Computational identification of mitochondrial dysfunction biomarkers in severe SARS-CoV-2 infection: Facilitating therapeutic applications of phytomedicine

Abstract

BackgroundCurrently, SARS-CoV-2 has not disappeared and continues to prevail worldwide, with the ongoing risk of mutations and the potential for severe COVID-19. The impairment of monocyte mitochondrial function caused by SARS-CoV-2, leading to a metabolic and immune dysregulation, is a crucial factor in the development of severe COVID-19. PurposeDiscover effective phytomedicines based on mitochondrial-related biomarkers in severe SARS-CoV-2 infection. MethodsFirstly, differential gene analysis and gene set enrichment analysis (GSEA) were conducted on monocytes datasets to identify genes and pathways distinguishing severe patients from uninfected individuals. Then, GO and KEGG enrichment analysis on the differentially expressed genes (DEGs) obtained. Take the DEGs and intersect them with the MitoCarta 3.0 gene set to obtain the differentially expressed mitochondrial-related genes (DE-MRGs). Subsequently, machine learning algorithms were employed to screen potential mitochondrial dysfunction biomarkers for severe COVID-19 based on score values. ROC curves were then plotted to assess the distinguish capability of the biomarkers, followed by validation using two additional independent datasets. Next, the effects of the identified biomarkers on metabolic pathways and immune cells were explored through Gene Set Variation Analysis (GSVA) and CIBERSORT. Finally, potential nature products for severe COVID-19 were screened from the expression profile dataset based on dysregulated mitochondrial-related genes, followed by in vitro experimental validation. ResultsThere are 1812 DEGs and 17 dysregulated mitochondrial processes between severe COVID-19 patients and uninfected individuals. A total of 77 DE-MRGs were identified, and the potential biomarkers were identified as RECQL4, PYCR1, PIF1, POLQ, and GLDC. In both the training and validation sets, the area under the ROC curve (AUC) for these five biomarkers was greater than 0.9. And they did not show significant changes in mild to moderate patients (p > 0.05), indicating their ability to effectively distinguish severe COVID-19. These biomarkers exhibit a highly significant correlation with the dysregulated metabolic processes (p < 0.05) and immune cell imbalance (p < 0.05) in severe patients, as demonstrated by GSVA and CIBERSORT algorithms. Curcumin has the highest score in the predictive model based on transcriptomic data from 496 natural compounds (p = 0.02; ES = 0.90). Pre-treatment with curcumin for 8 h has been shown to alleviate mitochondrial membrane potential damage caused by the SARS-CoV-2 S1 protein (p < 0.05) and reduce elevated levels of reactive oxygen species (ROS) (p < 0.01). ConclusionThe results of this study indicate a significant correlation between severe SARS-CoV-2 infection and mitochondrial dysfunction. The proposed mitochondrial dysfunction biomarkers identified in this study are associated with the disease progression, metabolic and immune changes in severe SARS-CoV-2 infected patients. Curcumin has a potential role in preventing severe COVID-19 by protecting mitochondrial function. Our findings provide new strategies for predicting the prognosis and enabling early intervention in SARS-CoV-2 infection.