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Population differences observed for complex traits may be attributed to the combined effect of socioeconomic, environmental, genetic and epigenetic factors. To better understand population differences in complex traits, genome-wide genetic and gene expression differences among ethnic populations have been studied. Here we set out to evaluate population differences in small non-coding RNAs through an evaluation of microRNA (miRNA) baseline expression in HapMap lymphoblastoid cell lines (LCLs) derived from 53 CEU (Utah residents with northern and western European ancestry) and 54 YRI (Yoruba people from Ibadan, Nigeria). Using the Exiqon miRcURYTM LNA arrays, we found that 16% of all miRNAs evaluated in our study differ significantly between these two ethnic groups (pBonferroni corrected < 0.05). Furthermore, we explored the potential biological function of these observed differentially expressed miRNAs by comprehensively examining their effect on the transcriptome and their relationship with cellular sensitivity to drug phenotypes. After multiple testing adjustment (false discovery rate (FDR) ≤0.1), we found that 55% and 88% of the differentially expressed miRNAs were significantly and inversely correlated with an mRNA expression phenotype in the CEU and YRI samples, respectively. Interestingly, a substantial proportion (64%) of these miRNAs correlated with cellular sensitivity to chemotherapeutic agents (FDR < 0.05). Lastly, upon performing a genome-wide association study between SNPs and miRNA expression, we identified a large number of SNPs exhibiting different allele frequencies that affect the expression of these differentially expressed miRNAs, suggesting the role of genetic variants in mediating the observed population differences.
Population differences are commonly observed for many complex traits including drug sensitivity,1,2 a phenomenon that has been termed pharmacoethnicity.3 Although genome-wide surveys have observed genetic4,5 and gene expression differences6–8 among different ethnic populations, there has been a paucity of data on non-coding RNAs.
MicroRNAs (miRNAs) are a set of non-coding small RNAs, which have been shown to play a significant role in gene expression.9 Thus far, over 4,000 miRNAs have been identified in various species, of which as many as 1,000 miRNAs may exist in humans.10,11 Furthermore, about one third of human genes may be regulated by miRNAs.10–12 Evidence suggests that miRNAs are directly involved in the pathogenesis of many human diseases, including cardiac arrhythmias,13 metabolic disorders, fragile X syndrome, asthma, DiGeorge syndrome, viral infection9 and cancers.14–16 MiRNAs have also been shown to mediate the activity of drug metabolizing enzymes17,18 and may play an essential role in drug response.19–22 However, the question of whether miRNA expression exhibits population differences has not been comprehensively and systematically evaluated.
The International HapMap consortium has collected samples from various ethnic populations around the world. To date, genetic variants in the form of millions of single nucleotide polymorphisms (SNPs) and thousands of copy number variants (CNVs), transcription level gene expression and other phenotypic data (e.g., cellular sensitivity to drugs) have been made publicly available for many of the HapMap samples.23,24 In this study, we set out to evaluate the extent of population differences in an important class of posttranscriptional regulators of gene expression, namely miRNAs. We explored the potential biological function of the observed differentially expressed miRNAs by comprehensively examining their relationship with target mRNAs and cellular sensitivity to chemotherapy. Furthermore, we examined the potential mechanism for the observed population differences in miRNA expression through population-specific genetic effects.
Among the 757 human miRNAs probed on the Exiqon miRCURYTM LNA arrays v10.0, 201 were reliably interrogated. Differential expression between CEU and YRI samples was observed for 107 miRNAs at a false discovery rate (FDR) <0.05 (Sup. Table 1) and 33 miRNAs at a FDR ≤ 0.0005 which meet a bonferroni corrected p < 0.05 (Fig. 1). We focused on this latter group of miRNAs that met the more stringent threshold.
Previously, we obtained genome-wide mRNA expression profile on all phase I/II HapMap CEU and YRI samples using the Affymetrix GeneChip® Human Exon 1.0 ST array (Affymetrix exon array).6 To gain insight into the biological significance of the observed population differences in miRNA expression, we comprehensively evaluated the effect of these differentially expressed miRNAs on the transcriptome.
Through regression analysis of the 33 differentially expressed miRNAs and genome-wide mRNA expression phenotypes, we identified 18 miRNAs (55%) significantly and inversely correlated with at least 1 of 138 mRNA expression phenotypes (FDR ≤ 0.1) in CEU and 29 differentially expressed miRNAs (88%) significantly and inversely correlated with at least 1 of 734 mRNA expression phenotypes (FDR ≤ 0.1) in YRI samples. Twenty-six such miRNA/mRNA correlation pairs (FDR < 0.1) were identified in both CEU and YRI samples consisting of 5 miRNAs and 22 mRNAs. Interestingly, miR-30b, 30d and 30e expression traits were correlated with the expression of ECE1, an endothelin converting enzyme, in both CEU and YRI samples (FDR < 0.1).
To further assess the global or system-level effect of these differentially expressed miRNAs, we evaluated enrichment in functional annotation categories for these genes whose expression levels correlated with the differentially expressed miRNAs. In CEU, the target mRNAs of the differentially expressed miRNAs are enriched in positive regulation of multicellular organismal process, regulation of vasoconstriction and blood pressure, immune response, circulatory system process, regulation of smooth muscle contraction, positive regulation of ion transport and protein amino acid phosphorylation in Biological Process (BP); in Molecular Function (MF), they are enriched in cytokine binding, purinergic nucleotide receptor activity and sulfotransferase activity; and in Cell Component (CC), the target mRNAs are enriched in plasma membrane (all categories with p < 0.05). In YRI, the target mRNAs of the differentially expressed miRNAs are enriched in regulation of cell death/apoptosis/programmed cell death and negative regulation of cellular protein metabolic process in BP (p < 0.001); they are enriched in protein heterodimarization activity, cytokine binding, transcription factor binding and cytokine receptor activity in MF (p < 0.02); and the target genes are enriched in endoplasmic reticulum, growth cone and Golgi apparatus/membrane in CC (p < 0.001).
We then focused on the relationships between differentially expressed miRNAs and previously identified 423 differentially expressed mRNAs (represented by 383 transcript clusters),6 to test the hypothesis that differentially expressed genes are regulated by differentially expressed miRNAs. Regression analysis shows that 14 of the 33 differentially expressed miRNAs were significantly correlated with at least one of 33 differentially expressed genes (constituting 9% of all differentially expressed genes) in either CEU or YRI samples (FDR < 0.10, corresponding to p < 0.0001 and < 0.0008 in CEU and YRI populations, respectively). In addition to our experimental data, we also utilized a publicly available miRanda prediction algorithm for additional bioinformatic support of our observed miRNA/mRNA relationships. Table 1 shows 18 differentially expressed miRNAs that are correlated with 29 differentially expressed genes at a nominally significant p < 0.05 from linear regression and that also meet the miRanda prediction at the p < 0.05 threshold. Several patterns of associations were observed, suggesting a variety of potential complex interactions between differentially expressed miRNAs and predicted differentially expressed target genes. There were 5 miRNA/mRNA pairs that showed significant inverse correlation in both CEU and YRI samples: miR-342-3p and ANKRD49, miR-30b and ZNRF1, miR-30d and ZNRF1, miR-30e and ZNRF1, and miR-140-3p and CTTN. Certain miRNA and mRNA relationships were observed to be unique to either CEU or YRI population (Table 1). In addition, some miRNAs were correlated with multiple mRNAs of which only some mRNAs overlapped in both populations (e.g., miR-140-3p expression is correlated with CTTN, TNIP1, TRMT12 expression in CEU and CTTN, PTGES2 expression in YRI). Finally, several mRNAs were found to correlate with multiple miRNAs (e.g., ZNRF1 expression is correlated with miR-30b, 30d, 30e expression in both CEU and YRI and it is correlated with miR-30c in YRI).
Functional validation. To validate these findings from our initial analyses, we quantified the expression levels of 3 miRNA/mRNA pairs (miR-342-3p and ANKRD49; miR-30b and ZNRF1; and miR-140-3p and CTTN) in an independent set of HapMap CEU III and YRI III samples. Differential expression of both miR-30b and ZNRF1 was confirmed in this replication set, with higher miR-30b and lower ZNRF1 expression in the YRI samples compared with CEU (Fig. 2 and p < 0.05). miR-342-3p expression also trended toward differential expression in the validation samples in the same direction as in the discovery samples (p = 0.08). Furthermore, the negative correlation between miR-342-3p and ANKRD49 was confirmed in the YRI III validation samples (Fig. 3 and p = 0.02). In the phase III CEU and YRI samples, we did not successfully validate the previously observed differential expression of miR-140-3p, CTTN and ANKRD49 from the discovery set; nor did we successfully validate the previously observed relationship between miR-30b and ZNRF1 expression (p > 0.05).
Furthermore, we conducted overexpression and knockdown experiments for 2 miRNA/mRNA pairs (miR-30b/ZNRF1 and miR-342-3p/ANKRD49). For miR-30b and ZNRF1, we found that the addition of miR-30b mimic resulted in an increase in the amount of miR-30b and a decrease in ZNRF1 expression in cells at 24 h. These findings were further supported by miR-30b inhibition experiments that led to a decrease in miR-30b expression level and an increase in ZNRF1 expression (Fig. 4). The magnitude of target gene ZNRF1 expression change is not as significant as that of miR-30b, which is in agreement with our findings that multiple miR-30 family members target ZNRF1 expression. For miR-342-3p and ANKRD49, miR-342-3p mimic increased the expression of miR-342-3p and decreased that of ANKRD49; however, the inhibition experiment failed to demonstrate an effect on ANKRD49, which may be the result of insufficient inhibition.
Our group has previously observed in vitro population differences in cellular sensitivity to carboplatin,25 daunorubicin25 and araC26 between the CEU and YRI samples. In this study, we evaluated whether differentially expressed miRNAs contribute to these observed ethnic differences in drug sensitivity. Twenty one of the 33 differentially expressed miRNAs were correlated with cellular sensitivity to at least one of the 3 chemotherapeutic agents in at least one population (Table 2, q < 0.05). Interestingly, the expression of many members of miR-30 family are correlated with carboplatin IC50 (Table 2), suggesting the functional importance of the mi-R30 family in platinum sensitivity. Furthermore, some miRNA expression levels were correlated with cellular sensitivity to multiple drugs (e.g., miR-768-3p).
We hypothesized that genetic variation may in part play a role in the observed in vitro miRNA differences between ethnic groups. Utilizing public resources including TargetScan and PITA, our lab previously performed a genome-wide search and identified 188 SNPs in 138 pre-miRNA regions.27 In addition, when evaluating the known copy number variation (CNV) coverage of human pre-miRNA genes using the CNVs deposited in the Database of Genomic Variants (DGV),28 we found that 193 pre-miRNAs were located in copy number variant (CNV) regions.27 Five of the 33 differentially expressed miRNAs have pre-miRNAs that contain SNPs (miR-140-3p, miR-183, miR-620, miR-92a and miR-939). Four pre-miRNAs are located in known CNV regions. Two of these pre-miRNAs (miR-939 and miR-620) contain both SNPs and are located in known CNV regions.
In this study, we identified 2,770 SNPs showing high levels of population differentiation between the CEU and YRI populations (Fst > 0.25). We evaluated these SNPs for their relationships with the list of differentially expressed miRNAs. Following linear regression, under an additive model, between these SNPs and the differentially expressed miRNAs, 272 of them were found to be associated with the expression of at least one of 33 differentially expressed miRNAs (Sup. Table 2, p < 10−5). Specifically, 122 such SNPs were associated with the expression of a differentially expressed miRNA (from 26 such miRNAs) in CEU while 150 such SNPs were associated with the expression of a differentially expressed miRNA (from 31 such miRNAs) in YRI (p < 10−5). Applying the Bonferroni correction for the number of tests performed, we found that 36% of all differentially expressed miRNAs are associated with a SNP showing high population differentiation (Sup. Table 2 bolded data for all 18 such relationships between 16 SNPs and 12 miRNAs, p < 2 × 10−7). Note that the majority of the SNP/miRNA associations from this analysis are trans relationships. Several SNPs with high Fst were found to associate with multiple miRNA expression phenotypes (e.g., rs11573962 and rs11707798); while multiple SNPs show associations with the same differentially expressed miRNA (e.g., SNPs and miR-628-3p).
Upon a genome-wide evaluation of the baseline miRNA expression levels in HapMap LCLs derived from CEU and YRI HapMap samples, we identified a set of 33 miRNAs (16% of all miRNAs reliably interrogated in our study), whose expression levels significantly differ in vitro between these 2 populations. To examine the functional significance of these differentially expressed miRNAs, we set out to comprehensively investigate the effect of these miRNAs on the transcriptome under the hypothesis that gene expression is an endophenotype or intermediate to more complex traits. We further identified a set of 29 mRNAs whose expression levels differed between CEU and YRI and were inversely correlated with the differentially expressed miRNAs. In addition, a high proportion (64%) of the differentially expressed miRNAs was found to correlate with cellular sensitivity to various chemotherapeutic agents (FDR < 0.05), for which population differences in drug sensitivity have been observed in vitro. All these suggest the biological and pharmacological significance of observed differences in miRNA expression.
Population differences have been observed in many human complex traits, including disease susceptibility,29,30 drug sensitivity31,32 and gene expression.6–8 Genetic polymorphisms have undergone extensive evaluation for their potential role in these observed population differences. In recent years, the significance of epigenetic factors in regulating complex traits has been actively pursued. MiRNAs have been shown to influence gene expression variation in the human genome33 and may affect other biological processes through their effect on the transcriptome. In addition, inter-ethnic differences in miR-26a expression have been observed in prostate cancer cell lines derived from African American men compared with those derived from Caucasians.34 In our study, we did not observe a significant difference in miR-26a expression; however, miR-26b expression does show a potential inter-ethnic difference with p value 0.00078 in LCLs derived from CEU and YRI (Sup. Table 1). However, to date, a comprehensive investigation of population differences in miRNA expression has not been done. Our results demonstrate that miRNA expression levels exhibit population differences. Furthermore, the differential miRNA expression may contribute to observed population differences in mRNA expression levels and possibly other complex human traits.
Our findings indicate that certain differentially expressed miRNAs consistently correlated with the same target mRNA in both CEU and YRI samples. For example, miR-30b, 30d and 30e expression were negatively correlated with the expression of ECE1 in both populations (FDR < 0.1). ECE1 protein is involved in proteolytic processing of endothelin precursors to biologically active peptides. The expression of this gene product has been linked to non-small cell lung cancer,35 diabetic nephropathy36 and Alzheimer disease.37 When we limited our analysis to differentially expressed mRNAs, we identified several miRNA-mRNA pairs that were inversely correlated in both CEU and YRI and have previously been implicated in studies of disease susceptibility: miR-342-3p and ANKRD49, miR-30b, d and e and ZNRF1. For example, miR-342-3p has been shown to be upregulated in neurodegenerative disease.38,39 ANKRD49 was identified as one of the 4 non-small cell lung cancer invasion-associated gene signatures, which can independently predict prognosis regardless of age, gender and stage of disease.40 miR-30b was found to be down-regulated in squamous cell lung carcinoma compared with normal lung tissue.41 It is also downregulated in multiple myeloma,42 in invasive urothelial carcinoma of the bladder,43 in polycythemia vera (a colonal hematopoietic stem cell disorder) reticulocytes.44 On the other hand, amplification and overexpression of miR-30b was observed in medulloblastoma.45 ZNRF proteins play a role in the establishment and maintenance of neuronal transmission and plasticity via their ubiquitin ligase activity.46 Overexpression of ZNRF1, which has been identified as a crucial molecule in nerve regeneration, causes morphological changes such as neurite-like elongation. ZNRF1 mediates regulation of neuritogenesis via interaction with tubulin.47
One caveat of the study is that these are LCLs collected at different times and cultured for different periods of time. Because of the differences in cell line collection time between the CEU and YRI samples,48,49 miRNA differences could be a combined effect of genetic and non-genetic factors. In addition, culture conditions or batch to batch variation could influence the observed differences in gene expression between the two populations.50 Therefore, to reduce these variables, cell culture protocols were optimized and samples (CEU and YRI) were randomized when cultured and evaluated for miRNA expression in this study.
In addition to their role in disease susceptibility, miRNAs have also been described to directly or indirectly affect drug efficacy and toxicity. For example, miR-214 was found to induce cell survival and cisplatin resistance by directly targeting and downregulating the PTEN gene in ovarian cancer.21 Studies have shown that a miRNA regulates the expression of CYP3A4, a major phase I drug metabolism enzyme, by targeting its major transcription factor, PXR.17 Furthermore, miRNAs have been shown to alter resistance to cytotoxic anticancer therapy.20 Our study supports the general conclusion of these findings with respect to miRNA function; we found that a substantial proportion (64%) of the differentially expressed miRNAs correlated with cellular sensitivity to at least one of the three chemotherapeutic agents evaluated in this study in one of the HapMap populations. These findings strongly argue for the inclusion of miRNAs in comprehensive pharmacogenomic studies.
Finally, we investigated the question of whether the population differences in miRNA expression are influenced by genetic polymorphisms. Of the 2,770 SNPs showing high levels of population differentiation between the CEU and YRI populations (Fst > 0.25), nearly 10% predict the expression level (p < 10−5) of at least one of 33 differentially expressed miRNAs. Furthermore, four of these differentially expressed miRNAs (12%) have pre-miRNAs in known CNV regions. Our findings thus highlight the potential role of genetic variants in mediating the observed population differences in miRNA expression.
HapMap phase I/II lymphoblastoid cell lines (LCLs) consisting of 54 CEU (Utah residents with ancestry from northern and western Europe, HAPMAPPT01; unrelated samples except for GM12878 which is a child of two unrelated patients included in the data set and was subsequent removed from further analysis) and 55 unrelated YRI (Yoruba in Ibadan, Nigeria, HAPMAPPT03). One sample was later excluded from analysis due to labeling error were used for the discovery study. An additional 58 unrelated CEU III (HAPMAPPT06) and 58 unrelated YRI III (HAPMAPPT04) LCLs were used for validation experiments. All cell lines were obtained from Coriell Institute for Medical Research. Cell lines were cultured in RPMI 1640 media (Mediatech) supplemented with 15% fetal bovine serum (HyClone) and 1% l-glutamine (MediaTech) in a 37°C, 5% CO2 and 95% humidity incubator.25
Each cell line was diluted four times at a concentration of 350,000 cells/mL prior to RNA collection. Cell pellets were harvested from exponentially growing cells. For the discovery samples, miRNA was isolated from LCLs that had a viability greater than 85% at the time of pelleting using miRNeasy Plus mini kit (Qiagen). For the validation samples, miRNA and mRNA were isolated from the same pellet of 58 CEU III and 58 YRI III LCLs. Qiagen's miRNeasy Plus mini kit was used for miRNA isolation while RNeasy Plus mini kit was used for mRNA isolation. Sample quality control was performed on the Agilent Bioanalyser 2100 and RNA quantity was measured using the NanoDrop 8000 instrument (Nanodrop Technologies Inc.).
Exiqon miRCURYTM LNA arrays v.10.0 were used to quantify genome-wide miRNA expression in the discovery HapMap phase I/II samples described above. Based on miRBase 11.0 annotation, a total of 757 human miRNA probes were interrogated on this platform. MicroRNA samples were labeled using the miRCURYTM Hy3TM/Hy5TM power labeling kit and hybridized on the LNA arrays. Measured signals were background corrected using normexp with offset value 10 based on a convolution model51 and normalized using the global Lowess regression algorithm.52 Of the interrogated miRNAs, 201 were consistently expressed in samples from both CEU and YRI populations (defined as having an expression intensity value on the arrays in at least 70% of all samples and having average Hy3 intensity value greater than 100). We identified miRNAs differentially expressed between the populations using a 2-tailed Student's t-test (unpaired with unequal variance). For multiple testing correction, FDR approach was used53 was used. In addition, Bonferroni-corrected p < 0.05 with the number of miRNAs examined was considered statistically significant.
The overall schema of the analysis is illustrated in Supplemental Figure 1. Our lab previously evaluated global baseline gene expression on 87 CEU and 89 YRI LCLs using the Affymetrix exon array.54 When processing Affymetrix array data, we removed all probes that contain more than one known polymorphism to remove potential artifacts of array hybridization.55 The Affymetrix transcriptional expression data have been deposited into GEO (Accession No: GSE7761). Utilizing these data, we have previously reported 383 differentially expressed transcript clusters between the CEU and YRI samples.6
Linear regression analyses were performed between the differentially expressed miRNAs and genome-wide mRNA expression in the CEU and YRI samples separately. Multiple testing correction was done through FDR.53 Inversely correlated miRNAs and mRNAs with FDR less than or equal to 0.1 were defined as statistically significant. Subsequently, we evaluated the relationships between the differentially expressed miRNAs and the 383 differentially expressed transcript clusters using linear regression analyses and miRanda miRBase prediction algorithm.56 To assess the function of all mRNAs that were correlated with differentially expressed miRNAs, DAVID bioinformatics tools were used to identify enriched functional annotation categories. Affymetrix transcript cluster was used to upload target gene lists in the CEU and YRI population separately. Entrez_gene_ID identifier was used to upload the background gene list, which contains 10,830 genes that are expressed in LCLs. Three classes of Gene Ontology (GO) terms [biological process (BP), cell component (CC) and molecular function (MF)] were evaluated. For pathway analysis, uncorrected p < 0.05 was used to identify potentially enriched functional pathways.
We have reported in vitro population differences in cellular sensitivity to carboplatin,25 daunorubicin25 and araC26 between the CEU and YRI samples. Cellular sensitivity to these chemotherapeutic agents—quantified by IC50 (the concentration required to inhibit 50% of cell growth) for carboplatin and daunorubicin, along with AUC (area under the drug concentration cellular percent survival curve) for araC—was evaluated. We performed linear regression analysis between the differentially expressed miRNAs and these drug sensitivity phenotypes in the separate CEU and YRI samples. Correlations between miRNA and cellular sensitivity with FDR less than 0.05 were reported in this study.
SNP genotypes for CEU and YRI samples were downloaded from the International HapMap database (Release 24). SNPs that have a minor allele frequency (MAF) greater than 5% in both CEU and YRI samples and no Mendelian inheritance transmission errors were included in the analysis. Population differentiation for the SNPs was measured by the F statistic (Fst) value,57 and the SNPs with Fst values over 0.25 were defined as differentiated SNPs. Genome-wide analyses (using an additive model) were performed between the genotypes of these differentiated SNPs and the expression of differentially expressed miRNAs. Associations with less than 10−5 p value were reported in this study; those associations that meet the Bonferroni correction of padj < 0.05 were highlighted.
To validate our findings from the Phase I/II CEU and YRI discovery samples, HapMap Phase III CEU and YRI LCLs were used. Quantitative PCR was performed to quantify 3 miRNAs (miR342-3p, miR30b and miR140-3p) and 3 mRNAs (ANKRD49, ZNRF1 and CTTN) using an ABI 7900 thermocycler (Applied Biosystems, Foster City, CA).
To convert miRNA to cDNA, total RNA was diluted to 1.786 ng/uL and reverse transcribed using Exiqon Universal cDNA synthesis kit (Exiqon catalog number 203300). Each reverse transcription was performed according to the manufacturer's instructions with positive and negative controls. Specifically, 20 µL sample was incubated for 60 min at 42°C followed by heat inactivation of the reverse transcriptase for 5 min at 95°C, then stored at 4°C. To convert mRNA to cDNA, total RNA was reverse transcribed using a High Capacity cDNA reverse transcription kit (Applied Biosystems catalog number 4368813). Specifically, 20 µL reaction was performed by a reaction incubating at 25°C for 5 min then 37°C for 120 min then 85°C for 5 min then stored at −20°C. All reverse transcription reactions were performed in a Peltier Thermal Cycler (MJ Research, Waltham MA).
Exiqon primer sets were utilized to quantify baseline expression levels of hsa-miR-342-3p, hsa-miR-30b and hsa-miR140-3p (Exiqon catalog numbers 204511, 204343 and 204304); while Applied Biosystems Taqman primer/probe sets were used to quantify mRNA expression of ANKRD49, ZNRF1 and CTTN (Applied Biosystems catalog numbers Hs00969433_g1, Hs00936381_m1 and Hs0112422_m1). All real-time PCR reactions were conducted according to the manufacturer's protocol for 384-well plates. Standard thermocycling condition was used and all reactions were performed in triplicate with the corresponding positive and negative controls. Relative quantitation of target expression was evaluated by the 2 (−ΔΔCt) method for each miRNA/mRNA of interest compared with a housekeeping gene.58 In this study, U6 RNA reference control (Exiqon catalog number 203907) was used as a miRNA housekeeping; while human β2M (Applied Biosystems catalog number 4326319E) was used as a mRNA housekeeping. All real-time PCR for miRNA quantification was completed within 24 h of miRNA reverse transcription.
A 2-tailed Student's t-test (unpaired with unequal variance) was performed between CEU III and YRI III LCLs for the 3 miRNAs and 3 mRNAs using the real-time PCR results. p < 0.05 was considered statistically significant. Furthermore, regression analyses were performed between miRNA and mRNA. Associations with p-values less than 0.05 were identified.
In addition, we conducted overexpression and knockdown experiments for 2 miRNA/mRNA pairs (miR-30b/ZNRF1 and miR-342-3p/ANKRD49) in a randomly selected CEU (GM07345) and YRI (GM18517) LCL sample. Specifically, miRNA mimics (cat. #MSY0000420 and MSY0000753), and miRNA inhibitors (cat. #MIN0000420 and MIN0000753) for miR-30b and miR-342-3p (respectively) and scrambled control (AllStars Negative control, cat. #1027292) were purchased from Qiagen. Two µL of 20 µM miRNA mimic or inhibitor or control (final concentration 2 µM) was transfected into 1.5 million cells suspended in 18 µL SF + Supplemental solution using Cell Line 96-well necleofector (Lonza, Basel, Switzerland). Each reaction was done in triplicates. After electroporation, a total of 60 µL cell suspension was pooled into a single well of a 6-well plate with additional 2 mL cell culture media. Cells were pelleted 24 and 48 h after electroporation. Total RNAs were isolated; qPCR was then performed for the target miRNAs and genes. Percent expression change of each experiment (either mimic or inhibitor) was calculated using the following equation: [(relative amount of expression from experiment − relative amount of expression from control)/relative amount of expression from control] × 100%. Results are plotted in Figure 4.
This work was supported by National Cancer Institute R21 [CA139278 to R.S.H. and M.E.D.], National Institute of Health/National Institute of General Medical Science [Pharmacogenomics of Anticancer Agents grant U01GM61393 to N.J.C. and M.E.D.] and by the University of Chicago Breast Cancer SPORE grant P50 [CA125183 to R.S.H. and M.E.D.]. R.S.H. also received support from National Institute of General Medical Science K08 [GM089941] and the University of Chicago Cancer Center Support Grant P30 [CA14599].