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GIOVANNA CILLUFFO

Feasibility of shotgun urinary proteomics for investigating prematurely born preschoolers (PBP)

  • Autori: Mauri, P.; Rossi, R.; De Palma, A.; Gagliardo, R.; Malizia, V.; Cilluffo, G.; Ferrante, G.; D'Arpa, S.; La Grutta, S.
  • Anno di pubblicazione: 2016
  • Tipologia: Abstract in atti di convegno pubblicato in rivista
  • Parole Chiave: Preterm, children
  • OA Link: http://hdl.handle.net/10447/243305

Abstract

Background: Preterms and twins are at higher risk of respiratory morbidity later in life. Advances in proteomic approaches may allow the characterization of biomarkers involved in respiratory diseases (Mauri et al. Imm. Lett. 2014;162:2-10). Gel-free approach quantitatively identify differentially expressed proteins in relation to physiopathological conditions (Mauri&Dehò, Meth Enzymology 2008;447:99-117). This can improve the clinical reliability of the next generation of biomarkers to discriminate multiple phenotypes of childhood respiratory diseases. Aim: To assess the ability of gel-free proteomics for identifying specific protein profiles related to PBP. Methods: Urine samples were collected from 3-5 years children: 43 prematurely born (33.7 mean gestational age - GA), including 20 twins (34.4 mean GA), and 23 full-term born children, enrolled in the Preterm Asthma and Rhinitis (PRE-AR) ongoing longitudinal study at IBIM. An innovative approach based on nanochip liquid chromatography combined to high resolution mass spectrometry was used to obtain the proteomics profiles. Results: Proteomics analyses permitted the identification of 300 proteins in each urine sample, with a good repeatability (R2>0.99) and these are used for grouping the samples in relation to the different disease phenotypes. Conclusions: Obtained data indicate the proteomics approach as a promising platform for characterizing specific profiles related to PBP. These results represent a proof of principle for the proteomics application to all PRE-AR cases: along with clinical information, FeNO and lung function tests, this approach will attempt to discover candidate biomarkers and related metabolic pathways.