Dataset Information

Identification of the pivotal role of SPP1 in kidney stone disease based on multiple bioinformatics analysis.

ABSTRACT:

Background

Kidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets.

Methods

Based on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall's plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein-protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis.

Results

A total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup.

Conclusions

There are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation.

SUBMITTER: Hong SY

PROVIDER: S-EPMC8751247 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Chronic kidney disease (CKD) is a high mortality disease and generally remains asymptomatic in the early stages. Long non-coding RNA (lncRNA) is defined as a non-protein-coding transcript more than 200 nucleotides which participate in numerous biological processes and have been identified as novel diagnostic markers for many diseases. Detection of circulating lncRNAs is a rapidly evolving, new area of molecular diagnosis. The purpose of our research was to identify circulating lncRNA expression profiles and possible molecular mechanisms involved in CKD. Blood samples were obtained from patients with CKD and healthy volunteers, and high-throughput sequencing was performed to identify differentially expressed (DE) lncRNAs and mRNAs. DE lncRNAs and mRNAs in peripheral blood mononuclear cells (PBMCs) were confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) to ensure the reliability and validity of RNA-seq data. Bioinformatics analysis was used to obtain biological functions and key pathways related to the pathogenesis of CKD. The interaction and co-expression functional networks for DE lncRNAs and mRNAs were also constructed. Our data showed that of the 425 DE lncRNAs detected, 196 lncRNAs were upregulated, while that of 229 lncRNAs were downregulated. A total of 433 DE mRNAs were identified in patients with CKD compared to healthy individuals. GO analysis revealed that DE lncRNAs were highly correlated with binding and pathway regulation. KEGG analysis suggested that DE lncRNAs were obviously enriched in regulatory pathways, such as antigen processing and presentation. We successfully constructed a potential DE lncRNA-mRNA co-expression network and analyzed the target genes of DE lncRNAs to predict cis- and trans-regulation in CKD. 100 lncRNAs that corresponded to 14 transcription factors (TFs) were identified in the TF-lncRNA binary network. Our findings on the lncRNA expression profiles and functional networks may help to interpret the possible molecular mechanisms implied in the pathogenesis of CKD; the results demonstrated that lncRNAs could potentially to be used as diagnostic biomarkers in CKD.

Project description:Individuals with autosomal dominant polycystic kidney disease have a higher incidence of stone formation than the general population. However, there are no cystic animal models known to develop stones. Cystic mice compound heterozygous for hypomorphic Pkd1V and Pkd1RC alleles develop cystic kidneys within a few weeks of birth but live beyond 20 wk of age, allowing for the study of cystic comorbidities including stone formation. Cystic Pkd1V/RC mice were euthanized at 3, 13, or 26 wk of age, and their kidneys were analyzed by microcomputed tomography (µCT) for stone formation. Mice had occasional mineral aggregates that could be detected by µCT analysis at 3 wk of age. At 13 or 26 wk of age, numerous white masses were visible beneath the kidney surface. µCT analysis confirmed the masses to be large mineral stone deposits throughout the renal cortex, with mineral content increasing with age. Staining of histological sections with alizarin red and von Kossa suggested that the stone deposits were composed primarily of calcium and phosphate. Microdissection confirmed stones localized within cyst lumens. Analysis of individual stones by µCT and infrared spectroscopy confirmed apatite mineral composition. Urinalysis revealed elevated levels of phosphate and citrate at 3 wk of age and lower pH and elevated levels of calcium and citrate at 13 wk of age, suggesting altered phosphate and calcium homeostasis as a potential cause of mineralization and renal stone formation. This is the first animal model exhibiting overt kidney stone formation in the context of cystic kidney disease.NEW & NOTEWORTHY Compound heterozygous Pkd1V/RC mice were found to form calcium phosphate-containing stones within cysts of the renal cortex by 13 wk of age. This is the first polycystic kidney disease animal model exhibiting spontaneous stone formation. A growing body of evidence suggests a link between renal stone formation and cystic kidney disease. This mouse model may be useful for studying the interplay between stone and cyst formation and the functional role of polycystins in mineral homeostasis.

Project description:BackgroundKidney stones or nephrolithiasis is a chronic metabolic disease characterized by renal colic and hematuria. Currently, a pathogenetic mechanism resulting in kidney stone formation remains elusive. We performed a multi-omic study investigating urinary microbial compositions and metabolic alterations during nephrolithiasis.MethodUrine samples from healthy and individuals with nephrolithiasis were collected for 16S rRNA gene sequencing and liquid chromatography-mass spectroscopy. Microbiome and metabolome profiles were analyzed individually and combined to construct interactome networks by bioinformatic analysis.ResultsDistinct urinary microbiome profiles were determined in nephrolithiasis patients compared with controls. Thirty-nine differentially abundant taxa between controls and nephrolithiasis patients were identified, and Streptococcus showed the most significant enrichment in nephrolithiasis patients. We also observed significantly different microbial compositions between female and male nephrolithiasis patients. The metabolomic analysis identified 112 metabolites that were differentially expressed. Two significantly enriched metabolic pathways, including biosynthesis of unsaturated fatty acids and tryptophan metabolism, were also identified in nephrolithiasis patients. Four potentially diagnostic metabolites were also identified, including trans-3-hydroxycotinine, pyroglutamic acid, O-desmethylnaproxen, and FAHFA (16:0/18:2), and could function as biomarkers for the early diagnosis of nephrolithiasis. We also identified three metabolites that contributed to kidney stone size. Finally, our integrative analysis of the urinary tract microbiome and metabolome identified distinctly different network characteristics between the two groups.ConclusionsOur study has characterized important profiles and correlations among urinary tract microbiomes and metabolomes in nephrolithiasis patients for the first time. These results shed new light on the pathogenesis of nephrolithiasis and could provide early clinical biomarkers for diagnosing the disease.