Hereditas最新文献

Background: Adult pneumonia is an infectious lung disease caused by bacteria, viruses, or other microorganisms and exhibits some degree of contagion. Tenuigenin, a bioactive compound derived from Polygala tenuifolia, possesses broad pharmacological effects, but its role in adult pneumonia remains incompletely understood.

Methods: Bioinformatics and database analysis were employed to screen and analyze the Tenuigenin target genes relevant to adult pneumonia. Cell functions were assessed using cell counting kit-8 (CCK8), 5-ethynyl-2'-deoxyuridine (EdU) staining, transwell, tube formation, Fluo-4 calcium assay, and transepithelial electrical resistance (TER) assays. Protein levels were measured by western blot. Network pharmacology and molecular docking were employed to screen core target genes and verify binding interactions.

Results: Tenuigenin targets in adult pneumonia were enriched in the pathways related to vascular permeability and calcium signaling. Tenuigenin mitigated lipopolysaccharide (LPS)-induced impairment of human pulmonary microvascular endothelial cell (HPMEC) viability, proliferation, migration, and angiogenesis, while attenuating LPS-induced increases in apoptosis, calcium ion, and reactive oxygen species (ROS) levels. Besides, Tenuigenin also attenuated the TER decrease and permeability increase caused by LPS exposure in HPMECs. Network pharmacology and molecular docking identified steroid receptor coactivator (SRC) as a core target of Tenuigenin, demonstrating binding to specific SRC amino acid residues. Tenuigenin also reduced LPS-induced increase in phosphor-SRC (p-SRC) expression. Crucially, after inhibition of SRC kinase activity, Tenuigenin no longer exerted significant protective effects against LPS-induced HPMEC injury and dysfunction.

Conclusion: Tenuigenin alleviates LPS-induced injury and dysfunction of HPMECs by targeting the SRC pathway, providing a target for managing adult pneumonia.

In this comprehensive study, we explored the molecular landscape C-X-C chemokine receptor (CXCR) family genes (CXCR1, CXCR2, CXCR3, CXCR4, CXCR5, and CXCR7) in osteosarcoma (OS) by scrutinizing the expression profiles and functional implications using Bioinformatics analyses and molecular experiments. We found significant up-regulation of these genes in OS cell lines compared to control cell lines, as assessed by RT-qPCR, with high diagnostic potential demonstrated by receiver operating characteristic (ROC) curve analysis. Cross-validation using the GSE12865 dataset revealed consistent up-regulation of CXCR family genes in OS samples, alongside decreased promoter methylation in tumor samples compared to normal tissues, as confirmed by the UALCAN database. Mutational analysis, conducted using data from 237 OS samples from the cBioPortal database, revealed minimal mutations in CXCR1 and CXCR2, with no alterations in CXCR3, CXCR4, CXCR5, and CXCR7. Copy number variation (CNV) analysis showed some level of amplification in CXCR1 and CXCR2, but no significant alterations in the copy numbers of the other genes. Survival analysis using meta-analysis across multiple independent studies showed that the expression of some CXCR genes were significantly associated with poor patient survival. Further exploration of the transcriptional regulation of CXCR genes using the ENCORI database revealed an intricate miRNA-mRNA network involving miR-130a, miR-146a, miR-155, miR-21, and miR-7, which regulate the expression of these genes. Elevated expression of these miRNAs in OS samples was validated by RT-qPCR, with promising diagnostic potential highlighted by ROC analysis. Additionally, the immunological analysis revealed a positive correlation between the expressions of CXCR genes and immune cell types, including macrophages and T cells, and CXCR genes were found to enhance drug responsiveness in OS patients. Gene enrichment analysis identified critical biological processes and pathways, such as chemokine-mediated signaling and immune response, linked to the CXCR family. Knockdown of CXCR1 in HOS and MG-63 cells confirmed that CXCR1 plays a crucial role in cell proliferation, colony formation, and migration. CXCR1 knockdown significantly reduced cell proliferation and colony formation, while enhancing cell migration, underscoring its functional importance in OS progression. Overall, our findings suggest that the CXCR family genes are potential diagnostic and prognostic markers in OS, with implications for therapeutic targeting and further investigation into their role in OS pathogenesis.

Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is characterized by hepatic lipid accumulation and metabolic disturbances. Caspase-2 cleaves site-1 protease (S1P), leading to the persistent activation of sterol regulatory element-binding proteins (SREBPs), which subsequently promote the progression of MASLD. Previous studies have demonstrated that the Qushi Huayu Decoction (QHD) significantly alleviates MASLD, particularly inhibiting the expression of SREBP-1 in hepatocytes of MASLD mouse models. However, its regulatory effect on the Caspase-2/SREBP-1 pathway and the dose-dependent nature of these effects remain unclear.

Objective: The regulatory effects of high, medium, and low doses of Qushi Huayu Decoction (QHD) on the Caspase-2/SREBP-1 pathway and their potential dose-dependent impacts was investigated.

Method: A MASLD model was induced in 28-week-old C57BL/6J mice using a high-fat diet (HFD). Mice were treated with QHD granules at high (3.466 g/kg), medium (1.733 g/kg), and low doses (0.867 g/kg), as well as a Caspase-2 inhibitor for a duration of 5 weeks. Pharmacodynamic indicators, including triglycerides (TG) and free fatty acids (FFA) in liver tissue, hepatic histopathology, and serum biochemical markers, were assessed. The expression of genes in the Caspase-2/SREBP-1 signaling pathway and its downstream targets was also analyzed.

Results: QHD at all doses effectively improved hepatic steatosis. The low-dose group significantly reduced hepatic TG levels (p < 0.01) and the insulin resistance index (p < 0.05). The high-dose group significantly inhibited the expression of Caspase-2 protein (p < 0.01) and nuclear SREBP-1 protein (p < 0.05), with a dose-dependent decrease in Caspase-2 activity.

Conclusion: QHD exhibits dose-dependent, complementary effects in MASLD, with low doses improving lipid metabolism and insulin sensitivity, and high doses more effectively suppressing Caspase-2/SREBP-1 and inflammatory signaling. This dual action underscores its broad regulation of ER stress and supports stage-specific, hierarchical dosing strategies aligned with traditional Chinese medicine principles.

Background: Renal cell carcinoma is characterized by immune and metabolic alterations. These metabolic reprogramming processes enhance tumor cell proliferation and infiltration. The purpose of this study was to investigate the characteristics of metabolism-related molecules and to identify potential prognostic biomarkers in kidney renal papillary renal cell carcinoma (KIRP).

Methods: We conducted a comprehensive analysis of metabolism-related genes using weighted gene co-expression network analysis and differential expression analysis. Subsequently, we constructed a metabolism-related signature (MRS) by integrating 90 machine learning algorithms. Based on Cox regression analyses, we developed a predictive nomogram. Functional enrichment analysis, genomic variant analysis, chemotherapy response evaluation, and immune cell infiltration profiling were then performed among the MRS subtypes. Finally, the MRS was further examined at the single-cell level, and quantitative PCR and immunohistochemical staining were conducted to validate the key genes.

Results: We identified 16 differentially expressed metabolic genes. The random survival forest (RSF) emerged as the optimal machine learning model in the TCGA-KIRP and GSE2748 cohorts. The MRS demonstrated robust predictive performance, with an AUC of 0.989 for 5-year survival predictions. The risk score was significantly correlated with T stage and pathological stage and was identified as an independent prognostic factor. Patients in the high-risk group exhibited higher tumor mutation burdens and derived greater benefits from sunitinib, pazopanib, lenvatinib, and temsirolimus. A four-genes nomogram was then constructed to predict overall survival. PYCR1, INMT, and KIF20A were highly expressed in KIRP according to scRNA-seq analysis and were validated in vitro.

Conclusion: This study revealed the heterogeneity of metabolic molecules in KIRP and established a prognostic machine learning model that enhances risk stratification and may optimize chemotherapy strategies in the management of KIRP.

Resistance to targeted cancer therapies is a significant barrier to favorable treatment outcomes. Malignant cells can tolerate and resist drug treatments due to their biological flexibility. Specifically, slow-cycling drug-resistant cells may achieve permanent resistance to the treatment or restore sensitivity upon cessation of therapy. Enhancing cancer treatment methodologies necessitates a deeper understanding of the adaptability of tumor cells. Drug resistance and cellular heterogeneity are closely associated with cancer cell adaptability. Alterations in cellular signaling, interactions with the tumor microenvironment, and genetic and epigenetic alterations are all implicated. Analyzing these pathways will enhance our understanding of how cancer cells evolve and evade treatment. Two effective strategies to address cancer cell adaptability are to target specific biological pathways and to employ combination therapies. The progression of cancer therapy methodologies relies on comprehending and exploring the concept of cancer cell adaptability. Understanding tumor heterogeneity and drug resistance necessitates identifying the cellular, molecular, and genetic processes that govern cancer cell plasticity. This understanding enables the development of more personalized and effective cancer therapies, leading to improved treatment outcomes. CLINICAL TRIAL NUMBER: Not applicable.

Background: This study examines the causal relationship between spermidine levels and coronary artery disease (CHD) risk using a bidirectional Mendelian Randomization (MR) approach.

Methods: We employed genetic variants as instrumental variables to assess the influence of genetically predicted spermidine levels on CHD risk and vice versa. Data for the MR analysis were sourced from the UK Biobank and genome-wide association study datasets, focusing on single nucleotide polymorphisms (SNPs) associated with spermidine levels and CHD. The study also utilized liquid chromatography-tandem mass spectrometry (LC-MS/MS) for accurate quantification of spermidine in plasma samples.

Results: Our analysis identified a significant association between lower genetically predicted spermidine levels and increased CHD risk. The LC-MS/MS results supported the accurate measurement of spermidine, highlighting its feasibility as a clinical biomarker.

Conclusions: The findings suggest that reduced spermidine levels may be a significant risk factor for CHD. This study supports the potential of spermidine as a biomarker for CHD risk assessment and its development as a therapeutic target. The integration of genetic and biochemical methodologies enhances our understanding of the role of spermidine in cardiovascular health and its utility in managing CHD risk.

Background: The endocannabinoid system (ES) plays a pivotal role in modulating central nervous system activity in response to emotional stimuli. This study aimed to identify and validate biomarkers associated with ES-related genes (ES-RGs) in major depressive disorder (MDD), providing insights into potential therapeutic targets.

Methods: Datasets GSE52790 and GSE38206 were analyzed in this study. Overlapping differential expression analysis and weighted gene co-expression network analysis (WGCNA) were integrated to identify intersecting genes. Candidate genes were selected through protein-protein interaction (PPI) analysis. Biomarker identification involved the integration of machine learning techniques, gene expression data, and receiver operating characteristic (ROC) analysis. A nomogram was developed and evaluated using these biomarkers as key indicators. Comprehensive analyses, including functional exploration, immune infiltration assessment, regulatory network construction, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation, were conducted.

Results: Mitochondrial ribosome protein S11 (MRPS11) and mitochondrial serine hydroxymethyltransferase2 (SHMT2) were identified as significant biomarkers for MDD, with markedly reduced expression in patient samples. These findings were validated by RT-qPCR analysis. The development of a biomarker-based nomogram successfully predicted MDD risk. Enrichment analysis highlighted the co-enrichment of both biomarkers in the "ribosome" pathway. Differential immune cell analysis revealed four immune cell types distinguishing MDD from control samples. Moreover, five key miRNAs targeting these biomarkers were predicted, along with 31 lncRNAs targeting the miRNAs, establishing an lncRNA-miRNA-mRNA network. Ten transcription factors (TFs) targeting the biomarkers were also identified, leading to the construction of a TF-mRNA network. Furthermore, 15 drugs targeting MRPS11 and 56 drugs targeting SHMT2 were identified, resulting in the formation of a biomarker-drug network. These findings may inform more precise and personalized therapeutic strategies for MDD.

Conclusion: MRPS11 and SHMT2 were identified as biomarkers for MDD through the validation of their expression patterns in clinical samples. This study provides a theoretical foundation for the development of targeted therapies for MDD.

Background: Ovarian cancer has poor treatment outcomes. This study aims to explore the clinical importance of LINC00886 and its effects on cancer cell behavior in ovarian cancer, potentially offering a new therapeutic target.

Materials and methods: RT-qPCR was used to detect LINC00886 expression in ovarian cancer tissue, with analysis of clinicopathological data and prognosis based on LINC00886 expression levels. CCK-8, Traswell, and Annexin V-FITC/PI flow cytometry assays were used to evaluate the impact of molecular expression on cell viability, invasiveness, and apoptosis. RIP and dual luciferase reporter gene assays were used to validate interactions among miR-423-5p, LINC00886, and TLR4. Western blot analysis was conducted to investigate downstream signaling proteins, and ELISA was used to measure TNF-α and IFN-γ levels in cell co-culture.

Results: LINC00886 is upregulated in ovarian cancer tissues and cell lines, and its high expression is associated with poor prognosis; downregulating LINC00886 inhibits cell viability and invasiveness while inducing apoptosis. miR-423-5p is downstream of LINC00886 and upstream of TLR4. Inhibiting miR-423-5p reverses the suppressive effects of LINC00886 downregulation on cancer cell behavior. Overexpressing TLR4 enhances cellular processes. Furthermore, downregulating LINC00886 reduces the expression of TLR4, Myd88, phosphorylated NF-κB p65, and PD-L1, while increasing TNF-α and IFN-γ levels and enhancing CD8 + T cell antitumor activity, thereby reducing tumor cell immune escape.

Conclusions: LINC00886 drives ovarian cancer progression and immune escape through themiR-423-5p/TLR4/Myd88/NF-κB/PD-L1 axis, establishing its potential as both a prognostic biomarker and therapeutic target.