Objective This study aimed to investigate the mechanism of artesunate-induced cardiotoxicity by using network toxicology and serum untargeted metabolomics.Methods The key targets and signaling pathways of artesunate-induced cardiotoxicity were expored by network toxicology technique.The toxic effect of artesunate on Healthy C57BL/6J mice was evaluated according to the pathological morphology of the heart and the levels of serum biochemical indexes included LDH and CKMB.Serum untargeted metabolomics analysis was combined with multivariate statistical analysis to search the key endogenous metabolites of artesunate cardiotoxic injury and related metabolic pathways.Results 64 targets including AKT1,EGFR,CASP3,VEGFA and 165 signaling pathways including Apoptosis,PI3K-Akt,FoxO were screened out by network toxicology,.Serum metabolomics analysis identified the serum metabolites affected by artesunate-induced cardiotoxicity included lysophosphatidylcholine,lysophosphatidylethanolamine,indoleacetaldehyde,etc.The results of metabolic pathways enrichment analysis were glycerophosphatidylcholine metabolism and tryptophan metabolism.Conclusion Artesunate 600 mg·kg-1 can induce cardiotoxicity in C57BL/6J mice after 1-week administration,and the mechanism is related to glycerophosphatidylcholine metabolism disorder and tryptophan metabolism disorder.This study provides a reference for the early prediction and evaluation of artesunate induced drug-induced cardiotoxicity.

Along with the develoipment of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called"rMVP"to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eX-pedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.