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1
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
2
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,
4
which further pushes neurons to become hyperexcitable and subsequently elicit more seizures.
5
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).
6
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
7
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.
8
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
9
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
10
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
11
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
12
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
13
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.
14
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
15
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.
16
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
17
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
18
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
19
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
20
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
21
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
22
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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1.5
2+
Low M
g +
CC 3d
Low M
g +
CC 7d
Low M
g +
CC 10d
2+2+ 2+
Fold
cha
nge
0.0
0.5
1.0
1.5
LDHA expression
Contro
l
Low M
g2+
Low M
g +
KC 3d
Low M
g +
KC 7d
Low M
g +
KC 10d
2+2+ 2+
0
500
1000
1500
Total AMPK expression
Contro
l2h
r
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
E
D
C
B
A
F
E
D
C
B
A
0
0.41
Control Low Mg2+
Base 2 hr Base 2 hr
0 5 10 15 20 25 30 0 5 10 15 20 25 30
Well E-6 Well E-6
TIme (sec) TIme (sec)
Cha
nnel
sp/s
Cha
nnel
sp/s
Base 2 hr
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
0.1
0.2
0.3
Burs
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z)
0.4
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ControlLow Mg2+
ControlLow Mg2+
ControlLow Mg2+
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
0.02
0.04
0.06
0.08
0.10
Burs
t Fre
quen
cy (H
z)
Baselin
e
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pre
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post
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ost
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post
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e
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pre0.00
0.02
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z)
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e
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pre
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pre
Day 1
Day 2
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0
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Fold
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*
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a) Single day burst frequency b) Single day burst frequency
c) Daily burst frequency ratios d) Burst frequency ratio
ControlLow Mg2+
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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.
122
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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