Accurate identification of the translation initiation codon in mRNA by ribosomes is crucial for expression of the correct cellular proteins. This process generally occurs in eukaryotic cells by a scanning mechanism, wherein the small (40S) ribosomal subunit recruits charged initiator tRNA (Met-tRNA_i^Met) in a ternary complex (TC) with eIF2-GTP, and the resulting 43S pre-initiation complex (PIC) attaches to the 5’ end of the mRNA and scans the 5’UTR for an AUG start codon. In the scanning PIC, the TC is bound in a relatively unstable state, dubbed ‘P_OUT’, suitable for inspecting successive triplets in the P decoding site for perfect complementarity with the anticodon of Met-tRNA_i. Nucleotides surrounding the AUG, particularly at the −3 and +4 positions (relative to the AUG at +1 to +3), the ‘Kozak context’, also influence the efficiency of start codon recognition. Hydrolysis of the GTP bound to eIF2 can occur, dependent on GTPase activating protein eIF5, but P_i release is blocked by eIF1, whose presence also prevents highly stable binding of Met-tRNA_i^Met in the ‘P_IN’ state. Start-codon recognition triggers dissociation of eIF1 from the 40S subunit, which in concert with other events allows P_i release from eIF2-GDP·P_i and accommodation of Met-tRNA_i^Met in the P_IN state of the 48S PIC (Figure 1A). Subsequent dissociation of eIF2-GDP and other eIFs from the 48S PIC enables eIF5B-catalyzed subunit joining and formation of an 80S initiation complex with Met-tRNA_i^Met base-paired to AUG in the P site (reviewed in [Hinnebusch, 2014] and [Hinnebusch, 2017]). eIF1 plays a dual role in the scanning mechanism, promoting rapid TC loading in the P_OUT conformation while blocking rearrangement to P_IN at non-AUG codons by clashing with Met-tRNA_i in the P_IN state (Rabl et al., 2011; Lomakin and Steitz, 2013)(Hussain et al., 2014), thus requiring dissociation of eIF1 from the 40S subunit for start codon recognition (Figure 1A). Consequently, mutations that weaken eIF1 binding to the 40S subunit reduce the rate of TC loading and elevate initiation at near-cognate codons (eg. UUG), or AUG codons in poor context, by destabilizing the open/P_OUT conformation and favoring rearrangement to the closed/P_IN state during scanning (Martin-Marcos et al., 2011; Martin-Marcos et al., 2013). Moreover, decreasing wild-type (WT) eIF1 abundance reduces initiation accuracy, whereas overexpressing eIF1 suppresses initiation at near-cognates or AUGs in poor context (Valásek et al., 2004; Alone et al., 2008; Ivanov et al., 2010; Saini et al., 2010; Martin-Marcos et al., 2011). The mechanistic link between eIF1 abundance and initiation accuracy is exploited to negatively autoregulate eIF1 expression, as the AUG codon of the eIF1 gene (SUI1 in yeast) occurs in suboptimal context and the frequency of its recognition is inversely related to eIF1 abundance (Ivanov et al., 2010; Martin-Marcos et al., 2011). Mutations that weaken 40S binding by eIF1 relax discrimination against the poor context of the SUI1 AUG codon and elevate eIF1 expression, overcoming autoregulation (Martin-Marcos et al., 2011). In contrast, mutations that enhance eIF1 binding to the 40S subunit impede rearrangement of the scanning PIC to the closed/P_IN conformation (Martin-Marcos et al., 2011; Martin-Marcos et al., 2014), which increases discrimination against the poor context of the SUI1 AUG codon, to reduce eIF1 expression, and also suppresses initiation at near-cognate UUG codons (Martin-Marcos et al., 2011; Martin-Marcos et al., 2014).

Figure 1

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eIF1A also has a dual role in scanning and start codon recognition. Scanning enhancer (SE) elements in the eIF1A C-terminal tail (CTT) promote TC binding in the open P_OUT conformation and impede rearrangement to the closed P_IN state, such that substitutions that impair the SE elements both impair TC recruitment and increase initiation at near-cognate start codons (Saini et al., 2010). Biochemical mapping experiments suggest that, like eIF1, the eIF1A CTT clashes with Met-tRNA_i in the P_IN state (Yu et al., 2009), and is displaced from the P site on start codon recognition (Zhang et al., 2015) to enable a functional interaction of the eIF1A CTT with the NTD of eIF5, the GTPase activating protein for eIF2, that facilitates P_i release from eIF2-GDP·P_i (Nanda et al., 2013). Scanning inhibitor elements SI₁ and SI₂ in the unstructured eIF1A N-terminal tail (NTT) and helical domain, respectively, antagonize SE function and stabilize the closed/P_IN conformation on start codon recognition (Figure 1A). Accordingly, substitutions that impair SI elements destabilize the closed complex and accelerate TC loading to the open complex in vitro, and promote continued scanning at UUG codons in hypoaccurate mutant cells (Fekete et al., 2007) (Saini et al., 2010). SI₁ mutations also increase the probability that the scanning PIC will bypass an upstream AUG codon (leaky scanning) (Fekete et al., 2007; Luna et al., 2013); and one such mutation, substituting NTT residues 17–21, decreases recognition of the suboptimal AUG codon of SUI1 mRNA to reduce eIF1 expression (Martin-Marcos et al., 2011).

Molecular insight into the deduced function of the eIF1A-NTT of promoting AUG recognition during scanning came from the cryo-EM structure of a partial yeast 48S PIC (py48S) containing eIF1, eIF1A, TC and mRNA, with the Met-tRNA_i base-paired to the AUG codon in a P_IN state. All but the first four residues of the eIF1A NTT were visible in this structure, and basic NTT residues Lys7, Lys10, Arg13, and Lys16 contact either the anticodon or the +4 to +6 mRNA nucleotides adjacent to the AUG codon, while Arg14 interacts with the 18S rRNA (Figure 1B) (Hussain et al., 2014). These findings suggest that the eIF1A NTT can directly stabilize the P_IN state, and help to explain how NTT substitutions in SI_1, which spans residues 1–26 (Saini et al., 2010), increase discrimination against non-AUG codons, which form less stable codon:anticodon duplexes than do AUG codons. Other studies have implicated NTT residues 7–11 and 12–16, encompassing the aforementioned basic NTT residues, in interactions with eIF1 and the eIF5-CTD that appear to promote assembly of the open, scanning PIC (Fekete et al., 2007; Luna et al., 2013). The β-subunit of eIF2 also harbors a highly basic NTT, which interacts with the eIF5-CTD to promote eIF1 dissociation from the closed complex at the start codon (Luna et al., 2012). It was suggested that interaction of the eIF5-CTD with the eIF1A-NTT would stabilize the open conformation of the PIC prior to AUG recognition, whereas alternative interaction of the eIF5-CTD with the eIF2β-NTT would stabilize the closed conformation of the PIC on AUG recognition (Luna et al., 2013). The proposed dissociation of the eIF1A-NTT from the eIF5-CTD on AUG recognition would free the eIF1A-NTT for interactions with the mRNA and anticodon evident in the py48S PIC (Hussain et al., 2014). Thus, the eIF1A-NTT would play a dual role of promoting the open conformation of the PIC through interaction with the eIF5-CTD and subsequently stabilizing the closed state by interacting with the mRNA and anticodon.

Somatic mutations in the human gene EIF1AX encoding eIF1A are frequently associated with uveal melanomas (UM) associated with disomy for chromosome 3, and all of the EIF1AX mutations sequenced thus far produce in-frame substitutions or deletions of one or more residues in the first 15 residues of the NTT (Martin et al., 2013). A subset of these mutations substitute or delete two of the four basic residues that contact mRNA or the tRNA_i anticodon in the yeast py48S PIC (Lys7 and Arg13), others introduce acidic residues that might electrostatically repel the phosphodiester backbone of the mRNA or tRNA_i, while others affect a Gly-Gly turn that is important for correct positioning of the basic residues in the PIC (Figure 1B–C) (Hussain et al., 2014). Thus, all of the UM mutations might affect eIF1A function by the same mechanism, of weakening the ability of the eIF1A NTT to stabilize the P_IN conformation of the tRNA_i. As such, they would be expected to increase discrimination against poor initiation sites in vivo. Alternatively, they could impair the proposed function of the eIF1A-NTT in stabilizing the open conformation (Luna et al., 2013), in which case they would have the opposite effect and relax discrimination against suboptimal start codons. We set out to distinguish between these possibilities by examining the consequences of seven yeast eIF1A-NTT substitutions equivalent to those associated with UM in residues Lys3, Lys4, Thr6, Gly8, Arg13 and Gly15, and also of altering the five NTT basic residues that interact with the mRNA or anticodon in the py48S PIC (Lys7, Lys10, Arg13, Arg14 and Lys16) (Figure 1C). Our genetic and biochemical analyses indicate that UM-associated eIF1A substitutions disrupt NTT interactions with the mRNA or tRNA_i to destabilize the closed/P_IN conformation of the PIC and increase discrimination against near-cognate codons or AUGs in suboptimal context, with particularly strong effects observed for substitutions of Arg13—one of five basic residues that interacts with the mRNA/tRNA_i anticodon. Ribosome profiling of the potent UM-associated mutant eIF1A-R13P reveals widespread increased discrimination against AUG codons in poor context, which can alter recognition of the start codon for the main coding sequences (CDS) or indirectly affect translation by modulating recognition of upstream open reading frames (uORFs) in the mRNA leader. These findings allow us to predict that eIF1A-NTT mutations alter gene expression in UM tumor cells by shifting translation initiation at main CDS and regulatory uORFs from poor to optimum initiation sites.

In this report we show that all seven substitutions in the NTT of yeast eIF1A associated with uveal melanoma in humans confer hyperaccuracy phenotypes in yeast cells. They suppress inappropriate initiation at a UUG start codon in his4-301 mRNA to prevent growth in the absence of a histidine supplement. They also reduce the UUG: AUG initiation ratio of a HIS4-lacZ reporter, in cells harboring the Sui^- mutation in eIF2ß (SUI3-2) that reduces accuracy and elevates UUG initiation. Like previously identified Ssu^- substitutions in eIF1 (Martin-Marcos et al., 2011), these eIF1A NTT substitutions also suppress the toxicity of SUI5 to cell growth at elevated temperatures. Moreover, they decrease initiation at the AUG codons of both SUI1 mRNA (encoding eIF1) and the GCN4 uORF1 specifically when they reside in unfavorable Kozak context. The recent structure of a yeast partial 48S PIC predicts that the UM-associated substitutions in the C-terminal portion of the NTT affect direct contacts of the NTT with mRNA nucleotides adjacent to the AUG codon, or in the anticodon of tRNA_i, and both interactions are thought to stabilize the PIC in the closed conformation with Met-tRNA_i accommodated in the P_IN state (Hussain et al., 2014; Llácer et al., 2015). Accordingly, the effects of the UM substitutions in reducing near-cognate UUG and poor-context AUG utilization can be attributed to destabilization of the P_IN state with attendant increased requirement for a perfect codon-anticodon duplex and optimal context. Two main lines of evidence support this interpretation. First, an identical set of phenotypes was observed for directed substitutions of conserved basic residues in the NTT that also make direct contacts with mRNA or anticodon nucleotides, namely K7, K10, K16, and R14. Substitutions of these residues with Asp have stronger phenotypes than Ala substitutions, consistent with replacing electrostatic attraction (Lys/Arg) with repulsion (Asp) for the phosphodiester backbone of mRNA or tRNA_i. The same was true for Asp and Ala substitutions of R13, which is replaced with Pro or His by UM-associated mutations. Second, biochemical experiments reveal that the R13P UM substitution and the directed K16D substitution specifically destabilize the P_IN state at UUG codons in vitro, increasing both the fraction of reconstituted PICs from which TC dissociates and the rate of this reaction (k_off) with a UUG, but not AUG, start codon in the mRNA. These substitutions also increase the rate of eIF1A dissociation, signifying a reduced fraction of PICs in the closed conformation and decreased overall stability of these complexes, with either UUG or AUG start codons. Together, these results help to account for the decreased usage of UUGs and AUGs in poor context conferred by these mutations in vivo, and support the notion that their hyperaccuracy phenotypes result from reduced occupancy and stability of the closed/P_IN state that, in turn, confers a heightened requirement for optimal initiation sites.

Although reduced initiation at near-cognate UUG codons in Sui^- mutants (Ssu^- phenotype) was reported previously for clustered alanine substitutions of eIF1A NTT residues 7–11, 12–16, and 17–21 (Fekete et al., 2007), belonging to the scanning inhibitor element designated SI₁ (Saini et al., 2010), it was unknown which residues in these three segments are most critical for increasing UUG initiation, nor whether any residues in the 7–11 and 12–16 intervals increase initiation at AUGs in poor context. Our findings establish that all five basic residues conserved between yeast and humans that contact mRNA, the anticodon, or 18S rRNA in the decoding center of the py48S PIC (K7, K10, R13, R14, and K16) are critical for efficient utilization of these suboptimal initiation sites, as is the conserved Gly8-Gly9 turn required for making these key contacts (Figure 1B–C) (Hussain et al., 2014). Accordingly, the Ala substitutions of K7 and K10 generated by the 7–11 mutation, and of R13, R14, and K16 by mutation 12–16 are likely responsible for the Ssu^- phenotypes of these multiple-residue substitutions (Fekete et al., 2007). Although the 17–21 mutation does not substitute any of the key basic residues, it might impair interactions of the C-terminal section of the eIF1A NTT with PIC components and indirectly prevent the basic residues in the N-terminal portion of the NTT from engaging with the decoding center (Figure 1B). Finally, our results implicate eIF1A residues K3 and K4 (N4 in humans), also substituted in UM, in controlling initiation accuracy, but their molecular functions remain to be determined, as they were not resolved in the py48S structure.

In addition to suppressing the elevated UUG initiation (Sui^- phenotype) conferred by the eIF2ß mutation SUI3-2, the eIF1A NTT substitutions we analyzed also suppress the derepressed GCN4-lacZ expression (Gcd^- phenotype) produced by SUI3-2. eIF1 stabilizes the open conformation of the PIC, to which TC binds most rapidly (P_OUT state) (Figure 1A) (Passmore et al., 2007). The Gcd^- phenotypes conferred by other Sui^- mutations affecting eIF1, eIF1A, and tRNA_i have been attributed to destabilization of this P_OUT state of TC binding. This interpretation was based partly on the finding that they are suppressed by Ssu^- substitutions in the SI₁ and SI₂ elements of eIF1A that destabilize the closed/P_IN conformation and thus shift the system in the opposite direction towards the open/P_OUT state, which should accelerate TC binding (Saini et al., 2010) (Dong et al., 2014; Martin-Marcos et al., 2014). Destabilization of the open/P_OUT state probably also contributes to the Gcd^- phenotype of SUI3-2 because it is suppressed by Ssu^- substitutions in SI₁ and SI₂ of eIF1A (Saini et al., 2010). Thus, the marked co-suppression of the Sui^- and Gcd^- phenotypes of SUI3-2 observed here for substitutions of the key basic residues K7, K10, R13, and K16 of the NTT, particularly for the acidic Asp replacements, provides additional genetic evidence that they preferentially destabilize the closed/P_IN state and shift the system towards the open conformation to which TC loads during assembly of scanning PICs.

We used ribosome footprint profiling to demonstrate that the R13P UM substitution confers a broad decrease in utilization of AUG codons with poor Kozak context in the yeast translatome, mimicking the effect of R13P in reducing eIF1 synthesis from SUI1 mRNA. R13P also reduced recognition of a subset of the ~100 uORFs whose translation we detected in both mutant and WT cells when their AUG codons reside in poor context, mimicking the effect of R13P of increasing leaky scanning through the elongated version of GCN4 uORF1 specifically when its AUG codon resides in poor context. R13P cells also display somewhat increased discrimination against the near-cognate ACG start codon of the ALA1 mRNA that initiates an N-terminal extension of the encoded alanyl tRNA synthetase, decreasing the ratio of reads in the extension versus the CDS by ~1/3^rd. ALA1 is one of only two annotated genes with non-AUG start codons in yeast (Chen et al., 2008; Chang et al., 2010), and the other such gene, GRS1, showed no reduction in initiation from the UUG codon initiating the N-terminal extension of glycyl-tRNA synthetase. This different behavior might be explained by the fact that the context of the GRS1 UUG matches closely the optimum consensus sequence in yeast in containing A’s at −4 to −1, and U at +4, whereas the ALA1 ACG deviates from this consensus by lacking A’s at −1 and −4 and containing A at +4. On the other hand, R13P modestly decreased initiation at the UUG codon of the HIS4-lacZ reporter, even though it contains preferred A’s at −4,–3, and −1. Thus, it remains to be seen whether poor context will be a significant determinant of increased usage of near-cognate start codons conferred by eIF1A NTT Ssu^- substitutions.

Considering that the sequence of the yeast and human eIF1A-NTT are quite similar, and that R13 is conserved between the two species (Figure 1C), our findings for the UM substitutions in yeast eIF1A lead us to propose that the corresponding substitutions in the human eIF1A NTT will favor oncogenic transformation by increasing discrimination against AUG codons with poor context or near-cognate start codons. If one or more tumor suppressor genes contains such a poor initiation site, the UM substitutions can be expected to increase its relative translation rate and thereby impair one or more control mechanisms governing cell proliferation. A recent study on UM tumor cell lines expressing either WT or the G6D variant of EIF1AX provided evidence that the G6D substitution reduces the critical function of EIF1AX in bulk translation initiation. Interestingly, sequencing of total polysomal mRNA indicated that ribosomal protein genes (RPGs) as a group have a heightened requirement for EIF1AX and that the translation of these mRNAs is reduced in G6D vs WT cells (Johnson et al., 2017). Given their high rates of translation during rapid cell growth, it seems likely that RPGs would exhibit favorable Kozak context, and by analogy with our findings in yeast on the eIF1A R13P substitution, the RPGs would not be expected to show decreased translation as the result of heightened discrimination against poor context. However, the yeast equivalent of G6D, T6D, did not significantly increase discrimination against the suboptimal eIF1 AUG codon in yeast in the manner observed for R13P. Moreover, unlike G6D in the tumor cells, we found no evidence that the UM-related substitutions in yeast eIF1A reduce bulk initiation. Thus, it is possible that the reduction in RPG expression in G6D tumor cells is a response to reduced bulk translation and cell growth; and it will be interesting to determine whether the R13P substitution in EIF1AX increases discrimination against AUGs in poor context in human cells.

Sequencing data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and the accession numbers are listed in the Additional Files under Major datasets.

1. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) eIF1A residues implicated in cancer stabilize translation preinitiation complexes and favor suboptimal initiation sites in yeast

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSE108334).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108334
2. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_WT(for SUI1-L96P)_with_CHX_1

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895470).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895470
3. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_WT(for SUI1-L96P)_with_CHX_2

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895471).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895471
4. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_SUI1-L96P_w/o_CHX_1

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895472).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895472
5. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_SUI1-L96P_w/o_CHX_2

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895473).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895473
6. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_SUI1-L96P_with_CHX_1

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895468).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895468
7. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_SUI1-L96P_with_CHX_2

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895469).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895469
8. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_WT(for SUI1-T15A)_1

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895458).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895458
9. 1. Pilar Martin-Marcos
   2. Fujun Zhou
   3. Charm Karunasiri
   4. Fan Zhang
   5. Jinsheng Dong
   6. Jagpreet Nanda
   7. Shardul D Kulkarni
   8. Neelam Dabas Sen
   9. Mercedes Tamame
   10. Michael Zeschnigk
   11. Jon R Lorsch
   12. Alan G Hinnebusch

   (2017) ribo_WT(for SUI1-T15A)_2

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895459.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895459
10. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_SUI1-T15A_1

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895456).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895456
11. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_SUI-T15A_2

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895457).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895457
12. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Mercedes Tamame
    9. Michael Zeschnigk
    10. Jon R Lorsch
    11. Alan G Hinnebusch

    (2017) ribo_SUI3-2(for SUI1_T15A)_1

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895460).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895460
13. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_SUI3-2(for SUI1_T15A)_2

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895461).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895461
14. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_SUI3-2/SUI-T15A_1

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895462).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895462
15. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_SUI3-2/SUI-T15A_2

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895463).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895463
16. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_20_WT_with_CHX_1

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895476).

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2895476
17. 1. Pilar Martin-Marcos
    2. Fujun Zhou
    3. Charm Karunasiri
    4. Fan Zhang
    5. Jinsheng Dong
    6. Jagpreet Nanda
    7. Shardul D Kulkarni
    8. Neelam Dabas Sen
    9. Mercedes Tamame
    10. Michael Zeschnigk
    11. Jon R Lorsch
    12. Alan G Hinnebusch

    (2017) ribo_WT(for TIF11_R13P)_1

    Publicly available at the NCBI Gene Expression Omnibus (accession no. GSM2895450).

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Author details

1. Pilar Martin-Marcos

   1. Laboratory of Gene Regulation and Development, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States
   2. Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8897-099X

2. Fujun Zhou

   Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

3. Charm Karunasiri

   Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Fan Zhang

   Laboratory of Gene Regulation and Development, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Formal analysis, Investigation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

5. Jinsheng Dong

   Laboratory of Gene Regulation and Development, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Investigation, Writing—original draft

   Competing interests

   No competing interests declared

6. Jagpreet Nanda

   Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Formal analysis, Investigation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

7. Shardul D Kulkarni

   Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Investigation, Formal analysis

   Competing interests

   No competing interests declared

8. Neelam Dabas Sen

   Laboratory of Gene Regulation and Development, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

   Contribution

   Resources, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

9. Mercedes Tamame

   Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain

   Contribution

   Investigation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

10. Michael Zeschnigk

    1. Institute of Human Genetics, University Duisburg-Essen, Essen, Germany
    2. Eye Cancer Research Group, Faculty of Medicine, University Duisburg-Essen, Essen, Germany

    Contribution

    Supervision, Writing—original draft, Writing—review and editing

    Competing interests

    No competing interests declared

11. Jon R Lorsch

    Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Validation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    jon.lorsch@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4521-4999

12. Alan G Hinnebusch

    Laboratory of Gene Regulation and Development, Eunice Kennedy Shriver National Institute of Child Health and Development, National Institutes of Health, Bethesda, United States

    Contribution

    Investigation, Formal analysis

    For correspondence

    ahinnebusch@nih.gov

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-1627-8395

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