ABSTRACT: Deregulation of the translational machinery is emerging as a critical contributor to lymphomagenesis. Various miRNA alterations have been identified in lymphoma, but their role in disrupting the cap-dependent translation regulation complex remains poorly understood. Here, we demonstrate the translation initiation factor, eIF4GII, as a direct target and major mediator of miR-520c-3p function through 3M-bM-^@M-^YUTR of eIF4GII mRNA. We established that elevated miR-520c-3p represses translation, initiates premature senescence and blocks cell proliferation in diffuse large B-cell lymphoma (DLBCL). Moreover, miR-520c-3p overexpression diminishes DLBCL cells colony formation and reduces tumor growth in a lymphoma xenograft mouse model. miR-520c-3p overexpressing cells display lowered eIF4GII levels. Consequently, downregulation of eIF4GII by siRNA induces cellular senescence, decreases cell proliferation and ability to form colonies. Our in vitro and in vivo findings we further validated in patient samples; DLBCL primary cells demonstrated low miR-520c-3p levels with reciprocally highly up-regulated eIF4GII protein expression. In contrast, normal donor B-cell lymphocytes had low levels of eIF4GII protein and elevated miR-520c-3p levels. Our results provide evidence that the tumor suppressor effect of miR-520c-3p is mediated through repression of cap-dependent translation while inducing senescence and that eIF4GII is a key effector of this anti-tumor activity. These findings may have implications for therapeutic interventions in patients with DLBCL. Human cervical carcinoma HeLa cells were transfected by using Oligofectamine (Invitrogen) with 50 nM small RNAs Pre-miR-520c-3p or control miRNA Pre-miR-Ctrl (Ambion). Linear sucrose gradient fractionation was performed on the transfected HeLa cells. Briefly, ~ 6 M-CM-^W 106 cells were incubated for 15 min with 100 mg/ml cycloheximide. Cells were lysed in cytoplasmic lysis buffer containing 20 mM Tris-HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, 0.3% IGEPAL CA-630, RNaseOUT (Invitrogen) and, protease inhibitor cocktail for 5 min on ice and centrifuged (10,000 M-CM-^W g, 10 min, 4M-BM-0C). Cytoplasmic extracts were loaded onto the 10M-bM-^@M-^S50% sucrose gradients and after centrifugation (Beckman SW41, 35,000 rpm, 3 h, 4M-BM-0C) the material was fractionated into eleven 1 ml aliquots labeled FR1-FR11 using a gradient fractionator (Brandel) and monitored by optical density measurement (A254). Total RNA was extracted from three biological replicates of each fraction numbered 1-11 as well as unfractionated cell pellets using Trizol (Invitrogen). Quality and quantity of the total RNA was checked with the Agilent 2100 bioanalyzer using RNA 6000 Nano chips. Total RNA samples were labeled using Illumina TotalPrep RNA Amplification Kit (Ambion; Austin, TX) 0.5g of total RNA was first converted into single-stranded cDNA with reverse transcriptase using an oligo-dT primer containing the T7 RNA polymerase promoter site and then copied to produce double-stranded cDNA molecules. The double stranded cDNA was cleaned and concentrated with the supplied columns and used in an overnight in-vitro transcription reaction where single-stranded RNA (cRNA) was generated and labeled by incorporation of biotin-16-UTP. A total of 0.75ug of this biotin-labeled cRNA was hybridized at 58 degrees C for 16 hours to Illumina's Sentrix Human HT-12, v3 Expression BeadChips (Illumina, San Diego, CA). Each BeadChip targets more than 25,000 annotated genes with more than 48,000 probes. Probes were designed using the RefSeq (Build 36.2, Rel 22) and the UniGene (Build 199) databases with an average of 15-fold redundancy. The arrays were washed, blocked and the biotin labeled cRNA detected by staining with streptavidin-Cy3. Arrays were scanned at a resolution of 0.8um using the Beadstation 500 X from Illumina. Data was extracted using the Illumina GenomeStudio software(v1.6.0). Any spots at or below the background were filtered out using an Illumina detection p-value of 0.02 and above. The natural log of all remaining scores was used to find the avg and std of each array and the z-score normalization was calculated and presented below. Z-score = (raw value - avg)/std.
