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The hexaploid genome of bread wheat (Triticum aestivum) is large (17 Gb) and repetitive, and this has delayed full sequencing and annotation of the genome, which is a prerequisite for effective quantitative proteomics analysis. Aware of these constraints we investigated the most effective approaches for shotgun proteomic analyses of bread wheat that would support large-scale quantitative comparisons using iTRAQ reagents. We used a data set that was generated by two-dimensional LC-MS of iTRAQ labeled peptides from wheat leaves. The main items considered in this study were the choice of sequence database for matching LC-MS data, the consistency of identification when multiple LC-MS runs were acquired, and the options for downstream functional analysis to generate useful insight. For peptide identification we examined the extensive NCBInr plant database, a smaller composite cereals database, the Brachypodium distachyon model plant genome, the EST-based SuperWheat database, as well as the genome sequence from the recently sequenced D-genome progenitor Aegilops tauschii. While the most spectra were assigned by using the SuperWheat database, this extremely large database could not be readily manipulated for the robust protein grouping that is required for large-scale, multirun quantitative experiments. We demonstrated a pragmatic alternative of using the composite cereals database for peptide spectra matching. The stochastic aspect of protein grouping across LC-MS runs was investigated using the smaller composite cereals database where we found that attaching the Brachypodium best BLAST hit reduced this problem. Further, assigning quantitation to the best Brachypodium locus yielded promising results enabling integration with existing downstream data mining and functional analysis tools. Our study demonstrated viable approaches for quantitative proteomics analysis of bread wheat samples and shows how these approaches could be similarly adopted for analysis of other organisms with unsequenced or incompletely sequenced genomes.

Background: Cabernet Sauvignon grapevines were exposed to a progressive, increasing water defict over 16 days. Shoot elongation and photosynthesis were measured for physiological responses to water deficit. The effect of water deficit over time on the abundance of individual proteins in growing shoot tips (including four immature leaves) was analyzed using nanoflow liquid chromatography - tandem mass spectrometry (nanoLC-MS/MS)., Results: Water deficit progressively decreased shoot elongation, stomatal conductance and photosynthesis after Day 4; 2277 proteins were identified by shotgun proteomics with an average CV of 9% for the protein abundance of all proteins. There were 472 out of 942 (50%) proteins found in all samples that were significantly affected by water deficit. The 472 proteins were clustered into four groups: increased and decreased abundance of early- and late-responding protein profiles. Vines sensed the water deficit early, appearing to acclimate to stress, because the abundance of many proteins changed before decreases in shoot elongation, stomatal conductance and photosynthesis. Predominant functional categories of the early-responding proteins included photosynthesis, glycolysis, translation, antioxidant defense and growth-related categories (steroid metabolism and water transport), whereas additional proteins for late-responding proteins were largely involved with transport, photorespiration, antioxidants, amino acid and carbohydrate metabolism., Conclusions: Proteomic responses to water deficit were dynamic with early, significant changes in abundance of proteins involved in translation, energy, antioxidant defense and steroid metabolism. The abundance of these proteins changed prior to any detectable decreases in shoot elongation, stomatal conductance or photosynthesis. Many of these early-responding proteins are known to be regulated by post-transcriptional modifications such as phosphorylation. The proteomics analysis indicates massive and substantial changes in plant metabolism that appear to funnel carbon and energy into antioxidant defenses in the very early stages of plant response to water deficit before any significant injury.

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.