One System for All: Is Mass Spectrometry a Future Alternative for Conventional Antibiotic Susceptibility Testing?

The Potential Clinical Applications of a Microfluidic Lab-on-a-Chip for the Identification and Antibiotic Susceptibility Testing of Enterococcus faecalis-Associated Endodontic Infections: A Systematic Review

This systematic review evaluated the potential clinical use of microfluidic lab-on-a-chip (LOC) technology in the identification and antibiotic susceptibility testing of E. faecalis in endodontic infections. The search methodology employed in this review adhered to the PRISMA guidelines. Multiple scientific databases, including PubMed/MEDLINE, SCOPUS, and SCIELO, were utilized, along with exploration of grey literature sources. Up to September 2023, these resources were searched using specific keywords and MeSH terms. An initial comprehensive search yielded 202 articles. Ultimately, this systematic review incorporated 12 studies. Out of these, seven aimed to identify E. faecalis, while the remaining five evaluated its susceptibility to different antibiotics. All studies observed that the newly developed microfluidic chip significantly reduces detection time compared to traditional methods. This enhanced speed is accompanied by a high degree of accuracy, efficiency, and sensitivity. Most research findings indicated that the entire process took anywhere from less than an hour to five hours. It is important to note that this approach bypasses the need for minimum inhibitory concentration measurements, as it does not rely on traditional methodologies. Microfluidic devices enable the rapid identification and accurate antimicrobial susceptibility testing of E. faecalis, which are crucial for timely diagnosis and treatment in endodontic infections.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

Fluorescent optotracers for bacterial and biofilm detection and diagnostics

Effective treatment of bacterial infections requires methods that accurately and quickly identify which antibiotic should be prescribed. This review describes recent research on the development of optotracing methodologies for bacterial and biofilm detection and diagnostics. Optotracers are small, chemically well-defined, anionic fluorescent tracer molecules that detect peptide- and carbohydrate-based biopolymers. This class of organic molecules (luminescent conjugated oligothiophenes) show unique electronic, electrochemical and optical properties originating from the conjugated structure of the compounds. The photophysical properties are further improved as donor-acceptor-donor (D-A-D)-type motifs are incorporated in the conjugated backbone. Optotracers bind their biopolymeric target molecules via electrostatic interactions. Binding alters the optical properties of these tracer molecules, shown as altered absorption and emission spectra, as well as ON-like switch of fluorescence. As the optotracer provides a defined spectral signature for each binding partner, a fingerprint is generated that can be used for identification of the target biopolymer. Alongside their use for in situ experimentation, optotracers have demonstrated excellent use in studies of a number of clinically relevant microbial pathogens. These methods will find widespread use across a variety of communities engaged in reducing the effect of antibiotic resistance. This includes basic researchers studying molecular resistance mechanisms, academia and pharma developing new antimicrobials targeting biofilm infections and tests to diagnose biofilm infections, as well as those developing antibiotic susceptibility tests for biofilm infections (biofilm-AST). By iterating between the microbial world and that of plants, development of the optotracing technology has become a prime example of successful cross-feeding across the boundaries of disciplines. As optotracers offers a capacity to redefine the way we work with polysaccharides in the microbial world as well as with plant biomass, the technology is providing novel outputs desperately needed for global impact of the threat of antimicrobial resistance as well as our strive for a circular bioeconomy.

Electrochemical Detection of Different Foodborne Bacteria for Point-of-Care Applications

Bacterial infections resulting from foodborne pathogenic bacteria cause millions of infections that greatly threaten human health and are one of the leading causes of mortality around the world. To counter this, the early, rapid, and accurate detection of bacterial infections is very important to address serious health issue concerns. We, therefore, present an electrochemical biosensor based on aptamers that selectively bind with the DNA of specific bacteria for the accurate and rapid detection of various foodborne bacteria for the selective determination of bacterial infection types. Different aptamers were synthesized and immobilized on Au electrodes for selective bindings of different types of bacterial DNA (Escherichia coli, Salmonella enterica, and Staphylococcus aureus) for the accurate detection and quantification of bacterial concentrations from 10¹ to 10⁷ CFU/mL without using any labeling methods. Under optimized conditions, the sensor showed a good response to the various concentrations of bacteria, and a robust calibration curve was obtained. The sensor could detect the bacterial concentration at meager quantities and possessed an LOD of 4.2 × 10¹, 6.1 × 10¹, and 4.4 × 10¹ CFU/mL for S. Typhimurium, E. Coli, and S. aureus, respectively, with a linear range from 10⁰ to 10⁴ CFU/mL for the total bacteria probe and 10⁰ to 10³ CFU/mL for individual probes, respectively. The proposed biosensor is simple and rapid and has shown a good response to bacterial DNA detections and thus can be applied in clinical applications and food safety monitoring.

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

The Development of Technology to Prevent, Diagnose, and Manage Antimicrobial Resistance in Healthcare-Associated Infections

There is a growing risk of antimicrobial resistance (AMR) having an adverse effect on the healthcare system, which results in higher healthcare costs, failed treatments and a higher death rate. A quick diagnostic test that can spot infections resistant to antibiotics is essential for antimicrobial stewardship so physicians and other healthcare professionals can begin treatment as soon as possible. Since the development of antibiotics in the last two decades, traditional, standard antimicrobial treatments have failed to treat healthcare-associated infections (HAIs). These results have led to the development of a variety of cutting-edge alternative methods to combat multidrug-resistant pathogens in healthcare settings. Here, we provide an overview of AMR as well as the technologies being developed to prevent, diagnose, and control healthcare-associated infections (HAIs). As a result of better cleaning and hygiene practices, resistance to bacteria can be reduced, and new, quick, and accurate instruments for diagnosing HAIs must be developed. In addition, we need to explore new therapeutic approaches to combat diseases caused by resistant bacteria. In conclusion, current infection control technologies will be crucial to managing multidrug-resistant infections effectively. As a result of vaccination, antibiotic usage will decrease and new resistance mechanisms will not develop.

Antibiotic resistance in plant growth promoting bacteria: A comprehensive review and future perspectives to mitigate potential gene invasion risks

Plant growth-promoting bacteria (PGPB) are endowed with several attributes that can be beneficial for host plants. They opened myriad doors toward green technology approach to reduce the use of chemical inputs, improve soil fertility, and promote plants’ health. However, many of these PGPB harbor antibiotic resistance genes (ARGs). Less attention has been given to multi-resistant bacterial bioinoculants which may transfer their ARGs to native soil microbial communities and other environmental reservoirs including animals, waters, and humans. Therefore, large-scale inoculation of crops by ARGs-harboring bacteria could worsen the evolution and dissemination of antibiotic resistance and aggravate the negative impacts on such ecosystem and ultimately public health. Their introduction into the soil could serve as ARGs invasion which may inter into the food chain. In this review, we underscore the antibiotic resistance of plant-associated bacteria, criticize the lack of consideration for this phenomenon in the screening and application processes, and provide some recommendations as well as a regulation framework relating to the development of bacteria-based biofertilizers to aid maximizing their value and applications in crop improvement while reducing the risks of ARGs invasion.

Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms

Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.

Using Targeted Liquid Chromatography-Tandem Mass Spectrometry to Rapidly Detect β-Lactam, Aminoglycoside, and Fluoroquinolone Resistance Mechanisms in Blood Cultures Growing E. coli or K. pneumoniae

New and rapid antimicrobial susceptibility/resistance testing methods are required for bacteria from positive blood cultures. In this study, a multiplex-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay was developed and validated for the detection of β-lactam, aminoglycoside, and fluoroquinolone resistance mechanisms in blood cultures growing Escherichia coli or Klebsiella pneumoniae complex. Selected targets were the β-lactamases SHV, TEM, OXA-1-like, CTX-M-1-like, CMY-2-like, chromosomal E. coli AmpC (cAmpC), OXA-48-like, NDM, VIM, and KPC; the aminoglycoside-modifying enzymes AAC(3)-Ia, AAC(3)-II, AAC(3)-IV, AAC(3)-VI, AAC(6′)-Ib, ANT(2′′)-I, and APH(3′)-VI; the 16S-RMTases ArmA, RmtB, RmtC, and RmtF; the quinolone resistance mechanisms QnrA, QnrB, AAC(6′)-Ib-cr; the wildtype quinolone resistance determining region of GyrA; and the E. coli porins OmpC and OmpF. The developed assay was evaluated using 100 prospectively collected positive blood cultures, and 148 negative blood culture samples spiked with isolates previously collected from blood cultures or isolates carrying less prevalent resistance mechanisms. The time to result was approximately 3 h. LC-MS/MS results were compared with whole-genome sequencing and antimicrobial susceptibility testing results. Overall, there was a high agreement between LC-MS/MS results and whole-genome sequencing results. In addition, the majority of susceptible and non-susceptible phenotypes were correctly predicted based on LC-MS/MS results. Exceptions were the predictions for ciprofloxacin and amoxicillin/clavulanic acid that matched with the phenotype in 85.9 and 63.7% of the isolates, respectively. Targeted LC-MS/MS based on parallel reaction monitoring can be applied for the rapid and accurate detection of various resistance mechanisms in blood cultures growing E. coli or K. pneumoniae complex.

Prediction of Mycobacterium tuberculosis drug resistance by nucleotide MALDI-TOF-MS

OBJECTIVES

To evaluate the performance of nucleotide matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in predicting the drug resistance of Mycobacterium tuberculosis.

METHODS

A total of 115 rifampin-resistant and 53 rifampin-susceptible tuberculosis (TB) clinical isolates were randomly selected from TB strains stored at -80℃ in the clinical laboratory of Shanghai Pulmonary Hospital. Nucleotide MALDI-TOF-MS was performed to predict the drug resistance using phenotypic susceptibility as the gold standard.

RESULTS

The overall assay sensitivities and specificities of nucleotide MALDI-TOF-MS were 92.2% and 100.0% for rifampin, 90.9% and 98.6% for isoniazid, 71.4% and 81.2% for ethambutol, 85.1% and 93.1% for streptomycin, 94.0% and 100.0% for amikacin, 77.8% and 99.3% for kanamycin, 75.0% and 93.3% for ofloxacin, and 75.0% and 93.3% for moxifloxacin. The concordances between nucleotide MALDI-TOF-MS antimicrobial susceptibility testing (AST) and phenotypic AST were 94.6% (rifampin), 90.1% (isoniazid), 79.2% (ethambutol), 89.9% (streptomycin), 99.4% (amikacin), 97.0% (kanamycin), 88.1% (ofloxacin), and 88.0% (moxifloxacin).

CONCLUSION

Nucleotide MALDI-TOF-MS could be a promising tool for rapid detection of Mycobacterium tuberculosis drug sensitivity to rifampin, isoniazid, ethambutol, streptomycin, amikacin, kanamycin, ofloxacin, and moxifloxacin.

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.

Recent Advances in Rapid Antimicrobial Susceptibility Testing

Background

Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.

Content

Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.

Summary

We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.

Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics

Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently from species identification. Sequencing-based approaches are possible alternatives for AMR detection, although the analysis of proteins should be superior to gene or transcript sequencing for phenotype prediction as the actual resistance to antibiotics is almost exclusively mediated by proteins. In this proof-of-concept study, we present an unbiased proteomics workflow for detecting both bacterial species and AMR-related proteins in the absence of secondary antibiotic cultivation within <4 h from a primary culture. The workflow was designed to meet the needs in clinical microbiology. It introduces a new data analysis concept for bacterial proteomics, and a software (rawDIAtect) for the prediction and reporting of AMR from peptide identifications. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms, which resulted in a sensitivity of 98% and a specificity of 100%.

Paving the Way to Overcome Antifungal Drug Resistance: Current Practices and Novel Developments for Rapid and Reliable Antifungal Susceptibility Testing

The past year has established the link between the COVID-19 pandemic and the global spread of severe fungal infections; thus, underscoring the critical need for rapid and realizable fungal disease diagnostics. While in recent years, health authorities, such as the Centers for Disease Control and Prevention, have reported the alarming emergence and spread of drug-resistant pathogenic fungi and warned against the devastating consequences, progress in the diagnosis and treatment of fungal infections is limited. Early diagnosis and patient-tailored therapy are established to be key in reducing morbidity and mortality associated with fungal (and cofungal) infections. As such, antifungal susceptibility testing (AFST) is crucial in revealing susceptibility or resistance of these pathogens and initiating correct antifungal therapy. Today, gold standard AFST methods require several days for completion, and thus this much delayed time for answer limits their clinical application. This review focuses on the advancements made in developing novel AFST techniques and discusses their implications in the context of the practiced clinical workflow. The aim of this work is to highlight the advantages and drawbacks of currently available methods and identify the main gaps hindering their progress toward clinical application.

Rapid and Accurate Detection of Aminoglycoside-Modifying Enzymes and 16S rRNA Methyltransferases by Targeted Liquid Chromatography-Tandem Mass Spectrometry

New and rapid diagnostic methods are needed for the detection of antimicrobial resistance to aid in curbing drug-resistant infections. Targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a method that could serve this purpose, as it can detect specific peptides of antimicrobial resistance mechanisms with high accuracy. In the current study, we developed an accurate and rapid targeted LC-MS/MS assay based on parallel reaction monitoring for detection of the most prevalent aminoglycoside-modifying enzymes and 16S rRNA methyltransferases in Escherichia coli and Klebsiella pneumoniae that confer resistance to aminoglycosides. Specific tryptic peptides needed for detection were selected and validated for AAC(3)-Ia, AAC(3)-II, AAC(3)-IV, AAC(3)-VI, AAC(6')-Ib, AAC(6')-Ib-cr, ANT(2″)-I, APH(3')-VI, ArmA, RmtB, RmtC, and RmtF. In total, 205 isolates containing different aminoglycoside resistance mechanisms that consisted mostly of E. coli and K. pneumoniae were selected for assay development and evaluation. Mass spectrometry results were automatically analyzed and were compared to whole-genome sequencing results. Of the 2,460 isolate and resistance mechanism combinations tested, 2,416 combinations matched. Discrepancies were further analyzed by repeating LC-MS/MS analysis and performing additional PCRs. Mass spectrometry results were also used to predict resistance and susceptibility to gentamicin, tobramycin, and amikacin in only the E. coli and K. pneumoniae isolates (n = 191). The category interpretations were correctly predicted for gentamicin in 97.4% of the isolates, for tobramycin in 97.4% of the isolates, and for amikacin in 82.7% of the isolates. Targeted LC-MS/MS can be applied for accurate and rapid detection of aminoglycoside resistance mechanisms.

Exploring antimicrobial resistance to beta-lactams, aminoglycosides and fluoroquinolones in E. coli and K. pneumoniae using proteogenomics

Antimicrobial resistance is mostly studied by means of phenotypic growth inhibition determinations, in combination with PCR confirmations or further characterization by means of whole genome sequencing (WGS). However, the actual proteins that cause resistance such as enzymes and a lack of porins cannot be detected by these methods. Improvements in liquid chromatography (LC) and mass spectrometry (MS) enabled easier and more comprehensive proteome analysis. In the current study, susceptibility testing, WGS and MS are combined into a multi-omics approach to analyze resistance against frequently used antibiotics within the beta-lactam, aminoglycoside and fluoroquinolone group in E. coli and K. pneumoniae. Our aim was to study which currently known mechanisms of resistance can be detected at the protein level using liquid chromatography-mass spectrometry (LC-MS/MS) and to assess whether these could explain beta-lactam, aminoglycoside, and fluoroquinolone resistance in the studied isolates. Furthermore, we aimed to identify significant protein to resistance correlations which have not yet been described before and to correlate the abundance of different porins in relation to resistance to different classes of antibiotics. Whole genome sequencing, high-resolution LC-MS/MS and antimicrobial susceptibility testing by broth microdilution were performed for 187 clinical E. coli and K. pneumoniae isolates. Resistance genes and proteins were identified using the Comprehensive Antibiotic Resistance Database (CARD). All proteins were annotated using the NCBI RefSeq database and Prokka. Proteins of small spectrum beta-lactamases, extended spectrum beta-lactamases, AmpC beta-lactamases, carbapenemases, and proteins of 16S ribosomal RNA methyltransferases and aminoglycoside acetyltransferases can be detected in E. coli and K. pneumoniae by LC-MS/MS. The detected mechanisms matched with the phenotype in the majority of isolates. Differences in the abundance and the primary structure of other proteins such as porins also correlated with resistance. LC-MS/MS is a different and complementary method which can be used to characterize antimicrobial resistance in detail as not only the primary resistance causing mechanisms are detected, but also secondary enhancing resistance mechanisms.

Rapid Methods for Antimicrobial Resistance Diagnostics

Antimicrobial resistance (AMR) is one of the most challenging threats in public health; thus, there is a growing demand for methods and technologies that enable rapid antimicrobial susceptibility testing (AST). The conventional methods and technologies addressing AMR diagnostics and AST employed in clinical microbiology are tedious, with high turnaround times (TAT), and are usually expensive. As a result, empirical antimicrobial therapies are prescribed leading to AMR spread, which in turn causes higher mortality rates and increased healthcare costs. This review describes the developments in current cutting-edge methods and technologies, organized by key enabling research domains, towards fighting the looming AMR menace by employing recent advances in AMR diagnostic tools. First, we summarize the conventional methods addressing AMR detection, surveillance, and AST. Thereafter, we examine more recent non-conventional methods and the advancements in each field, including whole genome sequencing (WGS), matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectrometry, Fourier transform infrared (FTIR) spectroscopy, and microfluidics technology. Following, we provide examples of commercially available diagnostic platforms for AST. Finally, perspectives on the implementation of emerging concepts towards developing paradigm-changing technologies and methodologies for AMR diagnostics are discussed.

Usefulness of the MALDI-TOF MS technology with membrane filter protocol for the rapid identification of microorganisms in perioperative drainage fluids of hepatobiliary pancreatic surgery

Surgical site infections (SSIs) are significant and frequent perioperative complications, occurring due to the contamination of the surgical site. The late detection of SSIs, especially organ/space SSIs which are the more difficult to treat, often leads to severe complications. An effective method that can identify bacteria with a high accuracy, leading to the early detection of organ/space SSIs, is needed. Ninety-eight drainage fluid samples obtained from 22 patients with hepatobiliary pancreatic disease were analyzed to identify microorganisms using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) with a new membrane filtration protocol and rapid BACpro® pretreatment compared to sole rapid BACpro® pretreatment. The levels of detail of rapid BACpro® pretreatment with or without filtration were also evaluated for the accuracy of bacterial identification. We found that reliable scores for E. coli and E. faecalis were obtained by inoculation with 1.0 × 104 CFU/ml after preparation of the membrane filter with rapid BACpro®, indicating approximately 10-folds more sensitive compared to sole rapid BACpro® pretreatment in drainage fluid specimens. Among 60 bacterial positive colonies in drainage fluid specimens, the MALDI-TOF MS and the membrane filtration with rapid BACpro® identified 53 isolates (88.3%) with a significantly higher accuracy, compared to 25 isolates in the rapid BACpro® pretreatment group (41.7%) (p < 0.001). Among the 78 strains, 14 enteric Gram-negative bacteria (93.0%) and 55 Gram-positive cocci (87.3%) were correctly identified by the membrane filtration with rapid BACpro® with a high reliability. This novel protocol could identify bacterial species within 30 min, at $2-$3 per sample, thus leading to cost and time savings. MALDI-TOF MS with membrane filter and rapid BACpro® is a quick and reliable method for bacterial identification in drainage fluids. The shortened analysis time will enable earlier selection of suitable antibiotics for treatment of organ/space SSIs to improve patients' outcomes.

Development of a Rapid, Antimicrobial Susceptibility Test for E. coli Based on Low-Cost, Screen-Printed Electrodes

Antibiotic resistance has been cited by the World Health Organisation (WHO) as one of the greatest threats to public health. Mitigating the spread of antibiotic resistance requires a multipronged approach with possible interventions including faster diagnostic testing and enhanced antibiotic stewardship. This study employs a low-cost diagnostic sensor test to rapidly pinpoint the correct antibiotic for treatment of infection. The sensor comprises a screen-printed gold electrode, modified with an antibiotic-seeded hydrogel to monitor bacterial growth. Electrochemical growth profiles of the common microorganism, Escherichia coli (E. coli) (ATCC 25922) were measured in the presence and absence of the antibiotic streptomycin. Results show a clear distinction between the E. coli growth profiles depending on whether streptomycin is present, in a timeframe of ≈2.5 h (p < 0.05), significantly quicker than the current gold standard of culture-based antimicrobial susceptibility testing. These results demonstrate a clear pathway to a low cost, phenotypic and reproducible antibiotic susceptibility testing technology for the rapid detection of E. coli within clinically relevant concentration ranges for conditions such as urinary tract infections.

Mass spectrometry-based microbiological testing for blood stream infection

BACKGROUND

The most successful application of mass spectrometry (MS) in laboratory medicine is identification (ID) of microorganisms using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) in blood stream infection. We describe MALDI-TOF MS-based bacterial ID with particular emphasis on the methods so far developed to directly identify microorganisms from positive blood culture bottles with MALDI-TOF MS including our own protocols. We touch upon the increasing roles of Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) as well.

MAIN BODY

Because blood culture bottles contain a variety of nonbacterial proteins that may interfere with analysis and interpretation, appropriate pretreatments are prerequisites for successful ID. Pretreatments include purification of bacterial pellets and short-term subcultures to form microcolonies prior to MALDI-TOF MS analysis. Three commercial protocols are currently available: the Sepsityper® kit (Bruker Daltonics), the Vitek MS blood culture kit (bioMerieux, Inc.), and the rapid BACpro® II kit (Nittobo Medical Co., Tokyo). Because these commercially available kits are costly and bacterial ID rates using these kits are not satisfactory, particularly for Gram-positive bacteria, various home-brew protocols have been developed: 1. Stepwise differential sedimentation of blood cells and microorganisms, 2. Combination of centrifugation and lysis procedures, 3. Lysis-vacuum filtration, and 4. Centrifugation and membrane filtration technique (CMFT). We prospectively evaluated the performance of this CMFT protocol compared with that of Sepsityper® using 170 monomicrobial positive blood cultures. Although preliminary, the performance of the CMFT was significantly better than that of Sepsityper®, particularly for Gram-positive isolates. MALDI-TOF MS-based testing of polymicrobial blood specimens, however, is still challenging. Also, its contribution to assessment of susceptibility and resistance to antibiotics is still limited. For this purpose, liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) should be more useful because this approach can identify as many as several thousand peptide sequences.

CONCLUSION

MALDI-TOF MS is now an essential tool for rapid bacterial ID of pathogens that cause blood stream infection. For the purpose of assessment of susceptibility and resistance to antibiotics of the pathogens, the roles of liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) will increase in the future.

Rapid Proteomic Profiling by MALDI-TOF Mass Spectrometry for Better Brain Tumor Classification

PURPOSE

Glioblastoma is one of the most aggressive primary brain cancers. The precise grading of tumors is important to adopt the best follow-up treatment but complementary methods to histopathological diagnosis still lack in achieving an unbiased and reliable classification.

EXPERIMENTAL DESIGN

To progress in the field, a rapid Matrix Assisted Laser Desorption Ionization - Time of Flight Mass spectrometry (MALDI-TOF MS) protocole, devised for the identification and taxonomic classification of microorganisms and based on the analysis of whole cell extracts, was applied to glioma cell lines.

RESULTS

The analysis of different human glioblastoma cell lines permitted to identify distinct proteomic profiles thus demonstrating the ability of MALDI-TOF to distinguish different malignant cell types.

CONCLUSIONS AND CLINICAL RELEVANCE

In the study, the authors showed the ability of MALDI-TOF profiling to discriminate glioblastoma cell lines, demonstrating that this technique could be used in complement to histological tumor classification. The proposed procedure is rapid and inexpensive and could be used to improve brain tumors classification and help propose a personalized and more efficient treatment.