TY - JOUR
T1 - Programmable trans-splicing riboregulators for complex cellular logic computation
AU - Gao, Yuanli
AU - Mardian, Rizki
AU - Ma, Jiaxin
AU - Li, Yang
AU - French, Christopher E.
AU - Wang, Baojun
N1 - We thank Y. Liu, X. Wan and F. Pinto for providing plasmid constructs and helpful suggestions. We thank R. Grima and S. Granneman for their support and advice. We thank C. Voigt (MIT) for providing the Marionette-Wild E.coli strain.
PY - 2025/1/2
Y1 - 2025/1/2
N2 - Synthetic genetic circuits program the cellular input–output relationships to execute customized functions. However, efforts to scale up these circuits have been hampered by the limited number of reliable regulatory mechanisms with high programmability, performance, predictability and orthogonality. Here we report a class of split-intron-enabled trans-splicing riboregulators (SENTRs) based on de novo designed external guide sequences. SENTR libraries provide low leakage expression, wide dynamic range, high predictability with machine learning and low crosstalk at multiple component levels. SENTRs can sense RNA targets, process signals by logic computation and transduce them into various outputs, either mRNAs or noncoding RNAs. We subsequently demonstrate that digital logic operation with up to six inputs can be implemented using multiple orthogonal SENTRs to regulate a single gene simultaneously and coupling SENTRs with split intein-mediated protein trans-splicing. SENTR represents a powerful and versatile regulatory tool at the post-transcriptional level in Escherichia coli, suggesting broad biotechnological applications.
AB - Synthetic genetic circuits program the cellular input–output relationships to execute customized functions. However, efforts to scale up these circuits have been hampered by the limited number of reliable regulatory mechanisms with high programmability, performance, predictability and orthogonality. Here we report a class of split-intron-enabled trans-splicing riboregulators (SENTRs) based on de novo designed external guide sequences. SENTR libraries provide low leakage expression, wide dynamic range, high predictability with machine learning and low crosstalk at multiple component levels. SENTRs can sense RNA targets, process signals by logic computation and transduce them into various outputs, either mRNAs or noncoding RNAs. We subsequently demonstrate that digital logic operation with up to six inputs can be implemented using multiple orthogonal SENTRs to regulate a single gene simultaneously and coupling SENTRs with split intein-mediated protein trans-splicing. SENTR represents a powerful and versatile regulatory tool at the post-transcriptional level in Escherichia coli, suggesting broad biotechnological applications.
U2 - 10.1038/s41589-024-01781-4
DO - 10.1038/s41589-024-01781-4
M3 - Article
SN - 1552-4469
JO - Nature Chemical Biology
JF - Nature Chemical Biology
ER -