Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

Integrated investigation and discovery of therapeutic targets for 3-hydroxybakuchiol against diabetes based on molecular docking studies and cell experiments

BACKGROUND

Diabetes mellitus is a prevalent endocrine condition. We aimed to investigate the anti-diabetic effects of 3-hydroxybakuchiol (HYD) by exploring its potential targets and molecular mechanisms through bioinformatics analysis and cell experiments.

METHODS

We performed an extensive search and screening of HYD and its potential targets for diabetes mellitus across various databases. Enrichment analyses were conducted using the ClusterProfiler package. PPI networks of the identified genes were constructed using STRING, and topological analysis was performed to identify core targets. The results were further confirmed through molecular docking. To validate the findings of our bioinformatics analysis, we conducted cell experiments using insulin resistance-induced HepG2 cells and C2C12 cells.

RESULTS

We discovered 260 common targets of HYD and diabetes mellitus, which were primarily related to the MAPK signaling pathway, PI3K-Akt signaling pathway, and endocrine resistance. A topological analysis of the PPI network identified four core targets (HSP90AA1, AKT1, SRC, and MAPK1). Molecular docking studies further confirmed the strong binding ability between HYD and these core targets. In cell experiments, we observed that HYD enhanced glucose uptake and suppressed gluconeogenesis in HepG2 cells and C2C12 cells. This resulted in an improvement in glucose metabolism, potentially through the regulation of the PI3K-Akt pathway.

CONCLUSIONS

This study provides valuable insights into the pharmacological effects of HYD on diabetes mellitus, suggesting its potential as a promising treatment option for the disease.

Serum Cytokines and Growth Factors in Subjects with Type 1 Diabetes: Associations with Time in Ranges and Glucose Variability

The detrimental effect of hyperglycemia and glucose variability (GV) on target organs in diabetes can be implemented through a wide network of regulatory peptides. In this study, we assessed a broad panel of serum cytokines and growth factors in subjects with type 1 diabetes (T1D) and estimated associations between concentrations of these molecules with time in ranges (TIRs) and GV. One hundred and thirty subjects with T1D and twenty-seven individuals with normal glucose tolerance (control) were included. Serum levels of 44 cytokines and growth factors were measured using a multiplex bead array assay. TIRs and GV parameters were derived from continuous glucose monitoring. Subjects with T1D compared to control demonstrated an increase in concentrations of IL-1β, IL-1Ra, IL-2Rα, IL-3, IL-6, IL-7, IL-12 p40, IL-16, IL-17A, LIF, M-CSF, IFN-α2, IFN-γ, MCP-1, MCP-3, and TNF-α. Patients with TIR ≤ 70% had higher levels of IL-1α, IL-1β, IL-6, IL-12 p70, IL-16, LIF, M-CSF, MCP-1, MCP-3, RANTES, TNF-α, TNF-β, and b-NGF, and lower levels of IL-1α, IL-4, IL-10, GM-CSF, and MIF than those with TIR > 70%. Serum IL-1β, IL-10, IL-12 p70, MCP-1, MCP-3, RANTES, SCF, and TNF-α correlated with TIR and time above range. IL-1β, IL-8, IL-10, IL-12 p70, MCP-1, RANTES, MIF, and SDF-1α were related to at least one amplitude-dependent GV metric. In logistic regression models, IL-1β, IL-4, IL-10, IL-12 p70, GM-CSF, HGF, MCP-3, and TNF-α were associated with TIR ≤ 70%, and MIF and PDGF-BB demonstrated associations with coefficient of variation values ≥ 36%. These results provide further insight into the pathophysiological effects of hyperglycemia and GV in people with diabetes.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy

Background

Diabetic nephropathy (DN), which is the main cause of renal failure in end-stage renal disease, is becoming a common chronic renal disease worldwide. Mendelian randomization (MR) is a genetic tool that is widely used to minimize confounding and reverse causation when identifying the causal effects of complex traits. In this study, we conducted an integrated multiple microarray analysis and large-scale plasma proteome MR analysis to identify candidate biomarkers and evaluate the causal effects of prospective therapeutic targets in DN.

Methods

Five DN gene expression datasets were selected from the Gene Expression Omnibus. The robust rank aggregation (RRA) method was used to integrate differentially expressed genes (DEGs) of glomerular samples between patients with DN and controls, followed by functional enrichment analysis. Protein quantitative trait loci were incorporated from seven different proteomic genome-wide association studies, and genetic association data on DN were obtained from FinnGen (3676 cases and 283,456 controls) for two-sample MR analysis. External validation and clinical correlation were also conducted.

Results

A total of 82 DEGs (53 upregulated and 29 downregulated) were identified through RRA integrated analysis. The enriched Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways of the DEGs were significantly enriched in neutrophil degranulation, neutrophil activation, proteoglycan binding, collagen binding, secretory granule lumen, gluconeogenesis, tricarboxylic acid cycle, and pentose phosphate pathways. MR analysis revealed that the genetically predicted levels of MHC class I polypeptide-related sequence B (MICB), granzyme A (GZMA), cathepsin S (CTSS), chloride intracellular channel protein 5, and ficolin-1 (FCN1) were causally associated with DN risk. Expression validation and clinical correlation analysis showed that MICB, GZMA, FCN1, and insulin-like growth factor 1 may participate in the development of DN, and carbonic anhydrase 2 and lipoprotein lipase may play protective roles in patients with DN.

Conclusion

Our integrated analysis identified novel biomarkers, including MICB and GZMA, which may help further understand the complicated mechanisms of DN and identify new target pathways for intervention.

Network pharmacology mechanism of Scutellarin to inhibit RGC pyroptosis in diabetic retinopathy

To investigate the effect of scutellarin (SCU) in diabetic retinopathy (DR) and explore the associated molecular network mechanism. The animal model of DR was established from diabetic mellitus (DM) rats by intraperitoneally injected streptozotocin (STZ) at dosage 55 mg/kg. Meanwhile, SCU was intraperitoneally administrated to protect retina from cell pyroptosis induced by DM, and cell pyroptosis was detected by using HE, Nissl staining, and immunofluorescence recognition. Moreover, the hub gene involving in pyroptosis in DR was screened by bioinformatics and network pharmacology, designated as Venny intersection screen, GO and KEGG analysis, PPI protein interaction, and molecular docking. Lastly, the expressional change of hub genes were validated with experimental detection. Cell pyroptosis of the DR, specifically in retina ganglion cells (RGC), was induced in DM rats; SCU administration results in significant inhibition in the cell pyroptosis in DR. Mechanically, 4084 genes related to DR were screened from GeneCards and OMIM databases, and 120 SCU therapeutic targets were obtained, by using GeneCards, TCMSP with Swiss Target Prediction databases. Moreover, 357 targets related to pyroptosis were found using GenenCards database, and Drug, disease and phenotypic targets were analyzed online using the Draw Venn Diagram website, and 12 cross targets were obtained. Through GO function and KEGG pathway enrichment analysis, 659 BP related items, 7 CC related items, 30 MF related items, and 70 signal pathways were screened out; Of these, eleven proteins screened from cross-target PPI network were subsequently docked with the SCU, and their expressions including caspase-1, IL-1β, IL-18, GSDMD and NLRP3 in RGC indicated by immunofluorescence, and the mRNA expression for caspase-1 in DR indicated by quantitative PCR, were successfully validated. SCU can effectively protect RGC pyroptosis in DR, and underlying mechanisms are involved in the inhibition of caspase-1, GSDMD, NLRP3, IL-1β and IL-18. Our findings therefore provide crucial evidence to support the clinic practice of SCU for the treatment of DR, and explained the underlying molecular network mechanism.

Long-term fasting glucose variability and risk of cancer in patients with type 2 diabetes mellitus: A retrospective population-based cohort study in Shanghai

BACKGROUNDS

Fasting blood glucose (FBG) variability may make an impact on adverse events in patients with diabetes mellitus. However, the association between long-term changes in FBG and cancer remains unclear. We aimed to investigate this association in a large-scale longitudinal study.

METHODS

Data were collected from 46 761 patients with type 2 diabetes mellitus aged 20-80 years who participated in the Diabetes Standardized Management Program in Shanghai, China. We adopted four indicators, including standard deviation (SD), coefficient of variation (CV), variation independent of the mean (VIM), and average real variability (ARV) to describe FBG variability. Adjusted multivariable Cox regression analyses and restricted cubic splines were used to investigate the association between long-term FBG variability and cancer risk. We also determined the interactive effect of FBG variability with hypertension and FBG-mean with hypertension on cancer risk, respectively.

RESULTS

In this study, we confirmed 2218 cancer cases (51.1% male) over a median follow-up of 2.86 years. In the multivariable-adjusted models, participants in the highest quartile of FBG variability had an increased risk of cancer compared with those in the lowest quartile. The nonlinear association was found when using FBG-VIM, FBG-ARV, and FBG-SD in restricted cubic spline plots. There was a significant interaction effect of FBG variability with hypertension on cancer, whereas the effect of FBG-mean with hypertension did not attain significance.

CONCLUSIONS

Our retrospective cohort study demonstrated a positive association between the long-term changes in FBG and cancer risk in patients with type 2 diabetes mellitus. FBG variability may independently predict cancer incidence.

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity.

Glucose variability in subjects with normal glucose tolerance: Relations with body composition, insulin secretion and sensitivity

BACKGROUND AND AIMS

To estimate the determinants of glucose variability (GV) in young and middle-aged non-obese subjects with normal glucose tolerance (NGT) we assessed relations between GV parameters, body composition, insulin secretion and sensitivity indices.

METHODS

Thirty individuals with normal body mass index (BMI) and twenty overweight subjects were included. 24-hour mean glucose, time in range, time above range (TAR), time below range (TBR), standard deviation (SD), coefficient of variation (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), J-index, lability index (LI), mean absolute glucose (MAG), M-value, high blood glucose index (HBGI), low blood glucose index (LBGI) were derived from continuous glucose monitoring. Body composition was assessed by DEXA. Insulin secretion and sensitivity was estimated by HOMA-IR and HOMA-B scores.

RESULTS

Overweight subjects demonstrated higher mean glucose, CONGA, J-index and lower TBR, M-value and LBGI values. Mean glucose correlated positively with total, trunk, gynoid and android fat mass, while M-value and LBGI demonstrated negative correlations with these parameters. In multiple stepwise regression analysis, android fat mass was a predictor of mean glucose, CONGA, J-index, SD and MAGE, gynoid fat mass predicted J-index only, and total fat mass was associated inversely with MAG. Fasting insulin was a predictor of TAR, SD, CV, MAGE, MAG, LI and HBGI. HOMA-B was associated with CONGA, M-value and LBGI.

CONCLUSION

In non-obese subjects with NGT mean glucose and GV parameters are related to fat mass and fat distribution. These relations can be mediated through insulin secretion and sensitivity.

Carotid Artery Disease in Subjects with Type 2 Diabetes: Risk Factors and Biomarkers

Carotid atherosclerosis (CA) and, especially, carotid artery stenosis (CAS), are associated with a high risk of cardiovascular events in subjects with type 2 diabetes (T2D). In this study, we aimed to identify risk factors and biomarkers of subclinical CA and CAS in T2D individuals. High-resolution ultrasonography of carotid arteries was performed in 389 patients. Ninety-five clinical parameters were evaluated, including diabetic complications and comorbidities; antihyperglycemic, hypolipidemic, and antihypertensive therapy; indices of glycemic control and glucose variability (GV); lipid panels; estimated glomerular filtration rate (eGFR); albuminuria; blood cell count; and coagulation. Additionally, serum levels of calponin-1, relaxin, L-citrulline, and matrix metalloproteinase-2 and -3 (MMP-2, -3) were measured by ELISA. In univariate analysis, older age, male sex, diabetes duration, GV, diabetic retinopathy, chronic kidney disease, coronary artery disease, peripheral artery disease, and MMP-3 were associated with subclinical CA. In addition to these factors, long-term arterial hypertension, high daily insulin doses, eGFR, and L-citrulline were associated with CAS. In multivariate logistic regression, age, male sex, BMI, GV, and eGFR predicted CA independently; male sex, BMI, diabetes duration, eGFR, and L-citrulline were predictors of CAS. These results can be used to develop screening and prevention programs for CA and CAS in T2D subjects.

Integrated analysis reveals the participation of IL4I1, ITGB7, and FUT7 in reshaping the TNBC immune microenvironment by targeting glycolysis

BACKGROUND

The overall response rate of immunotherapy in triple-negative breast cancer (TNBC) remains unsatisfactory. Accumulating evidence indicated that glucose metabolic reprogramming could modulate immunotherapy efficacy. However, transcriptomic evidence remains insufficient.

METHODS

Genes' relationship with glucose metabolism and TNBC-specific immune was demonstrated by weighted gene co-expression network analysis (WGCNA). The glucose metabolic capability was estimated by standardised uptake value (SUV), an indicator of glucose uptake in 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET), and a reflection of cancer metabolic behaviour. PD-(L)1 expression was used to reflect the efficacy of immunotherapy. Additionally, immune infiltration, survival, and gene coexpression profiles were provided.

RESULTS

Comprehensive analysis revealing that IL4I1, ITGB7, and FUT7 hold the potential to reinforce immunotherapy by reshaping glucose metabolism in TNBC. These results were verified by functional enrichment analysis, which demonstrated their relationships with immune-related signalling pathways and extracellular microenvironment reprogramming. Their expressions have potent positive correlations with Treg and Macrophage cell infiltration and exhausted T cell markers. Meanwhile, their overexpression also lead to poor prognosis.

CONCLUSION

IL4I1, ITGB7, and FUT7 may be the hub genes that link glucose metabolism, and cancer-specific immunity. They may be potential targets for enhancing ICB treatment by reprogramming the tumour microenvironment and remodelling tumour metabolism.

Hypoglycemia, Vascular Disease and Cognitive Dysfunction in Diabetes: Insights from Text Mining-Based Reconstruction and Bioinformatics Analysis of the Gene Networks

Hypoglycemia has been recognized as a risk factor for diabetic vascular complications and cognitive decline, but the molecular mechanisms of the effect of hypoglycemia on target organs are not fully understood. In this work, gene networks of hypoglycemia and cardiovascular disease, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline, and Alzheimer's disease were reconstructed using ANDSystem, a text-mining-based tool. The gene network of hypoglycemia included 141 genes and 2467 interactions. Enrichment analysis of Gene Ontology (GO) biological processes showed that the regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis, and cell signaling are significantly enriched for hypoglycemia. Among the network hubs, INS, IL6, LEP, TNF, IL1B, EGFR, and FOS had the highest betweenness centrality, while GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR, and AQP7 demonstrated the highest cross-talk specificity. Hypoglycemia-related genes were overrepresented in the gene networks of diabetic complications and comorbidity; moreover, 14 genes were mutual for all studied disorders. Eleven GO biological processes (glucose homeostasis, nitric oxide biosynthesis, smooth muscle cell proliferation, ERK1 and ERK2 cascade, etc.) were overrepresented in all reconstructed networks. The obtained results expand our understanding of the molecular mechanisms underlying the deteriorating effects of hypoglycemia in diabetes-associated vascular disease and cognitive dysfunction.

Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools

Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.

Glucose Variability: How Does It Work?

A growing body of evidence points to the role of glucose variability (GV) in the development of the microvascular and macrovascular complications of diabetes. In this review, we summarize data on GV-induced biochemical, cellular and molecular events involved in the pathogenesis of diabetic complications. Current data indicate that the deteriorating effect of GV on target organs can be realized through oxidative stress, glycation, chronic low-grade inflammation, endothelial dysfunction, platelet activation, impaired angiogenesis and renal fibrosis. The effects of GV on oxidative stress, inflammation, endothelial dysfunction and hypercoagulability could be aggravated by hypoglycemia, associated with high GV. Oscillating hyperglycemia contributes to beta cell dysfunction, which leads to a further increase in GV and completes the vicious circle. In cells, the GV-induced cytotoxic effect includes mitochondrial dysfunction, endoplasmic reticulum stress and disturbances in autophagic flux, which are accompanied by reduced viability, activation of apoptosis and abnormalities in cell proliferation. These effects are realized through the up- and down-regulation of a large number of genes and the activity of signaling pathways such as PI3K/Akt, NF-κB, MAPK (ERK), JNK and TGF-β/Smad. Epigenetic modifications mediate the postponed effects of glucose fluctuations. The multiple deteriorative effects of GV provide further support for considering it as a therapeutic target in diabetes.

Integrated Bioinformatics-Based Identification of Potential Diagnostic Biomarkers Associated with Diabetic Foot Ulcer Development

The present study was designed to detect possible biomarkers associated with diabetic foot ulcer (DFU) incidence in an effort to develop novel treatments for this condition. The GSE7014 and GSE29221 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, after which differentially expressed genes (DEGs) were identified between DFU and healthy samples. These DEGs were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. In total, 1192 DEGs were identified in the GSE7014 dataset (900 upregulated, 292 downregulated), while 1177 were identified in the GSE29221 dataset (257 upregulated, 919 downregulated). GO analyses revealed these DEGs to be significantly enriched in biological processes including sarcomere organization, muscle filament sliding, and the regulation of cardiac conduction, molecular functions including structural constituent of muscle, protein binding, and calcium ion binding, and cellular components including Z disc, myosin filament, and M band. These DEGs were also enriched in the adrenergic signaling in cardiomyoctes, dilated cardiomyopathy, and tight junction KEGG pathways. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with DFU development.