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

Intelligence encompasses various abilities, including logical reasoning, comprehension, self-awareness, learning, planning, creativity, and problem-solving. Extensive research and practical experience suggest that there are sex differences in intellectual development, with females typically maturing earlier than males. However, the key genes and molecular network mechanisms underlying these sex differences in intellectual development remain unclear. To date, Genome-Wide Association Studies (GWAS) have identified 507 genes that are significantly associated with intelligence. This study first analyzed RNA sequencing data from different stages of brain development (from BrainSpan), revealing that during the late embryonic stage, the average expression levels of intelligence-related genes are higher in males than in females, while the opposite is observed during puberty. This study further constructed interaction networks of intelligence-related genes with sex-differential expression in the brain, including the prenatal male network (HELP-M: intelligence genes with higher expression levels in prenatal males) and the pubertal female network (HELP-F: intelligence genes with higher expression levels in pubertal females). The findings indicate that the key genes in both networks are Ep300 and Ctnnb1. Specifically, Ep300 regulates the transcription of 53 genes in both HELP-M and HELP-F, while Ctnnb1 regulates the transcription of 45 genes. Ctnnb1 plays a more prominent role in HELP-M, while Ep300 is more crucial in HELP-F. Finally, this study conducted sequencing validation on rats at different developmental stages, and the results indicated that in the prefrontal cortex of female rats during adolescence, the expression levels of the intelligence genes in HELP-F, as well as key genes Ep300 and Ctnnb1, were higher than those in male rats. These genes were also involved in neurodevelopment-related biological processes. The findings reveal a sex-differentiated intelligence gene network and its key genes, which exhibit varying expression levels during the neurodevelopmental process.
Female ; Intelligence/physiology* ; Male ; Sex Characteristics ; Animals ; Brain/growth & development* ; E1A-Associated p300 Protein/physiology* ; beta Catenin/physiology* ; Transcriptome ; Rats ; Gene Expression Profiling ; Genome-Wide Association Study

The transcriptome sequencing based on Illumina Novaseq 6000 Platform was performed with the untreated seed embryo(DS), stratified seed embryo(SS), and germinated seed embryo(GS) of Zanthoxylum nitidum, aiming to explore the molecular mechanism regulating the seed dormancy and germination of Z. nitidum and uncover key differentially expressed genes(DEGs). A total of 61.41 Gb clean data was obtained, and 86 386 unigenes with an average length of 773.49 bp were assembled. A total of 29 290 DEGs were screened from three comparison groups(SS vs DS, GS vs SS, and GS vs DS), and these genes were annotated on 134 Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways. KEGG enrichment analysis revealed that the plant hormone signal transduction pathway is the richest pathway, containing 226 DEGs. Among all DEGs, 894 transcription factors were identified, which were distributed across 34 transcription factor families. These transcription factors were also mainly concentrated in plant hormone signal transduction and mitogen-activated protein kinase(MAPK) signaling pathways. Further real-time quantitative polymerase chain reaction(RT-qPCR) validation of 12 DEGs showed that the transcriptome data is reliable. During the process of seed dormancy release and germination, a large number of DEGs involved in polysaccharide degradation, protein synthesis, lipid metabolism, and hormone signal transduction were expressed. These genes were involved in multiple metabolic pathways, forming a complex regulatory network for dormancy and germination. This study lays a solid foundation for analyzing the molecular mechanisms of seed dormancy and germination of Z. nitidum.
Zanthoxylum/metabolism* ; Plant Dormancy/genetics* ; Seeds/metabolism* ; Gene Expression Regulation, Plant ; Plant Proteins/metabolism* ; Transcriptome ; Gene Expression Profiling ; Germination ; Transcription Factors/metabolism* ; Plant Growth Regulators/genetics* ; Signal Transduction

Tujia ethnic group medicine Xuetong is derived from Kadsura heteroclita, the stem of which has the medicinal value for anti-rheumatoid arthritis, liver protection, anti-tumor, anti-oxidation effects, and has been widely used in Hunan and Guangdong in China. The selection of reliable and stable reference genes is the basis for subsequent molecular research on K. heteroclita. In this study, GAPDH, TUA, Actin, UBQ, EF-1α, 18S-rRNA, CYP, UBC, TUB, H2A, and RPL were selected as candidate reference genes in Kadsura heteroclita. The gene expression levels of the 11 candidate reference genes of K. heteroclita in its 6 different parts(stem-inside of the cambium, stem-outside of the cambium, fruit, flower, root, and leaf) and under different intervention conditions [drought stress, salt stress, and methyl jasmonate(MeJA) treatment] were detected by quantitative real-time polymerase chain reaction(qRT-PCR). The expression stability of the 11 candidate reference genes was comprehensively analyzed and evaluated by geNorm, NormFinder, ΔCT algorithm, and RefFinder software. The results showed that the expression of UBC and RPL was relatively stable in 6 different parts, and UBC and GAPDH genes were relatively stable under different intervention conditions. To verify the reliability of reference genes for K. heteroclita, this study further examined the relative expression levels of KhFPS, KhIDI, KhCAS, KhSQE, KhSQS, KhSQS-2, KhHMGS, KhHMGR, KhMVD, KhMVK, KhDXR, KhDXS, KhPMVK, and KhGGPS in different parts and under different intervention conditions, which might relate to the synthesis of the main component(Xuetongsu) of K. heteroclita. The results showed that with UBC and RPL or UBC and GAPDH as the reference genes, the expression trends of these 14 genes were basically consistent in different parts or under different intervention conditions for K. heteroclita. In conclusion, UBC can be used as a reference gene of K. heteroclita for its different parts and different intervention conditions, which lays a foundation for further research on the biosynthetic pathway of main components in K. heteroclita.
Real-Time Polymerase Chain Reaction/methods* ; Reference Standards ; Gene Expression Regulation, Plant ; Gene Expression Profiling ; Plant Proteins/metabolism* ; Drugs, Chinese Herbal

Objective To explore the prognostic value of glycolysis-related genes in colorectal cancer (CRC) patients and formulate a novel glycolysis-related prognostic risk model. Methods Single-cell and bulk transcriptomic data of CRC patients, along with clinical information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Glycolysis scores for each sample were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Kaplan-Meier survival curves were generated to analyze the relationship between glycolysis scores and overall survival. Novel glycolysis-related subgroups were defined among the cell type with the highest glycolysis scores. Gene enrichment analysis, metabolic activity assessment, and univariate Cox regression were performed to explore the biological functions and prognostic impact of these subgroups. A prognostic risk model was built and validated based on genes significantly affecting the prognosis. Gene Set Enrichment Analysis (GSEA) was conducted to explore differences in biological processes between high- and low-risk groups. Differences in immune microenvironment and drug sensitivity between these groups were assessed using R packages. Potential targeted agents for prognostic risk genes were predicted using the Enrichr database. Results Tumor tissues showed significantly higher glycolysis scores than normal tissues, which was associated with a poor prognosis in CRC patients. The highest glycolysis score was observed in epithelial cells, within which we defined eight novel glycolysis-related cell subpopulations. Specifically, the P4HA1⁺ epithelial cell subpopulation was associated with a poor prognosis. Based on signature genes of this subpopulation, a six-gene prognostic risk model was formulated. GSEA revealed significant biological differences between high- and low-risk groups. Immune microenvironment analysis demonstrated that the high-risk group had increased infiltration of macrophages and tumor-associated fibroblasts, along with evident immune exclusion and suppression, while the low-risk group exhibited higher levels of B cell and T cell infiltration. Drug sensitivity analysis indicated that high-risk patients were more sensitive to Abiraterone, while low-risk patients responded to Cisplatin. Additionally, Valproic acid was predicted as a potential targeted agent. Conclusion High glycolytic activity is associated with a poor prognosis in CRC patients. The novel glycolysis-related prognostic risk model formulated in this study offers significant potential for enhancing the diagnosis and treatment of CRC.
Humans ; Colorectal Neoplasms/pathology* ; Glycolysis/genetics* ; Prognosis ; Transcriptome ; Tumor Microenvironment/genetics* ; Gene Expression Profiling ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Male ; Female ; Kaplan-Meier Estimate

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: To elucidate the underlying mechanisms of macrophage-mediated inflammation and tissue injury in diabetic foot ulcer (DFU). METHODS: Skin tissue samples were collected from patients with DFU and with non-DFU. A total of 79 272 high-quality cell transcriptomes were obtained using single-cell RNA sequencing. An unbiased clustering approach was employed to identify cell subpopulations. Seurat functions were used to identify differentially expressed genes between DFU and non-DFU groups, and gene ontology (GO) enrichment analysis was used to reveal gene function. Furthermore, cell-cell communication network construction and ligand-receptor interaction analysis were performed to reveal the mechanisms underlying cellular interactions and signaling regulation in the DFU microenvironment from multiple perspectives. RESULTS: The results revealed a significant expansion of myeloid cells in DFU tissues, alongside a marked reduction in structural cells such as endothelial cells, epithelial cells, and smooth muscle cells. Major cell types underwent functional reprogramming, characterized by immune activation and impaired tissue remodeling. Specifically, macrophages in DFU skin tissues exhibited a shift toward a pro-inflammatory M1 phenotype, with upregulation of genes associated with inflammation and oxidative stress. Cell communication analysis further demonstrated that M1 macrophages served as both primary signal receivers and influencers in the COMPLEMENT pathway mediated communication network, and as key signal senders and mediators in the secreted phosphoprotein 1 (SPP1) pathway mediated communication network, actively shaping the inflammatory microenvironment. Key ligand-receptor interactions driving macrophage signaling were identified, including C3-(ITGAM+ITGB2) and SPP1-CD44. CONCLUSIONS This study establishes a comprehensive single-cell atlas of DFU, revealing the role of macrophage-driven cellular networks in chronic inflammation and impaired healing. These findings may offer potential novel therapeutic targets for DFU treatment.
Humans ; Macrophages/immunology* ; Diabetic Foot/pathology* ; Single-Cell Analysis ; Transcriptome ; Gene Expression Profiling ; Inflammation ; Skin ; Cell Communication ; Signal Transduction ; Cellular Reprogramming

OBJECTIVE: To detect the expression level of T-complex polypeptide 1 (TCP1) in the bone marrow of newly diagnosed acute myeloid leukemia (AML) patients, and explore its correlation with clinical characteristics and prognosis. METHODS: The bone marrow samples from 80 newly diagnosed AML patients and 30 iron deficiency anemia (IDA) patients were collected, and real time fluorescence quantitative PCR was used to detect the expression level of TCP1 . The clinical data of AML patients were collected, and the correlation of TCP1 expression with clinical characteristics and prognosis of patients were analyzed. The impact of TCP1 on overall survival (OS) of AML patients was identified by using Kaplan-Meier curve analysis. Cox regression analysis was used to identify the factors affecting prognosis of AML patients. RESULTS: Compared with IDA patients, the expression of TCP1 was significantly increased in AML patients (P < 0.01). The high expression group of TCP1 showed a higher proportion of patients with ≥60 years and non-remission after treatment, more accompanied by TET2 mutation and poor prognosis but shorter OS compared to the low expression group (all P < 0.05). The results of multivariate Cox regression analysis showed that age, chromosomal abnormalities, therapeutic efficacy and TCP1 expression were independent risk factors affecting prognosis of AML patients (all P < 0.05). CONCLUSION TCP1 is significantly upregulated in AML patients, and its expression is associated with partial clinical features and poor prognosis. It can serve as a prognostic indicator and potential therapeutic target for AML patients.
Gene Expression Regulation, Leukemic ; Leukemia, Myeloid, Acute/metabolism* ; Humans ; Gene Expression Profiling ; Bone Marrow/metabolism* ; Anemia, Iron-Deficiency/metabolism* ; Polymerase Chain Reaction ; Prognosis ; Kaplan-Meier Estimate ; Proportional Hazards Models ; Multivariate Analysis ; Risk Factors ; Chaperonin Containing TCP-1