1. Peripheral changes in respiratory chain genes from the mitochondrial
genome in Alzheimer's disease as a potential biomarker
David Robinson.
Supervisors: Dr. Angela Hodges and Dr. Aoife Keohane.
Department of Neuroscience, Institute of Psychiatry, King's College London, University
of London.
Project report in partial fulfilment for the degree of MSc in Neuroscience August 2012.
2. 2
Statement of work
The following describes which individuals were responsible for the various aspects of the research.
Research design: Dr. Angela Hodges.
Diagnoses and sample collection: (see Lunnon et al., 2012).
RNA extraction: Dr. Katie Lunnon
cDNA synthesis: Dr. Katie Lunnon
Primer design: Dr. Angela Hodges, Phillip Mcguire, and David Robinson.
Production of standard solutions, including: PCR, gel electrophoresis, gel extraction, purification,
Nanodrop analyses of concentration and purity, and serial dilutions: Dr. Aoife Keohane, Dr. Katie
Lunnon, and David Robinson.
Production of standard curves: David Robinson.
qRT-PCR: David Robinson.
Statistical analyses: David Robinson.
3. 3
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that is predicted to affect as many as 90 million
people world-wide by 2040 (Ferri et al., 2005). The symptoms of AD include memory loss, psychosis,
depression, language defects, and general cognitive decline. Pathologically, it is characterised by
accumulations of extracellular amyloid plaques and intracellullar neurofibrilliary tangles of
hyperphosphorylated tau. Another prominent feature is mitochondrial dysfunction. Mitochondria contain
complexes that are responsible for oxidative phosphorylation (OXPHOS), the process via which
adenosine diphosphate (ADP) and inorganic phosphate (Pi) are used to produce adenosine triphosphate
(ATP) – considered to be the body’s universal energy currency. Mitochondria also contain their own
genome, which encodes 13 of the 88 subunits that constitute the OXPHOS complexes. The abundance of
OXPHOS transcripts encoded by mitochondrial DNA (mtDNA) is altered in AD brains (Chandrasekaran
et al., 1997; Aksenov et al., 1999; Manczak et al., 2004), as are OXPHOS transcripts encoded by nuclear
DNA in MCI and AD blood (Lunnon et al., 2012), indicating that peripheral responses are evident in
early AD. The current study used quantitative real-time PCR (qRT-PCR) to investigate whether a
selection of mtDNA-encoded OXPHOS transcripts (ND3, ND4, ND4L, ND5, and CYB) were
dysregulated in MCI and AD blood. These transcripts were found to be significantly more abundant in
MCI and AD relative to controls (p<.05). These results suggest that data regarding the abundance of
OXPHOS transcripts encoded by the mitochondrial genome might be used to develop biomarkers of early
AD. Biomarkers could be used to monitor responses to current or novel therapies, and to facilitate early
diagnosis, thereby capitalising on the effectiveness of treatments.
4. 4
Contents
1. Introduction.
1.1. Alzheimer’s disease: symptoms, pathology, and genetics.
1.2. The need for early diagnosis – biomarkers.
1.3. Mitochondria and reactive oxygen species in Alzheimer’s disease.
1.4. The mitochondrial genome.
1.5. Mitochondrial transcription.
1.6. Post-transcription.
1.7. Aims.
2. Methods and materials.
2.1. Subjects and samples.
2.2. RNA extraction and cDNA synthesis.
2.3. Primer design.
2.4. Principles of qRT-PCR and production of standard solutions.
2.5. Testing primers.
2.6. Producing standard solutions.
2.7. Standard curves.
2.8. Assaying samples.
3. Results.
3.1. The effect of disease status on mRNA abundance.
3.2. Relative abundance of mRNA species.
3.3. Correlations in mRNA abundance.
4. Discussion.
4.1. mRNA abundance in blood and brain and the implications for biomarkers.
4.2. Factors conferring relative abundance of mRNA species.
4.3. mRNA correlations and division of the precursor transcript.
4.4. Conclusions.
5. Acknowledgements.
6. References.
7. Appendices.
5. 5
1. Introduction
1.1. Alzheimer’s disease: symptoms, pathology, and genetics.
Alzheimer’s Disease (AD) is the most common form of dementia, estimated to affect as many as 30
million people world-wide, and predicted to affect 90 million by 2040 (Ferri et al., 2005). In addition to
memory defects and general cognitive decline, symptoms can include language deficits, depression, and
psychosis. Ultimately, it leads to death. Given the debilitating nature of AD, patients inevitably become
dependent on the assistance of others. It has been suggested that direct health and social care costs in the
UK are approximately £6 billion, whilst informal care might amount to an additional £8 billion. Costs are
predicted to rise with aging populations and the increases in prevelance that they entail (Lowin, Knapp,
and McCrone, 2001).
The hallmark pathological characteristics of AD are accumulations of extracellular amyloid plaques and
intracellullar neurofibrilliary tangles of hyperphosphorylated tau. Additional features include alterations
to synaptic plasticity, an increased abundance of reactive nitrogen and oxygen species,
neuroinflammation, and mitochondiral dysfunction, culminating in neuronal loss.
Early-onset AD is caused by mutations and duplications in the amyloid precursor protein (APP) gene
(Goate et al., 1991; Rovelet-Lecrux et al., 2006), and mutations in the presenilin 1 (PS1) and presenilin 2
(PS2) genes (Sherrington et al., 1995; Levy-Lehad et al., 1995), which are subunits of the gamma
secretase enzyme complex, which is involved in the cleavage of APP into beta amyloid (A) - found in
plaques. In sporadic, late-onset AD, which accounts for around 95% of cases (Kern and Behl, 2009), the
4 allelle of the apolipoprotein E (APOE) gene represents the largest risk factor (Corder et al., 1993).
Despite familial AD representing a small proportion of total AD cases, much weight has been given to the
postulation that A accumulations represent the start of sporadic pathogenesis, referred to as the ‘amyloid
cascade hypothesis’. At present, however, no comprehensive account of the aetiology is widely accepted.
6. 6
1.2. The need of early diagnosis – biomarkers.
Whilst currently no treatments are proven to prevent or reverse the pathophysiological features of AD,
there exist a number of pharmacological, psychosocial, and behavioural interventions that target
symptoms. Generally, the effectiveness of these is increased by early administration (Osborn and
Saunders, 2010). For example, with drugs that reduce rates of decline, such as acetylcholinesterase
inhibitors, their eventual impact positively correlates with earlier use. However, it is thought that
neurodegeneration related to AD might occur several decades prior to the appearance of clinical features
(Morris et al., 1996), and, therefore, symptom-based diagnositc methods are unlikely to capitalise on the
potential efficacy of treatments. Likewise, an inability to recognise the presence of nascent AD is
prohibitive of early intervention trials, which, when using large samples, provide the best hope for
establishing the effectiveness of new treatments (Lovestone et al., 2009).
A suitable surrogate biomarker that can distinguish between AD cases and controls prior to clinical
manifestations, or between controls and those that are at increased risk of developing AD, would be of
value. People with mild pre-clinical symptoms or Mild Cognitive Impairment (MCI) have an increased
likelihood of developing AD (Mitchell, 2009), and represent a population in which biomarkers could be
sought. As many as 70% of those with MCI might have prodromal AD. Whilst a substantial amount of
research has investigated whether diagnoses of AD might be posssible by assessing levels of A and tau
in cerebrospinal fluid (CSF) (e.g., Maruyama et al., 2001), a biomarker should ideally be detectable
within a medium for which the logistics of acquisition and processing are efficient and well-established.
Lunnon and colleagues (2012), using microarrays, compared levels of blood RNA in controls to those
with MCI and AD. Approximately a quarter of probes suggested significant differential gene expression
between controls and MCI/AD patients, indicative that peripheral changes are detectable at early stages of
pathogenesis. A number of nuclear-encoded transcripts required for the mitochondrial complexes
responsible for oxidative phosphorylation (OXPHOS), in addition to transcripts for subunits of the core
mitochondrial ribosomal complex, were down-regulated. Earlier research has demonstrated that
expression of mitochondrially-encoded OXPHOS transcripts is also dysregulated in AD brains
7. 7
(Chandrasekaran et al., 1997; Aksenov et al., 1999; Manczak et al., 2004). Assessing aberrant expression
of OXPHOS genes might provide a means of detecting pre-symptomatic AD or of monitoring responses
to treatments that target mitochondrial dysfunction in AD.
1.3. Mitochondria and reactive oxygen species.
Human cells contain multiple mitochondria, which are implicated in apoptotic signalling (Hengartner,
2000), calcium homeostasis (Rizutto, Bernadi, and Pozzan, 2000), and are the main cellular source of
adenosine triphosphate (ATP) – the body’s universal energy currency. ATP is generated by oxidative
phosphorylation (OXPHOS), which is orchestrated by five complexes embedded within the inner
mitochondrial membrane: NADH dehydrogenase (complex I), succinate dehydrogenase (complex II),
ubiquinol cytochrome c oxireductase (complex III), cytochrome c oxidase (complex IV), and ATP
synthase (complex V). In OXPHOS, electrons are transferred unidirectionally from one complex to
another, forming an electron transport chain (ETC). Electrons are donated from nicotinamide adenine
dinucleotide (NADH) and flavin adenine dinucleotide (FADH), which are produced in the mitochondrial
matrix by the Kreb’s cycle. As electrons are accepted, protons are transferred from the matrix to the
intermembrane space, producing a proton gradient, which is utilized by complex V to power the synthesis
of ATP from adenosine diphosphate (ADP) and inorganic phosphate (Pi).
Mitochondria have long been implicated in neurodegenerative diseases (e.g., AD, Parkinson’s disease,
Amyotrophic lateral sclerosis, Huntington’s disease). Dysregulation of OXPHOS produces reactive
oxygen species (ROS), which are capable of causing damage to proteins, lipids, and DNA when present
at high levels for prolonged periods, resulting in cell death. Neurons are especially susceptible to free
radical damage because they contain low levels of the antioxidant, glutathione, conferring upon them a
decreased capacity to remove oxygen free radicals (Christen, 2000).
1.4. The mitochondrial genome.
Due to its close proximity, a probable target of ROS generated by the ETC is mitochondrial DNA
(mtDNA). Indeed, its location, in addition to its lack of protective histones, is thought to contribute to the
high mutation rate in mtDNA relative to nuclear DNA (Maruszak and Żekanowski, 2011). MtDNA is
8. 8
organised in a single, circular, double-stranded 16.5kb molecule, containing 13 protein-coding genes
(Figure 1), encoding subunits of the OXPHOS complexes (the remaining 75 OXPHOS subunits are
encoded by nuclear DNA), 2 ribosomal RNAs (rRNA), 12s RNA and 16s RNA, which constitute
components of the small and large mitoribosomal subunits, respectively (Sharma et al., 2003), and 22
transfer RNAs (tRNA), required for mitochondrial genome gene translation.
Damage to the mitochondrial genome is likely to entail the production of aberrant OXPHOS subunits or
dysregulation of translational processes. It is conceivable that there exists a ‘vicious cycle’, whereby
mtDNA mutations, via their impact upon the functionality of the OXPHOS system, increase ROS
abundance, increasing the likelihood of new mutations (Linnane et al., 1989). It is possible that the altered
levels of RNA related to OXPHOS observed in AD patients in several studies (Chandrasekaran et al.,
1997; Aksenov et al., 1999; Manczak et al., 2004; Lunnon et al., 2012) reflect alterations to
transcriptional, post-transcriptional, or translational processes that occur in response to this putative cycle.
With regard to its initiation in AD, it has been reported that A interacts with a number of mitochondrial
proteins, including A binding alcohol dehydrogenase (ABAD) and cyclophilin D (CypD), which
promotes ROS generation (Lustbader et al., 2004; Du and Yan, 2010).
9. 9
Figure 1. The mitochondrial genome (modified from Kyriajouli et al., 2008). The mitochondrial genome encodes subunits
for 4 of the 5 OXPHOS complexes: NADH dehydrogenase (green),ubiquinol cytochrome c oxireductase (dark blue),
cytochrome c oxidase (orange), and ATP synthase (light blue). It also encodes 22 tRNAs (black) and 2 rRNAs (purple). The
location of the 3 promoter regions, HSP1, HSP2, and LSP1, are indicated towards the top-left (discussed below).
1.5. Mitochondrial transcription.
Reflecting its bacterial origins, transcription of the mitochondrial genome is largely polycistronic. It
consists of 3 promoter regions (Figure 1): 2 on the heavy strand – heavy strand promoter 1 (HSP1) and
heavy strand promoter 2 (HSP2) – and 1 on the light strand – light strand promoter 1 (LSP1). HSP1
transcription produces transcripts containing the 12s and 16s rRNAs, whilst HSP2 transcription produces
a transcript of the entire heavy strand, including 12 protein-coding genes (subunits of complex I: ND1,
ND2, ND3, ND4L, ND4, and ND5; a subunit of complex III: CYB; subunits of complex IV: CO1, CO2,
and CO3; and subunits of complex V: ATP6 and ATP8), both rRNAs, and 14 tRNAs. The LSP1
transcript incorporates the entire light strand, which includes the 1 remaining protein-coding gene (a
subunit of complex I: ND6) and 8 tRNAs (reviewed in Smits, Smeitink, and van den Huevel, 2010).
10. 10
Transcription of the human mitochondrial genome is maintained by 3 nuclear-encoded components: an
RNA polymerase (POLRMT) and 2 mitochondrial transcription factors, TFAM and TFB2M (Litonin et
al., 2010). Transcripts are divided into their constituent genes by the endonucleolytic excision of tRNAs
by mitochondrial RNase P and TRNase Z at the 5’ and 3’ ends, respectively (Ojala, Montoya, and
Attardi, 1981). Some genes are not separated by tRNAs and, therefore, are translated from a single
transcript: (1) ATP8/ATP6 (overlapping genes) and CO3, (2) ND4L/ND4 (overlapping genes), and (3)
ND5 and CYB.
1.6. Post transcription.
Human mitochondrial mRNA lack 5’ untranslated regions (Montoya, Ojala, and Attardi, 1981), so there
is limited potential for regulation of translation by gene-specific initiation factors. Translation is likely to
be mediated by steady-state levels of mRNA, which are determined by RNA stability and turnover
(Piechota et al., 2006). A small number of factors have, however, been identified, including a
translational activator of CO1 (TACO1) (Weraapachai et al., 2009).
Microdeletions affecting the stop codon of ND3 in a patient presenting with mtDNA disease resulted in
decreased levels of the transcript, suggesting that surveillance mechanisms coupled to translation promote
degradation of transcripts containing aberrant stop codons (Temperley et al., 2003). Turnover of ND3,
ND2, and CYB is faster than for other transcripts. Each of these genes only contain the first nucleotide of
their stop codons – the other 2 being added during adenylation. Conceivably, their stop codons are more
likely to be affected by the imperfect excision of tRNAs from the precursor. Increased stability of more
abundant transcripts (i.e., ATP6/8, CO2) might be conferred by secondary structures or by interactions
with stabilising proteins that are mediated by levels of OXPHOS (Leary et al., 1998; Piechota et al.,
2006).
1.7. Aims.
The current study employed quantitative real-time PCR (qRT-PCR) to investigate whether levels of
mitochondrial mRNA in blood differs between controls, those with MCI, and those with AD. As previous
research has observed altered levels of nuclear-encoded OXPHOS-related transcripts in blood in those
11. 11
with MCI and AD (Lunnon et al., 2012), it was anticipated that abundance of transcripts encoded by the
mitochondrial genome would also be altered. Given the high levels of conversion from MCI to AD,
identification of abnormalities in the former might be of use in the development of an early AD
diagnostic biomarker. Also, assuming that altered levels reflect disease processes, assessing abundance of
transcripts might provide a means of evaluating the efficacy of drugs that target pathological features of
AD.
Insights into the extent to which transcripts encoded by mtDNA correlate were also sought, as well as
information regarding the relative abundance of mtDNA-derived transcripts. Knowledge of these would
provide a basis for assessing current ideas regarding the nature of mitochondrial transcription and
turnover of the resultant mRNA. Understanding of these processes is of value for interpreting why
transcripts are more or less abundant in disease, including AD.
12. 12
2. Methods and Materials
2.1. Subjects and samples.
Blood samples (n = 437) were taken from individuals participating in the AddNeuroMed study
(Lovestone et al., 2009) (Table 1). Participants were from Kuopio, Lodz, London, Perugia, Thessaloniki,
and Toulouse. Diagnoses of probable AD were made using the National Institute of Neurological and
Communicative Disorders and Stroke and Alzheimer’s Disease and Related Disorders Association
Alzheimer’s criteria (McKhann et al., 1984) and the Diagnostic and Statistical Manual of Mental
Disorders (American Psychiatric Association, 2000). Diagnoses for MCI were made in accordance to the
Petersen’s criteria of amnestic MCI and scores on the Total Clinical Dementia Rating Scale (CDR).
Participants were excluded from the study if they were under 65 years of age, or if they suffered from any
other psychiatric or significant non-psychiatric illness, or depression. Further details of the diagnostic and
recruitment procedures are described by Lunnon and colleagues (2012). Standardized operating
procedures for sample collection and subject assessment were followed at all locations. Consent was
obtained in accordance to the declaration of Helsinki (1991) and ethical approval was obtained at all
locations.
Table 1. Age, gender, location, and number of APOE 4 alleles by disease status.
Control (n = 162) MCI (n = 134) AD (n = 141)
Mean age (years) 72.99 74.73 76.56
Number of each
gender
Males: 66
Females: 96
Males: 59
Females: 75
Males: 46
Females: 95
Number from each
location
Kuopio: 41
Lodz: 14
London: 47
Perugia: 41
Thessaloniki: 15
Toulouse: 4
Kuopio: 28
Lodz: 18
London: 19
Perugia: 43
Thessaloniki: 16
Toulouse: 10
Kuopio: 37
Lodz: 28
London: 14
Perugia: 36
Thessaloniki: 21
Toulouse: 5
Number of APOE
4 alleles
0: 114
1: 44
2: 4
0: 81
1: 47
2: 6
0: 60
1: 62
2: 19
13. 13
2.2. RNA extraction and cDNA synthesis.
Prior to the current study, 2.5ml blood samples were collected in PAXgene blood RNA vacutainer tubes
(BD Diagnostics) and RNA was extracted using the PAXgene bloodRNAkit (Qiagen), according to the
manufacturer’s protocol. A 2100 Bioanalyser (Agilent Technologies) was used to assess RNA integrity.
cDNA was synthesized using 250ng of total RNA using the QuantiTect Reverse Transcription Kit
(Qiagen) according to the manufacturer’s protocol from samples with RNA integrity numbers >7.
2.3. Primer design.
Primers were designed using Primer 3 (Table 2). The National Center for Biotechnology Information’s
(NCBI) Basic Local Alignment Search Tool (BLAST) was used to check for homology between target
and non-target sequences. Primers were redesigned if they exhibited complementarity for a given non-
target RNA, contained any single nucleotide polymorphisms (SNP), particuarly at the 5’ end, produced
more than 1 product, or an incorrect product (verified by sequencing), or could not be optimised to
produce a standard curve of the correct efficiency (see below). Primers were purchased from Sigma
Aldrich.
Table 2. Primers used in qRT-PCR to quantify levels of mitochondrial transcripts.
Target
product
Forward primer
5’ 3’
Reverse primer
5’ 3’
Product
size
ND3
ND4L
ND4
ND5
CYB*
TTACGAGTGCGGCTTCGACC
TAGTATATCGCTCACACCTC
CTAGGCTCACTAAACATTCTA
TCGAATAATTCTTCTCACCC
TATCCGCCATCCCATACATT
CCTAAGTCTGGCCTATGAGT
CACATATGGCCTAGACTAC
CGCAGTACTCTTAAAACTAGG
CGCAGGATTTCTCATTACTA
ACAACCCCCTAGGAATCACC
209bp
209bp
206bp
137bp
188bp
*Designed during the current project.
14. 14
2.4. Principles of qRT-PCR and production of standard solutions.
qRT-PCR utilises the exponential amplification of products by the PCR to quantify the original
abundance of a product (e.g., cDNA). It entails the use of dyes that emit a fluorescent signal, though only
when in the presence of double-stranded DNA (dsDNA). Therefore, as the level of a dsDNA product
doubles with every cycle of the reaction, the strength of the signal does too. There is, then, a negative
correlation between the number of cycles that are required for the signal to reach a given threshold –
referred to as the ‘cycle threshold’ (CT) value - and initial levels of the product. Before assaying samples,
it is necessary to establish CT values for solutions that contain a known number of target molecules (i.e.,
standard solutions). Subsequently, it is possible to estimate the quantity of target molecules in a sample
based on the CT value.
2.5. Testing primers.
Standard PCR, followed by gel electrophoresis, was used to test primers. For each set of primers, a 10μl
solution, containing 2μl 5x HOT FIREPol® EvaGreen® qPCR Mix Plus (ROX) (Solis Biodyne), 2μl
diluted cDNA, 1μl forward primer (10μM), 1μl reverse primer (10μM), and 4μl nuclease-free water, was
made up in a single well of a 384-well plate. No-template controls (NTC), in which cDNA was replaced
by nuclease-free water, were also produced for each set of primers to make ongoing checks for
contamination. All reagents (except the nuclease-free water), having been kept at -20C, were thawed on
ice, then vortexed (~5s) and centrifuged (~5s), before being aliquoted. An optical adhesive film
(Microamp) was used to cover the plate after reagents had been aliqoted. Plates were centrifuged for 1min
at 2000RPM before reactions were run.
Reactions were run in a 7900HT fast RT-PCR machine (Applied Biosystems) in conjunction with SDS
2.3 software (Applied Biosystems). In order to activate the DNA polymerase, plates were firstly heated to
95C for 15min. Next, 40 cycles of the following steps were run: 95C for 15s (denaturation), 60C for
20s (annealing), 72C for 20s (elongation). Finally, to check for homegeneity of PCR products, a
dissociation stage was run in which the temperature was raised to 95C for 15s, then lowered to 60C for
15s. A manual Ct threshold of 0.2 was used.
15. 15
PCRs were then run in an agarose gel to confirm whether the primers had worked. A 1% gel was created
by melting 1g of agarose powder (Invitrogen) with 100ml of 1X TAE buffer in a microwave (~2min).
Before it had set, 5μl of GelRed (Biotium) was added to the gel, which was poured into a casting tray
containing a comb with 8 wells. 1μl of 10X BlueJuice™ Gel Loading Buffer (Invitrogen) was added to
both the 10μl PCR and the 10μl NTC, before aliquoting them into separate wells in the gel. 10μl of 100bp
DNA ladder (Invitrogen) was aliquoted into a well adjacent to the PCR to allow for sizing of the PCR
product. Gels were run at 100V for ~1.5h.
Gels were inspected to see whether they contained a band in the column containing the PCR and to check
that bands were absent in the column containing the NTC. Bands in the former were compared to the
100bp ladder to confirm that the PCR product was of the expected size.
2.6. Producing standard solutions.
Having established that a set of primers worked, standard PCR was used to amplify cDNA for use in
standard solutions. A solution of 144μl, containing 36μl 5x HOT FIREPol® EvaGreen® qPCR Mix Plus
(ROX), 36μl diluted cDNA, 18μl forward primer (10μM), 18μl reverse primer (10μM), and 72μl
nuclease-free water, was aliquoted into a 384-well plate, divided across 8 wells, each containing 18μl of
the solution. The rest of the procedure for setting up and running the PCR was as described above. Gels
were also made, run, and inspected as described above, with the exception that larger gels were used,
containing 2g of agarose powder and 200ml 1X TAE buffer.
PCRs were purifed using a MinElute Gel Extraction Kit (Qiagen), followed by further purification and
concentrating using a MinElute PCR Purification Kit (Qiagen) according to the manufacturer’s protocols.
DNA concentration was assessed using a Nanodrop 1000 (Theremo Scientific) and was used to calculate
the volume needed for making a stock containing 1010 copies/1l (referred to as ‘standard 10’). All other
standard solutions (9-1) were made by a 1:10 serial dilution (i.e., standard solution 9, theoretically,
contains 109 copies/1l).
16. 16
2.7. Standard curves.
Standard curves were tested for all genes prior to assaying samples. 4l of a solution containing 0.5μl
forward primer (10μM), 0.5μl reverse primer (10μM), 2μl nuclease-free water, and 1μl HOT FIREPol®
EvaGreen® qPCR Mix Plus (ROX), was aliquoted using an electronic pipette into 22 wells of a 384-well
plate. Next, 1l of each standard solution was aliquoted using a multi-pipette into wells containing the
solution described above. Duplicates of each standard were assayed and a NTC (also duplicated) was
included in each plate. The rest of the procedure for setting up and running the PCR was as described
above.
Data were subsequently analysed in Microsoft Excel. For each gene, the mean CT values of duplicates for
each standard solution were calculated and used to generate a curve and a corresponding linear regression
equation. The coefficient from this equation was entered into an online efficiency calculator (Agilent
Technologies). Efficiencies between 90% and 110% were considered acceptable as they suggest that the
PCR product approximately doubled with each cycle of the reaction. Standards that produced values
outside of this range required futher optimisation and so were re-made, or assayed again having re-
designed primers. The differences between CT scores for duplicates were also examined; standard curves
were re-tested in instances where duplicates were in excess of one CT score from each other.
2.8. Assaying samples.
Samples were assayed in random order and the identities of the individuals from whom they came were
not known to the researcher. Standard solutions were assayed alongside samples in each plate as
described above. Samples, having been stored at -80C, were thawed on ice, vortexed (~5s), and
centrifuged (~5s), before aliquoting. cDNA was made prior to the current study and diluted 1:5. 1l of
this was aliquoted using a multi-pipette into wells containing 4l of the solution described above (i.e.,
primers, dye mix, and nuclease-free water). As with standard solutions, each sample was duplicated; if
CT values for duplicates were in excess of one cycle apart, the samples were assayed again. Reactions
were set up and run as described above.
17. 17
3. Results.
3.1. The effect of disease status on mRNA abundance.
Data were normalized to two house keeping genes, measured in the same samples, selected following
analysis of a panel of geNorm reference genes (Primer Design Limited), to account for technical
variations (e.g., pipetting errors, plate variation, etc.): ATP5B and SF3A1 (previously found to be the
most stable in these samples). To achieve normal distributions, data were log10 transformed and samples
with estimates in excess of 2 standard deviations from the overall mean for a given gene were removed
from the analysis. Samples were also removed if CT scores for duplicates were consistently over 1 apart
from each other, or if demographic information was missing. Normality was achieved for the majority of
data when categorised by gene and status (Appendix 1). All analyses were completed in SPSS (IBM).
Analyses of covariance (ANCOVA) were conducted to establish whether disease status (AD, MCI, or
control) could account for variations in RNA abundance for each gene, whilst controlling for age, gender,
number of APOE 4 alleles, and the location of the centre at which samples were taken. There were
significant effects of status on estimates for ND4, F(2, 430) = 9.354, p<.001, ND4L, F(2, 430) = 14.037,
p<.001, ND5, F(2, 430) = 19.382, p<.001, and CYB, F(2,430) = 33.651, p<.001. There was no such effect
for ND3, F(2, 430) = .643, p>.05. For all genes, effects of covariates were non-significant, except for
gender on ND4 (p<.05) and centre location on ND4L (p<.05).
Levene’s Test of Equality of Error Variances indicated equal variance between status groups in all genes
except ND3. Also, whilst kurtosis tests suggest that ND3 estimates resembled other genes, the ND3 data
appeared to be positively skewed to a greater extent than estimates for other genes (Table 3). A Kruskal-
Wallis non-parametric analysis of variance (ANOVA), however, also indicated that differences between
status groups for ND3 estimates were non-significant (p>.05), as did an ANCOVA, with the above
covariates, performed on square root transformed data (p>.05).
Table 3. Skewness and Kurtosis
ND3 ND4 ND4L ND5 CYB
Skewness .431 -.195 .341 .071 -.150
Kurtosis .123 .058 -.291 -.806 -.283
18. 18
Bonferroni tests were conducted to establish which status groups differed within individual genes. There
were no significant differences between status groups for ND3 (p>.05). For all other genes, estimates for
the MCI group were significantly higher than for the control group (p<.05 for ND4L; p<.001 for all
others), as were estimates for the AD group (p<.001). There were no significant differences between
estimates for MCI and AD groups for any genes (p>.05). On average, there was a 1.46 fold increase in
estimates of mRNA abundance between control and MCI groups, and a 1.72 fold increase between
control and AD groups (Table 4).
Table 4. Changes in abundance of mRNA from genes of the mitochondrial genome in MCI and AD groups relative to
controls.
Mean MCI Mean AD
ND3 1.39 1.6
ND4 1.21 1.03
ND4L 1.6 2.7
ND5 1.92 2.2
CYB 1.19 1.06
3.2. Relative abundance of mRNA species.
A one-way repeated ANOVA revealed a significant effect of gene on mRNA estimates, F(3.39, 1478.8) =
16786.23, p<.001 (Figure 2). Ratios of estimates for all genes compared to all others were also calculated,
based on pre-log10-transformed data (Table 5). Estimates for ND4 mRNA abundance were highest,
followed, in descending order, by ND4L, ND3, ND5, and CYB.
19. 19
Figure 2. Estimated marginal means for ND3 (1), ND4 (2), ND4L (3), ND5 (4), and CYB (5), based on log10
transformed data.
Table 5. Ratios of estimates of mRNA abundance for all genes (genes on the left are listed first).
ND3 ND4 ND4L ND5 CYB
ND3 1:241.1854 1:1.3773 1:0.4087 1:0.0983
ND4 1:0.0041 1:0.0057 1:0.0017 1:0.0004
ND4L 1:0.7261 1:175.1169 1:0.2968 1:0.0714
ND5 1:2.4465 1:590.0643 1:3.3695 1:0.2405
CYB 1:10.1723 1:2453.3983 1:14.0101 1:4.1578
A potential source of variation between estimates for different genes is variation in PCR product
concentrations between standards for different genes. These variations are probably attributable to
pipetting errors made whilst making the standard 10 solutions. To investigate this, mean CT scores,
calculated from CT scores from individual plates, were produced for standard 4 for each gene (standard 4
is present in most standard curves and typically produces CT scores that resemble sample CT scores). A
Pearson product-moment coefficient was computed to assess the correlation between standard 4 means
20. 20
and mean mRNA abundance estimates derived from log10 transformed data. There was a significant
positive correlation between the two variables, r = .851, n = 5, p<.05 (Figure 3), with standard 4 means
calculated to explain ~72% of the variance.
Figure 3. Correlation between standard 4 CT means and estimates of mRNA abundance.
From left to right: CYB, ND4L, ND3, ND5, ND4.
3.3. Correlations in mRNA abundance.
Pearson product-moment coefficients were computed to assess the correlation between estimates for all
genes. There were significant positive correlations between all genes (p<.001). Pearson product-moment
coefficients were also produced for all genes having split the data according to status. Within the control
group, there were significant positive correlations between the majority of estimates. ND3 estimates,
however, were not significantly correlated with any other gene (p>.05), except ND4L (p<.001) (Appendix
2). Within the MCI group, there were significant positive correlations between all estimates (Appendix
3). Within the AD group, there were significant positive correlations between the majority of estimates.
21. 21
ND4 estimates, however, were not significantly correlated with ND4L or ND5 (p>.05) (Appendix 4).
Since ND3 estimates did not exhibit homogeneity of variance (HOV), Spearman’s rank correlation
coefficients were also produced. The results resembled those described above, with the exception that,
within the control group, ND3 estimates were significantly, positively correlated with all other genes
(Appendix 4).
22. 22
4. Discussion
4.1. Abundance of mRNA by status groups.
The ANCOVA analyses indicate that, controlling for covariates, levels of transcripts for ND4, ND4L,
ND5, and CYB are significantly increased in blood in MCI and AD populations relative to controls.
Differences between MCI and AD groups, however, were not statistically significant. No differences
between any groups for ND3 estimates were significant. The fold changes for ND3 transcripts for MCI
and AD groups relative to controls do not appear to be markedly different to those for the other genes.
Failing to reach significance might be attributable to the distribution of ND3 data, which does not exhibit
HOV, as evidenced by the Levene’s test. A non-parametric ANOVA, however, also failed to produce a
significant result, as did an ANCOVA performed on square root transformed data. It is possible that the
removal of samples in excess of 2 standard deviations from the mean for each status group (i.e., not just
relative to the overall gene means) would produce HOV, though this would entail reductions in power,
which might affect whether other genes achieve significance.
Manczak and colleagues (2004) investigated differences in 11 transcripts encoded by the mitochondrial
genome in frontal cortex brain specimens from controls, and in those with early and definite AD. In
contrast to the current findings, they found that complex I (NADH dehydrogenase) subunit transcripts
were generally reduced in early and definite AD – an occurrence that has also been observed in the
hippocampus and inferior parietal lobule (Aksenov et al., 1999). All other transcripts, however, tended to
be more abundant. They suggest that the decreases in complex I transcripts might be attributable to an
increased susceptibility of complex I genes to mutation, which might mean that they are targeted for
degradation. It is possible that the brain, as an environment, entails a higher likelihood of mutations to
mtDNA than blood, which might explain the incongruence between findings. However, they also reported
an increase in abundance of ND6 (also a complex I subunit) in both AD groups, suggesting that
explanations need to be gene-specific. Moreover, whilst degradative mechanisms that target aberrant
mRNA might account, to some extent, for differences, the increased abundance of ND6, which is
transcribed separately from the other genes, implies that transcriptional processes are relevant.
23. 23
A number of nuclear-encoded OXPHOS genes and components of the core mitochondrial ribosome
complex are down-regulated in blood in MCI and AD (Lunnon et al., 2012). A shortage of translational
machinery might explain the increases in transcripts in the current study, since translation-coupled
degradation would be reduced. It is also conceivable that the increases reflect a compensatory response,
whereby transcription is up-regulated in response to decreases in OXPHOS resulting from the down-
regulation of the nuclear-encoded genes. However, this seems unlikely given that the ETC depends on the
correct functioning of all complexes, presumably rendering the up-regulation of only a small set of
subunits ineffective.
An alternative explanation is that the respective down- and up-regulations of nuclear and mtDNA genes
both occur in response to complex I mutations, whereby nuclear genes produce fewer subunits to match
the reduced number of functional complex I subunits encoded by mtDNA, and transcription of mtDNA is
up-regulated to compensate for the dysfunctional subunits. There are, however, are at least 2 potential
problems with this suggestion. Firstly, if mutations to complex I genes cause an up-regulation of mtDNA
transcription, this would not necessarily be reflected in steady-state levels, since aberrant transcripts are
more likely to be targeted for degradation (Temperley et al., 2003; Piechota et al., 2006). Secondly, since
it has been suggested that mutations to complex I genes in mtDNA are more likely to occur in the brain
than in blood, it would be expected that the increases in complex I mRNA abundance observed in blood
would be exaggerated in brain, though, in actuality, their presence is decreased (Aksenov et al., 1999;
Manczak et al., 2004). Regarding the first of these issues, since nuclear-encoded mitochondrial
translational components are down-regulated, degradation would also be reduced, allowing for mRNA
accumulation. Regarding the second point, it is conceivable that there exists a threshold in the level of
mutations beyond which transcription cannot occur, which would explain the up- and down-regulations
observed in blood and brain, respectively.
The initiation of these processes in AD might be related to interactions, which promote ROS generation
in mitochondria, between A and certain mitochondrial proteins, including ABAD and CypD (Lustbader
et al., 2004; Du and Yan, 2010). Factors that confer upon the brain a decreased capacity to remove ROS,
24. 24
such as the lack of glutathione in neurons (Christen, 2000), might result in the majority of mtDNA
molecules, in brain regions affected in AD, harbouring a mutation level that exceeds the putative
threshold, whilst in blood such levels are not reached.
According to this interpretation of the seemingly incongruent observations, made between blood and
brain, of mtDNA-derived mRNA abundance, both reflect the same disease response. Conceivably, then,
assessing abundance of these mRNA species in blood could provide a means of judging AD progression
in brain. On one hand, this may be of value in evaluating the efficacy of novel drugs that target
pathological features of AD, in which case a reduction in these mRNA would be anticipated. On the other
hand, assessing mtDNA-derived mRNA might provide a means for early diagnosis of AD. Detecting AD
before symptoms emerge would allow for the application of existing interventions sooner than is possible
with symptom-based diagnosistic methods, thereby capitalising on their potential. Early diagnosis would
also allow for early-intervention trials.
Future reseach should assess the abundance of other mRNA species encoded by mtDNA, in blood in MCI
and AD, to compare how abundance of these transcripts relates to those assayed here and to their
equivalents in brain (Chandrasekaran et al., 1997; Aksenov., 1999; Manczak et al., 2004). Patterns of
abundance of mtDNA-encoded transcripts in AD might resemble those of other neurodegenerative
diseases that exhibit mitochondrial dysfunction, and, therefore, information about a range of mRNA
species is likely to be needed to distinguish early AD from other diseases. Once data is available for all
mtDNA genes, classifiers should be tested to assess the degree of accuracy that can be achieved with this
data. It would also be interesting to compare their performance to other, more invasive, diagnostic
methods, such as the analysis of CSF for A or tau, which depends on lumbar punctures.
4.2. Abundance of mRNA between genes.
There are large differences between estimates for some transcripts and others. ND4 mRNA, for example,
is estimated to be ~2500 times more abundant than that of CYB . Other estimates are characterised by
similarity; ND4L transcripts are estimated to be only ~1.3 times more abundant than ND3 transcripts.
Variations in the concentrations of PCR products in standards between genes are likely to account for a
25. 25
substantial amount of the variation in estimates. It is probable that the concentration of the ND4 PCR
product, for instance, is reduced in the standards, leading to higher CT scores for standards, and higher
estimates for samples.
Whilst variation in standard concentrations make it difficult to produce accurate estimates, it is possible to
attempt to rank estimates for different genes. For instance, since the mean estimate for ND3 exceeds that
of ND5, despite ND3 having a lower mean for standard 4 CT scores, it is likely that ND3 transcripts are
more abundant than those of ND5. The same logic can be applied to infer that ND4L transcripts are
probably more abundant than those of ND3, and, therefore, of ND5. More generally, it is possible to get
an idea of their ranking, whilst controlling for standard concentrations, by looking at the graph and
observing the extent to which a dot falls below or above the fit line. For example, whilst ND3 and ND5
are both below the line, the distance is greater for ND5, suggesting that ND3 transcripts are, in actuality,
in greater abundance. According to this approach, the genes ranked in ascending order of transcript
abundance are: ND5, ND3, ND4L, CYB, and ND4. Though adopting an approach such as this might, to
an extent, help to control for standard PCR product concentration between genes, it would be preferable
to make ongoing checks of standard CT score variations to address the issue prior to analysis.
Assuming the ranking described immediately above is accurate, some of the relative levels are
incongruent with other accounts of mitochondrial mRNA relative abundance. For example, the model
proposed by Temperley and colleagues (2003) would suggest that ND3 and CYB transcripts would be
least abundant because of their susceptibility to containing aberrant stop codons, which make them targets
for translation-coupled degradation. Previous research has since demonstrated that these transcripts, in
addition to ND2, are less stable than other mtDNA-derived transcripts in HeLa cells (Piechota et al.,
2006). Conceivably, translation-coupled degradation of aberrant transcripts has less influence in MCI and
AD because of a down-regulation of mitochondrial translational components, though the control group do
not appear to exhibit patterns of relative abundance that diverge from the overall pattern. The fact that
levels of ND3 and CYB transcripts are not the lowest imply that factors other than the stop codon
position also influence gene-specific turnover rates.
26. 26
Prior research has shown that ATP6/8 and CO2 transcripts are more abundant than other mtDNA-derived
transcripts in HeLa cells, which might be related to their secondary structures or the presence of
stabilising proteins. Half-lives of these transcripts are reduced in the presence of ethidium bromide, which
is thought to interfere with secondary structures and mRNA-protein interactions (Piechota et al., 2006).
The binding of such proteins might be increased by reductions in OXPHOS as a way of preventing ATP
deficiency, as evidenced by the stabilisation of the CO2 transcript when complex IV (cytochrome c
oxidase) is inhibited by sodium azide (Leary et al., 1998). Differential abundance might also be conferred
by the presence of gene-specific translation initiation factors, whereby an increase in a specific factor
would promote turnover of its associated transcript. However, whilst initiation factors, such as TACO1
(Weraarpachai et al., 2009), have been identified in humans, there is limited potential for their
involvement in translation-coupled degradation since human mtDNA-derived transcripts lack 5’
untranslated regions (Montoya, Ojala, and Attardi, 1981).
It is apparent that a number of factors are likely to determine relative abundance of mRNA species
encoded by the mitochondrial genome, and that there is probably a complex set of interactions between
these factors. Moreover, the influence of these factors conceivably varies between tissue types, which
might partly explain the incongruence between the current findings and those of Piechota and colleagues
(2006). Differential abundance of OXPHOS transcripts is an interesting occurrence as it would appear
that the correct functioning of the complexes would depend on the presence of all subunits. One
explanation, of course, is that this is not true, and that some subunits are more essential than others, and
so measures are taken to enhance their stability. The relative importance of subunits could be investigated
by monitoring mitochondrial metabolic rates whilst silencing expression of individual OXPHOS genes
with, for example, small interfering RNA (siRNA). Understanding the relative importance of subunits, as
well as how their levels are regulated, will enrich interpretations of mitochondrial dysregulation in
neurodegenerative disease.
4.3. Gene correlations.
When not separated by status, all gene estimates exhibited significant positive correlation with all others.
27. 27
This is expected given that all of the genes assayed are transcribed into 1 polycistronic transcript. Within
the control group, ND3 was not found to be significantly correlated with any other genes, except ND4L,
when using a parametric test. Using a non-parametric test, however, returned significant results. Whilst
all estimates were significantly, positively correlated within the MCI group, ND4 was not significantly
correlated with ND4L and ND5 in the AD group, showing a weak, negative correlation with the former.
Since ND4 and ND4L overlap, this occurrence is unexpected. An inspection of the scatter plot (Appendix
6), however, reveals that a large number of the samples occupy a cluster that is indicative of a positive
correlation between the 2 genes, and that the coefficient will have been influenced by a smaller number of
samples that deviate extensively from this group. Nonetheless, the lack of an overall linear relationship is
surprising. A possible explanation is that degradation of cDNA occurred in the samples between the times
at which each of the genes were assayed, though this would have had to have affected samples
differentially for linearity to be compromised.
Across status groups, the strongest positive correlations were between estimates for ND5 and CYB,
which is expected given that they are not separated by a tRNA-encoding gene, and, therefore, occupy a
bicistronic transcript (Ojala, Montoya, and Attardi, 1981). Thus, it is unlikely that they would be
differentially affected by transcription or mRNA degradation. It would be interesting to assay CO3 and
ATP6/8 transcripts, which are also thought to be contained within a bicistronic molecule. Presumably, the
strength of the correlation between these 2 genes would closely resemble that of the correlation between
ND5 and CYB, though any differences might indicate the presence of degradative or stabilising
mechanisms that exhibit specificity for individual transcripts contained within a bicistronic molecule.
4.4. Conclusions.
It has been demonstrated that a number of transcripts encoded by the mitochondrial genome, namely
those derived from ND4, ND4L, ND5, and CYB, are significantly more abundant in blood in MCI and
AD. Though this finding contrasts to observations made in the brain (Aksenov., 1999; Manczak et al.,
2004), a model has been proposed that reconciles these differences, and which considers them to be part
of the same disease response. Consequently, it has been suggested that abundance of these mRNA species
28. 28
might be of value for early diagnosis of AD, or for monitoring responses to novel treatments. Future
research should obtain data regarding abundance of the other transcripts of the mitochondrial genome in
blood in MCI and AD, and investigate whether classifiers can be trained to distiguish AD from controls,
and from other neurodegenerative diseases characterised by mitochondrial dysfunction. Their
performance should be assessed in relation to classifiers that have been trained with data derived from
more invasive procedures (e.g., lumbar puncture).
Additionally, the relative abundance of gene transcripts has been investigated to assess existing accounts
of the processes that confer relative mRNA abundance. Some authors have argued that the location of a
gene’s stop codon increases its susceptibility to produce aberrant transcripts, thereby increasing their rate
of turnover by surveillance mechanisms that are coupled to translation (Temperley et al., 2003). However,
2 of the gene transcripts, ND3 and CYB, thought to be especially susceptible to aberrant expression, were
not the least abundant transcripts in the current study, emphasising the need to produce models of mRNA
turnover that take into account a range of factors, including secondary structures, stabilising proteins,
levels of OXPHOS, translation initiation factors, and tissue type. Moreover, such models will have to
consider the interactions between these factors. In doing so, researchers will be better placed to interpret
mitochondrial dysfunction in neurodegenerative disease.
The gene correlation data support the idea that transcription of the mitochodrial genome is a generally
uniform process, whereby initiation from the HSP2 produces a polycistronic transcript containing 12 of
the 13 protein-coding genes. The especially strong correlation between 2 of the genes, CYB and ND5,
also lends support to the tRNA punctuation model of RNA processing in human mitochondria (Ojala,
Montoya, and Attardi, 1981). In addition to the processes of stabilisation and degradation described
above, the nature of mitochondrial transcription is, of course, also a key determinant of expression, the
understanding of which is crucial for a comprehensive account of mtDNA-derived mRNA abundance,
whether in AD or not.
29. 29
5. Acknowledgements.
I would like to thank Dr. Angela Hodges and Dr. Aoife Keohane for their guidance throughout the
completion of this project.
30. 30
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34. 34
7. Appendices.
Appendix 1. Tests of normality for genes organized by status.
Tests of Normality
Status Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ND3_LOG
1.00 .100 171 .000 .951 171 .000
2.00 .042 141 .200* .992 141 .625
3.00 .066 144 .200* .986 144 .150
ND4_LOG
1.00 .085 171 .004 .986 171 .086
2.00 .060 141 .200* .990 141 .418
3.00 .045 144 .200* .995 144 .902
ND4L_LOG
1.00 .105 171 .000 .962 171 .000
2.00 .068 141 .200* .987 141 .187
3.00 .067 144 .200* .984 144 .090
ND5_LOG
1.00 .061 171 .200* .982 171 .024
2.00 .066 141 .200* .975 141 .011
3.00 .057 144 .200* .985 144 .113
CYB_LOG
1.00 .063 171 .095 .984 171 .053
2.00 .059 141 .200* .993 141 .731
3.00 .083 144 .017 .975 144 .009
* This is a lower bound of the true significance.
a. Lilliefors Significance Correction
The Kolmogorov-Smirnov test is typically applied when n>50.
35. 35
Appendix 2. Correlations between mRNA abundance between all genes in the control group.
Controls
ND3 ND4 ND4L ND5 CYB
ND3
Pearson Correlation 1 .083 .510** .117 .104
Sig. (2-tailed) .294 .000 .139 .188
N 162 162 162 162 162
ND4
Pearson Correlation .083 1 .275** .481** .484**
Sig. (2-tailed) .294 .000 .000 .000
N 162 162 162 162 162
ND4L
Pearson Correlation .510** .275** 1 .367** .198*
Sig. (2-tailed) .000 .000 .000 .011
N 162 162 162 162 162
ND5
Pearson Correlation .117 .481** .367** 1 .545**
Sig. (2-tailed) .139 .000 .000 .000
N 162 162 162 162 162
CYB
Pearson Correlation .104 .484** .198* .545** 1
Sig. (2-tailed) .188 .000 .011 .000
N 162 162 162 162 162
** Correlation is significant at the 0.001 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
36. 36
Appendix 3. Correlations between mRNA abundance between all genes in the MCI group.
MCI
ND3 ND4 ND4L ND5 CYB
ND3
Pearson Correlation 1 .292** .449** .418** .556**
Sig. (2-tailed) .001 .000 .000 .000
N 134 134 134 134 134
ND4
Pearson Correlation .292** 1 .262** .228** .406**
Sig. (2-tailed) .001 .002 .008 .000
N 134 134 134 134 134
ND4L
Pearson Correlation .449** .262** 1 .579** .514**
Sig. (2-tailed) .000 .002 .000 .000
N 134 134 134 134 134
ND5
Pearson Correlation .418** .228** .579** 1 .524**
Sig. (2-tailed) .000 .008 .000 .000
N 134 134 134 134 134
CYB
Pearson Correlation .556** .406** .514** .524** 1
Sig. (2-tailed) .000 .000 .000 .000
N 134 134 134 134 134
** Correlation is significant at the 0.01 level (2-tailed).
37. 37
Appendix 4. Correlations between mRNA abundance between all genes in the AD group.
AD
ND3 ND4 ND4L ND5 CYB
ND3
Pearson Correlation 1 .271** .448** .192* .266**
Sig. (2-tailed) .001 .000 .022 .001
N 141 141 141 141 141
ND4
Pearson Correlation .271** 1 -.062 .103 .326**
Sig. (2-tailed) .001 .463 .225 .000
N 141 141 141 141 141
ND4L
Pearson Correlation .448** -.062 1 .502** .318**
Sig. (2-tailed) .000 .463 .000 .000
N 141 141 141 141 141
ND5
Pearson Correlation .192* .103 .502** 1 .515**
Sig. (2-tailed) .022 .225 .000 .000
N 141 141 141 141 141
CYB
Pearson Correlation .266** .326** .318** .515** 1
Sig. (2-tailed) .001 .000 .000 .000
N 141 141 141 141 141
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
38. 38
Appendix 5. Correlations between mRNA abundance between all genes in the control group using Spearman’s rank
coefficients.
Controls
ND3 ND4 ND4L ND5 CYB
ND3
Correlation Coefficient 1.000 .194* .457** .210** .246**
Sig. (2-tailed) . .013 .000 .007 .002
N 162 162 162 162 162
ND4
Correlation Coefficient .194* 1.000 .429** .509** .479**
Sig. (2-tailed) .013 . .000 .000 .000
N 162 162 162 162 162
ND4L
Correlation Coefficient .457** .429** 1.000 .445** .271**
Sig. (2-tailed) .000 .000 . .000 .000
N 162 162 162 162 162
ND5
Correlation Coefficient .210** .509** .445** 1.000 .547**
Sig. (2-tailed) .007 .000 .000 . .000
N 162 162 162 162 162
CYB
Correlation Coefficient .246** .479** .271** .547** 1.000
Sig. (2-tailed) .002 .000 .000 .000 .
N 162 162 162 162 162
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
