Prospects & Overviews Problems & Paradigms

MicroRNA binding sites in the coding region of mRNAs: Extending the repertoire of post-transcriptional gene regulation Anneke Bru€mmer1) and Jean Hausser2)
It is well established that microRNAs (miRNAs) induce mRNA degradation by binding to 30 untranslated regions (UTRs). The functionality of sites in the coding domain sequence (CDS), on the other hand, remains under discussion. Such sites have limited impact on target mRNA abundance and recent work suggests that miRNAs bind in the CDS to inhibit translation. What then could be the regulatory benefits of translation inhibition through CDS targeting compared to mRNA degradation following 30 UTR binding? We propose that these domain-dependent effects serve to diversify the functional repertoire of post-transcriptional gene expression control. Possible regulatory benefits may include tuning the time-scale and magnitude of post-transcriptional regulation, regulating protein abundance depending on or independently of the cellular state, and regulation of the protein abundance of alternative splice variants. Finally, we review emerging evidence that these ideas may generalize to RNAbinding proteins beyond miRNAs and Argonaute proteins.

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Keywords: 30 UTR; CDS; miRNAs; mRNA degradation; posttranscriptional regulation; RBPs; translation

supporting information may be found in the : Additional online version of this article at the publisher’s web-site.

Introduction Post-transcriptional regulation is emerging as a major determinant of gene expression in metazoans [1, 2]. MicroRNAs (miRNAs) are prominent actors within the post-transcriptional regulatory layer, with over 1,000 miRNAs identified in human so far, which amounts to 5% of the total number of human genes. Their involvement is pervasive in cell biology, from early development [3–5] to organ function, and their perturbed expression has been associated with numerous human diseases such as diabetes [6], cancer [7], and viral infection [8]. Furthermore, miRNAs appear to be able to initiate, on their own, the process of reprogramming somatic cells into pluripotent stem cells [9]. At the molecular level, miRNAs are 23 nucleotides long, non-coding RNAs that load into Argonaute (Ago) proteins. They provide binding specificity to Ago proteins, which they guide to specific elements complementary to the miRNA sequence in the 30 untranslated regions (UTR) of mRNAs [10, 11]. Upon binding these elements, miRNAs repress the target genes by a combination of translation repression and mRNA degradation [12]. While cases in which miRNAs mainly repress target gene by translation inhibition have been reported [13–15], genome-wide studies show that mRNA deadenylation accounts for most changes in gene expression induced by miRNAs [16–19]. In contrast to the well characterized role of miRNA binding sites in 30 UTRs, the regulatory function of binding sites located in the coding domain sequence (CDS) remains more

DOI 10.1002/bies.201300104 1) 2)

Biozentrum, University of Basel, Basel, Switzerland Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

Abbreviations: CDS, coding domain sequence; RBP, RNA-binding protein; RPF, ribosome protected fragment; UTR, untranslated region.

*Corresponding author: Jean Hausser E-mail: [email protected]

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Figure 1. The regulatory effect of miRNAs depends on the domain of the mRNA – 30 UTR or CDS – in which binding occurs. A: Representation of an mRNA with the 50 cap structure (gray ball), the CDS (green rectangle), the 30 UTR and the poly-A tail (An). mRNA deadenylation occurs mainly when miRNAs bind to the 30 UTR while binding to CDS primarily inhibits translation. B: MiRNAs differ in their preference to target genes in the 30 UTR or in CDS. Each human miRNA appears as a dot. The scatter represents the number of binding sites in the CDS against the number of binding sites in 30 UTR. The number of sites was determined by the ElMMo algorithm, which predicts miRNA binding sites by modeling the evolution of orthologous target sites in related species [24, 45]. The dotted gray line shows the best-fitted scaling line between the number of CDS and 30 UTR sites, that goes through the origin and maximizes the projected variance. Colors represent the preference of each miRNA to target the 30 UTR (red) or the CDS (green), as determined by the distance to the dotted gray line.

elusive. These sites exhibit low evolutionary rates [20]. Comparative genomics analysis found that both CDS and 30 UTR sites can be under strong purifying selection [21, 22] and that there are as many miRNA conserved binding sites in the CDS as in the 30 UTR [23, 24]. These conclusions were reached by comparing the conservation of CDS miRNA binding sites to other positions in the CDS. Therefore, the observed purifying selection on CDS sites is not simply a consequence of the selective pressure on the encoded protein sequence. Crosslinking and immunoprecipitation (CLIP) experiments in human cells, which map mRNA stretches bound by miRNAs provided genome-wide evidence that miRNA binding is as frequent in CDS as in 30 UTRs [25, 26]. CDS-located sites identified by such experiments are also under specific selective pressure which suggests that they may have a function [26]. Indeed, studies found CDS-located sites to function in embryonic stem cell differentiation [27], DNA methylation [28], regulation of apoptosis [29], aortic development [30], and tumor suppression [31]. An interesting example involves the let-7 miRNA, which targets the CDS of the Dicer enzyme – an essential enzyme for miRNA biogenesis – hence establishing an auto-regulatory negative feedback loop [22, 32].

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Yet, in metazoa, mRNA deadenylation occurs mainly when miRNAs bind to sites in 30 UTRs (Fig. 1A). MiRNA binding sites in the CDS have puzzlingly little effect on target mRNA abundance [18, 26, 33], likely due to active translation of the target mRNA which interferes with miRNA-mediated regulation in the CDS [34]. Consistent with this mechanism, specific miRNAs whose RNA recognition motif occurs repeatedly in the CDS of the same gene to create multiple binding sites can effectively down-regulate target mRNAs [35, 36]. But what then could be the regulatory function of CDS-located binding sites for the vast majority of miRNAs whose recognition motifs do not occur repeatedly in the CDS?

miRNA binding to the CDS mainly leads to translation inhibition Ribosome protected fragment (RPF) sequencing experiments can be used to quantify and compare translation efficiency genome-wide across experimental conditions [37]. Compared to joint mRNA profiling and quantitative proteomics experiments [18, 32], RPF sequencing provides a broader coverage of the genome and directly measures the regulatory impact of miRNAs on translation, thereby circumventing the issue of protein stability which delays the regulatory impact of miRNAs [16, 18, 38]. Guo et al. [16] performed RPF sequencing following miRNA transfection in HeLa cells. In this study, the translation efficiency of mRNAs with miRNA binding sites in the CDS was significantly lower in the presence of the miRNA [24]. This suggests that translation inhibition is the main regulatory effect of miRNA binding sites located in the CDS (Fig. 1A). Under this hypothesis, protein abundance at steady-state is expected to change significantly more than the abundance of the cognate mRNA in the presence of a miRNA targeting the CDS (Box 1). A miR-223 knock-out experiment followed by protein profiling in mouse neutrophils supports this scenario [18]. Reporter gene assays in which miR-199a binding sites were cloned in the CDS of a Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

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Box 1

dm ¼ am  bm m dt dp ¼ ap m  bp p dt mRNA and protein abundances are at steady-state until t ¼ 0. This implies mð0Þ ¼ am =bm and pð0Þ ¼ ðap =bp Þmð0Þ. Then, a step-like increase in miRNA expression suddenly multiplies the mRNA decay rate bm by a factor fbm  1 and the translation rate ap by fap  1 (Fig. 2B). We can define temporal changes in mRNA abundance f m ðtÞ ¼ mðtÞ=mð0Þ and in protein abundance f p ðtÞ ¼ pðtÞ=pð0Þ. This leads to differential equations for fm(t) and fp(t) (see Supplementary Material) which we can solve to get analytical expressions for changes in mRNA and protein abundance as a function of time:
 1 1 f m ðtÞ ¼ 1  ebm f bm t þ f bm f bm f p ðtÞ ¼

f ap bp f ap f bm  1  ebm f bm t f bm f bm bm f bm  bp ! bp  bm þ 1 þ f ap ebp t bm f bm  bp

The shape of the two functions is illustrated in Fig. 2B. As the time t grows, protein abundance fp(t) reaches a new steady-state f ap =f bm , which combines the effects of the miRNA on translation and mRNA deadenylation.

luciferase in a miR-199a inducible human cell line provide further validation: following miR-199a induction, luciferase activity decreased in a way that could not be explained by changes in mRNA abundance alone as measured by qPCR [24]. Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

We can now use these expressions to compute the amount of time necessary to regulate protein abundance by CDS and 30 UTR targeting. To make the comparison meaningful, we set equal mRNA half-lives, equal protein half-lives, equal targeting efficiency, and examine the regulatory delay resulting from 30 UTR targeting. We define f  1 as the common fold change in protein abundance at steady-state due to the targeting miRNA. We model CDS targeting as having no effect on deadenylation, which implies fap ¼ f and fbm ¼ 1. Conversely, we assume that 30 UTR targeting only affects translation through deadenylation, that is fap ¼ 1 and f bm ¼ 1=f. In both cases, fp(t) ¼ f only occurs as t tends to infinity. To compute the time necessary to regulate protein abundance, we hence introduce a constant a which tracks the progression of protein regulation by the miRNA. We define a as

Problems & Paradigms

We describe a simple mathematical model to quantify the delay in regulating protein abundance through 30 UTR targeting compared to CDS targeting. We then study how mRNA and protein half-lives affect this delay. While the model we introduce here is not essential to understand the ideas presented in this paper, it allows to explicitly and unambiguously specificy our assumptions and quantitatively compare the kinetic properties of CDS and 30 UTR targeting. For the sake of conciseness, we only briefly outline the model here. The Supplementary Material reviews the litterature that justifies the model’s assumptions and provides step-by-step derivations of this model. It also introduces a more realistic and more complex model, which relaxes some of the assumptions of the model below. We model temporal changes in the abundance of a mRNA m and of its cognate protein p (Fig. 4). Following a commonly used formalism [2, 55, 87], the mRNA is transcribed at a rate am and deadenylates at a rate bm. ap is the translation rate per mRNA molecule and bp is the protein decay rate. We can formalize these assumptions by writing two differential equations for m(t) and p(t):

f p ðtÞ ¼ ð1  aÞf p ð0Þ þ af where fp(0) ¼ 1 by definition. a ranges from 0 to 1: the case a ¼ 0 corresponds to t ¼ 0 while a ¼ 1 corresponds to t ! þ1. In the text and in the figures of the present paper, we used a ¼ 1/2, which corresponds to the time necessary to perform half of the regulation. In the CDS case, solving the equation for t yields tCDS ¼ 

1 logð1  aÞ bp

That is, the time necessary to regulate protein abundance by CDS targeting scales inversely with protein decay. This expression does not depend on the fold change in protein abundance f. In the 30 UTR, solving the same equation yields 0 1 ðb eb m tUTR  b0 m ebp tUTR Þ ¼ 1  a bp  b0 m p

where we defined b0 m ¼ bm =f ¼ bm f bm as the mRNA decay rate in the presence of the miRNA. Unlike in the CDS case, this equation does not admit a closed expression for t as a function of a in the general case. However, we can solve it numerically given a specific value of a to find the time tUTR that is required to regulate protein abundance by 30 UTR targeting. From tCDS and tUTR in the last two equations, we can compute the speed-up due to CDS targeting tUTR–tCDS (Fig. 2C). Because f occurs neither in the equation for tCDS nor the equation for tUTR, the speed-up in protein regulation from CDS targeting does not dependent on the fold change in protein abundance, only on the protein decay rate and on the deadenylation rate in the presence of the miRNA.

The contribution of individual CDS sites to changes in protein abundance appears smaller than those from 30 UTR [26]. Nevertheless, several arguments suggest that important effects on translation can be achieved with CDS sites only. First, the amount of translation repression increases with the

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number of sites, from 10% with one site to 20% with two sites 12 h post transfection in the data of Guo et al. [16] for instance. Second, these figures group together potent and impotent CDS sites and may thereby underestimate the actual effect of potent sites on translation repression. Studies have sought to characterize the sequence and structure properties of potent sites based on their ability to induce Ago binding, deadenylate the mRNA or down-regulate the encoded protein [23, 24, 39, 40]. It will be interesting to see if excluding impotent sites by such means can lead to a better estimate of the extent of translation inhibition by CDS sites, as has already been done for 30 UTR sites [41, 42]. It is also possible that yet unexplored properties – e.g. mRNA stability, interactions with other RNA-binding proteins, or ribosomal density – may be crucial in characterizing potent CDS sites. In support of these possibilities, local translation efficiency appears to be lower close to CDS sites [26, 34], and stable mRNAs have been found to be more repressed by miRNAs, at least in 30 UTRs [43]. In summary, while additional work would be helpful to better characterize the magnitude and the time-scale of gene regulation by CDS sites, a new picture is currently emerging in which miRNA binding to 30 UTRs leads to changes in protein abundance mainly by mRNA deadenylation while binding to sites in the CDS mainly represses the translation with little impact on the polyA tail of the target mRNA.

CDS binding sites open new regulatory possibilities for miRNAs In the previous section, we reviewed the computational and experimental evidence supporting a function of CDS sites in translation repression. Since both CDS and 30 UTR targets appear to be functional, one may ask what could be the regulatory benefits of miRNA targeting in the CDS compared to targeting in the 30 UTR. At present time, any answer to this question entails a part of speculation because there has been little work on this problem so far. Nevertheless, certain findings hint at regulatory scenarios in which CDS sites may be more effective than 30 UTR sites as well as at regulatory functions that are best fulfilled by CDS sites. This section explores such scenarios and functions together with the supporting findings.

Individual miRNAs differ in their preference for targeting the CDS or the 30 UTR of mRNAs To understand the function of CDS binding sites, it is useful to examine the preference of individual miRNAs for CDS or 30 UTR targeting. Is there a common ratio of CDS targeting to 30 UTR targeting that is shared across miRNAs? Alternatively, do miRNAs tend to specialize, with some miRNAs preferentially targeting the CDS and other miRNAs targeting the 30 UTR? In support of the second hypothesis, Nelson et al. [44] performed Ago RNA immunoprecipitation experiments to show that miR-107 tends to target the CDS while miR-320 preferentially binds 30 UTRs.

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Computational miRNA target site predictions are well suited to the task of obtaining a genome-wide perspective on this question for all known miRNAs and genes. The ElMMo method [45] quantifies the amount of selective pressure on miRNA binding sites by modeling the evolution of orthologous binding sites to infer the likeliest target sites of known miRNAs and at the same time limit the number of false positive predictions. It is among the most accurate miRNA target prediction methods [26, 46, 47]. From ElMMo miRNA target site predictions in human 30 UTR and CDS regions, one can compare the number of sites under selection in the CDS with the number of sites under selection in the 30 UTR for all miRNAs [24]. The resulting scatter suggests that miRNAs commonly specialize (Fig. 1B). The miRNAs hsa-miR-16-5p, hsa-miR-15a/b-5p, hsa-miR-195-5p, hsa-miR-103-3p, hsa-miR107, hsa-miR-646, hsa-miR-424-5p, and hsa-miR-497-5p which share the AGCAGC targeting motif at the 50 end and have been shown to regulate cell cycle [48–51] predominantly target the CDS. On the other end of the spectrum are miRNAs expressed in the embryo such as hsa-miR-302a-3p, hsa-miR-369-3p, hsamiR-372, hsa-miR-373-3p, hsa-miR-374b/c-5p [52] together with oncogenic miRNAs of the miR-17 family – hsa-miR-17-5p, hsa-miR-20a/b-5p, hsa-miR-93-5p, hsa-miR-106a/b-5p [53]. It therefore appears that individual miRNAs differ in their preference to target the CDS or the 30 UTR. But if both 30 UTR and CDS targeting eventually lead to decreased protein levels, what could be the comparative benefits and drawbacks of CDS and 30 UTR sites in terms of gene expression regulation? One idea, which we will develop in the next section, is that CDS and 30 UTR sites may differ in how they affect the time-scale and magnitude of gene regulation. Another idea, which we will examine afterwards, is that CDS sites may enrich the regulatory repertoire of miRNAs because they interact with gene architecture, alternative splicing and alternative polyadenylation in a different way than 30 UTR sites.

CDS targeting may speed up silencing of stable mRNAs In the RPF sequencing experiments of Guo et al. [16], translation repression by CDS sites is observed before mRNAs are deadenylated by 30 UTR targeting [24]. Approaches based on kinetic modeling suggest that the regulatory delay observed with 30 UTR targeting is due to the time necessary for mRNAs to deadenylate [38]. At this time, a biochemical dissection of the mechanism of miRNA-mediated silencing in the CDS is still lacking. Yet, one can propose from these observations that CDS targeting decreases the translation rate while 30 UTR targeting increases the deadenylation rate. What are the consequences of these assumptions on the kinetics of miRNA-mediated gene regulation? To explore this question, we perform a computational comparison of the regulatory dynamics of two genes, targeted either in the CDS or in the 30 UTR (Fig. 2A). We explore the scenario in which miRNA expression suddenly steps up, and follow subsequent temporal changes in the mRNA and protein abundance of the target genes using a mathematical model (Box 1). The model has four parameters: the mRNA half-life, the protein half-life, and the changes in translation and deadenylation rates Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

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Problems & Paradigms

Figure 2. CDS sites are predicted to be more effective at regulating stable mRNAs than 30 UTR sites. A: Sketch of the model used to investigate the timing and magnitude of gene regulation by CDS and 30 UTR sites. We compare the regulatory dynamics of two proteins with 10 h half-lives encoded by two mRNAs with 7 h half-lives following a step increase in miRNA expression. The miRNA targets the mRNAs either in the CDS or in the 30 UTR. B: Changes in the per mRNA translation and deadenylation rates upon a step increase in miRNA expression (top panel). The middle and bottom panels show subsequent changes in mRNA and protein abundance in the mathematical model of Box 1. C: Delay in protein regulation resulting from 30 UTR targeting, as a function of polyAþ mRNA and protein half-life. The mRNA half-life on the x-axis is the one in the presence of the targeting miRNA. Red represents a small delay while larger delays appear in brighter colors. Contour lines represent pairs of mRNA and protein half-lives that lead to the same delay. Contours are labelled with the amount of delay, in hours.

caused by the miRNA. To make the comparison insightful, we make the two genes identical in all the parameters – mRNA half-life, protein half-life, and final decrease in protein abundance – except for the mechanism through which the Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

miRNA decreases protein abundance: inhibition of translation or mRNA deadenylation. We set the mRNA and protein halflives to 7 and 10 h, respectively – a configuration previously ¨usser et al. [2] – and consider for the observed by Schwanha sake of this example that stable miRNA induction leads to a twofold decrease in protein abundance at steady-state (i.e. fold change ¼ 0.5). We model miRNA binding in the CDS by a twofold decrease in the translation rate (green curve in the top panel of Fig. 2B). This leads to a progressive decrease in protein abundance while leaving polyadenylated mRNAs unaffected (Fig. 2B). In contrast, we model miRNA binding in the 30 UTR by a twofold increase in the deadenylation rate (red curve in the top panel of Fig. 2B). This causes a gradual decrease in the abundance of polyadenylated mRNAs (Fig. 2B). Eventually, protein abundance also decreases, but with a delay due to the time necessary for the mRNA to deadenylate. As a result, CDS targeting leads to faster regulatory kinetics in this model. How big a delay can one expect? In the example of the simulation of Fig. 2B, 30 UTR targeting doubles the deadenylation rate, which yields a half-life of 3.5 h – down from 7 h

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in the absence of the miRNA – and causes a 5.5 h delay. From the assumptions of the previous paragraph, one can prove that the deadenylation rate in the presence of the miRNA determines how fast mRNA abundance decreases upon 30 UTR targeting and hence sets the delay (Box 1). Hence, the more stable the mRNA, the larger the delay. The exact delay depends on two additional parameters, namely the protein decay rate, and at what point the delay is measured (Box 1). Varying mRNA half-lives within the range of values typically measured in cells [2, 54], we predict delays ranging from 5 to 25 h (Fig. 2C). For example, the predicted delay for an mRNA with a 10 h half-life would be around 15 h. What would happen if we assumed a different final decrease in protein abundance? Given the assumptions above, one can prove that the amount of delay does not depend on how much the miRNA affects gene expression (Box 1). Hence, this model suggests that CDS targeting could lead to faster kinetics, especially in regulating the abundance of proteins encoded by stable mRNAs. The influence of protein stability on the delay is smaller compared to that of mRNA stability (Fig. 2C). Nevertheless, it is important to consider protein stability in our reasoning because it determines how fast protein abundance responds to gene regulation [55]. For example, for a protein with a 100 h half-life, a 10 h delay implies 10% slower kinetics, a relatively mild slow-down. But for a protein with a half-life of 10 h, the same 10 h delay implies 100% slower kinetics than CDS targeting. Therefore, CDS targeting is expected to yield most dramatic speed-ups for genes with stable mRNAs encoding unstable proteins. Interestingly, such genes typically regulate cell proliferation and are involved in cell adhesion and cation homeostasis [2]. In rapidly dividing cells, CDS targeting may have significance beyond these specific genes. Because every cell division dilutes the number of proteins per cell by two, even extremely stable proteins effectively undergo decay through cell division. As a result, the cell cycle time sets an upper bound on protein half-lives [56]. In rapidly dividing cells, CDS targeting could therefore also speed-up the regulation of stable proteins. We have so far focused on a regulatory scenario in which the expression of a miRNA increases in a step-like manner. However, in cells, miRNA expression is unlikely to go through such abrupt changes because processes such as miRNA synthesis, miRNA decay and RISC loading have time-scales in the order of hours [38, 57, 58]. In a more realistic view, miRNA induction would lead to gradual changes in the translation and deadenylation rates of target mRNAs. We explore this scenario using a recently developed model which links miRNA biogenesis, decay and Ago loading to the mRNA and protein dynamics of target genes [38]. The model parameters were inferred from experiments and accurately describe timedependent changes in mRNA, protein, and ribosome density measured upon miRNA transfection and induction. Simulating CDS and 30 UTR targeting using this more realistic model yields comparable speed-ups and results as the model of Box 1 (see Supplementary Material). Using this model, we also relax the assumption that 30 UTR targeting only affects the deadenylation rate. Assuming a 20% contribution of translation repression in the case of 30 UTR targeting and a 20% contribution of deadenylation in the case of CDS targeting yields delays in the 3–20 h range (Supplementary Material),

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comparable to those of the model in Box 1. Note that this is a significant deviation from the assumptions of the simpler model: assuming a 50% contribution of translation repression with 30 UTR targeting and a 50% contribution of deadenylation from CDS targeting would effectively consider CDS and 30 UTR targeting as equivalent and therefore lead to no delay. In summary, CDS targeting may be recruited to speed-up miRNA-mediated gene regulation of stable mRNAs. In slow dividing cells, the speed-up may be mostly relevant for genes coding for unstable proteins. In rapidly dividing cells, CDS targeting would yield faster kinetics for stable mRNAs, independently of protein stability. However, most genes feature unstable mRNAs [2, 54], for which we predict speedups of 4–5 h. Depending on protein stability and on the cell cycle time, this may be significant or not. In the latter case, these genes may benefit from CDS targeting in other ways which we will explore in the next section.

CDS and 30 UTR sites interact differently with alternative splicing and alternative polyadenylation to expand the regulatory repertoire of post-transcriptional control Another corpus of ideas to rationalize the benefits of targeting the CDS compared to the 30 UTR relates to the architecture of protein coding genes. Reczko et al. [39] developed a computational miRNA binding site scoring scheme which predicts whether putative CDS and 30 UTR miRNA binding sites are functional based on the sequence and structure properties of these sites. The study shows that CDS site scores are significantly higher when these sites are located in genes with short 30 UTRs (500 nucleotides long or less). Although one could argue that it might be easier in terms of sequence evolution to extend the 30 UTR than create miRNA binding in the coding domain, this result suggests that spatial constraints may have led to favor CDS targeting for genes with short 30 UTRs (Fig. 3A). As a result, the preference of individual miRNAs to bind in the CDS or 30 UTR may be shaped by the length of the 30 UTRs of target genes. The findings of Sandberg et al. [59] that proliferating cells have shortened 30 UTRs hints at another function for CDS targeting. The length of mRNAs is determined by polyadenylation signals which reside in 30 UTRs. These signals are recognized by RNA-binding proteins and mark the location at which the pre-mRNA is cleaved [60]. A non-negligible fraction of protein coding genes possesses multiple polyadenylation signals. Such genes have 30 UTRs of varying length, depending on the polyadenylation signal used. In mammalian genomes, about 50% of protein coding genes are thought to have alternative 30 UTRs, due to the combined action of alternative polyadenylation and alternative splicing [61–63]. Activated immune cells and proliferating cells in general tend to favor upstream polyadenylation signals, leading to shorter 30 UTRs whereas quiescent cells tend to shift to downstream polyadenylation signals and hence have longer 30 UTRs [59]. As a result, gene expression regulation by miRNAs that bind 30 UTRs is reduced in proliferating cells (Fig. 3B). A similar Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

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post-transcriptional control as cells differentiate [65]. These findings suggest a complementary function for CDS and 30 UTR targeting. 30 UTR targeting may allow for contextdependent regulation of gene expression: repression in quiescent and in differentiated cells versus derepression in proliferating and embryonic cells. By contrast, CDS targeting would be recruited for general gene repression, to control gene expression independently of cell activation, proliferation, and differentiation. In other words, miRNAs that predominantly target the CDS – such as members of the miR-16 family which are well established cell-cycle regulators [48, 66] – may maintain gene regulation even if 30 UTRs shorten. A third possibility has been proposed by Duursma et al. [28] who found that miR-148 miRNA targets the DNA methyltransferase 3b gene (Dnmt3b) in the coding region. This gene has four splice variants: Dnmt3b1, Dnmt3b2, Dnmt3b3, and Dnmt3b4. The human miR-148 binding site is conserved in rhesus, mouse, rat, dog, horse, and armadillo but is absent from the Dnmt3b3 splice variant. MiR-148 over-expression leads to down-regulation of the Dnmt3b1 splice variant which carries the binding site and mutating the binding site abolishes miRNA-mediated repression. In contrast, Dnmt3b3 which lacks the binding site is not affected by miR-148 regulation. This suggests a function of CDS targeting in adjusting the relative abundance of splice variants (Fig. 3C). How systematically this mechanism is used in gene expression control remains open for exploration. To summarize, we have seen that CDS and 30 UTR targeting interact differently with alternative splicing and alternative polyadenylation to expand the regulatory repertoire of posttranscriptional regulation: circumventing space limitations in 30 UTRs, creating regulatory interactions that depend on or are independent of the state of the cell (cell activation, proliferation, and differentiation), and regulation of the protein abundance of alternative splice variants.

Domain specific regulatory effects may generalize to RNA-binding proteins beyond Ago

Figure 4. Sketch outlining a minimalistic mathematical model of how polyadenylated mRNA abundance m and protein abundance p change in response to miRNA targeting. The model accounts for transcription, mRNA deadenylation, translation, and protein decay. In the presence of the miRNA, the mRNA deadenylation rate changes by a factor fbm and the translation rate by an amount fap.

shortening of 30 UTR lengths occurs in cancer cells where it leads to the activation of oncogenes which are normally repressed by miRNAs [64]. In addition, 30 UTR length tends to increase as development progresses, leading to additional Bioessays 36: 0000–0000, ß 2014 WILEY Periodicals, Inc.

We have so far focused on domain specific gene regulation by miRNAs and Ago proteins. But CLIP experiments have shown that other RNA binding proteins (RBPs) beyond Ago associate with both the CDS and UTRs of mRNA [26, 67, 68]. While several studies showed that different RBPs bind in 30 UTRs to regulate the degradation, localization, or translation of mRNAs [69, 70], the functionality of CDS binding sites post mRNA processing has been disputed. A reason for this is the belief that CDS-located binding sites might not be capable of competing with the passing ribosomes, as indeed reported for UPF1 [68], or that the generally reduced structural accessibility in the CDS compared to the 30 UTR might interfere with RBP binding [71]. However, a well-documented example for functional binding to the coding-region is FMRP (fragile X mental retardation), an RBP that is involved in the regulation of human cognitive function, in particular in inhibiting fragile X syndrome [72].

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Figure 3. CDS and 30 UTR binding sites interact differently with gene architecture, alternative splicing, and alternative polyadenylation which may create new regulatory possibilities. A: Spatial constraints in 30 UTRs may lead miRNAs that target genes with constitutively short 30 UTRs to predominantly bind in the CDS [39]. B: 30 UTR length tends to be shorter in proliferating cells [59], in cancer cells [64] and in non-differentiated cells [65]. As a result, 30 UTR targeting tends to be context-dependent [59] whereas CDS targeting could repress gene expression independently from the cellular state. C: MiRNA binding sites located in alternatively spliced exons may be used to tune the relative protein abundance of alternative splice variants [28].

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CLIP experiments revealed that more than 60% of unique sequenced tags mapped to the CDS, while only 20% mapped to UTRs [67]. Upon binding in the CDS, FMRP causes stalling of ribosomes and thereby reduced protein output of specific components of macromolecular complexes at the synapse [67]. Translational repression after initiation has also been observed for other RBPs, however the precise molecular mechanism remains unclear, as well as the precise positions of the RBP binding sites involved [73, 74]. The GLD-1 (defective in germ line development) protein is a post-transcriptional regulator that represses hundreds of mRNAs in gonads of Caenorhabditis elegans [75] and provides another example of putative translational regulation by CDS sites. GLD-1 binding to a few target mRNAs has been studied intensively and binding sites in 30 UTRs have been shown to stabilize mRNA through inhibition of nonsense mediated decay [76, 77]. More recently, Jungkamp et al. [78] proposed that GLD-1 binding to 50 UTRs inhibits translation initiation, while Scheckel et al. [79] hypothesized a global role for GLD-1 in transcript stabilization. But, although a large fraction of sites identified by CLIP experiments are located in the CDS [80] – 15–50% depending on the experiment – a role for these has not been revealed yet. A recent computational study took site accessibility into account to suggest that GLD-1 binding in the CDS represses translation [80]. However, the underlying molecular mechanism remains to be defined and confirmed experimentally. Taken together, these findings suggest that GLD-1 could generally stabilize target mRNAs in addition to inhibiting translation upon binding in the CDS. In contrast to the repressing effect of miRNAs, the combination of transcript stabilization and translation repression by GLD-1 would amplify the regulatory range on protein abundance. For instance, 30 UTR targeting would stabilize the mRNA and hence lead to increased mRNA abundance while the resulting increase in protein synthesis would be circumvented through translation repression due to simultaneous binding of GLD-1 in the CDS. An appropriate tuning of the timing and the strength of stabilization and translation repression could hence lead to a rapid and strong increase of target protein abundance when GLD-1 disappears later during the C. elegans germ line development, without the need for new production of mRNA by transcription. In support of this scenario, GLD-1 abundance was shown to dramatically decrease during oogenesis [81], allowing the rapid release of target mRNAs from GLD-1 regulation. These two examples demonstrate that actors of posttranscriptional gene expression control beyond miRNAs may use domain-specific effects to create new regulatory possibilities. Additional examples are likely to emerge in the near future, with the field of RBPs presently thriving to establish the function of hundreds of RBPs encoded in metazoan genomes [69, 82, 83].

Conclusions Research on gene expression regulation by miRNAs in metazoa has mainly focused on sites located in 30 UTRs. CDS sites, on the other hand, have a limited effect on mRNA

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abundance which led to the view that CDS sites have negligible effect on gene regulation. However, accumulating experimental and computational evidence supports the view that miRNAs bind to CDS and 30 UTR with comparable frequency. While it is well established that miRNAs bind to 30 UTRs to destabilize the target mRNA, recent work suggests that miRNA binding to the CDS inhibits translation. This finding raises a new question: what could be the regulatory benefits of CDS targeting compared to 30 UTR targeting? We proposed that CDS and 30 UTR targeting have complementary benefits. One benefit of CDS targeting may be to speed-up the regulation of proteins encoded by stable mRNAs. A second group of benefits stems from the differences in how CDS and 30 UTR sites interact with gene architecture: CDS sites may be recruited when the 30 UTR of the target gene is too short, when targeting has to take place independently of the cell proliferation, or to tune the protein abundance of alternative splice variants. By contrast, binding in the 30 UTR would be preferred if the targeted mRNA has faster turnover, if space constraints allow it and for target genes that should be derepressed in proliferating cells. We therefore propose that domain specific regulatory effects extend the regulatory repertoire of post-transcriptional gene expression control, and that this conclusion may generalize to RBPs beyond miRNAs and Ago. The benefits of domainspecific effects in post-transcriptional regulation presented here are by no means exhaustive. Additional properties are likely to emerge from interactions with transcriptional regulatory networks [84–86], which opens promising avenues for future exploration.

Acknowledgments We thank Andreas Gruber from the Zavolan lab (University of Basel), Julien Roux (University of Chicago), Tabitha Bucher (Weizmann Institute of Science), and Marie Hausser for constructive comments on the scientific and graphical aspects of the manuscript. J.H. acknowledges the support of the Swiss National Science Foundation (PBBSP3_146961) and EMBO (ALTF 1160-2012).

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