Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

Integrating genomics and transcriptomics to identify candidate genes for high-altitude adaptation and egg production in Nixi chicken

1. This study combined genome-wide selection signal analysis with RNA-sequencing to identify candidate genes associated with high altitude adaptation and egg production performance in Nixi chickens (NXC).2. Based on the whole-genome data from 20 NXC (♂:10; ♀:10), the population selection signal was analysed by sliding window analysis. The selected genes were screened by combination with the population differentiation statistic (FST). The sequence diversity statistic (θπ). RNA-seq was performed on the ovarian tissues of NXC (n = 6) and Lohmann laying hens (n = 6) to analyse the differentially expressed genes (DEGs) between the two groups. The functional enrichment analysis of the selected genes and differentially expressed genes was performed.3. There were 742 genes under strong positive selection and 509 differentially expressed genes screened in NXC. Integrated analysis of the genome and transcriptome revealing 26 overlapping genes. The candidate genes for adaptation to a high-altitude environment, as well as for egg production, disease resistance, vision and pigmentation in NXC were preliminarily screened.4. The results provided theoretical guidance for further research on the genetic resource protection and utilisation of NXC.

Hypoglycemic Activity of Rice Resistant-Starch Metabolites: A Mechanistic Network Pharmacology and In Vitro Approach

Rice (Oryza sativa L.) is one of the primary sources of energy and nutrients needed by the body, and rice resistant starch (RRS) has been found to have hypoglycemic effects. However, its biological activity and specific mechanisms still need to be further elucidated. In the present study, 52 RRS differential metabolites were obtained from mouse liver, rat serum, canine feces, and human urine, and 246 potential targets were identified through a literature review and database analysis. A total of 151 common targets were identified by intersecting them with the targets of type 2 diabetes mellitus (T2DM). After network pharmacology analysis, 11 core metabolites were identified, including linolenic acid, chenodeoxycholic acid, ursodeoxycholic acid, deoxycholic acid, lithocholic acid, lithocholylglycine, glycoursodeoxycholic acid, phenylalanine, norepinephrine, cholic acid, and L-glutamic acid, and 16 core targets were identified, including MAPK3, MAPK1, EGFR, ESR1, PRKCA, FYN, LCK, DLG4, ITGB1, IL6, PTPN11, RARA, NR3C1, PTPN6, PPARA, and ITGAV. The core pathways included the neuroactive ligand-receptor interaction, cancer, and arachidonic acid metabolism pathways. The molecular docking results showed that bile acids such as glycoursodeoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid, deoxycholic acid, and cholic acid exhibited strong docking effects with EGFR, ITGAV, ITGB1, MAPK3, NR3C1, α-glucosidase, and α-amylase. In vitro hypoglycemic experiments further suggested that bile acids showed significant inhibitory effects on α-glucosidase and α-amylase, with CDCA and UDCA having the most prominent inhibitory effect. In summary, this study reveals a possible hypoglycemic pathway of RRS metabolites and provides new research perspectives to further explore the therapeutic mechanism of bile acids in T2DM.

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.

The Role of Cdc42 in the Insulin and Leptin Pathways Contributing to the Development of Age-Related Obesity

Age-related obesity significantly increases the risk of chronic diseases such as type 2 diabetes, cardiovascular diseases, hypertension, and certain cancers. The insulin-leptin axis is crucial in understanding metabolic disturbances associated with age-related obesity. Rho GTPase Cdc42 is a member of the Rho family of GTPases that participates in many cellular processes including, but not limited to, regulation of actin cytoskeleton, vesicle trafficking, cell polarity, morphology, proliferation, motility, and migration. Cdc42 functions as an integral part of regulating insulin secretion and aging. Some novel roles for Cdc42 have also been recently identified in maintaining glucose metabolism, where Cdc42 is involved in controlling blood glucose levels in metabolically active tissues, including skeletal muscle, adipose tissue, pancreas, etc., which puts this protein in line with other critical regulators of glucose metabolism. Importantly, Cdc42 plays a vital role in cellular processes associated with the insulin and leptin signaling pathways, which are integral elements involved in obesity development if misregulated. Additionally, a change in Cdc42 activity may affect senescence, thus contributing to disorders associated with aging. This review explores the complex relationships among age-associated obesity, the insulin-leptin axis, and the Cdc42 signaling pathway. This article sheds light on the vast molecular web that supports metabolic dysregulation in aging people. In addition, it also discusses the potential therapeutic implications of the Cdc42 pathway to mitigate obesity since some new data suggest that inhibition of Cdc42 using antidiabetic drugs or antioxidants may promote weight loss in overweight or obese patients.

Role of a small GTPase Cdc42 in aging and age-related diseases

A small GTPase, Cdc42 is evolutionarily one of the most ancient members of the Rho family, which is ubiquitously expressed and involved in a wide range of fundamental cellular functions. The crucial role of Cdc42 includes regulation of the actin cytoskeleton, cell polarity, morphology and migration, endocytosis and exocytosis, cell cycle, and proliferation in many different cell types. Many studies have provided compelling yet contradicting evidence that Cdc42 dysregulation plays an important role in cellular and tissue aging. Furthermore, Cdc42 is a critical factor in the development and progression of aging-related pathologies, such as neurodegenerative and cardiovascular disorders, diabetes type 2, and aging-related disorders of the joints and bones, and the inhibition of the Cdc42 demonstrates potentially significant therapeutic and anti-aging effects in animal models of aging and disease. However, regulation of Cdc42 expression and activity is very complex and depends on many factors, such as the origin and complexity of the tissues, hormonal status, etc. Therefore, this review is focused on current advances in understanding the underlying cellular and molecular mechanisms associated with Cdc42 activity and regulation of senescence in different cell types since they may provide a foundation for novel therapeutic strategies and targeted drugs to reverse the aging process and treat aging-associated disorders.

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.

Systems Network Pharmacology-Based Prediction and Analysis of Potential Targets and Pharmacological Mechanism of Actinidia chinensis Planch. Root Extract for Application in Hepatocellular Carcinoma

Purpose

Traditional Chinese medicine (TCM) sometimes plays a crucial role in advanced cancer treatment. Despite the significant therapeutic efficacy in hepatocellular carcinoma (HCC) that Actinidia chinensis Planch root extract (acRoots) has proven, its complex composition and underlying mechanism have not been fully elucidated. Therefore, this study analyzed the multiple chemical compounds in acRoots and their targets via network pharmacology and bioinformatics analysis, with the overarching goal of revealing the potential mechanisms of the anti-HCC effect.

Methods

The main ingredients contained in acRoots were initially screened from the traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the candidate bioactive ingredient targets were identified using DrugBank and the UniProt public databases. Second, the biological processes of the targets of active molecules filtered from the ingredients of acRoots were evaluated using gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Third, weighted gene coexpression network analysis (WGCNA) was performed to identify gene coexpression modules associated with HCC. The hub genes of acRoots in HCC were defined via contrasting the above module eigengenes with candidate target genes of acRoots. Furthermore, the target-pathway network was analyzed to explore the mechanism for anti-HCC effect of hub genes. Kaplan–Meier plotter database analysis was performed to validate the hub genes of acRoots correlation with prognostic values in HCC. In order to verify the results of the network pharmacological analysis, we performed a molecular docking approach on the active ingredients and key targets using the Discovery Studio software. The viability of SMMC-7721 and HL-7702 cells was determined by Cell counting kit-8 (CCK-8) after being treated with different concentrations of (+)-catechin (0, 50, 100, 150, 200, and 250 g/ml) for 24, 48, and 72 hours, respectively. Finally, qRT-PCR and Western blot involving human hepatocarcinoma cells were utilized to verify the impact of (+)-catechin on the hub genes associated with prognosis.

Results

6 out of 26 active ingredients extracted from TCMSP were deemed as the core ingredients of acRoots. 175 bioactive-ingredient targets of acRoots were obtained and a bioactive-ingredient targets network was established correspondingly. The biological processes (BP) of target genes mainly involved processes, such as toxic substance and wounding. The results of KEGG pathways indicated that the target genes were mainly enriched in pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and other pathways. Also, the two hub genes (i.e., ESR1 and CAT) were closely associated with the prognosis of HCC patients. As a consequence, we predicated a series of signaling pathways, including estrogen signaling pathway and longevity regulation pathway, through which acRoots could facilitate the treatment for HCC. The molecular docking experiment ascertained that ESR1 and CAT had an effective binding force with (+)-catechin, one of the core ingredients of acRoots. Furthermore, (+)-catechin inhibited SMMC-7721 cell growth in a dose-dependent manner and a time-dependent manner. Finally, we suggest that the expression level of ESR1 and CAT is positively related to the (+)-catechin concentrations in in-vitro experiments.

Conclusion

The bioactive ingredients of acRoots, including quercetin, (+)-catechin, beta-sitosterol, and aloe-emodin, have synergistic interactions in reinforcing the anticancer effect in HCC. Evidently, acRoots took effect by regulating multitargets and multipathways through its active ingredients. Further, (+)-catechin, the possible paramount anti-HCC active ingredient in acRoots, helped improve the prognosis of HCC patients by increasing the expression of ESR1 and CAT. Additionally, the findings yielded provide a conceptual guidance for the clinical treatment of HCC and the methods adopted are potentially applicable in the future comprehensive analysis of the underlying mechanisms of TCMs.

Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments

Objective

The incidence and prevalence of type 2 diabetes are increasing with age. Nevertheless, there is lack of sensitive diagnostic tools and effective therapeutic regimens. We aimed to establish and verify a practical and valid diagnostic tool for this disease.

Methods

WGCNA was presented on the expression profiling of type 2 diabetic and normal islets in combined GSE25724 and GSE38642 datasets. By LASSO Cox regression analyses, a gene signature was constructed based on the genes in diabetes-related modules. ROC curves were plotted for assessing the diagnostic efficacy. Correlations between the genes and immune cell infiltration and pathways were analyzed. BST2 and BTBD1 expression was verified in glucotoxicity-induced and normal islet β cells. The influence of BST2 on β cell dysfunction was investigated under si-BST2 transfection.

Results

Totally, 14 coexpression modules were constructed, and red and cyan modules displayed the correlations to diabetes. The LASSO gene signature (BST2, BTBD1, IFIT1, IFIT3, and RTP4) was developed. The AUCs in the combined datasets and GSE20966 dataset were separately 0.914 and 0.910, confirming the excellent performance in diagnosing type 2 diabetes. Each gene in the model was distinctly correlated to immune cell infiltration and key signaling pathways (TGF-β and P53, etc.). The abnormal expression of BST2 and BTBD1 was confirmed in glucotoxicity-induced β cells. BST2 knockdown ameliorated β cell dysfunction and altered the activation of TGF-β and P53 pathways.

Conclusion

Our findings propose a gene signature with high efficacy to diagnose type 2 diabetes, which could assist and improve early diagnosis and therapy.

A 646C > G (rs41423247) polymorphism of the glucocorticoid receptor as a risk factor for hyperglycaemia diagnosed in pregnancy-data from an observational study

AIM

Hyperglycaemia diagnosed in pregnancy (HiP) is a serious and frequent complication of pregnancy, increasing the risk for adverse maternal and neonatal outcomes. Investigate whether allelic variations of the glucocorticoid receptor are related to an increased risk of HiP.

METHOD

The following polymorphisms of the glucocorticoid receptor (GR) were investigated in the cohort study of N = 197 pregnant women with HiP and N = 133 normoglycemic pregnant controls: 646C > G (rs41423247), N363S (rs6195), ER23/22EK (rs6190, rs6189).

RESULTS

A GG variant of the rs41423247 polymorphism was associated with a significantly higher risk for HiP: OR 1.94 (1.18; 3.18), p = 0.009. The relationship remained significant after controlling for maternal age and prepregnancy BMI: OR 3.09 (1.25; 7.64), p = 0.014.

CONCLUSIONS

The allelic GG variant of the 646C > G (rs41423247) polymorphism is associated with an increased risk for hyperglycaemia in pregnancy.

Integrated analysis of transcriptome profiling of lncRNAs and mRNAs in livers of type 2 diabetes mellitus

Long noncoding RNAs (lncRNAs) influence the progression of almost all human diseases, but the participation of lncRNAs in type 2 diabetes mellitus (T2DM) has not been fully elucidated. The present study aimed to systematically compare the transcriptome profiling of lncRNAs and mRNAs in livers between T2DM patients and controls, to identify key genes associated with T2DM pathogenesis, and to predict the underlying molecular mechanisms. As a result, a total of 1,512 differentially expressed (DE) lncRNAs and 1,923 DE mRNAs were identified through microarray analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that multiple metabolic processes were dysregulated such as small molecule, organic acid, lipid and branched chain amino acid metabolism. Protein-protein interaction network was constructed and 10 hub mRNAs were identified, including EHHADH, ATM, ACOX1, PIK3R1, EGFR, UQCRFS1, HMGCL, UQCRC2, NDUFS3 and F2. RT-qPCR was conducted to verify the validity of microarray results. Then, coding-noncoding co-expression network and competing endogenous RNA (ceRNA) network were analyzed to predict the lncRNA-mRNA and lncRNA-miRNA-mRNA regulatory patterns. Subsequently, 10 key intermediating miRNAs in ceRNA networks with a node degree > 80 were identified, including hsa-miR-5692a, hsa-miR-12136, hsa-miR-5680, hsa-miR-1305, hsa-miR-6833-5p, hsa-miR-7159-5p, hsa-miR-548as-3p, hsa-miR-6873-3p, hsa-miR-1290 and hsa-miR-4768-5p. In conclusion, the present study evaluated the transcriptome profiling of lncRNAs and mRNAs in livers from T2DM patients, with a value for understanding the molecular mechanism of disease pathogenesis and identifying effective biomarkers in clinical diagnosis.

ASK1-Interacting Protein 1 Acts as a Novel Predictor of Type 2 Diabetes

Type 2 diabetes (T2D) mellitus is a chronic inflammatory disease characterized with high secretion of tumor necrosis factor (TNF)-α, but the regulatory pathway of TNF-α production in T2D has not been fully elucidated. ASK1-interacting protein 1 (AIP1) is a signaling scaffold protein that modulates several pathways associated with inflammation. In this study, we aimed to investigate the role of AIP1 in T2D development. Our results revealed that AIP1 was downregulated in omental adipose tissue (OAT) of obese patients with T2D compared with that in obese patients. In addition, Pearson's correlation test showed that AIP1 was negatively correlated with the homeostatic model assessment for insulin resistance (HOMA-IR, r = -0.4829) and waist-to-hip ratio (r = -0.2614), which are major clinical indexes of T2D. As revealed by the proteomic analysis, immunohistochemistry, and ELISA, the OAT and the serum of obese patients with T2D presented high inflammatory status. And the increased inflammatory factors TNF-α and C-reactive protein C (CRP) in the serum of obese patients with T2D showed a positive correlation with HOMA-IR (TNF-α, r = 0.4728; CRP, r = 0.5522). Interestingly, AIP1 deficiency in adipocytes facilitated TNF-α secretion and retarded glucose uptake. Mechanistically, AIP1 deletion in human adipocytes activated JNK, p38 MAPK, and ERK1/2 signaling. Furthermore, inhibition of these signaling pathways using specific inhibitors could suppress these signal activation and insulin resistance caused by AIP1 deficiency. In addition, AIP1 and TNF-α expression in the OAT of patients with T2D recovered to normal levels after laparoscopic Roux-en-Y gastric bypass (RYGB) surgery. These findings indicate that AIP1 is negatively correlated with the clinical indexes of T2D. It modulates TNF-α expression in OAT via JNK, p38 MAPK, and ERK1/2 signaling.

Pharmacoinformatics and UPLC-QTOF/ESI-MS-Based Phytochemical Screening of Combretum indicum against Oxidative Stress and Alloxan-Induced Diabetes in Long-Evans Rats

This research investigated a UPLC-QTOF/ESI-MS-based phytochemical profiling of Combretum indicum leaf extract (CILEx), and explored its in vitro antioxidant and in vivo antidiabetic effects in a Long-Evans rat model. After a one-week intervention, the animals' blood glucose, lipid profile, and pancreatic architectures were evaluated. UPLC-QTOF/ESI-MS fragmentation of CILEx and its eight docking-guided compounds were further dissected to evaluate their roles using bioinformatics-based network pharmacological tools. Results showed a very promising antioxidative effect of CILEx. Both doses of CILEx were found to significantly (p < 0.05) reduce blood glucose, low-density lipoprotein (LDL), and total cholesterol (TC), and increase high-density lipoprotein (HDL). Pancreatic tissue architectures were much improved compared to the diabetic control group. A computational approach revealed that schizonepetoside E, melianol, leucodelphinidin, and arbutin were highly suitable for further therapeutic assessment. Arbutin, in a Gene Ontology and PPI network study, evolved as the most prospective constituent for 203 target proteins of 48 KEGG pathways regulating immune modulation and insulin secretion to control diabetes. The fragmentation mechanisms of the compounds are consistent with the obtained effects for CILEx. Results show that the natural compounds from CILEx could exert potential antidiabetic effects through in vivo and computational study.

qPCR Analysis Reveals Association of Differential Expression of SRR, NFKB1, and PDE4B Genes With Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a heterogeneous, metabolic, and chronic condition affecting vast numbers of the world's population. The related variables and T2DM associations have not been fully understood due to their diverse nature. However, functional genomics can facilitate understanding of the disease. This information will be useful in drug design, advanced diagnostic, and prognostic markers.

AIM

To understand the genetic causes of T2DM, this study was designed to identify the differentially expressed genes (DEGs) of the disease.

METHODS

We investigated 20 publicly available disease-specific cDNA datasets from Gene Expression Omnibus (GEO) containing several attributes including gene symbols and clone identifiers, GenBank accession numbers, and phenotypic feature coordinates. We analyzed an integrated system-level framework involving Gene Ontology (GO), protein motifs and co-expression analysis, pathway enrichment, and transcriptional factors to reveal the biological information of genes. A co-expression network was studied to highlight the genes that showed a coordinated expression pattern across a group of samples. The DEGs were validated by quantitative PCR (qPCR) to analyze the expression levels of case and control samples (50 each) using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as the reference gene.

RESULTS

From the list of 50 DEGs, we ranked three T2DM-related genes (p < 0.05): SRR, NFKB1, and PDE4B. The enriched terms revealed a significant functional role in amino acid metabolism, signal transduction, transmembrane and intracellular transport, and other vital biological functions. DMBX1, TAL1, ZFP161, NFIC (66.7%), and NR1H4 (33.3%) are transcriptional factors associated with the regulatory mechanism. We found substantial enrichment of insulin signaling and other T2DM-related pathways, such as valine, leucine and isoleucine biosynthesis, serine and threonine metabolism, adipocytokine signaling pathway, P13K/Akt pathway, and Hedgehog signaling pathway. The expression profiles of these DEGs verified by qPCR showed a substantial level of twofold change (FC) expression (2-ΔΔCT) in the genes SRR (FC ≤ 0.12), NFKB1 (FC ≤ 1.09), and PDE4B (FC ≤ 0.9) compared to controls (FC ≥ 1.6). The downregulated expression of these genes is associated with pathophysiological development and metabolic disorders.

CONCLUSION

This study would help to modulate the therapeutic strategies for T2DM and could speed up drug discovery outcomes.