Global Analysis of Condition-specific Subcellular Protein Distribution and Abundance

A large number of proteomic studies have focused on profiling or quantifying protein components of specific subcellular compartments or macromolecular structures in a variety of cell types. These include proteomic studies of the nucleus, mitochondrion, Golgi apparatus, cell wall, peroxisome, plasma membrane, nucleolus, lysosome, and vacuole (1). These studies provide valuable insights into the biological functions of the organelles as well as individual protein components. Analysis of organelle protein inventories is complicated by the fact that a given protein may have multiple functional roles (2) and may have multiple subcellular locations as well as spatially and temporally dynamic distributions. For example, our previous study showed that glycerol-3-phosphate dehydrogenase 1 (Gpd1) localizes to not only cytosol but also peroxisomes (3, 4). Localization of Gpd1 to peroxisomes is dependent on both the metabolic status of cells and the phosphorylation of aminoacyl residues adjacent to the peroxisomal targeting signal. In addition, exposure of cells to osmotic stress induces subcellular redistribution of Gpd1 to the cytosol and nucleus. This example reinforces the fact that protein localization is an important and complex aspect of cellular response to environmental stimuli. For this reason, it is important to not only define the proteome of subcellular organelles, but also to measure dynamic distribution of proteins between different compartments. Shotgun mass spectrometry (MS) approaches have been widely used, especially for comparative and quantitative proteomics. Traditionally, shotgun proteomic analysis has been performed in a data-dependent manner where ion selection for collision induced dissociation relies on a preliminary precursor ion scan, from which peptides are selected and then subjected to collision induced dissociation (5). This general approach has become extremely powerful for determining the protein content of moderately complex mixtures. However, the ability to compare different samples is complicated by the semi-random sampling process of data-dependent acquisition (DDA)1, which under-samples available ions (5, 6). The bias in data-dependent ion selection is generally against ions of low signal to noise ratio leaving a portion of peptide ions detected (as precursor ions) but not identified. Recently, a novel tandem MS based proteome screen termed Precursor Acquisition Independent From Ion Count (PAcIFIC) was shown to identify proteins over an order of magnitude larger dynamic range than standard shotgun proteomic methods in a complex proteome (7, 8). This data-independent acquisition (DIA) MS technique systematically interrogates all available m/z channels for the presence of peptides, regardless of the observation of a precursor ion. PAcIFIC thus provides increased confidence in protein identification because all peptides present, irrespective of abundance, are eventually fragmented, which can dramatically improve protein sequence coverage. Therefore, this approach provides a simplified, direct, and systematic approach for proteomic screening. The yeast, Saccharomyces cerevisiae, can use a variety of carbon sources; glucose is catabolized mainly by fermentation, whereas nonfermentative carbon sources, such as ethanol, glycerol, and fatty acids are catabolized by respiration (9). In the presence of abundant glucose, fermentation is dominant and glycolysis in the cytosol generates all the necessary energy and biomass, and proteins required for utilization of nonfermentable substrates, including mitochondrial proteins involved in respiration, as well as peroxisomal proteins involved in β-oxidation of fatty acids, are repressed (10). In the absence of a fermentative carbon source, respiration dominates. Use of fatty acids such as oleic acid as the sole carbon source by S. cerevisiae requires the coordinated function of peroxisomes, where fatty acids are catabolized, and mitochondria, where oxidation is completed (11). We sought to establish a proteomic approach for global analysis of coordinated and conditional protein redistribution. A strategy combining a sensitive and quantitative DIA-MS method with subcellular fractionation was developed to measure changes in protein abundance and subcellular distribution under different conditions. We applied this strategy to globally characterize protein dynamics in response to a switch from glucose- to oleic acid-containing medium. These conditions represent two metabolic conditions of yeast, fermentation and respiration, and were selected because of the wealth of information available in the literature. The analysis generated data for more than 60% of the yeast proteome and identified over 1000 candidate proteins that responded to the stimulus. These data demonstrate that the strategy is capable of measuring condition-specific changes in abundance and redistribution of proteins on a global scale.