Multi-omics study on biomarker and pathway discovery of chronic obstructive pulmonary disease

Tang, Yongming; Chen, Zhengjun; Fang, Zhiling; Zhao, Jinshun; Zhou, Yuping; Tang, Chunlan

doi:10.1088/1752-7163/ac15ea

Cited by 10 publications

(7 citation statements)

References 66 publications

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“…In particular, this method presents poor patient compliance for people with underlying diseases and acute exacerbations. Currently, there is a deficiency in reliable genes or protein biomarkers to assess the diagnosis of COPD. ,, In contrast, metabolite biomarkers function as direct indicators of biochemistry activity, closely correlating the phenotype of disease . Here, we developed PdPt alloy-assisted LDI MS for the effective extraction of metabolic fingerprints in plasma due to the unique physiochemical property and porous structure.…”

Section: Results and Discussionmentioning

confidence: 99%

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Su,

Song,

Yang

et al. 2024

ACS Cent. Sci.

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Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

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Section: Results and Discussionmentioning

confidence: 99%

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Su,

Song,

Yang

et al. 2024

ACS Cent. Sci.

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“…KEGG analysis also identified aminoacyl-tRNA biosynthesis and glycine, serine, and threonine pathway overrepresentation in the metSS, which suggests disturbances in general amino acid metabolism. Aminoacyl-tRNAs are vital for protein synthesis and have been associated with oxidative stress [ 5 , 6 , 26 ]. Similarly, multiple studies have found enrichment of the glycine, serine, and threonine metabolism pathways, which have been associated with COPD exacerbation severity [ 4 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

A Metabolomic Severity Score for Airflow Obstruction and Emphysema

et al. 2022

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Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

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“…This method provides high sensitivity and specificity, frequently eliminating the need for sample derivatization. It is particularly suitable for investigating thermally unstable molecules that are not safe for GC-MS-based methodologies [ 17 ].…”

Section: Analytical Techniques In Metabolomicsmentioning

confidence: 99%

Exploring the Potential Role of Metabolomics in COPD: A Concise Review

Tirelli,

Mira,

Belmonte

et al. 2024

Cells

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Chronic Obstructive Pulmonary Disease (COPD) is a pathological condition of the respiratory system characterized by chronic airflow obstruction, associated with changes in the lung parenchyma (pulmonary emphysema), bronchi (chronic bronchitis) and bronchioles (small airways disease). In the last years, the importance of phenotyping and endotyping COPD patients has strongly emerged. Metabolomics refers to the study of metabolites (both intermediate or final products) and their biological processes in biomatrices. The application of metabolomics to respiratory diseases and, particularly, to COPD started more than one decade ago and since then the number of scientific publications on the topic has constantly grown. In respiratory diseases, metabolomic studies have focused on the detection of metabolites derived from biomatrices such as exhaled breath condensate, bronchoalveolar lavage, and also plasma, serum and urine. Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy are powerful tools in the precise identification of potentially prognostic and treatment response biomarkers. The aim of this article was to comprehensively review the relevant literature regarding the applications of metabolomics in COPD, clarifying the potential clinical utility of the metabolomic profile from several biologic matrices in detecting biomarkers of disease and prognosis for COPD. Meanwhile, a complete description of the technological instruments and techniques currently adopted in the metabolomics research will be described.

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Multi-omics study on biomarker and pathway discovery of chronic obstructive pulmonary disease

Cited by 10 publications

References 66 publications

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

A Metabolomic Severity Score for Airflow Obstruction and Emphysema

Exploring the Potential Role of Metabolomics in COPD: A Concise Review

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