VOC fingerprints: metabolomic signatures of biothreat agents with and without antibiotic resistance

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Metabolomic Profiles of Multidrug-Resistant Salmonella Typhimurium from Humans, Bovine, and Porcine Hosts

Simple Summary

The global threat that is imposed by the resistance the pathogens develop to antimicrobial drugs is escalating. Tools to detect the resistance (with evidence on molecular and cellular outcomes) would reveal intricate mechanisms through which novel drugs could be developed. Approaches such as metabolomics, which involve metabolite detection, provide scientific evidence of metabolite expression of antimicrobial-resistant pathogens. The current study involved metabolomics of antimicrobial-resistant Salmonella Typhimurium collected from various hosts (human, porcine, bovine) and were exposed to antimicrobial drugs—ampicillin, chloramphenicol, streptomycin, sulfisoxazole, and tetracycline—as one set of the experiment. The same isolates were also cultured with no drug exposure as a comparison. There are certain pathways of metabolite expression that are impacted by drug exposure when compared to no drug exposure, meaning that the expressed metabolites could be potential targets for drug companies for the treatment of antimicrobial-resistant pathogens.

Abstract

Antimicrobial resistance (AMR) is a global public health threat, yet tools for detecting resistance patterns are limited and require advanced molecular methods. Metabolomic approaches produce metabolite profiles and help provide scientific evidence of differences in metabolite expressions between Salmonella Typhimurium from various hosts. This research aimed to evaluate the metabolomic profiles of S. Typhimurium associated with AMR and it compares profiles across various hosts. Three samples, each from bovine, porcine, and humans (total n = 9), were selectively chosen from an existing library to compare these nine isolates cultured under no drug exposure to the same isolates cultured in the presence of the antimicrobial drug panel ACSSuT (ampicillin, chloramphenicol, streptomycin, sulfisoxazole, tetracycline). This was followed by metabolomic profiling using UPLC and GC–mass spectrometry. The results indicated that the metabolite regulation was affected by antibiotic exposure, irrespective of the host species. When exposed to antibiotics, 59.69% and 40.31% of metabolites had increased and decreased expressions, respectively. The most significantly regulated metabolic pathway was aminoacyl-tRNA biosynthesis, which demonstrated increased expressions of serine, aspartate, alanine, and citric acid. Metabolites that showed decreased expressions included glutamate and pyruvate. This pathway and associated metabolites have known AMR associations and could be targeted for new drug discoveries and diagnostic methods.

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.

Non-destructive mid-IR spectroscopy with quantum cascade laser can detect ethylene gas dynamics of apple cultivar 'Fuji' in real time

Many plants, including fruits and vegetables, release biogenic gases containing various volatile organic compounds such as ethylene (C₂H₄), which is a gaseous phytohormone. Non-destructive and in-situ gas sampling technology to detect trace C₂H₄ released from plants in real time would be attractive for visualising the ageing, ripening, and defence reactions of plants. In this study, we developed a C₂H₄ detection system with a detection limit of 0.8 ppb (3σ) using laser absorption spectroscopy. The C₂H₄ detection system consists of a mid-infrared quantum cascade laser oscillated at 10.5 µm, a multi-pass gas cell, a mid-IR photodetector, and a gas sampling system. Using non-destructive and in-situ gas sampling, while maintaining the internal pressure of the multi-pass gas cell at low pressure, the change in trace C₂H₄ concentration released from apples (Malus domestica Borkh.) can be observed in real time. We succeeded in observing C₂H₄ concentration changes with a time resolution of 1 s, while changing the atmospheric gas and surface temperature of apples from the ‘Fuji’ cultivar. This technique allows the visualisation of detailed C₂H₄ dynamics in plant environmental response, which may be promising for further progress in plant physiology, agriculture, and food science.