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Selective ribosome profiling to study interactions of translating ribosomes in yeast

ABSTRACT: A number of enzymes, targeting factors and chaperones engage ribosomes to support fundamental steps of nascent protein maturation, including enzymatic processing, membrane targeting and co-translational folding. The selective ribosome profiling (SeRP) method is a new tool for studying the co-translational activity of maturation factors that provides proteome-wide information on a factor’s nascent interactome, the onset and duration of binding and the mechanisms controlling factor engagement. SeRP is based on the combination of two ribosome-profiling (RP) experiments, sequencing the ribosome-protected mRNA fragments from all ribosomes (total translatome) and the ribosome subpopulation engaged by the factor of interest (factor-bound translatome). We provide a detailed SeRP protocol, exemplified for the yeast Hsp70 chaperone Ssb (stress 70 B), for studying factor interactions with nascent proteins that is readily adaptable to identifying nascent interactomes of other co-translationally acting eukaryotic factors. The protocol provides general guidance for experimental design and optimization, as well as detailed instructions for cell growth and harvest, the isolation of (factor-engaged) monosomes, the generation of a cDNA library and data analysis. Experience in biochemistry and RNA handling, as well as basic programing knowledge, is necessary to perform SeRP. Execution of a SeRP experiment takes 8–10 working days, and initial data analysis can be completed within 1–2 d. This protocol is an extension of the originally developed protocol describing SeRP in bacteria.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE123166 | GEO | 2019/07/10

REPOSITORIES: GEO

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Project description:Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution, synthetic biology, gene expression regulation, intracellular biophysics, and more. However, this phenomenon hasn't been quantified yet at a genomic level. Nevertheless, methodologies for studying translation (e.g. ribosome footprints) are usually calibrated to capture only single ribosome protected footprints (mRPFs) and thus limited in their ability to detect ribosome queuing. On the other hand, most of the models in the field assume and analyze a certain level of queuing. Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes (dRPFs) using a modified ribosome profiling protocol. We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic. The data are analyzed using computational models of translation dynamics. The approach was implemented on the Saccharomyces cerevisiae transcriptome. Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0.2-0.35, suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods. We found that specific regions are enriched with queued ribosomes, such as the 5’-end of ORFs, and regions upstream to mRPF peaks, among others. While queuing is related to higher density of ribosomes on the transcript (characteristic of highly translated genes), we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for. In addition, our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels. Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams. Thus, our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution.

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Selective ribosome profiling to study interactions of translating ribosomes in yeast

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