Objective To investigate the common molecular features of immunothrombosis, thus enhancing the comprehension of thrombosis triggered by immune and inflammatory responses and offering crucial insights for identifying potential diagnostic and therapeutic targets. Methods Differential gene expression analysis and functional enrichment analysis were conducted on datasets of systemic lupus erythematosus (SLE) and venous thromboembolism (VTE). The intersection of differentially expressed genes in SLE and VTE with those of neutrophil extracellular traps (NET) yielded cross-talk genes (CG) for SLE-NET and VTE-NET interaction. Further analysis included functional enrichment and protein-protein interaction (PPI) network assessments of these CG to identify hub genes. Venn diagrams and receiver operating characteristic (ROC) curve analysis were employed to pinpoint the most effective shared diagnostic CG, which were validated using a graft-versus-host disease (GVHD) dataset. Results Differential expression genes in SLE and VTE were associated with distinct biological processes, whereas SLE-NET-CG and VTE-NET-CG were implicated in pathways related to leukocyte migration, inflammatory response, and immune response. Through PPI network analysis, several hub genes were identified, with matrix metalloproteinase 9 (MMP9) and S100 calcium-binding protein A12 (S100A12) emerging as the best shared diagnostic CG for SLE (AUC: 0.936 and 0.832) and VTE (AUC: 0.719 and 0.759). Notably, MMP9 exhibited good diagnostic performance in the GVHD dataset (AUC: 0.696). Conclusion This study unveils the common molecular features of SLE, VTE, and NET, emphasizing MMP9 and S100A12 as the optimal shared diagnostic CG, thus providing valuable evidence for the diagnosis and therapeutic strategies related to immunothrombosis. Additionally, the expression of MMP9 in GVHD highlights its critical role in the risk of VTE associated with immune system disorders.
Humans ; Computational Biology/methods* ; Lupus Erythematosus, Systemic/immunology* ; Protein Interaction Maps/genetics* ; Venous Thromboembolism/therapy* ; Matrix Metalloproteinase 9/genetics* ; Extracellular Traps/metabolism* ; Gene Regulatory Networks ; Thrombosis/immunology* ; Graft vs Host Disease/genetics* ; Gene Expression Profiling

Objective To explore macrophage subpopulations in ischemic stroke (IS) by using single-cell RNA sequencing (scRNA-seq) data analysis and High-Dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Methods Based on single-cell sequencing data, transcriptomic information for different cell types was obtained, and macrophages were selected for subpopulation identification. hdWGCNA, cell-cell communication, and pseudotime trajectory analysis were used to explore the characteristics of macrophage subpopulations following IS. Key genes related to IS were identified using microarray data and validated for diagnostic potential through Receiver Operating Characteristic (ROC) analysis. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential functions of these genes. Results The scRNA-seq data analysis revealed significant changes in macrophage subpopulation composition after IS. A specific macrophage subpopulation enriched in the stroke group was identified and designated as MCAO-specific macrophages (MSM). Pseudotime trajectory analysis indicated that MSM cells were in an intermediate stage of macrophage differentiation. Cell-cell communication analysis uncovered complex interactions between MSM cells and other cells, with the CCL6-CCR1 signaling axis potentially playing a crucial role in neuroinflammation. Two gene modules associated with MSM were identified via hdWGCNA, significantly enriched in pathways related to NOD-like receptors and antigen processing. By integrating differentially expressed MSM genes with conventional transcriptomic data, three IS-related hub genes were identified: Arg1, CLEC4D, and CLEC4E. Conclusion This study reveals the characteristics and functions of macrophage subpopulations following IS and identifies three hub genes with potential diagnostic value, providing novel insights into the pathological mechanisms of IS.
Macrophages/metabolism* ; Computational Biology/methods* ; Single-Cell Analysis/methods* ; Transcriptome ; Ischemic Stroke/metabolism* ; Animals ; Gene Regulatory Networks ; Gene Expression Profiling ; Humans ; Male

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
Asthma/metabolism* ; Humans ; Epithelial Cells/metabolism* ; Animals ; Biomarkers/metabolism* ; Gene Expression Profiling ; Transcriptome ; Gene Regulatory Networks ; Rats ; Computational Biology

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

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

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.
Metabolic Networks and Pathways/physiology* ; Models, Biological ; Artificial Intelligence ; Systems Biology ; Kinetics ; Cell Physiological Phenomena ; Computational Biology ; Synthetic Biology ; Humans

Cancer is a serious global health problem and a major cause of human death. Conventional cancer treatments often run the risk of impairing vital organ functions. Anticancer peptides (ACPs) are considered to be one of the most promising therapeutic agents against common human cancers due to their small sizes, high specificity, and low toxicity. Since ACP recognition is highly limited to the laboratory, expensive, and time-consuming, we proposed pLM4ACP, a model for predicting ACPs based on machine learning and protein language models. In this model, the protein language model ProtT5 was used to extract the features of ACPs, and the extracted features were input into the support vector machine (SVM) classification algorithm for optimization and performance evaluation. The model showcased significantly higher accuracy than other methods, with the overall accuracy of 0.763, F1-score of 0.767, Matthews correlation coefficient of 0.527, and area under the curve of 0.827 on the independent test set. This study constructs an efficient anticancer peptide prediction model based on protein language models, further advancing the application of artificial intelligence in the biomedical field and promoting the development of precision medicine and computational biology.
Machine Learning ; Antineoplastic Agents/chemistry* ; Humans ; Peptides/chemistry* ; Support Vector Machine ; Algorithms ; Computational Biology/methods* ; Neoplasms/drug therapy*