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Tepus Petrsoric, M, 2025. Do urine vesicles offer a fingerprint of chronic kidney disease progression? PhD, Nottingham Trent University.
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Melanie Tepus Petrsoric 2025.pdf - Published version Download (14MB) | Preview |
Abstract
Diabetic nephropathy (DN) is the most prevalent form of chronic kidney disease (CKD), with symptoms typically appearing in the later stages. As a result, it frequently progresses to renal fibrosis and kidney failure. Despite an increasing number of studies is focusing on biomarkers for DN and other types of CKD, there is still a pressing need to discover non-invasive markers for monitoring the progression of DN. Urinary extracellular vesicles (uEVs) are a promising source of kidney disease biomarkers since they are secreted from kidney cells, play a role in inter-cellular communication, and carry molecular cargo that reflects pathophysiological conditions. The primary aim of this study was to use a multi omics approach to identify a multi-marker panel of DN progression employing uEVs as a source of biomarkers (proteins, miRNAs) and chemical-physical features such as surface charge. Exploring the interaction between uEVs miRNA and protein markers of DN was a further key objective expected to uncover new regulatory mechanisms involved in DN.
Cell-free urine samples of DN (n=53), diabetes with no CKD (n=20), and healthy (n=20) cohorts were sourced from Sheffield Kidney Institute (UK) and Patras University Hospital (Greece) with uniform standardised criteria and informed consent. DN cohorts were stratified in stable and progressive disease based on rate of eGFR loss, respectively 5 ml/min/year (n=25). uEVs were isolated and characterised with adherence to MISEV. Protein uEVs markers were identified through quantitative proteomics of uEVs lysates for each subject and a selection validated using single EV analysis directly in biofluids (pooled samples). miRNA markers were detected via miRNA sequencing on individual samples and by NanoString digital profiling (in pool) and validated by TaqMan Advanced qPCR. uEVs surface charge was measured in individual subjects uEVs preparations.
Quantitative proteomics revealed 72 proteins with differential expression in progressive versus stable DN when normalised to equal uEVs protein (46 proteins when normalised to equal volume of starting urine). The differential expression of five proteins was confirmed in progressive DN (Vasorin, Podocalyxin, Mucin 1, Ganglioside GM2 activator, 5 and Argininosuccinate synthase). Transcriptomic analyses identified four miRNAs with differential expression in progressive versus stable DN of which three (miR-99a-5p, miR 223-3p, miR-3613-5p) exhibited the same expression trend when validated. Moreover, a panel of proteins and miRNAs (L-lactate dehydrogenase C chain, Muscleblind-like protein-1, miR-891a-5p, miR-432-5p, miR-890, miR-892a) was found significantly altered in DN compared to diabetes. As miR-99a-5p and miR-223-3p were predicted to directly target and regulate the expression of Muscleblind-like protein 1, their direct interaction was confirmed by 3’UTR luciferase reporter system in transfected HK2 cells. Quantification of uEVs surface charge revealed a significantly less negative charge in diabetes without CKD than in DN uEVs (p<0.05), suggestive of glycomics changes.
In conclusion, this study has identified and validated a novel panel of uEV markers for DN progression, which could assist in stratifying DN, particularly patients who are at risk of progressing to the advanced progressive stage. The crosstalk between uEV miRNAs and proteins dysregulated in DN raises new research questions that could help uncover mechanisms of DN pathogenesis and increased severity.
| Item Type: | Thesis |
|---|---|
| Creators: | Tepus Petrsoric, M. |
| Contributors: | Name Role NTU ID ORCID |
| Date: | January 2025 |
| Rights: | This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the author. |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Jeremy Silvester |
| Date Added: | 04 Jul 2025 10:40 |
| Last Modified: | 04 Jul 2025 10:40 |
| URI: | https://irep.ntu.ac.uk/id/eprint/53882 |
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