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Journal Article

Endogenous stochastic decoding of the CUG codon by competing Ser- and Leu-tRNAs in Ascoidea asiatica.

MPS-Authors
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Schmitt,  H. D.
Research Group of Membrane Transport in Yeast, MPI for biophysical chemistry, Max Planck Society;

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Pan,  K. T.
Research Group of Bioanalytical Mass Spectrometry, MPI for biophysical chemistry, Max Planck Society;

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Plessmann,  U.
Research Group of Bioanalytical Mass Spectrometry, MPI for biophysical chemistry, Max Planck Society;

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Urlaub,  H.
Research Group of Bioanalytical Mass Spectrometry, MPI for biophysical chemistry, Max Planck Society;

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Fulltext (public)

2604505.pdf
(Publisher version), 2MB

Supplementary Material (public)

2604505_Suppl_1.pdf
(Supplementary material), 5MB

2604505_Suppl_2.xls
(Supplementary material), 47KB

2604505_Suppl_3.xls
(Supplementary material), 45KB

2604505_Suppl_4.pdf
(Supplementary material), 7MB

Citation

Mühlhausen, S., Schmitt, H. D., Pan, K. T., Plessmann, U., Urlaub, H., Hurst, L. D., et al. (2018). Endogenous stochastic decoding of the CUG codon by competing Ser- and Leu-tRNAs in Ascoidea asiatica. Current Biology, 28(13), 2046-2057. doi:10.1016/j.cub.2018.04.085.


Cite as: http://hdl.handle.net/21.11116/0000-0001-91D7-8
Abstract
Although the "universal" genetic code is now known not to be universal, and stop codons can have multiple meanings, one regularity remains, namely that for a given sense codon there is a unique translation. Examining CUG usage in yeasts that have transferred CUG away from leucine, we here report the first example of dual coding: Ascoidea asiatica stochastically encodes CUG as both serine and leucine in approximately equal proportions. This is deleterious, as evidenced by CUG codons being rare, never at conserved serine or leucine residues, and predominantly in lowly expressed genes. Related yeasts solve the problem by loss of function of one of the two tRNAs. This dual coding is consistent with the tRNA-loss-driven codon reassignment hypothesis, and provides a unique example of a proteome that cannot be deterministically predicted.