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生物信息学分析揭示了基质金属蛋白酶在免疫和尿路结石中的潜在作用。

Bioinformatics analysis reveals the potential role of matrix metalloproteinases in immunity and urolithiasis.

作者信息

Hong Sen-Yuan, Jiang Hong-Cheng, Xu Wen-Chao, Zeng He-Song, Wang Shao-Gang, Qin Bao-Long

机构信息

Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Front Immunol. 2023 Mar 15;14:1158379. doi: 10.3389/fimmu.2023.1158379. eCollection 2023.

DOI:10.3389/fimmu.2023.1158379
PMID:37006258
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10050583/
Abstract

BACKGROUND

The pathogenesis of urolithiasis remains unclear, making the development of medications for treatment and prevention stagnant. Randall's plaques (RPs) begin as interstitial calcium phosphate crystal deposits, grow outward and breach the renal papillary surface, acting as attachment for CaOx stones. Since matrix metalloproteinases (MMPs) can degrade all components of extracellular matrix (ECM), they might participate in the breach of RPs. Besides, MMPs can modulate the immune response and inflammation, which were confirmed to be involved in urolithiasis. We aimed to investigate the role of MMPs in the development of RPs and stone formation.

METHODS

The public dataset GSE73680 was mined to identify differentially expressed MMPs (DEMMPs) between normal tissues and RPs. WGCNA and three machine learning algorithms were performed to screen the hub DEMMPs. experiments were conducted for validation. Afterwards, RPs samples were classified into clusters based on the hub DEMMPs expression. Differentially expressed genes (DEGs) between clusters were identified and functional enrichment analysis and GSEA were applied to explore the biological role of DEGs. Moreover, the immune infiltration levels between clusters were evaluated by CIBERSORT and ssGSEA.

RESULTS

Five DEMMPs, including MMP1, MMP3, MMP9, MMP10, and MMP12, were identified between normal tissues and RPs, and all of them were elevated in RPs. Based on WGCNA and three machine learning algorithms, all of five DEMMPs were regarded as hub DEMMPs. validation found the expression of hub DEMMPs also increased in renal tubular epithelial cells under lithogenic environment. RPs samples were divided into two clusters and cluster A exhibited higher expression of hub DEMMPs compared to cluster B. Functional enrichment analysis and GSEA found DEGs were enriched in immune-related functions and pathways. Moreover, increased infiltration of M1 macrophages and enhanced levels of inflammation were observed in cluster A by immune infiltration analysis.

CONCLUSION

We assumed that MMPs might participate in RPs and stone formation through ECM degradation and macrophages-mediated immune response and inflammation. Our findings offer a novel perspective on the role of MMPs in immunity and urolithiasis for the first time, and provide potential biomarkers to develop targets for treatment and prevention.

摘要

背景

尿石症的发病机制仍不清楚,导致治疗和预防药物的研发停滞不前。兰德尔斑(RPs)最初是间质磷酸钙晶体沉积物,向外生长并突破肾乳头表面,成为草酸钙结石的附着点。由于基质金属蛋白酶(MMPs)可以降解细胞外基质(ECM)的所有成分,它们可能参与RPs的突破。此外,MMPs可以调节免疫反应和炎症,而免疫反应和炎症已被证实与尿石症有关。我们旨在研究MMPs在RPs形成和结石形成中的作用。

方法

挖掘公共数据集GSE73680,以鉴定正常组织和RPs之间差异表达的MMPs(DEMMPs)。进行加权基因共表达网络分析(WGCNA)和三种机器学习算法以筛选关键DEMMPs。进行实验验证。随后,根据关键DEMMPs的表达将RPs样本分类为不同簇。鉴定簇间差异表达基因(DEGs),并应用功能富集分析和基因集富集分析(GSEA)来探索DEGs的生物学作用。此外,通过CIBERSORT和单样本基因集富集分析(ssGSEA)评估簇间的免疫浸润水平。

结果

在正常组织和RPs之间鉴定出5种DEMMPs,包括MMP1、MMP3、MMP9、MMP10和MMP12,它们在RPs中均升高。基于WGCNA和三种机器学习算法,所有这5种DEMMPs均被视为关键DEMMPs。验证发现,在成石环境下肾小管上皮细胞中关键DEMMPs的表达也增加。RPs样本分为两个簇,与B簇相比,A簇中关键DEMMPs的表达更高。功能富集分析和GSEA发现DEGs富集于免疫相关功能和途径。此外,通过免疫浸润分析在A簇中观察到M1巨噬细胞浸润增加和炎症水平增强。

结论

我们推测MMPs可能通过ECM降解以及巨噬细胞介导的免疫反应和炎症参与RPs和结石形成。我们的研究结果首次为MMPs在免疫和尿石症中的作用提供了新的视角,并为开发治疗和预防靶点提供了潜在的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/cd05331ae350/fimmu-14-1158379-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/fbec4132d73c/fimmu-14-1158379-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/348e1127b09f/fimmu-14-1158379-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/61afebc3bedd/fimmu-14-1158379-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/cfc83f0aefe5/fimmu-14-1158379-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/aeb9f100c69a/fimmu-14-1158379-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/61f8ec5f3d74/fimmu-14-1158379-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/d5fb5b96e80a/fimmu-14-1158379-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/cd05331ae350/fimmu-14-1158379-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/fbec4132d73c/fimmu-14-1158379-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/348e1127b09f/fimmu-14-1158379-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/61afebc3bedd/fimmu-14-1158379-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/cfc83f0aefe5/fimmu-14-1158379-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/aeb9f100c69a/fimmu-14-1158379-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/61f8ec5f3d74/fimmu-14-1158379-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/d5fb5b96e80a/fimmu-14-1158379-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35ae/10050583/cd05331ae350/fimmu-14-1158379-g008.jpg

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