Metabolomics of volatile organic compounds in cystic fibrosis patients and controls

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

BACKGROUND

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

AIM OF REVIEW

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Exhaled breath analysis in adult patients with cystic fibrosis by real-time proton mass spectrometry

BACKGROUND

Proton-transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) is a promising tool for a rapid online determination of exhaled volatile organic compounds (eVOCs) profiles in patients with cystic fibrosis (CF).

OBJECTIVE

To detect VOC breath signatures specific to adult patients with CF compared with controls using PTR-TOF-MS.

METHODS

102 CF patients (54 M/48, mean age 25.6 ± 7.8 yrs) and 97 healthy controls (56 M/41F, mean age 25.8 ± 6.0 yrs) were examined. Samples from normal quiet breathing and forced expiratory maneuvers were analyzed with PTR-TOF-MS (Ionicon, Austria) to obtain VOC profiles listed as ions at various mass-to-charge ratios (m/z).

RESULTS

PTR-TOF-MS analysis was able to detect 167 features in exhaled breath from CF patients and healthy controls. According to cluster analysis and LASSO regression, patients with CF and controls were separated. The most significant VOCs for CF were indole, phenol, dimethyl sulfide, and not indicated: m/z = 297.0720 ([C12H13N2O7 and C17H13O5]H + ), m/z = 281.0534 ([C19H7NO2, C12H11NO7 and C16H9O5]H + ) during five-fold cross-validation both in forced expiratory maneuver and in normal quiet breathing.

CONCLUSION

PTR-TOF-MS is a promising method for determining the molecular composition of exhaled air specific to CF.

Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS)

The analysis of volatile organic compounds (VOCs) in exhaled air has attracted the interest of the scientific community because it provides the possibility of monitoring physiological and metabolic processes and non-invasive diagnostics of various diseases. However, this method remains underused in clinical practice as well as in research because of the lack of standardized procedures for the collection, storage and transport of breath samples, which would guarantee good reproducibility and comparability of results. The method of sampling, as well as the storage time of the breath samples in the polymer bags used for sample storage and transport, affect the composition and concentration of VOCs present in the breath samples. The aim of our study was to compare breath samples obtained using two methods with fully disposable equipment: a Haldane sampling tube intended for direct breath collection and breath samples exhaled into a transparent Tedlar bag. The second task was to monitor the stability of selected compounds of real breath samples stored in a Tedlar bag for 6 h. Gas chromatography coupled with ion mobility spectrometry (GC-IMS) implemented in the BreathSpec®device was used to analyse exhaled breath. Our results showed a significant difference in the signal intensity of some volatiles when taking a breath sample with a Haldane tube and a Tedlar bag. Due to its endogenous origin, acetone levels were significantly higher when the Haldane tube sampler was used while elevated levels of 2-propanol and unidentified VOC (designated as VOC 3) in the Tedlar bag samples likely originated from contamination of the Tedlar bags. The VOC stability study revealed compound-specific signal intensity changes of the selected VOCs with storage time in the Tedlar bags, with some volatiles showing increasing signal intensity during storage in Tedlar bags. This limits the use of Tedlar bags only for very limited time and carefully selected purpose. Our results highlight the importance of careful design and implementation of experiments and clinical protocols to obtain relevant and reliable results.

Changes in fecal lipidome after treatment with ivacaftor without changes in microbiome or bile acids

BACKGROUND

Alterations in gastrointestinal health are prominent manifestations of cystic fibrosis (CF) and can independently impact pulmonary function. Ivacaftor has been associated with robust improvements in pulmonary function and weight gain, but less is known about the impact of ivacaftor on the fecal microbiome, lipidome, and bile acids.

METHODS

Stool samples from 18 patients with CF and gating mutations (ages 6-61 years, 13 pancreatic insufficient) were analyzed for fecal microbiome and lipidome composition as well as bile acid concentrations at baseline and after 3 months of treatment with ivacaftor. Microbiome composition was also assessed in a healthy reference cohort.

RESULTS

Alpha and beta diversity of the microbiome were different between CF and reference cohort at baseline, but no treatment effect was seen in the CF cohort between baseline and 3 months. Seven lipids increased with treatment. No differences were seen in bile acid concentrations after treatment in CF. At baseline, 403 lipids and unconjugated bile acids were different between pancreatic insufficient (PI-CF) and sufficient (PS-CF) groups and 107 lipids were different between PI-CF and PS-CF after 3 months of treatment.

CONCLUSIONS

The composition and diversity of the fecal microbiome were different in CF as compared to a healthy reference, and did not change after 3 months of ivacaftor. We detected modest differences in the fecal lipidome with treatment. Differences in lipid and bile acid profiles between PS-CF and PI-CF were attenuated after 3 months of treatment.

Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis

BACKGROUND

Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown.

METHODS

In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits.

RESULTS

Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits.

CONCLUSIONS

Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.

Comparison of Cu+, Ag+, and Au+ Ions as Ionization Agents of Volatile Organic Compounds at Subatmospheric Pressure

Ionization of volatile organic compounds (VOCs) by coinage metal ions (Cu+, Ag+, and Au+) generated by laser desorption and ionization (LDI) of a metal nanolayer in subatmospheric conditions is explored. The study was performed in a commercial subatmospheric dual MALDI/ESI ion source. Five compounds representing different VOC classes were chosen for a detailed study of the metal ionization mechanism: ethanol, acetone, acetic acid, xylene, and cyclohexane. In the gas phase, ion molecular complexes of all three metal ions were formed, typically with two ligand molecules. The successful detection of the metal complexes with VOCs strongly depended on the applied voltages across the ion source, minimizing the in-source fragmentation. The employed orbital trap with ultrahigh resolving power and sub-parts-per-million mass accuracy allowed unambiguous identification of the formed complexes based on their molecular formulas. The detection limits of the studied compounds in the gas were in the range 0.1-1.4 nmol/L. Compared to Cu+ and Ag+ ions, Au+ ions exhibited the highest reactivity, often complicating spectra by side products of reactions. On the other hand, they also allowed detecting saturated hydrocarbons, which did not produce any signals with Ag+ and Cu+.

Endotypes of Paediatric Cough-Do They Exist and Finding New Techniques to Improve Clinical Outcomes

Chronic cough is a common symptom of many childhood lung conditions. Given the phenotypic heterogeneity of chronic cough, better characterization through endotyping is required to provide diagnostic certainty, precision therapies and to identify pathobiological mechanisms. This review summarizes recent endotype discoveries in airway diseases, particularly in relation to children, and describes the multi-omic approaches that are required to define endotypes. Potential biospecimens that may contribute to endotype and biomarker discoveries are also discussed. Identifying endotypes of chronic cough can likely provide personalized medicine and contribute to improved clinical outcomes for children.

Tuning phosphorene and MoS2 2D materials for detecting volatile organic compounds associated with respiratory diseases

Efficient identification of volatile organic compounds (VOCs) is essential for the rapid diagnostication of respiratory diseases. By detecting specific biomarkers associated with different pathologies one may distinguish between tuberculosis, nosocomial pneumonia, Aspergillus fumigatus, influenza and SARS-CoV-2 virus infections. Phosphorene and MoS2 are potential candidates from the class of 2D graphene-like materials, which can be used as active layers for sensing elements. However, as the target molecules poorly adhere to the pristine layers, binding centers are created by introducing substitutional impurities. The adsorbed VOCs induce modifications in the electrical properties of the customized active layers. For each biomarker and a sequence of substitutional impurities, a pattern of conductivities is obtained, which enables the detection of an unknown test specimen. Exploring multiple biosensor configurations we find an optimal design yielding a considerable selectivity for the five biomarker compounds.

Breath biomarkers in Non-Carcinogenic diseases

The analysis of volatile organic compounds (VOCs) from human matrices like breath, perspiration, and urine has received increasing attention from academic and medical researchers worldwide. These biological-borne VOCs molecules have characteristics that can be directly related to physiologic and pathophysiologic metabolic processes. In this work, gathers a total of 292 analytes that have been identified as potential biomarkers for the diagnosis of various non-carcinogenic diseases. Herein we review the advances in VOCs with a focus on breath biomarkers and their potential role as minimally invasive tools to improve diagnosis prognosis and therapeutic monitoring.

Exhaled breath profiles to detect lung infection with Staphylococcus aureus in children with cystic fibrosis

BACKGROUND

An electronic nose (eNose) can be used to detect volatile organic compounds (VOCs). Exhaled breath contains numerous VOCs and individuals' VOCs mixtures create distinct breath profiles. Previous reports have shown that eNose can detect lung infections. Whether eNose can detect Staphylococcus aureus airway infections in breath of children with cystic fibrosis (CF) is currently unclear.

METHODS

In this cross-sectional observational study, a cloud-connected eNose was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant and receiver operating characteristics (ROC) analyses.

RESULTS

Breath profiles from 100 children with CF (median predicted FEV1 91%) were obtained and analysed. CF patients with positive airway cultures for any CF pathogen were distinguishable from no CF pathogens (no growth or usual respiratory flora) with accuracy of 79.0% (AUC-ROC 0.791; 95% CI: 0.669-0.913) and between patients positive for Staphylococcus aureus (SA) only and no CF pathogen with accuracy of 74.0% (AUC-ROC 0.797; 95% CI: 0.698-0.896). Similar differences were seen for Pseudomonas aeruginosa (PA) infection vs no CF pathogens (78.0% accuracy, AUC-ROC 0.876, 95% CI: 0.794-0.958). SA- and PA-specific signatures were driven by different sensors in the SpiroNose suggesting pathogen-specific breath signatures.

CONCLUSIONS

Breath profiles of CF patients with SA in airway cultures are distinct from those with no infection or PA infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF.

Microbial Epidemiology of the Cystic Fibrosis Airways: Past, Present, and Future

Progressive obstructive lung disease secondary to chronic airway infection, coupled with impaired host immunity, is the leading cause of morbidity and mortality in cystic fibrosis (CF). Classical pathogens found in the airways of persons with CF (pwCF) include Pseudomonas aeruginosa, Staphylococcus aureus, the Burkholderia cepacia complex, Achromobacter species, and Haemophilus influenzae. While traditional respiratory-tract surveillance culturing has focused on this limited range of pathogens, the use of both comprehensive culture and culture-independent molecular approaches have demonstrated complex highly personalized microbial communities. Loss of bacterial community diversity and richness, counteracted with relative increases in dominant taxa by traditional CF pathogens such as Burkholderia or Pseudomonas, have long been considered the hallmark of disease progression. Acquisition of these classic pathogens is viewed as a harbinger of advanced disease and postulated to be driven in part by recurrent and frequent antibiotic exposure driven by frequent acute pulmonary exacerbations. Recently, CF transmembrane conductance regulator (CFTR) modulators, small molecules designed to potentiate or restore diminished protein levels/function, have been successfully developed and have profoundly influenced disease course. Despite the multitude of clinical benefits, structural lung damage and consequent chronic airway infection persist in pwCF. In this article, we review the microbial epidemiology of pwCF, focus on our evolving understanding of these infections in the era of modulators, and identify future challenges in infection surveillance and clinical management.

Identification of Exhaled Metabolites in Children with Cystic Fibrosis

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS² spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications.

The potential of volatile organic compound analysis for pathogen detection and disease monitoring in patients with cystic fibrosis

INTRODUCTION

Airway infection with pathogens and its associated pulmonary exacerbations (PEX) are the major causes of morbidity and premature death in cystic fibrosis (CF). Preventing or postponing chronic infections requires early diagnosis. However, limitations of conventional microbiology-based methods can hamper identification of exacerbations and specific pathogen detection. Analyzing volatile organic compounds (VOCs) in breath samples may be an interesting tool in this regard, as VOC-biomarkers can characterize specific airway infections in CF.

AREAS COVERED

We address the current achievements in VOC-analysis and discuss studies assessing VOC-biomarkers and fingerprints, i.e. a combination of multiple VOCs, in breath samples aiming at pathogen and PEX detection in people with CF (pwCF). We aim to provide bases for further research in this interesting field.

EXPERT OPINION

Overall, VOC-based analysis is a promising tool for diagnosis of infection and inflammation with potential to monitor disease progression in pwCF. Advantages over conventional diagnostic methods, including easy and non-invasive sampling procedures, may help to drive prompt, suitable therapeutic approaches in the future. Our review shall encourage further research, including validation of VOC-based methods. Specifically, longitudinal validation under standardized conditions is of interest in order to ensure repeatability and enable inclusion in CF diagnostic routine.

Metabolites From Trypanosome-Infected Cattle as Sensitive Biomarkers for Animal Trypanosomosis

Trypanosomes are important global livestock and human pathogens of public health importance. Elucidating the chemical mechanisms of trypanosome-relevant host interactions can enhance the design and development of a novel, next-generation trypanosomosis diagnostics. However, it is unknown how trypanosome infection affects livestock volatile odors. Here, we show that Trypanosoma congolense and Trypanosoma vivax infections induced dihydro-β- ionone and junenol, while abundance of dihydro-α-ionone, phenolics, p-cresol, and 3-propylphenol significantly elevated in cow urine. These biomarkers of trypanosome infection are conserved in cow breath and the urine metabolites of naturally infected cows, regardless of population, diet, or environment differences. Furthermore, treating trypanosome-infected cows reduced the levels of these indicators back to the pre-infection levels. Finally, we demonstrated that the potential of some specific biomarkers of phenolic origin may be used to detect active trypanosome infections, including low-level infections that are not detectable by microscopy. The sensitivity and specificity of biomarkers detection are suited for rapid, robust, and non-invasive trypanosomosis diagnosis under field conditions.

Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Preliminary method for profiling volatile organic compounds in breath that correlate with pulmonary function and other clinical traits of subjects diagnosed with cystic fibrosis: a pilot study

Cystic fibrosis (CF) is characterized by chronic respiratory infections which progressively decrease lung function over time. Affected individuals experience episodes of intensified respiratory symptoms called pulmonary exacerbations (PEx) which accelerate pulmonary function decline and decrease survival. There is no standard classification for PEx, which results in treatments that are heterogeneous. Improving PEx classification and management is a significant priority for people with CF. Previous studies have shown volatile organic compounds (VOCs) in exhaled breath can be used as biomarkers because they are products of metabolic pathways dysregulated by different diseases. To provide insights on PEx classification and other clinical factors, exhaled breath was collected from subjects with CF, with some experiencing PEx and others at baseline. Exhaled breath was collected in Tedlar bags during tidal breathing for VOC analysis by solid phase microextraction coupled to gas chromatography-mass spectrometry. Statistical significance testing between quantitative and categorical clinical variables displayed percent-predicted forced expiratory volume in one second (FEV1pp) was decreased in subjects experiencing PEx. VOCs correlating with other clinical variables (body mass index, age, use of highly effective modulator therapies, and need for antibiotics) were also explored. VOCs correlating to potential confounding variables were removed and analyzed by regression for correlations with FEV1pp measurements. The VOC with the highest correlation with FEV1pp (3,7-dimethyldecane) also gave the lowest p-value when comparing subjects at baseline and during PEx. Receiver operator characteristic curves showed 3,7-dimethyldecane had a higher ability to classify PEx (area under the curve (AUC) = 0.91) relative to FEV1pp values at collection (AUC = 0.83). However, normalized ΔFEV1pp values had the highest capability to distinguish PEx (AUC = 0.93). These results show that exhaled VOCs may be a source of biomarkers for various clinical traits of CF, including PEx, that should be explored in larger sample cohorts and validation studies.

Sweat metabolomics before and after intravenous antibiotics for pulmonary exacerbation in people with cystic fibrosis

BACKGROUND

People with cystic fibrosis (PWCF) suffer from acute unpredictable reductions in pulmonary function associated with a pulmonary exacerbation (PEx) that may require hospitalization. PEx symptoms vary between PWCF without universal diagnostic criteria for diagnosis and response to treatment.

RESEARCH QUESTION

We characterized sweat metabolomes before and after intravenous (IV) antibiotics in PWCF hospitalized for PEx to determine feasibility and define biological alterations by IV antibiotics for PEx.

STUDY DESIGN AND METHODS

PWCF with PEx requiring hospitalization for IV antibiotics were recruited from clinic. Sweat samples were collected using the Macroduct® Sweat Collection System at admission prior to initiation of IV antibiotics and after completion prior to discharge. Samples were analyzed for metabolite changes using ultra-high-performance liquid chromatography/tandem accurate mass spectrometry.

RESULTS

Twenty-six of 29 hospitalized PWCF completed the entire study. A total of 326 compounds of known identity were detected in sweat samples. Of detected metabolites, 147 were significantly different between pre-initiation and post-completion of IV antibiotics for PEx (average treatment 14 days). Global sweat metabolomes changed from before and after IV antibiotic treatment. We discovered specific metabolite profiles predictive of PEx status as well as enriched biologic pathways associated with PEx. However, metabolomic changes were similar in PWCF who failed to return to baseline pulmonary function and those who did not.

INTERPRETATION

Our findings demonstrate the feasibility of non-invasive sweat metabolomic profiling in PWCF and the potential for sweat metabolomics as a prospective diagnostic and research tool to further advance our understanding of PEx in PWCF.

Breath Metabolites to Diagnose Infection

BACKGROUND

Starkly highlighted by the current COVID-19 pandemic, infectious diseases continue to have an outsized impact on human health worldwide. Diagnostic testing for infection can be challenging due to resource limitations, time constraints, or shortcomings in the accuracy of existing diagnostics. Rapid, simple diagnostics are highly desirable. There is increasing interest in the development of diagnostics that use exhaled breath analysis as a convenient and safe diagnostic method, as breath sampling is noninvasive, secure, and easy to perform. Volatile organic compounds (VOCs) present in exhaled breath reflect the fingerprint of the underlying metabolic and biophysical processes during disease.

CONTENT

In this review, we overview the major biomarkers present in exhaled breath in infectious diseases. We outline the promising recent advances in breath-based diagnosis of respiratory infections, including those caused by influenza virus, SARS-CoV-2, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Aspergillus fumigatus. In addition, we review the current landscape of diagnosis of 2 other globally important infections: Helicobacter pylori gastrointestinal infection and malaria.

SUMMARY

Characteristic and reproducible breath VOCs are associated with several infectious diseases, suggesting breath analysis as a promising strategy for diagnostic development. Ongoing challenges include poor standardization of breath collection and analysis and lack of validation studies. Further research is required to expand the applicability of breath analysis to clinical settings.

An Investigation of Stability and Species and Strain-Level Specificity in Bacterial Volatilomes

Microbial volatilomics is a rapidly growing field of study and has shown great potential for applications in food, farming, and clinical sectors in the future. Due to the varying experimental methods and growth conditions employed in microbial volatilomic studies as well as strain-dependent volatilomic differences, there is limited knowledge regarding the stability of microbial volatilomes. Consequently, cross-study comparisons and validation of results and data can be challenging. In this study, we investigated the stability of the volatilomes of multiple strains of Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli across three frequently used nutrient-rich growth media. Volatilomic stability was assessed based on media-, time- and strain-dependent variation across the examined bacterial volatilomes. Strain-level specificity of the observed volatilomes of E. coli and P. aeruginosa strains was further investigated by comparing the emission of selected compounds at varying stages of cell growth. Headspace solid phase microextraction (HS-SPME) sampling coupled with gas chromatography mass spectrometry (GC-MS) was used to analyze the volatilome of each strain. The whole volatilomes of the examined strains demonstrate a high degree of stability across the three examined growth media. At the compound-level, media dependent differences were observed particularly when comparing the volatilomes obtained in glucose-containing brain heart infusion (BHI) and tryptone soy broth (TSB) growth media with the volatilomes obtained in glucose-free Lysogeny broth (LB) media. These glucose-dependent volatilomic differences were primarily seen in the emission of primary metabolites such as alcohols, ketones, and acids. Strain-level differences in the emission of specific compounds in E. coli and P. aeruginosa samples were also observed across the media. These strain-level volatilomic differences were also observed across varying phases of growth of each strain, therefore confirming that these strains had varying core and accessory volatilomes. Our results demonstrate that, at the species-level, the examined bacteria have a core volatilome that exhibits a high-degree of stability across frequently-used growth media. Media-dependent differences in microbial volatilomes offer valuable insights into identifying the cellular origin of individual metabolites. The observed differences in the core and accessory volatilomes of the examined strains illustrate the complexity of microbial volatilomics as a study while also highlighting the need for more strain-level investigations to ultimately elucidate the whole volatilomic capabilities of microbial species in the future.

Breath-Based Diagnosis of Infectious Diseases: A Review of the Current Landscape

Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.

Exhaled volatile organic compounds analysis in clinical pediatrics: a systematic review

BACKGROUND

Measured exhaled volatile organic compounds (VOCs) in breath also referred to as exhaled volatilome have been long claimed as a potential source of non-invasive and clinically applicable biomarkers. However, the feasibility of using exhaled volatilome in clinical practice remains to be demonstrated, particularly in pediatrics where the need for improved non-invasive diagnostic and monitoring methods is most urgent. This work presents the first formal evidence-based judgment of the clinical potential of breath volatilome in the pediatric population.

METHODS

A rigorous systematic review across Web of Science, SCOPUS, and PubMed databases following the PRISMA statement guidelines. A narrative synthesis of the evidence was conducted and QUADAS-2 was used to assess the quality of selected studies.

RESULTS

Two independent reviewers deemed 22 out of the 229 records initially found to satisfy inclusion criteria. A summary of breath VOCs found to be relevant for several respiratory, infectious, and metabolic pathologies was conducted. In addition, we assessed their associated metabolism coverage through a functional characterization analysis.

CONCLUSION

Our results indicate that current research remains stagnant in a preclinical exploratory setting. Designing exploratory experiments in compliance with metabolomics practice should drive forward the clinical translation of VOCs breath analysis.

IMPACT

What is the key message of your article? Metabolomics practice could help to achieve the clinical utility of exhaled volatilome analysis. What does it add to the existing literature? This work is the first systematic review focused on disease status discrimination using analysis of exhaled breath in the pediatric population. A summary of the reported exhaled volatile organic compounds is conducted together with a functional characterization analysis. What is the impact? Having noted challenges preventing the clinical translation, we summary metabolomics practices and the experimental designs that are closer to clinical practice to create a framework to guide future trials.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Exhaled volatile organic compounds detect pulmonary exacerbations early in children with cystic fibrosis: results of a 1 year observational pilot study

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 1 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 d, the RF model with the nine 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 d.

Discriminant Profiles of Volatile Compounds in the Alveolar Air of Patients with Squamous Cell Lung Cancer, Lung Adenocarcinoma or Colon Cancer

The objective of the present work was to analyze volatile compounds in alveolar air in patients with squamous cell lung cancer, lung adenocarcinoma or colon cancer, to prepare algorithms able to discriminate such specific pathological conditions. The concentration of 95 volatile compounds was measured in the alveolar air of 45 control subjects, 36 patients with lung adenocarcinoma, 25 patients with squamous cell lung cancer and 52 patients with colon cancer. Volatile compounds were measured with ion molecule reaction mass spectrometry (IMR-MS). An iterated least absolute shrinkage and selection operator multivariate logistic regression model was used to generate specific algorithms and discriminate control subjects from patients with different kinds of cancer. The final predictive models reached the following performance: by using 11 compounds, patients with lung adenocarcinoma were identified with a sensitivity of 86% and specificity of 84%; nine compounds allowed us to identify patients with lung squamous cell carcinoma with a sensitivity of 88% and specificity of 84%; patients with colon adenocarcinoma could be identified with a sensitivity of 96% and a specificity of 73% using a model comprising 13 volatile compounds. The different alveolar profiles of volatile compounds, obtained from patients with three different kinds of cancer, suggest dissimilar biological–biochemistry conditions; each kind of cancer has probably got a specific alveolar profile.

Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection

Respiratory tract infections are common, and when affecting the lower airways and lungs, can result in significant morbidity and mortality. There is an unfilled need for simple, non-invasive tools that can be used to screen for such infections at the clinical point of care. The electronic nose (eNose) is a novel technology that detects volatile organic compounds (VOCs). Early studies have shown that certain diseases and infections can result in characteristic changes in VOC profiles in the exhaled breath. This review summarizes current knowledge on breath analysis by the electronic nose and its potential for the detection of respiratory diseases with and without infection.

Multi-strain volatile profiling of pathogenic and commensal cutaneous bacteria

The detection of volatile organic compounds (VOC) emitted by pathogenic bacteria has been proposed as a potential non-invasive approach for characterising various infectious diseases as well as wound infections. Studying microbial VOC profiles in vitro allows the mechanisms governing VOC production and the cellular origin of VOCs to be deduced. However, inter-study comparisons of microbial VOC data remains a challenge due to the variation in instrumental and growth parameters across studies. In this work, multiple strains of pathogenic and commensal cutaneous bacteria were analysed using headspace solid phase micro-extraction coupled with gas chromatography-mass spectrometry. A kinetic study was also carried out to assess the relationship between bacterial VOC profiles and the growth phase of cells. Comprehensive bacterial VOC profiles were successfully discriminated at the species-level, while strain-level variation was only observed in specific species and to a small degree. Temporal emission kinetics showed that the emission of particular compound groups were proportional to the respective growth phase for individual S. aureus and P. aeruginosa samples. Standardised experimental workflows are needed to improve comparability across studies and ultimately elevate the field of microbial VOC profiling. Our results build on and support previous literature and demonstrate that comprehensive discriminative results can be achieved using simple experimental and data analysis workflows.

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

Pseudomonas 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.

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.

Spatial mapping of VOC exhalation by means of bronchoscopic sampling

Breath analysis holds promise for non-invasive in vivo monitoring of disease related processes. However, physiological parameters may considerably affect profiles of exhaled volatile organic substances (VOCs). Volatile substances can be released via alveoli, bronchial mucosa or from the upper airways. The aim of this study was the systematic investigation of the influence of different sampling sites in the respiratory tract on VOC concentration profiles by means of a novel experimental setup. After ethical approval, breath samples were collected from 25 patients undergoing bronchoscopy for endobronchial ultrasound or bronchoscopic lung volume reduction from different sites in the airways. All patients had total intravenous anaesthesia under pressure-controlled ventilation. If necessary, respiratory parameters were adjusted to keep P_ETCO₂ = 35-45 mm Hg. 30 ml gas were withdrawn at six sampling sites by means of gastight glass syringes: S1 = Room air, S2 = Inspiration, S3 = Endotracheal tube, S4 = Trachea, S5 = Right B6 segment, S6 = Left B6 segment (S4-S6 through the bronchoscope channel). 10 ml were used for VOC analysis, 20 ml for PCO₂ determination. Samples were preconcentrated by solid-phase micro-extraction (SPME) and analysed by gas chromatography-mass spectrometry (GC-MS). PCO₂ was determined in a conventional blood gas analyser. Statistically significant differences in substance concentrations for acetone, isoprene, 2-methyl-pentane and n-hexane could be observed between different sampling sites. Increasing substance concentrations were determined for acetone (15.3%), 2-methyl-pentane (11.4%) and n-hexane (19.3%) when passing from distal to proximal sampling sites. In contrast, isoprene concentrations decreased by 9.9% from proximal to more distal sampling sites. Blank bronchoscope measurements did not show any contaminations. Increased substance concentrations in the proximal respiratory tract may be explained through substance excretion from bronchial mucosa while decreased concentrations could result from absorption or reaction processes. Spatial mapping of VOC profiles can provide novel insights into substance specific exhalation kinetics and mechanisms.

Metabolomics profiling of tobacco exposure in children with cystic fibrosis

BACKGROUND

Inflammation is integral to early disease progression in children with CF. The effect of modifiable environmental factors on infection and inflammation in persons with CF is poorly understood. Our prior studies determined that secondhand smoke exposure (SHSe) is highly prevalent in young children with CF. SHSe is associated with increased inflammation, heightened bacterial burden, and worsened clinical outcomes. However, the specific metabolite and signaling pathways that regulate responses to SHSe in CF are relatively unknown.

METHODS

High-resolution metabolomics was performed on plasma samples from infants (n = 25) and children (n = 40) with CF compared to non-CF controls (n = 15). CF groups were stratified according to infant or child age and SHSe status.

RESULTS

Global metabolomic profiles segregated by age and SHSe status. SHSe in CF was associated with changes in pathways related to steroid biosynthesis, fatty acid metabolism, cysteine metabolism, and oxidative stress. CF infants with SHSe demonstrated enrichment for altered metabolite localization to the small intestine, liver, and striatum. CF children with SHSe demonstrated metabolite enrichment for organs/tissues associated with oxidative stress including mitochondria, peroxisomes, and the endoplasmic reticulum. In a confirmatory analysis, SHSe was associated with changes in biomarkers of oxidative stress and cellular adhesion including MMP-9, MPO, and ICAM-1.

CONCLUSIONS

SHSe in young children and infants with CF is associated with altered global metabolomics profiles and specific biochemical pathways, including enhanced oxidative stress. SHSe remains an important but understudied modifiable variable in early CF disease.

Proteomics and Metabolomics for Cystic Fibrosis Research

The aim of this review article is to introduce the reader to the state-of-the-art of the contribution that proteomics and metabolomics sciences are currently providing for cystic fibrosis (CF) research: from the understanding of cystic fibrosis transmembrane conductance regulator (CFTR) biology to biomarker discovery for CF diagnosis. Our work particularly focuses on CFTR post-translational modifications and their role in cellular trafficking as well as on studies that allowed the identification of CFTR molecular interactors. We also show how metabolomics is currently helping biomarker discovery in CF. The most recent advances in these fields are covered by this review, as well as some considerations on possible future scenarios for new applications.

Electronic nose in discrimination of children with uncontrolled asthma

Measuring biomarkers (e.g. volatile organic compounds [VOCs]) in exhaled breath is an attractive approach to monitor airway inflammation in asthma and other lung diseases. Olfactive technology by electronic nose (e-Nose) has been applied to identify VOCs in exhaled breath. We compared e-Nose respiratory patterns in a pediatric cohort with asthma classificate children with different asthma control. This cross-sectional study involved 38 children: 28 with asthma and 10 healthy controls . The asthmatic patients were categorized as having controlled (AC), partially controlled (APC) or uncontrolled asthma (ANC) based on level of asthma symptom control according to Global Initiative for Asthma (GINA). Clinical exams, exhaled breath collection for generating e-Nose VOC profiles, and spirometry were performed. Exhaled breath samples were obtained using a commercial electronic nose (Cyranose 320; Smith Detections, Pasadena, CA, USA). The discriminative ability of breathprints were investigated by principal component analysis and penalized logistic regression. The e-Nose was able to discriminate between the CON (controls) + AC and the ANC + APC group with an area under the curve [AUC] of 0.85 (95% confidence interval [CI] 0.72 to 0.98) and a cross-validated AUC of 0.80 (95% CI 0.70 to 0.85). Sensitivity and specificity calculated using the Youden index were 0.79 and 0.84, respectively. Exhaled biomarker patterns were easy to obtain with the device and were able to differentiate children with uncontrolled symptomatic asthma from asymptomatic controls.

Dried Blood Spot-Based Metabolomic Profiling in Adults with Cystic Fibrosis

Mucoviscidosis of the respiratory, gastrointestinal, and genitourinary tracts is the major pathology in patients with cystic fibrosis (CF), a lethal monogenic panethnic and multisystemic disease most commonly identified in Caucasians. Currently, the measurement of immuno reactive trypsinogen in dry blood spots (DBSs) is the gold-standard method for initial newborn screening for CF, followed by targeted CF transmembrane regulator (CFTR) mutation analysis, and ultimate confirmation with abnormally elevated sweat chloride. Previous metabolomics studies in patients with CF reported on different biomarkers such as breath 2-aminoacetophenone produced during acute and chronic infection in human tissues, including the lungs of CF patients. Herein, we used liquid and gas chromatography-mass spectrometry-based targeted metabolomics profiling to identify potentially reliable, sensitive, and specific biomarkers in DBSs collected from 69 young and adult people including CF patients (n = 39) and healthy control (n = 30). A distinctive metabolic profile including 26 significantly differentially expressed metabolites involving amino acids, glycolysis, mitochondrial and peroxisomal metabolism, and sorbitol pathways was identified. Specifically, the osmolyte (sorbitol) was remarkably downregulated in CF patients compared to healthy controls indicating perturbation in the sorbitol pathway, which may be responsible for the mucoviscidosis seen in patients with CF. The significance of our findings is supported by the clinical utility of inhaled mannitol and hypertonic saline in patients with CF. The systemic administration of sorbitol in such patients may confer additional benefits beyond the respiratory system, especially in those with misfolded CFTR proteins.

Exhaled Volatile Organic Compounds Are Able to Discriminate between Neutrophilic and Eosinophilic Asthma

Rationale: Analysis of exhaled breath for asthma phenotyping using endogenously generated volatile organic compounds (VOCs) offers the possibility of noninvasive diagnosis and therapeutic monitoring. Induced sputum is indeed not widely available and markers of neutrophilic asthma are still lacking.Objectives: To determine whether analysis of exhaled breath using endogenously generated VOCs can be a surrogate marker for recognition of sputum inflammatory phenotypes.Methods: We conducted a prospective study on 521 patients with asthma recruited from the University Asthma Clinic of Liege. Patients underwent VOC measurement, fraction of exhaled nitric oxide (Fe_NO) spirometry, sputum induction, and gave a blood sample. Subjects with asthma were classified in three inflammatory phenotypes according to their sputum granulocytic cell count.Measurements and Main Results: In the discovery study, seven potential biomarkers were highlighted by gas chromatography-mass spectrometry in a training cohort of 276 patients with asthma. In the replication study (n = 245), we confirmed four VOCs of interest to discriminate among asthma inflammatory phenotypes using comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. Hexane and 2-hexanone were identified as compounds with the highest classification performance in eosinophilic asthma with accuracy comparable to that of blood eosinophils and Fe_NO. Moreover, the combination of Fe_NO, blood eosinophils, and VOCs gave a very good prediction of eosinophilic asthma (area under the receiver operating characteristic curve, 0.9). For neutrophilic asthma, the combination of nonanal, 1-propanol, and hexane had a classification performance similar to Fe_NO or blood eosinophils in eosinophilic asthma. Those compounds were found in higher levels in neutrophilic asthma.Conclusions: Our study is the first attempt to characterize VOCs according to sputum granulocytic profile in a large population of patients with asthma and provide surrogate markers for neutrophilic asthma.

Volatile organic compound breath signatures of children with cystic fibrosis by real-time SESI-HRMS

Early pulmonary infection and inflammation result in irreversible lung damage and are major contributors to cystic fibrosis (CF)-related morbidity. An easy to apply and noninvasive assessment for the timely detection of disease-associated complications would be of high value. We aimed to detect volatile organic compound (VOC) breath signatures of children with CF by real-time secondary electrospray ionisation high-resolution mass spectrometry (SESI-HRMS). A total of 101 children, aged 4-18 years (CF=52; healthy controls=49) and comparable for sex, body mass index and lung function were included in this prospective cross-sectional study. Exhaled air was analysed by a SESI-source linked to a high-resolution time-of-flight mass spectrometer. Mass spectra ranging from m/z 50 to 500 were recorded. Out of 3468 m/z features, 171 were significantly different in children with CF (false discovery rate adjusted p-value of 0.05). The predictive ability (CF versus healthy) was assessed by using a support-vector machine classifier and showed an average accuracy (repeated cross-validation) of 72.1% (sensitivity of 77.2% and specificity of 67.7%). This is the first study to assess entire breath profiles of children with SESI-HRMS and to extract sets of VOCs that are associated with CF. We have detected a large set of exhaled molecules that are potentially related to CF, indicating that the molecular breath of children with CF is diverse and informative.

Proof of concept for identifying cystic fibrosis from perspiration samples

The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.

Metabolomic studies of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of P. aeruginosa have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by P. aeruginosa. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for Pseudomonas.

On-Line Analysis of Exhaled Breath Focus Review

On-line analysis of exhaled breath offers insight into a person's metabolism without the need for sample preparation or sample collection. Due to its noninvasive nature and the possibility to sample continuously, the analysis of breath has great clinical potential. The unique features of this technology make it an attractive candidate for applications in medicine, beyond the task of diagnosis. We review the current methodologies for on-line breath analysis, discuss current and future applications, and critically evaluate challenges and pitfalls such as the need for standardization. Special emphasis is given to the use of the technology in diagnosing respiratory diseases, potential niche applications, and the promise of breath analysis for personalized medicine. The analytical methodologies used range from very small and low-cost chemical sensors, which are ideal for continuous monitoring of disease status, to optical spectroscopy and state-of-the-art, high-resolution mass spectrometry. The latter can be utilized for untargeted analysis of exhaled breath, with the capability to identify hitherto unknown molecules. The interpretation of the resulting big data sets is complex and often constrained due to a limited number of participants. Even larger data sets will be needed for assessing reproducibility and for validation of biomarker candidates. In addition, molecular structures and quantification of compounds are generally not easily available from on-line measurements and require complementary measurements, for example, a separation method coupled to mass spectrometry. Furthermore, a lack of standardization still hampers the application of the technique to screen larger cohorts of patients. This review summarizes the present status and continuous improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider application.

Effects of pidotimod and bifidobacteria mixture on clinical symptoms and urinary metabolomic profile of children with recurrent respiratory infections: a randomized placebo-controlled trial

BACKGROUND

Many preschool children develop recurrent respiratory tract infections (RRI). Strategies to prevent RRI include the use of immunomodulators as pidotimod or probiotics, but there is limited evidence of their efficacy on clinical features or on urine metabolic profile.

OBJECTIVE

To evaluate whether pidotimod and/or bifidobacteria can reduce RRI morbidity and influence the urine metabolic profile in preschool children.

MATERIALS AND METHODS

Children aged 3-6 years with RRI were enrolled in a four-arm, exploratory, prospective, randomized, double-blinded, placebo-controlled trial. Patients were randomly assigned to receive pidotimod plus bifidobacteria, pidotimod plus placebo, bifidobacteria plus placebo or double placebo for the first 10 days of each month over 4 consecutive months. Respiratory symptoms and infections were recorded with a daily diary by parents during the study. Metabolomic analyses on urine samples collected before and after treatment were performed.

RESULTS

Compared to placebo, children receiving pidotimod, alone or with bifidobacteria, had more symptom-free days (69 versus 44, p = 0.003; and 65 versus 44, p = 0.02, respectively) and a lower percentage of days with common cold (17% versus 37%, p = 0.005; and 15% versus 37%, p = 0.004, respectively). The metabolomic analysis showed that children treated with Pidotimod (alone or in combination with bifidobacteria) present, respect to children treated with placebo, a biochemical profile characterized by compounds related to the pathway of steroids hormones, hippuric acid and tryptophan. No significant difference in the metabolic profile was found between children receiving bifidobacteria alone and controls.

CONCLUSIONS

Preschool children with RRI treated with pidotimod have better clinical outcomes and a different urine metabolomic profile than subjects receiving placebo. Further investigations are needed to clarify the connection between pidotimod and gut microbiome.

Feasibility and diagnostic accuracy of an electronic nose in children with asthma and cystic fibrosis

The measurement of volatile organic compounds (VOCs) in exhaled breath is a promising tool for diagnosing and monitoring various lung diseases in children. Gas chromatography mass spectrometry (GC-MS) analysis is a frequently used standard technique for VOCs analysis. However, as GC-MS is an expensive and time-consuming technique, hand-held devices or electronic noses have been developed. Recently, the Aeonose was introduced as an easy-to-use hand-held eNose capable of point-of-care testing. Although first results using this eNose in adults are promising, studies in children are lacking. We therefore performed a cross-sectional study in 55 children and adolescents ≥6 years of age (20 children with moderate to severe asthma, 13 children with CF, and 22 healthy controls). The feasibility of the Aeonose was high (>98% successful measurements). The diagnostic accuracy was high for discriminating asthma from CF (Area Under the Receiver Operating Characteristic Curve [AUC] 0.90 [95% Confidence Interval 0.78-1.00] sensitivity 89% [65%-98%], specificity 77% [46%-94%]), and for the distinction between CF and healthy controls (AUC 0.87 [0.74-1.00], sensitivity 85% [54%-97%], specificity 77% [54%-91%]). However, the diagnostic accuracy for the discrimination between asthma and healthy controls was modest (AUC 0.79 [0.63-0.94], sensitivity 74% [49%-90%], specificity 91% [69%-98%]). This is the first study to report test results of the Aeonose in children and adolescents ≥6 years. This eNose showed a high feasibility with modest to good diagnostic accuracies in asthma and CF. This study was registered at clinicaltrial.gov (NCT03377686).

Metabolic Signatures of Cystic Fibrosis Identified in Dried Blood Spots For Newborn Screening Without Carrier Identification

Cystic fibrosis (CF) is a complex multiorgan disorder that is among the most common fatal genetic diseases benefiting from therapeutic interventions early in life. Newborn screening (NBS) for presymptomatic detection of CF currently relies on a two-stage immunoreactive trypsinogen (IRT) and cystic fibrosis transmembrane conductance regulator (CFTR) mutation panel algorithm that is sensitive but not specific for identifying affected neonates with a low positive predictive value. For the first time, we report the discovery of a panel of CF-specific metabolites from a single 3.2 mm diameter dried blood spot (DBS) punch when using multisegment injection-capillary electrophoresis-mass spectrometry (MS) as a high-throughput platform for nontargeted metabolite profiling from volume-restricted/biobanked specimens with quality control. This retrospective case-control study design identified 32 metabolites, including a series of N-glycated amino acids, oxidized glutathione disulfide, and nicotinamide that were differentially expressed in normal birth weight CF neonates without meconium ileus ( n = 36) as compared to gestational age/sex-matched screen-negative controls ( n = 44) after a false discovery rate adjustment ( q < 0.05). Also, 16 metabolites from DBS extracts allowed for discrimination of true CF cases from presumptive screen-positive carriers with one identified CFTR mutation and transient neonatal hypertrypsinogenemic neonates ( n = 72), who were later confirmed as unaffected due to a low sweat chloride (<29 mM) test result. Importantly, six CF-specific biomarker candidates satisfying a Bonferroni adjustment ( p < 7.25 × 10^-5) from three independent batches of DBS specimens included several amino acids depleted in circulation (Tyr, Ser, Thr, Pro, Gly) likely reflecting protein maldigestion/malabsorption. Additionally, CF neonates had lower ophthalmic acid as an indicator of oxidative stress due to impaired glutathione efflux from exocrine/epithelial tissue and elevation of an unknown trivalent peptide that was directly correlated with IRT (ρ = 0.332, p = 4.55 × 10^-4). Structural elucidation of unknown metabolites was performed by high-resolution MS/MS, whereas biomarker validation was realized when comparing a subset of metabolites from matching neonatal DBS specimens independently analyzed by direct infusion-MS/MS at an accredited NBS facility. This work sheds new light into the metabolic phenotype of CF early in life, which is required for better functional understanding of CFTR mutations of unknown clinical consequence and the development of more accurate yet cost-effective strategies for CF screening.

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.

Breath analysis in respiratory diseases: state-of-the-art and future perspectives

INTRODUCTION

The vast majority of respiratory diseases are associated with the production of volatile organic compounds (VOCs), the analysis of which might improve our knowledge about these disorders and their clinical management. The aim of this narrative review is to provide a comprehensive summary of current evidence supporting the application of breath analysis in the field of respiratory diseases, as well as suggesting potential applications available in the near future. Areas covered: A computerized literature search was performed to identify relevant articles reporting original data on the clinical use of breath analysis in respiratory diseases. Papers focusing on diseases other than respiratory, technical issues of VOC sampling and analysis, in vitro experiments or exogenous compounds were excluded. Expert commentary: Currently available evidence on the application of breath analysis in respiratory diseases is encouraging; however, it is mostly based on single-center studies without external validation. The standardization of the technique, together with multicenter clinical trials with external validation, will ensure it is ready for clinical use. Current and new applications in respiratory diseases may represent a major breakthrough in the field, so much so as to deserve further efforts in outlining the most effective way to apply VOC analysis for clinical purposes.

Urinary metabolomics reveals unique metabolic signatures in infants with cystic fibrosis

BACKGROUND

Biologic pathways and metabolic mechanisms underpinning early systemic disease in cystic fibrosis (CF) are poorly understood. The Baby Observational and Nutrition Study (BONUS) was a prospective multi-center study of infants with CF with a primary aim to examine the current state of nutrition in the first year of life. Its secondary aim was to prospectively explore concurrent nutritional, metabolic, respiratory, infectious, and inflammatory characteristics associated with early CF anthropometric measurements. We report here metabolomics differences within the urine of these infants as compared to infants without CF.

METHODS

Urine metabolomics was performed for 85 infants with predefined clinical phenotypes at approximately one year of age enrolled in BONUS via Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Samples were stratified by disease status (non-CF controls (n = 22); CF (n = 63, All-CF)) and CF clinical phenotype: respiratory hospitalization (CF Resp, n = 22), low length (CF LL, n = 23), and low weight (CF LW, n = 15).

RESULTS

Global urine metabolomics profiles in CF were heterogeneous, however there were distinct metabolic differences between the CF and non-CF groups. Top pathways altered in CF included tRNA charging and methionine degradation. ADCYAP1 and huntingtin were identified as predicted unique regulators of altered metabolic pathways in CF compared to non-CF. Infants with CF displayed alterations in metabolites associated with bile acid homeostasis, pentose sugars, and vitamins.

CONCLUSIONS

Predicted metabolic pathways and regulators were identified in CF infants compared to non-CF, but metabolic profiles were unable to discriminate between CF phenotypes. Targeted metabolomics provides an opportunity for further understanding of early CF disease.

TRIAL REGISTRATION

United States ClinicalTrials.Gov registry NCT01424696 (clinicaltrials.gov).

Clinical metabolomics of exhaled breath condensate in chronic respiratory diseases

Chronic respiratory diseases (CRDs) are complex multifactorial disorders involving the airways and other lung structures. The development of reliable markers for an early and accurate diagnosis, including disease phenotype, and prediction of the response and/or adherence to treatment prescribed are essential points for the correct management of CRDs. Beside the traditional techniques to detect biomarkers, "omics" sciences have stimulated interest in clinical field as they could potentially improve the study of disease phenotype. Perturbations in a variety of metabolic and signaling pathways could contribute an understanding of CRDs pathogenesis. In particular, metabolomics provides powerful tools to map biological perturbations and their relationship with disease pathogenesis. The exhaled breath condensate (EBC) is a natural matrix of the respiratory tract, and is well suited for metabolomics studies. In this article, we review the current state of metabolomics methodology applied to EBC in the study of CRDs.

Metabolomic responses to lumacaftor/ivacaftor in cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) is a life-limiting disease caused by a defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Lumacaftor/Ivacaftor is a novel CFTR modulator approved for patients that are homozygous for Phe508del CFTR, but its clinical effectiveness varies amongst patients, making it difficult to determine clinical responders. Therefore, identifying biochemical biomarkers associated with drug response are clinically important for follow-up studies.

METHODS

Serum metabolomics was performed on twenty patients with CF pre- and 6-month post-Lumacaftor/Ivacaftor response via Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Correlation with clinical variables was performed.

RESULTS

Metabolomics analysis demonstrated 188 differentially regulated metabolites between patients pre- and post-Lumacaftor/Ivacaftor initiation, with a predominance of lipid and amino acid alterations. The top 30 metabolites were able to differentiate pre- and post-Lumacaftor/Ivacaftor status in greater than 90% of patients via a random-forest confusion matrix. Alterations in bile acids, phospholipids, and bacteria-associated metabolites were the predominant changes associated with drug response. Importantly, changes in metabolic patterns were associated with clinical responders.

CONCLUSIONS

Selected key lipid and amino acid metabolic pathways were significantly affected by Lumacaftor/Ivacaftor initiation and similar pathways were affected in clinical responders. Targeted metabolomics may provide useful and relevant biomarkers of CFTR modulator responses.