International Journal Of Pulmonary & Respiratory Sciences
Introduction to Metabolomics
Metabolites are the end products of enzymatic
activity inside the cells. Being in general characterized by molecular
masses less than 1 kDa, they are represented by amino acids,
carbohydrates, lipids, hormones, nucleotides and other small molecules.
Due to the proximity of metabolites to a phenotype or disease, detecting
and quantifying changes in the concentration of metabolites may reveal
the range of biochemical effects induced by a disease condition or its
therapeutic intervention. This is exactly the task of Metabolomics, the
newest ‘omics’ science whose term was coined in analogy with those of
the previously developed Genomics and Proteomics. Metabolomics is not
only complementary to the other two approaches but, being able to
provide information that allows a better understanding of cellular
biology, it looks like the perfect tool suitable to integrate them. In
fact, although genomics provides good fingerprints of hereditary
information, it should be underlined that not all human diseases are
associated with genetic defects. On the other hand, up- or
down-regulation in protein(s) expression revealed by proteomics not
necessarily correlates with a perturbation in their biological activity.
Thus, given that the metabolome reflects changes that occur in the
transcriptome, genome, or proteome, it can provide an instantaneous
snapshot of cell physiology.
NMR Technology
Given the huge number, the chemical diversity and the
dynamic range of metabolite concentration, analyzing their entire range
would require the combination of different analytical methods.
Nevertheless, very often a single technique is able to provide a good
snapshot of the system under investigation. The most used methods
applied are currently gas or liquid chromatography (GC or LC) in
combination with mass spectrometry (MS) and nuclear
magnetic resonance (NMR) spectroscopy. The peculiarities and advantages
of NMR (it is rapid, quantitative, non-destructive and requires minimal
sample pre-treatment) makes often this method more attractive than the
others for providing a rapid and accurate metabolic picture of the
sample. Briefly, the biological sample under investigation is placed in a
strong magnetic field to align nuclei (e.g., 1H, 13C, 15N, 31P)
contained in the analytes. The interaction of a high power short
duration radio frequency pulse leads to the generation of small NMR
signals which are translated into peaks that are displayed across a
spectrum. In principle, by comparing the position of these signals with
reference data present in the literature, NMR resonances of common
metabolites can be identified. When dealing with overlapping of signals,
the use of a more sophisticated procedure (two-dimensional NMR
experiments) that improve resolution is required.
NMR applied to Pulmonary Disorders
The rapid expansion of NMR metabolomics in the field
of lung disorders resulted in the publication of a wealth of articles
focused on the identification of metabolites associated with different
diseases including chronic obstructive pulmonary disease, asthma, cystic
fibrosis, tuberculosis, sarcoidosis, invasive pulmonary aspergillosis,
pulmonary arterial hypertension, pulmonary langerhans cell
hystiocytosis, high altitude pulmonary edema, adult respiratory distress
syndrome, bronchiolitis obliterans syndrome, pulmonary emphysema
associated with α1-antitrypsin deficiency. In most cases the matrices
investigated were those that much better than others reflect the local
environment they came from, i.e. bronchoalveolar lavage fluid (BALf),
induced sputum, exhaled breath condensate (EBC), epithelial lining fluid
(ELF). It was observed that profiles
of patients with different pathological conditions shared largely
the same panel of metabolites. However, while appearing
similar, the patterns of peaks evidenced distinctive differences
in terms of presence/absence of some specific metabolites
and prompted investigators at focusing on their quantitative
variations. In general, from among the numerous metabolites
detected, those that allowed to discriminate patients from
healthy controls were identified as Krebs cycle intermediates,
mono- and disaccharides, nucleotides, phospholipid precursors,
amino acids, alcohols, ketones, short-chain fatty acids. The fact
that these molecules were “heterogeneous”, other than being a
source of confusion, was an incentive to the search of a rationale
for reasoning on their potential role in the onset of the disorder.
Although belonging to different chemical classes, these analytes
contained a piece of information that was unequivocally useful
to distinguish profiles of health from those of disease states.
In most cases multivariate statistical analyses (i.e. principal
component analysis and/or orthogonal partial least squares
discriminant analysis) were carried out to confirm that data
concerning discrimination between cohorts of subjects under
investigation were statistically significant. Application of
appropriate platforms to the lists of metabolites also allowed
interpretation of acquired data and consequent generation of
biochemical pathways aimed at defining their relationships.
These studies allowed pointing out a good number of pathways
that played a critical role in different lung disorders. These
included cellular energy metabolism (alteration in β-oxidation of
fatty acids, glycolysis, pentose phosphate pathway), the pyruvate
and the taurine/hypotaurine pathways. The fact that a number
of metabolites identified in these studies were common to a
variety of pulmonary disorders could mean that they were not
very specific to a given pathology. However, given the common
clinical traits among several lung disorders, this finding was not
surprising. Taken together, all NMR experimental data so far
generated evidenced/confirmed that some relevant pathways
shown to be involved in a lung disorder, most likely were
deregulated also in other cognate pulmonary pathologies.
Conclusions
Although the application of NMR to metabolomic studies of respiratory disorders is still in its infancy, the data so far published represent a significant contribution to the identification of biomarkers which may aid in the diagnosis and/or treatment of lung diseases. Metabolome is characterized by a peculiar ability to change very quickly over time. Thus, succeeding in identifying molecules that nobody expected to be there (and the metabolic pathway they are involved in) would be an important contribution which may open the door to clinical studies.
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