N4-acetylcytidine (ac4C) is an ancient and highly conserved RNA modification that is present on tRNA and rRNA and has recently been investigated in eukaryotic mRNA1,2,3. However, the distribution, dynamics and functions of cytidine acetylation have yet to be fully elucidated. Here we report ac4C-seq, a chemical genomic method for the transcriptome-wide quantitative mapping of ac4C at single-nucleotide resolution. In human and yeast mRNAs, ac4C sites are not detected but can be induced—at a conserved sequence motif—via the ectopic overexpression of eukaryotic acetyltransferase complexes. By contrast, cross-evolutionary profiling revealed unprecedented levels of ac4C across hundreds of residues in rRNA, tRNA, non-coding RNA and mRNA from hyperthermophilic archaea. Ac4C is markedly induced in response to increases in temperature, and acetyltransferase-deficient archaeal strains exhibit temperature-dependent growth defects. Visualization of wild-type and acetyltransferase-deficient archaeal ribosomes by cryo-electron microscopy provided structural insights into the temperature-dependent distribution of ac4C and its potential thermoadaptive role. Our studies quantitatively define the ac4C landscape, providing a technical and conceptual foundation for elucidating the role of this modification in biology and disease4,5,6.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
CPEB1-dependent disruption of the mRNA translation program in oocytes during maternal aging
Nature Communications Open Access 26 January 2023
Actin cytoskeleton and complex cell architecture in an Asgard archaeon
Nature Open Access 21 December 2022
Detection technologies for RNA modifications
Experimental & Molecular Medicine Open Access 21 October 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Ac4C-seq datasets generated in this manuscript have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE135826. The cryo-EM density maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-10223 and EMD-10503 for the wild-type strain grown at 85 °C and 65 °C, respectively, and EMD-10224 for the TkNAT10-deletion strain. Model coordinates have been deposited in the Protein Data Bank (PDB) under accession numbers 6SKF, 6TH6 and 6SKG. Mass spectrometry proteomics data has been deposited to the ProteomeXchange Consortium via Pride77,78 partner repository with the dataset identifier PXD014814.
Code for the analyses described in this paper is available from the corresponding author upon request.
Sharma, S. et al. Yeast Kre33 and human NAT10 are conserved 18S rRNA cytosine acetyltransferases that modify tRNAs assisted by the adaptor Tan1/THUMPD1. Nucleic Acids Res. 43, 2242–2258 (2015).
Ito, S. et al. A single acetylation of 18S rRNA is essential for biogenesis of the small ribosomal subunit in Saccharomyces cerevisiae. J. Biol. Chem. 289, 26201–26212 (2014).
Ito, S. et al. Human NAT10 is an ATP-dependent RNA acetyltransferase responsible for N4-acetylcytidine formation in 18 S ribosomal RNA (rRNA). J. Biol. Chem. 289, 35724–35730 (2014).
Larrieu, D., Britton, S., Demir, M., Rodriguez, R. & Jackson, S. P. Chemical inhibition of NAT10 corrects defects of laminopathic cells. Science 344, 527–532 (2014).
Tschida, B. R. et al. Sleeping Beauty insertional mutagenesis in mice identifies drivers of steatosis-associated hepatic tumors. Cancer Res. 77, 6576–6588 (2017).
Zhang, H. et al. GSK-3β-regulated N-acetyltransferase 10 is involved in colorectal cancer invasion. Clin. Cancer Res. 20, 4717–4729 (2014).
Kotelawala, L., Grayhack, E. J. & Phizicky, E. M. Identification of yeast tRNA Um44 2′-O-methyltransferase (Trm44) and demonstration of a Trm44 role in sustaining levels of specific tRNASer species. RNA 14, 158–169 (2008).
Dewe, J. M., Whipple, J. M., Chernyakov, I., Jaramillo, L. N. & Phizicky, E. M. The yeast rapid tRNA decay pathway competes with elongation factor 1A for substrate tRNAs and acts on tRNAs lacking one or more of several modifications. RNA 18, 1886–1896 (2012).
Sharma, S. et al. Specialized box C/D snoRNPs act as antisense guides to target RNA base acetylation. PLoS Genet. 13, e1006804 (2017).
Arango, D. et al. Acetylation of cytidine in mRNA promotes translation efficiency. Cell 175, 1872–1886.e24 (2018).
Thomas, J. M. et al. A chemical signature for cytidine acetylation in RNA. J. Am. Chem. Soc. 140, 12667–12670 (2018).
Sinclair, W. R. et al. Profiling cytidine acetylation with specific affinity and reactivity. ACS Chem. Biol. 12, 2922–2926 (2017).
Tardu, M., Jones, J. D., Kennedy, R. T., Lin, Q. & Koutmou, K. S. Identification and quantification of modified nucleosides in Saccharomyces cerevisiae mRNAs. ACS Chem. Biol. 14, 1403–1409 (2019).
Kowalak, J. A., Dalluge, J. J., McCloskey, J. A. & Stetter, K. O. The role of posttranscriptional modification in stabilization of transfer RNA from hyperthermophiles. Biochemistry 33, 7869–7876 (1994).
Taoka, M. et al. Landscape of the complete RNA chemical modifications in the human 80S ribosome. Nucleic Acids Res. 46, 9289–9298 (2018).
Orita, I. et al. Random mutagenesis of a hyperthermophilic archaeon identified tRNA modifications associated with cellular hyperthermotolerance. Nucleic Acids Res. 47, 1964–1976 (2019).
Yu, N. et al. tRNA modification profiles and codon-decoding strategies in Methanocaldococcus jannaschii. J. Bacteriol. 201, e00690-18 (2019).
Sharma, S. & Lafontaine, D. L. J. ‘View from a bridge’: a new perspective on eukaryotic rRNA base modification. Trends Biochem. Sci. 40, 560–575 (2015).
Fischer, N. et al. Structure of the E. coli ribosome–EF-Tu complex at <3 Å resolution by Cs-corrected cryo-EM. Nature 520, 567–570 (2015).
Polikanov, Y. S., Melnikov, S. V., Söll, D. & Steitz, T. A. Structural insights into the role of rRNA modifications in protein synthesis and ribosome assembly. Nat. Struct. Mol. Biol. 22, 342–344 (2015).
Kawai, G. et al. Conformational rigidity of N4-acetyl-2′-O-methylcytidine found in tRNA of extremely thermophilic Archaebacteria (Archaea). Nucleosides Nucleotides 11, 759–771 (1992).
Bruenger, E. et al. 5S rRNA modification in the hyperthermophilic archaea Sulfolobus solfataricus and Pyrodictium occultum. FASEB J. 7, 196–200 (1993).
Kumbhar, B. V., Kamble, A. D. & Sonawane, K. D. Conformational preferences of modified nucleoside N4-acetylcytidine, ac4C occur at “wobble” 34th position in the anticodon loop of tRNA. Cell Biochem. Biophys. 66, 797–816 (2013).
Parthasarathy, R., Ginell, S. L., De, N. C. & Chheda, G. B. Conformation of N4-acetylcytidine, a modified nucleoside of tRNA, and stereochemistry of codon–anticodon interaction. Biochem. Biophys. Res. Commun. 83, 657–663 (1978).
Safra, M. et al. The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution. Nature 551, 251–255 (2017).
Li, X. et al. Base-resolution mapping reveals distinct m1a methylome in nuclear- and mitochondrial-encoded transcripts. Mol. Cell 68, 993–1005.e9 (2017).
Grozhik, A. V. et al. Antibody cross-reactivity accounts for widespread appearance of m1A in 5′ UTRs. Nat. Commun. 10, 5126 (2019).
Helm, M., Lyko, F. & Motorin, Y. Limited antibody specificity compromises epitranscriptomic analyses. Nat. Commun. 10, 5669 (2019).
Gehring, A. M., Sanders, T. J. & Santangelo, T. J. Markerless gene editing in the hyperthermophilic archaeon Thermococcus kodakarensis. Bio Protoc. 7, e2604 (2017).
Hileman, T. H. & Santangelo, T. J. Genetics techniques for Thermococcus kodakarensis. Front. Microbiol. 3, 195 (2012).
Santangelo, T. J., Cubonová, L., James, C. L. & Reeve, J. N. TFB1 or TFB2 is sufficient for Thermococcus kodakaraensis viability and for basal transcription in vitro. J. Mol. Biol. 367, 344–357 (2007).
Santangelo, T. J. & Reeve, J. N. Deletion of switch 3 results in an archaeal RNA polymerase that is defective in transcript elongation. J. Biol. Chem. 285, 23908–23915 (2010).
Lipscomb, G. L. et al. Natural competence in the hyperthermophilic archaeon Pyrococcus furiosus facilitates genetic manipulation: construction of markerless deletions of genes encoding the two cytoplasmic hydrogenases. Appl. Environ. Microbiol. 77, 2232–2238 (2011).
Oger, P. et al. Complete genome sequence of the hyperthermophilic archaeon Thermococcus sp. strain AM4, capable of organotrophic growth and growth at the expense of hydrogenogenic or sulfidogenic oxidation of carbon monoxide. J. Bacteriol. 193, 7019–7020 (2011).
Farkas, J. A., Picking, J. W. & Santangelo, T. J. Genetic techniques for the archaea. Annu. Rev. Genet. 47, 539–561 (2013).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Matzov, D. et al. The cryo-EM structure of hibernating 100S ribosome dimer from pathogenic Staphylococcus aureus. Nat. Commun. 8, 723 (2017).
Morlan, J. D., Qu, K. & Sinicropi, D. V. Selective depletion of rRNA enables whole transcriptome profiling of archival fixed tissue. PLoS ONE 7, e42882 (2012).
Shishkin, A. A. et al. Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325 (2015).
Engreitz, J. M. et al. The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science 341, 1237973 (2013).
Machnicka, M. A. et al. MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res. 41, D262–D267 (2013).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).
Jun, G., Wing, M. K., Abecasis, G. R. & Kang, H. M. An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data. Genome Res. 25, 918–925 (2015).
Piechotta, M., Wyler, E., Ohler, U., Landthaler, M. & Dieterich, C. JACUSA: site-specific identification of RNA editing events from replicate sequencing data. BMC Bioinformatics 18, 7 (2017).
Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).
Vainberg Slutskin, I., Weingarten-Gabbay, S., Nir, R., Weinberger, A. & Segal, E. Unraveling the determinants of microRNA mediated regulation using a massively parallel reporter assay. Nat. Commun. 9, 529 (2018).
Weingarten-Gabbay, S. et al. Systematic discovery of cap-independent translation sequences in human and viral genomes. Science 351, aad4939 (2016).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Tarazona, S., García, F., Ferrer, A., Dopazo, J. & Conesa, A. NOIseq: a RNA-seq differential expression method robust for sequencing depth biases. EMBnet.journal 17, 18–19 (2012).
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
Basanta-Sanchez, M., Temple, S., Ansari, S. A., D’Amico, A. & Agris, P. F. Attomole quantification and global profile of RNA modifications: Epitranscriptome of human neural stem cells. Nucleic Acids Res. 44, e26 (2016).
Yamauchi, Y. et al. Denaturing reversed phase liquid chromatographic separation of non-coding ribonucleic acids on macro-porous polystyrene-divinylbenzene resins. J. Chromatogr. A 1312, 87–92 (2013).
Taoka, M. et al. An analytical platform for mass spectrometry-based identification and chemical analysis of RNA in ribonucleoprotein complexes. Nucleic Acids Res. 37, e140 (2009).
Nakayama, H., Yamauchi, Y., Taoka, M. & Isobe, T. Direct identification of human cellular microRNAs by nanoflow liquid chromatography-high-resolution tandem mass spectrometry and database searching. Anal. Chem. 87, 2884–2891 (2015).
Nakayama, H. et al. Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data. Nucleic Acids Res. 37, e47 (2009).
Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Evaluating chromatographic approaches for the quantitative analysis of a human proteome on Orbitrap-based mass spectrometry systems. J. Proteome Res. 18, 1857–1869 (2019).
Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Effect of dynamic exclusion duration on spectral count based quantitative proteomics. Anal. Chem. 81, 6317–6326 (2009).
Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Improving proteomics mass accuracy by dynamic offline lock mass. Anal. Chem. 83, 9344–9351 (2011).
McDonald, W. H. et al. MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications. Rapid Commun. Mass Spectrom. 18, 2162–2168 (2004).
Xu, T. et al. ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J. Proteomics 129, 16–24 (2015).
Tabb, D. L., McDonald, W. H. & Yates, J. R., III. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1, 21–26 (2002).
Zhang, Y., Wen, Z., Washburn, M. P. & Florens, L. Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal. Chem. 82, 2272–2281 (2010).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Mindell, J. A. & Grigorieff, N. Accurate determination of local defocus and specimen tilt in electron microscopy. J. Struct. Biol. 142, 334–347 (2003).
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166(2018).
Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).
Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).
Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).
Pettersen, E. F., Goddard, T. D. & Huang, C. C. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).
Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D. electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation. Acta Crystallogr. D 65, 1074–1080 (2009).
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).
Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100–D1106 (2017).
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).
Ueda, T. et al. Mutation of the gene encoding the ribonuclease P RNA in the hyperthermophilic archaeon Thermococcus kodakarensis causes decreased growth rate and impaired processing of tRNA precursors. Biochem. Biophys. Res. Commun. 468, 660–665 (2015).
We thank S. Fox (Laboratory of Proteomics and Analytical Technology) for LC–MS analyses, C. Grose (Protein Expression Laboratory) for assisting with cloning and preparation of plasmid DNA, P. Blum (University of Nebraska) and B. Mukhopadhyay (Virginia Tech) for archaeal RNA samples, and N. Elad (Electron Microscopy unit, Weizmann Institute of Science) for assistance in setting up the cryo-EM data collection. We thank the Biophysics Resource in the Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute for assistance with DSC and circular dichroism spectroscopy studies. We thank E. Westhof for insights into RNA structure. S. Schwartz is funded by the Israel Science Foundation (543165), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 714023), and the Estate of Emile Mimran. S. Schwartz is the incumbent of the Robert Edward and Roselyn Rich Manson Career Development Chair in Perpetuity. M.S.B. is supported by the Zuckerman STEM Leadership Program, by Ilse Katz Institute for Material Sciences and Magnetic Resonance Research and by the Helen & Milton A. Kimmelman Center for Biomolecular Structure & Assembly. S. Schwartz and M.S.B. are jointly supported by the Weizmann-Krenter-Katz Interdisciplinary Research grant. J.L.M. is supported by the Intramural Research Program of the National Institutes of Health (NIH), the National Cancer Institute, The Center for Cancer Research (ZIA BC011488-06). M.P.W. is supported by the Stowers Institute for Medical Research and the National Institute of General Medical Sciences of the NIH (R01GM112639). T.J.S. is supported by the National Institute of General Medical Sciences of the NIH (R01GM100329) and the Department of Energy, Basic Energy Sciences Division (DE-SC0014597).
The authors declare no competing interests.
Peer review information Nature thanks Danica Fujimori and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 An optimized reaction for sequencing of N4-acetylcytidine in RNA.
a, Protonation under acidic conditions hyperactivates ac4C, increasing its reactivity with NaCNBH3. Efficient reduction manifests as quantitative misincorporation of deoxynucleotide triphosphates at ac4C upon reverse transcription. b, NaCNBH3-dependent misincorporation at the known ac4C site in human helix 45 is increased at more acidic pH. The percentage misincorporation at ac4C sites after chemical reduction, reverse transcription and PCR was quantified from Sanger sequencing data. One independent experiment. c, Kinetic analysis of ac4C reduction. Reaction progress was assessed by monitoring the disappearance of ac4C absorbance at 300 nm in the presence of first and second-generation hydride donors. Reaction conditions: ac4C (0.1 mM, free nucleoside), reductant (20 mM), H2O. NaBH4 reactions were carried out at pH 10, whereas NaCNBH3 reactions were adjusted to pH 1 using HCl before initiation. Representative of 3 independent experiments. d, Kinetic analysis of the hydrolysis of ac4C at pH values used in NaBH4 (pH 10) and NaCNBH3 (pH 1) reduction reactions. Reaction progress was assessed by monitoring the disappearance of ac4C absorbance at 300 nm. Acid- and base-catalysed hydrolysis occurred at similar rates, and were slow compared to ac4C reduction by NaBH4 and NaCNBH3. Representative of 3 independent experiments. e, LC–MS/MS analysis confirms reduction of ac4C to reduced ac4C in the presence of NaCNBH3. Reaction conditions: ac4C (0.1 mM, free nucleoside), NaCNBH3 (20 mM), HCl pH 1. Representative of 2 independent experiments. f, Exact mass of reduced ac4C and deamination product observed in LC–MS/MS experiments. g, Primer extension analysis of ac4C-containing RNAs after NaCNBH3 treatment (100 mM, pH 1, 37 °C, 1 h). h, Sanger sequence traces of a known ac4C site in helix 45 of human HAP1 cells. C>T misincorporation is exclusively observed at the ac4C site in reduced (NaBH4 and NaCNBH3) but not in mock-treated samples. ac4C sites are highlighted in yellow.
Extended Data Fig. 2 Ac4C in eukaryotic cells with wild-type NAT10 expression.
a–c, ac4C-seq was conducted on RNA from S. cerevisiae (a, c) and HeLa cells (b). Statistical significance from the χ2 test is plotted against the difference in misincorporation rates (corresponding to ac4C levels) between NaCNBH3-treated and mock-treated RNA from S. cerevisiae (a), RNA from wild-type and NAT10-depleted cells (b) or from wild-type S. cerevisiae cells and a strain expressing a catalytic mutant of Kre33 (c), treated with NaCNBH3. Sites with a differential misincorporation level >5% and a P value <0.05 are labelled and marked in red. For HeLa cells (b) an additional comparison between NaCNBH3 and deacetylation pre-treatment was conducted. Sites that do not pass significance under these conditions are marked with a plus sign (shown only for sites found significant between NaCNBH3 and mock treatment). Significant sites are labelled with the identity of the molecule and the relative position (or helix) of ac4C. n = 3 biologically independent samples for all but NAT10-depleted HeLa cells, in which case n = 2 biologically independent samples. d, e, Misincorporation level in the two known sites in 18S (helix 34 and helix 45), compared with controls in poly(A)-enriched RNA from wild-type S. cerevisiae cells and S. cerevisiae cells expressing a catalytic mutant of Kre33 (d) and from wild-type HEK-293T cells and HEK-293T cells overexpressing NAT10 and THUMPD1 (e). f, g, ac4C-seq data from poly(A)-enriched RNA from HEK-293T cells overexpressing NAT10 and THUMPD1 on ‘ac4C peaks’ that have been identified previously10 as harbouring ac4C. f, Distribution of misincorporation across each of 57 ‘ac4C peaks’ that had a coverage of more than 400 reads in more than 80% of the cytosines in the peak. For each peak the cytosine harbouring the highest misincorporation rate is indicated in colour, presented in blue if it harbours a CCG motif and red otherwise. g, Traces from the Integrative Genomic Viewer (IGV) browser of three such genes, with highest coverage in the ac4C-seq data. For each gene the 15 bases motif identified in ref. 10 is presented. The numbers above each cytidine indicate the number of bases (A, C, G and T) observed in our data at that position. h, Power analysis for ac4C detection, as a function of sequencing depths and stoichiometries. Each data point in each curve is based on 1,000 simulations. For each sampled depth, numbers in the legend indicate the sequencing depth, which was kept identical for treatment and control samples. In addition, the legend indicates the number of CCG sites found in wild-type HEK-293T samples that have such a minimal depth and the percentage of these detectable CCG sites from all CCG sites in the transcriptome.
Extended Data Fig. 3 Ac4C in eukaryotic cells with manipulated NAT10 expression.
a, RNA expression of NAT10 and THUMPD1 in HEK-293T cells overexpressing both genes compared to wild-type cells. Shown are TMM-normalized read counts. The numbers above the bars indicate fold increase compared with the wild type. b, Immunoblotting analysis of NAT10 and THUMPD1 overexpression in HEK-293T cells. Representative of 3 independent experiments with similar results. For gel source data, see Supplementary Data 3. c, Microscopy images of the eGFP–NAT10 construct, confirming nuclear and nucleolar localization of ectopically expressed N-terminally tagged protein. Representative of 3 independent experiments with similar results. d, RNA expression of Kre33 and Tan1 in wild-type yeast cells and in cells stably overexpressing Kre33 and either stably or inducibly overexpressing Tan1. The numbers above the bars indicate fold increase from the wild type. e, The number of sites displaying each of the 12 possible misincorporation patterns are displayed (bar plot, y axis) for sites found in poly(A)-enriched RNA from both wild-type S. cerevisiae cells and S. cerevisiae cells overexpressing both Kre33 and Tan1. The pie chart displays the proportion of sites harbouring C>T misincorporations that were embedded within a CCG motif (73 out of 74, 98.6%). f, Schematic of the known ac4C sites in human tRNAs (Leu and Ser) and in helix 34 (C1337) and helix 45 (C1842) of human 18S rRNA. The acetylated cytidine residue (highlighted in blue) is embedded within a CCG motif in all known sites. g, Fraction of ac4C sites found within the 5′ UTR, CDS and 3′ UTR (CDS, coding sequence; UTR, untranslated region). Results are shown for the set of ac4C sites in mRNA of HEK-293T cells overexpressing NAT10 and THUMPD1 (red bars, n = 139), and—as controls—for all CCG motifs present within all genes within which any ac4C was found (blue bars, n = 6,129). Error bars representing standard distribution of the binomial distribution. Data are based on 2 biologically independent samples. h, Fraction of ac4C sites at the first, second and third position of each codon, shown for ac4C sites and controls as in g. Data are mean ±s.d. of the binomial distribution and are based on two biologically independent samples.
Extended Data Fig. 4 Sequence and structure requirements for deposition of ac4C.
a, Oligonucleotides representing the wild-type sequence surrounding the acetylated site in BAZ2A mRNA, or variants with single mutations across the wild-type sequence, were synthesized as a pool and cloned into the 3′UTR of a reporter gene. The pool of plasmids was transfected into wild-type HEK-293T cells or cells transiently overexpressing NAT10 and THUMPD1. RNA extracted from cells was subjected to targeted ac4C-seq and ac4C levels were estimated on the basis of misincorporation rates. b, Misincorporation rate of oligonucleotides described in a, harbouring the wild-type sequence of BAZ2A (green triangles) or a sequence mutated at the CCG motif and at its surrounding bases (red and black, respectively). Box plot visualization parameters are as in Fig. 1h. n = 2 biologically independent samples. c, The difference in misincorporation rate of oligonucleotides with a single base mutation compared with the wild-type oligonucleotide is shown across all positions of the construct. d, De novo construction of the motifs surrounding the modified cytidine were built on the basis of the contribution of single-base mutations in the BAZ2A sequence to the reduction in misincorporation rate compared to wild-type BAZ2A sequence. e, Secondary structure of the BAZ2A mRNA fragment as predicted by RNAfold. Bases are colour-coded according to confidence level of the prediction. Regions highlighted by a blue and green line in c–e represent the CCG motif and a stem structure surrounding the modified cytidine, respectively.
Extended Data Fig. 5 Deletion of TkNAT10 and TkTHUMPD1 in T. kodakarensis.
a, Total RNA from T. kodakarensis was analysed via ac4C-seq. IGV browser traces display representative ac4C sites in rRNA, ncRNA, mRNA and tRNA of T. kodakarensis, visualized as in Fig. 1c. The number in parentheses indicates the number of sites identified for each class of molecules. b, Conserved domain architecture of human NAT10 and its homologue in T. kodakarensis, TK0754 (referred to as TkNAT10 in the text). c, Expression of TkNAT10 and TkTHUMPD1 (TK2097) in wild-type T. kodakarensis and the indicated deletion strains was quantified from ac4C-seq data. Shown are mean TMM-normalized values (n = 3 and 2 biological replicates in wild-type and deletion strains, respectively). d, Quantitative LC–MS/MS proteomics analysis of wild-type and ΔTkNAT10 T. kodakarensis. Fold-change in protein abundance was based on comparison of distributed normalized spectral abundance factor for individual proteins. Fold-change for proteins detectable exclusively in the wild-type or the knockout (KO) condition (fold-change = ∞) are graphed at 5.5 and 0.1, respectively, which represents the maximum and minimum of measured values. n = 3 LC–MS/MS runs for each condition. e, Anti-ac4C immuno-northern blot in T. kodakarensis total RNA. Ethidium bromide staining is used to visualize total RNA. Results are representative of two biological replicates. For gel source data, see Supplementary Data 3. f, Relative quantification of ac4C in total RNA isolated from wild-type and ΔTkNAT10 T. kodakarensis strains as measured by LC–MS. Mean of n = 3 technical replicates. n.d., not detectable. g, Scatter-plot depicting misincorporation rate of ac4C sites in wild-type T. kodakarensis is compared with the TkTHUMPD1-deletion strain, showing no effect of the deletion of the gene on the ac4C status. h, Correlation between misincorporation rates in T. kodakarensis compared to P. furiosus and T. sp. AM4 for the different types of ncRNAs identified by ac4C-seq. The Pearson’s correlation coefficient is indicated at the bottom of each plot. n = 4 and 1 independent biological samples for T. kodakarensis and other archaea, respectively. Shading indicates 95% confidence intervals for predictions from a linear model.
Extended Data Fig. 6 Ac4C accumulates in a temperature-dependent manner across all RNA species in archaea.
a, Relative quantification of ac4C in total RNA isolated from wild-type T. kodakarensis as a function of temperature as measured by LC–MS. Mean is shown along with individual data points. n = 3 technical replicates. For 65, 75, 85 °C: representative of 2 independent experiments, for 95 °C: 1 experiment. b, c, Quantitative LC–MS/MS proteomics analysis of T. kodakarensis temperature-dependent protein expression. Fold-change in T. kodakarensis protein abundance between growth conditions at 85 °C and 65 °C was based on comparison of distributed normalized spectral abundance factor for individual proteins. Fold-change for proteins detectable exclusively in the 85 °C or 65 °C condition (fold-change = ∞) was set at 7.8 and 0.1, respectively, which represents the maximum and minimum of measured values. n = 3 LC–MS/MS runs for each condition. Student’s t-test, paired, two tailed P = 0.012. d, Misincorporation rates of ac4C sites at distinct regions of T. kodakarensis tRNAs as a function of growth temperature (55–95 °C), segregated into distinct regions within the tRNA molecule. Only sites with a minimal stoichiometry of 5% in any sample are shown. Box plot visualization parameters are as in Fig. 1h. n = 4 biologically independent samples for 85 °C, n = 2 for 65 °C and 75 °C and n = 1 for 55 °C and 95 °C. e, Multiple alignment of 37 tRNA molecules, representing 19 distinct tRNAs in T. kodakarensis, plotted across three distinct temperatures. ac4C sites are coloured on the basis of misincorporation rate (see colour bar). The red–orange bar on the left segregates the aligned sequences into distinct tRNA molecules, identified by the single-letter abbreviation of their amino acid. Selected regions from the multiple alignment, where ac4C is particularly abundant, are shown and colour-coded according to the bottom colour bar. f, Schematic representation of RNaseP RNA in T. kodakarensis. ac4C sites (all in CCG) are marked with circles colour-coded by misincorporation rate measured in cells grown at 85 °C. Fine grey lines indicate regions that base pair in the folded structure of the molecule, according to the model in ref. 79. g, h, Distribution of 119 acetylated cytidine residues (in 86 mRNAs) in T. kodakarensis across different codons (g) and at specific position within codons (h) are shown, and compared to that of 2,245 control non-acetylated cytidines, found at CCG motifs of the same mRNAs. The y axis presents the fraction of cytidines in each position. n = 1 set of sites (comprising 119 ac4Cs and 2,245 Cs) with error bars representing standard deviation of the binomial distribution. i, Anti-ac4C immuno-northern blot in P. furiosus and T. sp. AM4 total RNA as a function of temperature. Ethidium bromide staining was used to visualize total RNA. Results are representative of two biological replicates. For gel source data, see Supplementary Data 3. j, A heat map showing misincorporation rates at conserved ac4C sites in 5S, 16S, 23S, RNase P RNA and SRP RNA of T. kodakarensis, P. furiosus and T. sp. AM4 grown at various temperatures. Rows are ordered according to misincorporation rates quantified in T. kodakarensis grown at 95 °C. The arrowhead indicates the conserved ac4C site at helix 45 (top site in the heat map).
Extended Data Fig. 7 Cryo-EM data processing and map reconstruction.
a, Schematic representation of electron microscopy data processing for the T. kodakarensis ribosomes. Data processing was performed in Relion 3 and included motion correction, contrast transfer function correction, particle picking and classification. Initial map reconstruction and post processing was performed by the 3D refinement algorithm implemented in Relion on the complete 70S particle, indicating high residual mobility of the SSU head domain (top left, grey). Further implementation of multibody refinement with individual masks prepared for the LSU (blue), SSU body (green) and SSU head (orange) resulted in the complete reconstruction of the 70S particle. The final map consisting of all three ribosomal domains for the wild-type ribosome derived from cells grown at 85 °C is presented at the bottom left. Fourier shell correlation curves indicating overall (black) and per domain (colour coded according to the relevant masks) resolutions are presented in b for the wild-type strain grown at 85 °C (WT85), c for the wild-type strain grown at 65 °C (WT65) and d for the ΔTkNAT10 ribosomes (mutant). FSC comparisons of full (grey) and half-maps (pink/cyan) to the final refined model are presented in e, f and g for WT85, WT65 and mutant strains, respectively. The excellent agreement between the cyan and pink curves indicates the lack of overfitting.
Extended Data Fig. 8 Cryo-EM data quality and ac4C visualization.
a, Surface (top) and cross-section (bottom) representations of the cryo-EM density maps coloured according to local resolution distribution. Growth conditions and T. kodakarensis strains used in the study along with PDB and EMDB accession codes are indicated. Resolution values are colour-coded according to the right index and are presented in Å. b, Model in density for multiple ac4C positions in wild-type T. kodakaresis grown at 85 °C (orange) and 65 °C (yellow) compared to an ac4C ΔTkNAT10 strain (mutant, blue) indicating the absence of acetate density (highlighted in light orange) in the mutant and in multiple positions of the strain grown at 65 °C. Positions highlighted with an asterisk are also acetylated in the 65 °C strain whereas positions that are unmarked are acetylated only in the archaea grown at 85 °C. These data are in good agreement with both the genomic sequencing and MS approaches described in this manuscript, that similarly indicate that ac4C distribution is highly dependent on growth temperature. A detailed list of ac4C distribution and comparison with other methods is provided in Supplementary Table 4. For a 2D map with ac4C distribution, see Extended Data Fig. 9d.
Extended Data Fig. 9 RNA modifications of T. kodakarensis ribosome and thermostability.
a, Misincorporation level as quantified by ac4C-seq across all ac4C sites identified in ribosomes of T. kodakarensis at 85 °C. Blue and red bars indicate sites that were and were not detected by cryo-EM, respectively. Dashed lines indicate median misincorporation of cryo-EM detected (upper, 13.7%) and not-detected (lower, 3.2%) sites. Acetylation detected by ac4C-seq and also observed in the cryo-EM were generally of medium to high stoichiometry whereas the majority of acetylation sites detected by ac4C-seq but not observed in the cryo-EM map density were of relatively low stoichiometry, rendering them invisible in the ensemble cryo-EM structure, which averages thousands of individual particles for map reconstruction. b–e, Combined cryo-EM and mass spectrometric analysis indicated six ac4C residues that are also methylated at their 2′-O position. Relative quantification of ac4C and ac4Cm detection in T. kodakarensis RNA via LC–MS is presented in b. Mean and individual data points are shown. n = 3 technical replicates. An example of ac4Cm in density is shown in c with acetate and methyl installations indicated by black arrows. A list of ac4Cms is indicated in Supplementary Table 4. 2D (d) and 3D (e) visualization of ac4C and ac4Cm distribution in the T. kodakarensis ribosome with ac4C highlighted orange and ac4Cm green. Data are presented for the T. kodakarensis grown at optimal growth temperature (85 °C). Ac4C positions highlighted in orange include genomic, mass spectrometry and electron microscopy data. Ac4Cm positions are a combination between cryo-EM and mass spectrometry data. In e, RNA and proteins are presented as grey ribbons, modified residues are highlighted as spheres. The protein exit tunnel (ET) is highlighted with a dashed black line, and tRNA is in yellow. The tRNA and mRNA coordinates are from PDB 4V5D. f, A comparative view of RNA modification distribution in E. coli, yeast (S. cerevisiae), human (H. sapiens) and T. kodakarensis. Base modifications are coloured blue, ac4Cs in red, tRNA and mRNAs in yellow. Ribosome functional regions are designated in black with decoding centre (DC), the peptidyl transferase centre (PTC) and the protein exit tunnel (ET) highlighted by a dashed black line. PDB codes for the structures used for comparison are 5AFI, 4V88 and 4UGO, for the E. coli, S. cerevisiae and human ribosome, respectively. g, Misincorporation rate as quantified by ac4C-seq for all ac4C sites in the T. kodakarensis ribosome. The bar colour indicates the lowest growth temperature at which the site was detected. h, 3D representation of the T. kodakarensis ribosome with ac4C sites detected at 55 °C and 85 °C shown and colour-coded according to misincorporation rate in each temperature. i, Ac4Cs were shown to stabilize the T. kodakarensis ribosome via direct interactions with protein and RNA residues. An example of stabilization through RNA–protein interactions is presented in Fig. 4g. RNA–RNA interactions correspond to interactions of ac4C1434 with OP2 of A1786 of LSU. j, Temperature-dependent circular dichroism spectra of synthetic RNAs containing cytidine (blue) or ac4C (red). Solid and dashed lines represent mean and individual measurements, respectively. n = 3 independent experiments. θ, ellipticity at 260 nm.
This file contains Supplementary note 1: Optimization of a reaction for sequencing N4-Acetylcytidine in RNA; Supplementary note 2: Statistical power of ac4C-seq and comparisons to previously published data; Supplementary note 3: Temperature-dependent patterns of ac4C in rRNA, tRNA, ncRNA and mRNA; Supplementary Data 1: The extracted mass chromatograms for the quantification of acetyl-4-cytidine (ac4C) in T. kodakarensis rRNAs; Supplementary Data 2: LC-MS traces of ac4C in human, yeast and archaea; and Supplementary Data 3. Raw gels.
This file contains Supplementary Data 4. Chemical structures of ac4C presented in Figure 1a presented in ChemDraw format. ac4C is reduced causing a misincorporation during reverse transcription. Chemical deacetylation of ac4C by mild alkali serves as a negative control.
Supplementary Table 1
| Sequences of oligonucleotides used in the study (a) Sequences of oligonucleotides used as synthetic ac4C spikes, for in vitro transcription or for reverse transcription of specific RNA targets. (b) oligonucleotides used to deplete ribosomal RNA from T. kodakarensis total RNA samples.
Supplementary Table 2
| ac4C sites identified by ac4C-seq in human, yeast and archaea. Spreadsheet “info”: general explanation of the file contents. spreadsheet “comparisons”: specific parameters used to create the “ac4C sites catalog” for each species. Ten spreadsheets detailing the specific catalog identified for each experiment.
Supplementary Table 3
| post-transcriptional RNA modification identified by tandem LC-MS Excel file with 2 spreadsheets quantifying RNA modifications in T. kodakarensis ribosomes grown at 85 ˚C (WT and TkNat10 depleted) and 65 ˚C. Used as raw data for analysis shown in Figure 2c.
Supplementary Table 4
| Comparison of ac4C sites identified in T. kodakarensis ribosomes by ac4C-seq, cryo-EM and tandem LC-MS ac4C sites identified by either ac4C -seq, LC-MS or cryo-EM. Used for the analysis presented in Figure 2c and Extended Data Figure 9a.
Supplementary Table 5
| Proteins identified from WT and TkNat10 deletion T. kodakarensis cells by mass spectrometry.
Supplementary Table 6
|Cryo-EM data collection and model refinement.
Supplementary Table 7
| Proteins and rRNA compositions for WT ribosomes derived of T. kodakarensis grown at 65˚C and 85˚C.
Supplementary Table 8
| Proteins and rRNA compositions for TkNat10 deletion ribosomes.
Supplementary Table 9
| LC-MS measurements of ac4C as an estimated percentage of total cytidine in human, yeast and archaea.
Rights and permissions
About this article
Cite this article
Sas-Chen, A., Thomas, J.M., Matzov, D. et al. Dynamic RNA acetylation revealed by quantitative cross-evolutionary mapping. Nature 583, 638–643 (2020). https://doi.org/10.1038/s41586-020-2418-2
This article is cited by
Lysine 2-hydroxyisobutyrylation of NAT10 promotes cancer metastasis in an ac4C-dependent manner
Cell Research (2023)
Navigating the pitfalls of mapping DNA and RNA modifications
Nature Reviews Genetics (2023)
N4-acetylcytidine modification of lncRNA CTC-490G23.2 promotes cancer metastasis through interacting with PTBP1 to increase CD44 alternative splicing
CPEB1-dependent disruption of the mRNA translation program in oocytes during maternal aging
Nature Communications (2023)
Actin cytoskeleton and complex cell architecture in an Asgard archaeon
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.