Posts Tagged ‘hypoxia’

Neurovascular pathways to neurodegeneration

Larry H. Bernstein, MD, FCAP, Curator




In addition to the many cellular insults which may contribute to neurodegeneration, there is also a wealth of evidence which suggests that dysfunction of the blood-brain barrier and other CNS vascular insults may also play a key role in Alzheimer’s Disease pathogenesis. This review from Berislav Zlokovic describes much of the recent work into understand how BBB dysfunction contributes to neurodegeneration.    By Tim Spencer on 24 Nov, 2015


Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders.

Berislav V. Zlokovic    About the author

Nat Rev Neurosci. 2011 Nov 3;12(12):723-38.


The neurovascular unit (NVU) comprises brain endothelial cells, pericytes or vascular smooth muscle cells, glia and neurons. The NVU controls blood–brain barrier (BBB) permeability and cerebral blood flow, and maintains the chemical composition of the neuronal ‘milieu’, which is required for proper functioning of neuronal circuits. Recent evidence indicates that BBB dysfunction is associated with the accumulation of several vasculotoxic and neurotoxic molecules within brain parenchyma, a reduction in cerebral blood flow, and hypoxia. Together, these vascular-derived insults might initiate and/or contribute to neuronal degeneration. This article examines mechanisms of BBB dysfunction in neurodegenerative disorders, notably Alzheimer’s disease, and highlights therapeutic opportunities relating to these neurovascular deficits.



Neurons depend on blood vessels for their oxygen and nutrient supplies, and for the removal of carbon dioxide and other potentially toxic metabolites from the brain’s interstitial fluid (ISF). The importance of the circulatory system to the human brain is highlighted by the fact that although the brain comprises ~2% of total body mass, it receives up to 20% of cardiac output and is responsible for ~20% and ~25% of the body’s oxygen consumption and glucose consumption, respectively1. To underline this point, when cerebral blood flow (CBF) stops, brain functions end within seconds and damage to neurons occurs within minutes2.

Neurodegenerative disorders such as Alzheimer’s disease and amyotrophic lateral sclerosis (ALS) are associated with microvascular dysfunction and/or degeneration in the brain, neurovascular disintegration, defective blood–brain barrier (BBB) function and/or vascular factors1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. Microvascular deficits diminish CBF and, consequently, the brain’s supply of oxygen, energy substrates and nutrients. Moreover, such deficits impair the clearance of neurotoxic molecules that accumulate and/or are deposited in the ISF, non-neuronal cells and neurons. Recent evidence suggests that vascular dysfunction leads to neuronal dysfunction and neurodegeneration, and that it might contribute to the development of proteinaceous brain and cerebrovascular ‘storage’ disorders. Such disorders include cerebral β-amyloidosis and cerebral amyloid angiopathy (CAA), which are caused by accumulation of the peptide amyloid-β in the brain and the vessel wall, respectively, and are features of Alzheimer’s disease1.

In this Review, I will discuss neurovascular pathways to neurodegeneration, placing a focus on Alzheimer’s disease because more is known about neurovascular dysfunction in this disease than in other neurodegenerative disorders. The article first examines transport mechanisms for molecules to cross the BBB, before exploring the processes that are involved in BBB breakdown at the molecular and cellular levels, and the consequences of BBB breakdown, hypoperfusion, and hypoxia and endothelial metabolic dysfunction for neuronal function. Next, the article reviews evidence for neurovascular changes during normal ageing and neurovascular BBB dysfunction in various neurodegenerative diseases, including evidence suggesting that vascular defects precede neuronal changes. Finally, the article considers specific mechanisms that are associated with BBB dysfunction in Alzheimer’s disease and ALS, and therapeutic opportunities relating to these neurovascular deficits.

The neurovascular unit

The neurovascular unit (NVU) comprises vascular cells (that is, endothelium, pericytes and vascular smooth muscle cells (VSMCs)), glial cells (that is, astrocytes, microglia and oliogodendroglia) and neurons1, 2, 13 (Fig. 1). In the NVU, the endothelial cells together form a highly specialized membrane around blood vessels. This membrane underlies the BBB and limits the entry of plasma components, red blood cells (RBCs) and leukocytes into the brain. The BBB also regulates the delivery into the CNS of circulating energy metabolites and essential nutrients that are required for proper neuronal and synaptic function. Non-neuronal cells and neurons act in concert to control BBB permeability and CBF. Vascular cells and glia are primarily responsible for maintenance of the constant ‘chemical’ composition of the ISF, and the BBB and the blood–spinal cord barrier (BSCB) work together with pericytes to prevent various potentially neurotoxic and vasculotoxic macromolecules in the blood from entering the CNS, and to promote clearance of these substances from the CNS1.

Figure 1 | Cerebral microcirculation and the neurovascular unit.

Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders

In the brain, pial arteries run through the subarachnoid space (SAS), which contains the cerebrospinal fluid (CSF). These vessels give rise to intracerebral arteries, which penetrate into brain parenchyma. Intracerebral arteries are separated from brain parenchyma by a single, interrupted layer of elongated fibroblast-like cells of the pia and the astrocyte-derived glia limitans membrane that forms the outer wall of the perivascular Virchow–Robin space. These arteries branch into smaller arteries and subsequently arterioles, which lose support from the glia limitans and give rise to pre-capillary arterioles and brain capillaries. In an intracerebral artery, the vascular smooth muscle cell (VSMC) layer occupies most of the vessel wall. At the brain capillary level, vascular endothelial cells and pericytes are attached to the basement membrane. Pericyte processes encase most of the capillary wall, and they communicate with endothelial cells directly through synapse-like contacts containing connexins and N-cadherin. Astrocyte end-foot processes encase the capillary wall, which is composed of endothelium and pericytes. Resting microglia have a ‘ramified’ shape and can sense neuronal injury.

Transport across the blood–brain barrier. The endothelial cells that form the BBB are connected by tight and adherens junctions, and it is the tight junctions that confer the low paracellular permeability of the BBB1. Small lipophilic molecules, oxygen and carbon dioxide diffuse freely across the endothelial cells, and hence the BBB, but normal brain endothelium lacks fenestrae and has limited vesicular transport.

The high number of mitochondria in endothelial cells reflects a high energy demand for active ATP-dependent transport, conferred by transporters such as the sodium pump ((Na++K+)ATPase) and the ATP-binding cassette (ABC) efflux transporters. Sodium influx and potassium efflux across the abluminal side of the BBB is controlled by (Na++K+)ATPase (Fig. 2). Changes in sodium and potassium levels in the ISF influence the generation of action potentials in neurons and thus directly affect neuronal and synaptic functions1, 12.

Figure 2 | Blood–brain barrier transport mechanisms.

Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders

Small lipophilic drugs, oxygen and carbon dioxide diffuse across the blood–brain barrier (BBB), whereas ions require ATP-dependent transporters such as the (Na++K+)ATPase. Transporters for nutrients include the glucose transporter 1 (GLUT1; also known as solute carrier family 2, facilitated glucose transporter member 1 (SLC2A1)), the lactate transporter monocarboxylate transporter 1 (MCT1) and the L1 and y+ transporters for large neutral and cationic essential amino acids, respectively. These four transporters are expressed at both the luminal and albuminal membranes. Non-essential amino acid transporters (the alanine, serine and cysteine preferring system (ASC), and the alanine preferring system (A)) and excitatory amino acid transporter 1 (EAAT1), EAAT2 and EAAT3 are located at the abluminal side. The ATP-binding cassette (ABC) efflux transporters that are found in the endothelial cells include multidrug resistance protein 1 (ABCB1; also known as ATP-binding cassette subfamily B member 1) and solute carrier organic anion transporter family member 1C1 (OATP1C1). Finally, transporters for peptides or proteins include the endothelial protein C receptor (EPCR) for activated protein C (APC); the insulin receptors (IRs) and the transferrin receptors (TFRs), which are associated with caveolin 1 (CAV1); low-density lipoprotein receptor-related protein 1 (LRP1) for amyloid-β, peptide transport system 1 (PTS1) for encephalins; and the PTS2 and PTS4–vasopressin V1a receptor (V1AR) for arginine vasopressin.

Brain endothelial cells express transporters that facilitate the transport of nutrients down their concentration gradients, as described in detail elsewhere1, 14 (Fig. 2). Glucose transporter 1 (GLUT1; also known as solute carrier family 2, facilitated glucose transporter member 1 (SLC2A1)) — the BBB-specific glucose transporter — is of special importance because glucose is a key energy source for the brain.

Monocarboxylate transporter 1 (MCT1), which transports lactate, and the L1 and y+ amino acid transporters are expressed at the luminal and abluminal membranes12, 14. Sodium-dependent excitatory amino acid transporter 1 (EAAT1), EAAT2 and EAAT3 are expressed at the abluminal side of the BBB15 and enable removal of glutamate, an excitatory neurotransmitter, from the brain (Fig. 2). Glutamate clearance at the BBB is essential for protecting neurons from overstimulation of glutaminergic receptors, which is neurotoxic16.

ABC transporters limit the penetration of many drugs into the brain17. For example, multidrug resistance protein 1 (ABCB1; also known as ATP-binding cassette subfamily B member 1) controls the rapid removal of ingested toxic lipophilic metabolites17 (Fig. 2). Some ABC transporters also mediate the efflux of nutrients from the endothelium into the ISF. For example, solute carrier organic anion transporter family member 1C1 (OATP1C1) transports thyroid hormones into the brain. MCT8 mediates influx of thyroid hormones from blood into the endothelium18 (Fig. 2).

The transport of circulating peptides across the BBB into the brain is restricted or slow compared with the transport of nutrients19. Carrier-mediated transport of neuroactive peptides controls their low levels in the ISF20, 21, 22, 23, 24 (Fig. 2). Some proteins, including transferrin, insulin, insulin-like growth factor 1 (IGF1), leptin25, 26, 27 and activated protein C (APC)28, cross the BBB by receptor-mediated transcytosis (Fig. 2).

Circumventricular organs. Several small neuronal structures that surround brain ventricles lack the BBB and sense chemical changes in blood or the cerebrospinal fluid (CSF) directly. These brain areas are known as circumventricular organs (CVOs). CVOs have important roles in multiple endocrine and autonomic functions, including the control of feeding behaviour as well as regulation of water and salt metabolism29. For example, the subfornical organ is one of the CVOs that are capable of sensing extracellular sodium using astrocyte-derived lactate as a signal for local neurons to initiate neural, hormonal and behavioural responses underlying sodium homeostasis30. Excessive sodium accumulation is detrimental, and increases in plasma sodium above a narrow range are incompatible with life, leading to cerebral oedema (swelling), seizures and death29.

Vascular-mediated pathophysiology

The key pathways of vascular dysfunction that are linked to neurodegenerative diseases include BBB breakdown, hypoperfusion–hypoxia and endothelial metabolic dysfunction (Fig. 3). This section examines processes that are involved in BBB breakdown at the molecular and cellular levels, and explores the consequences of all three pathways for neuronal function and viability.

Figure 3 | Vascular-mediated neuronal damage and neurodegeneration.

Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders

a | Blood–brain barrier (BBB) breakdown that is caused by pericyte detachment leads to leakage of serum proteins and focal microhaemorrhages, with extravasation of red blood cells (RBCs). RBCs release haemoglobin, which is a source of iron. In turn, this metal catalyses the formation of toxic reactive oxygen species (ROS) that mediate neuronal injury. Albumin promotes the development of vasogenic oedema, contributing to hypoperfusion and hypoxia of the nervous tissue, which aggravates neuronal injury. A defective BBB allows several potentially vasculotoxic and neurotoxic proteins (for example, thrombin, fibrin and plasmin) to enter the brain. b | Progressive reductions in cerebral blood flow (CBF) lead to increasing neuronal dysfunction. Mild hypoperfusion, oligaemia, leads to a decrease in protein synthesis, whereas more-severe reductions in CBF, leading to hypoxia, cause an array of detrimental effects.


Blood–brain barrier breakdown. Disruption to tight and adherens junctions, an increase in bulk-flow fluid transcytosis, and/or enzymatic degradation of the capillary basement membrane cause physical breakdown of the BBB.

The levels of many tight junction proteins, their adaptor molecules and adherens junction proteins decrease in Alzheimer’s disease and other diseases that cause dementia1, 9, ALS31, multiple sclerosis32 and various animal models of neurological disease8, 33. These decreases might be partly explained by the fact that vascular-associated matrix metalloproteinase (MMP) activity rises in many neurodegenerative disorders and after ischaemic CNS injury34, 35; tight junction proteins and basement membrane extracellular matrix proteins are substrates for these enzymes34. Lowered expression of messenger RNAs that encode several key tight junction proteins, however, has also been reported in some neurodegenerative disorders, such as ALS31.

Endothelial cell–pericyte interactions are crucial for the formation36, 37 and maintenance of the BBB33, 38. Pericyte deficiency can lead to a reduction in expression of certain tight junction proteins, including occludin, claudin 5 and ZO1 (Ref. 33), and to an increase in bulk-flow transcytosis across the BBB, causing BBB breakdown38. Both processes can lead to extravasation of multiple small and large circulating macromolecules (up to 500 kDa) into the brain parenchyma33, 38. Moreover, in mice, an age-dependent progressive loss of pericytes can lead to BBB disruption and microvasular degeneration and, subsequently, neuronal dysfunction, cognitive decline and neurodegenerative changes33. In their lysosomes, pericytes concentrate and degrade multiple circulating exogenous39and endogenous proteins, including serum immunoglobulins and fibrin33, which amplify BBB breakdown in cases of pericyte deficiency.

BBB breakdown typically leads to an accumulation of various molecules in the brain. The build up of serum proteins such as immunoglobulins and albumin can cause brain oedema and suppression of capillary blood flow8, 33, whereas high concentrations of thrombin lead to neurotoxicity and memory impairment40, and accelerate vascular damage and BBB disruption41. The accumulation of plasmin (derived from circulating plasminogen) can catalyse the degradation of neuronal laminin and, hence, promote neuronal injury42, and high fibrin levels accelerate neurovascular damage6. Finally, an increase in the number of RBCs causes deposition of haemoglobin-derived neurotoxic products including iron, which generates neurotoxic reactive oxygen species (ROS)8, 43 (Fig. 3a). In addition to protein-mediated vasogenic oedema, local tissue ischaemia–hypoxia depletes ATP stores, causing (Na++K+)ATPase pumps and Na+-dependent ion channels to stop working and, consequently, the endothelium and astrocytes to swell (known as cytotoxic oedema)44. Upregulation of aquaporin 4 water channels in response to ischaemia facilitates the development of cytotoxic oedema in astrocytes45.

Hypoperfusion and hypoxia. CBF is regulated by local neuronal activity and metabolism, known as neurovascular coupling46. The pial and intracerebral arteries control the local increase in CBF that occurs during brain activation, which is termed ‘functional hyperaemia’. Neurovascular coupling requires intact pial circulation, and for VSMCs and pericytes to respond normally to vasoactive stimuli33, 46, 47. In addition to VSMC-mediated constriction and vasodilation of cerebral arteries, recent studies have shown that pericytes modulate brain capillary diameter through constriction of the vessel wall47, which obstructs capillary flow during ischaemia48. Astrocytes regulate the contractility of intracerebral arteries49, 50.

Progressive CBF reductions have increasingly serious consequences for neurons (Fig. 3b). Briefly, mild hypoperfusion — termed oligaemia — affects protein synthesis, which is required for the synaptic plasticity mediating learning and memory46. Moderate to severe CBF reductions and hypoxia affect ATP synthesis, diminishing (Na++K+)ATPase activity and the ability of neurons to generate action potentials9. In addition, such reductions can lower or increase pH, and alter electrolyte balances and water gradients, leading to the development of oedema and white matter lesions, and the accumulation of glutamate and proteinaceous toxins (for example, amyloid-β and hyperphopshorylated tau) in the brain. A reduction of greater than 80% in CBF results in neuronal death2.

The effect of CBF reductions has been extensively studied at the molecular and cellular levels in relation to Alzheimer’s disease. Reduced CBF and/or CBF dysregulation occurs in elderly individuals at high risk of Alzheimer’s disease before cognitive decline, brain atrophy and amyloid-β accumulation10, 46, 51, 52, 53, 54. In animal models, hypoperfusion can induce or amplify Alzheimer’s disease-like neuronal dysfunction and/or neuropathological changes. For example, bilateral carotid occlusion in rats causes memory impairment, neuronal dysfunction, synaptic changes and amyloid-β oligomerization55, leading to accumulation of neurotoxic amyloid-β oligomers56. In a mouse model of Alzheimer’s disease, oligaemia increases neuronal amyloid-β levels and neuronal tau phosphophorylation at an epitope that is associated with Alzheimer’s disease-type paired helical filaments57. In rodents, ischaemia leads to the accumulation of hyperphosphorylated tau in neurons and the formation of filaments that resemble those present in human neurodegenerative tauopathies and Alzheimer’s disease58. Mice expressing amyloid-β precursor protein (APP) and transforming growth factor β1 (TGFβ1) develop deficient neurovascular coupling, cholinergic denervation, enhanced cerebral and cerebrovascular amyloid-β deposition, and age-dependent cognitive decline59.

Recent studies have shown that ischaemia–hypoxia influences amyloidogenic APP processing through mechanisms that increase the activity of two key enzymes that are necessary for amyloid-β production; that is, β-secretase and γ-secretase60, 61, 62, 63. Hypoxia-inducible factor 1α (HIF1α) mediates transcriptional increase in β-secretase expression61. Hypoxia also promotes phosphorylation of tau through the mitogen-activated protein kinase (MAPK; also known as extracellular signal-regulated kinase (ERK)) pathway64, downregulates neprilysin — an amyloid-β-degrading enzyme65 — and leads to alterations in the expression of vascular-specific genes, including a reduction in the expression of the homeobox protein MOX2 gene mesenchyme homeobox 2 (MEOX2) in brain endothelial cells5 and an increase in the expression of the myocardin gene (MYOCD) in VSMCs66. In patients with Alzheimer’s disease and in models of this disorder, these changes cause vessel regression, hypoperfusion and amyloid-β accumulation resulting from the loss of the key amyloid-β clearance lipoprotein receptor (see below). In addition, hypoxia facilitates alternative splicing of Eaat2 mRNA in Alzheimer’s disease transgenic mice before amyloid-β deposition67 and suppresses glutamate reuptake by astrocytes independently of amyloid formation68, resulting in glutamate-mediated neuronal injury that is independent of amyloid-β.

In response to hypoxia, mitochondria release ROS that mediate oxidative damage to the vascular endothelium and to the selective population of neurons that has high metabolic activity. Such damage has been suggested to occur before neuronal degeneration and amyloid-β deposition in Alzheimer’s disease69, 70. Although the exact triggers of hypoxia-mediated neurodegeneration and the role of HIF1α in neurodegeneration versus preconditioning-mediated neuroprotection remain topics of debate, mitochondria-generated ROS seem to have a primary role in the regulation of the HIF1α-mediated transcriptional switch that can activate an array of responses, ranging from mechanisms that increase cell survival and adaptation to mechanisms inducing cell cycle arrest and death71. Whether inhibition of hypoxia-mediated pathogenic pathways will delay onset and/or control progression in neurodegenerative conditions such as Alzheimer’s disease remains to be determined.

When comparing the contributions of BBB breakdown and hypoperfusion to neuronal injury, it is interesting to consider Meox2+/− mice. Such animals have normal pericyte coverage and an intact BBB but a substantial perfusion deficit5 that is comparable to that found in pericyte-deficient mice that develop BBB breakdown33 Notably, however,Meox2+/− mice show less pronounced neurodegenerative changes than pericyte-deficient mice, indicating that chronic hypoperfusion–hypoxia alone can cause neuronal injury, but not to the same extent as hypoperfusion–hypoxia combined with BBB breakdown.

Endothelial neurotoxic and inflammatory factors. Alterations in cerebrovascular metabolic functions can lead to the secretion of multiple neurotoxic and inflammatory factors72, 73. For example, brain microvessels that have been isolated from individuals with Alzheimer’s disease (but not from neurologically normal age-matched and young individuals) and brain microvessels that have been treated with inflammatory proteins release neurotoxic factors that kill neurons74, 75. These factors include thrombin, the levels of which increase with the onset of Alzheimer’s disease76. Thrombin can injure neurons directly40 and indirectly by activating microglia and astrocytes73. Compared with those from age-matched controls, brain microvessels from individuals with Alzheimer’s disease secrete increased levels of multiple inflammatory mediators, such as nitric oxide, cytokines (for example, tumour necrosis factor (TNF), TGFβ1, interleukin-1β (IL-1β) and IL-6), chemokines (for example, CC-chemokine ligand 2 (CCL2; also known as monocyte chemoattractant protein 1 (MCP1)) and IL-8), prostaglandins, MMPs and leukocyte adhesion molecules73. Endothelium-derived neurotoxic and inflammatory factors together provide a molecular link between vascular metabolic dysfunction, neuronal injury and inflammation in Alzheimer’s disease and, possibly, in other neurodegenerative disorders.

Neurovascular changes

This section examines evidence for neurovascular changes during normal ageing and for neurovascular and/or BBB dysfunction in various neurodegenerative diseases, as well as the possibility that vascular defects can precede neuronal changes.

Age-associated neurovascular changes. Normal ageing diminishes brain circulatory functions, including a detectable decay of CBF in the limbic and association cortices that has been suggested to underlie age-related cognitive changes77. Alterations in the cerebral microvasculature, but not changes in neural activity, have been shown to lead to age-dependent reductions in functional hyperaemia in the visual system in cats78 and in the sensorimotor cortex in pericyte-deficient mice33. Importantly, a recent longitudinal CBF study in neurologically normal individuals revealed that people bearing the apolipoprotein E (APOE) ɛ4 allele — the major genetic risk factor for late-onset Alzheimer’s disease79, 80, 81 — showed greater regional CBF decline in brain regions that are particularly vulnerable to pathological changes in Alzheimer’s disease than did people without this allele82.

A meta-analysis of BBB permeability in 1,953 individuals showed that neurologically healthy humans had an age-dependent increase in vascular permeability83. Moreover, patients with vascular or Alzheimer’s disease-type dementia and leucoaraiosis — a small-vessel disease of the cerebral white matter — had an even greater age-dependent increase in vascular permeability83. Interestingly, an increase in BBB permeability in brain areas with normal white matter in patients with leukoaraiosis has been suggested to play a causal part in disease and the development of lacunar strokes84. Age-related changes in the permeability of the blood–CSF barrier and the choroid plexus have been reported in sheep85.

Vascular pathology. Patients with Alzheimer’s disease or other dementia-causing diseases frequently show focal changes in brain microcirculation. These changes include the appearance of string vessels (collapsed and acellular membrane tubes), a reduction in capillary density, a rise in endothelial pinocytosis, a decrease in mitochondrial content, accumulation of collagen and perlecans in the basement membrane, loss of tight junctions and/or adherens junctions3, 4, 5, 6, 9, 46, 86, and BBB breakdown with leakage of blood-borne molecules4, 6, 7, 9. The time course of these vascular alterations and how they relate to dementia and Alzheimer’s disease pathology remain unclear, as no protocol that allows the development of the diverse brain vascular pathology to be scored, and hence to be tracked with ageing, has so far been developed and widely validated87. Interestingly, a recent study involving 500 individuals who died between the ages of 69 and 103 years showed that small-vessel disease, infarcts and the presence of more than one vascular pathological change were associated with Alzheimer’s disease-type pathological lesions and dementia in people aged 75 years of age87. These associations were, however, less pronounced in individuals aged 95 years of age, mainly because of a marked ageing-related reduction in Alzheimer’s disease neuropathology relative to a moderate but insignificant ageing-related reduction in vascular pathology87.

Accumulation of amyloid-β and amyloid deposition in pial and intracerebral arteries results in CAA, which is present in over 80% of Alzheimer’s disease cases88. In patients who have Alzheimer’s disease with established CAA in small arteries and arterioles, the VSMC layer frequently shows atrophy, which causes a rupture of the vessel wall and intracerebral bleeding in about 30% of these patients89, 90. These intracerebral bleedings contribute to, and aggravate, dementia. Patients with hereditary cerebral β-amyloidosis and CAA of the Dutch, Iowa, Arctic, Flemish, Italian or Piedmont L34V type have accelerated VSMC degeneration resulting in haemorrhagic strokes and dementia91. Duplication of the gene encoding APP causes early-onset Alzheimer’s disease dementia with CAA and intracerebral haemorrhage92.

Early studies of serum immunoglobulin leakage reported that patients with ALS had BSCB breakdown and BBB breakdown in the motor cortex93. Microhaemorrhages and BSCB breakdown have been shown in the spinal cord of transgenic mice expressing mutant variants of human superoxide dismutase 1 (SOD1), which in mice cause an ALS-like disease8, 94, 95. In mice with ALS-like disease and in patients with ALS, BSCB breakdown has been shown to occur before motor neuron degeneration or brain atrophy8, 11, 95.

BBB breakdown in the substantia nigra and the striatum has been detected in murine models of Parkinson’s disease that are induced by administration of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)96, 97, 98. However, the temporal relationship between BBB breakdown and neurodegeneration in Parkinson’s disease is currently unknown. Notably, the prevalence of CAA and vascular lesions increases in Parkinson’s disease99,100. Vascular lesions in the striatum and lacunar infarcts can cause vascular parkinsonism syndrome101. A recent study reported BBB breakdown in a rat model of Huntington’s disease that is induced with the toxin 3-nitropropionic acid102.

Several studies have established disruption of BBB with a loss of tight junction proteins during neuroinflammatory conditions such as multiple sclerosis and its murine model, experimental allergic encephalitis. Such disruption facilitates leukocyte infiltration, leading to oliogodendrocyte death, axonal damage, demyelination and lesion development32.

Functional changes in the vasculature. In individuals with Alzheimer’s disease, GLUT1 expression at the BBB decreases103, suggesting a shortage in necessary metabolic substrates. Studies using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) have identified reductions in glucose uptake in asymptomatic individuals with a high risk of dementia104, 105. Several studies have suggested that reduced glucose uptake across the BBB, as seen by FDG PET, precedes brain atrophy104, 105, 106, 107, 108.

Amyloid-β constricts cerebral arteries109. In a mouse model of Alzheimer’s disease, impairment of endothelium-dependent regulation of neocortical microcirculation110, 111occurs before amyloid-β accumulation. Recent studies have shown that CD36, a scavenger receptor that binds amyloid-β, is essential for the vascular oxidative stress and diminished functional hyperaemia that occurs in response to amyloid-β exposure112. Neuroimaging studies in patients with Alzheimer’s disease have shown that neurovascular uncoupling occurs before neurodegenerative changes10, 51, 52, 53. Moreover, cognitively normal APOE ɛ4 carriers at risk of Alzheimer’s disease show impaired CBF responses to brain activation in the absence of neurodegenerative changes or amyloid-β accumulation54. Recently, patients with Alzheimer’s disease as well as mouse models of this disease with high cerebrovascular levels of serum response factor (SRF) and MYOCD, the two transcription factors that control VSMC differentiation, have been shown to develop a hypercontractile arterial phenotype resulting in brain hypoperfusion, diminished functional hyperaemia and CAA66, 113. More work is needed to establish the exact role of SRF and MYOCD in the vascular dysfunction that results in the Alzheimer’s disease phenotype and CAA.

PET studies with 11C-verapamil, an ABCB1 substrate, have indicated that the function of ABCB1, which removes multiple drugs and toxins from the brain, decreases with ageing114 and is particularly compromised in the midbrain of patients with Parkinson’s disease, progressive supranuclear palsy or multiple system atrophy115. More work is needed to establish the exact roles of ABC BBB transporters in neurodegeneration and whether their failure precedes the loss of dopaminergic neurons that occurs in Parkinson’s disease.

In mice with ALS-like disease and in patients with ALS, hypoperfusion and/or dysregulated CBF have been shown to occur before motor neuron degeneration or brain atrophy8, 116. Reduced regional CBF in basal ganglia and reduced blood volume have been reported in pre-symptomatic gene-tested individuals at risk for Huntington’s disease117. Patients with Huntington’s disease display a reduction in vasomotor activity in the cerebral anterior artery during motor activation118.

Vascular and neuronal common growth factors. Blood vessels and neurons share common growth factors and molecular pathways that regulate their development and maintenance119, 120. Angioneurins are growth factors that exert both vasculotrophic and neurotrophic activities121. The best studied angioneurin is vascular endothelial growth factor (VEGF). VEGF regulates vessel formation, axonal growth and neuronal survival120. Ephrins, semaphorins, slits and netrins are axon guidance factors that also regulate the development of the vascular system121. During embryonic development of the neural tube, blood vessels and choroid plexus secrete IGF2 into the CSF, which regulates the proliferation of neuronal progenitor cells122. Genetic and pharmacological manipulations of angioneurin activity yielded various vascular and cerebral phenotypes121. Given the dual nature of angioneurin action, these studies have not been able to address whether neuronal dysfunction results from a primary insult to neurons and/or whether it is secondary to vascular dysfunction.

Increased levels of VEGF, a hypoxia-inducible angiogenic factor, were found in the walls of intraparenchymal vessels, perivascular deposits, astrocytes and intrathecal space of patients with Alzheimer’s disease, and were consistent with the chronic cerebral hypoperfusion and hypoxia that were observed in these individuals73. In addition to VEGF, brain microvessels in Alzheimer’s disease release several molecules that can influence angiogenesis, including IL-1β, IL-6, IL-8, TNF, TGFβ, MCP1, thrombin, angiopoietin 2, αVβ3 and αVβ5 integrins, and HIF1α73. However, evidence for increased vascularity in Alzheimer’s disease is lacking. On the contrary, several studies have reported that focal vascular regression and diminished microvascular density occur in Alzheimer’s disease4, 5, 73 and in Alzheimer’s disease transgenic mice123. The reason for this discrepancy is not clear. The anti-angiogenic activity of amyloid-β, which accumulates in the brains of individuals with Alzheimer’s disease and Alzheimer’s disease models, may contribute to hypovascularity123. Conversely, genome-wide transcriptional profiling of brain endothelial cells from patients with Alzheimer’s disease revealed that extremely low expression of vascular-restricted MEOX2 mediates aberrant angiogenic responses to VEGF and hypoxia, leading to capillary death5. This finding raises the interesting question of whether capillary degeneration in Alzheimer’s disease results from unsuccessful vascular repair and/or remodelling. Moreover, mice that lack one Meox2allele have been shown to develop a primary cerebral endothelial hypoplasia with chronic brain hypoperfusion5, resulting in secondary neurodegenerative changes33.

Does vascular dysfunction cause neuronal dysfunction? In summary, the evidence that is discussed above clearly indicates that vascular dysfunction is tightly linked to neuronal dysfunction. There are many examples to illustrate that primary vascular deficits lead to secondary neurodegeneration, including CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts), an hereditary small-vessel brain disease resulting in multiple small ischaemic lesions, neurodegeneration and dementia124; mutations in SLC2A1 that cause dysfunction of the BBB-specific GLUT1 transporter in humans resulting in seizures; cognitive impairment and microcephaly125; microcephaly and epileptiform discharges in mice with genetic deletion of a single Slc2a1 allele126; and neurodegeneration mediated by a single Meox2 homebox gene deletion restricted to the vascular system33. Patients with hereditary cerebral β-amyloidosis and CAA of the Dutch, Iowa, Arctic, Flemish, Italian or Piedmont L34V type provide another example showing that primary vascular dysfunction — which in this case is caused by deposition of vasculotropic amyloid-β mutants in the arterial vessel wall — leads to dementia and intracerebral bleeding. Moreover, as reviewed in the previous sections, recent evidence suggests that BBB dysfunction and/or breakdown, and CBF reductions and/or dysregulation may occur in sporadic Alzheimer’s disease and experimental models of this disease before cognitive decline, amyloid-β deposition and brain atrophy. In patients with ALS and in some experimental models of ALS, CBF dysregulation, BSCB breakdown and spinal cord hypoperfusion have been reported to occur before motor neuron cell death. Whether neurological changes follow or precede vascular dysfunction in Parkinson’s disease, Huntington’s disease and multiple sclerosis remains less clear. However, there is little doubt that vascular injury mediates, amplifies and/or lowers the threshold for neuronal dysfunction and loss in several neurological disorders.

Disease-specific considerations

This section examines how amyloid-β levels are kept low in the brain, a process in which the BBB has a central role, and how faulty BBB-mediated clearance mechanisms go awry in Alzheimer’s disease. On the basis of this evidence and the findings discussed elsewhere in the Review, a new hypothesis for the pathogenesis of Alzheimer’s disease that incorporates the vascular evidence is presented. ALS-specific disease mechanisms relating to the BBB are then examined.

Alzheimer’s disease. Amyloid-β clearance from the brain by the BBB is the best studied example of clearance of a proteinaceous toxin from the CNS. Multiple pathways regulate brain amyloid-β levels, including its production and clearance (Fig. 4). Recent studies127,128, 129 have confirmed earlier findings in multiple rodent and non-human primate models demonstrating that peripheral amyloid-β is an important precursor of brain amyloid-β130, 131, 132, 133, 134, 135, 136. Moreover, peripheral amyloid-β sequestering agents such as soluble LRP1 (ref.137), anti-amyloid-β antibodies138, 139,140, gelsolin and the ganglioside GM1 (Ref. 141), or systemic expression of neprilysin142, 143 have been shown to reduce the amyloid burden in Alzheimer’s disease mice by eliminating contributions of the peripheral amyloid-β pool to the total brain pool of this peptide.

Figure 4 | The role of blood–brain barrier transport in brain homeostasis of amyloid-β.

Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders

Amyloid-β (Aβ) is produced from the amyloid-β precursor protein (APP), both in the brain and in peripheral tissues. Clearance of amyloid-β from the brain normally maintains its low levels in the brain. This peptide is cleared across the blood–brain barrier (BBB) by the low-density lipoprotein receptor-related protein 1 (LRP1). LRP1 mediates rapid efflux of a free, unbound form of amyloid-β and of amyloid-β bound to apolipoprotein E2 (APOE2), APOE3 or α2-macroglobulin (not shown) from the brain’s interstitial fluid into the blood, and APOE4 inhibits such transport. LRP2 eliminates amyloid-β that is bound to clusterin (CLU; also known as apolipoprotein J (APOJ)) by transport across the BBB, and shows a preference for the 42-amino-acid form of this peptide. ATP-binding cassette subfamily A member 1 (ABCA1; also known as cholesterol efflux regulatory protein) mediates amyloid-β efflux from the brain endothelium to blood across the luminal side of the BBB (not shown). Cerebral endothelial cells, pericytes, vascular smooth muscle cells, astrocytes, microglia and neurons express different amyloid-β-degrading enzymes, including neprilysin (NEP), insulin-degrading enzyme (IDE), tissue plasminogen activator (tPA) and matrix metalloproteinases (MMPs), which contribute to amyloid-β clearance. In the circulation, amyloid-β is bound mainly to soluble LRP1 (sLRP1), which normally prevents its entry into the brain. Systemic clearance of amyloid-β is mediated by its removal by the liver and kidneys. The receptor for advanced glycation end products (RAGE) provides the key mechanism for influx of peripheral amyloid-β into the brain across the BBB either as a free, unbound plasma-derived peptide and/or by amyloid-β-laden monocytes. Faulty vascular clearance of amyloid-β from the brain and/or an increased re-entry of peripheral amyloid-β across the blood vessels into the brain can elevate amyloid-β levels in the brain parenchyma and around cerebral blood vessels. At pathophysiological concentrations, amyloid-β forms neurotoxic oligomers and also self-aggregates, which leads to the development of cerebral β-amyloidosis and cerebral amyloid angiopathy.

The receptor for advanced glycation end products (RAGE) mediates amyloid-β transport in brain and the propagation of its toxicity. RAGE expression in brain endothelium provides a mechanism for influx of amyloid-β144, 145 and amyloid-β-laden monocytes146 across the BBB, as shown in Alzheimer’s disease models (Fig. 4). The amyloid-β-rich environment in Alzheimer’s disease and models of this disorder increases RAGE expression at the BBB and in neurons147, 148, amplifying amyloid-β-mediated pathogenic responses. Blockade of amyloid-β–RAGE signalling in Alzheimer’s disease is a promising strategy to control self-propagation of amyloid-β-mediated injury.

Several studies in animal models of Alzheimer’s disease and, more recently, in patients with this disorder149 have shown that diminished amyloid-β clearance occurs in brain tissue in this disease. LRP1 plays an important part in the three-step serial clearance of this peptide from brain and the rest of the body150 (Fig. 4). In step one, LRP1 in brain endothelium binds brain-derived amyloid-β at the abluminal side of the BBB, initiating its clearance to blood, as shown in many animal models151, 152, 153, 154, 155, 156 and BBB models in vitro151, 157, 158. The vasculotropic mutants of amyloid-β that have low binding affinity for LRP1 are poorly cleared from the brain or CSF151, 159, 160. APOE4, but not APOE3 or APOE2, blocks LRP1-mediated amyloid-β clearance from the brain and, hence, promotes its retention161, whereas clusterin (also known as apolipoprotein J (APOJ)) mediates amyloid-β clearance across the BBB via LRP2 (Ref. 153). APOE and clusterin influence amyloid-β aggregation162, 163. Reduced LRP1 levels in brain microvessels, perhaps in addition to altered levels of ABCB1, are associated with amyloid-β cerebrovascular and brain accumulation during ageing in rodents, non-human primates, humans, Alzheimer’s disease mice and patients with Alzheimer’s disease66,151, 152, 164, 165, 166. Moreover, recent work has shown that brain LRP1 is oxidized in Alzheimer’s disease167, and may contribute to amyloid-β retention in brain because the oxidized form cannot bind and/or transport amyloid-β137. LRP1 also mediates the removal of amyloid-β from the choroid plexus168.

In step two, circulating soluble LRP1 binds more than 70% of plasma amyloid-β in neurologically normal humans137. In patients with Alzheimer’s disease or mild cognitive impairment (MCI), and in Alzheimer’s disease mice, amyloid-β binding to soluble LRP1 is compromised due to oxidative changes137, 169, resulting in elevated plasma levels of free amyloid-β isoforms comprising 40 or 42 amino acids (amyloid-β1–40 and amyloid-β1–42). These peptides can then re-enter the brain, as has been shown in a mouse model of Alzheimer’s disease137. Rapid systemic removal of amyloid-β by the liver is also mediated by LRP1 and comprises step three of the clearance process170.

In brain, amyloid-β is enzymatically degraded by neprilysin171, insulin-degrading enzyme172, tissue plasminogen activator173 and MMPs173, 174 in various cell types, including endothelial cells, pericytes, astrocytes, neurons and microglia. Cellular clearance of this peptide by astrocytes and VSMCs is mediated by LRP1 and/or another lipoprotein receptor66, 175. Clearance of amyloid-β aggregates by microglia has an important role in amyloid-β-directed immunotherapy176 and reduction of the amyloid load in brain177. Passive ISF–CSF bulk flow and subsequent clearance through the CSF might contribute to 10–15% of total amyloid-β removal152, 153, 178. In the injured human brain, increasing soluble amyloid-β concentrations in the ISF correlated with improvements in neurological status, suggesting that neuronal activity might regulate extracellular amyloid-β levels179.

The role of BBB dysfunction in amyloid-β accumulation, as discussed above, underlies the contribution of vascular dysfunction to Alzheimer’s disease (see Fig. 5 for a model of vascular damage in Alzheimer’s disease). The amyloid hypothesis for the pathogenesis of Alzheimer’s disease maintains that this peptide initiates a cascade of events leading to neuronal injury and loss and, eventually, dementia180, 181. Here, I present an alternative hypothesis — the two-hit vascular hypothesis of Alzheimer’s disease — that incorporates the vascular contribution to this disease, as discussed in this Review (Box 1). This hypothesis states that primary damage to brain microcirculation (hit one) initiates a non-amyloidogenic pathway of vascular-mediated neuronal dysfunction and injury, which is mediated by BBB dysfunction and is associated with leakage and secretion of multiple neurotoxic molecules and/or diminished brain capillary flow that causes multiple focal ischaemic or hypoxic microinjuries. BBB dysfunction also leads to impairment of amyloid-β clearance, and oligaemia leads to increased amyloid-β generation. Both processes contribute to accumulation of amyloid-β species in the brain (hit two), where these peptides exert vasculotoxic and neurotoxic effects. According to the two-hit vascular hypothesis of Alzheimer’s disease, tau pathology develops secondary to vascular and/or amyloid-β injury.

Figure 5 | A model of vascular damage in Alzheimer’s disease.

Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders

a | In the early stages of Alzheimer’s disease, small pial and intracerebral arteries develop a hypercontractile phenotype that underlies dysregulated cerebral blood flow (CBF). This phenotype is accompanied by diminished amyloid-β clearance by the vascular smooth muscle cells (VSMCs). In the later phases of Alzheimer’s disease, amyloid deposition in the walls of intracerebral arteries leads to cerebral amyloid angiopathy (CAA), pronounced reductions in CBF, atrophy of the VSMC layer and rupture of the vessels causing microbleeds. b | At the level of capillaries in the early stages of Alzheimer’s disease, blood–brain barrier (BBB) dysfunction leads to a faulty amyloid-β clearance and accumulation of neurotoxic amyloid-β oligomers in the interstitial fluid (ISF), microhaemorrhages and accumulation of toxic blood-derived molecules (that is, thrombin and fibrin), which affect synaptic and neuronal function. Hyperphosphorylated tau (p-tau) accumulates in neurons in response to hypoperfusion and/or rising amyloid-β levels. At this point, microglia begin to sense neuronal injury. In the later stages of the disease in brain capillaries, microvascular degeneration leads to increased deposition of basement membrane proteins and perivascular amyloid. The deposited proteins and amyloid obstruct capillary blood flow, resulting in failure of the efflux pumps, accumulation of metabolic waste products, changes in pH and electrolyte composition and, subsequently, synaptic and neuronal dysfunction. Neurofibrillary tangles (NFTs) accumulate in response to ischaemic injury and rising amyloid-β levels. Activation of microglia and astrocytes is associated with a pronounced inflammatory response. ROS, reactive oxygen species.


Amyotrophic lateral sclerosis. The cause of sporadic ALS, a fatal adult-onset motor neuron neurodegenerative disease, is not known182. In a relatively small number of patients with inherited SOD1 mutations, the disease is caused by toxic properties of mutant SOD1 (Ref. 183). Mutations in the genes encoding ataxin 2 and TAR DNA-binding protein 43 (TDP43) that cause these proteins to aggregate have been associated with ALS182, 184. Some studies have suggested that abnormal SOD1 species accumulate in sporadic ALS185. Interestingly, studies in ALS transgenic mice expressing a mutant version of human SOD1 in neurons, and in non-neuronal cells neighbouring these neurons, have shown that deletion of this gene from neurons does not influence disease progression186, whereas deletion of this gene from microglia186 or astrocytes187substantially increases an animal’s lifespan. According to an emerging hypothesis of ALS that is based on studies in SOD1 mutant mice, the toxicity that is derived from non-neuronal neighbouring cells, particularly microglia and astrocytes, contributes to disease progression and motor neuron degeneration186, 187, 188, 189, 190, whereas BBB dysfunction might be critical for disease initiation8, 43, 94, 95. More work is needed to determine whether this concept of disease initiation and progression may also apply to cases of sporadic ALS.

Human data support a role for angiogenic factors and vessels in the pathogenesis of ALS. For example, the presence of VEGF variations (which were identified in large meta-analysis studies) has been linked to ALS191. Angiogenin is another pro-angiogenic gene that is implicated in ALS because heterozygous missense mutations in angiogenin cause familial and sporadic ALS192. Moreover, mice with a mutation that eliminates hypoxia-responsive induction of the Vegf gene (Vegfδ/δ mice) develop late-onset motor neuron degeneration193. Spinal cord ischaemia worsens motor neuron degeneration and functional outcome in Vegfδ/δ mice, whereas the absence of hypoxic induction of VEGF in mice that develop motor neuron disease from expression of ALS-linked mutant SOD1G93A results in substantially reduced survival191.

Therapeutic opportunities

Many investigators believe that primary neuronal dysfunction resulting from an intrinsic neuronal disorder is the key underlying event in human neurodegenerative diseases. Thus, most therapeutic efforts for neurodegenerative diseases have so far been directed at the development of so-called ‘single-target, single-action’ agents to target neuronal cells directly and reverse neuronal dysfunction and/or protect neurons from injurious insults. However, most preclinical and clinical studies have shown that such drugs are unable to cure or control human neurological disorders2, 181, 183, 194, 195. For example, although pathological overstimulation of glutaminergic NMDA receptors (NMDARs) has been shown to lead to neuronal injury and death in several disorders, including stroke, Alzheimer’s disease, ALS and Huntington’s disease16, NMDAR antagonists have failed to show a therapeutic benefit in the above-mentioned human neurological disorders.

Recently, my colleagues and I coined the term vasculo-neuronal-inflammatory triad195to indicate that vascular damage, neuronal injury and/or neurodegeneration, and neuroinflammation comprise a common pathological triad that occurs in multiple neurological disorders. In line with this idea, it is conceivable that ‘multiple-target, multiple-action’ agents (that is, drugs that have more than one target and thus have more than one action) will have a better chance of controlling the complex disease mechanisms that mediate neurodegeneration than agents that have only one target (for example, neurons). According to the vasculo-neuronal-inflammatory triad model, in addition to neurons, brain endothelium, VSMCs, pericytes, astrocytes and activated microglia are all important therapeutic targets.

Here, I will briefly discuss a few therapeutic strategies based on the vasculo-neuronal-inflammatory triad model. VEGF and other angioneurins may have multiple targets, and thus multiple actions, in the CNS120. For example, preclinical studies have shown that treatment of SOD1G93A rats with intracerebroventricular VEGF196 or intramuscular administration of a VEGF-expressing lentiviral vector that is transported retrogradely to motor neurons in SOD1G93A mice197 reduced pathology and extended survival, probably by promoting angiogenesis and increasing the blood flow through the spinal cord as well as through direct neuronal protective effects of VEGF on motor neurons. On the basis of these and other studies, a phase I–II clinical trial has been initiated to evaluate the safety of intracerebroventricular infusion of VEGF in patients with ALS198. Treatment with angiogenin also slowed down disease progression in a mouse model of ALS199.

IGF1 delivery has been shown to promote amyloid-β vascular clearance and to improve learning and memory in a mouse model of Alzheimer’s disease200. Local intracerebral implantation of VEGF-secreting cells in a mouse model of Alzheimer’s disease has been shown to enhance vascular repair, reduce amyloid burden and improve learning and memory201. In contrast to VEGF, which can increase BBB permeability, TGFβ, hepatocyte growth factor and fibroblast growth factor 2 promote BBB integrity by upregulating the expression of endothelial junction proteins121 in a similar way to APC43. However, VEGF and most growth factors do not cross the BBB, so the development of delivery strategies such as Trojan horses is required for their systemic use25.

A recent experimental approach with APC provides an example of a neurovascular medicine that has been shown to favourably regulate multiple pathways in non-neuronal cells and neurons, resulting in vasculoprotection, stabilization of the BBB, neuroprotection and anti-inflammation in several acute and chronic models of the CNS disorders195 (Box 2).

Box 2 | A model of multiple-target, multiple-action neurovascular medicine

The recognition of amyloid-β clearance pathways (Fig. 4), as discussed above, opens exciting new therapeutic opportunities for Alzheimer’s disease. Amyloid-β clearance pathways are promising therapeutic targets for the future development of neurovascular medicines because it has been shown both in animal models of Alzheimer’s disease1 and in patients with sporadic Alzheimer’s disease149 that faulty clearance from brain and across the BBB primarily determines amyloid-β retention in brain, causing the formation of neurotoxic amyloid-β oligomers56 and the promotion of brain and cerebrovascular amyloidosis3. The targeting of clearance mechanisms might also be beneficial in other diseases; for example, the clearance of extracellular mutant SOD1 in familial ALS, the prion protein in prion disorders and α-synuclein in Parkinson’s disease might all prove beneficial. However, the clearance mechanisms for these proteins in these diseases are not yet understood.

Conclusions and perspectives

Currently, no effective disease-modifying drugs are available to treat the major neurodegenerative disorders202, 203, 204. This fact leads to a question: are we close to solving the mystery of neurodegeneration? The probable answer is yes, because the field has recently begun to recognize that, first, damage to neuronal cells is not the sole contributor to disease initiation and progression, and that, second, correcting disease pathways in vascular and glial cells may offer an array of new approaches to control neuronal degeneration that do not involve targeting neurons directly. These realizations constitute an important shift in paradigm that should bring us closer to a cure for neurodegenerative diseases. Here, I raise some issues concerning the existing models of neurodegeneration and the new neurovascular paradigm.

The discovery of genetic abnormalities and associations by linkage analysis or DNA sequencing has broadened our understanding of neurodegeneration204. However, insufficient effort has been made to link genetic findings with disease biology. Another concern for neurodegenerative research is how we should interpret findings from animal models202. Genetically engineered models of human neurodegenerative disorders inDrosophila melanogaster and Caenorhabditis elegans have been useful for dissecting basic disease mechanisms and screening compounds. However, in addition to having much simpler nervous systems, insects and avascular species do not have cerebrovascular and immune systems that are comparable to humans and, therefore, are unlikely to replicate the complex disease pathology that is found in people.

For most neurodegenerative disorders, early steps in the disease processes remain unclear, and biomarkers for these stages have yet to be identified. Thus, it is difficult to predict whether mammalian models expressing human genes and proteins that we know are implicated in the intermediate or later stages of disease pathophysiology, such as amyloid-β or tau in Alzheimer’s disease7, 181, will help us to discover therapies for the early stages of disease and for disease prevention, because the exact role of these pathological accumulations during disease onset remains uncertain. According to the two-hit vascular hypothesis of Alzheimer’s disease, incorporating vascular factors that are associated with Alzheimer’s disease into current models of this disease may more faithfully replicate dementia events in humans. Alternatively, by focusing on the comorbidities and the initial cellular and molecular mechanisms underlying early neurovascular dysfunction that are associated with Alzheimer’s disease, new models of dementia and neurodegeneration may be developed that do not require the genetic manipulation of amyloid-β or tau expression.

The proposed neurovascular triad model of neurodegenerative diseases challenges the traditional neurocentric view of such disorders. At the same time, this model raises a set of new important issues that require further study. For example, the molecular basis of the neurovascular link with neurodegenerative disorders is poorly understood, in terms of the adhesion molecules that keep the physical association of various cell types together, the molecular crosstalk between different cell types (including endothelial cells, pericytes and astrocytes) and how these cellular interactions influence neuronal activity. Addressing these issues promises to create new opportunities not only to better understand the molecular basis of the neurovascular link with neurodegeneration but also to develop novel neurovascular-based medicines.

The construction of a human BBB molecular atlas will be an important step towards understanding the role of the BBB and neurovascular interactions in health and disease. Achievement of this goal will require identifying new BBB transporters by using genomic and proteomic tools, and by cloning some of the transporters that are already known. Better knowledge of transporters at the human BBB will help us to better understand their potential as therapeutic targets for disease.

Development of higher-resolution imaging methods to evaluate BBB integrity, key transporters’ functions and CBF responses in the microregions of interest (for example, in the entorhinal region of the hippocampus) will help us to understand how BBB dysfunction correlates with cognitive outcomes and neurodegenerative processes in MCI, Alzheimer’s disease and related disorders.

The question persists: are we missing important therapeutic targets by studying the nervous system in isolation from the influence of the vascular system? The probable answer is yes. However, the current exciting and novel research that is based on the neurovascular model has already begun to change the way that we think about neurodegeneration, and will continue to provide further insights in the future, leading to the development of new neurovascular therapies.

  1. Zlokovic, B. V. The blood–brain barrier in health and chronic neurodegenerative disorders. Neuron 57, 178–201 (2008).

  2. Moskowitz, M. A., Lo, E. H. & Iadecola, C. The science of stroke: mechanisms in search of treatments. Neuron 67, 181–198 (2010).
    A comprehensive review describing mechanisms of ischaemic injury to the neurovascular unit.

  3. Zlokovic, B. V. Neurovascular mechanisms of Alzheimer’s neurodegeneration.Trends Neurosci. 28, 202–208 (2005).

  4. Brown, W. R. & Thore, C. R. Review: cerebral microvascular pathology in ageing and neurodegeneration. Neuropathol. Appl. Neurobiol. 37, 56–74 (2011).

  5. Wu, Z. et al. Role of the MEOX2 homeobox gene in neurovascular dysfunction in Alzheimer disease. Nature Med. 11, 959–965 (2005).
    A study demonstrating that low expression of MEOX2 in brain endothelium leads to aberrant angiogenesis and vascular regression in Alzheimer’s disease.



Read Full Post »

Mitochondrial Pyridine Nucleotides and Electron Transport Chain

Larry H Bernstein, MD, FCAP, writer and curator

2.1.5 Mitochondrial Pyridine Nucleotides and Electron Transport Chain Mitochondrial function in vivo evaluated by NADH fluorescence

Mayevsky A1, Rogatsky GG.
Am J Physiol Cell Physiol. 2007 Feb; 292(2):C615-40

Normal mitochondrial function is a critical factor in maintaining cellular homeostasis in various organs of the body. Due to the involvement of mitochondrial dysfunction in many pathological states, the real-time in vivo monitoring of the mitochondrial metabolic state is crucially important. This type of monitoring in animal models as well as in patients provides real-time data that can help interpret experimental results or optimize patient treatment. The goals of the present review are the following: 1) to provide an historical overview of NADH fluorescence monitoring and its physiological significance; 2) to present the solid scientific ground underlying NADH fluorescence measurements based on published materials; 3) to provide the reader with basic information on the methodologies used in the past and the current state of the art fluorometers; and 4) to clarify the various factors affecting monitored signals, including artifacts. The large numbers of publications by different groups testify to the valuable information gathered in various experimental conditions. The monitoring of NADH levels in the tissue provides the most important information on the metabolic state of the mitochondria in terms of energy production and intracellular oxygen levels. Although NADH signals are not calibrated in absolute units, their trend monitoring is important for the interpretation of physiological or pathological situations. To understand tissue function better, the multiparametric approach has been developed where NADH serves as the key parameter. The development of new light sources in UV and visible spectra has led to the development of small compact units applicable in clinical conditions for better diagnosis of patients.

UNDERSTANDING THE MITOCHONDRIAL function has been a challenge for many investigators, including cytologists, biochemists, and physiologists, since its discovery more than 120 years ago. In addition to many books regarding the mitochondria, Ernster and Schatz (79) reviewed the history of mitochondrial structure and function studies. In the past two decades, several studies have reported mitochondrial involvement in pathological processes such as stroke (225) or cytoprotection (77). Most of the information on the mitochondrial function has been accumulated from in vitro studies. A relatively small portion of published papers dealt with the monitoring of mitochondrial function in vivo and in real time. Presently, examination of the involvement of the mitochondrial function in many pathological states, such as sepsis, requires monitoring of patients treated in intensive care units. Unfortunately, real-time monitoring of the mitochondrial function in patients has rarely been performed. The current study presents a review of this issue. To evaluate the activity of the respiratory chain in vivo, it is possible to monitor the mitochondrial NADH, FAD, or the cytochrome oxidase oxidation-reduction state. The interference of blood with the monitoring of FAD and cytochrome oxidase is much higher than with NADH (48); therefore, we invest our effort into the monitoring of the mitochondrial NADH redox state. We do not know of any publication showing clearly that Fp fluorescence could be monitored in vivo in blood-perfused organs. In our preliminary report, we showed that in specific brain areas, one can see the fluorescence of Fp but we were not sure how to validate the results. During the past 33 years, we have published >140 papers in this very significant area, including the largest number of studies using NADH redox state monitoring in patients.

Since the discovery of pyridine nucleotides by Harden and Young (94), >1,000 papers have been published on the use of NADH (Fig. 1A) as a marker for mitochondrial function. In 2000, Schleffler et al. (217) reviewed mitochondrial research methods over the past century. A major aspect of mitochondrial function, namely monitoring the energy state of tissues in vivo, was not discussed in that review. Therefore, the present review will summarize 50 years of research, started in 1955 by Chance and Williams (5657), by defining the mitochondrial metabolic state in vitro. To understand mitochondrial function in vivo and under various pathophysiological conditions, it is important to monitor the redox state of the respiratory chain in real time. The present review will discuss the monitoring principles for one of the electron carriers, namely, nicotinamide adenine dinucleotide (NADH). It is well known that mitochondrial dysfunction is involved in many diseases, such as ischemia, hypoxemia, Parkinson’s disease, Alzheimer’s disease, and in the apoptotic process. Therefore, the possibility of monitoring the mitochondrial NADH redox state in experimental animals and patients is of great importance.

inter-conversion of NAD+ and NADH & difference in the absorption spectra of NAD+ and NADH

inter-conversion of NAD+ and NADH & difference in the absorption spectra of NAD+ and NADH

Fig. 1. A: molecular structure of NAD+ and the inter-conversion of NAD+ and NADH. B: difference in the absorption spectra of NAD+ and NADH. C: emission spectra of brain NADH excited by 366 nm light (A1, A2, B1, B2, C1) or 324 nm laser light (C2). C1 and C2 show measurements from a dead brain, for comparison of NADH spectra using two different light sources.

To assess the energy demand, it is necessary to measure different organ-specific parameters. In the brain, the energy demand can be evaluated by measuring the extracellular levels of K+ that reflect the activity of the major ATP consumer: Na+-K+-ATPase (152161). In the heart, most of the energy is consumed by the muscle contraction activity. On the other hand, the energy supply mechanism is the same in all tissues: oxygenated blood reaching the capillary bed releases O2that diffuses into the cells. Therefore, it is possible to evaluate tissue energy supply by monitoring the same four different parameters in all tissues.

The main function of the mitochondria is to convert the potential energy stored in various substrates (e.g., glucose) into ATP. The inner membrane of the mitochondria contains 5 complexes of integral membrane proteins, including NADH dehydrogenase (complex 1). Three of those proteins are involved in the respiratory chain activity. The main function of the respiratory chain is to gradually transfer electrons from NADH and FADH2 (originating from the TCA cycle) to O2. With the addition of protons (H+), H2O is generated in complex 4. NADH (Fig. 1Aright side) is a substrate or a coenzyme for the enzymatic activity of dehydrogenases that form part of the respiratory chain and reside in the inner membrane of the mitochondria.

Spectroscopic Monitoring of NADH: An Historical Overview

The discovery of the optical properties of reduced nicotinamide adenine dinucleotide (NADH; previously known as diphosphopyridine nucleotide or pyridine nucleotide) has led to a very intensive research since the early 1950s. The reduced form of this molecule, NADH, absorbs light at 320–380 nm (Fig. 1B) and emits fluorescent light at the 420–480 nm range (Fig. 1C).

Because the oxidized form NAD+ does not absorb light in this range, it was possible to evaluate the redox state of the mitochondria by monitoring the UV absorbance (see Monitoring UV absorbance by NADH) or blue fluorescence of NADH (see Monitoring NADH fluorescence).

Undoubtedly, the pioneering work of Britton Chance of the Johnson Research Foundation at the University of Pennsylvania in Philadelphia led to the establishment and development of the unique measurement technology and theoretical conceptualization of the mitochondrial function based on NADH redox state monitoring in vitro as well as in vivo.

The foundations for future NADH monitoring in vitro and in vivo were established mainly in the 1950s; thus this period will be discussed in this section.

Monitoring of NADH UV absorbance

In 1951, Theorell and Bonnichsen found a shift in the absorption spectrum of DPNH upon addition of alcohol dehydrogenase (238). In the same year, Theorell and Chance described a new spectrophotometric technique for measuring the formation and disappearance of the compound of alcohol dehydrogenase and NADH (239). In 1952, Chance showed the applicability of this new technique to the measurements of pyridine nucleotide enzymes of muscle homogenate or intact cells (25). In 1954, Chance and Williams briefly described new sensitive differential spectrophotometric methods applied to the study of reduced NADH in isolated rat liver mitochondria and the same approach was used by Connelly and Chance (61) in monitoring NADH in stimulated frog nerve and muscle preparations. The oxidation of NADH in the muscle was similar to its oxidation in isolated mitochondria upon addition of ADP. In a comprehensive paper, “Enzyme mechanisms in living cells,” Chance described in detail the measurements of the respiratory enzymes, including NADH (26).

A major milestone in NADH monitoring was the technique presented in 1954 by Chance (27) using a double beam spectrophotometer to determine the appropriate wavelengths in measurements of respiratory enzymes.

The detailed descriptions of the respiratory chain and oxidative phosphorylation in the mitochondria (published in 1955 by Chance and Williams) established our basic knowledge of the mitochondrial function (57). Chance and Williams defined, for the first time, the metabolic states of isolated mitochondria in vitro, depending on the substrate, oxygen, and ADP levels. In addition, they correlated those metabolic states to the oxidation-reduction levels of the respiratory enzymes. The physiological significance of those metabolic states was discussed in 1956 by Chance and Williams (58).

Monitoring NADH fluorescence

The fact that NADH was monitored by the difference in the absorption spectrum of its reduced form, limited the use of that technique to the study of mitochondria in vitro, and in very thin tissue samples (e.g., muscle) or in cell suspension. To provide a method more specific than absorption spectroscopy, fluorescence spectrophotometry in the near-ultraviolet range was applied for NADH measurement. The initial model of fluorescence recorder was described by Theorell and Nygaard in 1954 (240). The first detailed study using fluorescence spectrophotometry of NADH in intact Baker’s yeast cells and algae cells was published in 1957 by Duysens and Amesz (75).

In the next 5 years (1958–1962), the monitoring of NADH fluorescence was significantly expanded, led by Chance and collaborators. In a first preliminary study, Chance et al. (37) performed simultaneous fluorometric and spectrophotometric measurements of the reaction kinetics of bound pyridine nucleotides (PN) in the mitochondria. In the same year (1958), Chance and Baltscheffsky presented preliminary results of measuring the fluorescence of intramitochondrial PN (34). In this study, they proved the connection between the mitochondrial metabolic state and the redox state of NADH as measured by spectral fluorometry in mitochondria isolated from rat liver (57). The correlation between the enzymatic assay of PN and sensitive spectrophotometry was investigated by Klingeberger et al. (120) by using the rat liver, heart, kidney, and brain.

In 1959, Chance and collaborators were able to expand the use of NADH fluorometry to various experimental models, from isolated mitochondria to intact tissue. To monitor NADH localization in intact cells, Chance and Legallais (42) developed a unique differential microfluorimeter with a very high spatial resolution. This approach was used in various cells to identify the intracellular localization of NADH fluorescence signals (54201). The next step was to apply the fluorometric technique to the higher organization level of animal tissues. Together with Jobsis, Chance measured in vitro changes in muscle NADH fluorescence following stimulation (41). In another paper published by Chance and Theorell (55) the authors came to the very significant conclusion that “The oxidation and reduction state of mitochondrial pyridine nucleotide without a measurable change of cytoplasmic fluorescence suggest that compartmentalization of mitochondrial and cytoplasmic pyridine nucleotide occurs in vivo, at least in the grasshopper spermatid.”

An intensive use of the in vivo NADH monitoring approach started in 1962. The “classic” paper on in vivo monitoring of NADH was published in 1962 by Chance et al. (36). They were able to simultaneously monitor the brain and kidney of anesthetized rats using two microfluorometers. In 1962, Chance and collaborators elaborated on this kind of in vivo monitoring and used it in other rat organs (4350).

Scientific Background And Technological Aspects

The absorption and fluorescence spectra of NADH (the reduced form) have been well characterized at different levels of organization, i.e., in solution, mitochondria and cell suspensions, tissue slices, and organs in vitro and in vivo. NADH has an optical absorption band at about 300 to 380 nm and a fluorescence emission band at 420 to 480 nm (Fig. 1B and C). The spectra are considered the same, although there are small differences in the shape and maxima of the spectra for different environments and measurement conditions. However, there is a universal agreement that the intensity of the fluorescence band, independent of the organization level of the environment, is proportional to the concentration of mitochondrial NADH (the reduced form), particularly when measured in vivo from a tissue.

The biochemical and physiological significance of these spectral qualities is also universally accepted, that is, an increase in the fluorescence intensity indicates a more reduced state of NADH and of the rest of the mitochondrial electron transfer chain. Under various circumstances, changes in the redox state of the electron transport chain can be associated with various conditions.

To monitor NADH fluorescence, it is possible to use one of the two principles available. At the early stage, it was necessary to measure and identify the fluorescence spectrum of NADH. Fluorescence spectra were compared in different in vitro and in vivo preparations. In parallel, the second approach was adopted, namely, measuring the total fluorescence signal accumulated and integrated into a single intensity using appropriate filters. This approach was necessary to measure NADH fluorescence continuously. The following parts of this section describe the fluorescence spectra of NADH measured in various in vitro and in vivo models by different investigators. We present this review of the reported spectra to describe the foundations for the second monitoring approach, namely, the continuous monitoring of integrated spectra.

Fluorescence Emission Spectra of NADH

NADH in solution.

Several investigators have measured NADH fluorescence in solution. Very recently, Alfano’s group (62) performed a calibration test of pure β-NADH in solution, compared it to porcine myocutaneous flap, and found a very significant correlation. The NADH solution spectrum and mitochondrial spectrum were also compared by Chance and Baltscheffsky (34).

Similar spectra of NADH in solution were recorded by Schomacker et al. (219) using 337-nm excitation light for colonic tissue diagnosis.

NADH spectra in isolated mitochondria.

The excitation and emission spectra of NADH (PN) and flavoprotein were measured in frozen samples of pigeon heart mitochondria (52). Using rat liver mitochondria, Chance and Baltscheffsky (34) measured the fluorescence spectra in the three metabolic states defined by Chance and Williams (58). The 330-nm light excitation resulted in a fluorescence peak at 440–450 nm. The same kind of spectra was obtained by other investigators using different fluorometers or mitochondria isolated from various organs. Galeotti et al. (87) measured similar spectra from rat liver mitochondria. Using Rhodamine B as an internal standard for system calibration, Koretsky and Balaban (125) found the same spectra emitted from isolated rat liver mitochondria. Koretsky et al. (126) compared the emitted spectrum from heart homogenates (similar to isolated mitochondria) with that of dissolved heart homogenates (126).

Intact cells.

The use of microfluorimetry to study intact cell metabolism was described in several publications by Kohen and collaborators (see, for example, Ref. 123).

The typical NADH fluorescence spectrum was measured in suspension of ascite tumor cells (87). This study demonstrated that the spectrum of intact cells was similar to that of NADH solution.

Using isolated myocytes, Eng et al. (78) compared the spectra measured under various conditions of the mitochondria. They found that cyanide induced an increase in the spectrum difference, whereas FCCP, used as a typical uncoupler of oxidative phosphorylation, produced a marked decrease in the spectrum.


Principles of NADH monitoring.

As described in the introductory section, NADH can be measured by utilizing its absorption spectrum in the UV range, as well as by the blue fluorescence spectrum under UV illumination. In the early stages, NADH monitoring was based on the difference in the absorption of NADH and NAD+. At the range of 320 to 380 nm, only the reduced form; NADH absorbs light, while NAD+does not (Fig. 1B). Therefore, when a mixture of NADH and NAD+ is illuminated in a cuvette by 320–380 nm, only NADH will affect the absorption spectrum peak at 340 nm. This property of NADH was used in the early 1950s by several investigators, as reviewed in Spectroscopic Monitoring of NADH–Historical Overview. Chance and collaborators utilized this technique to measure NADH in muscle homogenates or intact cells (25) and published many papers concerning the unique absorption spectrum of NADH.

The absorption approach is not practical for measuring NADH in a thick tissue; hence, another property of NADH was used. Since the early 1950s, fluorescence spectrophotometry of NADH has been employed in various in vitro and in vivo models. The emission of NADH fluorescence, under illumination at 320–380 nm, has a very wide spectrum (420–480) with a peak at 450–460 nm (Fig. 1C). NADH fluorescence has been identified by Chance and his collaborators as a good indicator of the intramitochondrial oxidation-reduction state (48).

The review article on in vivo NADH fluorescence monitoring, published in 1992 by Ince et al. (102) included many other technical aspects of the methodology. Nevertheless, here we will elaborate on the historical development of the various models of NADH fluorometers. We recently (155) reported on a new type of NADH fluorometer based on a very small and stable UV light source: a 375-nm light-emitting diode.


In 1959, Chance and Legallais (42) described a differential fluorometer that heralded a new era in monitoring NADH fluorescence in vivo as an indicator of mitochondrial function. They used a microscope, serving as the fluorometer basis, with two light sources: tungsten and mercury lamps with appropriate filters. In 1959, Chance and Jobsis (41) proved that mechanical muscle activity is associated with NADH oxidation measured in excised muscle. This study was the bridge from the subcellular (mitochondria) and cellular (intact cell) monitoring approaches toward actual in vivo applications.

The first in vivo NADH monitoring device was presented in the early 1960s. At that stage, the effects of scattered light and tissue absorption due to blood were not taken into consideration when monitoring NADH fluorescence. The first detailed results of in vivo NADH fluorescence measurements were published in 1962 (36).

These classic papers described two microfluorometers that were modifications of previous designs (4254). This microfluorimeter type employed Leitz “Ultrapack” illumination, which had been used for many years by various groups until the appearance of UV transmitting optical fibers. To avoid movement artifacts, rats were anesthetized deeply and their heads were fixated in a special holder attached to the operation table. Numerous studies utilized the principles of the “Ultrapack” illumination system. The same instrumentation was used in other in vivo studies, including those of Chance’s group (38434459), Dora and Kovach’s group (7192), Rievich’s group (93), Jobsis and collaborators (108110111213), Gosalvez et al. (89), and Anderson and Sundt (5232). This is only a partial list.

Monitoring NADH fluorescence and reflectance.

The effect of blood on NADH fluorescence was discussed early by Chance et al. (36). To monitor NADH in vivo, Chance’s group had to avoid areas containing large blood vessels, which interfere with the emission and excitation light. The monitoring of a second channel in tissue fluorometry in vivo was reported by Chance and Legallais in 1963 (44). They showed that “changes due to the deoxygenation of oxyhemaglobin do not interfere with measurement of the time course of fluorescence changes in the tissue studies.”

The addition of a second monitoring signal, namely, tissue reflectance at the excitation wavelength was reported in 1968 by Jobsis and Stansby (112). It was based on a previous model described by Jobsis et al. in 1966 (107). In two more papers by Jobsis and collaborators (110,111), the measurement of 366-nm reflectance was used for the correction of the NADH fluorescence signal from the brain. The reflectance signal was subtracted from the fluorescence signal. The same type of instrumentation was used by various groups for the measurement of NADH in single cells (124) or in vitro preparations (1319).

Fiber optic fluorometer/reflectometer.

To enable the monitoring of NADH fluorescence in unanesthetized animals or other in vivo preparations, a flexible means was needed to connect the fluorometer with the tested organ, for example the brain. This was achieved in 1972, when UV transmitting quartz fibers became available (Schott Jena Glass). We have used the light guide-based fluorometer for in vivo monitoring of the brain (48157) subjected to anoxia or cortical spreading depression. The historical development of light guide-based fluorometery-reflectometry is shown in Fig. 2. The original device functioned on the time-sharing principle (Fig. 2A), where four filters were placed in front of a two-arms light guide. Filters 1 and 3 enabled the measurement of NADH fluorescence, while filters 2 and 4 were used to measure tissue reflectance at the excitation wavelength. The reflectance trace was used to correct the NADH signal for hemodynamic artifacts, and to indicate changes in the blood volume of the sampled tissue.

Fig. 2. The three stages in the development of the fiber optic fluorometer/reflectometer (started in the early 1970s).


development of the fiber optic fluorometer_reflectometer

development of the fiber optic fluorometer_reflectometer

Factors Affecting NADH Fluorescence and Reflectance Signals

The excitation and emission spectra of NADH are affected by the redox state of this fluorochrome and by other factors, leading to artifacts in the fluorescence measurements. This section will discuss various NADH-unrelated factors, affecting the measured signal. Since most fluorometers involve the measurement of the total backscattered light at the excitation wavelength (i.e., 366 nm), the discussion will concern changes in NADH fluorescence as well as in tissue reflectance.

The following factors may affect the two measured signals, 366-nm reflectance and 450-nm fluorescence: 1) tissue movement due to mechanical or intracranial pressure changes; 2) extracellular space events, such as volume changes or ion shifts between intra- and extracellular space; 3) vascular and intravascular events, for example, oxy-deoxy Hb changes, and blood volume changes due to autoregulatory vasoconstriction under pathological conditions; and 4) intracellular space factors, such as O2 level, ATP turnover rate, substrate availability, and mitochondrial redox state.

Fig. 3. Comparison between the mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply and brain activation.

mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply

mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply

Changes in mitochondrial NADH and tissue metabolic state

The pioneering work of Chance and Williams in the 1950s, led to the definition of the metabolic state of isolated mitochondria in vitro. The foundations for the use of NADH fluorescence as a marker of mitochondrial activity have been posited in detail by Chance and Williams (5657). The left portion of Fig. 6 is a modification of a published table, while the right hand segment demonstrates the responses of NADH fluorescence measured in the brain in vivo under various perturbations. The “resting state” of the mitochondria in vitro was defined as state 4, where NADH was 99% in the reduced form, and ADP was the rate limiting substance. If ADP is added to a suspension of mitochondria, ATP synthesis will be stimulated, O2 consumption will increase, and the rate limit will be determined by the activity of the respiratory chain. During this state 3, or the “active state,” the NADH redox state will decrease or become more oxidized (∼50%). When the resting mitochondria are deprived of O2, the activity of the mitochondria will stop and NADH will reach its maximum redox state (state 5).

A definitive description of the mitochondrial metabolic state has never been given for in vivo conditions. Therefore, we described the in vivo mitochondria conditions as recorded by NADH fluorescence in a representative tissue or organ; e.g., the brain. While the range between minimal NADH (∼0) and its maximal level was determined in vitro, it is almost impossible to determine in the intact brain or other organs in vivo. For example, state 2, with a substrate free medium, could not be achieved in vivo since the tissue would die. On the other hand, the maximal level of NADH (state 5) could be monitored in vivo under complete deprivation of O2 by anoxia or complete ischemia.

We used changes in NADH levels monitored in vivo to create a new scale ranging from a maximal definite point to the minimal level recorded in vivo. Details of this approach have been published (152). As shown in Fig. 3, the maximal NADH level is achieved under complete O2 deprivation that can be induced both under in vitro and in vivo conditions. This signifies that this definitive point can be used to determine state 5 in vivo as well. The problem is to determine the metabolic state of a tissue in an in vivo situation. If we adopt the in vitro value of a resting state (state 4), this would signify that the increase in NADH during state 5, induced by anoxia (0% O2), would be only 1%. According to all in vivo studies, this is not the case, and during anoxia the increase in NADH is lager than the decrease under state 4 to 3 transition. Figure 3right, illustrates that the observed level of NADH increase is indeed larger than the decrease. Therefore, we concluded that, under in vivo conditions, the “resting” metabolic state of the brain is found between states 4 and 3 rather than in state 4 as defined in vitro (152). To determine the maximal and minimal levels of NADH in vivo it is almost impossible to use cyanide or uncoupler (FCCP). Nevertheless, we were able to determine the maximal level by anoxia and the “minimal” level by nonfluorescing uncoupler. We injected the uncoupler pentachlorophenol into the ventricles of the rat’s brain while monitoring the NADH responses to anoxia and spreading depression (146). To perform a reliable study with cyanide, the animal would have to die and the results will not be helpful; therefore, we used the anoxia response to measure the maximal level of NADH. Using fiber optic fluorometry, we were able to monitor both anesthetized and awake rats. This figure will be discussed later on in this review. It is important to note that most of the published data on NADH monitoring, have been accumulated in brain studies. Therefore, we will present our data mainly relating to the brain, though results on other organs will be presented as well. Table 1 lists studies published by various investigators as well as our publications. The papers are classified according to the organ monitored and the type of perturbation used. This table does not include rarely studied types of organs or perturbations. Such studies are cited individually in the text.

Table 1. Effect of O2 delivery and consumption on NADH redox state measured in various intact organs by various investigators

Table 2. Historical milestones in monitoring NADH fluorescence in vivo


Anoxia and Hypoxia.

The responses to hypoxia and anoxia are very similar; therefore, they will be discussed together. According to the definition of Chance and Williams (5657), a shift toward state 5 involves an increase in NADH proportional to a decrease in O2 supply.

It is assumed that the response of NADH fluorescence to hypoxia or anoxia, induced in vivo, should be very similar to the response of isolated mitochondria. As shown in Fig. 4B, when the blood-free brain was exposed to N2, the fluorescence showed a clear increase-decrease cycle depending on the availability of O2. The reflectance trace was not affected at all. In autoregulated blood-perfused organs, it is expected that the lack of O2 will trigger compensation mechanisms that may lead to an increase in the blood flow and volume, or a decrease in thereflectancesignal. We tested, in the same rats, the response to anoxia of the normoxic blood-perfused brain. The results are shown in Fig. 4A. Indeed, reflectance exhibited a large decrease due to the increase in blood volume (vasodilatation of brain vessels). Figure 4C and D, presents the responses to anoxia measured via 2 mm and 1 mm light guides. A small variation can be seen in the reflectance response between the two light guides.

Ischemia, Or Decreased Blood Flow.

Under partial or complete ischemia, blood flow to the monitored organ is decreased and, as a result, O2 delivery is limited or even abolished. The use of ischemia in animal models provides information relevant to critical clinical situations such as brain stroke or heart attack. The primary factor starting the pathological state is the decrease in O2 supply, making the tissue energy balance negative, and preventing the tissue from performing its function. Figure 7 illustrates the effects of ischemia and anoxia on the NADH level in the brain of an anesthetized gerbil. The measurements of NADH in the cerebral hemispheres were correlated to the brain electrical activity (ECoG; electrocorticogram). To test and compare the measurements done in the two hemispheres, we exposed the gerbil to short-term anoxia. As shown, the two responses are very similar and correlate to the depression of the ECoG signal measured in the two hemispheres.


After the introduction of the light guide-based fluorometry, we were able to expose the awake brain to hyperbaric conditions. A clear decrease in NADH (oxidation) was recorded during the shift from 21% to 100% O2, as well as during compression of up to 10 atmospheres 100% O2 (150,152153167177). A similar oxidation was found upon CO2 addition to the gas mixture (94–99% O2) (149). We also found a correlation between the elevated brain PO2 and the oxidation of NADH in awake rats (151). The oxidation of NADH was also recorded under normobaric hyperoxia (113). Furthermore, we tested the effects of hyperbaric oxygenation on carbon monoxide intoxication (212) or cyanide exposure (235).


Responses to energy consumption changes

As shown by Chance and Williams (57, 58), the activation of the mitochondria by increased ADP is coupled with oxidation of NADH (decreased NADH levels) and is known as the state 4 to state 3 transition in isolated mitochondria. Most of the investigations in this field of tissue activation were made on neuronal tissue in vivo. However, studies of other organs, such as the heart or skeletal muscle, were conducted as well. The demand for energy (ATP) by various tissues is dependent on the specific tasks of each organ or tissue. Nevertheless, the stimulation of mitochondrial function is common in all tissues in the body. We will describe the effects of tissue activation on NADH fluorescence under normoxic conditions as well as during limitation of O2 supply in the tissue (hypoxia, ischemia).


Responses to energy consumption changes

As shown by Chance and Williams (5758), the activation of the mitochondria by increased ADP is coupled with oxidation of NADH (decreased NADH levels) and is known as the state 4 to state 3 transition in isolated mitochondria. Most of the investigations in this field of tissue activation were made on neuronal tissue in vivo. However, studies of other organs, such as the heart or skeletal muscle, were conducted as well. The demand for energy (ATP) by various tissues is dependent on the specific tasks of each organ or tissue. Nevertheless, the stimulation of mitochondrial function is common in all tissues in the body. We will describe the effects of tissue activation on NADH fluorescence under normoxic conditions as well as during limitation of O2 supply in the tissue (hypoxia, ischemia).


The effects of pharmacological agents on NADH redox state in various organs were published as well. Kedem et al. researched the influence of various inotropic agents (1) as well as nitroprusside (2), nitroglycerin (76), and propranolol (86).

Osbakken and collaborators (194195) also monitored NADH under various drug exposures. Baron et al. (17) described the effects of lidocaine on NADH, during ischemia in the dog heart. The effects of blood substitute emulsion on NADH in the kidney were reported (260). The influence of radioprotective chemicals on NADH in rat tissue was described in the 1960s (103). The action of various drugs (e.g., the uncoupler Amytal) was studied in the liver exposed to hyperbaric oxygenation (3140).

Monitoring Human Body Organs

The first attempt to apply NADH fluorometry to human tissues in vivo was made in 1971 by Jobsis et al. (111). Using NADH fluorescence microfluorometry, they monitored the exposed brain of neurosurgical patients undergoing treatments for focal cerebral seizures. They correlated the electrocorticographic data to the NADH redox state under direct cortical stimulation of the monitored area. The clear decrease in the NADH signal was interpreted as a change in oxidation. The recorded changes were very similar to those obtained in analogous procedures in the cat brain (213). A few years later, the collaboration between Austin and Chance (8) led to the recording of NADH in the brain of patients subjected to microanastomosis of the superficial temporal artery to the middle cerebral artery. The same group found an improvement of cerebral oxidative metabolism after the anastomosis, which was correlated to the elevated blood flow and increased tissue PO2 (9).

The next step was taken by Barlow et al. (16), who expanded this technique to monitor the heart and the brain. Using a different type of fluorometer, Van Buren et al. showed a decrease in NADH (oxidation) due to cortical stimulation in epileptic patients (251). In 1979, Fein and Jobsis (81) studied the changes in brain energetics in patients undergoing superficial temporal arterial-middle cerebral artery microanastomosis. Fein and Olinger (8283) monitored patients after transient ischemic attacks. The brain of these patients, who had undergone an extracranial-intracranial bypass, was stimulated, and changes in NADH were recorded.


The laser-based fluorimeter developed by Renault (207) was used to monitor NADH redox state in the heart muscle during pharmacological treatments (207), as well as in skeletal muscle (91). Attempts to apply NADH fluorometry in clinical practice (reported in a dozen short publications) did not lead to the development of a proper medical device applicable on a daily basis.

In 1990, our team started developing a unique multiparametric monitoring system that included the measurement of NADH fluorescence, using a light guide-based device. This system was initially applied to monitor neurosurgical patients undergoing brain surgery or those treated in the intensive care unit. In the first paper on the subject (published in 1991), we showed the feasibility of our approach. After a transient short occlusion of one common carotid artery, the increase in NADH was correlated to a decrease in cerebral blood flow (164). It took another 5 years to restart organized clinical testing of our monitoring system.

Monitoring Nadh And The Multiparametric Approach

The need for multiparametric monitoring of other parameters, additional to NADH, results from the basic understanding that NADH is affected by two major factors. The redox state of NADH reflects not only the availability of O2 inside the mitochondria but also the turnover rate of the ATP-ADP cycling activity (state 4 to state 3 transition). The interaction between these two factors affects the nature of NADH response to various conditions. For example, an increase in energy consumption (e.g., cortical spreading depression) under O2 restriction will be manifested as an increase in NADH rather than a decrease (oxidation) measured in normal well-oxygenated tissue. According to Chance and Williams, an increase in ATP production is always recorded as a decrease in NADH (5758). Therefore, the “reduction cycle” measured by the NADH signal in response to CSD can be interpreted as an artifact of some kind. This phenomenon and the fact that the mitochondrial NADH signal cannot yet be calibrated in absolute values prompted us to develop a multiparametric monitoring approach and a probe that could be used in various tissues exposed to different pathophysiological conditions. By this approach, two major advantages were gained. First, it provided the possibility of a better interpretation of the recorded results; second, nonphysiological responses could also be more easily detected. To elaborate on these points, we will consider the following typical example. In the early stage of NADH monitoring using a time sharing fluorometer, we found that a few minutes after complete ischemia was induced by decapitation in a rat model, a large increase in the reflectance signal was recorded in parallel to a clear NADH decrease in the dead monitored brain, apparently indicative of NADH oxidation. We termed this event “the Secondary Reflectance Increase-SRI” (147). It was clear to us that this late “oxidation” of NADH in the dead animal was an artifact of the monitoring system. The same response was recorded also when partial ischemia was induced in a gerbil’s brain. The “oxidation” of NADH in a dead or partially ischemic brain did not have any physiological or biochemical interpretation, so we suspected that this “oxidation” is due to the large increase in the reflectance signal, and to a failure of the fluorescence signal’s correction method. We speculated that the large increase in the reflectance trace (SRI) after ischemia or brain death, resulted from a spasm of blood vessels. Such spasms are known to occur in this type of conditions, namely during cortex depolarization. Only when monitoring other parameters, in addition to NADH, such as extracellular K+ and DC steady potential, were we able to give a substantial explanation for the SRI event (85). On the basis of these experiments, we concluded that the SRI phenomenon is always associated with a negative shift in the DC potential and a large increase in extracellular potassium when energy is not available.


NADH and electrical activity

The first attempt to combine NADH and electrical measurements was made by Chance and Schoener in 1962 (50). They showed the time relationship between the increase in NADH due to anoxia or hypoxia, and the disappearance of electrical activity (ECoG) in rat cerebral cortex. The same type of correlation was reported later by Jobsis et al. (110) for epileptic activity, and by Rosenthal and Somjen (163) and Mayevsky and Chance (157) for CSD. The accumulated results have made it clear that under limited energy or O2 supply, NADH becomes elevated in the brain, while the spontaneous ECoG activity is depressed. The ECoG begins to decelerate when NADH reaches 70%-80% of its maximal increase upon death (157159) or decapitation (160259). The recovery of ECoG after anoxia is completed much later than NADH oxidation, suggesting that energy availability is a prerequisite condition but not the only condition needed for a complete ECoG recovery. Depression of the ECoG is also recorded when the brain is exposed to depolarization due to CSD; however, it is not caused by a lack of O2. Similar correlations between NADH and ECoG were described in cat cerebral cortex exposed to seizures and hemorrhagic hypotension (100).

NADH and respiratory chain components

Since the activities of various respiratory chain components are strongly coupled, the tissue respiratory rate can be better evaluated by monitoring several such components. Very few attempts have been made to correlate NADH responses in vivo, with other components of the respiratory chain. The main reason for this was the stronger interference of blood with Fp or cytochrome oxidase measurements, compared with NADH. The effects of hypotension and anoxia on NADH and cytochrome aa3, were measured in the brain in vivo (99). LaManna et al. showed the effects of Ethanol on brain NADH and cytochrome aa3 in rats and cats (137). Therefore, almost all correlations between Fp and NADH were studied in blood-free organs (49). In 1976, we presented preliminary results indicating that in certain morphological areas of the brain, containing less blood vessels, a good correlation is recorded between NADH and Fp responses to anoxia in vivo (146). The only practical way to measure these two signals together was to freeze the tissue and then analyze the two parameters in the frozen state (168183). Another approach to correlating NADH and Fp redox state was suggested by Paddle et al. (198). They used a NADH/Fp scanning fluorometer to monitor the muscle (198) or rat diaphragm (197). A few papers have been published on the use of flying spot fluorometer to monitor the two fluorescent signals in the brain and other organs (35). Most of the data published in this field have been acquired in vitro (3349) or in blood-free organs such as the liver (218).

In this review, we tried to summarize the scientific background and technological aspects of in vivo NADH fluometry approach for the monitoring of mitochondrial functions. This technology still has some limitations including the need for better correction technique for hemodynamic artefacts as well as a new approach for quantitative calibration of the signals. During the past decade, the preliminary application of the NADH fluorometry to clinical environment was very promising. This stimulates us to improve the technology to provide a practical medical device that will be used by many clinicians after approval by the regulatory agencies around the world. A microscale mathematical model for metabolic symbiosis: Investigating the effects of metabolic inhibition on ATP turnover in tumors

Colin Phipps, Hamid Molavian, Mohammad Kohandel
J Theoret Biol 2015; 366: 103-114

Cancer cells are notorious for their metabolic adaptations to hypoxic and acidic conditions, and especially for highly elevated glycolytic rates in tumor tissues. An end product of glycolysis is lactate, a molecule that cells can utilize instead of glucose to fuel respiration in the presence of oxygen. This could be beneficial to those cells that do not have sufficient oxygen as it conserves glucose for glycolysis. To better quantify this phenomenon we develop a diffusion-reaction mathematical model for nutrient concentrations in cancerous tissue surrounding a single cylindrical microvessel. We use our model to analyze the interdependence between cell populations’ metabolic behaviors on a microscopic scale, specifically the emerging paradigm of metabolic symbiosis that exists between aerobic and glycolytic cells. The ATP turnover rates are calculated as a function of distance from the blood vessel, which exhibit a lactate-consuming population at intermediate distances from the vessel. We also consider the ramifications of the Warburg effect where cells utilize aerobic glycolysis along with this lactate consuming respiration. We also investigate the effect of inhibiting metabolic pathways on cancer cells since insufficient ATP can trigger cell apoptosis. Effects that could be induced by metabolic inhibitors are analyzed by calculating the total ATP turnover in a unit tissue annulus in various parameter regimes that correspond to treatment conditions where specific metabolic pathways are knocked out. We conclude that therapies that target glycolysis, e.g. lactate dehydrogenase inhibitors or glycolytic enzyme inhibition, are the keys to successful metabolic repression.

The extensive metabolic requirements for cancer cell proliferation coupled with the harsh microenvironment in solid tumors culminate in a highly adaptive and complex network for cellular energy production. The genetically altered metabolic behavior of cancer cells has led to a number of emerging metabolic paradigms, in addition to those that are universally exhibited in both cancerous and normal cells. We will investigate this complex metabolic behavior by formulating a minimal mathematical model that includes the essential metabolites of glucose, lactate and oxygen in the tissue surrounding a microvessel. The cylindrical geometry used here has been used in a similar context to consider interactions between metabolites and tumor cells with treatment effects in a simplified setting (e.g. Bertuzzi et al., 2000, 2007a). The model presented here will enable the quantification of various behaviors, such as the symbiotic relationship that exists between lactate producing glycolytic cells and lactate-consuming respiratory cells, and the analysis of metabolic dependence on various physiological conditions such as hypoxia and induced metabolic inhibition. Metabolic inhibition including glycolytic inhibitors among many others targets could be very important for cancer treatment since an ATP deficit can induce apoptosis (Izyumov et al., 2004). The key consideration for addressing this problem with mathematics is the formulation of nutrient consumption rates that encompass the various primary facets of cancer cell metabolism and their corresponding ATP yields. In normal well-oxygenated tissues the primary source of ATP is the process of cellular respiration. The complete conversion of glucose to carbon dioxide and water has an ideal yield of about 29 ATP, although realistically the yield is substantially lower (Brand, 2005). The preliminary stage of cellular respiration is glycolysis, the conversion of glucose to pyruvate; this process directly produces 2 ATP. In hypoxic conditions this pyruvate is preferentially converted into lactate via the enzyme lactate dehydrogenase (LDH) to regenerate the essential cofactor NAD+. In oxygenated conditions this pyruvate is transported across the inner mitochondrial matrix where it is decarboxylated and enters the citric acid cycle; the citric acid cycle directly generates 2 more ATP per glucose. The primary energy payoff is a result of cofactor oxidization that enables the electron transport chain to establish a proton gradient across the inner mitochondrial matrix. ATP synthase utilizes this electrochemical gradient to drive the phosphorylation of approximately 25 additional ATP per glucose molecule.

The aforementioned universal traits that cancer cells and normal cells share include cellular responses to various levels of oxygen, lactate or glucose. Examples include a Crabtree-like effect and a Pasteur-like effect (Casciari et al., 1992a). The Crabtree-like effect is when oxygen consumption decreases as glucose concentration increases. This can be explained by an increasing reliance on glycolysis for ATP when hyperglycemic conditions are encountered. The Pasteur-like effect is decreased glucose consumption as oxygen increases. This is due primarily to the inhibition of various metabolic steps by the presence of elevated ATP and other intermediaries. However, cancer cells are unique in that they preferentially utilize glycolysis, even in the presence of oxygen, coined aerobic glycolysis. This phenomenon is generally referred to as the Warburg effect whereby cells rely primarily on glycolysis even in the presence of sufficient oxygen to perform respiration (Warburg, 1956). There is a perceived inefficiency of this metabolic strategy, namely the dramatically reduced ATP yield, just 2 per glucose instead of 29, however, it has the benefits of faster ATP production and it is likely that much of this glucose is being consumed for proliferative (Vander Heiden et al., 2009) (e.g. by the pentose phosphate pathway) purposes. In addition to the typical glycolytic phenotype exhibited in many cancers, there is also a developing story of a co-operative relationship existing between aerobic and anaerobic  cancer cells. The lactate necessarily produced by glycolytic cells is being pushed back into the respiratory cycle by being converted into pyruvate (summarized in Feron, 2009; Nakajima and Van Houten, 2012); this spatial relationship is shown in Fig. 1. Lactate consumption has been observed in vitro in various models (Bouzier et al.,1998; Katz et al., 1974) as well as in vivo as early as the early 1980s (Sauer et al., 1982). However, a renewed interest in the topic was piqued when Sonveaux et al. (2008) showed that reducing lactate uptake by cancer cells led to hypoxic cell death, a particularly difficult subpopulation to target using traditional methods.

Metabolic phenomena have been studied in great detail by mathematical models, but models of tumor metabolism rarely include the interaction of the transport mechanisms of microvessels with the localized metabolic behavior of cells (with one recent exception McGillen et al., 2013). In the section to follow, we will develop a mathematical model that describes the concentrations of molecules that are important to cellular metabolism in the tissue around a single three-dimensional vessel that exhibits diffusion dominated interstitial transport. We will then use this model to demonstrate how the properties of the tumor cell population, such as glucose, lactate and oxygen consumption rates, affect tumor hypoxia and ATP production around a single vessel. The effects of metabolic inhibitors will be investigated by parameter changes that could be elicited by the application of glycolysis inhibitors, lactate dehydrogenase (LDH) inhibitors or respiratory inhibitors. We are interested in those metabolic inhibitors that could cripple the cells’ ability to produce ATP.

Fig.1. The spatial relationship between the cell populations in the model indicating dominant metabolism as we move away from the vessel. When the glucose and oxygen concentrations are highest near the vessel wall, the cells preferentially utilize glucose-fuelled respiration. When the oxygen supply is depleted far from the vessel, the cells rely on glycolysis. The glycolytic cells produce large quantities of lactate which are consumed by cells at intermediary distances and hypoxic oxygen concentrations. These cells are participating in a behavior that we will refer to as metabolic symbiosis.

A model to describe the concentrations of the major players in the metabolic pathways of respiration and glycolysis, will be outlined here. Its origins lie in a metabolic model developed by Casciari et al. (1992b) that was subsequently applied on the microscale by Molavian et al. (2009). The functional forms for the production rates are similar to those proposed by MendozaJuez et al. (2012) and subsequently extended to a spatial model by McGillen et al. (2013). In hypoxic and anoxic conditions, cells must partially or exclusively rely on metabolic pathways, such as glycolysis, that do not require oxygen for ATP production. In glycolysis, the preliminary stage of respiration, a single glucose molecule (C6H12O6) yields 2 ATP, which we will denote under the reaction arrow with a boxed ATP yield number, with the byproducts of lactate and a proton. Denoting glucose by G and lactate by L C3H5O3, the net reaction is G kG> 2L + 2H+; [2]

where kG (mM/s) is the rate of glucose consumption by glycolysis that results in lactate formation. The accumulation of these hydrogen ions in a solid tumor is a primary contributor to tumor acidosis. In the presence of oxygen (O2), glycolysis is typically followed by the rest of the respiratory process with an ideal energy yield of approximately 29 ATP molecules with carbon dioxide (CO2) and water (H2O) as the only byproducts. The simplified summary reaction is given by G kO à 6O2 6CO2 + 6H2O; [29]

where kO is the rate of glucose consumption that results in cellular respiration. To represent the metabolic symbiosis between cells primarily producing energy via glycolysis and those consuming lactate in well-oxygenated areas, we will link the above two reactions with the lactate-consuming net reaction: L+ H+ + 3O2 kLà  3CO2 + 3H2O; [13.5]

where kL is the rate of lactate consumption. This summarizes the re-entry of lactate, via conversion to pyruvate, into aerobic respiration that yields 13.5 ATP per lactate molecule. The relationships between the summary reactions included in the model are given in Fig. 2.

Fig. 2. The summary reactions included in the metabolism model. Glycolysis proceeds at rate kG and produces 2 ATP from the conversion of glucose to pyruvate. Glucose-fuelled respiration occurs at rate kO in the presence of oxygen, while lactate-fuelled respiration occurs at rate kL (2 kL is present in the diagram to remain consistent with the glycolytic yield of 2 lactate molecules).

Fig. 3. Solution to base case boundary value problem. Nondimensional oxygen o and glucose g concentrations decrease due to metabolic consumption. Lactate  ℓ  increases to almost double its vessel concentration since it is produced by glycolysis at a higher rate than it is consumed by respiration due to a limiting oxygen concentration. This image has been spatially truncated to 300 μm since the concentrations are approximately constant after this point.

Fig. 4. Consumption rates of oxygen, lactate and glucose (QO, QL and QG for the concentrations given in Fig. 3). The glucose and oxygen consumption rates are strictly positive while the consumption rate of lactate is predominantly negative. This indicates that even in regions where lactate is being consumed, it is being produced at a higher rate by glycolysis.

Fig. 5. The base case for ATP turnover (consumption/production) rates corresponding to consumption rates given in Fig. 4. The contributions of the pathways are bounded by the total ATP turnover rate  PATP . Glycolysis dominates in hypoxic/ anoxic regions while glucose-fuelled respiration occurs sparingly near the blood vessel. Lactate-fuelled cells are consuming the byproduct of the glycolytic cells where there is oxygen present.

Warburg effect

In the base case considered above glycolysis is inhibited until the oxygen consumption drops to values that prevent the production of sufficient ATP to maintain cell survival. However, cancer cells will commonly utilize glycolysis as a primary energy source even when there is enough oxygen to ensure cell survival. In the model we characterize the cell’s ability to hold off on utilizing glycolysis in oxygenated areas by the parameter ΛO. Reducing it 400-fold from the base case above (from 4000 to 100) results in spatial ATP turnover rate as given in Fig. 6. Cells near the vessel greedily consume the available resources leaving cells further from the vessel to die from insufficient ATP supply. The ATP production breakdown corresponds to the second bar in Fig. 8 and is slightly higher than the whole tissue considered in the base case above.

Fig. 6. ATP turnover (consumption/production) rates for cells exhibiting the Warburg effect (differs from base case because ΛO = 100 instead of 4000). The contributions of the pathways are bounded by the total ATP turnover rate PATP. Glycolysis is dominant in all regions of the tumor, however, glucose and lactate fueled respiration occur sparingly near the blood vessel where there is oxygen present.

Fig. 7. The optimal metabolic behavior on the microscale given an ATP turnover maximum of X mM=s. This shows glucose-fuelled respiration near the vessel, glycolysis far from the vessel and a lactate-consuming population in between these two

Instead of fixing all of the parameters to the values given in Table 1, we could leave some of the parameters free and optimize the amount of ATP generated from the given metabolites by imposing a maximum constraint on ATP production. For instance, setting all of the parameters initially to those given in Table 1, and then minimizing some function of Z¼PATP θ where θ is the maximum allowed ATP turnover rate would theoretically ensure that the available resources were not being selfishly consumed by oxidative cells near the vessel. Allowing cells to alter their glycolytic parameters: βg, δ and κg along with their lactate–glucose switch parameter λ yields the results shown in Fig. 7. While there was still enough constraint that the system still exhibited a non-constant ATP turnover where it could, this reinforces the suggested optimal strategy of glucose-fuelled respiration near the vessel, glycolysis far from the vessel and a lactate-consuming population in between these two. This optimization procedure most notably resulted in a reduced δ enabling the switch to glycolysis to happen closer to the vessel and a lower κg enabling a later and more drastic shut off of glycolysis; the parameter results of this simulation are presented in Table C1.

The mathematical model presented here can give insight into the effects of blocking various metabolic pathways. The three metabolic pathways that we have considered, namely (i) glucosefuelled respiration, (ii) lactate-fuelled respiration and (iii) glycolysis, could be inhibited by various agents, and the effects on local ATP production will be outlined below. For illustrative purposes we will consider complete inhibition of these pathways, but this will be followed by consideration of the more realistic scenario where these pathways are only partially inhibited. Entirely knocking out lactate metabolism could be achieved by inhibiting lactate dehydrogenase (LDH) which is responsible for the reentry of lactate into respiratory pathways by converting lactate into pyruvate. Successful inhibition would concurrently prevent the conversion of pyruvate to lactate as well, a crucial step for regenerating NAD+ in glycolytic cells. This has been shown to reduce ATP levels and consequently induce cell death in tumors (Le et al., 2010). The complete inhibition of lactate dehydrogenase would eliminate two of the three pathways considered here: lactate-fuelled respiration and glycolysis. Complete inhibition can be reflected in the model by setting BL¼0 and BG¼0, leaving only glucose-fuelled respiration to produce ATP, a physiologically normal condition. However, the hypoxic and hypoglycaemic conditions considered here do not leave enough fuel for cell survival. This scenario corresponds to the third bar in Fig. 8. We could also target glucose transport into the cell, an intermediary of glycolysis or one of the critical enzymes responsible for converting glucose to pyruvate. This is distinct from the strategy noted above of inhibiting LDH which prevents the conversion of lactate to pyruvate and vice versa. This has also been noted as a prime target for cancer therapy (Pelicano et al., 2006; Gatenby and Gillies, 2007) and there are currently many potential targets (Granchi and Minutolo, 2012). Here we will consider the shutdown of glycolysis as preventing both glucose-fuelled respiration and glycolysis since both of these require glucose to be converted into pyruvate. However, it leaves the lactate-fuelled respiratory pathway intact. This could be considered in the model by taking BO¼0 and BG¼0.Similar tothe caseof LDH inhibition this leads to a significant decrease in ATP production as shown in the fourth bar of Fig. 8. The final scenario that we consider corresponds to full inhibition of respiration somewhere along the chain between pyruvate transport into the mitochondria and the electron transport chain. There are numerous potential targets in the mitochondria (Costantini et al., 2000) and we will consider the complete shutdown of respiration by setting BL¼0 and BO¼0. This would result in negligible oxygen consumption and with our base case of ΛO 100-4000 this would lead to repressed glycolysis in the tissue.

Fig. 9. The effects of metabolic repression on total ATP production in a unit annulus of tumor tissue ðΦÞ. Cell metabolism is fully functioning when the relative rate is 1, while the cell metabolism is fully inhibited when the relative rateis 0. Intermediary values correspond to partial inhibition of both affected metabolic rates, e.g. for LDH half-inhibition: BG and BL are half the base case value, and BO remains at its base case value. The legend abbreviations are the same as those used in bars 3–6 in Fig. 8: LDH inhibition (LDH), glycolytic inhibition (Glyc), respiratory inhibition (Resp), and respiratory inhibition with Warburg effect (R-W). The total ATP production begins to significantly decrease for LDH and glycolytic inhibition only once more than half inhibition is reached. For respiratory inhibition, significant decreases are not detected until metabolic rates drop to one-tenth of the base case value.

The functional form for glycolysis given in (8) is similar to that used in McGillen et al. (2013), except where our form uses oxygen as the inhibitory molecule, they use lactate. McGillen et al. (2013) do not include glucose-fuelled respiration at all (as it was deemed to occur at negligible rates), and they use a similar lactate-fuelled respiratory term as used here, that was originally formulated by Mendoza-Juez et al. (2012). Instead of including a Michaelis– Menten oxygen dependence, they introduce a switch parameter that turns oxygen-fuelled metabolism on and off at a threshold oxygen concentration. However, they did enable the cells to use combinations of respiration and glycolysis as opposed to the strict switching between these two pathways modelled by Mendoza Juez et al. (2012). The novel aspects of our model include the introduction of a glycolytic inhibition parameter that can prevent or enable the Warburg effect, an explicit and smoothly defined oxygen dependence for the respiratory pathways, and the inclusion of an accurate ATP yield formula. While our results focus on the energetic consequences of metabolic inhibition, McGillen et al. (2013) focus on the interaction between metabolite consumption and tumor growth.


The mathematical model formulated and analyzed above can give insight into the metabolic behaviors of cancer cells on the microscale. The tumor microenvironment characterized by hypoxia and nutrient deprivation leads to the utilization of highly unregulated glycolytic pathways and the consumption by respiring cells of the lactate produced by these cells. These metabolic scenarios are encompassed by the functional forms proposed for glucose, lactate and oxygen consumption. To consider the effect of altering parameters in the model to the efficiency of energy production we must also consider the rate of ATP turnover in the tissue. To this end a detailed biochemical summary was performed in order to calculate estimates for ATP yields. These energetic landscapes were considered in tissues that utilize anaerobic glycolysis, thus keeping more cells alive, and those that experience the Warburg effect, performing glycolysis in oxygenated areas. The analysis shows that the latter does confer a proliferative advantage by producing more ATP. The effects of metabolic inhibition were taken into account by knocking out the pathways considered in our model. Glycolytic inhibition blocked glycolysis and glucose-fuelled respiration, LDH inhibition blocked glycolysis and lactate-fuelled respiration while respiration inhibition blocked both forms of respiration. Both strategies that block glycolysis lead to appreciable decreases in total ATP production, while those that block respiration are only effective in the base case where the cells are unable to elevate glycolytic rates due to the repressive effect of oxygen in the model. However, when considering a more realistic scenario where cells can adapt to blocked respiratory pathways by upregulating glycolysis via the Warburg effect, we observe that this treatment strategy allows sufficient ATP for cell survival. The work presented here should lead to a reconsideration of the importance of the spatial relationships between cells performing under specific metabolic regimes and provides a minimally parameterized and straightforward basis for future phenomenological metabolite consumption models. Localization and Kinetics of Reduced Pyridine Nucleotide in Living Cells by Microfluorometry J. Biol. Chem.-1959-Chance-3044-50

Britton Chance and Bo Thorell

J Biological Chemistry Nov 1959; 234(11)
On the basis of early studies of the blue fluorescence of living cells and tissues before chemical treatment, l Sjiistrand (1) suggested its association with the mitochondrial bodies. Microspectroscopic observations of prepared tissue sections revealed emission bands of the fluorescent material of axons (1) and acid treated groups of kidney cells; (2) critical evaluations of available spectrograms of purified materials lead to the identification of thiamin and riboflavin, respectively. Although some of the  of the kidney sections, before acid treatment, showed fluorescence bands in the spectrograms that are now regarded as suggestive of reduced pyridine nucleotide, the fluorescence of which was first observed by Warburg (2), insufficient data were available at that time to consider reduced pyridine nucleotide as a possible cause of the tissue fluorescence. Recent studies by Boyer and Theorell (3) and Duysens and Kronenberg (4) on alcohol dehydrogenase show clearly the great enhancement of DPNH fluorescence that is caused by a binding of the coenzyme to the enzyme surface. Furthermore, Duysens and Amesz (5) demonstrate that the intact yeast cell shows a fluorescence characteristic of bound reduced pyridine nucleotide. In more recent experiments, it has been found that intramitochondrial reduced pyridine nucleotide also exhibits the same characteristic fluorescence, calling attention to the possibility of a close relationship between this effect and the blue fluorescence of living cells and tissues (6). The fluorometric result agrees with the spectrophotometrically determined large RPN3 content of mitochondria (7). Furthermore, its binding to a mitochondrial component has been suggested by kinetic studies (7). More recent data show that the fluorescence of intact muscle diminishes upon electrically induced contraction, in agreement with the spectrophotometrically observed oxidation of RPN (8). Thus, there is good evidence that a considerable amount of tissue fluorescence is due to this component. To study the fluorescence of mitochondrial RPN independently of that of the cytoplasm, it has been desirable to develop a microfluorometric method, which, in conjunction with suitable biological materials showing isolation of the mitochondrial bodies, could be used to investigate cytoplasmic-mitochondrial interactions and also to permit the assay of RPN localized in different
1 The term “autofluorescence” is used by Sjostrand and other workers to indicate the fluorescence of a tissue before its treatment with stains, acids, and so forth.
2 F. S. Sjiistrand, unpublished experiments.
* The abbreviation used is: RPN, reduced pyridine nucleotide.

This paper describes such an instrument and its application to the observation of mitochondrial RPN, particularly in highly localized mitochondrial bodies such as the nebenkern4 of the grasshopper spermatid (11). It is now possible to investigate in viva the independent changes of mitochondrial and cytoplasmic pyridine nucleotide in the aerobic-anaerobic transition. In other cells, where mitochondrial localization is not sufficient for independent characterization of cytoplasmic and mitochondrial components of the fluorescence, assays of the oxidation-reduction state of the total pyridine nucleotide in individual cells in different states of metabolism and growth are possible. The combination of this differential fluorometer with the spectrophotometer described elsewhere (12, 13) for the localization of activities of respiratory and glycolytic enzymes in cells affords a new approach to the dynamic aspects of metabolic reactions.

The closure of the switch contacts and the wave form of the photocurrent and light intensity for an AC-operated light source (see below) are indicated in Fig. 1. The fluctuations of the light intensity (~100 per cent modulation) indicated on the top line cause synchronous variations which result in an asymmetrical wave form for the photocurrent, provided the fluorescent object coincides with the extremes of the excursions of the vibrating diaphragm. To measure the fluorescence intensity of the object (M) and that of a nearby “free space” (R), the switch circuit is adjusted so that it closes for a brief interval at the peaks of the photocurrent wave form (Fig. 1). The portions of the photocurrent selected by this switch are used to charge a condenser so that its potential represents the difference of the photocurrents at the two times. This potential is amplified by a “Millivac,” type 17C, and by an Esterline-Angus l-ma. recorder.

FIQ. 1. Wave forms of light intensity and photocurrent relative to the times of switch closure (alternating current operated by lamp). The vibrating diaphragm operates synchronously with the fluctuations in light intensity so that the extremes of its vibration correspond to maxima of light intensity

Fig. 2. Relative fluorescence maxima for suspensions of diploid bakers’ yeast, pentaploid yeast, and ascites tumor cells. These fluorescence emission spectra are obtained with excitation of the cell suspensions by the 366-rnr mercury line passed through the same filter used in the microfluorometer. The energy obtained through the Wratten 2A filter is analyzed by means of a grating monochromator and is plotted as a function of wave length. Significant features of the record are that no measurable energy at 366, 436, or 546 mp is received by the photocell. The cell suspensions are relatively concentrated (60 mg. per cc. for the yeast cell suspensions). The close correspondence of the amplitudes of the peaks is a consequence of adjusting the photocell dynode voltage appropriately (928).

Studies of mitochondria treated with ADP to cause the disappearance of RPN fluorescence show that a relatively small contribution of the flavoprotein of the respiratory chain remains and that flavoprotein fluorescence does not measurably change with its oxidation-reduction state. Thus, it is felt justified in these preliminary studies to attribute the major portion of the fluorescence observed to RPN. Evidence in favor of this view is indicated below, where chemical transitions affecting the oxidation-reduction state and hence the fluorescence of reduced pyridine nucleotide show that most of the fluorescence localized in the mitochondria is affected by this transition and hence is not a “fixed” background fluorescence.

Relative Intensities of Signals-A survey of various biological materials has been made to determine the relative intensities of the signals obtained and to demonstrate the feasibility of studies of their fluorescence. This study is largely incomplete, but the preliminary results summarized in Table I are rather encouraging. These fluorescence intensities range from a small value for the aerobic nebenkern of the grasshopper spermatid to a large value for the anaerobic pentaploid yeast cell. The larger currents give a signal-to-noise ratio of such magnitude that delicate indications are given, not only of the magnitude of the fluorescence, but also of changes that may occur in different metabolic states or in different parts of the cells. At higher currents, accuracies > 100 : 1 are possible. Localization of Fluorescence-The inadequate resolution of the optical microscope and the uniform distribution of the mitochondria throughout the cytoplasm of such cells as bakers’ or pentaploid yeast or ascites tumor cells offer little possibility for localizing the mitochondrial fluorescence as opposed to the cytoplasmic fluorescence.

Table I Summary of fluorescence intensities for various cell types and metabolic states

FIG. 7. Time course of the fluorescence changes of the nebenkern and of the cytoplasm in the aerobic-anaerobic transition. A, the ratio of nebenkern to cytoplasmic fluorescence, plotted as determined by records similar to those of Fig. 6. The numbers in the diagram refer to the cell studied. The abrupt upward discontinuity of the record at approximately 45 minutes occurs when anaerobiosis is expected. B, the individual measurements of the cytoplasmic (0-O) and nebenkern (0-C) fluorescence. The number of the cell used for measurement is also indicated along the scale of the abscissa (922a, b).

A fluorescence with spectral characteristics that are similar to those of reduced pyridine nucleotide of isolated mitochondria has been demonstrated to be localized in three cell configurations which cytologically show mitochondrial aggregation. The oxidation and reduction of mitochondrial pyridine nucleotide without a measurable change of cytoplasmic fluorescence suggest that compartmentalization of mitochondrial and cytoplasmic pyridine nucleotide occurs in viva, at least in the grasshopper spermatid. Studies of other material, particularly pentaploid yeast cells and ascites tumor cells, indicate that similar changes of fluorescence of the single cell are observed in the aerobic-anaerobic transition. In such cells, optical resolution does not permit localization of mitochondrial bodies. Nevertheless, the state of pyridine nucleotide in the individual cell can be investigated.


Radoslav S. Bozov (@Radobozov)

Your interpretations approximates wrong conclusions: 1. Oxygen is processed via mitochondrial Cu2+/3+ metalloproteins , H2O2 (King’s water) electro-negativity processing). 2. Lactate formation is an effect of cancerogenesis, a Lewis base., you lack fundamental understanding pKa issues in science and more accurately in moderns science. Such thing as protons have never been observed directly, that is a concept for explaining pH. All organic acids in bio systems are deprotonated carboxyl functional groups entering resonance state , which allows interpretation of spectra: There i s no way in real chemical science to have measured pH of a compartment and especially nano space! The physiological charge is -1:
Biophysical concepts might be applied in a wrong direction! which is the case of perceiving NADH/Pyruavte/Lactate triangle , you lack conceptual frames of systems applicability in expanded biological space/energies – proteins, nucleotides, and meta states! Pyridines have nothing to do with energy states, pyridines are nitrogen capacitors , they have nothing to do with origin and implications of mutation/evolution, regulation! You lack fundamental understanding of physical implications today!
Lactate is a compensatory mechanism of the genome for copying with disregulated supply of pyruvate for synthesizing negative methyl groups, energy processed in biospaces via compression/decompressing bio systems! Remember, quantum chemistry and implications of quantum physics is not one and the same! General Relativity is applied only towards statistical cloud delocalization, that implicates induction vs deductive reasoning! Classic mechanics of optics is a neat way to do math, nothing more than that of accepting reality of none observable parameters! Lactic acid was considered an end product of metabolism and physiological fatigue for a long time! Now, we know that is not true! To contrary lactic acid is used to have healthy pluripotecy differentiation of bone marrow derived cell lines. LDH proteins demonstrate high similarity motif selection with a range of transcriprion factors via blast studies. In general DATA IS MESSED UP and likely WRONGLY INTERPRETED!

Larry H Bernstein,

I am quite sure that what I presented is the best that science has produced.  Whether there is a theoretical issue in physical interpretation is another matter.  Two key papers are by Mayekovsky and by Britton Chance.  Britton Chance only died recently at nearly 100, but he was a giant in biochemistry, and my final exam question in freshman biochemistry was – should B Chance get the Nobel Prize.  His conception was then controversial, and the ETC won out.  Nevertheless, his contributions went far beyond the explanation for the H+ transfer role in ETC.  When I was a resident in pathology, my mentor (who identified the difference between myokinase and liver AK) commented that  the only reason that Chance had not been awarded was because his work was so technologically focused. I had studied the malate dehydrogenase reaction in Nate Kaplan’s lab, and I carried out stop-flow studies of the inhibition of the mitochondrial isoenzyme by oxaloacetate.  When I went to Washington, DC at the end of the Vietnam War and the time of Watergate, I had the good fortune to be introduced to Chance in a visit to Philadelphia.  I think that I do understand acidemia, cationic and anionic balance, which is not a simple matter – after some 35 years in pathology, with a main focus on clinical pathology.   If you could step back and give a point by point elucidation of where the experimental interpretation is in error, and a point by point highlight of your explanation, it would be very helpful. I know that I am quite knowledgable about the mechanism of reactions of the pyridine nucleotide linked dehydrogenases, and the isoenzymes, and the abortive ternary complexes.  I also published in the Brit J Cancer in the 1970’s on an abnormality in the cytoplasmic MDH in fast growing murine hepatomas, and in human cancer.  I spent many months purifying the heart mitochondrial MDH to purity, and established that there was no histidine residue at the active site.

Read Full Post »

Refined Warburg hypothesis -2.1.2

Writer and Curator: Larry H. Bernstein, MD, FCAP

Refined Warburg Hypothesis -2.1.2

The Warburg discoveries from 1922 on, and the influence on metabolic studies for the next 50 years was immense, and then the revelations of the genetic code took precedence.  Throughout this period, however, the brilliant work of Briton Chance, a giant of biochemistry at the University of Pennsylvania, opened new avenues of exploration that led to a recent resurgence in this vital need for answers in cancer research. The next two series of presentations will open up this resurgence of fundamental metabolic research in cancer and even neurodegenerative diseases. Cancer Cell Metabolism. Warburg and Beyond

Hsu PP, Sabatini DM
Cell, Sep 5, 2008; 134:703-707

Described decades ago, the Warburg effect of aerobic glycolysis is a key metabolic hallmark of cancer, yet its significance remains unclear. In this Essay, we re-examine the Warburg effect and establish a framework for understanding its contribution to the altered metabolism of cancer cells.

It is hard to begin a discussion of cancer cell metabolism without first mentioning Otto Warburg. A pioneer in the study of respiration, Warburg made a striking discovery in the 1920s. He found that, even in the presence of ample oxygen, cancer cells prefer to metabolize glucose by glycolysis, a seeming paradox as glycolysis, when compared to oxidative phosphorylation, is a less efficient pathway for producing ATP (Warburg, 1956). The Warburg effect has since been demonstrated in different types of tumors and the concomitant increase in glucose uptake has been exploited clinically for the detection of tumors by fluorodeoxyglucose positron emission tomography (FDG-PET). Although aerobic glycolysis has now been generally accepted as a metabolic hallmark of cancer, its causal relationship with cancer progression is still unclear. In this Essay, we discuss the possible drivers, advantages, and potential liabilities of the altered metabolism of cancer cells (Figure 1). Although our emphasis on the Warburg effect reflects the focus of the field, we would also like to encourage a broader approach to the study of cancer metabolism that takes into account the contributions of all interconnected small molecule pathways of the cell.

Figure 1. The Altered Metabolism of Cancer Cells

Drivers (A and B). The metabolic derangements in cancer cells may arise either from the selection of cells that have adapted to the tumor microenvironment or from aberrant signaling due to oncogene activation. The tumor microenvironment is spatially and temporally heterogeneous, containing regions of low oxygen and low pH (purple). Moreover, many canonical cancer-associated signaling pathways induce metabolic reprogramming. Target genes activated by hypoxia inducible factor (HIF) decrease the dependence of the cell on oxygen, whereas Ras, Myc, and Akt can also upregulate glucose consumption and glycolysis. Loss of p53 may also recapitulate the features of the Warburg effect, that is, the uncoupling of glycolysis from oxygen levels. Advantages (C–E). The altered metabolism of cancer cells is likely to imbue them with several proliferative and survival advantages, such as enabling cancer cells to execute the biosynthesis of macromolecules (C), to avoid apoptosis (D), and to engage in local metabolite-based paracrine and autocrine signaling (E). Potential Liabilities (F and G). This altered metabolism, however, may also confer several vulnerabilities on cancer cells. For example, an upregulated metabolism may result in the build up of toxic metabolites, including lactate and noncanonical nucleotides, which must be disposed of (F). Moreover, cancer cells may also exhibit a high energetic demand, for which they must either increase flux through normal ATP-generating processes, or else rely on an increased diversity of fuel sources (G).

The Tumor Microenvironment Selects for Altered Metabolism

One compelling idea to explain the Warburg effect is that the altered metabolism of cancer cells confers a selective advantage for survival and proliferation in the unique tumor microenvironment. As the early tumor expands, it outgrows the diffusion limits of its local blood supply, leading to hypoxia and stabilization of the hypoxia-inducible transcription factor, HIF. HIF initiates a transcriptional program that provides multiple solutions to hypoxic stress (reviewed in Kaelin and Ratcliffe, 2008). Because a decreased dependence on aerobic respiration becomes advantageous, cell metabolism is shifted toward glycolysis by the increased expression of glycolytic enzymes, glucose transporters, and inhibitors of mitochondrial metabolism. In addition, HIF stimulates angiogenesis (the formation of new blood vessels) by upregulating several factors, including most prominently vascular endothelial growth factor (VEGF).

The oxygen levels within a tumor vary both spatially and temporally, and the resulting rounds of fluctuating oxygen levels potentially select for tumors that constitutively upregulate glycolysis. Interestingly, with the possible exception of tumors that have lost the von Hippel-Lindau protein (VHL), which normally mediates degradation of HIF, HIF is still coupled to oxygen levels, as evident from the heterogeneity of HIF expression within the tumor microenvironment (Wiesener et al., 2001; Zhong et al., 1999). Therefore, the Warburg effect—that is, an uncoupling of glycolysis from oxygen levels—cannot be explained solely by upregulation of HIF.

Recent work has demonstrated that the key components of the Warburg effect—increased glucose consumption, decreased oxidative phosphorylation, and accompanying lactate production—are also distinguishing features of oncogene activation. The signaling molecule Ras, a powerful oncogene when mutated, promotes glycolysis (reviewed in Dang and Semenza, 1999; Samanathan et al., 2005). Akt kinase, a well-characterized downstream effector of insulin signaling, reprises its role in glucose uptake and utilization in the cancer setting (reviewed in Manning and Cantley, 2007), whereas the Myc transcription factor upregulates the expression of various metabolic genes (reviewed in Gordan et al., 2007). The most parsimonious route to tumorigenesis may be activation of key oncogenic nodes that execute a proliferative program, of which metabolism may be one important arm. Moreover, regulation of metabolism is not exclusive to oncogenes. Loss of the tumor suppressor protein p53 prevents expression of the gene encoding SCO2 (the synthesis of cytochrome c oxidase protein), which interferes with the function of the mitochondrial respiratory chain (Matoba et al., 2006). A second p53 effector, TIGAR (TP53-induced glycolysis and apoptosis regulator), inhibits glycolysis by decreasing levels of fructose-2,6-bisphosphate, a potent stimulator of glycolysis and inhibitor of gluconeogenesis (Bensaad et al., 2006). Other work also suggests that p53-mediated regulation of glucose metabolism may be dependent on the transcription factor NF-κB (Kawauchi et al., 2008).
It has been shown that inhibition of lactate dehydrogenase A (LDH-A) prevents the Warburg effect and forces cancer cells to revert to oxidative phosphorylation in order to reoxidize NADH and produce ATP (Fantin et al., 2006; Shim et al., 1997). While the cells are respiratory competent, they exhibit attenuated tumor growth, suggesting that aerobic glycolysis might be essential for cancer progression. In a primary fibroblast cell culture model of stepwise malignant transformation through overexpression of telomerase, large and small T antigen, and the H-Ras oncogene, increasing tumorigenicity correlates with sensitivity to glycolytic inhibition. This finding suggests that the Warburg effect might be inherent to the molecular events of transformation (Ramanathan et al., 2005). However, the introduction of similar defined factors into human mesenchymal stem cells (MSCs) revealed that transformation can be associated with increased dependence on oxidative phosphorylation (Funes et al., 2007). Interestingly, when introduced in vivo these transformed MSCs do upregulate glycolytic genes, an effect that is reversed when the cells are explanted and cultured under normoxic conditions. These contrasting models suggest that the Warburg effect may be context dependent, in some cases driven by genetic changes and in others by the demands of the microenvironment. Regardless of whether the tumor microenvironment or oncogene activation plays a more important role in driving the development of a distinct cancer metabolism, it is likely that the resulting alterations confer adaptive, proliferative, and survival advantages on the cancer cell.

Altered Metabolism Provides Substrates for Biosynthetic Pathways

Although studies in cancer metabolism have largely been energy-centric, rapidly dividing cells have diverse requirements. Proliferating cells require not only ATP but also nucleotides, fatty acids, membrane lipids, and proteins, and a reprogrammed metabolism may serve to support synthesis of macromolecules. Recent studies have shown that several steps in lipid synthesis are required for and may even actively promote tumorigenesis. Inhibition of ATP citrate lyase, the distal enzyme that converts mitochondrial-derived citrate into cytosolic acetyl coenzyme A, the precursor for many lipid species, prevents cancer cell proliferation and tumor growth (Hatzivassiliou et al., 2005). Fatty acid synthase, expressed at low levels in normal tissues, is upregulated in cancer and may also be required for tumorigenesis (reviewed in Menendez and Lupu, 2007). Furthermore, cancer cells may also enhance their biosynthetic capabilities by expressing a tumor-specific form of pyruvate kinase (PK), M2-PK. Pyruvate kinase catalyzes the third irreversible reaction of glycolysis, the conversion of phosphoenolpyruvate (PEP) to pyruvate. Surprisingly, the M2-PK of cancer cells is thought to be less active in the conversion of PEP to pyruvate and thus less efficient at ATP production (reviewed in Mazurek et al., 2005). A major advantage to the cancer cell, however, is that the glycolytic intermediates upstream of PEP might be shunted into synthetic processes.

Biosynthesis, in addition to causing an inherent increase in ATP demand in order to execute synthetic reactions, should also cause a decrease in ATP supply as various glycolytic and Krebs cycle intermediates are diverted. Lipid synthesis, for example, requires the cooperation of glycolysis, the Krebs cycle, and the pentose phosphate shunt. As pyruvate must enter the mitochondria in this case, it avoids conversion to lactate and therefore cannot contribute to glycolysis-derived ATP. Moreover, whereas increased biosynthesis may explain the glucose hunger of cancer cells, it cannot explain the increase in lactic acid production originally described by Warburg, suggesting that lactate must also result from the metabolism of non-glucose substrates. Recently, it has been demonstrated that glutamine may be metabolized by the citric acid cycle in cancer cells and converted into lactate, producing NADPH for lipid biosynthesis and oxaloacetate for replenishment of Krebs cycle intermediates (DeBerardinis et al., 2007).

Metabolic Pathways Regulate Apoptosis

In addition to involvement in proliferation, altered metabolism may promote another cancer-essential function: the avoidance of apoptosis. Loss of the p53 target TIGAR sensitizes cancer cells to apoptosis, most likely by causing an increase in reactive oxygen species (Bensaad et al., 2006). On the other hand, overexpression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) prevents caspase-independent cell death, presumably by stimulating glycolysis, increasing cellular ATP levels, and promoting autophagy (Colell et al., 2007). Whether or not GAPDH plays a physiological role in the regulation of cell death remains to be determined. Intriguingly, Bonnet et al. (2007) have reported that treating cancer cells with dichloroacetate (DCA), a small molecule inhibitor of pyruvate dehydrogenase kinase, has striking effects on their survival and on xenograft tumor growth.

DCA, a currently approved treatment for congenital lactic acidosis, activates oxidative phosphorylation and promotes apoptosis by two mechanisms. First, increased flux through the electron transport chain causes depolarization of the mitochondrial membrane potential (which the authors found to be hyperpolarized specifically in cancer cells) and release of the apoptotic effector cytochrome c. Second, an increase in reactive oxygen species generated by oxidative phosphorylation upregulates the voltage-gated K+ channel, leading to potassium ion efflux and caspase activation. Their work suggests that cancer cells may shift their metabolism to glycolysis in order to prevent cell death and that forcing cancer cells to respire aerobically can counteract this adaptation.

Cancer Cells May Signal Locally in the Tumor Microenvironment

Cancer cells may rewire metabolic pathways to exploit the tumor microenvironment and to support cancer-specific signaling. Without access to the central circulation, it is possible that metabolites can be concentrated locally and reach suprasystemic levels, allowing cancer cells to engage in metabolite-mediated autocrine and paracrine signaling that does not occur in normal tissues. So called androgen-independent prostate cancers may only be independent from exogenous, adrenal-synthesized androgens. Androgen-independent prostate cancer cells still express the androgen receptor and may be capable of autonomously synthesizing their own androgens (Stanbrough et al., 2006).

Metabolism as an Upstream Modulator of Signaling Pathways

Not only is metabolism downstream of oncogenic pathways, but an altered upstream metabolism may affect the activity of signaling pathways that normally sense the state of the cell. Individuals with inherited mutations in succinate dehydrogenase and fumarate hydratase develop highly angiogenic tumors, not unlike those exhibiting loss of the VHL tumor suppressor protein that acts upstream of HIF (reviewed in Kaelin and Ratcliffe, 2008). The mechanism of tumorigenesis in these cancer syndromes is still contentious. However, it has been proposed that loss of succinate dehydrogenase and fumarate hydratase causes an accumulation of succinate or fumarate, respectively, leading to inhibition of the prolyl hydroxylases that mark HIF for VHL-mediated degradation (Isaacs et al., 2005; Pollard et al., 2005; Selak et al., 2005). In this rare case, succinate dehydrogenase and fumarate hydratase are acting as bona fide tumor suppressors.

There are many complex questions to be answered: Is it possible that cancer cells exhibit “metabolite addiction”? Are there unique cancer-specific metabolic pathways, or combinations of pathways, utilized by the cancer cell but not by normal cells? Are different stages of metabolic adaptations required for the cancer cell to progress from the primary tumor stage to invasion to metastasis? How malleable is cancer metabolism? Cancer metabolism. The Warburg effect today

Ferreira LMR
Exp Molec Pathol 2010; 89:372-383.

One of the first studies on the energy metabolism of a tumor was carried out, in 1922, in the laboratory of Otto Warburg. He established that cancer cells exhibited a specific metabolic pattern, characterized by a shift from respiration to fermentation, which has been later named the Warburg effect. Considerable work has been done since then, deepening our understanding of the process, with consequences for diagnosis and therapy. This review presents facts and perspectives on the Warburg effect for the 21st century.

Research highlights

► Warburg first established a tumor metabolic pattern in the 1920s. ► Tumors’ increased glucose uptake has been studied since then. ► Cancer bioenergetics’ study provides insights in all its hallmarks. ► New cancer diagnostic and therapeutic techniques focus on cancer metabolism.

Contestation to Warburg’s ideas
Glucose’s uptake and intracellular fates
Lactate production and induced acidosis
Impairment of mitochondrial function
Tumour microenvironment
Proliferating versus cancer cells
More on cancer bioenergetics – integration of metabolism
Perspectives New aspects of the Warburg effect in cancer cell biology

Bensinger SJ, Cristofk HR
Sem Cell Dev Biol 2012; 23:352-361

Altered cellular metabolism is a defining feature of cancer [1]. The best studied metabolic phenotype of cancer is aerobic glycolysis–also known as the Warburg effect–characterized by increased metabolism of glucose to lactate in the presence of sufficient oxygen. Interest in the Warburg effect has escalated in recent years due to the proven utility of FDG-PET for imaging tumors in cancer patients and growing evidence that mutations in oncogenes and tumor suppressor genes directly impact metabolism. The goals of this review are to provide an organized snapshot of the current understanding of regulatory mechanisms important for Warburg effect and its role in tumor biology. Since several reviews have covered aspects of this topic in recent years, we focus on newest contributions to the field and reference other reviews where appropriate.


► This review discusses regulatory mechanisms that contribute to the Warburg effect in cancer. ► We list cancers for which FDG-PET has established applications as well as those cancers for which FDG-PET has not been established. ► PKM2 is highlighted as an important integrator of diverse cellular stimuli to modulate metabolic flux and cancer cell proliferation. ► We discuss how cancer metabolism can directly influence gene expression programs. ► Contribution of aerobic glycolysis to the cancer microenvironment and chemotherapeutic resistance/susceptibility is also discussed.

Regulation of the Warburg effect

PKM2 integrates diverse signals to modulate metabolic flux and cell proliferation

PKM2 integrates diverse signals to modulate metabolic flux and cell proliferation

Fig. 1. PKM2 integrates diverse signals to modulate metabolic flux and cell proliferation

Metabolism can directly influence gene expression programs

Metabolism can directly influence gene expression programs

Fig. 2. Metabolism can directly influence gene expression programs. A schematic representation of how metabolism can intrinsically influence epigenetics resulting in durable and heritable gene expression programs in progeny. Choosing between glycolysis and oxidative phosphorylation. A tumor’s dilemma

Jose C, Ballance N, Rossignal R
Biochim Biophys Acta 201; 1807(6): 552-561.

A considerable amount of knowledge has been produced during the last five years on the bioenergetics of cancer cells, leading to a better understanding of the regulation of energy metabolism during oncogenesis, or in adverse conditions of energy substrate intermittent deprivation. The general enhancement of the glycolytic machinery in various cancer cell lines is well described and recent analyses give a better view of the changes in mitochondrial oxidative phosphorylation during oncogenesis. While some studies demonstrate a reduction of oxidative phosphorylation (OXPHOS) capacity in different types of cancer cells, other investigations revealed contradictory modifications with the upregulation of OXPHOS components and a larger dependency of cancer cells on oxidative energy substrates for anabolism and energy production. This apparent conflictual picture is explained by differences in tumor size, hypoxia, and the sequence of oncogenes activated. The role of p53, C-MYC, Oct and RAS on the control of mitochondrial respiration and glutamine utilization has been explained recently on artificial models of tumorigenesis. Likewise, the generation of induced pluripotent stem cells from oncogene activation also showed the role of C-MYC and Oct in the regulation of mitochondrial biogenesis and ROS generation. In this review article we put emphasis on the description of various bioenergetic types of tumors, from exclusively glycolytic to mainly OXPHOS, and the modulation of both the metabolic apparatus and the modalities of energy substrate utilization according to tumor stage, serial oncogene activation and associated or not fluctuating microenvironmental substrate conditions. We conclude on the importance of a dynamic view of tumor bioenergetics.

Research Highlights

►The bioenergetics of cancer cells differs from normals. ►Warburg hypothesis is not verified in tumors using mitochondria to synthesize ATP. ►Different oncogenes can either switch on or switch off OXPHOS. ►Bioenergetic profiling is a prerequisite to metabolic therapy. ►Aerobic glycolysis and OXPHOS cooperate during cancer progression.

  1. Cancer cell variable bioenergetics

Cancer cells exhibit profound genetic, bioenergetic and histological differences as compared to their non-transformed counterpart. All these modifications are associated with unlimited cell growth, inhibition of apoptosis and intense anabolism. Transformation from a normal cell to a malignant cancer cell is a multi-step pathogenic process which includes a permanent interaction between cancer gene activation (oncogenes and/or tumor-suppressor genes), metabolic reprogramming and tumor-induced changes in microenvironment. As for the individual genetic mapping of human tumors, their metabolic characterization (metabolic–bioenergetic profiling) has evidenced a cancer cell-type bioenergetic signature which depends on the history of the tumor, as composed by the sequence of oncogenes activated and the confrontation to intermittent changes in oxygen, glucose and amino-acid delivery.

In the last decade, bioenergetic studies have highlighted the variability among cancer types and even inside a cancer type as regards to the mechanisms and the substrates preferentially used for deriving the vital energy. The more popular metabolic remodeling described in tumor cells is an increase in glucose uptake, the enhancement of glycolytic capacity and a high lactate production, along with the absence of respiration despite the presence of high oxygen concentration (Warburg effect) [1]. To explain this abnormal bioenergetic phenotype pioneering hypotheses proposed the impairment of mitochondrial function in rapidly growing cancer cells [2].

Although the increased consumption of glucose by tumor cells was confirmed in vivo by positron emission tomography (PET) using the glucose analog 2-(18F)-fluoro-2-deoxy-d-glucose (FDG), the actual utilization of glycolysis and oxidative phosphorylation (OXPHOS) cannot be evaluated with this technique. Nowadays, Warburg’s “aerobic-glycolysis” hypothesis has been challenged by a growing number of studies showing that mitochondria in tumor cells are not inactive per se but operate at low capacity [3] or, in striking contrast, supply most of the ATP to the cancer cells [4]. Intense glycolysis is effectively not observed in all tumor types. Indeed not all cancer cells grow fast and intense anabolism is not mandatory for all cancer cells. Rapidly growing tumor cells rely more on glycolysis than slowly growing tumor cells. This is why a treatment with bromopyruvate, for example is very efficient only on rapidly growing cells and barely useful to decrease the growth rate of tumor cells when their normal proliferation is slow. Already in 1979, Reitzer and colleagues published an article entitled “Evidence that glutamine, not sugar, is the major energy source for cultured Hela cells”, which demonstrated that oxidative phosphorylation was used preferentially to produce ATP in cervical carcinoma cells [5]. Griguer et al. also identified several glioma cell lines that were highly dependent on mitochondrial OXPHOS pathway to produce ATP [6]. Furthermore, a subclass of glioma cells which utilize glycolysis preferentially (i.e., glycolytic gliomas) can also switch from aerobic glycolysis to OXPHOS under limiting glucose conditions  [7] and [8], as observed in cervical cancer cells, breast carcinoma cells, hepatoma cells and pancreatic cancer cells [9][10] and [11]. This flexibility shows the interplay between glycolysis and OXPHOS to adapt the mechanisms of energy production to microenvironmental changes as well as differences in tumor energy needs or biosynthetic activity. Herst and Berridge also demonstrated that a variety of human and mouse leukemic and tumor cell lines (HL60, HeLa, 143B, and U937) utilize mitochondrial respiration to support their growth [12]. Recently, the measurement of OXPHOS contribution to the cellular ATP supply revealed that mitochondria generate 79% of the cellular ATP in HeLa cells, and that upon hypoxia this contribution is reduced to 30% [4]. Again, metabolic flexibility is used to survive under hypoxia. All these studies demonstrate that mitochondria are efficient to synthesize ATP in a large variety of cancer cells, as reviewed by Moreno-Sanchez [13]. Despite the observed reduction of the mitochondrial content in tumors [3][14][15][16][17][18] and [19], cancer cells maintain a significant level of OXPHOS capacity to rapidly switch from glycolysis to OXPHOS during carcinogenesis. This switch is also observed at the level of glutamine oxidation which can occur through two modes, “OXPHOS-linked” or “anoxic”, allowing to derive energy from glutamine or serine regardless of hypoxia or respiratory chain reduced activity [20].
While glutamine, glycine, alanine, glutamate, and proline are typically oxidized in normal and tumor mitochondria, alternative substrate oxidations may also contribute to ATP supply by OXPHOS. Those include for instance the oxidation of fatty-acids, ketone bodies, short-chain carboxylic acids, propionate, acetate and butyrate (as recently reviewed in [21]).

  1. Varying degree of mitochondrial utilization during tumorigenesis

In vivo metabolomic analyses suggest the existence of a continuum of bioenergetic remodeling in rat tumors according to tumor size and its rate of growth [22]. Peter Vaupel’s group showed that small tumors were characterized by a low conversion of glucose to lactate whereas the conversion of glutamine to lactate was high. In medium sized tumors the flow of glucose to lactate as well as oxygen utilization was increased whereas glutamine and serine consumption were reduced. At this stage tumor cells started with glutamate and alanine production. Large tumors were characterized by a low oxygen and glucose supply but a high glucose and oxygen utilization rate. The conversion of glucose to glycine, alanine, glutamate, glutamine, and proline reached high values and the amino acids were released [22]. Certainly, in the inner layers constituting solid tumors, substrate and oxygen limitation is frequently observed. Experimental studies tried to reproduce these conditions in vitro and revealed that nutrients and oxygen limitation does not affect OXPHOS and cellular ATP levels in human cervix tumor [23]. Furthermore, the growth of HeLa cells, HepG2 cells and HTB126 (breast cancer) in aglycemia and/or hypoxia even triggered a compensatory increase in OXPHOS capacity, as discussed above. Yet, the impact of hypoxia might be variable depending on cell type and both the extent and the duration of oxygen limitation.
In two models of sequential oncogenesis, the successive activation of specific oncogenes in non-cancer cells evidenced the need for active OXPHOS to pursue tumorigenesis. Funes et al. showed that the transformation of human mesenchymal stem cells increases their dependency on OXPHOS for energy production [24], while Ferbeyre et al. showed that cells expressing oncogenic RAS display an increase in mitochondrial mass, mitochondrial DNA, and mitochondrial production of reactive oxygen species (ROS) prior to the senescent cell cycle arrest [25]. Such observations suggest that waves of gene regulation could suppress and then restore OXPHOS in cancer cells during tumorigenesis [20]. Therefore, the definition of cancer by Hanahan and Weinberg [26] restricted to six hallmarks (1—self-sufficiency in growth signals, 2—insensitivity to growth-inhibitory (antigrowth) signals, 3—evasion of programmed cell death (apoptosis), 4—limitless replicative potential, 5—sustained angiogenesis, and 6—tissue invasion and metastases) should also include metabolic reprogramming, as the seventh hallmark of cancer. This amendment was already proposed by Tennant et al. in 2009 [27]. In 2006, the review Science published a debate on the controversial views of Warburg theory [28], in support of a more realistic description of cancer cell’s variable bioenergetic profile. The pros think that high glycolysis is an obligatory feature of human tumors, while the cons propose that high glycolysis is not exclusive and that tumors can use OXPHOS to derive energy. A unifying theory closer to reality might consider that OXPHOS and glycolysis cooperate to sustain energy needs along tumorigenesis [20]. The concept of oxidative tumors, against Warburg’s proposal, was introduced by Guppy and colleagues, based on the observation that breast cancer cells can generate 80% of their ATP by the mitochondrion [29]. The comparison of different cancer cell lines and excised tumors revealed a variety of cancer cell’s bioenergetic signatures which raised the question of the mechanisms underlying tumor cell metabolic reprogramming, and the relative contribution of oncogenesis and microenvironment in this process. It is now widely accepted that rapidly growing cancer cells within solid tumors suffer from a lack of oxygen and nutrients as tumor grows. In such situation of compromised energy substrate delivery, cancer cell’s metabolic reprogramming is further used to sustain anabolism (Fig. 1), through the deviation of glycolysis, Krebs cycle truncation and OXPHOS redirection toward lipid and protein synthesis, as needed to support uncontrolled tumor growth and survival [30] and [31]. Again, these features are not exclusive to all tumors, as Krebs cycle truncation was only observed in some cancer cells, while other studies indicated that tumor cells can maintain a complete Krebs cycle [13] in parallel with an active citrate efflux. Likewise, generalizations should be avoided to prevent over-interpretations.
Fig. 1. Energy metabolism at the crossroad between catabolism and anabolism.

Energy metabolism at the crossroad between catabolism and anabolism.

Energy metabolism at the crossroad between catabolism and anabolism.

The oncogene C-MYC participate to these changes via the stimulation of glutamine utilization through the coordinate expression of genes necessary for cells to engage in glutamine catabolism [30]. According to Newsholme EA and Board M [32] both glycolysis and glutaminolysis not only serve for ATP production, but also provide precious metabolic intermediates such as glucose-6-phosphate, ammonia and aspartate required for the synthesis of purine and pyrimidine nucleotides (Fig. 1). In this manner, the observed apparent excess in the rates of glycolysis and glutaminolysis as compared to the requirement for energy production could be explained by the need for biosynthetic processes. Yet, one should not reduce the shift from glycolysis to OXPHOS utilization to the sole activation of glutaminolysis, as several other energy substrates can be used by tumor mitochondria to generate ATP [21]. The contribution of these different fuels to ATP synthesis remains poorly investigated in human tumors.

  1. The metabolism of pre-cancer cells and its ongoing modulation by carcinogenesis

At the beginning of cancer, there might have been a cancer stem cell hit by an oncogenic event, such as alterations in mitogen signaling to extracellular growth factor receptors (EGFR), oncogenic activation of these receptors, or oncogenic alterations of downstream targets in the pathways that leads to cell proliferation (RAS–Raf–ERK and PI3K–AKT, both leading to m-TOR activation stimulating cell growth). Alterations of checkpoint genes controlling the cell cycle progression like Rb also participate in cell proliferation (Fig. 2) and this re-entry in the cell cycle implies three major needs to fill in: 1) supplying enough energy to grow and 2) synthesize building blocks de novo and 3) keep vital oxygen and nutrients available. However, the bioenergetic status of the pre-cancer cell could determine in part the evolution of carcinogenesis, as shown on mouse embryonic stem cells. In this study, Schieke et al. showed that mitochondrial energy metabolism modulates both the differentiation and tumor formation capacity of mouse embryonic stem cells [37]. The idea that cancer derives from a single cell, known as the cancer stem cell hypothesis, was introduced by observations performed on leukemia which appeared to be organized as origination from a primitive hematopoietic cell [38]. Nowadays cancer stem cells were discovered for all types of tumors [39][40][41] and [42], but little is known of their bioenergetic properties and their metabolic adaptation to the microenvironment. This question is crucial as regards the understanding of what determines the wide variety of cancer cell’s metabolic profile.

Impact of different oncogenes on tumor progression and energy metabolism remodeling.

Impact of different oncogenes on tumor progression and energy metabolism remodeling.

Fig. 2. Impact of different oncogenes on tumor progression and energy metabolism remodeling.

The analysis of the metabolic changes that occur during the transformation of adult mesenchymal stem cells revealed that these cells did not switch to aerobic glycolysis, but their dependency on OXPHOS was even increased [24]. Hence, mitochondrial energy metabolism could be critical for tumorigenesis, in contrast with Warburg’s hypothesis. As discussed above, the oncogene C-MYC also stimulates OXPHOS [30]. Furthermore, it was recently demonstrated that cells chronically treated with oligomycin repress OXPHOS and produce larger tumors with higher malignancy [19]. Likewise, alteration of OXPHOS by mutations in mtDNA increases tumorigenicity in different types of cancer cells [43][44] and [45].

Recently, it was proposed that mitochondrial energy metabolism is required to generate reactive oxygen species used for the carcinogenetic process induced by the K-RAS mutation [46]. This could explain the large number of mitochondrial DNA mutations found in several tumors. The analysis of mitochondria in human embryonic cells which derive energy exclusively from anaerobic glycolysis have demonstrated an immature mitochondrial network characterized by few organelles with poorly developed cristae and peri-nuclear distribution [47] and [48]. The generation of human induced pluripotent stem cell by the introduction of different oncogenes as C-MYC and Oct4 reproduced this reduction of mitochondrial OXPHOS capacity[49] and [50]. This indicates again the impact of oncogenes on the control of OXPHOS and might explain the existence of pre-cancer stem cells with different bioenergetic backgrounds, as modeled by variable sequences of oncogene activation. Accordingly, the inhibition of mitochondrial respiratory chain has been recently found associated with enhancement of hESC pluripotency [51].

Based on the experimental evidence discussed above, one can argue that 1) glycolysis is indeed a feature of several tumors and associates with faster growth in high glucose environment, but 2) active OXPHOS is also an important feature of (other) tumors taken at a particular stage of carcinogenesis which might be more advantageous than a “glycolysis-only” type of metabolism in conditions of intermittent shortage in glucose delivery. The metabolic apparatus of cancer cells is not fixed during carcinogenesis and might depend both on the nature of the oncogenes activated and the microenvironment. It was indeed shown that cancer cells with predominant glycolytic metabolism present a higher malignancy when submitted to carcinogenetic induction and analysed under fixed experimental conditions of high glucose [19]. Yet, if one grows these cells in a glucose-deprived medium they shift their metabolism toward predominant OXPHOS, as shown in HeLa cells and other cell types [9]. Therefore, one might conclude that glycolytic cells have a higher propensity to generate aggressive tumors when glucose availability is high. However, these cells can become OXPHOS during tumor progression [24] and [52]. All these observations indicate again the importance of maintaining an active OXPHOS metabolism to permit evolution of both embryogenesis and carcinogenesis, which emphasizes the importance of targeting mitochondria to alter this malignant process.

  1. Oncogenes and the modulation of energy metabolism

Several oncogenes and associated proteins such as HIF-1α, RAS, C-MYC, SRC, and p53 can influence energy substrate utilization by affecting cellular targets, leading to metabolic changes that favor cancer cell survival, independently of the control of cell proliferation. These oncogenes stimulate the enhancement of aerobic glycolysis, and an increasing number of studies demonstrate that at least some of them can also target directly the OXPHOS machinery, as discussed in this article (Fig. 2). For instance, C-MYC can concurrently drive aerobic glycolysis and/or OXPHOS according to the tumor cell microenvironment, via the expression of glycolytic genes or the activation of mitochondrial oxidation of glutamine [53]. The oncogene RAS has been shown to increase OXPHOS activity in early transformed cells [24][52] and [54] and p53 modulates OXPHOS capacity via the regulation of cytochrome c oxidase assembly [55]. Hence, carcinogenic p53 deficiency results in a decreased level of COX2 and triggers a shift toward anaerobic metabolism. In this case, lactate synthesis is increased, but cellular ATP levels remain stable [56]. The p53-inducible isoform of phosphofructokinase, termed TP53-induced glycolysis and apoptotic regulator, TIGAR, a predominant phosphatase activity isoform of PFK-2, has also been identified as an important regulator of energy metabolism in tumors [57].

  1. Tumor specific isoforms (or mutated forms) of energy genes

Tumors are generally characterized by a modification of the glycolytic system where the level of some glycolytic enzymes is increased, some fetal-like isozymes with different kinetic and regulatory properties are produced, and the reverse and back-reactions of the glycolysis are strongly reduced [60]. The GAPDH marker of the glycolytic pathway is also increased in breast, gastric, lung, kidney and colon tumors [18], and the expression of glucose transporter GLUT1 is elevated in most cancer cells. The group of Cuezva J.M. developed the concept of cancer bioenergetic signature and of bioenergetic index to describe the metabolic profile of cancer cells and tumors [18], [61], [64], [65]. This signature describes the changes in the expression level of proteins involved in glycolysis and OXPHOS, while the BEC index gives a ratio of OXPHOS protein content to glycolytic protein content, in good correlation with cancer prognostic[61]. Recently, this group showed that the beta-subunit of the mitochondrial F1F0-ATP synthase is downregulated in a large number of tumors, thus contributing to the Warburg effect [64] and [65]. It was also shown that IF1 expression levels were increased in hepatocellular carcinomas, possibly to prevent the hydrolysis of glytolytic ATP [66]. Numerous changes occur at the level of OXPHOS and mitochondrial biogenesis in human tumors, as we reviewed previously [67]. Yet the actual impact of these changes in OXPHOS protein expression level or catalytic activities remains to be evaluated on the overall fluxes of respiration and ATP synthesis. Indeed, the metabolic control analysis and its extension indicate that it is often required to inhibit activity beyond a threshold of 70–85% to affect the metabolic fluxes [68] and [69]. Another important feature of cancer cells is the higher level of hexokinase II bound to mitochondrial membrane (50% in tumor cells). A study performed on human gliomas (brain) estimated the mitochondrial bound HK fraction (mHK) at 69% of total, as compared to 9% for normal brain [70]. This is consistent with the 5-fold amplification of the type II HK gene observed by Rempel et al. in the rapidly growing rat AS-30D hepatoma cell line, relative to normal hepatocytes [71]. HKII subcellular fractionation in cancer cells was described in several studies [72][73] and [74]. The group led by Pete Pedersen explained that mHK contributes to (i) the high glycolytic capacity by utilizing mitochondrially regenerated ATP rather than cytosolic ATP (nucleotide channelling) and (ii) the lowering of OXPHOS capacity by limiting Pi and ADP delivery to the organelle [75] and [76].

All these observations are consistent with the increased rate of FDG uptake observed by PET in living tumors which could result from both an increase in glucose transport, and/or an increase in hexokinase activity. However, FDG is not a complete substrate for glycolysis (it is only transformed into FDG-6P by hexokinase before to be eliminated) and cannot be used to evidence a general increase in the glycolytic flux. Moreover, FDG-PET scan also gives false positive and false negative results, indicating that some tumors do not depend on, or do not have, an increased glycolytic capacity. The fast glycolytic system described above is further accommodated in cancer cells by an increase in the lactate dehydrogenase isoform A (LDH-A) expression level. This isoform presents a higher Vmax useful to prevent the inhibition of high glycolysis by its end product (pyruvate) accumulation. Recently, Fantin et al. showed that inhibition of LDH-A in tumors diminishes tumorigenicity and was associated with the stimulation of mitochondrial respiration [79]. The preferential expression of the glycolytic pyruvate kinase isoenzyme M2 (PKM2) in tumor cells, determines whether glucose is converted to lactate for regeneration of energy (active tetrameric form, Warburg effect) or used for the synthesis of cell building blocks (nearly inactive dimeric form) [80]. In the last five years, mutations in proteins of the respiratory system (SDH, FH) and of the TCA cycle (IDH1,2) leading to the accumulation of metabolite and the subsequent activation of HIF-1α were reported in a variety of human tumors [81], [82] and [83].

  1. Tumor microenvironment modulates cancer cell’s bioenergetics

It was extensively described how hypoxia activates HIF-1α which stimulates in turn the expression of several glycolytic enzymes such as HK2, PFK, PGM, enolase, PK, LDH-A, MCT4 and glucose transporters Glut 1 and Glut 3. It was also shown that HIF-1α can reduce OXPHOS capacity by inhibiting mitochondrial biogenesis [14] and [15], PDH activity [87] and respiratory chain activity [88]. The low efficiency and uneven distribution of the vascular system surrounding solid tumors can lead to abrupt changes in oxygen (intermittent hypoxia) but also energy substrate delivery. .. The removal of glucose, or the inhibition of glycolysis by iodoacetate led to a switch toward glutamine utilization without delay followed by a rapid decrease in acid release. This illustrates once again how tumors and human cancer cell lines can utilize alternative energy pathway such as glutaminolysis to deal with glucose limitation, provided the presence of oxygen. It was also observed that in situations of glucose limitation, tumor derived-cells can adapt to survive by using exclusively an oxidative energy substrate [9] and [10]. This is typically associated with an enhancement of the OXPHOS system. … In summary, cancer cells can survive by using exclusively OXPHOS for ATP production, by altering significantly mitochondrial composition and form to facilitate optimal use of the available substrate (Fig. 3). Yet, glucose is needed to feed the pentose phosphate pathway and generate ribose essential for nucleotide biosynthesis. This raises the question of how cancer cells can survive in the growth medium which do not contain glucose (so-called “galactose medium” with dialysed serum [9]). In the OXPHOS mode, pyruvate, glutamate and aspartate can be derived from glutamine, as glutaminolysis can replenish Krebs cycle metabolic pool and support the synthesis of alanine and NADPH [31]. Glutamine is a major source for oxaloacetate (OAA) essential for citrate synthesis. Moreover, the conversion of glutamine to pyruvate is associated with the reduction of NADP+ to NADPH by malic enzyme. Such NADPH is a required electron donor for reductive steps in lipid synthesis, nucleotide metabolism and GSH reduction. In glioblastoma cells the malic enzyme flux was estimated to be high enough to supply all of the reductive power needed for lipid synthesis [31].

Fig. 3. Interplay between energy metabolism, oncogenes and tumor microenvironment during tumorigenesis (the “metabolic wave model”).

Interplay between energy metabolism, oncogenes and tumor microenvironment

Interplay between energy metabolism, oncogenes and tumor microenvironment

While the mechanisms leading to the enhancement of glycolytic capacity in tumors are well documented, less is known about the parallel OXPHOS changes. Both phenomena could result from a selection of pre-malignant cells forced to survive under hypoxia and limited glucose delivery, followed by an adaptation to intermittent hypoxia, pseudo-hypoxia, substrate limitation and acidic environment. This hypothesis was first proposed by Gatenby and Gillies to explain the high glycolytic phenotype of tumors [91], [92] and [93], but several lines of evidence suggest that it could also be used to explain the mitochondrial modifications observed in cancer cells.

  1. Aerobic glycolysis and mitochondria cooperate during cancer progression

Metabolic flexibility considers the possibility for a given cell to alternate between glycolysis and OXPHOS in response to physiological needs. Louis Pasteur found that in most mammalian cells the rate of glycolysis decreases significantly in the presence of oxygen (Pasteur effect). Moreover, energy metabolism of normal cell can vary widely according to the tissue of origin, as we showed with the comparison of five rat tissues[94]. During stem cell differentiation, cell proliferation induces a switch from OXPHOS to aerobic glycolysis which might generate ATP more rapidly, as demonstrated in HepG2 cells [95] or in non-cancer cells[96] and [97]. Thus, normal cellular energy metabolism can adapt widely according to the activity of the cell and its surrounding microenvironment (energy substrate availability and diversity). Support for this view came from numerous studies showing that in vitro growth conditions can alter energy metabolism contributing to a dependency on glycolysis for ATP production [98].

Yet, Zu and Guppy analysed numerous studies and showed that aerobic glycolysis is not inherent to cancer but more a consequence of hypoxia[99].

Table 1. Impact of different oncogenes on energy metabolism

Impact of different oncogenes on energy metabolism.

Impact of different oncogenes on energy metabolism. Mitohormesis

Yun J, Finkel T
Cell Metab May 2014; 19(5):757–766

For many years, mitochondria were viewed as semiautonomous organelles, required only for cellular energetics. This view has been largely supplanted by the concept that mitochondria are fully integrated into the cell and that mitochondrial stresses rapidly activate cytosolic signaling pathways that ultimately alter nuclear gene expression. Remarkably, this coordinated response to mild mitochondrial stress appears to leave the cell less susceptible to subsequent perturbations. This response, termed mitohormesis, is being rapidly dissected in many model organisms. A fuller understanding of mitohormesis promises to provide insight into our susceptibility for disease and potentially provide a unifying hypothesis for why we age.

Figure 1. The Basis of Mitohormesis. Any of a number of endogenous or exogenous stresses can perturb mitochondrial function. These perturbations are relayed to the cytosol through, at present, poorly understood mechanisms that may involve mitochondrial ROS as well as other mediators. These cytoplasmic signaling pathways and subsequent nuclear transcriptional changes induce various long-lasting cytoprotective pathways. This augmented stress resistance allows for protection from a wide array of subsequent stresses.

Figure 2. Potential Parallels between the Mitochondrial Unfolded Protein Response and Quorum Sensing in Gram-Positive Bacteria. In the C. elegans UPRmt response, mitochondrial proteins (indicated by blue swirls) are degraded by matrix proteases, and the oligopeptides that are generated are then exported through the ABC transporter family member HAF-1. Once in the cytosol, these peptides can influence the subcellular localization of the transcription factor ATFS-1. Nuclear ATFS-1 is capable of orchestrating a broad transcriptional response to mitochondrial stress. As such, this pathway establishes a method for mitochondrial and nuclear genomes to communicate. In some gram-positive bacteria, intracellularly generated peptides can be similarly exported through an ABC transporter protein. These peptides can be detected in the environment by a membrane-bound histidine kinases (HK) sensor. The activation of the HK sensor leads to phosphorylation of a response regulator (RR) protein that, in turn, can alter gene expression. This program allows communication between dispersed gram-positive bacteria and thus coordinated behavior of widely dispersed bacterial genomes.

Figure 3. The Complexity of Mitochondrial Stresses and Responses. A wide array of extrinsic and intrinsic mitochondrial perturbations can elicit cellular responses. As detailed in the text, genetic or pharmacological disruption of electron transport, incorrect folding of mitochondrial proteins, stalled mitochondrial ribosomes, alterations in signaling pathways, or exposure to toxins all appear to elicit specific cytoprotective programs within the cell. These adaptive responses include increased mitochondrial number (biogenesis), alterations in metabolism, increased antioxidant defenses, and augmented protein chaperone expression. The cumulative effect of these adaptive mechanisms might be an extension of lifespan and a decreased incidence of age-related pathologies. Mitochondrial function and energy metabolism in cancer cells. Past overview and future perspectives

Mayevsky A
Mitochondrion. 2009 Jun; 9(3):165-79

The involvements of energy metabolism aspects of mitochondrial dysfunction in cancer development, proliferation and possible therapy, have been investigated since Otto Warburg published his hypothesis. The main published material on cancer cell energy metabolism is overviewed and a new unique in vivo experimental approach that may have significant impact in this important field is suggested. The monitoring system provides real time data, reflecting mitochondrial NADH redox state and microcirculation function. This approach of in vivo monitoring of tissue viability could be used to test the efficacy and side effects of new anticancer drugs in animal models. Also, the same technology may enable differentiation between normal and tumor tissues in experimental animals and maybe also in patients.

 Energy metabolism in mammalian cells

Fig. 1. Schematic representation of cellular energy metabolism and its relationship to microcirculatory blood flow and hemoglobin oxygenation.

Fig. 2. Schematic representation of the central role of the mitochondrion in the various processes involved in the pathology of cancer cells and tumors. Six issues marked as 1–6 are discussed in details in the text.

In vivo monitoring of tissue energy metabolism in mammalian cells

Fig. 3. Schematic presentation of the six parameters that could be monitored for the evaluation of tissue energy metabolism (see text for details).

Optical spectroscopy of tissue energy metabolism in vivo

Multiparametric monitoring system

Fig. 4. (A) Schematic representation of the Time Sharing Fluorometer Reflectometer (TSFR) combined with the laser Doppler flowmeter (D) for blood flow monitoring. The time sharing system includes a wheel that rotates at a speed of3000 rpm wit height filters: four for the measurements of mitochondrial NADH(366 nm and 450 nm)and four for oxy-hemoglobin measurements (585 nm and 577 nm) as seen in (C). The source of light is a mercury lamp. The probe includes optical fibers for NADH excitation (Ex) and emission (Em), laser Doppler excitation (LD in), laser Doppler emission (LD out) as seen in part E The absorption spectrum of Oxy- and Deoxy- Hemoglobin indicating the two wave length used (C).

Fig. 7. Comparison between mitochondrial metabolic states in vitro and the typical tissue metabolic states in vivo evaluated by NADH redox state, tissue blood flow and hemoglobin oxygenation as could be measured by the suggested monitoring system.

(very important) Metabolic Reprogramming. Cancer Hallmark Even Warburg Did Not Anticipate

Ward PS, Thompson CB.
Cancer Cell 2012; 21(3):297-308

Cancer metabolism has long been equated with aerobic glycolysis, seen by early biochemists as primitive and inefficient. Despite these early beliefs, the metabolic signatures of cancer cells are not passive responses to damaged mitochondria but result from oncogene-directed metabolic reprogramming required to support anabolic growth. Recent evidence suggests that metabolites themselves can be oncogenic by altering cell signaling and blocking cellular differentiation. No longer can cancer-associated alterations in metabolism be viewed as an indirect response to cell proliferation and survival signals. We contend that altered metabolism has attained the status of a core hallmark of cancer.

The propensity for proliferating cells to secrete a significant fraction of glucose carbon through fermentation was first elucidated in yeast. Otto Warburg extended these observations to mammalian cells, finding that proliferating ascites tumor cells converted the majority of their glucose carbon to lactate, even in oxygen-rich conditions. Warburg hypothesized that this altered metabolism was specific to cancer cells, and that it arose from mitochondrial defects that inhibited their ability to effectively oxidize glucose carbon to CO2. An extension of this hypothesis was that dysfunctional mitochondria caused cancer (Koppenol et al., 2011). Warburg’s seminal finding has been observed in a wide variety of cancers. These observations have been exploited clinically using 18F-deoxyglucose positron emission tomography (FDG-PET). However, in contrast to Warburg’s original hypothesis, damaged mitochondria are not at the root of the aerobic glycolysis exhibited by most tumor cells. Most tumor mitochondria are not defective in their ability to carry out oxidative phosphorylation. Instead, in proliferating cells mitochondrial metabolism is reprogrammed to meet the challenges of macromolecular synthesis. This possibility was never considered by Warburg and his contemporaries.

Advances in cancer metabolism research over the last decade have enhanced our understanding of how aerobic glycolysis and other metabolic alterations observed in cancer cells support the anabolic requirements associated with cell growth and proliferation. It has become clear that anabolic metabolism is under complex regulatory control directed by growth factor signal transduction in non-transformed cells. Yet despite these advances, the repeated refrain from traditional biochemists is that altered metabolism is merely an indirect phenomenon in cancer, a secondary effect that pales in importance to the activation of primary proliferation and survival signals (Hanahan and Weinberg, 2011). Most proto-oncogenes and tumor suppressor genes encode components of signal transduction pathways. Their roles in carcinogenesis have traditionally been attributed to their ability to regulate the cell cycle and sustain proliferative signaling while also helping cells evade growth suppression and/or cell death (Hanahan and Weinberg, 2011). But evidence for an alternative concept, that the primary functions of activated oncogenes and inactivated tumor suppressors are to reprogram cellular metabolism, has continued to build over the past several years. Evidence is also developing for the proposal that proto-oncogenes and tumor suppressors primarily evolved to regulate metabolism.

We begin this review by discussing how proliferative cell metabolism differs from quiescent cell metabolism on the basis of active metabolic reprogramming by oncogenes and tumor suppressors. Much of this reprogramming depends on utilizing mitochondria as functional biosynthetic organelles. We then further develop the idea that altered metabolism is a primary feature selected for during tumorigenesis. Recent advances have demonstrated that altered metabolism in cancer extends beyond adaptations to meet the increased anabolic requirements of a growing and dividing cell. Changes in cancer cell metabolism can also influence cellular differentiation status, and in some cases these changes arise from oncogenic alterations in metabolic enzymes themselves.

Metabolism in quiescent vs. proliferating cells nihms-360138-f0001

Metabolism in quiescent vs. proliferating cells: both use mitochondria.
(A) In the absence of instructional growth factor signaling, cells in multicellular organisms lack the ability to take up sufficient nutrients to maintain themselves. Neglected cells will undergo autophagy and catabolize amino acids and lipids through the TCA cycle, assuming sufficient oxygen is available. This oxidative metabolism maximizes ATP production. (B) Cells that receive instructional growth factor signaling are directed to increase their uptake of nutrients, most notably glucose and glutamine. The increased nutrient uptake can then support the anabolic requirements of cell growth: mainly lipid, protein, and nucleotide synthesis (biomass). Excess carbon is secreted as lactate. Proliferating cells may also use strategies to decrease their ATP production while increasing their ATP consumption. These strategies maintain the ADP:ATP ratio necessary to maintain glycolytic flux. Green arrows represent metabolic pathways, while black arrows represent signaling.

Metabolism is a direct, not indirect, response to growth factor signaling nihms-360138-f0002

Metabolism is a direct, not indirect, response to growth factor signaling nihms-360138-f0002

Metabolism is a direct, not indirect, response to growth factor signaling.
(A) The traditional demand-based model of how metabolism is altered in proliferating cells. In response to growth factor signaling, increased transcription and translation consume free energy and decrease the ADP:ATP ratio. This leads to enhanced flux of glucose carbon through glycolysis and the TCA cycle for the purpose of producing more ATP. (B) Supply-based model of how metabolism changes in proliferating cells. Growth factor signaling directly reprograms nutrient uptake and metabolism. Increased nutrient flux through glycolysis and the mitochondria in response to growth factor signaling is used for biomass production. Metabolism also impacts transcription and translation through mechanisms independent of ATP availability.

Alterations in classic oncogenes directly reprogram cell metabolism to increase nutrient uptake and biosynthesis. PI3K/Akt signaling downstream of receptor tyrosine kinase (RTK) activation increases glucose uptake through the transporter GLUT1, and increases flux through glycolysis. Branches of glycolytic metabolism contribute to nucleotide and amino acid synthesis. Akt also activates ATP-citrate lyase (ACL), promoting the conversion of mitochondria-derived citrate to acetyl-CoA for lipid synthesis. Mitochondrial citrate can be synthesized when glucose-derived acetyl-CoA, generated by pyruvate dehydrogenase (PDH), condenses with glutamine-derived oxaloacetate (OAA) via the activity of citrate synthase (CS). mTORC1 promotes protein synthesis and mitochondrial metabolism. Myc increases glutamine uptake and the conversion of glutamine into a mitochondrial carbon source by promoting the expression of the enzyme glutaminase (GLS). Myc also promotes mitochondrial biogenesis. In addition, Myc promotes nucleotide and amino acid synthesis, both through direct transcriptional regulation and through increasing the synthesis of mitochondrial metabolite precursors.

Pyruvate kinase M2 (PKM2) expression in proliferating cells is regulated by signaling and mitochondrial metabolism to facilitate macromolecular synthesis. PKM2 is a less active isoform of the terminal glycolytic enzyme pyruvate kinase. It is also uniquely inhibited downstream of tyrosine kinase signaling. The decreased enzymatic activity of PKM2 in the cytoplasm promotes the accumulation of upstream glycolytic intermediates and their shunting into anabolic pathways. These pathways include the serine synthetic pathway that contributes to nucleotide and amino acid production. When mitochondrial metabolism is excessive, reactive oxygen species (ROS) from the mitochondria can feedback to inhibit PKM2 activity. Acetylation of PKM2, dependent on acetyl-CoA availability, may also promote PKM2 degradation and further contribute to increased flux through anabolic synthesis pathways branching off glycolysis.

IDH1 and IDH2 mutants convert glutamine carbon to the oncometabolite 2-hydroxyglutarate to dysregulate epigenetics and cell differentiation. (A) α-ketoglutarate, produced in part by wild-type isocitrate dehydrogenase (IDH), can enter the nucleus and be used as a substrate for dioxygenase enzymes that modify epigenetic marks. These enzymes include the TET2 DNA hydroxylase enzyme which converts 5-methylcytosine to 5-hydroxymethylcytosine, typically at CpG dinucleotides. 5-hydroxymethylcytosine may be an intermediate in either active or passive DNA demethylation. α-ketoglutarate is also a substrate for JmjC domain histone demethylase enzymes that demethylate lysine residues on histone tails. (B) The common feature of cancer-associated mutations in cytosolic IDH1 and mitochondrial IDH2 is the acquisition of a neomorphic enzymatic activity. This activity converts glutamine-derived α-ketoglutarate to the oncometabolite 2HG. 2HG can competitively inhibit α-ketoglutarate-dependent enzymes like TET2 and the JmjC histone demethylases, thereby impairing normal epigenetic regulation. This results in altered histone methylation marks, in some cases DNA hypermethylation at CpG islands, and dysregulated cellular differentiation.

Hypoxia and HIF-1 activation promote an alternative pathway for citrate synthesis through reductive metabolism of glutamine. (A) In proliferating cells under normoxic conditions, citrate is synthesized from both glucose and glutamine. Glucose carbon provides acetyl-CoA through the activity of PDH. Glutamine carbon provides oxaloacetate through oxidative mitochondrial metabolism dependent on NAD+. Glucose-derived acetyl-CoA and glutamine-derived oxaloacetate condense to form citrate via the activity of citrate synthase (CS). Citrate can be exported to the cytosol for lipid synthesis. (B) In cells proliferating in hypoxia and/or with HIF-1 activation, glucose is diverted away from mitochondrial acetyl-CoA and citrate production. Citrate can be maintained through an alternative pathway of reductive carboxylation, which we propose to rely on reverse flux of glutamine-derived α-ketoglutarate through IDH2. This reverse flux in the mitochondria would promote electron export from the mitochondria when the activity of the electron transport chain is inhibited because of the lack of oxygen as an electron acceptor. Mitochondrial reverse flux can be accomplished by NADH conversion to NADPH by mitochondrial transhydrogenase and the resulting NADPH use in α-ketoglutarate carboxylation. When citrate/isocitrate is exported to the cytosol, some may be metabolized in the oxidative direction by IDH1 and contribute to a shuttle that produces cytosolic NADPH.

A major paradox remaining with PKM2 is that cells expressing PKM2 produce more glucose-derived pyruvate than PKM1-expressing cells, despite having a form of the pyruvate kinase enzyme that is less active and more sensitive to inhibition. One way to get around the PKM2 bottleneck and maintain/enhance pyruvate production may be through an proposed alternative glycolytic pathway, involving an enzymatic activity not yet purified, that dephosphorylates PEP to pyruvate without the generation of ATP (Vander Heiden et al., 2010). Another answer to this paradox may emanate from the serine synthetic pathway. The decreased enzymatic activity of PKM2 can promote the accumulation of the 3-phosphoglycerate glycolytic intermediate that serves as the entry point for the serine synthetic pathway branch off glycolysis. The little studied enzyme serine dehydratase can then directly convert serine to pyruvate. A third explanation may lie in the oscillatory activity of PKM2 from the inactive dimer to active tetramer form. Regulatory inputs into PKM2 like tyrosine phosphorylation and ROS destabilize the tetrameric form of PKM2 (Anastasiou et al., 2011; Christofk et al., 2008b; Hitosugi et al., 2009), but other inputs present in glycolytic cancer cells like fructose-1,6-bisphosphate and serine can continually allosterically activate and/or promote reformation of the PKM2 tetramer (Ashizawa et al., 1991; Eigenbrodt et al., 1983). Thus, PKM2 may be continually switching from inactive to active forms in cells, resulting in an apparent upregulation of flux through anabolic glycolytic branching pathways while also maintaining reasonable net flux of glucose carbon through PEP to pyruvate. With such an oscillatory system, small changes in the levels of any of the above-mentioned PKM2 regulatory inputs can cause exquisite, rapid, adjustments to glycolytic flux. This would be predicted to be advantageous for proliferating cells in the setting of variable extracellular nutrient availability. The capability for oscillatory regulation of PKM2 could also provide an explanation for why tumor cells do not select for altered glycolytic metabolism upstream of PKM2 through deletions and/or loss of function mutations of other glycolytic enzymes.

IDH1 mutations at R132 are not simply loss-of-function for isocitrate and α-ketoglutarate interconversion, but also acquire a novel reductive activity to convert α-ketoglutarate to 2-hydroxyglutarate (2HG), a rare metabolite found at only trace amounts in mammalian cells under normal conditions (Dang et al., 2009). However, it still remained unclear if 2HG was truly a pathogenic “oncometabolite” resulting from IDH1 mutation, or if it was just the byproduct of a loss of function mutation. Whether 2HG production or the loss of IDH1 normal function played a more important role in tumorigenesis remained uncertain.

A potential answer to whether 2HG production was relevant to tumorigenesis arrived with the study of mutations in IDH2, the mitochondrial homolog of IDH1. Up to this point a small fraction of gliomas lacking IDH1 mutations were known to harbor mutations at IDH2 R172, the analogous residue to IDH1 R132 (Yan et al., 2009). However, given the rarity of these IDH2 mutations, they had not been characterized for 2HG production. The discovery of IDH2 R172 mutations in AML as well as glioma samples prompted the study of whether these mutations also conferred the reductive enzymatic activity to produce 2HG. Enzymatic assays and measurement of 2HG levels in primary AML samples confirmed that these IDH2 R172 mutations result in 2HG elevation (Gross et al., 2010; Ward et al., 2010).

It was then investigated if the measurement of 2HG levels in primary tumor samples with unknown IDH mutation status could serve as a metabolite screening test for both cytosolic IDH1 and mitochondrial IDH2 mutations. AML samples with low to undetectable 2HG were subsequently sequenced and determined to be IDH1 and IDH2 wild-type, and several samples with elevated 2HG were found to have neomorphic mutations at either IDH1 R132 or IDH2 R172 (Gross et al., 2010). However, some 2HG-elevated AML samples lacked IDH1 R132 or IDH2 R172 mutations. When more comprehensive sequencing of IDH1 and IDH2 was performed, it was found that the common feature of this remaining subset of 2HG-elevated AMLs was another mutation in IDH2, occurring at R140 (Ward et al., 2010). This discovery provided additional evidence that 2HG production was the primary feature being selected for in tumors.

In addition to intensifying efforts to find the cellular targets of 2HG, the discovery of the 2HG-producing IDH1 and IDH2 mutations suggested that 2HG measurement might have clinical utility in diagnosis and disease monitoring. While much work is still needed in this area, serum 2HG levels have successfully correlated with IDH1 R132 mutations in AML, and recent data have suggested that 1H magnetic resonance spectroscopy can be applied for 2HG detection in vivo for glioma (Andronesi et al., 2012; Choi et al., 2012; Gross et al., 2010; Pope et al., 2012). These methods may have advantages over relying on invasive solid tumor biopsies or isolating leukemic blast cells to obtain material for sequencing of IDH1 and IDH2. Screening tumors and body fluids by 2HG status also has potentially increased applicability given the recent report that additional IDH mutations can produce 2HG (Ward et al., 2011). These additional alleles may account for the recently described subset of 2HG-elevated chondrosarcoma samples that lacked the most common IDH1 or IDH2 mutations but were not examined for other IDH alterations (Amary et al., 2011). Metabolite screening approaches can also distinguish neomorphic IDH mutations from SNPs and sequencing artifacts with no effect on IDH enzyme activity, as well as from an apparently rare subset of loss-of-function, non 2HG-producing IDH mutations that may play a secondary tumorigenic role in altering cellular redox (Ward et al., 2011).

Will we find other novel oncometabolites like 2HG? We should consider basing the search for new oncometabolites on those metabolites already known to cause disease in pediatric inborn errors of metabolism (IEMs). 2HG exemplifies how advances in research on IEMs can inform research on cancer metabolism, and vice versa. Methods developed by those studying 2HG aciduria were used to demonstrate that R(-)-2HG (also known as D-2HG) is the exclusive 2HG stereoisomer produced by IDH1 and IDH2 mutants (Dang et al., 2009; Ward et al., 2010). Likewise, following the discovery of 2HG-producing IDH2 R140 mutations in leukemia, researchers looked for and successfully found germline IDH2 R140 mutations in D-2HG aciduria. IDH2 R140 mutations now account for nearly half of all cases of this devastating disease (Kranendijk et al., 2010). While interest has surrounded 2HG due to its apparent novelty as a metabolite not found in normal non-diseased cells, there are situations where 2HG appears in the absence of metabolic enzyme mutations. For example, in human cells proliferating in hypoxia, α-ketoglutarate can accumulate and be metabolized through an enhanced reductive activity of wild-type IDH2 in the mitochondria, leading to 2HG accumulation in the absence of IDH mutation (Wise et al., 2011). The ability of 2HG to alter epigenetics may reflect its evolutionary ancient status as a signal for elevated glutamine/glutamate metabolism and/or oxygen deficiency.

With this broadened view of what constitutes an oncometabolite, one could argue that the discoveries of two other oncometabolites, succinate and fumarate, preceded that of 2HG. Loss of function mutations in the TCA cycle enzymes succinate dehydrogenase (SDH) and fumarate hydratase (FH) have been known for several years to occur in pheochromocytoma, paraganglioma, leiomoyoma, and renal carcinoma. It was initially hypothesized that these mutations contribute to cancer through mitochondrial damage producing elevated ROS (Eng et al., 2003). However, potential tumorigenic effects were soon linked to the elevated levels of succinate and fumarate arising from loss of SDH and FH function, respectively. Succinate was initially found to impair PHD2, the α-ketoglutarate-dependent enzyme regulating HIF stability, through product inhibition (Selak et al., 2005). Subsequent work confirmed that fumarate could inhibit PHD2 (Isaacs et al., 2005), and that succinate could also inhibit the related enzyme PHD3 (Lee et al., 2005). These observations linked the elevated HIF levels observed in SDH and FH deficient tumors to the activity of the succinate and fumarate metabolites. Recent work has suggested that fumarate may have other important roles that predominate in FH deficiency. For example, fumarate can modify cysteine residues to inhibit a negative regulator of the Nrf2 transcription factor. This post-translational modification leads to the upregulation of antioxidant response genes (Adam et al., 2011; Ooi et al., 2011).

There are still many unanswered questions regarding the biology of SDH and FH deficient tumors. In light of the emerging epigenetic effects of 2HG, it is intriguing that succinate has been shown to alter histone demethylase activity in yeast (Smith et al., 2007). Perhaps elevated succinate and fumarate resulting from SDH and FH mutations can promote tumorigenesis in part through epigenetic modulation.

Despite rapid technological advances in studying cell metabolism, we remain unable to reliably distinguish cytosolic metabolites from those in the mitochondria and other compartments. Current fractionation methods often lead to metabolite leakage. Even within one subcellular compartment, there may be distinct pools of metabolites resulting from channeling between metabolic enzymes. A related challenge lies in the quantitative measurement of metabolic flux; i.e., measuring the movement of carbon, nitrogen, and other atoms through metabolic pathways rather than simply measuring the steady-state levels of individual metabolites. While critical fluxes have been quantified in cultured cancer cells and methods for these analyses continue to improve (DeBerardinis et al., 2007; Mancuso et al., 2004; Yuan et al., 2008), many obstacles remain such as cellular compartmentalization and the reliance of most cell culture on complex, incompletely defined media.

Over the past decade, the study of metabolism has returned to its rightful place at the forefront of cancer research. Although Warburg was wrong about mitochondria, he was prescient in his focus on metabolism. Data now support the concepts that altered metabolism results from active reprogramming by altered oncogenes and tumor suppressors, and that metabolic adaptations can be clonally selected during tumorigenesis. Altered metabolism should now be considered a core hallmark of cancer. There is much work to be done. A Role for the Mitochondrial Pyruvate Carrier as a Repressor of the Warburg Effect and Colon Cancer Cell Growth

Schell JC, Olson KA, …, Xie J, Egnatchik RA, Earl EG, DeBerardinis RJ, Rutter J.
Mol Cell. 2014 Nov 6; 56(3):400-13

Cancer cells are typically subject to profound metabolic alterations, including the Warburg effect wherein cancer cells oxidize a decreased fraction of the pyruvate generated from glycolysis. We show herein that the mitochondrial pyruvate carrier (MPC), composed of the products of the MPC1 and MPC2 genes, modulates fractional pyruvate oxidation. MPC1 is deleted or underexpressed in multiple cancers and correlates with poor prognosis. Cancer cells re-expressing MPC1 and MPC2 display increased mitochondrial pyruvate oxidation, with no changes in cell growth in adherent culture. MPC re-expression exerted profound effects in anchorage-independent growth conditions, however, including impaired colony formation in soft agar, spheroid formation, and xenograft growth. We also observed a decrease in markers of stemness and traced the growth effects of MPC expression to the stem cell compartment. We propose that reduced MPC activity is an important aspect of cancer metabolism, perhaps through altering the maintenance and fate of stem cells.

Figure 2. Re-Expressed MPC1 and MPC2 Form a Mitochondrial Complex (A and B) (A) Western blot and (B) qRT-PCR analysis of the indicated colon cancer cell lines with retroviral expression of MPC1 (or MPC1-R97W) and/or MPC2. (C) Western blots of human heart tissue, hematologic cancer cells, and colon cancer cell lines with and without MPC1 and MPC2 re-expression. (D) Fluorescence microscopy of MPC1-GFP and MPC2-GFP overlaid with Mitotracker Red in HCT15 cells. Scale bar: 10 mm. (E) Blue-native PAGE analysis of mitochondria from control and MPC1/2-expressing cells. (F) Western blots of metabolic and mitochondrial proteins across four colon cancer cell lines with or without MPC1/2 expression

Figure 3. MPC Re-Expression Alters Mitochondrial Pyruvate Metabolism (A) OCR at baseline and maximal respiration in HCT15 (n = 7) and HT29 (n = 13) with pyruvate as the sole carbon source (mean ± SEM). (B and C) Schematic and citrate mass isotopomer quantification in cells cultured with D-[U-13C]glucose and unlabeled glutamine for 6 hr (mean ± SD, n = 2). (D) Glucose uptake and lactate secretion normalized to protein concentration (mean ± SD, n = 3). (E–G) (E) Western blots of PDH, phospho-PDH, and PDK1; (F) PDH activity assay and (G) CS activity assay with or without MPC1 and MPC2 expression (mean ± SD, n = 4). (H and I) Effects of MPC1/2 re-expression on mitochondrial membrane potential and ROS production (mean ± SD, n = 3). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

Figure 4. MPC Re-Expression Alters Growth under Low-Attachment Conditions (A) Cell number of control and MPC1/2 re-expressing cell lines in adherent culture (mean ± SD, n = 7). (B) Cell viability determined by trypan blue exclusion and Annexin V/PI staining (mean ± SD, n = 3). (C–F) (C) EdU incorporation of MPC re-expressing cell lines at 3 hr post EdU pulse. Growth in 3D culture evaluated by (D) soft agar colony formation (mean ± SD, n = 12, see also Table S1) and by ([E] and [F]) spheroid formation ± MPC inhibitor UK5099 (mean ± SEM, n = 12). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

Figure 7. MPC Re-Expression Alters the Cancer Initiating Cell Population (A) Western blot quantification of ALDHA and Lin28A from control or MPC re-expressing HT29 xenografts (mean ± SEM, n = 10). (B and C) Percentage of ALDHhi (n = 3) and CD44hi (n = 5) cells as determined by flow cytometry (mean ± SEM). (D) Western blot analysis of stem cell markers in control and MPC re-expressing cell lines. (E) Relative MPC1 and MPC2 mRNA levels in ALDH sorted HCT15 cells (n = 4,mean ± SEM). 2D growth of (F) whole-population HCT15 cells and (G) ALDH sorted cells. Area determined by ImageJ after crystal violet staining (mean ± SD, n = 6). (H and I) (H) Adherent and (I) spheroid growth of main population (MP) versus side population (SP) HCT15 cells. (mean ± SD, n = 6). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

Our demonstration that the MPC is lost or underexpressed in many cancers might provide clarifying context for earlier attempts to exploit metabolic regulation for cancer therapeutics. The PDH kinase inhibitor dichloroacetate, which impairs PDH phosphorylation and increases pyruvate oxidation, has been explored extensively as a cancer therapy (Bonnet et al., 2007; Olszewski et al., 2010). It has met with mixed results, however, and has typically failed to dramatically decrease tumor burden as a monotherapy (Garon et al., 2014;
Sanchez-Arago et al., 2010; Shahrzadetal.,2010). Is one possible reason for these failures that the MPC has been lost or inactivated, thereby limiting the metabolic effects of PDH activity? The inclusion of the MPC adds additional complexity to targeting cancer metabolism for therapy but has the potential to explain why treatments may be more effective in some studies than in others (Fulda et al., 2010; Hamanaka and Chandel, 2012; Tennant et al., 2010; Vander Heiden, 2011). The redundant measures to limit pyruvate oxidation make it easy to understand why expression of the MPC leads to relatively modest metabolic changes in cells grown in adherent culture conditions. While subtle, we observed a number of changes in metabolic parameters, all of which are consistent with enhanced mitochondrial pyruvate entry and oxidation. There are at least two possible explanations for the discrepancy that we observed between the impact on adherent and nonadherent cell proliferation. One hypothesis is that the stress of nutrient deprivation and detachment combines with these subtle metabolic effects to impair survival and proliferation.  ECM1 promotes the Warburg effect through EGF-mediated activation of PKM2

Lee KM, Nam K, Oh S, Lim J, Lee T, Shin I.
Cell Signal. 2015 Feb; 27(2):228-35

The Warburg effect is an oncogenic metabolic switch that allows cancer cells to take up more glucose than normal cells and favors anaerobic glycolysis. Extracellular matrix protein 1 (ECM1) is a secreted glycoprotein that is overexpressed in various types of carcinoma. Using two-dimensional digest-liquid chromatography-mass spectrometry (LC-MS)/MS, we showed that the expression of proteins associated with the Warburg effect was upregulated in trastuzumab-resistant BT-474 cells that overexpressed ECM1 compared to control cells. We further demonstrated that ECM1 induced the expression of genes that promote the Warburg effect, such as glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA), and hypoxia-inducible factor 1 α (HIF-1α). The phosphorylation status of pyruvate kinase M2 (PKM-2) at Ser37, which is responsible for the expression of genes that promote the Warburg effect, was affected by the modulation of ECM1 expression. Moreover, EGF-dependent ERK activation that was regulated by ECM1 induced not only PKM2 phosphorylation but also gene expression of GLUT1 and LDHA. These findings provide evidence that ECM1 plays an important role in promoting the Warburg effect mediated by PKM2.

Fig. 1.ECM1 induces a metabolic shift toward promoting Warburg effect. (A) The levels of glucose uptake were examined with a cell-based assay. (B) Levels of lactate production were measured using a lactate assay kit. (C) Cellular ATP content was determined with a Cell Titer-Glo luminescent cell viability assay. Error bars represent mean ± SD of triplicate experiments (*p b 0.05, ***p b 0.0005).

Fig.2. ECM1 up-regulates expression of gene sassociated with the Warburg effect. (A) Cell lysates were analyzed by western blotting using antibodies specific for ECM1, LDHA, GLUT1,and actin (as a loading control). The intensities of the bands were quantified using 1D Scan software and plotted. (BandC) mRNA levels of each gene were determined by real-time PCR using specific primers. (D) HIF-1α-dependent transcriptional activities were examined using a hypoxia response element (HRE) reporter indual luciferase assays. Error bars represent mean ± SD of triplicate experiments (*p b 0.05, **p b 0.005, ***p b 0.0005).

Fig.3. ECM1-dependent upregulation of gene expression is not mediated byEgr-1.

Fig.4. ECM1 activates PKM2 via EGF-mediated ERK activation

Fig. 5. TheWarburg effect is attenuated by silencing of PKM2 in breast cancer cells

Recently, a non-glycolytic function of PKM2 was reported. Phosphorylated PKM2 at Ser37 is translocated into the nucleus after EGFR and ERK activation and regulates the expression of cyclin D1, c-Myc, LDHA, and GLUT1[19,37]. Here, we showed that ECM1 regulates the phosphorylation level and translocation of PKM2 via the EGFR/ ERK pathway. As we previously showed that ECM1 enhances the EGF response and increases EGFR expression through MUC1-dependent stabilization [17], it seemed likely that activation of the EGFR/ERK pathway by ECM1 is linked to PKM2 phosphorylation. Indeed, we show here that ECM1 regulates the phosphorylation of PKM2 at Ser37 and enhances the Warburg effect through the EGFR/ERK pathway. HIF-1α is known to be responsible for alterations in cancer cell metabolism [38] and our current studies showed that the expression level of HIF-1α is up-regulated by ECM1 (Fig. 2C and D). To determine the mechanism by which ECM1 upregulated HIF-1α expression, we focused on the induction of Egr-1 by EGFR/ERK signaling [39]. However, although Egr-1 expression was regulated by ECM1 we failed to find evidence that Egr-1 affected the expression of genes involved in the Warburg effect (Fig. 3C). Moreover, ERK-dependent PKM2 activation did not regulate HIF-1α expression in BT-474 cells (Fig. 4D and5B). These results suggested that the upregulation of HIF-1α by ECM1 is not mediated by the EGFR/ERK pathway.


In the current study we showed that ECM1 altered metabolic phenotypes of breast cancer cells toward promoting the Warburg effect.

Phosphorylation and nuclear translocation of PKM2 were induced by ECM1 through the EGFR/ERK pathway. Moreover, phosphorylated PKM2 increased the expression of metabolic genes such as LDHA and GLUT1, and promoted glucose uptake and lactate production. These findings provide a new perspective on the distinct functions of ECM1 in cancer cell metabolism. Supplementary data to this article can be found online at


[1] R.A. Cairns, I.S. Harris, T.W. Mak, Cancer 11 (2011) 85–95.
[2] O. Warburg, Science 123 (1956) 309–314.
[3] G.L. Semenza, D.Artemov, A.Bedi, …, J. Simons, P. Taghavi, H. Zhong, Novartis Found. Symp. 240 (2001) 251–260 (discussion 260–254).
[4] N.C. Denko, Cancer 8 (2008) 705–713.
[5] C. Chen, N. Pore, A. Behrooz, F. Ismail-Beigi, A. Maity, J. Biol. Chem. 276 (2001) 9519–9525.
[6] J.Lum, T.Bui, M.Gruber, J.D.Gordan, R.J.DeBerardinis,.. ,C.B. Thompson, Genes Dev. 21 (2007) 1037–1049.
[7] J.T. Chi, Z. Wang, D.S. Nuyten, E.H. Rodriguez, .., P.O. Brown, PLoS Med.
3 (2006) e47.
[8] G.L. Semenza, Cancer 3 (2003) 721–732. Glutamine Oxidation Maintains the TCA Cycle and Cell Survival during impaired Mitochondrial Pyruvate Transport

Chendong Yang, B Ko, CT. Hensley,…, J Rutter, ME. Merritt, RJ. DeBerardinis
Molec Cell  6 Nov 2014; 56(3):414–424


  • Mitochondria produce acetyl-CoA from glutamine during MPC inhibition
    •Alanine synthesis is suppressed during MPC inhibition
    •MPC inhibition activates GDH to supply pools of TCA cycle intermediates
    •GDH supports cell survival during periods of MPC inhibition


Alternative modes of metabolism enable cells to resist metabolic stress. Inhibiting these compensatory pathways may produce synthetic lethality. We previously demonstrated that glucose deprivation stimulated a pathway in which acetyl-CoA was formed from glutamine downstream of glutamate dehydrogenase (GDH). Here we show that import of pyruvate into the mitochondria suppresses GDH and glutamine-dependent acetyl-CoA formation. Inhibiting the mitochondrial pyruvate carrier (MPC) activates GDH and reroutes glutamine metabolism to generate both oxaloacetate and acetyl-CoA, enabling persistent tricarboxylic acid (TCA) cycle function. Pharmacological blockade of GDH elicited largely cytostatic effects in culture, but these effects became cytotoxic when combined with MPC inhibition. Concomitant administration of MPC and GDH inhibitors significantly impaired tumor growth compared to either inhibitor used as a single agent. Together, the data define a mechanism to induce glutaminolysis and uncover a survival pathway engaged during compromised supply of pyruvate to the mitochondria.

Yang et al, Graphical Abstract

Yang et al, Graphical Abstract

Graphical abstract

Figure 1. Pyruvate Depletion Redirects Glutamine Metabolism to Produce AcetylCoA and Citrate (A) Top: Anaplerosis supplied by [U-13C]glutamine. Glutamine supplies OAA via a-KG, while acetylCoA is predominantly supplied by other nutrients, particularly glucose. Bottom: Glutamine is converted to acetyl-CoA in the absence of glucosederived pyruvate. Red circles represent carbons arising from [U-13C]glutamine, and gray circles are unlabeled. Reductive carboxylation is indicated by the green dashed line. (B) Fraction of succinate, fumarate, malate, and aspartate containing four 13C carbons after culture of SFxL cells for 6 hr with [U-13C]glutamine in the presence or absence of 10 mM unlabeled glucose (Glc). (C) Mass isotopologues of citrate after culture of SFxL cells for 6 hr with [U-13C]glutamine and 10 mM unlabeled glucose, no glucose, or no glucose plus 6 mM unlabeled pyruvate (Pyr). (D) Citrate m+5 and m+6 after culture of HeLa or Huh-7 cells for 6 hr with [U-13C]glutamine and 10 mM unlabeled glucose, no glucose, or no glucose plus 6 mM unlabeled pyruvate. Data are the average and SD of three independent cultures. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 2. Isolated Mitochondria Convert Glutamine to Citrate (A) Western blot of whole-cell lysates (Cell) and preparations of isolated mitochondria (Mito) or cytosol from SFxL cells. (B) Oxygen consumption in a representative mitochondrial sample. Rates before and after addition of ADP/GDP are indicated. (C) Mass isotopologues of citrate produced by mitochondria cultured for 30 min with [U-13C] glutamine and with or without pyruvate.

Figure 3. Blockade of Mitochondrial Pyruvate Transport Activates Glutamine-Dependent Citrate Formation (A) Dose-dependent effects of UK5099 on citrate labeling from [U-13C]glucose and [U-13C]glutamine in SFxL cells. (B) Time course of citrate labeling from [U-13C] glutamine with or without 200 mM UK5099. (C) Abundance of total citrate and citrate m+6 in cells cultured in [U-13C]glutamine with or without 200 mM UK5099. (D) Mass isotopologues of citrate in cells cultured for 6 hr in [U-13C]glutamine with or without 10 mM CHC or 200 mM UK5099. (E) Effect of silencing ME2 on citrate m+6 after 6 hr of culture in [U-13C]glutamine. Relative abundances of citrate isotopologues were determined by normalizing total citrate abundance measured by mass spectrometry against cellular protein for each sample then multiplying by the fractional abundance of each isotopologue. (F) Effect of silencing MPC1 or MPC2 on formation of citrate m+6 after 6 hr of culture in [U-13C]glutamine. (G) Citrate isotopologues in primary human fibroblasts of varying MPC1 genotypes after culture in [U-13C]glutamine. Data are the average and SD of three independent cultures. *p < 0.05; **p < 0.01; ***p < 0.001. See also Figure S1.

Figure 4. Kinetic Analysis of the Metabolic Effects of Blocking Mitochondrial Pyruvate Transport (A) Summation of 13C spectra acquired over 2 min of exposure of SFxL cells to hyperpolarized [1-13C] pyruvate. Resonances are indicated for [1-13C] pyruvate (Pyr1), the hydrate of [1-13C]pyruvate (Pyr1-Hydr), [1-13C]lactate (Lac1), [1-13C]alanine (Ala1), and H[13C]O3 (Bicarbonate). (B) Time evolution of appearance of Lac1, Ala1, and bicarbonate in control and UK5099-treated cells. (C) Relative 13C NMR signals for Lac1, Ala1, and bicarbonate. Each signal is summed over the entire acquisition and expressed as a fraction of total 13C signal. (D) Quantity of intracellular and secreted alanine in control and UK5099-treated cells. Data are the average and SD of three independent cultures. *p < 0.05; ***p < 0.001. See also Figure S2.

Figure 5. Inhibiting Mitochondrial Pyruvate Transport Enhances the Contribution of Glutamine to Fatty Acid Synthesis (A) Mass isotopologues of palmitate extracted from cells cultured with [U-13C] glucose or [U-13C]glutamine, with or without 200 mM UK5099. For simplicity, only even-labeled isotopologues (m+2, m+4, etc.) are shown. (B) Fraction of lipogenic acetyl-CoA derived from glucose or glutamine with or without 200 mM UK5099. Data are the average and SD of three independent cultures. ***p < 0.001. See also Figure S3.

Figure 6. Blockade of Mitochondrial Pyruvate Transport Induces GDH (A) Two routes by which glutamate can be converted to AKG. Blue and green symbols are the amide (g) and amino (a) nitrogens of glutamine, respectively. (B) Utilization and secretion of glutamine (Gln), glutamate (Glu), and ammonia (NH4+) by SFxL cells with and without 200 mM UK5099. (C) Secretion of 15N-alanine and 15NH4+ derived from [a-15N]glutamine in SFxL cells expressing a control shRNA (shCtrl) or either of two shRNAs directed against GLUD1 (shGLUD1-A and shGLUD1-B). (D) Left: Phosphorylation of AMPK (T172) and acetyl-CoA carboxylase (ACC, S79) during treatment with 200 mM UK5099. Right: Steady-state levels of ATP 24 hr after addition of vehicle or 200 mM UK5099. (E) Fractional contribution of the m+6 isotopologue to total citrate in shCtrl, shGLUD1-A, and shGLUD1-B SFxL cells cultured in [U-13C]glutamine with or without 200 mM UK5099. Data are the average and SD of three independent cultures. *p < 0.05; **p < 0.01; ***p < 0.001. See also Figure S4.

Figure 7. GDH Sustains Growth and Viability during Suppression of Mitochondrial Pyruvate Transport (A) Relative growth inhibition of shCtrl, shGLUD1A, and shGLUD1-B SFxL cells treated with 50 mM UK5099 for 3 days. (B) Relative growth inhibition of SFxL cells treated with combinations of 50 mM of the GDH inhibitor EGCG, 10 mM of the GLS inhibitor BPTES, and 200 mM UK5099 for 3 days. (C) Relative cell death assessed by trypan blue staining in SFxL cells treated as in (B). (D) Relative cell death assessed by trypan blue staining in SF188 cells treated as in (B) for 2 days. (E) (Left) Growth of A549-derived subcutaneous xenografts treated with vehicle (saline), EGCG, CHC, or EGCG plus CHC (n = 4 for each group). Data are the average and SEM. Right: Lactate abundance in extracts of each tumor harvested at the end of the experiment. Data in (A)–(D) are the average and SD of three independent cultures. NS, not significant; *p < 0.05; **p < 0.01; ***p < 0.001. See also Figure S5.

Mitochondrial metabolism complements glycolysis as a source of energy and biosynthetic precursors. Precursors for lipids, proteins, and nucleic acids are derived from the TCA cycle. Maintaining pools of these intermediates is essential, even under circumstances of nutrient limitation or impaired supply of glucose-derived pyruvate to the mitochondria. Glutamine’s ability to produce both acetyl-CoA and OAA allows it to support TCA cycle activity as a sole carbon source and imposes a greater cellular dependence on glutamine metabolism when MPC function or pyruvate supply is impaired. Other anaplerotic amino acids could also supply both OAA and acetyl-CoA, providing flexible support for the TCA cycle when glucose is limiting. Although fatty acids are an important fuel in some cancer cells (Caro et al., 2012), and fatty acid oxidation is induced upon MPC inhibition, this pathway produces acetyl-CoA but not OAA. Thus, fatty acids would need to be oxidized along with an anaplerotic nutrient in order to enable the cycle to function as a biosynthetic hub. Notably, enforced MPC overexpression also impairs growth of some tumors (Schell et al., 2014), suggesting that maximal growth may require MPC activity to be maintained within a narrow window. After decades of research on mitochondrial pyruvate transport, molecular components of the MPC were recently reported (Halestrap, 2012; Schell and Rutter, 2013). MPC1 and MPC2 form a heterocomplex in the inner mitochondrial membrane, and loss of either component impairs pyruvate import, leading to citrate depletion (Bricker et al., 2012; Herzig et al., 2012). Mammalian cells lacking functional MPC1 display normal glutamine-supported respiration (Bricker et al., 2012), consistent with our observation that glutamine supplies the TCA cycle in absence of pyruvate import. We also observed that isolated mitochondria produce fully labeled citrate from glutamine, indicating that this pathway operates as a self-contained mechanism to maintain TCA cycle function. Recently, two well-known classes of drugs have unexpectedly been shown to inhibit MPC. First, thiazolidinediones, commonly used as insulin sensitizers, impair MPC function in myoblasts (Divakaruni et al.,2013). Second, the phosphodiesterase inhibitor Zaprinast inhibits MPC in the retina and brain (Du et al., 2013b). Zaprinast also induced accumulation of aspartate, suggesting that depletion of acetyl-CoA impaired the ability of a new turn of the TCA cycle to be initiated from OAA; as a consequence, OAA was transaminated to aspartate. We noted a similar phenomenon in cancer cells, suggesting that UK5099 elicits a state in which acetyl-CoA supply is insufficient to avoid OAA accumulation. Unlike UK5099, Zaprinast did not induce glutamine-dependent acetyl-CoA formation. This may be related to the reliance of isolated retinas on glucose rather than glutamine to supply TCA cycle intermediates or the exquisite system used by retinas to protect glutamate from oxidation (Du et al., 2013a). Zaprinast was also recently shown to inhibit glutaminase (Elhammali et al., 2014), which would further reduce the contribution of glutamine to the acetyl-CoA pool.

Comment by reader –

The results from these studies served as a good
reason to attempt the vaccination of patients using p53-
derived peptides, and a several clinical trials are currently
in progress. The most advanced work used a long
synthetic peptide mixture derived from p53 (p53-SLP; ISA
Pharmaceuticals, Bilthoven, the Netherlands) (Speetjens
et al., 2009; Shangary et al., 2008; Van der Burg et al.,
2001). The vaccine is delivered in the adjuvant setting
and induces T helper type cells.

Read Full Post »

Larry H Bernstein, MD, FCAP, Author and Curator

Chief, Scientific Communication

Leaders in Pharmaceutical Intelligence

with contributions from JEDS Rosalis, Brazil
and Radislov Rosov, Univ of Virginia, VA, USA

A Brief Curation of Proteomics, Metabolomics, and Metabolism

This article is a continuation of a series of elaborations of the recent and
accelerated scientific discoveries that are enlarging the scope of and
integration of biological and medical knowledge leading to new drug
discoveries.  The work that has led us to this point actually has roots
that go back 150 years.  The roots go back to studies in the mid-nineteenth century, with the emergence of microbiology, physiology,
pathology, botany, chemistry and physics, and the laying down of a
mechanistic approach divergent from descriptive observation in the
twentieth century. Medicine took on the obligation to renew the method
of training physicians after the Flexner Report (The Flexner Report of
1910 transformed the nature and process of medical education in America
with a resulting elimination of proprietary schools), funded by the Carnegie
Foundation.  Johns Hopkins University Medical School became the first to
adopt the model, as did Harvard, Yale, University of Chicago, and others.

The advances in biochemistry, genetics and genomics, were large, as was
structural organic chemistry in the remainder of the centrury.  The advances
in applied mathematics and in instrumental analysis opened a new gateway
into the 21st century with the Human Genome Project, the Proteome Library,
Signaling Pathways, and the Metabolomes – human, microbial, and plants.

shall elaborate on how the key processes of life are being elucidated as
these interrelated disciplines converge.  I shall not be covering in great
detail the contribution of the genetic code and transcripton because they
have been covered at great length in this series.

Part I.  The foundation for the emergence of a revitalized molecular
and biochemistry.

In a series of discussions with Jose des Salles Roselino (Brazil) over a
period of months we have come to an important line of reasoning. DNA
to protein link goes from triplet sequence to amino acid sequence. The
realm of genetics. Further, protein conformation, activity and function
requires that environmental and microenvironmental factors should be
considered (Biochemistry).  This has been opened in several articles
preceding this.

In the cAMP coupled hormonal response the transfer of conformation
from protein to protein is paramount. For instance, if your scheme goes
beyond cAMP, it will show an effect over a self-assembly (inhibitor
protein and protein kinase). Therefore, sequence alone does not
explain conformation, activity and function of regulatory proteins.
Recall that sequence is primar structure, determined by the translation
of the code, but secondary structure is determined by disulfide bonds.
There is another level of structure, tertiary structure, that is molded by
steric influences of near neighbors and by noncovalent attractions
and repulsions.

A few comments ( contributed by Assoc. Prof. JEDS Roselino) are in
order to stress the importance of self-assembly (Prigogine, R. A
Marcus, conformation energy) in a subject that is the best for this
connection. We have to stress again that in the cAMP
coupled hormonal response the transfer of conformation from
protein to protein is paramount. For instance, in case the
reaction sequence follows beyond the production of the
second messenger, as in the case of cAMP, this second
messenger will remove a self-assembly of inhibitor protein
with the enzyme protein kinase. Therefore, sequence alone
does not explain conformation, activity and function of
regulatory proteins. In this case, if this important mechanism
was not ignored, the work of Stanley Prusiner would most
certainly have been recognized earlier, and “rogue” proteins
would not have been seen as so rogue as some assumed.
For the general idea of importance of self-assembly versus
change in covalent modification of proteins (see R. A Kahn
and A. G Gilman (1984) J. Biol. Chem.  259(10), pp 6235-
6240. In this case, trimeric or dimeric G does not matter.
“Signaling transduction tutorial”.
G proteins in the G protein coupled-receptor proteins are
presented following a unidirectional series of arrows.
This is adequate to convey the idea of information being
transferred from outside the cell towards cell´s interior
(therefore, against the dogma that says all information
moves from DNA to RNA to protein.  It is important to
consider the following: The entire process is driven by
a very delicate equilibrium between possible conform-
ational states of the proteins. Empty receptors have very
low affinity for G proteins. On the other hand, hormone
bound receptors have a change in conformation that
allows increasing the affinity for the G-trimer. When
hormone receptors bind to G-trimers two things happen:

  1. Receptors transfer conformation information to
    the G-triplex and
  2. the G-triplex transfers information back to the
    complex hormone-receptor.

In the first case , the dissociated G protein exchanges
GDP for GTP and has its affinity for the cyclase increased,
while by the same interaction receptor releases the
hormone which then places the first required step for the
signal. After this first interaction step, on the second and
final transduction system step is represented by an
opposite arrow. When, the G-protein + GTP complex
interacts with the cyclase two things happen:

  1. It changes the cyclase to an active conformation
    starting the production of cAMP as the single
    arrow of the scheme. However, the interaction
    also causes a backward effect.
  2. It activates the GTPase activity of this subunit
    and the breakdown of GTP to GDP moves this 
    subunit back to the initial trimeric inactive
     of G complex.

This was very well studied when the actions of cholera toxin
required better understanding. Cholera toxin changes the
GTPase subunit by ADP-ribosilation (a covalent and far more
stable change in proteins) producing a permanent conformation
of GTP bound G subunit. This keeps the cyclase in permanent
active conformation because ADP-ribosilation inhibits GTPase
activity required to put an end in the hormonal signal.

The study made while G-proteins were considered a dimer still
holds despite its limited vision of the real complexity of the
transduction system. It was also possible to get this very same
“freezing” in the active state using GTP stable analogues. This
transduction system is one of the best examples of the delicate
mechanisms of conformational interaction of proteins. Further-
more, this system also shows on the opposite side of our
reasoning scheme, how covalent changes are adequate for
more stable changes than those mediated by Van der Wall’s
forces between proteins. Yet, these delicate forces are the
same involved when Sc-Prion transfers its rogue
conformation to c-Prion proteins and other similar events.
The Jacob-Monod Model

A combination of genetic and biochemical experiments in
bacteria led to the initial recognition of

  1. protein-binding regulatory sequences associated with genes and
  2. proteins whose binding to a gene’s regulatory sequences
    either activate or repress its transcription.

These key components underlie the ability of both prokaryotic and
eukaryotic cells to turn genes on and off. The  experimental findings lead to a general model of bacterial transcription control.

Gene control serves to allow a single cell to adjust to changes in its
nutritional environment so that its growth and division can be optimized.
Thus, the prime focus of research has been on genes that encode
inducible proteins whose production varies depending on the nutritional
status of the cells. Its most characteristic and biologically far-reaching
purpose in eukaryotes, distinctive from single cell organisms is the
regulation of a genetic program that underlies embryological
development and tissue differentiation.

The principles of transcription have already been described in this
series under the translation of the genetic code into amino acids
that are the building blocks for proteins.

E.coli can use either glucose or other sugars such as the
disaccharide lactose as the sole source of carbon and energy.
When E. coli cells are grown in a glucose-containing medium,
the activity of the enzymes needed to metabolize lactose is
very low. When these cells are switched to a medium
containing lactose but no glucose, the activities of the lactose-metabolizing enzymes increase. Early studies showed that the
increase in the activity of these enzymes resulted from the
synthesis of new enzyme molecules, a phenomenon termed
induction. The enzymes induced in the presence of lactose
are encoded by the lac operon, which includes two genes, Z
and Y, that are required for metabolism of lactose and a third
gene. The lac Y gene encodes lactose permease, which spans the E. coli cell membrane and uses the energy available from
the electrochemical gradient across the membrane to pump
lactose into the cell. The lac Z gene encodes β-galactosidase,
which splits the disaccharide lactose into the monosaccharides
glucose and galactose, which are further metabolized through
the action of enzymes encoded in other operons. The third
gene encodes thiogalactoside transacetylase.

Synthesis of all three enzymes encoded in the lac operon is rapidly
induced when E. coli cells are placed in a medium containing lactose
as the only carbon source and repressed when the cells are switched
to a medium without lactose. Thus all three genes of the lac operon
are coordinately regulated. The lac operon in E. coli provides one
of the earliest and still best-understood examples of gene control.
Much of the pioneering research on the lac operon was conducted by
Francois Jacob, Jacques Monod, and their colleagues in the 1960s.

Some molecules similar in structure to lactose can induce expression
of the lacoperon genes even though they cannot be hydrolyzed by β-galactosidase. Such small molecules (i.e., smaller than proteins) are
called inducers. One of these, isopropyl-β-D-thiogalactoside,
abbreviated IPTG,is particularly useful in genetic studies of the lac
operon, because it can diffuse into cells and, it is not metabolized.
Insight into the mechanisms controlling synthesis of β-galactosidase
and lactose permease came from the study of mutants in which control
of β-galactosidase expression was abnormal and used a colorimetric
assay for β-galactosidase.

When the cells are exposed to chemical mutagens before plating on
X-gal/glucose plates, rare blue colonies appear, but when cells
from these blue colonies are recovered and grown in media containing
glucose, they overexpress all the genes of the lac operon. These cells
are called constitutive mutants because they fail to repress the lac
operon in media lacking lactose and instead continuously express the
enzymes, and the genes were mapped to a region on the E. coli
chromosome. This led to the conclusion that these cells had a defect
in a protein that normally repressed expression of the lac operon in
the absence of lactose, and that it blocks transcription by binding to
a site on the E. coli genome where transcription of the lac operon is
initiated. In addition, it binds to the lac repressor in the lactose
medium and decreases its affinity for the repressor-binding site
on the DNA causing the repressor to unbind the DNA. Thereby,
transcription of the lac operon is initiated, leading to synthesis of
β-galactosidase, lactose permease, and thiogalactoside

 regulation of the lac operon by lac repressor

Jacob and Monod model of transcriptional regulation of the lac operon

Next, Jacob and Monod isolated mutants that expressed the lac operon
constitutively even when two copies of the wild-type lacI gene
encoding the lac repressor were present in the same cell, and the
constitutive mutations mapped to one end of the lac operon, as the
model predicted.  Further, there are rare cells that carry a mutation
located at the region, promoter, that block initiation of transcription by
RNA polymerase.

lac I+ gene is trans-acting, & encodes a protein, which binds to a lac operator

 lac I+ gene is trans-acting, & encodes a protein, which
binds to a lac operator

They further demonstrated that the two types of mutations lac I and
lac I+, were cis- and trans-acting, the latter encoding a protein that
binds to the lac operator. The cis-acting Oc mutations prevent
binding of the lac repressor to the operator, and  mutations in the
lac promoter are cis-acting, since they alter the binding site for RNA
polymerase. In general, trans-acting genes that regulate expression
of genes on other DNA molecules encode diffusible products. In
most cases these are proteins, but in some cases RNA molecules
can act in trans to regulate gene expression.

According to the Jacob and Monod model of transcriptional control,
transcription of the lac operon, which encodes three inducible
proteins, is repressed by binding of lac repressor protein to the
operator sequence.

 (Section 10.1Bacterial Gene Control: The Jacob-Monod Model.)
This book is accessible by the search feature.

Comment: This seminal work was done a half century ago. It was a
decade after the Watson-Crick model for DNA. The model is
elaborated for the Eukaryote in the examples that follow.

(The next two articles were called to my attention by R. Bosov at
University of Virginia).

An acetate switch regulates stress erythropoiesis

M Xu,  JS Nagati, Ji Xie, J Li, H Walters, Young-Ah Moon, et al.
Nature Medicine 10 Aug 2014(20): 1018–1026.

message: 1- ( -CH3 ) = Ln ( (1/sqrt(1-Acetate^2) –
sqrt oxalate))/ Ln(oxygen) – K(o)

The hormone erythropoietin (EPO), synthesized in the kidney or liver
of adult mammals, controls erythrocyte production and is regulated by
the stress-responsive transcription factor hypoxia-inducible factor-2
 HIFα acetylation and efficient HIF-2–dependent EPO
induction during hypoxia requires  the lysine acetyltransferase CREB-binding protein (CBP) . These processes require acetate-dependent
acetyl CoA synthetase 2 (ACSS2) as follows.Acetate levels rise and
ACSS2 is required for HIF-2α acetylation, CBP–HIF-2α complex
formation, CBP–HIF-2α recruitment to the EPO enhancer and induction
of EPO gene expression
 in human Hep3B hepatoma cells and in EPO-generating organs of hypoxic or acutely anemic mice. In acutely anemic
mice, acetate supplementation augments stress erythropoiesis in an
ACSS2-dependent manner. Moreover, in acquired and inherited
chronic anemia mouse models, acetate supplementation increases
EPO expression
 and the resting hematocrit. Thus, a mammalian
stress-responsive acetate switch controls HIF-2 signaling and EPO
induction during pathophysiological states marked by tissue hypoxia.

Figure 1: Acss2 controls HIF-2 signaling in hypoxic cells.
Time course of endogenous HIF-2α acetylation during hypoxia following
immunoprecipitation (IP) of HIF-2α from whole-cell extracts and detection
of acetylated lysines by immunoblotting (IB).

Figure 2: Acss2 regulates hypoxia-induced renal Epo expression in mice.

Figure 3: Acute anemia induces Acss2-dependent HIF-2 signaling in mice.

Figure 4: An acetate switch regulates Cbp–HIF-2 interactions in cells.
(a) HIF-2α acetylation following immunoprecipitation of endogenous
HIF-2α and detection by immunoblotting with antibodies to acetylated
lysine or HIF-2α.

Figure 5: Acss2 signaling in cells requires intact HIF-2 acetylation.

Figure 6: Acetate facilitates recovery from anemia.

Acetate facilitates recovery from anemia

Acetate facilitates recovery from anemia

(a) Serial hematocrits of CD1 wild-type female mice after PHZ treatment, followed
by once daily per os (p.o.) supplementation with water vehicle (Veh; n = 7 mice),
GTA (n = 6 mice), GTB (n = 8 mice) or GTP (n = 7 mice) (single measurem…

see also-.
1. Bunn, H.F. & Poyton, R.O. Oxygen sensing and molecular adaptation to
hypoxia. Physiol. Rev. 76, 839–885 (1996).

  1. .Richalet, J.P. Oxygen sensors in the organism: examples of regulation
    under altitude hypoxia in mammals. Comp. Biochem. Physiol. A Physiol.
    118, 9–14 (1997).
  2. .Koury, M.J. Erythropoietin: the story of hypoxia and a finely regulated
    hematopoietic hormone. Exp. Hematol. 33, 1263–1270 (2005).
  3. Wang, G.L., Jiang, B.H., Rue, E.A. & Semenza, G.L. Hypoxia-inducible
    factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated
    by cellular O2 tension. Proc. Natl. Acad. Sci. USA92, 5510–5514 (1995).
  4. Chen, R. et al. The acetylase/deacetylase couple CREB-binding
    protein/sirtuin 1 controls hypoxia-inducible factor 2 signaling. J. Biol.
    Chem. 287, 30800–30811 (2012).
  5. .Papandreou, I., Cairns, R.A., Fontana, L., Lim, A.L. & Denko, N.C.
    HIF-1 mediates adaptation to hypoxia by actively down-regulating
    mitochondrial oxygen consumption. Cell Metab. 3,187–197 (2006).

14. Kim, J.W., Tchernyshyov, I., Semenza, G.L. & Dang, C.V. HIF-1-
mediated expression of pyruvate dehydrogenase kinase: a metabolic
switch required for cellular adaptation to hypoxia. Cell Metab. 3,
177–185 (2006).

16. Fujino, T., Kondo, J., Ishikawa, M., Morikawa, K. & Yamamoto, T.T.
Acetyl-CoA synthetase 2, a mitochondrial matrix enzyme involved in the
oxidation of acetate. J. Biol. Chem. 276,11420–11426 (2001).

17..Luong, A., Hannah, V.C., Brown, M.S. & Goldstein, J.L. Molecular
characterization of human acetyl-CoA synthetase, an enzyme regulated
by sterol regulatory element-binding proteins. J. Biol. Chem. 275,
26458–26466 (2000).

20 .Wellen, K.E. et al. ATP-citrate lyase links cellular metabolism to
histone acetylation. Science324, 1076–1080 (2009).

24. McBrian, M.A. et al. Histone acetylation regulates intracellular pH.
Mol. Cell 49, 310–321(2013).

Asymmetric mRNA localization contributes to fidelity and sensitivity
of spatially localized systems

Robert J Weatheritt, Toby J Gibson & M Madan Babu
Nature Structural & Molecular Biology 21, 833–839 (2014) 

Although many proteins are localized after translation, asymmetric
protein distribution is also achieved by translation after mRNA localization.
Why are certain mRNA transported to a distal location and translated
on-site? Here we undertake a systematic, genome-scale study of
asymmetrically distributed protein and mRNA in mammalian cells.
Our findings suggest that asymmetric protein distribution by mRNA
localization enhances interaction fidelity and signaling sensitivity
Proteins synthesized at distal locations frequently contain intrinsically
disordered segments. These regions are generally rich in assembly-
promoting modules and are often regulated by post-translational
modifications. Such proteins are tightly regulated but display distinct
temporal dynamics upon stimulation with growth factors. Thus, proteins
synthesized on-site may rapidly alter proteome composition and
act as dynamically regulated scaffolds to promote the formation
of reversible cellular assemblies. 
Our observations are consistent
across multiple mammalian species, cell types and developmental stages,
suggesting that localized translation is a recurring feature of cell
signaling and regulation.

Figure 1: Classification and characterization of TAS and DSS proteins.

The two major mechanisms for localizing proteins to distal sites in the cell

The two major mechanisms for localizing proteins to distal sites in the cell

(a)The two major mechanisms for localizing proteins to distal sites in the cell.
(b) Data sets used to identify groups of DSS and TAS transcripts, as well as
DSS and TAS proteins in mouse neuroblastoma cells

Figure 2: Structural analysis of DSS proteins reveals an enrichment
in disordered regions.

Distributions of the various structural properties of the DSS and TAS proteins of the mouse neuroblastoma data sets

Distributions of the various structural properties of the DSS and TAS proteins of the mouse neuroblastoma data sets

(a,b) Distributions of the various structural properties of the DSS and TAS
proteins of the mouse neuroblastoma data sets (a), the mouse pseudopodia,
the rat embryonic sensory neuron data set and the adult sensory neuron data set (b).…

Figure 3: Analysis of DSS proteins reveals an enrichment for linear motifs, phase-
transition (i.e., higher-order assembly) promoting segments and PTM sites that act
as molecular switches.

(a,b) Distributions of the various regulatory and structural properties of the DSS
and TAS proteins of the mouse neuroblastoma data sets

Figure 4: Dynamic regulation of DSS transcripts and proteins.

Dynamic regulation of DSS transcripts and proteins

Dynamic regulation of DSS transcripts and proteins

Genome-wide quantitative measurements of gene expression of DSS (n = 289)
and TAS (n = 1,292) proteins in mouse fibroblast cells. DSS transcripts and
proteins have a lower abundance and shorter half-lives

Figure 5: An overview of the potential advantages conferred by distal-site protein
synthesis, inferred from our analysis.

An overview of the potential advantages conferred by distal-site protein synthesis, inferred from our analysis

An overview of the potential advantages conferred by distal-site protein synthesis, inferred from our analysis

Turquoise and red filled circle represents off-target and correct interaction partners,
respectively. Wavy lines – a disordered region within a distal site synthesis protein.

The identification of asymmetrically localized proteins and transcripts.

The identification of asymmetrically localized proteins and transcripts

The identification of asymmetrically localized proteins and transcripts

An illustrative explanation of the resolution of the study and the concept of asymmetric
localization of proteins and mRNA. In this example, on the left a neuron is divided into
its cell body and axon terminal, and transcriptome/proteo…

Graphs and boxplots of functional and structural properties for distal site synthesis
(DSS) proteins (red) and transport after synthesis (TAS) proteins (gray).
See Online Methods for details and legend of Figure 2 for a description of boxplots
and statistical tests.

See also –
1. Martin, K.C. & Ephrussi, A. mRNA localization: gene expression in the spatial
dimension. Cell136, 719–730 (2009).

  1. Scott, J.D. & Pawson, T. Cell signaling in space and time: where proteins come
    together and when they’re apart. Science 326, 1220–1224 (2009).

4..Holt, C.E. & Bullock, S.L. Subcellular mRNA localization in animal cells
and why it matters.Science 326, 1212–1216 (2009).

  1. Jung, H., Gkogkas, C.G., Sonenberg, N. & Holt, C.E. Remote control of
    gene function by local translation. Cell 157, 26–40 (2014). 

Regulation of metabolism by hypoxia-inducible factor 1.   
Semenza GL.    Author information
Cold Spring Harb Symp Quant Biol. 2011;76:347-53.

The maintenance of oxygen homeostasis is critical for survival, and the
master regulator of this process in metazoan species is hypoxia-inducible
factor 1 (HIF-1), which

  • controls both O(2) delivery and utilization.

Under conditions of reduced O(2) availability,

  • HIF-1 activates the transcription of genes, whose protein products
  • mediate a switch from oxidative to glycolytic metabolism.

HIF-1 is activated in cancer cells as a result of intratumoral hypoxia
and/or genetic alterations.

In cancer cells, metabolism is reprogrammed to

  • favor glycolysis even under aerobic conditions.

Pyruvate kinase M2 (PKM2) has been implicated in cancer growth and
metabolism, although the mechanism by which it exerts these effects is
unclear. Recent studies indicate that

PKM2 interacts with HIF-1α physically and functionally to

  1. stimulate the binding of HIF-1 at target genes,
  2. the recruitment of coactivators,
  3. histone acetylation, and
  4. gene transcription.

Interaction with HIF-1α is facilitated by

  • hydroxylation of PKM2 at proline-403 and -408 by PHD3.

Knockdown of PHD3

  • decreases glucose transporter 1, lactate dehydrogenase A, and
    pyruvate dehydrogenase kinase 1 expression;
  • decreases glucose uptake and lactate production; and
  • increases O(2) consumption.

The effect of PKM2/PHD3 is not limited to genes encoding metabolic
enzymes because VEGF is similarly regulated.

These results provide a mechanism by which PKM2

  • promotes metabolic reprogramming and

suggest that it plays a broader role in cancer progression than has
previously been appreciated.   PMID: 21785006   


Cadherins are thought to be the primary mediators of adhesion
between the cells
 of vertebrate animals, and also function in cell
adhesion in many invertebrates. The expression of numerous cadherins
during development is highly regulated, and the precise pattern of
cadherin expression plays a pivotal role in the morphogenesis of tissues
and organs. The cadherins are also important in the continued maintenance
of tissue structure and integrity. The loss of cadherin expression appears
to be highly correlated with the invasiveness of some types of tumors. Cadherin adhesion is also dependent on the presence of calcium ions
in the extracellular milieu.

The cadherin protein superfamily, defined as proteins containing a
cadherin-like domain, can be divided into several sub-groups. These include

  • the classical (type I) cadherins, which mediate adhesion at adherens junctions;
  • the highly-related type II cadherins;
  • the desmosomal cadherins found in desmosome junctions;
  • protocadherins, expressed only in the nervous system; and
  • atypical cadherin-like domain containing proteins.

Members of all but the atypical group have been shown to play a role
in intercellular adhesion.

Part II.  PKM2 and regulation of glycolysis

PKM2 regulates the Warburg effect and promotes ​HMGB1
release in sepsis

L Yang, M Xie, M Yang, Y Yu, S Zhu, W Hou, R Kang, …, & D Tang
Nature Communic 14 July 2014; 5(4436)

Increasing evidence suggests the important role of metabolic reprogramming

  • in the regulation of the innate inflammatory response,

We provide evidence to support a novel role for the

  • ​pyruvate kinase M2 (​PKM2)-mediated Warburg effect,

namely aerobic glycolysis,

  • in the regulation of ​high-mobility group box 1 (​HMGB1) release. ​
  1. PKM2 interacts with ​hypoxia-inducible factor 1α (​HIF1α) and
  2. activates the ​HIF-1α-dependent transcription of enzymes necessary
    for aerobic glycolysis in macrophages.

Knockdown of ​PKM2, ​HIF1α and glycolysis-related genes

  • uniformly decreases ​lactate production and ​HMGB1 release.

Similarly, a potential ​PKM2 inhibitor, ​shikonin,

  1. reduces serum ​lactate and ​HMGB1 levels, and
  2. protects mice from lethal endotoxemia and sepsis.

Collectively, these findings shed light on a novel mechanism for

  • metabolic control of inflammation by
  • regulating ​HMGB1 release and

highlight the importance of targeting aerobic glycolysis in the treatment
of sepsis and other inflammatory diseases.

  1. Glycolytic inhibitor ​2-D G attenuates ​HMGB1 release by activated macrophages.
  2. Figure 2: Upregulated ​PKM2 promotes aerobic glycolysis and ​HMGB1
    release in activated macrophages.
  3. Figure 3: ​PKM2-mediated ​HIF1α activation is required for ​HMGB1
    release in activated macrophages.


ERK1/2-dependent phosphorylation and nuclear translocation of
PKM2 promotes the Warburg effect  

W Yang, Y Zheng, Y Xia, Ha Ji, X Chen, F Guo, CA Lyssiotis, & Zhimin Lu
Nature Cell Biology  2012 (27 June 2014); 14: 1295–1304
Corrigendum (January, 2013)

Pyruvate kinase M2 (PKM2) is upregulated in multiple cancer types and
contributes to the Warburg. We demonstrate that

  • EGFR-activated ERK2 binds directly to PKM2 Ile 429/Leu 431
  • through the ERK2 docking groove
  • and phosphorylates PKM2 at Ser 37, but
  • does not phosphorylate PKM1.

Phosphorylated PKM2 Ser 37

  1. recruits PIN1 for cis–trans isomerization of PKM2, which
  2. promotes PKM2 binding to importin α5
  3. and PKM2 translocates to the nucleus.

Nuclear PKM2 acts as

  • a coactivator of β-catenin to
  • induce c-Myc expression,

This is followed by

  1. the upregulation of GLUT1, LDHA and,
  2. in a positive feedback loop,
  • PTB-dependent PKM2 expression.

Replacement of wild-type PKM2 with

  • a nuclear translocation-deficient mutant (S37A)
  • blocks the EGFR-promoted Warburg effect
    and brain tumour development in mice.

In addition, levels of PKM2 Ser 37 phosphorylation

  • correlate with EGFR and ERK1/2 activity
    in human glioblastoma specimens.

Our findings highlight the importance of

  • nuclear functions of PKM2 in the Warburg effect
    and tumorigenesis.
  1. ERK is required for PKM2 nucleus translocation.
  2. ERK2 phosphorylates PKM2 Ser 37.
  3. Figure 3: PKM2 Ser 37 phosphorylation recruits PIN1.

 Pyruvate kinase M2 activators promote tetramer formation
and suppress tumorigenesis

D Anastasiou, Y Yu, WJ Israelsen, Jian-Kang Jiang, MB Boxer, B Hong, et al.
Nature Chemical Biology  11 Oct 2012; 8: 839–847

Cancer cells engage in a metabolic program to

  • enhance biosynthesis and support cell proliferation.

The regulatory properties of pyruvate kinase M2 (PKM2)

  • influence altered glucose metabolism in cancer.

The interaction of PKM2 with phosphotyrosine-containing proteins

  • inhibits PTM2 enzyme activity and
  • increases the availability of glycolytic metabolites
  • supporting cell proliferation.

This suggests that high pyruvate kinase activity may suppress
tumor growth

  1. expression of PKM1,  the pyruvate kinase isoform with high
    constitutive activity, or
  2. exposure to published small-molecule PKM2 activators
  • inhibits the growth of xenograft tumors.

Structural studies reveal that

  • small-molecule activators bind PKM2
  • at the subunit interaction interface,
  • a site that is distinct from that of the
    • endogenous activator fructose-1,6-bisphosphate (FBP).

However, unlike FBP,

  • binding of activators to PKM2 promotes
  • a constitutively active enzyme state that is resistant to inhibition
  • by tyrosine-phosphorylated proteins.

These data support the notion that small-molecule activation of PKM2
can interfere with anabolic metabolism

  1. PKM1 expression in cancer cells impairs xenograft tumor growth.
  2. TEPP-46 and DASA-58 isoform specificity in vitro and in cells.
    TEPP-46 and DASA-58 isoform specificity in vitro and in cells.

    TEPP-46 and DASA-58 isoform specificity in vitro and in cells.

    (a) Structures of the PKM2 activators TEPP-46 and DASA-58. (b) Pyruvate kinase (PK) activity in purified recombinant human
    PKM1 or PKM2 expressed in bacteria in the presence of increasing
    concentrations of TEPP-46 or DASA-58. M1, PKM1;…

  3. Activators promote PKM2 tetramer formation and prevent
    inhibition by phosphotyrosine signaling.
Activators promote PKM2 tetramer formation and prevent inhibition by phosphotyrosine signaling.

Activators promote PKM2 tetramer formation and prevent inhibition by phosphotyrosine signaling.

Sucrose gradient ultracentrifugation profiles of purified recombinant
PKM2 (rPKM2) and the effects of FBP and TEPP-46 on PKM2 subunit stoichiometry.

Figure 5: Metabolic effects of cell treatment with PKM2 activators.
(a) Effects of TEPP-46, DASA-58 (both used at 30 μM) or PKM1
expression on the doubling time of H1299 cells under normoxia
(21% O2) or hypoxia (1% O2). (b) Effects of DASA-58 on lactate
production from glucose. The P value shown was ca…

EGFR has a tumour-promoting role in liver macrophages during
hepatocellular carcinoma formation

H Lanaya, A Natarajan, K Komposch, L Li, N Amberg, …, & Maria Sibilia
Nature Cell Biology 31 Aug 2014

Tumorigenesis has been linked with macrophage-mediated chronic
inflammation and diverse signaling pathways, including the ​epidermal
growth factor receptor (​EGFR) pathway. ​EGFR is expressed in liver
macrophages in both human HCC and in a mouse HCC model. Mice
lacking ​EGFR in macrophages show impaired hepatocarcinogenesis,
Mice lacking ​EGFR in hepatocytes develop HCC owing to increased
hepatocyte damage and compensatory proliferation. EGFR is required
in liver macrophages to transcriptionally induce ​interleukin-6 following
interleukin-1 stimulation, which triggers hepatocyte proliferation and HCC.
Importantly, the presence of ​EGFR-positive liver macrophages in HCC
patients is associated with poor survival. This study demonstrates a

  • tumour-promoting mechanism for ​EGFR in non-tumour cells,
  • which could lead to more effective precision medicine strategies.
  1. HCC formation in mice lacking ​EGFRin hepatocytes or all liver cells.

2. EGFR expression in Kupffer cells/liver macrophages promotes HCC development.

EGFR c2a expression in Kupffer cells.liver macrophages promotes HCC development.

EGFR c2a expression in Kupffer cells.liver macrophages promotes HCC development.

Hypoxia-inducible factor 1 activation by aerobic glycolysis implicates
the Warburg effect in carcinogenesis

Lu H1, Forbes RA, Verma A.
J Biol Chem. 2002 Jun 28;277(26):23111-5. Epub 2002 Apr 9

Cancer cells display high rates of aerobic glycolysis, a phenomenon
known historically as the Warburg effect. Lactate and pyruvate, the end
products of glycolysis, are highly produced by cancer cells even in the
presence of oxygen

Hypoxia-induced gene expression in cancer cells

  • has been linked to malignant transformation.

Here we provide evidence that lactate and pyruvate

  • regulate hypoxia-inducible gene expression
  • independently of hypoxia
  • by stimulating the accumulation of hypoxia-inducible Factor 1alpha

In human gliomas and other cancer cell lines,

  • the accumulation of HIF-1alpha protein under aerobic conditions
  • requires the metabolism of glucose to pyruvate that
  1. prevents the aerobic degradation of HIF-1alpha protein,
  2. activates HIF-1 DNA binding activity, and
  3. enhances the expression of several HIF-1-activated genes
  4. erythropoietin,
  5. vascular endothelial growth factor,
  6. glucose transporter 3, and
  7. aldolase A.

Our findings support a novel role for pyruvate in metabolic signaling
and suggest a mechanism by which

  • high rates of aerobic glycolysis
  • can promote the malignant transformation and
  • survival of cancer cells.PMID: 11943784

Part IV. Transcription control and innate immunity

 c-Myc-induced transcription factor AP4 is required for
host protection mediated by CD8+ T cells

C Chou, AK Pinto, JD Curtis, SP Persaud, M Cella, Chih-Chung Lin, … & T Egawa Nature Immunology 17 Jun 2014;

The transcription factor c-Myc is essential for

  • the establishment of a metabolically active and proliferative state
  • in T cells after priming,

We identified AP4 as the transcription factor

  • that was induced by c-Myc and
  • sustained activation of antigen-specific CD8+ T cells.

Despite normal priming,

  • AP4-deficient CD8+ T cells
  • failed to continue transcription of a broad range of
    c-Myc-dependent targets.

Mice lacking AP4 specifically in CD8+ T cells showed

  • enhanced susceptibility to infection with West Nile virus.

Genome-wide analysis suggested that

  • many activation-induced genes encoding molecules
  • involved in metabolism were shared targets of
  • c-Myc and AP4.

Thus, AP4 maintains c-Myc-initiated cellular activation programs

  • in CD8+ T cells to control microbial infection.
  1. AP4 is regulated post-transcriptionally in CD8+ T cells.

Microarray analysis of transcription factor–encoding genes with a difference
in expression of >1.8-fold in activated CD8+ T cells treated for 12 h with
IL-2 (100 U/ml; + IL-2) relative to their expression in activated CD8+ T cells…

2. AP4 is required for the population expansion of antigen specific
CD8+ T cells following infection with LCMV-Arm.

Expression of CD4, CD8α and KLRG1 (a) and binding of an
H-2Db–gp(33–41) tetramer and expression of CD8α, KLRG1 and
CD62L (b) in splenocytes from wild-type (WT) and Tfap4−/− mice,
assessed by flow cytometry 8 d after infection

3. AP4 is required for the sustained clonal expansion of CD8+ T cells
but  not for their initial proliferation.

  1. AP4 is essential for host protection against infection with WNV, in
    a CD8+ T cell–intrinsic manner.
AP4 is essential for host protection against infection with WNV, in a CD8+ T cell–intrinsic manner.

AP4 is essential for host protection against infection with WNV, in a CD8+ T cell–intrinsic manner.

  •  Survival of Tfap4F/FCre− control mice (Cre−; n = 16) and
  • Tfap4F/FCD8-Cre+ mice (CD8-Cre+; n = 22) following infection with WNV.
    (b,c) Viral titers in the brain (b) and spleen (c) of Tfap4F/F Cre− and Tfap4F/F
    CD8-Cre+ mice  on day 9…

AP4 is essential for the sustained expression of genes that are targets of c-Myc.

Normalized signal intensity (NSI) of endogenous transcripts in
Tfap4+/+ and Tfap4−/− OT-I donor T cells adoptively transferred into
host mice and assessed on day 4 after infection of the host with LM-OVA
(top), and that of ERCC controls

Sustained c-Myc expression ‘rescues’ defects of Tfap4−/− CD8+ T cells.

AP4 and c-Myc have distinct biological functions.

Mucosal memory CD8+ T cells are selected in the periphery
by an MHC class I molecule

Y Huang, Y Park, Y Wang-Zhu, …A Larange, R Arens, & H Cheroutre

Nature Immunology 2 Oct 2011; 12: 1086–1095

The presence of immune memory at pathogen-entry sites is a prerequisite
for protection. We show that the non-classical major histocompatibility
complex (MHC) class I molecule

  • thymus leukemia antigen (TL),
  • induced on dendritic cells interacting with CD8αα on activated CD8αβ+ T cells,
  • mediated affinity-based selection of memory precursor cells.

Furthermore, constitutive expression of TL on epithelial cells

  • led to continued selection of mature CD8αβ+ memory T cells.

The memory process driven by TL and CD8αα

  • was essential for the generation of CD8αβ+ memory T cells in the intestine and
  • the accumulation of highly antigen-sensitive CD8αβ+ memory T cells
  • that form the first line of defense at the largest entry port for pathogens.

The metabolic checkpoint kinase mTOR is essential for IL-15 signaling during the development and activation of NK cells.

Marçais A, Cherfils-Vicini J, Viant C, Degouve S, Viel S, Fenis A, Rabilloud J,
Mayol K, Tavares A, Bienvenu J, Gangloff YG, Gilson E, Vivier E,Walzer T.
Nat Immunol. 2014 Aug; 15(8):749-757. Epub 2014 Jun 29  .    PMID: 24973821

Interleukin 15 (IL-15) controls

  • both the homeostasis and the peripheral activation of natural killer (NK) cells.

We found that the metabolic checkpoint kinase

  • mTOR was activated and boosted bioenergetic metabolism
  • after exposure of NK cells to high concentrations of IL-15,

whereas low doses of IL-15 triggered

  • only phosphorylation of the transcription factor STAT5.


  • stimulated the growth and nutrient uptake of NK cells and
  • positively fed back on the receptor for IL-15.

This process was essential for

  • sustaining NK cell proliferation during development and
  • the acquisition of cytolytic potential during inflammation
    or viral infection.

The mTORC1 inhibitor rapamycin 

  • inhibited NK cell cytotoxicity both in mice and humans;
    • this probably contributes to the immunosuppressive
      activity of this drug in different clinical settings.

The Critical Role of IL-15-PI3K-mTOR Pathway in Natural Killer Cell
Effector Functions.
Nandagopal NAli AKKomal AKLee SH.   Author information
Front Immunol. 2014 Apr 23; 5:187. eCollection 2014.

Natural killer (NK) cells were so named for their uniqueness in killing
certain tumor and virus-infected cells without prior sensitization.
Their functions are modulated in vivo by several soluble immune mediators;

  • interleukin-15 (IL-15) being the most potent among them in
    enabling NK cell homeostasis, maturation, and activation.

During microbial infections,

  • NK cells stimulated with IL-15 display enhanced cytokine responses.

This priming effect has previously been shown with respect to increased
IFN-γ production in NK cells

  • upon IL-12 and IL-15/IL-2 co-stimulation.
  • we explored if this effect of IL-15 priming 
  • can be extended to various other cytokines and
  • observed enhanced NK cell responses to stimulation
    • with IL-4, IL-21, IFN-α, and IL-2 in addition to IL-12.
  • we also observed elevated IFN-γ production in primed NK cells

Currently, the fundamental processes required for priming and

  • whether these signaling pathways work collaboratively or

    • for NK cell functions are poorly understood.

We examined IL-15 effects on NK cells in which

  • the pathways emanating from IL-15 receptor activation
    • were blocked with specific inhibitors
    • To identify the key signaling events for NK cell priming,

Our results demonstrate that

the PI3K-AKT-mTOR pathway is critical for cytokine responses
in IL-15 primed NK cells. 

This pathway is also implicated in a broad range of

  • IL-15-induced NK cell effector functions such as
    • proliferation and cytotoxicity.

Likewise, NK cells from mice

  • treated with rapamycin to block the mTOR pathway
  • displayed defects in proliferation, and IFN-γ and granzyme B productions
  • resulting in elevated viral burdens upon murine cytomegalovirus infection.

Taken together, our data demonstrate

  • the requirement of PI3K-mTOR pathway
    • for enhanced NK cell functions by IL-15, thereby
  • coupling the metabolic sensor mTOR to NK cell anti-viral responses.

KEYWORDS: IL-15; JAK–STAT pathway; mTOR pathway; natural killer cells; signal transduction

Part V. Predicting Therapeutic Targets 

New discovery approach accelerates identification of potential cancer treatments
 Laura Williams, Univ. of Michigan   09/30/2014

Researchers at the Univ. of Michigan have described a new approach to
discovering potential cancer treatments that

  • requires a fraction of the time needed for more traditional methods.

They used the platform to identify

  • a novel antibody that is undergoing further investigation as a potential
    treatment for breast, ovarian and other cancers.

In research published online in the Proceedings of the National Academy
of Sciences
, researchers in the laboratory of Stephen Weiss at the U-M Life
Sciences Institute detail an approach

  • that replicates the native environment of cancer cells and
  • increases the likelihood that drugs effective against the growth of
    tumor cells in test tube models
  • will also stop cancer from growing in humans.

The researchers have used their method

  • to identify an antibody that stops breast cancer tumor growth in animal models, and
  • they are investigating the antibody as a potential treatment in humans.

“Discovering new targets for cancer therapeutics is a long and tedious undertaking, and

  • identifying and developing a potential drug to specifically hit that
    target without harming healthy cells is a daunting task,” Weiss said.
  • “Our approach allows us to identify potential therapeutics
    • in a fraction of the time that traditional methods require.”

The researchers began by

  • creating a 3-D “matrix” of collagen, a connective tissue molecule very similar to that found
    • surrounding breast cancer cells in human patients.
  • They then embedded breast cancer cells into the collagen matrix,
    • where the cells grew as they would in human tissue.

The investigators then injected the cancer-collagen tissue composites into mice that then

  • recognize the human cancer cells as foreign tissue.
    • Much in the way that our immune system generates antibodies
      to fight infection,
  • the mice began to generate thousands of antibodies directed against
    the human cancer cells.
  • These antibodies were then tested for the ability to stop the growth
    of the human tumor cells.

“We create an environment in which cells cultured in the laboratory ‘think’
they are growing in the body and then

  • rapidly screen large numbers of antibodies to see if any exert
    anti-cancer effects,” Weiss said.
  • “This allows us to select promising antibodies very quickly and then

They discovered a particular antibody, 4C3, which was able to

  • almost completely stop the proliferation of the breast cancer cells.

They then identified the molecule on the cancer cells that the antibody targets.

The antibody can be further engineered to generate

  • humanized monoclonal antibodies for use in patients

“We still need to do a lot more work to determine how effective 4C3 might be as a
treatment for breast and other cancers, on its own or in conjunction with other
therapies,” Weiss said. “But we have enough data to warrant further pursuit,
and are expanding our efforts to use this discovery platform to find similarly promising antibodies.”

Source: Univ. of Michigan

  1. Jose Eduardo de Salles Roselino

    I think you have made a great effort in order to connect basic ideas of metabolic regulation with those of gene expression control “modern” mechanisms.
    Yet, I do not think that at this stage it will be clear for all readers. At least, for the great majority of the readers. The most important factor I my opinion, is derived from the fact that modern readers considers that metabolic regulation deals with so called “housekeeping activities” of the cell. Something that is of secondary, tertiary or even less level of relevance.
    My idea, that you have mentioned in the text when you write at the beginning, the word biochemistry, in order to resume it, derives from the reading of What is life together with Prof. Leloir . For me and also, for him, biochemistry comprises a set of techniques and also a framework of reasoning about scientific results. As a set of techniques, Schrodinger has considered that it will lead to better understanding of genetics and of physiology as a two legs structure supporting the future progress related to his time (mid-forties). For Leloir, the key was the understanding of chemical reactivity and I agree with him. However, as I was able to talk and discuss it with him in detail, we should also take into account levels of stabilities of macromolecules and above all, regulation of activities and function (this is where) Pasteur effect that I was studying in Leloir´s lab at that time, 1970-72, gets into the general picture.
    Regulation for complex living beings , that also have cancer cell as a great topic of research problem can be understood through the understanding of two quite different results when opposition with lack of regulation is taken into account or experimentally elicited. The most clearly line of experiments can follow the Pasteur Effect as the intracellular result best seen when aerobiosis is compared with anaerobiosis as conditions in which maintenance of ATP levels and required metabolic regulation (Energy charge D.E, Atkinson etc) is studied. Another line of experiments is one that takes into account the extracellular result or for instance the homeostatic regulation of blood glucose levels. The blood glucose level is the most conspicuous and related to Pasteur Effect regulatory event that can be studied in the liver taking into account both final results tested or compared regarding its regulation, ATP levels maintenance (intracellular) and blood glucose maintenance (extracellular).
    My key idea is to consider that the same factors that elicits fast regulatory responses also elicits the slow energetic expensive regulatory responses. The biologic logic behind this common root is the ATP economy. In case, the regulatory stimulus fades out quickly the fast regulatory responses are good enough to maintain life and the time requiring, energetic costly responses will soon be stopped cutting short the ATP expenditure. In case, the stimulus last for long periods of time the fast responses are replaced by adaptive responses that in general will follow the line of cell differentiation mechanisms with changes in gene expression etc.
    The change from fast response mechanisms to long lasting developmentally linked ones is not sharp. Therefore, somehow, cancer cells becomes trapped into a metastable regulatory mechanism that prevents cell differentiation and reinforces those mechanisms linked to its internal regulatory goals. This metastable mechanism takes advantage from the fact that other cells, tissues and organs will take good care of homeostatic mechanisms that provide for their nutritional needs. In the case of my Hepatology work you will see a Piruvate kinase that does not responds to homeostatic signals .

Read Full Post »