Sepiapterin reductase plays an enzymatic role in the biosynthesis of tetrahydrobiopterin, which is reported in limited studies to regulate the progression of several tumors. However, the role of sepiapterin reductase in hepatocellular carcinoma remains largely unknown. Here, we found that sepiapterin reductase was frequently highly expressed in human hepatocellular carcinoma, which was significantly associated with higher T stage, higher tumor node metastasis stage, and even shorter survival of hepatocellular carcinoma patients. Furthermore, cell and animal experiments showed that sepiapterin reductase depletion inhibited cancer cell proliferation and promoted cancer cell apoptosis. Importantly, the results suggested that sepiapterin reductase enzymatic activity was not necessary for the progression of hepatocellular carcinoma, based on the comparison between SMMC-7721 and SMMC-7721 containing sepiapterin reductase mutant. Moreover, we showed that sepiapterin reductase regulated the development of hepatocellular carcinoma via the FoxO3a/Bim-signaling pathway. Collectively, our study suggests that sepiapterin reductase controls hepatocellular carcinoma progression via FoxO3a/Bim signaling in a nonenzymatic manner, which provides a potential prognostic factor and therapeutic strategy for hepatocellular carcinoma.
Sepsis, a critical infectious disease, is intricately linked to the dysfunction of the intracellular Golgi apparatus. This study aims to explore the relationship between sepsis and the Golgi apparatus using bioinformatics, offering fresh insights into its diagnosis and treatment. To explore the role of Golgi-related genes in sepsis, we analyzed mRNA expression profiles from the NCBI GEO database. We identified differentially expressed genes (DEGs). These DEGs, Golgi-associated genes obtained from the MSigDB database, and key modules identified through WGCNA were intersected to determine Golgi-associated differentially expressed genes (GARGs) linked to sepsis. Subsequently, functional enrichment analyses, including GO, KEGG, and GSEA, were performed to explore the biological significance of the GARGs.A PPI network was constructed to identify core genes, and immune infiltration analysis was performed using the ssGSEA algorithm. To further evaluate immune microenvironmental features, unsupervised clustering was applied to identify immunological subgroups. A diagnostic model was developed using logistic regression, and its performance was validated using ROC curve analysis. Finally, key diagnostic biomarkers were identified and validated across multiple datasets to confirm their diagnostic accuracy. By intersecting DEGs, WGCNA modules, and Golgi-related gene sets, 53 overlapping GARGs were identified. Functional enrichment analysis indicated that these GARGs were predominantly involved in protein glycosylation and Golgi membrane-related processes. PPI analysis further identified eight hub genes: B3GNT5, FUT11, MFNG, ST3GAL5, MAN1C1, ST6GAL1, C1GALT1C1, and GALNT14. Immune infiltration analysis revealed significant differences in immune cell populations, mainly activated dendritic cells, and effector memory CD8+ T cells, between sepsis patients and healthy controls. A diagnostic model constructed using five pivotal genes (B3GNT5, FUT11, MAN1C1, ST6GAL1, and C1GALT1C1) exhibited predictive accuracy, with AUC values exceeding 0.96 for all genes. Validation with an independent dataset confirmed the differential expression patterns of B3GNT5, C1GALT1C1, and GALNT14, reinforcing their potential as robust diagnostic biomarkers for sepsis. This study elucidates the link between sepsis and the Golgi apparatus, introduces novel biomarkers for sepsis diagnosis, and offers valuable insights for future research on its pathogenesis and treatment strategies.
Studies of laminopathy-based progeria offer insights into aging-associated diseases and highlight the role of LMNA in chromatin organization. Mandibuloacral dysplasia type A (MAD) is a largely unexplored form of atypical progeria that lacks lamin A post-translational processing defects. Using iPSCs derived from a male MAD patient carrying homozygous LMNA p.R527C, premature aging phenotypes are recapitulated in multiple mesenchymal lineages, including mesenchymal stem cells (MSCs). Comparison with 26 human aging MSC expression datasets reveals that MAD-MSCs exhibit the highest similarity to senescent primary human MSCs. Lamina-chromatin interaction analysis reveals reorganization of lamina-associating domains (LADs) and repositioning of non-LAD binding peaks may contribute to the observed accelerated senescence. Additionally, 3D genome organization further supports hierarchical chromatin disorganization in MAD stem cells, alongside dysregulation of genes involved in epigenetic modification, stem cell fate maintenance, senescence, and geroprotection. Together, these findings suggest LMNA missense mutation is linked to chromatin alterations in an atypical progeroid syndrome. Atypical progeria syndromes provide insights into aging and chromatin organization. Here, authors show that a LMNA mutation in mandibuloacral dysplasia disrupt chromatin structure and accelerate senescence in mesenchymal stem cells.
Background.Moonlighting protein refers to a protein that can perform two or more functions.Since the current moonlighting protein prediction tools mainly focus on the proteins in animals and microorganisms , and there are differences in the cells and proteins between animals and plants, these may cause the existing tools to predict plant moonlighting proteins inaccurately.Hence, the availability of a benchmark data set and a prediction tool specific for plant moonlighting protein are necessary.Methods.This study used some protein feature classes from the data set constructed in house to develop a web-based prediction tool.In the beginning, we built a data set about plant protein and reduced redundant sequences.Then performed feature selection, feature normalization and feature dimensionality reduction on the training data.Next, machine learning methods for preliminary modeling were used to select feature classes that performed best in plant moonlighting protein prediction.And this selected feature was incorporated into the final plant protein prediction tool.After that, we compared five machine learning methods and used grid searching to optimize parameters, and the most suitable method was chosen as the final model. Results.The prediction results indicated that the eXtreme Gradient Boosting (XGBoost) performed best, which was used as the algorithm to construct the prediction tool, called IdentPMP (Identification of Plant Moonlighting Proteins).The results of the independent test set shows that the area under the precisionrecall curve (AUPRC) and the area under the receiver operating characteristic curve (AUC) of IdentPMP is 0.43 and 0.68, which are 19.44%(0.43 vs. 0.36) and 13.33% (0.68 vs. 0.60) higher than state-of-the-art non-plant specific methods, respectively.This further demonstrated that a benchmark data set and a plant-specific prediction tool was required for plant moonlighting protein studies.Finally, we implemented the tool into a web version, and users can use it freely through the URL: http://identpmp.aielab.net/.
To conduct a preliminary investigation that shows the possible correlation between the change of gut microbiota and missed abortions (MAs), which further provides a new potential insight for the prevention and therapy of MAs.One hundred women, including 50 patients with MAs (case group) and 50 normal pregnant women (control group), were enrolled in the study. Fecal specimens were collected in the first trimester. Bacterial DNA was extracted, hybridized with primers of specific genes, and then detected by bacterial chip. The composition and the relative abundance of the gut microbiota were compared and analyzed. Furthermore, Kyoto Encyclopedia of Genes and Genomes enrichment analysis was used to explore the relative pathways.(1) The α-diversity and β-diversity of the gut microbiota in patients with MAs were significantly lower than that those in normal pregnant women (P < 0.05). At the phylum level, Firmicutes, Proteobacteria, Actinomycetes, and Bacteroidetes accounted for the main proportion of intestinal flora in the 2 groups. Only Actinobacteria was high in the case group. Significant differences were found between the two groups at the phylum level (P < 0.05). Prevotella, Lactobacillus, and Paracoccus were significantly more abundant in the control group than in the case group at the genus level (P < 0.05). (2) KEGG pathway enrichment analysis found significant differences in 27 signaling pathways and metabolic pathways between the two groups of differentially expressed genes (all adjusted P < 0.05). (3) The positive rate of M. hominins (MH) detection in the control group was significantly higher in the MA group (χ2 = 7.853, P = 0.004).The high abundance of Actinobacteria in the MA group was the first time found and reported in the study. The dysbiosis of the gut microbiota correlates with MAs. This study provided insights into the potential change of gut microbiota of MAs and the potential underlying mechanisms through certain impaired lipid metabolism and aroused inflammation pathways. Comprehensive insights regarding gut microbiota may facilitate improved understanding and the development of novel therapeutic and preventive strategies for MAs.
The mosquito midgut harbors a highly dynamic microbiome that affects the host metabolism, reproduction, fitness, and vector competence. Studies have been conducted to investigate the effect of gut microbes as a whole; however, different microbes could exert distinct effects toward the host. This article provides the methodology to study the effect of each specific mosquito gut microbe and the potential mechanism. This protocol contains two parts. The first part introduces how to dissect the mosquito midgut, isolate cultivable bacteria colonies, and identify bacteria species. The second part provides the procedure to generate antibiotic-treated mosquitoes and reintroduce one specific bacteria species.
This paper proposes an event tree to analyze the risk of inadequate flight separation based on the HCR model. We explore how theconsequences of such an event depend on factors such as abilities and mental states of pilots and air traffic controllers, and the efficiencyof human-machine interaction. We also discuss possible practical measures to control risks in an effort to improve civil aviation safety.
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