Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Mar 1.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2013 Jan 18;22(3):327–336. doi: 10.1158/1055-9965.EPI-12-1131-T

Targetome profiling, pathway analysis and genetic association study implicate miR-202 in lymphomagenesis

Aaron E Hoffman 1, Ran Liu 2,3, Alan Fu 3, Tongzhang Zheng 3, Frank Slack 4, Yong Zhu 3
PMCID: PMC3905613  NIHMSID: NIHMS436798  PMID: 23334589

Abstract

Background

miRNAs have been implicated in numerous tumorigenic pathways, and previous studies have associated miR-202 dysregulation with various cancer types, including follicular lymphoma.

Methods

The miR-202 targetome was identified by ribonucleoprotein immunoprecipitation-microarray (RIP-Chip), and functional interactions among identified targets were investigated using the Ingenuity Pathway Analysis tool. We also performed a population-based genetic association study of a polymorphism within the miR-202 stem-loop sequence and risk of non-Hodgkin lymphoma. In vitro gain-of-function experiments were further conducted to elucidate the functional significance of the variant.

Results

141 potential members of the miR-202 targetome were identified by a transcriptome-wide RIP-Chip assay. Functional interactions among identified targets suggested that miR-202 regulated genes are involved in biological pathways relevant for hematological function and cancer. Consistent with this, a genetic association analysis using human blood samples revealed a significant association between a germline mutation (rs12355840) in the miR-202 precursor sequence and follicular lymphoma (FL) risk. An in vitro functional assay further demonstrated that the variant allele resulted in diminished miR-202 levels, possibly by altering precursor processing efficiency.

Conclusions

Taken together, our findings suggest that miR-202 is involved in follicular lymphomagenesis.

Impact

These findings implicate miR-202 as a potential tumor suppressor in FL and warrant the investigation of miR-202 as a novel biomarker of FL risk.

Keywords: miR-202, Targetome, lymphomagenesis, follicular lymphoma, pathway analysis

Introduction

The 1993 discovery of lin-4 in C. elegans brought about a fundamental change in the understanding of how gene activity is regulated, helping to uncover an additional element of the central dogma of molecular biology. The 22-nucleotide lin-4 transcript, with its post-transcriptional gene regulatory capacity, became the first identified member in what would later be the microRNA (miRNA) family of small non-protein-coding RNAs (1). Since this initial discovery, more than 1,900 mature miRNAs (miRBase 18) have been experimentally verified in humans, regulating the activity of perhaps more than a third of all human genes (2, 3).

The role of miRNA in tumorigenesis has been extensively studied in recent years. For example, miRNAs are commonly located within fragile sites on chromosomes (4) and act as both oncogenes and tumor suppressors (5, 6). Further findings have shown that a global reduction in miRNA processing promotes carcinogenesis, and miRNA profile data have been successfully applied to classify tumors and predict prognosis in several cancer types (7-10). In addition, epidemiologic and experimental findings have implicated a role for miRNAs in virtually every human cancer. Previously, our group performed a survey of miRNA-related SNPs, followed by a genetic association study and functional analysis in breast cancer (11). We identified miR-196a-2 as a potential breast cancer-relevant miRNA, which is consistent with later findings implicating its tumorigenic relevance in other cancer types (12-14). While a miR-202-associated SNP was identified in our survey, it was not associated with breast cancer risk in our population (11). However, prediction algorithms such as TargetScan (15) suggested that miR-202 may influence genes involved in hematological function, such as IL10 and ABL2 (TargetScan release 5.2; June 2011). In addition, altered expression of miR-202 has been recently demonstrated in several cancer types such as breast (9), cervical (16), colorectal (17) and gastric tumors (18). Interestingly, a recent miRNA profiling experiment comparing FL tumor cells to follicular hyperplasia cells identified a 44-miRNA signature of FL, which included miR-202 (19).

Like many miRNAs, miR-202 is located within a chromosomal fragile site, in the subtelomeric region of chromosome 10, the deletion of which has been associated with endometrial (20) and brain tumors (21), as well as developmental issues in children, including hyperactivity (22) and speech delay (23). A recent study also found that miR-202 may negatively regulate the expression of the proto-oncogene, MYCN, resulting in the inhibition of neuroblastoma cell proliferation (24).

In the current study, we experimentally identified the miR-202 targetome using a RIP-Chip-based approach. Functional interactions between identified targets were then investigated for disease-relevance using the Ingenuity Pathway Analysis tool. We also performed a population-based genetic association analysis of a polymorphism within the miR-202 stem-loop sequence and risk of non-Hodgkin lymphoma. In vitro gain-of-function experiments were further conducted to elucidate the functional significance of the variant.

Materials and Methods

Cell culture and miRNA transfection

HeLa cells were cultured in (+)-glutamine RPMI 1640 supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin/streptomycin (Invitrogen, Carlsbad, CA). Oligonucleotide transfection efficiencies were determined by fluorescence microscopy using the Cy3 DS Transfection Control (Integrated DNA Technologies, Inc), and cells were transfected with either a miR-202 mimic or a scrambled negative control (Qiagen) using the Lipofectamine™ RNAiMAX transfection reagent (Invitrogen), according to the manufacturer's protocol. Briefly, 120 pmol of miR-202 mimic or negative control was mixed with 20 μl of RNAiMAX reagent in 2 ml of OPTI-MEM (Invitrogen). The complex was then added into a 100mm dish and incubated for 20 min at room temperature. Approximately 4 million cells were then seeded into the dish to a total volume of 10 ml and incubated for 12 hours at 37°C before harvesting.

RIP-Chip analysis for miR-202 target identification

Ribonucleoprotein IP was performed using the RIP-Assay kit for microRNA (MBL) according to the protocol described by the manufacturer. Briefly, anti-EIF2C2/Ago2 monoclonal antibody (Novus Biologicals, LLC) was incubated with Protein G plus agarose beads (Pierce) at 4°C overnight to prepare antibody-immobilized beads. 20 million cells were harvested and washed four times with ice-cold DEPC-treated PBS. The cell pellet was lysed with 500 μl of lysis buffer and the supernatant was incubated with Protein G agarose beads without antibody to reduce nonspecific adsorption. The cell lysate was then transferred into a tube containing antibody-immobilized Protein G agarose beads and incubated for 3 hours at 4°C. The complex was washed five times with wash buffer prior to total RNA isolation. Mouse IgG1 isotype control (Novus Biologicals, LLC) was used as a negative control for the IP procedure. In order to confirm proper functioning of the RIP assay, an aliquot of precleared cell lysate, post-IP beads, and the flow-through fraction of antibody-immobilized Protein G agarose beads-RNP complexes were used for analysis of Ago2 quantity by Western blot. For use in the Western blot, mouse anti-EIF2C2/Ago2 monoclonal antibody was diluted 1:500 and chicken anti-mouse IgG conjugated with HRP was diluted 1:2000. SuperSignal West Pico Chemiluminescent Substrate (ThermoScientific, Rockford, USA) was used for visualization.

Genome-wide expression levels were analyzed in total RNA samples from total cell lysate and antibody-immobilized Protein G agarose beads-RNP complexes from cells transfected with miR-202 mimic or negative control using the Agilent 44K 60-mer human whole-genome microarray. Signal hybridization and scans were performed by MOGene, LC (St Louis, MO). The normalized signal intensities of probes from RNP-bead complexes (IP fraction) were divided by the signal intensities from total cell lysate (pre-IP fraction) in miR-202 mimic-transfected cells. Transcripts were identified as members of the global miRNA targetome if they exhibited an enrichment fold change ≥ 2.0. From this list, IP to pre-IP signal intensity ratios in miR-202 mimic-transfected cells were divided by IP to pre-IP signal intensity ratios in NC cells. Transcripts exhibiting normalized signal ratios of ≥ 1.5 were considered to be bound with miR-202 in the RNA-induced silencing complex (RISC), and therefore potential direct miR-202 targets. All microarray data were uploaded to the Gene Expression Omnibus database (accession # GSE42981).

Target screening

Enriched transcripts were screened for potential 3′ UTR miR-202 binding sites using four miRNA target prediction algorithms: miRanda (25), TargetScan/TargetScanS (26), PITA (27), and RNAhybrid (28). Prospective binding sites were predicted based on the number of base pair matches to the 5′ miRNA seed region, the degree of compensatory 3′ non-seed matches, and the number and nature of mismatched pairs (miRanda, TargetScan/TargetScanS, and PITA), as well as thermodynamic stability (miRanda and RNAhybrid).

Luciferase reporter assay

The 3′UTRs of two randomly selected target genes that have been cloned into pLightSwitch_3UTR GoClone vectors were purchased from Switchgear Genomics to confirm direct target binding. HeLa cells were seeded, in triplicate, onto a 96-well plate with a concentration of 10,000 cells/well in 100 μl of RPMI 1640 medium with 10% FBS and incubated for 24 hours. Cells were then co-transfected with a miR-202 mimic or a negative control and the GoClone vector (100 ng vector mixed with 1.5 pmol of miR-202 mimic or negative control in 10 μl of OPTI-MEM + 0.25 μl Lipofectamine 2000 transfection reagent (Invitrogen) in 10 μl of OPTI-MEM, followed by 20 minutes of incubation). 20 μl of transfection mixture and 80 μl of antibiotics-free RPMI1640 media were added into the 96-well plate and incubated at 37°C and 5% CO2 for 24 hours. The LightSwitch Assay System (Switchgear Genomics) was used for luciferase signal detection. After a 30 minute incubation period, the signal from each well was read in a GLOMAX 96 Microplate Luminometer (Promega), and a ratio of luciferase signal intensities (negative control:miR-202) was calculated for each construct.

Network analysis

miR-202 targets identified by RIP-chip were investigated for functional interrelatedness using the Ingenuity Pathway Analysis software tool (Ingenuity Systems). This tool scans the set of input genes to identify networks using information in the Ingenuity Pathways Knowledge Base, a manually curated database of functional interactions previously identified in peer-reviewed publications (29). A Fisher's exact test based on the hypergeometric distribution was then done for each identified network to determine the likelihood of obtaining at least the same number of interrelated molecules by chance as actually exists in the input gene set.

NHL study population

The study population, which consists of female residents of Connecticut, has been described in detail elsewhere (30). Briefly, 455 cases were identified through Yale Cancer Center's Rapid Case Ascertainment (RCA) between 1996 and 2000 as having incident, histologically-confirmed NHL (ICD-O, M-9590-9642, 9690-9701, 9740-9750). 527 Population-based controls were identified either through random digit dialing (for those younger than age 65) or through Health Care Financing Administration files (for controls 65 or older). Controls were frequency matched to cases on age (+/− five years) by occasionally adjusting the number of controls selected from each five year age strata. Participation rates were: 72% for cases, 69% for RDD controls, and 47% for controls identified by health care financing records. Tumor specimens were classified according to the Revised European-American Lymphoma (REAL) system using the original pathology slides obtained for each patient. Further information on population characteristics, including demographic data and case pathology distributions, are presented in Supplementary Table 1. All participation was voluntary, and the study was approved by the Institutional Review Boards at Yale University, the Connecticut Department of Public Health, and the National Cancer Institute. After obtaining written informed consent, those who agreed to participate were interviewed by trained study personnel either at the subject's home or at a mutually agreed upon location. All participants completed a questionnaire containing information on demographic characteristics, family history of cancer, past medical conditions and medication use, occupation, and lifestyle factors such as diet and smoking status. Following the interview, each participant provided a 10 ml peripheral blood sample from which genomic DNA was isolated.

Genotyping of SNP rs12355840 in the stem-loop of miR-202

The SNP (rs12355840) was identified using the miR-202 sequence and location data in the miRBase database (http://www.mirbase.org/) aligned with the SNP locations compiled in the HapMap database (HapMap genome browser release 22; http://www.hapmap.org/cgi-perl/gbrowse/hapmap_B36/). Genotyping was performed at Yale University's W.M. Keck Foundation Biotechnology Research Laboratory using the Sequenom MassARRAY multiplex genotyping platform (Sequenom, Inc., San Diego, CA) according to the manufacturer's protocol. Duplicate samples from 100 study subjects and 40 replicate samples from each of two blood donors were interspersed throughout each batch, and the concordance rates for QC samples was >99%. All genotyping scores, including quality control data, were re-checked by different laboratory personnel and the assay accuracy was confirmed. The secondary structure of miR-202 was predicted using the Vienna RNAfold prediction algorithm(31).

Statistical analysis of genetic association

All statistical analyses were performed using the SAS statistical software, version 9.1 (SAS Institute, Cary, NC), unless otherwise noted. For the case-control analyses, the allelic distribution of the SNP was tested in control subjects by goodness-of-fit Chi-square for compliance with Hardy-Weinberg equilibrium (HWE), and no departure from equilibrium was detected (P=0.930). Odds ratios and 95% confidence intervals were determined by unconditional multivariate logistic regression, including the following covariates: age, race, and family history of cancer in a first-degree relative.

Construction of the precursor miR-202 expression vector

Pre-mir-202-G and pre-mir-202-A constructs, consisting of the precursor miRNAs and a flanking region (299 bp on the left and 269 bp on the right), were synthesized using genomic DNA from individuals known to be homozygous for either the G or A allele of SNP rs12355840. Each construct was cloned into a pMIRNA1 lentivector containing a green fluorescent protein marker (System Biosciences). Vectors were then sequenced to verify the accuracy of the inserts and subsequently transfected into HeLa cells using the Lipofectamine 2000 transfection reagent (Invitrogen). Transfection efficiency was verified by fluorescence microscopy.

RNA isolation and miR-202 detection

Total RNA from precursor mir-202-G- and mir-202-A-transfected HeLa cells were isolated using the miRNeasy Mini Kit (QIAGEN) with on-column DNA digestion. Sequences for the forward and reverse primers used to detect mir-202 precursors were 5′-CCTCCCAGGCTCACGAGGCT-3′ and 5′-GGTGCAGGTGCACTGGTGCA-3′, respectively. Relative RNA abundance with respect to untransfected HeLa cells was assessed using the 2−ΔΔCt method with RNA content normalized to the housing keeping gene HPRT1. To determine levels of mature miRNA, polyadenylated mature miRNA sequences were first generated and converted to cDNA using the NCode miRNA First-Strand cDNA Synthesis Kit (Invitrogen). The cDNA was then amplified using a custom miR-202-specific forward primer (5′-AGAGGTATAGGGCATGGGAA-3′) and a universal reverse primer targeting on the polyadenylated region of the miRNA. Mature miRNA levels relative to untransfected HeLa cells were assessed using the 2−ΔΔCt method with normalization to miR-16. In order to more accurately quantify the effect of the A allele on mature miR-202 expression, mature miR-202 levels were additionally normalized to levels of precursor mir-202 using the 2−ΔΔCt method, where ΔΔCt = (Ctpre-mir-202 – CtHPRT1) – (CtmiR-202 – CtmiR-16). All qPCR reactions were performed in triplicate on an ABI 7500 Fast Real-Time PCR instrument (Applied Biosystems) using the QuantiFast SYBR Green PCR Kit (QIAGEN).

Luciferase target confirmation in Farage cells

Confirmation of miR-202 targets in the Farage lymphoma cell line was performed using the two luciferase-3′ UTR constructs previously used for target-confirmation in HeLa cells. Farage cells were transferred to a 96-well plate at a concentration of approximately 25,000 cells/well in 100 μl (+)-glutamine RPMI 1640 medium with 10% FBS. For each well, cells were treated with 100 ng of the GoClone vectors premixed with 0.6 μl of Lipofectamine LTX and 0.2 μl of PLUS Reagent (Invitrogen) stock solutions in OPTI-MEM followed by a 24-hour incubation period at 37°C and 5% CO2. After 24 hours, cells were treated in duplicate with 20 pmol of either the miScript Inhibitor Negative Control or the miScript miRNA Inhibitor Anti-hsa-miR-202 (QIAGEN), both premixed with 0.2 μL RNAiMAX transfection reagent (Invitrogen) in OPTI-MEM. Following treatment, cells were incubated for an additional 24 hours prior to evaluation in the luciferase assay, as described above, and an anti-miR-202 inhibitor:negative control luciferase signal intensity ratio was calculated for each gene.

Results

miR-202 targetome identification by RIP-Chip assay

Since miRNA function is mediated by argonaute (Ago) proteins in the RNA-induced silencing complex (RISC), co-immunoprecipitation of Ago with miRNA/mRNA complexes can be used to identify probable direct miRNA targets. First, we used an anti-Ago2 antibody to isolate global miRNA targets from HeLa cells (low expression of miR-202), which we identified using a genome-wide comparative hybridization microarray. Transcripts which were significantly enriched in the Ago2 antibody-treated fraction relative to total cell lysate (≥ 2.0x relative abundance) in cells treated with a miR-202 mimic were considered potential miRNA targets (N=857). These transcripts were then compared in miR-202 mimic-treated cells treated and cells treated with a scrambled double-stranded negative control to enrich for direct miR-202 targets. As a previous RT-qPCR assay revealed a low endogenous level of the mature miR-202 species in HeLa cells (data not shown), the introduction of miR-202 mimics allowed us to investigate miR-202 target-binding with minimal loss of assay sensitivity resulting from background miR-202 expression. Transfection efficiencies and Ago2 Western blot results showing significant Ago2 enrichment are presented in Figs. 1A and 1B, respectively. Transcripts which were enriched by 50% or greater in miR-202 mimic-treated cells relative to the negative control were considered potential direct miR-202 targets. In all, 141 unique transcripts from 216 probes were identified as potential members of the miR-202 targetome (Supplementary Table 2).

Figure 1. miR-202 Targetome identification using the RIP-Chip assay and validation with a luciferase reporter assay.

Figure 1

A, Transfection efficiencies were determined using a Cy3 DS transfection control. HeLa cells transfected with CY3 DS were screened using light microscopy (left panel) and fluorescence microscopy with a TRITC filter (right panel). The same transfection conditions were then used for miR-202 mimic and negative control (NC). B, An aliquot of post-IP beads, precleared cell lysate, and the flow-through fraction of the antibody-immobilized Protein G agarose beads-RNP complex from miR-202 mimic or negative control treated cells, and the equivalent from the mouse isotype IgG control were used in the analysis of Ago2 levels. C, A luciferase reporter assay for the 3′UTR of the RIP-chip-identified genes HAS2 and FAM135A was performed in order to validate these genes as direct miR-202 targets. Transfection of miR-202 mimic results in decreased signal intensity values relative to negative control.

Validation of miR-202 target genes by luciferase reporter assay

To confirm that the RIP-Chip approach provides valid candidates for the miR-202 targetome, we selected, at random, HAS2 and FAM135A, for further validation using a 3′UTR luciferase reporter assay. Fig. 1C shows the luciferase signal results from co-transfections of 3′UTR constructs of each gene with the miR-202 mimic or negative control. The expression of both luciferase-3′UTR constructs were significantly reduced when co-transfected with the miR-202 mimic, with a greater reduction seen for HAS2 (6.1-fold and 3.0-fold for HAS2 and FAM135A, respectively). Based on a bioinformatics search of both genes using the TargetScan miRNA target prediction tool (Release 5.2), HAS2 harbors an 8-mer site matched with the complementary sequence of the miR-202 seed region, while FAM135A carries a 7-mer site for the miR-202 seed sequence.

Interrogation of potential miR-202 targets using miRNA target prediction algorithms

137 out of 141 potential targets were screened for prospective 3′ UTR miR-202 binding sites. 4 transcripts (BBIP1, LOC100389602, LOC100506305, and LOC387895) could not be recognized by one or more databases and were thus excluded from the analysis. 92 of 137 (69%) of screened transcripts were identified as having miR-202 binding sites by 2 or more prediction algorithms based on the degree of base pairing complementarity to the seed region, the number of compensatory 3′ non-seed matches, and the thermodynamic stability of the predicted miR-202-mRNA hybrid. Fig. 2 shows the distribution of positive target predictions among the 137 screened transcripts. The number of positive predictions per transcript is listed in Supplementary Table 3.

Figure 2. Distribution of positive target predictions among identified potential miR-202 targets.

Figure 2

141 transcripts found to be enriched in the RIP-Chip assay were interrogated for 3′ UTR miR-202 binding sites using the miRanda, TargetScan/TargetScanS, PITA, and RNAhybrid miRNA target prediction algorithms. 92 of 137 (69%) screened transcripts were identified as having miR-202 binding sites by 2 or more prediction algorithms based on the degree of base pairing complementarity to the seed region, the number of compensatory 3′ non-seed matches, and the thermodynamic stability of the predicted miR-202-mRNA hybrid.

Lymphoma-related interaction network formed by identified miR-202 targets

The 141 identified transcripts comprising the miR-202 targetome were investigated for functional interrelatedness using the Ingenuity Pathway Analysis (IPA) software. The top interaction network identified using this method was designated by IPA as having relevance for “Hematological System Development and Function” (P=1.0E−46; Fig. 3) and the top disease associated with the transcripts was cancer (P=1.23E−4–4.98E−2). Functional descriptors assigned by the Ingenuity Knowledge Database to the molecules in the top network included: “hematological neoplasia”, “differentiation of lymphocytes”, “proliferation of hematopoietic cells”, and “lymphomagenesis”. Of the 35 genes, in the top network, 22 (B2M, CCL7, CRY1, ERCC8, FABP5, FUCA1, HAS2, HMGA2, HSPA14, IRF9, NKIRAS1, NLRP2, PFN2, RHEBL1, RIPK2, RPA2, RPS6KA5, SACS, SDC4, SLC5A5, SOCS1, and ZNF675) were identified by our RIP-chip assay as potential direct targets of miR-202. The full list of genes contained in the network, as well as notes on their relevance for lymphomagenesis, can be found in the Supplementary material (Supplementary Table 4).

Figure 3. Top interaction network formed by identified miR-202 targets and functionally related molecules.

Figure 3

The Ingenuity Pathway Analysis (IPA) software designed this network as having relevance for “Hematological System Development and Function” (P=1.0E−46). Functional descriptors assigned by the Ingenuity Knowledge Database to the molecules in this network included: “hematological neoplasia”, “differentiation of lymphocytes”, “proliferation of hematopoietic cells”, and “lymphomagenesis”. Of the 35 genes in this network, 22 were identified by our RIP-chip assay as potential direct targets of miR-202 (highlighted in gray).

Population-based genetic association

We further investigated miR-202′s potential relevance in lymphomagenesis using human samples. A previous in silico analysis identified a SNP (rs12355840) located within the precursor region of miR-202 (11), which we genotyped in a population-based case-control study of non-Hodgkin lymphoma (NHL) conducted in Connecticut (N=455 cases; 527 controls). Selected participant demographics and case pathology information for this population are presented in Supplementary Table 1. After stratification by NHL subtype, an unconditional logistic regression analysis showed that the variant alleles were strongly associated with follicular lymphoma risk (PTREND=0.006) (Table 1). Specifically, individuals harboring a single variant allele at this locus had a significantly elevated risk of FL (OR=1.77, 95% CI: 1.12-2.79; P=0.014), and the magnitude of the association was even stronger among individuals with two copies of the variant allele, although this association did not reach statistical significance due to the small number of individuals with the homozygous variant genotype (OR=2.62, 95%CI: 0.79-8.96; P=0.115). Harboring one or both variant alleles (i.e., assuming a dominant model) was also associated with a significantly elevated risk of FL (OR=1.83, 95% CI: 1.17-2.85; P=0.008). The full genotyping results for all subtypes can be found in Supplemental Table 5.

Table 1. Association of rs12355840 in miR-202 with follicular lymphoma risk.

Genotype Controls (%) Cases (%) Multivariate OR*
hsa-mir-202 AA 361 (70.4) 59 (57.8) Ref.
rs12355840 GA 139 (27.1) 39 (38.2) 1.77 (1.12-2.79)
GG 13 (2.5) 4 (3.9) 2.66 (0.79-8.96)
GA+GG 152 (29.6) 43 (42.2) 1.83 (1.17-2.85)
Trend P = 0.006
*

Adjusted for age, race, and family history of cancer

Functional effect of SNP rs12355840 on miR-202 levels in vitro

As SNP rs12355840 is located within the stem-loop sequence of the miR-202 precursor (Fig. 4), it may influence miR-202 levels in vivo by interfering with the formation of the secondary structure and/or affecting the processing of the pre-miRNA to its mature form. To test this hypothesis, precursor miR-202 expression vectors with either the G or A allele were transfected into HeLa cells, and levels of both precursor and mature miR-202 were quantified. Relative to the negative control, both pre-miR-202-G-transfected and pre-miR-202-A-transfected cells had approximately equal levels of the precursor miRNA (18.9- and 17.5-fold increase, respectively). In contrast, mature miR-202 levels were 6.0 times higher in pre-miR-202-A-transfected cells relative to the negative control but were approximately the same between pre-miR-202-G-transfected cells and the negative control (Fig. 5A). To further characterize the effect of the variant allele on pre-miR-202 processing, we conducted a second analysis in which mature miR-202 levels were normalized to precursor miR-202 levels. Based on equal quantities of precursor miRNA, pre-mir-202-A-transfected cells had 7.4 times more mature miRNA than pre-mir-202-G-transfected cells, suggesting that SNP rs12355840 influences processing of pre-miR-202 into the mature effector miRNA (Fig. 5B).

Figure 4. Schematic of secondary structure for miR-202 harboring a germline mutation rs12355840.

Figure 4

The structure image was adapted from the Vienna RNA secondary structure prediction algorithm (http://www.tbi.univie.ac.at/RNA/).

Figure 5. Functional assay to measure rs12355840's effect on pre-miR-202 processing efficiency and target validation in a lymphoma cell line.

Figure 5

A, Normalized pre-miR-202 levels were approximately the same between pre-miR-202-G- and pre-miR-202-A-transfected HeLa cells. In contrast, levels of mature miR-202 were several times higher in pre-miR-202-A-transfected cells compared to pre-miR-202-G transfected cells. B, Quantification of miR-202 levels following normalization to pre-miR-202 levels. The 7.4-fold increase in mature miR-202 levels in pre-miR-202-A-transfected cells relative to pre-miR-202-G-transfected cells is attributable to molecular processing events independent of initial precursor levels. These data indicate that the variant G allele may hinder the in vitro processing of precursor miR-202 transcripts into the mature form. C, Farage cells were transfected with vectors encoding luciferase fused to the 3′ UTR of HAS2 or FAM135A and either an anti-miR-202 or negative control (NC). Inhibition of miR-202 results in higher relative signal intensities for cells transfected with either 3′-UTR construct relative to negative control.

Luciferase miR-202 target confirmation in the Farage lymphoma cell line

Real-time RT-PCR revealed endogenous miR-202 levels to be 3.5-fold and 3.4-fold higher in Farage and Toledo B lymphoma cell lines, respectively, compared to levels in HeLa cells (data not shown). In order to validate our assumption that miR-202 targets are conserved between HeLa and B lymphocytes and investigate the endogenous functionality of miR-202 in lymphoma, we reaffirmed the target status of HAS2 and FAM135A in the Farage lymphoma cell line using luciferase-3′ UTR constructs and anti-miR-202 inhibitors. As expected, inhibiting miR-202 resulted in an increase in luciferase signal intensity for both HAS2 (2.5-fold) and FAM135A (3.1-fold) relative to negative controls (Fig. 5C).

Discussion

Our RIP-Chip analysis of direct miR-202 targets identified several transcripts with potential relevance for lymphoma-related processes, and the gene interaction network most strongly associated with the potential targets contained several molecules with known involvement in lymphomagenesis. Examples include: suppressor of cytokine signaling (SOCS1), a tumor suppressor (32) and potential miR-202 target that is epigenetically silenced in some B-cell lymphomas (33), and B2M, a subunit required for major histocompatibility complex (MHC) class I assembly that is frequently mutated in NHL (34, 35). Perhaps most interestingly, DICER1 was identified as a potential miR-202 target. According to TargetScan, the DICER1 3′UTR contains three conserved miR-202 binding sites. This protein is essential for the biogenesis and proper function of all miRNAs, and thus has widespread biological importance (36). Haploinsufficiency of DICER1 has been linked to susceptibility to multiple nonhematopoietic tumors (37, 38). However, in B cells, deletion of DICER1 results in suppression of lymphomagenesis in vivo, and inhibition of DICER1 in existing B-cell lymphomas results in cell death via apoptosis, indicating that DICER1 is required for B-cell lymphoma development and survival (39).

Another important miR-202 target is the cell cycle regulatory element and proto-oncogene SKP2. This protein regulates the G1-S transition and encourages cell proliferation through the degradation of the cyclin-dependent kinase inhibitor p27(40). SKP2 was shown to be causally involved in lymphomagenesis(41). In addition, inhibition of SKP2 in lymphoma cells leads to decreased growth and induces apoptosis, suggesting that SKP2 may be a potential target for therapeutic intervention in lymphoma patients(42). Indeed, bortezomib, which operates in part by degrading SKP2(43), was recently approved by the FDA for the treatment of refractory multiple myeloma and mantle cell lymphoma(44).

The results of our network-based analysis were consistent with the finding of a significant association between a SNP within the miR-202 stem-loop sequence and lymphoma in our NHL study population. Altogether, these finding suggests that rs12355840 may represent a novel risk biomarker for follicular lymphoma, and implicate miR-202 in follicular lymphomagenesis. Interestingly, the risk allele for rs12355840 (G) in our population-based genetic association analysis was shown to be associated with diminished pre-miR-202 processing capacity, and thus reduced mature miR-202 levels. Since both target genes DICER1 and SKP2 are lymphoma related, individuals harboring the risk allele for rs12355840 may have increased levels of both of these genes, which, in both cases, would be consistent with increased lymphoma risk. Thus, miR-202 may operate as a tumor suppressor in FL. However, an important limitation of our study was the lack RNA samples for the case-control population. Since only DNA was collected for this study, we are unable to directly correlate genotype to miRNA expression or processing directly, and are thus reliant on in vitro analyses.

So far, very few studies have investigated the role of miRNAs in lymphomagenesis, but there are a handful of previous observations with findings similar to the results of our association analysis. For example, the genomic region harboring miR-15a and miR-16 has been found to be commonly deleted in B-cell chronic lymphocytic leukemias (45), while a polycistronic miRNA cluster, miR-17–92, was found to be significantly amplified in B-cell lymphomas (46). In addition, miR-21 induction has been found to be sufficient to cause lymphoma in mouse models (6). Perhaps most the most relevant analysis comes from a recent miRNA profiling experiment comparing FL tumor cells to follicular hyperplasia (19). These investigators describe a FL signature comprised of three groups of miRNAs: 1) increased in FL; 2) increased in most FL, but decreased in a subset of FL; and 3) decreased in FL. miR-202 was one of only 11 miRNAs belonging to Group 3, lending further support to our hypothesis that miR-202 may operate as a tumor suppressor in FL.

It should be noted that, apart from FL, our genetic association data were not significant for any other lymphoma subtype, a pattern which is consistent with the results from previous NHL genetic association analyses, including two recent genome-wide association studies (47, 48) and a large pooled case-control analysis (49), all of which noted their strongest associations in the FL subtype. This indication that FL may have a stronger genetic component than exists for other NHL subtypes is of particular interest, given a recent examination of etiologic heterogeneity among NHL subtypes, which identified no significant risk factors which were unique to FL alone (50).

As the RIP-Chip assay was conducted using HeLa cells, our analysis was limited to only those genes with detectable levels of endogenous expression in this cell model. As such, it is certain that a portion of the miR-202 targetome remains unelucidated. Moreover, HeLa cells may have a gene expression program that differs from that which would be seen in lymphoma. Nonetheless, the fact that we were able to reaffirm HAS2 and FAM135A as miR-202 targets in Farage cells lends credence to the supposition that miR-202 targets are conserved between HeLa cells and B lymphocytes. Also, as a subset of identified targets has previously been implicated in B cell lymphomagenesis and hematopoiesis, their expression in B lymphocytes is almost certain at some stage of the lymphomagenic or hematopoietic process. Furthermore, it is evident that miR-202 has endogenous gene silencing activity in B lymphocytes, as implicated by our finding that inhibition of miR-202 activity resulted in increased expression of miR-202 targets in the Farage lymphoma cell line. Therefore, it is likely that miR-202 modulates the activity of at least the lymphoma-relevant targets identified in HeLa cells, providing yet more evidence of miR-202′s physiological significance in lymphomagenesis.

In summary, we identified 141 potential members of the miR-202 targetome. Interrogation enriched transcripts using miRNA target prediction algorithms revealed that a majority harbored predicted 3′ UTR miR-202 binding sites. Potential targets were then investigated for disease-relevance using a network-based analytical approach. We discovered that a large proportion of the molecular interactions between identified targets were relevant to lymphomagenesis and hematopoietic processes. We also investigated whether genetic variants within the miR-202 stem-loop could account for differences in susceptibility to lymphoma in a population of NHL patients. A miR-202 precursor SNP, rs12355840, was found to be significantly associated with follicular lymphoma. These findings need to be further confirmed in studies with larger sample sizes and expression levels of miR-202 and identified targets need to be examined in lymphoma clinical samples. A functional analysis of this polymorphism demonstrated that the risk allele was also associated with reduced pre-miR-202 processing capacity, which is consistent with the findings of our network analysis. Taken together, these findings suggest a role for miR-202 in lymphomagenesis and the full extent of miR-202′s role in lymphoma-related processes should be the focus of future investigations.

Supplementary Material

1

Acknowledgments

We thank Irina Tikhonova at Yale University's W.M. Keck Foundation Biotechnology Research Laboratory for Sequenom genotyping analysis. We also thank Daniel Jacobs and Fengqin Shi for laboratory assistant. This work was supported by the National Institutes of Health grants CA62006 and CA122676.

Footnotes

Conflicts of Interest: Frank Slack is one of founders of Mira Dx Company.

References

  • 1.Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843–54. doi: 10.1016/0092-8674(93)90529-y. [DOI] [PubMed] [Google Scholar]
  • 2.Bader AG, Brown D, Stoudemire J, Lammers P. Developing therapeutic microRNAs for cancer. Gene Ther. 2011 doi: 10.1038/gt.2011.79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Esquela-Kerscher A, Slack FJ. Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer. 2006;6:259–69. doi: 10.1038/nrc1840. [DOI] [PubMed] [Google Scholar]
  • 4.Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, Yendamuri S, et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A. 2004;101:2999–3004. doi: 10.1073/pnas.0307323101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Trang P, Medina PP, Wiggins JF, Ruffino L, Kelnar K, Omotola M, et al. Regression of murine lung tumors by the let-7 microRNA. Oncogene. 2010;29:1580–7. doi: 10.1038/onc.2009.445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Medina PP, Nolde M, Slack FJ. OncomiR addiction in an in vivo model of microRNA-21-induced pre-B-cell lymphoma. Nature. 2010;467:86–90. doi: 10.1038/nature09284. [DOI] [PubMed] [Google Scholar]
  • 7.Kumar MS, Lu J, Mercer KL, Golub TR, Jacks T. Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nat Genet. 2007;39:673–7. doi: 10.1038/ng2003. [DOI] [PubMed] [Google Scholar]
  • 8.Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A. 2006;103:2257–61. doi: 10.1073/pnas.0510565103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65:7065–70. doi: 10.1158/0008-5472.CAN-05-1783. [DOI] [PubMed] [Google Scholar]
  • 10.Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–8. doi: 10.1038/nature03702. [DOI] [PubMed] [Google Scholar]
  • 11.Hoffman AE, Zheng T, Yi C, Leaderer D, Weidhaas J, Slack F, et al. microRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysis. Cancer Res. 2009;69:5970–7. doi: 10.1158/0008-5472.CAN-09-0236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Schimanski CC, Frerichs K, Rahman F, Berger M, Lang H, Galle PR, et al. High miR-196a levels promote the oncogenic phenotype of colorectal cancer cells. World J Gastroenterol. 2009;15:2089–96. doi: 10.3748/wjg.15.2089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Peng S, Kuang Z, Sheng C, Zhang Y, Xu H, Cheng Q. Association of microRNA-196a-2 gene polymorphism with gastric cancer risk in a Chinese population. Dig Dis Sci. 2010;55:2288–93. doi: 10.1007/s10620-009-1007-x. [DOI] [PubMed] [Google Scholar]
  • 14.Tian T, Shu Y, Chen J, Hu Z, Xu L, Jin G, et al. A functional genetic variant in microRNA-196a2 is associated with increased susceptibility of lung cancer in Chinese. Cancer Epidemiol Biomarkers Prev. 2009;18:1183–7. doi: 10.1158/1055-9965.EPI-08-0814. [DOI] [PubMed] [Google Scholar]
  • 15.Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. doi: 10.1016/j.cell.2004.12.035. [DOI] [PubMed] [Google Scholar]
  • 16.Zhang Y, Dai Y, Huang Y, Ma L, Yin Y, Tang M, et al. Microarray profile of micro-ribonucleic acid in tumor tissue from cervical squamous cell carcinoma without human papillomavirus. J Obstet Gynaecol Res. 2009;35:842–9. doi: 10.1111/j.1447-0756.2009.01055.x. [DOI] [PubMed] [Google Scholar]
  • 17.Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, et al. Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut. 2009;58:1375–81. doi: 10.1136/gut.2008.167817. [DOI] [PubMed] [Google Scholar]
  • 18.Jiang Z, Guo J, Xiao B, Miao Y, Huang R, Li D, et al. Increased expression of miR-421 in human gastric carcinoma and its clinical association. J Gastroenterol. 2010;45:17–23. doi: 10.1007/s00535-009-0135-6. [DOI] [PubMed] [Google Scholar]
  • 19.Wang W, Corrigan-Cummins M, Hudson J, Maric I, Simakova O, Neelapu SS, et al. MicroRNA profiling of follicular lymphoma identifies microRNAs related to cell proliferation and tumor response. Haematologica. 2012;97:586–94. doi: 10.3324/haematol.2011.048132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Peiffer SL, Herzog TJ, Tribune DJ, Mutch DG, Gersell DJ, Goodfellow PJ. Allelic loss of sequences from the long arm of chromosome 10 and replication errors in endometrial cancers. Cancer Res. 1995;55:1922–6. [PubMed] [Google Scholar]
  • 21.Lee SH, Davison JA, Vidal SM, Belouchi A. Cloning, expression and chromosomal location of NKX6B TO 10Q26, a region frequently deleted in brain tumors. Mamm Genome. 2001;12:157–62. doi: 10.1007/s003350010247. [DOI] [PubMed] [Google Scholar]
  • 22.Courtens W, Wuyts W, Rooms L, Pera SB, Wauters J. A subterminal deletion of the long arm of chromosome 10: a clinical report and review. Am J Med Genet A. 2006;140:402–9. doi: 10.1002/ajmg.a.31053. [DOI] [PubMed] [Google Scholar]
  • 23.Irving M, Hanson H, Turnpenny P, Brewer C, Ogilvie CM, Davies A, et al. Deletion of the distal long arm of chromosome 10; is there a characteristic phenotype? A report of 15 de novo and familial cases. Am J Med Genet A. 2003;123A:153–63. doi: 10.1002/ajmg.a.20220. [DOI] [PubMed] [Google Scholar]
  • 24.Buechner J, Tomte E, Haug BH, Henriksen JR, Lokke C, Flaegstad T, et al. Tumour-suppressor microRNAs let-7 and mir-101 target the proto-oncogene MYCN and inhibit cell proliferation in MYCN-amplified neuroblastoma. Br J Cancer. 2011;105:296–303. doi: 10.1038/bjc.2011.220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.http://www.microrna.org
  • 26.http://www.targetscan.org
  • 27.http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html
  • 28.http://bibiserv.techfak.uni-bielefeld.de/rnahybrid
  • 29.http://www.ingenuity.com
  • 30.Zhang Y, Holford TR, Leaderer B, Boyle P, Zahm SH, Owens PH, et al. Blood transfusion and risk of non-Hodgkin's lymphoma in Connecticut women. Am J Epidemiol. 2004;160:325–30. doi: 10.1093/aje/kwh233. [DOI] [PubMed] [Google Scholar]
  • 31.Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res. 2003;31:3429–31. doi: 10.1093/nar/gkg599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Rottapel R, Ilangumaran S, Neale C, La Rose J, Ho JM, Nguyen MH, et al. The tumor suppressor activity of SOCS-1. Oncogene. 2002;21:4351–62. doi: 10.1038/sj.onc.1205537. [DOI] [PubMed] [Google Scholar]
  • 33.Chim CS, Wong KY, Loong F, Srivastava G. SOCS1 and SHP1 hypermethylation in mantle cell lymphoma and follicular lymphoma: implications for epigenetic activation of the Jak/STAT pathway. Leukemia. 2004;18:356–8. doi: 10.1038/sj.leu.2403216. [DOI] [PubMed] [Google Scholar]
  • 34.Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL, Corbett RD, et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature. 2011;476:298–303. doi: 10.1038/nature10351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Challa-Malladi M, Lieu YK, Califano O, Holmes AB, Bhagat G, Murty VV, et al. Combined genetic inactivation of beta2-Microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer cell. 2011;20:728–40. doi: 10.1016/j.ccr.2011.11.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Park JE, Heo I, Tian Y, Simanshu DK, Chang H, Jee D, et al. Dicer recognizes the 5′ end of RNA for efficient and accurate processing. Nature. 2011;475:201–5. doi: 10.1038/nature10198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Slade I, Bacchelli C, Davies H, Murray A, Abbaszadeh F, Hanks S, et al. DICER1 syndrome: clarifying the diagnosis, clinical features and management implications of a pleiotropic tumour predisposition syndrome. J Med Genet. 2011;48:273–8. doi: 10.1136/jmg.2010.083790. [DOI] [PubMed] [Google Scholar]
  • 38.Kumar MS, Pester RE, Chen CY, Lane K, Chin C, Lu J, et al. Dicer1 functions as a haploinsufficient tumor suppressor. Genes Dev. 2009;23:2700–4. doi: 10.1101/gad.1848209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Arrate MP, Vincent T, Odvody J, Kar R, Jones SN, Eischen CM. MicroRNA biogenesis is required for Myc-induced B-cell lymphoma development and survival. Cancer Res. 2010;70:6083–92. doi: 10.1158/0008-5472.CAN-09-4736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Carrano AC, Eytan E, Hershko A, Pagano M. SKP2 is required for ubiquitin-mediated degradation of the CDK inhibitor p27. Nat Cell Biol. 1999;1:193–9. doi: 10.1038/12013. [DOI] [PubMed] [Google Scholar]
  • 41.Latres E, Chiarle R, Schulman BA, Pavletich NP, Pellicer A, Inghirami G, et al. Role of the F-box protein Skp2 in lymphomagenesis. Proc Natl Acad Sci U S A. 2001;98:2515–20. doi: 10.1073/pnas.041475098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Uddin S, Hussain A, Ahmed M, Belgaumi A, Al-Dayel F, Ajarim D, et al. S-phase kinase protein 2 is an attractive therapeutic target in a subset of diffuse large B-cell lymphoma. J Pathol. 2008;216:483–94. doi: 10.1002/path.2433. [DOI] [PubMed] [Google Scholar]
  • 43.Uddin S, Ahmed M, Bavi P, El-Sayed R, Al-Sanea N, AbdulJabbar A, et al. Bortezomib (Velcade) induces p27Kip1 expression through S-phase kinase protein 2 degradation in colorectal cancer. Cancer Res. 2008;68:3379–88. doi: 10.1158/0008-5472.CAN-07-6109. [DOI] [PubMed] [Google Scholar]
  • 44.Molineaux SM. Targeting Proteasomal Protein Degradation in Cancer. Clin Cancer Res. 2011 doi: 10.1158/1078-0432.CCR-11-0853. [DOI] [PubMed] [Google Scholar]
  • 45.Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A. 2002;99:15524–9. doi: 10.1073/pnas.242606799. Epub 2002 Nov 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, et al. A microRNA polycistron as a potential human oncogene. Nature. 2005;435:828–33. doi: 10.1038/nature03552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Skibola CF, Bracci PM, Halperin E, Conde L, Craig DW, Agana L, et al. Genetic variants at 6p21.33 are associated with susceptibility to follicular lymphoma. Nat Genet. 2009;41:873–5. doi: 10.1038/ng.419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Conde L, Halperin E, Akers NK, Brown KM, Smedby KE, Rothman N, et al. Genome-wide association study of follicular lymphoma identifies a risk locus at 6p21.32. Nat Genet. 2010;42:661–4. doi: 10.1038/ng.626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Morton LM, Purdue MP, Zheng T, Wang SS, Armstrong B, Zhang Y, et al. Risk of non-Hodgkin lymphoma associated with germline variation in genes that regulate the cell cycle, apoptosis, and lymphocyte development. Cancer Epidemiol Biomarkers Prev. 2009;18:1259–70. doi: 10.1158/1055-9965.EPI-08-1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Morton LM, Wang SS, Cozen W, Linet MS, Chatterjee N, Davis S, et al. Etiologic heterogeneity among non-Hodgkin lymphoma subtypes. Blood. 2008;112:5150–60. doi: 10.1182/blood-2008-01-133587. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

RESOURCES