Computational inference of the structure and regulation of the lignin pathway in Panicum virgatum

Modeling lignin biosynthesis: a pathway to renewable chemicals

Plant biomass contains lignin that can be converted into high-value-added chemicals, fuels, and materials. The precise genetic manipulation of lignin content and composition in plant cells offers substantial environmental and economic benefits. However, the intricate regulatory mechanisms governing lignin formation challenge the development of crops with specific lignin profiles. Mathematical models and computational simulations have recently been employed to gain fundamental understanding of the metabolism of lignin and related phenolic compounds. This review article discusses the strategies used for modeling plant metabolic networks, focusing on the application of mathematical modeling for flux network analysis in monolignol biosynthesis. Furthermore, we highlight how current challenges might be overcome to optimize the use of metabolic modeling approaches for developing lignin-engineered plants.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via ¹³C-metabolic flux analysis (¹³C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated ¹³C labeling data were used with ¹³C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Enzyme Complexes of Ptr4CL and PtrHCT Modulate Co-enzyme A Ligation of Hydroxycinnamic Acids for Monolignol Biosynthesis in Populus trichocarpa

Co-enzyme A (CoA) ligation of hydroxycinnamic acids by 4-coumaric acid:CoA ligase (4CL) is a critical step in the biosynthesis of monolignols. Perturbation of 4CL activity significantly impacts the lignin content of diverse plant species. In Populus trichocarpa, two well-studied xylem-specific Ptr4CLs (Ptr4CL3 and Ptr4CL5) catalyze the CoA ligation of 4-coumaric acid to 4-coumaroyl-CoA and caffeic acid to caffeoyl-CoA. Subsequently, two 4-hydroxycinnamoyl-CoA:shikimic acid hydroxycinnamoyl transferases (PtrHCT1 and PtrHCT6) mediate the conversion of 4-coumaroyl-CoA to caffeoyl-CoA. Here, we show that the CoA ligation of 4-coumaric and caffeic acids is modulated by Ptr4CL/PtrHCT protein complexes. Downregulation of PtrHCTs reduced Ptr4CL activities in the stem-differentiating xylem (SDX) of transgenic P. trichocarpa. The Ptr4CL/PtrHCT interactions were then validated in vivo using biomolecular fluorescence complementation (BiFC) and protein pull-down assays in P. trichocarpa SDX extracts. Enzyme activity assays using recombinant proteins of Ptr4CL and PtrHCT showed elevated CoA ligation activity for Ptr4CL when supplemented with PtrHCT. Numerical analyses based on an evolutionary computation of the CoA ligation activity estimated the stoichiometry of the protein complex to consist of one Ptr4CL and two PtrHCTs, which was experimentally confirmed by chemical cross-linking using SDX plant protein extracts and recombinant proteins. Based on these results, we propose that Ptr4CL/PtrHCT complexes modulate the metabolic flux of CoA ligation for monolignol biosynthesis during wood formation in P. trichocarpa.

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa

Understanding the mechanisms behind lignin formation is an important research area with significant implications for the bioenergy and biomaterial industries. Computational models are indispensable tools for understanding this complex process. Models of the monolignol pathway in Populus trichocarpa and other plants have been developed to explore how transgenic modifications affect important bioenergy traits. Many of these models, however, only capture one level of biological organization and are unable to capture regulation across multiple biological scales. This limits their ability to predict how gene modification strategies will impact lignin and other wood properties. While the first multiscale model of lignin biosynthesis in P. trichocarpa spanned the transcript, protein, metabolic, and phenotypic layers, it did not account for cross-regulatory influences that could impact abundances of untargeted monolignol transcripts and proteins. Here, we present a multiscale model incorporating these cross-regulatory influences for predicting lignin and wood traits from transgenic knockdowns of the monolignol genes. The three main components of this multiscale model are (1) a transcript-protein model capturing cross-regulatory influences, (2) a kinetic-based metabolic model, and (3) random forest models relating the steady state metabolic fluxes to 25 physical traits. We demonstrate that including the cross-regulatory behavior results in smaller predictive error for 23 of the 25 traits. We use this multiscale model to explore the predicted impact of novel combinatorial knockdowns on key bioenergy traits, and identify the perturbation of PtrC3H3 and PtrCAld5H1&2 monolignol genes as a candidate strategy for increasing saccharification efficiencies while reducing negative impacts on wood density and height.

Overexpression of PtrMYB121 Positively Regulates the Formation of Secondary Cell Wall in Arabidopsis thaliana

MYB transcription factors have a wide range of functions in plant growth, hormone signaling, salt, and drought tolerances. In this study, two homologous transcription factors, PtrMYB55 and PtrMYB121, were isolated and their functions were elucidated. Tissue expression analysis revealed that PtrMYB55 and PtrMYB121 had a similar expression pattern, which had the highest expression in stems. Their expression continuously increased with the growth of poplar, and the expression of PtrMYB121 was significantly upregulated in the process. The full length of PtrMYB121 was 1395 bp, and encoded protein contained 464 amino acids including conserved R2 and R3 MYB domains. We overexpressed PtrMYB121 in Arabidopsis thaliana, and the transgenic lines had the wider xylem as compared with wild-type Arabidopsis. The contents of cellulose and lignin were obviously higher than those in wild-type materials, but there was no significant change in hemicellulose. Quantitative real-time PCR demonstrated that the key enzyme genes regulating the synthesis of lignin and cellulose were significantly upregulated in the transgenic lines. Furthermore, the effector-reporter experiment confirmed that PtrMYB121 bound directly to the promoters of genes relating to the synthesis of lignin and cellulose. These results suggest that PtrMYB121 may positively regulate the formation of secondary cell wall by promoting the synthesis of lignin and cellulose.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa

Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.

RNAi-mediated silencing of pinoresinol lariciresinol reductase in Linum album hairy roots alters the phenolic accumulation in response to fungal elicitor

Lignans are diphenolic compounds produced in plants via coupling of two coniferyl alcohol molecules with the aid of a dirigent protein to form pinoresinol (PINO). The latter is reduced via lariciresinol (LARI) to secoisolariciresinol by the bifunctional pinoresinol-lariciresinol reductase (PLR). In this study, we clarified the consequences of altered lignan biosynthesis on amino acids, phenolics compounds and lignin in the hairy roots of Linum album with an ihpRNAi construct to silence PLR gene expression. Down-regulation of PLR-La1 resulted in up to an 8.3 and 3.3-time increased PINO and LARI content respectively, and reduced levels of podophyllotoxin (PTOX) and 6-methoxy podophyllotoxin (6-MPTOX). By Suppression of PLR expression, the metabolites belonging to shikimate and phenylpropanoid pathways are conducted to phenolic compounds and lignin accumulations. Although PINO and LARI were induced in response to fungal elicitor, the accumulation of PTOX and 6-MPTOX did not occur in PLR down-regulated roots. Our result also demonstrated variation in amino acids, phenolic compounds and lignin levels in presence of the fungal elicitation in PLR down regulated-roots. This data assert the accumulation of aryltetralin lignans in interactions with plant pathogens by PLR activity and the importance this enzyme for defense against pathogens in L. album.

A dynamic model of lignin biosynthesis in Brachypodium distachyon

BACKGROUND

Lignin is a crucial molecule for terrestrial plants, as it offers structural support and permits the transport of water over long distances. The hardness of lignin reduces plant digestibility by cattle and sheep; it also makes inedible plant materials recalcitrant toward the enzymatic fermentation of cellulose, which is a potentially valuable substrate for sustainable biofuels. Targeted attempts to change the amount or composition of lignin in relevant plant species have been hampered by the fact that the lignin biosynthetic pathway is difficult to understand, because it uses several enzymes for the same substrates, is regulated in an ill-characterized manner, may operate in different locations within cells, and contains metabolic channels, which the plant may use to funnel initial substrates into specific monolignols.

RESULTS

We propose a dynamic mathematical model that integrates various datasets and other information regarding the lignin pathway in Brachypodium distachyon and permits explanations for some counterintuitive observations. The model predicts the lignin composition and label distribution in a BdPTAL knockdown strain, with results that are quite similar to experimental data.

CONCLUSION

Given the present scarcity of available data, the model resulting from our analysis is presumably not final. However, it offers proof of concept for how one may design integrative pathway models of this type, which are necessary tools for predicting the consequences of genomic or other alterations toward plants with lignin features that are more desirable than in their wild-type counterparts.

Mathematical models of lignin biosynthesis

BACKGROUND

Lignin is a natural polymer that is interwoven with cellulose and hemicellulose within plant cell walls. Due to this molecular arrangement, lignin is a major contributor to the recalcitrance of plant materials with respect to the extraction of sugars and their fermentation into ethanol, butanol, and other potential bioenergy crops. The lignin biosynthetic pathway is similar, but not identical in different plant species. It is in each case comprised of a moderate number of enzymatic steps, but its responses to manipulations, such as gene knock-downs, are complicated by the fact that several of the key enzymes are involved in several reaction steps. This feature poses a challenge to bioenergy production, as it renders it difficult to select the most promising combinations of genetic manipulations for the optimization of lignin composition and amount.

RESULTS

Here, we present several computational models than can aid in the analysis of data characterizing lignin biosynthesis. While minimizing technical details, we focus on the questions of what types of data are particularly useful for modeling and what genuine benefits the biofuel researcher may gain from the resulting models. We demonstrate our analysis with mathematical models for black cottonwood (Populus trichocarpa), alfalfa (Medicago truncatula), switchgrass (Panicum virgatum) and the grass Brachypodium distachyon.

CONCLUSIONS

Despite commonality in pathway structure, different plant species show different regulatory features and distinct spatial and topological characteristics. The putative lignin biosynthes pathway is not able to explain the plant specific laboratory data, and the necessity of plant specific modeling should be heeded.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

Stepwise inference of likely dynamic flux distributions from metabolic time series data

MOTIVATION

Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution.

RESULTS

We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method.

AVAILABILITY AND IMPLEMENTATION

The computational model was implemented in MATLAB (version R2014a, The MathWorks, Natick, MA). The source code is available at https://github.gatech.edu/VoitLab/Stepwise-Inference-of-Likely-Dynamic-Flux-Distributions and www.bst.bme.gatech.edu/research.php .

CONTACT

mojdeh@gatech.edu or eberhard.voit@bme.gatech.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development of Lignocellulosic Biorefinery Technologies: Recent Advances and Current Challenges

Recent developments of the biorefinery concept are described within this review, which focuses on the efforts required to make the lignocellulosic biorefinery a sustainable and economically viable reality. Despite the major research and development endeavours directed towards this goal over the past several decades, the integrated production of biofuel and other bio-based products still needs to be optimized from both technical and economical perspectives. This review will highlight recent progress towards the optimization of the major biorefinery processes, including biomass pretreatment and fractionation, saccharification of sugars, and conversion of sugars and lignin into fuels and chemical precursors. In addition, advances in genetic modification of biomass structure and composition for the purpose of enhancing the efficacy of conversion processes, which is emerging as a powerful tool for tailoring biomass fated for the biorefinery, will be overviewed. The continual improvement of these processes and their integration in the format of a modern biorefinery is paving the way for a sustainable bio-economy which will displace large portions of petroleum-derived fuels and chemicals with renewable substitutes.