RNA Nanostructures: Design, Characterization, and Applications (Methods in Molecular Biology, 2709) [1st ed. 2023] 107163416X, 9781071634165

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Methods in Molecular Biology 2709

Kirill A. Afonin Editor

RNA Nanostructures Design, Characterization, and Applications

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

RNA Nanostructures Design, Characterization, and Applications

Edited by

Kirill A. Afonin Department of Chemistry, UNC Charlotte, Charlotte, NC, USA

Editor Kirill A. Afonin Department of Chemistry UNC Charlotte Charlotte, NC, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3416-5 ISBN 978-1-0716-3417-2 (eBook) https://doi.org/10.1007/978-1-0716-3417-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface The study of RNA improves our understanding of cellular processes and the origin of various diseases. Rationally designed functional RNA nanostructures benefit from the inherent biological properties of RNA and its capacity to assemble from a diverse set of structural and interacting motifs. RNA nanostructures are attractive for a broad range of biomedical applications and clinical use because of their controllable architectures and proficiency in responding readily to biological environment changes. This book is an extensive resource of detailed protocols that renowned experts in computer-assisted design and characterization of RNA nanostructures, assessment of immunology of nanomaterials, biosensing, RNA nanotechnology, and drug delivery have organized. This collection of in-depth chapters addresses this field’s dimensions, covering RNA nanostructures’ design and characterization, which outlines their production, storage, and immunorecognition assessment protocols. This book also highlights a diverse set of biomedical applications and delivery approaches for therapeutic RNA nanoparticles. This collection will interest a broad audience due to its interdisciplinary nature aiming to address essential topics and concerns in the growing field of RNA nanotechnology. Charlotte, NC, USA

Kirill A. Afonin

v

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

v xi

COMPUTATIONAL DESIGN AND IN SILICO STUDIES RNA NANOSTRUCTURES

OF

1 Molecular Dynamics Simulations of RNA Motifs to Guide the Architectural Parameters and Design Principles of RNA Nanostructures . . . . . . . . . . . . . . . . . . . Valentina Abondano Perdomo and Taejin Kim 2 Computer-Assisted Design and Characterization of RNA Nanostructures. . . . . . Christina J. Bayard and Yaroslava G. Yingling 3 Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jian Wang, Congzhou M. Sha, and Nikolay V. Dokholyan 4 Structural Characterization of Nucleic Acid Nanoparticles Using SAXS and SAXS-Driven MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . James Byrnes, Kriti Chopra, Lewis A. Rolband, Leyla Danai, Shirish Chodankar, Lin Yang, and Kirill A. Afonin

PART II

3 31

51

65

PRODUCTION AND STORAGE OF FUNCTIONAL RNA NANOSTRUCTURES

5 Metalated Nucleic Acid Nanostructures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Douglas Zhang and Thomas Hermann 6 Bioconjugation of Functionalized Oligodeoxynucleotides with Fluorescence Reporters for Nanoparticle Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Erwin Doe, Hannah L. Hayth, and Emil F. Khisamutdinov 7 Light-Assisted Drying for the Thermal Stabilization of Nucleic Acid Nanoparticles and Other Biologics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Susan R. Trammell 8 Preparation of Nucleic Acid Aptamer Functionalized Silver/Gold Nanoparticle Conjugates Using Thiol-Substituted Oligonucleotides . . . . . . . . . . 131 Joshua D. Quarles, Allen T. Livingston, Ashley E. Wood, and Timea Gerczei Fernandez

PART III

CHARACTERIZATION OF RNA NANOSTRUCTURES

9 Thermodynamic Characterization of Nucleic Acid Nanoparticles Hybridization by UV Melting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Megan Teter, Ross Brumett, Abigail Coffman, and Emil F. Khisamutdinov

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viii

Contents

10

Structural Characterization of DNA-Templated Silver Nanoclusters by Energy Dispersive Spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Damian Beasock and Kirill A. Afonin 11 Small Volume Microrheology to Evaluate Viscoelastic Properties of Nucleic Acid-Based Supra-Assemblies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Akhilesh Kumar Gupta, Joel Petersen, Elizabeth Skelly, Kirill A. Afonin, and Alexey V. Krasnoslobodtsev 12 Characterization of RNA Nanoparticles and Their Dynamic Properties Using Atomic Force Microscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Alexander J. Lushnikov, Yelixza I. Avila, Kirill A. Afonin, and Alexey V. Krasnoslobodtsev

PART IV

INTRACELLULAR DELIVERY AND IMMUNORECOGNITION RNA NANOSTRUCTURES

OF

13

Synthesis of Mesoporous Silica Nanoparticles for the Delivery of Nucleic Acid Nanostructures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tamanna Binte Huq and Juan L. Vivero-Escoto 14 Assessment of Intracellular Compartmentalization of RNA Nanostructures . . . . Yasmine Radwan, Kirill A. Afonin, and M. Brittany Johnson 15 Discriminating Immunorecognition Pathways Activated by RNA Nanostructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leyla Danai, M. Brittany Johnson, and Kirill A. Afonin 16 Detection of Nanoparticles’ Ability to Stimulate Toll-Like Receptors Using HEK-Blue Reporter Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edward Cedrone and Marina A. Dobrovolskaia 17 Characterization of PAMAM Dendrimers for the Delivery of Nucleic Acid Nanoparticles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ oz, Yelixza I. Avila, Laura Rebolledo, Melanie Andrade-Mun and Kirill A. Afonin

PART V 18

205 211

229

241

253

RNA AND DNA NANOSTRUCTURES DESIGNED FOR BIOMEDICAL APPLICATIONS

Reverse Transfection of Functional RNA Rings into Cancer Cells Followed by in Vitro Irradiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Renata de Freitas Saito, Isabella Nevoni Ferreira, Maria Cristina Rangel, and Roger Chammas 19 Aptamer Conjugated RNA/DNA Hybrid Nanostructures Designed for Efficient Regulation of Blood Coagulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Lewis A. Rolband, Weina Ke, and Kirill A. Afonin 20 Detection of Multiplex NASBA RNA Products Using Colorimetric Split G Quadruplex Probes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Maria S. Rubel, Liubov A. Shkodenko, Daria A. Gorbenko, Valeria V. Solyanikova, Yulia I. Maltzeva, Aleksandr A. Rubel, Elena I. Koshel, and Dmitry M. Kolpashchikov

Contents

ix

21

Synthesis of DNA-Templated Silver Nanoclusters and the Characterization of Their Optical Properties and Biological Activity . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Elizabeth Skelly, Lewis A. Rolband, Damian Beasock, and Kirill A. Afonin 22 Dynamic Nanostructures for Conditional Activation and Deactivation of Biological Pathways. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Yasmine Radwan, Laura P. Rebolledo, Martin Panigaj, and Kirill A. Afonin 23 Anticoagulant Activity of Nucleic Acid Nanoparticles (NANPs) Assessed by Thrombin Generation Dynamics on a Fully Automated System . . . . . . . . . . . . 319 Renata de Freitas Saito, Ba´rbara Gomes Barion, Tania Rubia Flores da Rocha, Alex Rolband, Kirill A. Afonin, and Roger Chammas Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

333

Contributors KIRILL A. AFONIN • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA MELANIE ANDRADE-MUN˜OZ • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA YELIXZA I. AVILA • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA BA´RBARA GOMES BARION • Laboratorio de Hemostasia do Hospital das Clı´nicas da Faculdade de Medicina da Universidade de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil CHRISTINA J. BAYARD • Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC, USA DAMIAN BEASOCK • University of North Carolina at Charlotte, Charlotte, NC, USA ROSS BRUMETT • Chemistry Department, Ball State University, Muncie, IN, USA JAMES BYRNES • Brookhaven National Laboratory, Upton, NY, USA EDWARD CEDRONE • Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA ROGER CHAMMAS • Comprehensive Center for Precision Oncology, Centro de Investigac¸a˜o Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina da Universidade de Sa˜o Paulo and Instituto do Caˆncer do Estado de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil SHIRISH CHODANKAR • Brookhaven National Laboratory, Upton, NY, USA KRITI CHOPRA • Brookhaven National Laboratory, Upton, NY, USA ABIGAIL COFFMAN • Chemistry Department, Ball State University, Muncie, IN, USA LEYLA DANAI • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA MARINA A. DOBROVOLSKAIA • Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA ERWIN DOE • Department of Chemistry, Ball State University, Muncie, IN, USA NIKOLAY V. DOKHOLYAN • Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA; Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA; Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA; Department of Chemistry, Penn State University, State College, PA, USA; Department of Biomedical Engineering, Penn State University, State College, PA, USA TIMEA GERCZEI FERNANDEZ • Department of Chemistry, Physics, Geology and the Environment, Sims Building, Winthrop University, Rock Hill, SC, USA ISABELLA NEVONI FERREIRA • Comprehensive Center for Precision Oncology, Centro de Investigac¸a˜o Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina da Universidade de Sa˜o Paulo and Instituto do Caˆncer do Estado de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil

xi

xii

Contributors

RENATA DE FREITAS SAITO • Comprehensive Center for Precision Oncology, Centro de Investigac¸a˜o Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina da Universidade de Sa˜o Paulo and Instituto do Caˆncer do Estado de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil DARIA A. GORBENKO • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia AKHILESH KUMAR GUPTA • Department of Physics, University of Nebraska Omaha, Omaha, NE, USA HANNAH L. HAYTH • Department of Chemistry, Ball State University, Muncie, IN, USA THOMAS HERMANN • Department of Chemistry and Biochemistry, University of California, San Diego, CA, USA; Center for Drug Discovery Innovation, University of California, San Diego, CA, USA; Program in Materials Science and Engineering, University of California, San Diego, CA, USA TAMANNA BINTE HUQ • Department of Chemistry, Nanoscale Science Program, University of North Carolina, Charlotte, NC, USA M. BRITTANY JOHNSON • Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA WEINA KE • University of North Carolina at Charlotte, Charlotte, NC, USA EMIL F. KHISAMUTDINOV • Department of Chemistry, Ball State University, Muncie, IN, USA TAEJIN KIM • Physical Sciences Department, West Virginia University Institute of Technology, Beckley, WV, USA DMITRY M. KOLPASHCHIKOV • Department of Chemistry, University of Central Florida, Orlando, FL, USA; Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, USA; Center for Forensic Science, University of Central Florida, Orlando, FL, USA ELENA I. KOSHEL • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia ALEXEY V. KRASNOSLOBODTSEV • Department of Physics, University of Nebraska Omaha, Omaha, NE, USA ALLEN T. LIVINGSTON • Department of Chemistry, Physics, Geology and the Environment, Sims Building, Winthrop University, Rock Hill, SC, USA ALEXANDER J. LUSHNIKOV • Nanoimaging Core Facility at the University of Nebraska Medical Center, Omaha, NE, USA YULIA I. MALTZEVA • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia MARTIN PANIGAJ • Department of Chemistry, University of North Carolina, Charlotte, NC, USA VALENTINA ABONDANO PERDOMO • Physical Sciences Department, West Virginia University Institute of Technology, Beckley, WV, USA JOEL PETERSEN • Department of Physics, University of Nebraska Omaha, Omaha, NE, USA JOSHUA D. QUARLES • Department of Chemistry, Physics, Geology and the Environment, Sims Building, Winthrop University, Rock Hill, SC, USA YASMINE RADWAN • Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA

Contributors

xiii

MARIA CRISTINA RANGEL • Comprehensive Center for Precision Oncology, Centro de Investigac¸a˜o Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina da Universidade de Sa˜o Paulo and Instituto do Caˆncer do Estado de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil LAURA REBOLLEDO • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA LAURA P. REBOLLEDO • Department of Chemistry, University of North Carolina, Charlotte, NC, USA; Department of Biological Sciences, University of North Carolina, Charlotte, NC, USA TANIA RUBIA FLORES DA ROCHA • Laboratorio de Hemostasia do Hospital das Clı´nicas da Faculdade de Medicina da Universidade de Sa˜o Paulo, Sa˜o Paulo, SP, Brazil ALEX ROLBAND • University of North Carolina, Charlotte, NC, USA LEWIS A. ROLBAND • University of North Carolina at Charlotte, Charlotte, NC, USA ALEKSANDR A. RUBEL • Laboratory of Amyloid Biology, Saint-Petersburg State University, Saint Petersburg, Russia MARIA S. RUBEL • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia CONGZHOU M. SHA • Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA; Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA LIUBOV A. SHKODENKO • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia ELIZABETH SKELLY • Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, USA; University of North Carolina, Charlotte, NC, USA VALERIA V. SOLYANIKOVA • Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint Petersburg, Russia MEGAN TETER • Chemistry Department, Ball State University, Muncie, IN, USA SUSAN R. TRAMMELL • Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC, USA JUAN L. VIVERO-ESCOTO • Department of Chemistry, Nanoscale Science Program, University of North Carolina, Charlotte, NC, USA JIAN WANG • Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA ASHLEY E. WOOD • Department of Chemistry, Physics, Geology and the Environment, Sims Building, Winthrop University, Rock Hill, SC, USA LIN YANG • Brookhaven National Laboratory, Upton, NY, USA YAROSLAVA G. YINGLING • Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC, USA DOUGLAS ZHANG • Department of Chemistry and Biochemistry, University of California, San Diego, CA, USA

Part I Computational Design and In Silico Studies of RNA Nanostructures

Chapter 1 Molecular Dynamics Simulations of RNA Motifs to Guide the Architectural Parameters and Design Principles of RNA Nanostructures Valentina Abondano Perdomo and Taejin Kim Abstract Molecular dynamics (MD) simulations can be used to investigate the stability and conformational characteristics of RNA nanostructures. However, MD simulations of an RNA nanostructure is computationally expensive due to the size of nanostructure and the number of atoms. Alternatively, MD simulations of RNA motifs can be used to estimate the conformational stability of constructed RNA nanostructure due to their small sizes. In this chapter, we introduce the preparation and MD simulations of two RNA kissing loop (KL) motifs, a linear KL complex and a bent KL complex, and an RNA nanoring. The initial solvated system and topology files of each system will be prepared by two major force fields, AMBER and CHARMM force fields. MD simulations will be performed by NAMD simulation package, which can accept both force fields. In addition, we will introduce the use of the AMBER cpptraj program and visual molecular dynamics (VMD) for data analysis. We will also discuss how MD simulations of two KL motifs can be used to estimate the conformation and stability of RNA nanoring as well as to explain the vibrational characteristics of RNA nanoring. Key words Molecular dynamics simulations, RNA motif, RNA nanostructure, AMBER, CHARMM, NAMD

1

Introduction Since RNA tectoRNA [1–5] was built in the early 2000s, RNA nanotechnology has rapidly developed computationally and experimentally. The various shapes of RNA nanostructures have been built using numerous RNA motifs. The examples of RNA motifs are kink-turn motif, junction motif, pseudoknot, kissing loop hairpins, GNRA loop-receptor, triple helical scaffold, and G- quadruplex. Examples of RNA nanostructure shapes, which are constructed by RNA motifs, are triangle [6–8], square [9], hexagonal ring [10–12], cubes [13–16], and polyhedron [17, 18]. The various shapes of RNA nanostructures also have been investigated

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Valentina Abondano Perdomo and Taejin Kim

to develop diverse biomedical applications, such as drug delivery [19–21], gene therapy [14, 22], and molecular beacon [23–31]. Several computational methods have been developed to design RNA nanostructure. Examples of computational design tools are RNA2D3D [32], NanoTiler [33], Assemble2 [34], INFO-RNA [35], and NUPACK [36]. Once an initial RNA nanostructure is computationally generated, molecular dynamics (MD) simulations can be used to fix steric crashes in the nanostructure as well as to investigate the stability and conformational changes of nanostructure. Most commonly used MD simulation packages are Assisted Model Building with Energy Refinement (AMBER, https:// ambermd.org/) [37], Nanoscale Molecular Dynamics (NAMD, https://www.ks.uiuc.edu/) [38], Chemistry at Harvard Macromolecular Mechanics (CHARMM, https://www.charmm.org/) [39], and GROningen MAchine for Chemical Simulations (GROMACS, https://www.gromacs.org/) [40]. Each MD simulation package uses specific atomic interaction parameters and topological information, which are called force fields (FF). AMBER, CHARMM, and GROMACS have their own FF, and the NAMD platform can accept these FF to run MD simulations. In this chapter, we will introduce the use of AMBER and CHARMM FF to run MD simulations using NAMD. The atomic interactions in AMBER MD simulation are described by the below equation. V Amber =

kr r - r eq

2

bonds

þ

kθ θ - θeq

2

angles

qiqj B ij A ij Vn ½1 þ cosðn; - γ Þ þ - 6 þ 12 2 εRij Rij i < j R ij i KL-EMIN.pdb The converted pdb file (KL-EMIN.pdb) is used for solvation and ionization for explicit MD simulations. The solvated system can be generated by tleap as below. tleap, source leaprc.RNA.OL3 source leaprc.water.tip3p loadAmberParams frcmod.ions234lm_1264_tip3p molEMIN = loadpdb KL-EMIN.pdb solvateBox molEMIN TIP3PBOX 20.0 0.8 addions molEMIN K 0 addionsRAND molEMIN K 22 CL 22 addionsRAND molEMIN MG 1 CL 2 saveamberparm molEMIN KL-Solvated.prmtop KL-Solvated.inpcrd savepdb molEMIN KL-Solvated.pdb quit The command source loads RNA.OL3 force field and water. tip3p force field for KL complex and water, respectively. The command loadAmberParams loads ion force field, frcmod. ions234lm_1264_tip3p. The command solvateBox places water molecules around the KL complex with 0.8 Å gap and the thickness of 20 Å water layer from the KL complex. The significance of water box size is discussed in Note 1. The command addions adds K+ ions to neutralize the KL complex. Extra ions can be added to increase salt concentrations. In this example, extra K+, Cl-, and Mg2+ are added to set 50 mM of KCl and 2 mM of MgCl2. The number of extra ions can be calculated using formula below based on the volume of the water box. Number of ions = volume Å

3

× ðsalt concentrationÞ × 6:022 × 10 - 4

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Valentina Abondano Perdomo and Taejin Kim

AMBER tleap command generates a log file to record detailed information of tleap. The volume of the water box can be also found in the log file after the solvateBox command is executed. The solvated structure and topology files are saved by saveamberparm command. The resultant structure is also saved by pdb format using savepdb command. 3.1.2 Initial Minimization and Equilibration

The first step of MD simulation is the energy minimization of the entire system (KL complex, water, and ions). After minimization, equilibration is applied to water and ions for a given temperature, while the KL complex is fixed. VMD can be used to generate a pdb file, which identifies fixed atoms. To generate the pdb file for fixed atoms, in VMD, select File → New Molecule → Browse → select KL-Solvated.prmtop → select AMBER7 Parm → Browse → select KL-Solvated.inpcrd → select AMBER7 Restart → Load. Then, type below commands to VMD console window. vmd > set all [atomselect top all] vmd > set Fixatom [atomselect top "resname A C G U G5 C3"] vmd > $all set beta 0 vmd > $Fixatom set beta 1 vmd > $all writepdb KL-Fixed.pdb vmd > set center [measure center $all] The above commands assign index 1 to the beta column of atoms that belong to the residue names, A, C, G, U, G5, and C3. Atoms with index 1 in the beta column are recognized as fixed atoms during NAMD simulations. The result is saved to pdb format (KL-Fixed.pdb). The command set center detects the center of the water box in (x, y, z) coordinates. This coordinate will be used for the periodic boundary conditions in the NAMD config file (line 48 in the EminEQ-I.conf in Table 1). Initial minimization and equilibration are performed using NAMD by the below command with the config file, EminEQ-I. conf. namd2 +p number of cores EminEQ-I.conf > EminEQ-I.ene The above command is for using a CPU system to run MD simulation. Depending on the available computational resource, users can specify the number of CPU cores to run MD simulation after the flag +p. EminEQ-I.conf is an input config file (see Table 1 for the details). Note that the commands for AMBER in EminEQ-I.conf are specified in bold text. If a system is prepared by CHARMM FF, # AMBER Input section (line 6–9) must be removed. In addition, switching (line 17) must be on and switchdist (line 18), and pairlistdist (line 19) in the # Force-Field Parameters must be activated by removing # symbol. There are a few more

Table 1 The list of input files for minimization, equilibration, and MD simulations. These files can be used to run NAMD simulations with AMBER FF. If the system is prepared by CHARMM FF, use the config files listed in Table 2 EminEQ-I.conf

EminEQ-II.conf

##### MD Run with AMBER ##### set temperature 310 set outputname KL-EminEQ-I firsttimestep 0 # AMBER Input amber parmfile ambercoor

on KL-Solvated.prmtop KL-Solvated.inpcrd

temperature

$temperature

# Force-Field Parameters exclude 1-4scaling cutoff switching #switchdist #pairlistdist

scaled1-4 0.833333 12.0 off 10.0 14.0

# Integrator Parameters timestep rigidBonds rigidTolerance nonbondedFreq fullElectFrequency stepspercycle

2 all 0.0005 1 2 10

margin

0

# Fixed Atoms if {1} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces } # Harmonic Constraints if {0} { constraints consexp conskcol consref conskfile }

on KL-Fixed.pdb B on

on 2 B KL-Constraint.pdb KL-Constraint.pdb

# Periodic Boundary Conditions if {1} { cellBasisVector1 85.219 0.0 0.0 cellBasisVector2 0.0 111.86 0.0 cellBasisVector3 0.0 0.0 77.076 cellOrigin 42.491 56.06 38.671 } wrapwater on wrapAll on # PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 90 PMEGridSizeY 116 PMEGridSizeZ 80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

##### MD Run with AMBER ##### if {1} { binCoordinates KL-EminEQ-I.restart.coor binVelocities KL-EminEQ-I.restart.vel extendedSystem KL-EminEQ-I.restart.xsc } set outputname firsttimestep

KL-EminEQ-II 0

# AMBER Input amber parmfile ambercoor

on KL-Solvated.prmtop KL-Solvated.inpcrd

set temperature

310

# Force-Field Parameters exclude 1-4scaling cutoff switching #switchdist #pairlistdist

scaled1-4 0.833333 12.0 off 10.0 14.0

# Integrator Parameters timestep rigidBonds rigidTolerance nonbondedFreq fullElectFrequency stepspercycle

2 all 0.0005 1 2 10

margin

0

# Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces } # Harmonic Constraints if {1} { constraints consexp conskcol consref conskfile }

on KL-Fixed.pdb B on

on 2 B KL-Constraint.pdb KL-Constraint.pdb

# Periodic Boundary Conditions if {0} { cellBasisVector1 85.219 0.0 0.0 cellBasisVector2 0.0 111.86 0.0 cellBasisVector3 0.0 0.0 77.076 cellOrigin 42.491 56.06 38.671 } wrapwater wrapAll

on on

(continued)

Table 1 (continued) # Constant Pressure Control if {0} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 $temperature langevinTemp langevinHydrogen off # Output outputName #binaryoutput

$outputname NO

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

1000 1000 1000 1000 1000 YES YES

# Minimization if {1} { minimize reinitvels } run

20000 $temperature

100000

64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106

EQ-III.conf

# PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 90 PMEGridSizeY 116 PMEGridSizeZ 80 # Constant Pressure Control if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 langevinTemp $temperature langevinHydrogen off # Output outputName restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart # Minimization if {1} { minimize reinitvels } run

$outputname 5000 5000 5000 5000 5000 YES YES

20000 $temperature

250000

MD.conf

##### MD Run with AMBER ##### if {1} { binCoordinates KL-EminEQ-II.restart.coor binVelocities KL-EminEQ-II.restart.vel extendedSystem KL-EminEQ-II.restart.xsc } set outputname firsttimestep

KL-EminEQ-III 0

# AMBER Input amber parmfile ambercoor

on KL-Solvated.prmtop KL-Solvated.inpcrd

set temperature

310

# Force-Field Parameters exclude scaled1-4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

##### MD Run with AMBER ##### if {1} { binCoordinates KL-EminEQ-III.restart.coor binVelocities KL-EminEQ-III.restart.vel extendedSystem KL-EminEQ-III.restart.xsc } set outputname firsttimestep # AMBER Input amber parmfile ambercoor

KL-MD-1 0

on KL-Solvated.prmtop KL-Solvated.inpcrd

# Force-Field Parameters exclude scaled1-4 1-4scaling 0.833333 cutoff 12.0

(continued)

Table 1 (continued) 1-4scaling cutoff switching #switchdist #pairlistdist

0.833333 12.0 off 10.0 14.0

# Integrator Parameters timestep rigidBonds rigidTolerance nonbondedFreq fullElectFrequency stepspercycle

2 all 0.0005 1 2 10

margin

0

# Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces } # Harmonic Constraints if {0} { constraints consexp conskcol consref conskfile }

on KL-Fixed.pdb B on

on 2 B KL-Constraint.pdb KL-Constraint.pdb

# Periodic Boundary Conditions if {0} { cellBasisVector1 85.219 0.0 0.0 cellBasisVector2 0.0 111.86 0.0 cellBasisVector3 0.0 0.0 77.076 42.491 56.06 38.671 cellOrigin } wrapwater on wrapAll on # PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 90 PMEGridSizeY 116 PMEGridSizeZ 80 # Constant Pressure Control if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82

switching #switchdist #pairlistdist

off 10.0 14.0

# Integrator Parameters timestep rigidBonds rigidTolerance nonbondedFreq fullElectFrequency stepspercycle

2 all 0.0005 1 2 10

margin

0

# Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces }

on KL-Fixed.pdb B on

# Harmonic Constraints if {0} { on constraints consexp 2 conskcol B consref KL-Constraint.pdb conskfile KL-Constraint.pdb } # Periodic Boundary Conditions if {0} { cellBasisVector1 85.219 0.0 0.0 cellBasisVector2 0.0 111.86 0.0 cellBasisVector3 0.0 0.0 77.076 cellOrigin 42.491 56.06 38.671 } wrapwater wrapAll

on on

# PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 90 PMEGridSizeY 116 PMEGridSizeZ 80 # Constant Pressure Control (variable volume) -- ON if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control on langevin

(continued)

14

Valentina Abondano Perdomo and Taejin Kim

Table 1 (continued) langevin langevinDamping langevinTemp langevinHydrogen

on 10 $temperature off

# Output outputName

$outputname

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

5000 5000 5000 5000 5000 YES YES

# Minimization if {0} { minimize reinitvels } run

20000 $temperature

250000

83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105

langevinDamping langevinTemp langevinHydrogen

10 $temperature off

# Output outputName #binaryoutput

$outputname NO

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

2000 2000 2000 2000 2000 YES YES

# Minimization if {0} { minimize reinitvels } run

20000 $temperature

3000000

sections that should be noted. The fixed atoms in the KL complex are activated (line 32). The initial periodic box is defined (line 49), while the pressure control is deactivated (line 65). Under this condition, NVT simulation will be performed. To control temperature, Langevin dynamics (line 83–87) is employed. EminEQ-I.ene is an output file, which contains the molecular mechanics information, such as pressure, temperature, energy terms in bonding, angular, dihedral, van der Waals, and electrostatic interactions. The initial minimization and equilibration generate the following output files. KL-EminEQ-I.coor, KL-EminEQ-I.vel, KL-EminEQ-I.xst, KL-EminEQ-I.restart.xsc. KL-EminEQ-I.restart.coor, KL-EminEQ-I.restart.vel, nEQ-I.restart.xsc, and KL-EminEQ-I.dcd.

KL-Emi-

The first group of files are the final coordinates, velocities, and periodic boundary information (xst and xsc files), respectively. The second group of files are restart files of coordinates, velocities, periodic boundary information, and NAMD trajectory files (KL-EminEQ-I.dcd.), respectively. Restart files are periodically updated by NAMD command, restartfreq. More detailed information about config file and output file can be found in the recent NAMD manual (http://www.ks.uiuc.edu/Research/namd/).

MD Simulations of RNA Motifs and Nanostructures 3.1.3 Constrained MD Simulation

15

Once water and ion molecules are equilibrated at the target temperature, it is necessary to apply another minimization and then equilibrate the entire system while holding the KL complex with a weak constraint. To generate a constraint file, load initial solvated system and topology files (KL-Solvated.prmtop and KL-Solvated. inpcrd) to VMD as described in Subheading 3.1.2. To assign constraints to the KL complex, type the below commands to the VMD console. vmd > set all [atomselect top all] vmd > set ConstraintAtom [atomselect top "resname A C G U G5 C3"] vmd > $all set beta 0 vmd > $ConstraintAtom set beta 0.5 vmd > $all writepdb KL-Constraint.pdb Here, the beta column is set to zero, while atoms which belong to the residue names, A, C, G, U, G5, and C3, are set to 0.5 kcal/ (mol∙Å). If a biomolecular behavior is sensitive to equilibration, it may need to apply strong initial constraint to the molecule and gradually reduce constraints during multiple constrained MD simulations. The constrained MD simulation can be performed by the below command. namd2 +p number of cores EminEQ-II.conf > EminEQ-II.ene The details of EminEQ-II.conf are listed in Table 1. Note that coordinate, velocity, and periodic boundary files from the previous simulation are specified as input files (line 2–6) in the EminEQ-II. conf. In addition, the fixed KL complex is turned off (line 37), while the constrained KL complex is activated (line 45). For the constrained MD simulation, NVP is changed to NPT by turning off the initial boundary box in line 54 and activating Langevin pressure control in line 71–81.

3.1.4 Final Equilibration and Product MD Simulations

Once constrained MD simulation is completed, release constraints by turning it off (line 45) in the EQ-III.conf file (see Table 1) to run the final equilibration MD. This time, all components in the system, KL complex, ions, and water, will be equilibrated at the target temperature. The NAMD command for the final equilibration is below. namd2 +p number of cores EQ-III.conf > EQ-III.ene The details of the EQ-III.conf file are listed in Table 1. After the final equilibration MD simulation is completed, the product MD simulation can be performed using the below NAMD command.

16

Valentina Abondano Perdomo and Taejin Kim

Fig. 2 (a) The initial structure of the bent RNA, which is built by two dumbbell-shaped RNA motifs. The red dotted arrow indicates the length of the dumbbell-shaped RNA motif, and the blue dotted lines indicate the bending angle of the bent RNA. (b) The final MD structure of the bent RNA. (c) Top and side views of the initial structure of RNA nanoring. (d) Top and side views of the final structure of RNA nanoring

namd2 +p number of cores MD.conf > MD.ene The details of the MD.conf file are listed in Table 1. The MD. conf will run to produce a 6 ns-long MD trajectory (3,000,000 step × 2 fs = 6,000,000 fs = 6 ns). Longer MD trajectory can be produced by running consecutive MD simulations using coordinate, velocity, and periodic boundary files of the previous MD run. The results of MD simulations are discussed in Note 2. 3.2 MD Simulation of a Bent RNA Motif and RNA Nanoring

In this section, we introduce MD simulations of a bent RNA and an RNA nanoring (Fig. 2). The bent RNA is composed by kissing loop interactions between two dumbbell-shaped RNA motifs [11]. The RNA nanostructure has a ring shape, which is constructed by six dumbbell-shaped RNA motifs. Each dumbbell-shaped RNA has slightly different sequences in the stem. The initial solvated structure and topology will be prepared with CHARMM FF using VMD. Load the initial pdb structure (bentRNA.pdb) to VMD by the below procedure. File → New Molecule → Browse → select bentRNA.pdb → select file type as pdb → Load.

MD Simulations of RNA Motifs and Nanostructures

17

Generate structure and topology files with CHARMM FF as below. Extensions → Modeling → Automatic PSF Builder → define Output basename as bentRNA. Then, follow below steps in the Automatic PSF Builder window. Step 1: Load default CHARMM FF and structure files. If newer CHARMM FF and structure files are available, delete default FF and structure files, and click the Add button to load newer versions of FF. Click the Load input files button to complete Step 1. Step 2: Select the type of molecules. In this case, select Nucleic Acid. Then, click the Guess and split chains using current selections button. Step 3: Detailed information of the loaded structure will show up in the window box in Step 3. Select the strand, and click the Edit chain button to confirm the First Atom and the Last Atom index are correct as well as the 5′ (5TER) and the 3′ (3TER) ends being properly defined. If everything is okay, click the Create Chain button. Now, Automatic PSF Builder will generate bentRNA.pdb (structure) and bentRNA.psf (topology) files. To run explicit MD simulation, it is necessary to solvate the system with ions. To solvate the KL complex, in the VMD menu, select Extensions → Add Solvation Box → make sure that the previously generated structure and topology files (bentRNA.pdb and bentRNA.psf) are loaded under Input → Define the name of solvated system in the Output (e.g., bentRNA-sol) → Check Use Molecule Dimensions option. Enter the water box padding size in the Box Padding → Click the Solvate button. As discussed in Note 1, the size of water box padding should be carefully defined to avoid the violation of periodic boundary conditions. Now, the solvated structure and topology will be generated by pdb (bentRNA-sol.pdb) and psf (bentRNA-sol.psf) file formats, respectively. To ionize the solvated system, load the solvated system (bentRNA-sol.pdb and bentRNA-sol.psf) to VMD. In the VMD menu, select Extensions → Modeling → Add Ions → Define the name of the ionized system in the Output prefix (e.g., bentRNAsolion). VMD provides six default salt types, NaCl, KCl, CsCl, MgCl2, CaCl2, and ZnCl2. Choose the proper salt type for your simulation. In the section of Ion placement mode, users can select Only neutralize system with select salt type, Neutralize and set salt concentration to used defined salt concentration in mol/L, or Userdefined number of ions. Once the preferred salt conditions are determined, click the Autoionize button. The ionized structure and topology will be generated as pdb and psf file formats, respectively. When the solvated system with ionization is prepared, the explicit MD simulations can be performed with the same MD protocols described in Subheadings 3.1.2, 3.1.3 and 3.1.4.

Table 2 The list of input files for minimization, equilibration, and MD simulations. These files for NAMD run with CHARMM FF. If the system is prepared by AMBER FF, use the config files listed in Table 1 EminEQ-I.conf #######

EminEQ-II.conf

MD Run with CHARMM

#######

structure coordinates set temperature

bentRNAsolion.psf bentRNAsolion.pdb 310.15

firsttimestep

0

#Input paraTypeCharmm on parameters par_all36_na.prm temperature $temperature # Force-Field Parameters exclude scaled1-4 1-4scaling 1.0 cutoff 12.0 switching on switchdist 10.0 pairlistdist 14.0 # Integrator Parameters timestep 2 rigidBonds all nonbondedFreq 1 fullElectFrequency 2 stepspercycle 10 margin 0 # Fixed Atoms if {1} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces }

on fixedAtoms.pdb B on

# Harmonic Constraints if {0} { constraints on consexp 2 conskcol B consref Constraint.pdb conskfile Constraint.pdb } # Periodic Boundary Conditions if {1} { cellBasisVector1 137.47 0.0 0.0 cellBasisVector2 0.0 143.99 0.0 cellBasisVector3 0.0 0.0 135.24 cellOrigin 39.853 -8.634 6.321 } wrapwater on wrapAll on # PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 142 PMEGridSizeY 148 PMEGridSizeZ 140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

#######

MD Run with CHARMM #######

structure coordinates

bentRNAsolion.psf bentRNAsolion.pdb

if {1} { binCoordinates binVelocities extendedSystem }

bentRNA-EminEQ-I.restart.coor bentRNA-EminEQ-I.restart.vel bentRNA-EminEQ-I.restart.xsc

set outputname firsttimestep

bentRNA-EminEQ-II 0

#Input paraTypeCharmm parameters

on par_all36_na.prm

# Force-Field Parameters exclude scaled1-4 1-4scaling 1.0 cutoff 12.0 switching on switchdist 10.0 pairlistdist 14.0 # Integrator Parameters timestep 2 rigidBonds all nonbondedFreq 1 fullElectFrequency 2 stepspercycle 10 margin 0 # Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces }

on fixedAtoms.pdb B on

# Harmonic Constraints if {1} { constraints on consexp 2 conskcol B consref Constraint.pdb conskfile Constraint.pdb } # Periodic Boundary Conditions if {0} { cellBasisVector1 137.47 0.0 0.0 cellBasisVector2 0.0 143.99 0.0 cellBasisVector3 0.0 0.0 135.24 cellOrigin 39.853 -8.634 6.321 } wrapwater on wrapAll on # PME (for full-system periodic electrostatics)

(continued)

Table 2 (continued) # Constant Pressure Control if {0} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 langevinTemp $temperature langevinHydrogen off # Output outputName

bentRNA-EminEQ-I

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

1000 1000 1000 1000 1000 YES YES

# Minimization if {1} { minimize reinitvels } run

20000 $temperature

100000

64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103

EQ-III.conf #######

PME PMEGridSizeX PMEGridSizeY PMEGridSizeZ

yes 142 148 140

# Constant Pressure Control if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 $temperature langevinTemp langevinHydrogen off restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

# Minimization if {1} { minimize reinitvels } run

5000 5000 5000 5000 5000 YES YES

20000 $temperature

250000

MD.conf

MD Run with CHARMM

#######

structure coordinates

bentRNAsolion.psf bentRNAsolion.pdb

if {1} { binCoordinates binVelocities extendedSystem }

bentRNA-EminEQ-II.restart.coor bentRNA-EminEQ-II.restart.vel bentRNA-EminEQ-II.restart.xsc

set outputname firsttimestep

bentRNA-EQ-III 0

#Input paraTypeCharmm parameters temperature

on par_all36_na.prm $temperature

# Force-Field Parameters exclude scaled1-4 1-4scaling 1.0 cutoff 12.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

#######

MD Run with CHARMM

#######

structure coordinates

bentRNAsolion.psf bentRNAsolion.pdb

if {1} { binCoordinates binVelocities extendedSystem }

bentRNA-EQ-III.restart.coor bentRNA-EQ-III.restart.vel bentRNA-EQ-III.restart.xsc

set outputname firsttimestep

bentRNA-MD-1 0

#Input paraTypeCharmm parameters temperature

on par_all36_na.prm $temperature

# Force-Field Parameters exclude scaled1-4 1-4scaling 1.0 cutoff 12.0

(continued)

Table 2 (continued) switching switchdist pairlistdist

on 10.0 14.0

# Integrator Parameters timestep 2 rigidBonds all nonbondedFreq 1 fullElectFrequency 2 stepspercycle 10 margin 0 # Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces }

on fixedAtoms.pdb B on

# Harmonic Constraints if {0} { constraints on consexp 2 conskcol B consref Constraint.pdb conskfile Constraint.pdb } # Periodic Boundary Conditions if {0} { cellBasisVector1 137.47 0.0 0.0 cellBasisVector2 0.0 143.99 0.0 cellBasisVector3 0.0 0.0 135.24 cellOrigin 39.853 -8.634 6.321 } wrapwater on wrapAll on # PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 142 PMEGridSizeY 148 PMEGridSizeZ 140 # Constant Pressure Control if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 langevinTemp $temperature langevinHydrogen off

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87

switching switchdist pairlistdist

on 10.0 14.0

# Integrator Parameters timestep 2 rigidBonds all nonbondedFreq 1 fullElectFrequency 2 stepspercycle 10 margin 0 # Fixed Atoms if {0} { fixedAtoms fixedAtomsFile fixedAtomsCol fixedAtomsForces }

on fixedAtoms.pdb B on

# Harmonic Constraints if {0} { constraints on 2 consexp conskcol B consref Constraint.pdb conskfile Constraint.pdb } # Periodic Boundary Conditions if {0} { cellBasisVector1 137.47 0.0 0.0 cellBasisVector2 0.0 143.99 0.0 cellBasisVector3 0.0 0.0 135.24 cellOrigin 39.853 -8.634 6.321 } on wrapwater wrapAll on # PME (for full-system periodic electrostatics) PME yes PMEGridSizeX 142 PMEGridSizeY 148 PMEGridSizeZ 140 # Constant Pressure Control if {1} { useGroupPressure yes useFlexibleCell no useConstantArea no langevinPiston langevinPistonTarget langevinPistonPeriod langevinPistonDecay langevinPistonTemp }

on 1.01325 100.0 50.0 $temperature

# Constant Temperature Control langevin on langevinDamping 10 langevinTemp $temperature langevinHydrogen off

(continued)

MD Simulations of RNA Motifs and Nanostructures

21

Table 2 (continued)

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

# Minimization if {0} { minimize reinitvels } run

5000 5000 5000 5000 5000 YES YES

20000 $temperature

250000

88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104

restartfreq dcdfreq xstFreq outputEnergies outputPressure restartsave binaryrestart

# Minimization if {0} { minimize reinitvels } run

1000 1000 1000 1000 1000 YES YES

2000 $temperature

200000

However, the AMBER part in NAMD config files must be removed. In Table 2, NAMD input files, EminEQ-I.conf, EminEQ-II.conf, EQ-III.conf, and MD.conf, are listed. Notice that # AMBER Input is removed. In addition, commands for CHARMM FF are included in bold text. For example, in EminEQ-I.conf file, paraTypeCharmm is on (line 10); CHARMM parameter, par_all36_na.prm, is defined (line 11), switching is on (line 18); and switchdist (line 19) and pairlistdist (line 20) are defined. The results of MD simulation of the bent RNA will be discussed in Note 3. The same procedure can be applied to generate the initial solvated system and corresponding topology files of RNA nanoring. The MD simulation of RNA nanoring can be performed by the same protocols in Subheadings 3.1.2, 3.1.3 and 3.1.4. with NAMD config files in Table 2. A brief discussion about MD simulation of RNA nanostructure will be discussed in Note 3. 3.3 Data Analysis Using cpptraj and VMD

Cpptraj is one of the AMBER sub-programs that provides a wide range of data analysis tools. The basic command to run cpptraj is

3.3.1 cpptraj

cpptraj -p topology file cpptraj-output file Cpptraj-input file can contain multiple data analysis action commands. Below is an example of a cpptraj-input file. trajin MD-trajectory.dcd center : 9-16,34-41 strip :TIP3

22

Valentina Abondano Perdomo and Taejin Kim

strip :POT strip :CLA distance dist1 :2@N3 :20@N3 out DataOutPut.dat angle ang1 :4@N1 :5@N1 :6@N1 out DataOutPut.dat dihedral dihe1 :4@N1 :5@N1 :6@N1 :7@N1 out DataOutPut.dat hbond :9-16,34-41 avgout DataOutPut-hbond.dat rmsd first out DataOutPut.dat :1-88 trajout trajectory-NoWAT.dcd Trajin command loads MD trajectory. Trajin can load MD trajectories of major MD simulation packages, such as AMBER, NAMD, and GROMACS. The command center brings the RNA residues 9–16 and 34–41 to the center of the water box. The command strip removes the specified residues from the system. In this example, ions (POT and CLA) and TIP3 water molecules are removed from the MD trajectory, which can significantly reduce the size of MD trajectory file. The command distance measures the distance between N3 atoms at the residue 2 and 20. The command angle measures the angle of N1 atoms at the residue 4, 5, and 6. The command dihedral measures the dihedral angle along N1 atoms at the residue 4, 5, 6, and 7. Instead of specific atoms, the group of atoms, residues, or the group of residues can be used to measure their distance, angle, or dihedral angles. In this case, the center of mass of the selected regions will be used to measure the geometrical data. The command hbond detects HB interactions between acceptors and donors in residues 9–16 and 34–41, where the kissing loop interactions are established. The command avgout calculates the occupancy, average hydrogen bond distance, and average hydrogen bond angle between a donor and an acceptor. Note that the output file name of hbond is different from that of other commands. The command rmsd measures root-meansquare-deviation of the MD trajectory with respect to reference structure. Here, the rmsd of residue 1–88 is calculated with respect to the structure at the first frame. More detailed information of the cpptraj program can be found from the most recent AMBER manual (https://ambermd.org/Manuals.php). 3.3.2

VMD

VMD can visualize the final structure of MD simulation or MD trajectory. In addition, VMD provides basic data analysis tools to measure atomic distance, angle, dihedral angle, rmsd, and HB interactions. For data analysis, load the structure in a pdb or coor file with topology file or an MD trajectory with topology file. For example, in VMD, select File → New Molecule → Browse → select bentRNA-solion.psf → select CHARMM, NAMD, XPLOR PSF → Load → Browse → select coor (structure) or dcd (trajectory) file → select NAMD Binary Coordinate for coor file or CHARMM, NAMD, XPLOR DCD Trajectory for dcd file → Load. To measure

MD Simulations of RNA Motifs and Nanostructures

23

atomic distance, angle, and dihedral angle, follow the below procedures. Atomic distance: Press 1 on the keyboard and select two atoms using the mouse point. Atomic angle: Press 2 on the keyboard and select three atoms using the mouse point. Dihedral angle: Press 3 on the keyboard and select four atoms using the mouse point. To save measured distance, angle, or dihedral data, select Graphics in the VMD menu → Labels → select Bonds for atomic distance, Angles for angle, or Dihedrals for the dihedral angle → select the data in the window box to save → click the Save button → define the name of data file → click the OK button. The output data file contains frame numbers which start from 0 in the first column and list measured values in the second column. To measure RMSD using VMD, select Extensions → RMSD Visualizer Tool → Define Atom Selection. In this case, type nucleic → In the Reference, select self for Molecule ID and 0 for the Frame as Reference structure → click the RMSD button → click Plot Result button → In the MultiPlot window, select File → Export to ASCII vectors → Define the RMSD file name → Click the Save button. HB interactions can be analyzed by VMD. In the VMD menu, select Extensions → Analysis → Hydrogen Bonds → Type nucleic in the Selection 1 (Required) → Define Donor-Acceptor distance (Å) and Angle cutoff (degrees) for HB interaction scanning → In the section, Calculate detailed info for:, select All bonds → To save data, in the Output options, check Write output to files? → Define Output directory → define the name for output file → click Find hydrogen bonds! button. The hydrogen bond analysis tool will generate two data files. One file lists the number of hydrogen bonds in each frame, and the other file lists the donor residues, acceptor residues, and occupancies of HB interactions.

4

Notes 1. The Importance of Water Box Size It has been discovered that silver (Ag) atoms bound to cytosine-rich DNA hairpin and form DNA-Ag nanocluster (NC) [43–46]. The DNA-Ag NC motif can be used to build nucleic acid nanostructure to develop an antibacterial therapy [47–49]. Computationally, the structural stability and conformations of DNA-Ag NC can be investigated by MD simulations. Previous MD simulation study reported that the hairpin structure of nucleic acids can be significantly deformed due to ion bindings or salt concentrations [50]. In this section, we

24

Valentina Abondano Perdomo and Taejin Kim

Fig. 3 (a) The initial structure of the DNA hairpin. (b) The final structure of DNA hairpin. (c) The final MD structure of DNA hairpin in a small water box. (d) The final MD structure of the DNA hairpin in a large water box

introduce how MD simulations can be failed if a nucleic acid hairpin is elongated in a small water box. The NMR structure of a GAA-hairpin loop (PDB ID: 1JVE) [51] is used as a template to build a larger DNA hairpin, whose bases are replaced with cytosines using DSV. The initial solvated system and topology files of the DNA hairpin are prepared by CHARMM FF, as explained in Subheading 3.2. The MD simulation of DNA hairpin is performed by the same protocols described in Subheadings 3.1.2, 3.1.3 and 3.1.4 with NAMD config files in Table 2. In general, a solvated system for MD simulation is prepared with the minimum size of the water box to reduce computing time. However, if the DNA hairpin elongates during MD simulations, the DNA can break the periodic boundary conditions (PBC). During 50 ns MD simulation, the length of DNA hairpin increases from 25 to 32 Å. The initial and the final structures of DNA hairpin are plotted in Fig. 3a, b, respectively. To demonstrate the significance of water box size, two different sizes of water boxes are prepared. The dimensions of small and big water boxes are (58 Å × 59 Å × 85 Å) and (78 Å × 79 Å × 95 Å), respectively. Figure 3c is the result of a 30 ns-long MD simulation, where the DNA hairpin is initially placed in the small water box. The DNA hairpin is rotated during MD simulation, and the part of the DNA hairpin is placed beyond the primary PBC box as it elongates. The

MD Simulations of RNA Motifs and Nanostructures

25

elongated hairpin collides with the mirror image of the DNA in the neighbor PBC box. Under this condition, the DNA hairpin in the primary box and the 5′ and the 3′ ends of the DNA in the neighboring box interact with each other. This interaction remains for the rest of the simulations. However, when the DNA is placed in the bigger water box in Fig. 3d, there is enough space between the DNA and boundaries of the primary box so that the mirror images in the adjacent PBC boxes maintain enough distance from that of the primary box. Therefore, when a water box is prepared for a biomolecule, which can experience a large conformational change during MD simulations, it is necessary to prepare a large enough water box to avoid the interactions between the biomolecules in the primary and adjacent PBC boxes. 2. MD Simulations of Linear RNA Motifs To design RNA nanostructure, depending on the aim of nanostructure, it is necessary to implement specific sequences into the RNA nanostructure. However, modifying sequences can also alter the stability and conformation of RNA nanostructure. To demonstrate how RNA sequences affect the stability of RNA motifs, we prepared two mutated sequences in the DIS of HIV-1 RNA kissing loop complexes, which are named as KL-1 and KL-2 as shown in Fig. 1. The solvated system and topology files for each KL are prepared using AMBER FF, as described in Subheading 3.1.1. The MD simulations follow the protocols described in Subheadings 3.1.2, 3.1.3 and 3.1.4. Figure 1 shows the final structures of each KL complex at 200 ns. The stability of the KL complex is measured by RMSD using the cpptraj command (Subheading 3.3.1). The RMSD values of the overall KL-1 complex and its KL region are 4.5 ± 0.5 Å and 3.7 ± 0.1 Å, respectively. However, the RMSD values of the overall KL-2 complex and its KL region are 7.8 ± 0.8 Å and 5.2 ± 0.2 Å, respectively. These results indicate that the KL sequence of the KL-2 complex causes significant deformations. The capability of self-assembly between two RNA hairpins via KL interactions can be estimated by monitoring HB interactions. The formation and breaking down of HB interactions during MD simulations are monitored using the hbond command in the cpptraj program (see Subheading 3.3.1). The HB interaction analysis shows that KL bases in KL-1 form stable HB interactions in all six base pairs during the entire MD simulations, while KL-2 shows very weak HB interactions between G11:G38 and A16: G36. Therefore, these MD simulation results indicate that KL-1 can be used as a motif to build an RNA nanostructure, such as tectoRNA based on its structural stability and secure HB interactions, while the KL-2 motif may not form an RNA

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nanostructure due to severe structural deformation and very weak KL interactions. 3. MD Simulations of the Bent RNA Motif and RNA Nanoring The dumbbell-shaped RNA motif, which is introduced in Subheading 3.2, forms a KL complex, which is bent by 98° (Fig. 3a). In addition, a ring shape of RNA nanostructure can be constructed by six dumbbell-shaped RNA motifs (Fig. 3c). In this section, we briefly introduce the MD simulation results of the bent RNA and RNA nanoring. The initial solvated system and topology files of the bent RNA are prepared by CHARMM FF using the same protocol described in Subheading 3.2. The MD simulations are performed by the protocols described in Subheadings 3.1.2, 3.1.3 and 3.1.4 with NAMD config files in Table 2. The total number of atoms in the solvated system is 257,243. The final structure of the bent RNA at 100 ns is plotted in Fig. 2b. To measure the stability and geometrical characteristics of the bent RNA, the length of the dumbbell-shaped motif, the bending angle between two dumbbell-shaped motifs, the overall RMSD, and the RMSD of the KL region are measured by the cpptraj program. The length of the dumbbell-shaped RNA motif is measured by the distance between two phosphate atoms at the middle of each hairpin loop (red dotted arrows in Fig. 2a. The bending angle between two dumbbell-shaped motifs is measured at three locations, the center nucleotide in one hairpin loop, two center nucleotides at the KL region, and the center nucleotide in the other hairpin loop (blue dotted line in Fig. 2a). The initial length of a dumbbell-shaped motif is 54.1 Å, while the average lengths of each dumbbell-shaped motif in the bent RNA are 59.7 ± 2.5 Å and 61.7 ± 2.3 Å, respectively. The initial bending angle is 98° and the average bending angle is 126.7 ± 4.5°. The average length and bending angle are calculated from the last 50 ns trajectory. RMSD values are measured with respect to the initial structure. The overall RMSD of the bent RNA with respect to the first frame is 7.6 ± 0.7 Å. The most interesting results are the RMSD of the KL region and the stability of HB interactions. The average RMSD of the KL region with respect to the initial KL structure is only 1.2 ± 0.2 Å. The HB analysis by hbond command also shows that stable hydrogen bond interaction is established in the KL region. These results indicate strong and stable KL interactions between two dumbbell-shaped motifs. Therefore, the MD simulations of RNA motifs can be used to predict the stability of RNA nanoring. In addition, the large bending angle of the bent RNA can be used to explain the planar bending of RNA nanoring, which is described below.

MD Simulations of RNA Motifs and Nanostructures

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The RNA nanostructure in the Fig. 2c is constructed with six dumbbell-shaped RNA motifs. The preparation of solvated system and topology files and MD simulations of nanoring are performed by the same protocols with the bent RNA. The number of atoms in the solvated system is 534,271. The final structure of RNA nanoring at 100 ns is plotted in Fig. 2d. The lengths of each dumbbell-shaped RNA motifs are 57.4 ± 2.4 Å, 57.4 ± 2.2 Å, 57.3 ± 2.4 Å, 59.8 ± 2.0 Å, 58.1 ± 1.9 Å, and 58.3 ± 2.1 Å. These lengths are almost the same with those of the bent RNA. The overall RMSD with respect to the initial structure is 6.2 ± 0.2 Å. However, the RMSD values of each KL are 1.6 ± 0.2 Å, 1.9 ± 0.2 Å, 1.1 ± 0.2 Å, 2.2 ± 0.2 Å, 1.3 ± 0.2 Å, and 1.5 ± 0.2 Å, which are similar to those of the bent RNA. HB interaction analysis also shows stable KL interactions, which are observed in the KL interaction of the bent RNA complex. Therefore, the stability of KL interactions in the bent RNA is similar to that of nanoring. The bending angles of two consecutive dumbbell-shaped RNA motifs are 96.4 ± 4.3°, 96.5 ± 4.5°, 101.9 ± 4.6°, 91.5 ± 4.3°, 96.3 ± 4.4°, and 100.1 ± 3.4°. These angles are about 25° smaller than that of the bent RNA complex, which indicates less bending flexibility in the nanoring. However, the side view of nanoring shows the planarly bending mode, which is not observed in the bent RNA complex. The vibrational entropy along the bending angle in the bent RNA complex may be converted to planarly vibrational entropy in the nanoring. Therefore, these results indicate that the MD simulation of RNA motif can be used to explain the conformational characteristics of RNA nanostructure. References 1. Leontis NB, Stombaugh J, Westhof E (2002) The non-Watson-Crick base pairs and their associated isostericity matrices. Nucleic Acids Res 30:3497–3531 2. Jaeger L, Westhof E, Leontis NB (2001) TectoRNA: modular assembly units for the construction of RNA nano-objects. Nucleic Acids Res 29:455–463 3. Ishikawa J, Furuta H, Ikawa Y (2013) RNA tectonics (TectoRNA) for RNA nanostructure design and its application in synthetic biology. Wiley Interdiscip Rev RNA 4:651–664 4. Jaeger L, Chworos A (2006) The architectonics of programmable RNA and DNA nanostructures. Curr Opin Struct Biol 16:531–543 5. Westhof E, Masquida B, Jaeger L (1996) RNA tectonics: towards RNA design. Fold Des 1: 78–88

6. Bui MN, Johnson MB, Viard M et al (2017) Versatile RNA tetra-U helix linking motif as a toolkit for nucleic acid nanotechnology. Nanomed-Nanotechnol 13(3):1137–1146 7. Khisamutdinov EF, Jasinski DL, Guo P (2014) RNA as a boiling-resistant anionic polymer material to build robust structures with defined shape and stoichiometry. ACS Nano 8(5): 4771–4781 8. Bindewald E, Afonin KA, Jaeger L, Shapiro BA (2011) Multistrand RNA secondary structure prediction and nanostructure design including pseudoknots. ACS Nano 5(12):9542–9551 9. Severcan I, Geary C, Verzemnieks E, Chworos A, Jaeger L (2009) Square-shaped RNA particles from different RNA folds. Nano Lett 9(3):1270–1277

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10. Yingling YG, Shapiro BA (2007) Computational design of an RNA hexagonal nanoring and an RNA nanotube. Nano Lett 7(8): 2328–2334 11. Grabow WW, Zakrevsky P, Afonin KA, Chworos A, Shapiro BA, Jaeger L (2011) Self-assembling RNA nanorings based on RNAI/II inverse kissing complexes. Nano Lett 11(2):878–887 12. Sajja S, Chandler M, Fedorov D et al (2018) Dynamic behavior of RNA nanoparticles analyzed by AFM on a mica/air interface. Langmuir 34(49):15099–15108 13. Afonin KA, Bindewald E, Yaghoubian AJ et al (2010) In vitro assembly of cubic RNA-based scaffolds designed in silico. Nat Nanotechnol 5: 676–682 14. Afonin KA, Grabow WW, Walker FM, Bindewald E, Dobrovolskaia MA, Shapiro BA, Jaeger L (2011) Design and self-assembly of siRNA-functionalized RNA nanoparticles for use in automated nanomedicine. Nat Protoc 6:2022–2034 15. Afonin KA, Kasprzak W, Bindewald E et al (2014) Computational and experimental characterization of RNA cubic nanoscaffolds. Methods 67(2):256–265 16. Afonin KA, Viard M, Kagiampakis I et al (2015) Triggering of RNA interference with RNA–RNA, RNA–DNA, and DNA–RNA nanoparticles. ACS Nano 9(1):251–259 17. Elonen A, Natarajan AK, Kawamata I et al (2022) Algorithmic design of 3D wireframe RNA polyhedra. ACS Nano 16(10): 16608–16616 18. Severcan I, Geary C, Chworos A, Voss N, Jacovetty E, Jaeger L (2010) A polyhedron made of tRNAs. Nat Chem 2:772–779 19. Shu D, Shu Y, Haque F, Abdelmawla S, Guo P (2011) Thermodynamically stable RNA threeway junctions for constructing multifunctional nanoparticles for delivery of therapeutics. Nat Nanotechnol 6:658–667 20. Shu D, Li H, Yi S et al (2015) Systemic delivery of anti-MiRNA for suppression of triple negative breast cancer utilizing RNA nanotechnology. ACS Nano 9:9731–9740 21. Lee T, Haque F, Shu D et al (2015) RNA nanoparticles as a vector for targeted SiRNA delivery into glioblastoma mouse model. Oncotarget 6:14766–14776 22. Kim T, Viard M, Afonin KA et al (2020) Characterization of cationic bolaamphiphile vesicles for siRNA delivery into tumors and brain. Mol Ther Nucleic Acids 20:359–372 23. Chen AK, Behlke MA, Tsourkas A (2007) Avoiding false-positive signals with nuclease-

vulnerable molecular beacons in single living cells. Nucleic Acids Res 35:e105 24. Chen AK, Behlke MA, Tsourkas A (2008) Efficient cytosolic delivery of molecular beacon conjugates and flow cytometric analysis of target RNA. Nucleic Acids Res 36:e69 25. Mhlanga MM, Vargas DY, Fung CW, Kramer FR, Tyagi S (2005) TRNA-linked molecular beacons for imaging MRNAs in the cytoplasm of living cells. Nucleic Acids Res 33:1902– 1912 26. Rhee WJ, Santangelo PJ, Jo H, Bao G (2008) Target accessibility and signal specificity in livecell detection of BMP-4 MRNA using molecular beacons. Nucleic Acids Res 36:e30 27. Kim JK, Choi K, Lee M, Jo M, Kim S (2012) Molecular imaging of a cancer-targeting theragnostics probe using a nucleolin aptamer- and MicroRNA-221 molecular beacon-conjugated nanoparticle. Biomaterials 33:207–217 28. Peng X, Cao Z, Xia J et al (2005) Real-time detection of gene expression in cancer cells using molecular beacon imaging: new strategies for cancer research. Cancer Res 65:1909– 1917 29. Bryson JM, Fichter KM, Chu W, Reineke TM (2009) Polymer beacons for luminescence and magnetic resonance imaging of DNA delivery. Proc Natl Acad Sci U S A 106:16913–16918 30. Qiu L, Wu C, You M et al (2013) Targeted, self-delivered, and photocontrolled molecular beacon for MRNA detection in living cells. J Am Chem Soc 135:12952–12955 31. Liu TW, Akens MK, Chen J et al (2011) Imaging of specific activation of photodynamic molecular beacons in breast cancer vertebral metastases. Bioconjug Chem 22:1021–1030 32. Martinez HM, Maizel JV Jr, Shapiro BA (2008) RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA. J Biomol Struct Dyn 25:669–683 33. Bindewald E, Grunewald C, Boyle B, O’Connor M, Shapiro BA (2008) Computational strategies for the automated design of RNA nanoscale structures from building blocks using nanotiler. J Mol Graph Model 27:299– 308 34. Jossinet F, Ludwig TE, Westhof E (2010) Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels. Bioinformatics 26:2057–2059 35. Busch A, Backofen R (2007) INFO-RNA – a server for fast inverse RNA folding satisfying sequence constraints. Nucleic Acids Res 35: W310–W313

MD Simulations of RNA Motifs and Nanostructures 36. Zadeh JN, Steenberg CD, Bois JS et al (2011) nupack: analysis and design of nucleic acid systems. J Comput Chem 32:170–173 37. Case DA, Aktulga HM, Belfon K et al (2022) Amber 2022. University of California, San Francisco 38. Phillips JC, Hardy DJ, Maia JDC et al (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153: 044130 39. Brooks BR, Brooks CL III, Mackerell AD Jr et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545– 1615 40. Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25 41. Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095 42. Humphrey W, Dalke A, Schulten K (1996) VMD – visual molecular dynamics. J Mol Graph 14:33–38 43. Chandler M, Shevchenko O, Vivero-Escoto JL, Striplin CD, Afonin KA (2020) DNA-templated synthesis of fluorescent silver nanoclusters. J Chem Educ 97(7):1992–1996 44. Rolband L, Yourston L, Chandler M et al (2021) DNA-templated fluorescent silver nanoclusters inhibit bacterial growth while

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Chapter 2 Computer-Assisted Design and Characterization of RNA Nanostructures Christina J. Bayard and Yaroslava G. Yingling Abstract Molecular dynamics (MD) simulations can aid in the design and characterization of RNA nanomaterials, providing details about structural and dynamical properties as a function of sequence and environment. Here, we describe how to perform explicit and implicit solvent all-atom MD simulations for RNA nanoring systems. Key words In silico design of RNA nanostructures, Molecular dynamics simulations, RNA nanostructure dynamics

1

Introduction Biomolecular nanostructures and nanoparticles (NPs) hold tremendous promise for effective use in applications such as drug delivery [1], nanoelectromechanical systems, molecular sensors, and molecular lithography [2]. However, a comprehensive understanding of the underlying mechanisms and interactions that govern the self-assembly and properties of these architectures is needed for the design and construction of the next generation of stimulusresponsive, reconfigurable nanomaterials and nanodevices. Further progress in constructing molecular devices and patterned superstructures based on biopolymers will require the development of methods that can reduce errors in self-assembly processes and assess properties in detail. Computer simulations can provide predictive capabilities for the rational design of biomolecular architectures, such as RNA and DNA nanoparticles. Computational methods have been successful in characterizing and reaching close agreement with experimental studies on RNA conformations and structural responses to a change in ionic concentrations and upon the introduction of nucleotide mutations [3]. The most commonly used technique is molecular dynamics (MD) simulations, which involve

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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the time evolution of the chosen system starting from an initial set of coordinates and velocities calculated by integrating Newton’s classical equations of motion over a predefined timeframe. MD is a powerful tool that makes possible the dynamic characterization and exploration of the conformational energy landscape of biomolecules and their surroundings. For example, MD simulations can observe the RNA conformational landscapes and localizations, diffusive motions, structural effects of the counterions in atomic details [6], and can examine the real-time dynamics and atomicresolution details of solvated RNA nanostructures [4]. However, the primary limitations of MD simulations of RNA nanostructures are the relatively short timescales and accuracy of the force field [5]. The timescale limitation is related to the need for sufficiently sampling all of the RNA NP conformations in order to capture important the conformational transitions. Force field limitations are largely related to the specifics of approximations describing the pairwise interactions between atoms and the nature of parameterization and validation that are usually tuned toward a specific observation and/or environment. One of the known deficiencies of force fields used for RNA modeling is the description of interactions between RNA and divalent cations [6]. However, most RNA nanostructures depend on RNA loop-loop interactions [7], which result from the Watson-Crick base pairing between unpaired nucleotides in partially or fully complementary RNA loops. Kissing loop (KL) interactions control key processes of biological activity in all organisms and are essential for initiation of RNA folding into complex tertiary shapes, formation of protein recognition or catalytic sites, promotion of dimerization, or multimerization of RNAs and viral replication [8]. Specifically, most antisense control systems use KL interactions for the initial recognition [9], ColE1 plasmid replication is regulated via formation of KL interactions between the loops from RNAI and RNAII [10], and HIV-1 genomic RNA KL interactions initiate homodimer formation required for packaging genomic RNA into virion particle [11]. Previous experimental studies show that the presence of metal cations in solution is necessary for the formation of KL complexes [12–14]. Moreover, KL motifs were shown to be two to three orders of magnitude more stable than RNA duplexes with identical sequences in the presence of Mg2+ [14]. Thus, metal ions may have a particular task in stabilization of the loop-loop interactions. However, the accuracy of the current force fields is not yet ready to describe such interactions. Despite all these limitations, all-atom MD simulations were successfully used to describe structural dynamics of RNA nanoparticles and achieved reasonable agreement with the experimental observations [4].

Computer-Assisted Design of RNA Nanostructures

2

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Materials

2.1 Initial RNA 3D Structure Prediction

There are many computational methods that can assist in 3D RNA nanostructure building, which are described in recent reviews [15, 16]. Overall, building 3D complex RNA nanostructure is commonly performed using smaller fragments taken from solved RNA structures in the PDB databank. The available fragments can be found in databases, such as RNA Bricks2 [17] and RNA Junction [18], and assembled using, for example, RNA Composer [19]. Here, the coordinates of loop-loop complexes were taken from the PDB Databank (PDB id 2bj2.pdb) and assembled using in-house code into a 3D structure of the RNA nanoring [20] (Fig. 1).

2.2

Force Fields

Single-stranded nucleic acids were simulated using the OL3 force field, which provides a reparameterization of the glycosidic torsion in the ff99 force field for nucleic acids [21]. In explicit solvent, systems were solvated with TIP3P water and sodium chloride (NaCl) ions with force field parameters from Joung and Cheatham [22].

2.3

Software

All-atom molecular dynamics simulations were performed using the AmberTools22 software packages (which can be downloaded at https://ambermd.org/AmberTools.php). Minimization and solvent equilibration simulations were run using CPUs, while production simulations were run using Amber’s GPU-accelerated code. Simulations were analyzed using CPPTRAJ [23] and in-house scripts. Visualizations were performed using the Visual Molecular Dynamics (VMD) program [24].

Fig. 1 (a) sequence of one RNA strand used for ring RNA NP; (b) 3D structure of KL motif (PDB ID 2BJ2) used for NP corner and a 3D structure of one strand; (c) complete 3D ring RNA NP structure colored by strand; (d) RNA NP with Na+ (blue) and Cl- (yellow) ions; and (e) RNA NP and ions solvated in TIP3P water

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Christina J. Bayard and Yaroslava G. Yingling

Methods

3.1 Explicit Solvent Simulations of NANPs

First, we need to establish the coordinates of 3D RNA nanostructures (described above). Second, the RNA NP is immersed in a sufficiently large box of explicit water molecules and monovalent NaCl ions under periodic boundary conditions. Note that given the limitations of divalent cations description in the current force fields, MD simulations of RNA molecules are usually performed using monovalent ions (Fig. 1). Third, the system of RNA nanostructure, ions, and water must be minimized and equilibrated prior to production MD simulations. Fourth, MD production simulations are performed for a sufficiently long time. Fifth, the analysis of the trajectories is performed.

3.1.1 Preparation of the System

After generation of initial structure and to prepare the system for MD simulations, teLeap can be used to create a parameter topology (.prmtop) file and a restart coordinate file (.rst7) with the correct force field and added water molecules and NaCl ions. 1. Load the force field and water model into teLeap (see Notes 1 and 2): source leaprc.RNA.OL3 source leaprc.water.tip3p

The Amber force field ff99 with OL3 corrections is currently the recommended force field for RNA simulations. 2. Load the pdb file into a variable: saltyringng = loadpdb "rnaring.pdb"

3. Solvate your system: solvateoct saltyringng TIP3PBOX 8.0

Here, we solvated our system in a truncated octahedral box with an 8 Å buffer. 4. There are a number of methods available to calculate the correct number of explicit Na+ and Cl- ions to reach a desired salt concentration, but we used the screening layer tally by container average potential (SLTCAP) method [25]. To use this method, you need to know the total charge of the system and the number of water molecules that were added in step 3. By default, output from teLeap will be sent to the file “leap.log,” where after solvating the system, you can find information about your now solvated box that will look like this:

Computer-Assisted Design of RNA Nanostructures

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The number of boxes: x= 9 y= 9 z= 9 Volume: 1997551.572 A^3 (oct) Total mass 1121825.584 amu, Density 0.933 g/cc Added 57583 residues.

Here,we can see that 57,583 water molecules were added to the system. The charge on our system is -258, so using this information with the SLTCAP method for a target NaCl concentration of 0.1 M tells us we need to add 295 Na+ ions and 37 Cl- ions. Add them randomly to the system using the commands: addionsRand saltyringng Na+ 295 addionsRand saltyringng Cl- 0

Even though the SLTCAP method tells us the number of Cl- ions to add, we first add the calculated number Na+ ions and then add just enough Cl- ions to neutralize. From “leap. log,” we can see that the calculated number does match the number required to neutralize, but doing it this way can help avoid any rounding errors and ensure the system is neutralized. > addionsRand saltyringng Cl- 0 37 Cl- ions required to neutralize. Adding 37 counter ions to "saltyringng". 57251 solvent

molecules will remain.

5. To create the parameter topology file (sringoctng.prmtop) and restart coordinate file (sringoctng.rst7), issue the command: saveamberparm saltyringng sringoctng.prmtop sringoctng.rst7 quit

6. Load both files into VMD and check that the initial structure looks correct before proceeding to the next section. 3.1.2

Initial Minimization

Minimization is carried out using SANDER, which stands for simulated annealing with NMR-derived energy restraints. Here is an example minimization input file named “ring_min1. in”: Minimization

with Cartesian restraints

&cntrl imin=1, maxcyc=1000, ncyc=500, ntpr=50, ntwx=50,

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Christina J. Bayard and Yaroslava G. Yingling ntxo=1, ntr=1, restraint _wt=10.0, restraintmask=’!@%H*,OW’, /

Explanation of the variable choices: (a)

imin = 1:

(b)

ncyc = 500: 500 steps of steepest descent will be used, and since maxcyc = 1000, the remaining 500 steps will be conjugate gradient method.

(c)

ntpr = 50:

(d)

ntwx = 50:

(e)

ntxo = 1:

(f)

ntr = 1:

(g)

Flag that indicates minimization will be performed.

Every 50 steps, output is written to mdinfo file in human-readable format. Coordinates are written to the designated coordinate file every 50 steps. The format of the coordinate file will be ASCII.

Turns on restraints for the system. For the first minimization, the restraint weight (restraint_wt) is set to 10 kcal mol-1 Å-2.

restraintmask=’!@%H*,OW’: The restraint mask defines what part of the system will be restrained. Here, we specify that we are restraining all atoms that are not hydrogen atoms (H*) or oxygen atoms in water molecules (OW).

To run this file (ring_min1.in), issue the command in terminal or create a job script (see Note 3): mpirun -np 8 sander.MPI -O -i ring_min1.in -o ringmin1.out -p sringoctng.prmtop -c sringoctng.rst7 -r ringoctngmin1.rst7 -x ringoctngmin1.nc -ref sringoctng.rst7 -inf mdinfo &

We conducted four total stages of minimization. The only change between stages was the decrease in restraint_wt from 10 kcal mol-1 Å-2 in stage 1 to 5 kcal mol-1 Å-2 in stage 2, 1 kcal mol-1 Å-2 in stage 3, and 0 kcal mol-1 Å-2 in stage 4 (make sure to set ntr = 0 if you remove restraints, as we did here in stage 4). Each stage of minimization took 205–241 s (3–4 min) to run. Load PRMTOP and coordinate files into VMD to check the system before moving on. 3.1.3

Equilibration

Equilibration here is broken up into four main sections: heating up the system from 0 K while keeping the restraints the same, lowering the restraint weight and holding temperature constant at 300 K (stage 1), further lowering the restraint weight and now holding

Computer-Assisted Design of RNA Nanostructures

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temperature and pressure constant (stage 2), and again lowering the restraint weight and turning on SHAKE (stage 3). Input file to heat up the system: rnaring: 100 picosecond (ps) MD with restraint

on

RNA &cntrl imin = 0, irest = 0, ntx = 1, ntb = 1, cut = 10.0, tempi = 0.0, temp0 = 300.0, ntt = 3, gamma_ln = 1.0, nstlim = 200000, dt = 0.0005, ntpr = 100, ntwx = 2000, ntwr = 1000, ntr = 1, restraint _wt=10.0, restraintmask=’!@%H*, OW’ /

Each line is defined as follows: (a)

imin = 0, irest = 0: Minimization is off, and we are starting a new simulation instead of restarting an old one,

(b)

ntx = 1:

(c)

ntb = 1:

(d)

cut = 10:

(e)

Initial coordinates (but no velocities) will be read from formatted ASCII file (we will use the final coordinate file generated from minimization stage 4). Constant volume periodic boundaries. Nonbonded interactions will have a cutoff of 10 Å.

tempi = 0.0, temp0 = 300.0: Increase the

temperature from

0 to 300 K. (f)

Langevin dynamics with a 1 ps-1 collision frequency will be used. ntt = 3, gamma_ln = 1.0:

(g)

nstlim = 200000, dt = 0.0005:

(h)

ntpr = 100, ntwx = 2000, ntwr = 1000:

(i)

200,000 total steps with 0.5 femtosecond (fs) timestep (100 ps total).

Frequency of steps to write to mdinfo human-readable output, coordinate file, and restart “restrt” file, respectively,

ntr = 1, restraint _wt = 10.0, restraintmask = ’!@% H*,OW’: Restraints are turned on for specified atoms with a restraint weight of 10 kcal mol-1 Å-2.

It took 726 s (about 12 min) to heat up this system. Load the PRMTOP and coordinate file into VMD to check the system before moving on. Once the system is at the desired temperature, we can run our three stages of equilibration at constant temperature.

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Stage 1 Example script for the first stage: rnaring: 1ns MD with res on RNA &cntrl imin = 0, irest = 0, ntx = 5, ntb = 1, cut = 8.0, tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 1.0, nstlim = 1000000, dt = 0.001, ntpr = 100, ntwx = 10000, ntwr = 1000, ntr = 1, restraint _wt=5.0, restraintmask=’!@%H*, OW’ /

Most of the variables have been defined in the previous section, but here, we will be reading in initial coordinates as well as velocities (ntx = 5), the temperature remains constant (tempi = temp0), and we will have 1000,000 total steps with a time step of 1 fs, giving us 1 nanosecond (ns) of total production time. Stage 2 The major differences in stage 2 are the switch from constant volume to a constant pressure process and a decrease in the restraint weight. rnaring: 1ns MD with res on RNA &cntrl imin = 0, irest = 1, ntx = 5, ntb = 2, pres0 = 1, ntp = 1, taup = 2.0, barostat = 1, cut = 8.0, tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 1.0, nstlim = 1000000, dt = 0.001, ntpr = 100, ntwx = 10000, ntwr = 1000, ntr = 1, restraint _wt=1.0, restraintmask=’!@%H*, OW’ /

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39

Since we are running the equilibration stages together, is turned on to indicate that we will be continuing our MD run instead of beginning a new one. Pressure regulation:

irest = 1

(a)

ntb = 2:

(b)

pres0 = 1:

(c)

taup = 2.0:

(d)

barostat = 1:

Periodic boundary conditions. Pressure is maintained at 1 atm (default). Pressure relaxation time of 2 ps. Berendsen barostat is used to regulate

pressure. Stage 3 The last stage is very similar to stage 2, with two exceptions: the restraint weight is lowered to 0.5 kcal mol-1 Å-2, and SHAKE is turned on in this stage. The differences can be seen here: rnaring: 1ns MD with res on RNA &cntrl imin = 0, irest = 1, ntx = 5, ntb = 2, pres0 = 1, ntp = 1, taup = 2.0, barostat = 1, cut = 8.0, ntc = 2, ntf = 2, tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 1.0, nstlim = 500000, dt = 0.002, ntpr = 100, ntwx = 10000, ntwr = 1000, ntr = 1, restraint _wt=0.5, restraintmask=’!@%H*, OW’ /

To use SHAKE, ntc is set to 2 to constrain bonds involving hydrogen, meaning ntf must also be set to 2. Since we are using SHAKE, we can now use a 2 fs timestep, which cuts our number of steps (nstlim) in half. For this system, the equilibration stages took 53 min, 66 min, and 35 min to run, respectively. It’s easy to see the difference using SHAKE makes, as each run included 1 ns of total runtime, but the third stage took significantly less time to run than the first two. As always, load those coordinate files along with the PRMTOP file into VMD to check for issues before moving on to production.

40 3.1.4

Christina J. Bayard and Yaroslava G. Yingling Production Run

The production run is relatively straightforward, as all stages will use the same input file, and the job script will loop through them one at a time. rnaring: 1ns MD &cntrl imin = 0, irest = 1, ntx = 5, ntb = 2, pres0 = 1, ntp = 1, taup = 2.0, barostat = 1, cut = 8.0, ntr = 0, ntc = 2, ntf = 2, tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 1.0, nstlim = 500000, dt = 0.002, ntpr = 100, ntwx = 5000, ntwr = 1000, /

The only differences between the production script and the stage 3 equilibration script is that there are no longer restraints (ntr = 0), aside from SHAKE, and we’re writing to the coordinate file less frequently (ntwx is higher). Each stage was 1 ns, so 500 stages were used to achieve 500 ns total. Each stage took about 36 min to complete, and the whole production run took 13 days. 3.2 Implicit Solvent Simulations of NANPs

The overall process is the same for running implicit solvent simulations as it is for explicit solvent simulations, but there are major differences in the preparation of the system and the input files for each section.

3.2.1 Preparation of the System

The teLeap input file (“tleapimnogaps.in” in this case) looks almost identical to the explicit solvent input file: source leaprc.RNA.OL3 addPdbAtomMap { {"HO*2" "HO2’"} } imnogaps = loadpdb "rnaring.pdb" set default PBradii mbondi3 saveamberparm imnogaps imnogaps.prmtop imnogaps. rst7 quit

The only line that’s been added is “set default PBradii Implicit solvent is dependent on the generalized born (GB) model. Even though we have not yet specified to AMBER

mbondi3”.

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41

which GB model we’re going to use, we must know so that we can set the recommended radii set to match the chosen GB model. We are going to use igb = 8, which is a modification of the previous GBn model with further agreement between Poisson-Boltzmann and explicit solvent data [26]. Thus, with this model, the recommended radii set is “mbondi3,” and not specifying the radii set will lead to issues later. Since this is implicit solvent, there is no need to load in the TIP3P water module or add ions to the system. 3.2.2

Initial Minimization

The minimization file for implicit solvent looks a bit different than it did for the explicit solvent simulations: Minimization

with Cartesian restraints

&cntrl imin=1, maxcyc=10000, ncyc=2000, ntpr=100, ntwx=100, ntxo=1, igb=8, saltcon=0.1, cut=999, ioutfm=1, /

Almost all these variables are familiar from the previous minimization, with the addition of igb = 8, which specifies the GB model; saltcon = 0.1, which sets the salt concentration of the system to 0.1 M; and ioutfm = 1, which specifies that the coordinate file will be in binary (NetCDF) format. Since there are no calculations of long-range electrostatics like there are with explicit solvent simulations, having the cutoff only introduces additional approximations [27], so here the cutoff was set to 999 Å instead of 10 Å. Since we ran 10,000 steps total instead of the 1000 we ran for explicit, the run time was about 2 h. Load PRMTOP and coordinate files into VMD to check system before moving on. 3.2.3

Equilibration

Equilibration for the implicit system does not need to be as long and elaborate as for explicit systems, running only the initial heating up stage and then one stage of equilibration. Since we did not use any restraints for minimization, there is no need to slowly decrease the restraint weight in stages. Here is an example input file to heat up an implicit solvent system (see Note 4):

42

Christina J. Bayard and Yaroslava G. Yingling rnaring: 100ps MD with res on RNA &cntrl imin = 0, irest = 0, ntx = 1, ntb = 0, cut = 999.0, igb = 8, saltcon = 0.1, tempi = 0.0, temp0 = 300.0, ntt = 3, gamma_ln = 5.0, nstlim = 100000, dt = 0.001, ntpr = 100, ntwx = 1000, ntwr = 500, ntr = 1, restraint _wt=0.5, restraintmask=’!@%H*, OW’ /

As with minimization, we need to specify the GB model and salt concentration (if applicable), and the cut remains high. For the equilibrium stage, we only do 500 ps of MD: rnaring: 500ps MD with res on RNA &cntrl imin = 0, irest = 0, ntx = 5, ntb = 0, cut = 999.0, igb = 8, saltcon = 0.1, ntc = 2, ntf = 2, tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 5.0, nstlim = 250000, dt = 0.002, ntpr = 100, ntwx = 2500, ntwr = 250, ntr = 1, restraint _wt=0.1, restraintmask=’!@%H*, OW’ /

The restraint weight has been lowered, and SHAKE has been turned on to constrain all bonds involving hydrogen (ntc = 2), which allows us to use a 2 fs timestep. Load PRMTOP and coordinate files into VMD to check system before moving on. 3.2.4

Production Run

Example input file for the production run: rnaring: 1ns MD &cntrl imin = 0, irest = 1, ntx = 5, ntb = 0, igb = 8, saltcon = 0.1, cut = 999.0, ntr = 0, ntc = 2, ntf = 2,

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tempi = 300.0, temp0 = 300.0, ntt = 3, gamma_ln = 5.0, nstlim = 500000, dt = 0.002, ntpr = 100, ntwx = 5000, ntwr = 500, /

For the production run, restraints have been removed (except SHAKE), the temperature is being held constant at 300 K, and the simulation will not restart (irest = 1) but will read in coordinates and velocities from the equilibrium stage (ntx = 5). Each stage was 1 ns, so 500 stages were used to achieve a total of 500 ns. Here, we see the advantage of implicit solvent simulations, as each stage only took about 20 min, and the whole production run took 7 days, which is a little over half of the 13 days it took for the equivalent explicit solvent simulation of the same system. 3.3

Characterization

For simple analysis of the systems, we made plots of potential energy, radius of gyration, and root-mean-square deviation for each system.

3.3.1

Potential Energy

There are several ways to get potential energy (PE) data for a system, but the quickest is using a perl script that automates the process. In the directory where all the production output files are located, issue the command (replacing “srngprod*.out” with your output file names): process_mdout.perl srngprod*.out

This will produce several summary files, including one for potential energy (“summary.EPTOT”). From there, a simple python script (“PEplot.py”) can use numpy to read in the data, alter as necessary, and produce a plot using matplotlib, as shown below: import matplotlib.pyplot as plt import numpy as np df = np.loadtxt(’summary.EPTOT’, unpack=True) x= df[0,:] rms= df[1,:] xadj = np.subtract(x, 3000.2) xadj2 = np.divide(xadj, 1000) plt.plot(xadj2, rms) plt.title("Potential Energy Over Time") plt.xlabel("Time (ns)") plt.ylabel("PE (kcal/mol)") plt.tight_layout() plt.show()

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The summary file gives two columns (frame # and PE in kcal/ mol), so the first column (frame #) was adjusted to represent the time in ns. 3.3.2 Radius of Gyration and RMSD

For RMSD and radius of gyration, the coordinate files need to be read in by CPPTRAJ, AMBER’s coordinate file, and data processing software, to produce DAT files. This can be done with a simple input script (“rmsd_rg.in”): parm sringoctng.prmtop for i=1;i tleap_rna.out

if you want to redirect the output to a file other than leap. log. 2. Some extra steps may be required to prepare the PDB file for use in MD simulations. In our case, the PDB file was from several years ago, and we kept getting the error “atom does not have a type.” Amber ff99 showed that “HO*2” was the older syntax for “HO2’,” so addPdbAtomMap{} updated the atom type to account for that difference in the old and new syntaxes. addPdbAtomMap { {"HO*2" "HO2’"} }

Careful cleaning of the PDB files may be required before moving on. 3. Example job scripts have not been included, as the ones used for these simulations are specific to the group GPUs. However, several examples can be found in the AMBER tutorials (https://ambermd.org/tutorials/) and from the University of Florida (https://help.rc.ufl.edu/doc/Amber_Job_Sample_ Scripts). 4. GB models can only be run on nonperiodic systems, so ntb always must be =0 for implicit solvent simulations.

Christina J. Bayard and Yaroslava G. Yingling

a

30

b

25 RMSD (Å)

20 15 10 5 0 110

c

Rg Rg,max

100 90 Rg (Å)

80 70 60 50 40 -6.32

d ×105

-6.33 PE (kcal/mol)

46

-6.34 -6.35 -6.36 -6.37

PE PEavg

-6.38 0

100

200 300 Time (ns)

400

500

Fig. 2 Results from simulations of RNA NP in 0.1 M NaCl using a Berendsen thermostat. (a) Snapshots of RNA nanoring; temporal profiles of (b) RMSD; (c) radius of gyration; (d) potential energy

Computer-Assisted Design of RNA Nanostructures

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a

30

b

RMSD (Å)

25 20 15 10 5 0 110

c

Rg Rg,max

100

Rg (Å)

90 80 70 60 50 40

d

×105

-6.12

PE PEavg

PE (kcal/mol)

-6.13 -6.14 -6.15 -6.16 -6.17 -6.18 0

100

200 300 Time (ns)

400

500

Fig. 3 Results from simulations of RNA NP with six nucleotide shorter sequence in 0.1 M NaCl using a Berendsen thermostat. (a) Snapshots of RNA nanoring. Single-stranded gaps are shown using CPK representation in the initial snapshot. Temporal profiles of (b) RMSD; (c) radius of gyration; (d) potential energy

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Acknowledgments This work was supported by the National Science Foundation under Grant No. by DMR-2203979. References 1. Adams D, Gonzalez-Duarte A, O’Riordan WD et al (2018) Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis. N Engl J Med 379(1):11–21 2. Ho¨lz K, Schaudy E, Lietard J et al (2019) Multi-level patterning nucleic acid photolithography. Nat Commun 10(1):3805 3. Zhao C, Zhang D, Jiang Y et al (2020) Modeling loop composition and ion concentration effects in RNA hairpin folding stability. Biophys J 119(7):1439–1455 4. Kasprzak WK, Ahmed NA, Shapiro BA (2020) Modeling ligand docking to RNA in the design of RNA-based nanostructures. Curr Opin Biotechnol 63:16–25 5. McDowell SE, Spackova´ N, Sponer J et al (2007) Molecular dynamics simulations of RNA: an in silico single molecule approach. Biopolymers 85(2):169–184 6. Sˇponer J, Bussi G, Krepl M et al (2018) RNA structural dynamics as captured by molecular simulations: a comprehensive overview. Chem Rev 118(8):4177–4338 7. Brunel C, Marquet R, Romby P et al (2002) RNA loop-loop interactions as dynamic functional motifs. Biochimie 84(9):925–944 8. Brierley I, Pennell S, Gilbert RJC (2007) Viral RNA pseudoknots: versatile motifs in gene expression and replication. Nat Rev Microbiol 5(8):598–610 9. Eguchi Y, Itoh T, Tomizawa J (1991) Antisense RNA. Annu Rev Biochem 60(1): 631–652 10. Lee AJ, Crothers DM (1998) The solution structure of an RNA loop–loop complex: the ColE1 inverted loop sequence. Structure 6(8): 993–1007 11. Laughrea M, Jette´ L (1997) HIV-1 genome dimerization: kissing-loop hairpin dictates whether nucleotides downstream of the 5‘ splice junction contribute to loose and tight dimerization of human immunodeficiency virus RNA. Biochemistry 36(31):9501–9508 12. Gregorian RS Jr, Crothers DM (1995) Determinants of RNA hairpin loop-loop complex stability. J Mol Biol 248(5):968–984

13. Ohuchi SP, Nakamura Y (2007) Slight sequence modifications unexpectedly alter the metal-dependency of a kissing-loop interaction. Nucleic Acids Symp Ser 51(1):395–396 14. Jaeger L, Chworos A (2006) The architectonics of programmable RNA and DNA nanostructures. Curr Opin Struct Biol 16(4): 531–543 15. Ou X, Zhang Y, Xiong Y et al (2022) Advances in RNA 3D structure prediction. J Chem Inf Model 62(23):5862–5874 16. Miao Z, Westhof E (2017) RNA structure: advances and assessment of 3D structure prediction. Annu Rev Biophys 46(1):483–503 17. Chojnowski G, Walen´ T, Bujnicki JM (2014) RNA bricks – a database of RNA 3D motifs and their interactions. Nucleic Acids Res 42(D1): D123–D131 18. Bindewald E, Hayes R, Yingling YG et al (2008) RNAJunction: a database of RNA junctions and kissing loops for three-dimensional structural analysis and nanodesign. Nucleic Acids Res 36(suppl_1):D392–D397 19. Biesiada M, Pachulska-Wieczorek K, Adamiak RW et al (2016) RNAComposer and RNA 3D structure prediction for nanotechnology. Methods 103:120–127 20. Yingling YG, Shapiro BA (2007) Computational design of an RNA hexagonal nanoring and an RNA nanotube. Nano Lett 7(8): 2328–2334 21. Zgarbova´ M, Otyepka M, Sˇponer J et al (2011) Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J Chem Theory Comput 7(9):2886–2902 22. Joung IS, Cheatham TE (2008) Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B 112(30):9020–9041 23. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095

Computer-Assisted Design of RNA Nanostructures 24. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38 25. Schmit JD, Kariyawasam NL, Needham V et al (2018) SLTCAP: a simple method for calculating the number of ions needed for MD simulation. J Chem Theory Comput 14(4): 1823–1827

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26. Nguyen H, Roe DR, Simmerling C (2013) Improved generalized born solvent model parameters for protein simulations. J Chem Theory Comput 9(4):2020–2034 27. Tanner DE, Chan K-Y, Phillips JC et al (2011) Parallel generalized born implicit solvent calculations with NAMD. J Chem Theory Comput 7(11):3635–3642

Chapter 3 Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology Jian Wang, Congzhou M. Sha, and Nikolay V. Dokholyan Abstract Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction. Key words RNA nanotechnology, RNA nanoparticle, RNA 3D structure prediction, RNA restraints, Distance geometry, iFoldRNA, DMD, Discrete molecular dynamics, NMR

1

Introduction RNA nanotechnology [1–3] has been available for more than 20 years since the first RNA nanoparticle was constructed in 1998 [4]. Compared with DNA nanotechnology, RNA nanotechnology has many advantages. First, the structure of RNA is more thermally stable than DNA [5, 6]. Secondly, in addition to the canonical Watson-Crick pairs of A-U and G-C, RNA also exhibits nonstandard base pairing such as A-G and G-U, so RNA can form more unique structural modules than DNA [7–11]. Finally, due to extensive functional research on noncoding RNAs such as aptamers, ribozymes, riboswitches, miRNAs, and siRNAs, RNA nanotechnology can be used to feasibly integrate these RNAs into RNA nanoparticles to achieve specific functions. By integrating specific functional RNAs into RNA nanoparticles, RNA nanotechnology

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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has been applied in various fields, such as drug delivery [12] and immunotherapy [13]. The construction of RNA nanoparticles involves engineering challenges, such as designing functional binding, cross-linking RNA modules, labeling of subunits, and chemical modification of nucleotides. In the process of designing nanoparticles, the tertiary (i.e., 3D) structure of RNA constructed by computer modeling can be crucial in optimizing properties of the designed nanoparticles. For example, we collaborated with Dr. Afonin Kirill to predict the stability of nanoparticle designs, such as RNA/DNA fiber, ring, and cube structures, using the RNA tertiary structure prediction software iFoldRNA [14]. Over the past 10 years, the accuracy of RNA 3D structure prediction has improved greatly. In 2011, Liang and Schlick [15] evaluated existing methods and found that for RNAs longer than 50 nucleotides, the average RMSD values for RNA tertiary structure predictions at that time reached 20 Å. After more than 10 years of development, current methods have reduced the average RMSD to a max a min > b max:

Since RNA structure contains a large number of helical structures and these helical structures are usually very stable, DVASS firstly analyzes all the helices in the secondary structure of RNA to obtain the distances between nucleotides in each helix. Based on these initial distances, the maximum and minimum possible distances between each nucleotide pair can be obtained by iteratively applying the triangle inequality (Fig. 1).

2

Materials The DVASS algorithm was written in C++ with the standard STL library and compiled with gcc 4.8. DVASS can be installed in any modern OS including Windows, Linux, and Mac. iFoldRNA was written in C++ and compiled with gcc 4.8. For ease of access, we provide an iFoldRNA webserver, which can be accessed at https://dokhlab.med.psu.edu/ifoldrna/. To define restraints for iFoldNMR, we used data collected from imino proton nuclear Overhauser effect NMR experiments.

3

Methods

3.1 Coarse-Grained Molecular Dynamics for RNA: iFoldRNA

iFoldRNA iFoldRNA uses a coarse-grained force field for molecular dynamics simulations. This coarse-grained model simplifies each nucleotide into three virtual atoms, representing phosphate groups, sugar rings, and bases. iFoldRNA is based on discrete dynamics simulations [25, 34, 36, 52, 53] (DMD) developed in

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Dr. Nikolay Dokholyan’s lab to speed up predictions. We employed replica-exchange molecular dynamics simulations to enhance sampling. After DMD simulation, we perform clustering according to RMSD and select cluster centroids for all-atom reconstruction. If additional experimental data are available, such as hydroxyl radical reactivity probing [26] or NMR [33], restraints can be added to iFoldRNA to direct RNA structure sampling more toward native structures. Reconstruction of the all-atom model from the coarsegrained model was performed by randomly selecting the rotamer of the corresponding nucleotide from the all-atom downloaded from PDB. 3.2 All-Atom RNA Modeling

Using the DMD framework, we designed all-atom force fields in which all heavy atoms and polar hydrogens are represented. Van der Waals interactions were implemented as E

VDW

=

4ϵ ij j >i

σ ij r ij

12

σ ij r ij

6

where rij is the distance between particles i and j and the parameters ϵ ij and σ ij are fit to experimentally determined values as seen in RNA. To model solvation effects, we used the Lazaridis-Karplus model [54] E LK = fj > i

2ΔG free 2ΔG free j 2 i - p exp - x ij V i - p exp - x 2ji V j 4π π λi r 2ij 4π π λj r 2ij

with solvation energies (ΔGfree), correlation lengths λ, volume of atoms V , atomic radii x such that x ij = 12 x i þ x j , and interparticle distance rij as before. 3.3 Utilize NMR Restraints for RNA 3D Structure Prediction: iFoldNMR

To incorporate NMR restraints into all-atom RNA molecular dynamics simulation, we took coarse-grained trajectories, reconstructed all-atom models using the DMD package, and designed attractive potentials between residues, which satisfied a certain distance constraint. We added restraints corresponding to imino proton nuclear Overhauser effect (NOE) data. We simulated each RNA in three stages to allow for gradual annealing of the structure, with varying time steps, temperatures (T ), and heat exchange coefficients (ϵ): 1. 1000 time steps, T = 0.6 kcal/(mol kB), ϵ = 10 . 2. 1000 time steps, T = 0.6 kcal/(mol kB), ϵ = 1 . 3. 100,000 time steps or until all NMR restraints were satisfied, T = 0.3 kcal/(mol kB), with ϵ = 0.1 .

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Due to ambiguity in the distance geometry of reverse base interactions, in which base and sugar are oriented differently from a normal base pair, distance constraints were not sufficient to completely determine the presence of reverse base pairing. However, we hypothesized that the overall orientation of the RNA would determine the presence of reverse base pairing, so we performed simulations, enforcing both regular and reverse Hoogsteen base pair orientations using DMD, and confirmed that the all-atom force field chooses the correct orientation based on its energy. 3.4 Better Utilize Distance Restraints for RNA 3D Structure Prediction: DVASS

Restraints Definition In RNA structure, there are not only standard Watson-Crick pairings but also some nonstandard pairings, such as A-G and G-U, as well as some base-backbone and backbone and backbone interactions. Together, these interactions dictate the RNA tertiary structure. To apply the Go model to DMD, we need to extract the contacts in the native RNA structure as restraints. We chose to consider two nucleotides as forming contacts if the distance between their C1’ atoms is less than 15 Å, since this definition allows for all tertiary interactions inside RNA. In DMD, all restraints are implemented through an energy potential in the form of a step function. We design all contacts as step functions that constrain the distance between 3.75 Å and 15 Å to account for both attractive and repulsive forces. We only add restraints on nonadjacent nucleotides along the sequence and only consider the original force field for two adjacent nucleotides. The well depth for each such step function is -1.0 kcal/mol. We downloaded native RNA structures from the PDB database, and then used an in-house Python script to extract all contacts and generate step functions. DMD was run for 500,000 steps (approximately 25 ns) using eight replicas with a temperature distribution between 0.2 and 0.4 for replica exchange simulations. Hierarchical cluster analysis was used after the simulation to extract the most stable structure. The DVASS Algorithm Given a secondary structure as input, the DVASS algorithm can estimate the possible maximum and minimum distances between any two nucleotides, and the difference between the maximum and minimum distances is defined as the distance variation. There are many helical structures in RNA, and the structures of these helical structures are generally very stable, so the distance between each two nucleotides in a helical structure is a fixed value, and the distance variation value is 0. In order to accurately calculate the distance between nucleotides in the helical structure, we downloaded all RNA tertiary structures from the PDB database and analyzed all the helical structures. We calculated the distance distribution between any pair of nucleotides in the helical structure and obtained the formula shown in Fig. 1 through data fitting. With these formulas, we can calculate the distance

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Fig. 2 The geometries of helices and backbones in RNA structure. The unit of the distance is angstrom

between any pair of nucleotides in a helix of any length. For example, the distance between the C1’ atoms of two paired bases is 6.1 Å, and the distance between the C1’ atoms between two nucleotides, separated by one nucleotide, is 11.2 Å. Except nucleotides inside the helical structure, we set the distances between nucleotides outside of helices as a maximum of 999 Å and a minimum of 6.1 Å, so as to get an initial distance bound matrix. The upper triangle of this initial bound matrix represents the largest possible distance between any two nucleotides, and the lower triangle represents the smallest possible distance between any two nucleotides. For this initial distance bound matrix, we will iteratively apply the triangle inequality for optimization to obtain a more accurate bound matrix about the maximum and minimum distances between any two nucleotides. Through the maximum and minimum distances, we can calculate the distance variation between any two nucleotides. We show in Fig. 2 how to calculate all the distance variations of an RNA with a secondary structure as ((((. . . .((((. . .)))))..(((())))..)))). First, we construct a matrix of size N × N, where N is the number of residues. Entries in the upper triangle of the matrix are all assigned 999 Å, and entries in the lower triangle are assigned 6.1 Å, which is the distance between C4’ atoms in two adjacent residues in RNA.

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We then calculate the distance value between all residues inside helices according to the formula in Fig. 1. The triangle inequality is then applied iteratively until the matrix no longer changes, and finally, the distance variation is calculated by subtracting the lower triangle from the upper triangle. Once we have all the distance variation values, the next step is to cluster them, since we observe that some distance variations are correlated. Assuming that residue a and residue b have a considerable distance variation and residue a and residue c have a very small distance variation, then the distance variation between residue c and residue b is likely to be large. Based on this observation, we analyzed the associations among all the distance variations by clustering analysis. Initially, each distance variation is assigned to a different cluster, then we randomly select a distance variation and assign a certain value that satisfies the maximum and minimum distances to the distance of the corresponding residue pair, which makes the selected distance variation equal to 0. We then merged this distance variation and those that experienced drastic changes (greater than a custom cutoff) into a single cluster. By performing this process iteratively, we cluster all distance variations. The choice of cutoff is critical to the number of clusters produced. A small cutoff may result in too many clusters, while a large cutoff will only result in a small number of clusters.

4

Notes 1. Extend the DVASS algorithm for the prediction of the number of contacts. Although the DVASS was originally designed for ranking restraints for RNA 3D structure prediction, it can be broadly applied in the prediction of various structural properties of RNA. For example, here we extended the DVASS algorithm to predict the number of contacts given a secondary structure. First, we also set some initial distances by the geometric parameters of the RNA helix and backbone conformation. We then iteratively exploited the triangle inequality to infer distance variations for all pairs of residues. We iterate through all distance variations to find the one with the largest value and then assign a certain value to the distance between two residues belonging to the corresponding distance variation. Therefore, the new distance variation value for this residual pair is 0. Then, we iteratively apply the triangle inequality again to update all the distance variation values. We repeat this process 50 times and then count the number of residue pairs with a minimum distance less than 15.2 Å, which will be used as the predicted number of contacts.

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2. Comparison of iFoldNMR with other 3D structural modeling programs. iFoldNMR supports all-atom molecular dynamics, NMR constraints, and multistranded RNA 3D structure prediction. At present, no other RNA tertiary structure prediction method can support these three functions at the same time, but comparing the iFoldNMR platform with other structure modeling programs is still very interesting. We compare iFoldNMR with methods like FARFAR [55], 3dRNA-2.0 [18, 56], and RNAComposer [20]. These programs are fully automated RNA 3D structure prediction methods using different modeling approaches, all of which provide web servers and require an input sequence and a secondary structure as input. More specifically, the FARFAR program uses Rosetta force fields to model and rank RNA structures based on potential energies, and 3dRNA-2.0 and RNAComposer use fragments of crystal structures to build structural models, where 3dRNA-2.0 can also rank structures based on a statistical potential energy. It is worth noting that the FARFAR web server is currently limited to RNAs smaller than 32 nts, and both 3dRNA-2.0 and RNAComposer are limited to modeling single-stranded RNAs. We have tested these four programs on a test set consisting of 15 RNAs [33]. These 15 RNAs range in length from 16 nt to 62 nt. The average RMSD of structures predicted by iFoldNMR, FARFAR, 3dRNA-2.0, and RNAComposer are 5.5 Å, 4.6 Å, 15.4 Å, and 8.9 Å, respectively. It is worth noting that FARFAR only predicted the tertiary structures of 6 RNAs among the 15 RNAs. For the same 6 RNAs, the average RMSD of the structures predicted by iFoldNMR is 4.4 Å. It can be seen that the iFoldNMR platform is more versatile and more accurate than these programs, which is due to the improved sampling capabilities of discrete molecular dynamics simulation and high-resolution distance constraints. References 1. Jasinski D, Haque F, Binzel DW, Guo P (2017) Advancement of the emerging field of RNA nanotechnology. ACS Nano 11:1142–1164 2. Guo P (2010) The emerging field of RNA nanotechnology. Nat Nanotechnol 5:833 3. Chworos A, Severcan I, Koyfman AY et al (2004) Building programmable jigsaw puzzles with RNA. Science (80-) 306:2068–2072 4. Guo P, Zhang C, Chen C et al (1998) InterRNA interaction of phage φ29 pRNA to form a hexameric complex for viral DNA transportation. Mol Cell 2:149–155 5. Sugimoto N, Nakano S, Katoh M et al (1995) Thermodynamic parameters to predict stability

of RNA/DNA hybrid duplexes. Biochemistry 34:11211–11216 6. Searle MS, Williams DH (1993) On the stability of nucleic acid structures in solution: enthalpy-entropy compensations, internal rotations and reversibility. Nucleic Acids Res 21: 2051–2056 7. Jaeger L, Leontis NB (2000) Tecto-RNA: one-dimensional self-assembly through tertiary interactions. Angew Chem Int Ed 39:2521– 2524 8. Shu D, Moll W-D, Deng Z et al (2004) Bottom-up assembly of RNA arrays and

RNA 3D Structure Prediction with Restraints superstructures as potential parts in nanotechnology. Nano Lett 4:1717–1723 9. Ikawa Y, Tsuda K, Matsumura S, Inoue T (2004) De novo synthesis and development of an RNA enzyme. Proc Natl Acad Sci 101: 13750–13755 10. Matsumura S, Ohmori R, Saito H et al (2009) Coordinated control of a designed trans-acting ligase ribozyme by a loop–receptor interaction. FEBS Lett 583:2819–2826 11. Liu B, Baudrey S, Jaeger L, Bazan GC (2004) Characterization of tectoRNA assembly with cationic conjugated polymers. J Am Chem Soc 126:4076–4077 12. Shu Y, Pi F, Sharma A et al (2014) Stable RNA nanoparticles as potential new generation drugs for cancer therapy. Adv Drug Deliv Rev 66:74–89 13. Lin Y-X, Wang Y, Blake S et al (2020) RNA nanotechnology-mediated cancer immunotherapy. Theranostics 10:281 14. Ke W, Hong E, Saito RF et al (2018) RNA-DNA fibers and polygons with controlled immunorecognition activate RNAi, FRET and transcriptional regulation of NF-κB in human cells. Nucleic Acids Res 47:1350– 1361 15. Laing C, Schlick T (2011) Computational approaches to RNA structure prediction, analysis, and design. Curr Opin Struct Biol 21: 306–318. https://doi.org/10.1016/j.sbi. 2011.03.015 16. Miao Z, Adamiak RW, Blanchet M-FM-F et al (2015) RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA 21:1066– 1 0 8 4 . h t t p s : // d o i .o r g / 1 0 . 1 2 6 1 / r n a . 049502.114 17. Miao Z, Adamiak RW, Antczak M et al (2017) RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA 23:655–672. https://doi.org/10. 1261/rna.060368.116 18. Wang J, Mao K, Zhao Y et al (2017) Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis. Nucleic Acids Res 45:6299–6309 19. Wang J, Wang J, Huang Y, Xiao Y (2019) 3dRNA v2. 0: an updated web server for RNA 3D structure prediction. Int J Mol Sci 20:4116 20. Popenda M, Szachniuk M, Antczak M et al (2012) Automated 3D structure composition for large RNAs. Nucleic Acids Res 40:e112. https://doi.org/10.1093/nar/gks339

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Chapter 4 Structural Characterization of Nucleic Acid Nanoparticles Using SAXS and SAXS-Driven MD James Byrnes, Kriti Chopra, Lewis A. Rolband, Leyla Danai, Shirish Chodankar, Lin Yang, and Kirill A. Afonin Abstract Structural characterization of nucleic acid nanoparticles (NANPs) in solution is critical for validation of correct assembly and for quantifying the size, shape, and flexibility of the construct. Small-angle X-ray scattering (SAXS) is a well-established method to obtain structural information of particles in solution. Here, we present a procedure for the preparation of NANPs for SAXS. This procedure outlines the steps for a successful SAXS experiment and the use of SAXS-driven molecular dynamics to generate an ensemble of structures that best explain the data observed in solution. We use an RNA NANP as an example, so the reader can prepare the sample for data collection, analyze the results, and perform SAXS-driven MD on similar NANPs. Key words SAXS, WAXS, RNA, Molecular dynamics, SAXS-driven molecular dynamics

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Introduction 1.1 Nucleic acid nanoparticles (NANPs) are a highly versatile class of nanomaterials with significant biomedical potential [1– 3]. Through canonical and noncanonical Watson-Crick base pairing and naturally occurring RNA motif’s, NANPs can be rationally designed to adopt any number of architectures and carry multiple functional moieties in stoichiometric ratios [4– 6]. One of the key translational challenges, which is preventing the progression of NANPs from research to the clinical space, lies in their interactions with the innate immune system [1, 2, 7–9]. Two of the main factors that determine the immunostimulatory properties of NANPs are their physicochemical properties (geometry, composition, size, stabilities, etc.) and functionalization [10, 11]. Owing to this unique structurefunction relationship, a thorough understanding of NANP’s structure in solution, including examining factors such as their

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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flexibility where functional moieties have been added, is of the utmost importance to their translation to clinical settings. Structural characterization of functional biopolymers, such as proteins and nucleic acids in solution, is critical to elucidate the full behavior of the sample. Solution studies provide further insight into how biological samples behave in vivo and compliment high-resolution methods, such as X-ray crystallography and single-particle cryoEM [12]. Although the latter methods provide atomistic details, these methods are not necessarily optimal for capturing the dynamics of a system or, for example, concentration-dependent effects on oligomeric states [13]. Therefore, to obtain the best structural characterization of NANPs, solution-based methods should complement highresolution structural methods. The use of small-angle X-ray scattering (SAXS), in combination with wide-angle X-ray scattering (WAXS), is a method that provides information about the overall shape, size, maximal dimension, and flexibility of a particle in solution [10, 14, 15]. It is inherently a low-resolution technique, whereby the scattering contribution from all conformations tumbling in solution are rotationally averaged resulting in isotropic scattering, and thus, 3D (high resolution) structural information is lost. The small angles (SAXS) are most sensitive to overall properties of the particle, such as size and maximal dimensions, while the WAXS region can provide information about intramolecular structure. The resulting merged SAXS/ WAXS dataset is a 1D profile of scattering intensity (I) as a function of q (scattering vector), defined as q = 4πsinθ/λ, where θ is half the scattering angle and λ is the incident X-ray wavelength. A high-quality SAXS experiment can inform about particle size from the radius of gyration (Rg) value, distribution of scattering pairs via the pair distribution function P(r), and degree of flexibility from the Kratky analysis. There are several excellent reviews that provide an in-depth discussion of SAXS theory and practical applications [16–20]. To obtain meaningful results, one must properly prepare the samples and correctly analyze the results. Here, we present the procedure for preparing NANP components, particularly an RNA monomer construct that when combined with cognate partners can form planar RNA nanorings [3, 21–25], for solution scattering measurements, with an emphasis on preparation for the most common mode of operation, mail-in measurements. The advantage of mail-in measurements, and rapid access to LIX, is the reduction in turnaround of solution measurements and to lower the barrier for users to access beamlines. While the preparation steps are general for SAXS experiments collected at any facility, data analysis and modeling from data collected at LIX at NSLSII within Brookhaven National Laboratory is emphasized. For details about LIX, please refer to the following references [26–

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28]. Furthermore, we outline the procedures of SAXS-driven MD simulations to demonstrate the complementarity of SAXS and MD to derive meaningful results about the ensemble of structures that explain the RNA SAXS data. 1.2 SAXS-driven MD is a new methodology developed to refine the solution state structure of the biomolecule based on molecular dynamic simulations and experimental SAXS data. An extended version of commonly used molecular dynamics software, GROMACS, known as GROMACS-SWAXS, developed by Jochen Hub’s group at Saarland University in Germany, provides functionalities for the user to incorporate experimental SAXS data into the MD simulation by converting the I(q) versus q data into SAXS potentials. This method also utilizes an explicit solvent simulation to subtract background/solvent intensity and hence accurately calculate the scattering profile for the solute in solvent. Since the process involves utilizing both SAXS intensities as potentials as well as molecular forcefields, the method is used to identify the best possible ensemble structures of the solute in solution state, where the SAXS potentials drive the biomolecules to attain a conformation, which best describes the experimental data. This method provides a way to characterize flexibility in the biomolecules, which might exist in solution state and are often difficult to identify in other state of the art methods, such as X-ray crystallography and cryo-electron microscopy. SAXS-driven MD has been used to identify large domain conformational changes in proteins [29] and recently to refine the solution state structure of RNA molecules [30]. In Subheading 3.7, 3.8, 3.9 and 3.10, we describe how to use the SAXS-driven MD functionality from GROMACS-SWAXS software to refine the RNA model structure such that it best fits the experimental SAXS data. To explore the conformational space for the RNA molecule, one must compute the free MD simulation trajectory and perform its downstream analysis. 1.3 SAXS-driven MD simulation methodology is an amalgamation of forcefield-based MD simulations as well as experimental SAXS data. Both these methods independently have their advantages and short comings, which are, to some extent, addressed through this hybrid approach. While MD simulations are sufficient to identify the conformational space of any biomolecule under the influence of the specific forcefield, not all conformations identified are physically plausible. Introduction of experimental SAXS data in terms of potentials drive the simulations toward a more realistic conformational ensemble. The difference between the conformational space traversed by the RNA molecule in this study can be identified by comparing

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the RMSD changes during the free MD versus the SAXS-driven MD. The RMSD changes during the SAXS-driven MD are smaller than those observed during the free-MD, hence guiding the conformational space to attain a solution, which best represents the experimental data. The ensemble structure obtained from this method can improve the overall fit of the biomolecular structure into the bead model derived from the SAXS experiment. The analysis of the ensembles and associated trajectories can also help identify the flexible regions in the biomolecule. Thus, utilizing the strengths of both these methods through this hybrid approach might pave a path for identifying functional conformations of biomolecules in the solution state. Abbreviations: LIX Life Sciences X-ray scattering beamline, SAXS small-angle X-ray scattering, WAXS wide-angle X-ray scattering, NANP nucleic acid nanoparticle, DLS dynamic light scattering, MW molecular weight, HT-SAXS high-throughput static SAXS, SEC-SAXS size exclusion chromatography coupled with SAXS, SVD single value decomposition, MD molecular dynamics

2

Materials

2.1 SAXS Sample Preparation and Data Collection

1. CredoCube Series 4 L from Pelican BioThermal or similar. 2. Microdialysis chambers with an appropriate nominal molecular weight cutoff for the sample. 3. Sterile 0.2 μm filters for both microcentrifuge tubes and for larger containers (e.g., 50 mL conical tubes, bottles, etc.).

2.2 SAXS-Driven Molecular Dynamics Simulations

1. SAXS experimental data (I versus q data). 2. Model structure of the RNA molecule. 3. GROMACS-SWAXS software installation on a GPU-enabled machine for faster performance. A modified version of GROMACS was developed by Jochen Hub’s group at Saaraland University, Germany, which is available for download at https://gitlab.com/cbjh/gromacs-swaxs [31]. This software has all the same functionalities as the original GROMACS program with some additional functions, specifically designed for carrying out SAXS-driven MD simulations. MD simulations largely depend on the approximation of forcefields utilized for the biomolecule under study. Additionally, the SAXSdriven MD program depends on the calculation of Cromer-

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Mann parameters for all chemical/biochemical elements and ions for the forcefield being utilized for simulation. Most of the commonly used forcefields files contain the Cromer-Mann parameters in this version of the software (see Note 1). 4. Structure visualization software ChimeraX [32] or VMD [33]. 5. Xmgrace for plotting graphs [34].

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Methods

3.1 Preparation of Samples for Measurement at Beamline

1. Determine the type of question you seek to answer as outlined in Fig. 1 (see Note 2). 2. Determine the quality of sample. 3. Utilize non-denaturing gel electrophoresis, DLS, or other methods to determine sample stability and monodispersity (see Note 2). 4. Depending on the results from step 2, if the sample is stable and monodisperse, choose high-throughput static SAXS (HT-SAXS). If there are oligomeric assemblies present in the sample, choose SEC-SAXS.

Fig. 1 Flowchart outlining the phases of a SAXS experiment. To get the most out of your data, asking the right questions is critical

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3.2 For SEC-SAXS Measurement Preparation Skip to Subheading 3.3. For HT-SAXS:

1. Determine the number of unique samples and prepare ≥60 μl of pure, monodisperse sample. 2. Samples and buffers should minimize the use of organic solvents. 3. Concentration to obtain good signal-to-noise ratio depends on the size of the sample. A general rule of thumb for good signal at LIX is mg/mL = 50/MW, where MW is in kiloDaltons. 4. Prepare a concentration series around the value determined in step 3. This will be helpful to determine if Rg values are concentration-dependent. This can be performed manually by the researchers before shipping the samples or using the OT2 sample handling robot at the LIX beamline (see Note 3). 5. Prepare a matching buffer for each unique set of samples. This is critical for a proper subtraction; without a proper samplematched buffer, there is no data (see Note 4). The highestquality matched buffer is obtained by performing dialysis and using the dialysate as your matched buffer. Other options include the use of the flow-through from the final sample concentration step (e.g., spin concentrator flow-through) or collecting buffer from the SEC run during the polishing step of sample purification. At LIX, each buffer is classified as a sample and requires at least 60 μL. Each buffer, matched to a particular sample, should be repeated at least 2X (see Note 5). 6. To ensure there is no particulate contamination in the samples or buffer matches, filter each sample with 0.2 μm filters. By using filters designed to fit in microcentrifuge tubes, minimal sample loss from retention within the filter can be achieved. 7. Place samples in 96-well plates and prepare for shipping (step 7). For LIX-specific details about sending samples, refer to [26, 27]. In general, however, the plate should be sealed with a film that does not have adhesive over each sample well and the plate lid secured on top to protect the sealing film.

3.3 Preparing SECSAXS Samples

1. Determine expected chromatogram before sending samples for measurement. The type of column utilized will determine the volume and concentration needed. For well-polished samples, columns, such as the Superdex Increase 200 5/150 from Cytiva or Biozen dSec2 from Phenomenex or similar, should be employed due to reduced dilution and fast sample run time (typically 12 min/sample). The typical injection volumes are ≥60 μL, and dilution factors range from four- to sevenfold. Remember that the final concentration should be close to that determined in step 3 in Subheading 3.2. 2. Preparation of buffer. Columns must be equilibrated in at least two column volumes of buffer (or until UV 280 nm and refractive index is stable). Prepare a 5X or 10X stock to be diluted at the beamline, enough for ≥250 ml. It is important

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to send a pure, 0.2 μm degassed buffer. Particulates will clog and ruin column. Any bubbles that enter the system could potentially enter the X-ray flow cell and ruin the scattering experiment (see Note 6). 3. When measuring nucleic acids, particularly RNA, prepare the beamline staff, if sending samples, to work in an RNAse-free environment. Proper sterile tips, cleaning procedures for pipettes, and the working area at the beamline will be needed. 4. Shipping samples. It is best to avoid freeze thaw cycles. Ideally, send at 4 °C on ice. Always prepare for possible shipping delays, and add extra ice or utilize a reusable cooler, such as the CredoCube Series 4 L or larger from Pelican BioThermal. Ensure a next-day delivery service. 3.4 SAXS/WAXS Data Processing for HighThroughput Static Experiments

1. At LIX, all data will be shared in a standard HDF5 format. In addition, an HTML style report will be generated that will summarize the results for all your static samples (Fig. 2). 2. Examine the report for a linear Guinier region, good P(r) fit to the data, and evaluate the degree of compaction or flexibility from the Kratky plot. Figure 2a demonstrates a report from a high-quality sample. The left plot shows a linear Guinier region of high quality in blue, the experimental data (orange), and the fit of P(r) to the data (blue overlay on orange). The P (r) function behaves well, and the Kratky plot demonstrates some degree of flexibility within the system (center plot) (see Note 7). Proceed to step 8 to export data in DAT format. 3. If your report looks like Fig. 2b, there are significant issues with the measurement that resulted in a failure to perform basic analysis, such as Guinier; therefore, a closer examination of the data is required. The next steps will demonstrate the use

Fig. 2 Example output of the HTML style summary reports from static SAXS data. (a) Example output from a high-quality dataset. The left plot shows Guinier region (blue) and the merged SAXS/WAXS scaled data (orange). The middle plot shows Kratky analysis to assess the degree of flexibility. This sample shows a plateau in the Kratky analysis, suggesting a degree of flexibility within the sample. The right most plot is the P (r) or pair distance distribution function. The fit of P(r) to the scattering data is shown in the left plot as a blue line over the orange dots. All analysis were performed by using DATtools from the ATSAS suite of software. (b) An example of a report from a poor sample. No Guinier region was able to be determined

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Fig. 3 Example Jupyter Notebook GUI utilizing LIXtools software to visualize and analyze data from static SAXS experiments. (a) Section 1 (red box) is the region to select samples, sample frames, adjusting scaling factor, Guinier start region, and Rg value for a particular sample. Section 2 (purple box) allows for export of data (unsubtracted or subtracted) in DAT format and the ability to run the ATSAS report to generate the Kratky and pair distance distribution plots. (b) Unsubtracted raw I(q) vs q 1D scattering profile for ten frames. Each frame is separated by a scaling factor for visualization purposes. Note the presence of a water peak at 2.0 Å-1 and smooth fit of each colored profile to the average (gray dashed lines). Four frames were discarded and not used for the analysis (gray dots). (c) Scattering profile for high-quality data. Left I(q) vs q plot shows the subtracted data in blue, the scattering from sample + buffer in orange and buffer alone in green. Note the good signal-tonoise ratio and flat shape at low q. The “buf subtraction” window is a zoom in of the subtracted profile around the water peak at 2.0 Å-1 and is helpful for choosing the proper scaling factor. Finally, the Guinier region is displayed, showing a nice linear fit to the data. (d) Same as C, but for a sample that was too low in concentration and did not scatter strongly, resulting in no useful information

of LIXtools to find the reason for the poor result in Fig. 2b [27]. 4. At LIX, a central Jupyter Hub is available such that these next steps do not have to be performed on your local machine (see Note 8). This requires access to the LIX beamline and is setup for you by the beamline staff and is not covered here. Step 5 will assume that you have access to your data on the Jupyter Hub. 5. Figure 3a is the interactive GUI used to examine each of the samples in your HDF5 file(s). Figure 3a, Section 1 (red) contains a drop-down menu, next to “Sample:”, to display the raw 1D scattering profile of each sample and buffer measured. Select the buffer, in this case b20, to look for quality, i.e., a water peak at 2.0 Å-1, and a smoothly decaying intensity without any aberrant spikes. Figure 3b shows an example of a good scattering profile from buffer. 6. Figure 3b displays the I(q) vs q profile. Here, ten frames were collected with a 0.5 s exposure time. Note the colored lines fit the dashed lines (average of all selected frames) well and are of high quality. Some frames are missing due to poor quality (see Note 9).

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7. To see the missing frames or to exclude any frames that may deviate from the average, select the frames to exclude/include using the frame window shown in Fig. 3a, Section 1 (red box). Included frames will be highlighted gray. Then, click the update plot button (Fig. 3a, Section 2, purple box) to display the 1D, I(q) vs q scattering profiles with the updates. 8. Perform step 7 on each of the samples in the sample dropdown menu to ensure quality. If necessary, the raw, unsubtracted 1D profiles can be exported by clicking the Export button shown in Fig. 3a, Section 2. This will place a .DAT file in a “processed” directory of the format: sample_name.dat (see Note 10). 9. Select a sample (not buffer) in the drop-down menu and check the “show subtracted” button (Fig. 3a, Section 2). The subtracted data will be displayed as shown in Figs. 3c, d. 10. Figure 3c shows the high-quality data observed from the report (Fig. 2a). Note the blue line is subtracted data, orange is scattering from sample + buffer, and green is scattering from the buffer alone. 11. Adjust the scaling factor shown in Fig. 3a, Section 1, by clicking on the box or using the slider. A proper subtraction is shown in the window labeled “buf subtraction” in Fig. 3c. Details are described in [26]. 12. The Guinier panel shows a linear fit, and an Rg value will be displayed (~20 for the RNA monomer). For large NANPs, one might have to adjust the Guinier q-start (selecting the Guinier fit qs window) to include more points at low q. 13. To export these data, click the export subtracted button (Subheading 2) and click the export button. A subtracted. DAT file (appended suffix with an s) can then be found in the processed directory. 14. For the example in Fig. 3d, note the noisy, low-intensity subtracted signal. Here, the concentration of the sample was not high enough, and no meaningful data could be obtained. See Note 11 for an NANP example. 15. Click the ATSAS report button to export a GNOM output file (*.out) in the processed directory. 3.5 SAXS/WAXS Data Processing for SizeExclusion Chromatography Coupled with SAXS (SEC-SAXS)

1. If polydispersity is detected after a static SAXS measurement, as shown in Fig. 4 (red diamonds), SEC-SAXS may be an appropriate alternative to improve these data. Data analysis is more involved than high-throughput static SAXS processing, as a buffer region must be manually chosen from the chromatography run (see Note 12).

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Fig. 4 Comparison of SAXS data collected via the static or SEC-SAXS methods on the RNA monomer NANP. The static method (red diamonds) shows an upward trend at low q indicating polydispersity within the sample. Running SEC-SAXS on the RNA monomer (blue spades) improved the data quality drastically

2. Figure 5a shows the typical Jupyter Notebook GUI used at LIX to process SEC-SAXS data (see Note 13). There are two main types of control, one for the chromatograms (red box) and the other for subtraction (yellow box). 3. Under the chromatograms section, select the X-ray region of interest (ROI) desired, to view data. This is the q range that will be displayed. The default is 0.02, 0.03 Å-1; however, up to three different ROI’s can be overlaid. This can be useful to compare how the background is changing within certain q regions. 4. Under the subtraction box, you can select normal or SVD from the drop-down menu (see Note 14). 5. If using SVD, as determined from Note 14, select the number of background components (Nc) and the polynomial order (polyN) to fit the background under the peak, until the eigenvalues and eigenvectors stabilize. 6. Adjust the scaling factor so the heat map shows a reduced water peak (Fig. 5b, right panel). Check the 1D profile to confirm that there is no over- or under-subtraction (see Note 15).

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Fig. 5 Example of how LIXtools can be used in Jupyter Notebook GUI to analyze -SEC-SAXS data of the RNA monomer NANP. (a) Commands to run and display the GUI. Red box shows the controls for chromatograms, such as choosing a q-ROI (region of interest) and subtraction controls (yellow box), where the mode of subtraction can be chosen from a drop-down menu (normal or SVD), scaling factor, and subtracted frames to be exported. Note that upon the selection of normal mode, excluded frames becomes buffer frames, and these can be chosen manually. (b) Results of scattering of RNA monomer using Superdex Increase 200 5/150 GL column at a flow rate of 0.5 ml/min with 55 μl injection volume. The top part of both panels displays the X-ray scattering intensity (blue dots) for each frame. Each frame is a 2-second exposure for a 12-minute run; a total of 360 frames. The heat map on the bottom shows the q range (y-axis), frame # (horizontal axis), and intensity (colored) for the unsubtracted (left side) and SVD subtracted (right side). Note the intense band at 2.0 Å-1, which represents scattering from water. (c) Atsas output after selecting frames 131–145 to be averaged. Kratky plot shows some degree of flexibility

7. Select the frames under the peak to export (see Note 16). These frames will be averaged and saved as a subtracted DAT file in the processed directory. 8. Click the ATSAS report and confirm high-quality data as shown in Fig. 5c. The q range used for the ATSAS report can be adjusted using the skip and q-cutoff boxes (see Note 17).

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Fig. 6 Flowchart outlining the steps required for performing SAXS-driven molecular dynamics to generate ensemble structure 3.6 SAXS-Driven Molecular Dynamics

The free MD simulation of the RNA is performed using the following steps (Fig. 6): 1. Define the forcefield parameters: At this step, the appropriate forcefield is selected along with the recommended water model forcefield. Since only a few forcefields have defined parameters for RNA molecule, we used amber03 along with TiP3 water model for this simulation: $gmx pdb2gmx -f rna.pdb -o processed.gro -ff amber03 -water tip3p 2. Define a solvation box for the RNA: The size of the solvation box is defined using the parameter “d,” which is the distance from the surface of the biomolecule used to construct a box having user-defined geometry. In this case, we selected a dodecahedron geometry with a distance parameter of 3 nm. The box was then solvated with water (see Note 18). $gmx editconf -f processed.gro -o newbox.gro -c -d 3.0 -bt dodecahedron $gmx solvate -cp newbox.gro -cs spc216.gro -o solv.gro -p topol. top 3. Add appropriate ions to the system: Any biomolecule will have some amount to inherent charge on it, which should be neutralized before performing the MD simulation.

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$gmx grompp -f ions.mdp -c solv.gro -p topol.top -o ions.tpr $echo SOL | gmx genion -s ions.tpr -o solv_ions.gro -p topol.top -pname NA -nname CL-neutral If the experimental sample has additional salts/ions present, those ions should also be accounted for in the solvent box before performing MD simulation. For example, in this case, RNA was purified in 50 mM of KCl; hence, K and Cl ions were added, such that their concentration in the system is 0.05 M. $echo SOL | gmx genion -s ions.tpr -o solv_ions.gro -p topol.top -pname K -pq 1 -nname CL -nq - 1 -conc 0.05 -neutral Option “SOL” is used to replace water molecules with the appropriate number of ions to neutralize the system. The RNA molecule had a total charge of -43, which was neutralized by adding 84 K and 41 CL ions to the system. 4. Energy minimization of the modeled structure: The RNA molecule in the solvent with ions environment is energy minimized to ensure the system exhibits appropriate geometry and is free of any steric clashes. $gmx grompp -f minim.mdp -c solv_ions.gro -p topol.top -o em.tpr $gmx mdrun -v -deffnm em The convergence of the system in terms of energy is assessed by plotting the potential energy of the system (Fig. 7a). $gmx energy -f em.edr -o potential.xvg (Select 10 0) 5. Equilibration MD: To equilibrate the solvent and ion molecules around the solute, it is important to calibrate the system at a standard temperature and pressure. Hence, a 100 ps each of equilibration of the system is conducted at standard temperature (nvt) and pressure (npt). $gmx grompp -f nvt.mdp -c em.gro -r em.gro -p topol.top -o nvt.tpr $gmx mdrun -deffnm nvt $gmx grompp -f npt.mdp -c nvt.gro -r nvt.gro -t nvt.cpt -p topol.top -o npt.tpr $gmx mdrun -deffnm npt To check if the system is well equilibrated, temperature and pressure values throughout the 100 ps simulation are analyzed (Figs. 7b, c). $gmx energy -f nvt.edr -o temperature.xvg (select 16 0) $gmx energy -f npt.edr -o pressure.xvg (Select 18 0)) 6. Production MD: Once the system is equilibrated, a 30 ns free MD simulation of the RNA in solvent system is performed. $gmx grompp -f md.mdp -c npt.gro -t npt.cpt -p topol.top -o md_0_1.tpr $gmx mdrun -deffnm md_0_1

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Fig. 7 Analysis of free MD performed prior to SAXS-driven MD. (a) Energy minimization convergence plot depicting potential energy of the system. (b) Normal temperature equilibration. (c) Normal pressure equilibration. (d) RMSD with respect to the reference structure (black), moving average considering 100 ps time scale (red) during 30 ns simulation. (e) Radius of gyration (Rg:black) and moving average (Rgx: red) during 30 ns simulation

7. Analysis of MD convergence: MD simulation of a system is considered to have converged when it no longer shows variation in the root mean square deviation (RMSD) values across the simulation trajectory. Along with RMSD, it is important to check the radius of gyration (Rg) across all axes to determine if the molecule is not unfolding and is maintaining its compactness during the simulation. Both RMSD and Rg are calculated after removing the periodic boundary conditions and making the solute whole (Figs. 7d, e). $gmx trjconv -s md_0_1.tpr -f md_0_1.xtc -o md_0_1_noPBC. xtc -pbc mol -center $gmx rms -s md_0_1.tpr -f md_0_1_noPBC.xtc -o rmsd.xvg -tu ns $gmx gyrate -s md_0_1.tpr -f md_0_1_noPBC.xtc -o gyrate.xvg

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8. Analysis of the conformational space for the RNA: To identify the different conformations, present for the RNA molecule throughout the simulated trajectory, clustering analysis of the trajectory is performed based on RMSD values. Depending upon the basal level of RMSD in the molecule during the simulation, a cutoff value is determined. A lower cutoff value or the default value of 0.1 nm can sometimes lead to numerous clusters, which might not depict different conformations but only marginal backbone shifts. Here, for the RNA molecule, an RMSD cutoff value of 0.175 nm was used to identify a total of 4 clusters in the 30 ns simulation. $gmx cluster -f md_0_1_noPBC.xtc -s md_0_1.tpr -av yes -cutoff 0.175 An average structure, representative of each of the cluster was captured and compared to the starting model structure (Fig. 8). A representative structure from each of these clusters is further analyzed to determine the starting conformation for SAXS-driven MD. The .mdp files for free MD simulations were

Fig. 8 Trajectory cluster representative structures (cyan) from free MD simulation. (a) Average structure from cluster 1 representing 0 to 17.8 ns in the trajectory. (b) Average structure from cluster 2 representing 17.8 to 17.9 ns (c) Average structure from cluster 3 representing 17.9 to 22.5 ns and (d) average structure from cluster 4 representing 22.5 to 30 ns in comparison to model RNA structure (khaki)

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adopted from http://www.mdtutorials.com/gmx/lysozyme/ index.html. 3.7 Free MD Calculation for Solvent-Water and Ions

1. The SAXS-driven MD simulation requires an only solvent simulation to perform the approximation for calculating the SAXS profile from the simulated trajectory. Hence, an explicit solvent free MD simulation is performed using the following steps. 2. Defining the solvent box: A solvent box containing only water is generated by increasing the box size created in step 1b by a few nanometers. In general, the box size for solvent-only simulation should be greater than the solute in solvent simulation. $gmx solvate -box x y z -cs spc216.gro -o solvent_box.gro The dimensions for the box in the case of RNA in a solvent system were x = 12.435, y = 12.435, and z = 12.435. The solvent-only simulation box was created by adding 2 nm in each direction, with the final box size as x = 14.435, y = 14.435, and z = 14.435. 3. Define forcefield for solvent-only box: The same forcefield as used for RNA in the solvent box (Amber03) is used in the water-only box. $gmx pdb2gmx -f solvent_box.gro -o processed.gro -ff amber03 -water tip3p 4. Add ions to the system: The ions present in the RNA in solvent system were also to be added to the solvent-only box, i.e., 50 nM of KCl. $gmx grompp -f ions.mdp -c processed.gro -p topol.top -o ions.tpr $echo SOL | gmx genion -s ions.tpr -o solv_ions.gro -p topol.top -pname K -pq 1 -nname CL -nq - 1 -conc 0.050 -neutral 5. Energy minimization to production MD: Following the generation of a solvent system containing the same concentration of ions as for the solute in solvent free MD, energy minimization to production MD steps is performed for the solvent-only system using the same commands described in the section above (free MD calculations for solute-RNA molecule in solvent, 4–7). See Note 19.

3.8

SAXS-Driven MD

1. Generate index file: The first step for performing SAXS-MD is to generate an index file to define the solute in the system. If your system contains additional molecules, such as ligands, a group combining the biomolecule and ligand can be generated using the make index function of GROMACS. In this case, the solute is defined as only RNA; hence, defining additional groups is not required. $echo q | gmx make_ndx -f md.gro md.gro: output file from free MD simulation for RNA in solvent

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2. Calculating Cromer-Mann parameters for the solute in the system: Once the solute group is defined, a structure file (dummy.tpr) is generated to calculate the Cromer-Mann parameters for the solute group. This is one of the features specifically designed in the modified version of GROMACS (GROMACS-SWAXS). $touch empty.mdp $gmx grompp -f empty.mdp -c md.gro -p topol.top -o dummy.tpr $gmx genscatt -s dummy.tpr -vsites The solute group should be selected here as “RNA.” See Note 20. md.gro: output file from free MD simulation for RNA in solvent. topol.top: topology file generated from free MD simulation Output file: scatter_RNA_chain_A.itp The output file contains the scattering parameters for the solute molecule. For the program to read this scattering topology, the topol.top file is edited by adding the following lines just before the inclusion of the positional restrains. The topology.top file should read like: ;Include Scattering topology #ifdef SCATTER #include “scatter_RNA_chain_A.itp” #endif ;Include Position restrain file .... 3. Defining envelop boundaries for the SAXS-driven MD: For mimicking the in-solution experimental conditions, an envelope is defined around the RNA molecule to perform SAXSMD simulation. The trajectory obtained from the free MD simulation is utilized to construct this envelope so that all possible transitions in the solvent box can be traversed. The distance “d” defined here is the distance from the surface of the RNA molecule. In general, this distance should be greater than 0.6 nm, but for systems containing ions, the distance can be up to 3 nm for better convergence of experimental data. Here, for the RNA molecule, the distance was defined as 1.5 nm, and the trajectory without periodic boundary conditions (md_noPBC. xtc) was used to ensure the RNA molecule was whole (Fig. 9). $gmx genenv -d 1.5 -s dummy.tpr -f md_noPBC.xtc Output files: envelope-ref.gro, envelope.dat, envelope.py (see Note 21).

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Fig. 9 Envelope construction for SAXS-driven MD. (a) Envelope defined before SAXS-driven MD using the distance parameter “d” (1.5 nm in this example). (b) Envelope system containing RNA, water, and ions during SAXS-driven MD derived by merging RNA in solvent and explicit solvent free MD simulation systems

Two environment features are defined before processing SAXS-MD to read the envelope coordinates $export GMX_WAXS_FIT_REFFILE=/path/to/the/file/envelope-ref.gro $export GMX_ENVELOPE_FILE=/path/to/the/file/envelope. dat

While performing the calculation for generating the envelope, a global atom number (GOOD PBC atom) is generated, which is noted to be defined in the mdp file for SAXS-driven MD. For example: ############ G O O D P B C A T O M ############ #################### N.B.: Solute atom number 15 is near the center of the bounding sphere it

would make a (distance = 0.279619)

good

waxs-pbc

atom

Global atom number = 15 (name C5, residue G-1) ############################################ ########################### 4. Generate solvent structure file from free MD solvent-only simulation: To define the Cromer-Mann parameters for the solvent system for the SAXS-driven MD, a structure file (.tpr) from the free MD solvent-only simulation is generated. This structure file and trajectory file of the solvent from free MD is used as input in the final SAXS-driven production MD step. $gmx grompp -c solvent.gro -f solvent.mdp -p solvent-topol.top -o solvent.tpr

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5. Solute in solvent SAXS-driven production MD: Three input files were prepared for performing the solute in solvent SAXSdriven production MD. 6. SAXS experimental data file: The experimental data obtained collected for the RNA sample is first smoothened to reduce the noise as well as improve the computational efficiency of the SAXS-driven MD. A shell script is used to smoothen and re-bin the experimental data, which is based on the DATGNOM model of the ATSAS package [35] . Smoothed and re-binned data leads to better convergence of the computationally calculated SAXS profile. The values obtained after smoothening are then converted to nm-1 from Angstrom-1 by multiplying the q values by 10. The data is cut off around a q value of 10 nm-1. The lowest and highest value of q in the target dataset are noted. $./smooth-saxs-curve.sh -f saxs_experimental_data.dat -o target_smooth.xvg. 7. Parameter in the SAXS-driven.mdp file: • Waxs-pbc = Atom number obtained while generating envelope file (15 in this case). • Solute group = RNA (if ligand is present, mention the group create while defining the index file, e.g., RNA_LIG). • Solvent group = “Water_and_ions” in case ions are present, if not, “Water.” • Fc = 1–5 (for Bayesian solution, fc is defined as 1). • Number of q points: 30–50. • q start = slightly larger value than the smallest q point in the experimental data file. For example, if the first datapoint is 0.055, q start should be 0.06. • q end = slightly smaller than the largest q point in the experimental data file. For example, if the last datapoint is 10.15, q end should be 10. • Waxs-nstcalc = 250. The number of steps after which the SAXS curve is calculated. If the number of steps is given as 500, the SAXS curve is calculated after every 250 ps in the simulation. It is recommended to keep the nstcalc value as 250, i.e., calculate SAXS curve after every 125 ps, thus maintaining a 2 fs time step and avoiding any LINCS errors during simulations. • WAXS-tau = 250 ps. The number of frames utilized for calculating the SAXS curve. In simple terms, the SAXS curve calculated throughout the simulation takes into considerations this time window to generate an average curve. The SAXS curve obtained at the end of the SAXS-driven MD is also an approximation or on the fly curve generated

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by averaging the intensities in the last 250 ps of the simulation. • WAXS-t-target = 10,000 ps. This is the time up till which the SAXS experimentally derived potentials are introduced in the simulation. The SAXS potentials are not incorporated all together in the SAXS-driven MD; rather, the potentials are introduced slowly at each step such that the relative weight of the target curve to the calculated curve reaches 1 when the simulation reaches a time defined by Waxs-ttarget. For example, if the waxs-t-target is defined as 5000 ps, the relative weight of the target to calculated curve is 0.5 at 2500 ps and 1 at 5000 ps. The .mdp file for SAXS-driven MD were obtained from https://cbjh.gitlab. io/gromacs-swaxs-docs/tutorials.html. 8. Starting conformation of the RNA molecule: Determining the starting conformation for the SAXS-driven MD is a crucial step since the conformation of the biomolecule is not intended to change much following the SAXS-driven simulation. To identify the starting conformation, the rerun module of SAXSdriven MD is utilized. Through the clustering analysis performed for RNA in solvent free MD simulation, specific trajectory windows are obtained that depict major conformational changes. The rerun module essentially utilizes the trajectory generated from free MD simulation and generates a SAXS curve for the RNA molecule as well as provides an estimate of the Rg value. In this case, four clusters were identified from the 30 ns free MD trajectory analysis: cluster 1: 0–17.8 ns; cluster 2:17.8–17.9 ns, cluster 3: 17.9–22.2 ns; and cluster 4: 22.2–30 ns. Since the number of frames in cluster 2 were less, this cluster was not utilized for the SAXS curve calculation. The starting and ending frames of these clusters are defined as global environment features, i.e., GMX_WAXS_BEGIN and GMX_WAXS_END, and a SAXS curve is calculated by taking a non-weighted average across the free MD frames. $gmx grompp -f rerun.mdp -p topol.top -c md.gro -o saxs_01.tpr $gmx mdrun -sw solvent.tpr -fw solvent.xtc -rerun md_noPBC. xtc -deffnm saxs_01 The Rg value was calculated for each of the cluster trajectories, i.e., cluster 1: 22.5 Å, cluster 3: 23.8 Å, and cluster 4: 22.4 Å. Since the experimental value is close to 20, both cluster 1 and 4 can be utilized performing SAXS-driven MD. Hence, the first frame as well as the last frame of the free MD were considered as the starting structure of SAXS-driven MD simulation. Multiple starting conformations should be tested to reach a consensus for the experimental SAXS data. $gmx grompp -f SAXS-driven.mdp -p topol.top -c firstframe.gro -o saxs_driven.tpr

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Fig. 10 Analysis of SAXS-driven MD convergence. (a) RMSD with respect to reference starting structure (black), moving average over 100 ps (red) during the 30 ns SAXS-driven MD. (b) Radius of gyration (black) and moving average (red) during 30 ns simulation. (c) Energy contribution from SAXS potentials during 30 ns SAXS-driven MD simulation

-n index.ndx $gmx mdrun -s saxs_driven.tpr -is target_smooth.xvg -sw solvent. tpr -fw solvent.xtc -deffnm saxs_driven 3.9 Output Files and Analysis

1. The following output files are generated after the completion of SAXS-driven MD. These files are important to analyze the results and generate the average SAXS-driven MD structure. 2. Saxs_driven.gro: The structure of the RNA molecule obtained from the last frame of the SAXS-driven MD. 3. Saxs_driven.xtc: The trajectory file for the SAXS-driven MD. It can be visualized with the .gro file in software, such as ChimeraX [32] or VMD [33], to see the conformational changes the RNA molecule went through during the simulation. An analysis of the RMSD and Rg values throughout the simulation can be performed using the trajectory file in a similar way as performed for free MD simulation (Fig. 10a, b). 4. Saxs_driven.edr: The energy components of the SAXS-driven MD. This file is used to analyze the effect of SAXS potentials on the SAXS-driven MD simulation. The SAXS potentials through the simulation are calculated using the command: $gmx energy -f saxs_driven.xtc -o energy.xvg (Select option 16 0 – X.coupl energy) The overall effect of the SAXS potentials should not be enormous and minimal toward the end of the simulation. A lower energy contribution also indicates that the appropriate structural conformation, matching the experimental data, has been achieved through the SAXS-driven simulation. This analysis is also useful in determining which starting conformation is best suited for the downstream analysis. A starting conformation leading to

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Fig. 11 Analysis of SAXS curves generated from SAXS-driven MD and RERUN module. Red: maximum likelihood target SAXS curve (experimental curve). Blue: calculated SAXS curve from SAXS-driven MD. (a) On the fly SAXS curve generated toward the end of SAXS-driven MD. (b) SAXS curve spectra generated from the simulation by calculating SAXS profile after every 10 ps in the 30 ns simulation (gray). (c) Trajectory frames selected from spectra analysis to depict convergence of the system (27 ns to 30 ns: gray). (d) Average SAXS curve calculated using RERUN module considering 27 ns to 30 ns of trajectory frames

lower energy fluctuations through the simulation should be preferred. In this example, when the first frame from cluster 1 of free MD trajectory was used as the starting conformation, it yielded in an average energy of 50 kcal/mol (Fig. 10c). Whereas for the starting conformation as the last frame from cluster 4 of free MD, the average energy was 88 kcal/mol. Hence, the SAXS-driven MD performed using the first frame structure is best suited for further analysis, and generation of a SAXS-driven ensemble structure. 5. Saxs_driven_final.xvg: It contains 3 SAXS curves: 5.1 The input smoothened SAXS experimental data 5.2 Maximum Likelihood target data, scaled to match the calculated SAXS profile.

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5.3 On the fly SAXS curve: Obtained by averaging the last 250 ps or the memory value (waxs-tau) provided during the simulation. The on-the-fly curve might not necessarily match the experimental value, since it only considers the last few frames and also if the simulation has not converged (Fig. 11a). 6. Saxs_driven_spectra.xvg: It contains the SAXS curve calculated after every 10 ps during the simulation. This file is plotted along with the experimental SAXS curve, to understand the time frame at which the simulation converged for the RNA molecule. (Fig. 11b). The overlay of the ML target on the spectra can also help determine the minimum number of frames required to describe the ensemble structure from the SAXS profile (Fig. 11c). 7. Saxs_driven.log: Provides details about the energy calculation during the simulation and also the Guinier fit Rg value for the on-the-fly curve generated from the simulation. The Rg calculated by this module of SAXS-driven MD for the RNA molecule was 20.77 Å. 3.10 Rerun SAXS-MD Module

1. Since the on-the-fly curve obtained from SAXS-driven MD only takes into consideration the last 250 ps of the simulation, it might not be the correct estimate, depending upon when the simulation converged. To assess the correct Guinier approximation of Rg, the rerun module of this package is utilized. This module reruns the trajectory obtained from SAXS-driven MD and calculates an average curve for the overall trajectory. Depending upon the analysis and visualization of the trajectory file as well as the SAXS-driven spectra, an appropriate window of the trajectory can also be selected to reduce the number of frames being used to determine the average solution structure (Fig. 11c). 2. Based on the SAXS-driven spectra analysis, the global parameters GMX_WAXS_BEGIN and GMX_WAXS_END were defined as 27 ns and 30 ns, respectively. $export GMX_WAXS_BEGIN=27000 $export GMX_WAXS_END=30000 $gmx grompp -f rerun.mdp -p topol.top -c saxs_driven.gro -o waxs.tpr $gmx mdrun -sw solvent.tpr -fw solvent.xtc -rerun saxs_driven.xtc 3. The output files obtained from this module include a spectra file as well as an average calculated SAXS curve (Fig. 11d). The Guinier fit Rg approximation is reported in the log file. The Rg from the SAXS-driven MD rerun module was calculated as 20.18 Å.

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Fig. 12 Final frame structure obtained from SAXS-driven MD (cyan) superimposed onto model RNA structure (khaki)

4. Fitting estimated model from SAXS-MD trajectory to SAXS envelope: Once the best fitting window is determined from the analysis performed through the rerun module, an average structure can be determined and fitted into the envelope generated from the SAXS data analysis. The average structure generated from the best fit trajectory window is superimposed on the model RNA structure for comparison (Fig. 12).

4

Notes 1. Note: Possible error: “No default Xray coupl. Types.” Solution: Check forcefield.itp and ions.itp files of the particular forcefield being used in the share/top folder, and add the following to forcefield.itp file: #ifdef SCATTER. #include "cromer-mann-defs.itp" #include "neutron-scatt-len-defs.itp" #endif And add the following to the ions.itp file. The following block should be added for all the ions present in the system under study:

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#ifdef SCATTER [ scattering_params ] ; i ft a1-a4, b1-b4, c 1 1 CROMER_MANN_Cl_minus 1 2 NEUTRON_SCATT_LEN_Cl #endif 2. Monodispersity of sample is critical for a successful SAXS/ WAXS experiment. Interactions between the particles in your samples will show either an uptick or downturn in low q data and make it difficult to obtain a good Guinier fit. For this type of case, the use of SEC-SAXS or adjustments to buffer conditions (such as screening repulsive charges with salts) is appropriate to eliminate the effect of higher-order contaminants or to obtain SAXS data from additional oligomeric states. Moreover, upticks at low q can also indicate that one has a flexible sample, which exists in a wide range of conformations, altering the experimental approach. Therefore, understanding the behavior of your sample before sending it for SAXS measurement is critical. It may not always be optimal to run SEC-SAXS if you see an uptick at low q and have evidence of flexibility (such as an intrinsically disorder protein or IDP). One can then proceed to the SAXS-MD section. Finally, the beamline should not be used to measure partially purified samples. When discussing sample preparation with beamline scientists, be able to show chromatograms, gels, or other data to demonstrate sample stability so that an optimal experiment can be designed. 3. It can be useful to perform a concentration series starting with a wide range and narrowing the range if necessary (e.g., to focus on a structural transition around a particular concentration). For example, to ensure that you do not have concentration-dependent effects, such as aggregation, Rg values determined by Guinier analysis should be the same for changing concentration, and I(0) should increase linearly with increasing concentration. A concentration series can also be useful to observe transitions to various oligomeric states and to calculate Kd values. For an example and details, see [13]. At LIX, the Opentrons OT2 liquid handler can help to make concentration series easy to setup. A stock concentration of your sample protein is in a specific well of a 96-well plate and can be diluted with matched buffer using the OT2. This simplifies setup for both the user and beamline staff and increases sample throughput. For details, see [27]. 4. Scattering from your sample is relatively weak compared to that of the components of the buffer. A good buffer subtraction is critical to obtaining the scattering from your sample alone. If, for example, you are studying a system and add ligand to assess conformational changes, any unbound ligand must be

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removed. If there is free ligand in your sample, but not in your buffer, the scattering contributions from the buffer will be different, and the subtraction will not represent scattering from the sample alone. 5. Please refer to [26] for details about sample setup at LIX. Briefly, each protein or nucleic acid sample set should have at least two matched buffers, one before sample set measurement and immediately after, in order to verify the background does not change throughout the experiment (i.e., your sample fouls the flow cell). 6. Buffers should minimize the use of any organics or high concentrations of glycerol, sucrose, or similar viscous components (≤5%). Avoid high salt and buffer concentrations. Most SEC-SAXS runs may be performed at room temperature. Often, temperatures within the experimental hutch are higher than room temperature. At LIX, we have a temperature controlled multi-sampler, but the buffer and column must be chilled separately and thus requires advanced preparation. 7. The report is a Jupyter Notebook that employs parts of DATtools from the ATSAS suite [27, 36]. This report is a “first look” at your results and should be scrutinized. For example, the default values that are used in GNOM to calculate the P (r) fit might need adjustment. For the case of Fig. 2a, the Dmax value is slightly overestimated, and therefore, an adjustment is necessary. This can be done within GNOM or other software. Details are covered elsewhere and beyond the scope of this chapter. For NANPs, they can be very large, and estimating Dmax (if necessary) can be difficult if there are not enough data points collected in the low q range. Therefore, it is important to have some understanding of the overall size by other methods such as light scattering. 8. Data stored on Jupyter Hub (Brookhaven National Laboratory) can be downloaded to your local machine after processing to perform downstream analysis such as modeling. The details about Jupyter Hub change and, therefore, involve a conversation with beamline staff. Always check with the facility for details about data access and storage time. 9. Sometimes, not all frames are of high quality. These are thrown out if they deviate from the average intensities of the set. At least three high-quality frames are needed to obtain a confident average. Loss of frames could be a result of highly viscous samples that are difficult to load into the flow cell. Thus, planning with beamline staff before your experiment is critical to avoid such issues and explore alternative setups to accomplish your goals.

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10. The DAT files are ASCII format files of your 1D data that contain three columns. Column 1 is the q range, column 2 are the associated intensities, and the last column are the error values. These files can be used for import into downstream analysis software. 11. As mentioned in Subheading 1, an RNA NANP was generated. The monomeric form of the base structure was measured at LIX, and the resulting 1D scattering profile is shown in Fig. 4 (red diamonds). The experiment was performed with a good signal-to-noise ratio, but there was a significant uptick at low q indicating sample polydispersity. Thus, we cannot accurately determine what we are measuring since multiple species are present. To improve the dataset, the RNA monomer sample was rerun using SEC-SAXS on a Superdex Increase 200 5/150GL column from Cytiva. The improved SEC-SAXS results are shown as blue spades in Fig. 4. Note the flat region in the low-q range, where an accurate Rg value could now be determined. These data were used in the downstream analysis of SAXS-driven MD. 12. SEC-SAXS data will be shared in HDF5 format from LIX, like that found in step 1 of the high-throughput static saxs processing section. The file will be much larger as it contains a series of SAXS patterns in time. The number of frames is dependent on column utilized and exposure time. 13. HDF5 files can be directly read into RAW [37] and processed outside of the Jupyter Notebook if desired [21]. Use of other software will require exporting the desired frames in DAT format from the Jupyter Notebook and importing into desired software. 14. If the background is stable, as shown in Fig. 5b, normal subtraction can be used, where individual frames will be averaged and used for buffer subtraction. It is good practice to test various frame ranges before and after the peak of interest to see if it affects the subtraction and thus the 1D scattering profile. If there are significant changes, it would be beneficial to try SVD to interpolate the scattering contribution from the buffer. See reference [26] for details about SVD. If using SVD, be sure to select the frames of interest in the excluded frames window to ensure they are not included in the SVD analysis. 15. Over- or under-subtraction can be visualized using the heat maps in Fig. 5b, right panel. The left panel shows unsubtracted data, with a strong intensity at 2.0 Å-1 that corresponds to the water peak. Over/under-subtraction of the water peak by adjustment of the scaling factor can be visualized by observing this intensity decrease under the peak. A final look at the 1D scattering profile at 2.0 Å-1 will confirm if the scaling factor

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needs further adjustment. If the plot shows a large drop (over subtraction), decrease the scaling factor. On the other hand, if there is a water peak still present (under subtraction), increase the scaling factor. This is largely empirical and takes practice to get correct. 16. Selection of the optimal frames within a peak can be difficult. It is useful to plot Rg values across each frame of the peak to determine a stable region to use as your data. It is also useful to pay attention to the Kratky plot. If the graph does not plateau (as in Fig. 5c), the sample may have flexible regions. If this is the case, the hydrodynamic radius will vary, resulting in a single peak with multiple shapes, and thus, multiple models may explain these data. See the SAXS-driven MD section. 17. It is best to utilize other software, such as ATSAS, RAW or the like, to generate the P(r) function. These software packages have more control over the parameterization of the P(r) and details can be found [36, 37]. A note of caution: If you find you need to “skip” many datapoints at low q, there is an issue with the data, and no further downstream data interpretation should be attempted. 18. Note the dimensions of the newbox generated from this step. These dimensions will be used to generate solvent box for only solvent simulations. 19. The temperature groups (tc) were changed in the mdp files for the solvent-only simulation to “Water_and_ions” in place of “RNA ; Water_and_ion.” 20. If the solute system under consideration has more than one chain, then the scattering file is generated for each chain, and these lines must be added to each topol.top or topol_chain_A. itp file. 21. The envelope generated can be visualized by reading the file envelope-ref.gro and envelope.py in pymol. The surface of the envelope should look uniform and if spikes at certain points are observed, a larger distance parameter (d) should be provided to define the envelope.

Acknowledgments The LIX beamline is part of the Center for BioMolecular Structure (CBMS), which is primarily supported by the National Institutes of Health, by the National Institute of General Medical Sciences (NIGMS) through a P30 Grant (P30GM133893), and by the DOE Office of Biological and Environmental Research (KP1605010). LIX also received additional support from NIH Grant S10 OD012331. As part of NSLS-II, a national user facility

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at Brookhaven National Laboratory, work performed at the CBMS is supported in part by the US Department of Energy, Office of Science, Office of Basic Energy Sciences Program under contract number DE-SC0012704. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K.A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to thank Dr. Robert Sweet for insightful comments and suggestions for this chapter. KC is supported by Brookhaven National Laboratory LDRD (21-038). The authors would like to thank Dr. Hubertus JJ van Dam for setting up GROMACS-SWAXS on the institute cluster. References 1. Afonin KA, Dobrovolskaia MA, Church G, Bathe M (2020) Opportunities, barriers, and a strategy for overcoming translational challenges to therapeutic nucleic acid nanotechnology. ACS Nano 14(8):9221–9227 2. Afonin KA, Dobrovolskaia MA, Ke W, Grodzinski P, Bathe M (2022) Critical review of nucleic acid nanotechnology to identify gaps and inform a strategy for accelerated clinical translation. Adv Drug Deliv Rev 181:114081 3. Afonin KA, Viard M, Koyfman AY, Martins AN, Kasprzak WK, Panigaj M et al (2014) Multifunctional RNA nanoparticles. Nano Lett 14(10):5662–5671 4. Saito RF, Rangel MC, Halman JR, Chandler M, de Sousa Andrade LN, OdeteBustos S et al (2021) Simultaneous silencing of lysophosphatidylcholine acyltransferases 1-4 by nucleic acid nanoparticles (NANPs) improves radiation response of melanoma cells. Nanomedicine 36:102418 5. Rackley L, Stewart JM, Salotti J, Krokhotin A, Shah A, Halman JR et al (2018) RNA fibers as optimized Nanoscaffolds for siRNA coordination and reduced immunological recognition. Adv Funct Mater 28(48):1805959 6. Afonin KA, Kasprzak WK, Bindewald E, Kireeva M, Viard M, Kashlev M et al (2014) In silico design and enzymatic synthesis of functional RNA nanoparticles. Acc Chem Res 47(6):1731–1741 7. Chandler M, Jain S, Halman J, Hong E, Dobrovolskaia MA, Zakharov AV et al (2022) Artificial immune cell, AI-cell, a new tool to predict interferon production by peripheral blood monocytes in response to nucleic acid nanoparticles. Small 18:e2204941

8. Afonin KA, Dobrovolskaia MA, Ke W, Grodzinski P, Bathe M (2021) Critical review of nucleic acid nanotechnology to identify gaps and inform a strategy for accelerated clinical translation. Adv Drug Deliv Rev 181:114081 9. Dobrovolskaia MA, Afonin KA (2021) Use of human peripheral blood mononuclear cells to define immunological properties of nucleic acid nanoparticles. In: Therapeutic RNA nanotechnology. Jenny Stanford Publishing, pp 1091–1136 10. Chandler M, Rolband L, Johnson MB, Shi D, Avila YI, Cedrone E et al (2022) Expanding structural space for immunomodulatory nucleic acid nanoparticles via spatial arrangement of their therapeutic moieties. Adv Funct Mater 32(43):2205581 11. Hong E, Halman JR, Shah AB, Khisamutdinov EF, Dobrovolskaia MA, Afonin KA (2018) Structure and composition define Immunorecognition of nucleic acid nanoparticles. Nano Lett 18(7):4309–4321 12. Svergun DI, Koch MHJ (2003) Small-angle scattering studies of biological macromolecules in solution. Rep Prog Phys 66(10):1735–1782 13. Graziano V, McGrath WJ, Yang L, Mangel WF (2006) SARS CoV main proteinase: the monomer-dimer equilibrium dissociation constant. Biochemistry 45(49):14632–14641 14. Pilz I, Glatter O, Kratky O (1979) Small-angle X-ray scattering. Methods Enzymol 61:148– 249 15. Oliver RC, Rolband LA, Hutchinson-Lundy AM, Afonin KA, Krueger JK (2019) Smallangle scattering as a structural probe for nucleic acid nanoparticles (NANPs) in a dynamic solution environment. Nanomaterials 9(5):681

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16. Trewhella J, Duff AP, Durand D, Gabel F, Guss JM, Hendrickson WA et al (2017) 2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update. Acta Crystallogr Sect D Struct Biol 73(9):710–728 17. Jacques DA, Trewhella J (2010) Small-angle scattering for structural biology—expanding the frontier while avoiding the pitfalls. Protein Sci 19(4):642–657 18. Kikhney AG, Svergun DI (2015) A practical guide to small angle X-ray scattering (SAXS) of flexible and intrinsically disordered proteins. FEBS Lett 589(19):2570–2577 19. Rambo RP, Tainer JA (2013) Super-resolution in solution X-ray scattering and its applications to structural systems biology. Annu Rev Biophys 42(1):415–441 20. Weiel M, Reinartz I, Schug A (2019) Rapid interpretation of small-angle X-ray scattering data. PLoS Comput Biol 15(3):e1006900 21. Grabow WW, Zakrevsky P, Afonin KA, Chworos A, Shapiro BA, Jaeger L (2011) Self-assembling RNA nanorings based on RNAI/II inverse kissing complexes. Nano Lett 11(2):878–887 22. Afonin KA, Kireeva M, Grabow WW, Kashlev M, Jaeger L, Shapiro BA (2012) Co-transcriptional assembly of chemically modified RNA nanoparticles functionalized with siRNAs. Nano Lett 12(10):5192–5195 23. Afonin KA, Grabow WW, Walker FM, Bindewald E, Dobrovolskaia MA, Shapiro BA et al (2011) Design and self-assembly of siRNA-functionalized RNA nanoparticles for use in automated nanomedicine. Nat Protoc 6(12):2022–2034 24. Afonin KA, Viard M, Tedbury P, Bindewald E, Parlea L, Howington M et al (2016) The use of minimal RNA toeholds to trigger the activation of multiple functionalities. Nano Lett 16(3):1746–1753 25. Sajja S, Chandler M, Fedorov D, Kasprzak WK, Lushnikov A, Viard M et al (2018) Dynamic behavior of RNA nanoparticles analyzed by AFM on a mica/air interface. Langmuir 34(49):15099–15108 26. Yang L, Antonelli S, Chodankar S, Byrnes J, Lazo E, Qian K (2020) Solution scattering at the life science X-ray scattering (LiX) beamline. J Synchrotron Radiat 27(3):804–812

27. Yang L, Lazo E, Byrnes J, Chodankar S, Antonelli S, Rakitin M (2021) Tools for supporting solution scattering during the COVID-19 pandemic. J Synchrotron Radiat 28(4):1237–1244 28. Yang L (2013) Using an in-vacuum CCD detector for simultaneous small-and wideangle scattering at beamline X9. J Synchrotron Radiat 20(2):211–218 29. Chen P-c, Hub JS (2015) Interpretation of solution x-ray scattering by explicit-solvent molecular dynamics. Biophys J 108(10): 2573–2584 30. Chen Y-L, He W, Kirmizialtin S, Pollack L (2022) Insights into the structural stability of major groove RNA triplexes by WAXS-guided MD simulations. Cell Rep Phys Sci 3(7): 100971 31. Chen P-C, Hub JS (2014) Validating solution ensembles from molecular dynamics simulation by wide-angle X-ray scattering data. Biophys J 107(2):435–447 32. Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI et al (2021) UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 30(1):70–82 33. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38, 27–28 34. Turner P (2005) XMGRACE, version 5.1. 19. Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology, Beaverton 35. Chatzimagas L, Hub JS (2022) Structure and ensemble refinement against SAXS data: combining MD simulations with Bayesian inference or with the maximum entropy principle. bioRxiv. https://doi.org/10.1101/2022.04.05. 487171 36. Franke D, Petoukhov M, Konarev P, Panjkovich A, Tuukkanen A, Mertens H et al (2017) ATSAS 2.8: a comprehensive data analysis suite for small-angle scattering from macromolecular solutions. J Appl Crystallogr 50(4):1212–1225 37. Hopkins JB, Gillilan RE, Skou S (2017) BioXTAS RAW: improvements to a free opensource program for small-angle X-ray scattering data reduction and analysis. J Appl Crystallogr 50(5):1545–1553

Part II Production and Storage of Functional RNA Nanostructures

Chapter 5 Metalated Nucleic Acid Nanostructures Douglas Zhang and Thomas Hermann Abstract Nucleic acid nanotechnology takes advantage of the self-assembling property of nucleic acids to form a variety of shapes and structures. The incorporation of metal ions into these structures introduces functionality for sensor and molecular electronic applications. Here, we describe a protocol for the incorporation of silver ions into polygonal nanoshapes that self-assemble from RNA and DNA modules. Key words Nucleic acid nanostructures, RNA-DNA hybrid nanoshapes, Silver-mediated base pairs, Metalated nanostructures

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Introduction Nucleic acid nanotechnology uses nucleic acids as building blocks to create nanoscale structures. These nanostructures have uses in many disciplines, such as structural biology [1–3], nanomedicine [4–8], and molecular electronics [9]. The guiding principle of nucleic acid nanotechnology is the ability of nucleic acids to selfassemble in a predictable manner according to Watson-Crick base pairing rules as well as their ability to fold into complex structural motifs. Assembly of nucleic acid nanostructures requires the presence of metal cations that neutralize negative charge and play structural roles in folding of the structural motifs. Metal cations interact with nucleic acids on the phosphodiester backbone or the nucleobases [10–12]. Divalent cations, such as Mg2+, are important participants in precise folding of nucleic acids, while monovalent cations like Na+ serve to neutralize negative charge in bulk interactions. In addition to the alkali and alkaline earth metals, transition metals bind nucleic acids to provide distinct stabilizing effects [13–17]. Specifically, mercury and silver ions can mediate non-Watson-Crick base pairs in nucleic acids [17–23]. Here, we describe an experimental protocol for incorporating silver ions into polygonal RNA-DNA hybrid nanostructures. Silver nitrate is mixed

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with the oligonucleotides during annealing of the nanostructures. The affinity of silver ions for certain base mismatches naturally results in the formation of nanostructures associated with silver ions. The incorporation of metals into nucleic acid nanostructures through metal-mediated base pairing confers site-specific metallic properties that are useful in the design of metal arrays or molecular electronic devices.

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Materials Unless otherwise stated, all buffers and solutions should be diluted using ultrapure water.

2.1

Buffers

1. 10 mM sodium cacodylate buffer, pH 6.5: 1.0 M sodium cacodylate trihydrate pH 6.5, purchased from Hampton Research, Aliso Viejo, CA. Dilute to 10 mM with ultrapure water. Store at room temperature. 2. 10X 3-(N-morpholino)propanesulfonic acid (MOPS) buffer: Weigh 6.804 g sodium acetate trihydrate and 41.852 g MOPS. Combine and dissolve in ultrapure water to a final volume of 1 L solution. Store at room temperature. 3. 2X loading buffer: Combine 45 μL 100% glycerol with 45 μL 10 mM sodium cacodylate buffer, pH 6.5. Store at room temperature.

2.2 Nucleic Acid Stock Solutions

1. Custom-synthesized DNA and RNA were obtained from IDT, Coralville, IA. 2. Lyophilized oligonucleotides are rehydrated to stock solutions of 500 μM concentration with 10 mM sodium cacodylate buffer, pH 6.5. Store these stock solutions at
20  C. 3. Aliquots of the stock solutions are diluted to 100 μM using 10 mM sodium cacodylate buffer, pH 6.5. Store aliquots at
20  C.

2.3 Silver Nitrate Stock Solutions

1. Silver nitrate powder was purchased from Sigma Aldrich, St. Louis, MO. 2. Weigh out 0.16987 grams of silver nitrate and dissolve in 10 mL ultrapure water to form a 100 mM silver nitrate stock solution. Store at 4  C in the dark. 3. Dilute aliquots of the stock solution using ultrapure water to create a range of silver nitrate solutions from 100 μM to 1 mM (see Note 1). Store aliquots at 4  C in the dark.

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1. Five percent native polyacrylamide gel buffer: Mix 31.25 mL 40% 19:1 acrylamide/bis-acrylamide gel solution, purchased from Bio-Rad Laboratory, Hercules, CA, with 50 mL 10X MOPS buffer and 168.75 mL ultrapure water (see Note 2). 2. Ammonium persulfate, 10% w/w solution in ultrapure water. Store at 4  C. (see Note 3). 3. Tetramethylethylenediamine (TEMED): purchased Sigma Aldrich, St. Louis, MO. Store at 4  C.

from

4. Magnesium chloride solution: Prepare a 1.5 M solution of magnesium chloride in ultrapure water. 5. Buffer for native polyacrylamide gel electrophoresis (PAGE) running buffer: Mix 90 mL 10X MOPS buffer with 600 μL 1.5 M magnesium chloride solution, and bring the volume to 450 mL with ultrapure water.

3

Methods

3.1 Nucleic Acid Samples

The nucleic acid nanostructures are composed of four oligonucleotides: two RNA oligonucleotides and two DNA oligonucleotides (Table 1) (see Note 4). 1. Mix 1 μL stock solution of each oligonucleotide necessary to form the nanostructure. The total volume here is 4 μL. (see Note 5). 2. Add 2 μL of a 6 mM magnesium chloride solution to the mixture. The total volume here is 6 μL. (see Note 6). 3. Add 1 μL silver nitrate solution to the mixture. The total volume here is 7 μL. (see Note 7). 4. Anneal the nucleic acid mixture by heating to 65  C for 5 min, followed by incubating at 37  C for 20 min, and finally cooling to 5  C or placing the mixture on water ice (see Note 8).

Table 1 Sequences of the nucleic acids used to assemble the hybrid nanostructures Oligonucleotide

Sequence (50 -> 30 )

RNA-1

CCGAGGUCAGCCUG

RNA-2

CGAGACCAGGAACUACUGA

DNA-1

GTCTCG TGC CCCCCCCCC ACT CTACGT

DNA-2

CCTCGG ACGTAG AGT CCCCCCCCC GCA

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3.2 Native Gel Electrophoresis

1. Mix 15 mL 5% native polyacrylamide gel buffer with 20 μL 1.5 M magnesium chloride solution in a small beaker. Add 100 μL of 10% ammonium persulfate solution and 10 μL TEMED to the mixture. Mix the solution by swirling the beaker a few times. Cast the gel in a 10 cm  10 cm  1 mm gel cassette, and place a gel comb at the top to form the loading wells. 2. Once solidified, transfer the gel to a vertical gel box that can be cooled with recirculating water. Fill the chambers of the gel box with PAGE running buffer. Remove the comb and flush the wells with PAGE running buffer. Cool the gel box with constant recirculating ice water (see Note 9). 3. Conduct electrophoresis at 220 V and 22 mA. Pre-run the gel for 10 min. Mix nucleic acid samples with an equal volume of 2 native loading buffer (Sigma Aldrich, St. Louis, MO), and then load 6 μL of the solution to the gel. Perform electrophoresis for 1–1.5 h with ice water constantly recirculating. Upon completion of electrophoresis, remove the gel from the gel plates and soak in a solution of 0.01%(v/v) ethidium bromide for 20 min prior to imaging under UV light (302 nm) (Fig. 1) (see Note 10).

Fig. 1 Titration of the nanostructure oligonucleotides with silver nitrate. There is an ideal concentration of silver ions at which the structures are formed without aggregates retained in the gel well (200 μM). Higher concentrations of silver result in aggregation or degradation. Each band in the gel corresponds to a polygon increasing in size from triangles at the bottom to octagons at the top. AFM imaging is shown for representative species isolated from the gel

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1. Immerse freshly cleaved mica in 50 mM aqueous solution of 1-(3-aminopropyl)-silatrane for 30 min. 2. Dilute nucleic acid samples with 10 mM HEPES buffer containing 2 mM MgCl2 to 1.5 ng/μL concentration. 3. After 30 min, rinse mica with deionized water and dry under an argon stream. Deposit the diluted nucleic acid sample onto the mica and set for 2 min at 4  C. 4. Rinse the mica with ice deionized water and dry under an argon stream. AFM images were obtained using a MultiMode AFM Nanoscope IV system (Bruker Instruments, Billerica, MA) in tapping mode with silicon probes RTESPA-300 (Bruker Nano; resonance frequency ~ 300 kHz, spring constant ~40 N/m) at a scanning rate of ~2.0 Hz. Image processing was performed with the Advanced Technologies Center FemtoScan software package (Fig. 1).

4

Notes 1. Silver nitrate is light sensitive. Reduce exposure to light during experiments and store in the dark. 2. Unpolymerized acrylamide is a neurotoxin; care should be taken to avoid skin contact. The solution can be stored at 4  C for 1 month. 3. Ammonium persulfate should be made fresh after 1 month. 4. The RNA strand sequences should not be changed. The DNA sequences can be modified to include no mismatches or have a modified length. Refer to our previous method to determine an ideal DNA length to use [24]. 5. Larger volumes can be used as long as the ratio of each component is kept constant. 6. The final concentration of magnesium in the sample is around 2 mM. Higher concentrations of magnesium will also work, but this will require a higher concentration of silver ions to form the structures. 7. For the oligonucleotides presented here, silver nitrate has higher affinity for the RNA than for the chloride ions. If precipitation of silver chloride is an issue, magnesium chloride can be substituted with magnesium nitrate. We find the best results when the silver nitrate concentration is equal to or double the RNA concentration when in the presence of 2 mM magnesium chloride. Different concentrations of nucleic acids and magnesium will require a different silver nitrate concentration to form the structures. For optimum results, a

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titration should be performed (see Fig. 1). High concentrations of silver will result in degradation of the RNA due to interactions with the 20 hydroxyl group. Higher concentrations of magnesium can mitigate this effect. The final concentration of each individual oligonucleotide strand will be around 14 μM. The final concentration of magnesium chloride will be around 2 mM. The final concentration of silver nitrate will depend on the stock solutions used but should range from 14 μM – 142 μM. 8. Samples should be kept in low light until annealed. After annealing, samples no longer need to be kept in the dark. 9. It is important to ensure that the recirculating water remains chilled. The gel heats up during electrophoresis, and this can denature the nanostructures. Cycling iced water prevents the gel from reaching a temperature high enough to denature the nanostructures. 10. For best results, ensure that the current does not fall below 17 mA while pre-running the gel. If it does, add more magnesium to the running buffer or replace the running buffer with fresh buffer.

Acknowledgments This work was supported by the National Science Foundation, Division of Materials Research, grant DMR-2104335 to T.H. References 1. Liu D, The´lot FA, Piccirilli JA, Liao M, Yin P ˚ cryo-EM structure of RNA (2022) Sub-3-A enabled by engineered homomeric selfassembly. Nat Methods 19:576–585 2. Douglas SM, Chou JJ, Shih WM (2007) DNAnanotube-induced alignment of membrane proteins for NMR structure determination. Proc Natl Acad Sci 104:6644–6648 3. Aksel T, Yu Z, Cheng Y, Douglas SM (2020) Molecular goniometers for single-particle cryo-electron microscopy of DNA-binding proteins. Nat Biotechnol 39:378–386 4. Pi F et al (2017) Nanoparticle orientation to control RNA loading and ligand display on extracellular vesicles for cancer regression. Nat Nanotechnol 13:82–89 5. Shu D, Shu Y, Haque F, Abdelmawla S, Guo P (2011) Thermodynamically stable RNA threeway junction for constructing multifunctional nanoparticles for delivery of therapeutics. Nat Nanotechnol 6:658–667

6. Ke W et al (2019) RNA–DNA fibers and polygons with controlled immunorecognition activate RNAi, FRET and transcriptional regulation of NF-κB in human cells. Nucleic Acids Res 47:1350–1361 7. Johnson MB, Chandler M, Afonin KA (2021) Nucleic acid nanoparticles (NANPs) as molecular tools to direct desirable and avoid undesirable immunological effects. Adv Drug Deliv Rev 173:427–438 8. Guo P et al (2010) Engineering RNA for targeted siRNA delivery and medical application. Adv Drug Deliv Rev 62:650–666 9. Wang K (2018) DNA-based single-molecule electronics: from concept to function. J Funct Biomater 9:8 10. Draper DE (2004) A guide to ions and RNA structure. RNA 10:335–343 11. Draper DE, Grilley D, Soto AM (2005) Ions and RNA folding. Annu Rev Biophys Biomol Struct 34:221–264

Metalated Nucleic Acid Nanostructures 12. Shamsi MH, Kraatz HB (2013) Interactions of metal ions with DNA and some applications. J Inorg Organomet Polym Mater 23:4–23 13. Tanaka K et al (2006) Programmable selfassembly of metal ions inside artificial DNA duplexes. Nat Nanotechnol 1:190–194 14. Scharf P, Mu¨ller J (2013) Nucleic acids with metal-mediated base pairs and their applications. ChemPlusChem 78:20–34 15. Mandal S, Mu¨ller J (2017) Metal-mediated DNA assembly with ligand-based nucleosides. Curr Opin Chem Biol 37:71–79 16. Kanellis VG, Dos Remedios CG (2018) A review of heavy metal cation binding to deoxyribonucleic acids for the creation of chemical sensors. Biohys Rev 10:1401–1414 17. Takezawa Y, Shionoya M, Mu¨ller J (2017) Selfassemblies based on metal-mediated artificial nucleobase pairing. Compr Supramol Chem II 4:259–293 18. Kondo J et al (2017) A metallo-DNA nanowire with uninterrupted one-dimensional silver array. Nat Chem 9:956–960

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19. Kanazawa H, Kondo J (2017) Crystal structure of a novel RNA motif that allows for precise positioning of a single metal ion. J Inorg Biochem 176:140–143 20. Teller C, Willner I (2010) Functional nucleic acid nanostructures and DNA machines. Curr Opin Biotechnol 21:376–391 21. Liu H et al (2017) A DNA structure containing AgI-mediated G:G and C:C base pairs. Angew Chem Int Ed 129:9558–9562 22. Matsuura S et al (2018) Synthetic RNA-based logic computation in mammalian cells. Nat Commun 9:4847 23. Takezawa Y, Shionoya M (2012) Metalmediated DNA base pairing: alternatives to hydrogen-bonded Watson-Crick Base pairs. Acc Chem Res 45:2066–2076 24. Chen S, Monferrer A, Hermann T (2022) A simple screening strategy for complex RNA-DNA hybrid nanoshapes. Methods 197: 106–111

Chapter 6 Bioconjugation of Functionalized Oligodeoxynucleotides with Fluorescence Reporters for Nanoparticle Assembly Erwin Doe, Hannah L. Hayth, and Emil F. Khisamutdinov Abstract In the field of nucleic acid nanotechnology and therapeutics, there is an imperative need to improve the oligodeoxynucleotides’ (ODNs) properties by either chemical modification of the oligonucleotides’ structure or to covalently link them to a reporter or therapeutic moieties that possess biologically relevant properties. The chemical conjugation can thus significantly improve the intrinsic properties not only of ODNs but also reporter/therapeutic molecules. Bioconjugation of nucleic acids to small molecules also serves as a nano-delivery facility to transport various functionalities to specific targets. Herein, we describe a generalized methodology that deploys azide-alkyne cycloaddition, a click reaction to conjugate a cyanine-3 alkyne moiety to an azide-functionalized ODN 12-mer, as well as 3-azido 7-hydroxycoumarin to an alkyne functionalized ODN 12-mer. Key words Nucleic acid nanoparticles, Bioconjugation, Click chemistry, Fluorescence reporters

1

Introduction Nucleic acid nanotechnology involves the creation of artificial DNA- and RNA-based nanoparticles that exert an effect on various cellular, technological, engineering processes with the goal to reveal new useful information or improve our existing knowledge regarding the living matter. Central to this emerging field is the unique properties of nucleic acid biopolymers to undergo self-assembly driven by formation of complementary Watson Crick base-pairs, where guanine (G) pairs with cytosine (C) and adenine (A) pairs with thymine (T) or, in case of RNA, uracil (U). The assembly of the nucleic acid nanoarchitectures can be predicted with high level of accuracy to enable the programming of functional nanoparticles [1–3]. In the last two decades, a pool of elegantly designed DNA and RNA nanostructures have been created in the nucleic acid field of nanotechnology [1–4].

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In the specific application area of chemical biology, the nucleic acids are often required to undergo a chemical modification to enable their use for direct detection, visualization, and interaction with intra- and extracellular components. The green fluorescence protein [5] and fluorogenic RNA aptamers [6] have been used as genetically encoded reporters for protein and nucleic acid tagging, but other biomolecules, for example, lipids, glycans, and various posttranslational modifications, are not suitable for this type of labeling. Monoclonal antibodies are a great example at providing target specificity. However, they are laborious to generate, often incapable of cellular internalization, and immunogenic [7, 8]. The alternative strategy to introduce a functional moiety is by applying covalent chemical modification using a biorthogonal functional group pair. The biorthogonal pairs are mutually reactive (highly specific) but do not cross-react or interact, in noticeable ways, with other biological functionalities. The resulting products of such reactions are often nontoxic and stable due to the formation of a covalent bond. Click chemistry is a well-known example of such covalent modification [9, 10]. It was primarily “invented” by the Barry Sharpless research team in 1999 [11]. According to Sharpless, click chemistry refers to a category of chemical reactions that generate high yields, produce benign side products that are easily removed without chromatographic techniques, and are stereospecific and does not have to be necessarily enantioselective [12]. Generally, click chemistry reactions are grouped into four types: (i) cycloaddition reactions, (ii) nucleophilic substitution reactions, (iii) carbonyl reactions of the non-aldol type, and (iv) addition reactions across C–C multiple bonds. Of the four classes of click reactions, cycloadditions (especially Huisgen 1,3-dipolar cycloaddition (HDC) between azides and terminal alkynes, which leads to the formation of 1,4-disubstituted 1,2,3-triazoles, primarily catalyzed by CuI catalyst) are mostly utilized [13]. Particularly, a recent study led by Peixuan Guo’s team applied this reaction to covalently link the hydrophobic drug camptothecin, a topoisomerase I inhibitor [14], to a water-soluble RNA-based nanoparticle for targeted delivery, shown by human tumor xenograft models [15]. Such solubilization of hydrophobic drugs in aqueous buffer using a bioconjugation approach to hydrophilic oligonucleotides as a carrier is highly demanding to generate safe and effective therapeutics. In this chapter, we describe a methodology using click reactions involving the copper (I)-catalyzed azide-alkyne cycloaddition between functionalized oligomers and corresponding alkyne and azide functionalized fluorescent reporters for application in nucleic acid nanotechnology. Two fluorescence reporters–alkyne-modified cyanine dye (Cy-3) and azide-modified 7-hydroxycoumarin (coumarin or Cou)–are used to conjugate to corresponding azide- and

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Fig. 1 Synthetic reaction outline between 5′-end modified ODN alkyne (hexynyl) and ODN-azide with corresponding azido-coumarin and Cy3-alkyne leading to formation of the ODN-coumarin (a) and ODN-Cy3 products (b) products. (Figures (a) and (b) were adopted from Doe et al. [16])

alkyne-modified ODNs to obtain two clicked products (Fig. 1) [16]. The resulting products were subsequently hybridized with a DNA triangular nanoparticle to demonstrate their functionality.

2 2.1

Materials Chemicals

1. Two mM aqueous solutions of azide and/or alkyne functionalized 12-mer ODNs: /5AzideN/CGCGCTCTTACG-3′. /5Hexynyl/CGCGCTCTTACG-3′ 2. Dimethyl sulfoxide, analytical grade. 3. Triethylamine, analytical grade.

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4. N′,N′,N′,N′-tetramethylethylenediamine (TEMED). 5. Ammonium persulfate (APS). 6. Acrylamide/bis-acrylamide 37.5:1, 40 percent solution. 7. 1 mM stock of Cy3-alkyne in water. 8. 10 mM stock solution of 3-azido-7-hydroxychromen-2-one in DMSO. 9. Glacial acetic acid. 10. Tris[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl] amine (TBTA). 11. Boric acid. 12. EDTA, disodium salt. 13. Tris-acetate EDTA (TAE) buffer, pH = 8.0: Tris base of 40 mM, 20 mM of acetic acid, and 1 mM of EDTA. 14. Tris-Borate EDTA (TBE) buffer, pH = 8.0: 89 mM Tris base, 86 mM of boric acid, and 2 mM of EDTA. 15. Tris-borate magnesium (TBM) buffer, pH = 8.0: 89 mM Tris base, 86 mM of boric acid, and 5 mM of MgCl2. 16. Tris-magnesium saline (TMS) buffer, pH = 8.0: 50 mM Tri-HCl, 100 mM of NaCl, 10 mM of MgCl2. 17. 8 M UREA in TBE buffer pH = 8.0. 18. Amicon ultra-0.5 mL centrifugal filters with 3 KDa molecular weight cutoff membrane. 19. 5 mM ascorbic acid solution in water. 20. 2M triethylammonium acetate (TEAA) buffer pH 7.0: mix 2.78 mL of triethylamine (TEA) and 1.14 mL of glacial acetic acid (AA). Top up solution to 10 mL, and adjust the pH of the resulting solution to 7.0 with either TEA or AA. 21. CopperII–TBTA complex stock solution: 0.025 g of CuSO4.5H2O in 10 mL of Millipore water; 58 mg of TBTA in 11 mL of dimethyl sulfoxide. Mix thoroughly the resulting solutions. 2.2

Instrumentation

1. Gels imaging system, BIORAD Gel Doc™ XR+ molecular imager with Image Lab™ software. 2. Fluorescence spectrophotometer, Jasco FP-8350 spectrofluorometer with Spectra Manager™ Suite Spectroscopy Software. Light source: Xe lamp. 3. Scilogex Sci-24 Centrifuge with 24-place rotor from Balkowitsch Enterprises, Inc. 4. Power supply used for gel electrophoresis set up was PowerPac™ Basic, 400 mA, 75 W.

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Methods

3.1 Conjugation of ODN Alkyne with 3Azido-7Hydroxycoumarin

Table 1 provides a summary of the reaction components. 1. Pipette 50 μL of ODN alkyne into a clean 1.7 mL Eppendorf tube supplied with a magnetic bar. 2. Add 330 μL of 2M TEAA buffer (pH 7.0). Add 450 μL of (~100%) dimethyl sulfoxide and vortex the resulting mixture. 3. Add 20 μL of 10 mM coumarin azide stock solution followed by 100 μL of freshly prepared 5 mM ascorbic acid stock solution. 4. Vortex and bubble argon gas through the reaction mixture for several minutes to remove excess dissolved oxygen. 5. Pipette 50 μL of CuII-TBTA complex solution into the reaction mixture, and bubble argon gas for additional 1 min. 6. Place the Epitube on a magnetic stirrer and leave the reaction overnight (at least 12 h) at room temperature. 7. Stop the reaction by placing it in a freezer at -20 °C (see Note 1).

3.2 Conjugation of ODN Azide with Cy3 Alkyne

Table 2 provides a summary of the reaction components. 1. Pipette 400 μL of ODN azide into a clean Epitube supplied with a magnetic bar. 2. Pipette 70 μL of TEAA buffer with pH 7.0 (see Note 2). 3. Pipette 300 μL of (~100%) DMSO into the resulting mixture and vortex. 4. Add 80 μL of 1 mM Cy3 alkyne solution followed by 100 μL of freshly prepared 5 mM of ascorbic acid solution. Vortex and bubble argon gas through the reaction mixture for several minutes to remove excess of oxygen.

Table 1 Summary of ODN-alkyne coumarin-azide conjugation protocol Reagent

Final concentration

Concentration of stock

Volume of stock

ODN alkyne

100 μM

2 mM

50 μL

TEAA

0.66 M

2M

330 μL

DMSO

45%

100%

450 μL

Coumarin azide

200 μM

10 mM

20 μL

Ascorbic acid

0.5 mM

5 mM

100 μL

Cu –TBTA

0.5 mM

10 mM

50 μL

II

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Table 2 Summary of ODN-azide Cy3-alkyne conjugation protocol Reagent

Final concentration

Concentration of stock

Volume of stock

ODN azide

40 μM

100 μM

400 μL

TEAA

0.14 M

2M

70 μL

DMSO

30%

100%

300 μL

Cy3 alkyne

80 μM i.e., 2× [ODN]

1 mM

80 μL

Ascorbic acid

0.5 mM

5 mM

100 μL

Cu –TBTA

0.5 mM

10 mM

50 μL

II

5. Pipette 50 μL of CuII–TBTA solution into the mixture, and briefly purge argon through the solution. 6. Place the Epitube on a magnetic stirrer and leave the reaction overnight (at least 12 h) at room temperature. 7. Stop the reaction by placing it in a freezer at -20 °C. 3.3 Confirmation of Clicked Products on Denaturing Polyacrylamide Gel Electrophoresis (PAGE)

1. Pipette about 4 μL of the crude click chemistry products DNA-Cy3 or DNA-Coumarin into a clean Eppendorf tube. Ensure to label each tube appropriately. 2. Dilute each sample to 10 μL with Millipore water. 3. Add 10 μL of 8 M urea loading dye to each sample, heat to 90 ° C for 1 min to induce heat denaturation, and snap cool on ice. 4. Prepare control samples using unconjugated ODNs only and fluorescence dyes in a similar manner. As these samples will be analyzed by urea PAGE concurrently with the reaction mixture, ensure that similar concentrations are achieved to obtain comparable band intensities. 5. Resolve the products on a 32% denaturing PAGE containing 6 M urea. Load 2–3 μL of samples into each well, and run the gel at a constant 130 V for 150 min at ambient temperature (see Note 3). 6. Scan the gel prior to staining in an ethidium bromide solution and then after staining in the solution, as shown in Fig. 2 (see Notes 4 and 5). The application of the MWCO filtration approach is a rapid and convenient way to remove unconjugated Cy-3 and coumarin (used in excess in the cycloaddition reactions) as well as other lowmolecular-weight components, such as Cu1 catalyst. The calculated molecular weights of the DNA conjugates are 3960.6 g/mol and 4722.8 g/mol for DNA-Cou and DNA-Cy3, respectively. When a 3000 Da MWCO membrane is used, it is expected that conjugates will remain in the retentate fraction, which can be collected for

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Fig. 2 Images of denaturing 6 M urea PAGE of conjugated products and controls. The distinct electrophoretic retardation as a result of the formation of higher-molecular-weight ODNs are clearly visible on DNA-coumarin (a) and DNA-Cy3 (b) gel images with and without total DNA stain by EB. (Figure (b) adopted from Doe et al. [16]) 3.4 Purification of the DNA Conjugates Using Molecular Weight Cutoff Membrane (MWCO) and Analysis by Fluorescence Spectroscopy 3.4.1 Purification of Clicked Products

further application without further purification. This method is also useful if one decides to perform buffer exchange.

1. Pipette 400 μL of crude product into the filter membrane (3000 MWCO) and carry out centrifugation at 14,000× g for 10 min. 2. Add 200 μL of 2M TEAA buffer to retentate and repeat the centrifugation (Fig. 3). 3. Repeat step 2 and keep both filtrates and retentates for fluorescence analysis.

3.4.2 Fluorescence Assay of ODN Conjugates Before and After Purification

1. Perform the fluorescence analysis of the retentate and filtrate fractions as follows: Collect fluorescence emission spectra using fluorometer with the following settings: for the DNA-Cy3 conjugate, center the excitation wavelength at 553 nm, and collect emission from 560 to 700 nm; for the DNA-Coumarin conjugate, center the excitation wavelength at 404 nm, and collect emission from 430 to 650 nm. Figure 4 demonstrate examples of the spectra collected after three rounds of filtration.

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Fig. 3 Schematic view of purification of conjugates using a 3KDa molecular weight cutoff filtration device. (Figure was adopted from Doe et al. [16])

Fig. 4 Emission spectra of DNA conjugation products. (a) DNA-coumarin and (b) DNA-Cy3 spectra after three rounds of filtration. (Figure was adopted from Doe et al. [16])

3.5 DNA Nanoparticle SelfAssembly with the ODN-Coumarin and ODN-Cy3 Conjugates

The detailed protocol for nucleic acid nanoparticle self-assembly can be found in previous reports [17]: 1. Mix DNA strands to make a 1 μM final concentration in TMS from the corresponding 100 μM stock concentrations, and bring the final volume to 100 μL with Millipore H2O, as shown in Table 3. 2. Anneal the mixture of DNAs to 90 °C for 5 min and slowly cool down to 4 °C for 1 h. 3. Perform analysis of the assembled DNA polygons using native PAGE (6% acrylamide, in 1× TBM buffer), run the gel at 60 V for 60 min at room temperature [17]. Figure 5 demonstrates a typical result obtained for DNA triangular nanoparticle assembled with various concentrations of the ODN conjugates [1].

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Table 3 DNA nanoparticle self-assembly composition Nanoparticle: ODN conjugate ratio DNA

1:0

1:1

1:2

1:3

100 μM strand 1, μL

1

1

1

1

100 μM strand 2, μL

1

1

1

1

100 μM strand 3, μL

1

1

1

1

100 μM strand 4, μL

1

1

1

1

100 uM clicked products, μL

0

1

2

3

5× TMS

20

20

20

20

Millipore H2O

76

75

74

73

4. Perform fluorescence emission spectra to ensure the fluorescence activity of dyes. Use the example above to collect emission intensities using 553 nm and 404 nm excitation wavelengths for coumarin and Cy3 reporter, respectively Fig. 5. If necessary, proceed to the purification of the NANP decorated with fluorescence reports using native PAGE [17].

4

Notes 1. In carrying out the click chemistry, it is important to keep the click reaction away from light, especially when you leave it overnight and during storage after the click reaction is complete. Ensure to use a freshly prepared ascorbic acid for the click reaction. 2. Adjust the pH of TEAA buffer to 7.0 with triethylamine or acetic acid. 3. Ensure to wear hand gloves when handling gels. Avoid touching the surface of the gel in areas where you are likely to have bands. Touching those areas could leave fingerprints on the gel. Denaturing gels require higher voltages than native gels (e.g., 130–150 V are suitable for denaturing gels, while 90 V would be appropriate for native gels). 4. Clean tray very well with 70% ethanol followed by Millipore water. Wipe the tray clean. Carefully transfer gel to the tray for imaging. 5. Basis for electrophoretic separation: Electrophoresis is a separation technique used to separate charged molecules based on their different rates at which they migrate in an applied direct current electric field. The rate of migration, v (cm/s) of the

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Fig. 5 Electrophoretic mobility shift and fluorescence assays of DNA triangular nanoparticle hybridization with ODN-coumarin (panel a) and ODN-Cy3 (panel b). (Figure panels a and b were adopted from Doe et al. [16])

charged species is proportional to the strength of the applied electric field, E (V*cm-1). That is, v = μE; where μ is a proportionality constant called electrophoretic mobility. The electrophoretic mobility is also directly related to the ionic charge on the charged molecules and inversely related to the frictional retarding factors. The applied electric field affect only the charged species. So, if two molecules are different in charge or frictional forces, they will move at different rates and would therefore be separated. Molecules with higher mass-to-charge ratio would migrate faster. If molecules have the same size, those with greater charge would migrate faster, and if they have the same charge, those with smaller size would migrate faster. Uncharged molecules would not be affected by the electric field and would therefore not be separated.

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Acknowledgments This work was supported by NIH grant 1 R15EB031388-01 to E. F.K. References 1. Bui MN, Brittany Johnson M, Viard M, Satterwhite E, Martins AN, Li Z, Marriott I, Afonin KA, Khisamutdinov EF (2017) Versatile RNA tetra-U helix linking motif as a toolkit for nucleic acid nanotechnology. Nanomedicine 13:1137–1146 2. Johnson MB, Halman JR, Satterwhite E, Zakharov AV, Bui MN, Benkato K, Goldsworthy V, Kim T, Hong E, Dobrovolskaia MA et al (2017) Programmable nucleic acid based polygons with controlled neuroimmunomodulatory properties for predictive QSAR modeling. Small 13(42):1701255 3. Khisamutdinov EF, Jasinski DL, Li H, Zhang K, Chiu W, Guo P (2016) Fabrication of RNA 3D nanoprisms for loading and protection of small RNAs and model drugs. Adv Mater 28:10079–10087 4. Li H, Rychahou PG, Cui Z, Pi F, Evers BM, Shu D, Guo P, Luo W (2015) RNA nanoparticles derived from three-way junction of Phi29 motor pRNA are resistant to I-125 and Cs-131 radiation. Nucleic Acid Ther 25:188–197 5. Zimmer M (2002) Green fluorescent protein (GFP): applications, structure, and related photophysical behavior. Chem Rev 102:759– 781 6. Pothoulakis G, Ceroni F, Reeve B, Ellis T (2014) The Spinach RNA aptamer as a characterization tool for synthetic biology. ACS Synth Biol 3:182–187 7. Kollar E, Balazs B, Tari T, Siro I (2020) Development challenges of high concentration monoclonal antibody formulations. Drug Discov Today Technol 37:31–40 8. Samaranayake H, Wirth T, Schenkwein D, Raty JK, Yla-Herttuala S (2009) Challenges in monoclonal antibody-based therapies. Ann Med 41:322–331 9. Devaraj NK, Finn MG (2021) Introduction: click chemistry. Chem Rev 121:6697–6698

10. Bock VD, Perciaccante R, Jansen TP, Hiemstra H, van Maarseveen JH (2006) Click chemistry as a route to cyclic tetrapeptide analogues: synthesis of cyclo-[pro-Val-psi(triazole)-pro-Tyr]. Org Lett 8:919–922 11. Hein CD, Liu XM, Wang D (2008) Click chemistry, a powerful tool for pharmaceutical sciences. Pharm Res 25:2216–2230 12. Kolb HC, Finn MG, Sharpless KB (2001) Click chemistry: diverse chemical function from a few good reactions. Angew Chem Int Ed Engl 40:2004–2021 13. Breugst M, Reissig HU (2020) The Huisgen reaction: milestones of the 1,3-dipolar cycloaddition. Angew Chem Int Ed Engl 59:12293– 12307 14. Tesauro C, Simonsen AK, Andersen MB, Petersen KW, Kristoffersen EL, Algreen L, Hansen NY, Andersen AB, Jakobsen AK, Stougaard M et al (2019) Topoisomerase I activity and sensitivity to camptothecin in breast cancer-derived cells: a comparative study. BMC Cancer 19:1158 15. Piao X, Yin H, Guo S, Wang H, Guo P (2019) RNA nanotechnology to solubilize hydrophobic antitumor drug for targeted delivery. Adv Sci (Weinh) 6:1900951 16. Doe E, Hayth HL, Brumett R, Khisamutdinov EF (2023) Effective, rapid, and small-scale bioconjugation and purification of “clicked” small-molecule DNA oligonucleotide for nucleic acid nanoparticle functionalization. Int J Mol Sci 24:4797 17. Khisamutdinov EF, Bui MN, Jasinski D, Zhao Z, Cui Z, Guo P (2015) Simple method for constructing RNA triangle, square, pentagon by tuning interior RNA 3WJ angle from 60 degrees to 90 degrees or 108 degrees. Methods Mol Biol 1316:181–193

Chapter 7 Light-Assisted Drying for the Thermal Stabilization of Nucleic Acid Nanoparticles and Other Biologics Susan R. Trammell Abstract Cold-chain storage can be challenging and expensive for the transportation and storage of biologics, especially in low-resource settings. Nucleic acid nanoparticles (NANPs) are an example of new biological products that require refrigerated storage. Light-assisted drying (LAD) is a new processing technique to prepare biologics for anhydrous storage in a trehalose amorphous solid matrix at ambient temperatures. Small volume samples (10 μL) containing NANPs are irradiated with a 1064 nm laser to speed the evaporation of water and create an amorphous trehalose preservation matrix. In previous studies, samples were stored for 1 month at 4 °C or 20 °C without degradation. A FLIR SC655 mid-IR camera is used to record the temperature of samples during processing. The trehalose matrix was characterized using polarized light imaging to determine if crystallization occurred during processing or storage. Damage to LAD-processed NANPs was assessed after processing and storage using gel electrophoresis. Key words Anhydrous preservation, Thermal stabilization, Nucleic acid nanoparticles, Laser drying

1

Introduction A challenge in the development of a range of new biologics, including nanomedicine products, protein-based drugs, and vaccines, is that these products are temperature sensitive. As a consequence, these products must be stored and transported at specific recommended temperatures from the point of manufacture to the point of use to maintain potency and/or functionality. Cold storage strategies can be challenging and expensive for the transportation of biologics, especially in low-resource settings due to a lack of available infrastructure. The long-term preservation of biologics at near ambient temperatures is desirable for minimizing the cost and complexity of transportation and storage. Light-assisted drying (LAD) is a new, light-based processing technique to prepare biologics for dry-state (anhydrous) storage at near ambient temperatures

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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[1, 2]. LAD rapidly removes water from a sugar solution resulting in the formation of an amorphous, protective sugar matrix. Freeze-drying has achieved long-term preservation for some biological products [3–6]. However, freeze-drying is a lengthy, costly, and complex technique. In addition, the first step of the freeze-drying process involves freezing, which can damage many biologics, limiting the applicability of this process [7]. Research has demonstrated that anhydrous preservation in a trehalose amorphous solid matrix may be an alternative to freeze-drying for the preservation of biologics. Trehalose is used to form the amorphous sugar matrix because it can form an amorphous solid at room temperature and can also act as a bioprotectant [8, 9]. A substantial reduction of molecular mobility is necessary to ensure an extended shelf life for anhydrous samples. To ensure this is the case, samples need to be stored below the glass transition temperature, Tg, of the amorphous matrix to prevent degradation. The glass transition temperature for an amorphous trehalose solid formed by dehydration depends on the amount of water remaining in the sample after processing. Low-moisture contents are necessary for storage at higher temperatures. Light-assisted drying (LAD) is a new optical processing technique to create trehalose amorphous solids for the preservation of biologics [1, 2]. LAD uses illumination by near-infrared laser light to form a trehalose amorphous solid. For LAD processing, a biologic is suspended in a droplet (10–1000 μL) of a trehalose solution that is placed on a glass substrate. This droplet is then irradiated with the near-IR laser that selectively heats water in the sample to accelerate drying. As water is removed from the sample, the remaining sugars and salts become concentrated, and, if the solutes do not crystallize, the viscosity increases with progressive water loss until an amorphous solid protective matrix is achieved. LAD allows for the precise deposition of energy during processing not offered by other drying techniques. The precise energy deposition gives control over the sample temperature during processing, which is important for avoiding injury to thermally sensitive biologics. LAD processing also results in a uniform distribution of the trehalose matrix and uniform water content throughout the sample. Precise energy deposition enables repeatable, rapid attainment of the desired end moisture content of the sample, which dictates sample storage temperature. LAD is a one-step process that can dry samples to low-moisture content quickly (30 min–3 h compared to >24 h for other techniques). The process avoids extreme temperatures and pressures inherent to other methods, such as freezedrying. LAD is broadly applicable to a variety of biologics, including proteins, vaccines, and nanomedicine products [1, 2, 10–13]. NANPs are an attractive material for diverse applications in biomedical sciences because of their programmable multitasking and ability to respond dynamically to environmental changes

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[14–19]. Confirmed practical applications of NANPs include in vivo imaging and coordinated delivery of multiple therapeutic agents [20–26]. Currently, the standard for stabilization and storage of NANPs after synthesis is refrigeration in a buffer solution. Recently, LAD was used to thermally stabilize four types of representative NANPs, RNA cubes [27], RNA fibers [28–31], RNA rings [32–37], and DNA cubes [27, 38–41] for storage at ambient temperatures [10, 11, 13]. Here, the application of the LAD process to therapeutically relevant nucleic acid nanoparticles (NANPs) is described. The LAD process, the use of thermal imaging to monitor sample temperature and determine appropriate processing parameters, the use of polarized light imaging to access matrix quality, and sample storage techniques are described here. The preparation of the NANPs and techniques to assess the structure and functionality of the NANPs after processing are not included in this work.

2

Materials

2.1 LAD Processing and Storage

1. Drying solution (DS): 0.4 M disaccharide trehalose in 0.67 × phosphate buffer solution. 2. Continuous wave ytterbium fiber laser at 1064 nm with a maximum power output of 5 W and Gaussian beam with a FWHM spot size of 4.5 mm, resulting in an energy density of 26.9 W/cm2 (IPG Photonics). 3. Controlled humidity environment with source of dry air (see Note 1). 4. Temperature and RH logger for use inside the controlled humidity chamber. 5. 18 mm diameter borosilicate glass coverslips. 6. FLIR SC655 mid-IR camera (array size of 640 by 480 pixels, a frame rate of 6–60 fps, and a thermal resolution of
10 mK). 7. Laser beam profiler and power meter, BeamTrack 10A-PPS thermal sensor (Ophir Photonics). 8. Balance with 0.01 mg readability. 9. High-performance pipettors capable of dispensing 10 μL volumes. 10. 4″ × 6″ DRI-SHIELD reclosable static shielding moisture barrier bags. 11. 10-gram plastic, circular containers with lids (approximately 1.5-in. diameter by 0.8-in. depth).

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2.2 Polarized Light Imaging

1. Digital SLR camera with a 28–105 mm f/3.5–4.5 lens. 2. Two linear polarizers, 2-in. diameter, dichroic polarizing film between two AR-coated N-BK7 windows, operating wavelength range 400–700 nm. 3. Fixed mount for 2-in. polarizer. 4. Manual, continuous rotation mount for a 2-in. polarizer. 5. Borosilicate glass microscope slide. 6. White light fiber optic illuminator.

3

Methods

3.1 LAD Processing and Sample Storage

1. Prepare DS and add NANPs in buffer to the DS solution. Store at 4 °C (see Note 2). 2. Mount laser fiber and thermal camera inside the low humidity chamber as shown in Fig. 1 (see Note 3). 3. Check alignment and power output of the laser using the beam profiler/power meter. Laser power should be 5 W, and beam profile should be as described in the Materials section (see Note 4). 4. Check the alignment and focus of the thermal camera to ensure that the sample is centered in the field of view and is in focus (see Note 5).

Fig. 1 LAD experimental setup enclosed in the low relative humidity chamber

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5. Place a plastic container and a moisture barrier bag inside the chamber for later use (see Note 6). 6. Seal the chamber and start the flow of dry air. Monitor the RH and continue the flow of air until the RH inside chamber reaches 2% (see Note 7). 7. Place a glass coverslip on the balance and record the mass (this will be used to determine EMC see Subheading 3.2). 8. Pipette 10 μL of the DS + NANP solution onto the center of the coverslip (see Note 8). 9. Record the mass of the coverslip and droplet. 10. Move the coverslip into the low humidity chamber and position it so that the laser beam is incident on the center of the droplet (see Note 9). 11. Start recording a video with the thermal camera (see Note 10). 12. Begin laser illumination. Use the thermal image to confirm that the laser is incident on the center of the droplet as soon as laser illumination begins. Heating should be apparent in the first minute of processing. If the beam is off center, stop laser illumination and reposition. Centering the beam is necessary to ensure that the heating and drying of the sample are uniform. 13. Illuminate the sample with the laser for the appropriate time. In previous work, 10 μL samples were illuminated for 40 min. Procedures for determining an appropriate processing time are described in Subheading 3.3. 14. After processing is complete, turn off the laser, remove the coverslip from the chamber, immediately place it on the balance, and record the post-processing mass (see Note 11). 15. Immediately after determining the post-processing mass, complete the polarized light imaging (see Subheading 3.4 for instructions). 16. After PLI is complete, place the coverslip inside the humidity chamber, place the sample inside a plastic container, place the plastic container inside the moisture barrier bag, and seal. This will ensure that the RH of air inside the bag during storage is approximately 2% (see Note 12). 17. Remove the bag from the chamber and store at desired temperatures. In previous studies, samples were stored at 4 °C or 20 °C for 1 month (see Note 13). 18. After storage, remove the sample from the bag/plastic container, immediately place the coverslip on the balance, and record the post-storage mass. This will allow a determination of the water loss that occurred during storage. 19. Immediately complete PLI imaging.

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20. Immediately rehydrate samples with PBS buffer (or deionized water), and conduct tests of structure and functionally of the NANPs (not described here). 3.2 Calculation of Sample End Moisture Content

The length of time that the sample is irradiated with the laser determines the amount of water remaining in the sample after processing. This remaining water content directly determines the storage temperature of the amorphous solid [42]. The EMC is a measure of the amount of water relative to the dry mass of a sample and can be used to determine the appropriate LAD processing time. EMC is easy to measure and is helpful for monitoring relative water content during LAD processing. 1. Determine the dry weight of the sample (see Note 14). 2. End moisture content (EMC) is calculated using Eq. 1: m - ms - mdw EMC = f mdw

ð1Þ

where mf is the final mass of the sample including the mass of the substrate, ms is the mass of the substrate, and mdw is the dry weight of the initial sample. The dry weight is calculated by multiplying the initial mass of the sample by the percent dry weight. 3.3 Determination of Appropriate Processing Time

There are two tools to help determine the length of laser irradiation needed to dry samples to low EMC: drying curves (EMC vs processing time) and thermal histories (T vs time during processing). These tools provide an estimate of the time at which significant water removal ceases during laser irradiation. Processing beyond this time does not further dry a sample. The final water content of LAD samples is typical 15 min) will result in the slow reabsorption of moisture. 12. Store samples at low RH to prevent the reabsorption of water during storage. This reabsorption will cause the sample to crystallize, and this can damage embedded biologics. 13. In previous studies, samples were stored in a standard laboratory refrigerator at 4 °C for 1 month. Room temperature storage can be conducted on the benchtop or in a temperature-controlled cabinet. 14. Determine the dry weight using a bakeout method. Aliquots of the DS + NANPs solution are placed in an oven set to 105 °C and left to dry for 24+ h to remove all water. The weight of the dried aliquot is the dry weight of the sample. Percent moisture is determined by calculating the amount of mass lost during the drying process. 15. For example, process 3–4 samples for 10 min. At the end of the 10 min, determine EMC of the samples. A different set of samples will then be processed for 20 min and the EMC determined. For 10 μL samples, the maximum drying time for this testing should be 40 min based on previous work. 16. Extend the processing time 10 min beyond the time at which the curve flattens. This time can be estimated by eye or using a derivative analysis. 17. The y-axis in the thermal history is the change in temperature. The initial temperatures of samples can vary if the ambient temperature in the laboratory environment changes. The

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normalization to change in temperature in the graph allows easy comparison of the thermal histories of multiple samples. The overall shape of the thermal history should be the same for all types of NANPs processed. The initial rise in the temperature is the result of laser heating of the water in the sample. A maximum temperature is reached during the first minute of processing. After this peak in temperature, evaporative cooling reduces the sample temperature, indicating that LAD is effectively removing water from the sample. Near 20 min, the temperature reaches a minimum value and then again starts to increase. By 30 min, the temperature of the sample plateaus. On this plateau, the heating and cooling are balanced, resulting in a stabilization of sample temperature. This plateau marks the end of significant, rapid evaporation of water from the sample. This is consistent with the EMCs seen at 30 and 40 min. There was no significant decline in the water content between these processing times. In previous studies, the final processing time used was 10 min beyond time required to reach the plateau to ensure complete drying and to allow for small differences in water removal form sample to sample. In other work, larger volume droplets have been processed (up to 250 mL), and the same procedures were used to determine the appropriate irradiation times [1, 2, 43]. Small changes ( 18 MΩ at room temperature) and stored at room temperature unless otherwise specified. All solutions were filtered through a 0.2 μ filter to prevent bacterial growth.

2.1 Deprotection of Aptamers

1. Deprotecting agent (use either TCEP or DDT for deprotection not both): 1M tris(2-carboxyethyl)phosphine (TCEP), weigh 0.287 g of TCEP and add 1 mL of water. 1M 1,4 dithiothreitol (DTT), weigh 0.15 g DTT and add 1 mL water. Vortex to dissolve solids, and store 100 μL aliquot at 20 °C. Thaw aliquots as needed (see Note 1). 2. Desalting column (only if DTT used for deprotection). NAP-5 column or similar. 3. Chemically synthesized aptamers with 5′ or 3′ sulfhydryl groups (see Note 2) dissolved in pH ~ 8.0 buffer such as Trisacetate-EDTA (TE). 4. TE, 10 mM Tris-acetate, 10 mM EDTA. For 100 mL buffer, measure 0.2 mL 0.5M EDTA pH = 8.0 and 1 mL 1M Trisacetate pH = 8.0; add water to 100 mL.

Modifying Au/AgNPs with Thiol-Substituted DNA/RNA Aptamers

2.2 Generating Gold Nanoparticle-Aptamer Conjugates with 1:1 to 1:100 NanoparticleAptamer Ratio

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1. 0.4 mM and solid phosphine (4,4′-(Phenylphosphinidene)bis (benzenesulfonic acid)) dipotassium salt hydrate (see Note 3). 2. 1M sodium chloride, weigh 0.585 g of NaCl and add 10 mL of water. Vortex to dissolve solid. 3. Gold nanoparticles (see Note 4). 4. Methanol. 5. 5× TBE, Tris-borate-EDTA buffer. Weigh 54 g Tris base, 27.5 g boric acid, add 20 mL: 0.5M EDTA pH = 8.0, and adjust volume to 1 L (see Note 5).

2.3 Generating Gold Nanoparticle-Aptamer Conjugates with Many Aptamers per Particle

1. 1M NaCl. Weigh 0.585 g of NaCl and add 10 mL of water. Vortex to dissolve solid. 2. Gold nanoparticles (see Note 4). 3. 0.1M phosphate buffer pH = 7.0. To make 1 L buffer, mix 57.7 mL 1M Na2HPO4 and 42.3 mL NaH2PO4, and adjust volume to 1 L with water. The pH will be 7.0; no adjustment is necessary. 4. 0.1M PBS, 10 mM phosphate buffer, pH = 7.0 supplemented with 0.1M NaCl. To make 100 mL, measure 10 mL 0.1M phosphate buffer pH = 7.0, 10 mL 1M NaCl, and adjust volume to 100 mL with water. 5. 0.3M PBS, 10 mM phosphate buffer, pH = 7.0 supplemented with 0.3M NaCl. To make 100 mL, measure 10 mL 0.1M phosphate buffer pH = 7.0, 30 mL 1M NaCl, and adjust volume to 100 mL with water.

2.4 Generating Silver Nanoparticle-Aptamer Conjugates

1. 2M NaCl. Weigh 1.169 g of NaCl and add 10 mL of water. Vortex to dissolve solid. 2. 2 mM sodium citrate. Weigh 0.103 g trisodium citrate and add 200 mL of water. 3. AgNPs (see Note 6). 4. Centrifugal filter device.

2.5 Confirming attachment of aptamers to nanoparticles

Material need depends on method used to confirm attachment (see Note 7).

2.5.1 Agarose Gel Electrophoresis

1. 0.5× TBE buffer. Measure 100 mL 5× TBE and adjust volume to 1 L with water. 2. 1% agarose gel. To make agarose gel for a typical small agarose gel apparatus, weigh 0.5 g agarose into a 200 mL Erlenmeyer flask, and add 50 mL 0.5× TBE buffer. Heat in a microwave for

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1 min. Swirl flask and make sure agarose dissolved. Pour liquid into mold and add comb (see Note 8). 3. 30% glycerol. Weigh 30 g glycerol and adjust volume to 100 mL with water (see Note 9). 2.5.2 Denaturing Gel Electrophoresis

1. 0.5× TBE running buffer (see above). 2. 10% urea-polyacrylamide gel (see Notes 10–13). Set up glass plates. In a disposable 50 mL centrifuge tube, weigh 4 g urea, 4 mL 40% acrylamide-bisacrylamide solution (19:1), 1 mL 5× TBE (see recipe above); adjust volume to 10 mL using the markings on the tube. Vortex until solids dissolve, and then add 100 μL 10% ammonium persulfate and 10 μL N,N,N′, N′-Tetramethylethylenediamine (TEMED). Vortex to mix and then pour solution in between pre-assembled plates. Place comb. 3. 2× RNA loading dye (see Note 14). 4. UV-VIS spectrometer. 5. Dynamic light scattering instrument (Zetasizer).

2.5.3 Determining Aptamer-Nanoparticle Ratios

1. Aptamers that are labeled with a thiol group at one end and a fluorophore of choice at the other.

2.6 Facile Synthesis of AuNPs 15–20 nm in Size

1. 0.5 mM HAuCl4. Weigh 0.034 g HAuCl4 and add 200 mL water.

3

2. 2-mercaptoethanol.

2. 0.5% sodium citrate solution, weigh 0.25 g trisodium citrate and add 50 mL water.

Methods

3.1 Deprotection of Aptamers.

3.1.1 Prepare aptamer for attachment by deprotecting it freshly using either DTT or TCEP, and determine its concentration (see Note 15). In either case, use a pH ~ 8.0 buffer such as 1× TE for deprotection (not water). DTT must be removed from the aptamer before attachment, while TCEP can stay in the solution. Deprotected aptamers do not remain stable; hence, deprotect it as needed. 1. To deprotect with DTT make 100 μM aptamer solution in 100 mM DTT and 1× TE. Incubate 10 min at room temperature. Store this solution in the freezer, and remove DTT right before using the aptamer with a NAP-5 or other desalting column. 2. Removing DTT. Follow instructions provided by the manufacturer of the desalting column. Use water as buffer to equilibrate column and elute the aptamer.

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3. Speed-vac eluted aptamer to increase concentration if needed. 4. To deprotect with TCEP, add TCEP in 100× excess to the aptamer (e.g., if the aptamer concentration is 100 μM the final TCEP concentration should be 10 mM). 5. Incubate the aptamer-TCEP mixture for 2 h at room temperature. The aptamer is ready to be used – no removal of TCEP is needed. Store deprotected aptamer at -20 °C (see Note 16). 6. To determine aptamer concentration, use Beer’s law: A(260 nm) = ε*c*l, where l = 1 cm and ε is the molar absorptivity in L/mol*cm. You will get the concentration in M (may convert to μM). 3.2 Preparation of AuNP-RNA Conjugates 3.2.1 Preparation of Conjugates with Low AuNP-Aptamer Ratios (1:1, 1:10, or 1:100)

This protocol [12] was originally developed to make AuNPaptamer conjugates in 1:1: mol ratio, but we have used it to optimize aptamer-NP mole rations (1:125–1:1000). Varying mol ratios is often necessary to produce a stable conjugate. 1. To prepare nanoparticles, measure the absorbance of AuNPs at 520 nm. If it is >1, dilute nanoparticles so that the absorbance is below 1, using 2 mM citrate buffer. 2. To 10 mL nanoparticle solution, add 2 mg phosphine (final concentration 0.4 mM) and rotate on an orbital shaker for 10 h (see Note 17). 3. Add solid NaCl until the solution turns lighter blue (2–3M NaCl) (see Note 18). 4. Centrifuge particles for 30 min at 500× g to precipitate AuNPs. 5. Discard supernatant and resuspend particles in 1 mL 0.4 mM phosphine. 6. Add 0.5 mL methanol to precipitate particles again by centrifuging them for 30 min at 500× g. Discard supernatant and resuspend particles in 1 mL 0.4 mM phosphine. 7. Add 110 μL 0.5× TBE to this solution. 8. Determine AuNP concentration using Beer’s law A(520 nm) = ε*c*l, where l = 1 cm and ε of 10 nm AuNPs is 1 × 108 L/mol*cm. ε for different size nanoparticles, see Table 1. 9. To refold aptamer, use a refolding protocol that was optimized for the aptamer to maximize analyte binding. For most short aptamers (20–100 nt), the following “fast-cool” protocol works well: incubate aptamer at 95 °C for 2 min, followed by centrifugation for 30 s; then, chill aptamer on ice for at least 10 min. 10. Combine aptamer with the desired mol equivalent of AuNPs and mix well.

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Table 1 Extinction coefficients of AuNPs based on size (absorbance at 520 nm) [12] ε (M-1 cm-1)

Nanoparticle size (nm) 5

8 × 105

10

1 × 107

20

1 × 108

50

3 × 1010

11. Add 0.05 vol of 1M NaCl, and place the solution on an orbital shaker for 16 h. (overnight). For 10 mL AuNP, add 0.5 mL 1M NaCl. After the overnight incubation, the conjugate is ready to be purified. 3.2.2 Preparation of Conjugates with High Aptamer-AuNP Ratios (Coat the Entire NP Surface)

1. Deprotect aptamer and determine its concentration. 2. Determine the concentration of the AuNPs. 3. Calculate the amount of aptamer needed to fully coat the surface of AuNPs using the equation below. Mol of aptamer =ANP × cAuNP × D × Vreaction. Where ANP is the area of the nanoparticle, cAuNP is the nanoparticle concentration, D is the density of aptamers on the NP surface, and Vreaction is the reaction volume. A sample calculation is seen below for 10 nm diameter AuNPs, 10 nM NP concentration in 2.5 mL volume. 35 pmol aptamer per cm2 of NP surface is estimated based on [14] (see Note 19). 4π 5 × 10 - 9 m

2

× 10 × 10 - 9 mol=L × 35 × 10 - 12

× 1002 mol=m2 × 6:02 × 1023 × 0:0025L = 1:75nmol 4. Add 0.5 molar excess of aptamer to AuNPs (1.5 times the amount calculated in “3”), so 1.75 nmol × 1.5 = 2.63 nmol. 5. Incubate the reaction with continuous mixing for 16 h at room temperature (see Note 20). 6. Add 0.125 volume of each: 1M NaCl and 0.1M sodium phosphate buffer pH = 7.0 to the mixture. Incubate for 24 h at room temperature while nutating. 3.3 Purification of AuNP-Aptamer Conjugates

The purification protocol is similar regardless of aptamernanoparticle ratio, aptamer length, or nanoparticle size. Centrifugation speed (see Note 21–24) depends on the size of the conjugate; hence, measuring the size using dynamic light scattering is recommended.

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Table 2 Recommended centrifugation speed and time for AuNPs based on size [12] Nanoparticle size (nm)

RCF (× g)

Time (min)

5

64,000

60

10

20,000

30

20

10,000

20

50

4000

15

1. Measure the size of nanoparticles, and adjust centrifugation time and speed according to Table 2. 2. Centrifuge conjugates. When AuNPs are fully coated with aptamers, centrifugation will produce an oily layer, not a hard precipitate. 3. Carefully decant/remove supernatant. 4. Resuspend pellet (or oily layer) in 0.1M PBS. 5. Repeat steps 2–4 two more times (3 centrifugations steps total). 6. Resuspend the final product in 0.3M PBS. 7. Store conjugates in the refrigerator (cold/dark place). Conjugates are stable for at least 1 month. 3.4 Preparation of AgNP-Aptamer Conjugates

This protocol [11] works well with set (1:100 to 1:1000) NP-aptamer mol ratios as well as to fully coat the NPs with aptamers using 400 μL to 25 mL final volume. Wrap test tubes in aluminum foil during extended incubations as AgNPs are light sensitive. Optimize aptamer-AgNP mol ratios using small-scale reactions before attempting to prepare large amounts of conjugates (see Note 25). 1. Deprotect and refold aptamer. Determine aptamer concentration (see Subheading 2.1). 2. Determine AgNP concentration using Beer’s law A = ε*c*l, where l = 1 cm and ε of 10 nm AuNPs is 5.56 × 108 L/ mol*cm. ε for different size nanoparticles, see Table 3. 3. Prepare 100 μL aptamer aliquots by diluting the refolded aptamer with 2 mM citrate buffer (not water) to produce 1:1000, 1:500, 1:250, and 1:125 AgNP-aptamer mol ratios (see Note 26). 4. Add 300 μL AgNPs and incubate the mixture on an orbital shaker for 24 h (see Note 27). 5. Add 12.3 μL of 2M NaCl to each sample, and incubate on an orbital shaker for an additional 24 h (see Note 28).

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Table 3 Extinction coefficients of AgNPs based on size λ (nm)

ε (M-1 cm-1)

10

391.1

5.56 × 108

20

400.8

4.18 × 109

30

405.6

1.45 × 1010

40

412.3

3.36 × 1010

50

420.9

5.37 × 1010

60

431.5

7.39 × 1010

80

458.3

1.14 × 1011

100

492.8

1.55 × 1011

Nanoparticle size (nm)

Note redshift in absorbance as NP size increases [16]

Table 4 Recommended centrifugation speed and time for AgNPs based on size Nanoparticle size (nm)

RCF (× g)

Time (min)

10

21,000

60

20

17,000

30

30

11,000

30

40

3000

30

50

1800

30

60

900

30

80

500

30

100

300

30

6. Add 12.6 μL of 2M NaCl to the sample mix, and proceed directly to the purification step. Do not store salt aged conjugates. 7. Scale up production of AgNP-aptamer conjugates using the optimal NP-aptamer mol ratio. 3.5 Purification of AgNP-Aptamer Conjugates

1. Determine the size of conjugates using dynamic light scattering to select optimal centrifugation speed and time. 2. Centrifuge conjugates using the appropriate speed/time using a tabletop microcentrifuge at room temperature (see Table 4). For 30 nm AgNPs, we used 7000 rpm for 20 min. 3. After centrifugation there should be a visible yellow-brown pellet in the bottom of the centrifuge tube (see Fig. 1). Black pellet indicates aggregation of the nanoparticles.

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Fig. 1 Purification of AgNPs with centrifugation should produce a brown-yellow precipitate (tubes 1–3) and not a black/metallic-colored one (tube 4)

Fig. 2 Intact NP-aptamer conjugates retain the characteristic color of the NPs. (a) Intact silver aptamer-NP conjugates are yellow (tubes 1–2) and not gray (tubes 3–4) or transparent. (b) Intact AuNP-aptamer conjugates are burgundy in color (tubes 1–2); blue or metallic color indicates aggregation/disintegration of AuNPs (tubes 3–4)

4. Discard the supernatant and resuspend the pellet in 100 μL 2 mM citrate buffer. 5. Repeat steps 2–4 two more times (three centrifugation steps in total), and resuspend the conjugate in 100 μL 2 mM citrate buffer. 3.6 Confirmation of Aptamer Attachment

Conforming aptamer attachment can be challenging depending on NP size, aptamer size, and NP-aptamer mol ratio. Before attempting to confirm aptamer incorporation, visually inspect the conjugate solution to ensure that the NPs are still intact and did not aggregate. AuNP-aptamer conjugate solutions should be burgundy in color; blue/gray color indicates aggregation (see Fig. 2a). AgNPaptamer conjugates should be yellow. Gray discoloration or a

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Fig. 3 Representative agarose gel showing a shift in the AuNP band due to conjugation with a 76-nt DNA aptamer. The AuNPs were 10 nm in size; samples were resolved on a 1% agarose gel run with 60 V for 1 h at room temperature

transparent colorless solution indicates disintegration of the NPs (see Fig. 2b). Using at least two of these methods to confirm aptamer incorporation is advised, because each method is indirect. 3.6.1 Agarose Gel Electrophoresis (Nondenaturing)

Recommended for AuNP-aptamer conjugates as AuNP are deep burgundy in color and visible on an agarose gel to the naked eye. Look for shifting of the aptamer-NP conjugate band compared to NP alone. This method is not recommended for AgNPs, as their yellow color is not readily visible on an agarose gel. 1. Make 1% agarose gel using 0.5× TBE (not TAE). 2. Add 0.2 vol of 30% glycerol to each sample (5% final concentration). This step is necessary to make sure your sample sink in the well. 3. Run agarose gel using 0.5× TBE running buffer for ~60 min at 15 V/cm (we run our small agarose gel at 60 V to avoid overheating of the sample). 4. Red AuNP bands are visible to the naked eye. They should shift when aptamer is attached (see Fig. 3).

3.6.2 UV-VIS Spectrometry

1. NP peak shifts upon aptamer attachment. Both AuNP and AgNP exhibit a shifting when conjugated with nucleic acid aptamers. A shifting with 2–3 nm is small but consistently observed (see Fig. 4a, b) and caused by slight changes in surface plasmin resonance following nucleic acid attachment. 2. AuNPs absorb light at 520 nm, AgNPs at 410 nm, while nucleic acids at 260 nm. Even though it is anticipated that the 260 nm absorbance of NP-aptamer conjugates increase compared to NPs alone, often the baseline absorbance of the NP at 260 nm shadows the characteristic absorbance of nucleic acids (see Fig. 4c).

3.6.3 Denaturing Polyacrylamide Gel Electrophoresis (Urea Gel)

Resolve the purified aptamer-NP conjugates on a denaturing PAGE. When stained with nucleic acid stain (EtBr, SYBR stains), bands corresponding to the aptamer should appear on the gel. To resolve samples on a denaturing PAGE, follow the instructions below (see Note 29).

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Fig. 4 UV-VIS spectral analysis of NP-aptamer conjugates. (a) and (b) show a characteristic shift in peak wavelength upon attachment of a 76-nt DNA aptamer (red line) to 10 nm AuNPs and 30 nm AgNPs, respectively. (c) The appearance of a 260-nt peak (characteristic to nucleic acids) upon aptamer attachment is often overshadowed by the absorbance of the NP and/or analyte

1. Make 10% urea polyacrylamide gel. 2. Mix aptamer, AuNP (controls), and aptamer-NP conjugate with equal volume 2× RNA (or homemade denaturing) dye. Heat samples following the instruction of the manufacturer of the loading dye (5–10 min at 75 °C for commercially available 2× RNA dye). 3. Run gel with 15 W until dye front reaches the bottom of the gel. Stain with nucleic acid stain to visualize aptamers. 3.6.4 Dynamic Light Scattering

Upon aptamer attachment, the size of aptamer-NP conjugates should be greater than the NP alone. Even though NP size can change for many reasons, performing dynamic light scattering is advisable to ensure that the attachment produced a homogenous conjugate indicated by a single peak with low polydispersity that is larger in size than NP alone. Figure 5 shows the difference between homogenous and heterogenous conjugate samples.

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Fig. 5 Dynamic light scattering behavior of NP-aptamer conjugates indicates that NP-aptamer ratio needs to be optimized to produce stable, homogenous NP-aptamer conjugate. Intact NP-aptamer conjugates show a single, narrow peak that appears at a larger size than unconjugated NPs (blue curve). Multiple, broad peaks or high polydispersity indicates heterogenous conjugate that is prone to aggregation (red and green curves). Dynamic light scattering profiles were recorded using a Malvern Zetasizer Advance (Malvern Panalytical) 3.7 Determining NP-Aptamer Ratios

Accurate determination of NP-aptamer ratio is challenging and may call for instrumentation that requires significant expertise and investment. The method described here [13] is simple and based on removal of incorporated fluorescently labeled aptamers from NPs through reduction (see Note 30) and determining concentrations using fluorescence (nucleic acid aptamer) as well as absorbance at 410 nm (AgNPs) or 520 nm (AuNPs). A ratio of these concentrations gives an average NP-aptamer ratio. 1. Prepare a standard curve by measuring fluorescent emission of labeled aptamers of known concentration. This standard curve will be used to determine aptamer concentration based on fluorescence (see Note 31). 2. Disperse an aliquot of purified NP-aptamer conjugates (see Note 32) in their respective storage buffer (2 mM citrate for AgNPs and 0.3M PBS for AuNPs). 3. Determine NP concentration using Beer’s law A = ε*c*l, where l = 1 cm and ε is the molar absorptivity in L/mol*cm of the NPs (see Tables 1 and 3 for NP ε values). 4. Add 2-mercaptoethanol to a final concentration of 20 mM to the NP-aptamer conjugate. Incubate sample for 5 h at room temperature while rotating at low speed (see Note 33). 5. Separate the NPs from the aptamers by centrifugation. Adjust centrifugation speed to the size of the NPs (see Table 2 or 3). 6. Remove supernatant (aptamers).

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7. Measure fluorescence of the supernatant (aptamers) by using excitation and emission wavelengths corresponding to the fluorophore. Interpolate fluorescence values using the standard curve to determine aptamer concentration. 8. Divide aptamer concentration from “7” by the NP concentration determined in “3” to obtain an average aptamer-NP mole ratio. 3.8 Facile Synthesis of AuNPs

This is based on citrate reduction of Au referred to as the Turkevich method [15]. It is surprisingly simple but reproducibly yields AuNPs uniform size between 15 and 20 nm (see Notes 34–36). 1. Heat 200 mL 0.5 mM HAuCl4 solution and 10 mL 0.5% citrate solution in separate beakers until they both reach 95 ° C. The HAuCl4 solution should be heated on a stir plate with a magnetic stir bar, as the reaction will require vigorous stirring in step 3. 2. Add citrate solution to HAuCl4 solution and stir mixture vigorously with whirlpool reaching the bottom of the beaker. 3. Continue heating until color no longer changes; about 20 min. Color of the solution will change from transparent through yellow to burgundy. 4. Remove the solution from the heating block and place it on a stir plate. Continue mixing until the solution cools to room temperature. 5. Store NPs in a cool/dark place (refrigerator). NPs are stable for at least 6 months.

4

Notes 1. TCEP and DTT are not stable at room temperature, hence must be stored at -20 °C. 2. Sulfhydryl group can be placed either at the 5′ or 3′ of the aptamer, and both positions might need to be tested for successful attachment. Since nucleic acids form intricate 3D structures, a secondary structure prediction of the aptamer is recommended to ensure that the end with the modification is accessible for reacting with the nanoparticle. Initially chose the accessible end and/or the one that is not expected to interfere with analyte binding. 3. Phosphine is needed only if the nanoparticle-aptamer ratio is intended to be low; thus, the remaining surface area of the nanoparticle needs to be passivated by phosphine. Phosphine is anionic; hence, by covering the remaining surface of the nanoparticle, it prevents nanoparticle aggregation. Phosphine must be omitted if the intention is to load as many aptamers to the nanoparticle surface as possible.

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4. Gold nanoparticles 10–15 nm in size can be easily synthesized in house using the protocol described in this manuscript and do not necessarily need to be purchased. 5. 5× TBE is preferable to 10× TBE because it is more stable. Filtering the solution through a 0.2 μ filter will increase stability further by preventing precipitation. 6. Even though simple protocols to synthesize AgNPs are available in the literature, the author’s group had difficulty producing small (10–40 nm), stable AgNPs with narrow size distribution in house. As a result, we recommend purchasing the AgNPs. 7. Since each of these methods are indirect, the author uses at least two of these methods each time aptamer-nanoparticle conjugates are generated to confirm the success of the attachment process. 8. Agarose solution will be hot after microwaving; hence, handle solution with care. Mittens or corked Erlenmeyer flask should be used to prevent burning accidents. The Erlenmeyer flask should have at least 2× the volume of the agarose solution to prevent overspilling when the solution boils. 9. Due to the viscosity of 100% glycerol, it is easier to measure the wight of the solution than accurately pipette 30 mL 100% glycerol. 10. Acrylamide is a neurotoxin, a carcinogen, has reproductive toxicity, as well as a skin and eye irritant, hence PPE (gloves, goggles, lab coat) should be used when handling even small amounts of acrylamide. 11. Liquid acrylamide-bisacrylamide mixture is preferred to acrylamide powder, as the powder forms a fine aerosol that enters the body more easily (inhaled) than the liquid form; hence, the powder form is more toxic. 12. Disposable centrifuge tube is recommended to prepare gel solution as it allows easy disposing of solidified leftover gel solution. 13. A denaturing gel premix can be made using buffer, acrylamidebisacrylamide and water and stored in a dark glass bottle at room temperature for up to 1 year. To prepare 500 mL denaturing gel premix simply multiply quantities used for 10 mL solution by 50. Weigh 200 g urea, add 200 mL of 40% acrylamide-bisacrylamide solution (19:1) and 50 mL of 5× TBE. Adjust volume to 500 mL with water. Use stir-bar and plate to mix solution until all solids are dissolved. Use 10 mL of this solution when making denaturing gel; add 100 μL APS and 10 μL TEMED to initiate polymerization as previously described. 14. Homemade urea or formamide loading dye can also be used.

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15. The author’s group did not see any difference between deprotection with DTT or TCEP with respect to the success of aptamer attachment; hence, user may choose between these methods based on personal preference. 16. Deprotected aptamer will reoxidize over time and may need re-deprotection, so deprotect as needed or aliquot deprotected aptamers and store them at -20 °C. 17. Overnight incubation is sometimes convenient, and, in our hands, it did not affect incorporation efficiency. 18. This color transition is sudden, hence, weigh out enough solid NaCl to reach the 3M final concentration in solution. Add NaCl slowly, wait until each addition is dissolved and stop adding NaCl once color change occurred. Do not add more NaCl if solid does not dissolve; do not add more than the 3M equivalent of NaCl. 19. Typically, only the size, concentration, and reaction volume is changed between experiments; hence, the remaining terms can be grouped into a constant, and the equation can be simplified to naptamer = 2.65 × 1018 × (radius of NPs)2 × cNP × Vreaction. The radius of NP is in meter, the concentration in M, and the volume in L. 20. Longer incubation time is acceptable if more convenient but do not exceed 24 h. 21. Centrifugation speed is critical in producing stable aptamernanoparticle conjugates. 22. For 10 nm AuNP, we used 11,000 rpm, 30 min centrifugation steps at room temperature. 23. When centrifugation speed is too low, the supernatant will remain light red/pink. Increase centrifugation speed/time. 24. When centrifugation speed is too high, conjugates might disintegrate; hence, speed should be increased in increments. Use the values in Table 2 as starting points; then, carefully increase centrifugation speed with 2000 rpm at a time. 25. Optimizing AgNP-aptamer mol ratios is critical in producing a stable, homogenous conjugate. In our hands, 1:500 mol ratio worked best with 30 nm AgNP and aptamers 20–90 nt in size. 26. A sample calculation that yields 1:500 AgNp-aptamer mol ratio using 0.2 nM 30 nm AgNPs (concentration of AgNP listed in Materials) and 10 μM refolded aptamer is as follows. AgNP final concentration ½AgNPfinal = 0:2nM × 300μL 400μL = 0:12nM; hence, the final aptamer concentration should be 0.12nM × 500 = 60nM or 0.06 μM. Using the 10 μM aptamer stock, the volume of aptamer needed is 0:06μM 10μM × 400μL = 2:4μL: So to prepare conjugate with a 1:500 AgNp aptamer

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mol ratio, mix 2.4 μL of 10 μM refolded, deprotected aptamer, 97.6 μL of 2 mM citrate buffer, and 300 μL of 30 nm AgNps. 27. Protocols recommend addition of a phosphate buffer after initial incubation of the conjugate. In our hands, this step resulted in disintegration of AgNPs; hence, we recommend omitting this step. 28. Some protocols recommend a more gradual increase in salt concentration to achieve aging of the conjugate. A more gradual increase of salt concentration requires salt addition at shorter time intervals, which may be inconvenient. In the authors’ hands it did not improve the quality of the conjugates; hence, it is not necessary. 29. Percentage of polyacrylamide gel and run time used is dependent on the size of the aptamer. For aptamers 20–100 nt, a 10% gel run until dye front reaches the bottom of the gel produces adequate resolution of the samples. 30. It is tempting to skip separating the fluorescently labeled aptamers from the NPs and determine aptamer concentration by simply measuring the fluorescence of the aptamer-NP conjugate. This approach will lead to an underestimation of aptamer concentration because AuNPs quench the fluorescence emission of labeled aptamers through FRET. Therefore, separating the fluorescently labeled aptamers from the NPs is critical to obtain accurate results. 31. Care should be taken to ensure that the salt and 2-mercaptoethanol concentration as well as the pH of the buffer solution used to prepare the standard curve are the same as the buffer solution used for the experiments: 2 mM citrate (AgNPs) or 0.3M PBS (AuNPs), each supplemented with 20 mM 2-mercaptoethanol because fluorescence is dependent on salt concentration and pH. 32. It is critical that the NP-aptamer conjugates are purified and all unincorporated aptamer is removed. If there is any unincorporated aptamer still present, it will result in the overestimation of NP-aptamer ratio. 33. This step reduces the Au- and Ag-thiol linkages and liberates the aptamers that were attached to the NPs. They will remain in the supernatant after centrifugation. 34. Available protocols recommend using a reflux condenser to prevent evaporation and round-bottom flask to facilitate even heating to improve NP size distribution. In our hands, those additions did not improve NP quality. 35. Monitor temperature of the AuHCl4 and citrate solution with a thermometer, and add the citrate quickly when both solution reaches 95 °C.

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36. Continue heating reaction mixture with vigorous stirring until color is deep burgundy, and stop changing color (not until color changes begun).

Acknowledgments This work was supported by funding from SC INBRE #5P20GM103499 and MADE in SC EPSCoR, NSF #1655740. References 1. Wu W, Yu C, Wang Q, Zhao F, He H, Liu C, Yang Q (2020) Research advances of DNA aptasensors for foodborne pathogen detection. Crit Rev Food Sci Nutr 60:2353–2368 2. Trunzo NE, Hong KL (2020) Recent progress in the identification of aptamers against bacterial origins and their diagnostic applications. Int J Mol Sci 21:5074 3. Mehlhorn A, Rahimi P, Joseph Y (2018) Aptamer-based biosensors for antibiotic detection: a review. Biosensors (Basel) 8:54 4. Ahmadi S, Arab Z, Safarkhani M, Nasseri B, Rabiee M, Tahriri M, Webster TJ, Tayebi L, Rabiee N (2020) Aptamer hybrid nanocomplexes as targeting components for antibiotic/ gene delivery systems and diagnostics: a review. Int J Nanomedicine. https://doi.org/10. 2147/IJN.S248736 5. Gopinath SCB, Lakshmipriya T, Chen Y, Arshad MKM, Kerishnan JP, Ruslinda AR, Al-Douri Y, Voon CH, Hashim U (2016) Cell-targeting aptamers act as intracellular delivery vehicles. Appl Microbiol Biotechnol. https://doi.org/10.1007/s00253-0167686-2 6. Liu B, Zhang J, Liao J, Liu J, Chen K, Tong G, Yuan P, Liu Z, Pu Y, Liu H (2014) Aptamerfunctionalized nanoparticles for drug delivery. J Biomed Nanotechnol 10:3189–3203 7. Afrasiabi S, Pourhajibagher M, Raoofian R, Tabarzad M, Bahador A (2020) Therapeutic applications of nucleic acid aptamers in microbial infections. J Biomed Sci 27:6 8. Jalalian SH, Karimabadi N, Ramezani M, Abnous K, Taghdisi SM (2018) Electrochemical and optical aptamer-based sensors for detection of tetracyclines. Trends Food Sci Technol 73:45–57 9. Tavakkoli Yaraki M, Tan YN (2020) Recent advances in metallic nanobiosensors development: colorimetric, dynamic light scattering and fluorescence detection. Sensors Int.

https://doi.org/10.1016/j.sintl.2020. 100049 10. Vilela D, Gonza´lez MC, Escarpa A (2012) Sensing colorimetric approaches based on gold and silver nanoparticles aggregation: chemical creativity behind the assay. A review. Anal Chim Acta. https://doi.org/10.1016/j. aca.2012.08.043 11. Thompson DG, Enright A, Faulds K, Smith WE, Graham D (2008) Ultrasensitive DNA detection using oligonucleotide-silver nanoparticle conjugates. Anal Chem 80:2805–2810 12. Taton TA (2002) Preparation of gold nanoparticle–DNA conjugates. Curr Protoc Nucleic Acid Chem. https://doi.org/10. 1002/0471142700.nc1202s09 13. Li J, Zhu B, Yao X, Zhang Y, Zhu Z, Tu S, Jia S, Liu R, Kang H, Yang CJ (2014) Synergetic approach for simple and rapid conjugation of gold nanoparticles with oligonucleotides. ACS Appl Mater Interfaces. https://doi.org/10.1021/am504139d 14. Demers LM, Mirkin CA, Mucic RC, Reynolds RA, Letsinger RL, Elghanian R, Viswanadham G (2000) A fluorescence-based method for determining the surface coverage and hybridization efficiency of thiol-capped oligonucleotides bound to gold thin films and nanoparticles. Anal Chem. https://doi.org/ 10.1021/ac0006627 15. Turkevich J, Stevenson PC, Hillier J (1951) A study of the nucleation and growth processes in the synthesis of colloidal gold. Discuss Faraday S o c . h t t p s : // d o i . o r g / 1 0 . 1 0 3 9 / DF9511100055 16. Paramelle D, Sadovoy A, Gorelik S, Free P, Hobley J, Fernig DG (2014) A rapid method to estimate the concentration of citrate capped silver nanoparticles from UV-visible light spectra. Analyst. https://doi.org/10.1039/ c4an00978a

Part III Characterization of RNA Nanostructures

Chapter 9 Thermodynamic Characterization of Nucleic Acid Nanoparticles Hybridization by UV Melting Megan Teter, Ross Brumett, Abigail Coffman, and Emil F. Khisamutdinov Abstract The advances in nucleic acid nanotechnology have given rise to various elegantly designed structural complexes fabricated from DNA, RNA, chemically modified RNA strands, and their mixtures. The structural properties of NA nanoparticles (NANP) generally dictate and significantly impact biological function; and thus, it is critical to extract information regarding relative stabilities of the different structural forms. The adequate stability assessment requires knowledge of thermodynamic parameters that can be empirically derived using conventional UV-melting technique. The focus of this chapter is to describe methodology to evaluate thermodynamic data of NANPs complexation based on DNA 12 base-pair (bp) duplex formation as an example. Key words Nucleic acid thermodynamics, UV melting, Nucleic acid hybridization, RNA nanoparticles, DNA nanoparticles

1

Introduction The intrinsic properties of aromatic nitrogenous bases of both DNA and RNA to absorb ultraviolet light at around 260 nm has been indispensable to perform thermodynamic analysis for research, development, and application using the UV-melting technique [1–3]. The nucleic acid double helix is typically comprising of complementary bases (Watson-Crick base pairs), where G (guanine) pairs with C (cytosine) and A (adenine) pairs with T or U (thymine or uracil). The temperature-driven denaturation causes the double helix to dissociate to two single-stranded oligonucleotides. The elevated temperature disrupts non-covalent forces of attraction, including H-bonding, stacking interactions between individual strands. The individual nitrogenous bases, in singlestranded form, absorb a greater amount of light in the UV region than in double-stranded form. This phenomenon is the major principle behind UV absorbance melting experiment, where

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_9, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Fig. 1 Representative UV-melting curve of DNA duplex. Nonlinear melt curve fit (R2 > 0.99) for the [DNA] = 6.1 mM showing a distinct cooperative transition at 63.0 ± 3 °C. (Data were fit using the dose response function in OriginPro software)

absorption peak, typically at 260 nm, is monitored as a function of temperature. The resulting UV-melting curve possess a sigmoidal shape, analogous to a true phase transition, with the inflection point referring to as a melting temperature or Tm. At this point, the ratio of double-stranded NA form is equal to the singlestranded form as exemplified in Fig. 1. The important assumption for such thermal stability characterization is the presence of only two-state NA forms: duplex and single stranded (all-or-none), with no particular states significantly populated [4]. The Tm is an indication of thermal stability of the nucleic acid complex and often depends on multiple factors such as sequence composition, length of oligonucleotides, presence of monovalent and divalent metal ions, and polycationic amines [5–7]. Although the UV-melting method is often regarded as the most convenient and user-friendly approach, it is not the exclusive. Other methods exist for direct Tm and thermodynamic analysis of DNA and RNA, such as circular dichroism spectroscopy [8], temperature gradient gel electrophoresis [9], isothermal titration calorimeter (ITC) [10], differential titration calorimeter (DSC) [11], and indirect, for example, fluorescence emission spectroscopy using fluorescence reporters [12]. Herein, we report an experimental protocol to investigate the melting temperature of a 12-mer DNA duplex and perform thermodynamic analysis, which can be generalized to more sophisticated NANPs having multistrand compositions. The particular DNA duplex example was chosen as a standard molecule, enabling us to demonstrate reliability of the described methodology and compare the data with the calculated thermodynamic parameters of the same duplex using the structure prediction software mfold [13] and NUPAC [14].

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Materials Chemicals

Sequences of short DNA or RNA oligonucleotides (up to 60 nt.) for UV-melting experiments can be purchased directly from commercially available sources, for example, Integrated DNA Technology Inc. (http://www.idtdna.com/site) at different synthetic scales. The oligonucleotides are dissolved in ultrapure double deionized H2O (dd H2O) to obtain 100 μM concentrations and used without purification. 1. DNA oligonucleotide strand #1: 5′- CGT AAG AGC GCG -3′; MW = 3695.5 g/mol; extinction coefficient = 119,500 M-1 cm-1. 2. DNA oligonucleotide strand #2: 5′- CGC GCT CTT ACG -3′; MW = 3597.4 g/mol; extinction coefficient = 103,100 M-1 cm-1. 3. Sodium cacodylate pH 7.2. 4. Sodium chloride. 5. EDTA solution, pH = 8.0, RNase free.

2.2 UV-Visible Spectrophotometer

A commercially available UV-spectrophotometer capable of measuring absorbance at different temperature ranges covering a 4–99 ° C interval is suitable for the experiment. The protocol described herein utilizes a Shimadzu UV-Vis spectrophotometer (UV-2600i model) with the following critical supplements: 1. Temperature controlled block enabling multiple sample measurement: Thermal Melt System TMSPC-8. Enabling to measure eight samples simultaneously. 2. UV cuvettes with 1 cm and 0.5 cm path length: eight-series micro-cell for TMSPC-8. 3. Teflon cell stoppers to prevent sample evaporation: pack of eight stoppers for eight-micro-cell thermal melt. 4. Thermal melting software. 5. Water bath to deviate heat from TMSPC-8 thermal block.

2.3

Buffering System

Buffer choice is experiment-dependent. However, the main criteria for a buffer system are the following: (i) it must be inert to nucleic acids, (ii) the buffer components should not interfere with the absorption light of UV region, (iii) the buffer’s pKa should remain constant at various temperatures. The sodium cacodylate (CB, see Note 1) and phosphate buffers pH = 7.0 are good choices and are widely used in multiple UV-melting studies [15]. The Mg2+ in the presence of phosphate ions could lead to the formation of Mg3(PO4)2 at high temperatures, which can itself result in strong scattering of the UV signal and thus needs to be avoid. Other

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commonly used buffering systems, for example, Tris and/or HEPES, do not satisfy one or more buffer criteria (see above). For the UV-melting of RNA nanoparticles, a small amount of ethylene diamine tetra-acetate (EDTA) is included to bind the trace of divalent metals that otherwise might catalyze the hydrolysis of the ribose-phosphate backbone at elevated temperatures. In cases where NANPs require Mg2+, the experimental setting generally involves the collection of data from a low to a high temperature cycle. In the described protocol below, the following buffer is used: 1. Sodium cacodylate: 50 mM sodium cacodylate pH = 7.0, 1 M NaCl, 0.2 mM Na2EDTA.

3

Methods

3.1 Nucleic Acid Sample Preparation and Experimental Design

The concentration of nucleic acid is essential for the determination of thermodynamic data by UV-melting analysis. When collecting the temperature dependent curves, the absorption values measured at high temperatures (unfolded state ~90 ° C) should fall between 0.1 and 2.0 a.u; however, this depends on the type of instrumentation and its sensitivity. For the Shimazu spectrophotometer, the accurate maximum and minimum absorbance are 1.8 and 0.2, covering approximately tenfold concentration range. Typical percentage hyperchromicity, i.e., increase of absorbance, defined as (AF - AU) /AF, (where AF and AU are absorbances of folded and unfolded DNA states) is 15–20% but can vary depending upon wavelength. The Beer-Lambert law is used to determine oligonucleotide concentration and ensure that absorbance values will be optimal for melting: A = E
c
L

ð1Þ

where A is the absorbance function of temperature, E is the molar absorptivity (extinction) coefficient of oligonucleotides, C is the concentration of the sample, and L is the path length of the cell. The extinction coefficient, as it is dependent on base-base interactions, changes with the melting of the nucleic acid. Because the extinction coefficient is dominated by base-base interactions, a nearest neighbor model can be used to predict its value with good accuracy. Average extinction coefficient at 260 nm for the purines is 1.3 * 104 M-1 cm-1 and for pyrimidines is 0.8 * 104 M-1 cm-1 [16]. The large extinction coefficient enables the performance of melting experiments at sample concentrations as low as nanomolar. This high sensitivity is one of the advantages of the UV-melting technique, particularly when compared to ITC and NMR, which typically require mM concentrations.

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Due to advantage of the TMSPC-8 thermal melt system, eight samples can be prepared and analyzed simultaneously. Below is the sample preparation protocol to make eight different dilutions. 1. Prepare 400 μL of 10 μM of DNA duplex in CB buffer (see Note 2). 2. Prepare additional samples of 100 μL of 9, 8, 7, 6, 5, 3, and 2 μM concentrations from the 10 μM, using CB buffer. For example, in a corresponding Eppendorf tube labeled as 9 μM DNA, add 90 μL of 10 μM DNA and bring the total volume to 100 μL with 1× CB buffer. 3. Perform the annealing step to ensure the formation of DNA duplexes before collecting the first melting curve. Heat samples to 90 °C in a dry bath and allow them to cool to ambient temperature for about 1 h (see Note 3). 4. Using a speed vacuum concentrator, degas the resulting DNA samples for about 5 min to remove dissolved oxygen (see Note 4). Alternatively, the oxygen removal can be performed by bubbling nitrogen through the CB buffer prior to sample preparation. 5. Place samples into micro-cuvette 5 mm path length and seal with Teflon stoppers. The example of the multicell microcuvette is demonstrated in Fig. 2. 6. Follow Shimadzu’s manufacturing protocol to record absorbance vs temperature profiles [17]. Set the absorbance wavelength at 260 nm, the temperature ramp at 1 °C/min, and the temperature range from 15 °C to 95 °C (see Note 5). 7. Collect at least four spectra with different heating and cooling cycles to obtain an accurate Tm for the thermodynamic analysis (see Note 6).

Fig. 2 A picture of an eight-series micro-cell spectrophotometric cuvette (0.5 cm path length) enabling the analysis of eight samples simultaneously

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8. Save the data as a text file and proceed to data analysis to extract melting temperatures at different concentrations and thermodynamic parameters. 3.2

Data Analysis

The thermodynamic parameters, including enthalpy (ΔH), entropy (ΔS), and free energy changes (ΔG) of NA complexations from corresponding UV-melting data, can be calculated via Van’t Hoff analysis [1, 18]. These data are greatly dependent by the accuracy of melting transitions determined at different concentrations. An example of DNA 12-bp duplex thermal stability analysis is provided below (see Note 7). This is an example of a bimolecular (non-self-complementary) reaction that can be written as: SA þ SB $ Ds

ð2Þ

Let’s assume that there is a two-state equilibrium between single-stranded (SA and SB) and double-stranded (Ds) DNA or the presence of only folded and unfolded states and the concentrations of SA = SB. If the double-stranded DNA molar fraction is F and the total DNA concentration is Ct, the equilibrium constant for this reaction can be expressed as:


Keq = ½Ds=½SA ½SB  = 2F=ð1 - FÞ2 Ct

ð3Þ

Then, the Lamber-Beer Eq. (1) can be rearranged to express the observed absorbance at 260 nm: Abs = ðeDS þ eSS ð1 - FÞÞ
Ct
L

ð4Þ

where eDS and eSS are double- and single-stranded absorption molar coefficients. The eDS and eSS are temperature-dependent, as shown in the Fig. 1. Since the absorbance changes linearly in pre-transition and post-transition regions, the eDS and eSS can be expressed using a linear function as follows: eDS = MDS T þ BDS and eSS = MssT þ BSS

ð5Þ

where MDS and MSS are slopes for post-transition and pre-transition regions and rhe BDS and BSS correspond to Y intersects. The melting temperature (Tm) is calculated at the intersection of the median with the melting curve. A graphical software such as OriginPro (OriginLabR) can be used to fit the obtained data by using a dose response sigmoidal curve fitting model [19] or Meltwin software using the Marquardt-Levenberg method to find Tm [20, 21]. Once Tm is calculated, the thermodynamic data for the DNA complexation is extracted from the concentration-dependance melting temperatures using the Van’t Hoff equation, Table 1 (see Note 8).

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Table 1 Examples of nucleic acid reactions Reaction type

Equilibrium constants

Van’t Hoff equations

Monomolecular S$H

Keq = [H]/[S] = F/(1-F)

Do not depend

Bimolecular(selfcomplementary) 2S $ D

Keq = [D]/[S]2 = F/2(1-F)2*Ct 1/TM=(R/ΔHo)*lnCt + ΔSo/ΔHo

Bimolecular (non-selfcomplementary) SA + SB $ D

Keq = [D]/[SA][SB] = 2F/ (1-F)2 *Ct

1/TM=(R/ΔHo)*lnCt/4 + ΔSo/Δ Ho

Trimolecular (identical strands) 3S $ T

Keq = [T]/[S]3 = F/3Ct2* (1-F)3

1/TM=(2R/ΔHo)*lnCt + [ΔSoRln4/3]/ΔHo

Trimolecular (nonidentical strands) SA + SB + SC $ T

Keq = [T]/[SA][SB][SC] = 9F/ Ct2*(1-F)3

1/TM=(2R/ΔHo)*lnCt + [ΔSo2Rln6]/ΔHo

Tetramolecular (identical strands) 4S $ Q

Keq = [T]/[S]4 = F/4Ct2* (1-F)4

1/TM=(3R/ΔHo)*lnCt + [ΔSoRln2]/ΔHo

For the bimolecular non-self-complementary reaction, the equation: 1=TM = ðR=ΔHo Þ
lnCt=4 þ ΔSo =ΔHo

ð6Þ

where Tm is expressed in Kelvins and R is the gas constant = 1.987 cal*mol-1 K-1 (see Note 9). The equation follows a linear function, where ΔHo and ΔSo are calculated from the slope and intersect, respectively (see Note 10). The final free energy change ΔGo is calculated at 37 °C (310.14 K) according to the following relationship. ΔGo = ΔHo - TΔSo

ð7Þ

Table 2 summarizes the data acquired from eight different concentrations of DNA duplex 5′- CGTAAGAGCGCG -3′/5′CGCGCTCTTACG -3′, using a 5 mm path length cuvette. Figure 3 demonstrates the Van’t Hoff plot and linear equation used to extract the thermodynamic parameter for the DNA duplex. The calculated ΔHo = -94.6 kcal/mol; ΔSo = -254.3 cal/mol*K, and ΔGo at 37 °C = -15.8 kcal/mol. The obtained data is in good agreement with the theoretically computed values by the mfold web server [22] ΔHo = -98.5 kcal/mol; ΔSo = -266.3 cal/mol*K, and ΔGo at 37 °C = -15.9 kcal/mol.

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Table 2 Analysis of a 12-mer DNA duplex Ct (mM)

Tm (°C)a

ln(Ct/4)

1/Tm* 103 (K-1)

11.1

64.8 ± 0.3

-12.791

2.95902

9.3

64.1 ± 0.2

-12.967

2.96516

8

63.8 ± 0.2

-13.123

2.9678

7.3

63.4 ± 0.4

-13.217

2.97133

6.1

63.0 ± 0.3

-13.388

2.97486

5

62.4 ± 0.2

-13.604

2.98018

3.6

61.7 ± 0.1

-13.925

2.98641

2.4

61.1 ± 0.2

-14.316

2.99177

Error is the representation of four different curve fits, with R ≥ 0.99 in all cases a Tm calculated by using the dose response method in OriginPro v.2019b (OriginLab) 2

Fig. 3 UV-melting analysis of DNA 12 base pairs. (a) Representative melting curves showing concentration dependent melting transitions for 8 total concentrations (Ct) in 1 M NaCl, 50 mM CB buffer pH 7.0, 0.1 mM sodium EDTA. Data were fitted using a dose response function in OriginPro with R2 > 0.99. (b) Van’t Hoff analysis of the obtained Tm as function of ln(Ct/4)

4 Notes 1. Cacodylate buffer is based on arsenic acid dimethyl sodium salt. This is a very toxic carcinogen to humans and is on the Right to Know Hazardous Substance List because it is cited by OSHA, DOT, DEP, IARC, and EPA. 2. The single-stranded oligonucleotide concentrations were calculated based on their absorbance at 85 °C, and the extinction coefficients were calculated using the OligoAnalyzer tool (Integrated DNA Technologies).

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3. At this point, the annealed samples can be stored at -20 C for a prolonged period of time. It is a good practice to prepare all nucleic acid solutions at once and store them in the freezer until proceeding to the UV-melting assay. 4. The presence of oxygen could induce microscopic bubble formations at higher temperatures, scattering light during measurements. 5. This generalized parameter will greatly depend on the specifics of analyzed NA complexes. For sequences with high GC or AU content, the absorbance maxima at 260 or 280 nm are preferred to maximize the hyperchromicity. For the shorter duplexes or unstable complexes, the starting temperature range can be dropped to 4 °C. It is also advisable to provide enough equilibration for NA complexes between temperature ramps. Calibration of an instrument is necessary and should be done regularly by putting inside the cell a small thermocouple and reading the temperature using an electronic thermometer. Also, a very helpful calibration is to periodically measure the melting of a standard sample with well-fit parameters, such as tRNA [23]. 6. The following temperature cycles are implemented: Cycle 1: 15 °C - > 95 °C, cycle 2: 95 °C- > 15 °C, cycle 3: 15 °C - > 95 °C, and cycle 4: 95 °C- > 15 °C. 7. There are few methods available for an accurate Tm determination [18, 24, 25]. The approach described above is generally useful when there is only one melting transition observed; for melting profiles with multiple transitions, this method can be misleading for Tm determination. Another method uses the first derivative of the absorbance versus temperature data (dA/dT) [18]. It is advisable, however, to smooth the first derivative curve with a least square fit of the second order polynomial function to obtain reliable data. The resulting peak maxima correspond to Tm. Also, the TM can be calculated by implementing an equation for fractional saturation or dependance of the fractional occupation of folded molecules vs. temperature. F = ðA D - A 260 Þ=ðAD - A S Þ

ð8Þ

where F is the fraction of folded NA form and AD and AS are absorbances of the double-stranded (D) unfolded and singlestranded (S) populations, respectively. Herein, Tm is the temperature at F = 0.5 (50% of folded state). Figure 4 compares the first derivative plot and fractional saturation to evaluate Tm. 8. For intramolecular annealing only, the TM is independent of RNA concentration. In all other cases involving secondary and

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Fig. 4 Comparison of the first derivative and fractional saturation methods to calculate Tms

tertiary interactions between molecules, an increase in NA concentration will result in an increase TM with no change in percent hyperchromicity. Therefore, a concentration dependence of melting profile is a clear indicator of dimerization. 9. Data from a series of melting curves at different DNA concentrations is plotted as 1/Tm versus lnCt. Any precise determination supposes that Ct can be varied over a large range (μM to mM) while the absorbance remains within the linearity limits of the instrument. 10. If ΔHo is independent of temperature, the plot should be linear. A nonlinear Van’t Hoff plot can result from several factors: temperature dependence, poor choice of baselines, or a non-two-state transition [26]. However, this method depends on several assumptions: (1) baseline has been determined correctly; (2) a two-state model is valid; (3) the system is in perfect equilibrium at all temperatures; and lastly, ΔHo is temperature-independent and therefore ΔC = 0.

Acknowledgments This work was supported by NIH grant 1 R15EB031388-01 to E. F.K. References 1. Schroeder SJ, Turner DH (2009) Optical melting measurements of nucleic acid thermodynamics. Methods Enzymol 468:371–387 2. Rangadurai A, Shi H, Xu Y, Liu B, Abou Assi H, Boom JD et al (2022) Measuring thermodynamic preferences to form non-native conformations in nucleic acids using ultraviolet melting. Proc Natl Acad Sci U S A 119(24): e2112496119

3. Khisamutdinov EF, Sweeney BA, Leontis NB (2021) Context-sensitivity of isosteric substitutions of non-Watson-Crick basepairs in recurrent RNA 3D motifs. Nucleic Acids Res 49(16):9574–9593 4. Schurr JM (2021) A quantitative model of a cooperative two-state equilibrium in DNA: experimental tests, insights, and predictions. Q Rev Biophys 54:e5

Nucleic Acid Nanoparticles Thermal Melting Analysis 5. Yakovchuk P, Protozanova E, FrankKamenetskii MD (2006) Base-stacking and base-pairing contributions into thermal stability of the DNA double helix. Nucleic Acids Res 34(2):564–574 6. Dinis TBV, Sousa F, Freire MG (2020) Insights on the DNA stability in aqueous solutions of ionic liquids. Front Bioeng Biotechnol 8:547857 7. Owczarzy R, Moreira BG, You Y, Behlke MA, Walder JA (2008) Predicting stability of DNA duplexes in solutions containing magnesium and monovalent cations. Biochemistry 47(19):5336–5353 8. Clark CL, Cecil PK, Singh D, Gray DM (1997) CD, absorption and thermodynamic analysis of repeating dinucleotide DNA, RNA and hybrid duplexes [d/r(AC)]12.[d/r(GT/U)]12 and the influence of phosphorothioate substitution. Nucleic Acids Res 25(20):4098–4105 9. Benkato K, O’Brien B, Bui MN, Jasinski DL, Guo P, Khisamutdinov EF (2017) Evaluation of thermal stability of RNA nanoparticles by temperature gradient gel electrophoresis (TGGE) in native condition. Methods Mol Biol 1632:123–133 10. Ladbury JE, Sturtevant JM, Leontis NB (1994) The thermodynamics of formation of a three-strand, DNA three-way junction complex. Biochemistry 33(22):6828–6833 11. Duguid JG, Bloomfield VA, Benevides JM, Thomas GJ (1996) DNA melting investigated by differential scanning calorimetry and Raman spectroscopy. Biophys J 71(6):3350–3360 12. You Y, Tataurov AV, Owczarzy R (2011) Measuring thermodynamic details of DNA hybridization using fluorescence. Biopolymers 95(7): 472–486 13. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31(13):3406–3415 14. Zadeh JN, Steenberg CD, Bois JS, Wolfe BR, Pierce MB, Khan AR et al (2011) NUPACK: analysis and design of nucleic acid systems. J Comput Chem 32(1):170–173 15. SantaLucia J, Turner DH (1997) Measuring the thermodynamics of RNA secondary structure formation. Biopolymers 44(3):309–319

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16. Cavaluzzi MJ, Borer PN (2004) Revised UV extinction coefficients for nucleoside-5′-monophosphates and unpaired DNA and RNA. Nucleic Acids Res 32(1):e13 17. Obafemi Ajayi MT, Kinyanjui J, Head J (2014) Thermal analysis of DNA using the Shimadzu TMSPC-8 temperature controlled accessory. Shimadzu Excell Sci UV-0.13(SSI-UV-013): 1–2 18. Howard KP (2000) Thermodynamics of DNA duplex formation – a biophysical chemistry laboratory experiment. J Chem Educ 77(11): 1469–1471 19. Johnson MB, Halman JR, Miller DK, Cooper JS, Khisamutdinov EF, Marriott I et al (2020) The immunorecognition, subcellular compartmentalization, and physicochemical properties of nucleic acid nanoparticles can be controlled by composition modification. Nucleic Acids Res 48(20):11785–11798 20. McDowell JA, Turner DH (1996) Investigation of the structural basis for thermodynamic stabilities of tandem GU mismatches: solution structure of (rGAGGUCUC)2 by two-dimensional NMR and simulated annealing. Biochemistry 35(45):14077–14089 21. Hill AC, Schroeder SJ (2017) Thermodynamic stabilities of three-way junction nanomotifs in prohead RNA. RNA 23(4):521–529 22. Markham NR, Zuker M (2008) UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 453:3–31 23. Shiman R, Draper DE (2000) Stabilization of RNA tertiary structure by monovalent cations. J Mol Biol 302(1):79–91 24. Puglisi JD, Tinoco I Jr (1989) Absorbance melting curves of RNA. Methods Enzymol 180:304–325 25. Breslauer KJ, Frank R, Blocker H, Marky LA (1986) Predicting DNA duplex stability from the base sequence. Proc Natl Acad Sci U S A 83(11):3746–3750 26. Petersheim M, Turner DH (1983) Basestacking and base-pairing contributions to helix stability: thermodynamics of doublehelix formation with CCGG, CCGGp, CCGGAp, ACCGGp, CCGGUp, and ACCGGUp. Biochemistry 22(2):256–263

Chapter 10 Structural Characterization of DNA-Templated Silver Nanoclusters by Energy Dispersive Spectroscopy Damian Beasock and Kirill A. Afonin Abstract Here, a novel method of structural determination for DNA-templated silver nanoclusters (DNA-AgNCs) is introduced. This technique uses energy dispersive spectroscopy (EDS) coupled with a scanning electron microscope (SEM) to analyze a monodisperse solution of nucleic acid-based structures. Exploiting the consistent number of phosphate atoms in each structure, we determine the average number of silver atoms that make up the DNA-AgNCs. Proper sample preparation and fine-tuning of the SEM/EDS system settings were combined to achieve highly repeatable data. Key words EDS, Nucleic acid nanotechnology, AgNC, SEM

1

Introduction EDS is a semiquantitative technique used to determine the presence of elements and their relative abundance [1–3]. This protocol dives into the use of this characterization technique to reveal structural information of rationally designed nucleic acid structures and moieties. However, many stars had to align to adapt this method to our structure and obtain convincing data. If samples contain compatible elements and are prepared properly and instrument settings are carefully chosen, this method can reliably reveal vital structural information about nanostructures in solution [4]. This simple adaptation is highly advantageous, where other techniques are not applicable or available. EDS sensors are coupled with SEM systems, because the electron probe used for imaging generates many signals, including characteristic x-rays. The purpose of this combination is to simultaneously observe morphology and elemental composition of micro and nanostructures. These capabilities are highly sought after in materials research and nanolithography as composite structures are continually shrinking.

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_10, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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1.1 Identification and Quantification of Elements

Each element emits a unique spectrum of characteristic x-rays upon interaction with a sufficiently high energy radiation source [5]. The origins of characteristic x-rays are the energy differences of shifting inner electrons in atoms which are moved by the high energy electrons from the probe [6]. The primary electrons from the probe induce the excitation of tightly bound electrons close to the nucleus of an atom. This vacancy, or core hole, is now available for another electron from an outer shell to fall to this lower energy level. When an electron transitions to a lower energy level, a photon is emitted that is defined by the energy difference between the energy levels. In this case, the photon released is in the energy range of the x-ray spectrum. Each transition from one shell to another produces a photon with an exact energy, which defines the position of a peak. All elements have different but well-defined electron configurations and therefore a unique x-ray spectrum. Graphing the intensity and energy of x-rays reveals a spectrum of defined peaks, where elements can be qualitatively identified and quantitatively compared. The EDS software processes the data gathered and calculates quantitatively the differences between element signals. The intensity of the primary characteristic x-ray from an element is a function of the mass concentration [1]. Fortunately, the iterative numerical procedure to solve for mass concentration is taken care of automatically by the instrument software. These calculations are done by using expected intensities and peak positions of characteristic x-rays to identify and integrate relative concentrations. Each pixel in an SEM micrograph or EDS map represents an intensity measurement, where the signal is produced during a raster scan. The beam of electrons interacts with a specific volume of sample. The signal from this volume represents the intensity for the electron micrograph and also both the intensity and energy of x-rays for the EDS spectrum [7]. The shape and dimensions of the interaction volume resembles a pear with a narrow neck just below the surface of the sample and a wider round area below. As distance increases from the sample surface, the higher the probability the primary electron will interact with the atoms. This causes a deflection and/or production of a signal. The fundamental factor to determine the nature of the interaction volume is the mean free pass of the sample material; sometimes, this is presented as an area called the collision cross section [6]. The longer the mean free pass, the larger the dimensions of the interaction volume. As the mean free pass decreases and the interaction volume reduces, the volume more resembles a half sphere shape. The mean free pass is highly dependent on the atomic mass. Lighter elements scatter electrons less and will have a large volume of interaction [8]. A very important consideration for our analysis is the generation depth of characteristic x-rays. Characteristic x-rays originate from the bottom of the interaction volume. In Fig. 1, the portion

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Fig. 1 Electron probe interaction with the solid sample illustrating the interaction volume depth, where EDS signal is generated

of the total electron interaction is highlighted, where the characteristic x-ray originates. Therefore, preparing samples with adequate thickness is paramount for an appropriate spectrum containing the highest signal from the target elements as possible. Without sufficient sample thickness, the crucial x-ray generation depth of the interaction volume may propagate into the substrate. This will produce a superlinear increase in the substrate intensity over element signals from the sample. Any excess signal will reduce the signal-to-noise ratio of the sample quanta and diminish the reliability of atomic ratio calculations. 1.2 Importance of Beam Alignment

Before we have a chance to tune observation settings, the optical hardware must be checked and adjusted. Focus and beam alignment are important for a consistent signal and therefore important for reliable EDS data. Herein, image quality is not of upmost importance. Good image quality indicates a well aligned beam, but subtle offsets may not be perceived in the micrograph. The purpose of secondary electron imaging is simply to choose the sample area for gathering EDS data. However, proper beam alignment and focus will have positive impacts on EDS data quality and therefore repeatability.

1.3 Astigmatism Correction

The next step in beam alignment is correcting for astigmatism. In contrast to light microscopes, electron microscopes require magnetic lenses to apply a Lorentz force and focus the electron beam. Specifically, electromagnets are used, which are comprised of coils of wire with adjustable current. This design allows for control of the magnetic field and therefore control of the working distance, magnification, and convergence angle. An ideal magnetic lens system would have perfectly circular turns of the coil super imposed on one another. Unfortunately, this is a completely unphysical situation. In

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actuality, the coils are imperfect, and all are not precisely centered around the true optical axis. This introduces astigmatism effects that reduce image quality by distorting the probe shape. In practice, this results in elongated features during imaging and reduced resolution. A stigmator is integrated into the apparatus and is used to apply additional correction to the beam using a quadrupole [9]. From the perspective of the user, one must adjust the X and Y astigmatism correction to achieve the best image possible by eye. This is done most effectively at high magnification because the astigmatism distortion may not be noticeable at low magnification. Astigmatism effects are obvious when elongation of many features occur parallel to one another. For example, many perfectly rounded features will appear more elliptical, and all features in view will be stretched in the same direction. Viewing a nonuniform morphology or a featureless region, no matter the magnification, will not make image distortion obvious. If morphology is not known or recurrent, distortion also may not be evident at first glance. Astigmatism should always be checked in this case. This can be done by under and over focusing with the objective lens and paying attention to any image distortion. The more off focus an image, the greater the distortion will be. When passing between over and under focus, the direction of distortion will shift orthogonally. For good measure, one should always over and under adjust the objective focus to check if astigmatism correction is needed. The orthogonal shift in distortion is a dead giveaway that astigmatism effects are present. This check should be routine, especially if the sample morphology plays tricks on the eye. Adjusting the astigmatism correction is similar to adjusting the focus of the objective lens. A minor note for this step is to ensure that the objective focus is corrected in conjunction with the X and Y astigmatism correction. In some cases, astigmatism effects make it difficult to find the true objective focus. After optimizing the astigmatism correction, the objective focus can be checked for a possible (modest) improvement. 1.4 Aperture Alignment

Aperture alignment is important for image and EDS data quality. In the SEM apparatus, the aperture is a thin metal foil with openings of increasing diameter. Larger diameter aperture settings will allow more current to pass during operation. A typical SEM will have multiple aperture settings that require manual switching and manual alignment. Manual alignment is done while in the wobbler mode in the SEM software. This function oscillates the focus of the beam between overfocus and under focus. When the aperture is not aligned with the optical path, image shifting is visually apparent on screen. The direction of misalignments is revealed by the direction of image shifting. Image shifting in the horizontal direction will require adjustment of the X axis position. Vertical image shifting will require adjustment of the Y axis position. Each axis is done

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individually with separate knobs and should be done one at a time. Proper alignment here is vital because this ensures that the beam passes through the magnetic lenses as perpendicular as possible; all of the downstream hardware depends on this. 1.5 Choosing Ideal Instrument Settings for EDS

Typical settings for ideal EDS data depend on the nature of sample and the elements of interest. There are many parameters that determine the image quality and integrity of EDS data. Although many can be predicted, the complexity of these factors makes trial and error on each individual sample the most practical solution during analysis. Fine-tuning each setting by eye is required for systems without automatic settings. Although many can be predicted, the complexity of these factors together makes trial and error on each individual sample the most practical solution. Familiarity with the coaction of settings and understanding the physics behind each setting will allow you to achieve the best image quality and most dependable EDS data for individual samples. Changing the slightest processing parameters of samples can present noticeable differences during visualization. The most forgiving and effortless samples will typically consist of a material that is electrically conductive under the selected accelerating voltage, contains primarily heavy elements, and has good thermal conductivity. In our case, the samples analyzed herein consisted of primarily light elements and were nonconductive, and the production of dry mass was limited. All of these factors worked against us in terms of analysis conditions. Despite these factors, EDS proved to be a suitable method to analyze structural information about silver nanoclusters.

1.6 Accelerating Voltage

Accelerating voltage corresponds to the energy of electrons that make up the probe. This can have varying effects on the information revealed in electron micrographs. As mentioned in the interaction volume section, an increase in beam energy increases the dimensions of the interaction volume. Therefore, each pixel represents a signal from a volume under the sample surface. Edge effects and contrast can be more pronounced with higher beam energy as well. Alas, these image optimizations are not of our concern. Our primary business is excitation of characteristic x-rays and prevention of sample damage by the beam. The foremost consideration for the accelerating voltage choice is to provide the required energy to excite the characteristic x-rays corresponding to the elements of interest. Periodic tables specific to EDS are available, which list the energy of suitable characteristic x-rays for analysis and which shell transition they correspond to. The energy of the beam must be higher than the energy quantity of the characteristic x-ray. Without sufficient energy from the beam, the target fluorescent x-rays will not be produced. A higher probe voltage will not aid EDS data

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quality unless a more intense peak for the same element is available downrange. However, higher voltages should be avoided for our specific application. When possible, the lowest accelerating voltage should be chosen to avoid sample damage, mitigate signal depth, and reduce surface charging. 1.7 Working Distance

The working distance is the distance from the optic column to the focal point in an ideal lens, or to the circle of least confusion of a physical optical system. The focus setting on the SEM varies the objective lens strength which governs the working distance. The ideal working distance is defined by the manufacturer of the instrument. For EDS analysis, the working distance is highly relevant because of the nature of the EDS signal collection. For secondary electron imaging, the detector utilizes a positively charged grid that can focalize electrons emitted in any direction. In contrast, there are many irrelevant x-ray signals produced in the sample chamber, which cannot be manipulated by an electric field in the same way. The EDS sensor is a pulse processor behind a collimator to collect only x-rays emitted directly from the sample area excited by the probe. For the instrument employed for these studies, the fixed sensor is aimed to collect x-rays generated exactly 10 mm directly below the optic column. This requires the working distance to be set to the same distance. Only x-rays emitted from the sample with a take-off angle of 37° will reach the detector. Therefore, it is important to confirm or adjust focus between samples in a set, especially if sample height varies or the substrate is not perfectly level. This will ensure consistent signal generation and quality to maximize signal to noise ratio. Aberration in the working distance, stage height, or sample height will vary signal quality. There is a relatively large working distance range acceptable for routine secondary electron imaging. However, when gathering EDS data, the working distance is restricted based on the sensor position. The coarse focus adjustment is capable of changing the working distance significantly, potentially disrupting EDS signal collection. To avoid use of the coarse focus adjustment, a useful approach to focus the image prior to EDS analysis is to adjust the stage z position instead. The working distance is treated as a fixed parameter, and the sample is physically adjusted into the focal point. This is the same approach required with a light microscope and fixed lens systems. First, set the working distance to the ideal setting for the EDS system; adjusting the objective focus after this point will alter the working distance. From here, one should bring the image into focus by adjusting the stage height under high magnification. If needed, the fine focus adjustment may be used with care to make final optimizations.

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1.8 Dead Time and Process Time

Dead time during the capture of the EDS spectrum is important for repeatable data. For simplicity, an exact dead time may be defined by the manufacturer. Depending on the elements of interest, a higher or lower dead time percentage may be optimal. The EDS sensor is a pulse processor that uses a field effect transistor (FET) that converts x-rays into an electrical signal [10, 11]. Newer EDS sensors, such as silicon drift detectors, are capable of very high energy resolution [12]. If chosen wisely, x-rays of interest will be isolated from other peaks, and limited energy resolution of low end systems will not cause issue. Dead time is the percentage of x-rays that reach the detector but are not processed. The rate of x-rays that reach the detector will be effectively constant for a static scan. When the energy of an individual x-ray is measured by the field effect transistor (FET), a voltage step is created. Process time is the amount of time taken to average the noise before and after the step [13]. The average noise is then subtracted from the collected x-ray energy. Longer process times yield better energy resolution. This is advantageous for low atomic number elements, where the characteristic x-rays have smaller energy differences or if x-rays of interest are nearly overlapping. Lower process times allow for more x-rays to be processed during the scan time. For our purposes, more signal is advantageous for quantitative results, contingent upon no sample damage will be incurred.

1.9

Spot Size

Spot size is the area of the probe that illuminates the sample surface. Although the cross section of the electron beam is not a uniform circle, we can safely say 50% of the beam is contained in the full width half maximum, and 90% of the beam is contained within the full width tenth maximum of the Gaussian distribution [14]. Adjustment of the spot size is determined by the condenser lens. This lens varies the convergence before the aperture. Low convergence results in a larger spot size, more current, and higher signal. High convergence results in a smaller probe, better resolution, and lower current. For imaging, a larger spot size can mitigate noise; signal increases more quickly than noise with increased beam current. During an EDS scan, spot size is used to adjust the rate of characteristic x-ray generation. The user should adjust the spot size to achieve the required dead time for the sensor.

Aperture

The aperture of an electron microscope is a metal foil with precision drilled holes of varying diameter. For EDS analysis, resolution is not a priority, especially without a microstructure. Therefore, choosing the largest aperture setting available is recommended. This will maximize current for the set spot size to excite as much x-ray signal possible. This setting is a manual adjustment of the instrument and should be done before beam alignment. To fine-tune the beam current during data collection, the spot size should be adjusted.

1.10

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1.11 Beam Damage and Charging Effects

SEM/EDS systems are ideally used for conducting or semiconducting materials. If a material is able to conduct electrons away from the imaging site, charging is less likely. Materials with a high secondary electron yield reemit incident electrons as secondary electrons, which helps alleviate charge buildup. Materials prone to charging may suffer from reduced image quality caused by repulsion of the electron beam. Charging is ultimately caused when the number of electrons delivered by the probe exceed the number of electrons able to leave the sample. Electrons may dissipate from the scanning area as secondary electrons, as backscattered electrons, or may be conducted away to the apparatus ground. Similar to a static charge on a balloon, the electrons buildup on the surface and create a localized net negative charge. Samples that retain negative charge cause difficulties in aligning and focusing the electron beam and results in charging effects during image capture and loss of resolution/quality. Charging effects cause visual artifacts in the final image. These artifacts are caused by sudden discharge or image drift during the slow scan process of image capture. Such image artifacts are well-known and understood in the field and may not detract from qualitative conclusions made regarding morphology. However, charging issues in samples will reduce the reliability of EDS data. This is where knowledge and experience with instrument settings will greatly improve both electron micrograph presentation and EDS data reliability. High energy electrons with sufficient intensity can damage samples. A few different mechanisms may degenerate the sample material. Atoms in the sample may be displaced caused by momentum transfer from accelerated electrons. With lighter elements, especially those typical of organic materials, sample heating is a factor not to be overlooked. The sample chamber requires a vacuum, and therefore, heat is not transferred via convection. Heat dissipation is limited to radiation and conduction. Materials resilient to beam damage have high thermal shock resistance and high atomic number and are electrically conductive. Thermal shock resistance is a complex characteristic that depends on a combination of material quantities. Catastrophic material failure or sample deterioration is caused by transient mechanical load; the internal stress is introduced by thermal expansion by a drastic temperature gradient. A low coefficient of thermal expansion, high tensile strength, and a high thermal conductance increase resilience to temperature change [15]. Organic samples, however, do not provide the luxury of beam compatibility in electron microscopy and are commonly prone to damage. Lack of electrical conductivity can play a role as well in organic samples. Electron-atom interaction between probe and sample involve both a transfer of momentum and energy from the probe to the sample. Energy transfer causes ionization of the atom and momentum transfer is then able to displace the atom. Sample

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damage by ionization will only occur if the lifetime of the excited state is sufficiently long enough to allow enough momentum transfer to displace an atom. In electrical conductors, this excitation lifetime is orders of magnitude less than the time needed for the transfer of a significant amount of momentum. In contrast, an excited state in an electrical insulator can persist for much longer and opens the opportunity to accrue enough momentum for atomic displacement [16]. Again, the nature of organic materials works against us. Despite these obstacles, a good choice in sample substrate and proper adjustment of settings will preclude any constraining analysis challenges. 1.12 Sample Preparation

This method requires drying of solution and leaving a solid residue for analysis. The elements of interest should be decided upon beforehand. This method can determine stoichiometry of elements in a structure. For example, we looked at the ratio of phosphorous and silver in DNA-templated silver nanoclusters. This experiment sought to determine how many silver atoms made up each nanocluster. The rationale for choosing phosphorous was the consistent number of atoms per DNA template. There is one phosphorous atom per nucleotide, and this persists in all structures.

1.13 Sample Substrate Choice

Due to the fragility of our mostly carbon-based material, substrate choice can make remarkable differences in sample resiliency and in data quality. There are likely many compatible options to consider for our purposes. For simplicity, we choose a polished silicon wafer. The attractive feature of silicon is that it is a conductive substrate at the relevant voltages in SEM analysis. It also exhibits good thermal conductance to act as a heat sink if temperature is an issue. Polished silicon wafer is low cost, usually widely available, and even scrap pieces can be viable. To remove contaminants, piranha solution effectively cleans and renews the substrate for continued use. Other widely available and feasible options include aluminum SEM studs or copper tape. Substrates with good electrical conductivity and good thermal conductivity are highly encouraged. Insulating polymers or glass should be avoided if possible; grounding of the sample could be incorporated as an extraneous step. Care should be taken to choose a substrate that does not contain the same elements of interest in the sample structure. Proper sample preparation should prevent any significant substrate signal from persisting in the captured spectrum. To increase confidence in one’s choice, analyze the typical characteristic x-rays and Bremsstrahlung x-rays that may be emitted from a control scan of substrate only.

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1.14 Elements of Interest

2 2.1

Higher atomic number elements are most ideal for EDS. Light elements emit x-rays with small energy differences. Therefore, the peaks may overlap and will be difficult to differentiate. It is also important to mention that some SEM/EDS instruments position the EDS sensor behind a beryllium window. This engineering choice is advantageous to maintain ultrahigh vacuum separately from the sample chamber. The beryllium window also conducts away stray electrons directed toward the EDS sensor. Although the window is relatively transparent to hard x-rays, it is less transparent to soft x-rays. Elements lighter than oxygen can be unreliable if this is the case. Identifying elements of interest and their characteristic x-rays will determine whether a structure will be compatible with this method. The nano-architecture deciphered herein is a DNA-templated silver nanocluster. Rational design of this structure is enabled by the control over the exact DNA sequence of the template. Therefore, a known number of phosphorous atoms is constant per construct. The element phosphorous is not present in the assembly buffer or other reagents used for synthesis. Phosphorous is abundant in the structure. It represents >1% of the total atomic composition without water and will have a good signal to noise ratio. Phosphorous also produces a relatively high energy characteristic x-ray (Kα = 2.013 keV), which does not overlap with any existing elements in the sample but remains compatible with a low beam energy. Therefore, phosphorous is the best candidate for the known element of interest. The element of interest is silver. The average number of silver atoms that make up each silver nanocluster is in question. Varying the template parameters results in a variation of the observed biological function and optical properties. This indicates that the nanocluster size varies between templates. Silver is also abundant in the anhydrous sample and produces a distinct characteristic x-ray (Lα = 2.984 keV) compatible with a low beam tension. This analysis depends on a monodispersed sample with compatible elements.

Materials Sample Wash

1. Molecular weight cutoff (MWCO) filters (3 kDa cutoff). 2. 1× assembly buffer: 20 mM, NH4OAc, pH 6.9. 3. Refrigerated centrifuge.

2.2

Substrate

1. Silicon wafer (see Note 1), aluminum SEM sample studs, or copper tape. 2. Piranha cleaning solution for silicon wafer.

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Sample Drying

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1. Heat block. 2. Micropipette set. 3. AgNC assembly solution (see Note 2).

2.4 Sample Transportation

1. Petri dish with cover or wafer carrier with cover.

2.5 Sample Mounting

1. Copper tape (single-sided and double-sided adhesive).

2.6

1. The model used here is the JEOL JSM 6480 SEM equipped with an Oxford Instruments INCA EDS.

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SEM/EDS

Methods All method should be carried out in a clean environment to prevent contamination during microanalysis.

3.1 Sample Preparation and Deposition on Silicon Substrate

1. Add the AgNC sample solution to MWCO filter, and centrifuge at 14,000 g at 4 °C for 20 min. 2. Add ~400 μL of 1× assembly buffer, and repeat step 1 for a total of 3 times to remove any excess silver. 3. Clean the silicon wafer using piranha solution (see Note 3). Allow the silicon wafer to remain submerged for a few minutes. 4. Rinse with double deionized water. 5. Allow to dry in a clean area. 6. Using a heat block, heat the silicon wafer to 55 °C (see Notes 4 and 5). 7. With a micropipette, apply a 1 μL droplet of sample solution and allow to dry. Repeat this step on the same position 10 times (see Note 6). This will provide a film of solid residue with sufficient thickness to prevent substrate signal (see Note 7). Figure 2 represents the workflow to achieve film of sufficient thickness. 8. Repeat steps 1–7 for all repeats and all samples. 9. Store and transport in a petri dish with a cover. Use doublesided tape to secure the wafer.

3.2 SEM/EDS Analysis

1. Mount the wafer with deposited samples onto the SEM stage using copper tape (see Note 8). 2. Take a photo of the wafer and sample arrangement for reference (see Note 9).

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Fig. 2 Sample solution deposition on a silicon wafer and the solid residue for EDS analysis

3. Transfer the stage to the sample chamber and evacuate the atmosphere. 4. Set the aperture to the largest setting (see Note 10). 5. Set the beam tension to the required voltage for analysis (5 kV) and turn on the beam.

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6. Set the working distance to the ideal distance for EDS analysis (10 mm). 7. Using the stage Z height adjustment, adjust the sample surface to the working distance from the optic column. Visually, this will bring the sample into focus without use of the focus adjustment. Use high magnification to finely adjust the stage height and optimize the focus (see Note 11). 8. Adjust the contrast and brightness and the spot size if needed (see Note 12). 9. Bring into view any complex morphology to begin beam alignment of focus (see Note 13). 10. Use the wobbler imaging mode to align the aperture with the optical axis (see Notes 14 and 15). 11. Perform the astigmatism correction at high magnification (see Note 16). 12. Move the stage to view a clean location of substrate only. 13. Now in the EDS software, adjust the settings for optimal signal collection (see Note 17). 14. Perform a control scan. 15. During the scan, adjust the spot size to maintain the ideal deadtime. After the scan, return the spot size to the setting for imaging (see Note 18). 16. Locate the sample area and center the droplet residue in the viewing area and perform a scan, maintaining the deadtime. 17. Save the spectrum report with the atomic percentage calculations for data processing. 18. Repeat steps 16–17 for all samples. 19. Save the project file to maintain all raw data.

4

Notes 1. Many silicon wafers are doped with other elements for semiconductor purposes. Although proper sample preparation should minimize substrate signal, it should be known or checked with a control scan which elements are present in the substrate. 2. Sample solutions should have as high of a structure concentration as possible. This will reduce the elemental signal from the buffer and maximize the signals from the elements of interest. 3. This step is only recommended if the wafer is visibly unclean, is used or of unknown origin, or may introduce elemental contaminants that will produce interfering EDS signals.

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4. Heat blocks with a removable aluminum block can be flipped upside down to utilize the flat surface of the bottom. 5. Heating the sample is meant to evaporate water more quickly. It is unknown if the sample denatures in any way during this step. However, the atomic concentration of the elements of interest should remain unchanged. 6. There are many factors that influence the drying pattern. If heating is not an option, it is possible to proceed without heating of multiple droplets. However, this may require testing of multiple substrates (of different surface energy) and drying conditions. 7. The goal of sample deposition is to achieve a film thickness greater than the interaction volume depth. The thickness is usually on the order of a few microns. 8. If surface charging is an issue, grounding of the sample may help. Copper tape can be carefully placed near the sample to provide a more direct path for electrons to travel away from problem areas. 9. A photo will help with identification of samples (by droplet residue shape) and provide a reference to determine the overall position on the substrate during imaging. With a limited ability to zoom out, it can be easy to become lost without obvious landmarks. 10. For EDS, using the largest aperture setting will allow for the most current possible. If imaging is a priority, this should be done on a more ideal aperture setting separately. Realignment of the aperture will need to be performed each time the aperture setting is changed. 11. Fine-tuning may be used if no significant change to the working distance will be made. 12. If the image is noisy, increasing the spot size should be increased. More current will increase the secondary electron signal-to-noise ratio. 13. Focus is difficult to determine on a featureless specimen. It is recommended that this morphology be mostly conductive, such as the edge of the substrate or copper tape being the same height as the samples. Beam alignment and focus can take some time and charge a nonconductive area. A charged area on the sample could cause stage drift throughout the rest of the analysis. Performing beam alignment on a nonconductive sample could induce beam damage. 14. The wobbler view mode oscillates the focus over and under the current setting. When this setting is turned off, the focus is left at the current position in the oscillation. The working distance should be reset to 10 mm at this point.

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15. When using the physical X and Y aperture adjustment knobs on the SEM instrument, adjust each dimension over and under. The distortion should be symmetrical when over or under the center. This should help the user get a better understanding of where the true center is. 16. For both X and Y astigmatism adjustments, the focus should be adjusted over and under to develop a sense of where the ideal focus is. Sometimes the astigmatism can prevent proper objective focus. After the astigmatism focus has been optimized, the objective focus should be checked again for any room for improvement. 17. Depending on the elements of interest, process time and spectrum range should be chosen. 18. Leaving the spot size setting too high with the sample in view could induce excess charging.

Acknowledgments Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K. A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References 1. Castaing R (1951) Application of electron probes to local chemical and crystallographic analysis. PhD thesis, University of Paris 2. Shindo D, Oikawa T (2002) Energy dispersive x-ray spectroscopy. In: Analytical electron microscopy for materials science. Springer, pp 81–102 3. Hodoroaba V-D (2020) Energy-dispersive X-ray spectroscopy (EDS). In: Characterization of nanoparticles. Elsevier, pp 397–417 4. Rolband L, Yourston L, Chandler M, Beasock D, Danai L, Kozlov S et al (2021) DNA-templated fluorescent silver nanoclusters inhibit bacterial growth while being non-toxic to mammalian cells. Molecules 26(13):4045 5. Barkla C, Nicol J (1910) X-ray spectra. Nature 84(2127):139 6. Leng Y (2009) Materials characterization: introduction to microscopic and spectroscopic methods. Wiley

7. Zhou W, Apkarian R, Wang ZL, Joy D (2006) Fundamentals of scanning electron microscopy (SEM). In: Scanning microscopy for nanotechnology. Springer, pp 1–40 8. Gutierrez-Urrutia I, Zaefferer S, Raabe D (2009) Electron channeling contrast imaging of twins and dislocations in twinning-induced plasticity steels under controlled diffraction conditions in a scanning electron microscope. Scr Mater 61(7):737–740 9. Ong K, Phang J, Thong J (1997) A robust focusing and astigmatism correction method for the scanning electron microscope. Scanning 19(8):553–563 10. Instruments O (2006) INCA. https://scholar. google.com/scholar?hl=en&as_sdt=0%2C5& q=Instruments+O+%282006%29+INCA& btnG= 11. Harada Y, Ikuhara Y (2013) Chapter 1.1.1: The latest analytical electron microscope and its application to ceramics. In: Somiya S

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(ed) Handbook of advanced ceramics, 2nd edn. Academic, Oxford, pp 3–21 12. Stru¨der L, Lechner P, Leutenegger P (1998) Silicon drift detector–the key to new experiments. Sci Nat 85(11):539–543 13. Bloomfield D, Love G, Scott V (1983) Evaluation of dead-time corrections in EDS systems. X-Ray Spectrom 12(1):2–7

14. Ze-Jun D, Shimizu R (1989) Theoretical study of the ultimate resolution of SEM. J Microsc 154(3):193–207 15. Lu T, Fleck N (1998) The thermal shock resistance of solids. Acta Mater 46(13):4755–4768 16. Jiang N (2016) Electron beam damage in oxides: a review. Rep Prog Phys 79(1):016501

Chapter 11 Small Volume Microrheology to Evaluate Viscoelastic Properties of Nucleic Acid-Based Supra-Assemblies Akhilesh Kumar Gupta, Joel Petersen, Elizabeth Skelly, Kirill A. Afonin, and Alexey V. Krasnoslobodtsev Abstract Particle tracking (PT) microrheology is a passive microrheological approach that characterizes material properties of soft matter. Multicomponent materials with the ability to create extensive crosslinking, such as supra-assemblies, may exhibit a complex interplay of viscous and elastic properties with a substantial contribution of liquid phase still diffusing through the system. Microrheology analyzes the motion of microscopic beads immersed in a sample, making it possible to evaluate the rheological properties of biological supra-assemblies. This method requires only a small volume of the sample and a relatively simple, inexpensive experimental setup. The objective of this chapter is to describe the experimental procedures for the observation of particle motion, calibration of an optical setup for particle tracking, preparation of imaging chambers, and the use of image analysis software for particle tracking in viscoelastic nucleic acidbased supra-assemblies. Key words Nucleic acids, Supra-assemblies, Viscoelastic properties, Microrheology, Particle tracking

1

Introduction Rheology studies the flow and deformation of materials with complex viscoelastic properties under stress. Emulsions, suspensions, and supra-assemblies are a few examples of complex fluids. The flow of a material and its response to the applied stress can be predicted based on its rheological parameters. Furthermore, the rheological parameters offer a glimpse into the material’s underlying microstructure. Traditional rheometers typically assess the frequencydependent linear viscoelastic connection between strain and stress on the milliliter scale. Microrheology employs colloidal particles directly inserted in a soft material to measure rheological characteristics; the particles can be either externally driven by magnetic or optical tweezers (active microrheology) or rely on particle movement due to thermal motion (passive microrheology).

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_11, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Particle tracking (PT) passive microrheology uses an inexpensive optical setup utilizing a research-grade microscope, a video camera, an objective lens, and a computer. This method can also operate with small volume chambers providing, thus, the means of studying samples where classical rheology experiments become prohibitively expensive. In passive PT microrheology, particle’s motion is captured and further analyzed in terms of movement trajectories and particles’ positions with time. This motion reports on the spatiotemporal rheological characteristics of the fluid sample, thus assessing how fluid influences particle’s diffusive motion. This chapter focuses on particle tracking passive microrheology (PT) technique. We discuss typical experimental setup, calibration procedures, chamber design, and data analysis. Furthermore, we demonstrate the method’s utility on supra-assemblies made from mixtures of biotinylated DNA and streptavidin.

2

Materials Materials for Supra-Assembly Synthesis 1. Double biotinylated DNA duplexes, with one biotin attached to the 5′ end of each single-stranded complementary sequence (see Note 1). 2. Streptavidin. 3. Assembly buffer [final concentration of 89 mM tris-borate (pH 8.2), 50 mM KCl, and 2 mM MgCl2]. Beads and Suspensions All solutions were prepared using ultrapure water (~18 MΩ cm) and analytical-grade reagents. Glycerol/Water Viscous Mixture for Calibration of the Experimental Setup 4. Glycerol. 5. SecureSeal hybridization chamber with a nominal volume of 30 μL. 6. Polystyrene beads with nominal diameter of 1 μm (see Note 2). 7. Glass coverslips 24 by 50 mm in size. Particle Tracking 1. Olympus CX31 research-grade microscope (see Note 3). 2. Sentech CCD 4.0 MP camera with adjustable video capture rate (see Note 4).

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3. PC with installed Sentech software for capturing videos (see Note 5). 4. 100X objective lens (see Note 6). 5. Fiji (ImageJ, https://imagej.nih.gov/) software with installed Mosaic plugin (see Note 7).

3

Methods

3.1 Supra-Assembly Synthesis

For the synthesis of supra-assemblies with different concentrations of DNA, the procedure is as follows (see Note 8): 1. Prepare and dissolve DNA duplex with biotinylated 5′ strands at the desired concentration in assembly buffer. 2. Dilute streptavidin in assembly buffer in a 2:1 (duplex/streptavidin) molar ratio. 3. Add the streptavidin solution to the double biotinylated DNA duplex solution to prepare supra-assembly by rapidly pipetting up and down. 4. Vortex and centrifuge the final solution. 5. Incubate the solution at 37 °C for 30 min to complete binding. 6. For storage, keep the final solution at 4 °C.

3.2 Particle Tracking Setup and Its Calibration

The optical setup for observing the motion of the beads can be constructed around any research-grade microscope. The setup used in this study consists of CX31 Olympus upright microscope, 100× objective lens (air), and a variable scan speed camera Sentech CCD 4.0 MP, Fig. 1. To calibrate the optical setup, the motion of the beads is analyzed in a set of glycerol/water mixtures of various glycerolto-water ratios (as listed in Table 1—see Note 9) with known viscosity values, η. Calibration includes establishing the actual distance in nanometers between pixels in the video. Tracking of the particle’s motion returns trajectory measured in average distance traveled by the particle in pixels versus time, which is typically calculated using the time between two consecutive frames in the video. For two-dimensional or three-dimensional cases, the displacement is related to the diffusion coefficient simply by the following equation [1]: < Δr 2 > = 2pDΔt

ð1Þ

where p is dimensionality (for the optical setup and tracking procedure described here, p = 2 assuming the two-dimensional case of measurements performed in the x-y plane without consideration of the z-direction of particle’s diffusion), r is the displacement in

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Fig. 1 Small volume microrheology setup with particle tracking capabilities: (a) assembled setup, (b) source of light, (c) small sample chamber, (d) objective lens, (e) variable scan rate camera, (f) computer with video capture and particle tracking analysis software Table 1 Water/glycerol mixtures used for the calibration of the experimental setup Glycerol to water ratio by weight

Viscosity, η (P·s)

Diffusion coefficient, D (m2/s)

18.0% : 82.0%

1.66 × 10-3

2.64 × 10-13

32.2% : 67.8%

2.50 × 10-3

1.75 × 10-13

44.2% : 55.8%

4.65 × 10-3

0.94 × 10-13

64.5% : 35.5%

13.2 × 10-3

3.32 × 10-14

pixels in p dimensions, and Δt is the time interval. D is the diffusion coefficient related to the degree of particle’s motion; particles with large diffusion coefficients fluctuate more and vice versa. The Stokes–Einstein relationship for a spherical particle states that the fluctuation of a particle has the same origin as the dissipative frictional force the bead must work against to perturb the system in a particular direction: D=

kB T 6πηa

ð2Þ

where kBT is thermal energy, η is the fluid’s viscosity, and a is the particle’s radius [1, 2]. Increasing the viscosity of the solution decreases the amplitude of particle’s fluctuations which is explained by the increased friction experienced by the particle in a more viscous fluid. Known viscosity values for the mixtures presented in

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Table 1 allow for establishing the relationships between known diffusion coefficient, D values, and the measured fluctuations of particle’s position as a function of time. Pixel to nanometer coefficient, b, can be obtained using glycerol/water mixtures with known diffusion coefficients (Table 1). The average mean magnitude of the displacement (squared) between each frame measures how far the particle moves in time Δt (frame rate). The mean squared displacement of the particle then depends on the diffusion coefficient, D, and the time interval, (Δt) as: D=

< b 2 Δr 2 > 2Δt

ð3Þ

Here, b is the coefficient that relates displacement, r, measured in pixels with known diffusion coefficients, D (in m/s2). For the setup described in this study, the calibration yielded b = 32.5 × 10-9, indicating that the pixel-to-pixel distance is 32.5 nm. This coefficient, b, is used in all further calculations. 3.3 Bead Suspension Preparation and Video Acquisition 3.3.1 Glycerol/Water Mixtures

1. Weigh a moderate amount of glycerol into a vial. 2. Add the appropriate amount of water into the vial by measuring the volume of water with the pipette (recall the density of water is 1 g/mL) to make four mixtures with ratios listed in Table 1. Close the vial and shake it vigorously with a vortex mixer to ensure it is mixed uniformly. 3. Add 0.5 μL of stock bead solution (from the manufacturer’s vial) of the desired size (1 μm in diameter size is recommended) into the vial with the sample to be observed. 4. Close the vial and shake it vigorously with a vortex mixer to ensure it is mixed uniformly with beads (see Note 10). 5. Place a self-adhesive hybridization chamber onto the center of a clean glass coverslip. GraceBio hybridization chambers are pre-equipped with a transparent top cover and two holes for convenient fluid sample deposition into the chamber. 6. Use the pipette to transfer roughly 30 μL of the prepared beadmixed solution into the chamber, ensuring no bubbles are created. 7. Plug the holes with adhesive covers, keeping the solution from drying out. 8. Place the chamber onto the microscope stage. Move the objective lens away from the microscope stage before placing the slide onto the stage to avoid accidental damage. 9. Coverslip should be facing toward the objective lens. Turn on the light of the microscope. 10. Open the “Sentech” software to control the camera.

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11. Move the objective slowly toward the chamber by rotating the focus adjusting knob. 12. Stop when beads become clearly visible in the video window of the “Sentech” software. 13. Record a 40-second video of the bead motion with 25 frames per second rate. These parameters are set in the “advanced options” tab of the “Sentech” software. 14. Save the video in *.avi format. Several beads are typically captured within one video. 3.4 Particle Tracking and Data Analysis

Fiji (ImageJ) software package can be used to perform a single particle tracking procedure using the Mosaic plugin (with a single Particle Tracking Analysis option—Fig. 2). Particle tracking uses brightness contrast above the background to extract accurate positions of the bead. 1. Open your video file in Fiji (ImageJ) software package. 2. In the pop-up window—unclick “use visual stack” and choose “Convert to Grayscale” (see Note 11).

Fig. 2 Screenshot of the Fiji (ImageJ) and Mosaic (plugin) with input parameters for particle tracking analysis. (a) Main Fiji panel, (b) background corrected bead image, (C) tracked trajectory, (d) single particle tracking input parameters panel, (e) panel for defining of intensity for background correction, (f) results table, (g) saved table with trajectory data

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3. Crop the video to include only the particles of interest (see Note 12). 4. Click “Image”/“Adjust”/“Threshold,” and check “Dark background.” 5. Click “Apply” with “Dark background” selected. 6. Click “ok” in the pop-up window with the “Default” method, “Dark” background, and “calculate the threshold for each image” option selected. 7. Navigate to the Mosaic plugin. “Plugins”/“Mosaic”/“Particle tracker 2D/3D.” 8. Choose the “No” option for the “Are these 3D data?” question. 9. In the Particle Tracker window—choose Radius ~ 20 and click preview detected. 10. Make sure the radius ring fits well the entire particle. Click “ok” (see Note 13). 11. When the tracking process is finished, and the “Results Table” window opens, press “Visualize all trajectories.” If several particles were visible within the field of view—there will be several trajectories tracked. 12. Pick a trajectory of interest by clicking on it and save “Selected Trajectory to the Table.” 13. The table contains trajectory data as bead position in x and y in pixels recorded for each frame. Use the conversion factor, b, established previously to convert the units from pixels to meters for both x and y. 14. To obtain rheological information for supra-assemblies, construct the ensemble-averaged mean-squared displacement (MSD), , as a function of lag time, τ, from the unit corrected bead trajectories. Δr2(i) = Δx2(i) + Δy2(i) and < Δr2(τ) > =, where n is n = 1, 2, 3. . . N-10 and N is the total number of frames in the video (see Note 14). 15. MSD can be related to the diffusion coefficient, D, using the following equation [3]: < Δr 2 ðτÞ > = 2pDτα

ð4Þ

16. Plots of MSD vs. τ measured for supra-assemblies of different concentrations, 1, 5, and 7 μM, are shown in Fig. 3a–c, respectively. For comparison, Fig. 3d–g show MSD vs. τ plots for glycerol/water mixtures of the following viscosity values 1.66 mP·s, 2.5 mP·s, 4.65 mP·s, and 13.2 mP·s, respectively.

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Fig. 3 MSD vs. lag time (τ) plots. (a) 1 μM supra-assembly, (b) 5 μM supraassembly, (c) 7 μM supra-assembly, (d) 1.66 mP·s glycerol/water mixture, (e) 2.5 mP·s glycerol/water mixture, (f) 4.65 mP·s glycerol/water mixture, (g) 13.2 mP·s glycerol/water mixture Table 2 Parameters of the fit for MSD vs. τ plots Sample

Lag time power, α

Diffusion coefficient, D (m2/s)

A, 1 μM H-gel

0.92

5.14 × 10-13

B, 5 μM H-gel

0.92

4.95 × 10-13

C, 7 μM H-gel

0.93

4.75 × 10-13

D, 1.66 mP·s

1.02

2.84 × 10-13

E, 2.5 mP·s

1.00

1.64 × 10-13

F, 4.65 mP·s

1.00

1.08 × 10-13

G, 13.2 mP·s

1.00

3.30 × 10-14

17. The plots are fitted using Eq. 4 while setting p = 2 (2D case) with D and α as fitting parameters, Table 2. 18. Power of the lag time, α, identifies the state of the material. α = 1 corresponds to freely diffusing particles in a purely viscous environment, such as glycerol/water mixtures representing Newtonian fluids. For 0 < α < 1, particles move in a viscoelastic fluid, and the material’s structure restricts their motion. Lower values of α correspond to a greater restriction of the particle’s motion, becoming completely arrested when α –> 0 (see Note 15).

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Notes 1. The sequences used are listed in the supporting information of Chandler et al. (2021) as DNA for Sense_12_Biotin and DNA for Antisense_12_Biotin. Any biotinylated sequences should work with this procedure [4]. 2. Polystyrene microspheres are a good choice of beads since the density of polystyrene is similar to the density of water which allows these beads to be easily suspended in aqueous solutions without settling due to gravity. Also, the beads with no surface modification are preferred to avoid unnecessary surface–solute interactions so that it does not interfere with the purely diffusive motion of the beads. 3. Any other research-grade microscope, upright or inverted, would also be suitable for setting up a particle tracking optical system. We have also successfully used Olympus IX-71 inverted microscope with the same set of camera and objective lens. 4. Adjustable rate of the camera for video capturing provides more flexibility for a researcher to adjust how fast the motion of the beads is captured. It is beneficial for larger bead sizes and more viscous samples. 5. Captured videos may be very large; make sure the PC used has enough memory to store data. 6. We use high power 100× objective lens as part of the optical setup to provide better tracking capabilities. However, other objective lenses could be used; for example, 40× magnification is also suitable. 7. Mosaic plugin installed within the Fiji (ImageJ) software allows particle tracking to be performed in an easy and intuitive routine flow (http://mosaic.mpicbg.de/?q=downloads/imag eJor, https://sbalzarinilab.org/?q=downloads/imageJ) [5]. There are other plugins for ImageJ capable of similar particle tracking capabilities, for example, PTA (Particle Tracking and Analysis). 8. This procedure follows the DNA Duplex + QD assembly, but using streptavidin in place of quantum dots [4]. 9. Glycerol is highly viscous and cannot be measured precisely using the pipette as it will adhere to both the inside and outside of the pipette tip. To calculate the amount of glycerol, it is better to use a chemical balance to weigh the amount of glycerol and use a pipette to add water. 10. The size of the bead is an important parameter to consider, as the supra-assembly’s microstructure can impact the bead’s mobility. For example, supra-assemblies presented herein,

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while compliant for beads up to 2 μm in diameter, tend to arrest the motion of larger beads. Concentration-dependent arresting efficiency suggests complex microstructures of the supra-assemblies. The concentration of the beads and the manufacturer’s specifications may vary from batch to batch. Adjust the amount of added bead solution based on the desired amount of beads in the video’s field of view. 11. “Convert to grayscale” option allows the program to calculate the brightness of the bead correctly. If not chosen, the background will not be calculated properly. 12. Including only particles of interest reduces the time required to track a trajectory. Smaller cropped windows provide faster analysis. Scroll to the end of the video to ensure the particle of interest always stays within the cropped window. 13. Scroll through the entire video to check that the radius ~ 20 fits well throughout the entire length of the video. Sometimes, the particles also diffuse in and out of focus, and the particle size changes; if so, adjust the radius to fit the particle better. Also, due to in- and out-of-focus diffusion, the program may lose track of the particle for several frames. In this case, the tracking can be improved by selecting more than 2 (default) frames and larger than 10 (default) distances to link (Fig. 2d). 14. Limiting calculation to N-10 frames rather than N provides more reliable averaging of the last values of Δr2 with at least 10 values to average. 15. While supra-assemblies presented here show nice behavior of the MSD vs. τ plots, one has to keep in mind that more complex systems can be analyzed with the passive microrheology technique. More curved plots can be analyzed by including higher-order terms in the analysis of [3]. Another rheological parameter easily extractable from the measured MSDs is the creep compliance, J ðτÞ = dk3πr [3, 6], which in BT the case of supra-assemblies presented here returned very similar linear dependences suggesting linear increase of the strain rate for a constant applied stress for these materials [7].

Acknowledgments Research reported in this publication was supported by the National Science Foundation, Division of Material Research, Award Numbers 2203946 (to K.A.A.) and 2204027 (to A.V.K.), and the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K.A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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References 1. Mason TG (2000) Estimating the viscoelastic moduli of complex fluids using the generalized Stokes–Einstein equation. Rheol Acta 39(4): 371–378 2. Mason TG, Ganesan K, van Zanten JH, Wirtz D, Kuo SC (1997) Particle tracking microrheology of complex fluids. Phys Rev Lett 79(17):3282–3285 3. McGlynn JA, Wu N, Schultz KM (2020) Multiple particle tracking microrheological characterization: fundamentals, emerging techniques and applications. J Appl Phys 127(20):201101 4. Chandler M, Minevich B, Roark B, Viard M, Johnson MB, Rizvi MH et al (2021) Controlled

organization of inorganic materials using biological molecules for activating therapeutic functionalities. ACS Appl Mater Interfaces 13(33):39030–39041 5. Sbalzarini IF, Koumoutsakos P (2005) Feature point tracking and trajectory analysis for video imaging in cell biology. J Struct Biol 151(2): 182–195 6. Squires TM, Mason TG (2009) Fluid mechanics of microrheology. Annu Rev Fluid Mech 42(1): 413–438 7. Tweedie CA, Van Vliet KJ (2006) Contact creep compliance of viscoelastic materials via nanoindentation. J Mater Res 21(6):1576–1589

Chapter 12 Characterization of RNA Nanoparticles and Their Dynamic Properties Using Atomic Force Microscopy Alexander J. Lushnikov, Yelixza I. Avila, Kirill A. Afonin, and Alexey V. Krasnoslobodtsev Abstract The protocol described in this chapter allows for acquiring topography images of RNA-based nanoring structures and assessing their dynamic properties using atomic force microscopy (AFM) imaging. AFM is an indispensable tool for characterization of nucleic acid-based nanostructures with the exceptional capability of observing complexes in the range of a few nanometers. This method can visualize structural characteristics and evaluate differences between individual structurally different RNA nanorings. Due to the highly resolved AFM topography images, we introduce an approach that allows to distinguish the differences in the dynamic behavior of RNA nanoparticles not amenable to other experimental techniques. This protocol describes in detail the preparation procedures of RNA nanostructures, AFM imaging, and data analysis. Key words Atomic force microscopy, Topography imaging, RNA nanoparticle, Mica surface modification, Flexibility analysis, Mechanical stability

1

Introduction Nucleic acid nanotechnology is a research area that uses nucleic acid assembly principles as the bottom-up nanotechnological approach. This includes DNA and RNA, with the examples of nanoassemblies (e.g., RNA fibers, RNA and DNA cubes, and RNA rings) [1–7] and macro-assemblies (e.g., DNA origami [8]) that use specifically designed sequences to controllably build functional structures [9, 10]. The ability to visualize and characterize small nanostructures is critical in gaining control over the complex assembly process and constructing nanostructures of predicted design. Small variations in size and composition of structural elements can lead to large variations in properties of nanomaterials. One of the methods capable of nanoscale characterization of small nanostructures is atomic force microscopy (AFM). This chapter demonstrates the ability of AFM to assess subnanometer variations

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_12, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Fig. 1 Hexameric RNA nanostructure

in shapes of different nanostructures. Furthermore, we introduce a new approach to analysis of flexibility and dynamics of RNA-based hexameric nanorings modified with single strand gaps for potential functionalization of nanostructures. Using the six computationally programmed dumbbell building blocks, the hexameric RNA nanostructures self-assemble into two-dimensional nanostructures. Stabilized by the colE1-like kissing loop interactions [11, 12], the nanostructures are designed to form highly symmetric nanoring structures (Fig. 1) [13]. The potential for further functionalization of such nanorings with little impact on the nanostructure’s topology is increased by introducing gaps into the double-stranded regions of the RNA dumbbell monomers (Fig. 1). These gaps are only 6 bases long and, although small, can dramatically alter flexibility and dynamics of the entire nanoring structure. Herein, we describe a method to evaluate the flexibility of the RNA nanorings via static AFM imaging and deformation analysis. With high-resolution AFM topography images, we can trace structural arrangement of individual monomeric dumbbells in highly resolved nanostructures [13]. All nanoring structures assume a flat orientation on the atomically smooth mica surface, allowing for a comparative analysis of their shapes. Most stable nanostructures appear nearly circular due to their symmetric design. Less stable structures tend to adopt more of an elliptical shape. Nanoring deformation can then be analyzed via cross-sectional analysis of the structures from high-resolution AFM images. The ratio of orthogonal elliptical axes (major, DL, and minor, DSH) drawn through the center of a nanoring reports on the degree of deformation, the nanoring experienced due to flexibility of monomeric dumbbells. We describe technical aspects of sample preparation and AFM imaging technique. The protocol is intended to help a user with basic level of AFM skills to obtain good quality images. Post-

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acquisition image processing yields accurate information about distortion and deformation of the nanostructures reporting thus on their dynamic properties.

2

Materials and Equipment Water quality and reagent purity are critically important for RNA nanoparticle preparation and high-resolution AFM imaging. Prepare all buffer solutions using ultrapure water (Milli-Q, ~18 MΩ-cm) and analytical grade reagents.

2.1 RNA Nanoparticle Preparation

1. DNA oligo sequences as desalted products were used without further purification. 2. MyTaq™ Mix for PCR amplification of DNA oligos. 3. The DNA Clean and Concentrator™ kit for PCR product purification. 4. Agarose and ethidium bromide for verification of purified DNA solutions. 5. Bio-Rad ChemiDoc MP System and ethidium bromide for band visualization in gels for analysis. 6. T7 RNA Polymerase. 7. “Transcription” buffer: HEPES-KOH, pH 7.5; 2.5 mM spermidine; 50 mM DTT; 25 mM MgCl2; 5 mM rNTPs. 8. Acrylamide, bis-acrylamide, electrophoresis.

and

TEMED

for

PAGE

9. UV lamp for RNA band visualization. 10. Scalpel and tweezers for excision of gel pieces. 11. “Crush and soak” buffer: 300 mM NaCl, 89 mM Tris-borate (pH 8.2), 2 mM EDTA. 12. Nanodrop or spectrophotometer. 13. “Assembly” buffer: 89 mM tris-borate (pH 8.2), 2 mM MgCl2, 50 mM KCl. 14. Heat blocks that can be held at constant temperatures of 95 °C and 30 °C. 2.2 Mica Preparation and Sample Deposition

1. Sharp scissors for cutting mica sheets. 2. Vacuum cabinet such as vacuum oven for storing AFM samples deposited on APS-modified mica under vacuum or argon atmosphere. 3. Ultra-high purity (99.999%) compressed argon. 4. Scotch™ tape for preparation of smooth mica substrates. 5. Double-sided adhesive tape for mounting samples on metal pucks.

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6. 10 mm metal pucks for mounting mica substrates onto an AFM scanning stage. 7. Disposable plastic cuvettes for mica modification with APS and sample storage. 2.3 AFM Imaging and Data Analysis

1. MultiMode AFM Nanoscope IV system. 2. AFM probes RTESPA-300. 3. Data analysis FemtoScan Online Suite. See Note 1. 4. Software for statistical data analysis: MagicPlot Pro 3. See Note 2.

3

Methods

3.1 RNA Nanoparticle Preparation

1. DNA oligos were PCR amplified using the MyTaq™ Mix. 2. PCR products were purified using the DNA Clean and Concentrator™ kit. 3. Verification of PCR products was done using a 2% agarose gel stained with ethidium bromide. Samples were loaded and run for 10 min at 200 V, followed by visualization using a Bio-Rad ChemiDoc MP System. 4. RNAs for each of the six monomers were produced by in vitro run-off transcription using T7 RNA Polymerase in the buffer of the following composition: 80 mM HEPES-KOH, pH 7.5; 2.5 mM spermidine; 50 mM DTT; 25 mM MgCl2; 5 mM rNTPs. 5. RNAs were purified using an 8 M denaturing urea polyacrylamide gel electrophoresis (urea PAGE, 15%). RNA bands were visualized with a UV lamp (short wavelength), excised, and eluted overnight in “crush and soak” buffer. 6. RNAs were then precipitated by adding the “crush and soak” buffer to 2.5 volumes of 100% ethanol. The samples were then vortexed and placed in a – 20 °C freezer for 3 hours. Samples were spun down on a centrifuge at 14,000 rpm and rinsed with 90% ethanol twice, vacuum dried at a temperature of 55 °C, and then dissolved in ultra-pure water (17.8 MΩ·cm). 7. Concentration of each of the monomer strands was found by taking the absorbance of each strand using the NanoDrop. 8. To prepare the RNA nanoparticles (RNA rings), six RNA monomer strands were mixed at equimolar ratio in ddiH2O. The samples were heated to 95 °C for 2 min, snap-cooled to 4 ° C for 2 min by placing the sample in an ice bucket, and assembly buffer was added before incubation at 30 °C for 30 min.

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9. To verify the assembly of the RNA ring, an electrophoretic mobility shift assay was used. An 8% non-denaturing (native) PAGE (37.5:1) in the presence of 89 mM Tris-borate (pH 8.2), 2 mM MgCl2. 5 μL of 1 μM RNA rings per well were loaded and the gel was run for 60 min at 4 °C, 300 V. 10. The gel was then stained with ethidium bromide for 5 min and then visualized using a Bio-Rad ChemiDoc MP System. 3.2 Mica Surface Modification with 1-(3Aminopropyl)Silatrane (APS)

APS is not a commercially available reagent. It can be synthesized with standard equipment for organic chemistry as described in [5, 6]. 1. Dissolve APS in ultrapure water to prepare a stock solution of 50 mM (see Note 3). 2. Immediately prior to surface modification, dilute stock solution 300 times with ultrapure water to the final concentration of CAPS = 167 μM (see Note 4). 3. Cut mica substrates into pieces of 1 × 3 cm2 size using sharp scissors. 4. Cleave mica substrates using Scotch™ tape by peeling off layers on both sides of the substrate (see Note 5). 5. Place freshly cleaved mica substrates into a plastic cuvette filled with 167 μM APS solution, and leave for 30 min for modification. 6. Rinse mica substrates with ultrapure water a few times, and gently dry using ultra high purity argon. 7. Store APS-modified mica in vacuum overnight before sample deposition. Functionalized mica can be stored for several days under vacuum before use.

3.3 Deposition of RNA Nanoparticles on APS-Modified Mica Surface

Refer to Fig. 2 which illustrates the steps of sample preparation procedure outlined below. 1. Cut a small piece of APS-modified mica for sample deposition. Typical size necessary for AFM imaging is between 3 mm and 5 mm. 2. Place APS-modified piece of mica on a parafilm in a chamber designed to maintain constant humidity. 3. Deposit 2–5 μL of the sample solution in a dropwise manner (see Note 6). 4. Incubate for a total of 2 min. 5. Wash excess sample gently with copious amount of ultrapure water. 6. Gently dry the sample under a flow of ultra-high purity argon. 7. Store the deposited samples under vacuum overnight to remove residual water.

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Fig. 2 Illustration of sample preparation procedure: (1) APS-modified mica, (2) deposit 2–5 μL of sample solution, (3) incubate for 2 min, (4) rinse with copious amount of ultrapure water, (5) mount on metal puck for imaging

3.4 Atomic Force Microscopy Imaging 3.4.1

Brief Overview

3.4.2 Atomic Force Microscopy Imaging

This chapter is not intended to describe in full the details of AFM imaging and its technical subtleties. For detailed description of AFM imaging procedure, we will refer readers to the following reviews [14, 15]. We focus on the practical aspects of imaging which might be of interest to non-specialists wishing to visualize nucleic acid-based nanostructures using AFM imaging in air. The imaging mode used in this protocol is TappingMode™—a Brukerpatented technique which allows for gentle imaging of nanostructures with an oscillating AFM probe. A high-resolution 3D image of the sample’s surface topography is produced by monitoring the probe’s amplitude/frequency of oscillation. It is crucial for highresolution imaging to obtain a sample with clean background and optimal distribution of nanoparticles without crowding. It is important to note that chemical mica modification with APS provides stable fixation of nucleic acid-based nanostructures without their conformational distortion [14–17]. 1. Place the AFM probe in the probe holder (see Note 8). 2. Mount the mica substrate with deposited sample onto a metal puck using double-sided adhesive tape. 3. Mount the metal puck onto the scanning stage of the AFM microscope. Scanning stages of Bruker’s Nanoscope family of AFM are magnetic—designed to hold the metal pucks very well during imaging. The use of a scanner with small scan size is preferable, such as E-scanner from Bruker which is designed for up to 10 μm scan area. 4. Position and align the laser onto the AFM cantilever using laser adjustment knobs to maximize the sum. 5. Test if the laser is optimally reflected onto the quadrant detector by turning mirror’s lever, and adjust to maximize the sum.

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6. Adjust the horizontal deflection to a value as close to 0 V as possible by using adjustment knobs. 7. Open imaging program and run the “Autotune” feature. See Note 9. Adjust the scan size to 100 × 100 nm2, and start approaching the probe to the surface by pressing the “Engage” button. Scanning will start automatically once the probe is engaged. Change the scan size to 3 × 3 μm2, the pixel size to 128, and the scan rate to 1.5 Hz. These settings will allow for fast surveying of the sample. Move to a different area if needed. Reduce the set point until the surface is being tracked well. Nanostructures will appear as bright spots on the image. Proceed to the next step when optimal number of complexes is found within the size of the image. 8. Zoom to a scan size below 1 μm to start high-resolution imaging. 9. Increase the number of pixels to 512, and decrease the scan rate to 1 Hz to acquire a topography image with high resolution. Adjust scanning parameters such as the drive amplitude, set point, and gains to values which produce desired high-quality appearance of nanostructures. 10. To avoid image distortions, several restarts on the scanning area are required before final image is scanned and saved as the scanner should be completely equilibrated on the selected area. Figure 3a shows a representative half of the image obtained for RNA nanorings, 0.6 × 0.3 μm2, with 512 pixels. 11. If imaging of many nanostructures is required for statistical analysis, repeat steps 8–15 several times at different locations. 3.4.3 AFM Image Analysis to Evaluate Dynamicity of Nanoring Structures

AFM topography images can be processed using a variety of different software suites (see Note 1). We used the FemtoScan Online software package since it has a comprehensive set of tools for crosssectional analysis and distance measurements, and it served very well for analysis of RNA nanostructures. 1. Open a file saved in Bruker imaging software. FemtoScan Online can read raw data file without any additional conversions. 2. Adjust the image scan line to the same mean level for the whole image—“leveling the image.” 3. In the Z-scale settings, choose “brown” color palette and check “fixed scale” option. 4. Z-scale can be adjusted to achieve the desired visual contrast between RNA strands and background mica substrate. 5. For analysis of individual nanostructures, select rectangular area around a complex and choose “duplicate image” option. A separate window with the individual nanostructure of

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Fig. 3 (a) AFM topography image of RNA nanorings (600x300 nm2), no gap construct. Bottom panel shows individual nanorings extracted from the images for deformation factor analysis. (b) Deformation factor (DF) is calculated as a ratio of distances between NA strands (maxima in the cross section through the center of NA ring) for perpendicularly placed vertical and horizontal cross-sectional lines. (c) AFM topography image of RNA nanorings (600x300 nm2), 6 base gap construct. Bottom panel shows individual nanorings extracted from the images for deformation factor analysis. (d) Statistical histogram of DF values for “no gap” RNA nanoring. (e) Statistical histogram of DF values for “6 base gap” RNA nanoring

interest will appear. Examples of 30 × 30 nm2 areas with individual nanostructures are shown in Fig. 3a, c (bottom panels). 6. Using a “section” tool, build two orthogonal (long, L, along the major elliptical axis and short, SH, along the minor elliptical axis) cross-sectional lines (see Note 10). Each line will have a typical profile as shown in Fig. 3b. 7. Measure maximum-to-maximum distance in the crosssectional profile for both major (DL) and minor (DSH) axes which report on the distance between segments of nucleic acids in the corresponding direction of the RNA ring assembly. 8. By taking the ratio of distances (DL over DSH), calculate the deformation factor (DF) (see Note 11). 9. Combine DF values in a statistical histogram using MagicPlot software (see Note 2). An example of such a statistical histogram is shown in Fig. 3d, e. 10. Compare DF values for structurally different RNA nanorings if needed (see Note 12).

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Notes 1. Any other software with the capability of analyzing distance via cross-sectional line analysis could also be used including WSxM, Gwyddion, ImageSXM, and ImageJ. 2. Any other statistical software with the capability of plotting statistical histograms could also be used, for example, Excel (Microsoft, Inc.; Redmond, WA) or Origin Pro 2019 (OriginLab Corp., Northampton, MA). 3. There are several approaches utilized for the modification of mica substrate to obtain overall positively charged surface for effective binding of negatively charged RNA nanoparticles. One group of methods uses divalent cations such as Mg2+ and Ni2+ or other cations as a bridge between negatively charged surface groups and RNA nanoparticles. Another group of methods is based on chemical modification of the surface to create amino groups with reagents such as 3-aminopropyl-trietoxy silane (APTES) [14, 18–20] or 1-(3-aminopropyl)silatrane (APS) [5, 6]. Surface amino groups are positive at neutral pH providing strong enough binding for RNA. The modification procedure with APS is simple, is reproducible and provides a functionalized substrate for RNA or DNA sample deposition in a wide range of ionic conditions, temperatures, and pH values. APS allows to create positively charged surface for deposition of RNA, DNA, and DNA-protein complexes via electrostatic attraction between negatively charged nucleic acid backbone and positively charged APS-mica. This modification is very robust and reliable and has been extensively used in AFM studies. APS is an amorphous solid at normal conditions and is highly soluble in water. 1-(3-Aminopropyl)silatrane is not commercially available and requires custom synthesis [15, 16]. 4. Use freshly diluted APS solution every time APS modification of mica is needed. 5. Typically, mica cleaving process is repeated several times until required quality of mica top layer is achieved. The quality of mica substrate is monitored by the appearance of the separated mica layer on the Scotch™ tape. The cleaving procedure of mica sheets is repeated until the surface of the cleaved mica layer on the Scotch™ tape is glossy and has no visible defects. 6. The concentration of RNA nanoparticles should be adjusted to provide optimal surface coverage where nanoparticles do not overlap on the AFM image. This can be achieved by making several depositions with stepwise dilutions (e.g., 1/10, 1/100, 1/1000) to optimize concentration of RNA nanoparticles.

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7. Investigators who only begin using AFM imaging to visualize DNA and RNA nanostructures may encounter a few technical challenges. This chapter introduces specific details of AFM imaging and describes systematically the protocol and methods for obtaining good quality AFM topography images of RNA nanostructures. To obtain high-quality images, special attention should be paid to sample preparation procedures, the use of appropriate AFM probes, imaging mode, and imaging settings. These will contribute largely to the quality and reproducibility of AFM imaging. Also, we note that the AFM imaging approach described here is also broadly applicable to studying other nucleic acid nanostructures. 8. RTESPA-300 AFM probes were found to work reliably for imaging of DNA and RNA nanostructures in air. These probes have a nominal spring constant of 40 N/m and a resonance frequency at ~300 kHz. Although the nominal tip radius listed by the manufacturer is 8 nm, approximately one third of the probes are sharp enough to resolve subtle structural features of RNA nanostructures without any additional probe modifications. 9. The “autotune” feature in the imaging software will find the resonance peak of the cantilever and adjust the settings to the resonance frequency of the inserted AFM probe. Make sure that the target amplitude is at ~0.5 V and the drive amplitude is no larger than ~10 mV. 10. Using visual appearance of the nanostructures, determine the major (longest, L) and the minor (shortest, SH) axes of the elliptical nanostructures. Draw the lines such that they go through the center across the nanostructure. Make sure the lines maintain orthogonality. 11. Deformation factor is defined as DF = DL/DSH, where DL is the longer distance along the major axis and DSH is the shorter distance along minor axis of the elliptical nanostructure. The values of DF close to 1 will represent the least dynamic structure corresponding to a nearly circular shape of the nanostructure. Further improvement of the data analysis can be achieved via computer-assisted image analysis algorithm. This algorithm can identify circular objects on an AFM image and fit the nanostructures with an elliptical shape, automatically measuring major and minor axes of the ellipse. Such an approach allows for larger sets of structures to be analyzed [13]. 12. DF is plotted as a statistical histogram for various designs of the RNA ring nano-assemblies. Stable rings will be mostly circular, while less stable structures adopt an apparent elliptical shape. DF, therefore, can be used as a measure of the susceptibility of the rings to deformation and their dynamic properties. Two

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examples are shown in Fig. 3d, e to showcase the stark differences between stable RNA nanoring without any gaps and very flexible RNA nanoring which was modified with 6 base gaps in each monomeric dumbbell.

Acknowledgments Research reported in this publication was supported by the National Science Foundation, Division of Material Research, Award Numbers 2203946 (to K.A.A.) and 2204027 (to A.V.K.), and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K.A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AFM imaging of the RNA nanostructures was performed at the nanoimaging core facility (University of Nebraska Medical Center). References 1. Avila YI, Chandler M, Cedrone E, Newton HS, Richardson M, Xu J et al (2021) Induction of cytokines by nucleic acid nanoparticles (NANPs) depends on the type of delivery carrier. Molecules 26(3):625 2. Chandler M, Jain S, Halman J, Hong E, Dobrovolskaia MA, Zakharov AV et al (2022) Artificial immune cell, AI-cell, a new tool to predict interferon production by peripheral blood monocytes in response to nucleic acid nanoparticles. Small 18(46):e2204941 3. Chandler M, Johnson B, Khisamutdinov E, Dobrovolskaia MA, Sztuba-Solinska J, Salem AK et al (2021) The international society of RNA nanotechnology and nanomedicine (ISRNN): the present and future of the burgeoning field. ACS Nano 15(11): 16957–16973 4. Chandler M, Rolband L, Johnson MB, Shi D, Avila YI, Cedrone E et al (2022) Expanding structural space for immunomodulatory nucleic acid nanoparticles (Nanps) via spatial arrangement of their therapeutic moieties. Adv Funct Mater 32(43):43 5. Dobrovolskaia MA, Afonin KA (2020) Use of human peripheral blood mononuclear cells to define immunological properties of nucleic acid nanoparticles. Nat Protoc 15(11): 3678–3698 6. Rolband L, Beasock D, Wang Y, Shu YG, Dinman JD, Schlick T et al (2022) Biomotors, viral assembly, and RNA nanobiotechnology:

current achievements and future directions. Comput Struct Biotechnol J 20:6120–6137 7. Tran AN, Chandler M, Halman J, Beasock D, Fessler A, McKeough RQ et al (2022) Anhydrous nucleic acid nanoparticles for storage and handling at broad range of temperatures. Small 18(13):13 8. Dey S, Fan C, Gothelf KV, Li J, Lin C, Liu L et al (2021) DNA origami. Nat Rev Method Primer 1(1):13 9. Afonin KA, Dobrovolskaia MA, Church G, Bathe M (2020) Opportunities, barriers, and a strategy for overcoming translational challenges to therapeutic nucleic acid nanotechnology. ACS Nano 14(8):9221–9227 10. Afonin KA, Dobrovolskaia MA, Ke W, Grodzinski P, Bathe M (2022) Critical review of nucleic acid nanotechnology to identify gaps and inform a strategy for accelerated clinical translation. Adv Drug Deliv Rev 181:114081 11. Yingling YG, Shapiro BA (2007) Computational design of an RNA hexagonal Nanoring and an RNA nanotube. Nano Lett 7(8): 2328–2334 12. Afonin KA, Viard M, Koyfman AY, Martins AN, Kasprzak WK, Panigaj M et al (2014) Multifunctional RNA nanoparticles. Nano Lett 14(10):5662–5671 13. Sajja S, Chandler M, Fedorov D, Kasprzak WK, Lushnikov A, Viard M et al (2018) Dynamic behavior of RNA nanoparticles analyzed by

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AFM on a mica/air Interface. Langmuir 34(49):15099–15108 14. Lyubchenko YL, Gall AA, Shlyakhtenko LS (2014) Visualization of DNA and proteinDNA complexes with atomic force microscopy. Methods Mol Biol 1117:367–384 15. Lyubchenko YL, Shlyakhtenko LS, Gall AA (2009) Atomic force microscopy imaging and probing of DNA, proteins, and protein DNA complexes: silatrane surface chemistry. Methods Mol Biol 543:337–351 16. Shlyakhtenko LS, Gall AA, Lyubchenko YL (2013) Mica functionalization for imaging of DNA and protein-DNA complexes with atomic force microscopy. Methods Mol Biol 931:295–312 17. Lushnikov A, Hooy R, Sohn J, Krasnoslobodtsev A (2019) Characterization of DNA bound

cyclic GMP-AMP synthase using atomic force microscopy imaging. Methods Enzymol 625: 157–166 18. Pie´trement O, Pastre´ D, Fusil S, Jeusset J, David M-O, Landousy F et al (2003) Reversible binding of DNA on NiCl2-treated mica by varying the ionic strength. Langmuir 19(7): 2536–2539 19. Bezanilla M, Manne S, Laney DE, Lyubchenko YL, Hansma HG (1995) Adsorption of DNA to mica, Silylated mica, and minerals: characterization by atomic force microscopy. Langmuir 11(2):655–659 20. Vesenka J, Guthold M, Tang CL, Keller D, Delaine E, Bustamante C (1992) Substrate preparation for reliable imaging of DNA molecules with the scanning force microscope. Ultramicroscopy 42-44(Pt B):1243–1249

Part IV Intracellular Delivery and Immunorecognition of RNA Nanostructures

Chapter 13 Synthesis of Mesoporous Silica Nanoparticles for the Delivery of Nucleic Acid Nanostructures Tamanna Binte Huq and Juan L. Vivero-Escoto Abstract Nanomaterials have been extensively used for the delivery of nucleic acids. This is attributed to the unique features of nanoparticles to carry genetic material with different physiochemical properties. Mesoporous silica nanoparticles (MSNPs) are a versatile platform for the efficient delivery of nuclei acid-based materials. In this chapter, we describe the synthesis of MSNPs to efficiently transport nucleic acid nanoparticles. Key words Mesoporous silica nanoparticles, Nucleic acid nanoparticles, Delivery

1

Introduction Mesoporous silica nanoparticles (MSNPs) have attracted significant interest in various fields of science and engineering due to their unique properties [1, 2]. They offer several unique and advantageous properties, such as chemical and thermal stability, high surface area, tunable particle size, shape and porosity, chemically modifiable surfaces, and facile functionalization [3–7]. MSNPs are ideal carriers for nucleic acid nanoparticles (NANPs) because of their efficient internalization by mammalian cells and their ability to be modified to protect the NANPs’ cargo from enzymatic degradation, and they can be engineered to escape from endosomes or lysosomes to release their cargo into the cytoplasm [8–10]. Herein, we report on an easy and reproducible approach to synthesize MSNPs to efficiently carry NANPs.

2

Materials

2.1 CTAB Micelle Formation

1. Cetyltrimethylammonium bromide (CTAB) aqueous solution: Need 0.78 g of CTAB in 21.6 mL water and 3.32 mL ethanol mixture.

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2. Diethanolamine: 0.1 mL diethanolamine (0.4 mM). 3. Tetraethylorthosilicate: TEOS (98%, 2.19 mL) dropwise into the aqueous solution of CTAB. 2.2 Surface Modification of the Material

4. 3-(Trihydroxysilyl)propyl methylphosphonate monosodium salt solution (THPMP) (Sigma-Aldrich): 50 wt % in H2O for the surface modification of the material.

2.3 Surfactant Removal

5. Methanol acidic solution: An acidic solution of methanol (37% w/v) prepared for surfactant removal.

2.4 Post-grafting and Primary Amine Quantification

6. 3-Aminopropyl triethoxysilane (APTES). 7. Ninhydrin: 0.5 g ninhydrin in 10 mL of ethanol. 8. Sodium acetate buffer: 2 M in glacial acetic acid at pH 5.4. 9. Acetonitrile anhydrous. 10. Polyethylene imine: PEI, branched. 11. Methoxy-polyethylene-glycol succinimidyl carboxymethyl ester (mPEG-SCM): Creative PEGWorks. 12. Ethidium bromide. 13. DNA and short RNA oligos (Integrated DNA Technologies).

2.5 Nucleic Acid NP Formation

14. Longer ssRNAs entering the composition of NANPs are synthesized via in vitro run-off T7 transcription. 15. Transcription buffer: 80 mM N-(2-hydroxyethyl) piperazineN0 -ethanesulfonic acid (HEPES)  KOH, 2.5 mM spermidine, 50 mM dithiothreitol (DTT), and 25 mM MgCl2, and 5 mM each rNTP. 16. Urea polyacrylamide gel electrophoresis (PAGE): 8 M urea, 8% acrylamide. 17. Assembly buffer: 89 mM Tris-borate (pH 8.2), 2 mM MgCl2, 50 mM KCl.

3

Methods

3.1 Synthesis of Negatively Charged Mesoporous Silica Nanoparticles (MSNPs)

1. Dissolve 0.78 g of CTAB in a solution of 21.6 mL water and 3.32 mL ethanol [5]. 2. Add 0.1 mL diethanolamine (0.4 mM) and heat the solution to 60  C (see Note 1). 3. Add TEOS (98%, 2.19 mL) dropwise into the aqueous solution of CTAB (see Note 1), and allow the reaction to run for 18 h at 60  C to obtain as-made MSNPs [5]. 4. Once the reaction is complete, remove all the unreactive reagents via centrifugation (see Note 1).

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5. Modify the surface of MSNPs by grafting THPMP to impart the negative surface charge on the material. For phosphonate modification, add THPMP (150 μL diluted in 0.5 mL water) dropwise to the as-synthesized MSNPs, and stir for 6–8 h at 60  C. 6. Remove the surfactant CTAB from the pores using an acidic solution of methanol (37% w/v), which was heated to 60  C with stirring for 10 h. Repeat this process a second time to ensure all the surfactant is removed (see Note 2). 3.2 Synthesis of PEG-PEIModified MSNPs

1. Disperse 10 mg of negatively charged MSNPs (see Note 3) in an ethanolic solution (5 mL) containing 5 mg of PEI polymer [5]. 2. Stir for 2 h at room temperature. 3. Wash PEI-coated particles with ethanol. 4. Disperse 10 mg of PEI-MSNPs in 5 mL of acetonitrile anhydrous. 5. Add to this dispersion a solution of mPEG-SCM (3 mg/mL) in 1 mL of acetonitrile anhydrous. 6. Stir for 24 h. 7. Wash the final PEG-PEI-MSNPs with ethanol, and store them in the same solvent. 8. Kaiser’s test was used to quantify primary amines on the surface of PEG- and PEI-MSNPs [5].

3.3

Kaiser’s Assay

1. Ninhydrin solution is prepared by dissolving 0.5 g ninhydrin in 10 mL of ethanol [5]. 2. Kaiser’s assay is carried out by mixing 100 μL of the prepared sodium acetate buffer (pH 5.4) and 70 μL ninhydrin solution in a glass vial. 3. Add 10–20 μL of the MSN sample to the previous mixture. 4. Heat up the vials to 70  C in an oil bath for 10–15 min (see Note 4). 5. The vials are left to cool down at room temperature. 6. Add 3 mL of the ethanol-water mixture in a ratio of 3:2 (vol/vol) to each vial. 7. The absorbance of each solution was measured at 575 nm by a UV-Vis spectrophotometer.

3.4 In Vitro Run-Off T7 Transcription of ssRNAs

1. Incubate DNA templates (containing T7 promoter regions) at 37  C with homemade T7 RNA polymerase and transcription buffer for 4 h. 2. Stop the reaction with RQ1 DNase (Promega) for 30 min at 37  C.

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3. Purify the ssRNAs with denaturing gel electrophoresis (ureaPAGE) by visualizing bands under UV, extracting gel slices, and eluting the samples into 300 mM NaCl, 89 mM Trisborate (pH 8.2), and 2 mM ethylenediaminetetraacetic acid (EDTA) overnight at 4  C. 4. Mix the samples on the following day with 2 volume of anhydrous ethanol, and chill to 20  C for a minimum of 3 h. 5. The samples are then spun at 14000 g for 30 min, followed by disposing of the supernatant. 6. An additional washing step is carried out by adding 90% ethanol, centrifuging at 14000 g, and discarding the supernatant. 7. The samples are finally processed by drying using a SpeedVac concentrator followed by resuspension in double-deionized endotoxin-free water. 8. The concentrations are measured using a NanoDrop 2000. 3.5 Synthesis of NANPs

The synthesis of all NANPs is completed using a “one-pot” assembly method [11]. Cubes and rings (cNANPs and rNANPs) both consist of six scaffold ssRNAs with 30 -side dicer substrate (DS) antisense extensions and six complementary dicer substrate sense strands [8]. Fibers (fNANPs) consist of two ssRNAs with 30 -side dicer substrate antisense extensions and two complementary dicer substrate sense strands [8]. 1. Mix the ssRNA (cNANPs) strands in equimolar concentrations with 6 equivalents of dicer substrate sense strands, and heat to 95  C for 2 min and then cool down to 45  C for another 2 min. 2. Add assembly buffer and incubate the solution at 45  C for an additional 30 min. 3. Mix the ssRNA strands in equimolar concentrations with either 6 (for rNANPs) or 2 (for fNANPs) equivalent of dicer substrate sense strand. 4. Heat the mixture to 95  C for 2 min and then immediately cool on ice (4  C) for another 2 min. 5. Add the assembly buffer and incubate the solution at 30  C for 30 min. 6. To confirm the assembly of all structures, the NANPs are run in nondenaturing native-PAGE (8%, 37.5:1) in assembly buffer at 4  C followed by ethidium bromide total staining and visualization on a ChemiDoc MP Imaging System.

3.6 Complexation of NA-MS-NPs

1. Disperse 0.1 mg of MSNPs in 100 μL of 1 assembly buffer (89 mM Tris-borate (pH 8.2), 2 mM MgCl2, 50 mM KCl).

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2. Add slowly either DNA/RNA duplexes (10 μM) or NANPs (cube NANPs (1 μM), ring NANPs (1 μM), or fiber NANPs (3 μM)) in solution. 3. Add extra 1 assembly buffer to the mixture to make a final volume of 200 μL. 4. Mix the final solution by pipetting several times followed by incubation for 30 min at room temperature. 5. Separate the NA-MS-NPs from the dispersion by centrifugation at 810 k rpm, and redisperse in 100 μL of 1 assembly buffer.

4

Notes 1. Synthesis of Mesoporous Silica Nanoparticles (MSNPs) (a) After the diethanolamine addition, the solution should be immersed in the oil immediately, and wait a few minutes before TEOS addition. (b) Critical Step: TEOS addition is crucial for getting an ordered pore structure. The entire addition should be done in a dropwise fashion (a total of 2.19 mL should take around 5 min). (c) Stopping of the reaction: Once the reaction is done, separate out the unreactive solvents from the solution (Three cycles of centrifugation are recommended). (d) Sonication time may vary during solvent washes. 2. Surfactant Removal Calculate the amount of acidic solution of methanol carefully, and maintain the acid wash duration accordingly. 3. Post-Grafting of Mesoporous Silica Nanoparticles (MSNPs) Use the sample solution for post-grafting (the dried sample takes more time to redisperse in the solvents). 4. Kaiser’s Assay Getting a good calibration curve is necessary. Thus, maintain the temperature and time of heating cautiously and shake intermittently during heating (at least once in between the procedures).

References 1. Siddiqui B, Rehman A, I-ul H, Al-Dossary AA, Elaissari A, Ahmed N (2022) Exploiting recent trends for the synthesis and surface functionalization of mesoporous silica nanoparticles towards biomedical applications. Int J Pharm X 4:100116

2. Vallet-Regı´ M, Schu¨th F, Lozano D, Colilla M, Manzano M (2022) Engineering mesoporous silica nanoparticles for drug delivery: where are we after two decades? Chem Soc Rev 51: 5365–5451

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3. Alvarez-Berrı´os MP, Sosa-Cintron N, Rodriguez-Lugo M, Juneja R, Vivero-Escoto JL (2016) Hybrid nanomaterials based on iron oxide nanoparticles and mesoporous silica nanoparticles: overcoming challenges in current cancer treatments. J Chem 2016:1–15 4. Vivero-Escoto JL, Huxford-Phillips RC, Lin W (2012) Silica-based nanoprobes for biomedical imaging and Theranostic applications. Chem Soc Rev 41:2673 5. Tarannum M, Hossain MA, Holmes B, Yan S, Mukherjee P, Vivero-Escoto JL (2021) Advanced nanoengineering approach for target-specific, spatiotemporal, and ratiometric delivery of gemcitabine–cisplatin combination for improved therapeutic outcome in pancreatic cancer. Small 18:2104449 6. Tarannum M, Holtzman K, Dre´au D, Mukherjee P, Vivero-Escoto JL (2022) Nanoparticle combination for precise stroma modulation and improved delivery for pancreatic cancer. J Control Release 347:425–434 7. Alvarez-Berrı´os MP, Vivero-Escoto JL (2016) In vitro evaluation of folic acid-conjugated

redox-responsive mesoporous silica nanoparticles for the delivery of cisplatin. Int J Nanomedicine 11:6251–6265 8. Rackley L, Stewart JM, Salotti J et al (2018) RNA fibers as optimized nanoscaffolds for Sirna coordination and reduced immunological recognition. Adv Funct Mater 28:1805959 9. Juneja R, Vadarevu H, Halman J, Tarannum M, Rackley L, Dobbs J, Marquez J, Chandler M, Afonin K, ViveroEscoto JL (2020) Combination of nucleic acid and mesoporous silica nanoparticles: optimization and therapeutic performance in vitro. ACS Appl Mater Interfaces 12:38873–38886 10. Zhou Y, Quan G, Wu Q, Zhang X, Niu B, Wu B, Huang Y, Pan X, Wu C (2018) Mesoporous silica nanoparticles for drug and gene delivery. Acta Pharm Sin B 8:165–177 11. Afonin KA, Grabow WW, Walker FM, Bindewald E, Dobrovolskaia MA, Shapiro BA, Jaeger L (2011) Design and self-assembly of sirna-functionalized RNA nanoparticles for use in automated nanomedicine. Nat Protoc 6:2022–2034

Chapter 14 Assessment of Intracellular Compartmentalization of RNA Nanostructures Yasmine Radwan, Kirill A. Afonin, and M. Brittany Johnson Abstract Nucleic acid nanoparticles (NANPs) are extensively investigated as diagnostic and therapeutic tools. These innovative particles can be composed of RNA, DNA, and/or modified nucleic acids. Due to the regulatory role of nucleic acids in the cellular system, NANPs have the ability to identify target molecules and regulate expression of genes in disease pathways. However, translation of NANPs in clinical settings is hindered due to inefficient intracellular delivery, chemical instability, and off-target immunostimulatory effects following immune recognition. The composition of nucleic acids forming NANPs has been demonstrated to influence immunorecognition, subcellular compartmentalization, and physicochemical properties of NANPs. This chapter first outlines the methods used to generate a panel of NANPs with a uniform shape, size, charge, sequence, and connectivity. This includes the procedures for replacing the RNA strands with DNA or chemical analogs in the designated NANPs. Second, this chapter will also describe experiments to assess the effect of the chemical modification on enzymatic and thermodynamic stability, delivery efficiency, and subcellular compartmentalization of NANPs. Key words Nucleic acid nanoparticles (NANPs), Intracellular compartmentalization, Immunorecognition, NANPs synthesis

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Introduction Nucleic acid nanoparticles (NANPs) are rationally designed threedimensional nucleic acid structures. They hold the advantages of tunability of physicochemical properties, immune response regulation, and multifunctionality with targeting ligands or therapeutic nucleic acids. NANPs can also be exploited in diagnostic and therapeutic applications. However, NANPs have not been commercialized due to the following parameters that require further optimization: delivery efficiency, sensitivity to enzymatic degradation, and induction of off-target immune response [1]. Extensive research efforts are ongoing to overcome these hurdles: by optimizing the structure and composition of NANPs.

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Previous work demonstrated that the physicochemical properties of NANPs affect their immunostimulatory properties [2]. Previous studies highlighted that size, shape, and composition of NANPs are key players in immunostimulation [3, 4]. As for the size and globularity, it was reported that the increase in size and globularity of NANPs increases the stimulation of the immune response [3, 4]. Another study confirmed that altering the composition of NANPs modulated the degree of the immune response, as DNA NANPs have less immune activation than RNA NANPs [5]. Research indicates that thermostability, serum stability, and immunostimulatory activity can be optimized by incorporating chemically modified nucleic acid analogs in the NANPs [6– 14]. In this chapter, the methods for the synthesis and characterization of an array of NANPs with modified composition will be discussed. In our previous publication, NANPs composed of RNA, DNA, and 2’F modified oligonucleotides, but sharing the same size, shape, sequences, connectivity, and charge, were used to assess the effect of modified chemical composition on the intracellular compartmentalization and immunostimulatory potential. The temperature stability, serum stability, and structural integrity following release from a lipid carrier were determined via UV melting experiments, fetal bovine serum stability assays, and native-PAGE gels, respectively. Additionally, the receptors required for recognition of NANPs and the downstream immune responses were investigated using a combination of reporter cell lines, siRNA-based approaches, specific capture ELISAs, and immunoblot analysis. Finally, cellular fractionation experiments and fluorescent immunohistochemical analysis were used to assess the intracellular compartmentalization of the NANPs.

2

Materials (i) NANP Synthesis. 1. Synthetic oligonucleotides with 2′-fluoro-modified pyrimidines (2’F-U/C). 2. DNA strands oligonucleotides.

including

5′-end

cy-3-labeled

3. Polymerase chain reaction (PCR): 1. 100 μM Forward Primer for the desired strand. 2. 100 μM Reverse Primer for the desired strand. 3. 0.2 μM DNA Template for the desired strand. 4. 2X MyTaq Mix. 5. Endotoxin-free water. 6. PCR tubes (1 per strand).

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2. Purification of DNA samples: 1. Impure PCR products. 2. Endotoxin-free water. 3. 1.5 mL Eppendorf tubes (2 per PCR sample). 4. 2.0 mL Eppendorf tubes (1 per PCR sample). 5. Filtration columns (1 per PCR sample). 6. DNA Binding Buffer. 7. DNA Wash Buffer. 8. 1.5% agarose gel and supplies. 3. 2’F in vitro T7 RNA transcription kit. 4. 8 M urea 18% PAGE: 1. 20 mL 40% acrylamide/bis-acrylamide / 8 M urea. 2. 25 mL 8 M urea. 3. 5 mL 10X TBE; 108 g Tris base, 55 g boric acid, 7.4 g EDTA (disodium salt), 1 L Milli-Q water (17.5–17.8 MΩ). 4. 25 μL TEMED. 5. 500 μL 10% APS. 6. Beaker (100–150 mL). 7. Graduated cylinder (50–100 mL). 8. Glass gel plates (one front, one back). 9. 2 gel spacers. 10. Gel gasket. 11. Razor blade. 12. Gel comb. 13. 100% ethanol. 5. ddiH2O (17.5–17.8 MΩ). 6. Elution buffer: 89 mM Tris–HCl buffer (pH 8.0), 0.3 M sodium acetate, 0.1 mM EDTA). 7. Ethanol precipitation: (2.5× volume of 100% ethanol and 1/10 volume of 3 M sodium acetate): 1. 6 Eppendorf tubes (1.5 mL). 2. Eluates. 3. ddiH2O. 4. 100% ethanol. 5. 90% ethanol. 6. Equipment: Pipettes and pipette tips, microcentrifuge, vortex, refrigerated centrifuge, SpeedVac-20 °C, Freezer.

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8. Assembly buffer: 89 mM Tris-borate buffer (pH 8.3), 2 mM magnesium acetate, and 50 mM potassium chloride. 9. Native-PAGE gel: 1. ddiH2O (17.5–17.8 MΩ). 2. 40% 19:1 acrylamide/bis-acrylamide. 3. 10X TB. 4. 1 M Magnesium chloride. 5. TEMED. 6. 10% APS. 7. Beaker (50–100 mL). 8. Graduated cylinder (10 mL). 9. Mini gel glass plates (one front, one back). 10. Gel comb (of appropriate thickness for the chosen glass plate set). 11. Razor blade. 12. 100% ethanol. 13. Green gel casting frame. 14. Gel casting stand. 15. Buffer tank with lid. 16. Buffer dam. 17. Native loading buffer. 18. 1X TB, 2 mM Mg2+. 19. Mini gel spatula. 20. Glass tray. 21. Squirt bottle with ddiH2O. 22. Diluted ethidium bromide. 23. Equipment: Pipettes and pipette tips in boxes, refrigerator, UV lamp and transilluminator, power supply. (ii) UV-Melting Experiments. 1. UV-melting cells (micro-cuvette Starna Cells, 10 mm path length) equipped with a PTFE stopper. 2. Agilent spectrophotometer. (iii) Dynamic Light Scattering Analysis. 1. 50 kDa Ultracel-50 regenerated cellulose membrane. 2. DLS cells. 3. Zetasizer nano-ZS. (iv) Fetal Bovine Serum Stability Assay.

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1. 20% fetal bovine serum (FBS) solution. 2. 3% agarose gel; 50 mL 1x TBE buffer and 1.5 g agarose powder. (v) Integrity of NANP upon Release from a Carrier. 1. Lipofectamine 2000 (L2K). 2. 10% Triton-X. 3. Native PAGE (37.5:1, 8%): 1. ddiH2O (17.5–17.8 MΩ). 2. 40% 19:1 acrylamide/bis-acrylamide. 3. 10X TB. 4. 1 M magnesium chloride. 5. TEMED. 6. 10% APS. 7. Beaker (50–100 mL). 8. Graduated cylinder (10 mL). 9. Mini gel glass plates (one front, one back). 10. Gel comb (of appropriate thickness for the chosen glass plate set). 11. Razor blade. 12. 100% ethanol. 13. Green gel casting frame. 14. Gel casting stand. 15. Buffer tank with lid. 16. Buffer dam. 17. Native loading buffer. 18. 1X TB, 2 mM Mg2+. 19. Mini gel spatula. 20. Glass tray. 21. Squirt bottle with ddiH2O (17.5–17.8 MΩ). 22. Diluted ethidium bromide. 23. Equipment: Pipettes and pipette tips in boxes, refrigerator, UV lamp and transilluminator, power supply. (vi) Source and Propagation of Cell Lines. 1. Immortalized primary human microglia (hμglia) cells (gift from Dr. Jonathan Karn, Case Western Reserve University). 2. Dulbecco’s modified Eagle’s medium supplemented with 5% FBS and penicillin/streptomycin (100 U/mL–100 g/ mL).

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(vii) Transfection of Microglia. 1. L2K. 2. DOTAP (MilliporeSigma). 3. DMEM supplemented with 5% FBS. 4. Cell media supplemented with 100 U/mL penicillin100 g/mL streptomycin. (viii) Cellular Fractionation. 1. Cy3-labeled NANPs. 2. Cellular fractionation kit. 3. Cytosolic isolation buffer; CIB. 4. Membrane/organelle isolation buffer; MIB. 5. Nuclear/cytoskeleton isolation buffer; NIB. 6. Immunoblot for proteins GAPDH, COX IV, Rab7, and H3, as described in the immunoblot section. 7. SpectraMax iD5 plate reader. (ix) Flow Cytometric Analysis. 1. Cy3-labeled NANPs. 2. L2K or DOTAP. 3. 0.05% trypsin. 4. 1% paraformaldehyde. 5. Flow cytometer. (x) Fluorescent Immunohistochemical Analysis. 1. Poly-D-lysine-coated glass coverslips for huglia cell line. 2. Cy3-labeled NANPs. 3. L2K or DOTAP. 4. 4% paraformaldehyde. 5. 0.1% Triton-X-100. 6. 2% BSA. 7. Monoclonal rabbit antibody directed against EEA1. 8. Polyclonal goat anti-rabbit secondary antibody coupled to Alexa Fluor 647. 9. Prolong Diamond antifade mountant with DAPI. 10. Olympus Fluoview 1000 four-color confocal laser microscope. 2.1 Statistical Analysis

GraphPad Prism, GraphPad 15 Software.

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Methods (i) NANP Synthesis. 1. Amplification of DNA templates for in vitro transcription of 2’FU/C modified RNA strands by PCR: 1. All reagents are thawed and placed on ice. 2. All PCR tubes are labeled with the names of the samples to be amplified. 3. 25 μL of 2X MyTaq Mix is added to each PCR tube. 4. 22 μL of endotoxin-free water is added to each PCR tube. 5. 1 μL of each template, forward primer, and reverse primer specific for the desired strand are added to their respective tubes. 6. The total 50 μL of sample is mixed by pipetting up and down, and centrifuge briefly using a mini-centrifuge. 7. Tubes are placed into the thermocycler and lid is closed. Set a program to run from: • (94 °C for 90 sec) • (55 °C for 90 sec) • (72 °C for 90 sec) and repeat these three steps for 30 cycles. 8. Approximately 3 h are needed for the thermocycler to complete all 30 rounds of amplification. Afterward, the thermocycler can be programmed to keep all samples at 4 °C or placed in the 4 °C refrigerator until DNA purification. 2. Purify amplified DNA samples: 1. One 1.5 mL tube is labeled for each PCR sample. All of the 50 μL PCR products are moved from each sample to their respective 1.5 mL tubes. Afterward, the PCR tubes are discarded. 2. DNA Binding Buffer is added to each 1.5 mL tube to bring the total volume to 700 μL (For a 50 μL PCR reaction, add 650 μL of DNA Binding Buffer). 3. Vortex the tubes thoroughly and centrifuge. 4. Using scissors, cut off and discard the caps from the 2.0 mL tubes. 5. One filtration column is placed inside each 2.0 mL tube. The tab of each filtration column is labeled with the name of the sample. 6. All 700 μL of each sample is transferred into each filtration column.

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7. The filtration columns are placed in 2.0 mL tubes in a tabletop centrifuge so that they are evenly balanced. Set the centrifuge to 10,000 rcf for 30 s. 8. Afterward, the buffer which has run through the filter is discarded into a liquid waste container by briefly removing the filter column and dumping the 2.0 mL tubes. Be careful not to touch the white silica filter at the bottom before returning the filter to the 2.0 mL tube. 9. 200 μL of DNA Wash Buffer is added to each column to wash the DNA stuck in the filter. 10. The filtration columns in 2.0 mL tubes are placed back into the tabletop centrifuge so that they are evenly balanced. Spin the centrifuge again at 10,000 rcf for 30 s. 11. Next, the buffer which has run through the filter is discarded into a liquid waste container by briefly removing the filter column and dumping the 2.0 mL tubes. Be careful not to touch the white silica filter at the bottom before returning the filter to the 2.0 mL tube. 12. Wash the DNA a second time by adding 200 μL of DNA Wash Buffer to each column. 13. The filtration columns in 2.0 mL tubes are placed back into the tabletop centrifuge so that they are evenly balanced. Spin the centrifuge again at 10,000 rcf for 30 s. 14. The filtration columns are transferred into new 1.5 mL tubes. The 2.0 mL tubes are discarded by dumping the liquid into a liquid waste container and disposing of the tubes. 15. 50 μL of endotoxin-free water is added to each filtration column. (Or if 100 μL of PCR reaction was done, add 100 μL of endotoxin-free water.) 16. Place the filtration columns in 1.5 mL tubes back into the tabletop centrifuge so that they are evenly balanced with the caps towards the inside. Spin the centrifuge again at 10,000 rcf for 30 s. 17. The 1.5 mL tubes are labeled with the sample name and “purified DNA.” The filtration columns are discarded. 18. Verifying Purification on an Agarose Gel: a 1.5% agarose gel with ethidium bromide is prepared. 19. A piece of Parafilm is cut and placed paper-side down on the benchtop. 2 μL drops of agarose loading buffer are pipetted onto the Parafilm for each sample of purified DNA to be verified. 20. Add 2 μL of purified DNA to 2 μL of agarose loading buffer for each sample. Mix by pipetting 4 μL total up

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and down. Load the sample into one well of the agarose gel. Repeat for all purified DNA samples to be verified. 21. Put the lid onto the agarose gel chamber. (The samples will run from black to red; negative to positive). 22. Under UV light, look for the orange, fluorescent bands to see if purified DNA is present. Verified purified DNA should be stored at -20 °C until used for transcription. 3. Preparation of 2’F-modified RNA strand 2’F-T1 by 2’F in vitro T7 RNA transcription kit. 4. Purification of the prepared 2’F-modified RNA strand 2’FT1 by 8 M urea 18% PAGE. 1. Mix the reagents to create the urea gel: 25 mL 40% acrylamide/bis-acrylamide / 8 M Urea, 20 mL 8 M Urea, 5 mL 10X TBE, 25 μL of TEMED, and 500 μL of 10% APS. 2. Pour in the gel and let it polymerize for 15–20 min. 3. Add the gel into the tank and pour 1X TBE until it fills up past the wells of the gel. Pour 1X TBE into the bottom chamber of the gel stand until it fills up past the bottom of the gel assembly. 4. Rinse the wells twice with 1X TBE to clean the wells of the urea gel using a syringe and its needle. 5. In the first well, load 10 μL of the urea gel ladder and then 106 μL of each transcription sample into separate wells. 6. Run the gel until there is 3–4 inches of separation between the two dyes (bromophenol blue on the bottom and xylene cyanol on the top) found in the urea loading buffer. 5. UV shadow is used on the urea page to cut out the corresponding bands and elute them from the gel for 4 h at 37 °C in 700 μL elution buffer. 6. Precipitate the eluted bands by ethanol precipitation at -20 °C overnight: 1. Transfer 600 μL of the liquid from each elution tube into the corresponding “RNA strand.” Do not transfer any gel pieces. 2. Add 900 μL of 100% ethanol to each “RNA strand” tube. Vortex each tube for 5–10 s. 3. Place the tubes at -20 °C for 3 h. 4. Centrifuge the tubes at 4 °C for 30 min at 14,000 rpm. 5. Discard 900 μL of supernatant from each tube, and avoid the pellet which has formed at the bottom.

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6. Add 900 μL of 90% ethanol to each tube. 7. Centrifuge at 4 °C for 10 min at 14,000 rpm. 8. Repeat steps 5–7. 9. Remove ~1000 μL of supernatant from each tube, and avoid the pellet which has formed at the bottom leaving only ~50 μL of liquid with the pellet. 10. Place tubes with caps opened in the SpeedVac, and run at 55 ° C until all samples are dry ~30 min. 11. Visualize the dried pellet and add 30 μL of ddiH2O to each tube. Vortex each for 5–10 s and briefly centrifuge. 12. Measure the absorbance of each strand at 260 nm using the NanoDrop 2000. 13. Store samples at -20 °C. 7. To assemble all the DNA, RNA, and fluorinated structures, mix corresponding strands (1 μM final) in assembly buffer; following the one-pot self-assembly protocol using equimolar concentrations. Then heat the mixtures to 100 °C followed by slow cooling to 4 °C. 8. Use PCR thermal controller for annealing of 2’F-RNA/ DNA and 2’ F NANPs only for slow cooling (1 °C/ min) from 90 °C to 4 °C. 9. Store assemblies at 4 °C until needed. 10. Evaluate the purity of each batch by using AFM imaging. 11. Evaluate the purity of each batch by using 8% nativePAGE gel: 1. Mix the reagents to make the native gel: 10.5 mL ddiH2O, 3 mL 40% 19:1 acrylamide/bis-acrylamide (from the refrigerator), 1.5 mL 10X TB, 30 μL of 1 M MgCl2, 9 μL of TEMED, and 150 μL of 10% APS. 2. Pour in the gel and let it polymerize for 10–15 min. 3. Pre-run the gel in 1X TB (2 mM Mg2+) at 150 volts (and 25 mA) for 5 min on the “volt” setting. 4. Run gel after loading the samples, in 1X TB (2 mM Mg2+) at 300 volts (and 150 mA) for 20 min on the “volt” setting. 5. Stain the gel in diluted ethidium bromide for 5 min, and then wash twice in ddiH2O. 6. Image the gel using ChemiDoc to confirm pure assemblies (Fig. 1). (ii) UV-Melting Experiments. 1. Degas the assembled NANPs (C = 0.1–0.2 M, V = 100 μl) in 1× assembly buffer (AB) by SpeedVac for 5 min.

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Fig. 1 Native PAGE confirming the assemblies by EtBr total staining. (Reproduced from Ref. [5])

2. Place the mixtures of total volume of 100 μL in UV-melting cells. 3. Use Agilent spectrophotometer at 260 nm over a temperature range of 20–100 °C (ramp rate of 0.1 °C/min). 4. Replicate each experiment three times. 5. Fit the absorbance data by using the nonlinear dose response function of Origin Pro (see Note 1) (Fig. 2). (iii) Dynamic Light Scattering Analysis. 1. Filter the assembled NANPs through 50 kDa Ultracel-50 regenerated cellulose membrane by centrifugation at 12,000 × g for 2.5 min. 2. Transfer the remaining volume to DLS cells, and analyze at 25 °C using the Zetasizer nano-ZS (Fig. 3). (iv) Fetal Bovine Serum Stability Assay. 1. Incubate the assembled NANPs in 20% FBS solution. 2. Take aliquots at time intervals from 1 min to 12 h. 3. Evaluate the remaining particle fractions using 3% agarose gel electrophoresis. 4. For the control in this experiment, use corresponding NANPs incubated in buffer for 12 h at 37 °C in absence of FBS.

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Fig. 2 UV-melting experiment to measure the melting temperature of NANPs. (Reproduced from Ref. [5])

Fig. 3 Assessment of relative sizes of NANPs by DLS. (Reproduced from Ref. [5])

5. Quantify the remaining fraction of NANPs from the gel image using ImageJ software. 6. Plot the resulting fraction percentage as a function of incubation time using Origin Pro software (Fig. 4). (v) Integrity of NANP upon Release from a Carrier. 1. Mix the assembled NANPs to L2K to a final volume of 9 μl and incubate at room temperature. 2. After 30 min of incubation, add 1 μL of 10% Triton-X to the mixture and incubate for 30 min.

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Fig. 4 Chemical stability analysis by EMSA of NANPs in 20% FBS incubated at 37 °C from 1 to 720 min. (Reproduced from Ref. [5])

3. Separate the samples by native PAGE (37.5:1, 8%), and stain the gel with ethidium bromide for visualizing. Follow procedures as previously described in NANP synthesis section, step 10 (Fig. 5). (vi) Source and Propagation of Cell Lines. 1. Transformed primary human cells with lentiviral vectors expressing SV40 T antigen and hTERT (gifted from Dr. Jonathan Karn, Case Western Reserve University). 2. Maintain the cell line in Dulbecco’s modified Eagle’s medium supplemented with 5% FBS and penicillin/streptomycin (100 U/mL–100 g/mL). (vii) Transfection of Microglia. 1. Transfect the hμglia cell line with L2K or DOTAP following the manufacturer’s guidelines. 2. Incubate the NANPs with L2K or DOTAP for 30 min before transfection of hμglia with 5 nM NANPs for 4 h in DMEM supplemented with 5% FBS. 3. Change the cell culture medium with media supplemented with 100 U/mL penicillin-100 g/mL streptomycin. 4. Collect cell supernatants for analysis at the designated time points. (viii) Cellular fractionation. 1. Transfect cells with Cy3-labeled nanoparticles for 2 or 4 h. 2. Use the cellular fractionation kit according to the manufacturer guidelines to separate cells into three fractions:

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Fig. 5 EtBr total staining native PAGE confirming the structural integrity of NANPs upon release from Lipofectamine 2000 (L2K) complexation. (Reproduced from Ref. [5])

cytosolic (using the cytosolic isolation buffer; CIB), membrane/organelle (using the membrane/organelle isolation buffer; MIB), and nuclear/cytoskeleton (using the nuclear/cytoskeleton isolation buffer; NIB). 3. Determine the purity of the fractions via immunoblot for proteins GAPDH, COX IV, Rab7, and H3, as described previously in the immunoblot section. 4. Evaluate the fluorescence (Ex540/Em580) of each fraction using SpectraMax iD5 plate reader (Fig. 6). (ix) Flow Cytometric Analysis. 1. Transfect the hμglia cell line with 5 nM Cy3-labeled NANPs using L2K or DOTAP and incubate for 4 h. 2. Remove cells from tissue culture plates using 0.05% trypsin, and fix with 1% paraformaldehyde before flow cytometric analysis. 3. Evaluate NANP uptake by flow cytometric analysis using an Accuri C6 cytometer or similar instrument (Fig. 7). (x) Fluorescent Immunohistochemical Analysis. 1. Plate the hμglia cells on Poly-D-lysine-coated glass coverslips, and transfect cells with 5 nM Cy3-labled NANPs using L2K or DOTAP. Incubate cells for 4 h. 2. Fix cells with 4% paraformaldehyde, then permeabilize cells with 0.1% Triton-X-100, and block cells with 2% BSA. 3. Stain cells with a monoclonal rabbit antibody directed against EEA1. 4. Incubate cells with a polyclonal goat anti-rabbit secondary antibody coupled to Alexa Fluor 647.

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Fig. 6 Cellular fractionation into cytosolic (cytosolic isolation buffer, CIB), membrane/organelle (membrane/ organelle isolation buffer, MIB), and nuclear/cytoskeleton (nuclear/cytoskeleton isolation buffer, NIB) fractions. (a) Immunoblot analysis for evaluation protein expression of GAPDH, COX IV, histone 3 (H3), Rab7, and actin in fractions. (b) Illustration of cell fractionation protocol. Fluorescence of cell fractions at (c) 2 h and (e) 4 h. The (CIB/MIB) ratio showed at (d) 2 h and (f) 4 h. (Reproduced from Ref. [5])

5. Mount samples with Prolong Diamond antifade mountant with DAPI. 6. Image samples using an Olympus Fluoview 1000 fourcolor confocal laser microscope (Fig. 8). 3.1 Statistical Analysis

1. Present data as the mean ± standard error of the mean (SEM). 2. Perform statistical analyses on data using Student’s t-test, one-way analysis of variance (ANOVA) with Bonferroni’s or Tukey’s post hoc tests, or two-way ANOVA with Dunnet’s post hoc test using GraphPad Prism, GraphPad 15 Software. 3. Consider p-value of 90% viability. 3.2 Synthesis of RNA Rings

1. Prepare monomers of RNA rings through in vitro run-off transcription. For this, PCR-amplify DNA template (encoding RNA scaffold strand or short RNA antisense strand) and primer strands corresponding to each RNA ring monomer using 2× MyTaq Mix to net double-stranded DNA templates containing T7 RNA promotor sequences. 2. Purify these templates using DNA Clean & Concentrator kits prior to undergoing in vitro transcription with T7 RNA polymerase in the presence of 5 mM rNTPs, 50 mM DTT, 25 mM MgCl2, 2.5 mM spermidine, and 80 mM HEPES-KOH (pH 7.5) for 3.5 h at 37 °C. Stop transcription reactions with the addition of RQ1 RNase-Free DNase for 30 min at 37 °C. 3. Purify the RNA on a denaturing urea polyacrylamide gel, containing 8% polyacrylamide (w/v) and 8 M urea in 89 mM Trisborate (pH 8.20) buffer with 2 mM EDTA (1 × TBE) for 1.5 h at 75 mA. 4. Visualize RNA samples in the gel using a UV lamp to cut and elute them in 300 mM NaCl, 1 × TBE buffer overnight prior to precipitation in two volumes of 200 Proof ethanol. Centrifuge samples for 30 min at 10.0 G, rinse with 90% ethanol, centrifuge again at 10.0 G for 10 min twice, and vacuum dry. Resuspend RNA ring strands in endotoxin-free water (see Note 6). 5. Assemble hexameric RNA rings using equimolar amounts of the synthesized RNA ring strands and sense RNA strands (see Note 7). For GFP ring controls, add four equivalents of the GFP sense RNA strand. For LPCAT rings, add equivalents of LPCAT1, LPCAT2, LPCAT3 and LPCAT4 sense RNA strands. 6. Calculate each RNA strand concentration based on the absorbance at 260 nm using a NanoDrop 2000. Add equimolar amounts of RNA in endotoxin-free water, vortex, and heat the tubes at 95 °C for 2 min.

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7. Snap-cool by placing tubes on ice for 2 min (see Note 8). 8. Transfer samples to a 30 °C heat block and immediately add an assembly buffer at a final concentration of 89 mM Tris-borate (pH 8.20), 2 mM MgCl2, and 50 mM KCl. After incubating the samples at 30 °C for 30 min, transfer them to ice for immediate use or store samples at 4 °C (see Note 9 and Note 10). 3.3 Reverse Transfection of RNA Rings into Mammalian Cancer Cells

1. For each well to be transfected, prepare an RNA ring-liposome complex as follows (Fig. 1). Dilute RNA ring in medium without serum or improved Minimal Essential Medium (Opti-MEM™) (tube A). In a separate tube, dilute the cationic

Fig. 1 Representative scheme of reverse transfection protocol steps using a cationic lipid transfection reagent

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Table 1 Recommended transfection conditions for scaling up or down reverse transfection Surface area Plating media Transfection RNAi Culture plate (cm2) volume (mL) dilution media (mL) (nM) Transfection reagent (μL) 96-well

0.2

0.1

0.01

1–50 0.1–0.3

24-well

1.0

0.5

0.05

1–50 0.5–1.5

6-well

10.0

2.5

500

1–50 2.5–7.5

60 mm

20.0

5.0

1.0

1–50

5–15

100 mm

60.0

10.0

2.0

1–50

15–35

lipid transfection reagent in equal volume of medium without serum (tube B) (see Notes 11, 12, and 13). Mix gently each solution and incubate for 5 min at room temperature. 2. After incubation, add tube A to tube B, mix gently, and allow the complexes to form, undisturbed, at room temperature for 20 min (do not exceed 30 min). The two-step dilution method generates more reproducible results compared to adding the lipid directly to the diluted RNA. 3. Immediately add the RNA ring-liposome complexes, dropwise, to the cell culture well. Conditions are provided as a start point for optimization of transfection of RNA rings into mammalian cells in a 24-well plate, for scaling up or down transfection check Table 1 (see Note 14). 4. Dilute cells in complete growth media without antibiotics (see Notes 15 and 16) so that each well presents cells at 50–70% confluence 24 hours after plating. Add 400 μL of diluted cells to each well containing 100 μL RNA ring-liposome complexes, and mix gently by swirling (see Note 17). This gives a final RNA ring concentration of 10 nM in a final volume of 500 μL. 5. Incubate the cells 24–72 h at 37 °C in a CO2 incubator, and measure silencing efficiency by analyzing gene expression to determine the time point in which the target genes are downregulated (see Note 18). 3.4 Irradiation of Mammalian Cancer Cells Transfected with RNA Rings

1. After transfecting the RNA ring into the cells for the time necessary to downregulate the target genes, the culture medium needs to be replaced to discard any remaining RNA ring that has not been incorporated. This replenishment avoids possible interactions of free RNA rings with the radiation, which could signal to cancer cells.

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Fig. 2 Small animals and cells’ irradiator with six different chamber plate levels with each dose rate given by RS2000 160 kV manual, considering that dose decreases proportionally to 1/d2 (d is the distance between the X-rays window in X-ray tube until plate level). The X-ray tube accelerates and, posteriorly, forces the electrons to break with the tungsten anode. Therefore, the electrons deaccelerate releasing the kinetic energy given by the equipment in the form of X-rays (bremsstrahlung radiation)

2. Place the multiwell plate into the tray inside the exposure chamber of the biological X-ray irradiator. The tray can be manually adjusted for 6-height levels, and the plate must be placed within the circle (1–6) that corresponds to the number of tray height levels in order to receive optimal and uniform dosing (see Note 19). The circles correspond to the size of the radiation field at a corresponding height (Fig. 2). Caution: Handle the X-ray irradiator carefully according to the manufacturer’s instructions. 3. Set in the equipment the time of exposure that corresponds to the desired dose and plate level (Table 2) according to the following equation, and press Start (see Notes 20 and 21). Time of exposure ðmin Þ =

Desired dose ðGyÞ Dose rate ðGy= min Þ

4. Certify that the equipment door is well closed, and leave the room while your sample is being irradiated (see Note 22). After the irradiation ends, remove your sample, and maintain it in the incubator. After a predetermined period, perform a clonogenic assay, as follows.

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Table 2 Time of irradiation according to the dose rate given by the RS2000 160 kV brochure

Target Dose (Gy)

TRAY 1 Dose rate 1.1 Gy/min Time of irradiation (s)

TRAY 2 Dose rate 1.4 Gy/min Time of irradiation (s)

TRAY 3 Dose rate 2.0 Gy/min Time of irradiation (s)

TRAY 4 Dose rate 2.8 Gy/min Time of irradiation (s)

TRAY 5 Dose rate 4.5 Gy/min Time of irradiation (s)

TRAY 6 Dose rate 8.5 Gy/min Time of irradiation (s)

1

55

43

30

21

13

7

2

109

86

60

43

27

14

3

164

129

90

64

40

21

4

21S

171

120

86

53

28

5

273

214

150

107

67

35

6

327

257

180

129

80

42

7

332

300

210

150

93

49

8

436

343

240

171

107

56

9

491

386

270

193

120

64

10

545

429

300

214

133

71

20

1091

857

600

429

267

141

30

1636

1286

900

643

400

212

40

2182

1714

1200

857

533

282

50

2727

2143

1500

1071

667

353

5. Prepare a single-cell suspension by trypsinization, and count the number of cells per unit volume using a hemocytometer or an electronic cell counter. Seed a known number of cells in low density (e.g., 500) into a 60 cm2 petri dish or six-well plate at least in duplicate and incubate for 1–2 weeks until the surviving cells produce macroscopic colonies that can be counted. Fix with 6.0% glutaraldehyde, stain colonies with 0.5% crystal violet, and count the number of colonies (Fig. 3a). Calculate plate efficiency (PE), the ratio of the number of colonies to the number of cells seeded (i.e., unirradiated). After determining the PE, calculate the survival fraction, that is, the ratio of colonies formed by surviving cells after treatment, relative to cells plated with a correction for PE (Fig. 3b) [26]. Both formulas are described below. PE =

n ° of colonies formed in control × 100 n ° of cells seeded

Surviving fraction =

n ° of colonies formed after treatment ðn ° of colonies seeded × PEÞ

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Fig. 3 Cell survival assay workflow (a). Calculate plate efficiency (b upper panel) and comparison of petri dishes (b lower panel) of unirradiated and irradiated cancer cells transfected either with RNA ring control or RNA ring LPCATs

4

Notes 1. It is very important to check which culture medium and supplements are required for the specific cell line before starting the cell cultures. Cell lines maintained in a non-appropriated culture medium may alter their genotypic and phenotypic characteristics. 2. All reagents for cell growth and preservation must be sterile to prevent contamination by microbial growth. The reagents and supplements that are not purchased sterile will require filter sterilization at 0.2 μm. 3. Considering that most cancer cells are adherent, we described the protocol for adherent cells. The main difference for the maintenance of non-adherent or suspension cell lines is that they are subcultured based on volumes (cells/mL), and adherent cells are subcultured based on flask surface area (cells/cm2). 4. Cells should be monitored daily, checking under the microscopy their morphology, growth rates, confluency (do not exceed 80% of surface area covered with cell monolayer), and signals of contamination. Unusual changes in medium pH (yellow or purple color from the phenol red) and turbidity are often associated with contamination or unhealthy cells. Cells should be discarded if alterations in morphology and growth rates or unusual medium color changes are observed since these can negatively affect gene expression and cell

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viability. In addition to daily examinations, cell lines must also be authenticated by short-tandem repeat profiling and regularly tested and verified to be mycoplasma-negative. 5. Sequence design of sense RNA strands can be performed via the RNA secondary structure prediction and sequence design web server at http://matchfold.abcc.ncifcrf.gov/. It is important to compare the predicted secondary structure with the desired target structure of the RNA complex. The antisense sequences are programmed into hexagonal ring nanoscaffolds as an extension of either 3’or 5′ ends. 6. Resuspend freshly prepared or purchased RNA molecules in a Tris-EDTA (TE) or ddiH2O buffer, and keep on ice for several hours or freeze. Please note that if the samples are frozen, they must be vortex-mixed for 10 s before use. 7. For hexameric RNA rings, the final RNA concentrations (sense-concatenated strands and their corresponding short antisense strands) should be six times higher than the desirable concentration of functionalized RNA NANPs. For example, if the desirable RNA NANP concentration is 1 μM, the concentration of RNAs in the RNA rings should be 6 μM. 8. Do not incubate the mixture longer than 2 min as it can promote the degradation of RNAs. The first step in selfassembly by heating and immediately cooling before the addition of the assembly buffer is essential for the correct folding and formation of the desired secondary structure of RNA. 9. RNA rings should be kept on ice during the pipetting, and after assembling they can be stored at 4 °C for several weeks. Do not freeze the assemblies. 10. Quality control experiments using Native PAGE or DLS should be performed to check the success of duplex RNA formation. 11. Cellular transfection is the introduction of exogenous nucleic acids into mammalian cells by different methods. The method described here consists of using lipid-based transfection reagents to generate lipid-nucleic acid complexes. These complexes can be added through two different ways. In forward transfection, the transfection reagent mixture is added to cells already attached, while reverse transfection is conducted in the opposite way, by adding cells in suspension to pre-plated lipidnucleic acid complexes. Reverse transfection is defined as a mechanical method of introducing exogenous nucleic acids into eukaryotic cells while simultaneously plating them. This type of transfection has some advantages, such as higher transfection efficiency due to higher probability of cell-nucleic acid contact, it is less laborious, and it requires less nucleic acid for the success of transfection.

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12. There are different commercially available cationic lipid transfection reagents that provide some advantages over other transfection reagents, warranting higher transfection efficiencies, minimal toxicity, and achievement of high knockdown levels with low concentrations of RNAi duplexes. 13. To avoid pipetting too small volumes, prepare a mastermix in a tube. For example, to prepare a triplicate, pipette three times the volumes of each reagent in a single 1.5 mL tube. After the RNA ring-liposome complex incubation, add 106 μL of it to each well of a 24-well plate. 14. The concentration of RNAi duplex and transfection reagent may vary depending on the cell type, efficacy of the duplex, half-life of the target mRNA, and the turnover of the target protein. It is recommended to test different concentrations of RNAi duplex and transfection reagent, as shown in Table 1 to obtain the highest transfection efficiency. Additionally, cell density at the time of transfection can affect the transfection efficiency. Few cells can cause decrease in cell growth due to poor cell-to-cell contact; on the contrary, too many cells can result in contact inhibition and decrease the uptake of foreign nucleic acids. To optimize the transfection reaction, it is necessary to adjust the cell density to a log phase to have actively dividing cells. 15. The presence of antibiotics in the media during transfection can cause cell death. 16. There are some transfection reagents that are not compatible with serum once it can interfere with transfection reagents and compromise the transfection efficiency. Refer to the reagent manufacturer’s recommendations to assure that the chosen transfection reagent is compatible with serum. 17. It is not necessary to change the medium after transfection. 18. Gene silencing is usually measured between 24 and 72 h after transfection for measuring mRNA levels and 48 and 96 h to quantify proteins. As a quality control, check whether silencing the target gene does not affect cell viability. 19. Since the dose distribution inside the irradiator is not homogeneous neither in different heights nor ratios from the central irradiation axis [27], it is extremely important that the plates are placed in the same position in case of multiple irradiations, so that the measure will be reproducible. 20. Most equipment already comes with the dose rate per chamber plate level in its manual instructions; nevertheless, it is important that the responsible physicist execute periodic experiments to measure if the irradiator is working precisely.

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21. After confirming the dose rate given by the irradiator in each plate level, the physicist provides a “time of exposure” table. An example according to RS2000 160 kV dose rate can be seen in Table 2. 22. To execute the experiment with minimum risk of receiving scattered doses, it is recommended that the researcher stays at least 3 feet from the irradiator while it is on.

Acknowledgments This research was also supported in part by a FAPESP-USP SPRINT grant from The Graduate School at the University of North Carolina at Charlotte and Sao Paulo Research Foundation (FAPESP) under FAPESP Grants 2017/50029-6 and CNPq grants 426714/2016-0 and 305700/2017-0 (to R.C.) and a CAPES fellowship (to I.N.F). The authors would also like to thank Mara de Souza Junqueira, MSc, for performing cell irradiation at the University of Sa˜o Paulo. References 1. Jin J, Zhao Q (2020) Engineering nanoparticles to reprogram radiotherapy and immunotherapy: recent advances and future challenges. J Nanobiotechnol 18:75 2. Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE (2022) Opportunities for improving brain cancer treatment outcomes through imagingbased mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 187:114367 3. Jiao X, Yu Y, Meng J, He M, Zhang CJ, Geng W, Ding B, Wang Z, Ding X (2019) Dual-targeting and microenvironmentresponsive micelles as a gene delivery system to improve the sensitivity of glioma to radiotherapy. Acta Pharm Sin B 9:381–396 4. Huang C, Chen T, Zhu D, Huang Q (2020) Enhanced tumor targeting and radiotherapy by quercetin loaded biomimetic nanoparticles. Front Chem 8:225 5. Liyanage PY, Hettiarachchi SD, Zhou Y, Ouhtit A, Seven ES, Oztan CY, Celik E, Leblanc RM (2019) Nanoparticle-mediated targeted drug delivery for breast cancer treatment. Biochim Biophys Acta Rev Cancer 1871: 419–433 6. Afonin KA, Viard M, Koyfman AY, Martins AN, Kasprzak WK, Panigaj M, Desai R, Santhanam A, Grabow WW, Jaeger L,

Heldman E, Reiser J, Chiu W, Freed EO, Shapiro BA (2014 Oct 8) Multifunctional RNA nanoparticles. Nano Lett 14(10):5662–5671 7. Orth M, Lauber K, Niyazi M, Friedl AA, Li M, Maiho¨fer C, Schu¨ttrumpf L, Ernst A, Niemo¨ller OM, Belka C (2014) Current concepts in clinical radiation oncology. Radiat Environ Biophys 53:1–29 8. ESMO Interactive Guidelines. http://inter activeguidelines.esmo.org/esmo-web-app/ toc/index.php?subjectAreaID=13& loadPdf=1. Accessed 21 Sep 2022 9. Kirthi Koushik AS, Harish K, Avinash HU (2013) Principles of radiation oncology: a beams eye view for a surgeon. Indian J Surg Oncol 4:255–262 10. Pawlik TM, Keyomarsi K (2004) Role of cell cycle in mediating sensitivity to radiotherapy. Int J Radiat Oncol Biol Phys 59:928–942 11. Galluzzi L, Maiuri MC, Vitale I, Zischka H, Castedo M, Zitvogel L, Kroemer G (2007) Cell death modalities: classification and pathophysiological implications. Cell Death Differ 14:1237–1243 12. Velic D, Couturier A, Ferreira M, Rodrigue A, Poirier G, Fleury F, Masson J-Y (2015) DNA damage signalling and repair inhibitors: the long-sought-after Achilles’ heel of cancer. Biomol Ther 5:3204–3259

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13. Wang J-S, Wang H-J, Qian H-L (2018) Biological effects of radiation on cancer cells. Mil Med Res 5 14. Shao C, Folkard M, Michael BD, Prise KM (2004) Targeted cytoplasmic irradiation induces bystander responses. Proc Natl Acad Sci U S A 101:13495–13500 15. Carvalho H de A, Villar RC (2018) Radiotherapy and immune response: the systemic effects of a local treatment. Clinics 73:e557s 16. Saito RF, Rangel MC, Halman JR, Chandler M, de Sousa Andrade LN, OdeteBustos S, Furuya TK, Carrasco AGM, ChavesFilho AB, Yoshinaga MY, Miyamoto S, Afonin KA, Chammas R (2021) Simultaneous silencing of lysophosphatidylcholine acyltransferases 1-4 by nucleic acid nanoparticles (NANPs) improves radiation response of melanoma cells. Nanomedicine 36:102418 17. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646– 674 18. Rodrı´guez ML, Lo´pez Rodrı´guez M, Cerezo Padellano L (2007) Toxicity associated to radiotherapy treatment in lung cancer patients. Clin Transl Oncol 9:506–512 19. Cui C, Yang J, Li X, Liu D, Fu L, Wang X (2020) Functions and mechanisms of circular RNAs in cancer radiotherapy and chemotherapy resistance. Mol Cancer 19:58 20. Shu Y, Pi F, Sharma A, Rajabi M, Haque F, Shu D, Leggas M, Evers BM, Guo P (2014) Stable RNA nanoparticles as potential new

generation drugs for cancer therapy. Adv Drug Deliv Rev 66:74–89 21. Chen Z, Krishnamachary B, Pachecho-Torres J, Penet M, Bhujwalla ZM (2020) Theranostic small interfering RNA nanoparticles in cancer precision nanomedicine. WIREs Nanomed Nanobiotechnol 12:e1595 22. Aagaard L, Rossi JJ (2007) RNAi therapeutics: principles, prospects and challenges. Adv Drug Deliv Rev 59:75–86 23. Arshad R, Fatima I, Sargazi S, Rahdar A, Karamzadeh-Jahromi M, Pandey S, Dı´ez-Pascual AM, Bilal M (2021) Novel perspectives towards RNA-based nano-theranostic approaches for cancer management. Nanomaterials (Basel) 11:34. https://doi.org/10. 3390/nano11123330 24. Guo P (2010) The emerging field of RNA nanotechnology. Nat Nanotechnol 5:833–842 25. Hong CA, Nam YS (2014) Functional nanostructures for effective delivery of small interfering RNA therapeutics. Theranostics 4:1211– 1232 26. Franken NAP, Rodermond HM, Stap J, Haveman J, van Bree C (2006) Clonogenic assay of cells in vitro. Nat Protoc 1(5):2315–2319. https://doi.org/10.1038/nprot.2006.339 27. Bruno AC, Colello Bruno A, Mazaro SJ, Amaral LL, Rego EM, Oliveira HF, Pavoni JF (2017) Biological X-ray irradiator characterization for use with small animals and cells. Braz J Med Biol Res 50:9

Chapter 19 Aptamer Conjugated RNA/DNA Hybrid Nanostructures Designed for Efficient Regulation of Blood Coagulation Lewis A. Rolband, Weina Ke, and Kirill A. Afonin Abstract Disruptions to the hemostatic pathway can cause a variety of serious or even life-threatening complications. Situations in which the coagulation of blood has become disturbed necessitate immediate care. Thrombinbinding aptamers are single-stranded nucleic acids that bind to thrombin with high specificity and affinity. While they can effectively inhibit thrombin, they suffer from rapid degradation and clearance in vivo. These issues are resolved, however, by attaching the therapeutic aptamer to a nucleic acid nanostructure. The increased size of the nanostructure-aptamer complex elongates the post-infusion half-life of the aptamer. These complexes are also immunoquiescent. A significant benefit of using nucleic acids as anticoagulants is their rapid deactivation by the introduction of a nanostructure made fully from the reverse complement of the therapeutically active nanostructure. These advantages make nanoparticle conjugated antithrombin aptamers a promising candidate for a rapidly reversible anticoagulant therapy. Key words Thrombin, Anticoagulant, Aptamer, RNA/DNA hybrid

1

Introduction Aptamers are emerging as a clinically relevant category of therapeutic nucleic acids. Owing to their low cost, high affinity, high specificity, and biodegradability, aptamers have become increasingly researched for their therapeutic potential [1, 2]. By binding a protein with a specifically targeted aptamer, the function of that protein can be disrupted [1, 3, 4]. A major benefit of using aptamers for anticoagulation is that the restoration of coagulation can be readily accomplished by introducing the reverse complement oligo, which inactivates the aptamer by forming a duplex with it [4–7]. A key issue with the use of aptamers as anticoagulants has been their rapid clearance, which has hindered their broader clinical use [8–10]. By attaching aptamers to RNA/DNA hybrid nanostructures, fibers in this example (aptamer fibers), the post-infusion half-life, and blood stability of the aptamer can be improved by

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_19, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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increasing its size/molecular weight [1]. The nanostructureaptamer complexes also retain the ability to be deactivated by the introduction of the reverse complement nanostructure to form short duplexes which are rapidly excreted [1]. The deactivation of the aptamers by the appropriate antidote oligonucleotide, typically the reverse-complement strand of the aptamer, takes place, primarily, through Watson-Crick base pairing [1, 11, 12]. Through the disruption of the aptamer structure, the bound protein is released and able to return to its standard function [1]. In order to most effectively incorporate the aptamer to the nanostructure, the aptamer sequence can be added to the 5′ or 3′ end of one of the constituent oligos of the nanostructure or by using toe-hold regions [1, 13]. To ensure that no unintended structures will be formed, it is recommended that the sequences to be used are checked against each other and optimized, if necessary, with a secondary structure prediction tool, such as NUPACK [14]. The aptamer fiber used in this example is an adaptation of the NU172 formulation of ARC183, which was the first clinically tested thrombin-binding aptamer [1, 15–17]. This was done by extending the 3′ end of the DNA section of an RNA/DNA hybrid fiber with the NU172 sequence. The RNA fills gaps between the DNA toe-hold ends to maintain the double-helical structure of the construct. With one aptamer on each DNA strand in the fiber, the fiber is potentially able to bind multiple thrombin molecules. Due to the differences in thermodynamic stability of the fibrous structures as opposed to RNA/RNA and DNA/DNA duplexes that are fully complementary, the aptamer fiber is able to be deactivated by the administration of an antidote fiber, which is the reverse complement of the therapeutic fiber. This deactivation occurs through the isothermal association of fully complementary RNA and DNA duplexes as the aptamer and antidote fibers combine in solution [1]. Blood coagulation is a biochemical process that prevents blood loss due to blood vessel damage. It is complex as it involves not only the plasma proteins, thrombocytes, or platelets but other cells, such as leukocytes and endothelial cells [18]. The interaction among these components is critical in maintaining hemostasis and preventing life-threatening bleeding. When a nanoparticle formation is administrated systemically in blood, its effect on blood coagulation must be assessed. There are three main pathways involved in the coagulation cascade: intrinsic (refer to the contact activation pathway which is activated by damaged internal surfaces); extrinsic (refer to the tissue factor pathway which is activated by damaged external surfaces) [19]; and intrinsic and extrinsic pathways converge into the common pathway, specifically at thrombin. As thrombin is the enzyme where the intrinsic and extrinsic pathways meet, it is a promising target for anticoagulant medications and one

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of the most common targets for aptamer-based anticoagulants [1, 11]. The functionality of each pathway can be evaluated separately using specialized tests. The activated partial thromboplastin time (APTT) assay is used to assess the intrinsic pathway, the prothrombin time (PT) assay measures the extrinsic pathway, and the thrombin time (TT) indicates the functionality of the common pathway. RNA/DNA nanostructures are incubated with fresh collected and pooled human plasma. If the nanoparticle interacts with any plasma proteins responsible for the blood clot formation pathway, this interaction may alter the protein function, which will be reflected by an increase in coagulation time when comparing the coagulation time with standard controls for each assay, as is the case for aptamer fibers [1].

2

Materials 1. Computer with appropriate oligonucleotide secondary structure prediction software (e.g., NUPACK) [14]. 2. Human blood from at least three donors. 3. Neoplastine Cl. This reagent is supplied as lyophilized powder and the reconstitution buffer. Reconstitute condition according to the manufacturer’s instructions, and use within the time specified by the manufacturer [20] (see Note 1). 4. Thrombin. 5. 0.025 M CaCl2. 6. Owren-Koller buffer. 7. PTT-A reagent. 8. Normal and N + ABN).

abnormal

control

plasma

(CoagControl

9. RPMI-1640 Cell Culture Media. 10. Sterile and endotoxin-free phosphate buffered saline solution. 11. Pipettes covering the range of 0.05–10 mL. 12. 1.25 mL Finntip pipette tips. 13. 4-well cuvettes. 14. Metal balls for coagulometer. 15. Coagulometer (Diagnostica Stago Art4) (see Note 1). 16. Refrigerator. 17. Centrifuge capable of operating at 2500× g. 18. Purified oligonucleotides for the intended nanostructure.

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19. Hybridization buffer (89 mM Tris, 80 mM boric acid (pH 8.2), 2 mM magnesium chloride, 2 mM potassium chloride) (see Note 2). 20. Dry heat bath. 21. Gel imager or UV-transilluminator. 22. 8% non-denaturing polyacrylamide (19:1) gel. 23. Power source for gel electrophoresis. 24. Gel electrophoresis chamber. 25. Ethidium bromide (0.5 μg/mL). 26. LAL assay kit (if using samples for further immunological or in vivo testing) (see Note 3).

3

Methods

3.1 Preparation of Study Samples

1. Assemble the aptamer fibers by combining individual monomers at their appropriate concentrations in hybridization buffer, made with LAL grade water, and dilute to the appropriate volume with LAL grade water. 2. Heat the solution to 95 °C for 5 min. 3. Incubate the solution at room temperature for 20 min. 4. Analyze the assembly of the nanostructures at 4 °C on 8% non-denaturing native polyacrylamide (19:1) gel electrophoresis (native-PAGE) run for 30 min at 300 V in hybridization buffer. 5. Stain the gels with ethidium bromide (0.5 μg/mL) or by using the fluorescence of labeled oligonucleotides. Visualize the assemblies with a gel imager or UV-transilluminator. 6. All assemblies for immunological and in vivo studies should be further tested for bacterial endotoxins by a kinetic turbidity limulus amoebocyte lysate (LAL) assay.

3.2 Preparation of Test and Normal and Abnormal Control Plasmas

1. Freshly collected whole blood is used within 1 h after collection.

3.2.1 Blood Sample Preparation and General Testing Guidelines

3. Pooled plasma is stable for 8 h at room temperature. Do not refrigerate or freeze.

2. Spin the blood for 10 min, 2500× g at 20–22 °C; collect plasma and pool from at least two donors.

4. Analyze two duplicates (four total samples) of test plasma in each coagulation assay; to verify that the plasma functionality is not affected throughout the experiment, run one duplicate before the nanoparticle samples and the second duplicate at the end of each run.

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3.2.2 Test Plasma (With Aptamer-NANPs)

Combine 50 μL of aptamer-NANPs and 450 μL of test plasma in a microcentrifuge tube; mix well and incubate for 30 min at 37 °C.

3.2.3 Normal and Abnormal Control Plasmas

1. Use 1 mL of distilled water to reconstitute each of the lyophilized control plasmas. 2. Leave the solutions at room temperature for 30 min prior and mix thoroughly before use. 3. Keep the unused portion refrigerated, and use it within 48 h after reconstitution. These plasma samples are used as instrument controls.

3.2.4 Neoplastin, PTT-A Reagent, and Thrombin Preparation (Used to Initiate Plasma Coagulation)

3.3 Plasma Coagulation Assay Procedure

Lyophilized assay-specific reagents used in this assay are for initiating plasma coagulation, including Neoplastine for PT assay, PTT-A reagent for APTT assay, and thrombin for TT assay. Reconstitute the reagents according to the manufacturer’s instructions and use fresh or refrigerated, use them within the time specified by the manufacturer. 1. Allow the instrument to warm up for 5–10 min prior to use. Set up instrument test parameters for each assay (Table 1). 2. Prepare all reagents and warm them to 37 °C prior to use. All lyophilized reagents should be reconstituted at least 30 min prior to use. 3. Place cuvettes on the coagulometer into A, B, C, and D test rows (see Note 4.1). 4. Add one metal ball into each cuvette, and allow the cuvette and ball to warm for at least 3 min prior to use. 5. For PT or TT test, add 100 μL of control or test plasma to a cuvette. For APTT test, add 50 μL (Table 1). Prepare two wells for each test tube prepared in step 3.2.2. 6. This step is only for APTT: Add 50 μL of PTT-A reagent to plasma samples in cuvettes. 7. Pressing the A, B, C, or D timer buttons to start the timer for each test row. The timer will beep 10 s before the time is up. When this happens, immediately transfer cuvettes to the PIP row, and press the PIP button to activate the pipettor (see Note 4.1). 8. Depending on which assay is performed, add the corresponding coagulation activation reagent and volume to each cuvette, and record the coagulation time when the time is up (Table 1).

3.4 Calculations and Data Interpretation

1. For all three assays (PT, APTT, and TT), a percent coefficient of variation should be calculated for each control or test sample SD according to the following formula: %CV = Mean × 100%.

Control

Coag. control N + ABN

Coag. control N + ABN

Coag. control N + ABN

Assay

PT-prothrombin time (Neoplastine)

APTT-activated partial thromboplastin time

TT-thrombin time 60

120

60

60

180

120

Max Incubation time (s) time (s)

Instrument settings

Duplicate

Duplicate

Duplicate

5%

5%

5%

100 μL Neoplastine (PIP position 4)

Coagulation activation reagent

50 μL PTTA reagent

≤34.1

≤13.4

Normal Coagulation time (s)

100 μL thrombin (PIP ≤21 position 4)

50 μL plasma + 50 μL 50 μL CaCl2 (PIP position 2) PTTA reagent

100 μL plasma

Single/ Plasma and reagent Duplicate Precision volumns

Volumes

Table 1 Types of plasma coagulation assay, its control, instrument setting, plasma and coagulation activation reagent volumes, and normal coagulation time

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%CV between replicates of test plasma samples should be within 25%. If the test samples have a %CV greater than 25, the test samples should be reanalyzed. This assay performance requirement is based on the requirements for bioanalytical method validation guidance for the industry [21]. 2. Normal and abnormal control plasma should have coagulation time within the time established by the certifying laboratory (e.g., for the most batches of control plasmas, normal coagulation time in the PT assay is ≤13.4 s, APTT is ≤34.1 s, and TT is ≤21 s; abnormal control plasma coagulation time should be above these limits) (Table 2). When the untreated plasma sample coagulates within normal time limits, and normal and abnormal control perform as described above, both the instrument and the test plasma are qualified for use in this test. 3. When the coagulation time of the test plasma samples after exposure to nanoparticles is within normal limits, nanoparticles do not affect the assay coagulation cascade. 4. When the plasma coagulation time in plasma samples exposed to nanoparticles is prolongated, it suggests that the test particle either depletes or inhibits coagulation factors. There is no guidance on the degree of prolongation, but in general, a prolongation of twofold or more than that in the untreated control is considered physiologically significant.

4

Notes 1. This protocol is based on the semi-automatic STArt4 coagulometer from Diagnostica Stago [20]. If using a different instrument, please follow the operational guidelines recommended by the instrument manufacturer. 2. It is critical to ensure that the buffer contains a sufficient amount of K+ as this ion is critical for the formation of G-tetraplexes, which are commonly found in thrombin binding aptamers [22]. 3. While they are a necessary step in the development of any therapeutic agent, immunological and in vivo testing are outside of the scope of this chapter and, as such, will not be discussed here.

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Table 2 Plasma coagulation assessment: (Top) Results of Prothrombin Time (PT), Activated Partial Thromboplastin Time (APTT), and Thrombin Time (TT) of Anti-Thrombin Fibers for Their Abilities in Delaying the Coagulation in Donors from the United States and Brazil, Displaying Some Minor Regional Variations; (Bottom) Restoration of Normal Coagulation Time by Addition of Kill-Switch Fibers [1] PT

APTT

Sample

U.S. data (13.4 s)

Brazil data (12.1 s)

U.S. data (37.0 s)

NU fiber

17.3 ± 0.2

16.6 ± 0.3

>120.00

RA fiber

16.6 ± 0.4

19.7 ± 0.2

NU/RA fiber 16.8 ± 0.4 DNA-RNA fiber

TT Brazil data (36.5 s)

U.S. data (21.0 s)

Brazil data (16.6 s)

89.4 ± 1.4

>60.00

26.8 ± 0.2

70.9 ± 5.0

104.7 ± 1.9

>60.00

31.6 ± 0.6

17.7 ± 0.0

81.3 ± 4.3

100.4 ± 1.5

>60.00

28.6 ± 0.0

11.8 ± 0.3

13.5 ± 0.2

36.7 ± 0.8

40.2 ± 0.1

15.8 ± 0.7

19.3 ± 0.1

Plasma

10.8 ± 0.2

13.6 ± 0.2

33.4 ± 1.2

28.6 ± 0.3

16.6 ± 0.3

19.4 ± 0.1

NU172 aptamer

10.9 ± 0.2

14.1 ± 0.2

38.0 ± 1.5

39.5 ± 0.1

15.3 ± 0.8

19.0 ± 0.2

RA-36 aptamer

11.3 ± 0.4

15.3 ± 0.5

41.5 ± 3.2

45.5 ± 0.8

16.6 ± 0.8

26.5 ± 0.5

Sample

PT (13.4 s)

APTT (37.0 s)

TT (21.0 s)

Control (normal)

12.7 ± 0.2

36.8 ± 1.5

21.4 ± 0.6

Control (abnormal)

20.7 ± 0.4

71.8 ± 1.4

41.3 ± 1.4

9.7 ± 0.0

29.4 ± 0.2

16.1 ± 0.3

NU172 aptamer

16.1 ± 0.5

37.9 ± 0.7

25.3 ± 1.8

RA-36 aptamer

17.3 ± 0.3

44.2 ± 0.6

45.0 ± 1.4

NU fiber

18.6 ± 1.7

101.7 ± 2.0

59.5 ± 0.5

NU fiber + kill-switch

10.9 ± 0.1

36.7 ± 0.2

17.5 ± 0.4

RA fiber

20.8 ± 0.1

89.9 ± 1.1

>60.00

RA fiber + kill-switch

13.7 ± 0.1

42.9 ± 0.3

44.7 ± 0.1

NU/RA fiber

16.8 ± 0.9

95.3 ± 3.0

>60.00

NU/RA fiber + kill-switch

10.6 ± 0.3

38.5 ± 0.4

18.4 ± 0.6

Plasma

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Acknowledgments Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K.A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References 1. Ke W, Chandler M, Cedrone E, Saito RF, Rangel MC, De Souza JM et al (2022) Locking and unlocking thrombin function using Immunoquiescent nucleic acid nanoparticles with regulated retention in vivo. Nano Lett 22(14): 5961–5972 2. Panigaj M, Johnson MB, Ke W, McMillan J, Goncharova EA, Chandler M et al (2019) Aptamers as modular components of therapeutic nucleic acid nanotechnology. ACS Nano 13(11):12301–12321 3. Dupont DM, Thuesen CK, Bøtkjær KA, Behrens MA, Dam K, Sørensen HP et al (2015) Protein-binding RNA aptamers affect molecular interactions distantly from their binding sites. PLoS One 10(3):e0119207 4. Krissanaprasit A, Key CM, Froehlich K, Pontula S, Mihalko E, Dupont DM et al (2021) Multivalent aptamer-functionalized single-strand RNA origami as effective, targetspecific anticoagulants with corresponding reversal agents. Adv Healthc Mater 10(11): 2001826 5. Chan MY, Rusconi CP, Alexander JH, Tonkens RM, Harrington RA, Becker RC (2008) A randomized, repeat-dose, pharmacodynamic and safety study of an antidote-controlled factor IXa inhibitor. J Thromb Haemost 6(5): 789–796 6. Dyke CK, Steinhubl SR, Kleiman NS, Cannon RO, Aberle LG, Lin M et al (2006) First-inhuman experience of an antidote-controlled anticoagulant using RNA aptamer technology. Circulation 114(23):2490–2497 7. Woodruff RS, Sullenger BA (2015) Modulation of the coagulation Cascade using aptamers. Arterioscler Thromb Vasc Biol 35(10): 2083–2091 8. Becker R, Povsic T, Cohen MG, Rusconi C, Sullenger B (2010) Nucleic acid aptamers as antithrombotic agents: opportunities in extracellular therapeutics. Thromb Haemost 103(3):586–595

9. Zavyalova E, Samoylenkova N, Revishchin A, Golovin A, Pavlova G, Kopylov A (2014) Evaluation of antithrombotic activity of thrombin DNA aptamers by a murine thrombosis model. PLoS ONE 9(9):e107113 10. Mayer G, Rohrbach F, Po¨tzsch B, Mu¨ller J (2011) Aptamer-based modulation of blood coagulation. Hamostaseologie 31(04): 258–263 11. Vaganov AA, Taranushenko TE, Luzan NA, Shchugoreva IA, Kolovskaya OS, Artyushenko PV et al (2022) Aptamers regulating the hemostasis system. Molecules 27(23):8593 12. Gahlon HL, Sturla SJ (2013) Hydrogen bonding or stacking interactions in differentiating duplex stability in oligonucleotides containing synthetic nucleoside probes for alkylated DNA. Chem Eur J 19(33):11062–11067 13. Afonin KA, Viard M, Koyfman AY, Martins AN, Kasprzak WK, Panigaj M et al (2014) Multifunctional RNA nanoparticles. Nano Lett 14(10):5662–5671 14. Zadeh JN, Steenberg CD, Bois JS, Wolfe BR, Pierce MB, Khan AR et al (2011) NUPACK: analysis and design of nucleic acid systems. J Comput Chem 32(1):170–173 15. Kretz CA, Stafford AR, Fredenburgh JC, Weitz JI (2006) HD1, a thrombin-directed aptamer, binds exosite 1 on prothrombin with high affinity and inhibits its activation by Prothrombinase. J Biol Chem 281(49):37477–37485 16. Bock LC, Griffin LC, Latham JA, Vermaas EH, Toole JJ (1992) Selection of single-stranded DNA molecules that bind and inhibit human thrombin. Nature 355(6360):564–566 17. Di Cera E (2008) Thrombin. Mol Asp Med 29(4):203–254 18. Potter TM, Rodriguez JC, Neun BW, Ilinskaya AN, Cedrone E, Dobrovolskaia MA (2018) In vitro assessment of nanoparticle effects on blood coagulation. Methods Mol Biol 1682: 103–124

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19. Lakna: difference between intrinsic and extrinsic pathways in blood clotting. https://pediaa. com/difference-between-intrinsic-and-extrin sic-pathways-in-blood-clotting/ (2018) 20. STart4 standard operating procedure and training manual. Diagnostica Stago(June 2002)

21. HHS FDA/CDER/CVM. Bioanalytical method validation. Guidance for industry 22. Largy E, Mergny J-L, Gabelica V (2016) Role of alkali metal ions in G-quadruplex nucleic acid structure and stability. Springer, Cham, pp 203–258

Chapter 20 Detection of Multiplex NASBA RNA Products Using Colorimetric Split G Quadruplex Probes Maria S. Rubel, Liubov A. Shkodenko, Daria A. Gorbenko, Valeria V. Solyanikova, Yulia I. Maltzeva, Aleksandr A. Rubel, Elena I. Koshel, and Dmitry M. Kolpashchikov Abstract Structural RNA is a challenging target for recognition by hybridization probes. This chapter addresses the recognition problem of RNA amplicons in samples obtained by multiplex nucleic acid sequence-based amplification (NASBA). The method describes the design of G-quadruplex binary (split) DNA peroxidase sensors that produces colorimetric signal upon recognition of NASBA amplicons. Key words Folded RNA, Split (binary) probes, Peroxidase-based DNAzyme, Nucleic acid sequencebased amplification (NASBA), Visual detection

1

Introduction Complex and stable RNA structures can both reduce the yield and slow down the probe-RNA complex formation. One possible solution is to design long hybridization probes that tightly bind and unwind RNA secondary structure. However, such long probes are insensitive to single nucleotide variations (SNV) and can nonspecifically bind partially complementary fragments and form intramolecular structure themselves. The latter will reduce the efficacy of probe-RNA complex formation. Alternatively, nucleic acid analogues with high affinity to RNA (e.g., locked nucleic acid (LNA) or peptide nucleic acids (PNA)) can improve probe-RNA complex formation [1, 2]. At the same time, inclusion of fragments with high affinity can again affect probe specificity. Therefore, the design of the probe for sequence-specific recognition of RNA fragments is a challenging task [3, 4]. Our approach to the probe design starts with splitting the sequence of analyzed RNA in two adjacent fragments: a long and

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a) m probe

f probe

RNA Hybridization probes

Signal m probe

f probe

RNA

b)

DAB G hemin G Parallel G-quadruplex G G Linker m probe

[DAB]n G G G G G G f probe

RNA fragment

Fig. 1 (a) Demonstration of an effect of an asymmetric probe on unwinding complex secondary structures. (b) Binary peroxidase sensors (biPxD) in complex with complementary RNA fragment. Probe f hybridizes to RNA fragment and unwinds its secondary structure. Probe m with short analyte-binding arm is responsible for the specific recognition of the RNA sequence. DAB oxidation takes place only when both probes f and m bind RNA

a short one (Fig. 1). The long fragment is recognized by a long probe, which tightly binds and unwinds the RNA secondary structure. The short fragment is complementary to the second probe strand, which recognizes the analyte with high selectivity. Only when both of the probes are bound, the recognition signal is formed [5–8]. Here, we describe the design of three probes for detection of RNA amplicons obtained by nucleic acid sequencebased amplification (NASBA). NASBA is the amplification technique that produces singlestranded RNA amplicon using RNA as an initial input [9]. Unlike

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PCR, the method does not require thermocycler and is compatible with low-cost portable detection assays. During the first NASBA cycle, a DNA molecule complementary to the RNA input is synthesized by a reverse transcriptase; then a RNase H digests the RNA strand in the RNA/DNA complex. This is followed by synthesis of second DNA strand by the reverse transcriptase. At this point, a T7 promotor is introduced in the double-stranded DNA with one of the primers. A T7 RNA polymerase produces new RNA transcripts that can be turned back into the cycle. The process occurs at 41 °C. NASBA is used for the detection of such pathogens as Zika virus [10], SARS-CoV-2 [11], or HIV [12] and is a core technology in RTisochip™-W system and NUCLISENS EASYQ®. Gel electrophoresis and RNA staining dyes are normally used to detect the NASBA amplicons [13]. Luminescent [14] or fluorescent probes [15] have been applied in multiplex detection assays. The fluorescent probes require light sources and specific equipment for output readout. Colorimetric visualization is more suitable for point-of-care or field testing, since it can be analyzed with a naked eye. G-quadruplex-based binary peroxidase sensors (biPxD, Fig. 1) have been introduced earlier for visual detection of nucleic acids with selectivity down to a single base substitution [16, 17]. These probes consist of two RNA binding arms (probe sequence) linked to G-quadruplex-forming sequences. Once the arms bind the complementary sequence in adjacent positions, they form a G-quadruplex, which can oxidize a colorless organic substrate into a colored product in the presence of hemin and hydrogen peroxide [18]. Such sensors can successfully detect ssDNA targets including exonuclease-treated PCR products [19], RNA amplicons [20, 21], or even double-stranded DNA amplicons [22]. Here we describe a triplex NASBA for amplification of RNA fragments of viral and fungal pathogens: Candida albicans, Varicella zoster virus (VZV), and Herpesvirus sixth type (HH6). We designed three biPxD sensors for detection of the NASBA amplicons and demonstrated accurate sensor response for all possible combinations of pathogens in samples.

2

Materials

2.1 General Supplies and Equipment

1. Microcentrifuge tubes: 0.5 mL or 1.5 mL (DNase/RNasefree). 2. Deionized DNase/RNase-free water. 3. Pipets: 0.2–2 μL, 2–20 μL, 10–100 μL, 100–1000 μL. 4. Pipet tips (DNase/RNase-free). 5. Vortex. 6. Spinner.

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Table 1 List of NASBA primers used in this study Name

Sequence 5′-3’

Purification

Calb18SP2

GTACAGATTCATATT

SD

Calb18SP1

aattctaatacgactcactatagggGGTGCCAATAGGTTCA

SD

HH6U82P2

CTCTATTGACATC

SD

HH6U82P1

aattctaatacgactcactatagggTTGACAGACACAAAAG

SD

VZVORF31P2

GAATTTCTATCTCTC

SD

VZVORF31P1

aattctaatacgactcactatagggAAAGCCGCAACTTAAAG

SD

SD standard desalting; low case letters are the T7 promoter sequence

2.2 NASBA Amplification Reaction

1. PCR thermocycler (see Note 1). 2. NASBA reaction buffer (2.5×): 40 mM Tris-HCl, pH 8.5, 2.5 mM KCl, 12 mM MgCl2, 2.5 mM NTPs (ATP, UTP, CTP, GTP), 1.25 mM dNTPs (dATP, dTTP, dGTP, dCTP) (see Note 2). 3. NASBA enzyme mix (for one 25 μL reaction): 3 μg BSA, 30 U T7 RNA polymerase, 30 U MMLV or AMV reverse transcriptase, 0.3 U RNase H (see Note 3). 4. RNAse inhibitor (see Note 4). 5. RNA samples. Pathogen samples were kindly provided by Slyta A.V. from Pasteur’s Institute, Russia, and Sidorenko S.V. from the Institute of Pediatric Research and Clinical Center for Infectious Diseases, Russia. 6. NASBA primer buffer: 5 μM primer each, 15% DMSO. All the oligonucleotides are listed in Table 1 (see Note 5).

2.3 Visual Detection of Amplicons

1. Reaction buffer: 50 mM HEPES, pH 7.4, 50 mM MgCl2, 20 mM KCl, 120 mM NaCl, 0.03% Triton X-100, 1% DMSO. 2. 20 μM stock solutions of the sensor oligonucleotides (see Tables 2 and 3) (see Note 6). 3. 20 mM diaminobenzidine (DAB) in water (see Note 7). 4. 20 mM H2O2 in water. 5. 20 μM hemin in DMSO. 6. 20 μM synthetic analyte of the corresponding fragment. 7. 20 μM synthetic G-quadruplex.

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Table 2 List of biPxD oligonucleotides used in this study Name

Sequence 5′-3′

Purification

HH6_U82_f

GGGTAGGG /HEG/ GAAAAAACAACTATTGTAAC

SD

HH6_U82_m

TGCAGTTATT /HEG/ GGGTTGGG

SD

HH6_U82

GACTTTACAATTTGAGCTCTGGGCGTTACAATAGTT GTTTTTTCAATAACTGCATGCATTAAAAAAGCCCGC TTATAAAAGATGTCAATAGAG

SD

VZV_ORF31_f

taCTCAAATACTGCGACGTTCATATATGTTCTC /HEG/ GGGTAGGG

SD

VZV_ORF31_m GGGTTGGG /HEG/ TGCTCGACGCCCTATGT

SD

VZV_ORF31

AAAGCCGCAACTTAAAGTGACATCAGATCATTAGGA CATAGGGCGTCGAGCAGAGAACATATATGAACG TCGCCGTATTTGAGAGATAGAAATTC

SD

Calb_18S_f

cctAAACGATAACTGATTTAATGAG /HEG/ GGGTAGGG

SD

Calb_18S_m

GGGTTGGG /HEG/ CCATTCGCAGTTTC

SD

Calb_18S

CATGTCTAAGTATAAGCAATTTATACAGTGAAACTG CGAATGGCTCATTAAATCAGTTATCGTTTATTTGAT AGTACCTTACTACTTGGATAACCGTGG

SD

G4(control substrate)

GGGTAGGGCGGGTTGGG

SD

Low case letters, additional letters that form intentional secondary structure to decrease the background; HEG hexaethyleneglycol linker, SD standard desalting

Table 3 Sample composition of reaction for visual detection of the NASBA amplicons Reagent

Negative control

Positive control

G-quadruplex control

Sample

Reaction buffer

15

14

16

14

G-quadruplex

–

–

1

–

Synthetic analyte

–

1

–

–

NASBA product

–

–

–

1

f-chain

1

1

–

1

m-chain

1

1

–

1

DAB

1

1

1

1

Hemin

1

1

1

1

H202

1

1

1

1

Total

20

20

20

20

The volumes of each component are given in microliters

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Methods Sensor Design

1. Sensors are developed on the basis of previous reports [16–22] with the assistance of DINAMelt [23], mFold [24], and NuPack [25]. The sensor was designed using most stable (lowest Gibbs energy) secondary structure predicted by mFold (see Fig. 2 with the area of attachment marked in green and purple). 2. The central part of the binary probe, which contains the core, should be placed closer to the top of the hairpin. 3. The RNA binding arm of probe f should be designed to be long enough to bind one side of the RNA stem, enclosing the entire loop. It may also bind two to three nucleotides (nt) on the other side of the stem. 4. Probe m should be shorter but remain long enough to open the hairpin (min 3 nt longer than the final attachment point of the arm f). 5. Adjust the length of each arm to have their melting temperature at least 10 °C above the assay temperature: for a room temperature (22 °C) assay, Tm should be >32 °C. The temperature can reach up to 45 °C especially in CG-rich sequences. 6. The sensor can be additionally equipped with extra third of fourth arm connected via a dsDNA platform suitable for detection of complex structure with long stems [22]. In case of need to detect a mismatch, the altered nucleotide should be placed in the middle section of the arm m and the melting temperature of the arm should lowered down closer to the assay temperature. 7. Each part of the sensor contains a half of a G-quadruplex— 2 triplets (see Note 9). The triplets are divided via TT or TA interchange. The G-quadruplex part is separated from the detection arm via a linker—oligo dA or dT or tri- or hexaethyleneglycol [20]. 8. The sensor may also contain a short C-enriched fragment on the end of the arm that binds to the G-enriched sequence when the arms are not attached to the designated positions on the analyte, thus decreasing the background signal (see Note 9). 9. The sensor should not contain any stable secondary structure with ΔG lower than - 4 kcal/mol, except those directly involved in the core formation. Use UNAfold or NUPACK to check each strand.

3.2 NASBA Amplification Reactions

1. Primers are selected via Primer 3 tool [26] for fragments no longer than 120 nt. The fragment was selected due to the abundance in a cell, constitutive expression (for fungus), expression in late stages (for viral samples).

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Fig. 2 Secondary structure of the NASBA amplicon predicted by mFold [19]. Purple and green line present the site of the sensor’s recognition. (a) HH6, (b) VZV, (c) C. albicans

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2. Mix the 1 × NASBA reaction buffer, the primer mix, RNase inhibitors, and RNA sample. 3. Incubate at 65 °C for 5 min and at 41 °C for 10 min. 4. Add the enzyme mix for 1 reaction. 5. Incubate at 41 °C for 60 min. 6. (optional) Confirm the reaction with agarose gel electrophoresis (see Note 10). 3.3 Visual Detection of the NASBA Amplicons

1. Mix reaction components according to the table. First, mix the oligonucleotides and then add DAB, hemin, and H2O2. Be careful not to mix the compounds before the reaction set and change the pipette tips while assembling. The m and f strands should be paired according to the organism and fragment. The synthetic analyte should correspond to each pair of m and f strands. 2. Incubate the samples at room temperature (23–25 °C) for 10 min with the following visual assay (see Note 11). The G-quadruplex and positive controls should be colored brown if the quality of reagents is satisfactory. The negative control should remain transparent (Fig. 4a).

4

Notes 1. The temperature could be maintained by any convenient device including a water bath, incubator, or dry-block thermostat. The preliminary heating to 65 °C unfolds the RNA secondary structure and allows the primers to hybridize. The enzyme mix should be added after the temperature decreases to avoid inactivation. With MMLV, the reaction can work in temperatures as low as 37 °C. 2. The NASBA reaction buffer can be aliquoted and stored at – 20 °C for up to 3 months. We suggest trying several NTP and dNTP suppliers because the efficiency of NASBA reaction depends on the quality of these reagents. 3. The enzyme mix can be stored at – 20 °C for up to 3 months. AMV and MMLV reverse transcriptases can be used. We suggest increasing the amount of the enzymes added in multiplex reactions in comparison to the singleplex reactions. The amount of RNaseH can be decreased in the case of using MMLV. 4. RNAse inhibitors should inhibit RNAse types A, B, C. Add according to the manufacturer’s requirements. The presence of the inhibitors is essential for RNA stability due to the absence of dithiothreitol (DTT). It supports RNA stability but

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interferes with the peroxidase reaction: DTT is more active in reducing H2O2 than DAB. 5. The primer mix can be stored at – 20 °C for up to 6 weeks. DMSO should be fresh, non-defrosted, and non-oxidized. The quality of DMSO is crucial for reaction sensitivity. 6. The quality of oligonucleotides for sensors is important. We suggest testing the oligonucleotides via Bioanalyzer or PAGE gel electrophoresis prior to use to assess the degradation and fragment size. On the other hand, HPLC purification of the oligonucleotides is not required: standard desalting is sufficient. 7. DAB should be transparent. Brown-colored solution indicated accumulation of oxidation product and may increase the background color leading to lower S/B. 2,2′-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) can be used instead of DAB with green color formation. ABTS produces higher S/B but the color rapidly fades. 8. Hemin in 100% DMSO should be stored at –20 °C. Using fresh solution is recommended. 9. Alternatively, asymmetric splitting 3 + 1 was described, which can provide better results in some specific cases [17]. 10. Because the fragments that can be recognized by sensors are strictly selected to their size (up to 150 nts), they cannot be directly distinguished by the electrophoresis, but the successful reaction can be confirmed (Fig. 3).

Fig. 3 Analysis of NASBA products by 2% agarose gel electrophoresis, 80 V, 30 min. Ladder Evrogen 50 bp+ (1) all the pathogens in concentration 33 pg (~2100 genomic equivalents (GE) of C. albicans, ~185,000 GE of VZV, ~146,000 GE of HH6), (2) VZV 100 pg (~741,000 GE), (3) HH6 100 pg (~586,000 GE), (4) C. albicans 100 pg (~6500 GE), (5) negative control with no RNA added

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Fig. 4 Example of the product visualization on different substrates with G4 sensor. (a) Visualization in tubes, (b) spot visualization on sheet materials

11. The reaction can be optimized with the exposure in a timeframe of 5–20 min. The reaction can be analyzed in tubes and on TLC silica gel plates, nylon membranes, or filter paper sheets (see Fig. 4). For quantitative evaluation, the color can be analyzed via spectrophotometry at λ = 500 nm. Please note that the signal should be evaluated in relation to the positive control for each set of measurements, i.e. G4 (see Fig. 5). Alternatively, the signal can be detected via association of G-quadruplex to thioflavin T (fluorescence) [27] or polymerizing acrylamide (tactile detection) [28].

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Fig. 5 Visualization of products in multiplex. Composition of the multiplex reaction is marked under the tube. Numbers present the quantification of the signal via spectrometry at 500 nm

Acknowledgments We would like to thank group of experimental virology, Pasteur Institute, Saint-Petersburg, Russia, and group of molecular microbiology and medical epidemiology, Institute of Children Infection, Saint-Petersburg, Russia, for sample material. M.S.R., L.A.S., and D.A.G thanks Russian Science Foundation (project No 22-75-10073) and the Priority 2030 program. A.A.R. thanks St. Petersburg State University (project #94031363).

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References 1. Veedu RN, Wengel J (2010) Locked nucleic acids: promising nucleic acid analogs for therapeutic applications. Chem Biodivers 7(3): 536–542 2. Lai Q, Chen W, Zhang Y et al (2021) Application strategies of peptide nucleic acids toward electrochemical nucleic acid sensors. Analyst 146(19):5822–5835 3. Grimes J, Gerasimova YV, Kolpashchikov DM (2010) Real-time SNP analysis in secondarystructure-folded nucleic acids. Angew Chem Int Ed Engl 49(47):8950–8953 4. Kubota M, Tran C, Spitale RC (2015) Progress and challenges for chemical probing of RNA structure inside living cells. Nat Chem Biol 11(12):933–941 5. Kolpashchikov DM (2019) Evolution of hybridization probes to DNA machines and robots. Acc Chem Res 52:1949–1956 6. Nguyen C, Grimes J, Gerasimova YV et al (2011) Molecular beacon-based tricomponent probe for SNP analysis in folded nucleic acids. Chem Eu J 17:13052–13058 7. Sun S-C, Dou H-Y, Chuang M-C et al (2019) Multi-labeled electrochemical sensor for costefficient detection of single nucleotide substitutions in folded nucleic acids. Sens Act B Chem 287:569–575 8. Kikuchi N, Reed A, Gerasimova YV et al (2019) Split Dapoxyl aptamer for sequenceselective analysis of NASBA amplicons. Anal Chem 91:2667–2671 9. Compton J (1991) Nucleic acid sequencebased amplification. Nature 350:91–92 10. Pardee K, Green AA, Takahashi MK et al (2016) Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell 165:1255–1266 11. Xing W, Liu Y, Wang H et al (2020) A highthroughput, multi-index isothermal amplification platform for rapid detection of 19 types of common respiratory viruses including SARSCoV-2. Engineering 6:1130–1140 12. de Baar MP, van Dooren MW, de Rooij E et al (2001) Single rapid real-time monitored isothermal RNA amplification assay for quantification of human immunodeficiency virus type 1 isolates from groups M, N, and O. J Clin Microbiol 39:1378–1384 13. Asadi R, Mollasalehi H (2021) The mechanism and improvements to the isothermal amplification of nucleic acids, at a glance. Anal Biochem 631:114260 14. Jean J, D’Souza DH, Jaykus LA (2004) Multiplex nucleic acid sequence-based amplification for simultaneous detection of several enteric

viruses in model ready-to-eat foods. Appl Environ Microbiol 70:6603–6610 15. Li J, Macdonald J (2015) Advances in isothermal amplification: novel strategies inspired by biological processes. Biosens Bioelectron 64: 196–211 16. Kolpashchikov DM (2008) Split DNA enzyme for visual single nucleotide polymorphism typing. J Am Chem Soc 130:2934–2935 17. Connelly RP, Verduzco C, Farnell S et al (2019) Toward a rational approach to design split G-Quadruplex probes. ACS Chem Biol 14:2701–2712 18. Travascio P, Witting PK, Mauk AG et al (2001) The peroxidase activity of a hemin-DNA oligonucleotide complex: free radical damage to specific guanine bases of the DNA. J Am Chem Soc 123:1337–1348 19. Kovtunov EA, Shkodenko LA, Goncharova EA et al (2020) Towards point of care diagnostics: visual detection of meningitis pathogens directly from cerebrospinal fluid. Chemistry Select 5:14572–14577 20. Reed AJ, Connelly RP, Williams A et al (2019) Label-free pathogen detection by a deoxyribozyme cascade with visual signal readout. Sen Act B Chem 282:945–951 21. Lu X, Shi X, Wu G et al (2017) Visual detection and differentiation of classic swine fever virus strains using nucleic acid sequence-based amplification (NASBA) and G-quadruplex DNAzyme assay. Sci Rep 7:44211 22. Gorbenko DA, Shkodenko LA, Rubel MS et al (2022) DNA nanomachine for visual detection of structured RNA and double stranded DNA. Chem Commun (Camb) 58:5395–5398 23. Markham NR, Zuker M (2005) DINAMelt web server for nucleic acid melting prediction. Nucleic Acids Res 33:W577–W581 24. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415 25. Zadeh JN, Steenberg CD, Bois JS et al (2011) NUPACK: analysis and design of nucleic acid systems. J Comput Chem 32:170–173 26. Untergasser A, Cutcutache I, Koressaar T et al (2012) Primer3—new capabilities and interfaces. Nucleic Acids Res 40:e115 27. Jing S, Liu Q, Jin Y et al (2021) Dimeric G-Quadruplex: an effective nucleic acid scaffold for lighting up Thioflavin T. Anal Chem 93(3):1333–1341 28. Fedotova TA, Kolpashchikov DM (2017) Liquid-to-gel transition for visual and tactile detection of biological analytes. Chem Commun (Camb) 53:12622–12625

Chapter 21 Synthesis of DNA-Templated Silver Nanoclusters and the Characterization of Their Optical Properties and Biological Activity Elizabeth Skelly, Lewis A. Rolband, Damian Beasock, and Kirill A. Afonin Abstract DNA-templated silver nanoclusters (DNA-AgNCs) are a unique class of bioinorganic nanomaterials. The optical properties and biological activities of DNA-AgNCs are readily modulated by the minor adjustments in the sequence or structure of the templating oligonucleotide. Excitation-emission matrix spectroscopy (EEMS) enables the fluorescence of compounds to be measured in a way that examines the entirety of a material’s fluorescent properties. The use of EEMS for the characterization of DNA-AgNCs allows for multiple fluorescence peaks to be readily identified while providing the excitation and emission wavelengths of each signal. To assess the antibacterial and cytotoxic activities of DNA-AgNCs, two separate experimental approaches are used. Assessing the growth of bacteria over time is accomplished by measuring the optical density of the bacterial suspension with 600 nm light, which is directly related to the number of bacteria in suspension. In order to evaluate the DNA-AgNCs for cytotoxic activity, cell viability assays which probe mitochondrial activity were used. Herein, we describe protocols for the characterization of the fluorescent, antibacterial, and cytotoxic activities of DNA-AgNCs using EEM, optical density measurements, and cell viability assays. Key words DNA, Silver, AgNC, Nanocluster, Antibacterial, Fluorescence

1

Introduction

1.1 Structure and Function of DNAAgNCs

The single-stranded (ss) DNA-templated formation of silver nanoclusters (DNA-AgNCs) has been shown to have unique optical properties that change with the sequences of the ssDNA templates [1–6]. Silver cations have the highest binding affinity for the endocyclic N3 of cytosines out of all available coordination sites on nucleobases. As such, many DNA templates for DNA-AgNCs are cytosine-rich ssDNAs [4, 6–9]. To regulate the size and shape of DNA-AgNCs, the short DNA oligonucleotides are designed, guided by canonical Watson-Crick base pairing, with different numbers of single-stranded cytosines that are embedded in

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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secondary and tertiary DNA structures (e.g., hairpin loops, I-motifs, and G-quadruplexes) [7–9]. Both the size and shape of the DNA-AgNC complex change the optical properties, as the characteristic fluorescence is possible for nanoclusters with only a few silver atoms. Additionally, the charge state of the Ag within the DNA-AgNC impacts the fluorescent properties, as the DNA-AgNCs contain a mixture of Ag+ and Ag0 [10, 11]. With such a small size, around 2 nm or less, the electronic energy states that are continuous and present in bulk silver are broken up, and a band gap in the silver becomes apparent [1, 2, 7–9, 12–14]. This gap in the electronic density of states leads to the emergence of the observed fluorescence from DNA-AgNCs [3, 13, 14]. DNA-AgNCs are generally more resistant to photobleaching than organic fluorophores or fluorescent proteins, making DNA-AgNCs advantageous for use in a variety of nanophotonics and biosensing/ biomedical applications [15–17]. 1.2 Demand for New Antibacterial Treatments

Owing to the looming antibiotic resistance crisis, novel antibacterial agents must be explored [18–20]. Both atomic silver and silver nanomaterials are well-known to have antibacterial properties and are well studied [21, 22]. The antibacterial properties of DNA-AgNCs are lesser understood and in need of further study [23]. The correlations between the antibacterial properties of DNA-AgNCs and their fluorescence patterns may prove to be helpful in predicting the biological activity of novel DNA-AgNCs as they are developed.

1.3 Characterizing the Antibacterial Effectiveness and Testing Biocompatibility of Mammalian Cells

A straightforward and robust methodology for testing the effect of DNA-AgNCs on bacterial growth is to follow the optical density of a bacterial culture over time using a microplate reader [23, 24]. Furthermore, it is important to screen each DNA-AgNC being studied as an antibacterial agent for potential cytotoxicity in mammalian cells [24]. Using MTS, the relative cell viability of a mammalian cell culture can be determined through a colorimetric assay. Cells which are actively metabolizing will reduce this reagent to form a colored product whose concentration can be assessed by spectroscopy [25]. The protocols for each of these assays are presented herein, and the overall experimental design is outlined in Fig. 1.

2

Materials All experiments were done using DNA oligonucleotides (Integrated DNA Technologies) received as lyophilized desalted products and used without purification.

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Fig. 1 Experimental flow of DNA-silver nanocluster synthesis, purification, and analysis 2.1 Synthesis of DNA-AgNCs

1. DNA template resuspended in nuclease-free 18 MΩ water. 2. 1 M AgNO3. 3. Ammonium acetate buffer: 20 mM, NH4OAc, pH 6.9. Dissolve 0.154 g of anhydrous ammonium acetate in 90 mL of nuclease-free 18 MΩ water. Adjust the pH of the solution to 6.9 using glacial acetic acid or concentrated sodium hydroxide as necessary. 4. 3 Molecular-weight cut-off (MWCO) microcentrifuge tube filters (3 kDa).

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2.2 Fluorescence Experiment

1. Spectrofluorometer capable of recording EMS spectra. 2. Templating sequence: kept at 6 μM. 3. Quartz fluorometer cell. 4. Data analysis and graphing software.

2.3 Bacterial Growth Assays

1. K12 E. coli, lyophilized. 2. Lysogeny broth. 3. Incubated shaker. 4. Carbenicillin. 5. Microwell plate reader. 6. GraphPad Prism 9. 7. NanoDrop or spectrophotometer.

2.4 Mammalian Cell Viability Assays

3

1. Mammalian cells, maintained per source’s instructions. 2. CellTiter 96® AQueous One Solution Cell Proliferation Assay (3-(4,5-dimethylthiazol-2-yl)-5-(3-car-boxymethoxyphenyl)2-(4-sulfophe-nyl)-2H-tetrazolium – MTS).

Methods

3.1 Synthesis of DNA-AgNCs

1. DNA template and AgNO3 aqueous solutions are mixed with 1 M equivalent of AgNO3 for each cytosine in the template oligo. Buffer is added to be 20% of the total volume of the synthesis (e.g., for a 100 μL synthesis, add 20 μL of buffer) for the final concentration of 4 mM NH4OAc. 2. Heat the solutions to 95 °C for 2 min. For nanostructures whose design relies primarily on the formation of intramolecular base pairing, proceed to step 3. For nanostructures which primarily rely on intermolecular base pairing, proceed to step 4. 3. Immediately transfer the solutions to ice for 25 min (see Note 1). 4. Allow the solutions to cool to room temperature on the bench top for 25 min. 5. Add NaBH4 aqueous solution in a stoichiometric equivalent as the AgNO3, and mix thoroughly with a pipette (see Note 2). 6. Incubate the solution in the dark for 24 h at 4 °C. 7. Remove any excess AgNO3 and NaBH4 by bringing the solution volume to 500 μL with 4 mM NH4OAc, and add the solution to the MWCO filters. Centrifuge them at 14,000 rcf for 20 min at ambient temperature. Repeat this step a total of three times. 8. Evaluate the concentration of the DNA-AgNCs with the absorption 260 nm light, and dilute them to 50 μM with 4 mM NH4OAc.

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1. Dilute the DNA-AgNCs to ~6 μM. 2. Ensure the sample holder is at room temperature, roughly 22 °C. 3. Set the excitation-emission matrix spectra (EEMS) to record with 0.5 nm resolution, an emission wavelength range of 300 nm to 1000 nm, initial excitation wavelength set to 280 nm, and final excitation wavelength set to 800 nm with an increment of 0.5 nm.

3.3 Bacterial Growth Assays (Fig. 2)

1. Grow E. coli in LB from single colonies while shaking at 200 rpm at 37 °C in an incubated shaker. 2. Dilute bacteria in LB to an optical density at 600 nm (OD600) of 0.018–0.020 (see Note 3). 3. Add 50 μL of diluted bacteria to each well of a 96-well flat-bottom, black-walled plate. 4. Add purified DNA-AgNCs with LB to each well in the 96-well plate to reach a final volume of 100 μL each treatment (e.g., 4 μM final concentration of DNA-AgNCs, 50 μg/mL carbenicillin, 4 mM NH4OAc, etc.). 5. Hydrophobically treat the lids by filling them with 10 mL of 20% ethanol and 0.05% Triton X-100 for 30 s. Drain the excess liquid and let dry in a biosafety cabinet for a minimum of 30 min. 6. Use parafilm to secure the lids to the microwell plates to prevent evaporation (see Note 4). 7. Using a microwell plate reader, measure the OD600 every 15 min with 30 s of shaking between each measurement, with the plate kept at 37 °C, for at least 20 h.

3.4 Mammalian Cell Viability Assays (Fig. 3)

For all experiments, maintain and culture cells at 37 °C, 5% CO2 using the sources’ media and splitting recommendations. 1. Plate cells in a 96-well flat-bottom plate at a density of 40,000 cells per well and incubate for 24 h. 2. Aspirate media from each well and add DNA-AgNC solutions at final concentrations of 4 or 8 μM. Bring the final well volume to 100 μL with freshly warmed media. 3. Incubate cells with the DNA-AgNC treatment at 37 °C, 5% CO2 for 24 h. 4. After incubation, add 20 μL of warmed MTS to each well. 5. Incubate cells for 75 additional minutes at 37 °C, 5% CO2. 6. Measure absorbance at 490 nm using the microplate reader.

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Fig. 2 Experimental flow of bacterial growth inhibition assays with example data over 20 h

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Fig. 3 Experimental flow of MTS cell viability assays when treated with DNA-AgNCs with example normalized cell viability after 20 h of treatment

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Notes 1. To ensure solutions are cooled rapidly and encourage folding, water can be added to the ice to maximize heat spread from the sample container. 2. A very small amount of solid NaBH4 will be needed. A small rice grain-sized amount should be weighed out, and water should be added to achieve the correct concentration. 3. If taking multiple measurements in a cuvette, it is recommended to keep the window as clean as possible and refrain from wiping the window between measurements. 4. Parafilm will also prevent contamination of instruments not meant for bacterial cells. After the plate is sealed, ethanol can be used to sterilize the plate.

Acknowledgments Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM139587 (to K. A.A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References 1. Ritchie CM, Johnsen KR, Kiser JR, Antoku Y, Dickson RM, Petty JT (2007) Ag nanocluster formation using a cytosine oligonucleotide template. J Phys Chem C Nanomater Interfaces 111(1):175–181 2. Petty JT, Zheng J, Hud NV, Dickson RM (2004) DNA-templated Ag nanocluster formation. J Am Chem Soc 126(16):5207–5212 3. Chandler M, Shevchenko O, Vivero-Escoto JL, Striplin CD, Afonin KA (2020) DNA-templated synthesis of fluorescent silver nanoclusters. J Chem Educ 97(7):1992–1996 4. Yourston LE, Lushnikov AY, Shevchenko OA, Afonin KA, Krasnoslobodtsev AV (2019) First step towards larger DNA-based assemblies of fluorescent Silver nanoclusters: template design and detailed characterization of optical properties. Nano 9(4):613 5. Afonin KA, Schultz D, Jaeger L, Gwinn E, Shapiro BA (2015) Silver nanoclusters for RNA nanotechnology: steps towards visualization and tracking of RNA nanoparticle assemblies. In: Guo P, Haque F (eds) RNA

nanotechnology and therapeutics: methods and protocols. Springer, New York, pp 59–66 6. Yourston L, Rolband L, West C, Lushnikov A, Afonin KA, Krasnoslobodtsev AV (2020) Tuning properties of silver nanoclusters with RNA nanoring assemblies. Nanoscale 12(30): 16189–16200 7. New SY, Lee ST, Su XD (2016) DNA-templated silver nanoclusters: structural correlation and fluorescence modulation. Nanoscale 8(41):17729–17746 8. Lee TH, Gonzalez JI, Zheng J, Dickson RM (2005) Single-molecule optoelectronics. Acc Chem Res 38(7):534–541 9. Huard DJE, Demissie A, Kim D, Lewis D, Dickson RM, Petty JT et al (2019) Atomic structure of a fluorescent Ag8 cluster templated by a multistranded DNA scaffold. J Am Chem Soc 141(29):11465–11470 10. Copp SM, Schultz D, Swasey S, Pavlovich J, Debord M, Chiu A et al (2014) Magic numbers in DNA-stabilized fluorescent silver

Synthesis of DNA-Templated Silver Nanoclusters clusters Lead to magic colors. The Journal of Physical Chemistry Letters 5(6):959–963 11. Gwinn E, Schultz D, Copp S, Swasey S (2015) DNA-protected silver clusters for nanophotonics. Nano 5(1):180–207 12. O’Neill PR, Gwinn EG, Fygenson DK (2011) UV excitation of DNA stabilized Ag cluster fluorescence via the DNA bases. J Phys Chem C 115(49):24061–24066 13. Gwinn EG, O’Neill P, Guerrero AJ, Bouwmeester D, Fygenson DK (2008) Sequence-dependent fluorescence of DNA-hosted silver nanoclusters. Adv Mater 20(2):279–283 14. Gwinn E, Schultz D, Copp SM, Swasey S (2015) DNA-protected silver clusters for nanophotonics. Nanomaterials (Basel) 5(1): 180–207 15. Li J, Zhong X, Zhang H, Le XC, Zhu J-J (2012) Binding-induced fluorescence turn-on assay using aptamer-functionalized silver nanocluster DNA probes. Anal Chem 84(12): 5170–5174 16. Yang SW, Vosch T (2011) Rapid detection of micro RNA by a silver nanocluster DNA probe. Anal Chem 83(18):6935–6939 17. Yeh H-C, Sharma J, Han JJ, Martinez JS, Werner JH (2010) A DNA-silver nanocluster probe that fluoresces upon hybridization. Nano Lett 10(8):3106–3110

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Chapter 22 Dynamic Nanostructures for Conditional Activation and Deactivation of Biological Pathways Yasmine Radwan, Laura P. Rebolledo, Martin Panigaj, and Kirill A. Afonin Abstract Nucleic acid nanotechnology utilizes natural and synthetic structural motifs to build versatile nucleic acid nanoparticles (NANPs). These rationally designed assemblies can be further equipped with functional nucleic acids and other molecules such as peptides, fluorescent dyes, etc. In addition to nucleic acids that directly interact with the regulated target gene transcripts, NANPs can display decoys, wherein the oligonucleotide stretches with transcription factor binding sequences, preventing transcription initiation. The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is a group of five crucial transcription factors regulating the pathogenesis of inflammatory diseases and cancer; as such, they are relevant targets for therapy. One therapeutic approach involves interdependent self-recognizing hybridized DNA/RNA fibers designed to bind NF-κB and prevent its interaction with the promotor region of NF-κBdependent genes involved in inflammatory responses. Decoying NF-κB results in the inability to initiate transcription of regulated genes, showing a promising approach to gene regulation and gene therapy. The protocol described herein provides detailed steps for the synthesis of NF-κB decoy fibers, as well as their characterization using polyacrylamide gel electrophoresis (to confirm desired physicochemical properties and purity) and functional bioassays (to confirm desired biological activity). Key words Nucleic acid nanoparticles (NANPs), Hybrid fibers, Decoys, NF-κB regulation

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Introduction Nucleic acid nanoparticles (NANPs) are assemblies integrating various individual functional nucleic acids into their structure [1]. These assemblies can be further rationally designed to specifically respond to trigger molecules that activate their functionalities. The conditional activation of NANPs results from a toehold interaction between (i) a pair of cooperating sense/antisense NANPs [2] and (ii) recognition of endogenous complementary nucleic acid sequence [3] or (iii) by specific interaction to protein through NANP 3D structure [4]. The interplay between NANPs or cognate molecules subsequently releases functional DNA/RNA molecules with the potential for synergistic effects.

Kirill A. Afonin (ed.), RNA Nanostructures: Design, Characterization, and Applications, Methods in Molecular Biology, vol. 2709, https://doi.org/10.1007/978-1-0716-3417-2_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Reconfigurable NANPs offer a promising approach to providing decoys to regulate the nuclear factor kappa-light-chainenhancer of activated B cells (NF-κB), a crucial transcription factor in the pathogenesis of inflammatory diseases [5]. The NF-κB transcription factors form homo- or heterodimers in two distinct signaling pathways. While canonical signaling plays an important role in innate and adaptive immune responses, the alternative pathway is involved in the development of lymphoid organs, B cell survival and maturation, and osteoclast differentiation [6, 7]. The canonical pathway is triggered by pro-inflammatory cytokines and pathogenand damage-associated molecular patterns through their receptors. The regulation through the canonical pathway plays an important role in innate immunity. However constitutively activated NF-κB promotes tumorigenesis by proliferation and survival of cancer cells. The NF-κB is expressed in cancer cells as well as in cells of the tumor microenvironment [8]. The expression of NF-κB is ubiquitous in various types of mammalian cells. We can regulate the expression and activity of NF-κB with the nanoparticles functionalized with various molecules, such as small interfering RNAs (siRNA), aptamers, and antisense oligonucleotides [9–11]. Numerous functionalized nanoparticles or simpler chimeric therapeutic nucleic acids can be targeted to a specific cell type via aptamers [12]. However, aptamermediated targeting remains challenging. The conditional activation of functionalized NANPs represents an alternative approach to specific cell targeting. Conditional activation doesn’t rely on specific delivery but on the presence of activating molecule, which may be co-delivered or is of cellular origin. We developed a protocol focusing on interdependent selfrecognizing hybrid DNA/RNA fibers that, upon mutual interaction in the cytoplasm, re-associate into multiple dicer substrate (DS) RNAs and DNA duplexes containing NF-κB decoys that regulate NF-κB translocation to the nucleus and thus transcription initiation of NF-κB regulated genes [13]. The protocol described herein provides a detailed recipe for the assembly, as well as physicochemical and biological characterization of NF-κB decoy DNA/RNA fibers to confirm their integrity and intended biological function.

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Materials

2.1 Synthesis and Characterization of Hybrid RNA/DNA Fibers

1. Individual ssDNAs, ssRNAs, and fluorescently labeled oligonucleotides. 2. Hybridization buffer (HB): 89 mM Tris, 80 mM boric acid (pH 8.3), 2 mM magnesium acetate. 3. 8% Native-PAGE gel (19:1 acrylamide/bis-acrylamide).

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4. Native loading buffer (50% (vol/vol) glycerol, 1× Tris-borate buffer (pH 8.3), 0.01% (wt/vol) bromophenol blue, and 0.01% (wt/vol) xylene cyanol tracking dyes). 5. Native running buffer: 1X Tris buffer, 2 mM Mg2+. 6. 0.5 μg/ml ethidium bromide. 7. Bio-Rad ChemiDoc MP Imager. 8. Equipment: 1.5 mL microtubes, pipettes and pipette tips, microcentrifuge, vortex, heat block. 2.2 Assessment of Biological Activity of NF-κB Decoy Fibers

1. Freshly collected PBMC from healthy human donors. 2. Ultrapure K12 E. coli LPS.

2.2.1 Primary Human Peripheral Blood Mononuclear Cells (PBMCs) for Analysis of Interferon and Cytokine Secretion (to Assess Anti-inflammatory Potential) 2.2.2 Reporter CellBased Assay (to Assess Anti-inflammatory Potential)

1. HEK-Blue-hTLR4 cells. 2. Ultrapure K12 E. coli LPS. 3. Lipofectamine 2000 (L2K). 4. 96-well plates. 5. Quanti-Blue.

2.2.3 Immunofluorescence Analysis for Detection of NF-κB in Cancer Cell Line (to Assess Biological Activity)

1. A375 melanoma cells. 2. 24-well plates. 3. 1x PBS. 4. 5% BSA (bovine serum albumin). 5. Anti-NF-κB, p65 subunit monoclonal antibody (MAB3026, Millipore, 1:100). 6. AlexaFluor®488-conjugated secondary antibody (Molecular Probes, 1:1000). 7. Hoechst (25 ug/mL). 8. Rhodamine-conjugated phalloidin antibody (Sigma-Aldrich, 1:200). 9. Mounting solution: Glycerol: PBS (1:1). 10. Fluorescence microscope EVOS FL Auto Imaging System.

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2.3 Statistical Analysis

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1. GraphPad Prism software.

Methods

3.1 Synthesis and Physicochemical Characterization of Fibers

1. Mix equimolar concentrations of individual monomers of the hybrid fibers (see Note 1) in the hybridization buffer. 2. Heat mixture to 95 °C for 2 min. 3. Snap cool mixture in ice and then incubate at room temperature for 20 min. 4. Store fibers at 4 °C until needed. 5. Evaluate the assembly of fibers by using 8% native-PAGE gel (see Note 2): 1. Mix the reagents to make the native PAGE: 10.5 mL ddiH2O, 3 mL 40% 19:1 acrylamide/bis-acrylamide (from the refrigerator), 1.5 mL 10X TB, 30 μL of 1 M MgCl2, 9 μL of TEMED, and 150 μL of 10% APS, to prepare a Bio-Rad Mini 1.0 mm gel. 2. Pour in the gel and let polymerize for 10–15 min. 3. Pre-run the gel in 1X TB (supplemented with 2 mM Mg2+) at 150 volts (and 25 mA) for 5 min on the “volt” setting. 4. Mix samples with native loading buffer (1:1). 5. Load samples and run gel in 1X TB (2 mM Mg2+) at constant 300 volts (and 150 mA) for 20 min. 6. Stain the gel in diluted ethidium bromide for 5 min, and wash twice in ddiH2O. Skip this step for fluorescencelabeled oligos. 7. Image the gel using ChemiDoc to confirm pure assemblies (Fig. 1).

3.2 Assessment of NF-κB Biological Activity in Cell Models 3.2.1 Primary Human Peripheral Blood Mononuclear Cells (PBMCs) and Whole-Blood Culture for Analysis of Cytokine Secretion (See Note 3)

1. Collect blood from pre-screened healthy donors following institutional review board-approved protocol using BD Vacutainer tubes containing Li-heparin as the anticoagulant, and use collected blood within 2 h. 2. Use whole-blood cultures to analyze chemokine and cytokine induction. 3. Use PBMC cultures using myeloid cells for type I interferon analysis following the standardized protocol [14]. 4. Analyze supernatants using a chemiluminescence-based multiplex system. 5. Analyze duplicates of each supernatant on the multiplex plate. 6. Use 20 ng/mL LPS as positive control for the PBMC assay for cytokine analysis and whole-blood assay (Fig. 2).

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Fig. 1 Native PAGE confirming the formation of fibers. (Reproduced from Ref. [13])

Fig. 2 Assessment of NF-κB-dependent cytokines in PBMCs. First PBMCs were treated for the 24 h with controls or fibers, and after 24 hours they were stimulated with 20 ng/mL of ultrapure bacterial K12 LPS. Levels of IL-6 (a) and TNFα (b) were measured in the supernatants by multiplex ELISA. (Reproduced from Ref. [13]) 3.2.2 Reporter CellBased Assay for Assessment of NF-κBDependent SEAP (See Note 4)

1. Seed HEK-Blue-hTLR4 cells in a 96-well plate and incubate overnight for cell adherence. 2. Mix and pre-incubate NANP samples with L2K for 30 min before transfection at room temperature.

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Fig. 3 Assessment of NF-κB-dependent SEAP in the reporter cell line HEK-Blue hTLR4. Cells were transfected with fibers and, after 24 h, stimulated with ultrapure K12 LPS. Then, cells were incubated for another 24 h, and the levels of SEAP were measured in supernatants. (Reproduced from Ref. [13])

3. Transfect cells with NANPs and incubate for 24 h; triplicates are used for each sample. 4. Add 25 ng/mL LPS/well to cells 24 h post-transfection and incubate for 24 h. 5. Mix 20 μL of cell supernatant with 180 μL of Quanti-Blue in a 96-well plate and incubate at 37 °C for up to 3 h. 6. Read the absorbance at 620 nm using a plate reader to measure SEAP production (Fig. 3). 3.2.3 Immunofluorescence Analysis for Detection of NF-κB in Cancer Cells (See Note 5)

1. Seed A375 melanoma cells at 2.5 × 104 cells/well in a 24-well plate. 2. Transfect A375 melanoma cells onto uncoated glass coverslips. 3. Treat designated cells with LPS at 10 μg/mL for 4 h, and fix with 4% paraformaldehyde at room temperature for 15 min. 4. Wash with 1× PBS. 5. Permeabilize cells with 0.2% Triton X-100 in 1× PBS for 5 min. 6. Wash cells, and then block cells using 5% BSA for 1 h at room temperature. 7. Incubate cells overnight at 4 °C with anti-NF-κB, p65 subunit (1:100). 8. Wash cells with 1x PBS three times for 10 minutes each.

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Fig. 4 Immunofluorescence analysis for detection of NF-κB in cancer cells. A375 melanoma cells transfected with fibers were treated with LPS for 4 h. Cells were fixed and permeabilized and then processed for immunofluorescence staining with NF-κB (p65) using Alexa Fluor 488-conjugated secondary antibodies (green) and Hoechst (blue). Panel F(S) + F(A) reveals perinuclear accumulation of NF-κB (arrows), indicating that re-associated fibers impair NF-κB nuclear translocation induced by LPS (scale bar: 100 μm). (Reproduced from Ref. [13])

9. Incubate cells with Alexa Fluor®488-conjugated secondary antibody (1:1000) and Hoechst (25 μg/mL) at room temperature for 90 min. 10. Incubate cells with Rhodamine-conjugated phalloidin antibody (1:200) for F-actin staining for 15 min at room temperature. 11. Wash cells with 1× PBS three times for 10 minutes each. 12. Mount cells in glycerol: PBS (1:1) solution. 13. Visualize using the fluorescence microscope (Fig. 4). 3.3 Statistical Analysis

1. Present results as mean ± standard deviation (SD). 2. Perform statistical analyses on data using one-way analysis of variance (ANOVA) using GraphPad Prism software. 3. Consider a p-value of