Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry

Detection of volatile organic compounds as new paradigm to accelerate antimicrobial susceptibility testing: performance evaluation of VITEK® REVEAL™

OBJECTIVES

The measurement of VOCs release in the headspace of a bacterial culture represents a new approach to rapidly assess antimicrobial susceptibility. Herein, we evaluated the diagnostic performance of the VITEK® REVEAL™ system directly from a collection of Gram-negative positive blood cultures.

MATERIALS AND METHODS

One hundred and twenty-eight positive blood cultures were included in the analysis (Enterobacterales, n = 95; Pseudomonas aeruginosa, n = 21; Acinetobacter baumannii complex, n = 12). Samples were processed using VITEK® REVEAL™ according to the manufacturer's recommendations, and MICs of 22 antimicrobials were compared with those obtained using reference methods. Categorical agreement (CA), essential agreement (EA) and categorical errors were calculated.

RESULTS

Overall, 2220 strain/antibiotic pair combinations were analysed. Of these, most were classified as resistant by reference antimicrobial susceptibility testing (1091/2220; 48.7%). The overall CA and EA were 97.6% and 97.7%, respectively. CA ranged from 97.5% in Enterobacterales to 97.9% in both P. aeruginosa and A. baumannii complex. The overall number of categorical discrepancies were: 18 very major errors (1.6%), 13 major errors (1.2%) and 22 minor errors (2.4%). EA ranged from 95.2% in P. aeruginosa to 98.1% in Enterobacterales. Screening test for ESBL phenotype was positive, indeterminate and negative in 13.7%, 32.6% and 27.4% of Enterobacterales isolates tested by both VITEK® REVEAL™ and the reference method, showing 100% CA.

CONCLUSIONS

VITEK® REVEAL™ represents a reliable tool to obtain antimicrobial susceptibility results of the main Gram-negative species directly from positive blood cultures with time to results of less than 8 h.

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.

Faecal Volatile Organic Compound Analysis in De Novo Paediatric Inflammatory Bowel Disease by Gas Chromatography-Ion Mobility Spectrometry: A Case-Control Study

The gut microbiota and its related metabolites differ between inflammatory bowel disease (IBD) patients and healthy controls. In this study, we compared faecal volatile organic compound (VOC) patterns of paediatric IBD patients and controls with gastrointestinal symptoms (CGIs). Additionally, we aimed to assess if baseline VOC profiles could predict treatment response in paediatric IBD patients. We collected faecal samples from a cohort of de novo therapy-naïve paediatric IBD patients and CGIs. VOCs were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Response was defined as a combination of clinical response based on disease activity scores, without requiring treatment escalation. We included 109 paediatric IBD patients and 75 CGIs, aged 4 to 17 years. Faecal VOC profiles of paediatric IBD patients were distinguishable from those of CGIs (AUC ± 95% CI, p-values: 0.71 (0.64-0.79), <0.001). This discrimination was observed in both Crohn's disease (CD) (0.75 (0.67-0.84), <0.001) and ulcerative colitis (UC) (0.67 (0.56-0.78), 0.01) patients. VOC profiles between CD and UC patients were not distinguishable (0.57 (0.45-0.69), 0.87). Baseline VOC profiles of responders did not differ from non-responders (0.70 (0.58-0.83), 0.1). In conclusion, faecal VOC profiles of paediatric IBD patients differ significantly from those of CGIs.

GC-IMS facilitates identification of carbapenem-resistant Klebsiella pneumoniae in simulated blood cultures

This study aimed to identify carbapenem-resistant Klebsiella pneumoniae (CRKP) based on changes in levels of its volatile organic compounds (VOCs) in simulated blood cultures (BCs) using the gas chromatography-ion mobility spectrometry (GC-IMS) technique. A comprehensive analysis of volatile metabolites produced by Klebsiella pneumoniae (K. pneumoniae) in BC bottles was conducted using GC-IMS. Subsequently, the released VOCs were analyzed to examine differences in VOC release between CRKP and carbapenem-susceptible Klebsiella pneumoniae (CSKP). A total of 54 VOCs were detected, of which 18 (6 VOCs found in both monomer and dimer forms) were successfully identified. The VOCs produced by K. pneumoniae in BC bottles (BacT/ALERT® SA) were primarily composed of organic acids, alcohols, esters, and ketones. The content of certain VOCs was significantly different between CRKP and CSKP after the addition of imipenem (IPM). Moreover, the inclusion of carbapenemase inhibitors facilitated the identification of carbapenemase-producing K. pneumoniae based on the variations in VOCs. This study demonstrates the utility of GC-IMS technology in identifying CRKP, and reveals that changes in VOCs are closely related to the growth and metabolism of K. pneumoniae, indicating that they can be leveraged to promote early identification of CRKP bacteremia. However, further in-depth studies and experiments are needed to validate our findings.

Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe3O4 magnetic nanoparticle-based affinity mass spectrometry with a machine learning strategy

Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe3O4 MNPs were used as affinity probes for sample enrichment to eliminate the overnight culture and reduce the time in sample preparation. The limit of detection of the MNP-based affinity approach toward S. aureus combined with our machine learning strategy was as low as ~ 8 × 103 CFU mL-1. The feasibility of using the current approach for the identification of S. aureus in juice samples was also demonstrated.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria: Current state of the art

Pathogenic microorganisms produce numerous metabolites, including volatile organic compounds (VOCs). Monitoring these metabolites in biological matrices (e.g., urine, blood, or breath) can reveal the presence of specific microorganisms, enabling the early diagnosis of infections and the timely implementation of targeted therapy. However, complex matrices only contain trace levels of VOCs, and their constituent components can hinder determination of these compounds. Therefore, modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed. In this paper, we discuss bacterial VOC analysis under in vitro conditions, in animal models and disease diagnosis in humans, including techniques for offline and online analysis in clinical settings. We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis, in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species interactions, the kinetics of VOC metabolism, and species- and drug-resistance specificity.

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity

INTRODUCTION

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media.

METHODS

Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation.

RESULTS

The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature.

CONCLUSION

This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

Detection of Propionic Acids Trapped in Thin Zeolite Layer Using Thermal Desorption Analysis

Volatile organic compounds (VOCs) have recently received considerable attention for the analysis and monitoring of different biochemical processes in biological systems such as humans, plants, and microorganisms. The advantage of using VOCs to gather information about a specific process is that they can be extracted using different types of samples, even at low concentrations. Therefore, VOC levels represent the fingerprints of specific biochemical processes. The aim of this work was to develop a sensor based on a photoionization detector (PID) and a zeolite layer, used as an alternative analytic separation technique for the analysis of VOCs. The identification of VOCs occurred through the evaluation of the emissive profile during the thermal desorption phase, using a stainless-steel chamber for analysis. Emission profiles were evaluated using a double exponential mathematical model, which fit well if compared with the physical system, describing both the evaporation and diffusion processes. The results showed that the zeolite layer was selective for propionic acid molecules if compared to succinic acid molecules, showing linear behavior even at low concentrations. The process to define the optimal adsorption time between the propionic acid molecules was performed in the range of 5 to 60 min, followed by a thermal desorption process at 100 °C. An investigation of the relationship between the evaporation and diffusion rates showed that the maximum concentration of detected propionic acid molecules occurred in 15 min. Other analyses were performed to study how the concentration of VOCs depended on the desorption temperature and the volume of the analysis chamber. For this purpose, tests were performed using three analysis chambers with volumes of 25 × 10-6, 50 × 10-6, and 150 × 10-6 m3 at three different desorption temperatures of 20 °C, 50 °C, and 100 °C, respectively. The results demonstrated that the evaporation rate of the VOCs increased rapidly with an increasing temperature, while the diffusion rate remained almost constant and was characterized by a slow decay time. The diffusion ratio increased when using a chamber with a larger volume. These results highlight the capabilities of this alternative technique for VOC analysis, even for samples with low concentrations. The coupling of a zeolite layer and a PID improves the detection selectivity in portable devices, demonstrating the feasibility of extending its use to a wide range of new applications.

Influence of bacterial and alveolar cell co-culture on microbial VOC production using HS-GC/MS

Volatile organic compounds (VOCs) found in exhaled breath continue to garner interest as an alternative diagnostic tool in pulmonary infections yet, their clinical integration remains a challenge with difficulties in translating identified biomarkers. Alterations in bacterial metabolism secondary to host nutritional availability may explain this but is often inadequately modelled in vitro. The influence of more clinically relevant nutrients on VOC production for two common respiratory pathogens was investigated. VOCs from Staphylococcus aureus (S.aureus) and Pseudomonas aeruginosa (P.aeruginosa) cultured with and without human alveolar A549 epithelial cells were analyzed using headspace extraction coupled with gas chromatography-mass spectrometry. Untargeted and targeted analyses were performed, volatile molecules identified from published data, and the differences in VOC production evaluated. Principal component analysis (PCA) could differentiate alveolar cells from either S. aureus or P. aeruginosa when cultured in isolation based on PC1 (p = 0.0017 and 0.0498, respectively). However, this separation was lost for S. aureus (p = 0.31) but not for P. aeruginosa (p = 0.028) when they were cultured with alveolar cells. S. aureus cultured with alveolar cells led to higher concentrations of two candidate biomarkers, 3-methyl-1-butanol (p = 0.001) and 3-methylbutanal (p = 0.002) when compared to S. aureus, alone. P. aeruginosa metabolism resulted in less generation of pathogen-associated VOCs when co-cultured with alveolar cells compared to culturing in isolation. VOC biomarkers previously considered indicative of bacterial presence are influenced by the local nutritional environment and this should be considered when evaluating their biochemical origin.

Recent Advances on Peptide-Based Biosensors and Electronic Noses for Foodborne Pathogen Detection

Foodborne pathogens present a serious issue around the world due to the remarkably high number of illnesses they cause every year. In an effort to narrow the gap between monitoring needs and currently implemented classical detection methodologies, the last decades have seen an increased development of highly accurate and reliable biosensors. Peptides as recognition biomolecules have been explored to develop biosensors that combine simple sample preparation and enhanced detection of bacterial pathogens in food. This review first focuses on the selection strategies for the design and screening of sensitive peptide bioreceptors, such as the isolation of natural antimicrobial peptides (AMPs) from living organisms, the screening of peptides by phage display and the use of in silico tools. Subsequently, an overview on the state-of-the-art techniques in the development of peptide-based biosensors for foodborne pathogen detection based on various transduction systems was given. Additionally, limitations in classical detection strategies have led to the development of innovative approaches for food monitoring, such as electronic noses, as promising alternatives. The use of peptide receptors in electronic noses is a growing field and the recent advances of such systems for foodborne pathogen detection are presented. All these biosensors and electronic noses are promising alternatives for the pathogen detection with high sensitivity, low cost and rapid response, and some of them are potential portable devices for on-site analyses.

SARS-CoV-2 detecting rapid metasurface-based sensor

We have proposed a novel approach to detect COVID-19 by detecting the ethyl butanoate which high volume ratio is present in the exhaled breath of a COVID-19 infected person. We have employed a refractive index sensor (RIS) with the help of a metasurface-based slotted T-shape perfect absorber that can detect ethyl butanoate present in exhaled breath of COVID-19 infected person with high sensitivity and in-process SARS-CoV-2. The optimized structure of the sensor is obtained by varying several structure parameters including structure length and thickness, slotted T-shape resonator length, width, and thickness. Sensor's performance is evaluated based on numerous factors comprising of sensitivity, Q factor, detection limit, a figure of merit (FOM), detection accuracy, and other performance defining parameters. The proposed slotted T-shape RIS achieved the largest sensitivity of 2500 nm/RIU, Q factor of 131.06, a FOM of 131.58 RIU^-1, detection limit of 0.0224 RIU.

A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium

The detection and level assessment of microorganisms is a practical quality/contamination indicator of food and water samples. Conventional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reactions), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Electronic noses (E-noses), combined with chemometrics, provide a direct, green, and non-invasive assessment of the volatile fraction without the need for sample pre-treatments. The unique olfactory fingerprint generated during each microorganism's growth can be a vehicle for its detection using gas sensors. A lab-made E-nose, comprising metal oxide semiconductor sensors was applied, to analyze solid medium containing Gram-positive (Enterococcus faecalis and Staphylococcus aureus) or Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. The electrical-resistance signals generated by the E-nose coupled with linear discriminant analysis allowed the discrimination of the four bacteria (90% of correct classifications for leave-one-out cross-validation). Furthermore, multiple linear regression models were also established allowing quantifying the number of colony-forming units (CFU) (0.9428 ≤ R² ≤ 0.9946), with maximum root mean square errors lower than 4 CFU. Overall, the E-nose showed to be a powerful qualitative-quantitative device for bacteria preliminary analysis, being envisaged its possible application in solid food matrices.

GC-MS profiling of volatile metabolites produced by Klebsiella pneumoniae

Currently used methods for diagnosing ventilator-associated pneumonia (VAP) are complex, time-consuming and require invasive procedures while empirical antibacterial therapy applies broad spectrum antibiotics that may promote antimicrobial resistance. Hence, novel and fast methods based on alternative markers are needed for VAP detection and differentiation of causative pathogens. Pathogenic bacteria produce a broad range of volatile organic compounds (VOCs), some of which may potentially serve as biomarkers for microorganism identification. Additionally, monitoring of dynamically changing VOCs concentration profiles may indicate emerging pneumonia and allow timely implementation of appropriate antimicrobial treatment. This study substantially extends the knowledge on bacterial metabolites providing the unambiguous identification of volatile metabolites produced by carbapenem-resistant and susceptible strains of Klebsiella pneumoniae (confirmed with pure standards in addition to mass spectra match) but also revealing their temporary concentration profiles (along the course of pathogen proliferation) and dependence on the addition of antibiotic (imipenem) to bacteria. Furthermore, the clinical strains of K. pneumoniae isolated from bronchoalveolar lavage specimens collected from mechanically ventilated patients were investigated to reveal, whether bacterial metabolites observed in model experiments with reference strains could be relevant for wild pathogens as well. In all experiments, the headspace samples from bacteria cultures were collected on multibed sorption tubes and analyzed by GC-MS. Sampling was done under strictly controlled conditions at seven time points (up to 24 h after bacteria inoculation) to follow the dynamic changes in VOC concentrations, revealing three profiles: release proportional to bacteria load, temporary maximum and uptake. Altogether 32 VOCs were released by susceptible and 25 VOCs by resistant strain, amongst which 2-pentanone, 2-heptanone, and 2-nonanone were significantly higher for carbapenem-resistant KPN. Considerably more metabolites (n = 64) were produced by clinical isolates and in higher diversity compared to reference KPN strains.

Susceptibility Testing by Volatile Organic Compound Detection Direct from Positive Blood Cultures: A Proof-of-Principle Laboratory Study

Background: Bacteria produce volatile organic compounds (VOCs) during growth, which can be detected by colorimetric sensor arrays (CSAs). The SpecifAST® system (Specific Diagnostics) employs this technique to enable antibiotic susceptibility testing (AST) directly from blood cultures without prior subculture of isolates. The aim of this study was to compare the SpecifAST® AST results and analysis time to the VITEK®2 (bioMérieux) system. Methods: In a 12-month single site prospective study, remnants of clinical positive monomicrobial blood cultures were combined with a series of antibiotic concentrations. Volatile emission was monitored at 37 °C via CSAs. Minimal Inhibitory Concentrations (MICs) of seven antimicrobial agents for Enterobacterales, Staphylococcus, and Enterococcus spp. were compared to VITEK®2 AST results. MICs were interpreted according to EUCAST clinical breakpoints. Performance was assessed by calculating agreement and discrepancy rates. Results: In total, 96 positive blood cultures containing Enterobacterales, Staphylococcus, and Enterococcus spp. were tested (269 bug–drug combinations). The categorical agreement of the SpecifAST® system compared to the VITEK®2 system was 100% and 91% for Gram-negatives and Gram-positives, respectively. Errors among Gram-positives were from coagulase-negative staphylococci. Overall results were available in 3.1 h (±0.9 h) after growth detection without the need for subculture steps. Conclusion: The AST results based on VOC detection are promising and warrant further evaluation in studies with a larger sample of bacterial species and antimicrobials.

Multi-Strain and -Species Investigation of Volatile Metabolites Emitted from Planktonic and Biofilm Candida Cultures

Candida parapsiliosis is a prevalent neonatal pathogen that attains its virulence through its strain-specific ability to form biofilms. The use of volatilomics, the profiling of volatile metabolites from microbes is a non-invasive, simple way to identify and classify microbes; it has shown great potential for pathogen identification. Although C. parapsiliosis is one of the most common clinical fungal pathogens, its volatilome has never been characterised. In this study, planktonic volatilomes of ten clinical strains of C. parapsilosis were analysed, along with a single strain of Candida albicans. Headspace-solid-phase microextraction coupled with gas chromatography-mass spectrometry were employed to analyse the samples. Species-, strain-, and media- influences on the fungal volatilomes were investigated. Twenty-four unique metabolites from the examined Candida spp. (22 from C. albicans; 18 from C. parapsilosis) were included in this study. Chemical classes detected across the samples included alcohols, fatty acid esters, acetates, thiols, sesquiterpenes, and nitrogen-containing compounds. C. albicans volatilomes were most clearly discriminated from C. parapsilosis based on the detection of unique sesquiterpene compounds. The effect of biofilm formation on the C. parapsilosis volatilomes was investigated for the first time by comparing volatilomes of a biofilm-positive strain and a biofilm-negative strain over time (0–48 h) using a novel sampling approach. Volatilomic shifts in the profiles of alcohols, ketones, acids, and acetates were observed specifically in the biofilm-forming samples and attributed to biofilm maturation. This study highlights species-specificity of Candida volatilomes, and also marks the clinical potential for volatilomics for non-invasively detecting fungal pathogens. Additionally, the range of biofilm-specificity across microbial volatilomes is potentially far-reaching, and therefore characterising these volatilomic changes in pathogenic fungal and bacterial biofilms could lead to novel opportunities for detecting severe infections early.

Prediction of Inflammatory Bowel Disease Course Based on Fecal Scent

The early prediction of changes in disease state allows timely treatment of patients with inflammatory bowel disease (IBD) to be performed, which improves disease outcome. The aim of this pilot study is to explore the potential of fecal volatile organic compound (VOC) profiles to predict disease course. In this prospective cohort, IBD patients were asked to collect two fecal samples and fill in a questionnaire at set intervals. Biochemically, active disease was defined by FCP ≥ 250 mg/g and remission was defined by FCP < 100 mg/g. Clinically, active disease was defined by a Harvey Bradshaw Index (HBI) ≥ 5 for Crohn’s disease or by a Simple Clinical Colitis Activity Index (SCCAI) ≥ 3 for ulcerative colitis. Clinical remission was defined by an HBI < 4 or SCCAI ≤ 2. Fecal VOC profiles were measured using gas chromatography-ion mobility spectrometry (GC-IMS). The fecal samples collected first were included for VOC analysis to predict disease state at the following collection. A total of 182 subsequently collected samples met the disease-state criteria. The fecal VOC profiles of samples displaying low FCP levels at the first measurements differed between patients preceding exacerbation versus those who remained in remission (AUC 0.75; p < 0.01). Samples with FCP levels at the first time point displayed different VOC profiles in patients preceding remission compared with those whose disease remained active (AUC 0.86; p < 0.01). Based on disease activity scores, there were no significant differences in any of the comparisons. Alterations in fecal VOC profiles preceding changes in FCP levels may be useful to detect disease-course alterations at an early stage. This could lead to earlier treatment, decreased numbers of complications, surgery and hospital admission.

Simple, efficient and accurate method toward the monitoring of ethyl butanoate traces

We introduce in this research a simple, accurate, safe, and efficient design for the detection of ethyl butanoate that be present in the dry exhaled breath. In particular, the presence of ethyl butanoate in the dry exhaled breath could be utilized as a platform for the diagnosing of COVID 19. The main idea of this theoretical investigation is based on the inclusion of a cavity layer between a thin layer of Au and the well-known one-dimension photonic crystals. Accordingly, the cavity layer is filled with dry exhaled breath. The numerical results are investigated in the vicinity of the Drude model and transfer matrix method. The investigated results show the appearance of Tamm plasmon resonance in the reflectance spectrum of our design through the IR region. Such resonant mode provides very high sensitivity with the change in the concentration of ethyl butanoate. We have examined the performance of the proposed sensor by calculating its sensitivity, detection limit, detection accuracy, quality factor and figure of merit. The designed sensor could receive sensitivity of 0.3 nm/ppm or 260,486 nm/RIU, resolution of 7 ppm and quality factor of 969.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

An Investigation of Stability and Species and Strain-Level Specificity in Bacterial Volatilomes

Microbial volatilomics is a rapidly growing field of study and has shown great potential for applications in food, farming, and clinical sectors in the future. Due to the varying experimental methods and growth conditions employed in microbial volatilomic studies as well as strain-dependent volatilomic differences, there is limited knowledge regarding the stability of microbial volatilomes. Consequently, cross-study comparisons and validation of results and data can be challenging. In this study, we investigated the stability of the volatilomes of multiple strains of Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli across three frequently used nutrient-rich growth media. Volatilomic stability was assessed based on media-, time- and strain-dependent variation across the examined bacterial volatilomes. Strain-level specificity of the observed volatilomes of E. coli and P. aeruginosa strains was further investigated by comparing the emission of selected compounds at varying stages of cell growth. Headspace solid phase microextraction (HS-SPME) sampling coupled with gas chromatography mass spectrometry (GC-MS) was used to analyze the volatilome of each strain. The whole volatilomes of the examined strains demonstrate a high degree of stability across the three examined growth media. At the compound-level, media dependent differences were observed particularly when comparing the volatilomes obtained in glucose-containing brain heart infusion (BHI) and tryptone soy broth (TSB) growth media with the volatilomes obtained in glucose-free Lysogeny broth (LB) media. These glucose-dependent volatilomic differences were primarily seen in the emission of primary metabolites such as alcohols, ketones, and acids. Strain-level differences in the emission of specific compounds in E. coli and P. aeruginosa samples were also observed across the media. These strain-level volatilomic differences were also observed across varying phases of growth of each strain, therefore confirming that these strains had varying core and accessory volatilomes. Our results demonstrate that, at the species-level, the examined bacteria have a core volatilome that exhibits a high-degree of stability across frequently-used growth media. Media-dependent differences in microbial volatilomes offer valuable insights into identifying the cellular origin of individual metabolites. The observed differences in the core and accessory volatilomes of the examined strains illustrate the complexity of microbial volatilomics as a study while also highlighting the need for more strain-level investigations to ultimately elucidate the whole volatilomic capabilities of microbial species in the future.

Staphylococcus pasteuri (BCVME2) Resident in Buffalo Cervical Vaginal Mucus: A Potential Source of Estrus-Specific Sex Pheromone(s)

Mammals have microbes resident in their reproductive tract, some of which can be pathogenic while others may play a role in protecting the tract from infection. Volatile compounds play a role as sex pheromones that attract males for coitus during female estrus or heat. It is likely that these compounds themselves are secondary metabolites of bacterial flora resident in the vagina. In order to substantiate this hypothesis, bacteria were isolated from cervico-vaginal mucus (CVM) of buffalo during various phases of the estrous cycle and identified, using morphological, biochemical and molecular characteristics, as Bacillus during preestrus and diestrus, and as Staphylococcus during all three phases of the estrous cycle. Populations of Staphylococcus differed between different phases of the estrous cycle, the predominant forms being S. warneri (BCVMPE1_1) during preestrus, S. pastueri (BCVME2) during estrus and S. epidermis (BCVMDE3) during diestrus. Mice were used as chemosensors to differentiate the estrus-specific S. pasteuri (BCVME2) from the others. Chemical analysis showed that S. pasteuri (BCVME2) produced acetic, propanoic, isobutyric, butyric, isovaleric and valeric acids. In addition, it was shown that S. pasteuri (BCVME2) volatiles influenced the sexual behaviors, flehmen and mounting, of the bull. Thus, S. pasteuri (BCVME2) is a potential source of vaginal pheromone(s) during estrus in buffalo.

Volatile Organic Compounds to Identify Infectious (Bacteria/Viruses) Diseases of the Central Nervous System: A Pilot Study

BACKGROUND

Central nervous system (CNS) infectious diseases are common diseases in emergency rooms and neurology departments. CNS pathogen identification methods are time consuming and expensive and have low sensitivity and poor specificity. Some studies have shown that bacteria and viruses can produce specific volatile organic compounds (VOCs). The aim of this study is to find potential biomarkers by VOC analysis of cerebrospinal fluid (CSF) in patients with bacterial and viral meningitis/encephalitis (ME).

METHODS

CSF samples from 16 patients with bacterial ME and 42 patients with viral ME were collected, and solid-phase microextraction combined with gas chromatography-mass spectrometry was used to analyze the metabolites in the CSF.

RESULTS

There are 2 substances (ethylene oxide and phenol) that were found to be different between the 2 groups. Ethylene oxide was significantly greater in the group of bacterial ME patients than in the viral ME group of patients (p < 0.05). In addition, phenol was remarkably increased in the group of ME patients compared with the bacterial ME patients (p < 0.05).

CONCLUSIONS

Ethylene oxide and phenol may be potential biomarkers to distinguish bacterial ME and viral ME. VOC analysis of CSF may be used as a supporting tool for clinical diagnosis.

Untargeted Molecular Analysis of Exhaled Breath as a Diagnostic Test for Ventilator-Associated Lower Respiratory Tract Infections (BreathDx)

Patients suspected of ventilator-associated lower respiratory tract infections (VA-LRTIs) commonly receive broad-spectrum antimicrobial therapy unnecessarily. We tested whether exhaled breath analysis can discriminate between patients suspected of VA-LRTI with confirmed infection, from patients with negative cultures. Breath from 108 patients suspected of VA-LRTI was analysed by gas chromatography-mass spectrometry. The breath test had a sensitivity of 98% at a specificity of 49%, confirmed with a second analytical method. The breath test had a negative predictive value of 96% and excluded pneumonia in half of the patients with negative cultures. Trial registration number: UKCRN ID number 19086, registered May 2015.

Targeted exhaled breath analysis for detection of Pseudomonas aeruginosa in cystic fibrosis patients

BACKGROUND

Pseudomonas aeruginosa (PA) is an important respiratory pathogen for cystic fibrosis (CF) patients. Routine microbiology surveillance is time-consuming, and is best performed on expectorated sputum. As alternative, volatile organic compounds (VOCs) may be indicative of PA colonisation. In this study, we aimed to identify VOCs associated with PA in literature and perform targeted exhaled breath analysis to recognize PA positive CF patients non-invasively.

METHODS

This study consisted of 1) a literature review to select VOCs of interest, and 2) a cross-sectional CF study. Definitions used: A) PA positive, PA culture at visit/chronically; B) PA free, no PA culture in ≥12 months. Exhaled VOCs were identified via quadrupole MS. The primary endpoint was the area under the receiver operating characteristics curve (AUROCC) of individual VOCs as well as combined VOCs against PA culture.

RESULTS

241 VOCs were identified in literature, of which 56 were further evaluated, and 13 could be detected in exhaled breath in our cohort. Exhaled breath of 25 pediatric and 28 adult CF patients, PA positive (n=16) and free (n=28) was available. 3/13 VOCs were significantly (p<0.05) different between PA groups in children; none were in adults. Notably, a composite model based on 5 or 1 VOC(s) showed an AUROCC of 0.86 (CI 0.71-1.0) and 0.87 (CI 0.72-1.0) for adults and children, respectively.

CONCLUSIONS

Targeted VOC analysis appears to discriminate children and adults with and without PA positive cultures with clinically acceptable sensitivity values.

Rapid in vitro differentiation of bacteria by ion mobility spectrometry

Rapid screening of infected people plays a crucial role in interrupting infection chains. However, the current methods for identification of bacteria are very tedious and labor intense. Fast on-site screening for pathogens based on volatile organic compounds (VOCs) by ion mobility spectrometry (IMS) could help to differentiate between healthy and potentially infected subjects. As a first step towards this, the feasibility of differentiating between seven different bacteria including resistant strains was assessed using IMS coupled to multicapillary columns (MCC-IMS). The headspace above bacterial cultures was directly drawn and analyzed by MCC-IMS after 90 min of incubation. A cluster analysis software and statistical methods were applied to select discriminative VOC clusters. As a result, 63 VOC clusters were identified, enabling the differentiation between all investigated bacterial strains using canonical discriminant analysis. These 63 clusters were reduced to 7 discriminative VOC clusters by constructing a hierarchical classification tree. Using this tree, all bacteria including resistant strains could be classified with an AUC of 1.0 by receiver-operating characteristic analysis. In conclusion, MCC-IMS is able to differentiate the tested bacterial species, even the non-resistant and their corresponding resistant strains, based on VOC patterns after 90 min of cultivation. Although this result is very promising, in vivo studies need to be performed to investigate if this technology is able to also classify clinical samples. With a short analysis time of 5 min, MCC-IMS is quite attractive for a rapid screening for possible infections in various locations from hospitals to airports.

Key Points

• Differentiation of bacteria by MCC-IMS is shown after 90-min cultivation.

• Non-resistant and resistant strains can be distinguished.

• Classification of bacteria is possible based on metabolic features.

Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases

Exhaled breath analysis has the potential to provide valuable insight on the status of various metabolic pathways taking place in the lungs locally and other vital organs, via systemic circulation. For years, volatile organic compounds (VOCs) have been proposed as feasible alternative diagnostic and prognostic biomarkers for different respiratory pathologies.We reviewed the currently published literature on the discovery of exhaled breath VOCs and their utilisation in various respiratory diseasesKey barriers in the development of clinical breath tests include the lack of unified consensus for breath collection and analysis and the complexity of understanding the relationship between the exhaled VOCs and the underlying metabolic pathways. We present a comprehensive overview, in light of published literature and our experience from coordinating a national breathomics centre, of the progress made to date and some of the key challenges in the field and ways to overcome them. We particularly focus on the relevance of breathomics to clinicians and the valuable insights it adds to diagnostics and disease monitoring.Breathomics holds great promise and our findings merit further large-scale multicentre diagnostic studies using standardised protocols to help position this novel technology at the centre of respiratory disease diagnostics.

Belowground fungal volatiles perception in okra (Abelmoschus esculentus) facilitates plant growth under biotic stress

Microbial volatile organic compounds (mVOCs) have great potential in plant ecophysiology, yet the role of belowground VOCs in plant stress management remains largely obscure. Analysis of biocontrol producing VOCs into the soil allow detailed insight into their interaction with soil borne pathogens for plant disease management. A root interaction trial was set up to evaluate the effects of VOCs released from Trichoderma viride BHU-V2 on soil-inhabiting fungal pathogen and okra plant growth. VOCs released into soil by T. viride BHU-V2 inhibited the growth of collar rot pathogen, Sclerotium rolfsii. Okra plants responded to VOCs by increasing the root growth (lateral roots) and total biomass content. VOCs exposure increased defense mechanism in okra plants by inducing different enzyme activities i.e. chitinase (0.89 fold), β-1,3-glucanase (0.42 fold), peroxidase (0.29 fold), polyphenol oxidase (0.33 fold) and phenylalanine lyase (0.7 fold) when inoculated with S. rolfsii. In addition, T. viride BHU-V2 secreted VOCs reduced lipid peroxidation and cell death in okra plants under pathogen inoculated condition. GC/MS analysis of VOCs blend revealed that T. viride BHU-V2 produced more number of antifungal compounds in soil medium as compared to standard medium. Based on the above observations it is concluded that okra plant roots perceive VOCs secreted by T. viride BHU-V2 into soil that involved in induction of plant defense system against S. rolfsii. In an ecological context, the findings reveal that belowground microbial VOCs may play an important role in stress signaling mechanism to interact with plants.

Volatile organic compounds as disease predictors in newborn infants: a systematic review

Volatile organic compounds (VOC) detected in human breath, urine, stool, sweat, saliva, and blood result from metabolic processes in the body during health or disease. Using sophisticated measurement systems, small amounts of these compounds can be detected in the above bodily fluids. Multiple studies in adults and children have shown the potential of these compounds to differentiate between healthy individuals and patients by detecting profiles of compounds in non-invasively collected samples. However, the detection of biomarkers in VOCs from neonates is particularly attractive due to the non-invasive nature of its approach, and its ability to track disease progress by longitudinal sampling. In this work we have reviewed the literature on the use of VOCs in neonates and identified areas for future work.

Screening of Chorioamnionitis Using Volatile Organic Compound Detection in Exhaled Breath: A Pre-clinical Proof of Concept Study

Chorioamnionitis is a major risk factor for preterm birth and an independent risk factor for postnatal morbidity for which currently successful therapies are lacking. Emerging evidence indicates that the timing and duration of intra-amniotic infections are crucial determinants for the stage of developmental injury at birth. Insight into the dynamical changes of organ injury after the onset of chorioamnionitis revealed novel therapeutic windows of opportunity. Importantly, successful development and implementation of therapies in clinical care is currently impeded by a lack of diagnostic tools for early (prenatal) detection and surveillance of intra-amniotic infections. In the current study we questioned whether an intra-amniotic infection could be accurately diagnosed by a specific volatile organic compound (VOC) profile in exhaled breath of pregnant sheep. For this purpose pregnant Texel ewes were inoculated intra-amniotically with Ureaplasma parvum and serial collections of exhaled breath were performed for 6 days. Ureaplasma parvum infection induced a distinct VOC-signature in expired breath of pregnant sheep that was significantly different between day 0 and 1 vs. day 5 and 6. Based on a profile of only 15 discriminatory volatiles, animals could correctly be classified as either infected (day 5 and 6) or not (day 0 and 1) with a sensitivity of 83% and a specificity of 71% and an area under the curve of 0.93. Chemical identification of these distinct VOCs revealed the presence of a lipid peroxidation marker nonanal and various hydrocarbons including n-undecane and n-dodecane. These data indicate that intra-amniotic infections can be detected by VOC analyses of exhaled breath and might provide insight into temporal dynamics of intra-amniotic infection and its underlying pathways. In particular, several of these volatiles are associated with enhanced oxidative stress and undecane and dodecane have been reported as predictive biomarker of spontaneous preterm birth in humans. Applying VOC analysis for the early detection of intra-amniotic infections will lead to appropriate surveillance of these high-risk pregnancies, thereby facilitating appropriate clinical course of action including early treatment of preventative measures for pre-maturity-associated morbidities.

A hybrid electronic nose system for discrimination of pathogenic bacterial volatile compounds

A hybrid electronic nose comprising an array of three organic-inorganic nanocomposite gas sensors [zinc tetra tert-butyl phthalocyanine (ZnTTBPc), zinc tetra-phenyl porphyrin (ZnTPP), and cobalt tetraphenyl-porphyrin (CoTPP)] coupled with three commercial metal-oxide semiconductor gas sensors (TGS 2444, TGS 2603 and TGS 2620) was developed to discriminate bacterial volatile compounds. Each type of gas sensor had its own strengths and weaknesses in terms of its capability to detect complex odors from the five different bacterial species tested. Bacterial samples were controlled at a fixed initial bacterial concentration by measuring the optical density at 600 nm of the culture suspensions. A comparative evaluation of the volatile compound fingerprints from five bacterial species grown in Luria-Bertani medium was conducted to identify the optimal incubation time for detection of volatile biomarkers to discriminate among bacteria. The results suggest that the hybrid electronic nose was indeed able to discriminate among the bacterial species and culture media, with a variance based on contributions of 92.4% from PC1 and 7.2% from PC2, at an incubation time of 6 hours. Furthermore, the results of hierarchical cluster analysis showed that bacterial odor data formed two major bacterial groups, with the maximum cluster distance close to 25. Intra-group similarity was demonstrated as the two bacterial species (E. cloacae and P. aeruginosa) from among the Gram-negative bacteria had a greater similarity with a cluster distance close to 4. Finally, the minimum distance between E. cloacae and S. Typhi was approximately 1, at an equal distance from E. coli and S. aureus.

Influence of indoor chemistry on the emission of mVOCs from Aspergillus niger molds

People spend 80% of their time indoors exposed to poor air quality due to mold growth in humid air as well as human activities (painting, cooking, cleaning, smoking…). To better understand the impact of molds on indoor air quality, we studied the emission of microbial Volatile Organic Compounds (mVOCs) from Aspergillus niger, cultivated on malt agar extract, using a high-resolution proton transfer reaction- time of flight- mass spectrometer (PTR-TOF-MS). These emissions were studied for different cultivation time and indoor relative humidities. Our results show that the concentration of the known C₄-C₉ mVOCs tracers of the microbial activity (like 1-octen-3-ol, 3-methylfuran, 2-pentanone, dimethyl sulfide, dimethyl disulfide, nitromethane, 1,3-octadiene…) was the highest in the early stage of growth. However, these emissions decreased substantially after a cultivation time of 10-14 days and were highly affected by the relative humidity. In addition, the emissions of certain mVOCs were sensitive to indoor light, suggesting an impact of photochemistry on the relative amounts of indoor mVOCs. Based on this study, an estimation of the mVOC concentration for a standard living room was established at different air exchange rates and their indoor lifetimes toward hydroxyl radicals and ozone were also estimated. These findings give insights on possible mVOCs levels in moisture-damaged buildings for an early detection of microbial activity and new evidences about the effect of indoor light on their emission.

Multi-strain volatile profiling of pathogenic and commensal cutaneous bacteria

The detection of volatile organic compounds (VOC) emitted by pathogenic bacteria has been proposed as a potential non-invasive approach for characterising various infectious diseases as well as wound infections. Studying microbial VOC profiles in vitro allows the mechanisms governing VOC production and the cellular origin of VOCs to be deduced. However, inter-study comparisons of microbial VOC data remains a challenge due to the variation in instrumental and growth parameters across studies. In this work, multiple strains of pathogenic and commensal cutaneous bacteria were analysed using headspace solid phase micro-extraction coupled with gas chromatography-mass spectrometry. A kinetic study was also carried out to assess the relationship between bacterial VOC profiles and the growth phase of cells. Comprehensive bacterial VOC profiles were successfully discriminated at the species-level, while strain-level variation was only observed in specific species and to a small degree. Temporal emission kinetics showed that the emission of particular compound groups were proportional to the respective growth phase for individual S. aureus and P. aeruginosa samples. Standardised experimental workflows are needed to improve comparability across studies and ultimately elevate the field of microbial VOC profiling. Our results build on and support previous literature and demonstrate that comprehensive discriminative results can be achieved using simple experimental and data analysis workflows.

Pseudomonas aeruginosa Volatilome Characteristics and Adaptations in Chronic Cystic Fibrosis Lung Infections

Pseudomonas aeruginosa is a leading cause of chronic lung infections in cystic fibrosis (CF), which are correlated with lung function decline. Significant clinical efforts are therefore aimed at detecting infections and tracking them for phenotypic changes, such as mucoidy and antibiotic resistance. Both the detection and tracking of lung infections rely on sputum cultures, but due to improvements in CF therapies, sputum production is declining, although risks for lung infections persist. Therefore, we are working toward the development of breath-based diagnostics for CF lung infections. In this study, we characterized of the volatile metabolomes of 81 P. aeruginosa clinical isolates collected from 17 CF patients over a duration of at least 5 years of a chronic lung infection. We found that the volatilome of P. aeruginosa adapts over time and is correlated with infection phenotype changes, suggesting that it may be possible to track chronic CF lung infections with a breath test.

Pseudomonas aeruginosa chronic lung infections in individuals with cystic fibrosis (CF) significantly reduce quality of life and increase morbidity and mortality. Tracking these infections is critical for monitoring patient health and informing treatments. We are working toward the development of novel breath-based biomarkers to track chronic P. aeruginosa lung infections in situ. Using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC–TOF-MS), we characterized the in vitro volatile metabolomes (“volatilomes”) of 81 P. aeruginosa isolates collected from 17 CF patients over at least a 5-year period of their chronic lung infections. We detected 539 volatiles produced by the P. aeruginosa isolates, 69 of which were core volatiles that were highly conserved. We found that each early infection isolate has a unique volatilome, and as infection progresses, the volatilomes of isolates from the same patient become increasingly dissimilar, to the point that these intrapatient isolates are no more similar to one another than to isolates from other patients. We observed that the size and chemical diversity of P. aeruginosa volatilomes do not change over the course of chronic infections; however, the relative abundances of core hydrocarbons, alcohols, and aldehydes do change and are correlated with changes in phenotypes associated with chronic infections. This study indicates that it may be feasible to track P. aeruginosa chronic lung infections by measuring changes to the infection volatilome and lays the groundwork for exploring the translatability of this approach to direct measurement using patient breath.

IMPORTANCEPseudomonas aeruginosa is a leading cause of chronic lung infections in cystic fibrosis (CF), which are correlated with lung function decline. Significant clinical efforts are therefore aimed at detecting infections and tracking them for phenotypic changes, such as mucoidy and antibiotic resistance. Both the detection and tracking of lung infections rely on sputum cultures, but due to improvements in CF therapies, sputum production is declining, although risks for lung infections persist. Therefore, we are working toward the development of breath-based diagnostics for CF lung infections. In this study, we characterized of the volatile metabolomes of 81 P. aeruginosa clinical isolates collected from 17 CF patients over a duration of at least 5 years of a chronic lung infection. We found that the volatilome of P. aeruginosa adapts over time and is correlated with infection phenotype changes, suggesting that it may be possible to track chronic CF lung infections with a breath test.

Early detection and follow-up of colorectal neoplasia based on faecal volatile organic compounds

AIM

Early detection and removal of colorectal cancer (CRC) and advanced adenomas (AAs) decreases the incidence of and mortality from the disease. We aimed to evaluate the potential of faecal volatile organic compounds (VOCs) for detection and follow-up of colorectal adenoma using advanced electronic nose technology.

METHOD

This was a prospective multi-centre case-control cohort including two district hospitals and one tertiary referral hospital. Patients undergoing colonoscopy were instructed to collect a faecal sample prior to bowel cleansing and were included in the study when CRC, AAs, large adenomas (LAs; 0.5-1.0 cm), small adenomas (SAs; 0.1-0.5 cm) or no endoscopic abnormalities (controls) were observed. Patients undergoing polypectomy and controls were asked for a second sample after 3 months. Faecal VOCs were measured with gas chromatography-ion mobility spectrometry. Random forest, support vector machine, Gaussian process and neural net classification were used to evaluate accuracy.

RESULTS

In total, 14 patients with CRC, 64 with AAs, 69 with LAs, 127 with SAs and 227 controls were included. A second sample was collected from 32 polypectomy patients and 32 controls. Faecal VOCs discriminated CRC and adenomas from control [AUC (95% CI): CRC vs control 0.96 (0.89-1); AA vs control 0.96 (0.93-1); LA vs control 0.96 (0.92-0.99); SA vs control 0.96 (0.94-0.99)]. There were no significant differences between CRC and adenoma groups. Patients with adenomas and controls were discriminated prior to polypectomy, whereas 3 months after polypectomy VOC profiles were similar [T0 adenoma vs control 0.98 (0.95-1); T1 adenoma vs control 0.55 (0.40-0.69)].

CONCLUSIONS

Faecal VOC profiles may be useful for early detection of CRC and adenomas and the timing of polyp surveillance as polypectomy led to a normalization of the VOC profile.

Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition

In vitro cultivation of staphylococci is fundamental to both clinical and research microbiology, but few studies, to-date, have investigated how the differences in rich media can influence the volatilome of cultivated bacteria. The objective of this study was to determine the influence of rich media composition on the chemical characteristics of the volatilomes of Staphylococcus aureus and Staphylococcus epidermidis. S. aureus (ATCC 12600) and S. epidermidis (ATCC 12228) were cultured in triplicate in four rich complex media (brain heart infusion (BHI), lysogeny broth (LB), Mueller Hinton broth (MHB), and tryptic soy broth (TSB)), and the volatile metabolites produced by each culture were analyzed using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography—time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). When comparing the chemical compositions of the staph volatilomes by the presence versus absence of volatiles produced in each medium, we observed few differences. However, when the relative abundances of volatiles were included in the analyses, we observed that culturing staph in media containing free glucose (BHI and TSB) resulted in volatilomes dominated by acids and esters (67%). The low-glucose media (LB and MHB) produced ketones in greatest relative abundances, but the volatilome compositions in these two media were highly dissimilar. We conclude that the staphylococcal volatilome is strongly influenced by the nutritional composition of the growth medium, especially the availability of free glucose, which is much more evident when the relative abundances of the volatiles are analyzed, compared to the presence versus absence.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Unravelling the Potential of Salivary Volatile Metabolites in Oral Diseases. A Review

Fostered by the advances in the instrumental and analytical fields, in recent years the analysis of volatile organic compounds (VOCs) has emerged as a new frontier in medical diagnostics. VOCs analysis is a non-invasive, rapid and inexpensive strategy with promising potential in clinical diagnostic procedures. Since cellular metabolism is altered by diseases, the resulting metabolic effects on VOCs may serve as biomarkers for any given pathophysiologic condition. Human VOCs are released from biomatrices such as saliva, urine, skin emanations and exhaled breath and are derived from many metabolic pathways. In this review, the potential of VOCs present in saliva will be explored as a monitoring tool for several oral diseases, including gingivitis and periodontal disease, dental caries, and oral cancer. Moreover, the analytical state-of-the-art for salivary volatomics, e.g., the most common extraction techniques along with the current challenges and future perspectives will be addressed unequivocally.

Headspace gas chromatographic method for antimicrobial screening: Minimum inhibitory concentration determination

The headspace gas chromatographic method has been widely used to detect volatile metabolites to reflect the growth state of microorganisms, however, it has never been used for the determination of the minimum inhibitory concentration in antibacterial drugs. This paper reports a new method for evaluating the antimicrobial activity of drugs by measuring the amount of CO₂ produced by bacterial metabolism after treatment with drugs. According to the amount of CO₂ produced by bacterial metabolism, a proper amount of bacterial liquid is selected and added to a drug-containing culture medium as compared with bacteria without drugs in parallel. The amount of CO₂ produced by bacteria is measured by using a headspace gas chromatograph coupled with a thermal conductivity detector to measure the bacteriostasis rate and the minimum bacteriostasis concentration of the tested drug, so as to evaluate its antibacterial activity. The accuracy of this method was verified by comparison with the standard method (the OD method), which indicated that the precision was less than 3 % (expressed by relative standard deviation), the inhibition rate (R² = 0.968) was consistent with the reference method above. This method is simple in operation and can avoid the error caused by the properties of the sample such as volatility, solubility and color in the determination of the minimum inhibitory concentration. It is suitable for the determination of antibacterial activity of drugs, especially natural drugs.

A Filter Supported Surface-Enhanced Raman Scattering "Nose" for Point-of-Care Monitoring of Gaseous Metabolites of Bacteria

This work designs a convenient method for fabrication of surface-enhanced Raman scattering (SERS) devices by loading gold nanostars (AuNSs) on a flat filter support with vacuum filtration. The dense accumulation of AuNSs results in a strong sensitization to SERS signal and shows sensitive response to gaseous metabolites of bacteria, which produces a SERS "nose" for rapid point-of-care monitoring of these metabolites. The "nose" shows good reproducibility and stability and can be used for SERS quantitation of a gaseous target with Raman signal. The impressive performance of the proposed SERS "nose" for detecting gaseous metabolites of common foodborne bacteria like Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa from inoculated samples demonstrates its much higher sensitivity than that of human sense and application in distinguishing spoiled food at an early stage and real-time tracing of food spoilage degree. The strong point-of-care testing ability of the designed SERS "nose" and the miniaturization of whole equipment extend greatly the analytical application of SERS technology in food safety and public health.

Volatile scents of influenza A and S. pyogenes (co-)infected cells

Influenza A is a serious pathogen itself, but often leads to dangerous co-infections in combination with bacterial species such as Streptococcus pyogenes. In comparison to classical biochemical methods, analysis of volatile organic compounds (VOCs) in headspace above cultures can enable destruction free monitoring of metabolic processes in vitro. Thus, volatile biomarkers emitted from biological cell cultures and pathogens could serve for monitoring of infection processes in vitro. In this study we analysed VOCs from headspace above (co)-infected human cells by using a customized sampling system. For investigating the influenza A mono-infection and the viral-bacterial co-infection in vitro, we analysed VOCs from Detroit cells inoculated with influenza A virus and S. pyogenes by means of needle-trap micro-extraction (NTME) and gas chromatography mass spectrometry (GC-MS). Besides the determination of microbiological data such as cell count, cytokines, virus load and bacterial load, emissions from cell medium, uninfected cells and bacteria mono-infected cells were analysed. Significant differences in emitted VOC concentrations were identified between non-infected and infected cells. After inoculation with S. pyogenes, bacterial infection was mirrored by increased emissions of acetaldehyde and propanal. N-propyl acetate was linked to viral infection. Non-destructive monitoring of infections by means of VOC analysis may open a new window for infection research and clinical applications. VOC analysis could enable early recognition of pathogen presence and in-depth understanding of their etiopathology.

Monitoring of Bactericidal Effects of Silver Nanoparticles Based on Protein Signatures and VOC Emissions from Escherichia coli and Selected Salivary Bacteria

Escherichia coli and salivary Klebsiella oxytoca and Staphylococcus saccharolyticus were subjected to different concentrations of silver nanoparticles (AgNPs), namely: 12.5, 50, and 100 µg mL-1. Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) spectra were acquired after specified periods: 0, 1, 4, and 12 h. For study of volatile metabolites, headspace solid-phase microextraction coupled to gas chromatography/mass spectrometry (HS-SPME-GC-MS) was employed-AgNPs were added to bacteria cultures and the headspace was analyzed immediately and after 12 h of incubation. Principal components analysis provided discrimination between clusters of protein profiles belonging to different strains. Canonical correlation, network analysis, and multiple linear regression approach revealed that dimethyl disulfide, dimethyl trisulfide, 2-heptanone, and dodecanal (related to the metabolism of sulfur-containing amino acids and fatty acids synthesis) are exemplary molecular indicators, whose response variation deeply correlated to the interaction with bacteria. Therefore, such species can serve as biomarkers of the agent's effectiveness. The present investigation pointed out that the used approaches can be useful in the monitoring of response to therapeutic treatment based on AgNPs. Furthermore, biochemical mechanisms enrolled in the bactericidal action of nanoparticles can be applied in the development of new agents with enhanced properties.

A Novel Subspace Alignment-Based Interference Suppression Method for the Transfer Caused by Different Sample Carriers in Electronic Nose

A medical electronic nose (e-nose) with 31 gas sensors is used for wound infection detection by analyzing the bacterial metabolites. In practical applications, the prediction accuracy drops dramatically when the prediction model established by laboratory data is directly used in human clinical samples. This is a key issue for medical e-nose which should be more worthy of attention. The host (carrier) of bacteria can be the culture solution, the animal wound, or the human wound. As well, the bacterial culture solution or animals (such as: mice, rabbits, etc.) obtained easily are usually used as experimental subjects to collect sufficient sensor array data to establish the robust predictive model, but it brings another serious interference problem at the same time. Different carriers have different background interferences, therefore the distribution of data collected under different carriers is different, which will make a certain impact on the recognition accuracy in the detection of human wound infection. This type of interference problem is called "transfer caused by different sample carriers". In this paper, a novel subspace alignment-based interference suppression (SAIS) method with domain correction capability is proposed to solve this interference problem. The subspace is the part of space whose dimension is smaller than the whole space, and it has some specific properties. In this method, first the subspaces of different data domains are gotten, and then one subspace is aligned to another subspace, thereby the problem of different distributions between two domains is solved. From experimental results, it can be found that the recognition accuracy of the infected rat samples increases from 29.18% (there is no interference suppression) to 82.55% (interference suppress by SAIS).

Development of Open-Tubular-Type Micro Gas Chromatography Column with Bump Structures

Gas chromatography (GC) is the chemical analysis technique most widely used to separate and identify gas components, and it has been extensively applied in various gas analysis fields such as non-invasive medical diagnoses, indoor air quality monitoring, and outdoor environmental monitoring. Micro-electro-mechanical systems (MEMS)-based GC columns are essential for miniaturizing an integrated gas analysis system (Micro GC system). This study reports an open-tubular-type micro GC (μ-GC) column with internal bump structures (bump structure μ-GC column) that substantially increase the interaction between the gas mixture and a stationary phase. The developed bump structure μ-GC column, which was fabricated on a 2 cm × 2 cm μ-GC chip and coated with a non-polar stationary phase, is 1.5 m-long, 150 μm-wide, and 400 μm-deep. It has an internal microfluidic channel in which the bumps, which are 150 μm diameter half-circles, are alternatingly disposed to face each other on the surface of the microchannel. The fabricated bump structure μ-GC column yielded a height-equivalent-to-a-theoretical-plate (HETP) of 0.009 cm (11,110 plates/m) at an optimal carrier gas velocity of 17 cm/s. The mechanically robust bump structure μ-GC column proposed in this study achieved higher separation efficiency than a commercially available GC column and a typical μ-GC column with internal post structures classified as a semi-packed-type column. The experimental results demonstrate that the developed bump structure μ-GC column can separate a gas mixture completely, with excellent separation resolution for formaldehyde, benzene, toluene, ethylbenzene, and xylene mixture, under programmed operating temperatures.

The influence of lifestyle factors on fecal volatile organic compound composition as measured by an electronic nose

INTRODUCTION

Fecal volatile organic compounds (VOCs) are gaseous metabolic products which are increasingly considered potential non-invasive biomarkers for the detection of various (gastrointestinal) diseases. The influence of lifestyle factors on fecal VOC patterns remains unexplored but is of importance prior to implementation of VOC analysis as a diagnostic tool. The aim of this study was to investigate the effects of age, gender, body mass index, smoking status, dietary preferences, medication use and co-morbidity on fecal VOC patterns.

METHODS

For this study, fecal samples of patients undergoing a colonoscopy were collected prior to endoscopy. All participants completed a questionnaire on lifestyle factors, co-morbidity and medication use. Patients without colonic abnormalities were included in this study. Fecal VOC patterns were analyzed by means of an electronic nose (eNose) device (Cyranose^® 320).

RESULTS

From the 1039 participants willing to participate in the initial study, 211 were eligible as controls. All unique lifestyle variables investigated in this study affected the fecal VOC composition. The strongest influences were caused by low BMI, a vegetarian diet and an active smoking status, whereas the least influence was found for the variables gender, age > 55 years and previous smokers.

DISCUSSION

Age, gender, BMI, smoking habits, dietary preferences, co-morbidity and medication use all have unique effects on fecal VOC composition. Future studies should carefully consider this influence on VOC outcome when defining VOC signatures as biomarker for diagnostic purposes.

Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept study

Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae ( SP) or Pseudomonas aeruginosa ( PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography-mass-spectrometry (TD-GC-MS) and selected ion flow tube-mass spectrometry (SIFT-MS) of exhaled breath. Male adult rats ( n = 50) received an intratracheal inoculation of 1) 200 µl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TD-GC-MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73–0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94–1) and CCR of 99.9% for SP versus PA, 0.92 (0.83–1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92–1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens.

Sniffing out resistance - Rapid identification of urinary tract infection-causing bacteria and their antibiotic susceptibility using volatile metabolite profiles

Antibiotic resistance is set to be an unprecedented threat to modern medicine. 'Sniffing' bacteria potentially offers a rapid way to determine susceptibility. A successful proof-of-principle study is described, using thermal desorption-gas chromatography-mass spectrometry (TDGCMS) to 'smell' cephalexin and ciprofloxacin resistant and sensitive Urinary Tract Infection (UTI)-causing bacteria. 578 peaks at unique retention times were detected from 86 chromatograms of 18 bacterial isolates (E. coli, K. pneumoniae and P. aeruginosa). The isolates were grown with and without the presence of antibiotic. Chi-square analysis found 9 compounds that differed significantly between cephalexin sensitive and resistant isolates, and 22 compounds that differed significantly between ciprofloxacin sensitive and resistant isolates, at p ≤ 0.05. When antibiotic was added to the media, more differences were found in the cephalexin group, attributed to lysis, but not in the ciprofloxacin group. Further work with large sample sizes will potentially enable the development of diagnostic algorithms using presence/absence of particular compounds of interest.

Yeast Smell Like What They Eat: Analysis of Volatile Organic Compounds of Malassezia furfur in Growth Media Supplemented with Different Lipids

Malassezia furfur is part of the human skin microbiota. Its volatile organic compounds (VOCs) possibly contribute to the characteristic odour in humans, as well as to microbiota interaction. The aim of this study was to investigate how the lipid composition of the liquid medium influences the production of VOCs. Growth was performed in four media: (1) mDixon, (2) oleic acid (OA), (3) oleic acid + palmitic acid (OA+PA), and (4) palmitic acid (PA). The profiles of the VOCs were characterized by HS-SPME/GC-MS in the exponential and stationary phases. A total number of 61 VOCs was found in M. furfur, among which alkanes, alcohols, ketones, and furanic compounds were the most abundant. Some compounds previously reported for Malassezia (γ-dodecalactone, 3-methylbutan-1-ol, and hexan-1-ol) were also found. Through our experiments, using univariate and multivariate unsupervised (Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA)) and supervised (Projection to Latent Structures Discriminant Analysis (PLS-DA)) statistical techniques, we have proven that each tested growth medium stimulates the production of a different volatiles profile in M. furfur. Carbon dioxide, hexan-1-ol, pentyl acetate, isomer5 of methyldecane, dimethyl sulphide, undec-5-ene, isomer2 of methylundecane, isomer1 of methyldecane, and 2-methyltetrahydrofuran were established as differentiating compounds among treatments by all the techniques. The significance of our findings deserves future research to investigate if certain volatile profiles could be related to the beneficial or pathogenic role of this yeast.