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METABOLIC CONTROL OF NEURONAL ACTIVATION AND EPILEPSY

Alexander Ksendzovsky, MD

PhD Candidate

Department of Molecular Physiology and Biological Physics

Chief Resident

Department of Neurosurgery | University of Virginia

Surgical Neurology Branch | National Institutes of Health

PhD Mentor (NIH): Kareem Zaghloul, MD, PhD

PhD Mentor (UVA): Jaideep Kapur, MD, PhD

PhD Committee Chair: Avril Somlyo, PhD

Committee: Jeff Elias, MD

Mark Beenhakker, PhD

Brant Isakson, PhD

Dissertation

June 5, 2019

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TABLE OF CONTENTS

I. Abstract ............................................................................................................................. 3

II. Introduction ...................................................................................................................... 5

III. “Chronic activation leads to neuronal glycolysis through the AMPK/HIF1a pathway” ................................................................................................................... 14

IV. “A feedforward mechanism for epilepsy regulated by lactate dehydrogenase A” .... 55

V. Special Methods ............................................................................................................... 85

a. "Modeling epilepsy in a dish: mixed cortical cells cultured on a microelectrode array” ................................................................................................................................... 85

b. "A novel mouse model of cobalt-induced focal cortical epilepsy” …............................................................................................................................... 105

VI. Conclusions ....................................................................................................................... 115

VII. Future Directions ............................................................................................................. 117

VIII. Acknowledgements ...........................................................................................................122

IX. References ......................................................................................................................... 124

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I. Abstract

The fundamental role of metabolism in the regulation of neuronal activation is not well

understood. Glycolysis is thought to support active neurons as a supplement to mitochondrial respiration

in times of high metabolic demand which occurs through the astrocyte neuron lactate shuttle (ANLS).

Recent evidence, however, strongly refutes this claim and argues that acute neuronal stimulation directly

leads to neuronal glucose utilization through glycolysis, which becomes the primary source of ATP. Due

to this lack of clarity, the role of metabolism in epilepsy formation is also unknown. In the present work,

we explore the neuronal metabolic phenotype during times of high metabolic demand from chronic

stimulation. We extend these findings toward understanding metabolism’s role in regulating epilepsy.

In our first aim we used a novel model of chronic activation and resected human tissue to

demonstrate that chronic neuronal stimulation leads to neuronal metabolic reprogramming from aerobic

respiration to glycolysis through the upregulation of neuronal LDHA. Our results challenge the

prevailing ANLS hypothesis, which holds that the majority of metabolism occurs via supporting

astrocytes during times of high neuronal metabolic demand. The second aim of our study was to

describe the molecular pathway that regulates the transition from aerobic respiration to glycolysis during

chronic neuronal stimulation. Drawing from similarities of high energy demands during hypoxia, we

hypothesized that the AMPK/ HIF1a hypoxia pathway plays a role in regulating neuronal metabolism

during chronic stimulation. Using this model, we confirmed that neuronal metabolic reprogramming to

glycolysis is mediated by the AMPK/ HIF1a hypoxia pathway. For our third aim, we applied insights

gained from the neuronal metabolic phenotype during times of chronic stimulation from our first two

aims to more clearly elucidate the etiology of epilepsy formation. We showed that LDHA, regulated by

upstream HIF1a, leads to epileptiform activity in culture and in an animal model.

Collectively, the work presented here lays the foundation of an overarching hypothesis for

metabolically driven pathogenesis of epilepsy. We believe a feedforward loop exists wherein chronic

seizure activity shifts neurons into glycolysis through AMPK/HIF1a mediated upregulation of LDHA,

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which further pushes neurons to become hyperexcitable and subsequently elicit more seizures.

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II. Introduction Neuronal glucose utilization

The brain represents only 2% of human body mass but consumes more than 20% of the daily

energy requirement [1]. When performing cognitive tasks there is a wide variation in brain energy

consumption across cortical tissue as signals propagate from one node to another. On a macroscopic

scale, changes in cerebral blood flow accompanying brain activity account in part for the uncanny ability

of the brain to adapt to instantaneous shifts in metabolic demand [2]. On a more cellular level, where the

majority of these changes occur, intertwined biochemical and molecular pathways work together to

maintain a tightly regulated metabolic homeostasis.

The seemingly fundamental concept of neuronal metabolism, to this day, is poorly understood.

Current explanations for neuronal glucose utilization are controversial and have been based on

incomplete or inadequate underlying evidence. Over the last three decades the prevailing theory has

been the Astrocyte Neuron Lactate Shuttle (ANLS) hypothesis. A main part of this hypothesis rests on

understanding the cellular location of the metabolic processing required to provide neurons with the

adenosine tri-phosphate (ATP) necessary to perform homeostatic and, more importantly, dynamic

metabolic functions. According to the ANLS hypothesis, neurons receive a large amount of substrate

necessary for metabolism from neighboring astrocytes, which metabolize glucose into lactate via

astrocytic lactate dehydrogenase A (LDHA). Astrocytic lactate is subsequently shuttled into neurons for

further metabolism into pyruvate and eventually the tricarboxylic acid (TCA) cycle [3-5] (Figure 1).

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Figure 1: Astrocyte Neuron Lactate Shuttle (Adapted from Rho et al.) [5]. Glucose is provided to astrocytes either

endogenously through breakdown of glycogen or through peripheral circulation via glucose transporters. Glucose is then

converted to pyruvate and then lactate through astrocytic lactate dehydrogenase A (LDHA). Lactate is shuttled to neurons

through MCT transporters which is then converted back to pyruvate by neuronal lactate dehydrogenase B (LDHB). Several

of these enzymes are potential points for inhibition for decreased ATP production [5, 6].

The brain’s metabolic response to neuronal activation is even less clear. Early observations of a

mismatch between oxygen and glucose utilization during physiological [7, 8] and pathological (i.e.

epilepsy) [9, 10] neuronal stimulation wielded “aerobic glycolysis,” wherein glucose is ultimately

converted to lactate for energy in the presence of oxygen [3]. Despite a lower ATP yield with glycolysis,

the rate of ATP production is significantly higher compared to oxidative metabolism and thereby

compensates during high energy demands that occur with stimulation [3]. This concept was supported

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by observations of mismatch between glucose and oxygen utilization in vivo [11] as well as elevation of

brain lactate levels upon neuronal stimulation [12-14]. Initial explanations of ANLS’s involvement in

providing neuronal energy during times of activation suggested a link between neuronal stimulation and

astrocyte activation through glutamate and K+ release. This activation of astrocytes subsequently

produces lactate which is then shuttled back to neurons [15-17].

However, recent evidence has argued against the ANLS hypothesis. Most studies supporting

ANLS were performed in separate astrocyte and neuronal cultures and thus were unable to account for a

true cellular milieu or conclude on the directionality of lactate exchange [18-21]. Furthermore, in vivo

models have demonstrated mixed results [22, 23]. In 2017, Diaz-Garcia et al. examined fluorescent

NADH/NAD+ biosensors to illustrate an astrocyte-independent preference for glycolysis located within

stimulated neurons in hippocampal slice cultures and in vivo [19]. The group concluded that although

there is evidence to support the ANLS hypothesis in resting neurons [24, 25], stimulated neurons adopt a

more glycolytic phenotype in an acute metabolic response to stimulation [2, 19].

It is also unclear how neurons are able to support chronic states of high metabolic demand during

times of chronic neuronal stimulation. The first aim of our study was to define the neuronal metabolic

phenotype during times of chronic stimulation. We hypothesized that in the context of chronic activation

and frequent increases in metabolic demand, neurons upregulate glycolytic potential and alter their

overall metabolic phenotype. Indirect evidence supports this line of thought. Studies of patients with

epilepsy, a neurologic disorder involving chronic and pathologic neuronal activation, combined with

animal and culture models of epilepsy, have demonstrated high rates of glucose metabolism [26, 27],

ATP depletion [28], as well as LDHA activity and lactate production [27]. In our first aim, we showed

that chronic neuronal stimulation leads to neuronal metabolic reprogramming from aerobic respiration to

glycolysis through the upregulation of neuronal LDHA.

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Figure 2: Dueling hypotheses of neuronal metabolic response to activation (Adapted from Diaz-Garcia et al. 2018) [20]

(Blue - ANLS) Astrocytes are stimulated (by neuronal K+ or glutamate) and metabolize glucose to be shuttled into the neuron

as lactate which is then converted to pyruvate and used to produce ATP through oxidative phosphorylation. (Red – Neuronal

Glycolysis) Stimulated neurons themselves undergo glycolysis for ATP production during neuronal stimulation. Insets

represent data from Diaz-Garcia et al. 2017. Neuronal stimulation leads to elevation in neuronal NADH and lactate and

decreased neuronal glucose suggesting that neurons undergo glycolysis when stimulated. Time scale bar = 1 min [19, 20]

The second aim of our study was to describe the molecular pathway that regulates the shift from

aerobic respiration to glycolysis during chronic neuronal stimulation. During high cellular energy

demands associated with hypoxia, the AMP-activated protein kinase/hypoxia-inducible factor-1a

(AMPK/ HIF1a) pathway modulates cellular respiration and pushes cells into glycolysis [29-32]. In the

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setting of parallel energy demands during neuronal activation, this pathway becomes attractive as a

possible mechanism underlying metabolic reprogramming during these states. AMPK acts as a sensor of

cellular energy and becomes phosphorylated during ATP depletion [29-32]. Downstream consequences

of AMPK activation include HIF1a transcription and target gene expression, TCA cycle suppression,

and up-regulation of proteins (such as LDHA) responsible for shifting the cell into glycolysis (Figure 3)

[33]. Given similar energy demands with hypoxia and chronic neuronal stimulation, we hypothesized

that the AMPK/ HIF1a pathway regulates this transition from an aerobic to glycolytic phenotype during

chronic stimulation. Using a novel low Mg2+ culture model, we confirmed that neuronal metabolic

reprogramming to glycolysis is mediated by the AMPK/ HIF1a hypoxia pathway.

In the first two aims we investigated the fundamental nature of glucose utilization in neurons.

Understanding this interplay between neuronal activation and metabolism has wide implications for

human health and mechanisms of disease. For our third aim, we employed the models and applied the

information gained about glucose utilization from our first study in order to better elucidate the etiology

of epilepsy formation.

Seizures are regulated by LDHA

Epilepsy impacts approximately 70 million people or one percent of the global population [34].

Thirty percent of patients continue to experience seizures despite maximal medical therapy. In this

refractory group, uncontrolled seizures and polypharmacy have been associated with poor quality of life

[35]. Although novel therapeutic strategies are actively being investigated, a principle reason for the lack

of treatment options is a lack of understanding of the molecular mechanisms underlying epileptogenesis.

The link between pathological neuronal activation and attendant cellular and molecular changes is also

not well characterized, which further limits our approach to studying these diseases.

Despite mounting evidence of metabolic involvement in seizure activity, epilepsy as a disease of

energy metabolism is a relatively novel concept. Initial insights into involvement of metabolism

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emerged from the ketogenic diet (KD) as a treatment for children suffering from refractory epilepsy

[36]. Several mechanistic theories describe the anticonvulsant effects of KD including direct effects

from ketones [5] and glucose restriction [37] or upregulation of GABA neurotransmitters [38]. Some

have hypothesized that regulation of KATP channels through either the reduction of ATP levels [39] or

the accumulation of free fatty acids mediates the effects of the diet [40, 41]. Others have implicated

more direct metabolic reasons. Observations linking KD treatment to reduced glycolysis have propelled

this concept of metabolic control. Key glycolytic enzymes, such as fructose-1,6-bisphophate, are

decreased during ketosis [42, 43] while direct inhibitors of glycolysis, such as 2-deoxyglucose (2DG),

mimic its effect [44]. In parallel, there is growing data that suggests KD enhances oxidative

phosphorylation through upregulation of regulatory genes [45, 46] or through direct mitochondrial

biogenesis [45].

In our third aim, we extend our findings from our first two aims toward understanding the

pathogenesis of epilepsy. We explored LDHA and its role in seizure formation. The LDH enzyme is

responsible for the inter-conversion of pyruvate to lactate and NADH to NAD+ [47]. There are several

tetramer isoenzymes of LDH with differing kinetic properties, altering the direction of lactate to

pyruvate or vice versa [48]. Negatively charged LDHA has a higher affinity for pyruvate and

preferentially converts pyruvate to lactate (and NADH to NAD+) [47, 48]. This LDHA step is crucial for

replenishing NAD+ when the TCA cycle cannot due to low oxygen supply and is necessary for

continued glycolysis during anaerobic respiration [48, 49]. In our third aim, we showed that LDHA,

regulated by upstream HIF1a, leads to epileptiform activity in culture and in an animal model.

Epilepsy as a disease of energy metabolism

The same chronic low Mg2+-induced neuronal stimulation that upregulated LDHA in the first

aim of our study increased overall baseline neuronal bursting in the third aim of our study. As

chronically stimulated neurons shifted their metabolic phenotype to glycolysis to accommodate elevated

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energy requirements, they in essence became epileptic themselves. This feedforward loop provides the

foundation of our overarching hypothesis for metabolically driven pathogenesis of epilepsy (Figure 3).

We believe that chronic seizures shift neurons into glycolysis through AMPK/HIF1a mediated

upregulation of LDHA. Our results suggest that as neuronal LDHA expression increases, neurons

become hyperexcitable and begin to burst and elicit seizures.

Figure 3. Representative schematic of the feedforward loop that drives metabolic control of epilepsy. As neurons are

chronically activated or seize, they upregulate LDHA expression and thus glycolysis (top arrow) through the AMPK/HIF1a

pathway (middle arrow) which is activated by a high AMP:ATP ratio. AMP leads to phosphorylation of AMPK, which leads

to stabilization of HIF1a. HIF1a translocates into the nucleus as a transcription factor to upregulate LDHA transcription

and protein expression and thus glycolysis. HIF1a -regulated LDHA expression goes on to further cause pathologic

activation in neurons (bottom arrow).

Overall, our study provides insight into the fundamental interplay between neuronal activation

and glucose utilization. We exploit these findings to explain a mechanistic theory underlying the

metabolic control of epilepsy. Although we believe that our findings are noteworthy, we acknowledge

that these findings represent a small piece of a much larger puzzle that explains how neuronal

metabolism couples with neuronal activation and how this defines epilepsy formation. Future inquiry

into these interactions will further provide insight into the foundational components of the central

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nervous system and how their derangement shapes disease.

Research Objectives

The present research seeks to understand how glucose is metabolized in chronically activated

neurons and the role neuronal metabolism plays in epilepsy. We explored these questions in three

separate aims. Our first aim was to define the neuronal metabolic phenotype during times of chronic

stimulation. Using a low Mg2+ model and tissue resected from epilepsy patients we showed that, in the

context of chronic activation and frequent increases in metabolic demand, neurons upregulate their

glycolytic potential, thereby changing their overall metabolic phenotype. This metabolic shift occurs

through the upregulation of LDHA. The second aim of our study was to describe the molecular pathway

that regulates this shift from aerobic respiration to glycolysis. Using the above low Mg2+ model, we

showed that the AMPK/HIF1a hypoxia pathway regulates this transition. The findings of the first two

aims are presented in our first manuscript entitled “Chronic activation leads to neuronal glycolysis

through the AMPK/HIF1a pathway” (Section III).

The understanding of neuronal metabolism during chronic activation led to our third aim. In our

third aim, we explored LDHA and its role in seizure formation. We showed that LDHA, regulated by

upstream HIF1a, leads to epileptiform activity in culture as well as an animal model. LDHA is a

potential link between metabolism and epilepsy. The findings of aim three are presented in the

manuscript entitled “A feedforward mechanism for epileptogenesis regulated by lactate dehydrogenase

A” (Section IV).

In order to explore the above aims we created two novel models of neuronal activation and

epilepsy. We used a low Mg2+ in vitro model to explore chronic activation and epilepsy in culture. We

describe this model in the manuscript entitled “Modeling epilepsy in a dish: mixed cortical cells cultured

on a microelectrode array” (Section V). To understand the role of LDHA in epilepsy formation and for

future inquiry into epileptogenesis we created a chronic cobalt mouse model of frontal cortical epilepsy.

This model is described in the manuscript “A novel mouse model of cobalt-induced focal cortical

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epilepsy” (Section V).

Finally, we used the above findings to develop a central hypothesis of how metabolism may

control epilepsy. We believe a feedforward loop whereby chronic neuronal stimulation drives glycolysis

also drives neuronal activation and eventually epilepsy formation. This hypothesis will serve as the

driving focus for ongoing inquiry.

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III. Chronic activation leads to neuronal glycolysis through the AMPK/HIF1a pathway 1,5Alexander Ksendzovsky, MD; 1Marcelle Altshuler, BS; 2Muzna Bachani, BS; 1Stuart Walbridge, BS;

2Joseph Steiner, PhD; 1John Heiss, MD; 6Sara Inati, MD; 1Nancy Edwards, BS; 3,4Jaideep Kapur, MD,

PhD; 1Kareem Zaghloul, MD, PhD

1Surgical Neurology Branch, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

2Drug Development Unit, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

3Department of Neurology, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

4Neuroscience Department, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

5Department of Neurological Surgery, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

6EEG Section, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

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Abstract

Introduction

The metabolic consequences of neuronal activation are relatively unknown. It is thought that

glycolysis supports active neurons as a supplement to mitochondrial respiration in times of high

metabolic demand which occurs through the astrocyte neuron lactate shuttle. Recent evidence, however,

strongly refutes this claim and argues that acute neuronal stimulation directly leads to neuronal glucose

utilization through glycolysis, which becomes the primary source of ATP. Furthermore, the metabolic

phenotype of frequently stimulated neurons is also unknown. In this study we show that chronically

stimulated neurons switch from aerobic respiration to glycolysis through the AMPK/HIF1a hypoxia

pathway.

Methods

We activated neurons cultured on a multielectrode array with low Mg2+ media to probe for

lactate dehydrogenase A (LDHA), a marker for glycolysis and to determine neuron’s metabolic

phenotype after chronic stimulation. We analyzed human tissue for LDHA expression based on

electrographic characteristics of overlying subdural electrodes, as determined during intracranial

monitoring (epileptic vs normal cortex). Finally, we probed the AMKP/ HIF1a pathway to determine

its involvement in this metabolic switch.

Results

Treatment of cultured neurons with low Mg2+ increased neuronal bursting activity which caused

LDHA upregulation. In human tissue, LDHA expression was significantly upregulated in epileptic

neurons. Neuronal bursting caused depletion of intracellular ATP and subsequent activation of the

AMPK/HIF1a pathway through phosphorylation of AMPK. Furthermore, chronic activation of AMPK

led to HIF1a and LDHA upregulation and a subsequent switch from an aerobic to a glycolytic cellular

phenotype in neurons.

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Conclusion

In this study, we show that chronic neuronal activation leads to upregulation of LDHA and a

metabolic switch from aerobic respiration to glycolysis. This metabolic reprogramming occurs through

the canonical AMPK/ HIF1a hypoxia pathway.

Introduction

Neuronal activity is metabolically demanding and can exceed the energy sources available

through routine mitochondrial respiration, yet how neurons derive their energy during times of high

activation remains unclear. Early observations of a mismatch between oxygen and glucose utilization

during physiological [7, 8] and pathological (i.e. epilepsy) [9, 10] neuronal activity suggested that the

brain may rely upon aerobic glycolysis during times of high metabolic demand [3]. In aerobic

glycolysis, glucose is ultimately converted to lactate for energy even in the presence of oxygen. Despite

a lower ATP yield, the significantly more rapid production of ATP through glycolysis as compared to

oxidative metabolism may be better suited for supporting the energy demands of highly activated

neurons [3]. Recent studies have provided converging evidence supporting this possibility and have

demonstrated that neuronal activation in vivo leads to a mismatch between glucose and oxygen

utilization and elevated levels of lactate in the brain [11] [12-14].

The precise location where such aerobic glycolysis occurs in the brain, however, remains a

matter of debate. The location of aerobic glycolysis for increased neuronal energy demand was initially

described by the astrocyte neuron lactate shuttle (ANLS) hypothesis [16]. The ANLS hypothesis posits

that neuronal stimulation leads to astrocyte activation through glutamate and K+ release which

subsequently activates astrocytes to produce lactate through glycolysis [15-17]. Lactate is then shuttled

back into neurons, metabolized into pyruvate, and used in the TCA cycle [16, 20, 50]. Most studies

supporting the ANLS hypothesis, however, have been performed in separate astrocyte and neuronal

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cultures and thus are unable to account for the true cellular milieu or to make strong conclusions

regarding the directionality of lactate exchange [18-21]. Moreover, the evidence supporting the ANLS

hypothesis using in vivo models that address some of these limitations has been mixed [22, 23].

A different possibility is that active neurons themselves may generate lactate through aerobic

glycolysis in order to supplement mitochondrial respiration. Recent evidence supports this possibility, as

induced activation of neurons through electrical stimulation preferentially leads to an astrocyte-

independent utilization of glucose through glycolysis in neurons both in hippocampal slice cultures and

in vivo [19]. While the ANLS may support energy demands in resting neurons [24, 25], active neurons

therefore appear to rely upon glycolysis as their primary source for ATP production in an acute

metabolic response [2, 19].

How neurons are able to support chronic states of high metabolic demand, however, is less clear.

We hypothesized that in the context of chronic activation and frequent increases in metabolic demand,

neurons would upregulate their glycolytic potential, thereby changing their overall metabolic phenotype.

Indirect evidence supports this possibility, as studies of patients with epilepsy, a neurologic disorder

involving chronic and pathologic neuronal activation, and animal and culture models of epilepsy have

demonstrated high rates of glucose metabolism [26, 27], ATP depletion [28], and lactate dehydrogenase

(LDHA) activity and lactate production [27]. Elevated levels of LDHA are often taken as a marker of

glycolysis during anaerobic respiration since LDHA preferentially converts pyruvate to lactate and is

necessary for replenishing NAD+ that is required for continued glycolysis [48, 49]. We therefore were

interested in examining changes in neuronal LDHA expression that arise as a direct consequence of

chronic neuronal activation and that reflect the neurons’ metabolic phenotype.

If chronic neuronal activation leads to elevated LDHA expression, then we were also interested

in understanding the molecular pathways that underlie this transformation. We hypothesized that chronic

neuronal activation would lead to a metabolic switch to glycolysis through the AMP-activated protein

kinase/hypoxia-inducible factor-1a (AMPK/HIF1a) pathway. This pathway is activated in hypoxic cells

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to push cells into glycolysis by promoting the transcription of glycolytic enzymes such as LDHA [31,

33] [29-32]. Interestingly, hypoxia is not required nor is it the only circumstance under which glycolysis

is preferred over oxidative phosphorylation [47, 51, 52]. To accommodate for the energy demand

associated with rapid cellular proliferation, cancer cells also preferentially use glycolysis over oxidative

phosphorylation in the presence of oxygen, known as the Warburg effect [51, 52]. This metabolic switch

is also mediated through upregulation of LDHA [47, 51] and, as in hypoxia, LDHA expression in

aerobic glycolysis is also modulated through HIF1a signaling [52-54]. Given the parallel energy

demands during neuronal activation, this pathway becomes an attractive possible mechanism underlying

metabolic reprogramming during this state.

Here, we tested these hypotheses by examining LDHA expression in cultured neurons that we

chronically treated with low Mg2+ medium on a daily basis. Low Mg2+ has been previously used in cell

culture models of epilepsy [55, 56] and results in neuronal activation through NMDA-receptor activation

[57] and increased presynaptic hyperexcitability [58]. This model preserves neuron-astrocyte

interactions and upholds potential network effects from frequent stimulation [56]. We found that daily

treatments with low Mg2+ results in elevated neuronal excitability and a consequent increase in LDHA

expression that is modulated by the (AMPK/HIF1a) pathway. We confirmed that chronic neuronal

activation leads to elevated LDHA expression in vivo by examining human tissue resected from

participants with drug resistant and chronic seizures. Together, our data suggest that chronic neuronal

activation leads neurons to switch their metabolic profile from a quiescent aerobic phenotype to a

glycolytic phenotype through the AMPK/HIF1a pathway.

Methods

Experimental models and subject details

Animal Use

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To study the metabolic changes associated with neuronal activation, we cultured mixed rat

cortical cells on microelectrode arrays (MEAs) and standard plates. The use of animals in this protocol

was approved by the National Institute of Health Animal Care and Use Committee, followed all

regulatory requirements and guidelines, and was conducted in a facility that is accredited by the

Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC), International.

Cell culture and maintenance

We cultured rat cortical neurons from newborn P1 rat pups of any sex. We dissected cortices in a

modified Puck’s dissociation medium [100 mL 20X D1 (80g NaCl, 4g KCl, 0.45g Na2HPO4.7H2O, 0.3g

KH2PO4, 0.012g Phenol Red in 1L deionized water), 100mL glucose/sucrose solution (30g anhydrous

glucose + 74g sucrose to 500mL deionized water), 10mL 1M HEPES buffer, pH to 7.4, osm to 320-

330]. Once dissection was complete, we dissociated and tritiated the cells in a Puck’s/papain solution

(10mL D1, 100µL 150mM CaCl, 100 µL 50mM EDTA, 75µL papain (Worthington Biochemical

Corporation, Lakewood, NJ) and 0.01 µg cysteine. We then plated 200,000 cells per well in either a 96-

well standard plate (Grenier Bio-One, Frickenhausen, Germany) or a 48-well Axion CytoView

microelectrode array (MEA) plate (Axion Biosystems, Atlanta, GA) coated with 1mg/mL of poly-D-

lysine (PDL) in borate buffer (pH 8.4). On average, we harvested cells from 12 pups (male or female)

for culture in each plate. Twenty-four hours after plating, we performed a full media change for the

cells, and they were subsequently maintained in maintenance medium.

Human surgical specimens

Seven participants (4 male; 34.7 ± 2.88 years) with drug resistant epilepsy underwent a surgical

procedure in which platinum recording contacts (PMT Corporation, Chanhassen, MN) were implanted

for seizure monitoring. Data were collected at the Clinical Center at the National Institutes of Health

(NIH; Bethesda, MD). The research protocol (ClinicalTrials.gov identifier NCT01273129) was

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approved by the Institutional Review Board, and informed consent was obtained from all participants in

the study. In all cases, placement of the contacts was determined by the clinical team in order to best

localize epileptogenic regions for resection. We recorded continuous intracranial EEG (iEEG) from all

recording contacts, sampled at 1000 Hz using a Nihon Kohden EEG data acquisition system as

participants were monitored in the epilepsy monitoring unit to identify seizure activity. In each

participant, our epileptologist (SI) identified electrodes overlying the seizure onset zone (epileptogenic

electrodes) and electrodes that were not involved in the seizure network (non-epileptic electrodes) but

were within clinical bounds for resection (Figures 4a-c). It is often clinically indicated to remove areas

of the brain that are not necessarily within the immediate seizure focus or network [59] in order to fully

resect regions of epileptogenic cortex. Therefore, during subsequent surgical resection, we separately

resected tissue underlying electrodes identified as overlying the seizure onset zone and electrodes

overlying cortical regions that were not involved in seizures and segregated these tissue samples for

subsequent analysis comparing epileptic human brain samples to non-epileptic controls. No tissue was

removed solely for research purposes.

We collected surgical specimens using standard surgical technique. After initial analysis by our

staff pathologists, we divided resected tissue into frozen and fixed samples. For freezing, we set the

tissue samples in optimal cutting temperature compound (OCT) and submerged them in liquid nitrogen

for flash-freezing. We kept these samples in our tissue bank at -80°C. For tissue fixation, we first

directly placed tissue samples into 4% PFA for 48 hours. We then placed tissue samples into PBS

solution and maintained them at 4°C. All tissue was collected directly from the operating room and

fixed/frozen within five minutes of removal from blood supply.

Method Details

Neuronal activity in the MEA during low Mg2+ treatments

We recorded neuronal activity of the cell cultures in the 48-well MEA using the Maestro Pro

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MEA system (Axion BioSystems, Atlanta, GA). Each well contains 16 electrodes that record

extracellular voltage with a sampling rate of 12.5kHz. We identified action potentials (spikes) as time

points when the recorded trace exceeded a threshold of ±6 standard deviations from the baseline signal.

We defined neuronal bursts of spiking activity as events during which a minimum of 5 spikes were

detected on a single electrode with a maximal inter-spike interval of 100ms (Supplementary Figure 1a).

We used the Neural Metrics Tool (Axion BioSystems, Atlanta, GA) for spike and burst identification

and for subsequent analyses. During every 5-minute recording, we computed the rate of neuronal bursts

in every electrode in each well. In some cases, an individual electrode within a well did not record any

spiking activity for the duration of the recording. This often occurred because there were too few cells in

the vicinity of that electrode. We therefore also defined the number of active electrodes within each well

as all electrodes that demonstrated spiking activity with a minimum rate of 5 spikes/minute. We

computed the average rate of neuronal bursts across all electrodes within each well, and normalized by

the number of active electrodes in that well to account for any changes in bursting activity associated

with frequent media changes and the loss of cells. This generated an average burst rate for each well for

each 5-minute recording. In each MEA, we computed the average burst rate across 12 wells that were

designated for each treatment condition. We used at least 3 MEAs to perform each experiment

examining the changes in neuronal spiking and bursting activity to provide biologic replicates.

We began recording neuronal activity of the cell cultures in each MEA on day 10 in vitro (10

DIV) and continued daily recordings until the end of the experiment. We began daily 2-hour treatments

with low Mg2+ on 14 DIV since neuronal firing rates stabilized by that time. We therefore considered the

first four days of recording as the baseline neuronal activity of the cell cultures in each well. For all

reported effects, treatment with low Mg2+ began on 14 DIV, and the days on which each subsequent

effect was observed are referenced to this start date.

On every day of low Mg2+ treatment, we first obtained a 5-minute pretreatment recording prior to

changing the cell media. Then in every treatment well, we replaced the maintenance medium with low

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Mg2+ medium (98.75% deionized water, 1% 1M HEPES solution, 0.25% 1M KCl, 0.1% 2M CaCl,

0.0008% 0.25M glycine, 0.72g glucose, 3.38g NaCl) and immediately obtained a 5-minute recording.

We then incubated the MEA for two hours at 37C° following which we obtained another 5-minute

recording. We then replaced the low Mg2+ medium with fresh maintenance medium and obtained an

immediate post-media change 5-minute recording and then a two-hour post media change 5-minute

recording (2-hr washout). Because each media change causes an immediate and transient change in

spiking activity in the cell cultures, we restricted our analyses to the 5-minute recordings obtained two

hours following treatment with low Mg2+ and two hours following the washout. We obtained a final 5-

minute recording 16 hours after treatment as a new baseline.

To compare the changes in bursting activity observed during low Mg2+ treatment to controls, we

also recording neuronal burst activity from untreated wells that were treated with daily media changes

with maintenance medium. As with the well treated with low Mg2+, we performed these media changes

every day and recorded neuronal activity through recordings obtained during the pretreatment baseline,

two hours after the media change, and two hours after the subsequent washout. As a second control, we

also treated cells with daily media changes using ACSF (98.75% deinoized water, 1% 1M HEPES

solution, 0.25% 1M KCl, 0.1% 2M CaCl, 0.0008% 0.25M glycine, 0.72g glucose, 3.38g NaCl, 0.1% 1M

MgCl) and recorded neuronal activity during pretreatment, treatment, and washout.

To examine whether increased spiking and bursting activity leads to elevated expression of

LDHA, we treated cultured cells in the MEA with tetrodotoxin (TTX; Abcam, Cambridge, MA). We

used an identical method for culturing cells and recording neuronal activity in each MEA. In this case,

we added escalating doses of TTX (5, 10, 20 and 25nM) to the low Mg2+ medium used for treatment.

We recorded neuronal bursting activity from 12 wells for each treatment dose of TTX in each MEA. We

used an identical approach to examine the effects of 5-Aminoimidazole-4-carboxamide ribonucleotide

(AICAR; Sigma-Aldrich, St. Louis, MO), and dimethyloxalyglycine (DMOG; Sigma-Aldrich, St. Louis,

MO) on protein expression in cells cultured on the MEA. In these cases, we added escalating doses of

23

AICAR (10, 200, 500, 1000mM) and DMOG (10, 200, 500, 1000mM) in low Mg2+ medium. We treated

12 wells in each MEA with each treatment dose of each compound C, and examined protein expression

after 10 days of treatment in each MEA.

Cell culture on standard plates for metabolic assays

To understand the molecular mechanisms leading to elevated LDHA expression and the

metabolic consequences of low Mg2+ treatment, we cultured cells on standard 96-well plates. We

examined the effects of Compound C (Abcam, Cambridge, MA) or KC7F2 (Sigma-Aldrich, St. Louis,

MO) on protein expression in cell cultures treated with low Mg2+. In these cases, we added escalating

doses of Compound C (10, 20, 40, 100µM) or KC7F2 (10, 20, 50, 75µM) to the low Mg2+ medium. We

treated 12 wells in each plate with each treatment dose of each compound C, and examined protein

expression after 10 days of treatment. To examine the time course of protein expression over multiple

days, we treated cells with 100µM Compound C in low Mg2+ medium or 75µM KC7F2 in low Mg2+

medium and lysed cells and collected protein at 3, 7 and 10 days following the beginning of treatment.

We lysed cells and collected protein at similar time points for cells cultured in maintenance medium and

low Mg2+ medium as controls.

To measure ATP depletion during the two-hour low Mg2+ treatment and at two hours after

washout, we used the CellTiter-Glo Luminescent Cell Viability Assay (Promega Life Sciences,

Madison, WI). The details of this protocol are specified elsewhere [60]. Briefly, we treated 12 wells

each with low Mg2+ medium for 15 minutes, 30 minutes, 1 hour, 2 hours and after 2 hours of washout

(all in separate cell groups). We then lysed the cells and treated with luciferase and ATPase inhibitors

and recorded luminescence in relative light units (RLU). We computed the RLU in cells lysed from each

of three standard plates, and compared the levels across different time points.

In order to quantify the amount of total and phosphorylated AMPK during two hours of low

Mg2+ treatment, we used the Cisbio Total and Phospho-AMPK (THR172) 64MPKEG HTFR cell-based

24

sandwich assays (Cisbio Bioassays, Codolet, FR). The details of this protocol can be found in the

product insert. Briefly, in one standard plate we treated 4 separate wells containing cultured cells with

low Mg2+ medium for 2 hours, and used 4 untreated wells as a control. We then lysed the cells from the

two sets of treatment wells and added donor antibody/fluorophore, which binds to phosphorylated motif

on AMPK when probing for AMPK-P or is independent of AMPK-P when probing for total AMPK, and

acceptor antibody/fluorophore, which binds to AMPK independent of phosphorylation, to the lysate.

The proximity of these antibodies triggers a fluorescent resonant energy transfer (FRET) toward the

acceptor fluorophore causing it to fluoresce at 665nm. The donor fluorophore fluoresces at 620nM. We

excited the lysates at 320nm and measured the 665nm/620nm fluorescence ratio (HTFR ratio) using the

SpectraMax 5e microplate reader (Molecular Devices, San Jose, CA). This ratio is directly proportional

to phosphorylated AMPK or total AMPK. We compared this ratio across the 4 independent wells from

each treatment condition (low Mg2+ versus control).

We also used the Seahorse Bioscience XFe96 Extracellular Flux Analyzer (Agilent

Technologies, Santa Clara, CA) to measure the rate of change of dissolved oxygen in media (oxygen

consumption rate, OCR) and extracellular acidification rate (ECAR) immediately surrounding the

cultured neurons as previously described [61] and as described in the user guide. Briefly, we cultured

dissociated P1 rat cortices on a PDL coated XFe96 cell culture microplate (Agilent Technologies, Santa

Clara, CA) and maintained cell cultures until 14 DIV. We then initiated daily 2-hour low Mg2+

treatments in 6 wells for ten days. We used 12 wells of untreated cells as a control. We then washed

cells with Agilent Seahorse Base Medium (XF Base medium without phenol red 10nM glucose, 1mM

sodium pyruvate and 2mM L-glutamine, pH 7.4; Agilent Technologies, Santa Clara, CA) and measured

OCR and ECAR over one hour. We compared OCR and ECAR across the wells from each treatment

condition (low Mg2+ versus control) at each time point. To examine the changes in OCR and ECAR with

metabolic stress, we injected oligomycin (100µM) and FCCP (100µM) at 23.5 minutes to inhibit and

uncouple the electron transport chain.

25

Cell culture western blot

Once daily treatments were complete, we lysed and collected cells from both the standard plates

and from the MEAs for protein analysis. We performed Western blot analysis as previously described

[62]. Briefly, we collected cell lysates and quantified protein using a standard BSA curve. We loaded

equal amounts (15µg) of protein into a mini-PROTEAN TGX 10% gel (Bio Rad) and ran the gel in

tris/glycine buffer at 200V for 30 minutes for separation. We then transferred the samples to a

nitrocellulose membrane by electroblotting, and blocked the membranes in 5% non-fat milk diluted in

wash buffer (1X phosphate-buffered saline (PBS) with 0.1% Tween-20). After blocking, we incubated

the membranes with primary antibody diluted in 5% non-fat milk overnight at 4°C. We then washed the

membranes and applied a horseradish peroxidase conjugated secondary antibody (1:5000) at room

temperature for one hour. Finally, we exposed the membranes with Super Signal West Femto Maximum

Sensitive Substrate (Thermo-Fisher Scientific, Waltham, MA) and imaged them using the FluorChem

Imager (ProteinSimple, San Jose, CA). We processed and quantified the blots using ImageJ software

(NIH, Bethesda, MD). We used anti-LDHA monoclonal antibody (AF9D1) at a dilution of 1:1000. As a

loading control, we used anti-Vinculin monoclonal antibody (Abcam, Cambridge, MA) at a dilution of

1:2000. All antibodies used for the Western blots were validated in respective assays and species.

Human tissue section immunohistochemistry

We performed immunohistochemical (IHC) analysis on 4% PFA fixed tissue. We embedded

tissue samples in paraffin, sectioned them into 5µm slices, placed them on standard tissue slides. We

performed IHC on the Leica Bond Max automated stainer as previously described [63]. Briefly, we

deparaffinized and stained sections from each block using the Hematoxylin and Eosin (H&E) method.

We performed immunostaining using antibodies specific to each antigen. We used an anti-LDHA

antibody (Abgent, San Diego, CA) diluted to 1:300. We used an anti-NeuN antibody (Millipore,

26

Burlington, MA) diluted to 1:100, and performed antigen retrieval using NeuN in Citrate for 20 minutes.

We used an anti-GFAP antibody (Leica, Wetzlar, Germany) diluted to 1:100. Slides were then cover-

slipped.

To quantify cell staining and antibody expression, we digitized each individual slide containing

fixed and stained tissue with the Zeiss Axio Scan Z1 (Carl Zeiss AG, Oberkochen, Germany) and

analyzed cell counts using Zen Blue 2.3 software (Carl Zeiss AG, Oberkochen, Germany). We randomly

assigned ten 400 x 400µm regions of interest (ROIs) for analysis in each section of tissue and each stain

(LDHA, NeuN, and GFAP). ROIs from three consecutive sections (stained for LDHA, NeuN and

GFAP) were located at the same coordinates for each section (Supplementary figure 3) in order to

examine staining at the same location for each tissue block/sample. For each ROI, we performed

automated segmentation of stained cells using the Zen Blue 2.3 software package based on the color of

the stain and background thresholding. We distinguished brown staining from the blue counterstain

(hematoxylin) using hue thresholds and the saturation and intensity of color. We rejected all segmented

objects less than 3µm or greater than 30µm in diameter as these were unlikely to represent cells. We

applied the same automated segmentation procedure and thresholding to all of the samples for LDHA

and NeuN stained sections to obtain a cell count. This automated analysis was supervised by the

investigators and observed for errors in counting cells. The program was adjusted for sections with high

background staining. GFAP staining was not amenable to segmentation analysis (Supplementary figure

3b) and we therefore hand-counted GFAP-positive cells in each ROI. Importantly, all automated

segmentation, supervision, and both automated and manual cell counting was performed while the

investigator performing these analyses was blinded to the electrographic or pathological characteristics

of the section/ROI.

In order to identify LDHA-negative cells, we examined the LDHA-stained sections and ROIs

and similarly used hue, saturation and intensity to segment out brown staining objects. In this case,

however, we rejected the positive-staining brown cells (LDHA cells) and accepted only light blue-

27

staining nuclei. We used a size threshold between 1µm and 5µm to identify nuclei. To obtain a final

count of all cells within a particular ROI, we added the LDHA-negative nuclei count to the LDHA-

positive cell count on the same ROI (all cells = LDHA-positive cells + all other blue-staining nuclei).

Human section and cell culture immunofluorescence

For the human samples, we cut frozen tissue specimens into 10µm sections on a cryostat at -

24°C and placed them on standard glass slides. For immunostaining, we blocked samples in 5% normal

serum matching the host of the secondary antibody and incubated them with primary antibodies

overnight at 4°C. We then incubated sections with a fluorescent conjugated secondary antibody for 1

hour at room temperature. In co-staining experiments, we applied the second primary antibody overnight

at 4°C after incubation with normal serum matching the host of that secondary antibody. We mounted

coverslips with Vectashield mounting medium (Vector Laboratories, Burlingame, CA). We used an anti-

LDHA antibody (Abgent, San Diego, CA) at a dilution of 1:1000 and an anti-NeuN antibody (Millipore,

Burlington, MA) at a dilution of 1:1000 for immunofluorescence.

For cell cultures, once daily treatments were complete, we fixed cells with 4% paraformaldehyde

(PFA) then washed them with 1X DPBS (Thermo-Fisher Scientific, Waltham, MA) for immunostaining.

We then permeabilized the cells with 0.3% Triton-X (diluted in DPBS) and blocked them in 5% goat

serum at room temperature. We diluted primary antibody in 5% goat serum and applied the diluted

antibody to the fixed cells overnight at 4°C. We then incubated the cells with a fluorescent conjugated

secondary antibody and imaged them using confocal microscopy (Nikon Eclipse CI, Morrell). We used

the following primary antibodies and dilutions for cell culture immunofluorescence: anti-LDHA

monoclonal antibody (1;1000 dilution; AF9D1, Thermo-Fisher Scientific, Waltham, MA), b3-tubulin

(1:300, Cell Signaling, Danvers, MA). We used Hoechst dye (1:5000 dilution, Thermo-Fisher Scientific,

Waltham, MA) as a nuclear stain. All antibodies used for immunofluorescence were validated in

respective assays and species.

28

Quantification and statistical analysis

We used GraphPad Prism (San Diego, CA) for all statistical analyses. We performed paired or

unpaired students t-tests when comparing changes in neuronal burst rates or LDHA expression within or

across treatment groups, respectively. We used a one-way ANOVA to test for differences between

multiple treatment conditions and post-hoc Bonferroni’s multiple comparisons testing for each

individual group against control or low Mg2 baseline. We designated the level of significance for all

statistical tests as p < 0.05 or lower, depending on multiple comparison testing. All data are reported as

mean ± SEM unless otherwise noted.

Key Resources Table

REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies

Anti-LDHA monoclonal antibody Thermo-Fisher AF9D1

Cat MA5-17246

Anti-LDHA antibody Abgent Cat AP13542b

Anti-NeuN antibody Millipore Cat MAB377

Anti-GFAP antibody Leica Cat PA0026

Anti- b3-tubulin antibody Cell Signaling Cat 4466

Anti vinculin antibody Abcam Cat ab129002

Chemicals, Peptides, and Recombinant Proteins

Tetrodotoxin Abcam Ab 120055

CAS 18660-81-6

29

5-Aminoimidazole-4-carboxamide 1-β-D-

ribofuranoside, Acadesine, N1-(β-D-

Ribofuranosyl)-5-aminoimidazole-4-carboxamide

(AICAR)

Sigma-Aldrich A9978

CAS 2627-69-2

Dimethyloxalylglycine, N-(Methoxyoxoacetyl)-

glycine methyl ester (DMOG)

Sigma-Aldrich D3695

CAS 8946463

Dorsomorphin (Compound C) Abcam Ab 120843

CAS 866405-64-3

N,N′-(Dithiodi-2,1-ethanediyl)bis[2,5-dichloro-

benzenesulfonamide (KC7F2)

Sigma-Aldrich SML1043

CAS 927822-86-4

Critical Commercial Assays

CellTiter-Glo Luminescent Cell Viability Assay Promega G7570

Cisbio Total and Phospho-AMPK (THR172)

64MPKEG HTFR cell-based sandwich assays

Cisbio 63ADK060PEG

64MPKPEG

66PL96005

Seahorse Bioscience XFe96 Extracellular Flux

Analyzer

Agilent

Technologies

Seahorse XF- Cell Energy Phenotype Test Kit and

Microplate (includes oligomycin and FCCP)

Agilent

Technologies

103275-100

Software and Algorithms

Neural Metrics Tool Axion Biosystems

Nihon Kohden clinical EEG data acquisition

software

Nihon Kohden

Zen Blue 2.3 Carl Zeiss AG

Graphpad Prism 8 Graph Pad

30

ImageJ NIH https://imagej.nih.g

ov/ij/

Other

Axion CytoView MEA 48-Black plate Axion Biosystems M768-tMEA-48B-

5

Axion Maestro Pro MEA recording system Axion Biosystems

Surgical platinum recording contacts PMT Corporation

Nihon Kohden clinical EEG data acquisition system Nihon Kohden

Results

Neuronal activation leads to LDHA expression

To examine the metabolic changes associated with chronic neuronal activation, we cultured

mixed rat cortical cells on a microelectrode array (MEA) and treated them with low Mg2+ media. Low

Mg2+ media induces neuronal activation and has been previously used to study epilepsy through patch

clamp recordings and calcium imaging [56]. We cultured cells in each of the 48 wells in each MEA and

recorded neuronal spiking activity through 16 electrode contacts within each well (Figure 1a; see

Methods). After maturity and when firing rate stabilized (in vitro day 14), we treated the cultured cells

with low Mg2+ media on a daily basis for two hours. In each MEA, we captured neuronal burst

frequency across the electrode contacts in each of 12 wells that were treated with low Mg2+ and

compared that to neuronal burst frequency in a control set of 12 wells that did not receive low Mg2+

treatment (Figure 1b, Supplementary Figure 1a). We examined burst frequency every day at baseline,

immediately prior to treatment (pretreatment baseline, 0 hr), at the end of the two hours of treatment,

and two hours after the washout of low Mg2+ media with control media.

31

Low Mg2+ treatments results in an overall increase in burst frequency across several electrodes

within treatment wells when compared to electrodes within untreated control wells (Figure 1b,c). Across

wells in an exemplar MEA, we observed an approximately two-fold increase in burst frequency at the

end of low Mg2+ treatment (Figure 1d, n = 12 wells). This increase returns to baseline two hours after

the washout and is sustained at baseline levels until the following day (Supplementary Figure 1b,d). The

control cells do not exhibit this increase in burst frequency (Figure 1d; Supplementary Figure 1b,c). We

quantified the ratio of burst frequency at the end of two hours of treatment to the pretreatment baseline

burst frequency in each well to assess low Mg2+-induced changes across multiple days of treatment and

to compare these changes between conditions (burst frequency ratio; see Methods). This ratio reflects

the extent to which treatment causes an increase in burst activity over baseline and is consistently higher

during every day of treatment in the low Mg2+ wells compared to the control wells (Figure 1e). The

average burst frequency ratio across wells is significantly higher in the low Mg2+ treated wells compared

to the control wells across all days of treatment (t(18) = 8.155, p < .0001, unpaired t-test, n = 10 days;

Figure 1f). We also computed the ratio of burst frequency at the end of the two-hour treatment compared

to the burst frequency following two hours of washout (washout burst frequency ratio). Wells treated

with low Mg2+ also exhibit significantly higher washout burst frequency ratio than the control wells

(Supplementary Figure 1h). We confirmed that the increases in burst frequency are not related to the

media change or to the contents of the ACSF media used in the low Mg2+ treatment (Supplementary

Figure 1b,f-h). These data suggest that treatment with low Mg2+ directly leads to an increase in neuronal

burst frequency.

32

Figure 1. Low Mg2+ model of chronic neuronal stimulation

(a) Schematic (left) and immunofluorescence image (middle) of a mixed cell population cultured on a microelectrode array

(MEA). Cultures include neurons, astrocytes and glial cells. We recorded neuronal spiking activity from each of sixteen

electrode contacts within each well of the MEA (right). (b) We compared neuronal activity in 12 wells treated with low Mg2+

in each MEA (right) to activity in 12 control wells (left). The color of each well indicates the average burst rate across active

electrodes within that well during baseline and at the end of two hours of treatment (shaded). Burst frequency is unchanged

between baseline and the two-hour treatment in the control wells but increases with low Mg2+ treatment. Green and red

circles represent the wells used for visualization of spiking activity in (c). (c) Thirty second raster plots (bottom) and

histograms (top) of spiking activity in all 16 electrode channels in a single control well (green) and a single well treated with

33

low Mg2+ (red). Low Mg2+ treatment results in a greater number of bursts, defined as a minimum of 5 consecutive spikes with

a maximal inter-spike interval of 0.1s (Supplementary Figure 1a). (d) During a single day of treatment, burst frequency

increases from the pretreatment baseline (0 hours) to the end of the two-hour low Mg2+ treatment, and returns to baseline

following two hours of washout (n = 12 wells, mean ± SEM). Control wells exhibit no change. (e) The ratio of burst

frequency following two hours of treatment compared to the pretreatment baseline (burst frequency ratio) is higher in low

Mg2+ treated wells than in control wells every day of treatment (n = 12 wells, mean ± SEM). (f) Burst frequency ratio

averaged across wells is significantly higher across ten days of treatment in the low Mg2+ treated wells compared to control

wells (n = 10 days, mean ± SEM; * p < .0001, unpaired t-test). (g) LDHA protein expression is higher in cells aggregate from

the wells treated with low Mg2+. Protein expression normalized to vinculin.

We then examined whether this increase in burst frequency following the daily treatment with

low Mg2+ may be related to protein expression of LDHA. In an exemplar MEA, wells treated with daily

low Mg2+ exhibit higher levels of LDHA expression as compared to the control wells (Figure 1g). We

repeated our experiment across several MEAs and computed the average burst frequency ratio across all

wells from each condition and then across all treatment days in each MEA. Across several MEAs, we

found that the observed increase in burst frequency in the wells treated with daily low Mg2+ is

consistently and significantly higher than the control wells (n = 8 MEAs; t(14) = 3.22, p = .006,

unpaired t-test; Figure 2a). We examined protein expression and found that across MEAs, wells treated

with low Mg2+ exhibit significantly higher levels of LDHA expression as compared to the control wells

(n = 4 MEAs; t(6) = 16.8, p < .0001; Figure 2b). We confirmed that treatment with low Mg2+ results in

preferential overexpression of LDHA in neurons using immunofluorescence (Figure 2c). We then

examined the time course of LDHA expression through daily treatments with low Mg2+ by culturing

cells on standard 96 well plates and examining protein expression after progressively more days of

treatment (see Methods). Across several plates, we found that LDHA expression begins to increase

following three days of daily treatment, and continues to rise until reaching a maximum level after 10

days (Figure 2d,e).

34

0

2

4

6

8

Fold

Cha

nge

Control Low Mg2+0.0

0.5

1.0

1.5

2.0

2.5

c)

a) b)

0.0

0.5

1.0

1.5

2.0

2.5

Contro

l

Day

1

Day

3

Day

7

Day

10

d) e)

Control Low Mg2+

LDH

A30kD

aVinculin117kD

a

Contro

l

Day

1

Day

3

Day

7

Day

10

LDHA expressionProtein expression

**

Fold

Cha

nge

Fold

Cha

nge

LDHA expressionBurst frequency ratio

**

*

Control Low Mg2+

DAPI

DAPI/NeuN/LDHADAPI/NeuN/LDHA

DAPI NeuNNeuN LDHA LDHA

200uM 200uM

35

Figure 2. Burst frequency from low Mg2+ treatment is associated with increased LDHA expression

(a) Burst frequency ratio, averaged across wells and across all treatment days, is significantly higher in wells treated with low

Mg2+ compared to control wells across MEAs (n = 8 MEAs; * p < .05, unpaired t-test, mean ± SEM). (b) Western blot

quantification demonstrates increased LDHA expression in low Mg2+ treated cells compared to control cells (n = 4 MEAs; *

p < .05, unpaired t-test, mean ± SEM). (c) In an example well, immunofluorescence for NeuN and LDHA demonstrates

increased expression of LDHA after 10 days of low Mg2+ treatment (right) as compared to a control well (left). LDHA co-

localizes to NeuN expression in neurons. (d) Protein expression following a progressively greater number of daily treatments

with low Mg2+. Each Western blot represents protein expression aggregated from 12 wells in a standard 96 well plate that

were lysed at different time points. Protein expression normalized to vinculin. (e) Across plates, protein expression is

significantly different than the control wells following three days of low Mg2+ treatment and reaches a maximum at day 10 (n

= 3 plates per day of analysis; F(4,13) = 15.6, p < .0001, one-way ANOVA; post-hoc t-test Bonferroni multiple comparison

testing: day 1 versus control t(13) = .53, p > .05, day 3 versus control t(13) = 3.61, p = .013, day 7 versus control t(13) =

4.082, p = .0052, day 10 versus control t(13) = 7.323, p < .001; mean ± SEM). Data normalized to control levels of LDHA

expression.

Our data suggest that chronic neuronal activation as induced by daily low Mg2+ treatment is

correlated with overexpression of LDHA. To examine whether it is the neural bursting activity itself that

results in LDHA overexpression, we repeated our treatments with low Mg2+ while inhibiting neuronal

firing with tetrodotoxin (TTX). If repeated and frequent spiking activity is directly responsible for the

increase in LDHA expression, then concurrent treatment with TTX, which inhibits voltage-gated sodium

channels and therefore action potential, should mitigate any increases in LDHA. We therefore treated

cells cultured on an MEA plate with low Mg2+ and increasing doses of TTX. An example single well

treated with low Mg2+ exhibits an increase in neuronal bursting activity across several electrodes as

compared to a control well, but bursting activity is clearly reduced in the presence of 10 nM of TTX

even in the setting of low Mg2+ (Figure 3a; Supplementary Figure 2). We computed the average burst

frequency ratio across all wells in each condition within an MEA. Across ten days of treatment with low

Mg2+, TTX inhibited neuronal bursting in a dose-dependent fashion (n = 12 wells for each treatment

condition; F(5,51) = 16.12, p < .0001, one-way ANOVA; Figure 3b). We then examined LDH

36

expression in cells cultured on standard 96 well plates following ten days of daily low Mg2+ treatment

with progressively increasing doses of TTX (12 wells per treatment condition). In an example plate,

wells treated with higher doses of TTX have reduced expression of LDHA (Figure 3c). Across three

plates, we found a significant dose dependent relation between TTX dose and LDHA expression, with

the highest dose of TTX resulting in LDHA expression that was similar to the control wells (n = 3

plates; F(5,12) = 3.94, p = .021 one way ANOVA; Figure 3d). These data suggest that in the setting of

low Mg2+, neuronal spiking activity is necessary for LDHA expression and is the cause of neuronal

LDHA upregulation.

Figure 3. Burst frequency from low Mg2+ treatment causes an increase in LDHA expression

(a) Thirty second raster plots show increased bursting for each of the 16 electrodes channels in a control well (left), a well

treated with low Mg2+ (middle), and a well treated with low Mg2+ and 10 nM TTX (right). (b) Increasing doses of TTX added

to low Mg2+ media progressively decreases burst frequency ratios. Each data point represents the average burst frequency

ratio across 12 wells from each dose on each day (n = 12 wells; post-hoc t-test Bonferroni corrected multiple comparison

Contro

l

Low M

g2+

TTX 5nM

TTX 10nM

TTX 20nM

TTX 25nM

chan

nel

sp/s 200

chan

nel

sp/s 200

chan

nel

sp/s 200

Contro

l

Low M

g2+

TTX 5nM

TTX 10nM

TTX 20nM

TTX 25nM

Fold

Cha

nge

0.0

0.5

1.0

1.5

012345

Fold

Cha

ngeLD

HA

30kDa

Vinculin117kD

a

Control Low Mg2+a)

Time (30s) Time (30s)Time (30s)

Burst frequency ratio Protein expression Protein expressionb) c) d)

Low Mg 2++ 10nM TTX

Contro

l

Low M

g2+

TTX 5nM

TTX 10nM

TTX 20nM

TTX 25nM

* * ** *

* *

37

testing: low Mg2+ versus control t(51) = 6.42, p < .0001, low Mg2+ versus low Mg2+ + 5 nM TTX, t(51) = 4.335, p = .0003,

low Mg2+ versus low Mg2+ + 10 nM TTX, t(51) = 6.468, p < .0001, low Mg2+ versus low Mg2+ + 20 nM TTX, t(51) = 7.261,

p < .0001, low Mg2+ versus low Mg2+ + 25 nM TTX, t(51) = 6.42, p < .0001; mean ± SEM). (c) Protein expression of LDHA

in an example plate with increasing doses of TTX. Each Western blot represents protein expression in cells aggregated from

12 wells in a standard 96 well plate. Protein expression normalized to vinculin. (d) Across plates, protein expression

decreases with increasing doses of TTX in the setting of low Mg2+ (n = 3 plates; post-hoc t-test Bonferroni multiple

comparison testing: low Mg2+ versus control, t(12) = 3.634, p = .017, low Mg2+ versus low Mg2+ + 5 nM TTX, t(12) = 1.292,

p > .05, low Mg2+ versus low Mg2+ + 10 nM TTX, t(12) = 1.385, p > .05, low Mg2+ versus low Mg2+ + 20 nM TTX, t(12) =

1.682, p > .05, low Mg2+ versus low Mg2+ + 25 nM TTX, t(12) = 3.116, p = .045; mean ± SEM). Data are normalized to

protein expression in the low Mg2+ condition.

Neuronal bursting causes a metabolic switch from aerobic respiration to glycolysis

LDHA is instrumental to glycolysis and catalyzes the conversion of pyruvate to lactate for

continued glycolytic production of ATP [47]. Even though LDHA is widely considered a marker of

glycolysis [64], we sought to describe the direct metabolic switch from aerobic respiration to glycolysis

in chronically activated neurons. To examine this, we used a Seahorse metabolic assay to investigate the

bioenergetic profile of cultured cells after they were exposed to low Mg2+ media. Following daily two-

hour treatment for ten day, we measured baseline mitochondrial respiration using oxygen consumption

rate (OCR). We also assessed glycolysis using extracellular acidification rate (ECAR) in the surrounding

media, which is typically elevated following oxygen-independent conversion of pyruvate to lactate

(Figure 4a,c) [65]. At baseline following the daily treatments, cells treated with daily low Mg2+ exhibit

significantly lower OCR (n = 6 wells low Mg2+ and 12 control wells; t(16) = 2.12, p = .049, unpaired t-

test) and significantly higher ECAR (t(5) = 3.49, p = .014, unpaired t-test) than control cells (Figure

4b,d). We then introduced a state of high energy demand by inhibiting ATP synthase with oligomycin

and uncoupling the electron transport chain with p-trifluoromethoxy carbonyl cyanide hydrazone

(FCCP) and measured the resulting change in OCR and ECAR (Figure 4a,c). We used these stressors to

calculate the cell’s metabolic potential because they maximize cellular glycolysis and mitochondrial

38

respiration, respectively. In the stressed state, we found that cells treated with daily low Mg2+ also

exhibit significantly higher ECAR (t(5) = 3.28, p = .017, unpaired t-test) than control cells, but did not

exhibit a significant change in OCR (t(5) = 1.926, p = .0746, unpaired t-test) and (Figure 4b,d). Chronic

treatment with daily low Mg2+ shifts the metabolic profile of the cultured cells from a baseline aerobic

phenotype to a more glycolytic phenotype (Figure 4e). Given the increase in LDHA expression observed

with daily low Mg2+ treatments, these data suggest that the shift towards glycolysis is related to the

adaptive upregulation of the final enzyme in glycolysis, LDHA [47].

39

Figure 4. Low Mg2+ treatments drive neurons into glycolysis

(a) Oxygen consumption rate (OCR) in cells following treatment with

daily low Mg2+ for ten days is measured over one hour. We used the first

four time points (0-20 minutes) to extract a measure of baseline OCR.

We added oligomycin and FCCP at 23.5 minutes to stress the cells and

put them in a state of high energy demand. We used the subsequent five

time points (25-60 min) to measure the stressed OCR. OCR is decreased

in cells treated with low Mg2+ at baseline and during the first two time

points in the stressed state (n = 6 wells low Mg2+ and 12 control wells;

mean ± SEM). (b) Average OCR during baseline demonstrates a

decrease in OCR in cells treated with low Mg2+ at baseline (*p < .05,

unpaired t-test) but not in the stressed state; mean ± SEM). OCR values

are normalized to the total number of cells after treatment (c)

Extracellular acidification rate (ECAR) in cells following treatment with

daily low Mg2+ for ten days is measured over one hour. We used the first

four time points (0-20 minutes) to extract a measure of baseline ECAR.

We added oligomycin and FCCP at 23.5 minutes to stress the cells and

put them in a state of high energy demand. We used the following five

time points (25-60 min) to measure the stressed ECAR. ECAR is

increased in cells treated with low Mg2+ at baseline and in the stressed

state (n = 6 wells low Mg2+ and 12 control wells; mean ± SEM). (d)

Average ECAR during baseline and stressed conditions demonstrates an

increase in ECAR at baseline (*p < .05 unpaired t-test; mean ± SEM).

ECAR values are normalized to the total number of cells after treatment.

(e) ECAR and OCR for cells treated with low Mg2+ and for control cells

demonstrate the cell energy phenotype. Low Mg2+ treated cells

preferentially use glycolysis at baseline and when stressed.

5545352515

5

Oxygen Consumption Rate

Time (min)10 20 30 40 50 60

OCR

(pm

ol/m

in)

Extracellular Acidification Rate

35

25

15

5

Time (min)10 20 30 40 50 60

ECAR

(mpH

/min

)

Oxygen Consumption Rate

20

40

60

Baseline Stressed

Extracellular Acidification Rate

ECAR

(mpH

/min

)O

CR (p

mol

/min

)

Baseline Stressed

Cell Energy Phenotype

20

40

60

40

30

20

10

GlycolysisECAR (mpH/min)

10 20 30 400

0

0

Mito

chon

dria

l Res

pira

tion

OC

R (p

mol

/min

)

GlycolyticQuiescent

Aerobic Energetic

*

Oligo/FCCP

Oligo/FCCP

a)

b)

c)

d)

e)

Low Mg

Control2+

Low Mg

Control2+

Low Mg

Control2+

Low Mg

Control2+

Low Mg

Control2+

*

*

*

40

LDHA is upregulated in human epileptic neurons

To confirm that chronic neuronal activation can lead to similar changes in metabolism in vivo,

we analyzed epileptic tissue from seven human participants who underwent surgery for epilepsy

monitoring and resection of epileptic foci. Epilepsy is characterized by pathologically excessive

neuronal activity, and we hypothesized that regions of the brain exhibiting epileptic activity should also

demonstrate increased expression of LDHA. In each participant, using intracranial electrodes to monitor

for seizure activity, we prospectively identified regions of the brain that were epileptic (red) or non-

epileptic (green) that were within the planned subsequent surgical resection (Figure 5a-c). Five of the

participants exhibited seizures arising from the medial temporal lobe structures (Figure 5b), whereas two

had extratemporal lobe seizures. We analyzed resected surgical specimens, in each participant dividing

tissue into epileptic and non-epileptic, for LDHA protein expression.

Using immunohistochemistry and a semi-automated segmentation analysis (Figure 5d;

Supplementary Figure 3; see Methods), we computed the percentage of neurons (NeuN-positive cells)

that also exhibited positive staining for LDHA. We found that epileptic tissue exhibits a significantly

larger percentage of neurons that stain positively for LDHA compared to non-epileptic tissue (n = 7

participants; t(12) = 2.18, p = .049, unpaired t-test; Figure 5d,e). We confirmed that LDHA is

overexpressed in epileptic tissue by also computing the proportion of combined neurons and glial cells

(NeuN and GFAP positive staining), and also the proportion of all total cells, that stain positively for

LDHA and found that these percentages are also significantly higher in epileptic compared to non-

epileptic tissue (Supplementary Figure 3c,d). Using immunofluorescence, we confirmed that the

elevated expression of LDHA co-localizes to NeuN-positive neurons in epileptic tissue, suggesting that

LDHA upregulation in epileptic tissue is a phenomenon specific to neurons (Figure 5f,g).

41

0.0

0.2

0.4

0.6

0.8

1.0

Non-epileptic Epileptic

% L

DHA

(LDH

A/Ne

uN)

Human Tissue: LDHA

Non

-epi

lept

icEp

ilept

ic

LDHA NeuN

a) c)

d)

e)

f) g)Non-epileptic Epileptic

100uM 100uM

100uM 100uM

1sec1.5V

*

DAPI/NueN/LDHALDHA DAPI/NueN/LDHA

NueN

DAPI DAPI

NueN

LDHA 40x40x

b)

42

Figure 5. LDHA is upregulated in human epileptic neurons

(a) Temporal lobe of a participant with intracranial electrodes placed for seizure monitoring. During the monitoring period,

we identified electrodes that were or were not involved in seizure activity. In this example, electrode 14 (green) was not

involved in seizures. The underlying tissue was resected as part of the planned surgical procedure and analyzed as the non-

epileptic specimen for this participant. (b) Reconstructed brain map showing all intracranial electrodes implanted in this

participant. An electrode along the medial anterior temporal lobe (red) was over the epileptogenic zone. The underlying

tissue was resected and analyzed as the epileptic specimen for this participant. (c) Intracranial EEG recording corresponding

to epileptic and non-epileptic electrodes from (a) and (b). The epileptic electrode (red) demonstrates frequent spike and wave

discharges that are consistent with seizures. (d) Tissue underlying the epileptic (red) and non-epileptic (green) tissue from the

same participant were sectioned. Representative 400x400 µm regions of interest were analyzed for NeuN and LDHA using

semi-automated segmentation (shown). Epileptic tissue demonstrates significant LDHA staining compared to non-epileptic

tissue, while the neuronal marker NeuN was approximately the same between the two specimens. We normalized LDHA

staining to NeuN to account for differences in neuronal density across specimens. (e) Normalized LDHA staining, averaged

across ten 400x400 µm regions of interest in each participant, is significantly elevated across participants in epileptic

compared to non-epileptic tissue (n = 7 participants; *p < .05, unpaired t-test; mean ± SEM). (f) Immunofluorescence of

tissue demonstrates co-localization of LDHA and NeuN in epileptic tissue, and very minimal LDHA staining in the non-

epileptic tissue.

AMPK/HIF1a hypoxia pathway upregulation is responsible for LDHA upregulation

We were interested in understanding the molecular pathways and mechanisms that lead to

overexpression of LDHA in chronically active neurons. We hypothesized that this phenomenon involves

the AMPK/HIF1a pathway that has been previously shown to play a role in response to neuronal

activation and cellular hypoxia [54]. AMPK is a cellular energy-state sensor that is phosphorylated in

conditions of high AMP:ATP ratios, such as hypoxia or acute neuronal activation [29-32]. AMPK

phosphorylation leads to HIF1a protein expression and subsequent nuclear translocation, which

subsequently leads to several downstream changes including LDHA upregulation responsible for

metabolic reprogramming into glycolysis [29-32] (Figure 6a). We first confirmed the integrity of the

AMPK/HIF1a pathway leading to LDHA upregulation in the in vitro setting by demonstrating that

43

activation of AMPK and HIF1a leads to increased LDHA expression (Supplementary Figure 4). We

then examined the AMPK/ HIF1a pathway in a step-wise manner in our low Mg2+ in vitro model in

order to explore how neuronal activation leads to ATP depletion, downstream AMPK phosphorylation,

HIF1a regulation, and finally LDHA expression (Figure 6a).

To test ATP depletion, we treated our mixed cell culture with low Mg2+ media for two hours and

lysed the cells at several time points to determine intracellular ATP composition (see Methods). There is

a time-dependent decrease in intracellular ATP during treatment with low Mg2+ media, which peaks at a

greater than two-fold decrease in intracellular ATP at two hours (n = 3 plates; F(5,14) = 97.07, p <

.0001; Figure 6b). After washing out the low Mg2+ media, intracellular ATP levels return towards

baseline, suggesting that ATP utilization increases during neuronal bursting but begins to normalize

when neuronal activation ceases. This pattern of ATP utilization is similar to the observed pattern of

neuronal burst frequency that also reaches a peak at the end of two hours of low Mg2+ treatment and

returns to baseline following the washout.

To complement the observed changes in ATP, we also measured total AMPK and AMPK

phosphorylation (AMPK-P) during two hours of low Mg2+ treatment. After two hours of treatment,

AMPK-P but not total AMPK exhibit a significant increase (n = 4 wells; t(3) = 4.17, p = .025, unpaired

t-test; Figure 6c; Supplementary Figure 5a). AMPK phosphorylation at Thr172 is a known consequence

of ATP depletion [31], and therefore these data suggest that AMPK phosphorylation is a result of

neuronal activation. To determine if AMPK phosphorylation is necessary for downstream LDHA

upregulation when neurons are chronically activated, we inhibited phosphorylated AMPK with a small

molecule inhibitor (compound C) [66] while treating cell cultures with daily low Mg2+ over ten days. In

an example plate, escalating doses of compound C from 10 µM to 200 µM result in a decrease in

neuronal LDHA protein expression (Figure 6d,e). We found that the progressive decrease in LDHA

expression with increasing doses of compound C was consistent across experimental plates (n = 3 plates;

F(6,13) = 5.05, p = .007, one-way ANOVA; Figure 6e). We measured the time course of LDHA

44

expression over several days while administrating 100 µM of compound C in the setting of low Mg2+

treatment and found no difference in neuronal LDHA expression during any day compared to control

(Supplementary Figure 5b). These results suggest that compound C mitigates any changes in LDHA

expression changes associated with low Mg2+ treatment and the resulting increases in neuronal bursting.

Similarly, we examined if the immediately downstream HIF1a is directly responsible for LDHA

expression by inhibiting HIF1a activity with KC7F2 (KC), which is a potent inhibitor of HIF1a

synthesis at the translational level [67]. As with compound C, we treated cell cultures daily with low

Mg2+ and increasing doses of KC7F2 (10 µM to 75 µM) for ten days. In an example plate, escalating

doses of KC7F2 result in a decrease in neuronal LDHA protein expression (Figure 6d,f). We found this

relationship was consistent across experimental plates (n = 3 plates; F(5,15) = 2.92, p = .049, one-way

ANOVA; Figure 6h). We examined the time course of LDHA expression over several days while

treating cell cultures with low Mg2+ and 75 µM of KC7F2 and found no difference in LDHA expression

during any day compared to control (Supplementary Figure 5c), suggesting that HIF1a activity directly

leads to LDHA expression.

45

46

Figure 6. LDHA is upregulated through the AMPK/HIF1a hypoxia pathway

(a) Representative schematic of the AMPK/ HIF1a pathway that leads from chronic neuronal activation to LDHA

upregulation and glycolysis. Neuronal activation leads to energy depletion and a high AMP:ATP ratio. AMP leads to

phosphorylation of AMPK, which leads to stabilization of HIF1a. HIF1a translocates into the nucleus as a transcription

factor to upregulate LDHA transcription and protein expression. LDHA upregulation leads to a glycolytic metabolic profile

in neurons. Compound C is a small molecule used to inhibit AMPK phosphorylation. KC7F2 downregulates HIF1a protein

synthesis and has been shown to inhibit the activation of HIF1a targeted genes (ie: LDHA). DMOG upregulates HIF1a by

inhibiting prolyl-4-hydroxylase (regulates HIF1a). AICAR is an AMP analog and activates AMPK. (b) ATP is depleted in a

time-dependent manner during two hours of low Mg2+ treatment (n = 3 plates; post-hoc t-test Bonferroni multiple comparison

testing: control versus 15 min low Mg2+ t(14) = 6.566, p < .0001, control versus 30 min low Mg2+ t(14) = 10.13, p < .0001,

control versus 1 hour low Mg2+ t(14) = 16.79, p < .0001, control versus 2 hours low Mg2+ t(14) = 17.57, p < .0001, control

versus 2 hour washout t(14) = 7.015, p < .0001; mean ± SEM). (c) Phosphorylation of AMPK at Thr172 is significantly

elevated after two hours of low Mg2+ treatment (n = 4 wells, *p < .0-5, unpaired t-test, mean ± SEM). (d)

Immunofluorescence in example wells demonstrates that the increases in LDHA expression in neurons observed with low

Mg2+ are reduced in the setting of 100 mM of compound C and 75 µM of KC7F2. (e) In an example plate, LDHA expression

progressively decrease with escalating concentrations of compound C (CC) added to daily low Mg2+ treatments over 10 days.

(f) Across plates, there is a significant decrease in LDHA expression with increasing concentrations of CC added to daily low

Mg2+ treatments over 10 days (n = 3 plates; post-hoc t-tests Bonferroni multiple comparison testing low Mg2+ versus control,

t(13) = 3.947, p = .01, low Mg2+ versus low Mg2+ + 10 µM CC, t(13) = 3.202, p = .042, low Mg2+ versus low Mg2+ + 20 µM

CC t(13) = 3.583, p = .02, low Mg2+ versus low Mg2+ + 50 µM CC, t(13) = 3.927, p = .01, low Mg2+ versus low Mg2+ + 100

µM CC, t(13) = 4.477, p = .0037, low Mg2+ versus low Mg2+ + 200 µM CC, t(13) = 4.506, p = .0025; mean ± SEM). (g) In an

example plate, LDHA expression progressively decrease with escalating concentrations of KC7F2 added to daily low Mg2+

treatments over 10 days. (h) Across plates, there is a significant decrease in LDHA expression with increasing concentrations

of KC7F2 added to daily low Mg2+ treatments over 10 days (n = 3 plates; post-hoc t-tests Bonferroni multiple comparison

testing low Mg2+ versus low Mg2+ + 75 µM KC7F2, t(15) = 3.178, p = .031; mean ± SEM).

Discussion

In this study, we sought to evaluate metabolic changes associated with chronic neuronal

47

stimulation. In order to model chronic activation of neurons, mixed cortical rat cells were treated with

low Mg2+ medium for two hours on a daily basis. Treated cells demonstrated reliable daily increases in

neuronal burst frequency (Figures 1 and 2) which caused elevated expression of LDHA protein (Figures

1, 2 and 3). This observation was confirmed in vivo by examining human tissue that was resected from

patients with drug resistant epilepsy and chronic seizures. LDHA expression was significantly elevated

in epileptic tissue when compared to non-epileptic control tissue in the same patient (Figure 4). Finally,

using the low Mg2+ model, we showed that this metabolic shift occurs through the AMPK/HIF1a

hypoxia pathway. Although our cell culture model is a representative mix of neurons and supporting

cells, this model does not represent a true in vivo environment because it lacks normal brain architecture,

blood vessels and afferent input. Despite these limitations, the low Mg2+ culture model provided a

practical, reproducible and relatively long-lasting model to explore molecular mechanisms underlying

neuronal activation. Future similar studies may focus on other culture models such as brain organoids

[68] or further in vivo experiments.

The data presented in this study suggest a shift in contemporary dogma regarding glucose

utilization in neuronal tissue. In conjunction with Diaz-Garcia et al., the findings of the present study

depart from prevailing the ANLS hypothesis. According to the ANLS model, neuronal activation leads

to lactate production in astrocytes which is subsequently shuttled to neurons and used in the TCA cycle

[16, 20, 50]. In order for this to be valid, LDHA would become upregulated in astrocytes during chronic

neuronal stimulation to accommodate for increased neuronal energy demands. Data from both our

culture and human experiments contradict this point. In culture, elevated LDHA levels co-localized to

the neuronal marker, NeuN, on immunofluorescence (Figure 2c). Similarly, neurons in epileptic tissue

accounted for the majority of LDHA expression (Figure 5d, e) and LDHA also co-localized to NeuN

(Figure 5f) on immunofluorescence. Although evidence suggests that hypoxia potentiates EPO and

VEGF secretion from astrocytes [69] the role of astrocyte HIF1a in neuronal metabolism is not known.

Vangeison et al. performed cell-specific knock outs (KO) of HIF1a in a co-culture of neurons and

48

astrocytes. As expected, neuronal HIF1a KO potentiated cell death in hypoxic conditions (presumably

due to an inability to upregulate glycolytic processes) whereas HIF1a KO in astrocytes was

neuroprotective [70]. Our findings demonstrating LDHA upregulation through DMOG (Figure 4 c-d)

and downregulation through KC7F2 appears more congruent (Figure 6f, g) with impacting neuronal

HIF1a. If astrocytes truly supported neuronal energy demand during states of chronic stimulation (or

hypoxia), then astrocytic HIF1a KO (and thus decreased downstream glycolysis) would be detrimental

to neurons, not protective. Future investigation including similar selective inhibition of astrocyte HIF1a

could help to determine effects on neuronal LDHA during chronic stimulation.

Our findings also elucidate the neuronal response to chronic high energy demand through

neuronal activation. Although oxidative phosphorylation provides a comparatively more efficient means

of ATP generation, the pathway is exponentially slower than glycolysis [3]. For this reason, glycolysis

plays a compensatory role in providing ATP during states of high energy demand [2, 19]. As such, serial

states of high energy demand would hypothetically lead to accommodating neuronal changes on a

molecular level. As previously described, neurons transiently utilize glycolysis during times of acute

neuronal stimulation and oxidative phosphorylation during quiescence [2, 19]. However, neuronal

response to frequent stimulation has not been explored. We hypothesized that instead of continuously

oscillating between glycolysis and oxidative phosphorylation during chronic stimulation, neurons can

accommodate for more frequent shifts in metabolic demand by upregulating overall glycolytic potential

through LDHA. Overall, the LDH enzyme is responsible for the inter-conversion of pyruvate to lactate

and NADH to NAD+ [47]. Negatively charged LDHA has a higher affinity for pyruvate and thus

preferentially converts pyruvate to lactate (and NADH to NAD+) [47, 48]. This LDHA step becomes

necessary for replenishing NAD+ when the TCA cycle is exhausted during times of low oxygen supply

and therefore crucial for continued glycolysis during anaerobic respiration [48, 49]. From this

perspective, evaluating the bioenergetics of cultured cells allowed us to demonstrate that neurons do in

fact upregulate glycolysis and downregulate oxidative phosphorylation after chronic stimulation (Figure

49

4). This transition occurs through a shift in the proteomic profile of neurons to expressing LDHA

(Figure 1-3) and likely other glycolytic enzymes.

In addition, we explored in detail the molecular mechanisms responsible for transitioning the

neuronal metabolic phenotype in response to chronic activation. Increased energy demands from chronic

stimulation seemingly mimic the demands from hypoxia. For this reason, we investigated the

AMPK/HIF1a hypoxia pathway as a potential mediator of the metabolic shift due to chronic

stimulation. Under hypoxic conditions, high AMP concentrations promote the phosphorylation of

AMPK [31]. Activated AMPK stabilizes HIF1a which then translocates to the nucleus and upregulates

the expression of LDHA [53, 54, 71, 72] (Figure 6a). In order to analyze the role of this pathway in

neuronal metabolic change, we evaluated each pathway step in kind and analyzed the impact on

downstream LDHA expression. Elevated bursting behavior (Figure 1d) from low Mg2+ treatments

caused a time dependent depletion of ATP (Figure 5b). This depleted energy state caused an overall

increase in AMPK phosphorylation in a compatible fashion to the hypoxia pathway (Figure 5c).

Furthermore, AMPK phosphorylation was critical to downstream LDHA expression. Adding escalating

doses of Compound C, an AMPK inhibitor, to daily low Mg2+ treatments produced a concurrent

decrease in LDHA expression (Figure 5g). Finally, to demonstrate that HIF1a acts as the final

steppingstone in the modulation of LDHA expression during low energy states, we inhibited HIF1a with

KC7F2. This inhibition resulted in a clear inverse relationship between increased KC7F2 doses and

LDHA expression. The above experiments could conceivably be performed and evaluated with direct

bioenergetics of neurons through the Seahorse assay. However, unlike low Mg2+ treatments which

maintain pH, consistent manipulation of the extracellular environment with small molecule inhibitors

would artificially modify extracellular pH and introduce inaccuracies to the assay. The results from the

present study, in combination with reported literature, confirm that LDHA serves as an adequate marker

since it is so intimately tied to glycolysis (Valvona et al., 2016).

Overall, the present study provides insight into the fundamental interplay between neuronal

50

activation and glucose utilization Although notable, we understand our findings are likely a small piece

of the puzzle that couples metabolism and neuronal activation. Future inquiry into these interactions will

provide robust insight into the basic nature of the foundational components of the central nervous

system and how these relationships reshape into disease.

Supplemental Figures

Supplementary Figure 1

(a) Exemplar raster plot showing a single electrode with a spike (±6 standard deviations from the baseline signal), a burst

(minimum of 5 spikes were detected on a single electrode with a maximal inter-spike interval of 100ms) and burst frequency.

(b) During a single day of treatment, burst frequency increases from the pretreatment baseline (0 hours) to the end of the two-

hour low Mg2+ treatment, and returns to baseline following two hours of washout (n = 12 wells, mean ± SEM). Control wells,

wells treated with control media, and wells treated with Mg2+-enriched ACSF exhibit no change. (c-f) Daily burst frequency,

averaged across wells, during baseline, following two hours of treatment, and following two hours of washout in (c) control

wells, (d) wells treated with low Mg2+, (e) wells treated with media changes, and (f) wells treated with Mg2+-enriched ACSF.

Wells treated with low Mg2+ exhibit a significant increase in burst frequency following two hours of treatment compared to

b)

Base

0.0

0.1

0.2

Freq

uenc

y (H

z)

0hr

2hr

2hr W

asho

utBas

e

0.3

Single day burst frequency

Media changeACSF + Mg

ControlLow Mg2+

Contro

l

Media

chan

ge

ACSF + Mg

0

2

4

6

Low M

g2+ 2+

Burst frequency ratio (2hr vs base)g) Washout burst frequency

ratio (2hr vs 2hr washout)h)

Fold

Cha

nge

Fold

Cha

nge

0.00

0.02

0.04

0.060.08

0.10

Base

2hr

2hr W

asho

ut

Freq

uenc

y (H

z)

d) Averaged daily burst frequency: low Mg

0.00

0.02

0.04

0.060.08

0.10Fr

eque

ncy

(Hz)

Base

2hr

2hr W

asho

ut

c) Averaged daily burst frequency: control

Base

2hr

2hr W

asho

ut0.00

0.02

0.04

0.060.08

0.10

Freq

uenc

y (H

z)

e) Averaged daily burst frequency: media changes

Base

2hr

2hr W

asho

ut

f) Averaged daily burst frequency: ACSF + Mg

2+

2+

2+

0.00

0.02

0.04

0.060.08

0.10

Freq

uenc

y (H

z)

0

2

4

6

Contro

l

Media

chan

ge

ACSF + Mg

Low M

g2+ 2+

**

* * ** * *

BurstSpike

5 spikesISI = 100ms

BurstFrequency

Exemplar raster plota)

51

baseline (t(9) = 3.44, p = .0074, paired t-test) and the two hour washout (t(9) = 3.04, p = .014, paired t-test) across days (n =

10 days, mean ± SEM). (g) Burst frequency ratio, averaged across wells, is significantly higher in the low Mg2+ treated wells

compared to the control wells (t(18) = 8.16, p < .0001, unpaired t-test), wells treated with media change (t(18) = 6.07, p <

.0001), and wells treated with Mg2+-enriched ACSF (t(18) = 6.82, p < .0001) across 10 days of treatment (n = 10 days, mean

± SEM). (h) Washout burst frequency ratio comparing burst frequency at the end of treatment to burst frequency following

two hours of washout, averaged across wells, is significantly higher in the low Mg2+ treated wells compared to the control

wells (t(18) = 7.45, p < .0001, unpaired t-test), wells treated with media change (t(18) = 6.28, p < .0001), and wells treated

with Mg2+-enriched ACSF (t(18) = 4.89, p < .0001) across 10 days of treatment (n = 10 days, mean ± SEM).

Supplementary Figure 2

Thirty second raster plots show increased bursting for each of the 16 electrodes (channels) in a control well, in a well treated

with low Mg2+, and in wells treated with low Mg2+ and increasing doses of TTX. Bursting decreases with the addition of

increasing doses of TTX to low Mg2+ media.

chan

nel

sp/s

chan

nel

sp/s200 200

chan

nel

sp/s 200

chan

nel

sp/s 200

chan

nel

sp/s 200

chan

nel

sp/s 200

Time (30s)

Control Low Mg2+ Low Mg 2++ 5nM TTXa)

Time (30s)

Time (30s)

Time (30s)

Time (30s)Time (30s)

Low Mg 2++ 10nM TTX Low Mg 2++ 20nM TTX Low Mg 2++ 25nM TTX

52

Supplementary Figure 3.

(a) Resected specimens (epileptic and non-epileptic) stained for LDHA, NeuN and GFAP. Red boxes represent 400x400 µm

regions of interest used for analysis. (b) Representative 400x400 µm regions of interest were also segmented to analyze for

nuclei in cells that did not stain for LDHA. We analyzed GFAP stained sections by hand. (c) Normalized LDHA, averaged

across ten 400x400 µm regions of interest in each participant, is significantly elevated across participants in epileptic

compared to non-epileptic tissue when normalized to NeuN and GFAP cell count (n = 7 participants; t(12) = 7.72, p < .0001,

unpaired t-test; mean ± SEM). (d) Normalized LDHA, averaged across ten 400x400 µm regions of interest in each

participant, is significantly elevated across participants in epileptic compared to non-epileptic tissue when normalized to all

cells (n = 7 participants; t(12) = 5.08, p = .0003, unpaired t-test; mean ± SEM).

Non

-epi

lept

icEp

ilept

ic

Nuclei GFAPb)

100uM 100uM

100uM 100uM

Non

-epi

lept

icEp

ilept

ic

GFAPNeuNLDHAa)

400uM 400uM 400uM

400uM400uM400uM

0.0

0.2

0.4

0.6

0.8

1.0

% L

DHA

(LDH

A/Ne

uN+G

FAP)

Human Tissue: LDHA

*

Non-epileptic Epileptic

Non-epileptic Epileptic 0.0

0.2

0.4

0.6

0.8

1.0

% L

DHA

(LDH

A/Al

l cel

ls)

*

Human Tissue: LDHA

c)

d)

53

Supplementary Figure 4

(a) We used escalating doses (10µM to 1mM) of AICAR (an AMP analog known to phosphorylate AMPK at Thr 172 [29])

to activate AMPK daily for two hours over 10 days. In an example plate, we found a direct relationship between AICAR dose

and LDHA expression, with the highest dose of AICAR resulting in the highest LDHA expression. (b) Across experimental

plates, LDHA expression increases with escalating concentrations of AICAR over 10 days of treatment compared to control

(n = 3 plates; F(4,9) = 5.92, p = .013, one-way ANOVA; post-hoc t-test Bonferroni multiple comparison testing: control

versus 100 µM AICAR, t(9) = 3.176, p = .045; control versus 500 µM AICAR, t(9) = 3.884, p = 0.016; control versus

1000µM AICAR, t(9) = 4.391, p = .007; mean ± SEM). (c) We used dimethyloxalyglycine (DMOG) to test HIF1a activation

upstream from LDHA. DMOG up-regulates HIF1a transcription by inhibiting prolyl-4-hydroxylase, which is known to post-

translationally down-regulate HIF1a. In an example plate, increasing doses (10 µM to 1 mM) of DMOG had a direct

correlation with increasing LDHA protein expression. (d) Across experimental plates, LDHA expression increases with

escalating concentrations of DMOG over 10 days of treatment compared to control (n = 3 plates; F(4,14) = 21.99, p < .0001,

one-way ANOVA; post-hoc t-test Bonferroni multiple comparison testing: control versus 100 µM DMOG, t(14) = 2.397, p =

.0011, control versus 500 µM DMOG, t(14) = 6.978, p < .0001, control versus 1000 µM DMOG, t(14) = 8.353, p < .0001;

mean ± SEM).

LDH

A30kD

aVinculin117kD

a

Protein expression

LDH

A30kD

aVinculin117kD

a

Protein expression

Contro

l

10uM

AICAR

100u

M AICAR

500u

M AICAR

1mM AIC

ARCon

trol

10uM

DMOG

100u

M DMOG

500u

M DMOG

1mM D

MOG0

1

2

3

4

5LDHA expressionLDHA expression

0

2

4

6

Contro

l

10uM

AICAR

100u

M AICAR

500u

M AICAR

1mM AIC

AR

Contro

l

10uM

DMOG

100u

M DMOG

500u

M DMOG

1mM D

MOG

*

**

*

**

a) b) c) d)

54

Supplementary Figure 5

(a) Total AMPK expression does not change after two hours of treatment with low Mg2+ media (n = 4 wells). (b) Daily

treatment with low Mg2+ and 100 µM of compound C results in no significant increase in LDHA expression as compared to

controls (n = 3 plates; mean ± SEM). (c) Daily treatment with low Mg2+ and 75 µM of KC7F2 results in no significant

increase in LDHA expression as compared to controls (n = 3 plates; mean ± SEM).

Fold

cha

nge

LDHA expression

Contro

l

Low M

g0.0

0.5

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Total AMPK expression

Contro

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a) b) c)

55

IV. A feedforward mechanism for epilepsy regulated by lactate dehydrogenase A

1,5Alexander Ksendzovsky, MD*; 1Marcelle Altshuler, BS*; 1Stuart Walbridge, BS; 2Muzna Bachani,

BS; 4John Williamson, BS; 4Suchitra Joshi, PhD; 4Tanveer Singh, PhD; 2Joseph Steiner, PhD; 6Sara

Inati, MD, 1John Heiss, MD; 3,4Jaideep Kapur, MD, PhD; 1Kareem Zaghloul, MD, PhD

1Surgical Neurology Branch, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

2Drug Development Unit, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

3Department of Neurology, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

4Neuroscience Department, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

5Department of Neurological Surgery, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

6EEG Section, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

*Contributed equally to this work

56

Abstract Introduction

Despite the ketogenic diet’s successful use since the 1920’s, epilepsy as a disease of energy

metabolism is a novel concept. We previously established that seizures deplete neuronal energy stores

and reprogram neurons from an aerobic to glycolytic metabolic phenotype, marked by upregulation of

lactate dehydrogenase A (LDHA). LDHA has recently been shown to play a role in neuronal membrane

depolarization and epileptogenesis. We show here that LDHA upregulation through HIF1a leads to

seizure formation.

Methods

Resected tissue from 11 epileptic patients were probed for LDHA expression. To study the

electrophysiological consequences of LDHA, we used a mixed rat cortical cell culture model on a

microelectrode array (MEA). Furthermore, Fwe used a lentivirus vector to directly upregulate LDHA in

neurons cultured on an MEA to measure neuronal bursting. Finally, we developed a novel murine model

of chronic focal cortical epilepsy to establish HIF1a’s role in mediating LDHA expression and seizure

formation.

Results

We found that LDHA increased significantly in epileptic tissue versus non-epileptic tissue.

Induction of seizure activity in cultured neurons with low Mg2+ resulted in increased LDHA and

subsequently increased baseline bursting over ten days. Direct LDHA upregulation with an LDHA

lentivirus vecto resulted in increased bursting activity confirming that LDHA lead sto seizure formation.

Cells that were induced to upregulate LDHA via DMOG, an upstream HIF1a potentiator, showed a

significant increase in baseline bursting activity. Furthermore, placement of cobalt, a HIF1a stabilizer,

into the frontal cortex of mice caused seizures emanating from perilesion cortex which showed increased

LDHA.

Discussion

57

Overall, our data show that LDHA, regulated by HIF1a, can contribute to seizure development.

These data suggest a novel molecular mechanism for the pathogenesis of epilepsy where seizures cause

LDHA upregulation which then further drives seizures, leading to a cycle of epileptogenesis.

58

Introduction

Epilepsy impacts approximately 70 million people or one percent of the world’s population [34].

Thirty percent of patients continue to have seizures despite medical therapy. In this group, continued

seizures and polypharmacy have been associated with poor quality of life [35]. Although novel

therapeutic strategies are actively being investigated, a principle reason for the lack of treatment options

is a lack of understanding of the molecular mechanisms underlying epileptogenesis. Furthermore, the

link between pathological neuronal activation and ensuing cellular and molecular changes is also not

well characterized, further limiting our approach to studying this disease.

Despite mounting evidence of metabolic involvement in seizure activity, epilepsy as a disease of

energy metabolism is a relatively novel concept. Initial insights into metabolism’s involvement in

epilepsy came from the successful use of the ketogenic diet (KD) to manage refractory epilepsy in

children [36]. Since then there have been several mechanistic theories explaining the KD’s

anticonvulsant effects including direct effects from ketones [5] and glucose restriction [37] or

upregulation of GABA neurotransmitters [38]. Some have hypothesized that regulation of KATP channels

through either the reduction of ATP levels [39] or the accumulation of free fatty acids mediate the KD’s

effects [40, 41] while others have implicated more direct metabolic reasons. Observations linking KD

treatment to reduced glycolysis have propelled this concept of metabolic control. Key glycolytic

enzymes, such as fructose-1,6,bisphophate, are decreased during ketosis [42, 43] while direct inhibitors

of glycolysis, such as 2-deoxyglucose, mimic its effect [44]. In parallel, there is mounting evidence that

KD enhances oxidative phosphorylation through upregulation of regulatory genes [45, 46] or through

direct mitochondrial biogenesis [45].

In conjunction with data supporting increased aerobic respiration in KD’s treatment of epilepsy,

there is further evidence to suggest that enhanced glycolysis or glycolytic enzymes play a role in

neuronal excitability and epileptogenesis. High rates of glucose metabolism [26, 27], ATP depletion

59

[28], lactate dehydrogenase A (LDHA) activity and lactate production [27] have all been linked to

epilepsy in patients, animals and culture models. Furthermore, prolonged seizure activity has been

shown to impair mitochondrial bioenergetics [28]. A recent study by Sada et al. in 2015 described the

role of LDHA, the final enzymatic step and marker of glycolysis, in neuronal membrane depolarization

and seizure formation [6]. They showed that with LDHA inhibition, neurons were hyperpolarized and

kainate-induced seizures were reduced in mice [6].

We previously showed that chronically activated neurons sense a low energy environment and

begin to use glycolysis as a primary means of cellular energy metabolism. This switch from an aerobic

to a glycolytic phenotype is mediated by the AMPK/HIF1a hypoxia pathway and occurs through the

upregulation of LDHA. Given our previous findings and the above evidence implicating LDHA in

epilepsy, we sought to explore LDHA’s role in the pathogenesis of epilepsy and seizure formation. In

this study, we show that LDHA is upregulated in human epileptic surgical specimens and establish

LDHA as a potentiator of neuronal bursting in vitro. Finally, we show that upstream HIF1a mediates

LDHA expression and increases neuronal activity and seizures.

Methods

Human subjects and surgical specimens

Human subjects

Eleven medically refractory epilepsy patients (4 male; 43.5 ± 3.96 years) underwent preoperative

epilepsy evaluation which included structural MRI, functional MRI, scalp electroencephalography and

neuropsychiatric assessment [73]. Surgical candidates were referred for phase 2 intracranial monitoring

with the Surgical Neurology Branch at the Clinical Center at the National Institutes of Health. They

were enrolled under IRB-approved NIH protocol 11-N-0051 (ClinicalTrials.gov identifier

NCT01273129). Informed consent was obtained from all patients. We performed epilepsy surgery in

two separate stages. In the first stage, as indicated by preoperative planning, we placed platinum

60

subdural electrodes (PMT Corporation, Chanhassen, MN) over the temporal lobe and other brain areas

for recording of electrophysiological activity and seizure focus identification. Patients were monitored

for 1-3 weeks using continuous intracranial EEG (icEEG). We recorded iCEEG data from subdural

electrodes (PMT Corporation, Chanhassen, MN) sampled at 1000Hz using a Nihon Kohden EEG data

acquisition system. Once we identified the epileptic focus, we performed surgery to resect the focus and

other areas of the brain as clinically indicated. After the hospitalization, patients were followed for at

least 24 months postoperatively where their seizure burden (Engel classification), medication burden

and imaging was recorded (Table 1).

Surgical Sample Collection and Processing

We collected surgical specimens using standard surgical technique. After initial analysis by staff

pathologists, we divided resected tissue for both frozen and fixed samples. For frozen tissue, we set the

tissue samples in optimal cutting temperature compound (OCT) and submerged them in liquid nitrogen

for flash-freezing. We maintained these samples in our tissue bank at -80°C. For fixed tissue, we placed

tissue samples directly into 4% paraformaldehyde (PFA) for 48 hours. Following drop-fixation, we

placed the tissue into a phosphate-buffered saline (PBS) solution and maintained these samples at 4°C.

No tissue was removed solely for research purposes. Pathological diagnosis was obtained for all tissue

separately, including hippocampal and temporal cortex tissue.

Human tissue section immunohistochemistry

We performed immunohistochemical (IHC) analysis on 4% PFA fixed tissue. Prior to sectioning,

we embedded tissue samples in paraffin. Tissue was sectioned into 5µm slices and placed on standard

glass slides. We performed IHC on the Leica Bond Max automated stainer as previously described [63].

Briefly, we deparaffinized and stained sections from each block using the Hematoxylin and Eosin

(H&E) method. We performed immunostaining using antibodies specific to each antigen. We used an

61

anti-LDHA antibody (Abgent, San Diego, CA) diluted to 1:300, anti-NeuN antibody (Millipore,

Burlington, MA) diluted to 1:100, and anti-GFAP antibody (Leica, Wetzlar, Germany) diluted to 1:100.

Antigen retrieval was performed using anti-NeuN in citrate buffer for 20 minutes. Sections stained for

LDHA, NeuN and GFAP were consecutive within the same block.

Cell counting using semi-automated IHC segmentation analysis

To quantify cell staining and antibody expression we digitized individual slides with the Zeiss

Axio Scan Z1 (Carl Zeiss AG, Oberkochen, Germany) and analyzed cell counts using Zen Blue 2.3

software (Carl Zeiss AG, Oberkochen, Germany). We randomly assigned ten 400 x 400 µm regions of

interest (ROI) which we analyzed for each section of tissue and for each stain (LDHA, NeuN, and

GFAP). ROI’s from the three consecutive sections (stained for LDHA, NeuN and GFAP) were located

at the same coordinates for each section in order to obtain a representation of the same location within

the tissue block for each stained section for each tissue sample (Supplementary figure 1). For each ROI,

we performed automated segmentation of stained cells based on the color of the stain and background

thresholding. We distinguished brown staining from the blue counterstain (hematoxylin) using hue

thresholds and the saturation and intensity of color. We rejected all segmented objects less than 3µm or

greater than 30µm in diameter as these were unlikely to represent cells. We applied the same automated

segmentation procedure and thresholding to all of the samples for LDHA and NeuN stained sections to

obtain a cell count. This automated analysis was supervised by the investigators and observed for errors

in counting cells. The program was adjusted for sections with high background staining. GFAP staining

was not amenable to segmentation analysis (Supplementary Figure 1) and we therefore hand-counted

GFAP-positive cells in each ROI. Importantly, all automated segmentation, supervision, and both

automated and manual cell counting was performed while the investigator performing these analyses

was blinded to the pathological characteristics of the section/ROI.

62

Human section immunofluorescence

For the human samples, we cut frozen tissue specimens into 10µm sections on a cryostat at -

24°C and placed them on standard glass slides. For immunostaining, we blocked samples in 5% normal

serum matching the host of the secondary antibody and incubated them with primary antibodies

overnight at 4°C. We then incubated sections with a fluorescent conjugated secondary antibody for 1

hour at room temperature. In co-staining experiments, we applied the second primary antibody overnight

at 4°C after incubation with normal serum matching the host of that secondary antibody. We mounted

coverslips with Vectashield mounting medium (Vector Laboratories, Burlingame, CA). We used an anti-

LDHA antibody (Abgent, San Diego, CA) at a dilution of 1:1000 and an anti-NeuN antibody (Millipore,

Burlington, MA) at a dilution of 1:1000 for immunofluorescence.

Rat cortical in vitro model and applied experiments

To study the downstream electrophysiological consequences of LDHA, we utilized an

established mixed rat cortical cell culture model on a microelectrode array (MEA) and standard tissue

culture plates. The use of animals in this protocol was approved by the National Institute of Health

Animal Care and Use Committee, followed all regulatory requirements and guidelines, and was

conducted in a facility that is accredited by the Association for Assessment and Accreditation of

Laboratory Animal Care (AAALAC), International.

Cell culture and maintenance

We cultured rat cortical neurons from newborn P1 rat pups of any sex. We dissected cortices in a

modified Puck’s dissociation medium [100 mL 20X D1 (80g NaCl, 4g KCl, 0.45g Na2HPO4.7H2O, 0.3g

KH2PO4, 0.012g Phenol Red in 1L deionized water), 100mL glucose/sucrose solution (30g anhydrous

glucose + 74g sucrose to 500mL deionized water), 10mL 1M HEPES buffer, pH to 7.4, osm to 320-

330]. Once dissection was complete, we dissociated and tritiated the cells in a Puck’s/papain solution

63

(10mL D1, 100µL 150mM CaCl, 100µL 50mM EDTA, 75µL papain (Worthington Biochemical

Corporation, Lakewood, NJ) and 0.01µg cysteine. We then plated 200,000 cells per well in either a 96-

well standard tissue culture plate (Grenier Bio-One, Frickenhausen, Germany) or a 48-well Axion

CytoView microelectrode array (MEA) plate (Axion Biosystems, Atlanta, GA) coated with 1mg/mL of

poly-D-lysine (PDL) in borate buffer (pH 8.4). On average, we harvested cells from 12 pups (male or

female) for culture in each plate. Twenty-four hours after plating, we performed a full media change for

the cells, and they were subsequently maintained in maintenance medium.

Neuronal activity

We recorded neuronal activity of the cell cultures in the 48-well MEA using the Maestro Pro

MEA system (Axion BioSystems, Atlanta, GA). Each well contains 16 electrodes that record

extracellular voltage with a sampling rate of 12.5kHz. We identified action potentials (spikes) as time

points when the recorded trace exceeded a threshold of ±6 standard deviations from the baseline signal.

We defined neuronal bursts of spiking activity as events during which a minimum of 5 spikes were

detected on a single electrode with a maximal inter-spike interval of 100ms. We used the Neural Metrics

Tool (Axion BioSystems, Atlanta, GA) for spike and burst identification and for subsequent analyses.

During every 5-minute recording, we computed the rate of neuronal bursts in every electrode in each

well. In some cases, an individual electrode within a well did not record any spiking activity for the

duration of the recording. This often occurred because there were too few cells in the vicinity of that

electrode. We therefore also defined the number of active electrodes within each well as all electrodes

that demonstrated spiking activity with a minimum rate of 5 spikes/minute. We computed the average

rate of neuronal bursts across all electrodes within each well and normalized it by the number of active

electrodes in that well to account for any acute changes in bursting activity associated with frequent

media changes. In experiments with longitudinal daily media changes, we accounted for cell loss by

normalizing to an average cell loss ratio (Supplementary figure 2). To come up with this ratio, six MEA

64

plates were treated with daily with low Mg2+ media changes. DAPI counts were obtained in wells

without treatment and wells after 10 days of treatment. The average cell loss was 2.1-fold

(Supplementary figure 2). This ratio was used to normalize baseline burst frequency collected after 10-

day treatments involving media changes for low Mg2+ and dimethyloxalyglycine (DMOG) experiments

(below). We generated an average baseline burst rate for each well for each 5-minute recording. In each

MEA, we computed the average burst rate across 12 wells that were designated for each treatment

condition. We used at least 3 MEAs to perform each experiment examining the changes in neuronal

spiking and bursting activity to provide biological replicates.

We began recording neuronal activity of the cell cultures in each MEA on day 10 in vitro (10

DIV) to establish neuronal activity. We began daily 2-hour treatments with low Mg2+ medium on 14

DIV since neuronal firing rates stabilized by that time. We therefore considered the first pre-treatment

recording on the first day of low Mg2+ treatment as the day 1 baseline neuronal activity of the cell

cultures in each well. For all reported effects, treatment with low Mg2+ began on 14 DIV, and the days

on which each subsequent effect was observed are referenced to this start date. Twenty-four hours after

the tenth daily low Mg2+ treatment, a final baseline recording was obtained. This was used as the day 10

baseline burst frequency.

Low Mg2+ treatments

For each daily low Mg2+ treatment, we replaced the neuronal maintenance medium with low

Mg2+ medium (98.75% deinoized water, 1% 1M HEPES solution, 0.25% 1M KCl, 0.1% 2M CaCl,

0.0008% 0.25M glycine, 0.72g Glucose, 3.38g NaCl) and allowed the cells to undergo this treatment

incubated at 37°C for two hours. Following this two-hour treatment period, we replaced the low Mg2+

medium with fresh neuronal maintenance medium. Twenty-four hours after ten days of daily treatments

were complete, a baseline 5-minute recording was obtained, and cells were either lysed and collected for

protein analysis or fixed with 4% PFA for immunostaining. We compared baseline neuronal activity in

65

low Mg2+ treated cells to wells containing untreated control cells. Control cells underwent standard

biweekly ½ media changes to maintain the cells. We performed all experiments with a minimum of 3

biological replicates.

DMOG treatments

We used 1000µM DMOG (Sigma-Aldrich, St. Louis, MO) in neuronal maintenance medium to

upregulate LDHA via activation of the HIF1a pathway. Cultured cortical cells were treated with DMOG

daily for 2 hours for a period of 10 days. As with the above low Mg2+ treatments, we replaced the

neuronal maintenance medium with maintenance medium containing 1000µM DMOG and allowed the

cells to undergo this treatment incubated at 37°C for two hours. Following this two-hour treatment

period we replaced the 100µM DMOG medium with fresh neuronal maintenance medium. All

treatments were started on DIV 14. A baseline 5-minute recording was obtained prior to the first day of

DMOG treatment. This was considered the day 1 baseline. Twenty-four hours after ten days of daily

treatments were complete, a final baseline 5-minute recording was obtained. This was considered the 10-

day baseline. Cells were then lysed for protein analysis.

LDHA lentivirus treatment

To examine whether increased neuronal bursting itself is a result of the LDHA enzyme, we

overexpressed LDHA in cultured neurons using a lentivirus vector. To create the viral vector, we first

inserted the coding sequence of human LDHA into pLenti-C-myc-DDK-IRES-Puro vector (Origene,

Rockville, MD). We then validated the LDHA sequence and expression through Sanger’s sequencing

and immunoblotting. For lentivirus packaging, we co-transfected the LDHA plasmid with psPAX2

(Addgene, Watertown, MA 12260) and pMD2.G (Addgene, Watertown, MA 12259) into 293FT cells

using Lipofectamine 2000 (Thermo Fisher, Wlatham, MA). The supernatant was collected 24 and 48

hours after transfection. We then enriched the virus particles using Lenti-X concentrator (Clontech,

66

Shiga, Japan) and stored them at -80°C for future use. As a control, we used a pHIV-ZsGreen (Addgene,

Watertown, MA 18121) control virus prepared under the same conditions.

We treated dissociated cells with the LDHA lentivirus at a 1:1 cell to viral particle ratio prior to

plating on the MEA. In each well of the MEA there were 200,000 cells and 200,000 viral particles. As

above, the mixed cortical cultures were allowed to mature for 14 days (14) DIV prior to obtaining

recording. 14 DIV was the first baseline burst frequency recording. Daily 5-minute recordings were

obtained for 8 days. As above, all daily recordings were normalized to the number of active electrodes

during that recording. The control lentivirus was treated in the same manner.

In vivo Model

Animals

The use of animals in this protocol was approved by the University of Virginia Animal Care and

Use Committee, followed all regulatory requirements and guidelines, and was conducted in a facility

that is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care

(AAALAC), International.

We implanted ten mice with a cobalt wire and three mice with steel wire controls. All mice were

implanted with intracranial recording electrodes (below) and monitored for seizures with 24-hour video

electroencephalography (EEG). After the monitoring period, mice were sacrificed, perfused and

processed for evaluation. Six animals (three experimental and three control) were wild-type C57-black

mice (Jackson, Laboratories, Bar Harbor, ME) and the other seven were transgenic c-fos (cfos-tTA/cfos-

shEGFP, Jackson Laboratories, Bar Harbor, ME) mice. The c-fos animals allowed us to evaluate for

seizure propagation through a doxycycline-inhibited c-fos promoter region which modulates green

fluorescent protein (GFP) expression upon neuronal activation. This group was taken off doxycycline 24

hours before cobalt wire placement. Three animals received intraperitoneal homocysteine (841mg/kg) to

induce status epilepticus at the end of life, for a separate experiment.

67

Surgical procedure

The same surgical procedure was performed across all mice. A 500µm cobalt wire was placed

under stereotactic guidance anterolateral to the left bregma and deep to the outer table. Two stainless

steel recording electrodes were placed 1mm posterior and 1mm on either side of the bregma just under

the inner table of the skull. A left hippocampal depth electrode was placed along with a reference

electrode into the posterior fossa. Electrodes were fixed with dental cranioplast. Animals were

connected to continuous EEG monitoring.

Clinical and seizure monitoring

Seizures were evaluated for behavioral score, duration, location of initiation and pattern of

propagation, latency to first seizure, and total number of seizures and seizure frequency.

Clarity

C-fos mouse brains underwent modified CLARITY for evaluation of cellular fluorescence but

without electrophoresis to clear lipid molecules [74]. For lipid separation, tissue was incubated with

sodium dodecyl sulfate (SDS) and imaged using 2-photon microscopy.

General Methods

Cell culture Immunofluorescence

Prior to immunofluorescence, we fixed the cells in 4% PFA and washed them with 1X DPBS

(Thermo-Fisher Scientific, Waltham, MA). We permeabilized the cells with 0.3% Triton-X diluted in

1X DPBS and blocked them in 5% goat serum at room temperature for one hour. For nuclear stain, we

used DAPI dye (Thermo-Fisher Scientific, Waltham, MA) at 1:5000 in 1X DPBS.

68

Cell culture western blot

Once treatments were complete, we lysed and collected cells from both the standard tissue

culture plates and from the MEA plates for protein analysis. We performed Western blot analysis as

previously described [62]. Briefly, we collected cell lysates and quantified protein using a standard BSA

curve. We loaded equal amounts (15µg) of protein into a mini-PROTEAN TGX 10% gel (Bio Rad) and

ran the gel in tris/glycine buffer at 200V for 30 minutes for separation. We then transferred the samples

to a nitrocellulose membrane by electroblotting and blocked the membranes in 5% non-fat milk diluted

in wash buffer (1X PBS with 0.1% Tween-20). After blocking, we incubated the membranes with

primary antibody diluted in 5% non-fat milk overnight at 4°C. We then washed the membranes and

applied a horseradish peroxidase conjugated secondary antibody (1:5000) at room temperature for one

hour. Finally, we exposed the membranes with Super Signal West Femto Maximum Sensitive Substrate

(Thermo-Fisher Scientific, Waltham, MA) and imaged them using the FluorChem Imager

(ProteinSimple, San Jose, CA). We processed and quantified the blots using ImageJ software (NIH,

Bethesda, MD). We used anti-LDHA monoclonal antibody (AF9D1) at a dilution of 1:1000. As a

loading control, we used anti-Vinculin monoclonal antibody (Abcam, Cambridge, MA) at a dilution of

1:2000. All antibodies used for the Western blots were validated in respective assays and species.

Statistical analysis

We used GraphPad Prism (San Diego, CA) for all statistical analyses. We performed paired or

unpaired students t-tests when comparing changes in neuronal burst rates or LDHA expression within or

across treatment groups, respectively. We used a one-way ANOVA to test for differences between

multiple treatment conditions and post-hoc Bonferroni’s multiple comparisons testing for each

individual group against control or low Mg2 baseline. We designated the level of significance for all

statistical tests as p < 0.05 or lower, depending on multiple comparison testing. All data are reported as

mean ± SEM unless otherwise noted.

69

Results

LDHA is upregulated in human epileptic neurons

To examine the metabolic changes associated with epilepsy, we analyzed epileptic hippocampal

and non-epileptic cortical tissue from eleven participants who underwent surgery for epilepsy

monitoring and resection of epileptic foci. All patients were found to have hippocampal-onset seizures

and underwent an anterior temporal lobectomy with hippocampectomy. In all cases there was

pathological confirmation of hippocampal pathology and all patients had seizure freedom or seizure

reduction after surgery (Table 1). Resected temporal lobe tissue was pathologically normal in 8 out of 11

patients and 3 patients showed evidence of microdysgenesis, presumably from involvement in frequent

seizures. We analyzed resected surgical specimens in each participant, comparing the epileptic

hippocampus to non-epileptic temporal cortex. We hypothesized that regions of the brain exhibiting

epileptic activity should also demonstrate increased expression of LDHA.

Using immunohistochemistry and a semi-automated segmentation analysis (Figure 1a;

Supplementary Figure 1; see Methods), we computed the percentage of neurons (NeuN-positive cells)

that also exhibited positive staining for LDHA. We found that epileptic hippocampal tissue exhibits a

significantly larger percentage of neurons that stain positively for LDHA compared to non-epileptic

temporal cortex (n = 11 participants; t(20) = 4.461, p = .0002, unpaired t-test; Figure 1a, c). We further

confirmed that LDHA is overexpressed in epileptic tissue by computing the proportion of combined

neurons and glial cells (NeuN and GFAP positive staining) and found that these percentages are also

significantly higher in hippocampal compared to cortical tissue (n = 11 participants; t(20) = 3.092, p =

.0058 unpaired t-test; mean ± SEM) (Figure 1a, d). Using immunofluorescence, we confirmed that the

elevated expression of LDHA co-localizes to NeuN-positive neurons in epileptic tissue, suggesting that

LDHA upregulation in epileptic tissue is a phenomenon specific to neurons (Figure 1b).

70

Temporal Cortex Hippocampus

% L

DHA

(LDH

A/Ne

uN)

Normalized LDHA

Tem

pora

l Lob

eH

ippo

cam

pus

LDHA NeuNa)

c)

100uM

*

0.0

0.2

0.4

0.6

0.8

1.0

100uM

100uM 100uM 100uM

100uM

GFAP

% L

DH (L

DH/N

euN+

GFA

P)

0.0

0.2

0.4

0.6

0.8

1.0

Normalized LDHAd)

Temporal Cortex Hippocampus

b) Temporal Cortex

Hippocampus

DAPI/NueN/LDHALDHA

DAPI/NueN/LDHA

NueN

DAPI

DAPI

NueN

LDHA 40x

40x

71

Figure 1. LDHA is upregulated in human tissue.

(a) Tissue from epileptic hippocampus was compared to non-epileptic temporal cortex. Ten representative 400x400 µm

regions of interest were analyzed for NeuN, LDHA and GFAP using semi-automated segmentation. Epileptic hippocampal

tissue demonstrates significant LDHA staining compared to non-epileptic cortical tissue, while the neuronal marker (NeuN)

and glial marker (GFAP) were approximately the same between the two specimens. We normalized LDHA staining to NeuN

to account for differences in neuronal density across specimens and to NeuN and GFAP to account for neuronal and glial cell

density. (b) Immunofluorescence of tissue demonstrates co-localization of LDHA and NeuN in epileptic hippocampus

(bottom), and very minimal LDHA staining in the non-epileptic temporal cortex (top). (c) NeuN normalized LDHA staining,

averaged across ten 400x400 µm regions of interest in each participant, is significantly elevated across participants in

hippocampal compared to cortical tissue (n = 11 participants; t(20) = 4.461, p = 0.0002, unpaired t-test; mean ± SEM) (c)

NeuN + GFAP normalized LDHA staining, averaged across ten 400x400 µm regions of interest in each participant, is

significantly elevated across participants in hippocampal compared to cortical tissue

LDHA expression causes increased baseline neuronal bursting

In a previous study we showed that daily low Mg2+ treatments caused upregulation of LDHA in

cultured neurons. Here, we used this model to determine if LDHA expression is linked to baseline

neuronal firing. We cultured mixed rat cortical cells in each of the 48 wells on a microelectrode array

(MEA). After the cells matured and firing rate stabilized (in vitro day 14), we treated them with daily

low Mg2+ media for two hours. At the end of the 10-day treatment we recorded baseline neuronal

spiking activity through 16 electrode contacts within each well (Figure 2a; see Methods). In each MEA,

we captured baseline neuronal burst frequency in 6 - 12 treated wells prior to low Mg2+ treatment and 24

hours after ten days of low Mg2+ treatment (when bursting stabilized to a new baseline) (Figure 2c, d).

This was compared to control wells with untreated neurons (Figure 2c, d). After ten days of low Mg2+

treatment and a final baseline recording was obtained, we lysed the cells and probed for LDHA protein

expression.

In an exemplar MEA, wells treated with daily low Mg2+ exhibit higher levels of LDHA

expression as compared to the control wells (Figure 2b (top)). Across several MEAs, wells treated with

72

low Mg2+ exhibit significantly higher levels of LDHA expression as compared to the control wells (n =

5 plates, t(8) = 3.119, p = .0142, unpaired t-test) (Figure 2b (bottom)). In control wells there was no

change in baseline bursting activity on day 10 when compared to day 1 (Figure 2c (top), 2d (left)). In

treated cells, however, there was higher baseline bursting activity on the tenth day of measurement when

compared to day 1 (Figure 2c (bottom), 2d (right)).

We quantified the ratio of baseline burst frequency at the end of ten days of low Mg2+ treatment

to the day 1 pretreatment baseline burst frequency to assess low Mg2+-induced changes in burst

frequency and to compare these changes between conditions (Figure 2e). This ratio reflects the extent to

which ten days of treatment with low Mg2+ increases baseline bursting. The average baseline burst

frequency ratio across wells is significantly higher in the low Mg2+ treated wells compared to the control

wells (n = 6 MEA plates; t(5) = 3.657, p = .0146, unpaired t-test; mean ± SEM). In the context of

concurrently elevated LDHA expression, these data suggest that LDHA plays a role in regulating

neuronal bursting.

73

74

Figure 2. Overall burst frequency increases after ten days of LDHA upregulation

Cultured neurons were treated with low Mg2+ medium for ten days. LDHA expression and baseline neuronal burst frequency

was compared after 10 days. Bursting is defined as a minimum of 5 consecutive spikes with a maximal inter-spike interval of

0.1s. (a) Schematic of a mixed cell population cultured on a microelectrode array (MEA). Cultures include neurons,

astrocytes and glial cells. We recorded neuronal spiking activity from each of sixteen electrode contacts within each well of

the MEA (b) Protein expression of LDHA in a sample plate treated with low Mg2+ daily for ten days (top). Each Western blot

represents protein expression in cells pooled from 6-12 wells in a plate. Protein expression is normalized to vinculin in all

Western blots. Across plates, LDHA expression is significantly higher than the control wells following ten days of low Mg2+

treatment (bottom) (n = 5 plates, t(8) = 3.119, p = 0.0142, unpaired t-test). (c) Thirty second raster plots show unchanged

bursting for each of the 16 electrodes in a single well from day 1 to day 10 in the control setting (top). Daily treatment with

low Mg2+ causes increased baseline bursting after ten days (bottom). (d) We compared neuronal activity in 12 wells treated

with low Mg2+ in each MEA (right) to activity in 12 control wells (left). The color of each well indicates the average baseline

burst rate within that well on day 1 and after 10 days of treatment (box). Burst frequency is mostly unchanged between day 1

and day 10 in control wells but increases with low Mg2+ treatment. Green, dark green, red and dark red circles represent the

wells used for visualization of spiking activity in (c). (e) Baseline burst frequency on day 10 was normalized to active

electrodes, cell count and to baseline bursting on day 1 prior to treatment. Daily low Mg2+ treatment leads to a significantly

increased baseline bursting, correlates to an increase in LDHA expression seen in (b) (n = 6 MEA plates; t(5) = 3.657, p =

0.0146, unpaired t-test; mean ± SEM).

To further test this hypothesis and to examine whether increased neuronal bursting itself is a

result of the LDHA enzyme, we overexpressed LDHA in cultured neurons using a lentivirus vector. We

compared LDHA-lentivirus treated cells to neurons treated with control lentivirus. As expected, the

LDHA lentivirus vector increased LDHA expression compared to control lentivirus (Figure 1a – b). We

measured the daily change in baseline bursting by normalizing daily bursting to day 1 burst frequency.

Cells overexpressing LDHA had a significantly higher rise in daily burst frequency ratios compared to

control lentivirus treated cells (n = 2, F(1,28) = 136.75, p < .0001, ANCOVA). Taken together with the

low Mg2+ model and human data, these data suggest that LDHA expression directly causes an increase

in baseline bursting and is a possible mechanism underlying epileptic activity in pathological neurons.

75

Figure 3. LDHA overexpression leads to increased neuronal bursting

We over-expressed LDHA in neurons cultured on an MEA to directly study the electrophysiological consequences of LDHA

expression. (a) An LDHA coding sequence was inserted into neurons using a lentivirus vector. This caused higher LDHA

expression when compared to a control lentivirus vector. (b) Western blot quantification demonstrates increased LDHA

expression in LDHA lentivirus treated cells compared to control lentivirus treated cells (n = 4 MEAs, t(4) = 3.038, p =

0.0385, unpaired t-test, mean ± SEM). (c) We normalized daily baseline burst frequency to bursting on day 1 to obtain daily

burst frequency ratios. Daily burst frequency ratios were plotted against time and cells expressing LDHA had significantly

higher daily increases in burst frequency ratio compared to control cells (n = 2, F(1,28) = 136.75, p < .0001, ANCOVA).

c) Burst frequency ratio

1 2 3 4 5 6 7 81

2

3

4

5

6

Day

Control LentivirusLDHA Lentivirus

Fold

Cha

nge

LDH

A30kD

aVinculin117kD

a

ControlLentivirus

LDHALentivirus

Protein expression Protein expressiona) b)

0

1

2

3

Fold

Cha

nge

ControlLentivirus

LDHALentivirus

*

76

HIF1a regulates LDHA-induced neuronal bursting

We previously described that the AMPK/HIF1a hypoxia pathway regulates the transition from

aerobic respiration to glycolysis in chronically activated neurons. Based on these findings, we

hypothesized that HIF1a-regulated LDHA expression is also responsible for the above increased

bursting activity and possibly epilepsy. We first tested this hypothesis in neurons cultured on an MEA.

Cells were treated with daily DMOG and neuronal activity was recorded after ten days of treatment.

DMOG upregulates HIF1a transcription by inhibiting prolyl-4-hydroxylase, which is known to post-

translationally down-regulate HIF1a (Figure 5a). Ten days of DMOG treatment caused a concurrent

increase in LDHA expression as well as a significant increase from day 1 to day 10 in baseline neuronal

bursting when compared to control cells.

Figure 4. Upregulation of LDHA expression through HIF1a causes increased neuronal bursting

(a) Western blot showing that the addition of DMOG, an HIF1a stabilizer, causes the upregulation of LDHA expression after

10 days of treatment. (b) Western blot quantification showing significantly increased LDHA expression with DMOG

treatment (t(6) = 3.7277, p = 0.0098, unpaired t-test; n = 4 plates). (c) The baseline neuronal burst frequency after 10 days of

treatment with DMOG was compared to day 1. After ten days of DMOG treatment, the baseline 10 day neuronal burst

frequency ratio is significantly higher than in control, suggesting that DMOG leads to LDHA expression which leads to

increased baseline bursting (t(8) = 2.825, p = 0.0223, unpaired t-test; n = 5 plates)

Contro

l

DMOG

LDH

A30kD

aVinculin117kD

a

Protein expression Protein expressiona) b)

Fold

cha

nge

Fold

Cha

nge

c)

1

*

05

10152025

Control DMOG Control DMOG

*

0

2345

Day 10 / day 1baseline burst frequency

77

In order to expand on this concept and to further study the effect of HIF1a-regulated LDHA

expression in epilepsy, we used an in vivo cobalt model of focal cortical epilepsy. Cobalt dissociates

VHL and HIF1a and thus stabilizes HIF1a by preventing its proteasomal degradation [75] (Figure 5a).

Cobalt implantation has been used to induce focal cortical seizures for many years, however the

mechanism underlying these seizures is unknown. A recent study showed evidence of hypoxia and

downstream VEGF upregulation in tissue surrounding a seizure-inducing cobalt lesion [76]. Given that

LDHA is directly downstream of HIF1a and responsible for bursting in culture, we used this model to

explore LDHA’s role in cobalt-induced epileptogenesis.

Ten mice were implanted with a 500µM diameter cobalt wire into the prefrontal cortex. Animals

were also implanted with ipsilateral frontal, contralateral frontal, and hippocampal electrodes for 24-

hour video EEG monitoring for an average of 3.6 days. Three animals were wild-type C57-black mice

and the other seven were transgenic c-fos (cfos-tTA/cfos-shEGFP, Jackson Laboratories, Bar Harbor,

ME) mice. The c-fos animals allowed us to evaluate for seizure propagation through a doxycycline-

inhibited c-fos promoter region which modulates green fluorescence expression (GFP) upon neuronal

activation. Animals were taken off doxycycline 24 hours prior to cobalt implantation. After seizure

monitoring, animals’ brains either underwent CLARITY for GFP analysis or stained for LDHA

expression. Cobalt animals were compared to steel-wire control animals for LDHA expression.

78

Figure 5. Cobalt implantation causes perilesional LDHA upregulation and seizures

We implanted a 1mm cobalt wire into the frontal cortex of 10 mice. Using 24 hour vEEG we monitored these mice for an

average of 3.6 days. Three control mice were implanted with a steel wire control and were monitored for 7 days. (a)

Monitoring electrodes were implanted into the ipsilateral frontal cortex, contralateral frontal cortex and into the ipsilateral

hippocampus of the cobalt wire. A typical seizure is represented here. Seizures began in the cortex ipsilateral to the cobalt

wire and quickly spread to the contralateral frontal cortex and hippocampus. (b) Transgenic c-fos mice expressing a

doxycycline controlled GFP promoter which fluoresces upon neuronal activation were used to evaluate seizure initiation and

propagation. After two days there was significant fluorescence in the perilesional cortex surrounding cobalt wire (left).

Staining for LDHA (n = 3) shows significant staining also in the perilesional cortex, mimicking the pattern of neuronal

activation seen in c-fos animals. (c) Five representative 400x400 µm regions of interest (ROI) were analyzed for GFP

expression in c-fos cobalt animal (left) and stained and analyzed for LDHA in wild-type cobalt and control animals (right).

GFP expression in seizing animals mimicked LDHA expression in ROI’s. There was more perilesional LDHA expression in

cobalt wire implanted animals compared to steel wire control implanted animals. (d) Representative schematic of HIF1a

upstreatm from LDHA expression. Both cobalt and DMOG (Figure 4) upregulate LDHA expression which leads to seizure

activity. (e) Perilesional LDHA staining, averaged across five 400x400 µm regions of interest in each animal, is significantly

elevated across mice in cobalt compared to control animals. (n = 3 animals; t(4) = 2.927, p = .0429, unpaired t-test; mean ±

SEM)

79

The average time until the first seizure was 1.26 ± 0.68 days and the average seizure duration

was 28.1 ± 4.2s (Table 2). Animals with an implanted steel-wire control had no seizures. In the cobalt

animals, EEG and GFP expression showed that seizures began in the cortex surrounding the cobalt

lesion and quickly spread to the contralateral frontal cortex and then hippocampus (Figure 5b and c

(left)). LDHA staining showed concurrent LDHA expression in the perilesional cortex which mimicked

GFP expression (Figure 5b (right), c (top)). Significant LDHA staining was seen in the cortex

surrounding the cobalt lesion but not surrounding the steel wire control (Figure 5d (middle, right)).

There was a significantly higher number of LDHA-positive cells in cobalt treated animals compared to

animals implanted with the steel-wire control.

Taken together, the similarity between DMOG and cobalt-induced LDHA expression and

subsequent bursting or seizure formation in vivo suggests that upstream HIF1a regulates LDHA-induced

seizures.

Discussion

In this study, we found that the glycolytic enzyme LDHA is increased in epileptic neurons of

patients with intractable epilepsy (Figure 1). In primary culture, indirect upregulation of LDHA

expression through low Mg2+ stimulation and direct upregulation with a viral vector causes increased

baseline neuronal bursting (Figure 2, 3). Finally, we showed that LDHA upregulation is modulated by

upstream HIF1a, a regulatory enzyme in the AMPK/HIF1a hypoxia pathway. In vitro upregulation of

HIF1a with DMOG and HIF1a stabilization with cobalt led to increased LDHA expression and

subsequently increased bursting in cultured neurons (Figure 3) and seizures in mice (Figure 4),

respectively. These data suggest a fundamental role for LDHA in the production of seizures which is

modulated by canonical upstream proteins.

When Sada et al. (2015) removed glucose from patched STN cells and added ketones (b-

80

hydroxubuterate) to mimic the KD, they showed significant neuronal hyperpolarization [6]. This

hyperpolarization was reversed by neuronal administration of lactate but when oxamate (an LDH

inhibitor) was introduced, this reduced membrane potential could only be rescued by pyruvate, not

lactate. Furthermore, introduction of oxamate into astrocytes led to the hyperpolarization of neighboring

neurons but not in the presence of lactate in ACSF. Based on the assumption that LDHA, which

preferentially converts pyruvate to lactate, was found in astrocytes, Sada et al. concluded that neurons

were electrically regulated by the astrocyte neuron lactate shuttle through LDH [6]. Although

convincing, we believe this conclusion is only partially accurate.

For their hypothesis to be true, the state of neuronal metabolism must be static. However, the

neuron’s metabolic phenotype, and thus its expression of LDHA, is in fact dynamic [64] and adjusts not

only to immediate energy shifts [19] but also to changes in response to more long-term energy demands

(Ksendzovsky et al., unpublished data). In 2017, Diaz-Garcia et al. used fluorescent NADH/NAD+

biosensors to show an astrocyte-independent preference for glycolysis located within stimulated neurons

in hippocampal slice cultures and in mice [19]. In the setting of neuronal stimulation, their data

contradicted the ANLS dogma and elucidated the very dynamic nature of neuronal metabolism. In

accordance with Diaz et al.’s conclusions, we recently showed that neuronal stimulation leads to

immediate energy depletion and when stimulated chronically, neurons transition from a quiescent

aerobic phenotype to a glycolytic phenotype. This occurs through the AMPK/HIF1a hypoxia pathway

and is marked by neuronal upregulation of LDHA (Ksendzovsky et al., unpublished data).

Given the pathological nature of epileptic neurons, we believe that understanding this dynamic

metabolic phenotype and how it impacts neuronal activation is even more relevant in the context of

epilepsy. Driven by upstream HIF1a upregulation, neuronal LDHA leads to neuronal bursting (Figure 4)

in culture and seizures in mice. These seizures’ onset localizes directly to perilesional areas with LDHA

upregulation (Figure 5) and occur after 1.26 days of cobalt implantation suggesting that, in fact,

neuronal LDHA is responsible for seizure initiation.

81

The same chronic low Mg2+-induced neuronal stimulation that upregulated LDHA in our

previous study also increased overall baseline neuronal bursting in this study (Figure 1). As chronically

stimulated neurons shifted their metabolic phenotype to glycolysis in order to accommodate elevated

energy requirements, they in essence became epileptic through elevated LDHA. This feedforward loop

is the foundation of our overarching hypothesis for metabolically driven pathogenesis of epilepsy

(Figure 6). We believe that chronic seizures shift neurons into glycolysis through AMPK/HIF1a

mediated upregulation of LDHA. As neuronal LDHA expression increases, neurons become

hyperexcitable and begin to burst and elicit seizures, as evidenced by our current results.

How LDHA upregulation modulates neuronal membrane potential is not known, however, the

KATP channel is a potential target. The KATP channel is distributed widely throughout the central nervous

system [77-79]. Under normal conditions, it remains constitutively inhibited by ATP. In times of high

energy demand the channel opens and hyperpolarizes the cell acting to maintain a negative membrane

potential [80, 81]. However, shifts in neurons’ metabolic phenotype can alter this channel’s efficacy

[81]. Higher rates of ATP production through glycolysis could potentially inhibit KATP channels.

Furthermore, lactate itself could play a direct role in neuronal membrane potential modulation as it has

been shown to directly inhibit KATP channels in ventromedial hypothalamic neurons [82].

As evidenced by competing mechanistic theories for the KD, mechanisms underlying the

metabolic control of epilepsy are complicated and involve many converging intracellular pathways. Our

study provides evidence that a single enzyme, neuronal LDHA, can elicit seizures and is a hopeful target

for future treatment. A better fundamental understanding of neuronal glucose utilization, however, will

be important in further uncovering the interplay between epilepsy and metabolism.

82

Figure 6. Representative schematic of the feedforward loop that drives metabolic control of epilepsy. As neurons are

chronically activated or seize, they upregulate LDHA expression and thus glycolysis (top arrows) through the AMPK/HIF1a

pathway (middle arrows) which is activated by a high AMP:ATP ratio. AMP leads to phosphorylation of AMPK, which

leads to stabilization of HIF1a. HIF1a translocates into the nucleus as a transcription factor to upregulate LDHA

transcription and protein expression and thus glycolysis. HIF1a-regulated LDHA expression goes on to further cause

pathologic activation in neurons (bottom arrow).

Tables

83

Supplementary Figures

Supplementary Figure 1.

(a) Resected specimens (hippocampus and temporal cortex) stained for LDHA, NeuN and GFAP. Red boxes represent

400x400 µm regions of interest used for analysis. We analyzed GFAP stained sections by hand.

Tem

pora

l Lob

eH

ippo

cam

pus

LDHA NeuNa)

400uM 400uM

400uM 400uM 400uM

400uM

GFAP

84

Supplemental figure 2.

(a) Control and low Mg2+ treated cells after 10 days of treatment. There is no cell loss in the control group (left)) while

approximately half of the cells are lost after 10 days of media changes for low Mg2+ treatment (right). White arrows indicate

electrodes left uncovered after cell loss. (b) There is a 2.1-fold significant cell loss in low Mg2+ treated cells compared to

controls (n = 6 plates, t(5) = 3.379, p = 0.0197, paired t-test). This ratio was used to account for cell loss in burst frequency

measurements after 10 days of treatment.

0

10000

20000

30000

Control Low Mg2+

Cell C

ount

*2.1x

a) b) DAPI cell cout10 day DAPI Immunofluorescence

Control Low Mg2+

85

V. Special Methods

Modeling epilepsy in a dish: mixed cortical cells cultured on a microelectrode array

1Marcelle Altshuler, BS; 1,5Alexander Ksendzovsky, MD; 2Muzna Bachani, BS; 1Stuart Walbridge, BS;

2Joseph Steiner, PhD; 1John Heiss, MD; 3,4Jaideep Kapur, MD, PhD; 1Kareem Zaghloul, MD, PhD

1Surgical Neurology Branch, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

2Drug Development Unit, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

3Department of Neurology, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

4Neuroscience Department, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

5Department of Neurological Surgery, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

86

Abstract

The purpose of this method is to establish a robust model of neuronal activation and

epileptogenesis in vitro. With the use of a microelectrode array that allows for detailed, comprehensive

recordings of electrical activity as well as direct access to cells through microscopy and molecular

probing, this model allows for a comprehensive approach for investigating the mechanisms regulating

neuronal activation and its consequences. In this model, we use low magnesium (Mg2) treatments of

mixed rat brain cortical cultures monitored by a microelectrode array to model frequent neuronal

activation and epileptogenesis.

The method described in this protocol includes preparation of microelectrode arrays, cortical

brain tissue harvest from postnatal rat pups, brain tissue dissociation, cell plating onto the

microelectrode arrays, culture maintenance, low Mg2 treatment to induce neuronal activation and

epileptogenesis, electrical activity recording from the microelectrode arrays, treatment termination, and

electrophysiological data analysis.

The advantages of this method are the exhaustive and powerful electrophysiological data obtained

through the use of the microelectrode array. The relatively simple manipulation of the cellular

environment is easily monitored during the recording of the microelectrode arrays. This method can be

expanded to varying treatments used for exploration of regulatory mechanisms, co-culture of different

cell types, and the use of human induced pluripotent stem cell neurons. Furthermore, this model can be

used for high throughput screening of putative novel antiepileptic therapies.

87

Introduction

To investigate the pathogenesis of neurological disease it is necessary to understand how

neuronal activity, regulatory molecular pathways, and circuitry interact. Epilepsy affects approximately

70 million people around the world and despite medical therapy, thirty percent of patients continue to

have seizures.1 The World Health Organization’s 2010 Global Burden of Disease study ranks epilepsy

as the second most burdensome neurologic disorder worldwide in terms of disability-adjusted life years.2

Although novel therapeutic strategies are actively being investigated, a principle reason for the lack of

treatment options is due to a lack of understanding of the molecular mechanisms underlying neuronal

activation and epileptogenesis. Furthermore, the link between pathological neuronal firing and ensuing

cellular and molecular changes is not well characterized, further limiting the approach to studying this

disease.

In order to explore the mechanisms underlying pathological neuronal activation we developed an

in vitro low Mg2 model of epilepsy on a multielectrode array. In our model, seizures are defined by

neuronal bursting activity. The advantage of our in vitro model is that it allows for investigation of

molecular mechanistic correlates of neuronal electrophysiology and thus seizure activity.

One of the first in vitro low Mg2 models of epilepsy was introduced in 1995 by Sombati et al.

The investigators used cellular recordings and calcium imaging to analyze neuronal firing [3]. Even

though they gained significant insights into epilepsy they were limited by constraints that come with

single cell analysis. Our adaptation of their method allows for robust investigation of thousands of

neurons in a more representative environment, taking account for cell-cell interaction and network

effects. Our model constitutes a more realistic representation of the circuity formed by neurons along

with supporting cells natural to the in vivo environment. This model can be used to test various pathways

along with inhibitors and potentiators of those pathways that may be involved in neuronal activation or

epileptogenesis.

88

Protocol

The use of animals in this protocol was approved by the National Institute of Health Animal Care and

Use Committee, followed all regulatory requirements and guidelines, and was conducted in a facility

that is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care

(AAALAC), International.

1. Preparing microelectrode array (MEA)

(approximately 1 hour working time, 3.5 hours total time)

1.1. This step is to be completed the day before the rat pup harvest.

1.2. In the culture hood, sterile PolyD lysine (PDL) powder is mixed with sterile borate buffer

in 1mg:1ml ratio.

1.3. 40ul of the PDL:borate buffer mixture is dropped directly onto the center of each well on

the MEA plate.

1.4. Leave MEA plate with the lid on for one hour in the culture hood.

1.5. Wash three times with 1x sterile DPBS with 200 ul per well.

1.6. Leave the MEA drying with the lid off for two hours in the culture hood.

1.7. Replace the lid and wrap the MEA with parafilm. The plate can be left in the hood until

ready for use the next day.

NOTE: Plates can be wrapped in parafilm and stored in 4° until later use.

2. Rat pup harvest

(approximately 1.5 hours working time, 1.5 hours total time)

NOTE: Steps 2 and 3 of this protocol must be done consecutively on the same day

2.1. This design is based on a typical rat pups yield of approximately 12-15 pups which will

plate approximately 4-5 MEAs depending on cell count.

89

2.2. Preparation for harvest

NOTE: this harvest procedure is semi-sterile.

2.2.1. The tissue dissection will be done under a microscope. Set up a sterile pad under

the microscope where the tissue dissection will be performed.

2.2.2. Tools to be laid out on the pad include fine tip scissors, forceps, fine tip forceps,

penfield 4, and scalpel.

2.2.3. Spray the pad and the tools with 70% ethanol.

2.2.4. Place a small clear plastic dish in which the rat pup brain will be dissected. Fill

the dish with 1-2 ml of D1 dissection media to keep tissue from dying out while

dissecting.

2.2.4.1. Recipe for D1 dissection media:

2.2.4.2. 100 mL 20X D1 (80g NaCl, 4g KCl, 0.45g Na2HPO4.7H2O, 0.3g KH2PO4,

0.012g Phenol Red in 1L deionized water)

2.2.4.3. 100mL glucose/sucrose solution (30g anhydrous glucose + 74g sucrose to

500mL deionized water)

2.2.4.4. 10mL 1M HEPES buffer, pH to 7.4, osm to 320-330

2.2.5. Have D1 dissection media ready on ice. The collected brain tissue will be

deposited in this media. Have two 15 ml tubes with approximately 10 ml of D1

dissection media in each.

2.2.6. Use sterile gloves once handling the head of the rat pup.

2.3. Sacrificing

2.3.1. Rat pups for harvest should be between postnatal day 0-1.

2.3.2. Anesthetize the animals using isoflurane for approximately 5 minutes.

2.3.3. Using large scissors to remove the heads of the animals.

2.3.4. Perform immediate dissection of the brain tissue (within 15 minutes)

90

2.3.5. Animals can be sacrificed in batches to facilitate step 2.3.4.

2.4. Dissection of brain tissue

2.4.1. At this point, place the head on the sterile pad and use sterile gloves to handle the

tools.

2.4.2. Place the head on the pad or hold the head over the pad until brain is removed.

2.4.3. Use the fine tip scissors to cut the skin on the cranial surface along the sagittal

plane, in the midline, from the base of the neck to the tip of the nose. Peel the skill

back to expose the skull.

2.4.4. Use the fine tip scissors to cut the skull (very thin and delicate) on the cranial

surface along the sagittal plane, in the midline, on the same track as the skin

incision. Then close to the center of the sagittal cut, make an orthogonal cut along

the coronal plane (coronal suture) on each side.

2.4.5. Use forceps to peel away skull exposing the brain. Be careful not to injure the

brain.

2.4.6. Use the penfield 4 to remove the brain along the base of the skull. You will need

to cut the trigeminal nerve for the brain to fall out. Place the brain into the plastic

dish with the D1 dissection media.

2.4.7. Use the scalpel to cut off the cerebellum. Then use the scalpel to bisect the brain

sagittally into the two hemispheres.

2.4.8. Use the fine tip forceps to remove the hippocampus from both hemispheres.

2.4.9. Use the fine tip forceps to peel away the meninges.

2.4.10. Place the remaining brain cortical tissue into the tube with D1 dissection media.

2.5. Harvest the cortices into two 15 ml centrifuge tubes each containing 10 ml of D1

dissection media on ice. The tissue should be divided evenly between the two tubes.

3. Tissue dissociation

91

(approximately 1.5 hours working time, 2 hours total time)

3.1. The following steps are all sterile and conducted in the culture hood.

3.2. Prepare sterile D1 dissociation media immediately prior to use.

3.2.1. To facilitate tissue dissociation, prepare two 15 ml centrifuge tubes with 10 ml of

D1 dissociation media (total 20 ml).

3.2.2. Recipe for 10ml D1 dissociation media:

3.2.2.1. 10 ml of D1 media

3.2.2.2. 100 ul of 150 mM CaCl

3.2.2.3. 100 ul of 50 mM EDTA

3.2.2.4. 75 ul of Worthington Papain (liquid)

3.2.2.5. 5 grains of cysteine (drop into lid of centrifugal tube, close, shake)

3.2.3. Place in 37° for ten minutes.

3.2.3.1. After ten minutes at 37°, add NaOH for pH goal 7.6 (it is appropriate to

approximate 25-30 ul per 10 ml of D1 dissociation media).

3.3. Tissue Dissociation and Cell Plating

3.3.1. In the cell culture hood, remove as much as possible of the D1 dissection media

from the tissue tubes using a 5ml pipette without disturbing the tissue.

3.3.2. Transfer half of the harvested cortices into one tube with 10 ml D1 dissociation

media and the other half of harvested cortices into the other tube with 10 ml D1

dissociation media. This may be done by pouring the tissue from the tissue tube

into the D1 dissociation media tube or by sterile forceps.

NOTE: Two tubes increases the surface area for dissociation and

facilitates tissue dissociation.

3.3.3. Place the tissue in the D1 dissociation media into the incubator for 15-25 minutes.

NOTE: If the tissue becomes very viscous, it has been too long. The

92

viscous material represents DNA from lysing cells.

3.3.4. Remove as much D1 dissociation media as possible using a 5ml pipette without

disturbing the tissue or any cells.

3.3.5. Immediately add 1-2 ml of 2x antibiotic rat media onto cells in each tube to stop

the dissociation reaction.

3.3.6. Recipe of 2x antibiotic rat media 500 ml:

3.3.6.1. 454.5 ml neurobasal media (Gibco, Gaitherburg, MD)

3.3.6.2. Hepes (1M) 2.5 ml (0.5%)

3.3.6.3. B-27 Supplement (50x) 10 ml (2%)

3.3.6.4. Antibiotic-antimycotic (100x) 10 ml (2%), (10,000 units/mL penicillin,

10,000 ug/mL streptomycin and 25ug/mL amphotericin B).

3.3.6.5. Fetal bovine serum 25 ml (5%)

3.3.6.6. L-glutamine 3 ml (0.6%)

3.3.6.7. Filter

3.3.7. Dissociate the cells

3.3.7.1. Begin with a 5 ml pipette. Pipette up and down. Take care to be gentle or

the cells will lyse.

3.3.7.2. Next use a 1000 ul pipette. Pipette up and down until the cells are a thick

liquid.

3.3.7.3. Add 3 ml of 2x antibiotic rat media to each tube.

3.3.7.4. At this point, combine the two groups of cells into one 50ml tube

(alternatively the cell groups can remain separate to serve as biological

replicates).

3.4. Filter the cells

93

3.4.1. Using 70-micron filter sitting over a 50 ml tube, pipette cells into filter and let

them seep through (may need more than one filter if cell solution is thick).

3.4.2. Once the cells have filtered into the 50 m tube, bring the volume up to 10 ml with

2x antibiotic rat media.

3.4.3. Perform a cell count.

3.4.4. Plate cells onto microelectrode array

3.5. Plate cells onto MEA

3.5.1. Plate 200,000 cells per well for MEA

3.5.2. Cell solution is made up in 2x antibiotic rat media.

3.5.3. Using a single channel pipette, deposit 100 ul cell solution directly into the center

of each well.

3.5.4. Allow the cells settle for one hour. Then add 100 ul of 2x antibiotic rat media.

4. Maintenance of cell culture

(approximately 10 minutes working time, 10 minutes total time)

4.1. Full media change 24 hours following plating cells onto MEA. Replace with 200 ul 2x

antibiotic rat media.

4.2. Partial media change one week following plating cells onto MEA. Replace 100 ul (half

the well volume) with 1x antibiotic rat media.

4.3. Recipe of 1x antibiotic rat media 500 ml:

4.3.1. 454.5 ml neurobasal media neur(Gibco, Gaitherburg, MD)

4.3.2. Hepes (1M) 2.5 ml (0.5%)

4.3.3. B-27 Supplement (50x) 10 ml (2%) (Gibco, Gaitherburg, MD)

4.3.4. Antibiotic-antimycotic (100x) 5 ml (1%)

4.3.5. Fetal bovine serum 25 ml (5%)

4.3.6. L-glutamine 3 ml (0.6%)

94

4.3.7. Filter

4.4. Continue with partial media change once per week until treatment (and through treatment

on the untreated cells).

4.5. Approximately 14 days after plating cells onto MEA (14 days in vitro (DIV), cells are

ready for recording and treatments. We start recording from cells on 10 DIV to observe

normalization of baseline activity.

5. Recording MEA

(approximately 10 minutes working time, 10 minutes total time)

5.1. When recording, ensure Axion is recording Maestro (Axis Raw), Spike Detector (Axis

Spike), Spike Detector (Spike Counts), and Burst Detector (Electrode Burst List and

Network Burst List).

5.2. Each recording should be saved in its own folder. One recording per folder will ease

future data processing and analysis.

5.3. Prior to treatments, establish a baseline firing and bursting rate and ensure that cells are

firing appropriately.

5.4. Begin recording approximately 10 days after plating cells on MEA. On 14 DIV, neuronal

NMDA channels have matured and synapses between neurons are typically at baseline.

5.5. Record the cell firing rate for 5 minutes daily.

5.5.1. When beginning recording, allow Axion to read the cell firing rate for 30 seconds

before beginning the recording of the firing rate. This will allow the neuronal

culture time to stabilize from transient increases in firing due to movement of the

plate (Figure 1).

5.6. Once cells are firing in the range of 80-100 spikes/minute, the cells have matured to

establish appropriate synaptic connections and are ready to begin treatments.

6. Treatment with low Mg2 media to induce seizure activity

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(approximately 10 minutes working time, 10 minutes total time per MEA)

6.1. For each treatment, record the MEA immediately prior to treatment (pretreatment

recording day #) and then record the MEA after two hours of the treatment (posttreatment

recording day #).

6.1.1. Note: There will be a spike of activity immediately after media change. This

occurs from any media change and is not sustained. This is not representative of

low Mg2 effects. True low Mg2-induced bursting is sustained after two hours. 4

6.2. Prepare low Mg2 media. Can be stored in 4° and used for a treatment course (10 days).

6.3. Recipe of low Mg2 media 400 ml:

6.3.1. 395 ml of DI water

6.3.2. Sodium Chloride 3.38 g

6.3.3. Glucose 0.72 g

6.3.4. KCl (1M) 1 ml

6.3.5. Hepes (1M) 4 ml

6.3.6. Calcium Chloride (2M) 400 ul

6.3.7. Glycine (0.25M) 3.2 ul

6.3.8. Filter

NOTE: Artificial CSF media with Mg2 may be used as a control, recipe for 400 ml:

6.3.9. 395 ml of DI water

6.3.10. Sodium Chloride 3.38 g

6.3.11. Glucose 0.72 g

6.3.12. KCl (1M) 1 ml

6.3.13. Hepes (1M) 4 ml

6.3.14. Calcium Chloride (2M) 400 ul

6.3.15. Magnesium Chloride (1M) 400 ul

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6.3.16. Glycine (0.25M) 3.2 ul

6.3.17. Filter

NOTE: maintenance media (above) is also used as control as it contains magnesium

7. Record the MEA for 5 minutes (pretreatment recording).

7.1. Remove all media (200 ul) from wells that will be treated to induce seizure.

7.2. Replace with 200 ul of low Mg2 media and leave in incubator for 2 hours.

7.3. Record the MEA for 5 minutes (posttreatment recording).

7.4. Remove 200 ul of low Mg2 media and replace with 200 ul of 1x antibiotic rat media.

7.5. Continue this daily until conclusion of treatment (typically 10 days).

8. Treatment terminations

8.1. Perform a treatment termination recording (5 minutes).

8.2. The MEA can be fixed for staining, lysed for protein, collected for mRNA analysis, or

any other appropriate method for analysis.

9. Cell electrophysiology analysis

9.1. Neurometrics tool for analysis (Axion Biosystems)

9.1.1. Use the Axion neurometrics tool to convert the Axion spike files into data excel

spreadsheets.

9.1.2. Open Neurometrics. Click file → Load Axion spike file. Select any one file for

processing. Extension for these files are *.spk

9.1.3. Once the single file is processed, the system now allows for faster processing of

multiple files (batch processing).

9.1.4. Click file → Batch process multiple files. Click Add file and select the spike file

and add. Continue adding each file for processing. Once all files have been added,

click batch process.

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9.1.5. The spreadsheets will auto populate into the folder from which the spike file was

selected. Each spreadsheet will have all of the data listed in one spreadsheet tab.

9.2. Transfer data into master spreadsheet

9.2.1. Below is our lab’s technique for processing data. There is a wide variety of

processing techniques once recording data becomes available.

9.2.2. Create one spreadsheet that will combine all of the recording data.

9.2.3. Open each individual spreadsheet per recording, copy the entire tab and paste into

the master spreadsheet. The master spreadsheet will have a tab per each recording.

This best done in chronological order.

9.3. Important data to note

9.3.1. There are many data points provided in the spreadsheet that can be used for

analysis but are beyond the scope of this paper.

9.3.2. Important data points to note for this model include mean firing rate, burst

frequency and active electrodes.

9.3.3. The last tab of the spreadsheet should be a compiled data tab that incorporates the

mean firing rate, burst frequency and active electrodes for each well at each time

point.

9.4. Normalize the data

9.4.1. The burst frequency should be divided by active electrodes in order to account for

cell loss and decreased signal loss between treatments (no active electrodes = no

cells) (Figure 3a – c).

9.5. Compare pretreatment to posttreatment firing

9.5.1. The 2-hour posttreatment to pretreatment (baseline) change in firing is analyzed

by comparing the normalized burst frequency between the two (2-hour post-

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treatment/baseline). We perform this analysis to establish bursting activity

directly associated with low Mg2 treatment.

9.5.2. Posttreatment can be divided by pretreatment and compared between treatment

days (bar graph). The normalized burst frequency for each well can also evaluated

across time (line graph) (Figure 4c).

Representative Results

To study the cellular mechanisms underlying the regulation of neuronal activation, low Mg2

media was applied to a mixed rat neuronal culture on MEAs. Following the protocol, rat cortical neurons

were cultured in 48-well MEA plates with each well containing 16 electrodes. Recording data was

pooled from 12 wells per treatment condition. Following plating on the MEA, for 14 days neurons were

permitted to mature and form synapses (Figure 1). Following this time period, the firing rate stabilized

on DIV 14 and the neurons were ready to undergo treatments. The neurons were treated daily with low

Mg2 media for two hours while electrical activity was monitored. Burst frequency, representative of

seizure-like activity, was recorded pretreatment and posttreatment for control cells and low Mg2 treated

cells. Neuronal burst frequency is visualized using the Neural metrics tool (Axion Biosystems) as a heat

map (Figure 2a) or as a raster plot (Figure 2b). Burst frequency significantly increases two hours after

treating cells with low Mg2 media (Figure 2a – b).

Cultures were treated with low Mg2 media for two hours daily for a total of 10 days. Across ten

days, baseline bursting activity increases in both treated and untreated cells (Figure 3a). Furthermore,

because low Mg2 media changes require frequent media changes, the total number of active electrodes

decreases (Figure 3b). This decrease in active electrodes reflects cells that are lost due to frequent media

changes which affects the bursting rate that is recorded by the electrodes. In order to adjust for this, as

per the protocol, the bursting rate is normalized to active electrodes to ensure that only the electrodes

that have cells, and thus are recording, are monitored (Figure 3c). When normalized to active electrodes,

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low Mg2 treatment causes daily increased normalized burst frequency when compared to control (Figure

3c).

Extra caution should be exercised during media changes to ensure that the bottom of the wells

and the cells are minimally disturbed. However, if care is not taken when changing from culture media

to low Mg2 treatment media and vice versa, many cells can be lost and results in loss of firing and

suboptimal results. Additionally, no cells will remain for analysis at the conclusion of the experiment.

Figure 1. (a) Brightfield imaging of a mixed cell population cultured on a microelectrode array (MEA). Cultures include

neurons, astrocytes and glial cells. Cells matured to form synapses, pictured here at DIV 1, 3, 10, and 14. Neuronal spiking

activity was recorded from each of the sixteen electrode contacts within each well of the MEA.

Figure 2. (a) We recorded neuronal activity in 12 wells that underwent two-hour treatment with low Mg2+ in each MEA

(right) to neuronal activity in 12 control wells (left). The color of each well indicates the average burst rate across active

electrodes within that individual well during baseline and at the two hours following treatment with low Mg2+. Burst

400uM 400uM400uM400uM

DIV 1 DIV 3 DIV 10 DIV 14a)

F

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sp/s

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Low Mg2+a) b)

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frequency is unchanged between the baseline state and the two-hour treatment state in the control wells but increases with

low Mg2+ treatment. Green and red circles represent the wells used for analysis of spiking activity. (b) Thirty second raster

plots of spiking activity in all 16 electrode channels in a well before treatment with low Mg2+ (left) and after treatment with

low Mg2+ (right).

During a single daily low Mg2 treatment, each individual well demonstrates an increase in burst

frequency across electrodes when compared to untreated control cells (Figure 4a – c, Figure 3c). To

establish a baseline level of activity, firing rate and burst frequency were measured prior to treatment (0

min pre). Firing rate and burst frequency were measured again immediately after media change to low

Mg2 media (0 min pre), two hours after treatment with low Mg2 media (2-hour post), immediately after

washout and back to maintenance media (2.2-hour post) and then again two hours after washout

(baseline) (Figure 4a). Changing media causes a transient, unsustained spike in burst frequency (Figure

4a, 0-min post and 2.2-hour post). These spikes are not considered a true rise in burst frequency and thus

are not considered as part of the analysis (Figure 4c). Neurons treated with low Mg2 show a sustained

increase in burst frequency at two hours compared to baseline (Figure 4b). The observed increased

bursting activity returns to baseline two hours following termination of treatment (Figure 4b). We

quantified the ratio of burst frequency at the end of two hours of treatment to the pretreatment baseline

burst frequency in each well to assess low Mg2-induced changes across multiple days of treatment and to

compare these changes between conditions. This ratio reflects the extent to which treatment causes an

increase in bursting activity over baseline. We saw an increase in burst frequency every day for the

duration of the ten-day treatment (Figure 4c). Over the entire ten-day treatment period, treated neurons

had a significant increase in burst frequency when compared to control cells (Figure 4d) (n = 10 days,

t(9) = 7.053, p < .0001, unpaired t-test).

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Figure 3. (a) Baseline burst frequency recorded across ten days increases in both control and treated cells. (b) In treated cells,

the total number of active electrodes decreases due to cell loss associated with daily low Mg2 media changes. This resulting

cell loss affects the bursting rate that is recorded by the electrodes. (c) Burst frequency is normalized to active electrodes,

accounting for cell loss associated with daily media changes, and is shown to increase in the treated group compared to the

control group. Low Mg2 treatment causes daily increased normalized burst frequency when compared to control.

0.0

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a) Burst Frequency (non-normalized) Burst Frequency (normalized)Number of Active Electrodesb) c)

10987654321-1-3 10987654321-1-3 10987654321-1-3Day Day Day

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Figure 4. (a) Burst frequency was measured prior to treatment (0 min pre), immediately after media change to low Mg2

media (0 min pre), two hours after treatment with low Mg2 media (2-hour post), immediately after washout and back to

maintenance media (2.2-hour post), and then again two hours after washout (baseline). There is an unsustained spike in burst

frequency associated with media changes shown at 0min post and at 2.2-hour immediately after media change. This transient

increase in not sustained and returns to baseline two hours later. (b) Treated neurons, however, show a sustained increase in

burst frequency at two hours compared to controls. (c) The ratio of normalized burst frequency at two hours compared to

pretreatment is increased in the low Mg2+ treated group compared to controls every day. (d) This increase in burst frequency

is consistent across all ten days of treatment.

0.00

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c) Daily burst frequency ratios d) Burst frequency ratio

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Discussion

There are a few keys steps that are critical to the successful completion of this protocol. Since

this protocol describes harvesting neurons from postnatal rat pups, harvesting the rat pups within the

specified time frame is critical to ensure that neurons are not too mature to successfully survive and

create an electrical network in culture. Additionally, the tissue can be sensitive to dissociation and care

must be taken to stop the dissociation reaction within the time specified. Otherwise, the dissociation

media may lyse the cells and yield an insufficient cell count for full plating of the MEAs. The steps for

preparing and plating the cells in this protocol are similar to other methods typically used for cell

culture. However, due to the structure of the MEA well, it is critical that when coating the MEA with

PDL/borate mixture as well as when depositing the actual cells, a single channel pipette is used to

directly touch the center of the well. This ensures that cells will adhere to the area from where the MEA

electrodes will record activity. Adequate recordings from these electrodes are necessary for successful

experiments.

There is variability in the success of tissue dissociation and how many cells are lysed during the

dissociation period. This will become apparent when pipetting the tissue during mechanical dissociation.

The more cells that have lysed, the more viscous the cell mixture will be (representing DNA that is in

the mixture from the lysed cells). The dissociation time can be varied to optimize cell yield and

minimize lysis.

The method described in this protocol can be modified to explore regulatory pathways of

epilepsy. Drugs and compounds may be added within the low Mg2 treatment to investigate inhibition of

burst frequency in the setting of increased electrical activity. Pathways that potentiate epilepsy may be

explored by adding drugs or compounds hypothesized to increase burst frequency for the treatment time

instead of low Mg2 media. Additionally, cells can be left in a non-toxic treatment condition over days

and have electrical activity monitored. With respect to alternative techniques, this model provides a

more straightforward method of modifying conditions that may affect electrical activity and easily

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recording this activity to present comprehensive data on electrophysiology.

A limitation of this technique is the use of harvested cells from postnatal rat pups rather than the

use of a cell line. Harvested cells do not survive for more than a couple months which limits the length

of time they can be studied. Although our cell culture model is a representative mix of neurons and

supporting cells it is not a true in vivo environment in that it lacks normal brain architecture, blood

vessels, afferent input, among other things. However, despite these limitations, the low Mg2 culture

model provides a practical model to explore molecular mechanisms underlying neuronal activation and

epilepsy.

Future applications of this model can be used to explore varying cell types and co-cultured cells.

Human neurons, though less robust in electrical activity without the support of glial cells in culture, can

be used as well. Many exciting and novel modifications can be made to this protocol to further explore

mechanisms of neuronal activation and epileptogenesis.

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A novel mouse model of cobalt-induced focal cortical epilepsy

1,4Alexander Ksendzovsky, MD; 3John Williamson, BS; 4John Hantzmon, BS; 3Pravin Wagley, MS;

3Suchitra Joshi, PhD; 1John Heiss, MD; 3,4Jaideep Kapur, MD, PhD; 1Kareem Zaghloul, MD, PhD

1Surgical Neurology Branch, National Institute of Neurologic Disorders and Stroke,

National Institute of Health, Bethesda, Maryland

2Department of Neurology, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

3Neuroscience Department, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

4Department of Neurological Surgery, University of Virginia Health System

University of Virginia, Charlottesville, Virginia

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Abstract

Introduction

Despite previous efforts, there remains no currently accepted mouse model for focal cortical epilepsy, which

accounts for a significant burden of disease. In this study we present a novel mouse model of cobalt-induced

chronic focal cortical epilepsy. We describe seizure and clinical outcomes and elaborate on the nature and pattern

of seizure propagation.

Methods

We analyzed four separate treatment groups, including five mice with cobalt implanted into the prefrontal cortex,

sixteen mice injected with homocysteine (HT) and five mice concurrently implanted with cobalt and injected with

HT. Animals were continuously monitored with video-electroencephalography. CLARITY was used to evaluate

neuronal activation in a fourth group of five transgenic c-fos mice that housed a doxycycline-controlled promoter

responsible for expressing fluorescent protein in activated neurons.

Results

Animals implanted with cobalt and injected with HT showed increasing seizure behavior scores and seizure

frequency throughout the monitoring period. This contrasted with other groups that showed significant seizure

reduction after 1-2 weeks. All animals in the concurrent cobalt with HT group went into status epilepticus after

injection, which was staged and characterized. We believe induction of SE with HT is necessary to produce

chronic focal epilepsy in mice. In all four groups, seizures illustrated similar patterns of propagation on EEG. This

was further visualized in the c-fos mice demonstrating perilesional neuronal activation spreading to the ipsilateral

then contralateral motor cortex and finally to bilateral hippocampi.

Conclusion

In this study, we establish a chronic model of focal cortical epilepsy using cobalt wire implantation and

homocysteine injection. This model can be used to probe mechanisms and novel treatments for focal cortical

epilepsy.

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Introduction

A murine model of disease can provide robust insight into its molecular, genetic and electrophysiological

properties. Despite prior efforts, there remains no currently accepted mouse model for focal cortical epilepsy

which accounts for more than 60% of epileptic seizures [83-85]. A working model of focal cortical epilepsy can

potentially unearth tremendous insights into this high disease burden.

In this study we present a novel mouse model of cobalt-induced chronic focal cortical epilepsy. Seizure

are critically evaluated across three experimental groups of mice that were either implanted with cobalt, received

homocysteine injection or both. We describe seizure and clinical outcomes and elaborate on the nature and pattern

of seizure propagation using a transgenic c-fos mouse model.

Methods

Animals

The use of animals in this protocol was approved by the University of Virginia Animal Care and Use

Committee, followed all regulatory requirements and guidelines, and was conducted in a facility that is accredited

by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC), International.

To study cobalt-induced seizures we used four separate timing paradigms. The first included five adult

C57 wild-type (WT) mice implanted with cobalt wire (500mm diameter) into the prefrontal cortex. We also

implanted four intracranial recording electrodes for video-electroencephalography (video-EEG) monitoring. We

followed the mice with continuous video-EEG monitoring for 30 days and subsequently sacrificed, perfused and

processed them for histologic evaluation. We implanted a second group of 16 C57 WT mice with monitoring

electrodes, injected an intraperitoneal (841mg/kg) dose of HT and monitored for seizure activity for 30 days. A

third group of five C57 WT mice was implanted with a prefrontal cobalt wire and monitoring electrodes, injected

with homocysteine (841mg/kg) on post-operative day seven and followed with continuous video-EEG for 45

days. After monitoring we harvested the brains for histological analysis. Finally, we evaluated a fourth group of

seven transgenic c-fos (cfos-tTA/cfos-shEGFP, Jackson Laboratories, Bar Harbor, ME) mice for neuronal GFP

expression and thus seizure propagation. The c-fos mice had a doxycycline-mediated c-fos promoter region which

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allows for green fluorescence upon neuronal activation. This group was taken off doxycycline 24 hours before

cobalt placement and were monitored by video-EEG for a total of 48 hours.

Surgical procedure

The same surgical procedure was performed across all mice. We implanted A 500µm cobalt wire under

stereotactic guidance anterolateral to the left bregma and 1mm deep to the outer table. Two stainless steel

recording electrodes were placed 1mm posterior and 1mm on either side of the bregma just under the inner table

of the skull. We placed a left hippocampal depth electrode along with a reference electrode into the posterior

fossa. Electrodes were fixed with dental cranioplasty and animals were connected to continuous video-EEG

monitoring.

Clinical and seizure monitoring

We evaluated seizures for behavioral score (Table 1), duration, location of initiation and pattern of

propagation, latency to first and last seizure, total number of seizures and seizure frequency. We used a modified

Lothman scale [86] to monitor animals receiving homocysteine for status epilepticus. Status epilepticus was

analyzed for duration, duration and progression through each stage [86], frequency, total time, clinical morbidity

and mortality and power spectrum. We used a Fast Fourier Transformation to perform spectral analysis of power

in frequency bands during status epilepticus. The specifications of this analysis can be found in Phelan et al.

(2015) [87].

Clarity

C-fos mouse brains underwent modified CLARITY for evaluation of cellular fluorescence but without

electrophoresis to clear lipid molecules [74]. For lipid separation tissue was incubated with sodium dodecyl

sulfate (SDS) and imaged using 2-photon microscopy.

Results

Characteristics and activation pattern of acute cobalt-induced seizures

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We used seven transgenic c-fos mice to visualize seizure onset and to describe the pattern of seizure

propagation. In this group, the average total number of seizures was 7.7±3.02 with an average duration of

30.35±5s. Time to first and last seizure was 0.53±0.17hrs and 41.65±4.03hrs, respectively. The average

behavioral score was 2.7±0.17 (Table 2). There was no difference between average number of seizures, seizure

duration or behavioral score during the first two days of monitoring (Figure 2).

Electrographic analysis revealed a distinct pattern of seizure propagation. High frequency spiking activity

began in ipsilateral cortical electrodes and spread to the contralateral cortex and then hippocampus. This pattern

was observed across all mice and most seizures (Figure 1A, B). Two-photon microscopy of fluorescently labeled,

cleared tissue showed neuronal activation in a similar pattern (Figure 1 C - H). Neurons surrounding the cobalt

lesion showed intense fluorescence (Figure 1E), which continued to the ipsilateral primary motor area and

contralateral primary motor area (less intense) (Figure 1F). Some fluorescence was noted in the subiculum and

CA1 of the hippocampus (Figure 1H). There was no thalamic activation during seizure propagation (figure 1G).

The anterior olfactory nucleus served as an internal positive control and the lack of neuronal activation seen

around the lesion caused by a stainless-steel hippocampal recording electrode served as an internal negative

control (Figure 1H).

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Figure 1. Pattern of seizure initiation and propagation in transgenic c-fos mice. A and B. Intracranial EEG tracing for 2

separate seizures showing seizure initiation occurring first in the ipsilateral cortex (CTXi) then in the contralateral cortex

(CTXc) and then in the two hippocampal leads simultaneously (in same location within left hippocampus – twisted together).

C. Three-dimensional z-stack reconstruction of 300µm cleared tissue shows significant perilesional neuronal activation with

spread to bilateral motor cortices. E and F. Sections through the cobalt lesion (e) and 1mm posterior to the lesion (f) showing

anatomic distribution of neuronal activation in mice with cobalt-induced seizures. Neuronal activation is evident around the

site of the cobalt lesion and in bilateral primary motor cortices. Anterior olfactory nucleus neurons serve as an internal

control. G. Section through the thalamus of the same mouse showing lack of neuronal activation, questioning the thalamus’

involvement in seizure propagation in cobalt-induced focal cortical epilepsy. H. Section through the hippocampus of the

same mouse showing hippocampal activation in subiculum and CA1 on the right without neuronal activation in the dentate

gyrus. Of note, there is no perilesional neuronal activation around the site of the stainless steel (SS) hippocampal depth

electrodes, which serves as a negative internal control.

Figure 2. Comparison of seizure activity one and two days after cobalt wire placement in c-fos mice. A-C. There was

no statistically significant difference in the average amount of daily seizures, average behavioral scores and average seizure

duration on day 1 or day 2 after cobalt implantation.

Natural history of cobalt-induced seizures

To evaluate the long-term natural history of cobalt-wire implantation (without homocysteine), five WT

mice underwent cobalt wire implantation to the left premotor area. Two mice died after two days and underwent

separate analysis. The average total amount of seizures in the sacrificed group was 19.6±9.8 with an average

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duration of 15.19±1.7s and behavior score of 1.72±0.26. The mice that died had a total of 13.98±9.8 seizures in

two days which lasted longer (47.8±21.1) and had higher behavioral scores (2.127±0.37) than in the sacrificed

group (Table 2). These animals presumably succumbed to their seizures. Over a month of monitoring, seizure

number and behavioral scores decreases. Mice stopped having seizures two weeks after implantation. There was

no change in seizure duration over time (Figure 3A). The electrographic nature of the seizures mimicked the acute

c-fos animal cohort above.

Natural history of homocysteine-induced seizures

To evaluate the effects of homocysteine injection alone we treated 16 WT mice with 841mg/kg

homocysteine without cobalt implantation. Seizures were experience by 87.5% of mice and lasted an average

54.17 seconds. The mean number of seizures within 24hrs after injection was two, over 74.41 seconds with a

mean behavioral score of 5. After 24 hours only 55% of animals experienced seizures which lasted 30.9 seconds

and had a behavioral score of 3.5. All animals stopped seizing by day six (Figure 3B).

Cobalt and homocysteine-induced focal cortical epilepsy

Given the presumed additive effects of HT and cobalt we tested the combination of the two on five WT

animals. In these mice we implanted a 500µM cobalt wire into the left premotor area and injection homocysteine

(HT) (841mg/kg) seven days later. These animals were monitored for 45 days. The average pre- and post-HT

seizure number, duration and behavioral scores were: 2.4±1.69 and 25±9.7; 17.28±8.1 and 23.79±5.9; and

1.72±.077 and 2.5±0.7. Average latency to last seizure after HT therapy was 27±3.49 days (Table 2).

For the duration of the monitoring period after HT treatment, weekly seizure frequency and seizure

behavior scores increased (Figure 3C). This was in contrast to the cobalt group without HT and the animals who

received HT alone, suggesting that concurrent cobalt implantation and HT treatment was necessary to produce

chronic focal epilepsy. The animals who received HT in conjunction with cobalt had more seizures (25 vs 19.6)

which lasted longer (23.79s vs 15.19s) and had higher behavioral scores (2.5 vs 1.72 than the other two groups

(Table 2). The seizure onset zone and pattern of spread was the same as in the c-fos animals above.

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Figure 3. Comparative analysis of seizure characteristics in 3 treated groups. A. Cobalt only group. Weekly seizure

behavior scores, duration and frequency were monitored in mice implanted with a cobalt wire alone. There was a reduction in

weekly seizure frequency and behavior two weeks after implantation. There is no significant change in seizure duration

during the recording period. B. HT only group. Animals injected with homocysteine alone (without cobalt implantation) had

a significant reduction in seizure duration at week 2. No animals had seizures six days after implantation. C. Cobalt with

concurrent HT. Animals implanted with cobalt and injected with HT had an increase in seizure frequency and duration each

week with no change in behavior scores. These mice continued to have seizures throughout the entire monitoring period. This

is in contrast to the two previous groups and suggests that the addition of HT is necessary to induce chronic epilepsy in a

cobalt model of focal cortical epilepsy.

Characteristic of cobalt and homocysteine-induced status epilepticus

All five mice implanted with cobalt who received HT went into status epilepticus after HT administration.

The total duration of SE (from beginning of stage 1 to end of stage 4) was 1.74±0.2 hrs (Figure 4A-E). SE stage 1

lasted for an average of 9.3±3.2 minutes and consisted of high frequency intermittent spikes (Figure 4A). SE

stage 2 began with an initial seizure and lasted 30.1±8.4 minutes (Figure 4B). SE stages 3 and 4 consisted of

continuous high frequency seizure activity lasting 4.5±0.28min and 1.19±0.014hrs, respectively (Figure 4 C, D).

Power spectral analysis revealed 2-20Hz oscillations lasting for a total of 1.7hrs on average. This was consistent

with previous literature (Figure 4F) [76].

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Figure 4. Status epilepticus analysis in concurrent cobalt and HT group. A. SE stage 1 lasted for an average of 9.3±3.2

minutes and consisted of high frequency intermittent spikes. B. SE stage 2 began with the the initial seizure and lasted

30.1±8.4 minutes. C. SE stage 3 was a transitional stage and lasted for 4.5±0.28min. D. SE stage 4 consisted of continuous

high frequency seizure activity lasting 1.19±0.014hrs. E. Average time spent in each stage of status epileptics. The most time

was spent in SE stage 4. F) Power spectral analysis revealed 2-20Hz oscillations lasting for a total of 1.7hrs on average,

consistent with previous literature.

Discussion

Cobalt-induced epilepsy is not a novel concept and dates back over 50 years across several animal models

[76, 88-92]. From 1970 to 1992 cobalt implantation was used to produce focal seizures to examine novel medical

therapies. This model was instrumental in the discovery of carbamazepine [93, 94]. Interestingly, all

investigations noted seizure cessation after two weeks of implantation thus limiting the longevity of these models.

The cobalt model was reintroduced by Chang et al. in 2004. After three weeks of video-EEG monitoring

they noted significant seizure reduction and eventual seizure arrest after 18 days [95]. The present data confirms

that after two weeks of only cobalt implantation there is a significant reduction in seizure frequency and

behavioral score. This finding suggested that cobalt alone is limited to an acute model of focal epilepsy (Figure

3). Chang et al. subsequently showed that cobalt leaches into the brain parenchyma causing perilesional necrosis

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without otherwise widespread change [95]. It was hypothesized that this focal necrosis was likely responsible for

the focal nature of seizure initiation found in these animals. We confirmed focal perilesional seizure onset in our

animals, which was visualized in the c-fos group (Figure 1).

In 2011 Kim et al. used the cobalt model to evaluate the thalamus’ role in seizure propagation. Similar to

our approach, they implanted a frontal cortical cobalt wire and monitored for 30 days. Unlike our natural history

group and previous studies, however, they observed spiking activity 28 days after implantation [76]. This led the

investigators to believe that despite there being no clinical seizures, neurons around the cobalt lesion were still

hypeexcitable. Following the 1988 concept of homocysteine-induced SE in cobalt mice [96], Kim et al. injected a

cohort of cobalt mice with HT and noted convulsive seizures immediately after injection [76]. They did not

monitor these animals longitudinally, however, to see if chronic seizures developed.

In accordance with prevailing literature but in contrast to Kim et al., our data confirmed a significant

seizure reduction after two weeks in animals implanted with cobalt alone. We also noted seizure reduction only

six days after HT injection without cobalt implantation (Figure 3A,B). The combination of cobalt and HT,

however allowed our animals to develop consistent and chronic focal cortical seizures over the course of a month

(Figure 3C). Comparative analysis across groups suggests that both cobalt and homocysteine are necessary but

not individually sufficient to induce a chronic epileptic condition in cobalt-implanted mice. Furthermore, SE was

only observed in the cobalt with HT group suggesting the necessity of SE in production of chronic epilepsy

(Figure 4).

In all four groups, seizures followed similar patterns of propagation on EEG. This pattern was visualized

in transgenic c-fos mice showing perilesional neuronal activation spreading to the ipsilateral then contralateral

motor cortex and finally to bilateral hippocampi (Figure 1). EEG and c-fos neuronal activation confirmed focal

cortical epilepsy insofar as seizures propagated from the perilesional cobalt area.

In this study, we establish a chronic model of focal cortical epilepsy using cobalt wire implantation and

homocysteine injection. We describe the seizure characteristics and their pattern of propagation. This model can

be used in future studies to probe for mechanisms and potential treatments of focal cortical epilepsy.

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VI. Conclusions

The fundamental role of metabolism in the regulation of neuronal activation has long been

debated with several competing theories. Due to this lack of clarity, the role of metabolism in epilepsy is

also unknown. The present work describes the neuronal metabolic phenotype during chronic stimulation

and extends these findings toward understanding a more integrated pathogenesis of epilepsy.

In our first aim we used a novel model of chronic activation and resected human tissue to

demonstrate that chronic neuronal stimulation leads to neuronal metabolic reprogramming from aerobic

respiration to glycolysis through the upregulation of neuronal LDHA. Our results challenge the

prevailing ANLS hypothesis, which holds that the majority of metabolism occurs via supporting

astrocytes during times of high neuronal metabolic demand. The second aim of our study was to

describe the molecular pathway that regulates the transition from aerobic respiration to glycolysis during

chronic neuronal stimulation. Drawing from similarities of high energy demands during hypoxia, we

hypothesized that the AMPK/HIF1a hypoxia pathway plays a role in regulating neuronal metabolism

during chronic stimulation. Using our low Mg2+, we confirmed that neuronal metabolic reprogramming

to glycolysis is mediated by the AMPK/HIF1a hypoxia pathway. For our third aim, we applied insight

gained from the neuronal metabolic phenotype during times of chronic stimulation from our first two

aims to more clearly elucidate the etiology of epilepsy formation. We showed that LDHA, regulated by

upstream HIF1a, leads to epileptiform activity in culture and in an animal model.

The above three aims lay the foundation of an overarching hypothesis for metabolically driven

pathogenesis of epilepsy. We envision a feedforward loop in which chronic seizure activity shifts

neurons into glycolysis through AMPK/HIF1a mediated upregulation of LDHA, which pushes neurons

to become hyperexcitable and subsequently elicit more seizures.

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Figure 1. Representative schematic of the feedforward loop that drives metabolic control of epilepsy. As neurons are

chronically activated or seize, they upregulate LDHA expression and thus glycolysis (top arrows) through the AMPK/HIF1a

pathway (middle arrows) which is activated by a high AMP:ATP ratio. AMP leads to phosphorylation of AMPK, which

leads to stabilization of HIF1a. HIF1a translocates into the nucleus as a transcription factor to upregulate LDHA

transcription and protein expression and thus glycolysis. HIF1a-regulated LDHA expression goes on to further cause

pathologic activation in neurons (bottom arrow).

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VII. Future Directions

LDHA is responsible for cobalt-induced chronic seizures

Our research findings suggest an important role for LDHA in regulating neuronal firing and

potentiating seizures. However, we limited our cobalt model to observation in the acute period. In future

studies, we anticipate exploring the role of LDHA in cobalt-induced chronic seizures. We hypothesize

that chronic focal seizures are regulated by LDHA as well. In order to test this hypothesis, we will use

tamoxifen-dependent LDHA knockout mice. These mice will be implanted with cobalt and induced with

homocysteine as previously described in Section V in the manuscript “A mouse model of cobalt-induced

focal cortical epilepsy.” Mice will be monitored with continuous video-EEG for 30 days. LDHA will be

knocked down at different time points (24 hours, 48 hours, 3 days, 1 week, and 2 weeks) and seizure

frequency, severity, and timing will be recorded. We believe that LDHA inhibition will reduce seizure

burden in the cobalt model, lending further credence to its role in seizure formation.

Mechanisms underlying LDHA’s regulation of neuronal hyperexcitability

The mechanism by which LDHA modulates neuronal membrane potential is also unclear and

will provide motivation for our future research. The KATP channel provides a feasible target that may be

responsible for LDHA’s modulation of neuronal firing. The KATP channel is distributed widely

throughout the central nervous system [77-79]. Under normal conditions, this channel remains

constitutively inhibited by ATP. The channel maintains negative membrane potential in neurons by

staying open and hyperpolarizing the cell membrane in times of high energy demand [80, 81]. However,

shifts in neuronal metabolic phenotype can alter the efficacy of this channel [81]. Higher rates of ATP

production through glycolysis could potentially inhibit KATP channels. Moreover, lactate could play a

direct role in neuronal membrane potential modulation by directly inhibiting KATP channels, as described

in ventromedial hypothalamic neurons [82].

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In a series of future experiments, we plan to use similar models to the present work to explore

the role of the KATP channel in modulating electrical activity and metabolism. We plan to upregulate

LDHA using a lentivirus model on an MEA. We will monitor neuronal activity while selectively

activating the KATP channel with diazoxide, nicorandil, or P1075 [97]. If the KATP channel modulates

LDHA-dependent neuronal bursting, then we expect to observe decreased burst activity with channel

activation. In a similar setting, we will also inhibit the KATP channel with glyburide [97]. This will likely

potentiate burst activity in the context of elevated LDHA. In separate experiments, we anticipate using

genetic alterations of the KATP channel to further test this hypothesis. The KATP channel is an octamer

comprised of four pore-forming Kir6.2 subunits and four modulatory SUR subunits [98]. These subunits

are responsible for ATP’s inhibitory effects. We plan to use Kir6.2-/- neurons obtained from Kir6.2-/-

KO mice [98] to further test KATP channel’s role in LDHA induced seizures. We expect to observe

minimal neuronal bursting with LDHA upregulation in Kir6.2-/- neurons. Given that lactate itself may

play a role in modulating the KATP channel, we plan to modify intracellular lactate levels of cultured

neurons by inhibiting the MCT2 lactate transporter. AR-C155858 is a potent MCT2 inhibitor that binds

to intracellular MCT sites [99]. We plan to combine AR-C155858 with our culture model to determine

whether this results in increased neuronal bursting. We will combine this small molecule inhibitor with

our current LDHA lentivirus model and the proposed Kir6.2-/- cells to determine if lactate modulates the

KATP channel’s effect on neuronal bursting.

Finally, we plan to create tamoxifen-dependent Kir6.2-/- conditional knockout mice to use in

conjunction with our cobalt model to test the KATP channel’s regulation of chronic seizures after cobalt

implantation. In a similar experiment to our conditional LDHA KO mouse model, Kir6.2-/- will be

knocked down at 24 hours, 48 hours, 3 days, 1 week and 2 weeks to determine KATP channel’s role in

LDHA-induced seizures from cobalt.

LDHA plays a role in seizures associated with IDH-1 mutated low-grade gliomas

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Given the central role of LDHA in modulating metabolic regulation of neuronal activation and

epilepsy, we believe that LDHA is also involved in mechanisms underlying epilepsy associated with

isocitrate dehydrogenase-1 (IDH-1) mutated low-grade gliomas. The incidence of seizures in patients

with low-grade, IDH-1 mutated, primary brain tumors is extremely high and reaches 80-90%. Isocitrate

dehydrogenase is the second enzymatic step in the TCA cycle and typically catalyzes the conversion of

isocitrate to a-ketogluterate [100]. A mutation in this enzyme inhibits astrocytic oxidative

phosphorylation and likely drives tumor astrocytes into glycolysis. Furthermore, a-ketoglutarate is

necessary for hydroxyl-mediated degradation of HIF1a. As described above, HIF1a leads to LDHA

upregulation [100]. We believe the metabolic shift in IDH-1 mutated astrocytes leads to further

metabolic shifts in neighboring neurons similar to the shifts observed in chronically activated neurons.

In a preliminary clinical study, we used subdural electrodes to monitor five patients with IDH-1

mutated tumors for seizure localization. Briefly, patients underwent a two-stage operation for

intracranial grid-electrode placement and then tumor and epilepsy focus resection. Similar to the

technique described in Section III we evaluated cortical tissue based on overlying electrographic activity

and compared epileptic to non-epileptic peritumoral cortex for LDHA staining. In five participants, we

observed significantly higher LDHA staining in epileptic tissue compared to non-epileptic tissue (Figure

1).

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Figure 1. (a - b) Temporal lobe of a participant with intracranial electrodes placed over a low grade IDH-1 mutated tumors

for seizure monitoring. During the monitoring period, we identified electrodes that were or were not involved in seizure

activity. In this example, electrode 27 (green) was not involved in seizures while electrode 21 (red) was involved in seizures.

The underlying tissue was resected as part of the planned surgical procedure and analyzed as the epileptic or non-epileptic

specimen for this participant. (c) Tissue underlying the epileptic (red) and non-epileptic (green) tissue from the same

participant were sectioned. Representative 400x400 µm regions of interest were analyzed for IDH, NeuN (not shown) and

LDHA using semi-automated segmentation. Epileptic tissue demonstrates significant LDHA staining compared to non-

epileptic tissue, while the neuronal marker NeuN was approximately the same between the two specimens (not shown).

Mutant IDH staining was highly positive within the tumor (as expected) but decreased in tissue away from the tumor. (d) We

normalized LDHA staining to NeuN to account for differences in neuronal density across specimens. Normalized LDHA

staining, averaged across ten 400x400 µm regions of interest in each participant, is significantly elevated across participants

in epileptic compared to non-epileptic tissue (n = 5 participants; *p < .05, unpaired t-test; mean ± SEM).

4x

4x4x

4x

20x

20x

20x

20xEpileptic Non-epileptic

Epile

ptic

Non

-epi

lept

ic

IDH1 (R132H) mutation LDHA

IDH1 (R132H) mutation

a) b)

c) d)

TUMOR

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Given that the IDH-1 mutation lies in astrocytes and not neurons, we believe the underlying

mechanism for LDHA upregulation in peritumoral neurons arises from interacting with IDH-1 mutated

astrocytes or secreted cytokines. As described above, the IDH-1 mutation pushes astrocytes into

glycolysis and thus increases lactate release. Increased lactate release from tumor cells could directly

play a role in stimulating peritumoral bursting through KATP channel inhibition. This would

subsequently lead to neuronal LDHA expression in a similar fashion to the aforementioned feed-forward

epileptic-metabolic loop. In order to test this hypothesis, we plan to use IDH-1 mutated GL-261 glioma

cells grown in a transwell above mixed cortical cultures on an MEA. In preliminary transwell

experiments, we demonstrated that IDH-mutated GL-261 tumor cells increase neuronal bursting. We

plan to use this model to explore the role for LDHA in peritumoral neuronal activation and to unearth

the mechanism underlying LDHA expression and neuronal bursting (Figure 2).

Figure 2. Preliminary data from transwell experiment. IDH-mutated GL-261 were grown in a transwell above mixed cortical

cells grown on an MEA. Bursting activity was measured for 10 days. Cortical neurons with IDH-1 mutated GL-261 cells in

their respective transwell had significantly increased bursting than neurons associated with wild-type GL-261. These data

suggest that the IDH-1 mutation increases neuronal firing.

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VIII. Acknowledgements As the saying goes, “it takes a village to raise a child.” I came into the MPBP PhD program as a

third-year neurosurgery resident and a research infant. Over the last four years I have had the

tremendous fortune to be raised by mentors, colleagues, and friends across two huge academic

institutions. I would like to thank my mentors, Dr. Jaideep Kapur at UVA and Dr. Kareem Zaghloul at

NIH for taking this challenge on with me. I have learned a tremendous amount from both of them.

Mostly, however, I thank them for their patience with me. They embody what I always have and will

continue to strive for in my professional career – to be a true teacher, clinician, and scientist. I would

like to thank my PhD committee (Dr. Avril Somlyo, Dr. Brant Isakson, Dr. Mark Beenhakker and Dr.

Jeff Elias). I can remember initial looks of confusion when I presented my research objectives during

my first committee meeting. With their guidance, their faces changed over the years as I began to

achieve more concrete and consistent results. They always pushed me to better “my story,” which came

together into what is presented today.

I would also like to thank the leadership of the combined NIH/UVA neurosurgery residency

program: Dr. John Heiss, Dr. Mark Shaffrey and Dr. John Jane Jr. Five years ago I approached them

with a crazy scheme to leave neurosurgery residency with a PhD. Much to my surprise, they agreed to

let me do it. Since then, they never doubted my potential success and supported me throughout.

Furthermore, the NIH and UVA neurosurgery faculty were unanimously behind this experience as well.

They provided the intellectual and emotional support needed to see this endeavor through to the finish. I

certainly could not have done this without the support of my co-residents, especially my co-chiefs (Dr.

Dan Raper, Dr. James Nguyen, and Dr. Peter Christiansen). We came into the pit of residency together

and since the first day I could rely on them. For the completion of this thesis they took on the clinical

burden of our shared neurosurgery service at times when I needed to be in the lab. For this I am forever

grateful. From our neurosurgery program I would also like to thank Kaitlyn Benson, Camille Butler, and

Karen Saulle. They probably worked just as hard on the administrative portion of making this program

123

happen as I did on the actual research. This certainly could not have happened without their help.

From a laboratory perspective, I could not have achieved any of this work without learning the

myriad of techniques presented herein. In my UVA laboratory, I would like to thank John Williamson,

Pravin Wagley, and Dr. Suchitra Joshi. My first experience reading EEGs (in mice and humans) was

with this group and I will use this knowledge throughout my future research and clinical practice. At

NIH, I would like to thank Stuart Walbridge, Marcelle Altshuler, Joe Steiner, Muzna Bachani, Sara

Inati, and Nancy Edwards. Stuart has been teaching me how to do research since before I entered this

program. He harbors decades of experience and knowledge and was more than willing to pass it on. As a

testament to his lasting friendship, Stuart continued to perform certain aspects of animal experiments

that I did not find “appealing” until the very end. To Marcelle and Muzna, I certainly could not have

completed these experiments without your help. After I left NIH, they continued my work and have

taken it farther than I could have imagined. They will go on to accomplish great feats in medicine and

research and I am honored to have shared this time and these experiences with them.

Finally, and most importantly, I want to thank my family (my mother Nora, my father Pavel, my

sister Sofia, and my girlfriend Alyson). Throughout my life, they supported and encouraged me in every

decision I have made. During times of accomplishment they reminded me to stay humble and during

times of failure they reminded me to stay confident. They have always inspired me to do better. Thanks

to the village that raised me.

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