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KsgA facilitates ribosomal small subunit maturation by proofreading a key structural lesion

Nature Structural & Molecular Biology volume 30, pages 1468–1480 (2023)Cite this article

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Abstract

Ribosome assembly is orchestrated by many assembly factors, including ribosomal RNA methyltransferases, whose precise role is poorly understood. Here, we leverage the power of cryo-EM and machine learning to discover that the E. coli methyltransferase KsgA performs a ‘proofreading’ function in the assembly of the small ribosomal subunit by recognizing and partially disassembling particles that have matured but are not competent for translation. We propose that this activity allows inactive particles an opportunity to reassemble into an active state, thereby increasing overall assembly fidelity. Detailed structural quantifications in our datasets additionally enabled the expansion of the Nomura assembly map to highlight rRNA helix and r-protein interdependencies, detailing how the binding and docking of these elements are tightly coupled. These results have wide-ranging implications for our understanding of the quality-control mechanisms governing ribosome biogenesis and showcase the power of heterogeneity analysis in cryo-EM to unveil functionally relevant information in biological systems.

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Fig. 1: KsgA treatment of 30SΔksgA particles produces a heterogeneous structural ensemble.
Fig. 2: Analysis of KsgA-treated 30SΔksgA particles with MAVEn reveals large structural domains that cooperatively interconvert.
Fig. 3: KsgA binds a diverse array of assembly states.
Fig. 4: Substrate engagement by KsgA displaces a gatekeeping rRNA helix.
Fig. 5: Nearly mature SSUs accumulate in the absence of KsgA.
Fig. 6: KsgA recognizes and remodels inactive subunits.
Fig. 7: Network analysis reveals assembly dependency map for KsgA-treated SSUs.

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Data availability

The density map and the model for the KsgA-bound 30SΔksgA and untreated 30SΔksgA structures were deposited in the Electron Microscopy Data Bank under codes EMD-28720 and EMD-28692, respectively, and in the Protein Data Bank using codes 8EYT and 8EYQ, respectively. EMDB and PDB codes are also indicated in Table 1. Unfiltered particle stacks were deposited at EMPIAR with the following IDs: untreated dataset (EMPIAR-11529), small KsgA-treated dataset used for cryoDRGN and4MAVEn (EMPIAR-11526), and large KsgA-treated dataset used for high-resolution reconstruction of the KsgA-bound structure (EMPIAR-11528). Trained cryoDRGN models have been deposited at Zenodo at https://doi.org/10.5281/zenodo.7884215. Source data are provided with this paper.

Code availability

The MAVEn software, including scripts for on-the-fly reconstruction and analysis and voxel PCA, is available at: https://github.com/lkinman/MAVEn.

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Acknowledgements

We thank K. Sears, M. Strauss, K. Basu and other staff members at the Facility for Electron Microscopy Research (FEMR) at McGill University for help with microscope operation and data collection; the MIT-Satori administrative team for providing computational resources and support; and B. Powell and E. Zhong, and other members of the Davis and Ortega labs, for constructive feedback on this work. This work was funded by the Hugh Hampton Young Fellowship to L.F.K.; National Science Foundation CAREER grant 2046778 and National Institutes of Health grant R01-GM144542 to J.H.D.; and the Canadian Institutes of Health Research grant CIHR PJT-180305 to J.O. FEMR is supported by the Canadian Foundation for Innovation, Quebec Government and McGill University. Research in the Davis lab is supported by the Alfred P. Sloan Foundation, the James H. Ferry Fund, the MIT J-Clinic, and the Whitehead Family.

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Author notes

  1. These authors contributed equally to this work: Jingyu Sun, Laurel F. Kinman.

Authors and Affiliations

  1. Department of Anatomy and Cell Biology, McGill University, Montreal, Quebec, Canada

    Jingyu Sun, Dushyant Jahagirdar & Joaquin Ortega

  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Laurel F. Kinman & Joseph H. Davis

  3. Centre for Structural Biology, McGill University, Montreal, Quebec, Canada

    Joaquin Ortega

  4. Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA

    Joseph H. Davis

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Contributions

Investigation: J.S., L.F.K., D.J.; Software: L.F.K.; Writing – Original draft: J.S., L.F.K., J.O., J.H.D.; Writing – Review & editing: J.S., L.F.K., J.O., J.H.D.; Visualization: J.S., L.F.K., J.H.D.; Supervision, Project administration, and Funding acquisition: J.O., J.H.D. Peer reviewer reports are available.

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Nature Structural & Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Sara Osman and Dimitris Typas were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary Figures 1–17, Supplementary Video Legends 1 and 2, and Uncropped Gel

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Supplementary Video 1

Supplementary Video 1: Rotation of the head density in KsgA-treated volumes. For each of the KsgA-treated and untreated datasets, 500 volumes were resampled from the subset of latent space corresponding to particles exhibiting strong head density (see Methods). The full set of 1,000 volumes were aligned and amplitude-scaled, and a mask corresponding to the native H32 and H33 density in PDB model 4V9D was applied to the volumes. Principal component analysis was performed on the resulting voxel array (see Methods and Supplementary Figure 12). Volumes shown here are sampled from low to high values of the first principal component. Only the head portion of each volume is shown (orange); the mature 30S is overlaid for reference (gray).

Supplementary Video 2

Supplementary Video 2: H44 flexibility in the absence of KsgA. For each of the treated and untreated datasets, 500 volumes were resampled from the subset of latent space corresponding to particles exhibiting strong H44 density (see Methods). The full set of 1,000 volumes were aligned and amplitude-scaled, and a mask corresponding to the native H44 density in PDB model 4V9D was applied to the volumes. Principal component analysis (vPCA) was performed on the resulting voxel array (see Methods and Supplementary Figure 12). Volumes shown here were sampled from low to high value of the first principal component. Only the H44-masked portion of each volume is shown (orange); a volume lacking H44 is overlaid for reference (grey).

Source data

Source Data Fig. 2

Normalized MAVEn occupancies table for the KsgA-treated sample.

Source Data Fig. 5

Normalized MAVEn occupancies table for the untreated sample.

Source Data Extended Data Fig. 1

R-protein occupancy as measured by mass spectrometry. Data are plotted in Supplementary Figure 1

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Sun, J., Kinman, L.F., Jahagirdar, D. et al. KsgA facilitates ribosomal small subunit maturation by proofreading a key structural lesion. Nat Struct Mol Biol 30, 1468–1480 (2023). https://doi.org/10.1038/s41594-023-01078-5

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