The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells
Open accessAbstract
Post-transcriptional modification of RNA nucleosides occurs in all living organisms. Pseudouridine, the most abundant modified nucleoside in non-coding RNAs, enhances the function of transfer RNA and ribosomal RNA by stabilizing the RNA structure. 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 centre. 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 disease.
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Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E., & Jaffrey, S. R. (2012) · Cell · doi:10.1016/j.cell.2012.05.003 · Cited by 4,223
Coding-Sequence Determinants of Gene Expression in Escherichia coli
Kudla, G., Murray, A. W., Tollervey, D., & Plotkin, J. B. (2009) · Science · doi:10.1126/science.1170160 · Cited by 1,458
Heiss, N. S., Knight, S. W., Vulliamy, T. J., Klauck, S. M., Wiemann, S., Mason, P. J., Poustka, A., & Dokal, I. (1998) · Nature Genetics · doi:10.1038/ng0598-32 · Cited by 976
Rate-Limiting Steps in Yeast Protein Translation
Shah, P., Ding, Y., Niemczyk, M., Kudla, G., & Plotkin, J. B. (2013) · Cell · doi:10.1016/j.cell.2013.05.049 · Cited by 463
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mRNA vaccines — a new era in vaccinology
Pardi, N., Hogan, M. J., Porter, F. W., & Weissman, D. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.243 · Cited by 4,667
Sloan, K. E., Warda, A. S., Sharma, S., Entian, K.-D., Lafontaine, D. L. J., & Bohnsack, M. T. (2017) · RNA Biology · doi:10.1080/15476286.2016.1259781 · Cited by 707
Andries, O., Mc Cafferty, S., De Smedt, S. C., Weiss, R., Sanders, N. N., & Kitada, T. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.08.051 · Cited by 701
The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution
Safra, M., Sas-Chen, A., Nir, R., Winkler, R., Nachshon, A., Bar-Yaacov, D., Erlacher, M., Rossmanith, W., Stern-Ginossar, N., & Schwartz, S. (2017) · Nature · doi:10.1038/nature24456 · Cited by 646
Advances in COVID-19 mRNA vaccine development
Fang, E., Liu, X., Li, M., Zhang, Z., Song, L., Zhu, B., Wu, X., Liu, J., Zhao, D., & Li, Y. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00950-y · Cited by 601
Bohnsack, K. E., Höbartner, C., & Bohnsack, M. T. (2019) · Genes · doi:10.3390/genes10020102 · Cited by 517
METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation
Pandolfini, L., Barbieri, I., Bannister, A. J., Hendrick, A., Andrews, B., Webster, N., Murat, P., Mach, P., Brandi, R., Robson, S. C., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.03.040 · Cited by 491
“, 2016]) or chemical reactivity of the modification (for pseudouridine [Carlile et al., 2014; Schwartz et al., 2014], m5C [Schaefer, 2015], and 2 0-O-methylation [Dai et al.”
BackgroundEpitranscriptome sequencing technologies: decoding RNA modifications
Li, X., Xiong, X., & Yi, C. (2017) · Nature Methods · doi:10.1038/nmeth.4110 · Cited by 447
m 6 A in mRNA: An Ancient Mechanism for Fine-Tuning Gene Expression
Roignant, J.-Y., & Soller, M. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.04.003 · Cited by 426
New frontiers in translational control of the cancer genome
Truitt, M. L., & Ruggero, D. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2016.27 · Cited by 378
The m6A epitranscriptome: transcriptome plasticity in brain development and function
Livneh, I., Moshitch-Moshkovitz, S., Amariglio, N., Rechavi, G., & Dominissini, D. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0244-z · Cited by 330
iRNA-PseU: Identifying RNA pseudouridine sites
Chen, W., Tang, H., Ye, J., Lin, H., & Chou, K.-C. (2016) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2016.37 · Cited by 291
Svitkin, Y. V., Cheng, Y. M., Chakraborty, T., Presnyak, V., John, M., & Sonenberg, N. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx135 · Cited by 278
Jorjani, H., Kehr, S., Jedlinski, D. J., Gumienny, R., Hertel, J., Stadler, P. F., Zavolan, M., & Gruber, A. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw386 · Cited by 257
Wang, Y., Zhang, S., Jia, W., Fan, P., Wang, L., Li, X., Chen, J., Cao, Z., Du, X., Liu, Y., et al. (2022) · Nature Nanotechnology · doi:10.1038/s41565-022-01169-2 · Cited by 143
Profiling of m6A RNA modifications identified an age‐associated regulation of AGO 2 mRNA stability
Min, K., Zealy, R. W., Davila, S., Fomin, M., Cummings, J. C., Makowsky, D., Mcdowell, C. H., Thigpen, H., Hafner, M., Kwon, S., et al. (2018) · Aging Cell · doi:10.1111/acel.12753 · Cited by 131
Mapping epigenetic modifications by sequencing technologies
Chen, X., Xu, H., Shu, X., & Song, C.-X. (2025) · Cell Death & Differentiation · doi:10.1038/s41418-023-01213-1 · Cited by 105
Synthetic modified messenger RNA for therapeutic applications
Gao, M., Zhang, Q., Feng, X.-H., & Liu, J. (2021) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.06.020 · Cited by 89
Role of RNA modifications in brain and behavior
Jung, Y., & Goldman, D. (2018) · Genes, Brain and Behavior · doi:10.1111/gbb.12444 · Cited by 66
“in eukaryote mRNA with next-generation sequencing technology.(7,35,36) Ψ formation is initiated at the cellular level by environmental cues and is thought to be an irreversible modification of RNA.”
BackgroundPost-transcriptional pseudouridylation in mRNA as well as in some major types of noncoding RNAs
Adachi, H., De Zoysa, M. D., & Yu, Y.-T. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2018.11.002 · Cited by 60
PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays
Li, B., Tambe, A., Aviran, S., & Pachter, L. (2017) · Cell Systems · doi:10.1016/j.cels.2017.04.007 · Cited by 15
Metadata
- Authors
- Carlile, T. M., Rojas-Duran, M. F., Zinshteyn, B., Shin, H., Bartoli, K. M., & Gilbert, W. V.
- Venue
- Nature
- Published
- 2014-09-05
- Volume
- 515
- Issue
- 7525
- Pages
- 143-146
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2161890613
- Cited by
- 1,021
- References
- 9
- FWCI
- 28.84
- Type
- journal article
- Language
- en
- Open access
- green
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