Human Endogenous Natural Products
331
applications of targeted and untargeted analyses of human endogenous metabolite
identification.
5.1 Mass Spectrometry
Mass spectrometry (MS) plays a major role in metabolite profiling due to its high
sensitivity. The key objective is to enhance sample throughput and facilitate comprehensive metabolome identification. One approach is to perform rapid LC–MS runs
to increase sample throughput, but this comes at the expense of sensitivity and
overall metabolome coverage. The second option is to obtain a more comprehensive metabolome coverage through long analytical runs, for example, by making the
chromatographic peaks narrower to accommodate more peaks within a defined time
window. Narrower chromatographic peaks can be obtained by (1) increased peak
capacity; (2) lower ionization suppression; and (3) enhanced signal-to-noise ratio,
thus leading to increased sensitivity [30]. The current state of the art in LC–MSbased metabolic profiling is to use reversed-phase HILIC (hydrophilic interaction
chromatography), coupled to high-resolution mass spectrometry.
A key strategy for metabolomic profiling is to employ only a few methodologies in
order to cover as wide a range of metabolite classes as possible. For each compound
class, the correct analytical methodologies need to be selected and developed. The
group of Wilson has summarized the following required LC–MS conditions for a
specified compound class: (1) RPLC–MS for medium-polarity analytes; (2) HILIC–
MS for polar metabolites; (3) targeted HILIC–MS/MS or CE–MS/MS for targeted
primary metabolites; (4) GC/MS or RPLC/MS for lipidomic analysis, and (5) GC–
MS for volatile components [31].
There is now a number of applications emerging employing LC–MS in
metabolomics. Biomarker discovery often starts with the study of animal models,
for example, obesity, cancer hepatopathy, and cancer nephrotoxicity [31]. Metabolic
profiling has been used successfully to characterize metabolic pathways disrupted in
mouse models of human diseases, including cardiac disease and type 2 diabetes. The
implementation of metabolomics as a screening procedure in large-scale mutagenesis
programs has been successful in identifying mutants that possess clinically related
phenotypes. Using this approach, models of various human metabolic diseases have
been identified, such as a model for branched-chain ketoaciduria, and a model of lipotoxic cardiomyopathy. A substantial amount of work has focused on understanding
the role of lipotoxicity and insulin resistance in humans. Liquid chromatographyMS lipidomics has been used to investigate inflammation in the adipose tissue of
obese women, suggesting that the content of ceramides and long-chain fatty acids
in triglycerides in the tissues correlates with the degree of fatty liver by comparing
women with a similar body mass index but with different degrees of hepatic steatosis.
Additionally, LC–MS-based metabolite profiling has been used to identify serum
metabolic biomarkers of heart failure, where pseudouridine and 2-oxoglutaric acid
were found to be potential markers that are being assessed in further targeted work
331
applications of targeted and untargeted analyses of human endogenous metabolite
identification.
5.1 Mass Spectrometry
Mass spectrometry (MS) plays a major role in metabolite profiling due to its high
sensitivity. The key objective is to enhance sample throughput and facilitate comprehensive metabolome identification. One approach is to perform rapid LC–MS runs
to increase sample throughput, but this comes at the expense of sensitivity and
overall metabolome coverage. The second option is to obtain a more comprehensive metabolome coverage through long analytical runs, for example, by making the
chromatographic peaks narrower to accommodate more peaks within a defined time
window. Narrower chromatographic peaks can be obtained by (1) increased peak
capacity; (2) lower ionization suppression; and (3) enhanced signal-to-noise ratio,
thus leading to increased sensitivity [30]. The current state of the art in LC–MSbased metabolic profiling is to use reversed-phase HILIC (hydrophilic interaction
chromatography), coupled to high-resolution mass spectrometry.
A key strategy for metabolomic profiling is to employ only a few methodologies in
order to cover as wide a range of metabolite classes as possible. For each compound
class, the correct analytical methodologies need to be selected and developed. The
group of Wilson has summarized the following required LC–MS conditions for a
specified compound class: (1) RPLC–MS for medium-polarity analytes; (2) HILIC–
MS for polar metabolites; (3) targeted HILIC–MS/MS or CE–MS/MS for targeted
primary metabolites; (4) GC/MS or RPLC/MS for lipidomic analysis, and (5) GC–
MS for volatile components [31].
There is now a number of applications emerging employing LC–MS in
metabolomics. Biomarker discovery often starts with the study of animal models,
for example, obesity, cancer hepatopathy, and cancer nephrotoxicity [31]. Metabolic
profiling has been used successfully to characterize metabolic pathways disrupted in
mouse models of human diseases, including cardiac disease and type 2 diabetes. The
implementation of metabolomics as a screening procedure in large-scale mutagenesis
programs has been successful in identifying mutants that possess clinically related
phenotypes. Using this approach, models of various human metabolic diseases have
been identified, such as a model for branched-chain ketoaciduria, and a model of lipotoxic cardiomyopathy. A substantial amount of work has focused on understanding
the role of lipotoxicity and insulin resistance in humans. Liquid chromatographyMS lipidomics has been used to investigate inflammation in the adipose tissue of
obese women, suggesting that the content of ceramides and long-chain fatty acids
in triglycerides in the tissues correlates with the degree of fatty liver by comparing
women with a similar body mass index but with different degrees of hepatic steatosis.
Additionally, LC–MS-based metabolite profiling has been used to identify serum
metabolic biomarkers of heart failure, where pseudouridine and 2-oxoglutaric acid
were found to be potential markers that are being assessed in further targeted work