GSE202869
Genome-wide transcriptomic profiling of human iPS-derived cardiomyocytes in response to ectopic expression of SARS-CoV-2 proteins
In this study, we investigated the effects of SARS-CoV-2 subunits on cardiomyocytes derived from human stem cells. The human induced pluripotent stem cells (hiPSCs) was differentiated into cardiomyocytes, and then were transfected with plasmids carrying different SARS-CoV-2 subunits. The cardiac functions, such as contractility and metabolic profiles, were investigated. We found the subunits 9B and S proteins has significantly adversed effects on the cardiac functions, compared to other groups. Thus, changes in transcriptome of cardiomyocytes (day30) after over expression of 9B, S for two days (on day 32) and two weeks (on day 45) were investigated by RNA-seq. Overall design: The DNA sequences of SARS-CoV-2 (Wuhan-Hu-1 strain, GenBank: NC_045512.2) was used for DNA synthesis of each gene (General Bio, China). The viral genes were firstly cloned into pcDNA6B-Flag vector as described in our previous publication (PMID: 33203855). The constructs were further subcloned into a lentivirus transfer plasmid pCDH-CMV-MCS-EF1a-copGFP (System Biosciences, #CD511B-1, Palo Alto, CA, USA) with standard molecular cloning methods. An hiPSC line (generated from a health donor) obtained them from the Stanford Cardiovascular Institute (SCVI) Biobank was used for this study. The hiPSCs were differentiated into cardiomyocytes, and then were transfected with plasmids carrying SARS-CoV-2 subunits with a lentivirial transfection method.  After 4 days of treatment, the GFP expression was examined in the transfected cardiomyocytes. Cells after transfection with 9B, S, and control groups at day 32 and day 46 were harvested for RNA isolation and RNA-seq. The library preparation and RNA-sequencing were done by Novogene Corporation Inc (Sacramento, CA). The RNA-seq libraries were constructed using NEBNext UltraTM II RNA Library Prep Kit for Illumina and were sequenced by Novaseq 6000 PE150 system. Raw reads of FASTQ format were firstly processed through fastp, and clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. At the same time, Q20, Q30 and GC content of the clean data were calculated. All the downstream analyses were based on the clean data with high quality.The human gene symbols and their raw counts were calculated using the HTSeq (version 0.6.1p1) package in Python with the hg19 Homo sapiens gtf file. Differential gene-expression analysis was performed using the edgeR package in R, and the normalization was performed using a trimmed mean of M-values (TMM) method across all samples.