Serum Glycerophospholipid Profile in Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Causal relationship between Lipdome and Chronic Obstructive Pulmonary Disease and Asthma: Mendelian randomization

Genetic risk significantly influence susceptibility and heterogeneity of chronic obstructive pulmonary disease (COPD) and asthma, and increasing evidence suggests their close association with lipdome. However, their causal relationship remains unclear. In this study, we conducted a two-sample MR (Mendelian randomization) analysis using publicly available large-scale genome-wide association studies (GWAS) data to evaluate the causal impact of lipdome on COPD and asthma. The inverse variance weighted (IVW) method served as the primary analysis method, and multiple sensitivity and heterogeneity tests were performed to assess the reliability of the results. Finally, a Meta-analysis was conducted on lipdome with significant causal relationships to validate the robustness of the results. Our findings suggest that Sterol ester (27:1/18:2), Phosphatidylcholine (15:0_18:2), (16:0_18:2), (16:0_20:2), (17:0_18:2), (18:1_18:1), (18:1_18:2), (18:1_20:2), Triacylglycerol (54:3), and (56:4) levels are protective factors for COPD, while levels of Phosphatidylcholine (16:0_22:5), (18:0_20:4), and (O-16:0_20:4) are risk factors for COPD. Meta-analysis of lipids causally related to COPD also indicates significant results. Phosphatidylcholine (16:0_20:4), (16:0_22:5), and (18:0_20:4) levels are risk factors for asthma, while Phosphatidylcholine (18:1_18:2), (18:1_20:2), and Sphingomyelin (d38:1) levels are protective factors for asthma. However, the lack of statistical significance in the Meta-analysis may be due to heterogeneity in research methods and data statistics. This study indicates that 4 lipdome species have significant correlations with COPD and asthma. Phosphatidylcholine (18:1_18:2) and (18:1_20:2) are protective factors, while Phosphatidylcholine (16:0_22:5) and (18:0_20:4) are risk factors. Additionally, due to differences in molecular subtypes, phosphatidylcholine, sterol ester, and triacylglycerol exhibit differential effects on the diseases.

Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD‑FE) phenotype, identify potential metabolic biomarkers associated with COPD‑FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD‑FE and patients with non‑frequent exacerbation of COPD (COPD‑NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD‑FE and COPD‑NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D‑fructose 1,6‑bisphosphate (AUC=0.871), arginine (AUC=0.836), L‑2‑hydroxyglutarate (L‑2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine‑C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine‑C18:2 and L‑2HG were significantly different between patients with COPD‑FE and those with COPD‑NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD‑FE phenotype.

Comparative analysis of lysophospholipid metabolism profiles and clinical characteristics in patients with high vs. low C-reactive protein levels in acute exacerbations of chronic obstructive pulmonary disease

BACKGROUND

The precise role of lysophospholipids (LysoPLs) in the pathogenesis of acute exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) remains unclear. In this study, we sought to elucidate the differences in serum LysoPL metabolite profiles and their correlation with clinical features between patients with low versus high CRP levels.

METHODS

A total of 58 patients with AECOPD were enrolled in the study. Patients were classified into two groups: low CRP group (CRP < 20 mg/L, n = 34) and high CRP group (CRP ≥ 20 mg/L, n = 24). Clinical data were collected, and the LysoPL metabolite profiles were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS) and identified by matching with the LipidBlast library.

RESULTS

Nineteen differential LysoPLs were initially identified through Student's t-test (p < 0.05 and VIP > 1). Subsequently, four LysoPLs, LPC(16:0), LPE(18:2), LPC(22:0), and LPC(24:0), were identified by FDR adjustment (adjusted p < 0.05). These four lysoPLs had a significant negative correlation with CRP. Integrative analysis revealed that LPC (16:0) and LPC (22:0) correlated with less hypercapnic respiratory failure and ICU admission.

CONCLUSION

AECOPD patients with high CRP levels demonstrated a distinctive LysoPL metabolism profile, with LPC (16:0), LPE(18:2), LPC(22:0), and LPC(24:0) being the most significantly altered lipid molecules. These alterations were associated with poorer clinical outcomes.

The Role of Lysophospholipid Metabolites LPC and LPA in the Pathogenesis of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition characterized by persistent respiratory symptoms and airflow limitation. While there are some available treatment options, the effectiveness of treatment varies depending on individual differences and the phenotypes of the disease. Therefore, exploring or identifying potential therapeutic targets for COPD is urgently needed. In recent years, there has been growing evidence showing that lysophospholipids, namely lysophosphatidylcholine (LPC) and lysophosphatidic acid (LPA), can play a significant role in the pathogenesis of COPD. Exploring the metabolism of lysophospholipids holds promise for understanding the underlying mechanism of COPD development and developing novel strategies for COPD treatment. This review primarily concentrates on the involvement and signaling pathways of LPC and LPA in the development and progression of COPD. Furthermore, we reviewed their associations with clinical manifestations, phenotypes, and prognosis within the COPD context and discussed the potential of the pivotal signaling molecules as viable therapeutic targets for COPD treatment.

An integrated metabo-lipidomics profile of induced sputum for the identification of novel biomarkers in the differential diagnosis of asthma and COPD

BACKGROUND

Due to their complexity and to the presence of common clinical features, differentiation between asthma and chronic obstructive pulmonary disease (COPD) can be a challenging task, complicated in such cases also by asthma-COPD overlap syndrome. The distinct immune/inflammatory and structural substrates of COPD and asthma are responsible for significant differences in the responses to standard pharmacologic treatments. Therefore, an accurate diagnosis is of central relevance to assure the appropriate therapeutic intervention in order to achieve safe and effective patient care. Induced sputum (IS) accurately mirrors inflammation in the airways, providing a more direct picture of lung cell metabolism in comparison to those specimen that reflect analytes in the systemic circulation.

METHODS

An integrated untargeted metabolomics and lipidomics analysis was performed in IS of asthmatic (n = 15) and COPD (n = 22) patients based on Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS) and UHPLC-tandem MS (UHPLC-MS/MS). Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to resulting dataset. The analysis of main enriched metabolic pathways and the association of the preliminary metabolites/lipids pattern identified to clinical parameters of asthma/COPD differentiation were explored. Multivariate ROC analysis was performed in order to determine the discriminatory power and the reliability of the putative biomarkers for diagnosis between COPD and asthma.

RESULTS

PLS-DA indicated a clear separation between COPD and asthmatic patients. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, putrescine showed the highest score. A differential IS bio-signature of 22 metabolites and lipids was found, which showed statistically significant variations between asthma and COPD. Of these 22 compounds, 18 were decreased and 4 increased in COPD compared to asthmatic patients. The IS levels of Phosphatidylethanolamine (PE) (34:1), Phosphatidylglycerol (PG) (18:1;18:2) and spermine were significantly higher in asthmatic subjects compared to COPD.

CONCLUSIONS

This is the first pilot study to analyse the IS metabolomics/lipidomics signatures relevant in discriminating asthma vs COPD. The role of polyamines, of 6-Hydroxykynurenic acid and of D-rhamnose as well as of other important players related to the alteration of glycerophospholipid, aminoacid/biotin and energy metabolism provided the construction of a diagnostic model that, if validated on a larger prospective cohort, might be used to rapidly and accurately discriminate asthma from COPD.

Circular RNA_0025843 Alleviated Cigarette Smoke Extract Induced Bronchoalveolar Epithelial Cells Ferroptosis

Purpose

Circular RNA (circRNA) plays an important role in various biological processes. However, their functions in cigarette smoke extract (CSE) induced human normal lung epithelial cells (BEAS-2B) injury remain vague. The study aimed to explore circRNA expression profiles and reveal their potential roles in CSE-treated BEAS-2B cells.

Methods

5% CSE exposure for 24 hours were used to build the BEAS-2B cells ferroptosis model. Differentially expressed circRNAs (DECs) were identified by next-generation RNA sequencing. Six randomly selected DECs were validated via quantitative reverse transcription polymerase chain reaction (qRT-PCR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis were conducted to clarify the potential functions of the DECs. Furthermore, the role of hsa_circ_0025843 in CSE-related BEAS-2B cells ferroptosis was confirmed.

Results

5% CSE exposure induced BEAS-2B cells ferroptosis. Fifty-one up-regulated cirRNAs and 80 down-regulated circRNAs were revealed in CSE-treated BEAS-2B cells. Hsa_circ_0003461, hsa_circ_0007548, hsa_circ_0025843, hsa_circ_0068896, hsa_circ_0005832, and hsa_circ_0053378 were selected randomly to validate the reliability of next-generation RNA sequencing by qRT-PCR. After KEGG pathway analysis, DECs were found to participate in the process of EGFR tyrosine kinase inhibitor resistance and glycerophospholipid metabolism. The knockdown of hsa_circ_0025843 significantly alleviated CSE-induced BEAS-2B cells ferroptosis.

Conclusion

The study indicated the circRNA expression profiles in CSE-treated BEAS-2B cells. Hsa_circ_0025843 alleviated CSE induced BEAS-2B cells ferroptosis, which might be a potential therapeutic target of CSE related lung injury.

Differences in the lipid metabolism profile and clinical characteristics between eosinophilic and non-eosinophilic acute exacerbation of chronic obstructive pulmonary disease

Objective: In this study, we aimed to investigate the differences in serum lipid metabolite profiles and their relationship with clinical characteristics between patients with eosinophilic and non-eosinophilic AECOPD. Methods: A total of 71 AECOPD patients were enrolled. Eosinophilic AECOPD was defined as blood EOS% ≥ 2% (n = 23), while non-eosinophilic AECOPD, as blood EOS< 2% (n = 48). Clinical data were collected, and serum lipid metabolism profiles were detected by liquid chromatography-mass spectrometry (LC-MS). The XCMS software package was used to pre-process the raw data, and then, lipid metabolite identification was achieved through a spectral match using LipidBlast library. Differences in lipid profiles and clinical features between eosinophilic and non-eosinophilic groups were analyzed by generalized linear regression. The least absolute shrinkage and selection operator (LASSO) was applied to screen the most characteristic lipid markers for the eosinophilic phenotype. Results: Eosinophilic AECOPD patients had less hypercapnic respiratory failures, less ICU admissions, a shorter length of stay in the hospital, and a lower fibrinogen level. In the lipid metabolism profiles, 32 significantly different lipid metabolites were screened through a t-test adjusted by using FDR (FDR-adjusted p < 0.05 and VIP> 1). Nine differential lipid metabolites were found to be associated with the three clinical features, namely, hypercapnia respiratory failure, ICU admission, and fibrinogen in further integration analysis. The species of triacylglycerol (TAG), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and diacylglyceryl trimethylhomoserine (DGTS) were high in these eosinophilic AECOPD. The LASSO was applied, and three lipid metabolites were retained, namely, LPC (16:0), TAG (17:0/17:2/17:2), and LPC (20:2). The logistic regression model was fitted using these three markers, and the area under the ROC curve of the model was 0.834 (95% CI: 0.740-0.929). Conclusion: Patients with eosinophilic AECOPD had a unique lipid metabolism status. Species of TAGs and LPCs were significantly increased in this phenotype and were associated with better clinical outcomes.

Metabolic changes of glycerophospholipids during the reparative phase after myocardial infarction injury

Introduction

Myocardial infarction (MI) is a fatal manifestation of coronary heart disease, and its underlying mechanism is still largely unknown. Lipid levels and composition alterations predict the risk of MI complications. Glycerophospholipids (GPLs) are important bioactive lipids and play a crucial role in the development of cardiovascular diseases. However, the metabolic changes in the GPLs profile during post-MI injury remain unknown.

Methods

In the current study, we constructed a classic MI model by ligating the left anterior descending branch and assessed the alterations in both plasma and myocardial GPLs profiles during the reparative phase post-MI by liquid chromatography-tandem mass spectrometry analysis.

Results

We found that myocardial GPLs, but not plasma GPLs, were markedly changed after MI injury. Importantly, MI injury is associated with decreased phosphatidylserine (PS) levels. Consistently, the expression of phosphatidylserine synthase 1 (PSS1), which catalyzes the formation of PS from its substrate phosphatidylcholine, was significantly reduced in heart tissues after MI injury. Furthermore, oxygen-glucose deprivation (OGD) inhibited PSS1 expression and reduced PS levels in primary neonatal rat cardiomyocytes, while overexpression of PSS1 restored the inhibition of PSS1 and the reduction in PS levels caused by OGD. Moreover, overexpression of PSS1 abrogated, whereas knockdown of PSS1 aggravated, OGD-induced cardiomyocyte apoptosis.

Conclusions

Our findings revealed that GPLs metabolism was involved in the reparative phase post-MI, and cardiac decreased PS levels, resulting from inhibition of PSS1, are important contributor to the reparative phase post-MI. PSS1 overexpression represents a promising therapeutic strategy to attenuate MI injury.

A Novel Metabolic Score for Predicting the Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

Background

Chronic obstructive pulmonary disease (COPD) has higher mortality when developing to acute exacerbation (AECOPD); hence, the early intervention of COPD is critical for preventing AECOPD. Exploring the serum metabolites associated with acute exacerbation in patients with COPD will contribute to the early intervention of COPD.

Methods

In the study, a non-targeted metabolomics strategy combined with multivariate statistical methods was performed to explore the metabolic profiling of COPD developing acute exacerbation, to screen the potential metabolites associated with AECOPD and to analyze the potential value of these metabolites in predicting the development of COPD.

Results

Serum lysine, glutamine, 3-hydroxybutyrate, pyruvate and glutamate levels were significantly higher, while 1-methylhistidine, isoleucine, choline, valine, alanine, histidine and leucine levels were significantly lower in AECOPD patients, compared with stable COPD patients after normalization based on the healthy controls. Moreover, eight metabolic pathways were significantly altered (P<0.05) in the serum of AECOPD patients compared with the stable COPD population, including purine metabolism, glutamine and glutamate metabolism, arginine biosynthesis, butyrate metabolism, ketone body synthesis and degradation, and linoleic acid metabolism. In addition, the correlation analysis between metabolites and AECOPD patients demonstrated that an M-score based on a weighted sum of concentrations of four metabolites including pyruvate, isoleucine, 1-methylhistidine and glutamine were significantly associated with the acute exacerbation of pulmonary ventilation function in COPD patients.

Conclusion

Altogether, the metabolite score based on a weighted sum of concentrations of four serum metabolites was associated with an increased risk of COPD developing acute exacerbation, which will provide a new insight for the understanding of COPD development.

Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease

Microbiomics have significantly advanced over the last decade, driven by the widespread availability of next-generation sequencing (NGS) and multi-omic technologies. Integration of NGS and multi-omic datasets allow for a holistic assessment of endophenotypes across a range of chronic respiratory disease states, including chronic obstructive pulmonary disease (COPD). Valuable insight has been attained into the nature, function, and significance of microbial communities in disease onset, progression, prognosis, and response to treatment in COPD. Moving beyond single-biome assessment, there now exists a growing literature on functional assessment and host-microbe interaction and, in particular, their contribution to disease progression, severity, and outcome. Identifying specific microbes and/or metabolic signatures associated with COPD can open novel avenues for therapeutic intervention and prognosis-related biomarkers. Despite the promise and potential of these approaches, the large amount of data generated by such technologies can be challenging to analyze and interpret, and currently, there remains a lack of standardized methods to address this. This review outlines the current use and proposes future avenues for the application of NGS and multi-omic technologies in the endophenotyping, prognostication, and treatment of COPD.

Metabolomics of Respiratory Diseases

Metabolomics is an expanding field of systems biology that is gaining significant attention in respiratory research. As a unique approach to understanding and diagnosing diseases, metabolomics provides a snapshot of all metabolites present in biological samples such as exhaled breath condensate, bronchoalveolar lavage, plasma, serum, urine, and other specimens that may be obtained from patients with respiratory diseases. In this article, we review the rapidly expanding field of metabolomics in its application to respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and acute lung injury, along with its more severe form, adult respiratory disease syndrome. We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized occupational and environmental materials. With the latest advances in our understanding of the microbiome, we discuss microbiome-derived metabolites that arise from the gut and lung in asthma and COPD that have mechanistic implications for these diseases. Recent literature has suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe diseases which may be fatal for many patients each year.

Development of a Novel Targeted Metabolomic LC-QqQ-MS Method in Allergic Inflammation

The transition from mild to severe allergic phenotypes is still poorly understood and there is an urgent need of incorporating new therapies, accompanied by personalized diagnosis approaches. This work presents the development of a novel targeted metabolomic methodology for the analysis of 36 metabolites related to allergic inflammation, including mostly sphingolipids, lysophospholipids, amino acids, and those of energy metabolism previously identified in non-targeted studies. The methodology consisted of two complementary chromatography methods, HILIC and reversed-phase. These were developed using liquid chromatography, coupled to triple quadrupole mass spectrometry (LC-QqQ-MS) in dynamic multiple reaction monitoring (dMRM) acquisition mode and were validated using ICH guidelines. Serum samples from two clinical models of allergic asthma patients were used for method application, which were as follows: (1) corticosteroid-controlled (ICS, n = 6) versus uncontrolled (UC, n = 4) patients, and immunotherapy-controlled (IT, n = 23) versus biologicals-controlled (BIO, n = 12) patients. The results showed significant differences mainly in lysophospholipids using univariate analyses in both models. Multivariate analysis for model 1 was able to distinguish both groups, while for model 2, the results showed the correct classification of all BIO samples within their group. Thus, this methodology can be of great importance for further understanding the role of these metabolites in allergic diseases as potential biomarkers for disease severity and for predicting patient treatment response.