Identification of Novel Biomarkers of Bacterial Lipopolysaccharides in Diabetic Nephropathy via Transcriptomics and Mendelian Randomization.

IF 3.3 4区医学 Q2 GENETICS & HEREDITY

Current gene therapy Pub Date : 2025-06-03 DOI:10.2174/0115665232353811250527053550

Yaxian Ning, Jianqin Wang, Xiaochun Zhou, Gouqin Wang, Lili Zhang

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引用次数: 0

Abstract

Background: Dysbiosis of Intestinal Flora Lipopolysaccharides (LPS) is implicated in Diabetic Nephropathy (DN), yet the underlying mechanisms remain unclear. This study aims to elucidate the causal relationship between bacterial LPS and DN, with the goal of informing targeted therapeutic strategies.

Methods: DN datasets GSE30528 and GSE96804 were analyzed. Bacterial LPS-related genes (LPS-RGs) were retrieved from the Gene Set Enrichment Analysis (GSEA) database. Differential expression analysis identified differentially expressed genes (DEGs), which were cross-referenced with LPS-RGs to derive DE-LPS-RGs. Mendelian randomization (MR) was applied to explore correlations between exposure factors and outcomes using GWAS data. miRNA-mRNA and TFmRNA regulatory networks were constructed using data from the TarBase and ENCODE databases, and potential therapeutic agents were identified through the DGIdb database.

Results: Seven DE-LPS-RGs were identified, with CD14 and LY86 selected as biomarkers. GSEA and GeneMANIA analyses indicated that these genes participate in signal transduction and chargelike receptor signaling pathways. The regulatory networks demonstrated that LY86 interacts with miRNA hsa-mir-26a-5p, while TF ELK1 regulates both CD14 and LY86. Additionally, CD14 was associated with three potential drugs: VB-201, IC14, and Lovastatin.

Conclusion: CD14 and LY86 represent promising biomarkers for DN, offering new perspectives for its prediction, diagnosis, and therapeutic intervention.

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通过转录组学和孟德尔随机化鉴定糖尿病肾病细菌脂多糖的新生物标志物。

背景：肠道菌群失调脂多糖（LPS）与糖尿病肾病（DN）有关，但其潜在机制尚不清楚。本研究旨在阐明细菌LPS与DN之间的因果关系，为有针对性的治疗策略提供信息。方法：对DN数据集GSE30528和GSE96804进行分析。从基因集富集分析（GSEA）数据库中检索细菌lps相关基因（LPS-RGs）。差异表达分析鉴定出差异表达基因（Differential expression genes, DEGs），并与LPS-RGs进行交叉比对，得到DE-LPS-RGs。采用孟德尔随机化（MR）方法利用GWAS数据探讨暴露因素与结果之间的相关性。利用TarBase和ENCODE数据库的数据构建miRNA-mRNA和TFmRNA调控网络，并通过DGIdb数据库确定潜在的治疗药物。结果：鉴定出7个DE-LPS-RGs，选择CD14和LY86作为生物标志物。GSEA和GeneMANIA分析表明，这些基因参与信号转导和电荷样受体信号通路。调控网络表明LY86与miRNA hsa-mir-26a-5p相互作用，而TF ELK1同时调控CD14和LY86。此外，CD14与三种潜在药物相关：VB-201、IC14和洛伐他汀。结论：CD14和LY86是很有前景的DN生物标志物，为DN的预测、诊断和治疗干预提供了新的视角。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current gene therapy 医学-遗传学

CiteScore

6.70

自引率

2.80%

发文量

期刊介绍： Current Gene Therapy is a bi-monthly peer-reviewed journal aimed at academic and industrial scientists with an interest in major topics concerning basic research and clinical applications of gene and cell therapy of diseases. Cell therapy manuscripts can also include application in diseases when cells have been genetically modified. Current Gene Therapy publishes full-length/mini reviews and original research on the latest developments in gene transfer and gene expression analysis, vector development, cellular genetic engineering, animal models and human clinical applications of gene and cell therapy for the treatment of diseases. Current Gene Therapy publishes reviews and original research containing experimental data on gene and cell therapy. The journal also includes manuscripts on technological advances, ethical and regulatory considerations of gene and cell therapy. Reviews should provide the reader with a comprehensive assessment of any area of experimental biology applied to molecular medicine that is not only of significance within a particular field of gene therapy and cell therapy but also of interest to investigators in other fields. Authors are encouraged to provide their own assessment and vision for future advances. Reviews are also welcome on late breaking discoveries on which substantial literature has not yet been amassed. Such reviews provide a forum for sharply focused topics of recent experimental investigations in gene therapy primarily to make these results accessible to both clinical and basic researchers. Manuscripts containing experimental data should be original data, not previously published.