Metabolic Engineering Communications

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.

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.

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.

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.

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

The coprophilous agaricomycete Coprinopsis cinerea is a model organism for antagonistic fungal-bacterial interactions. Previous studies showed that C. cinerea responds to antagonistic bacteria with strong induction of a set of genes encoding secreted antibacterial molecules. However, little is known about the elicitors of this response. Key open questions in this respect include whether individual antibacterial defence genes are induced by different bacteria and/or by specific bacterial soluble molecules. Here, we present a new C. cinerea reporter system to monitor antibacterial defence induction and address related outstanding issues with minimal hands-on time. In this system, the promoter of the endogenous bacterial-induced gene cclys1 drives the expression of cnluc, which encodes a secreted variant of the deep-sea shrimp luciferase Nluc. We show that cNluc allows to detect and quantify cclys1 induction by measuring luminescence directly in the culture medium of reporter strain colonies. Building on these features, we successfully leveraged the inducible cNluc reporter strain for the development of a novel 96-well plate assay that allows the high-throughput screening of antibacterial defence elicitors. As cNluc can be subject to degradation by secreted proteases of fungal or bacterial origin in the culture medium, we coupled this assay to confirmatory qRT-PCR. Testing this set-up by confronting the reporter strain with several different bacteria revealed that cclys1 induction occurs independently of the bacterial ecological niche. Based on these results, we also recommend qRT-PCR exclusively for validation of negative results. We conclude that cNluc offers significant advantages over cytoplasmic reporter proteins, especially for preliminary rapid screening of multiple conditions.

Multi-stage continuous bioprocessing can increase volumetric productivity, operational consistency, and process throughput, but its design is complicated by coupling among dilution rate, reactor volume, feed allocation, and cellular physiology. Here, we present ContiDesigner, available at https://chemnettools.anc.univie.ac.at/ContiDesigner/, a mechanistic steady-state framework and interactive web tool for the system-level design of continuous fermentation cascades. Comparing one- and two-stage configurations at equal total reactor volume and outlet flow, ContiDesigner reveals how internal flow and reactor volume allocation shape space-time yield and identifies productivity-maximizing operating conditions. Compared with one-stage processes, two-stage cascades favor lower over-all dilution rates, thereby preserving residence time in the production stage. The first-stage dilution rate approaches the corresponding one-stage productivity optimum, but the cascade optimum occurs earlier, reflecting a system-level compromise between biomass generation and production-stage residence time. However, two-stage operation outperforms optimized one-stage operation only when non-growth-associated production in the second stage is sufficiently strong, whereas increasing growth coupling favors one-stage operation. Two case studies demonstrate both the potential and limits of process intensification. An optimized two-stage design is predicted to achieve a more than 1.5 fold increase in space-time yield for poly-R-3-hydroxybutyrate (PHB) production compared with a published experimental five-stage cascade, whereas the lactic acid case study identifies conditions under which staging offers no advantage. ContiDesigner translates these design principles into an accessible workflow to explore feasible operating regions and prioritize cascade designs for experimental evaluation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743657v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@ef58faorg.highwire.dtl.DTLVardef@1ba88a4org.highwire.dtl.DTLVardef@160edd3org.highwire.dtl.DTLVardef@9dda34_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIContiDesigner enables system-level design of continuous fermentation cascades C_LIO_LIHigh stage-one dilution supports biomass generation C_LIO_LILow stage-two dilution preserves productive residence time C_LIO_LIYet two-stage cascades favor lower overall dilution than one-stage systems C_LIO_LITwo-stage advantage requires strong non-growth-associated production in stage two C_LI

Rising demand for rare earth elements, including lanthanides (Lns), has intensified environmental pressures and supply-chain vulnerabilities, motivating the development of bio-based methods for their extraction and separation. However, the lack of high-throughput assays for analysing the selectivity of lanthanide-binding proteins remains a key bottleneck in engineering bio-based Ln-extraction systems. Here, we report the development of high-throughput assays based on Ln-responsive protein biosensors. These {beta}-lactamase-based biosensors contain receptors with a single Ln-binding site derived from either lanmodulin or the AI-designed protein RF2. We established multiplexed colourimetric assays that quantify biosensor activity and selectivity in vitro and in the periplasm of E. coli. We further demonstrate that E. coli cells expressing these biosensors exhibit Ln-dependent survival in the presence of {beta}-lactam antibiotics. These platforms enable large-scale testing of Ln biosensors and Ln-binding proteins.

O_LIVitamin B12, an essential micronutrient for many microalgae and humans, is synthesised only by certain prokaryotes. B12 is a complex tetrapyrrole that can exist in many forms (vitamers), some more bioavailable than others. Some microalgae are able to interconvert, or remodel, different B12 vitamers. As microalgae are important primary producers, it is crucial to understand how diverse microalgae acquire, utilise, and remodel this micronutrient. C_LIO_LIThrough the development of a novel algal bioassay for B12 quantification that distinguishes between B12 vitamers with different lower axial ligands, and the generation of targeted knock-out lines, we characterised the role of proteins involved in algal B12 uptake and remodelling. C_LIO_LIWe found that the previously characterised protein CoBalamin-Acquisition protein 1 (CBA1) is also necessary for the acquisition of pseudocobalamin, a less bioavailable form of B12. In addition, we provide the first experimental evidence that COBT is required for Chlamydomonas reinhardtii to remodel B12. C_LIO_LIWe apply the algal B12 bioassay to show that the edible alga Chlorella vulgaris can accumulate pseudocobalamin but is unable to remodel it, highlighting the need for thorough investigation of the metabolic requirements and capabilities of microalgae, especially given the growing interest in microalgae-based food additives. C_LI

Poly(ethylene terephthalate) (PET) is one of the most widely produced plastics, and enzymatic depolymerization offers a promising route to closed-loop recycling under mild conditions. However, most known bacterial PET hydrolases belong to a conserved canonical-fold cutinase family, leaving much of alpha/beta-hydrolase diversity unexplored. Here, we mapped bacterial cutinase sequence space by combining bioinformatics-guided sequence selection with high-throughput secretion screening in Bacillus subtilis. A library of 1,120 genes encoding 954 unique bacterial cutinases, spanning canonical- and minimal-fold families, was screened for activity on Impranil DLN and semicrystalline PET. We identified 156 secreted cutinases with polyester activity, broadly distributed across sequence space, but only ten showed detectable PET hydrolysis, all from the canonical-fold family. These PET hydrolases were active at 40-50{degrees}C, preferred alkaline pH, and showed moderate thermostability. Our results demonstrate that PET activity is rare among bacterial cutinases and provide a scalable workflow for discovering diverse enzyme starting points.

Multiple vectors and bacterial strains have been developed to enable cloning and amplification of DNA plasmids used in bioengineering applications when transgenic components are toxic to the host. These include plasmids that limit readthrough transcription into transgenic sequences and host strains carrying mutations to minimize recombination or plasmid copy number. However, these techniques are insufficient in cases where transgene expression elements are recognized by the bacterial transcriptional apparatus, or the translation products have functions in cellular metabolism. Here we demonstrate two platforms that mitigate bacterial expression of transgenes driven by the prokaryotic-like promoters of chloroplast transgenes destined for use in plant plastid genetic engineering applications. Both an engineered CRISPRi approach and utilization of the native E. coli Hfq repression system resulted in significant knockdown of plasmid-borne transgene expression, resulting in reproducibly successful cloning and plasmid amplification. The advancements reported here will facilitate synthetic biology studies generally, and enable complex transgenic studies in prokaryotic-like organelles.

Fed-batch production in Chinese hamster ovary cells is usually optimised empirically, yet the intracellular mechanisms that determine whether cells sustain productivity or enter terminal decline remain poorly resolved. Here, we combine longitudinal proteomics and intracellular metabolomics across differential ambr250 fed-batch processes to define the metabolic programmes associated with CHO cell viability and antibody production. Across media/feed combinations and an intensified seeding regime, culture progression followed a conserved trajectory from proliferation to metabolic transition and terminal stress. The high-stress state was characterised by a switch toward mitochondrial and peroxisomal fatty acid {beta}-oxidation, lipid remodelling, oxidative burden and activation of ferroptosis-associated pathways. Enriched feed conditions delayed this transition through enhanced redox and glutathione-linked defence, but did not proportionally increase antibody titre, revealing a trade-off in which cellular resources are diverted from recombinant protein production toward survival. These data identify fatty acid metabolism and ferroptosis as key constraints on late-stage CHO fed-batch performance and provide a mechanistic framework for rational feed design and host-cell engineering.

Plant cell walls are polysaccharide-rich extracellular matrices composed of multiple complex carbohydrate polymer networks, including cellulose, hemicelluloses, pectins, and glycosylated proteins. Polysaccharide deposition critically impacts cell wall structure, and structural microheterogeneity within cell wall glycans also influences polymer rigidity and polymer-polymer interactions. Collections of monoclonal antibodies (mAbs) have been developed to target unique carbohydrate epitopes within cell wall polysaccharides and to investigate how these structural changes impact cellular and plant development. Here, we implement generalizable methods to attach unique DNA barcodes to mAbs that recognize major cell wall polysaccharide classes. By applying these mAbs individually to polysaccharide standards, we demonstrate that bound DNA barcoded antibody abundance can be measured via quantitative PCR. Additionally, we demonstrate that DNA conjugated antibodies can be pooled to quantitatively analyze polysaccharide epitope composition of polysaccharide standards and fractionated cell wall material by amplifying their unique barcodes via qPCR. These results demonstrate that barcoded polysaccharide-directed mAbs offer sensitive, quantitative insights into cell wall polysaccharide composition and facilitate multiplexed profiling of cell wall polysaccharide abundance. This approach will also enable multiple future high-throughput applications, such as glycome profiling, spatial glycomics, and glycan interaction measurements, that will further our understanding of cell wall compositional impacts on plant physiology.

Isotopic Metabolic Flux Analysis (I-MFA) is a standard approach for estimating intracellular metabolic fluxes. I-MFA infers fluxes by comparing simulated and measured metabolite isotopologue distributions (MIDs) of metabolites from isotope labeling experiments. MIDs represent fractional abundances that strictly sum to one for any given metabolite, thus they are inherently compositional data. However, state-of-the-art estimation approaches rely on calculating standard Euclidean distances between MIDs in a non-compositional paradigm, introducing a systemic bias. To resolve this, our study proposes compositional I-MFA. We demonstrate how to construct a meaningful orthonormal basis for MIDs via ordered sequential binary partitioning, which can be used to perform isometric log-ratio (ILR) transformation. As a minimal change to existing I-MFA workflows, we suggest estimating fluxes by minimizing Euclidean distances between ILR-transformed MIDs. We validated this framework against traditional methods using both a toy model and a biologically realistic model, evaluating point estimates, sensitivity across varied true fluxes, and confidence intervals. In the two examples, compositional I-MFA consistently outperformed traditional approaches, reducing mean squared error of flux point estimates by an average of 42.6% and substantially narrowing confidence intervals. We conclude that compositional data analysis significantly improves I-MFA and can be implemented as a simple drop-in replacement for current pipelines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/742769v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1a53aa4org.highwire.dtl.DTLVardef@ad225aorg.highwire.dtl.DTLVardef@aa430eorg.highwire.dtl.DTLVardef@1880ca_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LINew compositional data approach improves metabolic flux estimation. C_LIO_LIThis data transformation requires minimal changes to existing workflows. C_LIO_LIThe new method reduced MSE of flux estimates by 42.6% in two examples tested. C_LIO_LIThe confidence intervals of the estimated fluxes were substantially narrowed. C_LIO_LIEstimation accuracy remained robust across a wide range of metabolic fluxes. C_LI

The domestication of Streptomyces species for antibiotic production involves long-term, iterative mutagenesis and selection, yet the genomic changes driving enhanced production remain unclear. Analysis of five strains from an industrial lineage of Streptomyces clavuligerus using comparative genomics, transcriptomics and phenotypic profiling for dynamic genome architectures with plasmid integration events and chromosomal rearrangements, alongside the accumulation of mutations affecting metabolic pathways and global gene regulation. These changes increased precursor supply and reprogrammed transcription leading to enhanced clavulanic acid production but reduced catabolic flexibility. Complementation experiments confirmed the functional impacts of specific mutations. These findings reveal that artificial selection shapes genome evolution in industrial strains, balancing production gains with metabolic trade-offs. This work will likely inform rational design of Streptomyces strains for improved natural product production in industry while highlighting the constraints imposed by domestication on metabolic versatility. More broadly it shows that many of the evolutionary processes in industrial strain improvement programmes mirror those at play during natural selection.

Sialic acids (Sias) and related nonulosonic acids are critical components of glycoconjugates involved in host-pathogen interactions, immune regulation, and cell signalling. Despite their biotechnological relevance, the diversity of enzymes involved in Sia biosynthesis remains largely underexplored due to limitations in culture-dependent methods and the lack of (ultra)high-throughput screening strategies. Here, we report the development of a highly sensitive droplet-based microfluidic screening platform enabling the functional discovery of sialic acid aldolases in environmental metagenomes. The method integrates a fluorescence-coupled enzymatic cascade compatible with fluorescence-activated droplet sorting (FADS), allowing the screening of >10 droplets per experiment, as well as a downstream validation strategy for the selected hits. Although some limitations were identified, the system demonstrated high sensitivity and was utilised for the screening of a metagenomic library from garden soil. During this campaign, a potential new sialic acid aldolase enzyme was identified. This work establishes a generalizable framework for measuring complex, multi-step enzymatic functions at ultrahigh throughput using coupled cascades in droplets

Pseudocercospora fijiensis is the causal agent of Black Leaf Streak Disease (BLSD), also known as black Sigatoka, in banana. The disease affects many banana varieties, including the highly susceptible Cavendish banana that dominates global production and the export trade, and several cooking bananas that are a staple food for hundreds of millions of people worldwide. Currently, the disease is controlled using preventative fungicide treatments with up to 70 applications per year in Cavendish plantations, which accounts for approximately 30% of the production costs. Resistant cultivars are required for more sustainable production, but no resistance gene to BLSD has been identified. This is partly due to the poor genetic amenability of P. fijiensis and the lack of methods for functional gene analysis. To address these limitations, we developed a CRISPR/Cas9-mediated transformation system specifically optimized for P. fijiensis. We established a protocol to produce protoplasts, evaluated their capacity to regenerate into new colonies, and assessed antibiotic sensitivity. Subsequently, we confirmed the integration of foreign DNA, including resistance markers, using PEG-mediated transformation. We demonstrated targeted transformation using CRISPR-Cas9 to knockout the polyketide synthase gene PKS10-1, which is responsible for the production of the pigment melanin, and the mitogen-activated protein kinase (MAPK) gene Fus3. Following the successful generation of knockout mutants for these genes, achieving gene targeting efficiencies of respectively 96% and 58%, we subsequently generated knockout mutants of the renowned effector Avr4 in P. fijiensis. The resulting mutants exhibited no reduction in virulence on the susceptible banana cultivar Cavendish. In addition, we used the wild-type isolate and Avr4 knockout strains to test the resistant banana accession Calcutta 4. Contrary to a previous study, we demonstrate that Avr4 does not explain the resistance of Calcutta 4, suggesting that resistance is instead triggered by the recognition of other hitherto unknown effectors. The established CRISPR/Cas9-mediated disruption system is highly efficient and enables routine functional gene characterization, which will help to elucidate genes involved in banana-P. fijiensis interaction, thereby supporting the discovery of resistance genes against BLSD.

Although metabolomics has shown considerable promise for biomarker discovery, and the development of diagnostic and prognostic applications, its translation into routine clinical practice remains limited by analytical complexity, cost, throughput, and standardization challenges. These limitations underscore the need for complementary tools, particularly in resource-limited settings. In this study, we developed a workflow for the engineering and characterization of growth-coupled metabolic sensors capable of disease detection (healthy vs. infected) and outcome prediction (mild vs. severe), which we illustrated using COVID-19 as a proof-of-concept application. We first generated a biomarker-guided library of 34 candidate sensors leveraging both auxotrophic phenotypes and less stringent metabolic dependencies. We then screened the library against patient plasma pools, identifying 19 sensor candidates with diagnostic and/or prognostic potential, including 14 with prognostic potential. Lastly, a selected subset of candidates was further evaluated on a patient cohort using two newly developed analytical frameworks designed to extract additional information from bacterial growth curves. The best-performing sensors achieved a balanced accuracy of 0.88{+/-} 0.06 for prognostic prediction (outer-test AUC = 0.89, 5-fold cross-validation, n = 37) and 1.00 for diagnostic classification (outer-test AUC = 1.00, 5-fold cross-validation, n = 56). Collectively, these findings establish a proof of concept for translating disease-associated plasmatic metabolic signatures into low-cost, growth-coupled biosensors with diagnostic and prognostic capabilities.

Acetate is an attractive renewable two-carbon substrate for microbial biotechnology, but its toxicity limits growth and carbon-use efficiency at process-relevant concentrations. Here, we used adaptive laboratory evolution to improve acetate tolerance in a genome-reduced strain of Pseudomonas putida and combined whole-genome sequencing, reverse engineering, transcriptomics, proteomics, and 13C-acetate fluxomics to resolve the underlying adaptation mechanisms. Evolution under increasing acetate concentrations selected recurrent mutations in gacA and fabB, which encode a global response regulator and a fatty acid biosynthesis enzyme, respectively. Reverse engineering of these mutations recovered most of the evolved phenotype, including shorter lag phase and substantially higher biomass yield from acetate. Multi-omic analyses showed repression of type VI secretion systems, carbohydrate storage functions, fatty acid metabolism, and oxidative stress-associated proteins, indicating resource reallocation away from costly stress and non-essential programs. Fluxomics further revealed reduced EDEMP cycling and increased glyoxylate shunt flux, consistent with improved acetate-carbon retention in biomass. These results establish acetate tolerance in P. putida as a resource-efficiency phenotype and identify gacA and fabB as actionable targets for acetate-based bioproduction.

Antimicrobial resistance has intensified the search for sustainable natural products with antimicrobial properties. Pleurotus ostreatus cultivated on lignocellulosic agro-wastes, including cassava peel, offers potential for bioactive-compound production and agricultural waste valorization. Conventional antimicrobial screening, however, can be labour-intensive when multiple extracts and pathogens are evaluated. This study evaluated whether extraction solvent, broad pathogen taxonomic category, and batch-level mycochemical composition could predict the antimicrobial response of P. ostreatus extracts cultivated on cassava peel and identified the variables contributing most strongly to prediction. Ethanolic and aqueous mushroom extracts were evaluated against seven microbial pathogens using agar well diffusion and broth microdilution assays. The dataset comprised 42 observations. A Random Forest model with leave-one-out cross-validation (LOOCV) was used to model zone of inhibition as a regression task and minimum inhibitory concentration (MIC) as a binary classification task. The Random Forest regression model showed moderate internal predictive performance for zone of inhibition (R2 = 0.68, MAE = 0.62 mm, RMSE = 0.75 mm). Extraction solvent was the strongest predictor, whereas batch-level mycochemical variables contributed minimally. In contrast, MIC classification performed poorly (accuracy = 0.43; F1-score = 0.33), indicating that the available predictors were insufficient to discriminate the two observed MIC groups. The findings support machine learning as an exploratory complement to antimicrobial screening of mushroom-derived natural products. Given the limited dataset and three cultivation batches, the results are preliminary. Larger, multi-substrate and multi-species datasets with replicate-resolved biochemical measurements will be required to develop robust predictive models.