Metabolic Engineering Communications

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

Synthetic allopolyploidization is a powerful strategy for generating strains with novel industrially relevant traits. During the design of select, hybridize, evolved and learn cycle, the mitochondrial inheritance is considered neutral and most studies overcome their phenotypic impact. Here, we generated 168 synthetic allotetraploid strains representing all pairwise parental combinations of eight wild Saccharomyces species, and systematically controlled mitochondrial inheritance in these crosses. These allopolyploids were phenotyped across 31 environmental conditions relevant to biotechnology. In ~50% of all condition-strain combinations conditions, allotetraploids exhibited mid-parent heterosis. In approximately 25% of conditions, we found crosses with hybrid vigor, particularly in combinations involving Saccharomyces arboricola x Saccharomyces mikatae . However, allotetraploids were highly unstable and rapidly sporulated. Our analyses revealed that environmental conditions accounted for the largest effects, follow by its interaction with mitotype. However, mitotype alone showed small effects but significantly affected 42% of the tested conditions, underscoring the importance of mitochondrial inheritance as a key determinant of allopolyploid performance. These findings demonstrate that mitochondrial inheritance is a key, environment-dependent determinant of allopolyploid fitness and should be considered alongside nuclear genome composition in the rational design of industrial allopolyploid strains

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

Azetidine-2-carboxylic acid (Aze) is a toxic, non-proteogenic amino acid produced in restricted plants. It can enter the human food chain through direct or indirect dietary exposure and has been linked to neurological disorders due to its ability to misincorporate for its structural analog proline (Pro) during translation and lead to protein misfolding. Although several microorganisms have evolved mechanisms to detoxify Aze, these pathways have not previously been exploited to engineer Aze tolerance in plants. Here, we heterologously expressed a bacterial Aze hydrolase (HAS) in Arabidopsis thaliana to establish a metabolic strategy for Aze detoxification in plants. Transgenic plants exhibited markedly enhanced tolerance to Aze, as demonstrated by improved root growth, cotyledon development, and survival compared with wild-type plants under Aze stress. We further showed that HAS localized to the cytosol, where it converted Aze into the non-toxic metabolite 2-hydroxy-4-aminobutyrate (HAB). HAB formation was confirmed both in planta and in vitro, demonstrating that HAS remained catalytically active in plant cells and effectively detoxified Aze. Enzyme kinetics revealed that HAS has a high affinity for Aze and is stereospecific for L-Aze, with no activity toward D-Aze. Multi-omics analysis including transcriptomics and misincorporation proteomics show that the transgenic lines misincorporate Aze significantly less compared to wild-type leading to enhanced Aze tolerance. Our findings establish bacterial HAS as an effective metabolic detoxification system that confers enhanced Aze tolerance in plants. This study provides a promising strategy for engineering plant resistance to toxic non-proteogenic amino acids.

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

Background: Long non protein coding (lnc)RNAs are ubiquitous transcripts known to be major regulators of cellular processes in eukaryotes. In diatoms, specific lncRNA expression is tightly correlated to nutrient fluctuations, which suggests function, but their biological relevance remains to be established. In order to fill this knowledge gap, we sought to design a protocol to generate diatom lncRNA knockout mutants by CRISPR-Cas9 enabling subsequent functional studies to shed light into their mechanistic roles. Since lncRNAs do not contain open reading frames that can be targeted by point mutations for the generation of functional knockouts, we designed a multiplexed CRISPR-Cas9 plasmid harboring two independent gRNA for the specific ablation of lncRNA loci in P. tricornutum. As proof of concept, we used our plasmid-based system to target three phosphate depletion responsive lncRNAs. Results: Using the golden gate based uLoop system, we assembled a single plasmid containing all the necessary components to generate CRISPR-Cas9 large genomic ablations of lncRNAs in P. tricornutum using bacterial conjugation. Three intergenic lncRNAs (Linc91, Linc77 and Linc24) responsive to phosphate fluctuations were selected and independently targeted. Around 32% of the exconjugants presented a monoallelic ablation of the targeted loci and 11% presented a biallelic ablation on the first PCR-screening round. Mutated lines, harboring biallelic lncRNAs ablations for each lncRNA targeted, were successfully generated for each of the targeted lncRNAs. Additionally, a first line of phenotypic analysis revealed significant growth rate differences for one of the mutants ({Delta}Lnc24) under both full and phosphate depleted conditions, suggesting its involvement in cell division regulation under nutrient fluctuations. Conclusions: This work provides a simple and efficient method for the generation of lncRNA knock-out mutants in P. tricornutum. Furthermore, this approach can be easily adapted to other microalgae amenable to transformation, enabling the enhancement of our understanding of these important cellular regulatory transcripts in the marine environment.

Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Enhancing grain carotenoid concentration to increase yellowness and provitamin A content in sorghum (Sorghum bicolor [L.] Moench) is a major breeding objective across sub-Saharan Africa. Carotenoid breeding is constrained by costly post-harvest phenotyping and can be accelerated through genetic markers that capture functional variation in key carotenoid biosynthesis genes. Zeaxanthin epoxidase (ZEP) is a major gene controlling sorghum grain carotenoid accumulation, and SNP-based KASP markers facilitate selection of high-carotenoid grain, although quantitative variation remains among lines carrying the favorable allele. We hypothesized that additional functional variation within ZEP and other carotenoid biosynthesis pathway genes contributes to this variation and can be revealed by a pangenome-informed analysis. Using a 33-member pangenome reference, we characterized sequence and structural variation at ZEP and other key carotenoid biosynthesis genes and applied pangenome-derived genotyping to study marker diversity associated with carotenoid accumulation. Evaluation of ZEP in the pangenome reference revealed previously uncharacterized structural variation that is absent from the BTx623 primary reference genome. A pangenome-based association analysis identified genetic markers associated with the accumulation of carotenoids which were specific to particular pangenome reference members, including in RTx430 and SRN39, both yellow endosperm lines. Machine learning identified markers in ZEP, {beta}-OH, ZDS, and Z-ISO as the most predictive for all carotenoid traits, suggesting a multi-locus genetic architecture underlies the accumulation of carotenoids in sorghum grain. By integrating sorghum pangenomic resources with machine learning, this study establishes a framework for pangenome-accelerated trait discovery and identifies new genetic targets for carotenoid biofortification in sorghum.

Mesoplasma florum is a fast-growing, near-minimal bacterium and an emerging model for systems and synthetic biology. However, its dependence on complex serum-containing media limits experimental control and complicates the interpretation of cellular phenotypes. Here, we developed CMRL-AT, a serum-free, quasi-defined medium that supports rapid growth comparable to the commonly used ATCC 1161 medium. Despite supporting similar biomass, CMRL-AT profoundly reshaped the M. florum transcriptome, with approximately one-third of the annotated protein-encoding genes being differentially expressed relative to ATCC 1161. These changes revealed distinct physiological programs associated with rapid growth in complex medium and higher nutrient acquisition in CMRL-AT, illustrating how medium composition alters the functional priorities of a near-minimal cell. Transcriptome profiling across six energy sources further uncovered distinct sugar-responsive expression programs. Combining these responses with transcription-unit organization, protein-domain predictions, and metabolic context resolved fructose- and sucrose-responsive modules, and allowed the assignment of previously ambiguous phosphotransferase system components to specific sugar-utilization pathways. CMRL-AT provides an experimental framework to help resolving gene functions, refining metabolic models, and designing reduced genomes adapted to defined environments.

Acinetobacter sp. strain Tol 5 is an attractive microorganism because of its ability to utilize various aromatic compounds, including toluene. However, because Tol 5 is a non-model microorganism, the utilization of carbon sources in central carbon metabolism remains unclear. In this study, the activity of the central carbon metabolism was characterized using 13C-metabolic flux analysis (13C-MFA). Tol 5 was cultured in synthetic medium using ethanol or acetate as the sole carbon source. In addition, because toluene is degraded into pyruvate and acetyl-CoA via the toluene dioxygenase pathway, this situation was mimicked by culturing with ethanol and lactate as co-substrates. The 13C-MFA confirmed flux through the glyoxylate shunt, which bypasses part of the TCA cycle when either ethanol or acetate was used as the sole carbon source. In ethanol, the flux ratio of isocitrate dehydrogenase at the isocitrate branch point decreased markedly. Unlike acetate, the conversion of ethanol to acetyl-CoA is accompanied by NADH production. Therefore, the observed change in this flux ratio was influenced by the excess NADH generated. Under ethanol-lactate conditions, Tol 5 simultaneously utilized ethanol and lactate. The ethanol consumption rate decreased to less than half of that observed when ethanol was the sole carbon source. Most of the lactate uptake was converted to pyruvate and metabolized via the TCA cycle. The glyoxylate shunt flux was lower than that in ethanol. These metabolic characteristics provide valuable insights into the use of Tol 5 as a next-generation microbial cell factory.

Carbohydrate inducible promoters on expression plasmids are widely used for biotechnology and basic science research. However, using carbohydrates for inducers has drawbacks including influencing bacterial metabolism, the inducer being used as a carbon source by the bacteria, high background expression, and only a subset of cells experiencing promoter activation. Furthermore, some bacteria including the pathogen Acinetobacter baumannii currently lack good carbohydrate-inducible promoters. To address this issue, a tetracycline-inducible promoter, PtetB, isolated from A. baumannii was cloned and evaluated in a number of important bacteria: Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Pseudomonas fluorescens, and Serratia marcescens. A highly sensitive luxCDABE reporter was used to evaluate promoter activity. S. marcescens strains with elevated and reduced cyclic-AMP levels were used to evaluate the impact of cAMP on PtetB function. The PtetB promoter was induced over 800-fold in A. baumannii and 10-750-fold inducible in the other tested species. Background expression was lower from PtetB in the pseudomonads than previously described carbohydrate inducible promoters. Data indicated that the PtetB promoter system may be a useful tool for future basic science research with the tested bacteria.

Chlorella sorokiniana is an industrially important microalga, but its genetic manipulation remains limited by poor transformation efficiency. Although several barriers have been proposed, the role of intracellular nuclease activity has received little attention. Here, we compared the degradation of exogenous double-stranded DNA in whole-cell lysates from transformation-recalcitrant (Chlorella sorokiniana and Tetradesmus obliquus) and transformation-proficient (Phaeodactylum tricornutum, Microchloropsis gaditana, and Nannochloropsis oceanica) microalgae. C. sorokiniana rapidly degraded both linear and plasmid DNA, while T. obliquus exhibited intermediate activity. In contrast, no detectable degradation was observed in the transformation-proficient species. DNA degradation in C. sorokiniana occurred within minutes, and nuclease activity in both recalcitrant species was inhibited by EDTA and Zn2+, consistent with a metal ion-dependent endonuclease. These findings suggest that intracellular nuclease activity may represent an underexplored barrier to exogenous DNA stability during transformation and provide a potential target for improving genetic transformation of recalcitrant microalgae.

Microalgae are promising biological platforms for sustainable energy production because they can convert photosynthetic activity into electricity in biophotovoltaic (BPV) systems. Identifying strains that grow well at elevated temperatures and under reduced irradiance is an important step toward BPV applications in seasonally variable environments. Microalgae were isolated from Seondong Lake (Seoul, Republic of Korea) during three sampling periods and screened for growth at different temperatures. Among 45 isolates, KHUC-11 showed the highest weighted growth index based on growth rates at 25, 30, and 35 under dark conditions and was selected for further characterization. Phylogenetic reconstruction of the 45S rRNA gene cluster, internal transcribed spacer 2 (ITS2) secondary-structure comparison, and morphological characterization supported the description of KHUC-11 as Desmodesmus ecotolerans sp. nov. D. ecotolerans produced more biomass than Chlamydomonas reinhardtii CC-125 under both moderate- and low-light conditions. Under low light, its final dry biomass concentrations were 1.81 and 1.58 times those of CC-125 under mixotrophic and photoautotrophic conditions, respectively. Despite its smaller cell size, D. ecotolerans showed higher photosynthetic performance than CC-125, and screen-printed electrode (SPE)-based electrochemical assays demonstrated enhanced anodic current generation and CV-derived estimated power output. Taken together, these findings identify D. ecotolerans as a novel climate-resilient Desmodesmus species with bioelectrochemical properties that support further evaluation for BPV applications.

To elucidate the resistance of hybrid larch (Larix spp.) clones to Dendrolimus superans and their chemical defense mechanisms, 46 clones were subjected to field feeding-infestation experiments. Insect resistance was comprehensively evaluated by the membership function method from three perspectives: insect weight change, frass weight and defoliation rate (damage consequences); pre- and post-infestation physiological indices (constitutive and induced resistance). High- and low-resistance materials screened from each perspective were further analyzed by widely targeted LC-MS metabolomics. The three damage indices differed highly significantly among clones (P<0.01) and were positively correlated, indicating abundant genetic variation in resistance. Clones such as -30-C, -35-A and -21-A showed strong overall resistance, whereas -28-E and -20-A showed high constitutive and induced resistance. Defence enzyme activities (POD, SOD, CAT, PPO and PAL) correlated closely with secondary metabolites (total phenolics, tannins and total flavonoids) and nutrients, and rose synergistically after feeding, reflecting synergy between constitutive and induced resistance. In total, 3,577 metabolites were identified, predominantly terpenoids (>90%). The three groups yielded 1,010, 1,086 and 922 differentially accumulated metabolites, and PCA separated high- from low-resistance materials in all groups. These metabolites were significantly enriched in metabolic pathways, biosynthesis of secondary metabolites, monoterpenoid and diterpenoid biosynthesis, and phenylpropanoid biosynthesis. In conclusion, resistance of hybrid larch to D. superans is jointly conferred by the defence enzyme system, secondary metabolite accumulation and a 'terpenoid-phenolic' metabolic network: terpenoids and phenolics act synergistically via the terpenoid and phenylpropanoid pathways, together forming the core of chemical defense and supporting the breeding of insect-resistant clones.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

Engineered microbial consortia offer a promising strategy for chemical production by distributing specialized functions among microbial strains, reducing metabolic burden, facilitating modular pathway optimization, reducing toxicity, and increasing strain stability. However, differences in growth rates can destabilize population composition, compromising productivity and limiting their applicability. Here, we developed a multichromatic optogenetic Toxin-Antitoxin (optogeneticTA) platform for dynamic control of Escherichia coli consortia of up to four members using blue, red, and near-infrared light and darkness. By varying light intensities or pulses, we precisely program and dynamically modulate the population composition of two-, three-, and four-membered consortia. We further developed a modular mathematical framework that captures and predicts population dynamics of these optogenetically controlled co-cultures. Applying dynamic control to a two-membered engineered consortium increased phenol production by ~69% relative to unregulated consortia. These results establish a programmable platform for stabilizing and dynamically optimizing microbial consortia, with potential applications across microbial biomanufacturing.

Ensuring the safety and suitability of proteins considered for food and biotechnology applications is an important challenge. Computational approaches can facilitate the prioritisation of candidate proteins for further investigation, particularly given the rapid growth of genomic and metagenomic sequence datasets. In this study, we developed computational models to distinguish edible plant tissue-associated (EPTA) proteins from non-EPTA proteins. The positive class comprised 939 proteins with experimentally validated evidence of expression in plant tissues commonly consumed by humans, hereafter referred to as EPTA proteins. The negative class comprised 939 proteins annotated in UniProt as toxic, allergenic, or antinutritional, hereafter referred to as non-EPTA proteins. Models were evaluated using five-fold cross-validation with a cluster-based partitioning strategy, ensuring that no protein in the validation set shared more than 40% sequence identity with proteins in the corresponding training set. First, composition-based sequence features were evaluated to establish a baseline for EPTA or non-EPTA classification, with the Extra Trees (ET) classifier achieving a validation AUROC of 0.848. To incorporate evolutionary information beyond amino acid composition, PSSM profiles were subsequently evaluated, yielding an AUROC of 0.842 with an SVM classifier. Finally, to capture richer sequence patterns and contextual information using protein language modelling, we evaluated ESM2-based models. The fine-tuned ESM2-T33 model achieved the highest validation AUROC of 0.925 and was selected as the final predictive model. The resulting approach was implemented as the EdiProPred web server (https://webs.iiitd.edu.in/raghava/edipropred/) and standalone software for computational classification of plant proteins. The EdiProPred framework provides a useful computational tool for prioritising candidate plant proteins and facilitating the exploration of novel protein resources for food and biotechnology applications.