MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the na？ve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies.
Lee I, Ajay SS, Yook JI, Kim HS, Hong SH, et al. (2009) New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome Res 19: 1175–1183. doi: 10.1101/gr.089367.108
Wang XW, El Naqa IM (2008) Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24: 325–332. doi: 10.1093/bioinformatics/btm595
Rusinov V, Baev V, Minkov IN, Tabler M (2005) MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res 33: W696–W700. doi: 10.1093/nar/gki364
Betel D, Wilson M, Gabow A, Marks DS, Sander C (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res 36: D149–D153. doi: 10.1093/nar/gkm995
Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, et al. (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 10: 295. doi: 10.1186/1471-2105-10-295
Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, et al. (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126: 1203–1217. doi: 10.1016/j.cell.2006.07.031
Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. Rna-a Publication of the Rna Society 10: 1507–1517. doi: 10.1261/rna.5248604
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nature Genetics 39: 1278–1284. doi: 10.1038/ng2135
Hammell M, Long D, Zhang L, Lee A, Carmack CS, et al. (2008) mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts. Nat Methods 5: 813–819. doi: 10.1038/nmeth.1247
Hua YJ, Tang ZY, Tu K, Zhu L, Li YX, et al. (2009) Identification and target prediction of miRNAs specifically expressed in rat neural tissue. BMC Genomics 10: 214. doi: 10.1186/1471-2164-10-214
Muniategui A, Nogales-Cadenas R, Vazquez M, Aranguren XL, Agirre X, et al. (2012) Quantification of miRNA-mRNA interactions. PLoS ONE 7: e30766. doi: 10.1371/journal.pone.0030766
Jayaswal V, Lutherborrow M, Ma DD, Yang YH (2011) Identification of microRNA-mRNA modules using microarray data. BMC Genomics 12: 138. doi: 10.1186/1471-2164-12-138
Saunders MA, Liang H, Li WH (2007) Human polymorphism at microRNAs and microRNA target sites. Proc Natl Acad Sci U S A 104: 3300–3305. doi: 10.1073/pnas.0611347104
Hong X, Hammell M, Ambros V, Cohen SM (2009) Immunopurification of Ago1 miRNPs selects for a distinct class of microRNA targets. Proceedings of the National Academy of Sciences of the United States of America 106: 15085–15090. doi: 10.1073/pnas.0908149106
Ruby JG, Stark A, Johnston WK, Kellis M, Bartel DP, et al. (2007) Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res 17: 1850–1864. doi: 10.1101/gr.6597907
Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, et al. (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141: 129–141. doi: 10.1016/j.cell.2010.03.009
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39: 1278–1284. doi: 10.1038/ng2135
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120: 15–20. doi: 10.1016/j.cell.2004.12.035
Djordjevic M, Sengupta AM, Shraiman BI (2003) A biophysical approach to transcription factor binding site discovery. Genome Res 13: 2381–2390. doi: 10.1101/gr.1271603
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, et al. (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77. doi: 10.1186/1471-2105-12-77
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940–3941. doi: 10.1093/bioinformatics/bti623
M？dder U, Sanyal A, Xu J, O'Malley B, Spelsberg T, et al. (2008) The skeletal response to estrogen is impaired in female but not in male steroid receptor coactivator (SRC)-1 knock out mice. Bone 42: 414–421. doi: 10.1016/j.bone.2007.10.017
M？dder U, Sanyal A, Kearns A, Sibonga J, Nishihara E, et al. (2004) Effects of loss of steroid receptor coactivator-1 on the skeletal response to estrogen in mice. Endocrinology 145: 913–921. doi: 10.1210/en.2003-1089
Rickard DJ, Subramaniam M, Spelsberg TC (1999) Molecular and cellular mechanisms of estrogen action on the skeleton. J Cell Biochem Suppl 32–33: 123–132. doi: 10.1002/(sici)1097-4644(1999)75:32+<123::aid-jcb15>3.0.co;2-k
Yamada T, Kawano H, Sekine K, Matsumoto T, Fukuda T, et al. (2004) SRC-1 is necessary for skeletal responses to sex hormones in both males and females. J Bone Miner Res 19: 1452–1461. doi: 10.1359/jbmr.040515
Kiel DP, Demissie S, Dupuis J, Lunetta KL, Murabito JM, et al. (2007) Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet 8 Suppl 1: S14. doi: 10.1186/1471-2350-8-s1-s14
Jin Y, Desta Z, Stearns V, Ward B, Ho H, et al. (2005) CYP2D6 genotype, antidepressant use, and tamoxifen metabolism during adjuvant breast cancer treatment. J Natl Cancer Inst 97: 30–39. doi: 10.1093/jnci/dji005
Jin Y, Hayes D, Li L, Robarge J, Skaar T, et al. (2008) Estrogen receptor genotypes influence hot flash prevalence and composite score before and after tamoxifen therapy. J Clin Oncol 26: 5849–5854. doi: 10.1200/jco.2008.16.8377
Ntukidem N, Nguyen A, Stearns V, Rehman M, Schott A, et al. (2008) Estrogen receptor genotypes, menopausal status, and the lipid effects of tamoxifen. Clinical pharmacology and therapeutics 83: 702–710.
Campo-Paysaa F, Semon M, Cameron RA, Peterson KJ, Schubert M (2011) microRNA complements in deuterostomes: origin and evolution of microRNAs. Evol Dev 13: 15–27. doi: 10.1111/j.1525-142x.2010.00452.x
Rivadeneira F, Styrkarsdottir U, Estrada K, Halldorsson B, Hsu Y-H, et al. (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41: 1199–1206. doi: 10.1038/ng.446
Brower-Sinning R, Carter DM, Crevar CJ, Ghedin E, Ross TM, et al. (2009) The role of RNA folding free energy in the evolution of the polymerase genes of the influenza A virus. Genome Biol 10: R18. doi: 10.1186/gb-2009-10-2-r18
Eulalio A, Rehwinkel J, Stricker M, Huntzinger E, Yang SF, et al. (2007) Target-specific requirements for enhancers of decapping in miRNA-mediated gene silencing. Genes & Development 21: 2558–2570. doi: 10.1101/gad.443107
Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of MicroRNA-target recognition. Plos Biology 3: 404–418. doi: 10.1371/journal.pbio.0030085
Zhang L, Ding L, Cheung TH, Dong MQ, Chen J, et al. (2007) Systematic identification of C. elegans miRISC proteins, miRNAs, and mRNA targets by their interactions with GW182 proteins AIN-1 and AIN-2. Molecular Cell 28: 598–613. doi: 10.1016/j.molcel.2007.09.014
Monroe DG, Getz BJ, Johnsen SA, Riggs BL, Khosla S, et al. (2003) Estrogen receptor isoform-specific regulation of endogenous gene expression in human osteoblastic cell lines expressing either ERalpha or ERbeta. J Cell Biochem 90: 315–326. doi: 10.1002/jcb.10633
Monroe DG, Secreto FJ, Hawse JR, Subramaniam M, Khosla S, et al. (2006) Estrogen receptor isoform-specific regulation of the retinoblastoma-binding protein 1 (RBBP1) gene: roles of AF1 and enhancer elements. J Biol Chem 281: 28596–28604. doi: 10.1074/jbc.m605226200
Monroe DG, Secreto FJ, Subramaniam M, Getz BJ, Khosla S, et al. (2005) Estrogen receptor alpha and beta heterodimers exert unique effects on estrogen- and tamoxifen-dependent gene expression in human U2OS osteosarcoma cells. Mol Endocrinol 19: 1555–1568. doi: 10.1210/me.2004-0381
Henry N, Nguyen A, Azzouz F, Li L, Robarge J, et al. (2009) Lack of association between oestrogen receptor polymorphisms and change in bone mineral density with tamoxifen therapy. British journal of cancer 102: 294–300.
