ZNF423 patient variants, truncations, and in-frame deletions in mice define an allele-dependent range of midline brain abnormalities
Autoři:
Ojas Deshpande aff001; Raquel Z. Lara aff001; Oliver R. Zhang aff001; Dorothy Concepcion aff001; Bruce A. Hamilton aff001
Působiště autorů:
Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, Rebecca and John Moores UCSD Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA, United States of America
aff001; Department of Medicine, Institute for Genomic Medicine, Rebecca and John Moores UCSD Cancer Center, University of California, San Diego School of Medicine, Gilman Drive, La Jolla, CA, United States of America
aff002
Vyšlo v časopise:
ZNF423 patient variants, truncations, and in-frame deletions in mice define an allele-dependent range of midline brain abnormalities. PLoS Genet 16(9): e32767. doi:10.1371/journal.pgen.1009017
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009017
Souhrn
Interpreting rare variants remains a challenge in personal genomics, especially for disorders with several causal genes and for genes that cause multiple disorders. ZNF423 encodes a transcriptional regulatory protein that intersects several developmental pathways. ZNF423 has been implicated in rare neurodevelopmental disorders, consistent with midline brain defects in Zfp423-mutant mice, but pathogenic potential of most patient variants remains uncertain. We engineered ~50 patient-derived and small deletion variants into the highly-conserved mouse ortholog and examined neuroanatomical measures for 791 littermate pairs. Three substitutions previously asserted pathogenic appeared benign, while a fourth was effectively null. Heterozygous premature termination codon (PTC) variants showed mild haploabnormality, consistent with loss-of-function intolerance inferred from human population data. In-frame deletions of specific zinc fingers showed mild to moderate abnormalities, as did low-expression variants. These results affirm the need for functional validation of rare variants in biological context and demonstrate cost-effective modeling of neuroanatomical abnormalities in mice.
Klíčová slova:
Alleles – Cerebellum – Deletion mutation – Heterozygosity – Homozygosity – Mouse models – Substitution mutation – corpus callosum
Zdroje
1. Kim YE, Ki CS, Jang MA. Challenges and Considerations in Sequence Variant Interpretation for Mendelian Disorders. Ann Lab Med. 2019;39(5):421–9. doi: 10.3343/alm.2019.39.5.421 31037860; PubMed Central PMCID: PMC6502951.
2. Rivera-Munoz EA, Milko LV, Harrison SM, Azzariti DR, Kurtz CL, Lee K, et al. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum Mutat. 2018;39(11):1614–22. doi: 10.1002/humu.23645 30311389; PubMed Central PMCID: PMC6225902.
3. 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.
4. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. bioRxiv. 2019:531210. doi: 10.1101/531210
5. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv. 2017:166298. doi: 10.1101/166298
6. Walsh R, Thomson KL, Ware JS, Funke BH, Woodley J, McGuire KJ, et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19(2):192–203. doi: 10.1038/gim.2016.90 27532257; PubMed Central PMCID: PMC5116235.
7. Chaki M, Airik R, Ghosh AK, Giles RH, Chen R, Slaats GG, et al. Exome capture reveals ZNF423 and CEP164 mutations, linking renal ciliopathies to DNA damage response signaling. Cell. 2012;150(3):533–48. doi: 10.1016/j.cell.2012.06.028 22863007; PubMed Central PMCID: PMC3433835.
8. Karaca E, Harel T, Pehlivan D, Jhangiani SN, Gambin T, Coban Akdemir Z, et al. Genes that Affect Brain Structure and Function Identified by Rare Variant Analyses of Mendelian Neurologic Disease. Neuron. 2015;88(3):499–513. doi: 10.1016/j.neuron.2015.09.048 26539891; PubMed Central PMCID: PMC4824012.
9. Hamilton BA. ZNF423 orthologs are highly constrained in vertebrates but show domain-level plasticity across invertebrate lineages. bioRxiv. 2020:2020.03.09.984518. doi: 10.1101/2020.03.09.984518
10. Alcaraz WA, Gold DA, Raponi E, Gent PM, Concepcion D, Hamilton BA. Zfp423 controls proliferation and differentiation of neural precursors in cerebellar vermis formation. Proc Natl Acad Sci U S A. 2006;103(51):19424–9. doi: 10.1073/pnas.0609184103 17151198; PubMed Central PMCID: PMC1748242.
11. Cheng LE, Zhang J, Reed RR. The transcription factor Zfp423/OAZ is required for cerebellar development and CNS midline patterning. Dev Biol. 2007;307(1):43–52. doi: 10.1016/j.ydbio.2007.04.005 17524391; PubMed Central PMCID: PMC2866529.
12. Warming S, Rachel RA, Jenkins NA, Copeland NG. Zfp423 is required for normal cerebellar development. Mol Cell Biol. 2006;26(18):6913–22. doi: 10.1128/MCB.02255-05 16943432; PubMed Central PMCID: PMC1592861.
13. Casoni F, Croci L, Vincenti F, Podini P, Massimino L, Cremona O, et al. ZFP423 regulates early patterning and multiciliogenesis in the hindbrain choroid plexus. bioRxiv. 2020:2020.03.04.975573. doi: 10.1101/2020.03.04.975573
14. Cheng LE, Reed RR. Zfp423/OAZ participates in a developmental switch during olfactory neurogenesis. Neuron. 2007;54(4):547–57. doi: 10.1016/j.neuron.2007.04.029 17521568; PubMed Central PMCID: PMC2866517.
15. Massimino L, Flores-Garcia L, Di Stefano B, Colasante G, Icoresi-Mazzeo C, Zaghi M, et al. TBR2 antagonizes retinoic acid dependent neuronal differentiation by repressing Zfp423 during corticogenesis. Dev Biol. 2018;434(2):231–48. doi: 10.1016/j.ydbio.2017.12.020 29305158.
16. Gupta RK, Arany Z, Seale P, Mepani RJ, Ye L, Conroe HM, et al. Transcriptional control of preadipocyte determination by Zfp423. Nature. 2010;464(7288):619–23. doi: 10.1038/nature08816 20200519; PubMed Central PMCID: PMC2845731.
17. Gupta RK, Mepani RJ, Kleiner S, Lo JC, Khandekar MJ, Cohen P, et al. Zfp423 expression identifies committed preadipocytes and localizes to adipose endothelial and perivascular cells. Cell Metab. 2012;15(2):230–9. doi: 10.1016/j.cmet.2012.01.010 22326224; PubMed Central PMCID: PMC3366493.
18. Shao M, Hepler C, Vishvanath L, MacPherson KA, Busbuso NC, Gupta RK. Fetal development of subcutaneous white adipose tissue is dependent on Zfp423. Mol Metab. 2017;6(1):111–24. doi: 10.1016/j.molmet.2016.11.009 28123942; PubMed Central PMCID: PMC5220400.
19. Plikus MV, Guerrero-Juarez CF, Ito M, Li YR, Dedhia PH, Zheng Y, et al. Regeneration of fat cells from myofibroblasts during wound healing. Science. 2017;355(6326):748–52. doi: 10.1126/science.aai8792 28059714; PubMed Central PMCID: PMC5464786.
20. Hong CJ, Hamilton BA. Zfp423 Regulates Sonic Hedgehog Signaling via Primary Cilium Function. PLoS Genet. 2016;12(10):e1006357. doi: 10.1371/journal.pgen.1006357 27727273; PubMed Central PMCID: PMC5065120.
21. Hata A, Seoane J, Lagna G, Montalvo E, Hemmati-Brivanlou A, Massague J. OAZ uses distinct DNA- and protein-binding zinc fingers in separate BMP-Smad and Olf signaling pathways. Cell. 2000;100(2):229–40. doi: 10.1016/s0092-8674(00)81561-5 10660046.
22. Huang S, Laoukili J, Epping MT, Koster J, Holzel M, Westerman BA, et al. ZNF423 is critically required for retinoic acid-induced differentiation and is a marker of neuroblastoma outcome. Cancer Cell. 2009;15(4):328–40. doi: 10.1016/j.ccr.2009.02.023 19345331; PubMed Central PMCID: PMC2693316.
23. Ku M, Howard S, Ni W, Lagna G, Hata A. OAZ regulates bone morphogenetic protein signaling through Smad6 activation. J Biol Chem. 2006;281(8):5277–87. doi: 10.1074/jbc.M510004200 16373339.
24. Masserdotti G, Badaloni A, Green YS, Croci L, Barili V, Bergamini G, et al. ZFP423 coordinates Notch and bone morphogenetic protein signaling, selectively up-regulating Hes5 gene expression. J Biol Chem. 2010;285(40):30814–24. doi: 10.1074/jbc.M110.142869 20547764; PubMed Central PMCID: PMC2945575.
25. Signaroldi E, Laise P, Cristofanon S, Brancaccio A, Reisoli E, Atashpaz S, et al. Polycomb dysregulation in gliomagenesis targets a Zfp423-dependent differentiation network. Nat Commun. 2016;7:10753. doi: 10.1038/ncomms10753 26923714; PubMed Central PMCID: PMC4773478.
26. Tsai RY, Reed RR. Cloning and functional characterization of Roaz, a zinc finger protein that interacts with O/E-1 to regulate gene expression: implications for olfactory neuronal development. J Neurosci. 1997;17(11):4159–69. doi: 10.1523/JNEUROSCI.17-11-04159.1997 9151733.
27. Casoni F, Croci L, Bosone C, D'Ambrosio R, Badaloni A, Gaudesi D, et al. Zfp423/ZNF423 regulates cell cycle progression, the mode of cell division and the DNA-damage response in Purkinje neuron progenitors. Development. 2017;144(20):3686–97. doi: 10.1242/dev.155077 28893945; PubMed Central PMCID: PMC5675449.
28. Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ, Baker D, et al. High-resolution mapping of protein sequence-function relationships. Nat Methods. 2010;7(9):741–6. doi: 10.1038/nmeth.1492 20711194; PubMed Central PMCID: PMC2938879.
29. Fowler DM, Fields S. Deep mutational scanning: a new style of protein science. Nat Methods. 2014;11(8):801–7. doi: 10.1038/nmeth.3027 25075907; PubMed Central PMCID: PMC4410700.
30. Majithia AR, Tsuda B, Agostini M, Gnanapradeepan K, Rice R, Peloso G, et al. Prospective functional classification of all possible missense variants in PPARG. Nat Genet. 2016;48(12):1570–5. doi: 10.1038/ng.3700 27749844; PubMed Central PMCID: PMC5131844.
31. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–D7. doi: 10.1093/nar/gkx1153 29165669; PubMed Central PMCID: PMC5753237.
32. Chong JX, Yu JH, Lorentzen P, Park KM, Jamal SM, Tabor HK, et al. Gene discovery for Mendelian conditions via social networking: de novo variants in KDM1A cause developmental delay and distinctive facial features. Genet Med. 2016;18(8):788–95. doi: 10.1038/gim.2015.161 26656649; PubMed Central PMCID: PMC4902791.
33. Alcaraz WA, Liu Z, Valdes P, Chen E, Valdovino Gonzalez AG, Wade S, et al. Strain-dependent modifier genes determine survival in Zfp423 mice. bioRxiv. 2020:2020.05.12.091629. doi: 10.1101/2020.05.12.091629
34. Herskowitz I. Functional inactivation of genes by dominant negative mutations. Nature. 1987;329(6136):219–22. doi: 10.1038/329219a0 2442619.
35. Cohen S, Kramarski L, Levi S, Deshe N, Ben David O, Arbely E. Nonsense mutation-dependent reinitiation of translation in mammalian cells. Nucleic Acids Res. 2019;47(12):6330–8. doi: 10.1093/nar/gkz319 31045216; PubMed Central PMCID: PMC6614817.
36. Lindeboom RG, Supek F, Lehner B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat Genet. 2016;48(10):1112–8. doi: 10.1038/ng.3664 27618451; PubMed Central PMCID: PMC5045715.
37. Hoek TA, Khuperkar D, Lindeboom RGH, Sonneveld S, Verhagen BMP, Boersma S, et al. Single-Molecule Imaging Uncovers Rules Governing Nonsense-Mediated mRNA Decay. Mol Cell. 2019;75(2):324–39 e11. doi: 10.1016/j.molcel.2019.05.008 31155380; PubMed Central PMCID: PMC6675935.
38. Cho YW, Hong CJ, Hou A, Gent PM, Zhang K, Won KJ, et al. Zfp423 binds autoregulatory sites in P19 cell culture model. PLoS One. 2013;8(6):e66514. doi: 10.1371/journal.pone.0066514 23762491; PubMed Central PMCID: PMC3675209.
39. Tsai RY, Reed RR. Identification of DNA recognition sequences and protein interaction domains of the multiple-Zn-finger protein Roaz. Mol Cell Biol. 1998;18(11):6447–56. doi: 10.1128/mcb.18.11.6447 9774661; PubMed Central PMCID: PMC109231.
40. Haeussler M, Schonig K, Eckert H, Eschstruth A, Mianne J, Renaud JB, et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016;17(1):148. doi: 10.1186/s13059-016-1012-2 27380939; PubMed Central PMCID: PMC4934014.
41. Labun K, Montague TG, Gagnon JA, Thyme SB, Valen E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016;44(W1):W272–6. doi: 10.1093/nar/gkw398 27185894; PubMed Central PMCID: PMC4987937.
42. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9. doi: 10.1038/nmeth0410-248 20354512; PubMed Central PMCID: PMC2855889.
43. Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40(Web Server issue):W452–7. doi: 10.1093/nar/gks539 22689647; PubMed Central PMCID: PMC3394338.
44. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7(10):e46688. doi: 10.1371/journal.pone.0046688 23056405; PubMed Central PMCID: PMC3466303.
45. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11(4):361–2. doi: 10.1038/nmeth.2890 24681721.
46. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39(17):e118. doi: 10.1093/nar/gkr407 21727090; PubMed Central PMCID: PMC3177186.
47. Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics. 2013;14 Suppl 3:S3. doi: 10.1186/1471-2164-14-S3-S3 23819870; PubMed Central PMCID: PMC3665549.
48. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–D94. doi: 10.1093/nar/gky1016 30371827; PubMed Central PMCID: PMC6323892.
49. Yang H, Wang K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc. 2015;10(10):1556–66. doi: 10.1038/nprot.2015.105 26379229; PubMed Central PMCID: PMC4718734.
50. Gray VE, Hause RJ, Luebeck J, Shendure J, Fowler DM. Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data. Cell Syst. 2018;6(1):116–24 e3. doi: 10.1016/j.cels.2017.11.003 29226803; PubMed Central PMCID: PMC5799033.
51. Carter RJ, Morton J, Dunnett SB. Motor coordination and balance in rodents. Curr Protoc Neurosci. 2001;Chapter 8:Unit 8 12. doi: 10.1002/0471142301.ns0812s15 18428540.
52. Crawley JN, Paylor R. A proposed test battery and constellations of specific behavioral paradigms to investigate the behavioral phenotypes of transgenic and knockout mice. Horm Behav. 1997;31(3):197–211. doi: 10.1006/hbeh.1997.1382 9213134.
53. Gurney ME, Pu H, Chiu AY, Dal Canto MC, Polchow CY, Alexander DD, et al. Motor neuron degeneration in mice that express a human Cu,Zn superoxide dismutase mutation. Science. 1994;264(5166):1772–5. doi: 10.1126/science.8209258 8209258.
54. Crawley JN. Behavioral phenotyping of transgenic and knockout mice: experimental design and evaluation of general health, sensory functions, motor abilities, and specific behavioral tests. Brain Res. 1999;835(1):18–26. doi: 10.1016/s0006-8993(98)01258-x 10448192.
55. Freitag S, Schachner M, Morellini F. Behavioral alterations in mice deficient for the extracellular matrix glycoprotein tenascin-R. Behav Brain Res. 2003;145(1–2):189–207. doi: 10.1016/s0166-4328(03)00109-8 14529817.
56. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149–60. doi: 10.3758/BRM.41.4.1149 19897823.
57. R Core Team. R: A language and environment for statistical computing.: R Foundation for Statistical Computing; 2017. Available from: https://www.R-project.org/.
58. Wickham H. ggplot2: Elegant Graphics for Data Analysis: Springer-Verlag New York; 2016.
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