HLA-B and TIMP1 as hub genes of the ventricular remodeling caused by hypertension.

RATIONALE: Myocardial fibrosis is an important pathological change that occurs during ventricular remodeling in patients with hypertension and is an important pathophysiological basis of cardiovascular disease. However, the molecular mechanism underlying this ventricular remodeling is unclear. METHODS: Bioinformatics analysis identified HLA-B and TIMP1 as hub genes in the process of myocardial fibrosis. Expression and correlation analyses of significant hub genes with ventricular remodeling were performed. Weighted gene co-expression network analysis (WGCNA) was performed to verify the role of HLA-B. ceRNA network was constructed to identify the candidate molecule drugs. Receiver operating characteristic (ROC) curves were analyzed. RESULTS: RT-qPCR was performed to verify the roles of HLA-B and TIMP1 in seven control individuals with hypertension and seven patients with hypertension and ventricular remodeling. The WGCNA showed that HLA-B was in the brown module and the correlation coefficient between HLA-B and ventricular remodeling was 0.67. Based on univariate logistic proportional regression analysis, HLA-B influences ventricular remodeling (P<0.05). RT-qPCR showed that the relative expression levels of HLA-B and TIMP1 were significantly higher in HLVR samples compared with their expression in the control group. CONCLUSIONS: HLA-B and TIMP1 might provide novel research targets for the diagnosis and treatment of HLVR.

Higher expression of TSR2 aggravating hypertension via the PPAR signaling pathway.

Hypertension is a complex disease with unknown causes. Therefore, it's crucial to deeply study its molecular mechanism. The hypertension dataset was obtained from Gene Expression Omnibus data base (GEO), and miRNA regulating central hub genes was screened via weighted gene co-expression network (DEGs) and gene set enrichment (GSEA). Cell experiments validated TSR2's role and the PPAR signaling pathway through western blotting. 500 DEGs were identified for hypertension, mainly enriched in actin cross-linking, insulin signaling, PPAR signaling, and protein localization. Eight hub genes (SEC61G, SRP14, Liy AR, NIP7, SDAD1, POLR1D, DYNLL2, TSR2) were identified. Four hub genes (LYAR, SDAD1, POLR1D, TSR2) exhibited high expression levels in the hypertensive tissue samples, while showing low expression levels in the normal tissue samples. This led us to speculate that they may have relevant regulatory effects on hypertension. When TSR2 was knocked down in the hypertension peripheral blood mononuclear cells (PBMC) model, the critical proteins in the PPAR signaling pathway (FABP, PPAR, PLTP, ME1, SCD1, CYP27, FABP1, OLR1, CPT-1, PGAR, CAP, ADIPO, MMP1, UCP1, ILK, PDK1 UBC AQP7) were downregulated. This also occurred in the hypertension peripheral blood mononuclear cells (PBMC) + TSR2_ OV model. TSR2 is highly expressed in individuals with hypertension and may play a significant role in the development of hypertension through the PPAR signaling pathway. TSR2 could serve as a molecular target for the early diagnosis and precise treatment of hypertension, providing a valuable direction for the mechanism research of this condition.

SGLT1/2 as the potential biomarkers of renal damage under Apoe-/- and chronic stress via the BP neural network model and support vector machine.

Background: Chronic stress (CS) could produce negative emotions. The molecular mechanism of SGLT1 and SGLT2 in kidney injury caused by chronic stress combined with atherosclerosis remains unclear. Methods: In total, 60 C57BL/6J mice were randomly divided into four groups, namely, control (CON, n = 15), control diet + chronic stress (CON+CS, n = 15), high-fat diet + Apoe-/- (HF + Apoe-/-, n = 15), and high-fat diet + Apoe-/- + chronic stress (HF+Apoe-/- + CS, n = 15) groups. The elevated plus maze and open field tests were performed to examine the effect of chronic stress. The expression of SGLT1 and SGLT2 in the kidney was detected. The support vector machine (SVM) and back propagation (BP) neural network model were constructed to explore the predictive value of the expression of SGLT1/2 on the renal pathological changes. The receiver operating characteristic (ROC) curve analysis was used. Results: A chronic stress model and atherosclerosis model were constructed successfully. Edema, broken reticular fiber, and increased glycogen in the kidney would be obvious in the HF + Apoe-/- + CS group. Compared with the CON group, the expression of SGLT1/2 in the kidney was upregulated in the HF + Apoe-/- + CS group (P < 0.05). There existed positive correlations among edema, glycogen, reticular fiber, expression of SGLT1/2 in the kidney. There were higher sensitivity and specificity of diagnosis of SGLT1/2 for edema, reticular fiber, and glycogen in the kidney. The result of the SVM and BP neural network model showed better predictive values of SGLT1 and SGLT2 for edema and glycogen in the kidney. Conclusion: In conclusion, SGLT1/2 might be potential biomarkers of renal damage under Apoe-/- and chronic stress, which provided a potential research direction for future related explorations into this mechanism.

A Potential Target for Clinical Atherosclerosis: A Novel Insight Derived from TPM2.

Atherosclerosis (AS) is a potential inducer of numerous cardio-cerebrovascular diseases. However, little research has investigated the expression of TPM2 in human atherosclerosis samples. A total of 34 clinical samples were obtained, including 17 atherosclerosis and 17 normal artery samples, between January 2018 and April 2021. Bioinformatics analysis was applied to explore the potential role of TPM2 in atherosclerosis. Immunohistochemistry, immunofluorescence, and western blotting assays were used to detect the expression of TPM2 and α-SMA proteins. The mRNA expression levels of TPM2 and α-SMA were detected using RT-qPCR. A neural network and intima-media thickness model were constructed. A strong relationship existed between the intima-media thickness and relative protein expression of TPM2 (P<0.001, R=-0.579). The expression of TPM2 was lower in atherosclerosis than normal artery (P<0.05). Univariate logistic regression showed that TPM2 (OR=0.150, 95% CI: 0.026-0.868, P=0.034) had clear correlations with atherosclerosis. A neural network model was successfully constructed with a relativity of 0.94434. TPM2 might be an independent protective factor for arteries, and one novel biomarker of atherosclerosis.

Citrate Synthase and OGDH as Potential Biomarkers of Atherosclerosis under Chronic Stress.

BACKGROUND: Pathological changes of the adrenal gland and the possible underlying molecular mechanisms are currently unclear in the case of atherosclerosis (AS) combined with chronic stress (CS). METHODS: New Zealand white rabbits were used to construct a CS and AS animal model. Proteomics and bioinformatics were employed to identify hub proteins in the adrenal gland related to CS and AS. Hub proteins were detected using immunohistochemistry, immunofluorescence assays, and Western blotting. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to analyze the expression of genes. In addition, a neural network model was constructed. The quantitative relationships were inferred by cubic spline interpolation. Enzymatic activity of mitochondrial citrate synthase and OGDH was detected by the enzymatic assay kit. Function of citrate synthase and OGDH with knockdown experiments in the adrenal cell lines was performed. Furthermore, target genes-TF-miRNA regulatory network was constructed. Coimmunoprecipitation (IP) assay and molecular docking study were used to detect the interaction between citrate synthase and OGDH. RESULTS: Two most significant hub proteins (citrate synthase and OGDH) that were related to CS and AS were identified in the adrenal gland using numerous bioinformatic methods. The hub proteins were mainly enriched in mitochondrial proton transport ATP synthase complex, ATPase activation, and the AMPK signaling pathway. Compared with the control group, the adrenal glands were larger and more disordered, irregular, and necrotic in the AS+CS group. The expression of citrate synthase and OGDH was higher in the AS+CS group than in the control group, both at the protein and mRNA levels (P < 0.05). There were strong correlations among the cross-sectional areas of adrenal glands, citrate synthase, and OGDH (P < 0.05) via Spearman's rho analysis, receiver operating characteristic curves, a neural network model, and cubic spline interpolation. Enzymatic activity of citrate synthase and OGDH increased under the situation of atherosclerosis and chronic stress. Through the CCK8 assay, the adrenal cell viability was downregulated significantly after the knockdown experiment of citrate synthase and OGDH. Target genes-TF-miRNA regulatory network presented the close interrelations among the predicted microRNA, citrate synthase and OGDH. After Coimmunoprecipitation (IP) assay, the result manifested that the citrate synthase and OGDH were coexpressed in the adrenal gland. The molecular docking study showed that the docking score of optimal complex conformation between citrate synthase and OGDH was -6.15 kcal/mol. CONCLUSION: AS combined with CS plays a significant role on the hypothalamic-pituitary-adrenal (HPA) axis, promotes adrenomegaly, increases the release of glucocorticoid (GC), and might enhance ATP synthesis and energy metabolism in the body through citrate synthase and OGDH gene targets, providing a potential research direction for future related explorations into this mechanism.

Identification of biomarkers in macrophages of atherosclerosis by microarray analysis.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages. METHODS: Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes. RESULTS: A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1. CONCLUSION: Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.