Saccharomyces cerevisiae
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA399079 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5945808.fastq.gz | Fastqsanger.gz | |||
| SRR5945809.fastq.gz | Fastqsanger.gz |
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Project description:Although protein synthesis dynamics has been studied both with theoretical models and by profiling ribosome footprints, the determinants of ribosome flux along open reading frames (ORFs) are not fully understood. Combining measurements of protein synthesis rate with ribosome footprinting data, we here inferred translation initiation and elongation rates for over a thousand ORFs in exponentially-growing wildtype yeast cells. We found that the amino acid composition of synthesized proteins is as important a determinant of translation elongation rate as parameters related to codon and tRNA adaptation. We did not find evidence of ribosome collisions curbing the protein output of yeast transcripts, either in high translation conditions associated with exponential growth, or in strains in which deletion of individual ribosomal protein genes leads to globally increased or decreased translation. Slow translation elongation is characteristic of RP-encoding transcripts, which have markedly lower protein output compared to other transcripts with equally high ribosome densities.
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Project description:Translation initiation is considered overall rate-limiting for protein biosynthesis, whereas the impact of non-uniform ribosomal elongation rates is largely unknown. Using a modified ribosome profiling protocol based on footprints from two closely packed ribosomes (disomes), we have mapped ribosomal collisions transcriptome-wide in mouse liver. We uncover that the stacking of an elongating onto a paused ribosome occurs frequently and scales with translation rate, trapping ~10% of translating ribosomes in the disome state. A distinct class of pause sites, independent of translation rate, is indicative of deterministic pausing signals. We find pause sites associated with specific codons, amino acids, and peptide motifs, and with structural features of the nascent polypeptide, suggestive of programmed pausing as a widespread mechanism associated with protein folding. Evolutionary conservation at disome sites and experiments indicate functional relevance of translational pausing. Collectively, our disome profiling approach allows novel and unexpected insights into gene regulation occurring at the step of translation elongation.
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Project description:This translation model consists of 274 biochemical reactions, including 119 reactions with non-linear kinetics. This mechanistic model accounts for the concentrations of mRNA, the ribosome, the different charged tRNAs, and the elongation factors Ts (EF-Ts) and Tu (EF-Tu). We fully parameterized the model with molecular masses and kinetic constants measured experimentally; the only exceptions are the initiation parameters, which were previously estimated from gene expression data, and the ribosomal Michaelis constant for the ternary complexes, which was estimated based on the diffusion limit and hence represents a lower bound. The model is based purely on biochemical and biophysical considerations; it contains no free parameters for fitting, nor does it include any explicit growth-rate dependencies. This model is used to test our hypothesis that, to maximize the E. coli growth rate in a given environment, natural selection minimizes the total cost of translation components utilized to achieve the required protein production rate.
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Project description:Ribosome profiling of whi3 mutant yeast
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Project description:Translation is the process by which genetic information from mRNA is decoded to produce proteins. Since mRNA levels alone do not predict protein amounts accurately, understanding translational control is essential. Ribosome profiling revealed that translation initiation is the main rate-limiting step, with rates varying up to 100-fold across mRNAs, while elongation rates vary less (~20-fold). Furthermore, only a small fraction of the variation in translation rates is explained by known determinants, indicating that much remains to be understood in the prediction of protein outputs of individual mRNAs. Current machine learning models would benefit from more data on endogenous mRNAs. To address this, here we report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically i) ribosome profiling of unperturbed cells and following translation initiation block to trace elongation (run-off ribosome profiling), ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). Predicting protein output from mRNAs is crucial for applications like protein expression engineering or mRNA vaccine designing.
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