RUNX1T1 rs34269950 is associated with obesity and metabolic syndrome

The regulatory role of m6A methylation modification in metabolic syndrome pathogenesis and progression

Metabolic syndromes are characterized by various complications caused by disrupted glucose and lipid metabolism, which are major factors affecting the health of a population. However, existing diagnostic and treatment strategies have limitations, such as the lack of early diagnostic and therapeutic approaches, variability in patient responses to treatment, and cost-effectiveness. Therefore, developing alternative solutions for metabolic syndromes is crucial. N6-methyladenosine (m6A) is one of the most abundant modifications that determine the fate of RNA. m6A modifications are closely associated with metabolic syndrome development and present novel prospects for clinical applications. Aberrant m6A modifications have been detected during inflammatory infiltration, apoptosis, autophagy, iron sagging, necrosis, and scorching during metabolic syndrome pathogenesis and progression. However, few reviews have systematically described the correlation between m6A modifications and these factors concerning metabolic syndrome pathogenesis and progression. This study summarizes the m6A methylation regulators and their roles in metabolic syndrome development, highlighting the potential of m6A modification as a biomarker in metabolic disorders.

Update on N6-methyladenosine methylation in obesity-related diseases

Obesity is a chronic metabolic disease that is closely related to type 2 diabetes mellitus, cardiovascular diseases, nonalcoholic fatty liver disease, obstructive sleep apnea, and osteoarthritis. The prevalence of obesity is increasing rapidly every year and is recognized as a global public health problem. In recent years, the role of epigenetics in the development of obesity and related diseases has been recognized and is currently a research hotspot. N6-methyladenosine (m6A) methylation is the most abundant epigenetic modification in the eukaryotic RNA, including mRNA and noncoding RNA. Several studies have shown that the m6A modifications in the target mRNA and the corresponding m6A regulators play a significant role in lipid metabolism and are strongly associated with the pathogenesis of obesity-related diseases. In this review, the latest research findings regarding the role of m6A methylation in obesity and related metabolic diseases are summarized. The authors' aim is to highlight evidence that suggests the clinical utility of m6A modifications and the m6A regulators as novel early prediction biomarkers and precision therapeutics for obesity and obesity-related diseases.

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Background

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM.

Methods

The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes.

Results

A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes.

Conclusion

The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

BACKGROUND

Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.

METHODS

We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.

RESULTS

A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).

CONCLUSIONS

This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

METTL3-Mediated lncRNA m6A Modification in the Osteogenic Differentiation of Human Adipose-Derived Stem Cells Induced by NEL-Like 1 Protein

OBJECTIVES

This study aimed to explore the regulatory mechanism of methyltransferase3 (METTL3) -mediated long non-coding RNA (lncRNA) N6-methyladenosine (m⁶A) modification in the osteogenic differentiation of human adipose-derived stem cells (hASCs) induced by NEL-like 1 protein (NELL-1).

MATERIALS AND METHODS

Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and high- throughput sequencing for RNA (RNA-seq) were performed on hASCs. Osteogenic ability was detected by alkaline phosphatase (ALP) staining, Alizarin Red S(ARS) staining, ALP quantification and Quantitative real-time polymerase chain reaction analysis (qRT-PCR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis predicted the osteogenesis-related pathways enriched for the lncRNAs and identified the target lncRNAs. After overexpression and knockdown of METTL3, methylated RNA immunoprecipitation-qPCR (MeRIP-qPCR) and qRT-PCR were used to detect the levels of m⁶A modification and the expression of the target lncRNA, and the binding of both was confirmed by RNA binding protein immunoprecipitation (RIP) assay. The effects of lncRNA and METTL3 on phosphorylation of the key proteins of the pathway were detected by western blot analysis.

RESULTS

In vitro experiments showed that METTL3 can promote osteogenic differentiation and that its expression level is upregulated. KEGG pathway analysis predicted that lncRNAs with differentially upregulated methylated peaks were enriched mostly in the mitogen-activated protein kinase (MAPK) signaling pathway, in which Serine/threonine protein kinase 3 (STK3) was the predicted target gene of the lncRNA RP11-44 N12.5. The m⁶A modification and expression of RP11-44 N12.5 were both regulated by METTL3. Subsequently, lncRNA RP11-44 N12.5 and METTL3 were found to regulate the phosphorylation levels of three key proteins in the MAPK signaling pathway, ERK, JNK and p38.

CONCLUSIONS

This study shows, for the first time, that METTL3 can activate the MAPK signaling pathway by regulating the m⁶A modification and expression of a lncRNA, thereby enhancing the osteogenic differentiation of hASCs.