1
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Kimchi O, Larsen BB, Dunkley ORS, te Velthuis AJ, Myhrvold C. RNA structure modulates Cas13 activity and enables mismatch detection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.05.560533. [PMID: 37987004 PMCID: PMC10659300 DOI: 10.1101/2023.10.05.560533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The RNA-targeting CRISPR nuclease Cas13 has emerged as a powerful tool for applications ranging from nucleic acid detection to transcriptome engineering and RNA imaging1-6. Cas13 is activated by the hybridization of a CRISPR RNA (crRNA) to a complementary single-stranded RNA (ssRNA) protospacer in a target RNA1,7. Though Cas13 is not activated by double-stranded RNA (dsRNA) in vitro, it paradoxically demonstrates robust RNA targeting in environments where the vast majority of RNAs are highly structured2,8. Understanding Cas13's mechanism of binding and activation will be key to improving its ability to detect and perturb RNA; however, the mechanism by which Cas13 binds structured RNAs remains unknown9. Here, we systematically probe the mechanism of LwaCas13a activation in response to RNA structure perturbations using a massively multiplexed screen. We find that there are two distinct sequence-independent modes by which secondary structure affects Cas13 activity: structure in the protospacer region competes with the crRNA and can be disrupted via a strand-displacement mechanism, while structure in the region 3' to the protospacer has an allosteric inhibitory effect. We leverage the kinetic nature of the strand displacement process to improve Cas13-based RNA detection, enhancing mismatch discrimination by up to 50-fold and enabling sequence-agnostic mutation identification at low (<1%) allele frequencies. Our work sets a new standard for CRISPR-based nucleic acid detection and will enable intelligent and secondary-structure-guided target selection while also expanding the range of RNAs available for targeting with Cas13.
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Affiliation(s)
- Ofer Kimchi
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, 08544, USA
| | - Benjamin B. Larsen
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, 08544, USA
| | - Owen R. S. Dunkley
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, 08544, USA
| | | | - Cameron Myhrvold
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, 08544, USA
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, 08544, USA
- Omenn-Darling Bioengineering Institute, Princeton University, Princeton, New Jersey, 08544, USA
- Department of Chemistry, Princeton University, Princeton, New Jersey, 08544, USA
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2
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Lin BC, Katneni U, Jankowska KI, Meyer D, Kimchi-Sarfaty C. In silico methods for predicting functional synonymous variants. Genome Biol 2023; 24:126. [PMID: 37217943 PMCID: PMC10204308 DOI: 10.1186/s13059-023-02966-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.
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Affiliation(s)
- Brian C Lin
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Upendra Katneni
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Katarzyna I Jankowska
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Douglas Meyer
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Chava Kimchi-Sarfaty
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA.
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3
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Chiang TK, Kimchi O, Dhaliwal HK, Villarreal DA, Vasquez FF, Manoharan VN, Brenner MP, Garmann RF. Measuring intramolecular connectivity in long RNA molecules using two-dimensional DNA patch-probe arrays. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.12.532302. [PMID: 36993626 PMCID: PMC10055002 DOI: 10.1101/2023.03.12.532302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We describe a simple method to infer intramolecular connections in a population of long RNA molecules in vitro. First we add DNA oligonucleotide "patches" that perturb the RNA connections, then we use a microarray containing a complete set of DNA oligonucleotide "probes" to record where perturbations occur. The pattern of perturbations reveals couplings between different regions of the RNA sequence, from which we infer connections as well as their prevalences in the population. We validate this patch-probe method using the 1,058-nucleotide RNA genome of satellite tobacco mosaic virus (STMV), which has previously been shown to have multiple long-range connections. Our results not only indicate long duplexes that agree with previous structures but also reveal the prevalence of competing connections. Together, these results suggest that globally-folded and locally-folded structures coexist in solution. We show that the prevalence of connections changes when pseudouridine, an important component of natural and synthetic RNA molecules, is substituted for uridine in STMV RNA.
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4
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Kimchi O, Brenner MP, Colwell LJ. Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User's Guide to the LandscapeFold Algorithm. Methods Mol Biol 2023; 2586:49-77. [PMID: 36705898 DOI: 10.1007/978-1-0716-2768-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Here we detail the LandscapeFold secondary structure prediction algorithm and how it is used. The algorithm was previously described and tested in (Kimchi O et al., Biophys J 117(3):520-532, 2019), though it was not named there. The algorithm directly enumerates all possible secondary structures into which up to two RNA or single-stranded DNA sequences can fold. It uses a polymer physics model to estimate the configurational entropy of structures including complex pseudoknots. We detail each of these steps and ways in which the user can adjust the algorithm as desired. The code is available on the GitHub repository https://github.com/ofer-kimchi/LandscapeFold .
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Affiliation(s)
- Ofer Kimchi
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. .,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
| | - Michael P Brenner
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Lucy J Colwell
- Department of Chemistry, University of Cambridge, Cambridge, UK
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5
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Implementing computational methods in tandem with synonymous gene recoding for therapeutic development. Trends Pharmacol Sci 2023; 44:73-84. [PMID: 36307252 DOI: 10.1016/j.tips.2022.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 12/24/2022]
Abstract
Synonymous gene recoding, the substitution of synonymous variants into the genetic sequence, has been used to overcome many production limitations in therapeutic development. However, the safety and efficacy of recoded therapeutics can be difficult to evaluate because synonymous codon substitutions can result in subtle, yet impactful changes in protein features and require sensitive methods for detection. Given that computational approaches have made significant leaps in recent years, we propose that machine-learning (ML) tools may be leveraged to assess gene-recoded therapeutics and foresee an opportunity to adapt codon contexts to enhance some powerful existing tools. Here, we examine how synonymous gene recoding has been used to address challenges in therapeutic development, explain the biological mechanisms underlying its effects, and explore the application of computational platforms to improve the surveillance of functional variants in therapeutic design.
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Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition. Nat Commun 2023; 14:332. [PMID: 36658112 PMCID: PMC9852226 DOI: 10.1038/s41467-023-35803-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 12/29/2022] [Indexed: 01/21/2023] Open
Abstract
RNA molecules aggregate under certain conditions. The resulting condensates are implicated in human neurological disorders, and can potentially be designed towards specified bulk properties in vitro. However, the mechanism for aggregation-including how aggregation properties change with sequence and environmental conditions-remains poorly understood. To address this challenge, we introduce an analytical framework based on multimer enumeration. Our approach reveals the driving force for aggregation to be the increased configurational entropy associated with the multiplicity of ways to form bonds in the aggregate. Our model uncovers rich phase behavior, including a sequence-dependent reentrant phase transition, and repeat parity-dependent aggregation. We validate our results by comparison to a complete computational enumeration of the landscape, and to previously published molecular dynamics simulations. Our work unifies and extends published results, both explaining the behavior of CAG-repeat RNA aggregates implicated in Huntington's disease, and enabling the rational design of programmable RNA condensates.
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Tang K, Roca J, Chen R, Ansari A, Liang J. Thermodynamics of unfolding mechanisms of mouse mammary tumor virus pseudoknot from a coarse-grained loop-entropy model. J Biol Phys 2022; 48:129-150. [PMID: 35445347 DOI: 10.1007/s10867-022-09602-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/19/2022] [Indexed: 11/26/2022] Open
Abstract
Pseudoknotted RNA molecules play important biological roles that depend on their folded structure. To understand the underlying principles that determine their thermodynamics and folding/unfolding mechanisms, we carried out a study on a variant of the mouse mammary tumor virus pseudoknotted RNA (VPK), a widely studied model system for RNA pseudoknots. Our method is based on a coarse-grained discrete-state model and the algorithm of PK3D (pseudoknot structure predictor in three-dimensional space), with RNA loops explicitly constructed and their conformational entropic effects incorporated. Our loop entropy calculations are validated by accurately capturing previously measured melting temperatures of RNA hairpins with varying loop lengths. For each of the hairpins that constitutes the VPK, we identified alternative conformations that are more stable than the hairpin structures at low temperatures and predicted their populations at different temperatures. Our predictions were validated by thermodynamic experiments on these hairpins. We further computed the heat capacity profiles of VPK, which are in excellent agreement with available experimental data. Notably, our model provides detailed information on the unfolding mechanisms of pseudoknotted RNA. Analysis of the distribution of base-pairing probability of VPK reveals a cooperative unfolding mechanism instead of a simple sequential unfolding of first one stem and then the other. Specifically, we find a simultaneous "loosening" of both stems as the temperature is raised, whereby both stems become partially melted and co-exist during the unfolding process.
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Affiliation(s)
- Ke Tang
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, 60607, IL, USA
| | - Jorjethe Roca
- Department of Physics, University of Illinois at Chicago, 845 W Taylor St, Chicago, 60607, IL, USA
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles St., Baltimore, 21218, MD, USA
| | - Rong Chen
- Department of Statistics, Rutgers University, 110 Frelinghuysen Rd, Piscataway, 08854, NJ, USA
| | - Anjum Ansari
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, 60607, IL, USA.
- Department of Physics, University of Illinois at Chicago, 845 W Taylor St, Chicago, 60607, IL, USA.
| | - Jie Liang
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, 60607, IL, USA.
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8
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Cheng Y, Zhang S, Xu X, Chen SJ. Vfold2D-MC: A Physics-Based Hybrid Model for Predicting RNA Secondary Structure Folding. J Phys Chem B 2021; 125:10108-10118. [PMID: 34473508 DOI: 10.1021/acs.jpcb.1c04731] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurate prediction of RNA structure and folding stability has a far-reaching impact on our understanding of RNA functions. Here we develop Vfold2D-MC, a new physics-based model, to predict RNA structure and folding thermodynamics from the sequence. The model employs virtual bond-based coarse-graining of RNA backbone conformation and generates RNA conformations through Monte Carlo sampling of the bond angles and torsional angles of the virtual bonds. Using a coarse-grained statistical potential derived from the known structures, we assign each conformation with a statistical weight. The weighted average over the conformational ensemble gives the entropy and free energy parameters for the hairpin, bulge, and internal loops, and multiway junctions. From the thermodynamic parameters, we predict RNA structures, melting curves, and structural changes from the sequence. Theory-experiment comparisons indicate that Vfold2D-MC not only gives improved structure predictions but also enables the interpretation of thermodynamic results for different RNA structures, including multibranched junctions. This new model sets a promising framework to treat more complicated RNA structures, such as pseudoknotted and intramolecular kissing loops, for which experimental thermodynamic parameters are often unavailable.
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Affiliation(s)
- Yi Cheng
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Sicheng Zhang
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design. Sci Rep 2020; 10:15643. [PMID: 32973171 PMCID: PMC7519053 DOI: 10.1038/s41598-020-72533-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/19/2020] [Indexed: 12/28/2022] Open
Abstract
As the SARS-CoV-2 pandemic is rapidly progressing, the need for the development of an effective vaccine is critical. A promising approach for vaccine development is to generate, through codon pair deoptimization, an attenuated virus. This approach carries the advantage that it only requires limited knowledge specific to the virus in question, other than its genome sequence. Therefore, it is well suited for emerging viruses, for which we may not have extensive data. We performed comprehensive in silico analyses of several features of SARS-CoV-2 genomic sequence (e.g., codon usage, codon pair usage, dinucleotide/junction dinucleotide usage, RNA structure around the frameshift region) in comparison with other members of the coronaviridae family of viruses, the overall human genome, and the transcriptome of specific human tissues such as lung, which are primarily targeted by the virus. Our analysis identified the spike (S) and nucleocapsid (N) proteins as promising targets for deoptimization and suggests a roadmap for SARS-CoV-2 vaccine development, which can be generalizable to other viruses.
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Kames J, Holcomb DD, Kimchi O, DiCuccio M, Hamasaki-Katagiri N, Wang T, Komar AA, Alexaki A, Kimchi-Sarfaty C. Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.03.30.016832. [PMID: 32511300 PMCID: PMC7217226 DOI: 10.1101/2020.03.30.016832] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
As the SARS-CoV-2 pandemic is rapidly progressing, the need for the development of an effective vaccine is critical. A promising approach for vaccine development is to generate, through codon pair deoptimization, an attenuated virus. This approach carries the advantage that it only requires limited knowledge specific to the virus in question, other than its genome sequence. Therefore, it is well suited for emerging viruses for which we may not have extensive data. We performed comprehensive in silico analyses of several features of SARS-CoV-2 genomic sequence (e.g., codon usage, codon pair usage, dinucleotide/junction dinucleotide usage, RNA structure around the frameshift region) in comparison with other members of the coronaviridae family of viruses, the overall human genome, and the transcriptome of specific human tissues such as lung, which are primarily targeted by the virus. Our analysis identified the spike (S) and nucleocapsid (N) proteins as promising targets for deoptimization and suggests a roadmap for SARS-CoV-2 vaccine development, which can be generalizable to other viruses.
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Affiliation(s)
- Jacob Kames
- Center for Biologics Evaluation and Research, Office of Tissues and Advanced Therapies, Division of Plasma Protein Therapeutics, Food and Drug Administration, Silver Spring, MD, USA
| | - David D. Holcomb
- Center for Biologics Evaluation and Research, Office of Tissues and Advanced Therapies, Division of Plasma Protein Therapeutics, Food and Drug Administration, Silver Spring, MD, USA
| | - Ofer Kimchi
- Harvard University School of Engineering and Applied Sciences
| | - Michael DiCuccio
- National Center of Biotechnology Information, National Institutes of Health, Bethesda, MD, USA
| | - Nobuko Hamasaki-Katagiri
- Center for Biologics Evaluation and Research, Office of Tissues and Advanced Therapies, Division of Plasma Protein Therapeutics, Food and Drug Administration, Silver Spring, MD, USA
| | - Tony Wang
- Center for Biologics Evaluation and Research, Office of Vaccines Research and Review, Division of Viral Products, Food and Drug Administration, Silver Spring, MD, USA
| | - Anton A. Komar
- Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH, USA
| | - Aikaterini Alexaki
- Center for Biologics Evaluation and Research, Office of Tissues and Advanced Therapies, Division of Plasma Protein Therapeutics, Food and Drug Administration, Silver Spring, MD, USA
| | - Chava Kimchi-Sarfaty
- Center for Biologics Evaluation and Research, Office of Tissues and Advanced Therapies, Division of Plasma Protein Therapeutics, Food and Drug Administration, Silver Spring, MD, USA
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