Splicing modulators elicit global translational repression by condensate-prone proteins translated from introns

Jagat K Chhipi-Shrestha; Tilman Schneider-Poetsch; Takehiro Suzuki; Mari Mito; Khalid Khan; Naoshi Dohmae; Shintaro Iwasaki; Minoru Yoshida

doi:10.1016/j.chembiol.2021.07.015

. Author manuscript; available in PMC: 2023 Feb 17.

Published in final edited form as: Cell Chem Biol. 2021 Sep 13;29(2):259–275.e10. doi: 10.1016/j.chembiol.2021.07.015

Splicing modulators elicit global translational repression by condensate-prone proteins translated from introns

Jagat K Chhipi-Shrestha ^1,², Tilman Schneider-Poetsch ¹, Takehiro Suzuki ³, Mari Mito ⁴, Khalid Khan ¹, Naoshi Dohmae ³, Shintaro Iwasaki ^4,^5,^6,^8,^*, Minoru Yoshida ^1,^2,^7,^*

PMCID: PMC8857039 NIHMSID: NIHMS1732741 PMID: 34520743

Summary

Chemical splicing modulators that bind to the spliceosome have provided an attractive avenue for cancer treatment. Splicing modulators induce accumulation and subsequent translation of a subset of intron-retained mRNAs. However, the biological effect of proteins containing translated intron sequences remains unclear. Here, we identify a number of truncated proteins generated upon treatment with the splicing modulator spliceostatin A (SSA) via genome-wide ribosome profiling and bio-orthogonal noncanonical amino-acid tagging (BONCAT) mass spectrometry. A subset of these truncated proteins has intrinsically disordered regions, forms insoluble cellular condensates, and triggers the proteotoxic stress response through JNK phosphorylation, thereby inhibiting the mTORC1 pathway. In turn, this reduces global translation. These findings indicate that creating an overburden of condensate-prone proteins derived from introns represses translation and prevents further production of harmful truncated proteins. This mechanism appears to contribute to the antiproliferative and proapoptotic activity of splicing modulators.

Keywords: Splicing modulator, spliceostatin A, intron, proteostasis, condensate, translation, ribosome profiling, BONCAT, mTORC1, JNK

eTOC Blurb

Chhipi-Shrestha et al. show that splicing modulation leads to widespread translation from retained introns, supplying intrinsically disordered proteins to form insoluble condensates and to induce cellular proteotoxicity. Activated JNK inhibits the mTORC1 pathway and ultimately reduces the global translation, suppressing the further generation of harmful truncated proteins.

Introduction

Splicing, the removal of intervening sequences from pre-mRNAs, is an essential step in eukaryotic mRNA maturation to maintain correct gene expression. Inadequate splicing is associated with a variety of diseases and tumorigenesis (Scotti and Swanson, 2016; Dvinge et al., 2016; Baralle and Giudice, 2017). Clinical genome sequencing revealed that mutations in spliceosome genes such as SF3B1, SRSF2, and U2AF1 occur at surprisingly high frequencies in hematological malignancies, including myelodysplastic syndromes (MDS) and chronic lymphocytic leukemia (CLL) (Yoshida et al., 2011; Obeng et al., 2016). Broadly, aberrant splicing patterns are frequently seen in cancers (Desterro et al., 2020). Even splicing pattern switching of a single gene (such as pyruvate kinase PKM) can lead to tumorigenesis (Christofk et al., 2008).

Owing to the strong association between tumors and splicing dysregulation, recently identified chemical modulators of splicing have drawn considerable interest to their therapeutic potential as spliceosome-targeted therapies (STTs) (Lee and Abdel-Wahab, 2016). Since the discovery of natural products FR901464 and pladienolide B (PlaB) as small molecules that specifically bind and inhibit SF3B1—a component of the SF3B subcomplex of the U2 snRNP (Kaida et al., 2007; Corrionero et al., 2011), a variety of structurally related splicing modulators, such as spliceostatin A (SSA), sudemycin, meayamycin, and E7107, have been developed. As candidates for cancer therapeutics [see review by (Butler, 2013; Bonnal et al., 2020)], these splicing modulators show the capacity to suppress cancer cells expressing mutant spliceosomal proteins in both in vitro and in animal models (Obeng et al., 2016; Teng et al., 2017; Shirai et al., 2017; Seiler et al., 2018). In particular, H3B-8800, an orally available molecule (Seiler et al., 2018), has begun clinical trials for treating both solid tumors and leukemias bearing spliceosome mutations. Even in the absence of splicing factor mutations, chemical splicing modulators appear to specifically induce apoptosis in a wide variety of tumor cells (Nakajima et al., 1996b; Nakajima et al., 1996a; Kotake et al., 2007; Eskens et al., 2013).

A number of mechanisms by which splicing modulators inhibit tumor cell survival and proliferation have been proposed: (1) synthetic lethality with splicing factor mutations (Seiler et al., 2018; Lee et al., 2018; Wang et al., 2019), (2) excess demand of spliceosome activity in MYC-activated tumor cells (Hsu et al., 2015), (3) splicing perturbation of BCL2 family antiapoptotic genes (Gao and Koide, 2013; Larrayoz et al., 2016; Aird et al., 2019), and (4) inhibition of angiogenesis by downregulating vascular endothelial growth factor (VEGF) expression in malignant tumors (Furumai et al., 2010; Amin et al., 2011; Sakai et al., 2004). However, these explanations apply only to certain types of cancer. The overall rationale for how splicing modulators suppress tumor growth remains unclear.

The outcome of splicing modulator treatment is not limited to the downregulation of spliceosome activity but alters downstream mRNA processing. Inhibition of splicing normally induces drop-off and/or elongation arrest of RNA polymerase II (Pol II) via dephosphorylation of Ser2 in the C-terminal domain (CTD) (Koga et al., 2014; Koga et al., 2015). The accumulation of splicing intermediates containing U1 and U2 snRNPs upon splicing modulator treatment inhibits the recycling of U1 snRNP from pre-mRNAs, leading to premature cleavage and polyadenylation (PCPA) of a subset of coding and noncoding RNAs due to the shortage of available U1 snRNPs that act to suppress PCPA (Yoshimoto et al., 2021). mRNAs containing introns are typically retained inside the nucleus (Kaida et al., 2007; Boutz et al., 2015; Yoshimoto et al., 2017; Carvalho et al., 2017), followed by degradation via the 3'-5' riboexonuclease exosome (Bousquet-Antonelli et al., 2000). However, a subset of mRNAs still leak into the cytoplasm (Kaida et al., 2007; Yoshimoto et al., 2017; Carvalho et al., 2017). This fraction of mRNAs is usually degraded via the nonsense-mediated decay (NMD) pathway, which recognizes premature termination codons (PTCs) inside intronic sequences [reviewed in (Shoemaker and Green, 2012)]. Moreover, intron-retained mRNAs in the cytosol form double-stranded RNA that drives antiviral immune signaling (Bowling et al., 2021).

Despite strict quality control mechanisms such as NMD, a substantial fraction of intron-retained mRNAs escape surveillance and thereby yield truncated, incomplete proteins with extensions from introns (Kaida et al., 2007; Trcek et al., 2013; Kim et al., 2017; Yoshimoto et al., 2021). We previously observed that FR901464 and SSA induce the production of truncated forms [we denote the truncated protein with an * following earlier nomenclature (Kaida et al., 2007)] of the tumor suppressor p27 (p27*) and the NF-κB signaling inhibitor IκBα (IκBα*) (Kaida et al., 2007), as well as a subset of mRNAs with PCPA in the middle of introns (Yoshimoto et al., 2021; Sousa-Luís et al., 2021). This suggests that a significant number of functionally active incomplete proteins are generated upon splicing modulator treatment. Although short transcripts with weaker 5′ splice sites are prone to leak from the nucleus (Yoshimoto et al., 2017), the generation of truncated proteins from such leaked mRNA by splicing modulators has not yet been systematically explored.

To address this issue, we implemented a global approach combining transcriptome, translatome, and proteome analysis upon chemical splicing perturbation. We found that under SSA treatment, a wide array of transcripts experience intron translation leading to proteins possessing intrinsically disordered and condensate-prone regions. This condensation activates the proteotoxic stress response via JNK phosphorylation and in turn inhibits the mTORC1 pathway. Inhibition of mTORC1-mediated translation activation significantly reduces the output of protein biosynthesis. Our results present an unexpected property of deleterious proteins originating from erroneous mRNA processing.

Results

SSA induces widespread intron retention and intron translation

To globally survey the truncated proteins generated by chemical splicing modulation beyond p27* and IκBα*, we performed simultaneous ribosome profiling and mRNA sequencing in the presence and absence of SSA (Figure S1A). Ribosome profiling is a powerful method based on deep sequencing of ribosome-protected mRNA fragments and provides the best overview of translation dynamics at subcodon resolution (Ingolia et al., 2009; Ingolia, 2016), enabling the global identification of translated introns. Our data displayed high experimental reproducibility (Figure S1B) and sample quality, including the expected size of ribosome footprints (Figure S1C) and three-nucleotide periodicity along the coding sequence (CDS) (Figure S1D). Simultaneously, we performed sequencing of cellular RNAs (RNA-Seq) to evaluate the occurrence of splicing changes under SSA treatment.

Among the different alternative splicing events observed in the RNA-Seq data, SSA induces exon skipping and intron retention (Figure 1A), confirming the results of previous studies on SF3B inhibitors (Kaida et al., 2007; Yoshimoto et al., 2017; Vigevani et al., 2017; Wu et al., 2018). Analysis of global intronic reads (see Materials and Methods for details) corroborated that a number of transcripts contained retained introns (Figure 1B). We define this subgroup of introns as “retained introns” and their source mRNAs as “intron-retained mRNAs”. We confirmed the findings by RT-PCR of a representative transcript (DNAJB1) that clearly showed intron retention upon SSA treatment (Figure S1E).

Ribosome profiling demonstrated that a large number of the retained introns did reach the translation machinery. Intronic ribosome footprints on the p27 mRNA were found until the first in-frame stop codon (Figure 1C), corresponding to the truncated protein seen by Western blotting (Figure 1D). Similarly, intron translation was widely observed across the transcriptome. Meta-gene analysis centered on the exon-intron junction showed a substantial increase in intronic reads upon SSA treatment, both in mRNA reads and in ribosome footprints (Figure 1E and 1F, left). Of the 5920 retained introns detected by RNA-Seq in the presence of SSA, we observed active translation of 1078 intronic sequences (Table S1). Moreover, we found that the number of footprints dropped off at the first in-frame stop codon (Figure 1F, right). Ribosome footprints upstream of the PTC showed 3-nt periodicity when analyzed by discrete Fourier transform, which indicates active translation from the retained introns (Figure 1G).

In line with previous reports, we observed that a significant fraction of mRNAs containing intronic sequences escaped NMD surveillance (Kaida et al., 2007; Trcek et al., 2013; Kim et al., 2017). The intron-translated mRNAs should have been targeted by the NMD pathway, since the distance from the intronic PTC to the downstream exon-exon junction exceeded ~50-55 nucleotides (nt), the minimal distance that triggers NMD (Figure S1G, right) (Maquat et al., 2010). This could be explained by the possibility that overproduction of target transcripts simply overwhelms the cellular NMD capacity. Alternatively, SSA may lead to PCPA in the middle of introns (Yoshimoto et al., 2021; Sousa-Luís et al., 2021). This would result in shorter isoforms without downstream exon-exon junctions and EJCs and thus escape NMD. These two scenarios are not mutually exclusive.

We further analyzed the products of intron translation by a proteome approach. Since pre-existing proteins perturb the detection of the translated peptides from intron-retained mRNAs generated during SSA treatment (6 h), we set out to enrich for newly synthesized protein during compound treatment by bio-orthogonal noncanonical amino-acid tagging (BONCAT). This technique is based on metabolic labeling of newly synthesized proteins by the noncanonical amino acid homopropargylglycine (HPG), which allows cycloaddition of azide-biotin by “click chemistry”, enrichment of the biotinylated proteins by streptavidin beads, and subsequent detection of de novo synthesized proteins by mass spectrometry (Dieterich et al., 2006; Dieterich et al., 2007). Based on a database of predicted chimeric introns in frame with the translation start from the 5920 retained introns (Figure 1B), BONCAT revealed a substantial number of stable chimeric proteins (n = 238) (Table S2) in the lysate prepared by SSA treatment (Figure 1H). The number of observed chimeric intron proteins is comparatively smaller (~20% only) than the number observed in ribosome profiling, likely due to the predominant presence of peptides from full-length proteins (2852 proteins detected) even in the BONCAT strategy and global repression of protein synthesis (see below).

Taken together, our results demonstrate that SSA treatment leads to the production of chimeric proteins containing both exon- and intron-derived sequences.

Characteristics of intron-translated transcripts and proteins

In the course of characterizing the intron-translated transcripts, we observed some general properties applying to the majority of the predicted chimeric intron-translated peptides and their source transcripts. The majority of the translated introns (71%) were derived from the first intron (Figure S2A, right), implying that translation halts on the first PTC (Figure 1F) and does not progress downstream (Figure S2A, left). These chimeric intron-translated proteins have a median length of 94 amino acids (Figure 2A).

Figure 2. — (A) Predicted length of translated introns upon SSA. The median length is shown.

(B) GO terms and IDs of the source genes of translated introns. Color indicates the statistical value.

(C-E) Cumulative distribution of relative disorderness (IUPred2A score) (C), net charge (Lehninger pKa scale) (D), and hydrophobicity score (Kyte-Doolittle scale) (E) of amino acid residues in translated introns, upstream CDS exons, and full CDSs. Ten amino acid windows were considered.

(F) Relative frequency of amino acids present in either translated introns, upstream CDS exons, or CDSs. Amino acids overrepresented in translated introns are highlighted in red.

In C-E, the significance was calculated by Wilcoxon’s test.

See also Figure S2.

To determine whether these intron-translated mRNAs have particular cellular functions, we conducted gene ontology analysis. Genes pertaining to mRNA catabolism, protein targeting to the membrane, and the cell cycle were significantly enriched (Figure 2B).

In contrast to full-length proteins, truncated peptides may lack well-defined and stable secondary and tertiary structures. IUPred2A (Mészáros et al., 2018) prediction of intrinsically disordered regions (IDRs) indicated that translated introns have significantly higher levels of IDRs than upstream CDS exons or the full-length CDS of the same transcript (Figure 2C). Moreover, the translated introns tended to be more positively charged (Figure 2D) than CDS and upstream exons. This agrees with established traits of IDRs (Müller-Späth et al., 2010; Wirth and Kühnel, 2017; Dyson and Wright, 2005). The variance in hydrophobicity was marginal (Figure 2E). We note that similar trends were observed regardless of analysis window sizes (Figure S2B-D).

The amino acids serine (S), proline (P), glycine (G), and arginine (R) were highly overrepresented in translated intron regions (Figure 2F). We observed this enrichment even when changing the reading frame by −1 or +1 nucleotides (Figure S2E). As P, G, and R are encoded by multiple numbers of GC rich codons (e.g., CCN for P, GGN for Gly, and CGN for R), we analyzed the GC content of the introns throughout the length. As expected (Zhang et al., 2011; Amit et al., 2012), the GC content is higher towards the 5′ end of the intron, thereby increasing the likelihood of encountering S, P, G, or R codons and therefore producing stretches of low complexity in the resulting proteins (Figure S2F). The presence of low complexity regions (LCRs) might lead to phase separation and/or to prion-like aggregation of the chimeric protein products (Oldfield and Dunker, 2014; Banani et al., 2017; Shin and Brangwynne, 2017; Franzmann and Alberti, 2019).

A subset of intron-derived peptides are condensation-prone

The enrichment of IDRs in intron-derived peptides prompted us to test the condensation propensity of the chimeric proteins. For this purpose, we performed BONCAT proteomic analysis of the cellular soluble and insoluble fractions. Regardless of the limited number of detected intron-derived peptides, we observed that the chimeric peptides detected in the pellet of SSA-treated cells appeared more prevalent than in the supernatant (Figure 3A). The protein ferritin heavy chain 1 (FTH1) represents a remarkable example, as ribosome profiling indicated intron translation (Figure 3B), and its truncated form was highly enriched in the pellet fraction of the BONCAT experiments (Figure 3A, left panel). The translated intron of FTH1 * possessed an LCR and showed a propensity for disorder (Figure 3C). Similar LCRs were found in other condensate-prone, translated introns (RAB5IF* and ABHD11*) (Figure 3A, right panel and Figure S3A).

The condensate-prone characteristic of the ectopically expressed truncated FTH1* was validated through fractionation by centrifugation followed by Western blotting (Figure 3D). Similar condensates were also obtained for ectopically expressed FADD* and IRF2BP2* (Figure S3B). Although RAB32* was not supported by BONCAT, we did find a likely condensate prone feature in this protein (Figure S3C) when seeking candidate sequences in our ribosome profiling data (Figure S3D). The identified truncated proteins were distinct from the previously reported p27*, as it constitutes a shortened but soluble and functional polypeptide (Figure S3E).

In addition, the recombinant protein FTH1*, but not FTH1, formed aggregates by concentration-dependent self-association in vitro (Figure 3E). Similar FTH1* condensates were also found in cells as, when expressed as a FLAG-tagged protein (Figure 3F). The presence of cellular FTH1* aggregates was also confirmed by filter trap assay (Figure 3G and 3H). We note that soluble p27* was not captured in this assay (Figure 3G and 3H), consistent with the centrifugation-based assay results (Figure S3E).

Condensation-prone intron-derived peptides are proteotoxic

Given that translation of improperly spliced transcripts leads to the production of truncated and cellular condensates/aggregates (Figure 3), it is feasible that these imperfect peptides produced upon SSA trigger a proteotoxic stress response. To test this idea, we investigated whether c-Jun N-terminal kinase (JNK), a multifaceted kinase that responds to different cues, including proteotoxic stress (Dhanasekaran and Reddy, 2008; Su et al., 2016), is activated upon SSA treatment. As reported in an earlier work (Su et al., 2016), we observed JNK activation through proteotoxic stress, as seen by its increased phosphorylation. Treatment with proteasome inhibitor MG132, which increases the cellular level of undegraded proteins and thus unbalances proteostasis, also lead to JNK phosphorylation (Figure 4A), indicating that the proteotoxicity is upstream of JNK activation. Similar to MG132, we detected that SSA could also induce JNK phosphorylation levels (Figure 4A). JNK activation was not detected when acetyl-SSA (Ac-SSA) (Figure S4A), an inactive SSA derivative (Kaida et al., 2007), was used. It did occur with another SF3B1 inhibitor, pladienolide B (PlaB) (Kotake et al., 2007; Finci et al., 2018; Cretu et al., 2018) (Figure S4B), which also induces intron retention (Figure S4C). We could therefore rule out off-target effects and conclude that splicing inhibition causes proteotoxic stress.

Figure 4. — (A) HeLa S3 cells were either treated with 100 ng/ml SSA or 0.5 μM MG132 for 10 h, and the cell lysates were immunoblotted with the indicated antibodies.

(B) HeLa S3 cells were transfected for 48 h with a Fluc-based sensor reporter construct encoding Fluc-EGFP or FlucDM-EGFP and subsequently incubated for 10 h with either 100 ng/ml SSA or solvent MeOH. Representative fluorescence micrographs show the distribution of EGFP signals (green) in cells. The nuclei were stained with Hoechst dye (blue). The scale bars are 7.5 μm.

(C) The number of aggregated GFP per cell in (B). A minimum of 26 cells were quantified in each condition.

(D and E) Filter trap assay of the total aggregated proteins in HeLa S3 cells along the time course of SSA treatment. Proteins on the membrane were detected by Revert 700 dye (D) and quantified (E). The data represent the mean and s.d. (n = 3). The high background (in MeOH treatment) may originate from the natural cellular aggregates and/or others cell debris. Significance was calculated using one-way ANOVA with the post hoc Tukey HSD test. *, P < 0.05.

(F) HeLa S3 cells were cotransfected with the Fluc-based sensor reporter construct encoding Fluc-EGFP or FlucDM-EGFP and either mock, FLAG-FTH1, or FLAG-FTH1* vectors for 48 h. Representative fluorescence micrographs show the distribution of EGFP signal (green) in cells. FLAG-tagged protein was immunostained (red). The nuclei were stained with Hoechst dye (blue). The scale bars are 7.5 μm.

(G) The number of aggregated GFP per cell in (F). A minimum of 26 cells were quantified in each condition.

See also Figure S4.

To visualize the proteotoxic stress response in individual cells, we used a firefly luciferase (Fluc) reporter and its conformationally unstable version (R188Q-R261Q double mutant or DM), which requires chaperone surveillance to fold, fused to GFP to assess a possible imbalance in proteostasis (Gupta et al., 2011). Under proteotoxic stress, chaperones become limiting, decreasing the solubility of the sensor protein and thereby preventing the luminescent reporter from proper protein folding. As expected, SSA treatment of cells harboring the FlucDM-EGFP reporter produced GFP aggregates (Figure 4B and 4C), as well as reduced luciferase activity (Figure S4D). These results corroborate the suspected proteostasis imbalance induced by splicing modulation.

In addition, we detected system-wide accumulation of aggregated proteins. Using a filter trap assay and subsequent total protein staining on the membrane demonstrated that SSA leads to an increase in aggregated proteins in a treatment time-dependent manner (Figure 4D and 4E). We note that the aggregate may consist of aggregation-prone full length proteins and the exon-intron chimeric proteins.

While proteotoxic stress under SSA treatment is likely the cumulative result of many truncated peptides, we investigated whether the ectopic expression of individual shortened proteins including their intron-derived sequences recapitulates the phenotype of SSA treatment at least in part. The expression of condensate-prone FTH1* induced an imbalance in proteostasis, as observed by condensate formation (Figure 4F and 4G), along with the reduction of firefly luciferase activity (Figure S4E), in the proteostasis reporter.

Our results suggest that an overload of toxic, condensate-prone proteins generated by splicing perturbation elicits proteotoxic stress, leading to activation of the stress-activated protein kinase JNK.

SSA induces global translation inhibition

During our analysis of ribosome profiling, we noticed that the impact of SSA was not restricted to intron translation. With the reported decrease in transcription and mRNA export upon SSA treatment (Kaida et al., 2007; Trcek et al., 2013; Kim et al., 2017), some concomitant decrease in protein synthesis would appear natural. Measuring the absolute change in translation and RNA abundance using mitochondrial ribosome footprints/RNA as an internal control (see the Materials and Methods for details), we found that splicing inhibition led to a far more drastic decrease (fourfold) in translation than could be explained by fewer transcripts alone (twofold) (Figure 5A). Metabolic labeling of nascent peptides using O-propargyl-puromycin (OP-puro) followed by fluorophore conjugation (Liu et al., 2012; Iwasaki and Ingolia, 2017) further corroborated the strong inhibition of protein synthesis in the presence of SSA (Figure 5B and Figure S5A).

Figure 5. — (A) Histograms showing absolute change in RNA-Seq reads (upper panel) and ribosomal footprints (lower panel) under SSA treatment. Both data were normalized to the total number of reads from mitochondrial genome-encoded transcripts used as internal spike-ins. The median-fold change is shown. The bin width is 0.1.

(B) Bulk translation change upon SSA treatment in HeLa S3 cells was monitored by OP-puro. Nascent peptides with incorporated OP-puro were visualized by click reaction with azide-conjugated IR-800 dye and quantified. The data represent the mean and s.d. (n = 3). Significance was calculated using Student's t-test (two-tailed). **, P < 0.01.

(C) MA plot of translation efficiency change during SSA treatment plotted against normalized RNA-Seq reads. FDR < 0.05 was set for the definition of low-sensitivity mRNAs and high-sensitivity mRNAs.

(D) KEGG pathway analysis based on the differential change in translation efficiency, visualized by iPAGE (Goodarzi et al., 2009). The terms and IDs of KEGG pathways are shown.

(E) Cumulative distribution of cytosolic ribosome mRNAs in translation efficiency change during SSA treatment. All the transcript isoforms of cytosolic ribosome mRNAs were considered. The significance was calculated by Wilcoxon’s test.

See also Figure S5.

We explored whether differential changes in translation efficiency of individual mRNAs would have functional implications. Translation efficiency, the ratio between footprint and mRNA-sequencing counts, serves as an effective means to quantify actually occurring translation, as it takes transcript abundance into account. We observed differential changes across several transcripts during SSA treatment (Figure 5C). On the basis of Kyoto Encyclopedia of Genes and Genomes (KEGG) term enrichment analysis, we found that the translation efficiencies of ribosomal proteins were particularly reduced (Figure 5D). Similar results were obtained through Gene Ontology (GO) analysis (Figure S5B). The downregulation of translation efficiency in mRNAs coding for components of the cytosolic ribosome stood out from the other affected genes (Figure 5E).

SSA-mediated JNK activation leads to mTORC1 inhibition

The translation decrease from ribosomal protein genes is a hallmark of mTORC1 inhibition (Jefferies et al., 1994; Thoreen et al., 2012). We then tested the possibility that SSA leads to mTORC1 inactivation, as mTORC1 can be deactivated upon sensing proteotoxic stress via JNK (Figure S6A) (Su et al., 2016). mTORC1—a complex consisting of mTOR kinase at its core and the accessory proteins DEPTOR, RAPTOR, PRAS40, and mLST8—is the master regulator of protein synthesis (Laplante and Sabatini, 2012; Kennedy and Lamming, 2016) through direct phosphorylation of several translational key regulators, such as eukaryotic translation initiation factor 4E (eIF4E) binding protein 1 (4EBP1) and ribosomal protein S6 kinase beta-1 (S6K1).

To test for mTORC1 inhibition by SSA, we examined the phosphorylation status of mTORC1 substrates 4EBP1 (at Thr37/46 and Ser65) and S6K1 (at Thr389). We observed dephosphorylation of these proteins in the presence of SSA (Figure 6A and Figure S6B and S6C). In contrast, Ac-SSA did not cause any dephosphorylation of mTORC1 substrates (Figure 6B).

Figure 6. — (A) Western blot for mTORC1 substrates S6K1 and 4EBP1 and their phosphorylated forms. HeLa S3 cells were either treated with MeOH solvent or 100 ng/ml SSA for different time periods.

(B and C) Western blot of the indicated proteins from HeLa S3 cells treated with 100 ng/ml Ac-SSA (B) and 1 μg/ml PlaB (C) for 10 h.

(D) HeLa S3 cells were either transfected with control siRNA or siRNA targeting SF3B1 mRNA for 36 h. The cell lysates were immunoblotted with the indicated antibodies.

(E and F) JNK1 (E) or JNK2 (F) was knocked down in HeLa S3 cells before treatment with 100 ng/ml SSA for 10 h. The cell lysates were immunoblotted with the indicated antibodies.

(G) HeLa S3 cells were either treated with 100 ng/ml SSA or MeOH solvent for 10 h, and the cell lysates were immunoblotted with the indicated antibodies.

(H and I) Representative Western blots for either phosphorylated or bulk S6K1 and 4EBP1 in stable HEK 293 cells, which express FLAG-FTH1 or FLAG-FTH1*, are shown (H) and quantified (I). The data represent the mean and s.d. (n = 3). Significance was calculated using one-way ANOVA with the post hoc Tukey HSD test. ***, P < 0.001.

See also Figure S6.

mTORC1 inhibition was not specific to SSA but could be reproduced when inhibiting the SF3B1 complex by other means. We repeated the above assays with PlaB and knockdown of SF3B1. Either method also induced intron retention (Figure S4C and S6D), and both conditions recapitulated the dephosphorylation of 4EBP1 and S6K1 (Figure 6C and 6D). Although this provided strong evidence that the observed effect on mTORC1 signaling must depend on aberrant splicing, we used an in vitro assay with recombinant mTORC1 protein to confirm that neither PlaB nor SSA directly inhibited its kinase activity (Figure S6E).

We assessed whether the observed JNK activity was responsible for mTORC1 inactivation. Individual knockdown of JNK variants JNK1 and JNK2 recovered 4EBP1 phosphorylation in the presence of SSA (Figure 6E, 6F, and Figure S6F).

Proteotoxic stress-activated JNK mediates the disassembly of the mTORC1 complex via phosphorylation of the mTORC1 component RAPTOR on S863 (Figure S6A) (Su et al., 2016). We observed increased phosphorylation of RAPTOR S863 in the presence of SSA (Figure 6G), supporting a functional link between JNK activation and mTORC1 inhibition.

JNK-mediated mTORC1 inhibition most likely originates from SSA-induced truncated peptide-derived condensates that cause proteotoxic stress. In line with our experiments on proteotoxic stress (Figure 4D-F), we observed that the expression of condensate-prone FTH1* inhibited mTORC1, as demonstrated by the dephosphorylation of 4EBP1 and S6K1 (Figure 6H and I). In sum, our results suggest that an overload of toxic, condensate-prone proteins generated by splicing perturbation leads to mTORC1 deactivation via activation of JNK.

SSA mimics translation repression by mTORC1 inactivation

Given the correspondence between 4EBP1 dephosphorylation (Figure 6) and translation repression (Figure 5), we inferred that SSA reduces protein synthesis through mTORC1 inactivation. To test this hypothesis, we directly compared the translation change effected by SSA and ATP-competitive mTOR inhibitor pp242. On the basis of our ribosome profiling data, transcripts sensitive to pp242 (Iwasaki et al., 2016) also proved sensitive to SSA (Figure 7A).

Figure 7. — (A) Cumulative distribution of pp242-sensitive mRNAs (Iwasaki et al., 2016) in translation efficiency change during SSA treatment. Significance was calculated by Wilcoxon’s test.

(B) Proteins copurified with m⁷G-cap beads were used for Western blotting. HeLa S3 cells were either treated with the MeOH control or SSA for 10 h.

(C) Reporter *Renilla* luciferase mRNA fused downstream to the TOP motif containing the 5′ UTR of *EIF2S3* or the non-TOP 5′ UTR of *ATP5O* (Figure S7A) and firefly luciferase mRNAs fused downstream to the HCV IRES were transfected into HeLa S3 cells for 4 h after 2 h of treatment with 100 ng/ml SSA. The data represent the mean and s.d. (n = 3). Significance was calculated using Student's t-test (two-tailed). *,P < 0.05.

(D) 5′ TOP reporter assay in HeLa S3 cells with 100 ng/ml SSA. The cells were transfected with siRNAs against JNK1 and 2. The data represent the mean and s.d. (n = 3). Significance was calculated using one-way ANOVA with the post hoc Tukey HSD test. **, P < 0.01.

(E and F) Cumulative distribution of 5′ TOP mRNAs [curated from defined 5' UTRs (Gandin et al., 2016)] and intron-retained mRNAs in translation efficiency change during SSA treatment

(E) and in intron read change during SSA treatment (F). The significance was calculated by Wilcoxon’s test.

(G) Model for translation attenuation upon splicing modulation. Under splicing attenuation, introns of pre-mRNAs are retained. A subset of RNAs is transported into the cytoplasm and is translated. Synthesized proteins from intron-retained RNA possessing a condensate-prone character lead to proteotoxic stress and JNK-mediated phosphorylation of mTORC1 components. Inhibition of mTORC1 results in reduced phosphorylation of its target proteins, including 4EBP and S6K1, and a subsequent decrease in protein biosynthesis for tumor toxicity.

See also Figure S7.

Since dephosphorylated 4EBP1 binds to the cap-binding protein eIF4E, thereby inhibiting eIF4F (a complex of eIF4E, G, and A) formation (Sonenberg and Hinnebusch, 2009), we tested whether SSA inhibits cap-dependent translation. Consistent with the dephosphorylation of 4EBP1 upon SSA treatment (Figure 6A), pulldown experiments on m⁷G-cap beads showed a stronger association between 4EBP and eIF4E in SSA-treated cells (Figure 7B).

Given that mRNAs coding for ribosomal proteins frequently possess a terminal oligopyrimidine motif (5' TOP) motif in their 5' untranslated regions (UTRs), which particularly sensitizes their translation to mTORC1 inhibition (Philippe et al., 2020), we investigated their translation efficiency under SSA treatment. To test the sensitivity of 5' TOP motif-containing mRNAs to SSA-induced translation repression, irrespective of their splicing status, we transfected in vitro synthesized intronless Renilla luciferase reporters into HeLa S3 cells. Firefly luciferase under the control of the HCV-internal ribosome entry site (IRES), which allows translation initiation independent of the eIF4F complex, served as a control (Hellen and Sarnow, 2001) (Figure 7C). In agreement with the mTORC1 inactivation, SSA strongly reduced expression of 5' TOP motif-containing mRNAs (Figure 7C) compared to non-TOP controls (Figure S7A). Furthermore, the SSA-induced reduction in 5' TOP reporter translation could be reversed when JNK1 and JNK2 were knocked down (Figure 7D and Figure S7B). Thus, our controlled reporter assay corroborated selective inhibition of mTORC1 by splicing modulation through activation of JNK.

These observations could not be explained by the action of SSA on splicing alone. The number of ribosomal footprints on intron-retained mRNAs naturally decreases, as translation halts at the first intronic stop codon, effectively decreasing the space available to ribosomes. Consistent with this scenario, we observed a reduction in the translation efficiency of intron-retained mRNAs upon SSA treatment (Figure 7E). However, the change in the translation efficiency of the 5' TOP mRNA was more prominent (Figure 7E). Conversely, 5' TOP mRNA hardly contained any retained introns even in the presence of SSA (Figure 7F). Therefore, these data demonstrate that selective inhibition of mTORC1-mediated translation upon SSA treatment, but not splicing defects per se, accounts for the observed decrease in protein output.

Our results indicate that toxic, condensate-prone proteins generated by splicing perturbation lead to mTORC1-dependent translational repression via JNK activation (Figure 7G).

Discussion

The function of peptides generated outside of the coding regions have long been overlooked. Recent reports have shed light on the role of noncanonical peptides. Substantial numbers of functional noncanonical human micropeptides have been reported from 5′ UTRs [or upstream ORFs (uORFs)] playing an essential physiological role (Chen et al., 2020). Amino acid sequences encoded in the 3′ UTRs were reported to suppress C-terminally extended proteins generated by stop codon readthrough (Arribere et al., 2016). Along with these studies, the functional exon-intron chimeric protein related in this study expands the cellular proteome.

The importance, benefits, and burdens of introns remain a topic of discussion (Jo and Choi, 2015). Even in intron-poor budding yeast, with only 300 introns across its entire genome, these intervening sequences bear important cellular functions, such as sensing and mediating the starvation response (Morgan et al., 2019; Parenteau et al., 2019). In yeast stationary growth phase, decreased TORC1 activity leads to intron stabilization, subsequent spliceosome sequestration, and attenuation of protein expression from intron-containing mRNAs such as ribosomal proteins. Although our study in mammals shows the opposite regulatory direction— intron retention triggering mTORC1 inhibition, the key molecular complex (TORC1) and regulatory targets (translation machinery) are the same. This may indicate that the basic properties of introns are conserved between yeast and humans.

The condensate-prone feature of low complexity peptides generated from intronic RNA sequences highlights a possible hidden function of exon-intron chimeric proteins. The high GC content around the 5′ splice site and its role in splicing have been reported in earlier studies (Zhang et al., 2011; Amit et al., 2012). Our data indicates an alternative role to encode condensation-prone P-, G-, and R-rich regions. Cellular condensates in the form of liquid, gel, and glass states have gained attention since they increase the concentration of a set of molecules thereby enhancing biochemical reaction rates, sequestering complexes in response to environmental stimuli, compartmentalizing cellular space as membrane-less organelles, and signaling switches (Oldfield and Dunker, 2014; Banani et al., 2017; Shin and Brangwynne, 2017; Franzmann and Alberti, 2019). While we demonstrated that FTH1* condensates function as a proteotoxic signal inducer, other translated introns with low complexity regions may exert alternative functions.

The mechanism by which splicing modulators exhibit their antitumor activity has remained speculative. Although the direct alteration of splicing in key genes offers a straightforward explanation, it is limited to a small number of cancers. For example, in a subset of tumors that depend on high levels of Mcl-1—a Bcl2 family apoptosis regulator—for survival, splicing modulators induce cell death by splice variant switching from the antiapoptotic Mcl-1L to the proapoptotic Mcl-1S isoform (Gao and Koide, 2013; Larrayoz et al., 2016; Aird et al., 2019). Splicing modulators are particularly potent in inhibiting hematological malignancies harboring splicing factor mutations, which supports the idea of synthetic lethality (Seiler et al., 2018; Lee et al., 2018; Wang et al., 2019). Similarly, Myc-activated tumor cells are vulnerable to splicing modulation due to a high demand for spliceosomal activity (Hsu et al., 2015). Additionally, splicing modulation drastically alters the transcription of particular genes: VEGF gene expression is downregulated (Furumai et al., 2010; Amin et al., 2011; Sakai et al., 2004), while NF-kB-dependent transcription becomes activated (Khan et al., 2014). In this study, we present a more general mechanism by which mTORC1 inhibition represses protein synthesis in response to splicing inhibition. As increased translation through hyperactive mTORC1 has been a hallmark of many human cancers, it is plausible that suppression of mTORC1 activity contributes to the anticancer activity of splicing modulators. mTOR inhibitors inhibit the expression of HIF-1 dependent genes including VEGF and activate apoptotic pathways in cells defective in tumor suppressors such as p53 and PTEN (Laplante and Sabatini, 2012; Saxton and Sabatini, 2017). Indeed, the mTOR inhibitors temsirolimus and everolimus have already been used for cancer treatment (Laplante and Sabatini, 2012; Saxton and Sabatini, 2017).

Along with mTORC1 repression, a recent finding that double-stranded RNAs formed in intron-retained mRNAs activate the antiviral immune pathway in triple-negative breast cancer and thus induce extrinsic apoptosis (Bowling et al., 2021) adds a possible general rationale to explain STTs by splicing modulators.

This scenario is not mutually exclusive with other possibilities. The downregulation of tumorigenic genes through reduced protein output could form the basis for antitumor activity. We observed strong translational repression of cancer-related genes such as CYR61, PABPC4, CDK9, SGK1, KIF23, and RAE1 under SSA treatment (Figure S7C). It is further possible that some other truncated proteins gain functions as “neopeptides” to induce cell death, cell senescence or differentiation. SSA-induced exon-intron chimeric proteins may provide a source for new antigens, which could form the basis for future cancer immunotherapy (Ott et al., 2017; Smart et al., 2018; Wirth and Kühnel, 2017). Antigen processing and presentation genes appeared upregulated in translation efficiency under SSA, possibly reflecting the production of the neoantigens (Figure 5D). Multiple mechanisms, including downregulation of mTORC1, may cooperate towards potent antitumorigenic activity when splicing is inhibited. Our genome-wide transcriptome/translatome data provide a useful resource for further investigation.

While we artificially induced intron retention, the widespread presence of IDR-encoded introns suggests a genuine physiological role in contexts where intron retention is induced, such as in cancer (Desterro et al., 2020), aging (Adusumalli et al., 2019), and heat shock (Shalgi et al., 2014). Under these conditions, mTORC1 inhibition by cellular condensates may feed back into translation and mitigate the synthesis of harmful proteins.

STAR Methods

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to the Lead Contact, Shintaro Iwasaki (shintaro.iwasaki@riken.jp).

Material availability

The materials generated in this study will be distributed upon request, pending a Material Transfer Agreement (MTA).

Data and code availability

The ribosome profiling and RNA-Seq data (GSE129305) obtained in this study were deposited in the National Center for Biotechnology Information (NCBI) database. This paper does not report the original code. Original images used for the figures are deposited in the Mendeley database (http://dx.doi.org/10.17632/srt2xpdt3f.1). Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Cell lines

HeLa S3 cells (RIKEN BioResource Research Center) were maintained in DMEM, high glucose (Wako Chemicals), supplemented with 10% FBS. HEK293 T-REx (Thermo Fisher Scientific) cells were maintained in DMEM, high glucose, GlutaMAX Supplement (Thermo Fisher Scientific) and supplemented with 10% FBS. All the cells were maintained in a humidified incubator at 5% CO₂ and 37°C. The stable cell lines of HEK293 T-REx were selected following the manufacturer’s instructions. The sex of the HeLa S3 and HEK293 cells is female.

Bacterial strains

For recombinant protein expression. E. coli BL21 Star (DE3) cells (Thermo Fisher Scientific) were transformed with plasmids (see below for details) and grown at 37°C in Luria-Bertani (LB) medium supplemented with an appropriate antibiotic.

METHOD DETAILS

Compounds

SSA (Kaida et al., 2007) and PlaB (a kind gift from Eisai Co., Ltd.) were dissolved in methanol (MeOH). MG132 (Wako Chemicals), rapamycin (Wako Chemicals), and pp242 (Sigma-Aldrich) were dissolved in DMSO.

Ribosome profiling and RNA-Seq

HeLa S3 cells were grown in 10-cm dishes at 70-80% confluency and treated with SSA (100 ng/ml) or its solvent MeOH for 6 h before lysis. The libraries for ribosome profiling were prepared as described earlier (Iwasaki et al., 2016; McGlincy and Ingolia, 2017) and sequenced by a HiSeq4000 sequencer (Illumina).

Total RNA was extracted from the same lysate used for ribosome profiling with TRIzol LS (Thermo Fisher Scientific) and Direct-zol RNA MicroPrep Kits (Zymo Research). The libraries were prepared with the TruSeq Stranded mRNA Library Prep Kit followed by rRNA removal with Ribo-Zero Gold (Illumina) and were sequenced on a HiSeq 4000 sequencer (Illumina).

Data analysis

Ribosome profiling:

First, the 3′ adaptor sequence (5′-AGATCGGAAGAGCACACGTCTGAA-3′) was trimmed using fastx clipper (Hannon lab, http://hannonlab.cshl.edu/fastx_toolkit/commandline.html) and then demultiplexed according to the barcode sequences in the linkers. We used Bowtie2 (Langmead and Salzberg, 2012) to map the clipped reads to a human rRNA, tRNA, snoRNA, snRNA, and microRNA reference database and captured unaligned reads. These reads were mapped to the human genome [hg19; known reference genes from University of California, Santa Cruz (UCSC)] using Tophat (Trapnell et al., 2009). The PCR duplicates were eliminated thanks to a randomized sequence stretch in the linkers. Empirical estimation of nucleotides on the ribosomal A-site was performed on the basis of footprint length: 15 for 27-28 nt reads and 16 for 29-31 nt reads. Translated introns were defined as follows: (i) intron length > 200 nt, (ii) at least one footprint on the intron, and (iii) 5 or more footprints on CDS exons. Except for the first and last 5 amino acids, the reads from CDS were counted. The relative enrichment of reads was calculated by DESeq (Anders and Huber, 2010).

We measured the change in overall translation by normalizing the number of cytosolic ribosome footprints to those from mitochondrial ribosomes (Iwasaki et al., 2016), as SSA treatment does not appear to impact mitochondrial protein synthesis.

RNA-Seq:

After clipping the adaptor sequence (5′-AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC-3′), the reads were processed as described above for ribosome profiling data analysis.

Mixture-of-isoforms (MISO) (Katz et al., 2010) was used to assess alternative splicing events upon splice inhibition across a database of annotated splice events (https://miso.readthedocs.io/en/fastmiso/) using the following filtering criteria: (i) both inclusion and exclusion reads were ≥ 1 such that (ii) the sum of inclusion and exclusion reads was ≥ 10, (iii) the absolute values of the difference for “Percent Spliced In” (ΔPSI) between vehicle and SSA were ≥ 0.2, and (iv) the Bayes factor was ≥ 10.

For differential intron retention analysis, reads from introns were analyzed by DESeq (Anders and Huber, 2010). The reads were processed as follows: (i) the number of reads was ≥ 5, (ii) introns within the ORF were considered, (iii) MAXENT score, strength of 5' splice sites (Yeo and Burge, 2004) was > 2.5 to minimize the background, and (iv) corresponding mRNAs had ≥ 5 reads in the ORFs. Retained introns were defined as FDR < 0.01 and read enrichment ≥ 4-fold.

For the differential expression analysis of transcripts, reads from transcripts (i.e., exons) were analyzed by DESeq (Anders and Huber, 2010). For the direct comparison of ribosome profiling data, we set the read offset as 15 nt for RNA-Seq reads and counted reads from CDS, excluding the first and last 5 amino acids. Then, the data were renormalized by mitochondrial RNA reads, as described above.

Translation efficiency:

The differential change in ribosome footprint reads over RNA-Seq reads was calculated using DESeq (Anders and Huber, 2010). Pathway enrichment analysis along with translation efficiency was performed by iPAGE (Goodarzi et al., 2009). For the definition of 5' TOP mRNAs, 5' end of mRNA was needed to be precisely assigned. We used the 5' end information obtained in nanoCAGE data (Gandin et al., 2016).

Translated intron features:

Prediction of intrinsic disorderness was performed using IUPred2A using the short option (Mészáros et al., 2018). The relative fraction of disordered residues was calculated by counting the total number of disordered amino acid residues (IUPred2A score > 0.5) normalized to the respective length of either translated introns, upstream CDS exons, or CDSs. Prediction of low-complexity regions (LCRs) was performed by SEG (http://mendel.imp.ac.at/METHODS/seg.server.html) (Wootton, 1994).

The hydrophobicity score (Kyte-Doolittle scale) and net charge (Lehninger pKa scale) of the translated introns and their respective upstream exons and CDSs were calculated with the Peptides package in R. The relative fraction of amino acids was calculated in R by counting the total number of particular amino acid residues normalized to the respective length of either translated introns, upstream exons or CDSs.

GO enrichment analysis for the genes showing intron translation was performed by GOrilla using the background of HeLa cell expressed genes in our experiment (Eden et al., 2009).

Detailed codes will be available upon request.

Western blotting

The cells were lysed using the same lysis buffer as used for the ribosome profiling experiment with 1× protease inhibitor cocktail (Roche), omitting cycloheximide. Proteins were transferred to nitrocellulose membranes (Bio-Rad), and the membrane was blocked with Odyssey blocking buffer (TBS) (LI-COR Biosciences, 927-50000). Anti-4EBP1 [Cell Signaling Technology (CST), 9452], anti-phospho-4EBP1 (Thr37/46) (236B4) (CST, 2855), anti-phospho-4EBP1 (Ser65) (174A9) (CST, 9456), anti-S6K1 (49D7) (CST, 2708), anti-phospho-S6K1 (Thr389) (108D2) (CST, 9234), anti-phospho-JNK (Thr183/Tyr185) (81E11) (CST, 4668), anti-JNK1 (2C6) (CST, 3708), anti-JNK2 (56G8) (CST, 9258), anti-pan-JNK (CST, 9252), anti-FLAG (M2) (Sigma-Aldrich, F1804), anti-β-actin (Medical & Biological Laboratories, M177-3 and LI-COR Biosciences, 926-42212), anti-α-tubulin (B-5-1-2) (Sigma-Aldrich, T5168), anti-phospho-RAPTOR (Ser863) (Sigma-Aldrich, SAB1305088), anti-histone H3 (Abcam, ab1791), anti-SF3B1 (D7L5T) (CST, 14434), and anti-GFP (Clontech, 632460) antibodies were used. IR-dye (680 or 800 nm)-conjugated secondary antibodies (LI-COR Biosciences, 925-68070/71 and 926-32210/11) were used for detection. Images were collected with the Odyssey CLx Infrared Imaging System (LI-COR Biosciences). Image studio version 5.2 (LI-COR Biosciences) was used for image quantification.

RT-PCR

HeLa S3 cells were treated with 100 ng/ml SSA, 100 ng/ml PlaB, or MeOH for 6 h. The cells were lysed using ribosome profiling lysis buffer without cycloheximide. Then, total RNA was extracted using TRIzol LS (Thermo Fisher Scientific) and purified by Direct-zol RNA MicroPrep Kits (Zymo Research). Six hundred thirty nanograms of total RNA was annealed to random 9-mer primers (TaKaRa) and reverse-transcribed using ProtoScript II (New England Biolabs). PCR was performed with an equal volume of the acquired cDNA in 25 μl of reaction mixture using PrimeSTAR Max Premix (TaKaRa) and each appropriate primer pair. The primers to detect DNAJB1 intron retention are as follows:

Exon 2-Fw: 5′-GAACCAAAATCACTTTCCCCAAGGAAGG-3′ and

Exon 3-Rv: 5′-AATGAGGTCCCCACGTTTCTCGGGTGT-3′.

The PCR conditions were 98°C for 3 min; 35 cycles of 98°C for 10 sec, 52°C for 15 sec, and 72°C for 60 sec; and then 72°C for 3 min. The PCR products were visualized by an MultiNA fragment analyzer (Shimadzu) using the DNA-1000 Reagent Kit (Shimadzu) and SYBR Gold (Thermo Fisher Scientific).

BONCAT

HeLa S3 cells (7.5 × 10⁶ cells) were grown for 24 h in a 10-cm dish in DMEM, high glucose (Wako Chemicals) supplemented with 10% FBS. The medium was removed from the dish and the cells were washed by PBS twice. Then, 10 ml of methionine-free medium [DMEM, high glucose, no glutamine, no methionine, no cystine (Thermo Fisher Scientific), supplemented with 862 mg/ml L-alanyl-L-glutamine (Nacalai Tesque), 48 mg/ml L-cysteine dihydrochloride (Nacalai Tesque), and 10% FBS] was added to the dish and the cells were incubated for 30 min at 37°C. The medium was replaced by methionine-free medium containing either 100 ng/ml SSA or solvent MeOH along with 50 μM HPG (Jena Bioscience) (3 replicates each). The cells were incubated for 6 h at 37°C. After the medium was removed, the cells were washed by ice-cold PBS thoroughly and lysed with 500 μl lysis buffer [20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 5 mM MgCl₂, and 1% Triton-X 100]. After centrifugation at 20,000 × g and 4°C for 10 min, the supernatant and pellet were separated. The pellet fraction was dissolved in lysis buffer along with manual grinding on the wall of tube and sonication.

HPG-labeled proteins in the supernatant and pellet fractions (~1 mg) were click-conjugated with 50 μM azide-PEG3-biotin (Sigma-Aldrich) by a Click-iT Cell Reaction Buffer Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. Free azide-PEG3-biotin was removed with MicroSpin G-25 Columns (GE Healthcare). The flow-through fraction was mixed with 300 μl of Dynabeads M280 streptavidin (Thermo Fisher Scientific), which were equilibrated in lysis buffer with 1 mM DTT and incubated overnight at 4°C with slow rotation. The beads were washed 3 times in lysis buffer with 1 mM DTT. The beads were finally dissolved in bead storage buffer [20 mM Tris-HCl (pH 7.5), 150 mM NaCl, and 5 mM MgCl₂] and LC-MS/MS was performed by on-bead digestion with trypsin (TPCK-treated, Worthington Biochemical).

LC-MS/MS

After reduction and S-carboxymethylation of the insoluble fraction, the proteins were precipitated by trichloroacetic acid (TCA) (PAGE Clean Up Kit, Nacalai Tesque) and then digested with trypsin (TPCK-treated, Worthington Biochemical). The protein amount of each solution was determined by amino acid analysis using the AQC pre-label method (Masuda and Dohmae, 2013). Peptides in one microgram of digest were separated on a nanoflow LC (Easy nLC 1200, Thermo Fisher Scientific) using a nanoelectrospray ionization spray column (NTCC analytical column; C18, φ75 μm × 100 mm, 3 μm; Nikkyo Technology) with a linear gradient of 0-40% buffer B (80% acetonitrile and 0.1% formic acid) in buffer A (0.1% formic acid) at a flow rate of 300 nl/min for 220 min, coupled online to a Q-Exactive HFX mass spectrometer (Thermo Fisher Scientific) that was equipped with a nanospray ion source. The mass spectrometer was operated in positive-ion mode, and MS and MS/MS spectra were acquired with a data-dependent TOP 10 method. Proteins were identified and quantified using Proteome Discoverer 2.2 (Thermo Fisher Scientific) with MASCOT program version 2.6 (Matrix science) using an in-house database.

DNA constructs

*pCDNA5/FRT/TO-FTH1, FTH1**, *RAB32, RAB32**, *FADD, FADD**, *IRF2BP2, IRF2BP2**, p27, and *p27**

DNA fragments containing the first coding exon and following the intron until the first in-frame stop codon from FTH1, RAB32, FADD, IRF2BP2, or p27 (CDKN1B) were PCR-amplified from the HEK cell genome. DNA fragments coding the FTH1, PAB32, FADD, IRF2BP2, or p27 (CDKN1B) CDS were PCR-amplified from HEK cell cDNA. An N-terminal 1× FLAG tag was inserted in frame upstream during PCR amplification. These PCR products were inserted into a pcDNA5/FRT/TO vector (Thermo Fisher Scientific) via the HindIII and BamHI sites by Gibson assembly (New England Biolabs). The sequences of the final constructs were verified by plasmid sequencing.

pColdI-mCherry-FTH1 and FTH1*

DNA fragments for FTH1 and FTH1* were PCR-amplified from the HEK cell cDNA and genome, respectively, and inserted into a pColdI vector (TaKaRa) with mCherry-tag by Gibson Assembly (New England Biolabs).

psiCHECK2-ATP5O

The non-TOP 5′ UTR sequence of ATP5O (5′-CGGGAGAAG-3′) was identified in published nanoCAGE data (Gandin et al., 2016) and inserted between the T7 promoter and Renilla luciferase in psiCHECK2 (Promega) by Gibson Assembly.

psiCHECK2-HCV-FL

The DNA region spanning from Renilla luciferase to the HSV-TK promoter was removed from psiCHECK2-HCV IRES (Iwasaki et al., 2016) by PCR and Gibson Assembly.

DNA transfection

Transfection of 1 μg of each individual DNA was performed in six-well plates using FuGENE HD (Promega) for HeLa S3 cells according to the manufacturer’s instructions.

Recombinant protein purification and imaging

E. coli BL21 Star (DE3) cells (Thermo Fisher Scientific) were transformed with pColdI-mCherry-FTH1 or FTH1* and cultivated to OD₆₀₀ ~0.5 in 11 of LB with ampicillin. The protein was inducted with 1 mM IPTG at 15°C overnight. The collected cell pellet was flash-frozen by liquid nitrogen and stored at −80°C.

The pellet was resuspended in Buffer A [20 mM HEPES-NaOH (pH 7.5), 1 M NaCl, 10 mM imidazole, 0.5% NP-40, and 10 mM 2-mercaptoethanol] and then sonicated on ice. The cell lysate was centrifuged at 10,000 × g for 20 min at 4°C. The supernatant was incubated with 1.5 ml bed volume of Ni-NTA Agarose (Qiagen) equilibrated with buffer C. Then, beads were washed with buffer D [20 mM HEPES-NaOH (pH 7.5), 1 M NaCl, 20 mM imidazole, and 10 mM 2-mercaptoethanol] on a econo-pac chromatography column (Bio-Rad). The bound proteins were eluted with elution buffer [20 mM HEPES-NaOH (pH 7.5), 1 M NaCl, 250 mM imidazole, 10 mM 2-mercaptoethanol, and 10% glycerol]. By the use of an NGC chromatography system (Bio-Rad), the proteins were loaded into HiLoad 16/600 Superdex 75 pg (GE Healthcare) in buffer E [20 mM HEPES-NaOH (pH 7.5), 1 M NaCl, and 1 mM DTT] and purified according to the size of the proteins. The proteins were concentrated using Vivaspin 6, 10,000 MWCO (Sartorius), flash-frozen by liquid nitrogen, and stored at −80°C.

Microscopic images of the recombinant proteins in chambered cover glass (Thermo Fisher Scientific, Nunc Lab-Tek II) were taken using a confocal microscope (Olympus, FV3000). The proteins were diluted in 20 mM HEPES-NaOH (pH 7.5), 200 mM NaCl, and 1 mM DTT.

Immunostaining and microscopic analysis

Cells were grown on coverslips, transfected with plasmids as described above, washed with PBS, fixed with 4% paraformaldehyde for 45 min, permeabilized with 0.2% Triton-X 100 in PBS, incubated with 10% normal goat serum (NGS) (Thermo Fisher Scientific, PCN5000) diluted in 0.2% Triton-X 100 in PBS as the blocking solution for 45 min, and then incubated with the anti-FLAG antibody (Sigma-Aldrich, F1804) in the blocking solution at 4°C overnight. The next day, cells were washed three times with 0.2% Triton-X 100 in PBS followed by incubation with the secondary antibodies conjugated with Alexa 594 (Thermo Fisher Scientific, A-11005) or Alexa 488 (Thermo Fisher Scientific, A-11008) in blocking solution for 50 min. Washing was performed three times for 10 min each with 0.2% Triton-X 100 including Hoechst 33342 (Thermo Fisher Scientific, H3570) during the second wash. Coverslips were mounted on microscope slides using ProLong Diamond Antifade Mountant (Thermo Fisher Scientific, P36965). Fluorescence imaging was obtained with a DeltaVision imaging system (Applied Precision) and deconvolved using SoftWoRx 5.5 (Applied Precision). Automated quantification of GFP fluorescence intensity and size of GFP aggregates were obtained using ImageJ. For the experiment with SSA treatment, immunostaining was not performed except for Hoechst staining.

Filter trap assay

HeLa S3 cells (2 × 10⁵) were grown in 2 ml of culture in six-well plates. The cells were transfected with 1 μg of pCDNA5/FRT/TO, pCDNA5/FRT/TO-FTH1, pCDNA5/FRT/TO-FTH1*, pCDNA5/FRT/TO-CDKN1B, or pCDNA5/FRT/TO-CDKN1B* using FuGENE HD (Promega) according to the manufacturer’s instructions. After 48 h of incubation, the cells were trypsinized, collected by centrifugation, washed by PBS, and resuspended in 125 μl of PBS and 1× protease inhibitor cocktail (Sigma-Aldrich). The cells were then lysed by sonication. Equal protein concentrations were mixed with 5-fold volume of 1% SDS in 1× PBS and loaded onto a cellulose acetate membrane with a 0.2-μm pore size (GE Healthcare) (pre-soaked in 1% SDS in 1× PBS) with a Slot blot microfiltration apparatus (Sanplatec Corp.). A vacuum was then applied to drain any residual liquid. Washing was performed with 1% SDS in 1× PBS. The membrane was used for the immune-detection of FLAG-tagged aggregates, as describe in the “Western blotting” section.

In the experiment for detecting system-wide accumulation of aggregated proteins, cell lysates were cleared by centrifugation at 400 × g for 5 min after sonication. Proteins on the membrane were stained by Revert 700 total protein stain (LI-COR Biosciences).

Images were collected with the Odyssey CLx Infrared Imaging System (LI-COR Biosciences). Image studio version 5.2 (LI-COR Biosciences) was used for image quantification.

Proteotoxic stress reporter transfection and luciferase activity assay

HeLa S3 cells (~40,000) were seeded in 24-well plates in 1 ml of culture in triplicate. The next day, transfection of 0.5 μg of either pCI-neo Fluc-EGFP (Addgene plasmid #90170; http://n2t.net/addgene:90170; RRID: Addgene_90170) or pCI-neo FlucDM-EGFP (Addgene plasmid #90172; http://n2t.net/addgene:90172; RRID: Addgene_90172), both gifts from Franz-Ulrich Hartl (Gupta et al., 2011), was carried out using FuGENE HD (Promega) according to the manufacturer’s instructions.

For the firefly luciferase activity assay, cells were washed with PBS and lysed with 1× passive lysis buffer (Promega). Luciferase assay reagent and GloMax (both Promega) were used to detect luminescence according to the manufacturer’s instructions. The relative luciferase activity was determined by normalizing the firefly luciferase activity by the respective total EGFP protein amount quantified by Western blot using an anti-GFP antibody (Clontech, 632460) in Image Studio version 5.2 (LI-COR Biosciences).

In vitro mTOR kinase assay

mTOR kinase activity was monitored by LANCE Ultra time-resolved fluorescence resonance energy transfer (TR-FRET, PerkinElmer) following the manufacturer’s instructions. mTOR enzyme (10 nM), ATP (90 μM), and ULight-S6K1 (Thr389) peptide (PerkinElmer, TRF0126-C) (25 nM) were incubated in kinase buffer [50 mM HEPES (pH 7.5), 1 mM EGTA, 3 mM MnCl₂, 10 mM MgCl₂, 2 mM DTT, and 0.01% Tween 20] at room temperature for 1 h in white 384-well opti-plates. The reaction was stopped by adding EDTA to a final concentration of 10 mM. A europium-labeled anti-phospho-S6K1 (Thr389) antibody (PerkinElmer, TRF0214-C) was then added to the reaction at 2 nM to detect the phosphorylated peptide. The signal intensity of the emitted light was measured with an EnVision Multilabel reader (PerkinElmer) in TR-FRET mode (excitation at 320 nm and emission at 665 nm).

RNA interference

ON-TARGETplus siRNAs against Nontargeting #1 (D-001810-01-05), human SF3B1 (L-020061-01-0005), human JNK1 (L-003514-00-0005), and human JNK2 (L-003505-00-0005) were obtained from Dharmacon. For the knockdown experiments, 2 × 10⁵ HeLa S3 cells per well were seeded in 6-well plates. The next day, 30 pmol of siRNA was transfected into cells using Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific) according to the manufacturer’s manual. Thirty-six hours after transfecting the SF3B1 siRNAs, cells were lysed as mentioned above in the Western blotting section. In the case of JNK knockdown experiments, 42 h after transfection, cells were treated with SSA for 6 h and then lysed.

Nascent peptide labeling by OP-puro

Nascent peptides were labeled with 20 μM OP-puro (Jena Bioscience) in 24-well plates for 30 min after 5.5 h of SSA or MeOH challenge. The cells were washed with ice-cold PBS and lysed with 60 μl buffer [20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 5 mM MgCl₂, and 1% Triton-X 100] and centrifuged at 20,000 × g and 4°C for 10 min. Supernatants were used for nascent peptide labeling with 50 μM azide-conjugated IR-800 dye (LI-COR Biosciences) by a Click-iT Cell Reaction Buffer Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions and were run on SDS-PAGE. Images were acquired on an Odyssey CLx Infrared Imaging System (LI-COR Biosciences) for the detection of nascent peptides at an IR 800 nm signal. The total proteins were measured by staining with Coomassie Brilliant Blue (CBB) (Wako Chemicals), giving a signal at IR 700 nm. The images were quantified with Image Studio version 5.2 (LI-COR Biosciences).

Reporter mRNA preparation

For Renilla luciferase reporter mRNAs, DNA fragments for in vitro transcription were PCR-amplified from psiCHECK2-EIF2S3 (Iwasaki et al., 2016) and psiCHECK2-ATP5O as templates using the following primers: 5′-TAATACGACTCACTATAGG-3′ and 5′-CTGTGTGTTGGTTTTTTGTGTGTG-3′. In vitro transcription, capping and polyadenylation of the reporter RNA were performed with the T7-Scribe Standard RNA IVT Kit, ScriptCap m⁷G Capping System, ScriptCap 2'-O-methyltransferase, and A-Plus Poly(A) Polymerase Tailing Kit (CELLSCRIPT) according to the manufacturer’s protocol.

For the HCV-firefly luciferase mRNA reporter, the DNA fragment was PCR-amplified from psiCHECK2-HCV-FL with the following primers: 5′-TGACTAATACGACTCACTATAGG-3′ and 5′-TGTATCTTATCATGTCTGCTCGAAG-3′. AP₃G (A-cap) (Jena Bioscience) was added to the in vitro transcription reaction, skipping the capping reaction.

Luciferase reporter assay

HeLa S3 cells (1 × 10⁵) were seeded onto 24-well plates in triplicate. Two hours after 100 ng/ml SSA treatment, 0.02 μg of each mRNA reporter per well was transfected the next day using the TransIT-mRNA Transfection Kit (Mirus) according to the manufacturer’s instructions. Four hours after transfection, cells were washed with PBS and lysed with 1× passive lysis buffer (Promega). The Dual-Luciferase Reporter Assay System and GLOMAX (both Promega) were used to detect luminescence according to the manufacturer’s instructions.

7-Methyl-guanosine (m⁷G) pulldown assays

HeLa S3 cells were grown until 70-80% confluency in a 15-cm dish. Following 10 h of 100 ng/ml SSA treatment, HeLa S3 cells were washed with ice-cold PBS and lysed with 1200 μl hypotonic lysis buffer [10 mM HEPES-NaOH (pH 7.5), 10 mM KCl, 1.5 mM MgCl₂, 1 mM DTT, and 1× protease inhibitor cocktail (Nacalai Tesque)]. After centrifugation at 20,000 × g and 4°C for 10 min, the lysate was precleared with 150 μl of Pierce control agarose resin (Thermo Fisher Scientific) equilibrated with hypotonic lysis buffer at 4°C for 1 h. The precleared lysate was incubated with 40 μl of agarose (blank) (Jena Bioscience) or γ-aminophenyl-m⁷GTP (C10-spacer)-agarose (Jena Bioscience) equilibrated with hypotonic wash buffer [10 mM HEPES-NaOH (pH 7.5), 10 mM KCl, 1.5 mM MgCl₂, 1 mM DTT, 0.02% Triton-X 100, and 50 μg/ml tRNA from baker’s yeast (Sigma-Aldrich)] at 4°C for 1 h and then washed with hypotonic wash buffer 3 times. Proteins were eluted with LDS sample buffer (Thermo Fisher Scientific) at 100°C for 4 min and examined by Western blotting.

QUANTIFICATION AND STATISTICAL ANALYSIS

The statistical details can also be found in the figure legends. Statistical significance was calculated in R.

For Figure 3H, 4E, 6I, and 7D, significance was calculated by one-way analysis of variance (ANOVA) with the post hoc Tukey honestly significant difference (HSD) test. For Figure 5B, 7C, and S6C, Student's t-test (two-tailed) was used. These data are presented as the mean and mean and s.d. (n = 3).

For Figures 2C, 2D, 2E, 5E, 7A, 7E, 7F, S2B, S2C, and S2D, significance was calculated by Wilcoxon’s test.

The fitting curve in Figure S6E was constructed by Igor Pro 8 (WaveMetrics) to calculate the IC₅₀ ± s.d.

Supplementary Material

Supplemental Figures

NIHMS1732741-supplement-Supplemental_Figures.pdf^{(1.8MB, pdf)}

Table S1

List of chimeric intron protein sequences and characteristics; related to Figure 1.

Each intron is listed with its UCSC identifier and individual scores (IUPred2A-based disordered fraction, hydrophobicity and net charge). Either a 10 or 20 amino acid window was considered.

NIHMS1732741-supplement-Table_S1.xlsx^{(154.5KB, xlsx)}

Table S2

List of chimeric intron proteins depicted by LC-MS/MS; related to Figure 1.

Each intron is listed with its UCSC identifier. The respective mRNA and gene names for the intron are listed.

NIHMS1732741-supplement-Table_S2.xlsx^{(20.1KB, xlsx)}

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Rabbit polyclonal anti-4EBP1 antibody	Cell Signaling Technology	Cat#9452; RRID: AB_331692
Rabbit monoclonal anti-phospho-4EBP1 (Thr37/46) (236B4) antibody	Cell Signaling Technology	Cat#2855; RRID: AB_560835
Rabbit monoclonal anti-phospho-4EBP1 (Ser65) (174A9) antibody	Cell Signaling Technology	Cat#9456; RRID: AB_823413
Rabbit monoclonal anti-S6K1 (49D7) antibody	Cell Signaling Technology	Cat#2708; RRID: AB_390722
Rabbit monoclonal anti-phospho-S6K1 (Thr389) (108D2) antibody	Cell Signaling Technology	Cat#9234; RRID: AB_2269803
Rabbit monoclonal anti-phospho-JNK (Thr183/Tyr185) (81E11) antibody	Cell Signaling Technology	Cat#4668; RRID: AB_823588
Mouse monoclonal anti-JNK1 (2C6) antibody	Cell Signaling Technology	Cat#3708; RRID: AB_1904132
Rabbit monoclonal anti-JNK2 (56G8) antibody	Cell Signaling Technology	Cat#9258; RRID: AB_2141027
Rabbit polyclonal anti-pan-JNK antibody	Cell Signaling Technology	Cat#9252; RRID: AB_2250373
Mouse monoclonal anti-FLAG (M2) antibody	Sigma-Aldrich	Cat#F1804; RRID: AB_262044
Mouse monoclonal anti-β-actin antibody	Medical & Biological Laboratories	Cat#M177-3; RRID: AB_10697039
Mouse monoclonal anti-β-actin antibody	LI-COR Biosciences	Cat#926-42212; RRID: AB_2756372
Mouse monoclonal anti-α-tubulin (B-5-1-2) antibody	Sigma-Aldrich	Cat#T5168; RRID: AB_477579
Rabbit polyclonal anti-phospho-RAPTOR (Ser863) antibody	Sigma-Aldrich	Cat#SAB1305088; RRID: AB_2891048
Rabbit polyclonal anti-histone H3 antibody	Abcam	Cat#ab1791; RRID: AB_302613
Rabbit monoclonal anti-SF3B1 (D7L5T) antibody	Cell Signaling Technology	Cat#14434; RRID: AB_2798479
Rabbit polyclonal anti-GFP antibody	Clontech	Cat#632460; RRID: AB_2314544
Goat polyclonal anti-mouse IgG antibody conjugated with IRDye 680RD	LI-COR Biosciences	Cat#925-68070; RRID: AB_2651128
Goat polyclonal anti-rabbit IgG antibody conjugated with IRDye 680RD	LI-COR Biosciences	Cat#925-68071; RRID: AB_2721181
Goat polyclonal anti-rabbit IgG antibody conjugated with IRDye 800CW	LI-COR Biosciences	Cat#926-32211; RRID: AB_621843
Goat polyclonal anti-mouse IgG antibody conjugated with IRDye800CW	LI-COR Biosciences	Cat#926-32210; RRID: AB_621842
Goat polyclonal anti-mouse IgG antibody conjugated with Alexa 594	Thermo Fisher Scientific	Cat#A-11005; RRID: AB_141372
Goat polyclonal anti-rabbit IgG antibody conjugated with Alexa 488	Thermo Fisher Scientific	Cat#A-11008; RRID: AB_143165
Mouse monoclonal anti-phospho-S6K1 (Thr389) antibody labeled with europium	PerkinElmer	Cat#TRF0214-C; RRID: AB_2891047

Bacterial and virus strains
BL21 Star (DE3)	Thermo Fisher Scientific	Cat#C601003

Biological samples
Normal goat serum	Thermo Fisher Scientific	Cat#PCN5000

Chemicals, peptides, and recombinant proteins
SSA	Kaida et al., 2007	N/A
PlaB	Eisai Co., Ltd.	N/A
MG132	Wako chemicals	Cat#135-18453
Rapamycin	Wako chemicals	Cat#R0161
pp242	Sigma-Aldrich	Cat#P0037
ProtoScript II	New England Biolabs	Cat#M0368L
PrimeSTAR Max Premix	TaKaRa	Cat#R045B
L-alanyl-L-glutamine	Nacalai Tesque	Cat#04260-64
L-cystine dihydrochloride	Nacalai Tesque	Cat#13003-12
HPG	Jena Bioscience	Cat#CLK-016-25
Azide-PEG3-biotin	Sigma-Aldrich	Cat#762024
Trypsin	Worthington Biochemical	Cat#LS003744
6-Aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC)	Synchem UG & Co. KG	Cat#S041
Amino Acid Mixture Standard Solution, Type H[CRM]	Wako Chemicals	Cat#018-27881
Gibson assembly	New England Biolabs	Cat#E2611L
mCherry-FTH1	This paper	N/A
mCherry-FTH1*	This paper	N/A
Hoechst 33342	Thermo Fisher Scientific	Cat#H3570
mTOR kinase	Millipore	Cat#14-770
ULight-S6K1 (Thr389) peptide	PerkinElmer	Cat#TRF0126-C
OP-puro	Jena Bioscience	Cat#NU-931-05
Azide-conjugated IR-800 dye	LI-COR Biosciences	Cat#929-60000

Critical commercial assays
DMEM, high glucose, GlutaMAX Supplement	Thermo Fisher Scientific	Cat#10566016
DMEM, high glucose	Wako chemicals	Cat#044-29765
DMEM, high glucose, no glutamine, no methionine, no cystine	Thermo Fisher Scientific	Cat#21013024
TRIzol LS	Thermo Fisher Scientific	Cat# 10296010
Direct-zol RNA MicroPrep Kits	Zymo Research	Cat#R2062
TruSeq Stranded mRNA Library Prep Kit	Illumina	Cat#RS-122-2102
Ribo-Zero Gold	Illumina	Cat#MRZG12324
Protease inhibitor cocktail	Roche	Cat#04693132001
Nitrocellulose membranes	Bio-Rad	Cat#1620145
Odyssey blocking buffer (TBS)	LI-COR Biosciences	Cat#927-50000
DNA-1000 Reagent Kit	Shimadzu	Cat#292-27911-91
SYBR Gold	Thermo Fisher Scientific	Cat#S11494
Click-iT Cell Reaction Buffer Kit	Thermo Fisher Scientific	Cat#C10269
MicroSpin G-25 Column	GE Healthcare	Cat#27-5325-01
Dynabeads M280 streptavidin	Thermo Fisher Scientific	Cat#DB11205
PAGE Clean Up Kit	Nacalai Tesque	Cat#06441-50
FuGENE HD	Promega	Cat#E2311
Ni-NTA Agarose	Qiagen	Cat#30210
ProLong Diamond Antifade Mountant	Thermo Fisher Scientific	Cat#P36965
0.2-μm cellulose acetate membrane filter	GE Healthcare	Cat#10404180
10% SDS Solution	Nacalai Tesque	Cat# 30562-04
Protease inhibitor cocktail	Sigma-Aldrich	Cat#P8340
Revert 700 total protein stain and wash solution kit	LI-COR Biosciences	Cat#926-11015
Passive lysis buffer	Promega	Cat#E1941
Luciferase assay reagent	Promega	Cat#E1483
Lipofectamine RNAiMAX	Thermo Fisher Scientific	Cat#13778030
Coomassie Brilliant Blue	Wako chemicals	Cat#299-50101
T7-Scribe Standard RNA IVT Kit	CELLSCRIPT	Cat#C-AS3107
ScriptCap m⁷G Capping System	CELLSCRIPT	Cat#C-SCCE0625
ScriptCap 2′-O-Methyltransferase Kit	CELLSCRIPT	Cat#C-SCMT0625
A-Plus Poly(A) Polymerase Tailing Kit	CELLSCRIPT	Cat#C-PAP5104H
AP₃G (A-cap)	Jena Bioscience	Cat#NU-941-5
TransIT-mRNA Transfection Kit	Mirus	Cat#MIR2250
Dual-Luciferase Reporter Assay System	Promega	Cat#E1960
Protease inhibitor cocktail	Nacalai Tesque	Cat#03969-21
Pierce control agarose resin	Thermo Fisher Scientific	Cat#26150
Agarose (blank) beads	Jena Bioscience	Cat#AC-001S
γ-aminophenyl-m7GTP (C10-spacer)-agarose beads	Jena Bioscience	Cat#AC-155S
tRNA from baker’s yeast	Sigma-Aldrich	Cat#10109495001
LDS sample buffer	Thermo Fisher Scientific	Cat#NP0007
Random 9-mer primers	TaKaRa	Cat#3802
Control siRNA	Dharmacon	Cat#D-001810-01-05
SF3B1 siRNA	Dharmacon	Cat#L-020061-01-0005
JNK1 siRNA	Dharmacon	Cat#L-003514-00-0005
JNK2 siRNA	Dharmacon	Cat#L-003505-00-0005

Deposited data
Ribosome profiling and RNA-Seq upon SSA treatment	This paper	GEO: GSE129305
5' sequence of mRNA obtained by nanoCAGE data	Gandin et al., 2016	https://genome.cshlp.org/content/26/5/636/suppl/DC1
Original images used for the figures	This paper	Mendeley Data: doi: 10.17632/srt2xpdt3f.1

Experimental models: Cell lines
HeLa S3	RIKEN BioResource Research Center	RCB: 1525
HEK293 T-REx	Thermo Fisher Scientific	Cat#R71007

Experimental models: Organisms/strains

Oligonucleotides
DNAJB1 Exon 2-Fw: 5′-GAACCAAAATCACTTTCCCCAAGGAAGG-3′	This paper	N/A
DNAJB1 Exon 3-Rv: 5′-AATGAGGTCCCCACGTTTCTCGGGTGT-3′	This paper	N/A
Rluc-Fw: 5′-TAATACGACTCACTATAGG-3′	This paper	N/A
Rluc-Rv: 5′-CTGTGTGTTGGTTTTTTGTGTGTG-3′	This paper	N/A
Fluc-Fw: 5′-TGACTAATACGACTCACTATAGG-3′	This paper	N/A
Fluc-Rv: 5′-TGTATCTTATCATGTCTGCTCGAAG-3′	This paper	N/A

Recombinant DNA
pCDNA5/FRT/TO	Thermo Fisher Scientific	Cat#V652020
pCDNA5/FRT/TO-FTH1	This paper	N/A
pCDNA5/FRT/TO-FTH1*	This paper	N/A
pCDNA5/FRT/TO-RAB32	This paper	N/A
pCDNA5/FRT/TO-RAB32*	This paper	N/A
pCDNA5/FRT/TO-FADD	This paper	N/A
pCDNA5/FRT/TO-FADD*	This paper	N/A
pCDNA5/FRT/TO-IRF2BP2	This paper	N/A
pCDNA5/FRT/TO-IRF2BP2*	This paper	N/A
pCDNA5/FRT/TO-p27	This paper	N/A
pCDNA5/FRT/TO-p27*	This paper	N/A
pColdI-mCherry-FTH1	This paper	N/A
pColdI-mCherry-FTH1*	This paper	N/A
psiCHECK2-ATP5O	This paper	N/A
psiCHECK2-HCV-FL	This paper	N/A
pCI-neo Fluc-EGFP	Gupta et al., 2011	Addgene plasmid #90170; RRID: Addgene_90170
pCI-neo FlucDM-EGFP	Gupta et al., 2011	Addgene plasmid #90172; RRID: Addgene_90172
psiCHECK2-EIF2S3	Iwasaki et al., 2016	N/A

Software and algorithms
fastx clipper	Hannon laboratory, Cancer Research UK Cambridge Institute	http://hannonlab.cshl.edu/fastx_toolkit/commandline.html
Bowtie2	Langmead et al., 2018	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
Tophat	Trapnell et al., 2009	https://ccb.jhu.edu/software/tophat/index.shtml
DESeq	Anders and Huber, 2010	https://bioconductor.org/packages/release/bioc/html/DESeq.html
MISO	Katz et al., 2010	https://miso.readthedocs.io/en/fastmiso/
iPAGE	Goodarzi et al., 2009	https://tavazoielab.c2b2.columbia.edu/iPAGE/
IUPred2A	Mészáros et al., 2018	https://iupred2a.elte.hu
SEG	Wootton, 1994	http://mendel.imp.ac.at/METHODS/seg.server.html
GOrilla	Eden et al., 2009	http://cbl-gorilla.cs.technion.ac.il
Image studio version: 5.2	LI-COR Biosciences	https://www.licor.com/bio/image-studio/
Proteome Discoverer version: 2.2	Thermo Fisher Scientific	https://www.thermofisher.com/order/catalog/product/OPTON-30955#/OPTON-30955
MASCOT server version: 2.6	Matrix science	https://www.matrixscience.com/
SoftWoRx version: 5.5	Applied Precision	www.appliedprecision.com
ImageJ version: 1.51	NIH	https://imagej.nih.gov/ij/
R	The R Foundation	https://www.r-project.org
Igor Pro version: 8.01	WaveMetrics	https://www.wavemetrics.com/products/igorpro

Other
Odyssey CLx Infrared Imaging System	LI-COR Biosciences	N/A
MultiNA fragment analyzer	Shimadzu	N/A
Easy nLC 1200	Thermo Fisher Scientific	N/A
NTCC analytical column	Nikkyo Technology	N/A
Q-Exactive HFX mass spectrometer	Thermo Fisher Scientific	N/A
Econo-pac chromatography column	Bio-Rad	Cat#7321010
NGC chromatography system	Bio-Rad	N/A
HiLoad 16/600 Superdex 75 pg	GE Healthcare	Cat#28989333
Vivaspin 6, 10,000 MWCO	Sartorius	Cat#28932296
Chambered cover glass Nunc Lab-Tek II	Thermo Fisher Scientific	Cat#155409PK
Confocal microscope FV3000	Olympus	N/A
DeltaVision imaging system	Applied Precision	N/A
Slot blot microfiltration apparatus	Sanplatec Corp.	N/A
GloMax 96 Microplate Luminometer w/Dual Injectors	Promega	Cat#E6521
EnVision Multilabel reader	PerkinElmer	Cat#21040010

Open in a new tab

Significance.

Small-molecule splicing modulators have attracted great interest as antitumor drugs. However, the exact mechanism through which splicing modulators induce antitumor activity remains unclear. Following intron retention induced by splicing modulators, it was shown that truncated proteins containing intron-derived peptides are produced from a subset of intron-retained transcripts. In this study, comprehensive proteome and translatome analysis gave a global overview of intron-derived peptides and their function. We demonstrated that treatment with the splicing modulator spliceostatin A (SSA) generates chimeric proteins with intrinsically disordered and condensation-prone properties. The proteotoxic condensates of peptides from intron translation leads to mTORC1 inhibition and subsequent global repression of translation. Since hyperactivated mTORC1 is a hallmark of cancer and its inhibition is a well-characterized option for tumor treatment, our results provide insight into the mechanisms underlying the antitumor activity of splicing modulators.

Highlights.

Retained introns induced by spliceostatin A, generate an array of truncated proteins
The truncated proteins are intrinsically disordered and form cellular condensates
SSA-induced proteotoxicity activates JNK and inhibits mTORC1 pathways
SSA ultimately attenuates global protein synthesis

Acknowledgments

We thank Rei Yoshimoto, Daisuke Kaida, Takeshi Ito, and all the members of the Iwasaki and Yoshida laboratories for constructive discussions, technical help, and critical reading of the manuscript. We are also grateful to the Support Unit for Bio-Material Analysis, RIKEN CBS Research Resources Division for technical help. PlaB was kindly provided by Eisai Co., Ltd. pCI-neo Fluc-EGFP and pCI-neo FlucDM-EGFP were kind gifts from Franz-Ulrich Hartl. M.Y. was supported in part by the Grant-in-Aid for Scientific Research (S) (JP19H05640) from the Japan Society for the Promotion of Science (JSPS) and the Grant-in-Aid for Scientific Research on Innovative Areas “Ubiquitin new frontier driven by Chemo-technologies” (JP18H05503) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT). S.I. was supported by the Grant-in-Aid for Scientific Research on Innovative Areas “Nascent Chain Biology” (JP17H05679) and the Grant-in-Aid for Transformative Research Areas (B) “Parametric Translation” (JP20H05784) from MEXT, the Grant-in-Aid for Young Scientists (A) (JP17H04998) and Challenging Research (Exploratory) (JP19K22406) from JSPS, AMED-CREST (JP20gm1410001) from the Japan Agency for Medical Research and Development (AMED), the Pioneering Projects (“Biology of Intracellular Environments”) and the Aging Project from RIKEN, and the Takeda Science Foundation. J.K.C.S. was supported by a Grant-in-Aid for Early-Career Scientists (JP20K15420) from JSPS. DNA libraries were sequenced by the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by the NIH S10 OD018174 Instrumentation Grant. Computations were supported by Manabu Ishii, Itoshi Nikaido, the Bioinformatics Analysis Environment Service on RIKEN Cloud, and the supercomputer HOKUSAI Sailing Ship in RIKEN ACCC. J.K.C.S. was a recipient of the Japanese Government (MEXT) Scholarship.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declaration of Interests

The authors declare that they have no competing interests.

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Associated Data

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

Supplementary Materials

Supplemental Figures

NIHMS1732741-supplement-Supplemental_Figures.pdf^{(1.8MB, pdf)}

Table S1

List of chimeric intron protein sequences and characteristics; related to Figure 1.

Each intron is listed with its UCSC identifier and individual scores (IUPred2A-based disordered fraction, hydrophobicity and net charge). Either a 10 or 20 amino acid window was considered.

NIHMS1732741-supplement-Table_S1.xlsx^{(154.5KB, xlsx)}

Table S2

List of chimeric intron proteins depicted by LC-MS/MS; related to Figure 1.

Each intron is listed with its UCSC identifier. The respective mRNA and gene names for the intron are listed.

NIHMS1732741-supplement-Table_S2.xlsx^{(20.1KB, xlsx)}

Data Availability Statement

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PERMALINK

Splicing modulators elicit global translational repression by condensate-prone proteins translated from introns

Jagat K Chhipi-Shrestha

Tilman Schneider-Poetsch

Takehiro Suzuki

Mari Mito

Khalid Khan

Naoshi Dohmae

Shintaro Iwasaki

Minoru Yoshida

Summary

eTOC Blurb

Introduction

Results

SSA induces widespread intron retention and intron translation

Figure 1. Retained introns that emerged upon splicing modulation are extensively subjected to translation.

Characteristics of intron-translated transcripts and proteins

Figure 2. Characterization of translated introns.

A subset of intron-derived peptides are condensation-prone

Figure 3. A subset of intron-derived peptides are condensation-prone.

Condensation-prone intron-derived peptides are proteotoxic

Figure 4. Condensation-prone intron-derived peptides are proteotoxic.

SSA induces global translation inhibition

Figure 5. Global translation was inhibited upon splicing modulation.

SSA-mediated JNK activation leads to mTORC1 inhibition

Figure 6. SSA-mediated JNK activation leads to mTORC1 inhibition.

SSA mimics translation repression by mTORC1 inactivation

Figure 7. Characterization of translation repression induced by SSA.

Discussion

STAR Methods

RESOURCE AVAILABILITY

Lead contact

Material availability

Data and code availability

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Cell lines

Bacterial strains

METHOD DETAILS

Compounds

Ribosome profiling and RNA-Seq

Data analysis

Ribosome profiling:

RNA-Seq:

Translation efficiency:

Translated intron features:

Western blotting

RT-PCR

BONCAT

LC-MS/MS

DNA constructs

pCDNA5/FRT/TO-FTH1, FTH1*, RAB32, RAB32*, FADD, FADD*, IRF2BP2, IRF2BP2*, p27, and p27*

pColdI-mCherry-FTH1 and FTH1*

psiCHECK2-ATP5O

psiCHECK2-HCV-FL

DNA transfection

Recombinant protein purification and imaging

Immunostaining and microscopic analysis

Filter trap assay

Proteotoxic stress reporter transfection and luciferase activity assay

In vitro mTOR kinase assay

RNA interference

Nascent peptide labeling by OP-puro

Reporter mRNA preparation

Luciferase reporter assay

7-Methyl-guanosine (m7G) pulldown assays

QUANTIFICATION AND STATISTICAL ANALYSIS

Supplementary Material

Significance.

Highlights.

Acknowledgments

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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*pCDNA5/FRT/TO-FTH1, FTH1**, *RAB32, RAB32**, *FADD, FADD**, *IRF2BP2, IRF2BP2**, p27, and *p27**

7-Methyl-guanosine (m⁷G) pulldown assays