Comprehensive Absolute Quantification of the Cytosolic Proteome of Bacillus subtilis by Data Independent,Parallel Fragmentation in Liquid Chromatography/Mass Spectrometry (LC/MSE)

In the growing field of systems biology, the knowledge of protein concentrations is highly required to truly understand metabolic and adaptational networks within the cells. Therefore we established a workflow relying on long chromatographic separation and mass spectrometric analysis by data independent, parallel fragmentation of all precursor ions at the same time (LC/MS^E). By prevention of discrimination of co-eluting low and high abundant peptides a high average sequence coverage of 40% could be achieved, resulting in identification of almost half of the predicted cytosolic proteome of the Gram-positive model organism Bacillus subtilis (>1,050 proteins). Absolute quantification was achieved by correlation of average MS signal intensities of the three most intense peptides of a protein to the signal intensity of a spiked standard protein digest. Comparative analysis with heavily labeled peptides (AQUA approach) showed the use of only one standard digest is sufficient for global quantification.The quantification results covered almost four orders of magnitude, ranging roughly from 10 to 150,000 copies per cell. To prove this method for its biological relevance selected physiological aspects of B. subtilis cells grown under conditions requiring either amino acid synthesis or alternatively amino acid degradation were analyzed. This allowed both in particular the validation of the adjustment of protein levels by known regulatory events and in general a perspective of new insights into bacterial physiology. Within new findings the analysis of “protein costs” of cellular processes is extremely important. Such a comprehensive and detailed characterization of cellular protein concentrations based on data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MS^E) data has been performed for the first time and should pave the way for future comprehensive quantitative characterization of microorganisms as physiological entities.In contrast to the rather static genome, composition of the proteome greatly varies with respect to environmental conditions (availability of nutrients, medium composition, stress, etc.) reflecting its key role in the adaptation of cells (1). Hence, proteome data for varying growth conditions should help to reach a comprehensive understanding of the physiology of adaptation to different nutritional conditions, which is the typical situation of bacterial cells in nature (2). In this context the availability of high quality absolute protein quantification data is of outstanding importance for the emerging field of systems biology because (a) proteins are major players for most biological processes and (b) their abundances decisively determine the adaptation rate of cellular processes. Additionally, an emerging set of theoretical and experimental works (reviewed in (3)) recently emphasized the importance of resource allocation in the growth rate management. Bacterial cells have to invest an available set of limited resources into biological processes to ensure growth and survival. Protein costs (or protein burden) of a biological process, defined as the total mass of proteins invested in the biological process, is then critical and must be finely tuned to sustain growth of bacteria. The determination of protein costs of different biological processes using genome-scale absolute protein quantification should thus represent a major breakthrough in understanding bacterial physiology and cellular design.For many years the gold standard for absolute protein quantification has been quantitative Western blotting and has been successfully applied, for example, to the yeast proteome (4). In recent years mass spectrometry based absolute proteome quantification techniques have become available allowing determination of cellular protein concentrations. The absolute protein amount can be precisely determined by spiking defined amounts of isotopically labeled synthetic peptides into a protein digest (5). Absolute protein amounts become available by detection and comparison of signal intensities of heavy and light peptides, but only for proteins related to the added synthetic peptides. This method was extended to a more global absolute quantification (AQUA)¹ by calibrating 2D gels with anchor proteins (6). Although the use of internal labeled standards for absolute protein quantification is very precise, availability and costs for such reference peptides are surely limiting. Therefore label free quantification techniques emerged. One of these methods is based on spectral counting. There, the number of sequenced peptides per protein is used to calculate the absolute quantity of one single protein in a complex sample (emPAI) (7). This can be refined by consideration of physicochemical properties of its peptides (APEX) (8, 9). Absolute protein quantification can also be achieved by comparing average signal intensities of the three most intense peptides per protein to an internally digested standard protein (Hi3 approach). Previous results showed that these average signal intensities per mole protein are constant within a tolerance of 10% (10). Recently the smart combination of AQUA technique and APEX approach was successfully applied to Leptospira interrogans covering about half of the proteome with an error less than threefold (11).In order to quantify the highest possible number of expressed proteins in an absolute manner mass spectrometry based methods seem to be the method of choice. Within the field of proteomics MS is often coupled with liquid chromatography to reduce sample complexity prior to MS analysis (LC/MS). Commonly applied data dependent acquisition (DDA) methods for peptide identification suffer from some limitations. Often low abundant peptides with a low MS signal intensity are discriminated and their isobaric precursor ions cannot be isolated leading to low scores in database search and wrong assignments (12, 13). These obstacles lead to lower protein sequence coverage in general and higher numbers of protein identifications based on a single peptide only. In contrast, with data independent acquisition (DIA) methods like LC/MS^E (14) all available precursor ions are fragmented in parallel without any selection by switching between low and high collision energy scans in high frequency. Therefore DIA can circumvent the disadvantages of DDA mentioned above. LC/MS^E utilizes chromatographic elution profiles of precursor masses to track the fragment ion spectra. Because all charge states and isotopic peaks of precursor ions are included for fragmentation (15) the LC/MS^E technique enables higher sequence coverage and has large advantages in the analysis of highly complex samples consisting of numerous co-eluting peptides (16). Combination of DIA methods with the approach based on Hi3 signal intensities was shown to be of potentially high performance for absolute protein quantification at global scale (17, 18) and is therefore used in this study.In this article, we applied a global absolute quantification approach based on the Hi3 method and data independent acquisition to the Gram-positive model bacterium Bacillus subtilis grown under two conditions for which large differences both in absolute protein amount per cell and in the predicted configurations of metabolic pathways were expected (19): a glucose and ammonia salts minimal medium (condition S) and a solely amino acid based medium (condition CH). This experimental set up enabled the physiologically meaningful comparison of profound consequences of growth under conditions requiring amino acid synthesis (S) or amino acid degradation (CH), as well as concerning the change between glycolytic (S) and gluconeogenic growth (CH). Moreover, as a model bacterium closely related to very important pathogens, B. subtilis is one of the best studied microorganisms. Particularly relevant for our study, the genomic organization of the chromosome, the regulatory network and metabolic pathways are well characterized. Based on this existing knowledge, global absolute protein quantification exemplary enabled (a) the large-scale investigation of protein distribution between cellular processes, (b) the systematic analysis of differential protein abundances for genes belonging to an operon (referred to as operon heterogeneity); and (c) the computation of the protein costs of cellular processes and of metabolic pathways in particular.