Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

Promising potential of machine learning-assisted MALDI-TOF MS as an effective detector for Streptococcus suis serotype 2 and virulence thereof

IMPORTANCE

To the best of our knowledge, this study reveals a strong correlation between mass spectra pattern and virulence phenotype among S. suis for the first time. In order to make the findings applicable and to excavate the intrinsic information in the spectra, the classifiers based on the machine learning algorithms were established, and RF (Random Forest)-based models have achieved an accuracy of over 90%. Overall, this study will pave the way for virulent SS2 (Streptococcus suis serotype 2) rapid detection, and the important findings on the association between genotype and mass spectrum may provide a new idea for the genotype-dependent detection of specific pathogens.

Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization - Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence

Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%-100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories.

Differentiation between Weissella cibaria and Weissella confusa Using Machine-Learning-Combined MALDI-TOF MS

Although Weissella cibaria and W. confusa are essential food-fermenting bacteria, they are also opportunistic pathogens. Despite these species being commercially crucial, their taxonomy is still based on inaccurate identification methods. In this study, we present a novel approach for identifying two important Weissella species, W. cibaria and W. confusa, by combining matrix-assisted laser desorption/ionization and time-of-flight mass spectrometer (MALDI-TOF MS) data using machine-learning techniques. After on- and off-plate protein extraction, we observed that the BioTyper database misidentified or could not differentiate Weissella species. Although Weissella species exhibited very similar protein profiles, these species can be differentiated on the basis of the results of a statistical analysis. To classify W. cibaria, W. confusa, and non-target Weissella species, machine learning was used for 167 spectra, which led to the listing of potential species-specific mass-to-charge (m/z) loci. Machine-learning techniques including artificial neural networks, principal component analysis combined with the K-nearest neighbor, support vector machine (SVM), and random forest were used. The model that applied the Radial Basis Function kernel algorithm in SVM achieved classification accuracy of 1.0 for training and test sets. The combination of MALDI-TOF MS and machine learning can efficiently classify closely-related species, enabling accurate microbial identification.

Lipid A modification-induced colistin-resistant Klebsiella variicola from healthy adults

Background. Klebsiella variicola was once recognised as a benign plant-endosymbiont but recent case reports suggest that it is a newly emerging Gram-negative pathogen related to opportunistic infection of multiple sites in humans.Methods. Antimicrobial susceptibility testing was performed using broth microdilution method. To identify colistin resistance mechanisms, phoPQ, pmrAB, and mgrB were sequenced and their mRNA expression was analysed using quantitative real-time PCR. In addition, we tried to detect crrAB and mcr. The lipid A moieties of colistin-susceptible and -resistant isolates were analysed using MALDI-TOF.Results. Among the two K. variicola isolates, one is colistin-resistant, and another is colistin-susceptible. The colistin-resistant K. variicola isolate showed no mutations in phoPQ, pmrAB, and mgrB, and crrAB and mcr were not identified. However, its phoQ and pbgP expression was significantly higher and amino-arabinosylated lipid A with hexa-acylated species in lipopolysaccharide was identified.Conclusions. We found that colistin resistance in K. variicola was mediated by the modification of lipid A. Although the isolate was obtained from faecal samples of healthy adults, colistin-resistant K. variicola challenges public health as an opportunistic pathogen.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Biomolecular Profiling by MALDI-TOF Mass Spectrometry in Food and Beverage Analyses

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has frequently been applied to the analysis of biomolecules. Its strength resides not only in compound identification but particularly in acquiring molecular profiles providing a high discriminating power. The main advantages include its speed, simplicity, versatility, minimum sample preparation needs, and a relatively high tolerance to salts. Other benefits are represented by the possibility of automation, high throughput, sensitivity, accuracy, and good reproducibility, allowing quantitative studies. This review deals with the prominent use of MALDI-TOF MS profiling in food and beverage analysis ranging from the simple detection of sample constituents to quantifications of marker compounds, quality control, and assessment of product authenticity. This review summarizes relevant discoveries that have been obtained with milk and milk products, edible oils, wine, beer, flour, meat, honey, and other alimentary products. Marker molecules are specified: proteins and peptides for milk, cheeses, flour, meat, wine and beer; triacylglycerols and phospholipids for oils; and low-molecular-weight metabolites for wine, beer and chocolate. Special attention is paid to sample preparation techniques and the combination of spectral profiling and statistical evaluation methods, which is powerful for the differentiation of samples and the sensitive detection of frauds and adulterations.

Antibiotic Abuse and Antimicrobial Resistance in Hospital Environment: A Retrospective Observational Comparative Study

Background and Objectives: Antimicrobial resistance represents a serious problem, and it may be life-threatening in the case of severe hospital-acquired infections (HAI). Antibiotic abuse and multidrug resistance (MDR) have significantly increased this burden in the last decades. The aim of this study was to investigate the distribution and susceptibility rates of five selected bacterial species (E. coli, K. pneumoniae, P. aeruginosa, S. aureus and E. faecium) in two healthcare settings located in the Apulia region (Italy). Materials and Methods: Setting n.1 was a university hospital and setting n.2 was a research institute working on oncological patients. All the enrolled patients were diagnosed for bacterial HAI. The observation period was between August and September 2021. Clinical samples were obtained from several biological sources, in different hospital wards. Bacterial identification and susceptibility were tested by using the software VITEC 2 Single system. Results: In this study, a higher incidence of multi-drug-resistant K. pneumoniae was reported (42,2% in setting n.1 and 50% in setting n.2), with respect to the Italian 2019 statistics report (30.3%). All the isolates of E. faecium and S. aureus were susceptible to linezolid. All the bacterial isolates of P. aeruginosa and most of K. pneumoniae were susceptible to ceftazidime–avibactam. Amikacin and nitrofurantoin represented a good option for treating E. coli infections. Multidrug-resistant (MDR) P. aeruginosa, methicillin-resistant S. aureus (MRSA) and vancomycin-resistantE. faecium (VRE) had a lower incidence in the clinical setting, with respect to E. coli and K. pneumoniae. Conclusions: The data obtained in this study can support clinicians towards a rational and safe use of antibiotics for treating the infections caused by these resistant strains, to enhance the overall efficacy of the current antibiotic protocols used in the main healthcare environments.

A Combination of MALDI-TOF MS Proteomics and Species-Unique Biomarkers' Discovery for Rapid Screening of Brucellosis

Brucellosis is considered to be a zoonotic infection with a predominant incidence in most parts of Iran that may even simply involve diagnostic laboratory personnel. In the present study, we apply matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) for rapid and reliable discrimination of Brucella abortus and Brucella melitensis, based on proteomic mass patterns from chemically treated whole-cell analyses. Biomarkers of the low molecular weight proteome in the MALDI-TOF MS spectra were assigned to conserved ribosomal and structural protein families that were found in genome assemblies of B. abortus and B. melitensis in the NCBI database. Significant protein mass signals successfully mapped to ribosomal proteins and structural proteins, such as integration host factor subunit alpha, cold-shock proteins, HU family DNA-binding protein, ATP synthase subunit C, and GNAT family N-acetyltransferase, with specific biomarker peaks that have been identified for each virulent and vaccine strain. Web-accessible bioinformatics algorithms, with a robust data analysis workflow, followed by ribosomal and structural protein mapping, significantly enhanced the reliable assignment of key proteins and accurate identification of Brucella species. Furthermore, clinical samples were analyzed to confirm the most dominant protein biomarker candidates and their relevance for the identifications of B. melitensis and B. abortus. With proper optimization, we envision that the presented MALDI-TOF MS proteomics analyses, coupled with special usage of bioinformatics, could be used as a cost-efficient strategy for the diagnostics of brucellosis and introduce a reliable identification protocol for species of dangerous bacteria.

Plasma and serum exosome markers analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with electron multiplier

Although matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a simple, rapid, and high-throughput assay, its microchannel plate (MCP) detector is limited by the low sensitivity and ion saturation effect when analyzing macromolecules. Herein, we introduced a strategy that combined MALDI-TOF MS with electron multiplier (EM) for the direct analysis of exosomal proteins isolated from human plasma and serum. The results demonstrated that EM yielded a higher sensitivity than MCP detector in high-mass range (m/z 5000-100000). Through the analysis of MALDI-TOF MS coupled with EM, chemokine (C-X-C motif) ligand 12 (CXCL12) ion at m/z 7960 and its degradation products at m/z 7927, 7587, and 7553 were identified as characteristic exosomal proteins in plasma. CXCL4 ion at m/z 7765 was identified as a characteristic protein in serum exosomes. Additionally, the peak intensity of CXCL12 and CXCL4 standards exhibited great linear relationship (CXCL12, R² = 0.989; CXCL4, R² = 0.986) with the concentrations (ranging from 0.1 to 20 μg/mL) when using EM as detector. In conjunction with ultrasonic assisted matrix coating technology (UAMCT), this assay repeatability in our lab has been excellent with coefficient of variation (CV%) of 4.6% for CXCL12 and 9.3% for CXCL4. Finally, the spectra demonstrated that the intensity of exosome related peaks was significantly enhanced in plasma and serum of patients with Parkinson's disease (PD) (m/z 7553, P < 0.01; m/z 7587, P < 0.01; m/z 7927, P < 0.001; m/z 7980, P < 0.001; m/z 7765, P < 0.01), Alzheimer's disease (AD) (m/z 7553, P < 0.001; m/z 7587, P < 0.001; m/z 7927, P < 0.001; m/z 7980, P < 0.001), and ischemic cerebrovascular disease (ICD) (m/z 7553, P < 0.05; m/z 7587, P < 0.05; m/z 7927, P < 0.01; m/z 7980, P < 0.05; m/z 7765, P < 0.05) compared to that in healthy persons. The fingerprint information of CXCL12 in plasma exosomes has better clinical relevance than serum exosome CXCL4 in MALDI-TOF MS analysis.

Differentiation of Bacillus cereus and Bacillus thuringiensis Using Genome-Guided MALDI-TOF MS Based on Variations in Ribosomal Proteins

Bacillus cereus and B. thuringiensis are closely related species that are relevant to foodborne diseases and biopesticides, respectively. Unambiguous differentiation of these two species is crucial for bacterial taxonomy. As genome analysis offers an objective but time-consuming classification of B. cereus and B. thuringiensis, in the present study, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was used to accelerate this process. By combining in silico genome analysis and MALDI-TOF MS measurements, four species-specific peaks of B. cereus and B. thuringiensis were screened and identified. The species-specific peaks of B. cereus were m/z 3211, 6427, 9188, and 9214, and the species-specific peaks of B. thuringiensis were m/z 3218, 6441, 9160, and 9229. All the above peaks represent ribosomal proteins, which are conserved and consistent with the phylogenetic relationship between B. cereus and B. thuringiensis. The specificity of the peaks was robustly verified using common foodborne pathogens. Thus, we concluded that genome-guided MALDI-TOF MS allows high-throughput differentiation of B. cereus and B. thuringiensis and provides a framework for differentiating other closely related species.

Improved approach based on MALDI-TOF MS for establishment of the fish mucus protein pattern for geographic discrimination of Sparus aurata

Food fraud is still a recurrent practice throughout food supply chains. In the case of seafood, misidentification of species and products repackaging constitute the most common frauds. Therefore, the development of appropriate analytical approaches to be used against food fraud is necessary. The present study goal is to explore for the first time, the possibility to differentiate between Sparus aurata from two different mariculture farms located in Madeira Island (Caniçal and Ribeira Brava), using the mass fingerprint of fish mucus obtained from MALDI-TOF MS and analyzed using Mass-UP software for multivariate statistical analysis and biomarker identification. It was possible to establish, from the mucus protein fraction, a set of potential biomarkers for each location in a total of 35 peaks, being 17 peaks specific to Caniçal located farm and 18 to Ribeira Brava. The proposed analytical approach revealed a useful strategy providing accurate and fast results for fish geographical origin discrimination.

Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin

Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

A Polyphasic Approach to Classification and Identification of Species within the Trichophyton benhamiae Complex

In recent years, considerable advances have been made in clearing up the phylogenetic relationships within the Arthrodermataceae family. However, certain closely related taxa still contain poorly resolved species boundaries. Here, we tried to elucidate the species composition of the Trichophyton benhamiae species complex using a combined approach consisting of multi-gene phylogenetic analysis based on internal transcribed spacer (ITS) and beta-tubulin (BT) gene regions, morphological analysis, and spectral comparison using MALDI-ToF. We confirmed the existence of 11 different monophyletic clades within the complex representing either species or genetically distinct groups within species. MALDI-ToF spectrometry analysis revealed that most of these clades were readily distinguishable from one another; however, some closely related sister clades, such as T. europaeum and T. japonicum, were often misidentified as their counterpart. The distinct “yellow” and “white” phenotypes of T. benhamiae do not have a clear genetic basis and should thus be considered as different morphotypes of the same species. Strains traditionally considered T. benhamiae can be divided into three main clades: (i) T. benhamiae, (ii) T. europaeum/T. japonicum, and (iii) the phylogenetically distant T. africanum. While T. europaeum and T. japonicum are distinguishable based on their genotype, spectral and morphological analysis did not provide clear delimiting characteristics.

Application and Perspectives of MALDI-TOF Mass Spectrometry in Clinical Microbiology Laboratories

Early diagnosis of severe infections requires of a rapid and reliable diagnosis to initiate appropriate treatment, while avoiding unnecessary antimicrobial use and reducing associated morbidities and healthcare costs. It is a fact that conventional methods usually require more than 24–48 h to culture and profile bacterial species. Mass spectrometry (MS) is an analytical technique that has emerged as a powerful tool in clinical microbiology for identifying peptides and proteins, which makes it a promising tool for microbial identification. Matrix assisted laser desorption ionization–time of flight MS (MALDI–TOF MS) offers a cost- and time-effective alternative to conventional methods, such as bacterial culture and even 16S rRNA gene sequencing, for identifying viruses, bacteria and fungi and detecting virulence factors and mechanisms of resistance. This review provides an overview of the potential applications and perspectives of MS in clinical microbiology laboratories and proposes its use as a first-line method for microbial identification and diagnosis.

Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning

Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.

Rapid Discrimination and Authentication of Korean Farmstead Mozzarella Cheese through MALDI-TOF and Multivariate Statistical Analysis

Geographical origin and authenticity are the two crucial factors that propel overall cheese perception in terms of quality and price; therefore, they are of great importance to consumers and commercial cheese producers. Herein, we demonstrate a rapid, accurate method for discrimination of domestic and import mozzarella cheeses in the Republic of Korea by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The protein profiles' data aided by multivariate statistical analysis successfully differentiated farmstead and import mozzarella cheeses according to their geographical location of origin. A similar investigation within domestic samples (farmsteads/companies) also showed clear discrimination regarding the producer. Using the biomarker discovery tool, we identified seven distinct proteins, of which two (m/z 7407.8 and 11,416.6) were specific in farmstead cheeses, acting as potential markers to ensure authentication and traceability. The outcome of this study can be a good resource in building a database for Korean domestic cheeses. This study also emphasizes the combined utility of MALDI-TOF MS and multivariate analysis in preventing fraudulent practices, thereby ensuring market protection for Korean farmstead cheeses.

Mass Spectrometry-Based Methods for Immunoglobulin G N-Glycosylation Analysis

Mass spectrometry and its hyphenated techniques enabled by the improvements in liquid chromatography, capillary electrophoresis, novel ionization, and fragmentation modes are truly a cornerstone of robust and reliable protein glycosylation analysis. Boost in immunoglobulin G (IgG) glycan and glycopeptide profiling demands for both applied biomedical and research applications has brought many new advances in the field in terms of technical innovations, sample preparation, improved throughput, and confidence in glycan structural characterization. This chapter summarizes mass spectrometry basics, focusing on IgG and monoclonal antibody N-glycosylation analysis on several complexity levels. Different approaches, including antibody enrichment, glycan release, labeling, and glycopeptide preparation and purification, are covered and illustrated with recent breakthroughs and examples from the literature omitting excessive theoretical frameworks. Finally, selected highly popular methodologies in IgG glycoanalytics such as liquid chromatography-mass spectrometry and matrix-assisted laser desorption ionization are discussed more thoroughly yet in simple terms making this text a practical starting point either for the beginner in the field or an experienced clinician trying to make sense out of the IgG glycomic or glycoproteomic dataset.

Unravelling neurological disorders through metallomics-based approaches

Understanding the biological process involving metals and biomolecules in the brain is essential for establishing the origin of neurological disorders, such as neurodegenerative and psychiatric diseases. From this perspective, this critical review presents recent advances in this topic, showing possible mechanisms involving the disruption of metal homeostasis and the pathogenesis of neurological disorders. We also discuss the main challenges observed in metallomics studies associated with neurological disorders, including those related to sample preparation and analyte quantification.

Creation of an Online Platform for Identification of Microorganisms: Peak Picking or Full-Spectrum Analysis

Identification of microorganisms by MALDI-TOF mass spectrometry is a very efficient method with high throughput, speed, and accuracy. However, it is significantly limited by the absence of a universal database of reference mass spectra. This problem can be solved by creating an Internet platform for open databases of protein spectra of microorganisms. Choosing the optimal mathematical apparatus is the pivotal issue for this task. In our previous study we proposed the geometric approach for processing mass spectrometry data, which represented a mass spectrum as a vector in a multidimensional Euclidean space. This algorithm was implemented in a Jacob4 stand-alone package. We demonstrated its efficiency in delimiting two closely related species of the Bacillus pumilus group. In this study, the geometric approach was realized as R scripts which allowed us to design a Web-based application. We also studied the possibility of using full spectra analysis (FSA) without calculating mass peaks (PPA), which is the logical development of the method. We used 74 microbial strains from the collections of ICiG SB RAS, UNIQEM, IEGM, KMM, and VGM as the models. We demonstrated that the algorithms based on peak-picking and analysis of complete data have accuracy no less than that of Biotyper 3.1 software. We proposed a method for calculating cut-off thresholds based on averaged intraspecific distances. The resulting database, raw data, and the set of R scripts are available online at https://icg-test.mydisk.nsc.ru/s/qj6cfZg57g6qwzN.

Parvularcula mediterranea sp. nov., isolated from marine plastic debris from Zakynthos Island, Greece

A Gram-negative, dark orange-pigmented, aerobic, non-spore-forming, coccoid-shaped bacterium designated as ZS-1/3^T was isolated from a floating plastic litter (polypropylene straw) sample, collected from shallow seawater near the public beach of Laganas on Zakynthos island, Greece. Phylogenetic analysis based on 16S rRNA gene sequences indicated that the isolate is affiliated with the genus Parvularcula in the family Parvularculaceae. Its closest relatives are Parvularcula lutaonensis (98.09  %) and Parvularcula oceanus (95.89  %). The pH and temperature ranges for growth are pH 5-10 and 20-38 °C (optima, pH 7.0 and 28 °C). The predominant fatty acids are C_18 : 1  ω7c (56.84  %), C_16 : 0 (27.51  %), C_18 : 0 (2.25  %) and C_12 : 0 (1.42  %). The predominant respiratory quinone detected in strain ZS-1/3^T is quinone-10 (Q10); the majority of detected polar lipids are glycolipid. The DNA G+C content is 62.5  mol%. Physiological and chemotaxonomic data further confirmed the distinctiveness of strain ZS-1/3^T from other members of the genus Parvularcula. Thus, strain ZS-1/3^T is considered to represent a novel species of the genus, for which the name Parvularcula mediterranea. sp. nov. is proposed. The type strain is ZS-1/3^T (=NCAIM B 02654^T=CCM 9032^T).

MALDI-TOF mass spectrometry for sub-typing of Streptococcus pneumoniae

BACKGROUND

Serotyping of Streptococcus pneumoniae is important for monitoring of vaccine impact. Unfortunately, conventional and molecular serotyping is expensive and technically demanding. This study aimed to determine the ability of matrix-assisted laser desorption-ionisation time-of-flight (MALDI-TOF) mass spectrometry to discriminate between pneumococcal serotypes and genotypes (defined by global pneumococcal sequence cluster, GPSC). In this study, MALDI-TOF mass spectra were generated for a diverse panel of whole genome sequenced pneumococcal isolates using the bioMerieux VITEK MS in clinical diagnostic (IVD) mode. Discriminatory mass peaks were identified and hierarchical clustering was performed to visually assess discriminatory ability. Random forest and classification and regression tree (CART) algorithms were used to formally determine how well serotypes and genotypes were identified by MALDI-TOF mass spectrum.

RESULTS

One hundred and ninety-nine pneumococci, comprising 16 serotypes and non-typeable isolates from 46 GPSC, were analysed. In the primary experiment, hierarchical clustering revealed poor congruence between MALDI-TOF mass spectrum and serotype. The correct serotype was identified from MALDI-TOF mass spectrum in just 14.6% (random forest) or 35.4% (CART) of 130 isolates. Restricting the dataset to the nine dominant GPSC (61 isolates / 13 serotypes), discriminatory ability improved slightly: the correct serotype was identified in 21.3% (random forest) and 41.0% (CART). Finally, analysis of 69 isolates of three dominant serotype-genotype pairs (6B-GPSC1, 19F-GPSC23, 23F-GPSC624) resulted in the correct serotype identification in 81.1% (random forest) and 94.2% (CART) of isolates.

CONCLUSIONS

This work suggests that MALDI-TOF is not a useful technique for determination of pneumococcal serotype. MALDI-TOF mass spectra appear more associated with isolate genotype, which may still have utility for future pneumococcal surveillance activities.

Identification and dereplication of endophytic Colletotrichum strains by MALDI TOF mass spectrometry and molecular networking

The chemical diversity of biologically active fungal strains from 42 Colletotrichum, isolated from leaves of the tropical palm species Astrocaryum sciophilum collected in pristine forests of French Guiana, was investigated. The collection was first classified based on protein fingerprints acquired by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) correlated with cytotoxicity. Liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) data from ethyl acetate extracts were acquired and processed to generate a massive molecular network (MN) using the MetGem software. From five Colletotrichum strains producing cytotoxic specialized metabolites, we predicted the occurrence of peptide and cytochalasin analogues in four of them by MN, including a similar ion clusters in the MN algorithm provided by MetGem software. Chemoinformatics predictions were fully confirmed after isolation of three pentacyclopeptides (cyclo(Phe-Leu-Leu-Leu-Val), cyclo(Phe-Leu-Leu-Leu-Leu) and cyclo(Phe-Leu-Leu-Leu-Ile)) and two cytochalasins (cytochalasin C and cytochalasin D) exhibiting cytotoxicity at the micromolar concentration. Finally, the chemical study of the last active cytotoxic strain BSNB-0583 led to the isolation of four colletamides bearing an identical decadienamide chain.

Usefulness of matrix-assisted laser desorption ionization/time of flight mass spectrometry for the identification of Streptococcus mutans

This study evaluated the reliability of MALDI-TOF MS coupled with statistical tools for the identification of Streptococcus mutans in comparison with PCR-based techniques. Bacterial isolates were identified and serotyped by conventional PCR, using S. mutans species and serotype-specific primers. For bacterial identification, mass spectra data from S. mutans and other streptococci were compared with Biotyper V 3.1 database and the mass peak lists were examined by cluster and principal component (PCA) analysis. Identification of potential biomarkers was performed using UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases and BLAST tool of the NCBI database. PCR identified 100% of the isolates as S. mutans. S. mutans strains were typed as serotypes c (85.6%), e (8.6%), k (4.8%), and f (0.9%). Although only the 70% of the strains tested were identified at species level by the Biotyper database, PCA and cluster analysis of mass peaks allowed the identification of 100% S. mutans isolates and its differentiation from the other oral and non-oral streptococci. One mass peak at m/z value of 9572.73 was identified as species-specific biomarker for S. mutans. No biomarkers were identified for S. mutans serotypes. KEY POINTS: • MALDI-TOF MS coupled with statistical tools for the identification of S. mutans. • Detection of species identifying biomarkers by MALDI-TOF MS. • PCR identification and serotyping of S. mutans from saliva samples.

Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.

Testing the Applicability of MALDI-TOF MS as an Alternative Stock Identification Method in a Cryptic Species Complex

Knowledge of intraspecific variability of a certain species is essential for their long-term survival and for the development of conservation plans. Nowadays, molecular/genetic methods are the most frequently used for this purpose. Although, the Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) technique has become a promising alternative tool to specify intraspecific variability, there is a lack of information about the limitations of this method, and some methodological issues need to be resolved. Towards this goal, we tested the sensitivity of this method on an intraspecific level, using genetically identified individuals of a cryptic fish species complex collected from five distinct populations. Additionally, some methodologic issues, such as the effect of (1) delayed sample preparation, (2) clove oil anaesthetization, and (3) different tissue types (muscle, and brain) were investigated using the MS analysis results. Our results show that the delayed sample preparation has a fundamental effect on the result of MS analysis, while at the same time the clove oil did not affect the results considerably. Both the brain and muscle samples were usable for cryptic species identification, but in our opinion this method has limited applicability for population-level segregation. The application of MALDI-TOF MS to the exploitable toolkit of phylogenetic and taxonomic researches could be used to broaden conclusions.

Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning

The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.

New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R²CV = 0.939, R²P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R²CV = 0.862, R²P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.

Rapid screening of marine bacterial symbionts using MALDI-TOF MS

Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) is a rapid, cost-effective and high-throughput method for bacteria characterization. However, most previous studies focused on clinical isolates. In this study, we evaluated the use of MALDI-TOF MS as a rapid screening tool for marine bacterial symbionts. A set of 255 isolates from different marine sources (corals, sponge, fish and seawater) was analyzed using cell lysates to obtain a rapid grouping. Cluster analysis of mass spectra and 16S rRNA showed 18 groups, including Vibrio, Bacillus, Pseudovibrio, Alteromonas and Ruegeria. MALDI-TOF distance similarity scores ≥ 60% and ≥ 70% correspond to ≥ 98.7% 16S rRNA gene sequence similarity and ≥ 95% pyrH gene sequence similarity, respectively. MALDI-TOF MS is a useful tool for Vibrio species groups' identification.

Intact cell MALDI-TOF mass spectrometry, a promising proteomic profiling method in farm animal clinical and reproduction research

The objective of this review is to provide new insights into the possible use of a proteomic method known as Intact Cell Matrix-Assisted Laser Desorption-ionization Time-Of-Flight Mass Spectrometry (ICM-MS) in animal clinical research. Here, we give an overview of the basics of this technique, its advantages and disadvantages compared with other proteomic approaches, past applications and future perspectives. A special emphasis on its implementation in animal reproduction science is given, including examples of the reliable use of ICM-MS on fertility screening. In mammals, the ICM-MS profiles from pig epididymal spermatozoa reflect the proteome changes that they undergo during epididymal maturation and could be associated with the acquisition of fertilizing ability. In chicken, using adequate pre-processing and bioinformatics analysis tools, sperm ICM-MS profiles showed characteristic spectral features that allowed their classification according to their actual fertilizing ability. The association of ICM-MS and Top-down proteomic strategies allowed the identification of chicken fertility biomarkers candidates such as protein vitelline membrane outer layer protein 1 (VMO-1) and avian beta-defensin 10 (AvBD10). In female reproduction, a similar approach on ovarian follicular cells allowed the identification of specific markers of oocyte maturation in the oocyte and surrounding cumulus cells. Altogether, these results indicate that ICM-MS profiling could be a suitable approach for molecular phenotyping of male and female gametes.

Identification of Brucella spp. isolates and discrimination from the vaccine strain Rev.1 by MALDI-TOF mass spectrometry

Brucellosis' surveillance and control programs require robust laboratory techniques that can reliably identify and biotype Brucella strains and discriminate between vaccine and field infection. In the recent years, Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) has revolutionized the routine identification of several microorganisms in clinical microbiology laboratories. Nevertheless, its application on Brucella spp. identification is limited since there are no reference spectra in the commercial databases, due to the microorganism's potential bioterrorist use. In this study, a custom MALDI-TOF MS reference library was constructed and its performance on identification at species level was evaluated using 75 Brucella spp. isolates. Furthermore, distinct peak biomarkers were detected for biovar assignment and discrimination from vaccine strain Rev.1. Analysis of mass peak profiles allowed Brucella accurate identification at genus and species level (100%) with no misidentifications. Despite the high intrageneric similarity, MALDI-TOF MS database succeeded in classifying at biovar level, 47 out of 62 B. melitensis bv. 3 isolates (75.81%), whereas all B. melitensis strains, except for one, were correctly discriminated from vaccine strain Rev.1. MALDI-TOF MS appeared to be a rapid, cost-effective and reliable method for the routine identification of brucellae which reduces time consumption in pathogen identification and could replace in the near future the current conventional and molecular techniques. Its ability to differentiate vaccine from field infection could facilitate brucellosis' monitoring systems contributing in the effective control of the disease.

Metabolomic tools used in marine natural product drug discovery

Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.

Detection of carbapenem-resistant Klebsiella pneumoniae on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach

BACKGROUND

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is emerging as a significant pathogen causing healthcare-associated infections. Matrix-assisted laser desorption/ionisation mass spectrometry time-of-flight mass spectrometry (MALDI-TOF MS) is used by clinical microbiology laboratories to address the need for rapid, cost-effective and accurate identification of microorganisms. We evaluated application of machine learning methods for differentiation of drug resistant bacteria from susceptible ones directly using the profile spectra of whole cells MALDI-TOF MS in 46 CRKP and 49 CSKP isolates.

METHODS

We developed a two-step strategy for data preprocessing consisting of peak matching and a feature selection step before supervised machine learning analysis. Subsequently, five machine learning algorithms were used for classification.

RESULTS

Random forest (RF) outperformed other four algorithms. Using RF algorithm, we correctly identified 93% of the CRKP and 100% of the CSKP isolates with an overall classification accuracy rate of 97% when 80 peaks were selected as input features.

CONCLUSIONS

We conclude that CRKPs can be differentiated from CSKPs through RF analysis. We used direct colony method, and only one spectrum for an isolate for analysis, without modification of current protocol. This allows the technique to be easily incorporated into clinical practice in the future.

Phenotypic and Molecular Characterization of Lacinutrix venerupis Isolated from Atlantic Horse Mackerel Trachurus trachurus

The aim of the present study was to characterize two gram-negative bacterial strains that were isolated from diseased Atlantic Horse Mackerel Trachurus trachurus in 2017. Based on the results obtained from the biochemical and chemotaxonomic characterization, the isolates were identified as Lacinutrix spp. The highest similarity of the 16S rRNA gene sequences was obtained with the strain L. venerupis CECT 8573^T (99.1%), while other species showed similarities of 98% (L. jangbogonensis) and 97% (L. algicola and L. mariniflava). Molecular characterization by repetitive element (REP)-PCR and enterobacterial repetitive intergenic consensus (ERIC)-PCR, as well as proteomic characterization by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS), demonstrated heterogeneity between the strains from the Atlantic Horse Mackerel and the type strain, CECT 8573^T . The virulence of one of the isolates for Turbot Scophthalmus maximus, European Sea Bass Dicentrarchus labrax, Senegalese Sole Solea senegalensis, and Rainbow Trout Oncorhynchus mykiss was assessed under experimental conditions. No mortalities were recorded after intraperitoneal injections with high doses of bacteria (1 × 10⁹  CFU/mL). Thus, further studies are necessary to elucidate the impact of this bacterial species as a fish pathogen.

Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics

In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.

Ultrasonic-assisted extraction and digestion of proteins from solid biopsies followed by peptide sequential extraction hyphenated to MALDI-based profiling holds the promise of distinguishing renal oncocytoma from chromophobe renal cell carcinoma

A novel analytical approach is proposed to discriminate between solid biopsies of chromophobe renal cell carcinoma (chRCC) and renal oncocytoma (RO). The method comprises the following steps: (i) ultrasonic extraction of proteins from solid biopsies, (ii) protein depletion with acetonitrile, (iii) ultrasonic assisted in-solution digestion using magnetic nanoparticle with immobilized trypsin, (iv) C18 tip-based preconcentration of peptides, (v) sequential extraction of the peptides with ACN, (vi) MALDI-snapshot of the extracts and (vii) investigation of the extract containing the most discriminating features using high resolution mass spectrometry. With this approach we have been able to differentially cluster renal oncocytoma and chromophobe renal cell carcinoma and identified 18 proteins specific to chromophobe and seven unique to renal oncocytoma. Chromophobes express proteins associated with ATP function (ATP5I & 5E; VATE1 & G2; ADT2), glycolysis (PGK1) and neuromedin whilst oncocytomas express ATP5H, ATPA, DEPD7 and TRIPB thyroid receptor interacting protein.

Identification and typing of Vagococcus salmoninarum using genomic and proteomic techniques

This study reports on the characterization of Vagococcus salmoninarum using phenotypic, serological, antigenic, genetic and proteomic methods. All strains of V. salmoninarum were resistant to most of the antimicrobials tested, and only 10% of strains were sensitive to florfenicol. Serological analysis demonstrated a high antigenic homogeneity within the species. No cross-reaction was detected with other fish pathogenic species causing streptococcosis (Lactococcus garvieae, Streptococcus parauberis, Streptococcus iniae, Streptococcus agalactiae, Carnobacterium maltaromaticum) using serum against V. salmoninarum CECT 5810. Electrophoretic analysis of cell surface proteins and immunoblot supported the antigenic homogeneity within V. salmoninarum strains. Moreover, limited diversity was detected using genomic (RAPD, ERIC-PCR and REP-PCR) and MALDI-TOF-MS analyses. The phenotypic, genomic and proteomic methods tested allowed the rapid differentiation of V. salmoninarum from the other species causing streptococcosis. However, MALDI-TOF-MS is the most promising method for typing and characterization of V. salmoninarum.

Novel site-specific PEGylated L-asparaginase

L-asparaginase (ASNase) from Escherichia coli is currently used in some countries in its PEGylated form (ONCASPAR, pegaspargase) to treat acute lymphoblastic leukemia (ALL). PEGylation refers to the covalent attachment of poly(ethylene) glycol to the protein drug and it not only reduces the immune system activation but also decreases degradation by plasmatic proteases. However, pegaspargase is randomly PEGylated and, consequently, with a high degree of polydispersity in its final formulation. In this work we developed a site-specific N-terminus PEGylation protocol for ASNase. The monoPEG-ASNase was purified by anionic followed by size exclusion chromatography to a final purity of 99%. The highest yield of monoPEG-ASNase of 42% was obtained by the protein reaction with methoxy polyethylene glycol-carboxymethyl N-hydroxysuccinimidyl ester (10kDa) in 100 mM PBS at pH 7.5 and PEG:ASNase ratio of 25:1. The monoPEG-ASNase was found to maintain enzymatic stability for more days than ASNase, also was resistant to the plasma proteases like asparaginyl endopeptidase and cathepsin B. Additionally, monoPEG-ASNase was found to be potent against leukemic cell lines (MOLT-4 and REH) in vitro like polyPEG-ASNase. monoPEG-ASNase demonstrates its potential as a novel option for ALL treatment, being an inventive novelty that maintains the benefits of the current enzyme and solves challenges.

Capsule Typing of Haemophilus influenzae by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Encapsulated Haemophilus influenzae strains belong to type-specific genetic lineages. Reliable capsule typing requires PCR, but a more efficient method would be useful. We evaluated capsule typing by using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Isolates of all capsule types (a−f and nontypeable; n = 258) and isogenic capsule transformants (types a−d) were investigated. Principal component and biomarker analyses of mass spectra showed clustering, and mass peaks correlated with capsule type-specific genetic lineages. We used 31 selected isolates to construct a capsule typing database. Validation with the remaining isolates (n = 227) showed 100% sensitivity and 92.2% specificity for encapsulated strains (a−f; n = 61). Blinded validation of a supplemented database (n = 50) using clinical isolates (n = 126) showed 100% sensitivity and 100% specificity for encapsulated strains (b, e, and f; n = 28). MALDI-TOF mass spectrometry is an accurate method for capsule typing of H. influenzae.

Sensitivity booster for mass detection enables unambiguous analysis of peptides, proteins, antibodies, and protein bioconjugates

A chemical tag enhances peptide detection by multiple orders in mass spectrometry.