Search

RationaleThe concentration of octane and acetaldehyde in exhaled breath has good diagnostic accuracy for Acute Respiratory Distress Syndrome (ARDS). We aimed to determine whether breath octane and acetaldehyde are able to distinguish the presence and absence of ARDS in critically ill patients suspected to have ventilator-associated pneumonia (VAP).MethodsThis is a secondary analysis of a prospective observational study into exhaled breath analysis using gas-chromatography-time of flight-mass spectrometry. Difference in the relative abundance of octane and acetaldehyde in exhaled breath was compared between patients with and without ARDS using the Mann-Whitney U-test and the association was quantified using logistic regression. The discriminative accuracy of octane and acetaldehyde, alone or in combination, was calculated using the area under the curve of the ROC (AUROCC).ResultsWe included 98 patients of whom 32 had ARDS and 66 did not. The area under the acetaldehyde peak was higher in patients with ARDS (p=0.03), and associated with the presence of ARDS (OR: 1.06 per 100000 count change (95% CI: 1.02–1.13), p=0.01). A combined model with octane and acetaldehyde showed a high specificity and low sensitivity (respectively, 90% and 40.6%), with a low accuracy (AUROCC: 0.65, 95% CI: 0.53–0.78).ConclusionPatients suspected to have VAP with ARDS had a higher acetaldehyde concentration in exhaled breath than patients suspected to have VAP without ARDS. However, in this patient population, discrimination of these breath biomarkers for ARDS was poor, indicating the difficulty of translating diagnostic tests between clinical settings.

Background and Aims: The prevalence of chronic liver disease in adults exceeds 30% in some countries and there is significant interest in developing tests and treatments to help control disease progression and reduce healthcare burden. Breath is a rich sampling matrix that offers non-invasive solutions suitable for early-stage detection and disease monitoring. Having previously investigated targeted analysis of a single biomarker, here we investigated a multiparametric approach to breath testing that would provide more robust and reliable results for clinical use. Methods: To identify candidate biomarkers we compared 46 breath samples from cirrhosis patients and 42 from controls. Collection and analysis used Breath Biopsy OMNITM, maximizing signal and contrast to background to provide high confidence biomarker detection based upon gas chromatography mass spectrometry (GC-MS). Blank samples were also analyzed to provide detailed information on background volatile organic compounds (VOCs) levels. Results: A set of 29 breath VOCs differed significantly between cirrhosis and controls. A classification model based on these VOCs had an area under the curve (AUC) of 0.95 +/- 0.04 in cross-validated test sets. The seven best performing VOCs were sufficient to maximize classification performance. A subset of 11 VOCs was correlated with blood metrics of liver function (bilirubin, albumin, prothrombin time) and separated patients by cirrhosis severity using principal component analysis. Conclusions: A set of seven VOCs consisting of previously reported and novel candidates show promise as a panel for liver disease detection and monitoring, showing correlation to disease severity and serum biomarkers at late stage.

Colorectal cancer (CRC) has been associated with changes in volatile metabolic profiles in several human biological matrices. This enables its non-invasive detection, but the origin of these volatile organic compounds (VOCs) and their relation to the gut microbiome are not yet fully understood. This systematic review provides an overview of the current understanding of this topic. A systematic search using PubMed, Embase, Medline, Cochrane Library, and the Web of Science according to PRISMA guidelines resulted in seventy-one included studies. In addition, a systematic search was conducted that identified five systematic reviews from which CRC-associated gut microbiota data were extracted. The included studies analyzed VOCs in feces, urine, breath, blood, tissue, and saliva. Eight studies performed microbiota analysis in addition to VOC analysis. The most frequently reported dysregulations over all matrices included short-chain fatty acids, amino acids, proteolytic fermentation products, and products related to the tricarboxylic acid cycle and Warburg metabolism. Many of these dysregulations could be related to the shifts in CRC-associated microbiota, and thus the gut microbiota presumably contributes to the metabolic fingerprint of VOC in CRC. Future research involving VOCs analysis should include simultaneous gut microbiota analysis.

Exhaled breath analysis has great potential in diagnosing various respiratory and non-respiratory diseases. In this study, we investigated the influence of inhaled corticosteroids (ICS) on exhaled volatile organic compounds (VOCs) of wheezing preschool children. Furthermore, we assessed whether exhaled VOCs could predict a clinical steroid response in wheezing preschool children. We performed a crossover 8-week ICS trial, in which 147 children were included. Complete data were available for 89 children, of which 46 children were defined as steroid-responsive. Exhaled VOCs were measured by GC-tof-MS. Statistical analysis by means of Random Forest was used to investigate the effect of ICS on exhaled VOCs. A set of 20 VOCs could best discriminate between measurements before and after ICS treatment, with a sensitivity of 73% and specificity of 67% (area under ROC curve = 0.72). Most discriminative VOCs were branched C11H24, butanal, octanal, acetic acid and methylated pentane. Other VOCs predominantly included alkanes. Regularised multivariate analysis of variance (rMANOVA) was used to determine treatment response, which showed a significant effect between responders and non-responders (p < 0.01). These results show that ICS significantly altered the exhaled breath profiles of wheezing preschool children, irrespective of clinical treatment response. Furthermore, exhaled VOCs were capable of determining corticosteroid responsiveness in wheezing preschool children.

Pancreatic ductal adenocarcinoma (PDAC), due to its genomic heterogeneity and lack of effective treatment, despite decades of intensive research, will become the second leading cause of cancer-related deaths by 2030. Step-wise acquisition of mutations, due to genomic instability, is considered to drive the development of PDAC; the KRAS mutation occurs in 95 to 100% of human PDAC, and is already detectable in early premalignant lesions designated as pancreatic intraepithelial neoplasia (PanIN). This mutation is possibly the key event leading to genomic instability and PDAC development. Our study aimed to investigate the role of the error-prone DNA double-strand breaks (DSBs) repair pathway, alt-EJ, in the presence of the KRAS G12D mutation in pancreatic cancer development. Our findings show that oncogenic KRAS contributes to increasing the expression of Polθ, Lig3, and Mre11, key components of alt-EJ in both mouse and human PDAC models. We further confirm increased catalytic activity of alt-EJ in a mouse and human model of PDAC bearing the KRAS G12D mutation. Subsequently, we focused on estimating the impact of alt-EJ inactivation by polymerase theta (Polθ) deletion on pancreatic cancer development, and survival in genetically engineered mouse models (GEMMs) and cancer patients. Here, we show that even though Polθ deficiency does not fully prevent the development of pancreatic cancer, it significantly delays the onset of PanIN formation, prolongs the overall survival of experimental mice, and correlates with the overall survival of pancreatic cancer patients in the TCGA database. Our study clearly demonstrates the role of alt-EJ in the development of PDAC, and alt-EJ may be an attractive therapeutic target for pancreatic cancer patients.

With the continued presence of COVID-19 worldwide, it has been a challenge for the breath research community to progress with clinical studies and travel restrictions have also limited the opportunities to meet up, share ideas and celebrate the latest advances. The Breath Biopsy Conference 2021 provided a much-needed opportunityoffered the chance to catch up with the latest breath research and to share the researchprogress that researchers in the community have been able to progress make in these difficult times. Limited opportunities for clinical research have provided opportunitiesled many in the field to look more closely at different methods for breath collection and have contributed to the growing calls for consistent standards in how results are reported, shared and even how breath studies themselves are carried out. As such, standardization was a key theme for this year's event and featured prominently in the keynotes, discussions and throughout many of the presentation sessions. With over 900 registrants, almost 400 live attendees and 16 speakers, the Breath Biopsy Conference continues to bring together breath research leaders from around the world. This article provides an overview of the highlights from this event.

Background & aims: Ageing is associated with an increased risk of frailty, intestinal microbiota perturbations, immunosenescence and oxidative stress. Prebiotics such as galacto-oligosaccharides (GOS) may ameliorate these ageing-related alterations. We aimed to compare the faecal microbiota composition, metabolite production, immune and oxidative stress markers in prefrail elderly and younger adults, and investigate the effects of GOS supplementation in both groups. Methods: In a randomised controlled cross-over study, 20 prefrail elderly and 24 healthy adults received 21.6 g/day BiotisTM GOS (containing 15.0 g/day GOS) or placebo. Faecal 16S rRNA gene-based microbiota and short-chain fatty acids were analysed at 0,1 and 4 weeks of intervention.Volatile organic compounds were analysed in breath, and stimulated cytokine production, CRP, malondialdehyde, trolox equivalent antioxidant capacity (TEAC) and uric acid (UA) in blood at 0 and 4 weeks. Results: Principle coordinate analysis showed differences in microbial composition between elderly and adults (P

While substantial efforts have been made to optimize and standardize fecal metabolomics for studies in adults, the development of a standard protocol to analyze infant feces is, however, still lagging behind. Here, we present the development of a hands-on and robust protocol for proton H-1 NMR spectroscopy of infant feces. The influence of extraction solvent, dilution ratio, homogenization method, filtration, and duration of centrifugation on the biochemical composition of infant feces was carefully evaluated using visual inspection of H-1 NMR spectra in combination with multivariate statistical modeling. The optimal metabolomics protocol was subsequently applied on feces from seven infants collected at 8 weeks, 4, and 9 months of age. Interindividual variation was exceeding the variation induced by different fecal sample preparation methods, except for filtration. We recommend extracting fecal samples using water with a dilution ratio of 1:5 feces-to-water to homogenize using bead beating and to remove particulates using centrifugation. Samples collected from infants aged 8 weeks and 4 months showed elevated concentrations of milk oligosaccharide derivatives and lactic acid, whereas short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs) were higher in the 9 month samples. The established protocol enables hands-on and robust analyses of the infant gut metabolome. The wide-ranging application of this protocol will facilitate interlaboratory comparison of infants' metabolic profiles and finally aid in a better understanding of infant gut health.

It is still unclear how airway inflammation affects the breath volatile organic compounds (VOCs) profile in exhaled air. We therefore analyzed breath following well-defined pulmonary endotoxin (lipopolysaccharide, LPS) challenges. Breath was collected from ten healthy non-smoking subjects at eight time points before and after segmental and whole lung LPS inhalation challenge. Four Tenax-TA® adsorption tubes were simultaneously loaded from an aluminum reservoir cylinder and independently analyzed by two research groups using gas chromatography—mass spectrometry. Airway inflammation was assessed in bronchoalveolar lavage (BAL) and in sputum after segmental and inhaled LPS challenge, respectively. Segmental LPS challenge significantly increased the median (interquartile range, IQR) percentage of neutrophils in BAL from 3.0 (4.2) % to 64.0 (7.3) %. The inhalation challenge increased sputum neutrophils from 33.9 (26.8) % to 78.3 (13.5) %. We observed increases in breath aldehydes at both time points after segmental and inhaled LPS challenge. These results were confirmed by an independent laboratory. The longitudinal breath analysis also revealed distinct VOC patterns related to environmental exposures, clinical procedures, and to metabolic changes after food intake. Changes in breath aldehydes suggest a relationship to LPS induced inflammation compatible with lipid peroxidation processes within the lung. Findings from our longitudinal data highlight the need for future studies to better consider the potential impact of the multiple VOCs from detergents, hygiene or lifestyle products a subject is continuously exposed to. We suspect that this very individual ‘owncloud’ exposure is contributing to an increased variability of breath aldehydes, which might limit a use as inflammatory markers in daily clinical practice.

Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the ex-amination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted. (c) 2021 Published by Elsevier B.V.

Liver diseases are currently diagnosed through liver biopsy. Its invasiveness, costs, and relatively low diagnostic accuracy require new techniques to be sought. Analysis of volatile organic compounds (VOCs) in human bio-matrices has received a lot of attention. It is known that a musty odour characterises liver impairment, resulting in the elucidation of volatile chemicals in the breath and other body fluids such as urine and stool, which may serve as biomarkers of a disease. Aims: This study aims to review all the studies found in the literature regarding VOCs in liver diseases, and to summarise all the identified compounds that could be used as diagnostic or prognostic biomarkers. The literature search was conducted on ScienceDirect and PubMed, and each eligible publication was qualitatively assessed by two independent evaluators using the SANRA critical appraisal tool. Results: In the search, 58 publications were found, and 28 were kept for inclusion: 23 were about VOCs in the breath, one in the bile, three in urine, and one in faeces. Each publication was graded from zero to ten. A graphical summary of the metabolic pathways showcasing the known liver disease-related VOCs and suggestions on how VOC analysis on liver impairment could be applied in clinical practice are given.

Stearoyl-CoA desaturase (SCD) is a rate-limiting enzyme that catalyzes the synthesis of monounsaturated fatty acids. It plays an important role in regulating skeletal muscle metabolism. Lack of the SCD1 gene increases the rate of fatty acid β-oxidation through activation of the AMP-activated protein kinase (AMPK) pathway and the upregulation of genes that are related to fatty acid oxidation. The mechanism of AMPK activation under conditions of SCD1 deficiency has been unclear. In the present study, we found that the ablation/inhibition of SCD1 led to AMPK activation in skeletal muscle through an increase in AMP levels whereas muscle-specific SCD1 overexpression decreased both AMPK phosphorylation and the adenosine monophosphate/adenosine triphosphate (AMP/ATP) ratio. Changes in AMPK phosphorylation that were caused by SCD1 down- and upregulation affected NAD+ levels following changes in NAD+ -dependent deacetylase sirtuin-1 (SIRT1) activity and histone 3 (H3K9) acetylation and methylation status. Moreover, mice with muscle-targeted overexpression of SCD1 were more susceptible to high-fat diet-induced lipid accumulation and the development of insulin resistance compared with wild-type mice. These data show that SCD1 is involved in nucleotide (ATP and NAD+ ) metabolism and suggest that the SCD1-dependent regulation of muscle steatosis and insulin sensitivity are mediated by cooperation between AMPK- and SIRT1-regulated pathways. Altogether, the present study reveals a novel mechanism that links SCD1 with the maintenance of metabolic homeostasis and insulin sensitivity in skeletal muscle.

In patients with Cystic fibrosis (CF), pulmonary exacerbations (PEx) have an important influence on well-being, quality of life, and lung function decline. Early detection combined with early treatment may prevent severe PEx.To determine whether early detection of PEx is possible by non-invasive markers (volatile organic compounds) in exhaled breath.In a one-year prospective observational pilot study, 49 children with CF were studied. At clinical visits with an interval of 2 months, lung function, volatile organic compounds (VOCs) in exhaled breath by means of gas chromatography-time-of-flight-mass spectrometry, and medication use were assessed. PEx were recorded. Random Forest (RF) classification modelling was used to select discriminatory VOCs, followed by building of Receiver Operating Characteristic curves.An inverse relation between the predictive power of a set of VOCs and time between exhaled breath sampling and the onset of PEx was found. When this time period was within 7 days, the RF model with the 9 most discriminatory VOCs was able to correctly predict 79% of the children with an upcoming PEx or remaining stable (sensitivity 79% and specificity 78%). This result was validated by means of bootstrapping within the RF classification model.PEx in children with CF can be detected at an early stage by means of exhaled VOCs. The highest predictive value was reached if time between sampling and the onset of an exacerbation was no longer than 7 days.

Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3–2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation.

Smokers are exposed to more than 6000 toxic smoke components including volatile organic compounds (VOCs). The objective is to investigate VOCs levels in blood and exhaled breath and their relation, upon smoking three types of cigarettes of one brand with varying declared tar, nicotine and carbon monoxide (TNCO) yields according to ISO 3308. Furthermore, we aim to identify the relevance of these declared yields for human smokers exposed to VOCs. Blood and exhaled breath were sampled from twelve male volunteers directly before and 10 minutes after smoking cigarettes on 3 days (day 1 Marlboro Red (regular), day 2 Marlboro Prime (highly ventilated, low-TNCO), day 3 Marlboro Prime with blocked filter ventilation (taped)). Our results show that levels of toluene, ethylbenzene, m/p-xylene, o-xylene, and 2,5-dimethylfuran in both blood and exhaled breath increase after smoking a cigarette. Overall, no strong correlation was found between VOC levels in exhaled breath and VOC levels in blood because of variations between the individual smoking volunteers. More research is required in order to use exhaled breath sampling as a non-invasive quantitative marker for volatile toxicants from cigarette smoke exposure in risk assessment. However, we also show that the increases in concentrations in blood and exhaled breath for each smoker are within the same range for all three cigarette types smoked. The differences in declared TNCO yields, as determined with machine smoking according to ISO 3308 regime, are thus irrelevant for the assessment of human exposure of VOCs, which is important for the risk assessment of inhaling mainstream cigarette smoke. Lastly, cigarettes with lower declared TNCO levels may expose smokers to similar or even higher levels of smoke toxicants. Clinical trial registration: NCT03498053.

Exhaled breath analysis has become a promising monitoring tool for various ailments by identifying volatile organic compounds (VOCs) as indicative biomarkers excreted in the human body. Throughout the process of sampling, measuring, and data processing, non-biological variations are introduced in the data leading to batch effects. Algorithmic approaches have been developed to cope with within-study batch effects. Batch differences, however, may occur among different studies too, and up-to-date, ways to correct for cross-study batch effects are lacking; ultimately, cross-study comparisons to verify the uniqueness of found VOC profiles for a specific disease may be challenging. This study applies within-study batch-effect-correction approaches to correct for cross-study batch effects; suggestions are made that may help prevent the introduction of cross-study variations. Three batch-effect-correction algorithms were investigated: zero-centering, combat, and the analysis of covariance framework. The breath samples were collected from inflammatory bowel disease ([Formula: see text]), chronic liver disease ([Formula: see text]), and irritable bowel syndrome ([Formula: see text]) patients at different periods, and they were analysed via gas chromatography-mass spectrometry. Multivariate statistics were used to visualise and verify the results. The visualisation of the data before any batch-effect-correction technique was applied showed a clear distinction due to probable batch effects among the datasets of the three cohorts. The visualisation of the three datasets after implementing all three correction techniques showed that the batch effects were still present in the data. Predictions made using partial least squares discriminant analysis and random forest confirmed this observation. The within-study batch-effect-correction approaches fail to correct for cross-study batch effects present in the data. The present study proposes a framework for systematically standardising future breathomics data by using internal standards or quality control samples at regular analysis intervals. Further knowledge regarding the nature of the unsolicited variations among cross-study batches must be obtained to move the field further.

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group.The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them. (c) 2020 Elsevier B.V. All rights reserved.

Biomarker discovery, i.e., finding disease or condition-specific biological markers, is a crucial aspect of biomedical research. Volatile organic compounds (VOCs) are excreted by various biofluids, cells and tissues, and bacteria, and these have been investigated extensively for their potential as markers of malfunctioning status in human. The number of VOCs excreted by those media - typically detected using sophisticated analytical instrumentation - are numerically large and biologically complex. Therefore, data preprocessing and analysis are crucial for successful identification of valid VOC markers for their application in clinical practice. This chapter provides an overview of various preprocessing approaches suitable for volatilome data of diverse nature. The importance of normalization and scaling, often neglected in the field, is discussed. The most common and promising machine learning techniques are presented and discussed, including unsupervised and supervised approaches, followed by a rarely used strategy in the volatilomics field, data fusion. The chapter aims to equip the reader with a basic overview of suitable techniques for treating and successfully exploiting volatilome data.

Microbiota composition and its metabolic capacity are very important for host health. Evidence suggests that gut microbiome is involved in the metabolites production by host-microbiome interaction. These metabolites can be absorbed in blood and excreted in exhaled air. Although, profiles of gut microbiota and exhaled metabolites were associated with gastrointestinal diseases, a direct link between them has not yet been investigated. The aim of the study was to investigate the relation between volatiles in breath and gut microbiome in active and quiescent Crohn's disease (CD) via a multivariate statistical approach. Canonical correlation analysis (CCA) was used to assess the relation between exhaled metabolites and faecal bacterial species.From 68 CD patients, 184 repeated faecal and breath samples were collected (92 active and 92 quiescent disease). The microbiota composition was assessed by the pyrosequencing of the 16 S rRNA V1-V3 gene region and breath metabolites by gas chromatography mass spectrometry.In active disease, CCA analysis identified 18 metabolites significantly correlated with 19 faecal bacterial taxa (R = 0.91 p-value 3.5*10-4). In quiescent disease 17 volatile metabolites were correlated with 17 bacterial taxa (R = 0.96 p-value 2.8*10-4). Nine metabolites and three bacteria taxa overlapped in active and inactive CD. This is the first study that shows a significant relation between gut microbiome and exhaled metabolites, and was found to differ between active and quiescent CD, indicating various underlying mechanisms. Unravelling this link is essential to increase our understanding on the functional effects of the microbiome and may provide new leads for microbiome-targeted intervention. (C) 2018 Elsevier B.V. All rights reserved.

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.

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.

The gold standard method for chronic liver diseases diagnosis and staging remains liver biopsy, despite the spread of less invasive surrogate modalities based on imaging and blood biomarkers. Still, more than 50% of chronic liver disease cases are detected at later stages when patients exhibit episodes of liver decompensation. Breath analysis represents an attractive means for the development of non-invasive tests for several pathologies, including chronic liver diseases. In this perspective review, we summarize the main findings of studies that compared the breath of patients with chronic liver diseases against that of control subjects and found candidate biomarkers for a potential breath test. Interestingly, identified compounds with best classification performance are of exogenous origin and used as flavoring agents in food. Therefore, random dietary exposure of the general population to these compounds prevents the establishment of threshold levels for the identification of disease subjects. To overcome this limitation, we propose the exogenous volatile organic compounds (EVOCs) probe approach, where one or multiple of these flavoring agent(s) are administered at a standard dose and liver dysfunction associated with chronic liver diseases is evaluated as a washout of ingested compound(s). We report preliminary results in healthy subjects in support of the potential of the EVOC Probe approach.

Comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) has become a mature technique. GC×GC-MS can now be used to conduct large scale studies, giving full access to its high-resolution power for targeted and mostly untargeted screening. The current challenges are now localized on the data management side, where powerful chemometric tools are required to unlock GC×GC-MS full potential. This manuscript reviews and discusses recent advances in the development of specific chemometrics for GC×GC-MS. It is designed as a guide to users who desire to establish robust and reproducible GC×GC-MS data processing workflows. Each of the critical steps of data preprocessing, feature selection, model building, and validation are described and considered in detail. Finally, some future perspectives on the development of the next generation of chemometric tools based on Artificial Intelligence, especially machine learning, are critically discussed.

SummaryBackground To optimise treatment of ulcerative colitis (UC), patients need repeated assessment of mucosal inflammation. Current non-invasive biomarkers and clinical activity indices do not accurately reflect disease activity in all patients and cannot discriminate UC from non-UC colitis. Volatile organic compounds (VOCs) in exhaled air could be predictive of active disease or remission in Crohn's disease. Aim To investigate whether VOCs are able to differentiate between active UC, UC in remission and non-UC colitis. Methods UC patients participated in a 1-year study. Clinical activity index, blood, faecal and breath samples were collected at each out-patient visit. Patients with clear defined active faecal calprotectin >250 μg/g and inactive disease (Simple Clinical Colitis Activity Index

Aging is accompanied with increased frailty and comorbidities, which is potentially associated with microbiome perturbations. Dietary fibers could contribute to healthy aging by beneficially impacting gut microbiota and metabolite profiles. We aimed to compare young adults with elderly and investigate the effect of pectin supplementation on fecal microbiota composition, short chain fatty acids (SCFAs), and exhaled volatile organic compounds (VOCs) while using a randomized, double-blind, placebo-controlled parallel design. Fifty-two young adults and 48 elderly consumed 15 g/day sugar beet pectin or maltodextrin for four weeks. Fecal and exhaled breath samples were collected before and after the intervention period. Fecal samples were used for microbiota profiling by 16S rRNA gene amplicon sequencing, and for analysis of SCFAs by gas chromatography (GC). Breath was used for VOC analysis by GC-tof-MS. Young adults and elderly showed similar fecal SCFA and exhaled VOC profiles. Additionally, fecal microbiota profiles were similar, with five genera significantly different in relative abundance. Pectin supplementation did not significantly alter fecal microbiota, SCFA or exhaled VOC profiles in elderly or young adults. In conclusion, aside from some minor differences in microbial composition, healthy elderly and young adults showed comparable fecal microbiota composition and activity, which were not altered by pectin supplementation.