Abstract
BackgroundFusion transcripts are found in many tissues and have the potential to create novel functional products. Here, we investigate the genomic sequences around fusion junctions to better understand the transcriptional mechanisms mediating fusion transcription/splicing. We analyzed data from prostate (cancer) cells as previous studies have shown extensively that these cells readily undergo fusion transcription.ResultsWe used the FusionMap program to identify high-confidence fusion transcripts from RNAseq data. The RNAseq datasets were from our (N = 8) and other (N = 14) clinical prostate tumors with adjacent non-cancer cells, and from the LNCaP prostate cancer cell line that were mock-, androgen- (DHT), and anti-androgen- (bicalutamide, enzalutamide) treated. In total, 185 fusion transcripts were identified from all RNAseq datasets. The majority (76 %) of these fusion transcripts were ‘read-through chimeras’ derived from adjacent genes in the genome. Characterization of sequences at fusion loci were carried out using a combination of the FusionMap program, custom Perl scripts, and the RNAfold program. Our computational analysis indicated that most fusion junctions (76 %) use the consensus GT-AG intron donor-acceptor splice site, and most fusion transcripts (85 %) maintained the open reading frame. We assessed whether parental genes of fusion transcripts have the potential to form complementary base pairing between parental genes which might bring them into physical proximity. Our computational analysis of sequences flanking fusion junctions at parental loci indicate that these loci have a similar propensity as non-fusion loci to hybridize. The abundance of repetitive sequences at fusion and non-fusion loci was also investigated given that SINE repeats are involved in aberrant gene transcription. We found few instances of repetitive sequences at both fusion and non-fusion junctions. Finally, RT-qPCR was performed on RNA from both clinical prostate tumors and adjacent non-cancer cells (N = 7), and LNCaP cells treated as above to validate the expression of seven fusion transcripts and their respective parental genes. We reveal that fusion transcript expression is similar to the expression of parental genes.ConclusionsFusion transcripts maintain the open reading frame, and likely use the same transcriptional machinery as non-fusion transcripts as they share many genomic features at splice/fusion junctions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2235-4) contains supplementary material, which is available to authorized users.
Highlights
Fusion transcripts are found in many tissues and have the potential to create novel functional products
Identification of fusion transcripts in prostate cancer A recent study indicates that the number of protein coding genes in the human genome is similar to lower vertebrates [21]
The majority of these fusion transcripts (140/185, 76 %) are derived from genes that are located next to each other in the genome, otherwise referred to as “read-through transcripts” [13], or transcription induced chimeras [23, 24] (Additional file 1). This observation is supported by a recent study in prostate cancer cells that indicates that a high percentage of fusion transcripts involve neighbouring genes {Qin, 2015 #33}
Summary
Fusion transcripts are found in many tissues and have the potential to create novel functional products. The latest estimates indicate that the human genome comprises only 20,687 protein coding genes [1] This number seems surprisingly low, considering the phenotypic complexity of humans. The most studied fusion in prostate cancer is formed between the TMPRSS2 and ERG genes, resulting in ERG transcription being driven by the androgen-responsive TMPRSS2 promoter [6,7,8]. This fusion is observed in ~50 % of primary prostate tumors, and ~41 % of lymph node metastatic tumors [8]. Other studies [15, 16] have since correlated SLC45A3-ELK4 expression with an unfavorable prostate cancer prognosis, resulting in a growing interest in fusion transcription in the prostate cancer biomarker field [17, 18]
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