{"title":"Characterizing Chemokine Signaling Pathways and Hub Genes in Calcium Oxalate-Induced Kidney Stone Formation: Insights from Rodent Models.","authors":"Boqiang Wang, Zhenkun Tan, Wusheng She, Xiang Wang, Xiaofeng Guan, Zhiwei Tao, Fuyou Guo, Hua Xu, Yaoliang Deng","doi":"10.1007/s10528-025-11036-z","DOIUrl":null,"url":null,"abstract":"<p><p>The predominant component of kidney stone is calcium oxalate monohydrate (COM), a fact widely acknowledged. Although rodent models are frequently used to induce calcium oxalate (CaOx) crystallization, further exploration of Randall's plaques (RPs) in these models is still needed. We first selected the GSE89028 and GSE75542 datasets from the Gene Expression Omnibus (GEO) database to identify commonly differentially expressed genes (co-DEGs). Based on co-DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify significantly enriched pathways. Additionally, we performed Gene Set Enrichment Analysis (GSEA) to validate the enriched pathways. In order to identify hub genes, we established a network of protein-protein interactions (PPI). Finally, we conducted real-time PCR and Western blot to validate the findings from the bioinformatics analysis. We selected 28 co-DEGs from two datasets. The enrichment analysis using GO, KEGG, and GSEA revealed significant enrichment of chemokine-related signaling pathways. The histogram analysis showed that three chemokine factor-related genes were involved in multiple pathways. We used Cytohubba to confirm the presence of three hub genes. Subsequently, analysis of external datasets and quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot demonstrated significant upregulation of CCL2, CXCL1, and CXCL2 in HK-2 cells following CaOx treatment compared to the control group (p < 0.05). Our study demonstrated that upon stimulation by CaOx, renal tubular epithelial cells release chemokines, including CCL2, CXCL1, and CXCL2. This release of chemokines is accompanied by the activation of signaling pathways such as TNF and IL-17. These findings may provide new directions for future research on Kidney Stone Disease.</p>","PeriodicalId":482,"journal":{"name":"Biochemical Genetics","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s10528-025-11036-z","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The predominant component of kidney stone is calcium oxalate monohydrate (COM), a fact widely acknowledged. Although rodent models are frequently used to induce calcium oxalate (CaOx) crystallization, further exploration of Randall's plaques (RPs) in these models is still needed. We first selected the GSE89028 and GSE75542 datasets from the Gene Expression Omnibus (GEO) database to identify commonly differentially expressed genes (co-DEGs). Based on co-DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify significantly enriched pathways. Additionally, we performed Gene Set Enrichment Analysis (GSEA) to validate the enriched pathways. In order to identify hub genes, we established a network of protein-protein interactions (PPI). Finally, we conducted real-time PCR and Western blot to validate the findings from the bioinformatics analysis. We selected 28 co-DEGs from two datasets. The enrichment analysis using GO, KEGG, and GSEA revealed significant enrichment of chemokine-related signaling pathways. The histogram analysis showed that three chemokine factor-related genes were involved in multiple pathways. We used Cytohubba to confirm the presence of three hub genes. Subsequently, analysis of external datasets and quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot demonstrated significant upregulation of CCL2, CXCL1, and CXCL2 in HK-2 cells following CaOx treatment compared to the control group (p < 0.05). Our study demonstrated that upon stimulation by CaOx, renal tubular epithelial cells release chemokines, including CCL2, CXCL1, and CXCL2. This release of chemokines is accompanied by the activation of signaling pathways such as TNF and IL-17. These findings may provide new directions for future research on Kidney Stone Disease.
期刊介绍:
Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses.
Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication.
Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses.
Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods.
Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.