The DNA in our cells contains the hereditary information that makes each of us unique. Molecules called mRNAs are copies of this information and are used as templates for making proteins. When a strand of incorrectly copied mRNA, or one including errors from the original DNA template, is recognized, our cells destroy the mRNA to prevent it from producing a damaged protein. Organisms from yeast to humans have evolved a mechanism for finding and destroying faulty mRNAs, called mRNA surveillance. Animals are particularly reliant on mRNA surveillance, as their proteins are often made from cutting and pasting together mRNA from different portions of DNA, in a process known as splicing. Despite being a vital process, we still lack a good understanding of how mRNA surveillance works.

Now, Baird et al. used human kidney cells that produced an error-containing mRNA that could be tracked. To investigate how efficient RNA surveillance is under different conditions, the levels of individual proteins were reduced one at a time. By tracking the amount of faulty mRNA, it was possible to find out if a single protein plays a role in human mRNA surveillance. If the number of faulty mRNAs is high when a protein is reduced, it suggests that this protein may be involved in mRNA surveillance.

Baird et al. screened more than 21,000 proteins, the majority of proteins made in human cells. Many of the proteins that stood out as important in mRNA surveillance were the ones already known to be relevant in yeast and worm cells. But the experiments also identified new proteins that appear to play a role specifically in human RNA surveillance. One of the proteins, ICE1, is essential for the relationship between mRNA splicing and mRNA surveillance. Without ICE1, the mRNA surveillance machinery can no longer find and destroy faulty mRNAs.

Nearly one-third of genetic diseases are caused by mutations that result in faulty mRNAs, which can be detected by mRNA surveillance pathways. Depending on the disease, destroying these error-containing mRNAs can either improve or worsen disease symptoms. A better understanding of the factors that control human RNA surveillance could one day help to develop treatments that affect mRNA surveillance to improve disease outcomes.

mRNA decay pathways have evolved to both maintain mRNA quality control and to regulate gene expression. For example, the nonsense-mediated mRNA decay (NMD) pathway was initially described for its role in targeting the destruction of messages harboring premature termination codons (PTCs; [Maquat et al., 1981; Peltz et al., 1993]). The introduction of a PTC can result from genetic mutations or errors in gene expression, ultimately generating a message encoding a potentially deleterious truncated protein. As such, the NMD pathway protects the integrity of the proteome by detecting and destroying PTC-containing messages. In addition to this well-characterized role in quality control surveillance, NMD regulates the expression of ~5–10% of the mammalian transcriptome, highlighting its importance in determining levels of non-aberrant mRNAs (Mendell et al., 2004; Rehwinkel et al., 2005; Tani et al., 2012; Weischenfeldt et al., 2008). These apparently normal mRNAs often contain NMD-triggering features such as upstream open-reading frames (uORFs) in the 5’-UTR, the presence of a long 3’-UTR, or a 3’-UTR that harbors an intron (Karousis et al., 2016; Rebbapragada and Lykke-Andersen, 2009).

NMD is conducted by a conserved set of factors primarily identified via genetic screens in model organisms. The UPF1, 2, and 3 proteins (up-frameshift), discovered in budding yeast, are required for NMD throughout eukaryotes, while the SMG (suppressor with morphogenetic effects on genitalia) proteins were originally identified in C.elegans and provide additional enzymatic and regulatory activities required for NMD in metazoans (Leeds et al., 1991; Pulak and Anderson, 1993); for a recent comprehensive review, see Karousis et al. (2016). UPF1, the central hub of the NMD pathway, is an ATP-dependent RNA helicase that acts at multiple steps in target discrimination and decay. UPF1’s ATPase activity and phosphorylation is enhanced by UPF2 binding, promoting decay (Chakrabarti et al., 2011; Chamieh et al., 2008; Kashima et al., 2006). UPF1 phosphorylation by the PI3K-related kinase SMG1 in the context of translation termination promotes the recruitment of the NMD-specific endonuclease SMG6, as well as the recruitment of the CCR4-NOT deadenylase complex via the SMG5-SMG7 heterodimer (Eberle et al., 2009; Gatfield and Izaurralde, 2004; Huntzinger et al., 2008; Kashima et al., 2006; Loh et al., 2013). UPF1-dependent RNA degradation can thus proceed through a combination of exo- and endonucleolytic pathways.

A second major elaboration of the NMD pathway in vertebrates is the use of the exon junction complex (EJC) to identify potential targets of NMD. The EJC is a tetrameric complex comprising the RNA helicase eIF4AIII, the MAGOH-Y14 heterodimer, and CASC3 (also known as MLN51 and Barentz) and is deposited 20–24 nt upstream of exon-exon junctions during splicing (Le Hir et al., 2000). The stable core participates in multiple stages of mRNA function by engaging in dynamic interactions with ‘peripheral’ EJC factors (Le Hir et al., 2016; Singh et al., 2012), including UPF3B, which binds EJCs consisting of at least eIF4AIII and MAGOH-Y14 through a conserved motif in its C-terminus (Buchwald et al., 2010; Gehring et al., 2003). Once bound in the nucleus, the EJC is believed to escort UPF3B into the cytoplasm, where the complex marks most splice sites prior to translation (Hauer et al., 2016; Saulière et al., 2012; Singh et al., 2012). During translation, scanning and elongating ribosomes displace EJCs in the 5’-leader and coding sequence, respectively (Gehring et al., 2009a), leaving only EJC-UPF3B complexes > 50 nts downstream of the termination codon bound to the mRNA. UPF3B thus provides a link between nuclear mRNA biogenesis and the cytoplasmic NMD machinery, as the N-terminal region of UPF3B binds UPF2, which in turn promotes UPF1 activity and phosphorylation (Chamieh et al., 2008; Kashima et al., 2006). Such mRNAs harboring an EJC sufficiently downstream of a stop codon are subject to ‘EJC-enhanced’ NMD, the branch of the NMD pathway responsible for the most prominent UPF1-dependent decay activities observed in mammalian cells (Bühler et al., 2006; Cheng et al., 1994; Metze et al., 2013; Singh et al., 2008; Wang et al., 2002; Zhang et al., 1998).

Notably, the primary genetic screens responsible for NMD factor identification were conducted in organisms that do not use the EJC to demarcate targets of decay. Further indicating a potential need for an expanded machinery, the human NMD pathway surveils a transcriptome more complex than that found in yeast and worms and has adopted regulatory roles in development and stress responses (Feng et al., 2017; Gong et al., 2009; Karam et al., 2015; Medghalchi et al., 2001; Wittkopp et al., 2009). To search for potential novel human NMD proteins, including those involved in ‘EJC-enhanced’ decay, we used a luciferase reporter to develop a gain-of-signal assay designed based on a well-characterized mRNA β-globin nonsense allele and performed a whole genome RNAi screen (Maquat et al., 1981; Thorne et al., 2010). By depleting >21,000 genes with three siRNAs/gene, we recovered core NMD and EJC factors as top hits, as well as a large cohort of potential NMD factors. From the latter, we used biochemical, genetic, and genomic approaches to validate ICE1 (interactor of little elongation complex ELL subunit 1, also known as KIAA0947) as a novel peripheral EJC factor essential for EJC-enhanced NMD. We show that ICE1 depletion leads to enhanced expression of many NMD targets, including those containing PTCs. Further, our data suggest that ICE1 uses a putative MIF4G domain to interact with mature EJCs and is required for proper association of UPF3B with the EJC core proteins. Importantly, overexpressing UPF3B to restore EJC-UPF3B assembly rescues the effects of ICE1 depletion on NMD, consistent with a model in which ICE1 serves an unanticipated role in linking nuclear EJC assembly to cytoplasmic detection of NMD substrates.

RNAi screens suffer from high rates of false positives arising from sequence-based off-target effects and false negatives due to incomplete protein depletion. Beyond these issues, multiple sources of experimental variance can complicate the identification of true positives. To limit confounding factors inherent to siRNA screens, we designed a bioluminescent gain-of-signal output to identify NMD-associated genes. We chose a luminescence-based reporter system over other assay formats such as fluorescence because luminescence-based assays have superior sensitivity with little or no non-basal background, a significant concern for high-throughput screening (Fan and Wood, 2007). In addition, similar assays using Renilla luciferase have proven to be sensitive and reliable for mechanistic studies of NMD (Boelz et al., 2006; Holbrook et al., 2006; Isken et al., 2008; Woeller et al., 2008). Importantly, our assay strategy results in an increase in bioluminescence with NMD inhibition, thus removing from consideration silenced genes affecting cell viability or gene expression (Hasson et al., 2015; Kaelin, 2017).

To develop the assay used for high-throughput RNAi screening, we constructed a pair of CMV promoter-driven reporters, a control construct encoding 3XFLAG-tagged firefly luciferase fused in-frame with the wild-type β-globin ORF and an NMD-sensitive construct in which a premature termination codon was introduced at β-globin position 39 (Figure 1A). Silencing of critical NMD components by siRNA was expected to inhibit decay of the PTC-containing mRNA, boosting luciferase expression. To confirm that this construct is indeed controlled by the NMD machinery, we co-transfected the plasmids with three UPF1 siRNAs into HEK-293 cells. After a 72 hr incubation, luciferase substrate was added and the luminescence signal was detected. As expected, UPF1 depletion caused a 2- to 3.5-fold increase in luciferase activity from the NMD reporter, relative to the non-silencing siRNA control (Figure 1B). This NMD construct expressed a significant basal level of luciferase activity, potentially allowing identification of factors that inhibit the NMD pathway as well as those required for its proper function. Clonal HEK-293 cell lines stably expressing this construct were prepared and used in subsequent characterization and siRNA library screening. To verify the proper functioning of the assay in this context, an siRNA targeting UPF1 was used as a positive control, exhibiting assay dynamic range suitable for high-throughput screening (z’-factors of >0.5).

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ICE1 depletion increases abundance of transcripts with NMD-inducing features

To assess a possible role for ICE1 in regulating NMD, we performed genome-wide RNAseq analyses of cells transfected with control (siNT), ICE1, and UPF1 siRNAs (Supplementary file 4). We began by evaluating the effect of ICE1 depletion on classes of transcripts with known NMD-inducing features. As the luciferase-β-globin reporter used to identify ICE1 undergoes EJC-stimulated decay, we first examined the impact of ICE1 depletion on transcript isoforms containing stop codons at least 50 nt upstream of the final exon-exon junction (PTCs). For these analyses, we focused on genes for which at least one alternative PTC-containing isoform and one normal isoform were represented in our RNAseq data, finding that ICE1 depletion specifically increased the abundance of the PTC-containing isoforms relative to the normal isoforms (Figure 2A). These data provide initial evidence that ICE1 is important for cellular elimination of many canonical NMD targets.

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To further investigate a role for ICE1 in EJC-dependent NMD, we tested its involvement in regulating the SR protein SRSF2 (also known as SC35). SRSF2 represents a classic example of NMD-based autoregulation of proteins involved in alternative splicing. Accumulation of SRSF2 protein leads to excision of two introns from its own 3’UTR, generating an NMD target mRNA referred to here as SRSF2(NMD+) ([Sureau et al., 2001]; Figure 2B, top schematic). Using previously characterized primers in the SRSF2 3’-UTR to distinguish SRSF2 transcript isoforms ([Hauer et al., 2016]; arrows denote region of amplification), our results recapitulate previous findings that SRSF2(NMD+) is a potent NMD substrate, as depletion of UPF1 resulted in a seven-fold increase in the NMD+ isoform compared to non-targeting controls (Figure 2B, bottom right bar graph). Consistent with our RNAseq studies, depletion of ICE1 resulted in a four-fold increase in the abundance of this NMD target (Figure 2B, bottom right graph), while levels of the unspliced 3’UTR isoform SRSF2(NMD-) were not increased. These findings indicate that increases in the SRSF2(NMD+) isoform are due to disruption of EJC-stimulated NMD rather than increased transcription from the SRSF2 locus.

Transcripts containing uORFs represent a second prominent class of NMD targets (Gardner, 2008; Hurt et al., 2013). If uORFs are efficiently used relative to the major transcript ORF, the uORF TC can be recognized as a premature termination codon and degraded by NMD. We examined genes represented in a translation initiation site (TIS) database based on ribosome profiling data from HEK-293 cells (Wan and Qian, 2014). Of the 4310 genes expressed in our RNAseq data that also met ribosome profiling read coverage cutoffs for inclusion in the TIS database, 1578 showed evidence of uORF translation. As expected of a putative NMD factor, depletion of ICE1 systematically led to increased abundance of mRNAs with empirically identified uORFs, compared to those lacking evidence of uORF translation (Figure 2C). The extent of up-regulation among uORF-containing mRNAs was less than that observed for PTC-containing mRNAs (Figure 2A), possibly because leaky scanning or efficient re-initiation at downstream ORFs is sufficient to stabilize many mRNAs undergoing some degree of uORF translation (Neu-Yilik et al., 2011; Zhang and Maquat, 1997). Because ICE1 participates in snRNA biogenesis as part of the little elongation complex, we asked whether the observed increases in uORF-containing mRNAs could be attributed to alterations in splicing. We detected alternative splicing events using two independent software packages, Majiq and Leafcutter, and excluded any genes found to undergo of splicing changes upon ICE1 depletion by either algorithm, using permissive cutoffs (Supplementary file 5; see Materials and methods for details; [Li et al., 2018; Vaquero-Garcia et al., 2016]). Removing these genes from the analysis of uORF-containing mRNAs had no apparent impact (Figure 2—figure supplement 1A). Likewise, removing the genes for which evidence of a PTC-containing isoform was detected (Figure 2A) had no effect on the relationship between ICE1 depletion and uORF mRNA abundance (Figure 2—figure supplement 1B).

Despite advances in uORF identification, there remain few human transcripts in which relative translation efficiencies of uORFs and primary ORFs are well characterized. For this reason, we chose to validate ICE1’s role in uORF-directed decay by studying key regulators of the Integrated Stress Response (ISR) known to be subject to translational regulation through uORFs that are inhibitory to translation of the downstream codon sequence (CDS). Under normal conditions, expression of both the transcription factor CHOP/DDIT3 and the phosphatase regulatory subunit GADD34/PPP1R15A are impaired by a mechanism involving translation of an inhibitory uORF that prevents downstream reinitiation at the CDS (Young and Wek, 2016). The inability of the ribosome to translate the CDS and displace UPF1 and/or EJCs predisposes these transcripts for NMD (Karam et al., 2015; Schmidt et al., 2015; Weischenfeldt et al., 2008). To investigate the effect of ICE1 depletion on transcript abundance of the ISR members CHOP/DDIT3 and GADD34/PPP1R15A, we performed RT-qPCR on RNA from cells treated with siICE1 or a non-targeting control siRNA. During ICE1 depletion, there was a 2–2.5 fold increase in the abundance of these uORF-containing transcripts (Figure 2D). To ensure that this was not an artifact of ICE1 depletion potentially inducing the ISR, we also measured levels of the downstream ISR member ASNS, which is transcriptionally induced during the ISR but not under translational control (Baird et al., 2014; Barbosa-Tessmann et al., 2000). ICE1 depletion did not increase ASNS levels (Figure 2D), and we did not observe alterations in mRNA processing of these and other genes induced by ICE1 depletion (see below; Figure 2—figure supplement 2), suggesting that the increase in CHOP/DDIT3 and GADD34/PPP1R15A levels was the product of reduced NMD activity.

Due to the large number of human NMD targets made susceptible to decay via long 3’UTRs, we also examined the impact of ICE1 depletion on abundance of transcripts with varying 3’UTR lengths. We stratified genes into quartiles on the basis of the 3’UTR length of their most highly expressed isoforms in siNT-treated cells. The responses of genes with 3’UTRs in the shortest two quartiles (<354 nt and 355–961 nt) were indistinguishable, but ICE1 depletion resulted in significantly increased abundance of genes with 3’UTRs in the longest two quartiles (962–2158 nt and >2158 nt; Figure 2E). The failure of ICE1 depletion to enhance abundance of mRNAs with 3’UTRs of moderate length (355–961 nt) differs from the significantly enhanced accumulation of such mRNAs in UPF1-depleted cells, but further work will be required to determine whether this is due to biological or technical differences (Figure 2—figure supplement 1C). As with the uORF-containing mRNAs, exclusion of mRNAs exhibiting ICE1-dependent splicing changes or expression of PTC-containing isoforms had no effect on the analysis of ICE1 depletion and 3’UTR-dependent changes in mRNA abundance (Figure 2—figure supplement 1D and E and Supplementary file 5). In subsequent qRT-PCR studies, ICE1 knockdown led to increased abundance of some long 3’UTR-containing mRNAs sensitive to UPF1 depletion, including ZNF667 (1855 nt 3’UTR), CDK6 (10,235 nt), and SETD7 (5461 nt) (Figure 2F and Figure 2—figure supplement 2). Together, our RNAseq analyses suggest that ICE1 depletion can disrupt NMD induced by uORFs, PTCs, and long 3’UTRs.

ICE1 depletion stabilizes mRNAs with NMD-inducing features

To investigate whether the observed changes in mRNA abundance were due to increases in mRNA stability consistent with NMD inhibition, we used REMBRANDTS software to infer changes in mRNA stability from the relative abundance of exonic and intronic reads from each gene (Alkallas et al., 2017). The REMBRANDTS algorithm is a refinement of earlier methods based on the idea that a change in exonic reads without a corresponding change in intronic reads is diagnostic of differential RNA stability, while concurrent changes in both exonic and intronic reads suggest altered transcription (Ameur et al., 2011; Gaidatzis et al., 2015; Zeisel et al., 2011). The effects of ICE1 depletion on mRNA abundance and relative stability were highly correlated (Spearman’s ρ=0.69; p<10⁻¹⁵; Figure 3—figure supplement 1A and Supplementary file 6), and transcripts that significantly increased in steady-state abundance with ICE1 knockdown were preferentially stabilized upon ICE1 or UPF1 depletion (Figure 3A and Figure 3—figure supplement 1B). Likewise, mRNAs that were increased in abundance upon UPF1 knockdown tended to be stabilized by ICE1 knockdown or UPF1 knockdown (Figure 3—figure supplement 1C and D). Comparison of relative mRNA stability upon ICE1 depletion with responses to UPF1 or UPF3B siRNAs revealed that the populations of mRNAs stabilized by depletion of these two core NMD factors were stabilized to a similar extent by ICE1 depletion (Figure 3B). Moreover, the mRNAs stabilized in both UPF1 and UPF3B knockdown cells exhibited an enhanced response to ICE1 depletion. Finally, we asked whether ICE1 depletion preferentially stabilizes mRNAs with uORFs or long 3’UTRs, with results that closely mirrored our findings based on steady-state mRNA abundance measurements (compare Figure 2C and E with Figure 3—figure supplement 1E and F).

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To independently assess whether ICE1 affects NMD target mRNA stability, we used a metabolic labeling approach to quantify mRNA half-lives in cells transfected with non-silencing control, UPF1, or ICE1 siRNAs (Dölken, 2013; Russo et al., 2017). Following gene depletion, cells were pulse-labeled with 5-ethynyluridine, a modified nucleoside that allows covalent biotinylation and quantitative recovery of newly transcribed mRNA. Transcript half-lives can then be calculated based on the relative abundance of nascent and total mRNA. As observed in our transcriptome-wide analyses, these metabolic labeling studies indicated that either ICE1 or UPF1 depletion increased the stability of several well-characterized NMD target mRNAs, while failing to stabilize the non-NMD target control ASNS (Figure 3C; Figure 3—figure supplement 2). Together, these results indicate that the effect of ICE1 depletion on steady-state levels of mRNAs with NMD-inducing features is due to increased mRNA stability, consistent with a role for the protein in promoting NMD through UPF1 and UPF3B.

ICE1 associates with the core EJC

Based on our observations implicating ICE1 in NMD, we next tested whether ICE1 associates with NMD or EJC proteins. Whole cell lysates from HEK-293 cells were subjected to immunoprecipitation assays using antibodies against ICE1, with UPF3B antibodies as a positive control for recovery of both the trimeric UPF complex and EJC components. Immunoblot analysis of samples purified with these specific antibodies or control IgGs revealed ICE1 co-purification with the core EJC component eIF4AIII, (Figure 4A), but not NMD proteins UPF1, UPF2, or UPF3B. Conversely, UPF3B co-purified with eIF4AIII, UPF1, and UPF2, but not ICE1. The immunoprecipitation protocol used involves hypotonic lysis and gentle buffer conditions to retain RNA-binding protein interactions with RNAs (see Materials and methods). To further investigate the nature of the interaction between ICE1 and eIF4AIII, we determined whether coprecipitation was disrupted by thorough digestion with an RNase A/T1 cocktail. Indicative of RNA-independent complex formation, ICE1 coprecipitation with eIF4AIII was not perturbed by RNase digestion. Importantly, in the UPF3B-positive control immunopurifications, RNase treatment abolished recovery of ribosomal protein S6 and PABPC1, suggesting that the RNase conditions used efficiently disrupted RNA-mediated interactions (Figure 4A).

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We also observed recovery of CASC3 and MAGOH in samples immunopurified with the antibody against ICE1, indicating that ICE1 interacts with fully assembled EJCs (Figure 4B). Reciprocally, we used monoclonal antibodies against eIF4AIII to coprecipitate ICE1, along with the expected partners UPF3B, MAGOH, and CASC3 (Figure 4C). Of note, we were unable to recover UPF3B or ICE1 from FLAG co-immunoprecipitations when the N- or C-termini of eIF4AIII were tagged, consistent with a previously reported mass spectrometry dataset (data not shown; [Singh et al., 2012]). Furthermore, while we did not observe co-purification of ICE1 and UPF3B using antibodies against endogenous proteins, stably overexpressed 3XMYC-UPF3B recovered ICE1, in a manner dependent on the UPF3B EJC-binding domain (Figure 4—figure supplement 1). These findings indicate that ICE1 can participate in a EJC-UPF3B complex but preferentially associates with fully assembled EJCs not bound to UPF3B.

A putative MIF4G domain mediates ICE1-EJC interactions and inhibits NMD when overexpressed

As described above, ICE1 encodes a putative C-terminal MIF4G domain (Figure 1—figure supplement 4 and Figure 5—figure supplement 1A). To determine if the C-terminus of ICE1 is important for EJC interaction, we transiently expressed 3XFLAG-tagged full-length ICE1 (Figure 5—figure supplement 1A; 3XF-ICE1), a variant lacking the C-terminus (3XF-ICE1 N-term), or the putative C-terminal MIF4G domain (3XF-MIF4G^ICE1). The full-length ICE1 protein and the isolated C-terminus recovered endogenous eIF4AIII and CASC3 above background levels, but the protein lacking the C-terminus failed to enrich for the EJC proteins (Figure 5—figure supplement 1B). To further probe the interaction between the putative MIF4G domain and the EJC, we employed stable Flp-In 293-TREx cell lines that allow for the integration of a single, inducible copy of a transgene of interest. Using stable expression of 3XF-MIF4G^ICE1, we again observed that putative ICE1 MIF4G domain was sufficient for association with eIF4AIII (Figure 5A). This interaction was retained during extensive RNase digestion, consistent with co-immunopurification studies using antibodies against endogenous proteins (Figure 4A).

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As the C-terminal ~30 kDa of ICE1 is sufficient for its interaction with EJC proteins, we asked whether overexpression of the putative MIF4G domain could have a dominant negative effect. Consistent with this hypothesis, stable overexpression of the (3XFLAG-MIF4G^ICE1) protein used for immunoprecipitation experiments caused a reproducible several-fold increase in levels of two mRNAs that responded strongly to both ICE1 and UPF1 knockdown in RNAseq and qRT-PCR experiments, ANXA1 (which contains a putative uORF; [Thierry-Mieg and Thierry-Mieg, 2006]) and CGA (previously identified as a strong target of UPF3B-dependent NMD; (Chan et al., 2007); Figure 5B). We note that this effect is not observed on all NMD targets tested, as the NMD substrate ATF3 was not stabilized during MIF4G overexpression, but is likely only apparent on highly NMD-sensitive transcripts such as ANXA1 and CGA (Figure 5B). Together, these data indicate that ICE1’s interaction with eIF4AIII occurs through its putative MIF4G domain and that its pro-NMD function is disrupted when this domain is expressed in trans.

ICE1 is required for the link between nuclear EJC assembly and cytoplasmic decay

Having observed ICE1 co-purification with endogenous core EJC proteins but not UPF3B, we next investigated whether NMD inhibition upon ICE1 depletion was due to disrupted EJC function. To determine whether ICE1 modulates EJC interactions with peripheral factors, we immunopurified fully assembled EJCs and associated proteins using an antibody against CASC3. In lysates from HEK-293 cells depleted of ICE1 (siICE1), CASC3 co-immunoprecipitated with EJC core members eIF4AIII and MAGOH at comparable levels as in the non-targeting (siNT) control (Figure 6A, left immunoblot, right bar graph illustrating eIF4AIII/CASC3 recovery efficiency quantified from three independent replicates), suggesting that ICE1 is not required for core EJC assembly. Interestingly, further immunoblotting of proteins co-purified with CASC3 showed a drastic reduction in the ability of the mature EJC to associate with NMD factors UPF3B and UPF2 when ICE1 was depleted. Following ICE1 depletion, CASC3 coprecipitated ~70% less UPF3B and, by association, nearly undetectable levels of UPF2 as compared to the non-targeting control (Figure 6A, left immunoblot, right bar graph illustrating UPF3B/CASC3 recovery efficiency quantified from three independent replicates). We observed similar disruption of UPF3B-EJC interactions upon overexpression of the putative ICE1 MIF4G domain, suggesting that the phenotypes arising from these interventions share a mechanistic basis (Figure 6—figure supplement 1).

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The defect in UPF3B-EJC association in the absence of ICE1 could be due to impaired RNP assembly in the nucleus or failure to retain interactions in the cytoplasm. To begin to explore the role of ICE1 in regulating EJC-UPF3B interactions, we examined the localization of stably expressed GFP-tagged UPF3B in cells treated with control or ICE1 siRNAs. Following siRNA transfection, we marked nuclei with Hoechst stain and subjected cells to imaging flow cytometry (Figure 6B). This approach allowed quantitative determination of the nuclear-cytoplasmic distribution of GFP-UPF3B in thousands of cells. In control cells, GFP-UPF3B was predominantly nuclear, but prominent diffuse cytoplasmic signal could also be observed, resulting in a median nuclear:cytoplasmic ratio of 2.89 ± 0.85 S.E.M. In contrast, ICE1 depletion caused UPF3B to accumulate in nuclei and be depleted from the cytoplasm. In knockdown cells, the median nuclear:cytoplasmic ratio shifted significantly to 5.59 ± 2.65 S.E.M. (Mann Whitney test, p<10⁻¹⁵), presumably decreasing UPF3B’s capacity to interact with mRNA targets of decay. It is thought that UPF3B is exported from the nucleus in association with mRNA-bound EJCs (Gehring et al., 2009b). To test whether reduced EJC binding contributes to increased nuclear accumulation of UPF3B, we analyzed the subcellular localization of the wild-type GFP-UPF3B and a mutant protein partially defective for EJC binding (GFP-UPF3BΔ412-434 [Chamieh et al., 2008; Gehring et al., 2003]). The EJC-binding impaired UPF3B exhibited a modest defect in co-immunoprecipitation with eIF4AIII and significantly enhanced nuclear localization, consistent with the results from ICE1 knockdown cells (Figure 6—figure supplement 2). These findings suggest that inability of UPF3B to interact with EJCs may in part account for lack of export to the cytoplasm in the absence of ICE1.

UPF3B overexpression overcomes ICE1 depletion

Previous studies have shown that a UPF3B peptide can interact directly with EJCs assembled in vitro, albeit with modest affinity (~10 μM; [Buchwald et al., 2010]). Our data suggest that ICE1 promotes association of UPF3B with the EJC, raising the possibility that ICE1 depletion may be overcome by boosting UPF3B levels. To test this hypothesis, we depleted ICE1 from parental HEK-293 cells or a cell line stably over-expressing 3XFLAG-UPF3B and assessed the ability of CASC3 to co-purify tagged and endogenous UPF3B. As shown in (Figure 6A), ICE1 depletion from parental cells caused a decrease in UPF3B recovery with CASC3 (Figure 7A, lanes 2 and 4). However, this defect in association could be rescued by overexpressing 3XFLAG-UPF3B (Figure 7A, lanes 6 and 8). To determine whether restoration of the EJC-UPF3B interaction could also enhance NMD, we assayed the effect of ICE1 depletion on several NMD target mRNAs in parental and UPF3B-overexpression lines. As above, ICE1 depletion caused an increase in levels of ATF3, GADD45B, GAS5, and ANXA1 mRNAs. Importantly, over-expressing exogenous UPF3B partially rescued the NMD phenotype, causing significant reductions in the abundance of the EJC-mediated substrates (Figure 7B). Consistent with previous findings, overexpression of UPF3B in the presence of ICE1 did not affect NMD target levels (Huang et al., 2011), suggesting that the rescue of ICE1 depletion was not simply due to enhanced NMD efficiency. Together, the biochemical and functional rescue of ICE1 depletion by UPF3B overexpression indicates that the loss of NMD in the absence of ICE1 is due to a failure in UPF3B-EJC assembly or stability.

Figure 7

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Beginning from whole-genome RNAi screening for proteins involved in the human NMD pathway, we present evidence that ICE1 is a EJC-associated protein that promotes UPF3B-EJC association and regulation of a large swath of NMD targets. This discovery was enabled by the CSA approach, which minimizes the impact of off-target effects mediated by siRNA seed sequences (Marine et al., 2012). Indicative of the potential to identify novel NMD factors, this strategy resulted in high levels of enrichment of proteins known to be involved in NMD. The screen was particularly well suited to identification of factors involved in EJC-enhanced NMD, with all four core EJC components and two previously known assembly factors exhibiting high median seed-corrected Z-scores, in addition to the newly identified EJC-interacting factor ICE1. We focus on the role of ICE1 in this study, but we expect that the screen data, particularly when combined with a recent CRISPR-based screen (Alexandrov et al., 2017), will provide a valuable resource for further investigation of novel NMD factors.

ICE1 depletion results in increased abundance of mRNAs selected for NMD on the basis of stop codons that violate the 50–55 nt rule, uORFs, and 3’UTR length. While decay of the former class is clearly stimulated by the presence of EJCs, the uORF class likely comprises a mix of transcripts that undergo EJC-dependent and EJC-independent decay, depending on transcript architecture, uORF translation frequency, and other factors. In our RNAseq studies, the largest effects of ICE1 loss appear to be on EJC-enhanced NMD, but our data also suggest a role for the protein in promoting decay of transcripts with longer than normal 3’UTRs. Even among the core NMD factors UPF2 and UPF3B, differential substrate preferences have been observed, leading to the proposal that there are multiple ‘branches’ of the NMD pathway, each with distinct cofactor requirements (Chan et al., 2007; Gehring et al., 2005; Huang et al., 2011; Metze et al., 2013). As yet, however, the RNA features underlying susceptibility to the various proposed NMD sub-pathways are not understood.

Biochemical and functional assays suggest that ICE1 functions in NMD by promoting the proper association of UPF3B with EJCs (Figure 7C). UPF2 association with EJCs is also decreased in ICE1 knockdown cells, presumably due to the inability of UPF3B to act as a bridging factor (Chamieh et al., 2008). In addition to the defect in UPF3B-EJC association, we also observed reduced accumulation of UPF3B in the cytoplasm of cells depleted of ICE1 (Figure 6B). Since UPF3B assembly with EJCs is thought to occur in the nucleus, enhanced UPF3B nuclear localization is unlikely to account for the observed defect in EJC binding. Instead, our data indicate that reduced export of UPF3B may be in part a consequence of decreased association with EJCs in the nucleus, preventing export of UPF3B with mRNPs. Alternatively, decreased stability of the UPF3B-EJC association could result in more rapid re-import of UPF3B. In turn, the reduced abundance of UPF3B in the cytoplasm may have the side-effect of impairing long 3’UTR-mediated NMD, explaining the apparent protection of such targets upon ICE1 depletion. Promotion of efficient UPF3B nuclear export by the EJC could also be necessary for its newly identified function in translation termination and help to explain why depletion of EJC factors has been observed to affect the decay of well-characterized 3’UTR length-dependent NMD targets (Huang et al., 2011; Neu-Yilik et al., 2017).

Together, our data are consistent with a model in which ICE1 helps to prepare newly assembled EJCs for association with UPF3B (Figure 7C). We find that ICE1 interacts with EJC components at endogenous levels, but have not observed an interaction between the endogenous ICE1 and UPF3B proteins. As these experiments were carried out under native conditions, we cannot exclude the possibility that this is in part due to dissociation during isolation. Arguing against this scenario, we readily observe UPF3B co-immunoprecipitation with the EJC under the same purification conditions and can detect co-purification of UPF3B with ICE1 when either UPF3B or the ICE1 C-terminus is overexpressed. Overexpressed UPF3B lacking the ability to bind EJCs also fails to associate with ICE1, leading us to propose that ICE1 and UPF3B can concurrently interact with the EJC but that this complex is scarce or absent under normal cellular conditions. Together, our data suggest that ICE1 transiently interacts with fully assembled EJCs to promote UPF3B-EJC association. Possible effects of ICE1 binding to EJCs could be increased efficiency of EJC maturation or altered post-translational modification of EJC proteins, each of which could have the effect of increasing UPF3B recruitment or the stability of the complex. Alternatively, transient interactions between ICE1 and UPF3B could enhance UPF3B recruitment to EJCs.

With increasing organismal complexity, the NMD pathway has evolved to use the exon junction complex to more efficiently discriminate between aberrant and normal mRNAs. The addition of components to the pathway in turn presents new opportunities for regulatory fine-tuning of decay. Interestingly, duplication of the ancestral UPF3 gene to yield UPF3A and UPF3B proteins has recently been shown to enable cell-type-specific control of NMD (Shum et al., 2016). UPF3A has a reduced capacity to interact with EJCs but retains important UPF2-interacting residues, allowing it to disrupt UPF3B activity by competing for UPF2 binding. Our observations suggest that the association of UPF3B with the EJC is also a potential target for regulatory control of NMD. In this case, interference with ICE1 function appears to leave the UPF2-UPF3B interaction intact (Figure 6—figure supplement 1), while reducing the ability of UPF3B to associate with EJCs (Figure 6 and Figure 6—figure supplement 1). Since UPF3A and ICE1 affect distinct UPF3B interactions, they could be independently or concurrently used for cell-type- or condition-specific regulation of NMD. Further work will be required to understand whether ICE1 is subject to regulation, but our findings clearly point to an opportunity for cells and/or therapeutic interventions to manipulate ICE1 to decouple the EJC’s role in NMD from its contributions to pre-mRNA splicing, export, localization, and translation.

1. 1. Baird TD
   2. Cheng KC-C
   3. Chen Y-C
   4. Buehler E
   5. Martin SE
   6. Inglese J
   7. Hogg JR

   (2018) ICE1 promotes the link between splicing and nonsense- mediated mRNA decay

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE105436).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105436
2. 1. Alexandrov A
   2. Shu MD
   3. Steitz JA

   (2017) Screening GeCKO lentiCRISPR knockout sgRNA library in human Fireworks HeLa cells to identify components and regulators of the human nonsense-mediated mRNA decay pathway

   Publicly available at NCBI BioProject (accession no: PRJNA353310).

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA353310/

1. 1. Alexandrov A
   2. Shu MD
   3. Steitz JA

   (2017) Fluorescence amplification method for forward genetic discovery of factors in human mRNA degradation

   Molecular Cell 65:191–201.

   https://doi.org/10.1016/j.molcel.2016.11.032

   • PubMed
   • Google Scholar
2. 1. Alkallas R
   2. Fish L
   3. Goodarzi H
   4. Najafabadi HS

   (2017) Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer's disease

   Nature Communications 8:909.

   https://doi.org/10.1038/s41467-017-00867-z

   • PubMed
   • Google Scholar
3. 1. Ameur A
   2. Zaghlool A
   3. Halvardson J
   4. Wetterbom A
   5. Gyllensten U
   6. Cavelier L
   7. Feuk L

   (2011) Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain

   Nature Structural & Molecular Biology 18:1435–1440.

   https://doi.org/10.1038/nsmb.2143

   • PubMed
   • Google Scholar
4. 1. Anders S
   2. Pyl PT
   3. Huber W

   (2015) HTSeq--a Python framework to work with high-throughput sequencing data

   Bioinformatics 31:166–169.

   https://doi.org/10.1093/bioinformatics/btu638

   • PubMed
   • Google Scholar
5. 1. Auld DS
   2. Inglese J

   (2016)

   Assay Guidance Manual

   Interferences with Luciferase Reporter Enzymes, Assay Guidance Manual, Bethesda, Eli Lilly & Company and the National Center for Advancing Translational Sciences.

   • Google Scholar
6. 1. Baird TD
   2. Palam LR
   3. Fusakio ME
   4. Willy JA
   5. Davis CM
   6. McClintick JN
   7. Anthony TG
   8. Wek RC

   (2014) Selective mRNA translation during eIF2 phosphorylation induces expression of IBTKα

   Molecular Biology of the Cell 25:1686–1697.

   https://doi.org/10.1091/mbc.E14-02-0704

   • PubMed
   • Google Scholar
7. 1. Barbosa I
   2. Haque N
   3. Fiorini F
   4. Barrandon C
   5. Tomasetto C
   6. Blanchette M
   7. Le Hir H

   (2012) Human CWC22 escorts the helicase eIF4AIII to spliceosomes and promotes exon junction complex assembly

   Nature Structural & Molecular Biology 19:983–990.

   https://doi.org/10.1038/nsmb.2380

   • PubMed
   • Google Scholar
8. 1. Barbosa-Tessmann IP
   2. Chen C
   3. Zhong C
   4. Siu F
   5. Schuster SM
   6. Nick HS
   7. Kilberg MS

   (2000) Activation of the human asparagine synthetase gene by the amino acid response and the endoplasmic reticulum stress response pathways occurs by common genomic elements

   Journal of Biological Chemistry 275:26976–26985.

   https://doi.org/10.1074/jbc.M000004200

   • PubMed
   • Google Scholar
9. 1. Boelz S
   2. Neu-Yilik G
   3. Gehring NH
   4. Hentze MW
   5. Kulozik AE

   (2006) A chemiluminescence-based reporter system to monitor nonsense-mediated mRNA decay

   Biochemical and Biophysical Research Communications 349:186–191.

   https://doi.org/10.1016/j.bbrc.2006.08.017

   • PubMed
   • Google Scholar
10. 1. Bray NL
    2. Pimentel H
    3. Melsted P
    4. Pachter L

    (2016) Near-optimal probabilistic RNA-seq quantification

    Nature Biotechnology 34:525–527.

    https://doi.org/10.1038/nbt.3519

    • PubMed
    • Google Scholar
11. 1. Buchwald G
    2. Ebert J
    3. Basquin C
    4. Sauliere J
    5. Jayachandran U
    6. Bono F
    7. Le Hir H
    8. Conti E

    (2010) Insights into the recruitment of the NMD machinery from the crystal structure of a core EJC-UPF3b complex

    PNAS 107:10050–10055.

    https://doi.org/10.1073/pnas.1000993107

    • PubMed
    • Google Scholar
12. 1. Buchwald G
    2. Schüssler S
    3. Basquin C
    4. Le Hir H
    5. Conti E

    (2013) Crystal structure of the human eIF4AIII-CWC22 complex shows how a DEAD-box protein is inhibited by a MIF4G domain

    PNAS 110:E4611–E4618.

    https://doi.org/10.1073/pnas.1314684110

    • PubMed
    • Google Scholar
13. 1. Bühler M
    2. Steiner S
    3. Mohn F
    4. Paillusson A
    5. Mühlemann O

    (2006) EJC-independent degradation of nonsense immunoglobulin-mu mRNA depends on 3' UTR length

    Nature Structural & Molecular Biology 13:462–464.

    https://doi.org/10.1038/nsmb1081

    • PubMed
    • Google Scholar
14. 1. Chakrabarti S
    2. Jayachandran U
    3. Bonneau F
    4. Fiorini F
    5. Basquin C
    6. Domcke S
    7. Le Hir H
    8. Conti E

    (2011) Molecular mechanisms for the RNA-dependent ATPase activity of Upf1 and its regulation by Upf2

    Molecular Cell 41:693–703.

    https://doi.org/10.1016/j.molcel.2011.02.010

    • PubMed
    • Google Scholar
15. 1. Chamieh H
    2. Ballut L
    3. Bonneau F
    4. Le Hir H

    (2008) NMD factors UPF2 and UPF3 bridge UPF1 to the exon junction complex and stimulate its RNA helicase activity

    Nature Structural & Molecular Biology 15:85–93.

    https://doi.org/10.1038/nsmb1330

    • PubMed
    • Google Scholar
16. 1. Chan WK
    2. Huang L
    3. Gudikote JP
    4. Chang YF
    5. Imam JS
    6. MacLean JA
    7. Wilkinson MF

    (2007) An alternative branch of the nonsense-mediated decay pathway

    The EMBO Journal 26:1820–1830.

    https://doi.org/10.1038/sj.emboj.7601628

    • PubMed
    • Google Scholar
17. 1. Cheng J
    2. Belgrader P
    3. Zhou X
    4. Maquat LE

    (1994) Introns are cis effectors of the nonsense-codon-mediated reduction in nuclear mRNA abundance

    Molecular and Cellular Biology 14:6317–6325.

    https://doi.org/10.1128/MCB.14.9.6317

    • PubMed
    • Google Scholar
18. 1. Dölken L

    (2013) High resolution gene expression profiling of RNA synthesis, processing, and decay by metabolic labeling of newly transcribed RNA using 4-thiouridine

    Methods in molecular biology 1064:91–100.

    https://doi.org/10.1007/978-1-62703-601-6_6

    • PubMed
    • Google Scholar
19. 1. Eberle AB
    2. Lykke-Andersen S
    3. Mühlemann O
    4. Jensen TH

    (2009) SMG6 promotes endonucleolytic cleavage of nonsense mRNA in human cells

    Nature Structural & Molecular Biology 16:49–55.

    https://doi.org/10.1038/nsmb.1530

    • PubMed
    • Google Scholar
20. 1. Fan F
    2. Wood KV

    (2007) Bioluminescent assays for high-throughput screening

    ASSAY and Drug Development Technologies 5:127–136.

    https://doi.org/10.1089/adt.2006.053

    • PubMed
    • Google Scholar
21. 1. Feng Q
    2. Jagannathan S
    3. Bradley RK

    (2017) The RNA surveillance factor UPF1 represses myogenesis via Its E3 ubiquitin ligase activity

    Molecular Cell 67:239–251.

    https://doi.org/10.1016/j.molcel.2017.05.034

    • PubMed
    • Google Scholar
22. 1. Gaidatzis D
    2. Burger L
    3. Florescu M
    4. Stadler MB

    (2015) Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

    Nature Biotechnology 33:722–729.

    https://doi.org/10.1038/nbt.3269

    • PubMed
    • Google Scholar
23. 1. Gardner LB

    (2008) Hypoxic inhibition of nonsense-mediated RNA decay regulates gene expression and the integrated stress response

    Molecular and Cellular Biology 28:3729–3741.

    https://doi.org/10.1128/MCB.02284-07

    • PubMed
    • Google Scholar
24. 1. Gatfield D
    2. Izaurralde E

    (2004) Nonsense-mediated messenger RNA decay is initiated by endonucleolytic cleavage in Drosophila

    Nature 429:575–578.

    https://doi.org/10.1038/nature02559

    • PubMed
    • Google Scholar
25. 1. Ge Z
    2. Quek BL
    3. Beemon KL
    4. Hogg JR

    (2016) Polypyrimidine tract binding protein 1 protects mRNAs from recognition by the nonsense-mediated mRNA decay pathway

    eLife 5:e11155.

    https://doi.org/10.7554/eLife.11155

    • PubMed
    • Google Scholar
26. 1. Gehring NH
    2. Kunz JB
    3. Neu-Yilik G
    4. Breit S
    5. Viegas MH
    6. Hentze MW
    7. Kulozik AE

    (2005) Exon-junction complex components specify distinct routes of nonsense-mediated mRNA decay with differential cofactor requirements

    Molecular Cell 20:65–75.

    https://doi.org/10.1016/j.molcel.2005.08.012

    • PubMed
    • Google Scholar
27. 1. Gehring NH
    2. Lamprinaki S
    3. Hentze MW
    4. Kulozik AE

    (2009b) The hierarchy of exon-junction complex assembly by the spliceosome explains key features of mammalian nonsense-mediated mRNA decay

    PLoS Biology 7:e1000120.

    https://doi.org/10.1371/journal.pbio.1000120

    • PubMed
    • Google Scholar
28. 1. Gehring NH
    2. Lamprinaki S
    3. Kulozik AE
    4. Hentze MW

    (2009a) Disassembly of exon junction complexes by PYM

    Cell 137:536–548.

    https://doi.org/10.1016/j.cell.2009.02.042

    • PubMed
    • Google Scholar
29. 1. Gehring NH
    2. Neu-Yilik G
    3. Schell T
    4. Hentze MW
    5. Kulozik AE

    (2003) Y14 and hUpf3b form an NMD-activating complex

    Molecular Cell 11:939–949.

    https://doi.org/10.1016/S1097-2765(03)00142-4

    • PubMed
    • Google Scholar
30. 1. Gong C
    2. Kim YK
    3. Woeller CF
    4. Tang Y
    5. Maquat LE

    (2009) SMD and NMD are competitive pathways that contribute to myogenesis: effects on PAX3 and myogenin mRNAs

    Genes & Development 23:54–66.

    https://doi.org/10.1101/gad.1717309

    • PubMed
    • Google Scholar
31. 1. Haque N
    2. Ouda R
    3. Chen C
    4. Ozato K
    5. Hogg JR

    (2018) ZFR coordinates crosstalk between RNA decay and transcription in innate immunity

    Nature Communications 9:.

    https://doi.org/10.1038/s41467-018-03326-5

    • Google Scholar
32. 1. Hasson SA
    2. Fogel AI
    3. Wang C
    4. MacArthur R
    5. Guha R
    6. Heman-Ackah S
    7. Martin S
    8. Youle RJ
    9. Inglese J

    (2015) Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter

    ACS Chemical Biology 10:1188–1197.

    https://doi.org/10.1021/cb5010417

    • PubMed
    • Google Scholar
33. 1. Hauer C
    2. Sieber J
    3. Schwarzl T
    4. Hollerer I
    5. Curk T
    6. Alleaume AM
    7. Hentze MW
    8. Kulozik AE

    (2016) Exon junction complexes show a distributional bias toward alternatively spliced mrnas and against mrnas coding for ribosomal proteins

    Cell Reports 16:1588–1603.

    https://doi.org/10.1016/j.celrep.2016.06.096

    • PubMed
    • Google Scholar
34. 1. Hogg JR
    2. Collins K

    (2007) RNA-based affinity purification reveals 7SK RNPs with distinct composition and regulation

    RNA 13:868–880.

    https://doi.org/10.1261/rna.565207

    • PubMed
    • Google Scholar
35. 1. Holbrook JA
    2. Neu-Yilik G
    3. Gehring NH
    4. Kulozik AE
    5. Hentze MW

    (2006) Internal ribosome entry sequence-mediated translation initiation triggers nonsense-mediated decay

    EMBO reports 7:722–726.

    https://doi.org/10.1038/sj.embor.7400721

    • PubMed
    • Google Scholar
36. 1. Hu D
    2. Smith ER
    3. Garruss AS
    4. Mohaghegh N
    5. Varberg JM
    6. Lin C
    7. Jackson J
    8. Gao X
    9. Saraf A
    10. Florens L
    11. Washburn MP
    12. Eissenberg JC
    13. Shilatifard A

    (2013) The little elongation complex functions at initiation and elongation phases of snRNA gene transcription

    Molecular Cell 51:493–505.

    https://doi.org/10.1016/j.molcel.2013.07.003

    • PubMed
    • Google Scholar
37. 1. Huang L
    2. Lou CH
    3. Chan W
    4. Shum EY
    5. Shao A
    6. Stone E
    7. Karam R
    8. Song HW
    9. Wilkinson MF

    (2011) RNA homeostasis governed by cell type-specific and branched feedback loops acting on NMD

    Molecular Cell 43:950–961.

    https://doi.org/10.1016/j.molcel.2011.06.031

    • PubMed
    • Google Scholar
38. 1. Huntzinger E
    2. Kashima I
    3. Fauser M
    4. Saulière J
    5. Izaurralde E

    (2008) SMG6 is the catalytic endonuclease that cleaves mRNAs containing nonsense codons in metazoan

    RNA 14:2609–2617.

    https://doi.org/10.1261/rna.1386208

    • PubMed
    • Google Scholar
39. 1. Hurt JA
    2. Robertson AD
    3. Burge CB

    (2013) Global analyses of UPF1 binding and function reveal expanded scope of nonsense-mediated mRNA decay

    Genome Research 23:1636–1650.

    https://doi.org/10.1101/gr.157354.113

    • PubMed
    • Google Scholar
40. 1. Hutten S
    2. Chachami G
    3. Winter U
    4. Melchior F
    5. Lamond AI

    (2014) A role for the Cajal-body-associated SUMO isopeptidase USPL1 in snRNA transcription mediated by RNA polymerase II

    Journal of Cell Science 127:1065–1078.

    https://doi.org/10.1242/jcs.141788

    • PubMed
    • Google Scholar
41. 1. Ideue T
    2. Sasaki YT
    3. Hagiwara M
    4. Hirose T

    (2007) Introns play an essential role in splicing-dependent formation of the exon junction complex

    Genes & Development 21:1993–1998.

    https://doi.org/10.1101/gad.1557907

    • PubMed
    • Google Scholar
42. 1. Isken O
    2. Kim YK
    3. Hosoda N
    4. Mayeur GL
    5. Hershey JW
    6. Maquat LE

    (2008) Upf1 phosphorylation triggers translational repression during nonsense-mediated mRNA decay

    Cell 133:314–327.

    https://doi.org/10.1016/j.cell.2008.02.030

    • PubMed
    • Google Scholar
43. 1. Kadlec J
    2. Izaurralde E
    3. Cusack S

    (2004) The structural basis for the interaction between nonsense-mediated mRNA decay factors UPF2 and UPF3

    Nature Structural & Molecular Biology 11:330–337.

    https://doi.org/10.1038/nsmb741

    • PubMed
    • Google Scholar
44. 1. Kaelin WG

    (2017) Common pitfalls in preclinical cancer target validation

    Nature Reviews Cancer 17:425–440.

    https://doi.org/10.1038/nrc.2017.32

    • PubMed
    • Google Scholar
45. 1. Karam R
    2. Lou CH
    3. Kroeger H
    4. Huang L
    5. Lin JH
    6. Wilkinson MF

    (2015) The unfolded protein response is shaped by the NMD pathway

    EMBO Reports 16:599–609.

    https://doi.org/10.15252/embr.201439696

    • PubMed
    • Google Scholar
46. 1. Karousis ED
    2. Nasif S
    3. Mühlemann O

    (2016) Nonsense-mediated mRNA decay: novel mechanistic insights and biological impact

    Wiley Interdisciplinary Reviews: RNA 7:661–682.

    https://doi.org/10.1002/wrna.1357

    • PubMed
    • Google Scholar
47. 1. Kashima I
    2. Yamashita A
    3. Izumi N
    4. Kataoka N
    5. Morishita R
    6. Hoshino S
    7. Ohno M
    8. Dreyfuss G
    9. Ohno S

    (2006) Binding of a novel SMG-1-Upf1-eRF1-eRF3 complex (SURF) to the exon junction complex triggers Upf1 phosphorylation and nonsense-mediated mRNA decay

    Genes & Development 20:355–367.

    https://doi.org/10.1101/gad.1389006

    • PubMed
    • Google Scholar
48. 1. Kelley LA
    2. Mezulis S
    3. Yates CM
    4. Wass MN
    5. Sternberg MJ

    (2015) The Phyre2 web portal for protein modeling, prediction and analysis

    Nature Protocols 10:845–858.

    https://doi.org/10.1038/nprot.2015.053

    • PubMed
    • Google Scholar
49. 1. Kim D
    2. Langmead B
    3. Salzberg SL

    (2015) HISAT: a fast spliced aligner with low memory requirements

    Nature Methods 12:357–360.

    https://doi.org/10.1038/nmeth.3317

    • PubMed
    • Google Scholar
50. 1. Kim YK
    2. Furic L
    3. Desgroseillers L
    4. Maquat LE

    (2005) Mammalian Staufen1 recruits Upf1 to specific mRNA 3'UTRs so as to elicit mRNA decay

    Cell 120:195–208.

    https://doi.org/10.1016/j.cell.2004.11.050

    • PubMed
    • Google Scholar
51. 1. Le Hir H
    2. Izaurralde E
    3. Maquat LE
    4. Moore MJ

    (2000) The spliceosome deposits multiple proteins 20-24 nucleotides upstream of mRNA exon-exon junctions

    The EMBO Journal 19:6860–6869.

    https://doi.org/10.1093/emboj/19.24.6860

    • PubMed
    • Google Scholar
52. 1. Le Hir H
    2. Saulière J
    3. Wang Z

    (2016) The exon junction complex as a node of post-transcriptional networks

    Nature Reviews Molecular Cell Biology 17:41–54.

    https://doi.org/10.1038/nrm.2015.7

    • PubMed
    • Google Scholar
53. 1. Leeds P
    2. Peltz SW
    3. Jacobson A
    4. Culbertson MR

    (1991) The product of the yeast UPF1 gene is required for rapid turnover of mRNAs containing a premature translational termination codon

    Genes & Development 5:2303–2314.

    https://doi.org/10.1101/gad.5.12a.2303

    • PubMed
    • Google Scholar
54. 1. Li W
    2. Xu H
    3. Xiao T
    4. Cong L
    5. Love MI
    6. Zhang F
    7. Irizarry RA
    8. Liu JS
    9. Brown M
    10. Liu XS

    (2014) MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

    Genome Biology 15:554.

    https://doi.org/10.1186/s13059-014-0554-4

    • PubMed
    • Google Scholar
55. 1. Li YI
    2. Knowles DA
    3. Humphrey J
    4. Barbeira AN
    5. Dickinson SP
    6. Im HK
    7. Pritchard JK

    (2018) Annotation-free quantification of RNA splicing using LeafCutter

    Nature Genetics 50:151–158.

    https://doi.org/10.1038/s41588-017-0004-9

    • PubMed
    • Google Scholar
56. 1. Loh B
    2. Jonas S
    3. Izaurralde E

    (2013) The SMG5-SMG7 heterodimer directly recruits the CCR4-NOT deadenylase complex to mRNAs containing nonsense codons via interaction with POP2

    Genes & Development 27:2125–2138.

    https://doi.org/10.1101/gad.226951.113

    • PubMed
    • Google Scholar
57. 1. Maquat LE
    2. Kinniburgh AJ
    3. Rachmilewitz EA
    4. Ross J

    (1981) Unstable beta-globin mRNA in mRNA-deficient beta o thalassemia

    Cell 27:543–553.

    https://doi.org/10.1016/0092-8674(81)90396-2

    • PubMed
    • Google Scholar
58. 1. Marine S
    2. Bahl A
    3. Ferrer M
    4. Buehler E

    (2012) Common seed analysis to identify off-target effects in siRNA screens

    Journal of Biomolecular Screening 17:370–378.

    https://doi.org/10.1177/1087057111427348

    • PubMed
    • Google Scholar
59. 1. Martin S
    2. Buehler G
    3. Ang KL
    4. Feroze F
    5. Ganji G
    6. Li Y

    (2012)

    Assay Guidance Manual

    Cell-Based RNAi Assay Development for HTS, Assay Guidance Manual, Bethesda, Eli Lilly & Company and the National Center for Advancing Translational Sciences.

    • Google Scholar
60. 1. Medghalchi SM
    2. Frischmeyer PA
    3. Mendell JT
    4. Kelly AG
    5. Lawler AM
    6. Dietz HC

    (2001) Rent1, a trans-effector of nonsense-mediated mRNA decay, is essential for mammalian embryonic viability

    Human Molecular Genetics 10:99–105.

    https://doi.org/10.1093/hmg/10.2.99

    • PubMed
    • Google Scholar
61. 1. Mendell JT
    2. Sharifi NA
    3. Meyers JL
    4. Martinez-Murillo F
    5. Dietz HC

    (2004) Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise

    Nature Genetics 36:1073–1078.

    https://doi.org/10.1038/ng1429

    • PubMed
    • Google Scholar
62. 1. Metze S
    2. Herzog VA
    3. Ruepp MD
    4. Mühlemann O

    (2013) Comparison of EJC-enhanced and EJC-independent NMD in human cells reveals two partially redundant degradation pathways

    RNA 19:1432–1448.

    https://doi.org/10.1261/rna.038893.113

    • PubMed
    • Google Scholar
63. 1. Neu-Yilik G
    2. Amthor B
    3. Gehring NH
    4. Bahri S
    5. Paidassi H
    6. Hentze MW
    7. Kulozik AE

    (2011) Mechanism of escape from nonsense-mediated mRNA decay of human beta-globin transcripts with nonsense mutations in the first exon

    RNA 17:843–854.

    https://doi.org/10.1261/rna.2401811

    • PubMed
    • Google Scholar
64. 1. Neu-Yilik G
    2. Raimondeau E
    3. Eliseev B
    4. Yeramala L
    5. Amthor B
    6. Deniaud A
    7. Huard K
    8. Kerschgens K
    9. Hentze MW
    10. Schaffitzel C
    11. Kulozik AE

    (2017) Dual function of UPF3B in early and late translation termination

    The EMBO Journal 36:2968–2986.

    https://doi.org/10.15252/embj.201797079

    • PubMed
    • Google Scholar
65. 1. Niknafs YS
    2. Pandian B
    3. Iyer HK
    4. Chinnaiyan AM
    5. Iyer MK

    (2017) TACO produces robust multisample transcriptome assemblies from RNA-seq

    Nature Methods 14:68–70.

    https://doi.org/10.1038/nmeth.4078

    • PubMed
    • Google Scholar
66. 1. Peltz SW
    2. Brown AH
    3. Jacobson A

    (1993) mRNA destabilization triggered by premature translational termination depends on at least three cis-acting sequence elements and one trans-acting factor

    Genes & Development 7:1737–1754.

    https://doi.org/10.1101/gad.7.9.1737

    • PubMed
    • Google Scholar
67. 1. Pertea M
    2. Pertea GM
    3. Antonescu CM
    4. Chang TC
    5. Mendell JT
    6. Salzberg SL

    (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads

    Nature Biotechnology 33:290–295.

    https://doi.org/10.1038/nbt.3122

    • PubMed
    • Google Scholar
68. 1. Pimentel H
    2. Bray NL
    3. Puente S
    4. Melsted P
    5. Pachter L

    (2017) Differential analysis of RNA-seq incorporating quantification uncertainty

    Nature Methods 14:687–690.

    https://doi.org/10.1038/nmeth.4324

    • PubMed
    • Google Scholar
69. 1. Pulak R
    2. Anderson P

    (1993) mRNA surveillance by the Caenorhabditis elegans smg genes

    Genes & Development 7:1885–1897.

    https://doi.org/10.1101/gad.7.10.1885

    • PubMed
    • Google Scholar
70. 1. Rebbapragada I
    2. Lykke-Andersen J

    (2009) Execution of nonsense-mediated mRNA decay: what defines a substrate?

    Current Opinion in Cell Biology 21:394–402.

    https://doi.org/10.1016/j.ceb.2009.02.007

    • PubMed
    • Google Scholar
71. 1. Rehwinkel J
    2. Letunic I
    3. Raes J
    4. Bork P
    5. Izaurralde E

    (2005) Nonsense-mediated mRNA decay factors act in concert to regulate common mRNA targets

    RNA 11:1530–1544.

    https://doi.org/10.1261/rna.2160905

    • PubMed
    • Google Scholar
72. 1. Russo J
    2. Heck AM
    3. Wilusz J
    4. Wilusz CJ

    (2017) Metabolic labeling and recovery of nascent RNA to accurately quantify mRNA stability

    Methods 120:39–48.

    https://doi.org/10.1016/j.ymeth.2017.02.003

    • PubMed
    • Google Scholar
73. 1. Saulière J
    2. Murigneux V
    3. Wang Z
    4. Marquenet E
    5. Barbosa I
    6. Le Tonquèze O
    7. Audic Y
    8. Paillard L
    9. Roest Crollius H
    10. Le Hir H

    (2012) CLIP-seq of eIF4AIII reveals transcriptome-wide mapping of the human exon junction complex

    Nature Structural & Molecular Biology 19:1124–1131.

    https://doi.org/10.1038/nsmb.2420

    • PubMed
    • Google Scholar
74. 1. Schmidt SA
    2. Foley PL
    3. Jeong DH
    4. Rymarquis LA
    5. Doyle F
    6. Tenenbaum SA
    7. Belasco JG
    8. Green PJ

    (2015) Identification of SMG6 cleavage sites and a preferred RNA cleavage motif by global analysis of endogenous NMD targets in human cells

    Nucleic Acids Research 43:309–323.

    https://doi.org/10.1093/nar/gku1258

    • PubMed
    • Google Scholar
75. 1. Shum EY
    2. Jones SH
    3. Shao A
    4. Dumdie J
    5. Krause MD
    6. Chan WK
    7. Lou CH
    8. Espinoza JL
    9. Song HW
    10. Phan MH
    11. Ramaiah M
    12. Huang L
    13. McCarrey JR
    14. Peterson KJ
    15. De Rooij DG
    16. Cook-Andersen H
    17. Wilkinson MF

    (2016) The antagonistic gene paralogs upf3a and upf3b govern nonsense-mediated RNA decay

    Cell 165:382–395.

    https://doi.org/10.1016/j.cell.2016.02.046

    • PubMed
    • Google Scholar
76. 1. Singh G
    2. Kucukural A
    3. Cenik C
    4. Leszyk JD
    5. Shaffer SA
    6. Weng Z
    7. Moore MJ

    (2012) The cellular EJC interactome reveals higher-order mRNP structure and an EJC-SR protein nexus

    Cell 151:750–764.

    https://doi.org/10.1016/j.cell.2012.10.007

    • PubMed
    • Google Scholar
77. 1. Singh G
    2. Rebbapragada I
    3. Lykke-Andersen J

    (2008) A competition between stimulators and antagonists of Upf complex recruitment governs human nonsense-mediated mRNA decay

    PLoS Biology 6:e111.

    https://doi.org/10.1371/journal.pbio.0060111

    • PubMed
    • Google Scholar
78. 1. Sureau A
    2. Gattoni R
    3. Dooghe Y
    4. Stévenin J
    5. Soret J

    (2001) SC35 autoregulates its expression by promoting splicing events that destabilize its mRNAs

    The EMBO Journal 20:1785–1796.

    https://doi.org/10.1093/emboj/20.7.1785

    • PubMed
    • Google Scholar
79. 1. Szklarczyk D
    2. Franceschini A
    3. Wyder S
    4. Forslund K
    5. Heller D
    6. Huerta-Cepas J
    7. Simonovic M
    8. Roth A
    9. Santos A
    10. Tsafou KP
    11. Kuhn M
    12. Bork P
    13. Jensen LJ
    14. von Mering C

    (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life

    Nucleic Acids Research 43:D447–D452.

    https://doi.org/10.1093/nar/gku1003

    • PubMed
    • Google Scholar
80. 1. Szklarczyk D
    2. Morris JH
    3. Cook H
    4. Kuhn M
    5. Wyder S
    6. Simonovic M
    7. Santos A
    8. Doncheva NT
    9. Roth A
    10. Bork P
    11. Jensen LJ
    12. von Mering C

    (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible

    Nucleic Acids Research 45:D362–D368.

    https://doi.org/10.1093/nar/gkw937

    • PubMed
    • Google Scholar
81. 1. Takahashi H
    2. Parmely TJ
    3. Sato S
    4. Tomomori-Sato C
    5. Banks CA
    6. Kong SE
    7. Szutorisz H
    8. Swanson SK
    9. Martin-Brown S
    10. Washburn MP
    11. Florens L
    12. Seidel CW
    13. Lin C
    14. Smith ER
    15. Shilatifard A
    16. Conaway RC
    17. Conaway JW

    (2011) Human mediator subunit MED26 functions as a docking site for transcription elongation factors

    Cell 146:92–104.

    https://doi.org/10.1016/j.cell.2011.06.005

    • PubMed
    • Google Scholar
82. 1. Tani H
    2. Imamachi N
    3. Salam KA
    4. Mizutani R
    5. Ijiri K
    6. Irie T
    7. Yada T
    8. Suzuki Y
    9. Akimitsu N

    (2012) Identification of hundreds of novel UPF1 target transcripts by direct determination of whole transcriptome stability

    RNA Biology 9:1370–1379.

    https://doi.org/10.4161/rna.22360

    • PubMed
    • Google Scholar
83. 1. Thierry-Mieg D
    2. Thierry-Mieg J

    (2006) AceView: a comprehensive cDNA-supported gene and transcripts annotation

    Genome Biology 7:S12–14.

    https://doi.org/10.1186/gb-2006-7-s1-s12

    • PubMed
    • Google Scholar
84. 1. Thorne N
    2. Inglese J
    3. Auld DS

    (2010) Illuminating insights into firefly luciferase and other bioluminescent reporters used in chemical biology

    Chemistry & Biology 17:646–657.

    https://doi.org/10.1016/j.chembiol.2010.05.012

    • PubMed
    • Google Scholar
85. 1. Vaquero-Garcia J
    2. Barrera A
    3. Gazzara MR
    4. González-Vallinas J
    5. Lahens NF
    6. Hogenesch JB
    7. Lynch KW
    8. Barash Y

    (2016) A new view of transcriptome complexity and regulation through the lens of local splicing variations

    eLife 5:e11752.

    https://doi.org/10.7554/eLife.11752

    • PubMed
    • Google Scholar
86. 1. Vitting-Seerup K
    2. Sandelin A

    (2017) The landscape of isoform switches in human cancers

    Molecular Cancer Research 15:1206–1220.

    https://doi.org/10.1158/1541-7786.MCR-16-0459

    • PubMed
    • Google Scholar
87. 1. Wan J
    2. Qian SB

    (2014) TISdb: a database for alternative translation initiation in mammalian cells

    Nucleic Acids Research 42:D845–D850.

    https://doi.org/10.1093/nar/gkt1085

    • PubMed
    • Google Scholar
88. 1. Wang J
    2. Gudikote JP
    3. Olivas OR
    4. Wilkinson MF

    (2002) Boundary-independent polar nonsense-mediated decay

    EMBO Reports 3:274–279.

    https://doi.org/10.1093/embo-reports/kvf036

    • PubMed
    • Google Scholar
89. 1. Weischenfeldt J
    2. Damgaard I
    3. Bryder D
    4. Theilgaard-Mönch K
    5. Thoren LA
    6. Nielsen FC
    7. Jacobsen SE
    8. Nerlov C
    9. Porse BT

    (2008) NMD is essential for hematopoietic stem and progenitor cells and for eliminating by-products of programmed DNA rearrangements

    Genes & Development 22:1381–1396.

    https://doi.org/10.1101/gad.468808

    • PubMed
    • Google Scholar
90. 1. Wittkopp N
    2. Huntzinger E
    3. Weiler C
    4. Saulière J
    5. Schmidt S
    6. Sonawane M
    7. Izaurralde E

    (2009) Nonsense-mediated mRNA decay effectors are essential for zebrafish embryonic development and survival

    Molecular and Cellular Biology 29:3517–3528.

    https://doi.org/10.1128/MCB.00177-09

    • PubMed
    • Google Scholar
91. 1. Woeller CF
    2. Gaspari M
    3. Isken O
    4. Maquat LE

    (2008) NMD resulting from encephalomyocarditis virus IRES-directed translation initiation seems to be restricted to CBP80/20-bound mRNA

    EMBO Reports 9:446–451.

    https://doi.org/10.1038/embor.2008.36

    • PubMed
    • Google Scholar
92. 1. Young SK
    2. Wek RC

    (2016) Upstream open reading frames differentially regulate gene-specific translation in the integrated stress response

    Journal of Biological Chemistry 291:16927–16935.

    https://doi.org/10.1074/jbc.R116.733899

    • PubMed
    • Google Scholar
93. 1. Zeisel A
    2. Köstler WJ
    3. Molotski N
    4. Tsai JM
    5. Krauthgamer R
    6. Jacob-Hirsch J
    7. Rechavi G
    8. Soen Y
    9. Jung S
    10. Yarden Y
    11. Domany E

    (2011) Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli

    Molecular Systems Biology 7:529.

    https://doi.org/10.1038/msb.2011.62

    • PubMed
    • Google Scholar
94. 1. Zhang J
    2. Maquat LE

    (1997) Evidence that translation reinitiation abrogates nonsense-mediated mRNA decay in mammalian cells

    The EMBO Journal 16:826–833.

    https://doi.org/10.1093/emboj/16.4.826

    • PubMed
    • Google Scholar
95. 1. Zhang J
    2. Sun X
    3. Qian Y
    4. Maquat LE

    (1998) Intron function in the nonsense-mediated decay of beta-globin mRNA: indications that pre-mRNA splicing in the nucleus can influence mRNA translation in the cytoplasm

    RNA 4:801–815.

    https://doi.org/10.1017/S1355838298971849

    • PubMed
    • Google Scholar

Author details

1. Thomas D Baird

   Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Designed, performed, and analyzed all experiments following the siRNA screen, Wrote the manuscript

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0280-9984

2. Ken Chih-Chien Cheng

   National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—review and editing, Conceived of and designed the assay used for high-throughput screening, Constructed and characterized the NMD reporter cell line, Assisted with preparation of the manuscript

   Competing interests

   No competing interests declared

3. Yu-Chi Chen

   National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, United States

   Contribution

   Investigation, Methodology, Helped to conduct the RNAi screen

   Competing interests

   No competing interests declared

4. Eugen Buehler

   National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Designed and analyzed the RNAi screen

   Competing interests

   No competing interests declared

5. Scott E Martin

   National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—review and editing, Designed and analyzed the RNAi screen, Assisted with preparation of the manuscript

   Competing interests

   No competing interests declared

6. James Inglese

   National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing—review and editing, Conceived of and designed the assay used for high-throughput screening, Designed and analyzed the RNAi screen, Assisted with preparation of the manuscript

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7332-5717

7. J Robert Hogg

   Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing, Designed gene depletion and high-throughput sequencing experiments and analyzed RNAseq data, Designed and analyzed all experiments following the siRNA screen, Wrote the manuscript

   For correspondence

   j.hogg@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5729-5135

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