A Future for Plasma Metabolomics in Cardiovascular Disease Assessment
Curator: Larry H Bernstein, MD, FCAP
Plasma metabolomics reveals a potential panel of biomarkers for early diagnosis
in acute coronary syndrome
CM. Laborde, L Mourino-Alvarez, M Posada-Ayala,
G Alvarez-Llamas, MG Serranillos-Reus, et al.
Metabolomics – manuscript draft
In this study, analyses of peripheral plasma from Non-ST Segment Elevation
Acute Coronary Syndrome patients and healthy controls by gas chromatography-
mass spectrometry permitted the identification of 15 metabolites with statistical
differences (p<0.05) between experimental groups.
In our study, 6 amino acids were found decreased in NSTEACS patients when
compared with healthy control group suggesting either a decrease in anabolic
activity of these metabolites or an increase in the catabolic pathways. Of both
possibilities, the increased catabolism of the amino acids can be explained
considering simultaneously the capacity of glycogenic and ketogenic amino
acids along with the gradual hypoxic condition to which cardiac muscle cells
have been exposed.
Additionally, validation by gas chromatography-mass spectrometry and liquid
chromatography-mass spectrometry permitted us to identify a potential panel
of biomarkers formed by 5-OH tryptophan, 2-OH-butyric acid and 3-OH-butyric
acid. Oxidative stress conditions dramatically increase the rate of hepatic
synthesis of glutathione. It is synthesized from the amino acids cysteine, glutamic
acid and glycine. Under these conditions of metabolic stress, the supply of cysteine
for glutathione synthesis become limiting and homocysteine is used to form
cystathionine, which is cleaved to cysteine and 2-OH-butyric acid. Thus elevated
plasma levels of 2-OH-butyric acid can be a good biomarker of cellular oxidative
stress for the early diagnosis of ACS. Another altered metabolite of similar
structure was 3-OH-butyric acid, a ketone body together with the acetoacetate,
and acetone. Elevated levels of ketone bodies in blood and urine mainly occur
in diabetic ketoacidosis. Type 1 diabetes mellitus (DMI) patients have decreased
levels of insulin in the blood that prevent glucose enter cells so these cells use
the catabolism of fats as energy source that produce ketones as final products.
This panel of biomarkers reflects the oxidative stress and the hypoxic state that
disrupts the myocardial cells and consequently constitutes a metabolomic
signature that could be used for early diagnosis of acute coronary syndrome.
We hypothesize that the hypoxia situation comes to “mimic” the physiological
situation that occurs in DMI. In this case, the low energy yield of glucose
metabolism “forces” these cells to use fat as energy source (through catabolism
independent of aerobic/anaerobic conditions) occurring ketones as final
products. In our experiment, the 3-OH-butyric acid was strongly elevated in
NSTEACS patients.
Current Methods Used in the Protein Carbonyl Assay
Nicoleta Carmen Purdel, Denisa Margina and Mihaela Ilie.
Ann Res & Rev in Biol 2014; 4(12): 2015-2026.
http://www.sciencedomain.org/download.php?f=Purdel4122013ARRB8763-1
The attack of reactive oxygen species on proteins and theformation of
protein carbonyls were investigated only in the recent years. Taking into
account that protein carbonyls may play an important role in the early
diagnosis of pathologies associated with reactive oxygen species
overproduction, a robust and reliable method to quantify the protein
carbonyls in complex biological samples is also required. Oxidative
stress represents the aggression produced at the molecular level by
the imbalance between pro-oxidant and antioxidant agents, in favor of
pro-oxidants, with severe functional consequences in all organs and
tissues. An overproduction of ROS results in oxidative damages
especially to proteins (the main target of ROS), as well as in lipids,or
DNA. Glycation and oxidative stress are closely linked, and both
phenomena are referred to as ‘‘glycoxidation’’. All steps of glycoxidation
generate oxygen-free radical production, some of them being common
with lipidic peroxidation pathways.
The initial glycation reaction is followed by a cascade of chemical
reactions resulting in the formation of intermediate products (Schiff base,
Amadori and Maillard products) and finally to a variety of derivatives
named advanced glycation end products (AGEs). In hyperglycemic
environments and in natural aging, AGEs are generated in increased
concentrations; their levels can be evaluated in plasma due to the fact
that they are fluorescent compounds. Specific biomarkers of oxidative
stress are currently investigated in order to evaluate the oxidative status
of a biological system and/or its regenerative power. Generaly, malondi-
aldehyde, 4-hydroxy-nonenal (known together as thiobarbituric acid
reactive substances – TBARS), 2-propenal and F2-isoprostanes are
investigated as markers of lipid peroxidation, while the measurement
of protein thiols, as well as S-glutathionylated protein are assessed
as markers of oxidative damage of proteins. In most cases, the
oxidative damage of the DNA has 8-hydroxy-2l-deoxyguanosine
(8-OHdG) as a marker. The oxidative degradation of proteins plays an
important role in the early diagnosis of pathologies associated with
ROS overproduction. Oxidative modification of the protein structure
may take a variety of forms, including the nitration of tyrosine residues,
carbonylation, oxidation of methionine, or thiol groups, etc.
The carbonylation of protein represents the introduction of carbonyl
groups (aldehyde or ketone) in the protein structure, through several
mechanisms: by direct oxidation of the residues of lysine, arginine,
proline and threonine residues from the protein chain, by interaction
with lipid peroxidation products with aldehyde groups (such as 4-
hydroxy-2-nonenal, malondialdehyde, 2-propenal), or by the
interaction with the compounds with the carbonyl groups resulting
from the degradation of the lipid or glycoxidation. All of these
molecular changes occur under oxidative stress conditions.
There is a pattern of carbonylation, meaning that only certain
proteins can undergo this process and protein structure determines
the preferential sites of carbonylation. The most investigated
carbonyl derivates are represented by gamma-glutamic
semialdehyde (GGS) generated from the degradation of arginine
residue and α-aminoadipic semialdehyde (AAS) derived from lysine.
A number of studies have shown that the generation of protein
carbonyl groups is associated with normal cellular phenomena like
apoptosis, and cell differentiation and is dependent on age, species
and habits (eg. smoking) or severe conditions’ exposure (as
starvation or stress). The formation and accumulation of protein
carbonyls is increased in various human diseases, including –
diabetes and cardiovascular disease.
Recently, Nystrom [7] suggested that the carbonylation process
is associated with the physiological and not to the chronological
age of the organism and the carbonylation may be one of the causes
of aging and cell senescence; therefore it can be used as the marker
of these processes. Jha and Rizvi, [15] proposed the quantification of
protein carbonyls in the erythrocyte membrane as a biomarker of aging
PanelomiX: A threshold-based algorithm to create panels of
biomarkers
X Robin, N Turck, A Hainard, N Tiberti, F Lisacek.
T r a n s l a t i o n a l P r o t e o m i c s 2 0 1 3; 1: 57–64.
http://dx.doi.org/10.1016/j.trprot.2013.04.003
The computational toolbox we present here – PanelomiX – uses
the iterative combination of biomarkers and thresholds (ICBT) method.
This method combines biomarkers andclinical scores by selecting
thresholds that provide optimal classification performance. Tospeed
up the calculation for a large number of biomarkers, PanelomiX selects
a subset ofthresholds and parameters based on the random forest method.
The panels’ robustness and performance are analysed by cross-validation
(CV) and receiver operating characteristic(ROC) analysis.
Using 8 biomarkers, we compared this method against classic
combination procedures inthe determination of outcome for 113 patients
with an aneurysmal subarachnoid hemorrhage. The panel classified the
patients better than the best single biomarker (p < 0.005) and compared
favourably with other off-the-shelf classification methods.
In conclusion, the PanelomiX toolbox combines biomarkers and evaluates
the performance of panels to classify patients better than single markers
or other classifiers. The ICBT algorithm proved to be an efficient classifier,
the results of which can easily be interpreted.
Multiparametric diagnostics of cardiomyopathies by microRNA
signatures.
CS. Siegismund, M Rohde, U Kühl, D Lassner.
Microchim Acta 2014 Mar.
http://dx.doi.org:/10.1007/s00604-014-1249-y
MicroRNAs (miRNAs) represent a new group of stable biomarkers
that are detectable both in tissue and body fluids. Such miRNAs
may serve as cardiological biomarkers to characterize inflammatory
processes and to differentiate various forms of infection. The predictive
power of single miRNAs for diagnosis of complex diseases may be further
increased if several distinctly deregulated candidates are combined to
form a specific miRNA signature. Diagnostic systems that generate
disease related miRNA profiles are based on microarrays, bead-based
oligo sorbent assays, or on assays based on real-time polymerase
chain reactions and placed on microfluidic cards or nanowell plates.
Multiparametric diagnostic systems that can measure differentially
expressed miRNAs may become the diagnostic tool of the future due
to their predictive value with respect to clinical course, therapeutic
decisions, and therapy monitoring.
Nutritional lipidomics: Molecular metabolism, analytics, and
diagnostics
JT. Smilowitz, AM. Zivkovic, Yu-Jui Y Wan, SM. Watkins, et al.
Mol. Nutr. Food Res. 2013, 00, 1–17.
http://dx.doi.org:/10.1002/mnfr.201200808
The term lipidomics is quite new, first appearing in 2001. Its definition
is still being debated, from “the comprehensive analysis of all lipid
components in a biological sample” to “the full characterization of
lipid molecular species and their biological roles with respect to the
genes that encode proteins that regulate lipid metabolism”. In principle,
lipidomics is a field taking advantage of the innovations in the separation
sciences and MS together with bioinformatics to characterize the lipid
compositions of biological samples (biofluids, cells, tissues, organisms)
compositionally and quantitatively.
Biochemical pathways of lipid metabolism remain incomplete and the
tools to map lipid compositional data to pathways are still being assembled.
Biology itself is dauntingly complex and simply separating biological
structures remains a key challenge to lipidomics. Nonetheless, the
strategy of combining tandem analytical methods to perform the sensitive,
high-throughput, quantitative, and comprehensive analysis of lipid
metabolites of very large numbers of molecules is poised to drive
the field forward rapidly. Among the next steps for nutrition to understand
the changes in structures, compositions, and function of lipid biomolecules
in response to diet is to describe their distribution within discrete functional
compartments lipoproteins. Additionally, lipidomics must tackle the task
of assigning the functions of lipids as signaling molecules, nutrient sensors,
and intermediates of metabolic pathways.
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