MBE Advance Access published April 15, 2015 No Evidence that MicroRNAs Coevolve with Genes Located in Copy Number Regions Richard Jovelin*,1 1 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada *Corresponding author: E-mail: [email protected]. Associate editor: Hideki Innan Abstract Key words: miRNA, copy number variation, gene regulation, regulatory networks, robustness. regulation needs to be investigated in multiple taxa to determine whether it represents an ancient evolutionary interaction or a derived function. To address this issue, I first used recent predictions of miRNA target sites in TargetScanHuman 6.2 (Garcia et al. 2011) and CNV annotations in the Database of Genomic Variants (MacDonald et al. 2014) to compare miRNA regulation between human CNV and non-CNV genes. On average, human CNV genes are regulated by 18% more miRNAs and have 23% more binding sites than non-CNV genes (fig. 1), consistent with published results (Felekkis et al. 2011). Second, I investigated the interaction between CNVs and miRNAs using predicted miRNA target sites from TargetScan 6.2 and CNV annotations in three other model organisms: C. elegans, Danio rerio, and Drosophila melanogaster (Ruby et al. 2007; Emerson et al. 2008; Maydan et al. 2010; Jan et al. 2011; Brown et al. 2012; Ulitsky et al. 2012). Similar to human, CNV genes in the fruit fly have greater miRNA regulation than non-CNV genes (fig. 1). However, worm and zebrafish show the opposite pattern with significantly more miRNAs and target sites per non-CNV genes (fig. 1). Similar results are obtained with predicted sites from miRanda (Betel et al. 2008) available in human, fly, and nematode (Supplementary Table S1). A potential drawback with miRNA target site predictors is the rate of false positives. Nevertheless, consistent differences among species are observed when using all predicted sites or a more stringent set of sites filtered by phylogenetic conservation or quality scores (Supplementary Table S1) and when using experimentally validated miRNA-target interactions from miRTarbase (Hsu et al. 2014) in human and worm (Supplementary Table S2). These results indicate that the relationship between ß The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: [email protected] Mol. Biol. Evol. doi:10.1093/molbev/msv073 Advance Access publication March 24, 2015 1 Letter MicroRNAs (miRNAs) are small noncoding regulatory RNAs playing essential roles by controlling gene expression and protein output (Bartel 2004). The functional characterization of miRNAs has fallen behind their discovery as a widespread class of regulators since miRNAs were identified in the nematode Caenorhabditis elegans by forward genetic screens (Lee et al. 1993; Reinhart et al. 2000). Few miRNAs have a mutant phenotype despite pervasive purifying selection, suggesting functional redundancy and/or that miRNA functions become apparent when organisms are subject to environmental and genetic perturbations (Miska et al. 2007; Li et al. 2009; Alvarez-Saavedra and Horvitz 2010; Brenner et al. 2010; Meunier et al. 2013; Jovelin and Cutter 2014). Indeed, regulatory circuits involving miRNAs may canalize phenotypes by reducing stochasticity inherent to gene expression and by using noise to create thresholds and stable switches (Hornstein and Shomron 2006; Herranz and Cohen 2010; Ebert and Sharp 2012; Siciliano et al. 2013). Although the origin of miRNAs in eukaryote lineages is still controversial (Tarver et al. 2012; Moran et al. 2013; Robinson et al. 2013), their function in tissue identity evolved early during animal history (Christodoulou et al. 2010). Yet, a recent study suggests that miRNAs may have evolved as a response to dosage imbalance due to structural variation (Felekkis et al. 2011). Human genes located in copy number regions (copy number variation [CNV] genes) have more miRNA regulators and corresponding sites than non-CNV genes, suggesting that miRNAs coevolve with CNVs (Felekkis et al. 2011). This result is consistent with the finding that miRNAs can buffer phenotypic variation against genomic diversity (Cassidy et al. 2013). Nevertheless, the relationship between structural variation and miRNA Downloaded from http://mbe.oxfordjournals.org/ at University of Toronto Library on April 15, 2015 MicroRNAs (miRNAs) are a widespread class of regulatory noncoding RNAs with key roles in physiology and development, conferring robustness to noise in regulatory networks. Consistent with this buffering function, it was recently suggested that human miRNAs coevolve with genes in copy number regions (copy number variation [CNV] genes) to reduce dosage imbalance. Here, I compare miRNA regulation between CNV and non-CNV genes in four model organisms. miRNA regulation of CNV genes is elevated in human and fly but reduced in nematode and zebrafish. By analyzing 31 human CNV data sets, careful analysis of human and chimpanzee orthologs, resampling genes within species and comparing structural variant types, I show that the apparent coevolution between CNV genes and miRNAs is due to the strong dependency between 30 -untranslated region length and miRNA target prediction. Deciphering the interplay between CNVs and miRNAs will likely require a deeper understanding of how miRNAs are embedded in regulatory circuits. MBE Jovelin . doi:10.1093/molbev/msv073 300 250 200 200 150 150 100 100 50 50 *** *** 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 miRNAs sites miRNAs sites *** 30 30 25 25 20 20 15 15 10 10 5 5 0 0 miRNAs sites Mean number of miRNAs *** 16 *** *** 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 miRNAs sites 0 Mean number of binding sites 35 Mean number of binding sites Mean number of miRNAs CNV non-CNV FIG. 1. Comparison of the mean number of miRNA regulators (left axis) and mean number of miRNA binding sites (right axis) predicted by TargetScan between CNV genes and non-CNV genes in four model organisms. CNV miRNA target genes have more miRNA regulators and target sites than nonCNV genes in human and fly. In contrast, non-CNV genes are regulated by more miRNAs and have more target sites than CNV genes in zebrafish and worm. Human: NCNV = 16,060, Nnon-CNV = 1,360; fly: NCNV = 1,580, Nnon-CNV = 10,170; worm: NCNV = 731, Nnon-CNV = 14,656; zebrafish: NCNV = 2,787, NnonCNV = 13,610; ***P < 0.0001, Wilcoxon rank-sum tests. Error bars represent 1 standard error of the mean. structural genomic variation and miRNA regulation is complex and does not necessarily lead to increased miRNA target sites for genes in CNV regions. Moreover, the opposite patterns observed within two protostomes and within two deuterostomes argue against the hypothesis that miRNAs may have evolved under selective pressure to accommodate the fluidity of genomes (Felekkis et al. 2011), or that the hypothesized evolutionary interaction is a unique derived function. What may be causing the observed differences among species? A simple explanation is that more target sites are predicted in longer 30 -untranslated regions (UTRs). Indeed, the number of miRNAs and the number of sites per gene are strongly correlated with the length of the 30 -UTR in all four species (human: miRNAs = 0.987, sites = 0.989; fly: miRNAs = 0.852, sites = 0.886; zebrafish: miRNAs = 0.905, sites = 0.937; worm: miRNAs = 0.844, sites = 0.871; Spearman’s rank correlation, P < 0.0001) (fig. 2). Importantly, the 30 -UTR length of CNV genes in human and fly is on average, respectively, 27% and 39% greater than the 30 -UTR length of non-CNV genes (fig. 2). In contrast, 2 the 30 -UTR of CNV genes in worm and zebrafish is, respectively, 30% and 8% shorter than the 30 -UTR of non-CNV genes (fig. 2). Including predicted sites located in the coding sequence (Schnall-Levin et al. 2010; Liu et al. 2015) gave consistent results (Supplementary Tables S3 and S4). Thus, the apparent coevolution between miRNAs and CNV genes in human and fly may be explained by the strong dependency between 30 -UTR length and miRNA target prediction. Transcripts with more intense posttranscriptional miRNA regulation may have longer 30 -UTRs, and so differences among species could result from functional differences between CNV and non-CNV genes. To test this possibility, I compared human CNV and non-CNV genes with their non-CNV orthologs using chimpanzee CNVs from Perry et al. (2008). Because human miRNAs benefit from more in-depth annotation (1,267 miRNA families in human vs. 423 miRNA families in chimp), both human CNV genes and non-CNV genes have greater miRNA regulation than their chimpanzee orthologs in non-CNV regions, despite no significant 30 -UTR length differences between orthologs Downloaded from http://mbe.oxfordjournals.org/ at University of Toronto Library on April 15, 2015 0 0 14 Mean number of miRNAs Mean number of miRNAs *** Mean number of binding sites *** Mean number of binding sites 250 MBE CNV Gene Regulation by miRNAs . doi:10.1093/molbev/msv073 1600 *** Mean UTR length (bp) Number of miRNAs 1200 1000 800 600 400 200 1400 1200 1000 800 600 400 200 0 0 0 3000 6000 9000 12000 15000 18000 21000 CNV non-CNV UTR Length (bp) 100 800 Mean UTR length (bp) 80 70 60 50 40 30 20 10 *** 700 600 Downloaded from http://mbe.oxfordjournals.org/ at University of Toronto Library on April 15, 2015 Number of miRNAs 90 500 400 300 200 100 0 0 0 CNV non-CNV 1000 2000 3000 4000 5000 6000 7000 8000 UTR Length (bp) 1400 Mean UTR length (bp) Number of miRNAs 160 140 120 100 80 60 40 20 1200 1000 800 600 400 200 0 0 0 2000 4000 6000 8000 *** 10000 CNV non-CNV UTR Length (bp) 200 140 Mean UTR length (bp) Number of miRNAs 120 100 80 60 40 20 0 0 1000 2000 3000 4000 5000 6000 *** 180 160 140 120 100 80 60 40 20 0 CNV non-CNV UTR Length (bp) FIG. 2. The number of miRNAs per target gene is strongly correlated with the length of the 30 -UTR in human, fly, zebrafish, and worm (left panels). Lines represent the linear regressions between the number of miRNAs inferred by TargetScan and the 30 -UTR length for each target gene. CNV genes have on average longer 30 -UTRs than non-CNV genes in human and fly. In contrast, the mean 30 -UTR length is shorter for CNV genes than for non-CNV genes in worm and zebrafish (right panels). ***P < 0.0001. (Supplementary fig. S1A and B). When the analysis is restricted to 406 conserved miRNA families (588 human miRNAs and 507 chimpanzee miRNAs), human CNV and non-CNV genes have 16% more miRNAs per gene than their non-CNV orthologs, but differences in target sites are very small (<1.2%) and not significant (Supplementary fig. S1C). Results are similar when both human and chimp CNV annotations are derived from Perry et al. (2008), although human CNV genes have significantly less miRNAs and target sites than human non-CNV genes in this study 3 MBE Jovelin . doi:10.1093/molbev/msv073 4 S7). Importantly, differential miRNA regulation between human CNV and non-CNV genes depends entirely on 30 -UTR length differences in all 31 studies (Supplementary Table S6), and the probability that miRNA regulation significantly differs given that the 30 -UTR length is significantly different is greater than 0.92 in all four species (Supplementary Table S7). These results do not support that CNV genes have longer 30 -UTRs. Instead, they indicate that greater miRNA regulation does not depend on a gene being in a CNV but on a gene having a longer 30 -UTR. To test whether miRNA regulation differs when controlling for 30 -UTR length differences, I first compared the number of miRNAs and binding sites normalized by the 30 -UTR length (Supplementary Table S8). Second, I predicted miRNA binding sites with TargetScan using 30 -UTR lengths from 100 bp to 1 kb (Supplementary Table S9). Differences between CNV and non-CNV genes are small (<4%) in all species except C. elegans, although some remain statistically significant after normalizing and when genes have the same 30 -UTR length (Supplementary Tables S8 and S9). Thus, these results do not provide support for increased miRNA regulation or miRNA avoidance for genes in regions of CNV. In conclusion, the apparent pattern of coevolutionary interactions noted in Felekkis et al. (2011) can be explained by the strong correlation between 30 -UTR length and target sites. Moreover, the hypothesis that natural selection favors a tighter posttranscriptional regulation of CNV genes rests on the assumption that miRNAs reduce expression levels to restore dosage balance (Felekkis et al. 2011). However, the relationship between expression level and CNVs is complex. Most CNV genes are dosage insensitive, whereas expression variation can either follow or be reversed with copy number decrease and increase for dosage sensitive genes (Zhou et al. 2011). In addition, genes encoding protein complexes, prone to dosage imbalance (Birchler and Veitia 2012), do not survive long in CNVs (Dopman and Hartl 2007; SchusterBockler et al. 2010; Zhou et al. 2011). The results presented here do not support a systemic and consistent relationship between CNVs and miRNAs. Instead, they suggest that deciphering the interplay between miRNAs and structural variants will likely require a deeper and precise understanding of the function of miRNAs within regulatory networks. For instance, miR-9 a but not miR-7 reduces the effect of genomic diversity on phenotypic variation in fly (Cassidy et al. 2013). Depending on their position within regulatory circuits and the type of loops they form with transcription factors, miRNAs can either attenuate or amplify expression variation (Hornstein and Shomron 2006; Herranz and Cohen 2010; Leung and Sharp 2010; Ebert and Sharp 2012; Siciliano et al. 2013). This may explain why expression variation within and among species is elevated for some miRNA target genes but reduced for others (Cui et al. 2007; Lu and Clark 2012). Supplementary Material Supplementary figure S1 and tables S1–S9 are available at Molecular Biology and Evolution online (http://www.mbe. oxfordjournals.org/). Downloaded from http://mbe.oxfordjournals.org/ at University of Toronto Library on April 15, 2015 (not shown). Thus, the hypothesis that miRNA regulation increases following CNV formation (Felekkis et al. 2011) is not supported when orthologous and presumably functionally conserved genes are compared, and when controlling for biased miRNA annotation between species. Differences in miRNA regulation of CNV genes among species could result from differential abundance of structural variant subtypes. For instance, miRNAs may preferentially regulate dosage sensitive genes located in regions of increased copy numbers to reduce gene expression levels, or may preferentially buffer stochastic variation of dosage sensitive genes with low expression in regions of decreased copy numbers. I tested this possibility by sorting CNVs that result exclusively in gain or loss of DNA, using information on structural variant types available for human, Drosophila and Caenorhabditis. The majority of miRNA CNV targets is located in CNVs with loss of DNA in human (4,762 CNV loss genes, 80.5 %) and worm (637 CNV loss genes, 87.14%) and in CNVs with gain of DNA in fly (977 CNV gain genes, 67.85%). Nevertheless, there is no clear relationship between miRNA regulation and the type of structural alteration. In fly, nonCNV genes have lower miRNA regulation than both CNV loss and CNV gain genes, whereas non-CNV genes in worm have a larger number of miRNA regulators and target sites than genes in either CNV subtype (P < 0.05). And non-CNV genes in human are less targeted than CNV loss genes (P < 0.0001) but more targeted than CNV gain genes (P < 0.05). In addition, CNV loss genes have more miRNA regulators and binding sites than CNV gain genes in human and in fly, but miRNAs and target sites are more abundant for CNV gain genes than for CNV loss genes in worm (Supplementary Table S5). Moreover, miRNA targeting differences between CNV loss and CNV gain genes are fully consistent with differences in 30 -UTR lengths (Supplementary Table S5). In summary, differential abundance of distinct CNV subtypes cannot explain the observed differences between CNV and non-CNV genes among species. Patterns of miRNA CNV gene regulation depend on the accuracy of CNV annotations, and so differential coverage of studies identifying CNVs could mask a potential evolutionary interaction between CNVs and miRNAs. Indeed, although the Database of Genomic Variants compiles CNV regions from 52 studies, CNV annotations in other species were derived from a single study. To evaluate how annotations may affect the inference of coevolution between CNV genes and miRNAs, I separately analyzed human CNV data sets from 31 studies with greater than 500 CNV miRNA target genes. Human CNV genes have greater miRNA regulation than non-CNV genes in 17 data sets (55%), lower regulation in eight data sets (26%), and no significant difference in six data sets (19%) (Supplementary Table S6). To further investigate the effect of CNV annotation, I generated 1,000 data sets for each species with 500 random CNV genes and 500 random non-CNV genes. Patterns of miRNA regulation for CNV and non-CNV genes are similar between the original data sets and those resulting from the resampling procedure, although less than 32% of the replicates in zebrafish show significantly lower miRNA regulation for CNV genes (Supplementary Table CNV Gene Regulation by miRNAs . doi:10.1093/molbev/msv073 Acknowledgments The author thank George Bell for providing the Summary Counts table for TargetScanFly and two anonymous reviewers for constructive comments on the manuscript. This work is supported by a grant from the National Health Institutes (R01-GM096008) to P.C.P. and A.D.C. References Jovelin R, Cutter AD. 2014. Microevolution of nematode miRNAs reveals diverse modes of selection. Genome Biol Evol. 6:3049–3063. Lee RC, Feinbaum RL, Ambros V. 1993. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843–854. Leung AK, Sharp PA. 2010. MicroRNA functions in stress responses. Mol Cell. 40:205–215. Li X, Cassidy JJ, Reinke CA, Fischboeck S, Carthew RW. 2009. A microRNA imparts robustness against environmental fluctuation during development. Cell 137:273–282. Liu G, Zhang R, Xu J, Wu CI, Lu X. 2015. Functional conservation of both CDS- and 30 -UTR-located microRNA binding sites between species. Mol Biol Evol. 32:623–628. Lu J, Clark AG. 2012. Impact of microRNA regulation on variation in human gene expression. Genome Res. 22:1243–1254. MacDonald JR, Ziman R, Yuen RK, Feuk L, Scherer SW. 2014. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 42: D986–D992. Maydan JS, Lorch A, Edgley ML, Flibotte S, Moerman DG. 2010. Copy number variation in the genomes of twelve natural isolates of Caenorhabditis elegans. BMC Genomics 11:62. Meunier J, Lemoine F, Soumillon M, Liechti A, Weier M, Guschanski K, Hu H, Khaitovich P, Kaessmann H. 2013. Birth and expression evolution of mammalian microRNA genes. Genome Res. 23:34–45. Miska EA, Alvarez-Saavedra E, Abbott AL, Lau NC, Hellman AB, McGonagle SM, Bartel DP, Ambros VR, Horvitz HR. 2007. Most Caenorhabditis elegans microRNAs are individually not essential for development or viability. PLoS Genet. 3:e215. Moran Y, Praher D, Fredman D, Technau U. 2013. The evolution of microRNA pathway protein components in Cnidaria. Mol Biol Evol. 30:2541–2552. Perry GH, Yang F, Marques-Bonet T, et al. 2008. Copy number variation and evolution in humans and chimpanzees. Genome Res. 18: 1698–1710. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. 2000. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403: 901–906. Robinson JM, Sperling EA, Bergum B, Adamski M, Nichols SA, Adamska M, Peterson KJ. 2013. The identification of microRNAs in calcisponges: independent evolution of microRNAs in basal metazoans. J Exp Zool B Mol Dev Evol. 320:84–93. Ruby JG, Stark A, Johnston WK, Kellis M, Bartel DP, Lai EC. 2007. Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res. 17: 1850–1864. Schnall-Levin M, Zhao Y, Perrimon N, Berger B. 2010. Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 30 UTRs. Proc Natl Acad Sci U S A. 107:15751–15756. Schuster-Bockler B, Conrad D, Bateman A. 2010. Dosage sensitivity shapes the evolution of copy-number varied regions. PLoS One 5: e9474. Siciliano V, Garzilli I, Fracassi C, Criscuolo S, Ventre S, di Bernardo D. 2013. MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise. Nat Commun. 4:2364. Tarver JE, Donoghue PC, Peterson KJ. 2012. Do miRNAs have a deep evolutionary history? Bioessays 34:857–866. Ulitsky I, Shkumatava A, Jan CH, Subtelny AO, Koppstein D, Bell GW, Sive H, Bartel DP. 2012. Extensive alternative polyadenylation during zebrafish development. Genome Res. 22:2054–2066. Zhou J, Lemos B, Dopman EB, Hartl DL. 2011. Copy-number variation: the balance between gene dosage and expression in Drosophila melanogaster. Genome Biol Evol. 3:1014–1024. 5 Downloaded from http://mbe.oxfordjournals.org/ at University of Toronto Library on April 15, 2015 Alvarez-Saavedra E, Horvitz HR. 2010. Many families of C. elegans microRNAs are not essential for development or viability. Curr Biol. 20:367–373. Bartel DP. 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297. Betel D, Wilson M, Gabow A, Marks DS, Sander C. 2008. The microRNA.org resource: targets and expression. Nucleic Acids Res. 36:D149–D153. Birchler JA, Veitia RA. 2012. Gene balance hypothesis: connecting issues of dosage sensitivity across biological disciplines. Proc Natl Acad Sci U S A. 109:14746–14753. Brenner JL, Jasiewicz KL, Fahley AF, Kemp BJ, Abbott AL. 2010. Loss of individual microRNAs causes mutant phenotypes in sensitized genetic backgrounds in C. elegans. Curr Biol. 20:1321–1325. Brown KH, Dobrinski KP, Lee AS, Gokcumen O, Mills RE, Shi X, Chong WWS, Chen JYH, Yoo P, David S, et al. 2012. Extensive genetic diversity and substructuring among zebrafish strains revealed through copy number variant analysis. Proc Natl Acad Sci U S A. 109:529–534. Cassidy JJ, Jha AR, Posadas DM, Giri R, Venken KJ, Ji J, Jiang H, Bellen HJ, White KP, Carthew RW. 2013. miR-9a minimizes the phenotypic impact of genomic diversity by buffering a transcription factor. Cell 155:1556–1567. Christodoulou F, Raible F, Tomer R, Simakov O, Trachana K, Klaus S, Snyman H, Hannon GJ, Bork P, Arendt D. 2010. Ancient animal microRNAs and the evolution of tissue identity. Nature 463: 1084–1088. Cui Q, Yu Z, Purisima EO, Wang E. 2007. MicroRNA regulation and interspecific variation of gene expression. Trends Genet. 23:372–375. Dopman EB, Hartl DL. 2007. A portrait of copy-number polymorphism in Drosophila melanogaster. Proc Natl Acad Sci U S A. 104: 19920–19925. Ebert MS, Sharp PA. 2012. Roles for microRNAs in conferring robustness to biological processes. Cell 149:515–524. Emerson JJ, Cardoso-Moreira M, Borevitz JO, Long M. 2008. Natural selection shapes genome-wide patterns of copy-number polymorphism in Drosophila melanogaster. Science 320:1629–1631. Felekkis K, Voskarides K, Dweep H, Sticht C, Gretz N, Deltas C. 2011. Increased number of microRNA target sites in genes encoded in CNV regions. Evidence for an evolutionary genomic interaction. Mol Biol Evol. 28:2421–2424. Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP. 2011. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 18: 1139–1146. Herranz H, Cohen SM. 2010. MicroRNAs and gene regulatory networks: managing the impact of noise in biological systems. Genes Dev. 24: 1339–1344. Hornstein E, Shomron N. 2006. Canalization of development by microRNAs. Nat Genet. 38(Suppl), S20–S24. Hsu SD, Tseng YT, Shrestha S, et al. 2014. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 42:D78–D85. Jan CH, Friedman RC, Ruby JG, Bartel DP. 2011. Formation, regulation and evolution of Caenorhabditis elegans 30 UTRs. Nature 469:97–101. MBE
© Copyright 2024