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Structural insights into higher-order natural RNA-only multimers | Nature Structural & Molecular Biology
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Article Published: 06 August 2025 Structural insights into higher-order natural RNA-only multimers Shengchun Zhang, Ran Yi, Linfeng An, Ji Liu, Xuebiao Yao, Shanshan Li & Kaiming Zhang
Nature Structural & Molecular Biology volume 32, pages 2012–2021 (2025)Cite this article
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Abstract
RNA-only complexes adopt intricate three-dimensional structures to fulfill diverse functions independently of protein partners. Although multimeric RNA-only structures have been engineered in synthetic RNA nanomaterials, naturally occurring RNA-only complexes have primarily been observed as monomers or dimers, leaving higher-order assemblies largely unexplored. ROOL (rumen-originating, ornate, large) and GOLLD (giant, ornate, lake- and Lactobacillales-derived) RNAs are conserved non-coding RNAs with complex secondary structures, but their high-resolution architectures remain unknown. Here, we determine the cryo-electron microscopy structures of UCC118-Rool RNA, Sag-Golld RNA and Env38-Golld RNA at 1.96–2.98 Å resolution, revealing their distinct hexameric, decameric and tetradecameric assemblies. These higher-order architectures are stabilized by an array of tertiary motifs such as kissing loops and tetraloop–receptor motifs, underscoring the conserved principles of RNA self-assembly. By elucidating the molecular details of these higher-order RNA-only assemblies, this study expands our understanding of RNA-based architectures and broadens the scope of RNA structural biology.
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RNA molecules are versatile macromolecules that have diverse roles in cellular processes, ranging from information storage and enzymatic catalysis to the regulation of gene expression. In addition to their well-established functions as intermediaries in protein synthesis, RNAs can adopt intricate three-dimensional (3D) architectures to form complex functional assemblies. These RNA-only complexes often exhibit remarkable structural versatility and can function independently of protein partners, as seen in ribozymes and riboswitches1,2,3,4,5,6,7,8. Despite extensive studies on smaller ribozymes and structured RNAs, the structural and functional diversity of large RNA-only complexes remains poorly understood, particularly with respect to their oligomerization mechanisms and biological activities.
Although multimeric RNA-only structures have been engineered in synthetic RNA nanomaterials9,10,11,12,13,14,15,16, naturally occurring RNA complexes have rarely been observed to form multimeric assemblies. To date, the highest level of oligomerization reported for naturally occurring RNA-only complexes is the dimer8,17,18,19,20,21,22,23. This gap in knowledge highlights the uncertainty regarding the ability of natural RNA-only complexes to form higher-order multimeric assemblies and the structural principles and functional implications underlying such assemblies. Multimerization is a well-known strategy used by macromolecules to achieve structural stability, cooperative functionality and dynamic regulation. For RNA, such multimeric structures may facilitate enhanced enzymatic activity, complex regulatory interactions or evolutionary adaptability, but these possibilities remain unexplored in natural systems.
Rumen-originating, ornate, large (ROOL) and giant, ornate, lake- and Lactobacillales-derived (GOLLD) RNAs represent large RNA-only complexes that serve as valuable model systems for studying the structural diversity and autonomous functionality of RNA. These conserved non-coding RNAs, found in various bacterial species, exhibit complex secondary structures and are often located near transfer RNA (tRNA) genes or bacterial prophages, suggesting a potential role in tRNA processing or viral processes24,25,26,27. Despite their intriguing features, the structural organization and assembly principles of these RNAs remain largely uncharacterized.
In this study, we report the discovery of naturally occurring multimeric RNA-only complexes exceeding the dimer. Using cryogenic electron microscopy (cryo-EM), we provide structural insights into the Lactobacillus salivarius UCC118 plasmid pMP118 Rool (UCC118-Rool) RNA, Streptococcus agalactiae 18RS21 Golld (Sag-Golld) RNA/Sag-Golld pretRNASer and marine metagenome JCVISCAF1096627009515 genomic scaffold Golld (Env38-Golld) RNA. These RNAs adopt compact hexameric, decameric and tetradecameric structures, respectively, stabilized by intricate tertiary interactions such as kissing-loop interactions and tetraloop–receptor interactions. These findings showcase the sophisticated structural strategies used by large RNAs to maintain structural integrity, not only advancing our understanding of RNA-based molecular systems but also guiding their potential applications in nanotechnology and synthetic biology.
Results Hexameric structure of UCC118-Rool RNA revealed by cryo-EM at 1.96 Å resolution
After in vitro transcription and folding optimization, RNAs were analyzed by native polyacrylamide gel electrophoresis (PAGE), which reveals distinct high-molecular-weight bands (Extended Data Fig. 1a,b), suggesting the formation of oligomeric assemblies. To further characterize the oligomeric states, we performed mass photometry, which showed minor peaks potentially compatible with the presence of multiple oligomeric species (Extended Data Fig. 1c). To gain structural insights into these assemblies, we conducted negative-staining electron microscopy, which reveals that UCC118-Rool RNA adopts a nanocage-like structure (Extended Data Fig. 1d), reminiscent of DNA or RNA origami constructs9,10,11,12,13. Despite concerns about resolving such structures at high resolution, cryo-EM analysis reveals a highly stable hexamer with D3 symmetry at 2.26 Å resolution with a Rosenthal–Henderson B-factor value28 of 108.4 (Fig. 1a–c, Table 1 and Extended Data Figs. 2 and 3). Further local refinement of an asymmetric subunit improved the resolution to 1.96 Å, and the map was subsequently optimized using the ‘ResolveCryoEM’ tool in Phenix29. This high resolution enables the clear differentiation between purines and pyrimidines, and even between adenine and guanine, with distinct ‘holes’ visible in the bases and ribose moieties in several regions, which is an important hallmark of high-resolution structural data (Fig. 1d–g and Extended Data Figs. 2 and 3). Such unprecedented detail allows for precise modeling, and most domains in the structure have a Q-score30 (a metric to assess atom resolvability) above that expected at the map’s overall resolution (Fig. 1f,g and Extended Data Figs. 2c and 3). The high quality of our cryo-EM density map underscores the robustness of our data-processing pipeline and structural modeling, substantially enhancing our understanding of RNA architecture and its stabilizing interactions.
Fig. 1: Sub-2 Å resolution structure of UCC118-Rool RNA.
a, Representative motion-corrected cryo-EM micrograph (number, 34,895). b, Reference-free 2D class averages. c, Plot of the particle number versus the reciprocal squared resolution. The B-factor was calculated as 2× the linear fitting slope. d, Resolution maps for the final 3D reconstructions. e, Resolution map for the monomer after local refinement. f, Model validation using Q-scores. The model is colored by Q-scores for nucleotides; a higher Q-score indicates better resolvability. g, Visualization of individual nucleotides at 1.96 Å resolution, with the map optimized using the ‘ResolveCryoEM’ tool in Phenix. Four representative nucleotides are shown at two orientations to highlight the characteristic ‘holes’ within bases and sugar rings. Q-scores for the nucleotide (green), backbone (purple) and base (blue) are shown.
Source data
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The secondary structure model derived from the cryo-EM data highlights an intricate network of helical elements (P1–P13) interconnected by flexible loops and junctions, with critical features such as pseudoknots serving as structural scaffolds and tetraloop–receptor interactions mediating long-range contacts (Fig. 2a–c). The monomeric unit assembles into the larger oligomer through extensive tertiary interactions. High-resolution visualization further reveals canonical and non-canonical tertiary motifs such as tetraloop–receptor motifs, kissing loops, coaxial stacking and triplex formations, all of which contribute to stabilizing the oligomeric structure (Fig. 2d–k and Supplementary Video 1). The oligomerization of the UCC118-Rool RNA is supported by three main types of interfaces: the dimer interface, the trimer interface and the dimer-of-trimer interface. In the trimeric interface, three sets of stacked G209–C210 base pairs in the J6/7 junction form a network of three G–C base pairs between adjacent subunits, effectively stabilizing the trimer structure (Fig. 2k). The dimeric interface is characterized by interactions such as the P7 stem-loop forming three base pairs with the J7/6 junction of the adjacent subunit (Fig. 2e), while the P9 GUGA tetraloop interacts specifically with the P3 tetraloop receptor (Fig. 2f). In addition, the P2 stem-loop pairs with the J12a/10 junction, connecting P10 and P2 to facilitate coaxial stacking of helices, further contributing to the structural integrity of the dimer (Fig. 2g). At the dimer-of-trimer interface, intricate tertiary interactions are observed, including the formation of a type-I A-minor motif (A/C–G interaction) by the P13 stem-loop and the P12a region (Fig. 2h), as well as the P11 stem-loop interacting with P4 to form two base triples (Fig. 2i). Another notable feature is the P12b–P12b kissing-loop interaction, which further stabilizes the oligomeric assembly (Fig. 2j).
Fig. 2: Cryo-EM structure of the UCC118-Rool RNA reveals a hexameric assembly stabilized by intricate tertiary interactions.
a, Cryo-EM reconstruction of UCC118-Rool RNA reveals a hexameric assembly with D3 symmetry. Each chain is colored individually to highlight the modular assembly. b, Side views of the monomeric unit highlighting secondary elements. c, Secondary structure model, with each helix colored differently. d, Close-up visualization of the hexameric assembly. Each monomer is represented with distinct colors, and key oligomeric interfaces are annotated. e–g, Interactions at the dimeric interface. Base pairing (e), tetraloop–receptor interactions (f) and coaxial stacking (g) stabilize the dimeric interface. h–j, Interactions at the dimer-of-trimer interface. A type-I A-minor motif (h), triplex formation (i) and kissing-loop interactions (j) contribute to structural reinforcement. k, Oligomer interface at the trimeric level, where three sets of stacked G209–C210 base pairs in the J6/7 junction form a network of G–C base pairs, stabilizing the trimer structure.
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Achieving sub-2 Å resolution in RNA-only cryo-EM structures remains rare and challenging. Our structure provides a valuable opportunity to directly visualize key RNA features, offering insights into hydrogen bond networks with improved accuracy. This level of detail not only enhances our understanding of RNA self-assembly mechanisms but also sets a benchmark for RNA structural biology and computational modeling. Together, these findings highlight the structural adaptability of UCC118-Rool RNA, reinforcing its status as a highly stable and autonomous RNA-only complex (Supplementary Video 1).
Decameric assemblies of Sag-Golld and Sag-Golld-pretRNASer RNAs
Building on the structural insights gained from UCC118-Rool RNA, we next investigated Sag-Golld RNA, which adopts a decameric structure with D5 symmetry at 2.51 Å resolution. Negative-staining and cryo-EM data reveal dynamic assembly behavior, with minor populations of alternative oligomeric states, probably forming D4-symmetric octamers and D6-symmetric dodecamers (Table 1 and Extended Data Figs. 1 and 4). The decameric structure (dimer-of-pentamers) is the predominant form and features a compact, pentagram-like architecture (Fig. 3a and Supplementary Video 2). The cryo-EM map shows well-resolved features across most regions, indicating a tightly packed and stable assembly. The secondary structure model highlights key architectural features, including a series of helices (P1–P11) that establish the modular assembly of the complex, multiple pseudoknots and E-loops that serve as critical scaffolds as well as bulges and internal loops mediating domain–domain contacts (Fig. 3b,c). Close-up views of the oligomer interfaces reveal three distinct types of interactions that are critical for stabilizing the decamer, including base pairing, kissing-loop interactions and tetraloop–receptor interactions (Fig. 3d–h). Within the dimer interface, the P7 stem-loop forms two base pairs with the single-stranded J1/3 region (Fig. 3e), which is further involved in a ribose zipper motif. This motif has a crucial role in stabilizing adjacent substructures by sealing the P1 and P2 helices together. Additionally, the P2 helix docks into the minor groove of the P8 helix via its AAA loop (Fig. 3f), providing substantial structural reinforcement. At the dimer-of-pentamer interface, the P8 helices engage in kissing-loop interactions (Fig. 3g). Furthermore, the P10a GGGA tetraloop interacts specifically with asymmetric internal loops within the P8 helix (Fig. 3h), contributing to the overall structural integrity of the complex. These interactions collectively ensure the compactness and stability of the decameric Sag-Golld RNA structure (Supplementary Video 2).
Fig. 3: Decameric structure of Sag-Golld RNA.
a, Cryo-EM reconstruction of Sag-Golld RNA at 2.51 Å resolution reveals a decameric structure with D5 symmetry. Each chain is individually colored. b, Side views of the monomeric unit. c, Secondary structure model. d, Overview of the decameric assembly, highlighting oligomer interfaces critical for stabilization. The decamer is formed through interactions at the dimer and dimer-of-pentamer interfaces. e,f, Interactions at the dimer interface. Base pairing (e) and helix docking (f) stabilize the dimeric interface. g,h, Interactions at the dimer-of-pentamer interface. Kissing-loop interactions (g) and tetraloop–receptor interactions (h) have a crucial role in stabilizing the decameric assembly.
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As GOLLD RNAs are often located near tRNA genes, we further analyzed the cryo-EM structure of Sag-Golld pretRNASer (Table 1 and Extended Data Figs. 1 and 5). The tRNA is connected to the 3′ terminus of the Sag-Golld RNA through a 10 nucleotide linker (Fig. 4a), imparting substantial flexibility to the pretRNA region. By lowering the threshold of the cryo-EM map to an appropriate level, the connection between the Sag-Golld RNA and tRNA becomes discernible (Fig. 4b). Model building confirms that the linker forms a short helix with J10/11 composed of three non-canonical base pairs, effectively extending the P11 helix and enhancing its stabilizing interactions (Fig. 4c). Comparing 3D structures of Sag-Golld and Sag-Golld-pretRNA reveals that the Sag-Golld RNA region remains structurally identical, while the tRNA deviates outward from the center of the decamer (Fig. 4b,d and Supplementary Video 2). This configuration probably positions the linker region for accessibility to tRNA-processing enzymes31,32,33, such as RNase P, facilitating efficient processing.
Fig. 4: Cryo-EM structure of Sag-Golld-pretRNASer reveals a flexible tRNA linker facilitating structural and functional adaptability.
a, Schematic of the Sag-Golld RNA and its association with tRNA. The tRNA is connected to the 3′ terminus of Sag-Golld RNA by a 10 nucleotide (nt) linker. b, Cryo-EM map of Sag-Golld-pretRNA at low threshold, showing the connection between Sag-Golld RNA and tRNA. The linker is indicated by an asterisk. c, Model of the linker between Sag-Golld RNA and tRNA, formed by a short helix with J10/11 composed of three non-canonical base pairs. d, Comparison of the 3D structures of Sag-Golld RNA (orange) and Sag-Golld-pretRNA (gray). The root-mean-square deviation (r.m.s.d.) map highlights regions of structural changes (color-coded from blue to red).
Full size image Tetradecameric Env38-Golld RNA
Extending our investigation to larger RNA-only complexes, we resolved the structure of Env38-Golld RNA at 2.93 Å resolution, revealing a tetradecameric assembly with D7 symmetry (Table 1 and Extended Data Figs. 1 and 6). Similar to UCC118-Rool RNA, Env38-Golld RNA exhibits a nanocage-like structure, specifically resembling a Chinese lantern (Fig. 5a–c and Supplementary Video 3). This tetradecameric structure is stabilized by a combination of base pairing, kissing-loop interactions and tetraloop–receptor interactions (Fig. 5d–n). At the heptameric interface, the P7 stem-loop is anchored by C–G base pairing with a symmetric bulge in the neighboring P7 helix (Fig. 5e), providing a foundational element for the oligomeric architecture. At the dimeric interface, kissing-loop interactions are prominent, with the P12c and adjacent P12b helices forming kissing-loop interactions involving three base pairs (Fig. 5f), and the P13 and adjacent P11 helices engaging in similar interactions stabilized by five base pairs (Fig. 5g). Tetraloop–receptor interactions also have a crucial role: the P10 GGAA tetraloop interacts with the P11 tetraloop receptor (Fig. 5h), the P3 GAAA tetraloop interacts with the P19 tetraloop receptor (Fig. 5i) and the P15 CAAA tetraloop engages the P21b tetraloop receptor (Fig. 5j). Additional base-pairing motifs contribute to structural reinforcement, including the P20 stem-loop, which forms base-pairing interactions with the J9/16 region (Fig. 5k). These interactions collectively reinforce the connections between subunits within the oligomer. At the dimer-of-heptamer interface, the P21a and P21b loops from adjacent subunits form G–C-based kissing-loop interactions with their counterparts (Fig. 5l,m), reinforcing inter-subunit stability. Further stabilization is provided by the P24 CAAA tetraloop, which interacts with neighboring P21 to form tetraloop–receptor interactions (Fig. 5n). Together, these intricate interactions—including canonical base pairing, tertiary structural motifs such as kissing loops and tetraloop–receptor motifs—ensure the compactness, stability and functional assembly of the Env38 oligomer (Supplementary Video 3).
Fig. 5: Cryo-EM structure of Env38-Golld RNA reveals a tetradecameric assembly.
a, Cryo-EM reconstruction of Env38-Golld RNA at 2.93 Å resolution reveals a tetradecameric assembly with D7 symmetry, adopting a nanocage-like structure resembling a Chinese lantern. Chains are individually colored to emphasize the modular organization. b, Secondary structure model. c, Side views of the monomeric unit. d, Overall organization of the oligomer, highlighting critical interfaces. The oligomer is stabilized by three main types of interfaces: the heptameric interface, dimeric interface and dimer-of-heptamer interface. e, C–G base pairing at the heptameric interface. f–k, Close-up views of the dimeric interface. Kissing-loop interactions (f,g), tetraloop–receptor interactions (h–j) and base pairing (k) reinforce inter-subunit connectivity. l–n, Interactions at the dimer-of-heptamer interface. G–C-based kissing-loop interactions (l,m) and tetraloop–receptor interactions (n) further stabilize the oligomeric assembly.
Full size image Shared structural features and genomic context of GOLLD RNAs
The UCC118-Rool RNA, Sag-Golld RNA and Env38-Golld RNA share common structural motifs, including pseudoknots, ribose zippers, coaxial helices, kissing loops, tetraloop–receptor motifs and base-pairing motifs, reflecting conserved principles underlying RNA architecture. Despite their shared motifs, these RNAs exhibit distinct oligomeric states and structural adaptations tailored to their specific architectures. Resolution map analysis reveals localized flexibility specifically within the dimer-of-trimer interface of UCC118-Rool RNA (Fig. 1d), suggesting a potential role in facilitating ligand binding or molecular recognition. By contrast, the interfaces of Sag-Golld RNA and Env38-Golld RNA appear more rigid (Extended Data Fig. 4e and 6e), indicating a shift towards structural stability in these larger oligomeric assemblies. The ability of these RNAs to self-assemble into distinct multimeric states, coupled with the structural flexibility of UCC118-Rool RNA and the robust stabilization of Sag-Golld and Env38-Golld RNAs, reflects the adaptability of RNA as a molecular scaffold for diverse functional requirements.
To explore the biological relevance of GOLLD RNAs, we conducted a bioinformatic analysis of their genomic context, focusing on tRNA and protein associations. Using a covariance model derived from conserved GOLLD 3′ ends, we screened bacterial, bacteriophage and environmental metagenome datasets, identifying nine bacterial, 303 bacteriophage and 81 environmental metagenome GOLLD sequences. Adjacent regions within ±500 bp were annotated (Extended Data Fig. 7a). Our results reveal a stronger association between GOLLD RNAs and tRNAs than proteins (Extended Data Fig. 7b), with tRNAs frequently co-occurring across all datasets, suggesting a potential functional link. Although proteins show less consistent associations, with most annotated proteins appearing only once, Terminase34,35 is enriched across datasets, suggesting a possible role in genome packaging or mobility (Extended Data Fig. 7c–e). Notably, no canonical tRNA-processing enzymes such as RNase P31,32,33 were identified, although a few, including PhoH-like domain-containing proteins (PHD-Protein)36,37, may have indirect roles in RNA metabolism. The lack of strong enrichment of tRNA-processing enzymes suggests that GOLLD RNAs, despite their proximity to tRNA genes, may recruit distant molecular partners to facilitate this process. These findings suggest that GOLLD RNAs may have context-dependent roles, with tRNA enrichment linking them to tRNA processing and diverse protein associations indicating variable functions. Further biochemical validation is needed, but our analysis provides insights into the structural and functional potential of GOLLD RNAs.
Discussion
Our study provides high-resolution structural insights into naturally occurring multimeric RNA-only assemblies surpassing dimers, revealing hexameric, decameric and tetradecameric architectures for UCC118-Rool RNA, Sag-Golld RNA and Env38-Golld RNA, respectively. RNA flexibility has historically posed a challenge for achieving sub-2 Å resolution in cryo-EM, yet our near-atomic structure of UCC118-Rool RNA underscores the growing capability of cryo-EM to resolve RNA architectures with unprecedented clarity (Fig. 1). These findings not only expand the landscape of RNA structural biology but also demonstrate the intrinsic ability of RNA to form stable, complex and functionally versatile macromolecular assemblies.
The nanocage-like structures observed in UCC118-Rool RNA and Env38-Golld RNA closely resemble artificial RNA or DNA origami constructs, but their naturally evolved architectures exhibit a higher degree of complexity and stability. Engineered RNA nanostructures often face challenges in achieving high-resolution structures owing to their inherent flexibility and stability issues9,10,11,12,13,14,15,16. By contrast, our cryo-EM analyses achieved 1.96–2.98 Å resolutions (Extended Data Fig. 2–6), providing unprecedented details of these naturally occurring multimeric assemblies. These findings suggest that evolutionary pressures have shaped these RNAs to adopt efficient folding pathways and stable architectures, which could serve as blueprints for designing artificial RNA nanomaterials with enhanced stability and functionality.
Sag-Golld RNA further demonstrates the versatility of RNA multimerization through its dynamic oligomerization behavior, with minor populations of octameric and dodecameric states in addition to its predominant decameric form (Extended Data Figs. 1 and 4b). This structural plasticity may enable adaptation to diverse biological environments, highlighting the evolutionary advantage of multimeric RNA assemblies. Furthermore, the incorporation of pretRNA into Sag-Golld RNA, facilitated by a flexible 10 nucleotide linker, underscores its potential role in tRNA processing by positioning the pretRNA for interaction with tRNA-processing enzymes (Fig. 4a–c). This structural feature suggests the adaptability of Sag-Golld RNA as a molecular platform for enhancing enzymatic activity and regulatory complexity.
The discovery of these higher-order RNA-only complexes sheds light on the evolutionary potential of RNA to form complex functional assemblies. Multimerization in RNA probably confers several advantages, including enhanced structural stability, cooperative interactions and functional diversification, which may have contributed to the versatility and adaptability of RNA in both ancient and modern biological contexts38,39,40. By revealing the structural principles underlying RNA multimerization, our findings provide a foundation for future research on RNA-based molecular systems and potential applications in nanotechnology and synthetic biology. Further studies investigating the functional roles of these multimeric RNAs in cellular and viral processes will be essential for fully understanding their biological significance.
Methods RNA sample preparation
The RNA sequences were obtained from NCBI (Supplementary Table 1) and cloned into the pUC57 plasmid. DNA templates in the pUC57 plasmid were amplified by PCR with the regular forward primer and 2′-O-methylated reverse primer to improve 3′-end homogeneity (Supplementary Table 1). The resulting DNA templates were isolated by ethanol precipitation for subsequent in vitro transcription reactions.
The in vitro transcription was conducted in a reaction mixture containing 0.2 μM DNA template, 40 mM Tris (pH 7.9), 20 mM MgCl2, 2 mM spermidine, 0.01% Triton X-100, 10 mM dithiothreitol, 4 mM NTPs, 1 U μl−1 recombinant ribonuclease inhibitor (TaKaRa) and 5 mg ml−1 laboratory-purified T7 RNA polymerase. The transcription reaction was carried out for 3 h at 37 °C and then mixed with 2× denaturing gel loading buffer containing 95% formamide, 0.025% SDS, 10 mM EDTA, 0.025% xylene cyanol and 0.025% bromophenol blue. The resulting mixture was then loaded onto a 5% urea–PAGE gel, run at 300 V for 3 h and briefly visualized under a 254 nm UV lamp at a safe distance to minimize RNA degradation. Target RNA bands were extracted by overnight spinning at 4 °C with a binding buffer containing 300 mM NaOAc (pH 5.3) and 1 mM EDTA. The resulting gel slurry was then filtered through 0.22 µm filters. The resulting RNA was concentrated using concentrator columns with a 30 kDa cutoff and precipitated with isopropyl alcohol to remove urea and salts. Finally, RNA was purified by isopropanol precipitation and dissolved in RNase-free water.
Protein expression and purification
The gene encoding the S. agalactiae RNase P protein subunit A (SaRnpA) with an amino-terminal 6×His tag was cloned into the Escherichia coli expression vector pET28a(+) (Novagen) and transformed into BL21(DE3) E. coli competent cells. A single colony containing the recombinant plasmid was grown in Luria–Bertani medium with 50 μg ml−1 carbenicillin at 37 °C until the optical density reached 0.8. Isopropyl-β-D-thiogalactopyranoside was then added to a final concentration of 0.3 mM, and the culture was incubated at 16 °C for 20 h to induce protein expression. The cell pellets were collected by centrifugation, resuspended in a lysis buffer containing 20 mM Tris-HCl (pH 7.8), 1 M NaCl and 5 mM imidazole and then lysed by sonication. The supernatant was collected by centrifugation at 20,000g for 30 min and loaded onto a pre-equilibrated Ni-NTA column. The column was washed with a lysis buffer containing 20 mM and 40 mM imidazole, respectively, and the target protein was eluted using a lysis buffer containing 500 mM imidazole. The eluted proteins were concentrated using an Amicon Ultra Centrifugal filter (10 kDa MWCO, EMD Millipore) and exchanged into a storage buffer containing 20 mM Tris-HCl (pH 7.8) and 150 mM NaCl. The purified proteins were flash-frozen in liquid nitrogen and stored at −80 °C for future use.
RNA folding optimization
To optimize the in vitro folding conditions for ROOL and GOLLD RNAs, two commonly used folding protocols were tested. In method one, RNA samples at a final concentration of 3 μM were incubated in a 50 mM Tris-HCl (pH 7.5) buffer system, heated at 90 °C for 3 min, cooled to 37 °C, followed by the addition of varying Mg2+ concentrations and incubated at 37 °C for 30 min. In method two, RNA samples under the same buffer conditions were heated at 90 °C for 3 min, cooled to 25 °C, followed by the addition of varying Mg2+ concentrations and incubated at 50 °C for 30 min. The refolded RNAs were assessed using 5% native PAGE. Samples folded at 50 °C showed stronger oligomeric bands, indicating enhanced oligomerization. We selected this condition for structural analysis, optimizing Mg2+ to 15 mM to ensure proper folding while minimizing aggregation.
Mass photometry analysis
Mass photometry experiments were performed using Refeyn TwoMP instruments. Microscope coverslips (Thorlabs) were pretreated with poly-lysine for 1 min, dried with nitrogen and stored until use. Samples were loaded onto a 3 × 2 well silicone gasket (Grace Bio-Labs) and placed into the flow chamber. The pretreated coverslips were carefully positioned over the gasket to ensure proper sealing. Contrast-to-mass calibration was performed using BSA, with a calibration curve fitted in Refeyn DiscoverMP software, yielding an R2 value of 0.999 and a maximum mass error of 5.5%. This calibration was applied to each sample measurement to determine the molecular mass distribution in subsequent analyses.
RNA samples were refolded in vitro at a concentration of 0.5 μM. Before sample analysis, 15 μl of room-temperature working buffer was added to the well, and the autofocus function of the instrument was used to lock the focal position. For each acquisition, 2 μl of RNA sample was added to the well, thoroughly mixed to achieve a final concentration of ~67 nM and recorded using Refeyn AcquireMP software in large image acquisition mode for 60 s (6,000 frames). Data processing and analysis were conducted using Refeyn DiscoverMP software. Each histogram was fitted with a Gaussian distribution to determine the molecular mass (kDa) and normalized counts. Each sample was measured in triplicate to ensure reproducibility.
Negative-staining electron microscopy and cryo-EM sample preparation
To prepare the RNA multimers for negative-staining electron microscopy analysis, 20 μM RNA samples were refolded in a buffer containing 50 mM Tris-HCl (pH 7.5). The refolding procedure involved denaturation at 90 °C for 3 min, followed by cooling to room temperature for 10 min. Next, 15 mM MgCl2 was added, and the samples were incubated at 50 °C for 30 min before a final 10 min cool-down to room temperature. For negative staining, 4 μl of sample was applied onto glow-discharged 200-mesh copper grids with continuous thin carbon coating for 90 s. Excess sample was removed using blotting paper, and the grids were stained with 4 µl of 1% (wt/vol) uranyl acetate for 150 s. Following staining, the grids were washed twice with 4 µl droplets of 1% (wt/vol) uranyl acetate. Any remaining stain was blotted away, and the grids were air-dried. The grids were then imaged using a Tecnai F20 electron microscope operated at 200 kV. Images were recorded at a nominal magnification of ×62,000, with a calibrated pixel size of 1.81 Å. For cryo-EM, the above samples were applied to glow-discharged 200-mesh R2/1 Quantifoil copper grids. The grids were blotted for 1.5 s and rapidly cryocooled in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) at 4 °C and 100% humidity.
Cryo-EM data collection and processing
For the Sag-Golld and Sag-Golld-pretRNAser, the grids were imaged on a Titan Krios G3i cryo-EM microscope (Thermo Fisher Scientific) at a magnification of ×105,000, corresponding to a calibrated pixel size of 0.86 Å. Micrographs were recorded by EPU software (Thermo Fisher Scientific) with a K3 camera. Each image consisted of 30 frames with an exposure time of 2 s and a total dose of 46 electrons per Å2. For the Env38-Golld, the grids were imaged on a Titan Krios G3i cryo-EM microscope (Thermo Fisher Scientific) at a magnification of ×81,000, corresponding to a calibrated pixel size of 1.06 Å. Micrographs were recorded by EPU software (Thermo Fisher Scientific) with a K3 camera. Each image consisted of 30 frames with an exposure time of 2 s and a total dose of 45 electrons per Å2. For UCC118-Rool RNA, the grids were imaged on a Titan Krios G4 cryo-EM microscope (Thermo Fisher Scientific) at a magnification of ×215,000, corresponding to a calibrated pixel size of 0.575 Å. Micrographs were recorded by EPU software (Thermo Fisher Scientific) with a Falcon4i camera, and an exposure time of 2.08 s and a total dose of 30 electrons per Å2 were used. A total of 4,903 movie stacks for Sag-Golld, 5,301 movie stacks for the Sag-Golld-pretRNAser, 5,133 movie stacks for the Env38-Golld and 36,386 movie stacks for the UCC118-Rool RNA were collected, with a defocus range of −0.5 to −1.3 μm. The details of data collection are summarized in Table 1.
Table 1 Cryo-EM data collection, refinement and validation statistics Full size table
Data processing was performed using the software package cryoSPARC41. Micrographs were motion-corrected with patch motion correction. The CTF parameters of micrographs were estimated using patch CTF estimation. All particles were autopicked using the template picker in cryoSPARC. After 2D classification, 100,000 randomly selected particles from well-defined 2D classes were used to generate an ab initio map, followed by multiple rounds of 3D heterogeneous refinement, both conducted without applying symmetry constraints. The particles from high-quality 3D classes were then subjected to non-uniform refinement, performed both with and without symmetry imposed. To validate the assigned symmetry, cross-correlation values were calculated between the symmetrized and non-symmetrized maps, with a high cross-correlation value confirming the correctness of the assigned symmetry. To further enhance cryo-EM map resolution, symmetry expansion and local refinement were applied to the asymmetric units of each symmetric nanocage, generating higher-resolution asymmetric unit maps. For improved visualization of the full nanocage map, map composition for Sag-Golld, Sag-Golld-pretRNAser and Env38-Golld was generated using Phenix29. In addition, for the UCC118-Rool RNA asymmetric subunit, after achieving 1.96 Å resolution, the map was further optimized using the ResolveCryoEM tool in Phenix29. Resolution for the final maps was estimated with the 0.143 criterion of the Fourier shell correlation curves. The resolution maps before map composition were calculated in cryoSPARC using ‘Local Resolution Estimation’, and the resolution of composite maps was calculated in Phenix using the ‘Local Resolution Map’. The processing details are shown in Extended Data Figs. 2 and 4–6 and are listed in Table 1.
Model building
Given that structural information for these molecules was unavailable, de novo model building was performed. Segmentation of the cryo-EM maps was conducted using Segger42 (a plugin for UCSF Chimera), and initial model building was based on the segmented monomer map. Backbone tracing was carried out using EMRNA43, while Coot44 was used to validate the nucleotide sequence registration of the initial model and assign nucleotides to unresolved cryo-EM density regions, incorporating sequence and predicted secondary structure information. The model was then refined with phenix.realspacerefine29. The refined atomic model of the monomer was fitted into the cryo-EM densities of other protomers in the oligomers using Chimera’s symmetry tools45. Optimization of these models was performed using Coot and phenix.realspacerefine, with final quality assessment conducted using MolProbity46. Detailed model statistics are provided in Table 1. Structural bioinformatics tools PISA and PDBsum were used to identify key nucleotides involved in protomer interactions47,48. Figures were prepared using Chimera or ChimeraX49. Secondary structure diagrams were made using RiboDraw, followed by manual optimization (https://github.com/ribokit/RiboDraw).
Genome-wide identification and annotation of GOLLD-associated elements
The 3′ end of GOLLD RNA is highly conserved, making it well-suited for covariance model construction. Using all sequences from one study24 and selected sequences from another25, we successfully built a covariance model and performed a genome-wide search using INFERNAL (v.1.1.4, default parameters)50. The genome datasets were sourced from three categories: (1) bacteria: bacterial reference genomes from GenBank (2019–2025); (2) bacteriophage: 189,680 metagenomic DNA viruses from the human gut microbiome (https://portal.nersc.gov/MGV/MGVv1.020210708.tar.gz); and (3) environmental metagenome: 45,599 species-level operational taxonomic units from the Earth’s Microbiomes catalog (https://portal.nersc.gov/GEM/otus/fna.tar). Candidate sequences were selected based on lengths of >300 bp and E-values of <10−3, yielding nine bacterial, 81 environmental and 303 bacteriophage sequences for further analysis. For genomic annotation, Prokka51 was used to identify tRNAs and protein-coding genes, with tRNAscan-SE52 used for secondary validation under default parameters. To analyze the distribution of tRNAs and proteins within 500 bp upstream and downstream of GOLLD sequences, protein sequences were clustered using mmseq2 (ref. 53), with sequence identity set to 0.3 and coverage threshold at 0.6.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The cryo-EM raw movie stacks of UCC118-Rool RNA have been deposited into the EMPIAR system under accession code EMPIAR-12792. Cryo-EM structures and atomic models generated in this study have been deposited in the wwPDB OneDep System, and the Electron Microscopy Data Bank (EMD) and Protein Data Bank (PDB) accession codes are as follows: EMD-63678 and PDB 9M78 for the consensus map of UCC118-Rool, EMD-63679 and PDB 9M79 for the focused asymmetric unit map of UCC118-Rool. EMD-63019 for the consensus map of Sag-Golld, EMD-63022 and PDB 9LEC for the focused asymmetric unit of Sag-Golld, EMD-63023 and PDB 9LEE for the composite map of Sag-Golld; EMD-63026 for the consensus map of Sag-Golld-pretRNASer, EMD-63028 and PDB 9LEL for the focused asymmetric unit map of Sag-Golld-pretRNASer, EMD-63029 and PDB 9LEM for the composite map of Sag-Golld-pretRNASer; EMD-63099 for the consensus map of Env38-Golld, EMD-63100 and PDB 9LHK for the focused asymmetric unit map of Env38-Golld, EMD-63101 and PDB 9LHL for the composite map of Env38-Golld. Raw data for sequence and structure conservation analyses are included in Supplementary Data 1. All other data are available from the authors on reasonable request. Please note that all unsharpened maps, along with the ResolveCryoEM-optimized map, have been deposited as additional maps under the same accession codes as the sharpened maps. Source data are provided with this paper.
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Acknowledgements
We thank the Cryo-EM Center at the University of Science and Technology of China (USTC) for the assistance with the data collection. This work was supported by the National Key R&D Program of China (2022YFA1302700 to K.Z. and 2022YFC2303700 to K.Z. and S.L.), the National Natural Science Foundation of China (32301044 and 32471301 to S.L., 32371345 to K.Z. and 32090040 and 92254302 to X.Y.), Anhui Provincial Natural Science Foundation (2308085QC80 to S.L.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0490000 to K.Z.), the Center for Advanced Interdisciplinary Science and Biomedicine of IHM (QYPY20220019 to K.Z.) and the Fundamental Research Funds for the Central Universities (YD9100002048 to K.Z. and YD9100002044 to S.L.).
Author information Author notes
These authors contributed equally: Shengchun Zhang, Ran Yi, Linfeng An.
Authors and Affiliations
Department of Urology, The First Affiliated Hospital of USTC, MOE Key Laboratory for Cellular Dynamics, Hefei National Research Center for Physical Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, The RNA Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
Shengchun Zhang, Ran Yi, Linfeng An, Ji Liu, Xuebiao Yao, Shanshan Li & Kaiming Zhang
Contributions
S.L. and K.Z. conceived the study and designed the experiments. R.Y. and L.A. prepared the samples and performed biochemical experiments. S.Z., R.Y. and L.A. carried out cryo-EM sample preparation, screening, data collection and image processing. K.Z. conducted final structure determination. S.Z. and S.L. built and refined the models. S.Z., R.Y., L.A., X.Y., S.L. and K.Z. analyzed the data. J.L. made the movie and performed the bioinformatic analysis. S.L. and K.Z. wrote and edited the manuscript.
Corresponding authors
Correspondence to Shanshan Li or Kaiming Zhang.
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Extended data Extended Data Fig. 1 ROOL and GOLLD RNA oligomer formation analysis.
a, Genomic organization of ROOL RNA, GOLLD RNA, and tRNA genes. b, RNA folding optimization (representative gel; n = 3 repeats). c, Mass spectrometry analyses of ROOL and GOLLD RNAs indicate that they exist in higher-order forms in solution from three independent experiments. The representative ratiometric image is also shown. d, The formation of oligomeric structures revealed by negative-staining electron microscopy from three independent experiments. Scale bars, 100 nm.
Source data
Extended Data Fig. 2 Single-particle cryo-EM analysis of the UCC118-Rool RNA.
a, Workflow of the data processing. b, Gold standard FSC plots for the 3D reconstructions, calculated in cryoSPARC. c, Model validation using Q-scores. The orange line represents the expected Q-score at respective resolution based on the correlation between Q-scores and map resolution.
Extended Data Fig. 3 Comparison of UCC118-Rool RNA structure resolvability at varying resolutions, obtained using different particle numbers.
Cryo-EM reconstructions of UCC118-Rool RNA at varying particle numbers, demonstrating the progressive improvement in resolution and map quality as the dataset size increases. Q-scores for four representative nucleotides (U277, G276, A336, and C337) are indicated in green to assess map resolvability and model quality. Notably, local refinement of the final dataset (5,687,172 particles) further improves the resolution to 1.96 Å, enhancing structural clarity, with base and ribose “holes” becoming visible.
Extended Data Fig. 4 Single-particle cryo-EM analysis of the Sag-Golld RNA.
a, Representative motion-corrected cryo-EM micrograph (Number = 4801). b, Reference-free 2D class averages. c, Workflow of the data processing. d, Gold standard FSC plots for the 3D reconstructions, calculated in cryoSPARC, e, Resolution maps for the final 3D reconstructions. f, Model validation using Q-scores. The orange line represents the expected Q-score at respective resolution based on the correlation between Q-scores and map resolution.
Extended Data Fig. 5 Single-particle cryo-EM analysis of the Sag-Golld-pretRNAser RNA.
a, Representative motion-corrected cryo-EM micrograph (Number = 5150). b, Reference-free 2D class averages. c, Workflow of the data processing. d, Gold standard FSC plots for the 3D reconstructions, calculated in cryoSPARC, e, Resolution maps for the final 3D reconstructions. f, Model validation using Q-scores. The orange line represents the expected Q-score at respective resolution based on the correlation between Q-scores and map resolution.
Extended Data Fig. 6 Single-particle cryo-EM analysis of the Env38-Golld RNA.
a, Representative motion-corrected cryo-EM micrograph (Number = 5117). b, Reference-free 2D class averages. c, Workflow of the data processing. d, Gold standard FSC plots for the 3D reconstructions, calculated in cryoSPARC, e, Resolution maps for the final 3D reconstructions. f, Model validation using Q-scores. The orange line represents the expected Q-score at respective resolution based on the correlation between Q-scores and map resolution.
Extended Data Fig. 7 Identification and analysis of tRNA and proteins associated with GOLLD RNA sequences across different genomic datasets.
a, Workflow for GOLLD RNA identification and annotation. The GOLLD RNAs’ 3’ ends exhibit high conservation, enabling the construction of a multi-covariance model based on 50 known GOLLD RNAs’ 3’ end sequences. Using INFERNAL, genome-wide searches were performed across three datasets. GOLLD RNA candidates exceeding 300 bp with an E-value threshold of <10⁻³ were retained. Prokka was used to annotate nearby tRNAs and proteins within 500 bp upstream and downstream of GOLLD RNA sequences.b, Distribution of tRNAs and proteins adjacent to GOLLD sequences. The pie charts illustrate the occurrence of tRNAs (top row) and proteins (bottom row) in bacterial (left), bacteriophage (middle), and environmental metagenome (right) datasets. The proportions indicate the number of sequences with at least one adjacent tRNA or protein. The distribution of tRNAs appears more concentrated compared to proteins, suggesting a stronger association between GOLLD RNA and tRNAs. c, Protein clustering analysis. To analyze proteins associated with GOLLD RNAs, protein sequences within ±500 bp were clustered using MMseq2 (30% sequence identity, 60% coverage). Representative sequences from each cluster were annotated using UniProt 90. Due to the diverse origins of sequences, enrichment analysis was not feasible, and protein occurrence is instead displayed in bar plots. d-e, Protein occurrence in different genomic backgrounds. Bar plots show the most frequently identified proteins in bacteriophage (d) and environmental metagenome (e) datasets. Only a subset of clustered proteins could be annotated (39% in bacteriophages and 60% in metagenomes), while 93% of unannotated sequences contained only single representatives, suggesting they may be non-functional ORFs. Most proteins appeared only once, indicating no strong conservation of GOLLD-associated proteins, except Terminase, which was enriched in both datasets. The differences in protein composition across datasets suggest that GOLLD RNA may serve distinct functions in different genomic contexts. Abbreviations used in (d-e): DBP-HU: DNA-binding protein HU; RDC-Protein: Resolvase domain-containing protein; MBL: MBL fold hydrolase; PHD-Protein: PhoH-like domain-containing protein; Terminase ss: Terminase small subunit; UnP: Uncharacterized protein; DUF2829: DUF2829 domain-containing protein; Put-terminase ss: Putative terminase small subunit; GLP-NrdH: Glycox-like protein NrdH; MCS-RE: 5-methylcytosine-specific restriction endonuclease; ADR-Protein: Alkylated DNA repair protein; PGPase: Phosphoglycerol phosphatase (EC 3.1.3.18); P-ACP-MCase: Pimeloyl-ACP methyl ester carboxylesterase; SBP: Small basic protein; ADCP: ATP-grasp domain-containing protein; Put-helicase: Putative helicase; IMBT-Protein: Integral membrane bound transporter domain-containing protein; PHNLigase: 3’-phosphate/5’-hydroxy nucleic acid ligase (EC 6.5.1.8); DI-Protein: Division initiation protein; GS-III: Glutamine synthetase type III; HDP-Pro: HD/PDEase domain-containing protein; Treg: Transcriptional regulator; FDH-Alpha: Formate dehydrogenase subunit alpha; ManDeh: Mannonate dehydratase (EC 4.2.1.8).
Source data
Supplementary information Supplementary Information
Supplementary Table 1.
Reporting Summary Peer Review File Supplementary Video 1
Structure of UCC118-Rool nanocage.
Supplementary Video 2
Structure of Sag-Golld and Sag-Golld-pretRNAser nanocage.
Supplementary Video 3
Structure of Env38-Golld nanocage.
Source data Source Data Fig. 1
Statistical source data.
Source Data Extended Data Fig. 1
Unprocessed gels.
Source Data Extended Data Fig. 7
Statistical source data.
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Zhang, S., Yi, R., An, L. et al. Structural insights into higher-order natural RNA-only multimers. Nat Struct Mol Biol 32, 2012–2021 (2025). https://doi.org/10.1038/s41594-025-01650-1
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Received 14 June 2025
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Published 06 August 2025
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DOI https://doi.org/10.1038/s41594-025-01650-1
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Subjects Cryoelectron microscopy Long non-coding RNAs RNA