**5. Conclusion**

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

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

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

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].

The large number of genomes, including human, that have been mapped and the knowledge of the genetic code, have increased the interest in other –OMICS technologies, such as proteomics and metabolomics. Both proteomics and metabolomics differ from genomics in terms of complexity and dynamic variability. Proteome and metabolome are constantly changing according to the genome and the environment, therefore the ultimate phenotype of cell, organ, and organism is reflected in proteomic and metabolomic profiles. This variability,

techniques provide will suppose a cornerstone for the study of this disease.

identification process.

162 Calcific Aortic Valve Disease

**3.3. Identification and data analysis**

same compound in several successive samples [92, 93].

**4. Towards a global understanding of physiology**

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 that are more active in AV. The characterization of physiological proteins and metabolites in this tissue and the creation of a complete database with the results from the descriptive studies, may serve as a reference material for further studies. This would facilitate the searching for potential markers for early diagnosis of the disease, thus being able to predict which people may develop aortic stenosis in the future.

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

http://dx.doi.org/10.5772/55589

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