Comprehensive Targeted Metabolomic Study in the Lung, Plasma, and Urine of PPE/LPS-Induced COPD Mice Model

Kim, Hyeon-Young; Lee, Hyeon‐Seong; Kim, In-Hyeon; Kim, Youngbae; Ji, Moongi; Oh, Songjin; Kim, Doo-Young; Lee, Won–Jae; Kim, Sunghwan; Paik, Man‐Jeong

doi:10.3390/ijms23052748

Cited by 10 publications

(2 citation statements)

References 53 publications

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“…Animal models of COPD such as generated by exposure to cigarette smoke (CS) can help better understand the underlying mechanisms of CS induced COPD and study the role of various inflammatory and metabolic pathways ( 12 , 13 ). Although COPD mouse can mimic some of the features of the disease, this model does not fully reflect all of the characteristic features of human disease due to the physiological, genetic and environmental differences between human and mouse ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Changes in targeted metabolomics in lung tissue of chronic obstructive pulmonary disease

Feng¹,

Xie²,

Liu³

et al. 2023

J Thorac Dis

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Background Chronic obstructive pulmonary disease (COPD) is a common chronic lung disease and its incidence is steadily increasing. COPD patients and mouse models of COPD share some similarities in lung pathology and physiology. We performed this study to explore the potential metabolic pathways involved in the pathogenesis of COPD and to discover the COPD-associated biomarkers. Furthermore, we aimed to examine how much the mouse model of COPD was similar and different to human COPD in terms of the altered metabolites and pathways. Methods Twenty human lung tissue samples (ten COPD and ten controls) and twelve mice lung tissue samples (six COPD and six controls) were analyzed by targeted HM350 metabolomics, and multivariate and pathway analysis were performed by Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results The counts of many metabolites such as amino acids, carbohydrates and carnitines were changed in both COPD patients and mice compared to controls, respectively. While lipid metabolism was changed only in COPD mice. After KEGG analysis, we found these altered metabolites involved in COPD through aging, apoptosis, oxidative stress and inflammation pathways. Conclusions The expressions of metabolites changed in both COPD patients and cigarette smoke exposed (CS-exposed) mice. And there were also some differences between COPD patients and mouse models due to the differences between species. Our study suggested the dysregulation in amino acid metabolism, energy production pathway and perhaps lipid metabolism may be significantly related to the pathogenesis of COPD.

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Section: Introductionmentioning

confidence: 99%

Changes in targeted metabolomics in lung tissue of chronic obstructive pulmonary disease

Feng¹,

Xie²,

Liu³

et al. 2023

J Thorac Dis

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“…Previous studies showed that central carbon metabolism played an essential role in the development of pulmonary diseases. For instance, in the PPE/ LPSinduced chronic obstructive pulmonary disease(COPD) mice model [11]., the differences in urinary succinic acid, isocitric acid, and pyruvic acid were statistically significant [11]. Another bleomycin model of pulmonary fibrosis in mice found that increased level of glycolysis, TCA cycle, and PPP associated with upregulated energy production for energy demand of the fibrotic lungs [12].…”

Section: Introductionmentioning

confidence: 99%

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules

Wu,

Shen,

et al. 2023

PLoS ONE

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With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.

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Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

Liu,

Shen,

Qin

et al. 2024

J Cancer Res Clin Oncol

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Background Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics. Methods The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results. Results Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification. Conclusion We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

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Comprehensive Targeted Metabolomic Study in the Lung, Plasma, and Urine of PPE/LPS-Induced COPD Mice Model

Cited by 10 publications

References 53 publications

Changes in targeted metabolomics in lung tissue of chronic obstructive pulmonary disease

Changes in targeted metabolomics in lung tissue of chronic obstructive pulmonary disease

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

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