Abstract
Post-transcriptional modification of RNA nucleosides occurs in all living organisms. Pseudouridine, the most abundant modified nucleoside in non-coding RNAs1, enhances the function of transfer RNA and ribosomal RNA by stabilizing the RNA structure2,3,4,5,6,7,8. Messenger RNAs were not known to contain pseudouridine, but artificial pseudouridylation dramatically affects mRNA function—it changes the genetic code by facilitating non-canonical base pairing in the ribosome decoding centre9,10. However, without evidence of naturally occurring mRNA pseudouridylation, its physiological relevance was unclear. Here we present a comprehensive analysis of pseudouridylation in Saccharomyces cerevisiae and human RNAs using Pseudo-seq, a genome-wide, single-nucleotide-resolution method for pseudouridine identification. Pseudo-seq accurately identifies known modification sites as well as many novel sites in non-coding RNAs, and reveals hundreds of pseudouridylated sites in mRNAs. Genetic analysis allowed us to assign most of the new modification sites to one of seven conserved pseudouridine synthases, Pus1–4, 6, 7 and 9. Notably, the majority of pseudouridines in mRNA are regulated in response to environmental signals, such as nutrient deprivation in yeast and serum starvation in human cells. These results suggest a mechanism for the rapid and regulated rewiring of the genetic code through inducible mRNA modifications. Our findings reveal unanticipated roles for pseudouridylation and provide a resource for identifying the targets of pseudouridine synthases implicated in human disease11,12,13.
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Acknowledgements
We thank I. Cheeseman, C. Burge, U. RajBhandary, D. Bartel and members of the Gilbert laboratory for comments and discussion. The sequencing was performed at the BioMicro Center under the direction of S. Levine. This work was supported by grants from The American Cancer Society – Robbie Sue Mudd Kidney Cancer Research Scholar Grant (RSG-13-396-01-RMC) and the National Institutes of Health (GM094303, GM081399) to W.V.G. T.M.C. was supported by the American Cancer Society New England Division (Ellison Foundation Postdoctoral Fellowship), and K.M.B. was supported by a Postdoctoral Fellowship (PF-13-319-01 – RMC) from the American Cancer Society. This work was supported in part by the NIH Pre-Doctoral Training Grant T32GM007287.
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T.M.C. and W.V.G. conceived and designed the experiments. T.M.C., M.F.R.-D., H.S., K.M.B. and W.V.G. performed the experiments. T.M.C., B.Z. and H.S. performed the bioinformatic analyses. T.M.C. and W.V.G. interpreted the results and wrote the paper with input from all authors.
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Extended data figures and tables
Extended Data Figure 1 Detection of specific snoRNA target sites by Pseudo-seq.
Pseudo-seq was performed on wild-type (n = 4), snr37Δ (n = 2), snr81Δ (n = 2), snr43Δ (n = 2) and snr49Δ (n = 2) yeast strains. Cultures were harvested at high density (a, b) or log phase (c, d). Ψs dependent on the deleted snoRNA are indicated in red. CMC-dependent peaks of reads are indicated with dashed red lines. Traces are representative of indicated number of biological replicates. a, Pseudo-seq reads in RDN25-1 (chrXII: 452221–452270) showing SNR37-dependence of 25S-Ψ2944. b, Pseudo-seq reads in RDN25-1 (chrXII: 454111–454160, left), and U2 snRNA (LSR1, chrII: 681791–681840, right) showing SNR81-dependence of 25S-Ψ1052 and U2-Ψ42. c, Pseudo-seq reads in RDN58-1 (chrXII: 455466–455515) showing SNR43-dependence of 5.8S-Ψ73. SNR43-dependent 25S-Ψ960 was not consistently detected in wild type owing to an overlapping CMC-independent reverse transcription stop. d, Pseudo-seq reads in RDN18-1 (chrXII: 457361–457610) showing SNR49-dependence of 18S-Ψ302, 18S-Ψ211, and 18S-Ψ120. 25S-Ψ990 was also detected as SNR49-dependent (data not shown).
Extended Data Figure 2 Technical variations of Pseudo-seq give similar results.
a–d, MetaPsi plots (left), and ROC curves (right) for various library prep conditions n = 1 for each condition. CMC-treated samples (solid) and mock-treated samples (dashed) are indicated. a, Comparison of AMV-RT (orange) and SuperScript III (blue) (0.2 M CMC; 115–130 nt, 100–115 nt fragments respectively). b, Comparison of 115–130 nt (orange), and 130–145 nt (blue) RNA fragment sizes (AMV-RT; 0.2 M CMC). c, Comparison of 0.2 M CMC (blue), and 0.4 M CMC (orange) (AMV-RT; 115–130 nt RNA). d, Comparison of shorter (orange) and longer (blue) truncated reverse transcription fragment sizes (AMV-RT; 115–130 nt RNA; 0.2 M CMC).
Extended Data Figure 3 Identification of pseudouridines in lowly expressed genes using multiple replicates.
a, Growth curves for wild-type yeast were grown in YPD. An A600 nm of 12 is indicated by the horizontal dotted line. b, c, Pseudo-seq was performed on polyA+ RNA isolated from high-density wild-type yeast strains. CMC-dependent peaks of reads are indicated with dashed red lines. b, Pseudo-seq reads from n = 4 biological replicates in a CDC39 (chrIII: 286226–286445, 12.3 average RPKMs), and c, IQG1 (chrXVI: 90655–90955, 12.4 average RPKMs) showing CDC39-Ψ6223 and IQG1-Ψ4367, respectively.
Extended Data Figure 4 Codons affected by mRNA pseudouridylation.
Pseudouridylation of mRNA preferentially affects GUA codons. Numbers of pseudouridines observed at the first (dark blue), second (blue), and third positions (light blue) of each codon are indicated.
Extended Data Figure 5 Expression levels minimally affect identification of yeast mRNAs displaying regulated pseudouridylation.
A plot of log-transformed average RPKMs in high-density versus log-phase yeast for all coding genes with a Ψ identified by Pseudo-seq n = 4 biological replicates for each condition. All genes (grey), genes with a high density induced Ψ (blue), and genes with a log phase induced Ψ (red) are indicated.
Extended Data Figure 6 Inducible pseudouridylation of ncRNAs.
a, b, Pseudo-seq was performed on wild-type (n = 4), snr81Δ (n = 2), pus1Δ (n = 2) and pus7Δ (n = 2) yeast strains grown to high density. CMC dependent peaks of reads are indicated with a dashed red line. Traces are representative indicated number of biological replicates. a, Pseudo-seq reads in U2 snRNA (LSR1; chrII: 681751–681790, left; chrII: 681769–681818, right) showing SNR81-dependence of U2-Ψ93, and PUS1-dependence of U2-Ψ56. Both are dependent on growth to high density. b, Pseudo-seq reads in U3a snoRNA (SNR17A, chrXV: 780461–780560) showing snR17A-Ψ369 (PUS7-dependent), snR17A-Ψ380, snR17A-Ψ391 and snR17A-Ψ425 (PUS1-dependent). c, Summaries of the numbers of Ψs called in ncRNAs by Pseudo-seq. Indicated are constitutive Ψs (top), and inducible Ψs (bottom).
Extended Data Figure 7 Analysis of potential snoRNA targets.
a–d, Pseudo-seq was performed on wild-type yeast in log phase, or grown to high density. Reads from n = 4 biological replicate libraries for each condition were pooled. b–d, Indicated are the predicted snoRNA target site (black, dashed), and the expected peak of CMC-dependent reads (black, dotted). a, b, Results of analysis on sets of random Us. a, A histogram of the differences (+CMC − −CMC) in mean normalized reads at the +1 peak position for 10,000 randomizations for high density (orange) and log phase (blue). The normalized read values for the computationally predicted Ψs in exponential and high density samples are indicated by arrows. b, An averaged metaPsi plot for all randomizations. c, d, +CMC (c) and −CMC (d) MetaPsi plots for computationally predicted Ψs separated by base pairing. Sites with 8 or more (red), 9 or more (blue), and 10 or more (orange) base pairs are indicated. Data for high density (left), and log phase (right) are indicated. e, Pseudo-seq reads for computationally predicted Ψs, CAT2 (chrXII: 193995–19450, left), and AIM6 (chrIV: 31135–31550, right). Traces are representative of six biological replicates.
Extended Data Figure 8 Mechanisms of Pus-dependent pseudouridylation.
a, b, Summaries of the PUS-dependence of called Ψs using higher stringency cut-offs (10/14 libraries) (a) and lower stringency cut-offs (9/14 libraries) (b). c, f, CMC-dependent peaks of reads are indicated with dashed red lines. Traces are representative of n = 4 (wild type), and n = 2 (pusΔ) biological replicates. Pseudo-seq reads for RPL14A (a, chrXI: 431901–432200) and PDI1 (d, chrIII: 49401–48760) showing PUS1- and PUS7-dependency, respectively. Both are dependent on growth to high density. d, e, g, h, WebLogo 3.3 was used to generate motifs for PUS1 (d), PUS2 (e), PUS7 (g) and PUS4 (h).
Extended Data Figure 9 Positive controls for human RNA Pseudo-seq.
a, Pseudo-seq reads for RDN28S5 (1516–1765) containing five known Ψs (28S-Ψ1536, 28S-Ψ1582, 28S-Ψ1677, 28S-Ψ1683 and 28S-Ψ1744). CMC-dependent peaks of reads are indicated with dashed red lines. Traces are representative of n = 5 biological replicates. b, A metaPsi plot of mean normalized reads (left axis) for +CMC libraries (orange), and −CMC libraries (blue). The number of Ψs at each position in the metaPsi window is indicated (black, right axis). c, A ROC curve of the Pseudo-seq signal for all known Ψs in rRNA and snRNA.
Extended Data Figure 10 New pseudouridines in human RNAs.
a, A Venn diagram showing the overlap of mRNA pseudouridylation events between serum-fed and serum-starved HeLa cells. b, A plot of log-transformed average RPKMs in serum-starved versus serum-fed HeLa for all coding genes with a Ψ identified by Pseudo-seq. All genes with a Ψ (grey), genes with a Ψ induced in plus serum cells (blue), and genes with a Ψ induced in serum-starved cells (red) are indicated. c, Pseudo-seq reads for RDN18S5 (184–411) (top, left), RDN18S5 (1015–1210) (top, right), RDN28S5 (2713–3108) (bottom, left), and RDN28S5 (4461–4618) (bottom, right). CMC-dependent peaks of reads are indicated with dashed red lines, and highlighted Ψs are indicated by red boxes. Traces are representative of n = 4 biological replicates.
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Carlile, T., Rojas-Duran, M., Zinshteyn, B. et al. Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells. Nature 515, 143–146 (2014). https://doi.org/10.1038/nature13802
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DOI: https://doi.org/10.1038/nature13802
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