Inducible transcription systems are essential tools in genetic engineering, where tight control, strong inducibility and fast response with cost-effective inducers are highly desired. However, existing systems in yeasts are rarely used in large-scale fermentations due to either cost-prohibitive inducers or incompatible performance. Here, we developed powerful xylose and arabinose induction systems in Saccharomyces cerevisiae, utilizing eukaryotic activators XlnR and AraR from Aspergillus species and bacterial repressors XylR and AraR. By integrating these signals into a highly-structured synthetic promoter, we created dual-mode systems with strong outputs and minimal leakiness. These systems demonstrated over 4000- and 300-fold regulation with strong activation and rapid response. The dual-mode xylose system was fully activated by xylose-rich agricultural residues like corncob hydrolysate, outperforming existing systems in terms of leakiness, inducibility, dynamic range, induction rate, and growth impact on host. We validated their utility in metabolic engineering with high-titer linalool production and demonstrated the transferability of the XlnR-based xylose induction system to Pichia pastoris, Candida glabrata and Candida albicans. This work provides robust genetic switches for yeasts and a general strategy for integrating activation-repression signals into synthetic promoters to achieve optimal performance.

We propose here that acylation modification of actinomycete proteins is a restrictive system that limits the excessive synthesis of secondary metabolites, its mechanism has not been clearly elucidated before. We used crotonylation as an example to investigate the acylation effect in the daptomycin biosynthesis by Streptomyces roseosporus. Our experiments revealed abundant crotonylation of numerous secondary metabolic enzymes in Streptomyces roseosporus, a daptomycin producer. DptE, which initiates daptomycin biosynthesis, is crotonylated at K454. We experimentally identified the corresponding DptE crotonyltransferase Kct1 and decrotonylase CobB. Further studies consistently confirmed that decrotonylation increases DptE activity. Decrotonylation functions like loosening a faucet knob, increasing substrate channel throughput and the initial flow of daptomycin biosynthesis. Moreover, DptE catalytic activity was enhanced via K454 and neighboring residues K184 and Q420 mutation, increasing daptomycin yield by 132%; daptomycin biosynthesis related metabolism activities also increased. Substrate channel prediction revealed 38% higher throughput for mutant DptE (K454I/K184Q/Q420N) than crotonylated DptE. Molecular dynamics (MD) simulations revealed significant increases in flexibility and substrate affinity of the mutant. In summary, we elucidated the faucet knob effect of DptE crotonylation on the initial flow of daptomycin biosynthesis and adopted decrotonylation to generate high-yield industrial strains.

Engineering of a specialized metabolic pathway in plants is a promising approach to produce high-value bioactive compounds to address the challenges of climate change and population growth. Understanding the interaction between the heterologous pathway and the native metabolic network of the host plant is crucial for optimizing the engineered system and maximizing the yield of the target compound. In this study, we performed transcriptomic, metabolomic and metagenomic analysis of tobacco (Nicotiana tabacum) plants engineered to produce betanin, an alkaloid pigment that is found in Caryophyllaceae plants. Our data reveals that, in a dose-dependent manor, the biosynthesis of betanin promotes carbohydrate metabolism and represses nitrogen metabolism in the leaf, but enhances nitrogen assimilation and metabolism in the root. By supplying nitrate or ammonium, the accumulation of betanin increased by 1.5-3.8-fold in leaves and roots of the transgenic plants, confirming the pivotal role of nitrogen in betanin production. In addition, the rhizosphere microbial community is reshaped to reduce denitrification and increase respiration and oxidation, assistant to suppress nitrogen loss. Our analysis not only provides a framework for evaluating the pleiotropic effects of an engineered metabolic pathway on the host plant, but also facilitates the development of novel strategies to balance the heterologous process and the native metabolic network for the high-yield and nutrient-efficient production of bioactive compounds in plants.

Chinese hamster ovary (CHO) cells, which are widely used for therapeutic protein production, have been genetically manipulated to enhance productivity. Nearly half of the genes in CHO cells are silenced, which are promising targets for CHO cell engineering. To identify novel gene targets among the silenced genes that can enhance productivity, we established a genome-wide clustered regularly interspaced short palindromic repeats activation (CRISPRa) screening platform for bispecific antibody (bsAb)-producing CHO (CHO-bsAb) cells with 110,979 guide RNAs (gRNAs) targeting 13,812 silenced genes using a virus-free recombinase-mediated cassette exchange-based gRNA integration method. Using this platform, we performed a fluorescence-activated cell sorting-based cold-capture assay to isolate cells with high fluorescence intensity, which is indicative of high specific bsAb productivity (q), and identified 90 significantly enriched genes. To verify the screening results, 14 high-scoring candidate genes were individually activated in CHO-bsAb cells via CRISPRa. Among these, 10 genes demonstrated enhanced fluorescence intensity of CHO-bsAb cells in the cold-capture assay when activated. Furthermore, the overexpression of the identified novel gene target Syce3 in CHO-bsAb cells resulted in a 1.4- to 1.9-fold increase in the maximum bsAb concentration, owing to improved q and specific growth rate. Thus, this virus-free CRISPRa screening platform is a potent tool for identifying novel engineering targets in CHO cells to improve bsAb production.

Raffinose, a trisaccharide abundantly found in soybeans, is a potential alternative carbon source for biorefineries. Nevertheless, residual intermediate di- or monosaccharides and low catabolic efficiency limit raffinose use through conventional microbial hosts. This study presents a Vibrio-based platform to convert raffinose efficiently. Vibrio sp. dhg was selected as the starting strain for the Adaptive Laboratory Evolution (ALE) strategy to leverage its significantly higher metabolic efficiency. We conducted ALE on a solid minimal medium supplemented with raffinose to prevent the enrichment of undesired phenotypes due to the shared effect of extracellular raffinose hydrolysis among multiple strains. As a result, we generated the VRA10 strain that efficiently utilizes raffinose without leaving behind degraded di- or monosaccharides, achieving a notable growth rate (0.40 h) and raffinose consumption rate (1.2 g/g/h). Whole genome sequencing and reverse engineering identified that a missense mutation in the melB gene (encoding a melibiose/raffinose:sodium symporter) and the deletion of the two galR genes (encoding transcriptional repressors for galactose catabolism) facilitated rapid raffinose utilization. The further engineered strain produced 6.2 g/L of citramalate from 20 g/L of raffinose. This study will pave the way for the efficient utilization of diverse raffinose-rich byproducts and the expansion of alternative carbon streams in biorefinery applications.

Culture conditions have a profound impact on therapeutic protein production and glycosylation, a critical therapeutic-quality attribute, especially for monoclonal antibodies (mAbs). While the critical culture parameter of pH has been known since the early 1990s to affect protein glycosylation and production, detailed glycan and metabolic characterization and mechanistic understanding are critically lacking. Here, Chinese Hamster Ovary (CHO) cells were grown in bioreactors at pH 6.75, 7 and 7.25 to examine how pH affects cell metabolism and site-specific N-linked glycosylation of the produced broadly neutralizing anti-HIV IgG1 mAb. VRC01 has N-linked glycosylation sites in both the Fc region and the Fab region, a situation not previously examined with respect to mAb glycosylation as affected by culture conditions. Using Parsimonious Flux Balance Analysis (pFBA) and Flux Variability Analysis (FVA), we dissect and quantitate the impact of pH on cell growth, glucose/lactate metabolism, accumulation of the toxic metabolite ammonia, IgG production rates, and nonessential amino acid metabolism. pFBA revealed thar that beyond the established mechanism of glutamine conversion to glutamate, ammonia is also produced by the reaction converting serine to pyruvate, especially in the later phases of culture. pFBA also provided insights into the switch from ammonia production to consumption, notably due to depletion of glutamine, and consumption of glutamate and aspartate. We document that culture duration and pH alter the complex bimodal patterns (production/uptake) of several essential and non-essential amino acids. Site-specific N-linked glycan analysis using glycopeptide mapping demonstrated that pH significantly affects the glycosylation profiles of the two IgG1 sites. Fc region glycans were completely fucosylated but did not contain any sialylation. The Fab region glycans were not completely fucosylated but contained sialylated glycans. Bioreactor pH affected both the fucosylation and sialylation indexes in the Fab region and the galactosylation index of the Fc region. However, fucosylation in the Fc region was unaffected thus demonstrating that the effect of pH on site-specific N-linked glycosylation is complex.

The non-conventional yeast Kluyveromyces marxianus is a promising microbial host for industrial biomanufacturing. With the recent development of Cas9-based genome editing systems and other novel synthetic biology tools for K. marxianus, engineering of this yeast has become far more accessible. Enzyme colocalization is a proven approach to increase pathway flux and the synthesis of non-native products. Here, we engineer K. marxianus to enable peroxisomal surface display, an enzyme colocalization technique for displaying enzymes on the peroxisome membrane via an anchoring motif from the peroxin Pex15. The native KmPex15 anchoring motif was identified and fused to GFP, resulting in successful localization to the surface of the peroxisomes. To demonstrate the advantages for pathway localization, the Pseudomonas savastanoi IaaM and IaaH enzymes were co-displayed on the peroxisome surface; this increased production of indole-3-acetic acid 7.9-fold via substrate channeling effects. We then redirected pathway flux by displaying the violacein pathway enzymes VioE and VioD from Chromobacterium violaceum, increasing selectivity of proviolacein to prodeoxyviolacein by 2.5-fold. Finally, we improved direct access to peroxisomal acetyl-CoA and increased titers of the polyketide triacetic acid lactone (TAL) by 2-fold through concurrent display of the proteins Cat2, Acc1, and the type III PKS 2-pyrone synthase from Gerbera hybrida relative to the same three enzymes diffusing in the cytosol. We further improved TAL production by up to 2.1-fold through engineering peroxisome morphology and lifespan. Our findings demonstrate that peroxisomal surface display is an efficient enzyme colocalization strategy in K. marxianus and applicable for improving production of a wide range of non-native products.

Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preferences and the design of mutants to switch cofactor specificity remain as complex tasks. Here, we introduce DISCODE (Deep learning-based iterative pipeline to analyze Specificity of Cofactors and to Design Enzyme), a novel transformer-based deep learning model to predict NAD(P) cofactor preferences. For model training, a total of 7,132 NAD(P)-dependent enzyme sequences were collected. Leveraging whole-length sequence information, DISCODE classifies the cofactor preferences of NAD(P)-dependent oxidoreductase protein sequences without structural or taxonomic limitation. The model showed 97.4% and 97.3% of accuracy and F1 score, respectively. A notable feature of DISCODE is the interpretability of its transformer layers. Analysis of attention layers in the model enables identification of several residues that showed significantly higher attention weights. They were well aligned with structurally important residues that closely interact with NAD(P), facilitating the identification of key residues for determining cofactor specificities. These key residues showed high consistency with verified cofactor switching mutants. Integrated into an enzyme design pipeline, DISCODE coupled with attention analysis, enables a fully automated approach to redesign cofactor specificity.

Escherichia coli Nissle 1917 (EcN), the probiotic featured with well-established safety in different host, is emerging as a favored chassis for the construction of engineered probiotics for disease treatment. However, limited by the low intestinal colonization ability of EcN, repeated administration is required to maximize the health benefits of the EcN-derived engineered probiotics. Here, using fecal metabolites as "metabolites pool", we developed a metabolomic strategy to characterize the comprehensive metabolic profile of EcN. Compared with Prevotella copri DSM 18205 (P. copri), one of the dominant microbes in gut flora, EcN exhibited minor growth advantage under the fecal metabolites-containing condition for its lower metabolic capability towards fecal metabolites. Further study indicated that EcN lacked the ability to import the oligopeptides containing more than two amino acids. The shortage of oligopeptides-derived amino acids might limit the growth of EcN by restricting its purine metabolism. Assisted with the bioinformatic and qRT-PCR analyses, we identified a tripeptides-specific importer Pc-OPT in P. copri, which was mainly distributed in genera Prevotella and Bacteroides. Overexpression of Pc-OPT improved the tripeptides importation of EcN and promoted its growth and intestinal colonization. Notably, 16S rRNA gene amplicon sequencing results indicated that strengthening the oligopeptides importation ability of EcN might promote its intestinal colonization by adjusting the gut microbial composition. Our study reveals that the growth and intestinal colonization of EcN is limited by its insufficient oligopeptides importation and paves road for promoting the efficacy of the EcN-derived synthetic probiotics by improving their intestinal colonization ability.

Salicin is a natural glycoside compound widely used to treat fever, inflammation, and analgesia. Currently, salicin is primarily extracted from willow bark, which is not only cumbersome in terms of extraction and separate steps, but also subject to seasonal and geographic limitations. In this study, a highly efficient biosynthetic pathway for salicin synthesis was designed and constructed in E. coli. The most important precursor in the synthetic pathway of salicin designed in this study is salicyl alcohol. Building on a previously constructed biosynthetic salicylic acid metabolic pathway, the production of salicyl alcohol in shake flask fermentation reached 1.7 g/L by increasing the supply of shikimic acid pathway precursor PEP and salicyl alcohol precursor chorismate. According to the principle of substrate similarity, this study identified the key enzyme OsSGT1 from Oryza sativa, which uses E. coli endogenous UDP-glucose as a glycosyl donor to glycosylate salicyl alcohol into salicin. By redefining the optimal substrate of OsSGT1, and balancing metabolic flux along with increasing the supply of UDP-glucose, salicin production in shake flasks reached 4 g/L. Finally, culturing the high-yield strain in a 3-L fermenter resulted in the synthesis of 14.62 g/L of salicin. To the best of our knowledge, this achievement marks the highest salicin production through microbial fermentation to date.

The butyrate biosynthetic pathway not only contributes to electron management and energy generation in butyrate forming bacteria, but also confers evolutionary advantages to the host by inhibiting the growth of surrounding butyrate-sensitive microbes. While high butyrate levels induce toxic stress, effects of non-toxic levels on cell growth, health, metabolism, and sporulation remain unclear. Here, we show that butyrate stimulates cellular processes of Clostridium acetobutylicum, a model butyrate forming Firmicute. First, we deleted the 3-hydroxybutyryl-CoA dehydrogenase gene (hbd) from the C. acetobutylicum chromosome to eliminate the butyrate synthetic pathway and thus butyrate formation. A xylose inducible Cas9 cassette was chromosomally integrated and utilized for the one-step markerless gene deletions. Non-toxic butyrate levels significantly affected growth, health, and sporulation of C. acetobutylicum. After deleting spo0A, the gene encoding the master regulator of sporulation, Spo0A, and conducting butyrate addition experiments, we conclude that butyrate affects cellular metabolism through both Spo0A-dependent and independent mechanisms. We also deleted the hbd gene from the chromosome of the asporogenous C. acetobutylicum M5 strain lacking the pSOL1 plasmid to examine the potential involvement of pSOL1 genes on the observed butyrate effects. Addition of crotonate, the precursor of butyrate biosynthesis, to the hbd deficient M5 strain was used to probe the role of butyrate biosynthesis pathway in electron and metabolic fluxes. Finally, we found that butyrate addition can enhance the growth of the non-butyrate forming Clostridium saccharolyticum. Our data suggest that butyrate functions as a stimulator of cellular processes, like a growth factor, in C. acetobutylicum and potentially evolutionarily related Clostridium organisms.

L-isoleucine, an essential amino acid, is widely used in the pharmaceutical and food industries. However, the current production efficiency is insufficient to meet the increasing demands. In this study, we aimed to develop an efficient L-isoleucine-producing strain of Escherichia coli. First, accumulation of L-isoleucine was achieved by employing feedback-resistant enzymes. Next, a growth-coupled L-isoleucine synthetic pathway was established by introducing the metA-metB-based α-ketobutyrate-generating bypass, which significantly increased L-isoleucine production to 7.4 g/L. Upon employing an activity-improved cystathionine γ-synthase mutant obtained from adaptive laboratory evolution, L-isoleucine production further increased to 8.5 g/L. Subsequently, the redox flux was improved by bypassing the NADPH-dependent aspartate aminotransferase pathway and employing the NADH-dependent pathway and transhydrogenase. Finally, L-isoleucine efflux was enhanced by modifying the transport system. After fed-batch fermentation for 48 h, the resultant strain, ISO-12, reached an L-isoleucine production titer of 51.5 g/L and yield of 0.29 g/g glucose. The strains developed in this study achieved a higher L-isoleucine production efficiency than those reported previously. These strategies will aid in the development of cell factories that produce L-isoleucine and related products.

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1). BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-k (TUST-k) module predicts the k values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

Atmospheric CO poses a major threat to life on Earth by causing global warming and climate change. On the other hand, it can be considered as a resource that is scalable enough to establish a circular carbon economy. Accordingly, technologies to capture and convert CO into reduced one-carbon (C) compounds (e.g. formic acid) are developing and improving fast. Driven by the idea of creating sustainable bioproduction platforms, natural and synthetic C-utilization pathways are engineered into industrially relevant microbes. The realization of synthetic C-assimilation cycles in living organisms is a promising but challenging endeavour. Here, we engineer the Serine Threonine Cycle, a synthetic C-assimilation cycle in Escherichia coli to achieve growth on formic acid. Our stepwise engineering approach in tailored selection strains combined with adaptive laboratory evolution experiments enabled formatotrophic growth of the organism. Whole genome sequencing and reverse engineering allowed us to determine the key mutations linked to pathway activity. The Serine Threonine Cycle strains created in this work use formic acid as a carbon and energy source and can grow at ambient CO cultivation conditions. This work sets an example for the engineering of complex C-assimilation cycles in heterotrophic microbes.

Maintaining a delicate balance of adenosine-5'-triphosphate (ATP) is crucial not only for optimal cellular functions but also for improved metabolite production, indicating the need for careful regulation of ATP demands in metabolic engineering. This study explored the modification of ATP metabolism to enhance adipic acid production in Escherichia coli, focusing on the reverse adipate degradation pathway (RADP), and ATP-consuming cycles were fine-tuned by controlling the overexpression of genes (panK and acs) to balance ATP consumption and adipic acid production. As a result, we successfully achieved a significant increase (19.5-fold) in adipic acid production, reaching 1093.11 mg/L in a shake flask, compared to that in the control strain (wild-type E. coli harboring the RADP). Our transcriptomic analysis indicated that modulation of ATP metabolism, along with a balanced supply of pathway precursors, affects metabolic fluxes, enhancing adipic acid biosynthesis in E. coli. This study suggests the potential of metabolic reprogramming of ATP to meet biosynthetic demands, which may improve the production of adipic acid and other ATP-derived chemicals.

Cytochrome B5s, or CytB5s, are small heme-binding proteins, ubiquitous across all kingdoms of life that serve mainly as electron donors to enzymes engaged in oxidative reactions. They often function as redox partners of the cytochrome P450s (CYPs), a superfamily of enzymes participating in multiple biochemical processes. In plants, CYPs catalyze key reactions in the biosynthesis of plant specialized metabolites with their activity dependent on electron donation often from cytochrome P450 oxidoreductases (CPRs or PORs). In eukaryotic microsomal CYPs, CytB5s frequently participate in the electron transfer process although their exact role remains understudied, especially in plant systems. In this study, we assess the role of CytB5s in the heterologous biotechnological production of plant specialized metabolites in yeast. For this, we used as a case-study the biosynthesis of forskolin - a bioactive diterpenoid produced exclusively from the plant Coleus forskohlii. The complete biosynthetic pathway for forskolin is known and includes three CYP enzymes. We reconstructed the entire forskolin pathway in the yeast Saccharomyces cerevisiae, and upon co-expression of the three CytB5s - identified in C. forskohlii transcriptomes - alleviation of a CYP-related bottleneck step was noticed only when a specific CytB5, CfCytB5A, was used. Co-expression of CfCytB5A in yeast, in combination with forskolin pathway engineering, resulted in forskolin production at titers of 1.81 g/L in a bioreactor. Our findings demonstrate that CytB5s not only play an important role in plant specialized metabolism but also, they can interact with precision with specific CYPs, indicating that the properties of CytB5s are far from understood. Moreover, our work highlights how CytB5s may act as indispensable components in the sustainable microbial production of plant metabolites, when their biosynthetic pathways involve CYP enzymes.

Mammalian cell cultures make a significant contribution to the pharmaceutical industry. They produce many of the biopharmaceuticals obtaining FDA-approval each year. Motivated by quality-by-design principles, various modelling methodologies are frequently trialled to gain insight into these bioprocesses. However, these systems are highly complex and uncertain, involving dynamics at different scales, both in time and space, making them challenging to model in a comprehensive and fully mechanistic manner. This study develops a machine-learning-supported multiscale modelling framework of cell cultures, linking the macroscale bioprocess dynamics to the microscale metabolic flux distribution. As a relevant biopharmaceutical case study, we consider the production of Trastuzumab by Chinese Hamster Ovary (CHO) cells in batch. A macroscale hybrid model is constructed by integrating macro-kinetic and machine-learning approaches. Enzyme-constrained Dynamic Metabolic Flux Analysis (ecDMFA) is adopted to calculate flux distributions based on the dynamic predictions of the hybrid model. Uncertainty estimation of the multiscale model is conducted through bootstrapping. Judging from experimental data, our hybrid model can reduce the modelling error of the macroscale dynamics to 8.0%; a 70% reduction from the purely mechanistic model. In addition, the predicted dynamic flux distribution aligns with observations seen in literature, highlighting important metabolic changes throughout the process. Model uncertainty is maintained at a low level, demonstrating the trustworthiness of the predictions. Overall, our comprehensive modelling framework has the potential to facilitate the development of digital twins in the biopharmaceutical industry.

Chimeric antigen receptor (CAR) T cells are an engineered immunotherapy that express synthetic receptors to recognize and kill cancer cells. Despite their success in treating hematologic cancers, CAR T cells have limited efficacy against solid tumors, in part due to the altered immunometabolic profile within the tumor environment, which hinders T cell proliferation, infiltration, and anti-tumor activity. For instance, CAR T cells must compete for essential nutrients within tumors, while resisting the impacts of immunosuppressive metabolic byproducts. In this review, we will describe the altered metabolic features within solid tumors that contribute to immunosuppression of CAR T cells. We'll discuss how overexpression of key metabolic enzymes can enhance the ability of CAR T cells to resist corresponding tumoral metabolic changes or even revert the metabolic profile of a tumor to a less inhibitory state. In addition, metabolic remodeling is intrinsically linked to T cell activity, differentiation, and function, such that metabolic engineering strategies can also promote establishment of more or less efficacious CAR T cell phenotypes. Overall, we will show how applying metabolic engineering strategies holds significant promise to improve CAR T cells for the treatment of solid tumors.

The trade-offs exist between microbial growth and bioproduct synthesis including intracellular polyester polyhydroxybutyrate (PHB). Under nitrogen limitation, more carbon flux is directed to PHB synthesis while growth is inhibited with diminishing overall carbon utilization, similar to the suboptimal carbon utilization during glycolysis-derived pyruvate decarboxylation. This study reconfigured the central carbon network of Halomonas bluphagenesis to improve PHB yield theoretically and practically. It was found that the downregulation of glutamine synthetase (GS) activity led to a synchronous improvement on PHB accumulation and cell growth under nitrogen non-limitation condition, increasing the PHB yield from glucose (g/g) to 85% of theoretical yield, PHB titer from 7.6 g/L to 12.9 g/L, and from 51 g/L to 65 g/L when grown in shake flasks containing a rich N-source, and grown in a fed-batch cultivation conducted in a 7-L bioreactor also containing a rich N-source, respectively. Results offer better metabolic balance between glucose conversion efficiency and microbial growth for economic PHB production.

Bioconversion of high-volume waste streams into value-added products will be an integral component of the growing bioeconomy. Volatile fatty acids (VFAs) (e.g., butyrate, valerate, and hexanoate) are an emerging and promising waste-derived feedstock for microbial carbon upcycling. Cupriavidus necator H16 is a favorable host for conversion of VFAs into various bioproducts due to its diverse carbon metabolism, ease of metabolic engineering, and use at industrial scales. Here, we report that a common strategy to improve product titers in C. necator, deletion of the polyhydroxybutyrate (PHB) biosynthetic operon, results in a significant growth defect on VFA substrates. Using adaptive laboratory evolution, we identify mutations to the regulator gene phaR, the two-component response regulator-histidine kinase pair encoded by H16_A1372/H16_A1373, and the tripartite transporter assembly encoded by H16_A2296-A2298 as causative for improved growth on VFA substrates. Deletion of phaR and H16_A1373 led to significantly reduced NADH abundance accompanied by large changes to expression of genes involved in carbon metabolism, balance of electron carriers, and oxidative stress tolerance that may be responsible for improved growth of these engineered strains. These results provide insight into the role of PHB biosynthesis in carbon and energy metabolism and highlight a key role for the regulator PhaR in global regulatory networks. By combining mutations, we generated platform strains with significant growth improvements on VFAs, which can enable improved conversion of waste-derived VFA substrates to target bioproducts.

Cell-free production systems are increasingly used for the synthesis of industrially relevant chemicals and biopharmaceuticals. Cell-free systems often utilize cell lysates, but biocatalytic cascades based on recombinant enzymes have emerged as a promising alternative strategy. However, implementing efficient enzyme cascades is a non-trivial task and mathematical modeling and optimization has become a key tool to improve their performance. In this work, we introduce a generic framework for the model-based optimization of cell-free enzyme cascades based on a given kinetic model of the system. We first formulate and systematize seven optimization problems relevant in the context of cell-free production processes including, for example, the maximization of productivity or product yield and the minimization of overall costs. We then present an approach that accounts for parameter uncertainties, not only during model calibration and model analysis but also when performing the actual optimization. After constructing a kinetic model of the enzyme cascade, experimental data are used to generate an ensemble of kinetic parameter sets reflecting their variabilities. For every parameter set, systems optimization is then performed and the resulting solution subsequently cross-validated for all other parameterizations to identify the solution with the highest overall performance under parameter uncertainty. We exemplify our approach for the cell-free synthesis of GDP-fucose, an important sugar nucleotide with various applications. We selected and solved three optimization problems based on a constructed dynamic model and validated two of them experimentally leading to significant improvements of the process (e.g., 50% increase of titer under identical total enzyme load). Overall, our results demonstrate the potential of model-driven optimization for the rational design and improvement of cell-free production systems. The developed approach for systems optimization under parameter uncertainty could also be relevant for the metabolic design of cell factories.