**3. Metabolomics**

according to their relation m/z. There are also different types of mass analyzer, such as quadrupole (Q), time of flight (TOF), ion trap (IT), Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap. The ion stream reaches to the detector and it is transformed into an electrical signal. These signals are then integrated by a computer system which generates a

Mass

m/z

There are two different ways to identify the molecules included in a complex mix using MS: peptide mass fingerprinting (PMF) and tandem mass spectrometry (MS/MS or MS2). Briefly, identification based on PMF identifies proteins according to the peptides generated after digestion with specific endoproteases, usually trypsine. Experimentally obtained peptide masses are compared to theoretical peptide masses of proteins stored in databases through mass search engines such as MASCOT (Matrix Science Ltd.) [45], MS-Fit [46] or Profound [47, 48]. These samples usually come from gel bands, 2-DE spots or LC fractions with low com‐ plexity and are analyzed through MALDI-TOF or ESI-TOF [48, 49]. In the case of MS/MS, two mass analyzers are used in tandem. The first one allows the measurement of the m/z values of the peptides and the second one, after the fragmentation of some peptides, allows the measurement of the m/z values of these fragments. Thus, a partial or even total sequence of the peptide can be obtained. Most usually used mass spectrometer for this purpose are MALDI-TOF-TOF (for low complex mixtures) and ESI coupled to different analyzers (Q3, Q-TOF, IT,

MALDI imaging mass spectrometry (IMS) is a powerful tool for investigating the distribution of proteins and small molecules present in thin tissue sections. This technique generates molecular profiles and two-dimensional ion density maps of peptide and protein signals

analyzer Detector

Computer system

mass spectrum which represents the abundance of the different generated ions.

Ionization source

Relative

**Figure 4.** Different elements of a mass spectrometer

Vacuum

abundance

Spectrum

**2.4. Tissue sections analysis: MALDI imaging mass spectrometry**

Sample

158 Calcific Aortic Valve Disease

Q-Q-LIT, FT y Orbitrap).

Metabolomics is the study of the set of final products and by-products of many metabolic pathways, called metabolites, which exist in humans and other living systems [8]. In order to perform a descriptive analysis of healthy valves, an untargeted approach, not focused on a specific group of metabolites, is the most recommended methodology. This method, also called metabolomic fingerprinting, permits to detect the largest number of metabolites optimizing different experimental conditions such as sample preparation and chromatographic and MS parameters. This approach usually compromises the sensitivity and specificity for identifica‐ tion of individual metabolites [59, 60]. Additionally, metabolomic fingerprinting involves less up-front method development when compared with targeted approaches but requires an exhaustive analysis due to the large number of identified metabolites. As well as in proteomics analyses, there are three main steps when performing a metabolomic study: [1] sample preparation; [2] metabolite detection and [3] data analysis.

#### **3.1. Sample preparation**

Optimization of sample-preparation protocol is extremely important in metabolomic studies because it affects both metabolite profile and quality data, leading to possible erroneous conclusions [61, 62]. An ideal sample-preparation method for metabolomic fingerprinting should have the following characteristics [1] non-selective to ensure adequate metabolite coverage; [2] simple and fast, with the minimum number of steps possible to minimize metabolite loss and/or degradation of the sample and enable high-throughput; [3] reproduci‐ ble and [4] incorporate a metabolism-quenching step (low temperatures, addition of acid, or fast heating) to represent true metabolome composition at the time of sampling (Figure 5) [63]. For tissue metabolomics, the protocol includes a previous step of manual homogenization at low temperatures follow by a quenching step in liquid nitrogen [64]. Although the tissue disruption technique can altered the precision and metabolite coverage, the selection of the extraction solvent have a stronger effect on the number of extracted metabolites [65]. Several aqueous and organic solvents can be used, such us methanol [66, 67], methanol/water [68], isopropanol/acetonitrile/water [69], acetonitrile/methanol/water [70], acidic acetonitrile/water [71] or methanol/acetonitrile/acetone [72] between others. However, there is no an established protocol for aortic valve so it is important the development of an adequate method before metabolic fingerprinting.

determining the structure of unknown metabolites. Therefore, each platform has inherent advantages and disadvantages for the metabolomic analysis of different compounds and only through their combined use the best understanding of the physiopathology of AV will be

Proteomics and Metabolomics in Aortic Stenosis: Studying Healthy Valves for a Better Understanding of the Disease

NMR Sensitivity

NMR is a spectroscopic analysis technique that exploits the specific magnetic spin or resonance frequency of the protons within atomic nuclei of specific molecules. When nuclei in a magnetic field are exposed to a radiofrequency pulse their protons temporarily move to a higher energy state, and then release a characteristic radiowave when they return to their normal energy state [75]. The resulting NMR spectrum is a collection of characteristic peaks and intensities of each compound that allow its identification [76]. NMR requires minimal sample preparation, it is a nondestructive and very reproducible technique and provides structural information of metabolites. These particular advantages confers NMR superior ability for the identification of unknown metabolites and constitutes a valuable approach for identification of unknown metabolites [77]. However, NMR is limited in terms of sensitivity and spectral resolution so it is not a good technology to identify metabolites that are found in low concentration [78].

NMR also allows the analyses of intact tissue using a technique called high-resolution magic angle spinning (HRMAS) NMR. The sample is spun at high speed about an axis at an angle of 54°44′(the so-called "magic angle") [79]. This rapid spinning at this precise angle has the effect of reducing dipolar coupling effects and narrowing of the broad lines found in this tissue. HRMAS-NMR has been applied successfully to analyze different intact cells and tissues

As well as in proteomic studies, MS uses m/z value to identify metabolites. When coupled with chromatographic methods, MS can analyze a wide number of metabolites with enhanced

[80-85], so it seems to be a powerful tool for the analyses of aortic valve tissue.

Spectral resolution

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Limited mass range

LC-MS No comprehensive spectral libraries

CG-MS Derivatization required

**Advantages PLATFORM Disadvantages**

**Table 1.** Comparison of the most common analytical methods used for metabolomics

achieved (Table 1).

Minimal sample preparation

No derivatization required Variety of metabolites

Comprehensive spectral libraries

*3.2.2. Mass spectrometry*

*3.2.1. Nuclear magnetic resonance*

Non-destructive Reproducible Structural information

Sensitivity Robust Reproducible

**Figure 5.** Protocol of tissue preparation for metabolomic analyses.

#### **3.2. Metabolite detection**

There is a great variability of metabolites in terms of polarity, solubility, and volatility with a wide dynamic range of concentration in biological samples. Therefore it is necessary to combine different chromatographic platforms for covering the largest range of metabolite possible. The two main platforms used in metabolomics analysis are nuclear magnetic resonance (NMR) and MS, usually coupled with a separation method for metabolites (LC or gas chromatography (GC)-MS) [73, 74]. The platform of choice will depend on the physico‐ chemical characteristics of the metabolites of interest. As it is explained below, CG-MS is a high-quality technique for analyzing hydrophobic compounds while LC-MS permits to determine a larger number of metabolites. Finally, NMR shows superior capability for determining the structure of unknown metabolites. Therefore, each platform has inherent advantages and disadvantages for the metabolomic analysis of different compounds and only through their combined use the best understanding of the physiopathology of AV will be achieved (Table 1).


**Table 1.** Comparison of the most common analytical methods used for metabolomics

#### *3.2.1. Nuclear magnetic resonance*

fast heating) to represent true metabolome composition at the time of sampling (Figure 5) [63]. For tissue metabolomics, the protocol includes a previous step of manual homogenization at low temperatures follow by a quenching step in liquid nitrogen [64]. Although the tissue disruption technique can altered the precision and metabolite coverage, the selection of the extraction solvent have a stronger effect on the number of extracted metabolites [65]. Several aqueous and organic solvents can be used, such us methanol [66, 67], methanol/water [68], isopropanol/acetonitrile/water [69], acetonitrile/methanol/water [70], acidic acetonitrile/water [71] or methanol/acetonitrile/acetone [72] between others. However, there is no an established protocol for aortic valve so it is important the development of an adequate method before

Supernatant

There is a great variability of metabolites in terms of polarity, solubility, and volatility with a wide dynamic range of concentration in biological samples. Therefore it is necessary to combine different chromatographic platforms for covering the largest range of metabolite possible. The two main platforms used in metabolomics analysis are nuclear magnetic resonance (NMR) and MS, usually coupled with a separation method for metabolites (LC or gas chromatography (GC)-MS) [73, 74]. The platform of choice will depend on the physico‐ chemical characteristics of the metabolites of interest. As it is explained below, CG-MS is a high-quality technique for analyzing hydrophobic compounds while LC-MS permits to determine a larger number of metabolites. Finally, NMR shows superior capability for

**METABOLOMIC ANALYSIS**

**Ideal Sample Preparation Method**

Non-selective Simple and fast Reproducible Metabolism-quenching step

Extraction solvent

**Figure 5.** Protocol of tissue preparation for metabolomic analyses.

**3.2. Metabolite detection**

Quenching: Liquid nitrogen

metabolic fingerprinting.

160 Calcific Aortic Valve Disease

NMR is a spectroscopic analysis technique that exploits the specific magnetic spin or resonance frequency of the protons within atomic nuclei of specific molecules. When nuclei in a magnetic field are exposed to a radiofrequency pulse their protons temporarily move to a higher energy state, and then release a characteristic radiowave when they return to their normal energy state [75]. The resulting NMR spectrum is a collection of characteristic peaks and intensities of each compound that allow its identification [76]. NMR requires minimal sample preparation, it is a nondestructive and very reproducible technique and provides structural information of metabolites. These particular advantages confers NMR superior ability for the identification of unknown metabolites and constitutes a valuable approach for identification of unknown metabolites [77]. However, NMR is limited in terms of sensitivity and spectral resolution so it is not a good technology to identify metabolites that are found in low concentration [78].

NMR also allows the analyses of intact tissue using a technique called high-resolution magic angle spinning (HRMAS) NMR. The sample is spun at high speed about an axis at an angle of 54°44′(the so-called "magic angle") [79]. This rapid spinning at this precise angle has the effect of reducing dipolar coupling effects and narrowing of the broad lines found in this tissue. HRMAS-NMR has been applied successfully to analyze different intact cells and tissues [80-85], so it seems to be a powerful tool for the analyses of aortic valve tissue.

#### *3.2.2. Mass spectrometry*

As well as in proteomic studies, MS uses m/z value to identify metabolites. When coupled with chromatographic methods, MS can analyze a wide number of metabolites with enhanced sensitivity. The two main strategies for metabolite separation are LC (for nonvolatile com‐ pounds in solution) or GC (for volatile samples or when the expected compounds can be easily made volatile by derivatization). LC-MS using ESI as ionization source is the most currently used platform for metabolomic studies since it permits the analysis of a wider variety of metabolites than GC-MS [86, 87]. Also, derivatization step prior the analysis is no necessary reducing losses of compounds during the sample preparation. Single quadrupole, ion trap and TOF analyzers are the most commonly used mass analyzers. GC-MS is a first-rate choice for the analysis of volatile compounds such as fatty acids and organic acids [88]. In most cases, it is necessary a prior step for chemical derivatization of non-volatile and thermally stable metabolites. The two more used derivatization reagents include BSTFA (N, O-Bis (trimethyl‐ silyl) trifluoroacetamide) and MSTFA (N-Methyl-N-(trimethylsilyl) trifluoroacetamide) [89-91]. Advantages of GC include higher resolution and more robust and reproducible retention times than LC, besides the existence of mass spectral libraries, which facilitates the identification process.

enhanced by the using of different methodologies and equipments, usually impedes obtaining reliable and reproducible results when comparing patients and healthy controls. The study of a large number of healthy valves using a standardized protocol may provide useful informa‐ tion about the heterogeneity of its profiles. Thus, different profiles could be assigned to different states such as sex, age or risk factors. This way, the creation of a protein atlas unmasking the expression and localization of proteins coupled to metabolomic results would function as a global knowledgebase with valuable information about normal cellular function [97, 98]. All these information can be storage in conventional web-based databases, in order to obtain a reference material to be used in further studies, when studying different valve diseases, helping to deepen the understanding of the beginning and development of the disease. It would also be possible to find some variation involving metabolic predisposition to develop the disease in the future. This will exponentially increase the possibilities to discover potential therapeutic targets and will open the door to develop a personalized medicine in this

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The major challenge of generating a complete database of AV tissue is obtaining enough biological material to be described, since it is not an easily accessible specimen. It is necessary a complex coordination of basic research activities, facilities and infrastructures, as well as the creation of an integrated and multidisciplinary environment with the participation of several different specialists in cardiovascular diseases, i.e. basic researchers, cardiologists, surgeons, pathologists, epidemiologists, patients, patient advocacy groups, funding agencies and industrial partners. Issues related to sample collection, handling and storage, standardization of protocols, common references, number of patients, availability of normal controls, access to bio-banks, tissue arrays, clinical information, follow-up clinical data, computational and statistical analysis, as well as ethical considerations are critical, and must be carefully consid‐ ered and dealt with from the beginning [99]. With such a big collaborating work, there will be

No matter how much ambitious and complete the descriptive study is, it is essential integration of the results through a system biology approach. This consists of placing proteins and molecules from experimental analysis in the context of a network of biological interactions, such as gene–gene, gene–protein or protein–protein interactions, followed by different 'guiltby-association' analyses [100]. Usually, these networks are deduced from previously published interactions or from computational prediction models. Different tools exist to perform these analyses, most of them based on Cytoscape Web, a freely available network visualization tool for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework [101-103]. Using an integrative approach, we can obtain a more holistic picture of the molecular mechanisms that occurs in

The study of healthy valves through proteomic and metabolomic approaches and the subse‐ quent integration of data can provide molecular level information of the metabolic pathways

a more effective translation of basic discoveries into clinical applications.

disease.

normal aortic valves.

**5. Conclusion**

In the course of the study of the physiopathology of AS the better comprehension of implicated and altered biochemical pathways will be acquired through the metabolomic study of biological samples, specially plasma and tissue. For this purpose NMR and MS represent the two most valuable techniques for metabolite identification thus the information that both techniques provide will suppose a cornerstone for the study of this disease.

#### **3.3. Identification and data analysis**

The data processing challenges in metabolomics are quite unique and often require specialized (and/or expensive) data analysis software and a complete knowledge of cheminformatics, bioinformatics and statistics for a correct interpretation of data. Ideally, data analysis softwares must be able to remove noise from the spectra, properly identify which metabolite generates every chromatographic peak and make a correct alignment of the peaks corresponding to the same compound in several successive samples [92, 93].

There are several commercial softwares available free such as MSFacts, MetAlign or Metab‐ oAnalyst which automatically import, reformat, align, correct the baseline, and export large chromatographic data sets to allow more rapid visualization of metabolomics data [94, 95]*.* However, most companies usually generate data that can be only read with own softwares which use mass spectral libraries for peak detection, identification and integration [96].
