Biology Reference
In-Depth Information
CHAPTER
5
Hannes Link
*
, Joerg Martin Buescher
*
,{
, Uwe Sauer
*
,1
⁎
Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
{
Biotechnology Research and Information Network AG, Zwingenberg, Germany
1
Corresponding author. e-mail address: sauer@imsb.biol.ethz.ch
Targeted and quantitative
metabolomics in bacteria
1
INTRODUCTION
Metabolites are small non-polymer molecules with an atomic mass typically in the
range of 50-1000 Da. The 1136 unique metabolites in the latest genome-scale met-
abolic model of
Escherichia coli
(
Orth
et al.
, 2011
) represent only a lower estimate
for the number of metabolites that we can expect in a typical bacterium. In total, the
KEGG database includes 5757 metabolites and other compounds in the metabolic
pathways of different organisms (
Hattori
et al.
, 2003
). Experimentally, over 1500
distinct metabolite ions were detected in
E. coli
extracts (
Fuhrer
et al.
, 2011
).
Two different methodological aims can be addressed when investigating the
metabolomic complexity of biological systems. Untargeted metabolic profiling aims
to detect as many metabolites as possible in a sample and recent methods are able to
detect a broad spectrum of 400-1500 metabolites (
Madalinski
et al.
, 2008; Fuhrer
et al.
, 2011
). Targeted metabolomics aims at the reliable and sensitive quantification
of a pre-selected subset of metabolites, and mass spectrometric methods typically
quantify absolute concentrations of around 100 metabolites (
Bennett
et al.
, 2008
;
Buescher
et al.
, 2010). Here, we focus on targeted quantitative metabolomics in
microorganisms, where the combination of gas- or liquid chromatography separa-
tion, coupled to mass spectrometry detection, is the most widely used analytical
approach.
Data obtained with such quantitative methods reveal that the intracellular con-
centration of many metabolites is in the micro-molar concentration range and only
few occur at a concentration greater than 10 mM. Depending on the environmental
conditions, these dominating metabolites include glutamate, glutathione, fructose
1,6-bisphosphate and ATP that participate in many reactions (
Bennett
et al.
,
2009; Buescher
et al.
, 2012
). In various yeast strains, the total amino acid concen-
tration represented up to 90% of the detected metabolites (
Christen and Sauer, 2011
).
While amino acid concentrations remain rather stable, many low abundance metab-
olites exhibit a high degree of fluctuation. These fluctuations are a consequence of
