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Belleville AE, Thomas JD, Tonnies J, Gabel AM, Borrero Rossi A, Singh P, Queitsch C, Bradley RK. An autoregulatory poison exon in Smndc1 is conserved across kingdoms and influences organism growth. PLoS Genet 2024; 20:e1011363. [PMID: 39150991 DOI: 10.1371/journal.pgen.1011363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 08/28/2024] [Accepted: 07/08/2024] [Indexed: 08/18/2024] Open
Abstract
Many of the most highly conserved elements in the human genome are "poison exons," alternatively spliced exons that contain premature termination codons and permit post-transcriptional regulation of mRNA abundance through induction of nonsense-mediated mRNA decay (NMD). Poison exons are widely assumed to be highly conserved due to their presumed importance for organismal fitness, but this functional importance has never been tested in the context of a whole organism. Here, we report that a poison exon in Smndc1 is conserved across mammals and plants and plays a molecular autoregulatory function in both kingdoms. We generated mouse and A. thaliana models lacking this poison exon to find its loss leads to deregulation of SMNDC1 protein levels, pervasive alterations in mRNA processing, and organismal size restriction. Together, these models demonstrate the importance of poison exons for both molecular and organismal phenotypes that likely explain their extraordinary conservation.
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Affiliation(s)
- Andrea E Belleville
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, United States of America
- Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America
| | - James D Thomas
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Jackson Tonnies
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Biology, University of Washington, Seattle, Washington, United States of America
| | - Austin M Gabel
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Andrea Borrero Rossi
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Priti Singh
- Preclinical Modeling Core, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Brotman Baty Institute for Precision Medicine, Seattle, Washington, United States of America
| | - Robert K Bradley
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
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2
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Sabbarini IM, Reif D, McQuown AJ, Nelliat AR, Prince J, Membreno BS, Wu CCC, Murray AW, Denic V. Zinc-finger protein Zpr1 is a bespoke chaperone essential for eEF1A biogenesis. Mol Cell 2023; 83:252-265.e13. [PMID: 36630955 PMCID: PMC10016025 DOI: 10.1016/j.molcel.2022.12.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/15/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023]
Abstract
The conserved regulon of heat shock factor 1 in budding yeast contains chaperones for general protein folding as well as zinc-finger protein Zpr1, whose essential role in archaea and eukaryotes remains unknown. Here, we show that Zpr1 depletion causes acute proteotoxicity driven by biosynthesis of misfolded eukaryotic translation elongation factor 1A (eEF1A). Prolonged Zpr1 depletion leads to eEF1A insufficiency, thereby inducing the integrated stress response and inhibiting protein synthesis. Strikingly, we show by using two distinct biochemical reconstitution approaches that Zpr1 enables eEF1A to achieve a conformational state resistant to protease digestion. Lastly, we use a ColabFold model of the Zpr1-eEF1A complex to reveal a folding mechanism mediated by the Zpr1's zinc-finger and alpha-helical hairpin structures. Our work uncovers the long-sought-after function of Zpr1 as a bespoke chaperone tailored to the biogenesis of one of the most abundant proteins in the cell.
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Affiliation(s)
- Ibrahim M Sabbarini
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Dvir Reif
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Alexander J McQuown
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Anjali R Nelliat
- Graduate Program in Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Prince
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Britnie Santiago Membreno
- RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Colin Chih-Chien Wu
- RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Andrew W Murray
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Vladimir Denic
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
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3
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Romanowski A, Schlaen RG, Perez-Santangelo S, Mancini E, Yanovsky MJ. Global transcriptome analysis reveals circadian control of splicing events in Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:889-902. [PMID: 32314836 DOI: 10.1111/tpj.14776] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 03/26/2020] [Accepted: 04/01/2020] [Indexed: 05/21/2023]
Abstract
The circadian clock of Arabidopsis thaliana controls many physiological and molecular processes, allowing plants to anticipate daily changes in their environment. However, developing a detailed understanding of how oscillations in mRNA levels are connected to oscillations in co/post-transcriptional processes, such as splicing, has remained a challenge. Here we applied a combined approach using deep transcriptome sequencing and bioinformatics tools to identify novel circadian-regulated genes and splicing events. Using a stringent approach, we identified 300 intron retention, eight exon skipping, 79 alternative 3' splice site usage, 48 alternative 5' splice site usage, and 350 multiple (more than one event type) annotated events under circadian regulation. We also found seven and 721 novel alternative exonic and intronic events. Depletion of the circadian-regulated splicing factor AtSPF30 homologue resulted in the disruption of a subset of clock-controlled splicing events. Altogether, our global circadian RNA-seq coupled with an in silico, event-centred, splicing analysis tool offers a new approach for studying the interplay between the circadian clock and the splicing machinery at a global scale. The identification of many circadian-regulated splicing events broadens our current understanding of the level of control that the circadian clock has over this co/post-transcriptional regulatory layer.
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Affiliation(s)
- Andrés Romanowski
- Comparative Genomics of Plant Development, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas Buenos Aires (IIBBA) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1405BWE, Buenos Aires, Argentina
| | - Rubén G Schlaen
- Comparative Genomics of Plant Development, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas Buenos Aires (IIBBA) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1405BWE, Buenos Aires, Argentina
| | - Soledad Perez-Santangelo
- Comparative Genomics of Plant Development, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas Buenos Aires (IIBBA) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1405BWE, Buenos Aires, Argentina
| | - Estefanía Mancini
- Comparative Genomics of Plant Development, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas Buenos Aires (IIBBA) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1405BWE, Buenos Aires, Argentina
| | - Marcelo J Yanovsky
- Comparative Genomics of Plant Development, Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas Buenos Aires (IIBBA) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1405BWE, Buenos Aires, Argentina
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4
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Osman EY, Bolding MR, Villalón E, Kaifer KA, Lorson ZC, Tisdale S, Hao Y, Conant GC, Pires JC, Pellizzoni L, Lorson CL. Functional characterization of SMN evolution in mouse models of SMA. Sci Rep 2019; 9:9472. [PMID: 31263170 PMCID: PMC6603021 DOI: 10.1038/s41598-019-45822-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/11/2019] [Indexed: 12/13/2022] Open
Abstract
Spinal Muscular Atrophy (SMA) is a monogenic neurodegenerative disorder and the leading genetic cause of infantile mortality. While several functions have been ascribed to the SMN (survival motor neuron) protein, their specific contribution to the disease has yet to be fully elucidated. We hypothesized that some, but not all, SMN homologues would rescue the SMA phenotype in mouse models, thereby identifying disease-relevant domains. Using AAV9 to deliver Smn homologs to SMA mice, we identified a conservation threshold that marks the boundary at which homologs can rescue the SMA phenotype. Smn from Danio rerio and Xenopus laevis significantly prevent disease, whereas Smn from Drosophila melanogaster, Caenorhabditis elegans, and Schizosaccharomyces pombe was significantly less efficacious. This phenotypic rescue correlated with correction of RNA processing defects induced by SMN deficiency and neuromuscular junction pathology. Based upon the sequence conservation in the rescuing homologs, a minimal SMN construct was designed consisting of exons 2, 3, and 6, which showed a partial rescue of the SMA phenotype. While a significant extension in survival was observed, the absence of a complete rescue suggests that while the core conserved region is essential, additional sequences contribute to the overall ability of the SMN protein to rescue disease pathology.
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Affiliation(s)
- Erkan Y Osman
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Madeline R Bolding
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Eric Villalón
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Kevin A Kaifer
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Zachary C Lorson
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Sarah Tisdale
- Center for Motor Neuron Biology and Disease, Department of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
| | - Yue Hao
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Gavin C Conant
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA.,Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.,Division of Biological Sciences, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - J Chris Pires
- Department of Biological Sciences, Program in Genetics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Livio Pellizzoni
- Center for Motor Neuron Biology and Disease, Department of Pathology and Cell Biology, Columbia University, New York, NY, 10032, USA
| | - Christian L Lorson
- Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA. .,Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
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5
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Pan Z, Li S, Liu Q, Wang Z, Zhou Z, Di R, Miao B, Hu W, Wang X, Hu X, Xu Z, Wei D, He X, Yuan L, Guo X, Liang B, Wang R, Li X, Cao X, Dong X, Xia Q, Shi H, Hao G, Yang J, Luosang C, Zhao Y, Jin M, Zhang Y, Lv S, Li F, Ding G, Chu M, Li Y. Whole-genome sequences of 89 Chinese sheep suggest role of RXFP2 in the development of unique horn phenotype as response to semi-feralization. Gigascience 2018; 7:4924504. [PMID: 29668959 PMCID: PMC5905515 DOI: 10.1093/gigascience/giy019] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 02/22/2018] [Indexed: 12/30/2022] Open
Abstract
Background Animal domestication has been extensively studied, but the process of feralization remains poorly understood. Results Here, we performed whole-genome sequencing of 99 sheep and identified a primary genetic divergence between 2 heterogeneous populations in the Tibetan Plateau, including 1 semi-feral lineage. Selective sweep and candidate gene analysis revealed local adaptations of these sheep associated with sensory perception, muscle strength, eating habit, mating process, and aggressive behavior. In particular, a horn-related gene, RXFP2, showed signs of rapid evolution specifically in the semi-feral breeds. A unique haplotype and repressed horn-related tissue expression of RXFP2 were correlated with higher horn length, as well as spiral and horizontally extended horn shape. Conclusions Semi-feralization has an extensive impact on diverse phenotypic traits of sheep. By acquiring features like those of their wild ancestors, semi-feral sheep were able to regain fitness while in frequent contact with wild surroundings and rare human interventions. This study provides a new insight into the evolution of domestic animals when human interventions are no longer dominant.
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Affiliation(s)
- Zhangyuan Pan
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.,College of Agriculture and Forestry Science, Linyi University, Linyi, China
| | - Shengdi Li
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qiuyue Liu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhen Wang
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhengkui Zhou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ran Di
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Benpeng Miao
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenping Hu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiangyu Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Ze Xu
- BasePair BioTechonology Co., Ltd., Suzhou, China
| | - Dongkai Wei
- BasePair BioTechonology Co., Ltd., Suzhou, China
| | - Xiaoyun He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Liyun Yuan
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaofei Guo
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Benmeng Liang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ruichao Wang
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoyu Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaohan Cao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xinlong Dong
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qing Xia
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongcai Shi
- Institute of Biotechnology, Xinjiang Academy of Animal Science, Urumqi, China
| | - Geng Hao
- Institute of Animal Science, Xinjiang Academy of Animal Science, Urumqi, China
| | - Jean Yang
- Research Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, China
| | - Cuicheng Luosang
- Research Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa, China
| | - Yiqiang Zhao
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Mei Jin
- College of Life Science, Liaoning Normal University, Dalian, China
| | - Yingjie Zhang
- College of Animal Science and Technology, Agricultural University of Hebei, Baoding, China
| | - Shenjin Lv
- College of Agriculture and Forestry Science, Linyi University, Linyi, China
| | - Fukuan Li
- College of Agriculture and Forestry Science, Linyi University, Linyi, China
| | - Guohui Ding
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai, China
| | - Mingxing Chu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yixue Li
- Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai, China
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6
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Mier P, Pérez-Pulido AJ, Andrade-Navarro MA. Automated selection of homologs to track the evolutionary history of proteins. BMC Bioinformatics 2018; 19:431. [PMID: 30453878 PMCID: PMC6245638 DOI: 10.1186/s12859-018-2457-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 10/31/2018] [Indexed: 11/26/2022] Open
Abstract
Background The selection of distant homologs of a query protein under study is a usual and useful application of protein sequence databases. Such sets of homologs are often applied to investigate the function of a protein and the degree to which experimental results can be transferred from one organism to another. In particular, a variety of databases facilitates static browsing for orthologs. However, these resources have a limited power when identifying orthologs between taxonomically distant species. In addition, in some situations, for a given query protein, it is advantageous to compare the sets of orthologs from different specific organisms: this recursive step-wise search might give an idea of the evolutionary path of the protein as a series of consecutive steps, for example gaining or losing domains. However, a step-wise orthology search is a time-consuming task if the number of steps is high. Results To illustrate a solution for this problem, we present the web tool ProteinPathTracker, which allows to track the evolutionary history of a query protein by locating homologs in selected proteomes along several evolutionary paths. Additional functionalities include locking a region of interest to follow its evolution in the discovered homologous sequences and the study of the protein function evolution by analysis of the annotations of the homologs. Conclusions ProteinPathTracker is an easy-to-use web tool that automatises the practice of looking for selected homologs in distant species in a straightforward way for non-expert users. Electronic supplementary material The online version of this article (10.1186/s12859-018-2457-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pablo Mier
- Faculty of Biology, Johannes Gutenberg University Mainz, Hans-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany.
| | | | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg University Mainz, Hans-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany
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orthoFind Facilitates the Discovery of Homologous and Orthologous Proteins. PLoS One 2015; 10:e0143906. [PMID: 26624019 PMCID: PMC4666658 DOI: 10.1371/journal.pone.0143906] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 10/07/2015] [Indexed: 11/19/2022] Open
Abstract
Finding homologous and orthologous protein sequences is often the first step in evolutionary studies, annotation projects, and experiments of functional complementation. Despite all currently available computational tools, there is a requirement for easy-to-use tools that provide functional information. Here, a new web application called orthoFind is presented, which allows a quick search for homologous and orthologous proteins given one or more query sequences, allowing a recurrent and exhaustive search against reference proteomes, and being able to include user databases. It addresses the protein multidomain problem, searching for homologs with the same domain architecture, and gives a simple functional analysis of the results to help in the annotation process. orthoFind is easy to use and has been proven to provide accurate results with different datasets. Availability: http://www.bioinfocabd.upo.es/orthofind/.
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8
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Molecular evolution of the moonlighting protein SMN in metazoans. COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS 2013; 8:220-30. [PMID: 23831553 DOI: 10.1016/j.cbd.2013.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 06/06/2013] [Accepted: 06/08/2013] [Indexed: 11/20/2022]
Abstract
The spinal muscular atrophy (SMA) associated protein survival of motor neuron (SMN) is known to be a moonlighting protein: having one primary, ancestral function (presumed to be involvement in U snRNP assembly) along with one or more secondary functions. One hypothesis for the evolution of moonlighting proteins is that regions of a structure under relatively weak negative selection could gain new functions without interfering with the primary function. To test this hypothesis, we investigated sequence conservation and dN/dS, which reflects the selection acting on a coding sequence, in SMN and a related protein, splicing factor 30 (SPF30), which is not currently known to be multifunctional. We found very different patterns of evolution in the two genes, with SPF30 characterized by strong sequence conservation and negative selection in most animal taxa investigated, and SMN with much lower sequence conservation, and much weaker negative selection at many sites. Evidence was found of positive selection acting on some sites in primate genes for SMN. SMN was also found to have been duplicated in a number of species, and with patterns that indicate reduced negative selection following some of these duplications. There were also several animal species lacking an SMN gene.
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