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Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth Francesc Puig-Castellvi, Yolanda Perez, Benjamin Piña, Romà Tauler, and Ignacio Alfonso Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01196 • Publication Date (Web): 15 Oct 2018 Downloaded from http://pubs.acs.org on October 20, 2018
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Analytical Chemistry
Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth Francesc Puig-Castellvía, Yolanda Pérezb, Benjamín Piñaa, Romà Taulera, Ignacio Alfonsoc* a
Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain; b NMR Facility, Institute of Advanced Chemistry of Catalonia (IQAC-CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain; c Department of Biological Chemistry, Institute of Advanced Chemistry of Catalonia (IQAC-CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain ABSTRACT: In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of onedimensional proton (1D 1H) NMR, while the analysis of other nuclei, such as 13C, or other NMR experiments are still underrepresented. The preference of 1D 1H NMR metabolomics lies on the fact that it has good sensitivity in a short acquisition time, albeit it lacks spectral resolution since it presents a high overlapping degree. In this study, the growth metabolism of yeast (Saccharomyces cerevisiae) has been analyzed by 1D 1H NMR and by two-dimensional (2D) 1H-13C HSQC (Heteronuclear Single Quantum Coherence) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two datasets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS-DA chemometric methods, and similar results were obtained regardless of the data type used. However, data analysis time for the 2D NMR dataset was substantially reduced when compared to the data analysis of the corresponding 1H NMR dataset because, for the 2D NMR data, signal overlapping was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide in the posterior data analysis of biomarker selection.
INTRODUCTION Metabolomics1 is the research field focused on the characterization of metabolites in cell extracts, tissues and living organisms for disease diagnosis,2 biomarker discovery,3 and phenotyping,4 among others.5-7 Metabolites are commonly detected using Nuclear Magnetic Resonance spectroscopy (NMR) or mass spectrometry (MS) hyphenated to a chromatographic technique.8 NMR is a nondestructive technique, with minimal sample preparation, and it can provide inherently quantitative measurements. On the other hand, MS is much more sensitive and therefore a larger number of metabolites (in the order of hundreds or even thousands) can be detected, although metabolite quantitation requires the use of metabolite standards and construction of calibration curves. Different active nuclei can be measured by NMR (such as 1H and 13C), expanding the possibilities of the technique for structural assignment.9,10 Regarding that, the most common NMR experiments correspond to one-dimensional (1D) spectra where a given nucleus is directly observed and analyzed. Additionally, two-dimensional (2D) experiments allow obtaining information about connectivity between nuclei, as a very powerful structural assignment tool. For instance, in a conventional 1H-13C HSQC (Heteronuclear Single Quantum Coherence) 2D NMR spectrum, the observed resonances reveal the 1H and the 13C nuclei connected through a direct C-H covalent bond.11
Even though an increasing number of NMR pulse sequences exists nowadays, most of the NMR metabolomics studies are based on the analysis of 1H NMR datasets,12-14 since they can be acquired relatively fast with a good sensitivity and resolution, while the analysis of datasets based on other NMR pulse sequences, such as homonuclear or heteronuclear 2D NMR, is still not so frequent.15-19 Nevertheless, recent strategies have emerged for increasing sensitivity in solution NMR,20 some of them specially designed for high-throughput 2D experiments.21 Despite few new 2D pulse sequences are quantitative22-24 (cross-peak intensity proportional to the concentration of magnetically equivalent nuclei), routine 2D NMR experiments are not quantitative for several reasons.25 First of all, 2D crosspeaks intensities depend on metabolite relaxation times and acquisition relaxation parameters, since relaxation delay is minimized in 2D experiments to reduce acquisition time for each t1 increment. Moreover, pulses excitation profiles and diversity in the actual values of J-couplings also produce differences in the intensity of the observed signals. We must bear in mind that the evolution time used for the experiments is optimized for an average 1JCH of 145 Hz, which is a compromise between the real values that can be observed for the different C-H groupings within molecule and between different molecules. For these routine 2D NMR spectra, absolute concentrations can still be obtained in a less straightforward approach using calibration curves.26
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Due to these limitations, 2D NMR spectra are used in metabolomics studies mostly for structure elucidation with selected samples27,28, while 2D NMR metabolomics studies are less common29,30. Nevertheless, 2D NMR metabolomics still has several advantages over 1D 1H NMR metabolomics, as they allow for a better structural analysis, and resonance overlapping is reduced due to the existence of the second dimension. The choice between 1D 1H NMR and any 2D NMR when designing metabolomics studies not only affects the number and characteristics of the detected resonances, but also the posterior processing workflow. From a mathematical point of view, a single 1D NMR spectrum is a vector of intensities, while a 2D NMR spectrum is a matrix. Due to the higher simplicity of the former, most commercial or open-access tools for deconvoluting NMR spectra only apply to 1D data (i.e. Chenomx (Chenomx Inc., Alberta, Canada), AMIX (Bruker, Billerica, MA, USA), Batman31), while 2D NMR deconvolution methods are almost inexistent.32 In addition, chemometric analysis of 2D NMR spectra by Principal Component Analysis (PCA) or Partial Least Squares – Discriminant Analysis (PLS-DA) is not as straightforward as for 1D NMR spectral datasets. In these methods, each sample must correspond to one vector. Accordingly, 2D NMR spectra data matrices can be also analyzed in the conventional way if they are unfolded to data vectors.33 Few 2D NMR metabolomics studies have been published until now, which used two main analytical strategies. In most of them, a careful resonance assignment was initially performed, and resonances were individually enclosed in Regions-Of-Interest (or ROI) segments34 that were bucketed afterwards. Then, these sets of buckets were analyzed with PCA.15-17,30,35,36 Moreover, in a reduced number of papers, samples were investigated using PCA or PLS-DA directly on the vectorized form of the 2D spectral datasets.29,33,37-39 Despite being less common, the chemometric analysis of all data points from the NMR spectra might provide more comprehensive results than the chemometric analysis of the buckets, since bucketing implies an important loss of spectral resolution (e.g., for 1H NMR spectra, buckets are typically constructed with a 0.04 ppm width). Comparative analyses between 1D and 2D NMR have been previously performed on different sets of metabolomics data, but the potential of the full data was unexploited, as the 2D NMR spectra were only analyzed either after bucketing36,40-42 or by just univariate analysis43, while any further exploration of the data (assignment of detected resonances, resonance integration, and the chemometric analysis of the resonance integrals) were left out in these studies. In this work, we have performed an exhaustive comparative study between 1H NMR and 1H-13C HSQC NMR analyses (combining both untargeted and targeted approaches) of metabolomics samples from Saccharomyces cerevisiae (yeast) extracts. Specifically, yeast was grown in two different liquid media and their metabolism was characterized at 8 different timepoints of a 3-day period. The two media used, YPD (Yeast Peptone Dextrose) and YSC (Yeast nitrogen base Synthetic Complete), are broadly used in yeast lab routines, and results from this analysis should be of interest for improving lab methodologies involving yeast. Finally, in addition to the evaluation of the biological results, we have also explored and discussed the similarities and differences on the analysis workflow and on the results that were ob-
tained from either 1D or 2D NMR metabolomics analysis, giving an insight of the particular benefits and weaknesses of the two approaches.
EXPERIMENTAL SECTION Yeast Growth. S. cerevisiae S288C cells were pre-cultured in YPD (1 % yeast extract, 1 % peptone, 2 % glucose) medium on an orbital shaker (150 rpm) at 30 °C overnight. All following cultures were cultured with these shaking and temperature conditions. 2 L of YNB Synthetic Complete medium (YSC, 1.7 g/L Yeast Nitrogen Base without amino acids and sulphate (Difco), 5 g/L (NH4)2SO4) were inoculated with 200 μl of the pre-culture sample and left at the same temperature and shaking conditions until the culture reached an absorbance at 600 nm (A600) of approximately 0.8 - 1. Pellets from these resulting cultures were collected by centrifuging the cultures, but not washed, at 613 g for 3 min and 4 °C. Pellets were used right after for inoculating erlenmeyers containing either YSC medium or YPD medium. Sample collection. 100 ml aliquots of every culture were collected eight times during three days (0h, 2h, 4h, 6h, 10h, 24h, 48h, and 72h). For every culture and time-point, four replicates were collected. Samples were arrested with a cold shock in ice and cell were harvested by centrifugation at 4,000 g for 3 min, discarding the supernatant. Cells were washed twice in Na2HPO4 100 mM pH 7.0 followed by a centrifugation at 4700 g for 3 min. Pellets were stored at -80 °C and lyophilized. Metabolite extraction. Metabolites were extracted by following the protocol published in a previous work.12 NMR sample preparation. Aqueous samples were dissolved in 650 μl of deuterated phosphate buffer (Na2DPO4 25 mM, pH 7.0) in D2O with DSS 0.2 mM as internal standard. Samples were centrifuged at 9,000 g for 5 min and the supernatant was collected and introduced into the NMR tube. NMR spectroscopy. All NMR data were acquired using a Bruker Avance-IIIHD 500 MHz spectrometer equipped with a z-axis pulsed field gradient triple resonance (1H, 13C, 15N) TCI cryoprobe. 1 H NMR experiments. 1D NOESY spectra were recorded using the noesygppr1d pulse sequence, using 256 scans, 4 seconds of relaxation delay, spectral width of 10 kHz and an acquired spectral size of 32k (final spectral size of 65 k after zerofilling). In total, every 1D NOESY experiment lasts for 30 minutes. The 90º pulse width (between 8.5 and 10.5 µs) and presaturation power for water suppression were measured for every sample before experiment acquisition. 1 H-13C HSQC NMR experiments. 2D 1H-13C HSQC NMR spectra were recorded using a pulse sequence with water presaturarion, sensitivity improvement and shaped adiabatic pulses for all 180-degree pulses on f2 channel (hsqcetgpprsisp2.2) and the following parameters: 12 scans, 3 seconds of relaxation delay, spectral width of 20.7 kHz in f1 and 7.9 kHz in f2, and an acquired spectral size of 548 data points in f1 dimension and of 1,536 data points in f2 dimension. After zero-filling, final spectral size in the 1H-13C HSQC NMR spectra were 1,024 data points in 13C dimension and 2,048 data points in f2 1H dimension. The total acquisition for each 1H-13C HSQC NMR experiment was 6 h. Preprocessing of NMR spectra. NMR spectra have been automatically referenced, phased and baseline corrected using
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Analytical Chemistry TopSpin (Bruker BioSpin GmbH, Billerica, MA, USA) routines. In addition, an exponential apodization of 0.2 Hz with MestreNova v.11.0 (Mestrelab Research, Spain) was applied to the set of 1H NMR spectra. The 1H NMR spectra were stored in ASCII file format and imported to Matlab R2014a (The Mathworks Inc., Natick, MA, USA) as a data matrix of 64 rows (samples) and 65,598 columns (ppm values). Then, regions of 4.41 - 5.16 ppm (water), 3.30 3.37 ppm (methanol), 7.64 - 7.69 ppm (chloroform), below 0.7 ppm (DSS), and above 9.7 ppm (empty) were excluded from the analysis. With this step, the number of variables was reduced to 26,633. Next, minor resonance misalignments were corrected with icoshift44. Finally, in order to eliminate sample size effects, 1H NMR spectra were normalized using the Probabilistic Quotient Normalization (PQN)45,46 method. On the other hand, the set of 1H-13C HSQC NMR spectra were imported to Matlab R2014a using the Matlab scripts from the BBIO Toolbox Matlab scripts provided by Bruker BioSpin GmbH. For every 2D spectrum, a matrix with 1,024 rows (δC) and 2,048 columns (δH) was generated. Next, for every spectrum, noise variables were discarded using the Variable-Of-Interest (VOI) strategy published recently.47 In VOI strategy, variables relative to noise are discarded, whereas variables with chemical meaning are kept. Selected variables needed to accomplish two criteria: first, they needed to be higher than a given threshold; and second, these intensity values should be agglomerated forming a cluster (resonance) of a minimum size. In our study, we set the intensity threshold at 6,000, and the minimum number of clustered variables was set to 24. A detailed description regarding the application of the VOI strategy can be consulted in the Supplementary methods. With VOI, the total number of selected variables was 65,881. Finally, the set of VOI-filtered 1H-13C HSQC NMR spectra were normalized using the same PQN quotient values obtained in the PQN normalization of the 1H NMR data. Metabolite identification. Metabolite assignment was performed by a detailed targeted metabolite profiling analysis of the 1H NMR or 1H-13C HSQC NMR signals using the Yeast Metabolome Data Base library,48 the Biological Magnetic Resonance Data Bank,49 and the NMR spectral library BBIOREFCODE from AMIX software (Bruker Inc.). Integration of 1H NMR resonances. Relative metabolite quantifications of the 1H NMR spectral matrix were performed using BATMAN R-package31. Integration of 1H-13C HSQC NMR resonances. Relative metabolite quantifications were performed by a first segmentation of the denoised 1H-13C HSQC NMR spectra, where each segment only includes a single resonance (for each metabolite, the most intense one or the one with less interfering overlap), followed by the sum of all the intensity values contained in each segment. For more information, see47. Principal Component Analysis (PCA). PCA was applied to the 1H NMR and 1H-13C HSQC NMR spectral matrices. Before PCA analysis, each data matrix was mean-centered. Analysis of resonance integral datasets. The two datasets of resonance integrals were analyzed with ANOVA – Simultaneous Component Analysis (ASCA) and Partial Least Squares
(PLS) chemometric methods. ASCA is a multivariate method that combines the power of ANOVA to separate variance sources with the advantages of Simultaneous Component Analysis (SCA) to the modeling of the individual separate effect matrices.50,51 In this work, ASCA was used to evaluate whether each of the individual factors (culture medium and time) produces a significant effect on yeast metabolism, and to evaluate whether the two factors interact (each medium produces a different metabolic response over time). Before ASCA analysis, these two matrices were scaled by diving them with the standard deviation of the corresponding metabolite signal in the yeast culture control group (time 0h),52 and the effect of each factor was evaluated using a permutation test with 10,000 permutations. PLS53 is a regression method that allows correlating a relatively small set of y variables to a large set of X-variables. As a result, a reduced number of new linear combination of the independent original X-values (called Latent Variables, LV) is obtained that correlates optimally with the variation in y. When the y variables are categorical for discriminant purposes, PLS is referred as PLS-DA (Discriminant Analysis). In this work, the most influent X variables in the model were calculated using their Variable Importance on Projection (VIP) scores54. X-variables associated with VIP scores greater than one are considered to be relevant on the PLS model.55 In this study, for each one of the two datasets, one discriminant PLS-DA model was built to distinguish between samples cultured in the two culture media (32 samples per class). PCA, ASCA. PLSR and PLS-DA were performed using PLS toolbox 7.8.0 (Eigenvector Research Inc., Wenatchee, WA, USA). For PCA and PLS analyses, Cross-Validation with Venetian Blinds was used.
RESULTS AND DISCUSSION Metabolite identification In general, resonance assignment in NMR metabolomics is troublesome for 1D 1H NMR and 2D 1H-13C HSQC datasets. In the case of 1D 1H NMR data, proton chemical shifts, multiplicity, and coupling constants can be measured. However, in some cases, the multiplicity pattern cannot be recognized because some of the resonances are masked by other neighboring intense signals due to the large signal overlapping. In addition, since some moieties are common for various metabolites (i.e. the trimethylammonium moiety in choline-containing metabolites, detected as a singlet at δH = ~3.2 ppm), the full characterization of a single isolated resonance may not be sufficient to confirm a metabolite. A stack plot of representative 1H NMR spectra of the studied samples is given in Figure 1A. In the case of 1H-13C HSQC data, resonances corresponding to direct C-H bonds (1JCH) are detected as cross-peaks between the two nuclei dimensions. Despite some resonances may overlap, the existence of an additional dimension attenuates this overlapping when compared with the corresponding 1H NMR data. For instance, one of the few examples of signal overlapping is found at δH = 3.742±0.024 ppm and δC = 57.16±0.46 ppm. This cluster includes resonances relative to the Cα-H of Llysine, L-ornithine, L-glutamine, L-glutamate, and L-arginine.
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Figure 1. Representative NMR spectra. A. Stack plot of 1H NMR samples (region showed δH= 0.7-4.7 ppm). B. Contour plot of a 1H-13C HSQC NMR spectrum (region shown: δH= 0.7-5 ppm, δC= 0-100 ppm). Listed compounds (metabolites and NMR standard): 1 DSS, 2 Lisoleucine, 3 L-valine, 4 L-threonine, 5 acetic acid, 6 L-lysine, 7 L-glutamine, 8 L-glutamate, 9 succinic acid, 10 L-ornithine, 11 L-alanine, 12 GABA, 13 L-methionine, 14 L-arginine, 15 GPC, 16 glycerol, 17 citric acid, 18 ethanol, 19 L-aspartate, 20 glucose, 21 trehalose, 22 choline, 23 GSSG, 24 L-histidine, 25 betaine, 26 L-leucine, 27 L-lactic acid, 28 L-asparagine, 29 AMP, 30 glycine, 31 citramalic acid, 32 GSH, 33 L-phenylalanine, 34 L-proline.
In addition, for some abundant metabolites, resonances of indirect C-H bonds (3JCH or longer) are detected, allowing for a better metabolite identification. 3JCH couplings were observed for glycerol, L-alanine, L-leucine, L-isoleucine, L-lysine, and L-valine. In Figure 1B, the contour plot of a single 1H-13C HSQC NMR is shown. From the analysis of the 1H NMR dataset, 53 metabolites were identified, comprising mostly amino acids, nucleotides, sugars and organic acids. On the other hand, when the 1H-13C HSQC dataset was analyzed, the corresponding cross-peaks for 55 metabolites were assigned, of which 50 were also detected by 1D 1H NMR. Overall, a total of 58 different metabolites were detected. Metabolites that were detected in the 1H NMR but not in 1H13 C HSQC NMR were (S)-2-isopropylmalate, fumaric acid, and a choline derivate. Metabolites detected in 1H-13C HSQC but not satisfactorily confirmed in 1H NMR were citramalic acid, malic acid, pyroglutamic acid, β-alanine, and cysteineglutathione disulfide (CYSSG). Metabolites that were only detected by 1H NMR corresponded to those that were found below the detection limits of 1H-13C HSQC. On the other hand, resonances from metabolites that were only detected by 1 H-13C HSQC NMR were masked by other resonances in 1H NMR. For CYSSG, it is worth mentioning that its characteristic signal (δH= 4.75 ppm and δC= 55.3 ppm) was masked by the
water peak (and partly suppressed by the 1D NOESY pulse sequence). The final list of assigned resonances for both datasets is given in Table S1. Metabolite quantitation Here, we have investigated the robustness of 2D NMR in providing reliable relative concentration (or fold-change) measurements by performing calibration curves between integrals from 1D data with integrals from 2D data. Integrals from 1H NMR data were obtained with Batman R-package31,56, whereas 2D integrals were obtained using our previously proposed VOI strategy47. After performing the integration for the NMR signals of the two datasets, we noticed that time spent on integration of 2D NMR data is drastically reduced when compared to the same analysis in the 1D NMR data integration. This is caused because deconvolution is a necessary preliminary step in the integration analysis of 1D NMR data but not for 2D NMR data integration (see methods section). Then, for every metabolite, the vector of 1D integrals was regressed with the corresponding vector of 2D integrals and the correlation coefficient between them was calculated. To avoid obtaining falsely low correlation coefficients, samples with integrated 2D cross-peak resonances below the detection limit
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were excluded in the regression. A table containing the correlation coefficients for every detected metabolite is provided in Table S2. Results from these analyses suggest that different methods may produce different outcomes. This is not an intrinsic problem of the NMR data type, but of its quality. For concentrated metabolites, a good regression was obtained, indicating that the two methods can provide equally satisfactory semi-quantitative measurements. However, for 1D NMR data, these measurements can be undermined if the peak integrals were obtained from an overlapped region. Even though a Lorentzian model can be used to deconvolute the integral from the analyzed overlapped region, results can differ from the true ones if the model captured wrongly intensities from other peaks. This has been observed, for instance, for L-lysine (Figure 2). L-lysine concentration was obtained from the 1H NMR data by deconvolution of the multiplet peak at 1.47 ppm and of the triplet peak at 3.01 ppm, which were simpler to deconvolute than the other L-lysine resonances. On the other hand, in the 2D 1H13 C HSQC NMR dataset, two integrals related to L-lysine were obtained from integration of the cross-peaks found at δH= 1.90 ppm and δC= 32.6 ppm, and at δH= 1.72 ppm and δC= 29.1 ppm, both perfectly isolated from the rest of the signals. When we compared the two sets of integrals, the regression using the 2D NMR data presented a convincing match (r2=0.986, Figure 2C), whereas it was considerably worse for 2D NMR data when it was compared with the 1D data (r2=0.842 and r2=0.926, Figure 2A and Figure 2B, respectively). This implies that both 1D and 2D data can be useful for semi-quantitation, and that the deconvolution may be a source of error and caution should be taken. In any case, regardless of the type of NMR data used, we consider that it is always preferable to calculate integrals from non-overlapped resonances than to use deconvolution tools to integrate overlapped resonances. From our data, the metabolites that can be much better integrated in the 2D 1H-13C HSQC NMR data are citric acid, oxidized glutathione, L-asparagine, L-aspartic acid and L-serine. For all of them, all their characteristic resonances lay in complex (and crowded) areas in the 1H NMR spectrum. Finally, due to a lower sensitivity in the HSQC, some metabolites were not detected in all samples. In our study, 4 metabolites were only detected in the 1H NMR samples, and some others (i.e. L-tryptophan, thiamine, nicotinamide mononucleotide)
were not detected for most of the 1H-13C HSQC spectra although they were measurable in the corresponding 1H NMR spectra. Thus, in these cases, it is preferable to use integral values from deconvoluting 1H NMR spectra. Explorative untargeted analysis with PCA PCA was used on the two NMR spectral (1D or 2D) datasets to assess, as an untargeted approach, whether the spectral differences produced a significant impact on the observed results. Variables from both assigned and unassigned resonances were considered in the two PCA analyses, while variables from noise were excluded before analysis. Before PCA analysis of spectral data containing an abundant number of noisy variables, such as for NMR data, so it is recommended to discard those noisy variables which could compromise data analysis and interpretation.33,39,47 For the 1H NMR dataset, variables from empty regions were removed, whereas for the 1H-13C HSQC dataset, noisy variables were discarded with the VOI strategy (see method section for more information about variable filtering47). First two principal components explained 62.95% of the data variance for the 1D NMR dataset (Figure 3A), and 55.08% for the 2D NMR dataset (Figure 3C). A similar score plots distribution was observed for the two PCA analyses (Figure 3A and Figure 3C). A linear evolution over time of the YPD-cultured sample scores (orange-red) was observed, whereas for YSCcultured sample scores (green), their trajectory changed after the first 24h. Positive scores on PC1 were associated with the early growth (0-24h) of YSC-cultured samples, while positive scores on PC2 were associated with the growth of YPDcultured samples. This comparison showed that chemometric analysis of 2D NMR spectral data is as informative as chemometric analysis of 1D NMR spectral data, although not so commonly recognized in the previous literature (with very few exceptions29,33,37-39). Furthermore, the computational times for the two analyses were also similar. In addition, for these two type of analyses, PCA loading plots highlighted the same metabolites, with resonances between δH= 1 ppm and δH= 5 ppm (loadings for PC1 are given in Figure 3B (for 1D NMR) and Figure 3D (for 2D NMR)). However, since 2D NMR data has less spectral overlapping, better-defined loadings were unambiguously obtained. Thus, 2D NMR data should be preferred when unambiguous assignment of the variable loadings is required.37
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Figure 3. PCA. A) and C) PCA scores plot of the (A) 1H NMR dataset and the (C) 1H-13C HSQC NMR dataset. B) and D) Loadings of PC1 of the (B) 1H NMR dataset and in the (D) 1H-13C HSQC NMR dataset. Samples in the scores plot (A, C) are colored according to the legend in A, while variables in the loading plots (B, D) are colored in red for positive loadings and in blue for negative loadings. In the loadings plots (B, D), only the proton region between 0.5 ppm and 4.5 ppm is shown. Green and orange arrow denote score progression over time of the YSC- and YPD-cultured samples, respectively.
Study of the effects of medium composition and time The two resonance integrals datasets were then analyzed by ASCA and PLS-DA chemometric methods. ASCA was first used to evaluate the importance of the two factors (culture medium and time) on yeast metabolism, as well as the interaction between them. The two ASCA analyses (one per NMR dataset) revealed that both factors and their interaction were significant (p= 0.0001). These results confirmed that using a different culture medium would affect the speed of the metabolic events occurring inside yeast cells. The effects of the medium composition on yeast metabolism were examined by PLS-DA analyses of the integral matrices obtained from each of the two NMR datasets. Metabolic differences caused by the two different media are summarized in Figure 4. In each of these two PLS-DA models, of the samples cultured at the two different media (Figure 4), 2 LVs were selected. 94.87% and 30.78% of the y- and X-variances were explained respectively for the 1D dataset (Figure 4A). In the case of the 2D dataset, 81.56% and 37.66% of the y- and X-variances were explained (Figure 4C). Interestingly, for the two types of analyses, metabolites associated to the highest VIPs (VIP > 1) were mostly the same (Figure 4B and Figure 4D). According to these two PLS-DA models, metabolites that allow optimal class separation were GABA, glycerol, L-arginine, L-lysine, and Lornithine, among others.
Despite the high degree of resemblance between the two PLS-DA, it should be noted that some differences exist. Most of these differences are derived from the fact that the lists of metabolites are not exactly the same in the two cases (50 metabolites in common, 3 only found in the 1D analysis, and 5 only in the 2D analysis), and because the concentration values for the coincident metabolites may present some divergences, as it was already commented in the ‘Metabolite quantitation’ section. Biological interpretation The observed metabolomic differences between cells grown in rich (YPD) and minimal (YSC) media are likely related to the different response to starvation occurring in these two different media. Prior to entry into stationary phase, yeast cultures progress through a series of growth phases, including the exponential phase (high glucose, fermentative metabolism), the diauxic shift (low glucose, transition to respiratory metabolism), the post-diauxic phase (low nutrients, respiratory metabolism) and, finally, the stationary phase (lack of nutrients, no growth), which typically occurs after 48-72 h of growth.57 It is at this point where the medium of the culture determines the fate of cells. Cells grown in YPD have a more prolonged period of hypo-metabolism in stationary phase, which allows them to remain viable for several weeks. In contrast, yeast cells grown in synthetic media, like YSC, maintain a high metabolism rate in the stationary phase, which results in a considerable loss of viability in only a few days after the exhaustion of the medium.58
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Analytical Chemistry
Figure 4. PLS-DA. A) and C) Scores plot for the PLS-DA analysis of the (A) 1H NMR dataset and the (C) 1H-13C HSQC NMR dataset. B) and D) VIP values for the variables in the (B) 1H NMR dataset and in the (D) 1H-13C HSQC NMR dataset. Metabolite list: 1 acetic acid, 2 adenosine, 3 AMP, 4 ATP, 5 betaine, 6 choline, 7 citraconic acid, 8 citric acid, 9 CMP, 10 ethanol, 11 GABA, 12 glycerol, 13 glycerophosphocholine, 14 glycine, 15 GMP, 16 GSSG, 17 L-alanine, 18 L-arginine, 19 L-asparagine, 20 L-aspartic acid, 21 L-glutamic acid, 22 Lglutamine, 23 L-histidine, 24 L-isoleucine, 25 L-lactic acid, 26 L-leucine, 27 L-lysine, 28 L-methionine, 29 L-ornithine, 30 L-phenylalanine, 31 L-proline, 32 L-serine, 33 L-threonine, 34 L-tryptophan, 35 L-tyrosine, 36 L-valine, 37 NAD+, 38 NADP, 39 NMN, 40 succinic acid, 41 taurine, 42 thiamine, 43 thiaminePP, 44 trehalose, 45 UDP-Glc, 46 UDP-Nac-glc-NH2, 47 uracil, 48 uridine, 49 α-glucose, 50 β-glucose, 51 2-(S)-isopropylmalic acid, 52 choline-derivative, 53 fumaric acid, 54 citramalic acid, 55 CYSSG, 56 malic acid, 57 pyroglutamic acid, 58 βalanine.
Our results confirmed very similar metabolic patterns for both cultures for the first 24h of incubation, roughly coinciding with the entry into stationary phase. After this point, the two cultures diverge, the differences becoming maximal after 72 h of culture. At this point, YPD-grown cells would likely enter into a low-metabolic, resilient state (therefore maintaining the physiological levels of essential metabolites), whereas those YSC-grown ones are at their limit of viability, after having consumed all available nutrients.57,58 Time of analysis Despite similar metabolomics results were obtained from the two datasets, it is important to stress the differences in the time needed for the execution of the two analyses. First, every 1D NOESY experiment was acquired in 30 minutes (~1.5 day for the whole 1D dataset), while the acquisition of each 2D 1H-13C HSQC NMR experiment required 6 h (16 days for the whole 2D dataset). Although this time may seem considerably long, it is important to consider that they can be substantially reduced by the application of the current instrumental advances in sensitivity enhancement of NMR spectroscopy.20 On the other hand, the much better clarity of the 1H-13C HSQC NMR dataset allowed assigning the resonances in only two weeks, while one month was dedicated to the analysis of the 1D 1H NMR dataset. Having said this, we were already familiar with the metabolomics fingerprint of yeast in 1H NMR spectra,12,46 meaning that the signal assignment from the 1D data should take longer to an inexperienced researcher. In this situation, it would be advisable to acquire some 2D NMR spectra on representative samples to assess the assignment step. On the other side, an inexperienced scientist may directly assign the
signals from the 1H-13C HSQC NMR dataset in a shorter time, since the resonances are very well resolved. Regarding the resonance integration step, more than one month was spent in the deconvolution of the proton resonances from the 1H NMR dataset. However, since the use of deconvolution approaches was not required for the analysis of the 1H13 C HSQC NMR dataset, this integration step was executed in less than one week. Finally, for the chemometric analyses, the same amount of time was dedicated to both datasets, consisting of approximately one week for each.
CONCLUSIONS The metabolism of Saccharomyces cerevisiae cultured under unrestricted conditions has been characterized indistinctively by 1D 1H NMR and 2D 1H-13C HSQC NMR spectroscopy. The intrinsic differences in signal resolution and sensitivity inherent to each of the NMR pulse sequences caused that the lists of metabolites detected by the two analyses were slightly different. These intrinsic differences also affected the estimation of the resonance integrals. For instance, resonance integrals from low concentrated metabolites were poorly estimated from the 2D NMR data, while deconvolution approaches may not perfectly integrate overlapped resonances, such as the ones in 1H NMR spectra of metabolomics samples. Untargeted analysis of 2D NMR data has been confirmed to be a reliable strategy to study yeast metabolism because the information present in the second dimension allows for a better signal resonance assignment, which results on an improvement in the understanding of the biological studied system (yeast in this case). In addition, for low overlapped 1H-13C HSQC NMR spectra, resonance integration after application of the VOI de-
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noising strategy is much faster than the corresponding integration of 1H NMR spectra needing their preliminary deconvolution. PCA, ASCA and PLS-DA analyses revealed to be specially useful to analyze both 1D and 2D NMR spectra, leading to similar results. Using both approaches, metabolic events occurring during yeast growth were confirmed to be highly influenced by the culture medium composition.
ASSOCIATED CONTENT Supporting Information Table S1. Resonance assignment for 1H NMR and 1H-13C HSQC NMR data (PDF). Table S2. Correlation coefficients between integrals from the 1H NMR analysis and the 1H-13C HSQC NMR analysis (PDF). Table S3. Resonance integrals from 1H NMR data (xlsx). Table S4. Resonance integrals from 1H-13C HSQC NMR data (xlsx). Figure S2. Plot of two overlaid 1H NMR spectra acquired from a representative sample at different times (PDF). The Supporting Information is available free of charge on the ACS Publications website.
AUTHOR INFORMATION Corresponding Author * Tel: +34-934006100 E-mail address:
[email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
Notes The authors declare no competing financial interest.
ACKNOWLEDGMENT The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 320737. The 500-MHz spectrometer was purchased in part through a Research Infraestructure MINECO-FEDER fund (Grant CSIC13-4E-2076).
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