1 Introduction
Systems biology, acquiring a holistic perspective to explain the
complexity of a biological system as a whole, is having and will
have an ever-greater impact on the current health care. We are
shifting from diagnosis and treatment of diseases by symptoms to
precision medicine in which each patient is treated taking into
account environmental factors, lifestyle habits, genetic information,
and molecular phenotype [1, 2].
One of the most challenging goals in the precision medicine era
is to develop an experimental approach to establish the phenotypic
properties of an individual in an objective and repeatable way as well
as the phenotypic changes related to genetic and environmental
factors [3].In this effort, metabolomics, i.e., the study of all low
molecular weight (~50–1500 Da) molecules or metabolites within
biological samples, plays an important role. By providing a compre-
hensive biochemical fingerprint of a biological system at the cell,
tissue, or organism level, metabolomics offers a systemic approach
to the study of various diseases without first having to identify
markers of the disease [4].Consequently, metabolomics can help
clinicians in the screening, diagnosis, treatment, and monitoring of
many diseases.
The main complementary high-throughput platforms for
metabolomics are Mass Spectrometry and Nuclear Magnetic Reso-
nance (NMR). NMR gives a direct fingerprint of the system, thus
providing a unified picture of whole metabolome across the identi-
fication of all major metabolite classes simultaneously. The
subsequent use of statistical and mathematical tools plays a key
role in extracting meaning from this “big data.” The metabolic
descriptors so identified become the coordinates of a new system
of reference represented by metabolomic maps on which patients
and their response to therapy are located.
NMR-based metabolomics has already been applied in many
disease studies [5].
Moreover, metabolomic analysis showed the potential to pre-
dict the prognosis or the response to treatments from baseline
metabolic profiles [6, 7], highlighting the potential of metabolo-
mics analysis in patient stratification and personalized medicine.
We propose here as a case study the analysis of fecal samples
from young patients with fibrosis cystic (CF) and healthy children
(controls) to characterize the metabolic impact of variations of the
gut microbiota. In fact, cystic fibrosis is a lethal hereditary disorder
involving respiratory infections, chronic inflammation, repeated
antibiotic treatments and hence influencing the gut microbiota
profiles.328 Luca Casadei et al.