Identification of genes and key pathways underlying the pathophysiological association between nonalcoholic fatty liver disease and atrial fibrillation

Identification of tryptophan metabolism-related biomarkers for nonalcoholic fatty liver disease through network analysis

Background

Increasing evidence demonstrates that tryptophan metabolism is closely related to the development of nonalcoholic fatty liver disease (NAFLD). This study aimed to identify specific biomarkers of NAFLD associated with tryptophan metabolism and research its functional mechanism.

Methods

We downloaded NAFLD RNA-sequencing data from GSE89632 and GSE24807, and obtained tryptophan metabolism-related genes (TMRGs) from the MsigDB database. The R package limma and WGCNA were used to identify TMRGs-DEGs, and GO, KEGG and Cytoscape were used to analyze and visualize the data. Immune cell infiltration analysis was used to explore the immune mechanism of NAFLD and the biomarkers. We also validated extended levels of biomarkers.

Results

We identified 375 NAFLD differentially expressed genes (DEGs) and 85 TMRGs-DEGs. GO/KEGG analysis revealed that TMRGs-DEGs were mainly enriched in triglyceride and cholesterol metabolism. ROC curves identified CCL20 (AUC = 0.917), CD160 (AUC = 0.933) and CYP7A1 (AUC = 1) as biomarkers of NAFLD. Immune infiltration analysis showed significant differences in ten immune cells, and the activation of dendritic cells and mast cells were highly positively correlated with NAFLD. CCL20, CD160 and CYP7A1 were highly correlated with M2 macrophage, neutrophil and mast cells activation, respectively. Twenty-seven TMRGs correlated with hub genes, and gene set enrichment analysis demonstrated their function in tryptophan- and lysine-containing metabolic process. We identified 41 therapeutic drug matches which corresponded to two hub genes and four drugs which co-targeted CCL20 and CYP7A1. Finally, three hub genes were validated in our mouse model.

Conclusions

CCL20, CD160 and CYP7A1 are tryptophan metabolism-related biomarkers of NAFLD, related to glycerol ester and cholesterol metabolism. We screened four compounds which co-target CCL29 and CYP7A1 to provide potential experimental drugs for NAFLD.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Network pharmacology-based approach to elucidate the pharmacologic mechanisms of natural compounds from Dictyostelium discoideum for Alzheimer's disease treatment

Alzheimer's disease (AD) is increasingly becoming a major public health concern in our society. While many studies have explored the use of natural polyketides, alkaloids, and other chemical components in AD treatment, there is an urgent need to clarify the concept of multi-target treatment for AD. This study focuses on using network pharmacology approach to elucidate how secondary metabolites from Dictyostelium discoideum affect AD through multi-target or indirect mechanisms. The secondary metabolites produced by D. discoideum during their development were obtained from literature sources and PubChem. Disease targets were selected using GeneCards, DisGeNET, and CTD databases, while compound-based targets were identified through Swiss target prediction and Venn diagrams were used to find intersections between these targets. A network depicting the interplay among disease, drugs, active ingredients, and key target proteins (PPI network) was formed utilizing the STRING (Protein-Protein Interaction Networks Functional Enrichment Analysis) database. To anticipate the function and mechanism of the screened compounds, GO and KEGG enrichment analyses were conducted and visually presented using graphs and bubble charts. After the screening phase, the top interacting targets in the PPI network and the compound with the most active target were chosen for subsequent molecular docking and molecular dynamic simulation studies. This study identified nearly 50 potential targeting genes for each of the screened compounds and revealed multiple signaling pathways. Among these pathways, the inflammatory pathway stood out. COX-2, a receptor associated with neuroinflammation, showed differential expression in various stages of AD, particularly in pyramidal neurons during the early stages of the disease. This increase in COX-2 expression is likely induce by higher levels of IL-1, which is associated with neuritic plaques and microglial cells in AD. Molecular docking investigations demonstrated a strong binding interaction between the terpene compound PQA-11 and the neuroinflammatory receptor COX2, with a substantial binding affinity of -8.4 kcal/mol. Subsequently, a thorough analysis of the docked complex (COX2-PQA11) through Molecular Dynamics Simulation showed lower RMSD, minimal RMSF fluctuations, and a reduced total energy of -291.35 kJ/mol compared to the standard drug. These findings suggest that the therapeutic effect of PQA-11 operates through the inflammatory pathway, laying the groundwork for further in-depth research into the role of secondary metabolites in AD treatment.

Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

BACKGROUND AND AIMS

Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD).

METHOD

The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes.

RESULTS

Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples.

CONCLUSION

Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.

TLR2 and CASP7 as the biomarkers associated with non-alcoholic fatty liver disease and chronic kidney disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease (CKD) are both highly prevalent worldwide. Studies have confirmed the association between them, but the underlying pathophysiological mechanisms are not clear yet. This study aims to identify the genetic and molecular mechanisms influencing both diseases through a bioinformatics approach.

RESULTS

Fifty-four overlapping differentially expressed genes associated with NAFLD and CKD were obtained by analysis of microarray datasets GSE63067 and GSE66494 downloaded from Gene Expression Omnibus. Next, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Nine hub genes were screened using protein-protein interaction network and Cytoscape software, including TLR2, ICAM1, RELB, BIRC3, HIF1A, RIPK2, CASP7, IFNGR1 and MAP2K4. The receiver operating characteristic curve results showed that all hub genes have good diagnostic values for patients with NAFLD and CKD. The mRNA expression of nine hub genes was detected in NAFLD and CKD animal models, and it was found that the expression of TLR2 and CASP7 was significantly increased in both disease models.

CONCLUSIONS

TLR2 and CASP7 can be used as biomarkers for both diseases. Our study provided new insights for identifying potential biomarkers and valuable therapeutic leads in NAFLD and CKD.

Identification of m6A regulator-mediated RNA methylation modification patterns and key immune-related genes involved in atrial fibrillation

The role of m6A in the regulation of the immune microenvironment in atrial fibrillation (AF) remains unclear. This study systematically evaluated the RNA modification patterns mediated by differential m6A regulators in 62 AF samples, identified the pattern of immune cell infiltration in AF and identified several immune-related genes associated with AF. A total of six key differential m6A regulators between healthy subjects and AF patients were identified by the random forest classifier. Three distinct RNA modification patterns (m6A cluster-A, -B and -C) among AF samples were identified based on the expression of 6 key m6A regulators. Differential infiltrating immune cells and HALLMARKS signaling pathways between normal and AF samples as well as among samples with three distinct m6A modification patterns were identified. A total of 16 overlapping key genes were identified by weighted gene coexpression network analysis (WGCNA) combined with two machine learning methods. The expression levels of the NCF2 and HCST genes were different between controls and AF patient samples as well as among samples with the distinct m6A modification patterns. RT-qPCR also proved that the expression of NCF2 and HCST was significantly increased in AF patients compared with control participants. These results suggested that m6A modification plays a key role in the complexity and diversity of the immune microenvironment of AF. Immunotyping of patients with AF will help to develop more accurate immunotherapy strategies for those with a significant immune response. The NCF2 and HCST genes may be novel biomarkers for the accurate diagnosis and immunotherapy of AF.