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.

Generative Artificial Intelligence GPT-4 Accelerates Knowledge Mining and Machine Learning for Synthetic Biology.

Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published information related to microbial performance under complex strain engineering and bioreactor conditions. As a proof of concept, we proposed prompt engineering for a GPT-4 workflow pipeline to extract knowledge from 176 publications on two oleaginous yeasts (Yarrowia lipolytica and Rhodosporidium toruloides). After human intervention, the pipeline obtained a total of 2037 data instances. The structured data sets and feature selections enabled ML approaches (e.g., a random forest model) to predict Yarrowia fermentation titers with decent accuracy (R2 of 0.86 for unseen test data). Via transfer learning, the trained model could assess the production potential of the engineered nonconventional yeast, R. toruloides, for which there are fewer published reports. This work demonstrated the potential of generative artificial intelligence to streamline information extraction from research articles, thereby facilitating fermentation predictions and biomanufacturing development.

Conditional protein degradation in Yarrowia lipolytica using the auxin-inducible degron.

Conditional protein degradation is a powerful tool for controlled protein knockdown. The auxin-inducible degron (AID) technology uses a plant auxin to induce depletion of degron-tagged proteins, and it has been shown to be functional in several non-plant eukaryotes. In this study, we demonstrated AID-based protein knockdown in an industrially important oleaginous yeast Yarrowia lipolytica. Using the mini-IAA7 (mIAA7) degron derived from Arabidopsis IAA7, coupled with an Oryza sativa TIR1 (OsTIR1) plant auxin receptor F-box protein (expressed from the copper-inducible MT2 promoter), C-terminal degron-tagged superfolder GFP could be degraded in Yarrowia lipolytica upon addition of copper and the synthetic auxin 1-Naphthaleneacetic acid (NAA). However, leaky degradation of the degron-tagged GFP in the absence of NAA was also noted. This NAA-independent degradation was largely eliminated by replacing the wild-type OsTIR1 and NAA with the OsTIR1F74A variant and the auxin derivative 5-Ad-IAA, respectively. Degradation of the degron-tagged GFP was rapid and efficient. However, Western blot analysis revealed cellular proteolytic cleavage within the mIAA7 degron sequence, leading to the production of a GFP sub-population lacking an intact degron. The utility of the mIAA7/OsTIR1F74A system was further explored in controlled degradation of a metabolic enzyme, ß-carotene ketolase, which converts ß-carotene to canthaxanthin via echinenone. This enzyme was tagged with the mIAA7 degron and expressed in a ß-carotene producing Y. lipolytica strain that also expressed OsTIR1F74A controlled by the MT2 promoter. By adding copper and 5-Ad-IAA at the time of culture inoculation, canthaxanthin production was found to be reduced by about 50% on day five compared to the control culture without adding 5-Ad-IAA. This is the first report that demonstrates the efficacy of the AID system in Y. lipolytica. Further improvement of AID-based protein knockdown in Y. lipolytica may be achieved by preventing proteolytic removal of the mIAA7 degron tag.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism.

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

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.

Biosystem design of Corynebacterium glutamicum for bioproduction.

Corynebacterium glutamicum, a natural glutamate-producing bacterium adopted for industrial production of amino acids, has been extensively explored recently for high-level biosynthesis of amino acid derivatives, bulk chemicals such as organic acids and short-chain alcohols, aromatics, and natural products, including polyphenols and terpenoids. Here, we review the recent advances with a focus on biosystem design principles, metabolic characterization and modeling, omics analysis, utilization of nonmodel feedstock, emerging CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) tools for Corynebacterium strain engineering, biosensors, and novel strains of C. glutamicum. Future research directions for developing C. glutamicum cell factories are also discussed.

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.

Photobiological production of high-value pigments via compartmentalized co-cultures using Ca-alginate hydrogels.

Engineered cyanobacterium Synechococcus elongatus can use light and CO2 to produce sucrose, making it a promising candidate for use in co-cultures with heterotrophic workhorses. However, this process is challenged by the mutual stresses generated from the multispecies microbial culture. Here we demonstrate an ecosystem where S. elongatus is freely grown in a photo-bioreactor (PBR) containing an engineered heterotrophic workhorse (either ß-carotene-producing Yarrowia lipolytica or indigoidine-producing Pseudomonas putida) encapsulated in calcium-alginate hydrogel beads. The encapsulation prevents growth interference, allowing the cyanobacterial culture to produce high sucrose concentrations enabling the production of indigoidine and ß-carotene in the heterotroph. Our experimental PBRs yielded an indigoidine titer of 7.5 g/L hydrogel and a ß-carotene titer of 1.3 g/L hydrogel, amounts 15-22-fold higher than in a comparable co-culture without encapsulation. Moreover, 13C-metabolite analysis and protein overexpression tests indicated that the hydrogel beads provided a favorable microenvironment where the cell metabolism inside the hydrogel was comparable to that in a free culture. Finally, the heterotroph-containing hydrogels were easily harvested and dissolved by EDTA for product recovery, while the cyanobacterial culture itself could be reused for the next batch of immobilized heterotrophs. This co-cultivation and hydrogel encapsulation system is a successful demonstration of bioprocess optimization under photobioreactor conditions.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production.

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. 13C-metabolic flux analysis (13C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition.

The microalga Chlorella sorokiniana has attracted much attention for lipid production and wastewater treatment. It can perform photosynthesis and organic carbon utilization concurrently. To understand its phototrophic metabolism, a biomass compositional analysis, a 13C metabolic flux analysis, and metabolite pool size analyses were performed. Under dark condition, the oxidative pentose phosphate pathway (OPP) was the major route for glucose catabolism (88% carbon flux) and a cyclic OPP-glycolytic route for glucose catabolism was formed. Under light condition, fluxes in the glucose catabolism, tricarboxylic acid (TCA) cycle, and anaplerotic reaction (CO2 fixation via phosphoenolpyruvate carboxylase) were all suppressed. Meanwhile, the RuBisCO reaction became active and the ratio of its carbon fixation to glucose carbon utilization was determined as 7:100. Moreover, light condition significantly reduced the pool sizes of sugar phosphate metabolites (such as E4P, F6P, and S7P) and promoted biomass synthesis (which reached 0.155 h-1). In addition, light condition increased glucose consumption rates, leading to higher ATP and NADPH production and a higher protein content (43% vs. 30%) in the biomass during the exponential growth phase.

Mineral-hydrogel composites for mitigating harmful algal bloom and supplying phosphorous for photo-biorefineries.

Harmful algal blooms (HAB) are a major environmental concern in eutrophic aquatic systems. To mitigate HABs and recover the phosphorus that drives algal growth, this study developed hydrogel composites seeded with calcium phosphate and wollastonite particles, which first adsorb phosphate (P) and then precipitate it as calcium phosphate. Using a fast-growing cyanobacterium, Synechococcus elongatus 2973, as a model microalga, we found that the mineral-hydrogel composites reduced dissolved P in BG11 media from 5.1 mg/L to 0.31 mg/L, initially reducing the biomass growth rate by up to 73 % and ultimately reducing the total biomass concentration by 75 %. When applied to municipal wastewater and agricultural run-off, the composites removed 96 % and 91 % of the dissolved P, respectively. Moreover, when the recovered P-enriched composites were reused as a slow-release bio-compatible fertilizer in a photobioreactor, they effectively supported algal growth without blocking light and interfering with photosynthesis. The P-enriched composites could tune the P concentration in the culture medium and significantly promote algal lipid accumulation. This study demonstrates the mineral-hydrogel composites' potential to treat point sources of P pollution and subsequently facilitate photoautotrophic biofuel production as a nutrient, effectively recycling the captured P.

Microbial electrochemical ammonia recovery from anaerobic digester centrate and subsequent application to fertilize Arabidopsis thaliana.

Although ammonia recovery from wastewater can be environmentally friendly and energy efficient compared to the conventional Haber-Bosch process, there is a lack of research on the reuse of the recovered ammonia to exhibit a complete picture of resource recovery. In this study, a microbial electrochemical system (MES) was used to recover ammonia from a mixture of anaerobic digester (AD) centrate and food wastewater at a volume ratio of 3:1. More than 60% of ammonia nitrogen was recovered with energy consumption of 2.7 kWh kg-1 N. The catholyte of the MES, which contained the recovered ammonia, was used to prepare fertilizers to support the growth of a model plant Arabidopsis thaliana. It was observed that A. thaliana grown on the MES generated fertilizer amended with extra potassium, phosphorus, and trace elements showed comparable sizes and an even lower death rate (0%) than the control group (24%) that was added with a commercial fertilizer. RNA-Seq analyses were used to examine A. thaliana genetic responses to the MES generated fertilizers or the commercial counterpart. The comparative study offered metabolic insights into A. thaliana physiologies subject to the recovered nitrogen fertilizers. The results of this study have demonstrated the potential application of using the recovered ammonia from AD centrate as a nitrogen source in fertilizer and identified the necessity of supplementing other nutrient elements.

Artificial intelligence: a solution to involution of design-build-test-learn cycle.

Iterative design-build-test-learn (DBTL) cycles are routinely performed during microbial strain development. This useful approach integrates computational strain design, genetic engineering, fermentation testing, and omics analysis to reveal and resolve production bottlenecks. However, the DBTL may enter involution, in which the numerous engineering cycles generate large amount of information and constructs without leading to breakthroughs. To avoid this problem, machine learning (ML) can be a promising yet not developed solution to multiscale modeling and process optimization. This review discusses the recent advances in ML applications, focusing on integrative metabolic models and knowledge engineering for guiding metabolic engineering and fermentation optimization. The ML-based strain development can eventually improve DBTL cycles to facilitate moving synthetic strains from laboratories to industries.

Harnessing electrical-to-biochemical conversion for microbial synthesis.

Electrical-to-biochemical conversion (E2BC) drives cell metabolism for biosynthesis and has become a promising way to realize green biomanufacturing. This review discusses the following aspects: 1. the natural E2BC processes and their underlying E2BC mechanism; 2. development of artificial E2BC for tunable microbial electrosynthesis; 3. design of electrobiochemical systems using self-powered, light-assisted, and nano-biohybrid approaches; 4. synthetic biology methods for efficient microbial electrosynthesis. This review also compares E2BC with electrocatalysis-biochemical conversion (EC2BC), as both strategies may lead to future carbon negative green biomanufacturing.

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.

Mitigation of host cell mutations and regime shift during microbial fermentation: a perspective from flux memory.

Microbial engineering forces flux redistribution to accommodate higher production rates, straining the cellular supply chain and leading to growth deficiency. Thus, there is a selective pressure to alleviate metabolic burden and revert towards the innate flux distribution ('flux memory') via mutations. Suboptimal fermentation exacerbates this phenomenon as increased number of generations prolong the selection window for the underlying flux memory to generate faster growing non-producers. New strategies to mitigate host genetic instability include laboratory evolution, high-resolution genome resequencing combined with phenotype screening, mismatch repair protein engineering, and advanced synthetic biology approaches (e.g. oscillators and biosensor regulators). Moreover, 13C-metabolic flux analysis can quantify flux suboptimality driven by metabolic burdens and cultivation stresses. Elucidation of correlations between metabolic suboptimality and host mutation rates/spectra may lead to early stage risk assessments of culture-population's regime shift during process scale-up as well as strategies to boost bioproductions.

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.

Analysis of Yarrowia lipolytica growth, catabolism, and terpenoid biosynthesis during utilization of lipid-derived feedstock.

This study employs biomass growth analyses and 13C-isotope tracing to investigate lipid feedstock utilization by Yarrowia lipolytica. Compared to glucose, oil-feedstock in the minimal medium increases the yeast's biomass yields and cell sizes, but decreases its protein content (<20% of total biomass) and enzyme abundances for product synthesis. Labeling results indicate a segregated metabolic network (the glycolysis vs. the TCA cycle) during co-catabolism of sugars (glucose or glycerol) with fatty acid substrates, which facilitates resource allocations for biosynthesis without catabolite repressions. This study has also examined the performance of a ß-carotene producing strain in different growth mediums. Canola oil-containing yeast-peptone (YP) has resulted in the best ß-carotene titer (121 ±â€¯13 mg/L), two-fold higher than the glucose based YP medium. These results highlight the potential of Y. lipolytica for the valorization of waste-derived lipid feedstock.