Analysis of the Zebrafish Proteome during Embryonic DevelopmentS

• Published in: Molecular & cellular proteomics Vol. 7; no. 5; pp. 981 - 994
• Main Authors: Lucitt, Margaret B., Price, Thomas S., Pizarro, Angel, Wu, Weichen, Yocum, Anastasia K., Seiler, Christoph, Pack, Michael A., Blair, Ian A., FitzGerald, Garret A., Grosser, Tilo
• Format: Journal Article
• Language: English
• Published: American Society for Biochemistry and Molecular Biology 01.05.2008
• ISSN: 1535-9476; 1535-9484
• DOI: 10.1074/mcp.M700382-MCP200

The model organism zebrafish ( Danio rerio ) is particularly amenable to studies deciphering regulatory genetic networks in vertebrate development, biology, and pharmacology. Unraveling the functional dynamics of such networks requires precise quantitation of protein expression during organismal growth, which is incrementally challenging with progressive complexity of the systems. In an approach toward such quantitative studies of dynamic network behavior, we applied mass spectrometric methodology and rigorous statistical analysis to create comprehensive, high quality profiles of proteins expressed at two stages of zebrafish development. Proteins of embryos 72 and 120 h postfertilization (hpf) were isolated and analyzed both by two-dimensional (2D) LC followed by ESI-MS/MS and by 2D PAGE followed by MALDI-TOF/TOF protein identification. We detected 1384 proteins from 327,906 peptide sequence identifications at 72 and 120 hpf with false identification rates of less than 1% using 2D LC-ESI-MS/MS. These included only ∼30% of proteins that were identified by 2D PAGE-MALDI-TOF/TOF. Roughly 10% of all detected proteins were derived from hypothetical or predicted gene models or were entirely unannotated. Comparison of proteins expression by 2D DIGE revealed that proteins involved in energy production and transcription/translation were relatively more abundant at 72 hpf consistent with faster synthesis of cellular proteins during organismal growth at this time compared with 120 hpf. The data are accessible in a database that links protein identifications to existing resources including the Zebrafish Information Network database. This new resource should facilitate the selection of candidate proteins for targeted quantitation and refine systematic genetic network analysis in vertebrate development and biology.