MALDI-TOF mass spectrometry typing for predominant serovars of non-typhoidal Salmonella in a Thai broiler industry

Fast detection of bacterial gut pathogens on miniaturized devices: an overview

INTRODUCTION

Gut microbes pose challenges like colon inflammation, deadly diarrhea, antimicrobial resistance dissemination, and chronic disease onset. Development of early, rapid and specific diagnosis tools is essential for improving infection control. Point-of-care testing (POCT) systems offer rapid, sensitive, low-cost and sample-to-answer methods for microbe detection from various clinical and environmental samples, bringing the advantages of portability, automation, and simple operation.

AREAS COVERED

Rapid detection of gut microbes can be done using a wide array of techniques including biosensors, immunological assays, electrochemical impedance spectroscopy, mass spectrometry and molecular biology. Inclusion of Internet of Things, machine learning, and smartphone-based point-of-care applications is an important aspect of POCT. In this review, the authors discuss various fast diagnostic platforms for gut pathogens and their main challenges.

EXPERT OPINION

Developing effective assays for microbe detection can be complex. Assay design must consider factors like target selection, real-time and multiplex detection, sample type, reagent stability and storage, primer/probe design, and optimizing reaction conditions for accuracy and sensitivity. Mitigating these challenges requires interdisciplinary collaboration among scientists, clinicians, engineers, and industry partners. Future efforts are essential to enhance sensitivity, specificity, and versatility of POCT systems for gut microbe detection and quantification, advancing infectious disease diagnostics and management.

Non-typhoidal Salmonella causing urinary tract infection in a young male with renal calculi - a case report and comprehensive review

Introduction

Non-typhoidal Salmonella (NTS) causes urinary tract infections infrequently and are usually associated with presence of genitourinary abnormalities.

Case presentation

We report a case of immunocompetent male in his early 20 s with phimosis presented with history of dysuria and burning micturition for 4 months. A renal ultrasonography showed presence of bilateral intrarenal calculi. Urine analysis revealed presence of non-typhoidal Salmonella. Automated identification systems performed poorly in identification of serotype. On serotyping, it was identified as Salmonella enteritidis in the referral centre. The patient was managed with oral antibiotics.

Conclusion

This report highlights the issues of inaccurate identification of NTS even with advanced automated systems and early initiation of therapy based on the knowledge of local susceptibility patterns. UTI in immunocompetent individuals by non-typhoidal Salmonella should always be investigated further to rule out genitourinary abnormalities and appropriate antibiotics must be started to avoid chronicity and complications.

Promising potential of machine learning-assisted MALDI-TOF MS as an effective detector for Streptococcus suis serotype 2 and virulence thereof

IMPORTANCE

To the best of our knowledge, this study reveals a strong correlation between mass spectra pattern and virulence phenotype among S. suis for the first time. In order to make the findings applicable and to excavate the intrinsic information in the spectra, the classifiers based on the machine learning algorithms were established, and RF (Random Forest)-based models have achieved an accuracy of over 90%. Overall, this study will pave the way for virulent SS2 (Streptococcus suis serotype 2) rapid detection, and the important findings on the association between genotype and mass spectrum may provide a new idea for the genotype-dependent detection of specific pathogens.

Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization - Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence

Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%-100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories.

An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens

Pathogens found in food are believed to be the leading cause of foodborne illnesses; and they are considered a serious problem with global ramifications. During the last few decades, a lot of attention has been paid to determining the microorganisms that cause foodborne illnesses and developing new methods to identify them. Foodborne pathogen identification technologies have evolved rapidly over the last few decades, with the newer technologies focusing on immunoassays, genome-wide approaches, biosensors, and mass spectrometry as the primary methods of identification. Bacteriophages (phages), probiotics and prebiotics were known to have the ability to combat bacterial diseases since the turn of the 20th century. A primary focus of phage use was the development of medical therapies; however, its use quickly expanded to other applications in biotechnology and industry. A similar argument can be made with regards to the food safety industry, as diseases directly endanger the health of customers. Recently, a lot of attention has been paid to bacteriophages, probiotics and prebiotics most likely due to the exhaustion of traditional antibiotics. Reviewing a variety of current quick identification techniques is the purpose of this study. Using these techniques, we are able to quickly identify foodborne pathogenic bacteria, which forms the basis for future research advances. A review of recent studies on the use of phages, probiotics and prebiotics as a means of combating significant foodborne diseases is also presented. Furthermore, we discussed the advantages of using phages as well as the challenges they face, especially given their prevalent application in food safety.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Research progress on detection techniques for point-of-care testing of foodborne pathogens

The global burden of foodborne disease is enormous and foodborne pathogens are the leading cause of human illnesses. The detection of foodborne pathogenic bacteria has become a research hotspot in recent years. Rapid detection methods based on immunoassay, molecular biology, microfluidic chip, metabolism, biosensor, and mass spectrometry have developed rapidly and become the main methods for the detection of foodborne pathogens. This study reviewed a variety of rapid detection methods in recent years. The research advances are introduced based on the above technical methods for the rapid detection of foodborne pathogenic bacteria. The study also discusses the limitations of existing methods and their advantages and future development direction, to form an overall understanding of the detection methods, and for point-of-care testing (POCT) applications to accurately and rapidly diagnose and control diseases.

A Specific and Sensitive Aptamer-Based Digital PCR Chip for Salmonella typhimurium Detection

Food poisoning and infectious diseases caused by Salmonella typhimurium (S. typhimurium) are serious public health concerns for human health and food safety. The diversity and complexity of food matrices pose great challenges for rapid and ultra-sensitive detection of S. typhimurium in food samples. A method capable of identification, detection, and quantification of S. typhimurium is essential for addressing these issues. In this study, aptamer-coated magnetic beads (Apt-MBs) are employed as capture bio-probes to specifically and selectively concentrate S. typhimurium in food samples. A self-priming chip-based digital PCR was then presented as another biosensor for on-site detection and quantification of S. typhimurium cells. The chip we developed was robust and did not require any external power for sample loading. The combination of Apt-MBs with an on-chip digital detection realized the integration into lab-on-a-chip-based biosensors for on-site monitoring of foodborne pathogens. It was possible to capture and detect S. typhimurium cells as low as 90 CFU/reaction with a capture efficiency of 94.5%. Additionally, the whole process only took about 2 h. This unique platform could also be used to monitor other target bacteria with high specificity and sensitivity by utilizing different aptamers. Furthermore, the platform has potential applications in point-of-care testing in the future.

Differentiation of Bacillus cereus and Bacillus thuringiensis Using Genome-Guided MALDI-TOF MS Based on Variations in Ribosomal Proteins

Bacillus cereus and B. thuringiensis are closely related species that are relevant to foodborne diseases and biopesticides, respectively. Unambiguous differentiation of these two species is crucial for bacterial taxonomy. As genome analysis offers an objective but time-consuming classification of B. cereus and B. thuringiensis, in the present study, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was used to accelerate this process. By combining in silico genome analysis and MALDI-TOF MS measurements, four species-specific peaks of B. cereus and B. thuringiensis were screened and identified. The species-specific peaks of B. cereus were m/z 3211, 6427, 9188, and 9214, and the species-specific peaks of B. thuringiensis were m/z 3218, 6441, 9160, and 9229. All the above peaks represent ribosomal proteins, which are conserved and consistent with the phylogenetic relationship between B. cereus and B. thuringiensis. The specificity of the peaks was robustly verified using common foodborne pathogens. Thus, we concluded that genome-guided MALDI-TOF MS allows high-throughput differentiation of B. cereus and B. thuringiensis and provides a framework for differentiating other closely related species.

Engineered Cell Line Imaging Assay Differentiates Pathogenic from Non-Pathogenic Bacteria

Cell culture systems have greatly expanded our understanding of how bacterial pathogens target signaling pathways to manipulate the host and cause infection. Advances in genetic engineering have allowed for the creation of fluorescent protein readouts within signaling pathways, but these techniques have been underutilized in pathogen biology. Here, we genetically engineered a lung cell line with fluorescent reporters for extracellular signal-related kinase (ERK) and the downstream transcription factor FOS-related antigen 1 (Fra1) and evaluated signaling after inoculation with pathogenic and non-pathogenic bacteria. Cells were inoculated with 100 colony-forming units of Acinetobacter baylyi, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus agalactiae, or Staphylococcus epidermidis and imaged in a multi-mode reader. The alamarBlue cell viability assay was used as a reference test and showed that pathogenic P. aeruginosa induced significant (p < 0.05) cell death after 8 h in both wild-type and engineered cell lines compared to non-pathogenic S. epidermidis. In engineered cells, we found that Fra1 signaling was disrupted in as little as 4 h after inoculation with bacterial pathogens compared to delayed disruption in signaling by non-pathogenic S. epidermidis. Overall, we demonstrate that low levels of pathogenic versus non-pathogenic bacteria can be rapidly and sensitively screened based on ERK-Fra1 signaling.

Predictive Potential of MALDI-TOF Analyses for Wine and Brewing Yeast

The potential of MALDI-TOF profiling for predicting potential applications of yeast strains in the beverage sector was assessed. A panel of 59 commercial yeasts (47 wine and 12 brewing yeasts) was used to validate the concept whereby 2 culture media (YPD agar and YPD broth), as well as two mass ranges m/z 500–4000 and m/z 2000–20,000, were evaluated for the best fit. Three machine learning-based algorithms, PCA, MDS, and UMAP, in addition to a hierarchical clustering method, were employed. Profiles derived from broth cultures yielded more peaks, but these were less well-defined compared with those from agar cultures. Hierarchical clustering more clearly resolved different species and gave a broad overview of potential strain utility, but more nuanced insights were provided by MDS and UMAP analyses. PCA-based displays were less informative. The potential of MALDI-TOF proteomics in predicting the utility of yeast strains of commercial benefit is supported in this study, provided appropriate approaches are used for data generation and analysis.

Advancement in Salmonella Detection Methods: From Conventional to Electrochemical-Based Sensing Detection

Large-scale food-borne outbreaks caused by Salmonella are rarely seen nowadays, thanks to the advanced nature of the medical system. However, small, localised outbreaks in certain regions still exist and could possess a huge threat to the public health if eradication measure is not initiated. This review discusses the progress of Salmonella detection approaches covering their basic principles, characteristics, applications, and performances. Conventional Salmonella detection is usually performed using a culture-based method, which is time-consuming, labour intensive, and unsuitable for on-site testing and high-throughput analysis. To date, there are many detection methods with a unique detection system available for Salmonella detection utilising immunological-based techniques, molecular-based techniques, mass spectrometry, spectroscopy, optical phenotyping, and biosensor methods. The electrochemical biosensor has growing interest in Salmonella detection mainly due to its excellent sensitivity, rapidity, and portability. The use of a highly specific bioreceptor, such as aptamers, and the application of nanomaterials are contributing factors to these excellent characteristics. Furthermore, insight on the types of biorecognition elements, the principles of electrochemical transduction elements, and the miniaturisation potential of electrochemical biosensors are discussed.

Rapid Detection of Salmonella Enteritidis, Typhimurium, and Thompson by Specific Peak Analysis Using Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry

Rapid detection of Salmonella serovars is important for the effective control and monitoring of food industries. In this study, we evaluate the application of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for the rapid detection of three serovars, Enteritidis, Typhimurium, and Thompson, that are epidemiologically important in Korea. All strains were identified at the genus level, with a mean score of 2.319 using the BioTyper database, and their protein patterns were confirmed to be similar by principal component analysis and main spectrum profile dendrograms. Specific peaks for the three serovars were identified by analyzing 65 reference strains representing 56 different serovars. Specific mass peaks at 3018 ± 1 and 6037 ± 1, 7184 ± 1, and 4925 ± 1 m/z were uniquely found in the reference strains of serovars Enteritidis, Typhimurium, and Thompson, respectively, and they showed that the three serovars can be differentiated from each other and 53 other serovars. We verified the reproducibility of these mass peaks in 132 isolates, and serovar classification was achieved with 100% accuracy when compared with conventional serotyping through antisera agglutination. Our method can rapidly detect a large number of strains; hence, it will be useful for the high-throughput screening of Salmonella serovars.

Characterization of Clostridium tyrobutyricum Strains Using Three Different Typing Techniques

Clostridium tyrobutyricum is well known as one of the main causative agents of severe cheese spoilage. The metabolism of this anaerobic bacterium during ripening leads to textural and sensory defects in cheese and consequential loss of product value. The potential to induce cheese spoilage, however, may vary among different strains of the same species. Therefore, a better understanding of the intra-species diversity of C. tyrobutyricum may be of practical relevance for the dairy industry. In the present study, we compared the ability of three typing techniques to differentiate 95 C. tyrobutyricum strains on the subspecies level: (1) repetitive element palindromic PCR (rep-PCR) fingerprinting combined with conventional agarose gel electrophoresis, (2) hexaplex-PCR followed by an automated capillary electrophoresis and (3) matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) typing. MALDI-TOF MS fingerprinting provided only moderate reproducibility and low discriminatory power. Both PCR-based methods were highly reproducible and discriminative, with hexaplex-PCR fingerprinting being slightly more discriminative than rep-PCR typing. Overall, a high intra-species diversity was observed among the tested strains, indicating that further investigations on the strain level may be of interest.