Abstract
BackgroundTo determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed.MethodsTen CLL specimens and five normal peripheral blood CD19+ B cells were analyzed by HTS RNA-seq. The library preparation was performed with Illumina TrueSeq RNA kit and analyzed by Illumina HiSeq 2000 sequencing system.ResultsAn average of 48.5 million reads for B cells, and 50.6 million reads for CLL specimens were obtained with 10396 and 10448 assembled transcripts for normal B cells and primary CLL specimens respectively. With the Cuffdiff analysis, 2091 differentially expressed genes (DEG) between B cells and CLL specimens based on FPKM (fragments per kilobase of transcript per million reads and false discovery rate, FDR q < 0.05, fold change >2) were identified. Expression of selected DEGs (n = 32) with up regulated and down regulated expression in CLL from RNA-seq data were also analyzed by qRT-PCR in a test cohort of CLL specimens. Even though there was a variation in fold expression of DEG genes between RNA-seq and qRT-PCR; more than 90 % of analyzed genes were validated by qRT-PCR analysis. Analysis of RNA-seq data for splicing alterations in CLL and B cells was performed by Multivariate Analysis of Transcript Splicing (MATS analysis). Skipped exon was the most frequent splicing alteration in CLL specimens with 128 significant events (P-value <0.05, minimum inclusion level difference >0.1).ConclusionThe RNA-seq analysis of CLL specimens identifies novel DEG and alternatively spliced genes that are potential prognostic markers and therapeutic targets. High level of validation by qRT-PCR for a number of DEG genes supports the accuracy of this analysis. Global comparison of transcriptomes of B cells, IGVH non-mutated CLL (U-CLL) and mutated CLL specimens (M-CLL) with multidimensional scaling analysis was able to segregate CLL and B cell transcriptomes but the M-CLL and U-CLL transcriptomes were indistinguishable. The analysis of HTS RNA-seq data to identify alternative splicing events and other genetic abnormalities specific to CLL is an added advantage of RNA-seq that is not feasible with other genome wide analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1708-9) contains supplementary material, which is available to authorized users.
Highlights
To determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA sequencing (RNA-seq)) analysis was performed
The total WBC counts for unmutated Immunoglobulin variable region heavy chain (IGVH) (U-Chronic lymphocytic leukemia (CLL)) were higher than mutated IGVH (M-CLL) specimens (Table 1) and the IGVH non-mutated CLL (U-CLL) specimens were noted to have a higher percentage of leukemic cells expressing CD38 and Zap-70 as described before [4, 5]
To assess the quality of mapping reads to the reference genome hg19, some key metrics were extracted from the TopHat output, and analyzed using the RNA-seq quality control package RseQC [37]
Summary
To determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed. To identify genetic alterations in CLL, a number of different methods have been employed including cytogenetic studies [6], and array comparative genomic hybridization CGH [7, 8] and recently whole exome sequencing [9]. With the development of massive parallel RNA sequencing (RNA-seq) technology, there have been a growing number of genome-wide studies that have analyzed the complete transcriptome cells in different malignancies [18,19,20,21,22] and non-malignant diseases [23, 24]. As all the RNA transcripts are being directly sequenced, this technology is ideally suited to study altered splicing pattern which is especially relevant in cancer cells as they are known to express unique RNA isoforms with varied biological effects [27, 28]
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