Skip navigation

A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans

A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans

Boateng, Joshua ORCID: 0000-0002-6310-729X, Kay, Richard, Lancashire, Lee, Brown, Pamela, Velloso, Cristiana, Bouloux, Pierre, Teale, Phil, Roberts, Jane, Rees, Robert, Ball, Graham, Harridge, Stephen, Goldspink, Geoffrey and Creaser, Colin (2009) A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans. Proteomics - Clinical Applications, 3 (8). pp. 912-922. ISSN 1862-8346 (Print), 1862-8354 (Online) (doi:10.1002/prca.200800190)

[img] PDF (Publisher's PDF)
3550 BOATENG_Proteomic_Approach_ 2009.pdf - Published Version
Restricted to Registered users only

Download (357kB)

Abstract

An integrated MS-based proteomic approach is described that combines MALDI-MS and LC-MS with artificial neural networks for the identification of protein and peptide biomarkers associated with recombinant human growth hormone (rhGH) administration. Serum from exercised males administered with rhGH or placebo was analysed using ELISA to determine insulin-like growth factor-I concentrations. Diluted serum from rhGH- and placebo-treated subjects was analysed for protein biomarkers by MALDI-MS, whereas LC-MS was used to analyse tryptically digested ACN-depleted serum extracts for peptide biomarkers. Ion intensities and m/z values were used as inputs to artificial neural networks to classify samples into rhGH- and placebo-treated groups. Six protein ions (MALDI-MS) correctly classified 96% of samples into their respective groups, with a sensitivity of 91% (20 of 22 rhGH treated) and specificity of 100% (24 of 24 controls). Six peptide ions (LC-MS) were also identified and correctly classified 93% of samples with a sensitivity of 90% (19 of 21 rhGH treated) and a specificity of 95% (20 of 21 controls). The peptide biomarker ion with the highest significance was sequenced using LC-MS/MS and database searching and found to be associated with leudne-rich alpha-2-glycoprotein.

Item Type: Article
Uncontrolled Keywords: Bioinformatics; ESI-MS/MS; LC, MALDI; Serum biomarkers
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
Q Science > QP Physiology
Q Science > QH Natural history > QH301 Biology
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Pharmaceutical, Chemical & Environmental Sciences
Related URLs:
Last Modified: 09 Dec 2016 12:55
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/3550

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics