MALDI-TOF MS Spectra Research Articles

Identification of Escherichia coli strains using MALDI-TOF MS combined with long short-term memory neural networks.

The current study aims to develop a new technique for the precise identification of Escherichia coli strains, utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with a long short-term memory (LSTM) neural network. A total of 48 Escherichia coli strains were isolated and cultured on tryptic soy agar medium for 24 hours for the generation of MALDI-TOF MS spectra. Eight hundred MALDI-TOF MS spectra were obtained per strain, resulting in a database of 38,400 spectra. Fifty percent of the data was utilized for LSTM neural network training, with fine-tuned parameters for strain-level identification. The other half served as the test set to assess model performance. Traditional PCA dimension reduction of MALDI-TOF MS spectra indicated 47 out of 48 strains to be unclassifiable. In contrast, the LSTM neural network demonstrated remarkable efficacy. After 20 training epochs, the model achieved a loss value of 0.0524, an accuracy of 0.999, a precision of 0.985, and a recall of 0.982. When tested on the unseen data, the model attained an overall accuracy of 92.24%. The integration of MALDI-TOF MS and LSTM neural network markedly enhances the identification of Escherichia coli strains. This innovative approach offers an effective and accurate tool for MALDI-TOF MS-based strain-level identification, thus expanding the analytical capabilities of microbial diagnostics.

Utility of MALDI-TOF MS for determination of species identity and blood meal sources of primary malaria vectors on the Kenyan coast.

Protein analysis using matrix-assisted laser desorption/ionisation time-of-flight mass-spectrometry (MALDI-TOF MS) represents a promising tool for entomological surveillance. In this study we tested the discriminative power of this tool for measuring species and blood meal source of main Afrotropical malaria vectors on the Kenyan coast. Mosquito collections were conducted along the coastal region of Kenya. MALDI-TOF MS spectra were obtained from each individual mosquito's cephalothorax as well as the abdomens of blood-engorged mosquitoes. The same mosquitoes were also processed using gold standard tests: polymerase chain reaction (PCR) for species identification and enzyme linked immunosorbent assay (ELISA) for blood meal source identification. Of the 2,332 mosquitoes subjected to MALDI-TOF MS, 85% (1,971/2,332) were considered for database creation and validation. There was an overall accuracy of 97.5% in the identification of members of the An. gambiae ( An. gambiae, 100%; An. arabiensis, 91.9%; An. merus, 97.5%; and An. quadriannulatus, 90.2%) and An. funestus ( An. funestus, 94.2%; An. rivulorum, 99.4%; and An. leesoni, 94.1%) complexes. Furthermore, MALDI-TOF MS also provided accurate (94.5% accuracy) identification of blood host sources across all mosquito species. This study provides further evidence of the discriminative power of MALDI-TOF MS to identify sibling species and blood meal source of Afrotropical malaria vectors, further supporting its utility in entomological surveillance. The low cost per sample (<0.2USD) and high throughput nature of the method represents a cost-effective alternative to molecular methods and could enable programs to increase the number of samples analysed and therefore improve the data generated from surveillance activities.

Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (ARIs) owing to its affordability and high-throughput capacity. MALDI-TOF MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-TOF MS in differentiating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vs non-COVID acute respiratory infections (NCARIs) in a clinical lab setting in Kazakhstan. Nasopharyngeal swabs were collected from inpatients and outpatients with respiratory symptoms and from asymptomatic controls (ACs) in 2020-2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-TOF MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARIs, and 39 ACs) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and machine learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. Applying the established MALDI-TOF MS pipeline "as is" resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARIs (48.0%) and ACs (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing support vector machine with radial basis function kernel model was at 88.0%, 95.0%, and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively, with a SARS-CoV-2 vs rest receiver operating characteristic area under the curve of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARIs. MALDI-TOF MS/ML is a feasible approach for the differentiation of ARI without specialized sample preparation. The implementation of MALDI-TOF MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.IMPORTANCEIn this proof-of-concept study, the authors used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning (ML) to identify and distinguish acute respiratory infections (ARI) caused by SARS-CoV-2 versus other pathogens in low-resource clinical settings, without the need for specialized sample preparation. The ML models were trained on a varied collection of MALDI-TOF MS spectra from studies conducted in Kazakhstan and South America. Initially, the MALDI-TOF MS/ML pipeline, trained exclusively on South American samples, exhibited diminished effectiveness in recognizing non-SARS-CoV-2 infections from Kazakhstan. Incorporation of spectral signatures from Kazakhstan substantially increased the accuracy of detection. These results underscore the potential of employing MALDI-TOF MS/ML in resource-constrained settings to augment current approaches for detecting and differentiating ARI.

Factors affecting the quality and reproducibility of MALDI-TOF MS identification for human Capnocytophaga species

Reproducibility and quality of MALDI-TOF MS spectra are critical in the identification process, however, information on the factors affecting the identification scores are scarce. Here, we studied the influence of various factors during the identification process of human oral Capnocytophaga species. The influence of two incubation times, plate-spotting reproducibility of two examiners, extraction technique, storage period of plates, and different laser repetition rates on the quality of MALDI-TOF MS identification of 34 human Capnocytophaga strains (including C. gingivalis, C. granulosa, C. haemolytica, C. leadbetteri, C. ochracea, C. sputigena, and Capnocytophaga genospecies AHN8471) was examined. The identification rate did not show a significant difference (P = 0.05) between the two incubation times, except that C. haemolytica needed a longer incubation time to be recognized at the genus level. The reproducibility of spotting between two examiners was ensured by following the manufacturer's instructions. At the species level, formic acid extraction improved the identification of species with limited representation in the database, such as C. haemolytica and C. granulosa. The storage of plates for one week decreased the identification scores. No significant difference (P = 0.39) was observed between the 60 Hz and 120 Hz laser repetition rates for identifying Capnocytophaga species to the genus or species level. In conclusion, the MALDI TOF MS offers a reliable Capnocytophaga identification after following the universal protocol, while the formic acid extraction is restricted to species with a limited number of strains in the database.

Antagonistic Effects of Lactic Acid Bacteria Isolated from Ethiopian Traditional Fermented Foods and Beverages Against Foodborne Pathogens.

Lactic acid bacteria (LAB) found in Ethiopian traditional fermented foods and beverages have potential antagonistic effects against foodborne pathogens due to their capacity to produce various antimicrobial metabolites. This study evaluated the antagonistic activity of LAB isolated from Ethiopian traditional fermented foods and beverages against foodborne pathogens and characterized their antimicrobial substances. A total of 180 traditional fermented foods and beverages were collected, and the antagonistic activities of LAB were evaluated against selected foodborne pathogens. The effects of pH, temperature, enzymes, and food additives on the antagonistic effects of cell-free supernatant produced by LAB were investigated. LAB identification and characterization were conducted using an integrated phenotypic approach and MALDI TOF MS spectrum analysis, and data were analyzed using one-way ANOVA and Tukey post hoc analysis. A total of 956 LAB were isolated, of which seventeen (17 LAB) isolates of Pediococcus pentosaceus (Pc. pentosaceus (n = 7)), Pediococcus acidilactici (Pc. acidilactici (n = 2)), Enterococcus faecium (Ec. faecium (n = 6)), and Lactococcus lactis (Lc. lactis (n = 2)) were screened for antagonistic activity based on their ability to produce bacteriocins, probiotic activity, and preservative potential. Pc. pentosaceus JULABB16, Pc. pentosaceus JULABB01, and Ec. faecium JULABBr39 showed strong antagonistic activity against all pathogens, with mean inhibition zone diameters ranging from 23.50 to 35.50mm. Lc. lactis, Pc. pentosaceus, Pc. acidilactici, and Ec. faecium produced bioactive metabolites that were sensitive to proteolytic enzymes and capable of withstanding high temperatures (80-100°C) and acid concentrations (pH 2-10). The CFS produced by Lc. lactis, Pc. pentosaceus, Pc. acidilactici, and Ec. faecium showed the most impending antagonistic activity against all pathogens. The bioactive substances produced by LAB isolates had promising effects against food spoilage and pathogenic bacteria, making them potential natural food preservatives.

Proteome and Peptidome Changes and Zn Concentration in Chicken after In Ovo Stimulation with a Multi-Strain Probiotic and Zn-Gly Chelate: Preliminary Research.

The aim of the study was to determine differences in the proteome and peptidome and zinc concentrations in the serum and tissues of chickens supplemented with a multi-strain probiotic and/or zinc glycine chelate in ovo. A total of 1400 fertilized broiler eggs (Ross × Ross 708) were divided into four groups: a control and experimental groups injected with a multi-strain probiotic, with zinc glycine chelate, and with the multi-strain probiotic and zinc glycine chelate. The proteome and peptidome were analyzed using SDS-PAGE and MALDI-TOF MS, and the zinc concentration was determined by flame atomic absorption spectrometry. We showed that in ovo supplementation with zinc glycine chelate increased the Zn concentration in the serum and yolk sac at 12 h post-hatch. The results of SDS-PAGE and western blot confirmed the presence of Cu/Zn SOD in the liver and in the small and large intestines at 12 h and at 7 days after hatching in all groups. Analysis of the MALDI-TOF MS spectra of chicken tissues showed in all experimental groups the expression of proteins and peptides that regulate immune response, metabolic processes, growth, development, and reproduction.

Enhancing saliva diagnostics: The impact of amylase depletion on MALDI-ToF MS profiles as applied to COVID-19

IntroductionHuman saliva contains a wealth of proteins that can be monitored for disease diagnosis and progression. Saliva, which is easy to collect, has been extensively studied for the diagnosis of numerous systemic and infectious diseases. However, the presence of amylase, the most abundant protein in saliva, can obscure the detection of low-abundance proteins by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-ToF MS), thus reducing its diagnostic utility. ObjectivesIn this study, we used a device to deplete salivary amylase from water-gargle samples by affinity adsorption. Following depletion, saliva proteome profiling was performed using MALDI-ToF MS on gargle samples from individuals confirmed to have COVID-19 based on nasopharyngeal (NP) swab reverse transcription quantitative polymerase chain reaction (RT-qPCR). ResultsThe depletion of amylase led to increased signal intensities of various peaks and the detection of previously unobserved peaks in the MALDI-ToF MS spectra. The overall specificity and sensitivity after amylase depletion were 100% and 85.17%, respectively, for detecting COVID-19. ConclusionThis simple, rapid, and inexpensive technique for depleting salivary amylase can reveal spectral diversity in saliva using MALDI-ToF MS, expose low-abundance proteins, and assist in establishing novel biomarkers for diseases.

A new fusion strategy for rapid strain differentiation based on MALDI-TOF MS and Raman spectra.

Typing of bacterial subspecies is urgently needed for the diagnosis and efficient treatment during disease outbreaks. Physicochemical spectroscopy can provide a rapid analysis but its identification accuracy is still far from satisfactory. Herein, a novel feature-extractor-based fusion-assisted machine learning strategy has been developed for high accuracy and rapid strain differentiation using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and Raman spectroscopy. Based on this fusion approach, rapid and reliable identification and analysis can be performed within 24 hours. Validation on a panel of important pathogens comprising Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii showed that the identification accuracies of k-nearest neighbors (KNNs), support vector machines (SVMs) and artificial neural networks (ANNs) were 100%. In particular, when benchmarked against a MALDI-TOF MS spectral dataset, the new approach improved the identification accuracy of Acinetobacter baumannii from 87.67% to 100%. This work demonstrates the effectiveness of combining MALDI-TOF MS and Raman spectroscopy fusion data in pathogenic bacterial subtyping.

Morphological, molecular, and MALDI-TOF MS identification of bed bugs and associated Wolbachia species from Cameroon

After vanishing from the public eye for more than 50 years, bed bugs have resurged to become one of the most widely discussed and heavily researched insect pests in the world. This study presents the basic information of infestations of tropical bed bugs, Cimex hemipterus (Hemiptera: Cimicidae), in Cameroon. A total of 248 immature stage and adult bed bug specimens were collected from households and a travel agency in Yaoundé and Douala, Cameroon. The ability of MALDI-TOF MS to identify bed bugs was tested using heads for adults and cephalothoraxes for immature stages. Microorganism screening was performed by qPCR and confirmed by regular PCR and sequencing. Based on morphometrical criteria, four stages of immature bed bugs are represented. Of the 248 bed bug specimens morphologically identified as Cimex hemipterus, 246 (77 males, 65 females and 104 immature specimens) were submitted to MALDI-TOF MS analysis. Of the 222 adults and immature specimens tested, 122 (59.9 %) produced good quality MALDI-TOF MS spectra (35 adults and 87 immature specimens). Blind testing allowed species level identification of 98.21 % of adult and immature C. hemipterus. Among the bacteria tested, only Wolbachia DNA was found in 12/246 (4.8 %) bed bugs. More surveys in the country are warranted to assess the true level of bed bug infestations, in order to take appropriate action for their control.

Rapid AMR prediction in Pseudomonas aeruginosa combining MALDI–TOF MS with DNN model

Abstract Background Pseudomonas aeruginosa is a significant clinical pathogen that poses a substantial threat due to its extensive drug resistance. The rapid and precise identification of this resistance is crucial for effective clinical treatment. Although matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) has been used for antibiotic susceptibility differentiation of some bacteria in recent years, the genetic diversity of P. aeruginosa complicates population analysis. Rapid identification of antimicrobial resistance (AMR) in P. aeruginosa based on a large amount of MALDI–TOF-MS data has not yet been reported. In this study, we employed publicly available datasets for P. aeruginosa, which contain data on bacterial resistance and MALDI–TOF-MS spectra. We introduced a deep neural network model, synergized with a strategic sampling approach (SMOTEENN) to construct a predictive framework for AMR of three widely used antibiotics. Results The framework achieved area under the curve values of 90%, 85%, and 77% for Tobramycin, Cefepime, and Meropenem, respectively, surpassing conventional classifiers. Notably, random forest algorithm was used to assess the significance of features and post-hoc analysis was conducted on the top 10 features using Cohen’s d. This analysis revealed moderate effect sizes (d = 0.5–0.8) in Tobramycin and Cefepime models. Finally, putative AMR biomarkers were identified in this study. Conclusions This work presented an AMR prediction tool specifically designed for P. aeruginosa, which offers a hopeful pathway for clinical decision-making.

Exploring Streptococcus pneumoniae capsular typing through MALDI-TOF mass spectrometry and machine-learning algorithms in Argentina: Identifying prevalent NON PCV13 serotypes alongside PCV13 serotypes

IntroductionLaboratory surveillance of Streptococcus pneumoniae serotypes plays a crucial role in effectively implementing vaccines to prevent invasive pneumococcal diseases. The conventional method of serotyping, known as the Quellung reaction, is both time-consuming and expensive. However, the emergence of MALDI-TOF MS technology has revolutionized microbiology laboratories by enabling rapid and cost-effective serotyping based on protein profiles. ObjectivesIn this study, we aimed to investigate the viability of utilizing MALDI-TOF MS technology as an adjunctive and screening method for capsular typing of Streptococcus pneumoniae. Our approach involved developing classification models based on MALDI-TOF MS to discern between Streptococcus pneumoniae strains originating from PCV13 (13-valent pneumococcal conjugate vaccine) and NON PCV13 isolates. MethodsFirstly, we established a comprehensive spectral database comprising isolates of serotypes present in the PCV13 vaccine, along with the top 10 most prevalent NON PCV13 serotypes based on local epidemiological data. This database served as a foundation for developing unsupervised models utilizing MALDI-TOF MS spectra, which enabled us to identify inherent patterns and relationships within the data. Our analysis involved a dataset comprising 215 new isolates collected from nationwide surveillance in Argentina. Our approach involved developing classification models based on MALDI-TOF MS to discern between Streptococcus pneumoniae strains originating from PCV13 (13-valent pneumococcal conjugate vaccine) and NON PCV13 isolates. ResultsAlthough our findings revealed suboptimal performance in serotype classification, they provide valuable insights into the potential of machine learning algorithms in this context. The sensitivity of the models ranged from 0.41 to 0.46, indicating their ability to detect certain serotypes. The observed specificity consistently remained at 0.60, suggesting a moderate level of accuracy in identifying non-vaccine serotypes. These results highlight the need for further refinement and optimization of the algorithms to enhance their discriminative power and predictive accuracy in serotype identification.By addressing the limitations identified in this study, such as exploring alternative feature selection techniques or optimizing algorithm parameters, we can unlock the full potential of machine learning in robust and reliable serotype classification of S. pneumoniae. Our work not only provides a comprehensive evaluation of multiple machine learning models but also emphasizes the importance of considering their strengths and limitations. ConclusionOverall, our study contributes to the growing body of research on utilizing MALDI-TOF MS and machine learning algorithms for serotype identification purposes.

Rapid Discrimination of Pseudomonas aeruginosa ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning

The goal of this study was to evaluate matrix-assisted laser desorption ionization–iime of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) as diagnostic alternatives to DNA-based methods for the detection of Pseudomonas aeruginosa sequence type (ST) 175 isolates involved in a hospital outbreak. For this purpose, 27 P. aeruginosa isolates from an outbreak detected in the Hematology department of our hospital were analyzed by the above-mentioned methodologies. Previously, these isolates had been characterized by pulse-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS). Besides, eight P. aeruginosa isolates were analyzed as unrelated controls. MALDI-TOF MS spectra were acquired by transferring several colonies onto the MALDI target and covering them with 1 µl of formic acid 100% and 1 µl of α-ciano-3,4-hidroxicinamic acid matrix. For the analysis with FTIR-S, colonies were resuspended in 70% ethanol and sterile water according to the manufacturer’s instructions. Spectra from both methodologies were analyzed using Clover Biosoft Software, which allowed data modeling using different algorithms and validation of the classifying models. Three outbreak-specific biomarkers were found at 5,169, 6,915, and 7,236 m/z in MALDI-TOF MS spectra. Classification models based on these three biomarkers showed the same discrimination power displayed by PFGE. Besides, K-nearest neighbor algorithm allowed the discrimination of the same clusters provided by WGS and the validation of this model achieved 97.0% correct classification. On the other hand, FTIR-S showed a discrimination power similar to PFGE and reached correct discrimination of the different STs analyzed. In conclusion, the combination of both technologies evaluated, paired with machine learning tools, may represent a powerful tool for real-time monitoring of high-risk clones and isolates involved in nosocomial outbreaks.

Solution-processable orange-red thermally activated delayed fluorescence hyper-structured molecular emitters containing diphenylamine substituted carbazole host units

Realizing solution-processable orange-red thermally activated delayed fluorescence (TADF) emitters is still challenging owing to the severe aggregation-caused quenching. In this study, N3,N3,N6,N6-tetraphenyl-9H-carbazole-3,6-diamine (DDPACz) with high triplet energy and good hole transport ability was chosen as host unit. And 1,8-naphthimide-9,9-dimethyl-9,10-dihydroacridine (NDMAC) was used as red TADF unit. Then, these two functional units were linked to the inactive calix[4]resorcinarene (CRA) core to construct a series of hyper-structured molecules (HSMs), CRA-NDMAC(x)-DDPACz(8-x) (x = 0, 1, 2, 4, 6, 7 or 8). 1H NMR, MALDI-TOF MS and FT-IR spectra indicated that HSMs were successfully synthesized. UV–Vis, PL and CV results demonstrated that these HSMs inherited the photophysical and electrochemical properties of functional units. Spin-coating CRA-NDMAC(x)-DDPACz(8-x) (x = 1, 2, 4, 6, 7 or 8) as light-emitting layer, all the non-doped solution processed organic light-emitting diodes (OLEDs) showed orange-red emission. Among them, CRA-NDMAC(1)-DDPACz(7)-based device showed the highest maximum external quantum efficiency (EQEmax) of 0.94% with the emission peak at 613 nm. Furthermore, bright orange emission at a central wavelength of 592 nm with EQEmax of 9.73% and luminance of 5428 cd/m2 was achieved in the doped OLED based on CRA-NDMAC(7)-DDPACz(1) using CBP as host. These results indicate that hyper-structured molecules could be a potential strategy for the construction of orange-red TADF emitters.

Utility of MALDI-TOF MS for determination of species identity and blood meal sources of primary malaria vectors on the Kenyan coast

Background: Protein analysis using matrix-assisted laser desorption/ionization time-of-flight mass-spectrometry (MALDI-TOF MS) represents a promising tool for entomological surveillance. In this study we tested the discriminative power of this tool for measuring species and blood meal source of main Afrotropical malaria vectors on the Kenyan coast. Methods: Mosquito collections were conducted along the coastal region of Kenya. MALDI-TOF MS spectra were obtained from each individual mosquito’s cephalothorax as well as the abdomens of blood-engorged mosquitoes. The same mosquitoes were also processed using gold standard tests: polymerase chain reaction (PCR) for species identification and enzyme linked immunosorbent assay (ELISA) for blood meal source identification. Results: Of the 2,332 mosquitoes subjected to MALDI-TOF MS, 85% (1,971/2,332) were considered for database creation and validation. There was an overall accuracy of 97.5% in the identification of members of the An. gambiae (An. gambiae, 100%; An. arabiensis, 91.9%; An. merus, 97.5%; and An. quadriannulatus, 90.2%) and An. funestus (An. funestus, 94.2%; An. rivulorum, 99.4%; and An. leesoni, 94.1%) complexes. Furthermore, MALDI-TOF MS also provided accurate (94.5% accuracy) identification of blood host sources across all mosquito species. Conclusions: This study provides further evidence of the discriminative power of MALDI-TOF MS to identify sibling species and blood meal source of Afrotropical malaria vectors, further supporting its utility in entomological surveillance. The low cost per sample (&lt;0.2USD) and high throughput nature of the method represents a cost-effective alternative to molecular methods and could enable programs to increase the number of samples analysed and therefore improve the data generated from surveillance activities.

Changes in the Protein Profile in Staphylococcal Strains from Patients Infected with the SARS-CoV-2 Virus.

Staphylococcus aureus strains are particularly often isolated from patients with SARS-CoV-2 infection. The aim of the current research was to determine whether the SARS-CoV-2 virus infection affects the protein profile of S. aureus. Bacteria were isolated from the forty swabs collected from the patients in the hospitals of the Pomeranian region. MALDI-TOF MS spectra were obtained using a Microflex LT instrument. Twenty-nine peaks were identified. The peak (2,430) is described here for the first time and was unique for the isolates from patients infected with the SARS-CoV-2 virus. These results support the hypothesis of bacterial adaptation to the conditions caused by viral infection.

Detection of Arthropod-Borne Bacteria and Assessment of MALDI-TOF MS for the Identification of Field-Collected Immature Bed Bugs from Mauritania.

Human infestations by bed bugs have upsurged globally in recent decades, including in African countries, where recent reports pointed out an increase in infestation. Sympatric dwelling has been described for two species of bed bug parasitizing humans: Cimex hemipterus (the tropical bed bug) and C. lectularius. Identification of these two species is based on morphological characteristics, and gene sequencing, and may also rely on Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS). The present work aimed to assess whether MALDI-TOF MS was applicable for species level identification of immature stages of Cimex. Arthropods were collected in domestic settings in Nouakchott, Mauritania. Identification used morphological keys and MALDI-TOF MS identification was assessed for immature stages. Quantitative PCR and sequencing assays were used to detect arthropod-associated bacteria in each specimen. A total of 92 arthropods were collected, all morphologically identified as C. hemipterus (32 males, 14 females and 45 immature stages). A total of 35/45 specimens produced good quality MALDI-TOF MS spectra. Analysis allowed species level identification of all immature C. hemipterus after their spectra were entered into our in-house MALDI-TOF MS arthropod spectra database. Molecular screening allowed detection of Wolbachia DNA in each specimen. These results suggested that MALDI-TOF MS is a reliable tool for species level identification of Cimex specimens, including immature specimens. Future studies should assess this approach on larger panels of immature specimens for different Cimex species and focus on the precise staging of their different immature developmental stages.

Preferred β-lactone synthesis can explain high rate of false-negative results in the detection of OXA-48-like carbapenemases

The resistance to carbapenems is usually mediated by enzymes hydrolyzing β-lactam ring. Recently, an alternative way of the modification of the antibiotic, a β-lactone formation by OXA-48-like enzymes, in some carbapenems was identified. We focused our study on a deep analysis of OXA-48-like-producing Enterobacterales, especially strains showing poor hydrolytic activity. In this study, well characterized 74 isolates of Enterobacterales resistant to carbapenems were used. Carbapenemase activity was determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), liquid chromatography/mass spectrometry (LC–MS), Carba-NP test and modified Carbapenem Inactivation Method (mCIM). As meropenem-derived β-lactone possesses the same molecular weight as native meropenem (MW 383.46 g/mol), β-lactonization cannot be directly detected by MALDI-TOF MS. In the spectra, however, the peaks of m/z = 340.5 and 362.5 representing decarboxylated β-lactone and its sodium adduct were detected in 25 out of 35 OXA-48-like producers. In the rest 10 isolates, decarboxylated hydrolytic product (m/z = 358.5) and its sodium adduct (m/z = 380.5) have been detected. The peak of m/z = 362.5 was detected in 3 strains co-producing OXA-48-like and NDM-1 carbapenemases. The respective signal was identified in no strain producing class A or class B carbapenemase alone showing its specificity for OXA-48-like carbapenemases. Using LC–MS, we were able to identify meropenem-derived β-lactone directly according to the different retention time. All strains with a predominant β-lactone production showed negative results of Carba NP test. In this study, we have demonstrated that the strains producing OXA-48-like carbapenemases showing false-negative results using Carba NP test and MALDI-TOF MS preferentially produced meropenem-derived β-lactone. We also identified β-lactone-specific peak in MALDI-TOF MS spectra and demonstrated the ability of LC–MS to detect meropenem-derived β-lactone.

Symbiotic Husbandry of Chickens and Pigs Does Not Increase Pathogen Transmission Risk.

A symbiotic or mixed animal husbandry (e.g., pigs and chickens) is considered to have a positive effect for animal welfare and sustainable agriculture. On the other hand, a risk of infection and transmission of microorganisms, especially of zoonotic pathogens, between animal species may potentially occur and thus might increase the risk of foodborne illnesses for consumers. To prove these assumptions, two groups of animals and their environmental (soil) samples were investigated in this study. Animals were kept in a free-range system. In the first group, pigs and chickens were reared together (pasture 1), while the other group contained only pigs (pasture 2). During a one-year study, fecal swab samples of 240 pigs and 120 chickens, as well as 120 ground samples, were investigated for the presence of Campylobacter spp., Salmonella spp. and E. coli. Altogether, 438 E. coli and 201 Campylobacter spp. strains were isolated and identified by MALDI-TOF MS. Salmonella spp. was not isolated from any of the sample types. The prevalences of Campylobacter coli and C. jejuni in pigs were 26.7% and 3.3% in pasture 1 and 30.0% and 6.7% in pasture 2, while the prevalences of C. coli and C. jejuni in chickens from pasture 1 were 9.2% and 78.3%, respectively. No correlation between the rearing type (mixed vs. pigs alone) and the prevalence of Campylobacter spp. was observed. All swab samples were positive for E. coli, while the average prevalences in soil samples were 78.3% and 51.7% in pasture 1 and 2, respectively. Results of similarity analysis of the MALDI-TOF MS spectra (for C. coli, C. jejuni and E. coli) and FT-IR spectra (for E. coli) of the same bacterial species showed no recognizable correlations, no matter if strains were isolated from chickens, pig or soil samples or isolated at different sampling periods. The results of the study indicate that the symbiotic husbandry of pigs and chickens neither results in an increased risk of a transmission of Campylobacter spp. or E. coli, nor in a risk of bacterial alteration, as shown by MALDI-TOF MS and FT-IR spectra. In conclusion, the benefits of keeping pigs and chickens together are not diminished by the possible transmission of pathogens.

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.

Distribution of Achromobacter Species in 12 French Cystic Fibrosis Centers in 2020 by a Retrospective MALDI-TOF MS Spectrum Analysis.

Achromobacter spp. are nonfermenting Gram-negative bacilli mainly studied among cystic fibrosis (CF) patients. The identification of the 19 species within the genus is time-consuming (nrdA-sequencing), thus data concerning the distribution of the species are limited to specific studies. Recently, we built a database using MALDI-TOF mass spectrometry (MS) (Bruker) that allows rapid and accurate species identification and detection of the multiresistant epidemic clones: A. xylosoxidans ST137 spreading among CF patients in various French and Belgium centers, and A. ruhlandii DES in Denmark. Here, we first assessed whether species identification could be achieved with our database solely by analysis of MS spectra without availability of isolates. Then, we conducted a multicentric study describing the distribution of Achromobacter species and of the clone ST137 among French CF centers. We collected and analyzed with our local database the spectra of Achromobacter isolates from 193 patients (528 samples) from 12 centers during 2020. In total, our approach enabled to conclude for 502/528 samples (95.1%), corresponding to 181 patients. Eleven species were detected, only five being involved in chronic colonization, A. xylosoxidans (86.4%), A. insuavis (9.1%), A. mucicolens (2.3%), A. marplatensis (1.1%) and A. genogroup 3 (1.1%). This study confirmed the high prevalence of A. xylosoxidans in chronic colonizations and the circulation of the clone A. xylosoxidans ST137 in France: four patients in two centers. The present study is the first to report the distribution of Achromobacter species from CF patients samples using retrospective MALDI-TOF/MS data. This easy approach could enable future large-scale epidemiological studies.