Metabolic Engineering Communications

BackgroundThe corn smut fungus Ustilago maydis is an important microbial model organism representing a genetically amenable and readily cultivable basidiomycete. Research in this fungus addresses a broad range of fundamental questions and its biotechnological exploitation is on the rise. Although genetic engineering in principle is well established, efficient methodology for synthetic biology approaches such as metabolic engineering or pathway transplantation has remained limited. ResultsHere, we present a comprehensive toolbox for U. maydis based on modular cloning and the characterization of more than 20 promoters. Careful comparative evaluation of insertion loci and terminator as well as reporter effects was conducted and a novel color-based strategy for straightforward genome integration was implemented. Moreover, the cloning and subsequent one-step integration of four transcriptional units into U. maydis was demonstrated by creating a "rainbow" strain producing four fluorescent proteins. ConclusionOverall, this next generation toolkit strongly advances genetic engineering and systems biology approaches in U. maydis, fostering its development into a valuable and competitive fungal chassis and prime model, particularly in applied research.

Muconic acid is an industrially valuable molecule that can be biologically produced from diverse biogenic and waste-derived feedstocks, including sugars and lignin- and plastic-derived aromatic compounds. However, accumulation of protocatechuate (PCA) has been observed in multiple microbes engineered for muconate production when the PCA decarboxylase, AroY, is used. This raises the question of whether PCA decarboxylation represents a rate-limiting step and how this bottleneck might be alleviated, especially given the toxicity and reactivity of PCA and catechol intermediates. To address this, we performed adaptive laboratory evolution (ALE) on a strain of Pseudomonas putida originally engineered for muconate production from aromatic compounds, but with catBC restored, to select for improved conversion of PCA and, in separate lineages, 4-hydroxybenzoate. Contrary to our expectations, the predominant beneficial mutations localized to the catA1 cassette encoding catechol 1,2-dioxygenase, rather than aroY or its associated cofactor biosynthesis genes. Transcriptomic analysis revealed elevated catA1 expression in evolved isolates from ALE, and introduction of these mutations improved productivity in strains designed for muconate production from both aromatic and sugar substrates. Quantitative proteomics and biochemical assays demonstrated that the mutations also led to increased CatA1 protein abundance and modest enhancements in catalytic efficiency, respectively, with strain phenotypes largely driven by high CatA1 levels and potentially synergistic kinetic improvements. Additional reverse-engineering studies identified variants with modest effects on muconate accumulation, including those with potential to enhance biosynthesis of the prenylated FMN cofactor of AroY. Collectively, these results indicate that catechol, not PCA, is the principal bottleneck in muconate production via the PCA decarboxylation route originally demonstrated by Draths et al., refining our understanding of pathway limitations and offering new strategies for improving rate, yield, and strain resilience in muconate bioproduction. HighlightsO_LIAccumulation of metabolic intermediates was alleviated by adaptive laboratory evolution C_LIO_LISequencing, proteomics, and enzyme kinetics revealed mechanisms for adaptation C_LIO_LIIncreased CatA1 expression reduced bottlenecks and improved muconate production C_LI

Muconic acid is a versatile platform chemical that can be biologically produced from lignocellulosic substrates, including from lignin-related aromatic compounds. Pseudomonas putida has been previously engineered to convert lignin-related aromatic compounds to muconate at quantitative molar yields. This high atom efficiency requires a supplemental carbon and energy source to support bacterial growth, and central carbon metabolic efficiency and its interaction with aromatic catabolism are underexplored. Here, we applied proteomics, metabolomics, and 13C-fluxomics to quantitatively compare central carbon and energy metabolism in wild-type P. putida KT2440 and a muconate-producing strain, P. putida CJ781. During cultivation on glucose and 4-hydroxybenzoate, CJ781 showed increased glucose uptake, reconfigured central fluxes, and increased extracellular leakage of aliphatic acids relative to wild type. These altered fluxes supported a 3-fold higher ATP pool, in excess of demand. Pyruvate and acetate secretion in CJ781 was mitigated by debottlenecking TCA-cycle entry via citrate synthase overexpression. Furthermore, tuned expression of the catechol dioxygenase and protocatechuate decarboxylase enabled the production of 36.3 g L-1 muconate at 1.1 g L-1 h-1. Overall, this work reveals how P. putida redirects carbon and energy fluxes to support aromatic bioconversion for improved bioproduction from renewable feedstocks.

While in vivo synthesis of biologic therapeutics has been broadly successful, it is limited by biological constraints of the cells and by the complexity, time, and cost of implementing the pipeline from discovery through manufacturing. Cell-free expression systems (CFES), which use cellular transcription and translation machinery to express proteins in vitro, offer a promising alternative approach that could improve robustness and modularity in that pipeline. However, current benchmark CFES productivity is well below the theoretical capacity of the input nucleotides and amino acids. Efforts to address this issue are hindered by limited understanding of the extent of enzymatic activity in CFES beyond gene expression, as previous work has shown that metabolic enzymes in cell-free lysates cause substantial background metabolic activity that influences protein expression. Here, we hypothesized that the inflection point of protein expression is a critical timescale for CFES metabolism. We performed metabolomics characterization of CFES reactions, finding significant metabolic changes at the inflection point. Driven by these findings, we sought to identify supplements that could be added to the cell-free reaction to avoid metabolic limitations. We found that amino acid supplementation increased expression productivity and lifetime only when added after the inflection point, and actually hurt expression when added before the inflection point. We found similar supplementation timing impacts for some other metabolites as well. These findings show that endogenous metabolism and supplementation timing are deeply interconnected and are critical considerations in CFES optimization, and that metabolomics-informed fed-batch supplementation is a potentially valuable strategy to improve reaction productivity.

There are huge variations in metabolic complexity between the different kingdoms of life. Whilst it has been shown that some simple, unicellular organisms such as E. coli direct their energetic resources towards maximising proliferation, the metabolic goals of more complex organisms are unclear. This is an especially important topic for engineered organisms, such as Chinese Hamster Ovary (CHO) cells, that have been modified to produce therapeutically relevant compounds. This metabolic goal is reflected in the objective function of a constraint-based model (CBM) and has a direct impact on the metabolic flux distribution that is predicted using Flux Balance Analysis (FBA). However, there is no broadly applicable approach to infer this objective function from experimental data, to ensure CBMs represent real growth conditions. Here, we developed SIMOFF (SIMulated annealing Objective Function Finder) to infer the most appropriate objective function from minimal experimental flux data. Our applications of SIMOFF to S. cerevisiae demonstrated that the most suitable objective function is dependent on key metabolic phenotypes, even when the same organism and conditions are being modelled. Furthermore, we demonstrated the translatability of SIMOFF through application to CHO cells, where we showed that a SIMOFF-inferred objective function improved the accuracy of gene essentiality simulations, resulting in more reliable experimental target predictions.

Anaerocellum (formerly Caldicellulosiruptor) bescii, an anaerobic, extremely thermophilic (Topt [~]78 {degrees}C) lignocellulolytic bacterium, is a promising chassis for metabolic engineering and next-generation bioprocessing. Yet, a lack of well-characterized genetic parts in A. bescii has hampered metabolic engineering efforts. Here, using a previously developed hyperthermophilic {beta}-galactosidase reporter system, we screened a diverse panel of putative A. bescii promoter sequences, identifying promoters that drove reporter output across a broad range. For a select subset, we mapped their transcriptional start sites (TSSs) and evaluated ribosome binding site (RBS) regions using chimeric promoter constructs. By constructing truncated promoter variants, we defined functional regions within the widely used, high-expression S-layer protein promoter (Pslp) and engineered a compact 99 bp variant that retained substantial reporter activity. Finally, we demonstrated that these new promoters can be used for metabolic engineering by using two newly characterized promoters to express an established thermostable alcohol dehydrogenase from Thermoclostridium stercorarium to drive ethanol production in A. bescii. Together, this work expands and diversifies the A. bescii genetic toolkit, opening doors to future metabolic engineering efforts in this species.

The pulp and paper industry produces large volumes of condensed kraft lignin, which is challenging to convert to single chemical products. For this purpose, tandem chemical depolymerization and bioconversion to a single atom-efficient product is a potentially promising strategy. In this study, we conducted copper-catalyzed oxidative depolymerization using pine-derived kraft lignin to generate multiple bioavailable aromatic monomers at a yield of 4.5 weight% (wt%; g monomers per g lignin) from both C--O and C--C bond cleavage, followed by counter-current extraction with a 52 wt% monomer recovery. This resulted in an oxidized lignin product containing vanillin, vanillate, 4-hydroxybenzaldehyde, 4-hydroxybenzoate, 5-formylvanillin, 5-carboxyvanillin, 5-carboxyvanillate, acetovanillone, and vanillyl glyoxylate. Based on this stream composition, we engineered the industrially relevant soil bacterium Pseudomonas putida KT2440 to catabolize the latter five compounds via overexpression of ten heterologous genes (acvABCDEFSYK-6, vceABSYK-6, ligW2SYK-6, and mdlCPP). We combined these engineered pathways with previously reported strategies for muconate production from G- and H-type monomers to generate P. putida KMM428, which utilized 93.6 {+/-} 0.2 mol% of the quantified aromatic monomers in a depolymerized kraft lignin mixture, and produced muconate at a yield of 99 {+/-} 3 mol%, on a quantified monomer basis. Together, this work increases the theoretical carbon conversion efficiency of this process by 37.6 {+/-} 0.1 mol% through incorporation of three {beta}-5 cleavage products, in addition to traditional G-type monomers.

BackgroundMicrobial chain-elongation by Clostridium kluyveri using the products (acetate and ethanol) derived from the electrocatalytic CO2 reduction reaction (CO2RR) represents a unique sustainable strategy for producing C4-C6 chemicals from CO2. However, direct integration of electrocatalytic effluents with anaerobic bioprocesses is often impeded by the physiological incompatibility between electrocatalytic product streams and microbial metabolism. Specifically, CO2RR effluents commonly contain formate, which cannot be utilized by C. kluyveri for chain elongation and therefore reduces the overall carbon efficiency of CO2 conversion to C4-C6 chemicals. Moreover, both formate and the elevated phosphate concentrations typical of electrochemical reaction solutions may inhibit microbial growth. ResultsWe show that formate at concentrations of up to 50 mM did not inhibit the growth of or the chain elongation by C. kluyveri. Based on this finding, we developed a modular two-step bioprocess. In the first step, the acetogen Clostridium ljungdahlii converts formate in CO2RR product mixtures into acetate, thereby generating additional substrates for second-step C. kluyveri-driven chain elongation, thus increasing the CO2RR carbon-conversion efficiency to C- C6 chemicals. To address the issue of C. ljungdahliis inhibition by high phosphate concentrations in electrocatalytic solutions, we explored the use of C. ljungdahlii biofilms for the first, i.e. the formate-conversion, step. C. ljungdahlii biofilms exhibit tolerance to concentrated electrolytes, enabling the conversion of up to 50 mM formate in CO2RR solutions. ConclusionsThe demonstrated two-step process constitutes the basis for the development of a robust and carbon-efficient biological process for the scalable upgrading of C1-C2 CO2RR products into higher-value C4-C6 chemicals.

Biological ethanol production is important for the circular carbon economy and makes up 73% of the U.S. biological fuels market. Previously, we produced ethanol by reversing methanogenesis and capturing methane by cloning methyl-coenzyme M reductase (Mcr) from an unculturable population of anaerobic methanotrophic archaea; this process was predicated on the generation of the intermediate acetate and its conversion by the methanogenic host to ethanol. Moreover, methanogens are generally thought to be detrimental for converting acetate to ethanol and are usually intentionally inhibited. Here, we demonstrate that direct growth on acetate as the sole carbon and energy source by the methanogen Methanosarcina acetivorans C2A results in 40% of the metabolized acetate becoming ethanol and that there is 430% more ethanol produced, compared to growth on methane via Mcr. In addition, we found growth on methanol results primarily in methane generation and low levels of ethanol. Therefore, acetate may be readily converted by the methanogen M. acetivorans to ethanol at high yields.

Many valuable plant metabolites are synthesized by type III polyketide synthases (PKS) that have become targets for the engineering of microbial production systems of these compounds. The rhizomes of turmeric (Curcuma longa) and ginger (Zingiber officinalis) are highly regarded for medicinal and culinary purposes and are the sources of bioactive curcuminoid and gingeroid polyketides. Fast growing demand for these compounds has sparked effort to identify their biosynthetic pathways to facilitate their heterologous production. In turmeric, a collaborative diketide synthase (DCS) and PKS (CURS) pair synthesizes curcumin from feruloyl- and malonyl-CoA. Yet, bona fide genes for the biosynthesis of gingeroids in Ginger are not known. Here we report the identification of two DCS/PKS pairs in Ginger that have different activity profiles in E. coli engineered to provide feruloyl- and hexanoyl-CoA as substrates. We show that one PKS (ZoPKS2) makes 6-dehydrogingerdione (6-DHG) as its major product while the other PKS (ZoPKS1) is a curcumin synthase. We found that ZoPKS2 becomes an efficient curcumin synthase when hexanoyl-CoA is not available, making it a dual-function enzyme that can be used to easily switch heterologous productions towards either of these two valuable products. Precursor feeding studies show that the substrate promiscuity of the collaborative DCS/PKSs may be exploited to access different dehydrogingerdione derivatives, while structural models of the Ginger PKSs offer insights for future engineering of product profiles. We believe that this work will add to the type III PKS toolbox and enable the development of efficient production platforms for gingeroids.

Building high-performance microbial cell factories requires dynamic coordination of resource allocation among cellular growth, target-product biosynthesis, and endogenous host metabolism. However, existing polyploid engineering strategies rely primarily on static manipulation of chromosome copy number. Although increasing gene dosage can enhance biosynthetic capacity, static designs cannot readily accommodate the changing metabolic demands encountered during fermentation. Here, we developed a metabolite-responsive dynamic polyploid engineering strategy that couples chromosome ploidy to the cellular metabolic state. We first constructed a high-performance L-threonine biosensor and used it to sense intracellular L-threonine levels and regulate ftsZ expression, a key cell-division gene, thereby establishing a dynamic polyploid system that requires neither exogenous inducers nor antibiotics. This system enabled engineered cells to progressively transition from polyploid to haploid during fermentation, accompanied by stage-specific remodeling of cellular physiology and metabolism. Physiological characterization revealed a marked increase in cell size and alterations in cell-envelope properties during the polyploid phase, followed by a gradual decrease in chromosome copy number as fermentation progressed. Transcriptomic and metabolomic analyses further demonstrated that dynamic ploidy transitions induced global metabolic network rewiring, remodeling the tricarboxylic acid cycle and amino acid metabolism while redirecting carbon flux toward the biosynthesis of aspartate-family amino acids. Ultimately, dynamic polyploid engineering substantially enhanced L-threonine production, enabling the engineered strain to achieve an L-threonine titer of 183.1 g/L and a yield of 0.67 g/g glucose in 5-L fed-batch fermentation without antibiotics or exogenous inducers. These findings show that dynamic regulation of chromosome ploidy can couple gene-dosage control with remodeling of cellular physiology and metabolic networks, providing a new engineering strategy to overcome the limitations of static polyploid designs and build high-performance microbial cell factories.

Bacillus subtilis is a core microbial chassis in biomanufacturing, and establishing efficient gene editing technologies is key to engineering this strain. In conventional CRISPR gene editing technologies, the large size of DNA nucleases leads to difficulties in plasmid construction, low transformation efficiency, and cumbersome multi-round editing operations; therefore, developing miniature gene editing tools can effectively address these issues. Although our group previously established a miniature gene editing tool based on IscB in B. subtilis SCK6, IscB relies on the 5'-CAGGAA-3' TAM recognition sequence, and 83.36% of the genes in the SCK6 genome harbor no or only one TAM sequence, indicating a bottleneck of restricted editing for IscB in this strain. The novel miniature DNA nuclease TasR does not require a TAM sequence and can thus compensate for the limitation of IscB; however, the applicability of TasR in B. subtilis remains unknown. Therefore, this study first constructed a single plasmid, pBsuTasR, capable of expressing TasR and its guide RNA (tigRNA), which enabled gene deletion of regular-sized fragments in SCK6 with editing efficiencies of 21.7%- 78.3%. Subsequently, the capacity of TasR to delete a long DNA fragment (169.9 kb) was evaluated, and it was found that under the guidance of a single tigRNA, the deletion efficiency was 21.73%, whereas after optimizing to two tigRNAs, the efficiency increased to 39.13%. Furthermore, the gene integration capability of pBsuTasR was further tested, and TasR was able to integrate the aprN gene into the amyE locus at an efficiency of 13.3% under the guidance of a single tigRNA, and after increasing to two tigRNAs, the integration efficiency increased to 91.3%. In terms of iterative genome editing, this study developed the pBsu-SRP (Scissors-Rock-Paper) iterative editing system, which automatically cures the editing plasmid from the previous round while performing a new round of gene editing, with sequential gene deletion efficiencies of 4.34%-26.08%, and using this system, the editing cycle can be shortened from 4N days by the conventional method to 3N+1 days. Subsequently, the pBsu-SRP system was successfully used to achieve the integration of two and three copies of the mCherry fluorescent reporter gene in SCK6, and it was found that the fluorescence intensity increased with the copy number. Finally, this study also explored the escape of SCK6 from TasR cleavage and found that mutations in the tigRNA sequence are the cause of the escape. In summary, this study constructed a novel miniature genome editing system in B. subtilis using the TAM-independent nuclease TasR as the core component. This system can not only provide an efficient technical tool for genetic manipulation of industrial microorganisms, but also offer new instrumental support for the iterative engineering and functional optimization of chassis cells in biomanufacturing.

Microbial metabolism can be represented as an energy-conserving process (catabolism) and a biomass-forming reaction (anabolism). Anabolism is traditionally measured through the turbidity of the culture, while catabolism is often assessed by the substrates consumed or the products formed. Standard measurements of biomass and products are intrusive and disrupt cultivation and headspace composition, potentially masking important analytical parameters and interactions. Online pressure and backscatter were combined in small-scale closed batch vials to obtain undisturbed real-time measurements of catabolic and anabolic rates, enabling mapping of metabolic phases throughout an entire batch cultivation cycle. The method identified discrete metabolic phases in yeast cultivation and thermophilic syngas fermentation. In nutrient-rich yeast cultivation, five metabolic phases were characterized, covering growth-associated and non-growth-associated gas formation. In a mixed community syngas fermentation, estimates of catabolic and anabolic rates distinguished early biomass increase from minimal net pressure change from two later gas-driven phases. An initial phase with a higher growth rate, linked to carboxydotrophy, followed by a phase with slightly lower growth and increased gas consumption, corresponding to hydrogenotrophic acetogenesis. The study demonstrates that a simple, affordable experimental setup with online pressure and backscatter measurements can be used to visualize phase-plane mapping of microbial metabolism. An additional advantage is the ability to detect sequential metabolic cascades in mixed microbial communities, which is not possible with gas-sparging bioreactor studies. Using a single simple batch culture, growth and maintenance data can be obtained, even when growth is low or absent, thereby yielding parameters applicable to phenotypic characterization and dynamic metabolic modelling.

Acetate is a promising carbon source for microbial biotechnology as it can be produced sustainably from lignocellulosic biomass or C1 gases. Since acetate is directly activated to acetyl-CoA, it is especially suitable for producing acetyl-CoA-derived products, showcased here with the production of 3-(3-hydroxyalkanoyloxy) alkanoic acids (HAAs). P. putida KT2440 can natively metabolize acetate, but the weak acid has also inhibitory effects on microbial growth. We present an in-depth study on the physiology of P. putida KT2440 using acetate as carbon and energy source and evaluate acetate as feedstock for the biosynthesis of HAAs. Initially, a rational engineering approach to overexpress acetyl-CoA synthetase for acetate activation resulted in an improved growth rate of 16% and reduced lag phase by six hours. To further increase the performance of P. putida KT2440 on acetate, adaptive laboratory evolution was performed. This resulted in an improvement in the growth rate from 0.4 h-1 to 0.6 h-1 and enabled growth on up to 12.5 g L-1 acetate with a shortened lag phase compared to the wild type. Whole-genome sequencing revealed mutations in proteins involved in gene expression regulation and signal transduction. This evolutionary engineering approach informed the deletions of gacS and crc, which resulted in a reduction in the lag phase from seven hours to one hour and an improvement of the growth rate by 25 %, matching the growth properties of the evolved clones. Using the evolved strains for the production of HAAs resulted in faster biomass and product formation with product titers reaching up to 94 % of that of the wild type. In conclusion, we identified mechanisms in the acetate metabolism of P. putida KT2440 and improved the growth performance of the strain by rational and evolutionary engineering, demonstrating the potential of the promising, but challenging 3rd generation feedstock acetate.

Methanol is a reduced, soluble one-carbon (C1) feedstock for sustainable bioproduction, but converting this potential into robust microbial growth remains difficult. Several synthetic C1 assimilation routes depend on autocatalytic cycles, whose operation requires coordinated control of redox balance, toxic intermediates, substrate regeneration, and host regulation. Here, we implemented the serine-threonine cycle (STC) in the soil bacterium Pseudomonas putida and used growth-coupled selection with adaptive laboratory evolution (ALE) to transition from mixotrophic C1 incorporation to strict methylotrophy. The evolved strain grew with methanol as the sole carbon and energy source under atmospheric CO2 with a doubling time of ca. 40 h. Whole-genome sequencing, reverse genetics, biosensors, isotope labelling, and comparative RNA sequencing showed that evolution repeatedly targeted native pyrroloquinoline quinone (PQQ)-dependent methanol oxidation, membrane-bound transhydrogenase activity, glycine regeneration, STC enzyme balance, and global regulatory nodes. Additional ALE under glycine-methanol co-feeding increased growth rates and exposed further targets for improving cycle flux. These results establish P. putida as a chassis for strict synthetic methylotrophy and define actionable engineering routes toward C1 biomanufacturing. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/739708v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1dce5a6org.highwire.dtl.DTLVardef@16910eaorg.highwire.dtl.DTLVardef@d59018org.highwire.dtl.DTLVardef@e7287f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Food production is a significant contributor to global greenhouse gas emissions and deforestation, exacerbated by substantial food waste. Converting food waste into yeast protein offers a sustainable solution to enhance food security and contribute to a circular economy. However, due to the diverse and variable nature of food waste substrates, numerous experimental trials are required to optimise the preprocessing steps, yeast strain selection, nutrient addition, and fermentation conditions. This study presents a hybrid modelling approach where data-driven machine learning is used to predict microbial growth kinetics from process parameters. The hybrid model was trained on a comprehensive dataset consisting of 963 fermentation experiments from 55 publications, enabling transfer learning across 46 yeast strains and 79 food waste substrates. The hybrid modelling method was integrated with Bayesian optimisation, a sequential strategy to optimise expensive-to-evaluate functions, to efficiently maximise yeast biomass growth from different food waste substrates. The utility of the hybrid model was evaluated using five test datasets selected from previous literature and was shown to facilitate an average reduction of 66% in the number of experimental trials required to identify optimal fermentation conditions compared to without using the hybrid model. This proved that the transfer of knowledge between yeast strains and food wastes improved the optimisation efficiency of real, previously published datasets compared to traditional optimisation methods. The novelty and contributions of this study include the collation of the extensive dataset, provided as supplementary material; and the demonstration that transfer learning by training the hybrid model on this heterogeneous dataset can improve the optimisation efficiency for yeast biomass growth on new strains and substrates.

Cell-free protein synthesis (CFPS) is a powerful platform for synthetic biology, yet the factors governing reaction longevity remain poorly understood despite their importance for high-throughput applications. Here, the three principal determinants of CFPS performance--DNA template design, reaction composition, and lysate genotype--were systematically optimized to extend reaction lifetime in a 384-well plate format. Different energy regeneration systems were evaluated through real-time pH monitoring and metabolomic analyses to identify the metabolic constraints limiting prolonged protein synthesis. Lysates prepared from engineered Escherichia coli BL21(DE3) strains were further examined to assess the contributions of DNA, RNA, and amino acid stabilization. Systematic optimization of amino acid, nucleoside triphosphate, polyethylene glycol, and lysate concentrations identified DNA template stability and amino acid preservation as the primary factors sustaining CFPS activity. Combining these improvements yielded reactions that remained productive for >14 h and produced 567 {+/-} 64 g mL-1 active deGFP. These findings establish practical strategies for extending CFPS lifetime and improving high-throughput cell-free platforms.

Nicotine accumulates in the gut and drives non-alcoholic steatohepatitis (NASH) via the gut-liver axis, yet no effective clinical intervention is currently available. To address this challenge, the probiotic Escherichia coli Nissle 1917 (EcN) was engineered for in situ nicotine clearance in the gut. Mutational screening of nicotine oxidoreductase 2 (PpNicA2) identified a highly active variant, PpNicA2A107R. Its incorporation into EcN together with an electron transfer protein (CycN) and a newly identified transporter (T3/T7) yielded 80% nicotine-degrading activity. Chromosomal integration of this module generated a stable strain, EcN-N12, which in NASH mouse models depleted intestinal nicotine, rescued hepatic lipid metabolism, alleviated tissue damage, and intercepted the nicotine-mediated gut-liver axis pathological progression. This work thus offers an effective and clinically translatable approach for nicotine-associated diseases.

Given the current global environmental challenges, waste biomass is an attractive renewable resource for circular economies. Gasification of biomass yields syngas (CO, CO2, and H2) that is a suitable feedstock for gas fermentation in biomanufacturing of fuels and chemicals using acetogen microbes. While it is generally known that syngas composition influences both acetogen growth and process performance, we are lacking a consistent dataset quantifying these effects under controlled fermentation conditions. Here, we mapped the metabolic response of the model-acetogen Clostridium autoethanogenum to seven synthetic syngas mixtures during exponential batch growth in bioreactor fermentations. Notably, distinct gas compositions resulted in different fermentation profiles, affecting both growth and metabolite production. Maximum specific growth rates ranged within 0.05 0.13 h-1, with slower growth for low-CO mixtures. While acetate and ethanol production yields varied between 20-133 and 76-353 mmol per gram dry cell weight, respectively, minor production of 2,3-butanediol was detected. All syngas mixtures supported co-utilization of CO and H2, though gas uptake stoichiometry only moderately correlated with syngas content. Importantly, gas uptake stoichiometry strongly influenced carbon partitioning, with higher relative H2 uptake reducing CO2 loss or even realizing CO2 fixation together with increasing carbon flow towards metabolites. Interestingly, higher syngas H2 content favored ethanol and 2,3-butanediol production, while higher H2:CO uptake ratios increased total flux through the Wood-Ljungdahl pathway rather than selectively favoring reduced by-products. Our results are valuable for a better understanding of syngas composition effects on the acetogen biocatalyst and for process engineering towards optimizing gas fermentation performance. HighlightsO_LISyngas composition affects acetogen growth, gas uptake, and carbon distribution C_LIO_LIHigher H2:CO uptake ratios increase carbon flow through the Wood-Ljungdahl pathway C_LIO_LIHigher relative H2 uptake reduces CO2 loss and increases metabolite production C_LI

Genome-scale metabolic models (GSMs) underpin pathway and strain engineering by linking genes to metabolic reactions and enabling system-level simulation of cellular fluxes and intervention effects, yet end-to-end analysis workflows remain fragmented, expert-demanding, and slow to adapt. Large language models (LLMs) could transform this landscape, lowering the barrier by explaining concepts, interpreting GSM files, and turning natural-language instructions into valid analysis code, thereby substantially mitigating the time, effort, and expertise required. However, their reliability for domain-specific tasks remains unexplored. Here, we delivered a systematic benchmark of four leading LLMs (GPT-4, Gemini, Claude, DeepSeek-R1) across four task areas central to metabolic engineering: domain knowledge, metabolic flux prediction, pathway construction, and flux optimization. For benchmarking, we introduced a standardized, rubric-based evaluation framework that uses multi-LLM automated scoring (an ensemble of LLM-as-a-judge assessments) and two distinct sets of nine task-tailored metrics (domain vs coding-focused tasks), rated on a 1-5 scale (up to 45 per task), covering scientific validity and code executability where applicable. Across tasks, we reveal consistent strengths (conceptual explanation, code synthesis) and critical failure modes (e.g., context window limitations, incorrect identifier assumptions, strain-dependent reasoning errors, and errors in domain-specific algorithms). In aggregate, DeepSeek-R1 led in domain tasks, narrowly edging GPT-4, Claude, and Gemini, demonstrating that conceptual biological logic remains highly invariant across architectures. In contrast, Gemini achieved the highest score for coding tasks, distinguished by functional execution and excelled in error handling, documentation, and readability, followed by GPT-4, Claude, and DeepSeek. We also evaluated LLM self-inspection capability by injecting subtle, consequential faults: a stoichiometric sign error causing mass imbalance and an omitted pathway reaction. We reveal that conversational "blind search" prompting completely fails to localize these network faults. Instead, robust error localization requires prompts reframed with domain-informed constraints that force the LLM to leverage tool-assisted code procedures, such as COBRApy mass-balance functions. Together, this work establishes an evidence-based baseline for LLM-enabled GSM analysis, providing actionable guidance for building reliable, automation-ready workflows for pathway and strain design. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/730004v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@442a55org.highwire.dtl.DTLVardef@1374927org.highwire.dtl.DTLVardef@a3b7a8org.highwire.dtl.DTLVardef@6ea308_HPS_FORMAT_FIGEXP M_FIG C_FIG