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Translation readthrough mitigation

Nature volume 534, pages 719–723 (2016)Cite this article

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Abstract

A fraction of ribosomes engaged in translation will fail to terminate when reaching a stop codon, yielding nascent proteins inappropriately extended on their C termini. Although such extended proteins can interfere with normal cellular processes, known mechanisms of translational surveillance1 are insufficient to protect cells from potential dominant consequences. Here, through a combination of transgenics and CRISPR–Cas9 gene editing in Caenorhabditis elegans, we demonstrate a consistent ability of cells to block accumulation of C-terminal-extended proteins that result from failure to terminate at stop codons. Sequences encoded by the 3′ untranslated region (UTR) were sufficient to lower protein levels. Measurements of mRNA levels and translation suggested a co- or post-translational mechanism of action for these sequences in C. elegans. Similar mechanisms evidently operate in human cells, in which we observed a comparable tendency for translated human 3′ UTR sequences to reduce mature protein expression in tissue culture assays, including 3′ UTR sequences from the hypomorphic ‘Constant Spring’ haemoglobin stop codon variant. We suggest that 3′ UTRs may encode peptide sequences that destabilize the attached protein, providing mitigation of unwelcome and varied translation errors.

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Figure 1: Translation into 3′ UTRs results in substantial loss of protein expression.
Figure 2: Identification of determinants for product loss upon translation into the 3′ UTR.
Figure 3: Translation into the 3′ UTR at an endogenous locus decreases protein levels.
Figure 4: Translation into 3′ UTRs results in protein loss for several genes in humans.

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Acknowledgements

We thank the Fire Laboratory for critical reading of the manuscript, C. Frøkjær-Jensen and K. Artiles for technical expertise, and T. Schedl, T. Inada, C. Joazeiro, L. Ling, A. Nager, and N. Spies for discussions. A. Sapiro and B. Li were instrumental in developing the RNA-seq2 protocol. This work was supported by grants from NIH R01GM37706, T32HG000044 (G.T.H.), 1DP2HD084069-01 (M.C.B.), 5F32GM112474-02 (J.A.A.), Walter and Idun Berry Foundation (E.S.C.), and NSF DGE-114747 (C.H.L.).

Author information

Authors and Affiliations

  1. Department of Pathology, Stanford University School of Medicine, Stanford, 94305, California, USA

    Joshua A. Arribere, Elif S. Cenik & Andrew Z. Fire

  2. Department of Bioengineering, Stanford University, Stanford, 94305, California, USA

    Nimit Jain

  3. Department of Genetics, Stanford University School of Medicine, Stanford, 94305, California, USA

    Gaelen T. Hess, Cameron H. Lee, Michael C. Bassik & Andrew Z. Fire

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  1. Joshua A. Arribere
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Contributions

J.A.A., E.S.C., and A.Z.F. designed C. elegans experiments. J.A.A. and E.S.C. conducted C. elegans experiments. N.J. developed the RNA-seq2 protocol. J.A.A. performed computational analyses. J.A.A. conducted experiments in human cell lines, as designed and aided by J.A.A., G.T.H., C.H.L., M.C.B., and A.Z.F. J.A.A. and A.Z.F. wrote the paper with help from all authors.

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Correspondence to Andrew Z. Fire.

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The authors declare no competing financial interests.

Additional information

Sequencing data are available at Sequence Read Archive (SRP064516).

Reviewer Information Nature thanks J. S. Butler, M. Yarus and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Distribution of C-terminal extensions upon stop codon readthrough.

Annotations and genomes were as described in Supplementary Methods. Each 3′ UTR was translated starting one codon after the stop codon until the next in-frame stop codon. For metazoans, counting was performed in three different ways: including only genes for which exactly one 3′ UTR was annotated (blue), counting each annotated 3′ UTR separately (green), or counting each gene once and splitting gene counts with multiple 3′ UTRs equally amongst the 3′ UTR isoforms (red). ‘Nonstop’ denotes 3′ UTRs for which no stop codon was encountered before the poly(A) tail. For each species the distribution of next in-frame stop codons was calculated for 1,000 nucleotide shuffling of 3′ UTR sequences for genes with a single 3′ UTR annotated, and 95% confidence interval shown (yellow). A similar ‘randomized’ distribution was obtained upon shuffling 3′ UTR sequences and preserving dinucleotide frequency. The frequency of stops immediately after the annotated stop codon (amino acid length 0) is highlighted with a blue arrow in each species. The distribution of peptide lengths follows an exponential decay curve, where the slope is related to the probability of encountering a stop codon at each position. In the simplest model, the probability of encountering a stop codon is constant throughout the 3′ UTR, accounting for the roughly linear shape of each plot (previously noted2,3). Notable exceptions are a tendency towards second in-frame stops in E. coli (blue arrow), and a tendency towards peptides >60 amino acids in length in all species. In E. coli, the enrichment towards longer downstream peptides is at least partially explained by the operonic layout of genes.

Extended Data Figure 2 Example quantification of the GFP:mCherry fluorescence ratios of images.

Images were taken under a broad excitation and emission filter to allow for simultaneous capture of GFP and mCherry fluorescence. Intensities of each pixel in the red and green channels were extracted in python. Unfiltered pixel intensities are shown as black dots. Pixels were filtered, background subtracted, and linear regression performed (red dots and line, see Methods). For simplicity, the green–red intensities from 1,000 random pixels are shown. The GFP:mCherry fluorescence ratio was taken as the slope of the linear regression line. 10 × objective.

Extended Data Figure 3 Readthrough regions confer a loss of superfolder GFP fluorescence.

Each of the indicated TerByP regions were inserted downstream of superfolder (sf) GFP, upstream of the let-858 3′ UTR. TerByP is the region after the annotated stop codon, up to and including the first in-frame stop codon in the 3′ UTR. Quantification was performed as described in Extended Data Fig. 2.

Extended Data Figure 4 Explanation of ‘shuffle’ sequences.

Trinucleotide codons from each TerByP region are colour-coded by gene (top). Codons were extracted and randomly shuffled in python. A codon was iteratively selected until a stop codon was encountered, defining shuffle1. The process was repeated twice more to define shuffle2 and shuffle3. The resulting shuffle peptides are a combination of all three TerByP regions. Lengths and colour-coding of codons for shuffle1–3 accurately reflect the sequences they are derived from.

Extended Data Figure 5 RNA-seq and ribo-seq from unc-54 mutants.

a–c, RNA-seq (a) and ribosome footprint profiling (ribo-seq) (b) library mRNA counts, with summary counts (c) for the indicated strains and mRNAs. Libraries were prepared from L4 animals, as described Methods. ‘N2’ is wild type (PD1074, VC2010 (ref. 53)). unc-54(cc3389) bears a TAA (stop) to AAT (Asn) mutation, unc-54(TerByP). unc-54(e1301) has a GGA (Gly387) to AGA (Arg387) point mutation that confers a temperature-sensitive Unc phenotype with minimal discernible effects on UNC-54 protein levels. unc-54(e1301) was included as a control for the Unc phenotype of unc-54(cc3389), though e1301 confers a less severe Unc phenotype than cc3389. Values for unc-54 mRNA (blue) are highlighted throughout, and for comparison, three additional transcripts known to be at least partly expressed in the body-wall muscles are also highlighted: unc-87 (pink), unc-15 (green), and unc-22 (red).

Extended Data Figure 6 Ribo-seq of unc-54(cc3389) shows an unexceptional progression of ribosomes in the readthrough region.

a, Raw ribo-seq reads for unc-54(+) (blue) and unc-54(cc3389) (green) animals, plotted as read pile-ups. Mismatched bases are indicated with black bars. Location of the normal stop codon and the first in-frame stop codon are indicated with ‘TAA’ and dotted lines. The extension in unc-54(cc3389) is 30 amino acids. b, The number of ribo-seq reads in the last 30 codons, compared to the previous 30 codons, for all mRNAs. Linear regression was performed on all points (solid line), and twofold difference shown (dashed lines). c, The distribution of ribo-seq reads in the last 30 codons (90 nt) of unc-54(cc3389) is shown in green, and the 95% confidence interval (CI) for all open reading frames in dashed lines. d, The fraction of in-frame ribo-seq reads in the last 30 codons is plotted as a function of read counts in the last 30 codons, and unc-54(cc3389) highlighted. e, The distribution of read lengths in the last 30 codons of unc-54(cc3389), and all open reading frames (95% confidence interval, dashed lines). For b–d, reads were restricted to 28, 29, 30 nt lengths. For b–e, a 12 nt offset was performed for the ribosomal P-site, and read counts were derived solely from the unc-54(cc3389) ribo-seq library. For c and e, a minimum 15 read counts was imposed to obtain the 95% confidence interval from ‘all genes’.

Extended Data Figure 7 Lack of general conservation of coding potential downstream of stop codons in Caenorhabditis.

Whole-genome alignment of six nematode species with C. elegans genome assembly ce10/WS220 was obtained from the UCSC genome browser. For each annotated transcript, the aligned bases from the multiple species alignment were extracted and compared to the reference (C. elegans) genome. The left plot shows summary information of the alignment centred on annotated stop codons; the right plot shows the same centred on the first in-frame stop codon in 3′ UTRs. In red is the substitution frequency, that is, the number of mismatched bases divided by the number of aligned bases at a given position. The enrichment of ‘wobble’ position mutations is apparent as an increase in substitutions at the third position of each codon in the CDS. In green is the synonymous substitution frequency, that is, for codons beginning at a given position, the fraction of mutations that yield a synonymous substitution divided by all mutations at that position (synonymous and non-synonymous). The tendency to conserve amino acids in the CDS is apparent as a green spike at every in-frame codon. The change in substitution frequency and synonymous substitution frequency about the first in-frame stop codon (right plot) is due to a tendency for NTR codons to be conserved, and for AAN/AGN/GAN codons to not be conserved in 3′ UTRs, regardless of frame.

Extended Data Figure 8 Nucleotide and amino acid composition of readthrough regions (C. elegans).

CDSs and 3′ UTRs were analysed for various sequence properties. For simplicity, only genes and 3′ UTRs for which a single 3′ UTR was annotated were considered. Similar results were obtained with genes with multiple 3′ UTRs. a, Nucleotide frequency of CDS, 3′ UTR, and TerByP (region between annotated stop codon and first in-frame stop codon). b, Frequency of amino acids in all three possible frames for the TerByP region. 3′ UTRs were translated one codon past the stop codon of the CDS until the next in-frame stop codon, with nonstop 3′ UTRs ignored. Highlighted are codons with high G content (GGN, Gly) and high T content (TTY, Phe). c, TerByP regions tend to be hydrophobic, regardless of frame. Kyte–Doolittle score was used as a measure of hydrophobicity54. To reduce noise, only TerByP regions at least 10 amino acids long were considered. P value is from Kolmogorov–Smirnov test comparing CDSs and TerByP sequences (each frame has P value < 10e-293 for this comparison). As the TerByP sequences are shorter than CDSs on average, the distribution of TerByP hydrophobicity scores will tend to have higher variance than CDSs. Random portions of CDSs were taken, length-matched to TerByP frame zero peptide lengths. This was repeated 100 times, and the 95% confidence interval is shown (dashed lines, ‘CDS rands’). d, Hydrophobicity of the inserts is correlated with a negative effect on GFP fluorescence. The GFP:mCherry fluorescence ratio (Fig. 2b) was plotted against the maximum Kyte–Doolittle score in a six amino acid window for each insert. (Similar results were obtained using the Kyte–Doolittle score averaged across the entire sequence.) Mean (circle) and s.d. (bars) are shown. 3′-UTR-derived sequences are in blue, and non-3′-UTR-derived sequences are in red. To avoid redundancy or skewing of the data, in cases where multiple constructs were present with the same peptide sequence (for example, unc-54(TerByP), unc-54(TerByP, syn1), and unc-54(TerByP, syn2)), only the first of these was used. e, Hydrophobicity analysis of the TerByP extensions obtained by CRISPR–Cas9 engineering at the unc-22 and unc-54 loci. ‘+1/-1 TerByP’ denotes the gain or loss of a nucleotide, generating a late frameshift and allowing translation to proceed past the annotated stop codon out of frame with the upstream open reading frame. In each case, Kyte–Doolittle hydropathy was used to analyse the C-terminal appendage. The least phenotypically affected strain of the three is shown in bold.

Extended Data Figure 9 Nucleotide and amino acid composition of readthrough regions (H. sapiens).

a, b, Similar analysis of hydrophobicity as in Extended Data Fig. 8c, d, performed in humans.

Extended Data Table 1 Translation into 3′ UTRs at endogenous loci tends to yield hypomorphs
Full size table

Supplementary information

Supplementary Table 1

This table contains a list of strains used in the study. (XLSX 14 kb)

Supplementary Table 2

This table contains a list of plasmids used in the study. (XLSX 17 kb)

Supplementary Table 3

This table contains DNA oligos for rRNA digestion by RNaseH. (XLSX 7 kb)

Supplementary Information

This file contains supplementary text and gel source data for figure 3d. (PDF 704 kb)

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Arribere, J., Cenik, E., Jain, N. et al. Translation readthrough mitigation. Nature 534, 719–723 (2016). https://doi.org/10.1038/nature18308

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