Maize adaptation across temperate climates was obtained via expression of two florigen genes


Autoři: Sara Castelletti aff001;  Aude Coupel-Ledru aff002;  Italo Granato aff002;  Carine Palaffre aff003;  Llorenç Cabrera-Bosquet aff002;  Chiara Tonelli aff001;  Stéphane D. Nicolas aff004;  François Tardieu aff002;  Claude Welcker aff002;  Lucio Conti aff001
Působiště autorů: Department of Biosciences, University of Milan, Milan, Italy aff001;  LEPSE, INRAe, Univ. Montpellier, SupAgro, Montpellier, France aff002;  Unité Expérimentale du Maïs, INRAe, Univ. Bordeaux, Saint Martin de Hinx, France aff003;  GQE—Le Moulon, INRAe, Université Paris-Saclay, CNRS, AgroParisTech, Gif-sur-Yvette, France aff004
Vyšlo v časopise: Maize adaptation across temperate climates was obtained via expression of two florigen genes. PLoS Genet 16(7): e32767. doi:10.1371/journal.pgen.1008882
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008882

Souhrn

Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas.

Klíčová slova:

Gene mapping – Genome-wide association studies – Heredity – Leaves – Maize – Plant genetics – Plant genomics – Quantitative trait loci – Single nucleotide polymorphisms


Zdroje

1. Matsuoka Y, Vigouroux Y, Goodman MM, Sanchez G. J, Buckler E, Doebley J. A single domestication for maize shown by multilocus microsatellite genotyping. Proc Natl Acad Sci. 2002;99: 6080–6084. doi: 10.1073/pnas.052125199 11983901

2. Parent B, Leclere M, Lacube S, Semenov MA, Welcker C, Martre P, et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. Proc Natl Acad Sci. 2018;115: 10642–10647. doi: 10.1073/pnas.1720716115 30275304

3. Colasanti J, Muszynski M. The Maize Floral Transition. Handbook of Maize: Its Biology. 2009. pp. 41–55. doi: 10.1007/978-0-387-79418-1_3

4. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The genetic architecture of maize flowering time. Science. 2009;325: 714–718. doi: 10.1126/science.1174276 19661422

5. Li D, Wang X, Zhang X, Chen Q, Xu G, Xu D, et al. The genetic architecture of leaf number and its genetic relationship to flowering time in maize. New Phytol. 2016;210: 256–268. doi: 10.1111/nph.13765 26593156

6. Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, et al. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics. 2004;168: 2169–2185. doi: 10.1534/genetics.104.032375 15611184

7. Li YX, Li C, Bradbury PJ, Liu X, Lu F, Romay CM, et al. Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population. Plant J. 2016;86: 391–402. doi: 10.1111/tpj.13174 27012534

8. Romero Navarro JA, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet. 2017;49: 476–480. doi: 10.1038/ng.3784 28166212

9. Guo L, Wang X, Zhao M, Huang C, Li C, Li D, et al. Stepwise cis-Regulatory Changes in ZCN8 Contribute to Maize Flowering-Time Adaptation. Curr Biol. 2018;28: 3005–3015.e4. doi: 10.1016/j.cub.2018.07.029 30220503

10. Kobayashi Y, Weigel D. Move on up, it’s time for change—mobile signals controlling photoperiod-dependent flowering. Genes Dev. 2007;21: 2371–2384. doi: 10.1101/gad.1589007 17908925

11. Lazakis CM, Coneva V, Colasanti J. ZCN8 encodes a potential orthologue of Arabidopsis FT florigen that integrates both endogenous and photoperiod flowering signals in maize. J Exp Bot. 2011;62: 4833–4842. doi: 10.1093/jxb/err129 21730358

12. Danilevskaya ON, Meng X, Hou Z, Ananiev E V, Simmons CR. A genomic and expression compendium of the expanded PEBP gene family from maize. Plant Physiol. 2008;146: 250–264. doi: 10.1104/pp.107.109538 17993543

13. Meng X, Muszynski MG, Danilevskaya ON. The FT-like ZCN8 gene functions as a floral activator and is involved in photoperiod sensitivity in maize. Plant Cell. 2011;23: 942–960. doi: 10.1105/tpc.110.081406 21441432

14. Yang Q, Li Z, Li W, Ku L, Wang C, Ye J, et al. CACTA-like transposable element in ZmCCT attenuated photoperiod sensitivity and accelerated the postdomestication spread of maize. Proc Natl Acad Sci. 2013;110: 16969–16974. doi: 10.1073/pnas.1310949110 24089449

15. Hung H-Y, Shannon LM, Tian F, Bradbury PJ, Chen C, Flint-Garcia SA, et al. ZmCCT and the genetic basis of day-length adaptation underlying the postdomestication spread of maize. Proc Natl Acad Sci. 2012;109: E1913–21. doi: 10.1073/pnas.1203189109 22711828

16. Stephenson E, Estrada S, Meng X, Ourada J, Muszynski MG, Habben JE, et al. Over-expression of the photoperiod response regulator ZmCCT10 modifies plant architecture, flowering time and inflorescence morphology in maize. PLoS One. 2019;14. doi: 10.1371/journal.pone.0203728

17. Alter P, Bircheneder S, Zhou L-Z, Schlüter U, Gahrtz M, Sonnewald U, et al. Flowering Time-Regulated Genes in Maize Include the Transcription Factor ZmMADS1. Plant Physiol. 2016;172: 389–404. doi: 10.1104/pp.16.00285 27457125

18. Liang Y, Liu Q, Wang X, Huang C, Xu G, Hey S, et al. Zm MADS 69 functions as a flowering activator through the ZmRap2.7‐ ZCN 8 regulatory module and contributes to maize flowering time adaptation. New Phytol. 2019;221: 2335–2347. doi: 10.1111/nph.15512 30288760

19. Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, et al. Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proc Natl Acad Sci. 2007;104: 11376–11381. doi: 10.1073/pnas.0704145104 17595297

20. Castelletti S, Tuberosa R, Pindo M, Salvi S. A MITE Transposon Insertion Is Associated with Differential Methylation at the Maize Flowering Time QTL Vgt1. G3; Genes|Genomes|Genetics. 2014;4: 805–812. doi: 10.1534/g3.114.010686 24607887

21. Hirsch CN, Foerster JM, Johnson JM, Sekhon RS, Muttoni G, Vaillancourt B, et al. Insights into the Maize Pan-Genome and Pan-Transcriptome. Plant Cell. 2014;26: 121–135. doi: 10.1105/tpc.113.119982 24488960

22. Ducrocq S, Madur D, Veyrieras J-BB, Camus-Kulandaivelu L, Kloiber-Maitz M, Presterl T, et al. Key Impact of Vgt1 on Flowering Time Adaptation in Maize: Evidence From Association Mapping and Ecogeographical Information. Genetics. 2008;178: 2433–2437. doi: 10.1534/genetics.107.084830 18430961

23. Colasanti J, Yuan Z, Sundaresan V. The indeterminate gene encodes a zinc finger protein and regulates a leaf-generated signal required for the transition to flowering in maize. Cell. 1998;93: 593–603. doi: 10.1016/s0092-8674(00)81188-5 9604934

24. Minow MAA, Ávila LM, Turner K, Ponzoni E, Mascheretti I, Dussault FM, et al. Distinct gene networks modulate floral induction of autonomous maize and photoperiod-dependent teosinte. J Exp Bot. 2018;69: 2937–2952. doi: 10.1093/jxb/ery110 29688423

25. Muszynski MG, Dam T, Li B, Shirbroun DM, Hou Z, Bruggemann E, et al. delayed flowering1 Encodes a Basic Leucine Zipper Protein That Mediates Floral Inductive Signals at the Shoot Apex in Maize. Plant Physiol. 2006;142: 1523–1536. doi: 10.1104/pp.106.088815 17071646

26. Dong Z, Danilevskaya O, Abadie T, Messina C, Coles N, Cooper M. A gene regulatory network model for Floral transition of the shoot apex in maize and its dynamic modeling. PLoS One. 2012;7: e43450. doi: 10.1371/journal.pone.0043450 22912876

27. Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, et al. Adaptation of Maize to Temperate Climates: Mid-Density Genome-Wide Association Genetics and Diversity Patterns Reveal Key Genomic Regions, with a Major Contribution of the Vgt2 (ZCN8) Locus. PLoS One. 2013;8: e71377. doi: 10.1371/journal.pone.0071377 24023610

28. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14: R55. doi: 10.1186/gb-2013-14-6-r55 23759205

29. Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, et al. Genomic prediction of maize yield across European environmental conditions. Nature Genetics. 2019. doi: 10.1038/s41588-019-0414-y

30. Zhang Y, Ngu DW, Carvalho D, Liang Z, Qiu Y, Roston RL, et al. Differentially Regulated Orthologs in Sorghum and the Subgenomes of Maize. Plant Cell. 2017;29: 1938–1951. doi: 10.1105/tpc.17.00354 28733421

31. Mascheretti I, Turner K, Brivio RS, Hand A, Colasanti J, Rossi V. Florigen-Encoding Genes of Day-Neutral and Photoperiod-Sensitive Maize Are Regulated by Different Chromatin Modifications at the Floral Transition. Plant Physiol. 2015;168: 1351–1363. doi: 10.1104/pp.15.00535 26084920

32. Miller TA, Muslin EH, Dorweiler JE. A maize CONSTANS-like gene, conz1, exhibits distinct diurnal expression patterns in varied photoperiods. Planta. 2008;227: 1377–1388. doi: 10.1007/s00425-008-0709-1 18301915

33. Negro SS, Millet EJ, Madur D, Bauland C, Combes V, Welcker C, et al. Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies. BMC Plant Biol. 2019;19: 318. doi: 10.1186/s12870-019-1926-4 31311506

34. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, et al. A powerful tool for genome analysis in maize: Development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics. 2014;15. doi: 10.1186/1471-2164-15-823

35. Kremling KAG, Chen S-Y, Su M-H, Lepak NK, Romay MC, Swarts KL, et al. Dysregulation of expression correlates with rare-allele burden and fitness loss in maize. Nature. 2018;555: 520–523. doi: 10.1038/nature25966 29539638

36. Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, et al. TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS One. 2014. doi: 10.1371/journal.pone.0090346

37. Chen X. A MicroRNA as a Translational Repressor of APETALA2 in Arabidopsis Flower Development. Science. 2004. doi: 10.1126/science.1088060

38. Wahl V, Ponnu J, Schlereth A, Arrivault S, Langenecker T, Franke A, et al. Regulation of flowering by trehalose-6-phosphate signaling in Arabidopsis thaliana. Science. 2013;339: 704–707. doi: 10.1126/science.1230406 23393265

39. Lin H ying, Liu Q, Li X, Yang J, Liu S, Huang Y, et al. Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS. Genome Biol. 2017. doi: 10.1186/s13059-017-1328-6

40. Swanson-Wagner R, Briskine R, Schaefer R, Hufford MB, Ross-Ibarra J, Myers CL, et al. Reshaping of the maize transcriptome by domestication. Proc Natl Acad Sci. 2012;109: 11878–11883. doi: 10.1073/pnas.1201961109 22753482

41. Rodgers-Melnick E, Vera DL, Bass HW, Buckler ES. Open chromatin reveals the functional maize genome. Proc Natl Acad Sci. 2016;113: E3177–84. doi: 10.1073/pnas.1525244113 27185945

42. Tenaillon MI, Charcosset A. A European perspective on maize history. Comptes Rendus—Biologies. 2011. pp. 221–228. doi: 10.1016/j.crvi.2010.12.015 21377617

43. Swarts K, Gutaker RM, Benz B, Blake M, Bukowski R, Holland J, et al. Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America. Science. 2017;357: 512–515. doi: 10.1126/science.aam9425 28774930

44. Boden SA, Cavanagh C, Cullis BR, Ramm K, Greenwood J, Jean Finnegan E, et al. Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat. Nat Plants. 2015;1: 14016. doi: 10.1038/nplants.2014.16 27246757

45. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, et al. A large maize (zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One. 2011;6. doi: 10.1371/journal.pone.0028334

46. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007. doi: 10.1086/521987

47. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: Complexity, diversity, and dynamics. Science. 2009;326: 1112–1115. doi: 10.1126/science.1178534 19965430

48. Parent B, Turc O, Gibon Y, Stitt M, Tardieu F. Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J Exp Bot. 2010;61: 2057–2069. doi: 10.1093/jxb/erq003 20194927

49. Cabrera-Bosquet L, Fournier C, Brichet N, Welcker C, Suard B, Tardieu F. High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol. 2016;212: 269–281. doi: 10.1111/nph.14027 27258481

50. Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, et al. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics. 2012. doi: 10.1534/genetics.112.141473

51. Manoli A, Sturaro A, Trevisan S, Quaggiotti S, Nonis A. Evaluation of candidate reference genes for qPCR in maize. J Plant Physiol. 2012;169: 807–815. doi: 10.1016/j.jplph.2012.01.019 22459324

52. Lin Y, Zhang C, Lan H, Gao S, Liu H, Liu J, et al. Validation of potential reference genes for qPCR in maize across abiotic stresses, hormone treatments, and tissue types. PLoS One. 2014;9. doi: 10.1371/journal.pone.0095445

53. Winterhalter WE, Fedorka KM. Sex-specific variation in the emphasis, inducibility and timing of the post-mating immune response in Drosophila melanogaster. Proc R Soc B Biol Sci. 2009;276: 1109–1117. doi: 10.1098/rspb.2008.1559

54. Steger K, Wilhelm J, Konrad L, Stalf T, Greb R, Diemer T, et al. Both protamine-1 to protamine-2 mRNA ratio and Bcl2 mRNA content in testicular spermatids and ejaculated spermatozoa discriminate between fertile and infertile men. Hum Reprod. 2008;23: 11–16. doi: 10.1093/humrep/dem363 18003625

55. Laumanns IP, Fink L, Wilhelm J, Wolff JC, Mitnacht-Kraus R, Graef-Hoechst S, et al. The noncanonical WNT pathway is operative in idiopathic pulmonary arterial hypertension. Am J Respir Cell Mol Biol. 2009;40: 683–691. doi: 10.1165/rcmb.2008-0153OC 19029018

56. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. 2013. doi: 10.1348/000712608X366867

57. Butler DG, Cullis BR, Gilmour AR, Gogel BJ. ASReml-R reference manual mixed models for S language environments. VSN International Ltd, Hemel Hempstead, HP1 1ES, UK www.vsni.co.uk. 2009.

58. Kruijer W, Boer MP, Malosetti M, Flood PJ, Engel B, Kooke R, et al. Marker-based estimation of heritability in immortal populations. Genetics. 2014;199: 379–398. doi: 10.1534/genetics.114.167916 25527288

59. Millet E, Welcker C, Kruijer W, Negro S, Nicolas S, Praud S, et al. Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios. Plant Physiol. 2016; pp.00621.2016. doi: 10.1104/pp.16.00621

60. Shipley B. Cause and Correlation in Biology. Cause and Correlation in Biology. 2016. doi: 10.1017/cbo9781139979573

61. McArdle J. Structural Equation Modeling: Applications in Ecological and Evolutionary Biology. Edited by Pugesek Bruce H, Adrian Tomer, and, Alexander von Eye. Cambridge and New York: Cambridge University Press. $75.00. xiii + 409 p; ill.; index. ISBN: 0–521–. The Quarterly Review of Biology. 2004. pp. 330–330. doi: 10.1086/425822

62. Lamb E, Shirtliffe S, May W. Structural equation modeling in the plant sciences: An example using yield components in oat. Can J Plant Sci. 2011;91: 603–619. doi: 10.4141/cjps2010-035

63. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012. doi: 10.18637/jss.v048.i02

64. Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, et al. Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics. 2014;197: 375–387. doi: 10.1534/genetics.113.159731 24532779

65. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8: 833–835. doi: 10.1038/nmeth.1681 21892150

66. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, et al. Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics. 2014;198: 1717–1734. doi: 10.1534/genetics.114.169367 25271305

67. Clayton D. Differences between snpStats and snpMatrix. Differences. 2011.

68. Shin J-H, Blay S, Graham J, McNeney B. LDheatmap: An R Function for Graphical Display of Pairwise Linkage Disequilibria Between Single Nucleotide Polymorphisms. J Stat Softw. 2015;16. doi: 10.18637/jss.v016.c03

69. Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics. 2008;180: 977–993. doi: 10.1534/genetics.108.092221 18780740

70. MacQueen J. Some Methods for classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability 1967. 1967. citeulike-article-id:6083430

71. Earley KW, Haag JR, Pontes O, Opper K, Juehne T, Song K, et al. Gateway-compatible vectors for plant functional genomics and proteomics. Plant Journal. 2006. pp. 616–629. doi: 10.1111/j.1365-313X.2005.02617.x 16441352

72. Pradal C, Dufour-Kowalski S, Boudon F, Fournier C, Godin C. OpenAlea: A visual programming and component-based software platform for plant modelling. Funct Plant Biol. 2008. doi: 10.1071/FP08084

73. Fournier C, Andrieu B. ADEL-maize: An L-system based model for the integration of growth processes from the organ to the canopy. Application to regulation of morphogenesis by light availability. Agronomie. 1999. doi: 10.1051/agro:19990311


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