For miR-34a, miR-298, and miR -892b, we observed cell phenotypes (such as cell retraction and detachment) suggestive of cell death. Therefore, we directly tested the ability of several candidate miRs to induce apoptotic signaling. Indeed, miR-34a, miR-298, and miR-892b overexpression activated caspase 3/7 by ~4- to 7-fold, as compared with the mock-transfected control (Figure 5B). The data suggested that these miRs suppress growth in part by inducing apoptotic cell death. The miRs that did not induce caspase activation may instead repress cell proliferation or promote caspase-independent cell death.
This proof-of-principle use of our miR-HTS identified a set of 59 miR candidates that inhibited growth of the IMR90 human fetal lung fibroblast cell line in our functional screen. Approximately 35% of the growth-inhibitory miR candidates identified herein have been implicated previously in growth suppression in IMR90 and/or other lung cancer cell lines, including reports that directly evaluated cellular mechanism experimentally (i.e., cell cycle, apoptosis, senescence assays), and reports that observed expression changes associated with growth-inhibitory function (i.e., upregulated expression in senescent cells versus young cells, or downregulated expression in lungcancer samples versus normal counterpart cells; summarized and referenced in Supplementary Table S4). Fifteen of these 59 candidate miRs are known to be upregulated in senescent IMR90 cells, including miR-449a/b (45). Ten of these 59 candidate miRs, including four upregulated in senescent IMR90 cells, are known to be downregulated in lung cancers, including miR-7 (46). Finally, four of the candidate miRs have been shown to promote apoptosis, promote senescence, or inhibit cell cycling in lung cancer cell lines (Supplementary Table S4). It will be interesting to examine whether any of the 11 novel growth-inhibitory miR candidates identified from IMR90 (i.e., the 11 miRs noted with “D” in the column labeled “Known Expression Pattern” of Supplementary Table S4) have tumor-suppressive activity in lung cancers that have downregulated expression of these specific miRs.
Intriguingly, the miR-HTS co-identified three pairs of candidates that are paralogs encoding the same miR hairpin sequence. Having exactly the same mature miR sequence, these paralogous miRs recognize the same targets and thus would be predicted to have the same effect on cell growth. These paralogous candidates include the miR-550a-1 and miR-550a-2 paralogs (miR-550a-3 was not included in the library used), miR-513a-1 and miR-513a-2 paralogs, and the miR-128-1 and miR-128-2 paralogs (Supplementary Table S4). In addition, multiple members of three additional miR families (i.e., beyond the above three sets of paralogs) were identified in the set of growth-inhibitory candidates [e.g., miR-888, miR-892a, and miR-892b of the miR-743 family (Supplementary Table S4)]. As a result of the miR hairpin sequence homology within each of these miR families (47), each miR family member may recognize many of the same targets and have similar functions. Moreover, the 59 growth-inhibitory candidates include miR-34a, miR-34c, and the miR-449a~449b cluster, which belong to two different miR families, but share identical seed sequence. We were surprised to find that miR-34b, which is also a member of the miR-34 family, was not among the candidates identified by the miR-HTS. Overexpression of miR-34b caused ≥8-fold decrease in abundance at the last time point in only one of the two miR-HTS replicate screens.
In summary, we have established miR-HTS as a novel methodology to conduct unbiased high-throughput miR functional screens. The 75% validation rate among the 12 selected candidates of a proof of-principle screen in the IMR90 cell line demonstrated the high fidelity of this approach. It should be possible to apply this miR-HTS technology to assess the roles of miRs in cellular processes other than growth-inhibition, targeting any depletion- or enrichment-based phenotype (e.g., one could identify the set of miRs that enhance differentiation using stage/lineage-specific surface markers to enrich the differentiated fraction from the entire cell population). The estimated costs per sample for miR-HTS is less than half that of other miR barcode quantification methods (see comparison in Supplementary Table S5). In addition, as the lenti-miR library expands, we can readily add more GRE-qPCR assays. Thus, this novel lenti-miR library–based and qPCR-based miR-HTS strategy provides a flexible and reliable platform for miR functional screening, with lower complexity and costs compared with published methods.
We thank Gerald Vandergrift at Applied Biosystems for expert advice on GRE-qPCR assay designs and development, Fernando Pineda at Johns Hopkins University for expert advice on statistics, and all Civin lab members for helpful discussions. This work was supported by NIH/NCI grant #P01CA70970 to C.C. This paper is subject to the NIH Public Access Policy.
The authors declare no competing interests.
Address correspondence to Curt I. Civin, Center for Stem Cell Biology & Regenerative Medicine University of Maryland School of Medicine, Baltimore, MD, USA. E-mail: [email protected]
1.) Pasquinelli, A.E. 2012. MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nat. Rev. Genet. 13:271-282. 2.) Chitnis, N.S., D. Pytel, E. Bobrovnikova-Marjon, D. Pant, H. Zheng, N.L. Maas, B. Frederick, J.A. Kushner. 2012. miR-211 is a prosurvival microRNA that regulates chop expression in a PERK-dependent manner. Mol. Cell 48:353-364. 3.) Benhamed, M., U. Herbig, T. Ye, A. Dejean, and O. Bischof. 2012. Senescence is an endogenous trigger for microRNA-directed transcriptional gene silencing in human cells. Nat. Cell Biol. 14:266-275. 4.) Friedman, R.C., K.K. Farh, C.B. Burge, and D.P. Bartel. 2009. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19:92-105. 5.) Alvarez-Garcia, I., and E.A. Miska. 2005. MicroRNA functions in animal development and human disease. Development 132:4653-4662. 6.) Sayed, D., and M. Abdellatif. 2011. MicroRNAs in development and disease. Physiol. Rev. 91:827-887. 7.) Esteller, M. 2011. Non-coding RNAs in human disease. Nat. Rev. Genet. 12:861-874. 8.) Mendell, J.T., and E.N. Olson. 2012. MicroRNAs in stress signaling and human disease. Cell 148:1172-1187. 9.) Hermeking, H. 2010. The miR-34 family in cancer and apoptosis. Cell Death Differ. 17:193-199. 10.) Trang, P., J.F. Wiggins, C.L. Daige, C. Cho, M. Omotola, D. Brown, J.B. Weidhaas, A.G. Bader, and F.J. Slack. 2011. Systemic delivery of tumor suppressor microRNA mimics using a neutral lipid emulsion inhibits lung tumors in mice. Mol. Ther. 19:1116-1122. 11.) Wiggins, J.F., L. Ruffino, K. Kelnar, M. Omotola, L. Patrawala, D. Brown, and A.G. Bader. 2010. Development of a lung cancer therapeutic based on the tumor suppressor microRNA-34. Cancer Res. 70:5923-5930. 12.) Bader, A.G., D. Brown, J. Stoudemire, and P. Lammers. 2011. Developing therapeutic microRNAs for cancer. Gene Ther. 18:1121-1126. 13.) Calin, G.A., and C.M. Croce. 2006. MicroRNA signatures in human cancers. Nat. Rev. Cancer 6:857-866. 14.) Bhattacharyya, S.N., R. Habermacher, U. Martine, E.I. Closs, and W. Filipowicz. 2006. Relief of microRNA-mediated translational repression in human cells subjected to stress. Cell 125:1111-1124. 15.) Glorian, V., G. Maillot, S. Poles, J.S. Iacovoni, G. Favre, and S. Vagner. 2011. HuR-dependent loading of miRNA RISC to the mRNA encoding the Ras-related small GTPase RhoB controls its translation during UV-induced apoptosis. Cell Death Differ. 18:1692-1701. 16.) Serva, A., C. Claas, and V. Starkuviene. 2011. A potential of microRNAs for high-content Screening. J. Nucleic Acids 2011:870903. 17.) Wang, Y., R. Medvid, C. Melton, R. Jaenisch, and R. Blelloch. 2007. DGCR8 is essential for microRNA biogenesis and silencing of embryonic stem cell self-renewal. Nat. Genet. 39:380-385. 18.) Poell, J.B., R.J. van Haastert, T. de Gunst, I.J. Schultz, W.M. Gommans, M. Verheul, F. Cerisoli, P.I. van Noort. 2012. A functional screen identifies specific microRNAs capable of inhibiting human melanoma cell viability. PLoS ONE 7:e43569. 19.) Li, Y., M. Zhang, H. Chen, Z. Dong, V. Ganapathy, M. Thangaraju, and S. Huang. 2010. Ratio of miR-196s to HOXC8 messenger RNA correlates with breast cancer cell migration and metastasis. Cancer Res. 70:7894-7904. 20.) Adams, B.D., S. Guo, H. Bai, Y. Guo, C.M. Megyola, J. Cheng, K. Heydari, C. Xiao. 2012. An in vivo functional screen uncovers miR-150-mediated regulation of hematopoietic injury response. Cell Rep. 4:1048-1060. 21.) Guo, S., H. Bai, C.M. Megyola, S. Halene, D.S. Krause, D.T. Scadden, and J. Lu. 2012. Complex oncogene dependence in microRNA-125a-induced myeloproliferative neoplasms. Proc. Natl. Acad. Sci. USA 109:16636-16641. 22.) Izumiya, M., K. Okamoto, N. Tsuchiya, and H. Nakagama. 2010. Functional screening using a microRNA virus library and microarrays: a new high-throughput assay to identify tumor-suppressive microRNAs. Carcinogenesis 31:1354-1359. 23.) Poell, J.B., R.J. van Haastert, F. Cerisoli, A.S. Bolijn, L.M. Timmer, B. Diosdado-Calvo, G.A. Meijer, A.A. van Puijenbroek. 2011. Functional microRNA screening using a comprehensive lentiviral human microRNA expression library. BMC Genomics 12:546. 24.) Voorhoeve, P.M., C. le Sage, M. Schrier, A.J. Gillis, H. Stoop, R. Nagel, Y.P. Liu, J. van Duijse. 2006. A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell 124:1169-1181. 25.) Mavrakis, K.J., J. Van Der Meulen, A.L. Wolfe, X. Liu, E. Mets, T. Taghon, A.A. Khan, M. Setty. 2011. A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL). Nat. Genet. 43:673-678. 26.) Huang, Q., K. Gumireddy, M. Schrier, C. le Sage, R. Nagel, S. Nair, D.A. Egan, A. Li. 2008. The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat. Cell Biol. 10:202-210. 27.) Ye, Z., X. Yu, and L. Cheng. 2008. Lentiviral gene transduction of mouse and human stem cells. Methods Mol. Biol. 430:243-253. 28.) He, L., X. He, L.P. Lim, E. de Stanchina, Z. Xuan, Y. Liang, W. Xue, L. Zender. 2007. A microRNA component of the p53 tumour suppressor network. Nature 447:1130-1134. 29.) Merritt, W.M., Y.G. Lin, L.Y. Han, A.A. Kamat, W.A. Spannuth, R. Schmandt, D. Urbauer, L.A. Pennacchio. 2008. Dicer, Drosha, and outcomes in patients with ovarian cancer. N. Engl. J. Med. 359:2641-2650. 30.) Thomson, J.M., M. Newman, J.S. Parker, E.M. Morin-Kensicki, T. Wright, and S.M. Hammond. 2006. Extensive post-transcriptional regulation of microRNAs and its implications for cancer. Genes Dev. 20:2202-2207. 31.) Davis-Dusenbery, B.N., and A. Hata. 2010. MicroRNA in cancer: the involvement of aberrant microRNA biogenesis regulatory pathways. Genes Cancer 1:1100-1114. 32.) Fields, B.N., D.M. Knipe, and P.M. Howley. 2007. Field's Virology. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia:41-43. 33.) Blakely, K., T. Ketela, and J. Moffat. 2011. Pooled lentiviral shRNA screening for functional genomics in mammalian cells. Methods Mol. Biol. 781:161-182. 34.) Silva, J.M., K. Marran, J.S. Parker, J. Silva, M. Golding, M.R. Schlabach, S.J. Elledge, G.J. Hannon, and K. Chang. 2008. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319:617-620. 35.) Schröder, A.R., P. Shinn, H. Chen, C. Berry, J.R. Ecker, and F. Bushman. 2002. HIV-1 integration in the human genome favors active genes and local hotspots. Cell 110:521-529. 36.) Ellis, J. 2005. Silencing and variegation of gammaretrovirus and lentivirus vectors. Hum. Gene Ther. 16:1241-1246. 37.) Livak, K.J., and T.D. Schmittgen. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402-408. 38.) Karrer, E.E., J.E. Lincoln, S. Hogenhout, A.B. Bennett, R.M. Bostock, B. Martineau, W.J. Lucas, D.G. Gilchrist, and D. Alexander. 1995. In situ isolation of mRNA from individual plant cells: creation of cell-specific cDNA libraries. Proc. Natl. Acad. Sci. USA 92:3814-3818. 39.) Guthrie, J.L., C. Seah, S. Brown, P. Tang, F. Jamieson, and S.J. Drews. 2008. Use of Bordetella pertussis BP3385 to establish a cutoff value for an IS481-targeted real-time PCR assay. J. Clin. Microbiol. 46:3798-3799. 40.) Chen, Y., J.A. Gelfond, L.M. McManus, and P.K. Shireman. 2009. Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. BMC Genomics 10:407. 41.) Caraguel, C.G., H. Stryhn, N. Gagne, I.R. Dohoo, and K.L. Hammell. 2011. Selection of a cutoff value for real-time polymerase chain reaction results to fit a diagnostic purpose: analytical and epidemiologic approaches. J. Vet. Diagn. Invest. 23:2-15. 42.) Ji, X., R. Takahashi, Y. Hiura, G. Hirokawa, Y. Fukushima, and N. Iwai. 2009. Plasma miR-208 as a biomarker of myocardial injury. Clin. Chem. 55:1944-1949. 43.) Kozomara, A., and S. Griffiths-Jones. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39:D152-D157. 44.) Griffiths-Jones, S., H.K. Saini, S. van Dongen, and A.J. Enright. 2008. miRBase: tools for microRNA genomics. Nucleic Acids Res. 36:D154-D158. 45.) Dhahbi, J.M., H. Atamna, D. Boffelli, W. Magis, S.R. Spindler, and D.I. Martin. 2011. Deep sequencing reveals novel microRNAs and regulation of microRNA expression during cell senescence. PLoS ONE 6:e20509. 46.) Xiong, S., Y. Zheng, P. Jiang, R. Liu, X. Liu, and Y. Chu. 2011. MicroRNA-7 inhibits the growth of human non-small cell lung cancer A549 cells through targeting BCL-2. Int. J. Biol. Sci. 7:805-814. 47.) Griffiths-Jones, S., S. Moxon, M. Marshall, A. Khanna, S.R. Eddy, and A. Bateman. 2005. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33:D121-D124.