based species identification an - Wiley Online Library

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DOI 10.1002/pmic.201200468

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REVIEW

MALDI-TOF-MS-based species identification and typing approaches in medical mycology Oliver Bader Institute for Medical Microbiology and German National Reference Center for Systemic Mycoses, University ¨ ¨ Medical Center Gottingen, Gottingen, Germany

MALDI-TOF MS-based species identification has found its place in many clinical routine diagnostic laboratories over the past years. Several well-established commercial systems exist and these allow precise analyses not only among bacteria, but also among clinically important yeasts. This methodology shows higher precision than biochemical and microscopic methods at significantly reduced turnaround times. Furthermore, the differentiation of different filamentous fungi including most dermatophytes and zygomycetes has been established. The direct identification of yeasts from blood culture bottles will be possible in a routine fashion with new standardized procedures. In addition to species identification, the MALDI-TOF MS technology offers several further possibilities, like assays to detect or predict resistance phenotypes in fungi as well as subtyping approaches to detect clinically relevant subgroups. The differences between the commercial systems are discussed with respect to fungi and an overview of their performances provided. Factors influencing outcome of MALDI-TOF-based species identification are discussed.

Received: October 13, 2012 Revised: November 17, 2012 Accepted: November 24, 2012

Keywords: Fungi / Intact cell MS / Microbiology / Mycology / Species identification

1

Introduction

Early onset of antifungal therapy is a critical step in the treatment of fungal infections. Fast, reliable, and definite identification of the pathogen can lead to the elimination of ineffective (e.g. azoles in the case of Candida glabrata or Candida krusei) or costly (echinocandins in the case of Candida albicans) therapies initiated in response to a microscopic or agar-based identification of a fungus-positive culture. Coming from phenotypic biochemical assays and microscopy, advancing to laborious and expensive DNA sequencing/hybridization approaches, the field of microbial species identification has now been taken over by the purely

Correspondence: Dr. Oliver Bader, Institute for Medical Microbiology and German National Reference Center for Systemic My¨ coses, University Medical Center Gottingen, Kreuzbergring 57, ¨ 37075 Gottingen, Germany E-mail: [email protected] Fax: +49 551 39 5861 Abbreviations: BC, blood culture; ITS, internally transcribed spacer.

C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

biophysical and bioinformatics-driven MALDI-TOF MS approach. MALDI-TOF MS-based species identification has been described as an “ongoing revolution in medical microbiology” [1]. In fact, it probably presents the major paradigm change of the past decades in the way species are determined in microbiology. The idea of such a method already dates back to the 1980s (summarized in [2, 3]) and has been first used to type yeasts in 2001 [4]. With respect to routine diagnostics, it has only started advancing in the past few years with the availability of commercialized systems. Computer hardware, database technologies, and MALDI-TOF MS machines have now reached a level at which the identification of complex mass spectra in databases has become possible in a standardized fashion and at speeds relevant for diagnostic procedures. With modern lasers and fast computer processors, mass spectrum acquisition and classification from a well-prepared analyte takes only a few seconds. Compared to biochemical procedures involving over night growth, this is a highly time-saving procedure and can significantly speed up diagnostic processes. Currently, there are four commercial systems in use: the MALDI Biotyper (Bruker Daltonics, Bremen, Germany), the AXIMA@SARAMIS database (AnagnosTec, Potsdam, www.proteomics-journal.com

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Germany and Shimadzu, Duisburg, Germany), and of late the Andromas (Andromas, Paris, France) and VITEK MS systems (bioMérieux, Marcy l’Etoile, France). For a general description of the algorithms used for spectrum identification, the reader is referred to [5]. The systems are well established in bacteriology [6–10], but several issues remain in mycology. The differences between the systems and their performances with respect to fungi are discussed below and a detailed analysis of factors influencing the outcome of MALDI-TOF-based fungal species identifications is provided.

2

abundance of the underlying analytes, with small molecules flying faster than large ones. The list of peak masses above the S/N threshold is classified in a central database by comparison to those lists obtained from reference spectra. These spectra are highly reproducible within a species (Fig. 1B), but sufficiently different even between highly related species (Fig. 1C) to allow a precise species determination. Where the identity of mass peaks has been investigated, they corresponded to highly abundant intracellular proteins such as ubiquitin in Saccharomyces cerevisiae [4] or ribosomal proteins in Escherischia coli [13].

Principle of intact cell MS 3

For all systems, pure cells prepared from cultured material (mainly agar plates, but also from liquids such as blood cultures) or in some cases even clinical material (e.g. urine) may serve as the analyte. The agar of choice is Sabouraud or Columbia blood, but also the analysis from Chromagar plates is possible [11, 12]. In the basic protocol, a small amount of intact cells is directly streaked onto the MALDI target plate (therefore called “intact cell mass spectrometry” (ICMS)) and overlaid with a small amount of matrix solution lysing the cells. Drying embeds the released cell components into a crystalline matrix, usually CHCA ( also termed “HCCA”), less frequently 2,5-DHB, or sinapinic acid (SA). Matrices serve as the chemical ionizing agent as well as for energy transfer from the laser to the analyte. The analyte is desorbed from the target plate by laser fire; the resulting ions are accelerated in an electric field and focused to fly along the flight tube (Fig. 1A). Time-resolved impact on the detector yields a characteristic spectrum where the observed peaks represent m/z ratio and

Yeast identification starting from colonies

Among fungi, ascomycetous and basidiomycetous yeasts including Candida, Pichia, and Cryptococcus genera are most easily processed and analyzed. They are efficiently lysed with the recommended procedures (see below) and their uniform growth on agar plates yields highly reproducible spectra (Fig. 1B). Several independent studies have looked at successes and pitfalls of MALDI-TOF MS identification of yeasts (Table 1). Most studies are of a retrospective character, at least in part, but several studies have also looked at the performance under routine conditions. All studies evaluating these databases’ performances for yeasts have used biochemical identification (either combinations of API strips and microscopy or VITEK II) as the gold standard. Only for discordant cases partial sequencing of the rDNA/ITS loci was used as a tie-breaker.

Figure 1. Principle of intact cell MS (A) schematic of a MALDI-TOF mass spectrometer. (B) Mass spectra from three independent Candida parapsilosis isolates taken during diagnostic routine identification. (C) Comparison of mass spectra from Candida glabrata (top) and Candida bracarensis (bottom), also taken during diagnostic routine identification.


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Table 1. Species identification system performances Study typea)

Typing systemb) (library version)

Study focus, comments

Ascomycetous and basidiomycetous yeasts R/P Biotyper (unknown) Database evaluation R/P Biotyper (V1.1.0.0d) ) Clinical routine identification P Biotyper (V2.0.4.0) Identification of bacteria R Biotyper (self-made) Database evaluation R/P Biotyper (V2.0.4.0) Database evaluation R Biotyper (V2.0.10) System comparison R SARAMIS (unknown) System comparison R Biotyper (unknown) Candida palmioleophila complex R/P Biotyper (V3.1.1.0) Clinical routine identification R SARAMIS (unknown) Database evaluation R/P Biotyper (unknown) Candida ortho/meta/ parapsilosis complex R/P Biotyper (V2.0.1.0) Cryptococcus identification R SARAMIS (unknown) Rare yeasts, DHB instead of HCCA matrix R/P Biotyper (V3.1.2.1) Clinical routine identification P Andromas (V2010) Clinical routine identification P Biotyper (V3.1.1.0) Clinical routine identification P VITEK-MS (V. 1.0) Clinical routine identification R Biotyper (unknown) Cryptococcus neoformans/ gattii subtyping R Biotyper (V3.1.1.0) Cryptococcus neoformans/ gattii subtyping R/P Biotyper (unknown) System comparison R/P SARAMIS (unknown) System comparison R Biotyper (unknown) Candida famata reclassification Ascomycetous yeasts, using lowered cutoffs for MALDI Biotyper R Biotyper (self-made) Database evaluation R Biotyper (unknown) Candida ortho/meta/ parapsilosis complex R/P Biotyper (V3.1.1.0) Clinical routine identification R Biotyper (unknown) Use of lowered scores R/P Biotyper (V3.1.2.0) Clinical routine identification R Biotyper (unknown) Growth on Chromagar Filamentous fungi R SARAMIS R SARAMIS R/P R

Andromas (unknown) Biotyper (self-made)

R

MS Excel (self-made)

R

Biotyper (self-made)

P R R/P R P R R

Andromas (V 2010) Biotyper Biotyper (self-made) Biotyper (self-made) VITEK-MS (V1.0) SARAMIS (unknown) Andromas (unknown)

Dermatophytes Fusarium proliferatur; DHB matrix Aspergillus spec. Pseudallescheria/ Scedosporium-complex Aspergillus spec, visual peak comparison Aspergillus, Fusarium, Mucorales spec. Clinical routine identification Dermatophytes Clinical routine identification Zygomycetes Clinical routine identification Dermatophytes; DHB matrix Dermatophytes

Species (+ not in DB)

Positive IDs of test isolates

Referencec)

Before (+ not in DB)

After addition to DB

14 (+11) 14 5 23 16 (+3) 25 (+7) 22 (+10) 4 (+2)

247/247 (+20) 18/19 24 / 24 192/194 257/297 (+4) 1163/1175 (+17) 1145/1152 (+40) 21/27 (+14)

267/267 n.d. n.d. n.d. 37/41

[14] [15] [16] [17] [18] [19] [19] [20]

33 (+12) 7 3

211/241 (+17) 67/73 103/103

n.d. -

[21] [30] [24]

8 10 (+3)

136/137 61/61 (+12)

73/73

[23] [31]

25 (+2) 19 (+1) 26 (+1) 14 (+3) 2

193/232 (+2) 160/160 (+2) 1176/1205 (+2) 184/187 (+5) 7/8

n.d. n.d. n.d. n.d. -

[22] [32] [25] [12] [26]

2

82/83

-

[27]

14 14 10 (+5)

182/201 190/201 44/47 (+6)

-

[28] [28] [29]

23 4

156/194 163/163

-

[17] [21]

33 (+13) 7 25 (+2) 13

232/241 (+17) 52/54 223/232 (+2) 174/183

n.d. n.d. -

[34] [34] [22] [11]

6 1

20/20 1/1

-

[100] [64]

24 2

138/140 2/3

n.d.

[57] [101]

3

10/10

-

[66]

24

91/94 (+9)

n.d.

[54]

6

63/64

-

17 (+16) 7 3 (+6) 21 10

154/156 (+21) 39/41 36/36 (+8) 379/381

n.d. n.d. 285/285e) -

[32] [33] [55] [65] [12] [68] [102]

a) R: retrospective (clinical culture collections); P: prospective clinical samples collected during study period. b) If not indicated otherwise, sample preparation according to the manufacturers’ guidelines (extraction for Biotyper, on-target-lysis for SARAMIS, VITEK-MS, and Andromas). “Self-made” denotes those studies, where initially a new database was created and evaluated, but, to the best of the author’s knowledge, the data was not integrated into the respective commercial product. c) Listed chronologically by date of manuscript acceptance. d) Database version number estimated from number of spectra given in the reference. e) Reidentification of strains used to build database only. DB, database. C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim


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Across all studies, isolates of species represented in the respective database could be readily identified with high accuracy, independently of instrument type: here, cumulated success rates were 95.7% with the MALDI Biotyper (4059/4247 isolates tested across 16 studies [14–29]), 98.4% with the SARAMIS system (1463/1487 isolates tested across four studies [19, 28, 30, 31]), and 98.4% and 100% with the VITEK-MS and Andromas databases, respectively, although these systems have not yet been tested to the same extent as the others (183/184 and 160/160 isolates in one study each partially funded [12] or fully performed [32] by the manufacturers). Most importantly, it was the general observation across all studies that no major errors were observed (i.e. false classifications), but that isolates of species not represented in the databases were rather interpreted as “unknown.” In some cases where identification was not possible at the first attempt, this could be achieved by simply repeating the analysis [19, 21, 22]. Only in one study [18] isolates were found that were not identifiable due to “database incompleteness,” meaning that the isolates were misidentified because intraspecies spectral variability was insufficiently represented by the set of reference spectra. In other cases, discordant identifications could be resolved by reinvestigating the type strains deposited in the database [19] or removing improper databases entries [33]. All yeast species commonly encountered in clinical routine procedures were included in the databases, and previously unrepresented species became identifiable after addition of spectra to the databases [14, 20, 31]. Where MALDI results and biochemical identification were discordant, rDNA sequencing of the internally transcribed spacer (ITS) region worked in favor of the MALDI results (e.g. [14, 19, 21, 22, 34]) demonstrating the superiority of this method. Furthermore, closely related yeast species which cannot be discriminated with common biochemical methods such as the Candida ortho-/meta-/parapsilosis [24, 30, 31, 35, 36], Candida glabrata/bracarensis/nivariensis [31], C. albicans/dubliniensis [31], Candida haemulonii group I and II [37] complexes, or the phenotypically similar species Candida palmioleophila, Candida famata, and Candida guilliermondii [20, 29, 38] can be resolved without difficulty by MALDI-TOF MS. The use of biochemistry as the gold standard and rDNA sequencing as a tiebreaker only leaves room for potential errors, where biochemical identification and MALDI may give concordant, but wrong identifications. However, since the same may be true for fully sequence-based identification procedures in the case of incorrect sequence database entries, this problem cannot be fully resolved and this approach represents the only one within reasonable work-load and financial constraints. This is demonstrated by previously falsely typed reference strains included into the databases [17, 19] as sources of error.


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Direct identification of yeasts from blood cultures

Blood cultures (BCs) have a special role in medical microbiology. They usually stem from the most critically ill patients and positive BCs have the most profound impact on antimicrobial therapy. As with bacteria, direct MALDI identification of microorganisms from positively flagged BCs is hampered by the fact that highly abundant substances from the culture media (e.g. charcoal [39] and cations other than H+ ) as well as from human blood (e.g. hemoglobin or albumin [40]) disturb the identification process. Some blood and blood culture medium components give rise to mass peaks that partially overlap with spectra from yeasts [40], necessitating a proper purification procedure before extraction. Also, in contrast to pure colonies picked from agars, multiple species may be present and their combined mass patterns may, if at all, result in low identification scores or ambiguous results [39,41]. While the second problem is still unsolved, several technical approaches to the first have been evaluated [42, 43]. To remove human blood components and culture medium, protocols involving gel matrices [42, 44], filtration devices [45], saponification [46], or differential centrifugation [41, 47] can be used. In addition to several washing steps with distilled water [40, 47] low concentrations of detergents such as SDS [40] or Tween80 [41] can increase analyte purity. For the lack of sufficient numbers of yeast-positive BCs in routine diagnostics, most studies evaluating such strategies have used artificially spiked BCs. With the exception of one study [41], results from positive BCs of clinical origin are low in numbers and represent 2.0 were counted as successful, as this cutoff allows automated validation on species level.

[41] 1/6 6/10 6/8 28/32 65/69 22/26 187/195

[43] 0/2 1/1 1/2 2/7 4/5

[40] [103] 5/5 1/1 5/5 5/5 5/5 5/5 8/8 5/5 5/5 28/28

[47] 0/1 0/9 0/8

10/10 20/20

Andromas (unknown) Biotyper (V2.0.4.0) Other Biotyper (V3.1.1.0) Biotyper (V3.1.1.0) Biotyper (unknown) BacT/Alert w/o charcoal BACTEC+ S/P

Extraction with TFA only

Other Candida guilliermondii Candida krusei Candida tropicalis Candida parapsilosis Candida glabrata Candida albicans BC type Exp. typea)

Sample preparation

Typing system (library version)b) Table 2. Direct identification from blood cultures

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Reference

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(usually 0.5–1 ␮L) of matrix solution. The solvent (usually 50% ACN and 0.1–2.5% TFA) in the matrix (CHCA, DHB, or sinapinic acid [50]) solution will sufficiently lyse the cells and once the analyte has dried, the released molecules are embedded in the crystallized matrix. This protocol is appropriate for most bacteria and several yeasts, but due to their stronger cell walls, fungal cells (as well as some bacterial species such as mycobacteria [51]) do not necessarily lyse to the same extent and may not sufficiently release their intracellular contents under these conditions. Therefore, several different preparation procedures (Fig. 2A), matrix compounds, and solvent compositions [52] have been tested for fungal cells with varying morphologies. Next to the simple smear protocol, several variants of extraction steps exist. In the simplest version (here referred to as “on-target-lysis,” in the literature also called “short extraction” [11, 34, 53]) the smear is overlaid with 0.5–1 ␮L 25–70% formic acid and left to dry before overlaying with the matrix. Others have also used absolute ethanol at this point [54] as ethanol fixation prior to analysis has previously been shown to increase peak numbers [50], probably aiding in cell lysis. In cases where on-target-lysis is not sufficient, cells can also be harvested and washed in 70% ethanol, dried, and then lysed in 70% formic acid (in the literature referred to as “extraction”) followed by ACN addition (increased protein solubilization). The debris is removed by centrifugation, and the extract spotted. This method also increases removal of residual ions from medium, extracellular matrix (e.g. from Cryptococci) and other potentially inhibiting substances. It generally leads to an increased number of distinct peaks (Fig. 2B) sufficient for classification in all systems. For cells that still do not lyse efficiently, other extraction methods like mechanical disruption in a bead-beater can be applied [4, 55, 56]. Obviously, the best preparation method to use for the system at hands is the same method with which the spectra for the database have been made, since this will lead to the highest concordance between acquired test spectra and reference spectra in the identification database. In the case of the MALDI Biotyper this is extraction, in the case of the SARAMIS, VITEK-MS, and Andromas systems this is ontarget-lysis. Since the on-target-lysis preparation method is significantly faster and requires less hands-on-time than extraction, several groups have started to evaluate applicability of this method to yeasts for use with the MALDI Biotyper system [9, 11, 17, 21, 22, 34]. The technical problem to be overcome here is that this method generates fewer peaks with a S/N ratio above the specified threshold ratio than extraction. As outlined above, the MALDI Biotyper reference spectra have been made by extraction and on-target-lysis leads to decreased “log-scores,” a “log-score” being the measure for quality of the database hit. In clinical routine, log-scores ≥2.000 are classified as “species level” identifications, log-scores between 1.700 and 1.999 are initially classified as “genus level only.” To achieve an automated “species level” identification in the current V3.0 MALDI Biotyper client, all spectra www.proteomics-journal.com

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Figure 2. Sample preparation methods. (A) Different sample preparation methods. (B) C. glabrata spectra derived from cells prepared with extraction (top), on-target-lysis (middle), and direct smear (bottom).

scoring ≥2.000 must be of the same species, all spectra scoring ≥1.700 of the same genus. However, the software also allows manual override of these automated classifications and it has been found that also lower log-scores starting from 1.800 [17, 21], 1.700 [11, 22, 34], or even 1.500 [44] can be accepted as species-specific for yeasts if certain criteria are met [9,11,44]. To this end, three additional criteria have been proposed: a list of identifications that (i) encompasses a certain number of database hits (n = 2 [11] or n = 3 [44]) of a single species at the top, that (ii) have no other species intermingled [11], and (iii) a certain difference in the log-score to the next species (e.g. 0.200 [9]). Using these criteria, correct identifications of ∼94% (1000/1067 isolates tested across six studies) were reported without false associations (Table 1). Only if the criteria are not met, the sample should then be extracted in a second step [9]. This approach using modified cutoffs has therefore value under routine conditions; however, time saved during preparation may be exceeded later, when each analysis needs to be manually validated. Of note, if an interpretation algorithm requires at least three consecutive database hits, it will not work for species of which there are less spectra deposited in the database. This, however, is only the case for rare species.

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Identification of molds and intraspecies subtyping

In contrast to yeasts, molds are more difficult to analyze. Partially, this is due to their more complicated morphology as well as even stronger cell walls preventing proper lysis during the extraction step, which makes sample preparation more error-prone. Depending on the presence/absence of conidia, the degree of agar invasion (e.g. leading to significant amounts of contaminating agar in the analyte) and degree of maturation of the colony [57], the analyte will yield differential spectra. This is also the case for the yeast and hyphal forms of C. albicans [50]. Furthermore, the presence of melanin in molds may inhibit ionization [58]. Other variables introducing even more variation in numbers and identities of mass peaks obtained are choice of preparation technique [52], choice of matrix compound and its solvent [52], and preanalytical steps [59]. Such a high degree of variation needs to be handled by diagnostic identification systems in some way. C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Generally, more mass peaks are much more appreciated if species are closely related. Taxonomical resolution and species identification increases if more peaks can be measured reproducibly. To construct and evaluate spectrum libraries, several groups have used different combinations of databases and preparation techniques. Early experiments using only conidia yielded only spectra with low discriminatory power [60–62]. Also, in strongly pigmented isolates such as those from Fusarium spp. or Aspergillus niger, the conidial melanin pigment inhibited ionization of the analyte [58, 59, 63]. This can be overcome by growth in liquid cultures suppressing pigment formation [58] or by preanalytical washing steps [59]. Consequently, better spectra are achieved using mycelial cells scraped from agar plates [46, 55, 64] or from liquid cultures. Extraction gives rise to excellent spectra [65], but boiling of the samples reduced spectrum quality [55]. The use of bead-beating protocols to mechanically disrupt Penicillum and Aspergillus cells and conidia also yielded spectra with high-discriminatory power [55, 56, 63]. However, also the use of acid-containing matrix solution for on-target-lysis [64, 66] may already be sufficient for many species. In principle, there are two approaches to address this heterogeneity for clinical routine diagnostics: either the growth conditions and sample preparation protocols need to be standardized or the database must be constructed in a way that encompasses most of these factors. In general, the databases are constructed with multiple entries for each species [5], reflecting the genetic (MALDI Biotyper) as well as the phenotypic heterogeneity introduced by varying growth conditions (VITEK-MS, SARAMIS [67,68] and Andromas [46] databases). In the SARAMIS system, an additional layer of so-called “superspectra” is introduced [5, 67], a single entry for each species containing only those masses that are reproducibly obtained on different media and at growth phases. For the MALDI Biotyper system a separate additional library with spectra obtained from filamentous fungi grown in a standardized Sabouraud liquid culture has been introduced (“Fungi library,” Bruker Daltonics), which incidentally also reduces pigment formation in most species. The heterogeneity of databases constructed for the individual studies prevents a proper comparison at this point, but in general the same applies as for the identification of yeast species: if a species is present in the database, isolates can be identified. A potential source of error is an insufficient number of peaks leading www.proteomics-journal.com

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to failure of identification. This is generally due to improper sample preparation and repetition of the extraction or entire analysis from fresh cultures can therefore significantly improve outcome [33, 57]. A central problem with the typing of molds is that their phylogenetic relationships are more complicated than in ascomycetous yeasts (e.g. [69]) and species boundaries are not drawn as easily. For typing on the DNA level, this is reflected by the need to sequence more than the rDNA locus, which is sufficient for typing of yeasts. Here, additional loci of choice are usually the conserved genes ␤-tubulin, calmodulin, or elongation factor 1␣. For mold species, identification therefore resembles more an intraspecies subtyping problem. This is also true for Cryptococci, where clinically relevant types are actually closely related subtypes with differential pathogenic impact. For Salmonella enterica subspecies, we have previously shown that cluster analyses of mass spectra can achieve the same depth of differentiation as—and general overlap with— serotyping [70]. In mycology, cluster analyses of mass spectra derived from Cryptococci with the PCA-based algorithms implemented in the respective diagnostic tools allows distinction of the different molecular types [26, 27], even down to hybrid strains [26], showing that similar may be true for fungi. An important but particularly difficult problem is the differentiation of Aspergillus flavus and Aspergillus oryzae. Many isolates of A. flavus produce the carcinogen aflatoxin as a secondary metabolite; A. oryzae never produces aflatoxin and is used in several food fermentation processes. In fact, A. oryzae is probably derived from an atoxigenic A. flavus lineage through human selection [71]. Several PCR-based methods exist for typing within this complex [72, 73], but discrimination with MALDI-TOF spectra made from A. flavus, A. oryzae, Aspergillus parasiticus, and Aspergillus sojae conidia was not possible as the few peaks seen were essentially identical [62]. In a later study employing cluster analyses, A. flavus and A. parasiticus clustered apart [63], but neither A. oryzae nor A. sojae was included into that particular study. Most interestingly, however, in the same study five distinct A. flavus isolates could be distinguished from each other [63]. This indicates that typing not just within this species complex, but also within the species themselves might be possible down to the strain level. This would be of particular interest for discriminating toxinogenic from atoxinogenic A. flavus strains [74]. Another clinically relevant issue is intraspecies typing in outbreak situations. For C. albicans the applicability of MALDI-TOF MS to discriminate different strains, growth conditions, and morphologies has been demonstrated [50] and a recent study looking at C. parapsilosis also allowed to discriminate isolates obtained during an outbreak situation in a neonatology department [75]. There, the comparison of different typing methods clearly showed that clustering of highquality mass spectra had the same discriminatory power as genetic typing methods such as microsatellite markers. In another study [36], the authors were able to distinguish biofilm forming from nonbiofilm forming subtypes of C. parapsilosis C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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and C. metapsilosis. Although in all these studies only relatively few isolates were looked at, this demonstrates the feasibility of the approach. While the clinical relevance for typing in outbreak situations is obvious, the discrimination of phenotypically different subtypes is a new idea. Where genetic typing, e.g. using MLST on C. glabrata, does not reveal any association with clinically relevant features [76], proteomic fingerprinting might indeed do so in the future.

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Prediction of antifungal drug resistance

For bacteria, several approaches to predict or analyze drug resistance using MS have been developed. This includes the detection of carbapenemase activity [44, 77, 78], or the detection of ribosomal mutations [79]. Furthermore, the phylogenetic detection of methicillin-resistant Staphylococcus aureus populations may be a future possiblity [80], especially as clonal lineages have arisen in this context [81]. The major antifungal drugs used in the clinic today are of the polyene (Amphotericin B), azole (e.g. Fluconazol, Voriconazol, and Posaconazol), and echinocandin (Caspofungin, Anidulafungin, and Micafungin) classes. With the notable exception of TR/L98H variants of A. fumigatus [82], the spread of drug resistant fungi of clinically relevance has not been described in the literature. Drug-degrading mechanisms are not known in fungi, neither are ribosomal proteins targeted by antimycotic drugs. Most cases of decreased antifungal drug susceptibility encountered in clinical samples are rather due to species with intrinsic drug resistance, such as C. glabrata or C. krusei in the case of azoles and C. parapsilosis in the case of echinocandins. Also, many molds [83] and zygomycetes [83, 84] show species-dependent distribution of drug susceptibility. A significant number of antifungal drug resistant isolates are therefore predicted by MALDI-TOF simply through the identification of the fungal species. However, under prolonged therapy or prophylaxis isolates may additionally acquire resistance traits. In the case of azoles, these are mainly due to mutations leading to increased drug efflux or in genes involved in the ergosterol biosynthesis pathway. In the case of echinocandins, mutations occur in the gene coding for the target enzyme glucan synthase (reviewed most recently in [85]). All of these proteins have molecular weights (∼60 kDa for Erg11 and Mdr1 proteins, ∼110–125 kDa for the transcription factors CaTac1/CgPdr1, ∼170 kDa for Cdr-type efflux pumps, and >200 kDa for the Fks1 subunit of the fungal glucan synthase complex) that are well outside of the detection range (2–20 kDa) of the current applications. The detection of glucan synthase and efflux pumps is further hampered by the fact that they are multidomain membrane proteins and will most likely not be measurable under MALDI conditions. Nevertheless, optimization of ionization matrices [52] and extraction procedures may yield approaches into this direction in the future. Two studies have suggested an alternative approach to speed up resistance detection for azoles [86] and www.proteomics-journal.com

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echinocandins [87]: both involve growth in the presence of the drug according to the CLSI protocol and subsequent MALDI analysis of cells recovered from the wells of the microdilution plate. Differences in the mass spectra, possibly reflecting changes in the proteome or improved lysis, were seen in cells grown above the minimal inhibitory concentrations of those particular drugs versus cells grown below. Although culture was still required and the time saving advantage was only minimal (15 h with MALDI-TOF versus 24 h for a first MIC reading), this approach may serve to better discriminate isolates with trailing growth from those with true resistance.

7

Cost effectiveness calculations

Cost calculations for improved diagnostic technologies are difficult. Direct costs for MALDI-TOF encompass purchase of the MS device, wear (mainly laser, detector, and vacuum pumps), and personnel, and these local factors are influenced by price negotiations before purchase and the local personnel costs. Consumables needed are matrix solution, pipette tips, toothpicks, chemicals for target cleaning, and an E. coli standard for instrument calibration. The costs for consumables are almost negligible, but the MS device and its repairs are considerably more expensive than those for the different cost models available for automated biochemical systems. Where such calculations have been done [21, 25], costs are generally predicted to be lower than conventional biochemical methods. However, two of the strongest cost-driving factors in clinics are prolonged hospital stays and application of expensive drugs within empirical therapy, which can both be significantly reduced through faster turnaround times of microbiologic diagnostics [88, 89]. For bacteremia, application of MALDI-TOF-MS directly to material from blood cultures has been shown to significantly reduce the time to species identification by ∼29 h in a clinical setting, leading to an 11% increase in patients receiving appropriate antibacterial therapy within the first 24 h after BC positivity [90]. This may also be expected for fungi, possibly even more so, since identification via biochemical methods may take more than just 24 h in extreme cases. Early onset of antifungal therapy is clearly correlated to outcome [91], and the mortality among patients infected with azole nonsusceptible species and receiving inadequate treatment is extremely high [92]. Taken together, with its highly reduced turnaround times, it is the hope that MALDI-TOF-based species identification applied to clinically relevant samples may significantly reduce antifungal consumption by allowing earlier targeted therapy.

8

Future perspectives

Next to the in vitro diagnostic certified closed systems, the research-use open libraries offer the possibility to include spectra on-site and they have been frequently updated in the C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

past with isolates contributed from the community. It can be reasoned that with the growth of the databases, a level of saturation will be reached where all clinically relevant species will be included. This will probably make many other culturedependent differentiation assays, such as agglutination, specific PCRs, or specialized agars, obsolete in the future. Most routine mycology laboratories will likely rely on a combination of MALDI-TOF and PCR/sequencing of marker genes to deal with the few isolates that remain after MALDI-TOF. Pushing the technology to the edge of what can be achieved by proteomic profiling/fingerprinting, the next level will be subtyping within species. This may even include the prediction of drug resistance. Furthermore, MS devices offer a fascinating repertoire of methods and are not limited to the analysis of proteins. Since all kinds of molecules can be ionized and analyzed under the right conditions, the detection of nucleic acids as well as metabolomic approaches are within reach. The analysis of nucleic acids by MALDI-TOF is, unfortunately, currently limited to short DNA oligonucleotides [93] or RNAs. Longer DNA fragments such as PCR products are not stable and need either translation to RNA [94,95], or analysis by other ionization methods, such as ESI-TOF [96, 97]. Metabolomic approaches already include the analysis of fungal lipids [98] as well as the detection of fungal toxins, e.g. aflatoxins [99] and may find their introduction into diagnostic mycology. I would like to thank Michael Weig (Göttingen), Thomas Maier (Bruker Daltonics), and Marcel Ehrhard (Anagnostec and bioMèrieux) for valuable discussions and critically reading the manuscript. The author has declared no conflict of interest.

9

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