Dynamic Omics Approach Identifies Nutrition-Mediated Microbial

Dynamic Omics Approach Identifies Nutrition-Mediated Microbial Interactions Yumiko Nakanishi,†,‡,§ Shinji Fukuda,†,§ Eisuke Chikayama,‡ Yayoi Kimura,†,| Hiroshi Ohno,*,†,§ and Jun Kikuchi*,†,‡,⊥ Graduate School of Nanobiosciences, Yokohama City University, 230-0045 Yokohama, Kanagawa, Japan, Advanced NMR Metabomics Research Team, RIKEN Plant Science Center, 230-0045 Yokohama, Kanagawa, Japan, Laboratory for Epithelial Immunobiology, RIKEN Research Center for Allergy and Immunology, 230-0045 Yokohama, Kanagawa, Japan, Laboratory for Immunogenomics, RIKEN Research Center for Allergy and Immunology, 230-0045 Yokohama, Kanagawa, Japan, and Graduate School of Bioagriculture Sciences, Nagoya University, 464-8601 Nagoya, Aichi, Japan Received September 28, 2010

“Omics” studies reported to date have dealt with either thoroughly characterized single species or poorly explored meta-microbial communities. However, these techniques are capable of producing highly informative data for the analysis of interactions between two organisms. We examined the bacterial interaction between Escherichia coli O157:H7 (O157) and Bifidobacterium longum (BL) as a pathogenic-commensal bacterial model creating a minimum microbial ecosystem in the gut using dynamic omics approaches, consisting of improved time-lapse 2D-nuclear magnetic resonance (NMR) metabolic profiling, transcriptomic, and proteomic analyses. Our study revealed that the minimum ecosystem was established by bacterial adaptation to the changes in the extracellular environment, primarily by O157, but not by BL. Additionally, the relationship between BL and O157 could be partially regarded as that between a producer and a consumer of nutrients, respectively, especially with regard to serine and aspartate metabolism. Taken together, our profiling system can provide a new insight into the primary metabolic dynamics in microbial ecosystems. Keywords: crosstalk • metabolomics • omics • time-lapse measurement • stable isotope labeling

Introduction “Omics” techniques, including genomics, transcriptomics, proteomics, and metabolomics, have been developed to understand the “whole picture” of biological reactions in cells and organisms.1 Comprehensive measurements of molecular components in model organisms have demonstrated a promising top-down strategy to solve puzzles in organisms that cannot be addressed by conventional bottom-up approaches.2 Recent advances in systems biology have also provided vast amounts of information, based on time-series observation at the genomic, proteomic, and metabolic levels.3,4 Time-series analyses based on these multi omics techniques have revealed novel protein-protein interactions and metabolic networks.5 These analyses have also enabled us to capture the overall changes * To whom correspondence should be addressed. Jun Kikuchi, RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan. Phone: +81455039490. Fax: +81455039489. E-mail: [email protected]. Hiroshi Ohno, RIKEN Research Center for Allergy and Immunology, 2300045 Yokohama, Kanagawa, Japan. Phone: +81455037031. Fax: +81455037068. E-mail: [email protected]. † Yokohama City University. ‡ RIKEN Plant Science Center. § Laboratory for Epithelial Immunobiology, RIKEN Research Center for Allergy and Immunology. | Laboratory for Immunogenomics, RIKEN Research Center for Allergy and Immunology. ⊥ Nagoya University.

824 Journal of Proteome Research 2011, 10, 824–836 Published on Web 11/09/2010

in intracellular molecular networks in response to external stimuli, as well as the periodicity of biological clocks.2,6,7 Furthermore, multivariate analyses have been adopted to comprehensively understand the dynamic changes of transcriptomic and metabolomic networks in time-series data of a cell or organism and to understand the biological behavior based on these networks.2,8,9 On the other hand, it is important to analyze more complex systems where two or more organisms interact with each other to understand their functions as a whole system. This would be particularly important in examining the microbial communities in industrial processes, environmental maintenance, and the human gut.10-13 Extracellular metabolites produced as a result of bacterial communication are considered key factors for understanding cell-cell interactions. Quormone and bacteriocin are well-known molecules involved in bacterial crosstalk in quorum-sensing and growth inhibition, respectively.14 In addition to these secondary metabolites, primary metabolites, such as organic acids and amino acids, are also considered important for understanding a microbial community.12,15,16 There have been several reports of one microorganism excreting precursors of vitamins, certain amino acids, or sugars to their symbiotic partners, not only to supply essential nutrients required for synthetic pathways, but also as a means of conserving biosynthetic energy.17 Although metabolic coopera10.1021/pr100989c

 2011 American Chemical Society

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Nutrition-Mediated Microbial Interactions tion has been assessed using coculture experiments, previous studies have focused only on specific metabolic events and thus omics-based comprehensive approaches are needed for an analysis of the whole picture of microbial symbiosis. The human gut is colonized with many commensal bacteria that have impacts on immune function, nutrient processing, and a broad range of other host activities, as well as providing competitive growth inhibition of several pathogens.15 However, the molecular basis of bacterial crosstalk, especially commensal-pathogenic bacterial crosstalk, has barely been examined, except with regard to bacterial growth inhibition by antibacterial peptides. It has been reported that Escherichia coli O157:H7 (O157) and Bifidobacterium longum (BL) as pathogenic and commensal bacteria live together in the germ-free murine gut without showing growth competition toward each other.18,19 O157 is a well-known major foodborne pathogen that causes diarrhea, hemorrhagic colitis, and hemolytic uremic syndrome.20 In contrast, BL is a commensal (or probiotic) bacterium, which has beneficial effects on human health.21,22 BL protects mice against lethal O157 infection in the germfree murine model. The protection mechanism by BL in this model was reported that the production of acetate from BL prevented the translocation of Shiga toxin from the gut lumen into the bloodstream through the up-regulation of intestinal epithelial barrier function and then protects from O157 lethal infection.18 The number of bacterial cells and the amount of Shiga toxin in the intestinal contents were comparable between diassociated mice (BL and O157) and O157 monoassociated mice; however, the details of bacterial interaction including primary metabolism between BL and O157 have been obscure. Thus, we selected the coexistence of O157 and BL as a unique model creating a microbial minimum ecosystem in the murine gut and analyzed primary metabolism of BL and O157 in vitro. Additionally, the coculture model of these bacteria is considered suitable for examining synergistic communications using omics approaches, because their genomic sequences are available.23,24 Several nuclear magnetic resonance (NMR)-based metabolic profiling methods have been reported.25-27 These methods involve observation of one-dimensional (1D) proton spectra, followed by statistical pattern recognition.28-31 Although 1Dbased methods are sufficient in some types of metabolic profiling studies,32 more robust methods are also required. In this study, we have improved our previous 1D-based method monitoring real-time bacterial metabolic dynamics33 to enhance the resolution of 2D-based NMR spectra in a bacterial coculture system. The 2D-based method allows us to analyze metabolic dynamics using uniform 13C-labeling techniques, which can provide high-resolution 1H-13C heteronuclear singlequantum coherence (HSQC) spectra.34-36 Rather than focusing on limited 13C-labeled substrates, such as 13C1-glucose and their metabolism,37,38 these uniform 13C-labeling techniques are more appropriate for untargeted metabolomics analysis. Here, we report the nutrition-mediated microbial interactions between BL and O157 in a coculture system, taking advantage of a newly developed time-lapse in vivo 2D-NMR profiling method with stable isotope (SI) labeling. The monitoring technique of living cells by NMR is called in vivo NMR which is core technique of our profiling method.38-42 Furthermore, in combination with transcriptomic and proteomic techniques, these methods provide insight to understand the intracellular responses of O157 and BL in the coculture system, as well as their ability to adapt to environmental changes. This

two-species system presents a unique coexistence model for examining bacterial crosstalk, and we partially regarded them as a producer and a consumer of nutrients, respectively, especially with regard to serine and aspartate metabolism.

Materials and Methods Bacterial Strains. B. longum subsp. longum JCM 1217T was purchased from the Japan Collection of Microorganisms (JCM). E. coli O157:H7 strain 44Rf, a rifampicin-resistant mutant strain, was originally isolated from bovine feces.43 Culture Conditions. The medium used for preparation of inocula and proteomics analysis was as follows (per liter): glucose (5 g), trypticase (2.5 g), peptone (2.5 g), yeast extract (5 g), K2HPO4 (0.9 g), KH2PO4 (0.9 g), NaCl (1.8 g), NH4SO4 (1.8 g), CaCl2 · 2H2O (0.24 g), MgSO4 · 7H2O (0.375 g), potassium acetate (1 g), sodium propionate (0.5 g), sodium n-butyrate (0.3 g), valeric acid (0.1 g), isobutyrate (0.1 g), isovaleric acid (0.1 g), 2-methyl butyrate (0.1 g), NH4Fe(III)-citrate (0.01 g), MnCl2 (0.006 g), CoCl2 (0.002 g), NiCl2 (0.0004 g), (NH4)6(Mo7O24) · 4H2O (0.0004 g), CuSO4 (0.00015 g), AlK(SO4)2 (0.0003 g), Na2B4O7 (0.0003 g), ZnSO4 (0.002 g), Na2SeO3 (0.0003 g), pyridoxal phosphate (0.002 g), p-amino benzoate (0.0005 g), biotin (0.0002 g), phenylpropionate (0.002 g), l-arginine (0.002 g), Panvitan (0.1 g; Takeda Chemical, Osaka, Japan), cysteine hydrochloride (0.001 g), and resazurin (7-hydroxy-3H-phenoxazin-3-one 10oxide) (0.005 g). Inoculum cultures were grown anaerobically in 10-mL serum vials at 37 °C and stopped at the midexponential phase (OD600 ) 1.0). NMR tubes 5 mm in diameter were inoculated with 1% of O157, 4% of BL, or 1% of O157 and 4% of BL of inoculum culture. These different inoculation volumes corresponded with the growth phase between the two strains. Labeling Experiment with in vivo NMR Measurements. The labeling media were optimized for in vivo NMR measurements. 1. Fully 13C-labeled medium; E. coli OD2 (13C,15N > 98%; Spectra, Andover, MA), 0.5% (w/v) 13C6-d-glucose (13C >98%; Spectra), 0.5% (w/v) 13C,15N Algal Amino Acid Mixture (10% (w/ w) Ala, 2.8% (w/w)Arg, 7.8% (w/w) Asp, 0.2% (w/w) Cys, 11% (w/w) Glu, 8.2% (w/w) Gly, 1.9% (w/w) His, 5.9% (w/w) Ile, 11.6% (w/w) Lys, 2.8% (w/w) Met, 2.1% (w/w) Phe, 6.3% (w/ w) Pro, 4.4% (w/w) Ser, 4.9% (w/w) Thr, 0.9% (w/w) Tyr, 7.9% (w/w) Val, 13C >97.4%, 15N > 97.9%; Chlorella Industry Co., Ltd., Tokyo), 10% (v/v) deuterium oxide (D2O) (Sigma-Aldrich, St. Louis, MO), 10% inoculum medium containing 0.5% (w/v) 13C6d-glucose, 0.1% (w/v) cysteine hydrochloride, and 0.05% (w/ v) resazurin with 5 mM sodium 2,2-dimethyl-2-silapentane-5sulfonate (DSS) (Sigma-Aldrich) as an internal standard. E. coli OD2 containing 13C,15N-labeled compounds, such as glucose, fructose, amino acids, and other compounds, measured by NMR; the results are shown in Supplementary Table 1 (Supporting Information). 2. [13C6] Glucose-labeled medium; E. coli OD2 (unlabeled; Spectra), 0.5% (w/v) 13C6-d-glucose (13C >98%; Spectra), 0.5% (w/v) Algal Amino Acid Mixture (unlabeled; Chlorella Industry Co., Ltd.), 10% (v/v) D2O, 10% inoculum medium containing 0.5% (w/v) 13C6-d-glucose, 0.1% (w/v) cysteine hydrochloride, and 0.05% (w/v) resazurin with 5 mM DSS. 3. 13C-Amino acid-labeled medium; E. coli OD2 (unlabeled; Spectra), 0.5% (w/v) d-glucose (unlabeled; Spectra), 0.5% (w/ v) 13C,15N Algal Amino Acid Mixture (13C >97.4%, 15N > 97.9%; Chlorella Industry Co., Ltd.), 10% (v/v) D2O, 10% inoculum medium containing 0.5% (w/v) 13C6-d-glucose, 0.1% (w/v) cysteine hydrochloride, and 0.05% (w/v) resazurin with 5 mM Journal of Proteome Research • Vol. 10, No. 2, 2011 825

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DSS. Addition of 0.5% (w/v) C, N Algal Amino Acid mixture and 10% inoculum promoted the growth of BL. Both BL and O157 were cultured in each medium at 37 °C for 8 h inside NMR. Additionally, the [13C6] Glucose-labeled and 13 C-Amino acid-labeled experiments, we cultured BL and O157 for 8 h in 5 mL of each medium in 10-mL serum vials at 37 °C in a laboratory incubator and then collected the culture supernatant at 0 and 8 h from the start of cultivation for NMR measurements in triplicate. The growth rates of BL, O157, and coculture were determined by measuring the optical density at 600 nm (OD600) using an NMR tube at 37 °C in a laboratory incubator. The OD600 after in vivo NMR experiments was also checked. For the bacterial counts, 10-fold serial dilutions of bacterial culture with phosphate buffer saline (PBS) were made and then 0.05 mL of an appropriate dilution was inoculated on Luria-Bertani broth (LB) (Becton, Dickinson and Co., Cockeysville, MD) and TOS propionate agar plates (Yakult Pharmaceutical, Tokyo). To assess the number of BL, TOS propionate agar plates were used while LB agar plates were prepared for the O157 count. The bacteria on the TOS propionate agar plates were cultured anaerobically at 37 °C for 48 h, and those on LB agar plates were cultured aerobically at 37 °C for 24 h. The colony-forming units (CFU) of each strain were counted. 13

C-Labeled Tracer Experiments using [13C4] Aspartate and [13C3] Serine. We used nonlabeled medium of which the composition was identical to fully 13C-labeled medium, containing E. coli OD2 (unlabeled), 0.5%(w/v) d-glucose, 0.5% (w/ v) Algal Amino Acid Mixture (unlabeled), 10% (v/v) D2O, 10% inoculum medium, and 5 mM DSS, and added to 1 mM [13C4,15N] aspartate (98% >13C, 98% >15N; Spectra) or [13C3,15N] l-serine (99% > 13C, 98% > 15N; Taiyo Nippon Sanso, Tokyo) for 13 C-labeled tracer experiments. We cultured O157 individually for 8 h in 10 mL of each medium in 30-mL serum vials at 37 °C. We collected the culture supernatant at 0, 2, 4, 6, and 8 h from the start of cultivation. 1D- and 2D-NMR Measurements. 1H NMR and 2D-1H-13C HSQC spectra were recorded on a DRX-500 spectrometer (Bruker, Billerica, MA) operating at 500.03 MHz for 1H, with the temperature of NMR samples maintained at 37 °C. 1H NMR and 1H-13C HSQC spectra were alternately observed every 30 min. For 1H NMR spectra, residual water signals were suppressed by the Watergate pulse sequence, with a repetition time of 1.2 s. 1H-13C HSQC spectra were measured according to the method of Kikuchi and Hirayama.44 All 1H-13C HSQC spectra were recorded on a DRX-500 spectrometer. In total, 32 complex f1 (13C) and 1024 complex f2 (1H) points were recorded with 16 scans per f1 increment. Spectral widths were 2640 and 5000 Hz for f1 and f2, respectively. NMR spectra were processed using the NMRPipe software.45,46 Proton-decoupled 13C NMR spectra were recorded on a Bruker Avance-700 spectrometer, equipped with an inverse triple resonance CryoProbe with a Z-axis gradient for a sample diameter of 5 mm, operating at 176.061 MHz for 13C, and the temperature of the NMR samples was maintained at 25 °C. 13C NMR spectra were acquired with a 30° flip angle and a recycle delay of 1.2 s under saturation. Proton broadband decoupling was achieved with the GARP sequence. In total, 2.8 k scans were collected with 64 k points/scan. NMR spectra were processed 826

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using the NMRPipe software and binned to 32 k points. The spectra were reconstructed using Excel (Microsoft, Redmond, WA). Quantitative Statistical Analysis of 1D- and 2D-1H NMR Spectra. For 1H NMR profiling, 1D-1H NMR data were reduced by subdividing spectra into sequential 0.03 ppm designated regions between 1H chemical shifts of 0.5 to 9.0 ppm. After exclusion of water resonance, each region was integrated and normalized relative to the DSS intensity and analyzed by PCA using the R software. For 1H-13C HSQC profiling, 1H-13C HSQC spectra were reduced by subdividing spectra into sequential bins of 0.3 ppm in the f1 direction and 0.03 ppm in f2 designated regions between 1H chemical shifts of 0.5 to 9.0 and 13 C chemical shifts of 40 to 90, respectively. After exclusion of water resonance, each region was integrated and normalized relative to DSS intensity as an internal standard. We next removed noise in NMR spectra due to its effect on the results of PCA.47 Noise removal was performed by adopting bins of z-score >0.1 as confirmed signals. The z-score matrix was defined as the matrix with each element of zij ) (sij - )/σj where sij is the intensity of the jth bin in the ith 1H-13C HSQC spectrum, is the average of all intensities of the jth bins in all the spectra, and σj is the standard deviation. The total signals were then reduced to approximately as large as 3000 bins. All bins were analyzed by PCA with the R software and plotted using GraphR. Loading plot analysis revealed the contribution of bins to the PCA scores and 2D spectral assignments were performed using SpinAssign software.48,49 Calculation of the Rate of Metabolism. The metabolism rate of key metabolites every half hour in our culture system was calculated as the normalized derivative value. The absolute values of each derivative were shown for the maximum to 1 as the rate of metabolism. Protein Extraction and Two-Dimensional Fluorescence Difference Gel Electrophoresis (2D-DIGE) Analysis. O157 and BL were precultured anaerobitically. In monoculture, BL and O157 were inoculated at 1% in 30 mL of nonlabeled medium in 100 mL serum vials and cultured at 37 °C. In coculture, precultured O157 was inoculated at 1% for 30 mL of nonlabeled medium and cultured at 37 °C. After 1 h, precultured BL was inoculated at 1% for O157 culture and cultured at 37 °C. We used nonlabeled medium for proteomic analysis, because we first analyzed 13C-labeled proteins using cultures in fully 13Clabeled medium in a preliminary experiment. However, 12Ccontaining proteins derived from 12CO2 used to replace O2 for anaerobic culture coexisted with 13C-containing proteins in a single band of a given protein, because they migrated together on electrophoresis and this mixture of 12C- and 13C-labeled proteins perturbs the subsequent TOF-MS identification of the protein eluted from the gel because 12C and 13C give broad peaks (Supplementary Figure 1, Supporting Information). Thus, we prepared medium of the same composition as E. coli OD2 medium (Spectra), except that it contained 12C instead of 13C (unlabeled medium). Additionally, gel-electrophoretic patterns of the proteins extracted from these bacteria at the midexponential phase of culture with unlabeled and OD2 media were also comparable (Supplementary Figure 2). Taken together, the transcriptomic and proteomic data in this study should be compatible and used nonlabeled medium for proteomic analy-

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Nutrition-Mediated Microbial Interactions sis. These cultures were stopped at the midexponential phase (OD600 ) 1.0), cooled immediately in an ice bath, and then centrifuged (10 000× g, 10 min, 4 °C). The pellet was washed with PBS, and the cells were suspended in lysis buffer (30 mM Tris-HCl, pH 8.5, 7 M urea, 2 M thiourea, 4% (w/v) 3-(3cholamidopropyl)dimethylammonio-1-propanesulfonate(CHAPS)). The cells were repeatedly disrupted using a sonicator until approximately 95% of the cells were broken. The disruption efficiency of the cells was assessed by Gram staining. After centrifugation (10 000× g, 10 min, 4 °C), the supernatant was subjected to 2D-DIGE analysis. Briefly, samples were labeled with 400 pmol per 50 µg of protein with Cy2, Cy3, or Cy5 protein labeling dye (GE Healthcare, Milwaukee, WI), according to the manufacturer’s protocol. The internal standard, an equimolecular mixture of all the protein extracts, was labeled with Cy2. Total protein labeled with Cy2, Cy3, and Cy5 for matched monocultures of BL and O157 and coculture samples were combined and mixed with lysis buffer. The combined samples were applied to 24-cm immobilized pH gradient (IPG) strips with a linear gradient of pH 4-7 (GE Healthcare). Isoelectric focusing in the first dimension was carried out on an Ettan IPGphor IEF system (GE Healthcare). After isoelectric focusing, strips were equilibrated and then loaded onto a 10% polyacrylamide gel (25 × 18 cm). After electrophoresis, proteins were digitally imaged using a Typhoon Trio Variable Mode Imager (GE Healthcare). The resulting images were then analyzed using the 2DE image analysis software package Progenesis SameSpots (Nonlinear Dynamics Ltd., Newcastle upon Tyne, UK), whereby individual spot volume ratios were calculated for each protein spot pair. To identify the proteins by MS, the gels were also stained with SYPRO Ruby staining (Molecular Probes, Eugene, OR) and then spots of interest were excised from the gels. After digestion with trypsin in 25 mM NH4HCO3 at 30 °C overnight, the resulting peptides were desalted using ZipTip-C18 µ (Millipore, Bedford, MA) and then eluted directly with matrix solution containing 4 mg/mL CHCA (Sigma-Aldrich) on a target plate. The obtained digests were analyzed on an ABI4800 proteomics analyzer (Applied Biosystems, Foster City, CA) operated in reflector positive mode. MS data were acquired over a mass range of m/z 800-4000. Peak lists were created using Peak to Mascot tool of the 4000 Series Explorer Software version 3.5.1 (Applied Biosystems). The obtained MS and MS/MS data were used for database searches using MASCOT Version 1.8.06 (Matrix Science, London, UK). The database (775 398 entries) used for this search consisted of amino acid sequences of bacterial proteins, which were retrieved from a subset of the NCBI nonredundant protein database (accessed 31 August 2006). The search parameters were as follows: each proteinase digestion with two missed cleavage permitted, variable modifications (N-terminal acetylation, oxidation and sulphonation of methionine, and propionamidation of cysteine), peptide mass tolerance for MS data (300 ppm, and fragment mass tolerance (0.3 Da. For data evaluation with MASCOT, only “bold red” peptides with a peptide score g30 were considered evaluation peptides. Search results that were proteins of E. coli or B. longum species and yielded at least one the evaluation peptide and a protein score that was greater than 50 were accepted as positive identifications. Extraction of RNA and Microarray Analysis. O157 and BL were precultured anaerobically for overnight. In monoculture, BL and O157 were inoculated at 1% (v/v) individually in the labeling medium in 100-mL serum vials and cultured at 37 °C. In coculture, precultured O157 was inoculated at 1% (v/v) in

labeled medium and cultured at 37 °C. After 1 h, precultured BL was inoculated at 1% (v/v) for O157 culture medium and cultured at 37 °C. These cultures were stopped at the midexponential phase (OD600 ) 1.0), centrifuged (10 min, 20 400× g), and the supernatants were removed. Total RNA was extracted using an RNAprotect Bacteria Reagent Kit, according to the manufacturer’s protocol (Qiagen, Valencia, CA). The disruption efficiency of the cells was assessed by Gram staining. Aliquots of approximately 10 µg of isolated RNA were treated with 5 units of RNase-free DNase (Ambion, Austin, TX) for 30 min at room temperature. The RNeasy MinElute Kit (Qiagen) was used to purify DNase-treated total RNA, removing degraded DNA, tRNA, rRNA, DNase, contaminating proteins, and potential inhibitors of the reverse transcription reaction. The A260 of the eluted RNA was measured, and aliquots of 4 µg of purified RNA were reserved to prepare labeled samples for microarray analysis. First-strand cDNA synthesis was primed from purified total RNA template using random primers for each of the O157 and BL genes. The reverse transcription reaction was performed using a reverse Poly-A RNA Control kit (Affymetrix, Santa Clara, CA) according to the manufacturer’s instructions. The synthesized cDNA was partially digested with DNase I (Affymetrix) for 10 min at 37 °C. The labeled cRNA was synthesized using GeneChip DNA Labeling Reagent (Affymetrix). Hybridization and data detection were performed according to the manufacturer’s recommendations (Affymetrix). The data were normalized and analyzed using the GeneSpring software (ver. 7.3.1; Silicon Genetics, Redwood, CA). Expression data were median-centered using the GeneSpring normalization option. Real-Time Quantitative RT-PCR. Total bacterial RNA was extracted and reverse-transcribed by using RNeasy (Qiagen) and RevaTra Ace (Toyobo Co. Ltd., Osaka), respectively. Realtime PCR was performed using SYBR Premix Ex Taq (Takara, Tokyo) and specific primers as follows: 5′-AACTGGAGGAAGGTGGGGAT-3′ (forward) and 5′- AGGAGGTGATCCAACCGCA3′ (reverse) for 16S rRNA; 5′-TCTGTTGGGTACCAGGGAAG-3′ (forward) and 5′-ATACCGCGAACAAATTCAGG-3′ (reverse) for aspA; and 5′-TGTACGCAGCTAACCACCTG-3′ (forward) and 5′ACGCCACGATAGAGAGCAGT-3′ (reverse) for pykA. Assays were performed in triplicate using a Thermal Cycler Dice Real Time System (Takara).

Results Strategies to Determine Bacterial Interactions by in vivo NMR Measurements with Stable Isotope (SI) Probing. As bacterial metabolism changes markedly in a time- and environment-dependent manner, we developed a time-lapse-based metabolic profiling method combined with 13C-stable-isotope (SI) probing (Figure 1) to understand the interactions between BL and O157. Although 13C-SI labeling-based metabolic profiling has been widely used for bacterial metabolomics analysis,50-52 we attempted to improve our understanding of whole bacterial metabolism, as well as fermentation and amino acid metabolism in two strains by using three 13C-labeled media: fully 13Clabeled medium, [13C6]-glucose-labeled medium, and 13C-amino acid-labeled medium. The compositions of these three types of 13C-labeled media were identical, with the exception of the supplemented 13C-labeled substrate (see Materials and Methods). The compositions of these media were optimized for normal growth of BL and O157 (Supplementary Figure 3 and Supplementary Table 2, Supporting Information). To study the interaction between BL and O157, we used a time-lapse in vivo Journal of Proteome Research • Vol. 10, No. 2, 2011 827

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Figure 1. Schematic overview of our time-lapse-based metabolic profiling method. Taking advantage of 13C-stable-isotope probing in combination with in vivo NMR (upper), specific metabolic dynamics in bacterial monoculture and coculture were monitored (left). To identify the specific molecules resulting from bacterial crosstalk, two-dimensional 1H,13C HSQC spectra of monoculture and coculture were subdivided into sequential bins and analyzed by principal component analysis (PCA; middle). Loading plot provided the concentration and metabolism rate of key metabolites (right).

2D-NMR profiling method by 1H-13C HSQC, which is an improved version of our previous 1D profiling method.33 The 2D-based method provides improved spectral resolution for statistical analyses. The PCA profiles and loadings provided feasible key metabolites, which changed dynamically during bacterial growth. To evaluate the advantages of our 1H-13C HSQC profiling method, we performed comparative metabolomic analyses with our previous 1D profiling and the 1H-13C HSQC profiling method in O157 monocultures grown in fully 13 C-labeled medium (Supplementary Figure 4, Supporting Information). Features of the PCA scores plots for the O157 monoculture were separated, based on time for principal component 1 (PC1) in both 1H-13C HSQC data and 1D data. Based on loading plots, the major contributing components of PC1 were assigned as acetate, lactate, succinate, formate, and ethanol. Assignments were based on data in our 1H,13C chemical shift database.48,49 However, glucose, fructose, and aspartate were only assigned by 1H-13C HSQC profiling because of their weak and overlapping signals on 1D profiling. Based on these results, the 1H-13C HSQC profiling method was superior to the 1D profiling method in terms of spectral resolution. Fermentative Metabolism of BL and O157 under Monoculture Conditions. To understand fermentative metabolism in O157 and BL, we examined monocultures of BL and O157 by 1 H-13C HSQC profiling with uniformly 13C-lableded media. The cultures were monitored every 30 min by 1H-13C HSQC measurements. On the basis of the results of 1H-13C HSQC profiling of BL culture, the PCA score plots were separated by time and the contributing components of PC1 were assigned as acetate, lactate, glucose, and fructose (Supplementary Figure 5, Supporting Information). These observations indicated that BL could consume glucose and fructose, and primarily produce acetate and lactate in fermentative metabolism. Bifidobacteria degrade hexoses using a specific pathway, the fructose-6-phosphate shunt, and primarily produce acetate and lactate,53-55 suggesting that our 1H-13C HSQC profiling method, analyzed by PCA, is useful for understanding microbial metabolic dynamics. Similarly, as described with 1D profiling (Supplementary Figure 4, Supporting Information), O157 used 828


glucose and fructose and produced acetate, lactate, succinate, and ethanol. These observations were consistent with the metabolism of anaerobic fermentation in E. coli. Under these conditions E. coli ferments sugars and their derivatives, and produces a mixture of organic acids and ethanol anaerobically.56 Metabolic Dynamics in the BL and O157 Coculture System. To clarify the bacterial crosstalk between BL and O157, we inoculated both bacterial precultures into an NMR tube with fully 13C-labeled fresh medium and analyzed by time-lapse based 1H-13C HSQC profiling (Figure 2 and 3). The PCA profiles and loadings of O157 and BL under monoculture and coculture conditions revealed that PC1 dominated the time course in monoculture and coculture conditions (Figure 2A). Loading plots indicated that glucose, fructose, acetate, lactate, succinate, and ethanol contributed to PC1 (Figure 2C), suggesting that glucose and fructose were consumed, while organic acids and ethanol were produced, as described previously. The features of the fermentation products of BL and O157 contributed strongly to the separation of PC2 clustering (Figure 2D). Acetate and lactate were major end products of both BL and O157 fermentative metabolism, whereas succinate and ethanol were produced primarily by O157 (Figure 3A). Additionally, aspartate and serine also contributed to PC2. Indeed, [13C6]-glucose labeling experiments indicated that acetate and lactate were produced primarily from glucose in both BL and O157, while aspartate and serine were produced partially from glucose in BL, but not in O157 (Supplementary Figure 6, Supporting Information). Bacterial crosstalk between BL and O157 in the coculture system was observed primarily in PC3 (Figure 2A). The metabolites contributing primarily to this separation were assigned as glucose, acetate, succinate, aspartate, serine, and lysine (Figure 2E). Acetate was one of the major fermentative products of the two species and showed significant accumulation under coculture, compared with monoculture conditions (Figure 3A). Additionally, the amount of succinate in coculture was higher than expected, although less than that in O157 monoculture; generally, the amounts of metabolites in coculture ranged to about half the sum of those in each monoculture, because the

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Figure 2. 1H-13C HSQC profiling of BL and O157 in monoculture and coculture using the time-lapse monitoring system. (A) PCA score plots of 1H-13C HSQC data; BL (blue), O157 (red), and coculture (purple) are shown. Contributions of PC1, PC2, and PC3 were 82.7%, 14.0%, and 1.75%, respectively. (B) Loading plots of 1H-13C HSQC data. Dot blotting shows the color indicating the loading values of PC1, PC2, and PC3 axes, and one dot indicates each binned spectrum, which includes the information of signal intensity and chemical shift. The loading of (C) PC1, (D) PC2, and (E) PC3 axes are shown on 1H-13C HSQC spectra and assigned bins with high loading values. The loading value levels are indicated in the inset at the middle right. Abbreviations: Ace, acetate; Asp, aspartate; EtOH, ethanol; Fru, fructose; Glc, glucose; Lac, lactate; Lys, lysine; Ser, serine; Suc, succinate.

number of O157 in coculture was almost same as that of BL and lower than half of that of O157 in monoculture (Supplementary Table 2, Supporting Information). The levels of succinate were 10 mM and 8 mM in E. coli monoculture and coculture, respectively (Figure 3A); therefore, the succinate level in coculture was greater than half that in E. coli monoculture. The metabolism rate of succinate in coculture was different from those seen in BL and O157 monocultures, but similar to that of aspartate in coculture (Figure 3B and Supplementary Figure 7, Supporting Information). This indicates that the metabolic dynamics of succinate and aspartate in coculture are correlated with each other. Aspartate and serine were rapidly consumed in coculture and O157 monoculture, but were accumulated in BL monoculture (Figure 3A). In addition to aspartate and serine metabolism, we also observed slight consumption of lysine only under coculture conditions, but not under monoculture conditions in either species (Figure 3A). Metabolic Dynamics of Aspartate and Serine in O157. Time-lapse in vivo 1H-13C HSQC profiling suggested that O157 uses aspartate and serine produced by BL in the coculture system. Additionally, the metabolic rate of succinate, which is a characteristic fermentative metabolite in O157, was similar to that of aspartate in the coculture system (Figure 3B). Because these results indicated that aspartate and serine consumption were related to succinate production in O157, we estimated the metabolic dynamics of aspartate and serine in O157 by 13Clabeled tracer experiments with [13C4] aspartate and [13C3] serine. After 6 h in 13C-labeled aspartate culture, 13C-13C coupling signals of succinate were detected (Figure 4A and C). The expanded view of the C2 signal of succinate showed a

double doublet peak (dd) indicating C1-C2-C2-C3 bondomer connectivity. This 13C-13C coupling pattern showed that [13C4] succinate was produced from [13C4] aspartate. The singlet peaks detected in this medium, such as acetate, ethanol, formate, and hydrogen carbonate, were probably derived from 12C carbon sources, including unlabeled glucose. In the case of serine metabolism, we detected 13C-13C coupling signals of acetate and ethanol in 1 mM [13C3] serine culture at 2 h (Figure 4B and D). The expanded views of the C2 signal of acetate and the C2 signal of ethanol showed doublet peaks (d), indicating C1-C2 bondomer connectivity. 13C-13C coupling pattern of acetate and ethanol indicated that [13C2] acetate and [13C2] ethanol were produced from [13C3] serine. The singlet peaks detected in this medium, such as succinate, formate, and hydrogen carbonate dissolved in medium, were probably derived from 12C carbon sources. The labeling patterns of [13C4] succinate, and [13C2] acetate and [13C2] ethanol indicated that aspartate and serine were metabolized to succinate, and to acetate and ethanol, respectively. The predicted metabolism routes of aspartate and serine in O157 are shown in Figure 4E and F, respectively. Transcriptomic and Proteomic Analyses of BL and O157 in the Coculture System. Time-lapse in vivo 1H-13C HSQC profiling revealed the characteristic kinetics of extracellular metabolites in the BL and O157 coculture system. To analyze changes in gene and protein expression of these two species in response to coculture, we compared the transcriptome and proteome data of the two species at logarithmic growth phase between monoculture and coculture. Gene expression profiling of O157 revealed that 3.00% (145 genes) and 0.80% (39 genes) Journal of Proteome Research • Vol. 10, No. 2, 2011 829

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Figure 3. Concentration and metabolism rate of the key metabolites resulted in BL-O157 crosstalk. Changes of the (A) concentration and (B) metabolism rate in monoculture and coculture incubated in fully13C-labeling medium are shown. The symbols are as follows: red, O157; blue, BL; violet, coculture. Means ( standard deviations of triplicate experiments are shown. Normalized derivative value every 30 min was calculated as the rate of metabolism (B).

of its genes were up- and down-regulated, respectively, by 2-fold or more, in coculture compared with monoculture (Table 1 and Supplementary Table 3, Supporting Information). In contrast, BL showed little change in gene expression profile in association with culture conditions; only 0.12% (2 genes) and 0.29% (5 genes) of the genes were up- and down-regulated, respectively, by 2-fold or more, in coculture compared with monoculture (Table 1 and Supplementary Table 4, Supporting Information). Analysis with the “Clusters of Orthologous Groups” database (COGs; http://www.ncbi.nlm.nih.gov/COG/) indicated that the genes up-regulated in O157 belonged primarily to O (posttranslational modification, protein turnover, chaperones), C (energy production and conversion), G (carbohydrate transport and metabolism), and E (amino acid transport and metabolism) categories (Figure 5A). In contrast, many of the down-regulated genes in O157 belonged to the N (cell motility and secretion) category, consisting mainly of genes encoding flagellar components (Figure 5B). To determine which proteins showed changes in expression in the coculture system, we performed 2D-DIGE analysis. Proteins showing changes in expression of 2-fold or more in coculture, compared with monoculture, were selected and identified by matrix-assisted laser desorption/ionization timeof-flight/time-of-flight (MALDI-TOF/TOF) MS. In total, 555, 830


527, and 576 proteins from O157 monoculture, BL monoculture, and coculture, respectively, were detected under our experimental conditions (Supplementary Figure 8, Supporting Information). All of the up-regulated proteins were derived from O157, whereas all of the down-regulated proteins were derived from BL (Figure 5C and D and Supplementary Table 5, Supporting Information). The up-regulated proteins in O157 belonged to the categories C, E, and F (nucleotide transport and metabolism). The down-regulated proteins in BL belonged to the categories G, E, and F. There is some overlap between the transcriptome and proteome data, but there are also several differences. A recent study indicated that protein abundance is generally not correlated with changes in mRNA abundance, because of posttranscriptional regulation.57 Thus, we assume that some proteins may be regulated by posttranscriptional mechanisms. Even if there are some differences, the general tendency of functional annotations of the transcriptome and proteome data indicated that intracellular carbohydrate and amino acid metabolism in O157 was activated under coculture conditions, whereas little change was observed in BL. Thus, in this coculture system, symbiotic conditions may be established by bacterial adaptation to changes in the extracellular environment, primarily by O157, but apparently not by BL.

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Figure 4. Aspartate and serine metabolism in O157 monitored by 13C NMR. O157 was cultured anaerobically with 1 mM [13C4] aspartate in unlabeled medium and 1 mM [13C3] serine in unlabeled medium. 1D [13C] spectrum of the supernatant from 6 h sample of the culture with [13C4] aspartate (A) and from 2 h sample of the culture with [13C3] serine (B). We assigned peaks of 1D 13C spectrum (A and B) as follows: a, C1 of succinate; b, C1 of formate; c, bicarbonate; d, C1of ethanol; f, C2 of succinate; g C2 of acetate; h, C2 of ethanol; i, C1of DSS; j, C1 of acetate; k, glucose. (Inset of A) Expanded view of the 13C-13C coupling peaks of succinate 1JC1-C2 ) 40 Hz, 1JC2-C3 ) 10 Hz, respectively. (Inset of B) Expanded view of the 13C-13C coupling peaks of acetate and ethanol, 1JC1-C2 ) 52 Hz, 1JC1-C2 ) 36 Hz, respectively. The sum of intensities of 13C-13C coupling peaks in C2 of succinate in 1D 13C spectrum from the culture with [13C4] aspartate is shown in C. The sum of intensities of 13C-13C coupling peaks in C2 of acetate and C2 of ethanol in 1D 13C spectrum from the culture with 13C3 is shown in D. Means ( standard deviations of triplicate experiments are shown. The predicted pathway of succinate from aspartate in O157 and 13C-13C coupling pattern (E). The predicted pathway of acetate and ethanol from serine in O157 and 13C-13C coupling pattern (F). Red lines indicate 13C-bond connectivity from the coupling pattern. Table 1. Properties of the Changes in Number of Genes and Proteins by Two-Fold or More in Coculture Compared to Those in Monoculturea transcriptome

a

proteome

selective genes

O157

BL

O157

BL

2-fold up-regulated 2-fold down-regulated

3.0% (145/4901 genes) 0.80% (39/4901genes)

0.12% (2/1730 genes) 0.29% (5/1730 genes)

21/555 spots 0/555 spots

0/527 spots 22/527 spots

All of the genes and proteins indicated here were detected under our experimental conditions.

Discussion Time-Lapse in vivo 1H-13C HSQC Profiling Method is Useful for Bacterial Systems Biology. Our 1H-13C HSQC profiling-based time-lapse metabolomic analysis revealed characteristic metabolites in the BL and O157 coculture system, compared with the monoculture; thus, the method described here can provide meaningful biological data related to metabolic dynamics. The advantage of our approach is that it allows in vivo time-lapse analysis of bacterial metabolic dynamics using 2D-NMR measurements. NMR-based metabolomics techniques have been developed for high-throughput analyses of metabolites in biological fluids and tissue extracts using 1DNMR methods, such as Carr-Purcell-Meiboom-Gill spin-echo, diffusion editing, and skyline projection of a two-dimensional J-resolved spectrum.58,59 However, 1D spectra suffer from overlapping signals. 2D-1H-13C HSQC allows the resolution of overlapping signals in the second dimension and is also superior to 1D 1H NMR for quantitative analyses of complex mixtures.7 Our 1H-13C HSQC measurements using fully-13C-

labeling techniques resulted in better signal-to-noise ratios of metabolites, which can provide much shorter acquisition time than the measurement of unlabeled metabolites. Moreover, calculation of the metabolic rates of some metabolites based on our profiling technique provides a new perspective on the metabolic pathways in bacterial cells.60,61 If the rates of metabolism of two metabolites are similar, these metabolites may be processed in the same pathway. Thus, calculation of the rate of metabolism may lead to the discovery of specific metabolic pathways that are activated or inactivated together under a given set of environmental conditions. Specific 13C-SIlabeling experiments using [13C6]-glucose and [13C,15N]-amino acids yielded more specific information regarding central carbon metabolism and amino acid metabolism. Succinate was one of the major fermentative metabolites in O157 from glucose and also produced from amino acids, whereas only a small amount of succinate was produced by BL (Supplementary Figure 6 and 9, Supporting Information). In aspartate metaboJournal of Proteome Research • Vol. 10, No. 2, 2011 831

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Figure 5. Functional annotations of the transcriptomic and proteomic data in coculture with O157 and BL. The pie charts show the distributions of differentially regulated Clusters of Orthologous Groups (COGs) functional modules. The left pie charts show transcriptome data in O157. (A) Genes showing 2-fold up-regulation and (B) those showing 2-fold down-regulation in coculture, compared with monoculture. The right pie charts show proteome data in O157 and BL. (C) Proteins showing 2-fold up-regulation in O157 and (D) 2-fold down-regulation in BL in coculture, compared with those in each monoculture.

lism, we assumed that BL and O157 acted as a producer and consumer, respectively. In research fields analyzing complex microbial communities, extracellular metabolites provide important information for intercellular communication,14 and the metabolite composition reflects the outcome of dynamic regulation of microbial constituents of the microbial communities by these metabolites, such as those found in industrial bioreactors.62 Additionally, combinations of bacterial strains can metabolize some molecules, which are difficult or even impossible for individual strains or species of bacteria; ethanol production from lignocellulose degradation requires organisms that can produce 5and 6-carbon sugars by lignocellulose degradation, and those that can metabolize them to ethanol.63 Our time-lapse in vivo 1 H-13C HSQC profiling method can provide meaningful biological data related to metabolic dynamics. Thus, our approach could become widely used in the field of systems biology for analyzing complex microbial communities. Microbial Interactions Involved in Primary Metabolism under Coculture Conditions. Characteristic changes in metabolic dynamics in O157 and BL were mainly observed in sugar fermentation and amino acid metabolism. Thus, we focused on the metabolic pathways associated with central carbon metabolism, fermentation, and amino acid metabolism (Figure 6). In the case of O157, several glycolytic enzymes, including pyruvate kinase (pkyA), a key enzyme of glycolysis, were upregulated at both the transcriptional and translational levels under coculture conditions, compared with monoculture conditions (Figure 6 and Supplementary Figure 10, Supporting Information). In BL, the genes involved in fructose-6-phosphate shunt, such as transaldolase (tal), and phosphoglycerate kinase (pgk) were slightly down-regulated (Figure 6). In addition, the expression levels of genes related to the fermentation pathway, including phosphoenolpyruvate carboxylase (pcc) and lactate dehydrogenase (ldh2) were also slightly down-regulated. No change was observed in the expression of key enzymes involved in the fructose-6-phosphate shunt, such as xylulose-5-phosphate fructose-6-phosphate phosphoketolase (BL0959), or glyceraldehyde-3-phosphate dehydrogenase C (gap), at the transcript or protein level, suggesting that central carbon metabolism in BL was little influenced by coculture conditions, compared with O157. 832


Time-lapse in vivo 1H-13C HSQC profiling indicated that a slight decrease in lysine level under coculture conditions, compared with monoculture conditions (Figure 3). In O157, several genes involved in lysine metabolism, such as lysine decarboxylase (cadA) and lysyl-tRNA synthetase (lysS), were upregulated by more than 2-fold, indicating the activation of lysine metabolism in O157 in the coculture system. Aspartate and Serine Metabolism in BL and O157. Timelapse in vivo 1H-13C HSQC profiling indicated that the levels of aspartate and serine consumption, and succinate and acetate production were increased in coculture, compared with O157 or BL monoculture. This seems to be due to the vigorous consumption by O157 of aspartate and serine produced by BL; O157 alone did not accumulate aspartate or serine, and exhibited less accumulation of aspartate and serine in coculture than BL monoculture (Figure 3A). Focusing on [13C,15N]-amino acid-labeled experiments, acetate, succinate, and ethanol were produced from amino acids in O157 monoculture and coculture conditions, but BL produced less of these metabolites than O157 (Supplementary Figure 9, Supporting Information), indicating that the amino acid utilization ability of O157 was higher than that of BL. A previous study showed that E. coli CP750 consumed serine exclusively in the lag phase followed by consumption of aspartate at log phase.64 This result agrees with our metabolic profiling data described above (Figure 3A and Supplementary Figure 9, Supporting Information). Consistent with the metabolic dynamics described above, transcripts associated with aspartate metabolism, purA and sdhC, were up-regulated and the level of AspA protein expression was also augmented in O157 during coculture (Figure 6). Aspartate is used as a precursor for other amino acids and for protein synthesis and can also be used in the synthesis of succinate via fumarate.65 Moreover, the aspA gene plays an important role in anaerobic metabolism in E. coli. The quantity of expression of aspA by RT-PCR tended to be high in coculture compare to monoculture (Supplementary Figure 10, Supporting Information). AspA generates fumarate and its expression is regulated by the global regulators FNR, CRP, and NarL.66,67 These regulatory proteins also regulate the DcuA and DcuB antiporters, which catalyze the exchange of fumarate, malate, and aspartate with succinate. In addition, aspA-dcuA and dcuBfumB (encoding fumarase B) form an operon. These systems


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Figure 6. Transcript and protein levels in central carbon metabolism and the diversion of key metabolites to biosynthetic pathways in the overall BL and O157 coculture system. The mRNA expression level was logarithmically transformed (base 2) response ratios according to the log2 ratio scale and the protein expression levels are indicated in the inset at the bottom right. Abbreviations: 1,3PG, 1,3-bisphospho-d-glycerate; β-Ala, β-alanine; AcCoA, acetyl coenzyme A; Ace, acetate; AcP, acetyl phosphate; AdSuc, adenylosuccinate; ArSuc, argininosuccinate; Asp, aspartate; E4P, erythrose-4-phosphate; EtOH, ethanol; Fru, fructose; F6P, fructose-6-phosphate; For, formate; Fum, fumarate; GA3P, glyceraldehyde-3-phosphate; G6P, glucose-6-phosphate; Glc, glucose; Lac, lactate; Mal malate; Oaa, oxaloacetate; PEP, phosphoenolpyruvate; Pyr, pyruvate; R5P, ribose-5-phosphate; S7P, sedoheptulose-7-phosphate; Ser, serine; Suc, succinate; Xu5P, xylulose-5-phosphate.

generate fumarate as an electron acceptor in anaerobic respiration, which also helps to maintain the overall redox balance by regenerating NAD+.65 Our 13C-labeled tracer experiments using [13C4] aspartate in O157 culture revealed that aspartate was metabolized to succinate (Figure 4A and C). Additionally, labeling patterns of succinate indicated that the metabolic pathway from aspartate to succinate was mediated by fumarate (Figure 4E). These data suggest that the increase in succinate level in coculture was derived from aspartate metabolism in O157. Serine can be metabolized to pyruvate in a single enzymatic step and also serves as a precursor of other amino acids in E. coli.68 There was no difference in the expression levels of the genes or proteins related to serine metabolism in O157 between monoculture and coculture conditions. It is possible that the expression levels of the molecules involved in serine metabolism were not further induced in O157 during coculture, because their expression level was already high in O157 under monoculture conditions. This is consistent with the observation that serine consumption occurred rapidly with similar kinetics in coculture and O157 monoculture (Figure 3A). Acetate was accumulated in coculture and is a major fermentative metabolite from glucose in both BL and O157. In BL, some metabolic enzymes associated with the fructose-6phosphate shunt were down-regulated at the levels of tran-

scription and translation in coculture, compared with monoculture. In O157, the protein expression level of acetate kinase (ackA), responsible for acetate synthesis, was up-regulated in coculture, compared with monoculture (Figure 6). E. coli grown in the presence of amino acids incorporates serine and aspartate, which causes the accumulation of acetate.64 Consistent with this report, in our 13C-labeled tracer experiments using [13C3] serine, we observed that serine was metabolized to acetate and ethanol after a 2-h incubation (Figure 4B and D). Although we found no difference in expression levels of genes or proteins associated with serine metabolism in O157 between coculture and monoculture, the labeling patterns of acetate and ethanol indicated that the metabolic route from serine to acetate and ethanol was mediated by pyruvate (Figure 4F). Up-Regulation of the Gene Expression Involved in Bacterial Chemotaxis and Acid Resistance in O157 in Coculture System. Aspartate and serine are important nutrients for E. coli with roles in chemotaxis; the organism adjusts its direction of movement on perception of changes in the concentrations of aspartate and serine, transferring the information to the flagellar motor.69 The gene expression levels of methyl-accepting chemotaxis protein II, aspartate sensor receptor (tar), and methyl-accepting chemotaxis protein I, serine sensor receptor (tsr), were about 2-fold down-regulated in coculture (Supplementary Table 3, Supporting Information). Journal of Proteome Research • Vol. 10, No. 2, 2011 833

research articles In addition, the gene expression levels of cheA, cheB, cheR, cheY, cheZ, and cheW, related to the mechanism of aspartate and serine stimulation, were also down-regulated (Supplementary Table 3, Supporting Information). This signaling system, mediated by the phosphorylation relay, has a negative feedback role in that increases in the density of attractants inhibit the phosphorylation of proteins. Previous studies showed that the kinase activities of Tar and Tsr, and sensitivity to the nonmetabolizable aspartate analog R-methylaspartate decreased when the expression level of Tar was reduced.69 Thus, it seems reasonable that the gene expression levels associated with chemotaxis were decreased in O157 in the coculture system due to the increase in level of aspartate produced by BL. We also found that the genes encoding glutamate decarboxylase isozymes (GadA and GadB) and arginine decarboxylase (AdiA) associated with acid resistance (AR) systems were up-regulated, 3-fold or more, in coculture, compared with those in monoculture. E. coli has several AR systems that confer protection against conditions of low pH,70 as enteric organisms must withstand extreme acid stress for colonization and pathogenesis when passing through the acidic environment of the stomach. However, pH in the culture medium was equivalent between monoculture and coculture (Supplementary Figure 11, Supporting Information). The mechanisms of this system involve consumption of intracellular protons through amino acid decarboxylation;71 therefore, the accumulation of extracellular amino acids in coculture may activate these AR systems. In particular, CadA and CadB function in a lysinedependent AR system that consumes one proton and then produces the diamine cadaverine from lysine.72 As described above, lysine metabolism in coculture was activated, compared with that in O157 monoculture (Figure 3A). Furthermore, the gene expression levels of cadA and lysS, encoding enzymes involved in lysine degradation, were increased in O157. Thus, up-regulation of the genes related to AR systems in coculture may be involved in the consumption of extracellular lysine.

Conclusions We have established a time-lapse in vivo 1H-13C HSQC profiling method using several 13C medium systems. The usefulness of this approach was validated by measuring the extracellular metabolites in the BL and O157 coculture system in combination with transcriptomic and proteomic analyses. Integrated omics data suggested that, in coculture, BL and O157 could be partially characterized as producer and consumer of extracellular nutrients, respectively, especially with regard to serine and aspartate metabolism. Thus, this profiling system can provide a new strategy for understanding dynamic microbial crosstalk. Our study is a fundamental step for clarifying microbial crosstalk from a minimal ecosystem toward more complex microbial communities, such as the gut, industrial bioreactors, and environmental microbial communities.

Acknowledgment. This research was supported in part by Grants-in-Aid for Scientific Research for challenging exploratory research (J.K.), Young Scientists (S.F.), Scientific Research (A) (J.K.), Scientific Research (B) (H.O.), and Scientific Research on Innovative Areas “Intracellular Logistics” (H.O.) from the Ministry of Education, Culture, Sports, Science, and Technology, Japan. This work was also supported, in part, by grants from Research and Development Program for New Bioindustry Initiatives of the Bio-oriented Technology Research Advancement Institution 834


Nakanishi et al. (BRAIN to J.K.), and New Energy and Industrial Technology Development Organization (NEDO to J.K.), RIKEN DRI Research Grant (S.F.), the Institute for Fermentation, Osaka (S.F.), the Kieikai Research Foundation (S.F.), the Naito Foundation (S.F.), the Nestle´ Nutrition Council, Japan (S.F.), a Sasakawa Scientific Research Grant from the Japan Science Society (Y.N. and S.F.), the Danone Institute of Japan (H.O.), and the Yakult Bio-Science Foundation (S.F.).

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