Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver
- Jonathan Schug†1,
- Lindsay B McKenna†1,
- Gabriel Walton1,
- Nicholas Hand2,
- Sarmistha Mukherjee3,
- Kow Essuman3,
- Zhongjie Shi3,
- Yan Gao4,
- Karen Markley3,
- Momo Nakagawa3,
- Vasumathi Kameswaran1,
- Anastassios Vourekas5,
- Joshua R Friedman2,
- Klaus H Kaestner1 and
- Linda E Greenbaum1Email author
© Schug et al.; licensee BioMed Central Ltd. 2013
Received: 23 January 2013
Accepted: 9 April 2013
Published: 18 April 2013
Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs.
miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine miRNA recruitment to the RISC in order to fully assess the impact of miRNA regulation. We incorporated RNA-seq quantification of total mRNA to identify expression-weighted Ago footprints, and developed a microRNA regulatory element (MRE) prediction algorithm that represents a greater than 20-fold refinement over computational methods alone. These high confidence MREs were used to generate candidate ‘competing endogenous RNA’ (ceRNA) networks.
HITS-CLIP analysis provide novel insights into global miRNA:mRNA relationships in the regenerating liver.
KeywordsLiver Hepatectomy HITS-CLIP ceRNA microRNA Cell cycle
microRNAs (miRNAs), 22–23 nucleotide noncoding RNAs, contribute to the control of diverse developmental, growth, and disease processes [1, 2]. Abnormal expression of miRNAs has been established in cancers, with miRNAs functioning as either tumor suppressors or oncogenes. MicroRNAs decrease expression of mRNA targets by either destabilization of mRNA or inhibition of protein translation . MicroRNAs are thought to target mRNAs through binding of nucleotides at position 2–8 of the miRNAs (the so-called ‘seed region’) to a complementary sequence in the mRNA [4, 5].
While differential expression of miRNAs has been determined in multiple contexts, the validation of physiologic miRNA targets has proven to be difficult. Modulation of miRNA levels using gain- and loss-of-function approaches have identified some mRNA targets; however, concerns exist regarding the potential for indirect and off-target effects, particularly in the case where a miRNA is overexpressed. Currently available miRNA target prediction algorithms produce large numbers of potential targets; however, very few of these targets have been experimentally validated. Many current algorithms mine sequences limited to the 3′UTR of mRNAs, and therefore do not identify miRNAs that target the coding region and 5′UTR, even though miRNA targeting to all mRNA regions has now been experimentally validated [5–11]. Furthermore, the ability of miRNAs to target mRNAs with only partial complementarity  indicates that in many cases identification of miRNA targets may not be possible based on seed match complementarity alone . Consequently, better methods are needed to identify bona fide miRNA targets.
Recently, Pandolfi and colleagues have proposed an ‘mRNA code’, in which competing endogenous mRNAs (ceRNAs) communicate with each other through crosstalk between shared microRNA regulatory elements (MRE) [10, 13]. An important prediction of this model is that competition for a common pool of miRNAs could result in de-repression of one mRNA following robust activation of a second mRNA containing the same MREs. Therefore, identification of MREs and mRNA:miRNA targeting relationships is an essential step towards understanding the role of miRNAs in the regulation of complex biological responses.
The partial hepatectomy model in rodents has been used extensively to investigate the mechanisms responsible for hepatic growth and proliferation, and is the best in vivo model of synchronous cell cycle progression in mammals [14, 15]. Following surgical removal of two thirds of the liver, hepatocytes and nonparenchymal cells rapidly reenter the cell cycle, replicate, and restore the original mass of the liver within 10–14 days . This process involves a complex regulatory cascade of cytokine signals and transcriptional regulators that coordinate cell cycle progression while maintaining homeostasis [15, 16]. An essential contribution of miRNAs in this regenerative response has been supported by a recent study in which mice with genetic deletion of the DROSHA cofactor DGCR8, a factor required for microRNA biogenesis, exhibited markedly impaired hepatocyte proliferation after partial hepatectomy . Although changes in expression of miRNAs after partial hepatectomy and in liver-graft models have been reported using array-based assays [17–24], only a small number of targets have been validated [22, 23, 25, 26]. Importantly, the identification of the subset of mRNAs that are regulated by miRNAs in the regenerating liver is far from complete, in part due to the large number of possible mRNA:miRNA targeting relationships predicted by computational approaches.
We hypothesized that recruitment of miRNAs to the RISC, or RNA-induced silencing complex, which contains the partially base-paired miRNA and its mRNA target, would be a more informative method to assess miRNA activity during liver regeneration, since changes in the overall expression level of miRNAs reported thus far do not take into consideration changes in target mRNA expression or MRE accessibility. To identify which miRNAs participate in the regenerative process and to identify their targets, we used a technique pioneered by the Darnell laboratory in which UV cross-linked miRNA:mRNA complexes are immunoprecipitated with an antibody to Argonaute, an essential component of the RISC, and then subjected to deep sequencing analysis [27–29]. We found that a subset of miRNAs is dynamically recruited to the RISC during liver regeneration, and that for the majority of these miRNAs, RISC recruitment did not correlate with changes in overall expression. Furthermore, we exploited the comparison of RNA-seq and RISC recruitment data to identify those mRNAs whose recruitment to the RISC was highly enriched relative to their overall abundance, and which are therefore likely to be regulated by miRNAs.
Results and discussion
Changes in expression of miRNAs, mRNAs and/or accessibility of microRNA regulatory elements (MREs) to their complementary miRNAs are likely to affect miRNA recruitment to the RISC . We hypothesized that miRNA activity would be more accurately determined by quantification of miRNA abundance in the RISC than by changes in miRNA levels alone. To this end, we determined miRNA recruitment at selected time points posthepatectomy previously shown to correspond to the G1 (1h), S (36H) and M (48H) phases of the hepatocyte cell cycle, and in the quiescent liver, corresponding to G0[30, 31]. Cell cycle stage was confirmed by activation of genes associated with G1 (jun, fos, myc), S (cyclin D1) and M (Foxm1) cell cycle phases determined by RNA-seq analysis of samples from these time points (Additional file 1: Figure S1). Following UV-crosslinking of the RISC to microRNA and mRNA, we quantified miRNAs immunoprecipitated with an antibody that recognizes Argonaute 1 through 4 proteins and therefore immunoprecipitates all RISCs  by ultra-high throughput sequencing. We calculated the loading microRNA relative to all others for each replicate as reads per million (RPM). We then employed a stringent cut-off of 100 RPM through which we detected 226 miRNAs in the RISC complex at one or more time points examined (Additional file 2: Table S1 contains loading data for the 632 miRNAs that had any aligning reads). An important issue for any comparison across time is global normalization of the data. Because the average levels of RISC-associated microRNAs were relatively unchanged over time, we employed additional global normalization, as well as a stricter 1,000 RPM cut-off, to identify differentially-loaded microRNAs.
The change in recruitment was extremely rapid for the majority of these miRNAs. For example, RISC recruitment of mir-215 increased approximately 5.6-fold by 1 h posthepatectomy, and was also similarly enriched at the time point corresponding to hepatocyte S phase (36 h) relative to quiescent liver. Surprisingly, mir-215 and mir-192, which contain the same seed sequence (Additional file 3: Figure S2), are both regulated by p53 [33, 34] and expressed at similar levels after hepatectomy (Figure 1B), followed different patterns of RISC recruitment. This discordance could reflect requirements for sequences outside of the seed region of the two miRNAs for binding to their targets or interaction with RNA binding proteins.
This rapid change in miRNA recruitment to the RISC is unlikely to be due solely to an increase in miRNA transcription, and likely reflects enhanced miRNA processing, stability, changes in miRNA subcellular localization, and/or changes in the abundance of specific mRNA targets or RNA binding proteins. Several miRNAs exhibited changes in RISC recruitment at later time points posthepatectomy. For example, significant RISC enrichment of mir-142-3p occurred 48 h posthepatectomy. This miRNA has been shown recently to inhibit RAC1-mediated colony formation migration and invasion in HCC cell lines  and could have a similar function in proliferating hepatocytes. We investigated whether changes in miRNA recruitment to the RISC were associated with corresponding changes in overall miRNA abundance during the regenerating time course (Figure 1A, B). Importantly, with the exception of mir-31, mir-144 and mir-378, we found no correlation between miRNA expression and RISC recruitment and for many of the miRNAs examined, changes in RISC recruitment and overall expression were inversely correlated. Overall, the dramatic changes in the levels of actively engaged miRNAs over a short time frame emphasize the necessity to determine miRNA recruitment to the RISC rather than relying on simple determination of miRNA abundance in order to fully assess the impact of miRNA regulation.
Regulation of mRNAs by miRNAs may either “fine-tune” or significantly inhibit target mRNA expression . To investigate the potential regulatory function of miRNAs for their targets in the regenerating liver, we plotted total gene expression and RISC recruitment for two clusters of genes known to be important in the regenerative process after partial hepatectomy . The total regulatory load (TRL) of Gas1, an inhibitor of proliferation, increases coincident with a decrease in its overall expression, suggesting that its inhibition by miRNAs may facilitate the G1/S transition  (Figure 2C). SPHK2 represses transcriptional activation of the immediate-early growth gene Fos via inhibition of histone acetylation . These findings suggest that Fos and SPHK2 inhibition by miRNAs could cooperatively contribute to the resolution of Fos activation posthepatectomy. In contrast, the TRL of mRNAs encoding proteins involved in DNA checkpoint regulation increases at 36 hours posthepatectomy, prior to the observed overall increase in mRNA at 48 hours posthepatectomy (Figure 2D). This temporal relationship suggests that miRNAs may limit and/or delay the increase in expression of replication checkpoint genes during hepatocyte S phase, allowing for completion of DNA replication.
To assess the accuracy of our predicted miRNA/mRNA pairs, we compared our results to a set of genes whose expression was altered in liver in response to miR-29a-3p antisense oligonucleotide treatment . Hand and colleagues identified 78 genes that were up-regulated upon blocking miR-29a-3p, and an additional 65 genes that were down-regulated. We identified 381 predicted targets of miR-29a-3p in quiescent liver using the methods summarized in Figure 4. Of these predicted targets, 10 (p = 4.16e-06; Fisher exact test, followed by Benjamini-Hochberg correction for multiple testing) were found in the up-regulated set in Hand and colleagues  whereas only 3 (p = 0.228) were found in the down-regulated set. Performing the same analysis for all miRNAs, we found the targets of miR-29a-3p to have the second most significant overlap with the up-regulated set obtained experimentally . The most significant overlap was with miR-328-3p that had a similar overlap (10 genes in common with 282 targets and 4 targets in common with miR-29a-3p). Next, we performed a similar comparison of the miR-122-5p targets identified in the quiescent liver [45, 46], and obtained a similar trend. We identified 748 target genes and 866 target mRNAs for miR-122-5p. The overlap between our targets and the microarray data were significant for the genes up-regulated upon interference with miR-122-5p activity (36 of 206 transcripts p = 3.87e-09 for Esau and 42 of 363 p = 3.69e-15 for Krutzfeldt), and not significant for the down-regulated genes (32 of 619 p = 1 for Esau and 16 of 305 p = 1 for Krutzfeldt). For both sets of up-regulated genes, miR-122-5p was the miRNA with the most significant overlap with the microarray data. Based on the differences between the experimental models, our results are likely to underestimate the true extent of mir-29a-3p or miR-122-5p target overlap. Nevertheless, these findings provide strong support for the validity of our target prediction approach.
A significant challenge in the field of microRNA biology is to understand physiologic miRNA:mRNA regulatory relationships in highly complex in vivo systems. We have demonstrated here that the HITS-CLIP assay can be applied to a complex model of growth and proliferation. We demonstrate that dynamic changes in miRNA recruitment to the RISC in most cases did not correlate with overall expression patterns in the regenerating liver, indicating that assessment of expression levels alone does not reflect miRNA activity. This lack of correspondence between RISC recruitment and miRNA expression is likely to be influenced by multiple factors including contributions of RNA binding proteins  ratio of target mRNA:targeting miRNA, flanking sequence homology  and changes in miRNA subcellular localization . Using miRNA sensory and decoy libraries, Mullokandov and colleagues recently showed that only highly abundant miRNAs showed significant target repression, and in some instances were also less active based on a high mRNA:miRNA ratio or nuclear localization . These findings suggest that the small changes in miRNA expression that occur in the regenerating liver may not contribute significantly to target mRNA regulation. We also show that inclusion of HITS-CLIP data enhances miRNA target predictions in hepatocytes by more than 20-fold over computational approaches alone. These findings provide a framework for understanding the relationship of miRNA regulation to changes in gene expression and will greatly facilitate the determination of candidate ceRNA networks.
8–10 week old male C57Bl/6 mice underwent partial hepatectomy as described  using isofluorane anesthesia between 8 AM and noon, and livers were harvested at the indicated time points after partial hepatectomy. All procedures involving mice were conducted in accordance with approved Institutional Animal Care and Use Committee protocols at the University of Pennsylvania and Thomas Jefferson University.
High-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP)
At the time of tissue harvest, approximately 50% of the left lateral lobe was coarsely homogenized with a Dounce homogenizer and the resulting suspension cross-linked three times on ice in a Stratagene Crosslinker at 400 mJ/cm2. HITS-CLIP was performed using the monoclonal argonaute antibody 2A8  as described . Libraries were generated for RNA-seq and Ago HITS-CLIP (both mRNA and miRNA fraction) for quiescent liver, and liver 1 hr, 36 hr and 48 hr post partial hepatectomy, using two biological replicates for each time point. Additional information is available in Additional file 8 on-line.
Quantification of miRNA expression
Taqman probes used for analysis of miRNA expression
RNA-seq heat map
To construct the heat map, the log2 of expression levels in reads per kilobase per million (RPKM) were quantile-normalized (R normalizeBetweenArrays from the limma package) and averaged across the two replicates. mRNAs expressed at a level of at least 2 RPKM were used. K-means clustering was then performed using a range of K values from 4 to 30. K = 12 yielded a set of mostly unique profiles with a few repeated profiles indicating that K was large enough to identify the major patterns. Functional analysis of the transcripts in each cluster was performed using the ‘Core Analysis’ function at the Ingenuity website (http://www.ingenuity.com).
Total regulatory load heat map
To construct the heat map, we calculated the total regulatory load (TRL) for all RefSeq transcripts, averaged over the two biological replicates, then followed the method used above for the RNA-Seq heat map. The strength of an individual footprint was calculated as the reads per million (RPM) in the footprint divided by the expression level of the mRNA (RPKM). The total regulatory load is the sum of the footprint strengths for all footprints on a given mRNA. We included all mRNAs with a TRL over 1 [RPM/RPKM]. A workflow diagram of computational methods is summarized in Additional file 9: Figure S3.
Contrasting mRNA and TRL levels
We selected genes in pathways enriched in the TRL k-means clusters and plotted both mRNA and TRL profiles for these genes. The k-means clustering ensures that the TRL profiles are similar, but mRNA levels are not constrained by this process.
miRNA-mRNA targeting prediction
Raw miRNA targeting relationships were predicted using miRanda with settings of ‘-en −10 -sc 140′ on all RefSeqs. A miRNA-mRNA targeting relationship was considered to be confirmed if the miRNA was present in the Ago-short library at a minimum of 100 RPM, and had a miRNA regulatory element that fell within 5 to 43 bp downstream of the start of a footprint with strength of at least 0.31 RPM/RPKM. This analysis was repeated separately for each replicate at each time point.
Overlap with miR-29a-3p and miR-122-5p regulated genes
The set of genes, up- or down-regulated upon antisense oligonucleotide-mediated inhibition [44–46], were intersected with the set of genes that we identified as regulated by miR-29a-3p or miR-122-5p as appropriate, as well as the sets for all other miRNAs for which we could identify targets. The statistical significance of the overlaps was computed using a one-sided Fisher exact test (R function fisher.exact), then corrected for multiple testing using the Benjamini-Hochberg correction (R function p.adjust.) Additional information is available in Additional file 8 on-line.
To identify potential ceRNA networks, we selected all mRNAs that had one major footprint targeted by a single miRNA. For this analysis, footprints within 20 bp of each other were merged. If (1) the second strongest distinct footprint was less than 25% of the strongest footprint, and (2) the most loaded miRNA in the strongest footprint was 10x higher than the second-most loaded miRNA in the footprint, then the mRNA and miRNA were included in a ceRNA network. This process was performed for each time point. Networks for each miRNA were then merged across all time-points to create summary networks.
Availability of supporting data
All sequencing data are available through ArrayExpress. Accession number: E-MTAB-1612.
High-throughput sequencing of RNA isolated by cross-linking immunoprecipitation
Competing endogenous microRNAs
microRNA regulatory elements
Total regulatory load.
This study was supported by NIH grants DK056669 to L.E.G. and P01DK049210 to K.H.K.
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