Elucidation of triacylglycerol catabolism in Yarrowia lipolytica: How cells balance acetyl-CoA and excess reducing equivalents.

Yarrowia lipolytica is an industrial yeast that can convert waste oil to value-added products. However, it is unclear how this yeast metabolizes lipid feedstocks, specifically triacylglycerol (TAG) substrates. This study used 13C-metabolic flux analysis (13C-MFA), genome-scale modeling, and transcriptomics analyses to investigate Y. lipolytica W29 growth with oleic acid, glycerol, and glucose. Transcriptomics data were used to guide 13C-MFA model construction and to validate the 13C-MFA results. The 13C-MFA data were then used to constrain a genome-scale model (GSM), which predicted Y. lipolytica fluxes, cofactor balance, and theoretical yields of terpene products. The three data sources provided new insights into cellular regulation during catabolism of glycerol and fatty acid components of TAG substrates, and how their consumption routes differ from glucose catabolism. We found that (1) over 80% of acetyl-CoA from oleic acid is processed through the glyoxylate shunt, a pathway that generates less CO2 compared to the TCA cycle, (2) the carnitine shuttle is a key regulator of the cytosolic acetyl-CoA pool in oleic acid and glycerol cultures, (3) the oxidative pentose phosphate pathway and mannitol cycle are key routes for NADPH generation, (4) the mannitol cycle and alternative oxidase activity help balance excess NADH generated from ß-oxidation of oleic acid, and (5) asymmetrical gene expressions and GSM simulations of enzyme usage suggest an increased metabolic burden for oleic acid catabolism.

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.

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.

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.