RNA sequencing of thirty-eight basal tumour samples from CLL patients
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina HiSeq 2500, Illumina HiSeq 4000
ORGANISM(S): Homo sapiens
SUBMITTER: Dean Bryant
PROVIDER: E-MTAB-12023 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Background: To determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed. Methods: Ten 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. Results: An 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). Conclusion: The 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. Ten 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.
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Project description:Chronic lymphocytic leukemia (CLL) is a biologically and clinically heterogeneous disease. The somatic hypermutation status of the immunoglobulin heavy chain variable (IGHV) genes has been identified as one of the most robust prognostic markers in CLL. Patients with unmutated IGHV status (U-CLL) typically experience an inferior outcome compared to those whose clones express mutated IGHV genes (M-CLL). We conducted a genome-wide DNA methylation analysis in CD19+ B-cells from a group of 43 CLL patients using reduced representation bisulfite sequencing (RRBS). Using base-pair resolution methylation sequencing, 2323 differentially methylated regions between CLL and normal B-cells (CLL-specific DMRs) and 569 between M-CLL and U-CLL samples (IGHV-specific DMRs) were identified in the CLL genomes. The IGHV-specific DMRs are mostly unique when compared to the CLL-specific DMRs. Less than 10% of the IGHV-specific DMRs are located in promoter regions; however, more than half of these overlap with known DNase I hypersensitive sites, enhancer regions marked by histone modification (H3K4Me1 and H3K27Ac), and transcription factor binding sites in the ENCODE datasets, which indicates that these DMRs contain regulatory sequences. Distinctive DNA methylation patterns were observed in M-CLL and U-CLL samples. Overall, U-CLL was found to contain 50% more hypermethylated regions than M-CLL samples. The hypermethylated loci observed in the U-CLL samples also appear to be hypermethylated in normal naïve B-cells as compared memory B-cells, suggesting that M-CLL and U-CLL differ in differentiation status corresponding to normal B-cell differentiation stages. RNA-seq analysis performed using matched samples (n=34), in which both DNA methylation and gene expression data were available, demonstrated excellent correlation between DNA methylation and gene expression. Several genes whose expression status was previously shown to be associated with CLL prognosis such as ZAP70, CRY1, LDOC1, SEPT10, LAG3, and LPL were differentially methylated in the promoter regions between M-CLL and U-CLL samples indicating that DNA methylation plays an important role in defining the gene expression patterns of these prognostic genes. We further validated 9 genes with IGHV-specific DMRs in the promoter regions using bisulfite pyrosequencing, and the results demonstrated excellent correlation between differential methylation and IGHV mutation status. These novel differentially methylated genes could be developed into biomarkers for CLL prognosis. In addition, DNA hypomethylation was observed in a significant number of genes involved in lymphocyte activation such as PDCD1, NFAT1, and CD5. DNA hypomethylation was observed in the proximal promoter and far up-stream enhancer regions of CD5, an important cell surface marker that uniquely identifies CLL. Overall, the DNA methylation landscape in CLL patients indicates that CLL B cells possess an active B-cell phenotype; at the same time, U-CLL and M-CLL are faithfully committed to their lineage resembling either naïve or memory B-cells. In summary, this comprehensive DNA methylation analysis has identified a large number of novel epigenetic changes in CLL patients. The results from this study will further advance our understanding of the epigenetic contribution to molecular subtypes in CLL. To perform a transcriptome analysis in CLL, we generated sequencing libraries from total RNA isolated from purified B-cells of CLL patients and healthy donnors. The RNA-seq libraries were sequenced using Illumina HiSeq2000 sequencer with a read length of 100bp. 11 CLL B-cell samples, 3 normal control samples including one each of normal CD19+ B cells were studied. We generated 20-30 million Illumina sequencing reads for each sample.
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Project description:Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status â which distinguishes the two major subtypes of CLL â was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we found widespread heterogeneity in the CLL chromatin landscape, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research. Genome-wide profiling of chromatin states and gene expression levels in 88 CLL samples from 55 individuals gave rise to 88 ATAC-seq profiles, 40 ChIPmentation profiles (10 samples, each with 3 different antibodies and matched immunoglobulin control), and 10 RNA-seq profiles. Raw sequence data has been deposited at the EBI's European Genome-phenome Archive (EGA) under the accession number EGAS00001001821 (controlled access to protect patient privacy).
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