Profiling and bioinformatics analyses reveal chronic lymphocytic leukemia cells share a unique circular RNA expression pattern
2020; Elsevier BV; Volume: 85; Linguagem: Inglês
10.1016/j.exphem.2020.04.001
ISSN1873-2399
AutoresOshrat Raz, Galit Granot, Metsada Pasmanik‐Chor, Pia Raanani, Uri Rozovski,
Tópico(s)Galectins and Cancer Biology
Resumo•Circular RNAs are noncoding RNAs.•Expression of 13,368 circRNAs was profiled in CLL patients and normal B cells.•CLL cells were found to share an expression profile distinct from that of normal B cells.•Eight hundred fifty-nine circRNAs from 592 genes were differentially expressed.•Whether dysregulated circRNAs contribute to CLL pathology remains to be determined. Approximately 10% of the human transcriptome is composed of circular RNAs (circRNAs). These are non-coding RNA molecules in which a covalent bond between the 3′ and 5′ forms a stable circular loop. Herein, we profiled the expression of 13,368 cricRNAS in 21 patients with chronic lymphocytic leukemia (CLL). Regardless of clinical, genetic, or prognostic characteristics, CLL cells share a unique expression profile distinguishable from that of normal B cells. Specifically, 859 circRNAs from 592 genes were differentially expressed (fold change ≥2 and false discovery rate ≤0.05). Whether dysregulation of circRNAs contributes to the pathogenesis of CLL remains to be determined. Approximately 10% of the human transcriptome is composed of circular RNAs (circRNAs). These are non-coding RNA molecules in which a covalent bond between the 3′ and 5′ forms a stable circular loop. Herein, we profiled the expression of 13,368 cricRNAS in 21 patients with chronic lymphocytic leukemia (CLL). Regardless of clinical, genetic, or prognostic characteristics, CLL cells share a unique expression profile distinguishable from that of normal B cells. Specifically, 859 circRNAs from 592 genes were differentially expressed (fold change ≥2 and false discovery rate ≤0.05). Whether dysregulation of circRNAs contributes to the pathogenesis of CLL remains to be determined. The role of noncoding RNA (ncRNAs) in the pathogenesis of human diseases was first discovered in chronic lymphocytic leukemia (CLL) [1Calin GA Dumitru CD Shimizu M et al.Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia.Proc Natl Acad Sci USA. 2002; 99: 15524-15529Crossref PubMed Scopus (4165) Google Scholar]. While trying to identify tumor suppressor genes located on chromosome 13q14, which is deleted in approximately 50% of patients, Calin et al. [1Calin GA Dumitru CD Shimizu M et al.Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia.Proc Natl Acad Sci USA. 2002; 99: 15524-15529Crossref PubMed Scopus (4165) Google Scholar] found that the minimal deleted region in patients with CLL and 13q14 deletion includes the coding sequence for microRNAs (miRs) miR-15a and miR-16-1. Since then, other studies have reported that dysregulation of the miR transcriptome is a hallmark of CLL cells [2Balatti V Pekarky Y Croce CM Role of microRNA in chronic lymphocytic leukemia onset and progression.J Hematol Oncol. 2015; 8: 12Crossref PubMed Scopus (52) Google Scholar]. The role of other ncRNAs in the pathobiology of CLL is less clear.Circular RNAs (circRNAs) constitute an additional class of ncRNA and are emerging as key members of the gene regulatory milieus. Originally, thought to represent an error in splicing activity and considered to occur in low abundance, circRNAs have recently been reported to be ubiquitously prevalent, forming approximately 10% of the human transcriptome [3Huang S Yang B Chen BJ et al.The emerging role of circular RNAs in transcriptome regulation.Genomics. 2017; 109: 401-407Crossref PubMed Scopus (116) Google Scholar]. In these newly identified circular ncRNAs, the 3′ and 5′ ends are joined by covalent bonds creating a circular stable structure. The cellular role of circRNAs is yet to be discovered, although circRNA–miR binding has been found to regulate transcriptome landscape and cellular function in normal and abnormal physiological processes [4Ebbesen KK Hansen TB Kjems J Insights into circular RNA biology.RNA Biol. 2017; 14: 1035-1045Crossref PubMed Scopus (282) Google Scholar]. For example, specific circRNAs have been reported to play an important role in the development and progression of different types of cancer [5Cui X Wang J Guo Z et al.Emerging function and potential diagnostic value of circular RNAs in cancer.Mol Cancer. 2018; 17: 123Crossref PubMed Scopus (114) Google Scholar].Although upregulation of specific circRNAs has been documented [6Xia L Wu L Bao J et al.Circular RNA circ-CBFB promotes proliferation and inhibits apoptosis in chronic lymphocytic leukemia through regulating miR-607/FZD3/Wnt/beta-catenin pathway.Biochem Biophys Res Commun. 2018; 503: 385-390Crossref PubMed Scopus (73) Google Scholar, 7Wu Z Sun H Liu W et al.Circ-RPL15: a plasma circular RNA as novel oncogenic driver to promote progression of chronic lymphocytic leukemia.Leukemia. 2020; 34: 919-923Crossref PubMed Scopus (38) Google Scholar, 8Dahl M Daugaard I Andersen MS et al.Enzyme-free digital counting of endogenous circular RNA molecules in B-cell malignancies.Lab Invest. 2018; 98: 1657-1669Crossref PubMed Scopus (61) Google Scholar], the expression profile of circRNAs in CLL lymphocytes is unknown. However, because the expression profile of circRNAs is tissue specific and varies between different pathological conditions [5Cui X Wang J Guo Z et al.Emerging function and potential diagnostic value of circular RNAs in cancer.Mol Cancer. 2018; 17: 123Crossref PubMed Scopus (114) Google Scholar], we hypothesized that the expression profile of circRNAs is unique to CLL cells and that circRNAs contribute to the pathogenesis of this disease.MethodsPatient samples and B-cell separationPeripheral blood cells were obtained from 27 untreated CLL patients (18 males and 9 females) who were followed at The Davidoff Cancer Center, Petah Tikva, Israel. Available upon request (online only, available at www.exphem.org) summarizes the patients' physiological details. This research was approved by the Rabin Medical Center Institutional Review Board and Ethics Committee. Cells were fractionated using Lymphoprep (Axis-Shield Diagnostics, Dundee, Scotland), followed by magnetic separation with anti-CD19 microbeads. Normal B cells were used as the control.circRNA expression profile and data analysisRNA was extracted from the cells using TRI reagent, amplified and transcribed into fluorescently labeled cRNA and hybridized into the Arraystar Human circRNA Array version 2.0 (Arraystar, Rockville, MD, USA). Arraystar circRNA Array uses specific circular junction probes to accurately detect each circRNA, even in the presence of their linear counterparts. Quantile normalization was performed using the R-software Limma package and further data processing with GeneSpring V7 Software (Agilent-Technologies, Santa Clara, CA, USA). The Benjamini–Hochberg false discovery rate (FDR) method with a cutoff of 0.05 was used to correct for multiple comparisons.Quantitative real-time polymerase chain reactionTotal RNA was extracted using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. cDNA was synthesized using the High-Capacity cDNA RT kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems) and the Fast SYBR Green Master Mix (Applied Biosystems). Primers for qRT-PCR are listed in available upon request (online only, available at www.exphem.org). Relative expression levels were calculated using 2–ΔΔCt.Construction of circRNA–miR–mRNA networksWe used Arraystar's microRNA (miR) target prediction software TargetScan (http://www.targetscan.org/vert_72/) and miRanda (https://omictools.com/miranda-tool) to predict circRNA–miR binding. Only miRs that were identified as circRNA targets by all tools were considered as putative circRNA targets. MiR target genes were obtained by the integration of mirNET and mIR target. Networks of circRNA–miR–mRNA were created using the target network from STRING V11 and presented by Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to predict target gene functions by the DAVID (Database for Annotation, Visualization, and Integrated Discovery) and WEBGESTALT (WEB-based Gene Set Analysis Toolkit) tools.Results and DiscussionTo compare the circRNA profiles of CLL cells and normal B cells, we quantified the expression of 13,368 circRNA transcripts in CLL cells from 21 treatment-naïve patients and in normal B cells from eight healthy individuals. We found 529 circRNAs from 364 genes that were upregulated and 330 circRNAs from 228 genes that were downregulated in CLL cells (fold change ≥2 and false discovery rate [FDR] 0.05) (Figure 1A). The number of genes that were transcribed into circRNAs was 1.9-fold higher than is expected by chance (p ≤ 0.0001). Using more stringent filtering (fold change ≥3 and FDR ≤1 × 10–5), we found 87 circRNA transcripts that were differentially expressed: 51 upregulated and 36 downregulated (Figure 1B; available upon request, online only, available at www.exphem.org). Based on fold-change difference (fold change ≥3, FDR ≤10–5) and putative biological significance (all selected circRNAs were bioinformatically shown to interact with miRs that are known to be dysregulated in CLL; in addition, circ_100251 originates from the ROR-1 gene known to be overexpressed in CLL, and circ_0001430 originates from a gene that codes only for noncoding RNAs, which we found to be interesting), we selected four circRNAs that were downregulated and two circRNAs that were upregulated and used qRT-PCR to validate the array results.Regardless of counts, clinical characteristics, and prognostic indicators in all six patients tested, the results were consistent with the results of the circRNA array (Figure 1C).Several authors have suggested that complementary binding of miRs to the single-stranded circRNA either inhibits miR-mediated regulatory activities (sponge effect) or enables the miRs to be kept as a "reservoir" until they are needed [9Wang K Sun Y Tao W Fei X Chang C Androgen receptor (AR) promotes clear cell renal cell carcinoma (ccRCC) migration and invasion via altering the circHIAT1/miR-195-5p/29a-3p/29c-3p/CDC42 signals.Cancer Lett. 2017; 394: 1-12Crossref PubMed Scopus (164) Google Scholar]. To model a possible "sponging" activity of circRNAs, we selected three circRNAs that were differentially expressed according to the array and constructed putative circRNA–miR–mRNA networks. Each network consists of one circRNA, five predicted miRs and mRNAs that are predicted to be regulated by at least one of these miRs (Figure 2A). Networks for circ_102680 and circ_001430 are provided in Supplementary Figures E1A and E2A (online only, available at www.exphem.org). One such example is circ_104424, which originates from the cyclin-dependent kinase 14 (CDK14) gene and is upregulated in CLL cells. MiR-516b-5p, miR-424-5p, miR-140-3p, miR15b-5p, and mR219a-1-3p were predicted to bind this circRNA. Collectively, these miRs regulate the expression of 1,463 mRNA transcripts, 1,014 of which are regulated by more than one miR (Figure 2A). Annotation analysis revealed several functions and pathways that are enriched with these mRNAs, including pathways in cancer, programed cell death, cell cycle, p53, mitogen-activated protein kinase (MAPK), and the wnt signaling pathway (Figure 2B). To exclude the impact of p53 mutation on circ_104424 expression, we analyzed this circRNA's expression with respect to the presence or absence of del17p. Circ_104424 was upregulated in patients with the 17p deletion (12-fold increase, p ≤ 0.0001) and in patients without the deletion (sevenfold increase, p ≤ 0.0001). In both cases the increased expression was statistically significant. The increase in circ_104424 expression was more prominent in the non-del17p group; However, this difference was not statistically significant, indicating that the del17p or p53 status of the cell does not have an impact on the expression level of circ_104424. Target pathway enrichment analyses for circ_102680 and circ_001430 are outlined in Supplementary Figures E1B and E2B). The six most relevant pathways/processes, in terms of statistical strength and biological significance, which were found to be enriched with circ_104424, are illustrated in detail in Figure 3.Figure 2(A) A network representing circRNA–miR–mRNA interactions based on the predicted target miRs of circ_104424 and their target mRNAs. The center circRNA, represented by a red diamond, is surrounded by a layer of 5 potentially targeted miRs, represented by light blue triangles, which is surrounded by another layer of potentially targeted mRNAs represented by gray rectangles. The potentially targeted mRNAs are displayed in two circles. The inner circle represents mRNAs that were found to be affected by more than one miR, and the outer circle represents mRNAs that were found to be affected by one miR only. (B) GOTERM BP (upper panel) and KEGG pathway (lower panel) enrichment analysis for all circ_104424 targeted genes (both inner and outer circle genes). Presented are the top 15 processes related to the circRNA. The numbers above the bars represent the amounts of genes/proteins potentially participating in each specific process or pathway.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 3Networks representing specific pathways/processes found to be affected by circ_104424. The center circRNA (circ_104424), represented by a red diamond, is surrounded by a layer of 5 potentially targeted miRs, represented by light blue triangles, which is surrounded by another layer of potentially targeted mRNAs represented by yellow (A, programmed cell death related, p ≤ 1.9 × 10–7 DAVID, GOTERM BP), green (B, cell cycle related, p ≤ 1.33 × 10–7, DAVID, GOTERM BP), pink fuchsia (C, Wnt signaling related, p ≤ 2.14 × 10–4, DAVID, GOTERM BP), orange (D, p53 signaling pathway related, p ≤ 4 × 10–5, DAVID, KEGG), pink (E, MAPK cascade related, p ≤ 0.02, DAVID, GOTERM BP), or purple (F, pathways in cancer related, p ≤ 9.3 × 10–21, WEBGESTALT, KEGG) rectangles. The potentially targeted mRNAs are displayed in two circles. The inner circle, if there, represents mRNAs that were found to be affected by more than one miR, and the outer circle represents mRNAs that were found to be affected by one miR only.View Large Image Figure ViewerDownload Hi-res image Download (PPT)The CDK14 protein is involved in the control of eukaryotic cell cycle via regulating pathways such as the Wnt signaling pathway [10Davidson G Niehrs C Emerging links between CDK cell cycle regulators and Wnt signaling.Trends Cell Biol. 2010; 20: 453-460Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. Our analysis indicates these same processes are enriched with circ_104424, suggesting that this circRNA, which is transcribed from the CDK14 gene, might act as an additional level of cell cycle regulation as does its gene of origin CDK14. Thus, circ_104424 and its parental gene may assist each other in the proper and accurate regulation of cell cycle-related pathways. The upregulated expression of circ_104424 could be a result of increased transcription of the CDK14 gene (as illustrated in Supplementary Figure E3, online only, available at www.exphem.org). Annotation analysis of the other two circRNAs tested highlighted similar pathways as well as the B-cell receptor activation/signaling known to play an imperative role in CLL pathobiology [11Rozovski U Harris DM Li P et al.Activation of the B-cell receptor successively activates NF-kappaB and STAT3 in chronic lymphocytic leukemia cells.Int J Cancer. 2017; 141: 2076-2081Crossref PubMed Scopus (15) Google Scholar] (Supplementary Figures E1 and E2).Previous studies had found that the mRNA and miR expression profiles of CLL cells distinguish them from normal B cells [12Calin GA Liu CG Sevignani C et al.MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias.Proc Natl Acad Sci USA. 2004; 101: 11755-11760Crossref PubMed Scopus (1146) Google Scholar,13Ferreira PG Jares P Rico D et al.Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia.Genome Res. 2014; 24: 212-226Crossref PubMed Scopus (138) Google Scholar]. Here we found that this unique transcriptome landscape also includes circRNAs. We found approximately a 90% increase in the number of genes that are transcribed into circRNAs in CLL cells compared with what is expected by chance alone. Others have observed global upregulation of the circRNA network in aging organisms [14Gruner H Cortés-López M Cooper DA Bauer M Miura P CircRNA accumulation in the aging mouse brain.Sci Rep. 2016; 6: 38907Crossref PubMed Scopus (207) Google Scholar]. As CLL is a disease of the elderly and the control B cells were derived from younger individuals, we cannot rule out that upregulation of circRNA transcription is merely a property of aging lymphocytes. The Pearson correlation coefficient, however, did not reveal a significant positive correlation (r = 0.05–0.37) between age of the CLL patients in this study and expression of the six circRNAs tested. Interestingly, these circRNA transcripts were differentially expressed across a wide range of samples with patients across all risk groups and disease characteristics, suggesting that the unique expression profile represents an early event possibly occurring at the premalignant cell of origin and thus possibly contributing to the activation of oncogenic pathways. Our analysis also revealed a complete shutdown of specific circRNAs in CLL cells. For example, of the top 10 differentially expressed circRNAs, eight were downregulated to undetectable levels in CLL cells. In CLL, as in many other cancers, DNA methylation inhibits transcriptional activity [15Cahill N Rosenquist R Uncovering the DNA methylome in chronic lymphocytic leukemia.Epigenetics. 2013; 8: 138-148Crossref PubMed Scopus (37) Google Scholar]. It is therefore possible that DNA methylation also inhibits the transcriptional activity of the circRNA transcripts. Downregulation of circRNAs may also result from errors in the back-splicing machinery in malignant tissues, from degradation of circRNAs by deregulated miRs, or from increased cell proliferation leading to a reduction in circRNA transcription.In conclusion, we found that the circRNA expression profile distinguishes CLL cells from healthy B cells. Although descriptive in nature, this study paves the way to understanding whether and how circRNA expression contributes to the pathogenesis of CLL. The role of noncoding RNA (ncRNAs) in the pathogenesis of human diseases was first discovered in chronic lymphocytic leukemia (CLL) [1Calin GA Dumitru CD Shimizu M et al.Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia.Proc Natl Acad Sci USA. 2002; 99: 15524-15529Crossref PubMed Scopus (4165) Google Scholar]. While trying to identify tumor suppressor genes located on chromosome 13q14, which is deleted in approximately 50% of patients, Calin et al. [1Calin GA Dumitru CD Shimizu M et al.Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia.Proc Natl Acad Sci USA. 2002; 99: 15524-15529Crossref PubMed Scopus (4165) Google Scholar] found that the minimal deleted region in patients with CLL and 13q14 deletion includes the coding sequence for microRNAs (miRs) miR-15a and miR-16-1. Since then, other studies have reported that dysregulation of the miR transcriptome is a hallmark of CLL cells [2Balatti V Pekarky Y Croce CM Role of microRNA in chronic lymphocytic leukemia onset and progression.J Hematol Oncol. 2015; 8: 12Crossref PubMed Scopus (52) Google Scholar]. The role of other ncRNAs in the pathobiology of CLL is less clear. Circular RNAs (circRNAs) constitute an additional class of ncRNA and are emerging as key members of the gene regulatory milieus. Originally, thought to represent an error in splicing activity and considered to occur in low abundance, circRNAs have recently been reported to be ubiquitously prevalent, forming approximately 10% of the human transcriptome [3Huang S Yang B Chen BJ et al.The emerging role of circular RNAs in transcriptome regulation.Genomics. 2017; 109: 401-407Crossref PubMed Scopus (116) Google Scholar]. In these newly identified circular ncRNAs, the 3′ and 5′ ends are joined by covalent bonds creating a circular stable structure. The cellular role of circRNAs is yet to be discovered, although circRNA–miR binding has been found to regulate transcriptome landscape and cellular function in normal and abnormal physiological processes [4Ebbesen KK Hansen TB Kjems J Insights into circular RNA biology.RNA Biol. 2017; 14: 1035-1045Crossref PubMed Scopus (282) Google Scholar]. For example, specific circRNAs have been reported to play an important role in the development and progression of different types of cancer [5Cui X Wang J Guo Z et al.Emerging function and potential diagnostic value of circular RNAs in cancer.Mol Cancer. 2018; 17: 123Crossref PubMed Scopus (114) Google Scholar]. Although upregulation of specific circRNAs has been documented [6Xia L Wu L Bao J et al.Circular RNA circ-CBFB promotes proliferation and inhibits apoptosis in chronic lymphocytic leukemia through regulating miR-607/FZD3/Wnt/beta-catenin pathway.Biochem Biophys Res Commun. 2018; 503: 385-390Crossref PubMed Scopus (73) Google Scholar, 7Wu Z Sun H Liu W et al.Circ-RPL15: a plasma circular RNA as novel oncogenic driver to promote progression of chronic lymphocytic leukemia.Leukemia. 2020; 34: 919-923Crossref PubMed Scopus (38) Google Scholar, 8Dahl M Daugaard I Andersen MS et al.Enzyme-free digital counting of endogenous circular RNA molecules in B-cell malignancies.Lab Invest. 2018; 98: 1657-1669Crossref PubMed Scopus (61) Google Scholar], the expression profile of circRNAs in CLL lymphocytes is unknown. However, because the expression profile of circRNAs is tissue specific and varies between different pathological conditions [5Cui X Wang J Guo Z et al.Emerging function and potential diagnostic value of circular RNAs in cancer.Mol Cancer. 2018; 17: 123Crossref PubMed Scopus (114) Google Scholar], we hypothesized that the expression profile of circRNAs is unique to CLL cells and that circRNAs contribute to the pathogenesis of this disease. MethodsPatient samples and B-cell separationPeripheral blood cells were obtained from 27 untreated CLL patients (18 males and 9 females) who were followed at The Davidoff Cancer Center, Petah Tikva, Israel. Available upon request (online only, available at www.exphem.org) summarizes the patients' physiological details. This research was approved by the Rabin Medical Center Institutional Review Board and Ethics Committee. Cells were fractionated using Lymphoprep (Axis-Shield Diagnostics, Dundee, Scotland), followed by magnetic separation with anti-CD19 microbeads. Normal B cells were used as the control.circRNA expression profile and data analysisRNA was extracted from the cells using TRI reagent, amplified and transcribed into fluorescently labeled cRNA and hybridized into the Arraystar Human circRNA Array version 2.0 (Arraystar, Rockville, MD, USA). Arraystar circRNA Array uses specific circular junction probes to accurately detect each circRNA, even in the presence of their linear counterparts. Quantile normalization was performed using the R-software Limma package and further data processing with GeneSpring V7 Software (Agilent-Technologies, Santa Clara, CA, USA). The Benjamini–Hochberg false discovery rate (FDR) method with a cutoff of 0.05 was used to correct for multiple comparisons.Quantitative real-time polymerase chain reactionTotal RNA was extracted using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. cDNA was synthesized using the High-Capacity cDNA RT kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems) and the Fast SYBR Green Master Mix (Applied Biosystems). Primers for qRT-PCR are listed in available upon request (online only, available at www.exphem.org). Relative expression levels were calculated using 2–ΔΔCt.Construction of circRNA–miR–mRNA networksWe used Arraystar's microRNA (miR) target prediction software TargetScan (http://www.targetscan.org/vert_72/) and miRanda (https://omictools.com/miranda-tool) to predict circRNA–miR binding. Only miRs that were identified as circRNA targets by all tools were considered as putative circRNA targets. MiR target genes were obtained by the integration of mirNET and mIR target. Networks of circRNA–miR–mRNA were created using the target network from STRING V11 and presented by Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to predict target gene functions by the DAVID (Database for Annotation, Visualization, and Integrated Discovery) and WEBGESTALT (WEB-based Gene Set Analysis Toolkit) tools. Patient samples and B-cell separationPeripheral blood cells were obtained from 27 untreated CLL patients (18 males and 9 females) who were followed at The Davidoff Cancer Center, Petah Tikva, Israel. Available upon request (online only, available at www.exphem.org) summarizes the patients' physiological details. This research was approved by the Rabin Medical Center Institutional Review Board and Ethics Committee. Cells were fractionated using Lymphoprep (Axis-Shield Diagnostics, Dundee, Scotland), followed by magnetic separation with anti-CD19 microbeads. Normal B cells were used as the control. Peripheral blood cells were obtained from 27 untreated CLL patients (18 males and 9 females) who were followed at The Davidoff Cancer Center, Petah Tikva, Israel. Available upon request (online only, available at www.exphem.org) summarizes the patients' physiological details. This research was approved by the Rabin Medical Center Institutional Review Board and Ethics Committee. Cells were fractionated using Lymphoprep (Axis-Shield Diagnostics, Dundee, Scotland), followed by magnetic separation with anti-CD19 microbeads. Normal B cells were used as the control. circRNA expression profile and data analysisRNA was extracted from the cells using TRI reagent, amplified and transcribed into fluorescently labeled cRNA and hybridized into the Arraystar Human circRNA Array version 2.0 (Arraystar, Rockville, MD, USA). Arraystar circRNA Array uses specific circular junction probes to accurately detect each circRNA, even in the presence of their linear counterparts. Quantile normalization was performed using the R-software Limma package and further data processing with GeneSpring V7 Software (Agilent-Technologies, Santa Clara, CA, USA). The Benjamini–Hochberg false discovery rate (FDR) method with a cutoff of 0.05 was used to correct for multiple comparisons. RNA was extracted from the cells using TRI reagent, amplified and transcribed into fluorescently labeled cRNA and hybridized into the Arraystar Human circRNA Array version 2.0 (Arraystar, Rockville, MD, USA). Arraystar circRNA Array uses specific circular junction probes to accurately detect each circRNA, even in the presence of their linear counterparts. Quantile normalization was performed using the R-software Limma package and further data processing with GeneSpring V7 Software (Agilent-Technologies, Santa Clara, CA, USA). The Benjamini–Hochberg false discovery rate (FDR) method with a cutoff of 0.05 was used to correct for multiple comparisons. Quantitative real-time polymerase chain reactionTotal RNA was extracted using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. cDNA was synthesized using the High-Capacity cDNA RT kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems) and the Fast SYBR Green Master Mix (Applied Biosystems). Primers for qRT-PCR are listed in available upon request (online only, available at www.exphem.org). Relative expression levels were calculated using 2–ΔΔCt. Total RNA was extracted using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. cDNA was synthesized using the High-Capacity cDNA RT kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems) and the Fast SYBR Green Master Mix (Applied Biosystems). Primers for qRT-PCR are listed in available upon request (online only, available at www.exphem.org). Relative expression levels were calculated using 2–ΔΔCt. Construction of circRNA–miR–mRNA networksWe used Arraystar's microRNA (miR) target prediction software TargetScan (http://www.targetscan.org/vert_72/) and miRanda (https://omictools.com/miranda-tool) to predict circRNA–miR binding. Only miRs that were identified as circRNA targets by all tools were considered as putative circRNA targets. MiR target genes were obtained by the integration of mirNET and mIR target. Networks of circRNA–miR–mRNA were created using the target network from STRING V11 and presented by Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to predict target gene functions by the DAVID (Database for Annotation, Visualization, and Integrated Discovery) and WEBGESTALT (WEB-based Gene Set Analysis Toolkit) tools. We used Arraystar's microRNA (miR) target prediction software TargetScan (http://www.targetscan.org/vert_72/) and miRanda (https://omictools.com/miranda-tool) to predict circRNA–miR binding. Only miRs that were identified as circRNA targets by all tools were considered as putative circRNA targets. MiR target genes were obtained by the integration of mirNET and mIR target. Networks of circRNA–miR–mRNA were created using the target network from STRING V11 and presented by Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to predict target gene functions by the DAVID (Database for Annotation, Visualization, and Integrated Discovery) and WEBGESTALT (WEB-based Gene Set Analysis Toolkit) tools. Results and DiscussionTo compare the circRNA profiles of CLL cells and normal B cells, we quantified the expression of 13,368 circRNA transcripts in CLL cells from 21 treatment-naïve patients and in normal B cells from eight healthy individuals. We found 529 circRNAs from 364 genes that were upregulated and 330 circRNAs from 228 genes that were downregulated in CLL cells (fold change ≥2 and false discovery rate [FDR] 0.05) (Figure 1A). The number of genes that were transcribed into circRNAs was 1.9-fold higher than is expected by chance (p ≤ 0.0001). Using more stringent filtering (fold change ≥3 and FDR ≤1 × 10–5), we found 87 circRNA transcripts that were differentially expressed: 51 upregulated and 36 downregulated (Figure 1B; available upon request, online only, available at www.exphem.org). Based on fold-change difference (fold change ≥3, FDR ≤10–5) and putative biological significance (all selected circRNAs were bioinformatically shown to interact with miRs that are known to be dysregulated in CLL; in addition, circ_100251 originates from the ROR-1 gene known to be overexpressed in CLL, and circ_0001430 originates from a gene that codes only for noncoding RNAs, which we found to be interesting), we selected four circRNAs that were downregulated and two circRNAs that were upregulated and used qRT-PCR to validate the array results.Regardless of counts, clinical characteristics, and prognostic indicators in all six patients tested, the results were consistent with the results of the circRNA array (Figure 1C).Several authors have suggested that complementary binding of miRs to the single-stranded circRNA either inhibits miR-mediated regulatory activities (sponge effect) or enables the miRs to be kept as a "reservoir" until they are needed [9Wang K Sun Y Tao W Fei X Chang C Androgen receptor (AR) promotes clear cell renal cell carcinoma (ccRCC) migration and invasion via altering the circHIAT1/miR-195-5p/29a-3p/29c-3p/CDC42 signals.Cancer Lett. 2017; 394: 1-12Crossref PubMed Scopus (164) Google Scholar]. To model a possible "sponging" activity of circRNAs, we selected three circRNAs that were differentially expressed according to the array and constructed putative circRNA–miR–mRNA networks. Each network consists of one circRNA, five predicted miRs and mRNAs that are predicted to be regulated by at least one of these miRs (Figure 2A). Networks for circ_102680 and circ_001430 are provided in Supplementary Figures E1A and E2A (online only, available at www.exphem.org). One such example is circ_104424, which originates from the cyclin-dependent kinase 14 (CDK14) gene and is upregulated in CLL cells. MiR-516b-5p, miR-424-5p, miR-140-3p, miR15b-5p, and mR219a-1-3p were predicted to bind this circRNA. Collectively, these miRs regulate the expression of 1,463 mRNA transcripts, 1,014 of which are regulated by more than one miR (Figure 2A). Annotation analysis revealed several functions and pathways that are enriched with these mRNAs, including pathways in cancer, programed cell death, cell cycle, p53, mitogen-activated protein kinase (MAPK), and the wnt signaling pathway (Figure 2B). To exclude the impact of p53 mutation on circ_104424 expression, we analyzed this circRNA's expression with respect to the presence or absence of del17p. Circ_104424 was upregulated in patients with the 17p deletion (12-fold increase, p ≤ 0.0001) and in patients without the deletion (sevenfold increase, p ≤ 0.0001). In both cases the increased expression was statistically significant. The increase in circ_104424 expression was more prominent in the non-del17p group; However, this difference was not statistically significant, indicating that the del17p or p53 status of the cell does not have an impact on the expression level of circ_104424. Target pathway enrichment analyses for circ_102680 and circ_001430 are outlined in Supplementary Figures E1B and E2B). The six most relevant pathways/processes, in terms of statistical strength and biological significance, which were found to be enriched with circ_104424, are illustrated in detail in Figure 3.Figure 3Networks representing specific pathways/processes found to be affected by circ_104424. The center circRNA (circ_104424), represented by a red diamond, is surrounded by a layer of 5 potentially targeted miRs, represented by light blue triangles, which is surrounded by another layer of potentially targeted mRNAs represented by yellow (A, programmed cell death related, p ≤ 1.9 × 10–7 DAVID, GOTERM BP), green (B, cell cycle related, p ≤ 1.33 × 10–7, DAVID, GOTERM BP), pink fuchsia (C, Wnt signaling related, p ≤ 2.14 × 10–4, DAVID, GOTERM BP), orange (D, p53 signaling pathway related, p ≤ 4 × 10–5, DAVID, KEGG), pink (E, MAPK cascade related, p ≤ 0.02, DAVID, GOTERM BP), or purple (F, pathways in cancer related, p ≤ 9.3 × 10–21, WEBGESTALT, KEGG) rectangles. The potentially targeted mRNAs are displayed in two circles. The inner circle, if there, represents mRNAs that were found to be affected by more than one miR, and the outer circle represents mRNAs that were found to be affected by one miR only.View Large Image Figure ViewerDownload Hi-res image Download (PPT)The CDK14 protein is involved in the control of eukaryotic cell cycle via regulating pathways such as the Wnt signaling pathway [10Davidson G Niehrs C Emerging links between CDK cell cycle regulators and Wnt signaling.Trends Cell Biol. 2010; 20: 453-460Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. Our analysis indicates these same processes are enriched with circ_104424, suggesting that this circRNA, which is transcribed from the CDK14 gene, might act as an additional level of cell cycle regulation as does its gene of origin CDK14. Thus, circ_104424 and its parental gene may assist each other in the proper and accurate regulation of cell cycle-related pathways. The upregulated expression of circ_104424 could be a result of increased transcription of the CDK14 gene (as illustrated in Supplementary Figure E3, online only, available at www.exphem.org). Annotation analysis of the other two circRNAs tested highlighted similar pathways as well as the B-cell receptor activation/signaling known to play an imperative role in CLL pathobiology [11Rozovski U Harris DM Li P et al.Activation of the B-cell receptor successively activates NF-kappaB and STAT3 in chronic lymphocytic leukemia cells.Int J Cancer. 2017; 141: 2076-2081Crossref PubMed Scopus (15) Google Scholar] (Supplementary Figures E1 and E2).Previous studies had found that the mRNA and miR expression profiles of CLL cells distinguish them from normal B cells [12Calin GA Liu CG Sevignani C et al.MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias.Proc Natl Acad Sci USA. 2004; 101: 11755-11760Crossref PubMed Scopus (1146) Google Scholar,13Ferreira PG Jares P Rico D et al.Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia.Genome Res. 2014; 24: 212-226Crossref PubMed Scopus (138) Google Scholar]. Here we found that this unique transcriptome landscape also includes circRNAs. We found approximately a 90% increase in the number of genes that are transcribed into circRNAs in CLL cells compared with what is expected by chance alone. Others have observed global upregulation of the circRNA network in aging organisms [14Gruner H Cortés-López M Cooper DA Bauer M Miura P CircRNA accumulation in the aging mouse brain.Sci Rep. 2016; 6: 38907Crossref PubMed Scopus (207) Google Scholar]. As CLL is a disease of the elderly and the control B cells were derived from younger individuals, we cannot rule out that upregulation of circRNA transcription is merely a property of aging lymphocytes. The Pearson correlation coefficient, however, did not reveal a significant positive correlation (r = 0.05–0.37) between age of the CLL patients in this study and expression of the six circRNAs tested. Interestingly, these circRNA transcripts were differentially expressed across a wide range of samples with patients across all risk groups and disease characteristics, suggesting that the unique expression profile represents an early event possibly occurring at the premalignant cell of origin and thus possibly contributing to the activation of oncogenic pathways. Our analysis also revealed a complete shutdown of specific circRNAs in CLL cells. For example, of the top 10 differentially expressed circRNAs, eight were downregulated to undetectable levels in CLL cells. In CLL, as in many other cancers, DNA methylation inhibits transcriptional activity [15Cahill N Rosenquist R Uncovering the DNA methylome in chronic lymphocytic leukemia.Epigenetics. 2013; 8: 138-148Crossref PubMed Scopus (37) Google Scholar]. It is therefore possible that DNA methylation also inhibits the transcriptional activity of the circRNA transcripts. Downregulation of circRNAs may also result from errors in the back-splicing machinery in malignant tissues, from degradation of circRNAs by deregulated miRs, or from increased cell proliferation leading to a reduction in circRNA transcription.In conclusion, we found that the circRNA expression profile distinguishes CLL cells from healthy B cells. Although descriptive in nature, this study paves the way to understanding whether and how circRNA expression contributes to the pathogenesis of CLL. To compare the circRNA profiles of CLL cells and normal B cells, we quantified the expression of 13,368 circRNA transcripts in CLL cells from 21 treatment-naïve patients and in normal B cells from eight healthy individuals. We found 529 circRNAs from 364 genes that were upregulated and 330 circRNAs from 228 genes that were downregulated in CLL cells (fold change ≥2 and false discovery rate [FDR] 0.05) (Figure 1A). The number of genes that were transcribed into circRNAs was 1.9-fold higher than is expected by chance (p ≤ 0.0001). Using more stringent filtering (fold change ≥3 and FDR ≤1 × 10–5), we found 87 circRNA transcripts that were differentially expressed: 51 upregulated and 36 downregulated (Figure 1B; available upon request, online only, available at www.exphem.org). Based on fold-change difference (fold change ≥3, FDR ≤10–5) and putative biological significance (all selected circRNAs were bioinformatically shown to interact with miRs that are known to be dysregulated in CLL; in addition, circ_100251 originates from the ROR-1 gene known to be overexpressed in CLL, and circ_0001430 originates from a gene that codes only for noncoding RNAs, which we found to be interesting), we selected four circRNAs that were downregulated and two circRNAs that were upregulated and used qRT-PCR to validate the array results. Regardless of counts, clinical characteristics, and prognostic indicators in all six patients tested, the results were consistent with the results of the circRNA array (Figure 1C). Several authors have suggested that complementary binding of miRs to the single-stranded circRNA either inhibits miR-mediated regulatory activities (sponge effect) or enables the miRs to be kept as a "reservoir" until they are needed [9Wang K Sun Y Tao W Fei X Chang C Androgen receptor (AR) promotes clear cell renal cell carcinoma (ccRCC) migration and invasion via altering the circHIAT1/miR-195-5p/29a-3p/29c-3p/CDC42 signals.Cancer Lett. 2017; 394: 1-12Crossref PubMed Scopus (164) Google Scholar]. To model a possible "sponging" activity of circRNAs, we selected three circRNAs that were differentially expressed according to the array and constructed putative circRNA–miR–mRNA networks. Each network consists of one circRNA, five predicted miRs and mRNAs that are predicted to be regulated by at least one of these miRs (Figure 2A). Networks for circ_102680 and circ_001430 are provided in Supplementary Figures E1A and E2A (online only, available at www.exphem.org). One such example is circ_104424, which originates from the cyclin-dependent kinase 14 (CDK14) gene and is upregulated in CLL cells. MiR-516b-5p, miR-424-5p, miR-140-3p, miR15b-5p, and mR219a-1-3p were predicted to bind this circRNA. Collectively, these miRs regulate the expression of 1,463 mRNA transcripts, 1,014 of which are regulated by more than one miR (Figure 2A). Annotation analysis revealed several functions and pathways that are enriched with these mRNAs, including pathways in cancer, programed cell death, cell cycle, p53, mitogen-activated protein kinase (MAPK), and the wnt signaling pathway (Figure 2B). To exclude the impact of p53 mutation on circ_104424 expression, we analyzed this circRNA's expression with respect to the presence or absence of del17p. Circ_104424 was upregulated in patients with the 17p deletion (12-fold increase, p ≤ 0.0001) and in patients without the deletion (sevenfold increase, p ≤ 0.0001). In both cases the increased expression was statistically significant. The increase in circ_104424 expression was more prominent in the non-del17p group; However, this difference was not statistically significant, indicating that the del17p or p53 status of the cell does not have an impact on the expression level of circ_104424. Target pathway enrichment analyses for circ_102680 and circ_001430 are outlined in Supplementary Figures E1B and E2B). The six most relevant pathways/processes, in terms of statistical strength and biological significance, which were found to be enriched with circ_104424, are illustrated in detail in Figure 3. The CDK14 protein is involved in the control of eukaryotic cell cycle via regulating pathways such as the Wnt signaling pathway [10Davidson G Niehrs C Emerging links between CDK cell cycle regulators and Wnt signaling.Trends Cell Biol. 2010; 20: 453-460Abstract Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. Our analysis indicates these same processes are enriched with circ_104424, suggesting that this circRNA, which is transcribed from the CDK14 gene, might act as an additional level of cell cycle regulation as does its gene of origin CDK14. Thus, circ_104424 and its parental gene may assist each other in the proper and accurate regulation of cell cycle-related pathways. The upregulated expression of circ_104424 could be a result of increased transcription of the CDK14 gene (as illustrated in Supplementary Figure E3, online only, available at www.exphem.org). Annotation analysis of the other two circRNAs tested highlighted similar pathways as well as the B-cell receptor activation/signaling known to play an imperative role in CLL pathobiology [11Rozovski U Harris DM Li P et al.Activation of the B-cell receptor successively activates NF-kappaB and STAT3 in chronic lymphocytic leukemia cells.Int J Cancer. 2017; 141: 2076-2081Crossref PubMed Scopus (15) Google Scholar] (Supplementary Figures E1 and E2). Previous studies had found that the mRNA and miR expression profiles of CLL cells distinguish them from normal B cells [12Calin GA Liu CG Sevignani C et al.MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias.Proc Natl Acad Sci USA. 2004; 101: 11755-11760Crossref PubMed Scopus (1146) Google Scholar,13Ferreira PG Jares P Rico D et al.Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia.Genome Res. 2014; 24: 212-226Crossref PubMed Scopus (138) Google Scholar]. Here we found that this unique transcriptome landscape also includes circRNAs. We found approximately a 90% increase in the number of genes that are transcribed into circRNAs in CLL cells compared with what is expected by chance alone. Others have observed global upregulation of the circRNA network in aging organisms [14Gruner H Cortés-López M Cooper DA Bauer M Miura P CircRNA accumulation in the aging mouse brain.Sci Rep. 2016; 6: 38907Crossref PubMed Scopus (207) Google Scholar]. As CLL is a disease of the elderly and the control B cells were derived from younger individuals, we cannot rule out that upregulation of circRNA transcription is merely a property of aging lymphocytes. The Pearson correlation coefficient, however, did not reveal a significant positive correlation (r = 0.05–0.37) between age of the CLL patients in this study and expression of the six circRNAs tested. Interestingly, these circRNA transcripts were differentially expressed across a wide range of samples with patients across all risk groups and disease characteristics, suggesting that the unique expression profile represents an early event possibly occurring at the premalignant cell of origin and thus possibly contributing to the activation of oncogenic pathways. Our analysis also revealed a complete shutdown of specific circRNAs in CLL cells. For example, of the top 10 differentially expressed circRNAs, eight were downregulated to undetectable levels in CLL cells. In CLL, as in many other cancers, DNA methylation inhibits transcriptional activity [15Cahill N Rosenquist R Uncovering the DNA methylome in chronic lymphocytic leukemia.Epigenetics. 2013; 8: 138-148Crossref PubMed Scopus (37) Google Scholar]. It is therefore possible that DNA methylation also inhibits the transcriptional activity of the circRNA transcripts. Downregulation of circRNAs may also result from errors in the back-splicing machinery in malignant tissues, from degradation of circRNAs by deregulated miRs, or from increased cell proliferation leading to a reduction in circRNA transcription. In conclusion, we found that the circRNA expression profile distinguishes CLL cells from healthy B cells. Although descriptive in nature, this study paves the way to understanding whether and how circRNA expression contributes to the pathogenesis of CLL. This study was supported by a grant from the CLL Global Research Foundation and by a grant from the Israel Cancer Association. Supplementary Data Download .pptx (2.27 MB) Help with pptx files Download .pptx (2.27 MB) Help with pptx files
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