PDF(Pubmed)
Abstract：
BACKGROUND: Lipids, as a fundamental cell component, play an regulating role in controlling the different cellular biological processes involved in viral infections. A notable feature of coronavirus disease 2019 (COVID-19) is impaired lipid metabolism. The function of lipophagy-related genes in COVID-19 is unknown. The present study aimed to investigate biomarkers and drug targets associated with lipophagy and lipophagy-based therapeutic agents for COVID-19 through bioinformatics analysis.
METHODS: Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications.
RESULTS: The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2, were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers.
CONCLUSIONS: The lipophagy-related genes ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2 can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19.
METHODS: Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications.
RESULTS: The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2, were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers.
CONCLUSIONS: The lipophagy-related genes ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2 can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19.
摘要：
背景：脂质，作为基本的细胞成分，在控制涉及病毒感染的不同细胞生物学过程中起调节作用。2019年冠状病毒病（COVID-19）的一个显着特征是脂质代谢受损。COVID-19中吸脂相关基因的功能未知。本研究旨在通过生物信息学分析研究与吸脂性相关的生物标志物和药物靶标以及基于吸脂性的COVID-19治疗剂。
方法：使用随机森林等机器学习算法鉴定了COVID-19的与脂肪吞噬相关的生物标志物，支持向量机-递归特征消除,广义线性模型，以及三个与COVID-19相关的GEO数据集的极端梯度提升：scRNA-seq（GSE145926）和批量RNA-seq（GSE183533和GSE190496）。在cMAP数据库中搜索潜在的COVID-19药物。
结果：脂质吞噬途径下调，脂滴形成途径上调，导致脂质代谢受损。七个吸脂相关基因，包括ACADVL,HYOU1,DAP,AUP1,PRXAB2,LSS,和PLIN2，被用作COVID-19的生物标志物和药物靶标。此外，吸脂性可能在COVID-19发病机制中起作用。作为治疗COVID-19的前瞻性药物，七个潜在的下调节剂(苯氧基苄胺，Helveticoside,lanatosideC,格尔德霉素,洛哌丁胺,吡格列酮,和曲古抑菌素A）被发现。这些候选药物显示出与七种生物标志物的显著结合能。
结论：脂质吞噬相关基因ACADVL，HYOU1,DAP,AUP1,PRXAB2,LSS,PLIN2可作为COVID-19的生物标志物和药物靶标。这七种生物标志物的七种潜在下调物可能对治疗COVID-19具有治疗作用。
方法：使用随机森林等机器学习算法鉴定了COVID-19的与脂肪吞噬相关的生物标志物，支持向量机-递归特征消除,广义线性模型，以及三个与COVID-19相关的GEO数据集的极端梯度提升：scRNA-seq（GSE145926）和批量RNA-seq（GSE183533和GSE190496）。在cMAP数据库中搜索潜在的COVID-19药物。
结果：脂质吞噬途径下调，脂滴形成途径上调，导致脂质代谢受损。七个吸脂相关基因，包括ACADVL,HYOU1,DAP,AUP1,PRXAB2,LSS,和PLIN2，被用作COVID-19的生物标志物和药物靶标。此外，吸脂性可能在COVID-19发病机制中起作用。作为治疗COVID-19的前瞻性药物，七个潜在的下调节剂(苯氧基苄胺，Helveticoside,lanatosideC,格尔德霉素,洛哌丁胺,吡格列酮,和曲古抑菌素A）被发现。这些候选药物显示出与七种生物标志物的显著结合能。
结论：脂质吞噬相关基因ACADVL，HYOU1,DAP,AUP1,PRXAB2,LSS,PLIN2可作为COVID-19的生物标志物和药物靶标。这七种生物标志物的七种潜在下调物可能对治疗COVID-19具有治疗作用。
