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Unleashing meiotic crossovers in crops

Nature Plants volume 4, pages 1010–1016 (2018)Cite this article

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Abstract

Improved plant varieties are important in our attempts to face the challenges of a growing human population and limited planet resources. Plant breeding relies on meiotic crossovers to combine favourable alleles into elite varieties1. However, meiotic crossovers are relatively rare, typically one to three per chromosome2, limiting the efficiency of the breeding process and related activities such as genetic mapping. Several genes that limit meiotic recombination were identified in the model species Arabidopsis thaliana2. Mutation of these genes in Arabidopsis induces a large increase in crossover frequency. However, it remained to be demonstrated whether crossovers could also be increased in crop species hybrids. We explored the effects of mutating the orthologues of FANCM3, RECQ44 or FIGL15 on recombination in three distant crop species, rice (Oryza sativa), pea (Pisum sativum) and tomato (Solanum lycopersicum). We found that the single recq4 mutation increases crossovers about three-fold in these crops, suggesting that manipulating RECQ4 may be a universal tool for increasing recombination in plants. Enhanced recombination could be used with other state-of-the-art technologies such as genomic selection, genome editing or speed breeding6 to enhance the pace and efficiency of plant improvement.

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Fig. 1: RECQ4, FANCM and FIGL1 mutations.
Fig. 2: Genetic maps in fancm and recq4 mutants compared with wild type.
Fig. 3: Average genetic size per chromosome in wild type, fancm and recq4 for Arabidopsis, rice, pea and tomato.
Fig. 4: Distribution of crossovers along the 12 rice chromosomes in Osrecq4l (blue), Osfancm (green) and wild type (grey) plants.

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Data availability

The data that support the findings of this study are provided as Supplementary Datasets. Genotyping data and derived recombination frequencies (Figs. 2, 3 and 4) for rice, pea and tomato are given in Supplementary Datasets 2–7, respectively. The protein sequences used for the phylogeny analyses (Supplementary Figs. 1–3) are provided as Supplementary Dataset 1.

References

  1. Wijnker, E. & de Jong, H. Managing meiotic recombination in plant breeding. Trends Plant Sci. 13, 640–646 (2008).

    Article  CAS  Google Scholar 

  2. Fernandes, J. B., Seguéla-Arnaud, M., Larchevêque, C., Lloyd, A. H. & Mercier, R. Unleashing meiotic crossovers in hybrid plants. Proc. Natl Acad. Sci. USA 115, 2431–2436 (2018).

    Article  CAS  Google Scholar 

  3. Crismani, W. et al. FANCM limits meiotic crossovers. Science 336, 1588–1590 (2012).

    Article  CAS  Google Scholar 

  4. Seguéla-Arnaud, M. et al. Multiple mechanisms limit meiotic crossovers: TOP3α and two BLM homologs antagonize crossovers in parallel to FANCM. Proc. Natl Acad. Sci. USA 4713–4718 (2015).

    Article  Google Scholar 

  5. Girard, C. et al. AAA-ATPase FIDGETIN-LIKE 1 and helicase FANCM antagonize meiotic crossovers by distinct mechanisms. PLoS Genet. 11, e1005369 (2015).

    Article  Google Scholar 

  6. Watson, A. et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat. Plants 4, 23–29 (2018).

    Article  Google Scholar 

  7. Berchowitz, L. E. & Copenhaver, G. P. Genetic interference: don’t stand so close to me. Curr. Genomics 11, 91–102 (2010).

    Article  CAS  Google Scholar 

  8. Ziolkowski, P. A. et al. Natural variation and dosage of the HEI10 meiotic E3 ligase control Arabidopsis crossover recombination. Genes Dev. 31, 306–317 (2017).

    Article  CAS  Google Scholar 

  9. Seguéla-Arnaud, M. et al. RMI1 and TOP3α limit meiotic CO formation through their C-terminal domains. Nucleic Acids Res. 45, 1860–1871 (2017).

    PubMed  Google Scholar 

  10. Girard, C. et al. FANCM-associated proteins MHF1 and MHF2, but not the other Fanconi anemia factors, limit meiotic crossovers. Nucleic Acids Res. 42, 9087–9095 (2014).

    Article  CAS  Google Scholar 

  11. Serra, H. et al. Massive crossover elevation via combination of HEI10 and recq4a recq4b during Arabidopsis meiosis. Proc. Natl Acad. Sci. USA 115, 2437–2442 (2018).

    Article  CAS  Google Scholar 

  12. Hartung, F. & Puchta, H. The RecQ gene family in plants. J. Plant Physiol. 163, 287–296 (2006).

    Article  CAS  Google Scholar 

  13. Hartung, F., Suer, S. & Puchta, H. Two closely related RecQ helicases have antagonistic roles in homologous recombination and DNA repair in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 104, 18836–18841 (2007).

    Article  CAS  Google Scholar 

  14. Ziolkowski, P. A. et al. Juxtaposition of heterozygous and homozygous regions causes reciprocal crossover remodelling via interference during Arabidopsis meiosis. Elife 4, 1–29 (2015).

    Article  Google Scholar 

  15. Blary, A. et al. FANCM limits meiotic crossovers in brassica crops. Front. Plant Sci. 9, 1–13 (2018).

    Article  Google Scholar 

  16. Zhang, P. et al. The rice AAA-ATPase OsFIGNL1 is essential for male meiosis. Front. Plant Sci. 8, 1639 (2017).

    Article  Google Scholar 

  17. Chase, M. W. et al. An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).

    Article  Google Scholar 

  18. Lloyd, A. H. et al. Meiotic gene evolution: can you teach a new dog new tricks?. Mol. Biol. Evol. 31, 1724–1727 (2014).

    Article  CAS  Google Scholar 

  19. Sallaud, C. et al. High throughput T-DNA insertion mutagenesis in rice: a first step towards in silico reverse genetics. Plant J. 39, 450–464 (2004).

    Article  CAS  Google Scholar 

  20. Larmande, P. et al. Oryza Tag Line, a phenotypic mutant database for the Génoplante rice insertion line library. Nucleic Acids Res. 36, 1022–1027 (2008).

    Article  Google Scholar 

  21. Zhou, T. et al. Genome-wide identification of NBS genes in japonica rice reveals significant expansion of divergent non-TIR NBS-LRR genes. Mol. Genet. Genomics 271, 402–415 (2004).

    Article  CAS  Google Scholar 

  22. Alves-Carvalho, S. et al. Full-length de novo assembly of RNA-seq data in pea (Pisum sativum L.) provides a gene expression atlas and gives insights into root nodulation in this species. Plant J. 84, 1–19 (2015).

    Article  CAS  Google Scholar 

  23. Fernandes, J. B. et al. FIGL1 and its novel partner FLIP form a conserved complex that regulates homologous recombination. PLoS Genet. 14, e1007317 (2018).

    Article  Google Scholar 

  24. Zapata, L. et al. Chromosome-level assembly of Arabidopsis thaliana Ler reveals the extent of translocation and inversion polymorphisms. Proc. Natl Acad. Sci. USA 113, E4052–E4060 (2016).

    Article  CAS  Google Scholar 

  25. Phillips, D. et al. The effect of temperature on the male and female recombination landscape of barley. New Phytol. 208, 421–429 (2015).

    Article  CAS  Google Scholar 

  26. Francis, et al. Pollen tetrad-based visual assay for meiotic recombination in Arabidopsis. Proc. Natl Acad. Sci. USA 104, 3913–3918 (2007).

    Article  CAS  Google Scholar 

  27. Lloyd, A. H., Morgan, C., Franklin, C. & Bomblies, K. Plasticity of meiotic recombination rates in response to temperature in . Arabidopsis. Genetics 4, 1409–1420 (2018).

    Google Scholar 

  28. Modliszewski, J. L. et al. Elevated temperature increases meiotic crossover frequency via the interfering (Type I) pathway in Arabidopsis thaliana. PLoS Genet. 14, e1007384 (2018).

    Article  Google Scholar 

  29. Wilfert, L., Gadau, J. & Schmid-Hempel, P. Variation in genomic recombination rates among animal taxa and the case of social insects. Heredity 98, 189–197 (2007).

    Article  CAS  Google Scholar 

  30. Kwon, Y.-I. et al. DNA replication arrest leads to enhanced homologous recombination and cell death in meristems of rice OsRecQl4 mutants. BMC Plant Biol. 13, 62 (2013).

    Article  CAS  Google Scholar 

  31. Nambiar, M. & Smith, G. R. Repression of harmful meiotic recombination in centromeric regions. Semin. Cell Dev. Biol. 54, 188–197 (2016).

    Article  CAS  Google Scholar 

  32. Choulet, F. et al. Structural and functional partitioning of bread wheat chromosome 3B. Science 345, 1249721 (2014).

    Article  Google Scholar 

  33. King, J. et al. A step change in the transfer of interspecific variation into wheat from Amblyopyrum muticum. Plant Biotechnol. J. 15, 217–226 (2017).

    Article  CAS  Google Scholar 

  34. Zickler, D. & Kleckner, N. E. Meiotic chromosomes: integrating structure and function. Annu. Rev. Genet. 33, 603–754 (1999).

    Article  CAS  Google Scholar 

  35. Yin, K., Gao, C. & Qiu, J.-L. Progress and prospects in plant genome editing. Nat. Plants 3, 17107 (2017).

    Article  CAS  Google Scholar 

  36. Van Bel, M. et al. PLAZA 4.0: an integrative resource for functional, evolutionary and comparative plant genomics. Nucleic Acids Res. 46, D1190–D1196 (2018).

    Article  Google Scholar 

  37. Fernandez-Pozo, N. et al. The Sol Genomics Network (SGN)—from genotype to phenotype to breeding. Nucleic Acids Res. 43, D1036–D1041 (2015).

    Article  CAS  Google Scholar 

  38. Dereeper, A. et al. Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. 36, W465–W469 (2008).

    Article  CAS  Google Scholar 

  39. Introduction to GATK Best Practices (Broad Institute, 2009); https://software.broadinstitute.org/gatk/best-practices.

  40. Droc, G. et al. OryGenesDB: a database for rice reverse genetics. Nucleic Acids Res. 34, D736–D7340 (2006).

    Article  CAS  Google Scholar 

  41. An, S. et al. Generation and analysis of end sequence database for T-DNA tagging lines in rice. Plant Physiol. 133, 2040–2047 (2003).

    Article  CAS  Google Scholar 

  42. Droc, G., Périn, C., Fromentin, S. & Larmande, P. OryGenesDB 2008 update: database interoperability for functional genomics of rice. Nucleic Acids Res. 37, D992–D995 (2009).

    Article  CAS  Google Scholar 

  43. Grelon, M., Vezon, D., Gendrot, G. & Pelletier, G. AtSPO11-1 is necessary for efficient meiotic recombination in plants. EMBO J. 20, 589–600 (2001).

    Article  CAS  Google Scholar 

  44. Heffelfinger, C., Fragoso, C. A. & Lorieux, M. Constructing linkage maps in the genomics era with MapDisto 2.0. Bioinformatics 33, 2224–2225 (2017).

    Article  CAS  Google Scholar 

  45. Tayeh, N. et al. Development of two major resources for pea genomics: the GenoPea 13.2K SNP Array and a high-density, high-resolution consensus genetic map. Plant J. 84, 1257–1273 (2015).

    Article  CAS  Google Scholar 

  46. Baldet, P. et al. Investigating the role of vitamin C in tomato through TILLING identification of ascorbate-deficient tomato mutants. Plant Biotechnol. J. 30, 309–314 (2013).

    Article  CAS  Google Scholar 

  47. Okabe, Y. et al. Tomato TILLING technology: development of a reverse genetics tool for the efficient isolation of mutants from micro-tom mutant libraries. Plant Cell Physiol. 52, 1994–2005 (2011).

    Article  CAS  Google Scholar 

  48. Garcia, V. et al. Rapid identification of causal mutations in tomato EMS populations via mapping-by-sequencing. Nat. Protoc. 11, 2401–2418 (2016).

    Article  CAS  Google Scholar 

  49. Shirasawa, K., Hirakawa, H., Nunome, T., Tabata, S. & Isobe, S. Genome-wide survey of artificial mutations induced by ethyl methanesulfonate and gamma rays in tomato. Plant Biotechnol. J. 14, 51–60 (2016).

    Article  CAS  Google Scholar 

  50. Petit, J. et al. Analyses of tomato fruit brightness mutants uncover both cutin-deficient and cutin-abundant mutants and a new hypomorphic allele of GDSL lipase. Plant Physiol. 164, 888–906 (2014).

    Article  CAS  Google Scholar 

  51. Smith, S. M. & Maughan, P. J. SNP genotyping using KASPar assays. Methods Mol. Biol. 1245, 243–256 (2015).

    Article  CAS  Google Scholar 

  52. Lorieux, M. MapDisto: fast and efficient computation of genetic linkage maps. Mol. Breed. 30, 1231–1235 (2012).

    Article  CAS  Google Scholar 

  53. Bollier, N. et al. At-MINI ZINC FINGER2 and Sl-INHIBITOR OF MERISTEM ACTIVITY, a conserved missing link in the regulation of floral meristem termination in Arabidopsis and tomato. Plant Cell 30, 83–100 (2018).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank J. Burstin, M. Causse, B. Courtois and C. Mézard for fruitful discussions; P. Sourdille and F. Benyahya for sharing wheat sequences before publication; C. Le Signor and M.-C. Le Paslier for offering their expertise and advice; and J.-F. Rami for his help with the SpiderMap software. The pea and tomato work was funded by HyperRec grants from INRA Transfert. The Institute Jean-Pierre Bourgin benefits from the support of the LabEx Saclay Plant Sciences-SPS (ANR-10-LABX-0040-SPS). This work was partly funded by the Investissements d’Avenir, France Génomique (10-INBS-0009) project IRIGIN (International Rice Genome Initiative) and the CGIAR research program on rice (RICE).

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Authors and Affiliations

  1. CIRAD, UMR AGAP, Montpellier, France

    Delphine Mieulet, Gaëtan Droc & Emmanuel Guiderdoni

  2. Université Montpellier, CIRAD, INRA Montpellier SupAgro, Montpellier, France

    Delphine Mieulet, Gaëtan Droc & Emmanuel Guiderdoni

  3. Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, Dijon, France

    Gregoire Aubert, Anthony Klein, Emilie Vieille, Celine Rond-Coissieux & Myriam Sanchez

  4. UMR 1332 BFP, INRA, Université Bordeaux, Villenave d’Ornon, France

    Cecile Bres, Jean-Philippe Mauxion & Christophe Rothan

  5. Institute of Plant Sciences, Paris-Saclay IPS2, CNRS, INRA, Université Paris-Sud, Université Evry, Université Paris-Saclay, Orsay, France

    Marion Dalmais

  6. Institut Jean-Pierre Bourgin, UMR1318 INRA–AgroParisTech, Université Paris-Saclay, Versailles, France

    Raphael Mercier

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Contributions

D.M., G.A., C.B., A.K., G.D., E.V., C.R.C., M.S., M.D. and J.P.M. produced the data. D.M., G.A., C.B., A.K., E.G. and R.M. analysed the data. D.M., G.A., C.B., C.R., E.G. and R.M. conceived and designed the experiments. D.M. and R.M. wrote the paper with inputs from G.A., C.B., C.R. and E.G.

Corresponding author

Correspondence to Raphael Mercier.

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Competing interests

Patents have been deposited by INRA on the use of RECQ4, FIGL1 and FANCM to manipulate meiotic recombination (EP3149027, EP3016506 and EP2755995).

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Supplementary information

Supplementary Information

Supplementary Figures 1–9 and Supplementary Tables 1–3.

Reporting Summary

Dataset 1

Sequences and accession numbers of RECQ4, FIGL1 and FANCM proteins analysed in Supplementary Figures 1–3.

Dataset 2

Genotyping data of rice populations.

Dataset 3

Recombination data in rice.

Dataset 4

Genotyping data of pea populations.

Dataset 5

Recombination data in pea.

Dataset 6

Genotyping data of tomato populations.

Dataset 7

Recombination data in tomato.

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Mieulet, D., Aubert, G., Bres, C. et al. Unleashing meiotic crossovers in crops. Nature Plants 4, 1010–1016 (2018). https://doi.org/10.1038/s41477-018-0311-x

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