Combining gene regulatory network and kinetic modeling of lignin biosynthesis in Arabidopsis

1. Combining gene regulatory network and kinetic modeling of lignin biosynthesis in Arabidopsis Longyun Guo1 John Morgan1,2 1Department of Biochemistry 2Davidson School of Chemical Engineering Purdue University May 2, 2019

2. Outline Background Lignin is an essential biopolymer in plant secondary cell walls Current knowledge of lignin biosynthesis in Arabidopsis Development of a multilayer kinetic model for lignin biosynthesis in Arabidopsis Gene regulatory network reconstruction Kinetic modeling of the lignin biosynthesis Applications of the multilayer kinetic model Flux maps of lignin biosynthesis in different genetic backgrounds Lignin phenotypes can be predicted by the model Summary Rao, X., & Dixon, R. A. (2018). Frontiers in plant science, 9, 399.

3. Lignin is an essential biopolymer in secondary cell wall for plant normal growth Rao, X., & Dixon, R. A. (2018). Frontiers in plant science, 9, 399. Li, M. et al. (2016) Frontiers in chemistry, 4, 45.

4. Current knowledge of lignin biosynthesis in Arabidopsis • 51 transcription factors with hundreds of regulatory interactions • 4 post-transcriptional regulatory proteins • 12 enzyme families catalyzing 34 chemical reactions

5. Different types of information are integrated together to formulate a multilayer kinetic model SND1 MED5a/b MYB20 4CL1

6. Reported regulatory interactions of lignin biosynthesis in Arabidopsis 438 transcriptional regulatory interactions and 12 post-transcriptional regulatory interactions between 72 genes Activation with direct evidence Inhibition with direct evidence Degradation with direct evidence Activation with indirect evidence Inhibition with indirect evidence Direct experimental evidence for presence of interaction: Steroid receptor-based inducible system Glucocorticoid receptor (GR)-mediated post-translational inducible system In vivo chromatin immunoprecipitation Electrophoretic mobility shift assay Indirect experimental evidence for presence of interaction: Transfection assay with GUS reporter Histochemical GUS staining assay Expression of target genes in transcription factor perturbed lines

7. Construction of a gene regulatory network (GRN) with linear differential equations Transcription GRN kinetic model where Translation mRNA decay rate from: Sorenson, R. S. et al. (2018) PNAS, 115, E1485-E1494.

8. Parameter estimation for GRN by iteratively solving linear equations Optimization Algorithm RNA-seq datasets from Arabidopsis basal stems with nine independent genetic backgrounds

9. Parameters of the GRN were estimated with RNA-seq datasets from nine genetic backgrounds Activation Inhibition Training: WT, ref3-2, ref3-3, pal1 pal2, 4cl1, cse-2, fah1, ref8 fah1 SmF5H, med5a/b ref8, med5a/b ref2 Validation: cadC cadD, F5H_ox, med5a/b

10. A hierarchical model was developed by combining the GRN and a kinetic model of lignin biosynthesis in Arabidopsis Markov Chain Monte Carlo sampling for a global parameter search + Haario, Heikki, et al. Statistics and computing 16.4 (2006): 339-354. Lignin flux, Vmax, metabolome datasets from nine genetic backgrounds in Arabidopsis basal stems

11. A hierarchical model was developed combining the GRN and the kinetic model of lignin biosynthesis in Arabidopsis Lignin Data For Validation Lignin Data For Training Predicted (nmol g FW-1 min-1) Measured lignin subunit flux (nmol g FW-1 min-1)

12. Flux map of lignin biosynthesis in wild type Arabidopsis using the hierarchical model Unit: nmol g FW-1 min-1 Lignin Deposition Rate Vmax For Training Measured Predicted (nmol g FW-1 min-1)

13. Flux map of lignin biosynthesis in cse-2 Arabidopsis using the hierarchical model Unit: nmol g FW-1 min-1

14. Lignin phenotypes of various genetically perturbated lines can be predicted by the hierarchical model S/G ratio Lignin deposition (nmol g-1 FW min-1) The synthesis of each gene was adjusted with a factor of zero for knockout, 0.5 for knockdown, and 2 for overexpression, individually.

15. Summary Large scale hierarchical modeling combining different types of information spanning transcriptomic and metabolomic studies • Substrate competition determines key flux partitions in lignin biosynthesis • Saturation of F5H activity ensures the robustness of S lignin biosynthesis • Hierarchical model can be used to generate in silico predictions for various genetic perturbations in lignin biosynthesis

16. Acknowledgements Collaborators Prof. Clint Chapple Prof. Natalia Dudareva Dr. Rohit Jaini Dr. Peng Wang Advisor Prof. John Morgan

17. Questions? Thank you for your attention!