Abstract
Riboswitches are non-coding RNA motifs that regulate gene expression in response to ligand binding. The glycine tandem riboswitch (GTR) contains two glycine aptamers that interact extensively, driving conformational changes in the downstream expression platform to control gene expression. Despite numerous studies, the role of glycine and RNA folding pathways in co-transcriptional regulation remains unclear. Here, we integrate single-molecule kinetic analysis, co-transcriptional RNA structure probing, and modeling to reveal that the GTR processes multiple molecular inputs sequentially, guided by polymerase pausing. Our findings elucidate its stepwise 5’-to-3’ folding pathway and demonstrate how sequential glycine binding to each aptamer, K+ binding to a kink-turn, non-native folding intermediates, inter-aptamer docking driving binding site pre-organization, and modulation by transcription factor NusA collectively orchestrate co-transcriptional gene regulation. These results support a model wherein glycine binding cooperativity arises through non-equilibrium mechanisms, rather than a classical concerted model.
Data availability
The raw sequencing data generated in this study have been deposited in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) with the BioProject accession code PRJNA938111. Individual BioSample accession codes are available in Table S9. The processed reactivity data have been deposited in the RNA Mapping Database (https://rmdb.stanford.edu/)74. Individual accession codes for each data set are available in Table S10. The ShapeMapper2 output files for these data have been deposited in Zenodo (https://doi.org/10.5281/zenodo.15264637)75.
The computational 3D simulation data generated in this study is available at GitHub (https://github.com/Vfold-RNA/Computational-3D-simulation-data-for-Bus-GTR)76. Source data for single-molecule and gel-based assays are available through the University of Michigan DeepBlue deposit (https://doi.org/10.7302/py5g-bm35)77. Source data are provided with this paper.
Code availability
TECtools can be accessed at https://github.com/e-strobel-lab/TECtools/releases/tag/v1.2.0. Data visualization scripts can be accessed at https://github.com/e-strobel-lab/TECprobe_visualization/releases/tag/v1.0.077,78. The Vfold3D-MD simulation code can be accessed at https://rna.physics.missouri.edu/vfold_software_download/vfold3D_download.html. The code used for the single-molecule experiments in this study are available through the DeepBlue deposit (https://doi.org/10.7302/py5g-bm35).
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Acknowledgements
We thank Chad Torgerson for helpful discussions. This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers R35GM131922 (to N.G.W.), R35GM147137 (to E.J.S.), and R35GM134919 (to S.-J.C.), by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number U54AI170660 (to S.-J.C.), by the National Science Foundation under Award Numbers MCB 2140320 (to N.G.W.) and CHE 2154924 (to S.-J.C.), by the Michigan Economic Development Corporation under Award Number RC112630 (to N.G.W.), and by start-up funding from the University at Buffalo (to E.J.S). R.A.R. was supported, in part, by T32GM149391 (Michigan Predoctoral Training in Genetics). We would also like to express our sincere gratitude to Dr. Tatiana Mishanina from the University of California, San Diego, for kindly providing core Bsu RNAP and SigA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Conceptualization: R.A.R., A.C., E.J.S., and N.G.W. Methodology: R.A.R., A.C., S.Z., and E.J.S. Investigation: R.A.R., A.C., S.S.T., V.A.R., S.Z., and C.E.S. Writing—original draft: R.A.R., A.C., S.Z., and E.J.S. Writing—review and editing: R.A.R., A.C., S.Z., S.-J.C., E.J.S., and N.G.W. Supervision: S.-J.C., E.J.S., and N.G.W. Funding acquisition: S.-J.C., E.J.S., and N.G.W. Correspondence to N.G.W., E.J.S., or S-J.C.
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Nature Communications thanks Xianyang Fang, who co-reviewed with Xiaolin Niu, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
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Romero, R.A., Chauvier, A., Teh, S.S. et al. Co-transcriptional folding orchestrates sequential multi-effector sensing by a glycine tandem riboswitch. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69648-x
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DOI: https://doi.org/10.1038/s41467-026-69648-x
