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The Energetic brain -Students to student review

July 2019

July 2019

Authors:

Ramesh Kumar Paidi

Rush University Medical Center

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Accepted Article

This article has been accepted for publication and undergone full peer review but has not

been through the copyediting, typesetting, pagination and proofreading process, which may

lead to differences between this version and the Version of Record. Please cite this article as

doi: 10.1111/jnc.14829

DR. MOOTAZ M. SALMAN (Orcid ID : 0000-0002-5683-1706)

DR. ARTEM V DIUBA (Orcid ID : 0000-0002-2885-4281)

MR. EMIL JAKOBSEN (Orcid ID : 0000-0001-6290-4222)

DR. ASHLEY P L MARSH (Orcid ID : 0000-0001-6049-6931)

DR. CONSTANZE I SEIDENBECHER (Orcid ID : 0000-0002-7433-2716)

Article type : Review

The energetic brain - A review from students to students

M.P. Bordone; Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Instituto de

Investigaciones Farmacológicas (ININFA), Buenos Aires, Argentina.

M.M. Salman; Department of Cell Biology, Harvard Medical School, and Program in Cellular and Molecular

Medicine, Boston Children’s Hospital, Boston, MA, USA

H.E. Titus; Northwestern University, Feinberg School of Medicine, Chicago, IL, USA

E. Amini; Department of Medicine, University Kebangsaan Malaysia Medical Centre (HUKM), Cheras,

Kuala Lumpur, Malaysia

J. V. Andersen; Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences,

University of Copenhagen, Denmark.

B Chakraborti; Manovikas Kendra, Kolkata, India

A.V. Diuba; Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University,

119992, Moscow, Russia

T. G. Dubouskaya; Institute of Biophysics and Cell Engineering National Academy of Sciences of Belarus,

Minsk, Belarus

E. Ehrke; Centre for biomolecular interactions, University of Bremen, Germany

A.E. Freitas; Neurobiology Section, Biological Sciences Division, University of California, San Diego, La

Jolla, California, USA

G.B. Freitas; institute of medical biochemistry, Federal University of Rio de Janeiro, Brazil

R.A. Gonçalves; Centre for neuroscience studies, Queen's University, Kingston, ON, Canada

D. Gupta; BITS Pilani, Pilani, India

R. Gupta; CSIR- Indian Institute of Toxicology Research, Lucknow, India

S.R. Ha; Baylor College of Medicine, Houston, TX, USA

I.A. Hemming; The Harry Perkins Institute of Medical Research, Brain Growth and Disease Laboratory,

Nedlands, Australia 2 The University of Western Australia, School of Medicine and Pharmacology,

Crawley, Australia

M. Jaggar; Department of biological sciences, Tata Institute of Fundamental Research, Mumbai, India

E. Jakobsen; Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences,

University of Copenhagen, Denmark

P. Kumari; Defense Institute of Physiology and allied sciences, Defense research and development

organization, Lucknow Timarpur, Delhi, India

N. Lakkappa; Department of Pharmacology, JSS college of Pharmacy, Ooty, India

A.P.L Marsh; Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute,

Royal Children’s Hospital, Parkville, Victoria, Australia.

J. Mitlöhner; Department of Neurochemistry and Molecular Biology, Leibniz Institute for Neurobiology

Magdeburg, Germany

Y. Ogawa; The Jikei University School of Medicine, Japan

R.K. Paidi; CSIR-Indian Institute of Chemical Biology, Kolkata, India

F.C. Ribeiro; Federal University of Rio de Janeiro, Brazil

A. Salamian; Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology,

Polish Academy of Sciences, Warsaw, Poland

S.Saleem; CSIR Indian Institute for Chemical Biology, India

Accepted Article

S. Sharma; Department of Pharmacy, Birla Institute of Technology and Science, Pilani, Rajasthan, India

J.M. Silva; Life and Health Sciences Research Institute (ICVS), Medical School, University of Minho,

Campus Gualtar, 4710-057 Braga, Portugal

S. Singh; CSIR-Indian Institute of Toxicology Research, India

K. Sulakhiya; Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, India

T.W. Tefera; School of Biomedical Sciences, The University of Queensland, Brisbane, Australia

B. Vafadari; Institute of environmental medicine, UNIKA-T, Technical University of Munich, Germany

A. Yadav; CSIR- Indian Institute of Toxicology Research, India

R. Yamazaki; Tokyo University of Pharmacy and Life Sciences, Tokyo, Japan

C. I. Seidenbecher; Department of Neurochemistry and Molecular Biology, Leibniz Institute for

Neurobiology Magdeburg, Germany

Corresponding author: Prof. Dr. Constanze I. Seidenbecher

Department of Neurochemistry and Molecular Biology

Leibniz Institute for Neurobiology Magdeburg

Brenneckestraße 6, 39118 Magdeburg

E-mail: seidenc@lin-magdeburg.de

Phone: +49-391-6263-92401

Keywords: neuronal energetic cost, metabolism, energy sources, neurometabolic coupling,

energy homeostasis, ANLS hypothesis, synapse.

Abbreviations used:

Aβ amyloid-beta

AD Alzheimer’s disease

ADP adenosine diphosphate

AGE advanced glycation end-product

AIS axon initial segment

ALS amyotrophic lateral sclerosis

AMP adenosine monophosphate

AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid

AMPK adenosine monophosphate-activated kinase

ANLS astrocyte-neuron lactate shuttle

AP action potential

APP amyloid precursor protein

ATP adenosine triphosphate

ATPmit mitochondrial adenosine triphosphate

ATPpresyn presynaptic adenosine triphosphate

BBB blood brain barrier

BDNF Brain-derived neurotrophic factor

CBF cerebral blood flow

CMRglu cerebral metabolic rate of glucose

CNS central nervous system

CoA coenzyme A

DA dopaminergic

DJ1/PARK7 protein deglycase DJ-1/Parkinson disease protein 7

Drp1 dynamin-related protein 1

EAAT 1/2 excitatory amino acid transporter 1/2

EEG electroencephalography

ER endoplasmic reticulum

Accepted Article

ETC electron transport chain

FDG fluorodeoxyglucose

FRET fluorescence resonance energy transfer

G6P glucose-6-phosphate

GAPDH glyceraldehyde-3-phosphate dehydrogenase

GDH glutamate dehydrogenase

GDNF glial cell line-derived neurotrophic factor

GLS glutaminase

GLUT glutamate transporter

GPR120 G protein-coupled receptor 120

GS glutamine synthase

GSH glutathione

GSSG glutathione disulfide

GTP guanosine triphosphate

H-H model Hodgkin-Huxley model

IGF-1 insulin-like growth factor-1

α-KG α-ketoglutarate

Km Michaelis-Menten constant

LDH lactate dehydrogenase

LPL lipoprotein lipase

LRRK2 leucine-rich repeat kinase 2

LTP long-term potentiation

MAS malate-aspartate shuttle

MCH melanin-concentrating hormone

MCT monocarboxylic acid transporter

MCTG medium chain triglyceride

Mfn1/2 Mitofusin 1/2

mRNA messenger ribonucleic acid

MS multiple sclerosis

mtDNA mitochondrial deoxyribonucleic acid

NAD+ oxidized nicotinamide adenine dinucleotide

NADPH reduced nicotinamide adenine dinucleotide phosphate

NBCe1 electrogenic sodium bicarbonate cotransporter 1

NE norepinephrine

NMDA N-methyl-D-aspartate

NMR nuclear magnetic resonance

NO nitric oxide

β-OHB β-hydroxybutyrate

OPA1 optic atrophy type 1

OxPhos oxidative phosphorylation

PC pyruvate carboxylase

PD Parkinon’s disease

PDGFRβ platelet-derived growth factor receptor β

PDH pyruvate dehydrogenase

PET positron emission tomography

PFK phosphofructokinase

PGC-1α peroxisome proliferator-activated receptor gamma coactivator 1-alpha

Accepted Article

PINK1 PTEN-induced kinase 1

PPAR peroxisome proliferator-activated receptor

PPP pentose phosphate pathway

PSC postsynaptic current

REM rapid eye movement

ROS reactive oxygen species

SIRT1 sirtuin-1

SLC1A5 neutral amino acid transporter B(0)

SLC6A8 sodium- and chloride-dependent creatine transporter 1

SLC38A1 sodium-coupled neutral amino acid transporter 1

SLC family solute carrier family

SNpc substantia nigra pars compacta

SPECT single-photon emission computed tomography

SWS slow wave sleep

T2DM type 2 diabetes mellitus

TBI traumatic brain injury

TCA tricarboxylic acid

TGF-β transforming growth factor β

VIP vasoactive intestinal polypeptide

Vmax maximum rate in Michaelis-Menten kinetics

Abstract

The past 20 years have resulted in unprecedented progress in understanding brain energy

metabolism and its role in health and disease. In this review, which was initiated at the 14th

International Society for Neurochemistry Advanced School, we address the basic concepts

of brain energy metabolism and approach the question of why the brain has high energy

expenditure. Our review illustrates that the vertebrate brain has a high need for energy

because of the high number of neurons and the need to maintain a delicate interplay

between energy metabolism, neurotransmission, and plasticity. Disturbances to the

energetic balance, to mitochondria quality control or to glia-neuron metabolic interaction may

lead to brain circuit malfunction or even severe disorders of the central nervous system

(CNS). We cover neuronal energy consumption in neural transmission and basic

(‘housekeeping’) cellular processes. Additionally, we describe the most common (glucose)

and alternative sources of energy namely glutamate, lactate, ketone bodies and medium

chain fatty acids. We discuss the multifaceted role of non-neuronal cells in the transport of

energy substrates from circulation (pericytes and astrocytes) and in the supply (astrocytes

and microglia) and usage of different energy fuels. Finally, we address pathological

consequences of disrupted energy homeostasis in the CNS.

1. Why does the brain have high energy expenditure?

The brain is a highly active and plastic organ with outstanding energetic needs. Thus, the

title of this review shall reflect both, the dynamic and adaptive nature of the brain with its

intense parallel information processing as well as its specific metabolic energy demands.

Accepted Article

In a majority of adult vertebrate species (excluding primates), the central nervous system

(CNS) uses 2-8% of the energy of total basal metabolism (Mink et al. 1981). The human

brain, however, accounts for 20% of oxygen (O2) consumption and 25% of glucose

utilization, although it amounts to only 2% of body weight (Sokoloff et al. 1977). Is the high

energetic expense of the human brain related to the higher cognitive abilities and wider

range of behaviors expressed by humans as compared to other vertebrates? Is it a

consequence of brain growth during evolution? During humanization, multifaceted social

behaviors evolved, such as formation of complex social groups, long-term parental

investment and cooperative foraging strategies. These outstanding social skills correlated

with increased intelligence and are highlighted as necessary for more effective foraging and

the exploitation of high-energy food, but at the same time the evolvement of these

capabilities also imposes higher nutrient requirements (summarized in Dunbar & Shulz,

2017). Traditionally, comparative studies of brain scaling take into account brain size and/or

the body-brain mass ratio to delineate an evolutionary explanation for the supposed human

brain exceptionality. While some authors consider the human brain as an outlier because it

deviates from the expected value even if compared to anthropoid primates (Jerison 2012;

Marino 1998), a more recent view, based on data obtained with isotropic fractionation

(Herculano-Houzel & Lent 2005), puts in focus the absolute number of neurons relative to

body-brain mass ratios. Studies on scaling brain metabolism according to brain size across

species or to neuronal number and/or density in a given structure were carried out to

examine this human brain peculiarity which could have implications in brain evolution and

could have exerted constraints for wiring patterns. According to Karbowski´s estimates,

which are based on the assumption of uniform scaling of neuronal density across species,

cerebral energy per neuron increases with brain size (Karbowski 2009). However, more

recently Herculano-Hozuel based her calculations on available glucose and O2 metabolic

rates in awake animals (mouse, rat, squirrel, macaque monkey, baboon and human) and the

total number of neurons determined by her group. She found that neuronal density in the

whole brain does not scale with brain mass across species, and that the energy budget of

the whole brain per neuron is fixed across species and brain sizes. Thus, the total glucose

use by the brain is a linear function of the number of neurons, and the remarkable energy

use in humans may be explained simply by its large number of neurons (Herculano-Houzel

2011).

Why do neurons have a high energetic demand? In addition to basic (‘housekeeping’)

cellular activity such as turnover of macromolecules, axoplasmic transport and mitochondrial

proton leak, neurons are highly specialized cells to perform energy-demanding

electrochemical signaling processes. Generation of action potentials (AP), postsynaptic ion

fluxes, presynaptic Ca2+ entry, transmitter reuptake and vesicle cycling but also maintenance

of the resting potential are costly cellular neuronal functions. In the following sections we will

provide a detailed description and explanation of each of the above-mentioned processes,

which altogether account for the neuronal energy demand.

1.1 Energy use in synaptic transmission and synaptic plasticity

Synaptic transmission imposes a large metabolic demand which is met through activity-

driven regulation of glycolysis and mitochondrial function (Rangaraju et al. 2014). Plasticity

of synapses can be mediated by changes in Ca2+ concentration and/or the number of

neurotransmitter receptors. Upon synaptic activity or plasticity, most of the adenosine

Accepted Article

triphosphate (ATP) is consumed when pumping ions to maintain resting membrane potential,

vesicle filling, vesicle transport, vesicle recycling and enzymatic processing of synaptic

transmitter within synapses (Harris et al. 2012) (Figure 1).

1.1.1 Presynaptic terminals

Activated presynaptic terminals are expected to place high ATP demands on energy

supplies. The presynaptic ATP (ATPpresyn) levels are mainly consumed by the Na+/K+

pump (Na+/K+-ATPase), the Ca2-ATPases in the plasma membrane and endoplasmic

reticulum (ER), the vacuolar H+-ATPase, motor proteins (Attwell & Laughlin 2001; Lennie

2003), and protein disassembly machineries (Rangaraju et al. 2014; Ly & Verstreken 2006).

The Na+/K+-ATPase imports 2 K+ and exports 3 Na+ ions involved in generating the AP and

powers Ca2+ removal by Na+/Ca2+ exchange, while Ca2+-ATPases in the plasma membrane

and ER reduce the elevated cytosolic Ca2+ concentration after membrane depolarization.

Vacuolar H+-ATPase energizes vesicular transmitter uptake and the motor proteins are

involved in intracellular transport of mitochondria and vesicles (Figure 1).

ATPpresyn levels are reduced by insufficiency of either glycolysis or mitochondrial function,

indicating the requirement of activity-driven ATP synthesis to meet the energy demands of

synaptic function. Most ATP produced in response to increased neuronal activity is

generated by mitochondria (Lin et al. 2010; Hall et al. 2012), highlighted by the positive

correlation between the signaling-related energy usage predicted by Attwell and Laughlin

(2001) and the distribution of mitochondria inside a cell, which was demonstrated to be

higher in soma plus dendrites (62%) than in axon terminals (36%) (Wong-Riley 1989). In

light of the distal location of nerve terminals from cell bodies, synapses must rely on local

ATP supplies, leaving them highly susceptible to mitochondriopathies. Studies in Drosophila

mutants affecting the mitochondrial localization as well as the mitochondrial ATP/ADP

translocase, confirmed the importance of mitochondria in synaptic function (Trotta et al.

2004; Guo et al. 2005; Verstreken et al. 2005).

ATPpresyn concentration is about 1.4 mM (corresponding to ~ 106 molecules for a typical

presynaptic varicosity) and ATP synthesis occurs through feedforward stimulation of both

glycolysis and oxidative phosphorylation (OxPhos) via electrical activity-driven Ca2+ influx

(Rangaraju et al. 2014). Completely blocking exocytosis does not significantly affect Ca2+

influx during neuronal APs, suggesting that the synaptic vesicle cycle consumes most

ATPpresyn. The monitoring of ATPpresyn during AP firing in the absence of external Ca2+

revealed that ATP utilized by the Na+/K+-ATPase alone represents a relatively small energy

burden compared to downstream vesicle cycle processes at presynaptic terminals and that

AP firing can persist even during phases of acute ATP synthesis blockade. Inhibition of

glycolysis is accompanied by a reduction in endocytosis and a shift in remaining vesicular

pH to more alkaline values. The arrest of endocytosis at energy deficit conditions depends

on the mechanochemical enzyme dynamin, which mediates membrane fission and depends

cooperatively on synthesized GTP using ATP (Rangaraju et al. 2014). By contrast,

compromised ATP synthesis does not immediately impact exocytosis. Therefore, ATPpresyn

demand and activity-induced ATP synthesis should be synchronous and are essential for the

maintenance of normal synaptic plasticity function. Any discrepancy or pathological condition

disturbing these metabolic functions will ultimately affect synaptic strength and plasticity.

Accepted Article

1.1.2 Postsynaptic terminals

At postsynaptic sites, ATP is mainly used for counterbalancing the ion fluxes through

postsynaptic receptors and, to a lesser extent, on rebounding Ca2+ to intracellular stores and

on mitochondrial trafficking. In inhibitory synapses, less energy is utilized to reverse

postsynaptic Cl- fluxes as the chloride reversal potential is close to the resting potential

(Attwell & Laughlin 2001; Howarth et al. 2010; Howarth et al. 2012). The number of activated

receptors, channel open time, channel conductance, and consequently ATP usage are

different at synapses throughout different regions of the brain (Silver et al. 1996; Spruston et

al. 1995; Markram et al. 1997). Based on analysis of Attwell and Laughlin (2001), when a

glutamatergic vesicle is released on a dendritic spine from non-cortical neurons, the number

of activated N-methyl-D-aspartate (NMDA) receptors is less than that of non-NMDA

receptors (Silver et al. 1996; Spruston et al. 1995; Markram et al. 1997). However, because

NMDA receptors have longer open times and higher conductance than non-NMDA

receptors, the ion influxes through those activated channels lead to hydrolysis of 5 % more

ATP molecules by the Na+/K+-ATPase, including the 3 Na+/Ca2+ exchange, for extrusion.

Moreover, activation of postsynaptic G protein-coupled receptors, such as metabotropic

glutamate receptors (mGluR), triggers downstream events, which constrain ATP usage.

However, the level of ATP consumption during G protein signaling is approximately 95 %

smaller than during NMDA and non-NMDA receptor activation (Attwell & Laughlin 2001).

1.1.3 Synaptic plasticity

Synaptic plasticity, increasing or decreasing of synaptic strength, can impact energy

expenditure. For example, long-term potentiation (LTP), which can be triggered by activation

of the postsynaptic NMDA receptors and insertion of more AMPA (α-amino-3-hydroxy-5-

methyl-4-isoxazolepropionic acid) receptors into the postsynaptic membrane, enhances ATP

supplement to potentiated synapses and energy consumption at postsynapses (Wieraszko

1982). It is suggested that induction of LTP, which is assumed as a fundamental mechanism

of learning and memory, is accompanied by increased energy usage at activated synapses.

Additionally, the level of lactate derived from glycogen in astrocytes is increased in response

to energy demand in animals performing learning and memory tasks (Suzuki et al. 2011;

Newman et al. 2011).

Mitochondrial ATP (ATPmit) production is noted to be essential for synaptic plasticity.

Inhibition of ATPmit production interfered with synaptic accumulation of mitochondria and

subsequently abolished synaptic potentiation at the Drosophila neuromuscular junction

(Tong 2007). Additionally, mutations in Drosophila Drp1 (dynamin-related protein 1) depleted

mitochondria from motor nerve terminals and interfered with mobilization of the reserve pool

of synaptic vesicles and maintenance of neurotransmission (Verstreken et al. 2005).

Moreover, transmission failure was rescued at high stimulation frequencies by adding ATP

exogenously and provided evidence that the reserve pool recruitment depends on ATPmit

production downstream of PKA (protein kinase A) signaling.

1.2. Action potential (AP) generation and propagation

The brain uses rapid electrical signaling in the form of APs as the primary means of

communication between neurons. This signaling is associated with substantial energetic

costs (Kole et al. 2008; Hallermann et al. 2012). Two sites, important for AP generation and

maintenance, due to their lowered excitation threshold, are the axon initial segment (AIS)

and the nodes of Ranvier (Figure 1), the sites between adjacent myelinated axonal

Accepted Article

segments (Palay et al. 1968; Peters 1966). It has been estimated that after one AP, rodent

cortical neurons require between ~ 4 – 8 x 108 ATP molecules to restore the Na+ and K+

gradient through the Na+/K+-ATPase, suggesting the metabolic cost of AP signaling is the

second largest after synaptic transmission (Lennie 2003; Hallermann et al. 2012). However,

energy usage within the brain depends in part on the AP rate. Moreover, because synaptic

energy cost is proportional to the transmitter release probability by an AP and to the number

of postsynaptic channels activated by transmitters, the estimated numbers for the synaptic

cost vary in the literature. The estimation of relative contributions of AP generation and

postsynaptic currents (PSC) to energy expenditure in the mammalian brain has varied in the

literature: while the classical Hodgkin-Huxley (H-H) model for AP generation in the squid

axon and a number of assumptions such as treating all neurons as identical (Attwell &

Laughlin 2001) led to an assumed energy expenditure ratio for AP and PSC of 58 % : 42 %,

direct experimental data in rat hippocampal non-myelinated mossy fibers showed a minimal

temporal overlap between the entry of sodium and the outflow of potassium during an AP

which results in one third less energy necessary to elicit depolarization than in the H-H

model, pointing to an AP : PSC ratio of 20 % : 80 % (Alle et al. 2009).

The approximately 50-fold higher densities of voltage-gated Na+ channels at AIS and nodes

of Ranvier, when compared to the soma and dendrites, are vital for producing the adequate

local current necessary to overcome membrane resistance and capacitance as well as to

initiate and propagate self-regenerating APs (Zhou et al. 1998; Kole et al. 2008). Moreover, it

has been shown that excess axonal Na+ influx at the AIS and nodes of Ranvier is critical for

AP conduction at high frequencies (Kole et al. 2008). Therefore, the high metabolic cost of

AP initiation by the AIS and propagation down the axon by nodes of Ranvier is seen as a

trade-off between minimizing energy costs and maximizing the conduction velocity of APs

(Hallermann et al. 2012).

1.3. Maintaining resting potential

Beyond spiking activity, a large portion of the energy sources in the brain is spent on

maintaining resting potential and non-signaling (‘housekeeping’) processes. Na+/K+-ATPases

are recruited to compensate non-zero cell membrane conductance for K+ and Na+. Atwell

and Laughlin (2001) calculated that at physiological spiking rate of 4 Hz, 15 % of the total

ATP in the grey matter is needed to maintain the resting potential of a typical neuron and an

associated glial cell (Attwell & Laughlin 2001). Lennie (2003) estimated that the spike-related

energy expenditure is only 13 % of total energy usage in human neocortex, attributing 28 %

and 10 % to maintain the resting potential in neurons and glia, respectively, and the

remaining to non-signaling (‘housekeeping’) processes (Lennie 2003). Maintaining resting

potential is estimated to take 30 % and ~ 44 % of the total energy used in partially and fully

myelinated white matter, respectively (Harris and Atwell, 2012), 34 % in the cerebellum

(Howarth et al. 2010), and 16 % to ~ 68 % of total energy used in the olfactory glomerulus,

depending on the number of activated olfactory receptor neurons (Nawroth et al. 2007).

1.4. Non-signaling (‘housekeeping’) processes

In the vertebrate brain, a majority of the total energy is spent on the removal of Na+ ions from

cells by Na+/K+-ATPases (Atwell and Laughlin, 2001; Lennie, 2003; Harris and Atwell, 2012).

Metabolic turnover is reduced by both, Na+/K+-ATPase inhibition (Whittam, 1961; Shibuki,

1989; Astrup et al., 1981) and deep anesthesia (Astrup et al. 1981; Sibson et al., 1998; Du

et al., 2008). The residual energy expenditure persisting after the suppression of spiking

Accepted Article

and/or Na+/K+-ATPase was attributed to non-signaling, or ‘housekeeping’, processes (Atwell

and Laughlin, 2001; Harris and Atwell, 2012; Engl and Atwell, 2015; Du et al., 2008). Using

13C NMR (nuclear magnetic resonance) spectroscopy, Sibson et al. (1998) estimated that

roughly 16 % of total glucose oxidation in the rat cortex are independent of synaptic activity.

The cerebral ATP metabolic rate is roughly reduced by half when rats are deeply

anesthetized and the electroencephalogram reads as “silent”, as compared with light

anesthesia using isoflurane (Du et al. 2008). Calculations for rodent brains attribute 25 % of

energy used in the grey matter to ‘housekeeping’ processes (Attwell and Laughlin 2001;

Lennie 2003; Harris et al. 2012), and ~ 63 % and ~ 56 % in partially or fully myelinated white

matter, respectively (Harris and Attwell 2012). The ‘housekeeping’ functions account for

19 % in the cerebellum (Howarth et al. 2010), and between 3 to 25 % in the olfactory

glomerulus (Nawroth et al. 2007). Particular non-signaling energy-consuming processes are

now under debate.

Actin cytoskeleton (re-)modeling underlies neuron morphology and modulates synaptic

function and structural plasticity (Luo 2002; Cingolani & Goda 2008), requiring ATP

hydrolysis (Wegner 1976; Carlier et al. 1988). Modeling and experimental estimates for

energy costs of actin treadmilling range from less than 1 % (Engl & Attwell 2015) of the total

brain energy usage to half of the energy used in neuronal culture (Bernstein & Bamburg

2003). The dynamic instability of microtubules instead requires energy in the form of GTP

(Margolis 1981; Zakharov et al. 2015).

Protein and phospholipid synthesis were estimated theoretically to account for no more than

2 % (Rolfe & Brown 1997; Attwell & Laughlin 2001) and 2 – 25 % (McKenna et al. 2012;

Purdon & Rapoport 2007; Purdon & Rapoport 1998) of the total ATP consumption in the

brain, respectively. Purdon and Rapoport (2007) estimated that ~ 26 % of the energy taken

by phospholipid metabolism in the rat brain is spent on fatty acid turnover,

phosphatidylinositol phosphorylation state and phospholipid bilayer asymmetry maintenance,

and on de novo synthesis of phosphatidylinositol and ether phospholipids (Purdon &

Rapoport 2007). Engl et al. (2017) provided first experimental single-assay measurements of

energy consumption by actin cycling, microtubule restructuring, and protein as well as

phospholipid metabolism (Engl et al. 2017). They applied specific blockers of these

processes to resting (i.e., without evoked signaling activity) hippocampal slices of developing

rat brains and traced the resulting changes in O2 consumption. O2 consumption decreased

by 25 % after actin cycling blockade (much higher than previously modelled by the same

group but lower than previous estimates), by 22 % after microtubule turnover blockade, and

by 18 % after lipid and protein metabolism blockade. However, blocking protein metabolism

alone did not change the O2 consumption significantly.

Proton leak across the inner mitochondrial membrane (IMM) uncoupled from ATP synthesis

was documented in isolated brain mitochondria (Rolfe et al. 1994); though, its contribution

was not quantified. The proton leak mechanism remains under debate. It may be attributed

to the proton conductance through the lipid bilayer, which depends on the fatty acid

composition of the mitochondrial membrane, to the non-specific proton conductance of IMM

carrier proteins such as adenine nucleotide translocase (Brand et al. 2005), or to the

functioning of the brain-specific uncoupling proteins UCP4 and UCP5 (Mao et al. 1999;

Sanchis et al. 1998). Deficits in mitophagy genes like in the PINK1 knockout mouse also

result in increased proton leak (Villeneuve et al. 2016).

Accepted Article

Finally, axonal transport contributes to ‘housekeeping’ energy expenditure (Harris et al.

2012; Lennie 2003) (Figure 1). Maday et al. (2014) calculated, as one kinesin-1 motor

spends one ATP molecule for every elementary 8 nm-step (Maday et al. 2014), the

anterograde transport of a vesicle along an average 40 mm axon would require ~ 5 x 106

ATP, which is small compared to the ~ 1 x 108 ATP consumed by the propagation of a single

spike along the same axon. However, this estimated cost of the axonal transport does not

account for tug-of-war events (Hendricks et al. 2010; Soppina et al. 2009) and dynein

retrograde transport. Further modeling is needed to estimate the actual energetic cost of

axonal transport processes.

The negligible gluconeogenesis activity of the brain raises the question of how the brain

uses energy in different physiological states and the importance of additional sources of

energy other than glucose.

2. Brain metabolism in sleep vs. wakefulness

Energy sources in the body are stored mainly in skeletal muscle, liver and adipose tissue

and maintain the reserve of energy during wakefulness (Brown & Ransom 2007). The

neurons of the lateral hypothalamus that express melanin-concentrating hormone (MCH)

and orexin/ hypocretin regulate body energy metabolism and the sleep-wakeful cycle. During

a high level of energy resources, MCH neurons are activated and promote conservation of

energy by inducing sleep, whereas a low level of glucose diminishes the excitability of MCH

neurons and promotes wakefulness. Alternatively, excitation of orexin neurons induces

wakefulness, while inhibition or loss of orexin neurons promotes sleep. Thus, MCH and

orexin neurons have opposing effects on sleep and wakefulness (Burdakov et al. 2005;

González et al. 2016).

Neuronal tissues are supplied with different sources of energy, such as glucose, lactate, and

acetate, through regulated mechanisms. Neurometabolic coupling is the mechanism by

which the brain energy metabolism and cerebral blood supply are modulated locally to meet

the needs of neuronal activity. The energy requirement of neurons can change rapidly during

wakefulness after sensory or motor stimulations, in sleep/waking and waking/sleep

transitions (Petit & Magistretti 2016). Different sleep stages can be distinguished based on

cortical activity, muscle tone, and eye movements. Deep, also called slow wave sleep

(SWS), is characterized by decreased heartbeat, breathing, and body temperature and

reduced cortical activity typically showing high-amplitude, low-frequency (0.5–4 Hz) EEG

activity. In rapid eye movements (REM) sleep cortical theta activity (4-11 Hz) is dominant

(Niethard et al. 2016). Studies have shown that the energy demand diminishes during SWS

sleep; thus, the utilization of glucose and O2 is reduced. Moreover, non-REM sleep promotes

anabolic processes such as biosynthesis of proteins, glycogen and fatty acids (Dworak et al.

2010). In contrast, sleep deprivation affects metabolic coupling and reduces glucose uptake

in all brain areas in humans and rodents and decreases ATP levels by enhancing energy

expenditure. Moreover, chronic sleep fragmentation reduced the uptake of 2-deoxyglucose

in cortex and hippocampus and decreased lactate levels in the cortex (Baud et al. 2016).

These results highlight the importance of non-REM sleep to restore brain energy levels

(Dworak et al. 2010) because it is during the non-REM sleep when the increase in ATP and

glycogen biosynthesis occurs; while REM sleep utilizes similar cerebral glucose as during

wakefulness (Maquet et al. 1990).

Accepted Article

Thus, synaptic potentiation, during the wakeful condition, increases consumption of ATP by

activating glycolysis, fatty acid oxidation, and glucose uptake; while during non-REM sleep,

ATP consumption decreases and leads to energy conservative processes, such as synaptic

scaling.

3. Sources of brain energy

Several regulatory mechanisms operate to regulate the production and usage of energy in

the brain. Though the brain requires a high amount of energy, it possesses minimal energy

reserve which can only satisfy a small portion of its energy demand and is dependent on the

supply of energy substrates from the blood through the blood brain barrier (BBB). Under

normal physiological conditions, the major energy fuel for the brain is glucose (Dienel,

2012a). However, several studies have shown that the brain can use alternative energy

substrates such as lactate, medium chain triglycerides (MCTGs), as well as ketone bodies,

during development and when glucose availability is limited (Owen et al. 1967; Hasselbalch

et al. 1994; Ebert et al. 2003).

Specific transporters allow the uptake of energy substrates, such as glucose and

monocarboxylic acids (lactate, pyruvate, β-hydroxybutyrate, acetoacetate and acetate) from

the circulation across the endothelial cell membrane and into brain cells. The uptake of

energy substrates by brain cells is dependent on the type and distribution of transporters

unique to each cell type and transport rate, on the number of transporters, and the catalytic

activity of each transporter. In the brain, several isoforms of the glucose transporter (GLUT)

and the monocarboxylic acid transporter (MCT) have been identified.

3.1 Glucose

Glucose metabolism provides the fuel for physiological brain function through the synthesis

of ATP via glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid

(TCA) cycle which serves as the basis for neuronal and non-neuronal cellular maintenance

as well as neurotransmitter and gliotransmitter production. Cells of the BBB, mainly

astrocytes and pericytes, act as the gatekeepers for glucose entry into the brain. Glucose

enters the brain tissue from the plasma by transport across the BBB mediated by the

GLUTs. The cytoarchitectural presence of astrocytic end feet enriched in the 45 kDa-isoform

of GLUT1 makes astrocytes an ideal cell type for the uptake of glucose. Glucose enters

neurons trans-cellularly through astrocytes via GLUT1 or directly via GLUT3, a neuronal

GLUT (Maher et al. 1994) (Figure 2).

Astrocytes are considered highly glycolytic due to low expression and activity of the E3

ubiquitin ligase APC/C-Cdh1, which in neurons mediates proteasomal degradation of the

glycolytic key enzyme phosphofructokinase (PFK) (Herrero-Mendez et al. 2009). Thus, high

expression and activity of PFK in astrocytes results in glucose and glucose-6-phosphate

(G6P) mainly being metabolized via the glycolytic pathway (Bélanger et al. 2011). Moreover,

the expression of lactate dehydrogenase (LDH) 5, which favors the conversion of

glycolytically derived pyruvate to lactate regenerating NAD+ (oxidized nicotinamide adenine

nucleotide) that is required as substrate in the reaction of glyceraldehyde-3-phosphate

dehydrogenase (GAPDH), also contributes to the high glycolytic activity of astrocytes

(Pellerin & Magistretti 2004; Hirrlinger & Dringen 2010).

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Unlike glycolytic astrocytes which synthetize lactate in aerobic conditions, neurons rely on

oxidative metabolism through the TCA cycle for their high energy needs. Neuronal activity

increases neurotransmitter concentration in the synaptic cleft (i.e glutamate) and

extracellular K+. This is met by intense mitochondrial activity putting neurons in risk of

oxidative stress, glutamate excitotoxicity, and apoptotic death. Increased glycolysis in the

brain can induce glutathione oxidized (GSSG) accumulation. Glutathione is a tripeptide that

serves not only as a scavenging antioxidant but also has been proposed as a reservoir for

glutamate (Koga et al. 2011). Therefore, as a neuroprotective mechanism, neurons

downregulate glycolysis and the use of glucose to maintain an antioxidant reduced

glutathione (GSH) pool. Additionally, glucose is used by neurons to restore reducing

equivalents of NADPH (reduced nicotinamide adenine dinucleotide phosphate) used in GSH

regeneration via the PPP which cannot be fueled by lactate (Gavillet et al. 2008; Bolaños et

al. 2010).

Moreover, glutamate released from presynaptic terminals into the synapse during neuronal

activation is taken up by astrocytes together with Na+ ions. Furthermore, it stimulates both

glucose uptake and glycolytic processing as well as lactate release in an ATP-dependent

manner, and as such plays a significant role in neuroenergetics (Magistretti et al. 1999).

Astrocytes convert glutamate to glutamine by glutamine synthetase (Norenberg & Martinez-

Hernandez 1979), which is then taken up by neurons and converted back to glutamate by

phosphate-activated glutaminase (GLS) (Figure 2), completing the glutamate-glutamine

cycle (Laake et al. 1999). Conversely, glutamate can be oxidized to enter into the TCA cycle

(Bak et al. 2006). Interestingly, astrocytes have the anaplerotic enzyme pyruvate

carboxylase (PC) (Yu et al. 1983), which carboxylates pyruvate to generate oxaloacetate.

This process is crucial to replace lost TCA cycle intermediates used in the synthesis of

neurotransmitters. Although sodium-coupled uptake of glutamate by astrocytes and the

ensuing activation of the Na+/K+-ATPase may trigger glycolysis (Fox et al. 1988; Magistretti

2006) in a time scale of minutes, extracellular K+ was demonstrated to stimulate astrocytic

glycolysis in vitro within seconds using a genetically encoded fluorescence resonance

energy transfer (FRET) -based nanosensor for glucose (Bittner et al. 2011), an effect

mediated by the Na+/HCO3- cotransporter NBCe1(Ruminot et al. 2011). This K+-mediated

mechanism was suggested to be a general hallmark of neurometabolic coupling, as K+ is

released not only by the postsynaptic terminal during glutamatergic neurotransmission but

also at nodes of Ranvier during AP propagation, at serotonergic synapses, or at the

cholinergic neuromuscular junction.

3.2 Glutamate

Glutamate acts as the major excitatory neurotransmitter in brain. As mentioned above, it

plays a role as a trigger in stimulating glucose utilization when taken up by astrocytes

leading to lactate production, acts as a recycled precursor for the neuronal neurotransmitter

pool and as an energy substrate in astrocytes. Hans Krebs was the first who recognized

glutamate as a brain energy substrate capable of increasing respiration in rabbit brain cortex

in the absence of glucose (Krebs, 1935). Later his group discovered that, in the absence of

glucose, extracellular glutamate was metabolized and mostly converted into aspartate in rat

brain homogenate (Haslam and Krebs, 1963). Further research discovered that glutamate is

not only the co-substrate in the aminotransferase catalyzed interconversion reaction into

aspartate but also into alanine, leucine, isoleucine, and valine (McKenna 2012; Schousboe

et al. 2014). Astrocytes convert the glutamate taken up from the extracellular milieu to α-

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ketoglutarate (α-KG) either primarily by the glutamate dehydrogenase (GDH)-catalyzed

energy-producing reaction or by transamination reactions in astrocytes (Bergles et al. 1999;

Zaganas et al. 2012). Further, the formed α-KG is metabolized into the four-carbon

compound oxaloacetate through sequential reactions of the TCA cycle and eventually

harvests nine ATP molecules (McKenna 2013).

Uptake of extracellular glutamate is an expensive process as astrocytes utilize one ATP for

the transfer of one glutamate. Evidence from in vitro and in vivo studies suggests that

mitochondrial mechanisms and multiprotein complexes are tightly associated with glutamate

uptake by astrocytes. Glial glutamate transporters are coupled to the first step in glycolysis

mediated by hexokinase, which ensures the oxidative energy metabolism of glutamate in

mitochondria yielding ample energy that pays the cost of glutamate uptake from the synaptic

cleft (Genda et al. 2011; Whitelaw & Robinson 2013). Oxidative metabolism of glutamate by

astrocytes, which results in complete glutamate oxidation and energy generation, has been

reported by several in vitro and in vivo studies (McKenna MC., 2013). Studies based on

substrate competition indicated that oxidation of glutamate in astrocytes as a source of

energy is preferred compared to glucose and other substrates (Figure 2), which suggests the

robustness of glutamate use as a substrate for energy in these cells (McKenna et al. 2012).

The steady and dynamic connection between the pre- and postsynaptic neuron and

astrocytes is efficiently involved in maintaining the low extracellular resting glutamate

concentration of ~1-10 μM to avoid excitotoxicity and maintain healthy brain states

(Waagepetersen et al. 1999; Hertz 1979; Schousboe 1981). In vitro experiments in primary

cultured neurons indicate that 10 mM of extracellular glutamate causes profound neuronal

cell death under normoglycemic and normoxic conditions (Khanna et al. 2003; Murphy et al.

1990). However, when glucose is removed from media, extracellular glutamate (10 mM) no

longer induces cell death (Rink et al. 2011).

3.3 Lactate and the Astrocyte-Neuron Lactate Shuttle (ANLS) hypothesis

Despite glucose being the major source of energy in the brain, under various circumstances,

lactate serves as an alternative energy substrate. Since lactate cannot diffuse passively

across the BBB, it needs to be produced in situ within the brain and efficiently transported

between cells (Figure 2). Additionally, as lactate cannot be utilized directly, conversion of

lactate to pyruvate, catalyzed by LDH, is essential as it then provides 18 ATPs via OxPhos

(Pellerin & Magistretti 1994; Magistretti 2006). The identification of MCTs further strengthens

the presence of lactate release and uptake in brain cells (Pellerin et al. 2005; Bröer et al.

1999). MCTs operate depending on the association with the glycoproteins basigin (MCTs 1,

3, 4) or embigin (MCT2) and the concentration gradients of monocarboxylates across the

plasma membrane (Halestrap 2013). While MCT2 and MCT4 are selectively expressed by

neurons and astrocytes, respectively, MCT1 is expressed in astrocytes, oligodendrocytes,

and endothelial cells of blood vessels (Debernardi et al. 2003; Pierre & Pellerin 2005;

Rinholm et al. 2011). Hence, lactate secreted by astrocytes through MCT4 and MCT1 is

assumed to be transported by MCT2 into neurons, where it can be converted to pyruvate

(Figure 2). Then, pyruvate can either enter the TCA cycle via pyruvate dehydrogenase

(PDH) and be metabolized via OxPhos in mitochondria to generate ATP or be converted to

lactate or alanine by LDH or aminotransferase, respectively. The distribution pattern of the

LDH isoforms (high expression of LDH5 in astrocytes and LDH1 in neurons) further provides

evidence for this concept. Furthermore, the muscle-type LDH5 having a greater Vmax, is

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better equipped at converting pyruvate to lactate and supports higher glycolytic rates, but

LDH1 exhibits a lower Km and is inhibited by low concentrations of pyruvate and by lactate.

Thus, efficient conversion of lactate to pyruvate and clearance of pyruvate are essential for

the enzymes to function properly. In summary, higher expression of LDH5 suggests a higher

rate of glycolysis in astrocytes and higher expression of LDH1 suggests lactate utilization as

an energy source in neurons. These data support the astrocyte-neuron lactate shuttle

(ANLS) hypothesis postulated in 1994 (Pellerin & Magistretti 1994). According to this,

astrocytes serve as a ‘lactate source’ whereas neurons serve as a ‘lactate sink’ (Figure 2).

In this context, when neuronal activity intensifies, astrocytes increase their glucose uptake,

thus increasing the rate of glycolysis and lactate release into the extracellular space.

Increased neuronal activity corresponds to an increased release of glutamate from

presynaptic vesicles into the synapse. Excessive glutamate is sensed and taken up by

astroglial glutamate transporters (i.e. EAAT1 and EAAT2). A sodium gradient drives this

glutamate exchange, where one glutamate is co-transported with three Na+ ions, thereby

increasing the concentration of Na+ within astrocytes. Glutamate uptake triggers glucose

uptake by the astrocytes in a stoichiometric ratio of 1:1. A higher Na+ concentration within

astrocytes leads to the activation of the α2 subunit of the Na+/K+ -ATPase which results in

glycolysis stimulation (Mason 2017). This stimulation leads to the production of lactate which

is used as a substitute energy substrate by neurons. Thus, lactate plays a role as an

alternative to meet the energetic demands of the CNS.

In opposition to the ANLS hypothesis, Bak and colleagues argue that oxidative metabolism

of lactate within neurons only occurs during repolarization (and in the period between

depolarizations) rather than during neurotransmission activity (Bak et al. 2009); and that

neurons use lactate as an ‘opportunistic’ substrate when it is present (Bak & Walls 2018). In

synaptic terminals, the use of lactate as energy source is tightly coupled to the activity of the

malate-aspartate shuttle (MAS), since its inhibition decreases the rate of lactate oxidation

(McKenna et al. 1993). According to the model proposed by Bak et al., elevated

neurotransmission may not increase oxidative metabolism of lactate; it decreases possibly

because of a depolarization-induced increase in intracellular Ca2+ concentration and a

putative limitation of the MAS, which transfers the reducing equivalents from the NADH

produced during glycolysis into mitochondria. Thus, when the activity of the MAS is limited

(due to Ca2+-induced activation of the α-KG dehydrogenase, which competes with the

malate/α-KG carrier for substrate), NAD+ cannot be regenerated for glycolysis and NADH is

not further oxidized in the electron transport chain (ETC), leading to an increase in cytosolic

NADH concentration and a decrease in glycolysis and OxPhos. At this point, pyruvate is

further converted into lactate with the concomitant regeneration of NAD+ for glycolysis; and it

is during repolarization (when cytosolic Ca2+ is low and the MAS is no longer limited) when

accumulated lactate is oxidized (Bak et al. 2009).

The ANLS concept explains the role of lactate as an important energy source for brain

function as well as defines the strong metabolic association between astrocytes and

neurons. It is suggested that most of the neurodegenerative diseases, as well as any other

adverse changes in the brain, have notable changes in the ANLS and lead to imbalances in

neurometabolic coupling (Bélanger et al. 2011). However, the ANLS concept has been

critically viewed and challenged due in part to contradictory results obtained in different

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experimental settings, and to the lack of a consensus-based method for real-time monitoring

of metabolic dynamics at cellular resolution. For example, while some authors argue that

neurons prefer lactate over glucose (Bouzier-Sore et al. 2003); others claim neurons are well

equipped to metabolize glucose in an activity-dependent manner (Bak et al. 2009), which

clearly opposes to the tight coupling of astrocytes to synaptic activity (Ruminot et al. 2017).

To further build upon these findings, for both research and diagnose purposes, positron

emission tomography (PET) with the glucose analogue 18F fluorodeoxyglucose (FDG) is

commonly used as clinical diagnostic measure for local glucose metabolism. Further

development of fluorescent protein-based sensors for specific, real-time readouts of

metabolites will fill this current technological gap (Zhang et al. 2018) and shed light on this

hypothesis which has long been a subject of debate (Chih & Roberts 2003; Dienel 2012;

Barros & Weber 2018a; Bak & Walls 2018; Barros & Weber 2018b). However, based on the

accumulated evidence in favor compared to that against this postulation, the ANLS

hypothesis seems now to be broadly accepted.

3.3.1 Glucose vs. Lactate at rest and while exercising

At rest, the predominant energy source for the brain is glucose (Dienel 2012; McEwen &

Reagan 2004). Strenuous physical activity increases O2 demand by the skeletal muscles

leading to increased heart rate and respiration. However, due to unmet O2 demand, plasma

lactate levels are enhanced by conversion of pyruvate to lactate, which is an important step

to regenerate NAD+, an essential substrate to carry out glycolysis and release ATP in the

muscle (Lucas-Cuevas et al. 2015). Rigorous physical activity increases lactate plasma

levels from 0.6 mmol/l to around 2 - 3 mmol/l, which then crosses the BBB via MCTs (Ide et

al. 1999). The cerebral uptake of lactate is thought to be 2-fold higher than that of glucose.

While performing extensive physical exercise, the cerebral metabolic ratio for carbohydrates,

defined as cerebral molar uptake of (O2/ (glucose +1/2 lactate)), decreases from a resting

value of 6 to <2 (Quistorff et al. 2008; Smith et al. 2003). Further, a clinical study involving

administration of sodium lactate reported a 3-4 fold increase in plasma lactate levels along

with an average of 17 % reduction in the rate of cerebral glucose uptake, indicative of a

preferential cerebral uptake of lactate over glucose (Smith et al. 2003). Lactate is thought to

be a favored cerebral energy source since the conversion of lactate to pyruvate does not

require ATP and is thermodynamically preferable compared to glucose that needs two

molecules of ATP (Dienel 2012; Quistorff et al. 2008).

Induced alteration in metabolic substrate concentration, through voluntary exercise, has

been shown to enhance metabolic enzymes involved in glycolysis, ATP synthesis, ATP

transduction, and glutamate turnover (Ding et al. 2006). Preclinical studies report exercise

training in mice augmented the expression of metabolically relevant genes (i.e. PGC-1α,

SIRT1, and citrate synthase) as well as increased mitochondrial DNA (mtDNA), suggestive

of mitochondrial biogenesis in most brain regions studied (Steiner et al. 2011). These

results, together with several converging lines of evidence suggest a critical role for

alterations in global and regional brain metabolism in the pathogenesis of neurodegenerative

diseases, indicating that physical activity could provide clinical benefit. However, the

mechanisms involved in exercise-induced cerebral mRNA expression of metabolically

relevant genes and mitochondrial biogenesis are not fully understood and need further

investigation.

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3.4. Ketone bodies

Ketone bodies provide the brain with an alternative source of energy during periods of

prolonged fasting and starvation. Under normal physiological conditions, monocarboxylates

cross the BBB with poor efﬁciency, but under starvation, the amount of ketones present in

the blood and expression of MCTs in cells forming the BBB are increased (Hasselbalch et al.

1995). Additionally, mild elevation of blood ketone bodies occurs during the process of

normal aging (Sengupta et al. 2010).

β-Hydroxybutyrate (β-OHB) is a metabolic intermediate that constitutes ~ 70 % of ketone

bodies produced in liver mitochondria mainly from the oxidation of fatty acids released from

adipose tissue (Persson 1970). The concentration of β-OHB in plasma under healthy, fasted

conditions is relatively low with reference values reported ~ 0.04 mM - 0.08 mM (Hansen &

Freier 1978). This may increase by fasting or starvation to 5 – 6 mM (Owen et al. 1967), and

to ~ 25 mM by dietary intervention or diabetic ketosis (Mitchell et al. 1995; Garber et al.

1974; Bonnefont et al. 1990; Saudubray et al. 1981). In non-diabetic subjects, a three-day

fast increases the concentration of β-OHB from an average of 0.03 to 3.15 mM in plasma,

and from 0.05 to 0.98 mM in the brain (Pan et al. 2000). Normal serum levels of ketone

bodies can be deﬁned as <0.5 mM, hyperketonemia in excess of 1.0 mM, and ketoacidosis

over 3.0 mM (Mitchell et al. 1995).

Regulation of ketone body metabolism is different in astrocytes and synaptic terminals.

Inhibition of the MAS and other transaminase reactions on the oxidation of energy substrates

increases the oxidation of lactate and β-OHB in astrocytes, but has no significant effect on

the rates of β-OHB oxidation and decreases the rate of lactate oxidation by synaptic

terminals (McKenna et al. 1993). Additionally, ketone bodies neither alter the plasma

membrane potential of presynaptic terminals nor the pH of synaptic vesicles. β-OHB

supports synaptic vesicle recycling; however, reduces both endocytosis and, to a smaller

extent, exocytosis (Hrynevich et al. 2016).

3.5 Medium chain triglycerides (MCTGs) as alternative brain energy fuel

MCTGs are fatty acids with 7-12 carbon chain length; the most common MCTGs include

heptanoate (C7), octanoate (C8) and decanoate (C10) (Schönfeld & Wojtczak 2016). Owing

to their smaller size, MCTGs can readily diffuse into the brain as they do not require carnitine

transporters unlike long chain fatty acids (Oldendorf 1973; Kuge et al. 1995). Additionally,

even MCTGs can be degraded in the liver to the 4 carbon (C4) ketone β-OHB. C4 ketones

and MCTGs can be further metabolized to acetyl-CoA and enter into the TCA cycle, primarily

in astrocytes (Edmond et al. 1987; Marin-Valencia et al. 2013). Octanoate was shown to

promote ketogenesis in astrocytes (Thevenet et al. 2016) and to inhibit brain glycolysis in

mice (McDonald et al. 2014) by reducing the maximal activity of the rate-limiting enzyme

PFK (Tan et al. 2017). On the other hand, decanoate facilitates glycolysis and lactate

formation in astrocytes thereby providing fuel to neurons (Thevenet et al. 2016) and

improving mitochondrial energy metabolism (Tan et al. 2017).

The odd chain MCTGs are unique because they can be β-oxidized to deliver propionyl-CoA

and acetyl-CoA. Additionally, they can be metabolized in the liver to 5 carbon (C5) ketone

bodies β-ketopentanoate and β-hydroxypentanoate (Kinman et al. 2006; Deng et al. 2009).

Triheptanoin, a triglyceride of heptanoate, generates three molecules of heptanoate upon

hydrolysis. Heptanoate can directly enter into the brain or can be degraded into C5 ketones

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in the liver (Marin-Valencia et al. 2013). The C5 ketones get converted into propionyl CoA,

an anaplerotic molecule which can be carboxylated to succinyl coenzyme A (CoA) to feed

the TCA cycle. Anaplerosis is the process by which TCA cycle intermediates are restored to

permit continuous function which is necessary during neurotransmission. Triheptanoin was

shown to partially replenish TCA cycle intermediates in epileptic mice (Hadera et al. 2014).

Additionally, numerous studies have demonstrated the beneficial effects of MCTGs as

energy substrates in several neurological disorders such as epilepsy (Willis et al. 2010; Wlaź

et al. 2012; Chang et al. 2013; Tan et al. 2017), amyotrophic lateral sclerosis (Zhao et al.

2012; Tefera et al. 2016), stroke (Schwarzkopf et al. 2015), and cognitive impairments

(Wang & Mitchell 2016). Overall, these studies corroborate the importance of alternative

fuels as a source of energy to improve brain energy metabolism with respect to

pathoconditions.

4. The role of non-neuronal cells in brain energy metabolism

4.1 Pericytes

The perivascular location and morphology of pericytes led to the suggestion that they may

be contractile cells involved in regulation of capillary blood flow in response to vasoactive

agents and neural activity (Sweeney et al. 2016). They may also be found around the

lymphatic capillaries in cases of developmental abnormalities (Petrova et al. 2004).

Additionally, pericytes exhibit macrophage-like activity, as shown by the presence of

numerous lysosomes within their cytoplasm (Allt & Lawrenson 2001), their efficient uptake

capacity for soluble tracer compounds (delivered into the blood, in the ventricular

cerebrospinal fluid, or in the extracellular fluid by direct injection into the tissue) (Rucker et

al. 2000), their phagocytic activity (Thomas 1999), and their capability to present antigens

(Rustenhoven et al. 2017).

4.1.1 Role of pericytes in the BBB and glucose homeostasis

Pericytes are imperative for normal CNS functioning and have important roles in

angiogenesis, vessel stabilization, endothelial cell regulation, and maintenance of the BBB

(Fisher 2009; Hill et al. 2014). These functions of pericytes are vital in maintaining the

homeostasis of the perivascular environment (Vezzani et al. 2016). Moreover, glucose intake

of pericytes is four times higher than of endothelial cells, but similar to that of astrocytes,

suggesting potential astrocyte/pericyte complementary roles in maintaining glucose

homeostasis in the brain. Furthermore, astrocytes and pericytes express comparable

amounts of GLUT1 and GLUT4 (Castro et al. 2018).

As endothelial cells comprise a portion of the BBB that regulates CNS transport of energy

metabolites, ions, and clearance of neurotoxic metabolites (Zhao et al. 2015), and since

there is a strong interaction between pericytes and endothelial cells by means of tight and

gap junctions found at their contact sites (Cuevas et al. 1984) allowing physical

communication and molecule exchange between this cells, pericytes have also been

reported to play an important role in the BBB (Daneman et al. 2010). Additionally, the

stabilization of these contact sites by adhesion plaques between the cells and fibronectin

from the extracellular matrix supports the fine-tuned distribution of the contractile force

caused by vascular smooth muscle cells (Vezzani et al. 2016). Reports suggest that the

pericyte/endothelial interaction regulates the basement membrane (Stratman et al. 2009;

Stratman et al. 2010) and the anatomical proximity between pericytes and endothelial cells

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indicates a probable role of pericytes in paracrine or juxtacrine signaling; as pericytes have

been proposed to be involved in several signaling pathways including Angiopoietin-1, TGF-

β, and PDGFRβ (Gaengel et al. 2009).

4.2. Astrocytes

Astrocytes are the most abundant glial cells in the brain and display a number of active roles

critical for CNS function (Nedergaard et al. 2003; Barros et al. 2018b), including regulation of

neurotransmitters (Anderson & Swanson 2000), supplying substrates to neurons for OxPhos

(Pellerin et al. 1998), maintaining water homeostasis (Simard & Nedergaard 2004; Salman

et al. 2017a) and regulating blood supply to meet neuronal energy demand (Zonta et al.

2003; Takano et al. 2006; Gordon et al. 2008). Additionally, astrocytes play a major role in

synapse formation, maintenance, and plasticity during brain development and in adulthood

(Araque et al. 1999; Han et al. 2013; Kim et al. 2017). Astrocytes have extensive processes

originating from the soma; some of these ensheath synapses (Ventura & Harris 1999)

forming the ‘tripartite synapse’, while others known as ‘endfeet’ surround the brain arteries

and capillaries (Araque et al. 1999; Oberheim et al. 2006; Barros et al. 2018b).

An intricate link exists between blood flow, glucose utilization, synaptic plasticity and

neuronal activity. This neurometabolic coupling is a salient physiological characteristic of the

brain function and has formed the basis for understanding neuroenergetics. Astrocytes take

up glucose via GLUT1 located at the endfeet covering brain microvessels (Mathiisen et al.

2010) (Figure 2). Following this, glucose is rapidly phosphorylated to G6P by hexokinase I

(Tabernero et al. 2006; Brown & Ransom 2007). G6P can then be channeled into the

glycolytic pathway to produce pyruvate, metabolized to glucose-1-phosphate for the

synthesis of glycogen (Cataldo & Broadwell 1986; Dienel & Cruz 2015), or used in the PPP

(Dringen et al. 2007). While the PPP is only a minor contributor to the total glucose

oxidation, it generates NADPH, an important molecule for maintaining the antioxidant

reduced glutathione, as well as precursors for nucleotide synthesis (Dringen et al. 2007). In

addition to glucose, astrocytes can efficiently use alternative energy substrates, such as

mannose (Dringen et al. 1993); however, other carbohydrates like fructose or galactose are

considered to be poor substrates in astrocytes (Dringen et al. 1993).

4.2.1 Glycogen metabolism

Alternatively, G6P can be used for the synthesis of glycogen, the main storage form of

glucose in the brain (Hertz & Dienel 2002). Glycogen is predominantly found in astrocytes

and seems to be connected to specific organelles and non-randomly distributed (Calì et al.

2016). The glycogen pool is dynamic and rapidly responds to changes in cerebral energetic

demands by undergoing constant degradation and re-synthesis (Swanson 1992), facilitated

by glycogen synthase and glycogen phosphorylase expressed by astrocytes (Brown &

Ransom 2007; Pellegri et al. 1996). Utilization of glycogen stores allows astrocytes to quickly

increase glycolytic flux independent of glucose availability and hexokinase activity in

response to local energy demands (Brown & Ransom 2015). Astrocytic glycogenolysis is

activated by extracellular increase in neurotransmitter concentrations as well as changes in

the K+ homeostasis following neuronal activity (Hof et al. 1988; Sickmann et al. 2009; Wang

et al. 2012). Interestingly, vasoactive intestinal polypeptide (VIP) as well as norepinephrine

(NE) induce glycogenolysis in murine cortex (Magistretti et al. 1981). This signal-induced

regulation of glucose supply is thought to act in a complementary way; while VIP acts locally

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within cortical columns, NE can control glycogen metabolism across adjacent columns.

Glycogen can also be mobilized under conditions such as glucose deprivation (Dringen &

Hamprecht 1992) or elevated cellular Ca2+ levels (Hamprecht et al. 1993). Functionally,

glycogenolysis in astrocytes and the subsequent release of lactate have been shown to play

a crucial role in formation of LTP and memory (Suzuki et al. 2011; Duran et al. 2013). The

important role of glycogen in supporting neuronal signaling is highlighted by the glycogen

shunt, where part of the glucose that enters the astrocyte is converted into glycogen before

entering the glycolytic pathway, despite this being energetically unfavorable compared to

classical glycolysis (Walls et al. 2009). Furthermore, astrocyte glycogen plays an important

role in maintaining neuronal survival during conditions of hypoglycemia in vitro (Swanson &

Choi 1993) and in vivo (Suh et al. 2007).

4.3 Oligodendrocytes

Oligodendroglia are specialized cells in the CNS that are responsible for generation and

maintenance of myelin sheath that surrounds CNS axons (Bradl & Lassmann 2010; Nave

2010). Myelin acts as an electrical insulator by increasing the membrane resistance and

decreasing membrane capacitance, resulting in increased conduction velocity while reducing

axonal size requirements and neuronal metabolic demand. Moreover, myelin enables rapid

saltatory propagation between nodes of Ranvier, allowing fast and efficient transduction of

electrical signals in CNS (Ransom & Sontheimer 1992; Edgar & Garbern 2004; Harris et al.

2012).

While myelination is the primary function of oligodendrocytes, they also provide trophic

support to neurons by secreting a wide variety of neurotrophins, including insulin-like growth

factor-1 (IGF-1), glial cell line-derived neurotrophic factor (GDNF), and brain-derived

neurotrophic factor (BDNF) (Bradl & Lassmann 2010; Saab et al. 2013). Moreover, though

astrocytes play a critical role in sustaining energy substrates through their glycogen stores

and production of lactate through glycolysis, recent evidence suggests that oligodendroglia

are a prominent site of lactate export to neuronal axons (Saab et al. 2013).

4.3.1 Oligodendrocytes as energy consumers

As outlined above, CNS white matter, primarily composed of myelinated axons, is estimated

to consume one third of the energy of grey matter. Conversely, myelination is energetically

costly and the metabolic costs of generating enough lipid and protein for myelin synthesis

may be higher than the energy saved by accelerated axonal conduction (Nave & Werner

2014). It has been reported that during peak myelination, oligodendrocytes increase their

weight three-fold per day (Ludwin 1997; McLaurin & Yong 1995). Remarkably, in the optic

nerve it has been estimated the initial energetic costs of myelin formation during

development can be repaid by 1-2 months normal activity (Harris & Attwell 2012). However,

the ATP needed to maintain myelin throughout life, including the cost of maintaining

oligodendroglia resting potential, likely negates the energy saved (Harris & Attwell 2012).

Therefore, the primary function of myelination is not to save energy, but rather to allow fast

nerve conduction, thereby increasing information processing and improving cognitive power

(Harris & Attwell 2012; Nave & Werner 2014).

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4.4. Microglia

Microglia are cells of mesodermal origin that migrate into the CNS during embryonic

development (Hickey & Kimura 1988). As immune cells, microglia serve both a supportive

and a protective function within the CNS. In their resting state, microglia act as the CNS’s

surveillance system, equipped with a wide range of receptors (i.e. neurotransmitter, cytokine,

chemokine, pattern recognition) (Kettenmann et al. 2011). Microglia interact with neurons

(i.e. scanning synapses) and thus contribute to the structure of neuronal networks and

connectivity (Kettenmann et al. 2011). When activated, as indicated by a change in

morphology towards an increased soma size and thicker proximal ramifications, microglia

can migrate to the site of injury and proliferate (Bernhart et al. 2010). Microglia can mount a

molecular defense through production of bioactive molecules that can be beneficial in some

circumstances (i.e. phagocytosis of aberrant cells posing a threat to the CNS) and

detrimental in others (i.e. when remaining primed in a disease state and being dysfunctional

in response to a secondary injury, which can lead to loss of neural circuits) (Kettenmann et

al. 2011; Daneman 2012; Koss et al. 2019). Proteomic changes in activated microglia

involve several glycolytic enzymes leading to enhanced ATP production; highlighting the

necessity of enhanced cellular metabolism to regulate their adaptability (Bernhart et al.

2010).

4.4.1 Microglial energy sources and metabolism

Microglia and macrophages within the CNS have a high energetic demand to function as

they monitor for abnormalities and make connections with neurons. The three major energy

substrates in microglia are glucose, fatty acids and glutamine. Glucose is imperative for

microglia survival and can enter microglia via transporters GLUT1, GLUT3, GLUT4 and

GLUT5 (Wang et al. 2019; Payne et al. 1997). Conversely, GLUT5, exclusively expressed in

microglial cells of the human and rat brain, is a very poor transporter of glucose and has

been shown to facilitate the passage of fructose across the plasma membrane (Payne et al.

1997; Horikoshi et al. 2003). After uptake, glucose undergoes glycolysis and full aerobic

breakdown or is used for formation and secretion of lactate.

Fatty acids are an alternative source of energy for microglia. After uptake by lipoprotein

lipase (LPL) long chain fatty acyl-CoA synthetase catalyzes the formation of fatty acyl-CoA,

which can only enter the mitochondria together with the carrier protein carnitine. Once inside

the mitochondria, fatty acyl CoA is β-oxidized into acetyl-CoA, which enters the TCA cycle

and subsequently the ETC in order to generate ATP. G protein-coupled receptor 120

(GPR120) is known to bind unsaturated fatty acids and is possibly involved in their anti-

inflammatory effects (Kalsbeek et al. 2016). Microglia highly express all components of the

NADPH oxidase complex, which has been shown to be stimulated by fatty acids to increase

production of reactive oxygen species (ROS) in macrophages.

Finally, glutamine is used as an energy substrate by microglia (Kalsbeek et al. 2016). The

glutamine transporters SLC1A5 and SLC38A1 are expressed by microglia and enable

microglia to take up glutamine. Inside the mitochondria glutamine is converted to glutamate

and ammonia (NH4+) by the GLS, glutamate is further metabolized by GDH1 to α-KG, which

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can enter the TCA cycle. Both microglial-produced glutamate and NH4+ have been shown to

have neurotoxic effects, which may contribute to neuronal cell death in inflammatory,

infectious, ischemic, and neurodegenerative diseases. Infection, for example, increases the

demand of energy of microglia (Kalsbeek et al. 2016). Tissue damage releases ATP which

attracts microglia at the site of infection and consumes a vast amount of energy (Engl &

Attwell 2015). Activation of microglia by ROS, especially nitric oxide (NO), modulates

metabolic assembly based on glucose uptake and up-regulates both anaerobic glycolysis

and OxPhos of PPP. Thus, NO plays a central role in the energy metabolism of microglia

(Gimeno-Bayón et al. 2014).

5. Disturbed energy homeostasis as a hallmark of CNS disorders and brain aging

Energy homeostasis is essential in maintaining a healthy state of the brain. Disrupted energy

homeostasis may either be a cause or an effect of a disease or a disease-like condition (i.e.

unhealthy aging). Metabolic changes contribute to the pathology of neurodegenerative

diseases, traumatic brain injury or stroke, but also cause neurological symptoms of diabetes

mellitus, and accompany normal aging.

5.1 Aging

During human brain development, energy metabolism declines with aging. Studies

calculating brain glucose utilization over the course of human development, from birth to

adulthood, have identified that energy metabolism peaks during early childhood (Kuzawa et

al. 2014a) and declines during aging (Kuzawa et al. 2014a; Skoyles 2014; Kuzawa et al.

2014b). A recent study from Goyal et al. showed that age-related decline in brain glucose

uptake exceeds that of O2 utilization, causing a loss of brain aerobic glycolysis (Goyal et al.

2017).

A suggestion for the decline in energy metabolism is the progression of metabolic deficiency

resulting in the age-associated cognitive decline and general brain function disturbance (Yin

et al. 2014; Ivanisevic et al. 2016). Metabolomics analysis-based studies have identified

compromised cellular energy status with metabolic imbalances suggesting a failure to

maintain metabolite homeostasis (Yin et al. 2014). These studies have identified increased

adenosine monophosphate (AMP), ATP, purine, and pyrimidine levels (Ivanisevic et al.

2016) with the accumulation of these metabolites as hallmarks of multiple neurodegenerative

diseases (Yin et al. 2014; Nyhan 2005).

A recent study has identified metabolism-regulating peroxisome proliferator-activated

receptor (PPAR) transcription factors as possible energetic metabolic switches during adult

neurogenesis (Di Giacomo et al. 2017). PPAR transcription factors have been shown to be

widely distributed within the mammalian brain and to be involved in regulating the expression

of genes involved in energy metabolism (Woods et al. 2003; Cimini et al. 2005; Cimini &

Cerù 2008); making them strong candidates for possible key regulators of metabolic

pathways impacted by brain aging.

Of note, impaired energy metabolism accompanying aging is a distinguishing factor of

neurodegeneration, highlighting aging as a predisposition destabilizing the “healthy” brain

energetics and making it more prone to neurodegenerative diseases.

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5.2 Type 2 Diabetes mellitus (T2DM)

Type 2 diabetes mellitus (T2DM) is a complex metabolic disease more prevalent with aging.

It is characterized by chronic elevated blood glucose levels. Although glucose is the main

energy substrate of the brain, chronic elevated blood glucose is not advantageous for the

brain, thus untreated T2DM can lead to severe cognitive dysfunction, termed diabetic

encephalopathy. Impeded cerebral glucose metabolism has been found in patients with

T2DM; a condition commonly observed in Alzheimer patients (see below) (Mosconi et al.

2008; Baker et al. 2011). This inadequate glucose metabolism can arise from disruptions in

supply, transport, or utilization, which likely all contribute to T2DM (Wardelmann et al. 2019).

Furthermore, cognitive impairment in T2DM patients is suspected to arise due to

hippocampal insulin resistance, which leads to several deleterious effects (Sims-Robinson et

al. 2010; Biessels & Reagan 2015; Correia et al. 2012). Interestingly, a specific hippocampal

decrease in glutamate and glutamine metabolism was recently described in a mouse model

of T2DM (Andersen et al. 2017b), supporting that metabolic changes at the interface of

glucose and neurotransmitter conversion may mediate cognitive hippocampal deficits in

T2DM.

Brain mitochondrial function was also found to be altered in T2DM (Sims-Robinson et al.

2010; Correia et al. 2012). Several studies have documented deleterious effects on

mitochondrial bioenergetics in mice models of T2DM; including altered activity and

expression of components of the ETC (Ernst et al. 2013; Andersen et al. 2017a); highlighting

mitochondrial deficits play a role in the decline of cerebral health in T2DM.

In addition to elevated plasma levels of glucose, increased amounts of ketone bodies,

acetoacetate, and β-OHB, are observed in T2DM model mice (Vannucci et al. 1997;

Poplawski et al. 2011). Along this line, it has been shown that cerebral fatty acids and β-

OHB metabolism are elevated in a mouse model of T2DM (Makar et al. 1995; Andersen et

al. 2017a). These observations correlate with an increased capacity of ketone body transport

into the brain (Pierre et al. 2007) and indicate that alternative substrates might be able to

compensate for the diminished glucose metabolism in T2DM.

Diabetic conditions can have a disease-modifying effect for progressive neurodegenerative

disorders of the brain like Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral

sclerosis or Multiple sclerosis, all of which involve diverse molecular and cellular

mechanisms, yet despite unrelated aetiology, they all share a metabolic component which

may be a consequence of the disorder or even causally contribute to its occurrence.

5.3 Alzheimer’s disease (AD)

AD is the most common chronic progressive neurodegenerative disorder that causes

dementia. There are two hypotheses of AD pathogenesis. On one hand, the amyloid

hypothesis proposes that the presence of extracellular amyloid-beta (Aβ) plaques and

intracellular neurofibrillary tangles causes brain atrophy and leads to nerve cell death

(Bhardwaj et al. 2017). On the other hand, the mitochondrial cascade hypothesis unifies the

biochemical, histological, and clinical features of sporadic AD (Swerdlow & Khan 2004).

T2DM is a risk factor for the development of AD (Ott et al. 1996; Exalto et al. 2012; Crane et

al. 2013). Indeed, increased brain Tau phosphorylation has been shown in T2DM mouse

models (Kim et al. 2009; Ramos-Rodriguez et al. 2013). It has been suggested that T2DM

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accelerates AD development through perturbations in brain energy metabolism, advanced

glycation end-product (AGE) and ROS production, and synaptic degeneration (Correia et al.

2012; Duarte 2015).

As dysfunctions in cerebral energy metabolism are considered a hallmark of AD, their

detection represents a tool for AD diagnosis and understanding of pathophysiological

mechanisms (Ferris et al. 1980; Mosconi 2013). As mentioned above, glucose metabolism

can be assessed by 18FDG uptake by PET. This technique has revealed a marked reduction

in FDG labeling that correlates with AD progression and is more prominent in brain areas

most affected by the disease, including the temporal and occipital lobes (Marcus et al. 2014).

One possible explanation for brain energetic impairment in AD could be reduced glucose

uptake. It has been observed that expression of GLUT1 and GLUT3 is reduced in AD brains

(Simpson et al. 1994). Furthermore, extracellular Aβ binds to several membrane receptors,

such as the NMDA receptor (De Strooper & Karran 2016). This interaction leads to an

impaired activity of AMP-activated kinase (AMPK), a protein that is normally induced by high

AMP/ATP ratios. Further, AMPK inhibition promotes a reduction of GLUT3 and GLUT4 in

plasma membranes of hippocampal neurons, reducing ATP production (da Silva et al. 2017).

Additionally, Aβ oligomers reduce hexokinase activity and ATP levels in neuronal cultures

(da Silva et al. 2017). Thus, extracellular Aβ contributes to the impairment of the energetic

metabolism.

On the other hand, as aging is a major risk factor for the onset of neurodegenerative

diseases like AD, the mitochondrial cascade hypothesis (Swerdlow & Khan 2004) suggests

that accumulated mutations in mtDNA caused by ROS play a pivotal role in mitochondrial

dysfunction (Lin & Beal 2006), which can be an early event in AD (Nunomura et al. 2001).

Amyloid precursor protein (APP) forms can accumulate in protein import channels of

mitochondria of human AD brains and thus contribute to mitochondrial dysfunction (Devi et

al. 2006). These alterations promote a reduction in energetic metabolism in neurons

(Caspersen et al. 2005). Though, some of these effects could be associated with variations

in protein levels related with mitochondrial dynamics (Itoh et al. 2013). It has been

demonstrated that protein levels of Drp1 are increased in AD and potentiate cell death (Park

et al. 2014). This protein is important for mitochondrial quality surveillance and induces

mitochondrial fission, which as a consequence reduces the protein levels of mitofusin 1/2

(Mfn1/2) and optic atrophy type 1 (OPA1); GTPases that promote the fusion process (Baek

et al. 2017). Additionally, the inhibition of Drp1 ameliorates Aβ deposition and synaptic

impairment. Therefore, reduction of mitochondrial fission leads to increased occurrences of

mitochondrial fusion and results in neuroprotective effects in AD models (Park et al. 2014;

Itoh et al. 2013; Baek et al. 2017).

5.4 Parkinson's disease (PD)

PD is a progressive neurodegenerative disorder characterized by a loss of dopaminergic

(DA) neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy

bodies (with α-synuclein inclusions) in the substantia nigra (Sveinbjornsdottir 2016).

Interestingly, in a large retrospective cohort study a higher prevalence of PD in patients with

T2DM was detected (De Pablo-Fernandez et al. 2018).

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However, dysfunctions in energy and/or redox homeostasis, along with oxidative stress

leading to mitochondrial dysfunction, are considered the initiators of a chain of events that

result in synaptic dysfunction, neuronal degeneration, and motor disabilities in PD

(Saravanan et al. 2006; Schapira & Jenner 2011). But, the observation that Lewy bodies are

not found in all neurons or are randomly expressed in the brain, as less than 1 % of brain

neurons are affected through the mid-stages of the disease, raised the question of why

SNpc DA neurons are the most vulnerable. It was proposed that there are intrinsic neuronal

features of generating and handling APs that render DA SNpc neurons more susceptible to

mitochondrial dysfunction related to PD (Surmeier et al. 2012).

Indeed, candidate gene and genome-wide association studies identified many genetic

mutations/polymorphisms associated with PD (i.e. in genes like PINK1, Parkin, DJ1/PARK7

and LRRK2) that compromise mitochondrial function and dynamics (Coleman 2012). DA

neurons lacking the mitochondrial fusion gene MFN2 display fragmented mitochondria and

their transport within the axon is hindered (Pham et al. 2012). Mutation in MFN2 leads to

severe locomotory behavioral deficits, which is accompanied by loss of striatal DA efferents.

Moreover, altered mitochondrial fission may result in synaptic loss and DA neuronal cell loss

(Berthet et al. 2014). In human patients, two heterozygous missense mutations in the

mitochondrial fusion promoter OPA1 protein show slow symptoms of PD and dementia

(Carelli et al. 2015).

Studies in various animal models of PD reveal that mitochondrial dysfunction in the brain is

linked to alterations in mitochondrial morphology, dynamics, mutation of mtDNA, increased

proton leak (Villeneuve et al. 2016) and decreased rates of electron transfer (Golpich et al.

2017; van der Merwe et al. 2017). These impairments in mitochondria are associated with:

accumulation of oxidation products of phospholipids and proteins, increased ROS

production, increased lipid peroxidation, decreased respiration and membrane potential,

decreased capacity for ATP production, and neuronal degeneration due to oxidative damage

and energy defects in the aged mammalian brain (Navarro & Boveris 2010).

5.5 Amyotrophic lateral sclerosis (ALS)

ALS is a progressive neurologic disorder, primarily characterized by the selective death of

lower and upper motor neurons in the spinal cord and cortical regions which finally leads to

muscle denervation, weakness and paralysis. Several pathogenic mechanisms are believed

to contribute to motor neuron death, including abnormal protein aggregation (Bruijn et al.

1997), oxidative stress (Barber et al. 2006), glutamate excitotoxicity (Rothstein 1995), and

impaired energy metabolism (Dupuis et al. 2011).

Numerous pre-clinical investigations in vitro and in vivo as well as in patients have revealed

metabolic irregularities in ALS brains which include reductions in glucose uptake (Miyazaki

et al. 2012) and reduced gene expression of enzymes involved in glycolysis (Ferraiuolo et al.

2011), PPP (Kirby et al. 2005), and TCA cycle (D’Arrigo et al. 2010), as well as the ETC

(Ferraiuolo et al. 2011). These metabolic defects, which comprise also disrupted metabolic

interactions between neurons and glial cells (reviewed in Tefera & Borges 2017), result in

impaired mitochondrial oxidative phosphorylation, declined generation of ATP, and

subsequent death of neurons as well as non-neuronal cells.

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Given the impairments in energy metabolism in ALS, several metabolic agents aimed at

correcting metabolic irregularities have been examined in ALS mouse models and/or

patients, including the ketogenic diet (Zhao et al. 2006), dichloroacetate (Miquel et al. 2012),

caprylic triglyceride (Zhao et al. 2012), triheptanoin (Tefera et al. 2016), and others

(reviewed in (Tefera & Borges 2017). These metabolic substrates were able to modify ALS

disease progression to varying degrees, signifying the contribution of CNS energy

metabolism towards the pathogenesis of the disease and the need for further studies to

correct metabolic defects.

5.6 Multiple sclerosis (MS)

MS is a chronic autoimmune neurodegenerative disease characterized by axonal

demyelination and impaired remyelination (Aslani et al. 2017). Activated T cell-mediated

autoimmune destruction of CNS myelin has been highlighted as the major underlying cause,

yet genetic predisposition, oxidative stress, mitochondrial dysfunction, and energy failure

likely contribute to accelerate the disease. Axonal energy failure and disrupted mitochondrial

energetics, especially in white matter astrocytes, have been implicated in MS (Cambron et

al. 2012); highlighted by the absence of β2-adrenoreceptors in the astrocytes of MS patients

(Cambron et al. 2012), which impairs the noradrenaline-mediated glycogenolysis necessary

to exploit this energy reservoir for neurons and their axons, thus leading to disrupted ion

gradients that disturb the excitability of axons. Also, disrupted metabolic support from

oligodendrocytes contributes to axonal damage and disease progression (summarized in

(Philips & Rothstein 2017).

Functionally, energy crisis-driven impaired ATP generation may lead to the failure of

important pumps and exchangers like the Na+/K+-ATPase maintaining the Na+ concentration

within the axon, which is ATP-dependent, and the Na+/Ca2+ exchanger, which further

sustains calcium homeostasis. This leads to an increased Na+ and Ca2+ influx in axons and

disrupted membrane potential. Intra-axonal accumulation of calcium activates

phospholipases and proteases like calpain, which bring about axonal disintegration, and

further lead to energy failure via mitochondrial disruption (Pennisi et al. 2011).

5.7 Traumatic brain injury (TBI) and stroke

Brain injury occurring under ischemic, hemorrhagic, or traumatic conditions is characterized

by metabolic and energy imbalances within cerebral cells. Structural damage causes

reduced cerebral blood flow and cell membrane disruption and is accompanied by

mitochondrial dysfunction, inflammatory responses and oxidative stress, ionic gradient

breakdown, glutamate-mediated excitotoxicity, stress signaling, and ultimately cell death (Lo

et al. 2003; Malik & Dichgans 2018). In addition, the integrity of the BBB is adversely

affected due to the disruption of tight junctions (Chodobski et al. 2011).

Autoradiographic studies have shown a transient increase followed by a prolonged decrease

in cerebral glucose metabolism (measured as cerebral metabolic rate of glucose, CMRglu) in

rodent models as well as in human patients with head injury (Glenn et al. 2003). It is

postulated that the acute phase of hyperglycolysis occurs in order to compensate the

increased cellular energy requirement to restore the ionic imbalance and membrane

potential (Karelina & Weil 2016).The following long-term depression of CMRglu, indicating

reduced glucose uptake and utilization, suggests the inability of the injured brain to meet the

increased metabolic demands which could be due to reduced glucose supply (as a

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consequence of hindered cerebral blood flow, CBF), defects in glucose transporter systems,

and/or reduced need for glucose. However, changes in CBF in patients with TBI show a

strong heterogeneity with some patients showing significantly reduced CBF, while in others

normal CBF values can be seen that persist for days to weeks after injury (Marion et al.

1991; Bouma et al. 1991). Nonetheless, the increased glucose metabolism despite the low

CBF during acute phase after TBI indicates that the metabolic crisis is not likely caused by

the reduced cerebral glucose supply. The other possibility includes the reduced glucose

uptake due to impaired glucose transport systems following TBI. Preclinical studies have

shown a decreased GLUT1 immunoreactivity indicating less transport of glucose molecules

into the brain. However, the data are highly variable requiring further studies. Finally,

shunting of glucose to other metabolic pathways within the cell may contribute to the energy

crisis after TBI. An animal study has shown an upregulation of the PPP with a 12 % increase

in cerebral glucose levels that is shunted towards the synthesis of pentose 24h after the

injury (Bartnik et al. 2005). Moreover, a reduction in glucose-metabolizing enzymes (such as

PDH) and cofactors (NAD+) indicating decreased efficiency of mitochondria to process

glucose for oxidative metabolism in rats (Xing et al. 2009).

Altered ionic balance results in a calcium influx (Kitchen et al. 2015b), which in turn leads to

the depolarization of the mitochondrial membrane due to the opening of the mitochondrial

permeability transition pore and cytochrome c-mediated caspase-dependent apoptosis.

Elevated levels of NO have been observed in animal models of head trauma (Görlach et al.

2015). NO hampers the enzymatic activity of the mitochondrial complex IV and thus

interferes with energy metabolism, too.

Brain edema, which is a hallmark of TBI and stroke, occur when water enters the CNS via

astrocytes at the BBB, primarily through the aquaporin 4 channel (Amiry-Moghaddam &

Ottersen 2003; Kitchen et al. 2015a). There is an increased interest in applying therapeutic

measures that could target TBI and stroke through decreasing the energy need of the brain

as well as act on limiting the devastating effects of edema (Salman et al. 2017b).

Therapeutic hypothermia is gaining popularity to prevent or improve a wide range of

neurological morbidities (Yenari & Han 2012; Salman et al. 2017a). The neuroprotective

effect of therapeutic hypothermia, which potentially involves the restoration of the BBB,

preservation of high-energy phosphate compounds and cellular metabolism, has been

confirmed in a number of clinical trials investigating the outcomes of patients suffering from

neonatal hypoxia-ischemia (Gluckman et al. 2005; Shankaran et al. 2005) and cardiac arrest

(Bernard et al. 2002).

Brain metabolism produces oxygen molecules that are converted into hydrogen peroxides

and finally to hydroxyl radicals (·OH). During normal physiological conditions, these highly

reactive free radicals are quenched by the cellular antioxidant defense systems. However,

during brain injury the levels of free radicals overwhelm the scavenging systems and result

in oxidative damage. ·OH has been shown to increase immediately after injury and peak at

30 minutes in severely injured mice (Hall et al. 1993). In addition, lipid peroxidation, a marker

of increased oxidative metabolism, has been shown to increase up to 24h post-injury (Hall et

al. 1993) .

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Moreover, TBI is characterized by the presence of high lactate concentrations in the

extracellular fluid, clearly indicating increased rates of glycolysis, mitochondrial impairment,

and/or hypoxia. A high lactate/pyruvate ratio (i.e. <25) has been well reported for TBI

patients (Patet et al. 2016). TBI is frequently accompanied by ischemia and hypoxia, major

but not the only causes of mitochondrial dysfunction after injury (Patet et al. 2016; Carpenter

et al. 2015).

6. Concluding remarks

6.1 Costly human brain: evolutionary implications

In humans, the brain consumes more energy relative to the rest of the body and has a higher

relative size than in other animals. However, the relative basal metabolism does not

correlate with the relative size of the brain (Navarrete et al. 2011). Some studies connected

the possibility of having such a costly brain with the relative reduction of gut (Aiello &

Wheeler 1995) or skeletal muscles (Leonard et al. 2003) in volume or with the decrease of

energy spent on locomotive tasks (Navarrete et al. 2011). According to this, the high

energetic demands of the human brain are met not due to elevated basal metabolism, but

rather due to the energy redirection from other metabolically expensive tissues. New findings

highlight the existence of molecular mechanisms of energy redirection. As shown by

Pfefferle et al. (2011) comparing GLUT1 and 4 expression patterns in apes, monkeys and

humans, the energy trade-off between the brain and other organs may be achieved by

redistribution of glucose uptake systems. GLUT1 expression in human brain was found to be

much higher as compared to chimpanzee and macaque brains, but GLUT4 expression is

higher in chimpanzee skeletal muscle than in human and macaque muscle. Another system

of fueling energy to tissue that likely facilitated the evolution of the costly human brain is the

phosphocreatine pathway (Pfefferle et al. 2011). The expression of the active creatine

transporter SLC6A8 and the brain-type cytosolic creatine kinase are higher in humans than

in chimpanzees and rhesus macaques in cerebral cortex and cerebellum, but not in skeletal

muscles (Pfefferle et al. 2011). Thus, shifts in the expression pattern of metabolic pathway

players could account for primate brain divergence in evolution.

6.2 The importance of maintaining brain energy homeostasis

The existence of many alternative energy sources to fuel the brain as well as the

pathological consequences of disturbed regional/global brain metabolism and energetics

highlight the importance of maintaining brain energy homeostasis. Glucose metabolism is

connected to cell death pathways by glucose-metabolizing enzymes. Thus, disruption of

glucose delivery and metabolism can lead to debilitating brain diseases. Furthermore, other

molecules that provide fuel or metabolic intermediates for the brain, for example ketones,

are used under pathological (diabetic ketosis) or physiological conditions such as fasting,

starvation or aging; and lactate during extensive physical exercise. A complex interplay

exists between the brain, in particular the hypothalamus, and peripheral systems that control

glucose uptake and supply to the brain (Lam et al. 2009), utilization, and feeding

(Joly‐Amado et al. 2012; Wu et al. 2009). When the body undergoes glucose deprivation,

fatty acids are broken down into ketones in the liver and the permeability of the BBB to

monocarboxylic acids increases in parallel; all of which allow ketone body utilization. Long

chain fatty acids, in contrast, do not serve as fuel for the brain because they are bound to

albumin in the plasma and do not traverse the BBB.

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6.3 A non-neurocentric view for neuroenergetics

Neuronal cells together with glia orchestrate the metabolic reactions to maintain the energy

demand and redox balance of neuronal activity. Glial cells are highly specialized in their

roles within the brain. Moreover, neurodegenerative disorders are not exclusively restricted

to neurons but glial cells are indeed a pivotal player. Future research is needed in order to

illuminate the roles of the individual glial cells in brain health and disease as there are still

many molecular mechanisms to define. For example, utilizing a combination of optogenetic

stimulation, electrophysiological recordings, and bioenergetic readouts (Barros et al. 2018a)

as well as further development of non-invasive imaging of brain metabolism with high-field

NMR spectroscopy, PET or SPECT (Hyder & Rothman 2017). Further studies are

warranted, not only because of the high prevalence of metabolic syndrome, but additionally

for a detailed understanding of brain energy metabolism and the link between mental and

physical health.

Acknowledgments

This review was initiated at the 14th International Society for Neurochemistry (ISN)

Advanced School held in Varennes Jarcy, France, in August 2017. We thank the ISN for the

financial and educational support provided, as well as all ISN School faculty members, who

devoted their time to share their views on neuroenergetics and to encourage young

scientists. Work in the lab of C.I.S. is funded by the DFG (SFB779-B14 and RTG2413

“SynAge” TP08), BMBF (IB-049 “PrePLASTic”) and EU (MC-ITN “ECMED”; LSA ESF

ZS/2016/08/80645 “ABINEP”). C.I.S. is an editor for Journal of Neurochemistry. All authors

declare no conflict of interest.

Brain energetics is a huge and growing field, which can hardly be covered in a single review

with satisfying depth. We would like to thank all the scientists and labs who contributed to

our current knowledge and we would also like to apologize to those whom we missed to

refer to, due to space limitations.

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Figure legends

Figure 1: Schematic representation of the main ATP consuming processes in neurons. (A) At the

synapse, most of neuronal signaling-related ATP is utilized by the Na+/K+-ATPase and in the synaptic

vesicle cycle. The Na+/K+-ATPase restores membrane potential after depolarization and maintains the

electrochemical gradient of Na+ used in the removal of Ca2+ by the Na+/Ca2+ exchanger and in the

uptake of neurotransmitters (i.e monoamines, GABA, glycine and glutamate). The synaptic vesicle

cycle uses ATP for neurotransmitter loading by vacuolar H+-ATPase and for protein disassembly

machineries during docking and priming. Also, extrusion of elevated cytosolic Ca2+ after exocytosis by

the Ca2+-ATPase (not shown) needs ATP. (B) AP generation and propagation at the axon initial

segment and the nodes of Ranvier as well as reversing the ion fluxes after depolarization are also

linked to high signaling-related ATP expenditure by Na+/K+-ATPase. (C) Basic cellular processes,

such as axonal transport involved in the trafficking of vesicles, mitochondria, and other cargo within

the neurites require motor proteins that hydrolyze ATP.

Figure 2: Schematic representation of the neurometabolic coupling illustrating the energy expenditure

in a glutamatergic synapse and the ANLS hypothesis. Glutamatergic vesicular release activates

postsynaptic receptors (AMPA and NMDA, blue and red, respectively) depolarizes the membrane by

opening channels, in turn triggering the influx of Na+ ions that constrain the use of ATP when they are

pumped out. Glutamate in the synaptic cleft is taken up into astrocytes via EAAT2 and metabolized

via glutamine synthase (GS). Glial glutamine is released via the SN-1 transporter, taken up by

neurons via SAT to replenish glutamate pools via GLS1 and vesicular reuptake via vGLUT. Most of

the energy required by those presynaptic and postsynaptic processes comes from glucose

metabolism. However, neurons can use alternative sources of energy. According to the ANLS

hypothesis, astrocytes take up glucose from the blood via GLUT1 and convert it into lactate through

LDH5 that is released and taken up by neurons through MCT transporters. In neurons, lactate is

converted to pyruvate by LDH1, enters the TCA cycle and is metabolized via oxidative

phosphorylation in the mitochondria.

Accepted Article

ResearchGate has not been able to resolve any citations for this publication.

In Vitro Priming and Hyper-Activation of Brain Microglia: an Assessment of Phenotypes

Article

Full-text available

Sep 2019
MOL NEUROBIOL

Microglia are the resident immune cells of the central nervous system that mediate the life and death of nervous tissue. During normal function, they exhibit a surveying phenotype and maintain vital functions in nervous tissue. In the event of injury or disease, chronic inflammation can result, wherein microglia develop a hyper-activated phenotype, shed their regenerative function, actively kill contiguous cells, and can partition injured tissue by initiating scar formation. With recoverable injury, microglia can develop a primed phenotype, where they appear to recover from an inflammatory event, but are limited in their support functions and show inappropriate responses to future injury often associated with neurodegenerative disorders. These microglial phenotypes were acutely recreated in vitro with potent pro- and anti-inflammatory treatments. Primary cultured microglia or mixed glia (microglia, astrocytes, and oligodendrocytes) were treated for 6 h with lipopolysaccharide (LPS). Recovery from an inflammatory state was modeled with 18-h treatment of the anti-inflammatory steroid dexamethasone. The cells were then treated for 24 h with interferon gamma (IFNγ) to detect inflammatory memory after recovery. Surveying was best represented in the untreated vehicle (Veh) cases and was characterized by negligible secretion of pro-inflammatory factors, limited expression of immune proteins such as induced nitric oxide synthase (iNOS), major histocompatibility complex class II (MHCII), relatively high expression of brain-derived and glial-derived neurotrophic factors (BDNF and GDNF), and thinly branched smaller microglia. Activation was noted in the LPS- and IFNγ-treated microglia with increased cytokines, NO, NGF, iNOS, proliferation, phagocytosis, reduced BDNF, and flattened round amoeboid-shaped microglia. Priming was observed to be an incomplete surveying restoration using dexamethasone from an activation comparison of LPS, IFNγ, and LPS/IFNγ. Dexamethasone treatments resulted in the most profound dysregulation of expression of NO, TNF, IL-1β, NGF, CD68, and MHCII as well as ramified morphology and uptake of myelin. These findings suggest microglial priming and hyper-activation may be effectively modeled in vitro to allow mechanistic investigations into these key cellular phenotypes.

Glucose transporter 1 critically controls microglial activation through facilitating glycolysis

Article

Full-text available

Dec 2019
MOL NEURODEGENER

Background: Uncontrolled microglial activation contributes to the pathogenesis of various neurodegenerative diseases. Previous studies have shown that proinflammatory microglia are powered by glycolysis, which relays on high levels of glucose uptake. This study aimed to understand how glucose uptake is facilitated in active microglia and whether microglial activation can be controlled by restricting glucose uptake. Methods: Primary murine brain microglia, BV2 cells and the newly established microglial cell line B6M7 were treated with LPS (100 ng/ml) + IFNγ (100 ng/ml) or IL-4 (20 ng/ml) for 24 h. The expression of glucose transporters (GLUTs) was examined by PCR and Western blot. Glucose uptake by microglia was inhibited using the GLUT1- specific inhibitor STF31. The metabolic profiles were tested using the Glycolysis Stress Test and Mito Stress Test Kits using the Seahorse XFe96 Analyser. Inflammatory gene expression was examined by real-time RT-PCR and protein secretion by cytokine beads array. The effect of STF31 on microglial activation and neurodegeneraion was further tested in a mouse model of light-induced retinal degeneration. Results: The mRNA and protein of GLUT1, 3, 4, 5, 6, 8, 9, 10, 12, and 13 were detected in microglia. The expression level of GLUT1 was the highest among all GLUTs detected. LPS + IFNγ treatment further increased GLUT1 expression. STF31 dose-dependently reduced glucose uptake and suppressed Extracellular Acidification Rate (ECAR) in naïve, M(LPS + IFNγ) and M(IL-4) microglia. The treatment also prevented the upregulation of inflammatory cytokines including TNFα, IL-1β, IL-6, and CCL2 in M(LPS + IFNγ) microglia. Interestingly, the Oxygen Consumption Rates (OCR) was increased in M(LPS + IFNγ) microglia but reduced in M(IL-4) microglia by STF31 treatment. Intraperitoneal injection of STF31 reduced light-induced microglial activation and retinal degeneration. Conclusion: Glucose uptake in microglia is facilitated predominately by GLUT1, particularly under inflammatory conditions. Targeting GLUT1 could be an effective approach to control neuroinflammation.

Insulin action in the brain regulates mitochondrial stress responses and reduces diet-induced weight gain

Article

Full-text available

Mar 2019

Objective Insulin action in the brain controls metabolism and brain function, which is linked to proper mitochondrial function. Conversely, brain insulin resistance associates with mitochondrial stress and metabolic and neurodegenerative diseases. In the present study, we aimed to decipher the impact of hypothalamic insulin action on mitochondrial stress responses, function and metabolism. Methods To investigate the crosstalk of insulin action and mitochondrial stress responses (MSR), namely the mitochondrial unfolded protein response (UPRmt) and integrated stress response (ISR), qPCR, western blotting, and mitochondrial activity assays were performed. These methods were used to analyze the hypothalamic cell line CLU183 treated with insulin in the presence or absence of the insulin receptor as well as in mice fed a high fat diet (HFD) for three days and STZ-treated mice without or with insulin therapy. Intranasal insulin treatment was used to investigate the effect of acute brain insulin action on metabolism and mitochondrial stress responses. Results Acute HFD feeding reduces hypothalamic mitochondrial stress responsive gene expression of Atf4, Chop, Hsp60, Hsp10, ClpP, and Lonp1 in C57BL/6N mice. We show that insulin via ERK activation increases the expression of MSR genes in vitro as well as in the hypothalamus of streptozotocin-treated mice. This regulation propagates mitochondrial function by controlling mitochondrial proteostasis and prevents excessive autophagy under serum deprivation. Finally, short-term intranasal insulin treatment activates MSR gene expression in the hypothalamus of HFD-fed C57BL/6N mice and reduces food intake and body weight development. Conclusions We define hypothalamic insulin action as a novel master regulator of MSR, ensuring proper mitochondrial function by controlling mitochondrial proteostasis and regulating metabolism.

Glia in brain energy metabolism: A perspective

Article

Full-text available

Feb 2018

Early views of glia as relatively inert, housekeeping cells have evolved, and glia are now recognized as dynamic cells that not only respond to neuronal activity but also sense metabolic changes and regulate neuronal metabolism. This evolution has been aided in part by technical advances permitting progressively better spatial and temporal resolution. Recent advances in cell-type specific genetic manipulation and sub-cellular metabolic probes promise to further this evolution by enabling study of metabolic interactions between intertwined fine neuronal and glial processes in vivo. Views of glia in disease processes have also evolved. Long considered purely reactive, glia and particularly microglia are now seen to play active roles in both promoting and limiting brain injury. At the same time, established concepts of glial energetics are now being linked to areas such as learning and neural network function, topics previously considered far removed from glial biology.

Transcriptome Analysis of Gene Expression Provides New Insights into the Effect of Mild Therapeutic Hypothermia on Primary Human Cortical Astrocytes Cultured under Hypoxia

Article

Full-text available

Dec 2017

Hypothermia is increasingly used as a therapeutic measure to treat brain injury. However, the cellular mechanisms underpinning its actions are complex and are not yet fully elucidated. Astrocytes are the most abundant cell type in the brain and are likely to play a critical role. In this study, transcriptional changes and the protein expression profile of human primary cortical astrocytes cultured under hypoxic conditions for 6 h were investigated. Cells were treated either with or without a mild hypothermic intervention 2 h post-insult to mimic the treatment of patients following traumatic brain injury (TBI) and/or stroke. Using human gene expression microarrays, 411 differentially expressed genes were identified following hypothermic treatment of astrocytes following a 2 h hypoxic insult. KEGG pathway analysis indicated that these genes were mainly enriched in the Wnt and p53 signaling pathways, which were inhibited following hypothermic intervention. The expression levels of 168 genes involved in Wnt signaling were validated by quantitative real-time-PCR (qPCR). Among these genes, 10 were up-regulated and 32 were down-regulated with the remainder unchanged. Two of the differentially expressed genes (DEGs), p38 and JNK, were selected for validation at the protein level using cell based ELISA. Hypothermic intervention significantly down-regulated total protein levels for the gene products of p38 and JNK. Moreover, hypothermia significantly up-regulated the phosphorylated (activated) forms of JNK protein, while downregulating phosphorylation of p38 protein. Within the p53 signaling pathway, 35 human apoptosis-related proteins closely associated with Wnt signaling were investigated using a Proteome Profiling Array. Hypothermic intervention significantly down-regulated 18 proteins, while upregulating one protein, survivin. Hypothermia is a complex intervention; this study provides the first detailed longitudinal investigation at the transcript and protein expression levels of the molecular effects of therapeutic hypothermic intervention on hypoxic human primary cortical astrocytes. The identified genes and proteins are targets for detailed functional studies, which may help to develop new treatments for brain injury based on an in-depth mechanistic understanding of the astrocytic response to hypoxia and/or hypothermia.

Association between diabetes and subsequent Parkinson disease: A record-linkage cohort study

Article

Jun 2018

Objective: To investigate the association between type 2 diabetes mellitus (T2DM) and subsequent Parkinson disease (PD). Methods: Linked English national Hospital Episode Statistics and mortality data (1999-2011) were used to conduct a retrospective cohort study. A cohort of individuals admitted for hospital care with a coded diagnosis of T2DM was constructed, and compared to a reference cohort. Subsequent PD risk was estimated using Cox regression models. Individuals with a coded diagnosis of cerebrovascular disease, vascular parkinsonism, drug-induced parkinsonism, and normal pressure hydrocephalus were excluded from the analysis. Results: A total of 2,017,115 individuals entered the T2DM cohort and 6,173,208 entered the reference cohort. There were significantly elevated rates of PD following T2DM (hazard ratio [HR] 1.32, 95% confidence interval [CI] 1.29-1.35; p < 0.001). The relative increase was greater in those with complicated T2DM (HR 1.49, 95% CI 1.42-1.56) and when comparing younger individuals (HR 3.81, 95% CI 2.84-5.11 in age group 25-44 years). Conclusions: We report an increased rate of subsequent PD following T2DM in this large cohort study. These findings may reflect shared genetic predisposition and/or disrupted shared pathogenic pathways with potential clinical and therapeutic implications.

Challenges and Opportunities in Stroke Genetics

Article

Mar 2018

Stroke, ischemic stroke and subtypes of ischemic stroke display substantial heritability. Compared to related vascular conditions, the number of established risk loci reaching genome-wide significance for association with stroke is still in the lower range, particularly for etiological stroke subtypes such as large artery atherosclerotic stroke or small vessel stroke. Nevertheless, for individual loci substantial progress has been made in determining the specific mechanisms mediating stroke risk. In this review, we present a roadmap for functional follow-up of common risk variants associated with stroke. First, we discuss in-silico strategies for characterizing signals in non-coding regions and highlight databases providing information on quantitative trait loci (QTL) for mRNA and protein expression, as well as methylation, focusing on those with presumed relevance for stroke. Next, we discuss experimental strategies for following up on non-coding risk variants and regions such as massively parallel reporter assays (MPRA), proteome-wide association studies (PWAS) and chromatin conformation capture (3C) assays. These and other approaches are relevant for gaining insight into the specific variants and mechanisms mediating genetic stroke risk. Finally, we discuss how genetic findings could influence clinical practice by adding to diagnostic algorithms and eventually improve treatment options for stroke.

CrossTalk opposing view: lack of evidence supporting an astrocyte-to-neuron lactate shuttle coupling neuronal activity to glucose utilisation in the brain: CrossTalk

Article

Jan 2018

CrossTalk proposal: an important astrocyte-to-neuron lactate shuttle couples neuronal activity to glucose utilisation in the brain: CrossTalk

Article

Jan 2018

We will argue here that there is net lactate transfer from astrocytes to neurons and that this transfer is important for brain function. Following CrossTalk guidelines, we will focus on data published over the last decade.

Alzheimer's Disease—Current Status and Future Directions

Article

Nov 2017

Alzheimer's disease (AD) is an age-related neurodegenerative disorder of the brain. The presence of amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), loss of neurons, synapses, and altered sensory perceptions, including memory loss, delineate AD. However, the cause of AD is not clearly known. Several genetic and nongenetic factors have been implicated in the disease. Of the genes, the ɛ4 allele of apolipoprotein E is the largest known genetic risk factor of AD. This review article focuses on the various genetic and other predisposing factors that account for AD, pathophysiology of the disease, and the mechanisms by which Aβ plaques and NFTs are formed and could affect AD brain. In addition, recent advances and current diagnostics available for AD patients are detailed. As oxidative stress has been implicated in the etiology of the disease, special emphasis is given for nutrition based antioxidant therapies and interventional strategies for reducing/treating AD.

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