The Voyager-DE PRO Biospectrometry Workstation is a benchtop MALDI-TOF mass spectrometer designed to accurately determine molecular weights on subpicomolar quantities of molecules. The system is used for routine non-expert operation and incorporates Delayed Extraction technology for excellent sensitivity, mass accuracy, and resolution.
R. Patel, Editor
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Reported matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) identification rates of Gram-positive rods (GPR) are low compared to identification rates of Gram-positive cocci. In this study, three sample preparation methods were compared for MALDI-TOF MS identification of 190 well-characterized GPR strains: direct transfer, direct transfer-formic acid preparation, and ethanol-formic acid extraction. Using the interpretation criteria recommended by the manufacturer, identification rates were significantly higher for direct transfer-formic acid preparation and ethanol-formic acid extraction than for direct transfer. Reducing the species cutoff from 2.0 to 1.7 significantly increased species identification rates. In a subsequent prospective study, 215 clinical GPR isolates were analyzed by MALDI-TOF MS, and the results were compared to those for identification using conventional methods, with discrepancies being resolved by 16S rRNA and rpoB gene analysis. Using the direct transfer-formic acid preparation and a species cutoff of 1.7, congruencies on the genus and species levels of 87.4% and 79.1%, respectively, were achieved. In addition, the rate of nonidentified isolates dropped from 12.1% to 5.6% when using an extended database, i.e., the Bruker database amended by reference spectra of the 190 GPR of the retrospective study. Our data demonstrate three ways to improve GPR identification by the Bruker MALDI Biotyper, (i) optimize sample preparation using formic acid, (ii) reduce cutoff scores for species identification, and (iii) expand the database. Based on our results, we suggest an identification algorithm for the clinical laboratory combining MALDI-TOF MS with nucleic acid sequencing.
Traditionally, identification of Gram-positive rods (GPR) in clinical diagnostic laboratories is based on morphological and biochemical criteria (). During the last 2 decades, molecular approaches such as 16S rRNA gene sequence analysis have been implemented to complement or replace conventional identification algorithms (). However, molecular techniques remain costly and are available in only a few routine clinical laboratories. Recently, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a novel and cost-saving approach in diagnostic microbiology for the identification of bacteria and fungi (, ). Different MALDI-TOF MS systems for microbial identification have been established, including the MALDI Biotyper (Bruker Daltonik GmbH, Bremen, Germany), Vitek MS (bioMérieux, Marcy l'Etoile, France), and the Andromas system (Andromas SAS, Paris, France). MALDI-TOF MS systems are based on the acquisition of a mass spectrum of an unknown organism and its comparison with reference databases for identification (,). The systems differ mainly in the sample preparation procedures, the species coverage of the reference database, and the identification algorithm of the software (,).
Most studies addressing the use of MALDI-TOF MS for the identification of bacteria in the diagnostic laboratory have focused on the analysis of Gram-negative bacteria and Gram-positive cocci and included only a few GPR (, , , ,). Identification by MALDI-TOF MS has been analyzed for single genera of GPR, such as Corynebacterium (,), Actinomyces (), Nocardia (), Listeria (), anaerobic GPR (), and difficult-to-identify GPR (, ). These studies were mostly done using the Bruker MALDI Biotyper system and indicated that sample preparation by the direct transfer procedure without additional modification is not sufficient for accurate identification of GPR but that a chemical extraction method is required. This extraction method, however, involves additional preparatory steps, significantly increasing processing time (), and seems, therefore, not suitable for high-throughput applications as required in clinical diagnostic laboratories. An adapted direct transfer method with on-target formic acid treatment leading to in situ cell lysis holds promise for increased identification rates without time-consuming extraction (). Similar identification rates were reported for Corynebacterium spp. and anaerobic bacteria when on-target formic acid preparation and extraction were compared (, ). An alternative preparation method with on-target ethanol treatment was described by Farfour et al. (), who analyzed a large collection of GPR using the Andromas system and reported accurate species identification. Few studies have systematically compared different sample preparation methods for the identification of GPR with MALDI-TOF MS (, , ).
In this study, we evaluated the MALDI Biotyper (Bruker Daltonik) for identification of clinical GPR strains. In the first, retrospective part of the study, we analyzed 190 well-characterized GPR strains from our strain collection, including 64 species from 21 genera. We compared identification rates of three sample preparation methods (direct transfer, direct transfer-formic acid preparation with on-target formic acid treatment, and ethanol-formic acid extraction) using standard interpretation criteria of the manufacturer and individual species and genus identification cutoff values. Based on the data from the retrospective study, we selected the direct transfer-formic acid preparation for the second, prospective part of the study, where we compared MALDI-TOF MS-based identification with the current identification algorithm used in our laboratory (n = 215 isolates) (). We defined reliable species and genus identifications for GPR, and we propose a practical algorithm of GPR identification for the routine laboratory integrating MALDI-TOF MS-based identification.
MATERIALS AND METHODS
Bacterial strains and culture conditions.
For the retrospective part of the study, we selected 190 Gram-positive rods (GPR) from the institute's strain collection, including the clinically most relevant genera, i.e., Actinomyces (n = 45 strains) and Corynebacterium (n = 81 strains), as well as more rarely encountered genera (n = 64 strains), such as Actinobaculum, Dermabacter, Nocardia, Rothia, and Trueperella (see Table S1 in the supplemental material). The strains were characterized by phenotypic methods and 16S rRNA gene analysis, which was considered the gold standard for identification. For Corynebacterium spp., additional rpoB gene sequencing was done when the discriminatory power of the biochemical testing and 16S rRNA gene analysis was insufficient (see the identification of Corynebacterium spp. below). In the prospective part of the study (February 2012 to September 2012), a total of 215 clinical GPR isolates were identified in parallel by (i) MALDI-TOF MS and (ii) our conventional identification algorithm (). Per this algorithm, identification was based on phenotypic characteristics only for 144 out of 215 isolates; for 71 out of 215 isolates, additional 16S rRNA gene sequence analyses were done. Bacteria were routinely cultivated on Columbia agar containing 5% sheep blood (bioMérieux) at 37°C with 7.5% CO2 for 24 to 48 h for MALDI-TOF MS measurement. Nocardia spp. were grown aerobically at 30°C, and Propionibacterium spp. were grown under anaerobic conditions.
Identification of Corynebacterium species.
Species assignments by 16S rRNA gene analysis of Corynebacterium spp. with high homology of 16S rRNA gene sequences (27), i.e., C. afermentans/C. coyleae/C. mucifaciens, C. aurimucosum/C. minutissimum/C. singulare, C. macginleyi/C. accolens, and C. propinquum/C. pseudodiphtheriticum, were confirmed by biochemical testing. When the biochemical reaction pattern did not allow unambiguous species assignment according to Bergey's Manual of Systematic Bacteriology (27), rpoB sequence analysis was done. rpoB analysis confirmed the identifications by 16S rRNA gene analysis for all isolates, except one C. mucifaciens isolate which was more closely related to C. afermentans by rpoB sequence analysis, and thus, was assigned to Corynebacterium sp. only.
GPR were identified by means of biochemical reactions according to those reported by von Graevenitz and Funke (), including catalase; acid production from glucose, maltose, sucrose, mannitol, and xylose in semisolid cysteine-Trypticase agar medium; motility; nitrate reduction; hydrolysis of urea; hydrolysis of esculin; the CAMP test; and a test for lipophilicity.
16S rRNA gene analysis.
Identification by partial 16S rRNA gene sequence analysis was done according to CLSI guidelines (29) and as described by Bosshard et al. (). A 16S rDNA fragment, corresponding to Escherichia coli positions 10 to 806, was amplified using primers BAK11w (5′-AGTTTGATC[A/C]TGGCTCAG) and BAK2 (5′-GGACTAC[C/T/A]AGGGTATCTAAT), and sequenced with the forward primer BAK11w (). For identification, the following criteria were used: (i) species identification when the determined sequence had a similarity score of ≥99% with that of a reference sequence of a classified species, (ii) genus assignment when the similarity score was <99% and ≥95%, and (iii) family assignment when the similarity score was <95% ().
rpoB gene analysis.
Sequence analysis of the partial rpoB gene of Corynebacterium spp. was done as previously described (). Species assignment required ≥95% sequence similarity ().
Sample preparation for MALDI-TOF MS.
Preparations of bacterial isolates for MALDI-TOF MS measurement were done as previously described (, ). Briefly, for the direct transfer method, fresh colony material was smeared on a polished steel MSP 96 target (Bruker Daltonik) using a toothpick, overlaid with 1 μl of a saturated a-cyano-4-hydroxy-cinnamic acid (HCCA) matrix solution in 50% acetonitrile-2.5% trifluoroacetic acid (Bruker Daltonik), and air dried at room temperature. For the direct transfer-formic acid method, 1 μl of 70% formic acid was added to the bacterial spot and allowed to air dry before the matrix solution was added. For the ethanol-formic acid extraction procedure, a loopful of bacterial material was suspended in 300 μl distilled water, and 900 μl ethanol was added. The cell suspension was centrifuged at 17,000 × g for 2 min, and the supernatant was discarded. The centrifugation was repeated, and the residual ethanol was discarded. The pellet was air dried and thoroughly resuspended in 5 to 50 μl formic acid-water (70:30 [vol/vol]) depending on the size, and finally, an equal volume of acetonitrile was added. After centrifugation at 17,000 × g for 2 min, 1 μl of the supernatant was transferred to the MALDI target plate and allowed to dry at room temperature before being overlaid with 1 μl of matrix solution.
MALDI-TOF MS analysis.
The acquisition and analysis of mass spectra were performed by a Microflex LT mass spectrometer (Bruker Daltonik) using the MALDI Biotyper software package (version 3.0) with the reference database version 184.108.40.206 (3,995 database entries) (Bruker Daltonik) and default parameter settings as published previously (). The Bruker bacterial test standard (Bruker Daltonik) was used for calibration according to the instructions of the manufacturer. For each strain, two preparations of colony/sample material were analyzed.
MALDI-TOF MS data interpretation.
The Biotyper software compares each sample mass spectrum to the reference mass spectra in the database, calculates an arbitrary unit score value between 0 and 3 reflecting the similarity between the sample and the reference spectrum, and displays the top 10 matching database records. Standard Bruker interpretative criteria were applied (). Briefly, scores of ≥2.0 were accepted for species assignment and scores of ≥1.7 but <2.0 for identification to the genus level. Scores below 1.7 were considered unreliable. Variations of the cutoff score values were done by reducing the species cutoff values to 1.9, 1.8, and 1.7, and the genus cutoff values to 1.6 and 1.5, followed by reinterpreting the top 10 matching database records.
Generation of in-house reference database.
Reference spectra were created for all 190 clinical strains of the retrospective study and added to the Bruker database version 220.127.116.11. For each strain, a set of 24 spectra was measured and checked manually for flat-line, outlier, and single spectra with peaks differing form the other spectra. Questionable spectra were removed, and a total of 20 to 24 spectra were used to calculate a reference spectrum, using the automated function of the Biotyper software.
Discrepancies between MALDI-TOF MS and phenotypic identification were resolved by 16S rRNA gene and rpoB gene sequence analyses, which were considered the gold standards for identification (, , ).
Statistical calculations were done using IBM SPSS Statistics software, version 20 (SPSS, Inc., Chicago, IL). Overall differences among the three MALDI-TOF MS preparation methods (direct transfer, direct transfer-formic acid preparation, and ethanol-formic acid extraction) and the tested cutoff score values were evaluated using the Kruskal-Wallis test. Follow-up tests were conducted using the Mann-Whitney U test for pairwise comparison of the three preparation methods and the different cutoff score values. Chance agreement between direct transfer-formic acid preparation and ethanol-formic acid extraction was evaluated with an interrater reliability analysis using the Kappa statistics (). Differences were considered statistically significant at P values of <0.05.
Nucleotide sequence accession numbers.
16S rRNA gene sequences of the retrospective and prospective parts of the study are accessible at GenBank under accession numbers KF925884 to KF926073 and KJ081453 to KJ081536. rpoB gene sequences are accessible at GenBank under accession numbers KJ081537 to KJ081544 and KJ150303 to KJ150321.
Retrospective study. (i) MALDI Biotyper identification rates of Gram-positive rods (GPR) are increased by formic acid treatment.
We evaluated the Bruker MALDI Biotyper system by analyzing 190 well-characterized clinical GPR isolates, including 64 species from 21 genera (see Table S1 in the supplemental material). When applying the direct transfer sample preparation method and the standard interpretation criteria of the manufacturer, i.e., a species cutoff score value of 2.0 and a genus cutoff score value of 1.7, the MALDI Biotyper correctly identified 137 of 190 strains (72.1%) at the genus level (Table 1). Correct identification at the species level was achieved for 70 of 190 strains (36.8%). Genus and species identification rates significantly increased to 84.7% and 61.1%, respectively, when we used direct transfer-formic acid sample preparation with on-target formic acid treatment (Z = −4.63, P < 0.001, and Z = −2.99, P = 0.003, for genus and species identification of direct transfer and direct transfer-formic acid preparation, respectively). Comparable increases in identification rates to 87.4% and 62.1% for genus and species identifications, respectively, were observed for the ethanol-formic acid extraction. The differences between direct transfer-formic acid preparation and ethanol-formic extractions were not significant (Z = −0.37, P = 0.710, and Z = −0.74, P = 0.460, for the comparison of species and genus identification, respectively). In order to calculate the strength of agreement between these two methods, kappa statistical analysis was performed. The kappa coefficient for the direct transfer-formic acid method and the ethanol-formic acid extraction was 0.81 (P < 0.001, 95% confidence interval [CI], 0.73, 0.88). This indicates almost perfect agreement between these two methods ().
Retrospective analysis of Gram-positive rods with MALDI-TOF MS: comparison of three sample preparation methods
|Genus (n)||Preparation methodb||No. (%) of isolates correctly identified at species level using a score value cutoff of:||No. (%) of isolates correctly identified at genus level using a score value cutoff of:|
|Actinobaculum (8)||DT||3 (37.5)c||7 (87.5)||8 (100)||8 (100)||8 (100)c||8 (100)||8 (100)|
|DT-FA||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)|
|EXT||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)||8 (100)|
|Actinomyces (45)||DT||16 (35.6)c||20 (44.4)||28 (62.2)d||31 (68.9)d||31 (68.9)c||31 (68.9)||34 (75.6)|
|DT-FA||25 (55.6)d||29 (64.4)d||30 (66.7)d||32 (71.1)d||32 (71.1)||33 (73.3)||37 (82.2)|
|EXT||24 (53.3)d||28 (62.2)d||30 (66.7)d||32 (71.1)d||32 (71.1)||32 (71.1)||34 (75.6)|
|Corynebacterium (81)||DT||31 (38.3)c||39 (48.1)||48 (59.3)d||50 (61.7)d||55 (67.9)c||60 (74.1)||61 (75.3)d|
|DT-FA||51 (63)||56 (69.1)||59 (72.8)d||55 (67.9)d||74 (91.4)d||75 (92.6)d||74 (91.4)d|
|EXT||52 (64.2)||57 (70.4)||62 (76.5)d||62 (76.5)d||77 (95.1)d||77 (95.1)d||79 (97.5)d|
|Dermabacter (5)||DT||0 (0)c||1 (20)||2 (40)||5 (100)||5 (100)c||5 (100)||5 (100)|
|DT-FA||0 (0)||2 (40)||5 (100)||5 (100)||5 (100)||5 (100)||5 (100)|
|EXT||1 (20)||2 (40)||4 (80)||5 (100)||5 (100)||5 (100)||5 (100)|
|Lactobacillus (7)||DT||2 (28.6)c||2 (28.6)||4 (57.1)||1 (14.3)||7 (100)c||7 (100)||7 (100)|
|DT-FA||3 (42.9)||4 (57.1)||2 (28.6)||1 (14.3)||7 (100)||7 (100)||7 (100)|
|EXT||5 (71.4)||5 (71.4)||5 (71.4)||1 (14.3)||7 (100)||7 (100)||6 (85.7)|
|Nocardia (8)||DT||0 (0)c||0 (0)||1 (12.5)||1 (12.5)||2 (25)c||3 (37.5)||2 (25)|
|DT-FA||2 (25)||2 (25)||2 (25)||2 (25)||3 (37.5)||4 (50)||3 (37.5)|
|EXT||1 (12.5)||2 (25)||2 (25)||2 (25)||3 (37.5)||3 (37.5)||4 (50)|
|Rothia (7)||DT||4 (57.1)c||6 (85.7)||6 (85.7)||7 (100)||7 (100)c||7 (100)||7 (100)|
|DT-FA||7 (100)||7 (100)||7 (100)||7 (100)||7 (100)||7 (100)||7 (100)|
|EXT||7 (100)||7 (100)||6 (85.7)||6 (85.7)||7 (100)||7 (100)||7 (100)|
|Trueperella (6)||DT||3 (50)c||6 (100)||6 (100)||6 (100)||6 (100)c||6 (100)||6 (100)|
|DT-FA||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)|
|EXT||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)||6 (100)|
|Other GPRa (22)||DT||11 (47.8)c||13 (56.5)||13 (56.5)||12 (52.2)||16 (69.6)c||16 (69.6)||16 (69.6)|
|DT-FA||14 (60.9)||14 (60.9)||14 (60.9)||14 (60.9)||19 (82.6)||19 (82.6)||16 (69.6)|
|EXT||14 (60.9)||16 (69.6)d||16 (69.6)d||16 (69.6)d||21 (91.3)||21 (91.3)||21 (91.3)|
|Total (190)||DT||70 (36.8)c||94 (49.5)d||116 (61.1)d||121 (63.7)d||137 (72.1)c||143 (75.3)||146 (76.8)d|
|DT-FA||116 (61.1)d||128 (67.4)d||133 (70)d||130 (68.4)d||161 (84.7)d||164 (86.3)d||165 (86.8)d|
|EXT||118 (62.1)d||131 (68.9)d||139 (73.2)d||138 (72.6)d||166 (87.4)d||166 (87.4)d||170 (89.5)d|
aGenera with ≤5 isolates tested, including Alloscardovia, Arcanobacterium, Arthrobacter, Bifidobacterium, Brevibacterium, Dietzia, Gardnerella, Gordonia, Listeria, Paenibacillus, Propionibacterium, Rhodococcus, and Terrabacter (for details, see Table S2 in the supplemental material).
bMALDI-TOF MS preparation method; DT, direct transfer; DT-FA, direct transfer with formic acid treatment; EXT, ethanol-formic acid extraction.
dValue significantly differs from the reference value. Agreement between the reference method (direct transfer species cutoff value 2.0, genus cutoff value 1.7) and the method evaluated was compared using a two-tailed McNemar test for paired samples. Differences were considered statistically significant at P values of <0.05.
Genus and species identification rates varied between different genera (Table 1; also see Table S2 in the supplemental material). For Actinobaculum, Dermabacter, Lactobacillus, Rothia, and Trueperella, genus identification was achieved for all strains tested, independently of the sample preparation method. In contrast, for the Nocardia strains, genus identification rates varied between 25.0% and 37.5% for the different preparation methods. Direct transfer-formic acid preparation and ethanol-formic acid extraction increased the average MALDI score values by 0.15 and 0.17 score units, respectively, as compared to direct transfer. No mass spectra (no peaks) were detected in 4.6% and 1.6% of the measurements when direct transfer or direct transfer-formic acid preparation was used, respectively. The majority of no-peaks results were observed for Nocardia spp. and two distinct species of the genus Corynebacterium, i.e., C. mucifaciens and C. pyruviciproducens. No-peaks results were not observed for the ethanol-formic acid extraction procedure.
(ii) Species coverage of the Bruker reference database.
Species of 21 genera covered by 904 reference entries in the Bruker database version 18.104.22.168 were analyzed in the retrospective part of the study. Four species tested in the present work were not included in the database, i.e., Actinomyces johnsonii, Corynebacterium pyruviciproducens, “Corynebacterium pseudogenitalium,”and Paenibacillus campinasensis. While A. johnsonii and P. campinasensis were not identified by MALDI-TOF MS, most of the C. pyruviciproducens and “C. pseudogenitalium” strains were assigned to the genus Corynebacterium when the direct transfer-formic acid or the ethanol-formic acid procedure was used.
(iii) Low discrimination at the species level using the MALDI Biotyper.
For some isolates, MALDI-TOF MS identification rank lists showed scores of ≥2.0 for >1 species, resulting in species inconsistency and identification to the genus level only. Such low discrimination at the species level was observed for the following strains: Corynebacterium aurimucosum (scores were ≥2.0 for C. aurimucosum/minutissimum), Corynebacterium simulans (scores were ≥2.0 for C. simulans/striatum), Lactobacillus gasseri (scores were ≥2.0 for L. gasseri/johnsonii), and Listeria monocytogenes (scores were ≥2.0 for L. monocytogenes/ivanovii/innocua). We observed low species discrimination more frequently when applying direct transfer-formic acid preparation (2.6%) and ethanol-formic acid extraction (2.6%) but less frequently when using the direct transfer method (1.6%). In all cases, the species with the highest score corresponded to the identification determined by phenotypic and molecular methods representing the gold standard.
(iv) Misidentifications using the MALDI Biotyper.
MALDI-TOF MS analysis of Rhodococcus gordoniae, Rhodococcus corynebacterioides, Terrabacter tumescens, and Gordonia terrae regularly produced poor-quality or no mass spectra, preventing identification. In addition, identification as Arthrobacter castelli was occasionally observed for these strains. The latter discrepancy is apparently due to the matching of a poor-quality spectrum with the reference spectrum of A. castelli. One Rhodococcus gordoniae strain (species assignment by 16S rRNA gene analysis) was identified by MALDI-TOF MS as Rhodococcus rhodochrous.
(v) Individual cutoff score values for species and genus identification.
Standard cutoff score values for species and genus identification are set to 2.0 and 1.7 by the manufacturer. We evaluated the effects of reducing the species and genus cutoff values from 2.0 to 1.9, 1.8, and 1.7, and from 1.7 to 1.6 and 1.5, respectively (Table 1; also see Table S2 in the supplemental material). Overall, reduction of the genus cutoff value marginally increased identification rates. The cutoff reduction was accompanied by increasing numbers of low-discrimination results at the genus level and by more misidentifications (low-discrimination rates of 0% versus 2% and misidentification rates of 0% versus 1% when applying genus-level cutoff scores of 1.7 and 1.5, respectively). In particular, Actinomyces isolates were identified as Acinetobacter sp., Arthrobacter sp., or Pseudomonas sp., and one Corynebacterium isolate was assigned to the genus Clostridium.
Significantly higher species identification rates were observed for all three preparation methods when lower species cutoff values were applied (Table 1; also see Table S2 in the supplemental material). Reducing the species cutoff from 2.0 to 1.7 increased identification rates from 36.8% to 63.7%, from 61.1% to 68.4%, and from 62.1% to 71.6% for direct transfer, direct transfer-formic acid preparation, and ethanol-formic acid extraction, respectively. Using a cutoff of 1.7, the species identification rates of direct transfer, direct transfer-formic acid preparation, and ethanol-formic acid extraction no longer differed significantly (χ2 [2, n = 570] = 5.65, P = 0.059, with the Kruskal-Wallis test). However, at the same time, the rate of low-discrimination results increased from 1.6% to 5.8% (species cutoff of 2.0 versus 1.7) for direct transfer, and from 2.6% to 10% for direct transfer-formic acid preparation and ethanol-formic acid extraction. The higher rates of low discrimination at the species level were mainly due to less differentiation between C. aurimucosum/minutissimum, C. striatum/simulans, L. gasseri/johnsonii, L. rhamnosus/casei, L. monocytogenes/innocua, and Rhodococcus corynebacterioides/kroppenstedtii. Reducing the species cutoff from 2.0 to 1.7 increased the rate of misidentifications from 0.5% to 5% for all three methods. The increase in misidentification rates was caused by (i) the misidentification of Rhodococcus corynebacterioides as Rhodococcus kroppenstedtii and (ii) the misidentification of “C. pseudogenitalium” as “Corynebacterium lipophile.”
Prospective study. (i) Comparison of MALDI-TOF MS identification with the conventional identification algorithm (phenotypic, genotypic) in the routine diagnostic laboratory.
In a prospective study, 215 clinically relevant GPR isolates, including 13 genera and 36 species, were identified by MALDI-TOF MS and according to the conventional identification algorithm as previously published (). The MALDI Biotyper system was used with the direct transfer-formic acid sample preparation method applying the Bruker database version 22.214.171.124. (3,995 entries), and a species-level identification cutoff score value of 2.0 (Table 2). Overall, for 188 of 215 isolates (87.4%), congruence on the genus level was observed between conventional and MALDI-TOF MS identification. Of 215 isolates, 133 (61.9%) yielded concordant species identification, and 26 of 215 isolates (12.1%) were not identified by MALDI-TOF MS.
MALDI-TOF MS identification versus conventional identification for 215 GPR clinically relevant isolates
|Conventional identificationa||No. of isolates||No. (%) of congruent identifications by MALDI-TOF MSb at:|
|Genus level (cutoff 1.7)||Species level (cutoff 2.0)||Species level (cutoff 1.7)|
|Corynebacterium glucuronolyticum||28||28 (100)||28 (100)||28 (100)|
|Corynebacterium amycolatum||22||22 (100)||22 (100)||22 (100)|
|Corynebacterium tuberculostearicum||20||16 (80)||2 (10)||15 (75)|
|Actinobaculum schaalii||12||11 (91.7)||9 (75)||11 (91.7)|
|Corynebacterium jeikeium||12||12 (100)||9 (75)||12 (100)|
|Turicella otitidis||12||12 (100)||10 (83.3)||12 (100)|
|Corynebacterium spp.||11||8 (72.7)||NAc||NA|
|Actinomyces odontolyticus||9||5 (55.6)||3 (33.3)||5 (55.6)|
|Dermabacter hominis||8||8 (100)||4 (50)||8 (100)|
|Actinomyces neuii subsp. anitratus||7||7 (100)||6 (85.7)||7 (100)|
|Actinomyces neuii subsp. neuii||6||6 (100)||4 (66.7)||6 (100)|
|Corynebacterium striatum||6||6 (100)||5 (83.3)||5 (83.3)|
|Corynebacterium pseudodiphtheriticum||5||5 (100)||4 (80)||4 (80)|
|Actinobaculum spp.||4||4 (100)||NA||NA|
|Alloscardovia omnicolens||4||3 (75)||2 (50)||3 (75)|
|Actinomyces spp.||3||0 (0)||NA||NA|
|Bifidobacterium breve||3||1 (33.3)||1 (33.3)||1 (33.3)|
|Bifidobacterium longum||3||3 (100)||3 (100)||3 (100)|
|Corynebacterium coyleae||3||3 (100)||3 (100)||3 (100)|
|Corynebacterium macginleyi||3||3 (100)||2 (66.7)||3 (100)|
|Corynebacterium propinquum||3||3 (100)||3 (100)||3 (100)|
|Propionibacterium avidum||3||3 (100)||2 (66.7)||2 (66.7)|
|Trueperella bernardiae||3||3 (100)||3 (100)||3 (100)|
|Rare isolatesd||25||16 (64)||8 (32)||14 (56)|
|Total||215||188 (87.4)||133 (61.9)||170 (79.1)|
aIdentification combining biochemical methods and 16S rRNA gene sequence analysis according to Bosshard et al. ().
bMALDI-TOF MS identification applying direct transfer-formic acid preparation, a genus cutoff value of 1.7, and species cutoff values of 2.0 and 1.7, respectively with the Bruker database version 126.96.36.199 (containing 3995 entries).
dRare isolates (with n ≤ 2), including Actinomyces spp. (A. funkei, A. israelii, A. radingae, A. turicensis, A. urogenitalis, and A. viscosus), Brevibacterium spp. (B. ravenspurgense), Corynebacterium spp. (C. accolens, C. afermentans, C. aurimucosum/minutissimum, C. bovis, C. imitans, C. mastitidis, C. mucifaciens, and C. ureicelerivorans), Nocardia spp., Paenibacillus spp., and Propionibacterium spp. (P. acnes).
Spectra that did not yield a score ≥2.0 were reanalyzed using a species cutoff of 1.7. This significantly increased the total number of congruent species results of conventional and MALDI-TOF MS identification from 133 (61.9%) to 170 (79.1%) (Table 2). In particular, a major increase of the identification rate from 10% to 75% was observed for Corynebacterium tuberculostearicum. We observed discrepancies on the genus and/or species level for 18 isolates (8.4%) when using a species cutoff of 1.7 (Table 3). For 3 of these 18 isolates, identification by molecular reference methods (16S rRNA gene and/or rpoB gene sequencing) confirmed species identification by MALDI-TOF MS. For 11 out of 18 isolates, MALDI-TOF MS allowed assignment to the species level, while 16S rRNA gene sequence analysis allowed assignment to the genus level only. Two biochemical misidentifications were observed. One C. propinquum was misidentified as C. pseudodiphtheriticum, and one “C. pseudogenitalium” was misidentified as C. tuberculostearicum. For one Propionibacterium avidum, low discrimination of P. avidum and Propionibacterium propionicum by MALDI-TOF MS was observed.
Resolution of 18 discrepant results comparing conventional pheno-/genotypic and MALDI-TOF MS GPR identifications
|Identification type and no. of isolates||Conventional identificationa||MALDI-TOF MS identificationb||Resolution of discrepancy|
|On genus level with MALDI-TOF MS|
|1||Propionibacterium avidum||Propionibacterium sp.||MALDI-TOF MS: low discrimination between P. avidum and P. propionicum|
|On genus level with conventional methods|
|4||Actinobaculum sp.||Actinobaculum schaalii||Genus assignment by 16S rRNA gene analysis: sequence similarities to A. schaalii, 97.5%, 97.6%, 97.8%, and 98.5%, respectively|
|1||Corynebacterium sp.||Corynebacterium accolens||Genus assignment by 16S rRNA gene analysis: sequence similarity to C. propinquum, 98.6%|
|3||Corynebacterium sp.||Corynebacterium aurimucosum||16S rRNA gene analysis: no discrimination of C. aurimucosum and C. minutissimum. rpoB gene sequence analysis confirmed C. aurimucosum (95.4%, 95.9%, and 96.9% sequence homology, respectively)|
|1||Corynebacterium sp.||Corynebacterium aurimucosum||Genus assignment by 16S rRNA gene analysis: sequence similarity to C. aurimucosum, 98.8%, and to C. minutissimum, 98.5%|
|2||Corynebacterium sp.||Corynebacterium afermentans||Genus assignment by 16S rRNA gene analysis: sequence similarity to Corynebacterium appendicis, 98.2% and 98.8%, respectively|
|1||Nocardia sp.||Nocardia higoensis||16S rRNA gene analysis: sequence homology to N. higoensis and Nocardia shimofusensis, 100%, no differentiation|
|1||Propionibacterium sp.||Propionibacterium avidum||Genus assignment by 16S rRNA gene analysis: sequence similarity to P. avidum, 96.7%|
|1||Corynebacterium sp.||“Corynebacterium lipophile”||16S rRNA gene analysis: sequence homology to “C. pseudogenitalium,” 100%|
|1||Corynebacterium pseudodiphtheriticum||Corynebacterium propinquum||Biochemical misidentification: 16S rRNA and rpoB gene analysis confirmed C. propinquum (100% and 98.1% sequence homology, respectively)|
|1||Corynebacterium tuberculostearicum||“Corynebacterium lipophile”||Biochemical misidentification: 16S rRNA gene sequence similarity to “C. pseudogenitalium,” 99.4%|
|1||Alloscardovia omnicolens||Actinobaculum schaalii||Mixed culture of A. omnicolens and A. schaalii|
aConventional identification according to Bosshard et al. . In addition, all discrepant isolates were analyzed by 16S RNA gene analysis. If not otherwise stated, identifications by 16S rRNA gene analysis and conventional identification were identical.
bMALDI-TOF MS identification using the direct transfer-formic acid protocol and a species cutoff of 1.7 with the Bruker database version 188.8.131.52.
One discrepancy on the genus level was observed (MALDI-TOF MS identification, Actinobaculum schaalii; 16S rRNA gene analysis, Alloscardovia omnicolens). Closer analysis revealed the presence of a mixed culture of A. schaalii and A. omnicolens.
Application of our amended Bruker-IMM database, i.e., an extended database combining the commercially available Bruker database with the in-house-generated reference spectra from the retrospective study, increased the number of congruent genus and species identifications from 188 to 202 (94.0%) and from 170 to 180 (83.7%), respectively. The rate of nonidentifications was reduced to 5.6%. In particular, 14 isolates that were previously not identified by MALDI-TOF MS were identified as Actinomyces odontolyticus (3 isolates), Actinomyces urogenitalis (1 isolate), Actinomyces sp. (3 isolates), Bifidobacterium breve (1 isolate), Corynebacterium imitans (1 isolate), Corynebacterium pyruviciproducens (1 isolate), and Corynebacterium tuberculostearicum (4 isolates).
MALDI-TOF MS is increasingly used in diagnostic laboratories for the identification of bacteria and fungi. We have analyzed the performance of the Bruker MALDI Biotyper system for the identification of GPR and evaluated possible ways to improve its performance, i.e., different sample preparation methods, data interpretation algorithms (particularly the variation of score cutoff values), and extension of the database by proprietary reference entries.
Using the manufacturer's standard interpretation criteria, i.e., a genus cutoff of 1.7 and a species cutoff of 2.0 in combination with the direct transfer method, identification rates of 72.1%, and 36.8% were achieved at the genus and species levels, respectively. Treatment of the sample with formic acid by either on-target overlay or tube-based extraction significantly increased genus and species identification rates to approximately 85% and 60%, respectively (Table 1). Identification rates for the direct transfer-formic acid method were comparable to those of the ethanol-formic acid extraction procedure that is considered the gold standard and which is used to generate the reference database. These findings are in agreement with previous reports which indicated that on-target treatment with formic acid or ethanol-formic acid extraction improved identification of GPR by MALDI-TOF MS (, , , ). However, the ethanol-formic acid procedure includes various manual preparation steps and is time-consuming (). In contrast, total time to result is only moderately increased by the direct transfer-formic acid method compared to direct transfer (), and thus seems more suited for routine use in diagnostic laboratories. While this study was in progress, Farfour et al. analyzed a large collection of GPR using the Andromas system () and reported accurate species identification of GPR using direct transfer with additional ethanol treatment.
Individual data interpretation algorithms.
Reducing the genus cutoff value from 1.7 to 1.5 only slightly increased overall genus identification rates for direct transfer and ethanol-formic acid extraction (from 72.1% to 76.3% and from 87.4% to 89.5%, respectively), while the identification rate for direct transfer-formic acid preparation remained constant at 84.7% (Table 1). At the same time, a genus cutoff score value of 1.5 resulted in an increased misidentification of 1% of the isolates. In contrast to genus identification, species identification was significantly enhanced by reducing the species cutoff value from 2.0 to 1.7 independent of the preparation method applied (36.8% versus 63.7%, 61.1% versus 68.4%, and 62.1% versus 71.6% for direct transfer, direct transfer-formic acid preparation, and ethanol-formic acid extraction, respectively). These results are in agreement with previous studies on MALDI-TOF MS identification of GPR, such as Corynebacterium spp., anaerobic GPR, and difficult-to-identify GPR, which all support the application of a lower species cutoff (mostly 1.7) to increase the MALDI-TOF MS identification rate (, , ).
Reducing the cutoff value increased the number of low discriminations and misidentifications. However, this effect was limited to certain species and reflects taxonomic inconsistencies rather than technical problems. The most frequent discrepancy was the misidentification of “C. pseudogenitalium” (per 16S rRNA gene analysis) as “C. lipophile.” “C. lipophile” is not an officially validated species, and the assignment of the single “C. lipophile” isolate in the Bruker database is unclear. Unfortunately, “C. pseudogenitalium” is not included in the Bruker database. It was described by Furness et al. () but has not yet been officially added to the list of recognized species, and would thus be better referred to as “Candidatus Corynebacterium pseudogenitalium.” As “C. lipophile” is not an officially accepted species name, we suggest that MALDI-TOF MS identifications of “C. lipophile” be ignored and that alternative identification methods be used for such isolates until additional entries have been added to the MALDI database and/or the taxonomy has been clarified. In conclusion, reducing the genus cutoff score value offers minimal gain of information but an increased number of misidentifications. In contrast, lower species cutoff values lead to significantly increased identification rates, thus, reducing the necessity for additional tests and increasing the efficiency of the laboratory workflow in terms of labor and costs. To avoid the problem of low discrimination at the species level, we suggest using a two-step interpretation: In a first step, data interpretation should be done according to the standard criteria of the manufacturer (species cutoff value 2.0). In a second step, the Biotyper results list should be reinterpreted, with the application of a lower species cutoff (preferably 1.7) for those isolates that did not yield species identification when using the standard species cutoff value of 2.0.
Coverage of the reference database.
Analyses of some more rarely encountered genera and species were limited to the retrospective part of the study. Several species are not included in the commercial Biotyper database, e.g., A. johnsonii, C. pyruviciproducens, “C. pseudogenitalium,” and P. campinasensis. Addition of those and other reference spectra from the extended in-house database that was generated in the retrospective study part increased the overall identification rate from 71.9% (commercial database) to 83.7% (commercial database amended with in-house database). As a consequence, the rate of nonidentifications was reduced to 5.6%. Thus, the addition of single spectra of rarely isolated species significantly improved the performance of the Biotyper system. Proprietary in-house reference spectra were provided to Bruker Daltonik for integrating selected data sets into the commercial database.
Identification of the clinically most common GPR isolates by MALDI-TOF MS in the prospective study yielded genus and species identifications of 87.4% and 79.1%, respectively, and was highly reliable. The identification rates were, however, lower than those reported for Gram-positive cocci (, , ). Apart from “C. lipophile,” no misidentifications occurred.
MALDI-TOF MS identification versus established identification algorithm.
The current aerobic GPR identification algorithm in our clinical laboratory relies on a two-step procedure that combines phenotypic and molecular methods (). In a first step, GPR isolates are identified by phenotypic and biochemical traits. For clinically relevant isolates, which are not identified to species level in this first step, 16S rRNA gene sequence analysis is done in a second step. Thirty-three percent of the isolates in the prospective study could not be identified to the species level by phenotypic methods and were subjected to 16S rRNA gene analysis. By MALDI-TOF MS, using the Bruker database and a species cutoff of 2.0, 33% of the isolates were also not identified to the species level (data not shown). Reduction of the species cutoff to 1.7 increased the species identification rate to 79.1%. The additional use of our extended in-house database further reduced the percentage of isolates not identified to the species level by MALDI-TOF MS to 16.3%. Thus, replacing phenotypic methods by MALDI-TOF MS has the potential to significantly reduce the amount of isolates which need to be sequenced for proper identification. We propose an adapted diagnostic algorithm for the clinical laboratory by combining MALDI-TOF MS and nucleic acid sequencing (Fig. 1). Isolates are first identified by MALDI-TOF MS, and 16S rRNA gene analysis is done only if no species identification is achieved. Additional rpoB gene sequencing is required for the identification of Corynebacterium spp. when the 16S rRNA target fails to discriminate closely related species. To optimize the species identification rate without increasing numbers of low discrimination, a species cutoff of 2.0 is first applied. For isolates that are not identified to the species level, a species cutoff of 1.7 is subsequently used.
Algorithm for the identification of Gram-positive rods in routine diagnostics using MALDI-TOF MS. Recommendations are based on the results of the prospective study. *1, Genera and species with <3 correctly identified isolates were not integrated in this algorithm. It is suggested that these isolates be identified by molecular analysis until sufficient data are available to update the approved lists; *2, 16S rRNA gene and rpoB gene sequencing as described previously (, , ).
In conclusion, this study showed that the identification of GPR by the Bruker MALDI Biotyper is highly reliable, although identification rates are generally lower than those for Gram-positive cocci or Gram-negative bacteria. Identification rates can be improved by (i) using direct transfer-formic acid sample preparation, (ii) reducing the species cutoff score value, and (iii) by expanding the commercial database with in-house-generated reference spectra. Based on our data, we suggest a practical algorithm combining MALDI-TOF MS with nucleic acid sequence analysis for the identification of GPR in clinical laboratories. This algorithm is based on the current manufacturers system setup (i.e., score value cutoffs and consistency rules), but includes cutoff value variations if no species identification can be achieved by the standard rules. This algorithm covers the most frequently found genera and species and can easily be complemented by validated new database entries.
We thank the laboratory technicians of the Institute of Medical Microbiology for their assistance and F. Mouttet for aid with statistical analyses.
The Institute of Medical Microbiology, University of Zurich, Switzerland, has a collaboration agreement with Bruker Daltonik GmbH (Bremen, Germany) for the purpose of improving the commercially available Bruker database. Bruker Daltonik had no influence on data collection or the interpretation of this study.
This study was supported by the University of Zurich.
Published ahead of print 22 January 2014
Supplemental material for this article may be found at http://dx.doi.org/10.1128/JCM.02399-13.
1. Bosshard PP, Abels S, Zbinden R, Böttger EC, Altwegg M.2003. Ribosomal DNA sequencing for identification of aerobic gram-positive rods in the clinical laboratory (an 18-month evaluation). J. Clin. Microbiol.41:4134–4140. 10.1128/JCM.41.9.4134-4140.2003 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
2. Neville SA, Lecordier A, Ziochos H, Chater MJ, Gosbell IB, Maley MW, van Hal SJ.2011. Utility of matrix-assisted laser desorption ionization–time of flight mass spectrometry following introduction for routine laboratory bacterial identification. J. Clin. Microbiol.49:2980–2984. 10.1128/JCM.00431-11 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
3. Tan EK, Ellis BC, Lee R, Stamper PD, Zhang SX, Carroll KC.2012. Prospective evaluation of a MALDI-TOF MS system in a hospital clinical microbiology laboratory for the identification of bacteria and yeasts: a bench-by-bench study to assess the impact on time-to-identification and cost-effectiveness. J. Clin. Microbiol.50:3301–3308. 10.1128/JCM.01405-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
4. Demirev PA, Fenselau C.2008. Mass spectrometry for rapid characterization of microorganisms. Annu. Rev. Anal. Chem.1:71–93. 10.1146/annurev.anchem.1.031207.112838 [PubMed] [CrossRef] [Google Scholar]
5. Croxatto A, Prod'hom G, Greub G.2012. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol. Rev.36:380–407. 10.1111/j.1574-6976.2011.00298.x [PubMed] [CrossRef] [Google Scholar]
6. Wieser A, Schneider L, Jung J, Schubert S.2012. MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review). Appl. Microbiol. Biotechnol.93:965–974. 10.1007/s00253-011-3783-4 [PubMed] [CrossRef] [Google Scholar]
7. Carbonnelle E, Grohs P, Jacquier H, Day N, Tenza S, Dewailly A, Vissouarn O, Rottman M, Herrmann JL, Podglajen I, Raskine L.2012. Robustness of two MALDI-TOF mass spectrometry systems for bacterial identification. J. Microbiol. Methods89:133–136. 10.1016/j.mimet.2012.03.003 [PubMed] [CrossRef] [Google Scholar]
8. Bille E, Dauphin B, Leto J, Bougnoux ME, Beretti JL, Lotz A, Suarez S, Meyer J, Join-Lambert O, Descamps P, Grall N, Mory F, Dubreuil L, Berche P, Nassif X, Feroni A.2012. MALDI-TOF MS Andromas strategy for the routine identification of bacteria, mycobacteria, yeasts, Aspergillus spp. and positive blood cultures. Clin. Microbiol. Infect.18:1117–1125. 10.1111/j.1469-0691.2011.03688.x [PubMed] [CrossRef] [Google Scholar]
9. Dubois D, Grare M, Prere MF, Segonds C, Marty N, Oswald E.2012. Performances of the Vitek MS matrix-assisted laser desorption ionization–time of flight mass spectrometry system for rapid identification of bacteria in routine clinical microbiology. J. Clin. Microbiol.50:2568–2576. 10.1128/JCM.00343-12.22593596 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
10. Martiny D, Busson L, Wybo I, El Haj RA, Dediste A, Vandenberg O.2012. Comparison of the Microflex LT and Vitek MS systems for routine identification of bacteria by matrix-assisted laser desorption ionization–time of flight mass spectrometry. J. Clin. Microbiol.50:1313–1325. 10.1128/JCM.05971-11 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
11. van Veen SQ, Claas EC, Kuijper EJ.2010. High-throughput identification of bacteria and yeast by matrix-assisted laser desorption ionization–time of flight mass spectrometry in conventional medical microbiology laboratories. J. Clin. Microbiol.48:900–907. 10.1128/JCM.02071-09 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
12. Bizzini A, Durussel C, Bille J, Greub G, Prod'hom G.2010. Performance of matrix-assisted laser desorption ionization–time of flight mass spectrometry for identification of bacterial strains routinely isolated in a clinical microbiology laboratory. J. Clin. Microbiol.48:1549–1554. 10.1128/JCM.01794-09 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
13. Alatoom AA, Cazanave CJ, Cunningham SA, Ihde SM, Patel R.2012. Identification of non-diphtheriae Corynebacterium by use of matrix-assisted laser desorption ionization–time of flight mass spectrometry. J. Clin. Microbiol.50:160–163. 10.1128/JCM.05889-11 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
14. Vila J, Juiz P, Salas C, Almela M, de la Fuente CG, Zboromyrska Y, Navas J, Bosch J, Aguero J, de la Bellacasa JP, Martínez-Martínez L.2012. Identification of clinically relevant Corynebacteriumspp., Arcanobacterium haemolyticum, and Rhodococcusequi by matrix-assisted laser desorption ionization–time of flight mass spectrometry. J. Clin. Microbiol.50:1745–1747. 10.1128/JCM.05821-11 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
15. Konrad R, Berger A, Huber I, Boschert V, Hormansdorfer S, Busch U, Hogardt M, Schubert S, Sing A.2010. Matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry as a tool for rapid diagnosis of potentially toxigenic Corynebacterium species in the laboratory management of diphtheria-associated bacteria. Euro Surveill.15:pii 19699 [PubMed] [Google Scholar]
16. Ng LS, Sim JH, Eng LC, Menon S, Tan TY.2012. Comparison of phenotypic methods and matrix-assisted laser desorption ionisation time-of-flight mass spectrometry for the identification of aero-tolerant Actinomyces spp. isolated from soft-tissue infections. Eur. J. Clin. Microbiol. Infect. Dis.31:1749–1752. 10.1007/s10096-011-1496-3 [PubMed] [CrossRef] [Google Scholar]
17. Verroken A, Janssens M, Berhin C, Bogaerts P, Huang TD, Wauters G, Glupczynski Y.2010. Evaluation of matrix-assisted laser desorption ionization–time of flight mass spectrometry for identification of Nocardia species. J. Clin. Microbiol.48:4015–4021. 10.1128/JCM.01234-10 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
18. Barbuddhe SB, Maier T, Schwarz G, Kostrzewa M, Hof H, Domann E, Chakraborty T, Hain T.2008. Rapid identification and typing of Listeria species by matrix-assisted laser desorption ionization–time of flight mass spectrometry. Appl. Environ. Microbiol.74:5402–5407. 10.1128/AEM.02689-07 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
19. Schmitt BH, Cunningham SA, Dailey AL, Gustafson DR, Patel R.2013. Identification of anaerobic bacteria by Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry with on-plate formic acid preparation. J. Clin. Microbiol.51:782–786. 10.1128/JCM.02420-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
20. Lau SK, Tang BS, Teng JL, Chan TM, Curreem SO, Fan RY, Ng RH, Chan JF, Yuen KY, Woo PC.18October2013. Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry for identification of clinically significant bacteria that are difficult to identify in clinical laboratories. J. Clin. Pathol.10.1136/jclinpath-2013-201818 [PubMed] [CrossRef] [Google Scholar]
21. Bizzini A, Jaton K, Romo D, Bille J, Prod'hom G, Greub G.2011. Matrix-assisted laser desorption ionization–time of flight mass spectrometry as an alternative to 16S rRNA gene sequencing for identification of difficult-to-identify bacterial strains. J. Clin. Microbiol.49:693–696. 10.1128/JCM.01463-10 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
22. Freiwald A, Sauer S.2009. Phylogenetic classification and identification of bacteria by mass spectrometry. Nat. Protoc.4:732–742. 10.1038/nprot.2009.37 [PubMed] [CrossRef] [Google Scholar]
23. Haigh J, Degun A, Eydmann M, Millar M, Wilks M.2011. Improved performance of bacterium and yeast identification by a commercial matrix-assisted laser desorption ionization–time of flight mass spectrometry system in the clinical microbiology laboratory. J. Clin. Microbiol.49:3441. 10.1128/JCM.00576-11 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
24. Theel ES, Schmitt BH, Hall L, Cunningham SA, Walchak RC, Patel R, Wengenack NL.2012. Formic acid-based direct, on-plate testing of yeast and Corynebacterium species by Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry. J. Clin. Microbiol.50:3093–3095. 10.1128/JCM.01045-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
25. Farfour E, Leto J, Barritault M, Barberis C, Meyer J, Dauphin B, Le Guern AS, Leflèche A, Badell E, Guiso N, Leclercq A, Le Monnier A, Lecuit M, Rodriguez-Nava V, Bergeron E, Raymond J, Vimont S, Bille E, Carbonnelle E, Guet-Revillet H, Lécuyer H, Beretti JL, Vay C, Berche P, Ferroni A, Nassif X, Join-Lambert O.2012. Evaluation of the Andromas matrix-assisted laser desorption ionization–time of flight mass spectrometry system for identification of aerobically growing Gram-positive bacilli. J. Clin. Microbiol.50:2702–2707. 10.1128/JCM.00368-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
26. McElvania Tekippe E, Shuey S, Winkler DW, Butler MA, Burnham CA.2013. Optimizing identification of clinically relevant Gram-positive organisms by use of the Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry system. J. Clin. Microbiol.51:1421–1427. 10.1128/JCM.02680-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
27. Goodfellow M, Parte A, Kämpfer P, Busse H-J, Trujillo ME, Suzuki K-I, Ludwig W, Whitman WB. (ed). 2012. Bergey's manual of systematic bacteriology, vol 5AThe Actinobacteria. Springer, New York, NY [Google Scholar]
28. Von Graevenitz A, Funke G.1996. An identification scheme for rapidly and aerobically growing gram-positive rods. Zentralbl. Bakteriol.284:246–254 [PubMed] [Google Scholar]
29. Clinical and Laboratory Standards Institute. 2008. Interpretative criteria for identification of bacteria and fungi by DNA target sequencing; Approved Guideline. CLSI document MM18-AClinical and Laboratory Standards Institute, Wayne, PA [Google Scholar]
30. Khamis A, Raoult D, La Scola B.2004. rpoB gene sequencing for identification of Corynebacterium species. J. Clin. Microbiol.42:3925–3931. 10.1128/JCM.42.9.3925-3931.2004 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
31. Khamis A, Raoult D, La Scola B.2005. Comparison between rpoB and 16S rRNA gene sequencing for molecular identification of 168 clinical isolates of Corynebacterium. J. Clin. Microbiol.43:1934–1936. 10.1128/JCM.43.4.1934-1936.2005 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
32. Schulthess B, Brodner K, Bloemberg GV, Zbinden R, Böttger EC, Hombach M.2013. Identification of Gram-positive cocci by use of matrix-assisted laser desorption-time of flight mass spectrometry: comparison of different preparation methods and implementation of a practical algorithm for routine diagnostics. J. Clin. Microbiol.51:1834–1840. 10.1128/JCM.02654-12 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
33. Viera AJ, Garrett JM.2005. Understanding interobserver agreement: the kappa statistic. Fam. Med.37:360–363 [PubMed] [Google Scholar]
34. Furness G, Sambury S, Evangelist AT.1979. Corynebacterium pseudogenitalium sp. nov. Commensals of the human male and female urogenital tracts. Invest. Urol.16:192–295 [PubMed] [Google Scholar]
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Phosphopeptides with one and four phosphate groups were characterized by MALDI mass spectrometry. The molecular ion of monophosphopeptide could be detected both as positive and negative ions by MALDI TOF with delayed extraction (DE) and in the reflector mode. The tetraphospho peptide could be detected in linear mode. When MS/MS spectra of the monophospho peptides were obtained in a MALDI TOF TOF instrument by CID, b and y ions with the intact phosphate group were observed, in addition the b and y ions without the phosphate group. Our study indicates that it is possible to detect phosphorylated peptides with out the loss of phosphate group by MALDI TOF as well as MALDI TOF TOF instruments with delayed extraction and in the reflector mode.
Keywords: phosphopeptides, mass spectrometry, positive ion, neutral loss, linear mode, reflector mode
Post-translational modification (PTM) of proteins by phosphorylation is extensively observed in biological systems. It is a dynamic process effecting the folding and function of proteins (; ). This process influences several cellular functions such as metabolic maintenance, cell division and signal transduction (; ; ). The large number of functions that are influenced by the phosphorylation indicates the diverse role played by phosphorylation. Several amino acids in proteins can be phosphorylated. These include the common and well-known O-phosphates of serine, threonine and tyrosine residues and also unusual amino acids such as hydroxyproline, hydroxylysine (). Some lesser-known phosphorylations on amino acids such as histidine, lysine (N-phosphates), cysteine (S-phosphate), aspartic, glutamic acids (acyl-phosphates) were also identified in some proteins (; ; ). In particular, phosphorylation of threonine, serine are known to play key roles in the regulation of the activities of the proteins involved in signal transduction in cells and is also important in the virulence mechanism of some pathogenic bacteria (; ; ).
Characterization of these wide varieties of phosphorylations demands the development of a large number of strategies and methodologies. In tune with the requirements, several strategies are being developed (; ; ; ; ; ).
Mass spectrometry is a powerful tool due its capability to analyze complex mixtures. It is versatile, easy to use, produces spectra with high mass accuracies and great sensitivity. It is being extensively used for the characterization of PTMs. MALDI TOF and ESI mass spectrometry have been used extensively to identify PTMs (; ; ; ; ; ; ; ). Some of these studies demonstrated neutral loss of phosphate group, HPO3 or H3PO4 from the peptides detected by a reduction of 80 or 98 Da in the mass, due to metastable decomposition during MALDI TOF experiments in the reflector mode of acquisition of the spectrum (; ; ). It was also shown that phosphoserine and phosphothreonine-containing peptides display significant intensity signals of the ions originating from the neutral loss of phosphoric acid, via gas phase β-elimination of the phosphate group (). It was observed that all the y and b daughter ions containing these phosphoresidues were associated with this loss of phosphoric acid (Paizs and Suhai, 2005). In this process, the phosphoserine and the phosphothreonine residues are converted to dehydroalanine and dehydroamino-2-butyric acid respectively (). Precursor ion scan and neutral ion scan methods can also be used to identify the phosphorylations in proteins and peptides (; ).
In source decay (ISD) and post source decay (PSD) have been used for the identification of the phosphorylation sites in peptides (; ; ; ; ). However, lack of spectral information in the mass range 100–700 due to the interference of matrix cluster ions in the ISD spectra limits the detection of phosphorylation sites in this mass region. PSD cannot be used routinely, due to poor fragmentation of the peptides.
Phosphopeptide identification from the tryptic digests of proteins was successfully carried out with the help of MALDI peptide mapping before and after phosphatase treatment that results in a mass shift of 80 Da due to the removal of phosphate moiety (; ; ; ). MALDI ion trap MS was also used for the identification of phosphopeptides from protein digests (). MALDI TOFMS has been used extensively for the detection of phosphopeptides. With delayed extraction of ions, it was shown to be a better choice as compared to the continuous extraction of ions (Vestal et al. 1995). Recent developments in the area of mass spectrometry, particularly with the availability of instrument such as MALDI TOF TOF could aid in not only detecting the phosphopeptides, but also mapping the site of phosphorylation.
In the present study, we show that mono phosphorylated peptides can be detected without loss of the phosphate group by MALDI mass spectrometry with delayed extraction, in the reflector mode. A tetra-phosphorylated peptide could be detected without loss of the phosphate groups in the linear mode. We have also observed that it is possible to detect peptide with the phosphate group intact when fragmented by CID in a MALDI TOF TOF instrument.
Materials and Methods
α-cyano-4-hydroxy cinnamic acid (HCCA), monophosphopeptide, FQ[pS]EEQQQTEDELQDK(F16Kp),tetraphosphopeptide,RELEENVPGEIVE[p S]L[pS][pS][pS]EESITR(R25R) of casein digest, enzymatically dephosphorylated casein were purchased from sigma chemical co(St.Louis, MO, USA). Peptides Ac-EGTHSFDG-am (E8G) and its phosphorylated form were synthesized using Fmoc chemistry and purified by reverse phase HPLC.
Trypsin digestion of protein
Casein (1picomole) was digested with trypsin using an enzyme to protein ratio of 1:50 in 25 mM ammonium bicarbonate buffer by incubating at 37 °C for 18 hours. The digest was dried on a speed vac concentrator and redissolved in 50% acetonitrile containing 0.1% trifluoroacetic acid before spotting on the MALDI target plate. The digest was also spiked with phosphopeptide (F16Kp) and spotted on the MALDI plate.
Preparation of Samples for MALDI Analysis
Peptides were dissolved in water (5 picomoles/μl) and 1 μl of the sample was spotted on the MALDI target plate. The sample was allowed to air dry and 1 μl of matrix was spotted (5 mg/ml of 50% ACN containing 0.1% TFA) allowed it to air dry. The MALDI plate was inserted in to the mass spectrometer to acquire the spectra.
A voyager DE-STR MALDI TOF mass spectrometer (Perceptive Biosystems, Framingham, MA, USA) was used for acquiring mass spectra in the positive ion reflector mode and linear mode. Negative ion spectra were also acquired. The mass spectrometer was fitted with a nitrogen laser (337 nm) for ionization. The laser-firing rate was 20 Hz. The grid voltage and delayed extraction time were optimized for obtaining good signals. The ion path length in linear and reflector mode is 2 meters and 3 meters respectively.
MALDI TOF TOF
The mass spectra of the phosphopeptides and the casein digest with and with out spiking with the phosphopeptide were also acquired using a 4800 MALDI TOF TOF analyzer obtained from Applied Biosystems (Foster city, CA). The mass spectrometer was fitted with a Nd:YAG laser (355 nm) to ionize samples. The laser-firing rate was 200 Hz. The ion path length of linear, reflector and MS/MS modes are 1.5 meters, 3 meters and 2.4 meters respectively. It consists of a high-energy collision induced (CID) cell and spectra were obtained using air as CID gas with 1 KV and 2 KV energy in the positive ion mode. TOF TOF or tandem mass spectrometry consists of two successive TOF accelerations. In tandem mass spectrometry the first acceleration selects, isolate and fragment (collision with neutral gas) a precursor ion selected for that purpose. The second acceleration accelerates the precursor ion and fragments, and measures masses and intensities of fragment ions. The instrument is also capable of deflecting matrix ions and suppresses metastable ions.
Results and Discussion
Several lines of evidence indicate that neutral loss of phosphate group occurs in phosphopeptides generated from ingel digest of proteins during the MALDI TOF acquisition of mass spectra in the reflector mode (; ). These peptides contain free amino group and carboxyl groups. In order to examine whether capping of the N and C termini modulates dephosphorylation during MALDI analysis, we examined the mass spectrum of E8G and E8Gp. The peptides with serine phosphorylated and non-phosphorylated stretching the region from residues 76 to 83 of the caveolin 1 (cav-1) were synthesized with capping of N and C terminals. Serine phosphorylation in this protein converts Cav-1 to a secretary protein from an integral membrane protein (). The peptides exhibited molecular ion at m/z 890.37(Theoretical MH+ 890.36) and 970.33(Theoretical MH+ 970.33) respectively. The positive ion mass spectra of these peptides E8G and E8Gp recorded in the reflector mode of analysis are shown in Figures 1A and and1B.1B. It is clear from Figure 1B that neutral loss of phosphate group was not observed in the reflector mode of spectrum acquisition with DE.
MALDI-TOF mass spectra of the peptides E8G (panel A) and E8Gp (panel B) recorded in the reflector mode using HCCA as matrix. The isotopic peaks of the peptides are also shown.
The mass spectrum of the phosphopeptide F16Kp with free amino and carboxyl groups exhibited molecular ion at m/z 2061.81 (Theoretical MH+ 2061.82) (Fig. 2). The molecular ion of this peptide was observed with good intensity and without any neutral loss of phosphate group. Hence it appears that presence or absence of blocking group at the N- or C- terminal has no effect on the neutral loss of phosphate group. Form these studies it is clear that phosphopeptides cannot be distinguished from non-phosphorylated peptides from the peptide mass fingerprints used in the identification of proteins using MALDI TOF analysis with delayed extraction and in the reflector mode.
The MALDI TOF mass spectrum of the phosphopeptide F16Kp showing the molecular ion. Spectrum was recorded in the reflector mode. The peaks arising from the loss of phosphoric acid and phosphate moiety from the parent ion (M) are also shown.
In order to examine whether multiply phosphorylated peptides can be detected, the mass spectrum of a tetraphospho peptide from casein digest was examined. The molecular ion of this peptide could not be obtained in the reflector mode. In the linear mode, the molecular ion could be detected at m/z 3124.49 (Theoretical MH+ 3123.92) using Voyager DE STR mass spectrometer. The MALDI TOF mass spectrum of this peptide is shown in Figure 3. However, molecular ion for the tetra phosphopeptide could not be detected even in the linear mode of analysis with 4800 MALDI TOF TOF (data not shown).
The MALDI TOF mass spectrum of the tetraphosphopeptide R25R recorded in the linear mode on Voyager DE STR MALDI TOF instrument. The loss of phosphoric acid from the parent ion (M) was indicated in the spectrum. The additional peaks are some impurities present in the sample.
In order to determine the stability of phosphate group during fragmentation, CID mass spectra of the peptidesE8Gp and F16Kp were recorded using 4800 MALDI TOF TOF mass analyzer using HCCA as matrix in the reflector mode. The results are shown in the Figures 4 and and5.5. The fragmentation of these peptides is shown in the inset of the mass spectra. The fragment ions of the peptides with loss of phosphate groups from y and b ions are shown in Table 1 and and2.2. Examination of the fragments of the bn ion series indicates that the mass difference between b5 and b4 is 167 Da, corresponding to the b-ion of phosphoserine. Therefore, this establishes that in peptide E8Gp there is no loss of phosphate from phosphoserine. Further, the analysis of the b-type ions indicates the presence b5, b6 and b7 of the phosphoserine containing fragments at m/z 634.13, 781.18 and 896.19 respectively. b ions which correspond to loss of phosphate are also observed. These ions are identified at m/z 536.18, 683.23 and 798.24 that arise from ions b5 -98, b6 -98 and b7 -98 respectively (see inset of Fig. 4). Similarly, the y-type ions after phosphoserine residue y4, y5, y6 and y7 are observed at m/z 504.14, 641.16, 742.20 and 799.20 respectively. The ions corresponding to the loss of phosphate group from these ions i.e. y4-98, y5-98, y7-98 are observed at m/z 406.15, 543.20, and 701.25 respectively. The loss of phosphate group from y6 ion is not observed. Taken together, the fragmentation of b type and y type ions coupled with the fragments arising from the loss of phosphate group the location of the phosphoserine could be localized at residue 5. The b-type ions are more intense as compared to the y-type ions (Table 1, Fig. 4). A similar analysis of F16Kp indicates the location of the phosphoserine at residue 3. Figure 5 shows the MS/MS spectrum of F16Kp and Table 2 shows the characteristic peaks for the identification of the sequence of the peptide. Analysis of yn series of ions of F16Kp reveals that the mass difference between y14 at m/z 1619.80 and y13 at m/z 1786.81 is 167 Da corresponding to the y ion of phosphoserine. Loss of H3PO4 from the molecular ion appeared at m/z 1963.97 and it is the most intense peak in the spectrum. This suggests the presence of a phosphoamino acid in the peptide. Loss of phosphate moiety from y14 ion detected at m/z 1688.86. A series of b ions of the peptide are observed at m/z 829.35, 957.46, 1085.50, 1186.57, 1315.59, 1430.62, 1559.30 and 1673.38 corresponding to b6 to b13 respectively. Loss of phosphate group from these fragments is also found at m/z 731.36, 859.44, 981.51, 1088.58, 1217.62, 1332.64, 1461.72 and 1574.77. The intensity of yn series of ions is more as compared to the bn series of ions (Table 2, Fig. 5). Thus, form these fragments the phosphorylation site in the peptides could be detected.
The CID mass spectrum of the phosphopeptide E8Gp recorded on 4800 MALDI TOF TOF mass analyzer. Panel A shows the fragmentation pattern of the peptide. Δ indicates the corresponding bn or yn ion minus H3PO4. Panel B shows the MS/MS spectrum of E8Gp. Number next to bn or yn ion indicates loss of H3PO4 .The inset shows expanded region of the spectrum showing b-type and y-type ions.
The CID mass spectrum of the monophospho peptide F16Kp recorded using 4800 MALDI TOF TOF mass analyzer. Panel A shows the fragmentation pattern of the peptide. Δ indicates the corresponding bn or yn ion minus H3PO4. Panel B shows the MS/MS spectrum of F16Kp. Number next to bn or yn ion indicates loss of H3PO4 .The inset shows some expanded region of the spectrum showing y-type and b-type ions.
Characteristic daughter ions obtained from MS/MS of the phosphopeptide E8Gp.
Characteristic daughter ions obtained from MS/MS of the phosphopeptide F16Kp.
Trypsin digest of Casein with and with out spiking with the phosphopeptide (F16Kp) was also analyzed. Figure 6 shows the PMF of the casein digest spiked with the phosphopeptide. The protein could be identified upon searching with database and the CID mass spectrum of phosphorylated peptide was shown in the inset Figure 6, which was same as the MS/MS of the purified phosphopeptide. This shows that the phosphopeptides could be identified form the peptide mass fingure print of the protein.
The trypsin digest of the protein casein spiked with the phosphopeptide. The inset shows the CID mass spectrum of the monophosphopeptide. The symbol (*) in the mass spectrum indicates the spiked peptide.
In conclusion, neutral loss of phosphate group was not observed in the mass spectrum of serine phosphorylated monophospho peptides in the reflector mode of analysis using either voyager DE STR MALDI TOF or 4800 MALDI TOF TOF mass spectrometers. The earlier analysis of phosphopeptides using MALDI TOF mass spectrometers contain single stage acceleration regions and have no control over the voltages that define the initial acceleration region and the fragmentation using different mass spectrometers might be different. However, introduction of delayed extraction (DE) in these mass spectrometers greatly helps in controlling the side chain fragmentation, improves mass accuracy and resolution (Juhasz et al. 1997; Vestal et al. 1995). The molecular ion of a tetra phosphate peptide could be detected in linear mode of analysis using Voyager MALDI TOF mass spectrometer. Low laser pulse rate (3 or 20 Hz), higher laser energy (337 nm) might have played a role in detecting the molecular ion of the tetra phosphopeptide in the Voyager DE STR MALDI TOF MS.
The phosphorylation sites could be detected with the help of CID MS spectra recorded with MALDI TOF TOF mass analyzer by direct observation of phosphorylated b and y ions in addition to their dephosphorylated counterparts. Thus, the instrumental design and the experimental conditions play an important role in the analysis of phosphopeptides.
We thank Mr. E. Bikshapathy, Ms. T. Lalita Prabha and Ms. Spardh Khera for their help in the synthesis and purification of the Peptides. Research grant to MVJ from Department of Biotechnology (DBT), Government of India, New Delhi is gratefully acknowledged.
- Annan RS, Carr SA. Phosphopeptide Analysis by Matrix-Assisted Laser Desorption Time-of-Flight Mass Spectrometry. Anal Chem. 1996;68:3413–21. [PubMed] [Google Scholar]
- Chaurand P, Luetzenkirchen F, Spengler B. Peptide and protein identification by matrix-assisted laser desorption ionization (MALDI) and MALDI-post-source decay time-of-flight mass spectrometry. J Am Soc Mass Spectrom. 1999;10:91–103. [PubMed] [Google Scholar]
- Domon B, Abersold R. Mass spectrometry and protein analysis. Science. 2006;312:212–17. [PubMed] [Google Scholar]
- Hardouin J, Hubere-Roux M, Delmas AF, et al. Identification of isoenzymes using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Rapid Commun Mass spectrum. 2006;20:725–32. [PubMed] [Google Scholar]
- Hoffmann R, Metzger S, Spengler B, et al. Sequencing of peptides phosphorylated on serines and threonines by post-source decay in matrix assisted laser desorption/Ionization time —of-flight mass spectrometry. J Mass Spectrom. 1999;34:1195–204. [PubMed] [Google Scholar]
- Hjerrild M, Gammeltoft S. Phosphoproteomics tool box: Computational biology, proteinchemistry and mass spectrometry. FEBS lett. 2006;580:4764–70. [PubMed] [Google Scholar]
- Jonscher KR, Yates JR., III Matrix-assisted laser desorption ionization/quadrupole ion trap mass spectrometry of peptides. Application to the localization of phosphorylation sites on the P protein from Sendai virus. J Biol Chem. 1997;272:1735–41. [PubMed] [Google Scholar]
- Juhasz P, Vestal ML, Martin SA. On the initial velocity of ions generated by matrix-assisted laser desorption Ionization and its effect on the calibration of delayed extraction time -of -flight mass spectra. J Amer Soc Mass spectrom. 1997;8:209–17.[Google Scholar]
- Kalume DE, Molina H, Pandey A. Tackling the phosphoproteome: tools and strategies. Curr Opin Chem Biol. 2003;7:64–9. [PubMed] [Google Scholar]
- Kinumi T, Niwa H, Matsumoto H. Phosphopeptide sequencing by in-source decay spectrum in delayed extraction matrix-assisted laser desorption ionization time-of -flight mass spectrometry. Anal Biochem. 2000;277:177–86. [PubMed] [Google Scholar]
- Larsen MR, Sørensen GL, Stephen JF, Larsen PM, et al. Phospho-proteomics: Evaluation of the use of enzymatic dephosphorylation and differential mass spectrometric peptide mass mapping for site specific phosphorylation assignment in proteins separated by gel electrophoresis. Proteomics. 2001;1:223–38. [PubMed] [Google Scholar]
- Larsen MR, Roepstorff P. Mass spectrometric identification of proteins and Characterization of their post translational modifications in proteome analysis. Fresenius J Anal Chem. 2000;366:677–90. [PubMed] [Google Scholar]
- Lee CH, McComb ME, Bromirski M, et al. On-membrane digestion of beta-casein for determination of phosphrylation sites by matrix-assisted laserdesorption/ionizationquadrupole/time of flight mass spectrometry. Rapid Commun Mass Spectrom. 2001;15:191–202. [PubMed] [Google Scholar]
- Lennon JJ, Walsh KA. Locating and identifying posttranslational modifications by in-source decay during MALDI-TOF mass spectrometry. Protein Sci. 1999;8:2487–93.[PMC free article] [PubMed] [Google Scholar]
- Liao PC, Leykam J, Andrews PC, et al. An Approach to Locate Phosphorylation Sites in a Phosphoprotein: Mass Mapping by Combining Specific Enzymatic Degradation with Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. Anal Biochem. 1994;219:9–20. [PubMed] [Google Scholar]
- Loyet KM, Stult JT, Arnott D. Mass spectrometric contributions to the practice of phosphorylation site mapping through 2003: a literature review. Moll Cell Proteomics. 2005;4:235–45. [PubMed] [Google Scholar]
- McLachilin DT, Chait BT. Analysis of phosphorylated proteins and peptides by mass spectrometry. Curr Opin Chem Biol. 2001;5:591–602. [PubMed] [Google Scholar]
- Molle V, Zanella-Cleon I, Robin JP, et al. Characterization of the Phosphorylation sites of Mycobacterium tuberculosis serine/threonine Protein kinases, PknA,PknD,PknE, and PknH by mass spectrometry. Proteomics. 2006;6:3754–66. [PubMed] [Google Scholar]
- Moran MF, Tong J, Taylor P, et al. Emerging applications for phosphoproteomics in cancer molecular therapeutics. Biochem Biophys Acta. 2006;1776:230–41. [PubMed] [Google Scholar]
- Morandell S, Stasyk T, Grosstessener-Hain K, et al. Phosphoproteomics strategies for the functional analysis of signal transduction. Proteomics. 2006;6:4047–56. [PubMed] [Google Scholar]
- Mumby M, Brekenn D. Phosphoproteomics: new insights in to cellular signaling. Genome boil. 2005;6:230.[PMC free article] [PubMed] [Google Scholar]
- Neubauer G, Mann M. Mapping of phosphorylation sites of gelisolated proteins by nanoelectrospray tandem mass spectrometry: potentials and limitations. Anal Chem. 1999;71:235–42. [PubMed] [Google Scholar]
- Papavinasasundaram KG, Chan B, Chung JHM, et al. Deletion of the Mycobacterium tuberculosis pknH Gene Confers a Higher Bacillary oad during the Chronic Phase of Infection in BALB/c Mice. J Bacteriol. 2005;87:5751–60.[PMC free article] [PubMed] [Google Scholar]
- Peirs P, Lefèvre P, Boarbi S, et al. Mycobacterium tuberculosis with Disruption in Genes Encoding the Binding Proteins PstS1 and PstS2 Is Deficient in Phosphate Uptake and Demonstrates Reduced In Vivo Virulence. Infect Immun. 2005;73:1898–902.[PMC free article] [PubMed] [Google Scholar]
- Piazs B, Suhai S. Fragmentation pathways of protonated peptides. Mass Spectrom Rev. 2005;24:508–48. [PubMed] [Google Scholar]
- Ptacek J, Synder M. Charging it up: Global analysis of protein phosphorylation. Trends Genet. 2006;22:545–54. [PubMed] [Google Scholar]
- Qin J, Chait BT. Identification and Characterization of Post-translational Modifications of Proteins by MALDI Ion Trap Mass Spectrometry. Anal Chem. 1997;69:4002–9. [PubMed] [Google Scholar]
- Reinders J, Sickmann A. State of the art in phosphoproteomics. Proteomics. 2005;5:4052–61. [PubMed] [Google Scholar]
- Salih E. Phosphoproteomics by mass spectrometry and classical protein chemistry approaches. Mass Spectrom rev. 2005;24:828–46. [PubMed] [Google Scholar]
- Schlegel A, Arvan P, Lisanti MP. Caveolin-1 binding to endoplasmic reticulam membranes and entry in to regulated secretary pathway are regulated by serine phosphorylation Protein sorting at the level of the endoplasmic reticulam. J Biol Chem. 2001;276:4398–408. [PubMed] [Google Scholar]
- Sickmann A, Meyer HE. Phosphoaminoacid analysis. Proteomics. 2001;1:200–06. [PubMed] [Google Scholar]
- Stock AM, Robinson VL, Goudreu PN. Two-component signal transduction. Ann Rev Biochem. 2000;69:183–215. [PubMed] [Google Scholar]
- Talbo GH, Suckau D, Malkoski M, et al. MALDI-PSD-MS analysis of the phosphorylation sites of caseinomacropeptides. Peptides. 2001;22:1093–8. [PubMed] [Google Scholar]
- Tholey A, Lindmann A, Kinzal V, et al. Direct effects of phosphorylation on the preferred backbone conformation of peptides: a nuclear magnetic resonance study. Biophys J. 1999a;76:76–87.[PMC free article] [PubMed] [Google Scholar]
- Tholey A, Reed J, Lehmann WD. Electrospray tandem mass spectrometric studies of phosphopeptides and phosphopeptides analogs. J mass spectrom. 1999b;34:117–23. [PubMed] [Google Scholar]
- Vestal ML, Juhasz P, Martin SA. Delayed extraction matrix-assisted laser desorption time -of-flight mass spectrometry. Rapid Commun Mass Spectrom. 1995;9:1044–50.[Google Scholar]
- Walburger A, Koul A, Ferrari G, et al. Protein Kinase G from Pathogenic Mycobacteria Promotes Survival Within Macrophages. Science. 2004;304:1800–04. [PubMed] [Google Scholar]
- Wang YK, Liao PC, Allison J, et al. Phorbol 12-myristate 13-acetate-induced phosphorylation of Op18 in Jurkat T cells. Identification of phosphorylation sites by matrix-assisted laser desorption ionization mass spectrometry. J Biol Chem. 1993;268:14269–77. [PubMed] [Google Scholar]
- Wang J, Zhang Y, Jiang H, et al. Phosphopeptide detection using automated on line IMAC-capillary LC-ESI-MS/MS. Proteomics. 2006;6:404–11. [PubMed] [Google Scholar]
- Yan JX, Packer NH, Gooley AA, Williams KL. Protein phosphorylation: Technologies for the identification of phosphoamino acids. J Chromatogr A. 1998;808:23–41. [PubMed] [Google Scholar]
- Zhang W, Czernik AJ, Yungwirth T, et al. Matrix-assisted laser desorption mass spectrometric peptide mapping of proteins separated by two-dimensional gel electrophoresis: determination of phosphorylation in synapsin I. Protein Sci. 1994;3:677–86.[PMC free article] [PubMed] [Google Scholar]
- Zhang X, Herring CJ, Romano PR, et al. Identification of phosphorylation sites in proteins separated by polyacrylamide gel electrophoresis. Anal Chem. 1998;70:2050–9. [PubMed] [Google Scholar]
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