Identification of co-expressed central genes and transcription factors in acute myocardial infarction and diabetic nephropathy.

BACKGROUND: Acute myocardial infarction (AMI) and diabetic nephropathy (DN) are common clinical co-morbidities, but they are challenging to manage and have poor prognoses. There is no research on the bioinformatics mechanisms of comorbidity, and this study aims to investigate such mechanisms. METHODS: We downloaded the AMI data (GSE66360) and DN datasets (GSE30528 and GSE30529) from the Gene Expression Omnibus (GEO) platform. The GSE66360 dataset was divided into two parts: the training set and the validation set, and GSE30529 was used as the training set and GSE30528 as the validation set. After identifying the common differentially expressed genes (DEGs) in AMI and DN in the training set, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein-protein interaction (PPI) network construction were performed. A sub-network graph was constructed by MCODE, and 15 hub genes were screened by the Cytohubba plugin. The screened hub genes were validated, and the 15 screened hub genes were subjected to GO, KEGG, Gene MANIA analysis, and transcription factor (TF) prediction. Finally, we performed TF differential analysis, enrichment analysis, and TF and gene regulatory network construction. RESULTS: A total of 46 genes (43 up-regulated and 3 down-regulated) were identified for subsequent analysis. GO functional analysis emphasized the presence of genes mainly in the vesicle membrane and secretory granule membrane involved in antigen processing and presentation, lipopeptide binding, NAD + nucleosidase activity, and Toll-like receptor binding. The KEGG pathways analyzed were mainly in the phagosome, neutrophil extracellular trap formation, natural killer cell-mediated cytotoxicity, apoptosis, Fc gamma R-mediated phagocytosis, and Toll-like receptor signaling pathways. Eight co-expressed hub genes were identified and validated, namely TLR2, FCER1G, CD163, CTSS, CLEC4A, IGSF6, NCF2, and MS4A6A. Three transcription factors were identified and validated in AMI, namely NFKB1, HIF1A, and SPI1. CONCLUSIONS: Our study reveals the common pathogenesis of AMI and DN. These common pathways and hub genes may provide new ideas for further mechanistic studies.

Causal association of circulating metabolites with diabetic retinopathy: a bidirectional Mendelian randomization analysis.

Introduction: The retina is a highly metabolically active tissue, and there is a lack of clarity about the relationship between metabolites and diabetic retinopathy (DR). This study used two-sample bidirectional Mendelian randomization (MR) analyses to identify causal relationships between metabolites and DR. Methods: Genetic variants were selected from the open-access Genome-Wide Association Studies (GWAS) summary database as proxies for the 1400 most recently published metabolites. MR analysis was performed to examine associations between these metabolite traits and DR. Single nucleotide polymorphism (SNP) data that were significantly associated with exposure were screened through association analysis. Validated instrumental variables (IVs) were obtained by removing SNPs with linkage disequilibrium (LD) and F-statistic values below 10. MR analyses were performed using the inverse variance weighted (IVW) method as the primary approach. The robustness of the results was verified by sensitivity analyses, including assessments of heterogeneity, horizontal pleiotropy, and the leave-one-out method. Results: In the IVW approach and in the primary analysis of several sensitivity analyses, genetically determined glycolithocholate sulfate levels, androstenediol (3 beta, 17 beta) monosulfate (1) levels, 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) levels, 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) levels, X-26109 levels, N6-methyllysine levels, (N6,N6-dimethyllysine levels), and (N2-acetyl,N6,N6-dimethyllysine levels) were negatively associated with the risk of DR. 5-hydroxymethyl-2-furoylcarnitine levels and the glutamate-to-alanine ratio were positively associated with the risk of DR. No reverse causal association was found between DR and metabolites. Discussion: This MR study suggests that nine metabolites may have a protective effect in DR, while two metabolites may be associated with an increased risk of DR. However, further research is needed to confirm these findings. Supplementation with beneficial metabolites may reduce DR risk and could potentially be a novel therapeutic approach to DR treatment.

Influence of glucagon-like peptide-1 receptor agonists on fat accumulation in patients with diabetes mellitus and non-alcoholic fatty liver disease or obesity: A systematic review and meta-analysis of randomized control trials.

AIM: This systematic review and meta-analysis aimed to comprehensively evaluate the impact of glucagon-like peptide 1 receptor agonists (GLP-1RAs) on visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in individuals with diabetes mellitus and non-alcoholic fatty liver disease (NAFLD) or obesity. METHODS: A search of PubMed, Embase, and Web of Science until October 2023 identified 13 Randomized Controlled Trials (RCTs) meeting the inclusion criteria. Bias risk was assessed using the Cochrane risk-of-bias instrument. Statistical analysis utilized standard mean differences (SMD) in Review Manager 5.4. Heterogeneity and publication bias were assessed. This study used the protocol registered with the Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY2023110020). RESULTS: GLP-1RA treatment significantly reduced VAT (SMD -0.55, 95 % CI [-0.90, -0.19]), SAT (SMD -0.59, 95 % CI [-0.99, -0.19]), body weight (SMD -1.07, 95 % CI [-1.67, -0.47]), and body mass index (BMI) (SMD -1.10, 95 % CI [-1.74, -0.47]) compared to controls. Heterogeneity was observed for VAT (I2 = 79 %, P < 0.01), SAT (I2 = 73 %, P < 0.01), body weight (I2 = 82 %, P < 0.01), and BMI (I2 = 82 %, P < 0.01). No publication bias was detected for VAT (P = 0.57) and SAT (P = 0.18). GLP-1RA treatment improved fasting blood glucose (FBG), postprandial glucose (PPG), hemoglobin A1c (HbA1c), Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), and fibrosis-4 (FIB-4). CONCLUSIONS: This meta-analysis highlights GLP-1RAs' potential to reduce fat accumulation, body weight, and BMI and improve glycemic control in individuals with diabetes mellitus and NAFLD or obesity. These findings supported using GLP-1RAs as promising therapeutic agents to address abnormal adipose tissue distribution and metabolic dysfunction.

A second-generation M1-polarized CAR macrophage with antitumor efficacy.

Chimeric antigen receptor (CAR) T cell therapies have successfully treated hematological malignancies. Macrophages have also gained attention as an immunotherapy owing to their immunomodulatory capacity and ability to infiltrate solid tumors and phagocytize tumor cells. The first-generation CD3ζ-based CAR-macrophages could phagocytose tumor cells in an antigen-dependent manner. Here we engineered induced pluripotent stem cell-derived macrophages (iMACs) with toll-like receptor 4 intracellular toll/IL-1R (TIR) domain-containing CARs resulting in a markedly enhanced antitumor effect over first-generation CAR-macrophages. Moreover, the design of a tandem CD3ζ-TIR dual signaling CAR endows iMACs with both target engulfment capacity and antigen-dependent M1 polarization and M2 resistance in a nuclear factor kappa B (NF-κB)-dependent manner, as well as the capacity to modulate the tumor microenvironment. We also outline a mechanism of tumor cell elimination by CAR-induced efferocytosis against tumor cell apoptotic bodies. Taken together, we provide a second-generation CAR-iMAC with an ability for orthogonal phagocytosis and polarization and superior antitumor functions in treating solid tumors relative to first-generation CAR-macrophages.

Integrative analyses of biomarkers and pathways for diabetic nephropathy.

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

Acarbose-metformin is more effective in glycemic variability control than repaglinide-metformin in T2DM patients inadequately controlled with metformin: a retrospective cohort study.

BACKGROUND: Acarbose and repaglinide are widely used either by themselves or in combination with other medications. However, their efficacy in diabetes control has not been compared when used in combination with metformin. METHODS: The present study aimed to compare their effects on glycemic variability (GV) control when taken with metformin for type 2 diabetes mellitus (T2DM) inadequately controlled with metformin alone. In this retrospective cohort study, T2DM patients who were treated with either acarbose-metformin or repaglinide-metformin combination were recruited. Either acarbose 100 mg or repaglinide 2 mg triple daily was taken for the subsequent 12 weeks in combination with metformin. Demographic data, biochemical data and 7-point glycemic self-monitoring conducted with capillary blood (SMBG) data were reviewed after one week and 12 weeks. The primary outcome including glucose control and changes in GV as well as other factors affecting GV and the incidence of hypoglycemia were also analyzed. RESULTS: Of the 305 T2DM patients enrolled, data from 273 subjects, 136 in the acarbose-metformin group (M+A) and 137 in the repaglinide-metformin group (M+R) were analyzed. Both regimens improved glycemic control at 12 weeks post commencement of new medications. GV, expressed as the mean amplitude of plasma glycemic excursions (MAGE, 5.0 ± 2.6 vs. 2.8 ± 1.6 mmol/L, p < 0.001 in M+A; 5.1 ± 2.5 vs. 2.9 ± 1.3 mmol/L, p < 0.001 in M+R), standard deviation of blood glucose (SDBG, 3.6 ± 1.3 vs. 2.0 ± 0.9 mmol/L, p < 0.001 in M+A; 3.7 ± 1.3 vs. 2.4 ± 1.3 p < 0.001 in M+R), coefficient of variation of blood glucose (CVBG, (0.30 ± 0.09 vs. 0.21 ± 0.1, p < 0.001 in M+A; 0.31 ± 0.09 vs. 0.24 ± 0.12, p < 0.001 in M+R), postprandial amplitude of glycemic excursions (PPGE, 5.2 ± 2.6 vs. 2.8 ± 1.6 mmol/L, p < 0.001 in M+A; 5.3 ± 2.5 vs. 2.9 ± 1.3 mmol/L, p < 0.001 in M+R) or largest amplitude of glycemic excursions (LAGE, 9.8 ± 3.6 vs. 5.4 ± 2.4 mmol/L, p < 0.001 in M+A; 10.1 ± 3.4 vs. 6.3 ± 3.2 mmol/L, p < 0.001 in M+R) decreased significantly after the addition of acarbose or repaglinide (p < 0.05 respectively). Compared with repaglinide-metformin, acarbose-metformin was more effective in GV control at 12 weeks post commencement of new medications (p < 0.05). This study indicates that both acarbose-metformin and repaglinide-metformin combinations could effectively reduce GV and the acarbose-metformin combination seems to be more effective than the repaglinide-metformin combination. However, this conclusion should be confirmed by future large-scaled and more comprehensive studies due to the limitations of the present study.