Aconiti Lateralis Radix Praeparata (Fuzi) represents a significant traditional Chinese medicine (TCM) that exhibits both notable pharmacological effects and toxicity. Various processing methods are implemented to reduce the toxicity of raw Fuzi by modifying its toxic and effective components, primarily diterpenoid alkaloids. To comprehensively analyze the chemical variations between different Fuzi products, ultra-high performance liquid chromatography-linear ion trap quadrupole Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) was employed to systematically characterize Shengfuzi, Heishunpian and Baifupian. A total of 249 diterpenoid alkaloids present in Shengfuzi were identified, while only 111 and 61 in Heishunpian and Baifupian were detected respectively, indicating substantial differences among these products. An untargeted metabolomics approach combined with multivariate statistical analysis revealed 42 potential chemical markers. Through subsequent validation using 52 batches of commercial Heishunpian and Baifupian samples, 8 robust markers distinguishing these products were identified, including AC1-propanoic acid-3OH, HE-glucoside, HE-hydroxyvaleric acid-2OH, dihydrosphingosine, N-dodecoxycarbonylvaline and three unknown compounds. Additionally, the MS imaging (MSI) technique was utilized to visualize the spatial distribution of chemical constituents in raw Fuzi, revealing how different processing procedures affect the chemical variations between Heishunpian and Baifupian. The distribution patterns of different diterpenoid alkaloid subtypes partially explained the chemical differences among products. This research provides valuable insights into the material basis for future investigations of different Fuzi products.
Diterpenes/chemistry* ; Alkaloids/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Aconitum/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Metabolomics ; Mass Spectrometry/methods* ; Plant Roots/chemistry* ; Molecular Structure

Sini Decoction (SNT) is a traditional formula recognized for its efficacy in warming the spleen and stomach and dispersing cold. However, elucidating the mechanism of action of SNT remains challenging due to its complex multiple components. This study utilized a synergistic approach combining two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE)-based drug affinity responsive target stability (DARTS) with label-free quantitative proteomics techniques to identify the direct and indirect protein targets of SNT in myocardial infarction. The analysis identified 590 proteins, with 30 proteins showing significant upregulation and 51 proteins showing downregulation when comparing the SNT group with the model group. Through the integration of 2D-DIGE DARTS with proteomics data and pharmacological assessments, the findings indicate that protein disulfide-isomerase A3 (PDIA3) may serve as a potential protein target through which SNT provides protective effects on myocardial cells during myocardial infarction.
Myocardial Infarction/genetics* ; Proteomics/methods* ; Drugs, Chinese Herbal/chemistry* ; Animals ; Protein Disulfide-Isomerases/genetics* ; Male ; Two-Dimensional Difference Gel Electrophoresis/methods* ; Humans ; Rats ; Rats, Sprague-Dawley ; Electrophoresis, Gel, Two-Dimensional

OBJECTIVES: This study aimed to investigate the impact of foam macrophages (FMs) on the intracellular survival of Mycobacterium tuberculosis (MTB) and identify the molecular mechanisms influencing MTB survival. METHODS: An in vitro FM model was established using oleic acid induction. Transcriptomic and metabolomic analyses were conducted to identify the key molecular pathways involved in FM-mediated MTB survival. RESULTS: Induced FMs effectively restricted MTB survival. Transcriptomic and metabolomic profiling revealed distinct changes in gene and metabolite expression in FMs during MTB infection compared with normal macrophages. Integrated analyses identified significant alterations in the cyclic adenosine monophosphate (cAMP) signaling pathway, indicating that its activation contributes to the FM-mediated restriction of MTB survival. CONCLUSIONS FMs inhibit MTB survival. The cAMP signaling pathway is a key contributor. These findings enhance the understanding of the role of FMs in tuberculosis progression, suggest potential targets for host-directed therapies, and offer new directions for developing diagnostic and therapeutic strategies against tuberculosis.
Mycobacterium tuberculosis/physiology* ; Transcriptome ; Metabolomics ; Foam Cells/microbiology* ; Humans ; Metabolome ; Tuberculosis/microbiology* ; Gene Expression Profiling

OBJECTIVES: This study aimed to explore the biological functions and clinical value of mothers against decapentaplegic homolog (SMAD) 7 in head and neck squamous cell carcinoma (HNSCC) through bioinformatics analysis and basic experiments. METHODS: The expression of SMAD7 in HNSCC in public databases was studied. Western blot was used to detect the expression of SMAD7 in HNSCC cell lines and normal epithelial cells. The SMAD7 highly expressed HNSCC cell line HSC-4 was silenced, and CCK-8, Transwell assays, and cell scratch experiments were conducted to study the effect of SMAD7 on the biological functions of HSC-4 cells. HNSCC expression profile data were obtained from UCSC xena, and genes related to SMAD7 were selected for gene ontology and Kyoto encyclopedia of genes and genomes gene enrichment analysis, construction of a co-expression gene interaction network, and screening of related cell signaling pathways. Western blot was used to detect the expression changes of proteins in the related cell signaling pathways in HNSCC cells with silenced SMAD7. cBioPortal was utilized to analyze the mutation rate of the SMAD7 gene, and the MethSurv database was used to analyze the methylation level of the SMAD7 gene and its correlation with prognosis. The receiver operating characteristic curve was used to assess the diagnostic value of SMAD7 for HNSCC. TIMER2.0 was used to analyze the correlation between SMAD7 expression and immune cell infiltration. RESULTS: SMAD7 was highly expressed in HNSCC tumor tissues and some cell lines. Silencing the expression of SMAD7 can significantly inhibit the proliferation, migration, and invasion of cancer cells. Silencing SMAD7 can induce the downregulation of vascular cell adhesion molecule 1 (VCAM-1). The bioinformatics analysis showed that the mutation rate of the SMAD7 gene and the methylation level were significantly correlated with the prognosis of patients with HNSCC. The expression of SMAD7 was related to the level of immune cell infiltration in HNSCC. CONCLUSIONS SMAD7 promotes the proliferation, migration, and invasion of HNSCC cells by regulating the expression of VCAM-1. It may be a potential tumor biomarker and therapeutic target for HNSCC.
Humans ; Smad7 Protein/metabolism* ; Prognosis ; Squamous Cell Carcinoma of Head and Neck ; Head and Neck Neoplasms/pathology* ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Signal Transduction ; Gene Expression Regulation, Neoplastic ; Gene Silencing ; Computational Biology

Ophiopogon japonicus, a precious medicinal plant endemic to Zhejiang Province. Its tuberous roots are rich in bioactive components such as flavonoids, possessing anti-inflammatory, antioxidant, and immunomodulatory properties. To elucidate the impact of cadmium (Cd) stress on the accumulation and biosynthetic pathway of flavonoids in O. japonicus, this study exposed O. japonicus to different concentrations of Cd stress and explored the changes through integrated transcriptomics and metabolomics analysis. The results demonstrated that Cd stress (1 mg/L and 10 mg/L) significantly increased the content of flavonoids in O. japonicus in a concentration-dependent manner. The metabolomics analysis revealed a total of 110 flavonoids including flavones, flavanols, flavonols, flavone and flavonol derivatives, flavanones, isoflavonoids, chalcones and dihydrochalcones, and anthocyanins in O. japonicus, among which flavones, flavonols, flavone and flavonol derivatives, and anthocyanins increased under Cd stress. The transcriptomics analysis identified several key flavonoid biosynthesis-associated genes with up-regulated expression under Cd stress, including 14 genes encoding 4-coumarate CoA ligase (4CL), 2 genes encoding chalcone isomerase (CHI), and 14 genes encoding phenylalanine ammonia lyase (PAL). The gene-metabolite regulatory network indicated significant positive correlations of 4CL (Cluster-21637.5012, Cluster-21637.90648, and Cluster-21637.62637) and CHI (Cluster-21637.111909 and Cluster-21637.123300) with flavonoid metabolites, suggesting that these genes promoted the synthesis of specific flavonoid metabolites, which led to the accumulation of total flavonoids under Cd stress. These findings provide theoretical support for the cultivation and utilization of medicinal plants in Cd-contaminated environments and offered new perspectives for studying plant responses to heavy metal stress.
Cadmium/toxicity* ; Flavonoids/biosynthesis* ; Metabolomics ; Ophiopogon/drug effects* ; Stress, Physiological ; Transcriptome ; Gene Expression Profiling ; Gene Expression Regulation, Plant

Flavonoids are key bioactive components for evaluating the pharmacological activities of Chimonanthus praecox. Exploring the potential flavonoids and pharmacological mechanisms of C. praecox lays a foundation for the rational development and efficient utilization of this plant. This study employed ultra-performance liquid chromatography-tandem mass spectrometry-based widely targeted metabolomics to comprehensively identify the flavonoids in C. praecox. Network pharmacology was employed to explore the bioactive flavonoids and their mechanisms of action. Molecular docking was adopted to validate the predicted results. Finally, the content of bioactive flavonoids in different varieties of C. praecox was measured. The widely targeted metabolomics analysis identified 387 flavonoids in C. praecox, and the flavonoids varied among different varieties. Network pharmacology predicted 96 chemical components including 19 bioactive compounds, 181 corresponding targets and 2 504 disease targets, among which 99 targets were shared by the active components and the disease. Thirty-three core targets were predicted, involving 229 gene ontology terms and 99 pathways (P≤0.05), which indicated that the flavonoids components of C. praecox exhibited pharmacological activities including antioxidant, anti-inflammatory, antimicrobial, and antiviral activities. Topological analysis screened out five core components (salvigenin, laricitrin, isorhamnetin, quercetin, and 6-hydroxyluteolin) and five core targets (SRC, PIK3R1, AKT1, ESR1, and AKR1C3). The predicted bioactive flavonoids from C. praecox stably bound to key targets, which indicated that these flavonoids possessed potential bioactivities in their interactions with the targets. The flavonoids in C. praecox exerted pharmacological activities in a multi-component, multi-target, and multi-pathway manner. The combined application of metabolomics and network pharmacology provides a theoretical basis for in-depth studies on the pharmacological effects and mechanisms of C. praecox.
Flavonoids/metabolism* ; Network Pharmacology ; Metabolomics/methods* ; Molecular Docking Simulation ; Calycanthaceae/chemistry* ; Tandem Mass Spectrometry ; Drugs, Chinese Herbal/chemistry*

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.
Artificial Intelligence ; Proteins/chemistry* ; Computer Simulation ; Software ; Computational Biology/methods*

Predicting protein mutation effects is a key challenge in bioinformatics and protein engineering. Recent advancements in deep learning, particularly the development of protein language models (PLMs), have brought new opportunities to this field. This review summarizes the application of PLMs in predicting protein mutation effects, focusing on three main types of models: sequence-based models, structure-based models, and models that combine sequence and structural information. We analyze in detail the principles, advantages, and limitations of these models and discuss the application of unsupervised and supervised learning in model training. Furthermore, this paper discusses the main challenges currently faced, including the acquisition of high-quality datasets and the handling of data noise. Finally, we look ahead to future research directions, including the application prospects of emerging technologies such as multimodal fusion and few-shot learning. This review aims to provide researchers with a comprehensive perspective to further advance the prediction of protein mutation effects.
Mutation ; Proteins/chemistry* ; Computational Biology/methods* ; Deep Learning ; Protein Engineering

With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review comprehensively summarizes the significant progress in applying artificial intelligence (AI) technologies to the design, mining, and modification of CRISPR-Cas systems. AI technologies, especially machine learning, have revolutionized sgRNA design by analyzing high-throughput sequencing data, thereby improving the editing efficiency and predicting off-target effects with high accuracy. Furthermore, this paper explores the role of AI in sgRNA design and evaluation, highlighting its contributions to the annotation and mining of CRISPR arrays and Cas proteins, as well as its potential for modifying key proteins involved in gene editing. These advancements have not only improved the efficiency and precision of gene editing but also expanded the horizons of genome engineering, paving the way for intelligent and precise genome editing.
CRISPR-Cas Systems/genetics* ; Artificial Intelligence ; Gene Editing/methods* ; RNA, Guide, CRISPR-Cas Systems/genetics* ; Machine Learning ; Humans ; Genetic Engineering/methods* ; Synthetic Biology