NMR Profiling of Exhaled Breath Condensate Defines Different Metabolic Phenotypes of Non-Cystic Fibrosis Bronchiectasis

Non-invasive detection of orthotopic human lung tumors by microRNA expression profiling of mouse exhaled breath condensates and exhaled extracellular vesicles

Aim

The lung is the second most frequent site of metastatic dissemination. Early detection is key to improving survival. Given that the lung interfaces with the external environment, the collection of exhaled breath condensate (EBC) provides the opportunity to obtain biological material including exhaled miRNAs that originate from the lung.

Methods

In this proof-of-principal study, we used the highly metastatic MDA-MB-231 subline 3475 breast cancer cell line (LM-3475) to establish an orthotopic lung tumor-bearing mouse model and investigate non-invasive detection of lung tumors by analysis of exhaled miRNAs. We initially conducted miRNA NGS and qPCR validation analyses on condensates collected from unrestrained animals and identified significant miRNA expression differences between the condensates of lung tumor-bearing and control mice. To focus our purification of EBC and evaluate the origin of these differentially expressed miRNAs, we developed a system to collect EBC directly from the nose and mouth of our mice.

Results

Using nanoparticle distribution analyses, TEM, and ONi super-resolution nanoimaging, we determined that human tumor EVs could be increasingly detected in mouse EBC during the progression of secondary lung tumors. Using our customizable EV-CATCHER assay, we purified human tumor EVs from mouse EBC and demonstrated that the bulk of differentially expressed exhaled miRNAs originate from lung tumors, which could be detected by qPCR within 1 to 2 weeks after tail vein injection of the metastatic cells.

Conclusion

This study is the first of its kind and demonstrates that lung tumor EVs are exhaled in mice and provide non-invasive biomarkers for detection of lung tumors.

Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments

Methanol is a respiratory biomarker for pulmonary diseases, including COVID-19, and is a common chemical that may harm people if they are accidentally exposed to it. It is significant to effectively identify methanol in complex environments, yet few sensors can do so. In this work, the strategy of coating perovskites with metal oxides is proposed to synthesize core-shell CsPbBr₃@ZnO nanocrystals. The CsPbBr₃@ZnO sensor displays a response/recovery time of 3.27/3.11 s to 10 ppm methanol at room temperature, with a detection limit of 1 ppm. Using machine learning algorithms, the sensor can effectively identify methanol from an unknown gas mixture with 94% accuracy. Meanwhile, density functional theory is used to reveal the formation process of the core-shell structure and the target gas identification mechanism. The strong adsorption between CsPbBr₃ and the ligand zinc acetylacetonate lays the foundation for the formation of the core-shell structure. The crystal structure, density of states, and band structure were influenced by different gases, which results in different response/recovery behaviors and makes it possible to identify methanol from mixed environments. Furthermore, due to the formation of type II band alignment, the gas response performance of the sensor is further improved under UV light irradiation.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Exhaled breath condensate efficacy to identify mutations in patients with lung cancer: A pilot study

Exhaled breath condensate (EBC) is used to investigate the efficacy of EBC to detect the genetic mutations in patients with lung cancer. Samples of 5 patients and 5 healthy volunteers were collected. DNA was extracted and used for amplification of hotspot regions of TP53 and KRAS genes by using PCR. We performed the mutation analysis by direct sequencing in all subjects. Detected mutations in EBC samples were compared with those of corresponding tumor tissues and there was complete agreement within the detected mutations in EBC and tumorous tissue. EBC can be used as an efficient and noninvasive source for the assessment of gene mutations in patients with lung cancer.

Malondialdehyde and anion patterns in exhaled breath condensate among subway workers

BACKGROUND

Underground transportation systems can contribute to the daily particulates and metal exposures for both commuter and subway workers. The redox and metabolic changes in workers exposed to such metal-rich particles have yet to be characterized. We hypothesize that the distribution of nitrosative/oxidative stress and related metabolic biomarkers in exhaled breath condensate (EBC) are modified depending on exposures.

RESULTS

Particulate number and size as well as mass concentration and airborne metal content were measured in three groups of nine subway workers (station agents, locomotive operators and security guards). In parallel, pre- and post-shift EBC was collected daily during two consecutive working weeks. In this biological matrix, malondialdehyde, lactate, acetate, propionate, butyrate, formate, pyruvate, the sum of nitrite and nitrate (ΣNO_x) and the ratio nitrite/nitrate as well as metals and nanoparticle concentrations was determined. Weekly evolution of the log-transformed selected biomarkers as well as their association with exposure variables was investigated using linear mixed effects models with the participant ID as random effect. The professional activity had a strong influence on the pattern of anions and malondialdehyde in EBC. The daily number concentration and the lung deposited surface area of ultrafine particles was consistently and mainly associated with nitrogen oxides variations during the work-shift, with an inhibitory effect on the ΣNO_x. We observed that the particulate matter (PM) mass was associated with a decreasing level of acetate, lactate and ΣNO_x during the work-shift, suggestive of a build-up of these anions during the previous night in response to exposures from the previous day. Lactate was moderately and positively associated with some metals and with the sub-micrometer particle concentration in EBC.

CONCLUSIONS

These results are exploratory but suggest that exposure to subway PM could affect concentrations of nitrogen oxides as well as acetate and lactate in EBC of subway workers. The effect is modulated by the particle size and can correspond to the body's cellular responses under oxidative stress to maintain the redox and/or metabolic homeostasis.

NMR metabolomic and microarray-based transcriptomic data integration identifies unique molecular signatures of hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP) is an immune-mediated granulomatous interstitial lung disease (ILD) that results from repeated inhalation of certain antigens. Despite major advances in research, pathophysiology of the disease remains poorly understood. The present study combines metabolomic and transcriptomic data to determine alterations in HP subjects as compared with healthy controls. Metabolic signatures were identified in serum, exhaled breath condensate (EBC) and bronchoalveolar lavage fluid (BALF) of HP patients using proton nuclear magnetic resonance (NMR) metabolomics. The expression of three metabolites, i.e., lactate, pyruvate, and proline, was found to be significantly altered in all three biofluids. The potential of differential diagnosis based on these three metabolites was investigated by including a group of patients with sarcoidosis, which is another type of granulomatous ILD. In addition, differentially expressed transcriptomic fingerprints in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. The transcriptomics analysis of these microarray-based data revealed 59 genes to be significantly dysregulated in patients with HP. Over representation analysis of the metabolites and genes of interest was performed using IMPaLA (Integrated Molecular Pathway Level Analysis) version 12. Integrated analysis of serum metabolite signatures and blood gene expression suggests dysregulation of PI3K-AKT signaling and TCA cycle pathways in these patients. This preliminary study is a step towards better understanding of the pathogenesis of HP by identification of differentially expressed metabolites and transcriptomic fingerprints. These molecular signatures may be explored as diagnostic markers for differentiating HP from other lung diseases.