| 1. |
- Abdel-Aziz, Mahmoud I., et al.
(author)
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A multi-omics approach to delineate sputum microbiome-associated asthma inflammatory phenotypes
- 2022
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In: European Respiratory Journal. - : European Respiratory Society. - 0903-1936 .- 1399-3003. ; 59:1
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Journal article (peer-reviewed)abstract
- A multi-omics approach revealed the underlying biological pathways in the microbiome-driven severe asthma phenotypes. This may help to elucidate new leads for treatment development, particularly for the therapeutically challenging neutrophilic asthma.
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| 2. |
- Brandsma, Joost, et al.
(author)
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Stratification of asthma by lipidomic profiling of induced sputum supernatant
- 2023
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In: Journal of Allergy and Clinical Immunology. - : Elsevier. - 0091-6749 .- 1097-6825. ; 152:1, s. 117-125
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Journal article (peer-reviewed)abstract
- Background: Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features.Objective: We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls.Methods: Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes.Results: Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts.Conclusion: We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator.
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| 3. |
- Brinkman, Paul, et al.
(author)
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Identification and prospective stability of electronic nose (eNose)-derived inflammatory phenotypes in patients with severe asthma
- 2019
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In: Journal of Allergy and Clinical Immunology. - : Elsevier. - 0091-6749 .- 1097-6825. ; 143:5, s. 1811-1820.e7
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Journal article (peer-reviewed)abstract
- Background: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using “omics” technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes.Objectives: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to withinpatient clinical and inflammatory changes.Methods: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability.Results: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNosedriven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045).Conclusions: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.
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| 4. |
- Burg, Dominic, et al.
(author)
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Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome
- 2018
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:6, s. 2072-2091
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Journal article (peer-reviewed)abstract
- Analysis of induced sputum supematant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in >= 40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in >= 3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.
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| 5. |
- Lefaudeux, Diane, et al.
(author)
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U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics
- 2017
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In: Journal of Allergy and Clinical Immunology. - : Elsevier BV. - 0091-6749 .- 1097-6825. ; 139:6, s. 1797-1807
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Journal article (peer-reviewed)abstract
- BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided.OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum.METHODS: Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data.RESULTS: Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels.CONCLUSION: Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.
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