The 3VmrMLM Method Provides New Genomic Variants Associated with Fiber Characteristics in Flax

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Abstract

Flax is an important agricultural crop grown for oil and fiber. Flax fiber is used in various industries, and breeding new flax varieties with better fiber characteristics is subject of interest. Genome-wide association studies (GWAS) can find variants associated with traits important for fiber quality, but differences in data due to different growing conditions in different years reduce the power of GWAS methods. The 3VmrMLM method allows searching for variants in data measured in several environments, allowing finding new variants not found by other methods. Measurements in different years were taken as different environments, and the method found a total of 205 variants characteristic of all or several environments, 37 of which fell into the body of known genes with important functions, the effect of some variants on fiber characteristics was also confirmed in an independent set of plants.

About the authors

M. A Duk

Peter the Great St. Petersburg Polytechnic University

Email: duk@mail.ioffe.ru
St. Petersburg, Russia

A. A Kanapin

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

M. P Bankin

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

M. G Samsonova

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

References

  1. Goudenhooft C., Bourmaud A., and Baley C. Flax (Linum usitatissimum L.) fibers for composite reinforcement: exploring the link between plant growth, cell walls development, and fiber properties. Front. Plant Sci., 10, 411 (2019). doi: 10.3389/fpls.2019.00411
  2. Fogorasi M. and Barbu I. The potential of natural fibres for automotive sector – review. IOP Conf. Ser. Mater. Sci. Eng., 252, 012044 (2017). doi: 10.1088/1757-899X/252/1/012044
  3. Goudenhooft C., Bourmaud A., and Baley C. Varietal selection of flax over time: Evolution of plant architecture related to influence on the mechanical properties of fibers. Ind. Crop. Prod., 97, 56–64 (2017).
  4. Rozhmina T., Bankin M., Samsonova A., Kanapin A., and Samsonova M. A comprehensive dataset of flax (Linum uitatissimum L.) phenotypes. Data Brief, 37, 107224 (2021). doi: 10.1016/j.dib.2021.107224
  5. Kanapin A., Rozhmina T., Bankin M., Surkova S., Duk M., Osyagina E., and Samsonova M. Genetic determinants of fiber-associated traits in flax identified by omics data integration. Int. J. Mol. Sci., 23, 14536 (2022). doi: 10.3390/ijms232314536
  6. Li M., Zhang Y. W., Xiang Y., Liu M. H., and Zhang Y. M. IIIVmrMLM: The R and C++ tools associated with 3VmrMLM, a comprehensive GWAS method for dissecting quantitative traits. Mol Plant., 15 (8), 1251–1253 (2022). doi: 10.1016/j.molp.2022.06.002, PMID: 35684963
  7. You F. and Cloutier S. Mapping quantitative trait loci onto chromosome-scale pseudomolecules in flax. Methods Protoc., 3 (2), 28 (2020). doi: 10.3390/mps3020028
  8. Yan Y., Ham B. K., Chong Y. H., Yeh S. D., and
  9. Lucas W. J. A plant SMALL RNA-BINDING PROTEIN 1 family mediates cell-to-cell trafficking of RNAi Signals. Mol. Plant., 13 (2), 321–335 (2020). doi: 10.1016/j.molp.2019.12.001
  10. Mei Y., Gao H. B., Yuan M., and Xue H. W. The Arabidopsis ARCP protein, CSI1, which is required for microtubule stability, is necessary for root and anther development. Plant Cell, 24 (3), 1066–1080 (2012). doi: 10.1105/tpc.111.095059
  11. Gu Y., Kaplinsky N., Bringmann M., Cobb A., Carroll A., Sampathkumar A., Baskin T. I., Persson S., and Somerville C. R. Identification of a cellulose synthase-associated protein required for cellulose biosynthesis. Proc. Natl. Acad. Sci. USA, 107 (29), 12866–12871 (2010). doi: 10.1073/pnas.1007092107
  12. Landrein B., Lathe R., Bringmann M., Vouillot C., Ivakov A., Boudaoud A., Persson S., and Hamant O. Impaired cellulose synthase guidance leads to stem torsion and twists phyllotactic patterns in Arabidopsis. Curr. Biol., 23 (10), 895–900 (2013). doi: 10.1016/j.cub.2013.04.013
  13. Werner A. K., Romeis T., and Witte C. P. Ureide catabolism in Arabidopsis thaliana and Escherichia coli. Nature Chem. Biol., 6 (1), 19–21 (2010). doi: 10.1038/nchembio.265
  14. Shin I., Percudani R., and Rhee S. Structural and functional insights into (S)-ureidoglycine aminohydrolase, key enzyme of purine catabolism in Arabidopsis thaliana. J. Biol. Chem., 287 (22), 18796–18805 (2012). doi: 10.1074/jbc.M111.331819
  15. Serventi F., Ramazzina I., Lamberto I., Puggioni V., Gatti R., and Percudani R. Chemical basis of nitrogen recovery through the ureide pathway: formation and hydrolysis of S-ureidoglycine in plants and bacteria. ACS Chem. Biol., 5 (2), 203–214 (2010). doi: 10.1021/cb900248n, PMID: 20038185
  16. Bencivenga S., Simonini S., Benková E., and Colombo L. The transcription factors BEL1 and SPL are required for cytokinin and auxin signaling during ovule development in Arabidopsis. Plant Cell, 24 (7), 2886–2897 (2012). doi: 10.1105/tpc.112.100164
  17. Li G., Zhang J., Li J., Yang Z., Huang H., and Xu L. Imitation Switch chromatin remodeling factors and their interacting RINGLET proteins act together in controlling the plant vegetative phase in Arabidopsis. Plant J., 72 (2), 261–270 (2012). doi: 10.1111/j.1365-313X.2012.05074.x
  18. Gámez-Arjona F. M., Li J., Raynaud S., Baroja-Fernández E., Muñoz F. J., Ovecka M., Ragel P., Bahaji A., Pozueta-Romero J., and Mérida Á. Enhancing the expression of starch synthase class IV results in increased levels of both transitory and long-term storage starch. Plant Biotechnol. J., 9 (9), 1049–1060 (2011). doi: 10.1111/j.1467-7652.2011.00626.x
  19. Seung D., Soyk S., Coiro M., Maier B. A., Eicke S., and Zeeman S. C. PROTEIN TARGETING TO STARCH is required for localising GRANULE-BOUND STARCH SYNTHASE to starch granules and for normal amylose synthesis in Arabidopsis. PLoS Biol., 13 (2), e1002080 (2015). doi: 10.1371/journal.pbio.1002080
  20. Smith S. M., Fulton D. C., Chia T., Thorneycroft D., Chapple A., Dunstan H., Hylton C., Zeeman S. C., and Smith A. M. Diurnal changes in the transcriptome encoding enzymes of starch metabolism provide evidence for both transcriptional and posttranscriptional regulation of starch metabolism in Arabidopsis. Plant Physiol., 136 (1), 2687–2699 (2004). doi: 10.1104/pp.104.044347
  21. Layat E., Cotterell S., Vaillant I., Yukawa Y., Tutois S., and Tourmente S. Transcript levels, alternative splicing and proteolytic cleavage of TFIIIA control 5S rRNA accumulation during Arabidopsis thaliana development. Plant J., 71 (1), 35–44 (2012). doi: 10.1111/j.1365-313X.2012.04948.x
  22. Barrero R. A., Umeda M., Yamamura S., and Uchimiya H. Arabidopsis CAP regulates the actin cytoskeleton necessary for plant cell elongation and division. Plant Cell, 14 (1), 149–163 (2002). doi: 10.1105/tpc.010301
  23. Hou B., Lim E. K., Higgins G. S., and Bowles D. J. N-Glucosylation of cytokinins by glycosyltransferases of Arabidopsis thaliana. J. Biol. Chem., 279 (46), 47822–47832 (2004). doi: 10.1074/jbc.M409569200
  24. Thomann A., Lechner E., Hansen M., Dumbliauskas E., Parmentier Y., Kieber J., Scheres B., and Genschik P. Arabidopsis CULLIN3 genes regulate primary root growth and patterning by ethylene-dependent and -independent mechanisms. PLoS Genet., 5 (1), e1000328 (2009). doi: 10.1371/journal.pgen.1000328
  25. Groß F., Rudolf E. E., Thiele B., Durner J., and Astier J. Copper amine oxidase 8 regulates arginine-dependent nitric oxide production in Arabidopsis thaliana. J. Exp. Bot., 68 (9), 2149–2162 (2017). doi: 10.1093/jxb/erx105
  26. Lolle S., Greeff C., Petersen K., Roux M., Jensen M. K., Bressendorff S., Rodriguez E., Sømark K., Mundy J., and Petersen M. Matching NLR immune receptors to autoimmunity in camta3 mutants using antimorphic NLR alleles. Cell Host Microbe, 21 (4), 518–529 (2017). doi: 10.1016/j.chom.2017.03.005
  27. Li H. and Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25 (14), 1754–1760 (2009). doi: 10.1093/bioinformatics/btp324
  28. Tello D., Gil J., Loaiza C. D., Riascos J. J., Cardozo N., and Duitama J. NGSEP3: accurate variant calling across species and sequencing protocols. Bioinformatics, 35 (22), 4716–4723 (2019). doi: 10.1093/bioinformatics/btz275
  29. Bradbury P. J., Zhang Z., Kroon D. E., Casstevens T. M., Ramdoss Y., and Buckler E. S. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 23 (19), 2633–2635 (2007). doi: 10.1093/bioinformatics/btm308
  30. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A., Bender D., Maller J., Sklar P., de Bakker P. I., Daly M. J., and Sham P. C. PLINK: A tool set for wholegenome association and population-based linkage analyses. Am. J. Hum. Genet., 81 (3), 559–575 (2007). doi: 10.1086/519795

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