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 ∼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.

A new study reveals a functional rule for N-terminal acetylation in higher eukaryotes called the (X)PX rule and describes a generic method that prevents this modification to allow the study of N-terminal acetylation in any given protein.

Bradyrhizobium japonicum, a gram-negative soil bacterium that establishes an N(2)-fixing symbiosis with its legume host soybean (Glycine max), has been used as a symbiosis model system. Using a sensitive geLC-MS/MS proteomics approach, we report the identification of 2315 B. japonicum strain USDA110 proteins (27.8% of the theoretical proteome) that are expressed 21 days post infection in symbiosis with soybean cultivated in growth chambers, substantially expanding the previously known symbiosis proteome. Integration of transcriptomics data generated under the same conditions (2780 expressed genes) allowed us to compile a comprehensive expression profile of B. japonicum during soybean symbiosis, which comprises 3587 genes/proteins (43% of the predicted B. japonicum genes/proteins). Analysis of this data set revealed both the biases and the complementarity of these global profiling technologies. A functional classification and pathway analysis showed that most of the proteins involved in carbon and nitrogen metabolism are expressed, including a complete set of tricarboxylic acid cycle enzymes, several gluconeogenesis and pentose phosphate pathway enzymes, as well as several proteins that were previously not considered to be present during symbiosis. Congruent results were obtained for B. japonicum bacteroids harvested from soybeans grown under field conditions.

Proteomes, the ensembles of all proteins expressed by cells or tissues, are typically analysed by mass spectrometry. Recent technical and computational advances have greatly increased the fraction of a proteome that can be identified and quantified in a single study. Current mass spectrometry-based proteomic strategies have the potential to reproducibly, accurately, quantitatively and comprehensively measure any protein or whole proteomes from cells and tissues at different states. Achieving these goals will require complete proteome maps and analytical strategies that use these maps as prior information and will greatly enhance the impact of proteomics on biological and clinical research.

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