Métricas para la validación del diseño

of the plant material, followed by the separation, purification and identification of the different constituents of plants. A brief description of important methods is made in this section.

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50 Sample preparation and extraction

Sample preparation is the crucial first step in the analysis of plants because it is necessary to extract the desired chemical components (Huie, 2002). Proper actions must be taken to assure that potential active constituents are not lost, distorted or destroyed (by the presence of water, for example), etc. Preparation of plant material usually includes the following steps: pre-washing, drying (or freeze drying) and grinding to obtain a homogenous sample and increase the contact of sample surface with the solvent system (Sasidharan et al., 2011). Then, extraction plays a significant and crucial role on the final result and outcome (Azmir et al., 2013). The most common factors affecting extraction processes are matrix properties of the plant part, solvent, temperature, pressure and time (Huie, 2002; Wang and Weller, 2006). The selection of the solvent system largely depends on the chemical properties of the target compounds. Different solvent systems are available to extract the bioactive compound from natural products: polar solvents such as methanol, ethanol or ethyl-acetate are more suitable for the extraction of hydrophilic compounds, while for more lipophilic compounds, dichloromethane or a mixture of dichloromethane/

methanol in ratio of 1:1 can be used (Sasidharan et al., 2011). Extraction of plant materials can be done by various extraction procedures and the suitability of the methods of extraction according to characteristics of the target compounds must be considered. Methods such as ultrasound, sonication, pulsed electric field, enzyme digestion, extrusion, ohmic heating, soxhlet, heating under reflux can be applied. More modern methods such as surfactant-mediated extraction, supercritical-fluid extraction, pressurized-liquid extraction, microwave-assisted extraction and solid-phase extraction possess certain advantages. These are, e.g. the reduction in organic solvent consumption and in sample degradation, elimination of additional sample clean-up and concentration steps before chromatographic analysis, improvement in extraction efficiency, selectivity, and/ kinetics of extraction (Huie, 2002; Sasidharan et al., 2011).

Characterization of bioactive compounds

After extraction, further separation, identification, and characterization of bioactive compounds is required. Metabolomics, one of the ‘omic’ sciences in systems biology, is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified within a biologic system (Zhang et al., 2012). Nowadays, ¹H NMR, Gas Chromatography–Mass Spectrometry (GC–MS) and Liquid Chromatography–Mass Spectrometry (LC–MS) are well-established powerful analytical methods for generating metabolomics profiles (Patel et al., 2010).

These techniques have their advantages and disadvantages. For instance, GC–MS requires sample derivatization, which lengthens the sample preparation time (O'Gorman et al., 2013). In general, LC–MS and GC–MS are more time-consuming concerning the sample preparation. On the other

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hand, GC–MS and LC–MS yield a higher sensitivity than NMR and therefore may detect metabolites that are present in a concentration below the detection limit of ¹H NMR (Scalbert et al., 2009). ¹H NMR is non-destructive, non-biased, and easily quantifiable, permits the identification of novel compounds and needs no chemical derivatization (Wishart, 2008). ¹H NMR may detect compounds that are too volatile for GC, while metabolites without proton (phosphoric acid) are not detected by ¹H NMR.

The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a targeted and non-destructive manner (Smolinska et al., 2012). NMR-based metabolomics are finding use in plant science in discovery-oriented natural products chemistry (Kim et al., 2010a). Activity guided fractionation, a common approach in natural products research, fails when several metabolites act synergistically. A metabolomics approach has proved to be very efficient in detecting synergistic compounds. Through the statistical analysis of NMR spectra of complex mixtures of metabolites, unique spectral features can be identified and correlated to a phenotype or biological property of interest (Larive et al., 2015), FIGURE 2.10.

FIGURE 2.10 | Scheme of NMR-based metabolomics used to identify metabolites in complex mixtures and correlate them to a phenotype or biological property of interest.

Multivariate data analysis (MVDA) methods aim to differentiate between classes in highly complex data sets, despite within class variability.

Data Analysis NMR-measurement

Phenotype 1 Phenotype 2

PC 1 PC 2

Phenotype 1 Phenotype 2

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52

Also, multidimensional NMR experiments spectra [such as 2D-NMR technique: J-resolved, ¹H-¹H correlation spectroscopy (COSY), ¹H-¹³C heteronuclear multiple bond correlation (HMBC), and ¹H-¹³C heteronuclear single quantum coherence (HSQC)] can aid in the process of assigning resonances, despite this strategy can be time-consuming (Kim et al., 2010a).

When the identity of the metabolites in a sample is known (or suspected), resonance assignments can be facilitated using libraries or databases. Public and commercial databases, such as HMDB, LipidMaps and Metlin (Bartel et al., 2013) now contain experimental 1D ¹H, ¹³C and 2D ¹H-¹³C spectra and extracted spectral parameters for over a thousand compounds and theoretical data for thousands more (Ellinger et al., 2013).

Multivariate Data Analysis (MVDA) in Metabolomics

Usually, scientific phenomena cannot be interpreted by a single variable but by multi ones. To handle the obtained multivariates, specific methods are required for data reduction, multicomponent statistics and prediction (Berrueta et al., 2007). A characteristic of metabolomics is the large amount of data generated and an important part of any metabolomics study is the analysis of these data using multivariate statistics (Brennan, 2013).

One approach to find meaning in metabolomics datasets involves MVDA methods that seek to capture not only changes of single metabolites between different groups, but also to utilize the dependency structures between the individual molecules. MVDA can be performed by unsupervised or supervised methods.

The unsupervised methods seek discriminating factors between the independent variables with the aim to obtain a graphical representation as the result of maximization of variances. For example, principal component analysis (PCA), is an unsupervised linear mixture mode. PCA is arguably the most widely used multivariate analysis method for metabolic fingerprinting and in chemometrics in general. Principal component analysis is often used as a starting point for data analysis, especially in a hypothesis free, exploratory experimental setup and attempts to identify inherent grouping of samples as a result of the similarity of the metabolic composition by a smaller number of mutually decorrelated principal components (PCs) (Bartel et al., 2013;

Brennan, 2013). So, principal component regression analyzes X in order to obtain components which can explain X in the best way.

Supervised methods find the best fitting relationship between independent and dependent variables. Examples of supervised techniques is partial least-squares (PLS) and orthogonal projection to latent structures (OPLS) modeling (Brennan, 2013; Worley and Powers, 2013). PLS is a model for relating two data matrices of X and Y by a multivariate linear model. PLS regression

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finds components of X which can predict Y in the best way. OPLS method is the improved form of PLS was first presented in 2002 and removes X changes that have no correlation with Y. Here the S-plot is proposed as a tool for visualization and interpretation of OPLS helping to identify statistically significant metabolites, based both on contributions to the model and their reliability (Sugimoto et al., 2012).

3. M ATERIALS AND M ETHODS

n this section, a description of all the biological and chemical materials used in this study is made, along with important observations necessary for their preparation. A protocol and a set of parameters adjusted for the identification of RMAs and other types of antibiotic adjuvants are defined. All the methods used in this thesis are described, highlighting crucial and important steps, appropriate conditions and a number of requirements that must be considered and fulfilled. A detailed chronological and methodological description of the project is given.

3

C ^hapter

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