Kaposi's sarcoma-associated herpesvirus (KSHV) is a large, oncogenic DNA virus belonging to the gammaherpesvirus subfamily. KSHV has been extensively studied with various high-throughput RNA-sequencing approaches to map the transcription start and end sites, the splice junctions, and the translation initiation sites. Despite these efforts, the comprehensive annotation of the viral transcriptome remains incomplete. In the present study, we generated a long-read sequencing data set of the lytic and latent KSHV transcriptome using native RNA and direct cDNA-sequencing methods. This was supplemented with Cap Analysis of Gene Expression sequencing based on a short-read platform. We also utilized data sets from previous publications for our analysis. As a result of this combined approach, we have identified a number of novel viral transcripts and RNA isoforms and have either corroborated or improved the annotation of previously identified viral RNA molecules, thereby notably enhancing our comprehension of the transcriptomic architecture of the KSHV genome. We also evaluated the coding capability of transcripts previously thought to be non-coding by integrating our data on the viral transcripts with translatomic information from other publications.IMPORTANCEDeciphering the viral transcriptome of Kaposi's sarcoma-associated herpesvirus is of great importance because we can gain insight into the molecular mechanism of viral replication and pathogenesis, which can help develop potential targets for antiviral interventions. Specifically, the identification of substantial transcriptional overlaps by this work suggests the existence of a genome-wide interference between transcriptional machineries. This finding indicates the presence of a novel regulatory layer, potentially controlling the expression of viral genes.
KSHV 3.0: a state-of-the-art annotation of the Kaposi's sarcoma-associated herpesvirus transcriptome using cross-platform sequencing.
KSHV 3.0:利用跨平台测序对卡波西肉瘤相关疱疹病毒转录组进行最先进的注释
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作者:Prazsák István, Tombácz Dóra, Fülöp Ãdám, Torma Gábor, Gulyás Gábor, DörmÅ Ãkos, Kakuk Balázs, McKenzie Spires Lauren, Toth Zsolt, BoldogkÅi Zsolt
| 期刊: | mSystems | 影响因子: | 4.600 |
| 时间: | 2024 | 起止号: | 2024 Feb 20; 9(2):e0100723 |
| doi: | 10.1128/msystems.01007-23 | 研究方向: | 肿瘤 |
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