Metabolic Engineering

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

Extracellular vesicles (EVs) are mediators of intercellular communication, yet their impact on Chinese Hamster Ovary (CHO) cell physiology and bioprocess performance remains poorly understood. Here, we investigated whether small EVs (sEVs) and large EVs (LgEVs) that accumulate during fed-batch and perfusion cultures modulate CHO cell growth, metabolism, apoptosis, and monoclonal antibody (mAb) production. EVs isolated from early- and late-stage cultures were added to fresh CHO cultures grown with or without glutamine supplementation. Only LgEVs had a significant impact. Late-stage LgEVs markedly altered CHO-cell behavior, reducing cell proliferation, increasing apoptosis under glutamine-limited conditions, and substantially enhancing mAb productivity in a dose-dependent manner. Glutamine supplementation largely alleviated the growth-inhibitory and pro-apoptotic effects of LgEVs while preserving their positive impact on productivity, suggesting that glutamine decouples EV-mediated stress from productivity enhancement. Metabolic analyses revealed increased glucose consumption, a glutamine-dependent shift between glycine and alanine overflow metabolism, and remodeling of amino-acid utilization. Metabolic flux analysis further demonstrated enhanced glycolytic overflow and increased reliance on amino acid-supported anaplerosis. Conversely, selective removal of LgEVs from perfusion medium significantly improved cell expansion without reducing antibody production, supporting an inhibitory role for late-stage LgEVs. These LgEVs were enriched in let-7 family miRNAs and miR-21, consistent with RNAseq analyses demonstrating stress-associated enrichment of these miRNAs in CHO EVs and with functional studies showing that let-7a and miR-21reduce CHO-cell growth. Together, these observations suggest that selective miRNA loading contributes to the growth, metabolic, and productivity phenotypes elicited by late-stage LgEVs. Our findings identify LgEVs as endogenous regulators of CHO-cell physiology and potential targets for optimizing high-density fed-batch and perfusion biomanufacturing processes. HighlightsO_LIEndogenous late-stage Large Extracellular Vesicles (LgEVs) reduce CHO cell growth but boost specific mAb productivity. C_LIO_LIGlutamine supplementation rescues LgEV-mediated growth inhibition and apoptosis. C_LIO_LIMetabolic Flux Analysis (MFA) based on the dynamic behavior of amino acid and other metabolite and substrate concentrations reveals the pyruvate node as a metabolic bottleneck and the associated lactate overflow metabolism as resulting from LgEV exposure. C_LIO_LIStress-associated let-7 and miR-21 microRNAs are highly enriched on a per-EV basis in late-stage LgEVs. C_LIO_LISelective removal of LgEVs improves perfusion cell growth without impacting antibody titer. C_LI

Engineering heterologous utilization of substrates requires selection of catabolic pathways that balance strain performance and product biosynthesis. Here, we compare the oxidative and isomerase arabinose utilization pathways in Pseudomonas putida strains engineered for cis,cis-muconic acid production from glucose and xylose. Based on the point of entry into central carbon metabolism, we hypothesized that the oxidative arabinose pathway would enable higher productivity while the arabinose isomerase pathway would enable higher muconate yield. In both strains, additional modifications were engineered to improve muconic acid production including sugar transporter tuning, catechol 1,2-dioxygenase overexpression, a feedback-resistant DAHP synthase, and a flux-stabilizing gltA variant. Consistent with our hypothesis, the oxidative arabinose pathway supported faster growth and higher productivity (0.58 g/L/h), whereas the arabinose isomerase pathway improved carbon efficiency, achieving muconate yields of up to 50 C-mol% in fed-batch bioreactors. Process modeling indicates that these performance metrics can reduce the minimum selling price of muconate-derived adipic acid to $2.74/kg and greenhouse gas emissions to 1.31 kg CO2e/kg, approaching cost parity and reducing emissions by 86% relative to fossil carbon-derived adipic acid. Overall, this study presents a systematic comparison of sugar catabolic pathways that enabled development of strains suited for the tradeoffs between rate and yield.

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.

Methanogenic archaea, such as Methanothermobacter marburgensis, represent a powerful biological platform for carbon capture and valorization, directly converting carbon dioxide (CO2) and molecular hydrogen (H2) into proteinogenic amino acids (AAs). In this study, we present a controlled and scalable strategy for tailoring AA production (biosynthesis and secretion) in continuous gas fermentation. By applying various Design of Experiments (DOE) techniques, we systematically identified and optimized key process parameters governing AA biosynthesis and shaping a targeted AA secretion profile. A hybrid modeling framework combining experimental data with scale-independent parameters derived from computational fluid dynamics (CFD) enabled robust performance prediction across bioreactor scales. This model-driven approach successfully translated the process from 120 mL glass bottles via 2 L to 150 L reactors, corresponding to a reaction-volume scale-up factor of 2000. These findings set the foundation for a robust and predictive platform for sustainable AA production, positioning archaea as a high-potential alternative in industrial biotechnology.

Sustainable hydrogen production from microalgae remains limited by intrinsic physiological constraints and the need to preserve biomass value for food and feed applications. Transgenic approaches to overcome these limitations were proven successful, yet result in genetically modified (GMO) strains that face major regulatory and deployment barriers. Here, we present a non-GMO experimental platform that enables systematic isolation of hydrogen-producing phenotypes through high-throughput UV mutagenesis pipline coupled with targeted physiological screening. Applying this approach across phylogenetically distinct algal species, including the industrial strain Chlorella vulgaris and the extremophile Chlorella ohadii, we achieve high discovery efficiency, recovering 0.4-0.6% validated hydrogen-producing mutants and achieving 6.7-25% validation rates among screen-positive candidates, indicating strong enrichment at the primary screening stage. We show that sustained hydrogen production represents a physiologically accessible state emerging across diverse genetic backgrounds. This state is consistently associated with reorganization of photosynthetic electron partitioning, yet arises through multiple distinct configurations that differentially balance hydrogen production, oxygen metabolism, and carbon fixation. This framework provides a scalable route to identify hydrogen-producing strains in industrially relevant algae without introducing foreign DNA and expands the accessible design space for photobiological hydrogen production.

Carbonic anhydrases are among the fastest known biocatalysts, reversibly facilitating the hydration of CO2 to HCO3- at rates up to 107 s-1, which warrants their investigation for industrial carbon capture technologies. However, engineering carbonic anhydrases to maintain stability under harsh industrial process conditions remains a key challenge, and sequence-to-function datasets compatible with machine learning to inform forward engineering are lacking. Here, we developed a high-throughput platform that couples cell-free gene expression with a gaseous CO2 colorimetric assay to map the fitness landscapes of carbonic anhydrases. From 96 diverse natural homologs, we identified a robust variant from the Aquificota phylum and conducted an exhaustive mutational scan and functional assessment of this enzyme at 70C and 90C, covering >99% of all single-amino acid substitutions (totaling 4,365 mutations assayed in 39,285 reactions). This biochemical landscape was used to benchmark 22 zero-shot protein fitness models and identify critical mutations that improved enzyme stability at 90C by more than three-fold. We then used both zero-shot protein language models and supervised learning to filter 419 model-generated variants from a ProteinMPNN library of 100,000 sequences, leading to a best-in-class enzyme that retained activity after incubation at 95C. This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.

Acetate metabolism drives mixotrophic and heterotrophic growth in some microalgae. Acetyl-CoA synthetase (ACS) and acetate kinase (ACK) are often considered the main enzymes involved in acetate catabolism in microalgae; however, their contributions to metabolic flux and carbon allocation are not fully understood. In this study, the functions of cytosolic ACS1 and mitochondrial ACK2 were characterized using two knockout mutants of the model microalga Chlamydomonas reinhardtii. The acs1 mutant exhibited a growth-oriented phenotype, characterized by 29.8% faster cell growth at 96 h and up to a 15.5% higher acetate depletion rate, yet showed a 38.3% lower triacylglycerol (TAG) content at 48 h under heterotrophic conditions. By contrast, the ack2 mutant exhibited an altered carbon-allocation phenotype under heterotrophic conditions. Despite an up to 32.4% lower respiratory oxygen consumption rate and a 27.7% reduction in cell density, ack2 exhibited a 39.3% higher biomass concentration and a 90.4% greater dry weight per cell than the wild type at 96 h. Biochemical analysis revealed that ack2 accumulated 23.3% more carbohydrate than the wild type at 120 h under heterotrophic conditions, whereas its TAG level remained comparable to that of the wild type. These findings suggest that, under heterotrophic conditions, the loss of cytosolic ACS1 facilitates cell growth and division at the expense of TAG biosynthesis, whereas the loss of mitochondrial ACK2 regulates growth by affecting carbon flux toward biomass and carbohydrate accumulation. This work provides insight into acetate catabolism in C. reinhardtii and suggests targets for engineering microalgae for production of biomass and bioproducts.

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.

Understanding stress-induced metabolic reprogramming in crop plants can inform breeding strategies and support the development of stress-resilient varieties. Genome-scale metabolic modelling has shown promise in elucidating network-level responses to changing environments, yet as an optimality-based approach it relies on the definition of an objective function, which is far from trivial for non-optimal conditions. To address this uncertainty, we used a time-resolved, data-informed metabolic model of rice (Oryza sativa L.) cold stress response as a test case, and explored two complementary approaches. We used sampling of the solution space combined with machine learning to identify reactions and pathways best characterizing the stress-induced metabolic shift, and used this information to perform Pareto analysis, placing growth and a stress-related objective in competition. This trade-off analysis identified key branch points in carbohydrate, amino acid, phenylpropanoid, nucleotide, and fatty acid biosynthesis, where resource reallocation towards stress-protection comes at the expense of growth. It further revealed differential flux modes across subcellular compartments and shifts in reducing equivalent provision as distinguishing features of the stress response. Together, these results provide a mechanistic understanding of the metabolic trade-offs and branch points governing cold stress response, and identify potential targets to optimize the cold response-growth trade-off in rice.

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.

Cyanobacteria manage photosynthetic and environmental stresses through transcriptional programs controlled by regulators also affecting carbon flux, growth states, and metabolic output that bioproduction seeks to optimize. This regulatory architecture and its most influential nodes remain incompletely characterized. We hypothesized two influential regulator layers: a conserved core responding to common stresses, and species-specific regulators mediating strain-level niche adaptations. Mapping both layers underpins understanding genome[->]regulatory-network[->]phenotype flow, enabling global transcription machinery engineering for reliable bioproduction. To test our hypothesis, we constructed conserved-core and species-specific gene regulatory networks (GRNs) for three cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803 and Picosynechococcus sp. PCC 7002, integrating a manually curated multi-pipeline regulator inventory with 1,098 harmonized transcriptome states for the 1,362-gene tri-homolog core genome. We quantified each regulator influence using local (degree, k-core), global (betweenness, closeness), and community-aware (eigenvector) centrality measures, and an Integrated Centrality score aggregating influence across complementary topological measures. High-influence regulators are predicted to exert broad metabolic effects when manipulated, making them priority candidates for single-target engineering interventions that modulate multiple genes and reprogram complex phenotypes. Across the three cyanobacteria, the two GRN layers proved topologically distinct: the conserved core concentrated influence in stress-related hubs (11 of its top 15 by Integrated Centrality were stress-related), while species-specific networks spread influence across functionally diverse regulators. Stress-coupled enrichment also held per individual centrality measure: regulators ranking top in both the core and species-specific GRNs by the same measure were mostly stress-related (15 of 19 instances), including the multi-stress regulators RpaB, Rre1, and BolA, the heat-shock HrcA, and the nitrogen NtcA. In species-specific GRNs, stress-related regulators remained the leading category alongside circadian, carbon-metabolism, morphology, and housekeeping regulators, including PlmA, Pex, TetR, and SrrB in PCC 7942; KaiC3, Sycrp1, Rre28, and Bhl in PCC 6803; and Zur, Sycrp1, and NarL in PCC 7002. High-influence putative regulators included the iron-stress AraC-family paralogs IutR1-IutR3, OmpR-family paralogs OmpR1-OmpR2, and chromosome- or plasmid-encoded Xre-family, AraC, and HypP. Stress regulation emerges as a recurring high-influence axis across these networks. The conserved core identifies universal regulatory programs, and species-specific layers reveal strain-level innovations for cross-strain transfer to support engineering of robust bioproduction. ImportanceCyanobacteria are studied as platforms for sustainable, carbon-recycling production of fuels and chemicals from sunlight, water, and atmospheric carbon dioxide. Their reliable deployment in industrial settings is limited by environmental stresses that depress photosynthetic efficiency and product yields. The same regulatory proteins that govern stress responses also control how cells partition carbon, switch growth states, and direct metabolic output, making them natural levers for engineering robust production strains. Yet systematic, cross-species maps of these regulators have been missing. We present the first comparative regulatory map spanning three biotechnologically important model cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803, and Picosynechococcus sp. PCC 7002, and identify the conserved regulators most influential across all three. The resulting catalog prioritizes candidate targets for experimental validation, and the supporting datasets and analytical framework are released for reuse to support efforts to engineer cyanobacterial strains for reliable industrial bioproduction.

High-quality plasmid DNA purification at high throughput remains a significant bottleneck in molecular biology and bioengineering. Current methods frequently fail to deliver sufficient yields of pure, transfection-grade DNA required for genetic engineering applications in mammalian cells. Here, we present a Biofoundry-based automated pipeline using the CyBio FeliX robotic liquid handling platform to rapidly purify plasmid DNA with minimal manual intervention. The protocol leverages Solid Phase Reversible Immobilisation (SPRI)-based magnetic bead technology to ensure consistency, scalability, and DNA purity suitable for downstream viral particle production and mammalian cell transfection. The pipeline supports flexible processing of between 8 and 96 samples per run, making it adaptable across a wide range of experimental scales. The protocol is openly available via Earlham Institute GitHub repository, enabling broad adoption across the bioscientific community and contributing to the growing toolkit of reproducible, scalable engineering biology workflows. In this work, we employed an integrated robotic pipeline to process 528 pooled DNA plasmids and built a Lentiviral DNA plasmid library for lineage tracing, validated the library by sequencing, and demonstrated efficacy in downstream mammalian cell transfection experiments.

Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly metabolite pools are renewed. As a result, physiologically consistent steady-state solutions should not only satisfy mass-balance and thermodynamic constraints but also exhibit turnover rates consistent with enzyme-mediated cellular dynamics. Current constraint-based approaches can admit many steady-state flux-concentration states that do not account for turnover rates, resulting in phenotypes incompatible with realistic metabolic dynamics, even when multiple types of data are imposed. Here, we present METEOR-K, an optimization framework that links steady-state metabolic fluxes to metabolite concentrations via turnover rate constraints to identify dynamically plausible flux-concentration reference states. Because these constraints reshape the feasible solution space, we also introduce turnover-rate-aware sampling strategies to efficiently explore the resulting feasible region. We applied METEOR-K to models of increasing scope and scale, including a reduced glycolysis pathway, anaerobic E. coli, and near-genome-scale ovarian cancer models. METEOR-K narrowed the admissible steady-state solution space, reduced uncertainty in feasible flux-concentration states, and improved local dynamic behavior. In nonlinear ODE simulations of bioreactor cultivation and drug-response scenarios, METEOR-K-derived states produced intracellular response times compatible with growth-supporting metabolic operation and perturbation recovery. Overall, these results establish metabolite turnover rates as scalable biophysical constraints that improve the physiological consistency of steady-state metabolic modeling. Because turnover rates encode flux-to-pool-size timescale constraints, METEOR-K moves part of physiological-consistency assessment upstream of kinetic parameterization, yielding better-suited flux-concentration reference states for kinetic modeling and dynamic prediction.

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.

Understanding transporter kinetics is essential for deciphering metabolite exchanges in biosystems, particularly for cells subject to substrate gradients. Nevertheless, the prediction of transporter kinetic parameters, maximum rate per gram protein (Vmax) and Michaelis-Menten constant (Km), has not yet been tackled. Here, we developed the first compound-protein interaction machine learning model of transporter Vmax and Km, MMTKPred, which achieved R2=0.553, RMSE=1.155 mmol/hr/g Protein and R2=0.330, RMSE=0.935 mM for log10-scaled Vmax and Km prediction, respectively. Moreover, we demonstrated MMTKPred's predictive power across biosystem scales, from capturing transporter kinetics modulated by point mutations and substrate changes at the molecular level, to enabling substrate-sensitive metabolic modelling of non-model yeasts at the cellular level, and rationalizing inter-species substrate competition in co-cultures. Collectively, MMTKPred effectively models metabolite transport spanning from molecular to multi-species scales, thereby offering a computational tool for rational microbial cell factory optimization.

Edible adipose tissue can enhance the sensory and nutritional qualities of cultivated and plant-based meats, yet efficient adipogenic differentiation remains a major bottleneck and synthetic PPAR{gamma} agonists are not approved for use in food production. Here, we report a natural compound screen in 3T3-L1 adipocytes that identifies magnolol and dicoumarol as enhancers of adipogenesis; this combination also robustly promotes lipid accumulation in primary porcine dedifferentiated fat cells and ovine preadipocytes. Transcriptomic analyses show that magnolol and dicoumarol induce adipogenesis in murine and porcine cell systems through canonical adipogenic pathways with a narrower transcriptional footprint than the potent PPAR{gamma} agonist rosiglitazone. These findings support the potential of naturally occurring compounds magnolol and dicoumarol as enhancers of adipogenesis for both mechanistic studies and food-relevant applications. More broadly, our findings establish a generalizable screening framework and identify small-molecule combinations that accelerate adipose tissue engineering across murine, porcine, and ovine culture systems.

Most proteins targeted to the organelles of endosymbiotic origin are encoded in the nuclear genome, placing them under the regulatory dominance of the nucleus. For photosynthetic eukaryotes, nuclear-encoded chloroplast proteins arise via two routes: First, genes of cyanobacterial origin were relocated to the nucleus through endosymbiotic gene transfer (EGT). Second, proteins of eukaryotic origin emerged to support chloroplast function and structure. These proteins are reimported into the chloroplast via an import machinery. Reversing the transfer of such genes from the nucleus to the chloroplast genome may offer insights into chloroplast regulation and evolution. In this study, we established a highly efficient and accessible electroporation protocol for chloroplast transformation in the green alga Chlamydomonas reinhardtii, and used it to reverse-transfer two nuclear-encoded genes encoding proteins arising via the two routes described above: the cyanobacteria-derived chloroplast division protein FtsZ1 and the Rubisco-linker EPYC1 of eukaryotic origin. Regardless of origin, both chloroplast-encoded FtsZ1 and EPYC1 showed proper localization and functionality comparable to their nuclear-encoded counterparts. Together, our study provides a robust protocol for chloroplast transformation, a platform for investigating the evolutionary drivers of EGT, and a foundation for advancing chloroplast bioengineering. SIGNIFICANCE STATEMENTO_LIEndosymbiotic gene transfer has resulted in the mass migration of genes from the chloroplast genome to the nuclear genome. Reversing the gene transfer could reveal the evolutionary significance of genome partitioning. C_LIO_LIUsing the green alga Chlamydomonas reinhardtii, this study developed an efficient, electroporation-based protocol for chloroplast transformation. Relocating the genes encoding two chloroplast-targeted proteins, FTSZ1 and EPYC1, to the chloroplast genome showed that the proteins maintained normal localization and function. C_LIO_LIThe established transformation protocol facilitates systematic testing of reverse gene transfer to elucidate the potential evolutionary advantages of genome partitioning and opens new avenues for chloroplast bioengineering. C_LI

Cells are the fundamental unit of life. Yet there is no natural cell for which all its life-essential functions are understood. Here we demonstrate a complete cell cycle for a synthetic cell undergoing selection, with genome replication, growth, resource acquisition via feeding, and genetically encoded division. The cell is encoded via a 90kb genome that includes functions needed for resource uptake, transcription, translation, growth, genome replication, and division. The resulting synthetic cell is sufficiently encouraging to support routinization of synthetic cell engineering workflows, and will ultimately underlie diverse applications across all of biotechnology.

Accurate gene-protein-reaction (GPR) associations are essential for the predictive performance of genome-scale metabolic models (GEMs),as they define the mapping between genes, enzymes, and metabolic reactions. However, GPR rules are often incomplete or inconsistent due to limitations in annotation transfer and the ambiguous representation of multi-subunit protein complexes, leading to errors in downstream analyses such as gene essentiality prediction. Here, I introduce Comp2GPR, an automated pipeline for reconstructing GPR rules that integrates curated protein complex information with sequence-level evidence. Protein complexes were sourced from the Complex Portal and subjected to an AI-assisted curation workflow to retain only metabolically relevant assemblies. Comp2GPR combines deterministic sequence similarity mapping with explicit rule construction to generate Boolean GPR expressions that accurately represent obligate subunit relationships and isoenzyme redundancy. I evaluated the impact of the reconstructed GPR rules by integrating them into the Yeast9 metabolic model and comparing gene essentiality predictions with the original model. While global performance metrics remained largely unchanged, the updated model achieved a net improvement in prediction accuracy through gene-level corrections. Overall, Comp2GPR demonstrates that combining curated protein complex data with sequence-based validation improves the accuracy, interpretability, and reproducibility of GPR rules. The method provides a robust framework for enhancing metabolic model annotations and supports more reliable simulation-based analyses.