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Gentile GM, Blue RE, Goda GA, Guzman BB, Szymanski RA, Lee EY, Engels NM, Hinkle ER, Wiedner HJ, Bishop AN, Harrison JT, Zhang H, Wehrens XH, Dominguez D, Giudice J. Alternative splicing of the Snap23 microexon is regulated by MBNL, QKI, and RBFOX2 in a tissue-specific manner and is altered in striated muscle diseases. RNA Biol 2025; 22:1-20. [PMID: 40207498 PMCID: PMC12064062 DOI: 10.1080/15476286.2025.2491160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Revised: 03/05/2025] [Accepted: 04/01/2025] [Indexed: 04/11/2025] Open
Abstract
The reprogramming of alternative splicing networks during development is a hallmark of tissue maturation and identity. Alternative splicing of microexons (small, genomic regions ≤ 51 nucleotides) functionally regulate protein-protein interactions in the brain and is altered in several neuronal diseases. However, little is known about the regulation and function of alternatively spliced microexons in striated muscle. Here, we investigated alternative splicing of a microexon in the synaptosome-associated protein 23 (Snap23) encoded gene. We found that inclusion of this microexon is developmentally regulated and tissue-specific, as it occurs exclusively in adult heart and skeletal muscle. The alternative region is highly conserved in mammalian species and encodes an in-frame sequence of 11 amino acids. Furthermore, we showed that alternative splicing of this microexon is mis-regulated in mouse models of heart and skeletal muscle diseases. We identified the RNA-binding proteins (RBPs) quaking (QKI) and RNA binding fox-1 homolog 2 (RBFOX2) as the primary splicing regulators of the Snap23 microexon. We found that QKI and RBFOX2 bind downstream of the Snap23 microexon to promote its inclusion, and this regulation can be escaped when the weak splice donor is mutated to the consensus 5' splice site. Finally, we uncovered the interplay between QKI and muscleblind-like splicing regulator (MBNL) as an additional, but minor layer of Snap23 microexon splicing control. Our results are one of the few reports detailing microexon alternative splicing regulation during mammalian striated muscle development.
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Affiliation(s)
- Gabrielle M. Gentile
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - R. Eric Blue
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Grant A. Goda
- Department of Chemistry, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bryan B. Guzman
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rachel A. Szymanski
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eunice Y. Lee
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nichlas M. Engels
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Emma R. Hinkle
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hannah J. Wiedner
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Aubriana N. Bishop
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jonathan T. Harrison
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hua Zhang
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xander H.T. Wehrens
- Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
| | - Daniel Dominguez
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- RNA Discovery Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jimena Giudice
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Genetics and Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- RNA Discovery Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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2
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Crowl S, Coleman MB, Chaphiv A, Jordan BT, Naegle KM. Systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms using PTM-POSE. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.01.10.575062. [PMID: 38260432 PMCID: PMC10802621 DOI: 10.1101/2024.01.10.575062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Post-translational modifications (PTMs) and splicing are important regulatory processes for controlling protein function and activity. Despite examples of interplay between alternative splicing and cell signaling in literature, there have been few detailed analyses of the impacts of alternative splicing on PTMs, partly due to difficulties in extracting PTM information from splicing measurements. We developed a computational pipeline, PTM Projection Onto Splice Events (PTM-POSE), to identify "prospective" PTM sites in alternative isoforms and splice events recorded in databases using only the genomic coordinates of a splice event or isoform of interest. Importantly, PTM-POSE integrates various PTM-specific databases and tools to allow for deeper analysis of the individual and global impact of spliced PTMs on isoform function, protein interactions, and regulation by enzymes like kinases. Using PTM-POSE, we performed a systematic analysis of PTM diversification across isoforms annotated in the Ensembl database. We found that 32% of PTMs are excluded from at least one Ensembl isoform, with palmitoylation being most likely to be excluded (49%) and glycosylation and crotonylation exhibiting the highest constitutive rates (75% and 94%, respectively). Further, approximately 2% of prospective PTM sites exhibited altered regulatory sequences surrounding the modification site, suggesting that regulatory or binding interactions might be different in these proteoforms. When comparing splicing of phosphorylation sites to measured phosphorylation abundance in KRAS-expressing lung cells, differential inclusion of phosphorylation sites correlated with phosphorylation levels, particularly for larger changes in inclusion (> 20%). To better understand how splicing diversification of PTMs may alter protein function and regulatory networks in specific biological contexts, we applied PTM-POSE to exon utilization measurements from TCGASpliceSeq of prostate tumor samples from The Cancer Genome Atlas (TCGA) and identified 1,489 PTMs impacted by ESRP1-correlated splicing, a splicing factor associated with worsened prognosis. We identified protein interaction and regulatory networks that may be rewired as a result of differential inclusion of PTM sites in ribosomal and cytoskeletal proteins. We also found instances in which ESRP1-mediated splicing impacted PTMs by altering flanking residues surrounding specific phosphorylation sites that may be targets of 14-3-3 proteins and SH2 domains. In addition, SGK1 signaling was found to be influenced by ESRP1 expression through increased inclusion of SGK1 substrates in ESRP1-expressing patients. Based on validation in a separate prostate cancer cohort from the Chinese Prostate Cancer Genome and EpiGenome Atlas (CPGEA), this correlated with increased phosphorylation of SGK1 substrates, particularly when SGK1 was predicted to be active. From this work, we highlighted the extensive splicing-control of PTM sites across the transcriptome and the novel information that can be gained through inclusion of PTMs in the analysis of alternative splicing. Importantly, we have provided a publicly available python package (PTM-POSE: https://github.com/NaegleLab/PTM-POSE) and all associated data for use by the broader scientific community to allow for continued exploration of the relationship between splicing and PTMs.
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Affiliation(s)
- Sam Crowl
- University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903
| | - Maeve Bella Coleman
- University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903
| | - Andrew Chaphiv
- University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903
| | - Ben T. Jordan
- University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903
| | - Kristen M. Naegle
- University of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA, 22903
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Titus MB, Chang AW, Popitsch N, Ebmeier CC, Bono JM, Olesnicky EC. The identification of protein and RNA interactors of the splicing factor Caper in the adult Drosophila nervous system. Front Mol Neurosci 2023; 16:1114857. [PMID: 37435576 PMCID: PMC10332324 DOI: 10.3389/fnmol.2023.1114857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 05/19/2023] [Indexed: 07/13/2023] Open
Abstract
Post-transcriptional gene regulation is a fundamental mechanism that helps regulate the development and healthy aging of the nervous system. Mutations that disrupt the function of RNA-binding proteins (RBPs), which regulate post-transcriptional gene regulation, have increasingly been implicated in neurological disorders including amyotrophic lateral sclerosis, Fragile X Syndrome, and spinal muscular atrophy. Interestingly, although the majority of RBPs are expressed widely within diverse tissue types, the nervous system is often particularly sensitive to their dysfunction. It is therefore critical to elucidate how aberrant RNA regulation that results from the dysfunction of ubiquitously expressed RBPs leads to tissue specific pathologies that underlie neurological diseases. The highly conserved RBP and alternative splicing factor Caper is widely expressed throughout development and is required for the development of Drosophila sensory and motor neurons. Furthermore, caper dysfunction results in larval and adult locomotor deficits. Nonetheless, little is known about which proteins interact with Caper, and which RNAs are regulated by Caper. Here we identify proteins that interact with Caper in both neural and muscle tissue, along with neural specific Caper target RNAs. Furthermore, we show that a subset of these Caper-interacting proteins and RNAs genetically interact with caper to regulate Drosophila gravitaxis behavior.
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Affiliation(s)
- M. Brandon Titus
- Department of Biology, University of Colorado Colorado Springs, Colorado Springs, CO, United States
| | - Adeline W. Chang
- Department of Biology, University of Colorado Colorado Springs, Colorado Springs, CO, United States
| | - Niko Popitsch
- Department of Biochemistry and Cell Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
| | | | - Jeremy M. Bono
- Department of Biology, University of Colorado Colorado Springs, Colorado Springs, CO, United States
| | - Eugenia C. Olesnicky
- Department of Biology, University of Colorado Colorado Springs, Colorado Springs, CO, United States
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Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022; 13:929736. [PMID: 35873469 PMCID: PMC9299079 DOI: 10.3389/fgene.2022.929736] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/25/2022] [Indexed: 12/13/2022] Open
Abstract
Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.
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Affiliation(s)
- Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mudassir Lodi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rihi Jain
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Achuth Nair
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Anirudh Pappu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Kush Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vignesh Venkat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Raghu Wable
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Matthew Dinatale
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Allyson Fu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vikram Iyer
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Ishan Kalove
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Marc Kleyman
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Joseph Koutsoutis
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - David Menna
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mayank Paliwal
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Nishi Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Thirth Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zara Rafique
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rothela Samadi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Roshan Varadhan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Shreyas Bolla
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States.,Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, United States
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5
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Li D, Mastaglia FL, Fletcher S, Wilton SD. Precision Medicine through Antisense Oligonucleotide-Mediated Exon Skipping. Trends Pharmacol Sci 2018; 39:982-994. [PMID: 30282590 DOI: 10.1016/j.tips.2018.09.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 08/30/2018] [Accepted: 09/04/2018] [Indexed: 12/11/2022]
Abstract
Clinical implementation of two recently approved antisense RNA therapeutics - Exondys51® to treat Duchenne muscular dystrophy (Duchenne MD) and Spinraza® as a treatment for spinal muscular atrophy (SMA) - highlights the therapeutic potential of antisense oligonucleotides (ASOs). As shown in the Duchenne and Becker cases, the identification and specific removal of 'dispensable' exons by exon-skipping ASOs could potentially bypass lethal mutations in other genes and bring clinical benefits to affected individuals carrying amenable mutations. In this review, we discuss the potential of therapeutic alternative splicing, with a particular focus on targeted exon skipping using Duchenne MD as an example, and speculate on new applications for other inherited rare diseases where redundant or dispensable exons may be amenable to exon-skipping ASO intervention as precision medicine.
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Affiliation(s)
- Dunhui Li
- Centre for Comparative Genomics, Murdoch University, Perth 6050, Australia; Perron Institute for Neurological and Translational Science, University of Western Australia, Perth 6000, Australia
| | - Frank L Mastaglia
- Perron Institute for Neurological and Translational Science, University of Western Australia, Perth 6000, Australia
| | - Sue Fletcher
- Centre for Comparative Genomics, Murdoch University, Perth 6050, Australia; Perron Institute for Neurological and Translational Science, University of Western Australia, Perth 6000, Australia
| | - Steve D Wilton
- Centre for Comparative Genomics, Murdoch University, Perth 6050, Australia; Perron Institute for Neurological and Translational Science, University of Western Australia, Perth 6000, Australia.
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6
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Faulty RNA splicing: consequences and therapeutic opportunities in brain and muscle disorders. Hum Genet 2017; 136:1215-1235. [DOI: 10.1007/s00439-017-1802-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 04/13/2017] [Indexed: 12/12/2022]
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7
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LOX-1 and Its Splice Variants: A New Challenge for Atherosclerosis and Cancer-Targeted Therapies. Int J Mol Sci 2017; 18:ijms18020290. [PMID: 28146073 PMCID: PMC5343826 DOI: 10.3390/ijms18020290] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 01/15/2017] [Accepted: 01/23/2017] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing (AS) is a process in which precursor messenger RNA (pre-mRNA) splicing sites are differentially selected to diversify the protein isoform population. Changes in AS patterns have an essential role in normal development, differentiation and response to physiological stimuli. It is documented that AS can generate both “risk” and “protective” splice variants that can contribute to the pathogenesis of several diseases including atherosclerosis. The main endothelial receptor for oxidized low-density lipoprotein (ox-LDLs) is LOX-1 receptor protein encoded by the OLR1 gene. When OLR1 undergoes AS events, it generates three variants: OLR1, OLR1D4 and LOXIN. The latter lacks exon 5 and two-thirds of the functional domain. Literature data demonstrate a protective role of LOXIN in pathologies correlated with LOX-1 overexpression such as atherosclerosis and tumors. In this review, we summarize recent developments in understanding of OLR1 AS while also highlighting data warranting further investigation of this process as a novel therapeutic target.
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9
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Abstract
The human transcriptome is composed of a vast RNA population that undergoes further diversification by splicing. Detecting specific splice sites in this large sequence pool is the responsibility of the major and minor spliceosomes in collaboration with numerous splicing factors. This complexity makes splicing susceptible to sequence polymorphisms and deleterious mutations. Indeed, RNA mis-splicing underlies a growing number of human diseases with substantial societal consequences. Here, we provide an overview of RNA splicing mechanisms followed by a discussion of disease-associated errors, with an emphasis on recently described mutations that have provided new insights into splicing regulation. We also discuss emerging strategies for splicing-modulating therapy.
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Affiliation(s)
- Marina M Scotti
- Department of Molecular Genetics and Microbiology, Center for NeuroGenetics and the Genetics Institute, University of Florida, College of Medicine, Gainesville, Florida 32610-3610 USA
| | - Maurice S Swanson
- Department of Molecular Genetics and Microbiology, Center for NeuroGenetics and the Genetics Institute, University of Florida, College of Medicine, Gainesville, Florida 32610-3610 USA
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11
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Wilton SD, Veedu RN, Fletcher S. The emperor's new dystrophin: finding sense in the noise. Trends Mol Med 2015; 21:417-26. [PMID: 26051381 DOI: 10.1016/j.molmed.2015.04.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 04/29/2015] [Accepted: 04/30/2015] [Indexed: 01/16/2023]
Abstract
Targeted dystrophin exon removal is a promising therapy for Duchenne muscular dystrophy (DMD); however, dystrophin expression in some reports is not supported by the associated data. As in the account of 'The Emperor's New Clothes', the validity of such claims must be questioned, with critical re-evaluation of available data. Is it appropriate to report clinical benefit and induction of dystrophin as dose dependent when the baseline is unclear? The inability to induce meaningful levels of dystrophin does not mean that dystrophin expression as an end point is irrelevant, nor that induced exon skipping as a strategy is flawed, but demands that drug safety and efficacy, and study parameters be addressed, rather than questioning the strategy or the validity of dystrophin as a biomarker.
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Affiliation(s)
- S D Wilton
- Centre for Comparative Genomics, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia; West Australian Neuroscience Research Institute, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia.
| | - R N Veedu
- Centre for Comparative Genomics, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia; West Australian Neuroscience Research Institute, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia
| | - S Fletcher
- Centre for Comparative Genomics, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia; West Australian Neuroscience Research Institute, Murdoch University, 90 South Street, Murdoch, WA 6009, Australia
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12
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Greer K, Mizzi K, Rice E, Kuster L, Barrero RA, Bellgard MI, Lynch BJ, Foley AR, O Rathallaigh E, Wilton SD, Fletcher S. Pseudoexon activation increases phenotype severity in a Becker muscular dystrophy patient. Mol Genet Genomic Med 2015; 3:320-6. [PMID: 26247048 PMCID: PMC4521967 DOI: 10.1002/mgg3.144] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 03/13/2015] [Accepted: 03/16/2015] [Indexed: 01/16/2023] Open
Abstract
We report a dystrophinopathy patient with an in-frame deletion of DMD exons 45-47, and therefore a genetic diagnosis of Becker muscular dystrophy, who presented with a more severe than expected phenotype. Analysis of the patient DMD mRNA revealed an 82 bp pseudoexon, derived from intron 44, that disrupts the reading frame and is expected to yield a nonfunctional dystrophin. Since the sequence of the pseudoexon and canonical splice sites does not differ from the reference sequence, we concluded that the genomic rearrangement promoted recognition of the pseudoexon, causing a severe dystrophic phenotype. We characterized the deletion breakpoints and identified motifs that might influence selection of the pseudoexon. We concluded that the donor splice site was strengthened by juxtaposition of intron 47, and loss of intron 44 silencer elements, normally located downstream of the pseudoexon donor splice site, further enhanced pseudoexon selection and inclusion in the DMD transcript in this patient.
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Affiliation(s)
- Kane Greer
- Centre for Comparative Genomics, Murdoch University 90 South St, Murdoch, Western Australia, 6150, Australia ; The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia
| | - Kayla Mizzi
- The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia
| | - Emily Rice
- The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia
| | - Lukas Kuster
- The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia
| | - Roberto A Barrero
- Centre for Comparative Genomics, Murdoch University 90 South St, Murdoch, Western Australia, 6150, Australia
| | - Matthew I Bellgard
- Centre for Comparative Genomics, Murdoch University 90 South St, Murdoch, Western Australia, 6150, Australia
| | - Bryan J Lynch
- Children's University Hospital Temple Street, Dublin, Ireland
| | | | | | - Steve D Wilton
- Centre for Comparative Genomics, Murdoch University 90 South St, Murdoch, Western Australia, 6150, Australia ; The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia ; Western Australian Neuroscience Institute Nedlands, Western Australia, 6009, Australia
| | - Sue Fletcher
- Centre for Comparative Genomics, Murdoch University 90 South St, Murdoch, Western Australia, 6150, Australia ; The University of Western Australia 35 Stirling Highway, Crawley, Western Australia, 6009, Australia ; Western Australian Neuroscience Institute Nedlands, Western Australia, 6009, Australia
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