MALDI-TOF MS Applications to the Detection of Antifungal Resistance: State of the Art and Future Perspectives

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.

Resistance Profile, Terbinafine Resistance Screening and MALDI-TOF MS Identification of the Emerging Pathogen Trichophyton indotineae

The emerging pathogen Trichophyton indotineae, often resistant to terbinafine (TRB), is known to cause severe dermatophytoses such as tinea corporis and tinea cruris. In order to achieve successful treatment for these infections, insight in the resistance profile of T. indotineae strains and rapid, reliable identification is necessary. In this research, a screening medium was tested on T. indotineae strains (n = 20) as an indication tool of TRB resistance. The obtained results were confirmed by antifungal susceptibility testing (AST) for TRB following the in vitro broth microdilution reference method. Additionally, AST was performed for eight other antifungal drugs: fluconazole, itraconazole, voriconazole, ketoconazole, griseofulvin, ciclopirox olamine, naftifine and amorolfine. Forty-five percent of the strains were confirmed to be resistant to terbinafine. The TRB resistant strains showed elevated minimal inhibitory concentration values for naftifine and amorolfine as well. DNA sequencing of the squalene epoxidase-encoding gene showed that TRB resistance was a consequence of missense point mutations in this gene, which led to amino acid substitutions F397L or L393F. MALDI-TOF MS was used as a quick, accurate identification tool for T. indotineae, as it can be challenging to distinguish it from closely related species such as Trichophyton mentagrophytes or Trichophyton interdigitale using morphological characteristics. While MALDI-TOF MS could reliably identify ≥ 95% of the T. indotineae strains (depending on the spectral library), it could not be used to successfully distinguish TRB susceptible from TRB resistant strains.

Combating antimicrobial resistance: the silent war

Once hailed as miraculous solutions, antibiotics no longer hold that status. The excessive use of antibiotics across human healthcare, agriculture, and animal husbandry has given rise to a broad array of multidrug-resistant (MDR) pathogens, posing formidable treatment challenges. Antimicrobial resistance (AMR) has evolved into a pressing global health crisis, linked to elevated mortality rates in the modern medical era. Additionally, the absence of effective antibiotics introduces substantial risks to medical and surgical procedures. The dwindling interest of pharmaceutical industries in developing new antibiotics against MDR pathogens has aggravated the scarcity issue, resulting in an exceedingly limited pipeline of new antibiotics. Given these circumstances, the imperative to devise novel strategies to combat perilous MDR pathogens has become paramount. Contemporary research has unveiled several promising avenues for addressing this challenge. The article provides a comprehensive overview of these innovative therapeutic approaches, highlighting their mechanisms of action, benefits, and drawbacks.

Closing the Gap in Proteomic Identification of Histoplasma capsulatum: A Case Report and Review of Literature

Histoplasmosis is a globally distributed systemic infection caused by the dimorphic fungus Histoplasma capsulatum (H. capsulatum). This fungus can cause a wide spectrum of clinical manifestations, and the diagnosis of progressive disseminated histoplasmosis is often a challenge for clinicians. Although microscopy and culture remain the gold standard diagnostic tests for Histoplasma identification, matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) has emerged as a method of microbial identification suitable for the confirmation of dimorphic fungi. However, to our knowledge, there are no entries for H. capsulatum spectra in most commercial databases. In this review, we describe the case of disseminated histoplasmosis in a patient living with HIV admitted to our university hospital that we failed to identify by the MALDI-TOF method due to the limited reference spectrum of the instrument database. Furthermore, we highlight the utility of molecular approaches, such as conventional polymerase chain reaction (PCR) and DNA sequencing, as alternative confirmatory tests to MALDI-TOF technology for identifying H. capsulatum from positive cultures. An overview of current evidence and limitations of MALDI-TOF-based characterization of H. capsulatum is also presented.

Detecting antibiotic resistance genes in anthropogenically impacted streams and rivers

Streams and rivers are widely impacted by human activities ranging from hydrological modifications to point and nonpoint pollution. Among the pollutants that enter lotic ecosystems are pharmaceuticals and personal care products, including antibiotics, that may play a role in the occurrence of antibiotic resistance genes (ARGs). Oftentimes, ARGs are detected based on culturing of bacteria or by using quantitative polymerase chain reaction; the limitations of these methods create barriers to our understanding. Use of more exhaustive methods, such as metagenomics, may overcome some of these barriers. The public health and ecological impacts of ARGs may be profound but are largely understudied. Antibiotic resistance is a growing concern for public health.

Rapid identification of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS: A meta-analysis

Invasive infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are associated with high mortality and morbidity. The sooner the pathogen is determined, the better it is beneficial to patient. However, routine laboratory inspections are time-consuming and laborious. A thorough research was conducted in PubMed and Web of Science (until June 2021) to identify studies evaluating the accuracy of MRSA identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). STATA 15.0 software was used to analyze the pooled results of sensitivity, specificity, and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) were utilized to show the overall performance of MALDI-TOF MS. Fifteen studies involving 2471 isolates were included in this study after the final selection in this meta-analysis. Using the random effects model forest plot to summarize the overall statistics, the sensitivity of MALDI-TOF MS for identifying MRSA was 92% (95% CI: 81%-97%), and the specificity was 97% (95% CI: 89%-99%). In the SROC curve, the AUC reached 0.99 (95% CI: 97%-99%). Deeks' test showed no significant publication bias in this meta-analysis. Compared with clinical reference methods, MALDI-TOF MS identification of MRSA shows a higher degree of sensitivity and specificity.

How MALDI-TOF Mass Spectrometry Technology Contributes to Microbial Infection Control in Healthcare Settings

Healthcare settings have been utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) since 2010. MALDI-TOF MS has various benefits over the conventional method of biochemical identification, including ease of use, speed, accuracy, and low cost. This approach can solve many of the obstacles to identifying bacteria, fungi and viruses. As technology advanced, more and more databases kept track of spectra, allowing species with similar morphological, genotypic, and biochemical traits to be identified. Using MALDI-TOF MS for identification has become more accurate and quicker due to advances in sample preparation and database enrichment. Rapid sample detection and colony identification using MALDI-TOF MS have produced promising results. A key application of MALDI-TOF MS is quickly identifying highly virulent and drug-resistant diseases. Here, we present a review of the scientific literature assessing the effectiveness of MALDI-TOF MS for locating clinically relevant pathogenic bacteria, fungi, and viruses. MALDI-TOF MS is a useful strategy for locating clinical pathogens, however, it also has some drawbacks. A small number of spectra in the database and inherent similarities among organisms can make it difficult to distinguish between different species, which can result in misidentifications. The majority of the time additional testing may correct these problems, which happen very seldom. In conclusion, infectious illness diagnosis and clinical care are being revolutionized by the use of MALDI-TOF MS in the clinical microbiology laboratory.

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.

MALDI-TOF Mass Spectroscopy Applications in Clinical Microbiology

There is a range of proteomics methods to spot and analyze bacterial protein contents such as liquid chromatography-mass spectrometry (LC-MS), two-dimensional gel electrophoresis, and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS), which give comprehensive information about the microorganisms that may be helpful within the diagnosis and coverings of infections. Microorganism identification by mass spectrometry is predicted on identifying a characteristic spectrum of every species so matched with an outsized database within the instrument. MALDI-TOF MS is one of the diagnostic methods, which is a straightforward, quick, and precise technique, and is employed in microbial diagnostic laboratories these days and may replace other diagnostic methods. This method identifies various microorganisms such as bacteria, fungi, parasites, and viruses, which supply comprehensive information. One of the MALDI-TOF MS's crucial applications is bacteriology, which helps identify bacterial species, identify toxins, and study bacterial antibiotic resistance. By knowing these cases, we will act more effectively against bacterial infections.

Recent Development of Rapid Antimicrobial Susceptibility Testing Methods through Metabolic Profiling of Bacteria

Due to the inappropriate use and overuse of antibiotics, the emergence and spread of antibiotic-resistant bacteria are increasing and have become a major threat to human health. A key factor in the treatment of bacterial infections and slowing down the emergence of antibiotic resistance is to perform antimicrobial susceptibility testing (AST) of infecting bacteria rapidly to prescribe appropriate drugs and reduce the use of broad-spectrum antibiotics. Current phenotypic AST methods based on the detection of bacterial growth are generally reliable but are too slow. There is an urgent need for new methods that can perform AST rapidly. Bacterial metabolism is a fast process, as bacterial cells double about every 20 to 30 min for fast-growing species. Moreover, bacterial metabolism has shown to be related to drug resistance, so a comparison of differences in microbial metabolic processes in the presence or absence of antimicrobials provides an alternative approach to traditional culture for faster AST. In this review, we summarize recent developments in rapid AST methods through metabolic profiling of bacteria under antibiotic treatment.

Rapid identification of fungi from respiratory samples by Bruker Biotyper matrix-assisted laser desorption/ionisation time-of-flight using ID-FUNGI plates

Identification of moulds is crucial for the clinical management of patients. The goal of this study was to evaluate the new ID-FUNGI plate (IDFP) for the identification of moulds by MALDI Biotyper. IDFP was compared with Sabouraud with gentamicin and chloramphenicol plate (SAB) for the identification of 80 moulds from respiratory samples and eight reference strains. With the direct transfer method, species identification rose from 6% with SAB to 68% with IDFP using score cut-off 2 and from 20 to 75% using cut-off 1.7 (p < 0.001). Our study highlights that the new IDFP improves mycological diagnostic and workflow in laboratories.

Rapid Antifungal Susceptibility Testing of Yeasts and Molds by MALDI-TOF MS: A Systematic Review and Meta-Analysis

Due to the growing burden of fungal infections and a recent rise in antifungal resistance, antifungal susceptibility testing (AFST) is of increasing importance. The common methods of AFST have turnaround times of 24 to 48 h, and the available rapid methods are limited by applicability, cost-efficiency or accuracy. Given the urgency of adequate antifungal treatment in invasive mycoses, the need for the rapid and reliable detection of resistance is evident. In this systematic review and meta-analysis, we evaluated the diagnostic accuracy of AFST based on matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). Twelve studies were reviewed, and data for the comparative analysis of their accuracy and methodology were systematically extracted. Compared to broth dilution as the gold standard, MALDI-TOF MS-based AFST reached a pooled sensitivity and specificity of 91% (95% Confidence Interval [CI], 84% to 96%) and 95% (95% CI, 90% to 98%), respectively. A comparative analysis showed that the sensitivity was higher for the semi-quantitative matrix-assisted laser desorption ionization Biotyper antibiotic susceptibility test rapid assay (MBT ASTRA) technique (96%) than for the correlate composite index (CCI) approach (85%), which is based on spectrum changes. Turnaround times below eight hours reached better diagnostic values than longer incubation periods, qualifying MALDI-TOF MS-based AFST as a rapid and accurate method for the detection of antifungal resistance.

Recent Advances and Novel Approaches in Laboratory-Based Diagnostic Mycology

What was once just culture and microscopy the field of diagnostic mycology has significantly advanced in recent years and continues to incorporate novel assays and strategies to meet the changes in clinical demand. The emergence of widespread resistance to antifungal therapy has led to the development of a range of molecular tests that target mutations associated with phenotypic resistance, to complement classical susceptibility testing and initial applications of next-generation sequencing are being described. Lateral flow assays provide rapid results, with simplicity allowing the test to be performed outside specialist centres, potentially as point-of-care tests. Mycology has responded positively to an ever-diversifying patient population by rapidly identifying risk and developing diagnostic strategies to improve patient management. Nowadays, the diagnostic repertoire of the mycology laboratory employs classical, molecular and serological tests and should be keen to embrace diagnostic advancements that can improve diagnosis in this notoriously difficult field.

Johnson E, Littlechild J, Winlove CP, Vollmer F, Gow NAR. Biosensors and Diagnostics for Fungal Detection

Early detection is critical to the successful treatment of life-threatening infections caused by fungal pathogens, as late diagnosis of systemic infection almost always equates with a poor prognosis. The field of fungal diagnostics has some tests that are relatively simple, rapid to perform and are potentially suitable at the point of care. However, there are also more complex high-technology methodologies that offer new opportunities regarding the scale and precision of fungal diagnosis, but may be more limited in their portability and affordability. Future developments in this field are increasingly incorporating new technologies provided by the use of new format biosensors. This overview provides a critical review of current fungal diagnostics and the development of new biophysical technologies that are being applied for selective new sensitive fungal biosensors to augment traditional diagnostic methodologies.

Detection of Antibiotic-Resistance by MALDI-TOF Mass Spectrometry: An Expanding Area

Several MALDI-TOF MS-based methods have been proposed for rapid detection of antimicrobial resistance. The most widely studied methods include assessment of β-lactamase activity by visualizing the hydrolysis of the β-lactam ring, detection of biomarkers responsible for or correlated with drug-resistance/non-susceptibility, and the comparison of proteomic profiles of bacteria incubated with or without antimicrobial drugs. Antimicrobial-resistance to a number of antibiotics belonging to different classes has been successfully tested by MALDI-TOF MS in a variety of clinically relevant bacterial species including members of Enterobacteriaceae family, non-fermenting Gram-negative bacteria, Gram-positive cocci, anaerobic bacteria and mycobacteria, opening this field to further clinically important developments. Early detection of drug-resistance by MALDI-TOF MS can be particularly helpful for clinicians to streamline the antibiotic therapy for a better outcome of patients with systemic infection, in all cases where a prompt and effective antibiotic treatment is essential to preserve organ function and/or patient survival.

Diagnostic Mycology: Xtreme Challenges

Developing any diagnostic assay that receives United States Food and Drug Administration (FDA) approval can be a slow and difficult process. FDA-approved assays for fungal diagnosis are generally few in number and are focused mainly on diagnosing candidiasis, which is caused by several species of Candida, in addition to a limited number of systemic mycotic agents. While all microbial diagnostic assays face challenges before they are FDA approved and reach the market, there are a number of challenges to fungal diagnostic assay development that have been difficult hurdles to overcome. These hurdles include template preparation, fungal morphology, how many fungi should be identified in a single assay (scope), taxonomy and nomenclature, discriminating colonizers from invasive infection, combining identification with antifungal susceptibility, and navigating the administrative hurdles required to integrate an assay into a clinical laboratory. Some of these challenges are easier to overcome than others, but all seem to be particularly difficult for fungal diagnostic assays.

A new culture-based method for rapid identification of microorganisms in polymicrobial blood cultures by MALDI-TOF MS

Background

The application of matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (MS) to microbial identification has allowed the development of rapid methods for identification of microorganisms directly in positive, blood cultures (BCs). These methods can yield accurate results for monomicrobial BCs, but often fail to identify multiple microorganisms in polymicrobial BCs. The present study was aimed at establishing a rapid and simple method for identification of bacteria and yeast in polymicrobial BCs from patients with bloodstream infection.

Results

The rapid method herein proposed is based on short-term culture in liquid media allowing selective growth of microorganisms recovered from polymicrobial BCs, followed by rapid identification by MALDI-TOF MS. To evaluate the accuracy of this method, 56 polymicrobial BCs were comparatively analyzed with the rapid and routine methods. The results showed concordant identification for both microbial species in 43/50 (86%) BCs containing two different microorganisms, and for two microbial species in six BCs containing more than two different species. Overall, 102/119 (85.7%) microorganisms were concordantly identified by the rapid and routine methods using a cut-off value of 1.700 for valid identification. The mean time to identification after BC positivity was about 4.2 h for streptococci/enterococci, 8.7 h for staphylococci, 11.1 h for Gram-negative bacteria, and 14.4 h for yeast, allowing a significant time saving compared to the routine method.

Conclusions

The proposed method allowed rapid and reliable microbial identification in polymicrobial BCs, and could provide clinicians with timely, useful information to streamline empirical antimicrobial therapy in critically ill patients.

MALDI-TOF Mass Spectrometry and Specific Biomarkers: Potential New Key for Swift Identification of Antimicrobial Resistance in Foodborne Pathogens

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is today the reference method for direct identification of microorganisms in diagnostic laboratories, as it is notably time- and cost-efficient. In the context of increasing cases of enteric diseases with emerging multi-drug resistance patterns, there is an urgent need to adopt an efficient workflow to characterize antimicrobial resistance (AMR). Current approaches, such as antibiograms, are time-consuming and directly impact the "patient-physician" workflow. Through this mini-review, we summarize how the detection of specific patterns by MALDI-TOF MS, as well as bioinformatics, become more and more essential in research, and how these approaches will help diagnostics in the future. Along the same lines, the idea to export more precise biomarker identification steps by MALDI-TOF(/TOF) MS data towards AMR identification pipelines is discussed. The study also critically points out that there is currently still a lack of research data and knowledge on different foodborne pathogens as well as several antibiotics families such as macrolides and quinolones, and many questions are still remaining. Finally, the innovative combination of whole-genome sequencing and MALDI-TOF MS could be soon the future for diagnosis of antimicrobial resistance in foodborne pathogens.