Towards an understanding of root system wide integration of environmental cues

Towards an understanding of root system wide integration of environmental cues

XVI Congreso Nacional de Bioquímica y Biología Molecular de Plantas y 9th Symposium México-USA. Querétaro Dec-2015
Here we propose a framework for analysis of root system architecture, gene expression and soil properties to leverage the development of tools that allow integration of these multidimensional information to better understand how roots are able to adapt root function and shape to heterogeneous spatial environmental cues.

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Rubén Rellán Álvarez

December 10, 2015
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  1. Towards an understanding of root system wide integration of environmental

    cues ubén Rellán Álvarez @rubenrellan rrellan@langebio.cinvestav.mx www.rrlab.org bit.ly/goo-roots-paper bit.ly/SMB-2015-slides www.rrlab.org/GLO-Roots
  2. Aaron Escobar: http://www.flickr.com/photos/aaronescobar/2569091622/ water Phosphate Roots integrate a variety of

    signals and adjust function and shape accordingly
  3. Drew et al. 1978 JXB 109:435 High N Low N

  4. A B C D E Contact Air lateral root aerenchyma

    aerenchyma lateral root macro- pore Soil macro- pore Soil Rellán-Álvarez, Lobet & Dinneny 2015 In Press Figure credit S. Mooney, C Sturrock, N. Robbins & J. Dinneny Roots integrate a variety of signals and adjust function and shape accordingly
  5. low N high N CEP1 LRR-RK CK CLE3-CLV1 NRT1.1 Auxin

    NRT1.1 Auxin N03 - ? HRS1 low P HRS1 NRT1.1 How can roots integrate & regulate local clues with whole root system responses? Adapted from Rellán-Álvarez, Lobet & Dinneny 2015 In Press Krouk et al 2010 PMID 20627075 Medici et al 2015 PMID 25723764 Ruffel et al 2011 PMID 22025711 Tabata et al. 2014 PMID: 25324386 Araya et al 2014 PMID 24449877
  6. Yun et al. 2014 PMID: 24927545 From metabolite in vivo

    imaging to 3D in soil root architecture Grossman et al. 2011 PMID: 22186371 Soil Artificial Substrate
  7. Yun et al. 2014 PMID: 24927545 From metabolite in vivo

    imaging to 3D in soil root architecture Grossman et al. 2011 PMID: 22186371 Soil Artificial Substrate
  8. Soil Microbe Colonization Soil Properties Time Root Structure Gene Expression

    We designed a root imaging system able to integrate different types of information Growth and Luminescence Observatory for Roots
  9. Transpiring environment Roots protected from light

  10. 30 cm 15 cm 2 mm 100 cm3 of soil

    Rhizotrons: a 2D sample of soil
  11. 30 cm 15 cm 2 mm 100 cm3 of soil

    Rhizotrons: a 2D sample of soil
  12. Rhizotrons: a 2D sample of soil

  13. Rhizotrons allow normal plant growth

  14. Roots grown in rhizotrons are similar to roots grown in

    pots Fresh Weight FW (mg) 0 60 120 180 240 MS Pot Rhizotron
  15. Aaron Escobar: http://www.flickr.com/photos/aaronescobar/2569091622/ water Phosphate

  16. Different environmental cues can be recreated and quantified in rhizotrons

    water
  17. Different environmental cues can be recreated and quantified in rhizotrons

    water
  18. Different environmental cues can be recreated and quantified in rhizotrons

    water
  19. Different environmental cues can be recreated and quantified in rhizotrons

    water Soil Moisture (%) 0 20 40 60 80 mean intensity 36 40 44 48 52 y = -0.0564x2 + 1.0466x + 111.09 y = -3.96x + 220.8 R2 = 0.88
  20. Different environmental cues can be recreated and quantified in rhizotrons

    water Soil Moisture (%) 0 20 40 60 80 mean intensity 36 40 44 48 52 y = -0.0564x2 + 1.0466x + 111.09 y = -3.96x + 220.8 R2 = 0.88
  21. Different environmental cues can be recreated and quantified in rhizotrons

    water Phosphate Soil Moisture (%) 0 20 40 60 80 mean intensity 36 40 44 48 52 y = -0.0564x2 + 1.0466x + 111.09 y = -3.96x + 220.8 R2 = 0.88
  22. Different environmental cues can be recreated and quantified in rhizotrons

    water Phosphate Soil Moisture (%) 0 20 40 60 80 mean intensity 36 40 44 48 52 y = -0.0564x2 + 1.0466x + 111.09 y = -3.96x + 220.8 R2 = 0.88
  23. Different environmental cues can be recreated and quantified in rhizotrons

    water Phosphate Soil Moisture (%) 0 20 40 60 80 mean intensity 36 40 44 48 52 y = -0.0564x2 + 1.0466x + 111.09 y = -3.96x + 220.8 R2 = 0.88
  24. None
  25. None
  26. We use luciferase to visualize roots in soil www.firefly.org/firefly-pictures.html Luciferase

    is great! •Does not require any light excitation •No background luminescence •LUC can be used in temporal studies •Dynamic response to gene expression •Multiple color reporters are possible luciferin + ATP + O2 → oxyluciferin +
  27. Prom mCherry Luciferase 35S Emami et al. 2013 PMID: 24032037

    Although we are also exploring the use of fluorescent proteins
  28. Prom mCherry Luciferase 35S Emami et al. 2013 PMID: 24032037

    Although we are also exploring the use of fluorescent proteins
  29. Prom mCherry Luciferase 35S Emami et al. 2013 PMID: 24032037

    Although we are also exploring the use of fluorescent proteins
  30. Although we are also exploring the use of fluorescent proteins

  31. Although we are also exploring the use of fluorescent proteins

  32. Roots are visualized with the GLO1 imaging system

  33. Roots are visualized with the GLO1 imaging system

  34. Both sides of the rhizotron are combined visualize the full

    root system ProUBQ10:LUC2o
  35. Both sides of the rhizotron are combined visualize the full

    root system ProUBQ10:LUC2o
  36. Images are data

  37. Images are data

  38. And there is a lot of data that can be

    gathered from this type of images DAS 0 21
  39. GLO-RIA is an ImageJ plugin developed to analyze GLO-Roots images

    bit.ly/GLO-RIA www.guillaumelobet.be
  40. 11 12 13 14 15 16 17 18 19 20

    21 GLO-RIA enables semi-automatic quantification of RSA traits
  41. GLO-RIA enables semi-automatic quantification of RSA traits 11 12 13

    14 15 16 17 18 19 20 21
  42. GLO-RIA enables semi-automatic quantification of RSA traits 11 12 13

    14 15 16 17 18 19 20 21 Count 0 45 90 Count 0 45 90 Count 0 45 90
  43. GLO-RIA enables semi-automatic quantification of RSA traits 11 12 13

    14 15 16 17 18 19 20 21 Angle Count 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90 0 45 90
  44. Low Light RSA (cm2) 0 100 200 300 400 Days

    After Sowing 10 20 30 40 50 23 DAS 33 DAS High Light
  45. Different accessions show distinct root traits including lateral root angle

  46. Different accessions show distinct root traits including lateral root angle

  47. Root system architecture is light regulated

  48. Root system architecture is light regulated

  49. Root system architecture is light regulated

  50. Root system architecture is light regulated

  51. Several degrees of water deficit can be induced

  52. Several degrees of water deficit can be induced

  53. Early withdrawal of water may affect lateral root emergence

  54. Early withdrawal of water may affect lateral root emergence

  55. Early withdrawal of water may affect lateral root emergence

  56. Early withdrawal of water may affect lateral root emergence

  57. + 24 h Early withdrawal of water may affect lateral

    root emergence
  58. If water deprivation starts later there is an induction of

    lateral root growth and a higher proliferation of tertiary roots
  59. Lateral root angle is affected differently in different ecotypes in

    response to water deprivation
  60. Root direction correlates with local soil moisture content WW WD

  61. 500 540 580 620 660 700 UBQ10:vLUC2 ACT2:PpyREo We have

    generated a golden gate ready collection of different luciferases Intensity 1005 1064 1123 1182 1241 1300 UBQ10:CBRo Intensity 1005 1064 1123 1182 1241 1300 500 540 580 620 660 700 UBQ10:LUC2o 500 540 580 620 660 700 UBQ10:CBGo LUC2o Maximum Intensity vLUC CBG99o PpyRE8o CBRo λ (nm)
  62. Plant-plant and plant-microbe interactions imaged using different luciferases

  63. Simultaneous imaging of root structure and gene is expression is

    possible Open 517-567 nm Merged ProACT2:PPyRE8o x ProZat12:LUC
  64. We can seamlessly integrate different types of information at the

    whole root system level Figure credit: Guillaume Lobet Rellán-Álvarez, Lobet & Dinneny 2015 In Press
  65. And then use this information to understand and model root

    architecture and function using functional structural models Adapted from Lobet et al 2015 PMID: 25614065 Root System Markup Language 1 2 3 4 5 Topology nested document structure Geometry polyline representation Functions associated with polyline - diameter - age - gene expression - … Low N High N Adapted from Rellán-Álvarez, Lobet & Dinneny 2015 In Press
  66. Soil Microbe Colonization Soil Properties Time Root Structure Gene Expression

    A multidimensional map of root responses to soil environmental cues
  67. Soil Microbe Colonization Soil Properties Time Root Structure Gene Expression

    A multidimensional map of root responses to soil environmental cues
  68. None
  69. Dinneny Lab Muh-Ching Yee Geng Yu Emilie Winfield Pierre-Luc Pradier

    Charlotte Trontin Thérèse LaRue Heike Lindner Guillaume Lobet Root Image Analysis and root modelling Jose Sebastian John Vogel Julin Maloof Other Species Cara Haney
  70. CINVESTAV

  71. Root system wide integration of environmental cues Phospholipid role in

    plant adaptation to low P and high altitude www.rrlab.org#projects
  72. Courtesy of Charlotte Trontin

  73. Towards an understanding of root system wide integration of environmental

    cues ubén Rellán Álvarez @rubenrellan rrellan@langebio.cinvestav.mx www.rrlab.org bit.ly/goo-roots-paper bit.ly/SMB-2015-slides www.rrlab.org/GLO-Roots