MALDI-TOF Mass Spectrometry Technology as a Tool for the Rapid Diagnosis of Antimicrobial Resistance in Bacteria

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.

Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.

Application of MALDI-TOF MS to Identify and Detect Antimicrobial-Resistant Streptococcus uberis Associated with Bovine Mastitis

Streptococcus uberis is a common bovine mastitis pathogen in dairy cattle. The rapid identification and characterization of antimicrobial resistance (AMR) in S. uberis plays an important role in its diagnosis, treatment, and prevention. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to identify S. uberis and screen for potential AMR biomarkers. Streptococcus uberis strains (n = 220) associated with bovine mastitis in northern Thailand were identified using the conventional microbiological methods and compared with the results obtained from MALDI-TOF MS. Streptococcus uberis isolates were also examined for antimicrobial susceptibility using a microdilution method. Principal component analysis (PCA) and the Mann-Whitney U test were used to analyze the MALDI-TOF mass spectrum of S. uberis and determine the difference between antimicrobial-resistant and -susceptible strains. Using MALDI-TOF MS, 73.18% (161/220) of the sampled isolates were identified as S. uberis, which conformed to the identifications obtained using conventional microbiological methods and PCR. Using PCR, antimicrobial-resistant strains could not be distinguished from antimicrobial-susceptible strains for all three antimicrobial agents, i.e., tetracycline, ceftiofur, and erythromycin. The detection of spectral peaks at 7531.20 m/z and 6804.74 m/z was statistically different between tetracycline- and erythromycin-resistant and susceptible strains, respectively. This study demonstrates a proteomic approach for the diagnosis of bovine mastitis and potentially for the surveillance of AMR among bovine mastitis pathogens.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Use of Matrix-Assisted and Laser Desorption/Ionization Time-of-Flight Technology in the Identification of Aeromonas Strains Isolated from Retail Sushi and Sashimi

The genus Aeromonas includes well-known pathogenic species for fishes and humans that are widely distributed in the aquatic environment and foods. Nowadays, one of the main issues related to wild Aeromonas isolates is their identification at the species level, which is challenging using classical microbiological and biomolecular methods. This study aims to test MALDI-TOF MS technology in the identification of Aeromonas strains isolated from n. 60 retail sushi and sashimi boxes using an implemented version of the SARAMIS software V4.12. A total of 43 certified Aeromonas strains were used to implement the SARAMIS database by importing the spectra obtained from their identification. The original SARAMIS version (V4.12) failed to recognize 62.79% of the certified strains, while the herein-implemented version (V4.12plus) allowed the identification of all the certified strains at least to the genus level with a match of no less than 85%. Regarding the sushi and sashimi samples, Aeromonas spp. was detected in n. 18 (30%) boxes. A total of 127 colonies were identified at the species level, with A. salmonicida detected as the most prevalent species, followed by A. bestiarum and A. caviae. Based on the results of the present study, we could speculate that MALDI-TOF technology could be a useful tool both for the food industry to monitor product contamination and for clinical purposes to make diagnoses effectively and quickly.

Yeast Diversity in Honey and Pollen Samples from Stingless Bees in the State of Bahia, Brazil: Use of the MALDI-TOF MS/Genbank Proteomic Technique

(1) Background: The identification of microorganisms includes traditional biochemical methods, molecular biology methods evaluating the conserved regions of rRNA, and the molecular biology of proteins (proteomics), such as MALDI-TOF MS mass spectrometry. This work aimed to identify the biodiversity of yeasts associated with stingless bee species' honey and pollen, Melipona scutellaris, Nannotrigona testaceicornes, and Tetragonisca angustula, from the region of São Gonçalo dos Campos-Bahia (BA) state, Brazil. (2) Methods: Cellular proteins were extracted from 2837 microbial isolates (pollen and honey) and identified via MALDI-TOF MS. The identified yeast species were also compared to the mass spectra of taxonomically well-characterized reference strains, available from the National Center of Biotechnology Information (NCBI) database. (3) Results: Nine yeast species were identified: Candida maltosa, Candida norvegica, Kazachstania telluris, Schizosaccharomyces pombe, Scheffersomyces insectosus, Meyerozyma guilliermondii, Brettanomyces bruxellensis, Kazachstania exigua, and Starmerella lactis-condensi. Nannotrigona testaceicornes pollen had the highest number of yeast colonies. The yeasts Brettanomyces bruxellensis and Kazachstania telluris showed high populations in the samples of Nannotrigona testaceicornes and Melipona scutellaris, respectively. This work shows that there is some sharing of the same species of yeast between honey and pollen from the same beehive. (4) Conclusions: A total of 71.84% of the identified species present a high level of confidence at the species level. Eight yeast species (Candida maltosa, Candida norvegica, Kazachstania telluris, Schizosaccharomyces pombe, Scheffersomyces insectosus, Meyerozyma guilliermondii, Kazachstania exigua, and Starmerella lactis-condensi) were found for the first time in the samples that the authors inspected. This contributes to the construction of new knowledge about the diversity of yeasts associated with stingless bee products, as well as to the possibility of the biotechnological application of some yeast species.

Biofilm antimicrobial susceptibility testing: where are we and where could we be going?

Our knowledge about the fundamental aspects of biofilm biology, including the mechanisms behind the reduced antimicrobial susceptibility of biofilms, has increased drastically over the last decades. However, this knowledge has so far not been translated into major changes in clinical practice. While the biofilm concept is increasingly on the radar of clinical microbiologists, physicians, and healthcare professionals in general, the standardized tools to study biofilms in the clinical microbiology laboratory are still lacking; one area in which this is particularly obvious is that of antimicrobial susceptibility testing (AST). It is generally accepted that the biofilm lifestyle has a tremendous impact on antibiotic susceptibility, yet AST is typically still carried out with planktonic cells. On top of that, the microenvironment at the site of infection is an important driver for microbial physiology and hence susceptibility; but this is poorly reflected in current AST methods. The goal of this review is to provide an overview of the state of the art concerning biofilm AST and highlight the knowledge gaps in this area. Subsequently, potential ways to improve biofilm-based AST will be discussed. Finally, bottlenecks currently preventing the use of biofilm AST in clinical practice, as well as the steps needed to get past these bottlenecks, will be discussed.

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

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

Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information

MOTIVATION

Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.

RESULTS

We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models.

AVAILABILITY AND IMPLEMENTATION

The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.

Clinical Impact of Rapid Bacterial Microbiological Identification with the MALDI-TOF MS

Rapid microbiological reports to clinicians are related to improved clinical outcomes. We conducted a 3-year quasi-experimental design, specifically a pretest-posttest single group design in a university medical center, to evaluate the clinical impact of rapid microbiological identification information using MALDI-TOF MS on optimizing antibiotic prescription. A total of 363 consecutive hospitalized patients with bacterial infections were evaluated comparing a historical control group (CG) (n = 183), in which the microbiological information (bacterial identification and antibiotic susceptibility) was reported jointly to the clinician between 18:00 h and 22:00 h of the same day and a prospective intervention group (IG) (n = 180); the bacterial identification information was informed to the clinician as soon as it was available between 12:00 h and 14:00 h and the antibiotic susceptibility between 18:00 h and 22:00 h). We observed, in favor of IG, a statistically significant decrease in the information time (11.44 h CG vs. 4.48 h IG (p < 0.01)) from the detection of bacterial growth in the culture medium to the communication of identification. Consequently, the therapeutic optimization was improved by introducing new antibiotics in the 10-24 h time window (p = 0.05) and conversion to oral route (p = 0.01). Additionally, we observed a non-statistically significant decrease in inpatient mortality (global, p = 0.15; infection-related, p = 0.21) without impact on hospital length of stay. In conclusion, the rapid communication of microbiological identification to clinicians reduced reporting time and was associated with early optimization of antibiotic prescribing without worsening clinical outcomes.

Antimicrobial Multidrug Resistance: Clinical Implications for Infection Management in Critically Ill Patients

The increasing incidence of antimicrobial resistance (AMR) worldwide represents a serious threat in the management of sepsis. Due to resistance to the most common antimicrobials prescribed, multidrug-resistant (MDR) pathogens have been associated with delays in adequate antimicrobial therapy leading to significant increases in mortality, along with prolonged hospital length of stay (LOS) and increases in healthcare costs. In response to MDR infections and the delay of microbiological results, broad-spectrum antibiotics are frequently used in empirical antimicrobial therapy. This can contribute to the overuse and misuse of antibiotics, further promoting the development of resistance. Multiple measures have been suggested to combat AMR. This review will focus on describing the epidemiology and trends concerning MDR pathogens. Additionally, it will explore the crucial aspects of identifying patients susceptible to MDR infections and optimizing antimicrobial drug dosing, which are both pivotal considerations in the fight against AMR. Expert commentary: The increasing AMR in ICUs worldwide makes the empirical antibiotic therapy challenging in septic patients. An AMR surveillance program together with improvements in MDR identification based on patient risk stratification and molecular rapid diagnostic tools may further help tailoring antimicrobial therapies and avoid unnecessary broad-spectrum antibiotics. Continuous infusions of antibiotics, therapeutic drug monitoring (TDM)-based dosing regimens and combination therapy may contribute to optimizing antimicrobial therapy and limiting the emergence of resistance.

Effect of environmental factors on expression of staphylococcal enterotoxin genes

Staphylococcal enterotoxins (SEs) secreted by Staphylococcus aureus (S. aureus) can cause foodborne disease, nausea, vomiting and diarrhea, and even death. Regulation of SE expression is related to accessory gene regulators (Agr). It is important to reveal which environmental factors influence regulation of SE expression to prevent SE food poisoning outbreak. Hence, natural environmental factors which may have an impact on SE expression were selected, such as temperature, food types, strains, and competing strains. Seven strains of S. aureus carrying different SE genes were collected from the Chinese Academy of Inspection and Quarantine (CAIQ) strain bank for study. Strains were cultured with different conditions. Temperature was 8 °C, 22 °C, and 30 °C. Food type was milk powder and nutrient broth. Competing strains were Vibrio parahaemolyticus (V. parahaemolyticus), Escherichia coli (E. coli), and Bacillus cereus (B. cereus). The expression culture solution was pretreated by centrifugation, then determined by using SDS-PAGE, and distinguished SEs apart from each other by HPLC-ESI-TOF. There are 168 samples collected from SE expression culture; the result of SDS-PAGE suggests 23 samples were positive for SEs, and the other 145 samples were negative for SEs. The result of HPLC-ESI-TOF suggests that SEs with similar molecular weight can be distinguished in terms of m/z. The most important factor contributing to regulate expression of SEs was estimated by logistic regressive analysis. The result shows that McFadden R2 is 0.213; p value is 0.000 (p < 0.05); this result illustrates that the model is valid and meaningful. Strains, food types, temperature, and competing strands can explain the 21% change in SE expression. Temperature (z = 3.029, p = 0.002 < 0.01), strains (z =  - 3.132, p = 0.002 < 0.01), and food types (z =  - 2.415, p = 0.016 < 0.05) have significant impact on SE expression, and the competing strains (z = 1.230, p = 0.219 > 0.05) have no impact on the SE expression. More important impact on SE expression was estimated by OR value; the result shows that strength of temperature influencing on SE expression is bigger than strains and food types in terms of values of OR, temperature (OR = 2.862), strains (OR = 0.641), and food types (OR = 0.561); consequently, temperature is a key factor for stimulating SE expression and had high expression at 30 °C. Therefore, food easily contaminated with S. aureus should be monitored intensively at early and late summer, when proper temperature for expressing SEs may result in S. aureus food poisoning prevalence.

Intracellular Dynamics-Resolved Label-Free Scattering Reveals Real-Time Metabolism of Single Bacteria

Nanoscopic investigation of bacterial cells is essential to reveal their physiological status, impacting all cellular functions. Currently, this requires labeled probes or targeted staining procedures. Herein, we report a new bacterial feature, intracellular dynamics-resolved Rayleigh scattering (IDRS), that visualizes spatiotemporal cytoplasmic transitions in unlabeled bacteria and characterizes their real-time physiological status in 10 s. From single-bacterium IDRS signals, we discovered unique spatial patterns and their multiple transitions in Gram-negative and Gram-positive bacteria. The magnitude of IDRS signal variation highly correlated with the metabolic status of bacteria, differentiating persistent subpopulations. This is also the first report demonstrating distinct real-time metabolic conditions of unlabeled drug-resistant bacteria that are exposed to different doses of antibiotics. Our strategy opens up a way to simultaneously trace in situ metabolic and antibiotic resistance statuses, which can be applied in single-cell level control of bacterial metabolism and efficacy with a heterogeneous nature.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical 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.

MALDI-TOF MS Indirect Beta-Lactamase Detection in Ampicillin-Resistant Haemophilus influenzae

Rapid identification of beta-lactamase-producing strains of Haemophilus influenzae plays key role in diagnostics in clinical microbiology. Therefore, the aim of this study was the rapid determination of beta-lactamase's presence in H. influenzae isolates via indirect detection of degradation ampicillin products using MALDI-TOF MS. H. influenzae isolates were subjected to antibiotic resistance testing using disk diffusion and MIC methodologies. Beta-lactamase activity was tested using MALDI-TOF MS, and results were compared to spectral analysis of alkaline hydrolysis. Resistant and susceptible strains of H. influenzae were distinguished, and strains with a high MIC level were identified as beta-lactamase-producing. Results indicate that MALDI-TOF mass spectrometry is also suitable for the rapid identification of beta-lactamase-producing H. influenzae. This observation and confirmation can accelerate identification of beta-lactamase strains of H. influenzae in clinical microbiology, which can have an impact on health in general.

Achieving Ultrasensitive Chromogenic Probes for Rapid, Direct Detection of Carbapenemase-Producing Bacteria in Sputum

Carbapenemase-producing bacteria (CPB) stand as the most dangerous "superbugs" in the clinic. Rapid point-of-care (POC) detection of CPB in clinical samples is key to timely and effective infection management. We herein report the first ultrasensitive chromogenic probe that allows direct POC detection of CPB in clinical sputum samples at a sample-to-result time of less than 15 min. This chromogenic probe is modularly designed by conjugating the carbapenem core with a benzene derivative bearing an electronegativity-tunable substituent. Unexpectedly high sensitivity was achieved simply by choosing strong electron-withdrawing substituents, such as -N⁺(CH₃)₃, without resorting to complex molecular design. Through integrating the probes with a portable paper chip, 24 out of 80 clinical sputum samples from sepsis patients with lung infections were quickly diagnosed as CPB-positive, exhibiting 100% clinical sensitivity and specificity. This low-cost paper chip assay can be readily performed on-site, breaking through the dilemma of rapid CPB detection, especially in resource-limited settings.

MALDI-TOF MS-Based Approaches for Direct Identification of Gram-Negative Bacteria and BlaKPC-Carrying Plasmid Detection from Blood Cultures: A Three-Year Single-Centre Study and Proposal of a Diagnostic Algorithm

The rapid identification of pathogens of bloodstream infections (BSIs) and the detection of antibiotic resistance markers are critically important for optimizing antibiotic therapy and infection control. The purpose of this study was to evaluate two approaches based on MALDI-TOF MS technology for direct identification of Gram-negative bacteria and automatic detection of Klebsiella pneumoniae carbapenemase (KPC) producers using the Bruker MBT Subtyping IVD Module in a large routine laboratory over a three-year period. MALDI-TOF MS analysis was performed directly from blood culture (BC) bottles following bacterial pellet recovery by Rapid MBT Sepsityper® Kit and on blood agar 4-h subcultures. Automated detection of blaKPC-carrying pKpQIL-plasmid by Bruker MBT Subtyping Module was evaluated in BCs tested positive to K. pneumoniae or E. coli. The results were compared with those obtained with conventional reference methods. Among the 2858 (93.4%) monomicrobial BCs, the overall species identification rates of the Rapid Sepsityper and the short-term subculture protocols were 84.5% (n = 2416) and 90.8% (n = 2595), respectively (p < 0.01). Excellent specificity for KPC-producers identification were observed for both MALDI-TOF MS protocols. The pKpQIL plasmid-related peak was detected in overall 91 of the 120 (75.8%) KPC-producing isolates. Notably, 14 out of the 17 (82.3%) K. pneumoniae isolates carrying blaKPC variants associated with ceftazidime/avibactam resistance and tested negative by the immunocromatography assay, were correctly identified as KPC-producers by MALDI-TOF MS. In conclusion, combination of both Rapid Sepsityper and short-term subculture protocols may represent an optimal solution to promptly identify more than 95% of Gram-negative bacteria causing BSIs. MALDI Biotyper® platform enabled a reliable and robust automated detection of KPC producers in parallel with species identification. However, integration of molecular or immunocromatographic assays are recommended according to local epidemiology.

Detection of carbapenemase-producing Enterobacterales by means of matrix-assisted laser desorption ionization time-of-flight mass spectrometry with ertapenem susceptibility-testing disks as source of carbapenem substrate

MALDI-TOF mass spectrometry has become widely used in clinical microbiology and has proved highly accurate for detection of carbapenemases in Gram-negative bacteria. However, the use of carbapenem-hydrolysis assays in routine diagnostics is hampered by the need for antibiotic substances and for making their fresh solutions each time an assay is conducted. Here, we evaluated the use of commercial antibiotic susceptibility-testing disks as source of ertapenem substrate in MALDI-TOF MS-based assay for detection of carbapenemase-producing Enterobacterales (CPE). The assay was validated on 48 CPE isolates of 8 different species expressing NDM-, VIM-, KPC- and OXA-48-type carbapenemases and exhibiting various levels of resistance to carbapenems (MIC range: 0.25- > 32 mg/l), as well as on 48 carbapenemase-non-producing isolates. The assay conditions were optimized as follows: 10-μl loopful of bacterial colonies was suspended in 150 μl 0.01 M Na-PBS buffer, pH 7.4, a 10 μg ertapenem susceptibility-testing disk was immersed in the suspension and incubated 3 h at 35°C, after which supernatant was obtained by centrifugation and applied on a target plate with alpha-cyano-4-hydroxycinnamic acid matrix. Mass spectra were analyzed between 440 and 560 m/z. Carbapenemase activity was detected in all tested CPE isolates by the appearance of m/z peaks corresponding to ertapenem hydrolysis products: [M_h + H]⁺:494.2, [M_h + Na]⁺:516.2, [M_h + 2Na]⁺:538.2, [M_h/d + H]⁺:450.2, [M_h/d + Na]⁺:472.2, and simultaneous decrease or loss of peaks of intact antibiotic: [M + H]⁺:476.2, [M + Na]⁺:498.1, [M + 2Na]⁺:520.1. No hydrolysis peaks or loss of intact ertapenem peaks were observed for carbapenemase-negative strains. We therefore report the development of a sensitive, specific and cost-effective MALDI-TOF MS-based assay for detection of CPE, which makes use of antibiotic disks readily available in most laboratories.

Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future

Clinical microbiology has possessed a marvellous past, an important present and a bright future. Western medicine modernization started with the discovery of bacterial pathogens, and from then, clinical bacteriology became a cornerstone of diagnostics. Today, clinical microbiology uses standard techniques including Gram stain morphology, in vitro culture, antigen and antibody assays, and molecular biology both to establish a diagnosis and monitor the progression of microbial infections. Clinical microbiology has played a critical role in pathogen detection and characterization for emerging infectious diseases as evidenced by the ongoing COVID-19 pandemic. Revolutionary changes are on the way in clinical microbiology with the application of “-omic” techniques, including transcriptomics and metabolomics, and optimization of clinical practice configurations to improve outcomes of patients with infectious diseases.

Recent Advances in the Use of Molecular Methods for the Diagnosis of Bacterial Infections

Infections caused by bacteria have a major impact on public health-related morbidity and mortality. Despite major advances in the prevention and treatment of bacterial infections, the latter continue to represent a significant economic and social burden worldwide. The WHO compiled a list of six highly virulent multidrug-resistant bacteria named ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) responsible for life-threatening diseases. Taken together with Clostridioides difficile, Escherichia coli, Campylobacter spp., (C. jejuni and C. coli), Legionella spp., Salmonella spp., and Neisseria gonorrhoeae, all of these microorganisms are the leading causes of nosocomial infections. The rapid and accurate detection of these pathogens is not only important for the early initiation of appropriate antibiotic therapy, but also for resolving outbreaks and minimizing subsequent antimicrobial resistance. The need for ever-improving molecular diagnostic techniques is also of fundamental importance for improving epidemiological surveillance of bacterial infections. In this review, we aim to discuss the recent advances on the use of molecular techniques based on genomic and proteomic approaches for the diagnosis of bacterial infections. The advantages and limitations of each of the techniques considered are also discussed.

Development and evaluation of a multiplex quantitative polymerase chain reaction assay for detecting bacteria associated with lower respiratory tract infection

OBJECTIVES

This study aimed to establish a multiplex quantitative polymerase chain reaction (MQ-PCR) assay for 12 bacterial pathogens found in lower respiratory tract infection (LRTI) and to evaluate its performance in a cohort of 211 patients with LRTI.

METHODS

The study was divided into two stages: a pilot study to establish the methodology and a clinical validation study to evaluate its performance. In the pilot study, we established the MQ-PCR and analyzed its performance regarding limits of detection, reproducibility, specificity, and efficiency. In the clinical validation study, we obtained 211 sputum and/or bronchoalveolar lavage fluid (BALF) samples and detected pathogens by MQ-PCR. The MQ-PCR time was 3 h from sample collection to complete pathogen detection.

RESULTS

The limit of detection was 1000 copies/ml, and the maximum efficiency was >95%. When cutoffs of ≥10⁵ copies/ml in sputum and ≥10⁴ copies/ml in BALF were applied, the sensitivity, specificity, and positive and negative predictive values of the MQ-PCR were 77% (95% confidence interval [CI] 67-88%), 94% (95% CI 93-95%), 25% (95% CI 19-31%), and 99% (95% CI 99-100%), respectively.

CONCLUSIONS

This study demonstrates that the new MQ-PCR assay is time-saving, more effective and sensitive, and brings us closer to mainstream adoption of quantitative molecular detection of bacteria.

Antimicrobial Susceptibility Testing: A Comprehensive Review of Currently Used Methods

Antimicrobial resistance (AMR) has emerged as a major threat to public health globally. Accurate and rapid detection of resistance to antimicrobial drugs, and subsequent appropriate antimicrobial treatment, combined with antimicrobial stewardship, are essential for controlling the emergence and spread of AMR. This article reviews common antimicrobial susceptibility testing (AST) methods and relevant issues concerning the advantages and disadvantages of each method. Although accurate, classic technologies used in clinical microbiology to profile antimicrobial susceptibility are time-consuming and relatively expensive. As a result, physicians often prescribe empirical antimicrobial therapies and broad-spectrum antibiotics. Although recently developed AST systems have shown advantages over traditional methods in terms of testing speed and the potential for providing a deeper insight into resistance mechanisms, extensive validation is required to translate these methodologies to clinical practice. With a continuous increase in antimicrobial resistance, additional efforts are needed to develop innovative, rapid, accurate, and portable diagnostic tools for AST. The wide implementation of novel devices would enable the identification of the optimal treatment approaches and the surveillance of antibiotic resistance in health, agriculture, and the environment, allowing monitoring and better tackling the emergence of AMR.

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

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

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

Rapid detection of KPC-producing Klebsiella pneumoniae in China based on MALDI-TOF MS

Klebsiella pneumoniae carbapenemase-producing K. pneumoniae (KPC-Kp) represent a serious threat to public health and their timely detection is essential for patient management and the prevention of nosocomial infections. Here, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to rapidly identify dominant KPC-Kp in China, by using an automated detection of a KPC-specific peak (at 4521 m/z) by a genetic algorithm using ClinProTools software. Whole-genome sequencing (WGS) was used to understand the genetic environment of the bla_KPC-2 gene. In this study, we analyzed 235 K. pneumoniae Chinese clinical isolates, of which 175 (93 KPC-positive isolates and 82 KPC-negative isolates) isolates were used to build a model to select a KPC-specific peak, and another 60 isolates for external validation. In addition, all the spectra were visually inspected by the FlexAnalysis software to evaluate the accuracy of the automated detection. The results showed a 4521 m/z peak found in all bla_KPC-2-positive isolates but absent in bla_KPC-2-negative isolates. Interestingly, all KPC-Kp belonged to ST11, the dominant clone in China. WGS analysis of a representative isolate showed that the genetic environment of KPC-2 was IS26-ISKpn27-bla_KPC-2-ΔISKpn6-Tn1721, similar to the KPC-2 genetic environment of ST11 KPC-Kp previously reported in China. Therefore, the 4521 m/z peak is closely related to ST11 KPC-Kp. In summary, we used MALDI-TOF MS to quickly detect KPC-Kp in the process of routine bacterial identification without increasing costs or requiring further knowledge, which has broad application prospects in drug resistance analysis and infection control.