Dataset Information

Quantitative proteomic analysis of urinary exosomes in kidney stone patients.

ABSTRACT: Background:Increased urinary exosomes are associated with kidney stones but how they work is unknown. In this study, we aim to identify dysregulated proteins in urinary exosomes from kidney stone patients and to explore the potential role of exosomal proteins in nephrolithiasis. Methods:First morning voids were collected from participants. Urinary exosomes were isolated via ultracentrifugation. Label free liquid chromatography-tandem mass spectrometry was performed to analyze the proteome of urine exosomes from three kidney stone patients and three age-/sex-matched healthy controls. Bioinformatics analysis was conducted to identify dysregulated proteins associated with stone formation. Results of proteomic analysis were verified by Western blotting in other three kidney stone patients and three healthy controls. Results:Nine hundred and sixty proteins were identified with proteomic analysis, of which 831 were identified in the control group and 879 in the stone group. Sixteen proteins in urinary exosomes were found most significantly different between kidney stone patients and healthy controls. Gene ontology (GO) analysis showed that dysregulated proteins were enriched in innate immune response, defense response to bacterium and calcium-binding. S100A8, S100A9 and S100A12 were common in above three GO terms and were chosen for further study. Western blotting confirmed that the expression of these three S100 proteins was higher in urinary exosomes from kidney stone patients. In addition, S100 proteins were aggregated in urinary exosomes and it was difficult to detect them in urine. Conclusions:Urinary exosomes from kidney stone patients are rich in S100 proteins and play a role in innate immune response, defense response to bacterium and calcium-binding.

SUBMITTER: Wang Q

PROVIDER: S-EPMC7475656 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative proteomic analysis of urinary exosomes in kidney stone patients.

Wang Qing Q Sun Yi Y Yang Yuanyuan Y Li Cong C Zhang Jiaqiao J Wang Shaogang S

Translational andrology and urology 20200801 4

<h4>Background</h4>Increased urinary exosomes are associated with kidney stones but how they work is unknown. In this study, we aim to identify dysregulated proteins in urinary exosomes from kidney stone patients and to explore the potential role of exosomal proteins in nephrolithiasis.<h4>Methods</h4>First morning voids were collected from participants. Urinary exosomes were isolated via ultracentrifugation. Label free liquid chromatography-tandem mass spectrometry was performed to analyze the ...[more]

PMID: 32944520

Similar Datasets

Project description:Background and objectivesMicrovesicles and exosomes are involved in the pathogenesis of autosomal dominant polycystic kidney disease. However, it is unclear whether they also contribute to medullary sponge kidney, a sporadic kidney malformation featuring cysts, nephrocalcinosis, and recurrent kidney stones. We addressed this knowledge gap by comparative proteomic analysis.Design, setting, participants, & measurementsThe protein content of microvesicles and exosomes isolated from the urine of 15 patients with medullary sponge kidney and 15 patients with autosomal dominant polycystic kidney disease was determined by mass spectrometry followed by weighted gene coexpression network analysis, support vector machine learning, and partial least squares discriminant analysis to compare the profiles and select the most discriminative proteins. The proteomic data were verified by ELISA.ResultsA total of 2950 proteins were isolated from microvesicles and exosomes, including 1579 (54%) identified in all samples but only 178 (6%) and 88 (3%) specific for medullary sponge kidney microvesicles and exosomes, and 183 (6%) and 98 (3%) specific for autosomal dominant polycystic kidney disease microvesicles and exosomes, respectively. The weighted gene coexpression network analysis revealed ten modules comprising proteins with similar expression profiles. Support vector machine learning and partial least squares discriminant analysis identified 34 proteins that were highly discriminative between the diseases. Among these, CD133 was upregulated in exosomes from autosomal dominant polycystic kidney disease and validated by ELISA.ConclusionsOur data indicate a different proteomic profile of urinary microvesicles and exosomes in patients with medullary sponge kidney compared with patients with autosomal dominant polycystic kidney disease. The urine proteomic profile of patients with autosomal dominant polycystic kidney disease was enriched of proteins involved in cell proliferation and matrix remodeling. Instead, proteins identified in patients with medullary sponge kidney were associated with parenchymal calcium deposition/nephrolithiasis and systemic metabolic derangements associated with stones formation and bone mineralization defects.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_04_24_CJASNPodcast_19_06_.mp3.

Project description:BackgroundProteins are the most abundant component of kidney stone matrices and their presence may reflect the process of the stone's formation. Many studies have explored the proteomics of urinary stones and crystals. We sought to comprehensively identify the proteins found in kidney stones and to identify new, reliable biomolecules for use in nephrolithiasis research.MethodsWe conducted bioinformatics research in November 2020 on the proteomics of urinary stones and crystals. We used the ClusterProfiler R package to transform proteins into their corresponding genes and Ensembl IDs. In each study we located where proteomic results intersected to determine the 20 most frequently identified stone matrix proteins. We used the Human Protein Atlas to obtain the biological information of the 20 proteins and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analysis to explore their biological functions. We also performed immunohistochemistry to detect the expression of the top five stone matrix proteins in renal tissue.ResultsWe included 19 relevant studies for analysis. We then identified 1,409 proteins in the stone matrix after the duplicates were removed. The 20 most-commonly identified stone matrix proteins were: S100A8, S100A9, uromodulin, albumin, osteopontin, lactotransferrin, vitamin K-dependent protein Z, prothrombin, hemoglobin subunit beta, myeloperoxidase, mannan-binding lectin serine protease 2, lysozyme C, complement C3, serum amyloid P-component, cathepsin G, vitronectin, apolipoprotein A-1, eosinophil cationic protein, fibrinogen alpha chain, and apolipoprotein D. GO and KEGG analysis revealed that these proteins were typically engaged in inflammation and immune response.Immunohistochemistry of the top five stone matrix proteins in renal tissue showed that the expression of S100A8, S100A9, and osteopontin increased, while uromodulin decreased in kidney stone patients. Albumin was rarely expressed in the kidney with no significant difference between healthy controls and kidney stone patients.ConclusionProteomic analysis revealed some common inflammation-related proteins in the kidney stone matrix. The role of these proteins in stone formation should be explored for their potential use as diagnostic biomarkers and therapeutic targets for urolithiasis.

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.

Dataset Information

Quantitative proteomic analysis of urinary exosomes in kidney stone patients.

Publications

Quantitative proteomic analysis of urinary exosomes in kidney stone patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets