The volatile molecule signature of four mycobacteria species

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.

How the volatile organic compounds emitted by corpse plant change through flowering

The corpse plant (Amorphophallus titanum) is so named because it produces a pungent, foul odor when flowering. Little is known about how the emitted volatiles change throughout the two-day flowering period. In this study, the comprehensive monitoring of the presence and change in volatile molecules during the female and the male flowering phases of A. titanum was conducted, and the plant temperature was monitored. A total of 422 volatile features were detected over the entire sampling period, of which 118 features were statistically significantly different between the pre-flowering and both flowering phases, and an additional 304 features were found present throughout the flowering period. A total of 45 molecules could be assigned putative names. The volatile profile of A. titanum changes over the two-day flowering period, with the S-containing molecules and aldehydes dominant in the female flowering phase, and the alcohols and hydrocarbons dominant in the male flowering phase. The two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) enabled us to identify 32 new molecules produced by A. titanum. Each of these molecules alone, and in combination, likely contribute to the different odors emitted during the flowering phase of A. titanum.

Breath biomarkers associated withnontuberculosis mycobacteriadisease status in persons with cystic fibrosis: a pilot study

Pulmonary infections caused by mycobacteria cause significant mortality and morbidity in the human population. Diagnosing mycobacterial infections is challenging. An infection can lead to active disease or remain indolent with little clinical consequence. In patients with pulmonarynontuberculosis mycobacteria(PNTM) identification of infection and diagnosis of disease can take months to years. Our previous studies showed the potential diagnostic power of volatile molecules in the exhaled breath samples to detect active pulmonaryM. tuberculosisinfection. Herein, we demonstrate the ability to detect the disease status of PNTM in the breath of persons with cystic fibrosis (PwCF). We putatively identified 17 volatile molecules that could discriminate between active-NTM disease (n= 6), indolent patients (n= 3), and those patients who have never cultured an NTM (n= 2). The results suggest that further confirmation of the breath biomarkers as a non-invasive and culture-independent tool for diagnosis of NTM disease in a larger cohort of PwCF is warranted.

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

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

Category A and B biothreat agents are deemed to be of great concern by the US Centers for Disease Control and Prevention (CDC) and include the bacteria Francisella tularensis, Yersinia pestis, Burkholderia mallei, and Brucella species. Underscored by the impact of the 2020 SARS-CoV-2 outbreak, 2016 Zika pandemic, 2014 Ebola outbreak, 2001 anthrax letter attacks, and 1984 Rajneeshee Salmonella attacks, the threat of future epidemics/pandemics and/or terrorist/criminal use of pathogenic organisms warrants continued exploration and development of both classic and alternative methods of detecting biothreat agents. Volatile organic compounds (VOCs) comprise a large and highly diverse group of carbon-based molecules, generally related by their volatility at ambient temperature. Recently, the diagnostic potential of VOCs has been realized, as correlations between the microbial VOC metabolome and specific bacterial pathogens have been identified. Herein, we describe the use of microbial VOC profiles as fingerprints for the identification of biothreat-relevant microbes, and for differentiating between a kanamycin susceptible and resistant strain. Additionally, we demonstrate microbial VOC profiling using a rapid-throughput VOC metabolomics method we refer to as ‘simultaneous multifiber headspace solid-phase microextraction’ (simulti-hSPME). Finally, through VOC analysis, we illustrate a rapid non-invasive approach to the diagnosis of BALB/c mice infected with either F. tularensis SCHU S4 or Y. pestis CO92.

Advances in the application of comprehensive two-dimensional gas chromatography in metabolomics

Due to excellent separation capacity for complex mixtures of chemicals, comprehensive two-dimensional gas chromatography (GC × GC) is being utilized with increasing frequency for metabolomics analyses. This review describes recent advances in GC × GC method development for metabolomics, organismal sampling techniques compatible with GC × GC, metabolomic discoveries made using GC × GC, and recommendations and best practices for collecting and reporting GC × GC metabolomics data.

Detection of high-risk carbapenem-resistant Klebsiella pneumoniae and Enterobacter cloacae isolates using volatile molecular profiles

Infections caused by carbapenem-resistant Enterobacteriaceae (CRE) are alarming in the clinical setting, as CRE isolates often exhibit resistance to most clinically-available antibiotics. Klebsiella pneumoniae carbapenemase (KPC) is the most common carbapenemase carried by CRE in North America and Europe, frequently detected in isolates of K. pneumoniae, Escherichia coli, and Enterobacter cloacae. Notably, KPC-expressing strains often arise from clonal lineages, with sequence type 258 (ST258) representing the dominant lineage in K. pneumoniae, ST131 in E. coli, and ST78 and ST171 in E. cloacae. Prior studies have demonstrated that carbapenem-resistant K. pneumoniae differs from carbapenem-susceptible K. pneumoniae at both the transcriptomic and soluble metabolomic levels. In the present study, we sought to determine whether carbapenem-resistant and carbapenem-susceptible isolates of K. pneumoniae, E. coli, and E. cloacae produce distinct volatile metabolic profiles. We were able to identify a volatile metabolic fingerprint that could discriminate between CRE and non-CRE with an area under the receiver operating characteristic curve (AUROC) as high as 0.912. Species-specific AUROCs were as high as 0.988 for K. pneumoniae and 1.000 for E. cloacae. Paradoxically, curing of KPC-expressing plasmids from a subset of K. pneumoniae isolates further accentuated the metabolic differences observed between ST258 and non-ST258.

SPME-GC×GC-TOF MS fingerprint of virally-infected cell culture: Sample preparation optimization and data processing evaluation

Untargeted metabolomics study of volatile organic compounds produced by different cell cultures is a field that has gained increasing attention over the years. Solid-phase microextraction has been the sampling technique of choice for most of the applications mainly due to its simplicity to implement. However, a careful optimization of the analytical conditions is necessary to obtain the best performances, which are highly matrix-dependent. In this work, five different solid-phase microextraction fibers were compared for the analysis of the volatiles produced by cell culture infected with the human respiratory syncytial virus. A central composite design was applied to determine the best time-temperature combination to maximize the extraction efficiency and the salting-out effect was evaluated as well. The linearity of the optimized method, along with limits of detection and quantification and repeatability was assessed. Finally, the effect of i) different normalization techniques (i.e. z-score and probabilistic quotient normalization), ii) data transformation (i.e. in logarithmic scale), and iii) different feature selection algorithms (i.e. Fisher ratio and random forest) on the capability of discriminating between infected and not-infected cell culture was evaluated.

Comparative analysis of volatile organic compounds for the classification and identification of mycobacterial species

Background

Species of Mycobacteriaceae cause serious zoonotic diseases in mammals, for example tuberculosis in humans, dogs, parrots, and elephants (caused by Mycobacterium tuberculosis) and in ruminants and humans (caused by M. bovis and M. caprae). Pulmonary diseases, lymphadenitis, skin diseases, and disseminated diseases can be caused by non-tuberculous mycobacteria (NTM). Diagnosis and differentiation among Mycobacterium species are currently done by culture isolation. The established diagnostic protocols comprise several steps that allow species identification. Detecting volatile organic compounds (VOCs) above bacterial cultures is a promising approach towards accelerating species identification via culture isolation. The aims of this project were to analyse VOCs in the headspace above 13 different species of mycobacteria, to define VOC profiles that are unique for each species, and to compile a set of substances that indicate the presence of growing mycobacteria in general.

Materials & methods

VOCs were measured in the headspace above 17 different mycobacterial strains, all cultivated on Herrold’s Egg Yolk Medium and above pure media slants that served as controls. For pre-concentration of VOCs, needle-trap micro-extraction was employed. Samples were subsequently analysed using gas chromatography-mass spectrometry. All volatiles were identified and calibrated by analysing pure reference substances.

Results

More than 130 VOCs were detected in headspace above mycobacteria-inoculated and control slants. Results confirmed significant VOC emissions above all mycobacterial species that had grown well. Concentration changes were measurable in vials with visually assessed bacterial growth and vials without apparent growth. VOCs above mycobacterial cultures could be grouped into substances that were either higher or equally concentrated, lower or equally concentrated, or both as those above control slants. Hence, we were able to identify 17 substances as potential biomarkers of the presence of growing mycobacteria in general.

Conclusions

This study revealed species-specific VOC profiles for eleven species of mycobacteria that showed visually apparent bacterial growth at the time point of analysis.

Towards the use of breath for detecting mycobacterial infection: a case study in a murine model

In the present research, the potential of breath analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was investigated for the discrimination between healthy and infected mice. A pilot study employing a total of 16 animals was used to develop a method for breath analysis in a murine model for studying Mycobacterium tuberculosis complex (MTBC) using the M. bovis bacillus Calmette-Guérin. Breath was collected in Tedlar bags and concentrated onto thermal desorption tubes for subsequent analysis by GC×GC-MS. Immunological test and bacterial cell count in bronchoalveolar lavage fluid and mice lung homogenate confirmed the presence of bacteria in the infected group. From the GC×GC-MS analysis, 23 molecules were found to mainly drive the separation between control and infected mice and their tentative identification is provided.This study shows that the overall used methodology is able to differentiate breath between healthy and infected animals, and the information herein can be used to further develop the mouse breath model to study MTBC pathogenesis, evaluate pre-clinical drug regimen efficacy, and to further develop the concept of breath-based diagnostics.

Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups

The identification of pathogen-specific volatile metabolic 'fingerprints' could lead to the rapid identification of disease-causing organisms either directly from ex vivo patient bio-specimens or from in vitro cultures. In the present study, we have evaluated the volatile metabolites produced by 100 clinical isolates belonging to ten distinct pathogen groups that, in aggregate, account for 90% of bloodstream infections, 90% of urinary tract infections, and 80% of infections encountered in the intensive care unit setting. Headspace volatile metabolites produced in vitro were concentrated using headspace solid-phase microextraction and analyzed via two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). A total of 811 volatile metabolites were detected across all samples, of which 203 were: (1) detected in 9 or 10 (of 10) isolates belonging to one or more pathogen groups, and (2) significantly more abundant in cultures relative to sterile media. Network analysis revealed a distinct metabolic fingerprint associated with each pathogen group, and analysis via Random Forest using leave-one-out cross-validation resulted in a 95% accuracy for the differentiation between groups. The present findings support the results of prior studies that have reported on the differential production of volatile metabolites across pathogenic bacteria and fungi, and provide additional insight through the inclusion of pathogen groups that have seldom been studied previously, including Acinetobacter spp., coagulase-negative Staphylococcus, and Proteus mirabilis, as well as the utilization of HS-SPME-GC×GC-TOFMS for improved sensitivity and resolution relative to traditional gas chromatography-based techniques.

Sniffing out the hypoxia volatile metabolic signature of Aspergillus fumigatus

Invasive aspergillosis (IA) is a life-threatening infectious disease caused by fungi from the genus Aspergillus, with an associated mortality as high as 90% in certain populations. IA-associated pulmonary lesions are characteristically depleted in oxygen relative to normal lung tissue, and it has been shown that the most common causal agent of IA, Aspergillus fumigatus, must respond to low-oxygen environments for pathogenesis and disease progression. Previous studies have demonstrated marked alterations to the Aspergillus fumigatus transcriptome in response to low-oxygen environments that induce a 'hypoxia response'. Consequently, we hypothesized that these transcriptomic changes would alter the volatile metabolome and generate a volatile hypoxia signature. In the present study, we analyzed the volatile molecules produced by A. fumigatus in both oxygen replete (normoxia) and depleted (hypoxia) environments via headspace solid-phase micro-extraction coupled to two-dimensional gas chromatography-time-of-flight mass spectrometry. Using the machine learning algorithm random forest, we identified 19 volatile molecules that were discriminatory between the four growth conditions assessed in this study (i.e., early hypoxia (1 h), late hypoxia (8 h), early normoxia (1 h), and late normoxia (8 h)), as well as a set of 19 that were discriminatory between late hypoxia cultures and all other growth conditions in aggregate. Nine molecules were common to both comparisons, while the remaining 20 were specific to only one of two. We assigned putative identifications to 13 molecules, of which six were most highly abundant in late hypoxia cultures. Previously acquired transcriptomic data identified putative biochemical pathways induced in hypoxia conditions that plausibly account for the production of a subset of these molecules, including 2,3-butanedione and 3-hydroxy-2-butanone. These two molecules may represent a novel hypoxia fitness pathway in A. fumigatus, and could be useful in the detection of hypoxia-associated A. fumigatus lesions that develop in established IA infections.