Cranos Williams

Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa

A proteomic-based predictive kinetic metabolic-flux model was developed for monolignol biosynthesis in Populus trichocarpa. Absolute quantities of all monolignol pathway proteins and 189 kinetic parameters were generated to construct the model, which was experimentally validated in transgenic P. trichocarpa and provides a comprehensive description of the monolignol biosynthetic pathway. We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants.

Systems Biology of Lignin Biosynthesis in Populus trichocarpa: Heteromeric 4-Coumaric Acid:Coenzyme A Ligase Protein Complex Formation, Regulation, and Numerical Modeling

This work shows that 4CL, an enzyme in monolignol biosynthesis, is found as a heterotetrameric complex of two isoforms in Populus trichocarpa. The activity of the heterotetramer can be described by a mathematical model that explains the effects of each isoform with mixtures of substrates and three types of inhibition, providing insights into the regulation of metabolic flux for this pathway. As a step toward predictive modeling of flux through the pathway of monolignol biosynthesis in stem differentiating xylem of Populus trichocarpa, we discovered that the two 4-coumaric acid:CoA ligase (4CL) isoforms, 4CL3 and 4CL5, interact in vivo and in vitro to form a heterotetrameric protein complex. This conclusion is based on laser microdissection, coimmunoprecipitation, chemical cross-linking, bimolecular fluorescence complementation, and mass spectrometry. The tetramer is composed of three subunits of 4CL3 and one of 4CL5. 4CL5 appears to have a regulatory role. This protein–protein interaction affects the direction and rate of metabolic flux for monolignol biosynthesis in P. trichocarpa. A mathematical model was developed for the behavior of 4CL3 and 4CL5 individually and in mixtures that form the enzyme complex. The model incorporates effects of mixtures of multiple hydroxycinnamic acid substrates, competitive inhibition, uncompetitive inhibition, and self-inhibition, along with characteristic of the substrates, the enzyme isoforms, and the tetrameric complex. Kinetic analysis of different ratios of the enzyme isoforms shows both inhibition and activation components, which are explained by the mathematical model and provide insight into the regulation of metabolic flux for monolignol biosynthesis by protein complex formation.

Monolignol Pathway 4-Coumaric Acid:Coenzyme A Ligases in Populus. trichocarpa: Novel Specificity, Metabolic Regulation, and Simulation of Coenzyme A Ligation Fluxes

Two 4-coumaric acid:CoA ligases regulate CoA flux with novel specificity. 4-Coumaric acid:coenzyme A ligase (4CL) is involved in monolignol biosynthesis for lignification in plant cell walls. It ligates coenzyme A (CoA) with hydroxycinnamic acids, such as 4-coumaric and caffeic acids, into hydroxycinnamoyl-CoA thioesters. The ligation ensures the activated state of the acid for reduction into monolignols. In Populus spp., it has long been thought that one monolignol-specific 4CL is involved. Here, we present evidence of two monolignol 4CLs, Ptr4CL3 and Ptr4CL5, in Populus trichocarpa. Ptr4CL3 is the ortholog of the monolignol 4CL reported for many other species. Ptr4CL5 is novel. The two Ptr4CLs exhibited distinct Michaelis-Menten kinetic properties. Inhibition kinetics demonstrated that hydroxycinnamic acid substrates are also inhibitors of 4CL and suggested that Ptr4CL5 is an allosteric enzyme. Experimentally validated flux simulation, incorporating reaction/inhibition kinetics, suggested two CoA ligation paths in vivo: one through 4-coumaric acid and the other through caffeic acid. We previously showed that a membrane protein complex mediated the 3-hydroxylation of 4-coumaric acid to caffeic acid. The demonstration here of two ligation paths requiring these acids supports this 3-hydroxylation function. Ptr4CL3 regulates both CoA ligation paths with similar efficiencies, whereas Ptr4CL5 regulates primarily the caffeic acid path. Both paths can be inhibited by caffeic acid. The Ptr4CL5-catalyzed caffeic acid metabolism, therefore, may also act to mitigate the inhibition by caffeic acid to maintain a proper ligation flux. A high level of caffeic acid was detected in stem-differentiating xylem of P. trichocarpa. Our results suggest that Ptr4CL5 and caffeic acid coordinately modulate the CoA ligation flux for monolignol biosynthesis.

Functional redundancy of the two 5-hydroxylases in monolignol biosynthesis of Populus trichocarpa: LC-MS/MS based protein quantification and metabolic flux analysis.

Flowering plants have syringyl and guaiacyl subunits in lignin in contrast to the guaiacyl lignin in gymnosperms. The biosynthesis of syringyl subunits is initiated by coniferaldehyde 5-hydroxylase (CAld5H). In Populus trichocarpa there are two closely related CAld5H enzymes (PtrCAld5H1 and PtrCAld5H2) associated with lignin biosynthesis during wood formation. We used yeast recombinant PtrCAld5H1 and PtrCAld5H2 proteins to carry out Michaelis–Menten and inhibition kinetics with LC–MS/MS based absolute protein quantification. CAld5H, a monooxygenase, requires a cytochrome P450 reductase (CPR) as an electron donor. We cloned and expressed three P. trichocarpa CPRs in yeast and show that all are active with both CAld5Hs. The kinetic analysis shows both CAld5Hs have essentially the same biochemical functions. When both CAld5Hs are coexpressed in the same yeast membranes, the resulting enzyme activities are additive, suggesting functional redundancy and independence of these two enzymes. Simulated reaction flux based on Michaelis–Menten kinetics and inhibition kinetics confirmed the redundancy and independence. Subcellular localization of both CAld5Hs as sGFP fusion proteins expressed in P. trichocarpa differentiating xylem protoplasts indicate that they are endoplasmic reticulum resident proteins. These results imply that during wood formation, 5-hydroxylation in monolignol biosynthesis of P. trichocarpa requires the combined metabolic flux of these two CAld5Hs to maintain adequate biosynthesis of syringyl lignin. The combination of genetic analysis, absolute protein quantitation-based enzyme kinetics, homologous CPR specificity, SNP characterization, and ER localization provides a more rigorous basis for a comprehensive systems understanding of 5-hydroxylation in lignin biosynthesis.

Hierarchical Modularization Of Biochemical Pathways Using Fuzzy-C Means Clustering

Biological systems that are representative of regulatory, metabolic, or signaling pathways can be highly complex. Mathematical models that describe such systems inherit this complexity. As a result, these models can often fail to provide a path toward the intuitive comprehension of these systems. More coarse information that allows a perceptive insight of the system is sometimes needed in combination with the model to understand control hierarchies or lower level functional relationships. In this paper, we present a method to identify relationships between components of dynamic models of biochemical pathways that reside in different functional groups. We find primary relationships and secondary relationships. The secondary relationships reveal connections that are present in the system, which current techniques that only identify primary relationships are unable to show. We also identify how relationships between components dynamically change over time. This results in a method that provides the hierarchy of the relationships among components, which can help us to understand the low level functional structure of the system and to elucidate potential hierarchical control. As a proof of concept, we apply the algorithm to the epidermal growth factor signal transduction pathway, and to the C3 photosynthesis pathway. We identify primary relationships among components that are in agreement with previous computational decomposition studies, and identify secondary relationships that uncover connections among components that current computational approaches were unable to reveal.

4-Coumaroyl and Caffeoyl Shikimic Acids Inhibit 4-Coumaric Acid:Coenzyme A Ligases and Modulate Metabolic Flux for 3-Hydroxylation in Monolignol Biosynthesis of Populus trichocarpa

Downregulation of 4-coumaric acid:coenzyme A ligase (4CL) can reduce lignin content in a number of plant species. In lignin precursor (monolignol) biosynthesis during stem wood formation in Populus trichocarpa, two enzymes, Ptr4CL3 and Ptr4CL5, catalyze the coenzyme A (CoA) ligation of 4-coumaric acid to 4-coumaroyl-CoA and caffeic acid to caffeoyl-CoA. CoA ligation of 4-coumaric acid is essential for the 3-hydroxylation of 4-coumaroyl shikimic acid. This hydroxylation results from sequential reactions of 4-hydroxycinnamoyl-CoA:shikimic acid hydroxycinnamoyl transferases (PtrHCT1 and PtrHCT6) and 4-coumaric acid 3-hydroxylase 3 (PtrC3H3). Alternatively, 3-hydroxylation of 4-coumaric acid to caffeic acid may occur through an enzyme complex of cinnamic acid 4-hydroxylase 1 and 2 (PtrC4H1 and PtrC4H2) and PtrC3H3. We found that 4-coumaroyl and caffeoyl shikimic acids are inhibitors of Ptr4CL3 and Ptr4CL5. 4-Coumaroyl shikimic acid strongly inhibits the formation of 4-coumaroyl-CoA and caffeoyl-CoA. Caffeoyl shikimic acid inhibits only the formation of 4-coumaroyl-CoA. 4-Coumaroyl and caffeoyl shikimic acids both act as competitive and uncompetitive inhibitors. Metabolic flux in wild-type and PtrC3H3 downregulated P. trichocarpa transgenics has been estimated by absolute protein and metabolite quantification based on liquid chromatography-tandem mass spectrometry, mass action kinetics, and inhibition equations. Inhibition by 4-coumaroyl and caffeoyl shikimic acids may play significant regulatory roles when these inhibitors accumulate.

Set membership experimental design for biological systems

BackgroundExperimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources.ResultsIn this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained.ConclusionsThe practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models.

Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells

Significance We developed a computational pipeline that uses gene expression datasets for inferring relationships among genes and predicting their importance. We showed that the capacity of our pipeline to integrate spatial and temporal transcriptional datasets improves the performance of inference algorithms. The combination of this pipeline with Arabidopsis stem cell-specific data resulted in networks that capture the regulations of stem cell-enriched genes in the stem cells and throughout root development. Our combined approach of molecular biology, computational biology, and mathematical biology, led to successful findings of factors that could play important roles in stem cell regulation and, in particular, quiescent center function. Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.

More than meets the eye: Emergent properties of transcription factors networks in Arabidopsis ☆

Uncovering and mathematically modeling Transcription Factor Networks (TFNs) are the first steps in engineering plants with traits that are better equipped to respond to changing environments. Although several plant TFNs are well known, the framework for systematically modeling complex characteristics such as switch-like behavior, oscillations, and homeostasis that emerge from them remain elusive. This review highlights literature that provides, in part, experimental and computational techniques for characterizing TFNs. This review also outlines methodologies that have been used to mathematically model the dynamic characteristics of TFNs. We present several examples of TFNs in plants that are involved in developmental and stress response. In several cases, advanced algorithms capture or quantify emergent properties that serve as the basis for robustness and adaptability in plant responses. Increasing the use of mathematical approaches will shed new light on these regulatory properties that control plant growth and development, leading to mathematical models that predict plant behavior. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Region of attraction estimation of biological continuous Boolean models

Quantitative analysis of biological systems has become an increasingly important research field as scientists look to solve current day health and environmental problems. The development of modeling and model analysis approaches that are specifically geared toward biological processes is a rapidly growing research area. Continuous approximations of Boolean models, for example, have been identified as a viable method for modeling such systems. This is because they are capable of generating dynamic models of biochemical pathways using inferred dependency relationships between components. The resulting nonlinear equations and therefore nonlinear dynamics, however, can present a challenge for most system analysis approaches such as region of attraction (ROA) estimation. Continued progress in the area of biosystems modeling will require that computational techniques used to analyze simple nonlinear systems can still be applied to nonlinear equations typically used to model the dynamics associated with biological processes. In this paper, we assess the applicability of a state of the art ROA estimation technique based on interval arithmetic to a subnetwork of the Rb-E2F signaling pathway modeled using continuous Boolean functions. We show that this method can successfully be used to provide an estimate of the ROA for dynamic models described using Hillcube continuous Boolean approximations.