Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus

Aim

Fatty acid metabolism is pivotal for lipid synthesis, cellular signaling, and maintaining cell membrane integrity. However, its diagnostic significance in type 2 diabetes mellitus (T2DM) remains unclear.

Materials and methods

Three datasets and fatty acid metabolism-related genes were retrieved. Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. Functional analysis, ceRNA network construction, immune microenvironment assessment, and drug prediction were conducted to explore the underlying molecular mechanisms.

Results

Six feature genes were identified with strong diagnostic performance and were involved in processes such as ribosome function and fatty acid metabolism. Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set.

Conclusion

This study investigated the potential molecular mechanisms of six fatty acid metabolism-related genes in T2DM, offering valuable insights that may guide future research and therapeutic development.

Decoding visceral adipose tissue molecular signatures in obesity and insulin resistance: a multi-omics approach

OBJECTIVE

Obesity-associated insulin resistance (IR) is responsible for considerable morbidity and mortality globally. Despite vast genomic data, many areas, from pathogenesis to management, still have significant knowledge gaps. We aimed to characterize visceral adipose tissue (VAT) in obesity and IR through a multi-omics approach.

METHODS

We procured data on VAT samples from the Gene Expression Omnibus (GEO) for the following two groups: 1) populations with obesity (n = 34) versus those without (n = 26); and 2) populations with obesity and IR (n = 15) versus those with obesity but without IR (n = 15). Gene set enrichment, protein-protein interaction network construction, hub gene identification, and drug-gene interactions were performed, followed by regulatory network prediction involving transcription factors (TFs) and microRNAs (miRNAs).

RESULTS

Interleukin signaling pathways, cellular differentiation, and regulation of immune response revealed a significant cross talk between VAT and the immune system. Other findings include cancer pathways, neurotrophin signaling, and aging. A total of 10 hub genes, i.e., STAT1, KLF4, DUSP1, EGR1, FOS, JUN, IL2, IL6, MMP9, and FGF9, 24 TFs, and approved hub gene-targeting drugs were obtained. A total of 10 targeting miRNAs (e.g., hsa-miR-155-5p, hsa-miR-34a-5p) were associated with obesity and IR-related pathways.

CONCLUSIONS

Our multi-omics integration method revealed hub genes, TFs, and miRNAs that can be potential targets for investigation in VAT-related inflammatory processes and IR, therapeutic management, and risk stratifications.

High expression of CNOT6L contributes to the negative development of type 2 diabetes

OBJECTIVE

Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by reduced responsiveness of body cells to insulin, leading to elevated blood sugar levels. CNOT6L is involved in glucose metabolism, insulin secretion regulation, pancreatic beta-cell proliferation, and apoptosis. These functions may be closely related to the pathogenesis of T2D. However, the exact molecular mechanisms linking CNOT6L to T2D remain unclear. Therefore, this study aims to elucidate the role of CNOT6L in T2D.

METHODS

The T2D datasets GSE163980 and GSE26168 profiles were downloaded from the Gene Expression Omnibusdatabase generated by GPL20115 and GPL6883.The R package limma was used to screen differentially expressed genes (DEGs). A weighted gene co-expression network analysis was performed. Construction and analysis of the protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis, and comparative toxicogenomics database (CTD) analysis were performed. Target Scan was used to screen miRNAs that regulate central DEGs. The results were verified by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), western blotting (WB), and blood glucose measurements in mice.

RESULTS

A total of 1951 DEGs were identified. GO and KEGG enrichment analysis revealed that differentially expressed genes were mainly enriched in the insulin signaling pathway, ECM-receptor interaction, and PPAR signaling pathway. Metascape analysis indicated enrichment primarily in the cAMP signaling pathway and enzyme-linked receptor protein signaling pathway. WGCNA analysis yielded 50 intersecting genes. PPI network construction and algorithm identification identified two core genes (CNOT6L and GRIN2B), among which CNOT6L gene was associated with multiple miRNAs. CTD analysis revealed associations of core genes with type 2 diabetes, diabetic complications, dyslipidemia, hyperglycemia, and inflammation. WB and RT-qPCR results showed that in different pathways, CNOT6L protein and mRNA levels were upregulated in type 2 diabetes.

CONCLUSION

CNOT6L is highly expressed in type 2 diabetes mellitus, and can cause diabetes complications, inflammation and other physiological processes by regulating miRNA, PPAR and other related signaling pathways, with poor prognosis. CNOT6L can be used as a potential therapeutic target for type 2 diabetes.

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Exploring early DNA methylation alterations in type 1 diabetes: implications of glycemic control

Background

Prolonged hyperglycemia causes diabetes-related micro- and macrovascular complications, which combined represent a significant burden for individuals living with diabetes. The growing scope of evidence indicates that hyperglycemia affects the development of vascular complications through DNA methylation.

Methods

A genome-wide differential DNA methylation analysis was performed on pooled peripheral blood DNA samples from individuals with type 1 diabetes (T1D) with direct DNA sequencing. Strict selection criteria were used to ensure two age- and sex-matched groups with no clinical signs of chronic complications according to persistent mean glycated hemoglobin (HbA1c) values over 5 years: HbA1c<7% (N=10) and HbA1c>8% (N=10).

Results

Between the two groups, 8385 differentially methylated CpG sites, annotated to 1802 genes, were identified. Genes annotated to hypomethylated CpG sites were enriched in 48 signaling pathways. Further analysis of key CpG sites revealed four specific regions, two of which were hypermethylated and two hypomethylated, associated with long non-coding RNA and processed pseudogenes.

Conclusions

Prolonged hyperglycemia in individuals with T1D, who have no clinical manifestation of diabetes-related complications, is associated with multiple differentially methylated CpG sites in crucial genes and pathways known to be linked to chronic complications in T1D.

Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

RFC2 promotes aerobic glycolysis and progression of colorectal cancer

BACKGROUND

Replication factor C subunit 2 (RFC2) participates in the growth and metastasis of various malignancies. Our study investigated the roles of RFC2 in colorectal cancer (CRC).

RESULTS

RFC2 expression was upregulated in CRC tissues and cells. High RFC2 expression was associated with poor prognosis. Knockdown RFC2 inhibited proliferation, induced apoptosis, and suppressed migration and invasion of CRC cells. CREB5 was a transcription factor of RFC2, and CREB5 knockdown suppressed RFC2 expression. Furthermore, RFC2 promoted aerobic glycolysis and MET/PI3K/AKT/mTOR pathway.

CONCLUSION

RFC2 promoted the progression of CRC cells via activating aerobic glycolysis and the MET/PI3K/AKT/mTOR pathway.

Comprehensive analysis of key m5C modification-related genes in type 2 diabetes

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear.

Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.

Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells.

Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.

Polygonatum sibiricum polysaccharides (PSP) improve the palmitic acid (PA)-induced inhibition of survival, inflammation, and glucose uptake in skeletal muscle cells

Polygonatum sibiricum polysaccharides (PSP) can decrease the levels of fasting blood glucose, total cholesterol, and triglyceride (TG) in hyperlipidemic and diabetic animals. It can also reduce inflammatory cytokines and promote glucose uptake in adipocytes. However, the underlying molecular mechanisms of PSP in improving insulin resistance (IR) in skeletal muscle remain unclear. In this study, palmitic acid (PA) induced an IR model in L6 myotubes. After treatment, cell proliferation was measured using the CCK8. miR-340-3p, glucose transporter 4 (GLUT-4), and interleukin-1 receptor-associated kinase 3 (IRAK3) expression was measured by qRT-PCR. IRAK3 protein levels were measured by Western blotting. Glucose in the cell supernatant, TG concentration in L6 myotubes, and the levels of IL-1β, IL-6, and TNF-α were measured by an ELISA. We found that cell survival, glucose uptake, and GLUT-4 expression in L6 myotubes were significantly suppressed, while lipid accumulation and inflammatory factor levels were enhanced by PA stimulation. Furthermore, PSP treatment markedly alleviated these effects. Interestingly, PSP also significantly reduced the upregulated expression of miR-340-3p in the L6 myotube model of IR. Furthermore, overexpression of miR-340-3p reversed the beneficial effects of PSP in the same IR model. miR-340-3p can bind to the 3′-untranslated regions of IRAK3. Additionally, PA treatment inhibited IRAK3 expression, whereas PSP treatment enhanced IRAK3 expression in L6 myotubes. Additionally, miR-340-3p also inhibited IRAK3 expression in L6 myotubes. Taken together, PSP improved inflammation and glucose uptake in PA-treated L6 myotubes by regulating miR-340-3p/IRAK3, suggesting that PSP may be suitable as a novel therapeutic agent for IR.

Transcriptome Analysis and Weighted Gene Co-expression Network Reveal Multitarget-Directed Antibacterial Mechanisms of Benzyl Isothiocyanate against Staphylococcus aureus

Staphylococcus aureus can cause many diseases and has a strong tendency to develop resistance to multiple antibiotics. In this study, benzyl isothiocyanate (BITC) was shown to have an excellent inhibitory effect on S. aureus ATCC25923 and methicillin-resistant S. aureus strains, with a minimum inhibitory concentration of 10 μg/mL. Under a scanning electron microscope, shrinkage and lysis of the cellular envelope were observed when exposed to BITC, and a bactericidal mode of BITC against S. aureus was further confirmed through flow cytometry. Additionally, the RNA profiles of S. aureus cells exposed to BITC indicated a violent transcriptional response to BITC. Through Kyoto Encyclopedia of Genes and Genomes analysis, it was found that many pathways involving bacterial survival were significantly affected, such as RNA degradation, oxidative phosphorylation, arginine biosynthesis, and so forth. A gene co-expression network was constructed using weighted gene co-expression network analysis, and six biologically meaningful co-expression modules and 125 hub genes were identified from the network. Among them, EfeB, GroES, SmpB, and Lsp were possibly targeted by BITC, leading to the death of S. aureus. Our results indicated a great potential of BITC to be applied in food safety and pharmaceuticals, highlighting its multitarget-directed bactericidal effects on S. aureus.

DNA primase subunit 1 deteriorated progression of hepatocellular carcinoma by activating AKT/mTOR signaling and UBE2C-mediated P53 ubiquitination

Background

DNA primase subunit 1 (PRIM1) has been reported as a novel oncogene in several cancer types. However, its roles in hepatocellular carcinoma (HCC) remain unclear. This study aimed to investigate underlying mechanisms of PRIM1 and identify it as a potential molecular target for HCC.

Methods

Hub genes were screened between HCC tissues and normal liver tissues in 3 gene expression omnibus (GEO) datasets and the cancer genome atlas (TCGA). The expression features and prognostic value of one of the hub genes PRIM1 were analyzed by bioinformatic analyses and immunohistochemistry. Loss-of-function and gain-of-function studies were used to investigate the regulatory role of PRIM1 in HCC cells. Real-time (RT)-qPCR, western blotting, and ubiquitin immunoprecipitation assays were performed to explore the underlying mechanisms. The xenograft model was employed to detect the roles of PRIM1 in tumor growth in vivo. Finally, the 3D spheroid model was conducted to validate the role of PRIM1 in tumor growth and sorafenib resistance.

Results

The hub genes of HCC were screened in multiple bioinformatic datasets. PRIM1, as one of the hub genes, was significantly overexpressed in HCC tissues in mRNA and protein levels. In addition, high expression of PRIM1 indicated poor prognosis of HCC patients in TCGA, ICGC, and Nantong cohorts. Overexpression of PRIM1 promoted the proliferation, migration/invasion, and sorafenib resistance of HCC cells, with the decrease in apoptosis and cell cycle arrest. Mechanically, PRIM1 facilitated epithelial-mesenchymal transition (EMT) process and the activity of PI3K/AKT/mTOR signaling of HCC cells. Additionally, PRIM1 could cause the ubiquitination and degradation of P53 by upregulating Ubiquitin Conjugating Enzyme E2 C (UBE2C). Furthermore, knockdown of PRIM1 significantly inhibited the growth of xenograft tumors and HCC cells-derived spheroids with enhanced sorafenib resistance.

Conclusion

This study implies that PRIM1 may play a key role in the progression of HCC and may serve as a potential target for HCC treatment.

Identification of Three Significant Genes Associated with Immune Cells Infiltration in Dysfunctional Adipose Tissue-Induced Insulin-Resistance of Obese Patients via Comprehensive Bioinformatics Analysis

BACKGROUND

Low-grade chronic inflammation in dysfunctional adipose tissue links obesity with insulin resistance through the activation of tissue-infiltrating immune cells. Numerous studies have reported on the pathogenesis of insulin-resistance. However, few studies focused on genes from genomic database. In this study, we would like to explore the correlation of genes and immune cells infiltration in adipose tissue via comprehensive bioinformatics analyses and experimental validation in mice and human adipose tissue.

METHODS

Gene Expression Omnibus (GEO) datasets (GSE27951, GSE55200, and GSE26637) of insulin-resistant individuals or type 2 diabetes patients and normal controls were downloaded to get differently expressed genes (DEGs), and GO and KEGG pathway analyses were performed. Subsequently, we integrated DEGs from three datasets and constructed commonly expressed DEGs' PPI net-works across datasets. Center regulating module of DEGs and hub genes were screened through MCODE and cytoHubba in Cytoscape. Three most significant hub genes were further analyzed by GSEA analysis. Moreover, we verified the predicted hub genes by performing RT qPCR analysis in animals and human samples. Besides, the relative fraction of 22 immune cell types in adipose tissue was detected by using the deconvolution algorithm of CIBERSORT (Cell Type Identification by Estimating Relative Subsets of RNA Transcripts). Furthermore, based on the significantly changed types of immune cells, we performed correlation analysis between hub genes and immune cells. And, we performed immunohistochemistry and immunofluorescence analysis to verify that the hub genes were associated with adipose tissue macrophages (ATM).

RESULTS

Thirty DEGs were commonly expressed across three datasets, most of which were upregulated. DEGs mainly participated in the process of multiple immune cells' infiltration. In protein-protein interaction network, we identified CSF1R, C1QC, and TYROBP as hub genes. GSEA analysis suggested high expression of the three hub genes was correlated with immune cells functional pathway's activation. Immune cell infiltration and correlation analysis revealed that there were significant positive correlations between TYROBP and M0 macrophages, CSF1R and M0 macrophages, Plasma cells, and CD8 T cells. Finally, hub genes were associated with ATMs infiltration by experimental verification.

CONCLUSIONS

This article revealed that CSF1R, C1QC, and TYROBP were potential hub genes associated with immune cells' infiltration and the function of proinflammation, especially adipose tissue macrophages, in the progression of obesity-induced diabetes or insulin-resistance.