PEA15 loss of function and defective cerebral development in the domestic cat
Autoři:
Emily C. Graff aff001; J. Nicholas Cochran aff004; Christopher B. Kaelin aff004; Kenneth Day aff004; Heather L. Gray-Edwards aff002; Rie Watanabe aff001; Jey W. Koehler aff001; Rebecca A. Falgoust aff002; Jeremy W. Prokop aff004; Richard M. Myers aff004; Nancy R. Cox aff001; Gregory S. Barsh aff004; Douglas R. Martin aff002;
Působiště autorů:
Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, United States of America
aff001; Scott-Ritchey Research Center, College of Veterinary Medicine, Auburn University, Auburn, Alabama, United States of America
aff002; Center for Neuroscience Initiative, Auburn University, Auburn, AL, United States of America Alabama, United States of America
aff003; HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States of America
aff004; Department of Anatomy Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, United States of America
aff005
Vyšlo v časopise:
PEA15 loss of function and defective cerebral development in the domestic cat. PLoS Genet 16(12): e1008671. doi:10.1371/journal.pgen.1008671
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008671
Souhrn
Cerebral cortical size and organization are critical features of neurodevelopment and human evolution, for which genetic investigation in model organisms can provide insight into developmental mechanisms and the causes of cerebral malformations. However, some abnormalities in cerebral cortical proliferation and folding are challenging to study in laboratory mice due to the absence of gyri and sulci in rodents. We report an autosomal recessive allele in domestic cats associated with impaired cerebral cortical expansion and folding, giving rise to a smooth, lissencephalic brain, and that appears to be caused by homozygosity for a frameshift in PEA15 (phosphoprotein expressed in astrocytes-15). Notably, previous studies of a Pea15 targeted mutation in mice did not reveal structural brain abnormalities. Affected cats, however, present with a non-progressive hypermetric gait and tremors, develop dissociative behavioral defects and aggression with age, and exhibit profound malformation of the cerebrum, with a 45% average decrease in overall brain weight, and reduction or absence of the ectosylvian, sylvian and anterior cingulate gyrus. Histologically, the cerebral cortical layers are disorganized, there is substantial loss of white matter in tracts such as the corona radiata and internal capsule, but the cerebellum is relatively spared. RNA-seq and immunohistochemical analysis reveal astrocytosis. Fibroblasts cultured from affected cats exhibit increased TNFα-mediated apoptosis, and increased FGFb-induced proliferation, consistent with previous studies implicating PEA15 as an intracellular adapter protein, and suggesting an underlying pathophysiology in which increased death of neurons accompanied by increased proliferation of astrocytes gives rise to abnormal organization of neuronal layers and loss of white matter. Taken together, our work points to a new role for PEA15 in development of a complex cerebral cortex that is only apparent in gyrencephalic species.
Klíčová slova:
Apoptosis – Astrocytes – Cats – Central nervous system – Cerebral cortex – Fibroblasts – Heterozygosity – Homozygosity
Zdroje
1. Schaefer GB, Sheth RD, Bodensteiner JB. Cerebral dysgenesis. An overview. Neurologic clinics. 1994;12(4):773–88. Epub 1994/11/01. 7845342.
2. Parrini E, Conti V, Dobyns WB, Guerrini R. Genetic Basis of Brain Malformations. Molecular syndromology. 2016;7(4):220–33. Epub 2016/10/27. doi: 10.1159/000448639 27781032; PubMed Central PMCID: PMC5073505.
3. Poirier K, Lebrun N, Broix L, Tian G, Saillour Y, Boscheron C, et al. Mutations in TUBG1, DYNC1H1, KIF5C and KIF2A cause malformations of cortical development and microcephaly. Nat Genet. 2013;45(6):639–47. Epub 2013/04/23. doi: 10.1038/ng.2613 23603762; PubMed Central PMCID: PMC3826256.
4. Defelipe J. The evolution of the brain, the human nature of cortical circuits, and intellectual creativity. Front Neuroanat. 2011;5:29. Epub 2011/06/08. doi: 10.3389/fnana.2011.00029 21647212; PubMed Central PMCID: PMC3098448.
5. Sun T, Hevner RF. Growth and folding of the mammalian cerebral cortex: from molecules to malformations. Nat Rev Neurosci. 2014;15(4):217–32. Epub 2014/03/22. doi: 10.1038/nrn3707 24646670; PubMed Central PMCID: PMC4107216.
6. Gregory MD, Kippenhan JS, Dickinson D, Carrasco J, Mattay VS, Weinberger DR, et al. Regional Variations in Brain Gyrification Are Associated with General Cognitive Ability in Humans. Curr Biol. 2016;26(10):1301–5. Epub 2016/05/03. doi: 10.1016/j.cub.2016.03.021 27133866; PubMed Central PMCID: PMC4879055.
7. Matsumoto N, Shinmyo Y, Ichikawa Y, Kawasaki H. Gyrification of the cerebral cortex requires FGF signaling in the mammalian brain. Elife. 2017;6. Epub 2017/11/15. doi: 10.7554/eLife.29285 29132503; PubMed Central PMCID: PMC5685484.
8. Shinmyo Y, Terashita Y, Dinh Duong TA, Horiike T, Kawasumi M, Hosomichi K, et al. Folding of the Cerebral Cortex Requires Cdk5 in Upper-Layer Neurons in Gyrencephalic Mammals. Cell Rep. 2017;20(9):2131–43. Epub 2017/08/31. doi: 10.1016/j.celrep.2017.08.024 28854363.
9. Griffin BBHJ. Domestic Cats as Laboratory Animal Models. 2nd ed. Fox JG, editor: Academic Press; 2002. 22 p.
10. Gurda BL, Bradbury AM, Vite CH. Canine and Feline Models of Human Genetic Diseases and Their Contributions to Advancing Clinical Therapies. Yale J Biol Med. 2017;90(3):417–31. Epub 2017/09/29. 28955181; PubMed Central PMCID: PMC5612185.
11. Martin DR, Cox NR, Morrison NE, Kennamer DM, Peck SL, Dodson AN, et al. Mutation of the GM2 activator protein in a feline model of GM2 gangliosidosis. Acta Neuropathol. 2005;110(5):443–50. Epub 2005/10/04. doi: 10.1007/s00401-005-1040-6 16200419.
12. Jezyk PF, Haskins ME, Patterson DF, Mellman WJ, Greenstein M. Mucopolysaccharidosis in a cat with arylsulfatase B deficiency: a model of Maroteaux-Lamy syndrome. Science. 1977;198(4319):834–6. Epub 1977/11/25. doi: 10.1126/science.144321 144321.
13. Lyons LA, Creighton EK, Alhaddad H, Beale HC, Grahn RA, Rah H, et al. Whole genome sequencing in cats, identifies new models for blindness in AIPL1 and somite segmentation in HES7. BMC Genomics. 2016;17:265. Epub 2016/04/01. doi: 10.1186/s12864-016-2595-4 27030474; PubMed Central PMCID: PMC4815086.
14. Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O'Keeffe S, et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci. 2014;34(36):11929–47. doi: 10.1523/JNEUROSCI.1860-14.2014 25186741; PubMed Central PMCID: PMC4152602.
15. Kitsberg D, Formstecher E, Fauquet M, Kubes M, Cordier J, Canton B, et al. Knock-out of the neural death effector domain protein PEA-15 demonstrates that its expression protects astrocytes from TNFalpha-induced apoptosis. J Neurosci. 1999;19(19):8244–51. Epub 1999/09/24. doi: 10.1523/JNEUROSCI.19-19-08244.1999 10493725.
16. Ascione F, Vasaturo A, Caserta S, D'Esposito V, Formisano P, Guido S. Comparison between fibroblast wound healing and cell random migration assays in vitro. Experimental cell research. 2016;347(1):123–32. Epub 2016/08/01. doi: 10.1016/j.yexcr.2016.07.015 27475838.
17. Buonomo R, Giacco F, Vasaturo A, Caserta S, Guido S, Pagliara V, et al. PED/PEA-15 controls fibroblast motility and wound closure by ERK1/2-dependent mechanisms. J Cell Physiol. 2012;227(5):2106–16. Epub 2011/07/23. doi: 10.1002/jcp.22944 21780113; PubMed Central PMCID: PMC3306794.
18. Renault F, Formstecher E, Callebaut I, Junier MP, Chneiweiss H. The multifunctional protein PEA-15 is involved in the control of apoptosis and cell cycle in astrocytes. Biochem Pharmacol. 2003;66(8):1581–8. Epub 2003/10/14. doi: 10.1016/s0006-2952(03)00514-8 14555237.
19. Estelles A, Yokoyama M, Nothias F, Vincent JD, Glowinski J, Vernier P, et al. The major astrocytic phosphoprotein PEA-15 is encoded by two mRNAs conserved on their full length in mouse and human. J Biol Chem. 1996;271(25):14800–6. Epub 1996/06/21. doi: 10.1074/jbc.271.25.14800 8662970.
20. Danziger N, Yokoyama M, Jay T, Cordier J, Glowinski J, Chneiweiss H. Cellular expression, developmental regulation, and phylogenic conservation of PEA-15, the astrocytic major phosphoprotein and protein kinase C substrate. J Neurochem. 1995;64(3):1016–25. Epub 1995/03/01. doi: 10.1046/j.1471-4159.1995.64031016.x 7861130.
21. Formisano P, Ragno P, Pesapane A, Alfano D, Alberobello AT, Rea VE, et al. PED/PEA-15 interacts with the 67 kD laminin receptor and regulates cell adhesion, migration, proliferation and apoptosis. J Cell Mol Med. 2012;16(7):1435–46. Epub 2011/09/08. doi: 10.1111/j.1582-4934.2011.01411.x 21895963; PubMed Central PMCID: PMC3823213.
22. Mace PD, Wallez Y, Egger MF, Dobaczewska MK, Robinson H, Pasquale EB, et al. Structure of ERK2 bound to PEA-15 reveals a mechanism for rapid release of activated MAPK. Nat Commun. 2013;4:1681. Epub 2013/04/12. doi: 10.1038/ncomms2687 23575685; PubMed Central PMCID: PMC3640864.
23. Exler RE, Guo X, Chan D, Livne-Bar I, Vicic N, Flanagan JG, et al. Biomechanical insult switches PEA-15 activity to uncouple its anti-apoptotic function and promote erk mediated tissue remodeling. Experimental cell research. 2016;340(2):283–94. Epub 2015/12/01. doi: 10.1016/j.yexcr.2015.11.023 26615958.
24. Fiory F, Spinelli R, Raciti GA, Parrillo L, D'Esposito V, Formisano P, et al. Targetting PED/PEA-15 for diabetes treatment. Expert Opin Ther Targets. 2017;21(6):571–81. Epub 2017/04/12. doi: 10.1080/14728222.2017.1317749 28395542.
25. Ramos JW, Townsend DA, Piarulli D, Kolata S, Light K, Hale G, et al. Deletion of PEA-15 in mice is associated with specific impairments of spatial learning abilities. BMC neuroscience. 2009;10:134. Epub 2009/11/18. doi: 10.1186/1471-2202-10-134 19917132; PubMed Central PMCID: PMC2781817.
26. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91. doi: 10.1038/nature19057 27535533; PubMed Central PMCID: PMC5018207.
27. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet journal. 2011;17(1):pp. 10–2.
28. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–60. doi: 10.1093/bioinformatics/btp324 19451168
29. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome res. 2010;20(9):1297–303. doi: 10.1101/gr.107524.110 20644199
30. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome res. 2012;22(3):568–76. doi: 10.1101/gr.129684.111 22300766
31. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92. doi: 10.4161/fly.19695 22728672
32. Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015;31(16):2745–7. doi: 10.1093/bioinformatics/btv195 25851949
33. Alonso A, Lasseigne BN, Williams K, Nielsen J, Ramaker RC, Hardigan AA, et al. aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing environments. Bioinformatics. 2017;33(11):1727–9. doi: 10.1093/bioinformatics/btx023 28108448
34. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886
35. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. doi: 10.1093/bioinformatics/btu638 25260700
36. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8 25516281
37. Rimmer A, Phan H, Mathieson I, Iqbal Z, Twigg SR, Wilkie AO, et al. Integrating mapping-, assembly-and haplotype-based approaches for calling variants in clinical sequencing applications. Nat Genet. 2014;46(8):912. doi: 10.1038/ng.3036 25017105
38. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30(1):97. doi: 10.1038/ng786 11731797
39. Pedersen BS, Collins RL, Talkowski ME, Quinlan AR. Indexcov: fast coverage quality control for whole-genome sequencing. GigaScience. 2017. doi: 10.1093/gigascience/gix090 29048539
40. Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012;28(18):i333–i9. doi: 10.1093/bioinformatics/bts378 22962449
41. Tan A, Abecasis GR, Kang HM. Unified representation of genetic variants. Bioinformatics. 2015;31(13):2202–4. Epub 2015/02/24. doi: 10.1093/bioinformatics/btv112 25701572; PubMed Central PMCID: PMC4481842.
42. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. doi: 10.1093/bioinformatics/btp352 19505943
43. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8. doi: 10.1093/bioinformatics/btr330 21653522
44. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46(3):310–5. doi: 10.1038/ng.2892 24487276; PubMed Central PMCID: PMC3992975.
45. Zhao H, Sun Z, Wang J, Huang H, Kocher J-P, Wang L. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics. 2013;30(7):1006–7. doi: 10.1093/bioinformatics/btt730 24351709
46. Prokop JW, Lazar J, Crapitto G, Smith DC, Worthey EA, Jacob HJ. Molecular modeling in the age of clinical genomics, the enterprise of the next generation. J Mol Model. 2017;23(3):75. Epub 2017/02/17. doi: 10.1007/s00894-017-3258-3 28204942; PubMed Central PMCID: PMC5529140.
47. Luna LG. Manual of Histologic Staining Methods of the Armed Forces Institute of Pathology.: Blakiston Division, McGraw-Hill; 1968.
48. Gray-Edwards HL, Regier DS, Shirley JL, Randle AN, Salibi N, Thomas SE, et al. Novel Biomarkers of Human GM1 Gangliosidosis Reflect the Clinical Efficacy of Gene Therapy in a Feline Model. Molecular therapy: the journal of the American Society of Gene Therapy. 2017;25(4):892–903. Epub 2017/02/27. doi: 10.1016/j.ymthe.2017.01.009 28236574; PubMed Central PMCID: PMC5383552.
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