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Title: | Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases | Authors: | Trindade, Fábio Barros, António Sousa Silva, Jéssica Vlahou, Antonia Falcão-Pires, Inês Guedes, Sofia Vitorino, Carla Ferreira, Rita Leite-Moreira, Adelino Amado, Francisco Vitorino, Rui |
Keywords: | urine; peptides; proteases; peptidomics; degradomics; biomarkers; predictive, preventive and personalized (3P) medicine; molecular patterns; individualized patient profiling | Issue Date: | 31-May-2021 | Publisher: | MDPI | Project: | UIDP/00051/2020 UIDB/04501/2020 POCI- 01-0145-FEDER-007628 UIDB/50006/2020 POCI-01-0145-FEDER-016385 FCT - SAICTPAC/0047/2015 IF/00286/2015 UIDP/00051/2020 UIDB/00051/2020 |
metadata.degois.publication.title: | International Journal of Molecular Sciences | metadata.degois.publication.volume: | 22 | metadata.degois.publication.issue: | 11 | Abstract: | Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine. | URI: | https://hdl.handle.net/10316/104805 | ISSN: | 1422-0067 | DOI: | 10.3390/ijms22115940 | Rights: | openAccess |
Appears in Collections: | FFUC- Artigos em Revistas Internacionais I&D CQC - Artigos em Revistas Internacionais I&D CNC - Artigos em Revistas Internacionais |
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