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. 2020 Apr 27;9:e56075. doi: 10.7554/eLife.56075

Srsf10 and the minor spliceosome control tissue-specific and dynamic SR protein expression

Stefan Meinke 1, Gesine Goldammer 1, A Ioana Weber 1,2, Victor Tarabykin 2, Alexander Neumann 1,†, Marco Preussner 1,✉, Florian Heyd 1,✉
Editors: Timothy W Nilsen3, James L Manley4
PMCID: PMC7244321  PMID: 32338600

Abstract

Minor and major spliceosomes control splicing of distinct intron types and are thought to act largely independent of one another. SR proteins are essential splicing regulators mostly connected to the major spliceosome. Here, we show that Srsf10 expression is controlled through an autoregulated minor intron, tightly correlating Srsf10 with minor spliceosome abundance across different tissues and differentiation stages in mammals. Surprisingly, all other SR proteins also correlate with the minor spliceosome and Srsf10, and abolishing Srsf10 autoregulation by Crispr/Cas9-mediated deletion of the autoregulatory exon induces expression of all SR proteins in a human cell line. Our data thus reveal extensive crosstalk and a global impact of the minor spliceosome on major intron splicing.

Research organism: Human, Mouse

Introduction

Alternative splicing (AS) is a major mechanism that controls gene expression (GE) and expands the proteome diversity generated from a limited number of primary transcripts (Nilsen and Graveley, 2010). Splicing is carried out by a multi-megadalton molecular machinery called the spliceosome of which two distinct complexes exist. The more abundant major spliceosome that consists of the U1, U2, U4, U5, U6 small nuclear ribonucleoprotein particles (snRNPs) and multiple non-snRNP splicing factors. Additionally, the cells of most eukaryotes contain the minor spliceosome, which is composed of the minor-specific snRNPs U11, U12, U4atac, U6atac, and the shared U5 snRNP. While the major spliceosome catalyzes splicing of around 99.5% of all introns, mainly so-called U2-type introns with the characteristic GT-AG splice sites, the minor spliceosome recognizes introns of the U12-type containing non-consensus AT-AC splice sites and distinct branch point and polypyrimidine sequences (Jackson, 1991; Turunen et al., 2013). However, despite its low abundance, the minor spliceosome plays a fundamental role in ontogenesis, as deficiencies in minor spliceosome activity or minor intron splicing are lethal or result in developmental defects and disorders (Doggett et al., 2018; Verma et al., 2018).

AS is highly regulated by cis-acting splicing enhancer and silencer elements, which are recognized by various RNA binding proteins, such as SR proteins and heterogeneous nuclear ribonucleoproteins (Hastings et al., 2001). The protein family of SR proteins, with 13 canonical members in humans, is characterized by an arginine and serine rich domain (RS domain) (Manley and Krainer, 2010). Aside from their role in mRNA nuclear export and GE they are essential regulators of AS (Long and Caceres, 2009). Every SR protein contains ultraconserved elements in alternative exons that control the presence of premature translation termination codons (PTC). This allows them to regulate their own abundance through nonsense-mediated decay (NMD) (Lareau et al., 2007). While many SR proteins and RBPs use autoregulation to maintain a stable expression level (Müller-McNicoll et al., 2019), their expression level changes in a tissue-specific manner (Wang et al., 2008; Olthof et al., 2019). Therefore, mechanisms aside from autoregulation are most likely employed to control SR protein levels under different conditions, for instance in different tissues or during differentiation. However, the mechanisms that coordinately regulate SR protein expression levels remain elusive.

SRSF10 is a unique SR protein, as it activates splicing in its phosphorylated state but becomes a general splicing inhibitor upon dephosphorylation (Feng et al., 2008; Zhou et al., 2014). We used SRSF10 as a case study of how tissue-specific differences in SR protein levels can be achieved by employing an autoregulatory feedback loop. We show that Srsf10 recognizes a highly conserved splicing enhancer element within its own pre-mRNA, which results in the production of a non-protein coding mRNA isoform and thereby the regulation of its own expression level. An additional layer of Srsf10 regulation is added by the presence of competing major and minor splice sites which control this autoregulatory AS event. The minor splice site leads to the formation of the protein-coding mRNA, whereas splicing mediated by the major spliceosome leads to the non-protein-coding mRNA. Consequently, Srsf10 levels correlate with the level of the minor spliceosome in a tissue- and developmental stage-specific manner. Surprisingly, we also found that the expression levels of most other SR proteins correlate with Srsf10 expression. This is directly mediated through the levels of Srsf10 and the competition between major and minor splice sites, as CRISPR/Cas9-mediated removal of the autoregulatory exon 3 of Srsf10 increases not only the expression of Srsf10, but also the expression of the other SR proteins. These data connect the minor spliceosome with Srsf10 and SR protein expression in a tissue- and differentiation state-specific manner. We thus reveal a mechanism that coordinately controls SR protein expression in different cellular conditions and that connects the minor spliceosome with global (alternative) splicing of major introns.

Results and discussion

Srsf10 autoregulates its own splicing and expression

Autoregulation has been described for many RBPs including most SR proteins (Lejeune et al., 2001; Sureau, 2001), but not for Srsf10. Srsf10 represents a particularly interesting example as its conserved region contains two competing 5’ splice sites in exon 2 (E2), which are recognized by either the minor or the major spliceosome. The upstream (up) minor splice site is coupled to E4 inclusion and production of a protein coding mRNA, while use of the downstream (dn) major splice site is coupled to E3 inclusion, the presence of a PTC and the use of an alternative polyadenylation site in E3 (Figure 1A). The dn-E3 variant is not a canonical NMD target, as the stop codon in E2 is less than 50 nucleotides upstream of the E2/3 junction (Nagy and Maquat, 1998) and could thus encode for a hypothetical short protein (Srsf10-s, see below). To investigate whether AS of the competing minor and major splice site in exon 2 of Srsf10 depends on an autoregulatory feedback loop, we generated an Srsf10 minigene containing mouse exons 2 to 4 (Figure 1B, top). We transfected this minigene into human HeLa cells and investigated AS after knocking down the endogenous SRSF10. These experiments revealed strong autoregulation, as SRSF10 knockdown decreased the dn-E3 isoform and increased the up-E4 product and retention of intron 2 (IR, Figure 1B, bottom). We confirmed the knockdown of SRSF10 (Figure 1—figure supplement 1A and B) and observed a reduced E3/E4 ratio for endogenous SRSF10 mRNA (Figure 1—figure supplement 1B), consistent with SRSF10 activating E3 inclusion.

Figure 1. Srsf10 autoregulates its own splicing through a conserved enhancer in exon 3.

(A) Schematic of the exon/intron structure of Srsf10. Usage of the downstream (dn) major splice site in Srsf10 exon 2 (GT.AG, U2-type) leads to exon 3 inclusion and a non-protein coding isoform, while usage of the upstream minor splice site (AT.AC, U12-type) results in exon 4 inclusion. A minor 5’ splice site in exon 3 is present but not used in the endogenous context (dotted lines). * indicate stop codons. (B) Srsf10 minigene splicing upon siRNA-mediated knockdown of endogenous Srsf10. Top: exon/intron structure of the Srsf10 minigene reporter containing mouse exons 2 to 4 (and complete intervening introns) with indicated primer binding sites (arrows). HeLa cells were transfected with control siRNA (siCtrl) or against human Srsf10 (siSrsf10), incubated for 24 hr followed by minigene transfection. After 48 hr splicing was analyzed with the indicated primers. Bottom: exemplary gel and quantification of the dn-E3 isoform (n = 5, mean ± SD). (C) Knockdown and rescue of SRSF10. Top: Western Blot of SRSF10 after siRNA-mediated knockdown and transfection with overexpression vectors for the different GFP-tagged Srsf10 isoforms. VINCULIN was used as loading control. Middle: Exemplary gel of Srsf10 minigene splicing upon knockdown and rescue. Bottom: Quantification of the dn-E3 isoform (n ≥ 3, mean ± SD). (D) Exon/intron structure of the Srsf10 minigenes used for mutational analysis. Exon and intron sequences were replaced by sequences containing a minor intron from glia maturation factor beta (Gmfb exons 4 to 5 including the minor intron 4, marked in red). Below the sequence of the identified ESE is shown. (E) Quantification of Srsf10 minigene splicing upon knockdown and rescue. HeLa cells were transfected with the mutated minigenes (D) and analyzed as in (B). Splicing of mutants is shown relative to the wt from (B) and for each mutant relative to the Ctrl siRNA (n = 5, mean ± SD). Student’s t test-derived p values *p<0.05, **p<0.01, ***p<0.001.

Figure 1.

Figure 1—figure supplement 1. Srsf10 autoregulates its own splicing.

Figure 1—figure supplement 1.

(A, B) Confirmation of the Srsf10 knockdown. In (A) SRSF10 protein levels were investigated by Western Blot using an SRSF10-specific antibody. Note that both SRSF10 variants (fl and −2) are strongly reduced. The remaining signal for SRSF10-fl could indicate some unspecific detection of a different (SR)-protein. HNRNP L served as a loading control. In (B) confirmation of the Srsf10 knockdown on mRNA level is shown. The expression of the different Srsf10 isoforms is shown relative to Gapdh and control siRNA (n ≥ 6, mean ± SD). Note the reduced dn-E3/up-E4 ratio after knockdown of Srsf10. (C) Overexpression of SRSF10 variants as in Figure 1C. Top: Overexpression of the SRSF10 variants was investigated with an antibody detecting the GFP epitope. SRSF10-s was not detectably expressed. VINCULIN served as a loading control. Bottom: Representative image of splicing analysis of co-transfected minigenes. (D) Dose-dependent Srsf10 autoregulation. HeLa cells were cotransfected with the minigene, decreasing amounts of SRSF10-fl or −2 (0.1, 0.05, or 0.025 µg plasmid) and adjusted amounts of GFP. Expression was confirmed using an antibody against GFP (top). Note the higher expression levels of SRSF10-2. VINCULIN served as a loading control. Bottom: Splicing analysis of cotransfected minigenes (n = 2, mean ± SD). Note that barely detectable amounts of SRSF10 already change splicing with a stronger effect of SRSF10-fl, especially prominent given the lower expression level of this variant.
Figure 1—figure supplement 2. Srsf10 autoregulates its own splicing through a conserved enhancer in exon 3.

Figure 1—figure supplement 2.

(A–D) Analysis of mutated minigene splicing via radioactive RT-PCR in knockdown (siSrsf10) and knockdown-rescue with SRSF10-fl (siSrsf10 +fl) conditions. In each panel on top the investigated minigene and the primers used are shown. Below, a representative gel with indicated splicing isoforms. The I3 mutant (A) was investigated as the WT in Figure 1C. After mutation of the polyA site in exon 3, mutants E3 and I2 (B and C) could be investigated with only BGH reverse, amplifying both splicing isoforms. The ESE mutant (D) was investigated with a second reverse primer in exon 3 (E3.2), and is shown in comparison to a transfected WT minigene in duplicates. The asterisk marks a degradation product most likely of dn-E3. Note that SRSF10 dependent generation of up-E4 and IR is abolished in the mutant. We observe less IR in the mutant in control condiitons, indicating that the SRSF10-dependent splicing enhancer sequence has an additional role in controlling splicing.
Figure 1—figure supplement 3. Alignment of the 5’ sequence of Srsf10 exon 3, which includes the identified ESE region (marked in red), showing high conservation across 7 mammals.

Figure 1—figure supplement 3.

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AS of Srsf10 results in three possible protein isoforms: SRSF10-fl (inclusion of exon 7a), SRSF10-2 (inclusion of exon 7b, see Figure 1A), and a hypothetical protein resulting from exon 3 inclusion (stop codon within the dn-E2 sequence, see Figure 1A). To investigate the activity of these protein isoforms in regulating AS, we performed rescue experiments with GFP-tagged Srsf10 mouse variants (not targeted by the human-specific siRNA). A Western blot against Srsf10 shows expression of GFP-Srsf10-fl and GFP-Srsf10-2 close to endogenous levels, while GFP-Srsf10-s was not detectable (Figure 1—figure supplement 1C, top). Consistently, Srsf10-fl and Srsf10-2 clearly rescue exon 3 mis-splicing caused by the knockdown of endogenous SRSF10, while transfecting Srsf10-s has no effect on Srsf10 AS (Figure 1C). We obtained the same result when overexpressing the different Srsf10 variants with no knockdown of the endogenous protein, confirming Srsf10-fl and Srsf10-2 as activators of exon 3 inclusion and Srsf10-s as a barely expressed protein (Figure 1—figure supplement 1C). Since the presence of Srsf10-s is hardly detectable, even with a stabilizing GFP tag, we assume that this protein variant is highly instable and does not have a biological function. To compare the activities of Srsf10-fl and Srsf10-2, we performed titration experiments. This demonstrated highly sensitive Srsf10 autoregulation, with Srsf10-fl, despite its lower expression levels, being a more potent activator of E3 inclusion than Srsf10-2 (Figure 1—figure supplement 1D). These data identify Srsf10 as an activator of the major intron between exons 2 and 3 and suggest that Srsf10 autoregulates its own expression level via a negative feedback loop, as higher Srsf10 levels result in the formation of the non-protein-coding dn-E3 isoform.

To identify the cis-regulatory element required for autoregulation, we used systematic mutational analysis of the Srsf10 minigene (Figure 1D, Figure 1—figure supplement 2, A–D). First, we replaced sequences downstream of exon 3 by human Gmfb sequences from exons 4 to 5, including a minor intron (mutant I3, see ‘Material and Methods’ for details). This reflects the endogenous situation, as Srsf10 exon 3 contains a minor 5’ splice site, which, however, is rarely used, since the polyadenylation site in exon 3 appears to be dominant (Figure 1A). The resulting minigene clearly remains responsive to SRSF10 knockdown and overexpression (Figure 1E, I3). In contrast, replacing sequences starting from exon 3 (E3) or in intron 2 (I2) by Gmfb sequences results in splicing unresponsive to SRSF10 knockdown and barely responsive to SRSF10 overexpression (Figure 1E; mutants E3 and I2). These data suggest that SRSF10 controls its own splicing via binding to exon 3, which, indeed, contains a GA-rich element representing the previously identified SRSF10 consensus binding site (Shin and Manley, 2002; Zhou et al., 2014). Replacing nucleotides 17 to 60 of exon 3 by GMFB exon 4 sequence was sufficient to abolish SRSF10-mediated AS (Figure 1D and E, ESE; Figure 1—figure supplement 3), thus identifying this GA-rich element as an SRSF10-dependent exonic splicing enhancer (ESE). Together, these data identify a highly conserved element in Srsf10 exon 3 which is necessary for an autoregulatory feedback loop that controls Srsf10 expression levels.

The minor spliceosome controls Srsf10 expression

Exon 2 of Srsf10 contains two competing 5’ splice sites, which are specifically recognized by either the minor or the major spliceosome. To investigate the relevance of these splice sites for Srsf10 autoregulation, we generated mutated minigenes containing either only major or only minor splice site (Figure 2A) and analyzed AS of the resulting minigenes. Mutated minigenes remained clearly responsive to SRSF10 knockdown and rescue, demonstrating that SRSF10 can regulate AS through both major and minor spliceosomes. However, in the control conditions (siCtrl), we observed that both minor-only or major-only minigenes show a strong increase in the use of exon 4 (Figure 2B). Exon 3 is hardly included at all in any of the two minigenes. Additionally, in the presence of a directly competing upstream splice site in exon 2, the downstream splice site is no longer used (Figure 2B, Figure 2—figure supplement 1). These data indicate that, in vivo, the use of the minor splice site that leads to productive Srsf10 splicing, is reduced through the presence of a competing major splice site. This arrangement could render Srsf10 expression susceptible to dynamic control through alterations in the activity of the minor spliceosome. To directly investigate this hypothesis, we inhibited minor spliceosome activity by performing an siRNA-mediated knockdown of the essential U11/U12 snRNP component RNPC3 (Figure 2C). Indeed, the expression of the coding SRSF10 mRNA (up-E4) was significantly decreased, while the levels of the non-coding dn-E3 mRNA are unaffected or slightly increased (Figure 2C). This result indicates that the abundance of the minor spliceosome directly correlates with Srsf10 GE through controlling productive vs. non-productive AS. Furthermore, regulation through the activity of the minor spliceosome appears to, at least partially, overrule the autoregulatory feedback loop. This suggests a model in which the activity of the minor spliceosome sets the expression level of Srsf10, which is then maintained through autoregulation.

Figure 2. The minor spliceosome controls Srsf10 expression.

(A) Schematic of the exon/intron structure of the Srsf10 WT and mutated minigenes harboring only major (GT.AG, blue) or minor (AT.AC, red) splice sites. Mutated splice sites are marked in red. (B) Minigenes from (A) were analyzed as in Figure 1. Quantification of splicing-sensitive RT-PCRs is shown relative to the WT from Figure 1B (n = 4, mean +/- SD). A representative gel is shown in Figure 2—figure supplement 1. (C) RT-qPCRs confirm siRNA-mediated knockdown of Rnpc3 (left) and changes in Srsf10 expression levels in HEK293. Expression relative to Gapdh and normalized to siCtrl (n = 3, mean +/- SD). Student’s t test-derived p values *p<0.05, **p<0.01, ***p<0.001.

Figure 2.

Figure 2—figure supplement 1. Competition of minor and major splice sites favors the downstream 5’ splice site A representative gel of only minor or major splice site containing minigenes.

Figure 2—figure supplement 1.

Minigene splicing of major and minor minigenes described and quantified in Figure 2A and B. Note that in the absence of competition between minor and major splice sites, we observe mainly splicing from the upstream 5’ splice site and the downstream 5’ splice site is rarely used.
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Minor spliceosome and SR protein expression correlate in a tissue-specific manner

To investigate the relevance of this mechanism in vivo, we analyzed the correlation of Srsf10 GE levels with expression of the minor spliceosome component Rnpc3 across 25 different mouse tissues (Figure 3A). Calculated transcripts per million (tpm) values using Whippet Quant (Sterne-Weiler et al., 2018) revealed clear tissue-specific expression patterns for both Srsf10 and Rnpc3. Both genes show the lowest GE in blood cells and the highest in thymus (Figure 3A). Notably, a linear regression fit revealed an almost perfect correlation of Srsf10 and Rnpc3 expression across the 25 investigated tissues (R²=0.85, p<0.0001, Figure 3B). Similarly, SRSF10 and RNPC3 GE levels correlate across 31 human tissues (R²=0.33, p=0.0006, Figure 3—figure supplement 1A). In contrast, the levels of the housekeeping gene Gapdh, which contains no minor intron, do not correlate with Rnpc3 (R²=0.01, p=0.4071, Figure 3C). Similar correlation coefficients were obtained with gene expression values determined independently using Salmon (Patro et al., 2017; Figure 3—figure supplement 1B and C). Globally, minor intron-containing genes correlate much more strongly with Rnpc3 levels than a randomly chosen group of expression level-matched genes containing only major introns (Figure 3D), indicating that Rnpc3 levels represent an adequate indicator for minor spliceosome activity. Consistently, Rnpc3 levels, and therefore Srsf10 levels, also correlate with the expression levels of two other minor spliceosome components, namely Snrnp25 and Snrp48 (Figure 3—figure supplement 1D and E). These in vivo GE data are consistent with our model that minor spliceosome activity controls Srsf10 levels. As an additional model system, we compared Rnpc3 and Srsf10 levels during neuronal differentiation of mouse embryonic stem cells (ES cells). GE levels correlate significantly (R²=0.34, p=0.0006, Figure 3—figure supplement 1F), while Gapdh levels do not correlate with Rnpc3 (R²=0.02, p=0.4365, Figure 3—figure supplement 1G). Again, globally, minor intron-containing genes show a stronger correlation with Rnpc3 levels than genes containing only major introns (Figure 3—figure supplement 1H). The Srsf10/Rnpc3 correlation in ES cell differentiation is less pronounced – also with Salmon derived tpm values (Figure 3—figure supplement 1I) – indicating other factors influencing gene expression. Normalization of gene expression using DESeq2 (Love et al., 2014) strongly increases this correlation (Figure 3—figure supplement 1J), which is consistent with a direct role of the minor spliceosome in regulating Srsf10 levels across different tissues and development stages. In summary, together with our minigene and knockdown results, these data indicate that the activity of the minor spliceosome controls productive vs unproductive Srsf10 splicing and expression levels during development and in a tissue-specific manner.

Figure 3. Minor spliceosome and Srsf10 expression correlate in a tissue-specific manner.

(A) Relative GE levels of Srsf10 and Rnpc3 across 25 mouse tissues (x-axis). Transcripts per million (tpm) values were calculated using Whippet (Sterne-Weiler et al., 2018) (n ≥ 2, mean +/- SEM). (B, C) Linear regression fit for comparison of Srsf10 (B) and Gapdh (C) GE/tpm values with Rnpc3 across the 25 different mouse tissues. Goodness of fit is represented by R2 and p-values. (D) Calculated p-values (left) and R2 values (right) of a global correlation analysis of Rnpc3 with minor intron containing genes (n = 587) or randomly chosen expression matched genes, containing only major introns (n = 629). Statistical significance was determined by an unpaired t-test ****p<0.0001.

Figure 3.

Figure 3—figure supplement 1. Minor spliceosome and Srsf10 expression correlate in a tissue-specific manner and during development.

Figure 3—figure supplement 1.

(A) Linear regression analysis of Srsf10 and Rnpc3 expression values (fpkm, fragments per kilobase of exon model per million reads mapped) across 31 human tissues (Uhlén et al., 2015). (B, C) Linear regression analysis of Srsf10 or Gapdh and Rnpc3 using Salmon derived tpm values. (D, E) Linear regression analysis of calculated tpm values for minor spliceosome components Snrnp25 (D) and Snrnp48 (E) against Rnpc3 in 25 mouse tissues. (F, G) Correlation analysis of calculated tpm values for Srsf10 (F) and Gapdh (G) against Rnpc3 in differentiating mouse ES cells (Hubbard et al., 2012). (H) Global correlation analysis of Rnpc3 to minor intron containing genes or randomly chosen expression matched genes containing only major introns. Calculated p-values (left) and R2 values (right) are shown. Statistical significance was determined by an unpaired t-test **p<0.01, ****p<0.0001. (I, J) Correlation analysis of calculated tpm values for Srsf10 against Rnpc3 in differentiating mouse ES cells using either Salmon-derived (I), or Salmon-derived and DESeq2-normalized (J) expression levels.
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SRSF10 and the minor spliceosome control tissue-specific and dynamic SR protein expression

To investigate whether the minor spliceosome exclusively controls Srsf10 expression, we next examined the expression levels of all other SR proteins in a tissue- and developmental stage-specific manner. Surprisingly, we found that the expression of all SR proteins correlates with the minor spliceosome, represented by Rnpc3, in the 25 investigated tissues (R²>0.42, p<0.0001, Figure 4A and Figure 4—figure supplement 1A). Additionally, we observed a highly similar expression pattern for all SR proteins during neuronal differentiation of mouse ES cells (Figure 4B and Figure 4—figure supplement 1B) and a significant correlation with Rnpc3 (Figure 4—figure supplement 1C). To experimentally confirm these results in vivo, we isolated RNA from mouse cerebral cortices from embryonic days (E) 12.5 and 15.5. RT-qPCR analysis revealed a significant increase of Rnpc3 GE from E 12.5 to E 15.5 and, in parallel, Srsf10 levels also increased (Figure 4C and D). This is consistent with our model that higher minor spliceosome activity (indicated by higher Rnpc3 abundance) results in preferential usage of the Srsf10 up-E4 minor splice site, which leads to an increase in protein coding Srsf10 mRNA. Consistent with SR protein expression patterns from different tissues or stem cell development in RNA-Seq data (Figure 4A and B), all other tested SR proteins are also upregulated during the transition from E 12.5 to E 15.5 (Figure 4D), which, again, indicates a co-regulation of SR proteins, even though Srsf10 is the only SR protein that contains a minor intron. As SR proteins are known to cross-regulate each other (Bradley et al., 2015), we hypothesized that a change in Srsf10 levels could directly influence the levels of other SR proteins. To test this, we generated a CRISPR/Cas9-edited cell line lacking the non-productive SRSF10 exon 3 (Figure 4E, left). Homozygous removal of exon 3 was confirmed by PCRs at the genomic level (Figure 4E, right) and, as expected, we observe increased SRSF10 expression (Figure 4F, see Figure 4—figure supplement 1D for protein expression). Interestingly, we observe a stronger increase in the less active SRSF10-2 isoform (Figure 4—figure supplement 1D), indicating that AS of the last exon could be used to partially compensate for the loss of autoregulation via exon 3. Next, to examine whether manipulation of SRSF10 autoregulation is sufficient to change the overall activity of SRSF10, we analyzed AS of SRSF10 target exons (Zhou et al., 2014; Wei et al., 2015). Consistent with increased SRSF10 expression, the inclusion of alternative exons in BCLAF1, PTBP2 and ZFP207 is promoted in the CRISPR/Cas9-edited cells, and the opposite is observed by knockdown of SRSF10 (Figure 4—figure supplement 1E). Notably, increased SRSF10 in our CRISPR/Cas9-edited cell line was sufficient for subtle upregulation of the mRNA levels of all other SR proteins (Figure 4F). In addition, reduced SRSF10 expression upon RNPC3 knockdown also correlates with slightly decreased expression of most other SR proteins (Figure 4—figure supplement 1F). In summary, these data suggest a model where minor spliceosome activity directly controls SRSF10 levels, which is sufficient to change expression levels of the other SR proteins in a tissue- and differentiation state-specific manner (Figure 4—figure supplement 2). An exciting question that remains is how SRSF10 is able to coordinately regulate the abundance of the other SR proteins. Cross-regulatory mechanisms are known for many RBPs (Kumar and Lopez, 2005; Rossbach et al., 2009) and we therefore speculate that SRSF10 could regulate other SR proteins (and potentially other RBPs) by repressing the formation of NMD-targeted isoforms from their pre-mRNAs. This could be mediated either directly, by binding to the respective pre-mRNAs, or indirectly, through interactions with other SR proteins. Additionally, differences in SR protein abundance could be achieved by changing their activity. Higher mRNA abundance could be the consequence of reduced SR protein activity, resulting in reduced autoregulatory NMD exon inclusion and therefore higher mRNA expression levels (Ni et al., 2007). The activity of SR proteins is, amongst others, controlled by their phosphorylation level (Goldammer et al., 2018), and could be controlled by a direct or indirect effect of SRSF10 on the regulating kinases and phosphatases. While these mechanistic details remain to be investigated, the control of SR protein levels through the minor spliceosome and SRSF10 is of fundamental importance, as SR protein levels will have consequences for the splicing of most major introns. Our data thus reveal a mechanism through which the activity of the minor spliceosome controls major intron splicing in a tissue- and differentiation state-specific manner. This may also be relevant for diseases caused by minor spliceosome deficiencies (Jutzi et al., 2018; Verma et al., 2018), as misregulation of SR proteins and consequently, defects in major intron splicing (Cologne et al., 2019), may contribute to the observed phenotypes.

Figure 4. SRSF10 and the minor spliceosome control tissue-specific and dynamic SR protein expression.

(A) Linear regression analysis of calculated tpm values for SR proteins against Rnpc3 in 25 different mouse tissues. R2 and p-values are shown on the right. See also Figure 4—figure supplement 1A with all SR proteins colored. Due to generally low expression levels Srsf12 was omitted. (B) Comparison of tpm values of SR proteins during mouse ES cell differentiation (n ≥ 3, mean ± SD). See also Figure 4—figure supplement 1B and C. (C) Rnpc3 GE levels in mouse cortex samples of the developmental stages E 12.5 and E 15.5 (relative to Gapdh, normalized to time point E 12.5) (n ≥ 4, mean ± SD). (D) SR protein expression in mouse cortex samples of the developmental stages E 12.5 and E 15.5. For Srsf10 only the functional up-E4 isoform was analyzed (relative to Gapdh, normalized to time point E 12.5) (n ≥ 4, mean ± SD). (E) Generation of clonal HEK293 cell lines lacking the regulatory Srsf10 exon 3. Left: Schematic of CRISPR/Cas9-mediated deletion of Srsf10 exon 3. Arrows indicate primer binding sites. Right: genotyping PCR on genomic DNA. (F) Relative SR protein expression in WT HEK293 cells or Srsf10ΔE3 cells. For Srsf10 only the functional up-E4 isoform was analyzed (relative to Gapdh, normalized to WT) (n = 3, mean +/- SD). Statistical significance was determined by an unpaired t-test *p<0.05, **p<0.01, ***p<0.001.

Figure 4.

Figure 4—figure supplement 1. SRSF10 and the minor spliceosome control tissue specific and dynamic SR protein expression.

Figure 4—figure supplement 1.

(A) Linear regression analysis of calculated tpm values for the SR proteins against Rnpc3 in 25 different mouse tissues as shown in Figure 4A with all SR proteins colored. (B) Comparison of GE/tpm values of SR proteins during mouse ES cell differentiation as shown in Figure 4B with all SR proteins colored. (C) Linear regression analysis of SR proteins against Rnpc3 in neuronal differentiating mouse ES cells. R2 and p-values are shown on the right. Quantifications are based on Salmon and DESeq2 based expression levels. (D) SRSF10-fl and SRSF10-2 protein levels in CRISPR/Cas9-edited HEK293 cells (ΔE3) were analyzed by Western Blot and quantified relative to HNRNP L (n = 3, mean ± SD). (E) SRSF10 target gene splicing (top: Bclaf1, middle: Ptbp2, bottom: Zfp207) in WT and CRISPR/Cas9-edited cells (ΔE3) and after Srsf10 knockdown. Representative gels for radioactive RT-PCRs and quantifications of SRSF10 target gene splicing in ΔE3 cells and upon siRNA-mediated knockdown, relative to WT or siCtrl, respectively (n = 3, mean ± SD). (F) mRNA expression levels of SR proteins in HEK293 cells upon Rnpc3 knockdown. SR protein expression levels are normalized to siCtrl (n = 3, mean +/- SD). Student’s t test-derived p values *p<0.05, **p<0.01, ***p<0.001.
Figure 4—figure supplement 2. Model for minor spliceosome-mediated control of SR proteins AS of SRSF10 mirrors minor spliceosome activity.

Figure 4—figure supplement 2.

Reduced minor spliceosome activity results in increased formation of an unproductive SRSF10 variant through exon 3 inclusion via the major spliceosome. High minor spliceosome activity results in splicing to exon 4 and formation of protein-coding isoforms. SRSF10 regulates both its own expression level and the expression of other SR proteins.
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Materials and methods

Key resources table.

Reagent type
(species) or
resource		 Designation Source or
reference		 Identifiers Additional
information
Gene (Mus musculus) Srsf10 ncbi GeneID:14105 Exons 2–4
Gene (Homo sapiens) Gmfb ncbi GeneID:2764 Exon 4–5
Gene
(Homo sapiens)		 Srsf10 ncbi GeneID:10772
Cell line (Homo sapiens) HeLa ATCC RRID:CVCL_0030
Cell line (Homo sapiens) HEK293 ATCC RRID:CVCL_0045
Cell line (Homo sapiens) HEK293ΔE3 This paper CRISPR/Cas9-mediated deletion of Srsf10 exon 3; see Figure 4
and Materials and methods Part
Biological sample (Mus musculus, NMRI strain) Cortices This paper developmental stages E 12.5 and E 15.5; see Figure 4 and Materials and methods Part
Antibody anti-FUSIP1 (T-18) (human, monoclonal) Santa Cruz Biotechnology RRID:AB_1123037 1:1000
Antibody Anti-GFP (B-2)(monoclonal) Santa Cruz Biotechnology RRID:AB_627695 1:5000
Antibody anti-Vinculin (H-300) (rabbit, polyclonal) Santa Cruz Biotechnology RRID:AB_2214507 1:1000
Antibody anti-hnRNP L (4D11) (human, monoclonal) Santa Cruz
Biotechnology		 RRID:AB_627736 1:10000
Recombinant DNA reagent Mouse Srsf10 minigene This paper See Figure 1 + with Figure 1—figure supplements 1 and 2; and Materials and methods part
Recombinant
DNA reagent		 mouse Srsf10-fl-GFP This paper See Figure 1 + with Figure 1—figure supplements 1 and 2; and Materials and methods part
Recombinant DNA reagent mouse Srsf10-2-GFP This paper See Figure 1 + with Figure 1—figure supplements 1 and 2; and Materials and methods part
Recombinant DNA reagent mouse Srsf10-s-GFP This paper See Figure 1 + with Figure 1—figure supplements 1 and 2; and Materials and methods part
Recombinant DNA reagent PX459 vector Kindly provided by Stefan Mundlos RRID:Addgene_62988 For sgRNA cloning and transfection
Recombinant DNA reagent pEGFP-N3 Clontech SRSF10 expression construct
Recombinant DNA reagent pcDNA3.1(+) Invitrogen Catalog no: V79020 Minigene cloning
Sequence-based reagent siRNA against human Srsf10 (siSrsf10) This paper GCGUGAAUUUGGUUAUdTdT Knockdown of endogenous Srsf10
Sequence-based reagent siRNA against human Rnpc3 (siRnpc3) This paper GAAAGAAGGUCGUAUGAAAdTdT Knockdown of endogenous Rnpc3
Sequence-based reagent Control siRNA (siCtrl) This paper UUUGUAAUCGUCGAUACCCdTdT
Sequence-based reagent sgRNA: SRSF10_E3_3fw Benchling Tool RRID:SCR_013955 Sequence: caccgctactttactcggtaagcca; CRISPR/Cas9-mediated deletion of Srsf10 exon 3
Sequence-based reagent sgRNA: SRSF10_E3_3rv Benchling Tool RRID:SCR_013955 Sequence: aaactggcttaccgagtaaagtagc; CRISPR/
Cas9-mediated deletion of Srsf10 exon 3
Sequence-based reagent sgRNA: SRSF10_E3_5fw Benchling Tool RRID:SCR_013955 Sequence: caccgtgagtttcagaagcatgaat; CRISPR/Cas9-mediated deletion of Srsf10 exon 3
Sequence-based reagent sgRNA:
SRSF10_E3_5rv		 Benchling Tool RRID:SCR_013955 sequence: aaacattcatgcttctgaaactcac; CRISPR/Cas9-mediated deletion of Srsf10 exon 3
Commercial assay or kit PowerUp SYBR Green Mastermix ThermoFisher Scientific A25742 RT-qPCR
Chemical compound,
drug		 Roti-Fect Carl Roth Order no:P001.1 Plasmid vector transfection
Software, algorithm GraphPad Prism 7.05 GraphPad RRID:SCR_002798 Statistical analysis
Software, algorithm ImageQuant TL GE Life Sciences RRID:SCR_014246 quantification
Software, algorithm Whippet v0.11 Sterne-Weiler et al., 2018 RRID:SCR_018349 Tpm calculation
Software, algorithm Salmon v1.2.0 Patro et al., 2017 RRID:SCR_017036 Tpm calculation
Software, algorithm TxImport v1.14.0 Soneson et al., 2015 RRID:SCR_016752 Import of transcript counts to R for
normalization wit DESeq
Software, algorithm DESeq2 v1.26.0 Love et al., 2014 RRID:SCR_015687 Transcript counts normalization
Open in a new tab

Tissue cell culture

HEK293 and HeLa cells were cultured in standard conditions. Transfections were done with Rotifect according to the manufacturer’s instructions. For siRNA sequences see Supplementary file 1. Minigenes were transfected 24 hr after knockdown and RNAs were isolated 48 hr later. For overexpression and rescue experiments, cells were transfected using 0.4 µg of minigenes and 0.4 µg expression vectors for GFP-tagged Srsf10-fl, Srsf10-2 or Srsf10-s (or GFP alone). 48 hr after transfection cells were harvested for protein and/or RNA preparation. We perform monthly test for mycoplasms using PCR. Cell have been tested negative in all tests during the experiments performed for the present study. The cells morphologically appear as expected for Hek293 and HeLa cells respectively. We have used these Hek293 cells in several RNA-Seq experiments and compared gene expression with published Hek293 datasets and found very good correlation.

Preparation of embryonic mouse cortices for RNA extraction

Colonies of wild type mice of the NMRI strain were maintained in the animal facilities of Charité-Universitätsmedizin Berlin. Tissue collection was performed in compliance with German Animal Welfare Law and regulations imposed by the State Office for Health and Social Affairs Council in Berlin/Landesamt für Gesundheit und Soziales (LAGeSo).

Mice were bred for timed pregnancies, and the date of vaginal plug detection was considered embryonic day 0.5. Pregnancies were timed accordingly, and embryos prepared at the indicated embryonic days. Prior to the preparation of embryos, all tools were thoroughly cleaned with RNAse AWAY solution (Thermo Fisher, cat. no. 10328011). The uteri were dissected into DEPC-treated PBS, and the embryonic brains quickly transferred to a solution of 2M NaCl in PBS for RNA stabilization. Cortices were dissected and then snap frozen in liquid nitrogen.

Molecular cloning

For cloning of the different Srsf10 overexpression constructs inserts were amplified from mouse cDNA and cloned into pEGFP-N3 (Clontech) using XhoI and BamHI restriction sites introduced through PCR primers. For minigene cloning, mouse genomic DNA was used as template to amplify Srsf10 exons 2 to 4 and the product was cloned into pcDNA3.1(+). See Supplementary file 1 for cloning primer sequences. For mutational analysis Srsf10 exons/introns were replaced by sequences from the human GMFB gene: exons 4 to 5, intron 4 is a minor intron. New inserts were amplified by 1-step or 2-step PCR and cloned into pcDNA3.1(+) using BamHI and XhoI or into the WT minigene using internal restrictions sites (BsrGI for exon 3 and XbaI for intron 3). In the I3 mutant 356 nt of Srsf10 exon 3 were maintained, the downstream sequence was replaced by the 5’ splice site of GMFB exon 4 (TCGatatcc…) and downstream sequence. For the E3 mutant sequences downstream of the BsrGI site (+16 in exon 3) were replaced by GMFB exon 4 to 5 sequence starting with (5’-CACCAGA…). For the ESE mutant nucleotids 17 to 60 of exon 3 were replaced by GMFB exon 4 sequence. For the minigene containing only minor splice sites we replaced the major splice sites of Srsf10 intron 2 by minor splice sites of GMFB intron 4 (5’ splice site: 5’-TCGatatcc; 3’ splice site: 5’-ttctttaacttgagaaaaacCTT). In the minigene containing only major splice sites the upstream 5’ splice site of exon 2 and the 3’ splice site of exon 4 were replaced by major splice sites from GMFB intron 5 or 3, respectively (5’ splice site: 5’-TTGgtaagt; 3’ splice site: 5’-gcttttctctgtggtgccagGGC). All constructs were confirmed by sequencing.

RT-PCR and RT-qPCR

RT-PCRs and RT-qPCRs were performed as previously described (Preußner et al., 2017). See Supplementary file 1 for primer sequences.

Western blot

Western Blot analyses were done as previously described (Preußner et al., 2017). The following antibodies were used: αSRSF10/FUSIP1 (T-18, Santa Cruz), αGFP (B-2, Santa Cruz), αVINCULIN (H-300, Santa Cruz) and αHNRNPL (4D11, Santa Cruz) antibodies. Western blots were quantified using the ImageQuant TL software.

RNA-Seq analysis

Transcripts per million (tpm) values were extracted from previously published RNA sequencing data using Whippet version 0.11 (Sterne-Weiler et al., 2018) and mouse reference genome mm10. Data for mouse tissues were obtained from WT control samples from SRA study DRP003641 (Tanikawa et al., 2017), data for neuronal differentiation of mouse ES cells from SRA study SRP017778 (Hubbard et al., 2012). Additionally, skin samples from SRP115206 were analyzed. For comparison, transcript abundances were additionally quantified using salmon version 1.2.0 (Patro et al., 2017). To obtain normalized expression counts, transcripts were imported to R using TxImport version 1.14.0 (Soneson et al., 2015) and count normalization was performed using DESeq2 version 1.26.0 (Love et al., 2014). DESeq2 based normalized expression counts dramatically increased the variance of replicate tissue samples (but not of ES cell differentiation), which is why we chose to analyze only the un-normalized values for the tissue comparisons. To investigate GE levels from different human tissues, fpkm values were directly used from Uhlén et al. (2015). Linear regression fits were performed in GraphPad Prism 7.05. A list of minor intron containing genes is based on Olthof et al. (2019). Genes with a comparable distribution of GE levels were randomly chosen from the remaining only major intron containing genes. The GE of each gene was compared to Rnpc3 levels across tissues or development stages. Unexpressed genes were excluded from the analysis. Downstream analyses were performed using standard Python 2 code and GraphPad Prism.

Generation of CRISPR/Cas9-edited cell lines

For CRISPR/Cas9-mediated deletion of SRSF10 exon 3 sgRNAs were designed using the Benchling tool (for sequences see Supplementary file 1) and cloned into the PX459 vector. Cells were transfected using Rotifect following the manufacturer's protocol. 48 hr after transfection, transfected cells were selected with 1 μg/ml puromycin and clonal cell lines were isolated by dilution (Ran et al., 2013). Genomic DNA from clonally expanded lines was extracted and analyzed by PCR.

Acknowledgements

The authors wish to thank members of the Heyd lab for constructive comments and the HPC Service of ZEDAT, Freie Universität Berlin, for computing time. This work was funded by DFG grant 278001972 - TRR 186 to FH and DFG grant TA303/8-1 to VT. IW was supported by a PhD fellowship of the Boehringer Ingelheim Fonds and the Charité Promotionsstipendium. MP is funded by a post-doc stipend of the Peter and Traudl Engelhorn Foundation.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Marco Preussner, Email: mpreussner@zedat.fu-berlin.de.

Florian Heyd, Email: florian.heyd@fu-berlin.de.

Timothy W Nilsen, Case Western Reserve University, United States.

James L Manley, Columbia University, United States.

Funding Information

This paper was supported by the following grants:

  • Deutsche Forschungsgemeinschaft 278001972 - TRR 186 to Florian Heyd.

  • Deutsche Forschungsgemeinschaft TA303/8-1 to Victor Tarabykin.

  • Boehringer Ingelheim Fonds PhD fellowship to A Ioana Weber.

  • Charité Promotionsstipendium to A Ioana Weber.

  • Peter and Traudl Engelhorn Foundation Postdoc stipend to Marco Preussner.

Additional information

Competing interests

No competing interests declared.

Author contributions

Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing.

Investigation, Methodology.

Formal analysis, Investigation, Writing - review and editing.

Resources.

Formal analysis.

Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing.

Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing.

Ethics

Animal experimentation: Colonies of wild type mice of the NMRI strain were maintained in the animal facilities of Charité-Universitätsmedizin Berlin. Tissue collection was performed in compliance with German Animal Welfare Law and regulations imposed by the State Office for Health and Social Affairs Council in Berlin / Landesamt für Gesundheit und Soziales (LAGeSo) under licence T102/11.

Additional files

Supplementary file 1. Oligonucleotide sequences of primers, siRNAs, and guide RNAs.
elife-56075-supp1.xlsx (13.2KB, xlsx)
Transparent reporting form

Data availability

All data are previously published and publicly available.

The following previously published datasets were used:

Human Genome Center, The University of Tokyo 2017. p53 mouse multi-tissue transcriptome analysis. NCBI Sequence Read Archive. DRP003641

USAMRICD 2012. Deep transcriptional profiling of longitudinal changes during neurogenesis and network maturation in vivo. NCBI Sequence Read Archive. SRP017778

References

  1. Bradley T, Cook ME, Blanchette M. SR proteins control a complex network of RNA-processing events. RNA. 2015;21:75–92. doi: 10.1261/rna.043893.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Cologne A, Benoit-Pilven C, Besson A, Putoux A, Campan-Fournier A, Bober MB, De Die-Smulders CEM, Paulussen ADC, Pinson L, Toutain A, Roifman CM, Leutenegger AL, Mazoyer S, Edery P, Lacroix V. New insights into minor splicing-a transcriptomic analysis of cells derived from TALS patients. RNA. 2019;25:1130–1149. doi: 10.1261/rna.071423.119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Doggett K, Williams BB, Markmiller S, Geng FS, Coates J, Mieruszynski S, Ernst M, Thomas T, Heath JK. Early developmental arrest and impaired gastrointestinal homeostasis in U12-dependent splicing-defective Rnpc3-deficient mice. RNA. 2018;24:1856–1870. doi: 10.1261/rna.068221.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Feng Y, Chen M, Manley JL. Phosphorylation switches the general splicing repressor SRp38 to a sequence-specific activator nature structural &amp. Molecular Biology. 2008;15:1040–1048. doi: 10.1038/nsmb.1485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Goldammer G, Neumann A, Strauch M, Müller-McNicoll M, Heyd F, Preußner M. Characterization of cis-acting elements that control oscillating alternative splicing. RNA Biology. 2018;15:1–12. doi: 10.1080/15476286.2018.1502587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Hastings ML, Wilson CM, Munroe SH. A purine-rich intronic element enhances alternative splicing of thyroid hormone receptor mRNA. RNA. 2001;7:859–874. doi: 10.1017/S1355838201002084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Hubbard KS, Gut IM, Lyman ME, Tuznik KM, Mesngon MT, McNutt PM. High yield derivation of enriched glutamatergic neurons from suspension-cultured mouse ESCs for neurotoxicology research. BMC Neuroscience. 2012;13:127. doi: 10.1186/1471-2202-13-127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Jackson IJ. A reappraisal of non-consensus mRNA splice sites. Nucleic Acids Research. 1991;19:3795–3798. doi: 10.1093/nar/19.14.3795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Jutzi D, Akinyi MV, Mechtersheimer J, Frilander MJ, Ruepp MD. The emerging role of minor intron splicing in neurological disorders. Cell Stress. 2018;2:40–54. doi: 10.15698/cst2018.03.126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Kumar S, Lopez AJ. Negative feedback regulation among SR splicing factors encoded by Rbp1 and Rbp1-like in Drosophila. The EMBO Journal. 2005;24:2646–2655. doi: 10.1038/sj.emboj.7600723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lareau LF, Inada M, Green RE, Wengrod JC, Brenner SE. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature. 2007;446:926–929. doi: 10.1038/nature05676. [DOI] [PubMed] [Google Scholar]
  12. Lejeune F, Cavaloc Y, Stevenin J. Alternative splicing of intron 3 of the serine/arginine-rich protein 9g8 gene identification of flanking exonic splicing enhancers and involvement of 9g8 as a trans-acting factor. The Journal of Biological Chemistry. 2001;276:7850–7858. doi: 10.1074/jbc.M009510200. [DOI] [PubMed] [Google Scholar]
  13. Long JC, Caceres JF. The SR protein family of splicing factors: master regulators of gene expression. Biochemical Journal. 2009;417:15–27. doi: 10.1042/BJ20081501. [DOI] [PubMed] [Google Scholar]
  14. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15:550. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Manley JL, Krainer AR. A rational nomenclature for serine/arginine-rich protein splicing factors (SR proteins) Genes & Development. 2010;24:1073–1074. doi: 10.1101/gad.1934910. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Müller-McNicoll M, Rossbach O, Hui J, Medenbach J. Auto-regulatory feedback by RNA-binding proteins. Journal of Molecular Cell Biology. 2019;11:930–939. doi: 10.1093/jmcb/mjz043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Nagy E, Maquat LE. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends in Biochemical Sciences. 1998;23:198–199. doi: 10.1016/S0968-0004(98)01208-0. [DOI] [PubMed] [Google Scholar]
  18. Ni JZ, Grate L, Donohue JP, Preston C, Nobida N, O'Brien G, Shiue L, Clark TA, Blume JE, Ares M. Ultraconserved elements are associated with homeostatic control of splicing regulators by alternative splicing and nonsense-mediated decay. Genes & Development. 2007;21:708–718. doi: 10.1101/gad.1525507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Nilsen TW, Graveley BR. Expansion of the eukaryotic proteome by alternative splicing. Nature. 2010;463:457–463. doi: 10.1038/nature08909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Olthof AM, Hyatt KC, Kanadia RN. Minor intron splicing revisited: identification of new minor intron-containing genes and tissue-dependent retention and alternative splicing of minor introns. BMC Genomics. 2019;20:686. doi: 10.1186/s12864-019-6046-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods. 2017;14:417–419. doi: 10.1038/nmeth.4197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Preußner M, Goldammer G, Neumann A, Haltenhof T, Rautenstrauch P, Müller-McNicoll M, Heyd F. Body temperature cycles control rhythmic alternative splicing in mammals. Molecular Cell. 2017;67:433–446. doi: 10.1016/j.molcel.2017.06.006. [DOI] [PubMed] [Google Scholar]
  23. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. Nature Protocols. 2013;8:2281–2308. doi: 10.1038/nprot.2013.143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Rossbach O, Hung L-H, Schreiner S, Grishina I, Heiner M, Hui J, Bindereif A. Auto- and Cross-Regulation of the hnRNP L proteins by alternative splicing. Molecular and Cellular Biology. 2009;29:1442–1451. doi: 10.1128/MCB.01689-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Shin C, Manley JL. The SR protein SRp38 represses splicing in M phase cells. Cell. 2002;111:407–417. doi: 10.1016/S0092-8674(02)01038-3. [DOI] [PubMed] [Google Scholar]
  26. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2015;4:1521. doi: 10.12688/f1000research.7563.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Sterne-Weiler T, Weatheritt RJ, Best AJ, Ha KCH, Blencowe BJ. Efficient and accurate quantitative profiling of alternative splicing patterns of any complexity on a laptop. Molecular Cell. 2018;72:187–200. doi: 10.1016/j.molcel.2018.08.018. [DOI] [PubMed] [Google Scholar]
  28. Sureau A. SC35 autoregulates its expression by promoting splicing events that destabilize its mRNAs. The EMBO Journal. 2001;20:1785–1796. doi: 10.1093/emboj/20.7.1785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Tanikawa C, Zhang YZ, Yamamoto R, Tsuda Y, Tanaka M, Funauchi Y, Mori J, Imoto S, Yamaguchi R, Nakamura Y, Miyano S, Nakagawa H, Matsuda K. The transcriptional landscape of p53 signalling pathway. EBioMedicine. 2017;20:109–119. doi: 10.1016/j.ebiom.2017.05.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Turunen JJ, Niemelä EH, Verma B, Frilander MJ. The significant other: splicing by the minor spliceosome. Wiley Interdisciplinary Reviews: RNA. 2013;4:61–76. doi: 10.1002/wrna.1141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Proteomics Tissue-based map of the human proteome. Science. 2015;347:1260419. doi: 10.1126/science.1260419. [DOI] [PubMed] [Google Scholar]
  32. Verma B, Akinyi MV, Norppa AJ, Frilander MJ. Minor spliceosome and disease. Seminars in Cell & Developmental Biology. 2018;79:103–112. doi: 10.1016/j.semcdb.2017.09.036. [DOI] [PubMed] [Google Scholar]
  33. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008;456:470–476. doi: 10.1038/nature07509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wei N, Cheng Y, Wang Z, Liu Y, Luo C, Liu L, Chen L, Xie Z, Lu Y, Feng Y. SRSF10 plays a role in myoblast differentiation and glucose production via regulation of alternative splicing. Cell Reports. 2015;13:1647–1657. doi: 10.1016/j.celrep.2015.10.038. [DOI] [PubMed] [Google Scholar]
  35. Zhou X, Wu W, Li H, Cheng Y, Wei N, Zong J, Feng X, Xie Z, Chen D, Manley JL, Wang H, Feng Y. Transcriptome analysis of alternative splicing events regulated by SRSF10 reveals position-dependent splicing modulation. Nucleic Acids Research. 2014;42:4019–4030. doi: 10.1093/nar/gkt1387. [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision letter

Editor: Timothy W Nilsen1

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Thank you for submitting your article "Srsf10 and the minor spliceosome control tissue-specific and dynamic SR protein expression" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor, Timothy Nilsen, and James Manley as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

As you will see, the reviewers were quite positive about the work and reviewer 3 was very enthusiastic. The reviewing editor was also enthusiastic about the manuscript. Nevertheless, the reviewers have raised some concerns regarding the data and its interpretation. Please address these points as thoroughly as possible via revision. It is not necessary to delete the impact of Srsf10 expression on other SR proteins.

Reviewer #2:

This paper describes an unusual regulatory feedback process in the alternative splicing of the Srsf10 gene involving competition between major and minor intron splice sites. Several RNA binding proteins self-regulate their protein levels by inclusion of alternative "poison exons" that include premature stop codons. Srsf10 appears to have a poison exon 3 who's exclusion requires minor spliceosome activity. This part of the paper seems solid. They then show that there is a general correlation of the expression levels of many SR proteins including Srsf10. This has been described before and similar correlations can be seen in ribosomal protein genes indicating that they are under coordinate regulation. The authors then remove the poison exon from the Srsf10 gene and argue that the results support a key role for Srsf10 and, by extension, the minor spliceosome in controlling the mRNA levels of SR genes in general. The key results shown in Figure 4E do not, to me, support this idea. The authors speculate that alterations in mRNA levels are due to NMD. A direct demonstration would be more convincing. With the data they show, I think they should focus on the Srsf10 case alone rather than pushing for a more global mechanism.

Reviewer #3:

In this work, Meinke et al. demonstrate that alternative splicing of Srsf10 is formed via competition between the major and the minor spliceosome using different splice sites. These in turn lead to functional isoforms that skip exon 3 (minor spliceosome) or inclusion of exon 3 (major spliceosome) in a mostly non functional isoform which encodes a PTC and utilizes an alternative 3' end. The authors show that similar to other Sr proteins Srsf10 regulates its own levels via the inclusion of exon 3 and find a short site in that exon that is sufficient to achieve that regulation. They also perform rescue experiments with SRSF10 mouse variants in human HeLa cells, and a titration experiment with the different isoforms detected (Figure 1). KD of Rnpc3 of the minor spliceosome changes the splicing and expression of Srsf10 as expected (Figure 2). Expression of Srsf10 correlates well with expression of Rnpc3 across diverse mouse tissues, and as expected Rnpc3 levels correlate much better with expression of intron containing genes than matched expression levels of genes with only major introns (Figure 3). When removing exon3 control of Srsf10 levels via CRISPR the expression of Srsf10 increase by ~3.5fold but six other SR genes expression levels rises significantly, and these too correlate well with expression of Rnpc3 (Figure 4). The overall regulatory model is summarized in Figure 4—figure supplement 2.

Overall, we really liked this work. The authors should be congratulated for a thorough line of thoughtful experiments in support of their regulatory model as mentioned above. The manuscript is clearly written and we enjoyed reading it. We have few general comments/suggestions that should be addressed/clarified.

1) Are the changes in Rnpc3 observed in tissues in the same range as done in the titration experiments?

2) Expression computation and correlations: It's not clear how these were computed and whether these were done properly. The authors state TPM were derived by Whippet (Figure 3—figure supplement 1) but Whippet is designed only for splicing changes. It's not clear how Whippet would give full transcripts, and more importantly weighted gene level TPM values. Furthermore, TPM is not a proper measure to compare across many different experiments/conditions (it's not as bad as RPKM but still not great). Between sample normalization should be applied as implemented in DESeq and TMM. See for example https://haroldpimentel.wordpress.com/2014/12/08/in-rna-seq-2-2-between-sample-normalization/

3) Figure 4: We understand where these p-values come from, but we are still worried about possible artifacts in the normalization procedures that might affect the results (also see above). Another way to compute a p-value and address the above concern is to compute an empirical p-value compared to sampling a large set of similarly expressed genes and computing the correlation values for them. True, some may be bona fide targets as well, but presumably this population of targets is rather small and the Sr proteins correlations stand out.

eLife. 2020 Apr 27;9:e56075. doi: 10.7554/eLife.56075.sa2

Author response

Reviewer #2:

This paper describes an unusual regulatory feedback process in the alternative splicing of the Srsf10 gene involving competition between major and minor intron splice sites. Several RNA binding proteins self-regulate their protein levels by inclusion of alternative "poison exons" that include premature stop codons. Srsf10 appears to have a poison exon 3 who's exclusion requires minor spliceosome activity. This part of the paper seems solid. They then show that there is a general correlation of the expression levels of many SR proteins including Srsf10. This has been described before and similar correlations can be seen in ribosomal protein genes indicating that they are under coordinate regulation. The authors then remove the poison exon from the Srsf10 gene and argue that the results support a key role for Srsf10 and, by extension, the minor spliceosome in controlling the mRNA levels of SR genes in general. The key results shown in Figure 4E do not, to me, support this idea. The authors speculate that alterations in mRNA levels are due to NMD. A direct demonstration would be more convincing. With the data they show, I think they should focus on the Srsf10 case alone rather than pushing for a more global mechanism.

We agree that the effects observed in Figure 4F are rather subtle. However, we find it quite remarkable that the expression of all SR proteins is increased in the Crispr cell line and for 8/12 this is statistically significant. We would therefore like to keep this figure, but have modified the text to say “…was sufficient for subtle upregulation…”. We have also weakened the conclusion, which now reads: “…which is sufficient to change expression levels of the other SR proteins in a tissue- and differentiation state-specific manner.” In the previous version it was “of all other SR proteins”. We hope that these adjustments address the reviewer’s concern.

Reviewer #3:

[…]

Overall, we really liked this work. The authors should be congratulated for a thorough line of thoughtful experiments in support of their regulatory model as mentioned above. The manuscript is clearly written and we enjoyed reading it. We have few general comments/suggestions that should be addressed/clarified.

1) Are the changes in Rnpc3 observed in tissues in the same range as done in the titration experiments?

In cell culture siRNA mediated knockdown of Rnpc3 results in reduction of the Rnpc3 mRNA levels to 50%. Under this conditions Srsf10 levels are reduced to ~75%. Using the slope of the linear regression fit for Rnpc3 and Srsf10 across different tissues we can calculate how strong a 2-fold reduction in Rnpc3 effects Srsf10. Based on this calculation Srsf10 expression is reduced to 55%, which is in good agreement with the 75% observed in cell culture.

2) Expression computation and correlations: It's not clear how these were computed and whether these were done properly. The authors state TPM were derived by Whippet (Figure 3—figure supplement 1) but Whippet is designed only for splicing changes. It's not clear how Whippet would give full transcripts, and more importantly weighted gene level TPM values. Furthermore, TPM is not a proper measure to compare across many different experiments/conditions (It's not as bad as RPKM but still not great). Between sample normalization should be applied as implemented in DESeq and TMM. See for example https://haroldpimentel.wordpress.com/2014/12/08/in-rna-seq-2-2-between-sample-normalization/

Thank you for raising these points, which are important for our conclusions and thus merit further clarification. First, the Whippet Quant function also calculates weighted TPM values on gene level (see Sterne-Weiler, 2018 and the GitHub documentation). This is a great advantage of the Whippet tool, as it allows alignment, splicing, and gene expression analysis in one step/one pipeline.

However, to be more confident with our results we repeated the analyses using Salmon to determine GE levels and DESeq2 to obtain normalized expression counts. These analyses were performed by an additional bioinformatician, Alexander Neumann, who is now included as co-author. Comparing expression across different tissues, we observe comparable correlations using Salmon derived gene expression levels, which are now included as Figure 3—figure supplement 1B (Srsf10) and 1C (Gapdh). When removing a single outlier value, we also observe a significant correlation between Rnpc3 and Srsf10 in the DESeq2 normalized values (R² = 0.7509, p < 0.0001) but as we observe large variations (e.g. the three Rnpc3 values in epididymis 1: 222; 2: 17921; 3: 2655 and Srsf10 1: 2522; 2: 28859; 3: 6516) we would rather not include these into our manuscript. This is also mentioned in the Materials and methods part: “DESeq2 based normalized expression counts dramatically increased the variance of replicate tissue samples (but not of ES cell differentiation), which is why we chose to analyze only the un-normalized values for the tissue comparisons.”

In ES cell differentiation, we also observe comparable correlations using Salmon derived gene expression levels (new Figure 3—figure supplement 1I). Here however, normalization via DESeq2 strongly increases the correlation (new Figure 3—figure supplement 1J), making us confident that the DeSeq2 analysis in principle works (but is not suitable for the different tissue samples). Taken together, these additional analyses are fully consistent with the conclusion that Rnpc3 and Srsf10 expression indeed correlate across different tissues and development stages.

3) Figure 4: We understand where these p-values come from, but we are still worried about possible artifacts in the normalization procedures that might affect the results (also see above). Another way to compute a p-value and address the above concern is to compute an empirical p-value compared to sampling a large set of similarly expressed genes and computing the correlation values for them. True, some may be bona fide targets as well, but presumably this population of targets is rather small and the Sr proteins correlations stand out.

The R² and p-values for correlations of SR-protein and RNPC3 expression across different tissues indeed stands out with most R² values above 0.6 (average 0.74) and each p-value < 0.0001. These correlations are slightly reduced with Salmon derived correlations (R² average 0.6) but remain always highly significant (each p-value < 0.0001). Additionally, we would like to point out that in Figure 3D we have calculated correlation coefficients for 587 minor intron containing genes and 629 only major intron containing genes using the same procedure. This resulted in mean R² values of 0.24 and 0.16, respectively. We are therefore confident that the correlation coefficients for SR proteins are indeed true, as they stand out from only major intron containing genes and also from other minor intron containing genes. Also in ES cell differentiation a mean R² of 0.37 for SR proteins is clearly higher than the correlation for all major intron genes (0.16).

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Human Genome Center, The University of Tokyo 2017. p53 mouse multi-tissue transcriptome analysis. NCBI Sequence Read Archive. DRP003641
    2. USAMRICD 2012. Deep transcriptional profiling of longitudinal changes during neurogenesis and network maturation in vivo. NCBI Sequence Read Archive. SRP017778

    Supplementary Materials

    Supplementary file 1. Oligonucleotide sequences of primers, siRNAs, and guide RNAs.
    elife-56075-supp1.xlsx (13.2KB, xlsx)
    Transparent reporting form
    elife-56075-transrepform.pdf (774.8KB, pdf)

    Data Availability Statement

    All data are previously published and publicly available.

    The following previously published datasets were used:

    Human Genome Center, The University of Tokyo 2017. p53 mouse multi-tissue transcriptome analysis. NCBI Sequence Read Archive. DRP003641

    USAMRICD 2012. Deep transcriptional profiling of longitudinal changes during neurogenesis and network maturation in vivo. NCBI Sequence Read Archive. SRP017778

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