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Sharma B, Torres MM, Rodriguez S, Gangwani L, Kumar S. MicroRNA-502-3p regulates GABAergic synapse function in hippocampal neurons. Neural Regen Res 2024; 19:2698-2707. [PMID: 38595288 PMCID: PMC11168514 DOI: 10.4103/nrr.nrr-d-23-01064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 01/05/2024] [Accepted: 01/22/2024] [Indexed: 04/11/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202412000-00026/figure1/v/2024-04-08T165401Z/r/image-tiff Gamma-aminobutyric acid (GABA)ergic neurons, the most abundant inhibitory neurons in the human brain, have been found to be reduced in many neurological disorders, including Alzheimer's disease and Alzheimer's disease-related dementia. Our previous study identified the upregulation of microRNA-502-3p (miR-502-3p) and downregulation of GABA type A receptor subunit α-1 in Alzheimer's disease synapses. This study investigated a new molecular relationship between miR-502-3p and GABAergic synapse function. In vitro studies were performed using the mouse hippocampal neuronal cell line HT22 and miR-502-3p agomiRs and antagomiRs. In silico analysis identified multiple binding sites of miR-502-3p at GABA type A receptor subunit α-1 mRNA. Luciferase assay confirmed that miR-502-3p targets the GABA type A receptor subunit α-1 gene and suppresses the luciferase activity. Furthermore, quantitative reverse transcription-polymerase chain reaction, miRNA in situ hybridization, immunoblotting, and immunostaining analysis confirmed that overexpression of miR-502-3p reduced the GABA type A receptor subunit α-1 level, while suppression of miR-502-3p increased the level of GABA type A receptor subunit α-1 protein. Notably, as a result of the overexpression of miR-502-3p, cell viability was found to be reduced, and the population of necrotic cells was found to be increased. The whole cell patch-clamp analysis of human-GABA receptor A-α1/β3/γ2L human embryonic kidney (HEK) recombinant cell line also showed that overexpression of miR-502-3p reduced the GABA current and overall GABA function, suggesting a negative correlation between miR-502-3p levels and GABAergic synapse function. Additionally, the levels of proteins associated with Alzheimer's disease were high with miR-502-3p overexpression and reduced with miR-502-3p suppression. The present study provides insight into the molecular mechanism of regulation of GABAergic synapses by miR-502-3p. We propose that micro-RNA, in particular miR-502-3p, could be a potential therapeutic target to modulate GABAergic synapse function in neurological disorders, including Alzheimer's disease and Alzheimer's disease-related dementia.
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Affiliation(s)
- Bhupender Sharma
- Center of Emphasis in Neuroscience, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Melissa M. Torres
- Center of Emphasis in Neuroscience, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Sheryl Rodriguez
- Center of Emphasis in Neuroscience, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Laxman Gangwani
- Bond Life Sciences Center and Department of Veterinary Pathobiology, University of Missouri, Columbia, MO, USA
| | - Subodh Kumar
- Center of Emphasis in Neuroscience, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
- L. Frederick Francis Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, El Paso, TX, USA
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Hara K, Iwano N, Fukunaga T, Hamada M. DeepRaccess: high-speed RNA accessibility prediction using deep learning. FRONTIERS IN BIOINFORMATICS 2023; 3:1275787. [PMID: 37881622 PMCID: PMC10597636 DOI: 10.3389/fbinf.2023.1275787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.
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Affiliation(s)
- Kaisei Hara
- Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo, Japan
| | - Natsuki Iwano
- Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Tokyo, Japan
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo, Japan
- Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
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Zhou T, Dai N, Li S, Ward M, Mathews DH, Huang L. RNA design via structure-aware multifrontier ensemble optimization. Bioinformatics 2023; 39:i563-i571. [PMID: 37387188 DOI: 10.1093/bioinformatics/btad252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.
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Affiliation(s)
- Tianshuo Zhou
- School of Electrical Engineering and Computer Science, Oregon State University, Corvalli OR 97330, United States
| | - Ning Dai
- School of Electrical Engineering and Computer Science, Oregon State University, Corvalli OR 97330, United States
| | - Sizhen Li
- School of Electrical Engineering and Computer Science, Oregon State University, Corvalli OR 97330, United States
| | - Max Ward
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - David H Mathews
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY 14642, United States
- Center for RNA Biology, University of Rochester Medical Center, Rochester, NY 14642, United States
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, United States
| | - Liang Huang
- School of Electrical Engineering and Computer Science, Oregon State University, Corvalli OR 97330, United States
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Comparative genomics, evolutionary epidemiology, and RBD-hACE2 receptor binding pattern in B.1.1.7 (Alpha) and B.1.617.2 (Delta) related to their pandemic response in UK and India. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2022; 101:105282. [PMID: 35427787 PMCID: PMC9005225 DOI: 10.1016/j.meegid.2022.105282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 04/04/2022] [Accepted: 04/08/2022] [Indexed: 02/07/2023]
Abstract
Background The massive increase in COVID-19 infection had generated a second wave in India during May–June 2021 with a critical pandemic situation. The Delta variant (B.1.617.2) was a significant factor during the second wave. Conversely, the UK had passed through the crucial phase of the pandemic from November to December 2020 due to B.1.1.7. The study tried to comprehend the pandemic response in the UK and India to the spread of the B.1.1.7 (Alpha, UK) variant and B.1.617.2 (Delta, India) variant. Methods This study was performed in three directions to understand the pandemic response of the two emerging variants. First, we served comparative genomics, such as genome sequence submission patterns, mutational landscapes, and structural landscapes of significant mutations (N501Y, D614G, L452R, E484Q, and P681R). Second, we performed evolutionary epidemiology using molecular phylogenetics, scatter plots of the cluster evaluation, country-wise transmission pattern, and frequency pattern. Third, the receptor binding pattern was analyzed using the Wuhan reference strain and the other two variants. Results The study analyzed the country-wise and region-wise genome sequences and their submission pattern, molecular phylogenetics, scatter plot of the cluster evaluation, country-wise geographical distribution and transmission pattern, frequency pattern, entropy diversity, and mutational landscape of the two variants. The structural pattern was analyzed in the N501Y, D614G L452R, E484Q, and P681R mutations. The study found increased molecular interactivity between hACE2-RBD binding of B.1.1.7 and B.1.617.2 compared to the Wuhan reference strain. Our receptor binding analysis showed a similar indication pattern for hACE2-RBD of these two variants. However, B.1.617.2 offers slightly better stability in the hACE2-RBD binding pattern through MD simulation than B.1.1.7. Conclusion The increased hACE2-RBD binding pattern of B.1.1.7 and B.1.617.2 might help to increase the infectivity compared to the Wuhan reference strain.
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Kang W, Fromm B, Houben AJ, Høye E, Bezdan D, Arnan C, Thrane K, Asp M, Johnson R, Biryukova I, Friedländer MR. MapToCleave: High-throughput profiling of microRNA biogenesis in living cells. Cell Rep 2021; 37:110015. [PMID: 34788611 DOI: 10.1016/j.celrep.2021.110015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 09/17/2021] [Accepted: 10/27/2021] [Indexed: 12/26/2022] Open
Abstract
Previous large-scale studies have uncovered many features that determine the processing of microRNA (miRNA) precursors; however, they have been conducted in vitro. Here, we introduce MapToCleave, a method to simultaneously profile processing of thousands of distinct RNA structures in living cells. We find that miRNA precursors with a stable lower basal stem are more efficiently processed and also have higher expression in vivo in tissues from 20 animal species. We systematically compare the importance of known and novel sequence and structural features and test biogenesis of miRNA precursors from 10 animal and plant species in human cells. Lastly, we provide evidence that the GHG motif better predicts processing when defined as a structure rather than sequence motif, consistent with recent cryogenic electron microscopy (cryo-EM) studies. In summary, we apply a screening assay in living cells to reveal the importance of lower basal stem stability for miRNA processing and in vivo expression.
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Affiliation(s)
- Wenjing Kang
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Bastian Fromm
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden; The Arctic University Museum of Norway, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Anna J Houben
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona (BIST), Catalonia, Spain
| | - Eirik Høye
- Department of Tumor Biology, Oslo Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Daniela Bezdan
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona (BIST), Catalonia, Spain; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
| | - Carme Arnan
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona (BIST), Catalonia, Spain
| | - Kim Thrane
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Michaela Asp
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Department for BioMedical Research, University of Bern, Bern, Switzerland; School of Biology and Environmental Science, University College Dublin, Dublin, Ireland; Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Inna Biryukova
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Marc R Friedländer
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.
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Zhang H, Zhang L, Mathews DH, Huang L. LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities. Bioinformatics 2021; 36:i258-i267. [PMID: 32657379 PMCID: PMC7355276 DOI: 10.1093/bioinformatics/btaa460] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Motivation RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime. Results Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart). Availability and implementation Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- He Zhang
- Baidu Research, Sunnyvale, CA 94089, USA.,School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97330, USA
| | - Liang Zhang
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97330, USA
| | - David H Mathews
- Department of Biochemistry & Biophysics, University of Rochester Medical Center, Rochester, NY 48306, USA.,Center for RNA Biology, University of Rochester Medical Center, Rochester, NY 48306, USA.,Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY 48306, USA
| | - Liang Huang
- Baidu Research, Sunnyvale, CA 94089, USA.,School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97330, USA
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Li TJX, Reidys CM. On an enhancement of RNA probing data using information theory. Algorithms Mol Biol 2020; 15:15. [PMID: 32782456 PMCID: PMC7413225 DOI: 10.1186/s13015-020-00176-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 07/31/2020] [Indexed: 12/21/2022] Open
Abstract
Identifying the secondary structure of an RNA is crucial for understanding its diverse regulatory functions. This paper focuses on how to enhance target identification in a Boltzmann ensemble of structures via chemical probing data. We employ an information-theoretic approach to solve the problem, via considering a variant of the Rényi-Ulam game. Our framework is centered around the ensemble tree, a hierarchical bi-partition of the input ensemble, that is constructed by recursively querying about whether or not a base pair of maximum information entropy is contained in the target. These queries are answered via relating local with global probing data, employing the modularity in RNA secondary structures. We present that leaves of the tree are comprised of sub-samples exhibiting a distinguished structure with high probability. In particular, for a Boltzmann ensemble incorporating probing data, which is well established in the literature, the probability of our framework correctly identifying the target in the leaf is greater than \documentclass[12pt]{minimal}
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Su C, Weir JD, Zhang F, Yan H, Wu T. ENTRNA: a framework to predict RNA foldability. BMC Bioinformatics 2019; 20:373. [PMID: 31269893 PMCID: PMC6610807 DOI: 10.1186/s12859-019-2948-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 06/12/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particular, RNA secondary structures, is critical for elucidating biological functions. Existing literature has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding problem where free energy has played a key role. RESULTS In this research, we propose a Positive-Unlabeled data- driven framework termed ENTRNA. Other than free energy and commonly studied sequence and structural features, we propose a new feature, Sequence Segment Entropy (SSE), to measure the diversity of RNA sequences. ENTRNA is trained and cross-validated using 1024 pseudoknot-free RNAs and 1060 pseudoknotted RNAs from the RNASTRAND database respectively. To test the robustness of the ENTRNA, the models are further blind tested on 206 pseudoknot-free and 93 pseudoknotted RNAs from the PDB database. For pseudoknot-free RNAs, ENTRNA has 86.5% sensitivity on the training dataset and 80.6% sensitivity on the testing dataset. For pseudoknotted RNAs, ENTRNA shows 81.5% sensitivity on the training dataset and 71.0% on the testing dataset. To test the applicability of ENTRNA to long structural-complex RNA, we collect 5 laboratory synthetic RNAs ranging from 1618 to 1790 nucleotides. ENTRNA is able to predict the foldability of 4 RNAs. CONCLUSION In this article, we reformulate the RNA design problem as a foldability prediction problem which is to predict the likelihood of the co-existence of a sequence-structure pair. This new construct has the potential for both RNA structure prediction and the inverse folding problem. In addition, this new construct enables us to explore data-driven approaches in RNA research.
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Affiliation(s)
- Congzhe Su
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85281 USA
| | - Jeffery D. Weir
- Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433 USA
| | - Fei Zhang
- Biodesign Center for Molecular Design and Biomimetics, The Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85281 USA
| | - Hao Yan
- Biodesign Center for Molecular Design and Biomimetics, The Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ 85281 USA
| | - Teresa Wu
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85281 USA
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Manzourolajdad A, Spouge JL. Structural prediction of RNA switches using conditional base-pair probabilities. PLoS One 2019; 14:e0217625. [PMID: 31188853 PMCID: PMC6561571 DOI: 10.1371/journal.pone.0217625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/15/2019] [Indexed: 11/23/2022] Open
Abstract
An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and alternative structures by partitioning structures sampled from the energy landscape into two clusters, but it is very time-consuming. Instead, we predict the alternative structure of an RNA switch from conditional probability calculations within the energy landscape. First, our method excludes base pairs related to the most stable structure in the energy landscape. Then, it detects stable stems (“seeds”) in the remaining landscape. Finally, it folds an alternative structure prediction around a seed. While having comparable riboswitch classification performance, the conditional-probability computations had fewer adjustable parameters, offered greater predictive flexibility, and were more than one thousand times faster than the sampling step alone in sampling-clustering predictions, the competing standard. Overall, the described approach helps traverse thermodynamically improbable energy landscapes to find biologically significant substructures and structures rapidly and effectively.
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Affiliation(s)
- Amirhossein Manzourolajdad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - John L. Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
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Rezazadegan R, Reidys C. Degeneracy and genetic assimilation in RNA evolution. BMC Bioinformatics 2018; 19:543. [PMID: 30587112 PMCID: PMC6307299 DOI: 10.1186/s12859-018-2497-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 11/16/2018] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The neutral theory of Motoo Kimura stipulates that evolution is mostly driven by neutral mutations. However adaptive pressure eventually leads to changes in phenotype that involve non-neutral mutations. The relation between neutrality and adaptation has been studied in the context of RNA before and here we further study transitional mutations in the context of degenerate (plastic) RNA sequences and genetic assimilation. We propose quasineutral mutations, i.e. mutations which preserve an element of the phenotype set, as minimal mutations and study their properties. We also propose a general probabilistic interpretation of genetic assimilation and specialize it to the Boltzmann ensemble of RNA sequences. RESULTS We show that degenerate sequences i.e. sequences with more than one structure at the MFE level have the highest evolvability among all sequences and are central to evolutionary innovation. Degenerate sequences also tend to cluster together in the sequence space. The selective pressure in an evolutionary simulation causes the population to move towards regions with more degenerate sequences, i.e. regions at the intersection of different neutral networks, and this causes the number of such sequences to increase well beyond the average percentage of degenerate sequences in the sequence space. We also observe that evolution by quasineutral mutations tends to conserve the number of base pairs in structures and thereby maintains structural integrity even in the presence of pressure to the contrary. CONCLUSIONS We conclude that degenerate RNA sequences play a major role in evolutionary adaptation.
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Affiliation(s)
- Reza Rezazadegan
- University of Virginia Biocomplexity Institute, 995 Research Park Boulevard, Charlottesville, 22911 USA
| | - Christian Reidys
- University of Virginia Biocomplexity Institute, 995 Research Park Boulevard, Charlottesville, 22911 USA
- Department of Mathematics, University of Virginia, 141 Cabell Drive, Charlottesville, 22904 USA
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Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes. Nat Commun 2018; 9:606. [PMID: 29426922 PMCID: PMC5807309 DOI: 10.1038/s41467-018-02923-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 01/09/2018] [Indexed: 11/23/2022] Open
Abstract
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies. Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.
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Garcia-Martin JA, Bayegan AH, Dotu I, Clote P. RNAdualPF: software to compute the dual partition function with sample applications in molecular evolution theory. BMC Bioinformatics 2016; 17:424. [PMID: 27756204 PMCID: PMC5069997 DOI: 10.1186/s12859-016-1280-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 09/26/2016] [Indexed: 12/01/2022] Open
Abstract
Background RNA inverse folding is the problem of finding one or more sequences that fold into a user-specified target structure s0, i.e. whose minimum free energy secondary structure is identical to the target s0. Here we consider the ensemble of all RNA sequences that have low free energy with respect to a given target s0. Results We introduce the program RNAdualPF, which computes the dual partition functionZ∗, defined as the sum of Boltzmann factors exp(−E(a,s0)/RT) of all RNA nucleotide sequences a compatible with target structure s0. Using RNAdualPF, we efficiently sample RNA sequences that approximately fold into s0, where additionally the user can specify IUPAC sequence constraints at certain positions, and whether to include dangles (energy terms for stacked, single-stranded nucleotides). Moreover, since we also compute the dual partition functionZ∗(k) over all sequences having GC-content k, the user can require that all sampled sequences have a precise, specified GC-content. Using Z∗, we compute the dual expected energy 〈E∗〉, and use it to show that natural RNAs from the Rfam 12.0 database have higher minimum free energy than expected, thus suggesting that functional RNAs are under evolutionary pressure to be only marginally thermodynamically stable. We show that C. elegans precursor microRNA (pre-miRNA) is significantly non-robust with respect to mutations, by comparing the robustness of each wild type pre-miRNA sequence with 2000 [resp. 500] sequences of the same GC-content generated by RNAdualPF, which approximately [resp. exactly] fold into the wild type target structure. We confirm and strengthen earlier findings that precursor microRNAs and bacterial small noncoding RNAs display plasticity, a measure of structural diversity. Conclusion We describe RNAdualPF, which rapidly computes the dual partition functionZ∗ and samples sequences having low energy with respect to a target structure, allowing sequence constraints and specified GC-content. Using different inverse folding software, another group had earlier shown that pre-miRNA is mutationally robust, even controlling for compositional bias. Our opposite conclusion suggests a cautionary note that computationally based insights into molecular evolution may heavily depend on the software used. C/C++-software for RNAdualPF is available at http://bioinformatics.bc.edu/clotelab/RNAdualPF. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1280-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Juan Antonio Garcia-Martin
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA.,Present Address: Systems Biology Program Centro Nacional de Biotecnología Consejo Superior de Investigaciones Científicas (CSIC) C/ Darwin 3, Madrid, 28049, Spain
| | - Amir H Bayegan
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA
| | - Ivan Dotu
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader, 88, Barcelona, Spain
| | - Peter Clote
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA.
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Abstract
We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a1, …, an. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.
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