K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data
Not provided.
Biotechnology & Applied Microbiology↗
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A quantitative lens on anaerobic life: leveraging the state-of-the-art fluxomics approach to explore clostridial metabolism
Quantitative understanding of clostridial metabolism is of longstanding interest due to the importance of Clostridia as model anaerobes, biotechnology workhorses, and contributors to evolutionary history and ecosystem. Current computational methods such as flux balance analysis-based construction of clostridial metabolism in genome scale provide a fundamental framework for metabolic analysis. However, this method alone is inadequate to characterize cellular metabolic activity. Experiment-driven approaches including isotope tracer-based fluxomics in association with genetic and biochemical methods are needed to gain a more comprehensive understanding. Here we focus on typical examples where these integrated approaches have contributed to the identification of new metabolic pathways and quantification of metabolic fluxes in Clostridia. Here, we also highlight the opportunities and challenges of cutting-edge fluxomics approaches such as machine learning modeling, deuterium tracer approach, and high throughput flux phenotyping in exploring clostridial metabolism with respect to inorganic carbon utilization, redox cofactor interconversion, and other key metabolic features.
09 BIOMASS FUELS↗
Computational Framework for Machine-Learning-Enabled 13 C Fluxomics
13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.
13C metabolic flux analysis↗
Final Technical Report--Quantitative analysis of metabolic regulation by integration of metabolomics, proteomics, and fluxomics
This project used an -omics driven systems biology approach to investigate metabolic regulation in a systematic, large-scale, and quantitative manner. Efforts involved two microorganisms, the biofuel producing yeast Saccharomyces cerevisiae and the cellulose degrading bacterium Clostridium celluloyticum. In yeast, we used a combination of metabolomics, proteomics, and fluxomics to measure enzyme concentrations, metabolite concentrations, and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three novel instances of cross-pathway metabolic regulation which we biochemically verified. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes. Thus, our studies in yeast argued for metabolism being substantially cell-regulating. In the cellulolytic bacterium, we developed and employed new tracer strategies to measure glycolytic reaction reversibility and thereby thermodynamics. This resulted in the striking observation that C. cellulolyticum glycolysis is nearly fully thermodynamically reversible, with no strongly forward driven step. The phosphofructokinase step, which is strongly forward driven in most species, is modified to use inorganic pyrophosphate as opposed to ATP as the phosphate donor. This increases the ATP yield of glycolysis in exchange for its becoming a slow, reversible pathway. Thus, our studies in cellulolytic bacteria argued for their having evolved a uniquely energy-efficient form of glycolysis. Collectively, these findings highlight the importance of metabolite concentrations and enzyme cofactor choices in controlling pathway fluxes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Fluxomic Analysis Reveals Central Carbon Metabolism Adaptation for Diazotroph Azotobacter vinelandii Ammonium Excretion
Diazotrophic bacteria are an attractive biological alternative to synthetic nitrogen fertilizers due to their remarkable capacity to fix atmospheric nitrogen gas to ammonium via nitrogenase enzymes. However, how diazotrophic bacteria tailor central carbon catabolism to accommodate the energy requirement for nitrogenase activity is largely unknown. In this study, we used Azotobacter vinelandii DJ and an ammonium excreting mutant, AV3 (ΔNifL), to investigate central carbon metabolism fluxes and central cell bioenergetics in response to ammonium availability and nitrogenase activity. Enabled by the powerful and reliable methodology of 13 C-metabolic flux analysis, we show that the respiratory TCA cycle is upregulated in association with increased nitrogenase activity and causes a monotonic decrease in specific growth rate. Whereas the activity of the glycolytic Entner-Doudoroff pathway is positively correlated with the cell growth rate. These new observations are formulated into a 13 C-metabolic flux model which further improves the understanding and interpretation of intracellular bioenergetics. This analysis leads to the conclusion that, under aerobic conditions, respiratory TCA metabolism is responsible for the supply of additional ATP and reducing equivalents required for elevated nitrogenase activity. This study provides a quantitative relationship between central carbon and nitrogen metabolism in an aerobic diazotroph for the first time.
09 BIOMASS FUELS↗
Chapter 14: Dynamic Flux Analysis: An Experimental Approach of Fluxomics
Metabolic flux analysis represents an essential perspective to understand cellular physiology and offers quantitative information to guide pathway engineering. A valuable approach for experimental elucidation of metabolic flux is dynamic flux analysis, which estimates the relative or absolute flow rates through a series of metabolic intermediates in a given pathway. It is based on kinetic isotope labeling experiments, liquid chromatography-mass spectrometry (LC-MS), and computational analysis that relate kinetic isotope trajectories of metabolites to pathway activity. Herein, we illustrate the mathematic principles underlying the dynamic flux analysis and mainly focus on describing the experimental procedures for data generation. This protocol is exemplified using cyanobacterial metabolism as an example, for which reliable labeling data for central carbon metabolites can be acquired quantitatively. This protocol is applicable to other microbial systems as well and can be readily adapted to address different metabolic processes.
BASIC BIOLOGICAL SCIENCES↗
Genome-Scale 13 C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae .
Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called "-omics" data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Furthermore, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a 13 C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering.The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, 13 C metabolic flux analysis, and two-scale 13 C metabolic flux analysis (2S- 13 C MFA). 2S- 13 C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMM library can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae ( S. cerevisiae ). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user's specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community.
-Omics data↗
Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13 C-Labeling Data
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59 BASIC BIOLOGICAL SCIENCES↗
A Combined Metabolomics and Fluxomics Analysis Identifies Steps Limiting Oil Synthesis in Maize Embryos
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
Unraveling metabolism underpinning biomass composition shift in Scenedesmus obliquus under simulated outdoor conditions using 13 C-fluxomics
To render the resulting biomass more attractive and amenable for utilization as the basis for low-carbon intensity bioproducts, single-celled algae need to be biochemically and metabolically poised to assimilate and store the delivered carbon in the fastest and most efficient manner. Accelerating biochemical carbon storage, as primarily carbohydrates or lipids, is critical to achieve the high carbon capture potential that is assigned to algae. To guide strain optimization and engineering for maximizing carbon capture and storage, it is essential to elucidate the link between carbon metabolism and biomass composition. Most published metabolomics work in algae remains largely restricted to ideal and simplified environmental conditions in model organisms, thereby limiting their translation to outdoor implementation. In this work, we utilize 13 C isotopic labeling to characterize distinct intracellular metabolic fluxes before, during, and after nitrogen depletion-induced compositional shifts in Scenedesmus obliquus UTEX 393. The results indicate that a transition to carbohydrates is characterized by diverting flux to starch instead of replenishing the Calvin cycle for CO 2 fixation whereas the subsequent transition to lipids is fueled by NADPH produced by upregulating the phosphoenolpyruvate carboxylase (PEPC)–malic enzyme (ME) cycle flux. Our work highlights bottlenecks to carbohydrate- and lipid-rich biomass and can guide implementable strategies to control the fate of fixed carbon in S. obliquus.
09 BIOMASS FUELS↗
Chapter 4: What We Can Learn from Measuring Metabolic Fluxes in Cyanobacteria
Cyanobacteria with the capability of oxygen-evolving photosynthesis play a vital role in the global carbon, oxygen, and nitrogen cycles. From a biochemical perspective, all phenotypic traits and functions of cyanobacteria are derived from the potential of their metabolism and mirrored in metabolic fluxes through an integrated metabolic network. Metabolic flux, the rate of turnover of molecules through a metabolic pathway, is therefore of great importance for an in-depth understanding of the role of cyanobacteria in the biosphere. Recent interest in cyanobacteria metabolic fluxes is also motivated by the prospect to utilize their light-driven CO2 fixation for sustainable production of valuable chemicals and biofuels. Valorization of cyanobacterial metabolism will require engineering of the native metabolic network - a challenge that can benefit from a quantitative understanding of metabolic reactions taking place intracellularly. In this chapter, we will discuss some intriguing questions relevant to metabolic fluxes in cyanobacteria: How are metabolic fluxes predicted or measured? What do we know about the fluxome inside various cyanobacteria thus far? How are they different between strains as well as in different conditions? And how can we use fluxomics information to guide metabolic engineering and synthetic biology? This chapter will highlight updated knowledge on cyanobacterial metabolism. Specifically, we will focus on providing readers a quantitative vision to understand how this photosynthetic model works.
BASIC BIOLOGICAL SCIENCES,INORGANIC, ORGANIC, PHYS↗
13 C-Isotope-Assisted Assessment of Metabolic Quenching During Sample Collection from Suspension Cell Cultures
Metabolomics and fluxomics are core approaches to directly profile and interrogate cellular metabolism in response to various genetic or environmental perturbations. In order to accurately measure the abundance and isotope enrichment of intracellular metabolites, cell culture samples must be rapidly harvested and cold-quenched to preserve the in vivo metabolic state of the cells at the time of sample collection. When dealing with suspension cultures, this process is complicated by the need to separate the liquid culture media from cellular biomass prior to metabolite extraction. Here, we examine the efficacy of several commonly used metabolic quenching methods, using the model cyanobacterium Synechocystis sp. PCC 6803 as an example. Multiple 13 C-labeled compounds, including 13 C-bicarbonate, 13 C-glucose and 13 C-glutamine, were used as tracers during the sample collection and cold-quenching process to assess the extent of metabolic turnover after cells were harvested from culture flasks. We show that the combination of rapid filtration followed by 100% cold (–80°C) methanol quenching exhibits the highest quenching efficiency, while mixing cell samples with a partially frozen 30% methanol slurry (–24°C) followed by centrifugation is slightly less effective at quenching metabolism but enables less laborious sample processing. By contrast, rapidly mixing the cells with a saline ice slurry (~0°C) is less effective as indicated by high isotope-labeling rates after sample harvest, while mixing the cells with 60% cold methanol (–65°C) prior to centrifugation causes significant metabolite loss. Furthermore, this study demonstrates a rigorous, quantitative, and broadly applicable method for assessing the metabolic quenching efficacy of protocols used for sample collection in metabolomics and fluxomics studies.
59 BASIC BIOLOGICAL SCIENCES↗
Data for Development of Vegetative Oil Sorghum: From Lab-to-Field
Biomass crops engineered to accumulate energy-dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar-accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high-yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push-pull-protect’ strategy. The top TAG-yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium-chain acyl-acyl carrier protein thioesterases. Though intended to generate oils with medium-chain fatty acids, engineered lines accumulated oleic acid-rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36-fold and 49-fold increases relative to wild-type (WT) plants, respectively. Under field conditions, the top-performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78-fold and 58-fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab-to-field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.
Biofuels↗
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↗
Cyanobacterial carboxysome mutant analysis reveals the influence of enzyme compartmentalization on cellular metabolism and metabolic network rigidity
Cyanobacterial carboxysomes encapsulate carbonic anhydrase and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Genetic deletion of the major structural proteins encoded within the ccm operon in Synechococcus sp. PCC 7002 ( ΔccmKLMN ) disrupts carboxysome formation and significantly affects cellular physiology. In this study, we employed both metabolite pool size analysis and isotopically nonstationary metabolic flux analysis (INST-MFA) to examine metabolic regulation in cells lacking carboxysomes. Under high CO 2 environments (1%), the ΔccmKLMN mutant could recover growth and had a similar central flux distribution as the control strain, with the exceptions of moderately decreased photosynthesis and elevated biomass protein content and photorespiration activity. Metabolite analyses indicated that the ΔccmKLMN strain had significantly larger pool sizes of pyruvate (>18 folds), UDPG (uridine diphosphate glucose), and aspartate as well as higher levels of secreted organic acids (e.g., malate and succinate). These results suggest that the ΔccmKLMN mutant is able to largely maintain a fluxome similar to the control strain by changing in intracellular metabolite concentrations and metabolite overflows under optimal growth conditions. When CO 2 was insufficient (0.2%), provision of acetate moderately promoted mutant growth. Interestingly, the removal of microcompartments may loosen the flux network and promote RuBisCO side-reactions, facilitating redirection of central metabolites to competing pathways (i.e., pyruvate to heterologous lactate production). This study provides important insights into metabolic regulation via enzyme compartmentation and cyanobacterial compensatory responses.
09 BIOMASS FUELS↗
Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis
A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.
09 BIOMASS FUELS↗
Nitrogen goes around
Variation in nutrient availability is a common challenge facing living organisms. Analysis of metabolomic and fluxomic responses in cyanobacteria to changes in nitrogen availability has led to the discovery of an ornithine-ammonia cycle.
09 BIOMASS FUELS↗
Quantitative decoding of coupled carbon and energy metabolism in Pseudomonas putida for lignin carbon utilization
Soil Pseudomonas species, which thrive on lignin derivatives, are widely explored for biotechnology applications in lignin valorization. However, how the native metabolism coordinates phenolic carbon processing with required cofactor generation remains poorly understood. Here, we achieve quantitative understanding of this metabolic balance through a detailed multi-omics investigation of Pseudomonas putida KT2440 grown on four common phenolic acid substrates: ferulate, p-coumarate, vanillate, and 4-hydroxybenzoate. Relative to succinate, proteomics reveals > 140-fold increase in transport and catabolic proteins for aromatics, but metabolomics identifies bottlenecks in initial catabolism to maintain favorable cellular energy charge, which is compromised in mutants with resolved bottlenecks. Up to 30-fold increase in pyruvate carboxylase and glyoxylate shunt proteins implies a metabolic remodeling confirmed by kinetic 13 C-metabolomics. Quantitative analysis by 13 C-fluxomics demonstrates coupling of this remodeling with cofactor production. Specifically, anaplerotic carbon recycling through pyruvate carboxylase promotes tricarboxylic acid cycle fluxes to generate 50-60% NADPH yield and 60-80% NADH yield, resulting in up to 6-fold greater ATP surplus than with succinate metabolism; the glyoxylate shunt sustains cataplerotic flux through malic enzyme for the remaining NADPH yield. This quantitative blueprint affords cofactor imbalance predictions in proposed engineering of key metabolic nodes in lignin valorization pathways.
09 BIOMASS FUELS↗