel electrophoresis (SCGE)
Sequencer capillary gel electrophoresis and fragment analysis was performed as previously described [24, 25]. PCR fragment analysis was performed using the ABI 3730xl DNA analyzer employing a 48-capillary 50 cm POP-7 gel (Applied Biosystems, Forster City, USA). PCR products were diluted 1:30 with molecular biology-grade H2O (Eppendorf) to a final volume of 30 μL. Sample injection was at 1.6 kV over 15 seconds with a total running time of 6,200 seconds at 15 kV run voltage. A 20- to 1200-bp LIZ 1200 ladder (Chimerx, Madison, WI, USA) was the internal size control.
SCGE interpretation and data analysis
Amplified fragments, represented by one or more peaks according to fragment size, were analyzed by Gene Mapper software (Applied Biosystems). A second software, Peak Scanner v1 (ThermoFisher Scientific, Cleveland, Ohio) was used to demonstrate consistency of peak position and size between different software. All peak with a height of <10% that of the highest peak in the individual profile were excluded as background signal rather than evidence of a major DNA fragment; when double peaks were observed ≤1.0 bp apart, only the larger peak was analyzed [24, 25]. Fragment lengths found in >10% of a species were called ‘common’ and fragment lengths found only in one species were defined as ‘unique’. Using IBM SPSS Statistics for Windows, Version 19.0, (Armonk, NY: IBM Corp) descriptive statistics was performed. The pattern of peaks was noted in addition to the number and numeric values of the fragments, and was rounded up or down to the nearest whole number for ATCC strains for “calling” a species. For clinical isolates, values were rounded to two decimal places; early experiments identified that this approach allowed for species identification with greater sensitivity without loss of resolution between known species (data not shown). Based on published work on the resolution of SCGE, peaks within +/- 0.10 bp difference from the average bp were classed as identical, whilst those that showed a difference of > 0.10 bp were considered dissimilar [30]; hence in our study if a peak between two known species was > 0.10 bp apart, the peaks were considered to be unique for their species.
Amongst clinical isolates, for certain species, small variations in bp lengths between strains of the same species were observed for individual SCGE peaks. Based on early experiments and optimizing resolution (data not shown), those fragments which had bp positions within +/- 0.3 from the average for that species were considered as having the same fragment length. If the length was outside +/- 0.3 bp, the fragments were classed as different fragment length types within that species.
Ethics statement
This study describes a laboratory validation of a novel method for the specific identification of SG-NTM pathogens. Bacterial isolates were collected and stored as part of routine laboratory practice. No additional isolates were collected or stored for the purpose of this study. All bacterial samples were de-identified for the purpose of study experiments, data analysis and reporting. The study does not involve the collection or reporting of patient data and no patient intervention occurred with the results obtained.
Results
Amplification of mycobacterial DNA with the primers ITS1F and ITS1R yielded a PCR product in all instances confirming good quality DNA for SCGE using our DNA extraction protocol.
SCGE analysis of ATCC strains
Table 1 shows the sizes of the SCGE ITS fragment lengths (or peaks) for 14 ATCC strains in comparison with HPLC results and Fig 2a illustrates the SCGE profiles of three example strains. Fragment lengths ranged from 362 to 456 bp; 13 strains exhibited one peak and one (M. scrofulaceum), had two fragments (370 bp, 372 bp) [25]. Twelve strains generated distinct ITS-SCGE electropherograms, but peaks were rounded up to the nearest whole number (to set a strict criterion to distinguish between species). M. marinum ATCC 927 (364.18 bp) and M. kansasii ATCC 12478 (363.97 bp) displayed an indistinguishable peak at 364 bp (Table 1). In the present study, the profile of M. avium ATCC 25291 when analysed by the two different softwares (see “Methods”), and on two independent test occasions yielded a single peak at 368 bp. All ATCC stains were of their species designation by 16S rRNA gene/ITS sequencing (data not shown). HPLC identified six species including M. kansasii but not M. marinum (Table 1).
a. SCGE peaks of three representative ATCC strains. (i) M. asiaticum ATCC 25276 369 bp. (ii) M. avium ATCC 25291 368 bp. (iii) M. intracellulare ATCC 13950, 371 bp. b. Five SCGE fragment length types of M. avium, M. gordonae and M. intracellulare. All isolates were obtained from clinical specimen. A) M. avium SCGE type I 367.44 bp B) M. avium SCGE type II 367.75 bp C) M. avium SCGE type III 368.44 bp D) M. avium SCGE type IV 368.49 bp E) M. avium SCGE type V 369.2 bp F) M. intracellulare SCGE type I 368.44 bp G) M. intracellulare SCGE type II 370.96 bp H) M. intracellulare SCGE type III 371.17 bp I) M. intracellulare SCGE type IV 371.60 bp J) M. intracellulare SCGE type V 372.15 bp K) M. gordonae SCGE type I 360.86 bp L) M. gordonae SCGE type II 361.20 bp M) M. gordonae SCGE type III 361.50 bp N) M. gordonae SCGE type IV 362.0 bp O) M. gordonae SCGE type V 363.29 bp
Analysis of clinical and environmental isolates
S1 Fig. shows representative patterns of peaks of 17 species of clinical SG-NTM. Average SCGE ITS fragment sizes ranged from 361.65 to 459.11 bp. All isolates had profiles comprising single peaks. The fragment size for the M. bovis strain was 364.11 bp.
Of 101 clinical isolates, 21 (14 species) had electropherograms characterized by only one fragment type (see below) of single peak (average bp positions are shown in Table 2). These were M. chimaera (n = 3 isolates), M. marinum (n = 3), M. kansasii (n = 2), M. paragordonae (n = 2 isolates), M. asiaticum (n = 1), M. branderi (n = 1), M. haemophilum (n = 1), M. kubicae (n = 1), M. lentiflavum (n = 1), M. parascrofulaceum (n = 2), M. terrae (n = 1), M. triplex (n = 1), M. ulcerans (n = 1) and M. vulneris (n = 1). Unique species-specific peaks (separation of species by >/ = 0.10 bp difference [30]) was observed for seven species. The clearest separation (range 0.20- >50 bp from the next closest species-specific fragment) was seen for M. haemophilum (390.13 bp), M. kubicae (365.57 bp), M. lentiflavum (373.22 bp), M. triplex (374.51 bp) and M. terrae (459.11 bp) but distinct peaks were also evident for M. asiaticum and M. kansasii. HPLC identified 3/14 species (M. kansasii, M. chimaera and M. lentiflavum; Table 2).
Electropherograms however, were not readily distinguishable between the following species or species length types (see below): M. chimera and M. parascrofulaceum, M. paragordonae, and 3/12 M. gordonae strains, M. branderi and M. vulneris (Table 2). Multiplex PCR grouped isolates as M. avium, M. intracellulare and M. gordonae amongst others (Table 2), but was not able to speciate isolates not identified by either SCGE or HPLC. 16S/ITS sequencing was the only methodology able to identify M. vulneris, M. chimaera, M. branderi and M. parascrofulaceum (Table 2). Of note, the SCGE peak of M. ulcerans (364.06 bp) was indistinguishable from M. bovis (364.11 bp) using the criteria for species distinction in the study. The latter readily identifies as “M. tuberculosis complex” by HPLC.
For the three species—M. avium, M. intracellulare and M. gordonae—more than one SCGE fragment length type of single-peak electropherograms was observed with the lengths outside +/- 0.3 bp of average bp length for that species. There were five different SCGE length types (or “subtypes, assigned as length type I-V) for each of these species (Table 2); Fig 2b illustrates examples of these subtypes for M. avium and M. intracellulare. The SCGE pattern of four M. gordonae subtypes (length types I, II IV and V) were unique as were those of M. avium type I, M. avium type II and M. intracellulare type IV.
Resolution of peak Overlaps
There were six instances of SCGE length overlap between species/species groups (Table 3). SCGE peaks of all M. paragordonae strains (average peak 361.65; range 361.65–361.66) overlapped with those of three M. gordonae fragment length type III strains (361.60; 361.46–361.70; Tables 2 and 3). Species identification for M. gordonae was achieved by the “KGSA 1” multiplex PCR whilst 16S sequencing identified M. paragordonae. Other species with fragment overlap were (i) M. chimaera, M. parascrofulaceum and M. intracellulare length types III and IV; (ii) M. marinum and M. ulcerans; (ii) M. intracellulare SCGE type I, and types III and IV of M. avium (iv) M. avium type V and M. branderi; and (v) M. vulneris and M intracellulare SCGE type V. Differentiation to species level for all the above was enabled by multiplex PCR or 16S/ITS sequencing (Table 3).
Reproducibility and consistency
Reproducibility between operators and between experiments using the CGE technique has been demonstrated previously [24, 25]. In the present study we performed PCR and SCGE on two separate occasions for 10 (10%) isolates and obtained near identical results (data not shown). The mean difference in SCGE peaks obtained between the two software products, for clinical isolates was 0.05 (IQR 0.01–0.08). The mean difference in peaks observed amongst the nine duplicate and one triplicate isolates was 0.06 (IQR 0.05–0.08).
Overall performance of SCGE in comparison with HPLC
Overall, SCGE yielded unique fragments for 7/17 (41%) of clinical/environmental SG-NTM species studied (Table 2), and for an additional six species of ATCC strains (M. nonchromogenicum, M. shimodei, M. scrofulaceum, M. szulgai and M. xenopi), hence a total of 13/24 (54%) species studied. In comparison, HPLC identified 3/17 (18%) species of clinical isolates, a further five ATCC species (total 8/24; 33% species). SCGE could not identify M. chimaera and M. paraffinicum, which was achieved by HPLC. Of note, neither technique produced unique profiles for the more common groups of SG-NTM: M. avium, M. intracellulare and M. gordonae; although the SCGE peaks of 4/5 M. gordonae, 2/5 M. avium and 1/5 M. intracellulare, were unique (Table 2). Combining the results of the present study with those SCGE profiles of four species of RGM [25], the fragment lengths of various species are shown in Fig 3 to provide a more global representation of SCGE profiles of nontuberculous mycobacteria.
Discussion
Accurate identification of SG-NTM in a diagnostic laboratory remains challenging due to absence of a robust, universal method for their speciation and in particular, to distinguish between closely related species [8]. Here, by studying a large number of SG-NTM isolates representing 24 species, we demonstrated that an ITS-targeted SCGE assay was able to rapidly and reliably identified 13 species and has potential as an alternative to HPLC-based identification of clinically relevant mycobacteria. That SCGE also has potential to distinguish between intra-species genetic types as demonstrated by different fragment length types within a number of SG-NTM species.
We previously observed that ITS SCGE patterns of ATCC strains of SG-NTM were distinct from those of other mycobacteria [25]. However, the utility of SCGE in the characterisation of clinical SG-NTM strains has not been explored. By re-analysing the 14 species of ATCC strains (identify confirmed by DNA sequencing), we confirmed the reproducibility of SCGE peaks over time. The single discrepant result with M. avium ATCC 25291 (a reproducible single peak in the present study vs. two peaks in the previous [25]) is likely to be caused by unsuspected contamination of the strain. We used strict criteria to reliably distinguish between species where fragment lengths were rounded to the nearest whole number, well above the limit of resolution of this technique (<0.06 bp) [30]. The two peaks for M. scrofulaceum ATCC 19981 are notable; further analysis of additional M. scrofulaceum strains would be worthwhile.
Of note, M. marinum ATCC 927 and M. kansasii ATCC 12478 produced a single 364 bp peak. However, when the peaks were rounded to two decimal places, they differed by 0.22 bp (364.18 and 363.96 bp, respectively; data not shown), being distinguishable using the criteria for clinical strains. M. kansasii is genetically closely related to M. marinum [31], and also to M. tuberculosis [25] and M. bovis (364.11 bp). Indeed, data indicate M. kansasii to represent the environmental ancestor of M. tuberculosis [31]. The inability of ITS-SCGE profiles on their own to readily differentiate between M. kansaii and M. marinum is broadly consistent with reports of ITS sequencing per se which is unable to resolve all NTM species [8, 32]. Hence any definitive identification method for SG-NTM should be evaluated using more than one genetic target to increase precision of identifications; one such target is the rpoB gene, which, by sequencing, may have superior discriminatory power over the ITS locus [19].
The complexity of SG-NTM identification is further borne out by the SCGE profiles for clinical/environmental isolates which illustrate the close evolutionary relationships of many SG-NTM. However, even using a less stringent rule to define uniqueness of peaks (bp rounded to two decimal places), certain closely related species had either very similar, or indistinguishable, fragment lengths, for example M. kansasii, M. marinum and M. ulcerans, where SCGE lengths were grouped at 364 bp. These species are phylogenetically closely related [4]. It has been suggested that M. ulcerans arose from M. marinum by horizontal gene transfer for a virulence plasmid [33]. In addition, M. bovis, included as a comparator species, was separated from M. ulcerans by only 0.05 bp. It is essential to distinguish SG-NTM from M. tuberculosis complex. At present, SCGE cannot differentiate M. tuberculosis/M. bovis from the above SG-NTM. Yet M. tuberculosis can be identified by HPLC in combination with the TB Ag MPT test (SD Bioline); this is a limitation of SCGE if it is to be offered as an alternative to HPLC-based identification. M. bovis (TB Ag MPT64 test negative) will require DNA sequencing for identification.
Fragment lengths of the M. gordonae/M. paragordonae group were generally indistinguishable; these two species share approximately 99% 16S rRNA gene sequence similarity [34]. Whilst they can be differentiated by growth characteristics (M. paragordonae grows at 25–30°C but not at 37°C, whereas M. gordonae grows at both temperatures), in our study and elsewhere, 16S rRNAgene /ITS sequencing were required for definitive identification [34]. Notably, SCGE profiles of M. paragordonae overlapped with those of M. gordonae length type III (see below).
Although some SCGE peaks of the more common species of MAC, M. avium and M. intracellulare, could be distinguished from one another (Table 2), profiles were not unique for either, nor was HPLC able to differentiate between M. avium and M. intracellulare. Of the other MAC species studied, M. chimaera (formerly M. intracellulare sequevar Mac-A [35]) and M. vulneris, HPLC correctly identified M. chimaera but its SCGE peak was not unique, overlapping with that of M. parascrofulaceum (Tables 2 and 3). M. parascrofulaceum belongs to the M. simiae complex [4] but for peak overlap with M. chimaera highlighted that the not closely related species could share a similar ITS fragment length. Neither HPLC, nor SCGE was able to identify M. vulneris, which required DNA sequencing [36].
Nonetheless, for clinical isolates it is noteworthy that SCGE clearly separated mycobacteria belonging to seven species. These species encompassed uncommon pathogens eg. M. kubicae, M. scrofulaceum, and M. terrae, as well as the more common M. haemophilum, M. lentiflavum, M. asiaticum and M. triplex [4]. HPLC was unable to identify M. kubicae, M. triplex, M. haemophilum and M. terrae for which SCGE had better discriminatory power. The ability to rapidly identify M. haemophilum is particularly relevant it is a significant pathogen in immunocompromised hosts [37]. That five species of M. simiae complex (M. simiae, M. lentiflavum, M. kubicae, M. shimodei and M. triplex) all had species-specific peaks further illustrate that SCGE can differentiate between closely related species.
A key finding of our study was that SCGE identified five different electropherogram patterns each for M. avium, M. gordonae and M. intracellulare. Within MAC, based on 16S rRNA gene analyses, there are four subspecies of M. avium and nine distinct species including M. chimaera, M. intracellulare and M. vulneris [38, 39]. It is unknown whether the five SCGE types of either M. avium or M. intracellulare identified here represent existing species or novel species. Subtypes of MAC are well studied by hsp65 sequencing; comparison of SCGE with both hsp65 and 16S rRNA gene sequencing, or of other targets such as rpoB would be of interest [38]. Genetic heterogeneity within M. gordonae species has also been described, particularly within the 5’ end of the 16S rRNA gene [40]. Based on PCR-REA of the hsp65 gene, five subspecies of M. gordonae have been identified whilst PCR-REA of the rpoB gene, classified M. gordonae into four major clusters [41]. Again comparison of SCGE with PCR-REA gene analyses may help categorise SCGE types within M. gordonae against known subtypes. Whole genome sequencing has become more accessible and affordable to reference laboratories and may offer the ultimate level of resolution for identification and characterisation of Mycobacteria [9, 42].
The benefits of SCGE are that it is simple, reproducible, has minimum reagent requirements, requires only 6–8 hours to perform, and is not reliant on labour intensive agar gel techniques. Other advantage of SCGE is that results can be visualised across centers using accessible and standardised database. In comparison, HPLC is time consuming (total time 10–12 hours), requires considerable expertise and is best suited to a high throughput reference laboratory [27]. Although the running cost of HPLC is lower (AU$8.00 per isolate vs. AU$15.00 for SCGE), set up costs of HPLC are significant. In our laboratory, up front use of SCGE will reduce the need for species-specific PCR assays on many isolates, further reducing TAT and costs. Further expansion of the SCGE database using well characterised strains will gradually resolve the limitation of fragment overlap between closely related species and the ability to identify less common species. Importantly, the possibility that isolates from disparate geographic regions may occasionally exhibit differing electropherograms cannot be ruled out.
In conclusion, ITS-SCGE of the Mycobacteria can accurately identify clinically relevant SG-NTM and can distinguish between closely related species. It can potentially identify SCGE length types within species, particularly MAC, and lends itself as an appealing alternative to HPLC for the initial identification of SG-NTM. Collaborative efforts to develop a comprehensive SCGE-ITS database to complement existing identification approaches for SG-NTM are warranted.
Supporting Information
S1 Fig. Representative SCGE patterns of 17 species of clinical SG-NTM.
A) M. paragordonae 361.65 bp B) M. gordonae SCGE type IV 362.03 bp C) M. kansasii 363.85 bp D) M. ulcerans 364.06 bp E) M. marinum 364.22 bp F) M. kubicae 365.57 bp G) M. avium SCGE type III 368.48 bp H) M. branderi 369.48 bp I) M. asiaticum 369.78 bp J) M. chimaera 370.60 bp K) M. intracellulare SCGE type V 371.98 bp L) M. parascrofulaceum 370.81 bp M) M. vulneris 371.88 bp N) M. lentiflavum 373.32 bp O) M. triplex 374.51 bp P) M. haemophilum 390.13 bp Q) M. terrae 459.11 bp
https://doi.org/10.1371/journal.pone.0164138.s001
(TIF)
Acknowledgments
The authors thank Andrea Bustmante and Sue Sleiman in the NSW Mycobacterium Reference Laboratory at Westmead Hospital for their technical assistance
Author Contributions
- Conceptualization: SS FK VS SCAC.
- Data curation: SS FK VS MX TJG SCAC.
- Formal analysis: SS FK VS SCAC.
- Funding acquisition: VS SCAC.
- Investigation: SS FK VS MX TJG SCAC.
- Methodology: SS FK VS PJ SCAC.
- Project administration: VS SCAC.
- Resources: VS SCAC.
- Software: SS FK MX.
- Supervision: FK VS SCAC.
- Validation: SS FK MX TJG.
- Visualization: SS FK VS SCAC.
- Writing – original draft: SS VS SCAC.
- Writing – review & editing: SS FK PJ VS SCAC MX.
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