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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Struct Mol Biol. Author manuscript; available in PMC 2013 August 27.
Published in final edited form as:
PMCID: PMC3753194

Intronic Splicing Enhancers, Cognate Splicing Factors and Context Dependent Regulation Rules


Most human genes produce multiple splicing isoforms with distinct functions. To systematically understand splicing regulation, we conducted an unbiased screen and identified >100 intronic splicing enhancers (ISEs) that were clustered by sequence similarity into six groups. All ISEs functioned in another cell type and heterologous introns, and their distribution and conservation patterns in different pre-mRNA regions are similar to exonic splicing silencers. Consistently all ISEs inhibited use of splice sites from exonic locations. The putative trans-factors of each ISE group were identified and validated. Five distinct ISE motifs were recognized by hnRNP H and F whose C-terminal domains were sufficient to render context-dependent activities of ISEs. The sixth group was controlled by factors that either activate or suppress splicing. This work provided a comprehensive picture of general ISE activities and provided new models of how a single element can function oppositely depending on its locations and binding factors.

Keywords: splicing regulation, splicing factors, RNA binding protein, context dependent activity


Most human genes produce multiple isoforms through alternative splicing, which is tightly controlled in different tissues and developmental stages 1-3. The splicing specificity is mainly determined by 5′ splice site (5′SS), 3′ splice site (3′SS) and branch point sequences, as well as by multiple cis-acting splicing regulatory elements (SRE) that are conveniently classified as exonic splicing enhancers (ESEs) or silencers (ESSs), and intronic splicing enhancers (ISEs) or silencers (ISSs). These SREs generally function by recruiting trans-factors to control splicing through diverse mechanisms 4-7. Activities of SREs are often location-dependent 6, however their underlying mechanisms are largely unclear. An important research goal is to study these SREs and their cognate factors on a global scale to derive a set of regulation rules for splicing (i.e. “splicing code”) 6,8.

Significant progress has been made to systematically identify exonic SREs (ESS and ESE) using experimental and computational approaches 9-17. These reports provided a global picture that ESEs are more enriched and conserved in exons to promote the exon definition 12,18-21, whereas the ESSs are more enriched in introns to suppress pseudoexons and help define alternative splice sites 19,22. In comparison, the intronic SREs are less well understood. Several computational approaches were developed to predict general intronic SREs in human based on intronic sequence conservation or distribution biases, most predicted elements resemble the RNA motifs recognized by tissue-specific splicing factors such as Fox1, Nova and nPTB 23-25.

Several ISEs were identified by analyzing sequences near alternative exons. For instance, the G-rich sequences containing at least one G-triplet were found to enhance splicing through recruiting hnRNP H and F to introns 26-29. When located in the downstream intron, the activity of G-runs is dependent on the strength of nearby 5′SS 30. There are also tissue specific ISEs, such as YCAY motifs (Y = C or U) that is recognized by the neuron-specific protein Nova to control many brain-specific splicing events 31 and the UGCAUG motif that is recognized by the brain- and muscle-specific factors Fox-1 and Fox-2 32,33.

To systematically study general ISEs, we developed a cell-based system to screen a random decamer library for sequences that promote splicing from an intronic location. We obtained 109 unique ISE 10mers, whose core motifs were clustered into six groups. We observed a systematic overlap between ISEs and ESSs and established a general rule that all ISE motifs consistently inhibited splicing in exons. The putative trans-factors for each ISE motif were further identified and analyzed. Altogether these data provided comprehensive rules of how ISEs regulate different alternative splicing events, and suggested two models of how the same SRE can either promote or suppress splicing at different pre-mRNA locations.


Identification of 109 ISEs that promote intron splicing

To unbiasedly identify ISEs, we developed a cell-based system called fluorescence-activated screen (FAS) that used a splicing reporter (pZW15C) with two exons and a weak intron (Fig. 1a and Supplementary Table 1). When spliced together, the exons formed an mRNA encoding the enhanced GFP. The intron is normally retained during splicing, and insertion of an ISE will promote splicing to generate a functional eGFP. We tested this reporter by inserting a G-rich ISE or a control sequence and transfecting them into 293T cells, and found that this ISE indeed promoted splicing to produce ~70% GFP-positive cells whereas the control reporter generated ~2% green cells (Fig. 1b). Using semi-quantitative RT-PCR, we confirmed that the green cells were indeed resulted from correct splicing (Fig. 1c).

Figure 1
Identification and validation of ISEs

Since the cores of ISEs are thought to be relatively short, we inserted a 10-nt random pool of sequences at 23 nt into the intron, far enough to avoid interference with the 5′SS (Supplementary Fig. 1a). We transformed enough E. coli to obtain ~2×106 colonies that provided at least 2-fold coverage of all possible DNA decamers. The quality of this library was examined to ensure that we started with an unbiased library. The resulting library was transfected into cultured 293-FlpIn cells that contain a single site-specific recombination site for stable integration. Similar to our previous screen for ESSs 13, this system ensured high sensitivity and unbiased sequence recovery.

We carried out 208 transfections to obtain enough (>106) stable clones that gave roughly one fold coverage of the entire decamer library. In total 117 FAS-ISE decamers were identified through the screen, 109 of which were unique (Supplementary Table 2). We identified seven decamers twice and one decamers three times in independent transfections, suggesting that the screen is self-converged (Supplementary Table 2). Based on the sequence similarity, the resulting ISE decamers can be clustered using CLUSTALW, indicating that they contained common core motifs (Fig. 1d). Although the initial library was essentially random with ~25% of each base, the identified FAS-ISE decamers had higher contents of G (40%) and T (35%) compared to A (18%) and C (7%) (Supplementary Fig. 1b). We also identified overrepresented (e.g, GG, TA) and underrepresented (e.g., GA, CG, GC) dinucleotides in these ISEs (Supplementary Fig. 1c). Such composition biases are similar to FAS-ESS but are different from RESCUE-ESE 11,13. Consistently, the ESS hexamers (FAS-hex2) 13 were overrepresented in the FAS-ISE decamers relative to random control sets (93 versus 35, P<10-4 based on random shuffles of ESS hexamers), whereas the RESCUE-ESE hexamers 11 were underrepresented compared to random hexamer sets (9 vs 45, P<10-4 based on random shuffles).

To validate our results, we arbitrarily selected 17 ISE decamers (marked by asterisks in Fig. 1d and Supplementary Table 2) to examine their activity in the original screen reporter. After transiently transfecting the 293T cells, all tested ISE decamers promoted splicing of the retained intron to generate 20-50% of green cells, whereas the control cells were essentially non-fluorescent (Supplementary Fig. 1d), suggesting our screen had a very low false positive rate.

Validate ISEs in a heterologous context and cell type

This screen was conducted using a constant intron from a particular gene, consequently certain ISEs identified may require sequence context specific to this intron for function. In addition, most alternative splicing events involve cassette exons and it was of interest to determine if these ISEs can function in other contexts. To directly address this, we generated a new splicing reporter (pZW2C) containing a cassette exon with its flanking introns, and inserted nine FAS-ISE sequences in the downstream intron (18 nt downstream of 5′SS). We found all the tested ISE decamers increased cassette exon inclusion in 293T cells as compared to the neutral sequence (Fig. 1e upper panel), suggesting that these ISEs generally function in a heterologous intron.

Most SREs are thought to function through recruiting specific trans-factors, whose expression levels or activities may vary in different cell types 5,6,34. Thus the splicing pattern of the same gene can change in different tissues. We further determined whether the recovered FAS-ISEs are active in another cell type. By transfecting HeLa cells with pZW2C reporters containing the same set of ISEs, we found that all tested ISEs led to a marked increase of exon inclusion (Fig. 1e lower panel). These results suggest that the systematically identified FAS-ISEs have general enhancer activity in different introns and in another cell type.

Identification and validation of core ISE motifs

To extract the core motifs with intrinsic ISE activities, we identified hexamers that are statistically overrepresented in the recovered FAS-ISE decamers. Each decamer was extended by appending 2 nt of the vector sequence and subsequently broken into overlapping hexamers 13. The number of hexamers occurring at least three times in the extended ISE set was more than 4-fold higher than what expected from random decamer sets (P<10-4, based on 10,000 samplings of 109 random decamers). This hexamer set, named as ISE-hex3 (Supplementary Table 3), was highlighted by the AGGTAT and GGGTGG that occurred 15 and 14 times respectively. Furthermore, 93 out of the 109 ISE decamers were covered by at least one hexamer, suggesting the ISE-hex3 represented a common pattern in ISE decamers. Based on sequence similarity, these ISE hexamers were clustered and multiply aligned to identify candidate motifs 11,13. At a dissimilarity cutoff of 2.45, most hexamers fell into one of six main clusters (Fig. 2a, groups A to F). We identified the consensus motif of each group by aligning all hexamers of that group, and found that group D resembled known ISEs bound by hnRNP H while others appeared to be novel.

Figure 2
Identification and validation of the core motifs of FAS-ISEs

We next used three strategies to test the intrinsic ISE activity of these significantly overrepresented hexamers. First, we selected six hexamers resembling the consensus of each group (i.e. exemplars) and inserted them into pZW15C. The exemplars were selected to represent the most common pattern from the consensus motifs, except for group D where the second most common pattern was selected to avoid synthesizing a string of six G’s. For controls we used mutants of the exemplars and neutral sequences not resembling known SREs. Upon transfection into 293T cells, all exemplars promoted intron splicing, whereas the splicing was barely detectable for the controls (Fig. 2b). In the second experiment, we examined whether each ISE group could promote splicing of a cassette exon from a heterologous intron. To increase the sensitivity, we inserted two tandem copies of exemplars from each group (Supplementary Table 4) at downstream of a cassette exon, and found all ISEs substantially increased the exon inclusion (Fig. 2c). Finally, we tested whether the ISE hexamers can promote splicing from upstream of a 3′SS despite their original identification near 5′SS, and found that all the six ISE groups considerably increased exon inclusion from upstream introns (Fig. 2d). We also found that these exemplars functioned in another cell type (HeLa), consistent with the finding that FAS-ISEs were active in different cell types (Supplementary Fig. 2).

Distribution and conservation patterns of FAS-ISEs

The FAS-ISEs had a base composition remarkably similar to the FAS-ESSs identified from an independent screen 13. The core motifs of both ISEs and ESSs have high G/U content and low content of C (Fig. 3a), which is very different from the purine-rich RESCUE-ESEs 11 and the AU rich FAS-ISSs (Wang et. al, data not shown). We next examined the positional distribution of ISE-hex3 in human exons and associated introns (Fig. 3b). All ISE groups are substantially enriched in introns vs. exons and most groups peaked at upstream of the 3′SS or downstream of the 5′SS, resembling the characteristic distribution pattern of ESSs 12,13. Even excluding the G-rich elements that are common for both ISEs and ESSs, they still had similar distribution patterns (Supplementary Fig. 3a). When calculating the frequency near exons with alternative 5′SS or 3′SS, we found that most ISEs were enriched in the exonic extension region compared to the core regions, again resembling that of FAS-ESSs 22 (Supplementary Fig. 3b).

Figure 3
Similarity between FAS-ISE and FAS-ESSs

We further analyzed the relative conservation of ISE-hex3 in different pre-mRNA regions using a previously developed scoring system 22. The exons conserved in human and mouse genomes were extracted and classified into skipped exons (SEs), constitutive exons (CEs) and alternative 5′SS or 3′SS exons (A5Es or A3Es). For each hexamer set we computed their P value of the conservation rate, thus a smaller P value representing more conserved hexamers in particular region (Fig. 3c). Remarkably, the ISE set had a similar conservation pattern to ESSs: both were more conserved in exons than in introns and were significantly conserved in exonic extension regions of the A5Es or A3Es (Fig. 3c). Different ISE groups had distinct patterns: four groups (A, D, E and F) were conserved in the extension region of A5Es and at downstream of CEs (Fig. 3c), whereas the others (B and C) were more conserved around A3Es than A5Es. In addition, most groups were conserved inside SEs and some groups, to a lesser extent, were conserved inside CEs.

Systematic overlap between ISEs and ESSs

Consistent with the similar distribution and conservation patterns between ISEs and ESSs, 46 hexamers were common to the 84 ISE-hex3 and the 176 FAS-ESS hexamers 13, 12 times more than expected by chance. To analyze the systematic overlap between the two sets, we used the sequential pattern mining to score the probability of a short element belonging to a common set (supplementary notes)35. This method considers both the nucleotide frequency and the dependency of different positions, thus served as a better classifier of SREs than position weight matrix. For all possible hexamers, we computed the ISE and ESS scores based on the sequential feature of FAS-ISE and FAS-ESS hexamer sets (Fig 3d). The hexamers with large ISE (or ESS) scores indicated that they are more likely to function as ISEs (or ESSs). We found a strong positive correlation between the ISE scores and ESS scores for all hexamers, suggesting that these two classes of SREs identified through unrelated screens of random sequences overlap systematically.

All ISEs consistently inhibit splicing from exonic contexts

Previous study suggested that ESSs play critical roles in regulating splicing by suppressing pseudoexons and inhibiting intron-proximal 5′SS or 3′SS 22. Based on the similarity between ISEs and ESSs, we predicted that the new FAS-ISEs might inhibit splicing in exons. To test this prediction, we inserted ISE exemplars as tandem copies into a cassette exon of a modular splicing reporter and found that, as expected, all ISE groups consistently inhibited inclusion of the cassette exon (Fig. 4a, Supplementary Table 4 and 5). When inserted between competing 5′SS, all ISE groups significantly inhibited the use of the proximal site compared to controls (Fig. 4b). The 3′SS choice was also controlled by ISEs in a similar fashion, with the same panel of representative ISEs causing a consistent inhibition of the proximal 3′SS usage (Fig. 4c). These results support a general rule of SRE activity: that sequences capable of enhancing exon inclusion from introns location usually have the ability to inhibit splicing from various exonic contexts. However, the opposite is not true as some sequences can inhibit splicing when inserted into either exons or introns (Wang et al. unpublished data). The context dependent activities for splicing regulation were previously observed for selected elements 6, our analyses established a general rule for an entire class of SREs.

Figure 4
FAS-ISEs function as splicing silencers in exonic contexts

Identify putative trans-factors for each ISE group

Because all ISE groups functioned in heterologous contexts, they probably act by specifically recruiting trans-factors. Thus we sought to unbiasedly identify such factors using an RNA affinity purification method 36. A 5′-biotin labeled short RNA “bait” (20 nt) containing three copies of ISE exemplars was synthesized and incubated with the extract of HeLa cells where these ISEs were consistently active (Fig. 1e). The RNA-protein complexes were purified and proteins specifically bound to the RNA “bait” were identified by mass spectrometry (Supplementary Fig. 4a). From all groups, we excised 30 bands and identified 17 known or predicted RNA binding proteins (Supplementary Fig. 4b and Table 6). Most identified proteins have RNA recognition motifs (RRM) that specifically bind to single-stranded RNA, and several proteins contain Zinc fingers or DEAH box that are known to bind nucleic acids (Fig. 5a).

Figure 5
HnRNP H and F recognize distinct ISE groups to control splicing through C-terminal domains

Remarkably, five groups (A to E) with distinct motifs were all bound by three major proteins around 52 kDa (Supplementary Fig. 4b), which were identified as hnRNP H1, F, and to a lesser extent GRSF1 (Fig. 5a). The binding of hnRNP H1 to the group D (G-runs) was expected based on previous studies 26,27,29, however it is surprising that these proteins were major factors to recognize diverse sequences (Fig. 2a). A recent CLIP-seq study also suggested that the binding specificity of hnRNP H/F may be rather promiscuous 37, consistent with our in vitro data using a SELEX-like methods (Dong et al, unpublished data). Alternatively, these factors can bind to different ISE motifs indirectly through other proteins. The finding that ISE groups A to E were strongly bound by common splicing factors hnRNP H1 and F suggested these factors may play a predominant role in promoting splicing from introns. The consensus motif of group F is very different from the other groups (Fig. 2a). Consistently, this group had a different protein interaction profile (Supplementary Fig. 4b) and was recognized by RNA-binding proteins including DAZAP1, RBM4 and hnRNP D0, some being demonstrated to regulate splicing 38,39.

HnRNP H and F enhance splicing by recognizing various ISEs

With a comprehensive list of putative trans-factors, we next examined if they were indeed responsible for the ISE activity. Since the ISEs in groups A to E were recognized by hnRNP H1 and F, we chose to test their function using splicing reporters containing cognate ISEs. We selected group B because its consensus motif was not previously shown to be recognized by hnRNP H or F 40. Over-expression of both hnRNP H1 and F promoted splicing of the intron inserted with the group B ISE, and co-expression of both proteins had synergistic effect (Fig. 5b left panel and Supplementary Fig. 5a). Consistently, the RNAi of hnRNP H1 or F decreased the intron splicing (Fig. 5b and Supplementary Fig. 5b). As controls, expressions or RNAi of hnRNP H1 or F had no effect on splicing of the same intron containing a non-cognate ISE (group F), suggesting they can promote splicing by specifically binding to the cognate targets.

HnRNP H1 control splicing through its C-terminal domain

hnRNP H1 represents a homologous protein family including hnRNP H2, F, 2H9 and GRSF1. Members of this family have similar domain organization (Fig. 5c), including three qRRM domains to specifically recognize G-runs 41 and two Gly/Tyr rich domains, with the exception of GRSF1 that lacks the Gly/Tyr rich domain. We next examined the functional domains of hnRNP H by fusing different fragments to a programmable RNA binding domain (PUF domain) and co-expressing with splicing reporters containing cognate targets (Fig. 5d and 5e). We selected three hnRNP H1 fragments covering the non-qRRM region (Fig. 5c) and tethered them at downstream of a cassette exon. All fragments enhanced exon splicing compared to the PUF domain alone (Fig. 5d), suggesting that the Gly/Tyr rich domains were the functional modules of hnRNP H1. As controls, the Gly-rich domain of hnRNP A1 and the RS domain of SRSF1 inhibited splicing, consistent with the findings that both proteins control splicing by recognizing ISSs (Wang, unpublished data). In addition, the control fusion proteins with a non-cognate PUF domain had no effect.

Remarkably, the same hnRNP H1 functional domains can inhibit exon splicing when recruited to the exon (Fig. 5e), supporting the finding that its cognate targets can function as either ISEs or ESSs in different contexts (Fig. 2 and and4).4). As controls, the Gly-rich domain of hnRNP A1 inhibited splicing and the RS domain of SRSF1 enhanced splicing, and fusion proteins with non-cognate PUF domain had no effect (Fig. 5e). Taken together, we proved that the recruitment of Gly/Tyr rich domains of hnRNP H1 (even the last 85 aa) was sufficient to cause the context dependent activity of cognate ISEs, suggesting that hnRNP H1 functions in a modular fashion similar to the SR proteins or hnRNP A1 42-44. The activities of these C-terminal domain are exactly opposite to that of RS domains 42-44, which may represent a new mechanism to control splicing.

Multiple antagonistic factors recognize ISEs in group F

The ISEs in group F were recognized by at least three putative splicing factors, DAZAP1, RBM4 and hnRNP D0 (Fig. 5a). We over-expressed each factor with the splicing reporter containing the group F exemplar, and found that DAZAP1 strongly promoted splicing to increase the PSI (percent spliced in) from 20% to 68%, whereas RBM4 and hnRNP D0 had an opposite effect to inhibit exon inclusion (Fig. 6a and Supplementary Fig. 6a). The activities of these factors were dependent on the presence of cognate ISE, as they did not affect the same reporter containing control ISE (group B) (Fig. 6a). These results suggested that DAZAP1 is responsible for the splicing enhancer activity of ISEs in group F, whereas the other factors may antagonize DAZAP1 activity.

Figure 6
Multiple factors with antagonistic activities bind ISE group F to control splicing

To examine whether the antagonistic factors can compete for the same element and switch its activity between ISE and ISS, we co-expressed DAZAP1 and RBM4 in different ratios with the splicing reporters containing the cognate ISE (Fig. 6b). The RBM4:DAZAP1 ratio indeed determined the splicing outcome: higher ratio inhibited splicing whereas a lower ratio promoted inclusion of the cassette exon (Fig. 6b). As controls, RBM4:DAZAP1 ratio had no effect on the reporter containing a non-cognate ISE. We noticed that the more abundant protein seems to “overshadow” the other protein in co-expression experiments (e.g., Fig. 6b lane 3 vs. lane 5, likely due to an over-expression artifact), however this does not change the main conclusion. The binding of the same SRE by antagonistic factors enables a delicate switch where the splicing outcome can be very sensitive to the subtle change of splicing factor levels.

Previous studies showed that DAZAP1 inhibited splicing through interaction with hnRNP A1/A2 45 and RBM4 could either activate or inhibit splicing in different genes 46-48. We searched the entire human genome for cassette exons with the group F ISEs in their adjacent introns and tested how DAZAP1 and RBM4 over-expression affect their splicing. To achieve a more consistent expression level, we generated stable cell lines using Flp-In™-293 T-Rex system where the expression of DAZAP1 or RBM4 can be induced by tetracycline (Supplementary Fig. 6b). We found that in three endogenous genes (COR06, SF1 and ANKS3) with the ISE sequence at down stream of a cassette exon (Supplementary Table 7), DAZAP1 expression indeed promoted the exon inclusion, whereas RBM4 had an opposite effect to inhibit splicing (Fig. 6c).

Antagonistic factors compete for same ISE in exonic context

Since the group F ISE inhibited splicing in exons (Fig. 4), we next examined how its cognate factors contribute to this activity. We co-expressed DAZAP1 or RBM4 with the splicing reporter that contains group F exemplar inside a cassette exon, and found that DAZAP1 promoted splicing whereas RBM4 had opposite activity to inhibit splicing (Fig. 6d, lanes 1 to 3). Over-expression of these factors had no effect on the reporter containing non-cognate ISE, suggesting such activities were due to specific recognition by DAZAP1 or RBM4. Co-expression of two proteins in different ratios changed the splicing outcome: higher RBM4:DAZAP1 ratio caused splicing inhibition whereas a decreased ratio promoted exon inclusion (Fig. 6d, lanes 4 to 6). Such effect was again likely due to the direct competition of these factors, because the RBM4:DAZAP1 ratio did not change splicing of the control reporter.

To determine if the same regulatory rule also applies to endogenous splicing, we searched endogenous human cassette exons containing the group F ISEs and tested how DAZAP1 and RBM4 affect their splicing. For three genes (ZBT17, LAMP1 and NOL8) with the ISE inside a cassette exon (Supplementary Table 7), induction of DAZAP1 and RBM4 had opposite effects with DAZAP1 promoting exon inclusion and RBM4 inhibiting splicing (Fig. 6e). When examining two non-small cells lung cancer lines (H157 and A549) with different RBM4 levels, we found that higher RBM4 level in A459 cells correlated with a reduced exon inclusion in most endogenous genes containing group F ISEs (Supplementary Fig. 6d and 6e), suggesting that the RBM4:DAZAP1 ratio can also explain some endogenous splicing variation in different cell lines.

The binding of the same SRE by antagonistic factors provided another model of the context dependent activity for some ISEs: DAZAP1 was the dominant factor when binding to the group F element in intron, leading to the ISE activity; however, the RBM4 outcompeted DAZAP1 when the same element was inserted into exons, resulting in the ESS activity. This model represents an exception to many cis-elements that have positional dependent activities by recruiting same trans-factor to different locations, and can probably be better described as a “factor dependent activity”.


This study was initiated from an unbiased screen for novel ISEs and generated several key conclusions. First, diverse sequences can function as general ISEs in different cell types and contexts. Second, we observed systematic overlaps between ISEs and ESSs identified through independent screens, suggesting a common rule for the location-dependent activity. Third, we identified multiple factors that specifically bind ISE motifs to promote splicing from introns. Fourth, hnRNP H1 and F play predominant roles in enhancing splicing from introns, the recruitment of hnRNP H1 Gly/Tyr rich domains to different locations explained the location-dependent activity of ISEs. Fifth, a novel class of ISEs (group F) can be recognized by antagonistic factors. Competition of these factors in introns vs. exons produced different splicing outcomes and caused the context-dependent activity of this group. Finally, single ISE element can be bound by multiple factors with distinct activities and the same factor can recognize multiple ISEs, suggesting that a complicated web of RNA-protein interactions control splicing to achieve certain degree of regulatory plasticity. Taken together, this study provided an integrated model in which the general ISEs can be considered as “intron-define elements” that support the splicing pathway to exclude the pre-mRNA region where they are located. These ISEs usually function through recruiting cognate splicing factors, however we cannot rule out the possibilities that some new ISEs may affect splicing by other mechanism such as by slowing down transcription rate.

Compared to the tissue-specific ISEs predicted by conservation 23,24 and the ISREs identified from experimental screens 49, motifs identified here have consistent activities among heterologous gene contexts and cell types. Since this screen was conducted in 293 cells, some known tissue specific ISEs (e.g. binding sites for Nova or Fox-1) were not recovered in our screen. The motifs whose cognate factors are expressed at low levels in 293T cells may also be missed (e.g. Fox-2). Similar strategies can be applied in the future to identify and study intronic SREs in other cell types. The ISEs identified in this study can promote splicing when located in either upstream or downstream intron, presenting a new trend that is distinct from some tissue specific SREs (e.g. Nova or Fox-1/2 binding site) that activate splicing from the downstream intron but repress splicing from upstream introns. This trend may reflect how general splicing factors interact with the core splicing machinery, which appears to be distinct from known tissue specific factors. Therefore motifs identified here have distinct features compared to previously reported motifs and significantly expanded the SRE repertoire.

We observed that multiple trans-factors can specifically recognize the same ISE and the distinct ISEs can be bound by the same protein (Supplementary Table 6). We further confirmed the functional relevance of such interaction network (Fig. 5 and and6).6). Diifferent ISEs could be recognized either by splicing factors with similar activity to generate a synergistic regulation (Fig. 5) or by antagonistic factors to produce a sensitive regulatory switch (Fig. 6). Consistent with the RNA-protein interaction network, the RRM-containing proteins often interact with RNA motifs with very short consensus and the binding motifs of splicing factors were very flexible as judged by different studies 50. The interaction network between cis-acting SREs and trans-factors provides a possible mechanism for regulatory plasticity. To understand the splicing code, new models considering the complex interaction network have to be implemented to simulate such plasticity. Such model will require the integration of information obtained from this study and other transcriptome-wide studies.

Online methods

Splicing reporter constructs

All of the splicing mini-gene reporters were modified from the same backbone construct, pZW1, which included multi-cloning sites between two GFP exons 13. To construct the reporter for FAS-ISE screen, a retained intron - intron 4 of C7orf26 (RefSeq: NM_024067)- was amplified with a PCR reaction using primers containing XhoI/ApaI restriction sites and inserted into the downstream of the 5′ splice site (5′SS) of the first GFP exon. The resulting reporter construct, pZW15, contained two exons and a retained intron () with a multicloning site at 21 nt downstream of exon 1. To increase the sensitivity of FAS-screen, we introduced additional mutations in the 5′SS to make the site stronger. The resulting minigene was subcloned into the site-specific integration plasmid, pcDNA5/FRT, by NheI/BamHI sites, generating the vector pZW15C that was stably transfected with pOG44 (in 1:9 ratio) into 293 FlpIn cell line.

To inserte candidate ISE sequences and controls into pZW15C, we used a forward primer CACCTCGAG(N6-12)GGGCCCCAC and reverse primer GTGGGGCCC(N6-12)CTCGAGGTG, which contained the candidate sequences (designated N6-12) flanked by XhoI and ApaI sites. The two primers were annealed, digested by XhoI/ApaI, and ligated into pZW15C pre-digested with XhoI/ApaI.

To make the random sequence library, we extended the foldback primer CACCTCGAG(N10)GGGCCCACACGTTTTTTTCGTGTGGGCCC with Klenow, cut the resulting DNA with XhoI and ApaI and ligated into pZW15C 13. The ligation product was used to transform ElectroMax DH-5α, and we transformed sufficient numbers of E. coli cells to obtain ~2-fold coverage of the possible DNA decamers. The resulting library was transfected into 293FlpIn cells in 15 batches to obtain enough stably transfected clones (>106 clones) that cover the entire decamer space.

To test ISE in a heterologous context, we constructed the reporter pZW2C by inserting exon 2 of Chinese hamster DHFR gene and portions of its flanking introns between the two GFP exons. This reporter was modified from pZW2 that was originally used in the FAS-ESS screen and contained an XhoI/ApaI restriction site inside the test exon 2 13. The pZW2 was digested with XhoI/ApaI and filled in with an oligonucleotide (obtained by annealing primers 1 and 2, Supplementary Table 1) to destroy the exonic restriction sites. We then introduced a new XhoI/ApaI restriction site at 18 nt downstream of the exon 2 by three consecutive PCR reactions. The resulting product was inserted into pZW2 digested with NheI/PstI to obtain the reporter pZW2B. To increase the ISE detection sensitivity, the pZW2C was further generated by weakening the 3′SS of exon 2 in pZW2B with site-directed mutagenesis so that the exon 2 was included in ~50% of mRNA in the absence of ISE.

To test ISE activity near the 3′SS of an alternative exon, we used a modular reporter pGZ3 19 and inserted ISEs at 33-nt upstream of the 3′SS for the test exon (Exon 12 of the human IGF-II mRNA-binding protein 1, IGF2BP1). We amplified the first GFP exon together with the following IGF2BP1 intron using the pGZ3 as template and the primers containing different ISE sequences (forward primer 5 and reverse primers 7-13, Supplementary Table 1). The resulting fragments were digested with HindIII/SacI and inserted into the pGZ3 vectors digested with same restriction enzymes.

The reporters with competing 5′SS and 3′SS were described previously, and we inserted ISEs and control sequences by annealing primers containing target sequence and cognate restriction sites 22. To test if ISEs can affect splicing when inserted into a skipped exon, we used the same modular splicing reporter pGZ3. The ISEs were inserted into this vector using XhoI/ApaI sites located inside the test exon 19.

To determine the functional modules of hnRNP H, we employed the pCI-new vector (Promega) to express fusion protein as described before 44. Briefly, we started with an expression construct that encodes from N- to C-terminals, Flag epitope, Gly-rich domain of hnRNP A1 (residues 195-320 of NP_002127), and the MS2 coat protein (gift of Dr. R. Breathnach form Institut de Biologie-CHR). The fragment encoding the MS2 coat protein fragment was removed using BamHI/SalI digestion and replaced with a fragment encoding a NLS (PPKKKRKV) and the PUF domain of human Pumilio1, resulting the PUF-Gly(hnRNP A1). To make an expression construct for PUF-RS(SRSF1), we replaced the fragment encoding the Flag/Gly-rich domain with a fragment that encodes the RS domain of SRSF1 protein with an N-terminal Flag epitope. To make expression constructs for PUF fused hnRNP H1 fragments, the RS domain was removed with XhoI/BamHI digestion and replaced with different fragments of hnRNP H1 (A, 188-449aa; B, 188-289aa; C, 364-449aa). To generate splicing reporters containing target sequences of PUF domains, we synthesized and annealed oligonucleotides containing UGUAUGUA sequence flanked by XhoI and ApaI sites, digested with XhoI/ApaI, and inserted into the exon of splicing reporter pGZ3 or the intron of splicing reporter pZW2C.

Cell culture and transfection

293T cells and HeLa cells were cultured with DMEM medium containing 10% of FBS. The ISE library stably transfected 293 FlpIn cells were maintained in DMEM medium with hygromycin at a final concentration of 100 μg/ml. For stable transfection, the random library was co-transfected with pOG44, which encodes the recombinase Flp, into 293 FlpIn cells. The stable transfectants were selected as previously described 13.

To generate stable cell line expressing DAZAP1 upon tetracycline induction, we used pCDNA5 FRT/TO vector and 293 FlpIn/T-Rex cells (Invitrogen). The FLAG-tagged full length DAZAP1 was cloned into the vector, and transfected with pOG44 in 1:9 ratio. The stably integrated cells were selected with 100 μg/ml hygromycin. One day before the induction, the cells were transferred to hygromycin-free medium. The inductions were carried out by adding tetracycline to a final concentration of 2 μg/ml. The induced cells were collected 48 hours after induction to extract RNA and protein for further analysis.

To overexpress trans-factors, cells were plated into 24-well plates 1 day before transfection. To determine the effect of over-expression of trans-factors on splicing changes, 0.2 μg of mini-gene reporters were co-tranfected with 0.4 μg of protein trans-factors using lipofectamine 2000 according to the manual. After 48 hours, cells were collected for further RNA and protein analysis. To knock down trans-factors, cells were transfected with 50 nM siRNA (siGENOME SMART pools, Dharmacon) according to the manual. At 48 hours post-transfection, cells were transfected with the splicing reporters containing ISE exemplars or control. The cells were harvested after another 24 hours for further analysis.

Assay of splicing reporters with semi-quantitative RT-PCR

The splicing reporters (pZW15C, pZW2C, pGZ3, pGZ3-intron, pEZ1B and pEZ2F) inserted with different ISEs in intronic or exonic locations were transfected into cultured cells (293T or HeLa) and samples were collected 24 hours after transfection. The total RNA were isolated from transfected cells with TRIzol reagent (Invitrogen) according to the manufacturer’s instructions, followed by 1 h DNase I (Invitrogen) treatment at 37 °C and then heat inactivation of DNase I. Total RNA (2 μg) was then reverse-transcribed with SuperScript III (Invitrogen) with poly T primer or gene specific primer (for GFP based splicing reporter), and one-tenth of the RT product was used as the template for PCR amplification (25 cycles of amplification, with trace amount of Cy5-dCTP in addition to non-fluorescent dNTPs). RT-PCR products were separated on 10% PAGE gels, and scanned with a Typhoon 9400 scanner (Amersham Biosciences). The amount of each splicing isoform was measured with ImageQuant 5.2. The primers used to amplify GFP based minigene reporters were AGTGCTTCAGCCGCTACCC for GFP exon 1 and GTTGTACTCCAGCTTGTGCC for exon 3.

Identification of trans-factors with RNA affinity purification approach

The RNA affinity purification method was adopted from the previously described protocol 36. For each biotin labeled ISE RNA sample, about 2.5 × 108 HeLa cells were collected and resuspended with 2.5 ml ice cold resuspension buffer (50mM Tris-HCl pH 8.0, 150 mM NaCl). Cells were mixed with 2.5ml 2x lysis buffer (50mM Tris-HCl pH 8.0, 150mM NaCl, 15 mM NaN3, 1%(V/V) NP-40, 2 mM DTT, 2 mM PMSF, 2x protease inhibitor mix) and lysed for 5 min, and then centrifuged at 12000 g for 20 min at 4°C. Then 0.75 nmol biotinylated RNA with two 18 atom spacers (Dharmacon) were added to the supernatants and incubated for 2 hrs at 4 °C. Next, 50 μl Streptavidin-agarose beads (Sigma) were added into the mixture and incubated for 2 hrs at 4 °C with slow rotation. The beads were washed 3 times using 4 ml lysis buffer (50 mM Tris-HCl pH 8.0, 150 mM NaCl, 15 mM NaN3, 0.5% NP-40, 1 mM DTT, 1 mM PMSF, 1x protease inhibitor mix), resuspended in 40 μl final volume, and mixed with 10 μl 5x SDS loading buffer. The proteins were then separated with a 10% SDS-PAGE gel and stained with coomassie blue. The gels was kept in 3% acetic acid for the further mass spectrometry analysis.

The interested bands that contained candidate protein trans-factors were cut and analyzed by ESI-MS/MS on a Q-Tof (Micromass) mass spectrometer. This analysis was conducted by the UNC Proteomic Center.

Supplementary Material


We thank the Drs. Jingyi Hui from Shanghai Institute of Biological Science and Woan-Yuh Tarn from Institute of Biomedical Sciences in Academia Sinica for providing the expression constructs, and Drs. Bill Marzluff and Chris Burge for critical reading of manuscripts. We thank Dr. Zbigniew Dominski for helping in RNA affinity purifications. This work is supported by an AHA grant (0865329E) and NIH grant (R01CA158283) to ZW.


Author contribution: Y W and Z W designed the research and performed the experiments. M M and X X developed computational methods to analyze the data. Y W and Z W wrote the paper.


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