Article Type : Research Article
Authors : Marcos Aurélio Gomes da Silva
Keywords : At present, humanity is experiencing a Cultural Revolution characterized by technological advances and the intense flow of information
At present, humanity is experiencing a Cultural Revolution
characterized by technological advances and the intense flow of information,
comparing impact on the emergence of agriculture approximately 10 thousand years
and the Industrial Revolution started in the 18th century. Some innovations
technological developments in the last three decades have led to a new form to
think about biological systems and, mainly, to research them. At biological
area, the starting point of this revolution was called the genomic era being
characterized by the development, standardization and optimization of genetic
engineering techniques.
At present, humanity is experiencing a Cultural Revolution
characterized by technological advances and the intense flow of information,
comparing impact on the emergence of agriculture approximately 10 thousand years
and the Industrial Revolution started in the 18th century. Some innovations
technological developments in the last three decades have led to a new form to
think about biological systems and, mainly, to research them. At biological
area, the starting point of this revolution was called the genomic era being
characterized by the development, standardization and optimization of genetic
engineering techniques. Genomic assays have evolved rapidly and the immense
volume of genetic data made it possible to deepen analysis of temporal and spatial
variations in the accumulation of trans-proteins, proteins and metabolites.
This new phase of the current evolution in the area biological was called
post-genomic era, or functional genomics. The term metabolome was coined in
1998, calling the analysis of relative concentration of metabolites resulting
from changes in the pattern of gene expression. The metabolomics assumes an
approach holistic and interactive analysis, according to which cellular
metabolism is effectively understood as a complex network of highly ordered
reactions interconnected, so that even small changes, such as decrease in the
concentration or activity of an enzyme, can cause simultaneous changes in the
concentration of hundreds of metabolites. Of this Thus, the metabolome provides
a direct link between the genome, the transcryptoma and proteome, which may
reveal which factors directly influence a given biological function.
The originality of the use of the term metabolome
proposed by Oliver and colleagues (1998) is not due to innovations in the
analytical techniques used, but rather the establishment of the joint use of
that analysis tool as a complement to the other “omic” areas. From then on, the
term started to design a very complex analysis strategy, which refers to the
qualitative and quantitative survey of the metabolites present in an organism,
or in a given component of it (tissues or cells, for example example), called
partial metabolome in the latter context. Changes induced genetically,
epigenetically or by influence of the environment are ultimately manifested
through changes in the composition and concentration of metabolites. Thus,
comparing the profiles generated in tissues that differ genetically or in their
state epigenetic functional genomic differences can be inferred. To date, the
use of a single analytical technique does not allow obtaining the complete
metabolomic picture of a given sample, being Adoption of an interdisciplinary
set of approaches that Biology, analytical chemistry, organic chemistry,
chemometrics and IT12. The analytical techniques commonly used in metabolomics.
Are liquid (CL) or liquid-gas (CG) chromatographies, whether or not mass
spectrometry (EM) and infrared spectroscopy (spectroscopy) medium infrared
vibrational troscopy, or Fourier-transform infrared spectroscopy - FTIR; and
near-infrared vibrational spectroscopy, or near infrared spectroscopy (NIR) and
Raman, as well as spectroscopy- hydrogen nuclear magnetic resonance (1H-NMR),
carbon (13C-NMR) and nitrogen (15N-NMR), in their experimental approaches unit
(1D) and two-dimensional (2D) and also quantitative, that is, spectroscopy
quantitative nuclear magnetic resonance imaging – qRMN.
First method applied
to spectral data of 1D-NMR was Principal Component Analysis (ACP). It is an unsupervised
method, that is, that does not require the prior definition of Groups for the
samples, being used essentially as a method of Dimensionality reduction. ACP,
created in 1901 by Karl Pearson80, Consists of an algebraic procedure that
converts the original variables (which are typically correlated) in a set of
non-cor-Related (linearly), which are called main components (CPs) or latent
variables. Metabolomics has occupied an important place in bioanalysis and Biomedical
research in recent years, positioning itself as the most of “omics”
technologies. Na important set of methods Emerged, and new analytical
approaches are made available at intervals of which often demonstrate the
potential for development and application that area. This scenario must be
expanded in future moments and quickly, due to the standardization and
validation of collection methods, Sample processing and analysis, in parallel
to the continuous development and improvement of hardware and software made
available for metabolic studies.