Longyun Guo 1 John Morgan 12 1 Department of Biochemistry 2 Davidson School of Chemical Engineering Purdue University May 2 2019 Background Lignin is an essential biopolymer in plant secondary cell walls ID: 910713
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Slide1
Combining gene regulatory network and kinetic modeling of lignin biosynthesis in Arabidopsis
Longyun Guo1John Morgan1,21Department of Biochemistry2Davidson School of Chemical EngineeringPurdue UniversityMay 2, 2019
Slide2Background
Lignin is an essential biopolymer in plant secondary cell walls Current knowledge of lignin biosynthesis in ArabidopsisDevelopment of a multilayer kinetic model for lignin biosynthesis in ArabidopsisGene regulatory network reconstructionKinetic modeling of the lignin biosynthesisApplications of the multilayer kinetic model
Flux maps of lignin biosynthesis in different genetic backgrounds
Lignin phenotypes can be predicted by the modelSummary
2
Outline
Rao, X., & Dixon, R. A. (2018).
Frontiers in plant science, 9, 399.
Slide33
Lignin is an essential biopolymer in secondary cell wall for plant normal growthLi, M. et al. (2016) Frontiers in chemistry, 4, 45.
Rao, X., & Dixon, R. A. (2018).
Frontiers in plant science, 9, 399.
Slide451
transcription factors with hundreds of regulatory interactions4 post-transcriptional regulatory proteins12 enzyme families catalyzing 34 chemical reactions4
Current knowledge of lignin biosynthesis in Arabidopsis
Slide55
Different types of information are integrated together to formulate a multilayer kinetic model
4CL1
MED5a/b
MYB20
SND1
Reference
Source
RNA-seq data
Current study
Regulation evidence
Literature + Estimated
None
Estimated
Lignin deposition rate
Current study + Estimated
Targeted metabolome
Current study
In vitro
assays
Literature + Estimated
Slide6Activation with direct evidence
Inhibition with direct evidenceDegradation with direct evidenceActivation with indirect evidenceInhibition with indirect evidence
6
Reported regulatory interactions of lignin biosynthesis in Arabidopsis
438
transcriptional regulatory interactions and
12
post-transcriptional regulatory interactions between
72
genes
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
Slide7GRN kinetic model
Translation
Transcription
where
Construction of a gene regulatory network (GRN) with linear differential equations
7
mRNA decay rate from: Sorenson, R. S. et al. (2018)
PNAS
,
115
, E1485-E1494.
Slide88
Parameter estimation for GRN by iteratively solving linear equations
Optimization Algorithm
RNA-seq datasets
from Arabidopsis basal stems with
nine independent genetic backgrounds
Slide9Parameters of the GRN were estimated with RNA-seq datasets from nine genetic backgrounds
ActivationInhibition 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
9
Slide1010
A hierarchical model was developed by combining the GRN and a kinetic model of lignin biosynthesis in Arabidopsis
Lignin flux, V
max
, metabolome datasets
from nine genetic backgrounds in Arabidopsis basal stems
Markov Chain Monte Carlo sampling for a global parameter search
Haario, Heikki, et al.
Statistics and computing
16.4 (2006): 339-354.
+
Slide1111
Lignin Data For TrainingLignin Data For Validation
Measured lignin subunit flux (nmol g FW
-1 min-1)
Predicted
(nmol g FW
-1 min
-1)A hierarchical model was developed combining the GRN and the kinetic model of lignin biosynthesis in Arabidopsis
Slide1212
Flux map of lignin biosynthesis in wild type Arabidopsis using the hierarchical model Unit: nmol g FW-1 min-1
V
max For Training
(nmol g FW
-1 min-1
)
Measured
Predicted
Lignin Deposition Rate
Slide13Flux map of lignin biosynthesis in cse-2
Arabidopsis using the hierarchical model Unit: nmol g FW-1 min-113
Slide14Lignin deposition (nmol g
-1 FW min-1)S/G ratio
The synthesis of each gene was adjusted with a factor of zero for knockout, 0.5 for knockdown, and 2 for overexpression, individually.
Lignin phenotypes of various genetically perturbated lines can be predicted by the hierarchical model
14
Slide1515
SummaryLarge scale hierarchical modeling combining different types of information spanning transcriptomic and metabolomic studiesSubstrate competition determines key flux partitions in lignin biosynthesisSaturation of F5H activity ensures the robustness of S lignin biosynthesisHierarchical model can be used to generate in silico predictions for various genetic perturbations in lignin biosynthesis
Slide16Acknowledgements
AdvisorProf. John Morgan
16
Collaborators Prof. Clint Chapple
Prof. Natalia DudarevaDr. Rohit Jaini
Dr. Peng Wang
Slide17Questions?
Thank you for your attention!17