Zhenguo Wen

Mycotoxins and the nucleic acid biomarkers of toxigenic fungi represent two complementary risk indicators in food safety; yet their parallel detection remains challenging because the two classes of analytes demand intrinsically incompatible signal-generation logic. Herein, we report a dual-channel DNAzyme (Dz) walker platform for the parallel detection of the toxigenic fungal marker gene aflD and aflatoxin B1 (AFB1). The platform is built on a Blocker-Dz-Substrate module that unifies two distinct recognition events into a common Dz-mediated cleavage output. Specifically, the aflD gene activates the Dz through competitive strand displacement, while AFB1 induces a conformational change in the aptamer-containing Blocker to release the Dz. The activated Dz then cleaves substrates labelled with Cy3 and FAM, enabling orthogonal dual-channel fluorescence readout. Gold nanoparticles serve as both scaffolds for localized walking and quenchers of fluorophores to suppress background, thereby amplifying the signal. The method achieved limits of detection of 0.39 nM for the aflD gene and 0.010 ng/mL for AFB1, with recoveries of 95.33-105.83% in spiked maize samples. By reconciling the signal-logic conflict between small-molecule toxins and nucleic acid markers, this strategy provides a generalizable route for parallel assessment of fungal contamination risk and mycotoxin burden in complex food matrices.

Manufacturing scalability and cost efficiency of Lentiviral vector (LV) production for gene therapy are hampered by slow offline detection of critical parameters and viral titer. To enable real‐time monitoring, this study proposes a process analytical technology (PAT) based on Raman spectroscopy and an innovative deep learning algorithm to achieve real‐time prediction of glucose, lactate, viable cell density, and viral titer during LV production. To address the limitations of existing Raman spectral analysis methods, this study integrated data augmentation, self‐supervised learning, and time‐series modeling strategies. A regression model integrating a one‐dimensional Convolutional Neural Network (1D‐CNN) with Squeeze‐and‐Excitation (SE) attention, enhanced by relative positional encoding (RPE), multi‐query attention (MQA), and Top‐K feature selection (termed ACNN), was constructed. This approach utilized vast amounts of unlabeled spectral data to precisely extract feature information from complex components, including viral particles. Experimental results demonstrated that the ACNN model significantly outperformed the traditional Partial Least Squares (PLS) model in predicting viral titer, cell density, glucose, and lactate concentrations (paired t ‐test, p < 0.05 for all parameters). Notably, titer prediction error was substantially reduced. Statistical analysis showed that the RMSE of ACNN for titer prediction was significantly lower than that of PLS ( t = 3.82, p < 0.01), and the R ² was significantly higher ( t = 4.15, p < 0.001). This study overcomes technical bottlenecks in real‐time quality monitoring and process optimization for LV production, providing a feasible transition from “black box operations” to transparent and intelligent monitoring throughout the entire process and establishing a robust foundation for large‐scale intelligent production of other gene therapy vectors.

Recombinant glucokinase (GLK), an important enzyme for blood glucose diagnostics, is commonly prepared in Escherichia coli (E.coli )for laboratory research and related biosynthetic applications. In small-scale laboratory cultivation, complex media are often used because of their convenience and strong growth-supporting capacity, but their ill-defined composition and batch-to-batch variability can compromise reproducibility and increase medium-related costs. Although minimal defined medium (e.g., M9 medium) are generally preferred in scalable E. coli bioprocesses because of their compositional consistency and process controllability, their relatively simple nutrient composition may limit recombinant protein expression performance under laboratory-scale conditions. Therefore, developing an improved chemically defined medium represents a practical strategy to balance expression efficiency, compositional control, and medium cost for recombinant GLK production. To address this, we developed a chemically defined medium including amino acids using high-throughput respiration activity monitoring system (RAMOS) and a high-throughput growth curve analysis system (Growth Profiler 960, GP960), alongside a machine learning-driven iterative optimization system (Machine-learning guided Experimental Trials for Improvement of Systems, METIS). The optimized medium enhanced soluble GLK expression by 33 fold compared to the pre-optimization medium, reaching 74% of the titer obtained in complex media, while reducing medium costs by 74%. Moreover, soluble protein expression per cell increased by 80% compared to the complex medium, lowering the medium usage cost per unit of bacterial protein by 85%. Validation in a 7.5 L bioreactor demonstrated that the optimized medium supported continuous growth to an optical density at 600 nm (OD₆₀₀) of 103, achieving 4-fold and 5.4-fold higher biomass than Luria-Bertina (LB) and M9 media, respectively, with soluble GLK titer reaching 920 mg L^- 1, which exceeded conventional media by more than 10-fold. This study tackles the challenge of suboptimal media in GLK fermentation, offering a cost-effective alternative and a transferable strategy for optimizing fermentation processes for other high-value enzymes.

The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, traditional protein design methods often depend on extensive datasets and detailed structural information, limiting their efficiency and applicability. To overcome these limitations, we propose a deep learning-based approach that directly targets desired binding functions by introducing mutations at selected SpA sites to optimize its properties. Specifically, we present a de novo protein design strategy that integrates a diffusion-based generative model with Kidera factor representations to create SpA variants. The framework comprises three modules: sequence generation, where protein sequences are encoded via Kidera factors and novel variants are generated using a diffusion model; computational screening, employing tools like AlphaFold3 to assess structural properties, solubility, and physicochemical characteristics, thereby selecting high-potential candidates; and experimental validation, involving wet-lab experiments to evaluate the biological activities and binding affinities of the designed proteins. The generated SpA variants demonstrated high success rates and strong binding affinities toward IgG. These findings confirm the effectiveness of our method in producing functional proteins comparable to natural counterparts, offering a scalable and data-efficient alternative to protein engineering.

In this study, a green and efficient extraction methodology was developed by leveraging the unique properties of chitosan—namely its non-toxicity, biocompatibility, and adhesive nature—to enhance the recovery of bioactive ingredients from Forsythia suspensa leaves. The core mechanism involves the formation of complexes between chitosan and the target bioactive ingredients, which significantly boosts their extraction efficiency. To substantiate this mechanism, comprehensive characterization was performed using Powder X-ray Diffraction (PXRD), Fourier Transform Infrared Spectroscopy (FT-IR), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and molecular docking analyses. The results provided robust evidence of a strong interaction between chitosan and the bioactive ingredients, leading to a marked enhancement in both the stability and aqueous solubility of the target compounds. For process optimization, a multi-objective approach was implemented using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously maximize the extraction yields of phillyrin and forsythoside A. The algorithm identified the optimal parameters as a leaf-to-chitosan mass ratio of 10:11.75, a solid-to-liquid ratio of 1:52 g/mL, a temperature of 80 °C, and a duration of 120 min. Under these optimized conditions, the corresponding extraction yields for phillyrin and forsythoside A were 1.68 ± 0.16% and 3.23 ± 0.27%, respectively. These findings collectively indicate that chitosan-assisted extraction represents a highly promising and advanced technology for the sustainable and effective extraction of bioactive ingredients from botanical sources.

In this study, an ultrasonic-assisted procedure for the extraction of mulberry leaf polysaccharides (MLPs) was investigated using response surface methodology with a 29-run Box–Behnken design. Four factors were investigated, and it was found that the factors influencing the process, in order of significance, were the extraction temperature > liquid-to-material ratio > ultrasonic power. Considering practical conditions, the parameters were adjusted to a liquid-to-material ratio of 16:1 mL/g, extraction time of 58 min, extraction temperature of 65 °C, and ultrasonic power of 500 W. Under these conditions, the yield of MLPs was 14.47%, which is close to the predicted value, indicating that the extraction process optimized by response surface methodology (RSM) is feasible. The separation and purification effects of macroporous resin and activated carbon on MLPs were investigated, with the D152 resin being considered the most suitable choice. The optimal separation conditions were found to be a sample concentration of 0.5 g/mL and an optimal flow rate of 1 mL/min. Thin-layer chromatography and infrared spectroscopy revealed that polysaccharides extracted from mulberry leaves are primarily composed of rhamnose, xylose, and arabinose. In conclusion, this study successfully optimized the ultrasonic-assisted extraction process of MLPs through response surface methodology, determined the optimal parameter combination, and verified its efficiency and stability. Under the optimal conditions obtained for ultrasonic-assisted extraction, the yield of MLPs is significantly higher than that reported in the previous literature. The decolorization process of crude mulberry leaf polysaccharide extract was also investigated, and the purified MLPs have clear monosaccharide composition and structural characteristics, providing a theoretical basis and technical support for their application in functional food or drug development.

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model’s ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation.

The virtual screening of synthetic proteins is to develop an effective method to predict and screen out synthetic proteins that are similar to the target sequences in terms of their structural and physicochemical properties by combining advanced computational tools and models, such as AlphaFold2 and ESM2. Through virtual screening, the experimental cost and time period can be reduced, and some guidelines for customized design of new proteins can be provided. All similar research efforts have promoted the advancement in the fields of synthetic biology and biomedical science. In this paper, we first calculate the backbone distances between synthetic proteins and target sequences using the AlphaFold2 tool to ensure that they are structurally similar. Secondly, based on the ESM2 model, the concept of feature distance is proposed to ensure that the two proteins are consistent in physicochemical properties. In the experiments, the steps of virtual screening of synthetic proteins are summarized, and the synthetic proteins are arranged in ascending order by backbone distance and feature distance. If the same synthetic protein appears in the first position of the two sorted tables, then select that protein. If different synthetic proteins appear in the first position of the two sorted tables, the synthetic protein with higher solubility will be selected. Repeat this process for other ranking positions. The experimental results show that this process derived from backbone distance and feature distance is a necessary and useful tool to select preferred synthetic proteins before entering the lab experimental session.