Circulating miR-3659 may be a potential biomarker of dyslipidemia in patients with obesity

Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis

BACKGROUND AND AIMS

This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS).

METHODS AND RESULTS

Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases.

CONCLUSION

We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients.

Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study

Epigenetics regulates gene expression and has been confirmed to play a critical role in a variety of metabolic diseases, such as diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), osteoporosis, gout, hyperthyroidism, hypothyroidism and others. The term 'epigenetics' was firstly proposed in 1942 and with the development of technologies, the exploration of epigenetics has made great progresses. There are four main epigenetic mechanisms, including DNA methylation, histone modification, chromatin remodelling, and noncoding RNA (ncRNA), which exert different effects on metabolic diseases. Genetic and non-genetic factors, including ageing, diet, and exercise, interact with epigenetics and jointly affect the formation of a phenotype. Understanding epigenetics could be applied to diagnosing and treating metabolic diseases in the clinic, including epigenetic biomarkers, epigenetic drugs, and epigenetic editing. In this review, we introduce the brief history of epigenetics as well as the milestone events since the proposal of the term 'epigenetics'. Moreover, we summarise the research methods of epigenetics and introduce four main general mechanisms of epigenetic modulation. Furthermore, we summarise epigenetic mechanisms in metabolic diseases and introduce the interaction between epigenetics and genetic or non-genetic factors. Finally, we introduce the clinical trials and applications of epigenetics in metabolic diseases.

Transcriptomic profiling of the telomerase transformed Mesenchymal stromal cells derived adipocytes in response to rosiglitazone

Background

Differentiation of Immortalized Human Bone Marrow Mesenchymal Stromal Cells - hTERT (iMSC3) into adipocytes is in vitro model of obesity. In our earlier study, rosiglitazone enhanced adipogenesis particularly the brown adipogenesis of iMSC3. In this study, the transcriptomic profiles of iMSC3 derived adipocytes with and without rosiglitazone were analyzed through mRNA sequencing.

Results

A total of 1508 genes were differentially expressed between iMSC3 and the derived adipocytes without rosiglitazone treatment. GO and KEGG enrichment analyses revealed that rosiglitazone regulates PPAR and PI3K-Akt pathways. The constant rosiglitazone treatment enhanced the expression of Fatty Acid Binding Protein 4 (FABP4) which enriched GO terms such as fatty acid binding, lipid droplet, as well as white and brown fat cell differentiation. Moreover, the constant treatment upregulated several lipid droplets (LDs) associated proteins such as PLIN1. Rosiglitazone also activated the receptor complex PTK2B that has essential roles in beige adipocytes thermogenic program. Several uniquely expressed novel regulators of brown adipogenesis were also expressed in adipocytes derived with rosiglitazone: PRDM16, ZBTB16, HOXA4, and KLF15 in addition to other uniquely expressed genes.

Conclusions

Rosiglitazone regulated several differentially regulated genes and non-coding RNAs that warrant further investigation about their roles in adipogenesis particularly brown adipogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01027-z.

Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity

Background: To explore the association of DNA methylation and gene expression in the pathology of obesity.

Methods: (1) Genomic DNA methylation and mRNA expression profile of visceral adipose tissue (VAT) were performed in a comprehensive database of gene expression in obese and normal subjects. (2) Functional enrichment analysis and construction of differential methylation gene regulatory networks were performed. (3) Validation of the two different methylation sites and corresponding gene expression was done in a separate microarray dataset. (4) Correlation analysis was performed on DNA methylation and mRNA expression data.

Results: A total of 77 differentially expressed mRNAs matched with differentially methylated genes. Analysis revealed two different methylation sites corresponding to two unique genes—s100a8-cg09174555 and s100a9-cg03165378. Through the verification test of two interesting different expression positions [differentially methylated positions (DMPs)] and their corresponding gene expression, we found that methylation in these genes was negatively correlated to gene expression in the obesity group. Higher S100A8 and S100A9 expressions in obese subjects were validated in a separate microarray dataset.

Conclusion: This study confirmed the relationship between DNA methylation and gene expression and emphasized the important role of S100A8 and S100A9 in the pathogenesis of obesity.

Integrated Weighted Gene Co-expression Network Analysis Identified That TLR2 and CD40 Are Related to Coronary Artery Disease

The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms. Array data from GSE90074 were downloaded from the Gene Expression Omnibus (GEO) database. Integrated weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene module and clinical characteristics. Gene Ontology annotation (GO), Disease Ontology (DO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by clusterProfiler and the DOSE package in R. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection (MCODE) to identify hub genes. Then, further functional validation of hub genes in other microarrays and population samples was performed, and survival analysis was performed to investigate the prognosis. A total of 660 genes were located in three modules and associated with CAD. GO functions identified 484 biological processes, 39 cellular components, and 22 molecular functions with an adjusted P < 0.05. In total, 38 pathways were enriched in KEGG pathway analysis, and 147 DO items were identified with an adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). There was a total of four modules with a score > 10 after PPI network analysis using the MCODE app, and two hub genes (TLR2 and CD14) were identified. Then, we validated the information from the GSE60993 dataset using the GSE59867 dataset and population samples, and we found that these two genes were associated with plaque vulnerability. These two genes varied at different time points after myocardial infarction, and both of them had the lowest prognosis of heart failure when they were expressed at low levels. We performed an integrated WGCNA and validated that TLR2 and CD14 were closely associated with the severity of coronary artery disease, plaque instability and the prognosis of heart failure after myocardial infarction.

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease-related proteins often interact with each other in functional modules, many advanced network-oriented algorithms were applied to patient-derived big data to identify the complex gene-environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin-1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence-based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non-invasive AI-derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI-radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network-based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

Weighted gene coexpression network analysis identifies the key role associated with acute coronary syndrome

The present study sought to identify potential hub genes and pathways of acute coronary syndrome (ACS). We downloaded the dataset (GSE56045) from the Gene Expression Omnibus (GEO) database and analyzed weighted gene coexpression networks (WGCNA). Gene Ontology annotation, Disease Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. The protein-protein interaction (PPI) network was constructed using Cytoscape, and the Molecular Complex Detection app was employed to identify significant modules and hub genes. The hub genes were then validated in other microarrays and patients by RT-PCR. Two modules were identified and associated with coronary artery disease (CAD) and included 219 genes. After function and PPI analyses, 24 genes were identified to be potentially associated with CAD. Linear correlation was performed to calculate the relationship between the gene expression levels and coronary artery calcification score and found that CCR7 (R = -0.081, P = 0.0065), CD2 (R = -0.075, P = 0.0012), CXCR5 (R = -0.065, P = 0.029) and IL7R (R = -0.06, P = 0.043) should be validated in other dataset. By comparing the gene expression levels in different groups in GSE23561, GSE34822, GSE59867, GSE60993 and GSE129935, only two genes (CCR7 and CXCR5) showed significance. The nomogram showed that CXCR5 showed the risk of ACS. Further analysis in chest patients found CXCR5 played a key role resulting in ACS. Our WGCNA analysis identified CXCR5 as a risk factor for ACS, and the potential pathogenesis may be associated with immune inflammation.

Circulating microRNA Associated to Different Stages of Liver Steatosis in Prader-Willi Syndrome and Non-Syndromic Obesity

BACKGROUND

Prader-Willi syndrome (PWS) is a rare and poorly characterized disease. Recent genomic and transcriptomic studies contributed to elucidate the molecular bases of the syndrome. In this study, we characterized the expression of circulating miRNAs in patients with PWS compared to those with non-syndromic obesity in association with liver steatosis.

METHODS

MiRNAs were studied by qRT-PCR in serum samples from 30 PWS and 30 non-syndromic obese subjects.

RESULTS

MiRNA expression was associated with the presence of the syndrome and to the grade of liver steatosis. MiR-122-5p, miR-151a, miR-92a-3p were up-regulated in obese (4.38-fold, p < 0.01; 2.72-fold, p < 0.05; 1.34-fold p < 0.05, respectively) and were able to differentiate obese from PWS (AUC = 0.81, sens/spec 78/71%). When stratifying groups according to the presence of steatosis, the expression of miR-151a-5p, miR-92a-3p, miR-106b-5p, and miR-93-5p were lower in PWS with steatosis grade 1. Within the group with steatosis grade 1, miR-151a-5p was significantly distinguished PWS from obese (AUC = 0.85, sens/spec 80/85%) and the combination of miR-106b-5p and miR-93-5p showed higher performances in discriminating different grades of steatosis in PWS (AUC = 0.84, sens/spec 93/74%).

CONCLUSIONS

MiRNAs represent a tool to better classify and characterize PWS, providing new information about the clinical picture and the extent of steatosis.

EHBP1 SNPs, Their Haplotypes, and Gene-Environment Interactive Effects on Serum Lipid Levels

The associations between single nucleotide polymorphisms (SNPs) rs2710642 and rs10496099 and their effect on the EH domain-binding protein 1 (EHBP1) gene and serum lipid profiles remain uncertain. This study was performed to investigate the two EHBP1 SNPs in Han and Maonan populations, including their association, haplotypes, and effects on serum lipid levels. Two EHBP1 SNPs in 564 Han and 796 Maonan participants were genotyped by high-throughput sequencing, and then the genotype and haplotype distributions of two EHBP1 SNPs were analyzed. Moreover, risk factors and their effects on serum lipid levels were analyzed using multivariable linear regression and logistic regression analyses. In Han and Maonan populations, a significant difference was found in the allelic and genotypic frequencies of the EHBP1 rs2710642 and rs10496099 SNPs and the alternate alleles of rs2710642A and rs10496099C might be potentially beneficial for healthy lipid levels. Medium linkage disequilibrium between the two SNPs was noted in each ethnic group, and four main haplotypes were detected. The rs2710642G-rs10496099C haplotype was associated with high triglycerides (TGs) and low high-density lipoprotein cholesterol, and the rs2710642A-rs10496099C haplotype was associated with low TGs and high apolipoprotein A1. The rs2710642G-rs10496099C haplotype was a high-risk factor for hyperlipidemia, and it interacted with smoking, fasting blood glucose, and hypertension to increase but with the female factor to decrease the prevalence of hyperlipidemia in Han individuals. The EHBP1 rs2710642 and rs10496099 SNPs and gene-environment interactions were associated with serum lipid profiles and hyperlipidemia, which is of ethnic specificity to our study populations.

LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

BACKGROUND

Long noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood.

METHODS

We performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve.

RESULTS

An AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (P _SNHG8-ICAM1 = 0.002; P _SNHG8-SOCS3 = 0.031). The results of a functional enrichment analysis of the interacting genes and microRNAs showed that the shared lncRNA SNHG8 may be a new factor in AMI.

CONCLUSIONS

Our investigation of the lncRNA-miRNA-mRNA regulatory networks in AMI revealed a novel lncRNA, lncRNA SNHG8, as a risk factor for AMI and expanded our understanding of the mechanisms involved in the pathogenesis of AMI.

miR-146a attenuates apoptosis and modulates autophagy by targeting TAF9b/P53 pathway in doxorubicin-induced cardiotoxicity

Clinical therapy of doxorubicin (DOX) is limited due to its cardiotoxicity. miR-146a was proved as a protective factor in many cardiovascular diseases, but its role in chronic DOX-induced cardiotoxicity is unclear. The objective of this study was to demonstrate the role of miR-146a in low-dose long-term DOX-induced cardiotoxicity. Experiments have shown that DOX intervention caused a dose-dependent and time-dependent cardiotoxicity involving the increased of apoptosis and dysregulation of autophagy. The cardiotoxicity was inhibited by overexpressed miR-146a and was more severe when miR-146a was downgraded. Further research proved that miR-146a targeted TATA-binding protein (TBP) associated factor 9b (TAF9b), a coactivator and stabilizer of P53, indirectly destroyed the stability of P53, thereby inhibiting apoptosis and improving autophagy in cardiomyocytes. Besides, miR-146a knockout mice were used for in vivo validation. In the DOX-induced model, miR-146a deficiency made it worse whether in cardiac function, cardiomyocyte apoptosis or basal level of autophagy, than wild-type. In conclusion, miR-146a partially reversed the DOX-induced cardiotoxicity by targeting TAF9b/P53 pathway to attenuate apoptosis and adjust autophagy levels.