MALDI-TOF MALDI-TOF Mass Spectrometry Research Articles

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning.

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

First Argentine database for the accurate identification of Brucella to species level by MALDI-TOF MS

MALDI-TOF mass spectrometry (MS) has proven to be a fast and reliable method for the identification of a large number of taxonomic groups. It offers the advantage of being able to incorporate protein spectra of microorganisms that are absent or poorly represented in commercial databases, such as the genus Brucella. The aim of the study was to build the first database of protein spectra of local biological variants of Brucella in Argentina and of standard strains. First, the identification performance of a panel of 135 strains was evaluated with the Swedish database ¨Folkhälsomyndigheten¨ (containing protein spectra of several international standards of the genus Brucella) imported from the open access site https://spectra.folkhalsomyndigheten.se/spectra/. With this library 100 % of the strains were correctly identified by mass spectrometry to genus level, but not to species level.Due to the limitation found, an in-house database was designed with local Brucella isolates from Argentina and standard strains used in routine bacteriological diagnosis. For its validation, a panel of strains, different from those used to develop the extended local database (n: 177), was used to, simultaneously, challenge both libraries. The samples were processed by triplicate and the results obtained were: 177 strains correctly identified to genus and species level compared to the gold standard method (phenotypic typing), meeting the criteria accepted by the literature and the manufacturer as reliable identification. Only 2 of these isolates had score values lower than 2 (1.862) and were therefore not included in the calculation of results.According to these results, MALDI-TOF MS is a fast and reliable method for the routine identification of the different Brucella species, and even has the advantage of reducing the time of exposure to pathogenic microorganisms for laboratorians. It could be considered a valuable technique to replace, in the near future, the current conventional techniques due to the ease of transferring protein spectra, avoiding the use of reference strains that are difficult to find commercially available and commonly used in phenotypic typing.

Evaluation of a Novel Benchtop Tool for Acceleration of Sample Preparation for MALDI-TOF Mass Spectrometry.

During the past decade, MALDI-TOF mass spectrometry (MS) has become a standard method for identification of bacteria and yeasts. Nonetheless, further optimization of the identification process is important to streamline workflows and save resources. This study evaluated the application of a multipurpose benchtop tool, MBT FAST Shuttle IVD, for accelerated drying of liquid assay components (matrix, formic acid, and/or sample) on a MALDI target. A total of 50 bacterial and fungal isolates were subjected to three different sample preparation procedures prior to the identification by MALDI-TOF MS: direct transfer (DT), extended direct transfer (eDT), and protein extraction (PE). Compared to conventional drying at room temperature, the preparation was performed with standardized heating of the MALDI target on the MBT FAST Shuttle. During DT, eDT, and PE, 56.7% (P < 0.001), 56.8% (P < 0.001), and 52.8% (P < 0.001) of time for matrix drying were saved by using the MBT FAST Shuttle, respectively. Applying the MBT FAST Shuttle, 57.5% (P < 0.001) of time for drying of formic acid were saved for eDT and 57.5% (P < 0.001) of time for sample drying were saved for PE. A significant improvement of the identification rates and scores was observed with MBT FAST Shuttle for eDT (P = 0.001) and PE (P = 0.008) methods, while the effect on identification quality for DT was not statistically significant (P = 0.16). In conclusion, the use of the MBT FAST Shuttle shortened the drying time of assay components by about a half for all preparation methods. Moreover, positive effect on identification success was observed.

Malassezia pachydermatis from brown bear: A comprehensive analysis reveals novel genotypes and distribution of all detected variants in domestic and wild animals.

Malassezia pachydermatis (phylum Basidiomycota, class Malasseziomycetes) is a zoophilic opportunistic pathogen with recognized potential for invasive infections in humans. Although this pathogenic yeast is widespread in nature, it has been primarily studied in domestic animals, so available data on its genotypes in the wild are limited. In this study, 80 yeast isolates recovered from 42 brown bears (Ursus arctos) were identified as M. pachydermatis by a culture-based approach. MALDI-TOF mass spectrometry (MS) was used to endorse conventional identification. The majority of samples exhibited a high score fluctuation, with 42.5% of isolates generating the best scores in the range confident only for genus identification. However, the use of young biomass significantly improved the identification of M. pachydermatis at the species confidence level (98.8%). Importantly, the same MALDI-TOF MS efficiency would be achieved regardless of colony age if the cut-off value was lowered to ≥1.7. Genotyping of LSU, ITS1, CHS2, and β-tubulin markers identified four distinct genotypes in M. pachydermatis isolates. The most prevalent among them was the genotype previously found in dogs, indicating its transmission potential and adaptation to distantly related hosts. The other three genotypes are described for the first time in this study. However, only one of the genotypes consisted of all four loci with bear-specific sequences, indicating the formation of a strain specifically adapted to brown bears. Finally, we evaluated the specificity of the spectral profiles of the detected genotypes. MALDI-TOF MS exhibited great potential to detect subtle differences between all M. pachydermatis isolates and revealed distinct spectral profiles of bear-specific genotypes.

Direct prediction of carbapenem-resistant, carbapenemase-producing, and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectra using machine learning and outcome evaluation

The objective of this study was to develop a rapid prediction method for carbapenem-resistant Klebsiella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results in order to formulate a suitable and rapid treatment strategy. A total of 830 CRKP and 1462 carbapenem-susceptible K. pneumoniae (CSKP) isolates were collected; 54 ColRKP isolates and 1592 colistin-intermediate K. pneumoniae (ColIKP) isolates were also included. Routine MALDI-TOF MS, antimicrobial susceptibility testing, NG-Test CARBA 5, and resistance gene detection were followed by machine learning (ML). Using the ML model, the accuracy and area under the curve for differentiating CRKP and CSKP were 0.8869 and 0.9551, respectively, and those for ColRKP and ColIKP were 0.8361 and 0.8447, respectively. The most important MS features of CRKP and ColRKP were m/z 4520-4529 and m/z 4170-4179, respectively. Of the CRKP isolates, MS m/z 4520-4529 was a potential biomarker for distinguishing KPC from OXA, NDM, IMP, and VIM. Of the 34 patients who received preliminary CRKP ML prediction results (by texting), 24 (70.6%) were confirmed to have CRKP infection. The mortality rate was lower in patients who received antibiotic regimen adjustment based on the preliminary ML prediction (4/14, 28.6%). In conclusion, the proposed model can provide rapid results for differentiating CRKP and CSKP, as well as ColRKP and ColIKP. The combination of ML-based CRKP with preliminary reporting of results can help physicians alter the regimen approximately 24 h earlier, resulting in improved survival of patients with timely antibiotic intervention.

Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis.

(1) Background: MALDI-TOF mass spectrometry (MS) is the gold standard for microbial fingerprinting, however, for phylogenetically closely related species, the resolution power drops down to the genus level. In this study, we analyzed MALDI-TOF spectra from 44 strains of B. melitensis, B. suis and B. abortus to identify the optimal classification method within popular supervised and unsupervised machine learning (ML) algorithms. (2) Methods: A consensus feature selection strategy was applied to pinpoint from among the 500 MS features those that yielded the best ML model and that may play a role in species differentiation. Unsupervised k-means and hierarchical agglomerative clustering were evaluated using the silhouette coefficient, while the supervised classifiers Random Forest, Support Vector Machine, Neural Network, and Multinomial Logistic Regression were explored in a fine-tuning manner using nested k-fold cross validation (CV) with a feature reduction step between the two CV loops. (3) Results: Sixteen differentially expressed peaks were identified and used to feed ML classifiers. Unsupervised and optimized supervised models displayed excellent predictive performances with 100% accuracy. The suitability of the consensus feature selection strategy for learning system accuracy was shown. (4) Conclusion: A meaningful ML approach is here introduced, to enhance Brucella spp. classification using MALDI-TOF MS data.

Immobilization of a Bifidobacterial Endo-ß-N-Acetylglucosaminidase to Generate Bioactive Compounds for Food Industry.

Conjugated N-glycans are considered next-generation bioactive prebiotic compounds due to their selective stimulation of beneficial microbes. These compounds are glycosidically attached to proteins through N-acetylglucosamines via specific asparagine residue (AsN-X-Ser/Thr). Certain bacteria such as Bifidobacterium longum subspecies infantis (B. infantis) have been shown to be capable of utilizing conjugated N-glycans, owing to their specialized genomic abilities. B. infantis possess a unique enzyme, Endo-ß-N-acetylglucosaminidase (EndoBI-1), which cleaves all types of conjugated N-glycans from glycoproteins. In this study, recombinantly cloned EndoBI-1 enzyme activity was investigated using various immobilization methods: 1) adsorption, 2) entrapment-based alginate immobilization, 3) SulfoLink-, and 4) AminoLink-based covalent bonding immobilization techniques were compared to develop the optimum application of EndoBI-1 to food processes. The yield of enzyme immobilization and the activity of each immobilized enzyme by different approaches were investigated. The N-glycans released from lactoperoxidase (LPO) using different immobilized enzyme forms were characterized using MALDI-TOF mass spectrometry (MS). As expected, regardless of the techniques, the enzyme activity decreased after the immobilization methods. The enzyme activity of adsorption and entrapment-based alginate immobilization was found to be 71.55% ± 0.6 and 20.32% ± 3.18, respectively, whereas the activity of AminoLink- and SulfoLink-based covalent bonding immobilization was found to be 58.05 ± 1.98 and 47.49% ± 0.30 compared to the free form of the enzyme, respectively. However, extended incubation time recovery achieved activity similar to that of the free form. More importantly, each immobilization method resulted in the same glycan profile containing 11 different N-glycan structures from a model glycoprotein LPO based on MALDI-TOF MS analysis. The glycan data analysis suggests that immobilization of EndoBI-1 is not affecting the enzyme specificity, which enables full glycan release without a limitation. Hence, different immobilization methods investigated in this study can be chosen for effective enzyme immobilization to obtain bioactive glycans. These findings highlight that further optimization of these methods can be a promising approach for future processing scale-up and commercialization of EndoBI-1 and similar enzymes.

Species Identification and In Vitro Antifungal Susceptibility of Paecilomyces/Purpureocillium Species Isolated from Clinical Respiratory Samples: A Multicenter Study.

Paecilomyces spp. are emerging fungal pathogens, where Paecilomyces lilacinus and Paecilomyces variotii are the most reported species. Taxonomic and phylogenetic revisions in this genus have shown that P. variotii represents a species complex, whereas P. lilacinus is related to another genus called Purpureocillium. The aims of this study were to identify clinical isolates of Paecilomyces spp. at the species level, and to determine their antifungal susceptibility profiles. 70 clinical Paecilomyces spp. isolates were identified by MALDI-TOF Mass Spectrometry (MS) and by multilocus rDNA genes sequencing including ITS and the D1/D2 genes. Among the 70 Paecilomyces spp. isolates, 28 were identified as P. lilacinum, 26 as P. variotii stricto sensu, and 16 as P. maximus. For antifungal susceptibility testing, Minimal Inhibitory Concentrations (MICs) or Minimal Effective Concentrations (MECs) were determined for 8 antifungals. All P. lilacinum isolates had high MICs and MECs of amphotericin B and echinocandins, respectively, unlike P. variotii and P. maximus. For azole drugs, MICs were molecule- and species- dependent. The differences in in vitro susceptibility to antifungals underline the importance of accurate species identification. The MALDI–TOF MS can be a good alternative in routine laboratory to ensure fast identification of Paecilomyces spp. and P. lilacinum.

Evaluation of MALDI-TOF MS for Identification of Species in the Candida parapsilosis Complex from Candidiasis Cases.

Phenotypically identified Candida parapsilosis is actually a complex of 3 member species named Candida parapsilosis sensu stricto (CPSS), Candida orthopsilosis (CO), and Candida metapsilosis (CM), which can be identified only by molecular methods and automated methods such as MALDI-TOF mass spectrometry (MS). This study was undertaken to evaluate the VITEK MS, which uses the principle of MALDI-TOF MS for the identification of member species of C. parapsilosis complex (CPC). In this cross-sectional study, 126 presumptively identified and stocked isolates of CPC were included. Definite identification to species level was done by VITEK MS and PCR as the gold standard method. Clinico-demographic characters and risk factors were analyzed. Antifungal susceptibility testing was performed for fluconazole and voriconazole. Twelve isolates were not identified as CPC either by VITEK MS or PCR and hence were excluded from the analysis. Out of 114 CPC isolates, 89 (78.1%), 18 (15.8%), and 7 (6.1%) isolates were identified as CPSS, CO, and CM, respectively, by VITEK MS. PCR identified 84 (79.2%), 15 (14.2%), and 7 (6.6%) isolates as CPSS, CO, and CM, respectively. However, PCR did not detect 8 isolates of CPSS detected by VITEK MS. VITEK MS showed 95.3% agreement in species identification and showed a kappa coefficient of 0.87, which is almost perfect agreement. Predominant isolations of all 3 species were from blood. Resistance was observed more in CPSS for both the azoles. MALDI-TOF MS can be used as a rapid, reliable, cost-effective method to identify the species of CPC.

Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp.

While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, less attention has been addressed to its ability for detection of antimicrobial resistance (AMR). Recently, some studies assessed its potential application together with machine learning for the detection of AMR in clinical pathogens. The scope of this study was to investigate MALDI-TOF MS protein mass spectra combined with a prediction approach as an AMR screening tool for relevant foodborne pathogens, such as Campylobacter coli and Campylobacter jejuni. A One-Health panel of 224 C. jejuni and 116 C. coli strains was phenotypically tested for seven antimicrobial resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin, streptomycin, and ampicillin, independently, and were submitted, after an on- and off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average spectra per isolate and type of extraction. Overall, high performance was observed for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were reached. No significant prediction performance differences were observed between on- and off-plate types of protein extractions. Finally, three putative AMR biomarkers for fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current study. Combination of MALDI-TOF MS and machine learning could be an efficient and inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may enable a rapid initiation of a precise, targeted antibiotic treatment.

Assessment of the main pathogens associated with clinical and subclinical endometritis in cows by culture and MALDI-TOF mass spectrometry identification

Clinical endometritis (CE) and subclinical endometritis (SCE) are diseases that affect dairy cows during the puerperium, causing negative effects on the animals' milk production and fertility. The objective of this study was to assess the main bacteria related to cases of CE and SCE from uterine samples of dairy cows in Brazilian herds. Selective and differential media were used for isolation of aerobic and anaerobic bacteria and further MALDI-TOF mass spectrometry (MS) identification. A total of 279 lactating dairy cows with 28 to 33 d in milk from 6 commercial farms were evaluated. Initially, cows were classified in 3 groups: cytologic healthy cows (n = 161), cows with CE (n = 83), and cows with SCE (n = 35). Healthy animals presented 97 species, followed by the CE group with 53 identified species, and SCE cows presented only 21 bacterial species. We found a significantly higher isolation rate of Trueperella pyogenes in CE (26.5%) cows compared with healthy and SCE cows. Some anaerobic species were exclusively isolated from the CE group, even though they presented lower frequency. Interestingly, 18.1% of samples from CE cows and 40% of SCE cows were negative to bacterial isolation. Despite the use of culture-dependent methods instead of molecular methods, the present study enabled the identification of a complex community of 127 different species from 48 genera, composed of aerobic and anaerobic bacterial species among the 3 different animal groups. The method of sample collection, culture, and identification by MALDI-TOF MS were essential for the success of the analyses.

MALDI-TOF Mass Spectrometry as a Rapid Screening Alternative for Non-tuberculous Mycobacterial Species Identification in the Veterinary Laboratory.

Non-tuberculous mycobacteria (NTM) are difficult to identify by biochemical and genetic methods due to their microbiological properties and complex taxonomy. The development of more efficient and rapid methods for species identification in the veterinary microbiological laboratory is, therefore, of great importance. Although MALDI-TOF Mass Spectrometry (MS) has become a promising tool for the identification of NTM species in human clinical practise, information regarding its performance on veterinary isolates is scarce. This study assesses the capacity of MALDI-TOF MS to identify NTM isolates (n = 75) obtained from different animal species. MALDI-TOF MS identified 76.0% (n = 57) and 4% (n = 3) of the isolates with high and low confidence, respectively, in agreement with the identification achieved by Sanger sequencing of housekeeping genes (16S rRNA, hsp65, and rpoB). Thirteen isolates (17.3%) were identified by Sanger sequencing to the complex level, indicating that these may belong to uncharacterised species. MALDI-TOF MS approximated low confidence identifications toward closely related mycobacterial groups, such as the M. avium or M. terrae complexes. Two isolates were misidentified due to a high similarity between species or due to the lack of spectra in the database. Our results suggest that MALDI-TOF MS can be used as an effective alternative for rapid screening of mycobacterial isolates in the veterinary laboratory and potentially for the detection of new NTM species. In turn, Sanger sequencing could be implemented as an additional method to improve identifications in species for which MALDI-TOF MS identification is limited or for further characterisation of NTM species.

Label-free cell assays to determine compound uptake or drug action using MALDI-TOF mass spectrometry.

Cell-based assays for compound screening and profiling are fundamentally important in life sciences, chemical biology and pharmaceutical research. Most cell assays measure the amount of a single reporter molecule or cellular endpoint, and require the use of fluorescence or other labeled materials. Consequently, there is high demand for label-free technologies that enable multiple biomolecules or endpoints to be measured simultaneously. Here, we describe how to develop, optimize and validate MALDI-TOF mass spectrometry (MS) cell assays that can be used to measure cellular uptake of transporter substrates, to monitor cellular drug target engagement or to discover cellular drug-response markers. In uptake assays, intracellular accumulation of a transporter substrate and its inhibition by test compounds is measured. In drug response assays, changes to multiple cellular metabolites or to abundant posttranslational protein modifications are monitored as reporters of drug activity. We detail a ten-part optimization protocol with every part taking 1-2 d that leads to a final 2 d optimized procedure, which includes cell treatment, transfer, MALDI MS-specific sample preparation, quantification using stable-isotope-labeled standards, MALDI-TOF MS data acquisition, data processing and analysis. Key considerations for validation and automation of MALDI-TOF MS cell assays are outlined. Overall, label-free MS cell-based assays offer speed, sensitivity, accuracy and versatility in drug research.

P-101: Detection of m-protein in acetonitrile precipitates of serum by MALDI-TOF mass spectrometry: a novel methodology

<h3>Background</h3> MALDI-TOF-mass spectrometry (MS) has demonstrated superior analytical sensitivity for the detection of M-protein. We present the results of an alternative methodology by MALDI-TOF mass spectrometry (MS) for M-protein analysis. <h3>Methods</h3> Serum samples from patients with newly diagnosed plasma cell dyscrasias (MGUS, multiple myeloma, plasmacytoma and AL amyloidosis) with evidence of monoclonal gammopathy underwent a direct reagent-based extraction process using acetonitrile (ACN) precipitation. Serum κ and λ were validated using immuno-enrichment by anti-κ and anti-λ biotin-labelled antibodies immobilised on streptavidin magnetic beads. MALDI-TOF MS measurements were obtained using alpha-cyano-4-hydroxycinnamic acid as a matrix. The images obtained were overlaid on apparently healthy donor serum samples to confirm the presence of an M-protein peak. <h3>Results</h3> Characteristic M-protein peaks were observed in the ACN precipitates of serum within the predicted κ and λ mass/charge (m/z) range. The κ and λ peaks were confirmed by immuno-enrichment analysis. Sixty-seven patient samples with monoclonal gammopathy were chosen for ACN precipitation and analysed by MALDI-TOF MS. There was a demonstrable peak suggestive of M-protein in all the samples. The characteristic "polytypic-like" M-protein was additionally observed in patient samples with AL amyloidosis. The Daratumumab peak was additionally identified in one patient on D-VRd induction therapy. The concordance rate with serum immunofixation electrophoresis and serum free light chain analysis was 95%. <h3>Conclusions</h3> We report the results of a low-cost process using ACN precipitation to enrich κ and λ light chains for MALDI-TOF MS analysis. <h3>Disclosure of Conflicts of Interest</h3> Gopal Gopisetty, Nikita Mehra, Subramani Jayavelu, Indian provisional patent application no: 202041009443 filed in March 2020

Comparing the phenotypic, genotypic, and proteomic identification of Trichosporon species: A globally emerging yeast of medical importance.

Trichosporon spp. are widely distributed in the nature, comprising species that inhabit different ecological niches and can be found in the water, soil, and body surface of animals and humans. Such microorganisms have been classically associated with superficial infections; however, in the last decades, they have also been related to disseminated infections in immunocompromised patients, behaving as opportunistic agents, which demands rapid and accurate species identification for efficient therapy. Concordance level between the traditional phenotypic method and the molecular technique (gold standard) in the identification of all 59 Trichosporon samples was 59.3%. Identification concordance between MALDI-TOF spectrometry and the molecular technique was 71.2%. No isolate of environmental origin was identifiable by MALDI-TOF mass spectrometry (MS), and 100% of such environmental isolates were discordant for IGS region sequencing and phenotypic characterization. Both comparisons evidenced greatest concordance in the identification of T. asahii. The species T. debeurmannianum, T. dermatis, T. venhuisii and T. insectorum were not properly identified by both MALDI-TOF MS and the phenotypic technique. MALDI-TOF MS, in particular, seems to be appropriate to investigate yeasts of the genus Trichosporon; however, database updates are still necessary, especially for species that are not common in the clinical routine. With the aim of helping understand the aspects involved in early and accurate diagnosis of infections caused by this opportunistic agent, the present study compared the phenotypic, molecular (IGS region) and mass-spectrometry (MALDI-TOF) identification of 59 yeasts of the genus Trichosporon which had clinical and environmental origin and were kept in a mycology collection.

DiagnoTop: A Computational Pipeline for Discriminating Bacterial Pathogens without Database Search.

Pathogen identification is crucial to confirm bacterial infections and guide antimicrobial therapy. Although MALDI-TOF mass spectrometry (MS) serves as foundation for tools that enable rapid microbial identification, some bacteria remain challenging to identify. We recently showed that top-down proteomics (TDP) could be used to discriminate closely related enterobacterial pathogens (Escherichia coli, Shigella, and Salmonella) that are indistinguishable with tools rooted in the MALDI-TOF MS approach. Current TDP diagnostic relies on the identification of specific proteoforms for each species through a database search. However, microbial proteomes are often poorly annotated, which complicates the large-scale identification of proteoforms and leads to many unidentified high-quality mass spectra. Here, we describe a new computational pipeline called DiagnoTop that lists discriminative spectral clusters found in TDP data sets that can be used for microbial diagnostics without database search. Applied to our enterobacterial TDP data sets, DiagnoTop could easily shortlist high-quality discriminative spectral clusters, leading to increased diagnostic power. This pipeline opens new perspectives in clinical microbiology and biomarker discovery using TDP.

Detection of M-Protein in Acetonitrile Precipitates of Serum Using MALDI-TOF Mass Spectrometry: A Novel Methodology

Background: Multiple myeloma (MM) and plasmacytoma(s) belong to a group of clonal plasma cell dyscrasias. In 97-98% of all cases, they are characterised by the detection of a monoclonal protein (M-protein) in the blood, and sometimes in the urine. MALDI-TOF-mass spectrometry (MS) has demonstrated excellent analytical sensitivity for the screening and detection of M-protein. We present the results of a novel methodology for M-protein analysis by MALDI-TOF MS. Patients and Methods: Blood samples from patients and controls were collected after obtaining Institutional Ethics Committee approval. The work was carried out in accordance with the Declaration of Helsinki after obtaining written informed consent. Patients with confirmed multiple myeloma or plasmacytoma and M-protein detected by serum protein electrophoresis (SPEP), serum immunofixation electrophoresis (IFE), and serum free light chains (FLC) were included for MALDI-TOF MS analysis. IFE and FLC analysis were sent to independent laboratories for external validation of the MALDI-TOF MS results. Reagent-based extraction The serum fraction was separated from whole blood by centrifugation at 5000 rpm for 15 minutes and stored at -80oC until further analysis. Twenty-five μL of the serum sample was mixed with 50% acetonitrile (ACN) to form a precipitate. After precipitation and incubation, the mixture was centrifuged. The protein precipitate was washed with 20% ACN. After centrifugation, the supernatant was discarded, and the precipitate was reconstituted in a buffer comprising 10% formic acid (FA) and 50 mmol/L tris(2-carboxyethyl)phosphine hydrochloride (TCEP). The MALDI-TOF MS results were validated using immunoenrichment by anti-kappa (κ) and anti-lambda (λ) biotin-labelled antibodies immobilised on streptavidin magnetic beads. MALDI-TOF MS measurements were obtained for intact proteins using alpha-cyano-4-hydroxycinnamic acid as a matrix. The images obtained were overlaid on apparently healthy serum samples to confirm the presence of M-protein. The samples were then analysed using UltraflexTM LT, Bruker MALDI/TOF-TOF mass spectrometer. The mass spectra for each sample was exported to FlexAnalysis 3.3 (Bruker Daltonics) and background subtracted. A sample was considered positive for M-protein if there was a sharp or broad peak within the κ or λ mass/charge (m/z) range- κ m/z- [M+2H]2+: 11550-12300 Da; [M+H]+: 23100-24600 Da), and λ m/z- [M+2H]2+: 11100-11500 Da; [M+H]+: 22200-23100 Da. All the images were acquired at a m/z range of 10000-29000 Da. Mass measurement was analysed with a summation of 500-5000 shots depending on the intensity of the M-peak. Results: Twenty-seven patient samples: 24- multiple myeloma, and 3- plasmacytoma with an M-protein identified by other biochemical tests, were chosen for ACN precipitation and analysed by MALDI-TOF MS. The median age was 62 years (range:44-72); males-12 (44%). A mass spectrometrist, S.J was blinded to the IFE and FLC results- blinded analyst. N.M was the unblinded analyst. Neat sample (without dilution) was spotted on the MALDI plate for all the control and patient samples. The Gaussian distribution of κ and λ light chains were obtained by analysing 20 serum samples of apparently healthy blood donors. All the 27 samples (100%) with M-protein confirmed by the other biochemical techniques, demonstrated a peak suggestive of M-protein with mass/charge (m/z) falling within the κ or λ range on MALDI-TOF MS: 24 patients with κ peak, and 3 with λ peak. (Fig. 1a and 1b) Immunoenrichment was performed on two samples- 1 with κ peak, and the other with λ peak, analysed by MALDI-TOF-MS by ACN precipitation. The mass spectra by immunoenrichment and ACN precipitation were found to be identical with the light chain m/z falling within their respective range. (Fig. 2 and 3) Three samples were labelled as confounders due to low peak intensity. However, their peaks matched their corresponding IFE and FLC reports. Concordance between MALDI-TOF MS and IFE was observed in 21/23 patients (91%); concordance between MALDI-TOF MS and FLC was observed in 23/24 patients (96%). Conclusions: We report the results of a low-cost, reagent-based extraction process using ACN precipitation to enrich for κ and λ light chains, which can be used for screening and for qualitative analysis of M-protein. Further studies are required to identify the immunoglobulin isotype, and to quantify the M-protein by this methodology. Disclosures No relevant conflicts of interest to declare.

Next-Generation Sequencing and MALDI Mass Spectrometry in the Study of Multiresistant Processed Meat Vancomycin-Resistant Enterococci (VRE).

Vancomycin-resistant enterococci (VRE), due to their intrinsic resistance to various commonly used antibiotics and their malleable genome, make the treatment of infections caused by these bacteria less effective. The aims of this work were to characterize isolates of Enterococcus spp. that originated from processed meat, through phenotypic and genotypic techniques, as well as to detect putative antibiotic resistance biomarkers. The 19 VRE identified had high resistance to teicoplanin (89%), tetracycline (94%), and erythromycin (84%) and a low resistance to kanamycin (11%), gentamicin (11%), and streptomycin (5%). Based on a Next-Generation Sequencing NGS technique, most isolates were vanA-positive. The most prevalent resistance genes detected were erm(B) and aac(6’)-Ii, conferring resistance to the classes of macrolides and aminoglycosides, respectively. MALDI-TOF mass spectrometry (MS) analysis detected an exclusive peak of the Enterococcus genus at m/z (mass-to-charge-ratio) 4428 ± 3, and a peak at m/z 6048 ± 1 allowed us to distinguish Enterococcus faecium from the other species. Several statistically significant protein masses associated with resistance were detected, such as peaks at m/z 6358.27 and m/z 13237.3 in ciprofloxacin resistance isolates. These results reinforce the relevance of the combined and complementary NGS and MALDI-TOF MS techniques for bacterial characterization.

Discrimination of bovine milk from non-dairy milk by lipids fingerprinting using routine matrix-assisted laser desorption ionization mass spectrometry

An important sustainable development goal for any country is to ensure food security by producing a sufficient and safe food supply. This is the case for bovine milk where addition of non-dairy milks such as vegetables (e.g., soya or coconut) has become a common source of adulteration and fraud. Conventionally, gas chromatography techniques are used to detect key lipids (e.g., triacylglycerols) has an effective read-out of assessing milks origins and to detect foreign milks in bovine milks. However, such approach requires several sample preparation steps and a dedicated laboratory environment, precluding a high throughput process. To cope with this need, here, we aimed to develop a novel and simple method without organic solvent extractions for the detection of bovine and non-dairy milks based on lipids fingerprint by routine MALDI-TOF mass spectrometry (MS). The optimized method relies on the simple dilution of milks in water followed by MALDI-TOF MS analyses in the positive linear ion mode and using a matrix consisting of a 9:1 mixture of 2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic acid (super-DHB) solubilized at 10 mg/mL in 70% ethanol. This sensitive, inexpensive, and rapid method has potential for use in food authenticity applications.

Putative Protein Biomarkers of Escherichia coli Antibiotic Multiresistance Identified by MALDI Mass Spectrometry.

The commensal bacteria Escherichia coli causes several intestinal and extra-intestinal diseases, since it has virulence factors that interfere in important cellular processes. These bacteria also have a great capacity to spread the resistance genes, sometimes to phylogenetically distant bacteria, which poses an additional threat to public health worldwide. Here, we aimed to use the analytical potential of MALDI-TOF mass spectrometry (MS) to characterize E. coli isolates and identify proteins associated closely with antibiotic resistance. Thirty strains of extended-spectrum beta-lactamase producing E. coli were sampled from various animals. The phenotypes of antibiotic resistance were determined according to Clinical and Laboratory Standards Institute (CLSI) methods, and they showed that all bacterial isolates were multi-resistant to trimethoprim-sulfamethoxazole, tetracycline, and ampicillin. To identify peptides characteristic of resistance to particular antibiotics, each strain was grown in the presence or absence of the different antibiotics, and then proteins were extracted from the cells. The protein fingerprints of the samples were determined by MALDI-TOF MS in linear mode over a mass range of 2 to 20 kDa. The spectra obtained were compared by using the ClinProTools bioinformatics software, using three machine learning classification algorithms. A putative species biomarker was also detected at a peak m/z of 4528.00.