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Tumor Engraftment in a Xenograft Mouse Model of Human Mantle Cell Lymphoma
Published on: March 30, 2018
Systematic fusion transcript discovery in mantle cell lymphoma using long-read sequencing
Lael Pasipamire1, Jacob Rashid1, Caiden Lukan1
1Department of Pharmaceutical Sciences, Butler University, Indianapolis, IN, USA.
Abstract:
Fusion transcripts are composed of hybrid RNA consisting of transcripts from two distinct genes and can arise from physical linking of genes at the DNA level, splicing or read-through transcription. In addition, there are also fusion transcripts that can occur between a protein coding gene and long non-coding RNAs. Systemic detection of all fusion transcripts at the RNA-level is important in the identification of potential therapeutic drug targets as well as biomarkers for detection, classification, and subtyping of cancer. We used long-read third-generation sequencing of RNA, Iso Sequencing to identify fusion transcripts in Mantle Cell Lymphoma (MCL) cell lines. Our results revealed widespread transcript diversity in MCL. The majority of the long-read transcripts were novel. Some of the thousands of novel transcripts we identified were fusion transcripts. These fusion transcripts had some of the longest transcripts in the MCL transcriptome. We identified the fusion junction of several select fusion transcripts involving protein coding genes including the well-known and widely expressed CTBS::GNG5 and validated their presence using other techniques. Furthermore, we also identified and validated a novel fusion transcript between the multifunctional, m6A methylation 'writer', RBM15, and LAMTOR5:AS, a long noncoding RNA. Use of the chemical compound, JT-607, an inhibitor of CPSF73/CPSF3 which affects both alternative polyadenylation and read-through transcription resulted in increased expression of the RBM15::LAMTOR5:AS fusion transcript. Our analysis suggests that RBM15::LAMTOR5:AS and many fusion transcripts we identified are intrachromosomal. Since the origin, significance and impact of many fusion transcripts remain unknown, our results support using an unbiased approach to identify fusion transcripts. This will help us to fully comprehend the complexity of the human transcriptome in normal biology and in disease.
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