Advanced RNA-Ligand Binding Analysis Using Microfluidic Modulation Spectroscopy

RNA molecules play numerous roles in the complicated network of intracellular molecular interactions that extend beyond their usual functions as gene expression mediators. Within these diverse functions, RNA riboswitches are fundamental regulatory elements that regulate gene expression in response to small molecule ligands.

Dynamic interactions between RNA and ligands are key to multiple biological processes, from human disease pathways to microbial pathogenesis.¹ Knowledge of RNA–ligand binding via riboswitches may aid in communicating the key mechanisms of gene regulation and open avenues for targeted therapeutic interventions.

RNA molecules have surpassed their conventional role as messengers of genetic data and grown into dynamic regulators of gene expression via complicated structural motifs known as riboswitches.

Amongst them, the S-adenosylmethionine (SAM)-I riboswitch regulates gene expression in response to fluctuations in intracellular SAM concentrations.² SAM is a critical metabolite involved in various cellular processes, playing a critical role as a cofactor and a regulatory molecule in multiple biological pathways, including transsulfuration, transmethylation, and polyamine synthesis.³

SAM-I riboswitches have distinct SAM specificity and affinity. They work as molecular sentinels and control gene expression to preserve homeostasis in cells and adapt to environmental stimuli.

The SAM-I riboswitch comprises two unique domains: the aptamer domain for SAM recognition (as shown in Figure 1) and the downstream expression platform that regulates gene expression.

During SAM binding, conformational rearrangements occur at the aptamer domain, which sends signals to the expression platform to control translational or transcriptional outputs (Figure 2).

This allosteric regulation mechanism is typical of riboswitches. It allows cells a precise and fast way to sense and react to changes in SAM levels, calibrating to metabolic pathways and securing cellular health.⁴

Figure 1. X-ray crystal structure of SAM-I riboswitch with a bound ligand (PDB: 2GIS) and the chemical structure of SAM. Adapted from ref. 4. Copyright 2017 by the RNA Society.

Understanding the structure and function of the SAM-I riboswitch has allowed for exceptional insights into the sophisticated link between RNA and ligand molecules.

Structural research utilizing nuclear magnetic resonance spectroscopy, X-ray crystallography, and cryo-electron microscopy has offered a detailed understanding of the SAM-binding pocket and allosteric communication pathways within the riboswitch.

Biochemical assays, together with biophysical approaches like single-molecule imaging and fluorescence spectroscopy, have uncovered the kinetic parameters that oversee the dynamics of SAM binding and riboswitch folding. However, these approaches only offer binding and structural data on the RNA–ligand complex.

Figure 2. Binding-induced conformational changes in SAM-I riboswitch. Upon binding of SAM at the four-way junction, the P1 domain rotates to bind the ligand, which in turn causes a large allosteric change on the P4 domain. Adapted from ref. 4. Copyright 2017 by the RNA Society.

This article demonstrates research that used microfluidic modulation spectroscopy (MMS) to assess SAM-I riboswitch structural variations following binding. MMS was utilized to probe the nucleic acid bases in the Amide-I infrared (IR) range to interrogate the base pairing/stacking throughout RNA folding and unfolding.

The amide-I band is typically utilized to research proteins’ secondary structures. However, this IR region also holds considerable structural data regarding nucleic acid bases, which are often overlooked.

MMS consistently modulates against a reference buffer to obtain precise background correction instantaneously. This approach’s sensitivity makes it especially applicable to quality control and fitting for several different formulation buffers.

This research used the Apollo MMS system, a tool that utilizes a high-power quantum cascade laser, which has a considerably higher intensity than traditional FTIR light sources.

The source of light and modulating background subtraction merge to make MMS 30 times more sensitive than FTIR and five times more sensitive than CD regarding structural variations.⁵

Methods

SAM-I riboswitches (apo and ligand-bound) were taken from Arrakis (Waltham, MA). The RNA samples were buffer-exchanged thrice to their formulation buffer (20 mM HEPES pH 7.5, 100 mM KCl, 3 mM MgCl₂, 0.05 % TWEEN-20, and 2 % DMSO), and the final eluent was used as the reference buffer.

The buffer-exchanged RNA samples were operated in triplicate on the Apollo, with an end concentration of 0.67 mg/mL (22 μM).

A backing pressure of 5 psi was used to transfer the samples into the flow cell, where they were modulated at 1 Hz between the sample and reference buffer for background subtraction.

The differential absorbance was assessed between 1580 and 1765 cm^-1. Replicates were averaged, and all samples were normalized for concentration and interpolated to produce the absolute absorbance spectra.

Data processing came after the procedures utilized in an earlier report.⁶ The raw differential absorbance was transformed to absolute absorbance, normalized by concentration and optical pathlength. The second derivatives of the absolute absorbance spectra were taken to improve spectral properties.

The second derivative was next inverted and baselined to generate a similarity plot, where the area of overlap is quantified and compared to a control to calculate the similarity between samples.⁷

Results

The MMS outcomes signaled discrete changes in structure, as evidenced by the spectral variations stemming from ligand binding on the SAM-I riboswitch, as demonstrated in Figure 3. The areas of change were the peaks positioned at 1690, 1640–1670, and 1604 cm^-1, predominantly assigned to guanine, uracil/cytosine, and adenine, as depicted in Figure 4.

The rise in intensity at 1690 cm^-1 was suggestive of the guanine C=O stretch in a double-strand or base-paired state. The crystal structure of ligand-bound SAM-I riboswitch (PDB: 2GIS) suggested that the binding used SAM interactions with U57, A45, A46, G11, and G58 in the riboswitch.

These base-ligand interactions probably contributed to spectral changes at the noted peaks in Figure 3 (with peak assignments displayed in Figure 4).⁸

Figure 3. MMS spectra (similarity plot) of the apo riboswitch and ligand-bound riboswitch. Image Credit: RedShiftBio 

Figure 4. MMS spectra (similarity plot) of the apo riboswitch and ligand-bound riboswitch with peak assignments. (ss: single strand. ds: double strand. ts: triple strand.). Image Credit: RedShiftBio

A dose-dependent titration between SAM and the SAM-I riboswitch was additionally carried out, and the samples were structurally defined via MMS. The MMS spectra in Figure 5 showcase two areas of gradual change upon SAM titration.

The first is the increase at 1695 cm^-1, signaling guanine base-pairing according to Figure 4. Here, the rising concentrations of SAM lead to more interaction among G11, G58, and SAM.

The second area is the 3-wavenumber shift from 1604 to 1607 cm^-1. This shift is less researched, but in this case, it probably stems from the interactions of the two adenines (A45 and A46) with SAM.

The spectral variation in the 1640–1670 cm^-1 area does not demonstrate a visible dependence on SAM concentration and thus requires further investigation.

Figure 5. Dose-dependent titration of SAM into SAM-I riboswitch. Gradual change in spectra has been observed as the SAM concentration increases. Image Credit: RedShiftBio

The area of overlap (AO) approach was used to quantify the spectral differences in similarity among samples to show that the previously observed spectral changes were real and not simply noise.^5,7

Table 1 highlights measurement repeatability against the sample-to-sample similarity via the AO approach. Overall, repeatability suggests how well the repeat spectra overlay, and a similarity under this value infers considerable spectral variation. All samples are considerably different from the reference (Apo riboswitch).

Table 1. Repeatability of measurement and sample-to-sample similarity (Apo riboswitch as reference: 100 % similarity). Source: RedShiftBio 

To answer the query of whether the conformational variation in the RNA stemming from ligand binding or the IR signal change from the bound ligand generated the spectral variations, the spectral difference (1—similarity) between the titrated samples and the apo riboswitch was plotted, as depicted in Figure 6.

The spectral variation (blue line) plateaus following ∼20 μM of SAM concentration, close to 1:1 ligand–riboswitch molar ratio, suggesting that the observed spectral change stems from ligand binding.

The first derivative of the spectral difference (displayed as the red line in Figure 6) underscores that the SAM concentration at which spectral change happened at the fastest rate was 4 μM.

Regardless of using riboswitch concentrations that exceeded the apparent dissociation constant (Kd) of the binding, this research shows the efficacy of MMS as a fast and easy-to-use instrument for detecting structural change as well as calculating Kd (in the micromolar range) of RNA–ligand binding.

Figure 6. Spectral change in area of overlap (AO) in response to SAM titration. Apo riboswitch was used as the reference. Spectral change increases as the SAM concentration increases. Blue: spectral change. Red: 1st derivative of spectral change. Image Credit: RedShiftBio

Conclusion

The research described here underscores the usefulness of MMS as a practical orthogonal assay for identifying RNA structural changes resulting from small-molecule ligands.

The research further shows the potential of MMS for calculating apparent Kd of RNA–ligand binding in the micromolar range by carefully differentiating structural variations in the RNA riboswitch stemming from ligand binding.

Acknowledgments

Produced from materials authored by Richard Huang and Valerie Collins from RedShiftBio and Scott Gorman from Arrakis Therapeutics.

References

1. Zhou, Y., Jiang, Y., Chen, S. J. (2022). RNA–Ligand Molecular Docking: Advances and Challenges. Wiley Interdisciplinary Reviews: Computational Molecular Science. https://doi.org/10.1002/wcms.1571
2. Montange, R. K., Batey, R. T. (2006). Structure of the S-Adenosylmethionine Riboswitch Regulatory MRNA Element. Nature, 441(7097), pp.1172–1175. https://pubmed.ncbi.nlm.nih.gov/16810258/
3. Ouyang, Y., Wu, Q., Li, J., Sun, S., Sun, S. (2020). S-Adenosylmethionine: A Metabolite Critical to the Regulation of Autophagy. Cell Proliferation. https://doi.org/10.1111/cpr.12891
4. Dussault, A.-M., Dubé, A., Jacques, F., Grondin, J. P., Lafontaine, D. A. (2017). Ligand Recognition and Helical Stacking Formation Are Intimately Linked in the SAM-I Riboswitch Regulatory Mechanism. RNA. https://pubmed.ncbi.nlm.nih.gov/28701520/
5. Kendrick, B. S., Gabrielson, J. P., Solsberg, C. W., Ma, E., Wang, L. (2020). Determining Spectroscopic Quantitation Limits for Misfolded Structures. J Pharm Sci, 109(1), pp.933–936. https://pubmed.ncbi.nlm.nih.gov/31521643/
6. Huang, R.; Collins, V.; Gillingham, D. Structural Comparisonof the Matrix Metalloproteinase Proenzymes Using MicrofluidicModulation Spectroscopy; 2023. www.redshiftbio.com.
7. Kendrick, B. S., Dong, A., Dean Allison, S., Manning, M. C., Carpenter, J. F. (1996). Quantitation of the Area of Overlap between Second-Derivative Amide I Infrared Spectra To Determine the Structural Similarity of a Protein in Different States. J Pharm Sci, 85(2), pp.155–158. https://pubmed.ncbi.nlm.nih.gov/8683440/
8. Banyay, M., Sarkar, M., Graslund, A. (2003). A Library of IR Bands of Nucleic Acids in Solution. Biophys Chem, 104, pp.477–488. https://pubmed.ncbi.nlm.nih.gov/12878315/

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RedShiftBio has developed a proprietary life sciences platform combining our Microfluidic Modulation Spectroscopy (MMS) and expertise in high-powered quantum cascade lasers that provide ultra-sensitive and ultra-precise measurements of molecular structure. These structural changes affect critical quality attributes governing the safety, efficacy, and stability of biomolecules and their raw materials. This combination of technologies is available to researchers in our fully-automated Aurora and Apollo systems and is backed by a global network of sales, applications, service, and support teams to address all market needs.

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