The pathogenesis of rheumatoid arthritis (RA) involves a complex interplay between immune dysregulation and metabolic disturbance, with the gut microbiota and its metabolites playing pivotal roles. However, integrated biomarkers based on the “gut microbiota–metabolite–host gene” regulatory axis that can quantify its impact on RA stratification and therapeutic response are lacking. We integrated multiple RA transcriptomic datasets to screen for host genes explicitly associated with the gut microbiota and metabolites using the gutMGene database. A gut microbiota–metabolite–host score (GMS) was constructed via principal component analysis (PCA). Immune infiltration patterns were evaluated using CIBERSORT. Furthermore, scRNA-seq was employed to map the cellular origins of key genes, and in silico knockout analysis using scTenifoldKnk was performed to simulate the functional ablation of core host genes within this regulatory axis. Additionally, molecular docking was conducted to validate the binding affinities between the key host proteins and the optimal small molecule. We identified seven microbiota–metabolite–host hub genes (FOS, JUN, IDO1, PEMT, EGFR, ABCC4, and FADS3) and constructed the GMS. The GMS stratified patients with RA into two distinct functional subtypes: an adaptive immune activation subtype and an inflammatory–metabolic composite subtype. Notably, the GMS demonstrated superior performance in predicting responses to anti-TNF and rituximab therapies. The scRNA-seq analysis revealed high expression of FOS and JUN in T cells and NK cells. In silico knockout analysis further revealed the central transcription factors FOS and JUN as key executors of this axis, as their ablation significantly affected osteoclast differentiation and the enrichment of TNF/MAPK inflammatory pathways. These computational findings support the core mechanism by which gut microbiota–metabolite signalling drives RA joint pathology via FOS/JUN. Molecular docking results confirmed strong binding interactions between the identified active molecules and their respective target proteins. This study establishes a GMS based on the gut microbiota–metabolite–host gene regulatory axis as a robust tool for molecular subtyping and predicting biological therapy responses in patients with RA. Crucially, through integrated computational modelling, including in silico knockout and molecular docking validation, we identified FOS/JUN as critical executor genes linking the gut microenvironment to RA bone destruction, providing novel targets for therapeutic strategies aimed at the gut–joint axis.
Agricultural and forestry waste is rich in renewable lignin, which holds natural potential for the production of fine chemicals and functional materials. However, lignin is tightly cross-linked with cellulose and hemicellulose. Traditional extraction processes using strong acids and bases can easily cause lignin molecular condensation and damage to the molecular framework. At the same time, there are problems such as high energy consumption and heavy environmental burden, which seriously restrict its resource utilization. Due to their advantages of simple synthesis, high tunability, and environmental friendliness, deep eutectic solvents (DES) have become a research hotspot for the green and efficient extraction of lignin. This paper systematically reviews the latest research findings on DES-based extraction of lignin from agricultural and forestry waste, summarizes the characteristics of different types of DES components, investigates the effects of process parameters on lignin extraction efficiency and structural retention, and provides an in-depth analysis of the lignin's characteristic bonds, functional group composition, and other microstructural features. The study confirms that DES can achieve selective dissolution of lignin and effectively cleave ether bonds such as β-O-4; the resulting lignin has a low molecular weight, a narrow distribution, and a high content of phenolic hydroxyl groups, laying an excellent structural foundation for subsequent chemical modification and deep processing. The paper also further summarizes the high-value application potential of this type of lignin in fields such as the preparation of phenolic compounds and the synthesis of functional materials. At the same time, it objectively analyzes the practical challenges currently faced by DES technology in process optimization, solvent recycling, and industrial-scale upscaling, and makes targeted predictions regarding future key development directions. The aim is to provide a theoretical basis and practical reference for the iteration of green lignin separation technologies and the construction of high-value conversion systems for waste biomass.
Renal fibrosis (RF) is a progressive and irreversible pathological process for which effective treatments are limited. This study explores the molecular targets and mechanisms through which kaempferol (Kae) ameliorates RF, with particular emphasis on cytochrome P450 1B1 (CYP1B1). Using a combination of transcriptomic profiling, weighted gene co‑expression network analysis (WGCNA), multi‑algorithm machine learning [least absolute shrinkage and selection operator (LASSO), support vector machine‑recursive feature elimination (SVM‑RFE), and Random Forest], and single‑cell transcriptomics, we identified the core targets of Kae intervention in RF. shapley additive explanations (SHAP) analysis evaluated gene contributions to the predictive model, while cell‑type identification by estimating relative subsets of RNA transcripts (CIBERSORT) estimated the immune microenvironment. The cell type-specific distribution of core targets was further characterized through single-cell RNA sequencing (scRNA-seq), and the scTenifoldKnk (a machine learning-based virtual gene knockout tool) algorithm simulated the effects of CYP1B1 loss-of-function on fibroblast activation and extracellular matrix (ECM) deposition. Finally, molecular docking was performed to predict the binding conformations of kaempferol with the proteins encoded by the six prioritized genes. Six core genes (VCAM1, NFKBIA, SFN, CCL5, CYP1B1, and AXL) were identified from 25 Kae-RF-related genes, with CYP1B1 as an upstream regulatory hub. Immune infiltration analysis revealed increased plasma cells and eosinophils, alongside decreased M0/M1 macrophages and activated NK cells. Single-cell analysis localized CYP1B1 to smooth muscle cells and fibroblasts, with significant upregulation during late-stage fibroblast-to-myofibroblast differentiation. Virtual knockdown of CYP1B1 upregulated 46 genes associated with fibrosis-related biological processes, including cell adhesion, G protein-coupled receptor (GPCR) signaling, ion transport, and cytoskeletal remodeling. The six-gene diagnostic model achieved area under the curves (AUCs) of 0.889 (training) and 0.759 (external validation). Molecular docking showed favorable Kae–CYP1B1 binding (− 6.5 kcal/mol). CYP1B1 may serve as a potential functional target of Kae in RF, likely through modulation of fibroblast activation and ECM remodeling. These findings are hypothesis‑generating and provide a computational basis for future experimental validation of CYP1B1‑targeted strategies in RF.
Gamma-linolenic acid (GLA) is a vital anti-inflammatory polyunsaturated fatty acid, yet its traditional supply from plant seeds such as evening primrose and borage is constrained by low yields and climatic instability. Consequently, microbial cell factories present a sustainable and season-independent alternative for scalable GLA production. This review critically synthesizes recent biotechnological advancements in utilizing oleaginous microorganisms as production platforms. It systematically elucidates the microbial lipid biosynthesis pathways and evaluates strategies to enhance GLA accumulation, with particular emphasis on rational metabolic engineering. Furthermore, emerging technologies for microbial lipid production, alongside the latest progress in industrial-scale applications, are thoroughly examined. Finally, comprehensive insights are provided to guide the optimization of the entire value chain, ranging from upstream strain engineering to downstream processing.
Cultivation under hypothermia has emerged as an interesting strategy for enhancing recombinant protein production in Komagataella phaffii, yet predictive approaches capable of integrating the effects of temperature and cell growth rate for rational bioprocess optimization remain limited. In this work, we investigated the influence of cultivation temperature on recombinant β-glucosidase (BGL1) production in the hybrid K. phaffii strain S467 by combining bioprocess characterization with dynamic mathematical modeling. Batch cultures performed at 20, 25 and 30 °C showed that hypothermia increased recombinant protein secretion while reducing biomass formation, revealing a trade-off between cell growth and product formation. To quantitatively describe these dynamics, a Droop-based model incorporating delayed coupling between substrate uptake and biomass synthesis was fitted using the experimental data. Model simulations predicted that continuous cultivation at 20 °C and a dilution rate of 0.045 h−1 maximized the total secreted protein volumetric productivity. Chemostat experiments at selected conditions yielded biomass concentrations and total secreted protein productivities close to the simulated values, supporting the model-predicted trends and its potential as a tool for process optimization. Interestingly, the highest specific BGL1 activity was observed at the highest dilution rate despite a lower bgl1 transcript level and secreted protein production, suggesting that cultivation conditions influence recombinant protein traits through mechanisms beyond transcriptional regulation. Collectively, these results show that integrating experimental bioprocess characterization, predictive dynamic modeling and targeted molecular analyses provide an effective framework for rational optimization of recombinant protein production in this biotechnologically relevant yeast.
