Identifying ALOX15-initiated lipid peroxidation increases susceptibility to ferroptosis in asthma epithelial cells.

Ferroptosis is a programmed form of cell death regulated by iron and has been linked to the development of asthma. However, the precise mechanisms driving ferroptosis in asthma remain elusive. To gain deeper insights, we conducted an analysis of nasal epithelial and sputum samples from the GEO database using three machine learning methods. Our investigation identified a pivotal gene, Arachidonate 15-lipoxygenase (ALOX15), associated with ferroptosis in asthma. Through both in vitro and in vivo experiments, we further confirmed the significant role of ALOX15 in ferroptosis in asthma. Our results demonstrate that ferroptosis manifests in an HDM/LPS-induced allergic airway inflammation (AAI) mouse model, mimicking human asthma, and in HDM/LPS-stimulated 16HBE cells. Moreover, we observed an up-regulation of ALOX15 expression in HDM/LPS-induced mice and cells. Notably, silencing ALOX15 markedly decreased HDM/LPS-induced ferroptosis in 16HBE cells. These findings indicate that ferroptosis may be implicated in the onset and progression of asthma, with ALOX15-induced lipid peroxidation raising the susceptibility to ferroptosis in asthmatic epithelial cells.

Integration of transcriptomics and metabolomics identify biomarkers of aberrant lipid metabolism in ulcerative colitis.

BACKGROUND: The incidence of ulcerative colitis (UC) continues to rise globally, but effective therapeutic targets are still lacking. In recent years, numerous studies have indicated that lipid therapies could offer a novel perspective for UC treatment. Given the absence of prior research utilizing high-throughput data to identify target genes associated with lipid metabolism, we conducted this work. METHODS: The training set for this study was derived from four datasets within the Gene Expression Omnibus (GEO), encompassing a total of 357 UC patients. We employed four machine learning methods (LASSO, SVM, RF, and Boruta) to jointly identify core biomarkers in these patients, whose aberrant expression needed to be validated in independent datasets and in dextrose sulfate sodium salt (DSS)-induced UC mouse models. Regarding metabolomics, we detected abnormal oxidized lipids in the serum of UC mouse using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in conjunction with orthogonal partial least squares-discriminant analysis (OPLS-DA). RESULTS: Phospholipase A2 Group IIA (PLA2G2A) was first identified as a possible biomarker for UC, with AUC values of 0.810 and 1.000 in the two validation sets, while in animal models the gene showed similarly significant up-regulation in damaged intestinal mucosa. Further analysis of this gene showed that it was positively correlated with 17 immune cell types and histological severity. Additionally, we pioneered the development of a lipid metabolism score in UC research, which outperformed all individual genes in terms of disease diagnostic efficacy (AUC values of 0.980 and 1.000 for the two validation sets, respectively). Finally, the metabolomics study also identified 31 significantly abnormal oxidized lipids, including 12-HHT and DHA. CONCLUSIONS: PLA2G2A is a key therapeutic target for UC, and oxidized lipids such as 12-HHT can serve as potential serologic indicators for diagnosis.

The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma.

BACKGROUND: Lipids, significant signaling molecules, regulate a multitude of cellular responses and biological pathways in asthma which are closely associated with disease onset and progression. However, the characteristic lipid genes and metabolites in asthma remain to be explored. It is also necessary to further investigate the role of lipid molecules in asthma based on high-throughput data. OBJECTIVE: To explore the biomarkers and molecular mechanisms associated with lipid metabolism in asthma. METHODS: In this study, we selected three mouse-derived datasets and one human dataset (GSE41665, GSE41667, GSE3184 and GSE67472) from the GEO database. Five machine learning algorithms, LASSO, SVM-RFE, Boruta, XGBoost and RF, were used to identify core gene. Additionally, we used non-negative matrix breakdown (NMF) clustering to identify two lipid molecular subgroups and constructed a lipid metabolism score by principal component analysis (PCA) to differentiate the subtypes. Finally, Western blot confirmed the altered expression levels of core genes in OVA (ovalbumin) and HDM+LPS (house dust mite+lipopolysaccharide) stimulated and challenged BALB/c mice, respectively. Results of non-targeted metabolomics revealed multiple differentially expressed metabolites in the plasma of OVA-induced asthmatic mice. RESULTS: Cholesterol 25-hydroxylase (CH25H) was finally localized as a core lipid metabolism gene in asthma and was verified to be highly expressed in two mouse models of asthma. Five-gene lipid metabolism constructed from CYP2E1, CH25H, PTGES, ALOX15 and ME1 was able to distinguish the subtypes effectively. The results of non-targeted metabolomics showed that most of the aberrantly expressed metabolites in the plasma of asthmatic mice were lipids, such as LPC 16:0, LPC 18:1 and LPA 18:1. CONCLUSION: Our findings imply that the lipid-related gene CH25H may be a useful biomarker in the diagnosis of asthma.

Machine learning identifies ferroptosis-related gene ANXA2 as potential diagnostic biomarkers for NAFLD.

Introduction: Non-alcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease, still lacks effective therapeutic targets today. Ferroptosis, a type of cell death characterized by lipid peroxidation, has been linked to NAFLD in certain preclinical trials, yet the exact molecular mechanism remains unclear. Thus, we analyzed the relationship between ferroptosis genes and NAFLD using high-throughput data. Method: We utilized a total of 282 samples from five datasets, including two mouse ones, one human one, one single nucleus dataset and one single cell dataset from Gene Expression Omnibus (GEO), as the data basis of our study. To filter robust treatment targets, we employed four machine learning methods (LASSO, SVM, RF and Boruta). In addition, we used an unsupervised consensus clustering algorithm to establish a typing scheme for NAFLD based on the expression of ferroptosis related genes (FRGs). Our study is also the first to investigate the dynamics of FRGs throughout the disease process by time series analysis. Finally, we validated the relationship between core gene and ferroptosis by in vitro experiments on HepG2 cells. Results: We discovered ANXA2 as a central focus in NAFLD and indicated its potential to boost ferroptosis in HepG2 cells. Additionally, based on the results obtained from time series analysis, ANXA2 was observed to significantly define the disease course of NAFLD. Our results demonstrate that implementing a ferroptosis-based staging method may hold promise for the diagnosis and treatment of NAFLD. Conclusion: Our findings suggest that ANXA2 may be a useful biomarker for the diagnosis and characterization of NAFLD.