Characterization of an outbreak caused by Elizabethkingia miricola using Fourier-transform infrared (FTIR) spectroscopy.

Fourier-transform infrared (FTIR) spectroscopy has the potential to be used for bacterial typing and outbreak characterization. We evaluated FTIR for the characterization of an outbreak caused by Elizabethkingia miricola. During the 2020-2021 period, 26 isolates (23 clinical and 3 environmental) were collected and analyzed by FTIR (IR Biotyper) and core-genome MLST (cgMLST), in addition to antimicrobial susceptibility testing. FTIR spectroscopy and cgMLST showed that 22 of the isolates were related to the outbreak, including the environmental samples, with only one discordance between both methods. Then, FTIR is useful for E. miricola typing and can be easily implemented in the laboratory.

Automatic Discrimination of Species within the Enterobacter cloacae Complex Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Supervised Algorithms.

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may have different acquired antimicrobial resistance and virulence mechanisms, and their identification is challenging. This study aims to develop predictive models based on matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) profiles and machine learning for species-level identification. A total of 219 ECC and 118 Klebsiella aerogenes clinical isolates from three hospitals were included. The capability of the proposed method to differentiate the most common ECC species (Enterobacter asburiae, Enterobacter kobei, Enterobacter hormaechei, Enterobacter roggenkampii, Enterobacter ludwigii, and Enterobacter bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with principal-component analysis (PCA) preprocessing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development data set. Thus, we developed supervised, nonlinear predictive models (support vector machine with radial basis function and random forest). The external validation of these models with protein spectra from two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species; with data analyzed in the three participating centers, the accuracy was close to 100%. Similar results were obtained with the Mass Spectrometric Identification (MSI) database developed recently (https://msi.happy-dev.fr) except in the case of E. hormaechei, which was more accurately identified with the random forest algorithm. In short, MALDI-TOF MS combined with machine learning was demonstrated to be a rapid and accurate method for the differentiation of ECC species.

Validation of an expanded, in-house library and an optimized preparation method for the identification of fungal isolates using MALDI-TOF mass spectrometry.

The goal of this study was to validate an optimized sample preparation method for filamentous fungal isolates coupled with the use of an in-house library for the identification of moulds using Matrix Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in a multicenter context. For that purpose, three Spanish microbiology laboratories participated in the identification of 97 fungal isolates using MALDI-TOF MS coupled with the Filamentous Fungi library 3.0 (Bruker Daltonics) and an in-house library containing 314 unique fungal references. The isolates analyzed belonged to 25 species from the genus Aspergillus, Fusarium, Scedosporium/Lomentospora, the Mucorales order and the Dermatophytes group. MALDI-TOF MS identification was carried out from hyphae resuspended in water and ethanol. After a high-speed centrifugation step, the supernatant was discarded and the pellet submitted to a standard protein extraction step. The protein extract was analyzed with the MBT Smart MALDI Biotyper system (Bruker Daltonics). The rate of accurate, species-level identification obtained ranged between 84.5% and 94.8% and the score values were 1.8 for 72.2-94.9% of the cases. Two laboratories failed to identify only one isolate of Syncephalastrum sp. and Trichophyton rubrum, respectively and three isolates could not be identified in the third center (F. proliferatum, n = 1; T.interdigitale, n = 2). In conclusion, the availability of an effective sample preparation method and an extended database allowed high rates of correct identification of fungal species using MALDI-TOF MS. Some species, such as Trichophyton spp. are still difficult to identify. Although further improvements are still required, the developed methodology allowed the reliable identification of most fungal species.

MALDI-TOF mass spectrometry has been improved as a diagnostic method for the rapid and reliable identification of filamentous fungi by means of the creation of an expanded database containing reference protein spectra of the most clinically impacting fungal species.

Identification of Mycobacterium abscessus Subspecies by MALDI-TOF Mass Spectrometry and Machine Learning.

Mycobacterium abscessus is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 325 clinical isolates of M. abscessus was used for MALDI-TOF MS analysis and for the development of machine learning predictive models based on MALDI-TOF MS protein spectra. Overall, using a random forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 96.5% of isolates were correctly identified at the subspecies level. Moreover, an improved model with Spanish isolates was able to identify 88.9% of strains collected in other countries. In addition, differences in culture media, colony morphology, and geographic origin of the strains were evaluated, showing that the latter had an impact on the protein spectra. Finally, after studying all protein peaks previously reported for this species, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of M. abscessus using MALDI-TOF MS.

Identification of Nocardia and non-tuberculous Mycobacterium species by MALDI-TOF MS using the VITEK MS coupled to IVD and RUO databases.

Identification of Nocardia and Mycobacterium species by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is still a challenging task that requires both suitable protein extraction procedures and extensive databases. This study aimed to evaluate the VITEK MS Plus system coupled with updated RUO (v4.17) and IVD (v3.2) databases for the identification of Nocardia spp. and Mycobacterium spp. clinical isolates. Sample preparation was carried out using the VITEK MS Mycobacterium/Nocardia kit for protein extraction. From 90 Nocardia spp. isolates analysed, 86 (95.6%) were correctly identified at species or complex level using IVD and 78 (86.7%) using RUO. Only two strains were misidentified as other species pertaining to the same complex. Among the 106 non-tuberculous Mycobacterium clinical isolates tested from a liquid culture medium, VITEK MS identified correctly at species or complex level 96 (90.6%) isolates in the IVD mode and 89 (84.0%) isolates in the RUO mode. No misidentifications were detected. Although the IVD mode was unable to differentiate members of the M. fortuitum complex, the RUO mode correctly discriminated M. peregrinum and M. septicum. The robustness and accuracy showed by this system allow its implementation for routine identification of these microorganisms in clinical laboratories.

Evaluation of the Vitek Ms system for the identification of filamentous fungi.

The implementation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) for the identification of fungal isolates remains challenging and has been limited to experienced laboratories in sample preparation and in-house libraries construction. However, the development of commercial kits for standardized fungal sample preparation and updated reference libraries can fill this gap. This study aimed to evaluate the performance of the commercial VITEK MS Mould Kit (bioMérieux, Marcy l'Etoile, France) and the VITEK MS system (bioMérieux) for identification using a panel of fungal species of clinical interest. Overall, 200 isolates belonging to 13 genera and 43 fungal species were analyzed with the VITEK MS system equipped with the v3.2 IVD database. Overall, 89.0% of the isolates were correctly identified, 41.5 and 43.5% at species and complex level, respectively. For an additional 4.0% of the identifications, correlation at the genus level was reported. The remaining 21 isolates (10.5%) could not be identified among which 85.0% (18/21) were species not claimed in the database. One Syncephalastrum isolate was misidentified as Rhizopus microsporus complex. Specifically, 100% of the Scedosporium/Lomentospora, 97.1% of the Fusarium, 65.7% of the Mucorales and 86.4% of the Aspergillus isolates were correctly identified at the species and complex level. The methodology described allows for an easy implementation of MALDI-TOF MS for routine identification of fungal species in a fast and reliable manner. Although further improvement in the databases is still required, an important number of fungal species can be correctly identified at the species level using this method. LAY SUMMARY: The use of MALDI-TOF for fungal identification remains a challenge. In this study, using a commercial protein extraction kit and updated database, VITEK MS system was able to identify up to 89.0% of a diverse collection of 200 filamentous fungi representing 43 fungal species.

Rapid and Reproducible MALDI-TOF-Based Method for the Detection of Vancomycin-Resistant Enterococcus faecium Using Classifying Algorithms.

Vancomycin-resistant Enterococcus faecium represents a health threat due to its ability to spread and cause outbreaks. MALDI-TOF MS has demonstrated its usefulness for E. faecium identification, but its implementation for antimicrobial resistance detection is still under evaluation. This study assesses the repeatability of MALDI-TOF MS for peak analysis and its performance in the discrimination of vancomycin-susceptible (VSE) from vancomycin-resistant isolates (VRE). The study was carried out on protein spectra from 178 E. faecium unique clinical isolates-92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Technical and biological repeatability were assayed. Unsupervised (principal component analysis, (PCA)) and supervised algorithms (support vector machine (SVM), random forest (RF) and partial least squares-discriminant analysis (PLS-DA)) were applied. The repeatability assay was performed with 18 peaks common to VSE and VRE with intensities above 1.0% of the maximum peak intensity. It showed lower variability for normalized data and for the peaks within the 3000-9000 m/z range. It was found that 80.9%, 79.2% and 77.5% VSE vs. VRE discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct internal differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.6% cases. The implementation of MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.

Multicentre study on the reproducibility of MALDI-TOF MS for nontuberculous mycobacteria identification.

The ability of MALDI-TOF for the identification of nontuberculous mycobacteria (NTM) has improved recently thanks to updated databases and optimized protein extraction procedures. Few multicentre studies on the reproducibility of MALDI-TOF have been performed so far, none on mycobacteria. The aim of this study was to evaluate the reproducibility of MALDI-TOF for the identification of NTM in 15 laboratories in 9 European countries. A total of 98 NTM clinical isolates were grown on Löwenstein-Jensen. Biomass was collected in tubes with water and ethanol, anonymized and sent out to the 15 participating laboratories. Isolates were identified using MALDI Biotyper (Bruker Daltonics). Up to 1330 MALDI-TOF identifications were collected in the study. A score ≥ 1.6 was obtained for 100% of isolates in 5 laboratories (68.2-98.6% in the other). Species-level identification provided by MALDI-TOF was 100% correct in 8 centres and 100% correct to complex-level in 12 laboratories. In most cases, the misidentifications obtained were associated with closely related species. The variability observed for a few isolates could be due to variations in the protein extraction procedure or to MALDI-TOF system status in each centre. In conclusion, MALDI-TOF showed to be a highly reproducible method and suitable for its implementation for NTM identification.

Determination of the ability of matrix-assisted laser desorption ionization time-of-flight mass spectrometry to identify high-biofilm-producing strains.

Background: The traditional method for assessing the capacity of a microorganism to produce biofilm is generally a static in vitro model in a multi-well plate using the crystal violet (CV) binding assay, which takes 96 h. Furthermore, while the method is simple to perform, its reproducibility is poor. Objective: We evaluated whether matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) could make it possible to differentiate between high-and low-biofilm-producing microorganisms on 24-h cultures of Staphylococcus aureus and Candida albicans. Methods: We included 157 strains of S. aureus and 91 strains of C. albicans obtained from the blood cultures of patients with bacteremia/candidemia. We tested biofilm production using the CV binding assay as the gold standard to classify strains as low or high biofilm producers. We then applied MALDI-TOF MS to create a machine learning-based predictive model using 40 strains of S. aureus and C. albicans, each with extreme absorbance values, and validated this approach with the remaining 117 and 51 strains using the random forest algorithm and the support vector machine algorithm, respectively. Results: Overall, 81.2% of the S. aureus strains (95/117) and 74.5% of the C. albicans strains (38/51) used for validation were correctly categorized, respectively, as low and high-biofilm-producing. Conclusion: Classification based on MALDI-TOF MS protein spectra enables us to predict acceptable information about the capacity of 24-h cultures of S. aureus and C. albicans to form biofilm.

Multicenter Evaluation of Rapid BACpro® II for the Accurate Identification of Microorganisms Directly from Blood Cultures Using MALDI-TOF MS.

The identification of microorganisms directly from blood cultures using MALDI-TOF MS has been shown to be the most impacting application of this methodology. In this study, a novel commercial method was evaluated in four clinical microbiology laboratories. Positive blood culture samples (n = 801) were processed using a rapid BACpro® II kit and then compared with the routine gold standard. A subset of monomicrobial BCs (n = 560) were analyzed in parallel with a Sepsityper® Kit (Bruker Daltonics, Bremen, Germany) and compared with the rapid BACpro® II kit. In addition, this kit was also compared with two different in-house methods. Overall, 80.0% of the monomicrobial isolates (609/761; 95% CI 71.5-88.5) were correctly identified by the rapid BACpro® II kit at the species level (92.3% of the Gram negative and 72.4% of the Gram positive bacteria). The comparison with the Sepsityper® Kit showed that the rapid BACpro® II kit generated higher rates of correct species-level identification for all categories (p > 0.0001), except for yeasts identified with score values > 1.7. It also proved superior to the ammonium chloride method (p > 0.0001), but the differential centrifugation method allowed for higher rates of correct identification for Gram negative bacteria (p > 0.1). The percentage of accurate species-level identification of Gram positive bacteria was particularly noteworthy in comparison with other commercial and in-house methods.

Evaluation of MALDI Biotyper Interpretation Criteria for Accurate Identification of Nontuberculous Mycobacteria.

Identification of mycobacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) requires not only a good protein extraction protocol but also an adequate cutoff score in order to provide reliable results. The aim of this study was to assess the cutoff scores proposed by the MALDI-TOF MS system for mycobacterial identification. A total of 693 clinical isolates from a liquid medium and 760 from a solid medium were analyzed, encompassing 67 different species of nontuberculous mycobacteria (NTM). MALDI-TOF MS identified 558 (80.5%) isolates from the liquid medium and 712 (93.7%) isolates from the solid medium with scores of ≥1.60. Among these, four (0.7%) misidentifications were obtained from the liquid medium and four (0.5%) from the solid medium. With regard to species diversity, MALDI-TOF MS successfully identified 64 (95.5%) different species, while PCR-reverse hybridization (GenoType Mycobacterium CM and AS assays) identified 24 (35.8%) different species. With MALDI-TOF MS scores of ≥2, all isolates were correctly identified, and with scores in the range from 1.60 to 1.99, most isolates were correctly identified, except for Mycobacterium angelicum, M. parascrofulaceum, M. peregrinum, M. porcinum, and M. gastri In conclusion, MALDI-TOF MS is a useful method for identifying a large diversity of NTM species. A score threshold of 1.60 proved useful for identifying almost all the isolates tested; only a few species required a higher score (≥2.00) to obtain a valid definitive identification.

Evaluation of the Xpert MTB/RIF Ultra Assay for Direct Detection of Mycobacterium tuberculosis Complex in Smear-Negative Extrapulmonary Samples.

The rapid detection of Mycobacterium tuberculosis complex (MTUBC) in clinical samples is essential for successful treatment. New techniques such as real-time PCR have been developed in order to facilitate rapid diagnosis, but their sensitivity is low in extrapulmonary specimens, due to the low bacillary load in such samples. A next-generation assay has recently been developed to try to overcome this limitation. The aim of this study was to analyze the effectiveness of the Xpert MTB/RIF Ultra (GX-Ultra) for the detection of MTUBC DNA in 108 smear-negative extrapulmonary specimens that were MTUBC culture positive. In addition, 40 extrapulmonary culture-negative samples and 20 samples with nontuberculous mycobacteria were tested to evaluate the specificity of the assay. All samples were collected between May 1999 and May 2017. The GX-Ultra detected DNA of MTUBC in 82 extrapulmonary specimens that were MTUBC culture positive (75.9% sensitivity; 95% confidence interval [CI], 66.6 to 83.4%). The assay was negative for all clinical specimens that were MTUBC culture negative and the samples with nontuberculous mycobacteria (100% specificity). Furthermore, two (1.8%) samples presented mutations related to rifampin resistance. The highest sensitivity was obtained in samples of lymph nodes (94.1%) and nonsterile fluids (93.7%), followed by tissue specimens (86.6%), stool material (80%), abscess aspirates (64.7%), and sterile fluids (60.5%). Pleural fluids, one of the least optimal samples for detecting DNA of MTUBC, were GX-Ultra positive in 10/21 (47.6%) of cases. In summary, GX-Ultra showed excellent specificity and high sensitivity in paubacillary specimens, making it a useful tool for rapid diagnosis of extrapulmonary tuberculosis.

Evaluation of Two Protein Extraction Protocols Based on Freezing and Mechanical Disruption for Identifying Nontuberculous Mycobacteria by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry from Liquid and Solid Cultures.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has proved to be a useful diagnostic method for identifying conventional bacteria. In the case of mycobacteria, a good protein extraction protocol is essential in order to obtain reliable identification results. To date, no such protocol has been definitively established. The aim of this study was to compare the manufacturer's recommended protein extraction protocol (protocol A) with two novel protocols (protocols B and C), which apply different freezing temperatures and mechanical disruption times using an automatic tissue homogenizer. A total of 302 clinical isolates, comprising 41 nontuberculous mycobacteria (NTM) species, were grown in parallel on solid and liquid media and analyzed: 174 isolates were slow-growing mycobacteria (SGM) and 128 isolates were rapid-growing mycobacteria (RGM). Overall, MALDI-TOF MS identified a higher number of NTM isolates from solid than from liquid media, especially with protocol C (83.4 and 68.2%, respectively; P < 0.05). From solid media, this protein extraction method identified 57.9 and 3.9% more isolates than protocols A (P < 0.001) and B (P < 0.05), respectively. In the case of liquid media, protocol C identified 49.7 and 6.3% more isolates than protocols A and B, respectively (P < 0.001). With regard to the growth rate, MALDI-TOF MS identified more RGM isolates than SGM isolates in all of the protocols studied. In conclusion, the application of freezing and automatic tissue homogenizer improved protein extraction of NTM and boosted identification rates. Consequently, MALDI-TOF MS, which is a cheap and simple method, could be a helpful tool for identifying NTM species in clinical laboratories.