Expression of iron metabolism - related genes in a

UNIVERSIDADE DE LISBOA
FACULDADE DE CIÊNCIAS
DEPARTAMENTO DE BIOLOGIA ANIMAL
Expression of iron metabolism - related genes in
a Portuguese population of Alzheimer disease
patients
Cláudia Cavaco Guerreiro
MESTRADO DE BIOLOGIA HUMANA E AMBIENTE
2013
UNIVERSIDADE DE LISBOA
FACULDADE DE CIÊNCIAS
DEPARTAMENTO DE BIOLOGIA ANIMAL
Expression of iron metabolism - related genes in a
Portuguese population of Alzheimer disease
patients
Dissertação de Mestrado orientada por:
Professora Maria Teresa Rebelo
(Faculdade de Ciências da Universidade de Lisboa)
Prof. Doutora Luciana Maria Gonçalves da Costa
(Instituto Nacional de Saúde Dr. Ricardo Jorge)
Cláudia Cavaco Guerreiro
MESTRADO DE BIOLOGIA HUMANA E AMBIENTE
2013
Agradecimentos
Existem várias pessoas a quem quero agradecer pois ao longo deste percurso tiveram
um papel crucial para que chegasse aqui…
- À minha orientadora externa, Prof. Doutora Luciana Maria Gonçalves da Costa, por
me ter aceitado para trabalhar num projecto tao interessante, no qual aprendi novas
técnicas e conceitos, por toda a dedicação e apoio e por ter estado sempre disposta a
ajudar no que fosse preciso. Não irei esquecer toda essa ajuda e será sempre um
exemplo a seguir pela extrema dedicação e profissionalismo ao seu trabalho;
- À minha orientadora interna Professora Maria Teresa Rebelo por ser uma pessoa
sempre disponível a ouvir e ajudar;
- À Doutora Madalena Cristina da Rocha Martins e ao Bruno Silva que foram o meu
braço direito relativamente a toda a aprendizagem no Real Time e orientação na análise
estatística e mostraram estar sempre disponíveis para as minhas dúvidas;
- À Doutora Isabel Picanço, à Liliana Marques e à Ana Mateus, responsáveis pela minha
formação a nível laboratorial onde me ajudaram com os protocolos e a orientar-me no
laboratório e deram a força necessária para nunca desmoralizar;
- A todos os meus amigos que sempre me deram força para acabar a tese
independentemente das várias circunstâncias, em especial um muito obrigada à Diana
Miguéns e ao Tiago Mendes que aturaram todas as minhas crises existenciais ao longo
deste percurso e souberam ouvir e reconfortar nas alturas mais difíceis.
- Aos meus pais, especialmente à minha mãe, pelo apoio incondicional e por me terem
feito chegar até aqui.
1
Abstract
Alzheimer’s disease (AD) is the most common neurodegenerative disorder and one of
the most frequent dementia worldwide. The main neuropathological hallmarks of AD
are of extracellular senile “plaques” (SP), and intracellular neurofibrillary “tangles”
(NFT) which are associated with the loss of cortical neurons. This disease also is
characterized by elevated brain Fe levels and accumulation of copper and zinc in
cerebral Aβ amyloid deposits, like SP. Thus, a dysfunctional homeostasis of transition
metals seems to play pivotal role in pathogenesis of this disease. In this work we
intended to: i) measure the expression of specific target Fe metabolism-related genes to
(TF; ACO1; HMOX-1; CP; FT; DMT1; APP and Nrf2); ii) search for a putative
association between gene expression studies and biochemical results and iii) identify
novel susceptibility factors associated with AD. Although we did not find significant
differences between patients and controls in Fe metabolism parameters measured in
periphery, a tendency for the decrease of serum Fe concentration, Tf, TIBC, Tf
saturation and Ft was observed in AD compared to healthy individuals. These
observations reinforce previous results that showed a low Fe status in periphery of AD
patients. On the other hand, gene expression studies showed a significant decrease in
ACO1 (p=0.011); SLC40A1 (p=0.000); CP (p=0.000); APP (p=0.007); TFR1 (p=0.000);
TFR2 (p=0.000), while an increase in expression of FT-L (p=0.038) was observed in
PMBCs of AD patients compared with healthy volunteers. These findings strongly
suggest a cellular Fe overload in AD individuals, which may lead to oxidative stress, a
typical feature of this disease. More research for peripheral blood Fe metabolism
markers and genetic variation in AD may be important to provide a low invasive and
earlier form for its diagnosis.
Keywords: Alzheirmer’s disease, Iron metabolism, gene expression, biochemical
parameters.
2
Resumo
A doença de Alzheimer (DA) é a mais comum doença neurodegenerativa e representa
um dos casos de demência mais frequente a nível mundial. As suas principais
características neuropatológicas são a formação de “placas senis” e de “emaranhados
neurofibrilares”, associados à perda de neurónios corticais. Esta doença é também
caracterizada pela presença de elevados níveis de ferro (Fe) no cérebro e pela
acumulação de cobre e de zinco nos agregados de β-amilóide, que constituem as “placas
senis”. Desta forma, uma disfunção na homeostase de metais de transição parece
desempenhar um papel crucial na patogénese da DA. Neste trabalho pretendemos: i)
avaliar a expressão de genes-alvo específicos, relacionados com o metabolismo do Fe,
tais como TF; ACO1; HMOX-1; CP; FT; DMT1; APP e Nrf2; ii) procurar uma
associação entre os estudos de expressão génica e os respectivos resultados bioquímicos
e iii) identificar novos factores de susceptibilidade associados à DA. Na análise
bioquímica, apesar de não termos encontrado diferenças significativas entre os doentes e
os controlos, observou-se a tendência para uma diminuição da concentração sérica, dos
níveis de Tf, TIBC, saturação da Tf e Ft nos doentes. Estas observações estão de acordo
com resultados anteriores que mostraram uma baixa concentração de Fe periférico em
doentes com a DA. Por outro lado, os resultados da expressão génica mostraram uma
diminuição significativa da ACO1 (p=0.011); SLC40A1 (p=0.000); CP (p=0.000) APP
(p=0.007); RTf1 (p=0.000) e do RTf2 (p=0.000) nas PMBCs dos doentes, quando
comparados com os indivíduos saudáveis. Os resultados obtidos neste estudo indicam
uma sobrecarga de Fe celular na DA, o que pode levar ao stress oxidativo tipicamente
sugerido para esta doença. Novas descobertas sobre marcadores do metabolismo do Fe
periférico e a variação genética na DA poderão ser essenciais para o fornecimento de
um diagnóstico mais precoce e menos invasivo da doença.
Palavras-chave: Doença de Alzheimer, metabolismo do ferro, expressão génica,
parâmetros bioquímicos.
3
Index
Agradecimentos………………………………………………………………....1
Abstract……………………………………………………………………...…..2
Resumo……………………………………………………………………..……3
1. Introduction…………………………………………………………….…….5
1.1Alzheimer´s disease………………………………………………………..5
1.1.2 Epidemiology of Alzheimer’s disease……………………………..….6
1.1.2 Biology and Pathophysiology of disease…………………………...…7
1.1.3 Genetic Factors of disease………………………………………….…8
1.2 Regulation of Iron metabolism…….……………………………………...9
1.2.1 Iron metabolism in brain…………………………………………...…11
1.3 Iron metabolism and Alzheimer’s disease…………………………….….14
1.4 Objectives………………………………………………………….……..14
2. Materials and Methods………………………………………………….…..15
2.1 Individuals – Recruitment and clinical characterization…………………15
2.1.1 Clinical evaluation of subjects…………………………………….…16
2.1.2 Sample collection and storage…………………………………….....16
2.13 Database and Biological Bank……………………………………..…16
2.2 Quantitative Real Time PCR………………………………………………..17
3. Results…………………………………………………………………..…...18
3.1 Population of study…………………………………………………………18
3.2 Measurement of biochemical parameters……………………………..……19
3.3 Gene expression studies……………………………………………………20
3.3.1
Real Time PCR optimization…………………………………...…20
3.3.2 Fe metabolism related gene expression in AD and controls…….....24
4. Discussion…………………………………………………………………..28
5. References………………………………………………………………….37
4
1. Introduction
1.1Alzheimer Disease
1.1.1
Epidemiology of Alzheimer’s disease
Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and one of
the most frequent dementia worldwide (1). The prevalence of AD in the world is
approximately 30 million people (2) and it’s estimated by 2040 that 80 million
individuals will have AD, accounting for 60% of all dementias (3). In Portugal,
estimates from Associação Portuguesa de Familiares e Amigos de Doentes de
Alzheimer indicate that more than 90 000 individuals suffer from AD (4). This disease
is characterized essentially by the decline in cognitive abilities and has as insidious
onset with a heterogeneous progression and several stages of severity that lead to death
in 7-10 years (2). Throughout the development of the disease there are coexisting
changes such as language dysfunction, visuospatial difficulty, loss of insight, and
personality changes (2). A useful staging system for dementia severity is the Clinical
Dementia Rating (CDR) (5). This system classifies individuals who are cognitively
normal (CDR=0), very mildly impaired (CDR=0.5) (6), mildly impaired (CDR=1),
moderately impaired (CDR=2), or severely impaired (CDR=3) and has been beneficial
for research studies as a global dementia rating scale and can track dementia
progression in the clinic or in research (3). However, it is noted that many of the
individuals classified as having very mild dementia also meet criteria for what is called
mild cognitive impairment (MCI) (6). This condition is an intermediate state between
healthy subjects and AD in which manifest memory disturbance is not accompanied by
dementia. Nevertheless, clinical diagnostic criteria are not easily able to distinguish
mild AD from MCI and normal aging (7).
It has been proposed that several structural and functional magnetic resonance (MR)
techniques could be used for the evaluation of progression and early diagnosis of AD.
However, such approaches are not exclusive for many of AD cases and clinicians are
still not able to improve the diagnostic potential of disease (8).
5
It has been proven through the structural magnetic resonance imaging (MRI) that the
loss or damage of brain tissue in characteristically vulnerable regions, such as the
hippocampus and entorhinal cortex, is predictive of progression of MCI to AD.
Furthermore, this technique allowed a distinction between AD and other pathologies
such as vascular or non-Alzheimer neurodegeneration (9). Thus, machine learning
techniques have recently been identified as promising tools in neuroimaging data
analysis, and such techniques are also able to concomitantly contribute to the
construction and validation of novel and effective disease biomarkers (8).
1.1.2 Biology and pathophysiology of disease
The main neuropathological hallmarks of AD are the presence of toxic insoluble
aggregates of amyloid-β peptide (Aβ) in the form of extracellular senile “plaques” (SP),
and hyperphosphorylation with subsequent aggregation of the microtubule-associated
protein tau in the form of intracellular neurofibrillary “tangles” (NFT) which are
associated with the loss of cortical neurons (1,10). Both SP and NFT are widespread in
the brain, however they occur mainly in regions involved in learning, memory and
emotional behaviors such as the entorhinal cortex, hippocampus, basal forebrain and
amygdale.
SP are associated to brain regions with reduced numbers of synapses and in these places
neuritis are often damaged, suggesting that Aβ damages synapses and neurites (11). The
peptide Aβ results from the proteolytic cleavage of the amyloid peptide precursor
(APP), a family of glycosylated transmembrane proteins which are ubiquitously
expressed, but most abundantly in the brain. (12). APP is cleaved by three types of
proteases: α-, β- and γ-secretases, and its proteolysis by β - and γ-secretase results in the
production of Aβ 1–40 and Aβ 1–42 isoforms and defines the amyloidogenic (toxic)
pathway, as opposed to the proteolytic cleavage of APP by α - and γ-secretase, which
produces noncytotoxic soluble fragments. These amyloidogenic isoforms are
hydrophobic and can oligomerize and form aggregates that precipitate and accumulate
in neurons (12, 13). This accumulation can be toxic to neurons and increase their
vulnerability to oxidative and metabolic stress (11).
On the other hand, tau is synthesized and produced in all neurons and is also present in
glia cells. In healthy neurons, tau binds to tubulin and stabilizes microtubules. In AD,
6
tau becomes hyperphosphorylated, and this form dissociates from microtubules and has
a tendency to self-aggregate, forming NFT in cell bodies and dystrophic neuritis.
Previous results suggest that neurofibrillary pathology contributes to neuronal
dysfunction and correlates with the clinical progression of this dementia (3).
1.1.3 Genetic Factors of disease
Both genetic and environmental factors can contribute to the development of AD. In
terms of age of onset, AD can be classified in late-onset and early-onset. A minority of
cases show an obvious genetic origin and demonstrate an autosomal dominant pattern of
inheritance, which is called early-onset AD. Linkage studies indicate that point
mutations in the gene for the APP, on chromosome 21, are associated with a subset of
early onset (less than 65 years) familial AD cases. However, most early onset cases
have been linked to alterations in two other genes: presenilin 1 (PSEN1) on
chromosome 14 and presenilin 2 (PSEN2) on chromosome 1 (14). Importantly, previous
results suggests that different mutations in these three genes lead to a common result: an
increase in Aβ accumulation in the brain, which forms the core of the neuritic plaques
found in AD (14). However, these genetic mutations make only a small contribution to
the risk of developing AD, since only a small percentage of all autossomal dominant
early-onset cases are linked to these genes (14). For late-onset cases one gene has been
consistently identified as a susceptibility factor for development of disease, the
apolipoprotein E gene (APOE) (15). This gene has 3 alleles, APOE ε2, APOE ε3 and
APOE ε4. It was discovered that APOE ε4 allele confers a genetic risk factor for lateonset AD and is associated with greater levels of cerebrovascular amyloid deposition
and hemorrhage in the central nervous system (CNS). Also, the APOE ε2 allele has
been linked to a greater risk of cerebral amyloid angiopathy associated to vasculopathy
and CNS hemorrhage. APOE is an important regulator of plasma lipoprotein
metabolism and is present at high levels within the CNS, where it plays a role in a
variety of processes including cholesterol transport, neuronal plasticity and
inflammation. Its exact function in health and disease, particularly in AD, remains still
unclear (15).
7
Thus, a large proportion of the heritability of AD continues to remain unexplained (1)
and it would be important beyond the identification of new genes target considers also
the risk linked to genetic and environmental factors.
1.2 Regulation of iron metabolism
Iron (Fe) is essential element in the human body and it’s particularly abundant in the
brain. It represents a cofactor for many proteins involved in the normal function of
neuronal tissue (16). Because Fe is
involved in almost every aspect of
normal cell function and because of
its role in the production of free
radicals (17), it is essential that cells
and organ systems have a welldefined
mechanism
for
binding,
storing, and delivering Fe. Several
proteins are involved in Fe uptake
and metabolism in the peripheral
tissues and particularly in brain.
Dietary Fe uptake takes place in the
duodenum,
which cells express a
proton coupled to a ferrous Fe (Fe2+)
transporter,
Figure 1 Schematic presentation of Fe metabolism.
http://www.scielo.br/img/revistas/rbhh/v30n5/a12fig01.jpg
the
divalent-metal
transporter 1 (DMT1), localized in
their apical membrane. In their
basolateral membrane, these cells express the only Fe exporter known in mammalians,
ferroportin (Fpn) (18). This protein exports Fe2+ from cells to circulation where is
carried through the bloodstream mostly by binding to transferrin (Tf), a Fe transport
protein that binds two Fe atoms of ferric Fe (Fe3+) (non-toxic) (Fe2-Tf) (19) (Fig.1). Tf
acts as the major Fe chelator and delivers the great majority of its Fe to developing
erythroid cells, which take up Fe via the Tf cycle. In this cycle, the Fe2-Tf complex
binds to its receptor (TfR1) and is endocytosed. The acid environment of endosomes
8
triggers Fe3+ release and the endosomal reductases convert it to ferrous Fe (Fe2+), before
it’s transport to cytosol by endosomal DMT1 (20). Once in cytosol, Fe2+ can be
transferred to the mitochondria or stored in cytosolic ferritin (Ft). About a quarter of
total body Fe stored in macrophages and hepatocytes, as a reserve for red blood cell
formation, is mostly in the form of Ft. Mammalian Ft are heteropolymers who consists
in two subunit types, heavy (H) and light (L) chains. H-subunits have a ferroxidase
activity, catalyzing the oxidation of two Fe2+ atoms to Fe3+ and L-subunits seems to be
involved in the nucleation of the mineral Fe core (19).
Cellular Fe homeostasis is to a large degree controlled at the level of the translation of
the mRNA of proteins which are involved in cellular Fe metabolism. This posttranscriptional regulation is made by two Fe regulatory proteins, IRP1, also called
Acotinase1 (ACO1), and IRP2, which act as cytosolic Fe sensors (19).
When cellular Fe concentration is low, IRPs bind with high affinity to Fe regulatory
elements (IREs), mRNA stem loops that encode the regulated proteins and are mainly
responsible for control post-transcriptional of the balance of cellular Fe storage and
transport. If IREs are in 5’-untranslated region (5’ UTR) of the mRNA, as in the case of
Ft and Fpn (SLC40A1) genes, your link to IRPs prevents initiation of translation. On the
other hand, if IREs are in 3’-untranslated region (3’ UTR), as in the case of TF and
DMT1, binding of the IRPs protects them against degradation by nucleases. Thus,
cellular Fe uptake increase and its storage and export are suppressed (19).
When cellular Fe concentration is high, there is a decrease in affinity of IRPs with IREs
and IRPs are no longer active in binding, which increases translation of Ft, Fpn and
APP while TFR1 and DMT1 mRNAs are degraded by nucleases. Under these
conditions, ACO1 acquires acotinase activity, associated with the incorporation of a
4Fe4S cluster and IRP2 is degraded in the proteasome (19).
Absorption of dietary Fe is tightly regulated by hepatocytes which secrete a regulatory
peptide hormone, hepcidin. The first signal of Fe loading is increase of Tf saturation,
which is detected in liver via a complex pathway involving hemochromatosis protein
(HFE), Tf receptor 2 (TfR2) and hemojuvelin (HJV). Hepatocytes respond increasing
HAMP gene expression and, consequently, increasing secretion of hepcidin that
prevents Fe absorption through Fpn internalization. Thus, this hormone also controls
levels of serum Fe (19).
9
1.2.1 Iron metabolism in brain
Abnormalities in
expression of Fe
metabolism
proteins, due to
genetic and nongenetic
factors,
are the cause of
the
increased
brain Fe (21 ya ke
2003).
The
regulation of Fe
metabolism in the
brain
Figure 2 Schematic representation of Fe uptake and export in the brain.
Adapted from Moos et al, 2007
is
independent from
liver because of
the blood-brain barrier (BBB), a poorly permeable vascular barrier which limits its
access to plasma nutrients, such as Fe. Fe transport into the brain involves the Tf- to-cell
cycle where Fe2-Tf binds to TfR1 expressed on the luminal membrane of the endothelial
cells of brain capillaries. Then Fe is released in the cytosol, probably dependent on
DMT1 (which is highly expressed in neurons) and it is exported to the CNS by Fpn.
Astrocytes in the BBB contain ceruloplasmin (Cp) attached to their end-feet membranes
by a glycosilphosphatidylinositol (GPI) linkage, generated by alternative splicing. Cp
exhibits ferroxidase activity that promotes Fpn activity by oxidizing the Fe2+ released to
Fe3+, which allows it to bind to Tf in the interstitial fluid (18) (Fig. 2).
Interestingly, it was observed in brain of AD patients that the stability of IRP-IRE
complex is increased, compared to healthy people (21). This can result in increase of
stability of TfR and in decrease of Ft, leading to Fe accumulation in brain (21).
Several studies seeking to understand if the intrinsic mechanisms that regulate Fe
homeostasis in the brain involve the same regulatory pathways used in the rest of the
body (22). For example, it was observed that hepcidin can be involved in regulating Fe
10
storage in the brain thought the reduction of Fpn expression levels, restricting the Fe
release from neural cells (23).
Since the regulation of brains expression of key proteins in response to rising levels of
Fe occurs first at transcriptional level (RNA), many of the brain dysfunctions are likely
to involve gene expression changes. Once candidate genes are identified (like IRP2,
HFE and FT-L in mice) further clues to explain the mechanisms involved in Fe
regulation in brain may be obtained from conditional genetic manipulations restricted to
particular regions, cell types and timepoints (22).
1.3 Iron metabolism and Alzheimer’s disease
AD is characterized by elevated brain Fe levels and accumulation of copper and zinc in
cerebral Aβ amyloid deposits, like SP (24). Thus, a dysfunctional homeostasis of
transition metals seems to play pivotal role in pathogenesis of this disease (25). The lack
of recycling of metals ions as a result of aging or disease leads to this dysregulation of
metal homeostasis and, consequently, to Aβ aggregation and deposition. In particular,
altered Fe metabolism is a widely accepted feature of AD. Enhanced Fe concentrations
occurs specific brain areas of AD patients, especially in the basal ganglia (26),
hippocampus (27), neocortex (28), and in or around SP and NFT (29). Also was
observed changes in the levels of Ft and Tf in areas of the AD brain associated with
neurodegeneration (30). Therefore, it becomes apparent that Fe progressively
accumulates
within
neurons,
with
age,
leading
to
oxidative
injury
and
neurodegeneration (25). Recently, an increasing number of studies have suggested that
this oxidative stress phenomenon may be at the basis of AD (31). The importance of
oxidative damage in AD could be suggested through the up-regulation of antioxidant
enzymes. Heme oxygenase-1 (HMOX-1) is a 32 kDa stress protein that degrades heme
to biliverdin, free Fe, and carbon monoxide and is among the most sensitive and
selective indicator of cellular oxidative response in AD. It has been demonstrated that
HMOX-1 immunoreactivity is greatly increased in neurons and astrocytes in brain of
AD patients and colocalises SP and NFT (32). Another key factor involved in
neurodegeneration processes is the nuclear factor (erythroid-derived 2)-like 2 (Nrf2).
This factor is essential in regulation of cellular homeostasis, mainly by exposure of cells
to oxidative and chemical stress. It regulates basal and induced expression of many
11
antioxidant proteins, detoxification enzymes and xenobiotic transporters. Aberrant
expression or function of Nrf2 can induce neurodegeneration, as occurs in AD (33).
SP and NFT are influenced by Fe and several studies has documented that an oxidative
event, like excess of Fe, is critical for Aβ neurotoxicity and aggregation (34). Fe
availability modulates the levels of cellular APP associated with protein processing by α
-secretase. In fact, α - and β-secretases generate the 40–42-aminoacid Aβ peptide that is
toxic to brain neurons, an event that is accelerated in the presence of Fe (35). Also, has
been reported that intracellular Fe levels control APP translation via IREs at 5’ UTR of
APP mRNA so that Fe can directly regulate its synthesis and expression (36).
Moreover, results showed that ACO1 is a central mediator of neural APP expression in
response to intracellular Fe (37). This event is similar to the interaction of ACO1 and
IRP2, with canonical IREs that controls Fe-dependent translation of the Ft subunits.
These two proteins perform a post-transcriptional regulation of expression of Ft and
TfR in response to cell Fe status (37).
Another protein that is involved in modulation of Fe homeostasis and possibly linked to
AD pathology is furin and its responsible for the cleavage of both α and β-secretases
and represents a member of the subtilisin-like pro-protein convertase family which
catalyses the cleavage of precursor proteins into their biologically active forms (38). It
is also implicated on production of soluble HJV (37), an antagonist of bone
morphogenic protein that regulates positively HPC expression (39). Transcription of
furin is regulated by cellular Fe levels and hypoxia, so when Fe is in excess furin
protein levels decrease, as well as production of HJV. When Fe is deficient or in
hypoxia the levels of Furin and soluble HJV increase and preventing HPC activation
(40). Once that furin modulate the processing of α- and -β secretases, a decrease in your
levels can lead to activation of the amyloidogenic pathway, resulting in Aβ production
and ultimately neurodegeneration (19). Thus, Fe regulation of furin may play an
important role in AD (41). On the other hand, and importantly, previous reports had
shown that APP has a functional ferroxidase similar to Cp and interacts with Fpn to
facilitate Fe export from cells. It was observed that the block of its ferroxidase activity
can contribute to neuronal Fe accumulation in AD cortex (42).
Fe homeostasis is meticulous regulated, particularly in the CNS, by concern action of
several Fe-related genes and proteins (43). Several studies suggest that highly prevalent
gene variants involved in Fe absorption, transport and metabolism could influence the
age at onset of AD (19). Also, C282Y and H63D HFE mutations (that codifies the
12
human hemochromatosis (HH) protein) have been suggested as being involved in AD
pathology (44). HFE protein forms a stable complex with the TfR and decreases the
affinity of Tf binding while these mutations seem to eliminate this interaction. The
interplay of TfR and Tf is very important for control of Fe absorption and, consequently
for Fe homeostasis maintenance. In this way, the loss of HFE-repressor function for Tf
uptake could result in increased cellular uptake of Fe by some tissues and contribute to
Fe deposition in HH. This can leads to an excess of cellular free Fe and free radicals
suggesting that these variants could have a role in AD pathophysiology (44) and thus,
leading to the search of associations between Fe metabolism related genes in this
disease.
The distribution of TF alleles differs in human populations and the most common
phenotype is designated as TF C. The two alleles TF C1 and TF C2 are present in
majority of the population (45). Previously, interactions of HFE gene with the TF C2
have been suggested as potential a risk of developing AD (13), especially in males (46).
The male carriers of HFE and Tf C2 variants may be at specially risk and the influence
of these genes can be particularly important in presymptomatic stages of AD (47). It
was observed that once acting together as bicarriers, TF C2 and C282Y give 5 times
greater risk of AD. Also mutation H63D may further increase the risk of developing the
disease (48). Furthermore, tricarriers of HFE C282Y, TF C2 and APOE4 may be at still
greater risk. Taking into account some technical limitations (like small number of
samples and conflicting results of several studies) it’s clear the need of further research
to clarify the role of these variants in risk of developing AD (49).
In previous genome-wide association studies (GWAS), evidence of genetic influence on
systemic Fe status has been reported in three loci (HFE, TFR2 and TMPRSS6) mapped
to genes known to be associated with Fe homeostasis (50). In recent years, using a
cohort of twins and their siblings, GWAS were conducted on genes codifying four
serum markers of Fe status (serum Fe, Tf, Tf saturation and serum Ft). Interestingly, it
seems that common variants in genes involved in Fe metabolism may modulate
susceptibility or resistance to AD pathology in humans (51, 52). However, GWAS
specifically designed to search for genetic associations in AD did not identify new
putative candidate Fe-metabolism genes implicated in this disease (53, 54). Moreover,
the predictive ability of some loci associated with AD to identify individuals at risk of
this disease is not yet clinically significant (54).
13
A study done by our own group found significant associations between AD and single
nucleotide polymorphisms (SNPs) in TF, transferrin receptor 2 (TFR2), ACO1 and Fpn
(SLC40A1) genes. In addition, in peripheral blood mononuclear cells (PBMCs) gene
expression studies a decrease of SLC40A1, TFR1 and TFR2 transcript levels in AD
patients was observed when compared to controls. The mapping of systemic Fe markers
and Fe-related gene expression levels with the AD significantly associated SNPs further
revealed the involvement of specific Fe-metabolism pathways in this disease (55).
In this context, since Fe dyshomeostasis in AD may have genetic and environmental
causes, its necessary a global approach to clarify the mechanisms by which iron
dysfunction could be involved in its pathophysiology (55). Although the effects of the
change in Fe metabolism in AD could be expected to be expressed particularly in the
brain, we can assume that such dysfunction could have a reflection on the systemic
profile of Fe-related proteins. The identification of biomarkers of peripheral blood could
be essential to achieve early diagnosis of a disease. Thus, a joint approach to phenotypic
and genotypic level could circumvent the heterogeneity of this disease and may be
crucial to understand the putative influence of Fe metabolism in AD pathofisiology.
1.4 Objectives
The main objective in this work is study the further putative role of Fe metabolism in
the molecular mechanisms associated with AD pathophysiology. In this context, we
specifically intend to:
1. Measure the expression of specific target genes related to Fe metabolism
which have been previously identified (53) as being associated with AD (TF
and ACO1).
Moreover, given its importance in Fe metabolism homeostasis, we also
intend to qualify the gene expression of HMOX-1; CP; FT; DMT1; APP and
Nrf2 genes.
2. Search for a putative association between gene expression studies and
biochemical results in our population.
3. Identify novel susceptibility factors associated with AD.
14
2. Materials and Methods
2.1 Individuals – Recruitment and clinical characterization
2.1.1 Clinical evaluation of subjects
In this study 67 AD patients were recruited [mean ± SD age: 75.3 ± 7.6 years; 11
men (70.6 ± 8.80), 56 women (76.3 ± 6.97)], which were attended in Outpatient Clinics
at Hospital de Santa Maria and Hospital Fernando Fonseca. The clinical evaluation and
characterization of these patients was performed through the criteria used in the
National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s
Disease and Related Disorders Association (NINCDS-ADRDA) (56).
Also, 72 control individuals were recruited [mean ± SD age: 68.1 ± 8.4 years; 31 men
(69.5 ± 8.38 years), 41 women (67.1±8.43 years)], mostly elderly people without any
sign of cognitive decline, from the two referred hospitals, Associação de Alzheimer de
Lisboa and attendees of Associação de Moradores do Campo Grande and Associação
de Reformados do Campo Grande. The criteria used for controls (57) were:
i)
age (≥ 50 years),
ii)
Mini Mental State Examination (MMSE) score above cut-off (range 0-30;
cut-offs: > 15 if illiterate, > 22 if with 1-11 years of education and > 27 if
with more than 11 years of education) (58),
iii)
community dwelling with maintained activities of daily living as evaluated
by normal Instrumental Activities of Daily Living (IADL) scale (59),
iv)
no evidence for cognitive deterioration or cognitive complaints,
v)
no neurological or psychiatric condition likely to interfere with cognition
and without familial history of dementia.
Individuals with any systemic disease and/or taking psychoactive medications with
possible impact on cognition, as well as chronic alcohol or drug abuse, were
excluded. For both groups were performed surveys with personal clinical data (only
for AD patients), education, habits, other diseases and medication (for all
individuals). Blood collection with informed consent for each blood donor was also
taken.
15
The study was presented and approved by local ethics committee of Hospital de
Santa Maria and Hospital Fernando Fonseca.
2.1.2 Sample collection and storage
The blood sample (approximately 20 ml) was obtained by venipuncture in vacuum
conditions and collected three blood tubes: a serum gel tube (for serum isolation), an
EDTA tube (for DNA extraction) and a CPT tube (for plasma and peripheral blood
mononuclear cells (PBMC) isolation). For PBMCs, they were isolated from CPT tubes
by centrifugation (30min, 1639g, room temperature), double washed with PBS (15min,
353g, 4ºC) and conserved on RNA later (Qiagen, Hilden, Germany). Total RNA was
extracted from PBMCs using the miRNeasy Mini kit (Qiagen) according to
manufacturer’s instructions, aliquoted and stored at -80ºC until further use.
2.1.3 Database and biological bank
Database was also previously created (55), and included all clinical information on
diagnosis (medical conditions, prescribed medication, cognitive performance and other
relevant clinical information, biochemical and genetic data). RNA were stored in
appropriate conditions, at Instituto Nacional de Saúde Dr. Ricardo Jorge and at Instituto
Gulbenkian de Ciência. This biobank was used only for research purposes and the
database is anonymous and restricted to researchers directly involved in this project.
2.2 Quantitative Real-Time PCR
The quantification of the Fe metabolism-related gene expression was performed by
quantitative Real Time PCR (RT-PCR) on a ViiA™ 7 RT-PCR System (Applied
Biosystems). Primers were designed to amplify specific amplicons for FURIN, HMOX1, TF, IREB1, NRF2, FT and APP. The software used was ABI Primer Express software
(Appleid Biosystems). The primer sets were tested for amplification efficiency through
a standart curve analysis by using successive cDNA diluitions.
In RT- qPCR, the values assigned to efficiency should be between 90% and 110% and
the standart curve slope should be equal to -3,32. However, slope values between -3,580
16
and -3,100 are accepted (60; 61; 62). The coefficient of determination (R2), which
explains the observed values and the quality of the linear function apllied should be
approaching a value of 1 for a quality of optimal adjustment (63).
The synthesis of cDNA was carried out by using the RNA extracted from the blood
mononuclear cells from 47 control and 50 AD individuals. Briefly, cDNA was
synthesized from total RNA in a reaction catalyzed by reverse transcriptase and DNA
polymerase. Each cDNA sample was diluted 5-fold and 5 µL added to 5 pmol of
primers and SYBR Green Master Mix (Applied Biosystems) to a final reaction volume
of 15 µL. The cycling parameters used were 10 min at 95ºC (hold stage), followed by
40 cycles of 15 sec at 95ºC and 30 sec at 60ºC (PCR stage), and finally a melting curve
stage.
Quantification of gene expression was performed by the 2^-ΔΔCt method using HPRT1 as
an endogenous control and a pool from several cDNA’s as a normalizing sample.
IBM SPSS Statistics 21 software was used to perform statistics descriptives, normality
tests and analysis of variance, adjusted for age and gender (ANCOVA), of gene
expression data.
17
3. Results
3.1 Population of study
The main demographic and clinical characteristics of population under study are
described in Table I.
Table I– Summary of the main demographic and clinical characteristics of the study
population.
Sample
Characteristics
AD patients
(n=67)
Controls
(n=72)
Age
75.3±7.6
68.1±8.4
Male (n=11)
Male (n=31)
Female (n=56)
Female (n=41)
Gender
Age at onset
Of disease
69.2±8.1
MMSE
14.0±6.9
Clinical Dementia
Rating
1.71±0.49
15.9±10.5
Legend: MMSE – Mini Mental State Examination; CDR – Clinical Dementia Rating
18
3.2 Measurement of Biochemical parameters
The results obtained of Fe metabolism markers in peripheral blood, that it was been
measured by our group, of AD patients and respective controls of study population are
described below in Table II.
Table II – Biochemical parameters measured in serum from AD cases and controls and
respective multivariate analysis of variance.
Parameters
[Iron] (µg/dL)
AD Cases
n
mean±SD
67 74.84±25.06
Controls
n mean±SD
72 89.42±28.36
[Transferrin] (mg/dL)
67 261.57±49.18
72 267.26±41.46
0.696(0.153)
[TIBC] (mg)
50 325.28±63.99
47 330.36±54.61
0.894(0.018)
67 23.51±8.83
72 27.37±9.02
0.361(0.841)
65 112.29±80.03
69 147.22±111.99
0.257(1.295)
48 36.05±7.92
41 34.43±6.88
0.935(0.007)
CPO
50 121.73±38.90
46 117.91±34.28
0.752(0.101)
CPF
30 723.47±164.38
11 694.37±154.78
0.982(0.001)
Transferrin Saturation (%)
Significance (F)
Overall
MANCOVA(F)
0.155(2.051)
0.292(1.282)
[Ferritin] (ng/µL)
[CP] (mg/dL)
Legend: [Fe] – serum iron concentration; [Tf] – serum transferrin concentration; [TIBC] – Total
iron-binding capacity; Tf Saturation – Transferrin saturation; [Ft] – serum ferritin concentration; [Cp]
– serum ceruloplasmin concentration; CPO – Ceruloplasmin oxidative activity; CPF – Ceruloplasmin
ferroxidase activity.
Although we did not find significant differences between patients and controls, a
tendency for the decrease of serum Fe concentration, Tf, TIBC, Tf saturation and Ft was
observed in AD compared to healthy individuals. In contrast, a tendency for an increase
19
in serum Cp concentration and both its ferroxidative and oxidative activities was found
for AD patients comparatively to controls.
3.3 Gene expression studies
3.3.1 Real Time PCR optimization
During the optimization of each primer set it was taken into account the slope of
standart curves, the R2 and efficiency values. They are presented in the table
below (Table 5).
Table 5 – Values of slope, R2 and efficiency of each primer set optimized in RT-qPCR.
Gene
FT-H
FT-L
TF
IREB1
HMOX-1
FURIN
Nrf2
Slope
-3.366
-3.651
-3.225
-3.419
-3.390
-3.144
-3.213
R2
0.996
0.999
0.874
0.998
0.999
0.998
0.999
Efficiency (%)
98.177
87.9
104.192
96.091
96.897
107.999
104.751
Legend- FT-H: ferritin heavy chain; FT-L: ferritin light chain; TT: transferrin; IREB1:
iron responsive element binding 1; HMOX-1: heme oxygenase 1; Nrf2: Nuclear factor
E2-related factor-2; R2: coefficient of determination.
20
3.3.2 Fe metabolism related gene expression in AD and controls
The expression of several Fe metabolism-related genes was measured in PBMCs
from AD patients and controls (Fig. 3). The results obtained showed a significant
decrease in CP (p=0.000) (Fig. 3A); SLC40A1 (p=0.000) (Fig. 3B); TFR1
(p=0.000) (Fig. 3C); TFR2 (p=0.000) (Fig. 3D) and ACO1 (p=0.011) (Fig. 3E),
while an increase in expression of FT-L (p=0.038) (Fig. 3F) was observed in AD
patients compared to controls. Importantly, a decrease in PMBCs of APP
expression (p=0.007) (Fig. 3H) was observed in AD patients compared with
healthy volunteers. Given the putative importance of this result we also measured
the expression of APP in MCI individuals (p=0.018) (Fig. 3O). The results
obtained showed that there is a significant difference between AD, MCI and
controls cases. This way, we observe a gradual increase in APP expression from
the more severe clinical group (AD patients) to controls, while the group of MCI
individuals shows one intermediate APP expression compared to the other groups.
A
CP mRNA expression levels
SLC40A1 mRNA expression levels
p=0.000
Control
n=51
AD
n=40
p=0.000
B
Control
n=58
AD
n=44
21
FT-L mRNA expression level
ACO1 mRNA expression level
TFR2 mRNA expression levels
TFR1 mRNA expression levels
p=0.000
Control
n=58
Control
n=47
E
p=0.000
AD
n=48
Control
n=58
C
p=0.011
AD
n=42
Control
n=45
AD
n=49
D
p=0.038
AD
n=43
F
22
p=0.193
FURIN mRNA expression level
FT-H mRNA expression level
p=0.425
G
Control
n=47
H
Control
n=47
AD
n=43
I
J
p=0.200
DMT1
mRNA
expression
APP
mRNA
expression
levellevels
APP
mRNA
expression
level levels
HAMP
mRNA
expression
p=0.688
P=0.007
Control
n=58
Control
n=47
p=0.018
AD
n=53
AD
n=49
Control
n=50
Control
n=47
K
MCI
n=16
AD
n=44
AD
n=49
L
TF mRNA expression level
Control
n=47
p=0.307
HMOX-1 mRNA expression level
p=0.288
M
AD
n=43
AD
n=42
23
N
Control
n=47
AD
n=50
NRF2 mRNA expression
level
O
P=0.227
Control
n=47
AD
n=50
Figure 3 - Boxplots showing the median and dispersion of the gene expression
observed in AD patients and controls, after of removal severe outliers. A – Ferritin
Heavy chain mRNA expression level; B – Furin mRNA expression level; C – Ferritin
Light chain mRNA expression level; D – Acotinase mRNA expression level; E –
Transferrin; F – Heme Oxigenase 1 mRNA expression level.
24
Discussion
The results obtained in this study provide additional evidence to strengthen the
hypothesis of an altered Fe homeostasis in AD. In fact, we found significant differences
between AD patients and controls in the expression of Fe metabolism-related genes.
Also, despite no significant differences were found at the biochemical level, some
peripheral Fe metabolism biomarkers like Fe, Tf and Ft, showed a tendency to decrease
in AD patients when compared to controls. These results support previous findings of a
peripheral blood low Fe status in this disease (55).
The quantification of mRNA expression of studied genes showed a significant decrease
in TFR1, TFR2, SLC40A1, ACO1, CP and APP transcripts in PBMCs of AD patients
compared to controls.
It has been described that IRPs regulates expression of several Fe metabolism-related
genes at the mRNA level (likely TFR1; TFR2; FT; DMT1; SLC40A1 and APP genes)
(64). It was previously described that when cellular Fe is in excess FT and Fpn
(SLC40A1) expression increases, while TFRs and DMT1 decreases. In our patients we
found a tendency for a decrease in Fe, thus one can speculate that Fe accumulation in
cells could own which is in agreement with the diminution of TFR1 and TFR2
expression in order to avoid Fe cellular influx. The results obtained showed that FT and
DMT1 expression also are in agreement with this line of reasoning. However, SLC40A1
expression is decreased in AD patients who could suggest that Fpn decrease could lead
to AD pathophysiology.
Pinero et al. 2000 show in brain of AD rats that protein level was decreased in TfR1
and increased in Ft in response to Fe overload, leading to block of Fe entry into the cells
and to enhance Fe store avoiding oxidative damage. In fact, expression results are in
agreement with this report but at systemic level, we observed that Ft has a tendency to
decrease. However, it was reported that FT-H has, essentially, a specific ferroxidase
activity for rapid iron uptake while FT-L appear to be involved in nucleation of mineral
Fe core (19). This indicate that FT-H exists predominantly in serum and FT-L in cell, so
our results suggests that FT mainly express in PMBC cells is FT-L (p=0.0038) and in
serum we observe principally FT-H activity.
Also, it was suggested that, when
considering molecular genetic studies of Fe homeostasis, its important remember that
25
gene transcript levels do not always correlate with protein levels due to IRE/IRP system
(65), which regulates post-transcriptional Fe homeostasis, which can influence
transcript stabilization or translational repression (66). Our results can suggest a
possible role of a disruption in the IRE/ACO1 in the development of AD.
It was also described that ACO1 is implicated in regulation of several Fe-related genes
expression (67). In this way, ACO1 is involved in regulation of several key players of
cellular Fe homeostasis and especially acts as a central mediator of neural APP
expression in response to intracellular Fe (68). Once that ACO1 strongly regulates APP
expression it make sense that in our population both APP and ACO1 expression is
decreased in AD patients compared to controls. Thus, one can hypothesize that ACO1
gene could have a direct role in Fe-metabolism related genes dysregulation and thereby
contribute to AD. In addition, the ferroxidase activity of APP stabilizes Fpn in the
neuronal membrane and was reported that in AD cases the inhibition of this activity
lead to Fpn internalization, causing neuronal Fe retention, increase of cellular oxidative
stress and cell death associated with the disease (69). Since APP possesses ferroxidase
activity similar to Cp and both interacts with Fpn to remove Fe from cells (69), and the
decrease in APP, CP and SLC40A1 expression suggests cellular Fe accumulation.
However, serum Cp did not show any significative differences between AD and
controls, with a slight tendency to be increased in AD individuals. CP it’s expressed by
two isoforms, Cp serum and GPI-Cp. Previously, it was shown that GPI-Cp is the
membrane form of Cp that is required in Fe efflux in CNS. Alterations in this activity
can lead to Fe accumulation in brain (70). GPI-Cp is physically associated with Fpn and
alone, in absence of GPI-Cp or Cp, is unable to efflux Fe from cells (70). So, if GPI-Cp
function in the brain is impaired, Fpn could be destabilized and prevent the output of Fe
from cells. In our study, there are no significant differences in serum Cp concentration
or oxidase activity between AD patients and controls, but the form of Cp that was
measured in serum is the secreted Cp. Once that CP expression was significantly
decreased in our patients, it’s possible that the expressed form is GPI-CP, thus leading
to Fe retention inside cells. Initially, we tried to quantify GPI-Cp expression by
quantitative RT-PCR, using the RNA extracted from PBMCs of our sample of AD
patients and controls but we can´t succeed.
Our results showed that serum Tf was a slight tendency to decrease. In previous study
was reported that Tf decrease and Cp increase concentrations, in AD patients compared
to controls, in diverse pathological conditions as an important defense mechanism
26
reflecting the body’s resistance to an oxidant insult (71). It was already described that
when systemic Fe is in excess the HAMP expression increases leading to high levels of
hepcidin secretion (19). Consequently, hepcidin bind to Fpn to internalize it and
posteriorly degraded it, inhibiting Fe efflux (72). In our results we observe that HAMP
expression was similar values between AD patients and controls, suggesting that serum
Fe is not increase and leading to the reasoning that Fe is mainly within cells. In our
study, serum Fe concentration, although were not found significant differences between
patients and controls, the tendency of population suggests that Fe accumulates in PMBC
cells of AD patients. Ozcankaya et al reported a significant higher concentration of Fe
in 27 AD patients’ serum compared with 25 controls (73) but Squitti et al also did not
find any significant differences between 51 AD patients and 53 controls for serum Fe,
Tf and Cp concentrations (74). Both studies were performed in European populations;
however, sample sizes and their constitution in terms of age and gender in these studies
it’s different. This is in agreement with our results, once that population study is also
bigger.
In general, these observations suggest an impaired cellular Fe efflux in AD leading to
cell damage by oxidative stress. Normally, oxidative damage in AD could be monitored
by the up-regulation of antioxindant enzimes, like HMOX-1 (75). Premkumar et al
demonstrated that both protein and mRNA of HMOX-1 are elevated in brains of AD
patients. It was also reported that an increase of HMOX-1 expression promotes the
aggregation and phosphorylation of tau (76), which becomes a vicious cycle where
HMOX-1 is induced in response to oxidative stress and phosphorylation of tau to
protect neurons (77), but at the same time if its expression increase, can trigger the tau
aggregation and subsequent cellular toxicity (76), characteristic events of AD. Herein,
although not statistically significant, we found a tendency to decrease of HMOX-1
expression levels in AD patients. Additionally, literature suggests that levels of Nrf2
protein under unstressed conditions in cells is maintained at very low levels (78) and
also regulates HMOX-1 expression (79). Our results show a tendency in Nrf2 increased
expression, which suggests that in AD patients cells are in stress conditions and once
that HMOX-1 is regulated by Nrf2 it make sense that both have been increased.
Furin was shown to be also implicated in Fe homeostasis in AD (80). It was been
described that excess of cellular Fe decreases furin protein levels and in a previous
study it was reported that expression of FURIN in the brains of AD patients were
significantly decreased compared to controls (80). This result follows the same
27
reasoning of our results, which shows that mRNA levels of FURIN have a tendency to
decrease in AD individuals.
The findings of this study show that Fe overload in cells of AD patients and are
consistent with other studies reported previously (81;82) which may lead to oxidative
stress, a common factor discussed in this disease.
More information about peripheral blood Fe metabolism markers and genetic variation
in AD may be important to provide a low invasive and earlier form for its diagnosis.
There is need not only continued refinement and development of prior biomarkers
which identify preclinical AD but also for markers that predict treatment effects for use
in future clinical trials.
28
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