2020
DOI: 10.1093/nar/gkaa786
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Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

Abstract: Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based bios… Show more

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Cited by 77 publications
(91 citation statements)
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“…Both of these changes reduce the number of A nucleotides and increase the number of C nucleotides. We acknowledge, however, that the effect of spacer sequence on translation efficiency is idiosyncratic, as recent high-throughput studies identified efficient RBS sequences that deviate from this rule [ 19 , 20 ].…”
Section: Resultsmentioning
confidence: 99%
“…Both of these changes reduce the number of A nucleotides and increase the number of C nucleotides. We acknowledge, however, that the effect of spacer sequence on translation efficiency is idiosyncratic, as recent high-throughput studies identified efficient RBS sequences that deviate from this rule [ 19 , 20 ].…”
Section: Resultsmentioning
confidence: 99%
“…Such switches are now useful for paper-based systems and materials to sense specific input RNA stimuli and to drive gene expression [ 325 ]. Further examples of using AI include the prediction of ribosome-binding sites providing desired translation rates [ 326 ], the de novo design of promoter elements in E. coli [ 327 ], or the design of an optimized CRISPR guide RNA for high-fidelity Cas9 variants [ 328 ]. Such approaches to construct genetic tools with predictable functions are highly relevant for the design of more complex expression systems as required for synthetic gene networks [ 321 ] or for orchestrating complex metabolic fluxes [ 329 , 330 ].…”
Section: Discussionmentioning
confidence: 99%
“…DcuS-DcuR is a two-component regulatory system that was identified in E. coli and functions as a classical two-component sensor-regulator in controlling gene expression. After optimizing the expression level of SK and RR and adjusting the kinase and phosphatase activity of SK by promoter engineering and DcuS engineering, respectively, the dynamic range (i.e., fold change in gene expression between the presence and absence of inducer [31]) was significantly improved. This work paves the way for high-throughput screening and dynamic regulation of fumaric acid cell factories.…”
Section: Introductionmentioning
confidence: 99%