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EMBO Molecular Medicine
cross-journal focus
Frontiers in Metabolism
cross-journal focus
emboj.embopress.org | embor.embopress.org | embomolmed.embopress.org | msb.embopress.org
Maria [email protected] | T +49 6221 8891 410
Maria received her PhD from the University of Heidelberg, where she studied the role of nuclear membrane proteins in development and aging. During her post-doctoral work, she focused on the analysis of tissue-specific regulatory functions of Hox transcription factors using a combination of computational and genome-wide methods.
Thomas LembergerChief [email protected] | T +49 6221 8891 413
Thomas completed his PhD at the University of Lausanne, where he studied hormonal regulation of gene expression by nuclear receptors. He moved then to Heidelberg where his research focused on the regulation of transcription in the brain.
Roberto [email protected] | T +49 6221 8891 310
Roberto Buccione completed his PhD at the University of l’Aquila, Italy studying the process of oogenesis in mammals. After continuing these studies as a post-doctoral researcher at the Jackson Laboratory, Bar Harbor ME, USA, he joined the Mario Negri Sud research institute in S. Maria Imbaro, Italy, where he lead a research group focused on the cell biology of tumour cell invasion. He joined EMBO Molecular Medicine as a Scientific Editor in October 2012.
EMBO Molecular Medicine
Céline [email protected] | T +49 6221 8891 310
Céline Carret completed her PhD at the University of Montpellier, France, characterising host immunodominant antigens to fight babesiosis, a parasitic disease caused by a unicellular Apicomplexan parasite closely related to the malaria agent Plasmodium. She further developed her post-doctoral career on malaria working at the Wellcome Trust Sanger Institute in Cambridge, UK and Instituto de Medicina Molecular in Lisbon, Portugal. Céline joined EMBO Molecular Medicine as a Scientific Editor in March 2011.
EMBO Molecular Medicine
Stefanie DimmelerChief [email protected] | T +49 6221 8891 310Stefanie Dimmeler is Professor of Experimental Medicine and Director of the Institute of Cardiovascular Regeneration, Center for Molecular Medicine at the University of Frankfurt, Germany. Her group elucidates the basic mechanisms underlying cardiovascular disease and vessel growth with the aim to develop new cellular and pharmacological therapies for improving the treatment of cardiovascular disease. Her current ongoing research focuses on epigenetic mechanisms that control cardiovascular repair, specifically the function of histone modifying enzymes and microRNAs. She received several international prizes including the Leibniz Award 2005, the Award of the Jung Foundation 2007 and the FEBS award in 2006.
EMBO Molecular Medicine
EDITORS
Frontiers in Metabolism
Molecular Systems Biology
A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migrationMolecular Systems Biology (2014) 10:744Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E.DOI: 10.15252/msb.20134993
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CONTENTS
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Genetic regulation of mouse liver metabolite levelsMolecular Systems Biology (2014) 10:730Ghazalpour A, Bennett BJ, Shih D, Che N, Orozco L, Pan C, Hagopian R, He A, Kayne P, Yang WP, Kirchgessner T, Lusis AJ.DOI: 10.15252/msb.20135004
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continued overleaf
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CONTENTS
continued overleaf
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Metabolic crosstalk between the heart and liver impacts familial hypertrophic cardiomyopathyEMBO Molecular Medicine (2014) 6 (4):482-95Magida JA, Leinwand LA.DOI: 10.1002/emmm.201302852
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Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modelingMolecular Systems Biology (2014) 10:721Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J.DOI: 10.1002/msb.145122
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Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxiaMolecular Systems Biology (2013) 9:712Fan J, Kamphorst JJ, Mathew R, Chung MK, White E, Shlomi T, Rabinowitz JD.DOI: 10.1038/msb.2013.65
CONTENTS
Article
A computational study of the Warburg effectidentifies metabolic targets inhibitingcancer migrationKeren Yizhak1,*,†, Sylvia E Le Dévédec2,†, Vasiliki Maria Rogkoti2, Franziska Baenke3, Vincent C de Boer4,
Christian Frezza5, Almut Schulze3, Bob van de Water2,‡ & Eytan Ruppin1,6,‡,**
Abstract
Over the last decade, the field of cancer metabolism has mainlyfocused on studying the role of tumorigenic metabolic rewiringin supporting cancer proliferation. Here, we perform the firstgenome-scale computational study of the metabolic underpin-nings of cancer migration. We build genome-scale metabolicmodels of the NCI-60 cell lines that capture the Warburg effect(aerobic glycolysis) typically occurring in cancer cells. The extentof the Warburg effect in each of these cell line models is quan-tified by the ratio of glycolytic to oxidative ATP flux (AFR),which is found to be highly positively associated with cancercell migration. We hence predicted that targeting genes thatmitigate the Warburg effect by reducing the AFR may specifi-cally inhibit cancer migration. By testing the anti-migratoryeffects of silencing such 17 top predicted genes in four breastand lung cancer cell lines, we find that up to 13 of these novelpredictions significantly attenuate cell migration either in all orone cell line only, while having almost no effect on cell prolifer-ation. Furthermore, in accordance with the predictions, a signifi-cant reduction is observed in the ratio between experimentallymeasured ECAR and OCR levels following these perturbations.Inhibiting anti-migratory targets is a promising future avenue intreating cancer since it may decrease cytotoxic-related sideeffects that plague current anti-proliferative treatments.Furthermore, it may reduce cytotoxic-related clonal selection ofmore aggressive cancer cells and the likelihood of emergingresistance.
Keywords cancer cell migration; cellular metabolism; genome-scale
metabolic modeling
Subject Categories Genome-Scale & Integrative Biology; Metabolism;
Computational Biology
DOI 10.15252/msb.20134993 | Received 18 November 2013 | Revised 6 July
2014 | Accepted 7 July 2014
Mol Syst Biol. (2014) 10: 744
Introduction
Altered tumor metabolism has become a generally regarded hall-
mark of cancer (Hanahan & Weinberg, 2011). The initial recognition
that metabolism is altered in cancer can be traced back to Otto
Warburg’s early studies, showing that transformed cells consume
glucose at an abnormally high rate and largely reduce it to lactate,
even in the presence of oxygen (Warburg, 1956). Over the last
decade, much of the field of cancer metabolism has focused on the
role of the Warburg effect in supporting cancer proliferation (Vander
Heiden et al, 2009). However, the role of this process in supporting
other fundamental cancer phenotypes such as cellular migration has
received far less attention.
Contemporary cytotoxic cancer treatment has been mainly
based on drugs that kill proliferating cells generally unselectively
and are therefore accompanied by many undesirable side effects.
Drug targets that can inhibit migration but leave cellular prolifer-
ation relatively spared may be able to avoid such side effects.
Such targets may have the additional benefit of reducing the
selection for more resistant clones that occurs due to the elimi-
nation of treatment-sensitive cells. The growing availability of
high-throughput measurements for a range of cancer cells
presents an opportunity to study a wider scope of dysregulated
metabolism across many different cancers. Here, we aim to
1 The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel2 Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands3 Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK4 Laboratory Genetic Metabolic Diseases, Academic Medical Center, Amsterdam, The Netherlands5 MRC Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK6 The Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
*Corresponding author. Tel: +972 3 6405378; E-mail: [email protected]**Corresponding author. Tel: +972 3 6406528; E-mail: [email protected]†These authors contributed equally to this study‡These authors contributed equally to this study
ª 2014 The Authors. Published under the terms of the CC BY 4.0 license Molecular Systems Biology 10: 744 | 2014 1
observed after all glycolysis inhibitors was lower than the corre-
sponding increase in A549 cells (Fig 1C).
Quantifying the Warburg effect and its relation to proliferationand migration across the NCI-60 cell lines
While ECAR and OCR are the commonly used measures for experi-
mentally quantifying the bioenergetic capacity of the cell and thus
the Warburg effect, the genome-wide scope of GSMMs enables us to
examine other putative measures as well. One promising such
measure we examined is the ratio between the ATP flux rate in the
glycolysis versus its flux rate in OXPHOS (AFR). Clearly, higher AFR
values denote more ‘Warburgian’ cell lines and vice versa. A
comparison of our new AFR metric versus the aforementioned
state-of-the-art ECAR/OCR ratio (EOR) (Materials and Methods and
Supplementary Dataset S2) showed a significant correlation across
the NCI-60 models (Spearman correlation R = 0.66, P-value = 2e�8).
Testing both measures using a genome-wide NCI-60 drug response
dataset (Scherf et al, 2000), we find that the model-predicted wild-
type AFR levels across all cell line models are significantly corre-
lated (Spearman P-value < 0.05; FDR corrected with a = 0.05) with
Gi50 values of 30% of the compounds across these cell lines
(empiric P-value < 9.9e�4), whereas the model-predicted EOR
measure accomplish this task for only 19% of the compounds
(Materials and Methods). Interestingly, we find that out of the 30%
AFR-Gi50-correlated compounds, 97% are positively correlated,
suggesting that the more ‘Warburgian’ cell lines are less responsive
and therefore require higher dosage of compound to suppress their
0 0.5 1
0 0.5
0.5
0.5
1
1
1
0
0
C
3BrPA(HK2)
Iodoacetate(G3PDH)
Fluoride (Enolase)
Glucose
G6P
G3P
2PG
PEP
Pyruvate
Lactate
Oxamate(LDH)
1,3BPG
A
B
Figure 1. A comparison between experimental and predicted in silicomeasurements of lactate secretion (or ECAR) and OCR across different cancer cell lines.
A Measured versus predicted lactate secretion rates across the 59 cell lines available at Jain et al (2012).B Measured versus predicted lactate secretion rates in hypoxic (red) and normoxic (blue) conditions for four breast cancer cell lines: T47D, MCF7, BT549, and Hs578T.
Bars represent the measured lactate secretion rates and the line represents the corresponding predicted rates. Error bars represent SD; number of samples forexperimental data (bars) is n = 7; number of samples for predicted data (line) is n = 1000.
C Predicted ECAR and OCR by the A549 and H460 cell line models following inhibitory perturbations in the glycolytic pathway. The models predictions show a decreasein ECAR (red line) and an increase in OCR (blue line). As found experimentally, the predicted OCR increase in H460 cells is lower than that found for A549 cells. Thex-axes represent the level of inhibition imposed, starting from a zero to a maximal inhibition (Materials and Methods). The specific perturbations include 3BpRA thatinhibits the enzyme hexokinase 2; Iodoacetate that inhibits the enzyme glycerol-3-phosphate dehydrogenase; Fluoride that inhibits the enzyme enolase; andOxamate that inhibits the enzyme lactate dehydrogenase.
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
3
integrate pertaining data with a genome-scale mechanistic model
of human metabolism to study the role of the Warburg effect in
tumor progression and its potential association with cellular
migration.
Genome-scale metabolic modeling is an increasingly widely
used computational framework for studying metabolism. Given
the genome-scale metabolic model (GSMM) of a species along-
side contextual information such as growth media and ‘omics’
data, one can obtain a fairly accurate prediction of numerous
metabolic phenotypes, including growth rates, nutrient uptake
rates, gene essentiality, and more (Covert et al, 2004). GSMMs
have been used for various applications (Oberhardt et al, 2009;
Chandrasekaran & Price, 2010; Jensen & Papin, 2010; Szappanos
et al, 2011; Wessely et al, 2011; Lerman et al, 2012; Nogales
et al, 2012; Schuetz et al, 2012) including drug discovery
(Trawick & Schilling, 2006; Oberhardt et al, 2013; Yizhak et al,
2013) and metabolic engineering (Burgard et al, 2003; Pharkya
et al, 2004). Over the last few years, GSMMs have been success-
fully used for modeling human metabolism as well (Duarte et al,
2007; Ma et al, 2007; Shlomi et al, 2008; Gille et al, 2010; Lewis
et al, 2010; Mardinoglu et al, 2013). Specifically, GSMM models
of cancer cells have been reconstructed and applied for predict-
ing selective drug targets, as well as for studying the role of
tumor suppressors and oxidative stress (Folger et al, 2011; Frezza
et al, 2011; Agren et al, 2012, 2014; Jerby et al, 2012; Goldstein
et al, 2013; Gatto et al, 2014). In the context of studying the
Warburg effect, the original human metabolic model does not
predict forced lactate secretion under maximal biomass produc-
tion rate, even when oxygen consumption rate equals zero.
This renders it unsuitable for studying the Warburg effect as is,
as already noted by (Shlomi et al, 2011). While the addition of
solvent capacity constraints has been shown to overcome this
hurdle in principle (Shlomi et al, 2011), this addition requires
enzymatic kinetic data which are still largely absent on a
genome-scale.
In this study, we utilize individual genome-scale metabolic
models tailored separately to each of the NCI-60 cancer cell lines
to study the role of the Warburg effect in supporting cancer cellu-
lar migratory capacity. We first test and validate the individual
models against both existing and novel bioenergetic experimental
data. Then, we examine the extent of the Warburg effect occur-
ring in a given cancer cell line, by quantifying the glycolytic to
oxidative ATP flux ratio (AFR). We find that the AFR is highly
positively correlated with cancer cell migration, emphasizing the
role of glycolytic flux in supporting the more aggressive meta-
static stages of tumor development. To determine whether a
causal relation exists between AFR levels and cell migration, we
predict gene silencing that reduce this ratio. These potential
targets are then filtered further to exclude those predicted to
result in cell lethality. Reassuringly, the predicted targets are
found to be significantly more highly expressed in metastatic and
high-grade breast cancer tumors. Experimental investigation of
the top predicted targets via siRNA-mediated knockdown shows
that a significant portion of them truly attenuate cancer cell
migration without inducing a lethal effect. Furthermore, in accor-
dance with the predictions, a significant reduction is observed in
the ratio between ECAR and OCR levels following these genes
silencing perturbations.
Results
Stoichiometric and flux capacity constraints successfully capturethe coupling of high cell proliferation rate to lactate secretionacross individual NCI-60 cancer models
As a starting point for this study, we developed a set of metabolic
models specific for each of the NCI-60 cell lines. We built these
models using a new algorithm we have recently developed termed
PRIME, for building individual models of cells from pertaining
omics data (Yizhak et al, submitted, Supplementary Information
and Supplementary Fig S1). PRIME uses the generic human model
as a scaffold and sets maximal flux capacity constraints over a
subset of its growth-associated reactions according to the expression
levels of their corresponding catalyzing enzymes in each of the
target cell lines.
An important hallmark of cancerous cells is the production of
lactate through the Warburg effect (Warburg, 1956). As a first step
in validating the basic function of our NCI-60 models, we assessed
whether maximizing biomass forces production of lactate, which
would signify proper coupling of biomass production with lactate
output as seen in cancer cells. We found that the models indeed
must secrete lactate under biomass maximization (Supplementary
Information and Supplementary Fig S2). Hence, in contrast to the
original generic model of human metabolism, they enable us to
systematically assess the extent of lactate secretion and study the
Warburg effect across a wide range of cancer cell lines without
needing to add (mostly unknown) solvent capacity constraints, thus
identifying its functional correlates on a genome scale.
Comparing predicted versus experimentally measuredbioenergetics capacity
We compared the predicted lactate secretion rates across all cell
lines to those measured experimentally by Jain et al (Jain et al,
2012), obtaining a moderate but significant correlation (Spearman
correlation R = 0.36, P-value = 5.7e�3, Fig 1A, Materials and Meth-
ods). To further test the models’ performance under different envi-
ronmental conditions, we measured lactate secretion rates in four
breast cancer cell lines, T47D, MCF7, BT549, and Hs578T (Supple-
mentary Dataset S1), under both normoxic and hypoxic conditions
(see Materials and Methods). Utilizing the corresponding cell line
models from the NCI-60 set, we found a high correlation between
measured and predicted lactate secretion levels across both condi-
tions (Spearman correlation R = 0.95, P-value = 1.1e�3, Fig 1B).
The ratio of glycolytic versus oxidative capacity in a cell can be
quantified using its extracellular acidification rate (ECAR, a proxy of
lactate secretion) and its oxygen consumption rate (OCR). To
further examine how well our cell line models capture measured
Warburg-related activity in response to genetic perturbations, we
utilized measured ECAR and OCR levels in response to perturba-
tions in two NCI-60 lung cancer cell lines (A549 and H460), and
compared the results to predictions from our models (Materials and
Methods) (Wu et al, 2007). Qualitatively similar ECAR and OCR
changes are found in response to various enzymatic perturbations
along the glycolytic pathway. Specifically, increased glycolytic inhi-
bition resulted in reduced ECAR and elevated OCR levels in both
cells, while the maximum cellular respiration increase in H460 cells
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
2
observed after all glycolysis inhibitors was lower than the corre-
sponding increase in A549 cells (Fig 1C).
Quantifying the Warburg effect and its relation to proliferationand migration across the NCI-60 cell lines
While ECAR and OCR are the commonly used measures for experi-
mentally quantifying the bioenergetic capacity of the cell and thus
the Warburg effect, the genome-wide scope of GSMMs enables us to
examine other putative measures as well. One promising such
measure we examined is the ratio between the ATP flux rate in the
glycolysis versus its flux rate in OXPHOS (AFR). Clearly, higher AFR
values denote more ‘Warburgian’ cell lines and vice versa. A
comparison of our new AFR metric versus the aforementioned
state-of-the-art ECAR/OCR ratio (EOR) (Materials and Methods and
Supplementary Dataset S2) showed a significant correlation across
the NCI-60 models (Spearman correlation R = 0.66, P-value = 2e�8).
Testing both measures using a genome-wide NCI-60 drug response
dataset (Scherf et al, 2000), we find that the model-predicted wild-
type AFR levels across all cell line models are significantly corre-
lated (Spearman P-value < 0.05; FDR corrected with a = 0.05) with
Gi50 values of 30% of the compounds across these cell lines
(empiric P-value < 9.9e�4), whereas the model-predicted EOR
measure accomplish this task for only 19% of the compounds
(Materials and Methods). Interestingly, we find that out of the 30%
AFR-Gi50-correlated compounds, 97% are positively correlated,
suggesting that the more ‘Warburgian’ cell lines are less responsive
and therefore require higher dosage of compound to suppress their
0 0.5 1
0 0.5
0.5
0.5
1
1
1
0
0
C
3BrPA(HK2)
Iodoacetate(G3PDH)
Fluoride (Enolase)
Glucose
G6P
G3P
2PG
PEP
Pyruvate
Lactate
Oxamate(LDH)
1,3BPG
A
B
Figure 1. A comparison between experimental and predicted in silicomeasurements of lactate secretion (or ECAR) and OCR across different cancer cell lines.
A Measured versus predicted lactate secretion rates across the 59 cell lines available at Jain et al (2012).B Measured versus predicted lactate secretion rates in hypoxic (red) and normoxic (blue) conditions for four breast cancer cell lines: T47D, MCF7, BT549, and Hs578T.
Bars represent the measured lactate secretion rates and the line represents the corresponding predicted rates. Error bars represent SD; number of samples forexperimental data (bars) is n = 7; number of samples for predicted data (line) is n = 1000.
C Predicted ECAR and OCR by the A549 and H460 cell line models following inhibitory perturbations in the glycolytic pathway. The models predictions show a decreasein ECAR (red line) and an increase in OCR (blue line). As found experimentally, the predicted OCR increase in H460 cells is lower than that found for A549 cells. Thex-axes represent the level of inhibition imposed, starting from a zero to a maximal inhibition (Materials and Methods). The specific perturbations include 3BpRA thatinhibits the enzyme hexokinase 2; Iodoacetate that inhibits the enzyme glycerol-3-phosphate dehydrogenase; Fluoride that inhibits the enzyme enolase; andOxamate that inhibits the enzyme lactate dehydrogenase.
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
3
integrate pertaining data with a genome-scale mechanistic model
of human metabolism to study the role of the Warburg effect in
tumor progression and its potential association with cellular
migration.
Genome-scale metabolic modeling is an increasingly widely
used computational framework for studying metabolism. Given
the genome-scale metabolic model (GSMM) of a species along-
side contextual information such as growth media and ‘omics’
data, one can obtain a fairly accurate prediction of numerous
metabolic phenotypes, including growth rates, nutrient uptake
rates, gene essentiality, and more (Covert et al, 2004). GSMMs
have been used for various applications (Oberhardt et al, 2009;
Chandrasekaran & Price, 2010; Jensen & Papin, 2010; Szappanos
et al, 2011; Wessely et al, 2011; Lerman et al, 2012; Nogales
et al, 2012; Schuetz et al, 2012) including drug discovery
(Trawick & Schilling, 2006; Oberhardt et al, 2013; Yizhak et al,
2013) and metabolic engineering (Burgard et al, 2003; Pharkya
et al, 2004). Over the last few years, GSMMs have been success-
fully used for modeling human metabolism as well (Duarte et al,
2007; Ma et al, 2007; Shlomi et al, 2008; Gille et al, 2010; Lewis
et al, 2010; Mardinoglu et al, 2013). Specifically, GSMM models
of cancer cells have been reconstructed and applied for predict-
ing selective drug targets, as well as for studying the role of
tumor suppressors and oxidative stress (Folger et al, 2011; Frezza
et al, 2011; Agren et al, 2012, 2014; Jerby et al, 2012; Goldstein
et al, 2013; Gatto et al, 2014). In the context of studying the
Warburg effect, the original human metabolic model does not
predict forced lactate secretion under maximal biomass produc-
tion rate, even when oxygen consumption rate equals zero.
This renders it unsuitable for studying the Warburg effect as is,
as already noted by (Shlomi et al, 2011). While the addition of
solvent capacity constraints has been shown to overcome this
hurdle in principle (Shlomi et al, 2011), this addition requires
enzymatic kinetic data which are still largely absent on a
genome-scale.
In this study, we utilize individual genome-scale metabolic
models tailored separately to each of the NCI-60 cancer cell lines
to study the role of the Warburg effect in supporting cancer cellu-
lar migratory capacity. We first test and validate the individual
models against both existing and novel bioenergetic experimental
data. Then, we examine the extent of the Warburg effect occur-
ring in a given cancer cell line, by quantifying the glycolytic to
oxidative ATP flux ratio (AFR). We find that the AFR is highly
positively correlated with cancer cell migration, emphasizing the
role of glycolytic flux in supporting the more aggressive meta-
static stages of tumor development. To determine whether a
causal relation exists between AFR levels and cell migration, we
predict gene silencing that reduce this ratio. These potential
targets are then filtered further to exclude those predicted to
result in cell lethality. Reassuringly, the predicted targets are
found to be significantly more highly expressed in metastatic and
high-grade breast cancer tumors. Experimental investigation of
the top predicted targets via siRNA-mediated knockdown shows
that a significant portion of them truly attenuate cancer cell
migration without inducing a lethal effect. Furthermore, in accor-
dance with the predictions, a significant reduction is observed in
the ratio between ECAR and OCR levels following these genes
silencing perturbations.
Results
Stoichiometric and flux capacity constraints successfully capturethe coupling of high cell proliferation rate to lactate secretionacross individual NCI-60 cancer models
As a starting point for this study, we developed a set of metabolic
models specific for each of the NCI-60 cell lines. We built these
models using a new algorithm we have recently developed termed
PRIME, for building individual models of cells from pertaining
omics data (Yizhak et al, submitted, Supplementary Information
and Supplementary Fig S1). PRIME uses the generic human model
as a scaffold and sets maximal flux capacity constraints over a
subset of its growth-associated reactions according to the expression
levels of their corresponding catalyzing enzymes in each of the
target cell lines.
An important hallmark of cancerous cells is the production of
lactate through the Warburg effect (Warburg, 1956). As a first step
in validating the basic function of our NCI-60 models, we assessed
whether maximizing biomass forces production of lactate, which
would signify proper coupling of biomass production with lactate
output as seen in cancer cells. We found that the models indeed
must secrete lactate under biomass maximization (Supplementary
Information and Supplementary Fig S2). Hence, in contrast to the
original generic model of human metabolism, they enable us to
systematically assess the extent of lactate secretion and study the
Warburg effect across a wide range of cancer cell lines without
needing to add (mostly unknown) solvent capacity constraints, thus
identifying its functional correlates on a genome scale.
Comparing predicted versus experimentally measuredbioenergetics capacity
We compared the predicted lactate secretion rates across all cell
lines to those measured experimentally by Jain et al (Jain et al,
2012), obtaining a moderate but significant correlation (Spearman
correlation R = 0.36, P-value = 5.7e�3, Fig 1A, Materials and Meth-
ods). To further test the models’ performance under different envi-
ronmental conditions, we measured lactate secretion rates in four
breast cancer cell lines, T47D, MCF7, BT549, and Hs578T (Supple-
mentary Dataset S1), under both normoxic and hypoxic conditions
(see Materials and Methods). Utilizing the corresponding cell line
models from the NCI-60 set, we found a high correlation between
measured and predicted lactate secretion levels across both condi-
tions (Spearman correlation R = 0.95, P-value = 1.1e�3, Fig 1B).
The ratio of glycolytic versus oxidative capacity in a cell can be
quantified using its extracellular acidification rate (ECAR, a proxy of
lactate secretion) and its oxygen consumption rate (OCR). To
further examine how well our cell line models capture measured
Warburg-related activity in response to genetic perturbations, we
utilized measured ECAR and OCR levels in response to perturba-
tions in two NCI-60 lung cancer cell lines (A549 and H460), and
compared the results to predictions from our models (Materials and
Methods) (Wu et al, 2007). Qualitatively similar ECAR and OCR
changes are found in response to various enzymatic perturbations
along the glycolytic pathway. Specifically, increased glycolytic inhi-
bition resulted in reduced ECAR and elevated OCR levels in both
cells, while the maximum cellular respiration increase in H460 cells
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
2
Fig 2D and Supplementary Table S1), it correlates even more
strongly in the positive direction with cancer cell migration
(Spearman correlation of R = 0.88, P-value = 0.03, Fig 2D and
Supplementary Table S1). Controlling for the cell lines’ measured
growth rates, this correlation becomes even more significant (partial
Spearman correlation of R = 0.96, P-value = 7e�3, Supplementary
Table S1). Overall, this finding suggests that glycolytic flux correlates
with migration rather than with growth, while OXPHOS flux exhibits
the opposite behavior. A similar association between lactate secretion
and growth rate has been recently found in an experimental study
by Jain et al (Jain et al, 2012) across the entire NCI-60 collection
(Spearman correlation of R = �0.22, P = 0.09). Furthermore,
previous studies have shown that high concentrations of lactate
correlate with a high incidence of distant metastasis (Hirschhaeuser
et al, 2011). The overall picture portrayed by these correlations is that
while glycolytic carbon diverted to biosynthetic pathways may
support cell proliferation, non-diverted glycolytic carbon supports cell
migration and metastasis (Supplementary Fig S4).
Predicting drug targets that revert the AFR and hence mayinhibit cancer migration
The congruence between AFR levels and disease severity led us to
ask if we could build upon this association to identify potential new
drug targets. We searched for drug targets predicted to reduce the
AFR ratio by simulating the knockout of each metabolic reaction
across the NCI-60 models, and examining the effects of the knock-
outs on biomass production, lactate secretion, and the AFR. As
lactate secretion is a basic indicator of the Warburg effect, we first
identified a set of 113 reactions whose knockout is predicted to
abolish lactate secretion rate in all cancer cell lines under biomass
maximization. Interestingly, the set of enzymes catalyzing these
reactions is significantly more highly expressed in the NCI-60 cell
lines than the background metabolic genes (one-sided Wilcoxon
P-value < 1.6e�8), indicating the potential oncogenic nature of
these genes.
To avoid selecting for drug-resistant clones it would be advanta-
geous to develop drugs that reduce the virulence of cancer cells but
avoid killing them. The knockout of 12 of 113 lactate-reducing reac-
tions reduces the AFR but relatively spares biomass production
(Materials and Methods and Supplementary Table S2). Importantly,
the knockout of these 12 reactions according to models of healthy
lymphoblast cells built by PRIME (Choy et al, 2008) also spares
their biomass production (Materials and Methods). Moreover, we
found that none of the lymphoblast cell lines show the forced lactate
secretion that is observed in cancer cells. While the Warburg effect
is sometimes referred in the literature as occurring in highly prolifer-
ating cells in general, our analysis finds that this phenomenon is
apparently more prominent in cancer cells, at least with regard to
the lymphoblastoid cell population studied here.
The final list of predicted gene targets includes 17 metabolic
enzymes that are associated with the final 12 reactions, spanning
glycolysis, serine, and methionine metabolism (Fig 3A). 10 of the
predicted targets have significantly higher expression levels in meta-
static versus non-metastatic breast cancer patients (Chang et al,
2005) (one-sided Wilcoxon P-value < 0.05, Fig 3B). Moreover, 9 of
the predicted targets exhibit higher expression levels in grade 3
tumors than in grade 1 tumors (Miller et al, 2005) (one-sided
Wilcoxon P-value < 0.05, Fig 3C). Finally, lower expression of nine
of the predicted targets is significantly associated with improved
long-term survival (Curtis et al, 2012) (log-rank P-value < 0.05,
Fig 3D), testifying for their potential role as therapeutic targets. All
P-values are corrected for multiple hypothesis using FDR with a = 0.05.
siRNA-mediated gene knockdown experiments testing thepredicted targets
To experimentally test our predictions we silenced the 17 predicted
AFR-reducing genes and examined their phenotypic effects in the
MDA-MB-231, MDA-MB-435, BT549, and A549 cell lines. Knock-
down experiments were performed with SmartPools from Dharma-
con using a live cell migration and fixed proliferation assays
(Materials and Methods). 8–13 out of the 17 enzymes (8–10 out of
12 metabolic reactions) were found to significantly attenuate migra-
tion speed in each cell line (two-sided t-test P-value < 0.05, FDR
corrected with a = 0.05, Fig 4, Materials and Methods and Supple-
mentary Dataset S4). This result is highly significant as only 17% of
the metabolic genes were found to impair cell migration in a siRNA
screen of 190 metabolic genes (Fokkelman M, Rogkoti VM et al,
unpublished data, Bernoulli P-value in the range of 3.9e�3 and
1.18e�7). Of note, the association between the gene expression of
the predicted targets and the measured migration speed is insignifi-
cant for all targets but one, testifying for the inherent value of our
model-based prediction analysis (Supplementary Table S3). It
should also be noted that the knockdown of the three splices of the
enolase gene have almost no significant effect on these cells’ migra-
tion speed, possibly because of isoenzymes backup mechanisms.
Importantly, most of the gene knockdown experiments do not
manifest any significant effects on cell proliferation (Fig 4). In
accordance with the findings of Simpson et al (Simpson et al,
2008), we found that the correlation between the reduction in
migration speed and reduction in proliferation rate is mostly
insignificant (Supplementary Dataset S4), suggesting that the
reduced migration observed is not simply a consequence of
common mechanisms hindering proliferation, but rather that it
occurs due to the disruption of distinct migratory-associated
metabolic pathways.
ECAR and OCR levels following selected gene silencing
To further study the association between reduced AFR levels and
impaired cell migration we used the Seahorse XF96 extracellular
flux analyzer to measure both ECAR and OCR fluxes in the MDA-
MB-231 cell line, following knockdown of a selected group of targets
(Materials and Methods and Supplementary Fig S6). As the AFR
measure is very difficult to measure experimentally, we tested the
conventionally measured EOR (ECAR/OCR) as its proxy. We
focused on a subset of seven genes (Fig 5) whose knockdown is
predicted to have the highest effect on cell migration and span all
three predicted metabolic pathways. As shown in Fig 5, a significant
EOR reduction versus the control is found for all seven examined
genes (two-sided t-test P-value < 0.05, FDR corrected with a = 0.05,
Materials and Methods and Supplementary Table S4). The silencing
of the four glycolytic genes (HK2, PGAM1, PGK2, and GAPDH)
results in both decreased ECAR and increased OCR levels, while
the silencing of the serine- and methionine-associated genes
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
5
growth. The effect of most of these compounds is also negatively
correlated with the cells’ growth rates, suggesting that slowly
proliferating cells are more resistant to treatment (similar results were
previously shown for compounds targeting cell growth (Penault-
Llorca et al, 2009; Vincent-Salomon et al, 2004)). Interestingly, the
response to many compounds in this dataset shows a significant
association with the AFR measure while having no association with
the cells’ growth rate. 133 such compounds were identified (Supple-
mentary Dataset S3), possibly suggesting that their mechanism
might be related to the Warburg level of the cells rather than to their
proliferation. Finally, predicted AFR values correctly separate
between epithelial and mesenchymal breast cancer cell lines (with
the more aggressive mesenchymal cell lines exhibiting larger
Warburg effect (Sarrio et al, 2008), Fig 2A). Once again, the AFR
was more predictive of this experimental observation than the EOR
(Supplementary Dataset S2).
We next turned to our primary objective of examining the rela-
tion between the Warburg effect and tumor proliferation and migra-
tion. To this end, we experimentally measured the migration speed
of six NCI-60 breast cancer cell lines (Fig 2B and C, Materials and
Methods, Supplementary Fig S3, and Supplementary Dataset S2)
and utilized publically available measured growth rates for these
cell lines. While the AFR correlates markedly negatively with cell
growth rate (Spearman correlation of R = �0.55, P-value = 4.53e�6,
* *
*
A
Basa
lLu
min
al
DB
C
Figure 2. Association between AFR levels and cell proliferation and migration.
A The 20 cell lines that are predicted to exhibit the Warburg effect to the greatest/least extent according to the AFR measure. The x-axis and y-axis represent the meanand SD of the normalized ATP flux rate in glycolysis and OXPHOS, respectively (Materials and Methods). The AFR measure correctly separates between mesenchymal(orange) and epithelial cell lines (green), showing that the former (which are known to be more aggressive) have higher AFR levels.
B We analyzed a panel of six breast cancer cell lines for their migration capacity using live cell imaging. Differential Interference Contrast (DIC) images of the six celllines in the order of their respective migration speed (from low to high), scale bar is 100 lm (Materials and Methods).
C The average migration speed of cells followed for 12 h in complete medium. Error bars represent SEM; the number of samples is between n = 100 and n = 200.D The correlation of predicted model-based EOR and AFR measures to growth and migration rates measured experimentally. Both measures represent a negative
correlation with growth and a positive correlation with migration rates. Significant results (P-value < 0.05) are marked with an asterisk.
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
4
Fig 2D and Supplementary Table S1), it correlates even more
strongly in the positive direction with cancer cell migration
(Spearman correlation of R = 0.88, P-value = 0.03, Fig 2D and
Supplementary Table S1). Controlling for the cell lines’ measured
growth rates, this correlation becomes even more significant (partial
Spearman correlation of R = 0.96, P-value = 7e�3, Supplementary
Table S1). Overall, this finding suggests that glycolytic flux correlates
with migration rather than with growth, while OXPHOS flux exhibits
the opposite behavior. A similar association between lactate secretion
and growth rate has been recently found in an experimental study
by Jain et al (Jain et al, 2012) across the entire NCI-60 collection
(Spearman correlation of R = �0.22, P = 0.09). Furthermore,
previous studies have shown that high concentrations of lactate
correlate with a high incidence of distant metastasis (Hirschhaeuser
et al, 2011). The overall picture portrayed by these correlations is that
while glycolytic carbon diverted to biosynthetic pathways may
support cell proliferation, non-diverted glycolytic carbon supports cell
migration and metastasis (Supplementary Fig S4).
Predicting drug targets that revert the AFR and hence mayinhibit cancer migration
The congruence between AFR levels and disease severity led us to
ask if we could build upon this association to identify potential new
drug targets. We searched for drug targets predicted to reduce the
AFR ratio by simulating the knockout of each metabolic reaction
across the NCI-60 models, and examining the effects of the knock-
outs on biomass production, lactate secretion, and the AFR. As
lactate secretion is a basic indicator of the Warburg effect, we first
identified a set of 113 reactions whose knockout is predicted to
abolish lactate secretion rate in all cancer cell lines under biomass
maximization. Interestingly, the set of enzymes catalyzing these
reactions is significantly more highly expressed in the NCI-60 cell
lines than the background metabolic genes (one-sided Wilcoxon
P-value < 1.6e�8), indicating the potential oncogenic nature of
these genes.
To avoid selecting for drug-resistant clones it would be advanta-
geous to develop drugs that reduce the virulence of cancer cells but
avoid killing them. The knockout of 12 of 113 lactate-reducing reac-
tions reduces the AFR but relatively spares biomass production
(Materials and Methods and Supplementary Table S2). Importantly,
the knockout of these 12 reactions according to models of healthy
lymphoblast cells built by PRIME (Choy et al, 2008) also spares
their biomass production (Materials and Methods). Moreover, we
found that none of the lymphoblast cell lines show the forced lactate
secretion that is observed in cancer cells. While the Warburg effect
is sometimes referred in the literature as occurring in highly prolifer-
ating cells in general, our analysis finds that this phenomenon is
apparently more prominent in cancer cells, at least with regard to
the lymphoblastoid cell population studied here.
The final list of predicted gene targets includes 17 metabolic
enzymes that are associated with the final 12 reactions, spanning
glycolysis, serine, and methionine metabolism (Fig 3A). 10 of the
predicted targets have significantly higher expression levels in meta-
static versus non-metastatic breast cancer patients (Chang et al,
2005) (one-sided Wilcoxon P-value < 0.05, Fig 3B). Moreover, 9 of
the predicted targets exhibit higher expression levels in grade 3
tumors than in grade 1 tumors (Miller et al, 2005) (one-sided
Wilcoxon P-value < 0.05, Fig 3C). Finally, lower expression of nine
of the predicted targets is significantly associated with improved
long-term survival (Curtis et al, 2012) (log-rank P-value < 0.05,
Fig 3D), testifying for their potential role as therapeutic targets. All
P-values are corrected for multiple hypothesis using FDR with a = 0.05.
siRNA-mediated gene knockdown experiments testing thepredicted targets
To experimentally test our predictions we silenced the 17 predicted
AFR-reducing genes and examined their phenotypic effects in the
MDA-MB-231, MDA-MB-435, BT549, and A549 cell lines. Knock-
down experiments were performed with SmartPools from Dharma-
con using a live cell migration and fixed proliferation assays
(Materials and Methods). 8–13 out of the 17 enzymes (8–10 out of
12 metabolic reactions) were found to significantly attenuate migra-
tion speed in each cell line (two-sided t-test P-value < 0.05, FDR
corrected with a = 0.05, Fig 4, Materials and Methods and Supple-
mentary Dataset S4). This result is highly significant as only 17% of
the metabolic genes were found to impair cell migration in a siRNA
screen of 190 metabolic genes (Fokkelman M, Rogkoti VM et al,
unpublished data, Bernoulli P-value in the range of 3.9e�3 and
1.18e�7). Of note, the association between the gene expression of
the predicted targets and the measured migration speed is insignifi-
cant for all targets but one, testifying for the inherent value of our
model-based prediction analysis (Supplementary Table S3). It
should also be noted that the knockdown of the three splices of the
enolase gene have almost no significant effect on these cells’ migra-
tion speed, possibly because of isoenzymes backup mechanisms.
Importantly, most of the gene knockdown experiments do not
manifest any significant effects on cell proliferation (Fig 4). In
accordance with the findings of Simpson et al (Simpson et al,
2008), we found that the correlation between the reduction in
migration speed and reduction in proliferation rate is mostly
insignificant (Supplementary Dataset S4), suggesting that the
reduced migration observed is not simply a consequence of
common mechanisms hindering proliferation, but rather that it
occurs due to the disruption of distinct migratory-associated
metabolic pathways.
ECAR and OCR levels following selected gene silencing
To further study the association between reduced AFR levels and
impaired cell migration we used the Seahorse XF96 extracellular
flux analyzer to measure both ECAR and OCR fluxes in the MDA-
MB-231 cell line, following knockdown of a selected group of targets
(Materials and Methods and Supplementary Fig S6). As the AFR
measure is very difficult to measure experimentally, we tested the
conventionally measured EOR (ECAR/OCR) as its proxy. We
focused on a subset of seven genes (Fig 5) whose knockdown is
predicted to have the highest effect on cell migration and span all
three predicted metabolic pathways. As shown in Fig 5, a significant
EOR reduction versus the control is found for all seven examined
genes (two-sided t-test P-value < 0.05, FDR corrected with a = 0.05,
Materials and Methods and Supplementary Table S4). The silencing
of the four glycolytic genes (HK2, PGAM1, PGK2, and GAPDH)
results in both decreased ECAR and increased OCR levels, while
the silencing of the serine- and methionine-associated genes
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
5
growth. The effect of most of these compounds is also negatively
correlated with the cells’ growth rates, suggesting that slowly
proliferating cells are more resistant to treatment (similar results were
previously shown for compounds targeting cell growth (Penault-
Llorca et al, 2009; Vincent-Salomon et al, 2004)). Interestingly, the
response to many compounds in this dataset shows a significant
association with the AFR measure while having no association with
the cells’ growth rate. 133 such compounds were identified (Supple-
mentary Dataset S3), possibly suggesting that their mechanism
might be related to the Warburg level of the cells rather than to their
proliferation. Finally, predicted AFR values correctly separate
between epithelial and mesenchymal breast cancer cell lines (with
the more aggressive mesenchymal cell lines exhibiting larger
Warburg effect (Sarrio et al, 2008), Fig 2A). Once again, the AFR
was more predictive of this experimental observation than the EOR
(Supplementary Dataset S2).
We next turned to our primary objective of examining the rela-
tion between the Warburg effect and tumor proliferation and migra-
tion. To this end, we experimentally measured the migration speed
of six NCI-60 breast cancer cell lines (Fig 2B and C, Materials and
Methods, Supplementary Fig S3, and Supplementary Dataset S2)
and utilized publically available measured growth rates for these
cell lines. While the AFR correlates markedly negatively with cell
growth rate (Spearman correlation of R = �0.55, P-value = 4.53e�6,
* *
*
A
Basa
lLu
min
al
DB
C
Figure 2. Association between AFR levels and cell proliferation and migration.
A The 20 cell lines that are predicted to exhibit the Warburg effect to the greatest/least extent according to the AFR measure. The x-axis and y-axis represent the meanand SD of the normalized ATP flux rate in glycolysis and OXPHOS, respectively (Materials and Methods). The AFR measure correctly separates between mesenchymal(orange) and epithelial cell lines (green), showing that the former (which are known to be more aggressive) have higher AFR levels.
B We analyzed a panel of six breast cancer cell lines for their migration capacity using live cell imaging. Differential Interference Contrast (DIC) images of the six celllines in the order of their respective migration speed (from low to high), scale bar is 100 lm (Materials and Methods).
C The average migration speed of cells followed for 12 h in complete medium. Error bars represent SEM; the number of samples is between n = 100 and n = 200.D The correlation of predicted model-based EOR and AFR measures to growth and migration rates measured experimentally. Both measures represent a negative
correlation with growth and a positive correlation with migration rates. Significant results (P-value < 0.05) are marked with an asterisk.
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
4
and the b-catalytic subunit of ATP synthase forming the BEC index
was found to have a prognostic value in assessing the clinical
outcome of patients with early-stage colorectal carcinomas. The
AFR measure and the BEC index (as computed by its corresponding
RNA levels) are significantly correlated (Spearman R = 0.58,
P-value = 1.6e�6) across the NCI-60 cell lines, and the BEC index is
perfectly correlated with migration speed across the six breast
cancer cell lines (Spearman R = 1, P-value = 2.8e�3). However, the
BEC index has inferior performance in predicting drug response
(Supplementary Table S1).
The finding that enhanced glycolytic activity plays a key role in
cancer cell migration is also in line with a very recent study by
De Bock et al, showing that glycolysis is the major source of ATP
production in endothelial cells and that the silencing of the glyco-
lytic regulator PFKFB3 impairs the cell migration capacity and inter-
feres with vessel sprouting (De Bock et al, 2013). In addition,
silencing of PFKFB3 was shown to suppress cell proliferation in
about 50% (De Bock et al, 2013). Overall, the results presented in
this study, as well as findings reported by others (Simpson et al,
2008), suggest that proliferation and migration are not mutually
exclusive, and the effect of potential targets on both processes
should be carefully examined.
Some of our predicted targets have been previously studied in
the context of cell proliferation as well (Cheong et al, 2012).
Possemato et al (Possemato et al, 2011) have showed that suppres-
sion of PHGDH in cell lines with elevated PHGDH expression, but not
Figure 4. Normalized to control mean speed per SmartPool gene silencing of the predicted targets.
A–D The four different cell lines that were analyzed: MDA-MB-231, MDA-MB-435s, BT549, and A549. Significant results (two-sided t-test, P-value < 0.05 after correctingfor multiple hypothesis using FDR with a = 0.05) are marked with an asterisk. Two different controls are used: (1) non-targeting siRNA (= negative control); and (2)a positive control DNM2 which is known to block both migration and proliferation (Ezratty et al, 2005). Left panel shows migration speed and right panel showsnuclear count. Error bars represent SD; the number of samples is n = 3.
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
7
(PSPH, AHCY, and PHGDH) results with decreased ECAR solely
(Fig 5A). Furthermore, a matching significant difference in
experimentally measured EOR levels is found between the lowest and
highest AFR-reducing genes (one-sided Wilcoxon P-value = 0.05).
Overall, taken together our results testify that, as predicted, the
knockdown of the top-ranked genes results in attenuated cell
migration that is accompanied by reduced EOR and AFR levels.
Discussion
In this study we explored the role of the Warburg effect in support-
ing tumor migration, going beyond recent investigations focusing on
its role in assisting cancer proliferation. A model-based investigation
across cancer cell lines shows that the ratio between glycolytic and
oxidative ATP flux rate is significantly associated with cancer migra-
tory behavior. Gene silencing perturbations predicted to reduce this
ratio were indeed found to attenuate cell migration, and result with
a significant reduction in ECAR to OCR levels. Of note, our modeling
approach relies on gene expression differences between the cells
and does not take into account specific uptake rates. It is therefore
more suited for capturing qualitative rather than exact quantitative
differences between the cells, as demonstrated throughout the
paper. Moreover, the lion share of our analysis is focused on the
simulations of perturbations where specific uptake rates are not
available. Nonetheless, utilizing such uptake measurements can
significantly increase the correlation to the measured lactate rates
(Spearman correlation R = 0.67, P-value = 1.5e�8), suggesting that
uptake rates measurements under perturbation states can signifi-
cantly increase the models’ prediction power.
Our AFR measure is conceptually analogous to a bioenergetic
(BEC) index previously introduced by Cuezva et al (Cuezva et al,
2002). In that study, the ratio between the expression of the glyco-
lytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
Glucose
G6P
F6P
FBP
G3PDHAP
1,3BPG
3PG
2PG
PEP
PyruvateLactateGln
Pyruvate
Ac-CoA
To PentosePhosphate Pathway
R5P
TCACycle
Glycolysis
Serine
Glycine
mTHF
THF
HK2
PKM
GAPDHTPI1
PGAM
PGK3PHP
NAD NADH
PHGDHSerineP-Serine
α-kgGlu
PSAT1 PSPH
L-Met adenosyl-Met
MAT1/2
Serine Biosynthesis
Methionine Metabolism
ENO
adenosyl-hcys
adn + HcysAHCY/AHCYL
A
P = 5.34e-4
P = 3.8e-5
P = 1.08e-7
P = 2.4e-3
P = 2.69e-5
P = 4.47e-5
P = 2.2e-6
P = 4.3e-4
P = 1.6e-2
P = 1.4e-2
P = 5.34e-5
P = 9.8e-3
P = 8.52e-5
P = 4.05e-4
P = 1.2-3
P = 2.78e-8
P = 7.08e-13
P = 4.61e-7
P = 1.83e-4
P = 4.61e-8
P = 1.96e-6
P = 6.08e-4
P = 6.12e-5P = 1.86e-5
P = 3.75e-4P = 1.5e-3
P = 1.1e-4P = 2.85e-4
B
C
D
Figure 3. Gene targets that are predicted to reduce the AFR and their association with prognostic markers of breast cancer patients.
A A schematic representation of the 12 predicted gene targets, marked in red.B Ten predicted targets that show a significantly higher expression in metastatic versus non-metastatic tumor samples (n = 295).C Nine predicted targets that show a significantly higher expression in grade 3 versus grade 1 tumor samples (n = 236).D Nine predicted targets whose lower expression is significantly associated with improved long-term survival (n = 1568).
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
6
and the b-catalytic subunit of ATP synthase forming the BEC index
was found to have a prognostic value in assessing the clinical
outcome of patients with early-stage colorectal carcinomas. The
AFR measure and the BEC index (as computed by its corresponding
RNA levels) are significantly correlated (Spearman R = 0.58,
P-value = 1.6e�6) across the NCI-60 cell lines, and the BEC index is
perfectly correlated with migration speed across the six breast
cancer cell lines (Spearman R = 1, P-value = 2.8e�3). However, the
BEC index has inferior performance in predicting drug response
(Supplementary Table S1).
The finding that enhanced glycolytic activity plays a key role in
cancer cell migration is also in line with a very recent study by
De Bock et al, showing that glycolysis is the major source of ATP
production in endothelial cells and that the silencing of the glyco-
lytic regulator PFKFB3 impairs the cell migration capacity and inter-
feres with vessel sprouting (De Bock et al, 2013). In addition,
silencing of PFKFB3 was shown to suppress cell proliferation in
about 50% (De Bock et al, 2013). Overall, the results presented in
this study, as well as findings reported by others (Simpson et al,
2008), suggest that proliferation and migration are not mutually
exclusive, and the effect of potential targets on both processes
should be carefully examined.
Some of our predicted targets have been previously studied in
the context of cell proliferation as well (Cheong et al, 2012).
Possemato et al (Possemato et al, 2011) have showed that suppres-
sion of PHGDH in cell lines with elevated PHGDH expression, but not
Figure 4. Normalized to control mean speed per SmartPool gene silencing of the predicted targets.
A–D The four different cell lines that were analyzed: MDA-MB-231, MDA-MB-435s, BT549, and A549. Significant results (two-sided t-test, P-value < 0.05 after correctingfor multiple hypothesis using FDR with a = 0.05) are marked with an asterisk. Two different controls are used: (1) non-targeting siRNA (= negative control); and (2)a positive control DNM2 which is known to block both migration and proliferation (Ezratty et al, 2005). Left panel shows migration speed and right panel showsnuclear count. Error bars represent SD; the number of samples is n = 3.
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
7
(PSPH, AHCY, and PHGDH) results with decreased ECAR solely
(Fig 5A). Furthermore, a matching significant difference in
experimentally measured EOR levels is found between the lowest and
highest AFR-reducing genes (one-sided Wilcoxon P-value = 0.05).
Overall, taken together our results testify that, as predicted, the
knockdown of the top-ranked genes results in attenuated cell
migration that is accompanied by reduced EOR and AFR levels.
Discussion
In this study we explored the role of the Warburg effect in support-
ing tumor migration, going beyond recent investigations focusing on
its role in assisting cancer proliferation. A model-based investigation
across cancer cell lines shows that the ratio between glycolytic and
oxidative ATP flux rate is significantly associated with cancer migra-
tory behavior. Gene silencing perturbations predicted to reduce this
ratio were indeed found to attenuate cell migration, and result with
a significant reduction in ECAR to OCR levels. Of note, our modeling
approach relies on gene expression differences between the cells
and does not take into account specific uptake rates. It is therefore
more suited for capturing qualitative rather than exact quantitative
differences between the cells, as demonstrated throughout the
paper. Moreover, the lion share of our analysis is focused on the
simulations of perturbations where specific uptake rates are not
available. Nonetheless, utilizing such uptake measurements can
significantly increase the correlation to the measured lactate rates
(Spearman correlation R = 0.67, P-value = 1.5e�8), suggesting that
uptake rates measurements under perturbation states can signifi-
cantly increase the models’ prediction power.
Our AFR measure is conceptually analogous to a bioenergetic
(BEC) index previously introduced by Cuezva et al (Cuezva et al,
2002). In that study, the ratio between the expression of the glyco-
lytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
Glucose
G6P
F6P
FBP
G3PDHAP
1,3BPG
3PG
2PG
PEP
PyruvateLactateGln
Pyruvate
Ac-CoA
To PentosePhosphate Pathway
R5P
TCACycle
Glycolysis
Serine
Glycine
mTHF
THF
HK2
PKM
GAPDHTPI1
PGAM
PGK3PHP
NAD NADH
PHGDHSerineP-Serine
α-kgGlu
PSAT1 PSPH
L-Met adenosyl-Met
MAT1/2
Serine Biosynthesis
Methionine Metabolism
ENO
adenosyl-hcys
adn + HcysAHCY/AHCYL
A
P = 5.34e-4
P = 3.8e-5
P = 1.08e-7
P = 2.4e-3
P = 2.69e-5
P = 4.47e-5
P = 2.2e-6
P = 4.3e-4
P = 1.6e-2
P = 1.4e-2
P = 5.34e-5
P = 9.8e-3
P = 8.52e-5
P = 4.05e-4
P = 1.2-3
P = 2.78e-8
P = 7.08e-13
P = 4.61e-7
P = 1.83e-4
P = 4.61e-8
P = 1.96e-6
P = 6.08e-4
P = 6.12e-5P = 1.86e-5
P = 3.75e-4P = 1.5e-3
P = 1.1e-4P = 2.85e-4
B
C
D
Figure 3. Gene targets that are predicted to reduce the AFR and their association with prognostic markers of breast cancer patients.
A A schematic representation of the 12 predicted gene targets, marked in red.B Ten predicted targets that show a significantly higher expression in metastatic versus non-metastatic tumor samples (n = 295).C Nine predicted targets that show a significantly higher expression in grade 3 versus grade 1 tumor samples (n = 236).D Nine predicted targets whose lower expression is significantly associated with improved long-term survival (n = 1568).
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
6
corresponding metabolic reaction to zero. The biomass function
utilized here is taken from (Folger et al, 2011). The media simu-
lated in all the analyses throughout the paper is the RPMI-1640
media that was used to grow the cell lines experimentally (Lee
et al, 2007; Choy et al, 2008).
Building cell-specific metabolic models and computing lactate secretion
Our method to reconstruct the NCI-60 cancer cell lines (see Supple-
mentary Material, based on the yet unpublished methods in Yizhak
et al, submitted) required several key inputs: (a) the generic human
model (Duarte et al, 2007), (b) gene expression data for each cancer
cell line from (Lee et al, 2007), and (c) growth rate measurements.
The algorithm then reconstructs a specific metabolic model for each
sample by modifying the upper bounds of growth-associated reac-
tions in accordance with their gene expression (Note: the growth
rates were used only to determine which reactions should be used
in constraining the models, in order to obtain models that were as
physiologically relevant as possible; they were not used to deter-
mine reaction bounds). A similar procedure was used to reconstruct
the lymphoblast metabolic models (Choy et al, 2008) for compari-
son against normal proliferating cells. A more detailed description is
found in the Supplementary Material.
Simulations of the Warburg effect include the examination of
minimal lactate production rate under different demands for
biomass production, glucose, glutamine, and oxygen uptake rates
(Supplementary Material). We examined the minimal value of
lactate secretion as it testifies whether or not the cell is enforced to
secrete lactate under a given condition (Supplementary Fig S1). All
the correlations reported in the paper are Spearman rank correla-
tions and their associated P-values are computed using the exact
permutation distribution.
Calculating wild-type and perturbed lactate secretion rates and
OCR levels
For simulating lactate secretion under normoxic conditions (when
comparing to Jain et al (Jain et al, 2012), Wu et al (Wu et al, 2007)
and the breast cancer data collected in this paper), oxygen maximal
uptake rate was set to the highest value under which minimal
lactate secretion is positive. Since metabolic models are designed to
maximize growth yield rather than growth rate, using an unlimited
amount of oxygen in GSMM simulations will result in a state where
the minimal lactate secretion rate equals zero. However, it’s impor-
tant to note that even under the limited oxygen levels simulated
here, the generic human model doesn’t show lactate secretion (as
opposed to the NCI-60 cancer cell line models described above). For
simulating the hypoxic conditions measured here for the breast
cancer cell lines, we lowered the oxygen maximal uptake rate by
50% of its normoxic state as described above. Under each of these
conditions, we sampled the solution space under maximal biomass
yield and obtained 1,000 feasible flux distributions (Bordel et al,
2010). The predicted lactate secretion rate is the average lactate
secretion flux over these samples. For emulating the perturbation
experiments in Wu et al we gradually lowered the bound of the
corresponding compound target (from the maximal bound to 0)
and repeated the procedure described above for computing
the ECAR (lactate secretion) and the OCR, which in a similar
manner is defined as the average oxygen consumption flux across
all samples.
Calculating the EOR and AFR measures for assessing the Warburg level
of the cell lines and using them to predict drug response
The EOR and AFR measures were calculated in a similar manner to
that described above. Specifically, the EOR is calculated as the mean
over lactate secretion across all samples divided by the mean over
oxygen consumption across all samples. Similarly, the AFR is calcu-
lated as the mean flux carried by the reactions producing ATP in
glycolysis versus the mean flux carried by the reaction producing
ATP in OXPHOS. To determine an empiric P-value in the drug
response analysis we randomly shuffled the drug response data
1,000 times, each time examining the resulting Wilcoxon P-value
over the original set of cell lines.
Predicting the effect of reaction knockouts
Each metabolic reaction in each cell line model is perturbed by
constraining its flux to zero. Under each perturbation the minimal
lactate secretion (under maximal growth rate) and the maximal
growth rate is calculated. The set of reactions that eliminate forced
lactate secretion while maintaining a level of cell growth that is
> 10% of the wild-type growth prediction is further tested for the
AFR level. The mean AFR level for each cell line under each of these
perturbations is calculated over 1,000 flux distribution samples as
described above. The final set of predicted reactions includes those
whose knockout reduces the AFR to below 60% of its wild-type level.
Datasets
Growth rate measurements and drug response data were down-
loaded from the NCI website.
Growth rate: http://dtp.nci.nih.gov/docs/misc/common_files/
cell_list.html
Drug response: http://discover.nci.nih.gov/nature2000/naturein
tromain.jsp
Experimentally measuring lactate secretion rates of breastcancer cell lines
Cell Culture
The MCF7, T47D, Hs578T and BT549 breast cancer cell lines were
obtained from the American Type Culture Collection and London
Research Institute Cell Services. Cells were cultured in DMEM/F12
(1:1), with 2 mM L-glutamine and penicillin/streptomycin. Medium
was supplemented with 10% FCS (GIBCO) for the cancer cell lines
and 5% horse serum, 20 ng/ml EGF, 5 lg/ml hydrocortisone,
10 lg/ml insulin, and 100 ng/ml cholera toxin for the non-
malignant cell lines.
Lactate secretion measurements
Cells were cultured under normoxic (20% O2) and hypoxic (0.5%
O2) conditions for 72 h. Cells were starved of glucose and glutamine
for 1 h and full medium was added for 1 h. Lactate secretion was
determined from normoxic and hypoxic cells and normalized to
cell growth (increase in total protein during the 72 h incubation in
normoxia). Lactate concentrations in media incubated with or
without cells were determined using lactate assay kits (BioVision).
Total protein content determined by Sulforhodamine B assay was
used for normalization. Two experiments were performed with
three or four biologically independent replicates (total of seven
replicates).
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
9
in those without, inhibits cell proliferation. Accordingly, as PHGDH
is not amplified in the cell line MDA-MB-231 which was examined
in both studies, its suppression is indeed non-lethal. However, we
show that its suppression significantly attenuates cell migration,
suggesting that metabolic enzymes can promote different cancerous
phenotypes in different cancer cells.
Remarkably, analyzing the model-predicted flux rates has
successfully uncovered a fundamental association between the AFR
and cancer migration, even given the relatively small set of cell lines
for which migration was measured. Our analysis has also revealed
other potential associations between individual fluxes and cell
migration (Supplementary Fig S4). However, future studies measur-
ing cellular migration data across a much wider array of cell lines
(of the order for which we already have proliferation data) are
needed to determine the actual significance of these potential leads.
As this study has shown, cellular proliferation and migration have
distinct underlying metabolite correlates; understanding the meta-
bolic correlates that are strongly associated with cell migration may
lead to new anti-metastatic treatment opportunities. It is important
to note, however, that while the inhibition of migration alone might
be a good strategy for avoiding the adverse side effects of cytotoxic
treatment, cell migration is a crucial process also in normal physiol-
ogy, for instance, in immune response and tissue repair (Forster
et al, 1999; Ridley et al, 2003). Therefore, future anti-migratory
drugs may pose different drug selectivity challenges that should be
carefully addressed in the future studies. Irrespectively, they may
result in lesser clonal selection, and as a result, their usage may be
accompanied with lesser rate of emergence of drug-resistant clones.
Materials and Methods
Computational methods
Genome-scale metabolic modeling (GSSM)
A metabolic network consisting of m metabolites and n reactions
can be represented by a stoichiometric matrix S, where the entry Sijrepresents the stoichiometric coefficient of metabolite i in reaction j
(Price et al, 2004). A CBM model imposes mass balance, directional-
ity, and flux capacity constraints on the space of possible fluxes in
the metabolic network’s reactions through a set of linear equations:
Sv ¼ 0 (1)
vmin � v� vmax (2)
where v stands for the flux vector for all of the reactions in the
model (i.e. the flux distribution). The exchange of metabolites with
the environment is represented as a set of exchange (transport)
reactions, enabling a pre-defined set of metabolites to be either
taken up or secreted from the growth media. The steady-state
assumption represented in equation (1) constrains the production
rate of each metabolite to be equal to its consumption rate. Enzy-
matic directionality and flux capacity constraints define lower and
upper bounds on the fluxes and are embedded in equation (2).
In the following, flux vectors satisfying these conditions will
be referred to as feasible steady-state flux distributions. Gene
knockouts are simulated by constraining the flux through the
Figure 5. ECAR and OCR levels of top predicted gene targets.
A Mean and SEM (normalized to nuclear count) ECAR and OCR levels after silencing of seven different genes (HK2, PGAM1, PGK2, GAPDH, PSPH, AHCY, and PHGDH)compared to the control. Silencing of the four glycolytic genes results in both a decrease in ECAR levels (x-axis) and an increase in OCR levels (y-axis), while theserine- and methionine-associated genes show only a decrease in ECAR levels. Error bars represent SEM. The number of samples is n = 18.
B Mean and SD of computed ECAR/OCR (EOR) levels for control and selected gene silencing (Materials and Methods). For all genes a significant reduction in EOR levelsis observed. Error bars represent SD. The number of samples is n = 18.
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
8
corresponding metabolic reaction to zero. The biomass function
utilized here is taken from (Folger et al, 2011). The media simu-
lated in all the analyses throughout the paper is the RPMI-1640
media that was used to grow the cell lines experimentally (Lee
et al, 2007; Choy et al, 2008).
Building cell-specific metabolic models and computing lactate secretion
Our method to reconstruct the NCI-60 cancer cell lines (see Supple-
mentary Material, based on the yet unpublished methods in Yizhak
et al, submitted) required several key inputs: (a) the generic human
model (Duarte et al, 2007), (b) gene expression data for each cancer
cell line from (Lee et al, 2007), and (c) growth rate measurements.
The algorithm then reconstructs a specific metabolic model for each
sample by modifying the upper bounds of growth-associated reac-
tions in accordance with their gene expression (Note: the growth
rates were used only to determine which reactions should be used
in constraining the models, in order to obtain models that were as
physiologically relevant as possible; they were not used to deter-
mine reaction bounds). A similar procedure was used to reconstruct
the lymphoblast metabolic models (Choy et al, 2008) for compari-
son against normal proliferating cells. A more detailed description is
found in the Supplementary Material.
Simulations of the Warburg effect include the examination of
minimal lactate production rate under different demands for
biomass production, glucose, glutamine, and oxygen uptake rates
(Supplementary Material). We examined the minimal value of
lactate secretion as it testifies whether or not the cell is enforced to
secrete lactate under a given condition (Supplementary Fig S1). All
the correlations reported in the paper are Spearman rank correla-
tions and their associated P-values are computed using the exact
permutation distribution.
Calculating wild-type and perturbed lactate secretion rates and
OCR levels
For simulating lactate secretion under normoxic conditions (when
comparing to Jain et al (Jain et al, 2012), Wu et al (Wu et al, 2007)
and the breast cancer data collected in this paper), oxygen maximal
uptake rate was set to the highest value under which minimal
lactate secretion is positive. Since metabolic models are designed to
maximize growth yield rather than growth rate, using an unlimited
amount of oxygen in GSMM simulations will result in a state where
the minimal lactate secretion rate equals zero. However, it’s impor-
tant to note that even under the limited oxygen levels simulated
here, the generic human model doesn’t show lactate secretion (as
opposed to the NCI-60 cancer cell line models described above). For
simulating the hypoxic conditions measured here for the breast
cancer cell lines, we lowered the oxygen maximal uptake rate by
50% of its normoxic state as described above. Under each of these
conditions, we sampled the solution space under maximal biomass
yield and obtained 1,000 feasible flux distributions (Bordel et al,
2010). The predicted lactate secretion rate is the average lactate
secretion flux over these samples. For emulating the perturbation
experiments in Wu et al we gradually lowered the bound of the
corresponding compound target (from the maximal bound to 0)
and repeated the procedure described above for computing
the ECAR (lactate secretion) and the OCR, which in a similar
manner is defined as the average oxygen consumption flux across
all samples.
Calculating the EOR and AFR measures for assessing the Warburg level
of the cell lines and using them to predict drug response
The EOR and AFR measures were calculated in a similar manner to
that described above. Specifically, the EOR is calculated as the mean
over lactate secretion across all samples divided by the mean over
oxygen consumption across all samples. Similarly, the AFR is calcu-
lated as the mean flux carried by the reactions producing ATP in
glycolysis versus the mean flux carried by the reaction producing
ATP in OXPHOS. To determine an empiric P-value in the drug
response analysis we randomly shuffled the drug response data
1,000 times, each time examining the resulting Wilcoxon P-value
over the original set of cell lines.
Predicting the effect of reaction knockouts
Each metabolic reaction in each cell line model is perturbed by
constraining its flux to zero. Under each perturbation the minimal
lactate secretion (under maximal growth rate) and the maximal
growth rate is calculated. The set of reactions that eliminate forced
lactate secretion while maintaining a level of cell growth that is
> 10% of the wild-type growth prediction is further tested for the
AFR level. The mean AFR level for each cell line under each of these
perturbations is calculated over 1,000 flux distribution samples as
described above. The final set of predicted reactions includes those
whose knockout reduces the AFR to below 60% of its wild-type level.
Datasets
Growth rate measurements and drug response data were down-
loaded from the NCI website.
Growth rate: http://dtp.nci.nih.gov/docs/misc/common_files/
cell_list.html
Drug response: http://discover.nci.nih.gov/nature2000/naturein
tromain.jsp
Experimentally measuring lactate secretion rates of breastcancer cell lines
Cell Culture
The MCF7, T47D, Hs578T and BT549 breast cancer cell lines were
obtained from the American Type Culture Collection and London
Research Institute Cell Services. Cells were cultured in DMEM/F12
(1:1), with 2 mM L-glutamine and penicillin/streptomycin. Medium
was supplemented with 10% FCS (GIBCO) for the cancer cell lines
and 5% horse serum, 20 ng/ml EGF, 5 lg/ml hydrocortisone,
10 lg/ml insulin, and 100 ng/ml cholera toxin for the non-
malignant cell lines.
Lactate secretion measurements
Cells were cultured under normoxic (20% O2) and hypoxic (0.5%
O2) conditions for 72 h. Cells were starved of glucose and glutamine
for 1 h and full medium was added for 1 h. Lactate secretion was
determined from normoxic and hypoxic cells and normalized to
cell growth (increase in total protein during the 72 h incubation in
normoxia). Lactate concentrations in media incubated with or
without cells were determined using lactate assay kits (BioVision).
Total protein content determined by Sulforhodamine B assay was
used for normalization. Two experiments were performed with
three or four biologically independent replicates (total of seven
replicates).
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
9
in those without, inhibits cell proliferation. Accordingly, as PHGDH
is not amplified in the cell line MDA-MB-231 which was examined
in both studies, its suppression is indeed non-lethal. However, we
show that its suppression significantly attenuates cell migration,
suggesting that metabolic enzymes can promote different cancerous
phenotypes in different cancer cells.
Remarkably, analyzing the model-predicted flux rates has
successfully uncovered a fundamental association between the AFR
and cancer migration, even given the relatively small set of cell lines
for which migration was measured. Our analysis has also revealed
other potential associations between individual fluxes and cell
migration (Supplementary Fig S4). However, future studies measur-
ing cellular migration data across a much wider array of cell lines
(of the order for which we already have proliferation data) are
needed to determine the actual significance of these potential leads.
As this study has shown, cellular proliferation and migration have
distinct underlying metabolite correlates; understanding the meta-
bolic correlates that are strongly associated with cell migration may
lead to new anti-metastatic treatment opportunities. It is important
to note, however, that while the inhibition of migration alone might
be a good strategy for avoiding the adverse side effects of cytotoxic
treatment, cell migration is a crucial process also in normal physiol-
ogy, for instance, in immune response and tissue repair (Forster
et al, 1999; Ridley et al, 2003). Therefore, future anti-migratory
drugs may pose different drug selectivity challenges that should be
carefully addressed in the future studies. Irrespectively, they may
result in lesser clonal selection, and as a result, their usage may be
accompanied with lesser rate of emergence of drug-resistant clones.
Materials and Methods
Computational methods
Genome-scale metabolic modeling (GSSM)
A metabolic network consisting of m metabolites and n reactions
can be represented by a stoichiometric matrix S, where the entry Sijrepresents the stoichiometric coefficient of metabolite i in reaction j
(Price et al, 2004). A CBM model imposes mass balance, directional-
ity, and flux capacity constraints on the space of possible fluxes in
the metabolic network’s reactions through a set of linear equations:
Sv ¼ 0 (1)
vmin � v� vmax (2)
where v stands for the flux vector for all of the reactions in the
model (i.e. the flux distribution). The exchange of metabolites with
the environment is represented as a set of exchange (transport)
reactions, enabling a pre-defined set of metabolites to be either
taken up or secreted from the growth media. The steady-state
assumption represented in equation (1) constrains the production
rate of each metabolite to be equal to its consumption rate. Enzy-
matic directionality and flux capacity constraints define lower and
upper bounds on the fluxes and are embedded in equation (2).
In the following, flux vectors satisfying these conditions will
be referred to as feasible steady-state flux distributions. Gene
knockouts are simulated by constraining the flux through the
Figure 5. ECAR and OCR levels of top predicted gene targets.
A Mean and SEM (normalized to nuclear count) ECAR and OCR levels after silencing of seven different genes (HK2, PGAM1, PGK2, GAPDH, PSPH, AHCY, and PHGDH)compared to the control. Silencing of the four glycolytic genes results in both a decrease in ECAR levels (x-axis) and an increase in OCR levels (y-axis), while theserine- and methionine-associated genes show only a decrease in ECAR levels. Error bars represent SEM. The number of samples is n = 18.
B Mean and SD of computed ECAR/OCR (EOR) levels for control and selected gene silencing (Materials and Methods). For all genes a significant reduction in EOR levelsis observed. Error bars represent SD. The number of samples is n = 18.
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
8
computations. SLD, VMR and VCB performed the experimental procedures. KY,
SLD, BvW, and ER wrote the paper.
Conflict of interestThe authors declare that they have no conflict of interest.
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functional analysis. Mol Syst Biol 3: 135
Mardinoglu A, Agren R, Kampf C, Asplund A, Nookaew I, Jacobson P, Walley
AJ, Froguel P, Carlsson LM, Uhlen M, Nielsen J (2013) Integration of clinical
data with a genome-scale metabolic model of the human adipocyte. Mol
Syst Biol 9: 649
ª 2014 The Authors Molecular Systems Biology 10: 744 | 2014
Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
11
Cell culture for live cell imaging and cell migration assays
T47D, MCF-7, MDA-MB-435, BT549, MDA-MB-231 and Hs578t were
cultured in RPMI (GIBCO, Life Technologies, Carlsbad, CA, USA)
supplemented with 10% FBS (PAA, Pashing Austria) and 100
International Units/ml penicillin and 100 lg/ml streptomycin
(Invitrogen, Carlsbad, CA, USA).
Gene silencing
Human siRNA SmartPools (a combination of four individual singles)
for the 17 predicted genes were purchased in siGENOME format
from Dharmacon (Lafayette, CO, USA). Plates were diluted to 1 lMworking concentration in complementary 1× siRNA buffer in a
96-well plate format. A non-targeting siRNA was used as negative
control. A 50 nM reverse transfection was performed according to
manufacturer’s guidelines. Complex time was 20 min and 5,000
cells were added. The plate was placed in the incubator overnight
and the medium was refreshed the following morning. After
48–72 h cells were used for various assays. Cell migration and meta-
bolic flux assay experiments were performed in duplicate while the
cell proliferation assay was performed in triplicate.
Live cell imaging random cell migration assay
Glass bottom 96-well plates (Greiner Bio-one, Monroe, NC, USA)
were coated with 20 lg/ll collagen type I (isolated from rat tails)
for 1 h at 37°C. 48 h after silencing, the MDA-MB-231 cells were re-
plated onto the collagen-coated glass bottom plate. 24 h after seed-
ing, cells were pre-exposed for 45 min to 0.1 lg/µl Hoechst 33342(Fisher Scientific, Hampton, NH, USA) to visualize nuclei. After
refreshing the medium, cells were placed on a Nikon Eclipse
TE2000-E microscope fitted with a 37°C incubation chamber, 20×
objective (0.75 NA, 1.00 WD) automated stage and perfect focus
system. Three positions per well were automatically defined, and
the Differential Interference Contrast (DIC) and Hoechst signals
were acquired with a CCD camera (Pixel size: 0.64 lm) every
20 min for a total imaging period of 12 h using NIS software
(Nikon). All data were converted and analyzed using custom-made
ImagePro Plus macros (Roosmalen et al, 2011). Cell migration was
quantified by tracking nuclei in time. Changes in migration speed
per knockdown were evaluated via a two-sided t-test comparing the
speed for every individual cell followed overtime for 16 h and the
corresponding control values. Data shown are normalized to control
and represent only one replicate. Of note, for all four cell lines both
replicates showed a R2 of reproducibility above 0.75. Genes achiev-
ing P-value < 0.05 after correcting for multiple hypothesis using
FDR with a = 0.05 are considered as hits.
Proliferation assay
Cells were directly transfected and plated onto micro-clear 96-well
plates (Greiner Bio-one). After 5 days of incubation, the cells were
stained with Hoechst 33342 and fixed with TCA (Trichloroacetic
acid) allowing both a nuclear counting and/or Sulforodamine B
(SRB) readout. Whole wells were imaged using epi-fluorescence
and the number of nuclei was determined using a custom-made
ImagePro macro. Plates were further processed for SRB staining as
described earlier (Zhang et al, 2011). SRB data showed a complete
overlap with the nuclear count so this measure is used in all
figures. Changes in proliferation rates upon knockdown when
compared to control were evaluated in triplicate via a two-sided
t-test. The mean proliferation rate after knockdown between all
three replicates was calculated and normalized to the non-targeting
siRNA (= control). Genes achieving P-value < 0.05 after correcting
for multiple hypothesis using FDR with a = 0.05 are considered as
hits.
Metabolic flux assay
The bioenergetics flux of cells in response to gene silencing was
assessed using the Seahorse XF96 extracellular flux analyzer
(Seahorse Bioscience). About 8,000 MDA-MB-231 cells per well
(Seahorse plate) were treated with siRNAs or control for 72 h. Each
gene (in total 7) was knockdown in six different wells and the
experiment was performed twice (so a total of six replicates per
plate and two plates). Prior to measurement, the medium was
replaced with unbuffered DMEM XF assay medium. The basal
oxygen consumption rate (OCR) and extracellular acidification rate
(ECAR) were then determined using the XP96 plate reader with the
standard program as recommended by the manufacturer: three
measurements per well were done (so for each gene 18 measure-
ments were obtained for both OCR and ECAR). After the measure-
ments were completed, the plates were live stained with Hoechst
33342 for 1 h and fixed with TCA allowing both a nuclear counting
and/or SRB readout. Whole wells were imaged using epi-fluores-
cence and the number of nuclei was determined using a custom-
made ImagePro macro. Plates were further processed for SRB stain-
ing as described earlier (Zhang et al, 2011). SRB data showed a
complete overlap with the nuclear count so this measure was used
for normalization. All values are normalized to nuclear count. EOR
for control and each gene knockdown is computed by dividing the
corresponding ECAR and OCR values. A two-sided t-test is applied
to examine significant changes between control and knockdown-
induced EOR.
Supplementary information for this article is available online:
http://msb.embopress.org
AcknowledgementsWe would like to thank Hans de Bont and Michiel Fokkelman for their technical
support, Yoav Teboulle, Matthew Oberhardt, Edoardo Gaude, Gideon Y. Stein
and Tami Geiger for their helpful comments on the manuscript. KY is partially
supported by a fellowship from the Edmond J. Safra Bioinformatics center at
Tel-Aviv University and is grateful to the Azrieli Foundation for the award of an
Azrieli Fellowship; SLD is supported by the Netherlands Consortium for Systems
Biology and the EU FP7 Systems Microscopy NoE project (258068) and BvdW
from the Netherlands Genomics Initiative. ER acknowledges the generous
support of grants from the Israeli Science Foundation (ISF), the Israeli Cancer
Research Fund (ICRF) and the I-CORE Program of the Planning and Budgeting
Committee and The Israel Science Foundation (grant No 41/11).
Author contributionsKY and ER conceived and designed the research. SLD, VCB, CF, and BvW
designed the experimental procedures. FB and AS contributed the lactate
secretion data. KY performed the computational analysis and the statistical
Molecular Systems Biology 10: 744 | 2014 ª 2014 The Authors
Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
10
computations. SLD, VMR and VCB performed the experimental procedures. KY,
SLD, BvW, and ER wrote the paper.
Conflict of interestThe authors declare that they have no conflict of interest.
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Keren Yizhak et al Identifying anti-migratory metabolic drug targets Molecular Systems Biology
11
Cell culture for live cell imaging and cell migration assays
T47D, MCF-7, MDA-MB-435, BT549, MDA-MB-231 and Hs578t were
cultured in RPMI (GIBCO, Life Technologies, Carlsbad, CA, USA)
supplemented with 10% FBS (PAA, Pashing Austria) and 100
International Units/ml penicillin and 100 lg/ml streptomycin
(Invitrogen, Carlsbad, CA, USA).
Gene silencing
Human siRNA SmartPools (a combination of four individual singles)
for the 17 predicted genes were purchased in siGENOME format
from Dharmacon (Lafayette, CO, USA). Plates were diluted to 1 lMworking concentration in complementary 1× siRNA buffer in a
96-well plate format. A non-targeting siRNA was used as negative
control. A 50 nM reverse transfection was performed according to
manufacturer’s guidelines. Complex time was 20 min and 5,000
cells were added. The plate was placed in the incubator overnight
and the medium was refreshed the following morning. After
48–72 h cells were used for various assays. Cell migration and meta-
bolic flux assay experiments were performed in duplicate while the
cell proliferation assay was performed in triplicate.
Live cell imaging random cell migration assay
Glass bottom 96-well plates (Greiner Bio-one, Monroe, NC, USA)
were coated with 20 lg/ll collagen type I (isolated from rat tails)
for 1 h at 37°C. 48 h after silencing, the MDA-MB-231 cells were re-
plated onto the collagen-coated glass bottom plate. 24 h after seed-
ing, cells were pre-exposed for 45 min to 0.1 lg/µl Hoechst 33342(Fisher Scientific, Hampton, NH, USA) to visualize nuclei. After
refreshing the medium, cells were placed on a Nikon Eclipse
TE2000-E microscope fitted with a 37°C incubation chamber, 20×
objective (0.75 NA, 1.00 WD) automated stage and perfect focus
system. Three positions per well were automatically defined, and
the Differential Interference Contrast (DIC) and Hoechst signals
were acquired with a CCD camera (Pixel size: 0.64 lm) every
20 min for a total imaging period of 12 h using NIS software
(Nikon). All data were converted and analyzed using custom-made
ImagePro Plus macros (Roosmalen et al, 2011). Cell migration was
quantified by tracking nuclei in time. Changes in migration speed
per knockdown were evaluated via a two-sided t-test comparing the
speed for every individual cell followed overtime for 16 h and the
corresponding control values. Data shown are normalized to control
and represent only one replicate. Of note, for all four cell lines both
replicates showed a R2 of reproducibility above 0.75. Genes achiev-
ing P-value < 0.05 after correcting for multiple hypothesis using
FDR with a = 0.05 are considered as hits.
Proliferation assay
Cells were directly transfected and plated onto micro-clear 96-well
plates (Greiner Bio-one). After 5 days of incubation, the cells were
stained with Hoechst 33342 and fixed with TCA (Trichloroacetic
acid) allowing both a nuclear counting and/or Sulforodamine B
(SRB) readout. Whole wells were imaged using epi-fluorescence
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siRNA (= control). Genes achieving P-value < 0.05 after correcting
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hits.
Metabolic flux assay
The bioenergetics flux of cells in response to gene silencing was
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(Seahorse Bioscience). About 8,000 MDA-MB-231 cells per well
(Seahorse plate) were treated with siRNAs or control for 72 h. Each
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Supplementary information for this article is available online:
http://msb.embopress.org
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Biology and the EU FP7 Systems Microscopy NoE project (258068) and BvdW
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Research Fund (ICRF) and the I-CORE Program of the Planning and Budgeting
Committee and The Israel Science Foundation (grant No 41/11).
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License: This is an open access article under the
terms of the Creative Commons Attribution 4.0
License, which permits use, distribution and reproduc-
tion in any medium, provided the original work is
properly cited.
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Molecular Systems Biology Identifying anti-migratory metabolic drug targets Keren Yizhak et al
12
Review
Principles of targeting endothelial cell metabolismto treat angiogenesis and endothelial celldysfunction in diseaseJermaine Goveia1,2, Peter Stapor1,2 & Peter Carmeliet1,2,*
Abstract
The endothelium is the orchestral conductor of blood vessel func-tion. Pathological blood vessel formation (a process termed patho-logical angiogenesis) or the inability of endothelial cells (ECs) toperform their physiological function (a condition known as ECdysfunction) are defining features of various diseases. Therapeuticintervention to inhibit aberrant angiogenesis or ameliorate ECdysfunction could be beneficial in diseases such as cancer andcardiovascular disease, respectively, but current strategies havelimited efficacy. Based on recent findings that pathological angio-genesis and EC dysfunction are accompanied by EC-specific meta-bolic alterations, targeting EC metabolism is emerging as a noveltherapeutic strategy. Here, we review recent progress in ourunderstanding of how EC metabolism is altered in disease anddiscuss potential metabolic targets and strategies to reverse ECdysfunction and inhibit pathological angiogenesis.
Keywords angiogenesis; endothelial cell dysfunction; metabolism
DOI 10.15252/emmm.201404156 | Received 8 April 2014 | Revised 14 June
2014 | Accepted 3 July 2014 | Published online 25 July 2014
EMBO Mol Med (2014) 6: 1105–1120
See also Glossary for abbreviations used in this article.
Introduction
Blood vessels perform many functions that are critical for tissue
homeostasis (Carmeliet, 2003). The endothelium, a single layer of
endothelial cells (ECs) that lines the blood vessel lumen, controls
vessel function. EC functions include the regulation of vascular tone
and barrier, leukocyte trafficking, blood coagulation, nutrient and
electrolyte uptake and neovascularization of hypoxic tissue, to name
only a few (Cines et al, 1998; Pober et al, 2009; Potente et al, 2011).
Many diseases are characterized by pathological blood vessel
responses or formation. The inability of ECs to perform their physio-
logical function (a condition termed EC dysfunction) contributes to
cardiovascular disease and diabetes (Davignon & Ganz, 2004),
whereas diseases such as cancer and age-related macula degenera-
tion are characterized by new blood vessel formation (a process
termed angiogenesis) (Carmeliet & Jain, 2011). Targeting ECs to
prevent dysfunction or inhibit pathological angiogenesis is poten-
tially beneficial for a wide variety of diseases, but current treatment
modalities, focusing primarily on growth factors, receptors, signal-
ing molecules and others have limited efficacy or specificity (Bergers
& Hanahan, 2008; Versari et al, 2009; Lee et al, 2012).
An emerging but understudied therapeutic target is EC metabo-
lism. It has been long known that risk factors for cardiovascular
disease (hypercholesterolemia, hypertension, dyslipidemia, diabe-
tes, obesity and aging) cause EC-specific metabolic perturbations
leading to EC dysfunction (Davignon & Ganz, 2004; Pober et al,
2009). Similarly, the links between EC metabolism and angiogene-
sis are apparent as angiogenic ECs migrate and proliferate in
metabolically challenging environments such as hypoxic and
nutrient-deprived tissue (Harjes et al, 2012). Moreover, the
growth factor-induced switch from a quiescent to an angiogenic
phenotype is mediated by important adaptations in EC energy
metabolism (De Bock et al, 2013a,b; Schoors et al, 2014a,b). EC
metabolic alterations are therefore not just innocent bystanders
but mediate pathogenesis. In this review, we summarize existing
data on the role of EC metabolism in mediating vascular disease
and discuss how metabolism may be targeted for therapeutic
benefit.
General endothelial metabolism
Despite their close proximity to oxygenated blood, ECs rely on
glycolysis instead of oxidative metabolism for adenosine triphos-
phate (ATP) production (Parra-Bonilla et al, 2010; De Bock et al,
2013b). In fact, under physiological conditions, over 80% of ATP is
produced by converting glucose into lactate (Fig 1). Less than 1% of
glucose-derived pyruvate enters the mitochondria for oxidative
metabolism through the tricarboxylic acid cycle (TCA) and subse-
quent ATP production via the electron transport chain (ETC) (Fig 1)
(Culic et al, 1997; De Bock et al, 2013b). However, ECs retain the
ability to switch to oxidative metabolism of glucose, amino acids
1 Laboratory of Angiogenesis and Neurovascular Link, Vesalius Research Center, Department of Oncology, University of Leuven, Leuven, Belgium2 Laboratory of Angiogenesis and Neurovascular Link, Vesalius Research Center, VIB, Leuven, Belgium
*Corresponding author. Tel: +32 16 37 32 02; Fax: +32 16 37 25 85; E-mail: [email protected]
ª 2014 The Authors. Published under the terms of the CC BY 4.0 license EMBO Molecular Medicine Vol 6 | No 9 | 2014 1105
ETC
MethylglyoxalAGEsMETHYLGLYOXAL PATHWAY
Glucose
GLUT1Glucose
G6P
F6P
F1,6P2
G3PDHAP
F2,6P2
PFKFB3PFK
TKT R5P
RPIPENTOSEPHOSPHATEPATHWAY
NUCLEOTIDESYNTHESIS
GFAT
3PG
G6PDRu5P
6PGDPOLYOL PATHWAY
Sorbitol
NADPH
AR
NADP+
3DG
NAD+NADH
Fructose
AGEs
ATP
ADPPGK
GAPDHNAD+
NADH
GlucN6PUDP-GlcNAcGlycosylation
HEXOSAMINE BIOSYNTHESIS PATHWAY
NADPHNADP+ NADPHNADP+OXIDATIVE
NON-OXIDATIVE
GLYCOLYSIS
NADPH REDOXREGULATION
Cysteine
Folate
PyruvateLactateLDHMCT
GLUTAMINE METABOLISM
ORNITHINE CYCLE
Pyruvate
GS
GLS
α-ketoglutarate
NADPH
NADP+IDH1
NADPH
NADP+
Isocitrate
Citrate
Aspartate
L-ornithine
ARG
Citrulline
Arginine
Aspartate
Oxaloacetate
Malate
NO
ODC
POLYAMINESYNTHESIS
PENTOSE PHOSPHATE PATHWAYONE-CARBON METABOLISM ORNITHINE CYCLE POLYAMINE SYNTHESIS
Fumarate
Ornithine
FA FACPT
Oxaloacetate
Malate
Acetyl-CoA
ME
TCA CYCLE
POLYOL PATHWAY TCA CYCLE
Citrate
Isocitrate
α-ketoglutarate
Succinate
Fumarate
Malate
Oxaloacetate
IDH2
NADPH NADP+
Glutamate-γ-semialdehydeProline
ATP
ADP
Lactate
FATTY ACIDβ-OXIDATION
HEXOSAMINE BIOSYNTHESIS PATHWAYGLYCOLYSISGLUTAMINE METABOLISMFATTY ACID β-OXIDATION
HMG-CoA
Cholesterol
MEVALONATEPATHWAY
METHYLGLYOXAL PATHWAY NUCLEOTIDE SYNTHESISNADPH REDOX REGULATIONMEVALONATE PATHWAY
eNOS
ATP
NADH
GSH
Acetyl-CoA
Glutamine
SLC1A5
HK
Serine
Glycine
Methylation
METHIONINEMETABOLISM
FOLATEMETABOLISM
ONE-CARBONMETABOLISM
SERINEMETABOLISM
meTHF
THF
hCYS
METSAM
SAH
MS
mTHF
GlutamineGlutamate Glutamate
Mitochondria
Figure 1. Overview of general EC metabolism.For clarity, not all metabolites and enzymes of the depicted pathways are shown. Abbreviations: 3DG: 3-deoxyglucosone; 3PG: 3-phosphoglycerate; 6PGD: 6-phosphogluconatedehydrogenase; AGE: advanced glycation end-product; AR: aldose reductase; ARG: arginase; ATP: adenosine triphosphate; CPT: carnitine palmitoyltransferase; DHAP:dihydroxyacetone phosphate; eNOS: endothelial nitric oxide synthase; ETC: electron transport chain; F6P: fructose 6-phosphate; F1,6P2: fructose 1,6-bisphosphate; F2,6P2:fructose 2,6 bisphosphate; FA: fatty acid; G6P: glucose 6-phosphate; G6PD: glucose 6-phosphate dehydrogenase; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; GFAT:glutamine-6-phosphate amidotransferase; GlucN6P: glucosamine-6-phosphate; GLS: glutaminase; GLUT: glucose transporter; GS: glutamine synthetase; GSH: glutathione:hCYS: homocysteine; HMG-CoA: hydroxymethylglutaryl coenzyme A; IDH; isocitrate dehydrogenase; LDH: lactate dehydrogenase; MCT: monocarboxylate transporter; ME:malic enzyme; MET: methionine; meTHF: 5.10-methylene-tetrahydrofolate; mTHF: 5-methyltetrahydrofolate; MS: methionine synthetase; NAD: nicotinamide adeninedinucleotide; NADPH: nicotinamide adenine dinucleotide phosphate; NO: nitric oxide; ODC: ornithine decarboxylase; PFK1: phosphofructokinase-1 PFKFB3: 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3; PGK: phosphoglycerate kinase; ROS: reactive oxygen species; RPI: ribose-5-phosphate isomerase; SAH: S-adenosylhomocysteine: SAM:S-adenosylmethionine; TCA cycle: tricarboxylic acid cycle; THF: tetrahydrofolate; TKT: transketolase; UDP-GlcNAc: uridine diphosphate N-acetylglucosamine.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1107
and fatty acids in case of reduced glycolytic rates (Krutzfeldt et al,
1990; Dranka et al, 2010).
ECs lining peripheral tissue vessels or the blood brain barrier
(BBB) express multiple members of the two major families of sugar
transporters, that is, glucose transporters (GLUT) and sodium/
glucose co-transporters (SGLTs), but the high-affinity GLUT1 is
considered to be the main route of glucose uptake in ECs (Fig 1)
(Mann et al, 2003; Gaudreault et al, 2004, 2008; Sahoo et al, 2014).
Phosphorylation of intracellular glucose by hexokinase (HK)
destines it for metabolic utilization, predominately by conversion to
lactate via glycolysis (Fig 1) (Paik et al, 2005; De Bock et al,
2013b). Glycolytic intermediates also serve as precursors for biosyn-
thetic pathways including the pentose phosphate pathway (PPP),
hexosamine biosynthesis and glycogenesis (Fig 1, for an extensive
review see (De Bock et al, 2013a,b)).
The PPP consists of oxidative and non-oxidative branches, and
its overall flux is determined by the rate-limiting enzyme glucose-6-
phosphate dehydrogenase (G6PD) (Fig 1). Partially regulated by
VEGF signaling, G6PD destines glucose-6-phosphate (G6P) for
utilization in the PPP (Pan et al, 2009). The oxidative branch of the
PPP converts G6P into ribulose-5-phosphate (Ru5P) and produces
NADPH from NADP+, thereby generating reducing power to main-
tain EC redox balance and biosynthetic reactions (Dobrina & Rossi,
1983; Jongkind et al, 1989; Spolarics & Spitzer, 1993; Spolarics &
Wu, 1997; Vizan et al, 2009). The non-oxidative branch converts
Ru5P into xylulose-5-phosphate (Xu5P) and ribose-5-phosphate
(R5P), the latter is necessary for nucleotide biosynthesis (Pandolfi
et al, 1995). However, PPP intermediates may also be converted
back into glycolytic intermediates via the action of transketolase
(TKT) and transaldolase. These reactions are reversible, allowing
biosynthesis of macromolecules from glycolytic metabolites via the
non-oxidative arm.
The hexosamine biosynthesis pathway starts with the conver-
sion of the glycolytic intermediate fructose-6-phosphate (F6P) into
glucosamine-6-phosphate (GlucN6P) (Fig 1). GlucN6P is then
metabolized to uridine diphosphate N-acetylglucosamine (UDP-
GlcNAc), a key substrate for glycosylation reactions that control
many aspects of EC function (Benedito et al, 2009; Laczy et al,
Glossary
1C metabolismA complex metabolic network characterized by the transfer of carbonfrom serine/glycine for folate compound chemical reactions andinvolved in nucleotide, lipid and protein biosynthesis, redoxhomeostasis and production of methylation substrates.Advanced glycation end products (AGEs)Proteins or lipids that have been non-enzymatically glycated, often asa result of hyperglycemia and/or oxidative stress, that causedamaging intracellular and extracellular dysfunction.AngiogenesisGrowth of new blood vessels from existing microvasculature.EndotheliumContinuous inner lining of all vasculature composed of endothelialcells (ECs), which regulates physiological vascular function andangiogenesis.EC dysfunctionInability of endothelial cells to fulfill their physiological role asmediators of the blood barrier and vasotone.Fatty acid oxidationMetabolism of fatty acids in mitochondria into acetyl-CoA to fuel theTCA cycle.GlycolysisAnaerobic metabolism of glucose producing ATP and pyruvateGlycosylationA post-translational modification that enzymatically adds glycans, oroligosaccharides, to proteins and lipids.Hexosamine biosynthesis pathwaySide pathway from glycolytic intermediate fructose 6-phosphate (F6P)that produces substrates for glycosylation.IsoprenoidMevalonate pathway intermediates used for the production ofcholesterol and as substrates for prenylation.Metabolic fluxFlow of metabolites through a given metabolic pathway.Metabolic flux analysisQuantification of metabolic flux by tracing the fate of Isotope-labeledsubstrates.MetabolismThe spectrum of organic and chemical cellular reactions dedicated tothe production of energy and building blocks for general maintenanceand functionality.
Methylglyoxal pathwayGlycolytic side pathway from dihydroxyacetone phosphate (DHAP)that results in production of methylglyoxal and/or AGEs.Oxidative metabolismAerobic metabolic pathways that break down substrates throughoxidation for energy production and biosynthesis.Pentose phosphate pathwayMetabolic pathway important for redox homeostasis and biosynthesiswhich utilizes glucose-derived glucose-6-phosphate (G6P) forproduction of NADPH through its oxidative branch, and fructose 6-phosphate (F6P) and 3-phosphoglycerate (3PG) for nucleotideproduction in its non-oxidative branch.Polyol pathwayPathway implicated in diabetic endothelial dysfunction by reductionof glucose into sorbitol and then fructose to fuel production of AGEs.PrenylationPost-translational addition of isoprenoids such as farnesyl or geranyl–geranyl to a protein.QuiescenceCell state defined by a lack of activity.Reactive nitrogen speciesHighly reactive nitrogen-containing molecules that often interact withROS, promote oxidative stress and reduce bioavailability of nitricoxide.Reactive oxygen species (ROS)Highly reactive molecules that contain oxygen (produced by aerobicmetabolic processes) and are involved in normal cell homeostasis andsignaling, but whose accumulation, termed oxidative stress, leads tocell damage.Stalk cellEndothelial cells that trail migratory tip cells and proliferate to extendgrowth of a new blood vessel during sprouting angiogenesis.Tip cellMigratory endothelial cells that lead spouting microvessels up achemokine gradient during angiogenesis.
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1106
ETC
MethylglyoxalAGEsMETHYLGLYOXAL PATHWAY
Glucose
GLUT1Glucose
G6P
F6P
F1,6P2
G3PDHAP
F2,6P2
PFKFB3PFK
TKT R5P
RPIPENTOSEPHOSPHATEPATHWAY
NUCLEOTIDESYNTHESIS
GFAT
3PG
G6PDRu5P
6PGDPOLYOL PATHWAY
Sorbitol
NADPH
AR
NADP+
3DG
NAD+NADH
Fructose
AGEs
ATP
ADPPGK
GAPDHNAD+
NADH
GlucN6PUDP-GlcNAcGlycosylation
HEXOSAMINE BIOSYNTHESIS PATHWAY
NADPHNADP+ NADPHNADP+OXIDATIVE
NON-OXIDATIVE
GLYCOLYSIS
NADPH REDOXREGULATION
Cysteine
Folate
PyruvateLactateLDHMCT
GLUTAMINE METABOLISM
ORNITHINE CYCLE
Pyruvate
GS
GLS
α-ketoglutarate
NADPH
NADP+IDH1
NADPH
NADP+
Isocitrate
Citrate
Aspartate
L-ornithine
ARG
Citrulline
Arginine
Aspartate
Oxaloacetate
Malate
NO
ODC
POLYAMINESYNTHESIS
PENTOSE PHOSPHATE PATHWAYONE-CARBON METABOLISM ORNITHINE CYCLE POLYAMINE SYNTHESIS
Fumarate
Ornithine
FA FACPT
Oxaloacetate
Malate
Acetyl-CoA
ME
TCA CYCLE
POLYOL PATHWAY TCA CYCLE
Citrate
Isocitrate
α-ketoglutarate
Succinate
Fumarate
Malate
Oxaloacetate
IDH2
NADPH NADP+
Glutamate-γ-semialdehydeProline
ATP
ADP
Lactate
FATTY ACIDβ-OXIDATION
HEXOSAMINE BIOSYNTHESIS PATHWAYGLYCOLYSISGLUTAMINE METABOLISMFATTY ACID β-OXIDATION
HMG-CoA
Cholesterol
MEVALONATEPATHWAY
METHYLGLYOXAL PATHWAY NUCLEOTIDE SYNTHESISNADPH REDOX REGULATIONMEVALONATE PATHWAY
eNOS
ATP
NADH
GSH
Acetyl-CoA
Glutamine
SLC1A5
HK
Serine
Glycine
Methylation
METHIONINEMETABOLISM
FOLATEMETABOLISM
ONE-CARBONMETABOLISM
SERINEMETABOLISM
meTHF
THF
hCYS
METSAM
SAH
MS
mTHF
GlutamineGlutamate Glutamate
Mitochondria
Figure 1. Overview of general EC metabolism.For clarity, not all metabolites and enzymes of the depicted pathways are shown. Abbreviations: 3DG: 3-deoxyglucosone; 3PG: 3-phosphoglycerate; 6PGD: 6-phosphogluconatedehydrogenase; AGE: advanced glycation end-product; AR: aldose reductase; ARG: arginase; ATP: adenosine triphosphate; CPT: carnitine palmitoyltransferase; DHAP:dihydroxyacetone phosphate; eNOS: endothelial nitric oxide synthase; ETC: electron transport chain; F6P: fructose 6-phosphate; F1,6P2: fructose 1,6-bisphosphate; F2,6P2:fructose 2,6 bisphosphate; FA: fatty acid; G6P: glucose 6-phosphate; G6PD: glucose 6-phosphate dehydrogenase; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; GFAT:glutamine-6-phosphate amidotransferase; GlucN6P: glucosamine-6-phosphate; GLS: glutaminase; GLUT: glucose transporter; GS: glutamine synthetase; GSH: glutathione:hCYS: homocysteine; HMG-CoA: hydroxymethylglutaryl coenzyme A; IDH; isocitrate dehydrogenase; LDH: lactate dehydrogenase; MCT: monocarboxylate transporter; ME:malic enzyme; MET: methionine; meTHF: 5.10-methylene-tetrahydrofolate; mTHF: 5-methyltetrahydrofolate; MS: methionine synthetase; NAD: nicotinamide adeninedinucleotide; NADPH: nicotinamide adenine dinucleotide phosphate; NO: nitric oxide; ODC: ornithine decarboxylase; PFK1: phosphofructokinase-1 PFKFB3: 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3; PGK: phosphoglycerate kinase; ROS: reactive oxygen species; RPI: ribose-5-phosphate isomerase; SAH: S-adenosylhomocysteine: SAM:S-adenosylmethionine; TCA cycle: tricarboxylic acid cycle; THF: tetrahydrofolate; TKT: transketolase; UDP-GlcNAc: uridine diphosphate N-acetylglucosamine.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1107
and fatty acids in case of reduced glycolytic rates (Krutzfeldt et al,
1990; Dranka et al, 2010).
ECs lining peripheral tissue vessels or the blood brain barrier
(BBB) express multiple members of the two major families of sugar
transporters, that is, glucose transporters (GLUT) and sodium/
glucose co-transporters (SGLTs), but the high-affinity GLUT1 is
considered to be the main route of glucose uptake in ECs (Fig 1)
(Mann et al, 2003; Gaudreault et al, 2004, 2008; Sahoo et al, 2014).
Phosphorylation of intracellular glucose by hexokinase (HK)
destines it for metabolic utilization, predominately by conversion to
lactate via glycolysis (Fig 1) (Paik et al, 2005; De Bock et al,
2013b). Glycolytic intermediates also serve as precursors for biosyn-
thetic pathways including the pentose phosphate pathway (PPP),
hexosamine biosynthesis and glycogenesis (Fig 1, for an extensive
review see (De Bock et al, 2013a,b)).
The PPP consists of oxidative and non-oxidative branches, and
its overall flux is determined by the rate-limiting enzyme glucose-6-
phosphate dehydrogenase (G6PD) (Fig 1). Partially regulated by
VEGF signaling, G6PD destines glucose-6-phosphate (G6P) for
utilization in the PPP (Pan et al, 2009). The oxidative branch of the
PPP converts G6P into ribulose-5-phosphate (Ru5P) and produces
NADPH from NADP+, thereby generating reducing power to main-
tain EC redox balance and biosynthetic reactions (Dobrina & Rossi,
1983; Jongkind et al, 1989; Spolarics & Spitzer, 1993; Spolarics &
Wu, 1997; Vizan et al, 2009). The non-oxidative branch converts
Ru5P into xylulose-5-phosphate (Xu5P) and ribose-5-phosphate
(R5P), the latter is necessary for nucleotide biosynthesis (Pandolfi
et al, 1995). However, PPP intermediates may also be converted
back into glycolytic intermediates via the action of transketolase
(TKT) and transaldolase. These reactions are reversible, allowing
biosynthesis of macromolecules from glycolytic metabolites via the
non-oxidative arm.
The hexosamine biosynthesis pathway starts with the conver-
sion of the glycolytic intermediate fructose-6-phosphate (F6P) into
glucosamine-6-phosphate (GlucN6P) (Fig 1). GlucN6P is then
metabolized to uridine diphosphate N-acetylglucosamine (UDP-
GlcNAc), a key substrate for glycosylation reactions that control
many aspects of EC function (Benedito et al, 2009; Laczy et al,
Glossary
1C metabolismA complex metabolic network characterized by the transfer of carbonfrom serine/glycine for folate compound chemical reactions andinvolved in nucleotide, lipid and protein biosynthesis, redoxhomeostasis and production of methylation substrates.Advanced glycation end products (AGEs)Proteins or lipids that have been non-enzymatically glycated, often asa result of hyperglycemia and/or oxidative stress, that causedamaging intracellular and extracellular dysfunction.AngiogenesisGrowth of new blood vessels from existing microvasculature.EndotheliumContinuous inner lining of all vasculature composed of endothelialcells (ECs), which regulates physiological vascular function andangiogenesis.EC dysfunctionInability of endothelial cells to fulfill their physiological role asmediators of the blood barrier and vasotone.Fatty acid oxidationMetabolism of fatty acids in mitochondria into acetyl-CoA to fuel theTCA cycle.GlycolysisAnaerobic metabolism of glucose producing ATP and pyruvateGlycosylationA post-translational modification that enzymatically adds glycans, oroligosaccharides, to proteins and lipids.Hexosamine biosynthesis pathwaySide pathway from glycolytic intermediate fructose 6-phosphate (F6P)that produces substrates for glycosylation.IsoprenoidMevalonate pathway intermediates used for the production ofcholesterol and as substrates for prenylation.Metabolic fluxFlow of metabolites through a given metabolic pathway.Metabolic flux analysisQuantification of metabolic flux by tracing the fate of Isotope-labeledsubstrates.MetabolismThe spectrum of organic and chemical cellular reactions dedicated tothe production of energy and building blocks for general maintenanceand functionality.
Methylglyoxal pathwayGlycolytic side pathway from dihydroxyacetone phosphate (DHAP)that results in production of methylglyoxal and/or AGEs.Oxidative metabolismAerobic metabolic pathways that break down substrates throughoxidation for energy production and biosynthesis.Pentose phosphate pathwayMetabolic pathway important for redox homeostasis and biosynthesiswhich utilizes glucose-derived glucose-6-phosphate (G6P) forproduction of NADPH through its oxidative branch, and fructose 6-phosphate (F6P) and 3-phosphoglycerate (3PG) for nucleotideproduction in its non-oxidative branch.Polyol pathwayPathway implicated in diabetic endothelial dysfunction by reductionof glucose into sorbitol and then fructose to fuel production of AGEs.PrenylationPost-translational addition of isoprenoids such as farnesyl or geranyl–geranyl to a protein.QuiescenceCell state defined by a lack of activity.Reactive nitrogen speciesHighly reactive nitrogen-containing molecules that often interact withROS, promote oxidative stress and reduce bioavailability of nitricoxide.Reactive oxygen species (ROS)Highly reactive molecules that contain oxygen (produced by aerobicmetabolic processes) and are involved in normal cell homeostasis andsignaling, but whose accumulation, termed oxidative stress, leads tocell damage.Stalk cellEndothelial cells that trail migratory tip cells and proliferate to extendgrowth of a new blood vessel during sprouting angiogenesis.Tip cellMigratory endothelial cells that lead spouting microvessels up achemokine gradient during angiogenesis.
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1106
retinal and hindbrain vascularization in mice, showing that
increased glycolytic flux is required for growth factor-induced angio-
genesis (De Bock et al, 2013b). Moreover, PFKFB3 overexpression
in zebrafish drives EC specification into sprout forming tip cells,
even in the presence of tip cell-inhibitory Notch signals that
promote proliferating stalk elongating cells (De Bock et al, 2013b).
Increased glycolysis not only provides energy for proliferation and
biosynthesis, but also for locomotion. Specifically, PFKFB3 and
other glycolytic enzymes co-localize with F-actin bundles in filopodia
and lamellipodia to produce ATP needed for rapid actin remodeling,
underlying locomotion and tip cell formation (De Bock et al,
2013b).
The important role of glycolysis in angiogenesis provides
opportunities for therapeutic targeting. Indeed, pharmacological
blockade with 3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO)
or EC-specific genetic silencing of PFKFB3 inhibits tumor growth in
vivo (Xu et al, 2014). In addition, 3PO inhibits glycolytic flux
partially and transiently and has recently shown efficacy in reducing
pathological angiogenesis in a variety of disease models (Schoors
et al, 2014b; Xu et al, 2014). The systemic harm caused by inhibit-
ing glycolysis is minimal, however, showing that even moderate,
short-term impairment of glycolysis renders ECs more quiescent
without overt detrimental side effects (Schoors et al, 2014b). The
finding that partial and transient reduction of glycolysis may be
sufficient to inhibit pathological angiogenesis provides a paradigm
shift in our thinking about anti-glycolytic therapies, away from
complete and permanent blockade of glycolysis, which can induce
undesired adverse systemic effects.
Aside from serving as an energy source or building blocks for
biosynthesis, glycolytic metabolites can also modulate angiogenesis
by acting as bona fide signaling molecules. This is evidenced by the
observation that glycolytic tumor cells secrete lactate, which is
taken up by ECs through the monocarboxylate transporter 1 (MCT1)
(Fig 2A) (Sonveaux et al, 2012). Instead of being metabolized,
A The glycolytic pathway drives pathological angiogenesis
TCA CYCLE
HEXOSAMINE BIOSYNTHESIS PATHA WAYAAGLYCOLL LYSISLL
PHD REGULATION
Pyruvate
Acetyl-CoA
Lactate Lactate
PHD2
PFKFB3
HIF1α
IL-8FGFVEGFR-2
MCT
Glucose
Glucose
GLUT1
F6P
F1,6P2
GlucN6P UDP-GlcNAc
VEGFR-2glycosylationPFK
LDH
Galectin-1 VEGFindependentsignaling
ReducedIncreased
αα-ketoglutarate--ketoglutarate
FATGFAT
Acetyl-CoAFatty acidoxidation
B Altered glucose metabolism and low NO levels are associatedwith pulmonary artery hypertension
PENTOSE PHOSPHATE PATHWAY
ORNITHINE CYCLE
POLYAMINEYY SYNTHESIS
TCA CYCLE
GLYCOLL LYSISLL
FATTYA ACID β-OXIDATIONA
Pyruvate
GLUT1
G6P
F6P
Lactate
Glucose
Citrate
Isocitrate
α-ketoglutarateIDH
MnSOD
ROS
Lactate
L-arginine
eNOS
Ornithine
ARG
NO
6PGXu5P
R5P
F6P
G3PG3P
Glucose
6PDG6PDRPIA
RPETKT
PutrescineSpermidineSpermineSRMSMS
ODC
PGK
ReducedIncreased
Ru5P
Figure 2. Metabolic pathways implicated in diseases characterized by pathological angiogenesis or hyperproliferative ECs.(A) Angiogenic ECs rely on glycolysis, instead of oxidative metabolism, for ATP production and upregulate PFKFB3 to increase the conversion of glucose into lactate throughglycolysis. Lactate is secreted and taken up through lactate transporters. High Lactate influx through MCT1 results in increased intracellular lactate levels that compete witha-ketoglutarate for PHD-2 binding, leading to HIF-1a stabilization and upregulation of pro-angiogenic genes. VEGFR-2 glycosylation is required for galectin-1-induced VEGF-independent signaling. (B) PAH ECs are metabolically characterized by high aerobic glycolysis and low oxidative metabolism. NO production through eNOS is impaired due toupregulation of arginase II and increased oxidative stress due to limited availability of MnSOD. In addition, several enzymes in the pentose phosphate pathway and polyaminebiosynthesis pathway are differentially expressed in PAH ECs, but the importance of these findings remains to be determined (B). Green font / bold line: upregulated, red font /broken line: downregulated. For clarity, not all metabolites and enzymes of the depicted pathways are shown. Abbreviations: as in Fig 1. FGF: fibroblast growth factor; HIF:hypoxia-inducible factor; IL: interleukin; PHD: prolyl hydroxylase domain; R5P: ribose-5-phosphate; RPE: ribulose-5-phosphate 3-epimerase; RPIA: ribose-5-phosphateisomerase; Ru5P: ribulose-5-phosphate; SRM: spermidine synthase; VEGFR: vascular endothelial growth factor receptor; Xu5P: xylulose-5-phosphate.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1109
2009; Croci et al, 2014). The polyol pathway and methylglyoxal
pathways are glycolysis side-pathways that are mostly known for
their role in cardiovascular disease (Fig 1; see below) (Goldin
et al, 2006).
Other metabolic pathways are less well characterized in ECs.
Fatty acid (FA) oxidation (FAO) and glutamine oxidation have been
implicated in replenishing the TCA cycle to produce ATP via oxida-
tive phosphorylation (Fig 1) (Leighton et al, 1987; Hinshaw &
Burger, 1990; Dagher et al, 1999, 2001; De Bock et al, 2013b).
However, since ECs predominately rely on glucose metabolism to
provide ATP, the energetic function of FAO and glutamine oxidation
is not clear (De Bock et al, 2013b). FAs and amino acids can serve
as precursors for biomass production, but such a role in ECs has not
been demonstrated using isotope tracer labeling studies. FAO
produces significant amounts of nicotinamide adenine dinucleotide
phosphate (NADPH), which is an important co-factor in many
biosynthetic reactions and essential to maintain redox balance. In
addition, FAO generates acetyl-coA which is another important
precursor for biomolecule production.
For example, acetyl-CoA is used, among other things, for the
synthesis of cholesterol via the mevalonate pathway (Fig 1).
Although endothelial cholesterol metabolism has been poorly stud-
ied, perturbations in cholesterol homeostasis are known to affect
key EC functions such as intracellular signaling, inflammatory acti-
vation, nitric oxide synthesis and angiogenesis (Boger et al, 2000;
Ivashchenko et al, 2010; Whetzel et al, 2010; Xu et al, 2010; Fang
et al, 2013). ECs express all the cholesterol biosynthesis enzymes
and the LDL receptor for extracellular uptake (Fig 1). These proteins
are under transcriptional control of the sterol regulatory element
binding protein (SREBP1 and -2) and liver X receptors (LXR)
(Noghero et al, 2012). SREBP1 and LXRs inhibit cholesterol synthe-
sis and absorption, whereas SREBP2 induces synthesis and inhibits
cholesterol efflux via transcriptional repression of the ATP-binding
cassette (ABC) transporter 1 ABCA1, which together with ABCG1
mediates cholesterol efflux from ECs (Hassan et al, 2006). Notably,
endothelial SREBP2 also controls expression of arginine metabolism
enzymes, although the physiological significance of this interaction
between cholesterol and arginine metabolism remains to be deter-
mined (Zeng et al, 2004).
Arginine and glutamine are the best studied amino acids
(AAs) in ECs. Arginine is a metabolite in the ornithine cycle and
converted into citruline and nitric oxide (NO) by endothelial
nitric oxide synthase (eNOS) (Fig 1) (Sessa et al, 1990). Altera-
tions in arginine and eNOS metabolism are among the best-
characterized causes of EC dysfunction and a prime therapeutic
target (Leiper & Nandi, 2011). Glutamine is the most abundant
AA in the peripheral blood and preferentially taken up by ECs
via the solute carrier family 1 member 5 (SLC1A5) trans-
porter (Fig 1) (Herskowitz et al, 1991; Pan et al, 1995).
Glutamine-utilizing pathways are mainly biosynthetic and can be
divided into those that utilize the c-nitrogen (nucleotide biosyn-
thesis, hexosamine biosynthesis, asparagine synthesis) and those
that use the a-nitrogen or carbon backbone (DeBerardinis &
Cheng, 2010). The latter reactions use glutamine-derived gluta-
mate rather than glutamine itself and include glutathione (GSH)
synthesis, anaplerotic refueling of the TCA cycle and biosynthesis
of polyamines, proline and other non-essential AAs (NEAAs)
(Fig 1) (DeBerardinis & Cheng, 2010).
Serine and glycine are especially interesting examples of gluta-
mine / glutamate-derived NEAAs, not only because of their direct
effects on ECs (Weinberg et al, 1992; Rose et al, 1999; Yamashina
et al, 2001; Mishra et al, 2008; den Eynden et al, 2009; McCarty
et al, 2009; Stobart et al, 2013), but also since their synthesis
requires both the glutamate a-nitrogen and the glycolytic intermedi-
ate 3-phosphoglycerate (3PG) (Fig 1) (Locasale, 2013). Hence,
serine and glycine metabolism integrates metabolic input from
central carbon (glycolysis) and nitrogen (glutamine) metabolism.
Moreover, the reversible interconversion of serine and glycine is
directly coupled to one-carbon metabolism, intermediates of which
are considered important targets to treat cardiovascular disease
(Fig 1; see below) (Locasale, 2013). In fact, while EC metabolism is
largely understudied, several of the above-mentioned metabolic
pathways have been implicated as mediators of pathological angio-
genesis or EC dysfunction.
EC metabolism in diseases characterized by angiogenesisand EC hyperproliferation
Cancer
Tumors need blood vessels to supply oxygen and detoxify waste
products (Jain, 1987; Papetti & Herman, 2002; Welti et al, 2013).
When tumors become too large to allow adequate diffusion of
oxygen and nutrients from local vasculature they secrete pro-
angiogenic growth factors to induce angiogenesis (Bergers &
Benjamin, 2003). Pharmacological inhibition of growth factor
signaling (primarily vascular endothelial growth factor (VEGF)
signaling) is the only clinically approved anti-angiogenic strategy,
but the benefits are limited as tumors acquire resistance within
months after treatment initiation (Bergers & Hanahan, 2008; Carme-
liet & Jain, 2011; Ebos & Kerbel, 2011; Welti et al, 2013). Escape
from anti-angiogenic therapy is mediated by increased secretion of
pro-angiogenic factors, activation of alternative angiogenic signaling
pathways, recruitment of pro-angiogenic accessory cells and other
mechanisms (Loges et al, 2010; Sennino & McDonald, 2012). A
recent report indicated that glycosylation-dependent interactions of
galectin-1 with VEGF receptor 2 (VEGFR2) could activate pro-angio-
genic signaling even when the VEGF ligand is blocked (Fig 2A)
(Croci et al, 2014). Hence, angiogenic signaling is robust and redun-
dant, and inhibition of individual signaling molecules and growth
factors can be overcome by escape mechanisms.
The switch from a quiescent to an angiogenic phenotype (as
occurs in cancer) is metabolically demanding and mediated by
adaptations in EC metabolism (Fig 2A). While the changes in meta-
bolic fluxes of ECs, freshly isolated from tumors, have not been
characterized yet, ECs in tumors and inflamed tissues likely
resemble highly activated ECs. Lactate dehydrogenase B (LDH-B) is
upregulated in tumor endothelium, and VEGF signaling increases
glycolytic flux by inducing GLUT1 and the glycolytic enzyme
6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3)
(Fig 2A) (van Beijnum et al, 2006; Yeh et al, 2008; De Bock et al,
2013b). PFKFB3 catalyzes the synthesis of fructose-2,6-bisphosphate
(F2,6P2), which is an allosteric activator of 6-phosphofructo-1-kinase
(PFK-1) (Van Schaftingen et al, 1982). PFK-1 converts fructose-6-
phosphate (F6P) to fructose-1,6-bisphosphate (F1,6P2) in the rate-
limiting step of glycolysis. EC-specific PFKFB3 deletion diminishes
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1108
retinal and hindbrain vascularization in mice, showing that
increased glycolytic flux is required for growth factor-induced angio-
genesis (De Bock et al, 2013b). Moreover, PFKFB3 overexpression
in zebrafish drives EC specification into sprout forming tip cells,
even in the presence of tip cell-inhibitory Notch signals that
promote proliferating stalk elongating cells (De Bock et al, 2013b).
Increased glycolysis not only provides energy for proliferation and
biosynthesis, but also for locomotion. Specifically, PFKFB3 and
other glycolytic enzymes co-localize with F-actin bundles in filopodia
and lamellipodia to produce ATP needed for rapid actin remodeling,
underlying locomotion and tip cell formation (De Bock et al,
2013b).
The important role of glycolysis in angiogenesis provides
opportunities for therapeutic targeting. Indeed, pharmacological
blockade with 3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO)
or EC-specific genetic silencing of PFKFB3 inhibits tumor growth in
vivo (Xu et al, 2014). In addition, 3PO inhibits glycolytic flux
partially and transiently and has recently shown efficacy in reducing
pathological angiogenesis in a variety of disease models (Schoors
et al, 2014b; Xu et al, 2014). The systemic harm caused by inhibit-
ing glycolysis is minimal, however, showing that even moderate,
short-term impairment of glycolysis renders ECs more quiescent
without overt detrimental side effects (Schoors et al, 2014b). The
finding that partial and transient reduction of glycolysis may be
sufficient to inhibit pathological angiogenesis provides a paradigm
shift in our thinking about anti-glycolytic therapies, away from
complete and permanent blockade of glycolysis, which can induce
undesired adverse systemic effects.
Aside from serving as an energy source or building blocks for
biosynthesis, glycolytic metabolites can also modulate angiogenesis
by acting as bona fide signaling molecules. This is evidenced by the
observation that glycolytic tumor cells secrete lactate, which is
taken up by ECs through the monocarboxylate transporter 1 (MCT1)
(Fig 2A) (Sonveaux et al, 2012). Instead of being metabolized,
A The glycolytic pathway drives pathological angiogenesis
TCA CYCLE
HEXOSAMINE BIOSYNTHESIS PATHA WAYAAGLYCOLL LYSISLL
PHD REGULATION
Pyruvate
Acetyl-CoA
Lactate Lactate
PHD2
PFKFB3
HIF1α
IL-8FGFVEGFR-2
MCT
Glucose
Glucose
GLUT1
F6P
F1,6P2
GlucN6P UDP-GlcNAc
VEGFR-2glycosylationPFK
LDH
Galectin-1 VEGFindependentsignaling
ReducedIncreased
αα-ketoglutarate--ketoglutarate
FATGFAT
Acetyl-CoAFatty acidoxidation
B Altered glucose metabolism and low NO levels are associatedwith pulmonary artery hypertension
PENTOSE PHOSPHATE PATHWAY
ORNITHINE CYCLE
POLYAMINEYY SYNTHESIS
TCA CYCLE
GLYCOLL LYSISLL
FATTYA ACID β-OXIDATIONA
Pyruvate
GLUT1
G6P
F6P
Lactate
Glucose
Citrate
Isocitrate
α-ketoglutarateIDH
MnSOD
ROS
Lactate
L-arginine
eNOS
Ornithine
ARG
NO
6PGXu5P
R5P
F6P
G3PG3P
Glucose
6PDG6PDRPIA
RPETKT
PutrescineSpermidineSpermineSRMSMS
ODC
PGK
ReducedIncreased
Ru5P
Figure 2. Metabolic pathways implicated in diseases characterized by pathological angiogenesis or hyperproliferative ECs.(A) Angiogenic ECs rely on glycolysis, instead of oxidative metabolism, for ATP production and upregulate PFKFB3 to increase the conversion of glucose into lactate throughglycolysis. Lactate is secreted and taken up through lactate transporters. High Lactate influx through MCT1 results in increased intracellular lactate levels that compete witha-ketoglutarate for PHD-2 binding, leading to HIF-1a stabilization and upregulation of pro-angiogenic genes. VEGFR-2 glycosylation is required for galectin-1-induced VEGF-independent signaling. (B) PAH ECs are metabolically characterized by high aerobic glycolysis and low oxidative metabolism. NO production through eNOS is impaired due toupregulation of arginase II and increased oxidative stress due to limited availability of MnSOD. In addition, several enzymes in the pentose phosphate pathway and polyaminebiosynthesis pathway are differentially expressed in PAH ECs, but the importance of these findings remains to be determined (B). Green font / bold line: upregulated, red font /broken line: downregulated. For clarity, not all metabolites and enzymes of the depicted pathways are shown. Abbreviations: as in Fig 1. FGF: fibroblast growth factor; HIF:hypoxia-inducible factor; IL: interleukin; PHD: prolyl hydroxylase domain; R5P: ribose-5-phosphate; RPE: ribulose-5-phosphate 3-epimerase; RPIA: ribose-5-phosphateisomerase; Ru5P: ribulose-5-phosphate; SRM: spermidine synthase; VEGFR: vascular endothelial growth factor receptor; Xu5P: xylulose-5-phosphate.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1109
2009; Croci et al, 2014). The polyol pathway and methylglyoxal
pathways are glycolysis side-pathways that are mostly known for
their role in cardiovascular disease (Fig 1; see below) (Goldin
et al, 2006).
Other metabolic pathways are less well characterized in ECs.
Fatty acid (FA) oxidation (FAO) and glutamine oxidation have been
implicated in replenishing the TCA cycle to produce ATP via oxida-
tive phosphorylation (Fig 1) (Leighton et al, 1987; Hinshaw &
Burger, 1990; Dagher et al, 1999, 2001; De Bock et al, 2013b).
However, since ECs predominately rely on glucose metabolism to
provide ATP, the energetic function of FAO and glutamine oxidation
is not clear (De Bock et al, 2013b). FAs and amino acids can serve
as precursors for biomass production, but such a role in ECs has not
been demonstrated using isotope tracer labeling studies. FAO
produces significant amounts of nicotinamide adenine dinucleotide
phosphate (NADPH), which is an important co-factor in many
biosynthetic reactions and essential to maintain redox balance. In
addition, FAO generates acetyl-coA which is another important
precursor for biomolecule production.
For example, acetyl-CoA is used, among other things, for the
synthesis of cholesterol via the mevalonate pathway (Fig 1).
Although endothelial cholesterol metabolism has been poorly stud-
ied, perturbations in cholesterol homeostasis are known to affect
key EC functions such as intracellular signaling, inflammatory acti-
vation, nitric oxide synthesis and angiogenesis (Boger et al, 2000;
Ivashchenko et al, 2010; Whetzel et al, 2010; Xu et al, 2010; Fang
et al, 2013). ECs express all the cholesterol biosynthesis enzymes
and the LDL receptor for extracellular uptake (Fig 1). These proteins
are under transcriptional control of the sterol regulatory element
binding protein (SREBP1 and -2) and liver X receptors (LXR)
(Noghero et al, 2012). SREBP1 and LXRs inhibit cholesterol synthe-
sis and absorption, whereas SREBP2 induces synthesis and inhibits
cholesterol efflux via transcriptional repression of the ATP-binding
cassette (ABC) transporter 1 ABCA1, which together with ABCG1
mediates cholesterol efflux from ECs (Hassan et al, 2006). Notably,
endothelial SREBP2 also controls expression of arginine metabolism
enzymes, although the physiological significance of this interaction
between cholesterol and arginine metabolism remains to be deter-
mined (Zeng et al, 2004).
Arginine and glutamine are the best studied amino acids
(AAs) in ECs. Arginine is a metabolite in the ornithine cycle and
converted into citruline and nitric oxide (NO) by endothelial
nitric oxide synthase (eNOS) (Fig 1) (Sessa et al, 1990). Altera-
tions in arginine and eNOS metabolism are among the best-
characterized causes of EC dysfunction and a prime therapeutic
target (Leiper & Nandi, 2011). Glutamine is the most abundant
AA in the peripheral blood and preferentially taken up by ECs
via the solute carrier family 1 member 5 (SLC1A5) trans-
porter (Fig 1) (Herskowitz et al, 1991; Pan et al, 1995).
Glutamine-utilizing pathways are mainly biosynthetic and can be
divided into those that utilize the c-nitrogen (nucleotide biosyn-
thesis, hexosamine biosynthesis, asparagine synthesis) and those
that use the a-nitrogen or carbon backbone (DeBerardinis &
Cheng, 2010). The latter reactions use glutamine-derived gluta-
mate rather than glutamine itself and include glutathione (GSH)
synthesis, anaplerotic refueling of the TCA cycle and biosynthesis
of polyamines, proline and other non-essential AAs (NEAAs)
(Fig 1) (DeBerardinis & Cheng, 2010).
Serine and glycine are especially interesting examples of gluta-
mine / glutamate-derived NEAAs, not only because of their direct
effects on ECs (Weinberg et al, 1992; Rose et al, 1999; Yamashina
et al, 2001; Mishra et al, 2008; den Eynden et al, 2009; McCarty
et al, 2009; Stobart et al, 2013), but also since their synthesis
requires both the glutamate a-nitrogen and the glycolytic intermedi-
ate 3-phosphoglycerate (3PG) (Fig 1) (Locasale, 2013). Hence,
serine and glycine metabolism integrates metabolic input from
central carbon (glycolysis) and nitrogen (glutamine) metabolism.
Moreover, the reversible interconversion of serine and glycine is
directly coupled to one-carbon metabolism, intermediates of which
are considered important targets to treat cardiovascular disease
(Fig 1; see below) (Locasale, 2013). In fact, while EC metabolism is
largely understudied, several of the above-mentioned metabolic
pathways have been implicated as mediators of pathological angio-
genesis or EC dysfunction.
EC metabolism in diseases characterized by angiogenesisand EC hyperproliferation
Cancer
Tumors need blood vessels to supply oxygen and detoxify waste
products (Jain, 1987; Papetti & Herman, 2002; Welti et al, 2013).
When tumors become too large to allow adequate diffusion of
oxygen and nutrients from local vasculature they secrete pro-
angiogenic growth factors to induce angiogenesis (Bergers &
Benjamin, 2003). Pharmacological inhibition of growth factor
signaling (primarily vascular endothelial growth factor (VEGF)
signaling) is the only clinically approved anti-angiogenic strategy,
but the benefits are limited as tumors acquire resistance within
months after treatment initiation (Bergers & Hanahan, 2008; Carme-
liet & Jain, 2011; Ebos & Kerbel, 2011; Welti et al, 2013). Escape
from anti-angiogenic therapy is mediated by increased secretion of
pro-angiogenic factors, activation of alternative angiogenic signaling
pathways, recruitment of pro-angiogenic accessory cells and other
mechanisms (Loges et al, 2010; Sennino & McDonald, 2012). A
recent report indicated that glycosylation-dependent interactions of
galectin-1 with VEGF receptor 2 (VEGFR2) could activate pro-angio-
genic signaling even when the VEGF ligand is blocked (Fig 2A)
(Croci et al, 2014). Hence, angiogenic signaling is robust and redun-
dant, and inhibition of individual signaling molecules and growth
factors can be overcome by escape mechanisms.
The switch from a quiescent to an angiogenic phenotype (as
occurs in cancer) is metabolically demanding and mediated by
adaptations in EC metabolism (Fig 2A). While the changes in meta-
bolic fluxes of ECs, freshly isolated from tumors, have not been
characterized yet, ECs in tumors and inflamed tissues likely
resemble highly activated ECs. Lactate dehydrogenase B (LDH-B) is
upregulated in tumor endothelium, and VEGF signaling increases
glycolytic flux by inducing GLUT1 and the glycolytic enzyme
6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3)
(Fig 2A) (van Beijnum et al, 2006; Yeh et al, 2008; De Bock et al,
2013b). PFKFB3 catalyzes the synthesis of fructose-2,6-bisphosphate
(F2,6P2), which is an allosteric activator of 6-phosphofructo-1-kinase
(PFK-1) (Van Schaftingen et al, 1982). PFK-1 converts fructose-6-
phosphate (F6P) to fructose-1,6-bisphosphate (F1,6P2) in the rate-
limiting step of glycolysis. EC-specific PFKFB3 deletion diminishes
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1108
of intracellular NADPH, which is necessary to convert oxidized
glutathione (GSSH) into reduced GSH, a critical ROS scavenger
(Fig 3A) (Leopold et al, 2003; Zhang et al, 2012). Therefore, by
reducing PPP flux, high glucose depletes NADPH levels and
contributes to ROS accumulation (Goldin et al, 2006). Interest-
ingly, G6PD overexpression restores redox homeostasis in high
glucose cultured ECs (Leopold et al, 2003; Zhang et al, 2012).
Some studies suggest that high glucose shifts the normally glyco-
lytic EC metabolism toward oxidative metabolism and increased
mitochondrial respiration (Fig 3). However, these results appear
contextual, as other studies did not report such an induction of
oxidative metabolism (Nishikawa et al, 2000; Koziel et al, 2012;
Pangare & Makino, 2012; Dymkowska et al, 2014). While the
precise effects on mitochondrial respiration require further study,
hyperglycemia-induced mitochondrial ROS induces DNA breaks
and thereby activates polyAPD-ribose polymerase (PARP-1) (Du
et al, 2000, 2003; Nishikawa et al, 2000; Giacco & Brownlee, 2010;
Blake & Trounce, 2013). PolyADP-ribosylation by PARP-1 inacti-
vates GAPDH and stalls glycolysis, allowing accumulation of glyco-
lytic metabolites (Du et al, 2003).
Accumulation of F6P increases the flux through the hexosamine
biosynthesis pathway (HBP), which produces UDP-GlcNac, an
important precursor of glycosylation reactions (Fig 3A) (Brownlee,
2001). While glycosylation is important for physiological EC func-
tion, hyperglycemia-induced protein glycosylation inhibits angio-
genic functions (Du et al, 2001; Federici et al, 2002; Luo et al,
2008). Other glycolytic intermediates are diverted into the polyol
and methylglyoxal pathways that produce damaging agents such
as ROS and advanced glycation end products (AGEs) (Fig 3A)
(Goldin et al, 2006). AGEs induce vascular dysfunction by altering
extracellular matrix protein function and dysregulating cytokine
expression (Yan et al, 2008). In addition, receptor of AGE (RAGE)
binding by AGEs in vascular cells causes inflammation and
reduced NO availability associated with vascular complications in
ReducedIncreased
Glucose
Acetyl-CoA
GLUT1
PPP
L-arginine
gnggi
npppupl
uplinin
up
ROS
NADH
ReducedIncreased
Figure 3. Metabolic pathways implicated in diseases characterized by EC dysfunction.(A) High glucose levels in diabetes pushes glycolytic flux and cause ROS production and AGE formation. (B) Metabolic alterations that cause eNOS dysfunction mediateatherosclerosis pathogenesis. Asymmetric dimethylarginine (ADMA) competes with arginine for binding to eNOS. Arginase expression is increased and eNOS expression isdecreased, leading to reduced eNOS activity. 1C metabolism and mevalonate metabolism provide eNOS coupling co-factors and inhibit ROS production. The mevalonatepathway also provides farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), required for GTPase prenylation. For clarity, not all metabolites and enzymesof the depicted pathways are shown. Green font / bold line: upregulated, red font / broken line: downregulated. Abbreviations: as in Figure 1. BH2: dihydrobiopterin; BH4:tetrahydrobiopterin; ADMA: asymmetric dimethylarginine; CoQ10: coenzyme Q10; DDAH: dimethylarginine dimethylaminohydrolase; DHF: dihydrofolate; DHFR:dihydrofolate reductase; FPP: farnesyl pyrophosphate; GGPP: geranylgeranyl pyrophosphate; GTP: Guanosine triphosphate; HMGCR: hydroxymethylglutaryl coenzyme Areductase; PRMT: protein arginine methyltransferase.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1111
lactate induces HIF-1a activation leading to increased expression of
VEGFR2 and bFGF (Sonveaux et al, 2012). Moreover, lactate
competes with a-ketoglutarate for binding to the oxygen sensing
prolyl hydroxylase-2 (PHD-2), resulting in diminished PHD-2 activ-
ity and subsequent hypoxia-inducible factor-1a (HIF-1a) stabiliza-
tion (Fig 2A). Stabilized HIF-1a induces pro-angiogenic signaling
pathways such as nuclear factor kappa-light-chain-enhancer of acti-
vated B-cells (NFkB)/interleukin 8 (IL-8) leading to increased angio-
genesis (Fig 2A) (Hunt et al, 2007; Vegran et al, 2011; Sonveaux
et al, 2012). Exploratory studies found that lactate induces angio-
genesis in vivo and that pharmacological blockade of MCT1 inhibits
angiogenesis and reduces tumor growth in mice (Sonveaux et al,
2012). Together, these data suggest an intricate relationship
between classical pro-angiogenic signals such as VEGF, HIF-1a and
hypoxia, and EC glucose metabolism. Targeting EC glucose metabo-
lism to inhibit tumor angiogenesis is in its infancy as a therapeutic
strategy, but recent evidence suggests its viability.
Pulmonary arterial hypertension
Idiopathic pulmonary arterial hypertension (PAH) is characterized
by heightened pressure in pulmonary arteries caused by excessive
EC proliferation and vascular dysfunction (Xu & Erzurum, 2011).
Emerging evidence indicates that metabolic abnormalities underlie
PAH (Fig 2B) (Sutendra & Michelakis, 2014; Zhao et al, 2014). In
line with recent findings that glycolysis regulates angiogenesis,
hyperproliferative PAH ECs rely on increased glycolytic flux and
reduced oxygen consumption, which may be related to HIF-1aoverexpression (Fig 2B) (Xu et al, 2007; Fijalkowska et al, 2010;
Majmundar et al, 2010; Tuder et al, 2012). Human pulmonary
ECs expressing mutated bone morphogenetic protein receptor 2
(BMPR2), which confers PAH, show altered expression of several
glycolytic enzymes including GLUT1 and phosphoglycerate kinase 1
(PGK1). PAH ECs also show increased expression of enzymes of the
PPP (R5P isomerase, Ru5P-3-epimerase) and polyamine biosynthe-
sis pathway (ornithine decarboxylase (ODC), spermine synthase
(SMS)). These metabolic changes may underlie the rapid prolifera-
tion of PAH ECs, since glycolysis, the PPP and mitogenic polyam-
ines all promote cellular proliferation (Morrison & Seidel, 1995).
However, the expression of other PPP and polyamine enzymes
[G6PD, TKT, spermidine synthase (SRM)] is reduced—a finding
that requires further explanation (Fig 2B) (Atkinson et al, 2002;
Rudarakanchana et al, 2002; Long et al, 2006; Fessel et al, 2012). In
addition, ECs isolated from EC-specific BMPR2 mutant mice show
similarly increased expression of PGK1, indicating altogether that
alterations in glycolysis as well as PPP likely underlie PAH (Majka
et al, 2011).
In addition to alterations in glycolysis, idiopathic PAH ECs have
fewer mitochondria and decreased mitochondrial metabolic activity
(Xu et al, 2007). BMPR2 mutant ECs have reduced quantities of TCA
cycle intermediates, reduced fatty acid oxidation and transcriptional
reduction of several enzymes involved in fatty acid metabolism,
including the rate-limiting enzyme of fatty acid oxidation carnitine
palmitoyltransferase 1 (CPT1) (Fig 2) (Fessel et al, 2012). Together,
these findings suggest reduced oxidative metabolism. Indeed, phar-
macological inhibition of hyper-activated pyruvate dehydrogenase
kinase (PDK), an enzyme that shunts glucose-derived pyruvate away
from oxidative TCA metabolism, has shown therapeutic efficacy.
However, whether these effects are mediated via ECs specifically
remains to be determined (McMurtry et al, 2004). For unexplained
reasons, PAH patients also show increased isocitrate dehydrogenase
(IDH)-1 and IDH-2 serum activity, a finding that corroborates with
the increased IDH activity observed in BPMR2 mutant ECs (Fessel
et al, 2012). Still, the mechanisms that alter metabolic pathways in
PAH ECs and the importance of some of these metabolic adaptations
in the pathogenesis of PAH remain unclear.
Reduced nitric oxide (NO) levels are another hallmark of PAH
ECs (Fijalkowska et al, 2010). Low NO levels may be related to the
reduced levels of the mitochondrial antioxidant manganese superox-
ide dismutase (MnSOD) (Fijalkowska et al, 2010). Indeed, MnSOD
increases NO availability by clearing superoxide anion, which inac-
tivates NO to form peroxynitrite (Fig 2) (Masri et al, 2008).
However, other factors likely contribute to the low NO levels in
PAH ECs (Xu et al, 2004). Indeed, human PAH ECs express high
levels of arginase II, which competes with endothelial nitric oxide
synthetase (eNOS) for their common substrate L-arginine (Fig 2)
(Xu et al, 2004). Inhibition of endothelial arginase II increases NO
production in vitro, suggesting that arginase II can be targeted to
prevent EC hyperproliferation and restore NO availability (Krotova
et al, 2010). While the mechanisms that induce abnormal metabolic
activity in PAH ECs are understudied, restoring NO may provide
dual benefits in preventing excessive EC proliferation as well as
restoring EC vasoactivity.
The metabolic adaptations in PAH (high glycolytic rates and
reduced oxidative metabolism) are partly reminiscent of the meta-
bolic profile of angiogenic ECs. It would be thus interesting to deter-
mine if reducing glycolysis by pharmacological blockade of PFKFB3
can reduce the hyperproliferative rate in PAH ECs. Alternatively, the
beneficial effects of PDK inhibition in PAH to induce oxidative
metabolism could also be beneficial to block angiogenesis by
preventing the glycolytic switch in ECs. Indeed, PDK blockade
with dichloroacetate inhibits angiogenesis in glioblastoma patients
(Michelakis et al, 2010).
EC metabolism in diseases characterized byEC dysfunction
Diabetes
Diabetes is characterized by high blood glucose levels that affect EC
metabolism and cause dysfunction (Fig 3A) (Blake & Trounce,
2013). Hyperglycemia induces peroxisome proliferator-activated
receptor-gamma coactivator 1a (PGC-1a), an important regulator of
metabolic gene expression and mitochondrial biogenesis (Puigserver
et al, 1998; Herzig et al, 2001; Lin et al, 2002). PGC1a increases
angiogenesis when expressed in heart and muscle cells (Arany et al,
2008; Patten et al, 2012). In contrast, diabetes-induced PGC-1aexpression in ECs renders them less responsive to angiogenic factors
and blunts angiogenesis (Sawada et al, 2014).
In addition to affecting gene expression, high glucose levels
alter metabolism to induce the production of reactive oxygen
species (ROS) and reactive nitrogen species (RNS), which might be
mediators of EC dysfunction (Fig 3) (Blake & Trounce, 2013).
High glucose levels cause ECs to produce ROS via activation of
NADPH-dependent oxidases (Inoguchi et al, 2003). In addition,
hyperglycemia inhibits PPP flux by down-regulation of G6PD, the
rate-limiting enzyme of the PPP. The PPP is an important source
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1110
of intracellular NADPH, which is necessary to convert oxidized
glutathione (GSSH) into reduced GSH, a critical ROS scavenger
(Fig 3A) (Leopold et al, 2003; Zhang et al, 2012). Therefore, by
reducing PPP flux, high glucose depletes NADPH levels and
contributes to ROS accumulation (Goldin et al, 2006). Interest-
ingly, G6PD overexpression restores redox homeostasis in high
glucose cultured ECs (Leopold et al, 2003; Zhang et al, 2012).
Some studies suggest that high glucose shifts the normally glyco-
lytic EC metabolism toward oxidative metabolism and increased
mitochondrial respiration (Fig 3). However, these results appear
contextual, as other studies did not report such an induction of
oxidative metabolism (Nishikawa et al, 2000; Koziel et al, 2012;
Pangare & Makino, 2012; Dymkowska et al, 2014). While the
precise effects on mitochondrial respiration require further study,
hyperglycemia-induced mitochondrial ROS induces DNA breaks
and thereby activates polyAPD-ribose polymerase (PARP-1) (Du
et al, 2000, 2003; Nishikawa et al, 2000; Giacco & Brownlee, 2010;
Blake & Trounce, 2013). PolyADP-ribosylation by PARP-1 inacti-
vates GAPDH and stalls glycolysis, allowing accumulation of glyco-
lytic metabolites (Du et al, 2003).
Accumulation of F6P increases the flux through the hexosamine
biosynthesis pathway (HBP), which produces UDP-GlcNac, an
important precursor of glycosylation reactions (Fig 3A) (Brownlee,
2001). While glycosylation is important for physiological EC func-
tion, hyperglycemia-induced protein glycosylation inhibits angio-
genic functions (Du et al, 2001; Federici et al, 2002; Luo et al,
2008). Other glycolytic intermediates are diverted into the polyol
and methylglyoxal pathways that produce damaging agents such
as ROS and advanced glycation end products (AGEs) (Fig 3A)
(Goldin et al, 2006). AGEs induce vascular dysfunction by altering
extracellular matrix protein function and dysregulating cytokine
expression (Yan et al, 2008). In addition, receptor of AGE (RAGE)
binding by AGEs in vascular cells causes inflammation and
reduced NO availability associated with vascular complications in
ReducedIncreased
Glucose
Acetyl-CoA
GLUT1
PPP
L-arginine
gnggi
npppupl
uplinin
up
ROS
NADH
ReducedIncreased
Figure 3. Metabolic pathways implicated in diseases characterized by EC dysfunction.(A) High glucose levels in diabetes pushes glycolytic flux and cause ROS production and AGE formation. (B) Metabolic alterations that cause eNOS dysfunction mediateatherosclerosis pathogenesis. Asymmetric dimethylarginine (ADMA) competes with arginine for binding to eNOS. Arginase expression is increased and eNOS expression isdecreased, leading to reduced eNOS activity. 1C metabolism and mevalonate metabolism provide eNOS coupling co-factors and inhibit ROS production. The mevalonatepathway also provides farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), required for GTPase prenylation. For clarity, not all metabolites and enzymesof the depicted pathways are shown. Green font / bold line: upregulated, red font / broken line: downregulated. Abbreviations: as in Figure 1. BH2: dihydrobiopterin; BH4:tetrahydrobiopterin; ADMA: asymmetric dimethylarginine; CoQ10: coenzyme Q10; DDAH: dimethylarginine dimethylaminohydrolase; DHF: dihydrofolate; DHFR:dihydrofolate reductase; FPP: farnesyl pyrophosphate; GGPP: geranylgeranyl pyrophosphate; GTP: Guanosine triphosphate; HMGCR: hydroxymethylglutaryl coenzyme Areductase; PRMT: protein arginine methyltransferase.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1111
lactate induces HIF-1a activation leading to increased expression of
VEGFR2 and bFGF (Sonveaux et al, 2012). Moreover, lactate
competes with a-ketoglutarate for binding to the oxygen sensing
prolyl hydroxylase-2 (PHD-2), resulting in diminished PHD-2 activ-
ity and subsequent hypoxia-inducible factor-1a (HIF-1a) stabiliza-
tion (Fig 2A). Stabilized HIF-1a induces pro-angiogenic signaling
pathways such as nuclear factor kappa-light-chain-enhancer of acti-
vated B-cells (NFkB)/interleukin 8 (IL-8) leading to increased angio-
genesis (Fig 2A) (Hunt et al, 2007; Vegran et al, 2011; Sonveaux
et al, 2012). Exploratory studies found that lactate induces angio-
genesis in vivo and that pharmacological blockade of MCT1 inhibits
angiogenesis and reduces tumor growth in mice (Sonveaux et al,
2012). Together, these data suggest an intricate relationship
between classical pro-angiogenic signals such as VEGF, HIF-1a and
hypoxia, and EC glucose metabolism. Targeting EC glucose metabo-
lism to inhibit tumor angiogenesis is in its infancy as a therapeutic
strategy, but recent evidence suggests its viability.
Pulmonary arterial hypertension
Idiopathic pulmonary arterial hypertension (PAH) is characterized
by heightened pressure in pulmonary arteries caused by excessive
EC proliferation and vascular dysfunction (Xu & Erzurum, 2011).
Emerging evidence indicates that metabolic abnormalities underlie
PAH (Fig 2B) (Sutendra & Michelakis, 2014; Zhao et al, 2014). In
line with recent findings that glycolysis regulates angiogenesis,
hyperproliferative PAH ECs rely on increased glycolytic flux and
reduced oxygen consumption, which may be related to HIF-1aoverexpression (Fig 2B) (Xu et al, 2007; Fijalkowska et al, 2010;
Majmundar et al, 2010; Tuder et al, 2012). Human pulmonary
ECs expressing mutated bone morphogenetic protein receptor 2
(BMPR2), which confers PAH, show altered expression of several
glycolytic enzymes including GLUT1 and phosphoglycerate kinase 1
(PGK1). PAH ECs also show increased expression of enzymes of the
PPP (R5P isomerase, Ru5P-3-epimerase) and polyamine biosynthe-
sis pathway (ornithine decarboxylase (ODC), spermine synthase
(SMS)). These metabolic changes may underlie the rapid prolifera-
tion of PAH ECs, since glycolysis, the PPP and mitogenic polyam-
ines all promote cellular proliferation (Morrison & Seidel, 1995).
However, the expression of other PPP and polyamine enzymes
[G6PD, TKT, spermidine synthase (SRM)] is reduced—a finding
that requires further explanation (Fig 2B) (Atkinson et al, 2002;
Rudarakanchana et al, 2002; Long et al, 2006; Fessel et al, 2012). In
addition, ECs isolated from EC-specific BMPR2 mutant mice show
similarly increased expression of PGK1, indicating altogether that
alterations in glycolysis as well as PPP likely underlie PAH (Majka
et al, 2011).
In addition to alterations in glycolysis, idiopathic PAH ECs have
fewer mitochondria and decreased mitochondrial metabolic activity
(Xu et al, 2007). BMPR2 mutant ECs have reduced quantities of TCA
cycle intermediates, reduced fatty acid oxidation and transcriptional
reduction of several enzymes involved in fatty acid metabolism,
including the rate-limiting enzyme of fatty acid oxidation carnitine
palmitoyltransferase 1 (CPT1) (Fig 2) (Fessel et al, 2012). Together,
these findings suggest reduced oxidative metabolism. Indeed, phar-
macological inhibition of hyper-activated pyruvate dehydrogenase
kinase (PDK), an enzyme that shunts glucose-derived pyruvate away
from oxidative TCA metabolism, has shown therapeutic efficacy.
However, whether these effects are mediated via ECs specifically
remains to be determined (McMurtry et al, 2004). For unexplained
reasons, PAH patients also show increased isocitrate dehydrogenase
(IDH)-1 and IDH-2 serum activity, a finding that corroborates with
the increased IDH activity observed in BPMR2 mutant ECs (Fessel
et al, 2012). Still, the mechanisms that alter metabolic pathways in
PAH ECs and the importance of some of these metabolic adaptations
in the pathogenesis of PAH remain unclear.
Reduced nitric oxide (NO) levels are another hallmark of PAH
ECs (Fijalkowska et al, 2010). Low NO levels may be related to the
reduced levels of the mitochondrial antioxidant manganese superox-
ide dismutase (MnSOD) (Fijalkowska et al, 2010). Indeed, MnSOD
increases NO availability by clearing superoxide anion, which inac-
tivates NO to form peroxynitrite (Fig 2) (Masri et al, 2008).
However, other factors likely contribute to the low NO levels in
PAH ECs (Xu et al, 2004). Indeed, human PAH ECs express high
levels of arginase II, which competes with endothelial nitric oxide
synthetase (eNOS) for their common substrate L-arginine (Fig 2)
(Xu et al, 2004). Inhibition of endothelial arginase II increases NO
production in vitro, suggesting that arginase II can be targeted to
prevent EC hyperproliferation and restore NO availability (Krotova
et al, 2010). While the mechanisms that induce abnormal metabolic
activity in PAH ECs are understudied, restoring NO may provide
dual benefits in preventing excessive EC proliferation as well as
restoring EC vasoactivity.
The metabolic adaptations in PAH (high glycolytic rates and
reduced oxidative metabolism) are partly reminiscent of the meta-
bolic profile of angiogenic ECs. It would be thus interesting to deter-
mine if reducing glycolysis by pharmacological blockade of PFKFB3
can reduce the hyperproliferative rate in PAH ECs. Alternatively, the
beneficial effects of PDK inhibition in PAH to induce oxidative
metabolism could also be beneficial to block angiogenesis by
preventing the glycolytic switch in ECs. Indeed, PDK blockade
with dichloroacetate inhibits angiogenesis in glioblastoma patients
(Michelakis et al, 2010).
EC metabolism in diseases characterized byEC dysfunction
Diabetes
Diabetes is characterized by high blood glucose levels that affect EC
metabolism and cause dysfunction (Fig 3A) (Blake & Trounce,
2013). Hyperglycemia induces peroxisome proliferator-activated
receptor-gamma coactivator 1a (PGC-1a), an important regulator of
metabolic gene expression and mitochondrial biogenesis (Puigserver
et al, 1998; Herzig et al, 2001; Lin et al, 2002). PGC1a increases
angiogenesis when expressed in heart and muscle cells (Arany et al,
2008; Patten et al, 2012). In contrast, diabetes-induced PGC-1aexpression in ECs renders them less responsive to angiogenic factors
and blunts angiogenesis (Sawada et al, 2014).
In addition to affecting gene expression, high glucose levels
alter metabolism to induce the production of reactive oxygen
species (ROS) and reactive nitrogen species (RNS), which might be
mediators of EC dysfunction (Fig 3) (Blake & Trounce, 2013).
High glucose levels cause ECs to produce ROS via activation of
NADPH-dependent oxidases (Inoguchi et al, 2003). In addition,
hyperglycemia inhibits PPP flux by down-regulation of G6PD, the
rate-limiting enzyme of the PPP. The PPP is an important source
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1110
atherosclerosis and promotes ROS production through eNOS
uncoupling (Fig 3B) (Pieper, 1997; Stroes et al, 1997; Heitzer et al,
2000). Endothelial BH4 levels are maintained by de novo biosyn-
thesis via the rate-limiting enzyme guanosine triphosphate
cyclohydrolase I (GTPCH) and by a salvage pathway from dihydro-
biopterin (BH2) via dihydrofolate reductase (DHFR) (Fig 3B)
(Bendall et al, 2014). Insufficient levels of GTPCH and DHFR,
important enzymes in GTP and folate metabolism, respectively, have
been associated with reduced BH4 availability, endothelial dysfunc-
tion and cardiovascular disease in several preclinical models
(Chalupsky & Cai, 2005; Crabtree et al, 2009b, 2011; Sugiyama
et al, 2009; Kidokoro et al, 2013). Interestingly, DHFR not only
regenerates active BH4 from oxidized inactive BH2 but is also a
key enzyme in folate and one-carbon metabolism, intermediates of
which in turn regulate BH4 biosynthesis and are associated with
cardiovascular disease (Humphrey et al, 2008).
One-carbon (1C) metabolism centers around the ability of
folate-derived co-enzymes to carry activated 1C units (Fig 3)
(Tibbetts & Appling, 2010). DHFR catalyzes the formation of
tetrahydrofolate (THF) from folate fueling 1C metabolism. THF
accepts 1C units from serine to produce 5,10-methylene-THF
(meTHF) and glycine. MeTHF is reduced to 5-methyl-THF (mTHF)
by methylenetetrahydrofolate reductase (MTHFR) (Fig 3).
Importantly, inactivating mutations in the MTHFR gene result in
hyperhomocysteinemia, which decreases GTPCH and DHFR levels
and may subsequently reduce BH4 levels (Bendall et al, 2014).
Indeed, MTHFR mutations have been associated with cardiovascu-
lar disease, but the exact association is still controversial (Kelly
et al, 2002; Klerk et al, 2002; Frederiksen et al, 2004; Yang et al,
2012). mTHF produced by MTHFR activity is required as a methyl
donor in the methionine synthase (MS) catalyzed reaction that
converts mTHF into THF (completing the folate cycle) and forms
methionine (MET) from homocysteine (hCYS) (Fig 3B) (Locasale,
2013). Methionine is used to generate S-adenosylmethionine
(SAM), which is an important methyl donor and plays a pivotal
role in methylation of lysine and arginine residues in proteins
(Fig 3B) (Leiper & Nandi, 2011). As discussed above, methylated
arginine residues are emerging as important mediators of EC
dysfunction. Moreover, SAM-mediated protein methylation
produces S-adenosylhomocysteine, which is converted back into
homocysteine. Homocysteine decreases the bioavailability of BH4
possibly through downregulation of GTPCH and DHFR, while BH4
supplementation alleviates homocysteine-induced EC dysfunction
(Dhillon et al, 2003; Topal et al, 2004). Together, these findings
suggest that dysregulation of endothelial 1C metabolism is
involved in the pathogenesis of cardiovascular disease, but the
VE
GG
VE
GG
VE
GG FGG
VE
GG
FGF
FGG
FGF
Anti-VEGFtreatment
Anti-VEGFtreatment
Angiogenic activityen a
ReducedIncreasedn
Figure 4. Targeting EC metabolism as an alternative to targeting growth factors in angiogenesis.(A) Vascular endothelial growth factor (VEGF) induces 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) and increases glycolytic flux, required forangiogenesis. (B) Anti-VEGF treatment reduces glycolytic flux and angiogenesis. (C) Increased growth factor signaling through alternative pathways, in this case fibroblastgrowth factor (FGF), mediates resistance to anti-angiogenic therapy. (D) Pharmacological targeting of PFKFB3 with (3PO) reduces angiogenesis irrespective of growth factorsignaling and is therefore possibly less prone to resistance. Abbreviations: as in Figure 1. 3PO: 3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one; FGF: fibroblast growth factor.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1113
diabetic patients (Bucala et al, 1991; Vlassara et al, 1995; Min
et al, 1999; Wautier & Schmidt, 2004; Goldin et al, 2006; Manigrasso
et al, 2014).
Excess glucose that cannot be metabolized by glycolysis enters
the polyol pathway when converted into sorbitol by aldose reduc-
tase (AR) at the expense of NADPH, increasing ROS. Sorbitol
is subsequently converted into fructose and the highly reactive
3-deoxyglucosone (3DG), which promotes the formation of AGEs
(Fig 3A) (Kashiwagi et al, 1994; Oyama et al, 2006; Giacco &
Brownlee, 2010; Sena et al, 2012; Yoshida et al, 2012). Transgenic
overexpression of human AR in the endothelium of diabetic mice
accelerates atherosclerosis formation and inhibition of endothelial
AR reduces intracellular ROS, EC migration and proliferation
(Obrosova et al, 2003; Tammali et al, 2011; Vedantham et al, 2011;
Yadav et al, 2012). Methylglyoxal is another AGE precursor and
produced from the glycolytic intermediates glyceraldehyde-3-phosphate
(G3P) and dihydroxyacetone phosphate (DHAP). Methylglyoxal is
detoxified by conversion into pyruvate via the multienzyme
glyoxalase system, of which glyoxalase-I (GloI) is rate-limiting
(Fig 3A) (Thornalley, 1993). Glyoxalase-I overexpression reverses
hyperglycemia-induced angiogenesis defects in vitro and transgenic
overexpression of glyoxalase-I in rats reduces vascular AGE
formation and improves vasoreactivity (Brouwers et al, 2010, 2014)
(Ahmed et al, 2008). Together, these observations indicate that
targeting AR and glyoxalase might confer a therapeutic benefit in
diabetic patients.
Atherosclerosis
Atherosclerosis is a chronic inflammatory process in the blood
vessel wall leading to luminal narrowing and subsequent cardio-
vascular events (Hopkins, 2013). Systemic metabolic perturbations
are among the most important risk factors of atherosclerosis.
However, metabolic flux changes have not been studied in ECs
isolated from atherosclerotic lesions, and the effects of atheroscle-
rosis on central metabolism of ECs thus remains to be character-
ized. Nonetheless, EC metabolism is strongly associated with a key
pathophysiological feature of atherosclerosis: reduced and uncou-
pled eNOS activity resulting in low NO bioavailability and high
ROS production (Fig 3B) (Kawashima & Yokoyama, 2004). eNOS
activity critically depends on the availability of L-arginine,
co-factor tetrahydrobiopterin (BH4) (Fig 3B) and possibly co-
enzyme Q10 (CoQ10) (Gorren et al, 2000; Crabtree et al, 2009a;
Mugoni et al, 2013). If L-arginine, BH4 or CoQ10 become limited,
eNOS no longer oxidizes L-arginine to form citrulline and NO, but
instead produces ROS (a condition termed eNOS uncoupling)
(Fig 3B) (Stroes et al, 1998; Mugoni et al, 2013). Targeting L-arginine
and BH4 metabolism to increase eNOS activity in patients with
cardiovascular disease is potentially beneficial, but available
evidence indicates that the picture is more complex than initially
anticipated.
Small-scale clinical trials indicate that administration of L-arginine
to patients with coronary heart disease improves vasoresponsive-
ness, possibly by increasing NO production by eNOS (Lerman et al,
1998). Interestingly, however, intracellular and plasma arginine
levels are sufficiently high to support NO biosynthesis via eNOS.
Therefore, the benefits of L-arginine supplementation on
elevating NO levels are not readily explained by increasing the
supply of L-arginine; however, it is possible that L-arginine is
compartmentalized in poorly interchangeable pools. Another possi-
ble explanation of the beneficial effects of L-arginine is competition
with asymmetric methylated arginines, which bind and inhibit
eNOS (Fig 3B) (Boger, 2004; Chen et al, 2013). More in detail, post-
translational methylation of arginine residues in proteins by protein
arginine methyltransferase (PRMT) results in the addition of up to
two methyl groups to arginine. Protein turnover releases these
post-translationally modified amino acids as asymmetric dimethyl-
arginine (ADMA) and symmetric dimethylarginine (SDMA). The
asymmetric dimethylarginines bind and uncouple eNOS resulting in
increased ROS production and reduced NO availability (Fig 3B)
(Dhillon et al, 2003; Leiper & Nandi, 2011). Hence by competing
with ADMAs, supplemented L-arginine could maintain eNOS activ-
ity to produce NO (Bode-Boger et al, 2003). Additional potential
interventions to reduce eNOS inhibition by ADMA include PRMT
inhibition (to reduce arginine methylation) and activation of methyl-
arginine catabolism by dimethylarginine dimethylaminohydrolase
(DDAH) (Fig 3B) (Leiper & Nandi, 2011). Interestingly, DDAH1 is
predominantly expressed in ECs and EC-specific deletion attenuates
NO production and induces hypertension, indicating that ADMA
scavenging by ECs is important to maintain homeostasis (Hu et al,
2009).
Because L-arginine is a substrate for both eNOS and arginase
(Wu & Meininger, 1995), NO production depends on the relative
expression levels of each enzyme (Fig 3) (Chang et al, 1998; Ming
et al, 2004; Ryoo et al, 2008). Endothelial arginase expression is
induced by many risk factors for cardiovascular disease, while
reducing arginase expression restores NO production in vitro
and improves vasodilatation in vivo (Ryoo et al, 2006, 2008;
Thengchaisri et al, 2006; Romero et al, 2008). The activity of
eNOS and arginase is regulated by the RhoA/ROCK signaling
cascade. RhoA and Rock decrease eNOS expression, while RhoA
also increases arginase activity (Fig 3B) (Laufs et al, 1998;
Takemoto et al, 2002). For proper activation and localization to
the cell membrane, RhoA must be prenylated (more specifically,
geranylgeranylated) by geranylgeranyltransferase (GGT) using
geranylgeranyl pyrophosphate (GGPP) as a substrate (Laufs &
Liao, 1998). This isoprenoid is an intermediate of the mevalonate
pathway, which produces cholesterol from acetyl-coA (Fig 3B).
Blocking the mevalonate pathway by inhibiting HMG-coA reduc-
tase using statins lowers cholesterol synthesis and is clinically
approved to prevent cardiovascular events in dyslipidemia
patients. In addition, HMG-coA blockade also decreases geranyl-
geranyl production, which reduces RhoA activity and restores a
more beneficial eNOS/arginase balance (Goldstein & Brown, 1990;
Liao & Laufs, 2005). Interestingly, UBIAD1 was recently identified
as a novel prenyltransferase that produces non-mitochondrial
CoQ10 from farnesyl pyrophosphate (FPP), another isoprenoid
produced in the mevalonate pathway (Fig 3) (Mugoni et al, 2013).
CoQ10 is an important anti-oxidant with beneficial effects on EC
function and hypothesized to be a novel co-factor required for
eNOS coupling (Gao et al, 2012; Mugoni et al, 2013). Hence, in
contrast to the above-mentioned beneficial effects, HMG-coA
reductase inhibition might thus also have a less favorable effect by
increasing ROS levels through reducing CoQ10 synthesis (Fig 3)
(Mugoni et al, 2013).
In addition to CoQ10, eNOS requires BH4 as a co-factor.
Reduced BH4 availability is found in patients at risk of
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1112
atherosclerosis and promotes ROS production through eNOS
uncoupling (Fig 3B) (Pieper, 1997; Stroes et al, 1997; Heitzer et al,
2000). Endothelial BH4 levels are maintained by de novo biosyn-
thesis via the rate-limiting enzyme guanosine triphosphate
cyclohydrolase I (GTPCH) and by a salvage pathway from dihydro-
biopterin (BH2) via dihydrofolate reductase (DHFR) (Fig 3B)
(Bendall et al, 2014). Insufficient levels of GTPCH and DHFR,
important enzymes in GTP and folate metabolism, respectively, have
been associated with reduced BH4 availability, endothelial dysfunc-
tion and cardiovascular disease in several preclinical models
(Chalupsky & Cai, 2005; Crabtree et al, 2009b, 2011; Sugiyama
et al, 2009; Kidokoro et al, 2013). Interestingly, DHFR not only
regenerates active BH4 from oxidized inactive BH2 but is also a
key enzyme in folate and one-carbon metabolism, intermediates of
which in turn regulate BH4 biosynthesis and are associated with
cardiovascular disease (Humphrey et al, 2008).
One-carbon (1C) metabolism centers around the ability of
folate-derived co-enzymes to carry activated 1C units (Fig 3)
(Tibbetts & Appling, 2010). DHFR catalyzes the formation of
tetrahydrofolate (THF) from folate fueling 1C metabolism. THF
accepts 1C units from serine to produce 5,10-methylene-THF
(meTHF) and glycine. MeTHF is reduced to 5-methyl-THF (mTHF)
by methylenetetrahydrofolate reductase (MTHFR) (Fig 3).
Importantly, inactivating mutations in the MTHFR gene result in
hyperhomocysteinemia, which decreases GTPCH and DHFR levels
and may subsequently reduce BH4 levels (Bendall et al, 2014).
Indeed, MTHFR mutations have been associated with cardiovascu-
lar disease, but the exact association is still controversial (Kelly
et al, 2002; Klerk et al, 2002; Frederiksen et al, 2004; Yang et al,
2012). mTHF produced by MTHFR activity is required as a methyl
donor in the methionine synthase (MS) catalyzed reaction that
converts mTHF into THF (completing the folate cycle) and forms
methionine (MET) from homocysteine (hCYS) (Fig 3B) (Locasale,
2013). Methionine is used to generate S-adenosylmethionine
(SAM), which is an important methyl donor and plays a pivotal
role in methylation of lysine and arginine residues in proteins
(Fig 3B) (Leiper & Nandi, 2011). As discussed above, methylated
arginine residues are emerging as important mediators of EC
dysfunction. Moreover, SAM-mediated protein methylation
produces S-adenosylhomocysteine, which is converted back into
homocysteine. Homocysteine decreases the bioavailability of BH4
possibly through downregulation of GTPCH and DHFR, while BH4
supplementation alleviates homocysteine-induced EC dysfunction
(Dhillon et al, 2003; Topal et al, 2004). Together, these findings
suggest that dysregulation of endothelial 1C metabolism is
involved in the pathogenesis of cardiovascular disease, but the
VE
GG
VE
GG
VE
GG FGG
VE
GG
FGF
FGG
FGF
Anti-VEGFtreatment
Anti-VEGFtreatment
Angiogenic activityen a
ReducedIncreasedn
Figure 4. Targeting EC metabolism as an alternative to targeting growth factors in angiogenesis.(A) Vascular endothelial growth factor (VEGF) induces 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) and increases glycolytic flux, required forangiogenesis. (B) Anti-VEGF treatment reduces glycolytic flux and angiogenesis. (C) Increased growth factor signaling through alternative pathways, in this case fibroblastgrowth factor (FGF), mediates resistance to anti-angiogenic therapy. (D) Pharmacological targeting of PFKFB3 with (3PO) reduces angiogenesis irrespective of growth factorsignaling and is therefore possibly less prone to resistance. Abbreviations: as in Figure 1. 3PO: 3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one; FGF: fibroblast growth factor.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1113
diabetic patients (Bucala et al, 1991; Vlassara et al, 1995; Min
et al, 1999; Wautier & Schmidt, 2004; Goldin et al, 2006; Manigrasso
et al, 2014).
Excess glucose that cannot be metabolized by glycolysis enters
the polyol pathway when converted into sorbitol by aldose reduc-
tase (AR) at the expense of NADPH, increasing ROS. Sorbitol
is subsequently converted into fructose and the highly reactive
3-deoxyglucosone (3DG), which promotes the formation of AGEs
(Fig 3A) (Kashiwagi et al, 1994; Oyama et al, 2006; Giacco &
Brownlee, 2010; Sena et al, 2012; Yoshida et al, 2012). Transgenic
overexpression of human AR in the endothelium of diabetic mice
accelerates atherosclerosis formation and inhibition of endothelial
AR reduces intracellular ROS, EC migration and proliferation
(Obrosova et al, 2003; Tammali et al, 2011; Vedantham et al, 2011;
Yadav et al, 2012). Methylglyoxal is another AGE precursor and
produced from the glycolytic intermediates glyceraldehyde-3-phosphate
(G3P) and dihydroxyacetone phosphate (DHAP). Methylglyoxal is
detoxified by conversion into pyruvate via the multienzyme
glyoxalase system, of which glyoxalase-I (GloI) is rate-limiting
(Fig 3A) (Thornalley, 1993). Glyoxalase-I overexpression reverses
hyperglycemia-induced angiogenesis defects in vitro and transgenic
overexpression of glyoxalase-I in rats reduces vascular AGE
formation and improves vasoreactivity (Brouwers et al, 2010, 2014)
(Ahmed et al, 2008). Together, these observations indicate that
targeting AR and glyoxalase might confer a therapeutic benefit in
diabetic patients.
Atherosclerosis
Atherosclerosis is a chronic inflammatory process in the blood
vessel wall leading to luminal narrowing and subsequent cardio-
vascular events (Hopkins, 2013). Systemic metabolic perturbations
are among the most important risk factors of atherosclerosis.
However, metabolic flux changes have not been studied in ECs
isolated from atherosclerotic lesions, and the effects of atheroscle-
rosis on central metabolism of ECs thus remains to be character-
ized. Nonetheless, EC metabolism is strongly associated with a key
pathophysiological feature of atherosclerosis: reduced and uncou-
pled eNOS activity resulting in low NO bioavailability and high
ROS production (Fig 3B) (Kawashima & Yokoyama, 2004). eNOS
activity critically depends on the availability of L-arginine,
co-factor tetrahydrobiopterin (BH4) (Fig 3B) and possibly co-
enzyme Q10 (CoQ10) (Gorren et al, 2000; Crabtree et al, 2009a;
Mugoni et al, 2013). If L-arginine, BH4 or CoQ10 become limited,
eNOS no longer oxidizes L-arginine to form citrulline and NO, but
instead produces ROS (a condition termed eNOS uncoupling)
(Fig 3B) (Stroes et al, 1998; Mugoni et al, 2013). Targeting L-arginine
and BH4 metabolism to increase eNOS activity in patients with
cardiovascular disease is potentially beneficial, but available
evidence indicates that the picture is more complex than initially
anticipated.
Small-scale clinical trials indicate that administration of L-arginine
to patients with coronary heart disease improves vasoresponsive-
ness, possibly by increasing NO production by eNOS (Lerman et al,
1998). Interestingly, however, intracellular and plasma arginine
levels are sufficiently high to support NO biosynthesis via eNOS.
Therefore, the benefits of L-arginine supplementation on
elevating NO levels are not readily explained by increasing the
supply of L-arginine; however, it is possible that L-arginine is
compartmentalized in poorly interchangeable pools. Another possi-
ble explanation of the beneficial effects of L-arginine is competition
with asymmetric methylated arginines, which bind and inhibit
eNOS (Fig 3B) (Boger, 2004; Chen et al, 2013). More in detail, post-
translational methylation of arginine residues in proteins by protein
arginine methyltransferase (PRMT) results in the addition of up to
two methyl groups to arginine. Protein turnover releases these
post-translationally modified amino acids as asymmetric dimethyl-
arginine (ADMA) and symmetric dimethylarginine (SDMA). The
asymmetric dimethylarginines bind and uncouple eNOS resulting in
increased ROS production and reduced NO availability (Fig 3B)
(Dhillon et al, 2003; Leiper & Nandi, 2011). Hence by competing
with ADMAs, supplemented L-arginine could maintain eNOS activ-
ity to produce NO (Bode-Boger et al, 2003). Additional potential
interventions to reduce eNOS inhibition by ADMA include PRMT
inhibition (to reduce arginine methylation) and activation of methyl-
arginine catabolism by dimethylarginine dimethylaminohydrolase
(DDAH) (Fig 3B) (Leiper & Nandi, 2011). Interestingly, DDAH1 is
predominantly expressed in ECs and EC-specific deletion attenuates
NO production and induces hypertension, indicating that ADMA
scavenging by ECs is important to maintain homeostasis (Hu et al,
2009).
Because L-arginine is a substrate for both eNOS and arginase
(Wu & Meininger, 1995), NO production depends on the relative
expression levels of each enzyme (Fig 3) (Chang et al, 1998; Ming
et al, 2004; Ryoo et al, 2008). Endothelial arginase expression is
induced by many risk factors for cardiovascular disease, while
reducing arginase expression restores NO production in vitro
and improves vasodilatation in vivo (Ryoo et al, 2006, 2008;
Thengchaisri et al, 2006; Romero et al, 2008). The activity of
eNOS and arginase is regulated by the RhoA/ROCK signaling
cascade. RhoA and Rock decrease eNOS expression, while RhoA
also increases arginase activity (Fig 3B) (Laufs et al, 1998;
Takemoto et al, 2002). For proper activation and localization to
the cell membrane, RhoA must be prenylated (more specifically,
geranylgeranylated) by geranylgeranyltransferase (GGT) using
geranylgeranyl pyrophosphate (GGPP) as a substrate (Laufs &
Liao, 1998). This isoprenoid is an intermediate of the mevalonate
pathway, which produces cholesterol from acetyl-coA (Fig 3B).
Blocking the mevalonate pathway by inhibiting HMG-coA reduc-
tase using statins lowers cholesterol synthesis and is clinically
approved to prevent cardiovascular events in dyslipidemia
patients. In addition, HMG-coA blockade also decreases geranyl-
geranyl production, which reduces RhoA activity and restores a
more beneficial eNOS/arginase balance (Goldstein & Brown, 1990;
Liao & Laufs, 2005). Interestingly, UBIAD1 was recently identified
as a novel prenyltransferase that produces non-mitochondrial
CoQ10 from farnesyl pyrophosphate (FPP), another isoprenoid
produced in the mevalonate pathway (Fig 3) (Mugoni et al, 2013).
CoQ10 is an important anti-oxidant with beneficial effects on EC
function and hypothesized to be a novel co-factor required for
eNOS coupling (Gao et al, 2012; Mugoni et al, 2013). Hence, in
contrast to the above-mentioned beneficial effects, HMG-coA
reductase inhibition might thus also have a less favorable effect by
increasing ROS levels through reducing CoQ10 synthesis (Fig 3)
(Mugoni et al, 2013).
In addition to CoQ10, eNOS requires BH4 as a co-factor.
Reduced BH4 availability is found in patients at risk of
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1112
Conflict of interestThe authors declare that they have no conflict of interest.
References
Ahmed U, Dobler D, Larkin SJ, Rabbani N, Thornalley PJ (2008) Reversal of
hyperglycemia-induced angiogenesis deficit of human endothelial cells
by overexpression of glyoxalase 1 in vitro. Ann N Y Acad Sci 1126:
262 – 264
Arany Z, Foo SY, Ma Y, Ruas JL, Bommi-Reddy A, Girnun G, Cooper M, Laznik
D, Chinsomboon J, Rangwala SM et al (2008) HIF-independent regulation
of VEGF and angiogenesis by the transcriptional coactivator PGC-1alpha.
Nature 451: 1008 – 1012
Atkinson C, Stewart S, Upton PD, Machado R, Thomson JR, Trembath RC,
Morrell NW (2002) Primary pulmonary hypertension is associated with
reduced pulmonary vascular expression of type II bone morphogenetic
protein receptor. Circulation 105: 1672 – 1678
van Beijnum JR, Dings RP, van der Linden E, Zwaans BM, Ramaekers FC,
Mayo KH, Griffioen AW (2006) Gene expression of tumor angiogenesis
dissected: specific targeting of colon cancer angiogenic vasculature. Blood
108: 2339 – 2348
Bendall JK, Douglas G, McNeill E, Channon KM, Crabtree MJ (2014)
Tetrahydrobiopterin in cardiovascular health and disease. Antioxid Redox
Signal 20: 3040 – 3077
Benedito R, Roca C, Sorensen I, Adams S, Gossler A, Fruttiger M, Adams RH
(2009) The notch ligands Dll4 and Jagged1 have opposing effects on
angiogenesis. Cell 137: 1124 – 1135
Bergers G, Benjamin LE (2003) Tumorigenesis and the angiogenic switch. Nat
Rev 3: 401 – 410
Bergers G, Hanahan D (2008) Modes of resistance to anti-angiogenic therapy.
Nat Rev 8: 592 – 603
Blake R, Trounce IA (2013) Mitochondrial dysfunction and complications
associated with diabetes. Biochim Biophys Acta 1840: 1404 – 1412
Bode-Boger SM, Muke J, Surdacki A, Brabant G, Boger RH, Frolich JC (2003)
Oral L-arginine improves endothelial function in healthy individuals older
than 70 years. Vas Med 8: 77 – 81
Boger RH, Sydow K, Borlak J, Thum T, Lenzen H, Schubert B, Tsikas D,
Bode-Boger SM (2000) LDL cholesterol upregulates synthesis of
asymmetrical dimethylarginine in human endothelial cells: involvement of
S-adenosylmethionine-dependent methyltransferases. Circ Res 87: 99 – 105
Boger RH (2004) Asymmetric dimethylarginine, an endogenous inhibitor of
nitric oxide synthase, explains the “L-arginine paradox” and acts as a novel
cardiovascular risk factor. J Nutr 134: 2842S – 2847S; discussion 2853S
Brouwers O, Niessen PM, Haenen G, Miyata T, Brownlee M, Stehouwer CD,
De Mey JG, Schalkwijk CG (2010) Hyperglycaemia-induced impairment of
endothelium-dependent vasorelaxation in rat mesenteric arteries is
mediated by intracellular methylglyoxal levels in a pathway dependent on
oxidative stress. Diabetologia 53: 989 – 1000
Brouwers O, Niessen PM, Miyata T, Ostergaard JA, Flyvbjerg A, Peutz-Kootstra
CJ, Sieber J, Mundel PH, Brownlee M, Janssen BJ et al (2014) Glyoxalase-1
overexpression reduces endothelial dysfunction and attenuates early renal
impairment in a rat model of diabetes. Diabetologia 57: 224 – 235
Brownlee M (2001) Biochemistry and molecular cell biology of diabetic
complications. Nature 414: 813 – 820
Bucala R, Tracey KJ, Cerami A (1991) Advanced glycosylation products quench
nitric oxide and mediate defective endothelium-dependent vasodilatation
in experimental diabetes. J Clin Investig 87: 432 – 438
Carmeliet P (2003) Angiogenesis in health and disease. Nat Med 9: 653 – 660
Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications
of angiogenesis. Nature 473: 298 – 307
Chalupsky K, Cai H (2005) Endothelial dihydrofolate reductase: critical for
nitric oxide bioavailability and role in angiotensin II uncoupling of
endothelial nitric oxide synthase. Proc Natl Acad Sci USA 102: 9056 – 9061
Chang CI, Liao JC, Kuo L (1998) Arginase modulates nitric oxide production in
activated macrophages. Am J Physiol 274: H342 –H348
Chen F, Lucas R, Fulton D (2013) The subcellular compartmentalization of
arginine metabolizing enzymes and their role in endothelial dysfunction.
Front Immunol 4: 184
Cines DB, Pollak ES, Buck CA, Loscalzo J, Zimmerman GA, McEver RP, Pober
JS, Wick TM, Konkle BA, Schwartz BS et al (1998) Endothelial cells in
physiology and in the pathophysiology of vascular disorders. Blood 91:
3527 – 3561
Clarke R, Halsey J, Lewington S, Lonn E, Armitage J, Manson JE, Bonaa KH,
Spence JD, Nygard O, Jamison R et al (2010) Effects of lowering
homocysteine levels with B vitamins on cardiovascular disease, cancer,
and cause-specific mortality: Meta-analysis of 8 randomized trials
involving 37 485 individuals. Arch Intern Med 170: 1622 – 1631
Crabtree MJ, Hale AB, Channon KM (2011) Dihydrofolate reductase protects
endothelial nitric oxide synthase from uncoupling in tetrahydrobiopterin
deficiency. Free Radical Biol Med 50: 1639 – 1646
Crabtree MJ, Tatham AL, Al-Wakeel Y, Warrick N, Hale AB, Cai S, Channon
KM, Alp NJ (2009a) Quantitative regulation of intracellular endothelial
nitric-oxide synthase (eNOS) coupling by both tetrahydrobiopterin-eNOS
stoichiometry and biopterin redox status: insights from cells with
tet-regulated GTP cyclohydrolase I expression. J Biol Chem 284:
1136 – 1144
Crabtree MJ, Tatham AL, Hale AB, Alp NJ, Channon KM (2009b) Critical role
for tetrahydrobiopterin recycling by dihydrofolate reductase in regulation
of endothelial nitric-oxide synthase coupling: relative importance of the
de novo biopterin synthesis versus salvage pathways. J Biol Chem 284:
28128 – 28136
Croci DO, Cerliani JP, Dalotto-Moreno T, Mendez-Huergo SP, Mascanfroni ID,
Dergan-Dylon S, Toscano MA, Caramelo JJ, Garcia-Vallejo JJ, Ouyang J et al
(2014) Glycosylation-dependent lectin-receptor interactions preserve
angiogenesis in anti-VEGF refractory tumors. Cell 156: 744 – 758
Culic O, Gruwel ML, Schrader J (1997) Energy turnover of vascular endothelial
cells. Am J Physiol 273: C205 –C213
Cunnington C, Van Assche T, Shirodaria C, Kylintireas I, Lindsay AC, Lee JM,
Antoniades C, Margaritis M, Lee R, Cerrato R et al (2012) Systemic and
vascular oxidation limits the efficacy of oral tetrahydrobiopterin treatment
in patients with coronary artery disease. Circulation 125: 1356 – 1366
Dagher Z, Ruderman N, Tornheim K, Ido Y (1999) The effect of AMP-activated
protein kinase and its activator AICAR on the metabolism of human
umbilical vein endothelial cells. Biochem Biophys Res Commun 265:
112 – 115
Dagher Z, Ruderman N, Tornheim K, Ido Y (2001) Acute regulation of fatty
acid oxidation and amp-activated protein kinase in human umbilical vein
endothelial cells. Circ Res 88: 1276 – 1282
Davignon J, Ganz P (2004) Role of endothelial dysfunction in atherosclerosis.
Circulation 109: III27 – III32
De Bock K, Georgiadou M, Carmeliet P (2013a) Role of endothelial cell
metabolism in vessel sprouting. Cell Metab 18: 634 – 647
De Bock K, Georgiadou M, Schoors S, Kuchnio A, Wong BW, Cantelmo AR,
Quaegebeur A, Ghesquiere B, Cauwenberghs S, Eelen G et al (2013b) Role
of PFKFB3-driven glycolysis in vessel sprouting. Cell 154: 651 – 663
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1115
exact mechanisms remain to be elucidated. Nonetheless, early
clinical and preclinical studies have found that therapeutic target-
ing of 1C metabolism, for example, via folate supplementation
lowers levels of homocysteinemia and increases BH4 regeneration
from BH2 (Verhaar et al, 2002). However, large-scale clinical trials
failed to show benefits of folate or BH4 supplementation to
prevent cardiovascular disease (Clarke et al, 2010; Cunnington
et al, 2012; Marti-Carvajal et al, 2013). These clinical and preclini-
cal findings suggest that while L-arginine, folate, methionine,
COQ10 and homocysteine metabolism are potential therapeutic
targets, a more detailed understanding of how these pathways
cause dysfunction is required to design more rational therapeutic
agents.
EC metabolism in the pathogenesis of other diseases
EC metabolism is best characterized in the diseases discussed
above. However, these represent only a minor fraction of the disor-
ders in which pathological EC responses are presumably involved.
Indeed, it is highly likely that EC metabolic alterations are also
involved in the pathogenesis of other diseases such as ischemia,
pre-eclampsia, vasculitis, vascular neoplasms and others although
this has hardly been studied.
On the other hand, many of the EC metabolic alterations that
lead to EC dysfunction are likely induced by cardiovascular risk
factors such as those that characterize metabolic syndrome, hyper-
homocysteinemia and hyperuricemia. For example, elevated serum
uric acid (a breakdown product of purine nucleotides generated by
xanthine oxidase with potent anti-oxidant activity) is common in
patients with hypertension and may even be a root cause of EC
dysfunction leading to cardiovascular disease (Feig et al, 2008).
Interestingly, while uric acid has been described as major anti-
oxidant in human plasma, ECs exposed to uric acid display
increased ROS production creating a paradox that has not been
resolved (Lippi et al, 2008; Sautin & Johnson, 2008). Regardless, in
cardiovascular disease models uric acid reduces mitochondrial
content, intracellular ATP and arginase activity (Zharikov et al,
2008; Sanchez-Lozada et al, 2012). In addition, uric acid inhibits
NO production in ECs in vitro, and in vivo levels of serum nitrites
(an indicator of NO production) are inversely proportional to serum
uric acid concentrations (Khosla et al, 2005). Interestingly, ECs
exposed to uric acid increase expression of AR and alter expression
of several other proteins linked to metabolism (Zhang et al, 2014).
These studies suggest that hyperuricemia induces EC dysfunction
through metabolic alterations. Whether the same is true for other
cardiovascular risk factors remains in question.
A broader characterization of EC metabolism in the future might
reveal novel therapeutic targets in metabolic pathways that are
generally not considered to be important in pathological EC func-
tion. Recent findings that endothelial cholesterol efflux to high-
density lipoprotein regulates angiogenesis (Fang et al, 2013), and
that EC-specific insulin receptor knock-out accelerates atheroscle-
rotic plaque formation (Gage et al, 2013) point to a key role for EC
metabolism in the pathogenesis of disease and indicate that many
more yet to be identified non-traditional but potentially druggable
metabolic enzymes, transporters and pathways may play a role in
vascular disease.
Therapeutic targeting of EC metabolism
Overall, it is clear that pathological blood vessel responses are
associated with metabolic alterations in ECs. These metabolic
adaptations are not just innocent bystanders, but in many cases
mediate important aspects of disease. Increased EC glucose metabo-
lism is emerging as a key feature of angiogenic and hyper-prolifera-
tive ECs. Targeting EC glucose metabolism has recently been shown
as a viable strategy to curb pathological angiogenesis, but is still in
its infancy (Schoors et al, 2014b). Recent technical and conceptual
advances, however, now make it possible to perform comprehen-
sive metabolic studies. These technical breakthroughs have led to a
resurgent interest in targeting cell metabolism for therapeutic gains.
As a proof of concept, targeting EC metabolism by pharmacological
inhibition of the glycolytic enzyme PFKFB3 has shown recent
success in inhibiting pathological angiogenesis (Fig 4) (De Bock
et al, 2013b; Schoors et al, 2014b; Xu et al, 2014). These results,
together with the observation that EC metabolism is altered in many
diseases, suggest that EC metabolism is an attractive and viable but
understudied therapeutic target.
For more informationAuthor website: http://www.vrc-lab.be/peter-carmeliet
AcknowledgementsWe apologize for not being able to cite the work of all other studies related to
this topic because of space restrictions. The authors gratefully acknowledge
Massimo M. Santoro and Richard C. Cubbon for their valuable comments that
helped improve the manuscript. J.G. is a PhD student supported by a BOF
fellowship from the University of Leuven. The work of P.C. is supported by a
Federal Government Belgium grant (IUAP P7/03), long-term structural
Methusalem funding by the Flemish Government, grants from the Research
Foundation Flanders (FWO), the Foundation of Leducq Transatlantic Network
(ARTEMIS), Foundation against Cancer, an European Research Council (ERC)
Advanced Research Grant (EU-ERC269073) and the AXA Research Fund.
Pending issues
The findings in this review suggest that blood vessel pathology is medi-ated, or at least characterized, by disease-specific alterations. However,at present, there are no studies that incorporate state-of-the-art meta-bolomics tools to characterize EC metabolism in disease. Metabolicprofiling using isotope incorporation studies and metabolic flux analysiscould greatly increase our understanding of the metabolic alterationsthat underlie EC pathology.
In vivo studies to characterize EC metabolism in animal models ofhuman disease could provide highly relevant insight in disease-specific metabolic alterations. However, this requires isolation of ECsfrom diseased tissue, which at present poses technical and interpreta-tional challenges for proper analysis of metabolism using advancedmetabolomics methods.
Another pressing issue is the lack of studies characterizing metabolism inpatient-derived tissue using either in or ex vivo models. The recent devel-opment of new protocols to isolate ECs from patient tissue offers thepossibility to study metabolism in clinically relevant systems. Accordingly,such studies could greatly advance the identification of novel biomarkersand therapeutic targets in EC metabolism.
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1114
Conflict of interestThe authors declare that they have no conflict of interest.
References
Ahmed U, Dobler D, Larkin SJ, Rabbani N, Thornalley PJ (2008) Reversal of
hyperglycemia-induced angiogenesis deficit of human endothelial cells
by overexpression of glyoxalase 1 in vitro. Ann N Y Acad Sci 1126:
262 – 264
Arany Z, Foo SY, Ma Y, Ruas JL, Bommi-Reddy A, Girnun G, Cooper M, Laznik
D, Chinsomboon J, Rangwala SM et al (2008) HIF-independent regulation
of VEGF and angiogenesis by the transcriptional coactivator PGC-1alpha.
Nature 451: 1008 – 1012
Atkinson C, Stewart S, Upton PD, Machado R, Thomson JR, Trembath RC,
Morrell NW (2002) Primary pulmonary hypertension is associated with
reduced pulmonary vascular expression of type II bone morphogenetic
protein receptor. Circulation 105: 1672 – 1678
van Beijnum JR, Dings RP, van der Linden E, Zwaans BM, Ramaekers FC,
Mayo KH, Griffioen AW (2006) Gene expression of tumor angiogenesis
dissected: specific targeting of colon cancer angiogenic vasculature. Blood
108: 2339 – 2348
Bendall JK, Douglas G, McNeill E, Channon KM, Crabtree MJ (2014)
Tetrahydrobiopterin in cardiovascular health and disease. Antioxid Redox
Signal 20: 3040 – 3077
Benedito R, Roca C, Sorensen I, Adams S, Gossler A, Fruttiger M, Adams RH
(2009) The notch ligands Dll4 and Jagged1 have opposing effects on
angiogenesis. Cell 137: 1124 – 1135
Bergers G, Benjamin LE (2003) Tumorigenesis and the angiogenic switch. Nat
Rev 3: 401 – 410
Bergers G, Hanahan D (2008) Modes of resistance to anti-angiogenic therapy.
Nat Rev 8: 592 – 603
Blake R, Trounce IA (2013) Mitochondrial dysfunction and complications
associated with diabetes. Biochim Biophys Acta 1840: 1404 – 1412
Bode-Boger SM, Muke J, Surdacki A, Brabant G, Boger RH, Frolich JC (2003)
Oral L-arginine improves endothelial function in healthy individuals older
than 70 years. Vas Med 8: 77 – 81
Boger RH, Sydow K, Borlak J, Thum T, Lenzen H, Schubert B, Tsikas D,
Bode-Boger SM (2000) LDL cholesterol upregulates synthesis of
asymmetrical dimethylarginine in human endothelial cells: involvement of
S-adenosylmethionine-dependent methyltransferases. Circ Res 87: 99 – 105
Boger RH (2004) Asymmetric dimethylarginine, an endogenous inhibitor of
nitric oxide synthase, explains the “L-arginine paradox” and acts as a novel
cardiovascular risk factor. J Nutr 134: 2842S – 2847S; discussion 2853S
Brouwers O, Niessen PM, Haenen G, Miyata T, Brownlee M, Stehouwer CD,
De Mey JG, Schalkwijk CG (2010) Hyperglycaemia-induced impairment of
endothelium-dependent vasorelaxation in rat mesenteric arteries is
mediated by intracellular methylglyoxal levels in a pathway dependent on
oxidative stress. Diabetologia 53: 989 – 1000
Brouwers O, Niessen PM, Miyata T, Ostergaard JA, Flyvbjerg A, Peutz-Kootstra
CJ, Sieber J, Mundel PH, Brownlee M, Janssen BJ et al (2014) Glyoxalase-1
overexpression reduces endothelial dysfunction and attenuates early renal
impairment in a rat model of diabetes. Diabetologia 57: 224 – 235
Brownlee M (2001) Biochemistry and molecular cell biology of diabetic
complications. Nature 414: 813 – 820
Bucala R, Tracey KJ, Cerami A (1991) Advanced glycosylation products quench
nitric oxide and mediate defective endothelium-dependent vasodilatation
in experimental diabetes. J Clin Investig 87: 432 – 438
Carmeliet P (2003) Angiogenesis in health and disease. Nat Med 9: 653 – 660
Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications
of angiogenesis. Nature 473: 298 – 307
Chalupsky K, Cai H (2005) Endothelial dihydrofolate reductase: critical for
nitric oxide bioavailability and role in angiotensin II uncoupling of
endothelial nitric oxide synthase. Proc Natl Acad Sci USA 102: 9056 – 9061
Chang CI, Liao JC, Kuo L (1998) Arginase modulates nitric oxide production in
activated macrophages. Am J Physiol 274: H342 –H348
Chen F, Lucas R, Fulton D (2013) The subcellular compartmentalization of
arginine metabolizing enzymes and their role in endothelial dysfunction.
Front Immunol 4: 184
Cines DB, Pollak ES, Buck CA, Loscalzo J, Zimmerman GA, McEver RP, Pober
JS, Wick TM, Konkle BA, Schwartz BS et al (1998) Endothelial cells in
physiology and in the pathophysiology of vascular disorders. Blood 91:
3527 – 3561
Clarke R, Halsey J, Lewington S, Lonn E, Armitage J, Manson JE, Bonaa KH,
Spence JD, Nygard O, Jamison R et al (2010) Effects of lowering
homocysteine levels with B vitamins on cardiovascular disease, cancer,
and cause-specific mortality: Meta-analysis of 8 randomized trials
involving 37 485 individuals. Arch Intern Med 170: 1622 – 1631
Crabtree MJ, Hale AB, Channon KM (2011) Dihydrofolate reductase protects
endothelial nitric oxide synthase from uncoupling in tetrahydrobiopterin
deficiency. Free Radical Biol Med 50: 1639 – 1646
Crabtree MJ, Tatham AL, Al-Wakeel Y, Warrick N, Hale AB, Cai S, Channon
KM, Alp NJ (2009a) Quantitative regulation of intracellular endothelial
nitric-oxide synthase (eNOS) coupling by both tetrahydrobiopterin-eNOS
stoichiometry and biopterin redox status: insights from cells with
tet-regulated GTP cyclohydrolase I expression. J Biol Chem 284:
1136 – 1144
Crabtree MJ, Tatham AL, Hale AB, Alp NJ, Channon KM (2009b) Critical role
for tetrahydrobiopterin recycling by dihydrofolate reductase in regulation
of endothelial nitric-oxide synthase coupling: relative importance of the
de novo biopterin synthesis versus salvage pathways. J Biol Chem 284:
28128 – 28136
Croci DO, Cerliani JP, Dalotto-Moreno T, Mendez-Huergo SP, Mascanfroni ID,
Dergan-Dylon S, Toscano MA, Caramelo JJ, Garcia-Vallejo JJ, Ouyang J et al
(2014) Glycosylation-dependent lectin-receptor interactions preserve
angiogenesis in anti-VEGF refractory tumors. Cell 156: 744 – 758
Culic O, Gruwel ML, Schrader J (1997) Energy turnover of vascular endothelial
cells. Am J Physiol 273: C205 –C213
Cunnington C, Van Assche T, Shirodaria C, Kylintireas I, Lindsay AC, Lee JM,
Antoniades C, Margaritis M, Lee R, Cerrato R et al (2012) Systemic and
vascular oxidation limits the efficacy of oral tetrahydrobiopterin treatment
in patients with coronary artery disease. Circulation 125: 1356 – 1366
Dagher Z, Ruderman N, Tornheim K, Ido Y (1999) The effect of AMP-activated
protein kinase and its activator AICAR on the metabolism of human
umbilical vein endothelial cells. Biochem Biophys Res Commun 265:
112 – 115
Dagher Z, Ruderman N, Tornheim K, Ido Y (2001) Acute regulation of fatty
acid oxidation and amp-activated protein kinase in human umbilical vein
endothelial cells. Circ Res 88: 1276 – 1282
Davignon J, Ganz P (2004) Role of endothelial dysfunction in atherosclerosis.
Circulation 109: III27 – III32
De Bock K, Georgiadou M, Carmeliet P (2013a) Role of endothelial cell
metabolism in vessel sprouting. Cell Metab 18: 634 – 647
De Bock K, Georgiadou M, Schoors S, Kuchnio A, Wong BW, Cantelmo AR,
Quaegebeur A, Ghesquiere B, Cauwenberghs S, Eelen G et al (2013b) Role
of PFKFB3-driven glycolysis in vessel sprouting. Cell 154: 651 – 663
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1115
exact mechanisms remain to be elucidated. Nonetheless, early
clinical and preclinical studies have found that therapeutic target-
ing of 1C metabolism, for example, via folate supplementation
lowers levels of homocysteinemia and increases BH4 regeneration
from BH2 (Verhaar et al, 2002). However, large-scale clinical trials
failed to show benefits of folate or BH4 supplementation to
prevent cardiovascular disease (Clarke et al, 2010; Cunnington
et al, 2012; Marti-Carvajal et al, 2013). These clinical and preclini-
cal findings suggest that while L-arginine, folate, methionine,
COQ10 and homocysteine metabolism are potential therapeutic
targets, a more detailed understanding of how these pathways
cause dysfunction is required to design more rational therapeutic
agents.
EC metabolism in the pathogenesis of other diseases
EC metabolism is best characterized in the diseases discussed
above. However, these represent only a minor fraction of the disor-
ders in which pathological EC responses are presumably involved.
Indeed, it is highly likely that EC metabolic alterations are also
involved in the pathogenesis of other diseases such as ischemia,
pre-eclampsia, vasculitis, vascular neoplasms and others although
this has hardly been studied.
On the other hand, many of the EC metabolic alterations that
lead to EC dysfunction are likely induced by cardiovascular risk
factors such as those that characterize metabolic syndrome, hyper-
homocysteinemia and hyperuricemia. For example, elevated serum
uric acid (a breakdown product of purine nucleotides generated by
xanthine oxidase with potent anti-oxidant activity) is common in
patients with hypertension and may even be a root cause of EC
dysfunction leading to cardiovascular disease (Feig et al, 2008).
Interestingly, while uric acid has been described as major anti-
oxidant in human plasma, ECs exposed to uric acid display
increased ROS production creating a paradox that has not been
resolved (Lippi et al, 2008; Sautin & Johnson, 2008). Regardless, in
cardiovascular disease models uric acid reduces mitochondrial
content, intracellular ATP and arginase activity (Zharikov et al,
2008; Sanchez-Lozada et al, 2012). In addition, uric acid inhibits
NO production in ECs in vitro, and in vivo levels of serum nitrites
(an indicator of NO production) are inversely proportional to serum
uric acid concentrations (Khosla et al, 2005). Interestingly, ECs
exposed to uric acid increase expression of AR and alter expression
of several other proteins linked to metabolism (Zhang et al, 2014).
These studies suggest that hyperuricemia induces EC dysfunction
through metabolic alterations. Whether the same is true for other
cardiovascular risk factors remains in question.
A broader characterization of EC metabolism in the future might
reveal novel therapeutic targets in metabolic pathways that are
generally not considered to be important in pathological EC func-
tion. Recent findings that endothelial cholesterol efflux to high-
density lipoprotein regulates angiogenesis (Fang et al, 2013), and
that EC-specific insulin receptor knock-out accelerates atheroscle-
rotic plaque formation (Gage et al, 2013) point to a key role for EC
metabolism in the pathogenesis of disease and indicate that many
more yet to be identified non-traditional but potentially druggable
metabolic enzymes, transporters and pathways may play a role in
vascular disease.
Therapeutic targeting of EC metabolism
Overall, it is clear that pathological blood vessel responses are
associated with metabolic alterations in ECs. These metabolic
adaptations are not just innocent bystanders, but in many cases
mediate important aspects of disease. Increased EC glucose metabo-
lism is emerging as a key feature of angiogenic and hyper-prolifera-
tive ECs. Targeting EC glucose metabolism has recently been shown
as a viable strategy to curb pathological angiogenesis, but is still in
its infancy (Schoors et al, 2014b). Recent technical and conceptual
advances, however, now make it possible to perform comprehen-
sive metabolic studies. These technical breakthroughs have led to a
resurgent interest in targeting cell metabolism for therapeutic gains.
As a proof of concept, targeting EC metabolism by pharmacological
inhibition of the glycolytic enzyme PFKFB3 has shown recent
success in inhibiting pathological angiogenesis (Fig 4) (De Bock
et al, 2013b; Schoors et al, 2014b; Xu et al, 2014). These results,
together with the observation that EC metabolism is altered in many
diseases, suggest that EC metabolism is an attractive and viable but
understudied therapeutic target.
For more informationAuthor website: http://www.vrc-lab.be/peter-carmeliet
AcknowledgementsWe apologize for not being able to cite the work of all other studies related to
this topic because of space restrictions. The authors gratefully acknowledge
Massimo M. Santoro and Richard C. Cubbon for their valuable comments that
helped improve the manuscript. J.G. is a PhD student supported by a BOF
fellowship from the University of Leuven. The work of P.C. is supported by a
Federal Government Belgium grant (IUAP P7/03), long-term structural
Methusalem funding by the Flemish Government, grants from the Research
Foundation Flanders (FWO), the Foundation of Leducq Transatlantic Network
(ARTEMIS), Foundation against Cancer, an European Research Council (ERC)
Advanced Research Grant (EU-ERC269073) and the AXA Research Fund.
Pending issues
The findings in this review suggest that blood vessel pathology is medi-ated, or at least characterized, by disease-specific alterations. However,at present, there are no studies that incorporate state-of-the-art meta-bolomics tools to characterize EC metabolism in disease. Metabolicprofiling using isotope incorporation studies and metabolic flux analysiscould greatly increase our understanding of the metabolic alterationsthat underlie EC pathology.
In vivo studies to characterize EC metabolism in animal models ofhuman disease could provide highly relevant insight in disease-specific metabolic alterations. However, this requires isolation of ECsfrom diseased tissue, which at present poses technical and interpreta-tional challenges for proper analysis of metabolism using advancedmetabolomics methods.
Another pressing issue is the lack of studies characterizing metabolism inpatient-derived tissue using either in or ex vivo models. The recent devel-opment of new protocols to isolate ECs from patient tissue offers thepossibility to study metabolism in clinically relevant systems. Accordingly,such studies could greatly advance the identification of novel biomarkersand therapeutic targets in EC metabolism.
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1114
DeBerardinis RJ, Cheng T (2010) Q’s next: the diverse functions of glutamine
in metabolism, cell biology and cancer. Oncogene 29: 313 – 324
Dhillon B, Badiwala MV, Maitland A, Rao V, Li SH, Verma S (2003)
Tetrahydrobiopterin attenuates homocysteine induced endothelial
dysfunction. Mol Cell Biochem 247: 223 – 227
Dobrina A, Rossi F (1983) Metabolic properties of freshly isolated bovine
endothelial cells. Biochim Biophys Acta 762: 295 – 301
Dranka BP, Hill BG, Darley-Usmar VM (2010) Mitochondrial reserve capacity
in endothelial cells: The impact of nitric oxide and reactive oxygen
species. Free Radical Biol Med 48: 905 – 914
Du XL, Edelstein D, Rossetti L, Fantus IG, Goldberg H, Ziyadeh F, Wu J,
Brownlee M (2000) Hyperglycemia-induced mitochondrial superoxide
overproduction activates the hexosamine pathway and induces
plasminogen activator inhibitor-1 expression by increasing Sp1
glycosylation. Proc Natl Acad Sci USA 97: 12222 – 12226
Du XL, Edelstein D, Dimmeler S, Ju Q, Sui C, Brownlee M (2001)
Hyperglycemia inhibits endothelial nitric oxide synthase activity by
posttranslational modification at the Akt site. J Clin Invest 108: 1341 – 1348
Du X, Matsumura T, Edelstein D, Rossetti L, Zsengeller Z, Szabo C, Brownlee
M (2003) Inhibition of GAPDH activity by poly(ADP-ribose) polymerase
activates three major pathways of hyperglycemic damage in endothelial
cells. J Clin Investig 112: 1049 – 1057
Dymkowska D, Drabarek B, Podszywalow-Bartnicka P, Szczepanowska J,
Zablocki K (2014) Hyperglycaemia modifies energy metabolism and
reactive oxygen species formation in endothelial cells in vitro. Arch
Biochem Biophys 542: 7 – 13
Ebos JM, Kerbel RS (2011) Antiangiogenic therapy: impact on invasion,
disease progression, and metastasis. Nat Rev Clin Oncol 8: 210 – 221
den Eynden JV, Ali SS, Horwood N, Carmans S, Brone B, Hellings N, Steels P,
Harvey RJ, Rigo JM (2009) Glycine and glycine receptor signalling in
non-neuronal cells. Front Mol Neurosci 2: 9
Fang L, Choi SH, Baek JS, Liu C, Almazan F, Ulrich F, Wiesner P, Taleb A, Deer
E, Pattison J et al (2013) Control of angiogenesis by AIBP-mediated
cholesterol efflux. Nature 498: 118 – 122
Federici M, Menghini R, Mauriello A, Hribal ML, Ferrelli F, Lauro D, Sbraccia P,
Spagnoli LG, Sesti G, Lauro R (2002) Insulin-dependent activation of
endothelial nitric oxide synthase is impaired by O-linked glycosylation
modification of signaling proteins in human coronary endothelial cells.
Circulation 106: 466 – 472
Feig DI, Kang DH, Johnson RJ (2008) Uric acid and cardiovascular risk. New
Engl J Med 359: 1811 – 1821
Fessel JP, Hamid R, Wittmann BM, Robinson LJ, Blackwell T, Tada Y, Tanabe
N, Tatsumi K, Hemnes AR, West JD (2012) Metabolomic analysis of bone
morphogenetic protein receptor type 2 mutations in human pulmonary
endothelium reveals widespread metabolic reprogramming. Pulm Circ 2:
201 – 213
Fijalkowska I, Xu W, Comhair SA, Janocha AJ, Mavrakis LA, Krishnamachary B,
Zhen L, Mao T, Richter A, Erzurum SC et al (2010) Hypoxia
inducible-factor1alpha regulates the metabolic shift of pulmonary
hypertensive endothelial cells. Am J Pathol 176: 1130 – 1138
Frederiksen J, Juul K, Grande P, Jensen GB, Schroeder TV, Tybjaerg-Hansen A,
Nordestgaard BG (2004) Methylenetetrahydrofolate reductase
polymorphism (C677T), hyperhomocysteinemia, and risk of ischemic
cardiovascular disease and venous thromboembolism: prospective and
case-control studies from the Copenhagen City Heart Study. Blood 104:
3046 – 3051
Gage MC, Yuldasheva NY, Viswambharan H, Sukumar P, Cubbon RM,
Galloway S, Imrie H, Skromna A, Smith J, Jackson CL et al (2013)
Endothelium-specific insulin resistance leads to accelerated
atherosclerosis in areas with disturbed flow patterns: a role for reactive
oxygen species. Atherosclerosis 230: 131 – 139
Gao L, Mao Q, Cao J, Wang Y, Zhou X, Fan L (2012) Effects of coenzyme Q10
on vascular endothelial function in humans: a meta-analysis of
randomized controlled trials. Atherosclerosis 221: 311 – 316
Gaudreault N, Scriven DR, Moore ED (2004) Characterisation of
glucose transporters in the intact coronary artery endothelium in rats:
GLUT-2 upregulated by long-term hyperglycaemia. Diabetologia 47:
2081 – 2092
Gaudreault N, Scriven DR, Laher I, Moore ED (2008) Subcellular
characterization of glucose uptake in coronary endothelial cells. Microvasc
Res 75: 73 – 82
Giacco F, Brownlee M (2010) Oxidative stress and diabetic complications. Circ
Res 107: 1058 – 1070
Goldin A, Beckman JA, Schmidt AM, Creager MA (2006) Advanced glycation
end products: sparking the development of diabetic vascular injury.
Circulation 114: 597 – 605
Goldstein JL, Brown MS (1990) Regulation of the mevalonate pathway. Nature
343: 425 – 430
Gorren AC, Bec N, Schrammel A, Werner ER, Lange R, Mayer B (2000)
Low-temperature optical absorption spectra suggest a redox role for
tetrahydrobiopterin in both steps of nitric oxide synthase catalysis.
Biochemistry 39: 11763 – 11770
Harjes U, Bensaad K, Harris AL (2012) Endothelial cell metabolism and
implications for cancer therapy. Br J Cancer 107: 1207 – 1212
Hassan HH, Denis M, Krimbou L, Marcil M, Genest J (2006) Cellular
cholesterol homeostasis in vascular endothelial cells. Can J Cardiol 22
(Suppl B): 35B – 40B
Heitzer T, Krohn K, Albers S, Meinertz T (2000) Tetrahydrobiopterin
improves endothelium-dependent vasodilation by increasing nitric oxide
activity in patients with Type II diabetes mellitus. Diabetologia 43:
1435 – 1438
Herskowitz K, Bode BP, Block ER, Souba WW (1991) Characterization of
L-glutamine transport by pulmonary artery endothelial cells. Am J Physiol
260: L241 – L246
Herzig S, Long F, Jhala US, Hedrick S, Quinn R, Bauer A, Rudolph D, Schutz G,
Yoon C, Puigserver P et al (2001) CREB regulates hepatic gluconeogenesis
through the coactivator PGC-1. Nature 413: 179 – 183
Hinshaw DB, Burger JM (1990) Protective effect of glutamine on endothelial
cell ATP in oxidant injury. J Surg Res 49: 222 – 227
Hopkins PN (2013) Molecular biology of atherosclerosis. Physiol Rev 93:
1317 – 1542
Hu X, Xu X, Zhu G, Atzler D, Kimoto M, Chen J, Schwedhelm E, Luneburg N,
Boger RH, Zhang P et al (2009) Vascular endothelial-specific
dimethylarginine dimethylaminohydrolase-1-deficient mice reveal that
vascular endothelium plays an important role in removing asymmetric
dimethylarginine. Circulation 120: 2222 – 2229
Humphrey LL, Fu R, Rogers K, Freeman M, Helfand M (2008) Homocysteine
level and coronary heart disease incidence: a systematic review and
meta-analysis. Mayo Clin Proc 83: 1203 – 1212
Hunt TK, Aslam RS, Beckert S, Wagner S, Ghani QP, Hussain MZ, Roy S,
Sen CK (2007) Aerobically derived lactate stimulates revascularization
and tissue repair via redox mechanisms. Antioxid Redox Signal 9:
1115 – 1124
Inoguchi T, Sonta T, Tsubouchi H, Etoh T, Kakimoto M, Sonoda N, Sato N,
Sekiguchi N, Kobayashi K, Sumimoto H, et al (2003) Protein kinase
C-dependent increase in reactive oxygen species (ROS) production in
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1116
vascular tissues of diabetes: role of vascular NAD(P)H oxidase. J Am Soc
Nephrol 14: S227 – S232
Ivashchenko CY, Bradley BT, Ao Z, Leiper J, Vallance P, Johns DG (2010)
Regulation of the ADMA-DDAH system in endothelial cells: a novel
mechanism for the sterol response element binding proteins, SREBP1c and
-2. Am J Physiol Heart Circ Physiol 298: H251 –H258
Jain RK (1987) Transport of molecules in the tumor interstitium: a review.
Cancer Res 47: 3039 – 3051
Jongkind JF, Verkerk A, Baggen RG (1989) Glutathione metabolism of human
vascular endothelial cells under peroxidative stress. Free Radical Biol Med
7: 507 – 512
Kashiwagi A, Asahina T, Ikebuchi M, Tanaka Y, Takagi Y, Nishio Y, Kikkawa R,
Shigeta Y (1994) Abnormal glutathione metabolism and increased
cytotoxicity caused by H2O2 in human umbilical vein endothelial cells
cultured in high glucose medium. Diabetologia 37: 264 – 269
Kawashima S, Yokoyama M (2004) Dysfunction of endothelial nitric oxide
synthase and atherosclerosis. Arterioscler Thromb Vasc Biol 24:
998 – 1005
Kelly PJ, Rosand J, Kistler JP, Shih VE, Silveira S, Plomaritoglou A, Furie KL
(2002) Homocysteine, MTHFR 677C–>T polymorphism, and risk of ischemic
stroke: results of a meta-analysis. Neurology 59: 529 – 536
Khosla UM, Zharikov S, Finch JL, Nakagawa T, Roncal C, Mu W, Krotova K,
Block ER, Prabhakar S, Johnson RJ (2005) Hyperuricemia induces
endothelial dysfunction. Kidney Int 67: 1739 – 1742
Kidokoro K, Satoh M, Channon KM, Yada T, Sasaki T, Kashihara N (2013)
Maintenance of endothelial guanosine triphosphate cyclohydrolase I
ameliorates diabetic nephropathy. J Am Soc Nephrol 24: 1139 – 1150
Klerk M, Verhoef P, Clarke R, Blom HJ, Kok FJ, Schouten EG (2002) MTHFR
677C–>T polymorphism and risk of coronary heart disease: a
meta-analysis. JAMA 288: 2023 – 2031
Koziel A, Woyda-Ploszczyca A, Kicinska A, Jarmuszkiewicz W (2012) The
influence of high glucose on the aerobic metabolism of endothelial
EA.hy926 cells. Pflugers Arch 464: 657 – 669
Krotova K, Patel JM, Block ER, Zharikov S (2010) Endothelial arginase II
responds to pharmacological inhibition by elevation in protein level. Mol
Cell Biochem 343: 211 – 216
Krutzfeldt A, Spahr R, Mertens S, Siegmund B, Piper HM (1990) Metabolism of
exogenous substrates by coronary endothelial cells in culture. J Mol Cell
Cardiol 22: 1393 – 1404
Laczy B, Hill BG, Wang K, Paterson AJ, White CR, Xing D, Chen YF,
Darley-Usmar V, Oparil S, Chatham JC (2009) Protein O-GlcNAcylation: a
new signaling paradigm for the cardiovascular system. Am J Physiol Heart
Circ Physiol 296: H13 –H28
Laufs U, La Fata V, Plutzky J, Liao JK (1998) Upregulation of endothelial nitric
oxide synthase by HMG CoA reductase inhibitors. Circulation 97: 1129 – 1135
Laufs U, Liao JK (1998) Post-transcriptional regulation of endothelial nitric
oxide synthase mRNA stability by Rho GTPase. J Biol Chem 273:
24266 – 24271
Lee R, Channon KM, Antoniades C (2012) Therapeutic strategies targeting
endothelial function in humans: clinical implications. Curr Vasc Pharmacol
10: 77 – 93
Leighton B, Curi R, Hussein A, Newsholme EA (1987) Maximum activities of
some key enzymes of glycolysis, glutaminolysis, Krebs cycle and fatty acid
utilization in bovine pulmonary endothelial cells. FEBS Lett 225: 93 – 96
Leiper J, Nandi M (2011) The therapeutic potential of targeting endogenous
inhibitors of nitric oxide synthesis. Nat Rev Drug Discovery 10: 277 – 291
Leopold JA, Zhang YY, Scribner AW, Stanton RC, Loscalzo J (2003)
Glucose-6-phosphate dehydrogenase overexpression decreases endothelial
cell oxidant stress and increases bioavailable nitric oxide. Arterioscler
Thromb Vasc Biol 23: 411 – 417
Lerman A, Burnett JC Jr, Higano ST, McKinley LJ, Holmes DR Jr (1998)
Long-term L-arginine supplementation improves small-vessel coronary
endothelial function in humans. Circulation 97: 2123 – 2128
Liao JK, Laufs U (2005) Pleiotropic effects of statins. Annu Rev Pharmacol
Toxicol 45: 89 – 118
Lin J, Wu H, Tarr PT, Zhang CY, Wu Z, Boss O, Michael LF, Puigserver P,
Isotani E, Olson EN et al (2002) Transcriptional co-activator PGC-1
alpha drives the formation of slow-twitch muscle fibres. Nature 418:
797 – 801
Lippi G, Montagnana M, Franchini M, Favaloro EJ, Targher G (2008) The
paradoxical relationship between serum uric acid and cardiovascular
disease. Clin Chim Acta 392: 1 – 7
Locasale JW (2013) Serine, glycine and one-carbon units: cancer metabolism
in full circle. Nat Rev 13: 572 – 583
Loges S, Schmidt T, Carmeliet P (2010) Mechanisms of resistance to
anti-angiogenic therapy and development of third-generation
anti-angiogenic drug candidates. Genes & cancer 1: 12 – 25
Long L, MacLean MR, Jeffery TK, Morecroft I, Yang X, Rudarakanchana N,
Southwood M, James V, Trembath RC, Morrell NW (2006) Serotonin
increases susceptibility to pulmonary hypertension in BMPR2-deficient
mice. Circ Res 98: 818 – 827
Luo B, Soesanto Y, McClain DA (2008) Protein modification by O-linked
GlcNAc reduces angiogenesis by inhibiting Akt activity in endothelial cells.
Arterioscler Thromb Vasc Biol 28: 651 – 657
Majka S, Hagen M, Blackwell T, Harral J, Johnson JA, Gendron R, Paradis H,
Crona D, Loyd JE, Nozik-Grayck E et al (2011) Physiologic and molecular
consequences of endothelial Bmpr2 mutation. Respir Res 12: 84
Majmundar AJ, Wong WJ, Simon MC (2010) Hypoxia-inducible factors and
the response to hypoxic stress. Mol Cell 40: 294 – 309
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(2013) Homocysteine-lowering interventions for preventing cardiovascular
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Masri FA, Comhair SA, Dostanic-Larson I, Kaneko FT, Dweik RA, Arroliga AC,
Erzurum SC (2008) Deficiency of lung antioxidants in idiopathic
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31ra34
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products induce apoptosis and procoagulant activity in cultured
human umbilical vein endothelial cells. Diabetes Res Clin Pract 46:
197 – 202
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1117
DeBerardinis RJ, Cheng T (2010) Q’s next: the diverse functions of glutamine
in metabolism, cell biology and cancer. Oncogene 29: 313 – 324
Dhillon B, Badiwala MV, Maitland A, Rao V, Li SH, Verma S (2003)
Tetrahydrobiopterin attenuates homocysteine induced endothelial
dysfunction. Mol Cell Biochem 247: 223 – 227
Dobrina A, Rossi F (1983) Metabolic properties of freshly isolated bovine
endothelial cells. Biochim Biophys Acta 762: 295 – 301
Dranka BP, Hill BG, Darley-Usmar VM (2010) Mitochondrial reserve capacity
in endothelial cells: The impact of nitric oxide and reactive oxygen
species. Free Radical Biol Med 48: 905 – 914
Du XL, Edelstein D, Rossetti L, Fantus IG, Goldberg H, Ziyadeh F, Wu J,
Brownlee M (2000) Hyperglycemia-induced mitochondrial superoxide
overproduction activates the hexosamine pathway and induces
plasminogen activator inhibitor-1 expression by increasing Sp1
glycosylation. Proc Natl Acad Sci USA 97: 12222 – 12226
Du XL, Edelstein D, Dimmeler S, Ju Q, Sui C, Brownlee M (2001)
Hyperglycemia inhibits endothelial nitric oxide synthase activity by
posttranslational modification at the Akt site. J Clin Invest 108: 1341 – 1348
Du X, Matsumura T, Edelstein D, Rossetti L, Zsengeller Z, Szabo C, Brownlee
M (2003) Inhibition of GAPDH activity by poly(ADP-ribose) polymerase
activates three major pathways of hyperglycemic damage in endothelial
cells. J Clin Investig 112: 1049 – 1057
Dymkowska D, Drabarek B, Podszywalow-Bartnicka P, Szczepanowska J,
Zablocki K (2014) Hyperglycaemia modifies energy metabolism and
reactive oxygen species formation in endothelial cells in vitro. Arch
Biochem Biophys 542: 7 – 13
Ebos JM, Kerbel RS (2011) Antiangiogenic therapy: impact on invasion,
disease progression, and metastasis. Nat Rev Clin Oncol 8: 210 – 221
den Eynden JV, Ali SS, Horwood N, Carmans S, Brone B, Hellings N, Steels P,
Harvey RJ, Rigo JM (2009) Glycine and glycine receptor signalling in
non-neuronal cells. Front Mol Neurosci 2: 9
Fang L, Choi SH, Baek JS, Liu C, Almazan F, Ulrich F, Wiesner P, Taleb A, Deer
E, Pattison J et al (2013) Control of angiogenesis by AIBP-mediated
cholesterol efflux. Nature 498: 118 – 122
Federici M, Menghini R, Mauriello A, Hribal ML, Ferrelli F, Lauro D, Sbraccia P,
Spagnoli LG, Sesti G, Lauro R (2002) Insulin-dependent activation of
endothelial nitric oxide synthase is impaired by O-linked glycosylation
modification of signaling proteins in human coronary endothelial cells.
Circulation 106: 466 – 472
Feig DI, Kang DH, Johnson RJ (2008) Uric acid and cardiovascular risk. New
Engl J Med 359: 1811 – 1821
Fessel JP, Hamid R, Wittmann BM, Robinson LJ, Blackwell T, Tada Y, Tanabe
N, Tatsumi K, Hemnes AR, West JD (2012) Metabolomic analysis of bone
morphogenetic protein receptor type 2 mutations in human pulmonary
endothelium reveals widespread metabolic reprogramming. Pulm Circ 2:
201 – 213
Fijalkowska I, Xu W, Comhair SA, Janocha AJ, Mavrakis LA, Krishnamachary B,
Zhen L, Mao T, Richter A, Erzurum SC et al (2010) Hypoxia
inducible-factor1alpha regulates the metabolic shift of pulmonary
hypertensive endothelial cells. Am J Pathol 176: 1130 – 1138
Frederiksen J, Juul K, Grande P, Jensen GB, Schroeder TV, Tybjaerg-Hansen A,
Nordestgaard BG (2004) Methylenetetrahydrofolate reductase
polymorphism (C677T), hyperhomocysteinemia, and risk of ischemic
cardiovascular disease and venous thromboembolism: prospective and
case-control studies from the Copenhagen City Heart Study. Blood 104:
3046 – 3051
Gage MC, Yuldasheva NY, Viswambharan H, Sukumar P, Cubbon RM,
Galloway S, Imrie H, Skromna A, Smith J, Jackson CL et al (2013)
Endothelium-specific insulin resistance leads to accelerated
atherosclerosis in areas with disturbed flow patterns: a role for reactive
oxygen species. Atherosclerosis 230: 131 – 139
Gao L, Mao Q, Cao J, Wang Y, Zhou X, Fan L (2012) Effects of coenzyme Q10
on vascular endothelial function in humans: a meta-analysis of
randomized controlled trials. Atherosclerosis 221: 311 – 316
Gaudreault N, Scriven DR, Moore ED (2004) Characterisation of
glucose transporters in the intact coronary artery endothelium in rats:
GLUT-2 upregulated by long-term hyperglycaemia. Diabetologia 47:
2081 – 2092
Gaudreault N, Scriven DR, Laher I, Moore ED (2008) Subcellular
characterization of glucose uptake in coronary endothelial cells. Microvasc
Res 75: 73 – 82
Giacco F, Brownlee M (2010) Oxidative stress and diabetic complications. Circ
Res 107: 1058 – 1070
Goldin A, Beckman JA, Schmidt AM, Creager MA (2006) Advanced glycation
end products: sparking the development of diabetic vascular injury.
Circulation 114: 597 – 605
Goldstein JL, Brown MS (1990) Regulation of the mevalonate pathway. Nature
343: 425 – 430
Gorren AC, Bec N, Schrammel A, Werner ER, Lange R, Mayer B (2000)
Low-temperature optical absorption spectra suggest a redox role for
tetrahydrobiopterin in both steps of nitric oxide synthase catalysis.
Biochemistry 39: 11763 – 11770
Harjes U, Bensaad K, Harris AL (2012) Endothelial cell metabolism and
implications for cancer therapy. Br J Cancer 107: 1207 – 1212
Hassan HH, Denis M, Krimbou L, Marcil M, Genest J (2006) Cellular
cholesterol homeostasis in vascular endothelial cells. Can J Cardiol 22
(Suppl B): 35B – 40B
Heitzer T, Krohn K, Albers S, Meinertz T (2000) Tetrahydrobiopterin
improves endothelium-dependent vasodilation by increasing nitric oxide
activity in patients with Type II diabetes mellitus. Diabetologia 43:
1435 – 1438
Herskowitz K, Bode BP, Block ER, Souba WW (1991) Characterization of
L-glutamine transport by pulmonary artery endothelial cells. Am J Physiol
260: L241 – L246
Herzig S, Long F, Jhala US, Hedrick S, Quinn R, Bauer A, Rudolph D, Schutz G,
Yoon C, Puigserver P et al (2001) CREB regulates hepatic gluconeogenesis
through the coactivator PGC-1. Nature 413: 179 – 183
Hinshaw DB, Burger JM (1990) Protective effect of glutamine on endothelial
cell ATP in oxidant injury. J Surg Res 49: 222 – 227
Hopkins PN (2013) Molecular biology of atherosclerosis. Physiol Rev 93:
1317 – 1542
Hu X, Xu X, Zhu G, Atzler D, Kimoto M, Chen J, Schwedhelm E, Luneburg N,
Boger RH, Zhang P et al (2009) Vascular endothelial-specific
dimethylarginine dimethylaminohydrolase-1-deficient mice reveal that
vascular endothelium plays an important role in removing asymmetric
dimethylarginine. Circulation 120: 2222 – 2229
Humphrey LL, Fu R, Rogers K, Freeman M, Helfand M (2008) Homocysteine
level and coronary heart disease incidence: a systematic review and
meta-analysis. Mayo Clin Proc 83: 1203 – 1212
Hunt TK, Aslam RS, Beckert S, Wagner S, Ghani QP, Hussain MZ, Roy S,
Sen CK (2007) Aerobically derived lactate stimulates revascularization
and tissue repair via redox mechanisms. Antioxid Redox Signal 9:
1115 – 1124
Inoguchi T, Sonta T, Tsubouchi H, Etoh T, Kakimoto M, Sonoda N, Sato N,
Sekiguchi N, Kobayashi K, Sumimoto H, et al (2003) Protein kinase
C-dependent increase in reactive oxygen species (ROS) production in
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1116
vascular tissues of diabetes: role of vascular NAD(P)H oxidase. J Am Soc
Nephrol 14: S227 – S232
Ivashchenko CY, Bradley BT, Ao Z, Leiper J, Vallance P, Johns DG (2010)
Regulation of the ADMA-DDAH system in endothelial cells: a novel
mechanism for the sterol response element binding proteins, SREBP1c and
-2. Am J Physiol Heart Circ Physiol 298: H251 –H258
Jain RK (1987) Transport of molecules in the tumor interstitium: a review.
Cancer Res 47: 3039 – 3051
Jongkind JF, Verkerk A, Baggen RG (1989) Glutathione metabolism of human
vascular endothelial cells under peroxidative stress. Free Radical Biol Med
7: 507 – 512
Kashiwagi A, Asahina T, Ikebuchi M, Tanaka Y, Takagi Y, Nishio Y, Kikkawa R,
Shigeta Y (1994) Abnormal glutathione metabolism and increased
cytotoxicity caused by H2O2 in human umbilical vein endothelial cells
cultured in high glucose medium. Diabetologia 37: 264 – 269
Kawashima S, Yokoyama M (2004) Dysfunction of endothelial nitric oxide
synthase and atherosclerosis. Arterioscler Thromb Vasc Biol 24:
998 – 1005
Kelly PJ, Rosand J, Kistler JP, Shih VE, Silveira S, Plomaritoglou A, Furie KL
(2002) Homocysteine, MTHFR 677C–>T polymorphism, and risk of ischemic
stroke: results of a meta-analysis. Neurology 59: 529 – 536
Khosla UM, Zharikov S, Finch JL, Nakagawa T, Roncal C, Mu W, Krotova K,
Block ER, Prabhakar S, Johnson RJ (2005) Hyperuricemia induces
endothelial dysfunction. Kidney Int 67: 1739 – 1742
Kidokoro K, Satoh M, Channon KM, Yada T, Sasaki T, Kashihara N (2013)
Maintenance of endothelial guanosine triphosphate cyclohydrolase I
ameliorates diabetic nephropathy. J Am Soc Nephrol 24: 1139 – 1150
Klerk M, Verhoef P, Clarke R, Blom HJ, Kok FJ, Schouten EG (2002) MTHFR
677C–>T polymorphism and risk of coronary heart disease: a
meta-analysis. JAMA 288: 2023 – 2031
Koziel A, Woyda-Ploszczyca A, Kicinska A, Jarmuszkiewicz W (2012) The
influence of high glucose on the aerobic metabolism of endothelial
EA.hy926 cells. Pflugers Arch 464: 657 – 669
Krotova K, Patel JM, Block ER, Zharikov S (2010) Endothelial arginase II
responds to pharmacological inhibition by elevation in protein level. Mol
Cell Biochem 343: 211 – 216
Krutzfeldt A, Spahr R, Mertens S, Siegmund B, Piper HM (1990) Metabolism of
exogenous substrates by coronary endothelial cells in culture. J Mol Cell
Cardiol 22: 1393 – 1404
Laczy B, Hill BG, Wang K, Paterson AJ, White CR, Xing D, Chen YF,
Darley-Usmar V, Oparil S, Chatham JC (2009) Protein O-GlcNAcylation: a
new signaling paradigm for the cardiovascular system. Am J Physiol Heart
Circ Physiol 296: H13 –H28
Laufs U, La Fata V, Plutzky J, Liao JK (1998) Upregulation of endothelial nitric
oxide synthase by HMG CoA reductase inhibitors. Circulation 97: 1129 – 1135
Laufs U, Liao JK (1998) Post-transcriptional regulation of endothelial nitric
oxide synthase mRNA stability by Rho GTPase. J Biol Chem 273:
24266 – 24271
Lee R, Channon KM, Antoniades C (2012) Therapeutic strategies targeting
endothelial function in humans: clinical implications. Curr Vasc Pharmacol
10: 77 – 93
Leighton B, Curi R, Hussein A, Newsholme EA (1987) Maximum activities of
some key enzymes of glycolysis, glutaminolysis, Krebs cycle and fatty acid
utilization in bovine pulmonary endothelial cells. FEBS Lett 225: 93 – 96
Leiper J, Nandi M (2011) The therapeutic potential of targeting endogenous
inhibitors of nitric oxide synthesis. Nat Rev Drug Discovery 10: 277 – 291
Leopold JA, Zhang YY, Scribner AW, Stanton RC, Loscalzo J (2003)
Glucose-6-phosphate dehydrogenase overexpression decreases endothelial
cell oxidant stress and increases bioavailable nitric oxide. Arterioscler
Thromb Vasc Biol 23: 411 – 417
Lerman A, Burnett JC Jr, Higano ST, McKinley LJ, Holmes DR Jr (1998)
Long-term L-arginine supplementation improves small-vessel coronary
endothelial function in humans. Circulation 97: 2123 – 2128
Liao JK, Laufs U (2005) Pleiotropic effects of statins. Annu Rev Pharmacol
Toxicol 45: 89 – 118
Lin J, Wu H, Tarr PT, Zhang CY, Wu Z, Boss O, Michael LF, Puigserver P,
Isotani E, Olson EN et al (2002) Transcriptional co-activator PGC-1
alpha drives the formation of slow-twitch muscle fibres. Nature 418:
797 – 801
Lippi G, Montagnana M, Franchini M, Favaloro EJ, Targher G (2008) The
paradoxical relationship between serum uric acid and cardiovascular
disease. Clin Chim Acta 392: 1 – 7
Locasale JW (2013) Serine, glycine and one-carbon units: cancer metabolism
in full circle. Nat Rev 13: 572 – 583
Loges S, Schmidt T, Carmeliet P (2010) Mechanisms of resistance to
anti-angiogenic therapy and development of third-generation
anti-angiogenic drug candidates. Genes & cancer 1: 12 – 25
Long L, MacLean MR, Jeffery TK, Morecroft I, Yang X, Rudarakanchana N,
Southwood M, James V, Trembath RC, Morrell NW (2006) Serotonin
increases susceptibility to pulmonary hypertension in BMPR2-deficient
mice. Circ Res 98: 818 – 827
Luo B, Soesanto Y, McClain DA (2008) Protein modification by O-linked
GlcNAc reduces angiogenesis by inhibiting Akt activity in endothelial cells.
Arterioscler Thromb Vasc Biol 28: 651 – 657
Majka S, Hagen M, Blackwell T, Harral J, Johnson JA, Gendron R, Paradis H,
Crona D, Loyd JE, Nozik-Grayck E et al (2011) Physiologic and molecular
consequences of endothelial Bmpr2 mutation. Respir Res 12: 84
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ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 9 | 2014
Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1117
Ming XF, Barandier C, Viswambharan H, Kwak BR, Mach F, Mazzolai L, Hayoz
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angiogenesis by impairing lipid raft localization and signaling of vascular
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2280 – 2288
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Frank RN, Stevens MJ (2003) Aldose reductase inhibitor fidarestat prevents
retinal oxidative stress and vascular endothelial growth factor
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is regulated through c-Src-mediated tyrosine phosphorylation in
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synthesis but essential for defense against oxidative stress. EMBO J 14:
5209 – 5215
Pangare M, Makino A (2012) Mitochondrial function in vascular endothelial
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angiogenesis. Am J Physiol Cell Physiol 282: C947 –C970
Parra-Bonilla G, Alvarez DF, Al-Mehdi AB, Alexeyev M, Stevens T (2010)
Critical role for lactate dehydrogenase A in aerobic glycolysis that sustains
pulmonary microvascular endothelial cell proliferation. Am J Physiol Lung
Cell Mol Physiol 299: L513 – L522
Patten IS, Rana S, Shahul S, Rowe GC, Jang C, Liu L, Hacker MR, Rhee JS,
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injury, and death. Annu Rev Pathol 4: 71 – 95
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angiogenesis. Cell 146: 873 – 887
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cold-inducible coactivator of nuclear receptors linked to adaptive
thermogenesis. Cell 92: 829 – 839
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793 – 798
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human genome-scale metabolic knowledgebase and their implications for
disease. Front Physiol 5: 91
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Cruz-Robles D, Nakagawa T, Yu MA, Kang DH, Johnson RJ (2012) Uric
acid-induced endothelial dysfunction is associated with mitochondrial
alterations and decreased intracellular ATP concentrations. Nephron Exp
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Sautin YY, Johnson RJ (2008) Uric acid: the oxidant-antioxidant paradox.
Nucleosides, Nucleotides Nucleic Acids 27: 608 – 619
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dysfunction. Pharmacol Res 65: 497 – 506
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Sonveaux P, Copetti T, De Saedeleer CJ, Vegran F, Verrax J, Kennedy KM,
Moon EJ, Dhup S, Danhier P, Frerart F et al (2012) Targeting the lactate
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1118
transporter MCT1 in endothelial cells inhibits lactate-induced HIF-1
activation and tumor angiogenesis. PLoS ONE 7: e33418
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Jermaine Goveia et al Targeting EC metabolism in disease EMBO Molecular Medicine
1119
Ming XF, Barandier C, Viswambharan H, Kwak BR, Mach F, Mazzolai L, Hayoz
D, Ruffieux J, Rusconi S, Montani JP et al (2004) Thrombin stimulates
human endothelial arginase enzymatic activity via RhoA/ROCK pathway:
implications for atherosclerotic endothelial dysfunction. Circulation 110:
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Mishra RC, Tripathy S, Desai KM, Quest D, Lu Y, Akhtar J, Gopalakrishnan V
(2008) Nitric oxide synthase inhibition promotes endothelium-dependent
vasodilatation and the antihypertensive effect of L-serine. Hypertension 51:
791 – 796
Morrison RF, Seidel ER (1995) Vascular endothelial cell proliferation:
regulation of cellular polyamines. Cardiovasc Res 29: 841 – 847
Mugoni V, Postel R, Catanzaro V, De Luca E, Turco E, Digilio G, Silengo L,
Murphy MP, Medana C, Stainier DY et al (2013) Ubiad1 is an antioxidant
enzyme that regulates eNOS activity by CoQ10 synthesis. Cell 152: 504 – 518
Nishikawa T, Edelstein D, Du XL, Yamagishi S, Matsumura T, Kaneda Y, Yorek
MA, Beebe D, Oates PJ, Hammes HP et al (2000) Normalizing
mitochondrial superoxide production blocks three pathways of
hyperglycaemic damage. Nature 404: 787 – 790
Noghero A, Perino A, Seano G, Saglio E, Lo Sasso G, Veglio F, Primo L, Hirsch
E, Bussolino F, Morello F (2012) Liver X receptor activation reduces
angiogenesis by impairing lipid raft localization and signaling of vascular
endothelial growth factor receptor-2. Arterioscler Thromb Vasc Biol 32:
2280 – 2288
Obrosova IG, Minchenko AG, Vasupuram R, White L, Abatan OI, Kumagai AK,
Frank RN, Stevens MJ (2003) Aldose reductase inhibitor fidarestat prevents
retinal oxidative stress and vascular endothelial growth factor
overexpression in streptozotocin-diabetic rats. Diabetes 52: 864 – 871
Oyama T, Miyasita Y, Watanabe H, Shirai K (2006) The role of polyol pathway
in high glucose-induced endothelial cell damages. Diabetes Res Clin Pract
73: 227 – 234
Paik JY, Lee KH, Ko BH, Choe YS, Choi Y, Kim BT (2005) Nitric oxide stimulates
18F-FDG uptake in human endothelial cells through increased hexokinase
activity and GLUT1 expression. J Nucl Med 46: 365 – 370
Pan M, Wasa M, Ryan U, Souba W (1995) Inhibition of pulmonary
microvascular endothelial glutamine transport by glucocorticoids and
endotoxin. JPEN J Parenter Enteral Nutr 19: 477 – 481
Pan S, World CJ, Kovacs CJ, Berk BC (2009) Glucose 6-phosphate dehydrogenase
is regulated through c-Src-mediated tyrosine phosphorylation in
endothelial cells. Arterioscler Thromb Vasc Biol 29: 895 – 901.
Pandolfi PP, Sonati F, Rivi R, Mason P, Grosveld F, Luzzatto L (1995)
Targeted disruption of the housekeeping gene encoding glucose
6-phosphate dehydrogenase (G6PD): G6PD is dispensable for pentose
synthesis but essential for defense against oxidative stress. EMBO J 14:
5209 – 5215
Pangare M, Makino A (2012) Mitochondrial function in vascular endothelial
cell in diabetes. J Smooth Muscle Res 48: 1 – 26
Papetti M, Herman IM (2002) Mechanisms of normal and tumor-derived
angiogenesis. Am J Physiol Cell Physiol 282: C947 –C970
Parra-Bonilla G, Alvarez DF, Al-Mehdi AB, Alexeyev M, Stevens T (2010)
Critical role for lactate dehydrogenase A in aerobic glycolysis that sustains
pulmonary microvascular endothelial cell proliferation. Am J Physiol Lung
Cell Mol Physiol 299: L513 – L522
Patten IS, Rana S, Shahul S, Rowe GC, Jang C, Liu L, Hacker MR, Rhee JS,
Mitchell J, Mahmood F et al (2012) Cardiac angiogenic imbalance leads to
peripartum cardiomyopathy. Nature 485: 333 – 338
Pieper GM (1997) Acute amelioration of diabetic endothelial dysfunction with
a derivative of the nitric oxide synthase cofactor, tetrahydrobiopterin. J
Cardiovasc Pharmacol 29: 8 – 15
Pober JS, Min W, Bradley JR (2009) Mechanisms of endothelial dysfunction,
injury, and death. Annu Rev Pathol 4: 71 – 95
Potente M, Gerhardt H, Carmeliet P (2011) Basic and therapeutic aspects of
angiogenesis. Cell 146: 873 – 887
Puigserver P, Wu Z, Park CW, Graves R, Wright M, Spiegelman BM (1998) A
cold-inducible coactivator of nuclear receptors linked to adaptive
thermogenesis. Cell 92: 829 – 839
Romero MJ, Platt DH, Tawfik HE, Labazi M, El-Remessy AB, Bartoli M,
Caldwell RB, Caldwell RW (2008) Diabetes-induced coronary vascular
dysfunction involves increased arginase activity. Circ Res 102: 95 – 102
Rose ML, Madren J, Bunzendahl H, Thurman RG (1999) Dietary glycine
inhibits the growth of B16 melanoma tumors in mice. Carcinogenesis 20:
793 – 798
Rudarakanchana N, Flanagan JA, Chen H, Upton PD, Machado R, Patel D,
Trembath RC, Morrell NW (2002) Functional analysis of bone
morphogenetic protein type II receptor mutations underlying primary
pulmonary hypertension. Hum Mol Genet 11: 1517 – 1525
Ryoo S, Lemmon CA, Soucy KG, Gupta G, White AR, Nyhan D, Shoukas A,
Romer LH, Berkowitz DE (2006) Oxidized low-density
lipoprotein-dependent endothelial arginase II activation contributes to
impaired nitric oxide signaling. Circ Res 99: 951 – 960
Ryoo S, Gupta G, Benjo A, Lim HK, Camara A, Sikka G, Lim HK, Sohi J,
Santhanam L, Soucy K et al (2008) Endothelial arginase II: a novel target
for the treatment of atherosclerosis. Circ Res 102: 923 – 932
Sahoo S, Aurich MK, Jonsson JJ, Thiele I (2014) Membrane transporters in a
human genome-scale metabolic knowledgebase and their implications for
disease. Front Physiol 5: 91
Sanchez-Lozada LG, Lanaspa MA, Cristobal-Garcia M, Garcia-Arroyo F, Soto V,
Cruz-Robles D, Nakagawa T, Yu MA, Kang DH, Johnson RJ (2012) Uric
acid-induced endothelial dysfunction is associated with mitochondrial
alterations and decreased intracellular ATP concentrations. Nephron Exp
Nephrol 121: e71 – e78
Sautin YY, Johnson RJ (2008) Uric acid: the oxidant-antioxidant paradox.
Nucleosides, Nucleotides Nucleic Acids 27: 608 – 619
Sawada N, Jiang A, Takizawa F, Safdar A, Manika A, Tesmenitsky Y, Kang
KT, Bischoff J, Kalwa H, Sartoretto JL et al (2014) Endothelial
PGC-1alpha mediates vascular dysfunction in diabetes. Cell Metab 19:
246 – 258
Schoors S, Cantelmo AR, Georgiadou M, Stapor PC, Wang X, Wong BW, Bifari
F, Quaegebeur A, Decimo I, Schoonjans L et al (2014a) Incomplete and
transitory decrease of glycolysis: a new paradigm for anti-angiogenic
therapy? Cell cycle 13: 16 – 22
Schoors S, De Bock K, Cantelmo AR, Georgiadou M, Ghesquiere B,
Cauwenberghs S, Kuchnio A, Wong BW, Quaegebeur A, Goveia J et al
(2014b) Partial and transient reduction of glycolysis by PFKFB3 blockade
reduces pathological angiogenesis. Cell Metab 19: 37 – 48
Sena CM, Matafome P, Crisostomo J, Rodrigues L, Fernandes R, Pereira P,
Seica RM (2012) Methylglyoxal promotes oxidative stress and endothelial
dysfunction. Pharmacol Res 65: 497 – 506
Sennino B, McDonald DM (2012) Controlling escape from angiogenesis
inhibitors. Nat Rev 12: 699 – 709
Sessa WC, Hecker M, Mitchell JA, Vane JR (1990) The metabolism of
L-arginine and its significance for the biosynthesis of
endothelium-derived relaxing factor: L-glutamine inhibits the generation
of L-arginine by cultured endothelial cells. Proc Natl Acad Sci USA 87:
8607 – 8611
Sonveaux P, Copetti T, De Saedeleer CJ, Vegran F, Verrax J, Kennedy KM,
Moon EJ, Dhup S, Danhier P, Frerart F et al (2012) Targeting the lactate
EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
1118
transporter MCT1 in endothelial cells inhibits lactate-induced HIF-1
activation and tumor angiogenesis. PLoS ONE 7: e33418
Spolarics Z, Spitzer JJ (1993) Augmented glucose use and pentose cycle
activity in hepatic endothelial cells after in vivo endotoxemia. Hepatology
17: 615 – 620
Spolarics Z, Wu JX (1997) Role of glutathione and catalase in H2O2
detoxification in LPS-activated hepatic endothelial and Kupffer cells. Am J
Physiol 273: G1304 –G1311
Stobart JL, Lu L, Anderson HD, Mori H, Anderson CM (2013) Astrocyte-induced
cortical vasodilation is mediated by D-serine and endothelial nitric oxide
synthase. Proc Natl Acad Sci USA 110: 3149 – 3154
Stroes E, Kastelein J, Cosentino F, Erkelens W, Wever R, Koomans H, Luscher
T, Rabelink T (1997) Tetrahydrobiopterin restores endothelial function in
hypercholesterolemia. J Clin Invest 99: 41 – 46
Stroes E, Hijmering M, van Zandvoort M, Wever R, Rabelink TJ, van Faassen
EE (1998) Origin of superoxide production by endothelial nitric oxide
synthase. FEBS Lett 438: 161 – 164
Sugiyama T, Levy BD, Michel T (2009) Tetrahydrobiopterin recycling, a key
determinant of endothelial nitric-oxide synthase-dependent signaling
pathways in cultured vascular endothelial cells. J Biol Chem 284:
12691 – 12700
Sutendra G, Michelakis ED (2014) The metabolic basis of pulmonary arterial
hypertension. Cell Metab 19: 558 – 573
Takemoto M, Sun J, Hiroki J, Shimokawa H, Liao JK (2002) Rho-kinase
mediates hypoxia-induced downregulation of endothelial nitric oxide
synthase. Circulation 106: 57 – 62
Tammali R, Reddy AB, Srivastava SK, Ramana KV (2011) Inhibition of aldose
reductase prevents angiogenesis in vitro and in vivo. Angiogenesis 14:
209 – 221
Thengchaisri N, Hein TW, Wang W, Xu X, Li Z, Fossum TW, Kuo L (2006)
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terms of the Creative Commons Attribution 4.0
License, which permits use, distribution and reproduc-
tion in any medium, provided the original work is
properly cited.
1120 EMBO Molecular Medicine Vol 6 | No 9 | 2014 ª 2014 The Authors
EMBO Molecular Medicine Targeting EC metabolism in disease Jermaine Goveia et al
Closeup
The heart-liver metabolic axis: defectivecommunication exacerbates diseaseKedryn K Baskin, Angie L Bookout & Eric N Olson
The heart has been recognized as an endo-crine organ for over 30 years (de Bold,2011); however, little is known about howthe heart communicates with other organsin the body, and even less is known aboutthis process in the diseased heart. In thisissue of EMBO Molecular Medicine, Magidaand Leinwand (2014) introduce the conceptthat a primary genetic defect in the heartresults in aberrant hepatic lipid metabo-lism, which consequently exacerbateshypertrophic cardiomyopathy (HCM). Thisstudy provides evidence in support of thehypothesis that crosstalk occurs betweenthe heart and liver, and that this becomesdisrupted in the diseased state.
See also: JAMagida& LA Leinwand (April 2014)
H CM is an inherited cardiovascular
disease primarily caused by muta-
tions in genes encoding proteins in
the sarcomere, the contractile apparatus of
cardiac myocytes. HCM is characterized by
increased heart mass and abnormal cardiac
function with susceptibility to arrhythmias
and sudden cardiac death. Histological
manifestations of the disease include cardiac
myocyte hypertrophy, myocardial fibrosis,
extracellular matrix disorganization, and
myocyte disarray. While many affected indi-
viduals are asymptomatic and remain undi-
agnosed, HCM is the most frequent cause of
sudden death in young athletes (Seidman &
Seidman, 2011; Maron & Maron, 2013).
To date, 13 genes containing over 900
distinct mutations have been identified as
genetic causes of HCM. Most of these genes
encode for proteins found within the thick
and thin filaments of sarcomeres, such as
b-myosin heavy chain (MYH7) and troponin
T (TTNT2). Mutations in MYH7 increase
force generation and actin-myosin sliding
velocity within sarcomeres. These findings
indicate that genetic mutations in HCM
patients are the primary cause of cardiac
hypertrophy (Wang et al, 2010).
Numerous animal models have been
generated to investigate HCM (Maass &
Leinwand, 2000), and much focus has been
given to an R403Q mutation in MYH7, which
causes an especially severe clinical pheno-
type (Seidman & Seidman, 2011). While the
various animal models of R403Q highlight
different aspects of HCM, they share
common traits of HCM including cardiac
hypertrophy and fibrosis (Maass & Leinw-
and, 2000). An interesting, and poorly
understood characteristic of hypertrophic
cardiomyopathy, as opposed to other types
of cardiomyopathies, is that systemic meta-
bolic alterations occur secondary to the
cardiomyopathy (Maron & Maron, 2013).
This is recapitulated in the R403Q model
used in the study published by Magida and
Leinwand (2014).
Clinical studies have revealed that HCM
patients harboring mutations in sarcomeric
genes present with deficient cardiac energet-
ics (Crilley et al, 2003). In the present study,
the authors demonstrate that the R403Q
HCM mouse model has diminished cardiac
ATP levels and impaired lipid utilization in
the heart, assessed by decreased cardiac
triglycerides and fatty acid content, and
decreased expression of fatty acid translo-
case (CD36), lipoprotein lipase (LPL), and
very low density lipoprotein receptor
(VLDLR). Notably, they observed an approx-
imate two-fold reduction in active CD36
protein at the plasma membrane, coupled
with a similarly decreased level of nonesteri-
fied fatty acid (NEFA) released from VLDL
by the left ventricle. The authors suggest
that this decreased lipid uptake in the heart
leads to the observed lipid elevation in the
plasma, ultimately resulting in hepatic lipid
accumulation and pathologically enhanced
gluconeogenesis. The authors propose that
this elevation in hepatic glucose production
creates a vicious cycle between the heart
and the liver in which the spillover of VLDL
triglyceride and oleic acid from the heart
insults the liver via elevated protein kinase
C signaling. The liver responds by increasing
blood glucose levels leading to exacerbation
of the primary cardiac disease (summarized
in Fig 1). Importantly, features of the dis-
eased state can be rescued either by restor-
ing the energetic deficit at the level of the
cardiomyocyte via AMPK agonism, or by
blocking the deleterious elevation in hepatic
glucose output using the phosphoenol-
pyruvate carboxykinase (PEPCK) inhibitor
3-MPA (Magida & Leinwand, 2014).
......................................................
“These findings raise the inter-esting concept that the lack ofuse of a specific metabolic sub-strate by one tissue directlyaffects another”......................................................
These findings raise the interesting con-
cept that the lack of use of a specific meta-
bolic substrate by one tissue directly affects
another, perhaps revealing an inter-tissue
homeostatic feedback mechanism. Namely,
Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA. E-mail: [email protected] 10.1002/emmm.201303800 | Published online 12 March 2014
EMBO Molecular Medicine Vol 6 | No 4 | 2014 ª 2014 The Authors. Published under the terms of the CC BY license.436
relationship between sarcomeric structural
integrity and metabolically-derived energy at
the organismal level, and opens up many
more avenues for future investigation.
Conflict of interestThe authors declare that they have no con-
flict of interest.
ReferencesBhatia LS, Curzen NP, Calder PC, Byrne CD (2012)
Non-alcoholic fatty liver disease: a new and
important cardiovascular risk factor? Eur Heart
J 33: 1190 – 1200
Blad CC, Tang C, Offermanns S (2012) G
protein-coupled receptors for energy
metabolites as new therapeutic targets. Nat
Rev Drug Discov 11: 603 – 619
de Bold AJ (2011) Thirty years of research on atrial
natriuretic factor: historical background and
emerging concepts. Can J Physiol Pharmacol 89:
527 – 531
Bordicchia M, Liu D, Amri EZ, Ailhaud G,
Dessi-Fulgheri P, Zhang C, Takahashi N, Sarzani
R, Collins S (2012) Cardiac natriuretic peptides
act via p38 MAPK to induce the brown fat
thermogenic program in mouse and human
adipocytes. J Clin Invest 122: 1022 – 1036
Bryzgalova G, Lundholm L, Portwood N,
Gustafsson JA, Khan A, Efendic S,
Dahlman-Wright K (2008) Mechanisms of
antidiabetogenic and body weight-lowering
effects of estrogen in high-fat diet-fed
mice. Am J Physiol Endocrinol Metab 295:
E904 – E912
Crilley JG, Boehm EA, Blair E, Rajagopalan B,
Blamire AM, Styles P, McKenna WJ,
Ostman-Smith I, Clarke K, Watkins H (2003)
Hypertrophic cardiomyopathy due to
sarcomeric gene mutations is characterized by
impaired energy metabolism irrespective of the
degree of hypertrophy. J Am Coll Cardiol 41:
1776 – 1782
D’Eon TM, Souza SC, Aronovitz M, Obin MS, Fried
SK, Greenberg AS (2005) Estrogen regulation of
adiposity and fuel partitioning. Evidence of
genomic and non-genomic regulation of
lipogenic and oxidative pathways. J Biol Chem
280: 35983 – 35991
Grueter CE, van Rooij E, Johnson BA, DeLeon SM,
Sutherland LB, Qi X, Gautron L, Elmquist JK,
Bassel-Duby R, Olson EN (2012) A cardiac
microRNA governs systemic energy homeostasis
by regulation of MED13. Cell 149: 671 – 683
Liu S, Brown JD, Stanya KJ, Homan E, Leidl M,
Inouye K, Bhargava P, Gangl MR, Dai L, Hatano
B et al (2013) A diurnal serum lipid integrates
hepatic lipogenesis and peripheral fatty acid
use. Nature 502: 550 – 554
Maass A, Leinwand LA (2000) Animal models of
hypertrophic cardiomyopathy. Curr Opin Cardiol
15: 189 – 196
Magida JA, Leinwand LA (2014) Metabolic
crosstalk between the heart and liver
impacts familial hypertrophic cardiomyopathy.
EMBO Mol Med 6: 482 – 495
Maron BJ, Maron MS (2013) Hypertrophic
cardiomyopathy. Lancet 381: 242 – 255
Rashed HM, Nair BG, Patel TB (1992) Regulation of
hepatic glycolysis and gluconeogenesis by
atrial natriuretic peptide. Arch Biochem Biophys
298: 640 – 645
Roberts LD, Bostrom P, O’Sullivan JF, Schinzel RT,
Lewis GD, Dejam A, Lee YK, Palma MJ, Calhoun
S, Georgiadi A et al (2014) Beta-Amino-
isobutyric Acid Induces Browning of White Fat
and Hepatic beta-Oxidation and Is Inversely
Correlated with Cardiometabolic Risk Factors.
Cell Metab 19: 96 – 108
Seidman CE, Seidman JG (2011) Identifying
sarcomere gene mutations in hypertrophic
cardiomyopathy: a personal history. Circ Res
108: 743 – 750
Wang L, Seidman JG, Seidman CE (2010) Narrative
review: harnessing molecular genetics for the
diagnosis and management of hypertrophic
cardiomyopathy. Ann Intern Med 152: 513 – 520,
W181
License: This is an open access article under the
terms of the Creative Commons Attribution License,
which permits use, distribution and reproduction
in any medium, provided the original work is prop-
erly cited.
EMBO Molecular Medicine Vol 6 | No 4 | 2014 ª 2014 The Authors
EMBO Molecular Medicine The heart-liver metabolic axis Kedryn K Baskin et al
438
that the heart signals to the liver to elevate
glucose production by selectively excluding
uptake and use of oleic acid and triglyceride
in VLDL particles. Indeed, an emerging
theme in homeostatic feedback is the recog-
nition of metabolites as signaling effectors
between tissues as means of physiologic
integration within the body [see (Blad et al,
2012; Liu et al, 2013; Roberts et al, 2014)
for examples]. However, in the setting of an
HCM genotype, the current work suggests
this relationship is injurious.
Many metabolic diseases, such as diabe-
tes and obesity, are ultimately detrimental to
cardiac function, but the reverse has yet to
be investigated. There is a clear relationship
between cardiac metabolism and cardiac
function, but diminished cardiac function,
per se, has thus far not been reported to
negatively influence systemic metabolism.
There is a clear link between liver dysfunc-
tion, specifically non-alcoholic fatty liver
disease, and cardiac dysfunction (Bhatia
et al, 2012), but new evidence reported in
this issue of EMBO Molecular Medicine sug-
gests the reverse is also true.
While the link between cardiac dysfunc-
tion, specifically the alteration of cardiac
metabolism, and deregulated hepatic lipid
metabolism is interesting, the mechanisms
regulating this crosstalk are not resolved by
the work of Magida and Leinwand (2014).
Further studies are required to clarify
whether HCM-induced metabolic abnormali-
ties are the primary cause of liver dysfunc-
tion. It remains unclear whether hepatic lipid
accumulation in this mouse model results
from decreased fatty acid uptake in the heart
alone. Certainly, the relationship between the
heart and liver is not monogamous, and
other tissues such as skeletal muscle, pan-
creas, and adipose are likely to be directly
affected by elevated circulating oleic acid and
VLDL triglyceride. Indeed it is likely that lipid
uptake, utilization, or storage in each of these
tissues contributes to the metabolic pheno-
type described by Magida and Leinwand
(2014) and would be influenced by systemic
agonism of AMPK. Further, PEPCK inhibition
not only affects glucose production by the
liver, kidney, and intestine, but also glycero-
neogenesis in adipocytes. Additionally, it
would be interesting to know if other sarco-
meric mutations also decrease liver function
in end-stage disease, and if so, if a similar
mechanism is involved.
Other aspects of HCM can also be
explored in the R403Q HCM mouse model
within the framework of metabolic abnor-
malities. For example, what role does
calcium homeostasis play in the develop-
ment of cardiac and metabolic dysfunction?
Calcium is an important regulator of energy
metabolism and calcium levels and homeo-
stasis are altered in human HCM patients
(Wang et al, 2010). Perhaps restoring cal-
cium homeostasis in the heart could restore
metabolism in this mouse as well? More-
over, what is the basis for the phenotypic
gender differences in HCM? Is there likely a
protective role for estrogen at the level of
cardiac energetics as well as liver metabo-
lism in the HCM patient? Estrogen certainly
has a role both as it relates to AMPK and
hepatic lipid metabolism (D’Eon et al, 2005;
Bryzgalova et al, 2008), properties which
could be therapeutically exploited.
......................................................
“Certainly, the relationshipbetween the heart and liver isnot monogamous”......................................................
The studies of Magida and Leinwand
(2014) add to a growing number of exam-
ples in which the heart modulates energy
homeostasis and metabolism in non-cardiac
tissues. In this regard, the cardiac natriuretic
peptides, ANP and BNP, have been shown
to improve metabolic parameters by induc-
ing the “browning” of white adipocytes
(Bordicchia et al, 2012). While the thermo-
genic action by ANP is restricted to human,
but not rodent adipocytes (Bordicchia et al,
2012), ANP was shown to induce gluconeo-
genesis in rat hepatocytes (Rashed et al,
1992). Therefore, it is curious that ANP
expression is dramatically enhanced in
HCM, but this mechanism for hepatic glu-
cose output was left unexplored in these
studies. Similarly, elevated expression of the
Mediator subunit MED13 in the heart con-
fers metabolic benefits in mice. MED13 is
negatively regulated by a cardiac specific
microRNA, miR-208, which plays a key role
in cardiac hypertrophy (Grueter et al, 2012).
Whether the miR-208/MED13 axis influ-
ences the metabolic consequences associ-
ated with HCM is an interesting question for
the future. Perhaps a miR-208 inhibitor can
remedy the metabolic defects observed in
HCM by activating cardiac MED13, thus
enhancing systemic metabolism, and revers-
ing or preventing liver steatosis.
In summary, the work of Magida and
Leinwand (2014) highlights the inextricable
Lipid storage
Gluconeogenesis
Plasma lipids Blood glucose
Other organs
Other organs
Lipid uptake
R403Q
HCM
?
?
Figure 1. Crosstalk between the heart and liver is altered in the setting of hypertrophiccardiomyopathy. The HCM-causing mutation in myosin (R403Q) decreases cardiac lipid uptake resulting inincreased plasma lipid content. Consequently, lipid storage is increased in liver, leading to increasedgluconeogenesis, increased blood glucose, ultimately exacerbating cardiac disease. It is still unclear whetherother organs are involved in this crosstalk in HCM (denoted in the figure as ‘?’).
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 4 | 2014
Kedryn K Baskin et al The heart-liver metabolic axis EMBO Molecular Medicine
437
relationship between sarcomeric structural
integrity and metabolically-derived energy at
the organismal level, and opens up many
more avenues for future investigation.
Conflict of interestThe authors declare that they have no con-
flict of interest.
ReferencesBhatia LS, Curzen NP, Calder PC, Byrne CD (2012)
Non-alcoholic fatty liver disease: a new and
important cardiovascular risk factor? Eur Heart
J 33: 1190 – 1200
Blad CC, Tang C, Offermanns S (2012) G
protein-coupled receptors for energy
metabolites as new therapeutic targets. Nat
Rev Drug Discov 11: 603 – 619
de Bold AJ (2011) Thirty years of research on atrial
natriuretic factor: historical background and
emerging concepts. Can J Physiol Pharmacol 89:
527 – 531
Bordicchia M, Liu D, Amri EZ, Ailhaud G,
Dessi-Fulgheri P, Zhang C, Takahashi N, Sarzani
R, Collins S (2012) Cardiac natriuretic peptides
act via p38 MAPK to induce the brown fat
thermogenic program in mouse and human
adipocytes. J Clin Invest 122: 1022 – 1036
Bryzgalova G, Lundholm L, Portwood N,
Gustafsson JA, Khan A, Efendic S,
Dahlman-Wright K (2008) Mechanisms of
antidiabetogenic and body weight-lowering
effects of estrogen in high-fat diet-fed
mice. Am J Physiol Endocrinol Metab 295:
E904 – E912
Crilley JG, Boehm EA, Blair E, Rajagopalan B,
Blamire AM, Styles P, McKenna WJ,
Ostman-Smith I, Clarke K, Watkins H (2003)
Hypertrophic cardiomyopathy due to
sarcomeric gene mutations is characterized by
impaired energy metabolism irrespective of the
degree of hypertrophy. J Am Coll Cardiol 41:
1776 – 1782
D’Eon TM, Souza SC, Aronovitz M, Obin MS, Fried
SK, Greenberg AS (2005) Estrogen regulation of
adiposity and fuel partitioning. Evidence of
genomic and non-genomic regulation of
lipogenic and oxidative pathways. J Biol Chem
280: 35983 – 35991
Grueter CE, van Rooij E, Johnson BA, DeLeon SM,
Sutherland LB, Qi X, Gautron L, Elmquist JK,
Bassel-Duby R, Olson EN (2012) A cardiac
microRNA governs systemic energy homeostasis
by regulation of MED13. Cell 149: 671 – 683
Liu S, Brown JD, Stanya KJ, Homan E, Leidl M,
Inouye K, Bhargava P, Gangl MR, Dai L, Hatano
B et al (2013) A diurnal serum lipid integrates
hepatic lipogenesis and peripheral fatty acid
use. Nature 502: 550 – 554
Maass A, Leinwand LA (2000) Animal models of
hypertrophic cardiomyopathy. Curr Opin Cardiol
15: 189 – 196
Magida JA, Leinwand LA (2014) Metabolic
crosstalk between the heart and liver
impacts familial hypertrophic cardiomyopathy.
EMBO Mol Med 6: 482 – 495
Maron BJ, Maron MS (2013) Hypertrophic
cardiomyopathy. Lancet 381: 242 – 255
Rashed HM, Nair BG, Patel TB (1992) Regulation of
hepatic glycolysis and gluconeogenesis by
atrial natriuretic peptide. Arch Biochem Biophys
298: 640 – 645
Roberts LD, Bostrom P, O’Sullivan JF, Schinzel RT,
Lewis GD, Dejam A, Lee YK, Palma MJ, Calhoun
S, Georgiadi A et al (2014) Beta-Amino-
isobutyric Acid Induces Browning of White Fat
and Hepatic beta-Oxidation and Is Inversely
Correlated with Cardiometabolic Risk Factors.
Cell Metab 19: 96 – 108
Seidman CE, Seidman JG (2011) Identifying
sarcomere gene mutations in hypertrophic
cardiomyopathy: a personal history. Circ Res
108: 743 – 750
Wang L, Seidman JG, Seidman CE (2010) Narrative
review: harnessing molecular genetics for the
diagnosis and management of hypertrophic
cardiomyopathy. Ann Intern Med 152: 513 – 520,
W181
License: This is an open access article under the
terms of the Creative Commons Attribution License,
which permits use, distribution and reproduction
in any medium, provided the original work is prop-
erly cited.
EMBO Molecular Medicine Vol 6 | No 4 | 2014 ª 2014 The Authors
EMBO Molecular Medicine The heart-liver metabolic axis Kedryn K Baskin et al
438
that the heart signals to the liver to elevate
glucose production by selectively excluding
uptake and use of oleic acid and triglyceride
in VLDL particles. Indeed, an emerging
theme in homeostatic feedback is the recog-
nition of metabolites as signaling effectors
between tissues as means of physiologic
integration within the body [see (Blad et al,
2012; Liu et al, 2013; Roberts et al, 2014)
for examples]. However, in the setting of an
HCM genotype, the current work suggests
this relationship is injurious.
Many metabolic diseases, such as diabe-
tes and obesity, are ultimately detrimental to
cardiac function, but the reverse has yet to
be investigated. There is a clear relationship
between cardiac metabolism and cardiac
function, but diminished cardiac function,
per se, has thus far not been reported to
negatively influence systemic metabolism.
There is a clear link between liver dysfunc-
tion, specifically non-alcoholic fatty liver
disease, and cardiac dysfunction (Bhatia
et al, 2012), but new evidence reported in
this issue of EMBO Molecular Medicine sug-
gests the reverse is also true.
While the link between cardiac dysfunc-
tion, specifically the alteration of cardiac
metabolism, and deregulated hepatic lipid
metabolism is interesting, the mechanisms
regulating this crosstalk are not resolved by
the work of Magida and Leinwand (2014).
Further studies are required to clarify
whether HCM-induced metabolic abnormali-
ties are the primary cause of liver dysfunc-
tion. It remains unclear whether hepatic lipid
accumulation in this mouse model results
from decreased fatty acid uptake in the heart
alone. Certainly, the relationship between the
heart and liver is not monogamous, and
other tissues such as skeletal muscle, pan-
creas, and adipose are likely to be directly
affected by elevated circulating oleic acid and
VLDL triglyceride. Indeed it is likely that lipid
uptake, utilization, or storage in each of these
tissues contributes to the metabolic pheno-
type described by Magida and Leinwand
(2014) and would be influenced by systemic
agonism of AMPK. Further, PEPCK inhibition
not only affects glucose production by the
liver, kidney, and intestine, but also glycero-
neogenesis in adipocytes. Additionally, it
would be interesting to know if other sarco-
meric mutations also decrease liver function
in end-stage disease, and if so, if a similar
mechanism is involved.
Other aspects of HCM can also be
explored in the R403Q HCM mouse model
within the framework of metabolic abnor-
malities. For example, what role does
calcium homeostasis play in the develop-
ment of cardiac and metabolic dysfunction?
Calcium is an important regulator of energy
metabolism and calcium levels and homeo-
stasis are altered in human HCM patients
(Wang et al, 2010). Perhaps restoring cal-
cium homeostasis in the heart could restore
metabolism in this mouse as well? More-
over, what is the basis for the phenotypic
gender differences in HCM? Is there likely a
protective role for estrogen at the level of
cardiac energetics as well as liver metabo-
lism in the HCM patient? Estrogen certainly
has a role both as it relates to AMPK and
hepatic lipid metabolism (D’Eon et al, 2005;
Bryzgalova et al, 2008), properties which
could be therapeutically exploited.
......................................................
“Certainly, the relationshipbetween the heart and liver isnot monogamous”......................................................
The studies of Magida and Leinwand
(2014) add to a growing number of exam-
ples in which the heart modulates energy
homeostasis and metabolism in non-cardiac
tissues. In this regard, the cardiac natriuretic
peptides, ANP and BNP, have been shown
to improve metabolic parameters by induc-
ing the “browning” of white adipocytes
(Bordicchia et al, 2012). While the thermo-
genic action by ANP is restricted to human,
but not rodent adipocytes (Bordicchia et al,
2012), ANP was shown to induce gluconeo-
genesis in rat hepatocytes (Rashed et al,
1992). Therefore, it is curious that ANP
expression is dramatically enhanced in
HCM, but this mechanism for hepatic glu-
cose output was left unexplored in these
studies. Similarly, elevated expression of the
Mediator subunit MED13 in the heart con-
fers metabolic benefits in mice. MED13 is
negatively regulated by a cardiac specific
microRNA, miR-208, which plays a key role
in cardiac hypertrophy (Grueter et al, 2012).
Whether the miR-208/MED13 axis influ-
ences the metabolic consequences associ-
ated with HCM is an interesting question for
the future. Perhaps a miR-208 inhibitor can
remedy the metabolic defects observed in
HCM by activating cardiac MED13, thus
enhancing systemic metabolism, and revers-
ing or preventing liver steatosis.
In summary, the work of Magida and
Leinwand (2014) highlights the inextricable
Lipid storage
Gluconeogenesis
Plasma lipids Blood glucose
Other organs
Other organs
Lipid uptake
R403Q
HCM
?
?
Figure 1. Crosstalk between the heart and liver is altered in the setting of hypertrophiccardiomyopathy. The HCM-causing mutation in myosin (R403Q) decreases cardiac lipid uptake resulting inincreased plasma lipid content. Consequently, lipid storage is increased in liver, leading to increasedgluconeogenesis, increased blood glucose, ultimately exacerbating cardiac disease. It is still unclear whetherother organs are involved in this crosstalk in HCM (denoted in the figure as ‘?’).
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 4 | 2014
Kedryn K Baskin et al The heart-liver metabolic axis EMBO Molecular Medicine
437
Closeup
Salvaging hope: Is increasing NAD+ a keyto treating mitochondrial myopathy?Robert N Lightowlers & Zofia MA Chrzanowska-Lightowlers
Mitochondrial diseases can arise frommutations either in mitochondrial DNA orin nuclear DNA encoding mitochondriallydestined proteins. Currently, there is nocure for these diseases although treat-ments to ameliorate a subset of the symp-toms are being developed. In this issue ofEMBO Molecular Medicine, Khan et al(2014) use a mouse model to test the effi-cacy of a simple dietary supplement ofnicotinamide riboside to treat and preventmitochondrial myopathies.
See also: NA Khan et al (June 2014)
G etting the right levels of vitamins is
essential for health. Those of us of a
certain age will remember in junior
school being taught about the consequences
of vitamin deficiency and having to memo-
rise those consequences. For example, one
deficiency, exotically named pellagra,
resulted in a combination of dermatitis, diar-
rhoea and dementia. The underlying cause
was identified as a lack of nicotinic acid or
nicotinamide (vitamin B3). Indeed, the
defect was exacerbated by a dietary lack of
tryptophan. This is now understood, as all
three components are important building
blocks for the production of nicotinamide
adenine dinucleotide, NAD, a redox-active
coenzyme and enzyme substrate. This
molecule is well known as a key player in
metabolism, being the primary electron
donor in the mitochondrial respiratory
chain. It is also utilised and broken-down by
a variety of proteins in other subcellular
compartments, such as the family of protein
deacetylases (sirtuins), the poly (ADP
ribose)-phosphorylases (PARPs) and NAD
glycohydrolases. De novo synthesis from
tryptophan is a complex 8-step enzymatic
process, so there are likely to be recycling
pathways that utilise NAD synthesis inter-
mediates as substrates. This is where nico-
tinamide and nicotinic acid feature. Both are
intermediates in NAD biosynthesis, requir-
ing enzymatic pathways of only 2 or 3 steps
respectively to generate NAD (Bogan &
Brenner, 2008). An additional salvage path-
way has been identified in eubacteria and
eukaryotes that is distinct from these nico-
tinic acid or nicotinamide recycling (or
salvaging) pathways; in a two-step process,
nicotinamide riboside (NR) can be first
phosphorylated and then adenylylated to
form NAD+ (Bieganowski & Brenner, 2004;
see Fig 1). Those of us who remember
memorising those vitamin deficiency
diseases at school, probably also remember
the compulsory bottle of milk to be drunk at
break time. Although we did not realise it
then, this was a good source of nicotinamide
riboside, which in addition to being a
normal metabolite in the body is also pres-
ent in cow’s milk.
NR can protect against mitochondrialmyopathy in mice
Defects of the mitochondrial (mt) respiratory
chain constitute one of the most common
forms of heritable metabolic disease. Clinical
presentation varies widely, and significantly,
there is no effective cure. Khan et al hypoth-
esised that under conditions of respiratory
chain deficiency, NADH utilisation is
partially blocked leading to a decrease in the
NAD+/NADH ratio. This constitutes a signal
in the cell that is translated as indicating
high nutrient availability, a condition
completely at odds with the defective mito-
chondrial function. Therefore, by repleting
levels of NAD+, the authors surmise that
mitochondrial dysfunction could be amelio-
rated. To challenge their hypothesis, the
authors have used their mt-Deletor mouse, a
model of mitochondrial myopathy, and
administered the NAD+ precursor, NR. The
Deletor mouse carries a dominant patho-
genic mutation in the major mitochondrial
DNA (mtDNA) replicative helicase, Twinkle,
that corresponds to a mutation found in
patients (Tyynismaa et al, 2005). In Deletor
mice, this causes increased levels of deleted
mtDNA and a subtle but chronically progres-
sive mitochondrial myopathy. Control mice
and pre- and post-symptomatic Deletor mice
were dosed with large (400 mg/kg/day)
amounts of NR for up to 4 months, a regime
previously documented to result in increased
levels of NAD+ in skeletal muscle of wild-
type mice (Canto et al, 2012). Khan et al
show that this treatment resulted in a
marked increase in mitochondrial biogenesis
in skeletal muscle and brown adipose tissue
compared to undosed controls. A similar
increase had been shown in the previous
experiments following NR treatment, both of
cultured cells and in various mice tissue
(Canto et al, 2012). Crucially, however, for
these new NR supplement experiments, the
mt-biogenesis was concomitant with a
decrease in markers of disease progression
in Deletor mice, which were also protected
from ultrastructural abnormalities of mito-
chondria. NR invoked a minor increase in
overall mtDNA levels in both control and
Deletor mice, but intriguingly caused a
decrease in the levels of deleted mtDNA that
accumulated in skeletal muscle of the Dele-
tors. Thus, data were consistent with NR
treatment and increasing NAD+ levels
protecting against mitochondrial disease in
the Deletor mice. In addition to promoting
mt-biogenesis, NR also appeared to enhance
Wellcome Trust Centre for Mitochondrial Research, Institute for Cell and Molecular Biosciences, Medical School, Newcastle University, Newcastle upon Tyne, UK.E-mail: [email protected] 10.15252/emmm.201404179
ª 2014 The Authors. Published under the terms of the CC BY 4.0 license EMBO Molecular Medicine Vol 6 | No 6 | 2014 705
the mitochondrial unfolded protein
response. This increase in a subset of mito-
chondrial chaperones and proteases is
believed to be beneficial to health and
promote an increased lifespan (Pellegrino
et al, 2013).
Why does HR treatment promotemitochondrial biogenesis?
Previous reports have implicated increased
NAD+ levels with increased sirtuin activity,
most notably SIRT1 and SIRT3 (Lagouge
et al, 2006; Hirschey et al, 2011). The conse-
quence is an activation of key transcription
factors including SIRT1 and SIRT3 (Canto
et al, 2012), which upregulate gene products
that are central to mt-biogenesis (Feige et al,
2008). In addition to enhancing oxidative
metabolism in a range of tissues, SIRT1 acti-
vation has also been reported to protect
against diet-induced metabolic disorders by
enhancing fatty acid oxidation (Feige et al,
2008). Consistent with this, Khan et al pres-
ent data to show an NR-mediated increase in
skeletal muscle mRNA levels encoding
proteins that are involved in fatty acid trans-
port or oxidation, namely CD36, ACOX1 and
MCAD. Increasing mitochondrial biogenesis
as a way of treating mitochondrial dysfunc-
tion is encouraging and has been previously
shown to be efficacious for mouse models of
mitochondrial disease (Wenz et al, 2008).
However, it has been well described that
mitochondrial proliferation can occur as a
consequence of mtDNA disease in man. It
will certainly be interesting to discover
whether drug-induced mitochondrial biogen-
esis can also be beneficial to patients with
mitochondrial dysfunction.
Why are these results so encouraging?
To date, there is no effective therapy for
patients with mitochondrial myopathy. Vita-
min cocktails including vitamin B3
(although at far lower doses than used here)
have often been used to treat such patients
for many years, with only sporadic reports
of efficacy. The rationale for increasing
NAD+ levels in order to increase mitochon-
drial mass is reasonable, and the results
reported here are compelling. What is partic-
ularly exciting is that NAD+ intermediates
such as NR are readily available and rela-
tively simple drugs. If the efficacy of NR is
entirely due to its effects as an NAD+
precursor, it is not absolutely clear why
neither nicotinamide nor nicotinic acid
themselves could not be used. Perhaps
because there is evidence that the former is
hepatotoxic at high concentrations and its
efficacy in increasing NAD+ levels in skele-
tal muscle is unclear (Bogan & Brenner,
2008)? Nicotinic acid, however, has been
used for many years to treat patients with
high serum cholesterol levels but can cause
irritating vasodilation (flushing). To counter
this, slow release formulations have been
available for some time. Of these NAD+
precursors, NR or its phosphorylated NAD+
precursor nicotinamide mononucleotide
(NMN) might be the therapeutic molecule of
choice by virtue of being able to access mito-
chondria and be converted to NAD+ by
mitochondrial-specific enzymes. Isoforms of
the NR kinase and NMN adenylyltransferase
are known, but there is conflicting evidence
on their mitochondrial location (Felici et al,
2013). Finally, side effects following admin-
istration of other NAD+ precursors supple-
ments have been reported (Sauve, 2008). It
will of course be necessary to evaluate the
NR dosage used by Khan et al, as it appears
O
O–
N+
Pribo
NaMN
NH+
COOH
NH2Trp
O
O–
N+
ADPribo
NaAD+
O
O–
NH+
Na
O
N+
NH2
Pribo
NMN
O
NH+
NH2
Nam
1a1c
1b2
O
N+
NH2
Ribo
NR
Nicotinamide ribosidesupplement
O
N+
ADPribo
NAD+
NH2
Figure 1. The salvage/recycling pathway for NAD+ biosynthesis from nicotinamide riboside (NR) in man.NR, taken in to the body, can be converted to nicotinamide mononucleotide (NMN) by one of two highly conserved NR kinases in the cytoplasm (pathway 1a). NAM(nicotinamide) can also be converted by NMN synthetase to NMN (pathway 1b). NMN is further converted to NAD+ by the action of one of three adenylyltransferases(NMNAT1-3) that also acts on NaMN (nicotinic acid mononucleotide) to produce NaAD+ (nicotinic acid adenine dinucleotide). The latter is subsequently converted by NADsynthase to NAD+. Nicotinic acid (Na) feeds into the pathway through conversion to NaMN by Na phosphoribosyltransferase (pathway 1c). Tryptophan is the de novoprecursor of NAD+ that also feeds into NaMN synthesis via a multistep pathway (2) described in Bogan and Brenner (2008).
EMBO Molecular Medicine Vol 6 | No 6 | 2014 ª 2014 The Authors
EMBO Molecular Medicine NAD+ treatment for mitochondrial myopathy? Robert N Lightowlers and Zofia M A Chrzanowska-Lightowlers
706
Closeup
Salvaging hope: Is increasing NAD+ a keyto treating mitochondrial myopathy?Robert N Lightowlers & Zofia MA Chrzanowska-Lightowlers
Mitochondrial diseases can arise frommutations either in mitochondrial DNA orin nuclear DNA encoding mitochondriallydestined proteins. Currently, there is nocure for these diseases although treat-ments to ameliorate a subset of the symp-toms are being developed. In this issue ofEMBO Molecular Medicine, Khan et al(2014) use a mouse model to test the effi-cacy of a simple dietary supplement ofnicotinamide riboside to treat and preventmitochondrial myopathies.
See also: NA Khan et al (June 2014)
G etting the right levels of vitamins is
essential for health. Those of us of a
certain age will remember in junior
school being taught about the consequences
of vitamin deficiency and having to memo-
rise those consequences. For example, one
deficiency, exotically named pellagra,
resulted in a combination of dermatitis, diar-
rhoea and dementia. The underlying cause
was identified as a lack of nicotinic acid or
nicotinamide (vitamin B3). Indeed, the
defect was exacerbated by a dietary lack of
tryptophan. This is now understood, as all
three components are important building
blocks for the production of nicotinamide
adenine dinucleotide, NAD, a redox-active
coenzyme and enzyme substrate. This
molecule is well known as a key player in
metabolism, being the primary electron
donor in the mitochondrial respiratory
chain. It is also utilised and broken-down by
a variety of proteins in other subcellular
compartments, such as the family of protein
deacetylases (sirtuins), the poly (ADP
ribose)-phosphorylases (PARPs) and NAD
glycohydrolases. De novo synthesis from
tryptophan is a complex 8-step enzymatic
process, so there are likely to be recycling
pathways that utilise NAD synthesis inter-
mediates as substrates. This is where nico-
tinamide and nicotinic acid feature. Both are
intermediates in NAD biosynthesis, requir-
ing enzymatic pathways of only 2 or 3 steps
respectively to generate NAD (Bogan &
Brenner, 2008). An additional salvage path-
way has been identified in eubacteria and
eukaryotes that is distinct from these nico-
tinic acid or nicotinamide recycling (or
salvaging) pathways; in a two-step process,
nicotinamide riboside (NR) can be first
phosphorylated and then adenylylated to
form NAD+ (Bieganowski & Brenner, 2004;
see Fig 1). Those of us who remember
memorising those vitamin deficiency
diseases at school, probably also remember
the compulsory bottle of milk to be drunk at
break time. Although we did not realise it
then, this was a good source of nicotinamide
riboside, which in addition to being a
normal metabolite in the body is also pres-
ent in cow’s milk.
NR can protect against mitochondrialmyopathy in mice
Defects of the mitochondrial (mt) respiratory
chain constitute one of the most common
forms of heritable metabolic disease. Clinical
presentation varies widely, and significantly,
there is no effective cure. Khan et al hypoth-
esised that under conditions of respiratory
chain deficiency, NADH utilisation is
partially blocked leading to a decrease in the
NAD+/NADH ratio. This constitutes a signal
in the cell that is translated as indicating
high nutrient availability, a condition
completely at odds with the defective mito-
chondrial function. Therefore, by repleting
levels of NAD+, the authors surmise that
mitochondrial dysfunction could be amelio-
rated. To challenge their hypothesis, the
authors have used their mt-Deletor mouse, a
model of mitochondrial myopathy, and
administered the NAD+ precursor, NR. The
Deletor mouse carries a dominant patho-
genic mutation in the major mitochondrial
DNA (mtDNA) replicative helicase, Twinkle,
that corresponds to a mutation found in
patients (Tyynismaa et al, 2005). In Deletor
mice, this causes increased levels of deleted
mtDNA and a subtle but chronically progres-
sive mitochondrial myopathy. Control mice
and pre- and post-symptomatic Deletor mice
were dosed with large (400 mg/kg/day)
amounts of NR for up to 4 months, a regime
previously documented to result in increased
levels of NAD+ in skeletal muscle of wild-
type mice (Canto et al, 2012). Khan et al
show that this treatment resulted in a
marked increase in mitochondrial biogenesis
in skeletal muscle and brown adipose tissue
compared to undosed controls. A similar
increase had been shown in the previous
experiments following NR treatment, both of
cultured cells and in various mice tissue
(Canto et al, 2012). Crucially, however, for
these new NR supplement experiments, the
mt-biogenesis was concomitant with a
decrease in markers of disease progression
in Deletor mice, which were also protected
from ultrastructural abnormalities of mito-
chondria. NR invoked a minor increase in
overall mtDNA levels in both control and
Deletor mice, but intriguingly caused a
decrease in the levels of deleted mtDNA that
accumulated in skeletal muscle of the Dele-
tors. Thus, data were consistent with NR
treatment and increasing NAD+ levels
protecting against mitochondrial disease in
the Deletor mice. In addition to promoting
mt-biogenesis, NR also appeared to enhance
Wellcome Trust Centre for Mitochondrial Research, Institute for Cell and Molecular Biosciences, Medical School, Newcastle University, Newcastle upon Tyne, UK.E-mail: [email protected] 10.15252/emmm.201404179
ª 2014 The Authors. Published under the terms of the CC BY 4.0 license EMBO Molecular Medicine Vol 6 | No 6 | 2014 705
the mitochondrial unfolded protein
response. This increase in a subset of mito-
chondrial chaperones and proteases is
believed to be beneficial to health and
promote an increased lifespan (Pellegrino
et al, 2013).
Why does HR treatment promotemitochondrial biogenesis?
Previous reports have implicated increased
NAD+ levels with increased sirtuin activity,
most notably SIRT1 and SIRT3 (Lagouge
et al, 2006; Hirschey et al, 2011). The conse-
quence is an activation of key transcription
factors including SIRT1 and SIRT3 (Canto
et al, 2012), which upregulate gene products
that are central to mt-biogenesis (Feige et al,
2008). In addition to enhancing oxidative
metabolism in a range of tissues, SIRT1 acti-
vation has also been reported to protect
against diet-induced metabolic disorders by
enhancing fatty acid oxidation (Feige et al,
2008). Consistent with this, Khan et al pres-
ent data to show an NR-mediated increase in
skeletal muscle mRNA levels encoding
proteins that are involved in fatty acid trans-
port or oxidation, namely CD36, ACOX1 and
MCAD. Increasing mitochondrial biogenesis
as a way of treating mitochondrial dysfunc-
tion is encouraging and has been previously
shown to be efficacious for mouse models of
mitochondrial disease (Wenz et al, 2008).
However, it has been well described that
mitochondrial proliferation can occur as a
consequence of mtDNA disease in man. It
will certainly be interesting to discover
whether drug-induced mitochondrial biogen-
esis can also be beneficial to patients with
mitochondrial dysfunction.
Why are these results so encouraging?
To date, there is no effective therapy for
patients with mitochondrial myopathy. Vita-
min cocktails including vitamin B3
(although at far lower doses than used here)
have often been used to treat such patients
for many years, with only sporadic reports
of efficacy. The rationale for increasing
NAD+ levels in order to increase mitochon-
drial mass is reasonable, and the results
reported here are compelling. What is partic-
ularly exciting is that NAD+ intermediates
such as NR are readily available and rela-
tively simple drugs. If the efficacy of NR is
entirely due to its effects as an NAD+
precursor, it is not absolutely clear why
neither nicotinamide nor nicotinic acid
themselves could not be used. Perhaps
because there is evidence that the former is
hepatotoxic at high concentrations and its
efficacy in increasing NAD+ levels in skele-
tal muscle is unclear (Bogan & Brenner,
2008)? Nicotinic acid, however, has been
used for many years to treat patients with
high serum cholesterol levels but can cause
irritating vasodilation (flushing). To counter
this, slow release formulations have been
available for some time. Of these NAD+
precursors, NR or its phosphorylated NAD+
precursor nicotinamide mononucleotide
(NMN) might be the therapeutic molecule of
choice by virtue of being able to access mito-
chondria and be converted to NAD+ by
mitochondrial-specific enzymes. Isoforms of
the NR kinase and NMN adenylyltransferase
are known, but there is conflicting evidence
on their mitochondrial location (Felici et al,
2013). Finally, side effects following admin-
istration of other NAD+ precursors supple-
ments have been reported (Sauve, 2008). It
will of course be necessary to evaluate the
NR dosage used by Khan et al, as it appears
O
O–
N+
Pribo
NaMN
NH+
COOH
NH2Trp
O
O–
N+
ADPribo
NaAD+
O
O–
NH+
Na
O
N+
NH2
Pribo
NMN
O
NH+
NH2
Nam
1a1c
1b2
O
N+
NH2
Ribo
NR
Nicotinamide ribosidesupplement
O
N+
ADPribo
NAD+
NH2
Figure 1. The salvage/recycling pathway for NAD+ biosynthesis from nicotinamide riboside (NR) in man.NR, taken in to the body, can be converted to nicotinamide mononucleotide (NMN) by one of two highly conserved NR kinases in the cytoplasm (pathway 1a). NAM(nicotinamide) can also be converted by NMN synthetase to NMN (pathway 1b). NMN is further converted to NAD+ by the action of one of three adenylyltransferases(NMNAT1-3) that also acts on NaMN (nicotinic acid mononucleotide) to produce NaAD+ (nicotinic acid adenine dinucleotide). The latter is subsequently converted by NADsynthase to NAD+. Nicotinic acid (Na) feeds into the pathway through conversion to NaMN by Na phosphoribosyltransferase (pathway 1c). Tryptophan is the de novoprecursor of NAD+ that also feeds into NaMN synthesis via a multistep pathway (2) described in Bogan and Brenner (2008).
EMBO Molecular Medicine Vol 6 | No 6 | 2014 ª 2014 The Authors
EMBO Molecular Medicine NAD+ treatment for mitochondrial myopathy? Robert N Lightowlers and Zofia M A Chrzanowska-Lightowlers
706
strikingly high (400 mg/kg/day) compared
to most commercially available supplements
(60–500 mg/person/day). Whether such a
large dosage is viable as a supplement needs
to be established; however, it will be excit-
ing to follow new pharmacokinetic data for
this potentially therapeutic nucleoside deriv-
ative.
AcknowledgementsRNL and ZCL would like to thank The Wellcome
Trust [096919/Z/11/Z] for continuing support.
Conflict of interestThe authors declare that they have no conflict of
interest.
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License: This is an open access article under the
terms of the Creative Commons Attribution 4.0
License, which permits use, distribution and
reproduction in any medium, provided the original
work is properly cited.
ª 2014 The Authors EMBO Molecular Medicine Vol 6 | No 6 | 2014
Robert N Lightowlers and Zofia M A Chrzanowska-Lightowlers NAD+ treatment for mitochondrial myopathy? EMBO Molecular Medicine
707
SynopsisAlthough short‑term anti‑miR‑33 therapy was reported to increase circulating HDL‑cholesterol and reduce atherosclerosis, long‑term adverse effects are here shown for the first time in mice fed a high‑fat diet to result in hypertriglyceridemia and moderate hepatic steatosis.• Theeffectoflong‑terminhibitionofmiR‑33wasdeterminedinmicefedachowdietand
high‑fatdiet.• ChronictherapeuticsilencingofmiR‑33increasedcirculatingtriglyceridesandlipid
accumulationintheliversofmicefedahigh‑fatdiet.• miR‑33inhibitionraisedtheexpressionofgenesinvolvedinfattyacidsynthesisandlipid
metabolism.• Furtherstudiesarewarrantedtounderstandthecomplexgeneregulatorynetwork
controlledbymiR‑33.
Long-term therapeutic silencing of miR-33 increases circulating triglyceride levels and hepatic lipid accumulation in miceLeighGoedeke1,2,3,4,†,AlessandroSalerno3,4,†,CristinaMRamírez1,2,3,4,LiangGuo3,4,RyanMAllen5,XiaokeYin6,SarahRLangley6,ChristineEsau7,AmarylisWanschel3,4,EdwardAFisher3,4,YajairaSuárez1,2,3,4,AngelBaldán5,ManuelMayr6andCarlosFernández‑Hernando*,1,2,3,4
1VascularBiologyandTherapeuticsProgram,YaleUniversitySchoolofMedicine,NewHaven,CT,USA,2IntegrativeCellSignalingandNeurobiologyofMetabolismProgram,SectionofComparativeMedicineYaleUniversitySchoolofMedicine,NewHaven,CT,USA,3LeonH.CharneyDivisionofCardiology,DepartmentofMedicine,NewYorkUniversitySchoolofMedicine,NewYork,NY,USA,4MarcandRutiBellVascularBiologyandDiseaseProgram,NewYorkUniversitySchoolofMedicine,NewYork,NY,USA,5EdwardA.DoisyDepartmentofBiochemistryandMolecularBiology,CenterforCardiovascularResearch,SaintLouisUniversitySchoolofMedicine,SaintLouis,MO,USA,6King'sBritishHeartFoundationCentre,King'sCollegeLondon,London,UK,7RegulusTherapeutics,SanDiego,CA,USA
*Correspondingauthor.Tel:+12037374615;Fax:+12037372290;E‑mail:[email protected]
DOI: 10.15252/emmm.201404046EMBO Molecular Medicine (2014) 6 (9):1133-1141
Plasma high‑density lipoprotein (HDL) levels show a strong inverse correlation with atherosclerotic vascular disease. Previous studies have demonstrated that antagonism of miR‑33 in vivo increases circulating HDL and reverse cholesterol transport (RCT), thereby reducing the progression and enhancing the regression of athero‑sclerosis. While the efficacy of short‑term anti‑miR‑33 treatment has been previously studied, the long‑term effect of miR‑33 antagonism in vivo remains to be elucidated. Here, we show that long‑term therapeutic silencing of miR‑33 increases circulating triglyceride (TG) levels and lipid accumulation in the liver. These adverse effects were only found when mice were fed a high‑fat diet (HFD). Mechanistically, we demonstrate that chronic inhibition of miR‑33 increases the expression of genes involved in fatty acid synthesis such as acetyl‑CoA carboxylase (ACC) and fatty acid synthase (FAS) in the livers of mice treated with miR‑33 antisense oligonucleotides. We also report that anti‑miR‑33 therapy enhances the expression of nuclear transcription Y subunit gamma (NFYC), a transcriptional regulator required for DNA binding and full transcriptional activation of SREBP‑responsive genes, including ACC and FAS. Taken together, these results suggest that persistent inhibition of miR‑33 when mice are fed a high‑fat diet (HFD) might cause deleterious effects such as moderate hepatic steatosis and hypertriglyceridemia. These unexpected findings highlight the importance of assessing the effect of chronic inhibition of miR‑33 in non‑human primates before we can translate this therapy to humans.
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SynopsisUpon nutrient change, a homogeneous E. coli population can split into a growing and a non‑growing persister phenotype. Stochastic variation in metabolic flux is responsible for this responsive diversification.
• Responsivediversificationoffersanexplanationforlagphasesinbacterialcultures• Flux‑inducedphenotypicbistabilitygeneralizestocentralmetabolism• Conditionalbet‑hedgingbalancesfastglycolyticgrowthandabilityforgluconeogenic
growth• Limitedcarboninfluxisamajortriggerforpersistence
Phenotypic bistability in Escherichia coli's central carbon metabolismOliverKotte1,†,BenjaminVolkmer1,†,JakubLRadzikowski2andMatthiasHeinemann*,1,2
1InstituteofMolecularSystemsBiology,ETHZurich,Zurich,Switzerland,2MolecularSystemsBiology,GroningenBiomolecularSciencesandBiotechnologyInstitute,UniversityofGroningen,Groningen,TheNetherlands
*Correspondingauthor.Tel:+31503638146;E‑mail:[email protected]†Theseauthorscontributedequallytothiswork
DOI: 10.15252/msb.20135022Molecular Systems Biology (2014) 10:736
Fluctuations in intracellular molecule abundance can lead to distinct, coexisting phenotypes in isogenic popula‑tions. Although metabolism continuously adapts to unpredictable environmental changes, and although bistabil‑ity was found in certain substrate‑uptake pathways, central carbon metabolism is thought to operate deterministi‑cally. Here, we combine experiment and theory to demonstrate that a clonal Escherichia coli population splits into two stochastically generated phenotypic subpopulations after glucose‑gluconeogenic substrate shifts. Most cells refrain from growth, entering a dormant persister state that manifests as a lag phase in the population growth curve. The subpopulation‑generating mechanism resides at the metabolic core, overarches the metabolic and transcriptional networks, and only allows the growth of cells initially achieving sufficiently high gluconeogenic flux. Thus, central metabolism does not ensure the gluconeogenic growth of individual cells, but uses a popula‑tion‑level adaptation resulting in responsive diversification upon nutrient changes.
Article
Acetate(Fumarate)
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SynopsisThis study reveals the interaction between inflammatory macrophages and β‑cells leading to the recruitment of diabetogenic neutrophils in the pancreas of neonatal mice via CXCR2/CXCR2 ligands. Inhibition of CXCR2 reduces the diabetogenic T‑cell response, insulitis, and incidence of diabetes.
• InyoungNODmice,CXCR2+‑neutrophilsarerecruitedfromthebloodintothepancreaticisletsandnotinthetwonon‑diabetesproneC57BL/6andBALB/cmice.
• ThetwoCXCR2ligands,CXCL1andCXCL2,aresecretedinthepancreaticisletsfromtheyoungandnotfromtheadultNODmiceorthetwonon‑diabetespronemice.
• Thepancreaticβ‑cellsarethemainsourceofCXCL1andCXCL2inthepancreaticisletsofyoungNODmice.
• TheproductionofCXCL1andCXCL2bytheβ‑cellsisinducedbyIL‑1b‑producingmacrophagesinfiltratingthepancreaticisletsofyoungNODmice.
• TheearlyblockageofneutrophilrecruitmentusingCXCR2antagonistreducestheinsulitis,theeffectoractivityofdiabetogenicCD8Tcells,andthedevelopmentofautoimmunediabetesinNODmice.
Macrophages and β-cells are responsible for CXCR2-mediated neutrophil infiltration of the pancreas during autoimmune diabetesJulienDiana*,1,2andAgnèsLehuen2,3,4
1InstitutNationaldelaSantéetdelaRechercheMédicale(INSERM),U1151,Necker‑EnfantsMaladesInstitute(INEM)NeckerHospital,Paris,France,2SorbonneParisCité,UniversitéParisDescartes,Paris,France,3InstitutNationaldelaSantéetdelaRechercheMédicale(INSERM),U1016,CochinInstituteCochinHospital,Paris,France,4Laboratoired'ExcellenceINFLAMEX,Paris,France
*Correspondingauthor.Tel:+33144495069;E‑mail:[email protected]
DOI: 10.15252/emmm.201404144EMBO Molecular Medicine (2014) 6 (8):1090-1104
Autoimmune type 1 diabetes (T1D) development results from the interaction between pancreatic β‑cells, and the innate and the adaptive immune systems culminating with the destruction of the insulin‑secreting β‑cells by auto‑reactive T cells. This diabetogenic course starts during the first postnatal weeks by the infiltration of the pancreatic islets by innate immune cells and particularly neutrophils. Here, we aim to determine the cellular and molecular mechanism leading to the recruitment of this neutrophils in the pancreatic islets of non‑obese diabetic (NOD) mice. Here, we show that neutrophil recruitment in the pancreatic islets is controlled by inflammatory macrophages and β‑cells themselves. Macrophages and β‑cells produce the chemokines CXCL1 and CXCL2, recruiting CXCR2‑express‑ing neutrophils from the blood to the pancreatic islets. We further show that pancreatic macrophages secrete IL‑1β‑inducing CXCR2 ligand production by the β‑cells. Finally, the blockade of neutrophil recruitment at early ages using CXCR2 antagonist dampens the diabetogenic T‑cell response and the later development of autoimmune diabetes, supporting the therapeutic potential of this approach.
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SynopsisMolecular bypass therapy with orally administered deoxythymidine monophosphate and deoxycytidine monophosphate produces clinical, molecular, biochemical, and histological improvements in a mitochondrial DNA depletion syndrome Tk2 knock‑in mouse model.
• DeoxypyrimidinemonophosphatescrossbiologicalbarriersinTk2‑deficientmice.• dCMP+dTMPtreatmentrestoresmtDNAlevelsandamelioratesthephenotypeof
Tk2‑mutantmice.• dCMP+dTMPhavedose‑relatedclinicalandbiochemicaleffectsinTk2‑deficientmice.
Deoxypyrimidine monophosphate bypass therapy for thymidine kinase 2 deficiencyCaterinaGarone1,2,BeatrizGarcia‑Diaz1,ValentinaEmmanuele1,3,LuisCLopez4,SabaTadesse1,HasanOAkman1,KurenaiTanji5,CatarinaMQuinzii1andMichioHirano*,1
1DepartmentofNeurology,ColumbiaUniversityMedicalCenter,NewYork,NY,USA,2HumanGeneticsJointPhDProgram,UniversityofBolognaandTurin,Turin,Italy,3PediatricClinicUniversityofGenoaIRCCSG.GasliniInstitute,Genoa,Italy,4InstitutodeBiotecnología,CentrodeInvestigaciónBiomédica,UniversidaddeGranadaParqueTecnológicodeCienciasdelaSalud,Armilla,Spain,5DepartmentofPathologyandCellBiology,ColumbiaUniversityMedicalCenter,NewYork,NY,USA
*Correspondingauthor.Tel:+12123051048;Fax:+12123053986;E‑mail:[email protected]
DOI: 10.15252/emmm.201404092EMBO Molecular Medicine (2014) 6 (8):1016-1027
Autosomal recessive mutations in the thymidine kinase 2 gene (TK2) cause mitochondrial DNA depletion, multiple deletions, or both due to loss of TK2 enzyme activity and ensuing unbalanced deoxynucleotide triphos‑phate (dNTP) pools. To bypass Tk2 deficiency, we administered deoxycytidine and deoxythymidine monophos‑phates (dCMP+dTMP) to the Tk2 H126N (Tk2−/−) knock‑in mouse model from postnatal day 4, when mutant mice are phenotypically normal, but biochemically affected. Assessment of 13‑day‑old Tk2−/− mice treated with dCMP+dTMP 200 mg/kg/day each (Tk2−/−200dCMP/dTMP) demonstrated that in mutant animals, the compounds raise dTTP concentrations, increase levels of mtDNA, ameliorate defects of mitochondrial respiratory chain enzymes, and significantly prolong their lifespan (34 days with treatment versus 13 days untreated). A second trial of dCMP+dTMP each at 400 mg/kg/day showed even greater phenotypic and biochemical improvements. In conclusion, dCMP/dTMP supplementation is the first effective pharmacologic treatment for Tk2 deficiency.
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SynopsisAn RNAi screen for genes needed in mtDNA copy number maintenance in Drosophila yielded 97 positives, including previously characterized mtDNA maintenance proteins, subunits of the cytoribosome, proteasome, and ATP synthase.
• AnRNAiscreenforgenesneededinmtDNAcopynumbermaintenanceinDrosophilayielded97positives.
• TheseincludedpreviouslycharacterizedcomponentsofthemtDNAmaintenancemachinery.• Othermajorclassesofpositiveswerethecytoribosome,proteasome,andATPsynthase.• ATPsynthasedeficiencyresultsinincreasedROSandactivationofmitochondrialturnoverby
pathway(s)distinctfromclassicalautophagy.
Screen for mitochondrial DNA copy number maintenance genes reveals essential role for ATP synthaseAtsushiFukuoh1,2,3,†,GiuseppeCannino1,†,MikeGerards1,SuzanneBuckley1,SelenaKazancioglu1,FilippoScialo1,EeroLihavainen4,AndreRibeiro4,EricDufour1andHowardTJacobs*,1,5
1BioMediTechandTampereUniversityHospital,UniversityofTampere,Tampere,Finland,2DepartmentofClinicalChemistryandLaboratoryMedicine,KyushuUniversityGraduateschoolofMedicalSciences,Fukuoka,Japan,3DepartmentofMedicalLaboratoryScience,JunshinGakuenUniversity,Fukuoka,Japan,4DepartmentofSignalProcessing,TampereUniversityofTechnology,Tampere,Finland,5ResearchProgramofMolecularNeurology,UniversityofHelsinki,Helsinki,Finland
*Correspondingauthor.Tel:+358335517731,+358503412894;E‑mail:[email protected]†Theseauthorsequallycontributedtothiswork.
DOI: 10.15252/msb.20145117Molecular Systems Biology (2014) 10:734
The machinery of mitochondrial DNA (mtDNA) maintenance is only partially characterized and is of wide interest due to its involvement in disease. To identify novel components of this machinery, plus other cellular pathways required for mtDNA viability, we implemented a genome‑wide RNAi screen in Drosophila S2 cells, assaying for loss of fluorescence of mtDNA nucleoids stained with the DNA‑intercalating agent PicoGreen. In addition to previ‑ously characterized components of the mtDNA replication and transcription machineries, positives included many proteins of the cytosolic proteasome and ribosome (but not the mitoribosome), three proteins involved in vesicle transport, some other factors involved in mitochondrial biogenesis or nuclear gene expression, > 30 mainly uncharacterized proteins and most subunits of ATP synthase (but no other OXPHOS complex). ATP synthase knockdown precipitated a burst of mitochondrial ROS production, followed by copy number depletion involving increased mitochondrial turnover, not dependent on the canonical autophagy machinery. Our findings will inform future studies of the apparatus and regulation of mtDNA maintenance, and the role of mitochondrial bioenergetics and signaling in modulating mtDNA copy number.
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SynopsisIt is well established that cognitive deficits go hand in hand with aging. Restoring cholesterol levels in the aged hippocampus to values found in the young can rescue learning and memory in the old, linking age‑dependent cholesterol decline with synaptic plasticity and neuronal function.
• Amildyetsignificantreductioninmembranecholesterolcharacterizestheagingrodenthippocampus.
• LowsynaptichippocampalcholesteroldeterminesreducedAktdephosphorylationafterNMDA‑inducedLTD,togetherwithreducedglutamate(AMPA)receptorlateraldiffusionandendocytosis.
• LowsynaptichippocampalcholesterolplaysaroleinthepoorLTDofoldmiceandrats,inex-vivoandin vivoparadigms.
• NormallevelsofpAktafterNMDA,properreceptorlateraldiffusion,andinternalizationandnormal(younganimals‑like)LTDintheoldcanberescuedbymembranecholesterolreplenishment.
• Cholesterolreplenishmentinlivingoldratsimproveslearningandmemory.
Constitutive hippocampal cholesterol loss underlies poor cognition in old rodentsMauricioGMartin*,1,2,TariqAhmed3,AlejandraKorovaichuk4,CesarVenero5,SilviaAMenchón2,6,IsabelSalas1,SebastianMunck2,OscarHerreras4,DetlefBalschun3andCarlosGDotti*,1,2
1CentroBiologíaMolecular“SeveroOchoa”CSIC‑UAM,Madrid,Spain,2VIBCenterfortheBiologyofDisease,CenterforHumanGenetics,UniversityofLeuven(KULeuven),Leuven,Belgium,3LaboratoryofBiologicalPsychology,FacultyofPsychologyandEducationalSciences,UniversityofLeuven(KULeuven),Leuven,Belgium,4DepartamentodeNeurobiologíaFuncionalydeSistemas,InstitutoCajal–CSIC,Madrid,Spain,5DepartamentodePsicobiología,FacultaddePsicología,UNED,Madrid,Spain,6IFEG‑CONICETandFaMAF,UniversidadNacionaldeCórdoba,Córdoba,Argentina
*Correspondingauthor.Tel:+543514681465;E‑mail:[email protected]*Correspondingauthor.Tel:+34911964401;E‑mail:[email protected]
DOI: 10.15252/emmm.201303711EMBO Molecular Medicine (2014) 6 (7):902-917
Cognitive decline is one of the many characteristics of aging. Reduced long‑term potentiation (LTP) and long‑term depression (LTD) are thought to be responsible for this decline, although the precise mechanisms underlying LTP and LTD dampening in the old remain unclear. We previously showed that aging is accompanied by the loss of cholesterol from the hippocampus, which leads to PI3K/Akt phosphorylation. Given that Akt de‑phosphorylation is required for glutamate receptor internalization and LTD, we hypothesized that the decrease in cholesterol in neuronal membranes may contribute to the deficits in LTD typical of aging. Here, we show that cholesterol loss triggers p‑Akt accumulation, which in turn perturbs the normal cellular and molecular responses induced by LTD, such as impaired AMPA receptor internalization and its reduced lateral diffusion. Electrophysiology recordings in brain slices of old mice and in anesthetized elderly rats demonstrate that the reduced hippocampal LTD associ‑ated with age can be rescued by cholesterol perfusion. Accordingly, cholesterol replenishment in aging animals improves hippocampal‑dependent learning and memory in the water maze test.
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SynopsisMouse liver metabolites were quantified by mass spectrometry and mapped by genome‑wide association. Genetic factors were shown to contribute substantially to metabolite levels and adenoviral overexpression validated several of the identified loci.
• Livermetabolitesexhibitawiderangeofvariation,indicatingstronggeneticinfluences.• Approximately40%ofmetabolitesareestimatedtoberegulatedbygeneticfactors.• Asignificantoverlapwasobservedbetweengeneticfactorsregulatingmouseliver
metabolitesandgeneticfactorsregulatinghumanserummetabolites.• Metabolitelevelscorrelatedsignificantlybothwitheachotherandwithotherphenotypes
suchastranscriptlevelsandphysiologicaltraits.
Genetic regulation of mouse liver metabolite levelsAnatoleGhazalpour1,†,BrianJBennett1,6,†,DianaShih1,NamChe1,LuzOrozco2,CalvinPan3,RaffiHagopian1,AiqingHe4,PaulKayne4,Wen‑pinYang4,ToddKirchgessner5,AldonsJLusis*1,3.
1DivisionofCardiology,DepartmentofMedicine,UCLA,LosAngeles,CA,USA,2DepartmentofMolecularCellandDevelopmentalBiology,UCLA,LosAngeles,CA,USA,3DepartmentofHumanGenetics,UCLA,LosAngeles,CA,USA,4DepartmentofAppliedGenomics,Bristol‑MyersSquibb,Princeton,NJ,USA,5DepartmentofAtherosclerosisDrugDiscovery,Bristol‑MyersSquibb,Princeton,NJ,USA,6DepartmentofGenetics,UniversityofNorthCarolinaatChapelHill,Kannapolis,NC,USA
*Correspondingauthor.Tel:+13108251359;E‑mail:[email protected]
DOI: 10.15252/msb.20135004Molecular Systems Biology (2014) 10:730
We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohy‑drates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome‑wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co‑variation across various biological scales.
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SynopsisMore and more evidence implicates ER stress in diabetes. Thioredoxin‑interacting protein (Txnip) is here shown to interact with and regulate protein disulfide isomerases (PDIs) activity and ER stress. This study highlights new therapeutic targets for treating diabetes.
• Anunbiasedproteomicsapproachaswellasspecificpulldownassaysrevealedaninteractionofthioredoxin‑interactingprotein(Txnip)withproteindisulfideisomerases(PDIs).
• TxnipincreasesPDIactivity,andTxnipknockoutleadstoincreasedproteinubiquitinationandincreasedlevelsofXbp1s,amarkerofERstress.
• IncreasedlevelsofXbp1sinTxnip‑KOmiceisreversedbytreatmentwithchemicalchaperones.
Thioredoxin-interacting protein regulates protein disulfide isomerases and endoplasmic reticulum stressSamuelLee1,2,3,SooMinKim1,2,JamesDotimas1,2,LetitiaLi1,2,EdwardPFeener4,StephanBaldus3,RonaldBMyers1,2,WilliamAChutkow2,ParthPatwari2,JunYoshioka2andRichardTLee*,1,2
1HarvardDepartmentofStemCellandRegenerativeBiology,HarvardStemCellInstituteHarvardMedicalSchoolBrighamandWomen'sHospital,Cambridge,MA,USA,2TheCardiovascularDivision,DepartmentofMedicine,HarvardMedicalSchoolBrighamandWomen'sHospital,Cambridge,MA,USA,3DepartmentIIIofInternalMedicine,UniversityHospitalofCologne,Cologne,Germany,4TheJoslinDiabetesCenter,HarvardMedicalSchool,Boston,MA,USA
*Correspondingauthor.Tel:+16177688282;Fax:+16177688280;E‑mail:[email protected]
DOI: 10.15252/emmm.201302561EMBO Molecular Medicine (2014) 6 (6):732-743
The endoplasmic reticulum (ER) is responsible for protein folding, modification, and trafficking. Accumulation of unfolded or misfolded proteins represents the condition of ER stress and triggers the unfolded protein response (UPR), a key mechanism linking supply of excess nutrients to insulin resistance and type 2 diabetes in obesity. The ER harbors proteins that participate in protein folding including protein disulfide isomerases (PDIs). Chang‑es in PDI activity are associated with protein misfolding and ER stress. Here, we show that thioredoxin‑interact‑ing protein (Txnip), a member of the arrestin protein superfamily and one of the most strongly induced proteins in diabetic patients, regulates PDI activity and UPR signaling. We found that Txnip binds to PDIs and increases their enzymatic activity. Genetic deletion of Txnip in cells and mice led to increased protein ubiquitination and splicing of the UPR regulated transcription factor X‑box‑binding protein 1 (Xbp1s) at baseline as well as under ER stress. Our results reveal Txnip as a novel direct regulator of PDI activity and a feedback mechanism of UPR signaling to decrease ER stress.
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SynopsisGlutamine plays an important role in cellular growth in several cancers. In this study, a further link between glutamine dependency and tumor invasiveness is established in ovarian cancer. Glutamine maintains the high‑invasive phenotype by regulating STAT3 signaling.
• High‑invasiveovariancancer(OVCA)cellsareglutaminedependentincontrasttolow‑invasivecellsthatareglutamineindependent.
• GlutamineregulatesSTAT3activationinhigh‑invasivecancercells.• Glutamine'sentryintoTCAcyclemodulatestheinvasivepotentialofhigh‑invasivecancercells.• Theratioofglutaminecatabolismoverglutamineanabolismisassociatedwithworseoverall
survivalinOVCApatients.
Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancerLifengYang1,2,TylerMoss3,LingegowdaSMangala4,5,JuanMarini6,HongyunZhao1,2,StephenWahlig1,7,GuillermoArmaiz‑Pena4,5,DahaiJiang4,5,AbhinavAchreja1,2,JuliaWin1,2,RajeshaRoopaimoole4,5,CristianRodriguez‑Aguayo5,7,ImeldaMercado‑Uribe8,GabrielLopez‑Berestein5,7,JinsongLiu8,TakashiTsukamoto9,AnilK.Sood4,5,PrahladTRam3andDeepakNagrath*,1,2,10
1LaboratoryforSystemsBiologyofHumanDiseases,RiceUniversity,Houston,TX,USA,2DepartmentofChemicalandBiomolecularEngineering,RiceUniversity,Houston,TX,USA,3DepartmentofSystemsBiology,UniversityofTexasMDAndersonCancerCenter,Houston,TX,USA,4DepartmentsofGynecologicalOncologyandCancerBiology,UniversityofTexasMDAndersonCancerCenter,Houston,TX,USA,5CenterforRNAInterferenceandNon‑CodingRNAUniversityofTexasMDAndersonCancerCenter,Houston,TX,USA,6BaylorCollegeofMedicine,Houston,TX,USA,7DepartmentofExperimentalTherapeutics,UniversityofTexasMDAndersonCancerCenter,Houston,TX,USA,8DepartmentofPathology,UniversityofTexasMDAndersonCancerCenter,Houston,TX,USA,9JohnsHopkinsUniversity,Baltimore,MD,USA,10DepartmentofBioengineering,RiceUniversity,Houston,TX,USA
*Correspondingauthor.Tel:+17133486408;Fax:+17133485478;E‑mail:[email protected]
DOI: 10.1002/msb.20134892Molecular Systems Biology (2014) 10:728.
Glutamine can play a critical role in cellular growth in multiple cancers. Glutamine‑addicted cancer cells are dependent on glutamine for viability, and their metabolism is reprogrammed for glutamine utilization through the tricarboxylic acid (TCA) cycle. Here, we have uncovered a missing link between cancer invasiveness and glutamine dependence. Using isotope tracer and bioenergetic analysis, we found that low‑invasive ovarian cancer (OVCA) cells are glutamine independent, whereas high‑invasive OVCA cells are markedly glutamine dependent. Consis‑tent with our findings, OVCA patients’ microarray data suggest that glutaminolysis correlates with poor survival. Notably, the ratio of gene expression associated with glutamine anabolism versus catabolism has emerged as a novel biomarker for patient prognosis. Significantly, we found that glutamine regulates the activation of STAT3, a mediator of signaling pathways which regulates cancer hallmarks in invasive OVCA cells. Our findings suggest that a combined approach of targeting high‑invasive OVCA cells by blocking glutamine's entry into the TCA cycle, along with targeting low‑invasive OVCA cells by inhibiting glutamine synthesis and STAT3 may lead to potential therapeutic approaches for treating OVCAs.
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SynopsisWeak mitochondrial uncouplers prevent neoangiogenesis in vitro and in vivo by depleting cellular energy reserves in proliferating but not normal quiescent endothelial cells (ECs).
• NewvesselformationduringtumorgrowthrequiresECproliferationandincreasedoxidativephosphorylationtomeetthegreaterenergydemandduringangiogenesis.
• WeakmitochondrialuncouplerspreventneoangiogenesisbydepletingcellularenergyreservesinproliferatingbutnotnormalquiescentECs.
• ProliferatingECsaresensitizedtomitochondrialuncouplersbyareductioninmembranepotentialandlowerrespiratoryreservecapacity.
• GeneticaccumulationofmitochondrialDNAmutationsinmitochondrialmutatormicehighlightsthelinkbetweenreducedOxPhosactivityandimpairedangiogenicresponse.
• Weakmitochondrialuncouplerscouldbeclinicallyvaluableincontrollingpathologicalneoangiogenesiswhilesparingnormalvasculatureandcomplementingcytostaticdrugsincancertreatment.
Embelin inhibits endothelial mitochondrial respiration and impairs neoangiogenesis during tumor growth and wound healingOliverCoutelle*,1,Hue‑TranHornig‑Do2,AxelWitt3,MariaAndree3,LarsMSchiffmann1,MichaelPiekarek4,KerstinBrinkmann3,JensMSeeger3,MaximLiwschitz1,SatomiMiwa5,MichaelHallek1,MartinKrönke3,6,7,AleksandraTrifunovic6,SabineAEming4,6,7,RudolfJWiesner2,6,7,UlrichTHacker1,†andHamidKashkar3,6,7,†
1DepartmentIforInternalMedicine,UniversityofCologne,Cologne,Germany,2InstituteforVegetativePhysiologyUniversityofCologne,Cologne,Germany,3InstituteforMedicalMicrobiology,ImmunologyandHygiene,MedicalFaculty,UniversityofCologne,Cologne,Germany,4DepartmentofDermatology,UniversityofCologne,Cologne,Germany,5InstituteforAgeingandHealthNewcastleUniversity,NewcastleuponTyne,UK,6CologneExcellenceClusteronCellularStressResponsesinAging‑AssociatedDiseases(CECAD),MedicalFacultyUniversityofCologne,Cologne,Germany,7CenterforMolecularMedicineCologne(CMMC),Cologne,Germany
*Correspondingauthor.Tel:+492214787285;Fax:+492214787288;E‑mail:[email protected]
DOI: 10.1002/emmm.201303016EMBO Molecular Medicine (2014) 6 (5):624-639
In the normal quiescent vasculature, only 0.01% of endothelial cells (ECs) are proliferating. However, this propor‑tion increases dramatically following the angiogenic switch during tumor growth or wound healing. Recent evidence suggests that this angiogenic switch is accompanied by a metabolic switch. Here, we show that prolifer‑ating ECs increasingly depend on mitochondrial oxidative phosphorylation (OxPhos) for their increased energy demand. Under growth conditions, ECs consume three times more oxygen than quiescent ECs and work close to their respiratory limit. The increased utilization of the proton motif force leads to a reduced mitochondrial membrane potential in proliferating ECs and sensitizes to mitochondrial uncoupling. The benzoquinone embe‑lin is a weak mitochondrial uncoupler that prevents neoangiogenesis during tumor growth and wound healing by exhausting the low respiratory reserve of proliferating ECs without adversely affecting quiescent ECs. We demonstrate that this can be exploited therapeutically by attenuating tumor growth in syngenic and xenograft mouse models. This novel metabolic targeting approach might be clinically valuable in controlling pathological neoangiogenesis while sparing normal vasculature and complementing cytostatic drugs in cancer treatment.
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SynopsisIn this review, the authors introduce the concept of understanding and further maintaining metabolic flexibility as a way to limit spreading of metabolic disorders. By modifying function or biogenesis, the mitochondria perfectly illustrates this point as they adapt to different stress situations.
Mitochondrial response to nutrient availability and its role in metabolic diseaseArwenWGao1,CarlesCantó*,2andRiekeltHHoutkooper*,1
1LaboratoryGeneticMetabolicDiseases,AcademicMedicalCenter,Amsterdam,TheNetherlands,2NestléInstituteofHealthSciences,Lausanne,Switzerland
*Correspondingauthor.Tel:+41216326116;Fax:+41216326499;E‑mail:[email protected]*Correspondingauthor.Tel:+31205666039;Fax:+31206962596;E‑mail:[email protected]
DOI: 10.1002/emmm.201303782EMBO Molecular Medicine (2014) 6 (5):580-589
Metabolic inflexibility is defined as an impaired capacity to switch between different energy substrates and is a hall‑mark of insulin resistance and type 2 diabetes mellitus (T2DM). Hence, understanding the mechanisms underlying proper metabolic flexibility is key to prevent the development of metabolic disease and physiological deterioration. An important downstream player in the effects of metabolic flexibility is the mitochondrion. The objective of this review was to describe how mitochondrial metabolism adapts to limited nutrient situations or caloric excess by changes in mitochondrial function or biogenesis, as well as to define the mechanisms propelling these changes. Altogether, this should pinpoint key regulatory points by which metabolic flexibility might be ameliorated in situ‑ations of metabolic disease.
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SynopsisModern cells possess a sophisticated metabolic network, but its origins remain largely unknown. Reconstructing scenarios of the Archean ocean, we observe chemical reactions reminiscent of modern metabolic sequences, indicating that metabolism could be of prebiotic origin.
• Metabolitesofglycolysisandthepentosephosphateundergonon‑enzymaticinterconversionreactions.
• MetalionsabundantlyfoundinsedimentsoftheprebioticArcheanocean,predominantlyFe(II),catalyseadditionalsugarphosphateinterconversionreactions.
• ReactionscatalysedbytheArcheanoceanmetalsresembleenzyme‑catalysedreactionsfoundinthemodernglycolyticandpentosephosphatepathways.
• TheobservedreactionsareacceleratedandgainspecificityinconditionssimulatingtheArcheanocean.
Non-enzymatic glycolysis and pentose phosphate pathway-like reactions in a plausible Archean oceanMarkusAKeller1,AlexandraVTurchyn2andMarkusRalser*,1,3
1DepartmentofBiochemistryandCambridgeSystemsBiologyCentre,UniversityofCambridge,Cambridge,UK,2DepartmentofEarthSciences,UniversityofCambridge,Cambridge,UK,3DivisionofPhysiologyandMetabolism,MRCNationalInstituteforMedicalResearch,MillHillLondon,UK
*Correspondingauthor.Tel:+441223761346;Fax:+441223766002;E‑mail:[email protected]
DOI: 10.1002/msb.20145228Molecular Systems Biology (2014) 10:725
The reaction sequences of central metabolism, glycolysis and the pentose phosphate pathway provide essential precursors for nucleic acids, amino acids and lipids. However, their evolutionary origins are not yet understood. Here, we provide evidence that their structure could have been fundamentally shaped by the general chemical environments in earth's earliest oceans. We reconstructed potential scenarios for oceans of the prebiotic Archean based on the composition of early sediments. We report that the resultant reaction milieu catalyses the intercon‑version of metabolites that in modern organisms constitute glycolysis and the pentose phosphate pathway. The 29 observed reactions include the formation and/or interconversion of glucose, pyruvate, the nucleic acid precursor ribose‑5‑phosphate and the amino acid precursor erythrose‑4‑phosphate, antedating reactions sequences simi‑lar to that used by the metabolic pathways. Moreover, the Archean ocean mimetic increased the stability of the phosphorylated intermediates and accelerated the rate of intermediate reactions and pyruvate production. The catalytic capacity of the reconstructed ocean milieu was attributable to its metal content. The reactions were particularly sensitive to ferrous iron Fe(II), which is understood to have had high concentrations in the Archean oceans. These observations reveal that reaction sequences that constitute central carbon metabolism could have been constrained by the iron‑rich oceanic environment of the early Archean. The origin of metabolism could thus date back to the prebiotic world.
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SynopsisA primary cardiac myocyte defect leads to aberrant lipid accumulation and signaling in the liver. The resulting hepatic phenotype impacts cardiac function. Normalization of heart lipid delivery or inhibition of gluconeogenesis improves ventricular function.
• Geneticheartdiseasecausesmetabolicabnormalitiesintheliver.• ThereisreducedtriglycerideclearancebytheHCMheart.• Accumulatingplasmatriglyceridesaresequesteredbyhepatocytes.• Activationofgluconeogenesisexacerbatescardiacpathology.
Metabolic crosstalk between the heart and liver impacts familial hypertrophic cardiomyopathyJasonAMagida1andLeslieALeinwand*,1
1DepartmentofMolecular,CellularandDevelopmentalBiology,BioFrontiersInstituteUniversityofColoradoatBoulder,Boulder,CO,USA
*Correspondingauthor.Tel:+13034927606;Fax:+13034928907;E‑mail:[email protected]
DOI: 10.1002/emmm.201302852EMBO Molecular Medicine (2014) 6 (4):482-495
Familial hypertrophic cardiomyopathy (HCM) is largely caused by dominant mutations in genes encoding cardi‑ac sarcomeric proteins, and it is etiologically distinct from secondary cardiomyopathies resulting from pressure/volume overload and neurohormonal or inflammatory stimuli. Here, we demonstrate that decreased left ventricular contractile function in male, but not female, HCM mice is associated with reduced fatty acid translocase (CD36) and AMP‑activated protein kinase (AMPK) activity. As a result, the levels of myocardial ATP and triglyceride (TG) content are reduced, while the levels of oleic acid and TG in circulating very low density lipoproteins (VLDLs) and liver are increased. With time, these metabolic changes culminate in enhanced glucose production in male HCM mice. Remarkably, restoration of ventricular TG and ATP deficits via AMPK agonism as well as inhibition of gluco‑neogenesis improves ventricular architecture and function. These data underscore the importance of the systemic effects of a primary genetic heart disease to other organs and provide insight into potentially novel therapeutic interventions for HCM.
Research article TRANSPARENTPROCESS
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SynopsisNicotinamide riboside (vitamin B3) delays the progression of mitochondrial myopathy by preventing pathology‑associated mitochondrial ultrastructure, improving mitochondrial DNA stability and further stimulating mitochondrial unfolded protein response.
• Nicotinamideriboside,vitaminB3,delaystheprogressionofmitochondrialmyopathy.• Nicotinamideribosidecurespathology‑associatedmitochondrialultrastructure.• NicotinamideribosideimprovesmitochondrialDNAstability.• Mitochondrialdiseaseinducesmitochondrialunfoldedproteinresponse,furtherenhancedby
nicotinamideriboside.• Nicotinamideribosideisapromisingtreatmentforadult‑onsetmitochondrialmyopathy.
Effective treatment of mitochondrial myopathy by nicotinamide riboside, a vitamin B3NahidAKhan1,MariAuranen1,2,†,IlsePaetau1,†,EijaPirinen3,4,LiliyaEuro1,SaaraForsström1,LottaPasila1,VidyaVelagapudi5,ChristopherJCarroll1,JohanAuwerx3andAnuSuomalainen*,1,2,6
1MolecularNeurology,ResearchProgramsUnit,UniversityofHelsinki,Helsinki,Finland,2DepartmentofNeurology,HelsinkiUniversityCentralHospital,Helsinki,Finland,3LaboratoryofIntegrativeSystemsPhysiology,ÉcolePolytechniqueFédéraledeLausanne,Lausanne,Switzerland,4BiotechnologyandMolecularMedicine,A.I.VirtanenInstituteforMolecularSciencesBiocenterKuopioUniversityofEasternFinland,Kuopio,Finland,5MetabolomicsUnit,InstituteforMolecularMedicineFinlandFIMM,Helsinki,Finland,6NeuroscienceResearchCentreUniversityofHelsinki,Helsinki,Finland
*Correspondingauthor.Tel:+358947171965;Fax:+358947171964;E‑mail:[email protected]†Theseauthorscontributedequallytothemanuscript.
DOI: 10.1002/emmm.201403943EMBO Molecular Medicine (2014) 6 (6):721-731
Nutrient availability is the major regulator of life and reproduction, and a complex cellular signaling network has evolved to adapt organisms to fasting. These sensor pathways monitor cellular energy metabolism, especially mitochondrial ATP production and NAD+/NADH ratio, as major signals for nutritional state. We hypothesized that these signals would be modified by mitochondrial respiratory chain disease, because of inefficient NADH utilization and ATP production. Oral administration of nicotinamide riboside (NR), a vitamin B3 and NAD+ precursor, was previously shown to boost NAD+ levels in mice and to induce mitochondrial biogenesis. Here, we treated mitochondrial myopathy mice with NR. This vitamin effectively delayed early‑ and late‑stage disease progression, by robustly inducing mitochondrial biogenesis in skeletal muscle and brown adipose tissue, prevent‑ing mitochondrial ultrastructure abnormalities and mtDNA deletion formation. NR further stimulated mitochon‑drial unfolded protein response, suggesting its protective role in mitochondrial disease. These results indicate that NR and strategies boosting NAD+ levels are a promising treatment strategy for mitochondrial myopathy.
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SynopsisPersonalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‑driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.
• Thepresenceofproteinsencodedby15,841genesintumorsfrom27HCCpatientsisevaluatedbyimmunohistochemistry.
• PersonalizedGEMsforsixHCCpatientsandGEMsfor83healthycelltypesarereconstructedbasedonHMR2.0andthetINITalgorithmfortask‑drivenmodelreconstruction.
• 101antimetabolitesarepredictedtoinhibittumorgrowthinallpatients.Antimetabolitetoxicityistestedusingthe83celltype‑specificGEMs.
• Anl‑carnitineanaloginhibitstheproliferationofHepG2cells.
Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modelingRasmusAgren1,†,AdilMardinoglu1,†,AnnaAsplund2,CarolineKampf2,MathiasUhlen3,4,JensNielsen*1,3.
1DepartmentofChemicalandBiologicalEngineering,ChalmersUniversityofTechnology,Gothenburg,Sweden,2DepartmentofImmunology,GeneticsandPathologyScienceforLifeLaboratory,UppsalaUniversity,Uppsala,Sweden,3ScienceforLifeLaboratoryKTH–RoyalInstituteofTechnology,Stockholm,Sweden,4DepartmentofProteomicsKTH–RoyalInstituteofTechnology,Stockholm,Sweden
*Correspondingauthor.Tel:+46317723804;Fax:+46317723801;E‑mail:[email protected]†Theseauthorscontributedequallytothiswork.
DOI: 10.1002/msb.145122Molecular Systems Biology (2014) 10:721
Genome‑scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for under‑standing the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‑driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabo‑lites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‑specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‑two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identi‑fied targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.
Article
Personalized GEMs
6 HCC patients
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SynopsisThe impact of oncogene activation and hypoxia on energy metabolism is analyzed by integrating quantitative measurements into a redox‑balanced metabolic flux model. Glutamine‑driven oxidative phosphorylation is found to be a major ATP source even in oncogene‑expressing or hypoxic cells.
• TheintegrationofoxygenuptakemeasurementsandLC‑MS‑basedisotopetraceranalysesinaredox‑balancedmetabolicfluxmodelenabledquantitativedeterminationofenergygenerationpathwaysinculturedcells.
• Intransformedmammaliancells,eveninhypoxia(1%oxygen),oxidativephosphorylationproducesthemajorityofATP.
• TheoncogeneRassimultaneouslyincreasesglycolysisanddecreasesoxidativephosphorylation,thusresultinginnonetincreaseinATPproduction.
• Glutamineisthemajorsourceofhigh‑energyelectronsforoxidativephosphorylation,especiallyuponRasactivation.
Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxiaJingFan1,JurreJKamphorst1,RobinMathew2,3,MichelleKChung1,EileenWhite2,3,4,TomerShlomi5,†andJoshuaDRabinowitz*,1,2,6,†
1DepartmentofChemistryandLewis‑SiglerInstituteforIntegrativeGenomics,PrincetonUniversity,Princeton,NJ,USA,2TheCancerInstituteofNewJersey,NewBrunswick,NJ,USA,3UniversityofMedicineandDentistryofNewJersey,RobertWoodJohnsonMedicalSchool,Piscataway,NJ,USA,4DepartmentofMolecularBiologyandBiochemistry,RutgersUniversity,Piscataway,NJ,USA,5DepartmentofComputerScience,Technion,Haifa,Israel,6DepartmentofMolecularBiology,PrincetonUniversity,Princeton,NJ,USA
*Correspondingauthor.DepartmentsofChemistryandIntegrativeGenomics,PrincetonUniversity,241CarlIcahnLaboratory,Princeton,NJ08544,USA.Tel.:+16092588985;Fax:+16092583565;E‑mail:[email protected]†Theseauthorscontributedequallytothiswork.
DOI: 10.1038/msb.2013.65Molecular Systems Biology (2013) 9:712
Mammalian cells can generate ATP via glycolysis or mitochondrial respiration. Oncogene activation and hypoxia promote glycolysis and lactate secretion. The significance of these metabolic changes to ATP production remains however ill defined. Here, we integrate LC‑MS‑based isotope tracer studies with oxygen uptake measurements in a quantitative redox‑balanced metabolic flux model of mammalian cellular metabolism. We then apply this approach to assess the impact of Ras and Akt activation and hypoxia on energy metabolism. Both oncogene activa‑tion and hypoxia induce roughly a twofold increase in glycolytic flux. Ras activation and hypoxia also strongly decrease glucose oxidation. Oxidative phosphorylation, powered substantially by glutamine‑driven TCA turning, however, persists and accounts for the majority of ATP production. Consistent with this, in all cases, pharmaco‑logical inhibition of oxidative phosphorylation markedly reduces energy charge, and glutamine but not glucose removal markedly lowers oxygen uptake. Thus, glutamine‑driven oxidative phosphorylation is a major means of ATP production even in hypoxic cancer cells.
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Glucose
Lactate
ATP
Glutamine
NADH
NADH
O2
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