Characterization of the chloroplast proteome is needed to understand the essential contribution of the chloroplast to plant growth and development. Here we present a large scale analysis by nanoLC-Q-TOF and nanoLC-LTQ-Orbitrap mass spectrometry (MS) of ten independent chloroplast preparations from Arabidopsis thaliana which unambiguously identified 1325 proteins. Novel proteins include various kinases and putative nucleotide binding proteins. Based on repeated and independent MS based protein identifications requiring multiple matched peptide sequences, as well as literature, 916 nuclear-encoded proteins were assigned with high confidence to the plastid, of which 86% had a predicted chloroplast transit peptide (cTP). The protein abundance of soluble stromal proteins was calculated from normalized spectral counts from LTQ-Obitrap analysis and was found to cover four orders of magnitude. Comparison to gel-based quantification demonstrates that ‘spectral counting’ can provide large scale protein quantification for Arabidopsis. This quantitative information was used to determine possible biases for protein targeting prediction by TargetP and also to understand the significance of protein contaminants. The abundance data for 550 stromal proteins was used to understand abundance of metabolic pathways and chloroplast processes. We highlight the abundance of 48 stromal proteins involved in post-translational proteome homeostasis (including aminopeptidases, proteases, deformylases, chaperones, protein sorting components) and discuss the biological implications. N-terminal modifications were identified for a subset of nuclear- and chloroplast-encoded proteins and a novel N-terminal acetylation motif was discovered. Analysis of cTPs and their cleavage sites of Arabidopsis chloroplast proteins, as well as their predicted rice homologues, identified new species-dependent features, which will facilitate improved subcellular localization prediction. No evidence was found for suggested targeting via the secretory system. This study provides the most comprehensive chloroplast proteome analysis to date and an expanded Plant Proteome Database (PPDB) in which all MS data are projected on identified gene models.

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A gronomy J our n al • Volume 10 0 , I s sue 3 • 2 0 0 8 517 ABSTRACT Th e improved soil N min -based N management is a promising approach to precision N management, which determines the optimum side-dress N rates based on N target values and measured soil nitrate N content in the root soil layer at diff erent growth stages. A total of 148 on-farm N-response experiments, in seven key summer maize (Zea mays L.) production regions of North China Plain (NCP) from 2003 to 2005, were conducted to evaluate the N min -based N management compared to traditional farmer's N practices. Th e recommended N rates based on the improved soil N min method were not signifi cantly diff erent ( ≤31 kg N ha -1 ) from those determined by yield response curves (n = 13). Th e average N rate determined with the soil N min method (157 kg N ha -1 ) was signifi cantly lower than farmer's practice (263 kg N ha -1 ), while maize grain yield was 0.4 Mg ha -1 higher than farmer's N practice (8.5 Mg ha -1 ) across all sites (n = 148). As a result, the improved soil N min -based N management signifi cantly increased net economic gains by $202 ha -1 , reduced residual nitrate N content and N losses by 44 kg N ha -1 and 65 kg N ha -1 , respectively, and improved recovery N effi ciency, agronomic N effi ciency and N partial factor productivity by 16%, 6 kg kg -1 and 36 kg kg -1 , respectively, compared with farmer's N practice. We conclude that the improved soil N min -based N management can be applied for summer maize production in NCP for improved N use effi ciency and reduced environmental contamination.

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