Association between IL-1B (-511)/IL-1RN (VNTR) polymorphisms and type 2 diabetes: a systematic review and meta-analysis

IL27 and IL1RN are causally associated with acute pancreatitis: a Mendelian randomization study

BACKGROUND

The interleukin (IL) plays a role in the development of acute pancreatitis (AP). However, the specific IL in AP has not been fully revealed. Therefore, the association between prospective IL and AP was studied via Mendelian randomization (MR).

METHODS

The HUGO Gene nomenclature committee (HGNC) database provided 47 interleukin related genes (ILRGs). ILRGs and differentially expressed genes (DEGs) from GSE194331 were overlapped to create differently expressed ILRGs (DE-ILRGs). The integrative epidemiology unit (IEU) open genome-wide association study (GWAS) database provided exposure and outcome datasets. Univariate MR (UVMR) analysis using MR-Egger, IVW, simple mode, and weighted mode was done. UVMR results were verified using sensitivity analysis. Drug prediction, MVMR analysis, and PPI network development were also performed.

RESULTS

Six DE-ILRGs were obtained. IL27 and IL1RN were substantially causally linked with AP by UVMR analysis (OR = 0.926, P < 0.001 and OR = 1.031, P = 0.023). Our sensitivity analysis showed the dependability of our results. Direct effect of IL27 was suggested by MVMR analysis. In the cytokine receptor binding pathway, IL27 and IL1RN interacted with IL36G and IL1R2. TAE-684, ARQ-680, and 12 other IL1RN and 14 IL27 medications were predicted.

CONCLUSIONS

IL1RN was identified as a risk factor for acute pancreatitis (AP), but IL27 was found to be a protective factor for AP.

Mechanisms of Action of Potentilla discolor Bunge in Type 2 Diabetes Mellitus Based on Network Pharmacology and Experimental Verification in Drosophila

Purpose

Type 2 diabetes mellitus (T2DM) is associated with reduced insulin uptake and glucose metabolic capacity. Potentilla discolor Bunge (PDB) has been used to treat T2DM; however, the fundamental biological mechanisms remain unclear. This study aimed to understand the active ingredients, potential targets, and underlying mechanisms through which PDB treats T2DM.

Methods

Components and action targets were predicted using network pharmacology and molecular docking analyses. PDB extracts were prepared and validated through pharmacological intervention in a Cg>InRK1409A diabetes Drosophila model. Network pharmacology and molecular docking analyses were used to identify the key components and core targets of PDB in the treatment of T2DM, which were subsequently verified in animal experiments.

Results

Network pharmacology analysis revealed five effective compounds made up of 107 T2DM-related therapeutic targets and seven protein-protein interaction network core molecules. Molecular docking results showed that quercetin has a strong preference for interleukin-1 beta (IL1B), IL6, RAC-alpha serine/threonine-protein kinase 1 (AKT1), and cellular tumor antigen p53; kaempferol exhibited superior binding to tumor necrosis factor and AKT1; β-sitosterol demonstrated pronounced binding to Caspase-3 (CASP3). High-performance liquid chromatography data quantified quercetin, kaempferol, and β-sitosterol at proportions of 0.030%, 0.025%, and 0.076%, respectively. The animal experiments revealed that PDB had no effect on the development, viability, or fertility of Drosophila and it ameliorated glycolipid metabolism disorders in the diabetes Cg>InRK1409A fly. Furthermore, PDB improved the body size and weight of Drosophila, suggesting its potential to alleviate insulin resistance. Moreover, PDB improved Akt phosphorylation and suppressed CASP3 activity to improve insulin resistance in Drosophila with T2DM.

Conclusion

Our findings suggest that PDB ameliorates diabetes metabolism disorders in the fly model by enhancing Akt activity and suppressing CASP3 expression. This will facilitate the development of key drug targets and a potential therapeutic strategy for the clinical treatment of T2DM and related metabolic diseases.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

Exploration of cross-talk and pyroptosis-related gene signatures and molecular mechanisms between periodontitis and diabetes mellitus via peripheral blood mononuclear cell microarray data analysis

OBJECTIVES

This bioinformatics study is aimed at identifying cross-talk genes, pyroptosis-related genes, and related pathways between periodontitis (PD) and diabetes mellitus (DM), which includes type 1 diabetes (T1DM) and type 2 diabetes (T2DM).

METHODS

GEO datasets containing peripheral blood mononuclear cell (PBMC) data of PD and DM were acquired. After batch correction and normalization, differential expression analysis was performed to identify the differentially expressed genes (DEGs). And cross-talk genes in the PD-T1DM pair and the PD-T2DM pair were identified by overlapping DEGs with the same trend in each pair. The weighted gene coexpression network analysis (WGCNA) algorithm helped locate the pyroptosis-related genes that are related to cross-talk genes. Receiver-operating characteristic (ROC) curve analysis confirmed the predictive accuracy of these hub genes in diagnosing PD and DM. The correlation between hub genes and the immune microenvironment of PBMC in these diseases was investigated by Spearman correlation analysis. The experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Subnetwork analysis helped identify the key pathway connecting DM and PD.

RESULTS

Hub genes in the PD-T1DM pair (HBD, NLRC4, AIM2, NLRP2) and in the PD-T2DM pair (HBD, IL-1Β, AIM2, NLRP2) were identified. The similarity and difference in the immunocytes infiltration levels and immune pathway scores of PD and DM were observed. ROC analysis showed that AIM2 and HBD exhibited pleasant discrimination ability in all diseases, and the subnetwork of these genes indicated that the NOD-like receptor signaling pathway is the most potentially relevant pathway linking PD and DM.

CONCLUSION

HBD and AIM2 could be the most relevant potential cross-talk and pyroptosis-related genes, and the NOD-like receptor signaling pathway could be the top candidate molecular mechanism linking PD and DM, supporting a potential pathophysiological relationship between PD and DM.

IL-6 gene rs1800795 polymorphism and diabetes mellitus: a comprehensive analysis involving 42,150 participants from a meta-analysis

BACKGROUND

Over the past two decades, several studies have focused on the association between a common polymorphism (rs1800795) from interleukin-6 (IL-6) gene and Diabetes Mellitus (DM) risk. However, the results remain ambiguous and indefinite.

METHODS

A comprehensive analysis was performed to explore this relationship. A search was conducted in the PubMed, Embase, Chinese (CNKI and Wanfang), and GWAS Catalog databases, covering all publications until February 10, 2022. Odds ratios (OR) with 95% confidence intervals (CI) were used to evaluate the strength of the association. Publication bias was assessed using both Begg and Egger tests.

RESULTS

Overall, 34 case-control studies with 7257 T2DM patients and 15,598 controls, and 12 case-control studies (10,264 T1DM patients and 9031 health controls) were included in the analysis. A significantly lower association was observed between the rs1800795 polymorphism and T2DM risk in Asians, mixed population, and hospital-based (HB) subgroups (C-allele vs. G-allele: OR = 0.76, 95% CI  0.58-0.99, P = 0.039 for Asians; CG vs. GG: OR = 0.74, 95% CI  0.58-0.94, P = 0.014 for mixed population; CC vs. GG: OR = 0.61, 95% CI  0.41-0.90, P = 0.014 for HB). However, increased associations were found from total, mixed population, and HB subgroups between rs1800795 polymorphism and T1DM susceptibility (CG vs. GG: OR = 1.32, 95% CI 1.01-1.74, P = 0.043 for total population, CC vs. GG: OR = 2.45, 95% CI 1.18-5.07, P = 0.016 for mixed individuals; C-allele vs. G-allele: OR = 1.29, 95% CI 1.07-1.56, P = 0.0009 for HB subgroup).

CONCLUSIONS

In summary, there is definite evidence to confirm that IL-6 rs1800795 polymorphism is associated with susceptibility to decreased T2DM and increased T1DM.