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Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function


Grobei, M A; Qeli, E; Brunner, E; Rehrauer, H; Zhang, R; Roschitzki, B; Basler, K; Ahrens, C H; Grossniklaus, U (2009). Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function. Genome Research, 19(10):1786-1800.

Abstract

Pollen, the male gametophyte of flowering plants, represents an ideal biological system to study developmental processes, such as cell polarity, tip growth, and morphogenesis. Upon hydration, the metabolically quiescent pollen rapidly switches to an active state, exhibiting extremely fast growth. This rapid switch requires relevant proteins to be stored in the mature pollen, where they have to retain functionality in a desiccated environment. Using a shotgun proteomics approach, we unambiguously identified approximately 3500 proteins in Arabidopsis pollen, including 537 proteins that were not identified in genetic or transcriptomic studies. To generate this comprehensive reference data set, which extends the previously reported pollen proteome by a factor of 13, we developed a novel deterministic peptide classification scheme for protein inference. This generally applicable approach considers the gene model-protein sequence-protein accession relationships. It allowed us to classify and eliminate ambiguities inherently associated with any shotgun proteomics data set, to report a conservative list of protein identifications, and to seamlessly integrate data from previous transcriptomics studies. Manual validation of proteins unambiguously identified by a single, information-rich peptide enabled us to significantly reduce the false discovery rate, while keeping valuable identifications of shorter and lower abundant proteins. Bioinformatic analyses revealed a higher stability of pollen proteins compared to those of other tissues and implied a protein family of previously unknown function in vesicle trafficking. Interestingly, the pollen proteome is most similar to that of seeds, indicating physiological similarities between these developmentally distinct tissues.

Pollen, the male gametophyte of flowering plants, represents an ideal biological system to study developmental processes, such as cell polarity, tip growth, and morphogenesis. Upon hydration, the metabolically quiescent pollen rapidly switches to an active state, exhibiting extremely fast growth. This rapid switch requires relevant proteins to be stored in the mature pollen, where they have to retain functionality in a desiccated environment. Using a shotgun proteomics approach, we unambiguously identified approximately 3500 proteins in Arabidopsis pollen, including 537 proteins that were not identified in genetic or transcriptomic studies. To generate this comprehensive reference data set, which extends the previously reported pollen proteome by a factor of 13, we developed a novel deterministic peptide classification scheme for protein inference. This generally applicable approach considers the gene model-protein sequence-protein accession relationships. It allowed us to classify and eliminate ambiguities inherently associated with any shotgun proteomics data set, to report a conservative list of protein identifications, and to seamlessly integrate data from previous transcriptomics studies. Manual validation of proteins unambiguously identified by a single, information-rich peptide enabled us to significantly reduce the false discovery rate, while keeping valuable identifications of shorter and lower abundant proteins. Bioinformatic analyses revealed a higher stability of pollen proteins compared to those of other tissues and implied a protein family of previously unknown function in vesicle trafficking. Interestingly, the pollen proteome is most similar to that of seeds, indicating physiological similarities between these developmentally distinct tissues.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
07 Faculty of Science > Institute of Molecular Life Sciences
07 Faculty of Science > Department of Plant and Microbial Biology
08 University Research Priority Programs > Systems Biology / Functional Genomics
08 University Research Priority Programs > Systems Biology / Functional Genomics
Dewey Decimal Classification:570 Life sciences; biology
580 Plants (Botany)
610 Medicine & health
Language:English
Date:2009
Deposited On:02 Dec 2009 15:44
Last Modified:05 Apr 2016 13:36
Publisher:Cold Spring Harbor Laboratory Press
ISSN:1088-9051
Publisher DOI:10.1101/gr.089060.108
PubMed ID:19546170
Permanent URL: http://doi.org/10.5167/uzh-25087

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