• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of jcmPermissionsJournals.ASM.orgJournalJCM ArticleJournal InfoAuthorsReviewers
J Clin Microbiol. Mar 2001; 39(3): 964–970.
PMCID: PMC87858

Application of the Sherlock Mycobacteria Identification System Using High-Performance Liquid Chromatography in a Clinical Laboratory

Abstract

There is a growing need for a more accurate, rapid, and cost-effective alternative to conventional tests for identification of clinical isolates of Mycobacterium species. Therefore, the ability of the Sherlock Mycobacteria Identification System (SMIS; MIDI, Inc.) using computerized software and a Hewlett-Packard series 1100 high-performance liquid chromatograph to identify mycobacteria was compared to identification using phenotypic characteristics, biochemical tests, probes (Gen-Probe, Inc.), gas-liquid chromatography, and, when necessary, PCR-restriction enzyme analysis of the 65-kDa heat shock protein gene and 16S rRNA gene sequencing. Culture, harvesting, saponification, extraction, derivatization, and chromatography were performed following MIDI's instructions. Of 370 isolates and stock cultures tested, 327 (88%) were given species names by the SMIS. SMIS software correctly identified 279 of the isolates (75% of the total number of isolates and 85% of the named isolates). The overall predictive value of accuracy (correct calls divided by total calls of a species) for SMIS species identification was 85%, ranging from only 27% (3 of 11) for M. asiaticum to 100% for species or groups including M. malmoense (8 of 8), M. nonchromogenicum (11 of 11), and the M. chelonae-abscessus complex (21 of 21). By determining relative peak height ratios (RPHRs) and relative retention times (RRTs) of selected mycolic acid peaks, as well as phenotypic properties, all 48 SMIS-misidentified isolates and 39 (91%) of the 43 unidentified isolates could be correctly identified. Material and labor costs per isolate were $10.94 for SMIS, $26.58 for probes, and $42.31 for biochemical identification. The SMIS, combined with knowledge of RPHRs, RRTs, and phenotypic characteristics, offers a rapid, reasonably accurate, cost-effective alternative to more traditional methods of mycobacterial species identification.

Mycobacteria from clinical specimens should be rapidly detected and identified to the species level whenever possible to initiate the isolation and adequate treatment of patients with pulmonary tuberculosis, prevent the spread of disease; help detect typically antimycobacterial drug-resistant species, thereby guiding physicians in the timely selection of appropriate therapy; reduce the risks of irreversible neurological damage from undiagnosed and untreated tubercular meningitis; and help determine the likelihood of infection based on the species identification (12, 21, 26, 30, 32). There are numerous recognized species of mycobacterial pathogens and additional species that have not yet been associated with human disease (7). Traditionally, a battery of conventional tests including determination of colony morphology and pigment production, growth rate, and biochemical reactions has been used to identify mycobacteria to the species level (7, 11, 14, 15, 21, 30). Disadvantages of this approach include the inherent delays in providing clinically relevant information, which may lead to increased health care costs, the extensive training and experience required to reliably perform many of the assays, interassay variability of results, strain variation within species causing both unexpected biochemical test results and atypical phenotypic characteristics, the possibilities of either misidentification or the inability to identify pathogens, and the limitation of this approach to the identification of only the more common mycobacterial species whose properties and characteristics have been fully documented (7, 11, 12, 17, 20, 21, 30, 32, 33). Some mycobacterial isolates cannot be accurately identified using standard biochemical tests alone (17, 20).

Rapid, commercially available tests have been developed for identification of clinical isolates of mycobacteria. These assays include capillary gas chromatography (GLC) of low-molecular-weight fatty acids; the NAP test as well as nucleic acid amplification for identification of the Mycobacterium tuberculosis complex; and nucleic acid probes for identification of the M. tuberculosis complex, Mycobacterium avium, Mycobacterium intracellulare, the M. avium-intracellulare complex (MAC), Mycobacterium kansasii, and Mycobacterium gordonae (7, 15, 18, 21, 26). The accuracy of the Microbial Identification System (MIS; MIDI, Inc., Newark, Del.) GLC method for identification of many, but not all, of the mycobacterial species is insufficient for routine clinical use of that system (28). The limited number of mycobacterial species or groups that can be identified with the other assays, considering their associated costs and requirements for expensive, specialized equipment, makes it increasingly desirable to look for a single technology with which a wide variety of both pathogenic and saprophytic mycobacterial species could be rapidly, reliably, and relatively inexpensively identified. Undoubtedly, molecular techniques such as sequencing of the 16S rRNA gene (16, 17, 30) and PCR-restriction enzyme analysis of the 65-kDa heat shock protein (hsp)-encoding gene (31) currently represent the most promising methods for mycobacterial identification. These techniques, however, require appropriate facilities for performing molecular biology assays and are both technically complex and expensive.

High-performance liquid chromatography (HPLC) has the potential for providing rapid, accurate, and cost-effective identification of clinical isolates of mycobacteria. This technology has facilitated early and accurate identification of rare Mycobacterium species, including M. haemophilum, M. malmoense, M. shimoidei, and M. fallax (26, 33). In 1984, Minnikin et al. (23) reported that both the number and types of cell wall mycolic acids associated with each mycobacterial species are constant for those species. Butler et al. later found that the number, relative retention times (RRTs), and relative peak height ratios (RPHRs) of mycolic acids in HPLC chromatograms were useful for the reliable identification of Mycobacterium species (3). Mycolic acids are high-molecular-weight, alpha-branched, beta-hydroxy fatty acids found in species within the genus Mycobacterium as well as in other genera, including Corynebacterium, Nocardia, and Rhodococcus (2). The number of carbon atoms found in mycobacterial mycolic acids ranges from C60 to C90 (2). The chromatographic separation of mycolic acids is based on chain length, degree of unsaturation, and functional groups in the fatty acids (2). The autoclaving-saponification steps in the HPLC procedure both frees mycolic acids and kills the mycobacteria, assuring laboratory safety (5).

Recently, the Sherlock Mycobacterial Identification System (SMIS; MIDI, Inc.) has been developed for the rapid, computer-assisted identification of mycobacterial species based on the separation and quantification of mycolic acids using HPLC technology (K. Jost, Jr., S. Chiu, K. Posey, T. Owens, D. Dunbar, and B. Elliott, Abstr. 96th Gen. Meet. Am. Soc. Microbiol. 1996, abstr. U-30, p. 106, 1996). The current SMIS database contains profiles for 26 mycobacterial species or groups as well as for Nocardia spp., Nocardia/Rhodococcus/Gordona, and Tsukamurella spp. The current study was undertaken to determine the accuracy and cost-effectiveness of the SMIS for identification of mycobacterial species.

MATERIALS AND METHODS

Fresh clinical isolates from York Hospital patients and stock cultures of clinical isolates (from the Pennsylvania Department of Health, Bureau of Laboratories, the American Type Culture Collection; the College of American Pathologists [proficiency test samples]; the Clinical Microbiology Laboratory of the Memorial Sloan-Kettering Cancer Center; and the Swiss National Center for Mycobacteria, University of Zurich) were used in the present study. Multiple isolates of the same species from the same patients were excluded from the study. Of 126 fresh clinical isolates, 105 (83%), 7 (6%), 5 (4%) and 9 (7%) were recovered from lower respiratory tract, skin, blood, and other specimens, respectively.

Reference method identification of mycobacterial isolates.

Nucleic acid probes (Accu-Probe; Gen-Probe, Inc., San Diego, Calif.) were used to identify all isolates of the M. tuberculosis complex and MAC (7, 15, 18, 21, 26). While niacin and nitrate tests (21) were also performed on many of the isolates of the M. tuberculosis complex, most isolates within this complex were not further identified during the study. The nucleic acid probe method was also used by the Pennsylvania Bureau of Laboratories to identify isolates of M. kansasii. MIS-GLC was used as the primary means of identifying isolates of only one species: M. gordonae (28). The identity of some isolates (including Mycobacterium lentiflavum, M. malmoense, Mycobacterium simiae, and members of the Mycobacterium terrae complex) was established or confirmed using 16S rRNA gene sequencing (16, 17, 30) and/or PCR-restriction enzyme analysis of the 65-kDa hsp-encoding gene (31). Biochemical tests and phenotypic characteristics including colony morphology, pigment, and growth rate (7, 11, 14, 15, 21, 30) were used to identify isolates of all other species.

SMIS identification of isolates.

At the York Hospital laboratory, most fresh clinical isolates and stock cultures were subcultured to Middlebrook 7H10 agar and incubated at 35°C (5 to 10% CO2) for 2 or more days until the growth of small, isolated colonies was established. Isolates of M. haemophilum were subcultured to chocolate agar and incubated at 30°C in 5 to 10% CO2 (27). Using a sterile loop, a small, barely detectable mass of mycobacterial cells was saponified, extracted, derivatized, clarified, and chromatographed according to instructions provided by MIDI, Inc. (22), except that the duration of extraction in the autoclave was shortened from 60 to 15 min to both speed up the process and avoid tying up the autoclave. Preliminary studies in the York Hospital laboratory indicated that a 15-min autoclave extraction interval gave results identical to those for the 60-min extraction interval. Mycolic acid extracts were chromatographically analyzed using the SMIS, which consists of a Hewlett-Packard (Avondale, Pa.) series 1100 HPLC system which included a degasser, pump, autosampler, and computer. The HPLC column was an XL series, 3-μm, 4.6 mm by 7.0 cm (Beckman, Fullerton, Calif.), particle column operated at 35 ± 1°C. The mobile phase was methanol and methylene chloride (HPLC grade; Fisher Scientific, Inc., Fair Lawn, N.J.), ranging from concentrations of 95 to 45% for methanol and 5 to 55% for methylene chloride during the 16.5-min runs. The flow rate of the mobile phase increased from 2.75 to 3.00 ml/min during the runs. Mycolic acids eluted from the column were detected using an FP-920 fluorescence detector (Jasco Inc., Easton, Md.) with an emission bandwidth of 40 nm, an excitation wavelength of 351 nm, an emission wavelength of 430 nm, and a gain of 1,000.

The SMIS was controlled using Sherlock software (version 2.95). An internal calibration standard (an extract of M. intracellulare, ATCC no. 13950) spiked with low- and high-molecular-weight internal standards (Ribi Immunochem, Hamilton, Mont.) was extracted and included in each day's HPLC run both before the first test sample and then again after every five test samples. Deviations from either the expected retention times or the quantity of each mycolic acid peak in the calibrator would result in a failure of the system to calibrate, followed by a SMIS system shutdown. As an external control, an extract of M. avium, ATCC no. 15769, was also included in each day's SMIS run. For each isolate, the SMIS computer printout either listed one or more possible species choices with a similarity index (SI) ranging from 0 to 1.000 for each choice or reported “no match,” indicating that the SMIS software was unable to identify the isolate. According to MIDI, Inc., the higher the SI value, the greater is the probability of an accurate species identification (22). RRTs and/or RPHRs of selected mycolic acids in the chromatograms of each isolate were calculated as previously described (3) to facilitate final species identification whenever the SMIS-computerized interpretation of the chromatograms resulted in either a misidentification or a failure to identify an isolate.

Data analysis.

The identification of an isolate using biochemical, GLC, or molecular methods (the reference methods) was considered the correct identification. The SMIS result was considered correct if the correct species name of an isolate was listed on the software report as the first choice, regardless of the magnitude of the SI value accompanying that species' name. If the SMIS and the reference method identifications differed or the SMIS was unable to identify an isolate, a fresh extract of a new subculture of the isolate on Middlebrook 7H10 agar (or chocolate agar for M. haemophilum) was analyzed in the SMIS a second time. In addition, species identification was confirmed using appropriate reference methods. If an isolate was misidentified the first time that it was analyzed in the SMIS, it was counted as a misidentification, regardless of whether it was correctly or incorrectly identified by the system when it was reanalyzed in the SMIS. If it was unidentified by the SMIS when first tested and then correctly or incorrectly identified when the chromatography was repeated, it was counted as a correct or an incorrect identification, respectively. In addition to calculating the percentage of isolates of each mycobacterial species that were correctly identified by the SMIS, the predictive values for the accuracy of SMIS software assignments of unknown mycobacterial isolates to each of the mycobacterial species or groups was determined by dividing the number of correct SMIS calls for a given species by the total number of times that isolates were correctly or incorrectly assigned by the SMIS to that species. A cumulative hard-copy library of HPLC chromatograms was begun in alphabetical order by Mycobacterium species' names. Each time another isolate of a species was either correctly identified, incorrectly identified, or unidentified by the SMIS, the chromatographic profile as well as selected RRTs and RPHRs of mycolic acid peaks were compared to those of previously identified isolates of the same species.

Material and labor costs associated with the SMIS, nucleic acid probes, and biochemical identification of the isolates were determined.

RESULTS

A total of 370 isolates (126 fresh and 244 stock cultures) were studied from 8 April 1998 until 14 April 2000. Of these, 327 (88%) and 43 (12%) were named and unidentified, respectively, by the SMIS software. The SMIS correctly named 279 isolates (75% of the total number of mycobacterial isolates and 85% of the isolates given a name by the system; Table Table1).1). Forty-eight isolates (13% of the total isolates studied and 15% of the isolates named by the system) were misidentified by the SMIS software, although none was assigned to a genus other than Mycobacterium (Table (Table2).2). When eight or more isolates of a species were studied, correct identification by the SMIS to the species level ranged from only 4% (for isolates of M. haemophilum) to 100% (for Mycobacterium marinum, M. simiae, and Mycobacterium xenopi). Only four isolates (one each of M. lentiflavum, Mycobacterium phlei, Mycobacterium smegmatis, and Mycobacterium vaccae) of mycobacterial species for which the SMIS software library had no data were tested. All four isolates were correctly called “no match” (unidentified) by the system.

TABLE 1
Comparison of SMIS-HPLC with phenotypic, biochemical, and molecular tests for identification of Mycobacterium species
TABLE 2
Misidentification of Mycobacterium species by the SMIS-HPLC system

The predictive value of the SMIS species identifications (the probability that SMIS assignments of unknown isolates to each of the mycobacterial species or groups was correct) was 85% overall but ranged from only 27% (of 11 SMIS identifications of isolates as Mycobacterium asiaticum, only 3 were correct) to 100% (for Mycobacterium flavescens, Mycobacterium celatum, M. haemophilum, M. malmoense, Mycobacterium nonchromogenicum, M. terrae, and Mycobacterium chelonae-abscessus complex [Table 1]). The SMIS does not differentiate between the species in the M. chelonae-abscessus complex. While the SMIS correctly named 100% (16 of 16) of the isolates of Mycobacterium marinum, the predictive value of a SMIS identification of an isolate as M. marinum was only 50% because, in addition to these 16 correct calls, 16 isolates of M. haemophilum were also called M. marinum by the system (Table (Table2).2). Conversely, while only 84% (21 of 25) of the isolates of M. chelonae-abscessus were correctly named by the SMIS, the predictive value of an SMIS identification of an isolate as belonging to that group was 100% because no isolate of any other species was misidentified by the system as M. chelonae-abscessus.

Of the 370 mycobacterial isolates studied, 115 (31%) had to be analyzed a second time because the initial SMIS results were either incorrect (38 isolates) or the isolates were unidentified (77 isolates). For these 115 retested isolates, the SMIS identification for 33 (29%) changed from incorrect or unidentified to correct, results from another 28 (24%) remained incorrect, results for 43 (37%) remained unidentified, results for only 1 (1%) changed from incorrect to unidentified, and results of 10 (9%) changed from unidentified to incorrect when analyzed a second time with the SMIS.

In many cases, a mycobacterial isolate which had been misidentified by the SMIS could easily be correctly identified by calculating the RPHRs and comparing those figures with known values for species with similar chromatographic profiles of mycolic acids (Table (Table3).3). For example, five isolates of M. kansasii were misidentified by the SMIS as M. asiaticum but each was correctly identified as M. kansasii by calculating the RPHR of peaks 3/6 and 3/8 in the chromatograms for each of these isolates and referring to the RPHR cutoffs for those two species in the table. Similarly, three isolates of M. gordonae were misidentified by the system as M. asiaticum and four isolates of Mycobacterium gastri were misidentified as M. tuberculosis complex but each was correctly identified by calculating its appropriate RPHRs (peaks 6/7 and 7/8 for M. asiaticum versus M. gordonae; peaks 3/8 for M. gastri versus M. tuberculosis complex) and comparing them to the cutoffs for these ratios in the table. Eight isolates of Mycobacterium scrofulaceum misidentified as MAC and two isolates of MAC misidentified as M. scrofulaceum were identified correctly by comparing the RPHR for peaks 6/7. When an isolate was unidentified by the SMIS or the identification was questioned, it could also often be correctly identified or the identification verified by numbering each peak, calculating the RRT of selected peaks, and comparing the chromatogram with those in the library of previously identified isolates of the suspected species. For example, M. celatum and M. xenopi have a similar HPLC profile with two clusters of mycolic acids. However, the RRTs of the first mycolic acid peak in the second cluster was 8.6 min for M. celatum and 9.5 to 9.6 min for M. xenopi.

TABLE 3
Relative peak height ratio cutoffs for selected Mycobacterium species

By determining RPHRs and RRTs; comparing the chromatograms of isolates in question with those in our library of mycobacterial species; and determining colony morphology, pigment, and growth rate; all 48 SMIS-misidentified isolates and 39 (91%) of the 43 SMIS-unidentified isolates could be correctly identified without additional tests. For each of the four SMIS-unidentified isolates (one each of M. lentiflavum, M. phlei, Mycobacterium smegmatis, and M. vaccae) that could not be identified using these methods, there were no previously identified isolates of the same species for comparison of the chromatographs and therefore, a full conventional identification would be required.

Material and labor costs associated with single SMIS, nucleic acid probe, and biochemical tests are shown in Table Table4.4. The actual cost-effectiveness of each type of assay depends on the number of isolates plus controls included in each test run as well as the number of times that each of these assays has to be repeated. The initial cost of SMIS hardware and software was just over $50,000 and an annual service contract is approximately $4,000.

TABLE 4
Cost analysis of mycobacterial identification methods

DISCUSSION

HPLC technology may largely replace biochemical methods for the routine identification of Mycobacterium species because of the ability of HPLC systems to facilitate the rapid, accurate identification of a large variety of species and to do so at a relatively low cost (11). To permit chromatographic identification of isolates as soon as their agar colonies were detectable, a fluorescence detector was used in the present study instead of a UV detector because the former is 200-fold more sensitive (13).

In the present study, 279 (75%) of the total number of isolates tested were correctly identified by SMIS software. In addition, all but 4 of the remaining 91 isolates that were either incorrectly identified or unidentified by the software could be correctly identified both quickly and easily, simply by using a knowledge of the isolate's colony morphology, pigment, and growth rate; by calculating RPHRs and RRTs of selected mycolic acid peaks; and/or by comparing the chromatograms of these isolates with those in our growing library of chromatograms for each mycobacterial species. For example, the predictive value of a SMIS identification of an isolate as M. marinum was only 50% because 16 isolates of M. haemophilum were misidentified by the system as M. marinum. Since isolates of no other species than M. haemophilum were misidentified by the SMIS as M. marinum (Table (Table2)2) and the former species is both nonpigmented and fails to grow or grows poorly on Middlebrook 7H10 agar (7, 27), a SMIS identification of a yellow-pigmented, photochromogenic isolate which grows on Middlebrook 7H10 agar as M. marinum can be accepted with a high degree of confidence. In a very experienced mycobacteriology laboratory which is very familiar with HPLC utilization, the SMIS software used to identify unknown isolates may not be as helpful or as necessary as it has been for less experienced, general clinical microbiology laboratories which isolate a large variety of mycobacterial species.

Approximately 7% of clinical isolates of mycobacteria at the Mayo Clinic have been difficult to identify using conventional tests (30). HPLC was found in one study to be more sensitive than probes for the identification of MAC, identifying 98.7 and 48.1% of probe-positive and probe-negative MAC isolates, respectively (33). Using HPLC technology, many mycobacterial isolates that are less frequently encountered, that may be more difficult to identify, or that may be misidentified using conventional morphological, biochemical, or probe tests can now be accurately and quickly identified to the species level. For example, M. celatum is a slow-growing, smooth, small, cream-colored, nonpigmented species that may become yellow with age (1,34,37). This species is biochemically similar to MAC, and the clinical picture of infection may resemble MAC infection (1). M. celatum may be misidentified as MAC (6, 30, 34) or as M. xenopi (34, 37) if biochemical tests are used and cannot be reliably identified by biochemical tests alone (6). In addition, the species has also been reported to give false-positive results (4, 29) or high-negative results (34) with an acridinium ester-labeled DNA probe for the M. tuberculosis complex (Gen-Probe). False-positive probe results for the M. tuberculosis complex may result not only in an incorrect diagnosis but also in inappropriate therapy for tuberculosis (4, 6) because isolates of M. celatum may be resistant to frequently used drugs for that disease, including isoniazid, rifampin, and ethambutol (1, 6, 34, 37). At the present time, only HPLC mycolic acid analysis or genomic sequencing can confirm the identification of an isolate as M. celatum (29, 34, 37). In the present study, all isolates of M. xenopi, which has a profile of mycolic acids similar to that of M. celatum (34, 37), were correctly identified by SMIS software. The one isolate of M. celatum which was misidentified as M. xenopi by the SMIS could be correctly identified by comparing its RRT (9.0 min) of the first mycolic acid peak from the second cluster of acids in its chromatogram with the RRTs for that same acid from previously identified isolates of M. celatum (9.0 min) and M. xenopi (9.5 to 9.6 min).

M. simiae is a photochromogenic, slow-growing, often niacin-positive species that may be misidentified as either MAC, M. scrofulaceum, or M. flavescens when biochemical tests are used (8, 25, 30, 33). About half of the isolates of M. simiae have been reported to be nonphotochromogenic (25, 33). In another study, about 10% of the mycobacterial strains identified as MAC using conventional culture and biochemical results turned out to be either M. simiae or M. malmoense based on their mycolic acid composition (19). M. simiae may be more common than currently thought. It has been reported to be the third most common mycobacterial species recovered over a 2-year interval in southern Arizona but was infrequently associated with disease (25). Until HPLC technology became available, M. simiae was infrequently identified (33). Isolates of this species have a triple cluster pattern of mycolic acid peaks by HPLC (25). This pattern is similar to the pattern of peaks from M. malmoense (8) but significantly different from the pattern of MAC (33). In the present study, the SMIS software correctly identified all 18 isolates of M. simiae and 8 of 9 (89%) of the isolates of M. malmoense. The one isolate of the latter species that was misidentified by SMIS software as M. simiae could be correctly identified by comparing the RPHR of peaks 2/3 (Table (Table3).3). If necessary, M. malmoense can also be separated from M. simiae by the niacin activity of the latter and the hydrolysis of Tween 80 by the former (8, 19).

As noted above, isolates of both M. celatum and M. simiae may be biochemically and phenotypically misidentified as MAC. At York Hospital prior to the use of SMIS-HPLC technology, approximately 4% of isolates reported as MAC (based on their phenotypic and biochemical properties) were DNA-probe negative. In the 2 years following implementation of the SMIS, not a single probe-negative isolate of MAC was reported. However, nine patients' isolates of M. simiae, the first ever identified at the hospital, were detected, confirmed, and reported.

HPLC has largely replaced the use of DNA probes for the identification of MAC at both York Hospital and at least one other site (33). In the current study, 46 (92%) of 50 MAC isolates were correctly named by the SMIS (2 others were called M. scrofulaceum and 2 were unidentified by the system) and 46 (85%) of the 54 SMIS calls of isolates as MAC were correct. Eight other isolates were misidentified by the system as MAC, but all of these were M. scrofulaceum isolates that could be separated and correctly identified by comparing their 6/7 RPHRs to those of MAC and M. scrofulaceum. When necessary, MAC can also be distinguished from M. scrofulaceum with the use of only two additional properties or tests: colonial pigment and the semiquantitative catalase assay (21). With few exceptions, strains of MAC have buff to pale yellow colonies and produce <45 mm of foam in the semiquantitative catalase assay while strains of M. scrofulaceum have bright yellow to orange colonies and produce >45 mm of foam in the semiquantitative catalase test (36). This switch from probes to HPLC for the identification of MAC provides a chromatographic “fingerprint” for the records of the patients' isolates and could save the hospital over $15.00 per isolate. In addition, the SMIS-HPLC technology has the potential to allow the rapid and accurate separation of BCG strains of Mycobacterium bovis from other strains of that species as well as from other species in the M. tuberculosis complex (9, 10, 33), although that separation was beyond the scope of the present study. Because M. bovis BCG is widely used for the treatment of superficial bladder cancer, dissemination and subsequent detection from the respiratory tract (26) as well as nosocomial infections (35) are possible. Therefore, these isolates should be specifically identified as M. bovis BCG rather than only as the M. tuberculosis complex (26). In a previous report (9), mycolic acid peaks of M. bovis BCG eluted from the column about 0.5 min earlier than those of other members of the M. tuberculosis complex. HPLC technology is used at the Centers for Disease Control and Prevention to separate M. bovis BCG from the other members of the M. tuberculosis complex without the use of additional tests (9).

The current SMIS software does not allow separation of M. chelonae from M. abscessus. Differentiation of these two species may be of clinical and epidemiological importance and can be accomplished with growth on citrate as the sole carbon source, drug susceptibility patterns, or PCR-restriction enzyme analysis of the hsp-encoding gene (21), as well as with HPLC (33). Calculation of a single RPHR (peaks 3/4) in the present study enabled the separation of these two species when the SMIS software identified an isolate as belonging to the M. abscessus-chelonae complex (Table (Table33).

In the present study, the material and labor costs per SMIS test of each mycobacterial isolate, internal standard, and control were substantially less than those for testing isolates and controls using either traditional biochemical tests or nucleic acid probes. Calibration failures necessitating repeated chromatographic runs rarely occured. However, if almost a third of the isolates have to be reextracted and reanalyzed in the SMIS, as occurred in the present study, SMIS cost advantages would be reduced. In addition, determination of the cost-effectiveness of the SMIS must take into account the purchase price of the system, the cost of an annual service contract, and the number of mycobacterial isolates recovered and chromatographically analyzed each month. The SMIS has been temporarily withdrawn from the market. When it returns (expected during 2001), the cost of software upgrades, which so far have been free, will not yet be determined (M. Sasser, MIDI Inc., personal communication). Previous studies of HPLC systems other than the SMIS have reported either equivalent or lower costs associated with HPLC technology (10, 13, 24, 33). In addition, the use of the sensitive fluorescence detector with the HPLC permitted very rapid identification of isolates, enabling reports to be made to physicians more quickly than had previously been possible.

Further studies with the SMIS should include strains of additional species or subspecies, such as M. bovis BCG, Mycobacterium genavense, M. lentiflavum, and Mycobacterium triplex, and should investigate the ability of the system to identify mycobacteria directly from broth cultures, as has been studied for another HPLC system (13). In addition, it could be helpful for a future study to confirm the SMIS identification of each mycobacterial isolate in the study using gene sequencing or PCR-restriction enzyme analysis of the 65-kDa hsp-encoding gene, since these latter assays are rapidly becoming the gold standards for mycobacterial species identification. It would also be helpful, especially to laboratories inexperienced with HPLC technology, if future generations of SMIS software were improved so that an even greater number of mycobacterial species could be accurately identified by the system. At the present time, however, the SMIS, together with determination of selected RPHRs and RRTs from the chromatograms; with a library of mycolic acid chromatographic profiles for each of the species; and with a basic knowledge of colony morphology, pigment, and growth rate; allows for the elimination of almost all biochemical testing and nucleic acid probes, a reduction in associated material and labor costs, and more rapid reporting times for most clinical mycobacterial isolates.

REFERENCES

1. Bonomo R A, Briggs J M, Gross W, Hassan M, Graham R C, Butler W R, Salata R A. Mycobacterium celatum in a patient with AIDS. Clin Infect Dis. 1998;26:243–245. [PubMed]
2. Butler W R, Ahearn D G, Kilburn J O. High-performance liquid chromatography of mycolic acids as a tool in the identification of Corynebacterium, Nocardia, Rhodococcus, and Mycobacterium species. J Clin Microbiol. 1986;23:182–185. [PMC free article] [PubMed]
3. Butler W R, Jost K C, Jr, Kilburn J O. Identification of mycobacteria by high-performance liquid chromatography. J Clin Microbiol. 1991;29:2468–2472. [PMC free article] [PubMed]
4. Butler W R, O'Connor S P, Yakrus M A, Gross W M. Cross-reactivity of genetic probe for detection of Mycobacterium tuberculosis with newly described species Mycobacterium celatum. J Clin Microbiol. 1994;32:536–538. [PMC free article] [PubMed]
5. Butler W R, Thibert L, Kilburn J O. Identification of Mycobacterium avium complex strains and some similar species by high-performance liquid chromatography. J Clin Microbiol. 1992;30:2698–2704. [PMC free article] [PubMed]
6. Bux-Gewehr I, Hagen H P, Rüsch-Gerdes S, Feurle G E. Fatal pulmonary infection with Mycobacterium celatum in an apparently immunocompetent patient. J Clin Microbiol. 1998;36:587–588. [PMC free article] [PubMed]
7. Cernoch P L, Enns R K, Saubolle M A, Wallace R J., Jr . Cumitech 16A. 1994. Laboratory diagnosis of the mycobacterioses. Coordinating ed., A. S. Weissfield. American Society for Microbiology, Washington, D.C.
8. Falkinham J O., III Epidemiology of infection by nontuberculous mycobacteria. Clin Microbiol Rev. 1996;9:177–215. [PMC free article] [PubMed]
9. Floyd M M, Silcox V A, Jones W D, Jr, Butler W R, Kilburn J O. Separation of Mycobacterium bovis BCG from Mycobacterium tuberculosis and Mycobacterium bovis by using high-performance liquid chromatography of mycolic acids. J Clin Microbiol. 1992;30:1327–1330. [PMC free article] [PubMed]
10. Guthertz L S, Lim S D, Jang Y, Duffey P S. Curvilinear-gradient high-performance liquid chromatography for identification of mycobacteria. J Clin Microbiol. 1993;31:1876–1881. [PMC free article] [PubMed]
11. Herold C D, Fitzgerald R L, Herold D A. Current techniques in mycobacterial detection and speciation. Crit Rev Clin Lab Sci. 1996;33:83–138. [PubMed]
12. Huebner R E, Good R C, Tokars J I. Current practices in mycobacteriology: results of a survey of state public health laboratories. J Clin Microbiol. 1993;31:771–775. [PMC free article] [PubMed]
13. Jost K C, Jr, Dunbar D F, Barth S S, Headley V L, Elliott L B. Identification of Mycobacterium tuberculosis and M. avium complex directly from smear-positive sputum specimens and BACTEC 12B cultures by high-performance liquid chromatography with fluorescence detection and computer-driven pattern recognition models. J Clin Microbiol. 1995;33:1270–1277. [PMC free article] [PubMed]
14. Kent P T, Kubica G P. Public health mycobacteriology. A guide for the level III laboratory. U.S. Atlanta, Ga: Department of Health and Human Services, Centers for Disease Control; 1985.
15. Kiehn T E. The diagnostic mycobacteriology laboratory of the 1990s. 1993. pp. S447–S454. [PubMed]
16. Kirschner P, Meier A, Bottger E C. Genotypic identification and detection of mycobacteria-facing novel and uncultured pathogens. In: Persing D H, Smith T F, Tenover F C, White T J, editors. Diagnostic molecular microbiology. Washington, D.C.: American Society for Microbiology; 1993. pp. 173–190.
17. Kirschner P, Springer B, Vogel U, Meier A, Wrede A, Kiekenbeck M, Bange F C, Bottger E C. Genotypic identification of mycobacteria by nucleic acid sequence determination: report of a 2-year experience in a clinical laboratory. J Clin Microbiol. 1993;31:2882–2889. [PMC free article] [PubMed]
18. Lebrun L, Espinasse F, Poveda J D, Vincent-Levy-Frebault V. Evaluation of nonradioactive DNA probes for identification of mycobacteria. J Clin Microbiol. 1992;30:2476–2478. [PMC free article] [PubMed]
19. Levy-Frebault V, Goh K-S, David H L. Mycolic acid analysis for clinical identification of Mycobacterium avium and related mycobacteria. J Clin Microbiol. 1986;24:835–839. [PMC free article] [PubMed]
20. Lumb R, Goodwin A, Ratcliff R, Stapledon R, Holland A, Bastian I. Phenotypic and molecular characterization of three clinical isolates of Mycobacterium interjectum. J Clin Microbiol. 1997;35:2782–2785. [PMC free article] [PubMed]
21. Metchock B G, Nolte F S, Wallace R J., Jr . Mycobacterium. In: Murray P R, Baron E J, Pfaller M A, Tenover F C, Yolken R H, editors. Manual of clinical microbiology. 7th ed. Washington, D.C.: American Society for Microbiology; 1999. pp. 399–437.
22. Miller J L, editor. Sherlock Mycobacteria Identification System by high performance liquid chromatography. Operating manual. Version 1. Newark, Del: MIDI, Inc.; 1997.
23. Minnikin D E, Minnikin S M, Parlett J H, Goodfellow M, Magnusson M. Mycolic acid patterns of some species of Mycobacterium. Arch Microbiol. 1984;139:225–231. [PubMed]
24. Ramos L S. Characterization of mycobacteria species by HPLC and pattern recognition. J Chromatogr Sci. 1994;32:219–227. [PubMed]
25. Rynkiewicz D L, Cage G D, Butler W R, Ampel N M. Clinical and microbiological assessment of Mycobacterium simiae isolates from a single laboratory in southern Arizona. Clin Infect Dis. 1998;26:625–630. [PubMed]
26. Salfinger M, Pfyffer G E. The new diagnostic mycobacteriology laboratory. Eur J Clin Microbiol Infect Dis. 1994;13:961–979. [PubMed]
27. Saubolle M A, Kiehn T E, White M H, Rudinsky M F, Armstrong D. Mycobacterium haemophilum: microbiology and expanding clinical and geographic spectra of disease in humans. Clin Microbiol Rev. 1996;9:435–447. [PMC free article] [PubMed]
28. Smid I, Salfinger M. Mycobacteria identification by computer-aided gas-liquid chromatography. Diagn Microbiol Infect Dis. 1994;19:81–88. [PubMed]
29. Somoskovi A, Hotaling J E, Fitzgerald M, Jonas V, Stasik D, Parsons L M, Salfinger M. False-positive results for Mycobacterium celatum with the AccuProbe Mycobacterium tuberculosis complex assay. J Clin Microbiol. 2000;38:2743–2745. [PMC free article] [PubMed]
30. Springer B, Stockman L, Teschner K, Roberts G D, Bottger E C. Two-laboratory collaborative study on identification of mycobacteria: molecular versus phenotypic methods. J Clin Microbiol. 1996;34:296–303. [PMC free article] [PubMed]
31. Telenti A, Marchesi F, Balz M, Bally F, Bottger E C, Bodmer T. Rapid identification of mycobacteria to the species level by polymerase chain reaction and restriction enzyme analysis. J Clin Microbiol. 1993;31:175–178. [PMC free article] [PubMed]
32. Tenover F C, Crawford J T, Huebner R E, Geiter L J, Horsburgh C R, Jr, Good R C. The resurgence of tuberculosis: is your laboratory ready? J Clin Microbiol. 1993;31:767–770. [PMC free article] [PubMed]
33. Thibert L, Lapierre S. Routine application of high-performance liquid chromatography for identification of mycobacteria. J Clin Microbiol. 1993;31:1759–1763. [PMC free article] [PubMed]
34. Tortoli E, Piersimoni C, Bacosi D, Bartoloni A, Betti F, Bono L, Burrini C, De Sio G, Lacchini C, Mantella A, Orsi P G, Penati V, Simonetti M T, Bottger E C. Isolation of the newly described species Mycobacterium celatum from AIDS patients. J Clin Microbiol. 1995;33:137–140. [PMC free article] [PubMed]
35. Waecker N J, Jr, Stefanova R, Cave M D, Davis C E, Danker W M. Nosocomial transmission of Mycobacterium bovis Bacille Calmette-Guerin to children receiving cancer therapy and to their health care providers. Clin Infect Dis. 2000;30:356–362. [PubMed]
36. Wayne L G, Good R C, Tsang A, Butler R, Dawson D, Groothuis D, Gross W, Hawkins J, Kilburn J, Kubin M, Schroder K H, Silcox V A, Smith C, Thorel M-F, Woodley C, Yakrus M A. Serovar determination and molecular taxonomic correlation in Mycobacterium avium, Mycobacterium intracellulare, and Mycobacterium scrofulaceum: a cooperative study of the International Working Group on Mycobacterial Taxonomy. 1993. Int J Syst Bacteriol. 1993;43:482–489. [PubMed]
37. Zurawski C A, Cage G D, Rimland D, Blumberg H M. Pneumonia and bacteremia due to Mycobacterium celatum masquerading as Mycobacterium xenopi in patients with AIDS: an underdiagnosed problem? Clin Infect Dis. 1997;24:140–143. [PubMed]

Articles from Journal of Clinical Microbiology are provided here courtesy of American Society for Microbiology (ASM)
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...