Bioinformation and deep-learning based model reveals norepinephrine inhibiting PRDX1 aggravates atherosclerosis
The regulation of metabolites comprises the fundamental biological processes of human. More new phenotypes of classic metabolites were revealed, while the devolution of functional targets and mechanisms of them still have many gaps due to diversity in structures and functions. Here, we report a bioinformation and attention mechanism based deep learning model leveraging abundant bioinformation of metabolites for functional target prediction of specific phenotypes. Taking advantage of this model, the potential target database of 3,382 common human metabolites were resourced publicly. Norepinephrine was identified as a selective peroxidase inhibitor of peroxiredoxin 1 (PRDX1) for atherosclerosis aggravation in model validation experiments. Salvianolic acid A (SAA) was screened out as PRDX1 exogenous stabilization compound for rescuing norepinephrine's aggravation effects in vivo. Besides, bulk RNA-Sequencing analysis indicated norepinephrine aggravates atherosclerosis through activating transcriptional activity of SREBP2 in a ROS dependent manner. Overall, our research provides a new computational regime for metabolites target identification besides experimental chemical proteomics and PRDX1 stabilization was validated a potential druggable target for cardiovascular diseases intervention. Overall design: The purpose is to elucidate the impact of SAA on cholesterol metabolism and atherosclerosis-related pathways by comparing gene expression profiles between the SAA treatment group and the control group.