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2. disease
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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: richard.jovelin@utoronto.ca.
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
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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
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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
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miRNAs sites
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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
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0
0
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Mean number of miRNAs
Mean number of miRNAs
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Mean number of binding sites
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Mean number of binding sites
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CNV Gene Regulation by miRNAs . doi:10.1093/molbev/msv073
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Mean UTR length (bp)
Number of miRNAs
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CNV non-CNV
UTR Length (bp)
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Mean UTR length (bp)
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Number of miRNAs
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CNV non-CNV
1000 2000 3000 4000 5000 6000 7000 8000
UTR Length (bp)
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Mean UTR length (bp)
Number of miRNAs
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CNV non-CNV
UTR Length (bp)
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Mean UTR length (bp)
Number of miRNAs
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6000
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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
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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.
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