Gene expression and splicing alterations analyzed by high throughput RNA sequencing of chronic lymphocytic leukemia specimens

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.

Comprehensive analysis of alternative splicing and transcriptome related to the prognosis of cervical squamous cell cancer

“…There is no doubt that aberrant splicing factors or abnormal expression of splicing factors directly leads to changes of splicing events pattern and splicing events expression [15,16]. Some studies reported that there was correlation between splicing factors and poor prognosis in chronic lymphocytic leukemia, lung adenocarcinoma, pancreatic cancer, breast cancer and melanoma [36][37][38]. In our study, splicing factors were collected from the SpliceAid2 database, and correlation network between SASEs and SFs was explored.…”

Altered expression of metabolic pathways in CLL detected by unlabelled quantitative mass spectrometry analysis

“…Lipid levels were measured using mass spectrometry and normalized to pmol/mg protein extracted. Lipid nomenclature and notation is based upon the paper by Liebisch et al (2013), depicting the fatty acyl/alkyl position level and bond type. Significance is indicated as * (P < 0Á05) and ** (P < 0Á005).…”

“…The origins of M-CLL and UM-CLL are not well known, with numerous hypotheses suggesting either a multiple-cell model of CLL derivation, where the two sub-groups originate from distinct cell types with differing antigen encounter histories or a onecell model, where both cell types are derived from marginal zone B cells (Chiorazzi & Ferrarini, 2011). Key microarray and RNA sequencing experiments analysing gene expression differences between M-CLL and UM-CLL have revealed that research paper only a small number of genes differ between the two (Klein et al, 2001;Rosenwald et al, 2001;Ferreira et al, 2014;Liao et al, 2015;Yepes et al, 2015), suggesting that the subgroups may have derived from a single originating cell. Of those genes that have been found to alter in expression between the two groups, pathways include the cell cycle, B cell development and inter-cellular communication (Ecker et al, 2015).…”

The landscape of the alternatively spliced transcriptome remains stable during aging across different species and tissues

“…We analyzed differential AS using rMATS (v3.0.8) 27,28 . This tool has been applied successfully in other publications and showed a high precision in AS identification 27,29 . Only results with a false discovery rate below 0.05 and an absolute inclusion level difference of AS events per sample (IncLevelDifference) above 0.1 were considered 29 .…”

“…This tool has been applied successfully in other publications and showed a high precision in AS identification 27,29 . Only results with a false discovery rate below 0.05 and an absolute inclusion level difference of AS events per sample (IncLevelDifference) above 0.1 were considered 29 . In addition, we manually controlled the results exemplary to ensure that AS takes place as predicted and whether it is already annotated as alternative transcript according to the respective species annotation.…”