A Machine-Learning Analysis of Flowering Gene Expression in the CDC Frontier Chickpea Cultivar

Abstract—We have analyzed the gene expression dynamics in floral transition in the CDC Frontier chickpea cultivar. We provide a model, in several versions, to predict the expression dynamics of five flowering genes, taking the expression of their regulators as an input. The models were trained using the random forest method on the previously published expression data for ten flowering genes under the short- and long-day growing conditions. The resulting models correctly predict the dynamics of the average expression levels under long days. We show that the models for CDC Frontier mainly reproduce the regulatory interactions between the key genes described for the Arabidopsis thaliana model plant. Based on the analysis, we hypothesize that the short-day data and the long-day data contain qualitatively different information, which may be due to different regulatory modules that function in different conditions. For the regulators of the flower meristem identity genes AP1 and LFY, our models predict FTa3 as the main activator and TFL1c as the main repressor under long days.

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  1. 1

    S. Jung and A. E. Muller, Trends Plant Sci. 14, 563 (2009). https://doi.org/10.1016/j.tplants.2009.07.005

    Article  Google Scholar 

  2. 2

    A. Kanth and M. Schmid, Cell Mol. Life Sci. 68, 2013 (2011). https://doi.org/10.1007/s00018-011-0673-y

    Article  Google Scholar 

  3. 3

    M. Khan, X Ai, and J. Zhang, Wiley Interdisc. Rev. RNA 5, 347 (2014). https://doi.org/10.1002/wma.l215

    Article  Google Scholar 

  4. 4

    J. A. Banta, I. M. Ehrenreich, S. Gerard, et al., Ecol. Lett. 15, 769 (2012). https://doi.org/10.1111/j.1461-0248.2012.01796.x

    Article  Google Scholar 

  5. 5

    F. Andres and G. Coupland, Nat. Rev. Genet. 13, 627 (2012). https://doi.org/10.1038/nrg3291

    Article  Google Scholar 

  6. 6

    V. Hecht, F. Foucher, C. Ferrandiz, et al., Plant Physiol. 137, 1420 (2005). https://doi.org/10.1104/pp.104.057018

    Article  Google Scholar 

  7. 7

    R. Benlloch, A. Berbel, L. Ali, et al., Front. Plant Sci. 6, 543 (2015). https://doi.org/10.3389/fpls.2015.00543

    Article  Google Scholar 

  8. 8

    J. L. Weller and R. Ortega, Front. Plant Sci. 6, 207 (2015). https://doi.org/10.3389/fpls.2015.00207

    Article  Google Scholar 

  9. 9

    C.-H. Jung, S. E. Wong, M. B. Singh, and P. L. Bhalla, PloS One 7, e38250 (2012). https://doi.org/10.1371/journal.pone.0038250

    ADS  Article  Google Scholar 

  10. 10

    H. D. Upadhyaya, D. Bajaj, S. Das, et al., Plant Mol. Biol. 89, 403 (2015). https://doi.org/10.1007/sl1103-015-0377-z

    Article  Google Scholar 

  11. 11

    J. L. Weller and R. C. Macknight, Methods Mol. Biol. 1822, 261 (2018). https://doi.org/10.1007/978-1-4939-8633-0_17

  12. 12

    R. Ortega, V. F. Hecht, J. S. Freeman, et al., Front. Plant Sci. 10, 824 (2019). https://doi.org/10.3389/fpls.2019.00824

    Article  Google Scholar 

  13. 13

    M. Blumel, N. Dally, and C. Jung, Curr. Opin. Biotech. 32, 121 (2015). https://doi.org/10.1016/j.copbio.2014.11.023

    Article  Google Scholar 

  14. 14

    S. Ridge, A. Deokar, R. Lee, et al., Plant Physiol. 175, 802 (2017). https://doi.org/10.1104/pp.l7.00082

    Article  Google Scholar 

  15. 15

    K. E. Jaeger, N. Pullen, S. Lamzin, et al., Plant Cell. 25, 820 (2013). https://doi.org/10.1105/tpc.113.109355

  16. 16

    F. C. Sussmilch, A. Berbel, V. Hecht, et al., Plant Cell. 27, 1046 (2015). https://doi.org/10.1105/tpc.115.136150

  17. 17

    F. Valentim, S. van Mourik, D. Pose, et al., PloS One 10 (2), e0l16973 (2015). https://doi.org/10.1371/journal.pone.0116973

  18. 18

    R. K. Varshney, C. Song, R. K. Saxena, et al., Nat. Biotechnol. 31, 240 (2013). https://doi.org/10.1038/nbt.2491

    Article  Google Scholar 

  19. 19

    V. V. Gursky, K. N. Kozlov, S. V. Nuzhdin, and M. G. Samsonova, Front. Genet. 9, 547 (2018). https://doi.org/10.3389/fgene.2018.00547

    Article  Google Scholar 

  20. 20

    F. Pedregosa, G. Varoquaux, A. Gramfort, et al., J. Machine Learning Res. 12, 2825 (2011).

    MathSciNet  Google Scholar 

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This study was supported by the Ministry of Science and Higher Education of the Russian Federation (task no. 1.8697.2017/BCh).

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Corresponding author

Correspondence to M. G. Samsonova.

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The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

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Translated by M. Batrukova

Abbreviations: LD, long day; SD, short day; NMSE, normalized mean square error.

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Podolny, B.S., Gursky, V.V. & Samsonova, M.G. A Machine-Learning Analysis of Flowering Gene Expression in the CDC Frontier Chickpea Cultivar. BIOPHYSICS 65, 225–236 (2020). https://doi.org/10.1134/S0006350920020189

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  • Keywords: chickpea
  • CDC Frontier
  • flowering gene network
  • random forest algorithm