RESUMO
BACKGROUND: Because of the increasing life expectancy in our society, aging-related neurodegenerative disorders are one of the main issues in global health. Most of these diseases are characterized by the deposition of misfolded proteins and a progressive cognitive decline. Among these diseases, Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are the most common types of degenerative dementia. Although both show specific features, an important neuropathological and clinical overlap between them hampers their correct diagnosis. In this work, we identified molecular biomarkers aiming to improve the misdiagnosis between both diseases. METHODS: Plasma extracellular vesicles (EVs) -from DLB, AD and healthy controls- were isolated using size-exclusion chromatography (SEC) and characterized by flow cytometry, Nanoparticle Tracking Analysis (NTA) and cryo-electron microscopy. Next Generation Sequencing (NGS) and related bibliographic search was performed and a selected group of EV-associated microRNAs (miRNAs) was analysed by qPCR. RESULTS: Results uncovered two miRNAs (hsa-miR-451a and hsa-miR-21-5p) significantly down-regulated in AD samples respect to DLB patients, and a set of four miRNAs (hsa-miR-23a-3p, hsa-miR-126-3p, hsa-let-7i-5p, and hsa-miR-151a-3p) significantly decreased in AD respect to controls. The two miRNAs showing decreased expression in AD in comparison to DLB provided area under the curve (AUC) values of 0.9 in ROC curve analysis, thus suggesting their possible use as biomarkers to discriminate between both diseases. Target gene analysis of these miRNAs using prediction online tools showed accumulation of phosphorylation enzymes, presence of proteasome-related proteins and genes involved in cell death among others. CONCLUSION: Our data suggest that plasma-EV associated miRNAs may reflect a differential profile for a given dementia-related disorder which, once validated in larger cohorts of patients, could help to improve the differential diagnosis of DLB versus AD.
RESUMO
BACKGROUND: Kidney transplantation (KTx) is the best therapeutic approach for chronic kidney diseases leading to irreversible kidney failure. Considering the origin of the graft, several studies have reported differences between living (LD) and deceased donors (DD) in graft and patient survival. These differences seem to be related to multiple factors including, donor age and time of cold ischemia among others. Many of transplanted organs come from old-aged DDs, in which pre-transplant biopsy is recommended. However, kidney biopsy has several limitations, and there is a need to develop alternatives to assess the status of a kidney before transplantation. As the analysis of urinary extracellular vesicles (uEVs) rendered promising results as non-invasive biomarkers of kidney-related pathologies, this pilot study aimed to investigate whether profiling uEVs of LDs and DDs may be of help to assess the quality of the kidney before nephrectomy. METHODS: uEVs from 5 living donors and 7 deceased donors were isolated by size-exclusion chromatography, and their protein and miRNA content were analysed by liquid chromatography followed by mass spectrometry and next generation sequencing, respectively. Then, hierarchical clustering and venn diagrams were done with Perseus software and InteractiVenn tool. Specific EVs data bases were also used for Gene Ontology analysis. RESULTS: Next generation sequencing revealed that uEVs from DDs contained less miRNAs than LDs, but most of the DD-expressed miRNAs were shared with LDs (96%). Only miR-326 (targeting the apoptotic-related Bcl2) was found significantly over-represented in LD. Focusing on the protein content, we detected a low intra-group correlation in both types of donors. Despite these differences, hierarchical clustering of either miRNA or protein data could not identify a differential profile between LDs and DDs. Of note, 90% of transplanted patients had a functional graft after a year from KTx. CONCLUSIONS: In this pilot study we found that, in normo-functional grafts, minor differences in uEVs profile could not discriminate between LDs and DDs.
