Gene Expression in Cord Blood and Tuberculosis in Early Childhood: A Nested Case-Control Study in a South African Birth Cohort.

BACKGROUND: Transcriptomic profiling of adults with tuberculosis (TB) has become increasingly common, predominantly for diagnostic and risk prediction purposes. However, few studies have evaluated signatures in children, particularly in identifying those at risk for developing TB disease. We investigated the relationship between gene expression obtained from umbilical cord blood and both tuberculin skin test conversion and incident TB disease through the first 5 years of life. METHODS: We conducted a nested case-control study in the Drakenstein Child Health Study, a longitudinal, population-based birth cohort in South Africa. We applied transcriptome-wide screens to umbilical cord blood samples from neonates born to a subset of selected mothers (N = 131). Signatures identifying tuberculin conversion and risk of subsequent TB disease were identified from genome-wide analysis of RNA expression. RESULTS: Gene expression signatures revealed clear differences predictive of tuberculin conversion (n = 26) and TB disease (n = 10); 114 genes were associated with tuberculin conversion and 30 genes were associated with the progression to TB disease among children with early infection. Coexpression network analysis revealed 6 modules associated with risk of TB infection or disease, including a module associated with neutrophil activation in immune response (P < .0001) and defense response to bacterium (P < .0001). CONCLUSIONS: These findings suggest multiple detectable differences in gene expression at birth that were associated with risk of TB infection or disease throughout early childhood. Such measures may provide novel insights into TB pathogenesis and susceptibility.

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.

Breath metabolome of mice infected with Pseudomonas aeruginosa.

INTRODUCTION: The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa. OBJECTIVES: To determine a discriminatory core of molecules in the "breath-print" of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific "breath-print" signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20-25, 2014. https://doi.org/10.1111/2049-632X.12107 ). METHODS: Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. RESULTS: Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. CONCLUSIONS: To the best of our knowledge, this is the first study to report on a 'core' murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

Investigation of mycobacteria fatty acid profile using different ionization energies in GC-MS.

Gas chromatography (GC) coupled with electron ionization (EI) mass spectrometry (MS) is a well-established technique for the analysis of volatile and semi-volatile compounds. The main advantage is the highly repeatable fragmentation of the compounds into the ion source, generating intense and diagnostic fragmentation when the ionization is performed at 70 eV; this is considered the standard ionization condition and has been used for creating many established databases, which are of great support in the analyte identification process. However, such an intense fragmentation often causes the loss of the molecular ion or more diagnostic ions, which can be detrimental for the identification of homologous series or isomers, as for instance fatty acids. To obtain this information chemical or soft ionization can be used, but dedicated ion sources and conditions are required. In this work, we explored different ionization voltages in GC-EI-MS to preserve the intensity of the molecular ion using a conventional quadrupole MS. Twenty, 30, 50, and 70 eV were tested using a mixture of fatty acid methyl esters standards. Intensity and repeatability of the most informative ions were compared. Twenty and 70 eV were then used to analyze the fatty acid composition of six different strains of mycobacteria. Two approaches were used for elaborating the data: (1) a single average spectrum of the entire chromatogram was derived, which can be considered (in terms of concept) as a direct EI-MS analysis; (2) the actual chromatographic separation of the compounds was considered after automatic alignment. The results obtained are discussed herein. Graphical abstract ᅟ.

Volatile metabolic diversity of Klebsiella pneumoniae in nutrient-replete conditions.

INTRODUCTION: Microorganisms 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. OBJECTIVES: To assess the core and media-specific components of the Klebsiella pneumoniae volatile metabolome via growth in four in vitro culture media. METHODS: Headspace 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 was assessed using hierarchical clustering analysis (HCA) and principal component analysis (PCA). RESULTS: A 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. CONCLUSION: K. 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.

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.

Use of a Multiplex Transcript Method for Analysis of Pseudomonas aeruginosa Gene Expression Profiles in the Cystic Fibrosis Lung.

The discovery of therapies that modulate Pseudomonas aeruginosa virulence or that can eradicate chronic P. aeruginosa lung infections associated with cystic fibrosis (CF) will be advanced by an improved understanding of P. aeruginosa behavior in vivo We demonstrate the use of multiplexed Nanostring technology to monitor relative abundances of P. aeruginosa transcripts across clinical isolates, in serial samples, and for the purposes of comparing microbial physiology in vitro and in vivo The expression of 75 transcripts encoded by genes implicated in CF lung disease was measured in a variety of P. aeruginosa strains as well as RNA serial sputum samples from four P. aeruginosa-colonized subjects with CF collected over 6 months. We present data on reproducibility, the results from different methods of normalization, and demonstrate high concordance between transcript relative abundance data obtained by Nanostring or transcriptome sequencing (RNA-Seq) analysis. Furthermore, we address considerations regarding sequence variation between strains during probe design. Analysis of P. aeruginosa grown in vitro identified transcripts that correlated with the different phenotypes commonly observed in CF clinical isolates. P. aeruginosa transcript profiles in RNA from CF sputum indicated alginate production in vivo, and transcripts involved in quorum-sensing regulation were less abundant in sputum than strains grown in the laboratory. P. aeruginosa gene expression patterns from sputum clustered closely together relative to patterns for laboratory-grown cultures; in contrast, laboratory-grown P. aeruginosa showed much greater transcriptional variation with only loose clustering of strains with different phenotypes. The clustering within and between subjects was surprising in light of differences in inhaled antibiotic and respiratory symptoms, suggesting that the pathways represented by these 75 transcripts are stable in chronic CF P. aeruginosa lung infections.

Elastin degradation product isodesmosine is a chemoattractant for Pseudomonas aeruginosa.

Previous studies have demonstrated that Pseudomonas aeruginosa PAO1 is chemotactic towards proteinogenic amino acids, however, the chemotaxis response of this strain towards non-proteinogenic amino acids and the specific chemoreceptors involved in this response are essentially unknown. In this study, we analysed the chemotactic response of PAO1 towards two degradation products of elastin, the lysine-rich, non-proteinogenic amino acids, desmosine and isodesmosine. We observed that isodesmosine, a potential biomarker for different diseases, served as a chemoattractant for PAO1. A screen of 251methyl-accepting chemotaxis proteins mutants of PAO1 identified PctA as the chemoreceptor for isodesmosine. We also showed that the positive chemotactic response to isodesmosine is potentially common by demonstrating chemoattraction in 12 of 15 diverse (in terms of source of isolation) clinical isolates, suggesting that the chemotactic response to this non-proteinogenic amino acid might be a conserved feature of acute infection isolates and thus could influence the colonization of potential infection sites.

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.

The role of gluconate production by Pseudomonas spp. in the mineralization and bioavailability of calcium-phytate to Nicotiana tabacum.

Organic phosphorus (P) is abundant in most soils but is largely unavailable to plants. Pseudomonas spp. can improve the availability of P to plants through the production of phytases and organic anions. Gluconate is a major component of Pseudomonas organic anion production and may therefore play an important role in the mineralization of insoluble organic P forms such as calcium-phytate (CaIHP). Organic anion and phytase production was characterized in 2 Pseudomonas spp. soil isolates (CCAR59, Ha200) and an isogenic mutant of strain Ha200, which lacked a functional glucose dehydrogenase (Gcd) gene (strain Ha200 gcd::Tn5B8). Wild-type and mutant strains of Pseudomonas spp. were evaluated for their ability to solubilize and hydrolyze CaIHP and to promote the growth and assimilation of P by tobacco plants. Gluconate, 2-keto-gluconate, pyruvate, ascorbate, acetate, and formate were detected in Pseudomonas spp. supernatants. Wild-type pseudomonads containing a functional gcd could produce gluconate and mineralize CaIHP, whereas the isogenic mutant could not. Inoculation with Pseudomonas improved the bioavailability of CaIHP to tobacco plants, but there was no difference in plant growth response due to Gcd function. Gcd function is required for the mineralization of CaIHP in vitro; however, further studies will be needed to quantify the relative contribution of specific organic anions such as gluconate to plant growth promotion by soil pseudomonads.

Characterization of Organic Phosphorus Form and Bioavailability in Lake Sediments using P Nuclear Magnetic Resonance and Enzymatic Hydrolysis.

Lake sediments are known to be a significant source of phosphorus (P) to plankton populations under certain biogeochemical conditions; however, the contribution of sediment organic P (P) to internal P loads remains poorly understood. We investigated P speciation and bioavailability in sediments collected over multiple months from a shallow, eutrophic bay in Lake Champlain (Missisquoi Bay, VT) using solution P nuclear magnetic resonance (NMR) spectroscopy and enzymatic hydrolysis (EH) analysis of sediments collected during years with (2008) and without (2007) algal blooms. Sediments collected during bloom onset (July) and peak bloom (August) months contained the largest proportion of enzyme-labile P, whereas pre- and postbloom sediments were primarily composed of nonlabile P. Monoester P to diester P ratios changed with respect to depth, particularly during bloom periods. Monoester P and DNA accumulation, likely from settling particulate matter, began at the onset of the bloom and continued into October 2008 during the postbloom period. The disappearance of inositol hexakisphosphate stereoisomers and the generation of orthophosphate at lower sediment depths was also evident in August 2008. Principal components analysis of EH and NMR species proportions confirmed differences between sediment cores collected during bloom onset and peak bloom, compared with pre- and postbloom sediments. Large enzyme-labile and P species proportions corresponded to increased sediment P flux and reduced manganese and iron species in porewater. These findings suggest that interseasonal changes in P speciation may influence P mobility in sediments and contribute to important feedback dynamics between biological productivity and sediment water interface geochemistry.

A feasibility study of sample re-collection in the analysis of selected volatile compounds in breath samples using GC×GC-TOFMS.

In this study, we aimed to assess the feasibility of re-collecting breath samples using the Centri® (Markes International, Bridgend, UK) followed by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) analysis. The work was conducted in two main phases. In the first phase, we evaluated the re-collection performance by analyzing two sets of standards, including a Grob mix primary solution and a standard mixture of 20 selected volatile compounds (VCs) covering different classes of organic species commonly found in breath samples. The intra-day and inter-day precision (reported as relative standard deviation (RSD),%) for the re-collection of the Grob mix primary solution were in the range of 1 % to14 % and 3 % to12 %, respectively. The re-collection accuracy ranged from 78 % to 97 %. The intra-day RSD for the re-collection of the standard mixture of selected VCs was within 20 % for all compounds, except for acetone and nonane. The precision was within 25 % for all compounds, except for nonane, n-hexane, 1,4-dichlorobenzene, and decane, which exhibited less than 36 % RSD. The re-collection accuracy was in the range of 67 % to 129 %. In the second phase of the study, the re-collection performance in breath analysis was evaluated via five repetitive splitting and re-collection of six breath samples obtained from healthy adults, realizing a total of 30 breath analyses. Initially, we evaluated the re-collection performance by considering all features obtained from breath analysis and then focused on the 20 VCs commonly found in breath samples. The re-collection accuracy for total breath features ranged from 86 to 103 %, and the RSDs were in the range of 1.0 % to 10.4 %. For the selected VCs, the re-collection accuracy of all compounds, except for undecane and benzene, was in the range of 71 % to 132 %.

Detection of Escherichia coli via VOC profiling using secondary electrospray ionization-mass spectrometry (SESI-MS).

Escherichia coli O157:H7 (EC O157:H7), as well as its recently emerging non-O157 relatives, are a notorious group of pathogenic bacteria associated with foodborne outbreaks. In this study, we demonstrated that secondary electrospray ionization mass spectrometry (SESI-MS) could be a rapid and accurate detection technology for foodborne pathogens. With SESI-MS volatile organic compound (VOC) profiling, we were able to detect and separate a group of eleven E. coli strains from two major foodborne bacteria, Staphylococcus aureus and Salmonella Typhimurium in three food modeling media. In addition, heatmap analysis of relative peak intensity show that there are six core peaks (m/z of 65, 91, 92, 117, 118 and 119) present and at a similar intensity in all eleven E. coli strains at the experimental conditions we tested. These peaks can be considered conserved VOC biomarkers for E. coli species (robustly produced after just 4 h of growth). Bacterial strain-level differentiation was also attempted via VOC profiling, and we found that EC O157:H7 and EC O145 were differentiable from all other EC strains under the conditions investigated.

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.

Profiling volatile organic compounds from human plasma using GC × GC-ToFMS.

Volatile organic compounds (VOCs) originating from human metabolic activities can be detected in, for example, breath, urine, feces, and blood. Thus, attention has been given to identifying VOCs from the above matrices. Studies identifying and measuring human blood VOCs are limited to those focusing on monitoring specific pollutants, or blood storage and/or decomposition. However, a comprehensive characterization of VOCs in human blood collected for routine diagnostic testing is lacking. In this pilot study, 72 blood-derived plasma samples were obtained from apparently healthy adult participants. VOCs were extracted from plasma using solid-phase microextraction and analyzed using comprehensive two-dimensional gas chromatography tandem time-of-flight mass spectrometry. Chromatographic data were aligned, and putative compound identities were assigned via spectral library comparison. All statistical analysis, including contaminant removal, data normalization, and transformation were performed usingR. We identified 401 features which we called the pan volatilome of human plasma. Of the 401 features, 34 were present in all the samples with less than 15% variance (core molecules), 210 were present in ⩾10% but <100% of the samples (accessory molecules), and 157 were present in less than 10% of the samples (rare molecules). The core molecules, consisting of aliphatic, aromatic, and carbonyl compounds were validated using 25 additional samples. The validation accuracy was 99.9%. Of the 34 core molecules, 2 molecules (octan-2-one and 4-methyl heptane) have been identified from the plasma samples for the first time. Overall, our pilot study establishes the methodology of profiling VOCs in human plasma and will serve as a resource for blood-derived VOCs that can complement future biomarker studies using different matrices with more heterogeneous cohorts.

Pseudomonas aeruginosa PAO1 Is Attracted to Bovine Bile in a Novel, Cystic Fibrosis-Derived Bronchial Epithelial Cell Model.

Cystic fibrosis (CF) is a life-threatening, inherited, multi-organ disease that renders patients susceptible throughout their lives to chronic and ultimately deteriorating protracted pulmonary infections. Those infections are dominated in adulthood by the opportunistic pathogen, Pseudomonas aeruginosa (Pa). As with other advancing respiratory illnesses, people with CF (pwCF) also frequently suffer from gastroesophageal reflux disease (GERD), including bile aspiration into the lung. GERD is a major co-morbidity factor in pwCF, with a reported prevalence of 35-81% in affected individuals. Bile is associated with the early acquisition of Pa in CF patients and in vitro studies show that it causes Pa to adopt a chronic lifestyle. We hypothesized that Pa is chemoattracted to bile in the lung environment. To evaluate, we developed a novel chemotaxis experimental system mimicking the lung environment using CF-derived bronchial epithelial (CFBE) cells which allowed for the evaluation of Pa (strain PAO1) chemotaxis in a physiological scenario superior to the standard in vitro systems. We performed qualitative and quantitative chemotaxis tests using this new experimental system, and microcapillary assays to demonstrate that bovine bile is a chemoattractant for Pa and is positively correlated with bile concentration. These results further buttress the hypothesis that bile likely contributes to the colonization and pathogenesis of Pa in the lung, particularly in pwCF.

The Arginine Catabolism-Derived Amino Acid l-ornithine Is a Chemoattractant for Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a common, opportunistic bacterial pathogen among patients with cystic fibrosis, asthma, and chronic obstructive pulmonary disease. During the course of these diseases, l-ornithine, a non-proteinogenic amino acid, becomes more abundant. P. aeruginosa is chemotactic towards other proteinogenic amino acids. Here, we evaluated the chemotaxis response of P. aeruginosa towards l-ornithine. Our results show that l-ornithine serves as a chemoattractant for several strains of P. aeruginosa, including clinical isolates, and that the chemoreceptors involved in P. aeruginosa PAO1 are PctA and PctB. It seems likely that P. aeruginosa's chemotactic response to l-ornithine might be a common feature and thus could potentially contribute to pathogenesis processes during colonization and infection scenarios.

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.

Volatile organic compound profiling to explore primary graft dysfunction after lung transplantation.

Primary graft dysfunction (PGD) is a major determinant of morbidity and mortality following lung transplantation. Delineating basic mechanisms and molecular signatures of PGD remain a fundamental challenge. This pilot study examines if the pulmonary volatile organic compound (VOC) spectrum relate to PGD and postoperative outcomes. The VOC profiles of 58 bronchoalveolar lavage fluid (BALF) and blind bronchial aspirate samples from 35 transplant patients were extracted using solid-phase-microextraction and analyzed with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. The support vector machine algorithm was used to identify VOCs that could differentiate patients with severe from lower grade PGD. Using 20 statistically significant VOCs from the sample headspace collected immediately after transplantation (< 6 h), severe PGD was differentiable from low PGD with an AUROC of 0.90 and an accuracy of 0.83 on test set samples. The model was somewhat effective for later time points with an AUROC of 0.80. Three major chemical classes in the model were dominated by alkylated hydrocarbons, linear hydrocarbons, and aldehydes in severe PGD samples. These VOCs may have important clinical and mechanistic implications, therefore large-scale study and potential translation to breath analysis is recommended.