Putting the Pieces Together: High-performance LC-MS/MS Provides Network-, Pathway-, and Protein-level Perspectives in Populus

Paul Abraham,Richard J Giannone,Rachel M Adams,Udaya Kalluri,Gerald A Tuskan,Robert L Hettich

doi:10.1074/mcp.m112.022996

Paul Abraham, Richard J Giannone + Show 4 more

Open Access

https://doi.org/10.1074/mcp.m112.022996

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Abstract

High-performance mass spectrometry (MS)-based proteomics enabled the construction of a detailed proteome atlas for Populus, a woody perennial plant model organism. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: (1) network-wide, (2) pathway-specific, and (3) protein-level viewpoints. Together, enhanced protein retrieval through a detergent-based lysis approach and maximized peptide sampling via the dual-pressure linear ion trap mass spectrometer (LTQ Velos), have resulted in the identification of 63,056 tryptic peptides. The technological advancements, specifically spectral-acquisition and sequencing speed, afforded the deepest look into the Populus proteome, with peptide abundances spanning 6 orders of magnitude and mapping to ∼25% of the predicted proteome space. In total, tryptic peptides mapped to 11,689 protein assignments across four organ-types: mature (fully expanded, leaf plastichronic index (LPI) 10-12) leaf, young (juvenile, LPI 4-6) leaf, root, and stem. To resolve protein ambiguity, identified proteins were grouped by sequence similarity (≥ 90%), thereby reducing the protein assignments into 7538 protein groups. In addition, this large-scale data set features the first systems-wide survey of protein expression across different Populus organs. As a demonstration of the precision and comprehensiveness of the semiquantitative analysis, we were able to contrast two stages of leaf development, mature versus young leaf. Statistical comparison through ANOVA analysis revealed 1432 protein groups that exhibited statistically significant (p ≤ 0.01) differences in protein abundance. Experimental validation of the metabolic circuitry expected in mature leaf (characterized by photosynthesis and carbon fixation) compared with young leaf (characterized by rapid growth and moderate photosynthetic activities) strongly testifies to the credibility of the approach. Instead of quantitatively comparing a few proteins, a systems view of all the changes associated with a given cellular perturbation could be made.

Highlights

From the ‡Graduate School of Genome Science and Technology, University of Tennessee, Knoxville Tennessee 37830; §Chemical Sciences Division at Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831; ¶Biosciences Division, at Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831
Similar to the filter-aided sample preparation (FASP) method, we demonstrate the power of sodium dodecyl sulfate (SDS) for proteomic sample preparation, in its ability to more-thoroughly lyse cells, and its ability to better solubilize both hydrophilic and hydrophobic proteins
Global Protein Identification in Populus—A protein sample derived from plant tissue is likely to consist of over 10,000 different protein species present at any time and the complexity far exceeds an analogous sample derived from any prokaryotic species

Summary

Introduction

From the ‡Graduate School of Genome Science and Technology, University of Tennessee, Knoxville Tennessee 37830; §Chemical Sciences Division at Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831; ¶Biosciences Division, at Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831. We performed multiple (5– 6 each) LTQ Velos ion-trap mass spectrometry measurements on proteome extracts from root, stem and both mature (fully expanded, leaf plastichronic index (LPI) 10 –12) and young leaf (LPI 4 – 6) samples. Spatial Proteomics: Profiling Organ-Specific Proteomes—A function-level view of the different Populus organ proteomes was generated by sorting protein groups into functional categories as defined in the eukaryotic clusters of orthologous groups (KOG) database and weighting by normalized spectral count (nSpC) [17] (Fig. 2).

Results

Conclusion