Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis

Disruption of specific metabolic pathways constitutes the mode of action of many known toxicants and it is responsible for the adverse phenotypes associated to human genetic defects. Conversely, many industrial applications rely on metabolic alterations of diverse microorganisms, whereas many therapeutic drugs aim to selectively disrupt pathogens' metabolism. In this work we analyzed metabolic changes induced by auxotrophic starvation conditions in yeast in a non-targeted approach, using one-dimensional proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and chemometric analyses. Analysis of the raw spectral datasets showed specific changes linked to the different stages during unrestricted yeast growth, as well as specific changes linked to each of the four tested starvation conditions (L-methionine, L-histidine, L-leucine and uracil). Analysis of changes in concentrations of more than 40 metabolites by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) showed the normal progression of key metabolites during lag, exponential and stationary unrestricted growth phases, while reflecting the metabolic blockage induced by the starvation conditions. In this case, different metabolic intermediates accumulated over time, allowing identification of the different metabolic pathways specifically affected by each gene disruption. This synergy between NMR metabolomics and molecular biology may have clear implications for both genetic diagnostics and drug development. © The Author(s) 2016.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. We also thank Dr. Yolanda Pérez for her helpful recommendations on setting up the acquisition parameters for some of the NMR experiments.Peer reviewe

A quantitative 1H NMR approach for evaluating the metabolic response of Saccharomyces cerevisiae to mild heat stress

Effect of growth temperature on the yeast (Saccharomyces cerevisiae) metabolome has been analysed by one-dimensional proton NMR spectroscopy (1H NMR). Potential biomarkers have been first identified by a non-targeted chemometric evaluation of the spectra, followed by a comprehensive analysis of bayesian estimated concentrations of target metabolites in extracts of cells growth either at 30 or 37 °C. Tentative identification of metabolites whose concentrations were affected by this mild heat-shock stress was attempted by partial least squares-discriminant analysis (PLS-DA) on 1H NMR data, combined with Statistical TOtal Correlation SpectroscopY, and further confirmed with empirical data. An extensive assignment for most of the detected NMR signals was performed, with a total number of 38 identified metabolites. Concentrations estimated using automatic BATMAN modelling revealed that bayesian integration is a sufficient approach for obtaining relevant concentration changes of metabolites and biological information of interest. In contrast to when it is applied directly on spectral data, the application of PLS-DA on BATMAN recovered metabolite concentration estimates allowed for a better overview of the investigated samples, since more metabolites were highlighted in the discriminatory model. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition. The strategy described in this work can thus be proposed as a powerful and easy tool to investigate complex biological processes, from biomarker screening and discovery to the study of metabolite network changes in biological processes.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.Peer reviewe

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different 1H NMR metabolomics datasets. In a first study, 1H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by 1H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using 1H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.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 and the Spanish Ministry of Economy and Competitiveness (CTQ2015-66254-C2-1-P).Peer reviewe

Comparative analysis of 1 H NMR and 1 H- 13 C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D 1 H) NMR, whereas the analysis of other nuclei, such as 13 C, or other NMR experiments are still underrepresented. The preference of 1D 1 H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast (Saccharomyces cerevisiae) was analyzed by 1D 1 H NMR and by two-dimensional (2D) 1 H- 13 C heteronuclear single quantum coherence (HSQC) 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 data sets (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 data set was substantially reduced when compared with the data analysis of the corresponding 1 H NMR data set because, for the 2D NMR data, signal overlap 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 the posterior data analysis of biomarker selection. © 2018 American Chemical Society.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 no. 320737. The 500 MHz spectrometer was purchased in part through a Research Infrastructure MINECO-FEDER fund (Grant CSIC13-4E-2076).Peer reviewe

Compression of multidimensional NMR spectra allows a faster and more accurate analysis of complex samples

We propose an approach to efficiently compress and denoise multidimensional NMR spectral data, improving their corresponding storage, handling, and analysis. This method has been tested with 2D homonuclear, 2D and 3D heteronuclear, and 2D phase-sensitive NMR spectral data and shown to be especially powerful for 2D NMR metabolomics studies. © The Royal Society of Chemistry.Peer reviewe

Deciphering the Underlying Metabolomic and Lipidomic Patterns Linked to Thermal Acclimation in Saccharomyces cerevisiae

Temperature is one of the most critical parameters for yeast growth, and it has deep consequences in many industrial processes where yeast is involved. Nevertheless, the metabolic changes required to accommodate yeast cells at high or low temperatures are still poorly understood. In this work, the ultimate responses of these induced transcriptomic effects have been examined using metabolomics-derived strategies. The yeast metabolome and lipidome have been characterized by 1D proton nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography-mass spectrometry at four temperatures, corresponding to low, optimal, high, and extreme thermal conditions. The underlying pathways that drive the acclimation response of yeast to these nonoptimal temperatures were evaluated using multivariate curve resolution-alternating least-squares. The analysis revealed three different thermal profiles (cold, optimal, and high temperature), which include changes in the lipid composition, secondary metabolic pathways, and energy metabolism, and we propose that they reflect the acclimation strategy of yeast cells to low and high temperatures. The data suggest that yeast adjusts membrane fluidity by changing the relative proportions of the different lipid families (acylglycerides, phospholipids, and ceramides, among others) rather than modifying the average length and unsaturation levels of the corresponding fatty acids. © 2018 American Chemical Society.The research that produced these results was supported by funding received from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 320737 and the Spanish Ministry of Economy and Competitiveness (CTQ2015-66254-C2-1-P).Peer reviewe

Rearrangement of incomplete multi-omics datasets combined with ComDim for evaluating replicate cross-platform variability and batch influence

International audienceMulti-omics studies can highlight the interrelationships among data across different layers of biological information. However, methods for the unsupervised analysis of multi-block data do not take the individual variability across batches into account and cannot deal with omics datasets when they present different numbers of replicates. We have explored three different data arrangement strategies to tackle these limitations. Several multiblock methods can be used to decipher the common variations across blocks and to determine the contribution of each block to each common component. In this study the ComDim method was used to compare these rearrangement strategies for three multi-omics datasets. We found that arranging the data using the 'replicate by blocks' strategy, where each block comprises data from only one replicate independently of its data type, provided the most insightful results. ComDim allowed the evaluation of the variability across the replicate blocks, confirming the existence of batch effects in some of the studies. Moreover, since the contributions of these batch effects were separated from the other contributions, the coordinated biological responses common across the different blocks was characterized for each data type