Broad-range direct detection and identification of fungi by use of the PLEX-ID PCR-electrospray ionization mass spectrometry (ESI-MS) system.

The PLEX-ID system is a novel technology that couples PCR amplification and electrospray ionization-mass spectrometry to identify pathogens directly in clinical specimens. The analytical performance of the PLEX-ID Broad Fungal assay was compared with that of traditional culture identification by using 91 characterized fungal culture isolates (64 manufacturer-claimed and 27 nonclaimed organisms) and directly by using 395 respiratory specimens. Discordant results were resolved by D2 large-subunit ribosomal DNA fungal sequencing. Environmental studies were performed to monitor for potential contamination. The PLEX-ID Broad Fungal assay correctly identified 95.6% (87/91) and 81.3% (74/91) of the culture isolates to the genus and species levels, respectively. Of the manufacturer-claimed organisms, 100% (64/64) and 92.2% (59/64) were correctly identified to the genus and species levels, respectively. Direct analysis of respiratory specimens resulted in 67.6% (267/395) and 66.6% (263/395) agreement with culture results to the genus and species levels, respectively, with 16.2% (64/395) of the results discordant with culture and 16.2% (64/395) not detected by the system. The majority (>95%) of the isolates not detected directly by the PLEX-ID system ultimately grew in low quantities in culture (≤ 20 colonies). In 20.3% (35/172) of the respiratory specimens where no growth was observed in culture, the PLEX-ID system identified a fungus, suggesting a potential increase in sensitivity over culture in some instances. The PLEX-ID system provides a rapid method for the detection of a broad array of fungi directly in respiratory specimens and has the potential of impacting turnaround times and patient care by reducing the need to wait for the growth of an organism in culture.

Formic acid-based direct, on-plate testing of yeast and Corynebacterium species by Bruker Biotyper matrix-assisted laser desorption ionization-time of flight mass spectrometry.

An on-plate testing method using formic acid was evaluated on the Bruker Biotyper matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry system using 90 yeast and 78 Corynebacterium species isolates, and 95.6 and 81.1% of yeast and 96.1 and 92.3% of Corynebacterium isolates were correctly identified to the genus and species levels, respectively. The on-plate method using formic acid yielded identification percentages similar to those for the conventional but more laborious tube-based extraction.

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics-Driven Machine Learning Approach.

OBJECTIVE: To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). METHODS: Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3-month follow-up. Supervised ML methods were trained with 5 repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort. RESULTS: Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (P = 0.05) in the training cohort and achieved a prediction accuracy of 76% (P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. CONCLUSION: Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

Candida auris Direct Detection from Surveillance Swabs, Blood, and Urine Using a Laboratory-Developed PCR Method.

Candida auris is an emerging fungal pathogen with cases reported in countries around the world and in 19 states within the United States as of August 2020. The CDC has recommended that hospitals perform active surveillance upon admission for patients with the appropriate risk factors. Currently, active surveillance requires that local hospitals send surveillance swabs to a public health laboratory for analysis. In this work, a real-time PCR assay was developed for the specific detection of C. auris from surveillance swabs, blood, and urine to enable rapid detection of this pathogen. The assay uses commercially available primers and reporter probes and it was verified on the LightCycler 480 PCR platform. Contrived specimens and prospectively collected composite groin/axilla surveillance swabs were used to validate the assay. The performance of the PCR assay on surveillance swabs was also compared to a second PCR assay targeting C. auris that was performed at the Minnesota Department of Health-Public Health Laboratory (MDH-PHL). Our PCR assay is able to detect and differentiate C. auris from closely related Candida species such as C. duobushaemulonii, C. haemulonii, and C. pseudohaemulonii on the basis of melting curve temperature differences.

Multicenter Study Demonstrates Standardization Requirements for Mold Identification by MALDI-TOF MS.

OBJECTIVES: Rapid and accurate mold identification is critical for guiding therapy for mold infections. MALDI-TOF MS has been widely adopted for bacterial and yeast identification; however, few clinical laboratories have applied this technology for routine mold identification due to limited database availability and lack of standardized processes. Here, we evaluated the versatility of the NIH Mold Database in a multicenter evaluation. METHODS: The NIH Mold Database was evaluated by eight US academic centers using a solid media extraction method and a challenge set of 80 clinical mold isolates. Multiple instrument parameters important for spectra optimization were evaluated, leading to the development of two specialized acquisition programs (NIH method and the Alternate-B method). RESULTS: A wide range in performance (33-77%) was initially observed across the eight centers when routine spectral acquisition parameters were applied. Use of the NIH or the Alternate-B specialized acquisition programs, which are different than those used routinely for bacterial and yeast spectral acquisition (MBT_AutoX), in combination with optimized instrument maintenance, improved performance, illustrating that acquisition parameters may be one of the key limiting variable in achieving successful performance. CONCLUSION: Successful mold identification using the NIH Database for MALDI-TOF MS on Biotyper systems was demonstrated across multiple institutions for the first time following identification of critical program parameters combined with instrument optimization. This significantly advances our potential to implement MALDI-TOF MS for mold identification across many institutions. Because instrument variability is inevitable, development of an instrument performance standard specific for mold spectral acquisition is suggested to improve reproducibility across instruments.