Skip to main content
NCBI home page
Journal of Clinical Microbiology logoLink to Journal of Clinical Microbiology
. 2022 Nov 3;60(12):e01032-22. doi: 10.1128/jcm.01032-22

A Practical Workflow for the Identification of Aspergillus, Fusarium, Mucorales by MALDI-TOF MS: Database, Medium, and Incubation Optimization

Kevin R Barker a,b,c,✉, Julianne V Kus b,d, Anne-Cecile Normand e, Farhad Gharabaghi f, Lisa McTaggart d, Coleman Rotstein g, Susan E Richardson b,f, Aaron Campigotto b,f, Manal Tadros b,f
Editor: Kimberly E Hansonh
PMCID: PMC9769873  PMID: 36326257

ABSTRACT

There is an increasing body of literature on the utility of MALDI-TOF MS in the identification of filamentous fungi. However, the process still lacks standardization. In this study, we attempted to establish a practical workflow for the identification of three clinically important molds: Aspergillus, Fusarium, and Mucorales using MALDI-TOF MS. We evaluated the performance of Bruker Filamentous Fungi database v3.0 for the identification of these fungi, highlighting when there would be a benefit of using an additional database, the MSI-2 for further identification. We also examined two other variables, namely, medium effect and incubation time on the accuracy of fungal identification. The Bruker database achieved correct species level identification in 85.7% of Aspergillus and 90% of Mucorales, and correct species-complex level in 94.4% of Fusarium. Analysis of spectra using the MSI-2 database would also offer additional value for species identification of Aspergillus species, especially when suspecting species with known identification limits within the Bruker database. This issue would only be of importance in selected cases where species-level identification would impact therapeutic options. Id-Fungi plates (IDFP) had almost equivalent performance to Sabouraud dextrose agar (SDA) for species-level identification of isolates and enabled an easier harvest of the isolates with occasional faster identification. Our study showed accurate identification at 24 h for Fusarium and Mucorales species, but not for Aspergillus species, which generally required 48 h.

KEYWORDS: mass spectrometry, molds, immunocompromised, immunosuppressed, Mucorales, IDFP, proteomics, clinical mycology, Aspergillus, Fusarium, MALDI-TOF, cryptic species, filamentous fungi, immunocompromised hosts, mycology

INTRODUCTION

The identification of filamentous fungi in a clinical mycology laboratory remains largely dependent on conventional phenotypic methods that require several days of investigation in addition to tremendous expertise. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) offers a promising potential to overcome the current gap in mycology diagnostics in the same way it has changed practice in clinical bacteriology (1–3). Several studies have investigated the utility of mass spectrometry for the accurate species identification of filamentous molds. However, performance has not been optimized (4–12). This may be attributed to nonstandardized protocols, different spectra generated by the same strain under different growth conditions (7), and the database used for identification.

In this study, we attempted to develop a standardized method that focused on issues of database utilization, media, and duration of incubation while being adaptable to a hospital-based diagnostic microbiology laboratory.

Regarding databases, we sought to validate the Bruker MBT Filamentous Fungi library v3.0 using well-characterized mold isolates. This version of the library includes spectra for 180 species, and 10 strains currently only identified to genus level. In total, 62 different genera are included in the library. We focused on Risk group 2 organisms commonly associated with invasive fungal infections (IFIs), namely, Aspergillus, Fusarium, and Mucorales. We employed a second database, Mass Spectrometry Identification-2 (MSI-2) (https://msi.happy-dev.fr/), an independent freely available online database that has been created in collaboration with the BCCM/IHEM (Belgian Coordinated Collections of Micro-organisms/Institute of Hygiene and Epidemiology Mycology) (13–15). This database includes 11,851 references representing 246 genera and 938 species of yeasts and filamentous fungi.

MATERIALS AND METHODS

Filamentous fungal isolates.

Sixty-nine isolates that were either from archived proficiency test specimens or clinical isolates were included in this study. Thirty-one isolates of Aspergillus species, 18 Fusarium species, and 20 from of the order Mucorales were used (see Table S1 in Supplemental File 1). The isolates were subcultured to Id-Fungi plates (IDFP; Conidia, Quincieux, France) and Sabouraud dextrose agar (SDA), and incubated aerobically at 27°C to 30°C. Example images of an Aspergillus, Fusarium, and Mucorales isolate, their growth and morphology on Sabouraud dextrose agar (SDA), Id-Fungi plates (IDFP) after 24, 48, and 72 h of incubation can be seen in Fig. S1 in Supplemental File 1.

Reference identification of isolates.

In addition to identification using phenotypic and growth characteristics sequence analysis was performed for molecular identification of most isolates. Internal transcribed spacer (ITS) region, β-tubulin, translation elongation factor (TEF), and D1/D2 region of the 25 to 28S rRNA gene amplification and sequence analysis were used for species-level identification of Aspergillus, Fusarium and Mucorales isolate. Detailed methodology, primer sequences, and interpretation were undertaken according to CLSI guidelines with further details in Supplemental File 1 (16). For increased specificity, NCBI nucleotide BLAST was undertaken using only sequences from type material and optimized for “megablast.” A phylogenetic tree-based approach and alignment using MAFFT (www.ebi.ac.uk/Tools/msa/mafft) were undertaken for species-level identification of Fusarium isolates. A detailed list of isolates and sequencing databases used for identification are provided in Table S1 and Table S2 in Supplemental File 1.

MALDI-TOF MS identification of isolates.

(i) Full extraction method. After 24 to 72 h of incubation, colonies were selected for MALDI-TOF MS processing. Approximately 0.5 cm to 1 cm of the surface of the colony was gently scrapped off from the leading edge of the SDA plate, or the surface of the membrane of the IDFP plate with a scalpel blade, while avoiding the inclusion of any agar. The material was placed into a 1.5 mL tube containing 900 μL absolute ethanol + 300 μL molecular grade water. The tube was vortexed for 30 s followed by centrifugation at 13,000 rpm for 10 min. Then, the supernatant was discarded without disrupting the pellet. The tube was then left open to dry the pellet and remove any residual alcohol. 10 μL of 70% formic acid was added to the pellet and mixed thoroughly by pipetting up and down and incubating for 5 min at room temperature. For larger-sized pellets, up to 50 μL of formic acid was added. Then, 100% acetonitrile was added at an equal volume to that of formic acid and mixed thoroughly by pipetting up and down. The tube was centrifuged at 13,000 rpm for 2 min. One microliter of the extract was deposited onto the target plate, in triplicate, allowed to dry, and then overlaid with 1 μL of α-cyano-4-hydroxycinnamic acid (HCCA) matrix (Bruker Daltonics Bremen, Germany).

(ii) Spectral analysis. While running MALDI-TOF MS, spectra were automatically compared to the Bruker MBT Filamentous Fungi Library version 3.0 (“Bruker database”). The top two scores and identifications were recorded. A score was the log of the number of peaks (signals) of the unknown that had a closely matching partner in the Bruker database. “A” meant species consistency of further matched to the unknown strain, whereas “B” meant genus consistency, and “C” reflected no consistency of genus or species of further matches. If the score was <1.7, full extraction was repeated the following day. The minimal score required was ≥2, consistency A for species identification and ≥1.7 for genus identification. Scores of ≥2 A were called to species level identification, or clade if there was a known limitation of the database as per MBT Filamentous Fungi Library 3.0 release notes. Scores of ≥2 B were called to section/species complex or clade levels according to known limitations of the database. Scores >1.7 but <2 were called to genus-level identification.

To analyze spectra using the MSI-2 database; the spectra folder was in the “Maldi biotyper Real-Time Classification” folder and saved as a compressed file (.zip), then uploaded to the MSI-2 database for identification. The MSI-2 database identified organisms to species (score A) if the (i) 1st score was >22 and the 1st to 2nd was >8; or (ii) 1st was >20 but ≤22 and the 1st to 2nd was >2. MSI-2 database identified up to section/species complex (score B) if the (i) 1st score was >22, the 2nd score was >20, and the 1st to 2nd was <8; or (ii) 1st was >20 but ≤22 and the 1st to 2nd score were <2. MSI-2 database identified as irrelevant (score C = no identification) if both the 1st and 2nd score were <20.

Statistical analysis.

We compared the performance of databases, medium, and time of incubation using descriptive analysis. Accuracy (correct identification) was defined as concordance with molecular identification or with the identification result provided in proficiency testing from the Institute of Quality Management in Healthcare (IQMH). Pearson's Chi-square test was performed to assess differences between the accuracy of identification of filamentous fungi using the Bruker and MSI databases, using SDA and IDFP media, and 24 h incubation compared to 48 h using R (R Core Team, 2021).

RESULTS

Database.

(i) Bruker filamentous fungi v3 versus MSI-2 database comparison. Database utilization was first assessed for its impact on the success of MALDI-TOF MS identification of filamentous fungi is the database used for identification. We compared the Bruker Filamentous Fungi v3 with the MSI-2 databases. The best score was taken over 24 to 72 h of incubation. Discrepant results can be found in Table S3 in Supplemental File 1.

(ii) Aspergillus. Thirty-one isolates were tested, with 21/31 identified to the species level and 10/31 identified to clade using molecular methods. Correct species identification was achieved by the Bruker and MSI-2 databases in 18/21 (85.7%) and 21/21 (100%) isolates, respectively, P = 0.07. Correct clade identification was achieved by the Bruker and MSI-2 databases in 7/10 (70%) and 10/10 (100%) of the isolates, respectively, P = 0.06. Correct section identification was achieved by the Bruker and MSI-2 databases in 30/31 (96.8%) and 31/31 (100%) isolates, respectively, P = 0.31. Misidentifications using Bruker included Aspergillus thermomutatus as A. fumigatus; A. calidoustus/pseudodeflectus as A. ustus; A. puniceus as A. ustus; and A. sydowii as A. versicolor. The MSI-2 database yielded 100% correct identification of species and clade, with no misidentifications (see Table 1).

TABLE 1.

Aspergillus species overall performance of databasese

Fumigati
(n = 10)
 Usti
(n = 5)
 Terrei
(n = 1)
 Nidulantes
(n = 3)
 Candidi
(n = 2)
 Nigri
(n = 2)
 Flavi
(n = 5)
 Versicolores
(n = 3)
 All Aspergillus spp.
(n = 31)
Level of ID BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)
Species 9 (90%) 10 (100%) 3 (60%) 5 (100%) 1 (100%) 1 (100%) 3 (100%) 3 (100%) 2 (100%) 2 (100%) - - - - - - 18 (85.7%) 21 (100%)
Clade - - - - - - - - - - 2 (100%) 2 (100%) 5 (100%) 5 (100%) 0 (0%) 3 (100%) 7 (70%) 10 (100%)
Section 10 (100%)a,b 10 (100%) 5 (100%)a,c 5 (100%) 1 (100%) 1 (100%) 3 (100%) 3 (100%) 2 (100%) 2 (100%) 2 (100%) 2 (100%) 5 (100%) 5 (100%) 2 (66.7%)a,d 3 (100%) 30 (96.8%) 31 (100%)
Genus 10 (100%) 10 (100%) 5 (100%) 5 (100%) 1 (100%) 1 (100%) 3 (100%) 3 (100%) 2 (100%) 2 (100%) 2 (100%) 2 (100%) 5 (100%) 5 (100%) 3 (100%) 3 (100%) 31 (100%) 31 (100%)
No ID 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Open in a new tab
a

Misidentification to the classification level above.

b

1/10 isolates were misidentified to species but correctly identified to section.

c

2/5 isolates were misidentified to species but correctly identified to section.

d

2/3 isolates were misidentified to a clade, but correctly identified to section.

e

Performance of each database (Bruker Filamentous Fungi v3 [BT] versus Mass Spectral Identification-2 [MSI-2]) by species (n = 21), clade (n = 10), section (n = 31), and genus (n = 31). The best score was taken from each replicate. The reference standard was β-tubulin sequencing. Dash indicates that there is no clade.

(iii) Fusarium. Eighteen isolates were tested, with 16/18 identified to the species level and 18/18 identified as species complex using molecular methods. Correct species complex identification was achieved by the Bruker and MSI-2 databases in 17/18 (94.4%) and 18/18 (100%) isolates, respectively, with P = 0.31 (see Table 2).

TABLE 2.

Fusarium species overall performance of databasesg

FSSCa (n = 6)
 FFSCa (n = 6)
 FOSCa (n = 3)
 FIESCa (n = 3)
 All Fusarium spp. (n = 18)
Level of
ID		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)
Species 1 (16.7%) 2 (33.3%) 6 (100%) 5 (83.3%) 0 (0%) 0 (0%) 0b (0%) 0b (0%) 7b (43.8%) 7b (43.8%)
Species Complex 5 (83.3%)c,d 6 (100%)c,d 6 (100%) 6 (100%) 3 (100%)c,e 3 (100%) 3 (100%)c,f 3 (100%)c,f 17 (94.4%) 18 (100%)
Genus 6 (100%) 6 (100%) 6 (100%) 6 (100%) 3 (100%) 3 (100%) 3 (100%) 3 (100%) 18 (100%) 18 (100%)
No ID 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Open in a new tab
a

FSSC, Fusarium solani species complex; FFSC, Fusarium fujikuroi species complex; FOSC, Fusarium oxysporum species complex; FIESC, Fusarium incarnatum-equiseti species complex.

b

Denominator is smaller for species versus others classification levels.

c

Misidentification to the classification level above.

d

2/5 isolates misidentified to species, but correctly identified to species complex.

e

3/3 isolates misidentified to species, but correctly identified to species complex.

f

1/3 isolates misidentified to species, but correctly identified to species complex.

g

Performance of each database (Bruker Filamentous Fungi v3 [BT] versus Mass Spectral Identification-2 [MSI-2]) by species (n = 16), species complex (n = 18), and genus (n = 18). The best score was taken from each replicate. The reference standard was translation elongation factor sequencing and DNA phylogenetic tree-based approach analysis.

(iv) Mucorales. Twenty isolates were tested, with 20/20 identified to species level using molecular methods. Correct species identification was achieved by the Bruker and MSI-2 databases in 18/20 (90%) and 19/20 (95%) isolates, respectively, with P = 0.54. Correct genus identification was achieved by both databases in 19/20 (95%) isolates. Bruker database gave no identification for a Syncephalestrum racemosum isolate and MSI-2 database gave no identification for a Lichtheimia ornata isolate (see Table 3).

TABLE 3.

Mucorales species overall performance of databasesa

Lichtheimia corymbifera
(n = 3)
 Lichtheimia ornata
(n = 1)
 Mucor circinelloides
(n = 4)
 Rhizomucor pusillus
(n = 4)
 Rhizopus microsporus
(n = 3)
 Rhizopus oryzae/arrhizus
(n = 4)
 Syncephalestrum racemosum
(n = 1)
 All Mucorales
(n = 2)
Level of ID BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)		 BT
n (%)		 MSI-2
n (%)
Species 3 (100%) 3 (100%) 0 (0%) 0 (0%) 4 (100%) 4 (100%) 4 (100%) 4 (100%) 3 (100%) 3 (100%) 4 (100%) 4 (100%) 0 (0%) 1 (100%) 18 (90%) 19 (95%)
Genus 3 (100%) 3 (100%) 1 (100%) 0 (0%) 4 (100%) 4 (100%) 4 (100%) 4 (100%) 3 (100%) 3 (100%) 4 (100%) 4 (100%) 0 (0%) 1 (100%) 19 (95%) 19 (95%)
No ID 0 (0%) 0 (0%) 0 (0%) 1 (100%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (100%) 0 (0%) 1 (5%) 1 (5%)
Open in a new tab
a

Performance of each database (Bruker Filamentous Fungi v3 [BT] versus Mass Spectral Identification-2 [MSI-2]) by species (n = 20), and genus (n = 20). The best score was taken from each replicate. The reference standard was phenotypic methods, internal transcribed spacer 2 regions and/or D1/D2 region of the25 to 28S rRNA gene sequencing.

Media.

(i) SDA versus IDFP. The second variable to evaluate was the medium, examined by performing simultaneous subcultures onto SDA and IDFP using both databases (Bruker and MSI-2; see Fig. 1). The best score was taken over 24 to 72 h of incubation. Overall species/clade/species complex level identification with the Bruker database was achieved in 58/69 (84.1%) of isolates harvested from IDFP versus 53/69 (76.8%) of those harvested from SDA, P = 0.28.

FIG 1.

FIG 1

Open in a new tab

Performance of Sabouraud dextrose agar (SDA) versus Id-fungi plates (IDFP). Aspergillus (A; n = 31), Fusarium (F; n = 18), and Mucorales (M; n = 20) isolates performance to species/clade, section/species complex, and genus level identification using the Bruker Filamentous Fungi v3 and Mass Spectral Identification-2 (MSI-2). The best score was taken from each replicate. The reference standard was β-tubulin, translation elongation factor, internal transcribed spacer 2 regions, and/or D1/D2 region of the 25 to 28S rRNA gene sequencing. Due to the limited number of Fusarium isolates that were successfully identified as species by reference sequencing (n = 16/18), only species complex, genus, and no ID was shown.

With the MSI-2 database, correct species/clade/species complex level identification was achieved in 65/69 (94.2%) of the isolates harvested from IDFP versus 64/69 (92.8%) of those harvested from SDA, P = 0.73.

(ii) Aspergillus. Of the 31 isolates tested, correct identification to species/clade level using the Bruker database was achieved in 25/31 (80.6%) of isolates harvested from IDFP identified versus 21/31 (67.7%) of those harvested from SDA, P = 0.24. However, similar rates of identification to species/clade level were noted using the MSI-2 database 28/31 (90.3%) versus 29/31 (93.5%), for the IDFP and SDA isolation, respectively, P = 0.64. The MSI-2 database outperformed and yielded significantly better performance than the Bruker database in the identification to species/clade level using SDA isolation (29/31 [93.5%] versus 21/31 [67.7%], respectively, P = 0.01). MSI-2 performed marginally better than Bruker in the identification to species/clade level using IDFP (28/31 [90.3%] versus 25/31 [80.6%], respectively, P = 0.27) (see Fig. 1).

(iii) Fusarium. Of the 18 isolates with species complex level identification by sequencing, when using the Bruker database for identification, IDFP performed better than SDA (16/18 [88.9%] versus 14/18 [77.8%], respectively, P = 0.37). IDFP and SDA were comparable when attempting identification using the MSI-2 database (18/18 [100%] versus 17/18 [94.4%], respectively, P = 0.31) (see Fig. 1).

(iv) Mucorales. Of the 20 isolates tested, IDFP performed similarly to SDA in the identification to species level when using the Bruker database (17/20 [85%] versus 18/20 [90%], respectively, P = 0.63), as well as when using the MSI-2 database (19/20 [95%] versus 18/20 [90%], respectively, P = 0.54). As for the identification to the genus level, comparability of IDFP and SDA was also noted whether using the Bruker (both 19/20 [95%]) or MSI-2 database (19/20 [95%] versus 18/20 [90%], respectively, P = 0.54). Both IDFP and SDA were unable to identify Syncephalestrum racemosum when using the Bruker database. Lichtheimia ornata was not identified with the MSI-2 database when IDFP or SDA was used for harvesting the organism. A Rhizopus oryzae/arrhizus isolate was unable to be identified from SDA using the MSI-2 database (see Fig. 1).

Duration of incubation.

(i) 24 h versus 48 h incubation. Incubation time is the third variable that may have a significant impact on the success of MALDI-TOF MS identification of filamentous fungi. Shorter incubation times and shorter turn-around times of identification may advantageous clinically and may improve specificity by reducing the acquisition of spectra from sporulating mature fungi. However, earlier growth may lead to less specific identification and reduced biomass for sampling. We further broke down the best score in the above section, to compare 24 with 48 h incubation times for isolates on IDFP using the Bruker and MSI-2 databases (see Fig. 2). Some isolates had an extended incubation of 72 h when required, but this was not included in the analysis.

FIG 2.

FIG 2

Open in a new tab

Performance of 24 h (24 HR) versus 48 h (48 HR) from Id-fungi plates (IDFP). Aspergillus (A; n = 31), Fusarium (F; n = 18), and Mucorales (M; n = 20) isolates performance to species/clade, section/species complex, and genus level identification using the Bruker Filamentous Fungi v3 and Mass Spectral Identification-2 (MSI-2). The best score was taken from each replicate. The reference standard was β-tubulin, translation elongation factor, internal transcribed spacer 2 regions, and/or D1/D2 region of the 25 to 28S rRNA gene sequencing. Due to the limited number of Fusarium isolates that were successfully identified as species by reference sequencing (n = 16/18), only species complex, genus, and no ID was shown.

(ii) Aspergillus. Of the 31 isolates tested, the incubation time of 48 h on IDFP had enhanced identification to the species or clade level compared to 24 h using the Bruker (20/31 [64.5%] versus 18/31 [58.1%], respectively, P = 0.60), and the MSI-2 databases (24/31 [77.4%] versus 18/31 [58.1%], respectively, P = 0.10). One A. fumigatus isolate and one A. sydowii clade isolate had no growth at 24 h, resulting in “No ID.” One A. calidoustus/pseudodeflectus isolate was only reported to the genus level at 48 h, as opposed to the species level at 72 h (see Fig. 2).

(iii) Fusarium. Of the 18 isolates tested, the incubation time of 24 h on IDFP performed slightly better to species complex level than 48 h using the Bruker database (13/18 [72.2%] versus 11/18 [61.1%], respectively, P = 0.47). However, 48 h performed equally well to 24 h incubation using the MSI-2 database (15/18 [83.3%]). Only one isolate, Fusarium lichenicola (F. solani species complex), was not identified to at least the genus level after 48 h incubation using the Bruker database but was successful after 72 h of incubation. There were no misidentifications to the species complex level (see Fig. 2).

(iv) Mucorales. Of the 20 isolates tested, 24 h of incubation on IDFP performed slightly better to the species level than 48 h using the Bruker database (14/20 [70%] versus10/20 [50%], respectively, P = 0.19). However, 48 was comparable to 24 h using the MSI-2 database (19/20 [95%] versus 17/20 [85%], respectively, P = 0.29). Both incubation durations performed equally well to genus level and above using the Bruker database (19/20 [95%]). A Syncephalestrum racemosum isolate was only able to be identified at the genus level. However, 48 h incubation performed better than 24 h when using the MSI-2 database (19/20 [95%] versus 17/20 [85%], respectively, P = 0.29). Only the Lichtheimia ornata was not identified at 48 h, and in addition, L. corymbifera and Rhizopus oryzae/arrhizus isolates were unable to be identified at 24 h. Interestingly, the only misidentification was L. ornata, identified as L. corymbifera at 24 h using the Bruker database (see Fig. 2). The estimated turnaround time from mold harvesting from media to getting identification results was 1 h.

DISCUSSION

In this study, we developed and utilized a standardized methodology for the detection of clinically relevant filamentous fungi (Aspergillus spp., Fusarium spp., and Mucorales spp.), using the Bruker MALDI-TOF MS, that may be adaptable to frontline or hospital-based microbiology laboratories. We investigated three variables that may have an impact on successful identification: (i) database utilization; (ii) culture medium; and (iii) duration of incubation.

Overall, the MSI-2 database performed better than the Bruker database for species/clade identification for Aspergillus isolates, but both performed equally well for section-level identification of Aspergillus. Misidentifications by the Bruker database included an A. thermomutatus (section Fumigati) isolate, misidentified as A. fumigatus; an A. calidoustus/pseudodeflectus (Usti) isolate and an A. puniceus (Usti) that were incorrectly identified as A. ustus (Usti section); and two A. sydowii clade (Versicolores section) isolates that were incorrectly identified as A. versicolor clade (Versicolores section). These isolates were correctly identified to species level using the MSI-2 database, likely due to multiple entries of these species in MSI-2 compared to few or no entries in the Bruker database (see Table S3 in Supplemental File 1). In addition to the misidentifications, the Bruker database had difficulty with the identification of Versicolores isolates to the clade level and Usti isolates to the species level.

Within the Aspergillus genus, several “cryptic” species of clinical significance were included in the evaluation to determine if the databases and methods studied could identify these isolates. These included A. lentulus (Fumigati), A. thermomutatus (Fumigati), A. calidoustus/pseudodeflectus (Usti), and A. tubingensis (Nigri). Cryptic species have been shown in large studies to be significant pathogens and a cause of the invasive disease (17, 18). These species often have also been shown to have high minimum inhibitory concentrations (MICs) to multiple antifungals (19–31). A. lentulus and A. thermomutatus are implicated in invasive diseases worldwide and consistently have high MICs to triazoles and in some cases, amphotericin B (20, 32–42). A. calidoustus is a species that was previously classified as A. ustus and has been implicated as a significant pathogen in the immunosuppressed, and treatment may be problematic (4, 8, 17, 18, 43, 44). A. pseudodeflectus is a newly described species that is very closely related to A. calidoustus, which has also been implicated in invasive aspergillosis (22, 44–47). Identification to species level for the Usti section has become important because A. calidoustus and A. pseudodeflectus have both been well described as having intrinsic very high MICs to triazoles, versus other species of the section such as A. puniceus or A. ustus senso stricto. A. niger clade consists of multiple species that morphologically look identical, including A. niger sensu stricto and cryptic species A. tubingensis, A. brasiliensis, and A. welwitschiae, most have been implicated in invasive aspergillosis (17, 18, 48). Higher itraconazole MICs have been described in A. tubingensis, suggesting that identification to species or at least clade may be useful to rule out the possibility of true infection and appropriate antifungal therapy (19, 28, 49). Based on our study findings, we recommended a process flow for the identification of Aspergillus isolate in a clinical microbiology laboratory (see Fig. 3).

FIG 3.

FIG 3

Open in a new tab

Flow chart for the workup of Aspergillus species after MALDI-TOF MS and the identification using the Bruker Filamentous Fungi v3 database. ID = identification; MBT = MALDI biotyper; MSI-2 = mass spectrometry identification-2.

Although Aspergillus species are the most common filamentous fungi isolated from immunosuppressed patients with invasive disease, Fusarium spp. are still a significant cause (17, 18). Fusarium spp. are also significant pathogens in the immunocompetent, causing superficial or locally invasive disease (50). Fusarium spp. can be divided into several species complexes, including F. solani (FSSC), F. oxysporum (FOSC), F. fujikuroi (FFSC), F. chlamydopsorum (FCSC), F. incarnatum-equiseti (FIESC), and several others (51, 52). FSSC, FOSC, and F. verticillioides, and F. proliferatum from FFSC are the most common clinical isolates (51, 53–55). Fusarium species are intrinsically resistant to 5-flucytosine, fluconazole, itraconazole, caspofungin, and anidulafungin but vary in their resistance to amphotericin B, voriconazole, and posaconazole depending on species complex (52, 56). Current guidelines recommend surgical debridement plus voriconazole as initial therapy, or with combination therapy of liposomal amphotericin B + voriconazole after confirmation of susceptibility, or posaconazole alone as salvage therapy (55, 57). The choice of an antifungal class could potentially be made based on species complex, with some species complex that has been described as having higher MICs to triazoles (e.g., FSSC and FCSC versus FOSC) (52, 58, 59). Species-level identification within the species complex may be beneficial in cases such as FFSC, where there have been described species-dependent MICs. F. nygamai and F. thapsinum have been described as having high MICs to all triazoles versus a more commonly described clinical isolate from FFSC, F. verticillioides. However, sequencing of even the TEF region frequently does not resolve to species-level identification in many cases, so multilocus sequence typing (MLST) is the recommended method for species-level identification (60–64) Additionally, we noticed some discrepancies in species-level identification in FOSC and FIESC when DNA phylogeny was used for identification compared to FUSARIOID-ID (www.fusarium.org) or CBS-KNAW (Westerdijk Fungal Biodiversity Institute; https://wi.knaw.nl). Overall, both databases performed very well for the identification to species complex level, which the authors believe in the current landscape is the level of identification that has the most significant impact. Within the species complex, the Bruker database was unable to identify F. lichenicola within the FSSC. However, there are no strains/entries within the Bruker database, whereas MSI-2 has five strains/entries (see Table S3 in Supplemental File 1).

We, therefore, recommend the following process flow for the identification of Fusarium isolates in a clinical microbiology laboratory (see Fig. 4).

FIG 4.

FIG 4

Open in a new tab

Flow chart for the workup of Fusarium species after MALDI-TOF MS and the identification using the Bruker Filamentous Fungi v3 database.

Mucorales are an order of filamentous fungi that impact the immunocompromised, including patients with diabetes, those undergoing chemotherapy or immunotherapy, as well as those undergoing hematopoietic stem-cell transplants (HSCT) and solid organ transplants (65–67). Mucormycosis has also recently been associated with COVID-19 (68–70). Mucormycosis carries high mortality rates, especially when diagnosis and further management are delayed (71–73). The most frequent causes of human disease are Rhizopus spp., Mucor spp., and Lichtheimia spp., while Rhizomucor spp., Cunninghamella spp., Apophysomyces spp., and Saksenaea spp. all cause mucormycosis less frequently (65, 74–76). Identification of the order Mucorales is useful to rule-in Mucorales and expedite appropriate management (debridement +/− antifungals). Identification to genus level may be useful to identify specific genera such as Cunninghamella spp., which may be associated with increased mortality rate and virulence in vivo (65, 77). Identification of species may be useful when investigating hospital-acquired outbreaks and for epidemiological reasons (78–80). Current guidelines recommend liposomal amphotericin B, with consideration of posaconazole or isavuconazole for maintenance therapy (57, 81). Some MIC data suggest higher MICs to amphotericin B for Rhizopus spp. and Cunninghamella and posaconazole for Mucor and Cunninghamella. A recent study however suggests that the activity of isavuconazole may be lower against Cunninghamella, Mucor, and Syncephalastrum, but higher against over genera (82). However, how these MICs can be interpreted is not yet known (83).

Both databases performed well in the identification of species for most Mucorales isolates. However, the Bruker database could not identify the Syncephalestrum racemosum isolate to genus. This may be due to its database containing only a single strain/entry for this species versus 12 with MSI-2. Both databases were able to identify all the Lichtheimia corymbifera, but both databases were not successful with species identification of the phenotypically indistinguishable L. ornata (83). The Bruker database misidentified the isolate as L. corymbifera on IDFP, but this is unlikely to pose any clinical significance. The MSI-2 database was however unable to identify even the genus level. This may be explained by the lack of strains/entries for this species in both databases (see Table S3 in Supplemental File 1). We propose the following process flow for the identification of Mucorales isolates in a clinical microbiology laboratory (see Fig. 5).

FIG 5.

FIG 5

Open in a new tab

Flow chart for the workup of Mucorales after MALDI-TOF MS and the identification using the Bruker Filamentous Fungi v3 database. ID, identification; MSI-2, mass spectrometry identification-2.

Overall, we observed a much “cleaner” and easier collection of biomass from IDFP versus SDA, which not only reduces the time required but reduces agar and spore contamination, which leads to reduced quality of spectra (84, 85). With the Bruker database, IDFP demonstrated improved performance versus SDA for identifying Aspergillus species/clade (80.6% versus 67.7%), and there was a minor improvement found using IDFP versus SDA for Fusarium but it was comparable for Mucorales. As a proxy for reproducibility, we acquired spectra for each isolate at 24, 48, and 72 (when required) hours of incubation. Acquiring spectra from IDFP after 48 versus 24 h incubation, improved the identification of Aspergillus isolates to species and section level for both databases. For Fusarium and Mucorales isolates, 24 h incubation performed slightly better using the Bruker database, but not with the MSI-2 database. The differences seen are likely attributable to growth rate, sporulation rate, and adherence to the agar. The differences between order/genera may be due to different expressions of proteins leading to different peaks as the fungus grows (86). This may explain why a rapidly growing filamentous fungus like Mucorales has a more successful identification after 24 versus 48 h, while a slower growing filamentous fungus like Aspergillus spp. after 48 versus 24 h. Although we did not investigate interlab, interoperator, and interinstrument variability, an early NIH multicenter study and others found it to be not ideal, and spectral acquisition required optimization for filamentous fungi (7, 10, 87). However, recent updates by Bruker may have improved this but is beyond the scope of this study. These findings, however, are consistent with other studies comparing IDFP with SDA for filamentous fungi (84, 88, 89).

In our study, we spotted each isolate in triplicate for MALDI-TOF MS analysis. It is our observation and recommendation that at least two replicate spots are made due to the lack of consistency between replicates (data not shown). This is consistent with early studies that recommended four replicates (90). This may be due to the nonhomogeneity of the extracted product from ethanol/formic acid/acetonitrile extraction applied to the target plate. A possible explanation is that biomass of the filamentous fungi may not sufficiently dissolve in the solvents used. Other extraction methodology could be investigated but was outside the scope of this study.

There were several limitations to this study. The inclusion of a larger number of strains from each genus would have been beneficial to confirm the findings in this study. Another limitation was the retrospective aspect of the study and working with well-characterized isolates rather than unknown isolates being identified in real time. More data could have been generated for extended incubation of 72 h to enhance precision but we wanted to emphasize reproducibility and speed of identification in this study.

Overall, we developed and utilized a standardized extraction method and compared three major variables: database utilization, culture medium, and duration of incubation. We determined that the Bruker database is generally a good database for the identification of Aspergillus, Fusarium, and Mucorales. In certain cases, where the identification of cryptic species of Aspergillus is clinically warranted, it would be useful to use MSI-2 as a supplementary database. Other systems/databases do exist such as the Vitek MS system, and it should be noted that MSI-2 is incompatible at this time with Vitek MS. IDFP does seem to improve performance and makes the process of obtaining biomass easier. The addition of IDFP as the medium for subculture would not increase turn-around time (TAT). The current status quo at our institution (and others) is to subculture onto a medium that induces sporulation, to assist in microscopic identification. This requires several more days than 24 to 48 h of incubation, which is required for MS identification, is less specific, and requires expertise. Moreover, sequencing, although more specific, is expensive (unless batched, which will increase TAT), still requires significant biomass on subculture and equal or longer incubation times, and frequently the more specific targets like beta-tubulin or transcription elongation factor are not available at most centers and require molecular expertise. However, the cost of IDFP is significant, so it can be used as a secondary medium for positive cultures. Based on our findings, we recommend attempting identification after at least 24 h of incubation, depending on the level of growth visually on the medium, and that 2 to 3 spots per isolate is generally warranted for identification.

Our study has highlighted a practical standardized approach that can be applied by a clinical microbiology laboratory for the identification of clinically significant filamentous fungi, by evaluating the main variables that may impact the correct identification of these fungi.

ACKNOWLEDGMENT

We declare no conflict of interest.

Footnotes

Supplemental material is available online only.

Supplemental file 1
Supplemental material. Download jcm.01032-22-s0001.pdf, PDF file, 0.5 MB (523.9KB, pdf)

Contributor Information

Kevin R. Barker, Email: kevin.barker@thp.ca, kevin.barker@utoronto.ca.

Kimberly E. Hanson, University of Utah

REFERENCES

  • 1.Clark AE, Kaleta EJ, Arora A, Wolk DM. 2013. Matrix-assisted laser desorption ionization-time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin Microbiol Rev 26:547–603. doi: 10.1128/CMR.00072-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Seng P, Drancourt M, Gouriet F, La Scola B, Fournier PE, Rolain JM, Raoult D. 2009. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin Infect Dis 49:543–551. doi: 10.1086/600885. [DOI] [PubMed] [Google Scholar]
  • 3.Bizzini A, Greub G. 2010. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry, a revolution in clinical microbial identification. Clin Microbiol Infect 16:1614–1619. doi: 10.1111/j.1469-0691.2010.03311.x. [DOI] [PubMed] [Google Scholar]
  • 4.Sanguinetti M, Posteraro B. 2017. Identification of molds by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol 55:369–379. doi: 10.1128/JCM.01640-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.De Carolis E, Posteraro B, Lass-Flörl C, Vella A, Florio AR, Torelli R, Girmenia C, Colozza C, Tortorano AM, Sanguinetti M, Fadda G. 2012. Species identification of Aspergillus, Fusarium and Mucorales with direct surface analysis by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin Microbiol Infect 18:475–484. doi: 10.1111/j.1469-0691.2011.03599.x. [DOI] [PubMed] [Google Scholar]
  • 6.Al-Hatmi AM, Normand AC, Van Diepeningen AD, Hendrickx M, De Hoog GS, Piarroux R. 2015. Rapid identification of clinical members of Fusarium fujikuroi complex using MALDI-TOF MS. Future Microbiol 10:1939–1952. doi: 10.2217/fmb.15.108. [DOI] [PubMed] [Google Scholar]
  • 7.Stein M, Tran V, Nichol KA, Lagacé-Wiens P, Pieroni P, Adam HJ, Turenne C, Walkty AJ, Normand AC, Hendrickx M, Piarroux R, Karlowsky JA. 2018. Evaluation of three MALDI-TOF mass spectrometry libraries for the identification of filamentous fungi in three clinical microbiology laboratories in Manitoba, Canada. Mycoses 61:743–753. doi: 10.1111/myc.12800. [DOI] [PubMed] [Google Scholar]
  • 8.Triest D, Stubbe D, De Cremer K, Piérard D, Normand AC, Piarroux R, Detandt M, Hendrickx M. 2015. Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of molds of the fusarium genus. J Clin Microbiol 53:465–476. doi: 10.1128/JCM.02213-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Ranque S, Normand AC, Cassagne C, Murat JB, Bourgeois N, Dalle F, Gari-Toussaint M, Fourquet P, Hendrickx M, Piarroux R. 2014. MALDI-TOF mass spectrometry identification of filamentous fungi in the clinical laboratory. Mycoses 57:135–140. doi: 10.1111/myc.12115. [DOI] [PubMed] [Google Scholar]
  • 10.Lau AF, Walchak RC, Miller HB, Slechta ES, Kamboj K, Riebe K, Robertson AE, Gilbreath JJ, Mitchell KF, Wallace MA, Bryson AL, Balada-Llasat JM, Bulman A, Buchan BW, Burnham CAD, Butler-Wu S, Desai U, Doern CD, Hanson KE, Henderson CM, Kostrzewa M, Ledeboer NA, Maier T, Pancholi P, Schuetz AN, Shi G, Wengenack NL, Zhang SX, Zelazny AM, Frank KM. 2019. Multicenter Study demonstrates standardization requirements for mold identification by MALDI-TOF MS. Front Microbiol 10:2098. doi: 10.3389/fmicb.2019.02098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Pinheiro D, Monteiro C, Faria MA, Pinto E. 2019. Vitek MS v3.0 system in the identification of filamentous fungi. Mycopathologia 184:645–651. doi: 10.1007/s11046-019-00377-0. [DOI] [PubMed] [Google Scholar]
  • 12.Rychert J, Slechta ES, Barker AP, Miranda E, Babady NE, Tang YW, Gibas C, Wiederhold N, Sutton DA, Hanson KE. 2018. Multicenter evaluation of the vitek ms v3.0 system for the identification of filamentous fungi. J Clin Microbiol 56:e01353-17. doi: 10.1128/JCM.01353-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Normand AC, Becker P, Gabriel F, Cassagne C, Accoceberry I, Gari-Toussaint M, Hasseine L, De Geyter D, Pierard D, Surmont I, Djenad F, Donnadieu JL, Piarroux M, Ranque S, Hendrickx M, Piarroux R. 2017. Validation of a new web application for identification of fungi by use of matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol 55:2661–2670. doi: 10.1128/JCM.00263-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Normand AC, Blaize M, Imbert S, Packeu A, Becker P, Fekkar A, Stubbe D, Piarroux R. 2021. Identification of Molds with Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: performance of the Newly Developed MSI-2 Application in Comparison with the Bruker Filamentous Fungi Database and MSI-1. J Clin Microbiol 59:e0129921. doi: 10.1128/JCM.01299-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Imbert S, Normand AC, Gabriel F, Cassaing S, Bonnal C, Costa D, Lachaud L, Hasseine L, Kristensen L, Schuttler C, Raberin H, Brun S, Hendrickx M, Stubbe D, Piarroux R, Fekkar A. 2019. Multi-centric evaluation of the online MSI platform for the identification of cryptic and rare species of Aspergillus by MALDI-TOF. Med Mycol 57:962–968. doi: 10.1093/mmy/myz004. [DOI] [PubMed] [Google Scholar]
  • 16.Clinical and Laboratory Standards Institute (CLSI). 2018. Interpretation criteria for identification of bacteria and fungi by targeted DNA sequencingCLSI guideline MM18, 2nd ed Clinical and Laboratory Standards Institute, Wayne, PA. [Google Scholar]
  • 17.Balajee SA, Kano R, Baddley JW, Moser SA, Marr KA, Alexander BD, Andes D, Kontoyiannis DP, Perrone G, Peterson S, Brandt ME, Pappas PG, Chiller T. 2009. Molecular identification of Aspergillus species collected for the transplant-associated infection surveillance network. J Clin Microbiol 47:3138–3141. doi: 10.1128/JCM.01070-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Alastruey-Izquierdo A, Alcazar-Fuoli L, Rivero-Menéndez O, Ayats J, Castro C, García-Rodríguez J, Goterris-Bonet L, Ibáñez-Martínez E, Linares-Sicilia MJ, Teresa Martin-Gomez M, Martín-Mazuelos E, Pelaez T, Peman J, Rezusta A, Rojo S, Tejero R, Anza DV, Viñuelas J, Zapico MS, Cuenca-Estrella M. 2018. Molecular identification and susceptibility testing of molds isolated in a prospective surveillance of triazole resistance in Spain (FILPOP2 Study). Antimicrob Agents Chemother 62. doi: 10.1128/AAC.00358-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Takeda K, Suzuki J, Watanabe A, Matsuki M, Higa K, Inoue E, Akashi S, Shimada M, Kawashima M, Ohshima N, Fukami T, Masuda K, Yamane A, Tamura A, Nagai H, Matsui H, Tohma S, Kamei K. 2020. Species identification, antifungal susceptibility, and clinical feature association of Aspergillus section Nigri isolates from the lower respiratory tract. Med Mycol 58:310–314. doi: 10.1093/mmy/myz072. [DOI] [PubMed] [Google Scholar]
  • 20.Nematollahi S, Permpalung N, Zhang SX, Morales M, Marr KA. 2021. Aspergillus lentulus: an under-recognized cause of antifungal drug-resistant aspergillosis. Open Forum Infect Dis 8:ofab392. doi: 10.1093/ofid/ofab392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Alastruey-Izquierdo A, Alcazar-Fuoli L, Cuenca-Estrella M. 2014. Antifungal susceptibility profile of cryptic species of aspergillus. Mycopathologia 178:427–433. doi: 10.1007/s11046-014-9775-z. [DOI] [PubMed] [Google Scholar]
  • 22.Aït-Ammar N, Levesque E, Murat JB, Imbert S, Foulet F, Dannaoui E, Botterel F. 2018. Aspergillus pseudodeflectus: a new human pathogen in liver transplant patients. BMC Infect Dis 18:648. doi: 10.1186/s12879-018-3527-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Van Der Linden JWM, Warris A, Verweij PE. 2011. Aspergillus species intrinsically resistant to antifungal agentsMedical Mycology. Med Mycol 49:S82–S89. doi: 10.3109/13693786.2010.499916. [DOI] [PubMed] [Google Scholar]
  • 24.Nedel WL, Pasqualotto AC. 2014. Treatment of infections by cryptic aspergillus species. Mycopathologia 178:441–445. doi: 10.1007/s11046-014-9811-z. [DOI] [PubMed] [Google Scholar]
  • 25.Lamoth F. 2016. Aspergillus fumigatus-related species in clinical practice. Front Microbiol 7:683. doi: 10.3389/fmicb.2016.00683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Imbert S, Normand AC, Cassaing S, Gabriel F, Kristensen L, Bonnal C, Lachaud L, Costa D, Guitard J, Hasseine L, Palous M, Piarroux M, Hendrickx M, Piarroux R, Fekkar A. 2020. Multicentric analysis of the species distribution and antifungal susceptibility of cryptic isolates from aspergillus section fumigati. Antimicrob Agents Chemother 64:e01374-20. doi: 10.1128/AAC.01374-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Iatta R, Nuccio F, Immediato D, Mosca A, De Carlo C, Miragliotta G, Parisi A, Crescenzo G, Otranto D, Cafarchia C. 2016. Species distribution and in vitro azole susceptibility of aspergillus section nigri isolates from clinical and environmental settings. J Clin Microbiol 54:2365–2372. doi: 10.1128/JCM.01075-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Howard SJ. 2014. Multi-resistant aspergillosis due to cryptic species. Mycopathologia 178:435–439. doi: 10.1007/s11046-014-9774-0. [DOI] [PubMed] [Google Scholar]
  • 29.Escribano P, Peláez T, Recio S, Bouza E, Guinea J. 2012. Characterization of clinical strains of Aspergillus terreus complex: molecular identification and antifungal susceptibility to azoles and amphotericin B. Clin Microbiol Infect 18:E24. doi: 10.1111/j.1469-0691.2011.03714.x. [DOI] [PubMed] [Google Scholar]
  • 30.Carrara B, Richards R, Imbert S, Morio F, Sasso M, Zahr N, Normand AC, Le Pape P, Lachaud L, Ranque S, Maubon D, Piarroux R, Fekkar A. 2020. Species distribution and comparison between eucast and gradient concentration strips methods for antifungal susceptibility testing of 112 aspergillus section nigri isolates. Antimicrob Agents Chemother 64:e02510-19. doi: 10.1128/AAC.02510-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Alcazar-Fuoli L, Mellado E, Alastruey-Izquierdo A, Cuenca-Estrella M, Rodriguez-Tudela JL. 2008. Aspergillus section Fumigati: antifungal susceptibility patterns and sequence-based identification. Antimicrob Agents Chemother 52:1244–1251. doi: 10.1128/AAC.00942-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Mortensen KL, Johansen HK, Fuursted K, Knudsen JD, Gahrn-Hansen B, Jensen RH, Howard SJ, Arendrup MC. 2011. A prospective survey of Aspergillus spp. in respiratory tract samples: prevalence, clinical impact and antifungal susceptibility. Eur J Clin Microbiol Infect Dis 30:1355–1363. doi: 10.1007/s10096-011-1229-7. [DOI] [PubMed] [Google Scholar]
  • 33.Parent-Michaud M, Dufresne PJ, Fournier E, Folch B, Martineau C, Moreira S, Doucet N, De Repentigny L, Dufresne SF. 2020. Prevalence and mechanisms of azole resistance in clinical isolates of Aspergillus section Fumigati species in a Canadian tertiary care centre, 2000 to 2013. J Antimicrob Chemother 75:849–858. doi: 10.1093/jac/dkz534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Negri CE, Gonçalves SS, Xafranski H, Bergamasco MD, Aquino VR, Castro PTO, Colombo AL. 2014. Cryptic and rare aspergillus species in brazil: prevalence in clinical samples and in vitro susceptibility to triazoles. J Clin Microbiol 52:3633–3640. doi: 10.1128/JCM.01582-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kidd SE, Goeman E, Meis JF, Slavin MA, Verweij PE. 2015. Multi-triazole-resistant Aspergillus fumigatus infections in Australia. Mycoses 58:350–355. doi: 10.1111/myc.12324. [DOI] [PubMed] [Google Scholar]
  • 36.Zbinden A, Imhof A, Wilhelm MJ, Ruschitzka F, Wild P, Bloemberg GV, Mueller NJ. 2012. Fatal outcome after heart transplantation caused by Aspergillus lentulus. Transpl Infect Dis 14:E60–E63. doi: 10.1111/j.1399-3062.2012.00779.x. [DOI] [PubMed] [Google Scholar]
  • 37.Prigitano A, Esposto MC, Grancini A, Biffi A, Innocenti P, Cavanna C, Lallitto F, Mollaschi EMG, Bandettini R, Oltolini C, Passera M, De Lorenzis G, Sargolzaei M, Crespan M, Cogliati M, Tortorano AM, Romanò L. 2021. Azole resistance in Aspergillus isolates by different types of patients and correlation with environment - An Italian prospective multicentre study (ARiA study). Mycoses 64:528–536. doi: 10.1111/myc.13241. [DOI] [PubMed] [Google Scholar]
  • 38.Yu SY, Guo LN, Xiao M, Zhou ML, Yuan Y, Wang Y, Zhang L, Sun TS, Ning YT, Jia PY, Kang W, Kong F, Chen SCA, Zhao Y, Xu YC. 2020. Clinical and microbiological characterization of invasive pulmonary aspergillosis caused by aspergillus lentulus in China. Front Microbiol 11:1672. doi: 10.3389/fmicb.2020.01672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Yoshida H, Seki M, Umeyama T, Urai M, Kinjo Y, Nishi I, Toyokawa M, Kaneko Y, Ohno H, Miyazaki Y, Tomono K. 2015. Invasive pulmonary aspergillosis due to Aspergillus lentulus: successful treatment of a liver transplant patient. J Infect Chemother 21:479–481. doi: 10.1016/j.jiac.2015.02.010. [DOI] [PubMed] [Google Scholar]
  • 40.de Azevedo Bastos VR, de Castro Lima Santos DW, Padovan ACB, Melo ASA, de Abreu Mazzolin M, Camargo LFA, Colombo AL. 2015. Early invasive pulmonary aspergillosis in a kidney transplant recipient caused by aspergillus lentulus: first Brazilian report. Mycopathologia 179:299–305. doi: 10.1007/s11046-014-9840-7. [DOI] [PubMed] [Google Scholar]
  • 41.Alhambra A, Catalán M, Moragues MD, Brena S, Pontón J, Montejo JC, Del Palacio A. 2008. Isolation of Aspergillus lentulus in Spain from a critically ill patient with chronic obstructive pulmonary disease. Rev Iberoam Micol 25:246–249. doi: 10.1016/s1130-1406(08)70058-5. [DOI] [PubMed] [Google Scholar]
  • 42.Yagi K, Ushikubo M, Maeshima A, Konishi M, Fujimoto K, Tsukamoto M, Araki K, Kamei K, Oyamada Y, Oshima H. 2019. Invasive pulmonary aspergillosis due to Aspergillus lentulus in an adult patient: a case report and literature review. J Infect Chemother 25:547–551. doi: 10.1016/j.jiac.2019.02.003. [DOI] [PubMed] [Google Scholar]
  • 43.Alastruey-Izquierdo A, Mellado E, Peláez T, Pemán J, Zapico S, Alvarez M, Rodríguez-Tudela JL, Cuenca-Estrella M, FILPOP Study Group . 2013. Population-based survey of filamentous fungi and antifungal resistance in Spain (FILPOP study). Antimicrob Agents Chemother 57:3380–3387. doi: 10.1128/AAC.00383-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Glampedakis E, Cassaing S, Fekkar A, Dannaoui E, Bougnoux ME, Bretagne S, Neofytos D, Schreiber PW, Hennequin C, Morio F, Shadrivova O, Bongomin F, Fernández-Ruiz M, Bellanger AP, Arikan-Akdagli S, Erard V, Aigner M, Paolucci M, Khanna N, Charpentier E, Bonnal C, Brun S, Gabriel F, Riat A, Zbinden R, Le Pape P, Klimko N, Lewis RE, Richardson M, İnkaya AC, Coste AT, Bochud PY, Lamoth F. 2021. Invasive aspergillosis due to Aspergillus section usti: a multicenter retrospective study. Clin Infect Dis 72:1379–1385. doi: 10.1093/cid/ciaa230. [DOI] [PubMed] [Google Scholar]
  • 45.Glampedakis E, Erard V, Lamoth F. 2020. Clinical relevance and characteristics of aspergillus calidoustus and other aspergillus species of section usti. J Fungi (Basel, Switzerland) 6:84. doi: 10.3390/jof6020084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Samson RA, Varga J, Meijer M, Frisvad JC. 2011. New taxa in Aspergillus section Usti. Stud Mycol 69:81–97. doi: 10.3114/sim.2011.69.06. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Varga J, Houbraken J, Van Der Lee HAL, Verweij PE, Samson RA. 2008. Aspergillus calidoustus sp. nov., causative agent of human infections previously assigned to Aspergillus ustus. Eukaryot Cell 7:630–638. doi: 10.1128/EC.00425-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Varga J, Frisvad JC, Kocsubé S, Brankovics B, Tóth B, Szigeti G, Samson RA. 2011. New and revisited species in Aspergillus section Nigri. Stud Mycol 69:1–17. doi: 10.3114/sim.2011.69.01. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Toyotome T, Saito S, Koshizaki Y, Komatsu R, Matsuzawa T, Yaguchi T. 2020. Prospective survey of Aspergillus species isolated from clinical specimens and their antifungal susceptibility: a five-year single-center study in Japan. J Infect Chemother 26:321–323. doi: 10.1016/j.jiac.2019.09.002. [DOI] [PubMed] [Google Scholar]
  • 50.Nucci M, Anaissie E. 2007. Fusarium infections in immunocompromised patients. Clin Microbiol Rev 20:695–704. doi: 10.1128/CMR.00014-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Al-Hatmi AM, Hagen F, Menken SB, Meis JF, De Hoog GS. 2016. Global molecular epidemiology and genetic diversity of Fusarium, a significant emerging group of human opportunists from 1958 to 2015. Emerg Microbes Infect 5:1–11. doi: 10.1038/emi.2016.126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Al-Hatmi AMS, Meis JF, de Hoog GS. 2016. Fusarium: molecular diversity and intrinsic drug resistance. PLoS Pathog 12:e1005464. doi: 10.1371/journal.ppat.1005464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Muhammed M, Anagnostou T, Desalermos A, Kourkoumpetis TK, Carneiro HA, Glavis-Bloom J, Coleman JJ, Mylonakis E. 2013. Fusarium infection: report of 26 cases and review of 97 cases from the literature. Medicine 92:305–316. doi: 10.1097/MD.0000000000000008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.van Diepeningen AD, Al-Hatmi AMS, Brankovics B, de Hoog GS. 2014. Taxonomy and clinical spectra of fusarium species: where do we stand in 2014? Curr Clin Micro Rpt 1:10–18. doi: 10.1007/s40588-014-0003-x. [DOI] [Google Scholar]
  • 55.Tortorano AM, Prigitano A, Esposto MC, Arsic Arsenijevic V, Kolarovic J, Ivanovic D, Paripovic L, Klingspor L, Nordøy I, Hamal P, Arikan Akdagli S, Ossi C, Grancini A, Cavanna C, Lo Cascio G, Scarparo C, Candoni A, Caira M, Drogari Apiranthitou M, ECMM Working Group . 2014. European Confederation of Medical Mycology (ECMM) epidemiological survey on invasive infections due to Fusarium species in Europe. Eur J Clin Microbiol Infect Dis 33:1623–1630. doi: 10.1007/s10096-014-2111-1. [DOI] [PubMed] [Google Scholar]
  • 56.Al-Hatmi AMS, Van Diepeningen AD, Curfs-Breuker I, Sybren de Hoog G, Meis JF. 2015. Specific antifungal susceptibility profiles of opportunists in the Fusarium fujikuroi complex. J Antimicrob Chemother 70:1068–1071. [DOI] [PubMed] [Google Scholar]
  • 57.Shoham S, Dominguez EA, the AST Infectious Diseases Community of Practice . 2019. Emerging fungal infections in solid organ transplant recipients: guidelines of the American Society of Transplantation Infectious Diseases Community of Practice. Clin Transplant 33:e13525. doi: 10.1111/ctr.13525. [DOI] [PubMed] [Google Scholar]
  • 58.Rosa PD, Heidrich D, Corrêa C, Scroferneker ML, Vettorato G, Fuentefria AM, Goldani LZ. 2017. Genetic diversity and antifungal susceptibility of Fusarium isolates in onychomycosis. Mycoses 60:616–622. doi: 10.1111/myc.12638. [DOI] [PubMed] [Google Scholar]
  • 59.Espinel-Ingroff A, Colombo AL, Cordoba S, Dufresne PJ, Fuller J, Ghannoum M, Gonzalez GM, Guarro J, Kidd SE, Meis JF, Melhem TMSC, Pelaez T, Pfaller MA, Szeszs MW, Takahaschi JP, Tortorano AM, Wiederhold NP, Turnidge J. 2016. International evaluation of MIC distributions and epidemiological cutoff value (ECV) definitions for fusarium species identified by molecular methods for the CLSI broth microdilution method. Antimicrob Agents Chemother 60:1079–1084. doi: 10.1128/AAC.02456-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.O'Donnell K, Sutton DA, Fothergill A, McCarthy D, Rinaldi MG, Brandt ME, Zhang N, Geiser DM. 2008. Molecular phylogenetic diversity, multilocus haplotype nomenclature, and in vitro antifungal resistance within the Fusarium solani species complex. J Clin Microbiol 46:2477–2490. doi: 10.1128/JCM.02371-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.O'Donnell K, Gueidan C, Sink S, Johnston PR, Crous PW, Glenn A, Riley R, Zitomer NC, Colyer P, Waalwijk C, Lee Tvd, Moretti A, Kang S, Kim H-S, Geiser DM, Juba JH, Baayen RP, Cromey MG, Bithell S, Sutton DA, Skovgaard K, Ploetz R, Corby Kistler H, Elliott M, Davis M, Sarver BAJ. 2009. A two-locus DNA sequence database for typing plant and human pathogens within the Fusarium oxysporum species complex. Fungal Genet Biol 46:936–948. doi: 10.1016/j.fgb.2009.08.006. [DOI] [PubMed] [Google Scholar]
  • 62.O'Donnell K, Sutton DA, Rinaldi MG, Gueidan C, Crous PW, Geiser DM. 2009. Novel multilocus sequence typing scheme reveals high genetic diversity of human pathogenic members of the Fusarium incarnatum-F. equiseti and F. chlamydosporum species complexes within the United States. J Clin Microbiol 47:3851–3861. doi: 10.1128/JCM.01616-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.O'Donnell K, Sutton DA, Rinaldi MG, Sarver BAJ, Balajee SA, Schroers H-J, Summerbell RC, Robert VARG, Crous PW, Zhang N, Aoki T, Jung K, Park J, Lee Y-H, Kang S, Park B, Geiser DM. 2010. Internet-accessible DNA sequence database for identifying fusaria from human and animal infections. J Clin Microbiol 48:3708–3718. doi: 10.1128/JCM.00989-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.van Diepeningen AD, Brankovics B, Iltes J, van der Lee TAJ, Waalwijk C. 2015. Diagnosis of Fusarium Infections: approaches to identification by the clinical mycology laboratory. Curr Fungal Infect Rep 9:135–143. doi: 10.1007/s12281-015-0225-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Roden MM, Zaoutis TE, Buchanan WL, Knudsen TA, Sarkisova TA, Schaufele RL, Sein M, Sein T, Chiou CC, Chu JH, Kontoyiannis DP, Walsh TJ. 2005. Epidemiology and outcome of zygomycosis: a review of 929 reported cases. Clin Infect Dis 41:634–653. doi: 10.1086/432579. [DOI] [PubMed] [Google Scholar]
  • 66.Prakash H, Ghosh AK, Rudramurthy SM, Singh P, Xess I, Savio J, Pamidimukkala U, Jillwin J, Varma S, Das A, Panda NK, Singh S, Bal A, Chakrabarti A. 2019. A prospective multicenter study on mucormycosis in India: epidemiology, diagnosis, and treatment. Med Mycol 57:395–402. doi: 10.1093/mmy/myy060. [DOI] [PubMed] [Google Scholar]
  • 67.Corzo-León DE, Chora-Hernández LD, Rodríguez-Zulueta AP, Walsh TJ. 2018. Diabetes mellitus as the major risk factor for mucormycosis in Mexico: epidemiology, diagnosis, and outcomes of reported cases. Med Mycol 56:29–43. doi: 10.1093/mmy/myx017. [DOI] [PubMed] [Google Scholar]
  • 68.Pal R, Singh B, Bhadada SK, Banerjee M, Bhogal RS, Hage N, Kumar A. 2021. COVID-19-associated mucormycosis: an updated systematic review of literature. Mycoses 64:1452–1459. doi: 10.1111/myc.13338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Dilek A, Ozaras R, Ozkaya S, Sunbul M, Sen EI, Leblebicioglu H. 2021. COVID-19-associated mucormycosis: case report and systematic review. Travel Med Infect Dis 44:102148. doi: 10.1016/j.tmaid.2021.102148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Garg D, Muthu V, Sehgal IS, Ramachandran R, Kaur H, Bhalla A, Puri GD, Chakrabarti A, Agarwal R. 2021. Coronavirus disease (Covid-19) associated mucormycosis (CAM): case report and systematic review of literature. Mycopathologia 186:289–298. doi: 10.1007/s11046-021-00528-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Sun HY, Singh N. 2011. Mucormycosis: its contemporary face and management strategies. Lancet Infect Dis 11:301–311. doi: 10.1016/S1473-3099(10)70316-9. [DOI] [PubMed] [Google Scholar]
  • 72.Vaughan C, Bartolo A, Vallabh N, Leong SC. 2018. A meta-analysis of survival factors in rhino-orbital-cerebral mucormycosis-has anything changed in the past 20 years? Clin Otolaryngol 43:1454–1464. doi: 10.1111/coa.13175. [DOI] [PubMed] [Google Scholar]
  • 73.Chamilos G, Lewis RE, Kontoyiannis DP. 2008. Delaying amphotericin B-based frontline therapy significantly increases mortality among patients with hematologic malignancy who have zygomycosis. Clin Infect Dis 47:503–509. doi: 10.1086/590004. [DOI] [PubMed] [Google Scholar]
  • 74.Jeong W, Keighley C, Wolfe R, Lee WL, Slavin MA, Kong DCM, Chen SCA. 2019. The epidemiology and clinical manifestations of mucormycosis: a systematic review and meta-analysis of case reports. Clin Microbiol Infect 25:26–34. doi: 10.1016/j.cmi.2018.07.011. [DOI] [PubMed] [Google Scholar]
  • 75.Sridhara SR, Paragache G, Panda NK, Chakrabarti A. 2005. Mucormycosis in immunocompetent individuals: an increasing trend. J Otolaryngol 34:402–406. doi: 10.2310/7070.2005.34607. [DOI] [PubMed] [Google Scholar]
  • 76.Skiada A, Pagano L, Groll A, Zimmerli S, Dupont B, Lagrou K, Lass-Florl C, Bouza E, Klimko N, Gaustad P, Richardson M, Hamal P, Akova M, Meis JF, Rodriguez-Tudela JL, Roilides E, Mitrousia-Ziouva A, Petrikkos G. 2011. Zygomycosis in Europe: analysis of 230 cases accrued by the registry of the European Confederation of Medical Mycology (ECMM) Working Group on Zygomycosis between 2005 and 2007. Clin Microbiol Infect 17:1859–1867. doi: 10.1111/j.1469-0691.2010.03456.x. [DOI] [PubMed] [Google Scholar]
  • 77.Petraitis V, Petraitiene R, Antachopoulos C, Hughes JE, Cotton MP, Kasai M, Harrington S, Gamaletsou MN, Bacher JD, Kontoyiannis DP, Roilides E, Walsh TJ. 2013. Increased virulence of Cunninghamella bertholletiae in experimental pulmonary mucormycosis: correlation with circulating molecular biomarkers, sporangiospore germination and hyphal metabolism. Med Mycol 51:72–82. doi: 10.3109/13693786.2012.690107. [DOI] [PubMed] [Google Scholar]
  • 78.Foster CE, Revell PA, Campbell JR, Marquez L. 2021. Healthcare-associated pediatric cutaneous mucormycosis at Texas Children’s Hospital, 2012-2019. Pediatr Infect Dis J 40:746–748. doi: 10.1097/INF.0000000000003153. [DOI] [PubMed] [Google Scholar]
  • 79.Gamarra S, Chaves MS, Cabeza MS, Macedo D, Leonardelli F, Franco D, Boleas M, Garcia-Effron G. 2018. Mucormycosis outbreak due to Rhizopus microsporus after arthroscopic anterior cruciate ligament reconstruction surgery evaluated by RAPD and MALDI-TOF Mass spectrometry. J Mycol Med 28:617–622. doi: 10.1016/j.mycmed.2018.09.002. [DOI] [PubMed] [Google Scholar]
  • 80.Rammaert B, Lanternier F, Zahar JR, Dannaoui E, Bougnoux ME, Lecuit M, Lortholary O. 2012. Healthcare-associated mucormycosis. Clin Infect Dis 54:S44. doi: 10.1093/cid/cir867. [DOI] [PubMed] [Google Scholar]
  • 81.Cornely OA, Alastruey-Izquierdo A, Arenz D, Chen SCA, Dannaoui E, Hochhegger B, Hoenigl M, Jensen HE, Lagrou K, Lewis RE, Mellinghoff SC, Mer M, Pana ZD, Seidel D, Sheppard DC, Wahba R, Akova M, Alanio A, Al-Hatmi AMS, Arikan-Akdagli S, Badali H, Ben-Ami R, Bonifaz A, Bretagne S, Castagnola E, Chayakulkeeree M, Colombo AL, Corzo-León DE, Drgona L, Groll AH, Guinea J, Heussel CP, Ibrahim AS, Kanj SS, Klimko N, Lackner M, Lamoth F, Lanternier F, Lass-Floerl C, Lee DG, Lehrnbecher T, Lmimouni BE, Mares M, Maschmeyer G, Meis JF, Meletiadis J, Morrissey CO, Nucci M, Oladele R, Pagano L. 2019. Global guideline for the diagnosis and management of mucormycosis: an initiative of the European Confederation of Medical Mycology in cooperation with the Mycoses Study Group Education and Research Consortium. Lancet Infect Dis 19:e405–e421. doi: 10.1016/S1473-3099(19)30312-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Badali H, Cañete-Gibas C, McCarthy D, Patterson H, Sanders C, David MP, Mele J, Fan H, Wiederhold NP. 2021. Epidemiology and antifungal susceptibilities of mucoralean fungi in clinical samples from the United States. J Clin Microbiol 59:e0123021. doi: 10.1128/JCM.01230-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Vitale RG, De Hoog GS, Schwarz P, Dannaoui E, Deng S, Machouart M, Voigt K, Van De Sande WWJ, Dolatabadi S, Meis JF, Walther G. 2012. Antifungal susceptibility and phylogeny of opportunistic members of the order mucorales. J Clin Microbiol 50:66–75. doi: 10.1128/JCM.06133-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Heireman L, Patteet S, Steyaert S. 2020. Performance of the new ID-fungi plate using two types of reference libraries (Bruker and MSI) to identify fungi with the Bruker MALDI Biotyper. Med Mycol 58:946–957. doi: 10.1093/mmy/myz138. [DOI] [PubMed] [Google Scholar]
  • 85.Paul S, Singh P, Rudramurthy SM, Chakrabarti A, Ghosh AK. 2017. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry: protocol standardization and database expansion for rapid identification of clinically important molds. Future Microbiol 12:1457–1466. doi: 10.2217/fmb-2017-0105. [DOI] [PubMed] [Google Scholar]
  • 86.Coulibaly O, Marinach-Patrice C, Cassagne C, Piarroux R, Mazier D, Ranque S. 2011. Pseudallescheria/Scedosporium complex species identification by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Med Mycol 49:621–626. doi: 10.3109/13693786.2011.555424. [DOI] [PubMed] [Google Scholar]
  • 87.Lau AF, Drake SK, Calhoun LB, Henderson CM, Zelazny AM. 2013. Development of a clinically comprehensive database and a simple procedure for identification of molds from solid media by matrix-assisted laser desorption ionization-Time of flight mass spectrometry. J Clin Microbiol 51:828–834. doi: 10.1128/JCM.02852-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Sacheli R, Henri AS, Seidel L, Ernst M, Darfouf R, Adjetey C, Schyns M, Marechal L, Meex C, Arrese J, Hayette MP. 2020. Evaluation of the new Id-Fungi plates from Conidia for MALDI-TOF MS identification of filamentous fungi and comparison with conventional methods as identification tool for dermatophytes from nails, hair and skin samples. Mycoses 63:1115–1127. doi: 10.1111/myc.13156. [DOI] [PubMed] [Google Scholar]
  • 89.Robert MG, Romero C, Dard C, Garnaud C, Cognet O, Girard T, Rasamoelin T, Cornet M, Maubon D. 2020. Evaluation of ID fungi plates medium for identification of molds by MALDI biotyper. J Clin Microbiol 58:e01687-19. doi: 10.1128/JCM.01687-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Gautier M, Ranque S, Normand A-C, Becker P, Packeu A, Cassagne C, L'Ollivier C, Hendrickx M, Piarroux R. 2014. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry: revolutionizing clinical laboratory diagnosis of mould infections. Clin Microbiol Infect 20:1366–1371. doi: 10.1111/1469-0691.12750. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental file 1

Supplemental material. Download jcm.01032-22-s0001.pdf, PDF file, 0.5 MB (523.9KB, pdf)

Articles from Journal of Clinical Microbiology are provided here courtesy of American Society for Microbiology (ASM)

RESOURCES