Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA640006 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12513910.fastq.gz | Fastqsanger.gz | |||
| SRR12513911.fastq.gz | Fastqsanger.gz | |||
| SRR12513912.fastq.gz | Fastqsanger.gz | |||
| SRR12513913.fastq.gz | Fastqsanger.gz | |||
| SRR12513914.fastq.gz | Fastqsanger.gz |
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Project description:When performing a computation, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to measure part function and identify the origins of failures. Here, we take “snapshots” of a large genetic circuit in different states. RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts are parameterized. The circuit behaves as designed; however, it is riddled with errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons and a failed gate. While serendipitously not impacting function, they reduce prediction accuracy and could lead to failures when used in other designs. Finally, the cellular power (RNAPs and ribosomes) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.
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Project description:To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.
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Project description:Protein translation depends on mRNA-specific initiation, elongation, and termination rates. While the regulation of ribosome elongation is well studied in bacteria and yeast, less is known in higher eukaryotes. Here, we combined ribosome and tRNA profiling to investigate the relations between ribosome elongation rates, (aminoacyl-) tRNA levels and codon usage in mammals. We modeled codon-specific ribosome dwell times and translation fluxes from ribosome profiling, considering pair-interactions between ribosome sites. In mouse liver, the model revealed site and codon specific dwell times, as well as codon pair-interactions clustering by amino acids. While translation fluxes varied significantly across diurnal time and feeding regimen, codon dwell times were highly stable, and conserved in human. Fasting had no effect on codon dwell times in mouse liver. Profiling of total and aminoacylated tRNAs revealed highly heterogeneous levels with specific isoacceptor patterns and a correlation with codon usage. tRNAs for isoleucine, asparagine, aspartate and arginine were lowly loaded and conserved in fasted mice. Finally, codons with low levels of charged tRNAs and high codon usage relative to tRNA abundance exhibited long dwell times. Together, these analyses pave the way towards understanding the complex relation between tRNA loading, codon usage and ribosome dwell times in mammals.
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Project description:Modeling ribosome dwell times and relationship with tRNA loading and codon usage in mammals (tRNA Profiling)
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Project description:In protein synthesis, ribosome movement is not always smooth, rather often impeded by numerous reasons. Although the deceleration of ribosome defines the fates of the mRNAs and the synthesizing proteins, fundamental questions remain to be addressed including where ribosomes pause in mRNAs, what kind of RNA/amino acid context causes the pausing, and how physiologically significant the slowdown of protein synthesis is. Here we surveyed the position of ribosome collisions, caused by ribosome pausing, at a genome-wide level using the modified ribosome profiling in human and zebrafish. The collided ribosomes, i.e. disome, emerge at various sites; the proline-proline-lysine motif, stop codons, and 3′ UTR. The number of ribosomes in a collision is not limited to two, rather four to five, forming a queue of ribosomes. Especially, XBP1, a key modulator of unfolded protein response, shows striking queues of collided ribosomes thus acts as a substrate for ribosome-associated quality control (RQC) to avoid the accumulation of undesired proteins in the absence of stress. Our results provide an insight into the causes and the consequences of ribosome slowdowns by dissecting the specific architecture of ribosomes.
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Project description:In protein synthesis, ribosome movement is not always smooth, rather often impeded by numerous reasons. Although the deceleration of ribosome defines the fates of the mRNAs and the synthesizing proteins, fundamental questions remain to be addressed including where ribosomes pause in mRNAs, what kind of RNA/amino acid context causes the pausing, and how physiologically significant the slowdown of protein synthesis is. Here we surveyed the position of ribosome collisions, caused by ribosome pausing, at a genome-wide level using the modified ribosome profiling in human and zebrafish. The collided ribosomes, i.e. disome, emerge at various sites; the proline-proline-lysine motif, stop codons, and 3′ UTR. The number of ribosomes in a collision is not limited to two, rather four to five, forming a queue of ribosomes. Especially, XBP1, a key modulator of unfolded protein response, shows striking queues of collided ribosomes thus acts as a substrate for ribosome-associated quality control (RQC) to avoid the accumulation of undesired proteins in the absence of stress. Our results provide an insight into the causes and the consequences of ribosome slowdowns by dissecting the specific architecture of ribosomes.
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