Diagnostic Performance of BD Phoenix CPO Detect Panels for Detection and Classification of Carbapenemase-Producing Gram-Negative Bacteria.

BD Phoenix CPO Detect panels can identify and classify carbapenemase-producing organisms (CPOs) simultaneously with antimicrobial susceptibility testing (AST) for Gram-negative bacteria. Detection and classification of carbapenemase producers were performed using the BD Phoenix CPO Detect panels NMIC/ID-441 for Enterobacterales, NMIC/ID-442 for nonfermenting bacteria, and NMIC-440 for both. The results were compared with those obtained using comparator methods. A total of 133 strains (32 Klebsiella pneumoniae, 37 Enterobacter cloacae complex, 33 Pseudomonas aeruginosa, and 31 Acinetobacter baumannii complex strains), including 60 carbapenemase producers (54 imipenemases [IMPs] and 6 OXA type), were analyzed. Using panels NMIC-440 and NMIC/ID-441 or NMIC/ID-442, all 54 IMP producers were accurately identified as CPOs (positive percent agreement [PPA], 100.0%; 54/54). Among the 54 IMP producers identified as CPOs using panels NMIC-440 and NMIC/ID-441, 12 and 14 Enterobacterales were not resistant to carbapenem, respectively. Among all 54 IMP producers, 48 (88.9%; 48/54) were correctly classified as Ambler class B using panel NMIC-440. Using panels NMIC-440 and NMIC/ID-442, all four OXA-23-like carbapenemase-producing A. baumannii complex strains (100.0%, 4/4) were correctly identified as CPOs, and three (75.0%, 3/4) were precisely classified as class D using panel NMIC-440. Both carbapenemase producers harboring the blaISAba1-OXA-51-like gene were incorrectly identified as non-CPOs using panels NMIC-440 and NMIC/ID-442. For detecting carbapenemase producers, the overall PPA and negative percent agreement (NPA) between panel NMIC-440 and the comparator methods were 96.7% (58/60) and 71.2% (52/73), respectively, and the PPA and NPA between panels NMIC/ID-441 or NMIC/ID-442 and the comparator methods were 96.7% (58/60) and 74.0% (54/73), respectively. BD Phoenix CPO Detect panels can successfully screen carbapenemase producers, particularly IMP producers, regardless of the presence of carbapenem resistance and can be beneficial in routine AST workflows. IMPORTANCE Simple and efficient screening methods of detecting carbapenemase producers are required. BD Phoenix CPO Detect panels effectively screened carbapenemase producers, particularly IMP producers, with a high overall PPA. As the panels enable automatic screening for carbapenemase producers simultaneously with AST, the workflow from AST to confirmatory testing for carbapenemase production can be shortened. In addition, because carbapenem resistance varies among carbapenemase producers, the BD Phoenix CPO Detect panels, which can screen carbapenemase producers regardless of carbapenem susceptibility, can contribute to the accurate detection of carbapenemase producers. Our results report that these panels can help streamline the AST workflow before confirmatory testing for carbapenemase production in routine microbiological tests.

High-throughput identification and screening of novel Methylobacterium species using whole-cell MALDI-TOF/MS analysis.

Methylobacterium species are ubiquitous α-proteobacteria that reside in the phyllosphere and are fed by methanol that is emitted from plants. In this study, we applied whole-cell matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis (WC-MS) to evaluate the diversity of Methylobacterium species collected from a variety of plants. The WC-MS spectrum was reproducible through two weeks of cultivation on different media. WC-MS spectrum peaks of M. extorquens strain AM1 cells were attributed to ribosomal proteins, but those were not were also found. We developed a simple method for rapid identification based on spectra similarity. Using all available type strains of Methylobacterium species, the method provided a certain threshold similarity value for species-level discrimination, although the genus contains some type strains that could not be easily discriminated solely by 16S rRNA gene sequence similarity. Next, we evaluated the WC-MS data of approximately 200 methylotrophs isolated from various plants with MALDI Biotyper software (Bruker Daltonics). Isolates representing each cluster were further identified by 16S rRNA gene sequencing. In most cases, the identification by WC-MS matched that by sequencing, and isolates with unique spectra represented possible novel species. The strains belonging to M. extorquens, M. adhaesivum, M. marchantiae, M. komagatae, M. brachiatum, M. radiotolerans, and novel lineages close to M. adhaesivum, many of which were isolated from bryophytes, were found to be the most frequent phyllospheric colonizers. The WC-MS technique provides emerging high-throughputness in the identification of known/novel species of bacteria, enabling the selection of novel species in a library and identification without 16S rRNA gene sequencing.

Cerebrospinal fluid proteomic patterns discriminate Parkinson's disease and multiple system atrophy.

The differential diagnosis of Parkinson's disease and multiple system atrophy can be challenging, especially in the early stages of the diseases. We developed a proteomic profiling strategy for parkinsonian diseases using mass spectrometry analysis for magnetic-bead-based enrichment of cerebrospinal fluid peptides/proteins and subsequent multivariate statistical analysis. Cerebrospinal fluid was obtained from 37 patients diagnosed with Parkinson's disease, 32 patients diagnosed with multiple system atrophy, and 26 patients diagnosed with other neurological diseases as controls. The samples were from the first cohort and the second cohort. Cerebrospinal fluid peptides/proteins were purified with C8 magnetic beads, and spectra were obtained by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Principal component analysis and support vector machine methods are used to reduce dimension of the data and select features to classify diseases. Cerebrospinal fluid proteomic profiles of Parkinson's disease, multiple system atrophy, and control were differentiated from each other by principal component analysis. By building a support vector machine classifier, 3 groups were classified effectively with good cross-validation accuracy. The model accuracy was well preserved for both cases, training by the first cohort and validated by the second cohort and vice versa. Receiver operating characteristics proved that the peak of m/z 6250 was the most important to differentiate multiple system atrophy from Parkinson's disease, especially in the early stages of the disease. A proteomic pattern classification method can increase the accuracy of clinical diagnosis of Parkinson's disease and multiple system atrophy, especially in the early stages.

Proteomic pattern analysis discriminates among multiple sclerosis-related disorders.

OBJECTIVE: To use a new, unbiased biomarker discovery strategy to obtain and assess proteomic data from cerebrospinal fluid (CSF) of patients with multiple sclerosis (MS)-related disorders. METHODS: CSF protein profiles were analyzed from 107 patients with either MS-related disorders (including relapsing remitting MS [RRMS], primary progressive MS [PPMS], anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder [SP-NMOSD], and seronegative-NMOSD with long cord lesions on spinal magnetic resonance imaging [SN-NMOSD]), amyotrophic lateral sclerosis (ALS), or other inflammatory neurological diseases (used as controls). CSF peptides/proteins were purified with magnetic beads, and directly measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The obtained spectra were analyzed with multivariate statistics and pattern matching algorithms. These analyses were replicated in an independent sample set of 84 patients composed of those with MS-related disorders or with other neurological diseases (the second cohort). RESULTS: MS-related disorders differed considerably in terms of CSF protein profiles. SP-NMOSD and SN-NMOSD, both of which fit within the NMO spectrum, were distinguishable from RRMS with high cross-validation accuracy on a support vector machine classifier, especially in relapse phases. Some peaks derived from samples of relapsed SP-NMOSD can discriminate RRMS with high area under curve scores (>0.95) and this was reproduced on the second cohort. The similarity of proteomic patterns between selected neurological diseases were demonstrated by pattern matching analysis. To our surprise, the spectral differences between RRMS and PPMS were much larger than those of PPMS and ALS. INTERPRETATION: Our findings suggest that CSF proteomic pattern analysis can increase the accuracy of disease diagnosis of MS-related disorders and will aid physicians in appropriate therapeutic decision-making.

Principal component analysis of direct matrix-assisted laser desorption/ionization mass spectrometric data related to metabolites of fatty liver.

Non-alcoholic steatohepatitis (NASH) is a common liver disease. NASH is characterized by fatty liver, along with inflammation. Most people with NASH are not aware of their condition, even though NASH can lead to hepatic cirrhosis. Several approaches have been tested to clarify the pathology of NASH. However, the mechanism of onset of NASH was not well-defined. In this study, a supervised multivariate analysis (principal component analysis) approach using direct matrix-assisted laser desorption/ionization mass spectrometry (dMALDI-MS) was applied to the analysis of metabolites in starvation-induced fatty liver tissue sections. This approach does not require complex pretreatments. We investigated the characteristic dynamics of metabolites in fatty liver. This approach can be applied to the analysis of human biopsy specimens of fatty liver in future studies.