MALDI-TOF MS method for differentiation of methicillin-sensitive and methicillin-resistant Staphylococcus aureus using (E)-Propyl α-cyano-4-Hydroxyl cinnamylate

In silico design of magnetic, polymeric synthetic receptor targeting clumping factor A, for the specific capture and detection of Staphylococcus aureus

Rapid diagnosis of Staphylococcus aureus (S. aureus) is critical for both therapy and infection control programs. Currently available rapid bacterial detection methods such as polymerase chain reaction (PCR) requires expensive equipment and trained personnel, whereas, enzyme-linked immunosorbent assay (ELISA) requires antibodies and thus, suffer from limitations such as limited reagent stability. Herein, we used stable, cost-effective alternates to the antibodies known as synthetic antibodies i.e., molecularly imprinted polymers (MIPs). In this study, polymeric synthetic receptor commonly known as MIPs were layered onto magnetic nanoparticles, specifically designed for the detection of S. aureus through the binding interaction with its surface biomarker- clumping factor A (ClfA). This approach offers a low limit of detection (LOD) of 102 colony-forming units per mL (CFU/mL) and a wide linear detection range (103 to 108 CFU/mL) for S. aureus. Briefly, ClfA gene was cloned, expressed and protein was purified using Ni-NTA affinity chromatography and anion-exchange chromatography. Magnetic nanoparticles were initially synthesized and coated with silica, followed by introduction of aldehyde groups for immobilization through imine bonding. ClfA was then immobilized onto the functionalized nanoparticles, serving as a template for MIP synthesis. To determine a monomer combination with high binding capacity and specificity for ClfA, docking studies were performed using Autodock 4.2. The polymerization process employed selected monomer combination, yielding MIP tailored to recognize ClfA. The binding properties of the MIP were extensively investigated, demonstrating specificity and selectivity for ClfA over non-specific proteins. Furthermore, the clinical utility of the MIP was assessed by examining its binding with ClfA in serum samples. The present study contributes to the advancement of specific and efficient tools for the S. aureus diagnostics, based on a virulence biomarker, ClfA, emphasizing the potential applications of molecularly imprinted magnetic nanoparticles for the detection of microorganisms and their virulence.

Detection of methicillin resistance of Staphylococcus aureus in vitreous humor using MALDI-TOF MS and Fc-MBL@Fe3O4 enrichment

Endophthalmitis is a serious infectious eye disease that causes permanent vision loss. This study developed a method for rapid identification and drug resistance analysis of pathogens in vitreous humor. After short-term rapid culture, 30 Staphylococcus aureus isolates were enriched and purified from the vitreous humor using Fc-MBL@Fe3O4, and then identified by MALDI-TOF MS. The bacterial solution was adjusted to 106 CFU/mL and mixed with CAMHB containing cefoxitin (4 µg/mL) at the same volume. After culture, it was enriched by Fc-MBL@Fe3O4 and identified by MALDI-TOF MS. MRSA was judged according to whether the bacteria could successfully be identified. The enrichment efficiency of Fc-MBL@Fe3O4 for S. aureus in CAMHB was 88.1%. The detection rate of S. aureus reached 100% after 8 h of vitreous humor culture. The best test performance was achieved with Fc-MBL@Fe3O4 enrichment after a 3 h incubation. At this time point, 96.7% validity, 100% sensitivity, and 100% specificity were achieved. Thus, the identification and drug resistance analysis of S. aureus (51-110 CFU) in vitreous humor was completed within 11 h. This study provides a new method for rapid clinical diagnosis of endophthalmitis and precise treatment with antibiotics.

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development

BACKGROUND

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current state of the art in using machine learning for the detection and classification of antimicrobial resistance from MALDI-TOF mass spectrometry data.

METHODS

A comprehensive review of the literature on machine learning applications for antimicrobial resistance detection was performed using databases such as Web Of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed. Only original articles in English were included. Studies applying machine learning without using MALDI-TOF mass spectra were excluded.

RESULTS

Forty studies met the inclusion criteria. Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli were the most frequently cited bacteria. The antibiotics resistance most studied corresponds to methicillin for S. aureus, cephalosporins for K. pneumoniae, and aminoglycosides for E. coli. Random forest, support vector machine and logistic regression were the most employed algorithms to predict antimicrobial resistance. Additionally, seven studies reported using artificial neural networks. Most studies reported metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) above 0.80.

CONCLUSIONS

Our study indicates that random forest, support vector machine, and logistic regression are effective for predicting antimicrobial resistance using MALDI-TOF MS data. Recent studies also highlight the potential of deep learning techniques in this area. We recommend further exploration of deep learning and multi-label supervised learning for comprehensive antibiotic resistance prediction in clinical practice.

Rapid Classification and Differentiation of Sepsis-Related Pathogens Using FT-IR Spectroscopy

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

Identification of Radix Bupleuri From Different Geographic Origins Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Support Vector Machine Algorithm

BACKGROUND

The geographic origin of Radix bupleuri is an important factor affecting its efficacy, which needs to be effectively identified.

OBJECTIVE

The goal is to enrich and develop the intelligent recognition technology applicable to the identification of the origin of traditional Chinese medicine.

METHOD

This article establishes an identification method of Radix bupleuri geographic origin based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and support vector machine (SVM) algorithm. The Euclidean distance method is used to measure the similarity between Radix bupleuri samples, and the quality control chart method is applied to quantitatively describe their quality fluctuation.

RESULTS

It is found that the samples from the same origin are relatively similar and mainly fluctuate within the control limit, but the fluctuation range is large, and it is impossible to distinguish the samples from different origins. The SVM algorithm can effectively eliminate the impact of intensity fluctuations and huge data dimensions by combining the normalization of MALDI-TOF MS data and the dimensionality reduction of principal components, and finally achieve efficient identification of the origin of Radix bupleuri, with an average recognition rate of 98.5%.

CONCLUSIONS

This newly established approach for identification of the geographic origin of Radix bupleuri has been realized, and it has the advantages of objectivity and intelligence, which can be used as a reference for other medical and food-related research.

HIGHLIGHTS

A new intelligent recognition method of medicinal material origin based on MALDI-TOF MS and SVM has been established.

MALDI-TOF MS Is an Effective Technique To Classify Specific Microbiota

MALDI-TOF MS is well-recognized for single microbial identification and widely used in research and clinical fields due to its specificity, speed of analysis, and low cost of consumables. Multiple commercial platforms have been developed and approved by the U.S. Food and Drug Administration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has been used for microbial identification. However, microbes can present as a specific microbiota, and detection and classification remain a challenge. Here, we constructed several specific microbiotas and tried to classify them using MALDI-TOF MS. Different concentrations of nine bacterial strains (belonging to eight genera) constituted 20 specific microbiotas. Using MALDI-TOF MS, the overlap spectrum of each microbiota (MS spectra of nine bacterial strains with component percentages) could be classified by hierarchical clustering analysis (HCA). However, the real MS spectrum of a specific microbiota was different than that of the overlap spectrum of component bacteria. The MS spectra of specific microbiota showed excellent repeatability and were easier to classify by HCA, with an accuracy close to 90%. These results indicate that the widely used MALDI-TOF MS identification method for individual bacteria can be expanded to classification of microbiota. IMPORTANCE MALDI-TOF MS can be used to classify specific model microbiota. The actual MS spectrum of the model microbiota was not a simple superposition of every single bacterium in a certain proportion but had a specific spectral fingerprint. The specificity of this fingerprint can enhance the accuracy of microbiota classification.

The Effectiveness of MALDI ToF Mass Spectrometry in Identification of <i>Francisella tularensis</i> Strains

The aim of the study was to evaluate the effectiveness of MALDI‑ToF mass spectrometry in the identification of collection and newly isolated strains of tularemia pathogen using the database “Protein profiles of mass spectra of microorganisms belonging to I–II pathogenicity groups for the MALDI Biotyper software”.

Materials and methods. We investigated 142 strains of Francisella tularensis, including 59 collection strains and 83 newly isolated ones. Bacteriological, molecular-genetic and proteomic research methods were used to identify them. The acquisition of mass spectra, analysis, generation and expansion of reference libraries were performed on a mass analyzer “Microflex LT” using FlexControl v. 3.3, FlexAnalysis v. 3.3, and MALDI Biotyper 3.0 software packages. The cluster analysis was performed using the BioNumerics 7.6 software.

Results and discussion. The possibility of identifying tularemia pathogen has been assessed using the extended database for MALDI Biotyper 3.0 “Protein profiles of mass spectra of microorganisms belonging to I–II pathogenicity groups for the MALDI Biotyper software”. During identification to the species level, the significance of mass spectrometry results for collection strains and newly isolated ones was 91.5 % and 97.6 %, respectively. In determining the genus appurtenance, the reliability of identification was 100 %. Thus, the MALDI‑ToF mass spectrometry method allows for accurate species and genus identification of F. tularensis strains. Based on the cluster analysis of 66 F. tularensis strains in BioNumerics 7.6 software using «Pearson correlation» and the UPGMA algorithm, the possibility of subspecies differentiation has been evaluated. Due to the similarity of protein profiles of F. tularensis strains, a clear differentiation into subspecies could not be achieved. It is necessary to use other options for sample preparation, new generation devices with higher resolution, as well as apply additional approaches and analysis tools for successful subspecific differentiation.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.