Current Limitations of Staph Infection Diagnostics, and the Role for VOCs in Achieving Culture-Independent Detection.

Staphylococci are broadly adaptable and their ability to grow in unique environments has been widely established, but the most common and clinically relevant staphylococcal niche is the skin and mucous membranes of mammals and birds. S. aureus causes severe infections in mammalian tissues and organs, with high morbidities, mortalities, and treatment costs. S. epidermidis is an important human commensal but is also capable of deadly infections. Gold-standard diagnostic methods for staph infections currently rely upon retrieval and characterization of the infectious agent through various culture-based methods. Yet, obtaining a viable bacterial sample for in vitro identification of infection etiology remains a significant barrier in clinical diagnostics. The development of volatile organic compound (VOC) profiles for the detection and identification of pathogens is an area of intensive research, with significant efforts toward establishing breath tests for infections. This review describes the limitations of existing infection diagnostics, reviews the principles and advantages of VOC-based diagnostics, summarizes the analytical tools for VOC discovery and clinical detection, and highlights examples of how VOC biomarkers have been applied to diagnosing human and animal staph infections.

Volatile Metabolites in Lavage Fluid Are Correlated with Cytokine Production in a Valley Fever Murine Model.

Coccidioides immitis and Coccidioides posadasii are soil-dwelling fungi of arid regions in North and South America that are responsible for Valley fever (coccidioidomycosis). Forty percent of patients with Valley fever exhibit symptoms ranging from mild, self-limiting respiratory infections to severe, life-threatening pneumonia that requires treatment. Misdiagnosis as bacterial pneumonia commonly occurs in symptomatic Valley fever cases, resulting in inappropriate treatment with antibiotics, increased medical costs, and delay in diagnosis. In this proof-of-concept study, we explored the feasibility of developing breath-based diagnostics for Valley fever using a murine lung infection model. To investigate potential volatile biomarkers of Valley fever that arise from host−pathogen interactions, we infected C57BL/6J mice with C. immitis RS (n = 6), C. posadasii Silveira (n = 6), or phosphate-buffered saline (n = 4) via intranasal inoculation. We measured fungal dissemination and collected bronchoalveolar lavage fluid (BALF) for cytokine profiling and for untargeted volatile metabolomics via solid-phase microextraction (SPME) and two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). We identified 36 volatile organic compounds (VOCs) that were significantly correlated (p < 0.05) with cytokine abundance. These 36 VOCs clustered mice by their cytokine production and were also able to separate mice with moderate-to-high cytokine production by infection strain. The data presented here show that Coccidioides and/or the host produce volatile metabolites that may yield biomarkers for a Valley fever breath test that can detect coccidioidal infection and provide clinically relevant information on primary pulmonary disease severity.

Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication.

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion─the first description and evaluation of this strategy─and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two GC × GC data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Spaceflight Analogue Culture Enhances the Host-Pathogen Interaction Between Salmonella and a 3-D Biomimetic Intestinal Co-Culture Model.

Physical forces associated with spaceflight and spaceflight analogue culture regulate a wide range of physiological responses by both bacterial and mammalian cells that can impact infection. However, our mechanistic understanding of how these environments regulate host-pathogen interactions in humans is poorly understood. Using a spaceflight analogue low fluid shear culture system, we investigated the effect of Low Shear Modeled Microgravity (LSMMG) culture on the colonization of Salmonella Typhimurium in a 3-D biomimetic model of human colonic epithelium containing macrophages. RNA-seq profiling of stationary phase wild type and Δhfq mutant bacteria alone indicated that LSMMG culture induced global changes in gene expression in both strains and that the RNA binding protein Hfq played a significant role in regulating the transcriptional response of the pathogen to LSMMG culture. However, a core set of genes important for adhesion, invasion, and motility were commonly induced in both strains. LSMMG culture enhanced the colonization (adherence, invasion and intracellular survival) of Salmonella in this advanced model of intestinal epithelium using a mechanism that was independent of Hfq. Although S. Typhimurium Δhfq mutants are normally defective for invasion when grown as conventional shaking cultures, LSMMG conditions unexpectedly enabled high levels of colonization by an isogenic Δhfq mutant. In response to infection with either the wild type or mutant, host cells upregulated transcripts involved in inflammation, tissue remodeling, and wound healing during intracellular survival. Interestingly, infection by the Δhfq mutant led to fewer transcriptional differences between LSMMG- and control-infected host cells relative to infection with the wild type strain. This is the first study to investigate the effect of LSMMG culture on the interaction between S. Typhimurium and a 3-D model of human intestinal tissue. These findings advance our understanding of how physical forces can impact the early stages of human enteric salmonellosis.

Life Cycle Dominates the Volatilome Character of Dimorphic Fungus Coccidioides spp.

Valley fever (coccidioidomycosis) is an endemic fungal pneumonia of the North and South American deserts. The causative agents of Valley fever are the dimorphic fungi Coccidioides immitis and C. posadasii, which grow as mycelia in the environment and as spherules within the lungs of vulnerable hosts. Current diagnostics for Valley fever are severely lacking due to poor sensitivity and invasiveness, contributing to a 23-day median time to diagnosis, and therefore, new diagnostic tools are needed. We are working toward the development of a breath-based diagnostic for coccidioidomycosis, and in this initial study, we characterized the volatile metabolomes (or volatilomes) of in vitro cultures of Coccidioides Using solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry (GC×GC-TOFMS), we characterized the volatile organic compounds (VOCs) produced by six strains of each species during mycelial or spherule growth. We detected a total of 353 VOCs that were at least 2-fold more abundant in a Coccidioides culture than in medium controls and found that the volatile metabolome of Coccidioides is more dependent on the growth phase (spherules versus mycelia) than on the species. The volatile profiles of C. immitis and C. posadasii have strong similarities, indicating that a single suite of Valley fever breath biomarkers can be developed to detect both species.IMPORTANCE Coccidioidomycosis, or Valley fever, causes up to 30% of community-acquired pneumonias in highly populated areas of the U.S. desert southwest where the disease is endemic. The infection is difficult to diagnose by standard serological and histopathological methods, which delays appropriate treatment. Therefore, we are working toward the development of breath-based diagnostics for Valley fever. In this study, we characterized the volatile metabolomes (or volatilomes) of six strains each of Coccidioides immitis and C. posadasii, the dimorphic fungal species that cause Valley fever. By analyzing the volatilomes during the two modes of growth of the fungus-mycelia and spherules-we observed that the life cycle plays a significant role in the volatiles produced by Coccidioides In contrast, we observed no significant differences in the C. immitis versus C. posadasii volatilomes. These data suggest that life cycle, rather than species, should guide the selection of putative biomarkers for a Valley fever breath test.

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

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.

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.

Inhibition of Fungal Growth and Induction of a Novel Volatilome in Response to Chromobacterium vaccinii Volatile Organic Compounds.

The study of chemical bioactivity in the rhizosphere has recently broadened to include microbial metabolites, and their roles in niche construction and competition via growth promotion, growth inhibition, and toxicity. Several prior studies have identified bacteria that produce volatile organic compounds (VOCs) with antifungal activities, indicating their potential use as biocontrol organisms to suppress phytopathogenic fungi and reduce agricultural losses. We sought to expand the roster of soil bacteria with known antifungal VOCs by testing bacterial isolates from wild and cultivated cranberry bog soils for VOCs that inhibit the growth of four common fungal and oomycete plant pathogens, and Trichoderma sp. Twenty one of the screened isolates inhibited the growth of at least one fungus by the production of VOCs, and isolates of Chromobacterium vaccinii had broad antifungal VOC activity, with growth inhibition over 90% for some fungi. Fungi exposed to C. vaccinii VOCs had extensive morphological abnormalities such as swollen hyphal cells, vacuolar depositions, and cell wall alterations. Quorum-insensitive cviR - mutants of C. vaccinii were significantly less fungistatic, indicating a role for quorum regulation in the production of antifungal VOCs. We collected and characterized VOCs from co-cultivation assays of Phoma sp. exposed to wild-type C. vaccinii MWU328, and its cviR - mutant using stir bar sorptive extraction and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (SBSE-GC × GC-TOFMS). We detected 53 VOCs that differ significantly in abundance between microbial cultures and media controls, including four candidate quorum-regulated fungistatic VOCs produced by C. vaccinii. Importantly, the metabolomes of the bacterial-fungal co-cultures were not the sum of the monoculture VOCs, an emergent property of their VOC-mediated interactions. These data suggest semiochemical feedback loops between microbes that have co-evolved for sensing and responding to exogenous VOCs.

Longitudinal Associations of the Cystic Fibrosis Airway Microbiome and Volatile Metabolites: A Case Study.

The identification of 16S rDNA biomarkers from respiratory samples to describe the continuum of clinical disease states within persons having cystic fibrosis (CF) has remained elusive. We sought to combine 16S, metagenomics, and metabolomics data to describe multiple transitions between clinical disease states in 14 samples collected over a 12-month period in a single person with CF. We hypothesized that each clinical disease state would have a unique combination of bacterial genera and volatile metabolites as a potential signature that could be utilized as a biomarker of clinical disease state. Taxonomy identified by 16S sequencing corroborated clinical culture results, with the majority of the 109 PCR amplicons belonging to the bacteria grown in clinical cultures (Escherichia coli and Staphylococcus aureus). While alpha diversity measures fluctuated across disease states, no significant trends were present. Principle coordinates analysis showed that treatment samples trended toward a different community composition than baseline and exacerbation samples. This was driven by the phylum Bacteroidetes (less abundant in treatment, log2 fold difference -3.29, p = 0.015) and the genus Stenotrophomonas (more abundant in treatment, log2 fold difference 6.26, p = 0.003). Across all sputum samples, 466 distinct volatile metabolites were identified with total intensity varying across clinical disease state. Baseline and exacerbation samples were rather uniform in chemical composition and similar to one another, while treatment samples were highly variable and differed from the other two disease states. When utilizing a combination of the microbiome and metabolome data, we observed associations between samples dominated Staphylococcus and Escherichia and higher relative abundances of alcohols, while samples dominated by Achromobacter correlated with a metabolomics shift toward more oxidized volatiles. However, the microbiome and metabolome data were not tightly correlated; examining both the metagenomics and metabolomics allows for more context to examine changes across clinical disease states. In our study, combining the sputum microbiome and metabolome data revealed stability in the sputum composition through the first exacerbation and treatment episode, and into the second exacerbation. However, the second treatment ushered in a prolonged period of instability, which after three additional exacerbations and treatments culminated in a new lung microbiome and metabolome.

A Metabolomic Approach for Predicting Diurnal Changes in Cortisol.

Introduction: The dysregulation of cortisol secretion has been associated with a number of mental health and mood disorders. However, diagnostics for mental health and mood disorders are behavioral and lack biological contexts. Objectives: The goal of this work is to identify volatile metabolites capable of predicting changes in total urinary cortisol across the diurnal cycle for long-term stress monitoring in psychological disorders. Methods: We applied comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry to sample the urinary volatile metabolome using an untargeted approach across three time points in a single day for 60 subjects. Results: The finalized multiple regression model includes 14 volatile metabolites and 7 interaction terms. A review of the selected metabolites suggests pyrrole, 6-methyl-5-hepten-2-one and 1-iodo-2-methylundecane may originate from endogenous metabolic mechanisms influenced by glucocorticoid signaling mechanisms. Conclusion: This analysis demonstrated the feasibility of using specific volatile metabolites for the prediction of secreted cortisol across time.

Influence of media on the differentiation of Staphylococcus spp. by volatile compounds.

Staphylococcus aureus asymptomatically colonizes a third of the world's population, and it is an opportunistic pathogen that can cause life threatening diseases. To diagnose S. aureus infections, it is necessary to differentiate S. aureus from the ubiquitous human commensal Staphylococcus epidermidis, which beneficially colonizes the skin of all humans. Efforts are underway to identify volatile biomarkers for diagnosing S. aureus infections, but to date no studies have investigated whether S. aureus and S. epidermidis can be reliably differentiated under a variety of growth conditions. The overall goal of this study was to evaluate the influence of growth medium on the ability to differentiate S. aureus and S. epidermidis based on their volatile profiles. We used headspace solid-phase microextraction (HS-SPME) and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS) to examine the headspace volatiles of S. aureus and S. epidermidis when aerobically grown in four different complex media. We detected 337 volatile features when culturing S. aureus and S. epidermidis in four complex media, termed the staph volatiles, and found only 20%-40% concurrence in the volatiles produced by these two species in any single medium. Using principal components analysis and hierarchical clustering analysis on the staph volatiles, we observed that S. aureus and S. epidermidis clustered independently from each other, and distinctly clustered by growth medium within species. Removing volatiles that are species and/or media-specific from the analysis reduced the resolution between species clusters, but in all models clustering by species overrode clustering by media type. These analyses suggest that, while volatile profiles are media-specific, species differences dominate the staph volatilome. These data enable future investigations into the identification of volatile biomarkers to discriminate staphylococcal pathogens versus commensals, which will improve staph diagnoses and provide insights into the biochemistry of staph infections and immunity.

Monitoring changes in the healthy female metabolome across the menstrual cycle using GC × GC-TOFMS.

Urinary metabolomics offers a non-invasive means of obtaining information about the system-wide biological health of a patient. Untargeted metabolomics approaches using one-dimensional gas chromatography (GC) are limited due to the chemical complexity of urine, which poorly detects co-eluting low-abundance analytes. Metabolite detection and identification can be improved by applying comprehensive two-dimensional GC, allowing for the discovery of additional viable biomarkers of disease. In this work, we applied comprehensive two-dimensional GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) to the analysis of urine samples collected daily across 28-days from 10 healthy female subjects for a personalized approach to female reproductive health monitoring. Through this analysis, we identified 935 unique volatile metabolites. Two statistical methods, a modified T-statistic and Wilcoxon Rank Sum, were applied to analyze differences in metabolome abundance on ovulation days as compared to non-ovulation days. Four metabolites (2-pentanone, 3-penten-2-one, carbon disulfide, acetone) were identified as statistically significant by the modified T-statistic but not the Rank Sum, after a false-discovery rate of 0.1 was set using a Benjamini-Hochberg correction. Subsequent analyses by boxplot indicated that the putative volatile metabolic biomarkers for fertility are expressed in increased or decreased abundance in urine on the day of ovulation. Individual analysis of metabolome expression across 28-days revealed some subject-specific features, which suggest a potential for long-term, personalized fertility monitoring using metabolomics.

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.

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.

Estimation of start and stop numbers for cluster resolution feature selection algorithm: an empirical approach using null distribution analysis of Fisher ratios.

Cluster resolution feature selection (CR-FS) is a hybrid feature selection algorithm which involves the evaluation of ranked variables via sequential backward elimination (SBE) and sequential forward selection (SFS). The implementation of CR-FS requires two main inputs, namely, start and stop number. The start number is the number of the highly ranked variables for the SBE while the stop number is the point at which the search for additional features during the SFS stage is halted. The setting of these critical parameters has always relied on trial and error which introduced subjectivity in the results obtained. The start and stop numbers are known to vary with each dataset. Drawing inspiration from overlapping coefficients, a method for comparing two probability density functions, empirical equations toward the estimation of start and stop number for a dataset were developed. All of the parameters in the empirical equations are obtained from the comparisons of the two probability density functions except the constant termed d. The equations were optimized using three real-world datasets. The optimum range of d was determined to be 0.48 to 0.57. An implementation of CR-FS using two new datasets demonstrated the validity of this approach. Partial least squares discriminant analysis (PLS-DA) model prediction accuracies increased from 90 and 96 to 100% for both datasets using start and stop numbers calculated with this approach. Additionally, there was a twofold increase in the explained variance captured in the first two principal components. Graphical abstract Here, we describe how to determine the start and stop numbers for an automated feature selection routine, ensuring that you get the best model you can for your data with minimal effort.

Comparative analysis of the volatile metabolomes of Pseudomonas aeruginosa clinical isolates.

Pseudomonas aeruginosa is a nearly ubiquitous Gram-negative organism, well known to occupy a multitude of environmental niches and cause human infections at a variety of bodily sites, due to its metabolic flexibility, secondary to extensive genetic heterogeneity at the species level. Because of its dynamic metabolism and clinical importance, we sought to perform a comparative analysis on the volatile metabolome (the 'volatilome') produced by P. aeruginosa clinical isolates. In this study, we analyzed the headspace volatile molecules of 24 P. aeruginosa clinical isolates grown in vitro, using 2D gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). We identified 391 non-redundant compounds that we associate with the growth and metabolism of P. aeruginosa (the 'pan-volatilome'). Of these, 70 were produced by all 24 isolates (the 'core volatilome'), 52 by only a single isolate, and the remaining 269 volatile molecules by a subset. Sixty-five of the detected compounds could be assigned putative compound identifications, of which 43 had not previously been associated with P. aeruginosa. Using the accessory volatile molecules, we determined the inter-strain variation in the metabolomes of these isolates, clustering strains by their metabotypes. Assessing the extent of metabolomic diversity in P. aeruginosa through an analysis of the volatile molecules that it produces is a critical next step in the identification of novel diagnostic or prognostic biomarkers.

Profiling aged artisanal Cheddar cheese using secondary electrospray ionization mass spectrometry.

A number of direct injection mass spectrometry methods that can sample foods nondestructively and without sample preparation are being developed with applications ranging from the rapid assessment of food safety to the verification of protected designations of origin. In this pilot study, secondary electrospray ionization mass spectrometry (SESI-MS) in positive- and negative-ion modes was used to collect volatile fingerprints of artisanal Cheddar cheeses aged for one to three years. SESI-MS fingerprints were found to change in an aging-dependent manner and can be used to descriptively and predictively categorize Cheddars by their aging period, identify volatile components that increase or decrease with aging, and robustly discriminate individual batches of artisanal cheese. From these results, it was concluded that SESI-MS volatile fingerprinting could be used by artisanal food producers to characterize their products during production and aging, providing useful data to help them maximize the value of each batch.

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.

Breathprints of model murine bacterial lung infections are linked with immune response.

In this model study, we explored the host's contribution of breath volatiles to diagnostic secondary electrospray ionisation-mass spectrometry (SESI-MS) breathprints for acute bacterial lung infections, their correlation with the host's immune response, and their use in identifying the lung pathogen. Murine airways were exposed to Pseudomonas aeruginosa and Staphylococcus aureus bacterial cell lysates or to PBS (controls), and their breath and bronchoalveolar lavage fluid (BALF) were collected at six time points (from 6 to 120 h) after exposure. Five to six mice per treatment group and four to six mice per control group were sampled at each time. Breath volatiles were analysed using SESI-MS and the BALF total leukocytes, polymorphonuclear neutrophils, lactate dehydrogenase activity, and cytokine concentrations were quantified. Lysate exposure breathprints contain host volatiles that persist for up to 120 h; are pathogen specific; are unique from breathprints of controls, active infections and cleared infections; and are correlated with the host's immune response. Bacterial lung infections induce changes to the host's breath volatiles that are selective and specific predictors of the source of infection. Harnessing the pathogen-specific volatiles in the host's breath may provide useful information for detecting latent bacterial lung infections and managing the spread of respiratory diseases.

Editorial: new analytical and statistical approaches for interpreting the relationships among environmental stressors and biomarkers.

The broad topic of biomarker research has an often-overlooked component: the documentation and interpretation of the surrounding chemical environment and other meta-data, especially from visualization, analytical and statistical perspectives. A second concern is how the environment interacts with human systems biology, what the variability is in "normal" subjects, and how such biological observations might be reconstructed to infer external stressors. In this article, we report on recent research presentations from a symposium at the 248th American Chemical Society meeting held in San Francisco, 10-14 August 2014, that focused on providing some insight into these important issues.