Quantitative Proteomics Reveals Significant Changes in Cell Shape

Technical Note pubs.acs.org/jpr

Quantitative Proteomics Reveals Significant Changes in Cell Shape and an Energy Shift after IPTG Induction via an Optimized SILAC Approach for Escherichia coli Lingyan Ping,†,‡,# Heng Zhang,†,# Linhui Zhai,† Eric B. Dammer,†,∥ Duc M. Duong,†,∥ Ning Li,† Zili Yan,⊥ Junzhu Wu,*,‡ and Ping Xu*,†,§ †

State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, P. R. China ‡ Department of Biochemistry, School of Medicine, Wuhan University, Wuhan, 430071, P. R. China § Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, 430071, P. R. China ⊥ Second High School Affiliated to Beijing Normal University, Beijing, 100088, P. R. China S Supporting Information *

ABSTRACT: Stable isotope labeling by amino acids in cell culture (SILAC) has been widely used in yeast, mammalian cells, and even some multicellular organisms. However, the lack of optimized SILAC media limits its application in Escherichia coli, the most commonly used model organism. We optimized SILACE medium (SILAC medium created in this study for E. coli) for nonauxotrophic E. coli with high growth speed and complete labeling efficiency of the whole proteome in 12 generations. We applied a swapped SILAC workflow and pure null experiment with the SILACE medium using E. coli BL21 (DE3) cells hosting a recombinant plasmid coding for glutathione-S-transferase (GST) and ubiquitin binding domain before and after isopropyl thiogalactoside (IPTG) induction. Finally, we identified 1251 proteins with a significant change in abundance. Pathway analysis suggested that cell growth and fissiparism were inhibited accompanied by the downregulation of proteins related to energy and metabolism, cell division, and the cell cycle, resulting in the size and shape change of the induced cells. Taken together, the results confirm the development of SILACE medium suitable for efficient and complete labeling of E. coli cells and a data filtering strategy for SILAC-based quantitative proteomics studies of E. coli. KEYWORDS: quantitative proteomics, mass spectrometry, Escherichia coli, SILACE, IPTG induction, QconCAT compatible labeling method

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interactome, metabolome, and physiome.11−13 It is predicted to have 4285 protein encoding genes.10,11 Previous studies have used proteomic approaches to elucidate metabolism and heterologous protein production mechanisms in E. coli.11,13 Recently, mass spectrometry (MS)-based proteomics has allowed quantitative analysis of large numbers of proteins in a single experiment by labeling samples with amino acids incorporating stable isotopes.14,15 Stable isotope labeling by amino acids in cell culture (SILAC) has increased the power of experiments to sensitively detect and quantify subtle protein changes16−18 and is used as the gold standard for MS-based relative protein quantification. The strength of this method lies in the ability to mix the light and heavy labeled samples prior to sample preparation, thus minimizing the introduction of

INTRODUCTION

Escherichia coli is not only a common pathogenic bacterium in humans and animals but also one of the most intensively studied model organisms. Because of easy cultivation, fast growth, and facile genetic manipulation, it has served as a preferred model bacterium in both basic research and industrial applications.1,2 Especially after the work of Stanley Norman Cohen and Herbert Boyer in the creation of recombinant DNA in the form of a plasmid,3 the E. coli bacterium has been well characterized at the molecular level and used to produce heterologous proteins for research and therapeutic purposes, such as therapeutic proteins for vaccines and pharmaceuticals.4−7 More than 65% of recombinant protein therapeutics or drugs were produced by the E. coli expression system.8,9 Since the completion of the E. coli genome-sequencing project in the 1990s,10 this organism has been characterized on the genome-wide scale in terms of its transcriptome, proteome, © XXXX American Chemical Society

Received: July 27, 2013

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dx.doi.org/10.1021/pr400775w | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

Technical Note

Growth Rate Assessment

different experimental errors for the differentially labeled samples during handling.19 To date, SILAC-based methods have been successfully applied to proteomic study in Saccharomyces cerevisiae,17 Drosophila melanogaster,14,20,21 Caenorhabditis elegans,20 cultured mammalian cells,22,23 and mouse.24,25 However, most SILAC applications in E. coli have utilized auxotrophic strains to avoid the reduction of heavy SILAC signal caused by amino acid interconversion.26−28 But this modification may also exacerbate changes in the background levels of metabolism and is therefore not suitable for direct applications using industrial strains. For example, in the research of SILAC labeled nematodes, the RNAi-feeding procedure targeting the ornithine transaminase enzyme orn-1 was employed, which is required for arginine-to-proline conversion.27 In this study, we developed a novel SILAC medium that achieved both complete labeling and fast growth without deletion of any gene associated with metabolism of amino acids. Furthermore, we applied this newly developed SILAC strategy to identify the effects of isopropyl thiogalactoside (IPTG) induction in an E. coli model, whereby we quantitatively analyzed the proteome and metabolic changes in E. coli after target protein expression induced by IPTG, which is the common means by which industrial strains produce heterologous proteins.

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In order to generate growth curves of E. coli in different SILAC media (M9 medium,30 DMEM medium,31 QconCAT medium (M9 medium plus with amino acids and vitamins),32 and SILACE medium (SILAC medium for E. coli created in this study), a single colony was inoculated into the LB medium to grow at 37 °C to log phase. The cells were then collected, washed, reseeded into these media mentioned above starting with A600 of 0.02, and cultured at 37 °C in a shaker (220 rpm). A600 of different cell cultures were monitored and compared during a time course. Protein Extraction from E. coli

Cells were lysed in a 1.5 mL centrifuge tube with lysis buffer (8 M urea, 50 mM NH4HCO3, 10 mM IAA) and 0.5 mm glass beads (Biospec Products Inc., Bartlesville, OK). The solution was vortexed at the highest speed for 1 min followed by 1 min interval on ice for 15 times. The lysate was clarified by centrifugation at 13 300 rpm for 5 min. Protein Digestion and Peptide Desalting

Total cell lysate was reduced with 5 mM DTT at 37 °C for 30 min and alkylated with 20 mM IAA in the dark at room temperature for 30 min. Protein was resolved on a 10% SDSPAGE gel and stained with Coomassie Blue G-250. The entire gel lane was then sliced into 32 bands followed by destaining and in-gel digestion with 10 ng/μL of Lys-C (Wako, Osaka, Japan) at 37 °C overnight according to the manufacturer’s protocol. The resulting peptides were dried and reconstituted with a sample desalting buffer (5% acetonitrile (ACN) and 1% formic acid (FA)) and desalted by passing through a small plug of C18 material embedded in a Teflon matrix (3M, St. Paul, MN) held within a 200 μL pipet tip (Corning Inc., Corning, NY) as previously reported.33 The eluted peptides were dried again and dissolved with sample loading buffer (1% ACN and 1% FA) for nano liquid chromatography−mass spectrometry/ mass spectrometry (nano-LC−MS/MS) analysis.

MATERIALS AND METHODS

Strain and Plasmid

E. coli strain BL21 (DE3) (pEGKT-4DSK2, Ampr)29 was stored at −80 °C and used to express a recombinant protein. Plasmid pEGKT-4DSK2 was an expression vector containing four tandem copies of ubiquitin binding domain of DSK2 fused with glutathione-S-transferase (GST) at the N-terminus. The target protein has no known interaction with endogenous proteins in E. coli, given that the bacteria did not contain any ubiquitin system. The cells were streaked on a Luria−Bertani (LB) plate containing 100 μg/mL of ampicillin as a selective marker and grown overnight at 37 °C.

Protein Analysis by nano-LC−MS/MS

The digested peptides were analyzed by nano-LC−MS/MS on an LTQ-OrbitrapVelos mass spectrometer (Thermo Electron, San Jose, CA). The instrument was equipped with a Waters nanoACQUITY ultra performance liquid chromatography (UPLC) (Waters, Milford, MA) and a 75 μm i.d. × 15 cm fused-silica capillary column packed with C18 reverse-phase HALO resin (MichromBioresources, Inc., Auburn, CA). The samples were loaded onto the column by the autosampler and then eluted with a 70 min gradient covering 8−40% of buffer B (buffer A: 0.1% FA and 2% ACN; buffer B, 0.1% FA and 100% ACN; flow rate: 300 nL/min). Eluted peptides were ionized under high voltage (2 kV) and analyzed by the Orbitrap MS in a survey scan (350−1600 m/z; 1× 106 automatic gain control (AGC) target; resolution 30 000 at m/z 400; maximum ion accumulation time, 150 ms) followed by data dependent scans for the 20 most abundant ions in the LTQ MS scan (2 m/z isolation width, 35% collision energy, 5000 AGC target, 25 ms maximum ion time, dynamic exclusion range of 30 s).

SILAC Labeling of E. coli

A fresh E. coli colony was picked from the LB plate to inoculate LB liquid medium and grown overnight as a seed culture. For SILAC labeling, the cells were spun down, washed twice with autoclaved distilled water, reseeded into SILAC medium formulated as indicated in the result part and figures containing heavy or light labeled amino acid (Cambridge Isotope Laboratories, Andover, MA) with a starting A600 of 0.00037, and cultured at 37 °C in a shaker (220 rpm). For labeling efficiency tests, the heavy labeled cells were harvested at different generations as indicated and stored at −80 °C. For the signal-to-noise (S/N) cutoff determination, the cells from heavy or light labeling medium were collected until the A600 reached 1.5 and mixed with an equivalent aliquot of the cells grown in the reciprocal labeling medium until reaching the same A600. For further SILAC analysis, the cells were retransferred into corresponding heavy or light medium with the starting A600 of 0.05 after 12 generations of labeling. When A600 reached about 0.6, two-thirds of E. coli cells in each culture were collected and marked as “uninduced”, while the remaining cells were kept growing in the medium with the addition of 1 mM IPTG for one more hour. These cells were harvested and marked as “induced” and stored at −80 °C for further analysis.

MS Data Processing

The MS/MS spectra from the E. coli samples were searched with the Sorcerer-SEQUEST (version 4.0.4 build, Sage-N Research, Inc.) against a composite target/decoy database to estimate false discovery rate (FDR).34 The target proteins were derived from a combination of E. coli database (downloaded on August 9, 2010) and common contaminants, such as porcine B



C

this study Growth better than in M9 and SILAC DMEM. Convenient & low cost. Suitable for study of physiological or pathological processes, and for industrial applications. SILACE medium (M9 plus amino acids, nucleic acids & vitamins, etc.)

C6 arginine and 13 C6 lysine 13 C6 arginine and 13 C6 lysine 13 C6 lysine SILACE

13

M9 NH4Cl

15

MARKET 9 (Spectra Stable Isotopes) NH4Cl

metabolic labeling metabolic labeling SILAC

base medium labeling reagent

15

E. coli is one of the most popular organisms for protein production in industry and basic research. Although SILACbased E. coli quantitative proteomics has been studied by several groups,35,36 most of them chose to use genetically modified auxotrophic strains, which need additional preparation, that is, the recombination of mutants, which might further affect their physiological state and phenotypes and prove difficult for industrial process optimization. In this study, we developed SILACE medium suitable for complete labeling of E. coli without any additional genetic modifications. We set out to compare this media to the existing options for SILAC labeling of E. coli. To date, several kinds of media have been used for SILAC labeling of E. coli, including M9 medium,30 MARKET 9 medium,20 DMEM medium,31 and modified M9 medium (M9 medium plus amino acids and vitamins) (Table 1).32 Among them, M9 medium is the least enriched because it contains only glucose and inorganic salts. Although modified M9 medium is supplemented with several amino acids and vitamins, it is still not ideal for optimal growth of E. coli (Figure 1). MARKET 9 medium is commercially available but has unspecified components which limit its application and make it unsuitable for physiological study in a SILAC approach. DMEM medium is more suitable for mammalian cell culture, and E. coli grows

labeling method

Table 1. Comparison of Different Metabolic Labeling Methods

Development of a Novel SILAC Labeling Medium

32 Growth is better than in M9. Convenient and cost-effective. Potential risk for partial labeling of proline.

30

QconCAT medium (M9 plus five kinds of amino acids (His, Tyr, Phe, Pro, Trp).

RESULTS AND DISCUSSION

SILAC

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Commercial medium, expensive. Medium is not made for bacteria. Slow growth. Adaptive phase is long and time-consuming. 31 Impractical for study of metabolic regulation.

Cell pellets collected before and after IPTG induction were cultured under the same conditions as with SILAC labeling except heavy lysine was replaced with the light form. Cells were fixed overnight in 3% glutaral dehyde resolved in 0.075 M phosphate buffer. After exposure to osmium tetroxide vapor, freeze-drying and sputter-coating with gold palladium, morphology was checked by scanning electron microscopy (SEM) (S-3400N, Hitachi, JAPAN).

SILAC DMEM (Invitrogen)

Morphology Analysis

Growth better than in M9. Commercial medium, expensive and with unknown components. Impractical for study of metabolic regulation. Accurate quantification difficult due to broad isotope distribution. Slow growth. Impractical for study of metabolic regulation. Accurate quantification difficult due to broad isotope distribution.

notes

reference

trypsin and human keratins. The decoy proteins were derived from pseudoreversed sequences of the target proteins. Searching parameters consisted of semitryptic restriction, fixed modification of Cys (+57.0215 Da, due to alkylation by IAA), and dynamic modifications of oxidized Met (+15.9949 Da) and stable isotope labeled Lys (+6.02013 Da), and a maximum of three modifications on a single peptide. Mass tolerance was set to 20 ppm for precursor ions. Only b and y ions were considered during the database match. Peptide matches were filtered by a minimal peptide length of six amino acids and then grouped by trypticity (only fully and partially tryptic peptides were accepted). In each group, the peptide matches were further filtered by a maximum of three modification sites (including SILAC-labeling), two maximal miscleavages, no mixed labeling of Lys in peptides with more than one Lys residue, and by dynamically increasing XCorr and ΔCn cutoffs until all decoy matches were discarded. Only proteins with at least two peptide matches were accepted to further minimize false discoveries. When matching peptides to proteins, we assigned the proteins sharing the same peptide(s) in one group and used the proteins with highest peptide matches to represent the group. Protein quantification was performed with an in-house program, in which peptide ion peaks in survey scans were first defined to include m/z, retention time, peak intensity, and S/N. The heavy and light ion peaks were matched using predicted m/z (±6 ppm) and identical retention time.

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Technical Note

Figure 1. Comparison of growth of E. coli cultured in various SILAC media. (A) Components of the SILACE medium developed in this study. (B) Comparison of the growth curves for E. coli grown in SILACE medium, DMEM medium, QconCAT medium, and M9 medium.

leading to incomplete labeling.42 As shown in Supplemental Figure S1B, lysine (13C) labeling efficiency of E. coli in medium with aspartic acid decreased to 85% after 10 generations. Therefore, we tested labeling efficiency in media without aspartic acid. We found that the E. coli was nearly completely labeled by this medium when cultured for 12 generations (Supplemental Figure S2A,B). We term this medium as SILACE, i.e., SILAC medium suitable for complete labeling of E. coli. In SILACE medium, glucose and amino acids are used as the preferred carbon and nitrogen sources, respectively (Figure 1A). Next, we compared the growth curves of E. coli cultured in different media mentioned above, including M9 medium, DMEM medium, QconCAT medium, and SILACE medium developed in this study. As shown in the Figure 1B, the cell adapts to SILACE in a much shorter time and has the shortest doubling time, which benefits labeling efficiency. In contrast, the cells in other media grow much more slowly. Among them, the growth in M9 medium is the slowest. Moreover, the growth curve for DMEM medium shown in the figure was obtained when the cells were transferred from LB directly without washing with water or PBS because there was almost no growth if a washing step was introduced, which was consistent with a lower maximum labeling efficiency of about 95%, probably because of the residual lysine or other nutrients from seed culture (data not shown). In summary, we have developed a

much more slowly in DMEM than in standard medium. Thus, in order to provide for optimal E. coli growth and higher labeling efficiency, we systematically developed SILACE medium beginning with M9 medium and adding most amino acids and a number of vitamins (Figure 1A), which has all required nutrients and is suitable for efficient growth of E. coli. Selection of the amino acid used for labeling is another key decision point in the development of SILAC medium. In previous methodologies, “heavy” arginine (13C, 15N4) and lysine (13C) were widely used as the labeled amino acids. However, the metabolic conversion of labeled arginine to other amino acids (especially proline) is an inherent problem in SILAC, which complicates quantitative analysis of peptides containing those amino acids.37−41 Lysine labeling alone was therefore considered a superior option. When we added all the amino acids included in yeast SC media (Supplemental Figure S1A, Supporting Information), the labeling efficiency increased gradually and reached a maximum of ∼90% labeling efficiency in the ninth generation. However, the labeling efficiency then decreased and continuously followed a periodic contour during further culture, which indicates that the strain could not be labeled completely (Supplemental Figure S1B). Abelson et al. found that the unlabeled carbon in aspartic acid added to medium might incorporate into lysine. This portion of light lysine would dilute the pool of pure heavy lysine in the cell, which could be incorporated into proteins during synthesis D



Technical Note

Figure 2. Analysis of accumulated labeling efficiency using stable isotope-labeled lysine in SILACE medium. (A) Labeling of a representative peptide across generations of E. coli grown in SILACE medium. E. coli proteins were extracted, in-gel digested, and analyzed by nanoLC-MS/MS to examine labeling efficiency. (B) Histograms of the population of proteins quantified by the log-transformed ratio of heavy versus light peptides representing protein labeling efficiency. (C) Detailed LC−MS/MS results. Labeling efficiency is given as the percentage calculated from the mean log2 (heavy/ light) ratio for all quantified proteins which in turn is calculated for each individual quantified protein as the average log2 (heavy/light) ratio for matched light and heavy peptides originating from that protein.

novel medium for nonauxotrophic E. coli. SILACE medium combined with “heavy” lysine (13C) permits fast growth and provides the highest saturation value of A600 over time. In addition, components of the SILACE medium are easily obtainable by any laboratory (Figure 1A), and the composition of this medium can be easily adjusted according to the characteristics of different E. coli mutants.

distribution, we were able to model the trend for isotope labeling. The heavy-labeled portion occupied about 48%, 73%, 85%, 98%, and 99% respectively for gen01, gen02, gen03, gen07, and gen12 (Figure 2B,C). After culturing for 12 generations (about 10 h), only six abundant proteins were identified with matched pairs of strong heavy peptides and barely detectable light ones. These six pairs of peptides were used for the calculation of labeling efficiency, which only occupied 0.7% of totally identified proteins from the sample of gen12 (Figure 2C). Considering the purity of L-lysine (13C6) employed here (∼99%), we interpreted that the labeling efficiency at gen12 is essentially complete. These results demonstrate complete labeling was achievable within 12 generations of nonauxotrophic E. coli culture without any genetic modification, and this should be ideal for quantitative protein analysis in a SILAC experiment. The high labeling efficiency may result from the high turnover rate of cells and proteins due to an increase in the reproduction rate of E. coli in the SILACE medium. In addition, the heavy isotope appears to have no effects on E. coli growth, consistent with a similar conclusion about SILAC labeling of Drosophila melanogaster21 and mice.24

Measurement of Labeling Efficiency of Heavy Stable Isotope-Labeled E. coli

As complete labeling of the E. coli proteome is a prerequisite for accurate quantitation of proteins within the proteome, we examined labeling efficiency by collecting samples from different generations to determine optimum labeling time. Here we used the log ratio of heavy over light peptides [log2 (heavy/light)] as an index for incorporation rate (Figure 2). The results showed that heavy lysine (13C6) incorporated rapidly, and a majority of the proteome from the first generation already had incorporated the heavy lysine. For instance, one abundant peptide (IQGIGAGFIPANLDLK) from cysteine synthase A in the sample cultured for one generation after inoculation into heavy SILACE medium (gen01) displayed about 52% light lysine and 48% heavy lysine (Figure 2A). The light form of this peptide decreased to less than 27% in sample gen02, became barely detectable after the third generation, and was not found in gen07or gen12 samples. We further analyzed global incorporation of heavy lysine (13C6) into the E. coli proteome. By fitting all data sets to a Gaussian

Determination of Ideal S/N Cutoff for SILAC Analysis (Pure Null Test)

The accuracy of quantitative information about proteins obtained using the SILAC platform is essential. To test the accuracy of our platform, we evaluated a pure null experiment, in which two identical SILAC labeled E. coli cultures were E



Technical Note

Figure 3. Determination of S/N cutoff in SILAC analysis (pure null test). (A) Work flow of pure null test; (B) trend for decreasing protein FDR with an increasing in S/N cutoff.

Figure 4. Quantitative proteomics comparison of E.coli before and after IPTG induction through a SILAC approach. (A) Work flow for quantitative comparison of protein amount change with IPTG treatment. (B) Total cell lysate (TCL) with or without IPTG induction was resolved by 10% tricine-SDS-PAGE. The sample for proteomic analysis was made by mixing the TCL from cells treated with or without IPTG treatment in a 1:1 ratio according to the A600.

technical replicates. Theoretically, no protein was anticipated to display an intensity ratio of light versus heavy other than 1, representing equal mixing of the two populations from the same E. coli seed cultures with identical medium and condition except for the difference between light and heavy lysine. However, in practice, the intensity ratio of light and heavy labeled peptide pairs may not equal one-to-one (log2 (heavy/ light) ratio equal to 0) due to the influence of noise produced during LC−MS/MS and incorporated into calculations via computational analysis. Thus, the log2 (heavy/light) ratios for all proteins quantified were distributed in the form of a Gaussian curve, with a mean of 0.03 and a standard deviation (SD) of 0.10 (Supplemental Figure S3). This suggested that the error range of log2 (heavy/light) ratios obtained from more than 95% proteins varied within a range of 7%. Quantification results were more likely to be inaccurate when the signal was

compared except one of them was labeled with light amino acid and another with heavy lysine. In order to determine and differentiate noise from signal for high accuracy of protein quantitation in subsequent large scale proteomics, we tried the S/N cutoff as a filter parameter.43 In this test, a single colony of E. coli was inoculated into a flask with LB liquid medium and grown at 37 °C to log phase. The culture was saved as a seed culture and split into two parts to inoculate either light or heavy SILACE media. After labeling for 12 generations, an equal amount of these two populations were mixed (Figure 3A), resulting in an equal amount of protein extracted from these SILAC labeled E. coli. The proteins were resolved by a 10% SDS−PAGE gel for a length of ∼3 mm, stained with Coomassie Blue G-250, and sliced into five gel bands. These gel pieces were then digested with Lys-C. The resulting peptides were analyzed by LC−MS/MS in four F



Technical Note

Figure 5. Large scale protein profiling of E. coli proteome after IPTG induction. (A and B) Representative isotope-labeled peptide pairs in the TCL from forward (A) and reverse labeling (B) with a mixture of an equal amount of light and heavy cells. The light- and heavy-labeled peptides were distinguishable by different m/z. The inset panels show representative isotope-labeled peptide pairs of ClpA. (C and D) Histograms of log ratios of abundance of quantified proteins in the TCL of forward labeled (n = 2178) and reverse labeled (n = 1845) experiments.

with light lysine, and the uninduced bacteria were labeled with heavy lysine. In each experiment, the cells labeled differentially were mixed at an equal value of A600. The total cell lysates were resolved by 10% SDS-PAGE, and the gel was excised into 32 gel bands based on molecular weight (Figure 4B), digested with Lys-C, and analyzed by LC-MS/MS (Figure 4A). For parsimonious identification, proteins with shared peptides were classified into one group that was represented by one top protein with the highest number of identified peptides. In the forward and reverse experiments, 2175 and 1845 proteins were identified respectively. For quantification, 2104 proteins remained in the forward test after filtering with S/N = 6. We confirmed the SILAC data by a manual check of intensity of a randomly selected peptide precursor ion (SIGLIHQDNSTDAMEEIKK) from the protein ClpA (Figure 5A,B). In both the forward and reverse experiments, the relative abundance of the peptide identified in the uninduced sample was about 2-fold higher than in the IPTG-induced sample. These results strongly supported the high accuracy and reproducibility of our SILAC labeling based quantitation approach. The distribution of log2 (uninduced/IPTG induced) ratio was symmetrical and could be fit to a Gaussian curve with a mean of 0.38 and a SD of 0.18 (Figure 5C). By the same means, in the reverse experiment, 1767 proteins were quantitated with a mean of 0.56 and a SD of 0.18 for the distribution of log2 (uninduced/IPTG induced) ratio (Figure 5D). Quantitative analysis of these two data sets

close to the noise level. We defined a measurement to be incorrect and contributing to FDR when the value difference of log2 (heavy/light) ratio between two repeats of pure null runs was more than 1. As shown in Figure 3B, the FDR for quantitative differences in individual proteins was reduced by increasing S/N cutoff. FDR was close to 1% when the S/N cutoff was increased to 6, which we used as a cutoff for all further analysis in this study. It is worth mentioning that the parameter may need adjustment when applied to different MS platforms as they may have different noise levels;43 the procedure here is expected to be highly instructive for conducting efficient proteomics analysis. Application of SILAC E. coli to Protein Profiling in an IPTG Induction Model

To further understand the physiological change in cells after IPTG induction, the protein expression patterns of E. coli induced by IPTG for 1 h was compared with uninduced cells by SILAC analysis. In order to improve the accuracy of analysis, we applied a swap labeling strategy, in which a pair of experiments (forward experiment and reverse experiment) was performed (Figure 4A). In the forward test, E. coli culture induced with IPTG was labeled with heavy lysine (13C6), and E. coli grown in the medium without IPTG was labeled with light lysine (12C6). In the reverse experiment, the IPTG-induced bacteria were labeled G



Technical Note

Figure 6. Global distribution of all the proteins in swap labeled SILAC samples. The histogram on the right shows the distribution of log2 (uninduced/IPTG induced) protein ratios in the forward labeled experiment (n = 2104). The histogram on top shows the distribution of log2 (uninduced/IPTG induced) protein ratios in the reverse labeled experiment (n = 1767). Proteins quantified in both experiments (n = 1471) are shown in the central scatter plot.

On the basis of DAVID analysis, KEGG analysis, Gene Ontology, BLAST alignment, and information from the literature, the 1251 differentially expressed IDs (except the three proteins GST-4DSK2, T7 polymerase and β-galactosidase (LacZ)) were classified into 15 functional categories: (1) carbohydrate and energy metabolism, (2) metabolism, (3) oxidation and/or reduction, (4) stress and defense, (5) transcription related, (6) protein synthesis, (7) protein modification, (8) protein degradation, (9) signaling, (10) membrane and transport, (11) cell structure, (12) cell motility, (13) cell cycle, (14) miscellaneous, and (15) function unknown (Supplemental Figure S4, Supplemental Table S1). Among these functional categories, carbohydrate and energy metabolism (15.7%), metabolism (24.9%), transcription related (13.2%), protein synthesis (11.7%), and membrane and transporter (11.8%) were over-represented (Supplemental Figure S4, Supplemental Table S1). IPTG, an analogue of galactose, binds to the Lac repressor, lowering affinity of the repressor for its cognate operator on the Lac operon by 300-fold.44 Addition of IPTG leads to the release of Lac repressor and starts the expression of genes under the control of Lac operator. In particular, T7 RNA polymerase, required for efficient recombinant protein transcription, is expressed from the BL21 (DE3) genome under the control of the lacUV5 promoter, which harbors a Lac operator. T7 polymerase in turn activates the expression of the target protein, GST-4DSK2 efficiently. These two proteins and a fragment of β-galactosidase were all significantly induced by IPTG, as shown by their position in the third quadrant of the

indicated that the average ratio of protein extracted from uninduced and induced samples is 1.30 and 1.47, respectively. In order to get more precise results, the values of log2 (uninduced/IPTG induced) ratio were adjusted to make the two means both equal to 0.47, the average of 0.38 and 0.56. After an additional manual check, 1471 proteins were left with a difference of adjusted log2 (uninduced/IPTG induced) ratio in both experiments larger than 0.4 (4 times the SD in the pure null experiment). These quantitative results were visualized in 2D plots (Figure 6). The log2 (uninduced/IPTG induced) ratios of proteins identified in each MS/MS run were plotted as the forward experiment value (x-axis) versus the reverse labeling experiment value (y-axis). The histograms to the top and right indicate protein ratio distributions in the forward and reverse experiments, respectively. Differentially expressed proteins with a similar trend on both forward and reverse experiments necessarily appear in the first quadrant or the third quadrant. By this means, we identified 1250 proteins that were consistently down-regulated by induction which located into the first quadrant with adjusted log2 (uninduced/IPTG induced) from both replicates greater than 0.2, and 11 upregulated proteins were in the third quadrant. The remaining 59 proteins were not significantly changed. In addition, as predicted by the E. coli genome-sequencing project, 4285 protein encoding genes existed in the E. coli,10,11 and our quantitative data (2104 and 1767 proteins were quantified in forward and reverse experiment respectively) cover about half of the complete proteome of E. coli. Thus, we thought lysine labeling strategy is suitable for E. coli. H



Technical Note

pathway.45 Down-regulated expression level of these proteins in our study may have resulted from the senescent growth status of E. coli caused by IPTG treatment, which in turn affects the motility and secretory system of bacteria. On the basis of these results, SILACE medium for labeling and subsequent quantitative proteomic analysis as presented here provides a highly sensitive and accurate global means for whole bacterial proteome quantification, which can be readily integrated with existing methods to elucidate biochemical pathways or functional protein−protein interactions as applied recently to cell cultures.46

2D distribution plot, consistent with expected results (Figure 6). These results also suggest that SILAC labeling of E. coli does not interfere with the efficient production of heterologous protein. The absolute amount of induced GST-4DSK2 was estimated by the intensity of target protein band and the whole gel lane with Quantity One 4.6.2 software (BIO-RAD, USA). We found that the expressed GST-4DSK2 occupied 16.1% of total cell protein. The massive increase in expression level of target protein GST-4DSK2, T7 RNA polymerase, and βgalactosidase takes up a large proportion of available resources. Finally, the induction of these proteins increased their contribution to the total protein from an insignificant to a significant percentage (Figure 4B, Supplemental Table S1), which resulted in the apparent reduction of expression of nearly all other endogenous proteins when they were compared by equal loading of uninduced and induced samples (Supplemental Table S1). Indeed, Coomassie Blue stained SDS-PAGE gel clearly shows that the staining intensity of most protein bands from induced sample was slightly lighter than the corresponding uninduced ones (Figure 4B). This raised the technical issue that protein abundances across the population might not be completely normalized to account for loading differences that occurred when comparing uninduced bacteria with those exposed to IPTG. Induced bacteria effectively activated the production of target protein, and no definitive solution presented itself which would entirely eliminate this normalization issue. Independent of the normalization issue, we asked if the growth of IPTG-induced bacteria is slower corresponding with depletion of and/or redirection of amino acids and other resources toward the production of target protein. Comparison of growth curves between the induced and uninduced cells indeed suggested that IPTG induction inhibits the growth of E. coli (Supplemental Figure S5A), and the significant reduction in the abundance of 196 carbohydrate and energy metabolism related proteins and 311 metabolism related proteins was consistent with this observation (Supplemental Table S1). Not only did cell number decrease in the same volume of culture but also the shape of individual cells, including the cell length, diameter, and superficial area of cells, was also changed by IPTG (Supplemental Figure S5B). As shown in Supplemental Figure S5C, the diameter and superficial area of induced cells were raised to 0.517 and 2.86 μm2 from 0.455 and 2.5 μm2, respectively. However, average cell length was not obviously changed after 1 h of IPTG induction. These changes in cell shape were consistent with the increased expression level of one group of proteins related to cell wall synthesis, including ftsI, mrcA and lpxC (Supplemental Table S1). Growth curves of E. coli before and after IPTG treatment showed that the IPTG induced cells grew more slowly than the uninduced ones, which was consistent with our quantitative proteomics data that the expression levels of cell cycle related genes were downregulated as well (Supplemental Table S1). These results might indicate that IPTG induction disturbed the process of fissiparism of E. coli and finally resulted in the slower growth rate. In addition, abundance of flagellar related proteins, such as basal-body rod modification protein flgD, flagellar basal-body rod protein flgG, flagellar hook protein flgE, and flagellar hookassociated protein (flgK and flgL), were also decreased by IPTG induction (Figure 6). Within the bacterial flagellum, the basal-body rod, the hook, the hook-associated proteins, and the helical filament together constitute an axial substructure whose elements share structural features and a common export

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CONCLUSION We have developed SILACE medium suitable for efficient and complete labeling of nonauxotrophic E. coli strains and have established a data filtering and evaluation strategy for SILACbased quantitative proteomic studies of E. coli. These tools are broadly applicable for quantitative proteomic studies with E. coli and perhaps other cultured bacteria in the future. The quantitative proteomics data sets may also be useful in metabolic pathway engineering studies or even in the optimization of processes in industry. In addition, we have demonstrated that metabolic labeling provides a particularly useful tool for sensitively identifying and quantifying differentially expressed proteins in varied conditions.

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ASSOCIATED CONTENT

S Supporting Information *

Table S1. Proteins and their relative change in E. coliafterone hour IPTG treatment. Figure S1. Testing the labeling efficiency of SILAC medium with aspartic acid. Figure S2. Optimization and evaluation of the labeling efficiency in a SILAC experiment using medium without aspartic acid (SILACE medium). Figure S3. Histograms of log2 (light/heavy) quantified proteins in four repeats of pure null runs. Figure S4. Functional analysis for proteins with significant expression level change after IPTG treatment. Figure S5. Comparison of the growth status of E. coli before and after IPTG induction. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*(P.X.) Tel/Fax: 8610-80705155; e-mail: xupingghy@gmail. com. *(J.W.) Tel: 8627-6875-9795; fax: 8627-6875-9222; e-mail: [email protected]. Present Address ∥

Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322. Author Contributions #

Authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors are grateful to colleagues in the Xu lab for helpful discussions and comments, especially Jingwei Wang for the E. coli strain and Lei Chang for technical support. This study was supported by the Chinese National Basic Research Programs (2011CB910600 & 2013CB911201), the National Natural I



Technical Note

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Science Foundation of China (Grant Nos. 31070673 & 31170780), the National High-Tech Research and Development Program of China (SS2012AA020502 & 2011AA02A114), National Megaprojects for Key Infectious Diseases (2013zx10003002), Key Projects in the National Science & Technology Pillar Program (2012BAF14B00), the China-EU International Collaboration Program (Grant No. 20111001), the National Natural Science Foundation of Beijing (Grant No. 5112012), and the Foundation of State Key Lab of Proteomics (SKLP-K200904 & SKLP-Y201102).

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ABBREVIATIONS ACN, acetonitrile; AGC, automatic gain control; FA, formic acid; FDR, false discovery rate; LB, Luria−Bertani; MS, mass spectrometry; nano-LC−MS/MS, nano-liquid chromatography−mass spectrometry/mass spectrometry; S/N, signal-tonoise; SEM, scanning electron microscopy; SILAC, stable isotope labeling by amino acid in cell culture; SILACE, SILAC medium for E. coli; UPLC, ultra performance liquid chromatography

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