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Start codon-associated ribosomal frameshifting mediates nutrient stress adaptation

Nature Structural & Molecular Biology volume 30, pages 1816–1825 (2023)Cite this article

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Abstract

A translating ribosome is typically thought to follow the reading frame defined by the selected start codon. Using super-resolution ribosome profiling, here we report pervasive out-of-frame translation immediately from the start codon. Start codon-associated ribosomal frameshifting (SCARF) stems from the slippage of ribosomes during the transition from initiation to elongation. Using a massively paralleled reporter assay, we uncovered sequence elements acting as SCARF enhancers or repressors, implying that start codon recognition is coupled with reading frame fidelity. This finding explains thousands of mass spectrometry spectra that are unannotated in the human proteome. Mechanistically, we find that the eukaryotic initiation factor 5B (eIF5B) maintains the reading frame fidelity by stabilizing initiating ribosomes. Intriguingly, amino acid starvation induces SCARF by proteasomal degradation of eIF5B. The stress-induced SCARF protects cells from starvation by enabling amino acid recycling and selective mRNA translation. Our findings illustrate a beneficial effect of translational ‘noise’ in nutrient stress adaptation.

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Fig. 1: Ezra-seq reveals prevailing out-of-frame footprints in the beginning of the CDS.
Fig. 2: Non-optimal codons induce ribosomal frameshifting.
Fig. 3: Characterizing start codon-associated ribosomal frameshifting.
Fig. 4: SCARF is dependent on sequence context.
Fig. 5: Detecting endogenous SCARF products.
Fig. 6: SCARF regulation by eIF5B.
Fig. 7: SCARF can be induced by amino acid starvation.

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Data availability

All sequencing data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession number GSE184825. Source data are provided with this paper.

Code availability

All Perl Scripts used in this study have been deposited in GitHub: https://github.com/QianLab-Cornell/Count_Ribo_Reads.

References

  1. Pelletier, J. & Sonenberg, N. The organizing principles of eukaryotic ribosome recruitment. Annu. Rev. Biochem. 88, 307–335 (2019).

    CAS  PubMed  Google Scholar 

  2. Hinnebusch, A. G. Structural insights into the mechanism of scanning and start codon recognition in eukaryotic translation initiation. Trends Biochem. Sci. 42, 589–611 (2017).

    CAS  PubMed  Google Scholar 

  3. Atkins, J. F. & Bjork, G. R. A gripping tale of ribosomal frameshifting: extragenic suppressors of frameshift mutations spotlight P-site realignment. Microbiol. Mol. Biol. Rev. 73, 178–210 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Dinman, J. D. Mechanisms and implications of programmed translational frameshifting. Wiley Interdiscip. Rev. RNA 3, 661–673 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Orr, M. W., Mao, Y., Storz, G. & Qian, S. B. Alternative ORFs and small ORFs: shedding light on the dark proteome. Nucleic Acids Res. 48, 1029–1042 (2020).

    CAS  PubMed  Google Scholar 

  6. Kearse, M. G. & Wilusz, J. E. Non-AUG translation: a new start for protein synthesis in eukaryotes. Genes Dev. 31, 1717–1731 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Pestova, T. V. et al. The joining of ribosomal subunits in eukaryotes requires eIF5B. Nature 403, 332–335 (2000).

    CAS  PubMed  Google Scholar 

  8. Wang, J. et al. eIF5B gates the transition from translation initiation to elongation. Nature 573, 605–608 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Brar, G. A. & Weissman, J. S. Ribosome profiling reveals the what, when, where and how of protein synthesis. Nat. Rev. Mol. Cell Biol. 16, 651–664 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147, 789–802 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Gao, X. et al. Quantitative profiling of initiating ribosomes in vivo. Nat. Methods 12, 147–153 (2015).

    CAS  PubMed  Google Scholar 

  13. Griss, J. et al. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat. Methods 13, 651–656 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hafner, M. et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA 17, 1697–1712 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee, S., Liu, B., Huang, S. X., Shen, B. & Qian, S. B. Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution. Proc. Natl Acad. Sci. USA 109, E2424–E2432 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Hecht, A. et al. Measurements of translation initiation from all 64 codons in E. coli. Nucleic Acids Res. 45, 3615–3626 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Kozak, M. Pushing the limits of the scanning mechanism for initiation of translation. Gene 299, 1–34 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Tuller, T. et al. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141, 344–354 (2010).

    CAS  PubMed  Google Scholar 

  20. Dersh, D., Yewdell, J. W. & Wei, J. A SIINFEKL-based system to measure MHC class I antigen presentation efficiency and kinetics. Methods Mol. Biol. 1988, 109–122 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Jia, L. et al. Decoding mRNA translatability and stability from the 5′ UTR. Nat. Struct. Mol. Biol. 27, 814–821 (2020).

    CAS  PubMed  Google Scholar 

  22. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Google Scholar 

  23. Xiao, Q., Zhang, F., Nacev, B. A., Liu, J. O. & Pei, D. Protein N-terminal processing: substrate specificity of Escherichia coli and human methionine aminopeptidases. Biochemistry 49, 5588–5599 (2010).

    CAS  PubMed  Google Scholar 

  24. Aksnes, H., Hole, K. & Arnesen, T. Molecular, cellular, and physiological significance of N-terminal acetylation. Int. Rev. Cell Mol. Biol. 316, 267–305 (2015).

    CAS  PubMed  Google Scholar 

  25. An, H., Ordureau, A., Korner, M., Paulo, J. A. & Harper, J. W. Systematic quantitative analysis of ribosome inventory during nutrient stress. Nature 583, 303–309 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Na, C. H. et al. Discovery of noncanonical translation initiation sites through mass spectrometric analysis of protein N termini. Genome Res 28, 25–36 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Hinnebusch, A. G. The scanning mechanism of eukaryotic translation initiation. Annu. Rev. Biochem. 83, 779–812 (2014).

    CAS  PubMed  Google Scholar 

  28. Thakur, A. & Hinnebusch, A. G. eIF1 Loop 2 interactions with Met-tRNAi control the accuracy of start codon selection by the scanning preinitiation complex. Proc. Natl Acad. Sci. USA 115, E4159–E4168 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Tang, L. et al. Competition between translation initiation factor eIF5 and its mimic protein 5MP determines non-AUG initiation rate genome-wide. Nucleic Acids Res. 45, 11941–11953 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Nag, N. et al. eIF1A/eIF5B interaction network and its functions in translation initiation complex assembly and remodeling. Nucleic Acids Res. 44, 7441–7456 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Dixon, A. S. et al. NanoLuc complementation reporter optimized for accurate measurement of protein interactions in cells. ACS Chem. Biol. 11, 400–408 (2016).

    CAS  PubMed  Google Scholar 

  32. Wang, J. et al. Structural basis for the transition from translation initiation to elongation by an 80S–eIF5B complex. Nat. Commun. 11, 5003 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Suraweera, A., Munch, C., Hanssum, A. & Bertolotti, A. Failure of amino acid homeostasis causes cell death following proteasome inhibition. Mol. Cell 48, 242–253 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Kilberg, M. S., Shan, J. & Su, N. ATF4-dependent transcription mediates signaling of amino acid limitation. Trends Endocrinol. Metab. 20, 436–443 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Lee, S. et al. Upregulation of eIF5B controls cell-cycle arrest and specific developmental stages. Proc. Natl Acad. Sci. USA 111, E4315–E4322 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Chukka, P. A. R., Wetmore, S. D. & Thakor, N. Established and emerging regulatory roles of eukaryotic translation initiation factor 5B (eIF5B). Front Genet 12, 737433 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Shu, X. E., Swanda, R. V. & Qian, S. B. Nutrient control of mRNA translation. Annu. Rev. Nutr. 40, 51–75 (2020).

    CAS  PubMed  Google Scholar 

  38. Wyant, G. A. et al. NUFIP1 is a ribosome receptor for starvation-induced ribophagy. Science 360, 751–758 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Adelman, K. & Lis, J. T. Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Netzer, N. et al. Innate immune and chemically triggered oxidative stress modifies translational fidelity. Nature 462, 522–526 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Consortium, T. R. RNAcentral: a hub of information for non-coding RNA sequences. Nucleic Acids Res. 47, D221–D229 (2019).

    Google Scholar 

  42. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Google Scholar 

  43. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, 1–10 (2009).

    Google Scholar 

  44. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  45. Reis, M. D., Savva, R. & Wernisch, L. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res. 32, 5036–5044 (2004).

    PubMed  PubMed Central  Google Scholar 

  46. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).

    Google Scholar 

  47. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    CAS  PubMed  Google Scholar 

  48. Xiao, Z. et al. De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res. 46, e61 (2018).

    PubMed  PubMed Central  Google Scholar 

  49. Noderer, W. L. et al. Quantitative analysis of mammalian translation initiation sites by FACS-seq. Mol. Syst. Biol. 10, 748 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. Lorenz, R. et al. ViennaRNA package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    PubMed  PubMed Central  Google Scholar 

  51. Cambray, G., Guimaraes, J. C. & Arkin, A. P. Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat. Biotechnol. 36, 1005–1015 (2018).

    CAS  PubMed  Google Scholar 

  52. Mutalik, V. K. et al. Quantitative estimation of activity and quality for collections of functional genetic elements. Nat. Methods 10, 347 (2013).

    CAS  PubMed  Google Scholar 

  53. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301 (2016).

    CAS  PubMed  Google Scholar 

  54. Vaudel, M., Barsnes, H., Berven, F. S., Sickmann, A. & Martens, L. SearchGUI: an open‐source graphical user interface for simultaneous OMSSA and X! Tandem searches. Proteomics 11, 996–999 (2011).

    CAS  PubMed  Google Scholar 

  55. Deutsch, E. W. et al. A guided tour of the Trans‐Proteomic Pipeline. Proteomics 10, 1150–1159 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. W. Yewdell (NIAID, NIH) for providing 25D1 reagents and HEK293-Kb cells. We thank the Cornell University Life Sciences Core Laboratory Center for sequencing and FACS support. This work was supported by US National Institutes of Health (R01GM1222814 and DP1GM142101) and HHMI Faculty Scholar (55108556) to S.-B.Q.

Author information

Author notes

  1. Yuanhui Mao

    Present address: Liangzhu Laboratory, Zhejiang University, Hangzhou, China

  2. These authors contributed equally: Yuanhui Mao, Longfei Jia.

Authors and Affiliations

  1. Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA

    Yuanhui Mao, Longfei Jia, Leiming Dong, Xin Erica Shu & Shu-Bing Qian

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Contributions

Y.M. and S.-B.Q. conceived the study and designed the experiments. Y.M. conducted the majority of data analysis, and L.J. performed the majority of experiments. L.D. contributed to the Ezra-seq development. X.E.S. helped with starvation Ribo-seq. S.-B.Q. and Y.M. wrote the manuscript. All authors discussed the results and edited the manuscript.

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Correspondence to Shu-Bing Qian.

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Competing interests

L.D., X.E.S. and S.-B.Q. are inventors of Ezra-seq technology (9217-02 PCT). X.E.S. and S.-B.Q. are co-founders of EzraBio Inc. The remaining authors declare no competing interests.

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Nature Structural & Molecular Biology thanks Jonathan Schatz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Sara Osman was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Development of Ezra-seq.

(a) The top panels show the comparison of Ezra-seq and two ligation-based Ribo-seq procedures. The bottom panels show in-frame ratio (IFR) of reads at codons with 1.0 indicating complete in-frame. The inserted bar plots show the fraction of reads in different reading frames. (b) An outline of Ezra-seq procedure (see Materials and Methods for the detail). (c) Aggregation plots show the ribosome density across the transcriptome. Transcripts are aligned to start and stop codons, respectively. Both 5’ end (green) and 3’ end (orange) of footprints are used for plotting. The heatmap shows the density of ribosome footprints with different length. (d) Aggregation plots show the ribosome density across the transcriptome in different cell lines and tissues. Transcripts are aligned to start and stop codons, respectively. Both 5’ end (green) are 3’ end (orange) of footprints are used for plotting. The right panel shows the range of codon IFR with 1.0 indicating complete in-frame reads. The inserted bar plot shows the fraction of reads in different reading frames. (e) Mean ribosome reads on individual triple-motifs. The top 10 motifs with the highest occupancy were listed. (f) Aggregation plots show ribosome densities around the motif PPD (left) and PPE (right). The 5’ end of the reads was used. (g) Variance analysis of ribosome density at individual P-sites when different offset values were considered. The pie chart shows the fraction of P-sites that have variation of ribosome density (12-nt offset) higher (red) or lower (blue) than the variation of ribosome density with random offset.

Extended Data Fig. 2 Comparison of 5’ end accuracy across representative Ribo-seq data sets.

(a)-(h) For each panel, the left bar plots show the number of mismatches at the 5’ end of footprints. The heatmap shows the distance of 5’ end to the start codon. All footprints were classified into different length (y-axis), the color represents the log2 count of footprint. For each length group, the submit peak was indicated by a star, which indicates the distance of P-site to the start codon (the number at the right side of heatmap). The number in parentheses is in-frame rate when the left P-site offset was used. (i) Schematic of IFR changes after a triplet with high or low initiation potential (left panel). A scatter plot shows initiation potential of 64 triplets based on IFR changes (right panel). (j) Correlation of initiation potential of 64 triplets between biological replicates (left), between HEK293 and MEF cell lines (middle), or between MEF cell lines and mouse heart (right). The AUG and 10 non-AUG triplets with highest initiation potential in HEK293 were highlighted in red.

Extended Data Fig. 3 Characterizing in-frame and out-of-frame RPFs.

(a) Top panel shows in-frame ratio of ribosome footprints across the transcriptome in cells with (left panel) or without (right panel) cycloheximide (CHX) treatment (100 μg/mL) for 30 min. Bottom panel shows in-frame ratio of ribosome footprints across the transcriptome in HEK293 and MEF cells. Ribo-seq data were obtained by ligation-based Ribo-seq methods. Due to the relatively low resolution, IFR values were calculated within a non-overlapping sliding window (30 nt). Grey shadow shows the variation of mean IFR estimated by bootstrap method. Transcripts are aligned to start and stop codons, respectively. (b) Heat maps show normalized IFR of CDS across different species and cell lines using published Ribo-seq data sets. IFR values were calculated within a non-overlapping sliding window (30 nt), which was subsequently normalized by CDS IFR. (c) The read length distribution for footprints at the start codon (Start), in the 5’ end of CDS (the first 60 nt, 5’ CDS), or in the CDS region. (d) In-frame ratio was calculated based on footprints with different lengths (from 27 nt to 30 nt), which represent the major groups of footprints. (e) An aggregation plot shows out-of-frame reads around the 1st frame 1 or frame 2 stop codons. The mean out-of-frame reads before and after the out-of-frame stop codons are indicated by dashed lines.

Extended Data Fig. 4 uORF and leaky scanning minimally contribute to reduced IFR.

(a) A heat map shows the IFR values at the first 333 codons of individual mRNAs. The right heat map shows ribosome densities in different regions of individual mRNAs. (b) Boxplots show RNA fold free energy (MFE) around start codon, GC fraction in 5’ UTR and 5’ UTR length between mRNAs with low or high IFR in the beginning of CDS. High and low IFR groups refer to mRNAs with top and bottom 15% of IFR values (n = 889 for each group) respectively. The median of MFE, GC and 5’ UTR length in each group is indicated by a center line, the box shows the upper and lower quantiles, the whisker shows the 1.5× interquartile range. The outliers are not shown. (c) Comparison of IFR between mRNAs with or without uORF. IFR values are calculated within a non-overlapping sliding window (30 nt). (d) Effects on uORF translation on in-frame ratio in the beginning of CDS. uORFs were identified by Ezra-seq data in this study. All uORFs were separated into different groups based on the initiators. Overlapping uORFs are defined if the stop codon of uORF is beyond the start codon of main CDS. The numbers indicate the number of mRNAs used for analysis. Of note, when uORFs strongly inhibit main CDS translation, those mRNAs were not included in analysis due to the lack of sufficient reads on the main CDS. (e) A heatmap shows IFR values between mRNAs with or without a stop codon UGA before start codon. V: not uracil, Y: pyrimidine. (f) Distance of the first downstream NTG (dNTG) in different reading frames relative to the annotated start codon.

Extended Data Fig. 5 Non-optimal codons induce ribosome frameshifting.

(a) A scatter plot shows the correlation between codon optimality and IFR when the A-site codon is considered. Spearman’s Rho correlation between tAI values and codon IFR, as well as the P value, was indicated. (b) A scatter plot shows the correlation of IFR values for (b) transcripts with or without uORFs. (c) A heat map shows the effect of P-site and A-site combinations on reading frame fidelity at ribosome A-sites. (d) Analysis of codon usage bias of the first codon after the start codon. Bar plot and the table show the relative synonymous codon usage (RSCU) of the most prevalence amino acid Alanine at the second codon of CDS. (e) Sequences of uORF reporter with either the 2nd codon or 14th codon replaced by a synonymous optimal (green) or non-optimal (orange).

Extended Data Fig. 6 SCARF regulation by the start codon sequence context.

(a) HEK293-Kb cells were transfected with massively parallel mRNA reporters followed by sucrose gradient separation into monosome (M) and polysome (P) fractions. The original frequency of triplets in different populations is shown as heat maps. (b – d) Relative contributions of the nucleotide identity in different positions to the uORF translation based on the M/P ratio. The highlighted numbers refer to the reading frame of the encoded SIINFEKL relative to the AUG codon.

Extended Data Fig. 7 Characterizing the regulatory role of translation initiation factors in SCARF.

(a) The left panel shows the western blots of HEK293 cells with or without eIF1 knockdown. The middle panel shows the comparison of polysome profiles of cells with or without eIF1 knockdown. The right panel shows the comparison of normalized IFR in cells with or without eIF1 knockdown. IFR values are calculated within a non-overlapping sliding window (45 nt), which was subsequently normalized by CDS IFR. The right panel shows the HiBit-based SCARF reporter assay. Error bars, mean ± s.e.m.; Two-tailed t-test, n = 3, n.s. no significant change. (b) Same as (a) using cells with eIF5 overexpression. (c) Same as (a) using cells with eIF1A knockdown. (d) Same as (a) using cells with eIF5B knockdown. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, * P = 0.04 for the comparison of Kozak context (left bars), *** P = 0.001 for the comparison of Non-Kozak context (right bars). (e) Bar plots show the relative GFP mean fluorescence intensity (MFI) of SCARF reporters over the in-frame control. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, n.s. no significant change.

Source data

Extended Data Fig. 8 Characterizing the regulatory role of translation initiation factors in SCARF.

(a) Comparison of normalized IFR for mRNAs with or without the Kozak sequence context on start codons in cells with or without eIF5B knockdown. IFR values are calculated within a non-overlapping sliding window (45 nt), which was subsequently normalized by CDS IFR. P value was calculated by a permutation test. (b) Sequence information of HiBit-based SCARF reporter. (c) Bar graphs show the HiBit-based SCARF reporter assays in HEK293 cells. Both Fluc (left panel) and HiBit (right panel) signals were measured from cells transfected with SCARF reporters bearing a uORF start codon or a uORF with start codon mutated to ACC. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, *** P = 2.3 × 10 − 5. n.s. no significant change. (d) Bar graphs show the HiBit-based SCARF reporter assays in cells with or without eIF5B knockdown. Both the Fluc (left panel) and HiBit (right panel) signals were measured from cells transfected with SCARF reporters bearing a uORF start codon with or without the Kozak sequence context. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, *** P = 1.2 × 10−6 and P = 3.1 × 10−6 for comparisons of Fluc activities in frame 0 and frame 1 of scramble cells (right panels); P = 8.6 × 10−8 and P = 1.1 × 10−7 for comparisons of Fluc activities in frame 0 and frame 1 of eIF5B knockdown cells (right panels). P = 9.5 × 10−7 and P = 3.0 × 10−6 for comparisons of Hibit activities in frame 0 of scramble and eIF5B knockdown cells (right panels). (e) A model depicting the role of eIF5B in stabilizing initiator tRNA at the P-site, thereby maintaining the reading frame during the transition from initiation to elongation.

Source data

Extended Data Fig. 9 Nutrient starvation induces SCARF via eIF5B degradation.

(a) Histogram showing the changes of ribosome density upon amino acid starvation. The mRNAs with top 25% increase (Inc) and decrease (Dec) are colored coded in red and blue, respectively. The right panels show the CDS IFR of both mRNA groups. P value was calculated by a permutation test. (b) Bar graphs show SCARF reporter assays in cells before and after amino acid starvation. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, *** P = 2.9 × 10−6 and P = 1.0 × 10−5 for comparison of Fluc levels in frame 0 or frame 1 of control cells; P = 2.3 × 10−5 and P = 1.5 × 10−4 for Fluc levels in cells with starvation. P = 5.6 × 10−6 and P = 6.5 × 10−5 for comparison of HiBit activities in frame 0 of cells with and without starvation. (c) The top panel shows the turnover of eIF5B in HEK293 cells before and after amino acid starvation. The bottom panel shows the turnover of eIF5B in starved HEK293 cells in the presence of 5 µM MG132. (d) Representative flow cytometry of HEK293-Kb cells transfected with SCARF reporters before and after amino acid starvation, in the absence or presence of 5 µM MG132. Bar plots show the relative 25D1 mean fluorescence intensity (MFI) of SCARF reporters. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, *** P = 1.2 × 10−5 and P = 8.6 × 10−4 for comparisons of 25D intensities in Frame 1; P = 7.5 × 10−4 and P = 1.3 × 10−3 for comparisons of 25D intensities in Frame 2. (e) Western blots of exogenous eIF5B in transfected HEK293 cells. (f) Representative flow cytometry of HEK293-Kb cells transfected with SCARF reporters before and after amino acid starvation, in the absence or presence of exogenous eIF5B. Bar plots show the relative 25D1 mean fluorescence intensity (MFI) of SCARF reporters. Error bars, mean ± s.e.m.; two-tailed t-test, n = 3, *** P = 5.8 × 10−4 for comparison of control vs starvation; P = 1.2 × 10−3 for comparison of exogenous eIF5B vs vector under starvation.

Source data

Extended Data Fig. 10 The rescuing effect of eIF5B in nutrient starvation induced SCARF.

(a) A schematic image of 80S-eIF5B complex adopted from Wang et al, Nat Commun 2020. (b) Bar graphs show the HiBit-based SCARF reporter assays in cells transfected with wild type eIF5B or mutants. Both the Fluc (left panel) and HiBit (middle panel) signals were measured from cells transfected with SCARF reporters bearing a uORF start codon with or without the Kozak sequence context. Error bars, mean ± s.e.m.; n = 3. (c) Representative western blots of polyubiquitinated species in HEK293 cells with or without eIF5B overexpression before and after amino acid starvation. The experiment was independently repeated three times with similar results. (d) Representative western blots of exogenous eIF5B in transfected HEK293 cells. The experiment was independently repeated three times with similar results. (e) Representative western blots of ATF4 in starved HEK293 cells transfected with eIF5B wild type or mutants. The experiment was independently repeated three times with similar results.

Source data

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Supplementary Information

Flow cytometry gating strategies.

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Supplementary Tables 1–4.

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Source Data Extended Data Fig. 7

Unprocessed Western Blots.

Source Data Extended Data Fig. 8

Statistical Source Data.

Source Data Extended Data Fig. 9

Statistical Source Data.

Source Data Extended Data Fig. 9

Unprocessed Western Blots.

Source Data Extended Data Fig. 10

Statistical Source Data.

Source Data Extended Data Fig. 10

Unprocessed Western Blots.

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Mao, Y., Jia, L., Dong, L. et al. Start codon-associated ribosomal frameshifting mediates nutrient stress adaptation. Nat Struct Mol Biol 30, 1816–1825 (2023). https://doi.org/10.1038/s41594-023-01119-z

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