Even though the genomes of many model species have already been sequenced, our knowledge of gene regulation in evolution is still very limited. One big obstacle is that it is hard to predict the target genes of transcriptional factors accurately from sequences. In this respect, microRNAs (miRNAs) are different from transcriptional factors, as target genes of miRNAs can be readily predicted from sequences. This feature of miRNAs offers an unprecedented vantage point for evolutionary analysis of gene regulation.
In this study, we analyzed a particular aspect of miRNA evolution, the differences in the “apparent repression effectiveness (ARE)” between human miRNAs of different conservational levels. ARE is a measure we designed to evaluate the repression effect of miRNAs on target genes based on publicly available gene expression data in normal tissues and miRNA targeting and expression data. We found that ARE values of more conserved miRNAs are significantly higher than those of less conserved miRNAs in general. We also found the gain in expression abundance and broadness of miRNAs in evolution contributed to the gain in ARE.
The ARE measure quantifies the repressive effects of miRNAs and enables us to study the influences of many factors on miRNA-mediated repression, such as conservational levels and expression levels of miRNAs. The gain in ARE can be explained by the existence of a trend of miRNAs in evolution to effectively control more target genes, which is beneficial to the miRNAs but not necessarily to the organism at all times. Our results from miRNAs gave us an insight of the complex interplay between regulators and target genes in evolution.
Citation: Huang Y, Gu X (2011) A Study of the Evolution of Human microRNAs by Their Apparent Repression Effectiveness on Target Genes. PLoS ONE 6(9): e25034. https://doi.org/10.1371/journal.pone.0025034
Editor: Sunil K. Ahuja, South Texas Veterans Health Care System, United States of America
Received: November 17, 2010; Accepted: August 26, 2011; Published: September 21, 2011
Copyright: © 2011 Huang, Gu. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a Pioneer Computational Biology Scholarship to YH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Even though the genome sequences of many model species are available now, our knowledge of gene regulation in evolution, including the changes in the regulatory genes, the regulatory sequence elements, and their interactions, is still very limited. The reason is that it is still very hard to accurately predict targets of transcriptional factors from sequences. Current methods for predicting transcription factor binding sites, such as those described in , are not yet sufficiently accurate. Furthermore, transcriptional factors usually act cooperatively with other factors, but the prediction of protein-protein interaction alone is still an unsolved problem.
In contrast, the target genes of microRNAs (miRNAs) can be readily predicted from sequences. MiRNAs are small RNAs that are encoded in genomes with stable expression patterns in tissues. MiRNA-mediated gene regulation seems to have originated very early in evolution, possibly before the emergence of the common ancestor of eukaryocytes , . They regulate a large number of target genes , , , in particular regulatory genes . MiRNAs repress the expression of their target genes by base-pairing to target sites usually in the 3′ UTR of the transcripts of the genes. Predicting target sites (and thus target genes) of miRNAs is relatively straight forward. In plants, the target sites usually are almost perfectly complementary to the mature sequence of miRNAs , while in animals the target sites usually are complementary at least to the seed region of miRNAs , , . In vitro experiments have shown that a single site alone is sufficient for miRNAs to exert the repressive effects . While the predicted target genes of miRNAs still contain many false positives, their accuracy is much higher than that of the transcriptional factors. These features of miRNAs offer us an unprecedented vantage point to study their evolution and give us insights into the evolution of gene regulation.
Of particular interest is the difference in repression effectiveness among miRNAs of different conservational levels. It is well understood now that, due to the repressive effect of miRNAs on target genes, the 3′UTRs of highly expressed genes are less likely to have target sites of miRNAs expressed in the same tissues , , , . On the other hand, phylogenetic studies have shown that miRNAs emerged in evolution progressively at time points matching major speciation events, such that the repertoire of miRNAs in an organism is a mixture of miRNAs of different conservational levels , . MiRNAs have been described as paradoxical regulators for being both evolutionary conserved and functionally dispensable (Wu et al., 2009). Their effects on the expression of target genes are often found to be relative (Baek et al., 2008; Selbach et al., 2008), than absolute, like on-off switches (Bartel, 2009). We found that a lot of confusions have been caused in miRNA studies due to the lack of a quantitative measurement of the repression effects of miRNAs.
In this study, we designed the measure of “apparent repression effectiveness (ARE)” to quantify the repressive effect of miRNAs on the expression of target genes at transcript level. Using the ARE measuer, we found that more conserved miRNAs have significantly higher repression effectiveness on the target genes than less conserved miRNAs on average, which can be explained by the existence of an increasing trend in miRNA evolution to secure more target genes. The ARE measure results of miRNAs gave us an insight into the interplay between regulator and target genes in evolution and also opened many new possibilities for further study of the interplay in cell and evolution.
Apparent repression effectiveness
The essential functionality of miRNAs is their repressive effect on the expression of target genes. However, this repressive effect is usually not complete. To quantify the repressive effect of miRNAs, we designed the “apparent repression effectiveness (ARE)” measure. As depicted in Table 1, for a particular miRNA in a tissue, genes are partitioned into target (T) and non-target genes (N), and into highly-expressed (H) and non-highly-expressed genes (L) (see Methods for detail). For a miRNA in a tissue, all the genes were partitioned into four categories, TH, TL, NH and NL. These symbols also denoted the number of genes in each category. The ARE of a miRNA in a tissue is the logarithm of the relative risk (RR) of finding genes highly expressed given not targeted versus targeted by the miRNA, which is expressed in the following formula, where the probabilities are calculated as frequencies from the data.(1)(2)(3)The ARE is the logarithm of a relative risk (RR) statistic, so it has an approximate normal distribution when the numbers of genes in each partition in Table 1 is not overly small. The genes that have no target sites of any miRNAs were not included in this study, as they are not informative to the differences of ARE between miRNAs. The reason that ARE measures the “apparent” repression effectiveness is that the expression levels of genes used in this study are from normal tissues, which means they are posterior (after miRNA-mediated regulations) expression levels. ARE thus measures the combined reduction in gene expression caused by both miRNA-mediated degradation of mRNAs and endogenous mutual exclusiveness in expression patterns between miRNAs and target genes. Since miRNAs repress gene expression mainly by blocking translation in animals, mutual exclusiveness in expression patterns is expected to contribute substantially to ARE. A miRNA with a higher ARE value in a tissue is less likely to have highly-expressed target genes in the tissue. This will indicate that the miRNA has stronger influences on the expression of its target genes in the tissues.
ARE values of human miRNAs in tissues
We calculated the ARE values of human miRNAs with both expression and targeting information available in twelve tissues, including cerebellum, frontal cortex, heart, liver, prostate, uterus, thyroid, placenta, pancreas, testis, ovary, and pituitary. We used the genes expression profiles in human tissues from GNF atlas 2 ; human miRNA expression profile from the mammalian miRNA expression atlas ; and miRNA target prediction from PicTar . In this study, all the gene expression data were at the transcript level.
A miRNA was viewed to be expressed in a tissue if its clone count was greater than 0 from the mammalian miRNA expression atlas. The target genes of miRNAs were retrieved from the PicTar database . For a chosen cutoff of high expression level, the numbers of genes in the TH, TL, NH, NL categories were counted and the ARE values were calculated as in Formula 1x1-37001r1. It should be noted that the choice of the expression cutoff is not trivial. Shown in Figure 1 are the ARE values for a typical miRNA, hsa-let-7a with different expression cutoff. The dots represent the mean ARE values across tissues where hsa-let-7a is expressed, and the error bars represent the standard error of the means. It is obvious from Figure 1 that ARE values are larger when the expression cutoff is set higher. This is expected, as miRNAs are negative regulators of gene expression and co-expression of miRNAs and highly expressed target genes are less likely to exist. It is also obvious that at relatively low expression cutoffs (such as 50%), the ARE values are very close to 0. This shows that, in general, the repressive effect of miRNAs on gene expression is more prominent at preventing their target genes from being highly expressed, than turning them off completely. It should be noted that, when the cutoff was set higher, the number of genes expressed above the cutoff was also smaller. As the result, the variances of the ARE values also became larger. In this study, we used 80% percentile as the cutoff for highly expressed genes, as the gene number above this cutoff is still big enough. Also, this is a commonly used cutoff in the literature.
A) Means and standard errors of ARE values across tissues of hsa-let-7a, B) ARE values of hsa-let-7a in different tissues.
Using the 80% percentile as the cutoff of high expression, the ARE values for each miRNA in each tissue were calculated and listed in Table S1. The ARE values in the tissues where the miRNA was not expressed were also calculated for control purposes. It is obvious from Figure 1B that ARE values of the same miRNA differ greatly in different tissues where the miRNAs is expressed. This is not unexpected, as not only the targeting and expression information of miRNAs is not yet perfectly accurate, but the expression levels of miRNAs also differ greatly among tissues. One representative example is the hsa-miR-1 case, as is shown in Table 2. The ARE value of hsa-miR-1 is 0.2961 in heart, where the miRNA is highly expressed (clone count 15). However, in tissues of prostate, uterus, thyroid, testis, and ovary where hsa-miR-1 is expressed at very low levels (clone counts 1 or 2), the ARE values are small and negative. The ARE values calculated from tissues where the miRNAs are highly expressed are more reliable, considering the potential errors in miRNA expression profiling (some miRNAs with clone count 1 or 2 may not be really expressed in the tissue). In this regard, for each miRNA, we chose the ARE value from the tissue where the miRNA was most highly expressed (take median in case of tie) as the representative ARE (rARE) of the miRNA and used it for comparisons between miRNAs.
Differences in rARE values among human miRNAs of different conservational categories
We compared the rARE (representative ARE) values among human miRNAs of different conservational categories. We classified human miRNAs into three categories based on the presence of their homologs in metazoan species, as was described in our previous report . The phylogenetic distribution of miRNAs was based on the miFam feature from miRBase. Briefly, a miRNA was classified into category I if it has homologs in mammals, non-mammal vertebrates, and invertebrates; category II if it has homologs only in mammals and non-mammal vertebrates; and to category III if it has homologs only in mammals (see Methods for details). We used only miRNAs with both targeting and expression information available in this study, which covered 38 human miRNAs in category I, 76 in category II and 19 in category III. All the miRNAs are conserved at least between human and mouse. The calculated rARE values and the conservational category classification of miRNAs are summarized in Table S2.
The comparison of the rARE values among miRNAs showed an interesting trend among different conservational categories. As is shown in Figure 2, the mean rARE values of miRNAs is the highest in the most conserved Category I and lowest in the least conserved Category III (see Table 3 for values of means and standard errors at cutoff 80%). We carried out ANOVA, using the rARE values as the response variable and conservational categories as the explanatory variable. The result showed that the conservational category is a significant factor for rARE values of miRNAs (F-test p-value = 0.0203). Comparison of the mean rARE values using Tukey's HSD test showed that the mean rARE value of Category I miRNAs is significantly larger than that of Category II (p-value = 0.0404) and Category III (p-value = 0.0459). The difference between Category II and III is not significant by Tukey's HSD test, likely resulted from the large standard error of mean rARE value in Category III (which has a small number of miRNAs).
The most conserved miRNAs in Category I has significantly higher rARE values than miRNAs in less conserved categories.
The finding of more conserved miRNAs having higher rARE values than less conserved miRNAs on average suggested the existence of an increasing trend in miRNAs to effectively control more target genes in evolution. It has been reported that the birth and death of new miRNAs happen frequently in evolution . In this respect, our result can be explained that in miRNA evolution, there may be continuous pressure for the miRNAs to secure enough target genes to ensure its own existence in the genome.
We carried out several control analyses to confirm that the finding of the increasing trend in rARE values among miRNAs of different conservational categories was not caused by chances or by the use of a particular set of data.
First, since the cutoff of high expression was arbitrarily set at 80% percentile in the previous section, we tested whether using a different cutoff might change the results. We tested different cutoff values including 60%, 70%,, 80% and 90%. The results are summarized in Table 3. We can see that the increasing trend in the mean rARE values in miRNAs of different conservational categories exists at all the expression cutoffs. At all the cutoff values, the p-values for the significance of the conservational category as a factor are smaller than 0.05. The p-value at 80% is actually the least significant compared to other cutoffs. Meanwhile, similar to the example of hsa-let-7a in Figure 1A, the mean rARE values are generally higher under higher expression cutoff. This is in accordance with the common wisdom that it is less likely to find highly expressed target genes in the same tissue with the miRNA. The standard errors also increase as the cutoffs increases. This is due to the smaller number of genes expressed above higher cutoffs. Taken together, the results are generally consistent under different cutoffs.
We also examined the ARE values in the tissues where the miRNAs were not expressed. For a miRNA, the mean of the ARE values in these tissues without expression was used to compare among different conservational categories. One complication here was that, in a tissue where a miRNA was not expressed, its target genes might still be targeted by other miRNAs expressed in that tissue. In this regard, for the control analysis, we limited the non-target genes (in NH and NL categories) of a miRNA in a tissue to be those that have no target sites of any other miRNAs that were expressed in the tissue. The results are shown in Table 4. It is clear that the ARE values are very low (below −0.09 in all categories). There is also not a significant difference between different conservational categories (F-test p-value = 0.5646). This analysis showed that the trend we observed in Figure 2 is not by chance.
We further analyzed the rARE values using a different set of predicted targets from TargetScan Human . All the other settings were the same, only the predicted targets were from TargetScan, instead of PicTar. The results are shown in Table 5. A similar trend in the rARE values is observed, with the more conserved miRNAs having higher rARE values on average. The result is consistent with those based on the PicTar set of predicted targets. One concern with the TargetScan set of target for this analysis was that the predicted targets in TargetScan were filtered by inter-species conservation. For the PicTar set this has not been a problem, since site conservation is only among mammal species in PicTar and all the miRNAs in this study are conserved at least between human and mouse. However, site conservation is more strictly requested to be among mammals and chicken in TargetScan. As the result, some bias may exist in Category III as some true targets of miRNAs from Category III may not be included in the TargetScan set. However, these differences in the target sets have not influenced the general trend we observed in the rARE values.
Covariates that may also influence ARE
We have shown that the conservational category of miRNAs to be a significant factor for the differences in rARE values among the miRNAs. We further analyzed several covariates that might influence ARE more directly, including expression abundance (as clone counts) and broadness (as number of tissues with expression) of miRNA, and the accessibility of target sites.
In Figure 3A, we plotted the rARE values of miRNAs against the expression abundance (as logarithm of clone counts) of the miRNAs in the tissues where the rARE values were calculated from. Using the rARE values of miRNAs as the response variable and expression abundance as explanatory variable, an ANOVA analysis showed that expression abundance to be a significant factor for rARE values (F-test p-value = 0.006373). It is obvious that the expression abundance of miRNAs has a significant effect on the rARE values, but it should also be noted that the variance of rARE is also large. In Figure 3B, we calculated the mean rARE values for miRNAs of different expression broadness (as number of tissues with expression). It is also obvious that miRNAs that have broad expression pattern (expressed in more than 8 tissues) have a mean rARE value much greater than miRNAs expressed in 8 or less tissues. The standard error of the mean of rARE in Figure 3B is also large. These results show that in tissues where a miRNA was highly expressed or if a miRNA was broadly expressed in tissues, the miRNA was more likely to have high rARE values. Both expression abundance and broadness of miRNAs are significantly different among miRNAs of different conservational categories, as is shown in Figure 4 (the p-values are from the F-test with conservational category as the explanatory factor). However, the variance of rARE for miRNAs with similar expression abundance or broadness is also large, suggesting many other factors may also be in play.
A) Regression of rARE on expression abundance of miRNAs (as log(clone count)), B) Mean rARE values for miRNAs of different expression broadness (as number of tissues).
A) Difference in expression abundance (in tissue with maximal expression) among miRNAs in different conservational categories, B) Difference in expression broadness among miRNAs of different conservational categories.
The accessibility of target site has also been reported to be an important factor for miRNA-mediated regulation . It has been suggested that deeply conserved miRNAs may have sites that are more accessible , . The TargetScan set of predicted miRNAs site has the “context score” feature integrating information based on 3′ paring, local AU content and distance from UTR ends of miRNA target sites, which servers as a measure of the accessibility of miRNA target sites . In this regard, we examined the differences in context score for sites of miRNAs in different conservational categories. The results are summarized in Figure 5. Different from what has been suggested in other reports, we found the distributions of the context score of target sites are almost identical for miRNAs in different conservational categories. It should be noted that the target sites we used in this study are the conserved target sites, as they are generally believed to be more reliable. In general, at least for the conserved target sites, the context score does not seem to differ greatly among miRNAs from different conservational categories. This may suggest that the evolution of the accessibility of target sites was fast in evolution, such that all target sites that are conserved in species have similar distribution in their accessibility.
Interactions between miRNAs
Another concern for this study was that how might the interaction between different miRNAs targeting the same gene influence our conclusions. We further analyzed the composition of targeting miRNAs for each gene. In Figure 6A, we plotted the gene expression levels (normalized at array level, from ) against the number of targeting miRNAs (each targeting miRNA count only once, even if there are multiple target sites) in each conservational categories and all categories combined. While the linear regression line in all four plots are almost all flat, it was interesting to find that genes that were targeted by larger number of miRNAs have smaller variance in expression. This is obviously seen in Figure 6A, as all the plots have a “>” shape. The pattern suggested that genes that are heavily targeted by many miRNAs tend to have a expression at medium level, which is in accordance to the canalization view of miRNA function . While this pattern is identical in all miRNA conservational categories, the distribution of the number of targeting miRNAs is different between the categories, as is shown in Figure 6B. We can see that the proportion of genes that are targeted by more than 6 (ln(count+1)>2) miRNAs are higher for Category I miRNAs than that for Category II and III miRNAs. Similar results were found with TargetScan data, which can be found in Figure S1.
A) Gene expression levels were plotted against the number of targeting miRNAs (as ln(Targeting miRNAs+1)), for miRNAs in each category or all together. The red line in each graph was the linear regression line based on the data points. B) For each miRNA category, the proportion of genes with ln(Targeting miRNAs+1) in 0–1, 1–2 or >2 were shown. Category I miRNAs have more interaction in targeting compared to other miRNAs.
This result suggested a mechanism of how interaction in miRNA targeting may contribute to the repression effectiveness of miRNAs. While genes targeted by many miRNAs tend to be expressed at medium level, more conserved miRNAs also tend to target common genes. As the result, genes targeted by more conserved miRNAs were less likely to be expressed at high levels. It should be noted that ARE measures the marginal property of miRNA-mediated repression, which concerns only what proportion of target gene were repressed given a miRNA was expressed in a tissue. This is a necessary simplification, as very few other regulation details of the genes are reliably understood now.
In this study, we designed the apparent repression effectiveness (ARE) measure to quantify the repressive effects of miRNAs on the expression of their target genes. We used the representative ARE (rARE) for comparison between miRNAs. An increasing trend in rARE was observed, in which more conserved miRNAs were found to have higher rARE in general. The results were confirmed by control analyses using different data sets and cutoffs.
It should be noted that the conservational level of miRNAs is an associated factor rather than a direct causative factor for the ARE of the miRNAs. For individual miRNAs in particular tissues, the ARE value may be influenced by more direct factors such as co-expressed miRNAs targeting the same mRNA, or the type and number of binding sites. What our results showed was that, on average, more conserved miRNAs have higher rARE values. Many evolutionary processes may have contributed to this outcome, such as the gain of stable expression pattern for conserved miRNAs in evolution and the co-evolution of genes and miRNAs to achieve mutual exclusiveness in expression.
It should also be noted that only the human miRNAs that are conserved at least in mouse, and have both expression and targeting information, were used in this study. While these miRNAs cover most of the classical miRNAs they still represented just a subset of all human miRNAs. Large number of new miRNAs were being added to the public database continuously, many of which were lineage specific. Expression and targeting information of the new miRNAs are mostly not available yet. Further analyses with a more complete set of human miRNAs can help to confirm the generality of the findings in this study, when data become available.
Our ARE results gave us an insight into the interplay between regulators and target genes in evolution. There are recent reports showing that lowly expressed miRNAs (mostly lineage specific miRNAs) evolve fast , and lineage specific miRNAs exert adverse effects when expressed in different species . Furthermore, more conserved miRNAs were generally found to be expressed more broadly and target more miRNAs . All these findings are consistent with our results in suggesting a simple model for miRNA evolution, in which there is a “selfish” tendency in miRNAs to secure more target genes in evolution. The “selfish” tendency is beneficial for the existence of the miRNAs in the species, but is not necessarily beneficial to the organism. However, the “selfish” tendency of miRNAs helped to provide more choices of gene regulation for selection in evolution, which may be critical to the organisms at the time of major speciation.
From many aspects, miRNA-mediated gene regulation is much simpler than that by transcriptional factors and lends itself to evolutionary studies. We have focused on miRNAs in this study. The insights we gained of gene regulation evolution from miRNA studies will eventually help us to understand the more complicated evolution of transcription factor mediated regulation when more data become available.
Materials and Methods
Calculating ARE values
For calculating the ARE value of a miRNA in a tissue, all the genes were partitioned into target (T) and non-target genes (N), based on whether a gene contained at least one target sites of the miRNA (based on the miRNA target prediction in use, PicTar or TargetScan in this study). The genes were also partitioned into highly-expressed (H) and non-highly-expressed genes (L), based on whether a gene has expression level above (including equal to) or below the cutoff in the tissue. The expression cutoff was chosen to be the 80% percentile (higher percentile for higher expression) of all the genes in a tissue, based on GNF atlas 2 (see below). The ARE value for a miRNA in a tissue was then calculated as described in Table 1 and Formula 1.
MiRNA sequences and phylogeny
The sequences of miRNAs were retrieved from miRBase (Release 12) . The phylogenetic distribution of miRNAs is based on the miFam feature from miRBase. We classified the human miRNAs, on a family basis, into three conservation categories base on the presence of their homologs in metazoan species . If a miRNA family contains homologs in mammals, non-mammal vertebrates, and invertebrates, it is in category I; if a miRNA family contains homologs only in mammals and non-mammal vertebrates, it is in category II; if a miRNA family contains homologs only in mammals, it is in category III. This categorization offered us a basic estimation of the evolutionary age (time since emergence) of miRNAs. We used only miRNAs with both targeting and expression information available in this study, which covered 38 human miRNAs in category I, 76 in category II and 19 in category III.
MiRNA expression profiles
We used the miRNA expression data of the mammalian miRNA expression atlas , which was one of the most comprehensive miRNA expression profiles available. The data were based on 256 small RNA libraries from 26 different organ systems and cell types in human and rodents. In our analysis, we used data from the 12 human tissues where gene expression data are also available. These tissues were cerebellum, frontal cortex, heart, liver, prostate, uterus, thyroid, placenta, pancreas, testis, ovary, and pituitary.
In this data set, the cloning frequencies of miRNAs were used as the measure of miRNA expression. In total, the data set covered 340 human miRNAs (by mature form), encoded by 395 miRNA genes (expression levels divided evenly between precursors that produce the same mature forms) from 214 transcript units.
Human gene expression data
Gene expression profiles in human were retrieved from the GNF expression atlas 2 dataset . A cutoff of 200 was applied to the raw readings to control noise. The expression data were already normalized at array level. To normalize between different tissues, we use a percentile-based approach. In each tissue, the percentiles (from 10% to 95% step by 5%) of gene expression levels were calculated and used in the analysis.
MiRNA target site prediction
The predicted miRNA target sites were retrieved from PicTar . We used the set of human miRNA targets in which the sites were conserved among five mammals (human, chimp, mouse, rat and dog) (the “Lall et al. 2006” set). The TargetScan (TS) set of targets were used as control. It was retrieved from TargetScan , Release 5.0. The targets in the TargetScan were conserved between human, mouse, rat, dog and chicken.
ANOVA and regression analyses were carried out using R (http://www.r-project.org/). Raw data and source codes are available upon request.
Interacting of miRNAs in targeting based on TargetScan data. This shows a similar pattern to that in Figure 6 of the interacting of miRNAs in targeting is also found using TargetScan data.
Full list of ARE values of all the miRNAs studied. Included in the table are the miRNA names, the tissue names, the expression levels of the miRNA in the tissues (based on ), the calculated ARE values, and the conservational categories of the miRNAs.
Full list of rARE values of miRNAs. Included in the table are the miRNA names, the median ARE values of miRNAs in all the tissues, the rARE value, the expression broadness of the miRNAs, and the maximal expression levels of miRNAs in the tissues.
Conceived and designed the experiments: YH XG. Performed the experiments: YH XG. Analyzed the data: YH XG. Contributed reagents/materials/analysis tools: XG. Wrote the paper: YH XG.
- 1. Liu LA, Bader JS (2009) Structure-based ab initio prediction of transcription factor-binding sites. Methods Mol Biol 541: 23–41.
- 2. Shabalina SA, Koonin EV (2008) Origins and evolution of eukaryotic RNA interference. Trends Ecol Evol 23: 578–587.
- 3. Voinnet O (2009) Origin, biogenesis, and activity of plant microRNAs. Cell 136: 669–687.
- 4. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297.
- 5. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136: 215–233.
- 6. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, et al. (2005) MicroRNA expression profiles classify human cancers. Nature 435: 834–838.
- 7. Cui Q, Yu Z, Purisima EO, Wang E (2006) Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol 2: 46.
- 8. Axtell MJ, Bowman JL (2008) Evolution of plant microRNAs and their targets. Trends Plant Sci 13: 343–349.
- 9. Friedman RC, Farh KK, Burge CB, Bartel DP (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19: 92–105.
- 10. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, et al. (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27: 91–105.
- 11. 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.
- 12. Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, et al. (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310: 1817–1821.
- 13. Sood P, Krek A, Zavolan M, Macino G, Rajewsky N (2006) Cell-type-specific signatures of microRNAs on target mRNA expression. Proc Natl Acad Sci U S A 103: 2746–2751.
- 14. Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM (2005) Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3′UTR evolution. Cell 123: 1133–1146.
- 15. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, et al. (2005) Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals. Nature 434: 338–345.
- 16. Hertel J, Lindemeyer M, Missal K, Fried C, Tanzer A, et al. (2006) The expansion of the metazoan microRNA repertoire. BMC Genomics 7: 25.
- 17. Huang Y, Gu X (2007) A bootstrap based analysis pipeline for efficient classification of phylogenetically related animal miRNAs. BMC Genomics 8: 66.
- 18. Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101: 6062–6067.
- 19. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al. (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129: 1401–1414.
- 20. Lall S, Grun D, Krek A, Chen K, Wang YL, et al. (2006) A genome-wide map of conserved microRNA targets in C. elegans. Curr Biol 16: 460–471.
- 21. Lu J, Shen Y, Wu Q, Kumar S, He B, et al. (2008) The birth and death of microRNA genes in Drosophila. Nat Genet 40: 351–355.
- 22. 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.
- 23. Chen K, Rajewsky N (2007) The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet 8: 93–103.
- 24. Wu CI, Shen Y, Tang T (2009) Evolution under canalization and the dual roles of microRNAs–A hypothesis. Genome Res 19: 734–743.
- 25. Liang H, Li W-H (2009) Lowly expressed human microRNA genes evolve rapidly. Mol Biol Evol 26: 1195–1198.
- 26. Tang T, Kumar S, Shen Y, Lu J, Wu ML, et al. Adverse interactions between micro-RNAs and target genes from different species. Proc Natl Acad Sci U S A 107: 12935–12940.
- 27. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36: D154–158.