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Simultaneous Qualitative and Quantitative Analysis of theEscherichia coli Proteome

2006; Elsevier BV; Volume: 5; Issue: 4 Linguagem: Inglês

10.1074/mcp.m500321-mcp200

1535-9484

Jeffrey C. Silva, Richard Denny, Craig A. Dorschel, M. V. Gorenstein, Guozhong Li, Keith Richardson, Daniel Wall, Scott Geromanos,

Genomics and Phylogenetic Studies

We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1- to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to 80%, consisting of one to 34 peptides per protein. The quantitative results obtained from our study were comparable to other existing proteomic and transcriptional profiling approaches. This study illustrates the robustness of this novel LCMS approach for the simultaneous quantitative and comprehensive qualitative analysis of proteins in complex mixtures. We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1- to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to 80%, consisting of one to 34 peptides per protein. The quantitative results obtained from our study were comparable to other existing proteomic and transcriptional profiling approaches. This study illustrates the robustness of this novel LCMS approach for the simultaneous quantitative and comprehensive qualitative analysis of proteins in complex mixtures. Escherichia coli is a microbial symbiote found in the colon and large intestine of most warm blooded animals that plays a critical role in vertebrate anabolism and catabolism. The environment in which E. coli lives is subject to rapid changes in the availability of the carbon and nitrogen compounds necessary to provide its energy and primary building blocks. E. coli survival hinges on the ability to successfully control the expression of genes coding for enzymes and proteins required for growth in response to environmental changes. Because of its simple cellular structure and its relative ease of maintenance and manipulation in the laboratory, E. coli has become the "workhorse host" for most research in molecular biology and microbiology. As a result, it is regarded as one of the most completely characterized organisms in all biology. The ease with which recombinant proteins can be expressed in E. coli has made this bacterium useful in the study of many basic biological processes as well as in the production of heterologous proteins for research and therapeutic purposes. For these reasons, E. coli has become a model system for testing new analytical technologies. For example, the relatively small genome size and prevalent laboratory use made E. coli genome one of the first to be completely sequenced (1Blattner F.R. Plunkett G. Bloch C.A. Perna N.T. Burland V. Tiley M. Collado-Vides J. Glasner J.D. Rode C.K. Mayhew G.F. Gregor J. Davis N.W. Kirkpatrick H.A. Goeden M.A. Rose D.J. Mau B. Shao Y. The complete genome sequence of Escherichia coli K-12.Science. 1997; 277: 1453-1474Crossref PubMed Scopus (6056) Google Scholar). Likewise E. coli genome microarrays were among the first to be commercially available with sequences for the complete set of open reading frames as well as intergenic regions (2Selinger D.W. Cheung K.J. Mei R. Johansson E.M. Richmond C.S. Blattner F.R. Lockhart D.J. Church G.M. RNA expression analysis using a 30 base pair resolution Escherichia coli genome array.Nat. Biotechnol. 2000; 18: 1262-1268Crossref PubMed Scopus (299) Google Scholar). The origins of proteomics can also be traced back to E. coli when pioneering two-dimensional gel electrophoresis experiments enabled the investigation of proteins on an organism-wide scale (3O'Farrell P.H. High resolution two-dimensional electrophoresis of proteins.J. Biol. Chem. 1975; 250: 4007-4021Abstract Full Text PDF PubMed Google Scholar). Resources such as CyberCell Database (4Sundararaj S. Guo A. Habibi-Nazhad B. Rouani M. Stothard P. Ellison M. Wishart D.S. The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli..Nucleic Acids Res. 2004; 32: D293-D295Crossref PubMed Google Scholar) and EchoBASE (5Misra R.V. Horler R.S.P. Reindl W. Goryanin I.I. Thomas G.H. EchoBASE: an integrated post-genomic database for Escherichia coli.Nucleic Acids Res. 2005; 33: D329-D333Crossref PubMed Scopus (62) Google Scholar) have been designed as central repositories of biochemical and genetic data from E. coli generated by a wide range of sources. These databases are periodically updated and annotated to facilitate a comprehensive understanding of this model organism. The knowledge gained through this organized effort can be applied to the understanding of other organisms for the development of antibiotics and/or antifungal agents. The availability of fully sequenced genomes has allowed construction of microarrays that are used to detect and quantify all postulated gene products by determining the levels of the corresponding transcribed mRNA. A study by Zimmer et al. (6Zimmer D.P. Soupene E. Lee H.L. Wendisch V.F. Khodursky A.B. Peter B.J. Bender R.A. Kustu S. Nitrogen regulatory protein C-controlled genes of Escherichia coli: scavenging as a defense against nitrogen limitation.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 14674-17679Crossref PubMed Scopus (291) Google Scholar) demonstrated the use of this method to identify those genes in E. coli whose expression is activated when replacing a preferred nitrogen source with a non-preferred nitrogen source. In a separate study, Oh et al. (7Oh M.K. Rohlin L. Kao K.C. Liao J.C. Global expression profiling of acetate-grown Escherichia coli..J. Biol. Chem. 2002; 277: 13175-13183Abstract Full Text Full Text PDF PubMed Scopus (248) Google Scholar) performed a similar analysis where E. coli were grown on different carbon sources. These studies not only identified those genes known to be associated with the specified metabolic pathway but also revealed many genes that had not been linked previously with the metabolic pathway under study. Although the measurement of transcribed mRNA by hybridization techniques has led to the discovery of molecular markers and the elucidation of biologic mechanisms, this technique is not sufficient for the complete characterization of biologic systems. The detection of a particular gene product in a microarray experiment does not confirm the presence or absence of the resulting protein product or related post-translationally modified isoforms. It is also understood that quantitative differences in the transcript of a particular gene or set of genes may not necessarily correlate with the corresponding protein abundance. This failure was illustrated by a study involving the effect of carbon source perturbation on steady-state gene expression in Saccharomyces cerevisiae. The authors reported that growing S. cerevisiae on either galactose or ethanol resulted in significant differences between the abundance ratio of the mRNA and the corresponding protein products (8Griffin T.J. Gygi S.P. Ideker T. Rist B. Eng J. Hood L. Aebersold R. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae..Mol. Cell. Proteomics. 2002; 1: 323-333Abstract Full Text Full Text PDF PubMed Scopus (565) Google Scholar). Several other studies have demonstrated the poor correlation between the relative abundance of a transcript and the corresponding protein (9Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol. Cell. Biol. 1999; 19: 1720-1730Crossref PubMed Scopus (3193) Google Scholar, 10Anderson L. Seilhamer J. Comparison of selected mRNA and protein abundances in human liver.Electrophoresis. 1997; 18: 533-537Crossref PubMed Scopus (1073) Google Scholar). To fully understand the cellular physiology of a particular organism or disease state, a comprehensive analytical survey of the cell must be completed. The information gathered by compiling data gained from multiple bioanalytical approaches (i.e. transcript, protein, and metabolite levels to name a few) on an organism in a variety of physiological states is the basis of the discovery science approach referred to as systems biology. Combining data for such a systems analysis leads to a degree of understanding in which "the whole is greater than the sum of the parts." Many studies involving analysis of complex protein mixtures have been accomplished by combining the well established separation capabilities of two-dimensional (2D) 1The abbreviations used are: 2D, two-dimensional; MSE, elevated energy MS; PLGS, ProteinLynx Global Server; RSD, relative standard deviation; AMRT, accurate mass, retention time detection; PMF, peptide mass fingerprint; DGAL, D-galactose-binding periplasmic protein; MDH, malate dehydrogenase; SIC, selected ion chromatogram; CI, confidence interval; ACS, acetyl-CoA synthetase; PGI, phosphoglucose isomerase; 2DGE, 2D gel electrophoresis; BPI, base peak intensity. PAGE with mass spectrometry-based sequence identification of selected, semipurified proteins (11Henzel W.J. Watanabe C. Stults J.T. Protein identification: the origins of peptide mass fingerprinting.J. Am. Soc. Mass Spectrom. 2003; 14: 931-942Crossref PubMed Scopus (208) Google Scholar). Although this technique is often applicable to comparative proteomics, 2D PAGE is notoriously insensitive to proteins that are not soluble during the isoelectric focusing stage of the separation. Moreover the staining methods required to visualize the proteins impose restraints on dynamic range and detection limits. Despite two-dimensional separation of the intact proteins, individual gel spots often contain many proteins, affecting the resulting quantitative analysis. This problem is exacerbated by the varying degrees of post-translational modifications that a particular protein may undergo, resulting in protein components appearing in multiple locations on the two-dimensional image. The development of automated, data-dependent ESI MS/MS in conjunction with microcapillary LC and database searching has significantly increased the sensitivity and speed of identification of gel-separated proteins. Alternative methods have subsequently been developed to maximize the duty cycle of the mass spectrometer with a concomitant increase in sensitivity that use a parallel ("broad band" acquisition) rather than a serial approach for the collision-induced dissociation of peptides (12Bateman, R. H., and Hoyes, J. B. (January 16, 2002) Methods and apparatus for mass spectrometry. UK Patent 2,364,168AGoogle Scholar, 13Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2004; 77: 2187-2200Crossref Scopus (522) Google Scholar, 14Purvine S. Eppel J.T. Yi E.C. Goodlett D.R. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer.Proteomics. 2003; 3: 847-850Crossref PubMed Scopus (134) Google Scholar). This method enhances the run-to-run reproducibility and yields high mass accuracy for both intact peptides and fragments, thereby improving sensitivity. A traditional approach to determine the relative quantities of peptides (or proteins) in a complex mixture involves using stable isotope-labeled peptides. This technique allows direct correlation of the naturally occurring peptide to its stable isotope-labeled analog (15Kuhn E. Wu J. Karl J. Liao H. Zolg W. Guild B. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards.Proteomics. 2004; 4: 1175-1186Crossref PubMed Scopus (369) Google Scholar, 16Ross P.L. Huang Y.N. Marchese J.N. Williamson B. Parker K. Hattan S. Khainovski N. Pillai S. Dey S. Daniels S. Purkayastha S. Juhasz P. Martin S. Bartlet-Jones M. He F. Jacobson A. Pappin D.J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.Mol. Cell. Proteomics. 2004; 3: 1154-1169Abstract Full Text Full Text PDF PubMed Scopus (3721) Google Scholar, 17Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat. Biotechnol. 1999; 17: 994-999Crossref PubMed Scopus (4362) Google Scholar). In these studies, an amino acid labeling strategy is incorporated into the protocol in which one of the biological samples is treated with the light isotope form of the chemical labeling reagent, and the other sample is treated with the heavy isotope form of the labeling reagent. When the samples are mixed and analyzed by LCMS, labeled peptide pairs from the two samples can be differentiated in the mass spectrometer by the virtue of their mass difference. The ratio of the signal intensities of the light to heavy peptide derivatives of the peptide pairs reflects the abundance ratio for the originating protein in the two different biological samples. Although this quantitative strategy is a useful method for determining the relative abundance of proteins between different samples, it can involve complex chemistry and require expensive reagents, and it is not particularly amenable to large scale relative quantitation studies. In this study, we used a simple, gel-free, label-free LCMS approach for qualitative and quantitative proteomic analysis (13Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2004; 77: 2187-2200Crossref Scopus (522) Google Scholar). This investigation involved the study of E. coli grown with single, specific carbohydrates. This approach provides an excellent model system to study subtle differences in the microbial proteome because there is a controlled environment in which only one parameter is varied. Using E. coli to better understand metabolic pathways and characterize previously unknown proteins helps validate this methodology and could lead to the discovery of novel antibiotics when applied to related virulent microbes. The results of this study correlate well with the known carbon source biochemistry and molecular biology of E. coli. The ease of use and efficiency of this new technique is demonstrated by the comparability of the results with those obtained from existing gene profiling and more traditionally obtained proteomic data available in the literature (7Oh M.K. Rohlin L. Kao K.C. Liao J.C. Global expression profiling of acetate-grown Escherichia coli..J. Biol. Chem. 2002; 277: 13175-13183Abstract Full Text Full Text PDF PubMed Scopus (248) Google Scholar, 18Peng L. Shimizu K. Global metabolic regulation analysis for Escherichia coli K12 based on protein expression by 2-dimensional electrophoresis and enzyme activity.Appl. Microbiol. Biotechnol. 2003; 61: 163-178Crossref PubMed Scopus (171) Google Scholar). Frozen E. coli (ATCC10798, K-12) cell stocks were streaked onto Luria-Bertani (LB) plates and grown at 37 °C. An individual colony was subsequently streaked onto M9 minimal medium plates supplemented with 0.5% sodium acetate and incubated at 37 °C. Seed cultures were generated by transferring single colonies into flasks of M9 minimal medium supplemented with 0.5% sodium acetate. Seed culture flasks were shaken at 250 rpm at 37 °C until midlog phase (A600 = 0.9–1.1). The seed culture was diluted 1 ml to 500 ml into separate M9 minimal media supplemented with one of three carbon sources (0.5% glucose, 0.5% lactose, or 0.5% sodium acetate). Flasks were shaken at 250 rpm at 37 °C until midlog phase (A600 = 0.9–1.1) and then harvested by centrifugation (5,000 × g for 15 min). Culture medium was discarded, and the cells were frozen at −80 °C until needed for protein extract preparation. Frozen cells were suspended in 5 ml of lysis buffer (Dulbecco's phosphate-buffered saline + 1/100 protease inhibitor mixture (Sigma catalog number 8340))/1 g of biomass in a 50-ml Falcon tube. The cells were lysed by sonication in a Microson XL ultrasonic cell disrupter (Misonix, Inc.) at 4 °C. The cell debris were removed by centrifugation at 15,000 × g for 30 min at 4 °C. The resulting soluble protein extract was dispensed into 1.0-ml cryotubes and stored at −80 °C for subsequent analysis. Each protein sample was denatured and reduced using a standard PAGE loading buffer mixture containing 1.0% SDS and 10 mm DTT. The denatured protein samples were run in a Bio-Rad Criterion gel apparatus into a 12% polyacrylamide gel at 160 V for 1 h. The polyacrylamide gel was stained with Coomassie Blue using standard protocols. Approximately 250 μg of total E. coli protein was suspended in 100 μl of 50 mm ammonium bicarbonate (pH 8.5) containing 0.05% Rapigest (19Yu Y.Q. Gilar M. Lee P.J. Bouvier E.S.P. Gebler J.C. Enzyme-friendly, mass spectrometry-compatible surfactant for in-solution enzymatic digestion of proteins.Anal. Chem. 2003; 75: 6023-6028Crossref PubMed Scopus (269) Google Scholar). Protein was reduced in the presence of 10 mm dithiothreitol at 60 °C for 30 min. The protein was alkylated in the dark in the presence of 30 mm iodoacetamide at room temperature for 30 min. Proteolytic digestion was initiated by adding modified trypsin (Promega) at a concentration of 50:1 (E. coli protein to trypsin) and incubated at 37 °C overnight. Tryptic digestion was terminated by diluting 1:1 with water and freezing immediately at −80 °C. The tryptic peptide solution (1.25 μg/μl total protein) was centrifuged at 10,000 × g for 10 min, and the supernatant was transferred into an autosampler vial for peptide analysis via LCMS. Capillary liquid chromatography of tryptic peptides was performed with a Waters CapLC system equipped with a Waters NanoEase™ Atlantis™ C18, 300-μm × 15-cm reverse phase column. The aqueous mobile phase (mobile phase A) contained 1% acetonitrile in 0.1% formic acid. The organic mobile phase (mobile phase B) contained 80% acetonitrile in 0.1% formic acid. Samples (5-μl injection, digested equivalent to 6.25 μg of total protein) were loaded onto the column with 6% mobile phase B. Peptides were eluted from the column with a gradient of 6–40% mobile phase B over 100 min at 4.4 μl/min followed by a 10-min rinse of 99% of mobile phase B. The column was immediately re-equilibrated at initial conditions (6% mobile phase B) for 20 min. The lock mass, [Glu1]fibrinopeptide at 100 fmol/μl, was delivered from the auxiliary pump of the CapLC system at 1 μl/min to the reference sprayer of the NanoLockSpray™ source. All samples were analyzed in triplicate. Mass spectrometry analysis of tryptic peptides was performed using a Waters/Micromass Q-TOF Ultima API system. For all measurements, the mass spectrometer was operated in V-mode with typical resolving power of at least 10,000. All analyses were performed using positive mode ESI using a NanoLockSpray source. The lock mass channel was sampled every 30 s. The mass spectrometer was calibrated with a [Glu1]fibrinopeptide solution (100 fmol/μl) delivered through the reference sprayer of the NanoLockSpray source. Accurate mass LCMS data were collected in an alternating, low energy (MS) and elevated energy (MSE) mode of acquisition. The spectral acquisition time in each mode was 1.85 s with a 0.15-s interscan delay. In low energy MS mode, data were collected at a constant collision energy of 10 eV. In MSE mode, collision energy was ramped from 28 to 35 eV during each 1.85-s data collection cycle. One cycle of MS and MSE data was acquired every 4.0 s. The radio frequency applied to the quadrupole mass analyzer was adjusted such that ions from m/z 300 to 2000 were efficiently transmitted, ensuring that any ions observed in the LC/MSE data less than m/z 300 were known to arise from dissociations in the collision cell. The continuum LCMSE data were processed and searched using ProteinLynx Global Server (PLGS) version 2.2. The resulting peptide and protein identifications were evaluated by the software using statistical models similar to those described by Skilling et al. (20Skilling J. Denny R. Richardson K. Young P. McKenna T. Campuzano I. Ritchie M. Probseq—a fragmentation model for interpretation of electrospray tandem mass spectrometry data.Comp. Funct. Genomics. 2004; 5: 61-68Crossref PubMed Scopus (24) Google Scholar). Results from replicate injections were collated for quantitative analysis to determine the relative -fold change using the glucose condition as the control experiment. Protein identifications were assigned by searching an E. coli protein database using the precursor and fragmentation data afforded by the LCMS acquisition method. The search parameter values for each precursor and associated fragment ion were set by the software using the measured mass error and intensity error obtained from processing the raw continuum data. The mass error tolerance values were typically under 5 ppm. Peptide identifications were restricted to tryptic peptides with no more than one missed cleavage and cysteine carbamidomethylation. The ion detection, clustering, and normalization were processed using PLGS as described earlier (13Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2004; 77: 2187-2200Crossref Scopus (522) Google Scholar). Additional data analysis was performed with Spotfire Decision Site 7.2 and Microsoft Excel. Due to the nature of the alternate scanning acquisition method, fragment ions produced from any given precursor will have the same chromatographic profile and apex retention time as the originating precursor ion. The data processing software produces an inventory of the measured monoisotopic mass of each detected precursor and fragment ion. The chromatographic peak area, chromatographic peak shape, combined charge state, and the apex retention time are also provided for each corresponding precursor and fragment ion. The chromatographic peak area is determined from the combined intensity of all the isotopes for all of the charge states associated to each precursor. Fragment ions are assigned to a parent precursor only if their apex retention times are within plus or minus the time associated with one acquisition scan (i.e. alternate scanning cycle). In these experiments, because the alternate scanning cycle time was 2 s, the ions found in the elevated energy channel to within ±0.05 min of a given precursor were assigned as associated fragments. A qualitative analysis of a protein mixture may produce instances where more than one precursor ion can be found at the same apex retention time. In this instance, the fragmentation data associated with a specific moment in time is shared among more than one co-eluting precursor ion; however, it is important to remember that precursor and fragment ion data are acquired at high mass accuracy (±5 ppm). At 5-ppm mass accuracy, there is enough mass specificity to resolve associated fragment ions with their appropriate precursor ion for a subsequent accurate mass stringent database search. With this level of mass accuracy and the ability to obtain time-resolved mass measurements, confident identifications can be made in the instances of co-eluting peptides. In instances where data from multiple injections of the same sample have been collected, the methodology utilizes chromatographic and analytical reproducibility to help confidently assign fragments to co-eluting precursors. A more thorough description of how the algorithms are used to "clean" the fragmentation data will be described in future work. 2J. C. Silva, C. Dorschel, M. V. Gorenstein, G.-Z. Li, and S. J. Geromanos, manuscript in preparation. Identical peptides from each of the replicate injections for all conditions were clustered by mass precision (typically <10 ppm) and a retention time tolerance (typically <0.25 min) using the PLGS clustering software. The clustered peptide data set was exported from PLGS and further evaluated with Excel and Spotfire. For each condition, those ion detections that occurred only in one of the three replicate injections were considered as noise and discarded from further analysis. The LCMS data were normalized to peptides originating from TUFA prior to determining the relative quantitation of identified proteins across the various conditions. The details regarding the normalization strategy is described later in greater detail under "Results and Discussion." A standard PAGE analysis was performed on the soluble protein extracts from E. coli grown on three different carbon sources. The protein loading was controlled to ensure that an equal amount of total protein from each condition was applied onto the gel. Two aliquots of total protein were loaded to obtain better resolution of the most abundant proteins. The protein profile patterns illustrated in Fig. 1A reveal similar patterns for the glucose and lactose growth conditions but a distinct pattern difference between the acetate growth condition and the other two growth conditions. The soluble protein extracts from each of the three growth conditions were treated with trypsin, and the resulting peptide mixtures were analyzed by LCMSE. Fig. 1B illustrates the BPI and the total ion chromatograms from the low energy and elevated energy MS data acquisition for each condition, respectively. Inspection of the BPI chromatograms indicates that the similarity between the glucose and lactose conditions is also observed at the level of the tryptic peptides. In addition, the distinction between the acetate condition and the other two carbon sources is evident. The LCMSE data from each condition were processed using the Protein Expression software to produce an inventory of peptides that can be used to determine the relative abundance of peptides/proteins across multiple conditions. The complexity of the samples are illustrated in Fig. 1C, which displays ∼8000 observed monoisotopic masses for each of the extracted peptide components (MH+) as a function of the observed retention time for each condition. The alternate scanning mode of the LCMS data acquisition is configured to detect the precursor peptides in the low energy channel while simultaneously obtaining the data from associated fragments for subsequent structural determination of each precursor. Replicate injections of tryptic peptides from soluble E. coli protein preparations were processed with the Protein Expression software, creating an inventory of the peptides obtained from the low energy data acquisition for each growth condition. Table I lists the number of peptide detections and summed ion intensities for each injection of the acetate, lactose, and glucose growth conditions. An average of 8102, 7959, and 8437 peptides were found in the acetate, lactose, and glucose growth conditions, respectively. The relative standard deviations (RSDs) for the number of peptide detections and the intensity sums ranged from 1.3 to 8.5% and from 3.1 to 5.7%, respectively. These RSDs indicate an acceptable degree of reproducibility of the data for the replicate injections of the three different growth conditions.Table ISummary of extracted peptides from E. coli grown in acetate, lactose, and glucoseInjectionAverageCVaCoefficient of variation.123%Acetate Peptide detections82188083800681021.3 Summed intensity1.2218E+081.2182E+081.2872E+081.2424E+083.1Lactose Peptide detections83577174834779598.5 Summed intensity9.9716E+079.3929E+071.0528E+089.9642E+075.7Glucose Peptide detections85618634811584373.3 Summed intensity1.91