Christiaan A Rees

Streptomyces exploration is triggered by fungal interactions and volatile signals

It has long been thought that the life cycle of Streptomyces bacteria encompasses three developmental stages: vegetative hyphae, aerial hyphae and spores. Here, we show interactions between Streptomyces and fungi trigger a previously unobserved mode of Streptomyces development. We term these Streptomyces cells ‘explorers’, for their ability to adopt a non-branching vegetative hyphal conformation and rapidly transverse solid surfaces. Fungi trigger Streptomyces exploratory growth in part by altering the composition of the growth medium, and Streptomyces explorer cells can communicate this exploratory behaviour to other physically separated streptomycetes using an airborne volatile organic compound (VOC). These results reveal that interkingdom interactions can trigger novel developmental behaviours in bacteria, here, causing Streptomyces to deviate from its classically-defined life cycle. Furthermore, this work provides evidence that VOCs can act as long-range communication signals capable of propagating microbial morphological switches. DOI: http://dx.doi.org/10.7554/eLife.21738.001

Characterization of the Clostridium difficile volatile metabolome using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Clostridium difficile is a bacterial pathogen capable of causing life-threatening infections of the gastrointestinal tract characterized by severe diarrhea. Exposure to certain classes of antibiotics, advanced age, and prolonged hospitalizations are known risk factors for infection by this organism. Anecdotally, healthcare providers have reported that they can smell C. difficile infections in their patients, and several studies have suggested that there may indeed be an olfactory signal associated with C. difficile-associated diarrhea. In this study, we sought to characterize the volatile molecules produced by an epidemic strain of C. difficile (R20291) using headspace solid-phase microextraction (HS-SPME) followed by two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). We report on a set of 77 volatile compounds, of which 59 have not previously been associated with C. difficile growth in vitro. Amongst these reported compounds, we detect both straight-chain and branched-chain carboxylic acids, as well as p-cresol, which have been the primary foci of C. difficile volatile metabolomic studies to-date. We additionally report on novel sulfur-containing and carbonyl-containing molecules that have not previously been reported for C. difficile. With the identification of these novel C. difficile-associated volatile compounds, we demonstrate the superior resolution and sensitivity of GC×GC-TOFMS relative to traditional GC-MS.

Expanding the Klebsiella pneumoniae volatile metabolome using advanced analytical instrumentation for the detection of novel metabolites.

The purpose of this study was to identify the volatile molecules produced by the pathogenic Gram‐negative bacterium Klebsiella pneumoniae (ATCC 13883) during in vitro growth using comprehensive two‐dimensional gas chromatography coupled to time‐of‐flight mass spectrometry (GC×GC‐TOFMS).

The volatile metabolome of Klebsiella pneumoniae in human blood

Klebsiella pneumoniae is an important cause of bloodstream infections in critically-ill patients, with mortality exceeding 50% for infections caused by antibiotic-resistant strains. Despite its importance as a bacteremia-causing agent, there is little known about the metabolism of K. pneumoniae during growth in pure human blood. Here, we comprehensively profile the volatile metabolites produced by K. pneumoniae during growth in human blood to approximately mid-exponential (7 h) and early stationary (12 h) phases using 2D gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS). We identified 33 volatile molecules that were significantly more abundant in K. pneumoniae cultures relative to sterile blood, of which 22 were detected in cultures only. We identified nine molecules that have not previously been reported as K. pneumoniae-associated headspace volatiles, four of which we believe to be novel bacterial-associated volatiles. We also identified a set of 17 volatile molecules that discriminate between 7 h and 12 h K. pneumoniae cultures, indicating either growth phase or cell density-associated changes in the composition of headspace volatiles. Our analysis of the volatile molecules produced by K. pneumoniae during growth in human blood using GC×GC-TOFMS has doubled the number of volatiles reported for this organism in blood-containing media, and increased the total number of K. pneumoniae-associated volatiles by 20%. The volatile molecules produced by K. pneumoniae in blood may represent novel biomarkers for the diagnosis of bacteremia.

Volatile metabolic diversity of Klebsiella pneumoniae in nutrient-replete conditions

IntroductionMicroorganisms catabolize carbon-containing compounds in their environment during growth, releasing a subset of metabolic byproducts as volatile compounds. However, the relationship between growth media and the production of volatile compounds has been largely unexplored to-date.ObjectivesTo assess the core and media-specific components of the Klebsiella pneumoniae volatile metabolome via growth in four in vitro culture media.MethodsHeadspace volatiles produced by cultures of K. pneumoniae after growth to stationary phase in four rich media (brain heart infusion broth, lysogeny broth, Mueller-Hinton broth, and tryptic soy broth) were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Differences in the composition of headspace volatiles as a function of growth media were assessed using hierarchical clustering analysis (HCA) and principal component analysis (PCA).ResultsA total of 365 volatile compounds were associated with the growth of K. pneumoniae across all media, of which 36 (10%) were common to all growth media, and 148 (41%) were specific to a single medium. In addition, utilizing all K. pneumoniae-associated volatile compounds, strains clustered as a function of growth media, demonstrating the importance of media in determining the metabolic profile of this organism.ConclusionK. pneumoniae produces a core suite of volatile compounds across all growth media studied, although the volatile metabolic signature of this organism is fundamentally media-dependent.

Volatile fingerprinting of human respiratory viruses from cell culture

Abstract Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.

Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography – Time of flight mass spectrometry and machine learning

Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB.

Volatile molecules from bronchoalveolar lavage fluid can ‘rule-in’ Pseudomonas aeruginosa and ‘rule-out’ Staphylococcus aureus infections in cystic fibrosis patients

Respiratory infections caused by Pseudomonas aeruginosa and Staphylococcus aureus are the leading cause of morbidity and mortality in cystic fibrosis (CF) patients. The authors aimed to identify volatile biomarkers from bronchoalveolar lavage (BAL) samples that can guide breath biomarker development for pathogen identification. BAL samples (n = 154) from CF patients were analyzed using two-dimensional gas chromatography time-of-flight mass spectrometry. Random Forest was used to select suites of volatiles for identifying P. aeruginosa-positive and S. aureus-positive samples using multiple infection scenarios and validated using test sets. Using nine volatile molecules, we differentiated P. aeruginosa-positive (n = 7) from P. aeruginosa-negative (n = 53) samples with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.71–1.00) and with positive and negative predictive values of 0.67 (95% CI 0.38–0.75) and 0.92 (95% CI 0.88–1.00), respectively. We were also able to discriminate S. aureus-positive (n = 15) from S. aureus-negative (n = 45) samples with an AUROC of 0.88 (95% CI 0.79-1.00) using eight volatiles and with positive and negative predictive values of 0.86 (95% CI 0.61–0.96) and 0.70 (95% CI 0.61–0.75), respectively. Prospective validation of identified biomarkers as screening tools in patient breath may lead to clinical application.

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.

The volatile molecular profiles of seven Streptococcus pneumoniae serotypes

In this study, the volatile molecule profile of Streptococcus pneumoniae serotypes was evaluated using solid phase microextraction (SPME) and two dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). Here, seven serotypes (6B, 14, 15, 18C, 19F, 9V, and 23F) were analyzed in an isogenic background. We identified 13 core molecules associated with all seven serotypes, and seven molecules that were differentially produced between serotypes. Serotype 14 was found to have the most distinct volatile profile, and could be discriminated from the other six serotypes in aggregate with an area under the curve (AUC) of 89%. This study suggests that molecules from S. pneumoniae culture headspace show potential for rapid serotype identification.