Sequential protein extraction as an efficient method for improved proteome coverage in larvae of Atlantic salmon ( Salmo salar )

Understanding diet- and environmentally induced physiological changes in fish larvae is a major goal for the aquaculture industry. Proteomic analysis of whole fish larvae comprising multiple tissues offers considerable potential but is challenging due to the very large dynamic range of protein abund...

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Bibliographic Details
Published in:PROTEOMICS
Main Authors: Nuez-Ortin, W, Carter, CG, Nichols, PD, Wilson, R
Format: Article in Journal/Newspaper
Language:English
Published: WILEY-VCH Verlag GmbH & Co. KGaA 2016
Subjects:
Online Access:https://doi.org/10.1002/pmic.201600051
http://www.ncbi.nlm.nih.gov/pubmed/27272914
http://ecite.utas.edu.au/109648
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Summary:Understanding diet- and environmentally induced physiological changes in fish larvae is a major goal for the aquaculture industry. Proteomic analysis of whole fish larvae comprising multiple tissues offers considerable potential but is challenging due to the very large dynamic range of protein abundance. To extend the coverage of the larval phase of the Atlantic salmon ( Salmo salar ) proteome, we applied a two-step sequential extraction (SE) method, based on differential protein solubility, using a nondenaturing buffer containing 150 mM NaCl followed by a denaturing buffer containing 7 M urea and 2 M thiourea. Extracts prepared using SE and one-step direct extraction were characterized via label-free shotgun proteomics using nanoLC-MS/MS (LTQ-Orbitrap). SE partitioned the proteins into two fractions of approximately equal amounts, but with very distinct protein composition, leading to identification of ∼40% more proteins than direct extraction. This fractionation strategy enabled the most detailed characterization of the salmon larval proteome to date and provides a platform for greater understanding of physiological changes in whole fish larvae. The MS data are available via the ProteomeXchange Consortium PRIDE partner repository, dataset PXD003366.