Metabolomics of B to Plasma Cell Differentiation - American Chemical

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Metabolomics of B to Plasma Cell Differentiation Jose Manuel Garcia-Manteiga,*,†,‡ Silvia Mari,§,|| Markus Godejohann,^ Manfred Spraul,^ Claudia Napoli,# Simone Cenci,†,‡ Giovanna Musco,§,||,z and Roberto Sitia†,‡,z †

Division of Genetics and Cell Biology, DiBiT, San Raffaele Scientific Institute, Milano, Italy Biomolecular NMR Laboratory, Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Milano, Italy Dulbecco Telethon Institute, San Raffaele Scientific Institute, Milano, Italy ^ Bruker BioSpin GmbH, Rheinstetten, Germany # Bruker Biospin Business Unit ‡ Universita Vita Salute San Raffaele, Milano, Italy

)

§

bS Supporting Information ABSTRACT: When small B lymphocytes bind antigen in the context of suitable signals, a profound geno-proteomic metamorphosis is activated that generates antibody-secreting cells. To study the metabolic changes associated with this differentiation program, we compared the exometabolome of differentiating murine B lymphoma cells and primary B cells by monodimensional proton nuclear magnetic resonance spectroscopy and mass spectrometry coupled to liquid chromatography. Principal component analysis, a multivariate statistical analysis, highlighted metabolic hallmarks of the sequential differentiation phases discriminating between the proliferation and antibody secreting phases and revealing novel metabolic pathways. During proliferation, lactate production increased together with consumption of essential amino acids; massive Ig secretion was paralleled by alanine and glutamate production, glutamine being used as carbon and energy sources. Notably, ethanol and 50 -methylthioadenosine were produced during the last phase of protein secretion and the proliferative burst, respectively. Our metabolomics results are in agreement with previous genoproteomics studies. Thus, metabolic profiling of extracellular medium is a useful tool to characterize the functional state of differentiating B cells and to identify novel underlying metabolic pathways. KEYWORDS: metabolomics, NMR, MS, metabolic profiling, B lymphocytes, plasma cells, protein secretion, immunoglobulin, footprinting

’ INTRODUCTION When suitably activated, B cells undergo a complex differentiation program involving proliferation, isotype switching, affinity maturation, generation of memory cells, and terminal differentiation into Ig secreting cells.1 The latter is characterized by expression of distinctive surface molecules (Syndecan-I/ CD138 and CXCR4) and transcription factors including Blimp-I, IRF-4, and XBP-1.1 Meanwhile, the endoplasmic reticulum (ER) and secretory organelles2 increase to prepare for massive antibody production and release. Apoptosis then ensues, unless plasma cells home to the bone marrow where they can persist for long times.2 Gene expression profiling and proteomics approaches provided a wealth of information onto the mechanisms underlying plasma cell differentiation.3,4 It was found that functionally related proteins show similar temporal expression profiles.3 As profound metabolic changes likely occur during the differentiation into Ig secreting professionals, we undertook an unbiased footprinting5,6 of the exometabolome to monitor the metabolites consumed or released during the progression from resting B lymphocytes to Ig secretors.711 Such a strategy can be r 2011 American Chemical Society

used to identify novel biochemical pathways and biomarkers or to evaluate the relevance of known ones.12,13 In this study, we used murine myeloma cell lines producing different amounts of Ig and in vitro inducible B lymphoma cells, I.29 μ+3,14 or primary B splenocytes,15 to investigate the metabolic hallmarks of B to plasma cell differentiation through principal component analysis (PCA) of 1D-1H NMR (monodimensional proton nuclear magnetic resonance spectroscopy) and LCMS (liquid chromatographymass spectrometry). Multivariate statistical analyses highlighted metabolic hallmarks of activation-proliferation and protein secretion during B to plasma cell differentiation.

’ EXPERIMENTAL PROCEDURES Cell Culture and LPS Induction

I.29 μ+ cells and B cells were cultured and induced to differentiate using LPS as described elsewhere.14,15 Cells were Received: April 8, 2011 Published: July 11, 2011 4165

dx.doi.org/10.1021/pr200328f | J. Proteome Res. 2011, 10, 4165–4176

Journal of Proteome Research

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Table 1. Summary of the Murine Myeloma Transfectants Used cell line

chain expressed

secretion

reference

NS0

J

Galfre and Milstein17

Nμ1 J558L

J, μ J, λ

λ

Sitia et al.18,19 Oi et al.16

NμλG

J, μ, λ

IgM

Reddy et al.20

J571b

J, μ, λ

IgM

Galfre and Milstein17

never allowed to reach a cell density higher than 2 106 cells/ mL. The media of 5 biological replicates were collected, aliquoted, and stored at 80 C until processed for NMR. Eight biological replicates were used for LCMS analysis. Myeloma cell lines used in this study are summarized in Table 1. In particular J558L16 cell lines express and secrete λ chains, while NS0 produce only J chains.17 When transfected with secretory Ig-μ (μs), J cells release IgM polymers (J571b),17 while N cells degrade orphan μ chains.18,19 For this reason, they do not secrete any Ig (Nμ1), unless λ chains are also expressed (NμλG).20 Three-hundred thousand cells/mL were seeded in T-75 flasks (10 mL) and cultured for 24 h. Spent media of 5 biological replicates of myeloma cell lines were analyzed. To discard the possibility that the presence of ethanol in extracellular medium was due to external contamination, we performed the following tests: (a) we performed PCR to discard mycoplasma infection of cell cultures, cells resulted negative to the test; (b) medium with and without serum was incubated without cells and inspected for microbial growth after 7 days at 37 C to exclude minimal infections. Neither microbial growth nor medium acidification were observed; (c) PCR for amplification of bacterial 16S rDNA of conditioned medium. The test did not highlight the presence of bacterial 16S rDNA. Secretion and ELISA Assay

Ig secretion assays were made as described previously.15 Briefly, after centrifugation, spent media aliquots of suitably diluted samples were added to anti-μ- and anti-λ-coated plates and incubated overnight at 4 C. After extensive washes, anti-μand anti-λ-HRP (1:1000 and 1:2000 respectively) (Southern Biotechnology, Birmingham, AL) were added and incubated for 1 h at room temperature. The assay was developed with Sigma OPD fast read or tetramethylbenzidine (Invitrogen) and the plates were read at 450 nm. NMR Acquisition

Five-hundred thirty microliters of cell culture medium was mixed with 60 μL of deuterated PBS solution containing DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) as chemical shift reference for both proton and carbon dimensions, plus 10 μL of 1.2% NaN3 water solution. Final sample volume was 600 μL with 50 mM PBS, pH 7, 0.02% NaN3 and 90 μM DSS. NMR spectra were acquired on a 600 MHz spectrometer (Bruker Avance 600 Ultra Shield TM Plus, Bruker BioSpin) equipped with a triple-resonance TCI cryoprobe with a z shielded pulsed-field gradient coil. All experiments were carried out at 298 K, spectrometer temperature was calibrated using pure methanol-d4 sample.21 Sample temperature inside the spectrometer was equilibrated for 5 min before data acquisition. For each sample noesygppr1d and Carr-Purcell-Meiboom-Gill T2 filter cpmgpr1d Bruker pulse sequences were used for 1D-1H spectra acquisition. For all experiments continuous water presaturation

with a RF of 35 Hz was applied during relaxation delay D1. Both the noesygppr1d and cpmgpr1d experiments were acquired using 80 scans, 98K complex data points, spectral width of 20 ppm, and relaxation delay of 6 s. A mixing time of 10 ms was used for the noesygppr1d experiment. FIDs were multiplied by an exponential function equivalent to that of a 1.0 Hz line-broadening factor and then Fourier transformed. Spectra were automatically phased; baseline corrected and referenced using the library Topspin AU program apk0.noe. To facilitate metabolites identification we acquired 2D J-resolved 1H NMR experiments, 2D-1H1H-TOCSY (Total Correlation spectroscopy) and 2D-1H13C-HSQC (Heteronuclear single quantum coherence). 2D-J-resolved experiments were acquired with 12 FIDs, accumulated over 40 increments; spectral widths were set to 16.7 ppm and 78 Hz for F2 and F1, respectively; during the relaxation delay (2 s) the water signal was suppressed using presaturation. 2D-1H1H-TOCSY experiments were acquired with a total of 8 FIDs for each of the 512 increments. Spectral widths were set at 12 ppm for both dimensions; water was suppressed with an excitation sculpting scheme, 2 s of relaxation delay was employed. 2D-1H13CHSQC spectra were acquired with a total of 44 FIDs for each of the 300 increments. Spectral widths were set to 16 and 185 ppm for 1H and 13C, respectively (with offsets equal to 4.7 and 75 ppm, respectively). The water signal was eliminated using a continuous wave presaturation during the 3 s of the relaxation delay. LCMS Acquisition

Five microliters of 8 replicates of the spent media were directly injected on the UPLC column and measured by Electrospray Ionization-Time of Flight-Mass Spectrometry (ESI-TOF-MS). LCMS (liquid chromatographymass spectrometry) data were acquired on a time-of-flight mass spectrometer in positive ionization mode using an electrospray ion source with a scan range with m/z between 50 and 1000 (Bruker Daltonic; Bremen, Germany). UPLC separation was done on a 100 2.1 mm BEH C18 column with a particle size of 1.7 μm from Waters (Milford, MA). The chromatographic separation was done on a Waters UPLC system (Milford, MA). The samples were separated using a mobile phase consisting of acetonitrile (A) and water (B) with 0.1% formic acid. The flow rate was set so 0.25 mL/min. A linear gradient was applied starting at 100% B held for 0.2 min. After 15 min the composition changes to 60% B and after 19 min it changes to 0% B to wash the column. Prior to the next injection, the column was equilibrated with 100% B. Calibration of the mass data was achieved by injecting a lithium formate solution at the beginning of each chromatographic run. Raw data were converted to the netCDF format for further statistical analysis using the AMIX software (Bruker, Rheinstetten, Germany). LC-SPE-NMR/MS

Components causing differentiation in PCA analysis which could not be identified by sum formula generation and identification in the mixture using the NMR results were subjected to LCSPE-NMR/MS analysis (Liquid Chromatography-Solid phase Extraction-NMR/MS). Prior to chromatography, the analytes were enriched from the growth medium by classical off-line SPE on Phenomenex Strata-X 3 mL SPE-cartridges. After washing with 5 mL of methanol and equilibration with 5 mL of water 10 mL of sample was passed through the cartridge with a flow of 5 mL/min. After drying with nitrogen for 20 min, the analytes 4166

dx.doi.org/10.1021/pr200328f |J. Proteome Res. 2011, 10, 4165–4176

Journal of Proteome Research were extracted with 2 mL of methanol. The sample was then evaporated with nitrogen to a residual volume of approximately 200 μL. Three 60 μL of this extract was injected on a 250 4 mm Agilent Eclipse C18 column with a particle size of 5 μm using an injection program. The separation was carried out at a flow rate of 0.6 mL/min and a gradient using the solvent system as described in the LC-MS section starting at 98% held for 5 min to achieve trapping of the compounds on the column head during the injection of the sample. The composition was changed linearly to 90% after 10 min and to 30% after 20 min. The peaks were trapped on Hysphere resin GP 2 10 mm cartridges after addition of 1.6 mL/min of mobile phase (B) to the affluent of the column using an automated cartridge exchanger from Bruker/Spark Holland (Rheinstetten, Germany). Directly after chromatography, 5% of the effluent was split to a Bruker Daltonic time-of-flight mass spectrometer (Bremen, Germany) for mass driven post column SPE trapping of the peaks of interest. After drying of the cartridges with nitrogen gas for 20 min, the peaks were eluted off the cartridges with deuterated methanol in a 3 mm NMR tube. The extract was measured on a 500 MHz NMR spectrometer (Bruker AVANCE III) equipped with a TCI cryo probe (Bruker, Rheinstetten, Germany) for identification of the unknown compounds. Statistical Analysis

For NMR footprinting analysis, monodimensional spectra were divided in regions, namely buckets, using the variable size bucketing procedure (Amix Software, Bruker) to take into account ppm shifts caused by pH and matrix effects. Bucket intensities were scaled to total intensity of the whole spectrum and the water region was excluded. The Pareto scaling of the variables was used prior to principal component analysis (PCA), a multivariate unsupervised statistical technique. PCA gives a global view of the systematic variation of the data while reducing its dimensionality to few principal components (PC), which account for a large amount of the total variance between the NMR fingerprints.22 The final aim of PCA is to enable easy visualization of any clustering or similarity of the various samples. The results of PCA are presented in terms of score and loading plots. A score plot summarizes the similarities and differences between each of the NMR fingerprints where each data point corresponds to one sample and two spaced points indicate a degree of metabolic similarity between those two samples in the new space defined by the PC. Samples with a similar metabolic footprint tend to cluster together in score plots. Each PC is a weighted linear combination of the original descriptors and this information is shown in a loading plot. If, for example, two groups of samples were shown to differ along the PC1 axis, then the loading plot for PC1 can be used to determine which signals in the NMR spectra are producing this metabolic difference. PCA was performed using Amix Software (Bruker). For the NMR profiling strategy, integration regions were defined, peaks assigned and integrated to obtain metabolite concentrations. For metabolite quantification we took advantage of the combination of (a) a new algorithm called GSD (global spectrum deconvolution), available in the Mnova software package of Mestrelab23 and (b) of a quantitative referencing strategy, known as PULCON.24 It is well-known that in biological samples containing lipids and proteins an internal standard cannot be used to perform absolute quantification of metabolites. This is due to the fact that all possible reference compounds, such as TSP (trimethylsilyl propionate) or DSS interact with large

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biological molecules, making the quantification error prone. By combining the GSD algorithm with a PULCON script we deconvolved overlapping regions and performed absolute quantification also of metabolites with resonances in crowded spectral areas. According to eq 1, each metabolite’s absolute area was compared with the one obtained from an aqueous sample (H2O/ D2O 90:10) containing 10 mM sucrose and 0.5 mM DSS and subsequently scaled according to the number of protons contributing to the signal. We used as reference area and concentration the average of the three methylene groups of DSS, respectively at 3.1, 1.9, and 0.8 ppm. A sample 3 calsample Aref 3 calref ¼ concref concsample

ð1Þ

where cali ¼

Ti 3 P1i RGi 3 NSi 3 SIi

and T is temperature, P1 is the 1 H 90 pulse length, RG is receiver gain, NS is the number of scans, SI is data size. Prior to PCA, data matrices were mean centered and scaled to unit variance with a correction of 0.1 to avoid 0 variance signals. Using a 95% confidence interval buckets with loadings with a value higher than 0.2 have been considered as main contributors for PCA. All the spectra were visually inspected. Spectra located outside the Hotelling/T2 plot (95% confidence) because of bad water suppression and/or low signal-to-noise ratio were considered as outliers and therefore eliminated from the statistical analysis. PCA on data matrices have been applied using R-statistical open source software (http://www.r-project.org/). For the UPLCESI-TOF-MS analysis, calibrated MS raw data were subjected to advanced bucketing in a mass range between m/z 50.5 and 650.5 and a time range from 0.5 to 8 min with a bucketing in the time dimension of 2 min. PCA was carried out after mean centering without scaling of the rows (spectra) and with Pareto scaling of the columns (variables). As MS raw data are directly submitted to the bucketing process, it cannot be determined from the loadings plot whether the mass response correspond to the pseudomolecular ion, an adduct, a fragment of an isotope peak of the discriminating analyte. As consequence, each mass-to-charge extracted from the loading plot ratio needs careful inspection in the corresponding original LCMS run. Once the mass-to-charge ratio of the pseudomolecular ion is determined the sum formula can be calculated from the exact mass position and the isotope ratio. This sum formula together with the exact mass information can be used for searching possible candidates in databases, for example, pubchem (http://pubchem.ncbi.nlm.nih.gov/), HMDB (www.hmdb.ca) or metlin database (http://metlin.scripps.edu/). In case of small molecules, tentative assignments was validated analyzing the 1D or 2D-J resolved NMR spectra of the individual samples and through a search against available NMR libraries, (HMDB and/ or BMRB http://www.bmrb.wisc.edu/metabolomics/metabolomics_standards.html).

’ RESULTS 1D-1H NMR Profiles Show that B- to Plasma Cell-Differentiation Presents a Characteristic Metabolic Pattern

To find metabolic hallmarks of B to plasma cell differentiation, we first compared the 1D-1H NMR spectra of the supernatants 4167



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Figure 1. Identification of metabolites in spent medium of I.29 μ+ differentiating cells. (A) 1D-1HCPMG spectrum with relative expanded views. 1 = leucine, 2 = valine, 3 = isoleucine, 4 = 3-hydroxybutyrate, 5 = ethanol, 6 = 3-hydroxyisovalerate, 7 = lactate, 8 = lysine, 9 = alanine, 10 = arginine, 11 = lysine, 12 = arginine/lysine/acetate, 13 = NAC/proline, 14 = glutamine/methionine, 15 = acetone, 16 = valine, 17 = glutamate/pyruvate/proline, 18 = succinate, 19 = glutamine, 20 = methionine, 21 = asparagine, 22 = lysine, 23 = citrulline, 24 = choline, 25 = β-glucose, 26 = R-glucose, 27 = threonine, 28 = valine, 29 = ethanol, 30 = asparagine, 31 = lactate/cystine, 32 = threonine, 33 = tyrosine, 34 = hystidine, 35 = tryptophane, 36 = phenylalanine, 37 = niacine, 38 = formic acid. Assignment details are listed in Supplementary Table 1 (Supporting Information). Recognition of ethanol proton peaks in 1D-1H NMR spectrum (methyl group at 1.18 ppm and methylene group at 3.66 ppm) is difficult because of high signals’ overlap. Assignment was confirmed by comparison of the 2D-1H13C-HSQC (B,C) and 2D-1H1H-TOCSY (D) of I.29 μ+ exometabolome at day 1 (black peaks) and standard ethanol spectrum (gray peaks) (HMBD code: HMDB00108). The assignment has been further confirmed by a 2D-J-resolved experiment (data not shown). 4168



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Figure 2. Metabolic patterns of B to Plasma cell differentiation by PCA. (A) Principal component analysis score plot (PC1 vs PC2) of I.29μ+ cells and (B) the corresponding loading plot. (C) Score plot of PC1 vs PC2 from primary B cells and (D) the corresponding loading plot. PCA shows that in both I.29 μ+ and primary B cells the different days of differentiation cluster according to the different lactate, glucose, glutamine, glutamate and alanine concentrations. Five replicates of 1D-1HCPMG spectra were used for bucketing and PCA. Outliers were removed as described in the Experimental Procedures. Assigned buckets with load higher than 0.2 (absolute value) are labeled. See Supplementary Figure 1 for the contribution of the third PC (PC3) in primary B cell samples clustering (Supporting Information).

of I.29 μ+, and B primary cells at various time points after LPS stimulation. A representative 1D-1HCPMG spectrum of I.29 μ+ supernatant, corresponding to day 1 in the differentiation process is shown in Figure 1A. Using Metabominer,25 2D-1H13C-HSQC and 2D-1H1H-TOCSY spectra and CCPN Metabolomic project,26,27 we identified and quantified 32 metabolites (Figure 1A). Analysis of 2D spectra highlighted the presence of resonances attributed to ethanol, an unexpected metabolite for mammalian cell cultures (Figure 1B, C, D). Importantly, a series of tests (as described in Experimental Procedures) were performed to exclude microbial contamination as a possible ethanol source. Data were next analyzed using principal component analysis (PCA), a multivariate unsupervised statistical technique. PCA was applied to either 1D-1H- NOESY or 1D-1HCPMG NMR spectra with similar results. Figure 2 shows PCA performed on 1D-1HCPMG spectra of cell culture supernatants of I.29 μ+ and primary B splenocytes induced to differentiate with LPS (Figure 2).

Remarkably, the score plots of both I.29 μ+ and primary B splenocytes showed clustering according to the different days of differentiation (Figure 2A, C). The loading plots of the first principal components (PC1 and PC2) indicate that glucose, lactate, glutamine, glutamate, and alanine are the main metabolites characterizing the differentiation phase in both models (Figure 2B, D). Signals corresponding to ornithine and/or lysine and ethanol and/or 3-hydroxybutyrate buckets are present only in I.29 μ+ (Figure 2B), while N-acetylated amino acids (NAC) are characteristic of the primary culture (Figure 2D). Analysis of the third principal component (PC3) allowed the identification of additional buckets in primary B cell cultures corresponding to ethanol and/or 3-hydroxybutyrate, hydroxyproline and pyruvate. These buckets contribute to better discriminate among the various differentiation steps in primary cultures (see Supplementary Figure 1, Supporting Information). Moreover, metabolic profiling analyses28 performed on assigned and quantified metabolites revealed that 4169



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Figure 3. Metabolites contributing to the metabolic profiles of plasma cell differentiation in I.29 μ+ and primary B cells. Concentrations of relevant metabolites characterizing the metabolic profiles of (A) I.29 μ+ and (B) primary B cells during LPS differentiation. Metabolite concentrations were determined as described in Experimental Procedures using 1D-1HCPMG. Bars represent average ( SD (n = 5).

the lactate to glucose ratio increases at days 1 and 2, whereas glutamine consumption versus glutamate and alanine production is increased in days 3 and 4, when antibody secretion becomes maximal. Also arginine and lysine contribute to the metabolic profile of the differentiation process. The concentrations of the metabolites contributing to PCA clustering shared both by I.29 μ+ cell line and the B cell primary culture are shown in Figure 3. Collectively, these results show that the exometabolome differences revealed by metabolic footprinting readily distinguish

between the initial activation-proliferation phase and the final antibody secreting phase that hallmark B to plasma cell differentiation. Antibody Synthesis and Secretion Influence the Metabolic Profile of Plasma Cells

To investigate whether the above metabolic changes correlated with Ig synthesis, we analyzed the exometabolome of murine myeloma lines with different protein secretion burdens 4170



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Figure 4. Metabolic profiles of murine myeloma cell lines. (A) Principal component analysis score plot (PC1 vs PC2) of the different myeloma cell lines and RPMI medium (control) and (B) the corresponding loading plot. Cell lines secreting immunoglobulins tend to cluster together according to high levels of glutamate, acetate and alanine, while the nonsecretory cell lines NS0 and Nμ1 cluster according to the higher lactate production. Five replicates of 1D-1HCPMG spectra were used for bucketing and PCA. Outliers were removed as described in Experimental Procedures. Assigned buckets with load higher than 0.2 (absolute value) together with alanine, acetate, NAC and glutamate are labeled.

Figure 5. Metabolic profiles of different Ig synthesis and secretion burdens in myeloma cell lines. (A) Principal component analysis score plot (PC2 vs PC4) of the different myeloma cell lines and RPMI medium (control) and (B) the corresponding loading plot. The combination of PC2PC4 allows the clustering of myeloma cell lines according to their ability to produce and secrete Ig. Assigned buckets with load higher than 0.2 (absolute value) together with alanine, acetate, NAC and glutamate are labeled.

(Table 1 and Experimental Procedures). PCA on 1D-1H NMR spectra confirmed that lactate and glucose buckets strongly contribute to PC1 (Figure 4), similarly to what observed in

I.29 μ+ (Figure 2A and B). Higher principal components (PC2, PC4, and PC5) allowed cell lines clustering according to their ability to produce and secrete complete antibodies (Figure 5, 4171



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Figure 6. Metabolites contributing to the metabolic profiles of myeloma cell lines. Concentrations of relevant metabolites characterizing the metabolic profiles of myeloma cell lines with different protein secretion burdens. Concentrations were determined as described in Experimental Procedures using 1D-1HCPMG- spectra of murine myeloma cell lines spent medium. Bars represent average ( SD (n = 5).

Supplementary Figure 2, Supplementary Figure 3, Supporting Information). In particular glutamine, glutamate, alanine, N-acetylated aminoacids (NAC), pyruvate, and acetate discriminate among cell lines (Figure 5, Supplementary Figure 3). Figure 6 summarizes the concentrations of the metabolites contributing to the clustering of the different myeloma transfectants. Taken together these results suggest that antibody synthesis and secretion entail a shift in energy metabolism into glutamine catabolism, glutamate and alanine becoming the main anaerobic products. The shift is evident in NSO and J558L, two lines with similar proliferation rates (Supplementary Figure 2, Supporting Information), and is therefore independent from cell division. In all likelihood, however, differences in proliferation rates are important in the metabolic rearrangements we detect in the first phases after LPS stimulation, when a proliferative burst takes place. Footprinting of the Exometabolome of I.29 μ+ Differentiation Using UPLC-TOF-MS

To complement the data obtained by NMR, the supernatants of differentiating I.29 μ+ cells were also analyzed by Mass Spectrometry (Figure 7 and Table 2). In agreement with the NMR profiling, glucose, glutamine (in form of pyroglutamic acid) and other essential amino acids (phenylalanine, tryptophan and tyrosine) are strongly consumed during the proliferation phase. Notably, by using a combination of high performance extraction and chromatographic method with the structural information obtained by NMR and/or MS (HPLC-SPENMR/MS), it was possible to identify new metabolites: 50 methylthioadenosine (50 MTA), which is produced and released during the activation phase, and uric acid, which was produced by undifferentiated cells and at day 1 after LPS. These metabolites were halved during the differentiation phase of plasma cells, whereas deoxycytidine release increased during the second phase of the differentiation process (Table 2).

’ DISCUSSION The stepwise differentiation of small, resting B lymphocytes into Ig secreting plasma cells provides a powerful model to investigate how morphological and functional changes are coordinated.14 In this study, we have analyzed the metabolic profiles of B cells as they differentiate, in order to shed light on the underlying metabolic changes. The proliferation phase entails activation of glycolysis with production of lactate, whereas the Ab secreting phase is characterized by: (a) decreased use of glucose; (b) sustained consumption of glutamine; (c) release of glutamate and alanine. The cellular metabolism switch characterized by increased anaerobic lactate production and reduced oxygen consumption is known as Warburg effect.29,30 This hallmarks highly proliferative cells,31,32 including many cancer cells that rely on glycolysis followed by lactic acid fermentation in the cytosol, rather than mitochondrial oxidation of pyruvate, for energy production.31,32 In agreement with this, our exometabolomics studies clearly demonstrate that activated spleen B cells undergo a typical Warburg effect. We also observe that as Ig secretion ensues, glucose oxidation to lactate diminishes and glutamate and alanine accumulate at the expense of glutamine (Figure 2), explaining why Ab secreting cells are glutamine dependent.33 While sustaining high secretion rates by supplying carbon for new amino acid and protein synthesis, glutamine can also provide high amounts of NADPH, promptly available to activate antioxidant responses, highly needed in nascent plasma cells.15 Changes in extracellular metabolites can be directly correlated to the activation or inhibition of biochemical pathways regulated by specific enzymes. Indeed, our metabolomic data perfectly fit with previous proteomic studies of plasma cell differentiation.3,4 For example, in accordance with the increased glycolytic flux observed upon LPS activation, the levels of the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase also increase during the first days of differentiation.4 Conversely, cytosolic and 4172



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Figure 7. Metabolic footprinting of B to plasma cell differentiation using UPLC-TOF-MS. (A) Principal component analysis score plot (PC1 vs PC4) of I.29 μ+ cells. Ellipsoids around groups correspond to the 67% confidence interval and (B) the corresponding loading plot (labels identify the m/z peaks responsible for the clustering).

Table 2. Metabolites Contributing to I.29μ+ Profiles As Determined by LCMSa t test (LPS

confirmation m/z

tR (min)

sum formula

assignment

by NMR

LPS

and day 1)b

t test (day 1 day 1

and day 2)b

t test (day 2 day 2

and day 3)b

t test (day 3 day 3

and day 4)b

day 4

180.07

6.3

C9H10NO3

Hippuric acid

yes

6.4

***

5.1

***

6.4

ns

6.3

**

5.6

298.10437

5.2

C11H16N5O3S

50 -Methylthio-

yes

24.3

***

35.7

***

28.3

***

13.4

ns

13.3

169.03973

1.9

C5H5N4O3

Uric acid

no

5.4

ns

5.6

ns

5.2

***

3.6

ns

3.0

228.103

2.2

C9H14N3O4

Deoxycytidine

no

1.9

ns

1.7

***

2.4

ns

2.6

**

2.1

166.0903

4.2

C9H12NO2

Phenylalanine

yes

66.8

***

59.0

ns

59.2

***

72.3

ns

74.7

203.0585

1.5

C6H12NaO6

Glucose

yes

107

***

92.6

ns

94.5

*

111

ns

117

182.08551

3.1

C9H12NO3

Tyrosine

yes

50.7

***

46.1

ns

43.9

*

51.1

ns

52.8

205.10229

5.1

C11H13N2O2

Tryptophan

yes

15.8

***

13.2

ns

12.6

**

16.7

ns

17.3

130.05

2.4

C10H16N2O6

Pyroglutamic

yes

43.8

**

41.0

ns

41.9

**

45.7

ns

47.4

no

11.2

ns

8.4

ns

8.0

ns

7.7

ns

6.2

adenosine

acid 600.288

0.9

C21H42N7O13

Streptamine derivative

a

List of discriminating metabolites taken from the loading plot in Figure 7B. Values reported for the different days of I.29μ+ differentiation correspond to peak intensity expressed in arbitrary units (103). b Students t tests were made between the different days that the columns are between (* p < 0.05, ** p < 0.01, *** p < 0.001, ns not significant).

mitochondrial aminotransferases and mitochondrial malate dehydrogenase levels increase during the last two days,4 supporting the idea that glutamine enters the Krebs cycle to obtain energy and building blocks through transamination with pyruvate. Primary B splenocytes may use glutamine and glutamate transamination with pyruvate to avoid the toxic accumulation of ammonium (Figures 2B and 3B) and to sustain basal autophagy,34 which is strongly activated during B to plasma cell differentiation (Pengo et al, manuscript in preparation). Lacking cystine/glutamic acid transporter (xCT), resting B cells rely on dendritic cells for cysteine supply.35 We therefore hypothesize that the increased xCT synthesis during differentiation is

related to the observed glutamate release, which in turn might be due to the increased cystine import to satisfy the higher glutathione demand.36 Moreover, extracellular glutamate accumulation as a byproduct of glutamine consumption may serve as a sensible signal of plasma cell activation which could amplify the antibody response. Interestingly, glutamate has been recently shown to enhance Ig production through the kainate receptor.37 Somehow unexpectedly, we detected ethanol in the media of differentiating primary and lymphoma B cells (Figures 1 and 2), which increased during differentiation. As described in the Experimental Procedures, we carefully excluded that ethanol accumulation resulted from microbial contamination. At present, 4173


Journal of Proteome Research the source and biological significance of ethanol release by activated B cells remains unclear. Ethanol has been previously found to be produced by plants and mammals, namely in mouse serum linked to hypoxia,38,39 in the vertebrate gold fish to escape anoxia and avoid lactic acid accumulation,40,41 and in human biofluids.42,43 In support of a physiologic role of ethanol in plasma cells, previous proteomics studies revealed a decrease in cytosolic malate dehydrogenase levels during B cell differentiation.4 This decrease points to an impairment in the malate/aspartate shuttle of NADH that might influence its cytosolic reoxidation. Moreover, aldehyde reductase (ALDR), an enzyme with alcohol dehydrogenase activity, is increased during the last days of differentiation.3,4 Endogenous ethanol could act as an intercellular immune signal. By reacting with peroxynitrite to produce ethyl nitrite, it may prolong nitric oxide half-life and its effects during the immune response.44,45 Using HPLC-SPE-NMR/MS, we identified 50 -MTA and uric acid during the proliferative phase in I.29 μ+, while deoxycytidine increased during terminal differentiation (Figure 7 and Table 2). 50 -MTA is a metabolite produced during polyamine synthesis from ornithine that is usually recycled back to adenine and methionine.46,47 When the synthesis of polyamines is activated 50 -methylthioadenosine phosphorylase (MTAP) recycles 50 MTA into methionine and adenosine.47 Interestingly, transformed cells, such as B and T lymphomas, are unable to recycle back 50 -MTA, which in turn accumulates.48 Secreted 50 -MTA in activated B lymphocytes could therefore act as a diffusible signal by modulating the proliferation burst of surrounding lymphocytes and could thus play a relevant role in the Ab response. Uric acid, produced by xanthine oxidase, could play a role in inflammation by activating the inflammasome49 and in vivo as an antioxidant.50 Finally, deoxycytidine51 is released at higher levels during the last phase of differentiation (Table 2) and could be linked to the onset of massive apoptosis, a crucial step to end Ab responses.52 Overall, our study provides numerous insights on the metabolic changes that accompany plasma cell differentiation, providing a more accurate knowledge on the underlying molecular pathways. This will help to formulate hypotheses and design further experiments aimed at dissecting this complex differentiation process, with obvious immunological and biomedical implications.

’ CONCLUSIONS By combining NMR and MS metabolic profiling of the extracellular medium of differentiating B lymphocytes, we have shown that exometabolome profiling can provide clues for discriminating the functional state of cells and identifying novel pathways, with no need to harvest huge amounts of cells. Unsupervised multivariate statistical analyses revealed profound changes in energy metabolism that match and extend our previous proteomics data. This approach can be applied to find novel biomarkers in human biofluids, such as peripheral or bone marrow plasma of patients harboring leukemias or multiple myeloma. ’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary Figure 1, PC3 contribution to primary B cells clustering. Supplementary Figure 2, Myeloma cell lines Ig secretion and doubling time. Supplementary Figure 3, Contribution of

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higher principal components to murine myeloma cell lines clustering. Supplementary Table 1, NMR metabolites assignment. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milano, Italy, [email protected], Tel. 00390226434738, Fax. 00390226434723. Notes z

These authors contributed equally to this manuscript.

’ ACKNOWLEDGMENT We thank Claudio Fagioli, Elena Pasqualetto, Francesca Benevelli, and Anna Minoja for crucial help, Anna Rubartelli for discussions, and Tina Scacciante for secretarial assistance. This work was supported through grants from Fondazione Cariplo to JGM and AIRC and from AIRC 5xmille- Special Program 9965 to RS, Fondazione Cariplo and Fondazione Telethon to GM. ’ REFERENCES (1) Shapiro-Shelef, M.; Calame, K. Regulation of plasma-cell development. Nat. Rev. Immunol. 2005, 5 (3), 230–42. (2) Manz, R. A.; Radbruch, A. Plasma cells for a lifetime? Eur. J. Immunol. 2002, 32 (4), 923–7. (3) van Anken, E.; Romijn, E. P.; Maggioni, C.; Mezghrani, A.; Sitia, R.; Braakman, I.; Heck, A. J. Sequential waves of functionally related proteins are expressed when B cells prepare for antibody secretion. Immunity 2003, 18 (2), 243–53. (4) Romijn, E. P.; Christis, C.; Wieffer, M.; Gouw, J. W.; Fullaondo, A.; van der Sluijs, P.; Braakman, I.; Heck, A. J. Expression clustering reveals detailed co-expression patterns of functionally related proteins during B cell differentiation: a proteomic study using a combination of one-dimensional gel electrophoresis, LC-MS/MS, and stable isotope labeling by amino acids in cell culture (SILAC). Mol. Cell. Proteomics 2005, 4 (9), 1297–310. (5) Kell, D. B.; Brown, M.; Davey, H. M.; Dunn, W. B.; Spasic, I.; Oliver, S. G. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol. 2005, 3 (7), 557–65. (6) Mapelli, V.; Olsson, L.; Nielsen, J. Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology. Trends Biotechnol. 2008, 26 (9), 490–7. (7) Nicholson, J. K.; Lindon, J. C. Systems biology: Metabonomics. Nature 2008, 455 (7216), 1054–6. (8) Keun, H. C.; Athersuch, T. J. Nuclear magnetic resonance (NMR)-based metabolomics. Methods Mol. Biol. 2011, 708, 321–34. (9) Pope, G. A.; MacKenzie, D. A.; Defernez, M.; Aroso, M. A.; Fuller, L. J.; Mellon, F. A.; Dunn, W. B.; Brown, M.; Goodacre, R.; Kell, D. B.; Marvin, M. E.; Louis, E. J.; Roberts, I. N. Metabolic footprinting as a tool for discriminating between brewing yeasts. Yeast 2007, 24 (8), 667–79. (10) Dunn, W. B.; Brown, M.; Worton, S. A.; Crocker, I. P.; Broadhurst, D.; Horgan, R.; Kenny, L. C.; Baker, P. N.; Kell, D. B.; Heazell, A. E. Changes in the metabolic footprint of placental explant-conditioned culture medium identifies metabolic disturbances related to hypoxia and pre-eclampsia. Placenta 2009, 30 (11), 974–80. (11) Villas-Boas, S. G.; Noel, S.; Lane, G. A.; Attwood, G.; Cookson, A. Extracellular metabolomics: a metabolic footprinting approach to assess fiber degradation in complex media. Anal. Biochem. 2006, 349 (2), 297–305. 4174


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Metabolomics of B to Plasma Cell Differentiation - American Chemical

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