Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis

脂质体 脂质代谢 脂类学 生物标志物 免疫系统 肺结核 结核分枝杆菌 磷脂酰胆碱 医学 免疫学 生物 生物信息学 生物化学 内科学 磷脂 病理
作者
Nguyen Phuoc Long,Nguyen Ky Anh,Nguyen Thi Hai Yen,Nguyen Ky Phat,Seongoh Park,Vo Thuy Anh Thu,Yong‐Soon Cho,Jae‐Gook Shin,Jee Youn Oh,Dong‐Hyun Kim
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:12 (1) 被引量:17
标识
DOI:10.1038/s41598-022-17521-4
摘要

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O-) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865-0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.
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