AptERA 2 targets ERA from Staphylococcus aureus and limits GTP hydrolysis

AptERA 2 targets ERA from Staphylococcus aureus and limits GTP hydrolysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AptERA 2 targets ERA from Staphylococcus aureus and limits GTP hydrolysis Katherin Peñaranda, Nicolle Pereira, Orestis Savva, Dezemona Petrelli, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6131212/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Ribosome assembly is a multistep process that ensures a functional ribosome structure. The molecular mechanism that ribosome­associated GTPases (RA­GTPases) use to enhance ribosome assembly accuracy, remains largely to be elucidated. Here, we use systematic evolution of ligands by exponential enrichment (SELEX), followed by sequencing, comprehensive bioinformatics analysis, and biochemical characterization to identify aptamers that target the RA-GTPase ERA of Staphylococcus aureus . ELONA and thermophoresis assays show that the Apt ERA 2 interaction with ERA is in the 200 nM range of affinity, displays a high level of specificity, and depends on the target structure. Docking to ERA suggests that Apt ERA 2 interacts with the protein's KH domain, consistent with the aptamer's similarities with helix 45 of the 16S rRNA. Apt ERA 2 did not interact with a similar RA-GTPase RbgA, conserved at the GTPase core but lacking the KH domain, confirming that the aptamer recognizes and binds the KH domain of ERA. This interaction leads to a significant reduction of 30S-dependent GTP hydrolysis, indicative of allosteric modulation of the enzyme activity or limiting the KH domain interaction with the 3’ end of the 16S rRNA rather than directly blocking GTP binding. Altogether, this work highlights the versatility of aptamers as tools to understand the complex processes of ribosome biogenesis further, offering new insights into bacterial protein synthesis mechanisms. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Molecular biology Ribosome Assembly Aptamer ERA GTPase SELEX Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The ribosome is an intricate macromolecular complex that synthesizes proteins in the cell and is essential for viability, growth, and proliferation. Ribosomes are assembled using a multi-step process that must compromise efficiency, fidelity, and velocity. In bacteria, the 70S prokaryotic ribosome consists of a large 50S and a small 30S subunit. The 50S subunit is composed of the 23S and 5S ribosomal RNAs (rRNAs) and 34 ribosomal proteins, while the 30S subunit is composed of the 16S rRNA and 21 proteins 1 – 3 . All these elements are involved in a controlled and dynamic choreography where ribosomal proteins are assembled on the three pre-rRNA transcripts, which are processed and modified during transcription 4 , 5 . Errors in this process could compromise translation fidelity, impair ribosome function, and activate quality control mechanisms to degrade defective ribosomes 6 , 7 . Thus, ribosome assembly is an energy-intensive and tightly regulated process that requires assembly factors to ensure proper function in vivo 8 . Ribosome associated GTPases (RA-GTPases) function as molecular switches, cycling between GTP-bound (active) and GDP-bound (inactive) states 9 . Active RA-GTPases bind to immature ribosomal subunits, facilitating their maturation. RbgA, HflX and Obg act on the 50S subunit, stabilizing critical helices and acting as a GTPase/ATPase for large subunit maturation 10 , 11 . RsgA and ERA, meanwhile, target the 30S subunit, helping in final subunit processing 12 . Once maturation is achieved, GTP is hydrolyzed to GDP, leading to the RA-GTPase dissociating from the ribosome. The importance of this GTPase activity is observed in the cell's ability to adapt under stringent conditions. During starvation, levels of the cellular signaling nucleotide (p)ppGpp rise and GTP concentrations fall 13 . (p)ppGpp can then outcompete GTP for RA-GTPase binding, which destabilizes the association of RA-GTPases with the ribosome subunits, negatively impacting ribosome biogenesis 14 – 16 . Therefore, RA-GTPases are molecular sensors of bacterial stress in addition to essential structural modulators of ribosome biogenesis. The GTPase ERA is an assembly factor involved in the biogenesis of the 30S ribosomal subunit in bacteria, playing a crucial role in ribosome availability and cell viability 17 . ERA is an essential protein in a number of bacterial species and its depletion is associated with severe pleiotropic phenotypes 18 , 19 . The protein’s structure has an N-terminal GTPase and a C-terminal KH domain. The NTD works as a molecular switch by GTP hydrolysis and GDP/GTP exchange 20 , 21 and binds to Protein S18 and helix h26 of the 16S rRNA on the 30S subunit. The KH domain, a distinct structural and functional unit of 85 amino acids is responsible for RNA binding and association with ribosomes by interacting with the 3′ end of the 16S rRNA 22 . Particularly, nucleotides G1530 and A1531 appear essential for ERA anchoring to the 30S 23 . ERA acts as an RNA chaperone, ensuring proper folding and maturation of the 16S rRNA and the assembly of the 30S subunit. GTPase activity appears to be essential for these processes, although it remains unclear whether GTP hydrolysis directly stimulates RNA processing or unlocks cycling between active and inactive states of the factor. Inhibiting ERA disrupts ribosome formation, making it a key target for studying ribosome assembly in bacteria and a potential target for drug development. Aptamers are short oligonucleotides that fold into unique three-dimensional structures and bind specifically and with high affinity to a given target molecule. Aptamers are selected by the SELEX (Systematic Evolution of Ligands by Exponential enrichment) method 24 . They are chemically synthesized and can be modified to enhance their stability, binding affinity, and specificity. In this work, we use SELEX coupled to next generation sequencing (NGS) and advanced bioinformatic tools to identify aptamers that bind specifically to the RA-GTPase ERA from S. aureus . We show that AptERA 2 binds the KH domain of ERA. This interaction leads to a significant reduction of 30S-dependent GTP hydrolysis, indicative of allosteric modulation of the enzyme activity or limiting the KH domain interaction with the 3’ end of the 16S rRNA rather than directly blocking GTP binding. Altogether, this work highlights the versatility of aptamers as tools to understand the complex processes of ribosome biogenesis further, offering new insights into bacterial protein synthesis mechanisms. Results Aptamers against the ERA GTPase Selection and computational analysis SELEX was utilized to select aptamer candidates with high affinity for the ribosome assembly factor ERA from Staphylococcus aureus , an RA-GTPase of 35 kDa recombinantly produced as per Bennison et al. 2021 25 (Supplementary Fig. 1). The selection process involved rounds of negative and positive selections that allowed the separation of binders from non-interacting ssDNA fragments, progressively isolating aptamers (Fig. 1 a) (Supplementary Fig. 2). Four final Enriched libraries were obtained, amplified, and sequenced by NGS (Next Generation Sequencing). These corresponded to a first round starting selection pool of binders (SP: initial aptamer library incubated with 20 µg ERA), followed by a high protein (HP) concentration (ERA: 200 nM) or a low protein (LP) concentration (ERA: 40 nM) of second selections, as well as a negative control selection using magnetic beads lacking ERA protein. The raw sets of sequences were curated using Galaxy project tools to discard reads showing amplification and sequencing artifacts. A comparison of enrichment analysis between curated and non-curated sequences showed a lower unique/total reads ratio after processing with Galaxy, suggesting better enrichment ratios (Supplementary Fig. 3). Following this, a bioinformatic analysis of the aptamer libraries was conducted using FASTAptamer 26 to identify abundant and enriched sequences that could be considered aptamer candidates. For this purpose, the libraries were first normalized to RPM (Reads Per Million), and individual and cluster analyses were performed comparing SP, HP, and LP conditions. Sequences that appeared in the negative control (without Era) were discarded as being potential unspecific binders. Individual analysis of each selection condition proved non-productive, with low enrichment ratios across libraries. Apt ERA 1 was the highest-ranked sequence in both the HP and LP selections, appearing at similar levels in both. However, it also ranked highest in the SP selection, suggesting no enrichment occurred in the second round; thus, it was excluded. As expected, SP showed poor sequence cluster formation, indicating heterogeneous sequence variety and it was excluded from further analysis. The HP and LP selection were then analyzed by cluster formation using the Levenshtein edit distance criteria, revealing 91 clusters for HP and 81 clusters for LP, from where cluster 1 including Apt ERA 1 was subtracted due to its high abundance in the initial SP library. The second and third most represented clusters by abundance were inspected in each library. The most abundant aptamer (highest RPM) was selected for Cluster 2 and Cluster 3 in each of the HP and LP selection conditions, respectively. Using this procedure, Apt ERA 2 (from cluster 2) and Apt ERA 3 (from cluster 3) were identified for the HP condition and Apt ERA 4 (from cluster 2) and Apt ERA 5 (from cluster 3) were found for the LP condition (Table 1 ). Enrichment analysis of clusters between the SP and LP or HP conditions showed that the ratios (LP/SP and HP/SP) were poor or not significantly different between clusters to be used as a selecting criterion. Sequence characterization of the four candidate aptamers revealed differential motifs patterns within each library (supplementary table S1 ). Motifs alignments against aptamers showed a general higher distribution of T-rich central motifs for Apt ERA 2 and Apt ERA 4, and G-rich motifs localized to the 3′ end for Apt ERA 3 and Apt ERA 5 (Fig. 1 b) (Supplementary Fig. 4–5). Nucleotide pairing analysis among the Apt ERA candidates showed that Apt ERA 2 was the most different between the candidates, while Apt ERA 4 exhibits the highest conservation at 67.5%, Apt ERA 5 at 60%, and Apt ERA 3 at 55%. (Supplementary Fig. 6a). Nucleotide composition analysis also revealed a predominance of pyrimidines in Apt ERA 2, 3, and 4, with a content of 60%, 62.5%, and 67.5%, respectively. Apt ERA 2 and 4 also have higher AT content, at 52.5% and 60%, respectively (Supplementary Fig. 6b). All Apt ERA aptamers showed minimal conservation of their primary sequence compared with the 16S rRNA (Supplementary Fig. 6c-d). Secondary structures of Apt ERA 2–5 were predicted using RNAfold and the best models were selected based on the lowest minimum free energy (ΔG) values, ranging from − 2.30 kcal/mol to -9.10 kcal/mol. These selected models were then analyzed structurally by visualization in Forna 27 . Predicted key structural elements that included stems, hairpins, interior loops, and unpaired nucleotides, showing distinct stem-loop and hairpin formations in all aptamers (Fig. 1 c). All aptamers contained an interior loop and a central hairpin, with Apt ERA 3, 4 and 5 having prominent stem regions. Apt ERA 2, exhibited the shortest structured region from positions 12 to 37, and Apt ERA 3 the longest from positions 3 to 40 (Fig. 1 c) (Supplementary Fig. 7a). All candidates had similar 2D and 3D structures to the helix 45 (H45) of the 16S rRNA, with Apt ERA 2 and 4 being the most similar (Supplementary Fig. 7a-b). Table 1 Aptamers Sequence † RPM § Cluster Selex Condition * Apt ERA 2 5´ TACTAGCCCTACCTGTACTCTCGAGCCGATTTTAAGGATC 3´ 2,484.92 2 HP (200 nM) Apt ERA 3 5´ TAGATCTCTGTTTGCCACTCTAGGCTGTTCTGCCAGGATC 3´ 464.05 3 HP (200 nM) Apt ERA 4 5´ TACTAGTCATGCCTGTCTATTCTTGTATTCTGCCATGATC 3´ 784.31 2 LP (40 nM) Apt ERA 5 5´ TACTAGTCCTACTGTCTGTGTAGAGCGTGCCGGAAGGATC 3´ 755.27 3 LP (40 nM) The sequence that ranked first for each cluster (LP and HP) was selected. †The central variable region shown (40nt) is flanked by the Forward 5´CAG GGG ACG CAC CAA GG 3´ and Reverse 5´CCA TGA CCC GCG TGC TG 3´primer annealing regions. §RPM: Reads per million evaluated as an abundance variable. *HP means High Protein and LP Low protein during Selex. GTPase ERA concentration is indicated in parenthesis. Aptamer binding and GTPase activity screening The binding capacities of the four aptamer candidates to the GTPase ERA were analyzed by a three-step screening process. All four aptamers were initially screened for their binding to the ERA protein using Label-free microscale thermophoresis (MST) under saturating conditions. A negative control from the initial selection library (SP) was used to set the MST signal cutoff in the absence of specific binders, determined as the mean MST plus two standard errors of the mean (s.e.m.). All four aptamers exhibited binding signals above the cutoff, indicating binding to the GTPase ERA (Fig. 2 a). This result led us to further perform testing of all the aptamers, evaluating their capacity to inhibit GTP hydrolysis, detected and quantified by Thin Layer Chromatography (TLC) using α32P-labeled GTP (Fig. 2 b) (Supplementary Fig. 8). The GTPase activity of ERA was normalized to a known GTPase activity inhibitor, ppGpp. In addition, the results were compared to a positive signal control of ERA in the absence of any GTPase inhibitor. Apt ERA 3 and Apt ERA 5 showed similar inhibitory activity as a random ssDNA control, dismissing them as possible GTPase activity inhibitors. However, Apt ERA 2 (P = 0.0001) and Apt ERA 4 (P = 0.0004) significantly reduced GTPase activity, showing a similar inhibitory effect as the positive control (ppGpp) (Fig. 2 b). To further confirm the binding of Apt ERA 2 and Apt ERA 4 to ERA, an Enzyme-Linked Oligonucleotide Assay (ELONA) assay was performed (Fig. 2 c). Both aptamers exhibited significantly (P ≤ 0.0001) higher binding signals to the ERA protein when compared to the negative control (biotinylated probe). Notably, Apt ERA 2 demonstrated the highest binding activity, correlating with its highest inhibition of GTPase hydrolysis. As such, Apt ERA 2 was selected for further characterization and evaluation. Characterization of Apt2 The specificity of Apt ERA 2 for the GTPase ERA was first evaluated under denaturing and native conditions to assess its dependence on the 3D conformation of the target protein. As shown in Fig. 3 a, Apt ERA 2 exhibited a strong binding signal to the native ERA protein, which was drastically reduced under denaturing conditions (2% SDS). Additionally, the binding signal to unrelated proteins, such as BSA and 1F8 (Chagas antigenic protein), was relatively lower (P ≤ 0.0001), indicating a high specificity for the correctly folded ERA protein. These results confirm that Apt ERA 2's binding is highly dependent on the intact 3D structure of ERA, with no significant interaction with unrelated proteins. The binding affinity of Apt ERA 2 to ERA was further characterized through various assays, including the ELONA assay and Label-free MST. These methods allowed for the determination of the dissociation constant (Kd) and the binding behavior across a range of aptamer concentrations. Using an ELONA assay, Apt ERA 2's binding affinity for ERA was assessed by titrating the aptamer concentrations from 62.5 nM to 2 µM. The results, depicted in Fig. 3 b, revealed a K d value of 243 ± 16 nM using non-linear regression analysis with a hyperbolic function for one site binding model. When titration experiment was performed with ERA, the ELONA signal displayed sigmoidal dependance on protein concentration. Thus, a dose-response analysis indicated an IC50 value of 185 ± 3 nM, showing a clear concentration dependence of Apt ERA 2 binding to ERA (Fig. 3 c). With the aim to complement the ELONA results, an MST analysis was performed to independently verify Apt ERA 2’s affinity for ERA, using an extended version of the aptamer that included selection flanking regions as indicated in Table 1 . As shown in Fig. 3 d, non-linear fitting using a sigmoidal function yielded an IC50 value of 117 ± 28 nM, consistent with the strong affinity of the aptamer for the ERA protein. Furthermore, this data ruled out a possible contribution/interference of the two flanking sequences used for the SELEX procedure. To assess Apt ERA 2’s performance in more complex environments, its binding specificity and affinity to ERA were evaluated in E. coli lysates expressing recombinant ERA protein from S. aureus (supplementary Fig. 9). Here, we show that the aptamer maintains its binding capacity for ERA under ex-vivo conditions and also shows 3D structure dependence of ERA binding (Fig. 4 a). Apt ERA 2’s specificity was tested alongside the related GTPase RbgA, which showed a low ELONA signal, similar to that obtained with an unrelated negative control protein (1F8) (Fig. 4 b). Apt ERA 2's binding behavior in lysates was also assessed through titration (Fig. 4 c). Lysates expressing the GTPases ERA and RbgA, the unrelated protein 1F8, and an empty vector over-expressing E. coli BL21 lysate were immobilized in 96 plates and tested against the aptamer for binding. When ERA was overexpressed, the measured absorbance increased with aptamer concentration as compared to negative controls and blanks. These controls showed a linear increase of absorbance with different slopes as obtained with a linear regression. Uninduced E. coli BL21 lysates showed a slope of 27 ± 1 nM − 1 , while for lysates containing overexpressed RbgA or 1F8 the slope was 34 ± 1 nM − 1 . Subtracting these backgrounds to the ERA data yielded a hyperbola consistent with a one-binding site model and a K d = 884 ± 58 nM. Overall, our work reveals that Apt ERA 2 binds specifically to both purified recombinant ERA or ERA-containing total cell lysates and shows very weak interactions with unrelated proteins or even with another RA-GTPase. Finally, the binding specificity is highly dependent on the 3D structure of ERA. The affinity of the aptamer-ERA interaction was consistent using two different methods, showing binding values ranging between 100 and 800 nM, depending on the complexity of the reaction. Both the specificity and binding affinity properties establish Apt ERA 2 as an aptamer against the GTPase ERA. Apt 2 Activity and Interaction Model To confirm the inhibitory activity of Apt ERA 2 on ERA's GTPase function, a dose-response GTPase hydrolysis activity assay was performed. The GTPase activity in the presence of Apt ERA 2, ppGpp, and a random oligo control was compared in a range of 100 to 6.25 µM. As shown in Fig. 5 a, Apt ERA 2 significantly reduced the GTPase activity, compared to the random oligo control, supporting its inhibitory effect. While the inhibition curve displayed weaker inhibitory activity as compared to the positive inhibitory control ppGpp, Apt ERA 2 still demonstrated a notable reduction in GTPase activity. The findings confirm that Apt ERA 2 effectively limits ERA's GTPase activity, making it a potentially useful tool for investigating GTP hydrolysis mechanisms and the function of ERA. To gain insights into the structural basis for Apt ERA 2's specificity and affinity for ERA, a computational 3D structural model of the aptamer-ERA complex was developed using HDOCK 30 molecular docking. Key protein-ligand interactions were suggested by PLIP analysis 31 . The resulting model, illustrated in Fig. 5 b, highlights the interface between Apt ERA 2 (blue) and the ERA protein (gray). The model shows the aptamer embracing the protein through contact with the KH Doman (Yellow) (supplementary Fig. 10). Docking shows key residues within ERA, particularly the sequence motif 'V – L - L W V K V', as possible contact points (supplementary Table S2). In comparison, Apt ERA 4 shows fewer possible contact points in this area (Fig. 5 b, comparison below the structure). This interaction could explain the aptamer's specificity and affinity towards ERA while its inhibitory activity suggests an allosteric mechanism. Discussion Aptamers are versatile molecules with a variety of applications, mostly used as molecular sensors or therapeutic agents 32 . Here we discuss the potential use of aptamers as molecular tools to further understand intra- and intermolecular interactions of the GTPase ERA and ribosome biogenesis. Our results show that Apt ERA 2 is a strong (Kd ~ 200 nM, Fig. 3 ) and specific binder of ERA. Once bound, the aptamer significantly reduces ERAs GTP hydrolysis activity on the 70S ribosome from S. aureus . Different inhibitory mechanisms may explain how Apt ERA 2 functions. For instance, the aptamer could block the guanosine nucleoside binding site, clash with the 30S subunit precluding ERA binding, or allosterically compromise ERA rearrangements that lead to GTP hydrolysis activation. Our data argues for an allosteric model since our structural models and experimental results indicate that Apt ERA 2 interacts with the KH domain of ERA (Fig. 5 ). Additionally, Apt ERA 2 shows no cross-reaction with the related protein RbgA, another GTPase that participates in the maturation of the 50S ribosomal. Both proteins, ERA and RbgA, have a very conserved G-domain, however, RbgA lacks a KH domain. Thus, the lack of binding to RbgA supports the potential interaction of Apt ERA 2 with the KH Domain. On the other hand, GTPase inhibition by blocking the KH domain may suggest that the Apt ERA 2 mechanism does not involve precluding GTP binding to the factor. Apt ERA 2 contains a motif near the 3' end, positions 37GATC40, which appears minimally structured in our 2D and 3D models (Supplementary Fig. 7). Although not identical to the 1530 GAUCA 1534 sequence found in 16S rRNA, the presence of 1530 GA 1531, known to be crucial for ERA interaction with the 30S, suggests that Apt ERA 2 may use a similar interaction 33 . This GA duplet, though important, does not seem to directly influence GTPase inhibition, as not all aptamers were able to inhibit the activity. This indicates that the dinucleotide GA is likely involved in ERA binding rather than in modulating its enzymatic function. Co-crystal structures show that ERA binds G1530 and interacts with helix 45 (h45) of the 16S rRNA 22 , 23 ​. Notably, G1530 does not stimulate GTPase activity, while A1531 and A1534 are essential for this stimulation, with position A1531 tolerating substitutions and A1534 being indispensable 23 . In our aptamers, the A at the second position of the consensus motif (1530 GAUC A 1534) is absent, similar to A1534 mutant (A1534U) that fails to stimulate ERA’s GTPase activity 23 . The presence of 2D structural motifs could mediate the interaction between the aptamer and the GTPase ERA. Hairpins are commonly associated with molecular recognition sites, while stems provide structural stability, which is crucial for effective binding 34 , 35 . The 2D structures of the aptamers show a characteristic hairpin loop, similar to h45, although the loop size varies and the double A repetition of h45 is absent in the aptamers. Additionally, h45 is known to stimulate ERA´s GTPase activity unlike the aptamer. The aptamers display loops of differing chemical composition and sizes. Apt ERA 2 and 4 show a tri- and tetraloops, respectively, while Apt ERA 3 and 5 have hexa- and pentaloops. 3D alignments of the Apt ERA aptamers with 16S rRNA show structural similarities to h45, with Apt ERA 2 and 4—the most active aptamers—showing the highest resemblance. Comparing the aptamer sequences with the 16S rRNA shows conservation of positions 3, 15, 19, 22, 23, and 36 in Apt ERA 2. Notably, positions 3, 19, 22, and 23 are involved in key contact points with ERA. Altogether, Apt ERA 2 may use a 3D structure similar to h45 for efficient binding yet preventing GTP hydrolysis rather than activating. The implications of this study extend beyond the initial findings. Aptamers like Apt ERA 2 show promise as versatile tools for probing the molecular mechanisms of ribosome biogenesis, especially by targeting key enzymes such as RA-GTPases. Proof of this are previous works where aptamers have been developed to target ribosomal proteins S8 and pepocin as well as small GTPases 36 – 38 . Moving forward, a structural investigation, such as cryo-EM or X-ray crystallography, could provide critical insights into the Apt ERA 2-ERA interaction, confirming the suggested KH domain binding and further clarifying the mechanism of inhibition 39 . Future research should also explore the adaptability of aptamers in modulating the function of other GTPases involved in ribosome assembly, providing a broader understanding of bacterial protein synthesis. The specific targeting and modulation capabilities of aptamers highlight their potential both as research tools and as a platform for developing new antimicrobial agents or “vehicles” to carry drugs to their specific target. Overall, our findings support a model in which Apt ERA 2 binds to the KH domain of the GTPase ERA and allosterically interferes with its 30S-dependent GTP hydrolysis—without directly blocking the GTP-binding site. By mimicking key rRNA elements, this aptamer effectively compromises ERA–ribosome interactions, thereby highlighting the importance of KH domain contacts for RA-GTPase activity. Beyond uncovering a novel mechanism for ERA inhibition, our study underscores the broader potential of aptamers as versatile molecular probes. Their high specificity, chemical versatility, and adaptability to complex molecular systems make them powerful tools for dissecting protein–RNA interactions, mapping conformational rearrangements, and modulating the functions of essential macromolecular assemblies—such as the bacterial ribosome—for both basic research and therapeutic innovation. Methods Aptamer Selection The X-Aptamer Selection Kit (AM Biotechnologies, LLC) 40 was used to select high-affinity aptamers. Selection buffers were prepared as follows: Buffer A: 1x PBS (pH 7.4) (524650-1EA, Sigma,USA) with 1 mM MgCl₂ (M8266-1KG, Sigma, USA), 0.05% Tween-20 (#P1379, Sigma,USA), and 0.5% BSA (#5000206, Biorad, USA); Buffer B: 1x PBS (pH 7.4) with 1 mM MgCl₂ and 0.05% Tween-20. The ssDNA library linked to polystyrene beads was rehydrated in 1.3 mL of Buffer B and activated by heating at 95°C for 5 minutes, followed by slow annealing at room temperature (RT) for 30 minutes. The negative selection was performed by incubating the aptamer library with pre-washed Ni-NTA-coated magnetic beads (#10103D, Invitrogen, USA) to remove nonspecific binders. The beads were resuspended in Buffer B and incubated with the library at RT for 1 hour with gentle rotation. The supernatant containing unbound aptamers was recovered and centrifuged to collect the library. The first positive selection involved coupling 20 µg of ERA to 25 µL of magnetic beads and incubating this with the library for 90 minutes at RT with rotation. The beads were then washed with Buffer A until the supernatant was clear, and the aptamer-polystyrene bead/ERA-magnetic bead complexes were separated using a magnetic stand. Selected bead-bound aptamers were eluted using 1 N NaOH (106469, Merck, USA) at 65°C for 30 minutes, followed by neutralization with 2 M Tris-HCl (#T5941, Sigma, USA). Eluted aptamers underwent a buffer exchange using columns, and the aptamer pool (Starting Pool, SP) was then used in a secondary pull-down selection with different ERA concentrations: 200 nM (High Protein, HP), 20 nM (Low Protein, LP), and 0 nM (SP and Negative control). For this selection, 15 µL of SP was distributed into four tubes in a final volume of 150 µL in Buffer A and incubated with the corresponding ERA concentrations. After 1 hour at RT, 5 µL of Ni-NTA-coated magnetic beads were added (except for the SP tube), incubated for 30 minutes with gentle rotation, and collected with a magnetic stand. After the final selection, magnetic beads were washed three times with 200 µL Buffer B and resuspended in 100 µL. The bound aptamers were amplified via PCR in a total volume of 100 µL containing 1x PCR buffer, 2.5 mM MgCl₂, 0.2 mM dNTPs, 1 U of Taq DNA polymerase (#M0320, NEB, USA), 0.4 µM of each primer, and 10 µL of the protein sample. The PCR conditions were as follows: an initial denaturation at 94°C for 1 minute, followed by 30 cycles of 94°C for 30 seconds, 50°C for 30 seconds, and 72°C for 1 minute, with a final extension at 72°C for 3 minutes. An initial analytic amplification was performed at 10, 14, 18, and 22 cycles to determine the optimal cycle number for each library, followed by final amplification using the selected cycle number. The PCR products were visualized on an 8–10% TBE polyacrylamide gel. After the first amplification, the libraries were prepared for high-throughput sequencing using the 16S Metagenomic Sequencing Library Preparation Kit (Illumina, USA) and sequenced on the MiSeq 2000 platform (Illumina, USA) for four libraries: SP, HP, LP, and the negative control. Bioinformatic Analysis Following the SELEX process, a comprehensive in silico workflow was employed to identify and characterize aptamers targeting the GTPase ERA. First, the High-Throughput Sequencing (HTS) data from the four libraries was curated using the Galaxy platform 41 , using tools for quality control (FASTQC-quality), adapter trimming (FASTQ Trimmer), and sequence filtering (Filter FASTQ). This process included cutting primers forward and reverse from all libraries and applying a sequence length filter (30–60 nt) to exclude shorter or longer sequences, artifacts of amplification and sequencing. To finalize sequencing reads (R1) and (R2) were merged to increase enrichment and the whole process was evaluated by unique/total sequences ratio. The curated libraries were then analyzed using FASTAptamer 42 . The commands count (to normalize to RPM, Reads per Million), enrich, compare and cluster were used to identify both individual and clustered enriched sequences. For clustering the fastaptamer_cluster command was used with the following parameters: edit distance of 4 (-d 4) and a minimum filter of RPM ≥ 9 (-f 9) to retain sequences with sufficient representation. Each cluster was analyzed by enrichment and abundance analysis, finally selecting candidates by ranking. Motif analysis was performed using FASTAptamer 2.0 43 , and the results were visualized through GraphPad Prism (Version 10; GraphPad Software, San Diego, CA, USA), plotting Z-scores (ZZ) for enrichment and p-values for statistical significance. Motif localization was assessed using a custom Python script ( https://github.com/nspereirab/APTs_Motifs ) , and Clustal Omega (EMBL-EBI, UK) 44 , which mapped motifs from HP and LP within candidate aptamers. Alignments between aptamers and 16S rRNA were done through MAFFT v7 45 . All alignments were visualized by Jalview v2 46 . Oligonucleotide synthesis and modifications Selected aptamers were synthesized by Macrogen (USA) with and without 5´biotin-TEG (triethylene glycol spacer) for binding evaluation in ELONA and MST, respectively. Additionally, non-modified aptamers were synthetized in an extended version, that included the flanking Forward 5´CAG GGG ACG CAC CAA GG 3´ and Reverse 5´CCA TGA CCC GCG TGC TG 3´ annealing regions. This extended version was used in label free-MST assays and ELONA initial screening using a 5´complementary biotinylated probe. Preparation of E. coli lysates The plasmids pET-28b for ERA and pET-24a for 1F8 were used for protein expression in E. coli BL21 (DE3) cultures. For each protein, a 1–2 liter LB culture was grown at 37°C until an OD 600 of 0.5–0.7 was reached. Protein expression was induced by adding IPTG (#R0392, Thermo Scientific, USA) to a final concentration of 0.5 mM, followed by overnight incubation at 18°C. Cells were harvested by centrifugation at 5,000 x g for 10 minutes at 4°C. The resulting cell pellets were resuspended in lysis buffer TAKM 7 containing 20 mM Tris-HCl (pH 7.5), 30 mM KCl, 7 mM MgCl₂, 70 mM NH₄Cl, and 0.2 mM Benzamidine (#12072, Merck, USA), and stored at -80°C until sonication. Cell pellets were thawed and kept in an ice bath to prevent overheating. Sonication was performed using a Pulse 150 Ultrasonic Homogenizer Sonicator (Benchmark Scientific, USA) with a microtube probe at the following conditions: power setting of 50%. 2 seconds sonication, 3-second intervals a total of 10 minutes. After this, the lysates were centrifuged at 8,500 x g for 30 minutes at 4°C to remove cell debris. The supernatant was collected, aliquoted, and stored at -80°C until further use. SDS-PAGE and ELONA confirmed the presence of ERA, RBGA, and 1F8 in the lysate with His-tag aptamers, while the overall protein concentration was determined using the Bradford assay. Production of recombinant proteins ERA was purified by nickel affinity chromatography using a 1 mL HisTrap HP column (#17524701, GE Healthcare, USA) as previously described 47 . The column was washed with wash buffer 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 5% glycerol, 10 mM imidazole, and the bound protein was eluted with a gradient of the elution buffer (50 mM Tris-HCl, pH 7.5, 200 mM NaCl, 5% glycerol, 500 mM imidazole). The gradient was run from 0–100% over 25 minutes to facilitate protein release. The eluted fractions were analyzed by SDS-PAGE, and the protein-containing fractions were pooled and dialyzed against storage buffer (50 mM Tris-HCl, pH 7.5, 200 mM NaCl, 5% glycerol). 1F8 purification was performed using a 1 mL HisTrap column, following the standard protocol provided by the manufacturer. Enzyme-Linked Oligonucleotide Assay (ELONA) For ELONA, pure proteins (1 µM) or lysates (2 µg/well) were immobilized on a 96-well plate using a carbonate/bicarbonate coating buffer (pH 9.6, #C3041, Sigma, USA) overnight at 4°C. Each well was then blocked with 200 µL of blocking buffer (1X PBS, 0.05% Tween, 5% milk powder) for 2 hours at room temperature (RT) with gentle agitation. For assays involving the anti-HisTag aptamer (6H5, Patent US10,934,856.), the aptamer was activated by heating at 95°C for 3 minutes, followed by immediate cooling on ice for 5 minutes. It was then incubated with the wells in binding buffer (1X PBS, 0.05% Tween, 0.1% milk, 1 mM MgCl₂) at a concentration of 100 pmol/well for 1 hour. For APT ERA aptamers, heating was followed by 15 minutes of cooling at RT and incubation in binding buffer (1X PBS, 0.05% Tween, 5% milk powder, 1 mM MgCl₂). After biotinylated-aptamer binding, 100 µL of streptavidin-HRP enzyme conjugate (1:1000 dilution, #N100, Thermo Scientific, USA) was added and incubated for 1 hour at RT. The signal was developed with 100 µL of One Step Slow TMB substrate (#34024, Thermo Scientific, USA), and the plate was read continuously at 620 nm for 30 minutes. The 100 ul reaction was then stopped with 50 µL of 2M sulfuric acid, and the final absorbance was measured at 450 nm. Positive and negative controls included 1F8 (positive) and BSA (negative). Between each incubation step, wells were washed four times with 200 µL/well of 1X PBS + 0.05% Tween. ELONA assays were performed to assess Apt ERA aptamer binding and specificity and to verify the presence of His-tagged proteins in lysates. All assays were performed in triplicate to ensure statistical reliability. One-way ANOVA with Dunnett's test was used for group comparisons, and an unpaired T-test for specific conditions. Hyperbolic non-linear regression and sigmoidal fitting were applied to dose-response curves. MST Label-free Microscale Thermophoresis (MST) assays were done using a Monolith NT.LabelFree instrument (NanoTemper Technologies, Munich, Germany) with LabelFree Capillaries (#MO-Z022, NanoTemper Technologies, Germany). The interaction between ERA (0.45 nM) and Apt ERA aptamers (3 µM) was assessed. Also, a 12-point titration of Apt ERA 2 was performed, with concentrations ranging from 3.4 µM to 1.66 nM. Experiments were conducted at 25°C with MST Power set to Medium and Excitation Power at 40% and data was collected from a cold region − 1 s to 0 s and a hot region from 14 s to 15 s. Data was analyzed using GraphPad software to determine binding affinities. GTPase Activity assay The ability of ERA to hydrolyze GTP in the presence or absence of aptamers was determined by incubating 1 µM ERA with 1 µM S. aureus 70S ribosomes, 2.78 nM α- 32 P-GTP and 50 µM aptamer (or ppGpp) in 40 mM Tris (pH 7.5), 100 mM NaCl, 10 mM MgCl 2 at 37°C for 1 hr. For GTPase dose response experiments, reactions were set up as described above but with increasing concentrations of ppGpp, Apt Era 2 or a control ssDNA oligo with concentrations ranging from 1.56 to 100 µM for 15 min at 37°C. All reactions were also set up in the absence or enzymes to monitor spontaneous GTP hydrolysis. Reactions were heat inactivated at 95°C for 5 min to precipitate proteins and release bound nucleotides. Proteins were pelleted by centrifugation at 17,000 x g for 10 min. Reaction products were visualized by TLC in PEI cellulose TLC plates (Macherey-Nagel) and separated using a 0.75 M KH 2 PO 4 (pH 3.6) buffer. The radioactive spots were exposed to a BAS-MS imaging plate (Fujifilm) and visualized using an LA7000 Typhoon PhosphoImager (GE Healthcare). Images were quantified using ImageQuant (GE Healthcare). Computational modeling The structures of the aptamers were predicted using a multi-step approach. First, the secondary structures of the aptamers were predicted using the RNAfold web server 48 and further visualized using Forna 27 to identify different structural motifs. After, RNAComposer 49 , 50 was employed for the tertiary structure predictions to generate initial models which were then converted into DNA replacing uridine for thymidine. Validation of aptamer sequences was done by the Webserver Xiaolab 51 . The structure of the GTPase ERA (UniProt ID: Q2FY06) was retrieved from the AlphaFold Protein Structure Database 52 . These predicted structures served as the foundation for subsequent docking simulations. Molecular docking simulations were done using the HDOCK server 30 , allowing for flexible docking of the aptamers with the target protein. Docking poses were ranked based on their docking scores, and the top poses were compared to a negative (BSA) and positive control (16S rRNA). These selected poses were then examined to identify potential interactions and binding motifs. Domain architecture and RNA-biding zones in the ERA GTPase (Q2FY06) were analyzed using the InterPro database 53 , while detailed protein-ligand interaction data were obtained through the Protein-Ligand Interaction Profiler (PLIP) 31 . Data Analysis Statistical analysis was performed using one-way ANOVA with Dunnett's multiple comparison test. P values are represented as follows: < 0.0001 (****, extremely significant), 0.0001 to 0.001 (***, extremely significant), 0.001 to 0.01 (**, very significant), 0.01 to 0.05 (*, significant), and ≥ 0.05 (ns, not significant). Results were visualized nad illustrated using GraphPad (Version 10; GraphPad Software, San Diego, CA, USA). Declarations Acknowledgements We are thankful to the Milon, Spurio, and Corrigan laboratories for their awesome scientific climate. Author contributions statement K.P., R.M.C., and P.M. conceived the experiment(s), K.P., N.P., O.S., and R.M.C. conducted experiments, all authors analyzed and discussed the results. K.P. and P.M. wrote the manuscript with the input from all authors. Funding This research was supported the Concytec Prociencia program grant PE501079419-2022 (to P.M.). This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 872869 (to P.M. and R.S.). The work was also supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society, grant number (104110/Z/14/A) (to R.M.C.); a Lister Institute Research Prize 2018 (to R.M.C.); and O.S. is funded by a BBSRC DTP studentship (grant BB/T007222/1). Open access costs are funded by the Universidad Peruana de Ciencias Aplicadas (UPC) to K.P. Additional information All data supporting the findings of this study are included in the manuscript and its Supplementary Information. The motif localization analysis was performed using a custom Python script, available at https://github.com/nspereirab/APTs_Motifs. Additional details are available upon request from the corresponding author. Competing interests : The authors declare no competing interests. References Traub, P. & Nomura, M. Structure and function of Escherichia coli ribosomes. I. Partial fractionation of the functionally active ribosomal proteins and reconstitution of artificial subribosomal particles. J. Mol. Biol. 34 , (1968). Traub, P. & Nomura, M. Structure and function of E. coli ribosomes. V. Reconstitution of functionally active 30S ribosomal particles from RNA and proteins. Proc. Natl. Acad. Sci. U S A . 59 , 777–784 (1968). Jomaa, A. et al. Understanding ribosome assembly: the structure of in vivo assembled immature 30S subunits revealed by cryo-electron microscopy. RNA 17 , 697 (2011). Gor, K. & Duss, O. Emerging Quantitative Biochemical, Structural, and Biophysical Methods for Studying Ribosome and Protein-RNA Complex Assembly. Biomolecules 13 , (2023). Davis, J. H. et al. 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The Stringent Response Inhibits 70S Ribosome Formation in Staphylococcus aureus by Impeding GTPase-Ribosome Interactions. mBio 12, (2021). Alam, K. K., Chang, J. L., Burke, D. H. & FASTAptamer A bioinformatic toolkit for high-throughput sequence analysis of combinatorial selections. Mol. Ther. Nucleic Acids . 4 , e230 (2015). Kerpedjiev, P., Hammer, S. & Hofacker, I. L. Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams. Bioinformatics 31 , 3377 (2015). Jeddi, I. & Saiz, L. Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors. Scientific Reports 2017 7:1 7, 1–13 (2017). Joseph, D. F. et al. DNA aptamers for the recognition of HMGB1 from Plasmodium falciparum. PLoS One . 14 , e0211756 (2019). Yan, Y., Zhang, D., Zhou, P., Li, B. & Huang, S. Y. HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 45 , W365 (2017). Salentin, S., Schreiber, S., Haupt, V. 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K., Xu, D. & Burke, D. H. FASTAptameR 2.0: A web tool for combinatorial sequence selections. Mol. Ther. Nucleic Acids . 29 , 862–870 (2022). Madeira, F. et al. The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024. Nucleic Acids Res. 52 , W521–W525 (2024). Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30 , 772–780 (2013). Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25 , 1189–1191 (2009). Corrigan, R. M., Bellows, L. E., Wood, A. & Gründling, A. PpGpp negatively impacts ribosome assembly affecting growth and antimicrobial tolerance in Grampositive bacteria. Proc. Natl. Acad. Sci. U S A . 113 , E1710–E1719 (2016). Gruber, A. R., Lorenz, R., Bernhart, S. H., Neuböck, R. & Hofacker, I. L. The Vienna RNA Websuite. Nucleic Acids Res. 36 , W70–W74 (2008). Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res. 40 , e112–e112 (2012). Sarzynska, J., Popenda, M., Antczak, M. & Szachniuk, M. RNA tertiary structure prediction using RNAComposer in CASP15. Proteins Struct. Funct. Bioinform. 91 , 1790–1799 (2023). Zhang, Y., Xiong, Y. & Xiao, Y. 3dDNA: A Computational Method of Building DNA 3D Structures. Molecules Vol. 27, Page 5936 27, 5936 (2022). (2022). Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596:7873 596, 583–589 (2021). Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51 , D418–D427 (2023). Supplementary Tables Supplementary Tables S1 and S2 are not available with this version. Additional Declarations No competing interests reported. Supplementary Files PenarandaetalSupplementaryInformation.docx Supplementary Information Figure S1: Production of Recombinant ERA. Figure S2: SELEX Workflow. Figure S3: Galaxy project curation analysis. Figure S4: Multiple Alignment of the HP Library using Clustal Omega. Figure S5: Multiple Alignment of the LP Library using Clustal Omega. Figure S6: Multiple alignment of all Aptamers and the 16S rRNA using MAFFT v.7. Figure S7: Structure of Apt ERA compared to h45 of the 16S rRNA. Figure S8: ERA GTPase activity in the presence of Apt ERA . Figure S9: Confirmation of His-Tagged Protein Presence in Lysates by ELONA and SDS-PAGE. Figure S10: Structural modelling of the ERA–Apt ERA 2 complex. Table S1: Motif alignments with Apt ERA aptamers. Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviews received at journal 27 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 21 Mar, 2025 Editor invited by journal 14 Mar, 2025 Submission checks completed at journal 13 Mar, 2025 First submitted to journal 28 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6131212","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424991896,"identity":"7291bd0d-1054-49a0-b745-745ead8b99c2","order_by":0,"name":"Katherin Peñaranda","email":"","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":false,"prefix":"","firstName":"Katherin","middleName":"","lastName":"Peñaranda","suffix":""},{"id":424991897,"identity":"36200b96-019a-47ee-9a92-c82b5707b925","order_by":1,"name":"Nicolle Pereira","email":"","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":false,"prefix":"","firstName":"Nicolle","middleName":"","lastName":"Pereira","suffix":""},{"id":424991898,"identity":"072d017f-7e88-4a1f-9f81-cc848bdcd910","order_by":2,"name":"Orestis Savva","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"prefix":"","firstName":"Orestis","middleName":"","lastName":"Savva","suffix":""},{"id":424991899,"identity":"79050e46-0594-4f85-8b3c-9f404d4b4896","order_by":3,"name":"Dezemona Petrelli","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Dezemona","middleName":"","lastName":"Petrelli","suffix":""},{"id":424991901,"identity":"3eb98dcc-f23e-4b10-8839-620377e074be","order_by":4,"name":"Roberto Spurio","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Spurio","suffix":""},{"id":424991903,"identity":"2b063fa2-5cf9-45e9-a2d7-af7eac6d9554","order_by":5,"name":"Rebecca M Corrigan","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"M","lastName":"Corrigan","suffix":""},{"id":424991904,"identity":"460f16cf-2c02-4438-a94a-578a9da346db","order_by":6,"name":"Pohl Milon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFACxsYDDAw2EDYPkVoagFrSSNLCwADUcpgELeZihxsOfNxxPnE7+wHGB2/bGPL4CWmxnJ3YcHDmmduJO3sSmA3ntjEUSzYQ0GJwO7HhMG/b7cQNNxjYpHnbGBI3HCBOyzmQFvbfIC37idRyAGwLM9gW4vzSlmy8syexWXLOOYliCUK2mEunP3zwsc1Odjv74YMf3pTZ5PE3EHIYgsEIUiuRQMhdyFoggLCWUTAKRsEoGHEAAI1SRmFiIDNLAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":true,"prefix":"","firstName":"Pohl","middleName":"","lastName":"Milon","suffix":""}],"badges":[],"createdAt":"2025-02-28 20:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6131212/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6131212/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-15180-9","type":"published","date":"2025-08-22T16:29:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78200839,"identity":"eb679b02-d5f0-4c60-b028-08c828bacedf","added_by":"auto","created_at":"2025-03-11 00:42:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAptamer selection and computational screening.\u003c/strong\u003e (a) Diagram of the SELEX methodology illustrating the DNA aptamer enrichment process. (b) Schematic representation of the most prevalent localization of motifs in the 4 aptamer candidates. G-rich motifs are indicated with an orange line, while AT-rich motifs are marked with a black line. Top: DNA library used, with 17 nt flanking regions complementary to primers for PCR amplification and a 40 nt central random region. (c) 2D and 3D structures of aptamer candidates, delta G values are shown. Secondary structures were predicted with RNAfold while 3D structures used a modified pipeline from \u003ca href=\"https://doi.org/10.1038/s41598-017-01348-5\"\u003eJeddi \u0026amp; Saiz, 2017\u003c/a\u003e\u003csup\u003e28\u003c/sup\u003e and \u003ca href=\"https://doi.org/10.1371/journal.pone.0211756\"\u003eJoseph et al., 201\u003csup\u003e9\u003c/sup\u003e\u003c/a\u003e\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/946623ffbfa98bcd8d54b16f.png"},{"id":78199716,"identity":"c348fea7-08e2-4f78-860c-5ea59693371f","added_by":"auto","created_at":"2025-03-11 00:34:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening and binding analysis of aptamer candidates.\u003c/strong\u003e (a) Binding screening of four aptamer candidates to purified ERA (0.45 µM) using Label-free MST under saturating conditions (3 μM). MST measures shifts in fluorescence signal upon differential thermal migration out from the observation spot as a function of the bound to unbound states of ligands, assessing aptamer-protein interactions. The binding cutoff was determined as the mean of the negative controls plus 2 s.e.m.\u0026nbsp; (b) GTP hydrolysis inhibition by aptamers. The assay allows the quantification of hydrolyzed and unhydrolyzed GTP. Activity was assessed by TLC (Thin Layer Chromatography) after incubation of 1 µM ERA with α32P-labeled GTP in the presence of aptamers and controls. Controls included ppGpp (positive control for inhibition), a random oligo (negative control for inhibition), and ERA in the absence of any inhibitor (Positive signal control). The Y-axis shows relative activity to the positive control for inhibition (ppGpp). (c) Verification of binding of Apt\u003csub\u003eERA\u003c/sub\u003e 2 and Apt\u003csub\u003eERA\u003c/sub\u003e 4 to ERA using the ELONA assay. In this assay, 1 µM ERA was immobilized onto a solid surface, and aptamer binding was detected through a streptavidin-HRP enzymatic reaction with TMB (3,3′,5,5′-tetramethylbenzidine) as the substrate. Signal was measured spectrophotometrically at 450 nm after stopping the reaction at 30 min. In this assay, extended versions of AptERA2 and AptERA4 with flanking regions were used, and both aptamers were indirectly biotinylated through a 5′ complementary probe. Two negative controls were used, biotinylated probe complementary to the 5´ end of the aptamer (Probe) and in the absence of any nucleic acid (Blank). 1 µM Bovine Serum Albumin (BSA) was used as negative control of protein in the assay in the same conditions used for ERA, Data represent mean ± sem from duplicate experiments (a, c) and mean ± sd from triplicate experiments for (b). Statistical analysis was performed using one-way ANOVA (****P ≤ 0.0001, ***P ≤ 0.001).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/edab5b77d73d6e0ceb69adb6.png"},{"id":78199641,"identity":"c7e3df6e-ccad-4c45-9ee0-fc4e029a040a","added_by":"auto","created_at":"2025-03-11 00:26:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Apt\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eERA\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e 2 binding specificity and affinity.\u003c/strong\u003e (a)\u0026nbsp; Apt\u003csub\u003eERA\u003c/sub\u003e 2 binding specificity on ERA structure. Binding of ERA was compared to BSA, 1F8 (Chagas antigenic protein) and to ERA under denaturing conditions (2% SDS). (b) Dissociation constant (Kd) determination for Apt\u003csub\u003eERA\u003c/sub\u003e 2 using the ELONA assay. ERA was used at a constant concentration of 1 µM, and the aptamer was titrated from 62.5 nM to 2 µM. The continuous line shows fitting with a hyperbolic non-linear regression function (\u003cem\u003eK\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e = 243 ± 16 nM). (c) Concentration dependence for ERA protein using ELONA assay. The continuous line shows a dose-response sigmoidal fitting (IC50 = 185 ± 3 nM). ERA was titrated from 78 nM to 1 µM.\u0026nbsp; (d) \u003cem\u003eK\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e determination using Label-free MST. The aptamer was titrated from 1.6 nM to 3.4 µM. Continuous line shows a non-linear fitting using a sigmoidal function (IC50 = 117 ± 28 nM). Error bars represent the standard error of the mean (s.e.m.) from duplicates. Error bars in (b,c) represent standard deviations (s.d.) from triplicates.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/837a1e189118c1df2dd66788.png"},{"id":78199717,"identity":"701573be-e83e-40cb-9220-bd5817c7679a","added_by":"auto","created_at":"2025-03-11 00:34:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApt\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eERA\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e 2 binding analysis under \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eex vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e conditions.\u003c/strong\u003e (a) Structural dependence for Apt\u003csub\u003eERA\u003c/sub\u003e 2 binding to \u003cem\u003eS. aureus\u003c/em\u003e ERA in\u003cem\u003e E. coli\u003c/em\u003e lysates expressing the protein. Native and 2% SDS-treated lysates were compared. \u0026nbsp;Mean ± s.d. from triplicate experiments are shown. Statistical analysis was performed using T-test (P=0,017). (b) Binding specificity of Apt\u003csub\u003eERA\u003c/sub\u003e 2 to ERA, RbgA, and 1F8 using total lysates of overexpressing \u003cem\u003eE. coli\u003c/em\u003e BL21 strains. Mean ± s.d. from triplicate experiments are shown. Statistical analysis was performed using one-way ANOVA (****P ≤ 0.0001). (c) Binding signal dependence on Apt\u003csub\u003eERA\u003c/sub\u003e 2 concentration in \u003cem\u003eE. coli\u003c/em\u003e lysates. The absorbance signal (450 nm) in the Y axis was corrected by subtracting a non-aptamer blank for all measurements. The continuous line shows the fitting with hyperbolic function for binding (\u003cem\u003eK\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e= 884 ± 58 nM).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/cc9004e17ab87eb7c04bec11.png"},{"id":78199642,"identity":"29b1c242-5222-48e5-9bac-4579d160ad03","added_by":"auto","created_at":"2025-03-11 00:26:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":170447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of GTP hydrolysis and structural model of Apt\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eERA\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e 2 with ERA protein. \u003c/strong\u003e(a) Evaluation of GTP hydrolysis inhibition of ERA. Apt\u003csub\u003eERA\u003c/sub\u003e 2 (blue), ppGpp (black, positive control for inhibition), and a random oligo (orange, negative control for inhibition) are compared at increasing concentrations Mean values ± s.d. from 5 independent measurements are shown. (b) The 3D molecular model illustrates ERA, where the G-domain is shown in gray, residues interacting with GTP in red, the KH domain in yellow, and Apt\u003csub\u003eERA\u003c/sub\u003e 2 in blue. Amino acids contacting Apt\u003csub\u003eERA\u003c/sub\u003e 2 as compared to Apt\u003csub\u003eERA\u003c/sub\u003e 4 are indicated below the structure. The 3D model of the complex was generated using HDOCK molecular docking and Protein-ligand interactions were analyzed using PLIP.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/aad3473b8b7514812268f492.png"},{"id":89847230,"identity":"51d8e71d-8968-4bf8-8281-063005e29ce9","added_by":"auto","created_at":"2025-08-25 16:42:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/8d12f226-4534-414a-b196-48202f3d12d0.pdf"},{"id":78199722,"identity":"9e143b42-17d4-4217-b006-568c96f66406","added_by":"auto","created_at":"2025-03-11 00:34:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4692448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e: Production of Recombinant ERA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e: SELEX Workflow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e: Galaxy project curation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e: Multiple Alignment of the HP Library using Clustal Omega.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e: Multiple Alignment of the LP Library using Clustal Omega.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S6\u003c/strong\u003e: Multiple alignment of all Aptamers and the 16S rRNA using MAFFT v.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S7\u003c/strong\u003e: Structure of Apt\u003csub\u003eERA \u003c/sub\u003ecompared to h45 of the 16S rRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S8\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eERA GTPase activity in the presence of Apt\u003csub\u003eERA\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S9\u003c/strong\u003e: Confirmation of His-Tagged Protein Presence in Lysates by ELONA and SDS-PAGE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S10\u003c/strong\u003e: Structural modelling of the ERA–Apt\u003csub\u003eERA\u003c/sub\u003e 2 complex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e: Motif alignments with Apt\u003csub\u003eERA\u003c/sub\u003e aptamers.\u003c/p\u003e","description":"","filename":"PenarandaetalSupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6131212/v1/884940c823eadb6828c5a65b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AptERA 2 targets ERA from Staphylococcus aureus and limits GTP hydrolysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ribosome is an intricate macromolecular complex that synthesizes proteins in the cell and is essential for viability, growth, and proliferation. Ribosomes are assembled using a multi-step process that must compromise efficiency, fidelity, and velocity. In bacteria, the 70S prokaryotic ribosome consists of a large 50S and a small 30S subunit. The 50S subunit is composed of the 23S and 5S ribosomal RNAs (rRNAs) and 34 ribosomal proteins, while the 30S subunit is composed of the 16S rRNA and 21 proteins \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. All these elements are involved in a controlled and dynamic choreography where ribosomal proteins are assembled on the three pre-rRNA transcripts, which are processed and modified during transcription \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Errors in this process could compromise translation fidelity, impair ribosome function, and activate quality control mechanisms to degrade defective ribosomes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Thus, ribosome assembly is an energy-intensive and tightly regulated process that requires assembly factors to ensure proper function \u003cem\u003ein vivo\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRibosome associated GTPases (RA-GTPases) function as molecular switches, cycling between GTP-bound (active) and GDP-bound (inactive) states \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Active RA-GTPases bind to immature ribosomal subunits, facilitating their maturation. RbgA, HflX and Obg act on the 50S subunit, stabilizing critical helices and acting as a GTPase/ATPase for large subunit maturation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. RsgA and ERA, meanwhile, target the 30S subunit, helping in final subunit processing \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Once maturation is achieved, GTP is hydrolyzed to GDP, leading to the RA-GTPase dissociating from the ribosome. The importance of this GTPase activity is observed in the cell's ability to adapt under stringent conditions. During starvation, levels of the cellular signaling nucleotide (p)ppGpp rise and GTP concentrations fall\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. (p)ppGpp can then outcompete GTP for RA-GTPase binding, which destabilizes the association of RA-GTPases with the ribosome subunits, negatively impacting ribosome biogenesis\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, RA-GTPases are molecular sensors of bacterial stress in addition to essential structural modulators of ribosome biogenesis.\u003c/p\u003e \u003cp\u003eThe GTPase ERA is an assembly factor involved in the biogenesis of the 30S ribosomal subunit in bacteria, playing a crucial role in ribosome availability and cell viability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. ERA is an essential protein in a number of bacterial species and its depletion is associated with severe pleiotropic phenotypes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The protein\u0026rsquo;s structure has an N-terminal GTPase and a C-terminal KH domain. The NTD works as a molecular switch by GTP hydrolysis and GDP/GTP exchange\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and binds to Protein S18 and helix h26 of the 16S rRNA on the 30S subunit. The KH domain, a distinct structural and functional unit of 85 amino acids is responsible for RNA binding and association with ribosomes by interacting with the 3\u0026prime; end of the 16S rRNA\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Particularly, nucleotides G1530 and A1531 appear essential for ERA anchoring to the 30S\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. ERA acts as an RNA chaperone, ensuring proper folding and maturation of the 16S rRNA and the assembly of the 30S subunit. GTPase activity appears to be essential for these processes, although it remains unclear whether GTP hydrolysis directly stimulates RNA processing or unlocks cycling between active and inactive states of the factor. Inhibiting ERA disrupts ribosome formation, making it a key target for studying ribosome assembly in bacteria and a potential target for drug development.\u003c/p\u003e \u003cp\u003eAptamers are short oligonucleotides that fold into unique three-dimensional structures and bind specifically and with high affinity to a given target molecule. Aptamers are selected by the SELEX (Systematic Evolution of Ligands by Exponential enrichment) method\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. They are chemically synthesized and can be modified to enhance their stability, binding affinity, and specificity. In this work, we use SELEX coupled to next generation sequencing (NGS) and advanced bioinformatic tools to identify aptamers that bind specifically to the RA-GTPase ERA from \u003cem\u003eS. aureus\u003c/em\u003e. We show that AptERA 2 binds the KH domain of ERA. This interaction leads to a significant reduction of 30S-dependent GTP hydrolysis, indicative of allosteric modulation of the enzyme activity or limiting the KH domain interaction with the 3\u0026rsquo; end of the 16S rRNA rather than directly blocking GTP binding. Altogether, this work highlights the versatility of aptamers as tools to understand the complex processes of ribosome biogenesis further, offering new insights into bacterial protein synthesis mechanisms.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAptamers against the ERA GTPase\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSelection and computational analysis\u003c/h2\u003e \u003cp\u003eSELEX was utilized to select aptamer candidates with high affinity for the ribosome assembly factor ERA from \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, an RA-GTPase of 35 kDa recombinantly produced as per Bennison \u003cem\u003eet al.\u003c/em\u003e 2021\u003csup\u003e25\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1). The selection process involved rounds of negative and positive selections that allowed the separation of binders from non-interacting ssDNA fragments, progressively isolating aptamers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) (Supplementary Fig.\u0026nbsp;2). Four final Enriched libraries were obtained, amplified, and sequenced by NGS (Next Generation Sequencing). These corresponded to a first round starting selection pool of binders (SP: initial aptamer library incubated with 20 \u0026micro;g ERA), followed by a high protein (HP) concentration (ERA: 200 nM) or a low protein (LP) concentration (ERA: 40 nM) of second selections, as well as a negative control selection using magnetic beads lacking ERA protein.\u003c/p\u003e \u003cp\u003eThe raw sets of sequences were curated using Galaxy project tools to discard reads showing amplification and sequencing artifacts. A comparison of enrichment analysis between curated and non-curated sequences showed a lower unique/total reads ratio after processing with Galaxy, suggesting better enrichment ratios (Supplementary Fig.\u0026nbsp;3). Following this, a bioinformatic analysis of the aptamer libraries was conducted using FASTAptamer\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e to identify abundant and enriched sequences that could be considered aptamer candidates. For this purpose, the libraries were first normalized to RPM (Reads Per Million), and individual and cluster analyses were performed comparing SP, HP, and LP conditions. Sequences that appeared in the negative control (without Era) were discarded as being potential unspecific binders. Individual analysis of each selection condition proved non-productive, with low enrichment ratios across libraries. Apt\u003csub\u003eERA\u003c/sub\u003e 1 was the highest-ranked sequence in both the HP and LP selections, appearing at similar levels in both. However, it also ranked highest in the SP selection, suggesting no enrichment occurred in the second round; thus, it was excluded. As expected, SP showed poor sequence cluster formation, indicating heterogeneous sequence variety and it was excluded from further analysis. The HP and LP selection were then analyzed by cluster formation using the Levenshtein edit distance criteria, revealing 91 clusters for HP and 81 clusters for LP, from where cluster 1 including Apt\u003csub\u003eERA\u003c/sub\u003e 1 was subtracted due to its high abundance in the initial SP library. The second and third most represented clusters by abundance were inspected in each library. The most abundant aptamer (highest RPM) was selected for Cluster 2 and Cluster 3 in each of the HP and LP selection conditions, respectively. Using this procedure, Apt\u003csub\u003eERA\u003c/sub\u003e 2 (from cluster 2) and Apt\u003csub\u003eERA\u003c/sub\u003e 3 (from cluster 3) were identified for the HP condition and Apt\u003csub\u003eERA\u003c/sub\u003e 4 (from cluster 2) and Apt\u003csub\u003eERA\u003c/sub\u003e 5 (from cluster 3) were found for the LP condition (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Enrichment analysis of clusters between the SP and LP or HP conditions showed that the ratios (LP/SP and HP/SP) were poor or not significantly different between clusters to be used as a selecting criterion.\u003c/p\u003e \u003cp\u003eSequence characterization of the four candidate aptamers revealed differential motifs patterns within each library (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Motifs alignments against aptamers showed a general higher distribution of T-rich central motifs for Apt\u003csub\u003eERA\u003c/sub\u003e 2 and Apt\u003csub\u003eERA\u003c/sub\u003e 4, and G-rich motifs localized to the 3\u0026prime; end for Apt\u003csub\u003eERA\u003c/sub\u003e 3 and Apt\u003csub\u003eERA\u003c/sub\u003e 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) (Supplementary Fig.\u0026nbsp;4\u0026ndash;5). Nucleotide pairing analysis among the Apt\u003csub\u003eERA\u003c/sub\u003e candidates showed that Apt\u003csub\u003eERA\u003c/sub\u003e 2 was the most different between the candidates, while Apt\u003csub\u003eERA\u003c/sub\u003e 4 exhibits the highest conservation at 67.5%, Apt\u003csub\u003eERA\u003c/sub\u003e 5 at 60%, and Apt\u003csub\u003eERA\u003c/sub\u003e 3 at 55%. (Supplementary Fig.\u0026nbsp;6a). Nucleotide composition analysis also revealed a predominance of pyrimidines in Apt\u003csub\u003eERA\u003c/sub\u003e 2, 3, and 4, with a content of 60%, 62.5%, and 67.5%, respectively. Apt\u003csub\u003eERA\u003c/sub\u003e 2 and 4 also have higher AT content, at 52.5% and 60%, respectively (Supplementary Fig.\u0026nbsp;6b). All Apt\u003csub\u003eERA\u003c/sub\u003e aptamers showed minimal conservation of their primary sequence compared with the 16S rRNA (Supplementary Fig.\u0026nbsp;6c-d).\u003c/p\u003e \u003cp\u003eSecondary structures of Apt\u003csub\u003eERA\u003c/sub\u003e 2\u0026ndash;5 were predicted using RNAfold and the best models were selected based on the lowest minimum free energy (ΔG) values, ranging from \u0026minus;\u0026thinsp;2.30 kcal/mol to -9.10 kcal/mol. These selected models were then analyzed structurally by visualization in Forna\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Predicted key structural elements that included stems, hairpins, interior loops, and unpaired nucleotides, showing distinct stem-loop and hairpin formations in all aptamers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). All aptamers contained an interior loop and a central hairpin, with Apt\u003csub\u003eERA\u003c/sub\u003e 3, 4 and 5 having prominent stem regions. Apt\u003csub\u003eERA\u003c/sub\u003e 2, exhibited the shortest structured region from positions 12 to 37, and Apt\u003csub\u003eERA\u003c/sub\u003e 3 the longest from positions 3 to 40 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) (Supplementary Fig.\u0026nbsp;7a). All candidates had similar 2D and 3D structures to the helix 45 (H45) of the 16S rRNA, with Apt\u003csub\u003eERA\u003c/sub\u003e 2 and 4 being the most similar (Supplementary Fig.\u0026nbsp;7a-b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAptamers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRPM\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSelex Condition\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApt\u003csub\u003eERA\u003c/sub\u003e 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute;\u003cem\u003eTACTAGCCCTACCTGTACTCTCGAGCCGATTTTAAGGATC\u003c/em\u003e 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,484.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHP (200 nM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApt\u003csub\u003eERA\u003c/sub\u003e 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute;\u003cem\u003eTAGATCTCTGTTTGCCACTCTAGGCTGTTCTGCCAGGATC\u003c/em\u003e 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e464.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHP (200 nM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApt\u003csub\u003eERA\u003c/sub\u003e 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute;\u003cem\u003eTACTAGTCATGCCTGTCTATTCTTGTATTCTGCCATGATC\u003c/em\u003e 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e784.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP (40 nM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApt\u003csub\u003eERA\u003c/sub\u003e 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026acute;\u003cem\u003eTACTAGTCCTACTGTCTGTGTAGAGCGTGCCGGAAGGATC\u003c/em\u003e 3\u0026acute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e755.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP (40 nM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sequence that ranked first for each cluster (LP and HP) was selected.\u003c/p\u003e \u003cp\u003e\u0026dagger;The central variable region shown (40nt) is flanked by the Forward 5\u0026acute;CAG GGG ACG CAC CAA GG 3\u0026acute; and Reverse 5\u0026acute;CCA TGA CCC GCG TGC TG 3\u0026acute;primer annealing regions.\u003c/p\u003e \u003cp\u003e\u0026sect;RPM: Reads per million evaluated as an abundance variable.\u003c/p\u003e \u003cp\u003e*HP means High Protein and LP Low protein during Selex. GTPase ERA concentration is indicated in parenthesis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eAptamer binding and GTPase activity screening\u003c/h3\u003e\n\u003cp\u003eThe binding capacities of the four aptamer candidates to the GTPase ERA were analyzed by a three-step screening process. All four aptamers were initially screened for their binding to the ERA protein using Label-free microscale thermophoresis (MST) under saturating conditions. A negative control from the initial selection library (SP) was used to set the MST signal cutoff in the absence of specific binders, determined as the mean MST plus two standard errors of the mean (s.e.m.). All four aptamers exhibited binding signals above the cutoff, indicating binding to the GTPase ERA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This result led us to further perform testing of all the aptamers, evaluating their capacity to inhibit GTP hydrolysis, detected and quantified by Thin Layer Chromatography (TLC) using α32P-labeled GTP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) (Supplementary Fig.\u0026nbsp;8). The GTPase activity of ERA was normalized to a known GTPase activity inhibitor, ppGpp. In addition, the results were compared to a positive signal control of ERA in the absence of any GTPase inhibitor. Apt\u003csub\u003eERA\u003c/sub\u003e 3 and Apt\u003csub\u003eERA\u003c/sub\u003e 5 showed similar inhibitory activity as a random ssDNA control, dismissing them as possible GTPase activity inhibitors. However, Apt\u003csub\u003eERA\u003c/sub\u003e 2 (P\u0026thinsp;=\u0026thinsp;0.0001) and Apt\u003csub\u003eERA\u003c/sub\u003e 4 (P\u0026thinsp;=\u0026thinsp;0.0004) significantly reduced GTPase activity, showing a similar inhibitory effect as the positive control (ppGpp) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). To further confirm the binding of Apt\u003csub\u003eERA\u003c/sub\u003e 2 and Apt\u003csub\u003eERA\u003c/sub\u003e 4 to ERA, an Enzyme-Linked Oligonucleotide Assay (ELONA) assay was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Both aptamers exhibited significantly (P\u0026thinsp;\u0026le;\u0026thinsp;0.0001) higher binding signals to the ERA protein when compared to the negative control (biotinylated probe). Notably, Apt\u003csub\u003eERA\u003c/sub\u003e 2 demonstrated the highest binding activity, correlating with its highest inhibition of GTPase hydrolysis. As such, Apt\u003csub\u003eERA\u003c/sub\u003e 2 was selected for further characterization and evaluation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCharacterization of Apt2\u003c/h3\u003e\n\u003cp\u003eThe specificity of Apt\u003csub\u003eERA\u003c/sub\u003e 2 for the GTPase ERA was first evaluated under denaturing and native conditions to assess its dependence on the 3D conformation of the target protein. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Apt\u003csub\u003eERA\u003c/sub\u003e 2 exhibited a strong binding signal to the native ERA protein, which was drastically reduced under denaturing conditions (2% SDS). Additionally, the binding signal to unrelated proteins, such as BSA and 1F8 (Chagas antigenic protein), was relatively lower (P\u0026thinsp;\u0026le;\u0026thinsp;0.0001), indicating a high specificity for the correctly folded ERA protein. These results confirm that Apt\u003csub\u003eERA\u003c/sub\u003e 2's binding is highly dependent on the intact 3D structure of ERA, with no significant interaction with unrelated proteins. The binding affinity of Apt\u003csub\u003eERA\u003c/sub\u003e 2 to ERA was further characterized through various assays, including the ELONA assay and Label-free MST. These methods allowed for the determination of the dissociation constant (Kd) and the binding behavior across a range of aptamer concentrations. Using an ELONA assay, Apt\u003csub\u003eERA\u003c/sub\u003e 2's binding affinity for ERA was assessed by titrating the aptamer concentrations from 62.5 nM to 2 \u0026micro;M. The results, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, revealed a \u003cem\u003eK\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e value of 243\u0026thinsp;\u0026plusmn;\u0026thinsp;16 nM using non-linear regression analysis with a hyperbolic function for one site binding model. When titration experiment was performed with ERA, the ELONA signal displayed sigmoidal dependance on protein concentration. Thus, a dose-response analysis indicated an IC50 value of 185\u0026thinsp;\u0026plusmn;\u0026thinsp;3 nM, showing a clear concentration dependence of Apt\u003csub\u003eERA\u003c/sub\u003e 2 binding to ERA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). With the aim to complement the ELONA results, an MST analysis was performed to independently verify Apt\u003csub\u003eERA\u003c/sub\u003e 2\u0026rsquo;s affinity for ERA, using an extended version of the aptamer that included selection flanking regions as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, non-linear fitting using a sigmoidal function yielded an IC50 value of 117\u0026thinsp;\u0026plusmn;\u0026thinsp;28 nM, consistent with the strong affinity of the aptamer for the ERA protein. Furthermore, this data ruled out a possible contribution/interference of the two flanking sequences used for the SELEX procedure.\u003c/p\u003e \u003cp\u003eTo assess Apt\u003csub\u003eERA\u003c/sub\u003e 2\u0026rsquo;s performance in more complex environments, its binding specificity and affinity to ERA were evaluated in \u003cem\u003eE. coli\u003c/em\u003e lysates expressing recombinant ERA protein from \u003cem\u003eS. aureus\u003c/em\u003e (supplementary Fig.\u0026nbsp;9). Here, we show that the aptamer maintains its binding capacity for ERA under \u003cem\u003eex-vivo\u003c/em\u003e conditions and also shows 3D structure dependence of ERA binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Apt\u003csub\u003eERA\u003c/sub\u003e 2\u0026rsquo;s specificity was tested alongside the related GTPase RbgA, which showed a low ELONA signal, similar to that obtained with an unrelated negative control protein (1F8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Apt\u003csub\u003eERA\u003c/sub\u003e 2's binding behavior in lysates was also assessed through titration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Lysates expressing the GTPases ERA and RbgA, the unrelated protein 1F8, and an empty vector over-expressing \u003cem\u003eE. coli\u003c/em\u003e BL21 lysate were immobilized in 96 plates and tested against the aptamer for binding. When ERA was overexpressed, the measured absorbance increased with aptamer concentration as compared to negative controls and blanks. These controls showed a linear increase of absorbance with different slopes as obtained with a linear regression. Uninduced \u003cem\u003eE. coli\u003c/em\u003e BL21 lysates showed a slope of 27\u0026thinsp;\u0026plusmn;\u0026thinsp;1 nM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while for lysates containing overexpressed RbgA or 1F8 the slope was 34\u0026thinsp;\u0026plusmn;\u0026thinsp;1 nM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Subtracting these backgrounds to the ERA data yielded a hyperbola consistent with a one-binding site model and a \u003cem\u003eK\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e = 884\u0026thinsp;\u0026plusmn;\u0026thinsp;58 nM.\u003c/p\u003e \u003cp\u003eOverall, our work reveals that Apt\u003csub\u003eERA\u003c/sub\u003e 2 binds specifically to both purified recombinant ERA or ERA-containing total cell lysates and shows very weak interactions with unrelated proteins or even with another RA-GTPase. Finally, the binding specificity is highly dependent on the 3D structure of ERA. The affinity of the aptamer-ERA interaction was consistent using two different methods, showing binding values ranging between 100 and 800 nM, depending on the complexity of the reaction. Both the specificity and binding affinity properties establish Apt\u003csub\u003eERA\u003c/sub\u003e 2 as an aptamer against the GTPase ERA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eApt 2 Activity and Interaction Model\u003c/h3\u003e\n\u003cp\u003eTo confirm the inhibitory activity of Apt\u003csub\u003eERA\u003c/sub\u003e 2 on ERA's GTPase function, a dose-response GTPase hydrolysis activity assay was performed. The GTPase activity in the presence of Apt\u003csub\u003eERA\u003c/sub\u003e 2, ppGpp, and a random oligo control was compared in a range of 100 to 6.25 \u0026micro;M. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Apt\u003csub\u003eERA\u003c/sub\u003e 2 significantly reduced the GTPase activity, compared to the random oligo control, supporting its inhibitory effect. While the inhibition curve displayed weaker inhibitory activity as compared to the positive inhibitory control ppGpp, Apt\u003csub\u003eERA\u003c/sub\u003e 2 still demonstrated a notable reduction in GTPase activity. The findings confirm that Apt\u003csub\u003eERA\u003c/sub\u003e 2 effectively limits ERA's GTPase activity, making it a potentially useful tool for investigating GTP hydrolysis mechanisms and the function of ERA. To gain insights into the structural basis for Apt\u003csub\u003eERA\u003c/sub\u003e 2's specificity and affinity for ERA, a computational 3D structural model of the aptamer-ERA complex was developed using HDOCK\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e molecular docking. Key protein-ligand interactions were suggested by PLIP analysis \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The resulting model, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, highlights the interface between Apt\u003csub\u003eERA\u003c/sub\u003e 2 (blue) and the ERA protein (gray). The model shows the aptamer embracing the protein through contact with the KH Doman (Yellow) (supplementary Fig.\u0026nbsp;10). Docking shows key residues within ERA, particularly the sequence motif 'V \u0026ndash; L - L W V K V', as possible contact points (supplementary Table S2). In comparison, Apt\u003csub\u003eERA\u003c/sub\u003e 4 shows fewer possible contact points in this area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, comparison below the structure). This interaction could explain the aptamer's specificity and affinity towards ERA while its inhibitory activity suggests an allosteric mechanism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAptamers are versatile molecules with a variety of applications, mostly used as molecular sensors or therapeutic agents\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Here we discuss the potential use of aptamers as molecular tools to further understand intra- and intermolecular interactions of the GTPase ERA and ribosome biogenesis. Our results show that Apt\u003csub\u003eERA\u003c/sub\u003e 2 is a strong (Kd\u0026thinsp;~\u0026thinsp;200 nM, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and specific binder of ERA. Once bound, the aptamer significantly reduces ERAs GTP hydrolysis activity on the 70S ribosome from \u003cem\u003eS. aureus\u003c/em\u003e. Different inhibitory mechanisms may explain how Apt\u003csub\u003eERA\u003c/sub\u003e 2 functions. For instance, the aptamer could block the guanosine nucleoside binding site, clash with the 30S subunit precluding ERA binding, or allosterically compromise ERA rearrangements that lead to GTP hydrolysis activation. Our data argues for an allosteric model since our structural models and experimental results indicate that Apt\u003csub\u003eERA\u003c/sub\u003e 2 interacts with the KH domain of ERA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, Apt\u003csub\u003eERA\u003c/sub\u003e 2 shows no cross-reaction with the related protein RbgA, another GTPase that participates in the maturation of the 50S ribosomal. Both proteins, ERA and RbgA, have a very conserved G-domain, however, RbgA lacks a KH domain. Thus, the lack of binding to RbgA supports the potential interaction of Apt\u003csub\u003eERA\u003c/sub\u003e 2 with the KH Domain. On the other hand, GTPase inhibition by blocking the KH domain may suggest that the Apt\u003csub\u003eERA\u003c/sub\u003e 2 mechanism does not involve precluding GTP binding to the factor.\u003c/p\u003e \u003cp\u003eApt\u003csub\u003eERA\u003c/sub\u003e 2 contains a motif near the 3' end, positions 37GATC40, which appears minimally structured in our 2D and 3D models (Supplementary Fig.\u0026nbsp;7). Although not identical to the 1530 GAUCA 1534 sequence found in 16S rRNA, the presence of 1530 GA 1531, known to be crucial for ERA interaction with the 30S, suggests that Apt\u003csub\u003eERA\u003c/sub\u003e 2 may use a similar interaction\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This GA duplet, though important, does not seem to directly influence GTPase inhibition, as not all aptamers were able to inhibit the activity. This indicates that the dinucleotide GA is likely involved in ERA binding rather than in modulating its enzymatic function. Co-crystal structures show that ERA binds G1530 and interacts with helix 45 (h45) of the 16S rRNA\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e​. Notably, G1530 does not stimulate GTPase activity, while A1531 and A1534 are essential for this stimulation, with position A1531 tolerating substitutions and A1534 being indispensable\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In our aptamers, the A at the second position of the consensus motif (1530 GAUC\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003e 1534) is absent, similar to A1534 mutant (A1534U) that fails to stimulate ERA\u0026rsquo;s GTPase activity\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe presence of 2D structural motifs could mediate the interaction between the aptamer and the GTPase ERA. Hairpins are commonly associated with molecular recognition sites, while stems provide structural stability, which is crucial for effective binding\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The 2D structures of the aptamers show a characteristic hairpin loop, similar to h45, although the loop size varies and the double A repetition of h45 is absent in the aptamers. Additionally, h45 is known to stimulate ERA\u0026acute;s GTPase activity unlike the aptamer. The aptamers display loops of differing chemical composition and sizes. Apt\u003csub\u003eERA\u003c/sub\u003e 2 and 4 show a tri- and tetraloops, respectively, while Apt\u003csub\u003eERA\u003c/sub\u003e 3 and 5 have hexa- and pentaloops. 3D alignments of the Apt\u003csub\u003eERA\u003c/sub\u003e aptamers with 16S rRNA show structural similarities to h45, with Apt\u003csub\u003eERA\u003c/sub\u003e 2 and 4\u0026mdash;the most active aptamers\u0026mdash;showing the highest resemblance. Comparing the aptamer sequences with the 16S rRNA shows conservation of positions 3, 15, 19, 22, 23, and 36 in Apt\u003csub\u003eERA\u003c/sub\u003e 2. Notably, positions 3, 19, 22, and 23 are involved in key contact points with ERA. Altogether, Apt\u003csub\u003eERA\u003c/sub\u003e 2 may use a 3D structure similar to h45 for efficient binding yet preventing GTP hydrolysis rather than activating.\u003c/p\u003e \u003cp\u003eThe implications of this study extend beyond the initial findings. Aptamers like Apt\u003csub\u003eERA\u003c/sub\u003e 2 show promise as versatile tools for probing the molecular mechanisms of ribosome biogenesis, especially by targeting key enzymes such as RA-GTPases. Proof of this are previous works where aptamers have been developed to target ribosomal proteins S8 and pepocin as well as small GTPases\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Moving forward, a structural investigation, such as cryo-EM or X-ray crystallography, could provide critical insights into the Apt\u003csub\u003eERA\u003c/sub\u003e 2-ERA interaction, confirming the suggested KH domain binding and further clarifying the mechanism of inhibition\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Future research should also explore the adaptability of aptamers in modulating the function of other GTPases involved in ribosome assembly, providing a broader understanding of bacterial protein synthesis. The specific targeting and modulation capabilities of aptamers highlight their potential both as research tools and as a platform for developing new antimicrobial agents or \u0026ldquo;vehicles\u0026rdquo; to carry drugs to their specific target.\u003c/p\u003e \u003cp\u003eOverall, our findings support a model in which Apt\u003csub\u003eERA\u003c/sub\u003e 2 binds to the KH domain of the GTPase ERA and allosterically interferes with its 30S-dependent GTP hydrolysis\u0026mdash;without directly blocking the GTP-binding site. By mimicking key rRNA elements, this aptamer effectively compromises ERA\u0026ndash;ribosome interactions, thereby highlighting the importance of KH domain contacts for RA-GTPase activity. Beyond uncovering a novel mechanism for ERA inhibition, our study underscores the broader potential of aptamers as versatile molecular probes. Their high specificity, chemical versatility, and adaptability to complex molecular systems make them powerful tools for dissecting protein\u0026ndash;RNA interactions, mapping conformational rearrangements, and modulating the functions of essential macromolecular assemblies\u0026mdash;such as the bacterial ribosome\u0026mdash;for both basic research and therapeutic innovation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAptamer Selection\u003c/p\u003e \u003cp\u003eThe X-Aptamer Selection Kit (AM Biotechnologies, LLC)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e was used to select high-affinity aptamers. Selection buffers were prepared as follows: Buffer A: 1x PBS (pH 7.4) (524650-1EA, Sigma,USA) with 1 mM MgCl₂ (M8266-1KG, Sigma, USA), 0.05% Tween-20 (#P1379, Sigma,USA), and 0.5% BSA (#5000206, Biorad, USA); Buffer B: 1x PBS (pH 7.4) with 1 mM MgCl₂ and 0.05% Tween-20. The ssDNA library linked to polystyrene beads was rehydrated in 1.3 mL of Buffer B and activated by heating at 95\u0026deg;C for 5 minutes, followed by slow annealing at room temperature (RT) for 30 minutes. The negative selection was performed by incubating the aptamer library with pre-washed Ni-NTA-coated magnetic beads (#10103D, Invitrogen, USA) to remove nonspecific binders. The beads were resuspended in Buffer B and incubated with the library at RT for 1 hour with gentle rotation. The supernatant containing unbound aptamers was recovered and centrifuged to collect the library. The first positive selection involved coupling 20 \u0026micro;g of ERA to 25 \u0026micro;L of magnetic beads and incubating this with the library for 90 minutes at RT with rotation. The beads were then washed with Buffer A until the supernatant was clear, and the aptamer-polystyrene bead/ERA-magnetic bead complexes were separated using a magnetic stand. Selected bead-bound aptamers were eluted using 1 N NaOH (106469, Merck, USA) at 65\u0026deg;C for 30 minutes, followed by neutralization with 2 M Tris-HCl (#T5941, Sigma, USA). Eluted aptamers underwent a buffer exchange using columns, and the aptamer pool (Starting Pool, SP) was then used in a secondary pull-down selection with different ERA concentrations: 200 nM (High Protein, HP), 20 nM (Low Protein, LP), and 0 nM (SP and Negative control). For this selection, 15 \u0026micro;L of SP was distributed into four tubes in a final volume of 150 \u0026micro;L in Buffer A and incubated with the corresponding ERA concentrations. After 1 hour at RT, 5 \u0026micro;L of Ni-NTA-coated magnetic beads were added (except for the SP tube), incubated for 30 minutes with gentle rotation, and collected with a magnetic stand. After the final selection, magnetic beads were washed three times with 200 \u0026micro;L Buffer B and resuspended in 100 \u0026micro;L. The bound aptamers were amplified via PCR in a total volume of 100 \u0026micro;L containing 1x PCR buffer, 2.5 mM MgCl₂, 0.2 mM dNTPs, 1 U of Taq DNA polymerase (#M0320, NEB, USA), 0.4 \u0026micro;M of each primer, and 10 \u0026micro;L of the protein sample. The PCR conditions were as follows: an initial denaturation at 94\u0026deg;C for 1 minute, followed by 30 cycles of 94\u0026deg;C for 30 seconds, 50\u0026deg;C for 30 seconds, and 72\u0026deg;C for 1 minute, with a final extension at 72\u0026deg;C for 3 minutes. An initial analytic amplification was performed at 10, 14, 18, and 22 cycles to determine the optimal cycle number for each library, followed by final amplification using the selected cycle number. The PCR products were visualized on an 8\u0026ndash;10% TBE polyacrylamide gel. After the first amplification, the libraries were prepared for high-throughput sequencing using the 16S Metagenomic Sequencing Library Preparation Kit (Illumina, USA) and sequenced on the MiSeq 2000 platform (Illumina, USA) for four libraries: SP, HP, LP, and the negative control.\u003c/p\u003e \u003cp\u003eBioinformatic Analysis\u003c/p\u003e \u003cp\u003eFollowing the SELEX process, a comprehensive \u003cem\u003ein silico\u003c/em\u003e workflow was employed to identify and characterize aptamers targeting the GTPase ERA. First, the High-Throughput Sequencing (HTS) data from the four libraries was curated using the Galaxy platform\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, using tools for quality control (FASTQC-quality), adapter trimming (FASTQ Trimmer), and sequence filtering (Filter FASTQ). This process included cutting primers forward and reverse from all libraries and applying a sequence length filter (30\u0026ndash;60 nt) to exclude shorter or longer sequences, artifacts of amplification and sequencing. To finalize sequencing reads (R1) and (R2) were merged to increase enrichment and the whole process was evaluated by unique/total sequences ratio. The curated libraries were then analyzed using FASTAptamer\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The commands count (to normalize to RPM, Reads per Million), enrich, compare and cluster were used to identify both individual and clustered enriched sequences. For clustering the fastaptamer_cluster command was used with the following parameters: edit distance of 4 (-d 4) and a minimum filter of RPM\u0026thinsp;\u0026ge;\u0026thinsp;9 (-f 9) to retain sequences with sufficient representation. Each cluster was analyzed by enrichment and abundance analysis, finally selecting candidates by ranking. Motif analysis was performed using FASTAptamer 2.0\u003csup\u003e43\u003c/sup\u003e, and the results were visualized through GraphPad Prism (Version 10; GraphPad Software, San Diego, CA, USA), plotting Z-scores (ZZ) for enrichment and p-values for statistical significance. Motif localization was assessed using a custom Python script (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nspereirab/APTs_Motifs\u003c/span\u003e\u003cspan address=\"https://github.com/nspereirab/APTs_Motifs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, and Clustal Omega (EMBL-EBI, UK)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, which mapped motifs from HP and LP within candidate aptamers. Alignments between aptamers and 16S rRNA were done through MAFFT v7\u003csup\u003e45\u003c/sup\u003e. All alignments were visualized by Jalview v2\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOligonucleotide synthesis and modifications\u003c/p\u003e \u003cp\u003eSelected aptamers were synthesized by Macrogen (USA) with and without 5\u0026acute;biotin-TEG (triethylene glycol spacer) for binding evaluation in ELONA and MST, respectively. Additionally, non-modified aptamers were synthetized in an extended version, that included the flanking Forward 5\u0026acute;CAG GGG ACG CAC CAA GG 3\u0026acute; and Reverse 5\u0026acute;CCA TGA CCC GCG TGC TG 3\u0026acute; annealing regions. This extended version was used in label free-MST assays and ELONA initial screening using a 5\u0026acute;complementary biotinylated probe.\u003c/p\u003e \u003cp\u003ePreparation of \u003cem\u003eE. coli\u003c/em\u003e lysates\u003c/p\u003e \u003cp\u003eThe plasmids pET-28b for ERA and pET-24a for 1F8 were used for protein expression in \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) cultures. For each protein, a 1\u0026ndash;2 liter LB culture was grown at 37\u0026deg;C until an OD\u003csub\u003e600\u003c/sub\u003e of 0.5\u0026ndash;0.7 was reached. Protein expression was induced by adding IPTG (#R0392, Thermo Scientific, USA) to a final concentration of 0.5 mM, followed by overnight incubation at 18\u0026deg;C. Cells were harvested by centrifugation at 5,000 x g for 10 minutes at 4\u0026deg;C. The resulting cell pellets were resuspended in lysis buffer TAKM 7 containing 20 mM Tris-HCl (pH 7.5), 30 mM KCl, 7 mM MgCl₂, 70 mM NH₄Cl, and 0.2 mM Benzamidine (#12072, Merck, USA), and stored at -80\u0026deg;C until sonication. Cell pellets were thawed and kept in an ice bath to prevent overheating. Sonication was performed using a Pulse 150 Ultrasonic Homogenizer Sonicator (Benchmark Scientific, USA) with a microtube probe at the following conditions: power setting of 50%. 2 seconds sonication, 3-second intervals a total of 10 minutes. After this, the lysates were centrifuged at 8,500 x g for 30 minutes at 4\u0026deg;C to remove cell debris. The supernatant was collected, aliquoted, and stored at -80\u0026deg;C until further use. SDS-PAGE and ELONA confirmed the presence of ERA, RBGA, and 1F8 in the lysate with His-tag aptamers, while the overall protein concentration was determined using the Bradford assay.\u003c/p\u003e \u003cp\u003eProduction of recombinant proteins\u003c/p\u003e \u003cp\u003eERA was purified by nickel affinity chromatography using a 1 mL HisTrap HP column (#17524701, GE Healthcare, USA) as previously described\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The column was washed with wash buffer 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 5% glycerol, 10 mM imidazole, and the bound protein was eluted with a gradient of the elution buffer (50 mM Tris-HCl, pH 7.5, 200 mM NaCl, 5% glycerol, 500 mM imidazole). The gradient was run from 0\u0026ndash;100% over 25 minutes to facilitate protein release. The eluted fractions were analyzed by SDS-PAGE, and the protein-containing fractions were pooled and dialyzed against storage buffer (50 mM Tris-HCl, pH 7.5, 200 mM NaCl, 5% glycerol). 1F8 purification was performed using a 1 mL HisTrap column, following the standard protocol provided by the manufacturer.\u003c/p\u003e \u003cp\u003eEnzyme-Linked Oligonucleotide Assay (ELONA)\u003c/p\u003e \u003cp\u003eFor ELONA, pure proteins (1 \u0026micro;M) or lysates (2 \u0026micro;g/well) were immobilized on a 96-well plate using a carbonate/bicarbonate coating buffer (pH 9.6, #C3041, Sigma, USA) overnight at 4\u0026deg;C. Each well was then blocked with 200 \u0026micro;L of blocking buffer (1X PBS, 0.05% Tween, 5% milk powder) for 2 hours at room temperature (RT) with gentle agitation. For assays involving the anti-HisTag aptamer (6H5, Patent US10,934,856.), the aptamer was activated by heating at 95\u0026deg;C for 3 minutes, followed by immediate cooling on ice for 5 minutes. It was then incubated with the wells in binding buffer (1X PBS, 0.05% Tween, 0.1% milk, 1 mM MgCl₂) at a concentration of 100 pmol/well for 1 hour. For APT\u003csub\u003eERA\u003c/sub\u003e aptamers, heating was followed by 15 minutes of cooling at RT and incubation in binding buffer (1X PBS, 0.05% Tween, 5% milk powder, 1 mM MgCl₂). After biotinylated-aptamer binding, 100 \u0026micro;L of streptavidin-HRP enzyme conjugate (1:1000 dilution, #N100, Thermo Scientific, USA) was added and incubated for 1 hour at RT. The signal was developed with 100 \u0026micro;L of One Step Slow TMB substrate (#34024, Thermo Scientific, USA), and the plate was read continuously at 620 nm for 30 minutes. The 100 ul reaction was then stopped with 50 \u0026micro;L of 2M sulfuric acid, and the final absorbance was measured at 450 nm. Positive and negative controls included 1F8 (positive) and BSA (negative). Between each incubation step, wells were washed four times with 200 \u0026micro;L/well of 1X PBS\u0026thinsp;+\u0026thinsp;0.05% Tween. ELONA assays were performed to assess Apt\u003csub\u003eERA\u003c/sub\u003e aptamer binding and specificity and to verify the presence of His-tagged proteins in lysates. All assays were performed in triplicate to ensure statistical reliability. One-way ANOVA with Dunnett's test was used for group comparisons, and an unpaired T-test for specific conditions. Hyperbolic non-linear regression and sigmoidal fitting were applied to dose-response curves.\u003c/p\u003e\n\u003ch3\u003eMST\u003c/h3\u003e\n\u003cp\u003eLabel-free Microscale Thermophoresis (MST) assays were done using a Monolith NT.LabelFree instrument (NanoTemper Technologies, Munich, Germany) with LabelFree Capillaries (#MO-Z022, NanoTemper Technologies, Germany). The interaction between ERA (0.45 nM) and Apt\u003csub\u003eERA\u003c/sub\u003e aptamers (3 \u0026micro;M) was assessed. Also, a 12-point titration of Apt\u003csub\u003eERA\u003c/sub\u003e 2 was performed, with concentrations ranging from 3.4 \u0026micro;M to 1.66 nM. Experiments were conducted at 25\u0026deg;C with MST Power set to Medium and Excitation Power at 40% and data was collected from a cold region \u0026minus;\u0026thinsp;1 s to 0 s and a hot region from 14 s to 15 s. Data was analyzed using GraphPad software to determine binding affinities.\u003c/p\u003e \u003cp\u003eGTPase Activity assay\u003c/p\u003e \u003cp\u003eThe ability of ERA to hydrolyze GTP in the presence or absence of aptamers was determined by incubating 1 \u0026micro;M ERA with 1 \u0026micro;M \u003cem\u003eS. aureus\u003c/em\u003e 70S ribosomes, 2.78 nM α-\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003eP-GTP and 50 \u0026micro;M aptamer (or ppGpp) in 40 mM Tris (pH 7.5), 100 mM NaCl, 10 mM MgCl\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C for 1 hr. For GTPase dose response experiments, reactions were set up as described above but with increasing concentrations of ppGpp, Apt\u003csub\u003eEra\u003c/sub\u003e 2 or a control ssDNA oligo with concentrations ranging from 1.56 to 100 \u0026micro;M for 15 min at 37\u0026deg;C. All reactions were also set up in the absence or enzymes to monitor spontaneous GTP hydrolysis. Reactions were heat inactivated at 95\u0026deg;C for 5 min to precipitate proteins and release bound nucleotides. Proteins were pelleted by centrifugation at 17,000 x g for 10 min. Reaction products were visualized by TLC in PEI cellulose TLC plates (Macherey-Nagel) and separated using a 0.75 M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (pH 3.6) buffer. The radioactive spots were exposed to a BAS-MS imaging plate (Fujifilm) and visualized using an LA7000 Typhoon PhosphoImager (GE Healthcare). Images were quantified using ImageQuant (GE Healthcare).\u003c/p\u003e \u003cp\u003eComputational modeling\u003c/p\u003e \u003cp\u003eThe structures of the aptamers were predicted using a multi-step approach. First, the secondary structures of the aptamers were predicted using the RNAfold web server\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and further visualized using Forna\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e to identify different structural motifs. After, RNAComposer\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e was employed for the tertiary structure predictions to generate initial models which were then converted into DNA replacing uridine for thymidine. Validation of aptamer sequences was done by the Webserver Xiaolab\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The structure of the GTPase ERA (UniProt ID: Q2FY06) was retrieved from the AlphaFold Protein Structure Database\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. These predicted structures served as the foundation for subsequent docking simulations. Molecular docking simulations were done using the HDOCK server\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, allowing for flexible docking of the aptamers with the target protein. Docking poses were ranked based on their docking scores, and the top poses were compared to a negative (BSA) and positive control (16S rRNA). These selected poses were then examined to identify potential interactions and binding motifs. Domain architecture and RNA-biding zones in the ERA GTPase (Q2FY06) were analyzed using the InterPro database\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, while detailed protein-ligand interaction data were obtained through the Protein-Ligand Interaction Profiler (PLIP)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using one-way ANOVA with Dunnett's multiple comparison test. P values are represented as follows: \u0026lt; 0.0001 (****, extremely significant), 0.0001 to 0.001 (***, extremely significant), 0.001 to 0.01 (**, very significant), 0.01 to 0.05 (*, significant), and \u0026ge;\u0026thinsp;0.05 (ns, not significant). Results were visualized nad illustrated using GraphPad (Version 10; GraphPad Software, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to the Milon, Spurio, and Corrigan laboratories for their awesome scientific climate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.P., R.M.C., and P.M. conceived the experiment(s), K.P., N.P., O.S., and R.M.C. conducted experiments, all authors analyzed and discussed the results. K.P. and P.M. wrote the manuscript with the input from all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported the Concytec Prociencia program grant PE501079419-2022 (to P.M.). This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 872869 (to P.M. and R.S.). The work was also supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society, grant number (104110/Z/14/A) (to R.M.C.); a Lister Institute Research Prize 2018 (to R.M.C.); and O.S. is funded by a BBSRC DTP studentship (grant BB/T007222/1). Open access costs are funded by the Universidad Peruana de Ciencias Aplicadas (UPC) to K.P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are included in the manuscript and its Supplementary Information. The motif localization analysis was performed using a custom Python script, available at https://github.com/nspereirab/APTs_Motifs. Additional details are available upon request from the corresponding author.\u003cstrong\u003e\u0026nbsp;Competing interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTraub, P. \u0026amp; Nomura, M. Structure and function of Escherichia coli ribosomes. I. Partial fractionation of the functionally active ribosomal proteins and reconstitution of artificial subribosomal particles. \u003cem\u003eJ. Mol. Biol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, (1968).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraub, P. \u0026amp; Nomura, M. Structure and function of E. coli ribosomes. V. Reconstitution of functionally active 30S ribosomal particles from RNA and proteins. \u003cem\u003eProc. Natl. Acad. Sci. 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InterPro in 2022. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, D418\u0026ndash;D427 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Tables S1 and S2 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ribosome Assembly, Aptamer, ERA, GTPase, SELEX","lastPublishedDoi":"10.21203/rs.3.rs-6131212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6131212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRibosome assembly is a multistep process that ensures a functional ribosome structure. The molecular mechanism that ribosome\u0026shy;associated GTPases (RA\u0026shy;GTPases) use to enhance ribosome assembly accuracy, remains largely to be elucidated. Here, we use systematic evolution of ligands by exponential enrichment (SELEX), followed by sequencing, comprehensive bioinformatics analysis, and biochemical characterization to identify aptamers that target the RA-GTPase ERA of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. ELONA and thermophoresis assays show that the Apt\u003csub\u003eERA\u003c/sub\u003e 2 interaction with ERA is in the 200 nM range of affinity, displays a high level of specificity, and depends on the target structure. Docking to ERA suggests that Apt\u003csub\u003eERA\u003c/sub\u003e 2 interacts with the protein's KH domain, consistent with the aptamer's similarities with helix 45 of the 16S rRNA. Apt\u003csub\u003eERA\u003c/sub\u003e 2 did not interact with a similar RA-GTPase RbgA, conserved at the GTPase core but lacking the KH domain, confirming that the aptamer recognizes and binds the KH domain of ERA. This interaction leads to a significant reduction of 30S-dependent GTP hydrolysis, indicative of allosteric modulation of the enzyme activity or limiting the KH domain interaction with the 3\u0026rsquo; end of the 16S rRNA rather than directly blocking GTP binding. Altogether, this work highlights the versatility of aptamers as tools to understand the complex processes of ribosome biogenesis further, offering new insights into bacterial protein synthesis mechanisms.\u003c/p\u003e","manuscriptTitle":"AptERA 2 targets ERA from Staphylococcus aureus and limits GTP hydrolysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 00:26:16","doi":"10.21203/rs.3.rs-6131212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-28T08:48:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T00:50:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-28T03:59:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278394517543670439630385712555464359445","date":"2025-03-23T13:12:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155809977063850027141066623366997719210","date":"2025-03-21T14:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238321417507465188087730239491230170290","date":"2025-03-21T09:40:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T07:55:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T07:33:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-14T14:26:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-13T14:29:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-28T20:06:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27c8c334-3054-4f74-80c0-b1a94c183d2a","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45294324,"name":"Biological sciences/Biochemistry"},{"id":45294325,"name":"Biological sciences/Biological techniques"},{"id":45294326,"name":"Biological sciences/Biotechnology"},{"id":45294327,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-08-25T16:34:57+00:00","versionOfRecord":{"articleIdentity":"rs-6131212","link":"https://doi.org/10.1038/s41598-025-15180-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-22 16:29:23","publishedOnDateReadable":"August 22nd, 2025"},"versionCreatedAt":"2025-03-11 00:26:16","video":"","vorDoi":"10.1038/s41598-025-15180-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-15180-9","workflowStages":[]},"version":"v1","identity":"rs-6131212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6131212","identity":"rs-6131212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}