Biotechnology and Bioengineering最新文献

Toluene o-xylene monooxygenase (ToMO) of Pseudomonas sp. OX1 was investigated as a drug-metabolizing enzyme for the first time and was found to metabolize chlorzoxazone and resveratrol to form human metabolites 6-chlorzoxazone (0.045 ± 0.016 nmol/hr/mg protein) and piceatannol (0.014 ± 0.009 nmol/hr/mg protein), respectively, though at low rates. ToMO also forms 2-acetamidophenol (2-AAP, 27%), 3-AAP (42%), and 4-AAP (31%) from acetanilide at 3.6 ± 0.3 nmol/hr/mg protein. Multiple-site saturation mutagenesis at positions I100/E103/A107 of the alpha-subunit along with site-directed mutagenesis approaches were used to isolate thirty-seven different ToMO variants with enhanced activities and/or fine-tuned specificities. Specifically, variant I100V/E103T was identified with 2.1- and 49-fold higher activities towards acetanilide and chlorzoxazone, respectively, compared to native ToMO. Variant I100V/E103T also had the regiospecificity of acetanilide change from 31% to 100% 4-AAP, mimicking human liver enzyme behavior. In addition, several variants showed up to 3.7-, 1.6-, and 3.2-fold improved selectivity for 2-, 3-, and 4-AAP formation, respectively. For resveratrol, variant I100T/E103L was a better catalyst than native ToMO, exhibiting 34-fold higher activity. The results presented here demonstrate the potential of nonhuman ToMO variants in drug metabolism and contribute to the list of research on probing this promising enzyme.

Bioelectronic systems that enable seamless communication between electronic devices and living systems represent a transformative frontier in biotechnology. Among available methodologies, redox based signaling offers unique advantages due to its ubiquity in biology and compatibility with standard electrochemical equipment, expanding on existing electrogenetic approaches while simplifying entry requirements for researchers. Here, we developed a modular phenazine-based system that enables bidirectional redox communication between electronic devices and engineered bacterial populations using commercially available electrodes. Our system integrates readily into existing synthetic biology frameworks and leverages phenazine modifications to modulate signal reception across biological and electronic domains. We structured our design around four modular components within a communication channel framework: (1) electronic signal encoding via electrochemically generated hydrogen peroxide that activates engineered cells to produce quorum sensing molecules, (2) biological signal transmission through phenazine biosynthesis controlled by a single regulatory target (PhzF), (3) dual-domain signal reception via both SoxRS-responsive biological circuits and direct electrochemical detection, and (4) controllable noise through phenazine-specific degradation enzymes. We demonstrate proportional control over phenazine production with linear relationships between electronic inputs and both biological and electrochemical outputs. This modular approach establishes phenazines as versatile bridges between electronic and biological information processing, providing accessible tools for practical bioelectronic systems with applications in environmental monitoring, adaptive biomanufacturing, and responsive biomedical devices.

Chromatography is a key unit operation in the biopharmaceutical manufacturing process used for protein purification and polishing. Design and optimization of these processes are resource-intensive resulting from the complex combinatorial design space. This constraint combined with the wide diversity in therapeutic formats and increased pressure for timely delivery to the market necessitates an efficient, fast, robust and generalized framework for process design and optimization. Here we present Gaussian processes as a potent machine learning methodology for predictive modeling in the context of QSAR modeling used for resin and solvent condition selection. We highlight the on-par predictive power of Gaussian Processes with other reported machine learning algorithms. Furthermore, we demonstrate additional properties of Gaussian processes such as its ability to provide confidence estimates for its prediction that makes it suitable for model-assisted optimization. Finally, we demonstrate the possibility to derive feature importances from Gaussian processes, making these models as interpretable as ensembled tree-based methods such as random forests.

1,5-Pentanediol (1,5-PDO) is a five-carbon aliphatic diol widely used as a raw material for synthesis of polyurethanes, polyesters, plastics, or fibers. Recently, the de novo synthesis of 1,5-PDO has been established, but the accumulation of intermediates and low yield of product limit its further application. In this study, based on the l-lysine high-producing Escherichia coli, an efficient microbial cell factory containing a 5-hydroxyvalerate synthesis module (5-HV) and a 1,5-PDO synthesis module was designed. By screening the enzymes of different modules, a 1,5-PDO-synthesizing recombinant strain with the best combination of MmCAR, Yahk, and GabT was obtained. The amino acid residues in the adenosine domain of CAR were rationally mutated to glutamic acid to obtain the variant MmCAR^Q302E, which had enhanced activity against 5-HV and reduced its accumulation. Subsequently, the accumulation of 5-HV was further reduced by enhancing the expression of CAR through RBS engineering and fixing CAR with the help of EutM protein scaffold. In addition, the endogenous gene ycjQ of E. coli was deleted to reduce the reoxidation of 1,5-PDO. Finally, the 1,5-PDO yield reached 12.9 g/L under the optimized fermentation conditions, achieving efficient biosynthesis of 1,5-PDO and lower accumulation of 5-HV, which is the highest yield reported in E. coli so far.

Protein aggregation poses a significant risk to biopharmaceutical product quality, as even minor amounts of oligomeric species can compromise efficacy and safety. Rapid and reliable detection of protein aggregates thus remains a major challenge in biopharmaceutical manufacturing. Although traditional offline methods such as size-exclusion chromatography provide accurate results, their inherent time delays limit real-time process control capabilities. Consequently, there is an urgent scientific need for inline analytical techniques capable of selectively quantifying protein monomers and aggregates in real time to facilitate immediate corrective actions and enhance overall process robustness. Raman spectroscopy, as a tool for a process analytical technology application, is especially suitable due to its molecular specificity, rapid data acquisition, and compatibility with aqueous solutions commonly used in biopharmaceutical manufacturing. Addressing this need, this study establishes a Raman spectroscopy-based strategy for the selective detection and quantification of monomeric and aggregated forms of a model protein (bovine serum albumin). Controlled stress conditions were applied to generate aggregated species reproducibly, and a Latin Hypercube sampling design was used to independently vary protein concentration and aggregate fraction, ensuring that observed spectral effects were attributable to aggregation rather than concentration differences. Furthermore, spectral markers identified in spectra acquired from multiple chromatographic runs were qualitatively compared with offline reference measurements from size-exclusion chromatography. This limitation in real-time applicability was circumvented by chemometric machine learning approaches. The use of convolutional neural networks enabled the selective quantification of the protein monomers and aggregates and delivered superior predictive performance and robustness across cross-validation, independent testing, and synthetic perturbation scenarios compared to traditional chemometric approaches. Collectively, these results demonstrate that the selected Raman spectral markers, combined with advanced chemometric modeling, enable reliable, real-time monitoring of protein size variants in biopharmaceutical downstream processes.

Fatty alcohols currently find use in areas such as surfactants, plasticizers, lubricants, fuels, and the cosmetics industry; however, traditional production methods rely on non-renewable petroleum-derived compounds or are harvested from non-sustainable oil-seed crops. Recently, conventional hosts including Escherichia coli and Saccharomyces cerevisiae have been used for fatty alcohol production with some success. Oleaginous yeasts, such as Yarrowia lipolytica, offer significant advantages to produce oleochemicals, as their native metabolism evolved for high-flux fatty acid biosynthesis. However, fatty alcohol production in the cytosol faces challenges, including toxicity, limited availability of acyl-CoA, and the presence of competing pathways. To overcome these limitations, we targeted fatty alcohol biosynthesis into the peroxisome, where fatty acyl-CoA flux is naturally directed toward beta-oxidation and with fewer competing pathways. Following media optimization, fatty acyl-CoA reductases (FAR) from bacterial and mammalian sources were screened using canonical peroxisome targeting sequences. Additionally, we implemented an enzyme fusion strategy to physically colocalize FAR next to the 3-ketoacyl-CoA thiolase (3KAT) enzyme in the peroxisome. 3KAT fusion resulted in nearly double the titer of fatty alcohols, irrespective of which FAR was overexpressed. We then systematically engineered the subcellular environment within peroxisomes by increasing peroxisome numbers and boosting localized NADPH availability via the peroxisomal malate pathway and NADH kinase. These strategies significantly improved the organelle capacity for fatty alcohol production. The highest titer we achieved in shake flask culture was over 1.6 g/L of fatty alcohols. Further, we scaled up the fatty alcohol production in a 2 L bioreactor, achieved 2.77 g/L of fatty alcohols, in which a peak production of 2.53 g/L of C16:0 hexadecanol was achieved.

Polysorbate-degrading host cell proteins (HCPs) represent a critical challenge in the manufacturing of monoclonal antibody therapeutics due to their potential to persist during downstream processing. While their enzymatic activity has been characterized, the role of direct HCP-mAb interactions, particularly those involving polysorbate degrading HCPs, remains poorly understood. In this study, we systematically investigated the binding behavior of four representative polysorbate-degrading HCPs (CES1F, LPLA2, PAF-AH, and PPT1) to a panel of mAbs using biolayer interferometry (BLI). All tested HCPs showed specific, transient interactions characterized by fast-on/fast-off kinetics, with apparent equilibrium dissociation constants (K_D) in the low nanomolar range (40-90 nM for strong binders) and rapid dissociation kinetics (k_d > 0.01 s^-1). This indicates a binding mode characterized by relatively high affinity but limited kinetic stability. Due to incomplete saturation and partially not meeting the quality criteria for kinetic fitting, we complemented model-based analysis with equilibrium-derived descriptors. The initial slope of the binding isotherm correlated well with kinetic parameters and enabled robust ranking of interaction strength. To assess hitchhiking relevance during downstream processing, we performed a Protein A chromatography experiment using PLBL2 as a model HCP and two mAbs with different interaction profiles. PLBL2 levels in Protein A elution pools correlated well with interaction propensity confirming that transient interactions can contribute to HCP co-elution. Our results provide the first systematic and quantitative comparison of polysorbate hydrolase-antibody interactions. They also demonstrate that direct mAb-HCP interaction is a relevant mechanism contributing to HCP persistence during downstream processing.