MCODE Algorithm Research Articles

Identification of pivotal genes and pathways in Chorea-acanthocytosis using comprehensive bioinformatic analysis.

Chorea-acanthocytosis (ChAc), an autosomal recessive disorder, is associated with cognitive and behavioral abnormalities. Previous studies were focused around exploring the functional annotation of VPS13A gene in ChAc, whereas the genetic labyrinth underlying this disease and plausible drug targets were underexplored. In the present study, we have identified the pivotal genes and molecular pathways implicated in ChAc using comprehensive bioinformatics analysis. In our analysis we found 27 distinct genes in Homo sapiens linked to ChAc, out of which 15 were selected as candidate genes for enrichment analysis based on their Gene Ontology (GO) annotations and involvement in relevant molecular pathways. By constructing a Protein-Protein Interaction (PPI) network consisting of 26 nodes and 62 edges, we identified two gene modules. Subsequently, using the MCODE algorithm, we identified 6 hub genes-ATN1, JPH3, TBP, VPS13A, DMD, and HTT-as core candidates. These hub genes are primarily associated with processes such as neuron development and differentiation, the CAMKK-AMPK signaling cascade, ion transmembrane transport systems, and protein localization. Furthermore, using drug gene databank we identified 23 FDA-approved drugs that possess the propensity to target 3 out of the 6 identified hub genes. We believe that our findings could open promising avenues for potential therapeutic interventions in ChAc.

The Prognostic Hub Gene POLE2 Promotes BLCA Cell Growth via the PI3K/AKT Signaling Pathway.

BLCA is a common urothelial malignancy characterized by a high recurrence rate. Despite its prevalence, the molecular mechanisms underlying its development remain unclear. This study aimed to explore new prognostic biomarkers and investigate the underlying mechanism of bladder cancer (BLCA). The objective of this study is to identify key prognostic biomarkers for BLCA and to elucidate their roles in the disease. We first collected the overlapping DEGs from GSE42089 and TCGA-BLCA samples for the subsequent weighted gene co-expression network analysis (WGCNA) to find a key module. Then, key module genes were analyzed by the MCODE algorithm, prognostic risk model, expression and immunohistochemical staining to identify the prognostic hub gene. Finally, the hub gene was subjected to clinical feature analysis, as well as cellular function assays. In WGCNA on 1037 overlapping genes, the blue module was the key module. After a series of bioinformatics analyses, POLE2 was identified as a prognostic hub gene in BLCA from potential genes (TROAP, POLE2, ANLN, and E2F8). POLE2 level was increased in BLCA and related to different clinical features of BLCA patients. Cellular assays showed that si-POLE2 inhibited BLCA proliferation, and si-POLE2+ 740Y-P in BLCA cells up-regulated the PI3K and AKT protein levels. In conclusion, POLE2 was identified to be a promising prognostic biomarker as an oncogene in BLCA. It was also found that POLE2 exerts a promoting function by the PI3K/AKT signaling pathway in BLCA.

Network Pharmacology Combined with GEO Analysis of the Mechanism of Qing-Jin-Hua-Tan Decoction in the Treatment of Non-small Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation. We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration. We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells. Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Mahuang Fuzi Xixin decoction: A potent analgesic for neuropathic pain targeting the NMDAR2B/CaMKIIα/ERK/CREB pathway

Neuropathic pain (NeP) is a condition charactesized by nervous system injury or dysfunction that affects a significant portion of the population. Current treatments are ineffective, highlighting the need for novel therapeutic approaches. Mahuang Fuzi Xixin decoction (MFXD) has shown promise for treating pain conditions in clinical practice; however, its potential against NeP and the underlying mechanisms remain unclear. This study identified 35 compounds in MFXD using ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). The analgesic effects of MFXD on chronic constriction injury (CCI) rats were evaluated through the detection of mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). The analgesic effects of MFXD in rats with chronic constriction injury (CCI) were evaluated by measuring the mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). Low-dose MFXD (L-MFXD) group (4.8 g/kg) and high-dose MFXD (H-MFXD) group (9.6 g/kg) exhibited significantly higher MWT and TWL values than the CCI group on days 11 and 15 post-CCI surgery, substantiating the remarkable analgesic efficacy of MFXD. Network pharmacology analysis identified 58 key targets enriched in pathways such as long-term potentiation (LTP) and glutamatergic synapse. The MCODE algorithm further identified core targets with significant enrichment in LTP. Molecular docking revealed that mesaconitine, rosmarinic acid, and delgrandine from MFXD exhibited high binding affinity with NMDAR2B (−11 kcal/mol), CaMKIIα (−14.3 kcal/mol), and ERK (−10.8 kcal/mol). Western blot and immunofluorescence confirmed that H-MFXD significantly suppressed the phosphorylation levels of NMDAR2B, CaMKIIα, ERK, and CREB in the spinal cord tissue of CCI rats. In conclusion, this study demonstrates that MFXD possesses potent analgesic effects on NeP by suppressing the NMDAR2B/CaMKIIα/ERK/CREB signalling pathway. This study unlocks a path toward potentially revolutionising NeP treatment with MFXD, encouraging further research and clinical development.

Hua-Feng-Dan Alleviates LPS-induced Neuroinflammation by Inhibiting the TLR4/Myd88/NF-κB Pathway: Integrating Network Pharmacology and Experimental Validation.

Neuroinflammation is the pathological basis of many neurological diseases, including neurodegenerative diseases and stroke. Hua-Feng-Dan (HFD) is a well-established traditional Chinese medicine that has been used for centuries to treat stroke and various other brain-related ailments. Our study aims to elucidate the molecular mechanism by which HFD mitigates neuroinflammation by combining network pharmacology and in vitro experiments. TCMSP and SymMap databases were used to extract active compounds and their related targets. The neuroinflammation-related targets were obtained from the GeneCards database. The common targets of HFD and neuroinflammation were used to construct a protein-protein interaction (PPI) network. MCODE plug-in was used to find the hub module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to dissect the hub module genes. The lipopolysaccharide (LPS)-induced BV2 microglial neuroinflammation model was utilized to assess the therapeutic effects of HFD on neuroinflammation. Western blotting analysis was performed to examine the core target proteins in the TLR4/My- D88/NF-κB signaling pathway, potentially implicated in HFD's therapeutic effects on neuroinflammation. Hoechst 33342 staining and JC-1 staining were employed to evaluate neuronal apoptosis. Through network pharmacology, 73 active compounds were identified, with quercetin, beta-sitosterol, luteolin, and (-)-Epigallocatechin-3-Gallate recognized as important compounds. Meanwhile, 115 common targets of HFD and neuroinflammation were identified, and 61 targets were selected as the hub targets utilizing the MCODE algorithm. The results of in vitro experiments demonstrated that HFD significantly inhibited microglial-mediated neuronal inflammation induced by LPS. Integrating the predictions from network pharmacology with the in vitro experiment results, it was determined that the mechanism of HFD in mitigating neuroinflammation is closely related to the TLR4/MyD88/NF-κB pathway. Furthermore, HFD demonstrated the capacity to shield neurons from apoptosis by curbing the secretion of pro-inflammatory factors subsequent to microglial activation. The findings demonstrated that HFD had an inhibitory effect on LPS-induced neuroinflammation in microglia and elucidated its underlying mechanism. These findings will offer a theoretical foundation for the clinical utilization of HFD in treating neurodegenerative diseases associated with neuroinflammation.

Revealing the active ingredients and mechanisms of Xiatianwu against hepatocellular carcinoma: a study based on network pharmacology and bioinformatics.

Xiatianwu is a traditional Chinese medicine. This study investigates the function of Xiatianwu in treating HCC through database analyses and in vitro experiments. The active ingredients of Xiatianwu were identified from TCMSP and HERB databases and their targets were predicted by Swiss TargetPrediction. The HCC dataset was screened using the GEO database, and the differentially expressed genes between HCC and non-tumor liver tissues were analyzed to identify overlapping targets with Xiatianwu. The intersecting targets underwent enrichment analysis using R software to elucidate the molecular mechanisms of Xiatianwu against HCC. Core targets were identified using the PPI network and MCODE algorithm. Clinical relevance and disease prognosis in HCC were verified using the TCGA database. Meanwhile, binding affinities among components and targets were validated with molecular docking. Finally, the anti-HCC efficacy of the active ingredient was validated in vitro. Our findings revealed that eight active ingredients of Xiatianwu interacted with 11 key targets, providing anti-HCC efficacy. Molecular docking indicated that bicuculline and fumarine exhibited superior binding abilities. Bicuculline, a representative ingredient of Xiatianwu, was chosen for in vitro validation. Results demonstrated that bicuculline, in a dose-dependent manner inhibited HCC cell viability, reduced migration, suppressed the G0/M cell cycle, and decreased core protein expression. Xiatianwu demonstrates significant potential for clinical application in treating HCC. Bicuculline, a key active ingredient of Xiatianwu, exerts anti-HCC effects by inhibiting the cell cycle.

Screening of potential hub genes involved in Kidney Wilms tumor via bioinformatics analysis and experimental validation

BackgroundWilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys.MethodsDifferential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes’ functions in WiT-49 cells.ResultsThe enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients’ prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells.ConclusionsEMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

Establishment of a Mitochondrial Metabolism-Related Diagnostic Model in Schizophrenia Based on LASSO Algorithm.

Schizophrenia is a common mental disorder, and mitochondrial function represents a potential therapeutic target for psychiatric diseases. The role of mitochondrial metabolism-related genes (MRGs) in the diagnosis of schizophrenia remains unknown. This study aimed to identify candidate genes that may influence the diagnosis and treatment of schizophrenia based on MRGs. Three schizophrenia datasets were obtained from the Gene Expression Omnibus database. MRGs were collected from relevant literature. The differentially expressed genes between normal samples and schizophrenia samples were screened using the limma package. Venn analysis was performed to identify differentially expressed MRGs (DEMRGs) in schizophrenia. Based on the STRING database, hub genes in DEMRGs were identified using the MCODE algorithm in Cytoscape. A diagnostic model containing hub genes was constructed using LASSO regression and logistic regression analysis. The relationship between hub genes and drug sensitivity was explored using the DSigDB database. An interaction network between miRNA-transcription factor (TF)-hub genes was created using the Network-Analyst website. A total of 1,234 MRGs, 172 DEMRGs, and 6 hub genes with good diagnostic performance were identified. Ten potential candidate drugs (rifampicin, fulvestrant, pentadecafluorooctanoic acid, etc.) were selected. Thirty-four miRNAs targeting genes in the diagnostic model (ANGPTL4, CPT2, GLUD1, MED1, and MED20), as well as 137 TFs, were identified. Six potential candidate genes showed promising diagnostic significance. rifampicin, fulvestrant, and pentadecafluorooctanoic acid were potential drugs for future research in the treatment of schizophrenia. These findings provided valuable evidence for the understanding of schizophrenia pathogenesis, diagnosis, and drug treatment.

Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis

Background Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete. Aim This study aims to identify shared ICH genes for both processes. Methods ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted via CMap database. Results 64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted via CMap. Conclusion Several shared genes between mitophagy and ferroptosis potentially drive ICH progression via TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.

Identification of potential biomarkers for sepsis based on neutrophil extracellular trap-related genes

Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors in the pathological progression of sepsis. This study aims to screen potential biomarkers for sepsis and delve into their regulatory function in the pathogenesis. We downloaded 6 microarray datasets from the Gene Expression Omnibus (GEO) database, with 4 as the training sets and 2 as the validation sets. NETs-related genes (NRGs) were obtained from relevant literature. Differential expression analysis was performed on four training sets separately. We intersected differentially expressed genes (DEGs) from the four training sets and NRGs, finally resulting in 19 NETs-related sepsis genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) unearthed that NETs-related sepsis genes were majorly abundant in functions and pathways such as defense response to bacterium and Neutrophil extracellular trap formation. Using the PPI network, the MCC algorithm, and the MCODE algorithm in the CytoHubba plugin, 7 sepsis hub genes (ELANE, TLR4, MPO, PADI4, CTSG, MMP9, S100A12) were identified. ROC curve for each Hub gene in the training and validation sets were plotted, which revealed that the Area Under Curve (AUC) values are all greater than 0.6, indicating good classification ability. A total of 349 miRNAs targeting Hub genes were predicted in the mirDIP database, and 620 lncRNAs targeting miRNAs were predicted in the ENCORI database. The ceRNA regulatory network was constructed using Cytoscape software. Finally, we employed the cMAP database to predict small molecular complexes as potentially effective drugs for the treatment of sepsis, such as chloroquine, harpagoside, and PD-123319. In conclusion, this project successfully identified 7 core genes, which may serve as promising candidates for novel sepsis biomarkers. Meanwhile, we constructed a related ceRNA network and predicted potential targeted drugs, providing potential therapeutic targets and treatment strategies for sepsis patients.

Identification of potential biomarkers of gout through weighted gene correlation network analysis.

Although hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity. Using the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT-qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments. According to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein-protein interaction (PPI) network. Using RT-qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins. In this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.

Metformin inhibits knee osteoarthritis induced by type 2 diabetes mellitus in rats: S100A8/9 and S100A12 as players and therapeutic targets

Abstract Type 2 diabetes mellitus (T2DM) is a significant risk factor for osteoarthritis (OA), and metformin, as the main therapeutic drug for T2DM, has shown positive effects on OA without a clear mechanism. This study aimed to explore the protective effects and mechanisms of oral metformin in T2DM-induced OA. We identified differentially expressed genes, using the GSE117999 and GSE98918 datasets, and protein–protein interaction networks were analyzed using the MCODE algorithm in cytospace to finalize the OA hub genes (S100A8, S100A9, and S100A12). To validate whether S100A8, S100A9, and S100A12 are potential targets of action for OA, we randomly divided 40 SD rats into a control group (CG, n = 10) and a T2DM group (n = 30). We modeled rats in the T2DM group with streptozotocin (35 mg/kg, i.p.) and a high carbohydrate and fat diet. Finally, 20 were randomly selected and divided into the T2DM group (n = 10) and the treated group (Met + T2DM, n = 10), and the treated group was given Met (180 mg/kg/day) by gavage for 8 weeks. We subsequently used histological assessment to show that oral metformin mitigated the development of T2DM-associated OA as indicated by the OA Research Society International score and articular cartilage thickness, and immunohistochemistry also confirmed that metformin significantly reduced the expression of S100A8, S100A9, and S100A12 in the knee joints of OA rats. In conclusion, metformin demonstrated a protective effect against OA in T2DM-induced rats, slowing knee OA progression by inhibiting S100A8, S100A9, and S100A12 expression. These findings suggest potential biological targets for future OA treatments.

Identification of a Novel Diagnosis Model based on 5 Hub Genes for Chronic Thromboembolic Pulmonary Hypertension.

As a type of precapillary pulmonary hypertension, chronic thromboembolic pulmonary hypertension (CTEPH) results from incomplete pulmonary embolism resolution. In this study, we aimed to determine biomarker genes for predicting the prognosis of CTEPH. RNAseq of CTEPH was collected from the public database, namely Gene Expression Omnibus (GEO), including GSE84538 and GSE188938, which combined a dataset (GSE). Differentially expressed genes (DEG) or miRNA (DEM) were identified by limma package. Functional enrichment analysis was performed by the WebGestaltR package. Then, the miRNA-mRNA network was presented by Cytoscape, and the protein-protein interactions (PPI) network was constructed by STRING. MCODE was mined by mature MCODE algorithm. Immune infiltration analysis was conducted by ESTIMATER and ssGSEA analysis. A diagnosis model was established by SVM algorithm. In the GSE dataset, CTEPH samples had a lower GOBP_RESPONSE_- TO_OXIDATIVE_STRESS score. A total of 628 DEGs and 31 DEMs were identified between CTEPH and normal samples. Afterward, DEGs were intersected with genes, which correlated with the GOBP_RESPONSE_TO_OXIDATIVE_STRESS score. A 26 DEMs-152 DEGs network was constructed, and a PPI network was established based on 152 DEGs to find 149 target genes. From the above 149 target genes, 3 modules were extracted to obtain 15 core targets. Finally, 5 hub genes were obtained by the intersection of 15 core targets and genes in MCODE2. A total of 5 hub genes were positively correlated with most immune cell scores as well as GOBP_RESPONSE_TO_OXIDATIVE_ STRESS. It was found that a diagnosis model based on 5 hub genes had a well diagnostic ability for CTEPH. We identified 5 hub genes associated with oxidative stress. It can be concluded that they may be beneficial in diagnosing CTEPH.

Regulation of lipid metabolism by APOE4 in intrahepatic cholangiocarcinoma via the enhancement of ABCA1 membrane expression.

Intrahepatic cholangiocarcinoma (ICC) is a malignancy with a dismal prognosis, thus the discovery of promising diagnostic markers and treatment targets is still required. In this study, 1,852 differentially expressed genes (DEGs) were identified in the GSE45001 dataset for weighted gene co-expression network analysis (WGCNA), and the turquoise module was confirmed as the key module. Next, the subnetworks of the 1,009 genes in the turquoise module analyzed by MCODE, MCC, and BottleNeck algorithms identified nine overlapping genes (CAT, APOA1, APOC2, HSD17B4, EHHADH, APOA2, APOE4, ACOX1, AGXT), significantly associated with lipid metabolism pathways, such as peroxisome and cholesterol metabolism. Among them, APOE4 exhibited a potential tumor-suppressive role in ICC and high diagnostic value for ICC in both GSE45001 and GSE32879 datasets. In vitro experiments demonstrated Apolipoprotein E4 (APOE4) overexpression suppressed ICC cell proliferation, migration, and invasion, knockdown was the opposite trend. And in ICC modulated lipid metabolism, notably decreasing levels of TG, LDL-C, and HDL-C, while concurrently increasing the expressions of TC. Further, APOE4 also downregulated lipid metabolism-related genes, suggesting a key regulatory role in maintaining cellular homeostasis, and regulating the expression of the membrane protein ATP-binding cassette transporter A1 (ABCA1). These findings highlighted the coordinated regulation of lipid metabolism by APOE4 and ABCA1 in ICC progression, providing new insights into ICC mechanisms and potential therapeutic strategies.

The mixed effect of Endocrine-Disrupting chemicals on biological age Acceleration: Unveiling the mechanism and potential intervention target

IntroductionAlthough previous studies investigated the potential adverse effects of endocrine-disrupting chemicals (EDCs) on biological age acceleration and aging-related diseases, the mixed effect of multiple types of EDCs on biological age acceleration, including its potential underlying mechanism, remains unclear. MethodsData from the National Health and Nutrition Examination Survey (NHANES) were used to analyze biological age measures, including Klemera-Doubal method biological age (KDM-BA), phenotypic age, and homeostatic dysregulation (HD). Weight quantile sum (WQS) regression was performed to screen biological age-related EDCs (BA-EDCs) and assess the mixed effect of BA-EDCs on biological age acceleration and aging-related disease. Targets of BA-EDCs were obtained from three databases, while heart aging-related genes were obtained from the Aging Anno database. Protein–protein interaction (PPI) network and MCODE algorithm were applied to identify potential interactions between BA-EDC targets and heart aging-related genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to identify related pathways. ResultsThis cross-sectional study included 1,439 participants. A decile increase in BA-EDCs co-exposure was associated with 0.31 years and 0.17 years of KDM-BA and phenotypic age acceleration, respectively. The mixed effect of BA-EDCs was associated with an increased prevalence of atherosclerotic cardiovascular disease (ASCVD). Vitamins C and E demonstrated a significant interaction effect on the association between BA-EDCs and KDM-BA acceleration. PPI network and functional enrichment analysis indicated that the AGE-RAGE signaling pathway in diabetic complications was significantly enriched. ConclusionOur results showed that the co-exposure effect of BA-EDCs was associated with biological age acceleration and ASCVD, with the AGE-RAGE signaling pathway being the underlying mechanism. Vitamins C and E may also be an actionable target for preventing EDC-induced biological aging.

SSR1 and CKAP4 as potential biomarkers for intervertebral disc degeneration based on integrated bioinformatics analysis.

Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms. The aim of this study was to identify potential IDD-related biomarkers using a comprehensive bioinformatics approach and validate them in vitro. In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single-cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD. we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and p.adj <0.05. Further analysis using both MCODE and LASSO algorithms enabled us to identify five key genes, of which CKAP4 and SSR1 were validated by GSE70362, demonstrating significant diagnostic value for IDD. Additionally, immune infiltrating analysis revealed that monocytes were significantly correlated with the two key genes. We also analyzed a single-cell sequencing dataset, GSE199866, which showed that both CKAP4 and SSR1 were highly expressed in fibrocartilage chondrocytes. Finally, we validated our findings in vitro by performing real time polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on 30 human disc samples. Our results showed that CKAP4 and SSR1 were upregulated in degenerated disc samples. Taken together, our findings suggest that CKAP4 and SSR1 have the potential to serve as disease biomarkers for IDD.

Bioinformatics-led discovery of liver-specific genes and macrophage infiltration in acute liver injury.

Acute liver injury (ALI) is an important global health concern, primarily caused by widespread hepatocyte cell death, coupled with a complex immune response and a lack of effective remedies. This study explores the underlying mechanisms, immune infiltration patterns, and potential targets for intervention and treatment ALI. The datasets of acetaminophen (APAP), carbon tetrachloride (CCl4), and lipopolysaccharide (LPS)-induced ALI were obtained from the GEO database. Differentially expressed genes (DEGs) were individually identified using the limma packages. Functional enrichment analysis was performed using KEGG, GO, and GSEA methods. The overlapping genes were extracted from the three datasets, and hub genes were identified using MCODE and CytoHubba algorithms. Additionally, PPI networks were constructed based on the String database. Immune cell infiltration analysis was conducted using ImmuCellAI, and the correlation between hub genes and immune cells was determined using the Spearman method. The relationship between hub genes, immune cells, and biochemical indicators of liver function (ALT, AST) was validated using APAP and triptolide (TP) -induced ALI mouse models. Functional enrichment analysis indicated that all three ALI models were enriched in pathways linked to fatty acid metabolism, drug metabolism, inflammatory response, and immune regulation. Immune analysis revealed a significant rise in macrophage infiltration. A total of 79 overlapping genes were obtained, and 10 hub genes were identified that were consistent with the results of the biological information analysis after screening and validation. Among them, Clec4n, Ms4a6d, and Lilrb4 exhibited strong associations with macrophage infiltration and ALI.

Unraveling the molecular mechanisms of nitrite-induced physiological disruptions in largemouth bass

Largemouth bass (LMB; Micropterus salmoides) is an economically consequential piscine species spanning North America, Europe, and China. Elevated nitrite concentrations in aquatic environments cause significant physiological changes in fish, including stunted growth, appetite changes, reduced feed intake, and impaired oxygen transport, collectively affecting overall fish health. This study embarked on uncovering the molecular mechanisms underscoring the influence of various nitrite concentrations (0 mg/L, 400 mg/L, 600 mg/L, and 600 mg/L followed by recovery in nitrite-free water) on LMB, leveraging transcriptomics and metabolomics. Our findings revealed that a nitrite exposure of 400/600 mg/L may incite temporary, recoverable damage to the spleen, heart, and gills, but potentially enduring, irreversible hepatic pathologies. The spleen emerged as the most vulnerable organ to nitrite-induced deleterious impacts, causing acute, reversible damage and widespread apoptosis. Nitrite reprogramed splenocyte gene expressions, and prompted apoptosis, autophagy, and changes in metabolic pathways. It also triggered cell-type transitions and promoted adhesion-related gene expressions, as well as inhibited nucleotide metabolism and cell cycle progression. Notably, the immune cell repertoire within the spleen was affected by different nitrite concentrations, possibly linking to nitrite-induced spleen tissue impairment. Following the construction of a LMB protein–protein interaction (PPI) network, several pivotal genes uniquely expressed in distinct nitrite concentrations were identified, such as hdac4, hdac5, and brg1 in the N2 group (400 mg/L), hdac1, hells, and nccrp1 in the N3 group (600 mg/L), and Actr1, brd4, atf7a, and kat2b in the N4 group (600 mg/L recovery). The LMB spleen transcriptome was most profoundly influenced in the N3 group. Applying the MCODE algorithm for network module segmentation, we discerned eight MCODE subnetworks. Detailed analyses of these subnetworks shed light on their core functional pathways and pivotal genes. In particular, the MCODE1 core network implicated mainly Notch-HLH transcription, histone deacetylation, and protein deacetylation pathways, with pivotal genes such as ESRRGB, ATF7A, JDP2A, BRD4, and THRB. Metabolomic analysis results paralleled those of transcriptomic examination, corroborating analogous metabolic pathway alterations. This study holds substantial implications for comprehending the toxicological impact of nitrite on economically important fish species like LMB, and provides insights for refining aquaculture practices to minimize this toxicity.

Identifying immuno-related diagnostic genes and immune infiltration signatures for periodontitis and alopecia areata

BackgroundAlthough there have been indications that periodontitis (PD) may be susceptible to alopecia areata (AA), the underlying mechanism of its pathogenesis remains poorly understood. The objective of our study is to conduct further research into the occurrence of this complication. MethodsThe gene expression omnibus (GEO) database was the source of acquisition for both PD and AA datasets. Various methods, including the differentially expressed genes (DEGs) analysis, functional enrichment analysis, protein–protein interaction (PPI) network construction, Cytohubba algorithms, and RandomForest algorithms, were utilized to identify candidate hub immuno-related genes (IRGs) for diagnosing AA with PD. The diagnostic efficacy was assessed by constructing receiver operating characteristic (ROC) curves. To further deepen our understanding, immune cell infiltration, flow cytometry assay, and immunofluorescence techniques were employed to uncover immune cell dysregulation in PD and AA. Results899 and 803 DEGs were detected in AA and PD, respectively, with an intersection of 150 common DEGs enriched in immune regulation. Further analysis of the junction of shared DEGs and IRGs was analyzed using the PPI network, Mcode, and Cytohubba algorithms. Three hub genes (CTSS, IL2RG, and ITGAL) were subsequently selected by Cytohubba and RandomForest algorithms and were found to be promising candidate hub genes with high diagnostic values (AUC ranging from 0.776 to 0.909) for diagnosing AA with PD. Additionally, various dysregulated immune cells were observed, with mast cells potentially serving as markers for AA and plasma for PD. ConclusionThree candidate hub IRGs (CTSS, IL2RG, and ITGAL) were identified with considerable diagnostic values. Besides, mast cells could serve as markers for AA, while plasma may indicate PD. Our research has the potential to identify shared diagnostic candidate genes and immune cells for AA and PD patients.

Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies.

Aging plays an essential role in the development of diabetic nephropathy (DN). This study aimed to identify and verify potential aging-related genes associated with DN using bioinformatics analysis. To begin with, we combined the datasets from GEO microarrays (GSE104954 and GSE30528) to find the genes that were differentially expressed (DEGs) across samples from DN and healthy patient populations. By overlapping DEGs, weighted co-expression network analysis (WGCNA), and 1357 aging-related genes (ARGs), differentially expressed ARGs (DEARGs) were discovered. We next performed functional analysis to determine DEARGs' possible roles. Moreover, protein-protein interactions were examined using STRING. The hub DEARGs were identified using the CytoHubba, MCODE, and LASSO algorithms. We next used two validation datasets and Receiver Operating Characteristic (ROC) curves to determine the diagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEARGs' expression levels in vitro. In addition, we investigated the relationships between immune cells and hub DEARGs. Next, Gene Set Enrichment Analysis (GSEA) was used to identify each biomarker's biological role. The hub DEARGs' subcellular location and cell subpopulations were both identified and predicted using the HPA and COMPARTMENTS databases, respectively. Finally, drug-protein interactions were predicted and validated using STITCH and AutoDock Vina. A total of 57 DEARGs were identified, and functional analysis reveals that they play a major role in inflammatory processes and immunomodulation in DN. In particular, aging and the AGE-RAGE signaling pathway in diabetic complications are significantly enriched. Four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) were further screened using the interaction network, CytoHubba, MCODE, and LASSO algorithms. The results above were further supported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infiltration, DN had significantly more resting mast cells and delta gamma T cells but fewer regulatory T cells and active mast cells. Four DEARGs have statistical correlations with them as well. Further investigation revealed that four DEARGs were implicated in immune cell abnormalities and regulated a wide range of immunological and inflammatory responses. Furthermore, the drug-protein interactions included four possible therapeutic medicines that target four DEARGs, and molecular docking could make this association practical. This study identified four DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) associated with DN, which might play a key role in the development of DN and could be potential biomarkers in DN.