Abstract
The Sec secretion pathway is found across all domains of life. A critical feature of Sec secreted proteins is the signal peptide, a short peptide with distinct physicochemical properties located at the N-terminus of the protein. Previous work indicates signal peptides are biased towards translationally inefficient codons, which is hypothesized to be an adaptation driven by selection to improve the efficacy and efficiency of the protein secretion mechanisms. We investigate codon usage in the signal peptides of E. coli using the Codon Adaptation Index (CAI), the tRNA Adaptation Index (tAI), and the ribosomal overhead cost formulation of the stochastic evolutionary model of protein production rates (ROC-SEMPPR). Comparisons between signal peptides and 5′-end of cytoplasmic proteins using CAI and tAI are consistent with a preference for inefficient codons in signal peptides. Simulations reveal these differences are due to amino acid usage and gene expression – we find these differences disappear when accounting for both factors. In contrast, ROC-SEMPPR, a mechanistic population genetics model capable of separating the effects of selection and mutation bias, shows codon usage bias (CUB) of the signal peptides is indistinguishable from the 5′-ends of cytoplasmic proteins. Additionally, we find CUB at the 5′-ends is weaker than later segments of the gene. Results illustrate the value in using models grounded in population genetics to interpret genetic data. We show failure to account for mutation bias and the effects of gene expression on the efficacy of selection against translation inefficiency can lead to a misinterpretation of codon usage patterns.
Original language | English |
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Pages (from-to) | 2479-2485 |
Number of pages | 7 |
Journal | Biochimica et Biophysica Acta - Biomembranes |
Volume | 1860 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2018 |
Funding
The authors acknowledge financial support from NSF grant MCB-1546402 (Primary Investigator: A. VonArnim), NSF grant MCB-1120370 (Primary Investigator: M.A. Gilchrist), the U.S. Department of Energy , Office of Science, and the Graduate School of Genome Science and Technology ( University of Tennessee , Knoxville). Additional support was provided by the National Institute for Mathematical and Biological Synthesis ( NSF:DBI-1300426 with additional support from the University of Tennessee). The authors acknowledge financial support from NSF grant MCB-1546402 (Primary Investigator: A. VonArnim), NSF grant MCB-1120370 (Primary Investigator: M.A. Gilchrist), the U.S. Department of Energy, Office of Science, and the Graduate School of Genome Science and Technology (University of Tennessee, Knoxville). Additional support was provided by the National Institute for Mathematical and Biological Synthesis (NSF:DBI-1300426 with additional support from the University of Tennessee).
Funders | Funder number |
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Graduate School of Genome Science and Technology | |
National Science Foundation | MCB-1546402, MCB-1120370 |
U.S. Department of Energy | |
Office of Science | |
University of Tennessee | |
National Institute for Mathematical and Biological Synthesis | DBI-1300426 |
National Stroke Foundation |
Keywords
- Adaptationist
- Codon usage bias
- Protein secretion
- Protein synthesis
- Signal peptides