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Programmable trans-splicing riboregulators for complex cellular logic computation

Nature Chemical Biology volume 21, pages 758–766 (2025)Cite this article

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Abstract

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.

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Fig. 1: Design, in vivo characterization and in silico prediction of SENTRs.
Fig. 2: Efficient and orthogonal trans-splicing of different EGSs, P1s, split sites and introns.
Fig. 3: Robust trans-splicing regulation in various genetic contexts by SENTRs.
Fig. 4: RNA-splicing-only logic circuitry.
Fig. 5: Split-intron and intein-enabled single-layer multi-input AND and NAND logic circuits.

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Data availability

The data that support the findings of this study were deposited at Zenodo56 and are publicly available as of the date of publication.

Code availability

All original code and NUPACK scripts were deposited at Zenodo56 and are publicly available as of the date of publication.

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Acknowledgements

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. This work was supported by the National Key R&D Program of China (2023YFF1204500 to B.W.), the ‘Pioneer’ and ‘Leading Goose’ R&D Program of Zhejiang (2024C03011 to B.W.), the National Natural Science Foundation of China (32271475 and 32320103001 to B.W.), the Fundamental Research Funds for the Central Universities (226-2022-00214 to B.W.) and the Kunpeng Action Program Award of Zhejiang Province (to B.W.). Y.G. acknowledges support by a Darwin Trust of Edinburgh scholarship.

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Authors and Affiliations

  1. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China

    Yuanli Gao, Jiaxin Ma, Yang Li & Baojun Wang

  2. ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, China

    Yuanli Gao, Jiaxin Ma, Yang Li & Baojun Wang

  3. School of Biological Sciences, University of Edinburgh, Edinburgh, UK

    Yuanli Gao, Rizki Mardian & Christopher E. French

  4. Zhejiang University–University of Edinburgh Joint Research Center for Engineering Biology, International Campus, Zhejiang University, Haining, China

    Christopher E. French

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  1. Yuanli Gao
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Contributions

B.W. and Y.G. conceptualized the study. B.W. and C.F. supervised the study. Y.G. designed and performed the majority of the experiments and data analysis. J.M. performed the experiments related to the six-input AND gate circuit. R.M. performed the ML analysis. Y.L. and Y.G. performed the RT–qPCR experiments and data analysis. All authors took part in interpreting the results and preparing materials for the paper. B.W. and Y.G. wrote the paper with input from all coauthors.

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Correspondence to Baojun Wang.

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B.W. and Y.G. have filed a patent application (number CN2024105834200) based on the presented work. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Characterizing, analyzing, and optimizing SENTR performance.

a, In vivo fluorescence of 1369 combinations of 5′ RNA-3′ RNA pairs. All 1296 combinations of 5′ RNAs with 36 5′ EGSs and 3′ RNAs with 36 3′ EGSs were validated, and another 73 groups of the 5′ RNA without 5′ EGS and 3′ RNAs without 3′ EGS were also characterized as control groups. b, SENTR performance with different lengths of EGSs. Different lengths of EGSs were designed by NUPACK with three trials, and the best-performing one of each length was shown. c, SENTR performance with EGS length gradient. The 50 nt EGS was shortened by every 10 nt (upper panel) and then every 5 nt (lower panel). d, ON state and OFF state fluorescence (upper panel) and ON/OFF ratios (lower panel) of 30 nt EGS library. e, The effect of 5′ spacer and 3′ spacer on SENTR performance. The 3′ spacer was designed to form P10 or not with the 3′ exon. Different lengths of 5′ spacers and 3′ spacers were tested. Error bars, mean values ± SD (n = 3). RPU, relative promoter units. Data in (a-e) were collected by flow cytometry. For (d), ON-state fluorescence was measured from cells expressing 5′ RNA and 3′ RNA, and OFF state fluorescence was determined from cells expressing only 3′ RNA. For (b-d), bars show mean values and error bars represent s.d. of n = 3 biological replicates. For (a, e), the heat maps show mean values of n = 3 biological replicates. Bacteria transformed with empty vectors and J23101-sfGFP were used as negative and positive controls for calculating fluorescence values in relative promoter units (RPU).

Extended Data Fig. 2 Evaluation of machine-learning (ML) models.

a, Two strategies to split the dataset into the training sets (purple) and the evaluation set (blue). Left panel shows the randomly splitting strategy where the data were randomly selected to train the models, and the rest data were used to test the models. Right panel shows the systematic splitting strategy where the data in quadrant 1 (Q1) were used for training the models, and those in Q2, Q3, or Q4 were used to test the models. b-c, R2 metrics between experimental and prediction values for ML models trained on sequence motifs (b) and thermodynamic parameters (c) using 16-fold cross-validation. Upper panels are R2 metrics of models trained on the randomly split dataset. Lower panels are R2 metrics of models trained on the systematically split dataset and the prediction performance for data points in quadrant 2 (Q2), quadrant 3 (Q3), and quadrant 4 (Q4). The linear regression results in lower panels of (b) are not plotted due to very poor R2 values. For (b-c), data are presented as mean values +/− SD of n = 16 cross validation. Three models (Neural Network, Bayesian Regression, and Ridge Regression) are omitted from panels (c) due to their poor performances in both scenarios.

Extended Data Fig. 3 Designing EGSs and intronic sequence components of SENTRs.

a, Comparison of orthogonal library size and library dynamic range for SENTRs and previous riboregulators. LIRA, loop-initiated RNA activators57. 3WJ, three-way junction. b-c, Truncating P6b (b) or P9 (c) to eliminate the trans-splicing activity of SENTRs without EGSs. w/o EGS: without EGS; w/ EGS: with EGS. d-e, Schematic of cis-splicing constructs with different P1s (d) and introns (e). The 5′ P1 sequence (d) and native exon-intron junction sequence (e) will remain in mature RNA. f, Cis-splicing activities of TT intron with native junction sequences at four splice sites of sfGFP. The junction sequences were also inserted without introns to mimic the translation of splicing products. g, Effect of native exon-intron junction sequences from 17 introns on sfGFP fluorescence. h, Cis-splicing activities of 17 introns. Thy intron was truncated to remove the open reading frame (ORF) in the P8 region. Data in (b-c) were collected by plate reader. Data in (f-h) were collected by flow cytometry. For (b-c), ON-state fluorescence was measured from cells expressing 5′ RNA and 3′ RNA, and OFF-state fluorescence was from cells expressing 3′ RNA. For (b-c, f-h), bars show mean values and error bars represent s.d. of n = 3 biological replicates. Bacteria transformed with empty vectors and J23101-sfGFP were used as negative and positive controls for calculating fluorescence in RPU.

Extended Data Fig. 4 Robust, tunable, and modular SENTR for gene regulation.

a, Trans-splicing performance for 16 base pair combinations at 5′ splice site. b, Choices of 5′ splice sites inside 5′ UTR (site −6 and −1) or start codon (site 2). c, Design schematic of mRNA sensor. The mRNA binds to 3′ EGS to repress the 3′ RNA, thus lowering the trans-splicing fluorescence. d, Transfer function for the mCherry sensor (blue curve, left y-axis) and mCherry (purple curve, right y-axis) as a function of rhamnose concentration. The sensor expression was induced by 3.2 µM AHL, and mCherry expression was induced by rhamnose. e, Splice site selection and performance for sgRNA trans-splicing. Combinations of RNA strands were achieved via the induction (+) and non induction (-) of certain RNA expression, and absence (∅) of certain RNA generator in circuits. The active (+) and deactivated (-) introns were used for assaying the sgRNA activity without trans-splicing reactions. Inset, sgRNA design and 5′ splice site. f, Genetic design architecture of modular SENTR. Yellow rectangles, split intron halves. Orange rectangles, EGSs. g, Tuning modular SENTRs by seven RBSs and four induction levels of 5′ RNAs. h, Modular SENTR from SunY intron. The split SunY intron, with its native junction sequences, was inserted into codon 1 of sfGFP CDS. Data in (a, d-e, g-h) were collected by flow cytometry. For (a, g), the heat maps show mean values of n = 3 biological replicates. For (e, h), bars show mean values and error bars represent s.d. of n = 3 biological replicates. For (d), the points show mean values and error bars represent s.d. of n = 3 biological replicates. Bacteria transformed with empty vectors and J23101-sfGFP/mCherry were used as negative and positive controls for calculating fluorescence values in relative promoter units (RPU).

Extended Data Fig. 5 RNA-splicing only logic gates.

a-c, Fluorescence for the two-input AND gates from ECF20 (a), sfGFP (b), and mCherry (c). d-e, Fluorescence for the two-input NAND gates from LmrA, PhlF (d), and BM3R1 (e). f, Fluorescence for the two-input NIMPLY gates. g, Fluorescence for the three-input NAND gate from BM3R1. h, Fluorescence for the four-input AND gate. Inset, fluorescence levels on logarithmic scales. Data in (a-b, d-h) were collected by flow cytometry. Data in (c) were collected by plate reader. Bars and errors represent the mean values and s.d. of six biological repeats (n = 6) in (g) and of three biological repeats (n = 3) in other panels. Bacteria transformed with empty vectors and J23101-sfGFP/mCherry were used as negative and positive controls for calculating fluorescence values in relative promoter units (RPU). The fold changes of logic gates were calculated by dividing the lowest TRUE-state fluorescence values by the highest FALSE-state fluorescence values, and labeled above the bars.

Extended Data Fig. 6 Split biomolecule enabled AND gates.

a, Design schematic of split biomolecule enabled three-input AND gate from ECF20. The 3′ RNA and 5′ RNA splice to produce mRNA encoding ECF20N-M86N, which splice with ECF20C-M86C to produce functional ECF20 protein. ECF20 activates downstream GFP expression. b, Fluorescence for split biomolecule enabled three-input AND gate from ECF20. Inset, fluorescence levels on logarithmic scales. Fold change: 102. c, Fluorescence for split biomolecule enabled three-input AND gate from mCherry. Fold change: 22. d, Fold changes of split biomolecule enabled four-input AND gate library. EGSs for RNA-splicing of each lobe are from the orthogonal EGS library. RBSs for C-lobe translation are from the iGEM RBS collection. RBS29, BBa_B0029. RBS32, BBa_B0032. RBS30 (BBa_B0030) was used for N-lobe peptide translation. The fluorescence for each gate was provided in source data file. e, Fluorescence for split biomolecule enabled four-input AND gates from ECF16. Fold change (left to right): 5, 5, 4. f, Design schematic of split intein enabled three-input AND gate from ECF20. Three peptides (ECF20N-M86N, M86C-ECF20M-sspDnaXN, and sspDnaXC-ECF20C) splice into functional ECF20 protein. g, Fluorescence for split intein enabled three-input AND gate from ECF20. Fold change: 13. Data in (b-c, e, g) were collected by flow cytometry, and bars show mean values and error bars represent s.d. of n = 3 biological replicates. Bacteria transformed with empty vectors and J23101-sfGFP were used as negative and positive controls for calculating fluorescence values in relative promoter units (RPU). The fold changes of logic gates were calculated by dividing the lowest TRUE-state fluorescence values by the highest FALSE-state fluorescence values.

Extended Data Fig. 7 Split biomolecule enabled NAND gates.

a, Design schematic of split biomolecule enabled three-input NAND gate from LmrA. The 3′ RNA and 5′ RNA splice to produce mRNA encoding LmrAN-sspGyrBN, which splice with LmrAC-sspGyrBC to produce functional LmrA protein. LmrA represses downstream GFP expression. b, Fluorescence for split biomolecule enabled three-input NAND gates from LmrA. Fold change: gate 1 (left), 51; gate 2 (right), 26. c, Design schematic of split biomolecule enabled three-input NAND gate from BM3R1. The 3′ RNA and 5′ RNA splice to produce mRNA encoding BM3R1C-Cth-TerC, which splice with BM3R1N-Cth-TerN to produce functional BM3R1 protein. BM3R1 represses downstream GFP expression. d, Fluorescence for split biomolecule enabled three-input NAND gates from BM3R1. Fold change: gate 1 (left), 15; gate 2 (right), 14. e, Fluorescence for the second split biomolecule enabled four-input NAND gate from LmrA. Fold change: 46. Data in (b, d, e) were collected by flow cytometry, and bars show mean values and error bars represent s.d. of n = 3 biological replicates. Bacteria transformed with empty vectors and J23101-sfGFP were used as negative and positive controls for calculating fluorescence values in relative promoter units (RPU). The fold changes of logic gates were calculated by dividing the lowest TRUE-state fluorescence values by the highest FALSE-state fluorescence values.

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Supplementary Figs. 1–13, Tables 1–30 and Notes 1–3.

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Supplementary Data 1

Sequences of the plasmids constructed in this work.

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Gao, Y., Mardian, R., Ma, J. et al. Programmable trans-splicing riboregulators for complex cellular logic computation. Nat Chem Biol 21, 758–766 (2025). https://doi.org/10.1038/s41589-024-01781-4

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