Assessing the impact of the 4CL enzyme complex on the robustness of monolignol biosynthesis using metabolic pathway analysis

<div><p>Lignin is a polymer present in the secondary cell walls of all vascular plants. It is a known barrier to pulping and the extraction of high-energy sugars from cellulosic biomass. The challenge faced with predicting outcomes of transgenic plants with reduced lignin is due in part to the presence of unique protein-protein interactions that influence the regulation and metabolic flux in the pathway. Yet, it is unclear why certain plants have evolved to create these protein complexes. In this study, we use mathematical models to investigate the role that the protein complex, formed specifically between Ptr4CL3 and Ptr4CL5 enzymes, have on the monolignol biosynthesis pathway. The role of this Ptr4CL3-Ptr4CL5 enzyme complex on the steady state flux distribution was quantified by performing Monte Carlo simulations. The effect of this complex on the robustness and the homeostatic properties of the pathway were identified by performing sensitivity and stability analyses, respectively. Results from these robustness and stability analyses suggest that the monolignol biosynthetic pathway is resilient to mild perturbations in the presence of the Ptr4CL3-Ptr4CL5 complex. Specifically, the presence of Ptr4CL3-Ptr4CL5 complex increased the stability of the pathway by 22%. The robustness in the pathway is maintained due to the presence of multiple enzyme isoforms as well as the presence of alternative pathways resulting from the presence of the Ptr4CL3-Ptr4CL5 complex.</p></div

Contour plot showing the variation of steady state flux (V₇) as a function of Ptr4CL3 and Ptr4CL5 concentration in the presence of a complex.

<p>The axis values represents the percentage of the protein concentration as a function of the wild type concentration. The color bar represents the flux values (μM/min).</p

The first order sensitivity index for monolignol flux with respect to Ptr4CL3 and Ptr4CL5 concentrations for the model without the complex.

<p>The first order sensitivity index for monolignol flux with respect to Ptr4CL3 and Ptr4CL5 concentrations for the model without the complex.</p

Cumulative distribution function plot of Eigenvalues for the model with and without the complex under WT enzyme concentrations.

<p>Cumulative distribution function plot of Eigenvalues for the model with and without the complex under WT enzyme concentrations.</p

Contour plot showing the variation of steady state flux (V₇) as a function of Ptr4CL3 and Ptr4CL5 concentration in the absence of a complex.

<p>The axis values represents the percentage of the protein concentration as a function of the wild type concentration. The color bar represents the flux values (μM/min).</p

Steady state flux pattern observed for the model without the complex WT enzyme concentrations.

<p>Colored arrows represent the magnitude of flux and the colors can be mapped to their flux values with the color bar.</p

Steady state flux pattern observed for the model with the complex under WT enzyme concentrations colored arrows represent the magnitude of the flux as shown in the color bar.

<p>Steady state flux pattern observed for the model with the complex under WT enzyme concentrations colored arrows represent the magnitude of the flux as shown in the color bar.</p

Heat Generation and Accumulation in Municipal Solid Waste Landfills

There have been reports of North American landfills that are experiencing temperatures in excess of 80–100 °C. However, the processes causing elevated temperatures are not well understood. The objectives of this study were to develop a model to describe the generation, consumption and release of heat from landfills, to predict landfill temperatures, and to understand the relative importance of factors that contribute to heat generation and accumulation. Modeled heat sources include energy from aerobic and anaerobic biodegradation, anaerobic metal corrosion, ash hydration and carbonation, and acid–base neutralization. Heat removal processes include landfill gas convection, infiltration, leachate collection, and evaporation. The landfill was treated as a perfectly mixed batch reactor. Model predictions indicate that both anaerobic metal corrosion and ash hydration/carbonation contribute to landfill temperatures above those estimated from biological reactions alone. Exothermic pyrolysis of refuse, which is hypothesized to be initiated due to a local accumulation of heat, was modeled empirically to illustrate its potential impact on heat generation

Expression validation of predicted targets in mutant regulator backgrounds.

<p>Root tissue was collected from seedlings grown 4 days on iron sufficient media and transferred to iron deficient media for 3 days. Expression values are normalized to <i><i>β</i>-tubulin</i> and to WT (Col-0) expression for each gene. Error bars indicate ±SEM (n = 4). Mutant backgrounds are (A)<i>obp4-1</i>, (B)<i>wrky57-3</i>, (C)<i>etf9-1</i>, (D)<i>col4-1</i>, (E)<i>asil2-1</i>, and (F)<i>myb55-1</i>. Asterisk indicates significant difference from WT (Student’s t-test, <i>p</i> < 0.05).</p

Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the <i>Arabidopsis thaliana</i> Iron Deficiency Response

<div><p>Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the <i>Arabidopsis thaliana</i> iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.</p></div