Glucotoxicity Activation of IL6 and IL11 and Subsequent Induction of Fibrosis May Be Involved in the Pathogenesis of Islet Dysfunction

Genome-wide association study of blood lipid levels in Southern Han Chinese adults with prediabetes

Background

Dyslipidemia is highly prevalent among individuals with prediabetes, further exacerbating their cardiovascular risk. However, the genetic determinants underlying diabetic dyslipidemia in Southern Han Chinese remain largely unexplored.

Methods

We performed a genome-wide association study (GWAS) of blood lipid traits in 451 Southern Han Chinese adults with prediabetes. Fasting plasma lipids, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were assayed. Genotyping was conducted using the Precision Medicine Diversity Array and Gene Titan platform, followed by genotype imputation using IMPUTE2 with the 1000 Genomes Project (Phase 3, Southern Han Chinese) as reference. Single nucleotide polymorphisms (SNPs) associated with lipid levels were identified using mixed linear regression, with adjustment for covariates.

Results

We identified 58, 215, 74 and 81 novel SNPs associated with TG, TC, HDL-C and LDL-C levels, respectively (P < 5×10-5). Several implicated loci were located in or near genes involved in lipid metabolism, including SRD5A2, PCSK7, PITPNC1, IRX3, BPI, and LBP. Pathway enrichment analysis highlighted lipid metabolism and insulin secretion.

Conclusion

This first GWAS of dyslipidemia in Southern Han Chinese with prediabetes identified novel genetic variants associated with lipid traits. Our findings provide new insights into genetic mechanisms underlying heightened cardiovascular risk in the prediabetic stage. Functional characterization of implicated loci is warranted.

Identification of core genes affecting IMF deposition in bovine

Intramuscular fat (IMF) content is an important economic factor in beef production. However, knowledge on the key factors controlling bovine IMF is limited. In this study, using weighted gene co-expression network analysis (WGCNA), nine modules were identified and the number of transcripts in these modules ranged from 36 to 3191. Two modules were found to be significantly associated with fat deposition and three genes (TCAP, MYH7, and TNNC1) were further identified by Protein-protein interaction (PPI), which may be the hub genes regulating bovine IMF deposition. In addition, considering the genetic variation, the PCK1 gene was found by functional enrichment analysis of overlapping genes, which was previously reported to be involved in IMF deposition. We noted that the core promoter region of buffalo PCK1 binds to transcription factors involved in lipid metabolism while cattle PCK1 binds transcription factors involved in muscle development. The results suggest that PCK1 participated in IMF deposition of buffalo and cattle in different ways. In summary, gene expression networks and new candidate genes associated with IMF deposition identified in this study. This would lay the foundation for further research into the molecular regulatory mechanisms underlying bovine IMF deposition.

Elucidating shared biomarkers and pathways in kidney stones and diabetes: insights into novel therapeutic targets and the role of resveratrol

BACKGROUND

The pathogenic mechanisms shared between kidney stones and diabetes at the transcriptional level remain elusive, and the molecular mechanisms by which resveratrol exerts its protective effects against these conditions require further investigation.

METHODS

To address these gaps in knowledge, we conducted a comprehensive analysis of microarray and RNA-seq datasets to elucidate shared biomarkers and biological pathways involved in the pathogenesis of kidney stones and diabetes. An assortment of bioinformatic approaches was employed to illuminate the common molecular markers and associated pathways, thereby contributing to the identification of innovative therapeutic targets. Further investigation into the molecular mechanisms of resveratrol in preventing these conditions was conducted using molecular docking simulation and first-principles calculations.

RESULTS

The study identified 11 potential target genes associated with kidney stones and diabetes through the intersection of genes from weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. Among these, Interleukin 11 (IL11) emerged as a pivotal hub gene and a potential diagnostic biomarker for both conditions, particularly in males. Expression analysis of IL11 demonstrated elevated levels in kidney stones and diabetes groups compared to controls. Additionally, IL11 exhibited correlations with specific cell types and differential expression in normal and pathological conditions. Gene set enrichment analysis (GSEA) highlighted significant disparities in biological processes, pathways, and immune signatures associated with IL11. Moreover, molecular docking simulation of resveratrol towards IL11 and a first-principles investigation of Ca adsorption on the resveratrol surface provided structural evidence for the development of resveratrol-based drugs for these conditions.

CONCLUSIONS

Overall, this investigation illuminates the discovery of common molecular mechanisms underlying kidney stones and diabetes, unveils potential diagnostic biomarkers, and elucidates the significance of IL11 in these conditions. It also provides insights into IL11 as a promising therapeutic target and highlights the role of resveratrol. Nonetheless, further research is warranted to enhance our understanding of IL11 targeting mechanisms and address any limitations in the study.

Bioinformatics and Experimental Analyses Reveal Immune-Related LncRNA-mRNA Pair AC011483.1-CCR7 as a Biomarker and Therapeutic Target for Ischemic Cardiomyopathy

Ischemic cardiomyopathy (ICM), which increases along with aging, is the leading cause of heart failure. Currently, immune response is believed to be critical in ICM whereas the roles of immune-related lncRNAs remain vague. In this study, we aimed to systematically analyze immune-related lncRNAs in the aging-related disease ICM. Here, we downloaded publicly available RNA-seq data from ischemic cardiomyopathy patients and non-failing controls (GSE116250). Weighted gene co-expression network analysis (WGCNA) was performed to identify key ICM-related modules. The immune-related lncRNAs of key modules were screened by co-expression analysis of immune-related mRNAs. Then, a competing endogenous RNA (ceRNA) network, including 5 lncRNAs and 13 mRNAs, was constructed using lncRNA-mRNA pairs which share regulatory miRNAs and have significant correlation. Among the lncRNA-mRNA pairs, one pair (AC011483.1-CCR7) was verified in another publicly available ICM dataset (GSE46224) and ischemic cell model. Further, the immune cell infiltration analysis of the GSE116250 dataset revealed that the proportions of monocytes and CD8⁺ T cells were negatively correlated with the expression of AC011483.1-CCR7, while plasma cells were positively correlated, indicating that AC011483.1-CCR7 may participate in the occurrence and development of ICM through immune cell infiltration. Together, our findings revealed that lncRNA-mRNA pair AC011483.1-CCR7 may be a novel biomarker and therapeutic target for ICM.

Identification and validation of HOXD3 and UNC5C as molecular signatures in keloid based on weighted gene co-expression network analysis

BACKGROUND

Keloid is a benign proliferative disease characterized by excessive deposition of extracellular matrix collagen during skin wound healing. The mechanisms of keloid formation have not been fully elucidated, and the current treatment methods are not effective for all keloid patients. Therefore, there is an urgent need to find more effective therapies, and our research focused on identifying characteristic molecular signatures of keloid to explore potential therapeutic targets.

METHODS

Gene expression profiles of keloid and control group samples were retrieved from the GEO database. Taking the GSE113619 dataset as the training set, the dataset collected skin tissues from non-lesion sites of healthy and keloid-prone individuals, denoted as Day0. The second sampling was performed 42 days later at the original sampling site of control and keloid groups, denoted as Day42.The 'limma' package and Venn diagram identified differentially expressed genes (DEGs) specific to keloid day42 versus day0 samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway functional enrichment, and annotation of the characteristic genes were conducted on the Metascape website. Ingenuity canonical pathways, disease & function enrichment analysis and gene interaction network were performed and predicted in Ingenuity Pathway Analysis (IPA) software. Key module genes related to keloid were filtered out by Weighted Gene Co-expression Network Analysis (WGCNA). We utilized the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to screen the characteristic genes in keloid by the 'glmnet' package. The area under the curve (AUC) of receiver operating characteristic (ROC) was utilized to determine the effectiveness of potential signatures in discriminating keloid samples from normal samples and performed by using the 'pROC' package. The enrich scores of 24 immune cells in each sample were calculated by the single-sample gene set enrichment analysis (ssGSEA) algorithm， and then the Gene Set Variation Analysis (GSVA) was performed. Finally, RNA from 4 normal and 6 keloid samples was extracted, and RT-qPCR and Western Blot validated the expression of characteristic genes.

RESULTS

A total of 640 DEGs specific to keloid day42 versus day0 samples were detected. 69 key module genes were uncovered and implicated in 'NCAM signaling for neurite out-growth', 'oncogenic MAPK signaling', 'transmission across chemical synapses' pathways, and the mitotic cell cycle-related processes. Five characteristic genes (MTUS1, UNC5C, CEP57, NAA35, and HOXD3) of keloid were identified by LASSO, and among which UNC5C and HOXD3 were validated by ROC plot in external dataset, RT-qPCR and Western Blot in validation samples. The result of ssGSEA indicated that the infiltration of neutrophils showed a relatively higher abundance and natural killer cells with relatively low enrichment in the keloid group compared to the control group. UNC5C was correlated with more immune cells compared with other characteristic genes.

CONCLUSION

In this study, characteristic genes associated with keloid were identified by bioinformatic approaches and verified in clinical validation samples, providing potential targets for the diagnosis and treatment of keloid.

Identification of Immune Markers in Dilated Cardiomyopathies with Heart Failure by Integrated Weighted Gene Coexpression Network Analysis

Dilated cardiomyopathy (DCM), a heterogeneous cardiomyopathy, is a major cause of heart failure and heart transplant. Currently, immunotherapy is believed to be an effective treatment method for DCM. However, individual differences are so obvious that the clinical effect is not satisfactory. In order to find immune-related biomarkers of DCM to guide treatment and improve clinical efficacy, we downloaded a GSE120895 dataset from the Gene Expression Omnibus (GEO) database using CIBERSORT and WGCNA algorithms in RStudio and visualizing the protein–protein interaction (PPI) network for key modules by Cytoscape, and finally obtained six hub genes. A GSE17800 dataset was downloaded from the GEO dataset to verify the diagnostic values of hub genes, MYG1, FLOT1, and ATG13, which were excellent. Our study revealed unpublished potential immune mechanisms, biomarkers, and therapeutic targets of DCM.