The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis

Biomarkers in exhaled breath condensate as fingerprints of asthma, chronic obstructive pulmonary disease and asthma-chronic obstructive pulmonary disease overlap: a critical review

Asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap are the third leading cause of mortality around the world. They share some common features, which can lead to misdiagnosis. To properly manage these conditions, reliable markers for early and accurate diagnosis are needed. Over the past 20 years, many molecules have been investigated in the exhaled breath condensate to better understand inflammation pathways and mechanisms related to these disorders. Recently, more advanced techniques, such as sensitive metabolomic and proteomic profiling, have been used to obtain a more comprehensive understanding. This article reviews the use of targeted and untargeted metabolomic methodology to study asthma, COPD and asthma-COPD overlap.

Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases

BACKGROUND

Although genome-wide association studies (GWAS) have identified many genomic regions associated with idiopathic pulmonary fibrosis (IPF), the causal genes and functions remain largely unknown. Many single-cell expression data have become available for IPF, and there is increasing evidence suggesting a shared genetic basis between IPF and other diseases.

METHODS

We conducted integrative analyses to improve the power of GWAS. First, we calculated global and local genetic correlations to identify IPF genetically associated traits and local regions. Then, we prioritised candidate genes contributing to local genetic correlation. Second, we performed transcriptome-wide association analysis (TWAS) of 44 tissues to identify candidate genes whose genetically predicted expression level is associated with IPF. To replicate our findings and investigate the regulatory role of the transcription factors (TF) in identified candidate genes, we first conducted the heritability enrichment analysis in TF binding sites. Then, we examined the enrichment of the TF target genes in cell-type-specific differentially expressed genes (DEGs) identified from single-cell expression data of IPF and healthy lung samples.

FINDINGS

We identified 12 candidate genes across 13 genomic regions using local genetic correlation, including the POT1 locus (p value=0.00041), which contained variants with protective effects on lung cancer but increasing IPF risk. We identified another 13 novel genes using TWAS. Two TFs, MAFK and SMAD2, showed significant enrichment in both partitioned heritability and cell-type-specific DEGs.

INTERPRETATION

Our integrative analysis identified new genes for IPF susceptibility and expanded the understanding of the complex genetic architecture and disease mechanism of IPF.

Respinos: A Portable Device for Remote Vital Signs Monitoring of COVID-19 Patients

The rapidly increasing number of COVID-19 patients has posed a massive burden on many healthcare systems worldwide. Moreover, the limited availability of diagnostic and treatment equipment makes it difficult to treat patients in the hospital. To reduce the burden and maintain the quality of care, asymptomatic patients or patients with mild symptoms are advised to self-isolate at home. However, self-isolated patients need to be continuously monitored as their health can turn into critical condition within a short time. Therefore, a portable device that can remotely monitor the condition and progression of the health of these patients is urgently needed. Here we present a portable device, called Respinos, that can monitor multiparameter vital signs including respiratory rate, heart rate, body temperature, and SpO2. It can also operate as a spirometer that measures forced vital capacity (FVC), forced expiratory volume (FEV), FEV in the first second (FEV1), and peak expiratory flow Rate (PEFR) parameters which are useful for detecting pulmonary diseases. The spirometer is designed in the form of a tube that can be ergonomically inflated by the patient, and is equipped with an accurate and disposable turbine based air flow sensor to evaluate the patient's respiratory condition. Respinos uses rechargeable batteries and wirelessly connects to a mobile application whereby the patient's condition can be monitored in real-time and consulted with doctors via chat. Extensive comparison against medical-grade reference devices showed good performance of Respinos. Overall results demonstrate the potential of Respinos for remote patient monitoring during and post pandemic.

Prediction of clinical efficacy of subcutaneous immunotherapy for Artemisia sieversiana pollen allergic rhinitis by serum metabolomics

BACKGROUND/PURPOSE

Specific immunotherapy is the only effective etiological treatment for allergic rhinitis, but subcutaneous immunotherapy has a slow onset and poor compliance. Predicting the clinical efficacy of subcutaneous immunotherapy in advance can reduce unnecessary medical costs and resource waste. This study aimed to identify metabolites that could predict the efficacy of subcutaneous immunotherapy on seasonal allergic rhinitis by serum metabolomics.

METHODS

Patients (n = 43) with Artemisia sieversiana pollen allergic rhinitis were enrolled and treated with subcutaneous immunotherapy for one year. Patients were divided into the ineffective group (n = 10) and effective group (n = 33) according to the therapeutic index. Serum samples were collected before treatment. Metabolomics was determined by liquid chromatography-mass spectrometry combined with gas chromatography-mass spectrometry and analyzed differential compounds and related metabolic pathways.

RESULTS

A total of 129 differential metabolites (P < 0.05) were identified and 4 metabolic pathways, namely taurine and hypotaurine metabolism, pentose and glucuronate interconversions, pentose phosphate pathway, and alanine, aspartate, and glutamate metabolism, were involved.

CONCLUSION

Some metabolites, such as hypotaurine, taurine, and l-alanine, have the potential to become predictive biomarkers for effective subcutaneous immunotherapy.

Gh BF, Taherkhani A, Rostami-Nejad M, Asri N, Haidari MH. Screening of Altered Metabolites and Metabolic Pathways in Celiac Disease Using NMR Spectroscopy

BACKGROUND

Celiac disease (CeD) is an autoimmune intestinal disorder caused by gluten protein consumption in genetically predisposed individuals. As biopsy sampling is an invasive procedure, finding novel noninvasive serological markers for screening of at-risk CeD population is a priority. Metabolomics is helpful in monitoring metabolite changes in body fluids and tissues. In the present study, we evaluated serum metabolite levels of CeD patients relative to healthy controls with the aim of introducing new biomarkers for population screening.

METHOD

We compared the serum metabolic profile of CeD patients (n = 42) and healthy controls (n = 22) using NMR spectroscopy and multivariate analysis.

RESULT

25 metabolites were identified by serum metabolic profiling. Levels of 3-hydroxyisobutyric acid and isobutyrate showed significant differences in CeD patients' samples compared with healthy controls (p < 0.05). According to pathway analysis, our data demonstrated that changes in nine metabolic pathways were significantly disrupted/affected in patients with CeD. These enriched pathways are involved in aminoacyl-tRNA biosynthesis; primary bile acid biosynthesis; nitrogen metabolism; glutamine and glutamate metabolism; valine, leucine, and isoleucine biosynthesis and degradation; taurine and hypotaurine metabolism; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and arginine biosynthesis.

CONCLUSION

In summary, our results demonstrated that changes in the serum level of 25 metabolites may be useful in distinguishing CeD patients from healthy controls, which have the potential to be considered candidate biomarkers of CeD.

A Systematic Review of Metabolic Alterations Underlying IgE-Mediated Food Allergy in Children

SCOPE

Immunoglobulin E-mediated food allergies (IgE-FA) are characterized by an ever-increasing prevalence, currently reaching up to 10.4% of children in the European Union. Metabolomics has the potential to provide a deeper understanding of the pathogenic mechanisms behind IgE-FA.

METHODS AND RESULTS

In this work, literature is systematically searched using Web of Science, PubMed, Scopus, and Embase, from January 2010 until May 2021, including human and animal metabolomic studies on multiple biofluids (urine, blood, feces). In total, 15 studies on IgE-FA are retained and a dataset of 277 potential biomarkers is compiled for in-depth pathway mapping. Decreased indoleamine 2,3-dioxygenase-1 (IDO- 1) activity is hypothesized due to altered plasma levels of tryptophan and its metabolites in IgE-FA children. In feces of children prior to IgE-FA, aberrant metabolization of sphingolipids and histidine is noted. Decreased fecal levels of (branched) short chain fatty acids ((B)SCFAs) compel a shift towards aerobic glycolysis and suggest dysbiosis, associated with an immune system shift towards T-helper 2 (Th2) responses. During animal anaphylaxis, a similar switch towards glycolysis is observed, combined with increased ketogenic pathways. Additionally, altered histidine, purine, pyrimidine, and lipid pathways are observed.

CONCLUSION

To conclude, this work confirms the unprecedented opportunities of metabolomics and supports the in-depth pathophysiological qualification in the quest towards improved diagnostic and prognostic biomarkers for IgE-FA.

Metabolomic profiling of extraesophageal reflux disease in children

Although respiratory symptoms in children are often attributed to gastroesophageal reflux disease, establishing a clear diagnosis of extraesophageal reflux disease (EERD) can be challenging, as there are no sensitive or specific EERD biomarkers. The aim of this study was to evaluate the metabolite profile in bronchoalveolar (BAL) fluid from children with suspected EERD and assess the impact of reflux treatment on these metabolites. In this prospective pilot study, we performed nontargeted global metabolomic profiling on BAL fluid from 43 children undergoing testing with bronchoscopy, upper endoscopy, and multichannel intraluminal impedance with pH (pH-MII) for evaluation of chronic respiratory symptoms. Twenty-three (54%) patients had an abnormal pH-MII study. Seventeen (40%) patients were on proton pump inhibitors (PPIs) for testing. Levels of histamine, malate, adenosine 5'-monophosphate, and ascorbate were significantly lower in subjects with abnormal pH-MII studies compared to those normal studies. Furthermore, in children off PPI therapy, those with abnormal pH-MII studies had robust increases in a number of glycerophospholipids within phospholipid metabolic pathways, including derivatives of glycerophosphorylcholine, glycerophosphoglycerol, and glycerophosphoinositol, compared to those with normal pH-MII studies. These findings offer insight into the impact of reflux and PPIs on the lungs and provide a foundation for future studies using targeted metabolomic analysis to identify potential biomarkers of EERD.

Untargeted metabolomics of human plasma reveal lipid markers unique to chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) is characterised by airway inflammation and progressive airflow limitation, whereas idiopathic pulmonary fibrosis (IPF) is characterised by a restrictive pattern due to fibrosis and impaired gas exchange. We undertook metabolomic analysis of blood samples in IPF, COPD and healthy controls (HC) to determine differences in circulating molecules and identify novel pathogenic pathways. An untargeted metabolomics using an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS) was performed to profile plasma of patients with COPD (n = 21), and IPF (n = 24) in comparison to plasma from healthy controls (HC; n = 20). The most significant features were identified using multiple database matching. One-way ANOVA and variable importance in projection (VIP) scores were also used to highlight metabolites that influence the specific disease groups. Non-polar metabolites such as fatty acids (FA) and membrane lipids were well resolved and a total of 4805 features were identified. The most prominent metabolite composition differences in lipid mediators identified at ∼2-3 fold higher in both diseases compared to HC were palmitoleic acid, oleic acid and linoleic acid; and dihydrotestosterone was lower in both diseases. We demonstrated that COPD and IPF were characterised by systemic changes in lipid constituents such as essential FA sampled from circulating plasma.

High-fiber diets attenuate emphysema development via modulation of gut microbiota and metabolism

Dietary fiber functions as a prebiotic to determine the gut microbe composition. The gut microbiota influences the metabolic functions and immune responses in human health. The gut microbiota and metabolites produced by various dietary components not only modulate immunity but also impact various organs. Although recent findings have suggested that microbial dysbiosis is associated with several respiratory diseases, including asthma, cystic fibrosis, and allergy, the role of microbiota and metabolites produced by dietary nutrients with respect to pulmonary disease remains unclear. Therefore, we explored whether the gut microbiota and metabolites produced by dietary fiber components could influence a cigarette smoking (CS)-exposed emphysema model. In this study, it was demonstrated that a high-fiber diet including non-fermentable cellulose and fermentable pectin attenuated the pathological changes associated with emphysema progression and the inflammatory response in CS-exposed emphysema mice. Moreover, we observed that different types of dietary fiber could modulate the diversity of gut microbiota and differentially impacted anabolism including the generation of short-chain fatty acids, bile acids, and sphingolipids. Overall, the results of this study indicate that high-fiber diets play a beneficial role in the gut microbiota-metabolite modulation and substantially affect CS-exposed emphysema mice. Furthermore, this study suggests the therapeutic potential of gut microbiota and metabolites from a high-fiber diet in emphysema via local and systemic inflammation inhibition, which may be useful in the development of a new COPD treatment plan.

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.

How to determine the mechanism of action of CFTR modulator compounds: A gateway to theranostics

The greatest challenge of 21st century biology is to fully understand mechanisms of disease to drive new approaches and medical innovation. Parallel to this is the huge biomedical endeavour of treating people through personalized medicine. Until now all CFTR modulator drugs that have entered clinical trials have been genotype-dependent. An emerging alternative is personalized/precision medicine in CF, i.e., to determine whether rare CFTR mutations respond to existing (or novel) CFTR modulator drugs by pre-assessing them directly on patient's tissues ex vivo, an approach also now termed theranostics. To administer the right drug to the right person it is essential to understand how drugs work, i.e., to know their mechanism of action (MoA), so as to predict their applicability, not just in certain mutations but also possibly in other diseases that share the same defect/defective pathway. Moreover, an understanding the MoA of a drug before it is tested in clinical trials is the logical path to drug discovery and can increase its chance for success and hence also approval. In conclusion, the most powerful approach to determine the MoA of a compound is to understand the underlying biology. Novel large datasets of intervenients in most biological processes, namely those emerging from the post-genomic era tools, are available and should be used to help in this task.

Lipidome Alterations Induced by Cystic Fibrosis, CFTR Mutation, and Lung Function

Cystic fibrosis is a genetic pathology characterized by abnormal accumulation of mucus in the respiratory, gastrointestinal, and reproductive tracts, caused by mutations in the CFTR gene. Although the classical presentation of the condition is well known, there is still a need for a better characterization of metabolic alterations related to cystic fibrosis and different genotypic mutations. We employed untargeted, comprehensive lipidomics of blood serum samples to investigate alterations in the lipid metabolism related to the pathology, mutation classes, and lung function decline. Six unique biomarker candidates were able to independently differentiate diseased individuals from healthy controls with excellent performance. Cystic fibrosis patients showed dyslipidemia for most lipid subclasses, with significantly elevated odd-chain and polyunsaturated fatty acyl lipids. Phosphatidic acids and diacylglycerols were particularly affected by different genotypic mutation classes. We selected a biomarker panel composed of four lipids, including two ceramides, one sphingomyelin, and one fatty acid, which correctly classified all validation samples from classes III and IV. A biomarker panel of five oxidized lipids was further selected to differentiate patients with reduced lung function, measured as predicted FEV1%. Our results indicate that cystic fibrosis is deeply related to lipid metabolism and provide new clues for the investigation of the disease mechanisms and therapeutic targets.

Mapping Metabolite and ICD-10 Associations

The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers’ performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Omics for the future in asthma

Asthma is a common, complex, multifaceted disease. It comprises multiple phenotypes, which might benefit from treatment with different types of innovative targeted therapies. Refining these phenotypes and understanding their underlying biological structure would help to apply precision medicine approaches. Using different omics methods, such as (epi)genomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, allowed to view and investigate asthma from diverse angles. Technological advancement led to a large increase in the application of omics studies in the asthma field. Although the use of omics technologies has reduced the gap between bench to bedside, several design and methodological challenges still need to be tackled before omics can be applied in asthma patient care. Collaborating under a centralized harmonized work frame (such as in consortia, under consistent methodologies) could help worldwide research teams to tackle these challenges. In this review, we discuss the transition of single biomarker research to multi-omics studies. In addition, we deliberate challenges such as the lack of standardization of sampling and analytical methodologies and validation of findings, which comes in between omics and personalized patient care. The future of omics in asthma is encouraging but not completely clear with some unanswered questions, which have not been adequately addressed before. Therefore, we highlight these questions and emphasize on the importance of fulfilling them.

Advances in Characterizing Recently-Identified Molecular Actions of Melatonin: Clinical Implications

Melatonin, N-acetyl-5-methoxy-tryptamine, was discovered to be a product of serotonin metabolism in the mammalian pineal gland where its synthesis is under control of the light:dark cycle. Besides its regulatory pathway involving ganglion cells in the retina, the neural connections between the eyes and the pineal gland include the master circadian clock, the suprachiasmatic nuclei, and the central and peripheral nervous systems. Since pineal melatonin is released into the blood and into the cerebrospinal fluid, it has access to every cell in an organism and it mediates system-wide effects. Subsequently, melatonin was found in several extrapineal organs and, more recently, perhaps in every cell of every organ. In contrast to the pinealocytes, non-pineal cells do not discharge melatonin into the blood; rather it is used locally in an intracrine, autocrine, or paracrine manner. Melatonin levels in non-pineal cells do not exhibit a circadian rhythm and do not depend on circulating melatonin concentrations although when animals are treated with exogenous melatonin it is taken up by presumably all cells. Mitochondria are the presumed site of melatonin synthesis in all cells; the enzymatic machinery for melatonin synthesis has been identified in mitochondria. The association of melatonin with mitochondria, because of its ability to inhibit oxidative stress, is very fortuitous since these organelles are a major site of damaging reactive oxygen species generation. In this review, some of the actions of non-pineal-derived melatonin are discussed in terms of cellular and subcellular physiology.

Bronchoalveolar Lavage Fluid from COPD Patients Reveals More Compounds Associated with Disease than Matched Plasma

Smoking causes chronic obstructive pulmonary disease (COPD). Though recent studies identified a COPD metabolomic signature in blood, no large studies examine the metabolome in bronchoalveolar lavage (BAL) fluid, a more direct representation of lung cell metabolism. We performed untargeted liquid chromatography-mass spectrometry (LC-MS) on BAL and matched plasma from 115 subjects from the SPIROMICS cohort. Regression was performed with COPD phenotypes as the outcome and metabolites as the predictor, adjusted for clinical covariates and false discovery rate. Weighted gene co-expression network analysis (WGCNA) grouped metabolites into modules which were then associated with phenotypes. K-means clustering grouped similar subjects. We detected 7939 and 10,561 compounds in BAL and paired plasma samples, respectively. FEV₁/FVC (Forced Expiratory Volume in One Second/Forced Vital Capacity) ratio, emphysema, FEV₁ % predicted, and COPD exacerbations associated with 1230, 792, eight, and one BAL compounds, respectively. Only two plasma compounds associated with a COPD phenotype (emphysema). Three BAL co-expression modules associated with FEV₁/FVC and emphysema. K-means BAL metabolomic signature clustering identified two groups, one with more airway obstruction (34% of subjects, median FEV₁/FVC 0.67), one with less (66% of subjects, median FEV₁/FVC 0.77; p < 2 × 10^-4). Associations between metabolites and COPD phenotypes are more robustly represented in BAL compared to plasma.

Pharmacological Management of Chronic Obstructive Lung Disease (COPD). Focus on Mutations - Part 1

BACKGROUND

We report a comprehensive overview of current Chronic Obstructive Lung Disease (COPD) therapies and discuss the development of possible new pharmacological approaches based on "new" knowledge. Specifically, sensitivity/resistance to corticosteroids is evaluated with a special focus on the role of gene mutations in drug response.

OBJECTIVE

Critically review the opportunities and the challenges occurring in the treatment of COPD.

CONCLUSION

Findings from "omics" trials should be used to learn more about biological targeted drugs, and to select more specific drugs matching patient's distinctive molecular profile. Specific markers of inflammation such as the percentage of eosinophils are important in determining sensitivity/resistance to corticosteroids. Specific gene variations (Single nucleotide polymorphisms: SNPs) may influence drug sensitivity or resistance. Clinicians working in a real-world need to have a suitable interpretation of molecular results together with a guideline for the treatment and recommendations. Far more translational research is required before new results from omics techniques can be applied in personalized medicine in realworld settings.

An Updated Overview of Metabolomic Profile Changes in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD), a common and heterogeneous respiratory disease, is characterized by persistent and incompletely reversible airflow limitation. Metabolomics is applied to analyze the difference of metabolic profile based on the low-molecular-weight metabolites (<1 kDa). Emerging metabolomic analysis may provide insights into the pathogenesis and diagnosis of COPD. This review aims to summarize the alteration of metabolites in blood/serum/plasma, urine, exhaled breath condensate, lung tissue samples, etc. from COPD individuals, thereby uncovering the potential pathogenesis of COPD according to the perturbed metabolic pathways. Metabolomic researches have indicated that the dysfunctions of amino acid metabolism, lipid metabolism, energy production pathways, and the imbalance of oxidations and antioxidations might lead to local and systematic inflammation by activating the Nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway and releasing inflammatory cytokines, like interleutin-6 (IL-6), tumor necrosis factor-α, and IL-8. In addition, they might cause protein malnutrition and oxidative stress and contribute to the development and exacerbation of COPD.

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy.

IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Anaerobes in cystic fibrosis patients' airways

Anaerobes are known to constitute an important part of the airway microbiota in both healthy subjects and cystic fibrosis (CF) patients. Studies on the potential role of anaerobic bacteria in CF and thus their involvement in CF pathophysiology have reported contradictory results, and the question is still not elucidated. The aim of this study was to summarize anaerobe diversity in the airway microbiota and its potential role in CF, to provide an overview of the state of knowledge on anaerobe antibiotic resistances (resistome), and to investigate the detectable metabolites produced by anaerobes in CF airways (metabolome). This review emphasizes key metabolites produced by strict anaerobic bacteria (sphingolipids, fermentation-induced metabolites and metabolites involved in quorum-sensing), which may be essential for the better understanding of lung disease pathophysiology in CF.

A pilot study of the effect of phospholipid curcumin on serum metabolomic profile in patients with non-alcoholic fatty liver disease: a randomized, double-blind, placebo-controlled trial

BACKGROUND/OBJECTIVES

Curcumin, a natural polyphenol compound in the spice turmeric, has been found to have potent anti-oxidative and anti-inflammatory activity. Curcumin may treat non-alcoholic fatty liver disease (NAFLD) through its beneficial effects on biomarkers of oxidative stress (OS) and inflammation, which are considered as two feature of this disease. However, the effects of curcumin on NAFLD have been remained poorly understood. This investigation evaluated the effects of administrating curcumin on metabolic status in NAFLD patients.

SUBJECTS/METHODS

Fifty-eight NAFLD patients participated in a randomized, double-blind, placebo-controlled parallel design of study. The subjects were allocated randomly into two groups, which either received 250 mg phospholipid curcumin or placebo, one capsule per day for a period of 8 weeks. Fasting blood samples were taken from each subject at the start and end of the study period. Subsequently, metabolomics analysis was performed for serum samples using NMR.

RESULTS

Compared with the placebo, supplementing phospholipid curcumin resulted in significant decreases in serum including 3- methyl-2-oxovaleric acid, 3-hydroxyisobutyrate, kynurenine, succinate, citrate, α-ketoglutarate, methylamine, trimethylamine, hippurate, indoxyl sulfate, chenodeoxycholic acid, taurocholic acid, and lithocholic acid. This profile of metabolic biomarkers could distinguish effectively NAFLD subjects who were treated with curcumin and placebo groups, achieving value of 0.99 for an area under receiver operating characteristic curve (AUC).

CONCLUSIONS

Characterizing the serum metabolic profile of the patients with NAFLD at the end of the intervention using NMR-based metabolomics method indicated that the targets of curcumin treatment included some amino acids, TCA cycle, bile acids, and gut microbiota.

Comparative Metabolomic Sampling of Upper and Lower Airways by Four Different Methods to Identify Biochemicals That May Support Bacterial Growth

Bacteria need nutrients from the host environment to survive, yet we know little about which biochemicals are present in the airways (the metabolome), which of these biochemicals are essential for bacterial growth and how they change with airway disease. The aims of this pilot study were to develop and compare methodologies for sampling the upper and lower airway metabolomes and to identify biochemicals present in the airways that could potentially support bacterial growth. Eight healthy human volunteers were sampled by four methods: two standard approaches - nasal lavage and induced sputum, and two using a novel platform, synthetic adsorptive matrix (SAM) strips—nasosorption and bronchosorption. Collected samples were analyzed by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Five hundred and eighty-one biochemicals were recovered from the airways belonging to a range of metabolomic super-pathways. We observed significant differences between the sampling approaches. Significantly more biochemicals were recovered when SAM strips were used, compared to standard sampling techniques. A range of biochemicals that could support bacterial growth were detected in the different samples. This work demonstrates for the first time that SAM strips are a highly effective method for sampling the airway metabolome. This work will assist further studies to understand how changes in the airway metabolome affect bacterial infection in patients with underlying airway disease.

Urinary organic acids as biomarkers in the assessment of pulmonary function in children with asthma

Childhood asthma prevalence continues to rise despite advancements in prevention and medical management strategies. The purpose of this study was to investigate correlations between urinary organic acids and pulmonary diagnostic tests, asthma control in Greek asthmatic children. We hypothesized that urinary organic acids are positively associated with poor pulmonary function in children with asthma. Seventy-two children, 5 to 12 years old with asthma were recruited from a pediatric asthma clinic in Athens, Greece. Pulmonary function was assessed using spirometry and exhaled nitric oxide analysis. Asthma control was measured qualitatively using the Asthma Control Questionnaire. Targeted metabolomic analysis of 34 urinary organic acids in children was conducted by gas chromatography-mass spectrometry. A statistically significant difference between girls and boys was found for asthma control score (P = .02), lactic acid (P = .03), but not for any other organic acids (P > .05). Statistically significant correlations were found between lactic acid and Forced Expiratory Volume in 1 second (FEV₁) (P = .02), Forced Vital Capacity (FVC) (P = .03); 4- hydroxyphenylacetic acid and FEV₁ (P = .01), FVC (P = .01); 5-hydroxyindoleacetic acid and FEV₁/FVC (P = .03), eNO (P = .05); glycolic acid with Peak Expiratory Flow (PEF) (P = .03); and malic acid with asthma control (P = .02). In conclusion, metabolomics was used to determine correlations between urinary organic acids and conventional pulmonary diagnostic tests in Greek asthmatic children. Metabolomics could be a promising approach for asthma research and in detection of novel biomarkers for asthma monitoring and therapeutic targets for childhood asthma. This study contributes towards a better understanding of the biochemical pathways involved in asthma.

Quantitative determination of potential urine biomarkers of respiratory illnesses using new targeted metabolomic approach

The diagnosis of asthma and chronic obstructive pulmonary disease (COPD) can be challenging due to the overlap in their clinical presentations in some patients. There is a need for a more objective clinical test that can be routinely used in primary care settings. Through an untargeted ¹H NMR urine metabolomic approach, we identified a set of endogenous metabolites as potential biomarkers for the differentiation of asthma and COPD. A subset of these potential biomarkers contains 7 highly polar metabolites of diverse physicochemical properties. To the best of our knowledge, there is no liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that evaluated more than two of the target metabolites in a single analytical run. The target metabolites belong to the families of monosaccharides, organic acids, amino acids, quaternary ammonium compounds and nucleic acids, rendering hydrophilic interaction liquid chromatography (HILIC) an ideal technology for their quantification. Since a clinical decision is to be made from patients data, a fully validated analytical method is required for biomarker validation. Method validation for endogenous metabolites is a daunting task since current guidelines were designed for exogenous compounds. As such, innovative approaches were adopted to meet the validation requirements. Herein, we describe a sensitive HILIC-MS/MS method for the quantification of the 7 endogenous urinary metabolites. Detection was achieved in the multiple reaction monitoring (MRM) mode with polarity switching, using quadrupole-linear ion trap instrument (QTRAP 6500) as well as single ion monitoring in the negative-ion mode. The method was fully validated according to the regulatory guidelines. Linearity was established between 6 and 21000 ng/mL and quality control samples demonstrated acceptable intra- and inter-day accuracy (85.7%-112%), intra- and inter-day precision (CV% <11.5%) as well as stability under various storage and sample processing conditions. To illustrate the method's applicability, the validated method was applied to the analysis of a small set of urine samples collected from asthma and COPD patients. Preliminary modelling of separation was generated using partial least square discriminant analysis (R² 0.752 and Q² 0.57). The adequate separation between patient samples confirms the diagnostic potential of these target metabolites as a proof-of-concept for the differentiation between asthma and COPD. However, more patient urine samples are needed in order to increase the statistical power of the analytical model.

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

INTRODUCTION

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

OBJECTIVE

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

METHODS

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n = 25) or COPD (n = 26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

RESULTS

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q² = 0.919, 0.705 vs R²Q² = 0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

CONCLUSION

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

Application of metabolomics to preeclampsia diagnosis

Preeclampsia is a multifactorial disorder defined by hypertension and increased urinary protein excretion during pregnancy. It is a significant cause of maternal and neonatal deaths worldwide. Despite various research efforts to clarify pathogenies of preeclampsia and predict this disease before beginning of symptoms, the pathogenesis of preeclampsia is unclear. Early prediction and diagnosis of women at risk of preeclampsia has not markedly improved. Therefore, the objective of this study was to perform a review on metabolomic articles assessing predictive and diagnostic biomarkers of preeclampsia. Four electronic databases including PubMed/Medline, Web of Science, Sciencedirect, and Scopus were searched to identify studies of preeclampsia in humans using metabolomics from inception to March 2018. Twenty-one articles in a variety of biological specimens and analytical platforms were included in the present review. Metabolite profiles may assist in the diagnosis of preeclampsia and discrimination of its subtypes. Lipids and their related metabolites were the most generally detected metabolites. Although metabolomic biomarkers of preeclampsia are not routinely used, this review suggests that metabolomics has the potential to be developed into a clinical tool for preeclampsia diagnosis and could contribute to an improved understanding of disease mechanisms.

ABBREVIATIONS

PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.

Changes in Metabolites Present in Lung-Lining Fluid Following Exposure of Humans to Ozone

Controlled human exposure to the oxidant air pollutant ozone causes decrements in lung function and increased inflammation as evidenced by neutrophil influx into the lung and increased levels of proinflammatory cytokines in the airways. Here we describe a targeted metabolomics evaluation of human bronchoalveolar lavage fluid (BALF) following controlled in vivo exposure to ozone to gain greater insight into its pulmonary effects. In a 2-arm cross-over study, each healthy adult human volunteer was randomly exposed to filtered air (FA) and to 0.3 ppm ozone for 2 h while undergoing intermittent exercise with a minimum of 4 weeks between exposures. Bronchoscopy was performed and BALF obtained at 1 (n = 9) or 24 (n = 23) h postexposure. Metabolites were detected using ultrahigh performance liquid chromatography-tandem mass spectroscopy. At 1-h postexposure, a total of 28 metabolites were differentially expressed (DE) (p < .05) following ozone exposure compared with FA-exposure. These changes were associated with increased glycolysis and antioxidant responses, suggesting rapid increased energy utilization as part of the cellular response to oxidative stress. At 24-h postexposure, 41 metabolites were DE. Many of the changes were in amino acids and linked with enhanced proteolysis. Changes associated with increased lipid membrane turnover were also observed. These later-stage changes were consistent with ongoing repair of airway tissues. There were 1.37 times as many metabolites were differentially expressed at 24 h compared with 1-h postexposure. The changes at 1 h reflect responses to oxidative stress while the changes at 24 h indicate a broader set of responses consistent with tissue repair. These results illustrate the ability of metabolomic analysis to identify mechanistic features of ozone toxicity and aspects of the subsequent tissue response.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

Metabolic Disorders in Chronic Lung Diseases

Chronic lung diseases represent complex diseases with gradually increasing incidence, characterized by significant medical and financial burden for both patients and relatives. Their increasing incidence and complexity render a comprehensive, multidisciplinary, and personalized approach critically important. This approach includes the assessment of comorbid conditions including metabolic dysfunctions. Several lines of evidence show that metabolic comorbidities, including diabetes mellitus, dyslipidemia, osteoporosis, vitamin D deficiency, and thyroid dysfunction have a significant impact on symptoms, quality of life, management, economic burden, and disease mortality. Most recently, novel pathogenetic pathways and potential therapeutic targets have been identified through large-scale studies of metabolites, called metabolomics. This review article aims to summarize the current state of knowledge on the prevalence of metabolic comorbidities in chronic lung diseases, highlight their impact on disease clinical course, delineate mechanistic links, and report future perspectives on the role of metabolites as disease modifiers and therapeutic targets.

Allergic asthma: an overview of metabolomic strategies leading to the identification of biomarkers in the field

Allergic asthma is a prominent disease especially during childhood. Indoor allergens, in general, and particularly house dust mites (HDM) are the most prevalent sensitizers associated with allergic asthma. Available data show that 65-130 million people are mite-sensitized world-wide and as many as 50% of these are asthmatic. In fact, sensitization to HDM in the first years of life can produce devastating effects on pulmonary function leading to asthmatic syndromes that can be fatal. To date, there has been considerable research into the pathological pathways and structural changes associated with allergic asthma. However, limitations related to the disease heterogeneity and a lack of knowledge into its pathophysiology have impeded the generation of valuable data needed to appropriately phenotype patients and, subsequently, treat this disease. Here, we report a systematic and integral analysis of the disease, from airway remodelling to the immune response taking place throughout the disease stages. We present an overview of metabolomics, the management of complex multifactorial diseases through the analysis of all possible metabolites in a biological sample, obtaining a global interpretation of biological systems. Special interest is placed on the challenges to obtain biological samples and the methodological aspects to acquire relevant information, focusing on the identification of novel biomarkers associated with specific phenotypes of allergic asthma. We also present an overview of the metabolites cited in the literature, which have been related to inflammation and immune response in asthma and other allergy-related diseases.

Metabolic changes of different high-resolution computed tomography phenotypes of COPD after budesonide-formoterol treatment

BACKGROUND

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system. Metabolic profiling of different high-resolution computed tomography (HRCT) phenotypes of COPD patients before and after treatment may identify discriminatory metabolites that can serve as biomarkers and therapeutic agents.

PATIENTS AND METHODS

1H nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomics was performed on a discovery set of plasma samples from 50 patients with stable COPD. Patients were assigned into two groups on the basis of HRCT findings including phenotype E (n=22) and phenotype M (n=28). After budesonide-formoterol treatment (160/4.5 µg ×2 inhalations twice daily for 3 months), clinical characteristics and metabolites were then compared between phenotype E pretreatment and posttreatment, phenotype M pretreatment and posttreatment, phenotype E pretreatment and phenotype M pretreatment, and phenotype E posttreatment and phenotype M posttreatment.

RESULTS

Inhaled budesonide-formoterol therapy for both phenotype E (emphysema without bronchial wall thickening) and phenotype M (emphysema with bronchial wall thickening) was effective. However, phenotype E and phenotype M were different in response to therapy. Patients with phenotype M in response to therapeutic effects were significantly greater compared with phenotype E. Certain metabolites were identified, which were closely related to the treatment and phenotype. Metabolic changes in phenotype E or phenotype M after treatment may be involved with adenosine diphosphate (ADP), guanosine, choline, malonate, tyrosine, glycine, proline, l-alanine, l-valine, l-threonine leucine, uridine, pyruvic acid, acetone and metabolism disturbance. Metabolic differences between phenotype E and phenotype M in pretreatment and posttreatment covered glycine, d-glucose, pyruvic acid, succinate, lactate, proline, l-valine and leucine.

CONCLUSION

Bronchial wall thickening in COPD may be an indicator for predicting the better response to the treatment with bronchodilator and corticosteroid. The identification of metabolic alterations provides new insights into different HRCT phenotypes and therapeutic assessment of COPD.

The respiratory microbiome of HIV-infected individuals

INTRODUCTION

The respiratory tract is constantly exposed to various environmental and endogenous microbes; however, unlike other similar mucosal surfaces, there has been limited investigation of the microbiome of the respiratory tract.

AREAS COVERED

In this review, we summarize the current state of knowledge of the bacterial, fungal, and viral respiratory microbiomes during HIV infection and how the microbiome might relate to HIV-associated lung disease. Expert commentary: HIV infection is associated with alterations in the respiratory microbiome. The clinical implications of lung microbial dysbiosis are however currently unknown. Mechanistic studies are needed to establish causality between shifts in the respiratory microbiome and pulmonary complications in HIV-infected individuals.

Associations of Nasopharyngeal Metabolome and Microbiome with Severity among Infants with Bronchiolitis. A Multiomic Analysis

RATIONALE

Bronchiolitis is the most common lower respiratory infection in infants; however, it remains unclear which infants with bronchiolitis will develop severe illness. In addition, although emerging evidence indicates associations of the upper-airway microbiome with bronchiolitis severity, little is known about the mechanisms linking airway microbes and host response to disease severity.

OBJECTIVES

To determine the relations among the nasopharyngeal airway metabolome profiles, microbiome profiles, and severity in infants with bronchiolitis.

METHODS

We conducted a multicenter prospective cohort study of infants (age <1 yr) hospitalized with bronchiolitis. By applying metabolomic and metagenomic (16S ribosomal RNA gene and whole-genome shotgun sequencing) approaches to 144 nasopharyngeal airway samples collected within 24 hours of hospitalization, we determined metabolome and microbiome profiles and their association with higher severity, defined by the use of positive pressure ventilation (i.e., continuous positive airway pressure and/or intubation).

MEASUREMENTS AND MAIN RESULTS

Nasopharyngeal airway metabolome profiles significantly differed by bronchiolitis severity (P < 0.001). Among 254 metabolites identified, a panel of 25 metabolites showed high sensitivity (84%) and specificity (86%) in predicting the use of positive pressure ventilation. The intensity of these metabolites was correlated with relative abundance of Streptococcus pneumoniae. In the pathway analysis, sphingolipid metabolism was the most significantly enriched subpathway in infants with positive pressure ventilation use compared with those without (P < 0.001). Enrichment of sphingolipid metabolites was positively correlated with the relative abundance of S. pneumoniae.

CONCLUSIONS

Although further validation is needed, our multiomic analyses demonstrate the potential of metabolomics to predict bronchiolitis severity and better understand microbe-host interaction.

Discriminant biomarkers of acute respiratory distress syndrome associated to H1N1 influenza identified by metabolomics HPLC-QTOF-MS/MS platform

Acute respiratory distress syndrome (ARDS) is a serious complication of influenza A (H1N1) virus infection. Its pathogenesis is unknown and biomarkers are lacking. Untargeted metabolomics allows the analysis of the whole metabolome in a biological compartment, identifying patterns associated with specific conditions. We hypothesized that LC-MS could help identify discriminant metabolites able to define the metabolic alterations occurring in patients with influenza A (H1N1) virus infection that developed ARDS. Serum samples from patients diagnosed with 2009 influenza A (H1N1) virus infection with (n = 25) or without (n = 32) ARDS were obtained on the day of hospital admission and analyzed by LC-MS/MS. Metabolite identification was determined by MS/MS analysis and analysis of standards. The specificity of the patterns identified was confirmed in patients without 2009 influenza A(H1N1) virus pneumonia (15 without and 17 with ARDS). Twenty-three candidate biomarkers were found to be significantly different between the two groups, including lysophospholipids and sphingolipids related to inflammation; bile acids, tryptophan metabolites, and thyroxine, related to the metabolism of the gut microflora. Confirmation results demonstrated the specificity of major alterations occurring in ARDS patients with influenza A (H1N1) virus infection.

Development of a validated LC- MS/MS method for the quantification of 19 endogenous asthma/COPD potential urinary biomarkers

Obstructive airways inflammatory diseases sometimes show overlapping symptoms that hinder their early and correct diagnosis. Current clinical tests are tedious and are of inadequate specificity in special population such as the elderly and children. Therefore, we are developing tandem mass spectrometric (MS/MS) methods for targeted analysis of urine biomarkers. Recently, proton-nuclear magnetic resonance (¹H-NMR) analysis proposed 50 urinary metabolites as potential diagnostic biomarkers among asthma and chronic obstructive pulmonary disease (COPD) patients. Metabolites are divided into 3 groups based on chemical nature. For group 1 (amines and phenols, 19 urinary metabolites), we developed and validated a high performance liquid chromatographic (HPLC)-MS/MS method using differential isotope labeling (DIL) with dansyl chloride. Method development included the optimization of the derivatization reaction, the MS/MS conditions, and the chromatographic separation. Linearity varied from 2 to 4800 ng/mL and the use of ¹³C₂-labeled derivatives allowed for the correction of matrix effects as well as the unambiguous confirmation of the identity of each metabolite in the presence of interfering isomers in urine. Despite the challenges associated with method validation, the method was fully validated as per the food and drug administration (FDA) and the European medicines agency (EMA) recommendations. Validation criteria included linearity, precision, accuracy, dilution integrity, selectivity, carryover, and stability. Challenges in selectivity experiments included the isotopic contributions of the analyte towards its internal standard (IS), that was addressed via the optimization of the IS concentration. In addition, incurred sample analysis was performed to ensure that results from patient samples are accurate and reliable. The method was robust and reproducible and is currently being applied in a cohort of asthma and COPD patient urine samples for biomarker discovery purposes.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

Comparison of Ambient and Atmospheric Pressure Ion Sources for Cystic Fibrosis Exhaled Breath Condensate Ion Mobility-Mass Spectrometry Metabolomics

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the gene that encodes the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The vast majority of the mortality is due to progressive lung disease. Targeted and untargeted CF breath metabolomics investigations via exhaled breath condensate (EBC) analyses have the potential to expose metabolic alterations associated with CF pathology and aid in assessing the effectiveness of CF therapies. Here, transmission-mode direct analysis in real time traveling wave ion mobility spectrometry time-of-flight mass spectrometry (TM-DART-TWIMS-TOF MS) was tested as a high-throughput alternative to conventional direct infusion (DI) electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) methods, and a critical comparison of the three ionization methods was conducted. EBC was chosen as the noninvasive surrogate for airway sampling over expectorated sputum as EBC can be collected in all CF subjects regardless of age and lung disease severity. When using pooled EBC collected from a healthy control, ESI detected the most metabolites, APCI a log order less, and TM-DART the least. TM-DART-TWIMS-TOF MS was used to profile metabolites in EBC samples from five healthy controls and four CF patients, finding that a panel of three discriminant EBC metabolites, some of which had been previously detected by other methods, differentiated these two classes with excellent cross-validated accuracy. Graphical Abstract ᅟ.

1H NMR To Evaluate the Metabolome of Bronchoalveolar Lavage Fluid (BALf) in Bronchiolitis Obliterans Syndrome (BOS): Toward the Development of a New Approach for Biomarker Identification

This report describes the application of NMR spectroscopy to the profiling of metabolites in bronchoalveolar lavage fluid (BALf) of lung transplant recipients without bronchiolitis obliterans syndrome (BOS) (stable, S, n = 10), and with BOS at different degrees of severity (BOS 0p, n = 10; BOS I, n = 10). Through the fine-tuning of a number of parameters concerning both sample preparation/processing and variations of spectra acquisition modes, an efficient and reproducible protocol was designed for the screening of metabolites in a pulmonary fluid that should reflect the status of airway inflammation/injury. Exploiting the combination of mono- and bidimensional NMR experiments, 38 polar metabolites, including amino acids, Krebs cycle intermediates, mono- and disaccharides, nucleotides, and phospholipid precursors, were unequivocally identified. To determine which signature could be correlated with the onset of BOS, the metabolites' content of the above recipients was analyzed by multivariate (PCA and OPLS-DA) statistical methods. PCA analysis (almost) totally differentiated S from BOS I, and this discrimination was significantly improved by the application of OPLS-DA, whose model was characterized by excellent fit and prediction values (R² = 0.99 and Q² = 0.88). The analysis of S vs BOS 0p and of BOS 0p vs BOS I samples showed a clear discrimination of considered cohorts, although with a poorer efficiency compared to those measured for S vs BOS I patients. The data shown in this work assess the suitability of the NMR approach in monitoring different pathological lung conditions.

A Review of Analytical Techniques and Their Application in Disease Diagnosis in Breathomics and Salivaomics Research

The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions.

Metabolomic analysis identifies potential diagnostic biomarkers for aspirin-exacerbated respiratory disease

BACKGROUND

To date, there has been no reliable in vitro test to diagnose aspirin-exacerbated respiratory disease (AERD).

OBJECTIVE

To investigate potential diagnostic biomarkers for AERD using metabolomic analysis.

METHODS

An untargeted profile of serum from asthmatics in the first cohort (group 1) comprising 45 AERD, 44 patients with aspirin-tolerant asthma (ATA), and 28 normal controls was developed using the ultra-high-performance liquid chromatography (UHPLC)/Q-ToF MS system. Metabolites that discriminate AERD from ATA were quantified in both serum and urine, which were collected before (baseline) and after the lysine-aspirin bronchoprovocation test (Lys-ASA BPT). The serum metabolites were validated in the second cohort (group 2) comprising 50 patients with AERD and 50 patients with ATA.

RESULTS

A clear discrimination of metabolomes was found between patients with AERD and ATA. In group 1, serum levels of LTE₄ and LTE₄ /PGF₂ α ratio before and after the Lys-ASA BPT were significantly higher in patients with AERD than in patients with ATA (P < 0.05 for each), and urine baseline levels of these two metabolites were significantly higher in patients with AERD. Significant differences of serum metabolite levels between patients with AERD and ATA were replicated in group 2 (P < 0.05 for each). Moreover, serum baseline levels of LTE₄ and LTE₄ /PGF₂ α ratio discriminated AERD from ATA with 70.5%/71.6% sensitivity and 41.5%/62.8% specificity, respectively (AUC = 0.649 and 0.732, respectively P < 0.001 for each). Urine baseline LTE₄ levels were significantly correlated with the fall in FEV₁ % after the Lys-ASA BPT in patients with AERD (P = 0.008, r = 0.463).

CONCLUSIONS AND CLINICAL RELEVANCE

Serum metabolite level of LTE₄ and LTE₄ /PGF₂ α ratio was identified as potential in vitro diagnostic biomarkers for AERD using the UHPLC/Q-ToF MS system, which were closely associated with major pathogenetic mechanisms underlying AERD.