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Oxidative Stress in Bipolar Disorder: a Meta-‐analysis of Oxidative Stress Markers and an Investigation of the

Hippocampus

by

Nicole C. Brown

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Pharmacology and Toxicology University of Toronto

© Copyright by Nicole C. Brown (2014)

ii

Oxidative Stress in Bipolar Disorder: a Meta-‐analysis of Oxidative

Stress Markers and an Investigation of the Hippocampus

Nicole C. Brown

Master of Science

Department of Pharmacology and Toxicology University of Toronto

2014

Abstract

Bipolar disorder is a prevalent and debilitating disease, however, its pathophysiology

remains unknown. There is substantial evidence of oxidative damage in bipolar disorder

from both peripheral and post-‐mortem samples, especially in the prefrontal cortex. It is the

objective of this study to consolidate research measuring oxidative stress biomarkers in

bipolar disorder through a meta-‐analysis and to determine if the hippocampus region is a

specific target of oxidative damage through a biochemical study using post-‐mortem

samples. Results from this study further confirm the presence of oxidative damage in

bipolar disorder, but suggest that the hippocampus is not a target. We have also shown a

global increase of 5-‐hydroxymethylcytosine in the hippocampus of patients with

schizophrenia, suggesting a possible alteration in the demethylation pathway. Determining

the exact targets of oxidative stress in bipolar disorder may lead to biomarker

development, better treatment and diagnostic options, and ultimately, a better quality of

life for patients.

iii

Acknowledgements

First and foremost I would like to thank my supervisors, Dr. Trevor Young and Dr. Ana

Andreazza, for their mentorship and for all of the opportunities they have given me. I am

extremely grateful to Dr. Young for his professional guidance, teaching me to think and

write logically, and to “tell a story.” I would like to express my deepest gratitude to Dr.

Andreazza for her understanding, encouragement, and for both professional and personal

support. Their knowledge and expertise has helped me grow as a scientist, and I could not

have hoped for better supervisors.

I would also like to thank my advisor, Dr. Ali Salahpour, for all of his support and advice,

especially during my first year seminar. Additionally, I would like to recognize all of the

members of the Young/Andreazza lab for their help, both inside and outside of the lab. A

special thank you to Larisse, Gustavo, and Helena, who welcomed me so warmly when I

moved to Toronto and began my Masters, and to Mathew, who I was fortunate enough to

work with over the summer and who “read my mind” during experiments. I am so

fortunate that I had the opportunity to work in such a kind and supportive lab.

Finally, I have to acknowledge my great group of friends, who have helped keep me

positive, especially my closest “Toronto friends”, Kayla, Dan, and Alex. And last, but

definitely not least, I want to thank my amazingly supportive parents who provided me the

inspiration and encouragement to keep following my heart and dreams, even when it took

me away from them.

iv

Table of Contents ABSTRACT .............................................................................................................................. II

ACKNOWLEDGEMENTS .......................................................................................................... III

TABLE OF CONTENTS ............................................................................................................. IV

LIST OF TABLES ...................................................................................................................... VI

LIST OF FIGURES ................................................................................................................... VII

LIST OF ABBREVIATIONS ...................................................................................................... VIII

LIST OF APPENDICES ............................................................................................................... X

CHAPTER 1. INTRODUCTION ................................................................................................... 1

1.1 Overview of bipolar disorder ................................................................................................................................. 1

1.2 The neurobiology of bipolar disorder: neuroimaging, inflammation, and neurotrophic factors .. 5 1.2.1 Neuroimaging studies and structural abnormalities ............................................................................................... 5 1.2.2 Inflammation ............................................................................................................................................................................. 7 1.2.3 Neurotrophic factors .............................................................................................................................................................. 8 1.2.4 Further biological abnormalities ...................................................................................................................................... 9

1.3 The neurobiology of bipolar disorder: oxidative stress and mitochondrial dysfunction .............. 11 1.3.1 Mechanisms of oxidative damage in brain tissue ................................................................................................... 11 1.3.2 Mitochondrial dysfunction in bipolar disorder ....................................................................................................... 14 1.3.3 Oxidative stress damage in patients with bipolar disorder ............................................................................... 17 1.3.4 Neuroprotective effects of medication ........................................................................................................................ 18 1.3.5 Brief overview of epigenetic findings in bipolar disorder .................................................................................. 21

1.4 The use of biomarkers in psychiatric disease ............................................................................................... 22

1.5 Aim of the thesis ....................................................................................................................................................... 25 1.5.1 Statement of problem ......................................................................................................................................................... 25 1.5.2 Purpose of the Study and Objective .............................................................................................................................. 25 1.5.3 Statement of Research Hypotheses .............................................................................................................................. 26

CHAPTER 2. METHODS AND RESULTS FOR THE META-‐ANALYSIS OF OXIDATIVE STRESS

MARKERS IN BIPOLAR DISORDER .......................................................................................... 27

2.1 Methods for the meta-‐analysis of oxidative stress markers in bipolar disorder .............................. 27 2.1.1 Search strategy ...................................................................................................................................................................... 27 2.1.2 Selection Criteria .................................................................................................................................................................. 27 2.1.3 Statistical Analysis ............................................................................................................................................................... 28

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2.2 Results for the meta-‐analysis of oxidative stress markers in bipolar disorder ................................ 29 2.2.1 Characteristics of included studies ............................................................................................................................... 29 2.2.2 Results of the meta-‐analysis ............................................................................................................................................ 30 2.2.3 Publication bias and sensitivity analysis .................................................................................................................... 34

CHAPTER 3. MATERIALS, METHODS, AND RESULTS FOR THE MEASUREMENT OF OXIDATIVE

STRESS DAMAGE IN POST-‐MORTEM HIPPOCAMPUS TISSUE ................................................. 35

3.1 Materials and methods for the measurement of oxidative stress damage and DNA alterations in the hippocampus ................................................................................................................................................................. 35 3.1.1 Postmortem brain tissue samples ................................................................................................................................. 35 3.1.2 Tissue homogenization ...................................................................................................................................................... 35 3.1.3 Mitochondrial subunit protein levels .......................................................................................................................... 36 3.1.4 Protein oxidative and nitrosative damage ................................................................................................................. 37 3.1.5 Lipid peroxidation ................................................................................................................................................................ 37 3.1.6 DNA methylation and hydroxymethylation .............................................................................................................. 38 3.1.7 Statistical analysis ................................................................................................................................................................ 39

3.2 Results of the measurement of oxidative stress damage and DNA alterations in the hippocampus ................................................................................................................................................................. 40 3.2.1 Patient demographics, family history, medications, and toxicology .............................................................. 40 3.2.2 Hippocampus may not be a target for mitochondrial dysfunction and oxidative damage to proteins

and lipids .................................................................................................................................................................................. 42 3.2.3 Increased levels of 5-‐hmC in hippocampus from patients with SCZ but not in BD ................................. 42 3.2.4 Toxicology and medication effects ................................................................................................................................ 45

CHAPTER 4. DISCUSSION ....................................................................................................... 46

4.1 Oxidative stress markers in patients with bipolar disorder ..................................................................... 46

4.2 Hippocampus and bipolar disorder .................................................................................................................. 50

4.3 Limitations of this study ........................................................................................................................................ 54

CHAPTER 5. CONCLUSIONS, SIGNIFICANCE, AND FUTURE DIRECTIONS .................................. 57

5.1 Overall conclusions from this study and significance ................................................................................. 57

5.2 Future directions ..................................................................................................................................................... 58

REFERENCES ......................................................................................................................... 59

APPENDIX A .......................................................................................................................... 85

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List of Tables

Table 1.1. Neuroprotective effects of lithium. There is substantial evidence showing chronic lithium treatment has many neuroprotective effects.

20

Table 2.1. Pooled statistics and meta-‐analysis of standardized mean group differences for oxidative stress markers in BD compared with healthy controls

31

Table 3.1. Demographic variables, postmortem interval, pH, and medications for bipolar disorder, schizophrenia, and control groups

41

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List of Figures

Figure 1.1. Factors contributing to differences in neuronal vulnerability to oxidative stress

13

Figure 1.2. Alterations of the λ subcomplex of mitochondrial complex I in bipolar disorder

16

Figure 1.3. Potential benefits of determining and validating an oxidative stress biomarker in bipolar disorder

24

Figure 1.4. Potential biomarkers in bipolar disorder

24

Figure 2.1. Forest plots of standardized mean differences and 95% confidence intervals for oxidative stress markers in patients with bipolar disorder compared to healthy controls

32-‐33

Figure 3.1. Mitochondrial protein subunit levels for NDUFS8, NDUFV2, and NDUFS7 in post-‐mortem hippocampus of patients with bipolar disorder, schizophrenia, and healthy controls

43

Figure 3.2. Protein oxidation and protein nitration in post-‐mortem hippocampus of patients with bipolar disorder or schizophrenia, and healthy controls

43

Figure 3.3. Levels of lipid peroxidation in post-‐mortem hippocampus of patients with bipolar disorder or schizophrenia, and healthy controls

44

Figure 3.4. Levels of DNA methylation and DNA hydroxymethylation in post-‐mortem hippocampus from patients with bipolar disorder or schizophrenia, and healthy controls

44

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List of Abbreviations

3-‐NT 3-‐nitrotyrosine

4-‐HNE 4-‐hydroxynonenal

5-‐hmC 5-‐hydroxymethlycytosine

5-‐mC 5-‐methylcytosine

ANOVA Analysis of variance

ATP Adenosine triphosphate

Bcl-‐2 B-‐cell lymphoma 2

BD Bipolar disorder

BDNF Brain-‐derived neurotrophic factor

CREB cAMP response element-‐binding protein

CTL Control

DNMT DNA methyl-‐transferase

DNP Dinitrophenyl

DSM Diagnostic and Statistical Manual of Mental Disorders

DTI Diffusion tensor imaging

DTT Dithiothreitol

ECL Enhanced chemiluminescence

EDTA Ethylenediaminetetraacetic acid

FA Fractional anisotropy

FMN Flavin mononucleotide

GPx Glutathione peroxidase

GWAS Genome wide association study

HBTRC Harvard Brain Tissue Resource Center

HPA Hypothalamic-‐pituitary-‐adrenal

HRP Horseradish peroxidase

ICD International Classification of Diseases

IL Interleukin

ix

LPH Lipid hydroperoxides

LSD Least significant difference

MBD Methyl-‐CpG-‐binding domain

MDD Major depressive disorder

NOS Not otherwise specified

PAGE Polyacrylamide gel electrophoresis

PCC Protein carbonyl content

PMI Post-‐mortem interval

PVDF Polyacrylamide gel electrophoresis

REDOX Reduction and oxidation

RNS Reactive nitrogen species

ROS Reactive oxygen species

SCZ Schizophrenia

SDS Sodium dodecyl sulfate

SOD Superoxide dismutase

TBARS Thiobarbituric acid reactive substances

TET Ten-‐eleven translocation

TNF Tumor necrosis factor

x

List of Appendices

Appendix A Table A1. Selected characteristics of all studies included in the meta-‐analysis of oxidative stress markers in bipolar disorder patients compared to healthy controls, sorted by sample type.

85

1

Chapter 1. Introduction

1.1 OVERVIEW OF BIPOLAR DISORDER

Bipolar disorder (BD) is within the top 10 leading causes of disability worldwide, is chronic

and recurrent, and is debilitating to both individuals and their families (Murray and Lopez,

1996). It is a mood disorder characterized by cycling episodes of depression and mania, an

elevated or agitated mood. Mania can present with different severities ranging from

hypomania, which is a mildly to moderately elevated or irritable mood, to psychosis, during

which the individual experiences some loss of contact with reality. Although BD is

considered a continuum, there are three subtypes and one non-‐specified type outlined in

the DSM-‐IV (Diagnostic and Statistical Manual of Mental Disorders). Briefly, diagnosis of BD

type I requires one or more manic episodes, BD type II requires at least one hypomanic and

at least one major depressive episode, and cyclothymia requires hypomanic episodes with

depressive episodes that do not meet the criteria for major depressive episodes. Finally the

DSM-‐IV provides another category called BD-‐NOS (not otherwise specified) when the

disorder does not fall into one of the other subtypes. The length of cycles in BD can vary

considerably with episodes lasting months or even cycling within a day or week. Onset and

development of BD can occur at any age; however 50% of cases are diagnosed by age 25

(Kessler et al., 2005), and the onset of BD tends to occur later for women than for men

(Arnold, 2003).

BD is diagnosed differentially through information obtained from patient self-‐reports,

structured interviews, family interviews, and clinician observation. BD can be difficult to

2

diagnose because of symptom overlap with other mood and psychotic disorders such as

major depressive disorder (MDD) and schizophrenia (SCZ). This may be clinically

significant since the initial presentation of BD is most often with depressive symptoms and

there may be a lag of months or years before mania presents and a diagnosis of BD is

determined (Berk et al., 2009). During this lag, a misdiagnosis of depression may lead to

ineffective treatment and potentially worse outcomes. For example, a misdiagnosis of BD as

unipolar depression may lead to inappropriate prescriptions, such as the use of

antidepressants without a mood-‐stabilizing drug, which may lead to mania and poor

clinical and functional outcomes (Phillips and Kupfer, 2013). Importantly, there is a current

push in psychiatry to find biological markers for BD (Frey et al., 2013). The development of

a biomarker would improve diagnostic accuracy and potentially allow intervention at early

stages of the illness, which may be critical to lowering the lifetime illness burden (Perry et

al., 1999, Miklowitz et al., 2013).

Bipolar disorder has a very high burden of illness with healthcare costs that are up to four

times higher than those of individuals without mental illness (Altamura et al., 2011). The

elevated healthcare costs of BD can be largely contributed to a very high prevalence of

medical comorbidities (Goetzel et al., 2003, Simon, 2003, Kupfer, 2005). In one study of

1379 patients with BD the most common comorbid medical illnesses were metabolic and

endocrine diseases (13.6%), diseases of the circulatory system (13.0%), and diseases of the

nervous system and sense organs (10.7%) (Beyer et al., 2005). There are significant gender

differences with the comorbidity of medical and psychiatric disorders; medical

comorbidities in women have more adverse effects on recovery from BD (Arnold, 2003). In

addition to increased medical illness in patients with BD, more than 60% of patients have a

3

psychopathological comorbidity (McElroy et al., 2001, Merikangas et al., 2007). BD is also

very highly associated with suicide. In one study comparing the lifetime rates of suicide

attempt between subjects with BD and subjects with any other DSM-‐III-‐defined Axis I

disorder (i.e. all psychological categories except mental retardation and personality

disorder), the authors found a strong relationship of suicide attempts in BD (29.2% and

4.2%, respectively)(Chen and Dilsaver, 1996). All of these factors, and many more,

contribute to a high burden of illness for BD including decreased quality of life and

decreased life expectancy (Angst et al., 2002, Fagiolini and Goracci, 2009).

BD is a chronic illness that requires long-‐term and multidisciplinary treatment.

Pharmacotherapy is a main treatment option for BD, however psychosocial interventions

such as cognitive behavior therapy, group psychoeducation, and interpersonal and social

rhythm therapy are also important aspects of management (Yatham et al., 2005). Due to

the episodic and chronic nature of BD, treatment is based on the current state of the

patient, while also considering maintenance for long-‐term success (Malhi et al., 2009).

Mood stabilizers, including lithium, anticonvulsants, and atypical antipsychotics, are often

the first line of medication for BD. In acute manic episodes, atypical antipsychotics such as

risperidone and olanzapine, may be beneficial alone or with lithium. Selective serotonin

reuptake inhibitors or other antidepressants may also be used in acute depression, with

caution, as an adjunct to lithium. Although there are a number of pharmacological options

for treatment, there is still an urgent need for more effective and tolerable medications to

improve overall functionality of patients with BD.

4

It is clear from family and twin studies that genetic factors play a role in BD and SCZ (Frey

et al., 2007). Studies have determined that there is a 10-‐fold increased risk of developing

BD if a first-‐degree relative is affected and heritability estimates are between 60-‐85% as

determined by monozygotic twin studies (Smoller and Finn, 2003). A meta-‐analysis of

whole genome linkage studies determined that the strongest evidence for susceptibility

loci is on 13q and 22q for BD (Badner and Gershon, 2002). Other meta-‐analyses using

genome wide association studies (GWAS) have found regions with genes implicated in the

cell cycle, neurogenesis, neuroplasticity, and neurosignaling (Scott et al., 2009, Thompson

et al., 2014). Of note, genetic epidemiology findings have provided evidence of many shared

genetic risk factors between BD, SCZ, and MDD (Craddock and Owen, 2005). Despite

decades of candidate gene association studies and whole-‐genome linkage scans, which have

found many regions of significance, no particular gene or alteration can explain BD. Very

large multi-‐group studies have shown shared polygenic contribution to risk between BD

and SCZ, illustrating the great complexity in the heritability of BD (Psychiatric GWAS

Consortium Bipolar Disorder Working Group, 2011).

Due to the biological complexity of BD, its pathophysiology is not known however,

abnormalities can be observed at all levels of physiology: tissue, cellular, molecular, and

genetic.

5

1.2 THE NEUROBIOLOGY OF BIPOLAR DISORDER: NEUROIMAGING, INFLAMMATION,

AND NEUROTROPHIC FACTORS

1.2.1 Neuroimaging studies and structural abnormalities

Neuroimaging studies have revealed structural differences and abnormalities in white

matter in BD. Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique

used to evaluate the diffusion of water in vivo, and can be utilized to determine

microstructural differences in white matter. The neural axons of white matter have an

internal fibrous structure that allows the rapid diffusion of water parallel to the tracts but

less diffusion perpendicularly; diffusion in axons is anisotropic, or directionally dependent.

The DTI measure of fractional anisotropy (FA) can be used to determine the direction of

water diffusion in white matter axons. Typically, white matter neuropathology causes the

anisotropy to decrease, which may be a result of either increased perpendicular (radial)

diffusion, or reduced parallel (axial) diffusion (Alexander et al., 2007). The biological

alteration leading to decreased FA is generally interpreted as changes in tract coherence

due to alterations in myelination, density, alignment, or diameter of the white matter

fibres.

A meta-‐analysis of whole brain DTI in BD found two clusters of decreased FA on the right

side of the brain; one in the right parahippocampal white matter and the second cluster

was close to the right anterior and subgenual cingulate cortex (Vederine et al., 2011). SCZ

is also associated with white matter abnormalities, but interestingly, different areas are

affected (Ellison-‐Wright and Bullmore, 2009). The clinical implications of these alterations

6

are not yet fully understood and, furthermore, larger studies with more subjects are

required to determine the effect of medications and disease characteristics. A meta-‐

analysis looking at what white matter tracts were most often implicated by changes in FA

in BD found changes in both anterior and posterior projection fibres, with larger tracts

implicated more frequently. These authors concluded that the anterior findings are

consistent with models of emotional regulation and hypothesized that the posterior

findings may be related to cognitive deficits (Nortje et al., 2013). Of clinical significance, a

recent study showed a strong association of FA abnormalities with increased serum lipid

peroxidation (Versace et al., 2014). Additionally, post-‐mortem studies have shown

reductions in the number, size, and density of glial cells as well as downregulation of

myelination and oligodendrocyte genes (Hakak et al., 2001, Uranova et al., 2001). Together

these demonstrate physiological cellular changes of white matter in BD, which may have

pathological implications.

There may also be structural changes in BD; the most consistently reported findings

include conservation of total cerebral volume with regional grey and white matter changes

in prefrontal, limbic, and midline networks, noncontingent ventriculomegaly, and increased

white matter hyperintensities (Emsell and McDonald, 2009). Decreased hippocampal

volume is characteristic of both schizophrenia and depression (Wright et al., 2000,

Campbell et al., 2004, Videbech and Ravnkilde, 2004, McDonald et al., 2006) however

multiple meta-‐analysis studies have not shown consistent changes in BD (Videbech and

Ravnkilde, 2004, Fusar-‐Poli et al., 2012, Thompson et al., 2014). Some other reported

structural findings in BD include lateral ventricular enlargement, decreased volume of the

subgenual cingulate gyrus and amygdala, and decreased grey matter in parietal lobe

7

(Swayze et al., 1990, Moorhead et al., 2007, Koo et al., 2008). Of interest, many studies have

demonstrated the effect of psychotropic medication on these structural changes in affective

disorders. For instance, lithium has been shown to increase hippocampal volumes in

patients that responded clinically to treatment (Bearden et al., 2008, Yucel et al., 2008,

Hajek et al., 2014). In contrast, antipsychotics and anticonvulsants generally had no

structural effect (Hafeman et al., 2012). The normalizing effect of lithium further

demonstrates the role of structural abnormalities, especially to the hippocampus, in the

pathophysiology of BD.

1.2.2 Inflammation

There is evidence that increased inflammation may play a role in the pathophysiology of

BD. Inflammation is part of a complex immune pathway to rid cells of foreign pathogens.

The process is highly regulated and depends on the balance between pro-‐ and anti-‐

inflammatory factors. BD patients in both manic and depressive states have increased

plasma levels of pro-‐inflammatory cytokines such as IL-‐2, IL-‐6, IL-‐8, and TNF-‐α. (O'Brien et

al., 2006, Kim et al., 2007, Brietzke et al., 2009). Furthermore, increased IL-‐1β, nuclear

factor-‐κB, and IL-‐1R proteins have been reported in post-‐mortem frontal cortex of patients

with BD (Rao et al., 2010). These alterations are also associated with disease state with

differences in cytokine levels arising consistently in mania or depression and euthymia

(Goldstein et al., 2009).

8

Importantly, increased cellular inflammation is linked to mitochondrial dysfunction and its

consequent oxidative stress. Inflammation and mitochondrial activity have a very complex

relationship: increased reactive oxygen species (ROS) from mitochondrial dysfunction can

activate redox-‐sensitive inflammatory pathways and pro-‐inflammatory factors can impair

mitochondrial function (Vaamonde-‐Garcia et al., 2012, Lopez-‐Armada et al., 2013).

1.2.3 Neurotrophic factors

Neurotrophins are a family of proteins that regulate the differentiation, proliferation, and

survival of neuronal cells. Alterations of neurotrophic factors are well documented in BD,

especially decreased levels of brain-‐derived neurotrophic factor (BDNF) in peripheral

samples (Chen et al., 2001, Cunha et al., 2006, Machado-‐Vieira et al., 2007b). In fact, a

polymorphism in the BDNF gene region (Val66Met) is frequently shown to be associated

with illness severity in many populations (Min et al., 2012, Miller et al., 2013, Chen et al.,

2014). There is evidence that BDNF levels reflect both current mood state and overall

illness progression. One review study found that BDNF levels were consistently decreased

in manic and depressive states, but not during euthymia, and furthermore, reflected

severity of the episodes (Fernandes et al., 2011). Comparing BDNF levels in patients with

different illness lengths showed a decrease of BDNF only in late-‐stage patients (Kauer-‐

Sant'Anna et al., 2009). There is also a lot of evidence indicating that alterations of

neurotrophic factors may underlie hippocampus atrophy in affective disorders (Frodl et al.,

2007, Schmidt and Duman, 2007, Son et al., 2014). In addition, oxidative stress may be

linked to BDNF activity through several pathways including cAMP response element-‐

9

binding protein (CREB), the NF-‐κB complex, MEK–Bcl-‐2 pathway (apoptotic), or through

endoplasmic reticulum stress responses (Kapczinski et al., 2008, Markham et al., 2012).

While neurotrophic factors probably play a role in pathophysiology of BD, they seem

unlikely to be a causal factor but may be more important in its neuroprogression (Berk et

al., 2011).

1.2.4 Further biological abnormalities

Many signal transduction processes may be involved in the neurobiology of BD, including

calcium signaling, glutamatergic and dopaminergic systems, the hypothalamic-‐pituitary-‐

adrenal (HPA) axis, and apoptotic pathways. Hyperactivity of the HPA axis is a consistent

finding in many psychiatric diseases (Pariante and Miller, 2001). The complex series of

interactions between the hypothalamus, anterior pituitary gland, and adrenal cortex are a

major part of the neuroendocrine system and play a role in stress response, among many

other body processes. Importantly, several brain regions involved in mood regulation,

including the prefrontal cortex and the hippocampus, regulate the HPA axis. Patients with

bipolar disorder have been shown to have an increased cortisol response to the

dexamethasone/corticotrophin-‐releasing hormone test, indicating HPA hyperactivity

(Watson et al., 2004, Duffy et al., 2012). Furthermore, decreased levels of glucocorticoid

receptor mRNA have been found in post-‐mortem brain samples (Webster et al., 2002).

These abnormalities may be secondary to stress in these disorders, and might not be a

causal factor in BD development, but could still be clinically relevant for treatment.

10

Alterations to apoptotic and cell-‐death pathways are well documented in BD and are

supported by genetic studies. There is increased pro-‐apoptotic and decreased anti-‐

apoptotic gene and protein expression in the postmortem brain samples from patients with

BD (Benes et al., 2006, Kim et al., 2010). The balance of pro-‐ and anti-‐apoptotic gene

expression is also affected by lithium treatment; one study found increased anti-‐apoptotic

and decreased pro-‐apoptotic gene expression in lithium responders one month after

treatment (Lowthert et al., 2012). Prior to apoptotic cell death there is shrinkage of the cell,

which may be the basis of the reduction of neuron size and density in BD (Gigante et al.,

2011b).

An elevated intracellular calcium level is one of the earliest reported biological

abnormalities in BD and since then, other studies have implicated components of calcium

signaling pathways (Dubovsky et al., 1989, Tan et al., 1990, Hough et al., 1999). One of the

strongest findings from GWAS are polymorphisms to a voltage-‐gate calcium channel. The

Bipolar Disorder Working Group of the Psychiatric GWAS Consortium combined data from

16,731 samples and a replication sample of 46,918 individuals and further confirmed

alterations in the calcium channel CACNA1C (Psychiatric GWAS Consortium Bipolar

Disorder Working Group, 2011). Furthermore, alterations to this gene region have been

predictive of brain activity, especially in the prefrontal cortex, hippocampus, and amygdala

(Bigos et al., 2010, Jogia et al., 2011). Interestingly, mitochondria play a role in sequestering

and releasing intracellular calcium, which may connect to endoplasmic reticulum

dysfunction, altered apoptotic pathways, altered neuroplasticity, and impaired adaptation

to stress (Kato, 2008, Quiroz et al., 2008).

11

In addition to the mechanisms outlined thus far, mitochondrial dysfunction and oxidative

stress play a very significant role in BD and will be discussed in the following section.

Clearly, the pathophysiology BD is very complex with many interacting systems and

pathways. While we may be a long way from uncovering every factor involved in BD

development, acquiring a further understanding of biological processes in BD is vital for

improved medications, diagnosis, and treatment.

1.3 THE NEUROBIOLOGY OF BIPOLAR DISORDER: OXIDATIVE STRESS AND

MITOCHONDRIAL DYSFUNCTION

1.3.1 Mechanisms of oxidative damage in brain tissue

Oxidation is a ubiquitous cellular process however modifications resulting in an imbalance

between antioxidants and pro-‐oxidants can cause damage. Reactive oxygen species (ROS)

and reactive nitrogen species (RNS) are signaling molecules that can cause cellular damage

to protein, lipids, and DNA when in abundance. The brain is especially vulnerable to

oxidative damage due to its high energy demand and reliance on oxidative metabolism. The

susceptibility is augmented: increased oxidative phosphorylation leads to increased ROS

and RNS and any decrease in aerobic metabolism due to damage will affect function of

these high-‐energy demand cells further. Importantly, neurons have multifactorial

differences in vulnerability to increased oxidative stress as shown in figure 1.1.

12

Mitochondria are the intracellular organelles responsible for the oxidative phosphorylation

pathway. This pathway, located on the inner mitochondrial membrane, supplies more than

95% of the total cellular energy requirement (Rezin et al., 2009). Briefly, the oxidative

phosphorylation pathway creates an electrochemical gradient by using energy from

multiple electron transfer (REDOX) reactions to pump protons from the inner

mitochondrial matrix to the intermembrane space. These protons then flow down their

electrochemical gradient to provide energy for ATP production. During this process, some

electrons “leak” from the pathway to form ROS and RNS. Importantly, this can lead directly

to the formation of the superoxide radical (O2•−) through the one-‐electron reduction of

oxygen (O2), which can dismutate to form hydrogen peroxide (H2O2) and further react to

form the hydroxyl radical (HO•). Although the production of ROS and RNS is necessary and

ubiquitous, alterations to any one of the many antioxidant systems may have impacts on

the molecular functions from subtle changes in signaling pathways to apoptosis or necrosis.

Increased oxidative stress can damage the macromolecules of the cell with many

consequences. For example, damage to proteins may affect the function of receptors,

enzymes, DNA replication and repair machinery, etc. Direct damage to DNA may cause

mutations or regulatory alterations and damage to lipids can cause impairment to

membrane function. Oxidative damage to brain tissue is a factor in normal aging processes,

neurodegenerative diseases such as Alzheimer’s, and in many psychiatric illnesses

including BD and SCZ (Barnham et al., 2004, Ng et al., 2008, Rezin et al., 2009). Increased

oxidative stress has damaging effects on signal transduction and cellular resilience, mainly

through damage to membrane lipids, proteins, and DNA/RNA.

13

Figure 1.1. Factors contributing to differences in neuronal vulnerability to oxidative stress.

Despite being exposed to the same levels of oxidative stress, some neurons will survive

while others die. This may help explain regional differences in the brain. Image adapted

from Wang and Michaelis, 2010.

14

1.3.2 Mitochondrial dysfunction in bipolar disorder

Substantial evidence from microarray and biochemical studies supports the presence of

mitochondrial dysfunction in BD. It is widely known that mitochondrial dysfunction leads

to increased oxidative stress (Cassarino and Bennett, 1999). In addition, mitochondrial

dysfunction has implications for many other cell processes that may contribute to the

pathophysiology of BD, including calcium regulation, cellular resiliency, and synaptic

plasticity (Quiroz et al., 2008). Expression of mRNA encoding mitochondrial genes is

altered in BD; studies have shown decreased gene expression in both the prefrontal cortex

(Iwamoto et al., 2005, Gigante et al., 2011a) and the hippocampus (Konradi et al., 2004).

Complex I is the entry point for electrons into the respiratory chain and can form

superoxide radicals in two ways: a reduced flavin mononucleotide (FMN) site on complex I

during decreased respiration reacts with O2 and reverse electron transfer due to high

potential energy (Murphy, 2009). The superoxide production during reverse electron

transfer is the highest that occurs in the mitochondria, making alterations to complex I of

great consequence. The altered mRNA expression of complex I subunits, determined by

microarray analysis, are mostly involved in the electron transfer process further

implicating mitochondrial complex I dysfunction and subsequent oxidative stress (Scola et

al., 2013). A summary of the altered subunits in the λ subcomplex of complex I is shown in

figure 1.2.

Importantly, decreased protein levels of mitochondrial complex I subunit NDUFS7 in post-‐

mortem prefrontal cortex samples was shown in our lab (Andreazza et al., 2010, Andreazza

et al., 2013). There are also functional mitochondrial changes in BD, including decreased

15

activity and morphological deviations (Cataldo et al., 2010, Gubert et al., 2013). Also of note

is the association of psychiatric illness with mitochondrial disorders, especially

mitochondrial encephalomyopathy with lactic acidosis and stroke-‐like episodes (Anglin et

al., 2012). Clearly, mitochondrial dysfunction is likely an important factor in the

pathophysiology of BD.

16

Figure 1.2. Alterations of the λ subcomplex of mitochondrial complex I in bipolar disorder,

found by microarray analysis. Importantly, the decreased subcomplexes are involved

directly with the electron transfer process, which may have an effect on oxidative stress in

the cell when the electrons cannot flow normally through the pathway. Adapted from Scola

et al., 2013.

17

1.3.3 Oxidative stress damage in patients with bipolar disorder

Oxidative stress damage has been measured in both peripheral samples, including

leukocytes, red blood cells, and serum, and in post-‐mortem brain samples, especially the

prefrontal cortex region. Multiple studies have shown increased lipid peroxidation,

DNA/RNA damage, protein damage, and altered antioxidant enzymes in blood samples

from patients with BD (Abdalla et al., 1986, Kuloglu et al., 2002, Savas et al., 2002, Yanik et

al., 2004, Savas et al., 2006, Andreazza et al., 2007a, Gergerlioglu et al., 2007, Selek et al.,

2008, Andreazza et al., 2009, Banerjee et al., 2012, Magalhaes et al., 2012, Raffa et al., 2012,

Soeiro-‐de-‐Souza et al., 2013, Versace et al., 2014). Although there are conflicting studies

measuring oxidative stress markers in BD, a 2008 meta-‐analysis from our laboratory found

a significant increase of lipid peroxidation and nitric oxide levels (Andreazza et al., 2008a).

Many studies suggest that the levels of oxidative damage in BD may be dependent on a

multitude of factors, including current mood episode, number of manic episodes, age of

onset, length of illness, and medications (Ozcan et al., 2004, Aliyazicioglu et al., 2007,

Andreazza et al., 2007a, Machado-‐Vieira et al., 2007b, Kunz et al., 2008, Kapczinski et al.,

2011).

Oxidative stress has also been examined in post-‐mortem brain tissue samples, especially

the prefrontal cortex. Increased lipid peroxidation, protein carbonylation and nitration, and

DNA damage has been shown in prefrontal cortex (Andreazza et al., 2010, Gawryluk et al.,

2011, Gigante et al., 2011a, Andreazza et al., 2013). Lipid peroxidation was shown to be

increased in the anterior cingulate cortex (Wang et al., 2009) and examinations of DNA

damage in this region had differing results (Benes et al., 2003, Buttner et al., 2007). Very

18

few studies of oxidative damage have been conducted on the hippocampus region. One

study of multiple brain areas found that the hippocampus was the only region with fully

intact DNA (Mustak et al., 2010) and another found a modest increase of oxidative damage

to nucleic acids in the cytoplasm of cells from the hippocampus, suggesting damage to RNA

(Che et al., 2010). It is important to determine the exact targets of oxidative stress damage

in BD to determine its pathophysiology.

1.3.4 Neuroprotective effects of lithium

There is substantial evidence that many effective medications for BD have neuroprotective

properties, especially lithium. Lithium has been shown to activate neurotrophic signaling

cascades, in particular through increasing the MAP kinase cascade activity, increasing

levels of bcl-‐2, and inhibiting glycogen synthase kinase (GSK)-‐3β (Manji et al., 2003). The

neuroprotective effects of lithium are outlined in table 1.1. In humans, one study found

increased lipid peroxidation and antioxidant enzyme activity in unmedicated, first-‐episode,

manic patients. In contrast, manic patients treated with lithium had a significant reduction

of antioxidant enzyme activity and lipid peroxidation levels (Machado-‐Vieira et al., 2007a).

In addition, mice studies have shown a positive effect of lithium on hippocampal

neurogenesis (Chen et al., 2000). The neuroprotective effects of lithium may also occur

through antioxidant mechanisms. For example, one study found a reduced superoxide

dismutase/catalase ratio in healthy volunteers after lithium treatment, which suggests

decreased oxidative stress by reducing hydrogen peroxide levels (Khairova et al., 2012).

Antidepressants also have a substantial neuroprotective effect; multiple studies have

19

shown that chronic antidepressant treatment increases neurogenesis in the dentate gyrus

cells of the hippocampus (D'Sa and Duman, 2002, Boldrini et al., 2012). The antioxidant and

neuroprotective properties of these medications further link oxidative stress to the

pathobiology of BD. Furthermore, the effects of these medications on the hippocampus

further suggest this regions involvement in BD and also demonstrate the importance of

considering medication status in biological human studies.

20

Table 1.1. Neuroprotective effects of lithium. There is substantial evidence showing chronic lithium treatment has many neuroprotective effects. Protects Cultured Cells of Rodent and Human neuronal Origin in Vitro+ from: Glutamate

High concentrations of calcium MPP+ β-‐amyloid Aging-‐induced cell death HIV regulatory protein, Tat HIV gp120 envelope protein Glucose deprivation Growth factor or serum deprivation Toxic levels of anticonvulsants Platelet activating factor (PAF) Aluminum toxicity Low potassium concentrations C2-‐ceramide Ouabain GSK-‐3β & staurosporine/heat shock β-‐bungarotoxin

Enhances Regeneration of Retinal Ganglion Cells Enhances Hippocampal Neurogenesis in Adult Mice Protects Rodent Brain in Vivo from: Cholingergic lesions

Radiation injury Middle cerebral artery occlusion (model of stroke) HIV gp120 envelope protein injection (model of HIV-‐associated dementia) Quinolinic Acid infusion (model of Huntington’s disease) Aluminum maltolate

Human Effects: No subgenual PFC gray matter volume reductions in cross-‐sectional MRI studies

No reductions in amygdala glial density in postmortem cell counting studies Increased total gray matter volumes on MRI compared to untreated BD patients in cross-‐sectional studies Increases in NAA (marker of neuronal viability) levels in BD patients in longitudinal studies Increased gray matter volumes in BD patients in longitudinal studies

Abbreviations: MPP+, 1-‐methyl-‐4-‐phenylpyridinium ion; HIV, human immunodeficiency virus; GSK, glucogen synthase kinase; PFC, prefrontal cortex; MRI, magnetic resonance imaging; BD, bipolar disorder; NAA, N-‐acetylaspartate. Table adapted from Manji et al., 2003.

21

1.3.5 Brief overview of epigenetic findings in bipolar disorder

The pathology of BD has genetic components, evidenced by family and twin studies that

show first-‐degree relatives of individuals with BD have a 10-‐fold increased risk of the

disorder compared to first-‐degree relatives of unaffected controls (Smoller and Finn,

2003). Decades of gene association studies have not revealed the genetic epidemiology

behind BD as it is very complex. Furthermore, epigenetic modifications may contribute to

the heritability of BD. Briefly, modifications to nucleotides in DNA have multiple effects, but

importantly, methylation at the 5’ position of cytosine, catalyzed by DNA methyl-‐

transferases (DNMTs), decreases gene transcription reducing expression. This “gene

silencing” occurs by impeding transcriptional proteins from binding, either directly or by

methyl-‐CpG-‐binding domain (MBD) proteins. MBDs can also recruit additional proteins

that alter chromatin structure, further silencing the gene. Hydroxylation of 5-‐

methylcytosine (5-‐mC), forming 5-‐hydroxymethylcytosine (5-‐hmC), is catalyzed by the ten-‐

eleven translocation (TET) proteins. This hydroxylation step is an intermediate to DNA

demethylation, and thus, DNA “activation” (Guo et al., 2011, He et al., 2011, Klug et al.,

2013). Epigenetic modifications, such as 5-‐mC, can persist in germ cells and can be passed

down to offspring, providing a mechanism for heritability (Petronis, 2010).

A number of epigenetic alterations have been shown in BD. Epigenetic alterations to loci

associated with mitochondrial function, brain development, and stress response, were

found in post-‐mortem frontal cortex of patients with psychosis and either SCZ or BD (Mill

et al., 2008, Grayson and Guidotti, 2013). It has also been shown that DNA methylation may

be different in monozygotic twins discordant for BD (Kuratomi et al., 2008). Interestingly,

22

oxidative stress may be associated with epigenetic changes, although few studies have been

conducted thus far. One study treated human neuroblastoma cells with hydrogen peroxide

to induce oxidative stress, which led to an imbalance between DNA methylation and

demethylation, between histone acetylation and deacetylation, and was also associated

with the activation of transcription factors (Gu et al., 2013).

1.4 THE USE OF BIOMARKERS IN PSYCHIATRIC DISEASE

Biomarkers are measurements that quantify biological processes and aid in diagnosis,

monitoring illness, and response to treatment (see figure 1.3). Psychiatry, unlike most

other fields of medicine, lacks specific and reliable biomarkers to diagnose and monitor

illness. Although clinician observation is important in many branches of medicine, most

also utilize diagnostic tests. BD can be especially difficult to diagnose because of symptom

overlap with other mood and psychotic disorders such as major depressive disorder and

schizophrenia and patients often present in a depressed state before mania first occurs. A

recent paper from the International Society for Bipolar Disorders Biomarkers Task Force

provides potential candidates for biomarkers in BD, including oxidative stress markers

(Frey et al., 2013; see figure 1.4). Thus far, oxidative stress markers have been utilized in a

research setting, however, with validation, some markers may become useful in clinical

settings. To be used as a clinical biomarker, certain criteria must be met including: 1) being

chemically stable, not prone to formation or loss during storage; 2) reflective of disease

23

onset/progression; 3) obtained by non-‐invasive sampling; 4) low intra-‐ and inter-‐

variability; and 5) measureable by accurate, precise, specific, sensitive, interference-‐free,

and validated assays (Dalle-‐Donne et al., 2006, Giustarini et al., 2009).

24

Figure 1.3. Potential benefits of determining and validating an oxidative stress biomarker in bipolar disorder. Adapted from Dalle-‐Donne et al., 2006.

Figure 1.4. Potential biomarkers in bipolar disorder. Adapted from Frey et al., 2013.

25

1.5 AIM OF THE THESIS

1.5.1 Statement of problem

Despite the prevalence and high burden of illness for BD, the pathophysiology of this

psychiatric illness remains unknown. It is vital to understand the biological mechanisms of

BD in order to develop better treatments, improve diagnosis, and ultimately increase the

quality of life for patients and their families. A meta-‐analysis of oxidative stress markers in

BD is vital to confirm biological differences. Previous meta-‐analyses of oxidative stress

markers in BD included peripheral samples only, however, we wanted to include post-‐

mortem brain samples in addition to increase sample size and to potentially reveal

additional oxidative damage. Most research conducted on oxidative stress markers in BD

used prefrontal cortex samples, however, it is necessary to determine whether other brain

regions are affected. The hippocampus is part of the limbic system, which is involved in

learning, memory formation and emotional regulation, and is also tightly connected to the

prefrontal cortex region. There is evidence from neuroimaging and microarray studies

implicating the hippocampus in BD. Importantly, microarray studies on samples from the

Harvard Brain Tissue Resource Center (HBTRC) revealed decreased mRNA expression of

several mitochondrial complex I and complex III subunits, suggesting abnormal energy

metabolism. Furthermore, results from our lab found increased oxidative stress damage to

the prefrontal cortex of this patient cohort.

1.5.2 Purpose of the Study and Objective

26

The purpose of this study is to consolidate recent literature of oxidative stress in BD and to

investigate mitochondrial dysfunction and oxidative stress damage to proteins, lipids, and

DNA modifications in the hippocampus. Specifically, two objectives were defined:

1. Consolidate data on oxidative stress in BD by conducting a meta-‐analysis on

oxidative stress markers in both peripheral and post-‐mortem brain samples.

2. Measure oxidative stress markers in post-‐mortem hippocampus samples from

patients with BD or SCZ, and healthy, non-‐psychiatric controls.

1.5.3 Statement of Research Hypotheses

1. Based on a previous meta-‐analysis from our laboratory and a review of the

literature, we hypothesized that increased levels of nitric oxide and lipid

peroxidation would be the most significant marker in BD.

2. The hippocampus has been implicated in the pathophysiology of BD and results

from microarray studies suggest abnormal energy metabolism. Furthermore,

biochemical studies in the prefrontal cortex region of this patient cohort revealed

increased oxidative stress damage. Based on this evidence, we hypothesized that the

hippocampus region would be a target of oxidative stress damage in BD.

27

Chapter 2. Methods and results for the meta-‐analysis of oxidative stress

markers in bipolar disorder

2.1 METHODS FOR THE META-‐ANALYSIS OF OXIDATIVE STRESS MARKERS IN BIPOLAR

DISORDER

2.1.1 Search strategy

A prospective protocol for this study was developed a priori with search terms and

inclusion criteria chosen in an attempt to include all relevant publications. Web of Science,

BIOSIS, and MEDLINE databases were searched for the term bipolar disorder with the

following: oxidative stress, reactive oxygen species, free radicals, antioxidant, nitric oxide,

lipid peroxidation, TBARS, protein carbonyl, 3-‐nitrotyrosine, catalase, glutathione, DNA

oxidation, DNA damage, or DNA fragmentation. References cited in publications found

using these search terms were also reviewed for any relevant studies not already identified

and all searches were conducted prior to May 2013 with no time span specified.

2.1.2 Selection Criteria

One reviewer screened all abstracts of potentially relevant publications. Studies were

included if they met the following criteria: (1) measured levels of one or more of the

following oxidative stress markers in both patients with bipolar disorder and healthy

controls: superoxide dismutase, catalase, glutathione peroxidase, protein carbonyl, 3-‐

28

nitrotyrosine, nitric oxide, DNA/RNA damage, and lipid peroxidation; (2) were reported in

an original research paper in a peer-‐reviewed journal; and (3) if they adequately described

their samples (e.g. diagnostic criteria, source of samples, and storage) and methods such

that the experiments could be replicated (or included appropriate references). Studies

were retained regardless of the measurement method or sample type (peripheral or post-‐

mortem brain). Additionally, authors were contacted for mean values and standard

deviations when their methods were appropriate but data was expressed in a graph or

figure only (Andreazza et al., 2009, Wang et al., 2009, Che et al., 2010, Mustak et al., 2010,

Gawryluk et al., 2011, Gigante et al., 2011a, Andreazza et al., 2013). For all included studies,

the disease state of BD patients, number of drug-‐free patients, sample type, type of

assay/measurement, and results were recorded.

2.1.3 Statistical Analysis

The meta-‐analysis of pooled standardized mean differences was conducted using Review

Manager software (Version 5.2, Copenhagen) from The Cochrane Collaboration. The effect

sizes for the standardized mean differences were expressed through Hedges’s G and a Z-‐

score; a p-‐value of <0.05 for Z was considered statistically significant. A random-‐effects

model was used and studies were weighted by the generic inverse variance method. The

between-‐study heterogeneity was determined using the Cochran Q statistic and expressed

using I2 and τ2. Publication bias was assessed by visually inspecting funnel plots and

applying Egger’s regression test with p<0.1 as statistically significant (Egger et al., 1997)

using the software program Comprehensive Meta-‐analysis (Borenstein et al., 2005). A one-‐

29

study removed sensitivity analysis was performed for each oxidation marker by manually

excluding each study included in the analysis to determine robustness. In cases where

patients were separated into subgroups (i.e. manic, depressed, or euthymic), the means and

standard deviations were pooled to compare all bipolar groups with healthy controls; 15

out of 27 studies included information about the patient disease state. All comparisons

were two-‐tailed and 95% confidence intervals are expressed where applicable. A weighted

linear meta-‐regression was conducted using SPSS Statistics for Windows, Version 22 (IBM)

with sample type as a comparator.

2.2 RESULTS FOR THE META-‐ANALYSIS OF OXIDATIVE STRESS MARKERS IN BIPOLAR

DISORDER

2.2.1 Characteristics of included studies

In total, 226 studies were screened and 29 fit the selection criteria. Of the 226 screened

papers, 68 were review articles, 48 were animal or cell studies, 51 did not measure an

included marker of oxidative stress, 28 were genetic studies, and 2 did not include a

healthy control group. Twenty-‐seven studies were included in the meta-‐analysis out of the

29 that fit the selection criteria; 2 studies were excluded due to missing means and

standard deviations (Benes et al., 2003, Buttner et al., 2007). All diagnoses, except for one,

were established based on DSM-‐IV criteria; the one exception was published by Abdalla et

30

al. in 1986 and used ICD-‐9 (International Classification of Diseases) criteria, which was

deemed appropriate for inclusion. After pooling the included studies, there were a total of

995 unique BD patients and 928 healthy controls. Appendix A outlines the characteristics

of these studies including the disease state of BD patients, number of drug-‐free patients,

sample type, type of assay, and overall results.

2.2.2 Results of the meta-‐analysis

A total of 8 oxidative stress markers were included in this analysis: superoxide dismutase,

catalase, glutathione peroxidase, protein carbonyl, 3-‐nitrotyrosine, nitric oxide, DNA/RNA

damage, and lipid peroxidation. Table 2.1 outlines the pooled statistics and meta-‐analysis

for the oxidative stress markers in patients with BD and controls. In total, 3 out of these 8

oxidative stress markers showed a statistically significant change in BD patients compared

to healthy controls: lipid peroxidation, nitric oxide level, and DNA/RNA damage. Forest

plots of all standardized mean differences and 95% confidence intervals are shown in

figure 2.1.

31

Table 2.1. Pooled statistics and meta-‐analysis of standardized mean group differences for oxidative stress markers in BD compared with healthy controls

Marker Number of studies

Total N Effect Heterogeneity BD CTL Hedges’s g (95% CI) Z P(Z) τ 2 I2

Lipid peroxidation * 12 517 426 1.62 (1.02, 2.22) 5.31 < 0.00001 1.07 93%

Nitric oxide * 6 203 153 0.93 (0.05, 1.82) 2.06 0.04 1.13 93%

DNA/RNA damage * 4 117 113 3.13 (1.42, 4.84) 3.59 0.0003 3.14 94%

Superoxide dismutase

12 440 376 0.12 (-‐0.82, 1.07) 0.26 0.80 2.71 97%

Catalase 5 200 154 -‐1.58 (-‐3.46, 0.30) -‐ 1.65 0.10 4.42 98%

Protein carbonyl 5 199 255 0.62 (-‐0.40, 1.64) 1.19 0.23 1.28 96%

Glutathione peroxidase

8 272 273 -‐0.05 (-‐0.47, 0.36) 0.26 0.80 0.27 79%

3-‐Nitrotyrosine 3 90 100 1.17 (-‐0.16, 2.50) 1.72 0.09 1.28 93%

Abbreviations: BD, Bipolar disorder; CTL, Controls * Statistically significant (P<0.05)

33

Figure 2.1. Forest plots of standardized mean differences and 95% confidence intervals for oxidative stress markers in patients with bipolar disorder compared to healthy controls 1 4-‐hydroxynonenal 2 Lipid hydroperoxides 3 Single-‐stranded DNA breaks 4 Double-‐stranded DNA breaks Note: Mustak 20103 and Mustak 20104 used the same study population and Andreazza 20131 and Andreazza 20132 used the same study population. In the meta-‐analysis each study was weighted as one, despite having two relevant measurements, to prevent one sample population from being overrepresented.

34

2.2.3 Publication bias, sensitivity analysis, and meta-‐regression

Given the small number of studies, we performed a one-‐study removed sensitivity analysis

by excluding each study individually. The Z-‐Value remained significant for DNA/RNA

damage and lipid peroxidation and the effect size for superoxide dismutase and glutathione

peroxidase remained essentially unchanged in direction and magnitude after the removal

of each study individually. The sensitivity analysis of protein carbonyl, 3-‐nitrotyrosine,

catalase, and nitric oxide showed that these results are not very robust and should be

interpreted cautiously: (1) for protein carbonyl, the removal of Andreazza et al. (2009)

caused the Z-‐Value to increase from 1.19 (p=0.23) to 2.13 (p=0.03); (2) for 3-‐nitrotyrosine,

the removal of Andreazza et al. (2013) caused the Z-‐Value to increase from 1.72 (p=0.09) to

4.32 (p=0.000015); (3) for catalase, the removal of Machado-‐Vieira et al. (2007) caused the

Z-‐Value to decrease from -‐1.65 (p=0.10) to -‐3.26 (p=0.024); and (4) for nitric oxide, the

removal of Ozcan et al. (2004) caused the Z-‐value to increase from 2.06 (p=0.04) to 5.39

(p<0.00001). Publication bias, measured by Egger’s regression test, was negative for all

markers: superoxide dismutase (95% CI=-‐38.7 to 6.0; p=0.13), catalase (95% CI=-‐43.0 to

24.9; p=0.45), glutathione peroxidase (95% CI=-‐9.8 to 7.4; p=0.74), lipid peroxidation

(95% CI=-‐7.2 to 8.5; p=0.87), protein carbonyl (95% CI=-‐50.7 to 58.4; p=0.79), nitric oxide

(95% CI=-‐53.7 to 55.9; p=0.95), 3-‐nitrotyrosine (95% CI=-‐146.4 to 130.6; p=0.60), and

DNA/RNA damage (95% CI=-‐2.7 to 14.4; p=0.12). A weighted linear regression analysis did

not reveal an interaction of sample type with any significant marker of oxidative stress:

lipid peroxidation (β=-‐0.119, p=0.781), nitric oxide (β=1.109, p=0.297), or DNA/RNA

damage (β=-‐0.054, p=0.575).

35

Chapter 3. Materials, methods, and results for the measurement of

oxidative stress damage in post-‐mortem hippocampus tissue

3.1 MATERIALS AND METHODS FOR THE MEASUREMENT OF OXIDATIVE STRESS

DAMAGE AND DNA ALTERATIONS IN THE HIPPOCAMPUS

3.1.1 Postmortem brain tissue samples

Hippocampus samples were generously donated by the Harvard Brain Tissue Resource

Center (HBTRC) and all patients provided consent to HBTRC prior to death. The 46

participants were divided into three groups: 19 non-‐psychiatric controls, 15 SCZ, and 12

BD. Two senior psychiatrists, using DSM-‐IV criteria, confirmed all diagnoses

retrospectively through patient records. Demographic information, post-‐mortem interval

(hours prior to freezing at -‐80°C; PMI), brain pH, and toxicology at time of death were

known. Medications prescribed within a year of death, family psychiatric history, and

comorbidities were determined through patient records. Medications were divided into

four groups: lithium, other mood stabilizers, antidepressants, or antipsychotics.

Antipsychotics were further divided into either typical or atypical. The investigators were

blind to diagnosis and all other variables throughout all experiments and measurements

with the random numeric code lifted only after all analysis was completed.

3.1.2 Tissue homogenization

36

Whole tissue homogenates were prepared as described by Smith (1967) with minor

modifications. Briefly, hippocampus tissues were homogenized in buffer (0.25 M sucrose,

2mM EDTA, 10mM Tris HCl; pH 7.2) at a ratio of 10 µL/mg of tissue. Samples were

homogenized using sonification (5 seconds at 25% amplitude; Branson Ultrasonics

Corporation, CT, USA) then centrifuged at 1000g and 4°C for 10 minutes. The supernatant

was kept on ice and the pellet was resuspended in buffer, mechanically homogenized, and

recentrifuged. Supernatants were combined and protein concentration was determined

using the Bradford assay (Bradford reagent, Sigma-‐Aldrich, MO, USA).

3.1.3 Mitochondrial subunit protein levels

Protein levels of complex I subunits NDUFS7, NDUFS8, and NDUFV2 were determined

through Western blotting followed by immunoblotting. Whole tissue homogenates were

mixed with SDS-‐PAGE sample buffer (50mM Tris HCl (pH 6.8), 100mM DTT, 2% SDS, 0.1%

bromophenol blue, 10% glycerol) to a concentration of 10 μg tissue/15 μL buffer. These

samples were loaded on 12% acrylamide sodium dodecyl sulfate-‐polyacrylamide

electrophoresis gels, separated, and transferred to PVDF membranes. Blots were incubated

with the primary antibody for 2 hours at room temperature (NDUFS7 [Santa Cruz

Biotechnology, #98644], 1:400 dilution; NDUFS8 [Abcam #67106], 1:640 dilution; NDUFV2

[Abcam #96117], 1:1000 dilution), followed by a secondary antibody conjugated to

horseradish peroxidase (1 hour at room temperature). Immunoreactive bands were

detected with Amersham ECL Prime Western Blotting Detection Reagent (GE Healthcare

37

Life Sciences, Ohio, USA) and analyzed densitometrically using VersaDoc (Bio-‐Rad, USA).

Blots were normalized by immunoblotting with anti-‐β-‐actin antibody, an established

loading control.

3.1.4 Protein oxidative and nitrosative damage

Protein oxidation damage was determined by measuring protein carbonyl content using

the OxyBlot Protein Oxidation Detection kit (Millipore Co, USA, #S7150) with

modifications. Briefly, protein side chains were derivatized with 2,4-‐

dinitrophenylhydrazone (DNP-‐hydrazone) through the reaction of 2,4-‐

dinitrophenylhydrazine (DNPH) with carbonyl groups. The DNP-‐derivatized proteins were

spotted onto PVDF membrane and immunoblotted with a primary antibody specific to the

DNP moiety, followed by HRP-‐conjugated secondary antibody, and detection with ECL

reagent. Nitrosative damage to proteins was determined by measuring the levels of 3-‐

nitrotyrosine using Western blotting as described above (anti-‐3-‐nitrotyrosine antibody,

Abcam #7048; 1:500 dilution). Blots were normalized using MemCode Reversible Protein

Stain Kit for PVDF Membranes (Thermo Scientific Pierce Protein Biology Products, Illinois,

USA).

3.1.5 Lipid peroxidation

38

The level of lipid peroxidation was determined by measuring total lipid hydroperoxides

and 4-‐hydroxynonenal, a degradation product. Total hydroperoxides were measured using

a colorimetric kit (Lipid Hydroperoxide (LPO) Assay Kit, Cayman Chemical, San Antonio,

USA) according to manufacturer’s instructions. Briefly, lipid hydroperoxides were

extracted from samples using a methanol/chloroform mixture (0°C, 1500g, 5 minutes) and

incubated with chromogen at room temperature. The absorbance was read at 500 nm and

compared to a standard curve. 4-‐hydroxynonenal was measured using OxiSelect HNE-‐His

Adduct ELISA Kit (Cell Biolabs, Inc., USA) according to manufacturer’s instructions. HNE-‐

His adducts are probed with an anti-‐HNE-‐His antibody followed by HRP conjugated

secondary antibody and chemiluminescence detection.

3.1.6 DNA methylation and hydroxymethylation

DNA methylation and hydroxymethylation were determined through the measurement of

5-‐mC and 5-‐hmC, respectively. Both were measured using an immuno-‐dot blot method

adapted from Ko et al. (2010) and Yang et al. (2013). Genomic DNA was extracted from

whole hippocampus tissue using GenElute Mammalian Genomic DNA Miniprep Kit (Sigma-‐

Aldrich, MO, USA), according to manufacturer’s instructions. The CpGenome 5-‐mC and 5-‐

hmC DNA Standard Set containing positive controls of 100% 5-‐mC or 5-‐hmC and a negative

control containing unmodified cytosine was used (Millipore, MA, USA), and a DNA curve to

standardize technique was completed. 50 ng genomic DNA in Tris-‐EDTA buffer and

denaturation solution was spotted onto PVDF membranes and allowed to dry, followed by

DNA UV cross-‐linking (120,000 microjoules per cm2 for 5 minutes; CL-‐1000, Ultra-‐Violet

39

Products). The blots were then incubated for 2 hours at room temperature with primary

antibody specific for 5-‐mC (Millipore clone 33D3; 1:1000 dilution) or 5-‐hmC (Millipore

clone AB3/63.3; 1:100 dilution). This was followed by a 40 minute incubation with HRP-‐

conjugated secondary antibody and detection with Amersham ECL Prime Western Blotting

Detection Reagent (GE Healthcare Life Sciences, Ohio, USA). Membranes were imaged using

VersaDoc system (Bio-‐Rad, USA) and relative levels of 5-‐mC and 5-‐hmC were determined

densitometrically using Image Lab software (Bio-‐Rad, USA).

3.1.7 Statistical analysis

All statistical analyses were performed using SPSS Statistics for Windows, Version 22

(IBM). Data distribution was determined by Kolmogorav-‐Smirnov test. The variables

NDUFS7 (Z= 0.203; p=0.000), NDUFS8 (Z= 0.241; p= 0.000), NDUFV2 (Z= 0.179; p= 0.001),

and 5-‐mC (Z= 0.140; p= 0.027), did not follow Gaussian distribution; lipid hydroperoxides

(Z= 0.102; p= 0.200), 4-‐hydroxynonenal (Z= 0.105; p= 0.200), protein carbonyl content (Z=

0.082; p= 0.200), 3-‐nitrotyrosine (Z=0.111; p= 0.200), and 5-‐hmC (Z= 110; p=0.200) did

have a normal distribution. To determine between-‐group differences we used one-‐way

ANOVA followed by LSD post-‐hoc test for parametric variables and Kruskal-‐Wallis test for

non-‐parametric variables. Differences were considered significant at p≤0.05. To determine

whether there was an effect of age, pH, and PMI on the results, Pearson’s correlation

coefficient for parametric variables and Spearman’s Rank Correlation for nonparametric

variables were used. One-‐way ANOVA was used to determine the relation of age with

biochemical data. Since there was no correlation between any of these factors with

40

biochemical data, we did not do further analysis. Medication effects were determined using

one-‐way ANOVA for parametric variables and Kruskal-‐Wallis test for non-‐parametric

variables.

3.2 RESULTS OF THE MEASUREMENT OF OXIDATIVE STRESS DAMAGE AND DNA

ALTERATIONS IN THE HIPPOCAMPUS

3.2.1 Patient demographics, family history, medications, and toxicology

Patient demographic and medication information can be found in table 3.1. Subjects in the

study included participants with BD (n=12), SCZ (n=15), and non-‐psychiatric controls

(n=19). Age ranged from 18-‐87, PMI from 13 hours to 38 hours, and pH from 5.6 to 7.0;

there were no significant differences between groups for these variables. Psychiatric

medications prescribed at time of death were known and included lithium (n=6), mood

stabilizers (n=5), antidepressants (n=4), and antipsychotics (n=20).

41

Table 3.1. Demographic variables, postmortem interval, pH, and medications for bipolar disorder, schizophrenia, and control groups

Bipolar disorder (n=12)

Schizophrenia (n=15)

Healthy controls (n=19)

Age, mean (SEM) [range] 58.5 (6.5) [18-‐86] 62.4 (3.5) [46-‐87] 59.5 (3.6) [18-‐80] F2,46 = 0.185; p = 0.8321

Gender, No. (%) Male 3 (25.0) 11 (73.3) 14 (73.7) Female 9 (75.0) 4 (26.7) 5 (26.3)

PMI, mean (SEM) [range] 20.67 (1.26) [13-‐27] 23.07 (1.78) [16-‐38] 22.16 (0.74) [16-‐30] F2,46 = 0.727; p = 0.4891

pH, mean (SEM) [range] 6.33 (0.08) [5.6-‐6.7] 6.45 (0.08) [5.72-‐7.0] 6.45 (0.05) [6.11-‐6.86] F2,46 = 0.987; p = 0.3811

Medication, No. (%) Lithium 5 (41.7) 1 (6.7) Mood-‐stabilizers 3 (25.0) 2 (13.3) Antidepressants 2 (16.7) 2 (13.3) Antipsychotics (any) 9 (75.0) 11 (73.3) Atypical 1 (8.3) 2 (13.3) Typical 5 (41.7) 8 (53.3) Unknown 3 (25.0) 1 (6.7)

Abbreviations: PMI, postmortem interval; SEM, standard error of the mean 1One-‐way ANOVA

42

3.2.2 Hippocampus may not be a target for mitochondrial dysfunction and oxidative

damage to proteins and lipids

We assessed the levels of mitochondrial complex I subunits NDUFS7, NDUFS8, and

NDUFV2, in homogenized tissue from the hippocampus and found no differences between

SCZ, BD, or control groups. These results are shown in figure 3.1. Two indicators of

oxidative damage to proteins were evaluated: 3-‐nitrotyrosine and protein carbonyl levels.

Lipid damage was evaluated by measuring total lipid hydroperoxides, and 4-‐

hydroxynonenal, which is a product of lipid peroxidation. In the hippocampus we did not

find any differences between groups for 3-‐nitrotyrosine, protein carbonyl, 4-‐

hydroxynonenal, or total lipid hydroperoxides. Results for protein damage are shown in

figure 3.2 and for lipid damage in figure 3.3.

3.2.3 Increased levels of 5-‐hmC in hippocampus from patients with SCZ but not in

BD

As an exploratory analysis, we evaluated the levels of global methylation and

hydroxymethylation. We found significant differences between groups for 5-‐hmC

(F2,43=4.397; p=0.018), with a significant increase in SCZ (p=0.028) compared to controls.

There were no between-‐group differences for 5-‐mC; these results are shown in figure 3.4.

After controlling for covariates (age, PMI, pH, and gender), the results for 5-‐hmC remained

significant.

43

Figure 3.1. Mitochondrial protein subunit levels for NDUFS8 (A), NDUFV2 (B), and NDUFS7 (C) in post-‐mortem hippocampus of patients with bipolar disorder (BD), schizophrenia (SCZ), and healthy controls (CTL). Between-‐group differences were analyzed using Kruskall-‐Wallis test.

Figure 3.2. Protein oxidation (protein carbonyl content; A) and protein nitration (3-‐nitrotyrosine, 3-‐NT; B) in post-‐mortem hippocampus of patients with bipolar disorder (BD) or schizophrenia (SCZ), and healthy controls (CTL).

A B C

A B

44

Figure 3.3. Levels of lipid peroxidation in post-‐mortem hippocampus of patients with bipolar disorder (BD) or schizophrenia (SCZ), and healthy controls (CTL). (A) 4-‐hydroxynonenal (4-‐HNE) levels; and (B) total lipid hydroperoxides.

Figure 3.4. Levels of DNA methylation (5-‐methylcytosine, 5-‐mC; A) and DNA hydroxymethylation (5-‐hydroxymethylcytosine, 5-‐hmC; B) in post-‐mortem hippocampus from patients with bipolar disorder (BD) or schizophrenia (SCZ), and healthy controls (CTL). Between-‐group differences were determined using Kruskal-‐Wallis test for 5-‐mC and one-‐way ANOVA followed by LSD post-‐hoc test for 5-‐hmC; *p<0.05.

A B

A B *

45

3.2.4 Toxicology and medication effects

We examined the effect of prescribed medications and toxicology at the time of death.

Medication effects were determined using one-‐way ANOVA for parametric variables and

Kruskal-‐Wallis test for non-‐parametric variables. No medication effects were found for any

measured variable. Toxicology at the time of death was also known and explored. We did

not find differences between subjects who were positive for opiates (n=10),

benzodiazepines (n=3), barbiturates (n=3), or amphetamines (n=2), compared to those

with no substance detected (n=28).

46

Chapter 4. Discussion

4.1 OXIDATIVE STRESS MARKERS IN PATIENTS WITH BIPOLAR DISORDER

A meta-‐analysis was conducted to consolidate and examine many different studies

measuring oxidative stress markers in BD. A total of twenty-‐seven papers were included in

the meta-‐analysis, which comprised of 971 unique patients with BD and 886 healthy

controls. Eight markers were analyzed: superoxide dismutase, catalase, protein carbonyl,

glutathione peroxidase, 3-‐nitrotyrosine, lipid peroxidation, nitric oxide, and DNA/RNA

damage. The meta-‐analysis of standardized means was conducted using a random-‐effects

model with generic inverse weighting. The results of the meta-‐analysis further supports the

presence of oxidative damage in BD; specifically, our analysis showed overall increased

lipid peroxidation, increased DNA/RNA damage, and increased levels of nitric oxide in BD

patients compared to healthy controls.

In the meta-‐analysis, there is a very strong effect size of lipid peroxidation in BD compared

to healthy controls and this increased lipid peroxidation is shown consistently in both

serum and post-‐mortem brain samples. Lipids are very prone to oxidative damage due to

their large size and high number of unsaturated bonds. Oxidative damage to these lipids

disrupts cell membranes and the end products of peroxidation are toxic. Since lipids

account for about 70% of the dry weight of myelin, the main component of white matter,

this damage may play a role in the pathophysiology of BD. Interestingly, a recent paper

examined whether peripheral lipid peroxidation levels were associated with white matter

abnormalities and showed that 59% and 51% of fractional anisotropy and radial diffusivity

47

differences, respectively, could be explained by variation in lipid hydroperoxide levels

(Versace et al., 2014). There is evidence that lipid peroxidation in serum is decreased with

medication (Aliyazicioglu et al., 2007); however, this was not accounted for in this analysis

and yet the effect size was strong despite this potentially lowering effect of medication.

Lipid peroxidation is a promising potential marker since it can be measured in serum and

holds promise to reflect brain alterations. If validated, there is a possibility for markers of

lipid peroxidation to be used as a prognostic biomarker along with neuroimaging tests.

Furthermore, the widely used TBARS or LPH assays for quantification do not require

specialized skills or equipment beyond that in a normal diagnostic laboratory.

There are many pathways through which increased oxidative stress can damage DNA or

RNA including scission or breaks and base modifications; these two types of oxidative

damage were included in this analysis. This is the first time a meta-‐analysis has examined

DNA and RNA oxidative damage in BD and our results show damage was increased in all

studies, which includes post-‐mortem brain samples and peripheral samples (Andreazza et

al., 2007b, Che et al., 2010, Mustak et al., 2010, Soeiro-‐de-‐Souza et al., 2013). This increase

in DNA scission and base hydroxylation may lead to increased cell necrosis and subsequent

inflammation of nearby tissues (Kim et al., 2001). Oxidative stress to cells may also induce

epigenetic changes through different mechanisms including DNA hypomethylation and

histone acetylation (Gu et al., 2013). Two of the included studies measured base

modifications in DNA and RNA (Che et al., 2010, Soeiro-‐de-‐Souza et al., 2013). The two

important nucleoside oxidation targets are guanosine and cytosine. Guanosine is the most

readily oxidizable base and its hydroxylation to 8-‐hydroxy-‐2-‐deoxyguanosine is often

considered an indicator of overall DNA and RNA damage. Cytoplasmic RNA is especially

48

vulnerable to this hydroxylation, and damage to mRNA causes improper translation and

protein aggregation (Shan et al., 2003). Hydroxylation of guanosine bases may also

promote hypomethylation through conformation changes in the DNA that may affect the

ability of methyl binding proteins to recognize their CpG island target. The oxidation of 5-‐

mC to 5-‐hmC is an important step for epigenetic regulation and is normally controlled by

the enzyme TET oxidase (Matarese et al., 2011). This hydroxylation step ultimately leads to

DNA demethylation and, therefore, often an increase in gene expression (Klug et al., 2013).

Upon review of the literature, it appears that DNA/RNA oxidation damage is very region

specific in post-‐mortem brain samples. For example, the 2010 study by Mustak et al. found

increased single-‐ and double-‐stranded breaks to genomic DNA in the parietal, temporal and

occipital lobes, thalamus, cerebellum, hypothalamus, medulla, pons, and frontal cortex, but

not in the hippocampus of bipolar patients. Another study that used post-‐mortem

hippocampus suggested that damage in this region occurs predominantly in the cytoplasm

of cells and thus affects RNA more than DNA (Che et al., 2010). One study, not included in

this meta-‐analysis, measured the methylation patterns of monozygotic twins discordant for

BD and found differences in 4 of the 10 explored regions (Kuratomi et al., 2008). Clearly,

these oxidative modifications to DNA and RNA may impact the heritability of bipolar

disorder and should be further investigated.

The two products of oxidative protein damage included in this meta-‐analysis, 3-‐

nitrotyrosine and protein carbonyl content, were not significant. 3-‐Nitrotyrosine is a

product of protein nitrosative damage that occurs when peroxynitrite/carbon dioxide-‐

derived radicals attack the hydroxyl group of tyrosine residues. Similarly, protein

carbonylation occurs when peroxide or oxygen radicals attack amine groups in amino acid

49

side-‐chains, often through a metal-‐cation catalyzed reaction. Oxidative damage to proteins

in BD is likely very transient due to the cell’s ability to remove these products and,

therefore, it is vital to study patients at different stages of the illness and in different

disease phases in order to fully determine the role protein damage may play in BD.

Nitration of proteins is dependent on levels of nitric oxide and an increase of nitric oxide in

BD patients was found in our meta-‐analysis. Nitric oxide is a widely used signaling

molecule in the nervous system; however, it can react with the free oxygen radical,

superoxide, to form the more unstable peroxynitrite. When the antioxidant system is

overwhelmed, peroxynitrite and its derivatives may cause damage to cellular lipids,

proteins, and nucleic acids. The increased nitric oxide levels in BD patients are discussed

further in the previous meta-‐analysis (Andreazza et al., 2008a) and no relevant papers

have since been published.

The antioxidant enzymes examined in the meta-‐analysis (glutathione peroxidase,

superoxide dismutase and catalase) did not show any overall significant changes in BD

compared to healthy controls, however, it still remains a possibility that there are changes

to larger antioxidant systems. Superoxide dismutase breaks the highly reactive and

damaging superoxide anion into molecular oxygen and hydrogen peroxide through a

copper-‐catalyzed redox reaction. The enzymes glutathione peroxidase and catalase can

then remove hydrogen peroxide from cells through further reduction. Two studies found

that the ratio of superoxide dismutase to glutathione peroxidase and catalase was

increased in manic and depressed patients but not in euthymic patients (Andreazza et al.,

2007a, Andreazza et al., 2007b). Consistent with these observations, the mood stabilizer

lithium that is typically effective in BD patients, significantly decreased the superoxide

50

dismutase/catalase ratio in healthy subjects (Khairova et al., 2012). Furthermore, a genetic

study showed a significant interaction between superoxide dismutase and glutathione

peroxidase haplotypes which increased risk for BD (Fullerton et al., 2010). All these

antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase) form

complicated relationships, and despite not being independently significant in this analysis,

they may still play an important role in the overall pathophysiology of BD.

In summary, the results of this meta-‐analysis further confirm the presence of oxidative

stress in BD patients. Compared to healthy controls, BD patients had higher levels of nitric

oxide, more DNA and RNA damage, and increased lipid peroxidation. Determining the

cause and effects of BD and its biological progression will lead to more effective treatments

and care. Furthermore, through the use of very large studies or meta-‐analyses, a biomarker

may be detected to aid in the diagnosis and treatment of BD. The large effect size and

robustness of increased lipid peroxidation in BD patients shown in this meta-‐analysis make

it a good candidate as a potential biomarker for BD. Limitations are discussed below.

4.2 HIPPOCAMPUS AND BIPOLAR DISORDER

Determining the exact brain regions affected by oxidative stress in BD and SCZ is vital to

improve treatment and outcomes. There is substantial evidence showing oxidative damage

to proteins, lipids, and DNA in the prefrontal cortex of patients with BD and SCZ. In this

study we investigated the involvement of oxidative stress in the hippocampus and found no

between-‐group differences in the oxidation of proteins or lipids. Of particular note,

51

however, we found preliminary evidence of increased global hydroxymethylation to DNA in

patients with SCZ. These results suggest that hippocampus is not a target region for

oxidative damage to proteins and lipids, however DNA modification in this region may be

important.

Microarray studies have shown many alterations in genes expression related to energy

metabolism and oxidative stress in patients with BD or SCZ, although there are differences

between the two diseases (Hakak et al., 2001, Vawter et al., 2002, Prabakaran et al., 2004,

Iwamoto et al., 2005, Sun et al., 2006, Choi et al., 2011). Alterations in the mRNA expression

of mitochondrial complex I subunits is especially relevant since complex I is a major source

of ROS. In fact, even partial inhibition of complex I leads to increased ROS in neurons

(Tretter et al., 2004, Fariss et al., 2005). Previous work from our laboratory, on the same

patient cohort from the HBTRC, found decreased protein levels of NDUFS7, a complex I

subunit, in the prefrontal cortex of patients with BD (Andreazza et al., 2010, Andreazza et

al., 2013). Although decreased mRNA expression of multiple complex I subunits has been

shown in hippocampus samples from the HBTRC (Konradi et al., 2004), our quantification

of protein levels in this region did not indicate any alterations. Additionally, results from

our laboratory on the prefrontal cortex of this patient cohort found increased protein

carbonylation in BD, increased 3-‐nitrotyrosine in BD and SCZ, and increased 4-‐

hydroxynonenal in SCZ and BD (Andreazza et al., 2013). We did not find any evidence of

protein or lipid damage in the hippocampus, however.

Since this is the first time oxidative damage to proteins and lipids were explored in the

hippocampus of patients with BD and SCZ, we looked at the literature for relevant animal

52

studies. Animal studies that link psychiatric distress to oxidative damage have shown that

the hippocampus may be less susceptible than other brain areas such as prefrontal cortex

(Lucca et al., 2009, Wang et al., 2009). One study found that although there was increased

ROS generated in the hippocampus of rats exposed to chronic mild stress, there was no

oxidative damage, which was in contrast to the cortex region (Lucca et al., 2009). In rats

with amphetamine-‐induced mania there was increased DNA damage, lipid peroxidation,

and an inhibition of mitochondrial respiratory chain complexes in the hippocampus;

however, some of these effects were prevented or reversed with mood stabilizers.

Interestingly, the therapeutic effects of the mood stabilizers lithium and valproate were

dependent on the brain region (Frey et al., 2006, Andreazza et al., 2008b, Feier et al., 2013).

These findings are not replicated in humans and it is unknown exactly how the human

hippocampus responds to oxidative stress. Additionally, oxidative and nitrosative damage

increases in neurons over time, as shown in the many studies where age correlates with

these measures (Venkateshappa et al., 2012). However, the hippocampus region is subject

to high cell turnover and is a region of high neurogenesis in humans (Eriksson et al., 1998),

therefore this damage may not accumulate as it does in other regions. A commonly

reported finding in BD and SCZ is changes in the gene expression and proteins levels of

BDNF, a mediator of hippocampal plasticity (Chen et al., 2001, Neves-‐Pereira et al., 2005,

Palomino et al., 2006, Frodl et al., 2007). There is also significant evidence that

antidepressant therapies work by increasing neurogenesis in the hippocampus (Santarelli

et al., 2003). Similarly, nitric oxide, which we determined to be increased in BD in the meta-‐

analysis, may also be involved with hippocampal neurogenesis (Zhang et al., 2001). These

factors could indicate that although there are no measurable oxidative changes to proteins

53

or lipids in hippocampus, this may be due to damage being repaired more efficiently than

in other brain regions through increased cell turnover; there remains a possibility that the

neuroplasticity in the hippocampus may mitigate oxidative damage.

It is clear from family and twin studies that genetic factors play a role in BD and SCZ (Frey

et al., 2007), however the genetic causes remain unknown. Modifications, such as

methylation or hydroxymethylation to cytosine, play a role in gene expression. 5-‐hmC is

formed by the oxidation of 5-‐mC, catalyzed by TET enzymes, and leading to demethylation

of cytosine (He et al., 2011, Ito et al., 2011, Kohli and Zhang, 2013). Very little is known

about these modifications in psychiatric disorders, however, one recent study showed

increased TET-‐1 mRNA expression and increased 5-‐hmC in the inferior parietal lobule of

psychotic patients (Dong et al., 2012). Here, we report increased 5-‐hmC in the

hippocampus of patients with SCZ, but not BD. This evidence further supports the

association of hydroxymethylation to psychosis since these features are more common in

SCZ, however future studies are necessary to evaluate the levels TET enzymes in the

hippocampus from patients with BD and SCZ.

Psychiatric illnesses, including BD and SCZ, are diagnosed based on patient reports and

observed behavior. Unlike other fields of medicine, there are currently no biological

markers for diagnosing and marking disease progression. Knowing the specific biological

alterations that occur in BD and SCZ will lead to better medications and a lower illness

burden for patients. This is especially significant considering that a very large proportion of

BD patients develop a chronic and refractory course and years lost to disability exceed

those from cancer patients (Altamura et al., 2011). Therefore, determining the molecular

54

mechanism underlying BD and SCZ, and identifying their affected brain regions, is crucial

for a more complete understanding of these psychiatric illnesses. Importantly, in this study,

we have shown that proteins and lipids in the hippocampus are not a target of oxidative

damage in BD or SCZ but DNA modifications in this region may be a contributing factor to

the pathophysiology of SCZ.

In summary, we have demonstrated that the hippocampus, unlike the prefrontal cortex

region, may not be subjected to increased oxidative stress damage in patients with BD or

SCZ. However, we found increased 5-‐hmC in the hippocampus of patients with SCZ,

suggesting alterations to the demethylation pathway. Limitations of this study are

discussed below.

4.3 LIMITATIONS OF THIS STUDY

The main limitations of the results from the meta-‐analysis are the high degree of

heterogeneity between studies and the small number of studies used in the analysis of

protein carbonyl content, RNA/DNA damage, and 3-‐nitrotyrosine. The sensitivity analysis

also revealed that the results of the analysis for catalase, nitric oxide, 3-‐nitrotyrosine, and

protein carbonyl content are not very robust. For catalase, the one study removed

sensitivity analysis showed that the lack of statistical significance was weak; removing the

study by Machado-‐Vieira et al. (2007) caused the negative effect size to become significant

which would indicate that BD patients have a lower activity of peripheral catalase. One

55

potential cause of this sensitivity could be the large drug-‐free population in the study by

Machado-‐Vieira et al. (2007) and this result may indicate the effect of medication use on

catalase activity. In the sensitivity analysis of nitric oxide, removal of the study by Ozcan et

al. (2004) caused a drastic increase in effect size and significance level. There are no

apparent differences in methods used since all included studies used the Greiss reaction to

measure nitric oxide however there is a lot of heterogeneity between patient samples

which is likely the main contributor to the sensitivity of this analysis. The sensitivity in the

results from 3-‐nitrotyrosine is likely due to the small number of studies and the sensitivity

in protein carbonyl content to the heterogeneity between patient populations. The

considerable between-‐study heterogeneity in this meta-‐analysis may be a reflection of the

heterogeneity in BD itself. Few papers report length of illness, age of onset, number of

mood episodes, illness phase, or BD phenotype; however, there is considerable evidence

that these are important factors in the level of oxidative stress (Andreazza et al., 2007a,

Andreazza et al., 2009, Kapczinski et al., 2011). There is also evidence that drug treatment

may partly alleviate increased oxidative stress, which is unaccounted for in this meta-‐

analysis due to few papers reporting drug status (Ozcan et al., 2004, Aliyazicioglu et al.,

2007, Frey et al., 2007, Machado-‐Vieira et al., 2007a). Laboratory methodology is also

another source of heterogeneity between studies in this meta-‐analysis. In addition, studies

were conducted in different geographical locations, which may add confounding factors

such as diet. Due to these limitations, interpretations of this meta-‐analysis must be

considered cautiously.

The use of postmortem brain samples is invaluable in psychiatric research since it allows

direct measurement of the affected tissue. There are potential confounding factors with

56

these samples, however, such as PMI, pH, and storage conditions. In general, DNA is

relatively resistant to post-‐mortem degradation, however protein nitration and oxidation

may be affected by PMI or pH (Ferrer et al., 2008). To control for these factors, we

correlated our data with PMI and pH and found no effect. Additionally, in this study, we

used whole tissue homogenates to measure protein and lipid damage, however damage

may be specific to certain cell types or fractions. A recent study in the prefrontal cortex of

this same patient cohort found differences specific to either the synaptosomal-‐ or

mitochondria-‐enriched fraction (Andreazza et al., 2013). Furthermore, there is evidence

that CA1 and CA3 pyramidal neurons in the hippocampus respond to oxidative stress very

differently. Multiple studies have found that when hippocampus cells are exposed to a

superoxide-‐generating compound, CA1 neurons are selectively destroyed while CA3

neurons mostly survive (Wilde et al., 1997, Wang and Michaelis, 2010). Nonetheless, most

studies in the literature use whole tissue. It is also important to consider the strong effect

of medications on oxidative parameters and neurogenesis in the hippocampus. Although

medication information was known and correlated to the biochemical measures with no

significance, it is important to note that our sample did not include drug-‐free patients. To

confirm that proteins and lipids in the hippocampus are not affected by oxidative stress, a

study with a larger drug-‐free population is necessary.

57

Chapter 5. Conclusions, significance, and future directions

5.1 OVERALL CONCLUSIONS FROM THIS STUDY AND SIGNIFICANCE

The principal objectives of this study were to investigate oxidative stress markers in BD

through a meta-‐analysis of published studies and quantitatively measure oxidative

biomarkers in post-‐mortem hippocampal tissue. Our hypothesis that the meta-‐analysis

would confirm the presence of increased nitric oxide and lipid peroxidation in BD has been

validated by our significant results. In addition, our analysis revealed a significant increase

of DNA/RNA damage in BD, a new finding. Our hypothesis that the hippocampus would be

a target of oxidative damage to proteins and lipids was not supported by our results,

however we did find an increase of 5-‐hmC in patients with SCZ. Although the results were

negative, it is important to determine which brain regions are affected to determine

neurological alterations in BD. Although the hippocampus has often been implicated in BD

through microarray and neuroimaging studies, this is the first study to measure oxidative

damage to proteins and lipids in this region. Overall, this study has further supported the

increased levels of peripheral oxidative stress damage in BD, especially to lipids and DNA,

and has demonstrated that the hippocampus may not be a target of oxidative alterations.

Furthermore, we have shown increased levels of 5-‐hmC in the hippocampus of patients

with SCZ, suggesting a possible alteration to the demethylation pathway.

58

5.2 FUTURE DIRECTIONS

Possible future directions of this study include:

• The quantification of 5-‐mC and 5-‐hmC in the prefrontal cortex

• Histological fractionation of the hippocampus and further examination of oxidative

stress markers

• Determining the gene-‐specific alterations of 5-‐hmC in SCZ

• Investigating oxidative stress markers in other brain regions

• Exploring lipid peroxidation as a potential biomarker in BD

Ultimately this study, together with ongoing studies in our laboratory, will guide the

development of better diagnostic and treatment tools to improve the quality of life for

patients with BD.

59

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Appendix A

Table A1. Selected characteristics of all studies included in the meta-‐analysis of oxidative stress markers in bipolar disorder patients compared to healthy controls, sorted by sample type.

Reference Number (patients/ controls)

Bipolar patients Sample

Marker Assay Results of BD compared to healthy controls Manic Depressed Euthymic First-‐

episode Drug-‐free Peripheral

Post-‐mortem brain

Sample Type: Blood

Abdalla et al., 1986

20/58 NA NA NA NA NA RBC -‐ SOD Nitroblue tetrazolium

Increased

GPx nmol NADPH oxidized/min

NS

Andreazza et al., 2007 (b)

32/32 NA NA NA NA 0 Whole blood

-‐ DNA/RNA dam.

Comet assay DNA damage increased

Kuloglu et al.,2002


Increased


NS

Lipid perox.

TBARS

Increased

Ozcan et al., 2004

30/21 16 2 0 0 0 RBC -‐ SOD Nitroblue tetrazolium

NS

CAT μmol of H2O2 consumed/min

Decreased in all BD groups

86


Decreased in pretreatment group

Raffa et al., 2012

30/40 8 5 17 0 NA RBC -‐ SOD Pyrogallol NS


Decreased


NS

Ranjekar et al., 2003


NS


Decreased


NS

Lipid perox.

TBARS NS

Soeiro-‐de-‐Souza et al., 2013

50/50 26 24 0 NA 50 Whole blood

-‐ DNA/RNA dam.

ELISA Increased hydroxylated guanine in DNA

Versace et al., 2013

24/18 0 0 24 0 0 Whole blood

-‐ Lipid perox.

Lipid hydroperoxides assay kit

Increased

Sample Type: Plasma

Ozcan et al., 2004

30/21 16 2 0 0 0 Plasma -‐ NO Greiss reaction Decreased in pretreatment group

87

Savas et al., 2002

44/21 44 0 0 0 0 Plasma -‐ NO Greiss reaction Increased

Yanik et al., 2004

43/31 43 0 0 0 0 Plasma -‐ NO Greiss reaction Increased

Sample Type: Serum

Andreazza et al., 2007

85/32 32 21 32 0 0 Serum -‐ SOD Adrenochrome Increased in depressed and manic patients


Decreased in euthymic and manic patients


Increased in euthymic patients

Lipid perox.

TBARS

Increased in manic, decreased in euthymic patients


30/30 (early BD) 30/30

(late BD)

NA NA NA NA 0 Serum -‐ GPx nmol NADPH oxidized/min

NS

PCC DNPH reaction

NS

3-‐NT ELISA

Increased in early and late stage patients

Banerjee et al., 2012

73/35 0 0 48 25 NA Serum -‐ Lipid perox.

TBARS Increased in all BD groups

Gergerlioglu et al., 2007

29/30 29 0 0 0 0 Serum -‐ SOD Nitroblue tetrazolium

Decreased

NO Greiss reaction Increased

88

Kapczinski et al., 2011

60/80 20 20 20 0 0 Serum -‐ Lipid perox.

TBARS Increased in manic and depressed patients

PCC DNPH reaction

Increased in manic and depressed

Kunz et al., 2008

83/32 32 19 32 0 NA Serum -‐ SOD Adrenochrome Increased in manic and depressed patients

Lipid perox.

TBARS Increased in all BD groups

Machado-‐Vieira et al., 2007

45/30 45 0 0 0 30 Serum -‐ SOD Adrenochrome Increased in drug-‐free manic patients


Increased

Lipid perox.

TBARS

Increased in drug-‐free manic patients

Magalhaes et al., 2012

53/89 (lipid perox.)

48/75 (PCC)

11 42 0 NA 44 Serum -‐ Lipid perox.

TBARS NS

PCC

DNPH reaction Increased

Ozcan et al., 2004

30/21 16 2 0 0 0 Serum -‐ Lipid perox.

TBARS

Increased in pre-‐ and post-‐treatment groups

89

Savas et al., 2006


Increased

NO Greiss reaction

Increased

Selek et al., 2008


Decreased

NO Greiss reaction

Increased

Sample Type: Post-‐mortem brain


15/15 NA NA NA NA 0 -‐ PFC (BA10)

PCC DNPH reaction Increased

3-‐NT

ELISA Increased


16/26 NA NA NA NA 0 -‐ PFC (BA10)

Lipid perox.

Lipid hydroperoxides assay kit and 4-‐HNE ELISA

4-‐HNE increased in synaptosomal section, no difference in LPH

PCC DNPH reaction Increased in synaptosomal proteins

3-‐NT Immunoblotting Increased in mitochondrial proteins

Benes et al., 20031

10/18 NA NA NA NA 4 -‐ ACC DNA/RNA dam.

Klenow method NS

Buttner et al., 20071

14/14 NA NA NA NA 2 -‐ ACC (BA24)

DNA/RNA dam.

Klenow method Scission increased in non-‐GABAergic cells only

90

Che et al., 20102

15/15 NA NA NA NA 3 -‐ Anterior hippo.

DNA/RNA dam.

Immunohisto-‐chemistry

RNA damage increased in patients more than DNA

Gawryluk et al., 2011

14/12 NA NA NA NA NA -‐ PFC (BA10)

GPx Immunoblotting NS

Gigante et al., 2011

35/35 NA NA NA NA NA -‐ Dorso-‐lateral PFC (BA9)

SOD Immunoblotting NS

Mustak et al., 2010

10/8 NA NA NA NA 10 -‐ Multiple brain regions

DNA/RNA dam.

Klenow method and incorporation of 3[H]-‐dTTP

Increased single and double stranded DNA breaks

Wang et al., 2009

15/15 NA NA NA NA NA -‐ ACC Lipid perox.

Immunnohisto-‐chemistry

Increased

Abbreviations: BD, bipolar disorder; RBC, red blood cells; SOD, superoxide dismutase; GPx, glutathione peroxidase; NS, not significant; CAT, catalase; TBARS, thiobarbituric acid reactive substances; NADPH, nicotinamide adenine dinucleotide phosphate; NA, not available; PCC, protein carbonyl content; 3-‐NT, 3-‐nitrotyrosine; DNPH, 2,4-‐Dinitrophenylhydrazine; ELISA, enzyme-‐linked immunosorbent assay; PFC, prefrontal cortex; LPH, lipid hydroperoxides; 4-‐HNE, 4-‐hydroxynonenal; ACC, anterior cingulate cortex; GABA, gamma-‐aminobutyric acid; NO, nitric oxide. 1. Not included in meta-‐analysis due to missing data 2. Patient info obtained from Dowlatshahi et al., 1999

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