1
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Chen CC, Han J, Chinn CA, Rounds JS, Li X, Nikan M, Myszka M, Tong L, Passalacqua LFM, Bredy T, Wood MA, Luptak A. Inhibition of Cpeb3 ribozyme elevates CPEB3 protein expression and polyadenylation of its target mRNAs and enhances object location memory. eLife 2024; 13:e90116. [PMID: 38319152 PMCID: PMC10919898 DOI: 10.7554/elife.90116] [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: 06/13/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024] Open
Abstract
A self-cleaving ribozyme that maps to an intron of the cytoplasmic polyadenylation element-binding protein 3 (Cpeb3) gene is thought to play a role in human episodic memory, but the underlying mechanisms mediating this effect are not known. We tested the activity of the murine sequence and found that the ribozyme's self-scission half-life matches the time it takes an RNA polymerase to reach the immediate downstream exon, suggesting that the ribozyme-dependent intron cleavage is tuned to co-transcriptional splicing of the Cpeb3 mRNA. Our studies also reveal that the murine ribozyme modulates maturation of its harboring mRNA in both cultured cortical neurons and the hippocampus: inhibition of the ribozyme using an antisense oligonucleotide leads to increased CPEB3 protein expression, which enhances polyadenylation and translation of localized plasticity-related target mRNAs, and subsequently strengthens hippocampal-dependent long-term memory. These findings reveal a previously unknown role for self-cleaving ribozyme activity in regulating experience-induced co-transcriptional and local translational processes required for learning and memory.
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Affiliation(s)
- Claire C Chen
- Department of Pharmaceutical Sciences, University of California, IrvineIrvineUnited States
| | - Joseph Han
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | - Carlene A Chinn
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | - Jacob S Rounds
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | - Xiang Li
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | | | - Marie Myszka
- Department of Chemistry, University of California, IrvineIrvineUnited States
| | - Liqi Tong
- Institute for Memory Impairments and Neurological Disorders, University of California, IrvineIrvineUnited States
| | - Luiz FM Passalacqua
- Department of Pharmaceutical Sciences, University of California, IrvineIrvineUnited States
| | - Timothy Bredy
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | - Marcelo A Wood
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, IrvineIrvineUnited States
| | - Andrej Luptak
- Department of Pharmaceutical Sciences, University of California, IrvineIrvineUnited States
- Department of Chemistry, University of California, IrvineIrvineUnited States
- Department of Molecular Biology and Biochemistry, University of California, IrvineIrvineUnited States
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2
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Sarzynska J, Popenda M, Antczak M, Szachniuk M. RNA tertiary structure prediction using RNAComposer in CASP15. Proteins 2023; 91:1790-1799. [PMID: 37615316 DOI: 10.1002/prot.26578] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/14/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023]
Abstract
As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.
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Affiliation(s)
- Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
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3
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Toch K, Buczek M, Labocha MK. Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans. Genes (Basel) 2023; 14:2080. [PMID: 38003023 PMCID: PMC10671385 DOI: 10.3390/genes14112080] [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: 10/24/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.
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Affiliation(s)
| | | | - Marta K. Labocha
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Ul. Gronostajowa 7, 30-387 Krakow, Poland; (K.T.); (M.B.)
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4
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Chen CC, Han J, Chinn CA, Rounds JS, Li X, Nikan M, Myszka M, Tong L, Passalacqua LFM, Bredy TW, Wood MA, Lupták A. Inhibition of CPEB3 ribozyme elevates CPEB3 protein expression and polyadenylation of its target mRNAs, and enhances object location memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.543953. [PMID: 37333407 PMCID: PMC10274809 DOI: 10.1101/2023.06.07.543953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
A self-cleaving ribozyme that maps to an intron of the cytoplasmic polyadenylation element binding protein 3 (CPEB3) gene is thought to play a role in human episodic memory, but the underlying mechanisms mediating this effect are not known. We tested the activity of the murine sequence and found that the ribozyme's self-scission half-life matches the time it takes an RNA polymerase to reach the immediate downstream exon, suggesting that the ribozyme-dependent intron cleavage is tuned to co-transcriptional splicing of the CPEB3 mRNA. Our studies also reveal that the murine ribozyme modulates maturation of its harboring mRNA in both cultured cortical neurons and the hippocampus: inhibition of the ribozyme using an antisense oligonucleotide leads to increased CPEB3 protein expression, which enhances polyadenylation and translation of localized plasticity-related target mRNAs, and subsequently strengthens hippocampal-dependent long-term memory. These findings reveal a previously unknown role for self-cleaving ribozyme activity in regulating experience-induced co-transcriptional and local translational processes required for learning and memory.
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Affiliation(s)
- Claire C. Chen
- Department of Pharmaceutical Sciences, University of California–Irvine, Irvine, California 92697, United States
| | - Joseph Han
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Carlene A. Chinn
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Jacob S. Rounds
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Xiang Li
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Mehran Nikan
- Ionis Pharmaceuticals, 2855 Gazelle Court, Carlsbad, CA 92010, USA
| | - Marie Myszka
- Department of Chemistry, University of California–Irvine, Irvine, California 92697, United States
| | - Liqi Tong
- Institute for Memory Impairments and Neurological Disorders, University of California–Irvine, Irvine, California 92697, United States
| | - Luiz F. M. Passalacqua
- Department of Pharmaceutical Sciences, University of California–Irvine, Irvine, California 92697, United States
| | - Timothy W. Bredy
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Marcelo A. Wood
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California–Irvine, Irvine, California 92697, United States
| | - Andrej Lupták
- Department of Pharmaceutical Sciences, University of California–Irvine, Irvine, California 92697, United States
- Department of Chemistry, University of California–Irvine, Irvine, California 92697, United States
- Department of Molecular Biology and Biochemistry, University of California–Irvine, Irvine, California 92697, United States
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5
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Roberts JM, Beck JD, Pollock TB, Bendixsen DP, Hayden EJ. RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes. eLife 2023; 12:80360. [PMID: 36655987 PMCID: PMC9901934 DOI: 10.7554/elife.80360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.
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Affiliation(s)
- Jessica M Roberts
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
| | - James D Beck
- Computing PhD Program, Boise State UniversityBoiseUnited States
| | - Tanner B Pollock
- Department of Biological Science, Boise State UniversityBoiseUnited States
| | - Devin P Bendixsen
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
| | - Eric J Hayden
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
- Computing PhD Program, Boise State UniversityBoiseUnited States
- Department of Biological Science, Boise State UniversityBoiseUnited States
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6
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Beck JD, Roberts JM, Kitzhaber JM, Trapp A, Serra E, Spezzano F, Hayden EJ. Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data. Front Mol Biosci 2022; 9:893864. [PMID: 36046603 PMCID: PMC9421044 DOI: 10.3389/fmolb.2022.893864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.
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Affiliation(s)
| | - Jessica M. Roberts
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, United States
| | - Joey M. Kitzhaber
- Department of Computer Science, Boise State University, Boise, ID, United States
| | - Ashlyn Trapp
- Department of Biological Sciences, Boise State University, Boise, ID, United States
| | | | | | - Eric J. Hayden
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, United States
- Department of Computer Science, Boise State University, Boise, ID, United States
- *Correspondence: Eric J. Hayden,
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7
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Peri G, Gibard C, Shults NH, Crossin K, Hayden EJ. Dynamic RNA fitness landscapes of a group I ribozyme during changes to the experimental environment. Mol Biol Evol 2022; 39:6502289. [PMID: 35020916 PMCID: PMC8890501 DOI: 10.1093/molbev/msab373] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl2). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.
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Affiliation(s)
- Gianluca Peri
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, USA
| | - Clémentine Gibard
- Department of Biological Science, Boise State University, Boise, ID, USA
| | - Nicholas H Shults
- Department of Biological Science, Boise State University, Boise, ID, USA
| | - Kent Crossin
- Department of Biological Science, Boise State University, Boise, ID, USA
| | - Eric J Hayden
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, USA.,Department of Biological Science, Boise State University, Boise, ID, USA
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