Background: Recent studies have disclosed the genome, transcriptome, and epigenetic
compositions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the
effect of viral infection on gene expression of the host cells. It has been demonstrated
that, besides the major canonical transcripts, the viral genome also codes for noncanonical
RNA molecules. While the structural characterizations have revealed a detailed transcriptomic
architecture of the virus, the kinetic studies provided poor and often misleading
results on the dynamics of both the viral and host transcripts due to the low temporal
resolution of the infection event and the low virus/cell ratio (multiplicity of infection
[MOI] = 0.1) applied for the infection. It has never been tested whether the alteration
in the host gene expressions is caused by aging of the cells or by the viral infection.Findings:
In this study, we used Oxford Nanopore's direct cDNA and direct RNA sequencing methods
for the generation of a highcoverage, high temporal resolution transcriptomic dataset
of SARS-CoV-2 and of the primate host cells, using a high infection titer (MOI = 5).
Sixteen sampling time points ranging from 1 to 96 hours with a varying time resolution
and 3 biological replicates were used in the experiment. In addition, for each infected
sample, corresponding noninfected samples were employed. The raw reads were mapped
to the viral and to the host reference genomes, resulting in 49,661,499 mapped reads
(54,62 Gbs). The genome of the viral isolate was also sequenced and phylogenetically
classified.Conclusions: This dataset can serve as a valuable resource for profiling
the SARS-CoV-2 transcriptome dynamics, the virus-host interactions, and the RNA base
modifications. Comparison of expression profiles of the host gene in the virally infected
and in noninfected cells at different time points allows making a distinction between
the effect of the aging of cells in culture and the viral infection. These data can
provide useful information for potential novel gene annotations and can also be used
for studying the currently available bioinformatics pipelines.
