Metabolic Engineering

Robust strain stability is essential for industrial-scale fatty alcohol production, as metabolic burden and toxicity not only constrain productivity but also create selective pressure for low-producing or non-producing subpopulations. This genetic instability leads to genetic heterogeneity and compromises strain performance during large-scale fermentation. In this study, we investigated the production stability of fatty alcohol-producing Yarrowia lipolytica strains and developed systematic strategies to improve the genetic stability of engineered strains for scale-up production. In a mock fermentation scale-up, a fatty acyl-CoA reductase (FAR)-expressing strain lost fatty alcohol production after five consecutive passages. To address this, we fused FAR to phosphoglycerate kinase I (PGK1), a gene essential to cell growth, to promote the stability of FAR expression and prevent production loss. This strategy extended fatty alcohol production by one passage. Additionally, FAR was fused to GFP and extended production stability by four additional passages. In parallel, competitive co-culture experiments, in which producing strains were cultured alongside non-producing mutants, revealed that when non-producers emerged with a frequency of 10-5, they dominated the fermentation population within six passages; at 10%, they took only two passages. Furthermore, deep sequencing of strains that demonstrate different levels of stability and fatty alcohol productivity identified mutation patterns that contribute to strain instability. These findings provide insights into engineering Y. lipolytica with enhanced genetic stability for scale-up fatty alcohol production.

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

Cultivated meat production is currently limited by high production costs and an incomplete understanding of cellular metabolism in agriculturally relevant species. Genome-scale metabolic models (GEMs) have successfully guided media optimization in biopharmaceutical systems but have not been widely applied to cultivated meat. In this study, we present iSsus3744, the first genome-scale metabolic reconstruction for Sus scrofa and demonstrate its application for rational media design in cultivated pork production. iSsus3744 was reconstructed using HumanGEM and Recon3D as template models and further constrained using experimentally determined biomass composition and uptake and excretion fluxes from a Duroc porcine muscle satellite cell line. The final model comprised 3,744 genes, 8,854 metabolites, and 12,248 reactions distributed across eight cellular compartments. Flux balance analysis (FBA) and flux variability analysis (FVA) were used to identify amino acids limiting cellular growth and predict media supplementation strategies. Experimental validation demonstrated that model-guided amino acid supplementation significantly improved proliferation. Supplementation with phenylalanine reduced doubling time from 31.9 hours to 17.2 hours, representing a 46% reduction, while lysine, methionine, tyrosine, leucine, and valine also improved growth performance. These results demonstrate the potential of genome-scale metabolic modeling as a powerful platform for rational media optimization in cultivated meat systems. iSsus3744 provides a foundational resource for future integration of omics, transcriptional regulation, and isotope-assisted metabolic flux analysis to further accelerate serum-free media development and cultivated meat bioprocess optimization.

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.

Malic acid is a C4 dicarboxylic acid traditionally produced from petroleum and widely used in the food industry. As a sustainable alternative, it can also be produced as a value-added platform chemical from biomass. Previously, the Escherichia coli strain XZ658 was engineered to produce L-malate via the carbon-fixation reductive branch of the TCA cycle. In this study, we further improved this system by relieving allosteric regulation of citrate synthase, addressing redox imbalance, and enhancing malate export. These modifications approximately doubled the L-malate titer in the final strain MO128 compared to XZ658 under simple batch fermentation conditions. The process achieved a high mass yield of 1.2 g malate g-{superscript 1} glucose, highlighting the carbon-fixation capacity of the reductive TCA pathway for fermentative malate production.

Budding yeast Saccharomyces cerevisiae is a workhorse chassis for producing added food and agricultural compounds. However, building multi-enzymatic pathways for these chemicals often requires iterative genomic integration, underscoring the need for efficient, rapid genome-editing tools that can reliably target transcriptionally active chromosomal regions. In this study, to accelerate strain construction, we established a genome-editing toolkit to rapidly engineer eight loci, highly expressed hot-spots, but nonessential genomic sites suitable for stable pathway assembly. Our approach integrates three key design features: (i) selectable markers to enable rapid screening of edited cells, (ii) extended homology arms that leverage the yeast homology-directed repair machinery for robust genomic integration, and (iii) co-delivery of Cas9 and guide RNAs to promote efficient double-stranded DNA breaks at specific integration sites. The sequence independence of FASTOP relies on the release of integration cassettes from integrative vectors, mediated by restriction digestion at two flanking multiple-cutting sites in the integration module to minimize the risk of introducing sequence errors during PCR amplification of the integration cassettes. Following the introduction of a fluorescent reporter cassette, we observed high integration efficiencies across the target sites. We then integrated the biosynthetic pathway of plant-derived flavonoid naringenin into the hot-spots of the yeast genome using the FASTOP toolkit. Our results demonstrated that upon expressing the five essential genes in simple shake flask culture, naringenin production reached 505.7 mg/L, representing a significant (69-fold) increase over previously reported titers for comparable minimal heterologous pathways in S. cerevisiae. Together, the FATSOP toolkit provides a user-friendly platform for reliably modifying hot-spot loci to rapidly construct multi-enzymatic metabolic pathways in S. cerevisiae, while achieving high production levels for high-value food-relevant metabolites.

The fast-growing cyanobacterium Synechococcus sp. PCC 11901 is emerging as a promising chassis for photosynthetic biomanufacturing. Here we report recombinant protein production in PCC 11901 via signal peptide-mediated secretion, enabling direct recovery of target proteins from the culture medium without cell disruption. Seven signal peptides spanning both Sec and Tat pathways are screened using eYFP as a reporter, with secretion quantified daily over seven days by fluorescence measurements. FutA, belonging to the Tat pathway from Synechocystis sp. PCC 6803, achieves 92.2% extracellular export by day 7, substantially outperforming all Sec candidates, including the best Sec signal peptide thermitase from Cyanobacterium aponinum PCC 10605 (55.7%). Signal peptide-bearing strains exhibit growth reductions of up to 26% relative to the wild-type, with FutA most affected, indicating a general metabolic cost correlated with secretion efficiency. The best-performing signal peptides from both pathways, FutA and thermitase, are validated with secretion of lichenase. Notably, the rank order of signal peptide performance is reversed for lichenase: thermitase demonstrates 2.6-fold higher extracellular activity than FutA, indicating that optimal signal peptide selection is cargo-dependent. These results establish PCC 11901 as a secretion-competent chassis and provide a rational framework for matching signal peptide pathways to target protein properties.

The optimization and control of bioprocesses require robust in silico models that can accurately capture the complex and dynamic behavior of living cells. While hybrid models that combine machine learning with mechanistic equations have emerged as a powerful tools, they often require relatively large datasets and might yield inconsistent predictions that violate the stoichiometry of metabolism. In this study, we introduce FBA-Hyb, a multi-scale hybrid modeling framework that tightly integrates genome-scale metabolic networks via flux balance analysis (FBA) into its architecture. In our FBA-Hyb framework, artificial neural networks predict key FBA inputs (substrate uptake rates and cellular objectives) while a surrogate FBA module translates them into the metabolic fluxes that govern the bioprocess. A key novelty is that the FBA optimization step is replaced by a surrogate generated with symbolic regression, which encapsulates the FBA model into a compact analytical expression. This allows easy backpropagation through the integration of the neural controlled differential equationbased FBA-Hyb bioprocess model. We validated FBA-Hyb against a standard hybrid model (Std-Hyb) using two Escherichia coli fedbatch case studies. In the first study, FBA-Hyb achieved a 42 % average improvement in predictive accuracy (R2) during a leave-one-process-out cross validation. Crucially, FBA-Hyb maintains strict stoichiometric feasibility even during extrapolation. Meanwhile, an alternative approach based on standard architectures leads to stoichiometrically inconsistent solutions in 22 % of the cases analyzed. In the second case study, we demonstrate how FBA-Hyb effectively simulates unmeasured chemical species and discovers a metabolic shift in sulfate-limited regimes during bioprocessing. By providing a modular, biologically consistent, and computationally efficient architecture, FBA-Hyb offers a robust foundation for the next generation of bioprocess models and sustainable process optimization. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/720062v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@16f011eorg.highwire.dtl.DTLVardef@b25b5borg.highwire.dtl.DTLVardef@18bd178org.highwire.dtl.DTLVardef@65274e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIFBA-Hyb integrates flux balance analysis (FBA) into hybrid bioprocess models. C_LIO_LISymbolic regression discovers a simple closed-form FBA surrogate model. C_LIO_LIThe FBA surrogate ensures accurate reaction stoichiometry. C_LIO_LIA neural network predicting the FBA objective keeps the model flexible. C_LIO_LIFBA-Hyb has superior capabilities and accuracy compared to the current standard. 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.

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.

Improving the reconstituted translation system is a key requirement for bottom-up synthetic biology. Here, we developed a two-step in vitro evolutionary method that can be used for improving translational proteins. In this method, two distinct conditions were sequentially applied while maintaining genotype-phenotype linkage in water-in-oil droplets. Using this method, we performed in vitro evolution of four translation factors, IleRS, PheRS, EF-G, and EF-Tu, and identified mutations that modestly enhanced translation activity in in vitro expression assays. One of the EF-G mutations (P610S) increased activity per protein approximately 2-fold for the recombinant protein purified from E. coli. This selection method is useful for improving translational proteins for bottom-up synthetic biology.

Genome streamlining and pathway refactoring are powerful strategies for constructing controllable microbial chassis for both fundamental studies and applications. While rational design benefits from reduced genetic complexity, adaptive laboratory evolution (ALE) thrives on metabolic redundancy, creating a mismatch between optimal hosts for design and evolution. Here, we introduce a dual chassis framework (DUET) in which rational pathway construction and adaptive evolution are first carried out in an evolution-competent host, and the resulting optimized designs are subsequently transferred into a genetically stable chassis for deployment. Using the naturally evolvable bacterium Acinetobacter baylyi ADP1 and its genome-stabilized derivative (ISx), we applied this framework to the {beta}-ketoadipate pathway, a central hub for aromatic compound catabolism. We first streamlined the native network by deleting individual pathway branches and then engineered a minimal synthetic route that merges protocatechuate and catechol metabolism. Subsequent ALE enabled efficient growth through this synthetic pathway, and reverse-engineering identified key adaptive mutations underlying functional recovery. Both the synthetic pathway and the mutations were transferred unchanged into ISx, where robust growth was maintained without further adaptation. These results demonstrate that DUET enables portable, host-independent deployment of rational metabolic streamlining combined with evolution, providing a generalizable strategy for building reduced yet robust microbial platforms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/729570v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@161e8b6org.highwire.dtl.DTLVardef@f5056forg.highwire.dtl.DTLVardef@37dcc0org.highwire.dtl.DTLVardef@17df4a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

Priestia megaterium SR7 is a promising candidate chassis for bioprocess engineering, but its development is limited by the availability of condition-grounded, mechanistic models that can translate experimental measurements into predictive design hypotheses. Here, we present PMSR7, a genome-scale metabolic model for SR7, and evaluate it under a growth-resolved chemostat framework spanning a dilution-rate series. Stable steady states were established across the growth regime, with the highest dilution rate (D = 1.1538 h-{superscript 1}) excluded from growth interpretation due to biomass collapse. Extracellular carbon fluxes were quantified by NMR and reported as mean {+/-} SD, providing an experimental basis for model comparison. PMSR7 was benchmarked using MEMOTE against representative reference reconstructions, supporting structural consistency suitable for constraint-based analyses. Under growth-resolved simulations, ATP demand scaled linearly with growth rate, enabling inference of maintenance-energy behavior across the regime. Growth-dependent feasibility and magnitude of overflow secretion were evaluated for acetate, lactate, and formate using feasible-space analyses, highlighting both agreement and regime-sensitive limitations. Finally, growth-resolved leucine and valine production was assessed in both raw and fold-change space, with experimental means compared against median-based summaries of sampled model distributions to account for feasible-space skew. Together, these results establish PMSR7 as a reproducible, quality-benchmarked platform for SR7 chassis development and provide a framework for iterative experimental integration in non-model organisms, where the dominant challenge is achieving congruence between measured physiology and model-feasible behavior.

Human milk fat (HMF) contains triacylglycerol (TAG) as its primary component, providing over 50% of the calories for infant nutrition, along with structural and bioactive lipids that are important for immune and nervous system development. Palmitic acid, comprising 20-25% of the fatty acid complement of HMF, is predominantly esterified to the sn-2 position on the glycerol backbone. This regiospecific positioning facilitates absorption as 2-palmitoyl-monoacylglycerol after hydrolysis of the fatty acids at sn-1 and sn-2 by gut lipases. Other features of HMF include enrichment in structured medium- and long-chain triglycerides (MLCTs), and variation in the ratio of oleic acid to linoleic acid with maternal diet and geography. We have engineered Auxenochlorella, an oleaginous green alga, for biosynthesis of an MLCT- and sn-2 palmitate-enriched HMF substitute for infant formula, matching the regioisomeric composition and proportions of the most abundant fatty acids in HMF.

In biotherapeutic manufacturing, product quality such as glycosylation profile is typically assessed only after harvest, limiting opportunities for corrective action during cell culture operation. Intracellular nucleotide sugar donors (NSD) directly determine glycosylation outcomes but are rarely measured, even offline, due to analytical complexity and process disruption. As a result, quality-related decisions remain constrained to fixed operating strategies. This work introduces a model-based soft sensing framework to infer NSD concentrations from readily available extracellular measurements. A Bayesian state estimation approach based on the Ensemble Kalman Filter (EnKF) is developed to reconstruct unmeasured intracellular states during CHO cell culture. An imperfect kinetic process model is combined with noisy extracellular measurements, explicitly accounting for process variability and measurement uncertainty through ensemble-based propagation and updates. The framework is validated using four independent experiments with distinct feeding perturbations that are not used for model calibration. Although the open-loop model exhibited substantial mismatch for both extracellular metabolites and intracellular NSDs, EnKF assimilation of extracellular measurements corrected key metabolic profiles. Building on these corrected extracellular dynamics, the EnKF demonstrated robust estimation of a growth-determining amino acid, asparagine, from correlated extracellular states. Based on the improved extracellular and amino acid estimates, the framework further enabled reliable inference of intracellular NSDs across all experiments.

The accumulation of plastic waste necessitates innovative strategies that convert polymer carbon into value-added products. Here, we present a sequential yeast-bacterial workflow for the integrated valorisation of glycerol and amorphous polyethylene terephthalate (amPET) into triacyl glyceride (TAGs) and polyhydroxyalkanoates (PHAs). First, an engineered obese strain of Yarrowia lipolytica was cultivated on glycerol, for the simultaneous production of intracellular lipids, up to 42.9% of its cell dry weight, and secretion of a PET-depolymerizing enzymatic cocktail, composed of the cutinase from Mycothermus thermophilus (HiC) and the lipase B from Moesziomyces antarcticus (CALB). The resulting enzymatic crude hydrolysed amPET, and the released degradation products--terephthalic acid (TPA) and ethylene glycol (EG)-- served as feedstocks for a bacterial consortium composed of Comamonas testosteroni RW31 and Pseudomonas putida JM37, which naturally assimilate TPA and EG, respectively. This consortium successfully upcycled the released monomers into intracellular polyhydroxybutyrate (PHB) and medium-chain-length PHAs. Furthermore, fluorescent strains of both bacteria enabled the development of a semi-quantitative method for monitoring the consortium population dynamics. Overall, this study provides a robust proof-of-concept for a circular bioeconomy approach, successfully coupling glycerol-based enzyme and lipid production with the downstream biological conversion of PET-derived monomers into valuable bioplastics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/729029v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1575eedorg.highwire.dtl.DTLVardef@1195aeborg.highwire.dtl.DTLVardef@1a01bb0org.highwire.dtl.DTLVardef@79f4f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Engineering the ethanologenic Gram-negative bacterium Z. mobilis for the secretion of hydrolytic enzymes is a key step towards establishing a biofuel cell factory that uses complex waste material as feedstock. Secretion strategies in Z. mobilis have exclusively relied on signal peptides, which limit protein transport to the periplasm. To achieve single-step secretion across the Z. mobilis double-layered membrane, we sought to identify a native Type I Secretion System (T1SS) tag for fusion to proteins of interest. While a T1SS operon had been identified in the Z. mobilis genome, its native cargo had remained unknown and the use of T1SS secretion tags had so far been unexplored. Here, bioinformatic analysis identified the Major Intrinsic Protein (MIP) as a putative T1SS cargo, and its role validated through fusion of C-terminal sequences of two lengths (61 and 141 amino acids) to a heterologous {beta}-galactosidase from Bacteroides thetaiotaomicron, expressed in Z. mobilis. The 141 amino acid tag, including two RTX domains, resulted in significantly higher secretion efficiency than the 61 amino acid tag lacking RTX repeats, consistent with the established role of RTX domains in preventing premature cytoplasmic folding, thus improving secretion. As extracellular secretion of hydrolytic enzymes has remained a major bottleneck in the development of Z. mobilis as a sustainable cell factory, the identification of a native T1SS secretion tag directly addresses this limitation, introducing a novel tool for enzyme delivery.

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.

Saccharomyces cerevisiae is an excellent microbial platform for sustainable production of next generation biofuels such as the branched chain higher alcohols (BCHAs) isobutanol and isopentanol. A cytosolic pathway for BCHA production is generated from expression of prokaryotic orthologs of branched-chain amino acid (BCAA) enzymes acetolactate synthase (ALS), mutant NADH-dependent ketol-acid reductoisomerase (KARIP2D1-A1), and dihydroxy-acid dehydratase (DHAD). The potential for this pathway has been hindered by the availability of iron-sulfur clusters, particularly the 2Fe-2S cluster, required for DHAD to function in the cytosol. ILV3, the endogenous yeast DHAD located in the mitochondria, can be deleted to create a valine auxotroph. In this study we use bioinformatics, heterologous gene library synthesis, and a valine complementation assay to find prokaryotic iron-sulfur cluster biosynthetic gene clusters (BGC) and accessory genes that aid DHAD function in the yeast cytosol. This work presents, to our knowledge, the first functional BGC that enhances the cytosolic activity of prokaryotic DHADs in S. cerevisiae. The SUF BGC from Bacillus subtilis combined with a ferritin-like protein (FTNB) from Escherichia coli and the Lactococcus lactis DHAD enhanced the production of BCHAs. Combined expression gave an average isobutanol titer of 412mg/L, 1.8-fold greater than L. lactis DHAD expressed alone. This work establishes a blueprint for better biofuel production by improving iron-sulfur cluster dependent enzyme activity in the yeast cytosol.