BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

OBJECTIVES: To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). METHODS: RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. RESULTS: We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. CONCLUSIONS Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.
Arthritis, Rheumatoid/genetics* ; Oxidative Stress/genetics* ; Humans ; Computational Biology ; Protein Interaction Maps ; Gene Expression Profiling ; Gene Regulatory Networks

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.
Humans ; Spondylitis, Ankylosing/complications* ; Systems Biology/methods* ; Myocardial Infarction/diagnosis* ; Biomarkers/metabolism* ; MicroRNAs/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Machine Learning

Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up approach to progressively design biological parts, devices, and circuits, aiming to create artificial biological systems not found in nature or to redesign existing biological systems for specific purposes. With the rapid development of the synthetic biology industry, there is an increasing demand for large complex genetic circuits. However, the traditional trial-and-error methods, heavily reliant on empirical knowledge, have limited efficiency and success rates of parts/circuits construction, thereby impeding the innovation and technology translation for synthetic biology. These limitations have prompted a paradigm shift from labor-intensive, experience-driven trial-and-error models towards standardized, intelligent engineering approaches. Machine learning, capable of uncovering hidden structures and relationships within biological data, offers robust support for the intelligent design of synthetic biological parts and genetic circuits. Here, we review commonly used machine learning algorithms and analyze their typical applications in designing biological parts (e.g., synthetic promoters, RNA regulatory elements, and transcription factors) and simple genetic circuits. Additionally, we discuss the primary challenges in machine learning-aided design and propose potential solutions. Lastly, we envision the future trend of integrating machine learning with synthetic biological system design, highlighting the importance of interdisciplinary collaboration.
Synthetic Biology/methods* ; Machine Learning ; Gene Regulatory Networks ; Algorithms

Objective To identify disulfidptosis-related gene(DRG)in acute myocardial infarction(AMI)by bioinformatics,analyze the molecular pattern of DRGs in AMI,and construct a DRGs-related prediction model.Methods AMI-related datasets were downloaded from the Gene Expression Omnibus database,and DRGs with differential expression were screened in AMI.CIBERSORT method was used to analyze the immune infiltration.Based on the differentially expressed DRGs,the AMI patients were classified into distinct subtypes via consensus clustering,followed by immune infiltration analysis,differential expression analysis,gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis,and gene set variation analysis.Weighted gene co-expression network analysis(WGCNA)was then performed to construct subtype-associated modules and identify hub genes.Finally,least absolute shrinkage and selection operator,random forest,and support vector machine-recursive feature elimination were used to screen feature genes to construct a DRGs-related prediction model.The model's diagnostic efficacy was evaluated by nomogram and receiver operating characteristic(ROC)curve analysis,followed by external validation.Results Nine differentially expressed DRGs were identified between AMI patients and controls.Based on the expression levels of these nine DRGs,AMI patients were divided into two DRGs subtypes,C1 and C2.Increased infiltration of monocytes,M0 macrophages,and neutrophils was observed in AMI patients and C1 subtype(all P<0.05),indicating a close correlation between DRGs and immune cells.There were 257 differentially expressed genes between the C1 and C2 subtypes,which were related to biological processes such as myeloid leukocyte activation and positive regulation of cytokines.Fcγ receptor-mediated phagocytosis and NOD-like receptor signaling pathway activity were enhanced in C1 subtype.WGCNA analysis suggested that the brown module exhibited the strongest correlation with DRG subtypes(r=0.67),from which 23 differentially expressed genes were identified.The feature genes screened by three machine learning methods were interpolated to obtain a DRGs-related prediction model consisting of three genes(AQP9,F5 and PYGL).Nomogram and ROC curves(AUC_train=0.891,AUC_test=0.840)showed good diagnostic efficacy.Conclusions DRGs were closely related to the occurrence and progression of AMI.The DRGs-related prediction model consisting of AQP9,F5 and PYGL may provide targets for the diagnosis and personalized treatment of AMI.
Humans ; Myocardial Infarction/diagnosis* ; Transcriptome ; Computational Biology ; Gene Expression Profiling ; ROC Curve ; Gene Regulatory Networks ; Nomograms ; Disulfidptosis

Heart failure(HF) is the terminal stage of various cardiovascular diseases, characterized by high morbidity and mortality, and it represents one of the major disease burdens for families and society. In recent years, as research on the molecular mechanisms of HF has deepened, a competing endogenous RNA(ceRNA) network mediated by long non-coding RNAs(lncRNAs) and circular RNAs(circRNAs) has been gradually constructed. Extensive research results have confirmed that the ceRNA network is widely involved in pathological processes such as inflammation, oxidative stress, myocardial hypertrophy, apoptosis, remodeling of extracellular matrix components and structure, and ferroptosis in HF. It reveals the complex pathological mechanisms of HF at the epigenetic level. Traditional Chinese medicine(TCM) plays a unique role in improving symptoms and prognosis of HF and intervenes in the ceRNA network in HF through multi-level and multi-target mechanisms. It improves key pathological processes such as myocardial fibrosis and inflammation, making progress in treating HF at the molecular level. This article summarized recent Chinese and international research on the regulatory mechanisms of ceRNA networks in HF, elaborated on the mechanisms of action of ceRNA networks in different pathological stages of HF, and summarized how effective components and compounds of TCM intervene in the ceRNA network to improve HF, so as to refine the molecular mechanisms of HF and provide directions for more precise molecular targeted therapeutic strategies.
Humans ; Heart Failure/metabolism* ; Medicine, Chinese Traditional ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; RNA, Circular/genetics* ; RNA, Long Noncoding/metabolism* ; Gene Regulatory Networks/drug effects* ; RNA/metabolism* ; RNA, Competitive Endogenous

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