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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism allows 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 current research using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13 C-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.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

The components of crop productivity: measuring and modeling plant metabolism

Several investigators in the CELSS program have demonstrated that crop plants can be remarkably productive in optimal environments where plants are limited only by incident radiation. Radiation use efficiencies of 0.4 to 0.7 g biomass per mol of incident photons have been measured for crops in several laboratories. Some early published values for radiation use efficiency (1 g mol-1) were inflated due to the effect of side lighting. Sealed chambers are the basic research module for crop studies for space. Such chambers allow the measurement of radiation and CO2 fluxes, thus providing values for three determinants of plant growth: radiation absorption, photosynthetic efficiency (quantum yield), and respiration efficiency (carbon use efficiency). Continuous measurement of each of these parameters over the plant life cycle has provided a blueprint for daily growth rates, and is the basis for modeling crop productivity based on component metabolic processes. Much of what has been interpreted as low photosynthetic efficiency is really the result of reduced leaf expansion and poor radiation absorption. Measurements and models of short-term (minutes to hours) and long-term (days to weeks) plant metabolic rates have enormously improved our understanding of plant environment interactions in ground-based growth chambers and are critical to understanding plant responses to the space environment.

NASA Discipline Life Support Systems↗
DOE OSTI2023

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13 C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was 169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST–MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast–growing algae, and produced a synergistic multi–organ flux map that is a first in MFA for any biological system. Finally, these insights could not be elucidated through other approaches and show the potential of INST–MFA to correct an oversimplified understanding of plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A KBase Case Study on Genome-wide Transcriptomics and Plant Primary Metabolism in Sorghum

A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by allowing them to upload private or public data into the KBase Narrative Interface, empowering them to analyze this data using a rich, extensible array of computational and data-analytics tools, and allows them to securely share scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes that are differentially expressed during drought-stress in Sorghum bicolor, which is an important sustainable production crop plant. We then show how to use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sorghum bicolor Core Metabolism Model

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

Feedstock Production↗
DOE OSTI2022

An automated workflow that generates atom mappings for large‐scale metabolic models and its application to Arabidopsis thaliana

SUMMARY Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large‐scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large‐scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana . We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)‐containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N‐MFA studies of A. thaliana . The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph‐theoretic structural analyses with large‐scale metabolic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Plant terpene specialized metabolism: complex networks or simple linear pathways?

From the perspectives of pathway evolution, discovery and engineering of plant specialized metabolism, the nature of the biosynthetic routes represents a critical aspect. Classical models depict biosynthesis typically from an end-point angle and as linear, for example, connecting central and specialized metabolism. As the number of functionally elucidated routes increased, the enzymatic foundation of complex plant chemistries became increasingly well understood. The perception of linear pathway models has been severely challenged. With a focus on plant terpenoid specialized metabolism, we review here illustrative examples supporting that plants have evolved complex networks driving chemical diversification. The completion of several diterpene, sesquiterpene and monoterpene routes shows complex formation of scaffolds and their subsequent functionalization. These networks show that branch points, including multiple sub-routes, mean that metabolic grids are the rule rather than the exception. This concept presents significant implications for biotechnological production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Kinetic modeling of anaerobic degradation of plant-derived aromatic mixtures by Rhodopseudomonas palustris

Abstract Rhodopseudomonas palustris is a model microorganism for studying the anaerobic metabolism of aromatic compounds. While it is well documented which aromatics can serve as sole organic carbon sources, co-metabolism of other aromatics is poorly understood. This study used kinetic modeling to analyze the simultaneous degradation of aromatic compounds present in corn stover hydrolysates and model the co-metabolism of aromatics not known to support growth of R. palustris as sole organic substrates. The simulation predicted that p -coumaroyl amide and feruloyl amide were hydrolyzed to p -coumaric acid and ferulic acid, respectively, and further transformed via p -coumaroyl-CoA and feruloyl-CoA. The modeling also suggested that metabolism of p -hydroxyphenyl aromatics was slowed by substrate inhibition, whereas the transformation of guaiacyl aromatics was inhibited by their p -hydroxyphenyl counterparts. It also predicted that substrate channeling may occur during degradation of p -coumaroyl-CoA and feruloyl-CoA, resulting in no detectable accumulation of p -hydroxybenzaldehyde and vanillin, during the transformation of these CoA ligated compounds to p- hydroxybenzoic acid and vanillic acid, respectively. While the simulation correctly represented the known transformation of p -hydroxybenzoic acid via the benzoyl-CoA pathway, it also suggested co-metabolism of vanillic acid and syringic acid, which are known not to serve as photoheterotrophic growth substrate for R. palustris .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metabolic engineering of Caldicellulosiruptor bescii for 2,3-butanediol production from unpretreated lignocellulosic biomass and metabolic strategies for improving yields and titers

ABSTRACT The platform chemical 2,3-butanediol (2,3-BDO) is used to derive products, such as 1,3-butadiene and methyl ethyl ketone, for the chemical and fuel production industries. Efficient microbial 2,3-BDO production at industrial scales has not been achieved yet for various reasons, including product inhibition to host organisms, mixed stereospecificity in product formation, and dependence on expensive substrates (i.e., glucose). In this study, we explore engineering of a 2,3-BDO pathway inCaldicellulosiruptor bescii, an extremely thermophilic (optimal growth temperature = 78°C) and anaerobic bacterium that can break down crystalline cellulose and hemicellulose into fermentable C 5 and C 6 sugars. In addition, C. besciigrows on unpretreated plant biomass, such as switchgrass. Biosynthesis of 2,3-BDO involves three steps: two molecules of pyruvate are condensed into acetolactate; acetolactate is decarboxylated to acetoin, and finally, acetoin is reduced to 2,3-BDO.C. besciinatively produces acetoin; therefore, in order to complete the 2,3-BDO biosynthetic pathway,C. besciiwas engineered to produce a secondary alcohol dehydrogenase (sADH) to catalyze the final step. Two previously characterized, thermostable sADH enzymes with high affinity for acetoin, one from a bacterium and one from an archaeon, were tested independently. When either sADH was present inC. bescii,the recombinant strains were able to produce up to 2.5-mM 2,3-BDO from crystalline cellulose and xylan and 0.2-mM 2,3-BDO directly from unpretreated switchgrass. This serves as the basis for higher yields and productivities, and to this end, limiting factors and potential genetic targets for further optimization were assessed using the genome-scale metabolic model ofC. bescii. IMPORTANCE Lignocellulosic plant biomass as the substrate for microbial synthesis of 2,3-butanediol is one of the major keys toward cost-effective bio-based production of this chemical at an industrial scale. However, deconstruction of biomass to release the sugars for microbial growth currently requires expensive thermochemical and enzymatic pretreatments. In this study, the thermo-cellulolytic bacteriumCaldicellulosiruptor besciiwas successfully engineered to produce 2,3-butanediol from cellulose, xylan, and directly from unpretreated switchgrass. Genome-scale metabolic modeling ofC. besciiwas applied to adjust carbon and redox fluxes to maximize productivity of 2,3-butanediol, thereby revealing bottlenecks that require genetic modifications.

Biotechnology & Applied Microbiology↗
DOE OSTI2022

Genome-scale modelling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Reconfiguring Plant Metabolism for Biodegradable Plastic Production

For decades, plants have been the subject of genetic engineering to synthesize novel, value-added compounds. Polyhydroxyalkanoates (PHAs), a large class of biodegradable biopolymers naturally synthesized in eubacteria, are among the novel products that have been introduced to make use of plant acetyl-CoA metabolic pathways. It was hoped that renewable PHA production would help address environmental issues associated with the accumulation of nondegradable plastic wastes. However, after three decades of effort synthesizing PHAs, and in particular the simplest form polyhydroxybutyrate (PHB), and seeking to improve their production in plants, it has proven very difficult to reach a commercially profitable rate in a normally growing plant. This seems to be due to the growth defects associated with PHA production and accumulation in plant cells. Here, we review major breakthroughs that have been made in plant-based PHA synthesis using traditional genetic engineering approaches and discuss challenges that have been encountered. Then, from the point of view of plant synthetic biology, we provide perspectives on reprograming plant acetyl-CoA pathways for PHA production, with the goal of maximizing PHA yield while minimizing growth inhibition. Specifically, we suggest genetic elements that can be considered in genetic circuit design, approaches for nuclear genome and plastome modification, and the use of multiomics and mathematical modeling in understanding and restructuring plant metabolic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis

In the future, plants may encounter increased light and elevated CO 2 levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light ( R L ) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and R L under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring R L under high light and high CO 2 (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate RL and investigate photosynthetic carbon flux, unveiling nuanced adjustments in Camelina sativa under HLHC. Despite numerous flux alterations in HLHC, RL remained stable. HLHC affects several factors influencing RL, such as starch and sucrose partitioning, v o /v c ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of A/C i curve operational points reveals that HLHC’s major changes primarily stem from CO 2 suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during R L , offering insights into manipulating labeling through adjustments in photosynthetic rates.

Elevated CO2↗
DOE OSTI2021

On the modeling paradigm of plant root nutrient acquisition

Plant root nutrient acquisition, and to a lesser extent foliar nutrient uptake, maintain plant metabolism and strongly regulate terrestrial biogeochemistry and carbon-climate feedbacks. However, terrestrial biogeochemical models differ in their representations of plant root nutrient acquisition, leading to significantly different, and uncertain, carbon cycle and future climate projections. Here we first review biogeochemical principles and observations relevant to three essential plant root nutrient acquisition mechanisms: activity of nutrient acquiring proteins, maintenance of nutrient stoichiometry, and energy expenditure for these processes. We next examine how these mechanisms are considered in three existing modeling paradigms, and conclude by recommending the capacity-based approach, the need for observations, and necessary modeling developments of plant root nutrient acquisition to improve carbon-climate feedback projections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13 C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

09 BIOMASS FUELS↗