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A.A. Mensah et al.
miRNA to its recognition sequence in the 3’ untranslated region of its target gene.10 A single miRNA can recognize multiple target genes and, conversely, different miRNAs can target an individual gene.11 Thus, in the context of cancer, miRNAs can intricately and markedly influence individual driver genes and entire signaling pathways cru- cial to the survival of cancer cells. Furthermore, a number of miRNAs have been shown to participate in a feedback loop with the protein product of their target gene.11
Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma that accounts for approximately 35-40% of all lymphoma cases.12 DLBCL frequently harbors mutations in chromatin-modifying enzymes indicating that pertur- bation of epigenetic regulation is an important trigger for B-cell transformation.13,14 A class of epigenetic drugs that has recently shown promising results in pre-clinical and clinical settings, and particularly in DLBCL, inhibits mem- bers of the bromodomain and extra-terminal domain (BET) protein family.15-25 In mammals, the BET family comprises four proteins, BRD2, BRD3, BRD4 and BRDT, which all share two highly conserved N-terminal bro- modomains (BRD) and a C-terminal extra-terminal (ET) domain. BET proteins specifically bind to acetylated lysine residues via their dual BRD motifs, acting as epige- netic readers of acetyl-lysine marks. They therefore con- stitute an important component of the write-read-erase model via which epigenetic information is interpreted by cells.17 BET inhibitors act by preventing the interaction of BRD4 with acetylated histones.26 Here we show direct and indirect regulation of miRNA expression in DLBCL by a BET inhibitor.
Methods
Cell lines and molecules
Established human cell lines derived from DLBCL were cul- tured according to recommended conditions. Two germinal-cen- ter B-cell type DLBCL (GCB-DLBCL) cell lines, DOHH-2 and OCI-LY-1, were cultured in Roswell Park Memorial Institute medium and Iscove's Modified Dulbecco's Medium, respective- ly. The activated B-cell–like DLBCL (ABC-DLBCL) cell lines SU- DHL-2 and HBL-1 were cultured in Roswell Park Memorial Institute medium. Cell lines were obtained as previously described,27 and their identity was authenticated by short tan- dem repeat DNA profiling (IDEXX BioResearch, Ludwigsburg, Germany). All media were supplemented with fetal bovine serum (10%; DOHH-2 and OCI-LY-1 or 20%; SU-DHL-2 and HBL-1), penicillin-streptomycin-neomycin (5,000 units peni- cillin, 5 mg streptomycin and 10 mg neomycin/mL, Sigma) and L-glutamine (1%). OTX015 (MK-8628, birabresib) was provided by Oncoethix (Lausanne, Switzerland).
In vivo xenograft model
The xenograft model used here has been described
elsewhere.15 Total RNA, previously extracted from these tumors, was used to analyze OTX015-mediated modulation of miRNA expression in vivo.
Western blotting analysis
Protein extractions, sodium dodecylsulfate polyacrylamide gel electrophoresis and immunoblotting were performed as previ- ously described.15 The antibodies used were anti-PRMT5 (A1520; NeoBiolab), anti-GAPDH (9131; Cell Signaling) and anti-BRD4 (A301-985A; Bethyl).
One-step quantitative reverse transcription - polymerase chain reaction
Total RNA was extracted from cells treated with dimethyl sul- foxide (DMSO) or OTX015 using TRIzol (Thermo Scientific, Lausanne, Switzerland). One-step quantitative reverse transcrip- tion - polymerase chain reaction (qRT-PCR) was performed as pre- viously described15 using 20 ng of RNA for each reaction. Forward and reverse primers used for quantification of PRMT5 mRNA were, respectively, 5’-TCTCATGGTTTCCCATCCTC-3’ and 5’- ACACAGATGGTTTGGCCTTC-3’. Quantification of GAPDH expression served as an endogenous control. GAPDH primer sequences were, 5’-CGACCACTTTGTCAAGCTCA-3’ (forward) and 5’-CCCTGTTGCTGTAGCCAAAT-3’ (reverse). Expression of GAPDH was verified to be stable between the analyzed groups.
MicroRNA expression profiling
Total RNA was extracted as previously described.15 miRNA expression profiling was performed on RNA from DLBCL cell lines treated with DMSO or OTX015 using the Agilent Human microRNA microarray v. 3 or Nanostring nCounter Human V3A miRNA Expression Assay Kits. Profiling was done on RNA extracted from untreated lymphoma cell lines27,28 using the Nanostring nCounter Human V2. All samples were processed as previously described.29,30 Profiling data are available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) database under the GEO project number GSE99208.
MicroRNA quantification with TaqMan microRNA assays
Profiling results for selected miRNAs were validated using the following TaqMan MicroRNA Assays (Applied Biosystems): hsa- miR-96-5p, assay ID: 000186; hsa-miR-92a-1-5p, assay ID: 002137, hsa-miR-21-3p, assay ID: 002438; hsa-miR-155-5p, assay ID: 002623; RNU6B, assay ID: 001093. Reverse transcription and quantitative PCR were performed using the TaqMan MicroRNA Reverse Transcription Kit and the TaqMan Universal PCR Master Mix according to the manufacturer’s instructions. Briefly, for each sample, 10 ng of total RNA was used for reverse transcription and 1.33 mL of the reverse transcription product was used in triplicate wells for the quantitative PCR (qPCR). All qPCR reactions were performed on an Applied Biosystems StepOnePlus System. Amplification of RNU6B served as a normalizing control for RNA quantity. Data were analyzed using the ΔΔCt method to obtain relative quantities. Expression of RNU6B was verified as stable between the analyzed groups.
Data mining
miRNA expression data obtained from each profiling platform were analyzed independently. For Agilent arrays, the hybridiza- tion signal values for the multiple probes were obtained using the Agilent Feature Extraction Software 10.7.3 (Agilent Technologies). For the Nanostring nCounter, raw expression data were log-trans- formed and normalized by the quantile method after application of manufacturer-supplied correction factors. For both platforms, differentially expressed miRNAs were defined using R/Bioconductor with the linear model for microarray data analysis (limma) with a contrast matrix for the comparisons of interest on the datasets filtered to exclude features below the detection threshold (defined for each sample by a cut-off corresponding to twice the standard deviation of negative control probes plus the means) in at least half of the samples. The transcripts bearing an absolute log-fold change greater than 0.2 and a P-value less than 0.05 at any experimental time point were defined as differentially expressed. Overlapping among lists was performed using the VENNY on-line tool.31 Experimentally validated transcript targets
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haematologica | 2018; 103(12)