BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

Role of carboxysomes in cyanobacterial CO2 assimilation: CO2 concentrating mechanisms and metabolon implications.

Many carbon-fixing organisms have evolved CO2 concentrating mechanisms (CCMs) to enhance the delivery of CO2 to RuBisCO, while minimizing reactions with the competitive inhibitor, molecular O2 . These distinct types of CCMs have been extensively studied using genetics, biochemistry, cell imaging, mass spectrometry, and metabolic flux analysis. Highlighted in this paper, the cyanobacterial CCM features a bacterial microcompartment (BMC) called 'carboxysome' in which RuBisCO is co-encapsulated with the enzyme carbonic anhydrase (CA) within a semi-permeable protein shell. The cyanobacterial CCM is capable of increasing CO2 around RuBisCO, leading to one of the most efficient processes known for fixing ambient CO2 . The carboxysome life cycle is dynamic and creates a unique subcellular environment that promotes activity of the Calvin-Benson (CB) cycle. The carboxysome may function within a larger cellular metabolon, physical association of functionally coupled proteins, to enhance metabolite channelling and carbon flux. In light of CCMs, synthetic biology approaches have been used to improve enzyme complex for CO2 fixations. Research on CCM-associated metabolons has also inspired biologists to engineer multi-step pathways by providing anchoring points for enzyme cascades to channel intermediate metabolites towards valuable products.

Silk fibroin production in Escherichia coli is limited by a positive feedback loop between metabolic burden and toxicity stress.

To investigate the metabolic elasticity and production bottlenecks for recombinant silk proteins in Escherichia coli, we performed a comprehensive characterization of one elastin-like peptide strain (ELP) and two silk protein strains (A5 4mer, A5 16mer). Our approach included 13C metabolic flux analysis, genome-scale modeling, transcription analysis, and 13C-assisted media optimization experiments. Three engineered strains maintained their central flux network during growth, while measurable metabolic flux redistributions (such as the Entner-Doudoroff pathway) were detected. Under metabolic burdens, the reduced TCA fluxes forced the engineered strain to rely more on substrate-level phosphorylation for ATP production, which increased acetate overflow. Acetate (as low as 10 mM) in the media was highly toxic to silk-producing strains, which reduced 4mer production by 43% and 16mer by 84%, respectively. Due to the high toxicity of large-size silk proteins, 16mer's productivity was limited, particularly in the minimal medium. Therefore, metabolic burden, overflow acetate, and toxicity of silk proteins may form a vicious positive feedback loop that fractures the metabolic network. Three solutions could be applied: 1) addition of building block supplements (i.e., eight key amino acids: His, Ile, Phe, Pro, Tyr, Lys, Met, Glu) to reduce metabolic burden; 2) disengagement of growth and production; and 3) use of non-glucose based substrate to reduce acetate overflow. Other reported strategies were also discussed in light of decoupling this positive feedback loop.

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction.

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R2 = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R2 = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.

Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H.

Colwellia psychrerythraea 34H is a model psychrophilic bacterium found in the cold ocean-polar sediments, sea ice, and the deep sea. Although the genomes of such psychrophiles have been sequenced, their metabolic strategies at low temperature have not been quantified. We measured the metabolic fluxes and gene expression of 34H at 4 °C (the mean global-ocean temperature and a normal-growth temperature for 34H), making comparative analyses at room temperature (above its upper-growth temperature of 18 °C) and with mesophilic Escherichia coli When grown at 4 °C, 34H utilized multiple carbon substrates without catabolite repression or overflow byproducts; its anaplerotic pathways increased flux network flexibility and enabled CO2 fixation. In glucose-only medium, the Entner-Doudoroff (ED) pathway was the primary glycolytic route; in lactate-only medium, gluconeogenesis and the glyoxylate shunt became active. In comparison, E. coli, cold stressed at 4 °C, had rapid glycolytic fluxes but no biomass synthesis. At their respective normal-growth temperatures, intracellular concentrations of TCA cycle metabolites (α-ketoglutarate, succinate, malate) were 4-17 times higher in 34H than in E. coli, while levels of energy molecules (ATP, NADH, NADPH) were 10- to 100-fold lower. Experiments with E. coli mutants supported the thermodynamic advantage of the ED pathway at cold temperature. Heat-stressed 34H at room temperature (2 hours) revealed significant down-regulation of genes associated with glycolytic enzymes and flagella, while 24 hours at room temperature caused irreversible cellular damage. We suggest that marine heterotrophic bacteria in general may rely upon simplified metabolic strategies to overcome thermodynamic constraints and thrive in the cold ocean.

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data.

Synechococcus elongatus UTEX 2973 (Synechococcus 2973) has the shortest reported doubling time (2.1 h) among cyanobacteria, making it a promising platform for the solar-based production of biochemicals. In this meta-analysis, its intracellular flux distribution was recomputed using genome-scale isotopic nonstationary 13C-metabolic flux analysis given the labeling dynamics of 13 metabolites reported in an earlier study. To achieve this, a genome-scale mapping model, namely imSyu593, was constructed using the imSyn617 mapping model for Synechocystis sp. PCC 6803 (Synechocystis 6803) as the starting point encompassing 593 reactions. The flux elucidation revealed nearly complete conversion (greater than 96%) of the assimilated carbon into biomass in Synechococcus 2973. In contrast, Synechocystis 6803 achieves complete conversion of only 86% of the assimilated carbon. This high biomass yield was enabled by the reincorporation of the fixed carbons lost in anabolic and photorespiratory pathways in conjunction with flux rerouting through a nondecarboxylating reaction such as phosphoketolase. This reincorporation of lost CO2 sustains a higher flux through the photorespiratory C2 cycle that fully meets the glycine and serine demands for growth. In accordance with the high carbon efficiency drive, acetyl-coenzyme A was entirely produced using the carbon-efficient phosphoketolase pathway. Comparison of the Synechococcus 2973 flux map with that of Synechocystis 6803 revealed differences in the use of Calvin cycle and photorespiratory pathway reactions. The two species used different reactions for the synthesis of metabolites such as fructose-6-phosphate, glycine, sedoheptulose-7-phosphate, and Ser. These findings allude to a highly carbon-efficient metabolism alongside the fast carbon uptake rate in Synechococcus 2973, which explains its faster growth rate.

Production and excretion of astaxanthin by engineered Yarrowia lipolytica using plant oil as both the carbon source and the biocompatible extractant.

This study aimed to develop a bioprocess using plant oil as the carbon source for lipid-assimilating yeast to produce high-value astaxanthin. Using high-oleic safflower oil as a model, efficient cell growth and astaxanthin production by the engineered Yarrowia lipolytica strain ST7403 was demonstrated, and a considerable portion of astaxanthin was found excreted into the spent oil. Astaxanthin was the predominant carotenoid in the extracellular oil phase that allowed facile in situ recovery of astaxanthin without cell lysis. Autoclaving the safflower oil medium elevated the peroxide level but it declined quickly during fermentation (reduced by 84% by day 3) and did not inhibit cell growth or astaxanthin production. In a 1.5-L fed-batch bioreactor culture with a YnB-based medium containing 20% safflower oil, and with the feeding of casamino acids, astaxanthin production reached 54 mg/L (53% excreted) in 28 days. Further improvement in astaxanthin titer and productivity was achieved by restoring leucine biosynthesis in the host, and running fed-batch fermentation using a high carbon-to-nitrogen ratio yeast extract/peptone medium containing 70% safflower oil, with feeding of additional yeast extract/peptone, to attain 167 mg/L astaxanthin (48% excreted) in 9.5 days of culture. These findings facilitate industrial microbial biorefinery development that utilizes renewable lipids as feedstocks to not only produce high-value products but also effectively extract and recover the products, including non-native ones.Key Points• Yarrowia lipolytica can use plant oil as a C-source for astaxanthin production.• Astaxanthin is excreted and accumulated in the extracellular oil phase.• Astaxanthin is the predominant carotenoid in the extracellular oil phase.• Plant oil serves as a biocompatible solvent for in situ astaxanthin extraction. Graphical abstract.

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. Here 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 CO2 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 CO2 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.

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630.

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, we performed 13C-pathway tracing, 13C-pulse-tracing, transcriptional profiling, biomass composition analysis, and metabolite profiling in conjunction with 13C-metabolic flux analysis (13C-MFA) of phenol metabolism. We found that 1) phenol is metabolized mainly through the ortho-cleavage pathway; 2) phenol utilization requires a highly active TCA cycle; 3) NADPH is generated mainly via NADPH-dependent isocitrate dehydrogenase; 4) active cataplerotic fluxes increase plasticity in the TCA cycle; and 5) gluconeogenesis occurs partially through the reversed Entner-Doudoroff pathway (EDP). We also found that phenol-fed R. opacus PD630 generally has lower sugar phosphate concentrations (e.g., fructose 1,6-bisphosphatase) compared to metabolite pools in 13C-glucose-fed Escherichia coli (set as internal standards), while its TCA metabolites (e.g., malate, succinate, and α-ketoglutarate) accumulate intracellularly with measurable succinate secretion. In addition, we found that phenol utilization was inhibited by benzoate, while catabolite repressions by other tested carbon substrates (e.g., glucose and acetate) were absent in R. opacus PD630. Three adaptively-evolved strains display very different growth rates when fed with phenol as a sole carbon source, but they maintain a conserved flux network. These findings improve our understanding of R. opacus' metabolism for future lignin valorization.

Engineering microbial consortia by division of labor.

During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and 13C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.

Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization.

Sphingobium sp. SYK-6 is a soil bacterium boasting a well-studied ligninolytic pathway and the potential for development into a microbial chassis for lignin valorization. An improved understanding of its metabolism will help researchers in the engineering of SYK-6 for the production of value-added chemicals through lignin valorization. We used 13C-fingerprinting, 13C metabolic flux analysis (13C-MFA), and RNA-sequencing differential expression analysis to uncover the following metabolic traits: (i) SYK-6 prefers alkaline conditions, making it an efficient host for the consolidated bioprocessing of lignin, and it also lacks the ability to metabolize sugars or organic acids; (ii) the CO2 release (i.e., carbon loss) from the ligninolysis-based metabolism of SYK-6 is significantly greater than the CO2 release from the sugar-based metabolism of Escherichia coli; (iii) the vanillin catabolic pathway (which is the converging point of majority of the lignin catabolic pathways) is coupled with the tetrahydrofolate-dependent C1 pathway that is essential for the biosynthesis of serine, histidine, and methionine; (iv) catabolic end products of lignin (pyruvate and oxaloacetate) must enter the tricarboxylic acid (TCA) cycle first and then use phosphoenolpyruvate carboxykinase to initiate gluconeogenesis; and (v) 13C-MFA together with RNA-sequencing differential expression analysis establishes the vanillin catabolic pathway as the major contributor of NAD(P)H synthesis. Therefore, the vanillin catabolic pathway is essential for SYK-6 to obtain sufficient reducing equivalents for its healthy growth; cosubstrate experiments support this finding. This unique energy feature of SYK-6 is particularly interesting because most heterotrophs rely on the transhydrogenase, the TCA cycle, and the oxidative pentose phosphate pathway to obtain NADPH.

BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods.

Motivation: Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability. Results: We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools. Availability and Implementation: MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ). Contacts: toyetunde@wustl.edu or muhan@wustl.edu . Supplementary information: Supplementary data are available at Bioinformatics online.

Engineering the oleaginous yeast Yarrowia lipolytica to produce the aroma compound ß-ionone.

BACKGROUND: ß-Ionone is a fragrant terpenoid that generates a pleasant floral scent and is used in diverse applications as a cosmetic and flavoring ingredient. A growing consumer desire for natural products has increased the market demand for natural ß-ionone. To date, chemical extraction from plants remains the main approach for commercial natural ß-ionone production. Unfortunately, changing climate and geopolitical issues can cause instability in the ß-ionone supply chain. Microbial fermentation using generally recognized as safe (GRAS) yeast offers an alternative method for producing natural ß-ionone. Yarrowia lipolytica is an attractive host due to its oleaginous nature, established genetic tools, and large intercellular pool size of acetyl-CoA (the terpenoid backbone precursor). RESULTS: A push-pull strategy via genome engineering was applied to a Y. lipolytica PO1f derived strain. Heterologous and native genes in the mevalonate pathway were overexpressed to push production to the terpenoid backbone geranylgeranyl pyrophosphate, while the carB and biofunction carRP genes from Mucor circinelloides were introduced to pull flux towards ß-carotene (i.e., ionone precursor). Medium tests combined with machine learning based data analysis and 13C metabolite labeling investigated influential nutrients for the ß-carotene strain that achieved > 2.5 g/L ß-carotene in a rich medium. Further introduction of the carotenoid cleavage dioxygenase 1 (CCD1) from Osmanthus fragrans resulted in the ß-ionone production. Utilization of in situ dodecane trapping avoided ionone loss from vaporization (with recovery efficiencies of ~ 76%) during fermentation operations, which resulted in titers of 68 mg/L ß-ionone in shaking flasks and 380 mg/L in a 2 L fermenter. Both ß-carotene medium tests and ß-ionone fermentation outcomes indicated the last enzymatic step CCD1 (rather than acetyl-CoA supply) as the key bottleneck. CONCLUSIONS: We engineered a GRAS Y. lipolytica platform for sustainable and economical production of the natural aroma ß-ionone. Although ß-carotene could be produced at high titers by Y. lipolytica, the synthesis of ß-ionone was relatively poor, possibly due to low CCD1 activity and non-specific CCD1 cleavage of ß-carotene. In addition, both ß-carotene and ß-ionone strains showed decreased performances after successive sub-cultures. For industrial application, ß-ionone fermentation efforts should focus on both CCD enzyme engineering and strain stability improvement.

Integrating MS1 and MS2 Scans in High-Resolution Parallel Reaction Monitoring Assays for Targeted Metabolite Quantification and Dynamic 13C-Labeling Metabolism Analysis.

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. Initially developed for proteomic analysis, the parallel reaction monitoring (PRM) technique exploiting high-resolution MS2 fragment ion data has shown high promise for targeted metabolite quantification. Notably, MS1 ion intensity data acquired independently as part of each PRM scan cycle are often underutilized in the PRM assay. In this study, we developed an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids and shikimate pathway-related metabolites on an orthogonal QqTOF system. Concentration curve assessment revealed that exploiting both MS1 and MS2 scans in PRM analysis afforded higher sensitivity, wider dynamic range and better reproducibility than relying on either scan mode for quantification. Furthermore, Skyline was incorporated into our workflow to process the MS1/MS2 ion intensity data, and eliminate noisy signals and transitions with interferences. This integrated MS1/MS2 PRM approach was applied to targeted metabolite quantification in engineered E. coli strains for understanding of metabolic pathway modulation. In addition, this new approach, when first implemented in a dynamic 13C-labeling experiment, showed its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary 13C-labeling metabolism analysis.

Deciphering flux adjustments of engineered E. coli cells during fermentation with changing growth conditions.

Microbial fermentation conditions are dynamic, due to transcriptional induction, nutrient consumption, or changes to incubation conditions. In this study, 13C-metabolic flux analysis was used to characterize two violacein-producing E. coli strains with vastly different productivities, and to profile their metabolic adjustments resulting from external perturbations during fermentation. The two strains were first grown at 37°C in stage 1, and then the temperature was transitioned to 20°C in stage 2 for the optimal expression of the violacein synthesis pathway. After induction, violacein production was minimal in stage 3, but accelerated in stage 4 (early production phase) and 5 (late production phase) in the high producing strain, reaching a final concentration of 1.5mmol/L. On the contrary, ~0.02mmol/L of violacein was obtained from the low producing strain. To have a snapshot of the temporal metabolic changes in each stage, we performed 13C-MFA via isotopomer analysis of fast-turnover free metabolites. The results indicate strikingly stable flux ratios in the central metabolism throughout the early growth stages. In the late stages, however, the high producer rewired its flux distribution significantly, which featured an upregulated pentose phosphate pathway and TCA cycle, reflux from acetate utilization, negligible anabolic fluxes, and elevated maintenance loss, to compensate for nutrient depletion and drainage of some building blocks due to violacein overproduction. The low producer with stronger promoters shifted its relative fluxes in stage 5 by enhancing the flux through the TCA cycle and acetate overflow, while exhibiting a reduced biomass growth and a minimal flux towards violacein synthesis. Interestingly, the addition of the violacein precursor (tryptophan) in the medium inhibited high producer but enhanced low producer's productivity, leading to hypotheses of unknown pathway regulations (such as metabolite channeling).

Enhancing fatty acid production in Escherichia coli by Vitreoscilla hemoglobin overexpression.

Our recent 13 C-metabolic flux analysis (13 C-MFA) study indicates that energy metabolism becomes a rate-limiting factor for fatty acid overproduction in E. coli strains (after "Push-Pull-Block" based genetic modifications). To resolve this bottleneck, Vitreoscilla hemoglobin (VHb, a membrane protein facilitating O2 transport) was introduced into a fatty-acid-producing strain to promote oxygen supply and energy metabolism. The resulting strain, FAV50, achieved 70% percent higher fatty acid titer than the parent strain in micro-aerobic shake tube cultures. In high cell-density bioreactor fermentations, FAV50 achieved free fatty acids at a titer of 7.02 g/L (51% of the theoretical yield). In addition to "Push-Pull-Block-Power" strategies, our experiments and flux balance analysis also revealed the fatty acid over-producing strain is sensitive to metabolic burden and oxygen influx, and thus a careful evaluation of the cost-benefit tradeoff with the guidance of fluxome analysis will be fundamental for the rational design of synthetic biology strains. Biotechnol. Bioeng. 2017;114: 463-467. © 2016 Wiley Periodicals, Inc.

Cyanobacterial carbon metabolism: Fluxome plasticity and oxygen dependence.

Synechocystis sp. strain PCC 6803 has been widely used as a photo-biorefinery chassis. Based on its genome annotation, this species contains a complete TCA cycle, an Embden-Meyerhof-Parnas pathway (EMPP), an oxidative pentose phosphate pathway (OPPP), and an Entner-Doudoroff pathway (EDP). To evaluate how Synechocystis 6803 catabolizes glucose under heterotrophic conditions, we performed 13 C metabolic flux analysis, metabolite pool size analysis, gene knockouts, and heterologous expressions. The results revealed a cyclic mode of flux through the OPPP. Small, but non-zero, fluxes were observed through the TCA cycle and the malic shunt. Independent knockouts of 6-phosphogluconate dehydrogenase (gnd) and malic enzyme (me) corroborated these results, as neither mutant could grow under dark heterotrophic conditions. Our data also indicate that Synechocystis 6803 metabolism relies upon oxidative phosphorylation to generate ATP from NADPH under dark or insufficient light conditions. The pool sizes of intermediates in the TCA cycle, particularly acetyl-CoA, were found to be several fold lower in Synechocystis 6803 (compared to E. coli metabolite pool sizes), while its sugar phosphate intermediates were several-fold higher. Moreover, negligible flux was detected through the native, or heterologous, EDP in the wild type or Δgnd strains under heterotrophic conditions. Comparing photoautotrophic, photomixotrophic, and heterotrophic conditions, the Calvin cycle, OPPP, and EMPP in Synechocystis 6803 possess the ability to regulate their fluxes under various growth conditions (plastic), whereas its TCA cycle always maintains at low levels (rigid). This work also demonstrates how genetic profiles do not always reflect actual metabolic flux through native or heterologous pathways. Biotechnol. Bioeng. 2017;114: 1593-1602. © 2017 Wiley Periodicals, Inc.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Synthetic biology for manufacturing chemicals: constraints drive the use of non-conventional microbial platforms.

Genetically modified microbes have had much industrial success producing protein-based products (such as antibodies and enzymes). However, engineering microbial workhorses for biomanufacturing of commodity compounds remains challenging. First, microbes cannot afford burdens with both overexpression of multiple enzymes and metabolite drainage for product synthesis. Second, synthetic circuits and introduced heterologous pathways are not yet as "robust and reliable" as native pathways due to hosts' innate regulations, especially under suboptimal fermentation conditions. Third, engineered enzymes may lack channeling capabilities for cascade-like transport of metabolites to overcome diffusion barriers or to avoid intermediate toxicity in the cytoplasmic environment. Fourth, moving engineered hosts from laboratory to industry is unreliable because genetic mutations and non-genetic cell-to-cell variations impair the large-scale fermentation outcomes. Therefore, synthetic biology strains often have unsatisfactory industrial performance (titer/yield/productivity). To overcome these problems, many different species are being explored for their metabolic strengths that can be leveraged to synthesize specific compounds. Here, we provide examples of non-conventional and genetically amenable species for industrial manufacturing, including the following: Corynebacterium glutamicum for its TCA cycle-derived biosynthesis, Yarrowia lipolytica for its biosynthesis of fatty acids and carotenoids, cyanobacteria for photosynthetic production from its sugar phosphate pathways, and Rhodococcus for its ability to biotransform recalcitrant feedstock. Finally, we discuss emerging technologies (e.g., genome-to-phenome mapping, single cell methods, and knowledge engineering) that may facilitate the development of novel cell factories.