Acute and chronic toxicity of

methamphetamine exposure in cultured

neuronal cells

Elise Cook

BForensics (Hons) Forensic Biology and Toxicology

BSc Biomedical Science and Molecular Biology

This thesis is presented for the Honours degree in Biological Science at Murdoch

University, School of Biological Sciences

Western Australia

November 2013

Declaration

I declare that this thesis is my own account of my research and has not been previously

submitted for a degree at any tertiary educational institution.

Elise Cook

Thesis word count: 15,588

iii

Abstract

Methamphetamine is a highly addictive psychostimulant drug with serious health consequences

that include long-term neurotoxic effects. While the neurotoxic mechanisms are still not fully

understood, monoamine release, production of reactive oxygen species and excitotoxicity are

believed to be involved. There is currently no effective treatment to prevent these effects. Using

metabolomic analysis to explore the effect of methamphetamine on neuronal cells with dose and

time may help to elucidate the biochemical pathways affected, and provide an insight into

methamphetamine neurotoxicity.

A B50 neuroblastoma cell culture model was used in these experiments. Cell viability was assessed

by lactate dehydrogenase assay and Trypan blue exclusion testing after 48 hours exposure to 1 mM

methamphetamine. A dose curve was conducted exposing cells to a range of methamphetamine

doses (100 nM, 1 µM, 10 µM, 100 µM and 1 mM) over 48 hours. A time course examined the 6-,

24- and 48-hour time points after B50 exposure to 1 mM methamphetamine. A gas

chromatography-mass spectrometry metabolomic method was used to analyse the treated cells and

cell media of the dose curve and time course. The metabolites found to contribute most to the

variance between the samples were chosen for further study.

Methamphetamine caused observable damage to B50 cells and cell viability which was found to be

dose-dependent by Trypan blue testing, however, LDH results were inconclusive. The metabolites

found to change over dose and time during methamphetamine exposure included amino acids,

carbohydrates and fatty acids. The dose curve showed a build-up of carbohydrates, a decrease in

octadecenoate and alterations to many amino acids with increasing dose. The results from the time

course found an increase in L-glutamate and related metabolites, an increase in antioxidant amino

acids and a decrease in carbohydrates over time. The changes suggest glutamate release, reactive

oxygen species and disturbances to energy utilisation may be involved in the effect of

methamphetamine upon neuronal cells.

The study has confirmed that methamphetamine causes dose-dependent damage and death of

neurons. Methamphetamine exposure resulted in quantifiable biochemical changes over dose and

iv

time with the metabolite changes reflecting the known mechanisms of methamphetamine

neurotoxicity. The result of this study furthers our understanding of neurochemical processes in

response to methamphetamine and could potentially lead to the identification of therapeutic targets.

v

Table of Contents 1. Introduction ................................................................................................................................ 1

1.1 The methamphetamine problem ….…………………………………………………… 2

1.2 The effects of methamphetamine……………………………………………………….4

1.2.1 Cardiovascular effects………………………………………………………..4

1.2.2 Cognitive effects……………………………………………………………..4

1.2.3 Developmental effects………………………………………………………..5

1.2.4 "Meth mouth"………………………………………………………………...5

1.2.5 Neurotoxic effects……………………………………………………………6

1.2 Neurons and neurotransmitters…………………………………………………………7

1.2.1 The neuron…………………………………………………………………...7

1.2.1 Dopamine…………………………………………………………………….8

1.2.2 Serotonin……………………………………………………........................10

1.2.3 Glutamate………………………………………………………...................11

1.4 Neurotoxicity of methamphetamine…………………………………………………...12

1.41 Metabolism of methamphetamine…………………………………………...17

1.42 Methamphetamine addiction………………………………………………...17

1.5 Metabolomics………………………………………………………………………….19

1.6 Gas chromatography-mass spectrometry……………………………………………...22

1.7 Aims…………………………………………………………………………………...25

2. Materials and methods………………………………………………………………………..26

2.1 Materials……………………………………………………………………………….26

2.1.1 Chemicals and kits………………………………………………………….26

2.1.2 Cells………………………………………………………………………...26

2.2 Methods……………………………………………………………………………….26

2.2.1 Cell culture…………………………………………………………………26

2.2.2 Cell counting……………………………………………………………….27

2.2.3 LDH assay………………………………………………………………….27

2.2.4 Harvesting………………………………………………………………….28

vi

2.2.5 Extraction…………………………………………………………………..29

2.2.6 Derivatization………………………………………………………………29

2.2.7 Instrumental analysis……………………………………………………….30

2.2.8 Data analysis………………………………………………………………..31

2.3 Experiments…………………………………………………………………………...31

2.3.1 LDH assay………………………………………………………………….31

2.3.2 Dose curve………………………………………………………………….31

2.3.3 Time course………………………………………………………………....32

3. Results ...................................................................................................................................... 33

3.1 Cell viability testing…………………………………………………………………...33

3.2 Dose curve…………………………………………………………………………….37

3.2.1 Intracellular metabolites……………………………………………………37

3.2.2 Extracellular metabolites…………………………………………………...41

3.3 Time course…………………………………………………………………………...46

3.3.1 Intracellular metabolites…………………………………………………....46

3.3.2 Extracellular metabolites…………………………………………………...50

4. Discussion ................................................................................................................................ 55

4.1 Cell viability testing…………………………………………………………………...55

4.2 Dose curve…………………………………………………………………………….56

4.2.1 Overall discussion of dose curve…………………………………………...59

4.3 Time course……………………………………………………………………………60

4.3.1 Overall discussion of time course…………………………………………..63

4.4 Effect of methamphetamine…………………………………………………………...63

4.5 Limitations and expansions……………………………………………………………64

4.6 Conclusion……………………………………………………………………………..66

5. References .................................................................................................................................. 67

vii

Index to figures

Figure 1.0 Chemical structures of methamphetamine and other psychostimulants…………………1

Figure 1.1 The two major routes of methamphetamine synthesis…………………………………..3

Figure 1.21 Dental caries due to methamphetamine abuse…………………………………………6

Figure 1.31 The basic structure of a neuron…………………………………………………………7

Figure 1.32 The dopamine and serotonin pathways through the brain……………………………...9

Figure 1.33 The dopamine biosynthesis pathway………………………………………………….10

Figure 1.34 A serotonergic synapse………………………………………………………………..11

Figure 1.41 Pathways involved in methamphetamine neurotoxicity………………………………12

Figure 1.42 Mechanisms whereby methamphetamine causes the release of dopamine and

catecholamines……………………………………………………………………………………..13

Figure 1.43 The main metabolic pathways of methamphetamine…………………………………18

Figure 1.51 Comparison of publications on metabolomic applications……………………………20

Figure 1.61 Overview of the GC-MS instrument………………………………………………….23

Figure 3.11 Photographs of B50 cells after 48 hours of methamphetamine treatment……………34

Figure 3.12 Cytotoxicity of methamphetamine measured by LDH assay…………………………35

Figure 3.13 Cell viability after methamphetamine exposure measured by Trypan blue exclusion

testing………………………………………………………………………………………………36

Figure 3.21 PCA of GC-MS metabolomics data from B50 cells following exposure to

methamphetamine at a range of doses……………………………………………………………..38

Figure 3.22 Changes in intracellular amino acids of B50 cells with dose of methamphetamine….39

Figure 3.23 Changes in intracellular metabolites of B50 cells with dose of methamphetamine…..40

Figure 3.24 PCA of GC-MS metabolomic data from medium of B50 cells following exposure to

methamphetamine at a range of doses………………………………………………………….…..42

Figure 3.25 Changes in extracellular amino acids of B50 cells with dose of methamphetamine….43

Figure 3.26 Changes in extracellular amino acids of B50 cells with dose of methamphetamine….44

Figure 3.27 Changes in extracellular metabolites of B50 cells with dose of methamphetamine….45

Figure 3.31 PCA of GC-MS metabolomics data from B50 cells following 6, 24 or 48 hours

exposure to methamphetamine…………………………………………………………………….47

Figure 3.32 Changes over time of intracellular amino acids of B50 cells…………………………48

viii

Figure 3.33 Changes over time of intracellular metabolites of B50 cells………………………….49

Figure 3.34 PCA of GC-MS metabolomics data from medium of B50 cells following 6, 24 or 48

hours exposure to methamphetamine………………………………………………………………51

Figure 3.35 Changes over time of extracellular amino acids of B50 cells…………………………52

Figure 3.36 Changes over time of extracellular metabolites of B50 cells…………………………53

Index to tables

Table 1.5 A comparison of NMR, GC-MS and LC-MS………………………………………….22

ix

List of abbreviations

5-HIAA 5-hydroxyindoleacetic acid

5-HT 5-hydroxytryptamine

ATP Adenosine triphosphate

B50 B50 rat neuroblastoma cell line

Ca2+

Calcium

CNS Central nervous system

DAT Dopamine transporter

DMEM Dulbecco‟s modified Eagle‟s medium

EAA Excitatory amino acid

GABA γ-aminobutyric acid

GC Gas chromatography

IL-1 Interleukin 1

LC Liquid chromatography

LDH Lactate dehydrogenase

m/z Mass to charge ratio

MAO Monoamine oxidase

MS Mass spectrometry

MSTFA N-methyl-N-(trimethylsilyl)trifluroacetamide

NMDA N-methyl-D-aspartate

NMR Nuclear magnetic resonance

nNOS Nitric oxide synthase

P2P Phenyl-2-propanone

PBS Phosphate buffered solution

PCA Principal component analysis

PKC Protein kinase C

ROS Reactive oxygen species

SERT Serotonin transporter

VMAT-2 Vesicular monoamine transporter

x

Index to units

oC Degrees centigrade

µg Microgram

µL Microlitre

µm Micrometre

µM Micromolar

cm Centimetre

g Gram

g G-force

m Metre

mg Microgram

mL Millilitre

mm Millimetre

mM Millimolar

rpm Revolutions per minute

xi

Acknowledgements

Although challenging, my Honours year has been one of the most rewarding experiences of my

university education. It has given me an invaluable insight into the process of conducting research

and taught me that persistence and hard work pays off. I have gained valuable knowledge from

experienced supervisors and from being part of a fantastic team.

I would like to sincerely thank my supervisors, Dr. Garth Maker and A/Prof. Ian Mullaney, for all

their help throughout this year, for the support, patience and guidance they have given me. I would

especially like to thank Garth for his proof reading, constructive feedback and always taking the

time to answer my questions. I have enjoyed working with and, am immensely grateful to both of

them.

I extend my thanks to the metabolomics team: A/Prof. Robert Trengove, for his suggestions;

Catherine Rawlinson, for her help and guidance with the GC-MS and my data processing; Maria

Wenner, for her advice about my cells; Hayley Abbiss, for her assistance with the GC-MS; and to

Dr. Joel Gummer, for answering my questions in the lab.

To my fellow Honours students, thank you for listening to my troubles, for offering support and

advice and most of all for making this year enjoyable.

Lastly, I would like to thank my family for their support and understanding. I couldn‟t have

completed this without them.

1

1. Introduction

The amphetamine derivative, methamphetamine, also known as “meth”, “speed” and “crank” is a

powerful psychoactive drug. Whilst nearly identical in structure, methamphetamine is more potent

than amphetamine (Figure 1.0), with effective doses of 0.06 and 0.28 mg/kg respectively

(Bondareva et al., 2002). This is thought to be due to the presence of an additional methyl group on

the amine making the compound more stable and lipid soluble, aiding its transport across the blood

brain barrier (Carvalho et al., 2012). Two isomeric forms of methamphetamine exist; (+)

methamphetamine and (-) methamphetamine, with the former being dominant and five times more

biologically active (Schep et al., 2010). Methamphetamine has stimulant, euphoric, anorectic and

hallucinogenic properties. It is highly abused for its effects of increased concentration, reduced

need for food and sleep and for feelings of well-being. In addition to these sought after effects,

methamphetamine causes anxiety, psychosis, cognitive and motor impairments, stroke, cardiac

irregularities and death (Cadet et al., 2007).

Figure 1.0: Chemical structures of methamphetamine and other psychostimulants ephedrine,

MDMA (methylenedioxymethamphetamine or ecstasy), amphetamine, pseudoephedrine and

cocaine (adapted from Vearrier et al., 2012).

2

1.1 The methamphetamine problem

While methamphetamine‟s beginnings were innocent, misuse of the drug has developed into a

global problem. Since its synthesis in 1919 by Japanese chemist Akira Ogata, methamphetamine

has had a range of uses. Similar to amphetamine, methamphetamine was first put to use for nasal

decongestion and for the treatment of asthma (Vearrier et al., 2012). It was also used as a diet aid

and to combat depression and alcoholism (Watanabe-Galloway et al., 2009). During World War II

methamphetamine was favoured by the German and Japanese militaries for its stimulant properties,

keeping soldiers awake and alert. However, adverse effects soon made themselves known with

reports of increased blood pressure and heart rate, palpitations, convulsions, tremor and difficulty

breathing. Restrictions were put in place, limiting the availability of methamphetamine to

prescription only, eventually outlawing the drug completely. These measures were mostly

ineffective for soon after these restrictions were put in place, the first clandestine laboratories

appeared (Vearrier et al., 2012).

There are two main methods for the production of methamphetamine: the phenyl-2-propanone

(P2P) method, which uses P2P and methylamine as precursors and the ephedrine/pseudoephedrine

reduction method (Figure 1.1). Clandestine labs mostly use the ephedrine/pseudoephedrine method

as it is simple and is based on easily available household products such as acetone, lithium (lithium

batteries), sodium hydroxide (lye), toluene (paint thinner) and ephedrine/pseudoephedrine (cold

and allergy medication). This has resulted in the restricted sale of some of these goods (Weisheit,

2008); however, its effectiveness in reducing production is questionable due to lack of

comprehensive control (Schloenhardt, 2007). Substitution of new ingredients to replace those

restricted has resulted in a reduction in purity with the median purity being between 1.9 and 49.9%

in Australia (Australian Crime Commission, 2012). Some of these impurities and adulterants can

result in adverse consequences or death. A report by the US Centres for Disease Control (1990)

discovered eight cases of lead poisoning due to intravenous methamphetamine use where the drug

samples tested contained up to 60% lead by weight.

3

Figure 1.1: The two major routes of methamphetamine synthesis; the ephedrine/pseudoephedrine

reduction method (top) and the phenyl-2-propanone (P2P) method (bottom) (Mendelson et al.,

2006).

As a result of its availability, low cost and long duration of action methamphetamine has a high

potential for abuse. The United Nations Office on Drugs and Crime 2008 report estimated that

there were 25 million methamphetamine users worldwide, exceeding the use of cocaine and heroin

(Panenka et al., 2012). In Australia, estimates place the number of people over 14 years of age who

have used methamphetamine in the last 12 months at 2.1 % (Australian Institute of Health and

Welfare, 2011). Associated with a high risk of unemployment and financial trouble, psychological

and health problems and violent and criminal behaviour, methamphetamine addiction places a

great burden on society (Brecht et al., 2004). Amphetamine stimulants account for 15% of national

illicit drug arrests second only to cannabis (Australian Crime Commission, 2012).

4

1.2 Effects of methamphetamine

Methamphetamine users seek the drug‟s pleasurable effects such as euphoria, decreased appetite

and increased alertness, concentration and libido. However, in seeking these effects, users are

risking their lives as methamphetamine use has a dark side. The damaging effects of

methamphetamine on the body are multiple and wide spread including cardiovascular, cognitive,

developmental, dental caries and neurotoxic effects (Barr et al., 2006).

1.2.1 Cardiovascular effects

Methamphetamine is frequently associated with severe cardiovascular symptoms, the

complications from which are often a cause of death in users. The cardiovascular effects of

methamphetamine are due to the release of the catecholamine neurotransmitters adrenaline and

noradrenaline. These compounds activate β-adrenergic receptors causing an increase in heart rate.

The oxidative stress associated with methamphetamine abuse can also cause cardiotoxicity.

Combined, these effects interfere with cardiac homeostasis and impair heart function. Immediate

cardiovascular effects include increased heart rate, tachycardia and increased blood pressure,

sometimes lasting several hours. Vascular effects include hypertension, stroke and haemorrhage.

Methamphetamine abuse is also linked with an increased risk of myo-cardial infarction, ventricular

hypertrophy, arrhythmias and cardiomyopathy. A study of cardiomyopathy patients found that 4 of

10 patients under 45 years of age had a history of methamphetamine abuse. This suggests that

young people who use methamphetamine are at higher risk of developing cardiomyopathy than

non-users. (Carvalho et al., 2012).

1.2.2 Cognitive effects

While a single low dose of methamphetamine may improve attention and cognitive processing,

chronic use leads to severe neuropsychological problems. Many processes are affected by

methamphetamine use, with the most consistent being impairments in working memory, executive

function and attention (Barr et al., 2006). Methamphetamine users may also experience problems

with learning and decision making (Cadet et al., 2007). Specificity to these areas is thought to be

5

due to the rich dopaminergic innervation of pathways controlling cognitive processes which

methamphetamine is known to target (Barr et al., 2006).

One of the most serious cognitive effects associated with methamphetamine abuse is the

development of drug-related psychosis. Very similar in nature to schizophrenia, this enduring

effect results from high dose or continuous methamphetamine use (Levi et al., 2012). A Japanese

survey of young people who regularly used methamphetamine found 80% had suffered from

psychotic symptoms. This has raised concern as those who suffer repeated psychotic episodes are

less responsive to treatment (Barr et al., 2006).

1.2.3 Developmental effects

Methamphetamine is increasingly becoming the drug of choice for drug-dependent women during

pregnancy. In a study of drug use in pregnant women, only 35% decreased their methamphetamine

use, while 10% increased and 55% made no change. These women were also less likely to seek

prenatal advice (Della Grotta et al., 2010). Methamphetamine is able to quickly cross the placenta

and distributes to the foetal organs where it causes changes such as increased heart weight and

myocardial damage. Other problems include hypoxia, hyperglycemia and hypertension. At birth,

these infants often show a decreased weight, length and head circumference (Schep et al., 2010).

Children who have been exposed in utero perform poorly on attention and verbal memory tests.

They also display changes in brain development, with smaller striatal and hippocampal volumes

which may account for their cognitive problems (Chang et al., 2007).

1.2.4 “Meth mouth”

Methamphetamine has devastating effects on oral health. “Meth mouth” has become the term used

to describe the decay and oral problems associated with methamphetamine abuse (Figure 1.21).

Methamphetamine users often neglect their oral hygiene and frequently favour sugary drinks

during times of drug use. Dehydration and stimulation of the sympathetic nervous system reduce

the amount of saliva produced leading to development of dental caries. Also contributing to tooth

decay are the acidic substances used in the production of methamphetamine. Users of

6

methamphetamine are known to display bruxism, clenching of the jaw and grinding of the teeth,

causing wear. Often patients do not feel any pain but seek dental treatment for cosmetic reasons

(Donaldson et al., 2006).

Figure 1.21: Dental caries due to methamphetamine abuse (Vearrier et al., 2012).

1.2.5 Neurotoxic effects

Neurotoxicity is an adverse effect on the structure or function of the central or peripheral nervous

system caused by exposure to toxic substances called neurotoxins. These neurotoxic effects can

range from short-term to permanent changes. In serious cases neurotoxicity can result in neuron

loss and changes to brain structure. Neurotoxicity is associated with a number of disorders

including behavioural problems, deficit regulation of emotion, impaired intelligence and

depression (Cunha-Oliveira et al., 2008).

Methamphetamine is a neurotoxin which, in high doses, causes the lasting depletion of the

monoamines dopamine, serotonin, adrenaline and noradrenaline and damage to nerve terminals

and cell bodies. While not completely understood, the neurotoxic effects of methamphetamine are

believed to stem from interaction with the dopaminergic and serotonergic systems. Implicated in

the neurotoxicity of methamphetamine are oxidative stress, excitotoxicity, hyperthermia,

7

mitochondrial dysfunction, inflammation and endoplasmic reticulum induced apoptosis (Cadet et

al., 2007).

1.3 Neurons and neurotransmitters

1.3.1 The neuron

The principal components of the nervous system, the neurons or nerve cells sense changes in the

environment, then communicate these changes to other neurons and stimulate the body‟s response.

The neuron is comprised of two parts: the cell body or soma, which contains the cell nucleus and

organelles, and the neurites, numerous thin tubes radiating away from the cell body (Figure 1.31).

The neurites are made up of multiple dendrites, and the axon, which is usually singular, branching

and travels over great distances (Brown et al., 2007), for example, the sciatic nerve which runs

from the base of the spine to the big toe (Encyclopaedia Britannica, 2014).

Figure 1.31: The basic structure of a neuron consisting of the cell body or soma, the axon and

dendrites (adapted from Brown et al., 2007).

The synapse is the site where the end of an axon, or axon terminal, comes into contact with and

passes information to other neurons. At the synapse, information in the form of electrical impulses

is converted into a chemical signal that crosses the space between the neurons, called the synaptic

cleft. These chemical signals, known as neurotransmitters of which there are several different

types, activate receptors on the receiving or post-synaptic neuron creating a response or further

8

propagation of the electrical signal (Bear et al., 2007). Of these neurotransmitters, the most

important are dopamine, serotonin and glutamate.

1.3.2 Dopamine

Dopamine is a catecholamine neurotransmitter within the CNS, where it controls a number of

functions such as cognition, emotion, locomotion, endocrine regulation and reward processes.

Dopamine is also active in the peripheral nervous system (the nerves and ganglia outside the CNS),

with roles including catecholamine release, hormone secretion and the regulation of renal, heart

and gastrointestinal function (Missale et al., 1998). Dopamine has been associated with disorders

such as schizophrenia and Parkinson‟s disease and is known to be involved in the neurotoxicity of

methamphetamine and a number of other drugs (Salamone et al., 2005).

Accounting for less than 1% of the total neuronal population (Arias-Carrión et al., 2010),

dopaminergic neurons are localized in the mesolimbic, mesocortical and nigrostriatal pathways

(Schep et al., 2010) (Figure 1.32). These pathways encompass the nucleus accumbens, amygdala,

hippocampus and striatum (Völlm et al., 2004), which are involved in emotion, motor control and

reward (Schep et al., 2010). There are five subtypes of dopaminergic receptors. Two are D1-like

receptors (D1 and D5) which are coupled to the G protein, Gs, and activate adenylyl cyclase. The

other receptors are D2-like (D2, D3 and D4) and are G protein coupled receptors that act to

activate potassium ion channels and inhibit adenylyl cyclase (Missale et al., 1998). The receptors

have a similar distribution to the dopaminergic neurons (Arias-Carrión et al., 2010).

9

Figure 1.32: The dopamine (yellow) and serotonin (red) pathways through the brain. Some

functions of each neurotransmitter are listed (NIDA, 2008).

Dopamine is produced in a two step process. The rate limiting enzyme tyrosine hydroxylase

converts the amino acid tyrosine to L-3,4-dihydroxyphenylalanine (DOPA), which then undergoes

decarboxylation to dopamine (Figure 1.33; Daubner et al., 2011). After synthesis, dopamine is

stored in presynaptic vesicles until release. Under normal physiological conditions dopamine is

released from the pre-synaptic neuron into the synapse in response to relevant stimuli. Once in the

synapse it diffuses in the extracellular fluid and activates its receptors (Arias-Carrión et al., 2010).

Dopamine is cleared from the synapse as a result of reuptake via the dopamine transporter (DAT)

and is metabolised by monoamine oxidase (MAO) (Riddle et al., 2006).

10

Figure 1.33: The dopamine biosynthesis pathway. Phenylalanine is converted to tyrosine by

phenylalanine hydroxylase, tyrosine to DOPA by tyrosine hydroxylase, and DOPA to dopamine by

aromatic acid decarboxylase. Dopamine is able to undergo further reactions to produce the

catecholamines norepinephrine and epinephrine (Daubner et al., 2011).

1.3.3 Serotonin

Serotonin or 5-hydroxytryptamine (5-HT) has a wide range of functions, including regulation of

the sleep/wake cycle, learning, appetite and aggression. In the brain, serotonin acts as a

neurotransmitter and is involved in emotional responses, movement and the reward system (Homer

et al., 2008). The serotonergic system is one of the most diffuse, reaching almost every part of the

CNS, but is mostly concentrated in the cerebral cortex, limbic structures, basal ganglia and

brainstem (Figure 1.34) (Kranz et al., 2010).

The amino acid tryptophan is converted in a two-step process to serotonin, involving the enzyme

tryptophan hydroxylase, which is considered the rate-limiting enzyme. Serotonin is then stored in

presynaptic vesicles until release into the synaptic cleft upon depolarisation. Serotonin can bind to

post-synaptic serotonin receptors or to auto-receptors which act as negative feedback against

further serotonin release. Reuptake of serotonin from the synapse is performed by the serotonin

11

transporter (SERT) located on the presynaptic neuron. Metabolism of serotonin occurs by MAO to

produce 5-hydroxyindoleacetic acid or 5-HIAA (Figure 1.34) (Mohammad Zadeh et al., 2008).

Figure 1.34: A serotonergic synapse showing the synthesis, storage, release, metabolism and

reuptake of serotonin (Mohammad Zadeh et al., 2008).

1.3.4 Glutamate

Glutamate is the principal excitatory neurotransmitter and is ubiquitously distributed throughout

the brain. Glutamate is important in the synthesis of proteins and peptides including glutathione

(Fonnum, 1984). Due to its role in neuronal plasticity, glutamate is also involved in learning and

memory functions. While glutamate is essential to brain function, excessive glutamate release has

been implicated in neurotoxicity (Nakanishi, 1992).

Glutamate can be synthesised from a number of different precursors: glutaminase can convert

glutamine, aspartate aminotransferase converts 2-oxoglutarate and aspartate, and ornithine δ-

aminotransferase converts 2-oxoglutarate (Fonnum, 1984). Like dopamine and serotonin,

glutamate is stored in vesicles after synthesis and is released upon depolarization by a calcium-

dependent mechanism. It may also be released in a non-calcium dependent process by reverse

operation of the glutamate transporters, responsible for glutamate reuptake from extracellular

12

space. This occurs when the sodium and potassium ion gradient across the membrane is reduced in

situations such as ischemic stroke (Meldrum, 2000).

Until recently glutamate was thought to exclusively activate glutamate-gated cation channels called

ionotropic glutamate receptors such as N-methyl-D-aspartate (NMDA) receptor. It has now been

determined that glutamate also acts through metabotropic receptors bound to a G protein. It is the

Ca2+

permeability of NMDA that is responsible for the neuronal plasticity and the neurotoxic

properties of glutamate (Nakanishi, 1992).

1.4 Neurotoxicity of methamphetamine

Administration of methamphetamine causes damage to the dopaminergic and serotonergic nerve

terminals, as well as some cell body degeneration. While the areas vulnerable to dopaminergic

toxicity are relatively few (the striatum, frontal cortices and subcortical areas) (Oh et al., 2005), the

areas susceptible to serotonergic damage are numerous and include the hippocampus, nucleus

accumbens, amygdala, caudate nucleus and cingulate cortex (Panenka et al., 2012). Although the

exact mechanisms are still unclear, there is agreement on the involvement of monoamine release,

excitotoxicity, hyperthermia, free radical formation and oxidative stress (Figure 1.41).

Figure 1.41: Pathways involved in methamphetamine neurotoxicity that lead to apoptosis and

terminal degeneration in neurons (Cadet et al., 2007).

13

With prolonged use, methamphetamine causes a significant depletion of monoamines, dopamine

and serotonin, an effect which has been shown to last several years after abstinence in primates

(Friedman et al., 1998). Initially, methamphetamine causes an increase in the extracellular

concentrations of dopamine and serotonin by stimulating their release from presynaptic terminals

and preventing their reuptake or breakdown. This is achieved by disrupting several points in the

monoamine release and reuptake pathway (Figure 1.42) (Riddle et al., 2006).

Figure 1.42: Mechanisms whereby methamphetamine causes the release of dopamine and

catecholamines from neuronal terminals into the synapse: (i) methamphetamine (triangles) enters

the cell via diffusion or (ii) by dopamine transporters; (iii) methamphetamine reverses transport of

dopamine (circles) from the vesicles to the cytosol; and (iv) and promotes the expression of

tyrosine hydroxylase, increasing dopamine production; (v); decreases the expression of dopamine

transporters at cell membrane; and (vi) inhibits monoamine oxidase activity (Schep et al., 2010).

Due to its structural similarity to the monoamines, methamphetamine acts as a substrate for the

membrane-bound dopamine and serotonin transporters (DAT and SERT), and is transported into

the cytosol (Cho, 1990). Methamphetamine can also enter the cell via diffusion (Schep et al.,

2010). Once inside, methamphetamine causes reversal of the vesicular monoamine transporter

14

(VMAT-2), responsible for the storage of dopamine and serotonin in vesicles by reducing the pH

of the vesicle. This compromises the ability of VMAT-2 to sequester monoamines (Panenka et al.,

2012), resulting in the displacement of the monoamines to the cytoplasm, where they are at risk of

auto-oxidation. Methamphetamine is also responsible for the blockage of further uptake into the

vesicles (Carvalho et al., 2012) and the redistribution of VMAT-2 to an as-yet unidentified cellular

location (Panenka et al., 2012).

The build-up of monoamines in the cytosol is further enhanced by the inhibition of the enzyme

monoamine oxidase (MAO). MAO is present on the outer mitochondrial membrane and is

responsible for monamine catabolism in presynaptic terminals (Panenka et al., 2012).

Methamphetamine further increases the concentration of dopamine by promoting its synthesis

through the up-regulation of tyrosine hydroxylase, the enzyme which catalyses the transformation

of the dopamine precursor, tyrosine, into dopamine (Cunha-Oliveira et al., 2008). However, with

chronic use of methamphetamine this effect is reversed, reducing the activity of tyrosine

hydroxylase as well as the enzyme tryptophan hydroxylase involved in serotonin production

(Hotchkiss, 1980).

The binding of methamphetamine to the monoamine transporters causes the reverse transport of

dopamine and serotonin into the synapse, where they are able to activate the postsynaptic receptor

causing hyperstimulation. The reversal of the transporters is thought to occur through a facilitated

exchange process, by which the inward transport of methamphetamine increases the inward facing

binding sites for monoamine efflux (Khoshbouei et al., 2004). Methamphetamine also prevents

reuptake of the monoamines from the synapse by reducing the number of transporters at the cell

membrane. This effect is more likely due to internalisation or phosphorylation than destruction

(Riddle et al., 2006). This has been confirmed with the finding that protein kinase C (PKC) activity

increases with methamphetamine administration and can lead to phosphorylation of DAT

(Khoshbouei et al., 2004).

15

Methamphetamine enhances the formation of reactive oxygen species (ROS) which cause damage

to amino acids, phospholipids and nucleic acids (Cunha-Oliveira et al., 2008). The released

dopamine is a potential source of ROS, as dopamine can undergo auto-oxidation via the Fenton

reaction, using iron as a co-factor. Dopamine is also thought to be oxidised to dopamine quinones

which can be converted to toxic radicals through redox cycling (Thrash et al., 2009). While the

mechanisms are less well known, serotonin toxicity is also believed to be mediated by free radicals

(Barr et al., 2006).

The involvement of ROS has been confirmed by reports of increased oxidised and reduced

glutathione levels after methamphetamine treatment (Gluck et al., 2001). Glutathione protects

against oxidising environments by acting as a reducing agent and an antioxidant (Deneke et al.,

1989). Furthermore, mice over-expressing the antioxidant enzyme CuZn superoxide dismutase

show reduced neurotoxicity (Gluck et al., 2001).

The mitochondria present another source of intracellular ROS. Mitochondrial ROS are generated

as a result of ATP production. The electron transport chain, located on the inner mitochondrial

membrane, produces ROS as a side product during ATP formation. Methamphetamine is also

known to inhibit complexes within the electron transport chain, increasing free radical production.

The hydrogen peroxide produced during the breakdown of dopamine via MAO also interferes with

electron transport chain activity. Electron transport chain complexes II and III seem to be the most

affected, showing rapid inhibition within 1 hour of methamphetamine administration (Brown et al.,

2003). Using an infusion of methamphetamine and the complex II inhibitor malonate, Brown and

colleagues (2005) found lasting depletions of dopamine in the striatum greater than that seen with

malonate alone. The breakdown of the electron transport chain and associated depletion of ATP

initiates transcription factor p53. p53, responsible for the apoptotic death pathway, has been shown

to accumulate following methamphetamine administration (Thrash et al., 2009).

Decrease in ATP production can also lead to excitotoxicity, a process of neuronal death brought

about by excessive and prolonged activation of the excitatory amino acid (EAA) receptors. Release

16

of glutamate, the major mammalian excitatory amino acid, is enhanced by methamphetamine

administration and is responsible for its excitotoxic effect (Pederson et al., 2000). The increase in

glutamate availability causes over stimulation of the N-methyl-D-aspartate (NMDA) receptor which

is spatially linked to neuronal nitric oxide synthase (nNOS), which can produce the nitric oxide

involved in neuronal degeneration (Lau et al., 2010). Glutamate release is highly dependent on

calcium influx which is mediated by the activation of ionotropic and metabotropic glutamate

receptors. High calcium levels can lead to activation of kinases, proteases and lipases, the

breakdown of cytoskeletal proteins and the production of free radicals and resulting DNA damage

(Quinton et al., 2006).

Much of the intracellular calcium released by excitotoxicity enters the mitochondria where it

causes de-energisation and the release of pro-apoptotic factor cytochrome C (Lankiewicz et al.,

2000). Calcium influx can also activate ROS formation, further inhibiting mitochondrial function

(Brown et al., 2003). The involvement of glutamate excitotoxicity in mitochondrial damage has

been confirmed by the administration of NMDA antagonist MK-801, which prevented the decrease

of complex II-III activity induced by methamphetamine (Brown et al., 2005). Whether the

mitochondria recovers after an excitotoxic injury determines the mechanism by which the neuron

dies. Necrosis occurs immediately after glutamate exposure and is associated with loss of

mitochondrial function. Death of surviving neurons with functional mitochondria occurs by

apoptosis and is a delayed process (Ankarcrona et al., 1995). The hyperthermic response brought

on by methamphetamine may also contribute to the decreased activity of the electron transport

chain (Brown et al., 2003).

While not directly responsible for the neurotoxicity of methamphetamine, hyperthermia greatly

potentiates neurotoxic effects. Lowering the ambient temperature using substances such as

haloperidol, diazepam and MK-801, is effective in preventing neurotoxicity, however, this effect

was negated with an increase an ambient temperature. Importantly, methamphetamine treatment is

able to produce neurotoxic damage without a hyperthermic response and in hypothermic animals.

17

While the exact mechanisms of methamphetamine-induced hyperthermia are unknown, the release

of monoamines is thought to be involved (Albers et al., 1995). Also implicated in the hyperthermia

response are vasoconstriction produced by methamphetamine release of noradrenaline and

interleukin-1 (IL-1) produced during microglia stimulation (Bowyer et al., 1994).

1.41 Metabolism of methamphetamine

Methamphetamine has a relatively long half-life of approximately 12 hours in humans (Cho, 1990)

and undergoes a number of metabolic reactions within the liver, mainly involving the enzyme

CYP2D6 (Figure 1.43; Schep et al., 2010). Primary reactions include aromatic hydroxylation, N-

demethylation and aliphatic hydroxylation, although, the extent of these reactions varies widely

with species (Caldwell et al., 1972). Excretion is mainly via the urine and to a small extent by

sweat and in the faeces (Schep et al., 2010). In man, methamphetamine is not well metabolised

with 30-40% of the drug being eliminated unchanged compared to 15% in the rat. Other

metabolites found in urine include 4-hydroxymethamphetamine (15 %), hippuric acid (5 %),

amphetamine (2-3 %) norephedrine (2 %) and 4-hydroxynorephedrine (1-2 %) (Caldwell et al.,

1972).

1.42 Methamphetamine addiction

Methamphetamine is often described as the most addictive drug and can cause dependence after

the first use. While methamphetamine is highly addictive, addiction is a long term consequence

and results from changes in brain chemistry that compromise top-down control (Salo et al., 2006).

It is the euphoria experienced after intake to which users become addicted. This effect comes about

due to the stimulation of the mesolimbic and nigrostriatal reward pathways by the release of

dopamine (Thrash et al., 2009).

18

Figure 1.43: The main metabolic pathways of methamphetamine: (i) aromatic hydroxylation, (ii)

N-demethylation, (iii) β-hydroxylation, (iv) oxidative deamination (v) side-chain oxidation. In

man, the main pathways are (i) and (ii) resulting in 4-OH methamphetamine and amphetamine

respectively (Schep et al., 2010).

When questioned about their reasons for taking methamphetamine or relapsing to

methamphetamine, 27% of the study participants claimed to take the drugs impulsively with 31%

being unable to stop once they began preparation. Cravings were a highly rated motivation and

27% claimed to use drugs to lessen withdrawal symptoms (Newton et al., 2009). Withdrawal is

associated with disturbances of cognition and mood characterised by depression, anxiety, cravings

and increased sleeping and eating. These symptoms begin to lessen within 7 to 10 days of

abstinence (Berman et al., 2008).

The work of Day and colleagues (2008) revealed that mice which had been repeatedly exposed to

methamphetamine showed a decreased rate of glutamate release indicating presynaptic depression

at corticostriatal terminals. Withdrawal from the drug did not return glutamate to normal levels;

however, the effect was reversed upon the re-administration of the drug. Similarly, Bamford et al.

(2008) found a reduction of acetylcholine causing corticostriatal terminal depression which was

reversed with methamphetamine re-administration. These studies provide new insight into the

changes within the brain and account for some of the physiological components of addiction and

19

tolerance to the pleasurable effects which can develop after repeated use. As methamphetamine

causes a sustained depletion of dopamine, users have difficultly feeling any pleasure other than that

gained using the drug (NIDA, 2013).

The neurotoxic effects of methamphetamine severely impact the mechanisms of cellular function

such as the mitochondrial electron transport chain. This would result in changes to the biochemical

make up of the cell.

1.5 Metabolomics

The metabolome, as first described by Oliver and colleagues (1998), is the set of all low-

molecular-weight compounds, or metabolites, present within a cell or organism, that participate in

metabolic reactions. As the metabolome is the downstream product of gene expression, it shows

more diversity in physical and chemical properties than the transcriptome (RNA) or proteome

(protein) (Dunn et al., 2005). Any changes in the transcriptome or proteome, would consequently

be expected to be amplified in the metabolome (Goodacre et al., 2004). Metabolomics, therefore, is

the study of metabolites and how they are affected by genetic modification, stimuli or changes in

the environment. Metabolomics aims to identify and measure all metabolites in a system

(Almstetter et al, 2012).

Although metabolomics has been in use for a number of years, metabolomic studies using

mammalian cells are limited. A review of the literature available shows that cell culture analysis,

while on the rise, still lags behind body fluids metabolomics (Figure 1.51; Cuperlović-Culf et al.,

2010). While they offer more limited metabolic functions, clonal cell lines are often preferred over

primary cells in metabolomic studies for their availability, simple handling and reduced variability

(Leon et al., 2013). In spite of this, the application of metabolomics to cell lines has been impeded

by several factors, key among them being the fact that the composition of the medium, seeding

density and age of the cells can all influence the metabolome. Common quenching processes, such

as the use of methanol, can cause damage to cell membranes, leading to leakage of metabolites into

20

the extracellular fluid (Leon et al., 2013) and extraction processes are time-consuming

(Cuperlović-Culf et al., 2010). Despite these limitations, mammalian cell metabolomics has proved

useful in numerous areas including biomarker identification, drug testing, pharmaceutical research

and toxicology (Leon et al., 2013).

Figure 1.51: Comparison of the number of publications (1999-2008) available on various

applications of metabolomics. Blue, cell culture/line; red, blood/serum and green, urine. Search

was performed in PubMed using the keywords outlined in the legend (Cuperlović-Culf et al.,

2010).

Metabolomics provides a snap shot of a cell‟s activity at a certain point in time and under given

conditions. In order to achieve this, metabolism must be stopped as quickly as possible to avoid

compositional changes in the metabolome. In addition, the analysis ideally requires high recovery,

reproducibility and sensitivity and a definite identification and quantification of metabolites (De la

Luz-Hdez, 2012).

As metabolites are very labile and diverse, there is currently no single analytical system that is able

to detect and quantify all of the metabolites within an organism (Desbrosses et al., 2005). The most

commonly used systems are nuclear magnetic resonance (NMR) spectroscopy and gas- or liquid

21

chromatography (GC; LC) coupled with mass spectrometry (MS). A comparison of various aspects

of these technologies is given in Table 1.5 (Mayr, 2008).

NMR detects the nuclear spin of molecules with uneven numbers of protons and neutrons such as

1H, which is present in high abundance in organic compounds. NMR is therefore able to detect a

wide range of metabolites, and is also highly reproducible and robust. NMR has the added

advantages of not destroying the sample during analysis and has the ability to perform in-vivo

analysis. However, NMR does have poor sensitivity when compared with mass spectrometry

(Mayr, 2008) and many functional groups are proton-deficient making identification challenging

(Villas-Boas et al., 2004).

GC-MS is a popular alternative to NMR due to its sensitivity. This technique separates compounds

by GC, based on their affinity for a gas or solid phase, before they undergo ionization,

fragmentation and detection in the MS (Desbrosses et al., 2005). Metabolites are identified by

comparison of retention time with known standards. GC-MS is most useful in the analysis of

volatile compounds, meaning that non-volatile metabolites (e.g. amino acids, sugars, fatty acids

and other polar metabolites) must be made thermally stable through chemical derivatisation before

being analysed by GC-MS (Dunn et al., 2005). This process is time-consuming and can introduce

confounding factors to the chromatogram (Halket et al., 2005). A key draw back of GC-MS is that

samples are destroyed during analysis (Dunn et al., 2005).

Functionally, LC-MS is similar to GC-MS, however LC separates metabolites based on their

affinity for a liquid or solid phase. LC-MS is suited to the analysis of non-volatile compounds that

are soluble in solution. An advantage of LC-MS is the ability to directly analyse many polar

metabolites without derivatisation (De la Luz-Hdez et al., 2012).

22

Table 1.5: A comparison of NMR, GC-MS and LC-MS in relation to metabolomics analysis

(Mayr, 2008).

1.6 Gas Chromatography-Mass Spectrometry

GC-MS is well established in mammalian cell metabolomics research (Leon, 2013). For this

reason, as well as its good sensitivity, separation power and ability to be applied to a wide range of

metabolites (Mayr, 2008), it is the platform utilised in the following experiments.

The GC consists of a gas supply, an injector, flow and pressure regulators and a column contained

within an oven (Figure 1.61). The sample is injected into the injector site, which is maintained at a

set temperature, and is evaporated. A carrier gas flows into the injector port at a constant and

controlled rate and carries the vaporised sample into the column. The column is usually an open

tubular, narrow bore fused silica tube to which the stationary phase is chemically bound. Within

the column, the evaporated compounds are separated according to their distribution between the

solid and gaseous phases. After separation, the sample is transferred to the MS (Villas-Boas et al.,

2007).

The MS performs the functions of analysing, detecting and determining structural information of

the sample. It consists of an ion source, mass analyser, detector and data system (Figure 1.61). The

most common ionization method used in GC-MS is electron impact. The ion source emits

energized electrons which collide with the sample molecules, producing ions in a fragmentation

23

pattern highly unique to that compound. The ions are transferred to a vacuum for determination of

the mass-to-charge ratio (m/z) in the mass analyser. The quadrupole mass analyser is commonly

used in the GC-MS system for its simplicity and reliability. It consists of four parallel rods with a

voltage supplied. The voltage creates a cylinder through which the molecules pass to reach the

detector. Only ions within a certain m/z range are allowed to pass through the entire cylinder;

others hit the rods and are neutralised. The detector measures the current or the number of ions

passing through as a function of time. This information is then read by the data system which

processes the information (Villas-Boas et al., 2007).

Figure 1.61: Overview of the GC-MS instrument. (Adapted from Dunnivant and Ginsbach, 2008)

There are seven main steps involved in GC-MS metabolomics. Firstly, quenching of the sample to

prevent further metabolic activity. This usually involves the separation of cells from the culture

24

medium and detachment from the culture plates which can lead to cell leakage. The quenching

process must be rapid and avoid metabolite destruction or modification (Desbrosses et al., 2005).

Secondly, metabolites must be extracted from the cells in an unbiased manner. This can be difficult

due to the range of metabolite polarities. Again, extraction must aim to minimise metabolite

changes (Dettmer et al., 2011). Next, the samples must undergo derivatisation to create thermal

stability for the GC-MS analysis. The samples are then separated and detected by GC-MS and,

lastly the chromatographic output is analysed by software to identify fragmentation patterns using

reference libraries (Desbrosses et al., 2005).

25

1.7 Aims

The impact of methamphetamine‟s neurotoxic effects is evident in the altered biochemistry of the

neuronal cell. The GC-MS metabolomics method provides an insight into these changes through

the identification and quantification of intra- and extracellular metabolites.

The aims of this project were to utilise an established in-vitro neuronal cell culture model to

investigate cellular mechanisms of methamphetamine neurotoxicity, specifically:

1) To demonstrate, measure and compare the decrease in neuronal viability after exposure to a

range of methamphetamine concentrations;

2) To analyse the metabolite profile of neuronal cells after administration of a range of

methamphetamine concentrations, and;

3) To study the changes to the metabolite profile of neuronal cells over time after administration of

a toxic dose of methamphetamine

26

2. Materials and methods

2.1 Materials

2.11 Chemicals and kits

D-sorbitol-6-13

C, Dulbecco‟s modified Eagle‟s medium (DMEM), foetal calf serum, L-glutamine,

methamphetamine hydrochloride, methoxyamine hydrochloride, n-alkanes (C10, C12, C15, C19, C22,

C28, C32, C36), N-methyl-N-(trimethylsilyl)trifluroacetamide (MSTFA), penicillin/streptomycin

solution, phosphate buffered solution (PBS), pyridine and trypsin-EDTA solution were all

purchased form Sigma-Aldrich, (Sydney Australia) in the highest purity available. LDH assay was

performed using a CytoTox 96 Non-Radioactive Cytotoxicity Assay kit purchased from Promega

Corporation, (Sydney, Australia). LC-MS grade methanol and HPLC grade water were purchased

from Fisher Scientific (Fair Lawn, USA). n-hexane (95%) was purchased from LabScan, (Gliwice,

Poland). Carbon dioxide (CO2) (food grade) and helium (ultra-high purity) were obtained from

BOC Gases, (Perth, Australia).

2.12 Cells

Cells used for all experiments were a B50 rat neuroblastoma cell line obtained through the

European Collection of Cell Cultures and purchased from Sigma Aldrich. Cells were grown in

75cm2 flasks (Corning, NY,USA) and maintained at 37

oC in a humidified atmosphere of 95% air

and 5% CO2 using a Thermo Scientific Heraeus Function Line incubator (Scoresby, Australia).

2.2 Methods

2.21 Cell culture

B50 cells were maintained in DMEM (12 mL) supplemented with 5% foetal calf serum, 1% L-

glutamine and 1% penicillin/streptomycin. Cells were cultured until 70-80% confluent before

being passaged or seeded onto 6-well plates for experimentation. Cells were dislodged from the

flask for passaging by trypsinisation. This involved the removal of DMEM and the addition of 2 ml

1 x trypsin-EDTA, incubation for 2 minutes at 37oC and 5% CO2 followed by inactivation of

trypsin by the addition of DMEM containing the above supplements. Cells were then resuspended

with fresh DMEM, aliquoted into new flasks and made up to 12 mL with DMEM.

27

Photos were taken using a Moticam 2300 camera (Motic Instruments Inc., Hong Kong) and Motic

Images Plus software (v2.0) in combination with an Olympus CKX41 microscope (Tokyo Japan)

immediately prior to harvesting to record morphological differences between control and

methamphetamine-treated cells.

2.22 Cell counting

One well on each plate was reserved for cell counting. In this process, which was conducted at the

same time as harvesting (2.24), the medium was removed from the well and 500 µL of trypsin

added to detach cells from the plate. The plate was incubated at 37oC for 5 minutes before the

addition of 2 mL of DMEM to terminate trypsinisation. An aliquot of 10 µL was transferred to a

haemocytometer and counted using an Olympus inverted research microscope (model IMT-2,

Tokyo, Japan.). The cells in the four corner squares were counted, excluding those on the outside

borders. The average of these counts was used to estimate the number of cells per well for each

plate.

The Trypan blue exclusion test was conducted during the cell counting process. In this assay, the

media was removed and 500 µL trypsin added to a single well as per the counting method above.

After 5 minutes incubation at 37oC, 2 mL of fresh DMEM was added. A 2 mL aliquot of the cell

suspension was centrifuged at 150 x g for 3 minutes and the supernatant discarded. The resulting

pellet was resuspended in 1 mL of PBS. Equal amounts (10 µL) of the cell suspension and 0.4%

Trypan blue were mixed in a 96-well plate and applied to the haemocytometer for counting. The

unstained (viable) and blue stained (dead or damaged) cells were counted separately and used to

estimate the percentage cell viability for each experimental condition.

2.23 LDH Assay

A lactate dehydrogenase (LDH) assay is a useful way to measure cytotoxicity mediated by

chemicals. Using colorimetric methods, the assay quantitatively measures lactate dehydrogenase,

an enzyme released upon cell lysis. Due to a coupled enzymatic reaction, the amount of colour

produced is directly proportional to the amount of LDH present (Promega, 2012).

28

Before experimental use, the method was optimised for use with B50 cells and Dulbecco‟s

modified Eagle‟s medium to determine correct dilution factors.

A 500 µL sample of media was collected from each well and placed in separate tubes before being

centrifuged at 1092 x g and 4oC for 5 minutes. These samples were diluted 1:50 with DMEM,

vortexed and kept on ice.

6-well plates containing cells and remaining media then underwent a freeze-thaw lysis process: 30

minutes at -80oC followed by a 15-minute incubation at 37

oC. During freeze-thaw lysis, the cells

break open as ice crystals formed during the freeze stage contract when thawing (Tansey, 2006).

Cells were scraped from each well into the media and both media and cells collected into tubes.

The tubes were vortexed and centrifuged at 1092 x g for 4 minutes. Each sample was diluted 1:100

with DMEM, vortexed and placed on ice.

Triplicate media and cell-media lysis samples was transferred to a 96-well plate at 50 µL per well.

Triplicates of media and treatment blanks were also included to ensure no background absorbance

or interaction of methamphetamine with the assay substrate. The assay substrate mix was added

(50 µL) to each well and the plate was covered and incubated at room temperature for 30 minutes.

After the incubation period, the assay stop solution was added (50 µL) and, after ensuring the

absence of bubbles, the absorbance was read at 490 nm using an iMark microplate absorbance

reader (Bio-rad; Gladesville, Australia).

2.24 Harvesting

The harvesting process is a crucial step in metabolomics studies. In order to stop enzymatic

activity, the samples must be quenched as quickly as possible after the treatment period. Without

this quenching step, degradation of the metabolites may occur and the composition of the sample

may alter. Harvesting also acts to detach adherent cells from culture plates (Dettmer et al., 2010).

From each plate, a medium sample of 40 µL was taken from five wells, pooled in a 1.5 mL tube

and stored on ice. Any remaining medium from these wells was then discarded. Cold PBS was

used as a cell wash buffer with the addition of 1 mL to each well. PBS was carefully removed and

29

discarded to avoid disturbing the cells. To quench the cells, a further 100 µL of cold PBS was

added and cells scraped into the buffer using a plastic cell scraper. The contents of the wells was

collected into a single 1.5 mL tube and stored on ice until freezing. All samples were transferred to

-80oC until frozen, after which they were freeze-dried using a Labconco FreeZone 2.5 Plus (Kansas

City, USA) and stored at -80oC until extraction.

2.25 Extraction

For metabolomic analysis to be successful, efficient metabolite extraction is required. Extraction is

the process by which metabolites are released from the cell sample and separated from other,

undesired, compounds such as proteins (Sana et al., 2007).

An extraction solution was prepared by dissolving the internal standard of 13

C6-sorbitol in LC-MS

grade methanol at 2.6 µg/mL. 500 µL of extraction solution was added to each cell and media

sample. The cell samples were vortexed and the contents transferred to lysis tubes. The contents of

the cells were extracted using the Precellys 24 lysis and homogenisation tissue lyser (Bertin

Technologies, Aix-en-Provence, France), run for two 20-second cycles at 6500 rpm.

Both the medium and cell samples were centrifuged at 16,100 x g for 10 minutes and 300 µL of the

resulting supernatant transferred to fresh 1.5 mL tubes. The volume was reduced using the

Eppendorf Concentrator Plus rotary vacuum concentrator (North Ryde, Australia) for 90 minutes

to reduce the volume by at least 75% to facilitate freezing. 300 µL HPLC grade water was added to

each sample and tubes were frozen at -80oC. Once frozen, the samples were freeze-dried and stored

at -80oC until derivatisation.

2.26 Derivatisation

In order to improve the sensitivity and specificity of analysis using gas chromatography, a

derivatisation step is required. This enables the analysis of non-volatile compounds and improves

separation on GC columns (Almstetter et al., 2012). The derivatisation method used in this project

is well-established (Abbiss et al., 2012).

30

A solution of 20 mg/mL methoxyamine hydrochloride in pyridine was prepared and 20 µL added

to each extracted sample. By reacting with carbonyl groups to form oxime derivatives,

methoxyamine acts as a protective agent, ensuring compounds are able to be derivatised (Sellers,

2007). Using the Eppendorf Thermomixer comfort (North Ryde, Australia) samples were agitated

at 1200 rpm and 30oC for 90 minutes. Samples were then centrifuged at 16,100 x g for 1 minute

and 40 µL of MSTFA was added to each. The samples were further agitated at 300 rpm at 37oC for

30 minutes. Each sample was transferred to a 2 mL GC vial, 100 µL standard insert and aluminium

combination seal, and 5 µL of a mixture of n-alkanes in n-hexane were added. This alkane mixture

(C10-C36, 0.625 mg/mL) is added to allow the calculation of a Kovat‟s index.

2.27 Instrumental analysis

The gas chromatography-mass spectrometry (GC-MS) system used for these experiments was an

Agilent 6890 Series gas chromatograph with an Agilent 7683 autosampler and injector, coupled

with an Agilent 5973 Series single quadrupole mass selective detector (Agilent Technologies,

Santa Clara, USA) using an established metabolomic analysis method (Abbiss et al., 2012). The

GC carrier gas was helium and a deactivated inlet liner with glass wool was used. The column used

for analyte separation in these experiments was an Agilent FactorFour VF-5ms fused silica

capillary column (30 m length x 0.25 mm internal diameter 0.25µm film thickness + 10 m EZ-

Guard; Agilent Technologies).

The GC program was as follows: inlet temperature was set to 230oC and the oven had an initial

temperature of 70oC. The temperature was ramped at 1

oC per minute for 6 minutes and then

increased a further 5.63oC per minute to reach a final temperature of 330

oC, which was maintained

for 10 minutes. The GC to MS transfer line was held at 330oC and the ion source was set to 230

oC

and 70 eV, with an 8-minute solvent delay. The detector performed a full scan for the mass range

of m/z 45 to 600 at 1 scan per second.

31

2.28 Data analysis

GC-MS data was imported into Analyzer Pro (v2.7.0; Spectral Works Ltd., Runcorn, United

Kingdom). This program assigns peaks to the data and identifies metabolites by comparison with a

target component library (Metabolomics Node, Murdoch University and National Institute of

Standards and Technology (NIST) 2005), matching according to mass spectrum and retention

index information. The results of this analysis were exported as a matrix into Microsoft Excel

(v12.0; Microsoft Corporation, Redmond, USA) to allow for manual inspection and data clean-up.

The Excel matrix was imported into The Unscrambler X (v10.1; CAMO, Oslo, Norway) to allow

for comparison between samples. The matrix data was transformed by log10 before performing a

principal component analysis, using non-linear iterative partial squares algorithm, cross validation

and no rotation. For the dose curve and time course experiments, data was normalised to the total

ion chromatogram (TIC) for each sample.

2.3 Experiments

2.31 LDH assay

B50 cells were grown in 75cm2 flasks until 70-80% confluent before being seeded onto 6-well

plates at 3 mL per well. Methamphetamine solution was prepared at 14.9 mg/mL

methamphetamine hydrochloride in HPLC grade H2O and serially diluted to achieve the desired

dose. After 24 hours incubation at 37oC and 5% CO2, cells were treated with 30 µL

methamphetamine to reach final concentrations of 1 µm, 10 µM, 100 µM and 1 mM which were

tested in triplicate. Three wells were used as a vehicle control using HPLC grade H2O. Plates were

incubated for 48 hours and after the treatment period, LDH cytotoxicity assay was performed as

described in 2.23.

2.32 Dose curve

To examine the influence of methamphetamine concentration on the neuronal metabolite profile,

an experimental dose curve was conducted. A reduction in cell viability following exposure to

methamphetamine at various concentrations has been well recorded within the literature. In

neuronal cells, Stumm and colleagues (1999) found 1 mM methamphetamine led to approximately

32

50% reduction in viability after 96 hours exposure. Similar results were found by Shao and

colleagues (2012), who found a 50% drop in the viability of endothelial cells compared to control

after 48 hours exposure to methamphetamine. At methamphetamine concentrations below 0.125

mM, little to no change in neuronal viability was reported after 24 hours incubation (Sinchai et al.,

2011). Using a range of concentrations would allow us to compare the metabolic changes

occurring at toxic and sub-toxic doses of methamphetamine.

Cells were grown in 75cm2 flasks until 70-80% confluent before being plated in 6-well plates.

Methamphetamine solution was prepared at 14.9 mg/mL methamphetamine hydrochloride in

HPLC grade H2O and serially diluted to achieve the desired dose. After 24 hours incubation at

37oC and 5% CO2, cells were treated with 30 µL methamphetamine solution to final concentrations

of 100 nM, 1 µM, 10 µM, 100 µM and 1 mM. Each concentration was tested in triplicate with an

additional three plates used as controls with the addition of HPLC grade H2O. The plates were

incubated for 48 hours before harvesting and this experiment was undertaken in duplicate.

2.33 Time Course

The biochemical changes over time due to methamphetamine administration were investigated

using a time course experiment. Cells were cultured in 75cm2 flasks until 70-80% confluent, plated

into 6-well plates and incubated for 24 hours at 37oC and 5% CO2. Methamphetamine solution was

prepared at 14.9 mg/mL methamphetamine hydrochloride in HPLC grade H2O. Six plates were

administered 30 µL of methamphetamine for a final concentration of 1 mM, with the remaining six

kept as controls by adding HPLC grade H2O. At 6, 24 and 48 hours after drug administration,

duplicate treated and control plates were harvested. This time course experiment was conducted in

duplicate.

33

3. Results 3.1 Cell viability testing

To investigate the effect of methamphetamine dose on B50 neuroblastoma cells, cells were

exposed to a range of methamphetamine concentrations (100 nM, 1µM, 10 µM, 100 µM and 1

mM) for 48 hours. The photographic evidence of observed changes is shown in Figure 3.11.

At 48 hours, all cells including controls displayed some indications of clumping or aggregation.

The cells demonstrated little change in appearance from controls at concentrations below 100 µM.

At 100 µM and 1 mM methamphetamine, the B50 cells showed excessive aggregation and retained

little normal neuronal morphology.

To determine if the morphological changes coincided with cell death, toxicity was quantified using

a LDH cytotoxicity assay. B50 rat neuroblastoma cells were incubated with methamphetamine (1

µM, 10 µM, 100 µM and 1 mM) for 48 hours and cytotoxicity compared to controls was

determined by LDH assay (see 2.23). The results, expressed as percentage cytotoxicity are

presented in Figure 3.12. The cytotoxicity of 1 mM methamphetamine (21.9% ± 4.8) was found to

be considerably higher than the control (14.9% ± 1.6) while the lowest concentration tested, 1 µM,

showed very little change from the control (15.3% ± 0.9). Conversely, the mid-range

concentrations of 10 µM and 100 µM showed less toxicity than the control and 1 µM

methamphetamine.

As a comparison to the LDH method, Trypan blue exclusion was also used to assess cell viability.

B50 cells treated with methamphetamine at a range of concentrations (100 nM, 1 µM, 10 µM, 100

µM and 1 mM) were compared to control cells after 48 hours of exposure. The results, given as a

percentage of the control, are shown in Figure 3.13. This shows a dose-dependent decrease in the

viability of neuronal cells as compared to controls, with the viability of the highest concentration (1

mM) being 69.9% ± 3.4 of the control.

34

Figure 3.11 Photographs of B50 cells after 48 hours of methamphetamine treatment at various

doses: A) control B) 100 nM C) 1 µM D) 10 µM E) 100 µM F) 1 mM. Photos were taken using a

Moticam 2300 camera in combination with an Olympus CKX41 microscope at 100 x

magnification.

35

Figure 3.12: Cytotoxicity of methamphetamine after 48 hours exposure to neuronal cells as

measured by LDH assay. The percentage cytotoxicity of methamphetamine (concentration 1 µM,

10 µM, 100 µM and 1 mM) to cultured neuronal cells compared to controls. % cytotoxicity

represents mean ± standard error (n=3). Percentage cytotoxicity was calculated by comparing LDH

release of medium samples taken from each concentration or control with its corresponding total

cell fraction.

0

5

10

15

20

25

30

Control 1uM 10uM 100uM 1mM

% C

yto

toxi

city

Methamphetamine concentration

36

Figure 3.13: Cell viability after 48 hours exposure to methamphetamine as measured by Trypan

blue exclusion testing. The viability of neuronal cells after methamphetamine exposure

(concentrations 100 nM, 1 µM, 10 µM, 100 µM and 1 mM) compared to controls. Viability is

given as a percentage of the control value. % viability represents mean ± standard error (n=4).

0

20

40

60

80

100

120

Control 100nM 1uM 10uM 100uM 1mM

% V

iab

ility

Methamphetamine concentration

37

3.2 Dose curve

The fact that methamphetamine causes neuronal death is well known, however, the biochemical

response of cells to methamphetamine administration is less well documented. A dose curve

experiment was undertaken to compare the changes occurring in the metabolome under

administration of methamphetamine at toxic and sub-toxic doses from 100 nM to 1 mM. In order

to compare the results from duplicate experiments, the data was normalized to the total ion

chromatogram (TIC) for each sample.

3.2.1. Intracellular metabolites

Principal component analysis (PCA) is a common method for data mining in metabolomics

analysis (Putri et al., 2013). The scores plot (Figure 3.21-A) shows the relationship between the

treatment groups. Variance in the cell sample dataset is best described by principal component 1

(PC-1), which explains 48%, while PC-2 explains 28%. The control samples grouped most closely

with the 1 µM samples and separate from the higher concentrations, which grouped together.

Triplicate samples at 100 nM methamphetamine were included as part of the experiment, but were

excluded from analysis due to the large variance between the replicates.

The loadings plot (Figure 3.21-B) identifies the metabolites which contribute most to the difference

observed between samples. The loadings data was extracted from this plot and twelve metabolites

which had the most impact on the variance between samples were chosen from the loadings plot

for further study. The peak area at each concentration was then expressed as a percentage of the

control. Figures 3.22 and 3.23 display the results of this analysis, showing the amino acids and

other metabolites, respectively.

A number of amino acids including L-isoleucine, showed a decrease in peak area as dose increased

from 100 µM to 1 mM. L-proline followed a similar trend, with peak area increasing from 0.76 ±

0.10 to 1.26 ± 0.07 between 1 µM and 100 µM before dropping below control at 1 mM. Overall, L-

38

Figure 3.21: Principal component analysis of GC-MS metabolomics data generated from B50 rat

neuroblastoma cells following exposure to methamphetamine. Cells were incubated with or

without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for 48 hours. (A) Scores plot, with

sample groupings identified. (B) Loadings plot, showing metabolites and their influence on the

sample groupings in (A). Data was deconvoluted, compounds identified and normalised to internal

standard prior to multivariate analysis.

39

Figure 3.22: Changes in intracellular amino acids of B50 cells with dose of methamphetamine

identified by PCA as contributing most to the variance observed between samples. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of 48

hours. Amino acids were chosen from the GC-MS metabolite data, normalised to total ion

chromatogram and presented as a percentage of the control value. Peak area is mean ± standard

error. n=3

40

Figure 3.23: Changes in intracellular metabolites of B50 cells with dose of methamphetamine

identified by PCA as contributing most to the variance observed between samples. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of 48

hours. Metabolites were chosen from the GC-MS metabolite data, normalised to total ion

chromatogram and presented as a percentage of the control value. Peak area is mean ± standard

error. n=3

41

leucine, L-threonine and L-valine showed very little change from the controls. Only L-serine

showed an upwards trend, with a dose-dependent increase in peak area.

The “other” metabolite group displayed more variety in trends compared to the amino acids. Of all

the metabolites analysed, galactose had the greatest increase in peak area with a substantial

increase from 0.13 ± 0.01 at 1 µM to 333.64 ± 2.68 at 10 µM, remaining high until a slight

decrease at 1 mM. Cholesterol and glycerol 3-phosphate levels decreased to below controls at 1

mM methamphetamine. γ-aminobutyric acid (GABA) showed a similar trend, except instead of

decreasing at 1 mM, GABA levels increased sharply. Levels of N-acetylglutamate slowly

increased from 3.15 ± 1.17 to 3.79 ± 0.71 between 1 µM and 100 µM and sharply decreased to 1.56

± 0.95 at 1mM of methamphetamine. Octadecenoate levels increase from 0.66 ± 0.15 at 1 µM to

1.31 ± 0.21 at 10 µM then gradually declined.

3.2.2 Extracellular metabolites

In order to have a full understanding of metabolic changes, it is important to consider both the

intracellular metabolome, the metabolites within the cell, and the extracellular metabolome,

consisting of the metabolites present within the culture medium. Changes within the cell may be

reflected in the extracellular metabolome, and there may also be the exchange of metabolites

between cells and the surrounding medium due to uptake or excretion (Leon, 2013).

A PCA was generated from the GC-MS data of media samples and is presented in Figure 3.24. The

scores plot (A) shows clear differences between the treatment groups. The variance between

groups is best explained by PC-1 (43%) and PC-2 (20%). Compared to the cell PCA in Figure

3.21, there appears to be a greater overlap between samples treated with low and high

concentrations of methamphetamine. The loadings plot (B) was used to identify the metabolites

representing the greatest source of variance between the groupings, which were further analysed

and plotted as dose curves (Figures 3.25, 3.26 and 3.27). Triplicates samples at 100 nM

methamphetamine were included as part of the experiment, but were excluded from analysis due to

the large variance between the replicates.

42

Figure 3.24: Principal component analysis of GC-MS metabolomic data generated from medium

obtained from B50 rat neuroblastoma cells following exposure to methamphetamine. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of for 48

hours. (A) Scores plot, with sample groupings identified. (B) Loadings plot, showing metabolites

and their influence on the sample groupings in (A). Data was deconvoluted, compounds identified

and normalised to internal standard prior to multivariate analysis.

43

Figure 3.25: Changes in extracellular amino acids of B50 cells with dose of methamphetamine

identified by PCA as contributing most to the variance observed between samples. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of 48

hours. Amino acids were chosen from the GC-MS metabolite data, normalised to total ion

chromatogram and presented as a percentage of the control value. Peak area is mean ± standard

error. n=3

44

Figure 3.26: Changes in extracellular amino acids of B50 cells with dose of methamphetamine

identified by PCA as contributing most to the variance observed between samples. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of 48

hours. Amino acids were chosen from the GC-MS metabolite data, normalised to total ion

chromatogram and presented as a percentage of the control value. Peak area is mean ± standard

error. n=3

45

Figure 3.27: Changes in extracellular metabolites of B50 cells with dose of methamphetamine

identified by PCA as contributing most to the variance observed between samples. Cells were

incubated with or without methamphetamine (1 µM, 10 µM, 100 µM, 1 mM) for a period of 48

hours. Metabolites were chosen from the GC-MS metabolite data, normalised to total ion

chromatogram and presented as a percentage of the control value. Peak area is mean ± standard

error. n=3

46

The extracellular metabolites chosen for further review have been placed into three groups for

analysis according to type and direction of change. The first group (Figure 3.25) containing the

amino acids L-alanine, glycine, L-proline, L-serine and L-threonine, showed an increase in peak

area as the dose of methamphetamine increased. The second group (Figure 3.26) is the amino acids

L-isoleucine, L-leucine, L-methionine and L-valine, which followed a trend of decreasing peak

area with increasing dose. Lastly, non-amino acid metabolites (Figure 3.27). Arabitol and urea

levels decreased with increasing methamphetamine concentration. The levels of galactofuranose

remained steady before a sharp decrease in peak area from 1.20 ± 0.11 at 100 µM to 0.88 ± 0.41 at

1 mM. Both myo-inositol and octadecenoate followed a general increase in peak area as the

methamphetamine concentration increased. Similarly, uracil levels also increased with dose;

however, a drop in peak area was recorded at 100 µM (0.86 ± 0.14) before rising again at 1 mM

(1.71 ± 0.35).

3.3 Time course

To measure the biochemical response of B50 neuroblastoma cells to methamphetamine over time,

a time course experiment was conducted. This analysis compared incubation periods of various

lengths: a short incubation of 6 hours, an intermediate 24 hours and an extended 48-hour

incubation.

3.3.1 Intracellular metabolites

The PCA created from the intracellular GC-MS dataset is presented in Figure 3.31. The scores plot

(A), shows the difference between the groups as best modelled by PC-1 (44%) and PC-2 (20%).

The scores plot shows distinct differences between the different time groups with the control and

treatment groups for each time period close together. This shows there was more variance based on

time than based on treatment. The loadings plot (B) identifies the metabolites which accounted for

the variation between samples. From this data, the change in peak area over time has been

graphically represented for the metabolites representing the most variance (Figures 3.32 and 3.33).

47

Figure 3.31: Principal component analysis of GC-MS metabolomics data generated from B50 rat

neuroblastoma cells following exposure to methamphetamine. Cells were incubated with or

without 1 mM methamphetamine for 6, 24 or 48 hours. (A) Scores plot, with sample groupings

identified. (B) Loadings plot, showing metabolites and their influence on the sample groupings in

(A). Data was deconvoluted, compounds identified and normalised to internal standard prior to

multivariate analysis.

48

Figure 3.32: Changes over time of intracellular amino acids of B50 cells incubated with

(treatment) or without (control) methamphetamine (1 mM) for periods of 6, 24 or 48 hours.

Metabolites were identified from the GC-MS metabolite data by PCA as contributing most to the

variance observed between samples. Data was normalised to total ion chromatogram. Peak area is

mean standard ± error. n=3

49

Figure 3.33: Changes over time of intracellular metabolites of B50 cells incubated with

(treatment) or without (control) methamphetamine (1 mM) for periods of 6, 24 or 48 hours.

Metabolites were identified from the GC-MS metabolite data by PCA as contributing most to the

variance observed between samples. Data was normalised to total ion chromatogram. Peak area is

mean standard ± error. n=3

50

The time course results for intracellular amino acids are displayed in Figure 3.32. The amino acids

L-alanine, L-glutamate, L-methionine and L-threonine showed a similar trend of an increase in

peak area with increasing incubation time. Of these, only L-glutamate recorded a decrease in levels

relative to control at 48 hours with peak areas of 1.13 x 108 ± 4.04 x 10

7 and 6.78 x 107 ± 3.05 x 10

7

respectively. L-valine remained steady throughout the time course with fairly similar control and

treatment levels.

The other intracellular metabolites identified for further analysis are shown in Figure 3.33. Both

fructose and glucose followed a trend of decreasing peak area over time. Also displaying a

decrease over time was hexdecanoate, whose treatment samples were consistently higher than

control. Myo-inositol and succinate showed a very similar trend of increasing peak area over time

(from 1.49 x 107 ± 7.50 x 10

6 to 2.21 x 10

7 ± 3.40 x 10

6 for myo-inositol and from 5.70 x 10

6 ± 2.03

x 106 to 7.58 x 10

6 ± 2.63 x 10

6 for succinate, from 6 hour to 48 hour treatments respectively) with

treatment levels beginning high relative to control and finishing below control at 48 hours. Levels

of treatment samples were generally low compared to control for tryptamine, which displayed an

increasing trend over time (from 6.60 x 106 ± 3.53 x 10

6 at 6 hours treatment to 15.81 x 10

7 ± 6.81

x 106 at 48 hours treatment).

3.3.2 Extracellular metabolites

A PCA was also constructed for the extracellular metabolomic data (Figure 3.34). As shown in the

scores plot (A), this data is described by PC-1 (33%) and PC-2 (27%). There is clear distinction

between the groups, with the treatment and control group for each time period clustering together.

The metabolites distinguishing the groups are given in the loadings plot (B). A number of these

metabolites were examined further through graphical representation of the changes over time

(Figures 3.35 and 3.36).

An increase in peak area over time was found for the amino acids L-alanine, L-phenylalanine and

L-proline. The treatment sample for L-alanine was considerably lower than control level at the 48

51

Figure 3.34: Principal component analysis of GC-MS metabolomics data generated from medium

obtained from B50 rat neuroblastoma cells following methamphetamine exposure for 6, 24 or 48

hours. (A) Scores plot, with sample groupings identified. (B) Loadings plot, showing metabolites

and their influence on the sample groupings in (A). Data was deconvoluted, compounds identified

and normalised to internal standard prior to multivariate analysis.

52

Figure 3.35: Changes over time of extracellular amino acids of B50 cells incubated with

(treatment) or without (control) methamphetamine (1 mM) for periods of 6, 24 or 48 hours. Amino

acids were identified from the GC-MS metabolite data by PCA as contributing most to the variance

observed between samples. Data was normalised to total ion chromatogram. Peak area is mean

standard ± error. n=3

53

Figure 3.36: Changes over time of extracellular metabolites of B50 cells incubated with

(treatment) or without (control) methamphetamine (1 mM) for periods of 6, 24 or 48 hours.

Metabolites were identified from the GC-MS metabolite data by PCA as contributing most to the

variance observed between samples. Data was normalised to total ion chromatogram. Peak area is

mean standard ± error. n=3

54

hour mark (48 hours control, 1.55 x 106 ± 2.62 x 10

5 and 48 hours treatment, 5.42 x 105 ± 3.08 x

105). L-phenylalanine treatment samples were above control at all incubation times. A trend of

decreasing peak area over time was shown by L-isoleucine, L-serine and L-valine. At the 24- and

48-hour time periods (1.32 x 107 ± 6.82 x 10

6 and 1.05 x 10

7 ± 6.35 x 10

6 respectively), the levels

of L-serine treatment samples were noticeably higher than control (6.32 x 106 ± 4.52 x 10

6 and 4.22

x 106 ± 4.75 x 10

6 respectively).

Of the extracellular metabolites presented in Figure 3.36, only myo-inositol followed a trend of

decreasing peak area over time (7.22 x 107 ± 2.19 x 10

7 at 6 hours treatment and 5.40 x 107 ± 1.02

x 107 at 48 hours treatment) with the remaining metabolites increasing over time. The glycerol

samples show a noticeable increase in peak area from 24 to 48 hours (2.64 x 105 ± 3.61 x 10

4 at 24

hours treatment to 7.06 x 105 ± 2.52 x 10

5 at 48 hours treatment). The levels of treatment samples

compared to control remained fairly similar for all metabolites analysed.

55

4. Discussion 4.1 Cell viability testing

To demonstrate the neurotoxic effects of methamphetamine at a range of doses, neuronal cells

were exposed to the drug and the cells subsequently photographed (Figure 3.11). The clumping and

aggregation observed upon methamphetamine administration shows that the drug caused visible

morphological damage to these cells. This damage would affect neuronal function and result in the

loss of neuronal viability.

The B50 cells displayed increased aggregation and altered morphology when exposed to high

concentrations of methamphetamine compared to low concentrations. This shows that the degree

of damage due to methamphetamine was dose-dependent, with damage increasing with increased

dose.

To test whether the visible damage translated into cytotoxicity and cell death, an LDH assay was

performed. It was expected that cytotoxicity would increase as the dose of methamphetamine

increased. However, the results of the LDH assay were unusual, with the 10 µM and 100 µM doses

of methamphetamine resulting in cytotoxicity below control and the 1 µM levels (Figure 3.12).

These results are in conflict with those of Huang et al. (2009) and Zhou et al. (2004), who found a

dose-dependent increase in cytotoxicity in response to methamphetamine by quantifying LDH.

Zhou and colleagues (2004) reported a cytotoxicity level 30.2% above control for 1 mM

methamphetamine after 48 hours. This is considerably higher than the result of 6.96% above

control for the same dosage achieved in this experiment. In keeping with the results of this

experiment, Huang and colleagues (2009) found that methamphetamine concentrations at and

below 1 mM did not have a significant cytotoxic effect after 24 hours of exposure. However,

Welder (1992) reported a significant LDH release for 1 mM methamphetamine in the same time

frame. This disparity may be due to a difference in method or cell type used, as some cell types

may be more susceptible to the effects of methamphetamine than others. To discount the

possibility of interference on the LDH results, both the modified DMEM and methamphetamine

used in the experiment were analysed using the procedure given in 2.23. Neither was found to

56

cause interference effects or react with the reagents. It may be that this LDH test kit is unsuitable

for the cell culture model used in this experiment.

Due to the inconsistent results obtained using LDH assay, Trypan blue exclusion testing was

conducted as a comparison (Figure 3.13). The results of this test showed a decrease in cell viability

with increased dose of methamphetamine. This result is backed up by similar findings by Wu et al.

(2007). However, the decrease in viability found in this experiment was greater than that reported

by Wu. After 48 hours exposure to 1 mM methamphetamine the viability was found to be 69.9%

compared to control. In contrast, Wu and colleagues (2007) found a viability of 85% compared to

control for 1 mM. The decrease in viability for the other concentrations tested was also greater than

that found by Wu et al. (2007) who saw very little change in viability for concentrations below 1

mM using Trypan blue. Possibly the B50 cell type used in this experiment was more sensitive to

methamphetamine than that used by Wu. A difference in the method used may also account for

these changes, as the vortex used in this experiment to separate cells from medium may have

caused additional damage to cell membranes. Also, as Trypan blue counting techniques rely on

observing the change in colour of the cells, human error could play a part in the discrepancies.

4.2 Dose curve

In order to study the biochemical response of neuronal cells to methamphetamine administration, a

dose curve was established, comparing the changes within the cell and the surrounding medium at

a range of doses. Those metabolites that showed the most difference between doses were chosen

for further analysis, a large proportion of which were amino acids. Other metabolites affected

included carbohydrates, fatty acids and organic compounds.

The release of the excitatory amino acid and neurotransmitter glutamate is known to occur as part

of the neurotoxic action of methamphetamine. This event is highly dependent on the influx of Ca2+

and causes the continued activation of glutamate receptors, leading to excitotoxicity and neuronal

death (Jayanthi et al., 2005). Further damage is caused by the influx in Ca2+

through the activation

57

of kinases, proteases and lipases, the breakdown of cytoskeletal proteins and the production of free

radicals and resulting DNA damage (Quinton et al., 2006).

The metabolite N-acetylglutamate is a product of glutamate and therefore is reflective of glutamate

levels within the cell (Caldovic and Tuchman, 2003). Upon exposure to methamphetamine, the

amount of N-acetylglutamate within the cell was found to increase relative to control, before

decreasing at the highest methamphetamine concentration. Release of glutamate is enhanced by

methamphetamine administration (Pederson et al., 2000) and may be responsible for the increase

in N-acetylglutamate. The reduction in N-acetylglutamate at high doses of methamphetamine may

be due to protective mechanisms lowering the amount of free glutamate available and thereby

reducing excitotoxicity.

The amount of L-threonine detected within the treated cell followed a bell shaped curve, with

levels increasing until 100 µM and then decreasing. The decrease of L-threonine seen with higher

doses could be due to the amino acid being transported out of the cell to the media. This coincides

with the increase in extracellular L-threonine at 1 mM methamphetamine. The reduction of

intracellular L-threonine may also be accounted for by conversion to glycine via serine

hydroxymethyl transferase followed by export to the medium (Fraser and Newman, 1975). This is

supported by an increased detection of glycine within the medium. Glycine has neurotransmitter

properties and acts as a co-agonist with glutamate for the NMDA receptor. Excessive stimulation

of this receptor can result in excitotoxic damage (Laube et al., 1997).

The amino acid L-proline is an intracellular metabolite that was found to be affected by

methamphetamine. Proline is synthesised from its precursor, L-glutamate, via a number of steps

(Jones, 1985). The decrease in availability of glutamate as a precursor could explain the reduced

levels of proline at high doses of methamphetamine. Export of proline to the extracellular space

could provide another possible explanation for the decrease. This is supported by a corresponding

increase in proline levels in the medium at 1 mM methamphetamine.

58

The level of urea within the medium was found to decrease with increasing methamphetamine

concentration. In the urea cycle, urea is produced as a by-product during the conversion of arginine

to ornithine. Ornithine is then able to undergo further changes to produce proline (Brown and

Cohen, 1959). The increased intracellular proline at low concentrations of methamphetamine could

cause increased production of urea. As a waste product, urea would then be transported out of the

cell to the medium. Also, as proline levels decreased, so would urea production, further

establishing the production of urea as a result of proline synthesis.

Another neurotoxic event known to occur in response to methamphetamine is the production of

damaging ROS of which there are a number of potential sources. Firstly, excess dopamine released

into the cytosol can undergo auto-oxidation to dopamine quinones, which are further converted to

toxic radicals (Thrash et al., 2009). A second source of ROS is derived from serotonin toxicity

(Barr et al., 2006) and lastly, the mitochondria are known to produce ROS as a side product of

ATP synthesis and as a result of electron transport chain dysfunction (Brown et al., 2003).

To combat the oxidative stress caused by methamphetamine-induced ROS the cell may increase

the release of antioxidant substances. The level of L-serine within the treated cell was found to

increase with increasing dose of methamphetamine. This could be a protective response to the

oxidative damage as serine has been shown to possess anti-oxidant effects. It has been suggested

that L-serine supports the growth and survival of neurons and is essential for specific brain

functions (Wang et al., 2010). Within the medium, the levels of L-serine were found to increase at

1 mM methamphetamine. This may be caused by expulsion from within the cell due to high

intracellular L-serine causing transport to the medium in an attempt to restore homeostasis.

Octadecenoate, a salt or ester of the fatty acid oleic or octadecenoic acid, was also affected by

methamphetamine administration. The increased production of ROS within the cell associated with

methamphetamine exposure, could lead to the breakdown of fatty acids by per-oxidation (Thrash et

al., 2009; Catala, 2009). This may account for the initial increase in octadecenoate seen within the

treated cell. As a fatty acid derivative, octadecenoate may also be utilised as a fuel source causing

59

levels of the metabolite to decrease with increased methamphetamine concentration. Another

possibility is that octadecenoate was transported out of the cell, reducing intracellular levels. This

idea is plausible given the increase in octadecenoate within the medium.

Other intracellular amino acids which decreased as methamphetamine dose increased were L-

isoleucine, L-leucine and L-valine. These amino acids all rely on pyruvate as a precursor for their

synthesis (Freundlich et al., 1962). Pyruvate is produced as a product of glycolysis, the metabolic

pathway employed by tissues to produce energy in the form of ATP via the oxidation of glucose

(Ferrier, 2011). The reduction of these metabolites suggests that less pyruvate was available within

the cell as methamphetamine damage increased, which may be due to inference with energy

production.

Galactose levels greatly increased within the cell as the dose of methamphetamine increased.

Metabolism of galactose via the Leloir pathway results in the production of UDP-glucose which is

able to participate in glycolysis (Ferrier, 2011). A build-up of galactose within the cell suggests

dysfunction of the mechanisms responsible for clearing galactose and dysfunctional energy

production pathways.

4.2.1 Overall discussion of dose curve

Most affected by increased methamphetamine concentration were metabolites related to energy

production and utilisation. These included octadecenoate, amino acids L-isoleucine, L-leucine and

L-valine, and galactose.

Changes in N-acetylglutamate, L-proline and glycine with the dose of methamphetamine were

observed which may be in response to increased glutamate release and excitotoxicity associated

with methamphetamine administration.

The overall picture of methamphetamine toxicity is that glutamate is released in response to

methamphetamine administration, which causes excitotoxicity and oxidative stress. These cause

damage to the cell, disrupting energy production. The cell utilises a number of mechanisms to

60

attempt to reduce oxidative stress, but as dose increases, the degree of damage also increases,

making it difficult for the cell to survive.

4.3 Time course

The time dependency of the biochemical response of B50 cells to methamphetamine was analysed

using a time course experiment. Using a dose of methamphetamine known to be toxic to neuronal

cells, a number of time points were targeted, 6-, 24- and 48 hours. The metabolites identified as

showing the most variance between the samples included, amongst others, amino acids,

carbohydrates and fatty acids.

Release of glutamate, an amino acid neurotransmitter, was increased at 24 hours as a result of

methamphetamine administration. The released glutamate would be able to cause excessive

stimulation of glutamate receptors such as NMDA, leading to excitotoxicity and nitric oxide

production, which can be highly damaging to the cell (Pederson et al., 2000).

The levels of intracellular L-glutamate changed greatly over the 48-hour period with the largest

change occurring between 6 and 24 hours. At 6 hours, the amount of glutamate present in treated

cells was less than that in control. This reversed at 24 hours, with treated cells having greater

glutamate than control. A possible reason for this change is the release of glutamate triggered by

methamphetamine. This excess glutamate could lead to excitotoxic damage through the over-

activation of glutamate receptors (Pederson et al., 2000). N-acetylglutamate was found to increase

in the medium over time. Release of glutamate, the precursor of N-acetylglutamate, is enhanced by

methamphetamine exposure (Caldovic and Tuchman, 2003). As excess glutamate can have

detrimental effects due to the activation of NMDA receptors (Pederson et al., 2000), it would

benefit the cell to remove glutamate via conversion to N-acetylglutamate and expulsion from the

cell.

The increase in intracellular glutamate could account for the changes in L-alanine in

methamphetamine treated cells. L-alanine levels showed an increase over time, increasing greatly

when compared to control at 48 hours. This could be due to glutamate being converted to alanine

61

via the enzyme alanine transaminase in an attempt to reduce intracellular glutamate and thereby

excitotoxicity (Matthews et al., 2000). This effect was noted by Matthews et al. (2000), who

reported increased neuronal viability after glutamate exposure in the presence of alanine

transaminase. Uptake of alanine from outside the cell could also contribute to this increase in

alanine. This is supported by the corresponding decrease in extracellular alanine at 48 hours.

An extracellular metabolite found to increase over time after methamphetamine treatment was

myo-inositol. This result is confirmed by Ernst et al. (2000), who detected an increase in myo-

inositol in the frontal grey matter after methamphetamine exposure. Myo-inositol is considered an

indirect marker of glial cell integrity (Perrine et al., 2010). Inhibition of myo-inositol containing

IP3 receptors reduces Ca2+

release suggesting that it is also involved in regulation of intracellular

calcium concentration (Gerasimenko et al., 2006). Myo-inositol could be increased in an effort to

reduce the Ca2+

influx brought about by methamphetamine-induced excitotoxicity. The increase in

intracellular myo-inositol could also be accounted for by an intake from the medium as shown by a

decrease in extracellular myo-inositol.

Methamphetamine exposure is associated with an increased production of ROS. This is brought

about through the auto-oxidation of monoamines such as dopamine, excitotoxicity, and the

production of ATP and the inhibition of the electron transport chain within the mitochondria

(Thrash et al., 2009; Barr et al., 2006; Brown et al., 2003). ROS can have damaging effects on

amino acids, phospholipids and nucleic acids (Cunha-Oliveira et al., 2008).

The amino acids L-methionine and L-serine were also influenced by methamphetamine

administration. The levels of methionine were found to increase over time while serine decreased.

These metabolites are known to have antioxidant properties (Levine et al., 2000; Wang et al.,

2010) and could be produced in response to the production of ROS brought about by

methamphetamine toxicity (Barr et al., 2006). Serine levels were highest at 6 hours, while

methionine was greatest at 48 hours. This suggests that there are two ROS events occurring, an

initial event occurring soon after exposure and a delayed event.

62

Levels of glycerol were also shown to be elevated over time in the medium of methamphetamine-

treated cells. Glycerol forms an important structural component of cell membranes (Nelson and

Cox, 2013). Methamphetamine is known to cause the release of ROS through monoamine

autooxidation and mitochondrial damage (Trash et al., 2009; Brown et al., 2003). The

consequential oxidative stress may cause damage to cellular structures including cell membranes

(Cuhna-Oliveira et al., 2008) which would result in the release of glycerol and the death of the cell.

Both glucose and fructose followed a trend of decreasing peak area over time within the cell when

exposed to methamphetamine. The ROS produced in response to methamphetamine would have an

adverse effect on mitochondrial function. Damage to the mitochondria could result in a decrease in

aerobic respiration which could lead to the cell using carbohydrates at an accelerated rate. This

may explain the decrease in both glucose and fructose over the time course. A metabolomic study

by McClay and colleagues (2013) also reported a decrease in fructose concentration after

methamphetamine administration.

Levels of intracellular succinate, a TCA cycle intermediate, also increased over time.

Mitochondrial damage from ROS could also have resulted in TCA cycle dysfunction. This could

lead to a build-up of succinate over time. The finding of increased succinate levels after

methamphetamine administration is supported by the work of McClay et al, (2013).

The release of catecholamines such as dopamine and serotonin by methamphetamine is well

established. Methamphetamine administration results in the accumulation of catecholamines

through redistribution from storage vesicles to the cytosol, transport to the synapse and prevention

of reuptake, increasing the risk auto-oxidation of catecholamines to harmful compounds (Thrash et

al., 2009).

Levels of L-phenylalanine within the medium were elevated compared to control at all time points

tested. The catecholamine precursor tyrosine is produced from the hydroxylation of phenylalanine

(Scarnà et al., 2003). The release and production of catecholamines is triggered by the actions of

methamphetamine and is known to cause long-term damage to nerve terminals (Friedman et al.,

63

1998). Therefore the increased release of phenylalanine into the medium may be an attempt to

reduce the production of catecholamines.

4.3.1 Overall discussion of time course

The majority of metabolites affected by methamphetamine over time were related to the increased

release of glutamate brought about by methamphetamine treatment. These were the increase in L-

glutamate, N-acetylglutamate and L-alanine, and the decrease in myo-inositol. These changes

occurred slowly, building to peak at 48 hours.

Increased levels of L-methionine at 48 hours, L-serine at 6 hours and glycerol, which greatly

increased between 24- and 48-hours may be in response to the damaging effects of ROS, known to

be caused by methamphetamine.

A disruption to energy production by methamphetamine is reflected in the changes to glucose,

fructose and succinate and an attempt to reduce catecholamines is shown by the reduction of L-

phenylalanine. This effect occurred early in the time course, with changes evident as early as 6

hours.

4.4 Effect of methamphetamine

The exposure of B50 cells to methamphetamine affected several aspects of metabolism, including

changes to glutamate, carbohydrates, fatty acids and amino acids.

Changes were observed in cellular glutamate production as shown by increased L-glutamate, N-

acetylglutamate, L-alanine and L-proline. These changes are reflective of the release of glutamate

and subsequent excitotoxic effects that are known to be associated with methamphetamine

administration (Pederson et al., 2000). The increase in glutamate is highly dependent on the influx

of Ca2+

, the majority of which is taken up by mitochondria, causing further damage (Brown et al.,

2003). The increase in N-acetylglutamate, L-alanine and L-proline, as products of glutamate

(Caldovic and Tuchman, 2003; Matthews et al., 2000; Jones, 1985), suggests attempts by the cell

to reduce intracellular glutamate through diversion to other pathways.

64

A number of carbohydrates and fatty acids were also affected; suggesting alterations in energy

pathways are brought about by methamphetamine treatment. Decreased levels of fructose and

glucose within the cell may have resulted from the break down of aerobic respiration brought about

by mitochondrial damage (Brown et al., 2003), causing an accelerated rate of carbohydrate use.

The increase in octadecenoate, a derivative of oleic acid, suggests that the cell was increasingly

utilising fatty acid as an energy source. The fatty acid glycerol was also found to increase. This

may be caused by an increased production of ROS in response to methamphetamine treatment.

ROS may arise from mitochondrial damage and dopamine and serotonin oxidation causing damage

to cellular structures such as the glycerol-containing cell membranes (Cuhna-Oliveira et al., 2008).

The group most widely affected by methamphetamine was the amino acids. Amino acids are

involved in numerous pathways and have a vast variety of functions, making it difficult to

determine the reasons for their involvement with confidence. The increase in L-serine and L-

methionine, which are known to have antioxidant properties, could be brought about to combat the

increased ROS associated with methamphetamine toxicity (Brown et al., 2003). Another protective

response to methamphetamine was seen in the changes to L-phenylalanine levels. As

catecholamines are known to rise to damaging levels after methamphetamine administration, a

decrease in phenylalanine, a precursor for catecholamines, would reduce further formation (Scarnà

et al., 2003).

The neurotoxic effects of methamphetamine include monoamine release, oxidative stress,

excitotoxicity, mitochondrial damage and alterations to energy utilisation. The changes in

metabolite levels over dose and time of methamphetamine treated cells and medium were

reflective of these effects.

4.5 Limitations and expansions

As a preliminary study, this work has a number of factors which may limit the interpretation of the

data contained within.

65

The study employed the use of cell cultures to simulate the neurotoxic effects of methamphetamine

upon the brain. Whilst this method is convenient and eliminates any differences between test

subjects, as may be found in animal studies, it uses a single cell type to represent the entire CNS.

To examine the biochemical changes of the CNS as a whole, an in-vivo method could be applied,

or alternatively, a mixed brain cell culture as used by Fabian and Rea (1993).

An untargeted metabolomics approach was used in this study to identify metabolites of interest.

While limited information of specific metabolites affected by methamphetamine made this

necessary, further studies could benefit from a targeted approach. This would allow a more precise

quantification and a more in-depth look at the metabolites (Almstetter et al., 2012). The

metabolomic data obtained from the dose curve and time course experiments showed considerable

variance between the replicates, evident in the size of the standard error. As this is preliminary

work, some variation in the results is to be expected until methods are consolidated. The sample

size used for these experiments was also small, adding to the variance. To improve this aspect, a

larger number of replicates might be used and all experiments should be performed on the same

passage of cells at the same time, instead of in separate batches as was done here.

This study was limited to the administration of methamphetamine at a single time point.

Continuation of this work may utilise repeated doses of methamphetamine to simulate real-life

drug use. In addicts, patterns of methamphetamine administration often show repeated, high dose

binging, taken within a short period of time (Segal and Kuczenski, 1997). This could be easily

emulated, in vitro, as a comparison to a single toxic dose.

4.6 Conclusion

This work has shown that methamphetamine causes visible and dose-dependent damage to

neuronal cells. The viability of cells treated with methamphetamine at a range of doses was able to

66

be quantified via Trypan blue exclusion testing which demonstrated that cell death occurred in

response to methamphetamine administration.

These experiments found that methamphetamine exposure resulted in measurable biochemical

changes over dose and time. The use of a metabolomic method provided a snap shot of the activity

of the cell giving new information on the biochemical response of the cell. These metabolite

changes were reflective of the known mechanisms of methamphetamine toxicity including

glutamate release and excitotoxicity, changes to energy utilisation and production of ROS. The

result of this study furthers our understanding of neurochemical processes in response to

methamphetamine and could potentially lead to the identification of therapeutic targets.

67

5. References

Abbiss, H., Maker, G. L., Gummer, J., Sharman, M. J., Phillips, J. K., Boyce, M., &

Trengove, R. D. (2012). Development of a non-targeted metabolomics method to

investigate urine in a rat model of polycystic kidney disease: Use of metabolomics to

investigate PKD. Nephrology, 17(2), 104–110. doi:10.1111/j.1440-1797.2011.01532.x

Almstetter, M. F., Oefner, P. J., & Dettmer, K. (2012). Comprehensive two-dimensional gas

chromatography in metabolomics. Analytical and Bioanalytical Chemistry, 402(6), 1993–2013.

doi:10.1007/s00216-011-5630-y

Ankarcrona, M., Dypbukt, J. M., Bonfoco, E., Zhivotovsky, B., Orrenius, S., Lipton, S. A., &

Nicotera, P. (1995). Glutamate-induced neuronal death: a succession of necrosis or apoptosis

depending on mitochondrial function. Neuron, 15(4), 961–973.

Arias-Carrión, O., Stamelou, M., Murillo-Rodríguez, E., Menéndez-González, M., & Pöppel, E.

(2010). Dopaminergic reward system: a short integrative review. International archives of

medicine, 3(1), 24.

Australian Crime Commission (2012). Illicit Drug Data Report 2010-11. ACC, Canberra

Australian Institute of Health and Welfare (AIHW) 2011,„2010 National Drug Strategy Household

Survey report: detailed findings‟, Drug statistics series, Number 25, Cat. No. PHE 145. AIHW,

Canberra

Bamford, N. S., Zhang, H., Joyce, J. A., Scarlis, C. A., Hanan, W., Wu, N.-P., Sulzer, D. (2008).

Repeated Exposure to Methamphetamine Causes Long-Lasting Presynaptic Corticostriatal

Depression that Is Renormalized with Drug Readministration. Neuron, 58(1), 89–103.

doi:10.1016/j.neuron.2008.01.033

Barr, A.M., Panenka, W.J., MacEwan, W., Thornton, A.E., Lang, D.J., Honer, W.G., Lecomte, T.

(2006). The need for speed: an update on methamphetamine addiction. Journal of Psychiatry &

Neuroscience, 31(5), 301-13

Bear, M.F., Connors, B.W., Paradiso, M.A. (2007). Neuroscience: Exploring the Brain. 3rd

Edition.

Lippincott Williams & Wilkins, Philadelphia

Berman, S. M., Voytek, B., Mandelkern, M. A., Hassid, B. D., Isaacson, A., Monterosso, J.,

London, E. D. (2007). Changes in cerebral glucose metabolism during early abstinence from

chronic methamphetamine abuse. Molecular psychiatry, 13(9), 897–908.

Bondareva, T.S., Young, R., Glennon, R.A. (2002). Central stimulants as discriminative stimuli:

Asymmetric generalization between (-)ephedrine and S(+)methamphetamine. Pharmacology,

Biochemistry and Behavior 74, 157 – 162

Brecht, M.-L., O‟Brien, A., von Mayrhauser, C., & Anglin, M. D. (2004). Methamphetamine use

behaviors and gender differences. Addictive Behaviors, 29(1), 89–106. doi:10.1016/S0306-

4603(03)00082-0

Brown, G.W. and Cohen, P.P. (1959). Comparative Biochemistry of Urea Synthesis. Journal of

Biological Chemistry, 234(7), 1769-1774

68

Brown, J. M., & Yamamoto, B. K. (2003). Effects of amphetamines on mitochondrial function:

role of free radicals and oxidative stress. Pharmacology & Therapeutics, 99(1), 45–53.

doi:10.1016/S0163-7258(03)00052-4

Brown, J. M., Quinton, M. S., & Yamamoto, B. K. (2005). Methamphetamine-induced inhibition

of mitochondrial complex II: roles of glutamate and peroxynitrite. Journal of Neurochemistry,

95(2), 429–436. doi:10.1111/j.1471-4159.2005.03379.x

Buchsbaum, M. S. (2004). Frontal Cortex Function. American Journal of Psychiatry, 161, 2178-

2178. doi:10.1176/appi.ajp.161.12.2178

Cadet, J.L., Krasnova, I.N., Jayanthi, S., Lyles, J. (2007). Neurotoxicity of Substituted

Amphetamines: Molecules and Cellular Mechanisms. Neurotoxicity Research, 11(3,4), 183-202

Caldovic, L. and Tuchman, M. (2003). N-Acetylglutamate and its changing role through evolution.

Biochemistry Journal, 372, 279–290

Caldwell, J., Dring, L. G., & Williams, R. T. (1972). Metabolism of [14

C] methamphetamine in

man, the guinea pig and the rat. Biochemical Journal, 129, 11–22.

Carvalho, M., Carmo, H., Costa, V. M., Capela, J. P., Pontes, H., Remião, F., Bastos, M. de L.

(2012). Toxicity of amphetamines: an update. Archives of Toxicology, 86(8), 1167–1231.

doi:10.1007/s00204-012-0815-5

Centers for Disease Control and Prevention (1990). Lead poisoning associated with intravenous-

methamphetamine use - Oregon, 1988. Journal of the American Medical Association, 263(6), 797-

798

Chang, L., Alicata, D., Ernst, T., & Volkow, N. (2007). Structural and metabolic brain changes in

the striatum associated with methamphetamine abuse. Addiction, 102(s1), 16–32.

Cho, A.K. (1990). Ice: A New Dosage Form of an Old Drug. Science; 249, 4969; 631-634

Cunha-Oliveira, T., Rego, A. C., & Oliveira, C. R. (2008). Cellular and molecular mechanisms

involved in the neurotoxicity of opioid and psychostimulant drugs. Brain Research Reviews, 58(1),

192–208. doi:10.1016/j.brainresrev.2008.03.002

Cuperlović-Culf, M., Barnett, D. A., Culf, A. S., & Chute, I. (2010). Cell culture metabolomics:

applications and future directions. Drug discovery today, 15(15), 610–621.

Daubner, S. C., Le, T., & Wang, S. (2011). Tyrosine hydroxylase and regulation of dopamine

synthesis. Archives of Biochemistry and Biophysics, 508(1), 1–12. doi:10.1016/j.abb.2010.12.017

Day, J. J., & Carelli, R. M. (2008). Methamphetamine Induces Chronic Corticostriatal Depression:

Too Much of a Bad Thing. Neuron, 58(1), 6–7. doi:10.1016/j.neuron.2008.03.014

De la Luz-Hdez, K. (2012). Metabolomics and Mammalian Cell Culture. Retrieved from

http://cdn.intechopen.com/pdfs/28001/InTech-Metabolomics_and_mammalian_cell_culture.pdf

Della Grotta, S., LaGasse, L. L., Arria, A. M., Derauf, C., Grant, P., Smith, L. M., Lester, B. M.

(2009). Patterns of Methamphetamine Use During Pregnancy: Results from the Infant

Development, Environment, and Lifestyle (IDEAL) Study. Maternal and Child Health Journal,

14(4), 519–527. doi:10.1007/s10995-009-0491-0

69

Deneke, S. M., & Fanburg, B. L. (1989). Regulation of cellular glutathione. American Journal of

Physiology-Lung Cellular and Molecular Physiology, 257(4), L163–L173.

Desbrosses, G., Steinhauser, D., Kopka, J., Udvardi, M. (2005). Metabolome analysis suing GC-

MS, chapter 4.6. Lotus japonicus Handbook, 165-174

Deshmukh, S. S., & Knierim, J. J. (2012). Hippocampus. Wiley Interdisciplinary Reviews:

Cognitive Science, 3(2), 231–251. doi:10.1002/wcs.1164

Donaldson, M., & Goodchild, J. H. (2006). Oral health of the methamphetamine abuser. American

Journal of Health-System Pharmacy, 63(21), 2078–2082. doi:10.2146/ajhp060198

Dunn, W. B., Bailey, N. J. C., & Johnson, H. E. (2005). Measuring the metabolome: current

analytical technologies. The Analyst, 130(5), 606. doi:10.1039/b418288j

Dunnivant and Ginsbach, (2008). GC-MS and LC-MS: A basic introduction. Whitman College

Eichenbaum, H. (2004). Hippocampus: cognitive processes and neural representations that underlie

declarative memory. Neuron, 44(1), 109–120.

Encyclopaedia Britannica (2014). Sciatic Nerve. Retrieved January 18, 2014 from

http://www.britannica.com/EBchecked/topic/528745/sciatic-nerve.

Ernst, T., Chang, L., Leonido-Yee, M., Speck, O, (2000). Evidence for long-term neurotoxicity

associated with methamphetamine abuse. Neurology, 54, 1344-1349

Fabian, R.L., Rhea, H.C. (1993). Neuronal toxicity by macrophages in mixed brain cell culture is

augmented by antineuronal IgG and dependent upon nitric oxide synthesis. Journal of

Neuroimmunology, 44(1): 95-102.

Ferrier, D.R. (2011). Lippincott‟s Illustrated Reviews: Biochemistry, 6th Edition. Lippincott

Williams & Wilkins, Philadelphia

Fonnum, F. (1984). Glutamate: A Neurotransmitter in Mammalian Brain. Journal of

Neurochemistry, 42(1), 1-12

Fraser, J., Newman, E.B., 1975. Derivation of Glycine from Threonine in Escherichia coli K-12

Mutants. Journal of Bacteriology, 122(3), 810-817

Freundlich, M., Burns, R.O., Umbarger, H.E. (1962). Control of isoleucine, valine, and leucine

Biosynthesis, I. Multi-valent repression. Biochemistry, 48, 1804-1808

Friedman, S.D., Castaneda, E., Hodge, G.K. (1998). Long-Term Monoamine Depletion,

Differential Recovery, and Subtle Behavioral Impairment Following Methamphetamine-Induced

Neurotoxicity. Pharmacology Biochemistry and Behavior, 61(1), 35–44

Gerasimenko, J. V., Flowerdew, S. E., Voronina, S. G., Sukhomlin, T. K., Tepikin, A. V., Petersen,

O. H., & Gerasimenko, O. V. (2006). Bile Acids Induce Ca2+ Release from Both the Endoplasmic

Reticulum and Acidic Intracellular Calcium Stores through Activation of Inositol Trisphosphate

Receptors and Ryanodine Receptors. Journal of Biological Chemistry, 281(52), 40154–40163.

doi:10.1074/jbc.M606402200

Gluck, M. R., Moy, L. Y., Jayatilleke, E., Hogan, K. A., Manzino, L., & Sonsalla, P. K. (2001).

Parallel increases in lipid and protein oxidative markers in several mouse brain regions after

methamphetamine treatment. Journal of neurochemistry, 79(1), 152–160.

70

Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G., & Kell, D. B. (2004).

Metabolomics by numbers: acquiring and understanding global metabolite data. Trends in

Biotechnology, 22(5), 245–252. doi:10.1016/j.tibtech.2004.03.007

Halket, J. M. (2005). Chemical derivatization and mass spectral libraries in metabolic profiling by

GC/MS and LC/MS/MS. Journal of Experimental Botany, 56(410), 219–243.

doi:10.1093/jxb/eri069

Halket, J.M, Waterman, D., Przyborowska, A.M, Patel, R.K.P, Fraser, P.D, Bramley, P.M. (2005).

Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and

LC/MS/MS. Journal of Experimental Botany. 56 (410): 219-243.

Homer, B. D., Solomon, T. M., Moeller, R. W., Mascia, A., DeRaleau, L., & Halkitis, P. N.

(2008). Methamphetamine abuse and impairment of social functioning: a review of the underlying

neurophysiological causes and behavioral implications. Psychological bulletin, 134(2), 301.

Huang, Y.-N., Wu, C.-H., Lin, T.-C., & Wang, J.-Y. (2009). Methamphetamine induces heme

oxygenase-1 expression in cortical neurons and glia to prevent its toxicity. Toxicology and Applied

Pharmacology, 240(3), 315–326. doi:10.1016/j.taap.2009.06.021

Jayanthi, S., Deng, X., Ladenheim, B., McCoy, M. T., Cluster, A., Cai, N., & Cadet, J. L. (2005).

Calcineurin/NFAT-induced up-regulation of the Fas ligand/Fas death pathway is involved in

methamphetamine-induced neuronal apoptosis. Proceedings of the National Academy of Sciences

of the United States of America, 102(3), 868–873

Kranz, G. S., Kasper, S., & Lanzenberger, R. (2010). Reward and the serotonergic system.

Neuroscience, 166(4), 1023–1035. doi:10.1016/j.neuroscience.2010.01.036

Lankiewicz, S., Luetjens, C. M., Bui, N. T., Krohn, A. J., Poppe, M., Cole, G. M., Prehn, J. H.

(2000). Activation of calpain I converts excitotoxic neuron death into a caspase-independent cell

death. Journal of Biological Chemistry, 275(22), 17064–17071.

Lau, A., & Tymianski, M. (2010). Glutamate receptors, neurotoxicity and neurodegeneration.

Pflügers Archiv - European Journal of Physiology, 460(2), 525–542. doi:10.1007/s00424-010-

0809-1

Laube, B., Hirai, H., Sturgess, M., Betz, H., & Kuhse, J. (1997). Molecular determinants of agonist

discrimination by NMDA receptor subunits: analysis of the glutamate binding site on the NR2B

subunit. Neuron, 18(3), 493–503

LeDoux, J. (2007). The amygdala. Current Biology, 17(20), R868–R874.

León, Z., García-Cañaveras, J. C., Donato, M. T., & Lahoz, A. (2013). Mammalian cell

metabolomics: experimental design and sample preparation. Electrophoresis, 34(19):2762-75.

doi:10.1002/elps.201200605

Levi, M. S., Divine, B., Hanig, J. P., Doerge, D. R., Vanlandingham, M. M., George, N. I.,

Bowyer, J. F. (2012). A comparison of methylphenidate-, amphetamine-, and methamphetamine-

induced hyperthermia and neurotoxicity in male Sprague–Dawley rats during the waking (lights

off) cycle. Neurotoxicology and Teratology, 34(2), 253–262. doi:10.1016/j.ntt.2012.01.007

Levine, R. L., Moskovitz, J., & Stadtman, E. R. (2000). Oxidation of methionine in proteins: roles

in antioxidant defense and cellular regulation. IUBMB life, 50(4-5), 301–307.

71

Matthews, C. C., Zielke, H. R., Wollack, J. B., & Fishman, P. S. (2000). Enzymatic degradation

protects neurons from glutamate excitotoxicity. Journal of neurochemistry, 75(3), 1045–1052

Mayr, M. (2008). Metabolomics: Ready for the Prime Time? Circulation: Cardiovascular

Genetics, 1(1), 58–65. doi:10.1161/CIRCGENETICS.108.808329

McClay, J. L., Adkins, D. E., Vunck, S. A., Batman, A. M., Vann, R. E., Clark, S. L., Oord, E. J.

C. G. (2013). Large-scale neurochemical metabolomics analysis identifies multiple compounds

associated with methamphetamine exposure. Metabolomics, 9(2), 392–402. doi:10.1007/s11306-

012-0456-y

Meldrum, B. S. (2000). Glutamate as a neurotransmitter in the brain: review of physiology and

pathology. The Journal of nutrition, 130(4), 1007S–1015S.

Mendelson, J., Uemura, N., Harris, D., Nath, R.P., Fernandez, E., Jacob, P., Everhart, E.T., and

Jones, R.T. (2006). Human Pharmacology of the methamphetamine stereoisomers. Clinical

Pharmacology & Therapeutics 80, 403–420

Missale, C., Nash, S. R., Robinson, S. W., Jaber, M., & Caron, M. G. (1998). Dopamine receptors:

from structure to function. Physiological reviews, 78(1), 189–225.

Mohammad-Zadeh, L. F., Moses, L., & Gwaltney-Brant, S. M. (2008). Serotonin: a review.

Journal of Veterinary Pharmacology and Therapeutics, 31(3), 187–199. doi:10.1111/j.1365-

2885.2008.00944.x

Nakanishi, S. (1992). Molecular Diversity of Glutamate Receptors and Implications for Brain

Function. Science, New Series, 258(5082), 597-603

National Institute on Drug Abuse. “Methamphetamine Abuse and Addiction.” NIDA Research

Report Sept. 2006: NIH Publication No. 06-4210. 2006. National Institutes of Health, Bethesda,

MD. <www.drugabuse.gov/ResearchReports/methamph/methamph.html>.

Nelson, D.L and Cox, M.M. (2013). Principles of Biochemistry, 6th Edition. W. H. Freeman and

Company, New York

Newton, T. F., De La Garza, R., Kalechstein, A. D., Tziortzis, D., & Jacobsen, C. A. (2009).

Theories of Addiction: Methamphetamine Users‟ Explanations for Continuing Drug Use and

Relapse. American Journal on Addictions, 18(4), 294–300. doi:10.1080/10550490902925920

NIDA (2008). Addiction Science: From Molecules to Managed Care. National Institute on Drug

Abuse. Available http://www.drugabuse.gov/publications/addiction-science (accessed 31.10.2013)

NIDA (2013). Methamphetamine: abuse and addiction. National Institute on Drug Abuse.

Available: http://www.drugabuse.gov/publications/research-reports/methamphetamine-abuse-

addiction

Oh, J. S., Lyoo, I. K., Sung, Y. H., Hwang, J., Kim, J., Chung, A., Song, I. C. (2005). Shape

changes of the corpus callosum in abstinent methamphetamine users. Neuroscience Letters, 384(1-

2), 76–81. doi:10.1016/j.neulet.2005.04.082

Oliver S.G., Winson, M.K., Kell, D.B., Baganz, F. (1998). Systematic functional analysis of the

yeast genome. Trends in Biotechnology 16, 373–378

Panenka, W. J., Procyshyn, R. M., Lecomte, T., MacEwan, G. W., Flynn, S. W., Honer, W. G., &

Barr, A. M. (2013). Methamphetamine use: A comprehensive review of molecular, preclinical and

72

clinical findings. Drug and Alcohol Dependence, 129(3), 167–179.

doi:10.1016/j.drugalcdep.2012.11.016

Perrine, S. A., Ghoddoussi, F., Michaels, M. S., Hyde, E. M., Kuhn, D. M., & Galloway, M. P.

(2010). MDMA administration decreases serotonin but not N-acetylaspartate in the rat brain.

NeuroToxicology, 31(6), 654–661. doi:10.1016/j.neuro.2010.08.005

Quinton, M. S., & Yamamoto, B. K. (2006). Causes and consequences of methamphetamine and

MDMA toxicity. The AAPS journal, 8(2), E337–E337.

Riddle, E. L., Fleckenstein, A. E., & Hanson, G. R. (2006). Mechanisms of methamphetamine-

induced dopaminergic neurotoxicity. The AAPS journal, 8(2), E413–E418.

Rolls, E.T. (1994). Neurophysiology and cognitive functions of the striatum. Revue neurologique.

(Paris),150(8-9),648-60

Salamone, J., Correa, M., Mingote, S., & Weber, S. (2005). Beyond the reward hypothesis:

alternative functions of nucleus accumbens dopamine. Current Opinion in Pharmacology, 5(1),

34–41. doi:10.1016/j.coph.2004.09.004

Salo, R., Nordahl, T. E., Natsuaki, Y., Leamon, M. H., Galloway, G. P., Waters, C., Buonocore, M.

H. (2007). Attentional Control and Brain Metabolite Levels in Methamphetamine Abusers.

Biological Psychiatry, 61(11), 1272–1280. doi:10.1016/j.biopsych.2006.07.031

Sana, T. and Fisher, S. (2007). Maximizing Metabolite Extraction for Comprehensive

Metabolomics Studies of Erythrocytes. Agilent Technologies, Inc. Santa Clara, CA, USA

Scarna, A., Gijsman, H. J., McTavish, S. F. B., Harmer, C. J., Cowen, P. J., & Goodwin, G. M.

(2003). Effects of a branched-chain amino acid drink in mania. The British Journal of Psychiatry,

182(3), 210–213.

Schep, L.J., Slaughter, R.J., Beasley, D.M.G. (2010). The clinical toxicology of metamfetamine.

Clinical Toxicology, 48, 675-694.

Schloenhardt, A. (2007). „The market for amphetamine-type stimulants and their precursors in

Oceania‟, Research and Public Policy Series, No. 81. Australian Institute of Criminology,

Canberra.

Segal, D.S. and Kuczenski, R. (1997). Repeated Binge Exposures to Amphetamine and

Methamphetamine: Behavioral and Neurochemical Characterization. The Journal of

Pharmacology and Experimental Therapeutics, 282(2), 561-573

Sellers, K. (2007). Why Derivatize? Improve GC separations with derivatization. Restek

Corporation: Clinical/Forensic vol 3, available: www.restek.com www.restek.com

Shao, X., Hu, Z., Hu, C., Bu, Q., Yan, G., Deng, P., Lv, L., Wu, D., Deng, Yi., Zhao, J., Zhu, R.,

Li, Y., Li, H., Xu, Y., Yang, H., Zhao, Y., Cen, X. (2012). Taurine protects methamphetamine-

induced developmental angiogenesis defect through antioxidant mechanism. Toxicology and

Applied Pharmacology 260, 260–270

Sinchai, T., Plasen, S., Sanvarinda, Y., Jaisin, Y., Govitrapong, P., Phumala Morales, N.,

Ratanachamnong, P., Plasen, D. (2011). Caffeine potentiates methamphetamine-induced toxicity

both in vitro and in vivo. Neuroscience Letters 502: 65– 69

73

Stumm, G., Schlegel, J., Schafer, T., Wurz, C., Mennel, H.D., Krieg, J.C., Vedder, H. (1999).

Amphetamines induce apoptosis and regulation of bcl-x splice variants in neocortical neurons.

FASEB Journal 13, 1065–1072

Tansey, WP. (2006). Freeze-Thaw Lysis for Extraction of Proteins from Mammalian Cells. Cold

Spring Harbor Protocols.

Thrash, B., Thiruchelvan, K., Ahuja, M., Suppiramaniam, V., & Dhanasekaran, M. (2009).

Methamphetamine-induced neurotoxicity: the road to Parkinson‟s disease. Pharmacol Rep, 61(6),

966–77.

Vearrier, D., Greenberg, M. I., Miller, S. N., Okaneku, J. T., & Haggerty, D. A. (2012).

Methamphetamine: History, Pathophysiology, Adverse Health Effects, Current Trends, and

Hazards Associated with the Clandestine Manufacture of Methamphetamine. Disease-a-Month,

58(2), 38–89. doi:10.1016/j.disamonth.2011.09.004

Villas-Bôas, S. G., Mas, S., Åkesson, M., Smedsgaard, J., & Nielsen, J. (2005). Mass spectrometry

in metabolome analysis. Mass Spectrometry Reviews, 24(5), 613–646. doi:10.1002/mas.20032

Villas-Bôas, S.G., Roessner, U., Hansen, M.A.E., Smedsgaard, J., Nielsen, J. (2007). Metabolome

Analysis, An Introduction. John Wiley & Sons, Inc, New Jersey.

Völlm, B. A., de Araujo, I. E., Cowen, P. J., Rolls, E. T., Kringelbach, M. L., Smith, K. A.,

Matthews, P. M. (2004). Methamphetamine Activates Reward Circuitry in Drug Naïve Human

Subjects. Neuropsychopharmacology, 29(9), 1715–1722. doi:10.1038/sj.npp.1300481

Wang, G.-H., Jiang, Z.-L., Chen, Z.-Q., Li, X., & Peng, L.-L. (2010). Neuroprotective effect of L-

serine against temporary cerebral ischemia in rats. Journal of Neuroscience Research, NA–NA.

doi:10.1002/jnr.22365

Watanabe-Galloway, S., Ryan, S., Hansen, K., Hullsiek, B., Muli, V., Malone, C. (2009). Effects

of Methamphetamine Abuse Beyond Individual Users. Journal of Psychoactive Drugs, 41(3), 241-

248

Weisheit, R. (2008). Making Methamphetamine. Southern Rural Sociology, 25(2), 78-107

Welder, A.A. (1992). A primary culture system of postnatal rat heart cells for the study of cocaine

and methamphetamine toxicity. Toxicology Letters, 60(2), 183–196

Wu, C.-W., Ping, Y.-H., Yen, J.-C., Chang, C.-Y., Wang, S.-F., Yeh, C.-L., Lee, H.-C. (2007).

Enhanced oxidative stress and aberrant mitochondrial biogenesis in human neuroblastoma SH-

SY5Y cells during methamphetamine induced apoptosis. Toxicology and Applied Pharmacology,

220(3), 243–251. doi:10.1016/j.taap.2007.01.011

Zhou, J.-L., Liang, J.-H., Zheng, J.-W., & Li, C.-L. (2004). Nerve growth factor protects R2 cells

against neurotoxicity induced by methamphetamine. Toxicology Letters, 150(2), 221–227.

doi:10.1016/j.toxlet.2004.01.007